Inflammation in type 2 diabetes and the neuroendocrine role of IL-18
Chris Booker
A thesis submitted for the degree of PhD at the University of Otago, Dunedin, New Zealand
Date: May 2012
Abstract:
Cardiovascular disease is one of the leading causes of morbidity and mortality in type 2 diabetes, and yet, recent randomised controlled trials investigating the effect of different regimens of blood glucose control failed to show any benefit of more intensive control on rates of cardiovascular mortality, and minimal benefit on levels of morbidity. This thesis examines the theoretical understanding of type 2 diabetes, including the historical process which led to the above trials being conducted. A review of the historical and modern evidence surrounding the role of chronic inflammation suggests that this may be a common pathogenic alteration in obesity, diabetes and cardiovascular disease which may contribute to the development of cardiovascular disease in persons with type 2 diabetes, and even the pathogenesis of type 2 diabetes itself. This thesis therefore investigates the role of inflammation in type 2 diabetes, by a two-pronged approach: one investigates the effects of a dietary intervention on levels of chronic inflammation in participants with type 2 diabetes in the Lifestyle Over and Above Drugs in Diabetes (LOADD) randomised controlled trial. In this trial, chronic inflammation is assessed by a range of inflammatory markers and adipokines including leptin, neopterin, tumor necrosis factor α and its soluble receptors, the interleukins 1, 6, 10, and 18, and interleukin 1 receptor antagonist. The dietary intervention group exhibited a significant reduction in circulating levels of interleukin-18 (IL-18) and neopterin relative to a 'care as usual' control group; reductions which further analyses suggest were unrelated to concomitant changes in adiposity or glycaemia. In the second branch of the thesis, the neuroendocrine effects of IL-18 are further investigated in rodents, as previous evidence has linked this cytokine with effects on appetite, body weight, and blood glucose control. Clear evidence of central production of IL-18 in the medial habenula is presented, with transport along fibres in the fasciculus retroflexus terminating in the interpeduncular nucleus (IPN). However, no evidence for a corresponding IPN focus of expression of the genes encoding the IL-18 receptor was found. In light of this discrepancy, further investigations were conducted to assess other potential receptors for central IL-18 action; bioinformatics approaches suggest that the interleukin-1 receptor accessory protein like (IL1RAPL) receptors may form alternative central receptors for IL-18, and immunohistochemistry experiments localize IL1RAPL1 in the interpeduncular nucleus. A model is thus proposed whereby habenula IL- 18 expression may influence appetite via actions on IL1RAPL1 in the IPN. The thesis concludes with a discussion of the hypothesis of inflammation as a cause of diabetes and cardiovascular disease, and the potential for an improved understanding of the pathological processes involved in type 2 diabetes to produce greater improvements in clinical outcomes than has been attained thus far.
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Acknowledgements:
It is difficult to know where to start when it comes to writing an acknowledgements section, as not only have so many assisted during the process of completing this thesis itself and the research contained therein, but also all the others who have provided guidance, mentoring and just simply bouncing-of-ideas in the years beforehand while working as a young scientist, have all in some way or other contributed to the current thesis in your hands now.
As little science can be done without money these days, I should perhaps first thank the funders, especially the National Heart Foundation of New Zealand who provided me with a postgraduate scholarship without which, quite simply, this thesis never would have been completed. In many ways it's quite a strange experience to have an organisation you've never previously been involved with contribute a sizeable financial sum to allow you to pursue a higher education and spend your days in research and contemplation, and I am extremely grateful for having had the opportunity to do so. The Southern Trust, Lotteries New Zealand, and Health Research Council of New Zealand have all contributed to the funding of the LOADD study at various stages, and the Maurice and Phyllis Paykel Trust provided pilot funding to pursue our ideas around investigating the neuroendocrine role of IL-18, which led to further funding from the Health Research Council, and these funders jointly contributed to the completion of the work included here.
I obviously owe many thanks to my supervisors Jim and Dave. Jim, it's been a long time since I first started working at the Edgar National Centre for Diabetes Research shortly after its formation and had but a vague idea that there was a 'type 1' and 'type 2' diabetes, but your mentoring and inspiration along the way has been invaluable, and I certainly wouldn't have got so far as to contemplate a thesis on diabetes without it. It's a rare experience to work with someone with such a wealth of experience and perspective, and I'm sure your talk of things such as seeing fibre come in and out of nutrition 'fashion' has rubbed off in some way in my appreciation of the historical diabetes literature and thinking of longer-term timescales of research. To Dave, after discussing the problems of obesity and diabetes over many an ale-based beverage I remember coming to you with a paper from Nature Medicine and some vague ideas of trying to figure out what IL-18 was doing in the brain. Maybe you should have said 'aim for something a little less ambitious son!', but I'm glad that instead you were on board with giving it a go and were willing to put your name out there as PI on a project which could well have failed (and did seem like it was for a while). With a bit of ii
hindsight maybe I can say that like any good discovery science project, it led us down completely different lines of thought than when we started, and yet despite ending up outside the lab's comfort zone with completely different receptors and brain regions than we'd usually investigate, you've stayed supportive throughout. Many thanks.
To all the team involved in the LOADD study: Kirsten, Minako, Sue, Alex, Ashley, Sheila, Margaret, and many others, thanks for all your input and help, you've all certainly made my job the easy one in this project. Thank you Sheila for your additional help in the statistical analysis and interpretation, and I'd especially like to thank Ashley, Holly and Michelle, from the Lipid Laboratory for help with all the LOADD study assays, as well as Drs Sarah Young and Clare Fitzpatrick for their assistance in planning and conducting the multiplex analyses. Many thanks as well to the wider ENCDOR team for their support.
There are so many to thank from the 'Grattan lab' that I can't name you all, but special thanks to Ilona, Sharon, Jet and Rachael Augustine who took the time to teach me various hands-on lab procedures during the course of this PhD. To all the wider members of the lab and CNE group, many thanks for providing a great 'science geek' working environment and comraderie in general.
I'd also like to thank the members of my PhD committee, who took the time to read over various reports and updates, and provide feedback during our annual meetings: Marilyn, Kirsten, Sheila, Jim, Dave and, for a spell, Dr Alex Tups.
Y por último, gracias a mi amor Paulina por soportar mi ausencia durante las noches y fines de semana mientras que estaba ocupado haciendo las investigaciones y escribiendo este pequeño libro. Tal vez es más duro la vida de la pareja de un científico, que la del dicho científico. Entonces espero que valdría la pena para ti también, aunque no puedo garantizar que en el futuro cuando me preguntes en que pienso, que igual no voy a responder 'trabajo'. Gracias por tu apoyo.
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Table of contents:
Page Preliminaries Abstract……………………………………………………………………………….. i Acknowledgements…………………………………………………………………... ii Table of contents……………………………………………………………………... iv List of tables…………………………………………………………………………... viii List of figures…………………………………………………………………………. x List of biological and chemical abbreviations………………………………………. xiv List of trial and organisation acronyms……………………………………………... xvi Interleukin-1 family nomenclature…………………………………………………. xvii References for list of figures and interleukin-1 family nomenclature table...... xviii
Section 1: Introduction Chapter 1: Introduction.………………………………………………………………………….. 3 1.1 Diabetes: the glucose disease.………………………………………………………... 4 1.2 The (attempted) prevention of diabetes complications by glucose lowering therapy………………………………………………………………………………... 6 1.3 End of the glucose apotheosis?...... 11 1.4 The pathogenesis of diabetes and its complications revisited……………………… 12 1.5 Lifestyle effects on inflammatory markers………………………………………….. 27 1.6 Inflammation and diabetes: a key role of NF-κB…………………………………… 37 1.7 A role for IL-18 in metabolic disease………………………………………………... 39 1.8 Patterns and dynamics of cytokine release………………………………………….. 43
Section 2: Inflammatory marker and adipokines in the Lifestyle Over and Above Drugs in Diabetes (LOADD) Study Chapter 2: Overview and methods of the LOADD study 2.1 The LOADD study: rationale………………………………………………………… 47 2.2 Inflammatory markers and adipokines of interest………………………………….. 50 2.3 Overview of the LOADD study……………………………………………………… 54 iv
2.4 Summary of previously published LOADD study findings………………………… 56 2.5 The current work: measurement of inflammatory markers and adipokines……… 59 2.6 Statistical analysis…………………………………………………………………….. 62 Chapter 3: Results 3.1 Effect of dietary intervention on inflammatory markers and adipokines………… 66 3.2 Correlation of inflammatory markers and adipokines with anthropometric variables and glycaemic control……………………………………………………... 72 Chapter 4: Discussion 4.1 Discussion of LOADD study findings……………………………………………….. 77 4.2 Interpreting the effects of the LOADD study dietary intervention; theoretical considerations of establishing cause and effect in studies of metabolic disease…... 85
Section 3: Animal studies into the biological role of IL-18 Chapter 5: Introduction 5.1 CNS control of appetite and blood glucose………………………………………….. 89 5.2 Architecture of the hypothalamus…………………………………………………... 91 5.3 Inflammation in the hypothalamus…………………………………………………. 94 5.4 Key questions regarding the biological actions of interleukin-18: how are the central effects of IL-18 mediated?...... 95 5.5 Hypotheses……………………………………………………………………………. 96 Chapter 6: General laboratory methods 6.1 Animals……………………………………………………………………………….. 97 6.2 Reverse transcription polymerase chain reaction (RT-PCR)………………………. 97 6.3 Gel electrophoresis: visualization of PCR products………………………………… 100 6.4 Product sequencing: verification of amplification of desired gene products……… 101 6.5 In situ hybridisation methods……………………………………………………….. 101 6.6 Immunohistochemistry………………………………………………………………. 105 Chapter 7: Brain-derived IL-18 as a mediator of the metabolic effects of IL-18 7.1 Overview……………………………………………………………………………… 109 7.2 Methods……………………………………………………………………………….. 110 7.3 In situ hybridisation to examine IL-18 mRNA expression…………………………. 111 7.4 Immunohistochemistry to examine the distribution of IL-18 protein…………….. 113 7.5 Examining expression of an IL-18 splice variant in the rat habenula……………... 120
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7.6 Discussion……………………………………………………………………………... 122 Chapter 8: Central expression of IL-18 receptors 8.1 Overview……………………………………………………………………………… 125 8.2 Methods……………………………………………………………………………….. 126 8.3 Pilot examination of the expression of components of the IL-18 signalling system in the rat hypothalamus……………………………………………………………… 133 8.4 Examination of central expression of IL-18 receptor subunits using in situ hybridisation………………………………………………………………………...... 135 8.5 Examination of expression of IL-18 receptor splice variants by RT-PCR in rat brain micropunches…………………………………………………………………... 137 8.6 Discussion……………………………………………………………………………... 143 Chapter 9: Examining potential unknown mediators of IL-18 signalling in the CNS 9.1 Overview……………………………………………………………………………… 148 9.2 Examining a putative central receptor for IL-18: BLAST searches for proteins related to IL-18r1 and IL-18 BP……………………………………………………... 150 9.3 Examining conservation of residues necessary for IL-18 binding in the IL-1 receptor family……………………………………………………………………….. 158 9.4 Examining conservation of residues shared between IL1RAPL and IL-18 BP……. 163 9.5 Immunohistochemistry for IL1RAPL proteins……………………………………... 166 9.6 Preliminary investigation of IL1RAPL gene expression across rat tissues………… 173 9.7 Discussion……………………………………………………………………………... 177 Chapter 10: A model of habenula IL-18 function...... 187 10.1 Pre-existing evidence for a role of the habenula in metabolism………………….. 187 10.2 A theoretical model for a neuroendocrine role of IL-18…………………………... 191
Section 4: Discussion Chapter 11: Advancing theory and practice in the treatment and prevention of cardiometabolic diseases 11.1 The inflammatory hypothesis………………………………………………………... 203 11.2 Theoretical considerations for future research into, and pharmacological approaches to, the treatment of type 2 diabetes……………………………………. 205
References…………………………………………………………………………………………….. 213 vi
Section 5: Appendices Appendix 1: Chapter 3, LOADD study supplementary information……………………………… 289 Appendix 2: Section 3 supplemental information, detecting IL-18 system splice variants by RT-PCR………………………………………………………………………………… 317 Appendix 2.1: Establishing methodology to examine expression of an IL-18 splice variant………………………………………………………………… 318 Appendix 2.2: Establishing methodology to examine expression of an IL-18r1 splice variant………………………………………………………….. 322 Appendix 2.3: Establishing methodology to examine expression of sIL-18rap……. 326 References, Appendix 2………………………………………………………………... 329 Appendix 3: Section 3 supplementary information, images of micropunch procedure………….. 331 Appendix 4: Chapter 8 supplementary information: IL1RAPL1 and IL1RAPL2 bioinformatics comparisons……………………………………………………………………………. 337 References, Appendix 4………………………………………………………………... 350
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List of tables:
Page Preliminaries: Nomenclature Table i Nomenclature of IL-1 and IL-18 family members...... xvi
Section 1: Introduction Chapter 1: Introduction Table 1 Research into the effects of salicylates in diabetes and insulin resistance over the 19th and 20th centuries……………………………………………………… 22
Section 2: Inflammatory markers and adipokines in the Lifestyle Over and Above Drugs in Diabetes (LOADD) Study Chapter 2: Methods Table 2a Measured intra- and inter-assay coefficients of variation (CV) for RIAs and multiplexes……………………………………………………………………….... 64 Table 2b Calculated intra- and inter-assay coefficients of variation (CV) for ELISAs….. 65 Chapter 3: Results Table 3 Comparison of effect sizes of control and dietary interventions on adipokines and inflammatory markers……………………………………...... 68 Table 4 Pairwise correlation coefficients for ∆ BMI, HbA1c, IL-18 and neopterin……. 70 Table 5 Intervention effect sizes for IL-18 and neopterin with additional adjustment for changes in HbA1c and BMI…………………………………………………... 71 Table 6 Pearson correlation coefficients of adipokines and inflammatory markers with anthropometric, clinical and biochemical measures at baseline…………...... 74 Table 7 Partial correlation coefficients for leptin when adjusting for BMI………...... 76
Section 3: Animal studies into the biological role of IL-18 Chapter 6: General Laboratory Methods Table 8 Primer sequences for use in generating in situ hybridisation probes for use in rat and mouse tissues……………………………………………………………... 102 Chapter 8: Central expression of IL-18 receptors viii
Table 9 Primer sequences for amplification of IL-18 system transcripts in rat hypothalamic micropunch samples……………………………………..…...... 127 Table 10 Primers used for amplifying IL-18 receptor transcripts in brain micropunches………………………...... 128 Table 11 PCR settings for amplifying IL-18 and receptor transcripts from brain micropunches………………………………………………………………...... 130 Chapter 9: Examining potential unknown mediators of IL-18 signalling in the CNS Table 12 BLAST search results using amino acids encoding the extracellular portion of IL-18r1 as a query against reference protein sequence databases for rat, mouse and human species…………………...... 151 Table 13 BLAST search results using IL-18BP protein sequence as a query against reference protein sequence databases for rat, mouse and human species...... 153 Table 14 Primer combinations and reaction conditions for RT-PCR of IL-18r1, IL1RAPL1, IL1RAPL2 and Actb transcripts across various rat tissues…...... 174
Appendices Appendix 1: Chapter 3, LOADD study supplementary information Appendix 1, Table 1 Baseline characteristics of LOADD study participants……………... 291 Appendix 1, Table 2 Dietary intake across intervention and control groups…………….. 292 Appendix 1, Table 3 Pairwise correlation coefficients of ∆ BMI, HbA1c, IL-18, etc…….. 293 Appendix 1, Table 4 Pearson correlation coefficients, p-values, and number of observations for correlations shown in Table 6, Chapter 3………… 300 Appendix 2: Section 3 supplemental information, detecting IL-18 system splice variants by RT-PCR Appendix 2, Table 1 Primer details for establishing methodology for amplifying an IL- 18 splice variant and examining its expression in the habenula…... 319 Appendix 4: Chapter 8 supplementary information, IL1RAPL1 and IL1RAPL2 bioinformatics Appendix 4, Table 1 Consensus amino acid residue codes………………………………… 338 Appendix 4, Table 2 Reference protein and nucleotide sequences used for multiple sequence alignment in Figures 23 and 24…………………………… 339 Appendix 4, Table 3 Sources of amino acid sequences for 23 species used in multiple sequence alignment of IL1RAPL, IL-18BP and IL-18 proteins...... 340
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List of figures:
Page Section 2: Inflammatory markers and adipokines in the Lifestyle Over and Above Drugs in Diabetes (LOADD) Study Chapter 2: Overview and methods of the LOADD study Figure 1 LOADD study participant flow chart……………………………………………. 58 Chapter 3: Results Figure 2 Forest plot of LOADD dietary intervention effect sizes on adipokines and inflammatory markers…………………………………………………………….. 69 Figure 3 Scatter plots of change in IL-18 levels versus change in neopterin levels……... 70
Section 3: Animal studies into the biological role of IL-18 Chapter 5: Introduction Figure 4 Schematic diagram showing the position of hypothalamic nuclei in sagittal section in the rat brain……………………………………………………………. 92 Chapter 6: General Laboratory Methods Figure 5 Schematic diagram showing sagittal rat brain with approximate plane of sectioning of coronal oblique sections…………………………………………… 107 Chapter 7: Brain-derived IL-18 as a mediator of the metabolic effects of IL-18 Figure 6 In situ hybridisation for expression of the IL-18 gene in the rat brain………... 112 Figure 7 Immunohistochemistry for IL-18 in the rat brain showing coronal sections focused on the habenula and surrounding brain regions……………………….. 114 Figure 8 Sagittal views of IL-18 immunostaining in the rat brain……………………….. 117 Figure 9 Staining of ependymal cells surrounding the 3rd ventricle during immunohistochemistry for IL-18………………………………………………… 119 Figure 10 RT-PCR for IL-18 splice variants in the rat habenula………………………….. 121 Chapter 8: Central expression of IL-18 receptors Figure 11 Schematic diagram of positions of primers used to amplify IL-18r1 splice variants…………………………………………………………………………….. 131 Figure 12 Schematic diagram of positions of primers used to amplify IL-18rap splice variants…………………………………………………………………………….. 132 x
Figure 13 Expression of IL-18 signalling components in rat hypothalamic micropunches……………………………………………………………………… 134 Figure 14 In situ hybridisation for IL-18r1 expression in the mouse brain………………. 136 Figure 15 In situ hybridisation for IL-18rap expression in the mouse brain……………… 136 Figure 16 RT-PCR for the detection of IL-18r1 transcripts in rat brain micropunches….. 138 Figure 17 RT-PCR for the detection of IL-18rap transcripts in rat brain micropunches… 141 Chapter 9: Examining potential unknown mediators of IL-18 signalling in the CNS Figure 18 Alignment of rat IL-18r1 vs IL1RAPL1 proteins………………………………... 156 Figure 19 Alignment of rat IL-18BP vs IL1RAPL2 proteins……………………………….. 157 Figure 20 Alignment of members of the human IL-1 receptor family with human and viral IL-18BP………………………………………………………………………. 160 Figure 21 Manual alignment of multi-species consensus sequences for IL-18BP, IL1RAPL1 and IL1RAPL2………………………………………………………… 165 Figure 22 Immunostaining for IL-18 and IL1RAPL1 in sagittal sections through the interpeduncular nucleus………………………………………………………….. 167 Figure 23 Immunostaining for IL1RAPL1 in the cortex and hippocampus……………….. 169 Figure 24 Immunostaining for IL1RAPL1 in the cerebellar cortex with and without primary antibody preabsorption………………………………………………….. 170 Figure 25 RT-PCR of IL1RAPL1, IL1RAPL2 and IL-18r1 expression across rat tissues….. 176 Figure 26 In situ hybridisation for IL1RAPL1 expression in the mouse brain from Carrié et al. 1………………………………………………………………………………. 179 Chapter 10: A model of habenula IL-18 function Figure 27 A model of IL-1RAcP function as proposed by Yoshida et al. 2………………... 194 Figure 28 A model of IL-1RAcPb function…………………………………………………. 195 Figure 29 A model of IL-18 and IL1RAPL function………………………………………... 196 Figure 30 Schematic diagrams depicting a model of habenula function governing food intake………………………………………………………………………………. 199
Appendices Appendix 1: Chapter 2, LOADD study supplementary information Appendix 1, Figure 1 Scatter graphs showing the correlation of change in neopterin levels with various dietary components……………………………... 294 Appendix 1, Figure 2 Scatter graphs showing the correlation of change in IL-18 levels
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with various dietary components……………………………………. 296 Appendix 1, Figure 3 Distribution of baseline measures of adipokines and inflammatory markers in the LOADD study………………………………………. 298 Appendix 1, Figure 4 Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and age & sex……… 306 Appendix 1, Figure 5 Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and smoking……….. 308 Appendix 1, Figure 6 Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and anthropometric measures………………………………………………………………. 309 Appendix 1, Figure 7 Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and markers of cardiovascular risk…………………………………………………… 311 Appendix 1, Figure 8 Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and markers of glycaemia……………………………………………………………… 313 Appendix 1, Figure 9 Scatter plots of statistically significant pairwise correlations between inflammatory markers and adipokines……………………. 314 Appendix 2: Chapter 6, Detecting IL-18 system splice variants by RT-PCR, supplemental information Appendix 2, Figure 1 Schematic diagram showing location of primers designed to amplify rat IL-18 splice variants……………………………………... 320 Appendix 2, Figure 2 RT-PCR testing the amplification of IL-18 transcripts……………... 321 Appendix 2, Figure 3 Alignment of rat and mouse intronic sequences transcribed in the IL18r1 type II splice variant…………………………………………. 323 Appendix 2, Figure 4 Sequencing results for rat IL-18r1 type II…………………………… 324 Appendix 2, Figure 5 Sequencing results for rat sIL-18rap…………………………………. 327 Appendix 3: Chapter 6 supplementary information, images of micropunch procedure Appendix 3, Figure 1 Micropunches of rat brain habenula and arcuate nuclei…………… 332 Appendix 3, Figure 2 Micropunches of rat brain habenula and ventromedial hypothalamic nuclei………………………………………………….. 333 Appendix 3, Figure 3 Micropunches of rat brain paraventricular hypothalamic nuclei….. 334 Appendix 3, Figure 4 Micropunches of rat brain interpeduncular nuclei and ventral xii
tegmental area………………………………………………………… 335 Appendix 4: Chapter 8 supplementary information, IL1RAPL1 and IL1RAPL2 bioinformatics Appendix 4, Figure 1 Multiple species alignment of IL-18BP and the third IgG domain of the IL1RAPL protein sequences in multiple species...... 340 Appendix 4, Figure 2 Alignment of rat vs human IL1RAPL1 amino acid sequences...... 347 Appendix 4, Figure 3 Alignment of rat vs human IL1RAPL2 amino acid sequences...... 348 Appendix 4, Figure 4 Alignment of rat IL1RAPL1 with IL1RAPL2...... 349
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List of biological and chemical abbreviations:
A2 (R)...... angiotensin 2 (receptor) ACE...... angiotensin converting enzyme adr…………... adrenal ApoE...... apolipoprotein E arc…………… arcuate nucleus of the hypothalamus BMI…………. body mass index CART……….. cocaine and amphetamine regulated transcript CDK-5……….. cyclin-dependent kinase 5 CI……………. confidence interval CIMT...... carotid intimal medial thickness CNS...... central nervous system CRP…………. C-reactive protein CVD...... cardiovascular disease DEPC……….. diethylpyrocarbonate dNTP………... deoxynucleotide triphosphate DPP-IV……... dipeptidyl-peptidase IV DTT…………. dithiothreitol FBG…………. fasting blood glucose GIP………….. gastrointestinal inhibitory peptide GLP…………. glucagon-like peptide HbA1c………. haemoglobin A1c, also known as glycated haemoglobin HDL...... high density lipoprotein hyp…………... hypothalamus ICAM-1...... intercellular adhesion molecule 1 IFN-γ………... interferon gamma IgG…………... immunoglobulin G IKK...... inhibitor of kappa beta kinase IL-1………….. interleukin-1 IL-10………… interleukin-10 IL-18………… interleukin-18 xiv
IL-18 BP...... interleukin-18 binding protein IL-1ra……….. interleukin-1 receptor antagonist IL-6………….. interleukin-6 IL1RAPL(1/2). interleukin-1 receptor accessory protein-like (1/2) IPN…………... interpeduncular nucleus IκB...... inhibitor of kappa beta Kir6.2………... inwardly rectifying potassium channel family member, encoded by KCJN11 gene LDL...... low density lipoprotein LPS…………... lipopolysaccharide MAPK……….. mitogen activated protein kinase MRI………….. magnetic resonance imaging NF-κB...... nuclear factor kappa beta NIDDM……… non-insulin dependent diabetes mellitus NPY…………. neuropeptide Y NSAID...... non-steroidal anti-inflammatory drug PCI...... percutaneous coronary intervention PCR………….. polymerase chain reaction POMC……….. proopiomelanocortin PPAR-γ……… peroxisome proliferator activated receptor gamma PVN…………. paraventricular hypothalamus RT…………… reverse transcription/reverse transcriptase SGLT2……….. sodium glucose co-transporter 2 spl……………. spleen SUR1………… sulphonylurea receptor, encoded by ABCC8 gene TCF7L2…….... transcription factor 7-like 2 gene % TE………… percent of total energy intake TIR...... toll/interleukin-1 receptor TLR...... toll-like receptor TNFα………... tumour necrosis factor alpha VMH……….... ventromedial hypothalamus VTA…………. ventral tegmental nucleus
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List of trial and organisation acronyms:
ACCORD………. Action to Control Cardiovascular Risk in Diabetes ADVANCE...... Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation DCCT…………... Diabetes Control and Complications Trial DEHKO……….... Development Programme for the Prevention and Care of Diabetes (acronym refers to Finnish translation) DIGAMI………... Diabetes Mellitus, Insulin Glucose infusion in Acute Myocardial Infarction EASD...... European Association for the Study of Diabetes GWAS...... Genome-Wide Association Studies HEART2D...... Hyperglycemia and Its Effect After Acute Myocardial Infarction on Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus JUPITER...... Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin LOADD………… Lifestyle Over and Above Drugs in Diabetes NZGG…………... New Zealand Guidelines Group PROactive……… PROspective pioglitAzone Clinical Trial In macroVascular Events Steno-2…………. Refers to a study conducted at Steno Diabetes Centre, Copenhagen UGDP…………... University Group Diabetes Program UKPDS...... United Kingdom Prospective Diabetes Study VADT…………... Veteran’s Affairs Diabetes Trial WCRF…………... World Cancer Research Fund WHO…………… World Health Organisation
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Interleukin-1 family nomenclature:
A number of different names have been proposed for members of the interleukin-1 family and are utilised in the existing literature. For clarity, the range of different naming practices are summarized below in Table i. Names in the left hand column will be used in this thesis.
Table i: Nomenclature of IL-1 and IL-18 family members.
Ligand or Other names New IL-1 family receptor naming system 3
IL-1β Interleukin 1 beta IL-1F2 IL-1ra Interleukin 1 receptor antagonist IL-1F3 IL-18 Interleukin 18 IL-1F4 Interferon gamma inducing factor (IGIF) IL-18s short form or spliced variant of IL-18 (as described in the rat 4 and mouse in Yang et al. 5) IL-18r1 IL-18R - Interleukin-18 receptor subunit IL-1R5 IL-1Rrp - Interleukin-1 Receptor related protein IL-18r1 type I 6 IL-18rap IL-18Rβ - Interleukin-18 receptor β subunit IL-1R7 IL-18RII – Interleukin-18 receptor II IL-18r1 type II (truncated IL-18r1 splice variant described in Alboni et al. 6) sIL-18rap truncated IL-18rap IL-18rap short 7 sIL-18Rβ 8,9 IL1RAPL1 Interleukin-1 receptor accessory protein like 1 IL-1R8 IL1RAPL2 Interleukin-1 receptor accessory protein like 2 IL-1R9
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References for list of figures and interleukin-1 family nomenclature table:
1. Carrié A, Jun L, Bienvenu T, et al. A new member of the IL-1 receptor family highly expressed in hippocampus and involved in X-linked mental retardation. Nat Genet, 23 (1): 25–31. (1999). 2. Yoshida T, Shiroshima T, Lee S-J, et al. Interleukin-1 receptor accessory protein organizes neuronal synaptogenesis as a cell adhesion molecule. J Neurosci, 32 (8): 2588–2600. (2012). 3. Dinarello CA. Immunological and inflammatory functions of the interleukin-1 family. Annu Rev Immunol, 27: 519–550. (2009). 4. Conti B, Jahng JW, Tinti C, et al. Induction of interferon-gamma inducing factor in the adrenal cortex. J Biol Chem, 272 (4): 2035–2037. (1997). 5. Yang Y-JJ, Wang Z-YY, Chen S-HH, et al. Cloning and characterization of a new isoform of mouse interleukin-18. Acta Biochim Biophys Sin (Shanghai), 37 (12): 826–834. (2005). 6. Alboni S, Cervia D, Ross B, et al. Mapping of the full length and the truncated interleukin-18 receptor alpha in the mouse brain. J Neuroimmunol, 214 (1-2): 43–54. (2009). 7. Fiszer D, Rozwadowska N, Rychlewski L, et al. Identification of IL-18RAP mRNA truncated splice variants in human testis and the other human tissues. Cytokine, 39 (3): 178–183. (2007). 8. Andre R, Wheeler R, Collins P, et al. Identification of a truncated IL-18R beta mRNA: a putative regulator of IL-18 expressed in rat brain. J Neuroimmunol, 145 (1-2): 40–45. (2003). 9. Alboni S, Montanari C, Benatti C, et al. Constitutive and LPS-regulated expression of interleukin-18 receptor beta variants in the mouse brain. Brain Behav Immun, 25 (3): 483–493. (2011).
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Section 1:
Introduction
- 1 - - 2 - Chapter 1. Introduction
“…we need to return to science to understand the metabolic pathways underlying the development of diabetes and cardiovascular disease”. K. Borch-Johnsen & N. Wareham, "The rise and fall of the metabolic syndrome". Diabetologia, 2010 1
Rates of obesity, cardiovascular disease and type 2 diabetes are steadily on the rise throughout the world 2, and New Zealand is no exception. Data from the latest New Zealand Health Survey show that 1 in 4 New Zealand adults are obese, 1 in 20 have been diagnosed with ischaemic heart disease, and 1 in 20 with diabetes 3. That the prevalence of these three conditions should rise simultaneously is no surprise; obesity and overweight are major risk factors for the development of both heart disease and diabetes 4–6, and diabetes itself is a major risk factor for heart disease 7–10. This thesis examines the link between these three diseases, and focuses in particular on evidence that chronic sub-clinical inflammation could play a role in the pathogenesis of all three. The principal focus will be on the role of inflammation in diabetes, both as a mechanism by which diabetes may increase the risk of cardiovascular diseases, and as a cause of diabetes pathogenesis itself.
The experimental work in the thesis takes a two-pronged approach to examining the role of inflammation: on the one hand describing the effects of dietary intervention in people with type 2 diabetes on a range of inflammatory markers which have been linked to cardiovascular risk and insulin resistance; on the other detailing investigations in animals into the physiological role of one inflammatory marker in particular, interleukin-18 (IL-18), focusing on potential mechanisms by which it could influence appetite and glucose homeostasis.
To begin, it is necessary that we cast our eyes back somewhat, into the history of obesity, heart disease and diabetes and the scientific and clinical thinking as to their cause and pathogenesis, in order to arrive at the modern-day picture of the evidence linking inflammation with each, and to the future promise of addressing chronic inflammation in order to curb the increasing prevalence of these conditions.
- 3 - 1.1 Diabetes: the glucose disease
Diabetes is a disease which has been recognised throughout the course of human history. In former times the major cause for concern was the wasting, lethargy, high urine production and thirst characteristic of what is now called type 1 diabetes; as a physician in the ancient Roman empire noted in the second century AD “a melting down of the flesh and limbs into urine” 11. Historically, dietary treatment aimed to avoid ketoacidosis in those with ‘severe diabetes’ a by prescribing an extremely low carbohydrate, or even “starvation”, diet (thus ameliorating the post-prandial rise in blood glucose); however, the dietary regimens were harsh and mortality rates high 12,13. In 1921-22 Banting and Best performed their famous experiments involving ligating the pancreatic ducts of dogs 14,15, leading to the discovery of insulin, which was heralded as “a miraculous development in the treatment of diabetes” 16, having remarkable effects on glycosuria and blood glucose.
However, subsequent to the introduction and increasingly widespread use of insulin, it became apparent to physicians of the time that other problems were becoming manifest; Elliot Joslin is said to have noted: "The era of coma as the central problem of diabetes has given way to the era of complications. People with diabetes are at increased risk for the development of serious complications, including blindness, kidney failure, heart disease, stroke, and amputations" 16. A link between diabetes and cardiovascular disease had been previously recognized in the late 1800’s (prior to the introduction of insulin) 17. Yet after the introduction of insulin in the early 20th century, rates of death from arteriosclerosis amongst those with diabetes rapidly rose to become a leading cause of mortality. As Joslin was to note in 1954: "insulin hid the tragic symptoms and signs of the disease and thus allowed to burrow, unobserved, the slowly developing arteriosclerosis that finally killed the patient" 18, and Ewen Downie, in 1955, foresaw the growing problem of diabetes-related cardiovascular disease: “the grim realization that long-standing diabetes predisposes to premature
a Part of the difficulty of interpreting the historical literature relating to diabetes is the changing definitions of ‘diabetes’ which have been used a different times. It is likely that what is referred to as ‘severe diabetes’ in childhood is what is now known as type 1 diabetes; in older patients it could be inferred that they were probably in the later stages of what is now diagnosed as type 2 diabetes, with insulin insufficiency due to pancreatic degradation seen in the advanced stages. Along with changing classification systems and glycaemic thresholds for diagnosis, the word ‘diabetes’ across different time periods has referred to disease definitions which capture differing groups of patients. Nevertheless, preventing the complications of diabetes, be it in what is now termed ‘type 1’ or ‘type 2’ diabetes, has remained a challenge throughout history. - 4 - arteriosclerosis has posed a problem second only to that of the causation of diabetes” 19. Indeed, these words still ring true over 50 years later. In particular, given that the vast majority of cases of diabetes in the world are what is now classified as ‘type 2’ diabetes, it is the complications of this type of diabetes which form much of the diabetes-related health-care, mortality, and morbidity burden facing modern societies, and the costs of type 2 diabetes management are well documented as placing an incredible strain on health systems worldwide 20.
The question thus arose as to how to prevent the onset of these diabetes complications, particularly the rates of early death due to cardiovascular causes. An obvious suspect was the elevation of blood glucose seen in diabetes, and with the widespread availability of insulin and increasing lengths of survival of people with diabetes, the question of whether lowering blood glucose levels in people with diabetes could influence the development of these longer-term complications came to be the foremost question in subsequent diabetes research. However, during much of the 20th century, evidence addressing this issue consisted mainly of expert opinion or limited retrospective reviews of patient records. It is only in recent times that larger, well designed, randomised controlled trials have been conducted investigating this question; these have typically involved a prospective comparison of treatments aimed at the sustained lowering of blood glucose to different levels and follow-up of participants for micro- and/or macro-vascular end points, with the aim of comparing rates of disease across different levels of blood glucose control or different treatment strategies.
- 5 - 1.2 The (attempted) prevention of diabetes complications by glucose lowering therapy
In the context of type 1 diabetes, exogenous doses of insulin were shown in the Diabetes Complications Control Trial (DCCT) to result in impressive long-term improvements in diabetes microvascular outcomes in the range of 40 % - 70 % risk reductions with a more rigorous ‘intensive’ treatment schedule being more efficacious (at the inevitable trade-off of more dose-related adverse effects, i.e. hypoglycaemic episodes) 21 b.
Insulin resistance is a characteristic feature of type 2 diabetes and thus in a similar fashion treatment of type 2 diabetes with exogenous doses of insulin, thereby providing greater amounts of insulin than the body can produce, represented a logical progression in evaluating the potential prevention of complications in this second, more abundant, form of diabetes. Indeed the first study to examine this question was published just a few years after the original DCCT report 22; the small Kumamoto study in Japanese participants with type 2 diabetes (then known as NIDDM c) published by Ohkubo et al., which showed a more intensive insulin treatment regimen to be associated with lower rates of microvascular complications 23.
In terms of studies examining the prevention of macrovascular complications of diabetes, the first prospective study to examine the potential of blood glucose lowering to reduce mortality in individuals with type 2 diabetes was the University Group Diabetes Program (UGDP) 24. Here, the use of a variable-dose insulin regimen provided the best long-term fasting blood glucose control, and yet neither it nor a fixed-dose insulin regimen provided any significant reduction in cardiovascular mortality compared to a placebo group treated with diet only. While this may certainly not be a fault of insulin therapy but a beacon pointing towards the potential of dietary treatment of diabetes, the study received extensive criticism of its randomisation, conduct, and analysis 25–37, as well as coverage in the lay media (discussed in 38). One memorable result of the study was that the use of
b It should be noted that the paper cited here is a re-analysis of the DCCT data published 13 years after the original report, in which it was shown that a number of the results of the original analysis were in fact an artifact of the statistical modeling originally used (see in particular pages 996 & 997 of the paper cited here). c Abbreviation: NIDDM – non-insulin dependent diabetes mellitus - 6 - tolbutamide (the sulphonylurea used in the study) resulted in significantly greater cardiovascular mortality than either placebo or insulin treatment groups, despite equivalent or better fasting glucose control than fixed-dose insulin treatment, and better postprandial glucose control than either fixed-dose or variable insulin treatment for the majority of the study d. Likewise, in analyses of the two insulin treatment and placebo groups “in spite of differences in blood glucose levels among the treatment groups, there were only minor differences in the occurrence of fatal or nonfatal events” 40. The authors noted that, despite the study’s weaknesses, “the possibility that control of blood glucose levels will not alter the course of vascular disease in the adult-onset diabetic should not be overlooked as one possible explanation” 40 e.
Perhaps the most well-known study of blood glucose lowering and its effect on the incidence of diabetes complications is the United Kingdom Prospective Diabetes Study (UKPDS). This study and its subsequent analyses have not escaped their share of controversy either, with various criticisms of study design and analysis 42–45. However, a beneficial effect of intensive glucose lowering on the incidence of retinopathy was reported in the key publications describing the results of the study 46 (that this was coupled with a suspicious lack of effect on other microvascular complications and could have been due to physician behaviour given the lack of blinding was subsequently noted by others 42–44). A later epidemiological analysis of the mean HbA1c over a period of 10 years of follow- up showed that higher mean glycaemia was associated with an increased incidence of microvascular and macrovascular disease 47. However, this finding does not delineate that blood glucose itself is the causative mechanism, as the intervention could bring about changes which result in a lower incidence of diabetes complications and lower blood glucose without the former arising from the latter. Yet despite these limitations the UKPDS has been cited in many position statements and
d The variable insulin regimen group had the highest proportion of patients classified as exhibiting 'good' control based on fasting glucose, and the lowest fasting blood glucose levels over the course of the study. However, one hour glucose tolerance measures in the tolbutamide group were clearly lower than other groups for most of the follow-up period, only surpassed by the variable insulin regimen towards the end of the study 39. e Another treatment arm using the biguanide phenformin, which was started 18 months after the initiation of the trial, was also terminated early due to toxic side effects. In the wake of the trial the drug’s manufacturer attempted to obtain extensive records from the UGDP researchers via a Freedom of Information Act request 41. - 7 - treatment guidelines as providing evidence of good glycaemic control reducing microvascular complications in type 2 diabetes 48.
More recently, three separate studies have examined approaches of intensive blood glucose control compared with ‘normal’ or ‘conventional’ blood glucose control and studied their effects on the incidence of macrovascular endpoints – the ACCORD 49,50, ADVANCE 51 and VA Diabetes Trial 52 f. Contrary to expectation, however, these found either no difference or higher levels of cardiovascular morbidity and mortality in the treatment (lower average blood glucose) arms, prompting a large amount of media coverage 53–56, and comment, criticism and discussion in various medical journals42,57–68; repercussions now-familiar from the earlier release of similar findings from the UGDP and UKPDS studies. These studies would result in the unprecedented release of a joint position statement from respected diabetes and cardiovascular disease authorities reproduced in three high impact journals 69–71 discussing the results of these and earlier studies.
Meta-analyses of trials involving intensive glucose lowering and follow-up for cardiovascular endpoints have been undertaken analysing the combined UKPDS, ACCORD, ADVANCE, VA Diabetes Trial 72, and PROactive studies 73–77. These analyses come to similar conclusions that targeting intensive vs. conventional glycaemic control in these studies reduces incident cardiovascular events and myocardial infarctions in the range of 11-15 % (depending on outcome and meta-analysis), however, produces no reduction in cardiovascular mortality nor all-cause mortality. Thus, we are left with the somewhat odd conclusion that the effect of ‘tight’ control of blood glucose (to the extent of the reduction in HbA1c achieved in these studies and by way of the medication regimens they employed) appears to be that it avoids only non-fatal cardiovascular events; in any event it does not save lives, and the question of whether the reduction in non-fatal events is worthwhile from the perspective of adverse events, quality of life, or healthcare costs is another matter entirely, which has not yet been addressed.
Indeed, to date, the only trial which has conclusively shown a reduced incidence of macrovascular events in people with type 2 diabetes involving blood glucose lowering is the Steno-2 study. However, while the Steno-2 intervention included blood glucose lowering, this was in fact a
f Trial abbreviations: ACCORD - Action to Control Cardiovascular Risk in Diabetes; ADVANCE - Action in Diabetes and Vascular Disease-Preterax and Diamicron Modified Release Controlled Evaluation; VA Diabetes Trial - Veterans Affairs Diabetes Trial. - 8 - multifactorial intervention involving ACE (angiotensin converting enzyme) inhibitors, aspirin use, diet therapy and glycaemic control in which a number of cardiovascular risk factors were modified78. Given the known benefit of controlling other traditional cardiovascular risk factors on macrovascular disease outcomes (even in diabetes) it cannot be known from the design of this trial whether lower glycaemia in the treatment group was responsible for any of the beneficial effect. Of note, the proportion of patients receiving different forms of oral hypoglycaemic agents did not differ between treatment groups, nor the proportion taking insulin, nor mean insulin dose, and thus it is likely that the lower glycaemia in the treatment group resulted from the documented change in dietary behaviour.
1.2.1 Could future trials redeem glycaemic control as a strategy for cardiovascular benefit?
Despite the lacklustre support from the above trials for the hypothesis that glucose lowering can reduce cardiovascular events, some have attempted to salvage the hypothesis with a revised ‘glycaemic memory’ theory whereby initial poor control of blood glucose produces longer term detrimental effects on the pathogenesis of diabetes 57,58 (which are seemingly irreversible given the lack of success or even harm in the above trials), and some simply downplay the results and question the early termination of the ACCORD study 59 (with notable drug company conflicts of interest). One might muse that perhaps glucose lowering does have a beneficial effect on cardiovascular disease in type 2 diabetes but that, however, due to various factors such as study weaknesses, lower than expected cardiovascular event rates, or the rapidity of glucose lowering, this beneficial effect has not been evident in trials thus far, and indeed various commentators have suggested that this could be the case 57,58,79–83. Thinking along such lines is a worthwhile endeavour as it presents us with considerations to take heed of in pursuing further the ‘glucose-lowering as cardiovascular benefit’ hypothesis. If we assume that indeed glucose lowering can in fact improve cardiovascular outcomes in diabetes, the following questions come to light: i. What would it take to prove it?
Clearly, at least another study would be necessary, of sufficient duration (at least 5 years) and sufficient size (in the thousands if not tens of thousands) to observe the presumed cardiovascular benefit. Such a study would incur a substantial cost and also involve hard ethical questions as to whether participants should be subjected to such glucose-lowering treatment when there are clear risks of hypoglycaemia, which analyses from both the previous ADVANCE and ACCORD studies
- 9 - suggest is associated with increased risks of mortality. Power calculations based on the expected benefit level would be greatly problematic given the already largely negative results from the existing studies, and additionally in light of these prior studies one trial with a ‘positive result’ would most likely not be deemed sufficient to prove the hypothesis, necessitating further studies. ii. Would it change clinical practice?
The mainstay of diabetes treatment around the world is currently glycaemic control. Even in the absence of substantial supportive evidence for a beneficial effect of good glycaemic control on macrovascular and microvascular endpoints, good glycaemic control has been pursued as a means to reduce their incidence. In addition, more attention has been given in recent years to states of ‘pre- diabetes’, in recognition of blood glucose being a continuous variable upon which the diagnosis of diabetes is made by a pre-defined cut-off. Thus, the thrust of clinical practice is currently to identify individuals with, or at risk of, diabetes as early as possibly in the disease process and establish good glycaemic control. It is unlikely that clinical practice would, or even could, greatly change in a hypothetical future in which glycaemic control was also shown to produce cardiovascular benefit. iii. Does it offer anything new?
The current pharmacopeia of diabetes drugs is exclusively devised around lowering blood glucose levels. Proving a cardiovascular benefit of glucose lowering would not offer any new drug targets, neither would it offer any hope to those already with diabetes, given not only that their clinical treatment would be unlikely to change, but also that data from current trials would suggest that for those with established diabetes there is no benefit of good glycaemic control on cardiovascular mortality.
Perhaps the best way to close this part of the discussion on whether glucose control exerts any benefit on cardiovascular disease in those with type 2 diabetes is the words of Jay Skyler et al. in the release of joint position statements from the American Diabetes Association, American Heart Association and American College of Cardiology Foundation: “It is likely that a real benefit of glucose lowering on CVD in type 2 diabetes, even if it could be proven, is modest compared with and incremental to treatment of other CVD risk factors” 69–71.
- 10 - 1.3 End of the glucose apotheosis?
“The history of modern knowledge is concerned in no small degree with man’s attempt to escape from his previous concepts” H. P. Himsworth, “The syndrome of diabetes and its causes”. Lancet, 1949 84
Einstein famously noted: “it can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience” 85, often paraphrased as ‘things should be made as simple as possible, but not simpler’. However, to the detriment of advancement in the understanding and treatment of diabetes, it appears that in the present day state of affairs this dictum has been breached (one wonders possibly in the hope of profits from drug revenue, or due to the ever- increasing importance and centrality in scientific and medical circles of the prestige of ‘high impact’ publications, rather than the primacy of advancing scientific understanding) and thus despite evidence to the contrary, the populist view of diabetes pathogenesis has surrendered ‘the adequate representation of a single datum of experience’, and we are left with various oversights in the widely accepted theory of diabetes pathogenesis; of particular relevance to the current discussion is that the focus on the symptom of elevated blood glucose results in a simplification which largely casts aside the plethora of concomitant comorbidities characteristic of this disease (e.g. insulin resistance, hyperuricaemia, ectopic lipid deposition, alterations in circulating adipokines, elevated reactive oxygen species, endoplasmic reticulum stress, chronic subclinical inflammation) as curiosities or epiphenomena of little importance.
In the absence of a clear rationale for focusing on reducing blood glucose levels in the future to control the macrovascular complications of type 2 diabetes, it is starkly clear that if we are to improve the burden of cardiovascular disease in individuals with type 2 diabetes, and the wider impact this has on society (especially in the face of projected increases in the prevalence of obesity and type 2 diabetes in the immediate and medium-term future 86–89), then new avenues and hypotheses need to be explored.
- 11 - 1.4 The pathogenesis of diabetes and its complications revisited
“…we see around us every day the growth of new beliefs, which think themselves new; and which are yet but the old, which pretend to be young” the character Van Helsing, from Bram Stoker's 'Dracula' 90
“It is certainly a sobering thought for those entering this area of research to learn that little is truly new: rediscovery and explanation of previously believed but unproven hypotheses are often the rule” A. Boulton, “Why ‘diabetes and history’?”, Diabetic Medicine, 1994 91
1.4.1 The obesity, cardiovascular disease and diabetes trinity
As stated in the opening paragraph of this thesis, there is a rising incidence of obesity, diabetes and heart disease worldwide, and thus considerable interest in identifying key pathophysiological components which could form a common ground for their development. A number of metabolic abnormalities exist in type 2 diabetes (listed on the previous page), and these same aberrations are often also observed in obese individuals. Far from the state of despair raised by some commentators in the wake of the failure of blood glucose lowering treatments to prevent cardiovascular disease in diabetes 55, the array of metabolic abnormalities in the diseases of obesity and diabetes could be viewed as offering an abundance of potential therapeutic targets, and in addition, open the way for the advancement of alternative theories of the underlying pathogenesis of diabetes and its complications; theories in which blood glucose need not be a central player.
Theories regarding a common predisposing factor underlying these three diseases are often referred to as a ‘common soil’ hypothesis, and evidence of this idea is found mentioned throughout the modern history of diabetes: cf. “Perhaps it would be more nearly correct to regard the problem in the light of an abiotrophy affecting both the insulin-producing tissues and the vascular systems alike, and to suggest that a condition is present which requires merely an additional stimulus to bring about arterial lesions of pathogenic proportions, just as it requires an additional stimulus, such as obesity, overfeeding, or an infectious illness, for diabetes to become manifest” (Dry and Hines, 1941); and: “Is it the high blood sugar? The elevated lipids? Or may there be a common factor which produces both the diabetes and the complications as two branches of the same tree?” (Ricketts, 1947)92. Early studies of retinopathy in diabetes noted a surprising correlation with the presence of elevated blood pressure 93, with however, a higher prevalence also seen in those with diabetes than those without in the absence of hypertension, leading the authors to conclude: “the very existence of - 12 - retinitis in cases in which patients have no other signs of vascular disease must mean that diabetes alone does something to injure the finer arterioles or venules of the retina” 93. Notably, they also raised the possibility that other complications of diabetes could all arise from the same phenomena, stating: “The possibility occurs to us that lesions like those observed in the retina may also occur in the peripheral nerves and thus account for the high incidence of peripheral neuritis among patients with retinitis. Also, similar lesions… might contribute to the atherosclerosis of the larger arteries”.
One of the most well-known theories regarding the link between cardiometabolic diseases is the hypothesis from Gerald Reaven that insulin resistance could be a central underlying causative factor94. He proposed that various surrogate measures of risk of diabetes and heart disease could be grouped together as one ‘syndrome’. The so-called ‘metabolic syndrome/syndrome X’ has been highly controversial and the clinical utility of the syndrome later questioned by the author himself95, who is, however, still supportive of the role of insulin resistance as an underlying cause of these conditions 95,96.
Although the plethora of metabolic alterations in diabetes and obesity are numerous, and any of these could be potentially causative or merely associative, there is a growing pool of evidence suggesting that chronic activation of the innate immune system and the resulting low grade systemic inflammation associated with obesity, diabetes and heart disease may be a major contributing factor to the joint pathophysiology of these conditions 97–99 g. Hints of this hypothesis can be seen much earlier, with Himsworth noting in 1939, for example: “diet is not the only factor influencing insulin- sensitivity. It is probable that sepsis and obesity have a similar influence, and for this reason they also may precipitate diabetes” 103. The role of inflammation in the development of insulin resistance and pathogenesis of diabetes, and the relevance of inflammatory processes as a link between obesity, diabetes and cardiovascular disease is the focus of the remainder of this thesis h. The evidence linking inflammation of the innate immune system to each of these diseases is discussed below, along with
g This state has been termed ‘metaflammation’ by some 97,100–102. h There is also a wealth of evidence that obesity is a risk factor for many cancers 104,105, increasing recognition of a link between diabetes and certain cancers 106–108, and evidence that chronic inflammation may play a role as a mediator between these diseases and risk of cancer 109–115. However, an additional examination of the link between inflammation, metabolic diseases and cancer, while clinically relevant, is outside the scope of this thesis. - 13 - the existing evidence as to whether targeting inflammation in these conditions can reduce the risk of cardiovascular disease, or improve diabetes control or the development of diabetes complications.
1.4.2 Inflammation and obesity
There is emerging evidence of a central role of adipose tissue biology 97,116, including appropriate adipocyte differentiation 117–120 in the pathogenesis of obesity-related insulin resistance. Adipocyte size has been shown to relate to degree of insulin resistance 119,121, and, in a study in Pima Indians, was shown to predict incident diabetes better than percent body fat 122. Adipose tissue function impacts on the release of pro-inflammatory cytokines and adipokines which act locally via paracrine actions, and systemically via release into the circulation 116,123. Obesity has been shown to be associated with increased macrophage infiltration into adipose tissue 124,125, and higher levels of circulating inflammatory markers 97,116,126, and thus obesity-associated inflammation could provide a mechanism by which obesity increases the risk of type 2 diabetes and heart disease.
1.4.3 Inflammation and cardiovascular disease
The development of atherosclerosis is a multi-factorial process dependent on genetic influences and nutrition and lifestyle effects, although the key chain of events is thought to be similar in all cases: circulating lipoproteins accumulate in the vascular endothelial wall and these, in turn, cause the release of chemoattractant factors to cause monocyte accumulation, their differentiation into macrophages, and subsequent uptake of embedded lipoproteins into the macrophage. These heavily lipid-laden macrophages are classified as foam cells and, primarily due to their size, become embedded in the vascular wall. Accumulated monocytes, macrophages and foam cells themselves also secrete chemoattractant and pro-inflammatory cytokines and thus the process becomes self- amplifying, until such point as the blood vessel becomes occluded, and depending on the concentration of other thrombolytic factors such as matrix metalloproteinases (which degrade the vascular wall), the atheroma either bursts causing thrombosis or becomes so occluded as to limit blood flow, with the small vessels supplying the heart itself often ironically the first to fail, resulting in the typical symptoms of angina and ‘heart attack’ 127,128. Thus, current theory is that as well as traditional factors such as lipoprotein concentration (commonly, lipid or cholesterol levels), blood pressure and family history of disease, inflammatory factors also contribute to the pathogenesis of cardiovascular disease 127, and thus a clear mechanism exists by which the subclinical inflammation associated with obesity and type 2 diabetes may contribute to heart disease.
- 14 - Given the acknowledged role of inflammation on the pathogenesis of atherosclerosis and cardiovascular disease there are surprisingly few studies specifically examining anti-inflammatory medications and cardiovascular outcomes. In part this may represent the relatively recent nature of the hypothesis of cardiovascular disease as an inflammatory disorder; while the origins of the hypothesis can be traced as far back as the works of Virchow 129,130, an association of cardiovascular disease with elevated levels of CRP was shown in the 1950's 131–137, modern reviews on the pathogenesis of atherosclerosis begin to discuss inflammatory mechanisms in the 1980’s 138, and the major modern work affirming the hypothesis of atherosclerosis as an inflammatory disorder was published in 1999 139 i.
Low dose aspirin is a recommended treatment for reducing cardiovascular risk as both a primary and secondary prevention measure, however this has entered clinical practice more as an anti-platelet rather than an anti-inflammatory agent. In addition, the data from studies examining the use of aspirin specifically in patients with type 2 diabetes are inconsistent 140–146, and a recent meta-analysis on the use of aspirin for the primary prevention of cardiovascular events in people with type 2 diabetes found a relative risk (95 % CI) of events of 0.91 (0.79 to 1.05) 140. Combined with an increased risk of gastrointestinal and retinal bleeding with aspirin therapy it appears that its use as a cardioprotective agent in type 2 diabetes is thus not warranted. Whether this difference of an apparent lack of effect in those with type 2 diabetes compared to benefits in those without arises from a greater level of ‘aspirin resistance’ in people with type 2 diabetes is a possibility 147, although it is not certain that this underlies the discrepancy. In addition, whether the low doses of aspirin used for their anti-platelet effects are sufficient to bring about anti-inflammatory effects 148 is not clear.
Another major class of cardioprotective agents is the ‘blockbuster’ statins (hydroxymethylglutaryl- coenzyme A reductase inhibitors). Although designed to inhibit a specific enzyme in the cholesterol synthesis pathway, they show pleiotropic effects, including anti-inflammatory properties 149. Thus,
i The relatively ‘young’ age of this hypothesis still does not provide a great deal of assurance as regards the paucity of studies examining anti-inflammatory medications and cardiovascular disease outcomes; notably the work by Ross in 1999 has been cited some 11,310 times [source: Scopus database, 28-11-11] showing an intense interest in this hypothesis, which would be expected to translate into experimental works testing it. The possibility exists therefore that publication bias may be present and that studies have been undertaken which have had ‘negative’ findings and remain unpublished. - 15 - while they show cardioprotective effects (in those with diabetes as well as those without 150,151) the extent to which the observed cardioprotective benefits can be ascribed to their anti-inflammatory properties cannot be easily ascertained j. One study has examined their use more specifically as an anti-inflammatory agent, the JUPITER trial k, by targeting individuals with moderate cholesterol levels but C-reactive protein (CRP l) levels in the ‘intermediate’ risk range or above 154. The study reported an impressive level of benefit, with almost half the odds of the primary end point in those randomised to rosuvastatin. The study has raised a large amount of controversy regarding the stated benefits, early termination and external validity of the trial 155–161 (also including the role of drug company ties and the lead author possessing a patent on the measurement of CRP). One commentary analysing data in the published reports comes to the conclusion that rosuvastatin in fact produced no difference in cardiovascular mortality 162. The wider adverse effects of statins in general must also be taken into account, with their cardioprotective benefit counterbalanced by the findings of a meta- analysis on statin use and incident diabetes which revealed a significant 9 % increase in risk of diabetes in those randomised to statin therapy 163. Given acknowledged anti-inflammatory effects of statins, this increase in risk is in fact in the opposite direction from that expected by the hypothesis of inflammation as a cause of insulin resistance; however, the mechanism by which statins elevate diabetes risk is unclear. There is also a certain cruel irony that a cardioprotective agent raises the risk of a disease which has been branded a ‘cardiovascular disease equivalent’ 164. Given the pleiotropic nature of statins and controversy regarding this study, the findings do not provide clear evidence regarding the hypothesis of whether inhibiting inflammation produces cardiovascular benefit.
In contrast to the studies of aspirin and statins, which exert effects via diverse pathways not limited to anti-inflammatory effects, a recent trial investigated the cardioprotective benefit of a medication specifically targeting p38-MAPK (mitogen activated protein kinase) 165,166, which is activated by a
j While statins have become a mainstay treatment for the prevention of cardiovascular events, two recent meta-analyses of their use in primary prevention have described rather modest effect sizes on risk of all- cause mortality, with one reporting a non-significant reduction in risk of all-cause mortality of 9 % 152, and another a significant 12 % reduction in odds of all-cause mortality 153. k Abbreviation: JUPITER – Justification for Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin l Many articles talk of high sensitivity CRP, or hs-CRP, however the ‘high sensitivity’ prefix refers to the properties of the assay used to measure CRP, rather than any difference in the properties of the CRP protein itself. Thus, only CRP is used to describe this protein throughout the text. - 16 - number of pro-inflammatory pathways 167. This study examined the potential of blocking p38-MAPK activation specifically after percutaneous coronary intervention (PCI, or ‘stent’ implantation), which is typically accompanied by a post-surgical rise in inflammatory markers. Working on the hypothesis that post-PCI increases in inflammation could contribute to stent restenosis, 93 patients were randomised to receive a p38-MAPK inhibitor or placebo for 28 days following PCI. The study was designed principally as a test of efficacy (as measured by post-PCI increases in inflammatory markers) and safety, however longer term follow up at 30 months post-trial was conducted in some individuals and showed promising results in terms of lower cardiovascular event rates among participants who received p38-MAPK inhibitor treatment 166.
Chronic inflammation is also a recognized hallmark feature of rheumatoid arthritis, with a number of anti-inflammatory agents now used regularly as part of an evidence-based treatment regimen 168. A pilot study involving the use of the anti-IL-6 antibody, Tocilizumab, showed improvements in endothelial function and arterial stiffness across a period of 6 months in patients with rheumatoid arthritis 169,170, suggesting that modification of the underlying inflammatory process may translate into cardiovascular benefit for patients with this disease.
In summation, while there is a great amount of interest in the role of inflammation in the development of cardiovascular disease, there are currently few trials examining whether inhibition of inflammation provides cardioprotective benefit; interest in this research question appears to have only arisen in recent times.
1.4.4 Inflammation and diabetes
While inflammation is recognised to play a role in the pathogenesis of heart disease, the possibility of a link to the pathogenesis of type 2 diabetes has received much less attention. However, this does not imply that such evidence does not exist. Higher levels of inflammatory markers have been observed in persons with diabetes than in those without 171–174, even at comparable levels of adiposity174–178; such cross-sectional analyses raise the question as to whether inflammation plays a role in the development of diabetes. However, in cross-sectional data the direction of causation cannot be verified. Longitudinal analyses would overcome this weakness and provide a better test of the hypothesis that inflammation contributes to insulin resistance.
- 17 - 1.4.4.1 Inflammatory markers and worsening glycaemic control
If a factor were to be a causal variable in the development of a disease, it would logically follow that elevations of that variable would be associated with higher incidences of disease. In the case of inflammation as a potential cause of diabetes, various studies have identified inflammatory markers in non-diabetic individuals as being independently predictive of onset of new diabetes or worsening glycaemic control.
CRP has typically been the most intensively studied marker of sub-clinical inflammation, and high levels of CRP have been associated with the incidence of type 2 diabetes or deterioration in glycaemic control in various studies and population groups 179–182, including a meta-analysis 183. Other inflammatory markers have also been reported to have positive associations with the incidence of diabetes or declining glycaemic control: including interleukin-6 (IL-6) 184–186, interleukin-1 beta (IL-1β) 185; tumour necrosis factor alpha () 187; interleukin-18 (IL-18) 188,189; and interleukin-1 receptor antagonist 190,191. Not all studies have reported positive associations however, with a study in Pima Indians, for example, showing no association between IL-6 or CRP levels and incidence of type 2 diabetes 192.
In addition, while the above experiments examine a more long-term timeframe of prediction, more immediate short-term elevations of inflammatory markers occur in infectious illness, and in established diabetes the presence of concomitant illness and infection has long been recognized to cause hyperglycamia, with this statement written in the British Medical Journal at around the time of the discovery of insulin: “It is of interest to note that often in these infections the return of glycosuria occurs before the signs and symptoms of the disease [infection] are manifest or before the temperature rises. This is not difficult to understand when we remember that the injection of a few million dead microbes into a diabetic will cause a temporary hyperglycaemia” (O. Leyton, 1923) 193. Later works imply that such knowledge had been widespread for many decades, with a physician noting in 1937: "That an infection in a diabetic patient upsets the diabetic state, raises the blood sugar and brings on acidosis is a well-known fact" 194. In the present day, the problem is noted in diabetes treatment guidelines, with those from the American Diabetes Association stating: “The stress of illness, trauma, and/or surgery frequently aggravates glycemic control and may precipitate diabetic ketoacidosis” 195. Thus, acute illness, which is associated with short-term elevations of pro- inflammatory cytokines, exacerbates the glucose intolerance of diabetes, and this has been well- recognized for some time. The same response can also be observed in non-diabetic individuals, with Emil Schnée providing an extensive review of observations of fever and acute illness associated with - 18 - glycosuria in his work of 1889 196 (e.g.: "In malaria, according to Verneuil, and according to Bourdel, glycosuria is caused during the fever-stage" - E Schnée, 1889). He also quotes the system of classifications of diabetes mellitus devised by the physician Frerichs, in which one class of diabetes is listed as "diabetes in consequence of infectious diseases". Modern studies involving experimental induction of endotoxemia by injection of LPS m in non-diabetic individuals have shown (albeit with more 'advanced' techniques) similar effects as those noted above by Leyton, with LPS administration causing insulin resistance 197,198.
Evidence therefore suggests that elevated inflammatory markers can predict deterioration of glycaemic control, whether it be in non-diabetic individuals progressing to overt diabetes, or in those with established diabetes progressing further into higher levels of glycaemia. However, analyses of this nature are not sufficient to delineate causation, as despite providing predictive ability, the factor of interest may be a risk marker rather than a risk factor directly involved in the causal pathway of disease. Other evidence from the ‘natural experiment’ of infectious illness shows that a state of high levels of inflammation is associated with changes in glycaemia, and while recent studies of LPS administration allow this relationship to be ascribed cause-and-effect, rather than simply association, such changes could represent a physiological response which in some way assists in alleviating the short-term challenge of infectious disease, rather than a pathological process. Emil Schnée, for example, in his work on diabetes, went to great lengths to dissociate short-term glycosuria from what he saw as 'true' diabetes 196. Another ‘natural experiment’ is to examine variation in genes encoding inflammatory markers and incidence of diabetes, and indeed various studies have reported that polymorphisms in genes encoding inflammatory markers show relationships with the incidence of diabetes, the metabolic syndrome, or insulin resistance, including: IL-6 promoter polymorphisms 199–201; promoter polymorphisms of the TNF gene 199; IL- 18 polymorphisms 202; IκB genotype 203 n, and the interleukin-1 (IL-1) gene family 204. A recent study using the genetic approach of Mendelian randomisation concluded that although CRP was associated with the incidence of type 2 diabetes, analysis of genetic variation in the gene encoding CRP
m Abbreviation: LPS - lipopolysaccharide; a component of the bacterial cell wall which is recognized by the immune system and initiates an immune response. n Abbreviation: IκB – inhibitor of kappa beta, involved in the nuclear factor kappa beta signalling pathway discussed further on in the text. - 19 - suggests that CRP itself is not the causative factor involved in this association 205,206 and the authors conclude that the causative factor must lie upstream in the inflammatory pathway o.
With the above studies suggesting that increases in inflammatory markers, or variation in inflammatory marker genes, are associated with changes in glycaemia, the question which arises then is whether targeting inflammation can improve diabetes control or the development of diabetes complications in those with established diabetes.
1.4.4.2 Inflammation and diabetes: early works on the effects of salicylates
As far back as 1874 the German physician Wilhelm Ebstein published two case reports on the use of sodium salicylate in the treatment of diabetes, where “in the first case, sodium salicylate completely got rid of the diabetic symptoms, and in the second case helped the patient significantly improve” 210. Furthermore, he stated “it must be left to further research to learn about cases of diabetes mellitus in which the application of sodium salicylate… has prospects for success. In any case, it appears to me to be worth including sodium salicylate in the group of medications which deserve to be tested in the treatment of diabetes mellitus. Prompting [interest] in this direction has been the purpose of this text” 210. Sadly this call went largely unheard and but a scattering of papers appeared over the following 80 years which investigated salicylate use in diabetes. Indeed, over the 140-odd years since the publication of this work, it is obvious that research into the effects of salicylates in diabetes and insulin resistance has occurred in bursts, until recently characterized by a small number of interested researchers during each phase. These works are summarized in Table 1 and briefly described below. A number of research articles appeared by German physicians and academics (cited in 211), including a report by another physician four years after Ebstein’s account 212. Another German physician, Emil Schnée, discusses their use by prominent diabetes physicians around the turn of the century 196. Interest in the salicylates as a treatment for diabetes appears not to have spread further afield, however, and but four articles appeared in the British Medical Journal addressing their potential for glucose lowering in the following quarter century 213–216.
Reports suggest that the clinical use of aspirin as a treatment for diabetes was more widespread in the early 1900’s, but appears to have been abandoned even prior to the introduction of insulin due to the severity and toxicity of side effects 211,217 (no doubt arising from the doses used, quoted as being
o It is noted that the concept of Mendelian randomisation is not without its limitations, see for example discussions by Sheehan et al. 207, Lawlor et al. 208, and Little and Khoury 209. - 20 - as high as 14-16 g/day 215) . The 1950s-60s marked the period of greatest interest and research with studies in humans and animals into the mechanisms by which salicylates influence glucose metabolism and insulin action 217–231. Although studies from this era were in general small and typically intervention-only studies lacking control groups or placebo use, they consistently observed that high dose aspirin or salicylate had the ability to lower blood glucose levels and reduce glycosuria and ketonuria 217–223. The efficacy of salicylates was observed with either oral or intravenous administration 219, and most studies reported an effect of improving blood glucose levels in persons with diabetes but lesser or no effect in those without 219,221, and also noted an absence of hypoglycaemic episodes with salicylate therapy 217. Case reports described the use of aspirin for periods of up to 28 months with durable clinical efficacy 220, and dose equivalents to replace insulin in diabetes treatment were even investigated 223.
Adverse effects however were common and severe, including hearing problems such as tinnitus 217,218 and deafness 217, nausea and vomiting 217,218, hyperventilation 223, dehydration 223, and neurological symptoms such as headache 218 and confusion 223. As noted by researchers at the time: “These toxic effects of aspirin, particularly the early transient symptoms due to overdosage with the drug, present a real problem, and they will have to be avoided if aspirin is to find a place in the treatment of diabetes” (Reid and Lightbody 223).
Again, scant research occurred for the following decades until investigations into the effects of aspirin or salicylates on insulin action surface in the literature again, principally driven from a group led by D. Giugliano from the University of Naples 232–241. These document improvements in glucose- stimulated insulin release with the administration of lysine acetylsalicylate in patients with diabetes, showing that these effects were additive to the improvement of insulin release seen with sulphonylurea administration (suggestive of differing modes of action), and also showing the potential for salicylates to normalise blood glucose levels in response to hypoglycaemia. These studies hypothesized a potential role for endogenous prostaglandins in exerting a deleterious effect on glucose tolerance.
A more recent proof-of-principle study established that a short course of high-dose aspirin improved surrogate biomarkers of diabetes and cardiovascular disease, including insulin sensitivity as measured
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Table 1: Research into the effects of salicylates in diabetes and insulin resistance over the 19 th and 20th centuries. Note how the literature falls into bursts of research activity, typically with multiple works emanating from a small number of interested researchers within each period of activity.
1800 – early 1900's:
1876 Ebstein W. On the therapy of diabetes mellitus, in particular on the application of sodium salicylate. (Reprinted in part in English 210) 1878 Bartels K. Uber die Therapeutische Verwertung der Salicylsaure und ihres Natronsalzes in der inneren medicin. Dtsch Med Wschr, 4, 423-5;35-7. (1878). 1881 Paget GE. Reports of societies: Cambridge medical society. Br Med J, 1, 274-8. (1881). 1886 Holden JS. The salicylic treatment of glycosuria. Br Med J, 1, 816-7. (1886). 1901 Williamson RT. On the treatment of glycosuria and diabetes mellitus with sodium salicylate. Br Med J, 1, 760-762. (1901). 1902 Williamson RT. On the treatment of diabetes mellitus and glycosuria with aspirin. Br Med J, 2, 1946-1948. (1902).
1950's – early 1960's:
1952 Ingle DJ & Meeks RC. Suppression of glycosuria during administration of large doses of aspirin to force-fed partially depancreatectomized rats. Am J Physiol, 171, 600-603. (1952). 1952 Bornstein J, Meade BW & Smith MJH. Salicylates and carbohydrate metabolism. Nature, 169, 115-116. (1952). 1953 Ingle DJ. Effects of aspirin, aminopyrine, and HPC upon glycosuria in the diabetic rat. J Am Pharm Assoc, 42, 247-249. (1953). 1957 Reid J, Macdougall AI & Andrews MM. Aspirin and diabetes mellitus. Br Med J, 2, 1071-1074. (1957). 1958 Reid J. Dinitrophenol and diabetes mellitus: a comparison with salicylate. Br Med J, 2, 724-727. (1958). 1958 Aspirin and diabetes. Nutr Rev, 16, 167-168. (1958). 1958 Lista GA, Nunez OH & Vozzi IR. Tratamiento de la diabetes sacarina con ácido acetilsalicílico. Prensa Med Argent, 45, 2909-2911. (1958). 1959 Hecht A & Goldner MG. Reappraisal of the hypoglycemic action of acetylsalicylate. Metabolism, 8, 418-428. (1959). 1959 Reid J & Lightbody TD. The insulin equivalence of salicylate. Br Med J, 1, 897-900. (1959). 1960 Segal S, Blair A & Weinberg A. In vitro effects of salicylate on carbohydrate metabolism. Metabolism, 9, 1033-1046. (1960).
Table 1 cont:
1960 Gilgore SG. The influence of salicylate on hyperglycemia. Diabetes, 9, 392-393. (1960). 1961 Gilgore SG & Rupp JJ. Response of blood glucose to intravenous salicylate. Metabolism, 10, 419-421. (1961). 1962 Gilgore SG & Rupp JJ. The long-term response of diabetes mellitus to salicylate therapy: report of a case. JAMA, 180, 65-66. (1962). 1963 Kabanchik M, Levin H & Tuchszer S. Acción hipoglucemiante del ácido acetilsalicílico en la diabetes de la vejez. Prensa Med Argent, 50, 501-504. (1963).
Late 1970’s - 1980's:
1978 Micossi P, Pontiroli AE, et al. Aspirin stimulates insulin and glucagon secretion and increases glucose tolerance in normal and diabetic subjects. Diabetes, 27, 1196-1204. (1978). 1978 Giugliano D, Torella R, et al. The effect of acetylsalicylic acid on insulin response to glucose and arginine in normal man. Diabetologia, 14, 359-362. (1978). 1979 Giugliano D, Torella R, et al. Acetylsalicyclic acid restores acute insulin response reduced by furosemide in man. Diabetes, 28, 841-845. (1979). 1982 Giugliano D, Sacca L, et al. Influence of acetylsalicylic acid on glucose turnover in normal man. Diabete Metab, 8, 279-282. (1982). 1983 Newman WP & Brodows RG. Aspirin causes tissue insensitivity to insulin in normal man. J Clin Endocrinol Metab, 57, 1102-1106. (1983). 1985 Giugliano D, Giannetti G, et al. Normalization by sodium salicylate of the impaired counterregulatory glucagon response to hypoglycemia in insulin-dependent diabetes. A possible role for endogenous prostaglandins. Diabetes, 34, 521-525. (1985). 1985 Giugliano D, Ceriello A, et al. Effects of salicylate, tolbutamide, and prostaglandin E2 on insulin responses to glucose in non-insulin-dependent diabetes mellitus. J Clin Endocrinol Metab, 61, 160-166. (1985). 1985 Giugliano D, Ceriello A, et al. Interaction between epinephrine, prostaglandin E, and met-enkephalin in the regulation of insulin release in man. Diabete Metab, 11, 295-302. (1985). 1987 Giugliano D, Quatraro A, et al. Sodium salicylate restores the impaired insulin response to glucose and improves glucose tolerance in heroin addicts. Acta Diabetol Lat, 24, 205-212. (1987). 1988 Giugliano D, Cozzolino D, et al. Studies on the mechanism of salicylate-induced increase of insulin secretion in man. Diabete Metab, 14, 431-436. (1988).
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by a euglycaemic hyperinsulimaemic clamp 242 p. Another recent small study in healthy men showed that aspirin was able to ameliorate the insulin resistance caused by acute lipid infusion, without altering insulin sensitivity prior to lipid infusion 243. In terms of primary prevention of type 2 diabetes, a recent analysis of the Physicians’ Health Study examining self-reported use of aspirin and NSAIDs and the incidence of diabetes suggested a protective effect of NSAID use 244,245.
Despite these promising results, however, the tendency for high dose aspirin to cause major gastrointestinal bleeding obviously limits its application for widespread use. Another compound named salsalate (a dimer of salicylic acid, marketed under the trade names Disalcid, Amigesic, and Salflex), which unlike aspirin does not influence gastrointestinal prostaglandins 246, cause gastropathy247, nor alter normal platelet function 248, is currently undergoing clinical trials to determine efficacy in the treatment of type 2 diabetes and reducing cardiovascular and diabetes risk.
1.4.4.3 Inflammation and diabetes: current studies on salsalate, related salicylates, and other anti- inflammatory agents
One study has examined different doses of salsalate as an add-on therapy in 104 adults with type 2 diabetes, showing mean decreases of 0.4 % in HbA1c and 1.8 mmol/l in fasting blood glucose 249 over 14 weeks with the lowest dose studied (3.0 g/day) q. Another small randomised controlled trial including 17 people with type 2 diabetes showed improvements in glycaemic control and insulin measures over a 4 week period 250, and another conducted in treatment-naïve newly diagnosed patients with type 2 diabetes showed improvements in fasting and postprandial glucose levels over 3 months of treatment compared to placebo 251. Three trials in obese but otherwise healthy volunteers have been completed and published: a one month course of salsalate resulted in improved measures of glycaemia and inflammation 252; another double-blind, randomised cross-over trial of a salicylate derivative triflusal resulted in a dose-dependent increase in insulin secretion, which was also observed in vitro in human and mouse islets 253; and a randomised trial of one week’s salsalate
p Notably at the time of writing this study has been cited by subsequent publications 273 times, despite offering very little in the way of new findings over previous studies in the 1970’s - 1980’s. Source: Scopus database, 10-1-11. q Especially in view of the evidence discussed earlier regarding the utility of glucose lowering as a prevention for diabetes complications, it could be argued that examining the potential of anti- inflammatory medications as diabetes therapies and evaluating their efficacy by measuring the extent to which they decrease blood glucose is a rather illogical approach. Only studies with clinical endpoints will reveal whether anti-inflammatory medications are truly of use in the treatment of diabetes. - 24 -
therapy in obese individuals without diabetes showed improved glucose disposal, due to lower insulin clearance as revealed during a hyperglycaemic-euglycaemic clamp 254. A study of the effects of 4 days of salsalate administration in healthy volunteers showed no change in lipid-induced insulin resistance, but increased basal insulin levels, and elevated resting energy expenditure and insulin- stimulated carbohydrate oxidation 255.
At least thirteen clinical trials are currently registered for the examination of salsalate in metabolic and cardiovascular disorders, including: as a treatment in people with type 2 diabetes [www.clinicaltrials.gov registration numbers NCT00392678, NCT00799643] 256,257; to reduce diabetes and cardiovascular risk in individuals with impaired glucose tolerance [NCT00330733] 258 or overweight [NCT00258115] 259; to examine its effect on coronary artery calcification in subjects with the metabolic syndrome [NCT00624923] 260; for effects on vascular function and insulin sensitivity in healthy volunteers [NCT00553995] 261; for effects on glucose disposal and flow-mediated vasodilation in lean and obese individuals without diabetes [NCT00837590] 262; for effects on insulin-mediated and endothelium-dependent vasodilation in subjects with metabolic syndrome [NCT00762827] 263 or atherosclerosis [NCT00760019] 264; to counteract the free-fatty acid induced impairment of blood pressure and endothelial function in obese normotensive subjects [NCT00721617] 265; and on insulin resistance and endothelial dysfunction in people with HIV [NCT01046682] 266. A new trial currently registered is also investigating the potential of different doses of aspirin on markers of diabetes control in individuals with type 2 diabetes [NCT00898950] 267.
The adverse effects of aspirin and salicylates, reported most notably in the studies of the 1950s and 60s, although seen also as far back as the original report by Ebstein 210, remain a serious barrier to the effective use of salicylate-based therapies as a modern day diabetes medication. Textbooks from the same era as many of these early studies describe extensive monitoring and studies into toxicities from salicylate-based medications, also noting that some individuals exhibit allergic reactions to aspirin211. Recent reviews document the same array of adverse effects noted above, including life-threatening consequences 268. Although it is difficult to discern to what extent adverse effects may differ between aspirin and salsalate (given that the former possesses an acetyl group and differing chemical properties), since salsalate is a dimer of salicylic acid it seems inevitable that the same range of adverse effects and difficulties noted by researchers in the 1950s-60s will again arise in the current clinical trials. Indeed, in the recent studies to report adverse effects, two of eight patients randomised to salsalate therapy reported tinnitus, dizziness or headache in a study of obese but otherwise healthy volunteers 252 (although so did one of eight patients randomized to placebo), and
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subjects with type 2 diabetes randomised to differing doses of salsalate were more likely to report nausea, vomiting, diarrhoea, heartburn and tinnitus in another 249. The only consolation in this instance is that salsalate has been available as a therapy for rheumatoid arthritis for decades, and thus has a well-established safety profile which is unlikely to result in the kind of surprise adverse effects seen in recently introduced diabetes and obesity medications 269–274. Whether other salicylate-based therapies such as nitric oxide releasing aspirins 275–278, will fare any better is yet to be seen. Trials utilising other anti-inflammatory medications such as diclofenac [NCT00221052] 279 and diflunisal [NCT00506298] 280 with a view to improve glucose metabolism are also registered and currently underway.
Other recent randomised controlled trials specifically target the inhibition of interleukin-1 with the use of subcutaneous injections of recombinant interleukin-1 receptor antagonist (IL-1ra) in participants with type 2 diabetes, and show improvements in markers of diabetes control such as fasting blood glucose, HbA1c, and pro-insulin to insulin ratio for up to 39 weeks of therapy 281,282.
1.4.5 Targeting inflammation in cardiovascular disease and type 2 diabetes
Surprisingly, given the amount of literature linking inflammation to obesity, type 2 diabetes and cardiovascular disease there is but a small body of evidence that targeting inflammation can improve the outcomes of these diseases, at least as far as studies conducted in humans are concerned. The historical literature described above forms perhaps the largest pool of evidence that inflammation may be a viable target for medical therapy in diabetes, and currently registered clinical trials (mentioned above) should soon provide more definitive evidence – given the lack of randomised controlled trials in the historical literature – of whether anti-inflammatory medications can form part of the diabetes pharmacopeia. Thus, evidence that specifically targeting inflammation via the use of medications produces benefit in cardiovascular disease or type 2 diabetes appears in its infancy.
However, just as the known risk factors for obesity, type 2 diabetes and heart disease include lifestyle-associated variables such as diet and exercise, there is evidence that levels of inflammation in obesity and type 2 diabetes can be modified by these same factors, raising the possibility that inflammation could be targeted by diet and exercise strategies.
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1.5 Lifestyle effects on inflammatory markers
In order for a factor such as inflammation to be plausibly related to the pathogenesis of diabetes and cardiovascular disease, changes in that factor must also be in alignment with what is already known regarding established risk factors for these diseases. As discussed above, obesity is now well recognized as a state of chronic sub-clinical inflammation, such that obesity-associated elevations in inflammation are in agreement with the role of obesity as a risk factor for cardiometabolic diseases. Given the established role for lifestyle modification in the prevention and treatment of diabetes and cardiovascular disease, changes in inflammatory markers would also be expected, therefore, to show changes in a similar direction as the change in risk of cardiometabolic disease elicited by lifestyle intervention.
1.5.1 Weight loss:
There are a large number of studies which have examined changes in levels of inflammatory markers with lifestyle intervention where the intervention produced weight loss, and it appears from the bulk of evidence that weight loss in the obese or those with type 2 diabetes has the ability to reduce circulating inflammatory markers 283–293 or measures of activation of inflammatory pathways 294,295. Reviews of trials 101,296 and systematic reviews and meta-analyses of weight loss studies 283,297 attest to the relationship between weight loss and inflammation, at least as judged by the inflammatory marker CRP. The importance of calorie restriction in controlling levels of inflammation is also underscored by a small study showing that the increases in inflammatory markers typically observed with inactivity can be ameliorated via reducing energy intake in healthy volunteers 298. These results are also in agreement with studies showing the opposite phenomenon of increases in levels of inflammation with short-term overfeeding in healthy humans 299 or the consumption of a high fat diet in laboratory animals 300,301. Clearly then, in terms of dietary studies conducted in obesity and type 2 diabetes, weight loss is a potential confounding factor which makes ascribing cause-and-effect to specific deitary factors difficult.
1.5.2 Individual dietary components:
There are many studies which have examined the association of specific dietary components to circulating inflammatory markers or the potential effects of altering dietary components on markers of inflammation, with a large proportion examining the relationship with obesity-related inflammation. Particularly in depth reviews have been conducted by Calder et al. 302,303, Basu et al. 296, and Hamer and Steptoe 304. Below, with a focus on randomised controlled trials conducted in - 27 -
participants with metabolic diseases such as obesity, the metabolic syndrome, or type 2 diabetes, a brief review is made of the current status of the literature regarding trials which have involved altering dietary intake and examining inflammatory markers as outcomes. n-3 fatty acids:
Although there have been a number of animal and in vitro studies into the possible anti- inflammatory role of dietary n-3 fatty acids (see, for example, discussions by Calder 305 and Oliver et al. 306), there have been few randomised controlled trials in humans assessing whether n-3 fatty acid supplementation can influence circulating inflammatory markers in type 2 diabetes. In a small study in women with type 2 diabetes, dietary supplementation with n-3 fatty acids (given as fish oil) reduced circulating PAI-1 levels, as well as reducing in the expression of inflammation-related genes in subcutaneous adipose tissue 307. One study of omega-3 supplementation through capsules showed reductions in circulating TNF-α and IL-2 levels, with no change in CRP levels 308. Similarly, another randomised controlled trial of fish oil supplementation in type 2 diabetes reported no change in circulating CRP 309, and another no change in CRP, IL-6 or TNF-α levels with n-3 fatty acid supplementation 310. In obese, non-diabetic subjects, supplementation with fish oil showed no effect on CRP levels in one study 311 or IL-6, CRP, sTNF-R1, sTNF-R2, and PAI-1 levels in another 312. However, one study of n-3 fatty acid supplementation in men at high risk of cardiovascular events reported a reduction in IL-18 287. These findings suggest that in vivo in states of obesity, type 2 diabetes, or high cardiovascular risk - which are associated with chronic, low-grade inflammation - that n-3 fatty acids have little ability to alter CRP levels. The results for other circulating inflammatory markers are less concordant, which could relate either to between-study differences in participants, or distinct effects on individual inflammatory markers which may be of relevance to their particular biological roles. n-6 fatty acids:
A systematic review published in 2012 of randomised controlled trials in humans examining the influence of dietary n-6 fatty acids on markers of inflammation concluded that the current body of evidence did not suggest an influence of n-6 fatty acids on circulating markers of inflammation 313. Similarly, a randomised controlled trial published the same year comparing an n-6 fatty acid and a saturated fat diet found no differences in circulating IL-1ra or sTNF-R2 between groups 314.
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Soy:
A randomised cross-over trial in post-menopausal women with the metabolic syndrome comparing responses to control (DASH diet), soy nut or soy protein dietary regimens reported reductions in circulating CRP with both soy-based diets, with the effect greater after the soy nut diet, which additionally produced significant reductions in IL-18 and TNFα 315. However, another study with overweight and obese participants found no effect of a soy diet on a range of inflammatory markers 316. Trials in post-menopausal women without diabetes suggest that soy has no effect on circulating inflammatory markers 317–322, including the post-exercise increase in inflammatory markers 323. Similarly a trial in middle-aged men and women examining diets with differing isoflavone content showed little effect on circulating inflammatory markers, which the exception of an increase in IL-6 in women only 324, and another in hypercholesterolaemic subjects found no effect on circulating CRP levels but a reduction in TNFα 325. In terms of studies involving participants with type 2 diabetes, one randomised controlled trial has been conducted in individuals with type 2 diabetes and nephropathy, finding a significant decrease in CRP with a high soy protein diet 326.
Nuts:
A randomised cross-over trial in Chinese participants with type 2 diabetes compared isocaloric diets where 20 % of the total energy intake was substituted with almonds, finding almond intake produced a significant decrease in IL-6, TNFα, and CRP measures 327. This appears to be the only trial in subjects with type 2 diabetes. Other studies enrolling participants with the metabolic syndrome have compared a walnut, cashew nut, or control diet and report no effect of either nut diet on CRP levels 328, and another including a comparison of a mixed nut diet (consisting of walnuts, hazelnuts and almonds) with a control diet in participants with the metabolic syndrome observed no change in circulating CRP, PAI-1 or IL-18 levels, but a decrease in IL-6; however, this was attenuated after adjustment for weight loss 329. Studies in hypercholesterolaemic subjects report differing effects, with decreases in circulating CRP 330 and TNFα 331, as well as reduced production of IL-1β, IL-6 and TNFα by peripheral blood mononuclear cells 331 with diet characterized by a high intake of walnuts, and in contrast no change in CRP with a walnut diet 332. A study of pistachio nut supplementation in healthy young men observed a decrease in IL-6 but no change in CRP or TNFα levels 333. On the whole, these works paint a picture that nuts may reduce circulating inflammatory markers, but effects appear to vary greatly across different studies; at any rate, no studies have reported worsening levels of inflammation with diets involving high levels of nut consumption.
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Dietary advanced glycation endproducts (AGEs):
Two studies have examined the effect of dietary AGEs in randomised controlled trials in subjects with type 2 diabetes with small sample sizes: one study including a 2-week randomised cross-over design study in 11 subjects and one 6-week randomised parallel trial in 13 subjects (Vlassara et al. 334), and another a metabolic study assessing the effect of acute intake after a high or low AGE meal involving 20 subjects (Negrean et al. 335). In both studies, diets were balanced for macronutrient composition but altered dietary AGE content via different cooking methods. Across both trials reported by Vlassara et al., higher dietary AGE content produced higher measures of inflammatory markers or inflammatory gene expression in peripheral blood mononuclear cells. In that of Negrean et al., post-meal levels of circulating CRP and IL-6 did not differ between high and low AGE content meals.
Macronutrient composition:
One study with a low total fat composition (based on the National Cholesterol Education Program stepped diet) found little effect of a low fat dietary intervention on circulating CRP levels, with a significant effect of diet limited only to female participants with the metabolic syndrome 336. One study in participants with type 2 diabetes showed that a high protein diet reduced circulating measures of CRP and IL-6, but not a low calorie diet based on liquid meal replacements, despite a greater weight loss on the latter 337. In a randomised controlled trial in participants with type 2 diabetes, a 6-month low fat dietary regimen produced a significant reduction in circulating CRP levels 338, which was not observed on a low carbohydrate diet, despite similar reductions in body weight in both dietary arms.
Alcoholic beverages:
With particular regard to type 2 diabetes, a study of secondary prevention (enrolling subjects post- myocardial infarction) randomised participants to moderate red wine consumption for a year, finding significant reductions in CRP, IL-6, TNFα and IL-18 after red wine consumption 339. This appears to be the only randomised controlled trial investigating the effect of alcohol intake on inflammation in type 2 diabetes.
Studies in healthy subjects are generally supportive of an effect of moderate alcohol consumption on circulating inflammatory markers. In a randomised cross-over study of moderate wine consumption in healthy women, CRP, IL-6, ICAM-1 and CD-40 ligand were all reduced by consumption of either white or red wine 340. Similar effects have been reported by moderate alcohol consumption in men
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with a study by Vázquez-Agell et al. showing reduced CRP, IL-6, ICAM-1, and CD-40 ligand after moderate consumption of white wine for 3 months 341. A randomised cross-over trial of healthy individuals involving moderate red wine consumption found decreased CRP, but increased PAI-1 after 4 weeks of intake 342, with another reporting no change in CRP levels with 3 weeks of moderate red wine intake 343. A similar randomised cross-over study in healthy men compared moderate red wine intake with gin (matched for total alcohol consumption) finding reductions in ICAM-1, VCAM-1, and CRP with wine intake 344.
There is suggestion that some of the effects observed with wine consumption could arise from other, non-alcoholic components of the wine, as studies which have involved matched alcohol intake from gin have generally not observed the same anti-inflammatory effects as wine 341,344 . In addition, a randomised controlled trial in male smokers involving supplementation with flavanols extracted from grapes produced significant decreases in the expression of inflammation-related genes in leukocytes, however, in the absence of a change in circulating CRP 345.
Non-alcoholic beverages:
One RCT has found no effect of green tea consumption on CRP in participants with type 2 diabetes or states of ‘prediabetes’ 346. These results are supported by another RCT finding no effect of green tea on CRP or IL-6 levels in subjects with type 2 diabetes 347, or another finding no effect on CRP, IL-1β or IL-6 in subjects with the metabolic syndrome 348. In contrast, a study involving obese participants with hypertension found a green tea extract produced significantly lower CRP and TNFα than placebo 349. An RCT in healthy men found no effect of green tea extract supplementation on CRP and IL-6 350. Less research appears to have been done into the effects of black tea, however one RCT in healthy men reported a significantly lower CRP with randomisation to black tea vs placebo 351.
In a randomised controlled trial of coffee consumption, 4 weeks of coffee consumption resulted in lower IL-18 levels compared to no coffee in subjects at risk of type 2 diabetes 352, with no differences in IL-1ra, IL-6 or CRP levels. A study of acute caffeine administration (in the form of tablets) in people with coronary artery disease or healthy controls found a post-caffeine decrease in CRP levels in both groups 353, suggesting that caffeine itself may contribute to the effects of tea and coffee. However, another acute study in healthy men found no effect of caffeinated or de-caffeinated coffee on serum IL-6 or IL-18 levels 354.
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Dietary fibre:
With regard to the effects of dietary fibre on inflammation, a cross-sectional relationship between higher habitual dietary fibre intake and lower CRP levels has been shown in many 355–358 but not all 359 studies (although the latter did report an association with lower IL-6 and sTNF-R2 levels). These findings are suggestive of an anti-inflammatory effect of dietary fibre; an effect is also supported by data from one longitudinal study 358. With regard to intervention studies, a systematic review conducted in 2009 noted that trials involving an increase in dietary fibre generally resulted in a decrease in circulating CRP levels 360, however, with the caveat that interventions which modulated dietary fibre often also reported changes in carbohydrate and saturated fat intakes, or failed to report whether changes in dietary macronutrient composition had occurred. Such factors limit the ability to draw conclusions regarding cause and effect of specific dietary components, and indeed, reflect an intractable difficulty when conducting dietary intervention studies; when conducting isocaloric alterations in dietary components, a reduction in one component must be accompanied by an increase in another to maintain the same overall calorie intake. As such, while questions along the lines of “does dietary factor ‘x’ influence markers of inflammation?” are easy to ask, in reality discerning cause and effect becomes much more difficult once put into practice, especially in the case of the link between obesity and inflammation where changes in body weight may mask or confound relationships between dietary factors and inflammatory markers. In response to such problems, and out of a growing acknowledgement of real life eating habits, where people usually do not set out with the aim of ‘increasing dietary β-carotene intake’, but increase their dietary fruit and vegetable intake (as a hypothetical example), many research studies now do away with attempting to identify specific cause-and-effect relationships from a reductionist perspective, but focus more holistically on dietary patterns and their effects on surrogate markers of metabolic health. (These problems in ascribing cause and effect in dietary interventions are expanded upon further in Chapter 4, Section 4.2).
1.5.3 Dietary patterns:
‘Meditteranean’ diet:
In such works examining dietary patterns and markers of inflammation, many intervention studies have focused on the so-called ‘Mediterranean diet’, which has been shown to improve a number of circulating inflammatory markers in elderly subjects at increased risk of coronary heart disease 361,362, participants with the metabolic syndrome 286, obesity 284, hypercholesteraemia 363, and reduce NF-κB gene expression in peripheral blood mononuclear cells in elderly subjects 364,365. However, not all - 32 -
studies report improvements in inflammatory markers with a Mediterranean diet, such as two studies conducted in participants at a high risk of cardiovascular events showing no improvement in CRP 366,367, or another observing no effect in healthy subjects 368. While the disparate results of these studies may reflect differences in diet composition or study duration, they may also point to differences in effect dependent on the metabolic health of enrolled participants; indeed, it would be perfectly reasonable to expect that the potential anti-inflammatory effect of a dietary intervention would depend on the initial inflammatory status of the participants involved.
Other dietary patterns:
Similar to studies involving the Mediterranean diet, other dietary regimens involving the alteration of multiple components have been investigated. An improvement in CRP levels was reported in participants with impaired glucose tolerance following a diet characterized by fish, bilberries and wholegrains 369, and likewise reduced CRP levels in adults with hyperlipidaemia have been observed with a diet high in plant sterols, almonds, viscous fibre and soy protein 370.
Investigations into the DASH (Dietary Approaches to Stop Hypertension) diet have demonstrated the potential for this dietary pattern to influence circulating cytokine measures, with a study in participants with type 2 diabetes showing a reduction in CRP during a randomised cross-over study 371. Although cross-over arms were only of an 8-week duration, significant differences in body weight were produced from the DASH diet relative to control, and thus it is difficult to decipher whether responses were due to changes in diet or body composition.
A randomised controlled trial in persons with type 2 diabetes (n = 162) who managed their blood glucose with diet only examined the effect of a low vs. high glycaemic index diet on inflammation. Importantly, the dietary group exhibited no significant differences in body weight or HbA1c at the completion of the study, but the low glycaemic-index diet group showed reduced CRP levels 372, which suggests that in type 2 diabetes dietary glycaemic index in itself may modulate inflammation in the absence of changes in body weight.
Although not a ‘dietary pattern’ study per se, Bakker et al. conducted a randomised controlled trial involving supplementation with a number of substances simultaneously, on the hypothesis that each had shown promise as anti-inflammatory agents found in different foods. Overweight men with mildly elevated CRP were randomised to receive supplementation containing resveratrol, tomato extract, green tea extract, α-tocopherool, fish oil and n-3 fatty acids, cellulose, and soy lethicin. No
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change was observed in circulating CRP levels, although reductions in circulating IL-18 and sTNF- R2 were observed 373.
1.5.4 Dietary patterns and components - summary
One aspect of the above studies which becomes evident is the diversity of effects reported. As mentioned throughout, the different findings of these works could easily be due to factors such as study design, duration, or differences in the dietary modifications employed. In addition, for purely statistical reasons, the effect of dietary factors on inflammatory markers would be easier to detect in individuals with higher baseline levels of inflammation. Furthermore, there may be biological factors which influence the effect of specific dietary alterations on levels of inflammatory markers, such that alterations, for example, as increasing fibre intake may exert an anti-inflammatory effect in obesity-related inflammation, but not in healthy individuals. One finding which is evident is that there is little evidence that implementing dietary changes such as those reviewed above would result in increases in markers of inflammation.
1.5.5 Exercise:
Regarding the specific effects of exercise, a number of studies report cross-sectional relationships between levels of inflammatory markers and measures of fitness or physical activity 374–378, and that physical activity lowers levels of inflammatory markers in obesity, type 2 diabetes and heart disease 293,379–387. Not all interventions have shown reductions, however 388,389, and the effects of exercise on inflammation appear complex; in the short term exercise induces increases in a number of inflammatory markers 390,391, and different types of exercise or physical activity may exert differing influences on chronic inflammation 374,392,393, with genotype interactions also operating 379,394.
1.5.6 Clinical outcomes:
Studies examining the influence of lifestyle intervention on chronic low-grade inflammation typically have only gone so far as to investigate whether lifestyle interventions can influence markers of inflammation, and not tied the reduction in inflammation to solid clinical outcomes such as event rates of cardiovascular disease or diabetes complications. Clearly the effects of diet and exercise are diverse, and if future studies do arise which follow up participants for clinical events, the extent to which the anti-inflammatory effects of lifestyle modifications underlie a potential clinical benefit versus other metabolic effects of the interventions will be extremely difficult to
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quantify r. One study which is an exception in this regard is an analysis of exercise and cardiovascular events in the Women’s Health Initiative 395, which followed 27,055 women over a 10- year period and attempted to quantify the effect of baseline measures of cardiovascular risk on cardiovascular event rates by regression modelling adjusting for different sets of a priori risk factors. They estimated that between approximately 20 - 30 % of cardiovascular events could be explained by haemostatic or inflammatory pathways, evaluated in their modelling by the grouping of baseline C-reactive protein, fibrinogin and sICAM-1 (soluble intercellular adhesion molecule-1) measures.
1.5.7 Lifestyle effects on inflammation in type 2 diabetes:
With particular relevance to the emphasis of this thesis on inflammation in type 2 diabetes, there is a paucity of studies examining changes in inflammatory markers with lifestyle intervention in people with type 2 diabetes. A study comparing lifestyle intervention and insulin treatment in type 2 diabetes by Aas et al. (n = 28) showed that while both were able to give similar reductions in glycaemia, only lifestyle intervention reduced circulating inflammatory markers 396, although to what extent this was caused by differences in body weight with insulin vs. lifestyle therapy is unknown. As mentioned above, a low-glycaemic index diet was investigated for changes in CRP in participants with type 2 diabetes (n = 162) by Wolever et al., however, this only included patients whose diabetes was managed by diet and not medications. One study of 33 women with type 2 diabetes compared the effects of diet, exercise, or diet plus exercise interventions on selected inflammatory markers, and included subjects whose diabetes was treated with either diet only, metformin, or sulphonylureas (Giannopoulou et al. 397); they found a significant reduction in CRP levels in all three treatment groups. A small randomised cross-over trial (n = 12) by Bozzetto et al. compared the acute effects of a meal high in monounsaturated fatty acids (MUFA) or one high in fibre and low in glycaemic index on circulating inflammatory markers, finding a significant post- meal decrease in CRP after a MUFA diet but not a high fibre/low glycaemic index diet 398. Two randomised controlled trials have been published which exclusively enrolled subjects with type 2 diabetes and modified one specific dietary variable: that of Azadbakht et al. in 41 people with type 2 diabetes and nephropathy, which examined the effect of soy protein, finding a significant decrease in CRP with a high soy protein diet 326; and that of Kabir et al., which enrolled 27 women with type 2 diabetes who were randomised to 2 months of n-3 fatty acid supplement, finding a reduction in
r The LOADD study, which forms part of this thesis, also examines only surrogate markers of diabetes and cardiovascular risk. In the event of a follow up study involving clinical endpoints, this same difficulty of ascribing causation will apply. - 35 -
circulating PAI-1 307. There have been some studies of the effect of dietary alteration on levels of inflammatory markers in obese individuals which have included some individuals with type 2 diabetes, but these have generally only included small subsets of people with type 2 diabetes, such as Bastard et al. (n = 7) 399, Monzillo et al. (n = 8) 400, and Bjermo et al. (n = 9) 314
Thus, only the studies of Wolever et al. (examining the effect of glycaemic index), Giannopoulou et al. (diet and/or exercise) and Aas et al. (lifestyle and/or insulin), Azadbakht et al. (soy protein in individuals with nephropathy), and Kabir et al. (n-3 fatty acid supplementation) have examined whether long-term lifestyle changes cause alterations in circulating inflammatory markers in individuals with type 2 diabetes. The studies of Aas et al. and Giannopolou et al. consisted solely of individuals on oral hypoglycaemic agents, while that of Wolever et al. enrolled participants treated with diet only; thus, the subjects enrolled were not reflective of the broader population of individuals with type 2 diabetes who often receive a more intensive regimen of concurrent diabetes pharmacotherapy. That of Kabir et al. consisted of an intervention involving solely of supplementing one specific dietary component. The study of Azadbakht et al. is interesting: on the one hand it enrolled solely participants with nephropathy, which may limit the ability to generalize results to other patients with type 2 diabetes, and involved a highly prescriptive dietary regimen of increased soy protein intake; on the other, enrolled participants were treated with any form of diabetes medication, and additionally followed subjects for 4 years, far longer than the other works mentioned here. They found 4-year levels of circulating CRP were lower in those randomised to soy protein intake.
Given the hypothesized role of inflammation in the development of cardiovascular disease, and even in the pathogenesis of diabetes itself, an experiment to add to these works was therefore undertaken as part of the current thesis; an investigation into whether dietary modification alters selected adipokines and inflammatory markers in individuals with type 2 diabetes who are treated according to current therapeutic guidelines. This study and analysis form Section 2 of the current thesis, as part of the Lifestyle Over and Above Drugs in Diabetes (LOADD) study. This study was a randomised controlled trial of a 6-month dietary intervention in participants with type 2 diabetes who were concomitantly treated with a range of cardioprotective, renoprotective, and hypoglycaemic medications according to the current New Zealand Guidelines Group recommendations 150.
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1.6 Inflammation and diabetes: a key role of NF-κB
As evidenced from the discussion above, many different inflammatory markers have been implicated or associated with changes occurring in obesity and type 2 diabetes. In general these highlight a shift towards a more pro-inflammatory phenotype in these disease states. One key downstream effect of a number of these inflammatory pathways is the activation of the transcription factor ‘nuclear factor kappa beta’ (NF-κB). NF-κB is present in the cell cytoplasm and is typically bound to members of an inhibitory protein family IκB (inhibitor of kappa beta). NF-κB translocates to the nucleus and influences gene transcription upon activation from a range of pro-inflammatory stimuli, including IL-1, IL-18, and toll-like receptor (TLR) signalling 401,402. Research predominantly conducted over the course of the last decade has implicated the activation of NF-κB as a key event in the development of obesity-related insulin resistance and shown inhibition of NF-κB to be a potential target for the treatment of insulin resistance. Various animal studies have demonstrated that manipulation of NF-κB signalling by a variety of means improves or exacerbates insulin resistance403– 410, and that NF-κB is upregulated in the liver 408 and hypothalamus 300 during a high-fat diet. Likewise studies in humans have shown that hepatic NF-κB binding activity correlates with insulin resistance 411 and weight loss in obese individuals has been shown to downregulate NF-κB activity in mononuclear cells 294. Genetic polymorphisms of the gene encoding NF-κB have been shown to be associated with risk of type 2 diabetes in Czech and German subjects 203. In in vitro studies, activation of NF-κB has been shown to induce insulin resistance in muscle cells 412,413.
In 1959, Reid and Lightbody concluded the discussion of one of their studies on the glucose lowering effects of aspirin by stating: “These results place salicylate in the class of promising oral antidiabetic substances, and should stimulate investigations to remove its undesirable effects and to find out how it acts.” 223. Thirty-five years later, a study in Science revealed that aspirin and salicylates inhibit NF- κB 148, which combined with the data outlined above for a role of NF-κB in insulin signalling, suggest that this could indeed be the mechanism of their anti-diabetic actions. Sadly, however, ‘removing the undesirable effects’ of salicylates has yet to be achieved; indeed whether the typical symptoms of ‘salicylism’ observed with high doses of salicylate-based medications 268 can be avoided, and mechanistically separated from their glucose-lowering properties, is currently unknown. Later studies also confirmed that salicylates inhibit the NF-κB pathway 408,414–417, and in addition, that other diabetes medications such as metformin and pioglitazone also exhibit NF-κB inhibiting properties 416,418.
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1.6.1 Interleukin-18 as an upstream activator of NF-κB: links with obesity, diabetes and cardiovascular disease
Exactly how NF-κB activity is upregulated in obesity and diabetes is not known, or at least, given that many possible pathways exist including the direct activation of toll-like receptors by saturated fatty acids 419–421, and the influence of elevated circulating inflammatory factors in obesity and diabetes, the relative contributions of each to the increased NF-κB activity seen in these conditions is difficult to disentangle. While a number of pro-inflammatory cytokines induce NF-κB activity 422, at the outset of the investigations involved in this thesis, interleukin-18 (IL-18) was regarded as an inflammatory marker of interest as it activates the NF-κB signalling pathway 423, is elevated in obesity and type 2 diabetes 424–431, released from fat tissue 432, and evidence existed suggestive of an active role in the pathogenesis of heart disease 433–437; prerequisite qualities necessary to contribute to a possible link between inflammation and the triad of metabolic diseases of obesity, diabetes and heart disease.
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1.7 A role for IL-18 in metabolic disease
IL-18 was originally described as interferon-γ (IFN-γ) inducing factor (IGIF), and given a recognised role for IFN-γ in the pathogenesis of atherosclerosis 438,439, a plausible biological role for IL-18 exists in the pathogenesis of cardiovascular disease via induction of downstream IFN-γ, if not additionally by other mechanisms. Moreover, IL-18 is involved in the hypothalamo-pituitary-adrenal stress axis 440, and there is abundant evidence of stress exerting detrimental effects on health outcomes associated with obesity, diabetes and cardiovascular disease. A number of animal in vivo, and human in vitro, or ex vivo studies suggest that IL-18 plays a causative role in the development of atherosclerosis.
Human endothelial cells, smooth muscle cells, and mononuclear phagocytes have been shown to express the IL-18 receptor, with increased receptor and ligand levels in atherosclerotic plaques 441, and with significantly higher levels in symptomatic or ulcerated plaques than in asymptomatic or unulcerated plaques 442. Various animal experiments in ApoE -/- mice, a highly utilised rodent model of atherosclerosis, have shown that IL-18 contributes to the pathogenesis of atherosclerosis and that its reduction results in improved outcomes 443–447. Evidence for a direct effect of IL-18 on the myocardium also comes from studies in rats where improvements in left ventricular ejection fraction were seen after myocardial infarction followed by treatment with the natural endogenous inhibitor of IL-18, IL-18 binding protein (IL-18 BP) 448. A study in a rodent model of the metabolic syndrome also showed that adenoviral-induced IL-18 overexpression resulted in increased expression of cellular adhesion molecules in the aorta, greater aortic thickness and insulin resistance 449. Studies of tissue from human subjects document higher IL-18 and IL-18 receptor gene expression and protein levels, and lower levels of IL-18 BP gene expression in ventricular tissue from heart failure patients versus control tissue from organ donors 450, with evidence of higher IL-18 and IL-18 receptor levels in nonrheumatic aortic valve stenosis 451 (with the caveat of comparison to tissue extracted from cadavers, which may be a poor control).
However, other work in vivo in humans has been inconsistent regarding a causative role for IL-18 in atherosclerosis. High IL-18 levels have been observed in individuals with cardiovascular disease 425, and elevated IL-18 levels have been reported after recent myocardial infarction 452–455. A number of studies have documented IL-18 as being predictive of future cardiovascular events, but with associations being no longer significant after adjustment for traditional cardiovascular risk factors 456– 458, or of borderline significance 459. Others report that associations of IL-18 with future cardiovascular events remain significant after adjustment for multiple confounders 434,460–462, while - 39 -
others find no association at all 463. However, while a number of individual prospective studies have not shown a statistically significant relationship of baseline IL-18 levels with the development of cardiovascular disease, a meta-analysis of prospective cohort studies found an increased risk of developing cardiovascular disease or coronary heart disease with elevated baseline IL-18 levels, with a relative risk (95% CI) of 1.34 (1.17, 1.53) after adjustment for age, sex, classical cardiovascular risk factors and other inflammatory markers 464. In studies which have focused on patients recruited after myocardial infarction or percutaneous coronary intervention, IL-18 has been shown to be predictive of future mortality although not cardiovascular mortality 465, future adverse coronary events 452,453, and IL-18/IL-10 ratio predictive of future adverse coronary events 433,466.
Studies of subclinical atherosclerosis have produced heterogeneous results for an association of IL-18 independent of traditional cardiovascular risk factors. Two studies examining the association of IL-18 with carotid intimal medial thickness (CIMT) found that IL-18 was associated with CIMT, with one study reporting that this remained significant after adjustment for traditional risk factors 467, and one that the association was no longer significant 468. Another study similarly reports that associations with coronary artery calcium were no longer significant after adjustment for traditional risk factors469. Other studies have reported positive associations for IL-18 and coronary artery disease severity after adjustment for confounders 437 and in other studies with incomplete adjustment for other cardiovascular risk factors positive associations are reported for IL-18 and pulse wave propagation time 470, thin plaque fibrous cap thickness 471 or coronary plaque area 472.
Various works have examined genetic polymorphisms of genes in the IL-18 system and risk of cardiovascular disease. Multiple studies have been published examining polymorphisms of the IL-18 gene. These studies show associations of polymorphisms of the gene encoding IL-18 with sudden cardiac death 473,474, coronary artery disease 475, myocardial infarction 476, cardiovascular disease and mortality 477–479, the metabolic syndrome 202, insulin sensitivity 202,480,481, higher levels of inflammation and longer hospital after percutaneous coronary intervention 482, post-prandial increase in triglycerides 480, and BMI 483. In terms of studies examining other components of the IL- 18 signalling system, two studies have examined polymorphisms of genes encoding the IL-18 receptor and neither found any association with cardiovascular events or death 477,484. One of these also examined polymorphisms of the IL-18 BP gene, although only found one polymorphism of low frequency (less than 2% of participants), which was therefore not examined further for associations with cardiovascular events477.
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In terms of evidence linking IL-18 with diabetes pathogenesis, IL-18 has been shown in various cross-sectional analyses to show a positive correlation with blood glucose levels 485, insulin or insulin resistance 426,486–491 and the metabolic syndrome 492,493. Two prospective studies report baseline IL-18 levels to be predictive of new-onset type 2 diabetes even when controlling for multiple risk factors and other inflammatory markers and adipokines 188,189. A decrease in IL-18 was also observed to be an independent predictor of improvement in insulin sensitivity in patients treated with rosiglitazone or metformin 494. As far as links with type 1 diabetes and β-cell failure are concerned, evidence also exists that IL-18 plays a role in β-cell apoptosis induced by streptozotocin 495, a common animal model of type 1 diabetes, and polymorphisms of the β subunit of the IL-18 receptor (IL-18rap) have emerged in genome-wide association studies as being associated with type 1 diabetes 496.
Studies show that lifestyle modification has the ability to modify IL-18 levels, with exercise interventions reducing plasma IL-18 levels in either low-level 431 or extreme exercise conditions 380, including in patients with type 2 diabetes 381. Lifestyle intervention studies resulting in weight loss in obese individuals report reductions in IL-18 levels 284,285,428,497, as do studies of gastric weight- reducing surgery 498,499.
Metabolic studies show that acute hyperglycaemia results in marked elevations of circulating IL- 18500, and that IL-18 levels are acutely modulated by diet composition with a high-fat diet increasing, and high-fibre diet decreasing, IL-18 levels 501. Thus, IL-18 levels appear to be moderated by diet in the same direction as risk of cardiovascular disease and diabetes, i.e. a high fat diet has been linked with the development of cardiovascular disease and diabetes, and increases IL-18, while a high fibre diet has been linked with protection from cardiovascular disease and diabetes, and reduces IL-18.
One hypothesis is that IL-18 is in fact not a causative agent as regards the associations with glucose metabolism and insulin sensitivity described above, but acts as a compensatory mechanism and many studies could be viewed as lending support to this hypothesis. IL-18 enhances glucose uptake in adipocytes 502, decreases hepatic gluconeogenesis and prolongs the hypoglycaemia induced during an insulin tolerance test 503 and thus, for example, elevations of IL-18 during hyperglycaemia 500 or elevated IL-18 associated with higher blood glucose and insulin levels or poor insulin sensitivity485,487,488 could be viewed as a mechanism by which the body attempts to reduce blood glucose. Similarly, reports of elevated IL-18 in obese individuals coupled with animal data suggesting IL-18 can act centrally to decrease food intake (discussed further below) indicate that an elevation of IL-18 in obesity could play a compensatory role, much akin to the manner in which leptin, also an
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anorectic cytokine, is elevated in obesity and appears to be associated with ‘leptin resistance’ s. In a similar fashion a degree of ‘IL-18 resistance’ was reported in a study comparing obese vs. normal weight individuals, where peripheral blood mononuclear cells from obese individuals were shown to exhibit a reduced production of interferon-γ (IFN-γ) in response to IL-18 stimulation 505. On the other hand, the involvement of IL-18 in activating the NF-κB pathway, which has been shown to be involved in the pathogenesis of insulin resistance and diabetes (as discussed in the Introduction, Chapter 1), suggests that higher levels of IL-18 would in fact be a causative agent in the development of insulin resistance.
A key study which led to the development of experiments into the role of IL-18 included in this thesis is that by Netea et al., which was the first to report an obese phenotype of IL-18 knockout mice 503, supported a year later by similar findings from another laboratory 506. Together these studies show that IL-18 knockout animals display a phenotype of overeating leading to obesity and its associated comorbidities including glucose intolerance. In these animals, delivery of IL-18 into the brain reduced appetite to normal levels, an effect also observed in ‘wild type’ mice with normal IL- 18 levels. These studies thus unveiled a previously unknown neuroendocrine effect of IL-18 on energy homeostasis, mediated by unknown mechanisms in the brain.
Thus, on the basis of the evidence linking IL-18 with the risk and pathogenesis of cardiovascular disease and diabetes, observations surrounding its elevation in obesity and modification by lifestyle intervention (discussed further in Chapter 4), its role as an activator of the NF-κB signalling pathway implicated in dysglycaemia and insulin resistance, and evidence from animal studies for a role in appetite and glucose homeostasis, another avenue of investigation in this thesis was to investigate further the neuroendocrine effects of IL-18, with a view to aid in understanding the mechanisms by which it reduces appetite and affects glucose metabolism, and thus contribute further to the emerging evidence of a role of inflammation in the pathogenesis of diabetes and insulin resistance. These works form Section 3 of this thesis.
s Although obesity has often been referred to as a state of leptin resistance, there is little consensus as regards to how this term is used or such resistance is measured. The widespread, but ill-defined, use of the phrase 'leptin resistance' was such that it was the topic of a recent workshop convened by the US National Institutes of Health aimed at deriving a clinically useful consensus in order to encourage clearer application of the term in the future 504. - 42 -
1.8 Patterns and dynamics of cytokine release
One aspect of cytokine biology which confounds efforts at understanding the link between circulating cytokines and health outcomes is the nature of cytokine release. Cytokines are usually activated and released upon some form of inflammatory stimulus, such as physical tissue injury, viral or bacterial infection, or other forms of tissue injury such as exposure to ultraviolet light 507–510. In some cases such inflammatory reactions can remain localised in a particular tissue, while in others alterations in circulating cytokine levels can be observed. For example, after an acute illness 511–513, following myocardial infarction 137,514,515, exposure to air pollution 516, or an artificial inflammatory stimulus in a research setting such as injection of bacterial lipopolysaccharide 198,517,518, levels of circulating cytokines can be seen to follow increase and subsequently decline, with time courses dependent on the type and extent of inflammatory event and inter-individual variation. Interleukin- 6, for example, is known as a ‘myokine’ – a cytokine released from muscle tissue during physical exertion and thus circulating levels increase dramatically post-exercise 519–521.
As such, measuring circulating cytokines at one time point, as is commonly done in studies involving humans (for example epidemiological studies, or dietary intervention studies), may not provide an accurate reflection of the more complex biology of cytokine action. Nevertheless, in terms of research into the role of inflammation in obesity and type 2 diabetes, even in the absence of an acute inflammatory stimulus elevations of cytokines can be observed, which in itself, is the basis for these elevations being named ‘chronic sub-clinical’ inflammation. Given that the focus of this thesis is chronic inflammation in the absence of an acute inflammatory stimulus, and the participant burden which would be required for a more intensive analysis of cytokine dynamics, in the LOADD study described in Section 2 only single measures of circulating cytokines have been made. Nevertheless, such measures should be interpreted keeping in mind the potential limitations of examining one time point.
In addition, in Section 3, which focuses on the neuroendocrine role of IL-18, this cytokine is constitutively present in the circulation, as opposed to the more sporadic release of many other inflammatory markers. Furthermore, and as detailed in Chapter 7, IL-18 production in the medial habenula is constitutive and can be observed in the absence of any inflammatory stimulus. Data from knockout animals with an absence of IL-18 suggest that this constitutive presence plays a role in body weight regulation 503,506, and it is this constitutive aspect of IL-18 biology which is the focus of the works in Section 3, as opposed to the typical dynamic of cytokine induction followed by remission observed with acute inflammatory stimuli. - 43 -
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Section 2:
The effect of dietary intervention on inflammatory markers and adipokines in type 2 diabetes: an analysis in the Lifestyle Over and Above Drugs in Diabetes (LOADD) study
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Chapter 2. Overview and methods of the LOADD study
2.1 The LOADD study: rationale
In contrast to the studies discussed in the Introduction examining the effect of glucose-lowering agents on cardiovascular endpoints, there have been no studies conducted in which the effect of dietary or lifestyle modification in patients with type 2 diabetes has been examined for potential benefits on clinical endpoints of macrovascular disease. While the evidence is not conclusive of lowering of blood glucose per se via medication-driven strategies for the prevention of macrovascular disease in type 2 diabetes, there is a sound body of evidence that lifestyle modification and dietary intervention can improve surrogate markers of diabetes and cardiovascular risk in those with diabetes, to the extent that evidence-based nutritional guidelines for the treatment of diabetes have been released by a number of authorities 522,523 t. The Diabetes and Nutrition Study Group of the European Association for the Study of Diabetes (EASD) released guidelines in 2004 which were derived principally from studies which included outcomes of pre-determined surrogate markers of diabetes and cardiovascular risk (blood pressure, lipoprotein profile, glycaemia, insulin sensitivity, body composition and renal function) 522. Many of the nutritional studies involving people with type 2 diabetes upon which these guidelines were based were conducted prior to the widespread use of agents such as statins and ACE inhibitors which are recommended as part of a modern diabetes treatment regimen. The New Zealand Guidelines Group (NZGG) 150 for example recommends patients with type 2 diabetes be treated with:
t Whether the improvement in clinical outcomes derives from an improvement in glycaemic control itself, or whether lifestyle modification induces improvement in other metabolic variables (which are associated with changes in glycaemic control) which themselves influence the pathogenesis of micro- and macrovascular diabetes complications is not known; the relative contributions of the glucose lowering vs. other effects of dietary interventions (e.g. changes in macronutrient composition or nutrient quality) cannot be easily disentangled. The scientific difficulty of ascribing cause and effect of a dietary intervention to an endpoint via intermediate markers is discussed further in Chapter 4 while addressing the potential benefit of the LOADD dietary intervention on clinical endpoints. - 47 -
aspirin, a beta-blocker, statin and ACE (angiotensin converting enzyme) inhibitor after a previous cardiovascular event (irrespective of blood pressure or lipid levels) and/or ACE inhibitor, A2 (angiotensin 2) receptor blocker, aspirin and lipid-modifying agent in the event of evidence of renal disease (including microalbuminuria and irrespective of baseline lipid levels) and/or fibrates in the event of high triglycerides and low HDL u
Thus, the current clinical management of those with type 2 diabetes typically involves not only agents for the control of glycaemia, but also renoprotective, cardioprotective and lipid-modifying agents. This presents a situation in which recommendations for the nutritional management of diabetes were based upon studies in which participants were often taking a much simpler range of medications than would now be regarded as acceptable diabetes management. Researchers at the Edgar National Centre for Diabetes Research v therefore posed the question as to whether implementation of these evidence-based guidelines would be able to further improve cardiovascular risk and glycaemic control in diabetes patients treated according to guidelines such as those above. Although some studies had conducted comparisons of the relative, or additive, effects of statins and dietary intervention to lower blood cholesterol levels following the introduction of statins 526–528, similar studies did not exist for the comparison of the relative or additive effects of dietary intervention and the currently recommended range of diabetes medications on diabetes complications.
In addition, at a local level, data collected by the Otago Diabetes Project w also showed that in spite of an increasing use of recommended medications, glycaemic control and body weight of enrolled patients (estimated to be approximately 70 % of those with diabetes in the Otago region) were worsening, suggesting that lifestyle measures were either not being encouraged or that compliance was poor 529.
u These diabetes management guidelines have been updated at the time of writing 524, however, the guidelines cited above were those current during the planning and conduct of the LOADD study. v Edgar National Centre for Diabetes Research – based at the University of Otago, one of the host institutions for this PhD thesis; now known as the Edgar National Centre for Diabetes and Obesity Research 525. w Otago is a region in the South Island of New Zealand, where the Edgar National Centre for Diabetes Research is based. Staff at the centre were involved in the running of the Otago Diabetes Project which has since ceased. - 48 -
As noted above, there have also been no studies which have examined the potential for dietary modification to reduce macro- or microvascular events in persons with type 2 diabetes. The Steno-2 study comes close to examining this question; however, while the Steno-2 study involved lifestyle and dietary modification in the intervention arm, rates of medication use also differed between treatment groups such that a more intensive medication regimen was used in the intervention group. The currently recommended range of medications for diabetes listed above by the NZGG is similar in many respects to those used in the intervention group in the Steno-2 study 78, with the control arm in that study receiving what would be considered a sub-standard level of care according to NZGG guidelines. Thus, the extent to which the observed benefit in macrovascular endpoints in the Steno-2 study is due to medication or lifestyle effects cannot be easily determined.
From these multiple concerns the Lifestyle Over and Above Drugs in Diabetes (LOADD) study was initiated. The aim of this study was to examine the extent to which implementation of the nutritional guidelines recommended by the EASD could improve cardiovascular risk indicators in people with type 2 diabetes. All participants would be treated with the full gamut of medications as recommended by national treatment guidelines, and would be derived from the same population that was assessed by the Otago Diabetes Project, the results of which suggested poor adherence to lifestyle and nutrition advice. Those randomised to the intervention group would receive intensive dietary advice as well as initiating an optimized drug regimen, whereas those in the control group would not be given special nutrition-related advice.
While ultimately clinical endpoints of microvascular and macrovascular complications are preferred outcomes for measuring the effectiveness of an intervention aimed at improving diabetes control (as has been particularly pertinent in recent history regarding evaluating the effectiveness of diabetes medications 269), this would obviously require a substantial time and cost investment. The LOADD study was designed to examine outcomes based on surrogate measures in the first instance. This could possibly be followed by a larger study based on clinical endpoints in the event of positive findings in the initial study on surrogate measures. The primary endpoint in the LOADD study was glycaemic control as measured by HbA1c (glycaemic control having often been regarded as the principal factor of interest in the management of type 2 diabetes). However, this study provided an opportunity to examine the effect of the LOADD study dietary intervention on a range of additional outcome measures including adipokines and inflammatory markers, the study of which forms part of the work contained in this thesis.
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2.2 Inflammatory markers and adipokines of interest:
There is a vast array of inflammatory markers and adipokines which could potentially be of relevance to the pathogenesis of type 2 diabetes and its complications. However, for practical and budgetary reasons a selection of analytes of interest was made. These were: interleukin-1 beta (IL- 1β), interleukin-1 receptor antagonist (IL-1ra), interleukins 6, 10, and 18 (IL-6, IL-10, IL-18), tumour necrosis factor (TNF), the soluble TNF receptors sTNF-R1 and sTNF-R2, neopterin, and leptin. A brief summary of the reasons why these were chosen is given below. i. Interleukin-18 (IL-18)
The study of IL-18 was the basis of the animal work undertaken as a major part of this thesis, and the scientific basis for interest in the cytokine in relation to type 2 diabetes and metabolic disease is described in the introduction of this thesis (Section 1.6.1) and Chapter 5. In brief, a number of studies suggest IL-18 plays an active role in the development of cardiovascular disease (discussed in Chapter 5) and a meta-analysis of prospective studies found elevated baseline IL-18 was associated with an increased risk of developing coronary heart disease, even after adjustment for traditional cardiovascular risk factors 464. In addition, two prospective cohort studies have shown elevated IL-18 levels to be predictive of the development of type 2 diabetes 188,189, suggesting a role for this cytokine in diabetes pathogenesis. ii. Neopterin
Neopterin is released from macrophages in response to interferon-gamma (IFN-γ) 530, which is in turn released in response to IL-18 (initially named IFN-γ inducing factor). A number of studies have shown neopterin levels to be predictive of cardiovascular events, cardiovascular mortality and all- cause mortality 531–535, including in individuals with type 2 diabetes 536. Serum levels correlate with BMI 537, and are elevated in stress 538 and depression 539. Neopterin levels have also been shown to relate to plaque morphology 540, and to correlate with the extent and severity of coronary artery disease 541, and carotid intimal medial thickness 542. In addition, levels are predictive of left ventricular remodelling in myocardial infarction survivors 543,544 and of low left ventricular ejection fraction 545. Human endothelial cells have been shown to release tissue factor, ICAM-1 and VCAM-1 upon exposure to neopterin, by a mechanism which involves NF-κB activity 546. Given that IL-18 induces IFN-γ, and that in turn neopterin is produced in response to IFN-γ, it is not surprising that administration of recombinant IL-18 was observed in primates to increase circulating neopterin
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levels 547,548, such that IL-18 and neopterin can be regarded as different points in the same inflammatory pathway. iii. IL-1β and IL-1ra
IL-1β is a pro-inflammatory cytokine which stimulates NF-κB activity, and there are many similarities in the processing and signalling pathways activated by IL-18 and IL-1β, and in phenotypes resulting from their manipulation. Variants in the IL-1 gene have been shown to be associated with fat mass in men 549, obesity and the metabolic syndrome in persons with coronary heart disease 550 and diabetes and blood glucose homeostasis in men and women of European descent204. In animal studies, knockout of the interleukin-1 receptor has been shown to protect against high-fat diet induced insulin resistance 551 and increased blood pressure 552, and administration of an IL-1β antibody improves insulin resistance in mice on a high fat diet 553,554. IL- 1β is produced from the immune system inflammasome and inflammasome activation and IL-1β release has been identified as a key component of the development of diet-induced insulin resistance in animal studies 555.
IL-1ra acts as a natural circulating antagonist able to bind the interleukin-1 receptor but not induce signal transduction. The use of IL-1ra as a potential diabetes therapy has also been examined in randomised controlled trials in humans with type 2 diabetes, and has been shown to be a potential therapy with improvements in glycaemia 281,282, a finding also observed in animal models 556. Elevation of IL-1ra has been shown to be a predictor of future development of diabetes in prospective cohort studies 190,191,557, thought to reflect an underlying pathogenic process relating to changes in inflammatory signalling early in the pre-clinical stage of disease. iv. Interleukin-6 (IL-6)
Interleukin-6 is a pro-inflammatory cytokine which is released from a range of tissues and cell types including adipocytes, skeletal muscle and macrophages 558. It is the most immediate upstream regulator of circulating CRP levels, as IL-6 action on hepatocytes induces the release of CRP 559. Studies which have compared the predictive power of IL-6 and CRP on clinical outcomes such as cardiovascular disease have often found IL-6 to be a more powerful predictor than CRP 184. IL-6 has been also named a ‘myokine’ due to large amounts being released from skeletal muscle following exercise 560,561. In keeping with other pro-inflammatory cytokines, IL-6 levels are chronically elevated in those with obesity and diabetes 399,562. Up to 30 % of circulating IL-6 in humans may be produced by adipose tissue 563 and circulating IL-6 levels can be reduced by weight loss 284,399,400. IL-6
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has also been shown to induce hepatic insulin resistance 564, a key pathogenic feature of type 2 diabetes. v. Interleukin-10 (IL-10)
Interleukin-10 is an anti-inflammatory cytokine which suppresses the release of pro-inflammatory cytokines from macrophages 565. Thus, not surprisingly, given the relationship between inflammation and pathways of insulin resistance, this anti-inflammatory cytokine typically shows associations with insulin sensitivity which are inverse to those of pro-inflammatory cytokines. IL-10 levels have been shown to be associated with insulin sensitivity determined by clamp 566, and just as a number of studies have shown a reduction in pro-inflammatory cytokines with weight loss, a combined medication and diet regimen in obese individuals was shown to increase IL-10 levels 567. The ratio of IL-18 to IL-10 has been documented as being predictive of coronary events 433,466, and IL-10 is associated with the metabolic syndrome and type 2 diabetes 568. Variations in the IL-10 gene have been shown to associate with obesity and insulin resistance, although not diabetes, in people of European descent 569 and with type 2 diabetes in people of Asian descent 570. In animal studies transgenic overexpression of IL-10 in skeletal muscle was shown to prevent high-fat diet induced insulin resistance 571, inhibition of IL-10 worsened hepatic insulin resistance induced by a high-fat diet 572, and treatment with IL-10 was found to prevent the insulin resistance induced by lipids or IL- 6 564. vi. TNF and its soluble receptors
TNF is a pro-inflammatory cytokine which activates the NF-κB signalling pathway 573 and also interferes with insulin signalling pathways by inducing serine phosphorylation of the insulin receptor substrate 1 (IRS-1) 574. TNF exerts its effects by the membrane bound receptors TNF-R1 and TNF-R2. Binding of TNF induces shedding of the extracellular portion of the receptor, forming the soluble receptors sTNF-R1 and sTNF-R2. These soluble receptors are thought to modulate TNF activity by acting as a buffer system to bind TNF and prevent activation of membrane-bound receptors, and also as a means of prolonging TNF activity by binding TNF (preventing degradation) and later release. As such, it has been suggested that levels of the soluble TNF receptors may be a more accurate marker of TNF activity than the ligand itself 575,576. In the in vivo situation in humans, the use of monoclonal antibodies against TNF in the treatment of rheumatoid arthritis has been shown to be associated with an improvement in insulin sensitivity 577–582, and a case report details a sudden deterioration of glycaemia upon termination of TNFα antibody treatment 583. There is also evidence that pulsatility of TNF determines the responsiveness of
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TNF-induced NF-κB activation, and thus chronically elevated TNF in obesity may exert differing effects on NF-κB activity than TNF activity in healthy persons 584. TNF and its receptors play a role in heart disease585, including myocardial infarction 586 and heart failure 587,588 with evidence for opposing effects of the TNF receptor types on heart failure pathogenesis 587. TNF-induced serine phosphorylation of IRS-1 (which reduces downstream insulin signalling) has also been shown to be inhibited by salicylic acid 589, thus implicating this as a possible mechanism involved in salicylate- induced improvements in glycaemic control. vii. Leptin
Leptin is often referred to as the archetypal adipokine, as it was the first cytokine released from adipose tissue to be discovered 590. Leptin levels are elevated in obesity, which appears to be a state of pathogenic leptin resistance 591 x, although evidence exists that while central (brain) responsiveness is impaired in obesity, leptin signalling in the periphery may be spared 592. Leptin shares many downstream signalling mechanisms with insulin and various studies have shown that central leptin signalling modulates peripheral insulin sensitivity 593–595. In addition, rodents made insulin-deficient with streptozotocin or through autoimmune processes have been shown to be viably maintained with normal glucose homeostasis with the administration of leptin (even in the absence of insulin)596,597, highlighting the powerful effects of this adipokine on glycaemic control. High leptin levels have been proposed to exert adverse cardiovascular effects 598,599 and thus may contribute to the excess cardiovascular morbidity observed in obesity, although this relationship may be confounded by other processes related to adiposity 600.
x although obesity has often been referred to as a state of leptin resistance, there is little consensus regarding how this term is used or how such resistance is measured. The widespread, but ill-defined, use of the phrase 'leptin resistance' was such that it was the topic of a recent workshop convened by the US National Institutes of Health aimed at deriving a clinically useful consensus in order to encourage clearer application of the term 504. - 53 -
2.3 Overview of the LOADD study:
Participants, inclusion and exclusion criteria:
The Lifestyle Over and Above Drugs in Diabetes study was a randomised controlled trial conducted by the Edgar National Centre for Diabetes Research, Dunedin, New Zealand. The study enrolled participants with type 2 diabetes within 1 to 5 years of diagnosis who were treated with oral hypoglycaemic agents. Inclusion criteria stipulated that participants should be less than 70 years of age, have unsatisfactory glycaemic control (defined as an HbA1c of ≥ 7 %) and in addition have two of the following three cardiovascular risk factors: overweight or obesity, hypertension or dyslipidaemia. Hypertension was defined as the use of antihypertensive medications or blood pressure ≥ 140/90 mmHg with or without antihypertensive medication. Dyslipidaemia was defined as either a total cholesterol level of greater than 5.2 mmol/l, LDL cholesterol > 3.5 mmol/l, triglycerides > 2.0 mmol/l, HDL cholesterol < 1.0 mmol/l or a combination of the above, or the use of lipid modifying agents. Overweight was defined as a BMI ≥ 25 kg/m2. The baseline characteristics of participants randomised to control and intervention groups is shown in Appendix 1, Table 1.
Pre-randomisation period and randomised treatment groups:
The study commenced with a pre-randomisation period in which participants saw a study physician who ensured medication regimens were in accordance with the NZGG recommendations 150. Medication regimens were altered where necessary and participants saw the study physician on multiple occasions if needed during this run-in phase. Once it was deemed that participants were receiving an optimal level of treatment according to NZGG recommendations they were then randomised to either an intensive dietary treatment arm, or a control arm in which participants continued with their usual standard of care provided by their General Practitioner and/or Diabetes Specialist. Participants in the usual care group were therefore in theory receiving the same level of diabetes care as afforded to all individuals with diabetes in New Zealand, though arguably the level of care was somewhat higher due to contact with the study physician and medication review prior to randomisation. The intensive dietary treatment arm consisted of a six month dietary intervention supervised by the study dietitian, both in one-on-one and group visits. Dietary advice was tailored to assist participants to follow a diet in line with recommendations in the evidence-based dietary guidelines for the treatment of diabetes issued by the Diabetes and Nutrition Study Group of the European Association for the Study of Diabetes 522, but also took into account individual preference.
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Dietary intake:
Dietary intake was assessed by the use of three-day weighed diet records collected by participants at baseline and six months. Diet records were used to calculate total energy, macronutrients and fat content, and fibre intake, as shown in Appendix 1, Table 2.
Study visits and blood sampling:
Participants were invited for two baseline visits (titled S0 and S1) for blood collection and anthropometric measurements. An average of the blood tests and clinical results for both S0 and S1 visits was taken as the baseline level for measures such as blood lipid levels, HbA1c, blood pressure, and anthropometric measures. Six month end-of-study measures were taken in a similar manner, with repeat measures taken one week apart (labelled S10 and S11). For the purposes of the current analysis of inflammatory markers and adipokines, blood samples taken at S0 (baseline) and S10 (6- months) were used, as the use of repeat measures would have exceeded the available budget.
Power studies, endpoints and ethical approval:
The main endpoint for the study was glycaemic control, and the study was designed to have 80 % power to detect a 0.5 % difference in HbA1c between treatment arms at a 5 % level of significance. A number of additional measures of interest were included as secondary outcomes. These were: anthropometric variables and markers of diabetes control and cardiovascular risk, an analysis of which has been published 601 (note that this is the work of other researchers - the author of the current thesis did not contribute to the collection or analysis of those variables); and a selection of inflammatory markers and adipokines, which were measured from stored plasma samples and analysed as part of the current thesis. The study was approved by the Lower South Regional Ethics Committee of New Zealand (project key LRS/05/07/026), and registered prior to commencing at www.clinicaltrials.gov under the accession number NCT00124553.
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2.4 Summary of previously published LOADD study findings:
A flow diagram of participants through the LOADD study is shown below in Figure 1, as published previously 601. A total of 94 participants completed the 6-month study (n = 46 for the intervention group, n = 48 control group), which was completed through 2006 to 2009. One of the intervention participants was subsequently diagnosed with slow onset type 1 diabetes and was excluded from further analysis.
An analysis of the clinical, anthropometric, biochemical and dietary data from the LOADD study has already been made and published 601. Although that work does not form part of the current thesis, some of these measures are included in analyses here, or are referred to during interpretation of the present analyses. Therefore, by way of explanation, the collection of these variables and the results presented in the published work are summarized below in order to assist in interpreting the findings presented here.
Collection of clinical, anthropometric, biochemical and dietary data:
Briefly, the methods used for the collection and measurement of these data were as follows: plasma total cholesterol, HDL cholesterol, and triglycerides were measured using enzymatic test kits (Roche Diagnostics, USA) on a Cobas Mira Plus autoanalyser (Roche Diagnostics, USA). Low-density- lipoprotein cholesterol was subsequently calculated using the Friedewald formula. Fasting glucose was measured using the hexokinase method using a commercially available kit (Roche Diagnostics, USA) and HbA1c was measured enzymatically (Roche Diagnostics, USA). Uric acid was measured using the Unimate 5 enzymatic colorimetric test (Roche Diagnostics, USA). Anthropometric and blood pressure measurements were made by trained nurses, with anthropometric measures made in duplicate and blood pressure measurements in triplicate. Dietary intake was assessed by 3-day weighed diet records.
Results: dietary intake:
Dietary intakes of control and intervention groups are shown in Appendix 1, Table 2. In brief, at the end of the 6-month study period participants in the intensive dietary intervention group were consuming a diet significantly higher in protein (difference (95 % CI) in percent energy intake adjusted for age, sex and baseline measures: 1.6 (0.04, 3.1) %), significantly lower in saturated fat content (- 1.9 (- 3.3, - 0.6) %), with a trend towards a higher fibre intake, of borderline statistical significance (3.0 (- 0.2, 6.1) g). No other measures of dietary composition differed between groups. - 56 -
Results: glycaemic control and surrogate markers of cardiovascular risk:
The intensive dietary intervention resulted in statistically significant reductions in HbA1c (difference (95 % CI) adjusted for age, sex and baseline measures: - 0.4 (- 0.1, - 0.7) %), and measures of anthropometry in the intervention group compared to control (weight: - 1.3 (- 0.1, - 2.4) kg; waist circumference: - 1.6 (- 0.5, - 2.7) cm; BMI: - 0.5 (- 0.1, - 0.9) kg/m2).
No statistically significant changes in other markers of glycaemic control and surrogate measures of cardiovascular risk were observed, including fasting glucose, blood pressure, lipid profile, triglycerides, uric acid, and urinary albumin to creatinine ratio.
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Figure 1: LOADD study participant flow chart, adapted from Coppell et al. 601.
165 assessed for eligibility
43 not meeting inclusion criteria
122 had clinical assessment
17 excluded: 13 HbA1c ≤ 7 % 1 diet only treatment 1 too busy 2 unspecified reasons
105 eligible
1 withdrew
104 randomised
52 intensive dietary 52 usual care interventionble
6 discontinued: 4 discontinued: 2 withdrew 1 withdrew 2 lost to follow-up 1 died 1 moved out of area 1 loss of family member 1 admitted to hospital 1 pregnancy
46 completed 48 completed (1 excluded from analysis due to misdiagnosis)
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2.5 The current work: measurement of inflammatory markers and adipokines:
Plasma samples from LOADD study participants were stored at - 80 ˚C until processing. Two aliquots of approximately 300 µl each were initially set aside for analysis of inflammatory markers and adipokines. IL-18 was initially measured by ELISA from Bender MedSystems (Austria, catalogue number BMS267/2), however the results were found to have an unacceptably high inter-assay coefficient of variation (data not shown). IL-18 measurement was subsequently redone with an ELISA kit from another manufacturer (MBL, Japan). This kit has been used in a number of other publications (e.g. see 426,434,437,460,461,499,602–604). Leptin and neopterin were measured by 125I- radioimmunoassay (Leptin: Millipore, MA, USA, catalogue number HL81K; Neopterin: BRAHMS Diagnostica, Germany, B·R·A·H·M·S Neopterin RIA). The human IL-18BP gene encodes four different isoforms of IL-18BP, labelled ‘a’ through ‘d’. IL-18BPa and IL-18BPc appear to have the greatest ability to inhibit IL-18, with IL-18BPa having a much greater affinity 605. Much of the research into the role of IL-18BP has focused on the IL-18BPa isoform (e.g. 448,454,606–611), including the generation of transgenic mice which overexpress the IL-18BPa isoform 612,613, and therefore IL- 18BP was measured using a commercial ELISA designed to bind IL-18BPa (RayBiotech, Inc., GA, USA, catalogue number ELH-IL-18 BPA-001). The remaining cytokines were measured in a Lumenix™ bead array multiplex system with multiplex beads and plates from Millipore (MA, USA) which were read on a Bio-Plex suspension array instrument (Bio-Rad Laboratories, CA, USA). Multiplex arrays were performed with two separate plates: one for analysis of IL-1β, IL-1ra, IL-6, IL- 10 and TNFα (catalogue number MPXHCYTO-60K), and another for analysis of sTNF receptors (sTNF-R1 and sTNF-R2, catalogue number HSCR-32K). Because of the practical issues involved in conducting a number of different measurements on frozen aliquots, and to ensure consistent freeze- thaw cycles across samples, a schedule was set up in which the two aliquots were thawed for use in assays and refrozen as follows: aliquot 1: leptin RIA, neopterin RIA, sTNF receptor (sTNF-R1, sTNF-R2) multiplex aliquot 2: cytokine multiplex (IL-1β, IL-1ra, IL-6, IL-10, TNFα), initial failed IL-18 ELISA (BenderMedSystems) (note - results not included in this thesis), IL-18BP ELISA As noted above, the IL-18 ELISA was subsequently repeated using kits from MBL (Japan). This was performed on a separate plasma sample which had previously been through one freeze-thaw cycle for the measurement of C-reactive protein from the same clinic visit.
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Analyses were conducted at the Department of Microbiology (for Luminex™ bead multiplex assays), or Lipid and Diabetes Laboratory, Department of Human Nutrition (all other assays), both within the University of Otago, Dunedin, New Zealand. Assay plates were loaded to have a balanced number of samples from control and intervention groups to reduce any systematic error introduced from interassay variability. In addition, the repeat IL-18 ELISA assay (MBL) was also balanced to have the same number of baseline and 6-month follow-up samples for each randomisation group. Assays were conducted according to manufacturer’s instructions and where instructions allowed optional overnight incubations (Millipore multiplex plates, RayBiotech IL-18BPa ELISA), this was done for greater assay sensitivity. Participant samples were analysed in duplicate in RIAs and ELISAs, and with single samples in multiplex assays. Duplicate samples from RIAs with a coefficient of variation of 20 % or greater were excluded from further analysis, as were samples from ELISAs where raw absorbance readings of duplicates differed from their mean by 20 % or more. One participant in the control group was found to have extremely high levels of neopterin, sTNF-R1 and sTNF-R2 both at baseline and follow-up visits, for unknown reasons, and these values were excluded from further analysis (baseline: neopterin 155.6 nmol/l, sTNF-R1 16,624.0 pg/ml, sTNF-R2 21,594.6 pg/ml; 6- months: neopterin 259.16 nmol/l, sTNF-R1 14,138.2 pg/ml, sTNF-R2 21,790.1 pg/ml).
2.5.1 Intra- and inter-assay variability:
Pooled plasma samples, which consisted of plasma left over from other unrelated analyses conducted at the Department of Nutrition Lipid Laboratory, were added as unknowns in the assays in order to aid in the calculation intra- and inter-assay coefficients of variation (CVs), shown in Tables 2a and 2b. A number of assays also included internal controls provided by the manufacturer and thus where possible CVs were calculated using pooled plasma samples and control(s) as CV calculations can differ across the range of the standard curve. Using samples at various points in the curve provides a more accurate measure of overall variability. Controls from different plate kits had the same manufacturing lot number and thus could be included in inter-assay CV calculations. In some assays pooled plasma samples were either accidentally omitted (Leptin RIA), or had undetectable levels of analytes in some or all assay plates (IL-18 BPa ELISA, Millipore cytokine multiplex – IL-1β, IL-1ra, IL-6, IL-10, TNFα). Thus, for the Millipore cytokine multiplex internal control samples were used as the basis for CV calculation. To test the validity of calculating CV values on the basis of internal control samples, CV calculations for the other multiplex performed here (Millipore soluble cytokine receptor multiplex – sTNF-R1 and sTNF-R2) were made on the basis of both pooled plasma samples and controls as a comparison, shown in Table 2a. Both methods gave comparable results. The IL-18 BPa ELISA used, manufactured by RayBiotech Ltd, lacked internal control samples, and in addition
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pooled plasma samples were below the limit of detection (for reasons unknown). This excluded the possibility of evaluating coefficients of variation based on traditional methods. Thus, alternative methods to calculate assay CV values were investigated, and the same calculations made with the other ELISA used (IL-18, MBL) as a comparison to gauge the validity of these different methods, which were: (i) using the variation in absorbance readings of the standards used to create the standard curve, and (ii) the intra-assay average CV values calculated from duplicates of all unknown samples (participant samples - IL-18 and IL-18 BP ELISAs; and plasma pools - IL-18 ELISA). Comparisons are shown in Table 2b. For the IL-18 ELISA assay, intra-assay CV values calculated with these alternative methods were lower, and the inter-assay CV value marginally higher, than those calculated using plasma pools. This suggests that if calculating CV values of the IL-18 BPa ELISA by using pooled plasma samples had been possible, it would likely give values somewhat different than those shown in Table 2b by these two alternative methods, although would likely remain within an acceptable range of variance. Limits of detection for ELISAs are stated by the manufacturers as 12.5 pg/ml for MBL IL-18 ELISA, and < 20 pg/ml for Raybiotech Ltd. IL-18 BPa ELISA. Limits of detection for the remaining assays are shown in Table 2a.
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2.6 Statistical analysis:
Statistical analysis was performed under the direction of the study statistician (Assoc Prof S Williams, University of Otago), using Stata software (Stata Corporation, Texas, USA) or GraphPad Prism 5.0d for Mac OS X (GraphPad Software, Inc., CA, USA).
Effect of dietary intervention:
The effect of the intervention was assessed by multiple regression adjusting for baseline measures. As the majority of analytes showed skewed distribution, measures were log-transformed before analysis. Differences are represented as the ratio of difference at 6-month follow-up of the intervention group over the difference at 6-month follow-up of the control group, using log-transformed values of each variable. For some variables, further analyses were conducted with additional adjustment for changes in BMI and HbA1c, as indicated in the text. The effect of dietary intervention on levels of inflammatory markers and adipokines was corrected for multiple testing with Bonferroni adjustment. The relationship between changes in neopterin and IL-18 levels, or between these cytokines and changes in measures of dietary composition, was performed using linear regression in GraphPad Prism 5.0d for Mac OS X.
Correlation analysis:
In order to examine the relationships between the different adipokines and inflammatory markers, as well as with other surrogate markers of cardiovascular risk, pairwise correlation coefficients were generated using baseline measures of participants from both intervention and control groups (using non-log transformed measures) with Stata. As leptin is highly correlated with measures of adiposity partial correlation coefficients corrected for BMI were generated. No corrections were made for multiple testing of baseline correlations.
For variables showing significant pairwise correlation coefficients, further analyses and visualisation were made by examining scatter plots with the use of GraphPad Prism. Given the large number of pairwise correlations generated, and that outlying points can generate false positive associations, tests were made to examine the robustness of these associations by excluding possible outliers from scatter plots (Appendix 1, Figure 2). On the one hand, the exclusion of potential outliers can highlight whether an association is driven by one or a few select points, on the other, excluding variables without an a priori condition can result in biased selection of data points. Nevertheless, these analyses were performed to check for false positive associations driven by outlying data points. No attempt was made to perform a similar analysis with non-significant pairwise correlations to - 62 -
check for false negative associations. Note that due the differing methods GraphPad Prism and Stata software use to generate linear regression of scatter plots, and pairwise correlation coefficients, respectively, the r and p values differ slightly for the same associations between variables.
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Table 2a: Measured intra- and inter-assay coefficients of variation (CV) for RIAs and multiplexes, and limits of detection as stated by the manufacturer.
Assay & method Limit of detection Intra-assay CV (%) Inter-assay CV Method of CV calculation (Company) (%)
Neopterin RIA n.s. 7.8 7.8 pooled plasma and control samples (BRAHMS Diagnostica)
Leptin RIA 0.5 ng/ml 2.9 10.7 control samples (Millipore)
Multiplex – cytokine 5-plex (Millipore) IL-1β 0.4 pg/ml 8.0 31.1 control samples IL-1ra 2.9 pg/ml 10.7 26.0 control samples IL-6 0.3 pg/ml 6.5 33.1 control samples IL-10 0.3 pg/ml 9.9 32.9 control samples TNFα 0.1 pg/ml 7.7 28.0 control samples
Multiplex – soluble TNFα receptors (Millipore) sTNF-R1 9.6 pg/ml 9.6 13.4 pooled plasma samples 7.0 9.3 control samples sTNF-R2 16.9 pg/ml 5.5 6.4 pooled plasma samples 4.7 8.5 control samples
n.s - not stated. Lowest standard is 3.0 nmol/l.
Table 2b: Calculated intra- and inter-assay coefficients of variation (CV) for ELISAs, showing the comparison of different methods for IL-18 BPa and IL-18 ELISAs.
Method of CV calculation IL-18 ELISA IL-18 BPa ELISA Intra-assay CV (%) Inter-assay CV (%) Intra-assay CV (%) Inter-assay CV (%)
pooled plasma samples 8.0 9.9 * * average variance of absorbance of 3.1 10.9 6.7 8.1 standard curve points average variance of absorbance of all 2.2 - 13.4 - unknown samples
* Assay lacks internal control samples and pooled plasma samples were below the limit of detection, such that CV values cannot be calculated with this method.
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Chapter 3: Results
3.1 Effect of dietary intervention on inflammatory markers and adipokines
The effect of the 6-month LOADD study dietary intervention on levels of adipokines and inflammatory markers is shown in Table 3, with group differences presented in graphical format in Figure 2. Because of the skewed distribution of the data, values for each measure were log- transformed and effect sizes calculated as the ratio between groups of change from baseline.
The intervention resulted in significant reductions in IL-18 and neopterin levels relative to control, with difference ratios (95 % CI) of 0.91 (0.83, 0.99) and 0.87 (0.77, 0.98), respectively. No significant changes were observed in other measured inflammatory markers and adipokines. Bonferroni adjustment for multiple testing rendered these differences non-significant.
Given that the intervention produced significant reductions in anthropometric measures (weight: 1.3 (0.1, 2.4) kg, waist circumference: 1.6 (0.5, 2.7) cm, BMI: 0.5 (0.1, 0.9) kg/m2) and HbA1c (0.4 (0.1, 0.7) %) (data from 601), the relationship between these changes and those seen here in IL-18 and neopterin was further investigated by examining the correlation between change in BMI, HbA1c, IL- 18 and neopterin across all participants (Table 4 and Appendix 1, Table 3). Changes in HbA1c were correlated with change in BMI (r = 0.30, p = 0.003), however, there was no association between changes in IL-18 or neopterin and change in BMI or HbA1c. In contrast, changes in IL-18 were correlated with changes in neopterin, with correlation of a similar magnitude to that observed between HbA1c and BMI (r = 0.299, p = 0.009). A scatter plot of change in neopterin levels vs. change in IL-18 levels for all participants revealed a selection of data points which could lend undue weight to this correlation (Figure 3, Panel A). Sequential removal of two participants with large increases in neopterin values reduced this association to r = 0.225 (p = 0.053, n = 74; Figure 3, Panel B).
Another method of examining the relationship between changes in multiple variables arising from the intervention is to perform multiple regression with adjustment for the additional variables as
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confounders. Intervention effect sizes were therefore calculated for changes in IL-18 and neopterin with additional adjustment for changes in BMI and/or HbA1c, as shown in Table 5. Effect sizes for change in IL-18 and neopterin were minimally altered by further adjustment for BMI or HbA1c, with effect sizes either marginally larger (after adjustment for change in BMI) or smaller (after adjustment for HbA1c) for the effect of the intervention on IL-18 levels, or slightly larger after adjustments for the effect of the intervention on neopterin levels.
The association of changes in neopterin and IL-18 levels with dietary components was further analysed in Appendix 1, Figures 1 and 2. Changes in neopterin levels (henceforth Δ neopterin) showed a significant correlation with changes in saturated fat intake (Δ SFA: Appendix 1, Figure 1, Panel D; r = 0.29, p = 0.03). Examining changes in intervention vs. control participants separately showed that this was entirely driven by an association between Δ neopterin and Δ SFA in intervention participants (r = 0.43, p = 0.01; Appendix 1, Figure 1, Panel F) which was not observed in control participants (r = 0.01, p = 0.94; Appendix 1, Figure 1, Panel E). No other significant associations were observed between dietary variables and changes in neopterin or IL-18 levels.
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Table 3: Comparison of effect sizes of control and dietary interventions on adipokines and inflammatory markers. Differences are depic ted in graphical format in Figure 2.
Intervention Control baseline 6 months baseline 6 months Difference * Measure n median (IQR) n median (IQR) n median (IQR) n median (IQR) (95 % CI) p-value leptin (ng/ml) 36 36.4 (28.8, 48.0) 36 37.4 (20.9, 54.5) 34 30.2 (14.7, 45.6) 34 33.2 (12.8, 51.6) 1.10 (0.80, 1.50) 0.56
neopterin(nmol/l) 39 5.36 (4.55, 6.09) 39 5.04 (4.42, 5.47) 37 5.08 (4.51, 5.83) 37 5.21 (4.56, 5.85) 0.87 (0.77, 0.98) 0.03
sTNF-R1 (pg/ml) 40 836 (615, 1019) 40 901 (671, 1127) 43 857 (611, 961) 43 854 (661, 1093) 1.00 (0.89, 1.11) 0.94
sTNF-R2 (pg/ml) 40 2727 (2413, 3300) 40 2919 (2418, 3254) 42 2616 (2156, 3157) 42 2648 (1909, 3613) 1.10 (0.87, 1.40) 0.42
IL-1β (pg/ml) 12 1.60 (0.52, 4.16) 12 0.51 (0.20, 4.03) 8 3.87 (0.22, 13.40) 8 1.29 (0.15, 16.42) 0.56 (0.11, 2.74) 0.45
IL-1ra (pg/ml) 10 12.7 (7.4, 55.2) 10 41.3 (3.8, 102.5) 9 11.2 (7.4, 24.7) 9 21.3 (8.6, 43.7) 1.20 (0.45, 3.15) 0.70
IL-6 (pg/ml) 29 4.94 (1.43, 9.76) 29 5.61 (1.88, 23.21) 26 4.68 (1.64, 10.32) 26 4.44 (1.78, 20.30) 1.12 (0.51, 2.44) 0.78
IL-10 (pg/ml) 20 3.08 (1.74, 6.61) 20 2.42 (1.60, 8.63) 21 3.81 (2.01, 5.27) 21 5.42 (2.39, 11.96) 0.57 (0.30, 1.10) 0.09
TNFα (pg/ml) 44 9.7 (6.7, 16.3) 44 14.0 (10.2, 21.8) 46 10.0 (6.7, 13.9) 46 15.0 (12.0, 18.9) 1.00 (0.79, 1.27) 1.00
IL-18 (pg/ml) 45 506 (430, 666) 45 449 (354, 553) 46 527 (418, 657) 46 504 (419, 652) 0.91 (0.83, 0.99) 0.04
IL-18 BP (pg/ml) 32 455 (140, 857) 32 362 (163, 768) 27 473 (255, 751) 27 426 (229, 782) 0.82 (0.53, 1.26) 0.36
* analyses adjusted for baseline but no other variables. Difference represents the ratio of change in intervention group to c hange in control group.
Figure 2: Forest plot of LOADD dietary intervention effect sizes on adipokines and inflammatory markers. x- axis represents the effect size as shown in Table 3, being the ratio of the change from baseline in the intervention group over the change from baseline in the control group.
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Table 4: Pairwise correlation coefficients of the change in BMI, HbA1c, IL-18 and neopterin for participants from both control and intervention groups.
∆ BMI ∆ HbA1c ∆ IL-18 ∆ HbA1c 0.30 ** 1 -
∆ IL-18 -0.04 0.14 1
∆ neopterin -0.11 -0.02 0.30 **
* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001
Figure 3: Scatter plots of change in IL-18 levels (x axis) versus change in neopterin levels (y axis) across all participants with linear regression correlations sho wn with a red dashed line. A: r = 0.274 (p = 0.017, n = 76) *; B: exclusion of two uppermost Δ neopterin values (circled in red in A), r = 0.225 (p = 0.053, n = 74).
* note there are slight differences in the r and p values shown here versus those in Table 4 and Appendix 1, Table 4, due to differences in the software used (Stata pairwise correlation function in Table 4 and Appendix 1, Table 4; GraphPad Prism regression analysis in Figure 3).
Table 5: Intervention effect sizes for IL-18 and neopterin with additional adjustment for changes in HbA1c and BMI.
Multiple regression model IL-18 (pg/ml) neopterin(nmol/l) Difference (95 % CI) p-value Difference (95 % CI) p-value
Base model * 0.91 (0.83, 0.99) 0.04 0.87 (0.77, 0.98) 0.03
+ adjustment for change in BMI 0.90 (0.82, 0.99) 0.03 0.85 (0.75, 0.96) 0.01
+ adjustment for change in HbA1c 0.92 (0.83, 1.01) 0.07 0.85 (0.74, 0.96) 0.01
+ adjustment for changes in both BMI & HbA1c 0.91 (0.83, 1.00) 0.06 0.84 (0.73, 0.96) 0.01
* multiple regression adjusted for baseline measures, expressed as the ratio of change in intervention group over change in c ontrol group. As shown in Table 3 above.
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3.2 Correlation of inflammatory markers and adipokines with anthropometric variables and glycaemic control
In order to examine the relationship of the selected inflammatory markers and adipokines with other clinical and anthropometric measures of cardiovascular risk, the baseline (pre-intervention) measures from both control and intervention arms were pooled. Distribution of measures are shown in scatter graphs in Appendix 1, Figure 3. Pairwise correlation coefficients are shown in Table 6 and Appendix 1, Table 4 for relationships among adipokines and inflammatory markers and with other clinical variables. In order to account for the potential confounding effect of outlying data points, those variables with significant correlations were plotted in scatter graphs (Appendix 1, Figures 4 through 9) to check for outlying data points and reassess correlations excluding these points.
Leptin was significantly correlated with waist circumference, body weight and BMI, and was also correlated with measures of glycaemia and various lipid measures. Leptin was also lower in men and current smokers. Scatter graphs of these relationships did not reveal any outlying data points to skew these correlations (Appendix 1, Figures 4 – 9). To further investigate the correlation of leptin with measures of glycaemia and blood lipids, partial correlation coefficients were generated for leptin corrected for BMI as a measure of adiposity (Table 7). These showed that the association of leptin with measures of glycaemia appeared to be mediated by adiposity, as the correlations of leptin with glucose or HbA1c were no longer statistically significant after taking into account the association with BMI. In contrast, the partial correlation of leptin with total cholesterol remained statistically significant (p = 0.015) and of similar magnitude after accounting for adiposity (r = 0.29 for pairwise correlation, r = 0.27 for partial correlation adjusted for BMI).
IL-18 was significantly associated with measures of anthropometry, with associations with waist circumference (r = 0.31, p = 0.002) and weight (r = 0.28, p = 0.007) comparable to those seen with leptin (with values of r = 0.30, p = 0.006 for waist circumference and r = 0.27, p = 0.013 for weight; see Appendix 1, Table 4 for full pairwise correlation data). These associations appeared robust in scatter graphs with no outlying data points (Appendix 1, Figures 4 – 9). Other associations reaching a high level of significance (p ≤ 0.01) include lower levels of sTNF-R2 with increasing fasting glucose, and lower TNFα levels with increasing age. Associations of smoking with the proinflammatory cytokines TNFα and IL-1β appeared to be driven by outlying data points (Appendix 1, Figure 5). Neopterin was significantly correlated with age and diabetes duration. While these associations would also be expected, since diabetes duration also correlates with age, the association of neopterin - 72 -
levels with diabetes duration appeared to be unduly influenced by one outlying data point (Appendix 1, Figure 8, Panels C and C’).
In correlations amongst adipokines and inflammatory markers, levels of the soluble TNFα receptors were highly correlated (Appendix 1, Figure 9, Panel G). A number of correlations amongst inflammatory markers and adipokines appeared to have outlying data points, and correlations were even stronger after their exclusion, including the associations of the soluble TNFα receptors and neopterin, the anti-inflammatory cytokines IL-10 and IL-1ra, IL-6 and TNFα, and IL-10 and TNFα. The association of IL-1ra with neopterin, however, appeared to be influenced by select outliers and was no longer significant after their exclusion (Appendix 1, Figure 9, Panels A and A’).
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Table 6: Pearson correlation coefficients of adipokines and inflammatory markers with anthropometric, clinical and biochemical measures at baseline. Full data including number of observations and p-values for each correlation are shown in Appendix 1, Table 2.
leptin neopterin TNF sTNF-R1 sTNF-R2 IL-1β IL-1ra IL-6 IL-10 IL-18 IL-18 BP (ng/ml) (nmol/l) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) Glycaemia: glucose (mmol/l) 0.25 * -0.03 -0.04 -0.07 -0.27 ** -0.04 -0.09 -0.10 -0.12 -0.04 0.09 HbA1c (%) 0.30 ** 0.05 0.03 -0.05 -0.16 0.01 0.03 -0.10 -0.05 0.13 0.11 diabetes duration (yrs) 0.14 0.22 * † -0.08 0.03 0.13 -0.12 0.26 -0.19 -0.06 0.14 0.07 Anthropometry: waist (cm) 0.30 ** 0.14 0.10 0.22 * 0.12 -0.05 -0.04 -0.11 -0.07 0.31 ** 0.12 weight (kg) 0.27 * 0.06 0.18 0.14 0.04 -0.02 -0.12 -0.15 -0.01 0.28 ** 0.13 BMI (kg/m2) 0.48 *** 0.13 0.14 0.17 0.08 0.05 -0.14 -0.10 -0.11 0.26 * 0.10 Biochemistry: total chol. (mmol/l) 0.29 ** -0.14 0.03 0.00 -0.06 0.16 0.16 -0.01 0.03 -0.08 0.00 HDL (mmol/l) 0.24 * -0.01 -0.17 -0.04 0.01 0.17 0.30 0.19 0.29 * † -0.23 * 0.00 LDL (mmol/l) 0.26 * -0.16 0.00 -0.06 -0.07 0.21 0.10 -0.03 0.05 -0.15 0.00 TAG (mmol/l) 0.00 0.01 0.20 0.17 -0.01 -0.16 -0.01 -0.10 -0.24 * † -0.03 0.00 uric acid (µmol/l) -0.04 0.02 -0.04 0.07 0.19 -0.16 -0.12 -0.17 -0.06 0.14 -0.03 Blood pressure: Systolic (mmHg) -0.09 0.09 -0.18 0.06 0.09 0.09 0.16 -0.09 -0.07 0.03 0.19 Diastolic (mmHg) -0.14 -0.04 -0.08 -0.15 -0.18 0.20 0.07 -0.07 0.05 0.03 0.23 *
Table 6 cont: leptin neopterin TNF sTNF-R1 sTNF-R2 IL-1β IL-1ra IL-6 IL-10 IL-18 IL-18 BP (ng/ml) (nmol/l) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) (pg/ml) Demographic: age (yrs) -0.04 0.32 ** -0.28 ** 0.22 * 0.24 * -0.25 * † 0.04 0.02 0.01 -0.10 0.12 male -0.38 *** -0.02 0.10 0.03 -0.11 -0.10 0.16 -0.19 0.16 0.18 0.09 current smoker -0.24 * 0.07 0.30 ** † 0.11 0.22 * 0.38 ** † -0.15 -0.01 -0.10 0.05 -0.02 Adipokines and inflammatory markers: leptin (ng/ml) - neopterin (nmol/l) 0.02 - TNFα (pg/ml) 0.00 -0.06 - sTNF-R1 (pg/ml) 0.16 0.22 * 0.13 - sTNF-R2 (pg/ml) 0.12 0.25 * -0.09 0.53 *** - IL-1β (pg/ml) -0.01 0.00 0.13 -0.03 -0.16 - IL-1ra (pg/ml) -0.20 0.50 ** † -0.06 0.01 -0.10 -0.04 - IL-6 (pg/ml) -0.05 0.00 0.28 * 0.11 0.03 0.05 0.12 - IL-10 (pg/ml) -0.02 0.06 0.28 * -0.01 -0.14 0.01 0.58 *** 0.08 - IL-18 (pg/ml) 0.03 0.18 0.18 0.15 0.07 0.30 -0.11 0.16 0.05 - IL-18 BP (pg/ml) 0.00 0.17 0.09 0.15 0.07 -0.06 0.11 -0.05 0.24 0.12 -
* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001
† Association was not robust to exclusion of outlying data points as shown in Appendix 1, Figures 4 – 9.
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Table 7: Partial correlation coefficients for leptin when adjusting for BMI.
Variable X Pairwise correlation Variable X partial correlation BMI partial correlation (as shown in Table 1) p-value adjusting for BMI p-value adjusting for variable X p-value glucose (mmol/l) 0.25 0.024 0.14 0.207 0.44 0.000 HbA1c (%) 0.30 0.006 0.15 0.185 0.42 0.000 total chol. (mmol/l) 0.29 0.008 0.27 0.015 0.47 0.000 BMI (kg/m2) 0.48 0.000 - - - -
Chapter 4: Discussion
4.1 Discussion of LOADD study findings
This study examined the effects of a randomised controlled trial of dietary intervention in patients with type 2 diabetes on a range of adipokines and inflammatory markers. Analyses included an examination of the associations between these adipokines and inflammatory markers and other markers of cardiovascular risk, and an assessment of the effect of dietary intervention on circulating levels of these analytes. The key finding from this study was that the 6-month dietary intervention in the LOADD study produced significant reductions in circulating IL-18 and neopterin levels. Adjustment of the results shown in Table 3 for multiple testing reduces the required p value to 0.0047, and thus none of these results are significant after Bonferroni correction. There is, however, debate about the utility of correction for multiple testing 614,615. In particular, the Bonferroni test assumes all measures are independent. However, substantial interaction would be expected amongst inflammatory markers due to their biological roles and often production and release from the same cells, and this is suggested by the analyses of correlation of inflammatory markers and adipokines at baseline (Table 6 and Appendix 1, Figures 4 – 9). Under these circumstances, Bonferroni correction will substantially reduce the available statistical power to detect differences between groups; in other words, correction for multiple testing would reduce the chance of a type I error, but is likely to increase the chance of a type II error. In the of the results here, it is perhaps best concluded that significant reductions in neopterin and IL-18 were observed in the intervention group, but that it is possible these reflect a type I error.
There have been no previous studies where the effect of a dietary intervention on inflammatory markers has been assessed in participants with type 2 diabetes. A number of other publications in persons with obesity have shown that weight loss can reduce circulating IL-18 levels, including studies of liposuction 616, bariatric surgery 498,499,617,618, and diet and/or exercise interventions 284,285,428,562,619,620. In this regard, the results of the LOADD study in people with type 2 diabetes are in accordance with the findings of previous lifestyle studies in persons with obesity. However, analyses
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of the correlation of change in IL-18 or neopterin with the change in BMI or HbA1c, or further adjustment of regression models for changes in BMI or HbA1c, suggest that the reductions in IL-18 and neopterin were largely independent of the observed reductions in BMI and HbA1c which also resulted from the intervention, and thus arise from other factors relating to the LOADD study intervention aside from weight loss. Given that previous metabolic studies by Esposito et al. have shown that IL-18 increases after a high fat meal and decreases after a high fibre meal 501, and that the dietary intervention in the LOADD study resulted in a significant increase in protein intake, significant reduction in saturated fat intake, and an increase in dietary fibre intake of borderline significance 601, it seemed plausible that the reduction in IL-18 could thus be reflective of changes in dietary composition. However, a post-hoc analysis of the correlation of changes in neopterin and IL- 18 levels with various aspects of diet composition largely failed to show any correlations between these variables. The only exception was a significant association of changes in saturated fat intake with changes in circulating neopterin levels. This association was only observed amongst participants in the intervention group. It is not clear how this effect could be biologically plausible; if neopterin levels were regulated by saturated fatty acid intakes then changes in intake levels in control group participants would also be expected to show the same relationship, unless somehow the intervention participants were directed towards, for example, a different profile of saturated fatty acid composition and specific fatty acids exhibited differing effects on neopterin release, or the changes in saturated fatty acid intake were correlated with intake of a specific dietary variable which influences neopterin levels. Given the post-hoc nature of this assessment, it is possible that this is a spurious result. It should be noted, however, that the lack of association between changes in IL-18 and neopterin and other measures of dietary composition does not necessarily rule out an effect of diet composition on these variables; even weighed diet records, as used here in the LOADD study, are prone to error and based on self-reported intake, and an effect could exist which we are unable to detect with the LOADD study data (i.e. a type II error).
If the lack of association between changes in IL-18 and neopterin, and changes in anthropometric, glycaemic, or dietary variables is taken as an accurate finding, this then leaves open then question as to what mediates these changes in cytokine levels in the LOADD study. One hypothesis relates to the role of gut bacteria in generating inflammatory cytokines. A number of studies have shown a role for IL-18 in the pathogenesis of intestinal inflammatory disorders 621,622, and there is recent interest in the role of gut microbiota in mediating obesity-associated metabolic changes 623,624. A recent study by Maslanik et al. in rats showed that stress-induced changes in circulating IL-18 can be blunted by oral administration of antibiotics 625, and this raises the possibility that diet-induced - 78 -
alterations in gut microbiota could be one mechanism underlying the reduction in IL-18 observed here. This could also potentially form a mechanism by which changes in diet composition acutely regulate circulating IL-18 levels, as reported by Esposito et al. 501 (discussed above). Other possible explanatory mechanisms exist, such as the role of IL-18 in the hypothalamic-pituitary-adrenal stress axis 440. A number of studies in animals have shown that acute stress increases circulating or saliva IL-18 levels 626–632, and while the results of Maslanik et al. suggest that this may involve gut microbiota in part, their results also reinforce the influence of psychological stress on circulating IL- 18 levels. While the LOADD study was designed as a dietary intervention, it is important to recognize the unplanned 'non-intervention' effects of involvement in a research project. Whether the increased interpersonal contact with a dietitian and discussion of diet and diabetes-related problems by intervention group participants had psychological effects which influenced the stress axis is unknown.
A significant reduction in neopterin was also observed in the current study. Given that: (i) IL-18 induces neopterin release from macrophages (via induction of IFN-γ 530), (ii) levels of neopterin are increased in monkeys after an acute injection of IL-18 547,548, it seemed likely that the observed reductions in neopterin could occur downstream of the reductions in IL-18. Correlation analyses showed that changes in neopterin levels in the LOADD study correlated with changes in IL-18 (r = 0.299, p = 0.009, Table 4), however, the significance of this relationship was sensitive to the exclusion of a few data points with large delta values (Figure 3). Thus, it appears possible that the reduction in neopterin may be dependent in part on a reduction in circulating IL-18 levels, although there are clearly other factors which have a greater contribution to changes in neopterin levels.
Given that the LOADD dietary intervention resulted in significant changes in anthropometric measures, and that leptin levels at baseline correlated with these anthropometric measures, it was somewhat surprising that these anthropometric changes were not paralleled by a change in circulating leptin. While changes in BMI, waist circumference and weight were statistically significant, the effect sizes of 0.5 kg/m2, 1.6 cm, and 1.3 kg represent changes of 1.4 %, 1.4 %, and 1.3 %, respectively, relative to the mean baseline values in the intervention group. Thus, it is possible that the effect sizes were simply not sufficient to detect a change in circulating leptin levels.
In addition to examining the effect of the LOADD study dietary intervention on selected inflammatory markers and adipokines, analyses were conducted here to examine the association of these variables with glycaemic control and surrogate markers of cardiovascular risk. These analyses
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showed a number of expected associations, such as lower leptin levels in males and non-smokers 633– 636 and an association with anthropometric measures, a correlation between of soluble TNFα receptors and neopterin 576, and associations of IL-18 with measures of anthropometry 289,428,490,616,619,637. Interestingly, the correlation of IL-18 with baseline body weight and anthropometric measures was of a similar magnitude to those seen between leptin and anthropometric measures (IL-18 correlations r = 0.31, p = 0.002 for waist circumference and r = 0.28, p = 0.007 for weight; leptin correlations r = 0.30, p = 0.006 for waist circumference and r = 0.27, p = 0.013 for weight; see Appendix 1, Table 4 for full pairwise correlation data). The correlation of IL-18 with measures of anthropometry has been previously noted in other studies 289,428,490,616,619,637, although not all have observed this relationship 638. Whether or not IL-18 is a true 'adipokine' and actually released from adipose cells is controversial432,639, however, this argument is largely academic as while IL-18 may not be produced by adipose cells, it is produced from adipose tissue with production from non-adipose stromal cells289 and studies have reported higher adipose tissue IL-18 expression production in obesity in humans and animals 289,427,431,640. The association of IL-18 with anthropometric measures at baseline presents a scenario which, at face value, appears at odds with the finding that IL-18 levels were reduced by the dietary intervention but were not associated with changes in BMI. It is not known how such an effect arises and presents an interesting topic of enquiry for further research.
A few previous studies have found a positive association of neopterin with age 531,534,537,538,602,641, as seen here, and an association of neopterin with the soluble TNFα receptors has also been found in a number of studies (reviewed in 576). Studies have shown higher levels of neopterin in patients with diabetes than in those without 642 or conversely, a higher proportion of participants with diabetes across increasing quarters of neopterin 534. One study in patients with type 1 diabetes noted a trend towards a significant association of neopterin with diabetes duration (r = 0.205; p < 0.0539) 643, and other studies in patients with type 2 diabetes have shown increased levels in patients with nephropathy 644, and microalbuminurea 645. An association of neopterin with diabetes duration could therefore be indicative of an underlying pathogenic process, such as the development of renal disease or atherosclerosis. However, given age and duration of diabetes are inevitably correlated, the observed positive association of neopterin with duration of diabetes could arise from confounding by age; indeed in the aforementioned study which showed higher neopterin levels in patients with microalbuminurea 645, these patients were significantly older than the comparison group without. In the results presented here, neopterin was significantly positively associated with age, and yet the association with diabetes duration appeared to be influenced by one data point. Clearly, one of the - 80 -
difficulties in examining factors which show a correlation with age is delineating whether the association is reflective of an underlying pathogenic process such as atherosclerosis, or part of a normal, healthy, alteration associated with aging. Forming a distinction between the two would require a large amount of scientific study, and depend to large extent on the definitions used to differentiate between 'normal' aging and a pathogenic process associated with aging. However, at least in the case of neopterin, there is sufficient evidence of an association or even active role in the development of cardiovascular disease 531–536,540,541,543,545,546,646–658 to suggest that the association with age is unlikely to reflect a process of 'healthy' aging, and probably arises due to the association with subclinical or clinical atherosclerosis.
Leptin levels correlated with anthropometric measures of adiposity, as expected based on its production from adipose tissue, as well as associations with glycaemia and cholesterol. While the association of leptin with glycaemia appeared to be explained by differences in adiposity, the association with cholesterol was not affected by controlling for BMI. A number of studies have shown direct effects of leptin on aspects of lipid metabolism, such as changes in fat oxidation 659,660 and changes in lipolysis 661. However, changes in lipid metabolism seen in people with congenital leptin deficiency, or leptin-deficient rodents are confounded by changes in body weight with leptin treatment, and in addition, these subjects are leptin-sensitive, in contrast to the leptin resistance associated with excess adiposity which would be expected in the participants of the LOADD study. While much research has been conducted into the blunted effects of leptin on appetite, energy expenditure and glucose metabolism with obesity (so-called 'leptin resistance'), whether the effects of leptin on lipid metabolism are also affected is less clear 662. A study in diet-induced obese mice showed that leptin reduces liver triglyceride content in livers from normal-weight but not diet- induced obese animals, supporting the notion of a more global leptin resistance which also blunts the effects of leptin on lipid metabolism. In contrast, a study involving acute disruption of leptin signalling in normal weight mice showed no effects on circulating lipids 663. Given a number of studies in humans showing that leptin therapy can reduce circulating lipid levels in leptin deficiency664,665 and lipodystrophy 666–668, the positive association of leptin levels with cholesterol seen in the LOADD study participants could be taken as evidence of resistance to the effects of leptin on lipid metabolism, as based on the aforementioned works higher circulating leptin levels would be expected to result in lower circulating lipid levels. Of note, a recent randomised controlled trial of leptin therapy in obese patients with type 2 diabetes enrolled subjects with a similar mean BMI (33.2 ± 3.8 kg/m2), age (53.3 ± 11.4 yrs), and leptin levels (38.0 ± 6.4 ng/ml placebo group, 35.2 ± 3.5 ng/ml leptin treatment group at baseline) as the LOADD study participants. The authors of that study
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found no effect of leptin treatment on a variety of metabolic parameters, consistent with a diagnosis of leptin resistance, which supports the notion that the LOADD study participants were also likely to have been leptin resistant.
This study had a number of weaknesses. Firstly, it is acknowledged that a number of analyses have been conducted here and there is a high probability of a type I error producing spurious 'positive results'. Indeed, for any randomised controlled trial, analysis of 20 variables would be expected to produce one false positive result by chance at the 5 % level of significance. The key analysis included here, of examining the effect of the LOADD study dietary intervention on circulating levels of a selection of inflammatory markers, was however, planned a priori, and the finding of significant reductions in both IL-18 and neopterin, which form part of the same inflammatory signalling pathway, possesses an additional biological plausibility suggestive of being a 'true' positive result. . The results of multiple pairwise correlation comparisons, involving correlations between a large number of variables, are best viewed with some caution as these could similarly be subject to a type I error. However, the use of scatter graphs and exclusion of outlying data points shown in Appendix 1 provides some measure of the robustness of the reported associations.
Another weakness of the study study is that for a number of the inflammatory markers and adipokines measured (IL-1β, IL-1ra, IL-6, IL-10 and IL-18 BP) samples were outside the range of measurement or excluded on the basis of large coefficients of variation, resulting in a number of missing values. This is particularly evident in the measures of IL-1β, IL-1ra, IL-6, and IL-10, which were all measured as part of the same multiplex (in addition to TNFα, for which more reliable measures were obtained, suggesting there was not simply a more global problem with this multiplex assay). The principal analysis examining the effect of the dietary intervention was also limited to participants with values for each analyte at baseline and 6-month end of study visits. The resulting modest sample gave rise to wide confidence intervals and hence greatly reduced statistical power to detect any alterations arising from the intervention. A number of studies have been published which have aimed to systematically investigate the use of multiplex technology versus ELISAs, or assess the accuracy of different multiplex panels for the measurement of cytokines 669–679. These studies paint a picture generally in agreement that some problems exist in the use of multiplex technologies, such as the presence of heterophilic antibodies (i.e. antibodies for the detection of different analytes interacting with each other during the assay), and that the overall agreement of absolute values can differ widely between multiplex technologies and ELISAs, depending on the analytes in question, and this can subsequently interfere with cross-study comparisons. However, despite these - 82 -
differences multiplex assays have generally been confirmed as providing the ability to provide discrimination between relative levels across samples, and thus, while the absolute levels detected may differ from those seen by ELISA assays, they are still capable of being used for a number of the same comparisons offered by ELISA or RIA methods, such as ranking samples and providing correlations, and for assessing relative changes across time. Both Martos-Moreno et al. 672 and Khan et al. 680 have compared changes in cytokine levels across time (Martos-Moreno et al. in a weight loss intervention in obese children, and Khan et al. following an acute inflammatory challenge) and found changes in cytokine concentrations measured by multiplex comparable to those measured with ELISA or RIA in the same study. In their comparison across multiple laboratories, Breen et al. concluded that "in the context of smaller studies in a single laboratory or when all samples have been collected and can be run at a single time and place, multiplex kits may be useful and offer the benefit of greatly reduced sample volumes" 670. Given that the analyses conducted here in the LOADD study conform to these criteria and present a comparison of relative changes with a dietary intervention, or correlation with other cardiovascular risk variables based on ranking, the use of the multiplex values of IL-1β, IL-1ra, IL-6 and IL-10 in this analysis seems justified. However, given the number of samples outside of the detectable range, results should be interpreted with caution as the presence of a systematic difference between samples within or outside of the detectable range cannot be excluded. In addition, current consensus appears to suggest the comparison of absolute levels across studies should also be made with caution as differences in antibody pairs and laboratory procedures can give highly differing levels for some analytes.
In conclusion, the LOADD study dietary intervention resulted in significant reductions in circulating levels of IL-18 and neopterin. IL-18 has been associated with the development of cardiovascular disease, including a meta-analysis of prospective studies showing a relative risk (95 % CI) of developing of coronary heart disease of 1.34 (1.17, 1.53) after adjustment for age, gender, classical CHD risk factors, and inflammatory markers. Similarly, neopterin levels have been evaluated as a risk marker in numerous studies and associated with the development of a variety of cardiovascular events 532,535,540,541,543,545,602,646–654, including an association with ischaemic heart disease in people with type 2 diabetes 536. Although evidence exists for both of these factors for an association with, or even active role in, the development of cardiovascular disease, there are no trials demonstrating that a reduction in either of these markers results in a reduced rate of clinical endpoints. As such, the change in inflammatory markers seen in the current study appears to move in a favourable direction, although only a larger, longer trial of the LOADD dietary intervention with follow-up for clinical events would provide sufficient evidence of an effect on risk of
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cardiovascular disease or diabetes complications. Subsequently delineating whether such a reduction in risk derives from changes in inflammatory markers would be subject to numerous limitations; interpretating cause-and-effect relationships and future expectations of benefit with lifestyle intervention studies is discussed below.
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4.2 Interpreting the effects of the LOADD study dietary intervention; theoretical considerations of establishing cause and effect in studies of metabolic disease
One of the main driving ideas behind the research in this thesis was that sub-clinical inflammation associated with type 2 diabetes could be a causative factor in the high rates of heart disease in this population, if not even a cause of type 2 diabetes and insulin resistance itself. The question was posed as to whether dietary intervention in people with type 2 diabetes, who are treated according to current guidelines, could alleviate sub-clinical inflammation as assessed by a panel of inflammatory markers. The study in which this question was examined, the LOADD study, was itself designed principally as a pilot study, to assess the effects of dietary intervention on a range of markers of cardiovascular risk and diabetes control, with the intention of following this study with a larger trial with clinical endpoints; as recent trials in diabetes have underscored, these are the outcomes truly of interest in the treatment of diabetes.
In the event then that a hypothetical future study were to be conducted and show a reduction, for example, of diabetes complications (whatever endpoint that may include) with the LOADD study dietary intervention in people with type 2 diabetes, the question many would ask is how is this effect is achieved? Most likely, people would look to changes in physiological variables such as blood pressure or surrogate markers of cardiovascular risk such as lipid profile or inflammatory markers, and put forward the idea that the hypothetical risk reduction could be mediated by changes in such factors. In scenarios such as these, people often look to ‘basic research’ in order to try and understand the physiological processes taking place and deduce how they could impact on disease progression or rates of diabetes complications. In the main analysis of the LOADD study, statistically significant reductions in HbA1c, waist circumference, weight, and BMI were seen, without any changes in the conventional cardiovascular risk factors of lipid measures, blood pressure, or kidney function as measured by urinary albumin:creatinine ratio 601. The analysis included here also showed reductions in the inflammatory markers of neopterin and IL-18. Clearly, there are also a large number of other possible inflammatory markers not considered here, as well as markers reflective of other pathological processes currently being investigated for active roles in the development of diabetes complications, such as ectopic lipid deposition, endoplasmic reticulum stress, or levels of reactive oxygen species. Any hypothetical reduction in diabetes complications could also be derived from as yet unknown mechanisms or biological functions. Indeed, the rate of scientific progress in - 85 -
the understanding of metabolism is presently giving rise to an increasing number of potential mediators including endocrine effects of bone 681, increasing numbers of adipokines 682, and differing levels of metabolites 683,684 and “lipokines” 685 in people with type 2 diabetes. In addition, other studies highlight the role of factors carried by one’s diet but not conventionally thought of as ‘dietary’, such as cooking method 686, or co-ingested factors including pesticide residues 687 or plastics such as bisphenol A 688,689. Therefore, were the LOADD study dietary intervention to be shown in the future to have any effects on diabetes complications, ascribing causal mechanisms to changes in any of these biomarkers, whether they be ‘classical cardiovascular risk factors’, or newer ‘novel’ risk factors, or delineating the relative contributions of changes in multiple biomarkers to a reduction in disease risk would be incredibly difficult to put into practice. Any such conclusions would also be theoretically marred by the possibility that other unmeasured variables mediate changes in risk (in effect, the biological equivalent of the ‘unknown/unmeasured confounder’ in epidemiological terms).
With the multitude of biological processes and measurable chemical entities in the human body, ascribing cause-and-effect to changes in a variable such as a blood-borne analyte quickly leads one into a quagmire fraught with the difficulties of discerning association from causation. This is the quagmire into which research into the role of glucose as a cardiovascular risk factor, discussed in the Introduction, has marched head first by attempting to hail glucose reduction as the curative factor of the diverse interventions employed. The key factor necessary to bear in mind is that descriptions of cause-and-effect must hold the intervention in question as the ‘cause’, which in the case of the glucose-lowering studies discussed earlier are the medication regimens employed (as opposed to their effect; a reduction in glycaemia). Discerning the possible biological mediators which lie in between the administered ‘cause’ and measured ‘effect’ is another scientific venture altogether, whether the intervention be lifestyle modification, as used here, or drug therapy as in the glucose-lowering trials discussed in the Introduction. Thus, were the LOADD study dietary intervention to be shown to influence rates of diabetes complications, the dietary intervention would be the cause (including its associated ‘non-dietary’ components including, but not limited to, the psychological effects of visiting a study dietitian, or inadvertent alterations in the intake of pesticide or plastics residues); we could posit theories regarding reductions in glycaemia, adiposity, IL-18 or neopterin mediating risk reductions (assuming that future hypothetical interventions would show the same effects on surrogate markers as measured here), however, these would remain purely theoretical postulations.
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Section 3:
Animal studies into the biological role of IL-18
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Chapter 5: Introduction
5.1 CNS control of appetite and blood glucose
The idea that the CNS may exert a substantial effect on peripheral glucose homeostasis is not at all new, and Claude Bernard, the ‘father of modern physiology’ suggested this to be so as far back as 1849 690. More recently the career trajectory of Robert Sherwin, a physician-scientist with an interest in hypoglycaemia, is one which began in the 1970’s with investigations into the peripheral regulation of blood glucose and progressed to focus increasingly on CNS mechanisms as the key events involved in sensing and responding to hypoglycaemia 691.
By the early twentieth century it had been known for some time that tumors or injuries of the hypothalamic region could produce a cluster of symptoms including adiposity, fever, polyuria and reproductive disorders 692,693, and a few experimental studies of this era showed that lesions of the hypothalamus could produce obesity 693. However, it was not until the development of stereotaxic equipment in the 1930’s by Horsley-Clarke, which allowed for precise, reproducible lesions of the brain, that research into the role of the hypothalamus in the control of appetite and blood glucose began in earnest 692–696. Researchers quickly realised that the bilateral ventromedial hypothalamic nuclei play an important role in weight maintenance, and were labelled a ‘satiety centre’, as lesioning of these nuclei would produce weight gain 695,696. It was also established that this was not due to food intake alone, as pair-feeding of lesioned animals to the same amount of food consumed by non-lesioned controls did not completely impede weight gain 693. The neighbouring lateral hypothalamic nuclei were soon after documented as producing the opposite effect and became known as a ‘feeding centre’ 695,696. At the same time as these studies, dramatic effects of the brain on blood glucose control were also uncovered, including effects of the pituitary and ventromedial hypothalamic nuclei on blood glucose homeostasis 694,697, and the role of descending signals from the vagus nerve 693.
Further works into the control of food intake over the following decades focused on results obtained from ‘parabiosis’ experiments where rodents were surgically joined to allow subcutaneous transport
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of extracellular and blood-borne proteins from one animal to another. In combination with the serendipitous occurrence of spontaneous mutations resulting in heritable obesity in strains of mice at the Jackson Laboratories 698,699, various parabiosis experiments involving these mutant strains or overfeeding of normal rodents 700–709, lesioning of the ventromedial hypothalamus 710 or electrical stimulation of the lateral hypothalamus 711, suggested that “the hypothalamus may respond to body fat content by way of a slowly turned over blood-borne factor” (from Hervey et al., 1977 711). Later, painstaking positional cloning experiments led to the discovery of leptin, for which Douglas Coleman and Jeffrey Friedman – key figures in the parabiosis and cloning works, respectively – were awarded the 2010 Albert Lasker Basic Medical Research Award 700–702. A wealth of research into the role of leptin and hypothalamic feeding pathways has since followed; while the twentieth century marked a slow but sure development of knowledge regarding the role of the hypothalamus in the control of appetite and blood glucose homeostasis, the final years of the twentieth century and initial decade of the twenty-first have seen the rate of progress greatly accelerated by modern molecular biology and genetic engineering techniques, as well as a heightened scientific interest in elucidating the mechanisms by which metabolism is regulated. Recent works in particular show that the actions of leptin and other metabolic signals in the hypothalamus influence peripheral insulin sensitivity and glucose uptake 594,712–718, and that the hypothalamic effects of nutrients also influence food intake, metabolism and hepatic glucose production 719–726. Leptin administration into the hypothalamus has even been shown to reverse the glycaemic abnormalities of rodents with type 1 diabetes 727 y, apparently by a mechanism involving increased peripheral glucose uptake and depressed hepatic glucose output 728. These findings reinforce the discovery first elucidated by Claude Bernard; that the brain plays a powerful role in the regulation of peripheral glucose levels. In today’s modern setting of diabetes research, recent reviews discuss the CNS as having potentially overlooked powerful effects on peripheral insulin action and glucose homeostasis which could be of clinical relevance to the pathogenesis and treatment of diabetes 729–731.
y This experiment is similar in many regards to findings approximately 80 years earlier, in which pancreatectomized dogs were kept alive for months by removal of the pituitary 697. These studies underscore the key role of CNS mechanisms in controlling glucose homeostasis; even, as is the case in these works, in the face of a complete absence of insulin (in both studies) and glucagon (in pancreatectomized dogs). - 90 -
5.2 Architecture of the hypothalamus
Given the relatively short time frame of recent intense research into the functioning of the hypothalamus and mechanisms of energy homeostasis, it is not surprising that there remain many unanswered questions with regard to precisely how different metabolic signals influence the hypothalamus, and how signalling mechanisms involved in energy homeostasis interact in the living animal. Nevertheless, there is general consensus on some basic aspects of hypothalamic function and architecture. The arcuate nucleus at the base of the hypothalamus has been a focal point of research, and it appears that it forms a unique nucleus in the mammalian brain; evolved to be capable of detecting signals in the peripheral circulation or cerebrospinal fluid in the 3rd ventricle, which could be achieved via a more permeable blood-brain barrier and/or direct projections outside of the blood- brain barrier 732–736. Although these mechanisms remain somewhat controversial, it is generally agreed that the arcuate is particularly responsive to changes in circulating metabolic signals. For example, leptin receptors are found in high density in the arcuate nucleus 732, which respond to physiological levels of circulating leptin 733 (with other hypothalamic nuclei responding at greater concentrations) and a loss of response to leptin in the arcuate nucleus is a key feature of diet-induced obesity 591,737. Leptin signalling in the arcuate also appears to play a role in synaptic development and plasticity 738–741, thus demonstrating that metabolic signals have a role beyond the early signalling events which are typically monitored as outcomes in most research experiments. The arcuate nucleus contains two populations of neurons that have been particularly well-studied as targets of leptin action, one which expresses proopiomelanocortin (POMC) and cocaine and amphetamine regulated transcript (CART), activation of which causes a decrease in food intake, and another expressing agouti-related peptide (AgRP) and neuropeptide Y (NPY), activation of which increases food intake. Another key population of neurons is those expressing the melanocortin receptors (MCRs), which are particularly dense in the paraventricular nucleus of the hypothalamus (PVN) 742, and which receive input from POMC neurons in the arcuate nucleus. The key role of MCR expressing neurons in energy homeostasis has been underscored by a number of studies 743 and their relevance in human physiology is evident in studies showing that mutations of the melanocortin receptors are the most prevalent known genetic cause of obesity in people 744.
Figure 4 shows a basic schematic diagram of the location of these nuclei and neuronal populations in the hypothalamus. While the existing knowledge of hypothalamic function is more complex than that shown in the diagram, and will no doubt become increasingly more so as further research is
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Figure 4: Nissl-stained coronal rat brain section, showing the position of hypothalamic nuclei. Paraventricular (PVN), ventromedial (VMH), and arcuate (ARC) hypothalamic nuclei are labelled. The arcuate nucleus contains two neuronal populations, expressing POMC/CART and NPY/AgRP, which are involved in energy homeostasis and metabolism. Although found throughout the hypothalamus, the paraventricular nucleus contains a particularly high concentration of cells expressing melanocortin receptors, which are also important regulators of food intake and metabolism. POMC-expressing cells in the arcuate nucleus project to the paraventricular nucleus, and much of their effects on food intake and metabolism may arise from downstream effects on melanocortin receptor-expressing cells. Image adapted from http://brainmaps.org.
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conducted, the components of this model and their importance in energy homeostasis stand up well to the evidence presented by the existing literature.
Of note, the bulk of the characterisation of these metabolic pathways has been conducted in rodents, with some work in other species (particularly sheep) 745–751. Thus, to what extent the current knowledge of these metabolic signalling pathways also holds true in humans is not known. Similarities in findings across mammalian species (for example sheep and rodents), the phenotypes observed in humans with genetic mutations of melanocortin receptors or leptin signalling (which largely agree with expected effects based on animal studies), and genetic homology between humans and rodents in genes encoding many of these metabolic factors, suggest that the basic system of metabolic control by the hypothalamus also operates in a similar fashion in humans. However, differences obviously exist, such as the processing of POMC transcripts 743, and the precise role of any uniquely human traits will be incredibly difficult to ascertain.
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5.3 Inflammation in the hypothalamus
Just as there are links between inflammatory markers in the periphery and insulin resistance (as discussed in the Introduction), a number of works have also shown that activation of inflammatory pathways in the brain are linked to central responses to leptin and insulin. Activation of NF-κB and transcription of pro-inflammatory cytokines is upregulated in the hypothalamus upon consumption of a high fat diet 300,419,752, which appears to involve the inflammatory pathways of TLR-4 (toll-like receptor 4) 419 z and MyD88 (myeloid differentiation primary response gene 88) 758 signalling. Increased NF-κB signalling in the hypothalamus reduces responses to the hormones insulin 300,752 and leptin 300, and also appears to contribute to diet-induced weight gain, as manipulations to reduce hypothalamic inflammation also decrease weight gain on a high fat diet 300,752,758. Leptin-induced changes in appetite, or anorectic sickness behaviour induced by common inflammatory signals, have also been implicated to involve activation of the NF-κB signalling pathway in the hypothalamus 759, and NF-κB activation induces hypothalamic suppressor of cytokine signalling-3 (SOCS-3), a negative regulator of leptin signalling 300, thus appearing to form a classical negative feedback loop (in much the same way as leptin-induced STAT-3 signalling). Other studies show that the beneficial effects of exercise on insulin sensitivity may involve, at least in part, cytokine effects on hypothalamic inflammatory signalling 760. In addition, a number of proinflammatory cytokines are known to influence appetite (although it has not been demonstrated that their mechanisms of action all involve activation of hypothalamic inflammatory pathways), and their elevation is believed to underlie cancer cachexia 761.
z There is some evidence that saturated fatty acids directly activate TLR-4 receptors 421 although this has been contested 753. In vivo it appears that TLR-4 receptors do play a role in diet-induced obesity as mice with a gene ‘knock-out’ or manipulation of TLR-4 are resistant to diet-induced obesity 419, and the in vivo situation could involve other, indirect, means of TLR-4 activation such as the generation of ‘endotoxemia’ by gut microbiota in response to a high fat or hypercaloric diet 301,754–757. - 94 -
5.4 Key questions regarding the biological actions of interleukin-18: how are the central effects of IL-18 mediated?
What is the expression and distribution of the IL-18 receptor in the brain?
The studies of Netea et al. 503 and Zorrilla et al. 506, which showed that intracerebroventricular injections of IL-18 could act centrally to reduce food intake, present a ‘black box’ scenario where IL- 18 acts within the brain but the mechanism by which it produces a reduction in appetite is unknown. Neither of these studies offer hypotheses or putative mechanisms by which these effects could be mediated. Given the evidence linking the hypothalamus with appetite, an obvious hypothesis was that IL-18 would act on neuronal populations in the hypothalamus known to control food intake. In addition, this line of reasoning is supported by similarities between manipulation of IL-18 and manipulation of its related cytokine family member IL-1: mice lacking IL-1 receptors display a phenotype remarkably similar to that reported for mice lacking IL-18 receptors, with mature-onset obesity leading to impairment of glucose homeostasis (IL-18 receptor knockouts: 503; IL-1 receptor knockouts: 762); IL-1 receptor antagonist (an endogenous antagnosit of IL-1) knockout mice have also been characterised and show protection from diet-induced obesity and a lean phenotype 763; and two papers by the same group report the expression of IL-1 receptors on proopiomelanocortin (POMC) and agouti-related peptide (AgRP) expressing neurons in the hypothalamus 764,765. Therefore, it was reasoned that given similarities in phenotypes resulting from manipulations of IL-1 and IL-18 genes, and in the signalling pathways of these cytokines 423,766, that IL-18 receptors may also be expressed on POMC and AgRP neurons in the hypothalamus.
Could IL-18 contribute to hypothalamic inflammation?
Given the key role of the hypothalamus in influencing energy homeostasis and glucose control, links between inflammation and insulin resistance, studies suggesting a link between IL-18 and measures of insulin resistance and development of diabetes, and the fact that IL-18 is a pro-inflammatory cytokine which acts through NF-κB signalling, it was hypothesized that IL-18 could act in the hypothalamus to influence food intake and glucose homeostasis via NF-κB activation.
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5.5 Hypotheses:
Two hypotheses arose from interpretations of the current literature on the role of IL-18:
(i) The brain as a target of peripheral IL-18
One key hypothesis or model of IL-18 action in this thesis is that elevated circulating IL-18, as seen in obesity and diabetes, could drive NF-κB signaling in the hypothalamus, which has been linked to hypothalamic responsiveness to insulin and leptin. In this scenario, circulating IL-18 could influence appetite by interacting with hypothalamic pathways which govern energy homeostasis. Thus, experiments were planned in this thesis to examine the distribution of the IL-18 receptor in the rodent brain by in situ hybridisation and polymerase chain reaction (PCR) experiments.
(ii) Brain-derived IL-18 as a mediator of the metabolic effects of IL-18
Another hypothesis, is that IL-18 production within the brain itself could influence metabolic pathways involved in appetite. Such a possibility could explain observations from animal experiments showing modulation of appetite by IL-18 gene knockout or intracerebroventricular IL- 18 administration.
Note that these two hypotheses are not necessarily mutually exclusive; a scenario could exist whereby both peripheral and brain-derived IL-18 jointly influence metabolism. Nevertheless, the chapters in this section of the thesis are organised around investigations into these two hypotheses.
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Chapter 6: General laboratory methods
6.1 Animals
Animals were obtained from the Hercus Taieri Resource Unit, Dunedin, New Zealand and housed under Specific Pathogen Free (SPF) conditions until use in the current experiments. Animals used were either adult male Sprague-Dawley rats or adult male CJ57BL/6 mice. All procedures were performed with ethical approval from the Animal Welfare Office, University of Otago (approval numbers 58/07 and 64/08).
6.2 Reverse transcription polymerase chain reaction (RT-PCR)
Tissue collection of whole hypothalamus, liver, spleen and adrenal samples:
Samples of liver, spleen, adrenal and hypothalamus were collected by gross dissection after sacrificing animals by decapitation. Samples were wrapped in tin foil and placed on dry ice until frozen, and then transferred to a freezer at -80 C as soon as possible. For extraction of hypothalamic tissue, brains were placed on tin foil with the inferior surface facing upwards, and the hypothalamus grossly dissected with a razor blade. For liver, spleen and adrenal samples, segments of tissue small enough to be dissolved in 1 mL of Trizol were dissected.
Collection of brain micropunches:
Brains were collected following decapitation as above. Tissue was kept at -80 °C until needed. Brains were transferred to a cryostat set to -10 C and mounted onto sectioning chucks using Tissue-Tek® (Sakura Finetek USA, OCT Compound 4583). Coronal sections were cut at a thickness of 300 μm and mounted onto glass slides, which were placed onto a steel block kept cool with dry ice and viewed under dissecting microscope. Micropunches through nuclei of interest were collected by punching circular holes using modified 21-gauge hypodermic needles 500 μm in diameter.
For collection of ventromedial hypothalamic and paraventricular nuclei, two punches per section were made either side of the third ventricle. Collection of arcuate nuclei was done by a single punch, centred on the middle of the third ventricle at its lowest point. Two micropunches per section were - 97 -
collected through the habenula, interpeduncular nucleus and ventral tegmental area. The rat brain atlas of Paxinos and Watson was used as a guide 767. Appendix 3, Figures 1 through 4 show images of micropunch collection from the various nuclei for experiments described in Chapter 7, Section 7.5 and Chapter 8, Section 8.5.
RNA extraction:
Collected tissue samples or brain micropunches were placed into 1.7 ml pre-sterilized microcentrifuge tubes (Axygen, CA, USA; product number: MCT-175-C-S) containing Trizol reagent (Invitrogen, USA, SKU# 15596-026). Spleen, liver, adrenal and whole hypothalamus samples were digested in 1 ml Trizol, and micropunches in 800 μl of Trizol. Samples were homogenised by using a plunger which had been treated with RNase Zap (Ambion, USA, Catalogue #AM9780) according to manufacturers instructions, and samples were subsequently passed through a sterile 26 gauge needle to further break up tissue. Following homogenisation, samples were sonicated for approximately 10 minutes and 10 μg of glycogen added. Samples were incubated at room temperature for 5 minutes and at this stage some samples were optionally frozen at -80 C for later RNA extraction. For RNA extraction, 0.2 ml of chloroform solution was added per 1 ml of Trizol and tubes shaken for approximately 15 seconds, then incubated at room temperature for a further 3 minutes. Samples were then spun in a refrigerated centrifuge set at 4 C for 15 minutes at 12,000 g to produce a clear upper phase containing RNA. This clear phase (between 300 - 600 μl) was transferred to fresh tubes using sterile pipette tips, and isopropyl alcohol added at a quantity of 0.5 ml for every 1 ml of Trizol originally used for extraction. Tubes were vortexed and incubated at room temperature for 10 minutes, after which tubes were spun in a centrifuge for 10 minutes at 12,000 g at a temperature of 4 C, leaving a pellet of RNA at the bottom of the tube. The supernatant was discarded and 1 ml of 75 % ethanol added. Ethanol solution was diluted from 100 % Analar grade ethanol using DEPC- treated water. The RNA pellet was washed by vortexing and spun in a centrifuge at 7,500 g for 5 minutes at a temperature of 4 C. The resulting supernatant was poured off and any remaining excess supernatant removed by pipette. Samples were left in open tubes at room temperature for approximately 10 minutes to further air dry the RNA pellet, after which 20 μl of DEPC-treated water was added and tubes incubated for 10 minutes at 60 C with occasional vortexing to resuspend the collected RNA. Samples were either used for reverse transcription immediately or were kept frozen at -80 C for reverse transcription at a later time.
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Measurement of DNA and RNA quantity and quality:
Quantity and quality of DNA and RNA was measured using a Nanodrop spectrophotometer (Nanodrop® ND-1000 Spectrophotometer), and assessed via the ratio of absorptions at wavelengths of 260 nm to 230 nm (260/230 ratio), and 260 nm to 280 nm (260/280 ratio).
Reverse transcription:
Reverse transcription was performed using Superscript III reverse transcriptase (Invitrogen, Catalogue number 18080-044). Tubes were prepared with up to 11 μl of RNA solution, 50 ng of random hexamers, 1 nmol of dNTP, and where necessary made up to 13 μl of total solution with DEPC-treated water. The amount of RNA added was calculated using previous measurements obtained from spectrophotometer analysis. Tubes were incubated for 5 minutes at 65 C and then placed on ice. To each tube 7 μl of a second solution was added which contained 4 μl of 5 × first strand buffer, 0.1 ng of DDT, 40 U of RNase OUT, and either 200 U of Superscript III enzyme for reverse transcription positive (RT+) samples, or an equal volume (1 μl) of DEPC-treated water for reverse transcription negative (RT-) samples. Samples were placed in a thermocycler machine and incubated for 5 minutes at a temperature of 25 C, followed by 50 C for 50 minutes and 70 C for 15 minutes. Following reverse transcription samples were stored at -20 C until further use. Calculation of approximate cDNA concentrations following reverse transcription were made based on the amount of RNA added and assumed a complete 100 % conversion of RNA to cDNA.
Primer design for polymerase chain reactions:
Primers were designed against target gene sequences using either Invitrogen’s OligoPerfect™ Designer 768 or Primer3 769 online software systems, by targeting desired segments of the reference gene sequences, or using the National Center for Biotechnology Information PrimerBLAST (Basic Local Alignment Search Tool) after this was released 770. Gene sequences were accessed from the National Center for Biotechnology Information online Genbank database 771 where available, or otherwise from the Ensembl Genome Browser 772. Primers were checked for possible amplification of non-target genes by the use of the online BLAST 773 or PrimerBLAST 770 tools from the National Center for Biotechnology Information, and checked for the probability of primer dimers using the online software Netprimer 774.
Polymerase chain reaction (PCR):
Polymerase chain reaction was carried out using either 1.1 or 2 Reddymix™ PCR Master Mix (Thermo Scientific, USA, product numbers AB-0575/LD/A and AB-0575/DC/LD/A respectively) - 99 -
which incorporates loading buffers and dyes to enable direct loading of PCR products onto electrophoresis gels. Reactions were carried out in either 25 μl or 50 μl total reaction volumes, which incorporated: the appropriate amount of Reddymix as per manufacturer's instructions; cDNA, added with the aim of achieving a concentration of 1-4 ng of cDNA per µl of reaction mix up to a maximum of 4 μl/50 µl reaction (or 2 μl/25 µl reaction), on the assumptions that all RNA as measured by spectrophotometer (above) was converted to cDNA during reverse transcription, and that no cDNA was subsequently degraded. Forward and reverse primers were added at 20 pmol each, with the exception of amplification of rat IL-18r1 using the primers of Andre et al. when 50 pmol of primers was added (as per reported methodology of Andre et al. 775). Primers and target sequences of desired transcripts are given later in each chapter. Reactions were carried out in appropriate thermocycler 200 μl tubes (either Abgene product number ABGAB-0337 from Thermo Scientific, USA or Axygen, USA, product number PCR-02D-C) and using any of three thermocycler machines: Minicycler™, MJ Research Inc, Watertown, MA, USA, Model PTC-150HB Biometra TProfessional Basic, Biometra, Germany, order number 070-701 Corbett Research Thermocycler, Corbett Research, USA, Model CG1-96
Unless stated otherwise, PCR settings were an initial denaturing step of 3 minutes at 94 °C, followed by 35 cycles of a denaturing step of 94 C for 45 s, an annealing step at 60 C for 45 s, and an extension step of 72 C for 60 s, followed by a final extension step of 72 C for 5 minutes.
Nested and semi-nested PCR:
Nest and semi-nested PCR reactions were carried out as above, with first round PCR products diluted 1:200 in DEPC-treated water, and a 1 μl volume of the resulting diluted PCR product used per 50 μl total reaction as the cDNA template in the second round of amplification.
6.3 Gel electrophoresis: visualization of PCR products
Products of polymerase chain reactions were loaded onto 1.5 % agarose gels, prepared with Ultrapure™ agarose (Invitrogen, SKU #16500500) and either Tris-acetate-EDTA (TAE) or Tris- borate-EDTA (TBE) buffers, and ethidium bromide at a concentration of 0.3 μg per ml of buffer. Samples were run with a 100 bp DNA ladder (Invitrogen, SKU #15628-019), at a fixed voltage ranging from 70 V to 120 V depending on the size of the gel used, and for a variable length of time until sufficient separation of DNA bands was observed with an ultraviolet gel viewer. Gels were viewed on a viewing platform lit with ultraviolet bulbs, and photographed with a camera with Polaroid film 667 (Polaroid Corporation UK), or using a digital camera connected to a computer. - 100 -
Polaroid photographs were scanned into digital images for archiving and use in the current thesis document. Images were colour inverted to give black bands on a white background for clearer print reproduction. For some gel images, brightness and contrast were adjusted to give clearer definition of bands, but no other digital manipulation of images was performed.
6.4 Product sequencing: verification of amplification of desired gene products
Products were sequenced either at the Allan Wilson Centre for Molecular Ecology and Evolution (AWCMEE, Massey University, New Zealand) or the Genetic Analysis Services at the Department of Anatomy, University of Otago, New Zealand. PCR products were first purified by column purification, using QIAquick® Column Extraction kit (Cat No 28104, QIAGEN) according to manufacturer’s instructions.
6.5 In situ hybridisation methods
Preparation of tissue sections for in situ hybridisation procedure:
Whole brains were collected from animals following decapitation and stored at -80 °C until use. Sections through rat or mouse brain were cut in a cryostat set between -20 C and -16 C at a thickness of 30 μm in either coronal or sagittal orientations. Sections of liver and spleen collected from the same animals were used as positive control tissues, and placed on cryostat chucks at an orientation which provided a reasonable ease of mounting and sectioning. Sections were collected onto either Superfrost®Plus microscope slides (Biolab New Zealand, Cat No. LBS4951) or ATS- coated glass slides prepared in house. Slides were stored at -80 C until used in in situ hybridisation procedures.
Generation of hybridisation probe cDNA templates:
RT-PCR was used to amplify targeted transcripts using the primers shown in Table 8, using cDNA reverse transcribed from total RNA extracted from tissues of the respective species. PCR products were sequenced to ensure amplification of the desired sequences. New primers were then made with either ‘SP6’ (AATTAGGTGACACTATAGAATAG) or ‘T7’ (TAATACGACTCACTATAGGGAGA) promoter nucleotide sequences added to the original primers, both placed at the 5’ end of 'forward' and
'reverse' primers, respectively, in order to generate anti-sense probes from a T7 promoter, and sense probes from an SP6 promoter. Templates for use in in situ experiments were made by performing PCR amplification using these primers with either RT product or diluted PCR product from previous
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10
2
- Table 8: Primer sequences for use in generating in situ hybridisation probes for use in rat and mouse tissues
Gene product of interest Primer Primer sequence Designed to bind † Expected probe length ‡ Rat: IL-18 F TGGCTGTGACCCTATCTGTG NM_019165.1, binding nt 287 - 543 256 bp R ATCCCCATTTTCATCCTTCC Mouse: IL-18 rap F ACTGGCTCCATTCATTGTCC NM_010553.2, binding nt 941 - 1195 254 bp R CAGAATTTCCGGCTTCACAT IL-18r1 F TGATATCCCAGGCCATGTCT NM_008365.1, binding nt 1331 - 1461 130 bp R CCGCTATGCGCCTATAGAAC F - forward primer; R - reverse primer
† reference codes are for the NCBI Genbank database 771,776
‡ Excludes SP6 and T7 promoter nucleotide sequences
reactions which utilised the original primers without SP6/T7 promoter extensions as a reaction template. Subsequently, DNA templates were sequenced at the Allan Wilson Centre for Molecular Ecology and Evolution or Genetic Analysis Services, Department of Anatomy to ensure appropriate amplification of desired gene sequences prior to use in in situ hybridisation experiments.
Generation of hybridisation probes:
Radiolabelled hybridisation probes were generated using a Promega Riboprobe® Combination System SP6/T7 RNA Polymerase kit (Promega, USA, product number P1460) and Easytides® S35- labelled UTP (Perkin Elmer, Boston, MA, USA, product number NEG739H001MC). Probes were made by drying 9 μl of Easytides S35-labelled UTP in a 1.5 mL screw top centrifuge tube (Axygen Scientific, USA, product number SCT-150-C-S) in a vacuum centrifuge (ThermoSavant DNA120 SpeedVac®) until very little solution remained. The volume of S35-labelled UTP was adjusted as deemed necessary depending on the age of the stock and how far through its half-life the S35-labelled UTP had progressed, using 9 l for a fresh stock and greater amounts for older radiolabelled UTP supplies. Subsequently 1 μl of 100 nmol of DTT, 1.25 nmol each of ATP, GTP and CTP nucleotides, approximately 200 ng of cDNA hybridisation template, and DEPC-treated water were added to give a final volume of 8.5 μl. Tubes were vortexed to resuspend radiolabelled UTP nucleotides and then 20 U of RNaseIn Ribonuclease inhibitor and 10 - 20 U of T7 or SP6 RNA polymerase added. Tubes were incubated for one and a half hours at 37 C in a dry bath, after which RQ1 DNase was added to a concentration of 1 U per microgram of template cDNA and tubes incubated for a further 15 minutes. Reactions were terminated with the addition of 1 μl of 0.2 M EDTA. Samples were diluted to 50 μl total volume with TE buffer and duplicate measures of 1 μl of solution set aside to determine the total radioactivity present.
Unincorporated nucleotides were removed using Roche mini Quick Spin columns (Roche Applied Science, catalogue number 11 814 427 001) according to manufacturer’s instructions and duplicate 1 μl measures removed for measuring of incorporated radioactivity.
Radioactivity was measured using 4 mL of scintillation fluid (Biodegradable Counting Scintillant, Product code NBCS104, Amersham) in 5 mL scintillation tubes, and counted using a 1450 microbeta Trilux system (Wallac) running Microbeta Windows Workstation software. Probes were used for subsequent in situ hybridisation if they showed at least 25 % incorporation of radiolabelled UTP.
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Following removal of unincorporated nucleotides, samples were made up to 100 μl with TE buffer and 5 μl removed for visualising the probe on an electrophoresis gel. To the remaining solution 10 μl of 3 M sodium acetate was added, followed by 250 μl of cold 100 % Analar grade ethanol. Tubes were then placed in a -80 C freezer for at least 30 minutes prior to use.
Gel electrophoresis of radiolabelled probes:
Radiolabelled probes were run on an RNA-denaturing 1.5 % agarose gel. Prior to adding probe samples to the gel wells, to the 5 μl of the transcription solution (removed in steps above), 15 μl of sample buffer was added, containing deionised formamide, formaldehyde and MOPS buffer and heated for 10 minutes at 65 C. Samples were then loaded with loading buffer and run with a DNA ladder to facilitate evaluation of band size. Following electrophoresis, gels were photographed under UV light and then loaded into light-proof cassettes (Hypercassette ™, Amersham Life Science, UK) and autoradiograph film (Kodak Medical X-ray film, supplied by Radiographic Supplies Ltd, Christchurch, NZ) loaded into the cassettes in a dark room. Cassettes were left at 4 C overnight and developed the following day. Autoradiographs were developed as described below and lined up against photos of the electrophoresis gel to estimate the size of bands visible on the autoradiograph film, in order to verify that they were in accordance with the expected length of RNA probes.
In situ hybridisation procedure:
Slides were thawed at 55 C for 5 minutes in an oven and loaded into slide racks. Racks were placed in 4 % paraformaldehyde solution and left for 20 minutes in a fridge at 4 C. Slides were washed in 0.5 SSC buffer for 5 minutes and placed into RNase buffer containing 2 μg/ml of proteinase K for 10 minutes for membrane permeabilisation, after which slides were washed again in 0.5 SSC buffer for 10 minutes with agitation. Slides were then placed in a 1 TEA solution for 3 minutes with agitation followed by 1 TEA solution with 0.25 % acetic anhydride for 10 minutes with agitation to decrease non-specific binding. Slides were subsequently washed twice in 2 SSC solution for 2 minutes per wash. Slides were dried using Kimwipe tissues (Kimberly Clark Professional, NSW) and placed elevated on Perspex bars in an air-tight box which was kept humid by lining the bottom of the box with paper towels which had been moistened with buffer containing SSC buffer, formamide and DEPC-treated water. Sections were covered with a hybridisation buffer solution containing DTT, NaCl, Tris buffer, EDTA, Denhardt’s solution, dextran sulphate and formamide. Slides were then incubated in an oven at 42 C for 3 hours for pre-hybridisation. Afterwards, 20 μl of a hybridisation mix containing the radiolabelled probe (2 l), tRNA (1 l) and buffer (17 l) as listed
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above was added to each section. Slides were covered with glass coverslips and incubated at 55 C overnight for hybridisation of radiolabelled probes to target mRNA.
Following hybridisation, slides were loaded into slide racks and washed twice in 2 SSC solution containing 10 mM β-mercaptoethanol and 1 mM EDTA for 10 minutes each, and then placed in RNase A solution containing 20 μg/l of RNase A for 30 minutes to remove any remaining unbound RNA. Slides were again washed twice in 2 SSC solution containing 10 mM β-mercaptoethanol and 1 mM EDTA for 10 minutes each, and slide racks were then placed in a 1 L container of 0.1 SSC solution and incubated for an hour at 55 C as a high stringency wash, after which the incubation solution was replaced with fresh 0.1 SSC solution and left for a further hour. Slides were washed twice in 0.5 SSC for 10 minutes each.
Visualisation of binding using autoradiographs:
Following the in situ hybridisation procedure, sections were dried by submersion in a graded alcohol series by placing slides for 3 minutes each in solutions of 50 % ethanol with 0.3 M NH4, 70 % ethanol with 0.3 M NH4, 95 % ethanol, and 100 % ethanol. Any remaining alcohol solution was dried off by placing slides on a bench in front of a fan. Slides were placed into light-proof cassettes and radiographic film (Kodak Medical X-ray film, supplied by Radiographic Supplies Ltd, Christchurch, NZ) placed over slides in a dark room. Cassettes were shut and additionally wrapped in black polyurethane if deemed necessary to inhibit the leakage of light onto radiographic film. Cassettes were placed in a 4 C fridge and left to expose for 1 - 3 weeks prior to developing.
Developing autoradiographs:
Films were developed in a dark room using either an automated developer machine (Ecomax X-ray film processor, Protec Medical Systems, Oberstenfeld, Germany), or manually by immersion for 2 minutes in developer (Kodak D19 developer, Eastman Kodak, USA), rinsing for 30 seconds in running cold water, and 1 minute in fixative (Ilford Hypam fixer, Ilford Photo, England). Films were scanned into digital images for archiving and inclusion in this thesis document.
6.6 Immunohistochemistry
Sample preparation:
Adult male Sprague-Dawley rats were anaesthetized by interperitoneal injection of sodium pentobarbitone (Pentobarb 300, Provet NZ Ltd, Auckland, NZ) combined with sodium heparin
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(Multiparin, CP Pharmaceuticals Ltd, UK) to aid in blood letting during perfusion. Rats were perfused by transcardiac perfusion, initially with sterile saline containing 500 IU/L of heparin, followed by 4 % paraformaldehyde. Brains were removed, along with spleens for use as a positive control, and were post-fixed in 4 % paraformaldehyde overnight at 4 ˚C and then transferred to sucrose infiltration solution. After 24 hours infiltration solution was refreshed and tissues left until samples had sunk (typically another 24 to 48 hours), and were then either sectioned immediately or transferred to a -80 ˚C freezer until sectioning. Tissues were sectioned by microtome, with the brains of three animals cut into different planes: coronal, sagittal or coronal oblique. The coronal oblique plane was oriented with the ventral portion of the brain more posterior than the dorsal, with the aim of sectioning along the length of the fasciculus retroflexus, as previous studies had shown IL- 18-positive fibres running within this fasciculus 777. The approximate plane of sectioning is shown in Figure 5. Brains were cut into 30 µm sections in the coronal and coronal oblique planes, and 40 µm sections in the sagittal plane. Spleen sections used as positive controls in IL-18 immunohistochemistry were cut into 30 µm sections. All sections were placed in cryoprotectant solution and stored at -20 ˚C until processing for immunohistochemistry. Cryoprotectant solution consisted of 0.05 M phosphate buffer with sodium chloride at physiological concentration, 30 % ethylene glycol, 30 % weight by volume sucrose, and 1 % weight by volume polyvinylpyrrolidone.
Immunohistochemistry procedure:
Tris-buffered saline (TBS) with 0.1 % triton was used as the wash buffer throughout, and immunohistochemistry was performed with free-floating sections in incubation wells. Sections were washed three times for 10 minutes each in wash buffer to remove cryoprotectant, and then placed in distilled water with 30 % hydogen peroxidase and 1 % sodium hydroxide for 20 minutes to deplete endogenous peroxidase activity. Sections were washed for 5 minutes in wash buffer and then endogenous biotin blocked using a method adapted from Wood and Warnke 778 to reduce labelling of endogenous biotin in the rat brain 779 by incubation in 0.01 % avidin solution (Invitrogen, catalogue number 43-4401) followed by 0.001 % biotin solution (Invitrogen, catalogue number B-1595) for 15 minutes each, followed by 5 minute washes. For IL-18 immunohistochemistry described in Chapter 7, a mild antigen retrieval step was used to enhance staining in fasciculus retroflexus fibres by incubation in 0.03 % SDS (sodium dodecyl sulphate) for 10 minutes followed by a 5 minute wash in wash buffer. Non-specific antibody binding was blocked by incubating sections in 4 % serum from the appropriate species in TBS for 1 to 2 hours at room temperature. Since some of the secondary antibodies used exhibit cross-reactivity with bovine IgG, the use of bovine serum albumin was
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Figure 5: Schematic diagram showing sagittal rat brain with approximate plane of sectioning of coronal oblique sections indicated by dashed line. Image adapted from http://brain-maps.org. avoided in all experiments. Primary and secondary antibodies, concentrations, and controls are as described in each chapter. Following primary antibody incubation, sections were washed three times for 10 minutes each in wash buffer and then incubated in biotinylated secondary antibody (as indicated). Sections were developed using an avidin-biotin complex (ABC)/ 3,3'-diaminobenzidine (DAB) reaction, both performed according to manufacturer’s instructions (both from Vectorlabs: VECTASTAIN ABC standard kit, PK-4000; DAB Peroxidase Substrate Kit, SK-4100). Nickel was added to the DAB reaction to produce a black precipitate. Following DAB development, sections were refrigerated in TBS until mounting on slides. Glass gelatin-coated slides were used for mounting sections, which were then passed through an alcohol dehydrating series, incubated in xylene and coverslipped in DPX (dibutyl phthalate xylene) microscopy medium.
Images of sections included here were obtained by photographing with a digital camera attached to an Olympus AX70 light microscope. Each image was white-balanced against a blank section of the slide. Due to uneven background levels across different sections and magnifications, images were later processed through Adobe Photoshop CS5, adjusting the input levels for each image until background portions of each image appeared white.
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Chapter 7: Brain-derived IL-18 as a mediator of the metabolic effects of IL-18
7.1 Overview
One potential explanation for both the phenotype of IL-18 knockout animals, and observations of intracerebroventricular IL-18 administration reducing appetite reported by Netea et al. 503 and Zorrilla et al. 506, is that there is production of IL-18 within the brain which acts to modulate appetite.
A few studies have shown increases in IL-18 in the brain in various models of brain injury 780–783, where IL-18 seems to fulfill a role typically expected of pro-inflammatory cytokines, which are often upregulated following an acute injury. In other studies examining constitutive presence of IL-18 under more physiological conditions, IL-18 has been reported in a number of brain areas: the first study to clone rat IL-18 cDNA reported its expression in the cerebellum, hippocampus, hypothalamus, striatum and cortex 784; other works have shown amplification of IL-18 mRNA by PCR in various brain regions, such as the hypothalamus 785–787, amygdala 788, pituitary 789, and cerebellum 790. Studies using cell cultures have shown expression of IL-18 in microglia and astrocytes791, which has also been observed in vivo 783,792, and IL-18 knockout mice have been shown to exhibit reduced microglial activation following a neurotoxic insult 793 or acute short-term stress794, demonstrating a role for IL-18 in microglial function. A study by Prinz and Hanisch shows reducing brain IL-18 content with age in mice 795 with little evidence of brain IL-18 in adult mice, which is in agreement with data from the Allen Brain Atlas showing no detectable expression of the IL-18 gene in the adult mouse 796. On the other hand, the Eurexpress project, which examines gene expression across the entire developing embryo, shows no evidence of IL-18 brain expression at 14.5 days post- conception 797. Of particular importance to the current thesis is one previous report from 2002 in which Sugama et al. conducted in situ hybridisation and immunohistochemistry for IL-18 in the Sprague-Dawley rat 777. They reported that IL-18 is produced within the medial habenula and ependymal cells lining the 3rd ventricle. Although the reproduction of immunohistochemistry
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images in that paper is somewhat poor, they present evidence from dark field images of immunohistochemistry experiments showing that IL-18 is transported down fibres within the fasciculus retroflexus, a major fibre tract which connects the habenula (medial and lateral), stria terminalis, interpeduncular nucleus and ventral tegmental area and forms part of the dorsal diencephalic conduction system 798–800. This was the first paper to describe such a selective distribution of IL-18 in the brain, and no other studies have been published since describing similar findings.
Given the conflicting evidence presented by the above studies, we sought to characterize the expression of IL-18 mRNA and its translation into protein in the adult rat, through in situ hybridisation with radioactive probes and immunohistochemistry, respectively, in order to examine whether central expression of IL-18 could play a role in the phenotype of IL-18 knockout animals and associations of IL-18 with metabolic diseases.
7.2 Methods
7.2.1 In situ hybridisation
In situ hybridisation for expression of the IL-18 gene was performed in adult male Sprague-Dawley rat tissue, with methods and probes as described in Methods section, Chapter 6, Section 6.3. Radiographic film was left to develop for 2 weeks.
7.2.2 Immunohistochemistry
Immunohistochemistry was performed as described in the General Laboratory Methods, Chapter 6. Anti-IL-18 primary antibody (Santa Cruz, sc-6179) was added to 4 % normal horse serum diluted in TBS with 0.1 % triton at a ratio of 1:1000 and incubated at 4 ˚C for approximately 36 hours. Secondary antibody (Vectorlabs horse anti-goat IgG, BA-9500) was diluted 1:1000 in blocking solution and incubated for one hour at room temperature. Sections from the same brains were run through three negative control procedures at the same time, where either: the primary antibody was omitted, the primary antibody was replaced with non-immune IgG from the same species (goat IgG, Vector Labs, I-5000), or the primary antibody was pre-incubated during the blocking step in 500 µl of TBS to which a 5-fold concentration of the immunizing antigen was added (pre-absorption control; antigen from Santa Cruz, sc-6179P).
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7.2.3 IL-18 and IL-18s RT-PCR
Appendix 2, Section 2.1 describes the design and testing of primers to amplify the full-length IL-18 and IL-18s transcripts. Primers were designed to bind to either the 57 bp segment present only in the full-length IL-18 transcript, or to span the nucleotides either side of this segment to amplify the IL- 18s transcript, shown in Appendix 2, Table 1. Primers were tested as shown in Appendix 2 on spleen and adrenal tissue. Micropunches of the habenula were made as described in General Laboratory Methods, Chapter 6; images of sections before and after punching the habenula nuclei are shown in Appendix 3, Figures 1 and 2. RNA extraction and reverse transcription were performed as detailed in Chapter 6, and PCR reactions carried out as described in Appendix 2, Section 2.1.
7.3 In situ hybridisation to examine IL-18 mRNA expression
Autoradiographs from in situ hybridisation experiments are shown in Figure 6. No signal was noted in the liver, which had previously proved a good positive control during PCR amplification of IL-18 mRNA (not shown). Brain sections showed no signal in sections incubated with sense probes, and a specific strong signal in the habenula with anti-sense probes, as had been reported previously by others 777. A lighter signal was also observed more anteriorly in the brain in a region within the triangular septal nucleus.
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Figure 6: In situ hybridisation for expression of the IL-18 gene in the rat brain. Left and middle panels show autoradiograph images of sections incubated with antisense and sense probes, as indicated. To the right are shown Nissl-stained coronal sections of the rat brain with features of interest labelled (adapted from http://brainmaps.org). Note focal expression is seen in the medial habenula but is absent in other brain areas, with the exception of some evidence of radiolabelling in the triangular septal nucleus.
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7.4 Immunohistochemistry to examine the distribution of IL-18 protein
Immunohistochemistry experiments for IL-18 produced strongly specific staining in the habenula, principally in the dorsal region of the medial habenula bordering the dorsal 3rd ventricle, but some IL-18 positive cells were also present more laterally in the medial habenula (Figure 7, Panels A - D). Sections where the primary antibody had been pre-absorbed with a 5-fold excess of antigen showed light background staining as well as brown staining along the fibres of the stria terminalis, which runs along the dorsal edge of the habenula (Panel E); this non-specific signal was also present in sections incubated with primary antibody, where a similar brown pattern of staining is visible superior to the darker stained IL-18 positive cell bodies (Panels A and B). Sections through the triangular septal nucleus, which gave in situ hybridisation results suggestive of IL-18 expression (above, Figure 6), showed no signs of positive staining (not shown). Examination of the distribution of IL-18 positive cells in sagittal (Figure 8) and coronal (Figure 7) planes reveals that anteriorly, IL- 18 positive cells begin to appear interspersed with afferent fibres from the stria terminalis. Cell numbers increase more posteriorly in the habenula as the stria terminalis fibres terminate, with the heaviest immunolabelling found in the dorsal medial habenula. Throughout the habenula, IL-18 positive cells are concentrated around the most medial aspect of the habenula, lining the dorsal 3rd ventricle.
Fibres within the fasciculus retroflexus also stained positive for the presence of IL-18. These could be seen in coronal (Figure 7), and sagittal planes (Figure 8). The fasciculus retroflexus projects to the interpeduncular nucleus, and postively-stained fibres could be seen throughout the fasciculus, terminating in a densely stained projection field in the interpeduncular nucleus (Figure 8, Panel C).
Staining of ependymal cells was noted (Figure 9), which was also observed by Sugama et al. when conducting immunohistochemistry for IL-18 in the Sprague-Dawley rat 777. The positive staining observed during incubation with anti-IL-18 primary antibody was, however, also observed when sections were incubated with anti-IL-18 antibody which had been preabsorbed with a 5-fold excess of antigen (Figure 9, Panel B); the intensity of staining was lighter in these negative control sections, but still present. Given that staining of ependymal cells was visible with antibody preabsorption, and that the preabsorption procedure used successfully blocked antibody binding to the more numerous habenula IL-18-containing cells (Figure 8, Panel E), the staining seen in ependymal cells with anti- IL-18 antibody appears non-specific. - 113 -
Figure 7 (following pages): Immunohistochemistry for IL-18 in the rat brain showing coronal sections focused on the habenula and surrounding brain regions. A - D show sections incubated with primary antibody demonstrating IL-18 immunostaining anteriorly (A) through posteriorly (D) in the medial habenula. E shows a section at the level of that shown in Panel B where primary antibody was pre-absorbed with antigen prior to incubation. Clear presence of IL-18 immunoreactive cells are seen in the medial habenula, principally in the dorsomedial portion. Fibres exiting the medial habenula can be seen (as indicated by arrows) joining the emerging fasciculus retroflexus more ventrolaterally. Scale bars = 500 µm.
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Figure 8 (following page): Sagittal view of IL-18 staining in the rat habenula. A: IL-18 positive cells in the medial habenula are interspersed among afferent stria terminalis fibres. B: More laterally, IL-18-containing fibres can be seen in the emerging fasciculus retroflexus. C: Terminating fasciculus retroflexus fibres entering the interpeduncular nucleus, which stains densely for IL-18. Scale bars = 500 µm.
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Figure 9: Staining of ependymal cells surrounding the 3rd ventricle during immunohistochemistry for IL-18. Ependymal cells stain positively when incubated with anti-IL-18 antibody (A), but also with preabsorbed antibody (B). Scale bars = 200 µm.
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7.5 Examining expression of an IL-18 splice variant in the rat habenula
Previous studies have reported the existence of a truncated IL-18 splice variant, IL-18s in the rat and mouse 801,802. In the study showing its existence in the mouse, expression in brain homogenates was seen by RT-PCR 802. In the study by Conti et al., which discovered the IL-18s transcript in the rat 801, the existence of the IL-18s transcript was detected in the adrenal gland by RT-PCR. In that study, expression of IL-18 mRNA in the adrenal gland was also observed to increase after cold stress, or treatment with the antihypertensive reserpine. Similarly, Sugama et al. 626 have also shown in mice that immersion and immobilisation stress causes an upregulation of IL-18 mRNA and protein in the adrenal gland, and adrenalectomy experiments showed that the adrenal gland was the source of stress-induced elevated circulating IL-18. In a separate study, Sugama et al. also showed that restraint stress in rats induces an upregulation of IL-18 in the habenula 777. Given the similar upregulation of IL-18 transcription and translation in the adrenal gland and habenula in response to stress, the presence of an IL-18 splice variant reported in the adrenal gland, and detected in brain homogenates in the mouse, experiments were undertaken to determine if this splice variant is similarly present in the rat habenula. Previous in situ hybridisation and immunohistochemistry experiments reported above had shown the habenula to be the only site of brain IL-18 production aa.
Results of RT-PCR experiments utilising habenula samples are shown in Figure 10. As observed previously with adrenal RT samples (Appendix 2, Figure 2), the primer combination designed to amplify the IL-18s transcript also showed amplification of the full length IL-18 transcript. The IL- 18s transcript was present spleen, included as a positive control, and the three habenula micropunch samples showed the presence of the IL-18s transcript, which was however much lighter in samples 2 and 3 than sample 1.
aa ‘only site of brain IL-18 production’ – these experiments show that the overwhelming amount of radioactive signal from IL-18 mRNA in situ hybridisation experiments arises from the habenula, and likewise immunohistochemistry experiments show specific staining for the IL-18 protein in the habenula. These findings do not necessarily definitively rule out IL-18 production in other areas, particularly for example during other disease conditions, as IL-18 production in other areas has been observed during ischaemic brain injury 780. They do, however, show that in a typical healthy male Sprague-Dawley rat the habenula is the only substantial source of IL-18 production. - 120 -
Figure 10: RT-PCR for IL-18 splice variants in the rat habenula. Primer combinations were used, as indicated, to amplify IL-18 and IL-18s transcripts in habenula micropunches. Numbers above lanes represent samples from different animals. spl - spleen (control); H20 - water only sample without RT product (negative control, in RT- panel only). RT+ samples are shown on the image above and RT- samples below as indicated; RT+ and RT- samples were run on the same gel. Expected product sizes - IL-18: 352 bp, IL-18s: 182 bp (239 bp for amplification of full-length IL-18), full IL-18 only: 231 bp.
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7.6 Discussion:
Results from both the in situ hybridisation and immunohistochemistry experiments included here revealed an intense focus of IL-18 expression in the brain in the medial habenula. Some signal for the presence of IL-18 mRNA in the triangular septal nucleus was seen in the in situ hybridisation experiment (Figure 6). This, however, was not seen at the protein level in immunohistochemistry experiments, although given only a few sections collected at the level of the triangular septal nucleus were included in the experiment, the expression of IL-18 protein in this nucleus cannot be ruled out from these results. While IL-18 mRNA had previously been amplified from liver samples during primer design and testing, liver sections included as positive control sections in the IL-18 in situ hybridisation experiments showed no discernable signal of IL-18 expression. There could be a number of reasons for this observations, such as that there were problems with the liver sections included as a positive control in this assay, that liver samples previously used during RT-PCR were from an animal exposed to an unrecognized inflammatory stimulus, or that the in situ hybridisation sensitivity was too low to detect liver IL-18 mRNA expression.
Other works in the literature have also detected IL-18 expression by qPCR and transcriptome sequencing in brain regions other than the habenula, such as the hypothalamus and medial prefrontal cortex (Gregg et al. 785). One must bear in mind that PCR is a powerful technique allowing amplification of even very low levels of gene expression, and tissue samples collected in that study, and here for RT-PCR, used fresh tissue samples and thus also contain mRNA present in circulating blood-borne cells such as lymphocytes. Additionally, in the work of Gregg et al., IL-18 expression is reported in the medial prefrontal cortex and hypothalamus based on qPCR and Illumina and Sequenom transcriptome sequencing experiments. However, while the data are presented as reads (Illumina and Sequenom transcriptome sequencing) and IL-18 transcript levels normalised to GAPDH expression (qPCR), the relative amounts compared to other transcripts (such as the control transcript GAPDH) is not clear and it is entirely possible from those experiments that the actual levels of IL-18 detected were low. This certainly appears to be the case from experience with a pilot RT-PCR conducted as part of this thesis (described below in Chapter 8, Section 8.1). In these hypothalamic micropunch samples, while IL-18 was detectable, the resulting bands visible in gel electrophoresis of PCR products were very faint. Brain tissue used here for in situ hybridisation was also collected fresh, and in sections of this tissue the overwhelming bulk of radioactive hybridisation signal was seen in the habenula, with very little signal present in other brain areas (with the
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exception of some sections showing a positive signal in the triangular septal nucleus). In addition, the results of both in situ hybridisation and immunohistochemistry experiments here present a consistent finding of IL-18 expression almost exclusively in the medial habenula. Together, these findings suggest that while it is possible to detect IL-18 transcripts in other brain regions, the only sizeable source of IL-18 in the brain of a healthy rat is the habenula. Other studies examining disease states have shown IL-18 expression in the brain in other regions, with Hedtjärn et al., for example, showing clear presence of IL-18 by immunohistochemistry in other brain regions following hypoxic- ischaemic brain injury 780. In that study IL-18 appears to be playing a role regarded as typical for pro- inflammatory cytokines – that of being upregulated by injury or infection – whilst the strong constitutive expression present in the habenula in healthy rodents shown here appears to be fulfilling an entirely distinct function.
The above experiments also show that the habenula expresses the IL-18s splice variant. The role this splice variant plays in IL-18 signalling is largely unknown, and indeed the only study which examines the functional role of this variant is a report by Yang et al. who examine its ability to induce IFN-γ in mouse splenocytes 802, showing that IL-18s appears to amplify the production of IFN-γ induced by IL-18, however only does so at low IL-18 levels.
The implications of, and further questions which arise from, these findings and how they relate to the hypothesis of brain-derived IL-18 playing a role in the previously observed metabolic effects of IL-18 are discussed further in the final discussion for Section 2, Chapter 10, which also examines previous research into the function of the medial habenula.
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Chapter 8: Central expression of IL-18 receptors
8.1 Overview
At the time of beginning this thesis there was little information available regarding the central distribution of the IL-18 receptor. A few studies had reported amplification of transcripts of the IL- 18 receptor subunits in the rodent brain 775,786,803, showing expression across a number of brain regions including the cortex, striatum, hippocampus and hypothalamus. One of the hypotheses proposed for the associations of IL-18 with metabolic disorders and its effect on appetite in rodents was that circulating IL-18 could act in a neuroendocrine manner by activating pro-inflammatory signals within the hypothalamus.
Experiments were therefore planned to examine the distribution of the IL-18 receptor subunits, IL- 18r1 and IL-18rap, in the rodent brain by in situ hybridisation, as (i) this had not been published previously, and (ii) in situ hybridisation images would enable visualisation of specific regions of expression in the hypothalamus, which could guide further experiments examining expression in distinct cell types such as POMC, NPY, or melanocortin-receptor expressing neurons which are largely demarcated into different hypothalamic nuclei.
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8.2 Methods
8.2.1 Pilot examination of the expression of components of the IL-18 signalling system in the rat hypothalamus
Primers were designed to amplify the IL-18, IL-18 BP, IL-18r1, IL-18rap, and Myd88 transcripts in the rat, as shown in Table 9. Primers for the detection of IL-18 spanned a 57 bp segment missing in the IL-18s transcript described above in Chapter 7, and thus could theoretically amplify both transcripts. Design and testing of primers for the amplification of IL-18r1 and IL-18rap splice variants are described in Appendix 2, Sections 2.2 and 2.3, respectively.
8.2.2 In situ hybridisation
In situ hybridisation for IL-18r1 and IL-18rap was performed as described in the General Laboratory Methods chapter, Chapter 6, Section 6.5. Probes for the detection of IL-18r1 in the mouse were designed to cross the portion of the IL-18r1 mRNA transcript which encodes the transmembrane region of IL-18r1 protein, upstream of the site of divergence of a reported splice variant (IL-18r1 type II) with a truncated intracellular portion 804, and would therefore be expected to bind mRNA transcripts for both splice variants. Probes for the detection of IL-18rap in mice spanned a region downstream of a reported IL-18rap splice variant which includes an unspliced intron 805, and would also be expected to detect both transcripts. Hybridisation experiments were performed on brain tissue from adult male C57BL/6 mice, with spleen sections included as a positive control.
8.2.3 Examination of IL-18 receptor splice variants across various nuclei of the rat brain
Primers were designed to amplify splice variants of the IL-18r1 and IL-18rap transcripts, using the primers and PCR conditions described in Tables 10 and 11. Primer positions are depicted in schematic diagrams in Figures 11 and 12. Prior to conducting the experiment, methodology was established to amplify different splice variants of the IL-18r1 and IL-18rap transcripts in the rat, particularly as the IL-18r1 type II transcript had not been described previously in the rat, and is described in Appendix 2. Following primer testing, micropunches of brain nuclei and spleen tissue samples were collected and processed through Trizol-based RNA extraction as described in the Methods section, Chapter 6, Section 6.2. Appendix 3, Figures 1 through 4 show images of micropunch collection from the various nuclei.
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Table 9: Primer sequences for amplification of IL-18 system transcripts in rat hypothalamic micropunch samples, shown in Figure 13.
Gene product of Primer Primer sequence Designed to bind † Expected product length interest IL-18 F TGGCTGTGACCCTATCTGTG NM_019165, binding 287 – 306 256 bp
R ATCCCCATTTTCATCCTTCC NM_019165, binding 524 – 543
IL-18 BP F GCTCCTTCATTGAGCACCTC NM_053374.1, binding 306 – 325 189 bp
R TGGGCCAGAATGACATGATA NM_053374.1, binding 476 – 495
IL-18r1 F CCAACGAAGAAGCCACAGACA NM_001106905.2, binding 1269 – 1289 259 bp
R CTCAGGATGACACTCTCTCAG NM_001106905.2, binding 1508 – 1528
IL-18 rap F ACGTCCAGTGGTACATGCAA NM_184047, binding 245 – 264 / 454 bp ENRSRNOT00000020260, binding 245 – 264
R GACGGTCCAGGAACTCACAT ENRSRNOT00000020260, binding 680 – 699
Myd88 F GAGATCCGCGAGTTTGAGAC NM_198130.1, binding 260 – 279 191 bp
R CTGTTTCTGCTGGTTGCGTA NM_198130.1, binding 432 – 451
F – forward primer
R – reverse primer
† reference codes are for either NCBI Genbank database (for sequences beginning in ‘NM’) 771 or Ensembl gene database (for sequences beginning in ‘EN’) 772
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127 127
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128 128
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Table 10: Primers used for amplifying IL-18 receptor transcripts in brain micropunches.
Transcript(s) Primer pair Primer sequences Primers designed to bind Expected product
IL-18r1 transcripts
Total IL-18r1 Forward A ATCCTAGCTATGGAGAGCTG ENSRNOT00000020194, 804 - 823; 196 bp IL18r1 reference sequence within exon 4
Total IL18r1 reverse ACTGTTCCCTTCAATAACCGT ENSRNOT00000020194, 980 - 1000; IL18r1 reference sequence crossing exons 5-6.
Total IL-18r1 Forward TGCATGAAGACAGGAACGAG ENSRNOT00000020194, 1149 - 1168; 237 bp IL18r1 reference sequence within exon 7 Primers from Reverse CTGCCACAGAGGTGAGAACA ENSRNOT00000020194, 1366 - 1385; Wheeler et al. 786 IL18r1 reference sequence within exon 9
IL-18r1 type I Forward C CCAACGAAGAAGCCACAGACA ENSRNOT00000020194, 1269 - 1289; 360 bp IL18r1 reference sequence within exon 8
IL18r1 type I reverse CAGGCACCACATCTCTTTCA ENSRNOT00000020194, 1609 - 1628; IL18r1 reference sequence within exon 10
IL-18r1 type II, 1st Forward B GCAAACTGCATGCTTCAAAA 1188 - 1192/1194 - 1207; 544 bp IL18r1 reference sequence within exon 8* round PCR IL18r1 type II reverse AGCACGGGACATGTGAGGAGA ENSRNOG00000015027, 19589 - 19609; IL18r1 reference sequence, intron between exons 9 and 10.
IL-18r1 type II, 2nd Forward C CCAACGAAGAAGCCACAGACA ENSRNOT00000020194, 1269 - 1289; 463 bp IL18r1 reference sequence within exon 8 round PCR § IL18r1 type II reverse AGCACGGGACATGTGAGGAGA ENSRNOG00000015027, 19589 - 19609; IL18r1 reference sequence, intron between exons 9 and 10.
Table 10 cont:
IL-18rap transcripts
IL-18rap† IL18rap F ACGTCCAGTGGTACATGCAA ENSRNOG00000015062, 1746 - 1765; 454 bp IL18rap reference gene sequence, within exon 2 (sIL18rap: 543 bp)
IL18rap R GACGGTCCAGGAACTCACAT ENSRNOG00000015062, 9509 - 9528; IL18rap reference gene sequence, within exon 4 sIL-18rap§ sIL18rap F CAAATCATCCCAGTCTGAAAA ENSRNOG00000015062, 1904 - 1924; 386 bp IL18rap reference gene sequence, intron between exons 2 and 3.
IL18rap R GACGGTCCAGGAACTCACAT ENSRNOG00000015062, 9509 - 9528; IL18rap reference gene sequence, within exon 4
Positive control transcript
β-actin F CTTGCAGCTCCTCCGTCGCC NM_031144.2, binding 37 - 56 337 bp
R GGGGCCACACGCAGCTCATT NM_031144.2, binding 355 - 374
* Primer was originally designed against mouse IL-18r1 reference sequence and thus contains a one base pair mismatch, underlined in the primer sequence.
† Primer pair can theoretically also amplify sIL-18rap transcript which includes a transcribed intron between the forward and reverse primer. The sIL18rap transcript amplifie d would be 543 bp, although in practice this transcript is not observed and is revealed in a second round of semi-nested PCR
§ Semi-nested PCRs. For detection of IL-18r1 type II, the PCR product from the 1st round primer combination was diluted and used as the template for amplification with the
2nd round primer pair. Likewise for IL-18rap, the PCR product for IL-18rap was diluted DEPC-treated water and this used as the template for a second round of semi-nested PCR utilising the primer pair stated.
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129 129
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130 130
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Table 11: PCR settings for amplifying IL-18 and receptor transcripts from brain micropunches
Transcript Initial Denaturing step Cycles Denaturing Annealing Extension Final Extension
β-actin 94˚C for 3 min 35 94˚C for 30 s 58˚C for 30 s 72˚C for 45 s 72˚C for 5 min
Total IL18r1 94˚C for 3 min 35 94˚C for 45 s 54˚C for 45 s 72˚C for 60 s 72˚C for 5 min
Total IL18r1, 94˚C for 3 min 35 94˚C for 45 s 57˚C for 45 s 72˚C for 60 s 72˚C for 5 min Wheeler et al. primers
IL18r1 type I 94˚C for 3 min 35 94˚C for 45 s 55˚C for 45 s 72˚C for 60 s 72˚C for 5 min
IL18r1 type II 1st round 94˚C for 3 min 35 94˚C for 45 s 55˚C for 45 s 72˚C for 60 s 72˚C for 5 min
IL18r1 type II 2nd round * 95˚C for 3 min 35 95˚C for 30 s 62˚C for 30 s ** 72˚C for 45 s 72˚C for 5 min
IL-18rap 94˚C for 3 min 35 94˚C for 45 s 54˚C for 45 s 72˚C for 60 s 72˚C for 5 min
sIL-18rap † 94˚C for 3 min 35 94˚C for 30 s 57˚C for 30s 72˚C for 45 s 72˚C for 5 min
* using as template the diluted product from IL18r1 type IIa first round, diluted 1:200 with DEPC-treated water. ** touchdown step dropping 0.2 ˚C per cycle † using as tempalte the diluted product from IL18rap, diluted 1:200 with DEPC-treated water.
Figure 11: Schematic diagram of positions of primers used to amplify IL-18r1 splice variants. Depicted is the IL- 18r1 type I reference transcript. Exons are depicted as boxes, with open boxes representing untranslated regions. Introns are shown as lines. Primer positions are indicated, with forward primers above the sequence and reverse primers below. Primers correspond to those listed in Table 10. Also shown is the position of the intron insert present in IL-18r1 type II transcript. In the mouse, the type II transcript includes an intron sequence located between exons 9 and 10, but not the remaining downstream exons 804. Assuming the same occurs for the type II transcript in the rat, reverse primers placed downstream of this segment (such as ‘IL-18 type I reverse’) will not amplify the type II transcript. Primers upstream would be expected to amplify both.
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Figure 12: Schematic diagram of positions of primers used to amplify IL-18rap splice variants. Depicted above is the IL-18rap reference transcript (ENSRNOT00000020260). Exons are depicted as boxes, introns as lines. Shown as a grey box is the intron which is inserted to form the sIL-18rap transcript. Primer positions are indicated, with forward primers above the sequence and reverse primers below. Primers correspond to those listed in Table 10. Also shown is the Genbank entry NM_184047.1 and the region this covers of the sIL-18rap transcript. The IL-18rap transcript was amplified by the primers indicated on the sequence above. For amplification of the sIL-18rap transcript, a semi-nested PCR was performed where the 2nd PCR used a primer within the intron insert, as shown on the sequence below.
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8.3 Pilot examination of the expression of components of the IL-18 signalling system in the rat hypothalamus
Prior to conducting experiments involving in situ hybridisation to examine receptor distribution in the hypothalamus, a preliminary pilot experiment was conducted to ensure that components of the IL-18 signalling system could be detected by RT-PCR. Hypothalamic micropunches were performed as described in the Methods section using brain tissue extracted from an adult male Sprague-Dawley rat. Primer combinations were used to amplify subunits of the IL-18 receptor (IL-18r1 and IL-18rap), as well as IL-18 itself, IL-18 BP (the natural antagonist of IL-18), and Myd88 which is involved in downstream signalling of the IL-18 receptor. Gel electrophoresis of PCR products is shown in Figure 13.
Very low levels of expression of IL-18 were observed in micropunches of the ventromedial and paraventricular hypothalamic nuclei. IL-18 BP expression was observed in all nuclei, as was the intracellular signalling component Myd88 (which is also involved in signal transduction from the interleukin-1 receptor). Expression of IL-18 receptor subunits IL-18r1 and IL-18rap was detected in micropunches of hypothalamic nuclei. The primer combination used to amplify IL-18r1 produced a band of the expected size in the RT- sample of the hypothalamus homogenate. A subsequent PCR using the same sample gave a clear RT- lane and band of the expected size in the RT+ lane (not shown), and it was concluded that the whole hypothalamus homogenate IL-18r1 RT- tube used here was contaminated in this PCR run only.
Although not a quantitative PCR experiment, approximately 200 ng of cDNA was added to each reaction tube and results suggest expression could be higher in the arcuate and ventromedial hypothalamic nuclei than in the paraventricular nucleus. It was noted that both of the primer combinations used to amplify IL-18r1 and IL-18rap produced clear bands in the whole hypothalamus and paraventricular nucleus samples, but extra bands in the arcuate and ventromedial hypothalamus samples. It is not known why this occurred, as if these bands were to represent, for example, additional splice variants or non-specific binding, it would be expected that this would also be present in the whole hypothalamus homogenates as these include both the arcuate and ventromedial hypothalamic nuclei. Regarding the additional bands present in the arcuate and ventromedial hypothalamus IL-18rap lanes, the sIL-18rap splice variant includes an unspliced intron and therefore would in fact be heavier than the product for IL-18rap, not lighter as is the case for the additional
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bands present. Heavier bands present in the IL-18 BP and Myd88 samples are the expected size for amplification of the target DNA band, as these RT samples were not DNase treated.
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Figure 13: Expression of IL-18 signalling components in rat hypothalamic micropunches. Amplified transcripts are indicated above gel segments. Samples are as follows: arc – arcuate nucleus; pvn – paraventricular nucleus; vmh – ventromedial hypothalamus; hyp – whole hypothalamus homogenate. Hypothalamic micropunch samples (arc, pvn, vmh) shown are RT+, run alongside whole hypothalamus RT+ (hyp +) and RT- (hyp -) control samples as indicated.
8.4 Examination of central expression of IL-18 receptor subunits using in situ hybridisation
Following the detection of mRNA encoding IL-18 receptor subunits in the RT-PCR pilot experiment above, in situ hybridisation was performed to examine the distribution of expression of the IL-18 receptor genes in the mouse brain.
8.4.1 Interleukin-18 receptor alpha subunit (IL-18R/IL-18r1)
Radiographic film was left opposed to sections for 16 days prior to development for hybridisation with IL-18r1 probes (Figure 14). In situ hybridisation for the interleukin-18 receptor alpha subunit produced clear pockets of staining in the spleen, which was observed with antisense but not sense probes. Brain sections, however, produced no specific staining in either coronal or sagittal sections.
8.4.2 Interleukin-18 receptor beta subunit (IL-18Rβ/IL-18rap)
In situ hybridisation using probes for IL-18rap resulted in little visible signal in an autoradiograph developed after one week of exposure. A subsequent film exposed for 25 days resulted in visible foci of staining in spleen sections (Figure 15), with a similar staining pattern in the spleen to that seen in in situ hybridisation experiments for mouse IL-18r1 (compare Figure 14), with pockets of expression visible. However, no specific staining was seen in brain sections, as was the case in in situ hybridisation experiments for IL-18r1.
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Figure 14: In situ hybridisation for IL-18r1 expression in the mouse brain. Sections were incubated with sense and antisense probes for the IL-18r1 transcript and left exposed for 16 days. Middle row shows coronal (top) or sagittal (middle row) brain and spleen (bottom row) sections incubated with antisense probes. Corresponding positions in the mouse brain are shown to the left with Nissl-stained coronal sections (adapted from http://brainmaps.org). Right hand column shows sagittal brain and spleen sections incubated with sense probes.
Figure 15: In situ hybridisation for IL-18rap expression in the mouse brain. Middle and right hand columns show mouse brain and spleen sections incubated with anti-sense probe for IL-18rap and left exposed for 25 days against radiographic film. Left-hand column shows corresponding Nissl-stained coronal and sagittal sections with major features indicated (adapted from http://brainmaps.org). Note the spleen section included as a positive control, which shows foci of expression corresponding to the white pulp.
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8.5 Examination of expression of IL-18 receptor splice variants by RT- PCR in rat brain micropunches
The results from the in situ hybridisation experiments presented above in Section 8.4, suggest that in fact there is very little or no detectable IL-18 receptor expression in the brain. Given these experiments were preceded by a pilot study examining IL-18 receptor expression by RT-PCR (Section 8.3), which did demonstrate amplification of IL-18 receptor transcripts, these results presented seemingly contradicting results. A further experiment was planned to repeat that pilot experiment in more animals with a revised experimental plan. Different brain nuclei were chosen to reflect the differing hypotheses of possible IL-18 involvement in eating behaviour; hypothalamic micropunches were collected through the arcuate, ventromedial and paraventricular hypothalamic nuclei, on the hypothesis that circulating IL-18 could influence signalling in these areas. Given that habenula-derived projections were shown to terminate in the interpeduncular nucleus, micropunches through the interpeduncular nucleus and neighbouring ventral tegmental area were made. In addition, additional primer sets and amplification strategies were designed to amplify different portions of the IL-18r1 and IL-18rap mRNA transcripts, including primers to specifically amplify different splice variants for IL-18r1 (IL-18r1 type I and type II described in the mouse in 804, and in the rat as described in Appendix 2) and IL-18rap (IL-18rap and sIL-18rap, described in the rat in 775).
Photographs of agarose gels following electrophoresis for each PCR reaction are collated in Figures 16 and 17. It was apparent that bands of the expected product size were present in RT- samples for β-actin, as well as the presence of products of expected size in some RT- samples for amplification of IL-18r1, showing the presence of contamination. Bands were also present in RT- samples during semi-nested PCR for the amplication of IL-18r1 type II, however, these were not of the expected product size (approx 180 bp vs. 463 bp) and thus likely represent non-specific amplification of unknown sequences; similar non-specific bands were also present in some RT+ samples.
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Figure 16 (following pages): RT-PCR for the detection of IL-18r1 transcripts in rat brain micropunches. Various primer combinations were used, as indicated, to amplify IL-18r1 transcripts or β-actin. arc - arcuate nucleus; pvn - paraventricular nucleus; vmh - ventromedial hypothalamus; ipn - interpeduncular nucleus; vta - ventral tegmental area; spl - spleen (control); H20 - water only sample without RT product (negative control). Panel A - RT+ samples; Panel B - RT- samples. The darker band present toward the centre of the 100 bp ladder is 600 bp.
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Figure 17 (following pages): RT-PCR for the detection of IL-18rap transcripts in rat brain micropunches. Primer combinations were used, as indicated, to amplify IL-18rap, sIL-18rap transcripts or β-actin. arc - arcuate nucleus; pvn - paraventricular nucleus; vmh - ventromedial hypothalamus; ipn - interpeduncular nucleus; vta - ventral tegmental area; spl - spleen (control); H20 - water only sample without RT product (negative control). Panel A - RT+ samples; Panel B - RT- samples.
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8.6 Discussion:
Despite an initial pilot experiment showing amplification of IL-18 receptor subunits in the rat hypothalamus, subsequent in situ hybridisation in the mouse brain showed no signals suggesting central expression of the genes which encode the IL-18 receptor. A further RT-PCR experiment which was conducted across a wider range of brain regions showed a low level of expression across all brain regions sampled. The results of the RT-PCR experiments here are reminiscient of those published by Andre et al. who published RT-PCR experiments which resulted in bands of similar intensity across samples from different regions of the rat brain 775. In those experiments, and those performed here, interpretation of the relative amounts of each transcript type and across different brain areas is complicated by the fact that the amplifications performed were not done by qPCR; thus, different intensities of bands of different transcripts could represent differing amplification efficiencies of the various primer combinations. Likewise, differing intensities of bands across different brain regions could reflect differing amounts of starting material (added cDNA per reaction tube); the intensity of β-actin amplification products from samples from Rat 3 appear lighter than those from Rats 1 and 2 in the latter experiment included here, and could therefore support the interpretation that less RNA was reverse transcribed from these samples.
Bearing these limitations in mind, and notwithstanding the limitations imposed by contamination of RT- samples, these experiments and previously published works 775,785,786,803 generally show that IL- 18r1 and IL-18rap transcripts can be amplified from samples of various brain regions in the mouse or rat brain. Regarding the expression of the IL-18r1 type II splice variant, this was only detectable in brain micropunches by a second round of semi-nested PCR in the present study. In contrast, Alboni et al. found that this transcript could be reliably amplified by a single round PCR strategy in the mouse 804. Whether these differences relate to a species difference between the mouse and rat, or differences in amplification efficiencies of the primers used, is unknown. From the results here it could be concluded that (i) given semi-nested PCR is an incredibly powerful technique able to amplify minute levels of gene expression, and (ii) positive bands are only seen in approximately half of the micropunch samples, with no particular clear pattern across different brain regions, it appears that this variant is probably not expressed at sufficient levels to be physiologically relevant in the rat brain, at least under the physiological conditions of the animals used here.
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The IL-18rap transcript, which encodes the receptor subunit necessary for signal transduction, was also detectable, albeit produced bands of much lesser intensity than that seen in the positive control tissue of spleen. Three out of three arcuate nucleus samples showed IL-18rap expression, as did three of three interpeduncular nucleus samples. Other brain regions showed less consistent results, with only one ventral tegmental area micropunch showing evidence of expression. Given that in general many bands produced on electrophoresis were faint, and the inherent weakness noted above of comparing transcripts across different samples, it is difficult to determine from these results if these findings could translate into differences in receptor activity levels across the brain regions sampled. The most notable finding that is clear is that there is no central expression of the sIL-18rap transcript. These results were also replicated with another semi-nested PCR strategy utilising different primers, which also failed to detect any expression across brain micropunch samples, despite strong positive control results in the spleen (data not shown). Thus, these results suggest that in the rat this splice variant is not expressed in the brain. The original study to report the existence of the sIL-18rap splice variant detected its expression in various brain regions by RT-PCR 775, and similarly another study in mice reports the presence of murine sIL-18rap in the brain using RT- PCR805, and the equivalent human splice variant has been reported to be expressed in the brain 806, and the results here are therefore not in accordance with other reports in the literature across various species.
The expression of the IL-18rap transcript in the brain has been shown to be upregulated on exposure to LPS in two studies conducted in mice, also using RT-PCR 803,805. One of these also examined the expression of the murine sIL-18rap splice variant and found that this was also upregulated after LPS exposure, but that the ratio of expression of the sIL-18rap splice variant to the normal full length IL- 18rap transcript was not changed 805. One possible explanation to reconcile the results here with other works showing sIL-18rap expression in the brain in rats 775 and mice 805 is the presence of inflammatory stimuli; expression levels of IL-18rap in the brain appear to be regulated by inflammatory stimuli, and the rats used in the present study were housed in SPF (Specific Pathogen Free) conditions where even food is autoclaved prior to consumption. Animals studied here may therefore exhibit a lack of sIL-18rap expression due to a lower level of inflammation than the animals used in other works by Andre et al. 775 and Alboni et al. 805. However, whether this could account for the lack of sIL-18rap expression seen here is unknown, as the normal IL-18rap transcript was readily detectable in brain samples, and even if sIL-18rap were expressed in relatively lower amounts than the normal IL-18rap transcript (which was observed to be the case in the two other reports by Andre et al. 775 and Alboni et al. 805), the semi-nested PCR technique here would be more
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than capable of amplifying even minute levels of sIL-18rap expression. Thus, the reason for the discrepancy between the results of this experiment and other reports in the literature are not easily reconciled.
Regarding the results of in situ hybridisation studies shown here, at the outset of this thesis there were no published results of in situ hybridisation experiments conducted to detect the expression of the genes encoding the IL-18 receptor in the brain. The experiments shown above in Section 8.4 showed no signal for expression of IL-18 receptor subunits in the mouse brain, despite positive signals being detected in the spleen. During the course of conducting this thesis, the results of similar experiments have been published by others, which include: (i) the Allen Brain Atlas 807, which shows little or no central expression of IL18r1 (probe number 75831726) nor IL18rap (probe number 71529941) in the mouse brain; (ii) studies from Bruno Conti's laboratory, which examine the central expression of IL18r1 804 and IL18rap 805 in the mouse using both in situ hybridisation and immunohistochemistry techniques, suggesting a relatively low-level but ubiquitous expression throughout the brain; and (iii) the Eurexpress developmental transcriptome which shows no IL- 18r1808 nor IL-18rap 809 in the developing mouse embryo by in situ hybridisation 810. Each of these other studies have utilized digoxigenin (DIG) probes, which would be expected to have a lower sensitivity than the radioactive probes utilized in this study. On the one hand, the utility of radioactive probes is best observed once slides incubated with radioactive probes are dipped in photographic emulsion and left to develop for a number of weeks. In this study, slides incubated with radioactive probes were exposed to autoradiographs, which have a lower sensitivity than emulsion-dipped slides, although in the case of in situ hybridisation experiments for the expression of IL-18r1 and IL-18rap these were left to develop for 16 and 25 days respectively, longer than autoradiographs would often be left to develop, in order to increase the possibility of detecting a low radioactive signal. Thus, the methods used here could arguably be more sensitive than the above studies utilizing DIG probes. However, there is obvious discrepancy amongst these works, with the experiments presented here, the Allen Brain Atlas, and the Eurexpress project showing no evidence of expression of the genes encoding the IL-18 receptor by in situ hybridisation, and the works from Alboni et al. showing a ubiquitous, low-level expression 804,805.
Other papers have used RT-PCR to detect expression of receptor subunits in the brain (e.g. 775,803), as has also been performed here. The various results from these studies are not necessarily in concordance with each other. The issue of whether there is a ubiquitous low-level expression vs. none in the brain could be viewed as a question of sensitivity or specificity, particularly where a lack
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of sensitivity could result in a negative result (no expression). Even the results presented here in this thesis are not necessarily concordant across different techniques – whereas we have been unable to detect any reliable signal of receptor expression by in situ hybridisation using sensitive radioactive probes, multiple RT-PCR experiments have amplified IL-18 receptor subunit transcripts from various brain regions, albeit producing bands of weak intensity bb. One explanation for the descrepancy between methodologies found here and reported in the wider literature could be that RT-PCR experiments detect a much lower level of transcript than can be visualized with in situ hybridisation such that RT-PCR experiments produce positive signals which are not observed by in situ hybridisation; this would certainly be expected based on the ability of PCR experiments to amplify even very small levels of mRNA transcripts.
One potential confounding factor which could go some way to explaining the results presented across different studies is the presence of IL-18 receptor mRNA in blood-borne cells. The experiments presented here were performed on brain micropunches collected from tissue which were collected immediately after decapitation, and not perfused with paraformaldehyde or saline. This method of tissue collection is often used for examining mRNA expression as tissue can be collected and frozen prior to degradation of RNA, and it avoids the use of drugs for deeply anaesthetizing animals which may influence gene expression in unknown ways. However, a potential downside from this method is the presence of blood within tissue samples. Following the initial report by Alboni et al. describing the IL-18r1 type II transcript in the mouse 804, the presence of this transcript was investigated in the human by the author by RT-PCR, using RNA purified from whole blood (unpublished results from the author), in which both IL-18r1 type I and II were amplified from whole blood RNA. Thus, these transcripts are likely to also be present in cells within the bloodstream in rodent species and this could account for their detection in RT-PCR experiments. Other studies, such as Andre et al. 775,803 and Wheeler et al. 786 report the amplification of IL-18 receptor subunit transcripts from various brain regions in the Sprague-Dawley rat following saline perfusion prior to RNA extraction 775,786,803. This experimental approach would decrease, although not entirely eliminate, the possibility of false positive results through amplification of transcripts present in blood-borne cell types, and it is possible that the expression reported in those studies could also arise from amplification of IL-18 receptor expression in blood-borne cells remaining in
bb Since RT-PCR was performed here and not qPCR, it cannot be known whether, for example, the weak intensity bands produced are reflective of a low level of transcript, or of a poor amplification efficiency of the primer pairs used. - 146 -
small capillary vessels. The residual presence of such cells following perfusion could also explain the relatively homogenous expression across different brain regions reported by Alboni et al. by in situ hybridisation 804,805. However, contrary to this argument are reports of receptor expression in cell cultures of neurons, microglia and astrocytes 803, in which such contamination would not be present.
Regardless of the above inconsistencies, one finding which emerges is that the results from these experiments and other published studies is that none of these works suggest a focus of IL-18 receptor in the interpeduncular nucleus. Given the clear results obtained here (Chapter 7) and others (Sugama et al. 777 and Aizawa et al. 811) of IL-18 being present in the dorsomedial subnucleus of the medial habenula, and of IL-18 containing fibres projecting down the fasciculus retroflexus and terminating in the interpeduncular nucleus (Chapter 7 and Sugama et al. 777), a receptor for IL-18 would be expected to be present at increased levels in this nucleus. In light of the inconsistency, it seemed possible that habenula-produced IL-18 could act via another, separate, receptor system. This led to the examination of possible unknown mediators of IL-18 signalling in the brain detailed in the following chapter.
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Chapter 9: Examining unknown mediators of IL-18 signalling in the CNS
9.1 Overview
IL-18 and its receptor belong to the interleukin-1 family of cytokines, and this family is ever- evolving with additional ligands and receptors being discovered. A number of unanswered questions remain about how members of the family interact 812,813; for example, it has recently been elucidated that the IL-18 receptor itself, IL-18r1, is also used by IL-37 and can combine with a different accessory protein, Single IgG IL-1 Related Receptor (SIGIRR), in order to exert anti-inflammatory actions 814. IL-18 forms a relatively weak bond with the IL-18r1 receptor subunit, and binding to IL- 18rap is required before a high affinity bond is formed. Curiously, IL-18BP, which acts as a natural circulating antagonist, forms a high affinity bond with IL-18 and yet bears little resemblence to the IL-18 receptor subunits. For example, while IL-18r1 (and most other ligand-binding members of the IL-1 receptor family) contain three immunoglobin (Ig)-like domains, IL-18BP contains only one 813, and nucleotide and protein sequences share little similarity with the interleukin-18 receptor subunits. Experiments utilizing IL-18 and IL-18r1 knockout animals in the study of autoimmune processes demonstrate that the two exhibit divergent phenotypes 815,816. These have been interpreted as evidence of another ligand for the IL-18 receptor (a possible explanation for these findings is the recent discovery, noted above, of IL-37 exerting anti-inflammatory actions via IL-18r1 814), although these findings could also signify an alternative receptor for IL-18. To what extent these results could be explained in hindsight by other reports suggesting that aspects of the IL-18r1 knockout mouse phenotype arise from other co-inherited genetic material 817 is unclear.
Thus, given (i) the acknowledged incomplete understanding of the IL-1 cytokine family, (ii) prior evidence within the IL-1 cytokine family of multiple ligands acting on different members of the IL-1 family of receptors 813, (iii) the fact that IL-18 is known to bind to both the IL-18 receptor and IL-18 BP, two disparate and seemingly unrelated proteins, (iv) the apparent lack of central expression of the IL-18 receptor despite strong central expression, and (v) the published behavioural effects of IL-
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18 on appetite, the notion that IL-18 could bind a separate centrally-expressed receptor seemed plausible.
This chapter details bioinformatics approaches and immunohistochemistry experiments undertaken to investigate this hypothesis. Given the exploratory nature of this line of investigation, initial results from bioinformatics approaches led to further hypotheses which were examined in subsequent experiments. In order to preserve the logical flow from one experiment to the other, each of these experiments is described in a separate section, with the background, methodological detail, results, and discussion of results and available literature contained within each section.
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9.2 Examining a putative central receptor for IL-18: BLAST searches for proteins related to IL-18r1 and IL-18 BP
The initial line of enquiry began by examining the possibility of another putative central receptor for IL-18 using BLAST searches of reference human, mouse and rat protein sequences.
9.2.1 Bioinformatics methodology
BLAST searches:
To examine the hypothesis that IL-18 could act via a different receptor, the extracellular sequence of IL-18r1 (which forms the primary site of ligand binding during IL-18 signal transduction) and the sequence of IL-18 BP were entered into BLAST searches for related proteins for the respective protein sequences of rat, mouse and human species. Searches were conducting using the blastp interface to mine the database of reference sequence proteins (refseq_protein) with searches limited to each respective organism. Pairwise alignment between selected sequences was performed using the 'blastp' query interface set to align two sequences.
9.2.2 BLAST search results
The ten results with the lowest E-values from BLAST searches for proteins related to the extracellular portion of human, rat and mouse IL-18r1 are shown in Table 12, and those related to IL-18BP of each species in Table 13. Searches for proteins related to IL-18r1 consistently produced similarities for various members of the IL-1 receptor family across the three species, including the IL-1 receptor (interleukin-1 receptor type I and type II, interleukin-1 receptor accessory protein), IL-33 receptor (interleukin-1 receptor-like 1 812), interleukin-1 receptor-like 2 (which binds IL-1 family members IL-1F6 and IL-1F8 813), and interleukin-1 receptor associated protein-like 1 (IL1RAPL1, until recently an orphan receptor 818,819).
Searches for proteins similar to IL-18 BP produced much more varied lists across species (Table 13). Indeed, only one protein was consistently within the first ten results for all three species, interleukin-1 receptor accessory protein-like 2 (IL1RAPL2).
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Table 12: BLAST search results using amino acids encoding the extracellular portion of IL-18r1 as a query against reference protein sequence databases for rat, mouse and human species. The first ten results are listed for each species using the amino acid queries as listed. Searches were limited to proteins from each respective species. Numerou s members of the IL-1 receptor family showed similarity for each species. Shown shaded is IL1RAPL1, which was examined further in the following experiments.
Accession Description Total score Query coverage E value
Rat: proteins similar to amino acids 1 – 326 of rat IL-18r1 (NP_001100375)
NP_001100375.1 interleukin 18 receptor 1 687 100% 0.0
NP_001121161.1 interleukin-1 receptor-like 1 Fit-1M 67.8 85% 2e-14
NP_037169.1 interleukin-1 receptor-like 1 Fit-1S 66.2 82% 5e-14
NP_037255.3 interleukin-1 receptor type 1 63.5 85% 7e-13
NP_001161312.1 interleukin-1 receptor accessory protein isoform b 53.9 88% 8e-10
NP_037100.1 interleukin-1 receptor accessory protein precursor 51.6 88% 4e-09
NP_808796.1 interleukin-1 receptor accessory protein-like 1 precursor 49.7 89% 2e-08
NP_598259.1 interleukin-1 receptor-like 2 precursor 45.8 89% 3e-07
NP_446405.1 interleukin-1 receptor type 2 precursor 45.4 66% 3e-07
XP_001055013.2 PREDICTED: leucine-rich repeats and immunoglobulin-like domains 3 40.8 52% 1e-05
Mouse: proteins similar to amino acids 1 – 325 of mouse IL-18r1 (NP_032391.1)
NP_001155314.1 interleukin-18 receptor 1 isoform b 677 100% 0.0
NP_032391.1 interleukin-18 receptor 1 isoform a 680 100% 0.0
NP_034873.2 interleukin-1 receptor-like 1 isoform b 78.6 76% 4e-18
NP_001020773.1 interleukin-1 receptor-like 1 isoform a 78.2 74% 1e-17
-
151 151
-
-
152 152
-
Table 12 cont: NP_032388.1 interleukin-1 receptor type 1 isoform 1 65.9 83% 1e-13
NP_001116854.1 interleukin-1 receptor type 1 isoform 2 65.9 83% 1e-13
NP_001153875.1 interleukin-1 receptor accessory protein-like 1 57.8 88% 7e-11
XP_003086889.1 PREDICTED: interleukin-1 receptor accessory protein-like 1-like 57.4 88% 9e-11
NP_001152789.1 interleukin-1 receptor accessory protein isoform c precursor 55.8 89% 3e-10
NP_001152790.1 interleukin-1 receptor accessory protein isoform d precursor 55.8 89% 3e-10
Human: proteins similar to amino acids 1 – 330 of human IL-18r1 (NP_003846.1)
NP_003846.1 interleukin-18 receptor 1 precursor 690 100% 0.0
NP_000868.1 interleukin-1 receptor type 1 precursor 73.6 91% 6e-16
NP_003847.2 interleukin-1 receptor-like 1 isoform 2 precursor 67.8 80% 2e-14
NP_057316.3 interleukin-1 receptor-like 1 isoform 1 precursor 67.0 80% 8e-14
NP_055086.1 interleukin-1 receptor accessory protein-like 1 precursor 66.2 94% 2e-13
NP_608273.1 interleukin-1 receptor accessory protein isoform 2 62.8 88% 2e-12
NP_001161403.1 interleukin-1 receptor accessory protein isoform 3 62.8 71% 3e-12
NP_002173.1 interleukin-1 receptor accessory protein isoform 1 61.6 86% 5e-12
NP_004624.1 interleukin-1 receptor type 2 precursor 54.7 64% 7e-10
NP_003845.2 interleukin-1 receptor-like 2 precursor 54.3 56% 1e-09
Table 13: BLAST search results using IL-18BP protein sequence as a query against reference protein sequence databases for rat, mouse and human species. First ten results are listed for each species using the protein reference sequences as listed. Searches were limited to proteins from each respecti ve species. Shown shaded is the only protein result common to the first ten results in all three species, IL1RAPL2.
Accession Description Total score Query coverage E value
Rat: proteins similar to rat IL-18 BP (NP_445826.1)
NP_445826.1 interleukin-18-binding protein 392 100% 1e-143
NP_446405.1 interleukin-1 receptor type 2 precursor 39.3 37% 6e-06
NP_001159814.1 X-linked interleukin-1 receptor accessory protein-like 2 33.1 45% 8e-04
NP_058912.1 neutral and basic amino acid transport protein rBAT 28.5 27% 0.029
NP_001008846.1 RT1 class II, DO beta 28.1 52% 0.030
NP_062160.2 apolipoprotein B-100 precursor 28.5 31% 0.031
NP_958738.1 SH3 and multiple ankyrin repeat domains protein 2 isoform a 27.3 52% 0.068
NP_786939.1 immunoglobulin superfamily member 1 precursor 26.6 46% 0.10
NP_001102261.1 PHD finger protein 3 26.6 46% 0.11
NP_001100338.1 protein-cysteine N-palmitoyltransferase HHAT-like protein 26.6 37% 0.11
Mouse: proteins similar to mouse IL-18 BP (NP_034661.1)
NP_034661.1 interleukin-18-binding protein precursor 393 100% 1e-144
NP_034685.1 interleukin-1 receptor type 2 precursor 33.5 37% 3e-04
XP_923633.3 PREDICTED: tyrosine-protein phosphatase non-receptor type substrate 1-like 33.1 39% 3e-04
NP_001002898.1 signal-regulatory protein beta 1 32.3 53% 7e-04
NP_001166931.1 signal-regulatory protein beta 1B 32.0 24% 8e-04
-
153 153
-
-
154 154
-
Table 13 cont: NP_109613.1 X-linked interleukin-1 receptor accessory protein-like 2 precursor 31.6 48% 0.001
NP_001166930.1 signal-regulatory protein beta 1-like 30.4 24% 0.003
NP_034265.2 protein enabled homolog isoform 1 27.3 33% 0.031
NP_932138.1 SCY1-like protein 2 27.3 28% 0.035
NP_034519.2 histocompatibility 2, O region beta locus 26.9 52% 0.038
Human: proteins similar to human IL-18 BP (NP_766630.2)
NP_001034748.1 interleukin-18-binding protein isoform a precursor 390 100% 1e-142
NP_005690.2 interleukin-18-binding protein isoform c precursor 341 87% 2e-123
NP_766632.2 interleukin-18-binding protein isoform d precursor 261 65% 4e-92
NP_001138527.1 interleukin-18-binding protein isoform b precursor 157 40% 7e-52
NP_059112.1 X-linked interleukin-1 receptor accessory protein-like 2 36.2 60% 1e-04
XP_003119930.1 PREDICTED: protein FAM132B, partial 30.8 18% 0.003
NP_001035214.1 PQ-loop repeat-containing protein 2 isoform 1 27.7 17% 0.052
NP_443137.2 formin-like protein 2 27.7 40% 0.066
NP_037421.2 killer cell immunoglobulin-like receptor 3DL1 precursor 27.3 14% 0.077
NP_001077008.1 killer cell immunoglobulin-like receptor 3DS1 26.9 14% 0.092
Alignment of IL-18BP with IL1RAPL2 revealed a series of regions showing differing degrees of similarity spaced across different portions of the respective amino acid sequences (shown for rat sequences in Figure 18). Given the similarities between IL-18BP and IL1RAPL2 revealed by BLAST searches, IL-18r1 was aligned with the related protein IL1RAPL1, which also featured in lists of similar proteins from BLAST searches of the extracellular portion of IL-18r1. Alignment revealed a certain degree of similarity across almost the entire length of both proteins (shown in Figure 19 with rat sequences). Comparing rat sequences reveals 148 of 544 (27 %) of amino acids are identical, with a further 100 (18 %) exhibiting similar polarity ('positives'). (Similar relationships are found with human and mouse species).
9.2.3 Discussion
The interleukin-1 family of ligands and receptors consists of a number of different members, all with varying degrees of similarity 813. The observed similarities between IL-18r1 and other IL-1 family members shown above was therefore expected, given the high degree of homology between family members, and the current understanding that these various family members appear to have arisen by gene duplication and deletion events. However, despite their similarity the IL-1 family members obviously exhibit a certain degree of specificity, such that IL-18, for example, does not activate the highly homologous IL-1 or IL-33 receptors. Therefore, similarity alone would not be sufficient to determine IL-18 binding to related family members. The similarity of IL-18 BP to IL1RAPL2, however, was not expected, and has not been reported previously.
IL1RAPL1 and IL1RAPL2 are highly homologous and are thought to have evolved by gene duplication. They are also interesting members of the IL-1 family by virtue of their chromosomal location; almost all members of the IL-1 receptor family reside within a cluster in the same autosomal region (chromosome 2 in humans, chromosome 1 in the mouse, and chromosome 9 in the rat; the interleukin-1 receptor accessory protein is the only exception, residing on a different autosomal chromosome in each species). The two genes for IL1RAPL1 and IL1RAPL2, however, both reside on the X chromosome in all three species. The mammalian X chromosome is enriched for genes involved in reproduction and brain function 820–822, and in accordance with this enrichment studies have identified IL1RAPL1 and IL1RAPL2 expression in the brain 823–825 with little expression elsewhere. Given the bioinformatics searches conducted here were undertaken to identify a putative centrally expressed receptor for IL-18, the IL1RAPL proteins thus exhibit the required gross anatomical localisation necessary to fulfil this role. The IL1RAPL proteins were also regarded as orphan receptors until very recently 818, and at the time of conducting these investigations had no known ligands. - 155 -
Figure 18: Alignment of rat IL-18BP (NP_445826.1) vs IL1RAPL2 (NP_001159814.1) proteins produces four regions of modest homology. Panels A - D: Schematic diagrams show the regions of similarity between the two proteins, followed by the aligned amino acid sequences for each of the four regions. The region of alignment for IL1RAPL2 shown in Panel C sits in the cytoplasmic domain, the remainder are extracellular.
A.
Segment 1: Identities = 26/90 (29%), Positives = 38/90 (42%), Gaps = 15/90 (17%)
IL-18BP 25 LARATSAPLTTATVLTRSSKDPCSSWSPAVPTKQYPTLDVIWPEKEVPLNGTLTLSCTACSRFP L R T+ TA + + K P P + P++ +V L L + C A F IL1RAPL2 222 LVRRTTELKVTALLTDKPPK------PLFPMENQPSV------IDVQLGKPLNIPCKAFFGFS
IL-18BP 89 NFS--ILYWLGNGSFIEHLPGRLREG 112 S ++YW+ FIE L G +REG IL1RAPL2 273 GESGPMIYWMKGEKFIEELAGHIREG 298
B.
Segment 2: Identities = 16/46 (35%), Positives = 21/46 (46%), Gaps = 6/46 (13%)
IL-18BP 17 LLYVHVVILARATSAPLTTATVLTRSSKDPCSSWSPAVPTKQYPTL 62 LL VV A +T+ + + R+S D C WS V K Y L IL1RAPL2 5 LLLALVVCSAVSTNLKMVS----KRNSVDGCIDWS--VDLKTYMAL 44
C.
Segment 3: Identities = 10/33 (30%), Positives = 15/33 (45%), Gaps = 1/33 (3%)
IL-18BP 52 PAVPTKQYPTLDVIWPEKEVPLNGTLTLSCTAC 84 P V K Y + PE+++ +GT T C IL1RAPL2 432 PDVLEKHY-GYKLFIPERDLIPSGTYIEDLTRC 463
D.
Segment 4: Identities = 4/12 (33%), Positives = 7/12 (58%), Gaps = 0/12 (0%)
IL-18BP 124 WLHRALVLEELS 135 W+ +EEL+ IL1RAPL2 281 WMKGEKFIEELA 292
- 156 -
Figure 19: Alignment of rat IL-18r1 (NP_001100375.1) vs IL1RAPL1 (NP_808796.1) proteins. Identities = 148/544 (27 %), Positives = 248/544 (46 %), Gaps = 58/544 (11 %).
IL-18r1 23 IRRSQIHVVEGEPFYLKPCDMSAPMHNNETA------TMRWFKGNASHGYRELNMRSSPRIAFHGHALEFWPVELEDKGTYFSQVGND----RQNW 112 I++ Q+ V GEP +K + N T ++ W+K + + E R++ ++ F P L+D G Y + N + + IL1RAPL1 38 IKKYQVLV--GEPVRIKCALFYGYIRTNYTLAQSAGLSLMWYKSSGPGDFEEPIAFDGSRMSKEEDSIWFRPTLLQDSGLYACVIRNSTYCMKVSI 131
IL-18r1 113 TLNVTKRNKHSCFSEKLVTNRDVEVKKSLWITCENPSYGELINH---TLLYKNCKEISKTP------MILKDAEFGDEGYYSCVFSVHHNGQQY 197 +L V + + C++ K+ E+ KS I+C + L L YK C+ + P +++K+ + D G Y+C + + G + IL1RAPL1 132 SLTVGENDTGLCYNSKMKYFEKAELSKSKEISCRDIEDFLLPTREPEILWYKECRTKTWRPSIVFKRDTLLIKEVKEDDIGNYTC--ELKYGG--F 223
IL-18r1 198 NITKTVNITVIEGNSKITPAIFGSKSAKVGVE---LGEDVELNCSAVLNRND----LFYWSIRKE--DSLDPN-VHEDRNETTWTFEGKLHASKIL 283 + +T +TV + P + +K+ ++ LG L C A + L YW ++ + LD N V E G+ S L IL1RAPL1 224 VVRRTTELTVTAPLTDKPPKLLYPMESKLTIQETQLGGSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISL 319
IL-18r1 284 RIQKVTEKYLNVLYNCTVANEEATDTKSFILVRKETPDIQGHVFMRGITMVVLTSVAAVCVVILCIIYKVDLVLFYRRVAERDETLTDGKTYDAFV 379 + V E L Y+C V N S +L ++E + + + VC V + YK++++LFYR +E D K YDA++ IL1RAPL1 320 IVDSVEEGDLGN-YSCYVENGNGRRHASVLLHKREL-----MYTVELAGGLGAILLLLVCSVTIYKCYKIEIMLFYRNHFGAEELDGDNKDYDAYL 409
IL-18r1 380 SYLR----ECHPENGEEYTFAVETLPRVLEKQFGYKLCIFERDVVPGGAVVDEIHSLIEKSRRLIIVLSKSYMT--NGSRLELESGLHQALVERKI 465 SY + + + E GEE FA+E LP +LEK +GYKL I +RD++P G ++++ +++S+RLIIV++ +Y+ S ELE+ L LV +I IL1RAPL1 410 SYTKVDPDQWNQETGEEERFALEILPDMLEKHYGYKLFIPDRDLIPTGTYIEDVARCVDQSKRLIIVMTPNYVVRRGWSIFELETRLRNMLVTGEI 505
IL-18r1 466 KIILIEFTPASNIT------FLPPSLKLLKSYRVLRWKADKPLSSKSRFWKNLLYLMPAKAVKP 523 K+ILIE + I L ++KLL V++W K S+FWK L Y MP K ++P IL1RAPL1 506 KVILIECSELRGIMNYQEVEALKHTIKLLT---VIKWHGPKCNKLNSKFWKRLQYEMPFKRIEP 566
-
157 157
-
9.3 Examining conservation of residues necessary for IL-18 binding in the IL-1 receptor family
The actions of a number of IL-1 family members are regulated by soluble versions of their respective receptors. IL-18, however, is unusual among cytokines in that it's activity is regulated by a distinct circulating binding protein, which shows little resemblance to the IL-18 receptors other than the fact that they each contain immunoglobulin (Ig)-like domains. Given a role for IL-18 in the immune response and viral clearance, many forms of Poxviridae have evolved a mechanism to increase their virulence by producing their own IL-18 binding proteins (including Variola virus, the causative agent of smallpox 826), having possibly done so by horizontal gene transfer from their host species. Various studies have examined human or viral forms of IL-18 BP through mutation analysis and functional assays, and determined amino acid residues necessary for inhibition of IL-18 action 605,826– 828. In order to examine whether IL1RAPL1 or IL1RAPL2 could form central receptors for IL-18, the similarity of each to various forms of IL-18BP was studied by multiple sequence alignment.
9.3.1 Bioinformatics methodology
Multiple protein sequence alignment:
In order to examine the similarity of human and viral IL-18 BP protein sequences with those of the IL1RAPL1 and IL1RAPL2 proteins, multiple protein sequence alignment was undertaken using the MAFFT (Multiple Alignment using Fast Fourier Transform) alignment tool available online from the European Bioinformatics Institute 829. For comparison, other members of the IL-1 receptor family were included to assess the sequence similarity between other homologous proteins from the same receptor family which do not bind IL-18, in order to attempt to descriminate between residues common amongst IL-1 family members and residues common between proteins capable of binding IL-18 (human and viral IL-18BP, human IL-18r1). Results were fed into 'Mview', a multiple sequence alignment visualisation tool, also available from the European Bioinformatics Institute 830, in order to generate the included figures and assess amino acid consensus sequences. Consensus amino acid sequences generated by 'Mview' are based on those reported and used by Taylor 831 and Bork et al. 832 and are shown for reference in Appendix 4, Table 1. Reference sequence and source codes for human and viral sequences are shown in Appendix 4, Tables 2a and 2b.
- 158 -
9.3.2 Results: IL1RAPL proteins share residues with IL-18BP not present in other IL-1 receptor family members
Comparison of IL-18 binding proteins in the multiple sequence alignment reveals that the studies by Kim et al. 605, Krumm et al. 827, Esteban et al. 833, and Xiang and Moss 828 identify many similar critical residues on human and viral IL-18 binding proteins (Figure 20, Panel A). Examining the similarity between sequences there are a number of conserved amino acids, and at the 70 % consensus level this group of proteins was observed to share either common individual residues or residues with similar physicochemical properties. IL1RAPL proteins can be seen to share a characteristic 'pFIEpL' sequence (where 'p' represents an amino acid with a positive charge) with a number of forms of IL- 18BP. As shown underlined in the sequences of human and viral IL-18 binding proteins, residues in this region were identified by the above studies as forming one of the sites necessary for inhibition of IL-18.
Removing the other IL-1 receptor family members from the multiple sequence alignment (Figure 19, Panel B), it can be seen that the IL1RAPL proteins share a large number of residues at the 100 % consensus level which are either identical to, or possess similar physicochemical properties as, those present in human and viral forms of IL-18BP. Intriguingly, at the nucleotide level, the coding sequences of these proteins show that while they encode similar proteins, the IL1RAPL sequences share many nucleotides across the sequence encoding the 'pFIEpL' motif with the sequences of Ectromelia, Variola and Cowpox viruses, which form a conserved viral IL-18 binding protein domain cc, while the coding sequence for the MCV054L protein (forming a separate conserved domain dd ) exhibits much more similarity with the coding sequence for human IL-18BP, such that these viruses appear to possess sequences forming two groups of 'human IL1RAPL-like' and 'human IL-18BP-like' nucleotide sequences across this 'pFIEpL' region (Figure 20, Panel C).
9.3.3 Discussion
The analyses conducted here show a number of conserved residues between human IL1RAPL proteins and both human and viral forms of IL-18 BP, which suggest that the former could act as receptors for IL-18. Importantly, the portion of the IL1RAPL proteins showing similarity to IL-18 binding proteins depicted in Figure 19 maps to the extracellular region of these receptors,
cc pfam05566, see: http://www.ncbi.nlm.nih.gov/Structure/cdd/cddsrv.cgi?uid=pfam05566 dd PHA03112, see: http://www.ncbi.nlm.nih.gov/Structure/cdd/cddsrv.cgi?uid=PHA03112 - 159 - -
160 160
-
Figure 20: (following pages) Alignment of members of the human IL-1 receptor family with human and viral IL-18BP (Panel A) or solely IL1RAPL sequences with human and
viral proteins capable of binding IL-18 (IL-18r1, IL-18rap and IL-18BP sequences, Panel B). Underlined amino acids show: residues in EVM013 identified by Krumm et al. 827 or
Esteban et al. 833, residues in human IL-18BPa identified by Kim et al. 605 and Xiang and Moss 828, and residues in MC054L identified by Xiang and Moss 828, as being necessary for IL-18 binding or functional inhibition. Protein sequences have been truncated to only show the region of interest around the res idues identified by these studies. Percentages next to protein sequence names refer to the similarity of the entire protein sequence as compared to IL1RAPL1 as a reference. Consensus sequences shown underneath alignments are
derived from 'Mview' multiple sequence alignment tool 830. Panel C shows the consensus 'pF.EpL' portion of the sequences common to IL-18BP and IL1RAPL sequences, with nucleotide sequences added, highlighting the apparent existence of two nucleotide sequence groups.
A
IL1RAPL1 100.0% ETQLGDSANLTCRAFF-GYSGDVSPLIYWMKGE------KFIE------DLDENRVWESDIRILKEHLGEQEVSIS-LIVDSVEEGDL-GNYSCYVENGNGR--RHASVLL--H-KRE IL1RAPL2 61.2% DVQLGKPLNIPCKAFF-GFSGESGPMIYWMKGE------KFIE------EL-AGHIREGEIRLLKEHLGEKEVELA-LIFDSVVEADL-ANYTCHVENRNGR--KHASVLL--R-KKD IL1rap 30.4% EKEPGEELLIPCTVYF-SFLMDSRNEVWWTIDG------KKPD------DITIDVTINESISHSRTE-DETRTQIL-SIKKVTSEDLK-RSYVCHARSAKGEVAKAAKVKQ--K-VPA IL18rap 20.1% EVELGKPLTISCKARF-GFERVFNPVIKWYIKD------S------DL-EWEVSVPEAKSIKSTLKDEIIERN-IILEKVTQRDLRRKFVCFVQNSIGN--TTQSVQL--KEKRG IL18r1 25.9% AVELGKNVRLNCSALL-----NEEDVIYWMFGE------ENGS------DPNIHE--EKEMRIMTPE-GKWHASKV-LRIENIGESNLNVLYNCTVASTGGT--DTKSFIL--V-RKA IL1r1 21.3% EVDLGSQIQLICNV---TGQLSDIA--YWKWNG------SVIDEDDPVLGEDYY------SVENPANKRRSTLITV-LNISEIESRFYKHPFTCFAKNTHGI--DAAYIQL--I-YPV IL1rl2 20.5% EVQLGTTLIVDCNVTD-TKDNTNLR--CWRVNN------TLVD------DYYDESKRIREGVETHVSFREHNLYTVNITFLEVKMEDYGLPFMCHA----GV--STAYIIL--Q-LPA IL1rl1 21.1% EVEIGKNANLTCSACF-GKGTQFLAAVLWQLNG------TKIT------DFGEPRIQQEEGQNQSFSNGLACLDMV-LRIADVKEEDLLLQYDCLALNLHGL--RRHTVRLSRK-NPI IL1r2 21.2% SASLGSRLTIPCKVFL-GTGTPLTTMLWWTAND------THIE------SAYPGGRVTEGPRQEYSENNENYIEVP-LIFDPVTREDLHMDFKCVVHNTLSF--QTLRTTV--K-EAS SIGIRR 13.5% RPALGSSVALNCTAWVVSGPHCSLPSVQWLKDG------LPLG------IGGHYSLHEYSWVKANLSEVLVSSV-LGVNVTSTEVY-GAFTCSIQNI-----SFSSFTL--Q-RAG EVM013 3.5% ----TSSGEFHCSGC--VEHMPEFSYMYWLAKDMKSDEDTKFIE------HLGDGINEDETVRTT--DGGTTTLRKV-LHVTDTNKFAH-YRFTCVLTTLDGV--SKKEYLA--EV--- VV IL18BP 6.2% ----TSSGEFYCSGC--VEHMPKFSYMYWLAKDMKSDEYAKFIE------HLGDGIKEDETIRTT--DGGITTLRKV-LHVTDTNKFAH-YRFTCVLTTLNGV--SKKNIWL--K---- CPXV024 5.6% ----TSSGEFHCSGC--VEHMPDFSYMYWLAKDMKSDEATKFIE------HLGDGIKEDETVRTT--DGGITTLRKV-LHVTDTNKFAH-YRFTCVLTTIDGV--SKKNIWL--K---- MC054L 6.9% QVGPNGTTILTCLGC--TNH-THVSLIYWIVNE------SFPE------QLDSSLSEGRTHKHKFPNQSLTEISTN-LTVGPDV-ATHSTNFSCVLVDPEQV--VQRHLAL--TP--- IL18BPa 5.6% EVPLNGTLSLSCVAC--SRF-PNFSILYWLGNG------SFIE------HLPGRLWEGSTSRER--GSTGTQLCKA-LVLEQLTPALHSTNFSCVLVDPEQV--VQRHVVL--AQLWA
consensus 100% ....st.h.h.C.s...... W.htt...... t...... t.t...h...... h...... h.h.a.C.h...... h...... consensus 70% pst.spshplsCpuh..s.thsphshhaWhhss...... phl-...... ch.tthh.tpphpph..p.stpplphs Lhlpsssptsh.hpasChltshtGh..pptplhL..p....
B
IL1RAPL1 100.0% ETQLGDSANLTCRAFFGYSGDVSPLIYWMKGE------KFIEDLD-ENRVWESDIRILK-EHLGEQEVSISLIVDSVEEGDL-GNYSCYVENGNGRRHASVLLH-K IL1RAPL2 61.2% DVQLGKPLNIPCKAFFGFSGESGPMIYWMKGE------KFIEEL--AGHIREGEIRLLK-EHLGEKEVELALIFDSVVEADL-ANYTCHVENRNGRKHASVLLR-K IL18RAP 19.9% EVELGKPLTISCKARFGFERVFNPVIKW------YIKDSDLEWEVSVPEAKSIK-STLKDEIIERNIILEKVTQRDLRRKFVCFVQNSIGNTTQSVQLKEK IL18R1 24.0% AVELGKNVRLNCSALL----NEEDVIYWMFGE------ENGSD---PNIHEEKEMRIMT-PE-GKWHASKVLRIENIGESNLNVLYNCTVASTGGTDTKSFILVRK IL-18BPa 5.5% EVPLNGTLSLSCVACSRF-PNFS-ILYWLGNG------SFIEHL--PGRLWEGSTSRER--GSTGTQLCKALVLEQLTPALHSTNFSCVLVDPEQVVQRHVVLA-- MC054L 6.4% QVGPNGTTILTCLGCTNH-THVS-LIYWIVNE------SFPEQL--DSSLSEGRTHKHKFPNQSLTEISTNLTVGPDV-ATHSTNFSCVLVDPEQVVQRHLALT-- EVM013 3.3% ----TSSGEFHCSGCVEHMPEFS-YMYWLAKDMKSDEDTKFIEHL--GDGINEDETVRTT--DGGTTTLRKVLHVTDTNKFAH-YRFTCVLTTLDGVSKKEYLAE-- VV 6.2% ----TSSGEFYCSGCVEHMPKFS-YMYWLAKDMKSDEYAKFIEHL--GDGIKEDETIRTT--DGGITTLRKVLHVTDTNKFAH-YRFTCVLTTLNGVSKKNIWLK-- CPXV024 6.2% ----TSSGEFHCSGCVEHMPDFS-YMYWLAKDMKSDEATKFIEHL--GDGIKEDETVRTT--DGGITTLRKVLHVTDTNKFAH-YRFTCVLTTIDGVSKKNIWLK-- consensus/100% ....stsh.h.C.uh...... t.hhhW...... pp...... h...ph...p..t.t...hphslhht...... h.h.asChl.s..t..ptph.h... consensus/70% ....spshphpCpuhhta.sphu.hhYWhht-...... pFIEcL sstlpEs-hhhhp .p.u.pplppsLhlpsssphsh.hpaoChlts.pGssppplhLp .
C
IL1RAPL1 -K--F--I--E--D--L- AAATTTATTGAAGATCTG IL1RAPL2 -K--F--I--E--E--L- AAGTTTATTGAAGAACTG EVM013 -K--F--I--E--H--L- 'human IL1RAPL-like' nucleotide sequence AAGTTTATAGAACATCTG VV IL18BP -K--F--I--E--H--L- AAGTTTATAGAACATCTG CPXV024 -K--F--I--E--H--L- AAGTTTATAGAACATCTG
MC054L -S--F--P--E--Q--L- TCCTTCCCCGAGCAGCTC 'human IL-18BP-like' nucleotide sequence IL18BPa -S--F--I--E--H--L- TCCTTCATTGAGCACCTC
-
161 161
-
specifically, the third Ig-like domain of both IL1RAPL1 and IL1RAPL2. Notably, the third Ig-like domain of IL-18r1 forms a key binding site for IL-18r1 binding of IL-18 826,834. The multiple sequence alignments above and by others 833 suggest that both human and Poxviridae IL-18 binding proteins align exclusively to this third Ig-like domain.
If the hypothesis that IL1RAPL proteins form central receptors for IL-18 were correct, one would expect the following to also hold true: (i) that key residues necessary for IL-18 binding would be conserved throughout the process of evolution in the IL1RAPL proteins, as with IL-18 BP itself (provided that motifs in the ligand IL-18 were also conserved), and (ii): that IL1RAPL proteins would be found at sites of action of IL-18. Moreover, since it is already known that the IL1RAPL proteins are predominantly found in the brain 823–825, these should be located in the interpeduncular nucleus; the terminal field of IL-18 containing fibres projecting from the medial habenula.
- 162 -
9.4 Examining conservation of residues shared between IL1RAPL proteins and IL-18 BP
In order to assess the question (i) raised above, of conservation of key motifs throughout evolution, the protein sequences of IL-18BP, IL-18, IL1RAPL1, and IL1RAPL2 for a number of different species were aligned to assess the extent to which key residues identified as playing a role in IL-18BP or IL- 18 action were conserved throughout mammalian evolution.
9.4.1 Bioinformatics methodology
Multiple protein sequence alignment:
Alignment of multiple protein sequences of IL1RAPL1, IL1RAPL2, IL-18 BP and IL-18 from various species was undertaken using the MAFFT and Mview alignment tools as described above. Selection of species was performed by examining the listings of Homo sapiens IL1RAPL1, IL-18 and IL-18BP in the Ensembl database to identify homologous genes in other species (listed under 'Gene tree' tab of Ensembl database), or searching the NCBI Genbank database for available protein sequences in non-human species. A total of 23 species were identified for which reliable protein sequences (i.e. not containing long ambiguous 'X' read segments) were available across the NCBI or Ensembl databases. Of these, only 20 had available sequences for IL1RAPL2. Reference sequence and source codes are shown in Appendix 4, Table 3.
9.4.2 Results
Residues shared between IL1RAPL proteins and IL-18 BP are highly conserved:
Multiple sequence alignment of IL1RAPL proteins from various species showed a high degree of conservation of almost the entire coding sequences of the IL1RAPL proteins (Appendix 4, Figure 1, Panels A and B). Interestingly, a number of the species sampled appear to have IL1RAPL proteins which contain only one extracellular Ig-like domain (as opposed to three in human, mouse, and rat species), and this one Ig-like domain corresponds to the third Ig-like domain found in humans which contains the conserved sequences shown in Figure 20 above. Sequences of both IL-18BP and IL-18 also showed a high degree of conservation across the species sampled (Appendix 4, Figure 1, Panels C and D, respectively).
- 163 -
Alignment of the 80 % consensus sequences across species for IL1RAPL1, IL1RAPL2, and IL-18BP is shown in Figure 21 for the region corresponding to the third Ig-like domain of human IL1RAPL proteins. It can be seen that a number of residues are shared between the consensus sequences, with two cysteine residues at either end of the third Ig-like domain which are believed to form a disulphide bridge and are necessary for IL-18 BP function 833, and various residues between these two, including the 'pFIEpL' motif identified earlier, an upstream 'YW' sequence, and downstream glutamic acid (E) which has been identified as a key residue for IL-18 binding or inhibition in Ectromelia virus, Molluscum contagiosum virus and human IL-18 binding proteins.
9.4.3 Discussion
It is now recognised that the three dimensional structure of folded proteins is arguably more important for evaluating protein function than amino acid residue sequences, as even proteins with low levels of amino acid similarity can show similar function and three-dimensional structure. In the case of the IL1RAPL proteins, however, consideration of the three-dimensional structure yields little further insight beyond that already known, as the portions of the proteins shown in Figures 20 and 21 are already recognised as forming Ig-like domains, a feature common to all members of the IL-1 receptor family, and also IL-18 BP. Thus, given IL1RAPL proteins are known to exhibit a similar three dimensional structure to other proteins capable of binding IL-18 (the IL-18 receptor subunits and IL-18BP), and that they share a number of key residues with IL-18BP, the anatomical location of the IL1RAPL proteins in the rat brain was investigated by immunohistochemistry in order to address question (ii) raised above, and examine if the IL1RAPL proteins are localised in the interpeduncular nucleus.
- 164 -
Figure 21: Manual alignment of multi-species consensus sequences for IL-18BP, IL1RAPL1 and IL1RAPL2. Below is shown key shared residues present across each protein in many species, and below the dividing line the sequences of Ectromelia virus, Molluscum contagiosum virus and human IL-18 binding proteins with key residues identified by
Krumm et al. 827, Esteban et al. 833, Kim et al. 605 and Xiang and Moss 828, as being necessary for binding of IL-18.
IL1RAPL1 80% consensus ..GsSAN----LTCRA----FFGYSGDVSPLIYWMKGE------KFIEDLDENRVWESDIRILKEHLGEQEVS-ISLIVDSVEEGD-LGNYSCYVENGNGRRHASVLLH IL1RAPL2 80% consensus QLGKPLN----IPCKA----FFGFSGESGPMIYWMKGE------KFIEELAGHIR-EGEIRLLKEHLGEKEVE-LALIFDSVVEAD-LANYTCHVENRNGRKHASVLLR IL18BP 80% consensus .-h-PhNGTLoLSCsACS-+Fs.FS-----lLYWL-GNG------SFIE+LPGpL-hEGpTphp.tsttT.L.+sLVLEpLoPsL+--sTNFSClhhDPtpssphHllLA shared residues N C A S YW G FIE L E L N C EVM013 TSSGEFHCSGCVEHMPEFS-----YMYWLAKDMKSDEDTKFIEHLGDGIN-EDETVRTT--DGGTTTLRKVLHVTDTNKFAH-YRFTCVLTTLDGVSKKEY-LA MC054L QVG-P-NGTTILTCLGCTNH-THVS-----LIYWIVNE------SFPEQLDSSLS-EGRTHKHKFPNQSLTEISTNLTVGPDVAT-HSTNFSCVLVDPEQVVQRH--LA Human IL18BPa EV--PLNGTLSLSCVACS-RFPNFS-----ILYWL-GNG------SFIEHLPGRL-WEGSTSRERGSTGTQLCKALVLEQLTPALH--STNFSCVLVDPEQVVQRHVVLA
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9.5 Immunohistochemistry for IL1RAPL proteins in the rat CNS
In order to assess whether the IL1RAPL proteins could form central receptors for IL-18, immunohistochemistry experiments were undertaken in the rat, as previous work identifying IL-18 projections from the habenula had been performed in this species.
9.5.1 Methodology: immunohistochemistry
Brains were collected from adult male Sprague-Dawley rats and processed in preparation for immunohistochemistry as described in the General Laboratory Methods chapter, Section 6.6. Given that fibres within the interpeduncular nucleus are largely oriented in a rostral-caudal direction 835, brains were sliced sagitally in 40 µm sections in order to cut through fibres running along the sagittal plane.
Immunohistochemistry for the detection of IL1RAPL1 and IL1RAPL2 was performed using goat polyclonal primary antibodies raised against human IL1RAPL proteins (IL1RAPL1: sc-161736, IL1RAPL2: sc-131569, Santa Cruz Biotechnology, USA), using the immunohistochemistry procedure described in General Laboratory Methods, Chapter 6, Section 6.6.
Immunohistochemistry for IL-18 was performed as described previously in Section 7.2.2. For IL1RAPL1 immunohistochemisty, sections were incubated overnight at 4 °C in a primary antibody solution containing either a 1:1000 or 1:2000 dilution of anti-IL1RAPL1 antibody in wash buffer with 5 % normal horse serum. The two antibody concentrations produced similar results and images from either concentration are included here. For IL1RAPL2 immunohistochemistry, sections were incubated overnight at 4 °C in a 1:2000 dilution of primary antibody. Secondary antibody (Santa Cruz horse anti-goat biotinylated secondary antibody, catalogue number BA-9500) was added at 1:2000 dilution and sections incubated at room temperature for one hour. Antigen pre-absorption of primary antibody was performed to test the specificity of staining, with sections processed in parallel with the addition of either primary antibody at 1:2000 dilution, or primary antibody which had been pre-absorbed by incubation in PBS with a 5-fold concentration of their respective blocking peptides (Santa Cruz Biotechnologies, USA, product numbers sc-161736P and sc-131569P) overnight at 4 °C. Sections were incubated in pre-absorbed primary antibody added to provide the same final concentration of primary antibody (1:2000) as used in positive control sections. Although the antibodies were raised against peptides from the human protein sequences, IL1RAPL1 and
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IL1RAPL2 are highly homologous between human and rat species (as illustrated in Appendix 4, Figures 2 and 3), and given the high homology it was expected that both antibodies would therefore also be suitable for use on rat tissue. Although the precise antigen sequences are not given by the manufacturer, the antibodies were raised against peptides mapping within the C-terminal cytoplasmic domains of the respective proteins 836,837. The carboxy tail of the IL1RAPL1 and IL1RAPL2 receptors features a unique region of approximately 120 amino acids, which, although similar between these two proteins, shares little resemblence with any other known protein. Given that IL1RAPL1 and IL1RAPL2 are thought to have arisen via gene duplication and show a high degree of homology, it was feared that antibodies raised against one of these proteins could also produce positive staining for the other. However, despite their homology, the protein sequences of the carboxy tails of these two proteins are not greatly similar (Appendix 4, Figure 4), and the manufacturers claim no cross-reactivity of each antibody with the respective homologue proteins.
9.5.2 Results
Immunohistochemistry for IL1RAPL1 produced positive staining across a number of brain regions. In the interpeduncular nucleus, the region of interest in this study, staining corresponded well to the pattern of immunostaining of IL-18 terminal fibres entering the interpeduncular nucleus (Figure 22). At higher magnification, the postive immunostaining could be seen to label fibres and processes in the interpeduncular nucleus, with some staining of cell bodies (Figure 22, Panel C).
In other brain regions, positive immunostaining was also observed throughout the cerebral cortex and hippocampus (Figure 23), and in the molecular layer of the cerebellar cortex (Figure 24), where immunostaining was the most intense of the regions labelled, and was localised to Purkinje cell bodies and processes. Antigen pre-absorption of the primary antibody reduced, but did not entirely eliminate all of the observed immunostaining. No reliable staining could be seen in the forebrain or midbrain, however, some residual staining was observed in the cerebellar cortex (Figure 24), with Purkinje cell bodies and some processes visible.
Figure 22 (following page): Immunostaining for IL-18 (A), as shown in Figure 7, Panel C, and IL1RAPL1 (B and C) in sagittal sections through the interpeduncular nucleus. The boxed region in B is enlarged in C. Images are oriented similar to the schematic diagram depicted earlier in Figure 5 with the rostral portion of the brain to the right, and the caudal portion to the left of the image. Scale bars: A and B = 500 μm, C = 100 μm.
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Figure 23: Immunostaining for IL1RAPL1 in the cortex (A and B) and hippocampus (C and D). Boxed regions in A and C are enlarged in B and D, respectively. Scale bars: A and C = 500 μm, B and D = 100 μm.
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Figure 24: Immunostaining for IL1RAPL1 in the cerebellar cortex after incubation with primary antibody (A and B) or primary antibody following preabsorption with binding peptide (C and D). Boxed regions in A and C are enlarged in B and D, respectively. The intensity of staining in the cerebellum is reduced but not entirely eliminated by peptide preabsorption. Scale bars = 500 μm.
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IL1RAPL2 immunohistochemistry produced no identifiable positive staining. Although some faint labelling of Purkinje cell bodies was observed in sections incubated with anti-IL1RAPL2 antibody, this was also present in sections incubated with pre-absorbed primary antibody (not shown).
9.5.3 Discussion
The immunohistochemistry results presented here suggest expression of IL1RAPL1 in the interpeduncular nucleus. Sections processed for immunostaining of IL-18 showed a marked correspondence of the terminal field of IL-18 fibres with IL1RAPL1 immunostaining. Although pre- absorption of primary antibody with its binding peptide did not entirely prevent immunostaining, a reduction in the intensity of immunostaining is generally viewed as indicative of a staining signal which reflects binding to the desired antigen sequence. Assessing whether immunostaining reflects a signal specific to the antigen of interest in the tissue is an area fraught with problems, and interpretation of immunostaining is best done by relying on a variety of methods 838,839. Ideally, a lack of immunostaining in tissue from a transgenic animal with a deletion of the gene encoding the protein of interest provides one of the best pieces of evidence in support of specific immunostaining. However, in neither the case of IL-18 immunohistochemistry (reported in Chapter 7), nor IL1RAPL1 immunohistochemistry reported here, did we have access to tissues from transgenic knockout rats devoid of the IL18 or IL1RAPL1 genes to test antibody specificity. Other methods, such as antigen preabsorption, can add to a body of evidence regarding antibody specificity but also have their shortcomings; non-specific staining is still possible with antigen preabsoprtion, as is specific staining when the binding of antibody to peptide used in preabsorption is reversable. Perhaps one of the best sources of evidence for antibody specificity is demonstrating mRNA expression in the same distribution as the observed immunostaining through in situ hybridisation techniques. This, for example, was performed earlier in Chapter 7 and provides strong support for the specificity of IL-18 immunostaining reported in this thesis. In the case of IL1RAPL1, this has not been performed here due to time constaints. However, experiments have been reported by others examining IL1RAPL1 protein distribution by Western blot or mRNA expression by in situ hybridisation, and these show a reasonable correspondence with the observed immunostaining (discussed further below in Section 9.7). Of particular importance is whether there is also IL1RAPL1 mRNA expression in the interpeduncular nucleus, and this is more difficult to discern from available studies. Thus, the immunostaining observed here could be specific to IL1RAPL1, but a firmer body of evidence regarding antibody characterization is clearly necessary to properly interpret the observed immunostaining.
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Immunohistochemistry with the anti-IL1RAPL2 antibody used here failed to produce any positive staining. However, it is possible that further experimentation with incubation conditions or antigen retrieval steps could result in reliable staining from this antibody. There is a paucity of commercially available antibodies raised against human or rodent IL1RAPL2 proteins, making investigations into their localization difficult. At present, based on the results obtained here, the presence of IL1RAPL2 in the interpeduncular nucleus can neither be confirmed nor ruled out. The results of other studies examining IL1RAPL2 mRNA expression and protein presence in the brain utilizing different methodologies are discussed further below in Section 9.7.
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9.6 Preliminary investigation of IL1RAPL gene expression across rat tissues
The findings above, that IL1RAPL proteins share a number of key residues with IL-18 BP, and that IL1RAPL1 receptor expression in the interpeduncular nucleus is in complete accordance with IL-18 projections, strongly suggest that IL1RAPL forms a central receptor for IL-18. Given IL-18 is constitutively present as a circulating cytokine, these results also raise the question as to whether IL- 18 could also act on IL1RAPL receptors in the periphery. A pilot investigation was thus undertaken using tissue samples collected from the same animal in order to examine the expression of the IL1RAPL genes across a number of different peripheral tissues. RT-PCR was conducted using these samples to examine the expression of IL1RAPL1, IL1RAPL2, and by comparision, IL-18r1, gene expression across tissues.
9.6.1 Methodology
RT-PCR
A range of tissues were collected from an adult male Sprague-Dawley rat on dry ice following sacrifice by decapitation and stored at -80 °C until use. Hypothalamus and cortex samples were collected by sectioning the brain coronally at the anterior and posterior limits of the hypothalamus as viewed from the inferior surface of the brain. From the resulting coronal section, the hypothalamus was grossly dissected from the inferior surface and a sample of the cortex taken from superior surface of the same section. Skeletal muscle samples were collected from the quadriceps and heart samples from the wall of the left ventricle. Tissues were homogenized using the Qiagen TissueLyser II system (catalogue number 85300, Qiagen, USA) according to manufacturers' instructions and total RNA extracted from the homogenate using the Qiagen RNeasy Plus Universal Mini Kit (catalogue number 73404, Qiagen, USA). Generation of cDNA was performed with Superscript III (product number 18080-044, Invitrogen, USA) as described in 'General Laboratory Methods' (Section 6.2) with a final addition of RNase H to remove remaining RNA strands. Amplification of desired transcripts was performed using the primer combinations and thermocycler conditions listed in Table 14 below. Approximately 150 ng of cDNA was added per 25 µl reaction for each sample.
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174 174
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Table 14: Primer combinations (A) and reaction conditions (B) for RT-PCR of IL-18r1, IL1RAPL1, IL1RAPL2 and Actb transcripts across various rat tissues.
A.
Transcript Forward primer binds Reverse primer binds nucleotides on Expected nucleotides on desired transcript product desired size transcript IL-18r1 ATCCTAGCTATGGAGAGCTG 804 - 823 ACTGTTCCCTTCAATAACCGT 980 – 1000 196 bp (NM_001106905.2) IL1RAPL1 TGGAGCTGAGGAACTGGACGGA 1186 - 1207 AGATGCTCCAGCCCCTTCGGAC 1454 - 1475 290 bp (NM_177935.2) IL1RAPL2 GCAAGCCAAAAATGTGGAGAAGCA 1472 - 1495 TGGCTTGGGAGGCTTGTCTGT 1630-1650 179 bp (NM_001166342.1) Actb TACAACCTTCTTGCAGCTCCTCCG 28 - 51 TGTAGCCACGCTCGGTCAGG 657 - 676 649 bp (NM_031144.2)
B.
Initialisation Denaturing Annealing Extension Cycles Final extension 94 °C for 3 minutes 94 °C for 30 seconds 57 °C for 30 seconds 72 °C for 45 seconds 35 72 °C for 5 minutes
9.6.2 Results
Images from gel electrophoresis of PCR products are shown in Figure 25. As seen previously, IL-18r1 expression was particularly prominent in the spleen, and also the lung, with lesser expression seen in the brain, liver and heart. The expression of both IL1RAPL genes was almost entirely restricted to the brain. However, some expression in peripheral tissues was present, with IL1RAPL2 expression also seen in the adrenal gland and lung, and IL1RAPL1 expression detected in the spleen sample. However, a very faint IL1RAPL1 band could also be seen in the RT- sample of the spleen, such that the positive signal seen in the spleen RT+ sample is best interpreted with some caution.
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Figure 25: RT-PCR of IL1RAPL1, IL1RAPL2 and IL-18r1expression across rat tissues. A: RT positive samples; B: RT negative control samples. Bands of the expected size for IL-18r1, IL1RAPL1 and IL1RAPL2 are shown, with control Actb (encoding β-actin) bands below. Samples were run against a 100 bp ladder (100 bp) in the left-most lane, with the remaining lanes showing: spl - spleen, adr - adrenal, kid - kidney, liv - liver, lun - lung, hea - heart, mus - muscle, hyp - hypothalamus, cor - cortex, pan - pancreas.
A.
B.
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9.7 Discussion
The bioinformatics approaches used here have identified that amino acid residues which form part of the IL-18 binding domains of human and viral IL-18 BP are also present in the third Ig domain of the human IL1RAPL1 and IL1RAPL2 sequences, although are generally not shared amongst homologous IL-1 receptor family members. In addition, immunohistochemistry experiments in the rat brain showed the presence of IL1RAPL1 protein in the interpeduncular nucleus, providing anatomical evidence of co-expression of the putative receptor/ligand combination. Together these results are consistent with the hypothesis that IL1RAPL1 forms a centrally-expressed receptor for habenula-produced IL-18. IL1RAPL2 may also function in a similar manner, however, further experimentation with immunohistochemistry procedures or Western blots of interpeduncular nucleus specimens would be necessary to deduce whether this receptor is also present in the interpeduncular nucleus. Amino acid sequences of the IL1RAPL proteins were also observed to be highly conserved amongst a selection of mammalian species assessed here, suggesting that these putative receptor/ligand interactions may be very old in evolutionary terms, and may currently operate in a wide variety of animals.
Reviewing the literature related to IL1RAPL1 and IL1RAPL2 genes shows that both were first discovered through analyses in humans with X-linked mental retardation 823,824, and are particularly highly expressed in the brain. Surprisingly, given their identified role in mental retardation, and studies examining cerebellar defects in IL1RAPL1 knockout mice 840, there are no previous reports which have examined the overall distribution of the IL1RAPL proteins in the brain using immunohistochemistry, such that the results presented here are novel findings. A few papers have examined IL1RAPL1 expression by immunohistochemistry in cultured cortical 819 or hippocampal 818,841 neurons, showing the presence of IL1RAPL1 in both. However, these results are of limited value in determining the wider distribution throughout the brain in vivo. A number of other studies have examined IL1RAPL gene expression and protein distribution by other methods.
IL1RAPL1/2 localisation within the brain:
One of the first papers describing IL1RAPL1 and its association with mental retardation conducted in situ hybridisation in the mouse brain, describing the presence of IL1RAPL1 in the hippocampus, olfactory bulb and mammillary bodies (Carrié et al. 824). A closer inspection of the figures presented in that paper, however, suggests that the positive signal reported in the mammillary bodies may be at a more caudal level of sectioning. Figure 26 shows Figure 5d from that study, along with edited
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images of coronal sections from the Paxinos and Franklin mouse brain atlas 842 at the level of the mammillary bodies and more caudally at the level of the interpeduncular nucleus. The level of sectioning shown in Figure 5d of Carrié et al. in fact appears to align better with the more caudal reference image. A common error which can occur in producing brain sections for experimental work is sectioning at an off-plane angle, such that different sides of the sections produced correspond to different reference images in a rodent brain atlas. As is shown in Figure 26, this does not seem to have happened in this case, as features of both sides of the ventral aspect of the section (e.g. the form of the hippocampus bilaterally) are similar to the reference atlas image at the level of the interpeduncular nucleus. Thus, although Carrié et al. report a positive signal for mRNA in the mammillary bodies, their results could be interpreted as showing a high density of the IL1RAPL1 gene transcript within the interpeduncular nucleus. The remainder of their results, showing IL1RAPL1 gene expression in the cerebellum, hippocampus and olfactory bulb are consistent with the immunohistochemistry results here (with the caveat that the olfactory bulb was not included in brain sections for immunohistochemistry experiments described here).
The Allen Brain Atlas presents in situ hybridisation for IL1RAPL1 in sagittal sections (image series 70278045 843), which is largely in concordance with the findings of Carrié et al., showing relatively wide expression across the brain, with particularly high expression in the hippocampus, olfactory bulb and cortex. It does, however, also show signals corresponding to the mammillary bodies (which is more convincing in the sagittal plane given the clear demarcation of the cerebral peduncular fossa) and hypothalamus. Only two sagittal sections are presented in the Allen Brain Atlas which include the interpeduncular nucleus, covering the lateral portion of the nucleus. A possible explanation to reconcile these in situ hybridisation results is that, while the coronal image presented by Carrié et al. appears to show the interpeduncular nucleus (shown in Figure 26), the authors may indeed have observed IL1RAPL1 expression in the mammillary bodies, as they report in the text, while the Allen Brain Atlas shows expression in the mammillary bodies but does not provide evidence to clearly refute the presence of IL1RAPL1 in the interpeduncular nucleus. Results of a preliminary RT-PCR experiment presented here (Section 9.6) and the Allen Brain Atlas suggest there is IL1RAPL1 mRNA present in the hypothalamus, although protein was not oberved immunohistochemistry experiments.
The Eurexpress project 810, which has examined the expression of a wide number of genes in the developing mouse embryo, shows widespread IL1RAPL1 expression throughout the brain and spinal
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Figure 26: In situ hybridisation in the mouse brain for IL1RAPL1 expression from Carrié et al., Nat
Genet, 1999, 23: 25-31 824 ee. Top panel shows image presented as Figure 5d of Carrié et al., with panels below showing reference atlas images at the level of the interpeduncular nucleus and mammillary bodies for comparison. Reference atlas images adapted from Paxinos and
Watson 842 showing coronal sections at Bregma -3.64 mm (interpeduncular nucleus) and Bregma -2.92 mm (mammillary bodies).
ee Figure from Carrié et al. reprinted by permission from Macmillan Publishers Ltd: Carrié A, Jun L, Bienvenu T, et al. A new member of the IL-1 receptor family highly expressed in hippocampus and involved in X-linked mental retardation. Nat Genet, 23 (1): 25–31. (1999). License Number: 2831001462342 - 179 -
cord during development 844, which, however, does help discern the location of its expression at later stages of adult development. Other studies have utilised Western blot to show the presence of IL1RAPL1 protein in hippocampal neurons 845, the cerebellum 846, the olfactory bulb 847, and all three of these regions 841. However, there are no studies which provide Western blot data to confirm or refute the presence of IL1RAPL1 in the interpeduncular nucleus.
In sum, the results of these studies provide some support for the same distribution of IL1RAPL1 gene expression and translation into protein as the immunohistochemistry results presented here, with the presence of IL1RAPL1 in the cortex, hippocampus and cerebellum, which supports the validity of these results. Regions with discordant results are the interpeduncular nucleus (the area of interest for the current work), hypothalamus and mammillary bodies.
Regarding the localisation of IL1RAPL2 in the brain, mRNA expression has been detected in the human and mouse brain by Nothern blot 823,825, and in situ hybridisation performed in the mouse brain by Ferrante et al. 823 shows expression in the cortex, olfactory bulb, and cerebellum, similar to the IL1RAPL1 transcript. In contrast, the Allen Brain Atlas presents results for IL1RAPL2 from two different probes (image series 73994672 and 69735389) which show little expression of IL1RAPL2 across the mouse brain (with the exception of some weak signal in the pontine reticulotegmental nucleus), and Born et al. show only weak expression in the fetal brain 848. Likewise, the Eurexpress project shows no expression of IL1RAPL2 in the developing mouse embryo 849. Immunohistochemistry experiments conducted for this thesis in the adult rat brain failed to produce any specific staining. Thus, currently available studies present disparate results and cannot currently be easily reconciled to form a consensus picture as to the localisation of expression of IL1RAPL2 in the brain.
IL1RAPL1/2 function and potential ligands:
The IL1RAPL proteins have been largely regarded as orphan receptors since their discovery around the turn of the millenium 824,825,848,850, although one paper has presented evidence that IL-1 can signal via IL1RAPL1 to induce phosphorylation of JNK 845.
Late in 2011 two papers simultaneously reported that IL1RAPL1 forms a trans-synaptic ligand- receptor complex with pre-synaptic protein tyrosine phosphatase receptor delta (PTPRD; Valnegri et al. 818 and Yoshida et al. 819). These studies not only identify a ligand for the IL1RAPL receptors, but also greatly advance the understanding of how these receptors function. Whereas the functioning of
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the IL1RAPL receptors was initially assumed to follow that of the other IL-1 family members, these and other studies now provide sufficient insight to reveal that these function in an entirely separate manner, and in fact seem to regulate the formation and maintenance of excitatory (glutamatergic) synapses both via pre-synaptic and post-synaptic effects. The post-synaptic effects of IL1RAPL proteins can also be isolated to different portions of the cytoplasmic domain; the toll/interleukin-1 receptor (TIR) domain is necessary for inducing changes in dendrite architecture (although the signalling mechanisms involved are unknown), and the effect of IL1RAPL proteins on synaptic localisation occurs via the C-terminal domain, which induces phosphorylation of JNK and phosphorylation of post-synpatic density protein 95 (PSD-95). Importantly, studies in mice suggest that PTPRD is not expressed in the interpeduncular nucleus 851,852, and in fact the only overlap between PTPRD and IL1RAPL1 expression in the brain appears to be in the hippocampus. Given both the studies of Valnegri et al. and Yoshida et al. identified the interaction of IL1RAPL1 and PTPRD by utilizing whole brain homogenates it seems likely that their results arise from identifying an interaction which occurs in the hippocampus. Valnegri et al. mention approximately 200 proteins identified by their co-precipitation assays, although subsequent experiments only focused on PTPRD, and this suggests that a number of other IL1RAPL1-interacting proteins exist; phrased in a different manner, their results in no way exclude the existence of other IL1RAPL1 ligands. In addition, given that PTPRD and IL1RAPL1 could only interact in areas of co-expression, and furthermore, that the interaction only appears to occur with specific PTPRD splice variants, these studies raise the question of what potential ligands could interact with IL1RAPL1 in other areas of the brain with no evidence of PTPRD expression, such as in the cortex or cerebellum. The argument is made here that, based on the work described in this chapter, IL-18 is a prime candidate for forming an IL1RAPL1 ligand in the interpeduncular nucleus.
One study thus far has examined whether the IL1RAPL proteins could form receptors for IL-18 (Born et al. 848), utilizing two different experimental methods. In one, COS-7 cells were transfected with IL1RAPL proteins as well as an NF-κB luciferase reporter, finding no effect of IL-18 on luciferase reporter activity. Given most members of the IL-1 receptor family function by the combined action of ligand-binding and accessory protein chains (a.k.a. α and β chains), in another experiment the cytoplasmic domain of various members of the IL-1 receptor family, including the IL1RAPL proteins, were fused to the extracellular portion of the IL-1 receptor to generate chimeric receptors. Cells transfected with these receptors were stimulated with IL-1 using NF-κB luciferase activity as a marker of signal transduction. This experiment showed no effect of IL-1 stimulation on NF-κB activity when various members of the IL-1 receptor family were coupled with the
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cytoplasmic domains of IL1RAPL proteins. These experimental procedures, however, are based on the assumption that the IL1RAPL receptors function in a similar manner to other members of the IL- 1 receptor family and result in NF-κB activation, which now does not appear to be the case. At the time of conducting these experiments, it had been suggested that the longer C-terminal amino acid sequences of the IL1RAPL proteins could be inhibitory, and thus further experiments were conducted in which truncated versions of the IL1RAPL proteins, lacking the C-terminal domain, were utilised, again showing no activation of NF-κB. However, it is difficult to know from these experiments whether (i) this is simply because the toll-like receptor domain of the IL1RAPL proteins is unable to activate NF-κB even in the absence of the C-terminal domain, (ii) whether the IL1RAPL proteins simply do not function as ligand binding/accessory protein diads in the same manner as other members of the IL-1 receptor family, or (iii) whether they are in fact capable of activating NF- κB but simply require another accessory protein or ligand binding chain different from the known IL-1 receptor family members tested in this study. In support of a lack of possible NF-κB activation from IL1RAPL receptors are the findings of Khan et al., who have published evidence that IL1RAPL1, but not IL1RAPL2, can activate the JNK pathway, with no evidence that either activates the NF-κB, nor the p38 or ERK MAPK kinases 853. Despite its recent discovery, the TIR domain of IL1RAPL1 was the first member of the IL-1 family of receptors to have a crystal structure produced and published 853. The crystal structure shows a number of interesting insights, such as the apparent ability of IL1RAPL1, and presumably IL1RAPL2, to form homodimers, which is not seen with other TIR domains and may be involved in initiating the unique signalling pathways of these receptors.
The unique C-terminal cytoplasmic domain of IL1RAPL proteins has also been shown to interact with neuronal calcium sensor-1 (NCS-1) and can regulate exocytosis in endocrine PC12 cells via an effect on voltage-gated calcium channels 840,847. A recent analysis of gene sequence conservation across 29 mammals identified conserved regions within the final exon of IL1RAPL1 and IL1RAPL2, the portion identified as encoding the neuronal calcium sensor 1 (NCS-1) interacting domain 854, indicating that this interaction with NCS-1 probably forms a key, highly conserved, biological function of the IL1RAPL proteins.
Interactions of the homologous IL-1 cytokine with IL1RAPL1/2:
Of particular importance to the proposed interaction of IL-18 with IL1RAPL proteins is the recent findings of another study by Yoshida et al. 855, which offer supportive evidence that the highly related cytokine IL-1 binds to a CNS-specific receptor which interacts with PTPRD, analogous to the
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proposed action of IL-18 binding to IL1RAPL receptors (which are highly expressed within the brain and also interact with PTPRD).
A few key works prior to that study had discovered the existence of a centrally-expressed splice variant of the accessory receptor for IL-1 (known as IL-1RAcPb) 856–858. This receptor is formed by the use of a separate final exon, downstream of the IL1RAcP transcript, which is specifically transcribed in the CNS, and appears to mediate a number of the central effects of IL-1. The work of Yoshida et al. takes these results further and shows that the extracellular portion of IL-1RAcP/IL- 1RAcPb (the extracellular portion is common to both isoforms) forms a trans-synaptic interaction with PTPRD 855, functioning in a manner very similar to that described for IL1RAPL1; the extracellular interaction with PTPRD is necessary for presynaptic differentiation of excitatory synapses, while the neuron-specific IL-1RAcPb isoform, with its longer intracellular domain, induces post-synaptic differentiation. They also report that no synaptogenic activity was observed for IL-18rap; an observation in accordance with the hypothesis presented here, i.e. that the neuronal effects of IL-18 are likely to be mediated via the IL1RAPL proteins and not the IL-18 receptor. Interestingly, the existence of a separate centrally-expressed receptor system was predicted for IL-1 prior to the recent discovery of IL-1RAcPb 859, and similarly in the case of IL-18, a separate centrally-expressed receptor system would be expected based on the limited central expression of IL- 18 receptor subunits and the mismatch this creates with abundant habenula IL-18 expression and projections into the interpeduncular nucleus.
Conclusions:
The goal at the outset of this thesis was to examine the neuroendocrine role of IL-18; experiments published in two high profile journals (Netea et al. in Nature Medicine 503 and Zorrilla et al. in PNAS506) had described a role for IL-18 in controlling food intake, mediated by effects in the brain. The founding hypotheses upon which the experiments in this thesis were undertaken revolved around the role of IL-18 as a pro-inflammatory cytokine and a large body of evidence linking changes in inflammatory signalling with alterations in metabolism; more specifically, that inflammation within the brain could alter hypothalamic function. As such, it was initially proposed that IL-18 could exert its effects via activating IL-18 receptors in the hypothalamus.
A number of experiments were then undertaken to examine the central distribution of the subunits of the IL-18 receptor. Although the results of those experiments are not entirely concordant across experimental techniques (as described earlier in Chapter 8), those experiments present results which
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could be interpreted as showing either a lack of central IL-18 receptor expression, or a low-level, relatively even expression across different brain regions. Indeed, other published works appear to fall into either of these categories. These results are rather puzzling; on the one hand, a lack of focused expression in the interpeduncular nucleus is at odds with observed IL-18 projections to this brain region, on the other, there also appears to be no focus of IL-18 receptor expression in the arcuate nucleus, the most likely site of action for circulating IL-18 exerting a metabolic role through activation of hypothalamic inflammatory pathways (as hypothesized in this thesis).
In light of these findings, it seemed plausible that IL-18 could act through a distinct centrally- expressed receptor. The works in this Chapter have identified IL1RAPL receptors as potential central receptors for IL-18. Although this thesis does not provide evidence of IL-18 initiating signal transduction events downstream of the IL1RAPL receptors, nor provide evidence from binding studies, a number of pieces of evidence converge to suggest the IL1RAPL receptors may in fact form a centrally-expressed receptor for IL-18. These are: (i) the aforementioned mismatch between central IL-18 expression and a lack of central IL-18 receptor expression reported here and by other published studies; (ii) the overlapping presence in the interpeduncular nucleus of IL-18 containing projections and IL1RAPL1; (iii) conserved amino acid motifs shared between IL-18 BP and the IL1RAPL receptors; and (iv) that likewise, a CNS-specific receptor exists for the highly homologous IL-1 cytokine, which acts in a similar fashion to IL1RAPL1.
Clearly the proposed binding of IL-18 to IL1RAPL proteins will ultimately need to be experimentally tested, and in order to do so appropriate downstream signalling events as markers of IL-18 activation of IL1RAPL receptors will need to be identified. How, or if, these signalling events could translate downstream into effects on appetite and explain the hyperphagic phenotype of IL-18 knockout animals is currently unknown. One piece of evidence against this hypothesis is the phenotype of IL1RAPL1 knockout mice; the authors of the paper describing the generation of these mice report no gross abnormalities distinguishing them from wild-type mice 846. This could be interpreted as evidence that even if habenula-produced IL-18 does act on interpeduncular nucleus IL1RAPL proteins, this may be unrelated to the effects of IL-18 on appetite. However, IL-18 knockout animals appear to have been in existence for some time before their hyperphagia and overweight phenotype was published ff. In terms of phenotypes and associations with disease reported in humans, there are
ff See Jackson Laboratories information page for IL-18 knockout mice 860, which shows numerous publications prior to the study of Netea et al. describing their overweight phenotype 503. - 184 -
also no reports of IL1RAPL proteins being associated with obesity or alterations in body weight. This uncertainty aside, in the next Chapter a putative model of how IL-18 action on IL1RAPL receptors in the interpeduncular nucleus could mediate alterations in appetite is proposed and discussed, including future experiments which could test the proposed model. Although this model goes beyond the experimental evidence presented, building models such as these, and attempting to combine the proposed interaction between IL-18 and IL1RAPL receptors with other existing evidence regarding the anatomy and function of the habenula-interpeduncular nucleus connection, is a necessary key step in shaping future research questions and experimental approaches.
While the experiments described here were conducted with the principal focus on a neuroendocrine role of IL-18, they do raise other tangential questions relevant to related fields and other aspects of the biological role of IL-18. Of note, when conducting a mutation analysis of residues necessary for the Molluscum contagiosum virus IL-18 binding protein, MC054L, to bind human IL-18, Xiang and Moss identified that substituting the valine present in MC054L upstream of the 'pFIEpL' consensus sequence ('VSLIYW' shown in Figure 20, Panel A) for a phenylalanine, as is present in the other viral and human IL-18 BP sequences, caused the dissociation constant to decrease approximately 10- fold, to a value similar to human IL-18 BP 828. They concluded: "our finding that MC054L contained a nonoptimal valine in place of phenylalanine was puzzling, since we intuitively expected a virus to encode the most potent antagonist. There are several possibilities that might explain this apparent discrepancy. Although MCV replicates exclusively in humans, MC54L could have been acquired at an earlier stage of evolution from a host that had a valine instead of a phenylalanine in the IL-18 binding site. However, even the mouse IL-18BP has phenylalanine in this position, and there is no evidence for an ancestral IL-18BP with valine. Furthermore, the orthopoxvirus IL-18BP homologs have phenylalanine, not valine, in corresponding positions." (from pg 9950 of 828). Comparison of the respective amino acid sequences for each of these proteins shows that IL1RAPL1 also contains a valine in this position ('VSPLIYW' shown in Figure 20, Panel A gg). This could suggest, firstly, that the hypothesized binding of IL-18 to IL1RAPL exhibits a higher dissociation constant than that seen with IL-18BP hh. Secondly, an interesting research question which arises from the works above is the
gg Note that two amino acids upstream of these residues, aligned by MAFFT in Figure 19 Panels A and B, are 'YS' (for IL1RAPL1) or 'FS' (for IL1RAPL2) residues which could also correspond to the respective VS or YS residues in viral IL-18BPs; these are shown aligned manually in Figure 21. hh At least for caspase-1 processed IL-18, which was tested in these studies. Discussed later is that IL-18 processed by other enzymes would be expected to exhibit different binding affinities. - 185 -
suggestion that Poxviridae may have obtained their own forms of IL-18BP not through horizontal transfer of the mammalian IL-18BP gene, but given similarities noted in the nucleotide coding sequences above, and the question raised by Xiang and Moss, possibly via incorporation of gene sequences encoding the third Ig-like domain of IL1RAPL proteins.
The position of the IL1RAPL proteins on the X chromosome also raises many interesting questions as regards their origins and function. The IL1RAPL1 gene in the human lies in the so-called 'short' arm of the X chromosome, also known as the 'X added region' (XAR) 861, which is believed to have been transferred to the primordial X chromosome after the divergence of the sex chromosomes from autosomes. The IL1RAPL2 gene, in contrast, is located on the 'long arm' or 'X conserved region' (XCR) 861 which is an evolutionarily older portion of the X chromosome. It has been hypothesized that as genes were transferred to the X chromosome their expression patterns became more restricted, tending towards brain-specific expression as opposed to wider expression in the body, (with the obvious exception of sexually-dimorphic expression of testis-specific genes being located on the X chromosome 822); in other words, that pre-existing genes transferred to the X chromosome evolved increasingly brain and reproduction-specific roles, and studies specifically investigating this hypothesis present data in support of this pattern 821,862. In studies which have examined the expression of IL1RAPL proteins across various tissues, their expression appears to conform to this trend, with IL1RAPL2 (presumably having been present on the X chromosome for longer due to its location in the XCR) expression reported as restricted only to the brain in two studies 823,825 or also in liver and placenta in one study 848. In contrast, IL1RAPL1 (transferred to the X chromosome at a later stage based on its presence in the XAR) expression has been reported in the brain as well as peripheral organs such as the heart, kidney, skin, lung, thryoid, placenta, intestine, testis and ovarian tissues 824,825,848. The pilot RT-PCR experiment conducted here in fact appears to show a more brain- specific expression of IL1RAPL1 than IL1RAPL2, i.e. in the direction opposite to the above hypothesis. Regardless of this difference, if the hypothesis presented here that IL-18 is able to act on the IL1RAPL receptors proves correct, given the association of IL-18 with a variety of diseases, including noncommunicable diseases such as cardiovascular disease 464, and autoimmune diseases such as rheumatoid arthritis 863, inflammatory bowel disease 622 and asthma 864,865, then IL-18 action on IL1RAPL1 and IL1RAPL2 receptors in the periphery may be of importance for the normal biological functions of IL-18, and their aberration in disease states.
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Chapter 10: A model of habenula IL-18 function
Two papers published prior to, or early on in, the course of this thesis showed that IL-18 knockout mice exhibit hyperphagia, and that administration of IL-18 directly into the brain could reduce appetite 503,506. These studies established a metabolic role for IL-18, suggesting that this effect arises out of action within the brain. This gave rise to two hypotheses regarding IL-18 action: (i) that peripheral circulating IL-18 acts within the brain and thus functions in an endocrine manner, most likely exerting it’s influence via actions in the hypothalamus, and (ii) that IL-18 produced within the brain is responsible for its metabolic effects. Note that these two hypotheses are not necessarily mutually exclusive and cross-talk between peripheral and central IL-18 production could exist.
It is well established that IL-18 is constitutively expressed in the periphery, and can be readily detected in the circulation. The work in this thesis has shown that IL-18 is also constitutively expressed in brain, specifically within the habenula (at least in the Sprague-Dawley rat, the animal model used here). These findings are in agreement with those of Sugama et al. 777, and raise the possibility that the hyperphagia exhibited by IL-18 knockout mice derives from a lack of habenula IL-18 expression. When examining the available evidence of habenula function, discussed further below, a picture emerges suggesting the habenula could function as a metabolic sensor.
10.1 Pre-existing evidence for a role of the habenula in metabolism:
10.1.1 Circadian rhythm, seasonal change, and the habenula:
Indirect evidence for a potential role of the habenula in metabolism arises from considering the evolutionary role of the habenula across various species. Various fish, lizards, and amphibians show assymetrical habenula nuclei, with either the right or left side larger than the contralateral nucleus 866. This assymetry appears to be driven by an assymetrical input from the parietal, or ‘third’, eye present in these species; the parietal eye forms a mid-line, translucent membrane, which contains
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photosensitive cells which project to the habenula 866,867. Given the fact that the parietal eye is photosensitive, but appears not to encode visual information, it is theorised to play a role in encoding and entraining circadian rhythms, and thus the habenula, as one of the major efferent nuclei of fibres from the parietal eye, is likely to play a role in responding and adapting to diurnal rhythms or changes in day-night length. In accordance with this phenomenon, the habenula of some species of frog has been shown to undergo seasonal size variation 868. Habenular assymetry appears to a much lesser extent in chickens, and is absent in other mammals such as rats and humans 866. Despite the obvious absence of a parietal eye in mammalian species, the possibility remains that the habenula in these species also receives non-visual light information, with studies in mice showing that melanopsin-containing neurons present in the retina project to a wide range of neuronal targets, including the habenula 869. In rats, the spontaneous firing rate of neurons in the medial and lateral habenula have been shown to be modulated by retinal illumination, as well as exhibiting diurnal variation in firing rate 870. The lateral habenula has been shown to be a target organ of the superchiasmatic nucleus in hamsters 871, and exhibits diurnal variations in stress-induced fos immunoreactivity 872. Interactions between the habenula and pineal gland also exist, with evidence for projections from the habenula to the pineal gland 873,874. Intracerebroventricular IL-18 administration in rats and rabbits has also been shown to modulate aspects of sleep, such as length of non-REM sleep 875, which raises the possibility that habenula-derived IL-18 may act in a similar fashion.
Thus, there is some evidence from a range of species that the habenula may function as a sensor of circadian rhythm. This may not necessarily implicate the habenula in the regulation of metabolism, particularly as seasonal changes in habenula size or function observed in some species could be related to the seasonality of reproduction rather than seasonal metabolic adaptations.
10.1.2 The habenular ependymal layer:
The distribution of IL-18 producing cells observed here (Chapter 7), and previously by Sugama et al.777, were seen not only to be present in the medial habenula, which lies either side of the dorsal third ventricle, but moreover were specifically clustered around the most medial surface of the medial habenula, directly adjacent to the ventricle, corresponding to the superior medial habenula subnucleus (MHbS) described by Andres et al. 876. Such a constrained distribution so close to the ventricle raises the question as to whether these IL-18 producing cells in fact participate in sensing the composition of, or secreting factors into, the cerebrospinal fluid. The ventricular ependymal layer of the medial habenula of the rat has been investigated by a one laboratory in detail. Their
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findings show that the ventricular surface of the habenulae exhibit microvilli and cilia 877,878, whose exact function is unknown, but appear similar to microvilli present on the choroid plexus 879. In addition, the ventricular surface is host to a number of tanycytes 880, similar to other circumventricular organs known to exhibit endocrine roles such as the area postrema and median eminence. The ependymal surface of the medial habenula also shows properties of being ‘leaky’, with for example, entrance of horseradish peroxidase into the habenula from the cerebrospinal fluid 881. Thus, a number of features of the habenular ventricular wall suggest that the habenula could act as a sensor of cerebrospinal fluid contents, which influence brain function at a wide level 882. Notably, the position of the medial habenula either side of the dorsal third ventricle is also immediately adjacent to the choroid plexus of the third ventricle, placing these IL-18 producing cells in a prime position to monitor, or secrete into, the neighbouring cerebrospinal fluid.
10.1.3 The habenula as mediator of reward and drug addiction:
Animals with electrodes placed in the habenula show self-stimulatory behaviour 883, a classic example of the involvement of a brain region in reward. A number of studies have also implicated the habenula or connected interpeduncular nucleus in the intake of drugs of abuse, including morphine 884,885, heroin 886,887, cocaine 888–892 and nicotine 893–897, as well as a possible role in in alcohol intake and addiction 898–900. The habenula is the site of the heaviest denisty of nicotinic receptors in the brain 901,902, and manipulation of habenular nictotinic receptors in rodents alters self- administration of nicotine 897,903. Under chronic nicotine administration the habenula and its projections to the interpeduncular nucleus undergo degeneration 896,904,905. Similar degeneration is also observed upon exposure to other drugs of abuse, which has led some authors to describe the habenula-interpeduncular nucleus pathway as a ‘weak link’ 896,906 which may potentially underlie addiction to many drugs of abuse.
A number of studies have shown interactions between the habenula and ventral tegmental area (VTA) 907–910, and median raphe 883. Habenula projections can modulate dopamine activity in the VTA 907 and other brain regions including the nucleus accumbens and striatum 911. Efferent projections from the habenula to the midbrain also appear to be reciprocal, with evidence for VTA projections along the fasciculus retroflexus to the habenula 912–914, and from the median raphe to the habenula 912,915. Interestingly, the distribution of IL-18 producing cells in the habenula occupy the exact same distribution as the terminal fields of dopamine projections from the VTA 913 (and in addition habenula substance P expression 916,917). Such connections thus provide an anatomical basis for an interaction of habenula-derived IL-18 with brain reward systems. Both the median raphe 918
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and the VTA 919,920 are known to influence feeding behaviour, and thus interactions with IL-18 producing cells could form a mechanism by which IL-18 influences appetite. In addition, lesions of the habenula (both medial and lateral nuclei) produce a sustained activation of mesocortical dopamine neurons 921. Although the above evidence of interactions with reward pathways does not necessarily implicate the habenula in the control of food intake, there is currently a large amount of scientific interest in evidence showing cross-over of the reward of eating behaviour with reward pathways involved in addictive behaviour 922–924. Also of note is that alterations in VTA dopaminergic activity is an established effect of other metabolic hormones such as leptin and insulin922,925–928.
10.1.4 Metabolic signalling, food intake, and the habenula:
More specifically with regard to a role in metabolism, the habenula has been observed to exhibit expression of receptors for leptin 929, insulin 930, NPY 899, GLP-1 931, melanocortins (MC3R) ii and sweet taste (Tas1r2, Tas1r3) receptors 932, as well as the peptide adropin 933. The habenula has also been shown to respond to metabolic stimuli, with studies showing changes in habenula leptin receptor expression with fasting 929, habenula opioid receptor expression in obesity 934 or fasting 935, and changes in habenula expression of c-fos with intake of a palatable meal 936. Clearly, showing that the habenula responds to metabolic signals may not necessarily imply that it influences eating behaviour, as metabolic signals could be integrated in this nucleus but any subsequent output utilised for other means, such as effects on reproduction and fertility, growth, or the immune system, which need to be tied to the metabolic health and functioning of an organism. More direct evidence for a role for the habenula in food intake comes from a study showing lesions of the habenula or interpeduncular nucleus influence the intake of water laced with the bitter substance quinine 937. Electrical stimulation of the lateral habenula has also been observed to reduce sucrose intake 889. Furthermore, with regard to the effects of IL-18 on appetite, Zorrilla et al. report that IL-18 knockout mice overeat on a normal chow, or low-fat diet, but not on a high fat diet 506, suggesting that some form of taste or food preference may underlie their hyperphagia, rather than a simple drive to overeat per se.
ii Unpublished in situ hybridisation experiments in the rat, H Phillipps, 'Grattan laboratory', Dept of Anatomy, Univeristy of Otago. - 190 -
10.2 A theoretical model for a neuroendocrine role of IL-18
10.2.1 Projections and transport from the habenula:
The primary output from the habenula is the fasciculus retroflexus, with fibres from the medial habenula travelling down the core of the fasciculus and lateral habenula fibres forming the surrouding outer sheath 938. Once they reach the interpeduncular nucleus, fibres can be seen to undergo a somewhat unusual pathway of crossing to the contralateral side and then returning ipsilaterally 835. Whether these fibres ultimately terminate ipsilaterally or also form en passant axonal connections on the contralateral side is not clear. It has been shown here, and by Sugama et al. 777, that IL-18 containing fibres can be observed within the fasciculus retroflexus, terminating in the IPN.
10.2.2 Processing of IL-18 in the habenula:
IL-18 has typically been investigated with a focus on its role following caspase-1 processing. Indeed the IL-18 precursor is often referred to as 'pro-IL-18' which is cleaved by caspase-1 to 'active' or 'mature' IL-18. However, there is also clear evidence that the IL-18 precursor can be processed by mast cell chymase-1 939 (as can IL-1 940), proteinase-3 941, granzyme-B (at the same site as caspase-1) 942 and caspase-3 943. Given the forms of IL-18 produced by processing from these enzymes form shorter amino acid sequences (with the exception of granzyme-B), it would be expected that these forms of IL-18 would show differing affinities to the IL-18 receptor and IL-18 BP. There appears to be little available literature regarding the expression of these other IL-18-processing enzymes in the brain, and while the Allen Brain Atlas shows no experiments for detecting caspase-1 or proteinase-3 as yet, they have performed experiments for the detection of mast cell chymase-1 (encoded by the Cma1 gene) showing a high density of expression in the habenula 944, and caspase-3 945, showing light expression in the habenula. It is proposed that differential processing of the percursor IL-18 protein has evolved as a mechanism which allows, on the one hand, for the deactivation of 'mature' IL-18 (as has been shown for caspase-3 processing of IL-18 943), but also for the IL-18 precursor to be directed toward greater or lesser affinity to differing receptors or isoforms of the IL-18 binding protein. Given the high expression levels of mast cell chymase-1 in the habenula, it is proposed that chymase- processed IL-18 shows a greater affinity for the IL1RAPL receptors than caspase-1-processed 'mature' IL-18. Indeed, mast cell chymase-1-processed IL-18 has be shown to exhibit a reduced ability to elicit IFN-γ production in cell culture (approximately 20 % of that observed for caspase-1- processed IL-18) 939, suggesting a reduced affinity or ability to activate downstream signalling for the IL-18 receptor.
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10.2.3 Proposed actions of IL-18 on interpeduncular IL1RAPL receptors
Of particular relevance to proposed effects of IL-18 on IL1RAPL receptors are findings from Yoshida et al. who describe the interaction of another member of the IL-1 receptor family, IL-1RAcP(b), with PTPRD 855. They present a model of the functioning of this receptor as shown in Figure 27. The binding of the extracellular portion of IL-1RAcP or IL-1RAcPb (since both share the same extracellular domain) with PTPRD forms a trans-synaptic adhesion resulting in differentiation of the pre-synaptic terminal, specifically in excitatory synapses. Binding of IL-1, however, is theorized to disrupt this trans-synaptic interaction and result in the typical pro-inflammatory effects of IL-1 (Figure 27, Panel B). However, missing from the proposed model is that, while the interaction of the extracellular portion of IL1-RAcP(b) with PTPRD is sufficient to induce pre-synaptic differentiation, only the longer neuron-specific IL-1RAcPb isoform is capable of inducing post-synaptic differentiation of excitatory synapses, as shown in Figure 28, Panel A. Importantly, IL-1RAcPb also appears to lack the ability to induce pro-inflammatory signalling 858 (Figure 28, Panel B), and thus, based on current knowledge it appears that the binding of IL-1 may not be important in terms of inducing novel downstream signalling, but through disrupting the trans-synaptic interaction with PTPRD.
In terms of the actions of the IL1RAPL receptors, the majority of research has focused on IL1RAPL1. The trans-synaptic interaction of this receptor with PTPRD has been shown to induce both pre- and post-synaptic differentation of excitatory synapses (Figure 29, Panel A), in a similar fashion to IL- 1RAcPb. Effects on a number of downstream signalling molecules have been shown across various studies, including phosphorylation of JNK, which leads to phosphorylation of PSD-95, as well as recruitment of Rho-GAP2 to excitatory synapses and interaction with NCS-1. Trans-synaptic interaction of PTPRD with IL1RAPL2 also induces pre-synaptic differentiation of excitatory synapses, although based on the works of Valnegri et al. this appears to be of a lesser extent than that seen for IL1RAPL1 818. In addition, while they show that IL1RAPL2 is capable of inducing post- synaptic effects, such as an increase in dendritic spine number, it is unclear how these effects are mediated, as they found that IL1RAPL2 does not activate Rho-GAP2, unlike IL1RAPL1, and in fact appeared to reduce IL1RAPL1-induced Rho-GAP2 activation, presumably by competitive binding with pre-synaptic PTPRD. Thus, while the signalling effects of IL1RAPL2 are less clear, it is known that this receptor also exerts effects which result in the strengthening of excitatory synapses (Figure 28, Panel B). As detailed above, it is hypothesized here that IL-18 binds to the third Ig domain of both IL1RAPL receptors. Currently, it is unknown what the stoichiometry of this interaction is; given evidence from the crystal structure of IL1RAPL1 it appears that the TIR domains of these - 192 -
receptors are capable of forming homodimers 853, and thus IL-18 could putatively bind either single IL1RAPL receptors, or form dimers with either IL1RAPL1, IL1RAPL2, or both (Figure 29, Panel C). What the downstream signalling effects of these interactions could be cannot be easily hypothesized. However, the effects of IL-18 binding may not lie in any specific signalling mechanisms induced, but in the resulting lack of interaction with pre-synaptic PTPRD, which would be expected to reduce the strength of excitatory synapses; in this manner, IL-18 binding to the IL1RAPL proteins may be similar to IL-1 binding of the neuron-specific IL-1RAcPb isoform. IL-18 binding to IL1RAPL receptors could therefore form a mechanism by which alterations in habenula IL-18 expression (such as those observed by Sugama et al. following an acute stress 777) could dynamically regulate the strength of interpeduncular nucleus excitatory synapses.
The cytokines IL-1 and IL-18 are known to share a number of similarities in their processing, protein sequences, mechanisms of receptor activation and downstream signalling 423,813,946, and the model proposed here would therefore also extend their similarities to include the manner in which they regulate the strength of excitatory synapses via neuron-specific receptors. A key difference between the case of IL-1 and the proposed relationship here of IL-18 with the IL1RAPL receptors, is that in the case of IL-1 the centrally-expressed IL-1RAcPb receptor is formed by alternative splicing of a gene previously known to mediate IL-1 actions in the periphery, whereas the case is made here that IL-18 functions in the CNS via separate receptor(s), which show(s) similarity to IL-18 BP. Clearly, the IL-1 signalling system does not possess a similar circulating binding protein, such that a completely analogous process is impossible. Nevertheless, the existence in the case of IL-1 of a distinct receptor isoform specifically expressed in the CNS which mediates excitatory synaptic strength via PTPRD gives strong support to the possibility that IL-18 acts in a similar manner via interactions with the IL1RAPL proteins.
10.2.4 A model of habenula IL-18 function governing food intake:
From the literature summarized above and other published works, a model of habenula IL-18 function is proposed here, which is described below and depicted in Figure 30.
Many works linking habenula function to reward focus on the lateral habenula 947–951, although interestingly the medial habenula possesses projections to the lateral habenula, yet the reverse does not appear to be the case (the lateral habenula does not project to the medial habenula) 952. Therefore it is proposed that the medial habenula possesses the ability to influence lateral habenula activity, but not vice versa. Exactly what the nature of these fibres are is unknown, and therefore whether
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Figure 27: A model of IL-1RAcP function as proposed by Yoshida et al. 855. Panel A - IL-1RAcP interaction with pre-synaptic PTPRD induces the pre-synaptic differentiation
of excitatory synapses. Panel B - binding of IL-1 results in the formation of an IL-1r1 and IL-1RAcP complex inducing pro-inflammatory signalling.
Figure 28: The model of Yoshida et al. 855 altered to account for the specific actions of IL-1RAcPb. Panel A - Only IL-1RAcPb is capable of also inducing post-synaptic differentiation of excitatory synapses. Panel B - Recruitment of IL-1RAcPb to the IL-1/IL-1r1 receptor complex does not induce pro-inflammatory signalling 858.
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Figure 29: A similar model of IL-18 and IL1RAPL function. Panel A - The trans-synaptic interaction of IL1RAPL1 with PTPRD has been shown to induce both pre- and post- synaptic differentiation of excitatory synapses, with a range of post-synaptic signalling events implicated. Panel B - The trans-synaptic interaction of IL1RAPL2 with PTPRD appears to exert effects on pre-synaptic, but not post-synaptic, differentiation. Panel C - Binding of IL-18 to the third Ig domain of the IL1RAPL proteins is theorized to disrupt the interaction with PTPRD, but whether this results in any unique post-synaptic signalling effects is unknown. Also unknown is whether IL-18 binds to just one of the IL1RAPL proteins, or can form homo- or hetero-dimers.
they are excitatory or inhibitory of lateral habenula neurons is depicted in Figure 30 with a question mark. Fasciculus retroflexus projections from the lateral habenula are principally glutamatergic908,909, while the medial habenula projections contain glutamate 953, substance P 916, acetylcholine 916, and IL-18. Fibres from both the lateral and medial habenula exhibit distinct topography both within the fasciculus retroflexus and in the location of their terminations in the interpeduncular nucleus906,910,916, suggesting that any direct effect of IL-18 (and indeed, IL1RAPL presence) is likely to be limited to a distinct sub-region of the interpeduncular nucleus. Within this system, the specific role of IL-18 release in the interpeduncular nucleus is proposed to act on IL1RAPL1, and possibly IL1RAPL2 receptors, as described above, resulting in a reduction in the strength of excitatory synapses, and thus forms a mechanism by which the strength of excitatory glutamatergic inputs from the medial habenula are regulated. In this manner, IL-18 may function in many respects as a type of co-transmitter, whose function in-and-of-itself is simply to modulate the strength of co-localized and co-existent glutamatergic synapses. Interpeduncular nucleus efferents project to the ventral tegmental area and median raphe 799,954, and thus decreased habenular glutamatergic input is proposed to influence in turn ventral tegmental area dopamine neurons, alterations in forebrain dopaminergic activity, and ultimately effects on appetite. Other projections from the lateral habenula to the ventral tegmental area and median raphe 907,915,938 may also play an indirect role through projections from the medial to lateral habenula. Importantly, blocking fasciculus retroflexus transmission has been shown to increase forebrain dopaminergic activity 911, providing experimental support for an effect of fasciculus retroflexus fibres in modulating forebrain dopamine release in the manner proposed here. In addition, effects in the ventral tegmental area, and downstream changes in forebrain dopamine content and feeding behaviour have been previously observed as a mechanism of leptin action 926,927.
Dopamine neurons project from the ventral tegmental area to the same portion of the medial habenula which expresses IL-18 913, and serotonergic fibres from the median raphe form intraventricular axons lining the ependymal layer of the medial habenula 955, and thus these may form a feedback system which regulates the overall activity of the combined signalling circuit formed by these nuclei.
This hypothesis could be tested by the use of viral-mediated RNA knockdown to reduce habenula IL-18 expression over a medium term period (days to weeks depending on the longevity of the virus employed). This would allow sufficient time for changes in appetite, and possibly also body weight, to manifest, and would be predicted to recapitulate the phenotype of increased appetite observed by
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Netea et al. 503 and Zorrilla et al. 506 in global IL-18 knockout animals. The nature of IL-18 effects in the interpeduncular nucleus and proposed actions on midbrain dopaminergic systems could also be analysed by such experiments, which would avoid potential confounding by possible developmental effects of a lack of IL-18 across the lifespan in global knockout animals. A similar approach could be used to reduce IL1RAPL expression in the interpeduncular nucleus, which would be expected to bring about similar changes in behaviour.
Within this proposed system, it is also important to take note of additional results presented by Netea et al. 503, as IL-18r1 knockout animals and transgenic animals overexpressing IL-18 BP showed similar phenotypes to IL-18 knockout animals. Given results from Sugama et al., showing an increase in IL-18 in the medial habenula 777, and also an increase in peripheral IL-18 levels 626 following an acute stress, it is proposed that there is a neuroendocrine mechanism which couples habenula IL-18 expression with circulating IL-18. Other metabolic hormones such as cholecystokinin (CCK) and glucagon-like peptide-1 (GLP-1) have been shown to exert central effects via activation of receptors on vagal afferents 956,957 jj. A similar mechanism could exist whereby circulating IL-18 activates neural signals which feed back to regulate habenula IL-18 levels. In other words, habenula IL-18 expression may be driven by, and dependent on, peripheral IL-18 signals. If such a signalling mechanism were to occur via activation of the IL-18 receptor (IL-18r1), this would form a coherent explanation for the model proposed here where habenular mast cell chymase processed IL-18 regulates appetite via effects on IL1RAPL1 receptors, and also explain the reported phenotypes of IL- 18r1 and IL-18 BP genetically modified animals. This hypothesis could be tested by administration of IL-18 (to raise circulating IL-18 levels), or by the use of monoclonal antibodies or IL-18 BP (to reduce circulating IL-18 levels), and examining the expression of IL-18 in the habenula. If an effect were found whereby peripheral IL-18 levels modulate central IL-18 production, repetition of similar experiments in IL-18r1 knockout animals could help to eludicate whether this effect is mediated by the IL-18 receptor, and potentially explain the reported phenotype of IL-18r1 knockout animals kk.
While there are many unknowns with regard to the role and action of brain-derived IL-18, it is clear that it is constitutively produced in a specific group of cells in the dorsomedial habenula and
jj Although both also act directly on receptors in the brain. kk It is difficult to know to what extent the phenotype of IL-18r1 KO mice could be explained by later evidence suggesting that there are additional genetic alterations in this strain beyond the deletion of IL - 18r1 817. - 198 -
Figure 30: Schematic diagrams depicting a model of habenula function governing food intake. A – line diagram showing a coronal view of the medial (MHb) and lateral (LHb) habenula. Black dots represent the location of IL-18 expressing cells in the dorsomedial habenula. Glutamatergic projections travelling in the fasciculus retroflexus (fr) are shown terminating in the interpeduncular nucleus (IPN). Fibres from the medial habenula form the ‘core’ of the fasciculus retroflex us, with lateral habenula fibres surrounding these. B – outline of the rat brain in sagittal section. The approximate positions of the habenula (Hab), interpeduncular nucleus (IPN), ventral tegmental area (VTA), and median raphe (Raphe) are all shown, although these nuclei in fact exist in different planes of sagittal section. The fasciculus retroflexus is shown as a solid arrow, with additional arrows representing fibres seen to project to the VTA and median raphe from the lateral habenula. The hypothesized final downstream effect of IL-18 action is predicted to be an increase in dopamine (DA) release in forebrain targets, which is shown by dotted lines. See text for a full description and explanation of the model with supporting references.
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transported along the length of the fasciculus retroflexus. In this regard, centrally-produced IL-18 appears to be functioning in a fashion typically reserved for neurotransmitters or neuropeptides; although the downstream effects are unknown, these qualities grant it a unique role in brain function, as no other cytokine is known to exhibit such characteristics.
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Section 4:
Discussion
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Chapter 11: Advancing theory and practice in the treatment and prevention of cardiometabolic diseases
11.1 The inflammatory hypothesis
The Introduction to this thesis focused on the history of research into the treatment of diabetes and its complications and the theory of their causation. During the 20th century, the prevailing theory of diabetes pathogenesis revolved around the central role of blood glucose as a cause of diabetes complications, and therefore unsurprisingly this was also the central focus of the major diabetes- related treatment trials of this time period. In the wake of these studies, questions have been raised regarding the underlying assumptions inherent in the focus on blood glucose 61,63,958,959, and evident in the medical literature is an increasing interest in examining other possible pathological mechanisms of diabetes 63,99,960,961.
Claude Bernard noted in 1877: "The cause of diabetes is more profound that the causes of (hyper)glycaemia… the real etiological element of this affliction is that cause, as yet unknown, which causes the weakening of the primitive organs. It is this cause which should be addressed and not the symptoms of (hyper)glycaemia and glycosuria" (pg 414 of 962 ll).
Over the course of the last 140 years since this statement an alternative interpretation of the pathogenesis of diabetes is evident in the medical literature; an interpretation in which inflammation is posited as a cause of insulin resistance and diabetes. As discussed in the Introduction (Chapter 1), a number of investigations have been conducted over the course of the 20th century into the potential benefits of anti-inflammatory medications on insulin sensitivity. However, these works have been largely overlooked. There has been a recent resurgence in earnest into the potential use of anti-inflammatory agents in the treatment of diabetes, which appears to have arisen from a
ll Translated from the original French by the author. - 203 -
'discovery science' route; with an increasing focus on, and understanding of, the intracellular pathways of inflammatory and insulin signalling, and observations of increased inflammation in obesity and diabetes, the idea was arrived at that anti-inflammatory medications could form a potential therapy for diabetes. Researchers involved in some of the modern clinical trials of anti- inflammatory agents in diabetes themselves state that they arrived at such investigations via this ‘discovery science’ route, unaware of previous clinical research 963. This alternative interpretation has therefore certainly not been at the forefront of the diabetes literature, and given the scarce attention paid to this theory it is not surprising that there has been little real progress in the development or application of this theory; as is evident in the sobering lack of progress seen in the pages of the following publication excerpts, where the same links between rheumatism, diabetes and salicylates are re-stated over the course of 128 years:
1881: “Dr Latham had been led to try salicylic acid in cases of diabetes, from theoretical considerations, arising out of a hypothesis as to the curative action of the same drug in acute rheumatism”. G.E. Paget, British Medical Journal, January 1881. 215
1957: "Disappearance of glycosuria and the return of fasting blood sugar to normal in a young diabetic during aspirin treatment for acute rheumatism led to reinvestigation of the effect of salicylate in diabetes mellitus". J Reid et al. British Medical Journal, November 1957. 217
2009: "Salicylates are commonly used to treat rheumatic pain and have an established safety profile. As chronic, subclinical inflammation is often associated with obesity and increases the risks of obesity-related insulin resistance and type 2 diabetes mellitus, researchers suggest that salicylates could prevent or ameliorate obesity-related diabetes mellitus." J Vig, Nature Reviews Endocrinology, April 2009 964.
With a recently renewed interest in pursuing the theory of inflammation as a cause of insulin resistance, a number of clinical trials are now currently registered which aim to examine the potential of anti-inflammatory agents to treat diabetes and related metabolic conditions 256–267,279,280; whether these will result in new treatment indications for these pre-existing agents is yet to be seen.
Given the failure then of recent trials to show reductions in cardiovascular mortality in diabetes, the withdrawal of diabetes medications 965,966, and a continued inability to design safe drugs for the treatment of obesity 967–971, a major diabetes risk factor, one of the key questions which arises is - 204 -
whether there may be better methods and ways of identifying potential drug targets in type 2 diabetes. Specifically, moving away from a focus on glycaemic control, with the plethora of biochemical alterations associated with diabetes how could we identify biochemical parameters which could form good treatment targets, and therefore could hold the best hope of treatment success, and warrant increased focus and investigation?
11.2 Theoretical considerations for future research into, and pharmacological approaches to, the treatment of type 2 diabetes
Although few researchers or drug manufacturers explicitly state that by designing drugs for use in diabetes they are directly attempting to mimic the beneficial effects of lifestyle modification, in effect this is by default the unstated aim of any medical therapy for a disease in which the principal risk factors are lifestyle-related; drug treatment aims to either reverse the metabolic abnormalities induced by a ‘poor lifestyle’ or at least manage the pathogenic changes sufficiently to prolong life. This is indeed the aim of prescribing hypoglycaemic agents to patients with diabetes – to reverse the biochemical abnormality on which the diagnosis of diabetes is made. In the case of management of diabetes by diet or lifestyle modifications only, this approach makes sense. Dietary and lifestyle factors are among the principal risk factors for the development of diabetes, and various studies in individuals with ‘pre-diabetes’ have shown that alterations in diet and increasing physical activity can avoid or delay progression to overt diabetes 972 mm. Notably, the effectiveness of lifestyle intervention in improving glycaemia in people with diabetes was first demonstrated in 1939 974, around the same time in which diabetes was being recognized as including insulin sensitive and insulin resistant phenotypes.
One of the most important, yet scarcely mentioned, logical progressions arising from measuring the effectiveness of diet and lifestyle modification by using measures of glycaemia is that this creates a scenario whereby the same risk factors which are associated with worsening glycaemic control are those being modified to improve glycaemic control. As noted earlier, type 2 diabetes is associated with changes in a range of biochemical and physiological parameters and, indeed, the effectiveness
mm That these studies have been undertaken in participants with ‘pre-diabetes’ as opposed to frank diabetes does not undermine the argument here, as it is well known that the diagnosis of diabetes itself is based on ‘drawing a line in the sand’ and establishing a cut-off for a continuous risk factor, a cut-off which itself traces back to rates of development of the diabetes complication retinopathy 973. - 205 -
of lifestyle modification could also theoretically be assessed by measuring changes any of these variables, alone or in concert. In contrast, in the case of utilising pharmacological therapy as a treatment for type 2 diabetes, the dynamics of the above scenario are changed, such that the self- same risk factors which underlie the pathogenesis of the disease are no longer the factors being modified as part of the treatment of the disease; instead an exogenous pharmacological agent is introduced, and its effectiveness is monitored on the basis of changes in one of the biochemical abnormalities associated with type 2 diabetes. However, as Archie Cochrane noted, “it may well be the wrong parameter that is being altered” 975, and importantly, the above logical progression no longer holds. Of particular concern to the current day management of type 2 diabetes is that new drug agents for the treatment of type 2 diabetes have been licensed and approved on the basis of their ability to modify this one biochemical parameter nn.
The question which arises then is how to best identify other metabolic variables that could serve as potential drug targets. If one accepts the premise that diabetes medication, by definition, must attempt to reverse or alleviate the metabolic derangements brought about by the disease, then clearly the examination of these derangements and how they relate to the incidence of complications will highlight potential drug targets. This is obviously in many ways the method by which elevated blood glucose came to be investigated as a potential cardiovascular risk factor, and serves as a reminder that this method of investigation may throw up ‘false postives’ – factors which are associated with the incidence of diabetes complications but whose manipulation will not appreciably influence rates of disease. Chronic sub-clinical inflammation is another diabetes-related metabolic derangement which forms part of this thesis and the potential for anti-inflammatory agents to improve diabetes outcomes is discussed in the Introduction. Likewise, studies examining the role of endoplasmic reticulum stress suggest that its alleviation may show promise in diabetes 100,976–988. There remain a variety of other known alterations associated with diabetes, and indeed we may soon discover additional metabolic derangements. Whether the manipulation of these factors will alter
nn This is not necessarily to say that these drugs are completely ineffective, however; although the logic underpinning their approval and how their effectiveness is monitored may be erroneous, they may still exert favourable metabolic changes which are clinically relevant. The problem arises as to how to assess this on the basis of the currently completed trials, as their designs reflect the underlying hypothesis, which was not to examine whether ‘medication x’ is useful for reducing diabetes complications, but whether a common effect of various drug agents (the reduction of blood glucose) alters diabetes-related end points. - 206 -
rates of diabetes complications is, of course, a completely separate question, and it is here that an improved understanding of the 'basic science' of the pathogenesis of type 2 diabetes will be necessary.
11.2.1 Combining hypothesis-free and hypothesis-driven research to identify molecular targets in type 2 diabetes
Recently, in many areas of medicine and research, more ‘broad brush’ approaches to examining factors influencing disease onset have been carried out. Most notably, and perhaps most well-known, has been in the field of genetics, which has moved from a candidate gene approach based on the hypothesized role of a gene of interest, to genome-wide association studies (GWAS).
In the case of type 2 diabetes, this approach has yielded a number of novel insights, including establishing the role of the Wnt signalling pathway in glucose control with the TCF7L2 gene emerging as the strongest genetic risk marker, an unexpected finding 989. Similarly, GWAS in cardiovascular diseases has highlighted a section of DNA on chromosome 9q21 as showing a strong influence on risk 990 oo, which was of completely unknown function (studies conducted since implicate a role in IFN-γ signalling 991).
A similar change in experimental approach has also occurred in examining the association of environmental risk factors with type 2 diabetes; whereas environmental exposures are typically derived from validated questionnaires to assess exposure to specific chemicals, a recent study performed an ‘environment wide association study’ examining the association of type 2 diabetes with a number of chemical entities directly measured in bodily tissue samples 687. This approach identified the association of diabetes with a number of previously-recognized environmental exposures, such as pesticides and polychlorinated biphenyls, thus confirming the validity of this approach, as well as novel associations such as γ-tocopherol (a component of vitamin E).
Another example of a broader mass-screening approach to investigate the acute metabolic effects of insulin was also recently undertaken in individuals with type 1 diabetes using proton magnetic
oo Interestingly, this same locus has been identified in GWAS of type 2 diabetes. - 207 -
resonance spectroscopy and liquid chromatography tandem mass spectrometry 992 pp, and similar studies have been done in type 2 diabetes to identify changes in common metabolites associated with the disease 683,993, as well as metabolic predictors of diabetes incidence 994. Of note, these studies have identified alterations in amino acid and lipid metabolite profiles, reaffirming the scientific consensus of the early 20th century that diabetes involves not just alterations in glucose metabolism, but also disorders of protein and fat metabolism qq. These approaches highlight not only the technological advances of recent high-throughput analytical techniques, and the validity of such approaches – in that many have reliably identified previously known associations – but, more importantly, the promise of such techniques to identify many novel, unexpected associations, and the promise of such 'hypothesis-free' research to uncover unknown associations and mechanisms.
This form of hypothesis-free investigation is more in line with the method of scientific investigation known as ‘inductive reasoning’ supported by Francis Bacon, who believed it offered a number of advantages over hypothesis-drived research 995,996. In terms of the application to type 2 diabetes, such approaches have already delivered, and in the future could offer more, new insights into the pathogenesis of diabetes.
Clearly, however, it is one thing to be able to identify metabolic, genetic, or biochemical differences between states of health and disease, and another to understand how those changes ‘work’, both in terms of the biological pathways involved and how they influence the incidence and progression of complications arising from the disease. It is these aspects which will require a substantial investment of ‘discovery science’ research into the basic functions of metabolic pathways, and will arguably require the greater length of time and amount of research funding before observed differences in biochemical or metabolic parameters can be converted into viable therapies. A research approach of this nature will also require a great deal of collaboration and cross-pollination between epidemiological and clinical research methods and discovery science laboratory-based investigations.
pp The results of this study are, in itself, testiment to the fact that insulin has a diverse range of metabolic effects which extend far beyond a reduction in blood glucose levels. qq An important logical extension of acknowledging that diabetes is a disease of protein, lipid and carbohydrate metabolism, is that HbA1c and glucose, which form the basis of the diagnosis and monitoring of diabetes, could just as validly be replaced by other biomarkers related to other aspects of metabolism, which could be used independently, or in combination with, markers of glucose metabolism. - 208 -
This thesis represents in many ways an application of the investigative approach outlined above, utilizing both a clinical research approach to examine changes in inflammatory markers brought about by lifestyle modification in people with type 2 diabetes, in concert with a discovery science approach to attempt to further understand the biological role of one of the inflammatory markers modified by dietary intervention rr. This research has identified that a dietary intervention in people with type 2 diabetes, who are concurrently treated according to national guidelines, produces reductions in IL-18 and neopterin. These reductions, however, do not appear to be related to changes in adiposity or glycaemia, which raises many interesting questions for further research. The 'discovery science' aspect of this thesis has investigated the neuroendocrine role of IL-18, coming to the conclusion that IL-18 is unlikely to act through its currently recognized receptor (composed of the IL-18r1 and IL-18rap subunits) but may instead act through related receptor(s) (IL1RAPL1 and possibly IL1RAPL2) which are almost exclusively expressed in the brain and show similar amino acid motifs as seen in IL-18 BP. Specifically, the action of habenula-derived IL-18 on these receptors is hypothesized to modulate the strength of glutamatergic synapses within the interpeduncular nucleus. Exactly how this may relate to previously reported effects of IL-18 on appetite is unknown, although it is interesting to note that the habenular-interpeduncular nucleus pathway interacts with dopaminergic reward pathways, and modulation of these pathways is known to alter eating behaviour. Indeed, altering dopaminergic transmission has been shown to be one of the central effects of other metabolic hormones such as leptin and insulin.
11.2.2 The need for joint clinical and discovery science initiatives
While the above blueprint represents a logical template and way forward with regard to the medical management of type 2 diabetes, investigations of this nature will clearly necessitate a substantial investment of research personnel, participants, funding, and time before any discoveries find their way into routine clinical use. However, the need for such approaches is highlighted by the recent example of the case of rosiglitazone, which was licensed for use in diabetes and released on the basis of its ability to lower blood glucose levels. In documentation concerning the initial licensing of rosiglitazone, the pharmacology review states: "Rosiglitazone is a thiazolidinedione derivative that has antihyperglycemic action which might be a useful antidiabetic agent. The mechanism of action has not been clearly established, although its stimulatory effect on peroxisomal proliferator activated
rr However, in this case a more traditional hypothesis-driven approach has been used by studying a selection of cytokines based on prior evidence suggesting potential roles for these cytokines in metabolic disorders, and examining the neuroendocrine role of one of these cytokines in greater detail. - 209 -
receptor gamma (PPARγ) is well known. The primary molecular target for the action of rosiglitazone might be the nuclear receptors, but how its activation of the receptors improves glycemic control remains to be elucidated" (pgs 2-3 of Pharmacology Review Part 1 from 997). The medication was subject to much controversy 998, and is now largely withdrawn, and yet, further research has highlighted the lack of original understanding as to this drug's effects and the foolhardiness of embracing the use of medications whose mechanisms of action are poorly understood. 'Discovery science' research published after the use of rosiglitazone began to fall out of favour suggest that it's profile of activity was far broader than necessary 999–1001, with it's glucose-lowering effect correlating specifically with Cdk5 (cyclin-dependent kinase 5)-mediated phosphorylation of PPAR-γ in adipose tissue 1001, and that effects on appetite, body weight, and to some extent glycaemic control, arise via actions in the hypothalamus 1002,1003. Scenarios such as these highlight the need for a greater understanding of the molecular mechanisms and systemic alterations in metabolism present in type 2 diabetes prior to attempting to modulate them for clinical benefit.
A disturbing consequence of the failure of trials such as the ACCORD, ADVANCE and VADT to reduce clinical diabetes-related endpoints is that it is subsequently clear that the current pharmacological management of type 2 diabetes is not founded on firm evidence; the majority of the present day medications indicated for use in diabetes have not been shown to lower micro- or macrovascular endpoints. Furthermore, given the progressive nature of diabetes, many patients are given ‘add on’ therapies with the aim of maintaining glycaemic control and the safety and efficacy of different drug combinations has received even less attention. Given, in addition, a current estimated worldwide adult prevalence of diabetes of approximately 7 % and projected increases in the number of people living with diabetes 1004, we are faced then with a large population of individuals with diabetes receiving treatment which is not evidence-based 1005, and will not become so without a substantial research investment to investigate the utility of these medications in reducing the incidence of diabetes complications.
And yet, research shows us that substantial improvements could be made in the treatment and prevention of type 2 diabetes (and associated non-communicable diseases) through greater, more widespread use and uptake of lifestyle modifications, which could translate into improvements in public health were it not for want of uptake and execution. Indeed, in the current absence of sufficient action to reduce the mean population risk of development of diabetes, and in turn its complications, the weight of diabetes prevention and treatment falls therefore by default on the shoulders of discovery science, when clearly the greatest gains in health could be achieved if both - 210 -
discovery science and population health measures worked together in concert – and the full array of available resources and strategies were utilized to reduce the burden of cardiometabolic diseases.
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References:
1. Borch-Johnsen K & Wareham N. The rise and fall of the metabolic syndrome. Diabetologia, 53 (4): 597–599. (2010). 2. World Health Organisation. Diet, nutrition and the prevention of chronic diseases. Report of a joint FAO/WHO expert consultation. (2003). 3. Ministry of Health. A portrait of health: key results of the 2006/07 New Zealand health survey. (Ministry of Health, 2008). 4. Rana JS, Li TY, Manson JE, et al. Adiposity compared with physical inactivity and risk of type 2 diabetes in women. Diabetes Care, 30 (1): 53–58. (2007). 5. Haffner SM. Relationship of metabolic risk factors and development of cardiovascular disease and diabetes. Obesity (Silver Spring), 14 Suppl 3: 121S–127S. (2006). 6. Wild SH & Byrne CD. ABC of obesity. Risk factors for diabetes and coronary heart disease. BMJ, 333 (7576): 1009–1011. (2006). 7. Stamler J, Vaccaro O, Neaton JD, et al. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care, 16 (2): 434–444. (1993). 8. Manson JE, Colditz GA, Stampfer MJ, et al. A prospective study of maturity-onset diabetes mellitus and risk of coronary heart disease and stroke in women. Arch Intern Med, 151 (6): 1141–1147. (1991). 9. Kannel WB & McGee DL. Diabetes and cardiovascular disease. The Framingham study. JAMA, 241 (19): 2035–2038. (1979). 10. The Emerging Risk Factors Collaboration. Diabetes mellitus, fasting glucose, and risk of cause- specific death. N Engl J Med, 364 (9): 829–841. (2011). 11. Aretaeus. The extant works of Aretaeus, the Cappadocian. (Translated by Francis Adams. Printed for the Sydenham Society, London, 1856). Available at: http://www.archive.org/details/extantworksaret00adamgoog, accessed January 20, 2012. 12. Joslin EP. The treatment of diabetes mellitus. Can Med Assoc J, 6 (8): 673–684. (1916). 13. Mazur A. Why were “starvation diets” promoted for diabetes in the pre-insulin period? Nutr J, 10 (1): 23. (2011). 14. Simoni RD, Hill RL & Vaughan M. The discovery of insulin: the work of Frederick Banting and Charles Best. J Biol Chem, 277 (26): e15+. (2002).
- 213 -
15. Banting FG & Best CH. The internal secretion of the pancreas. J Lab Clin Med, 7 (5): 251–266. (1922). 16. Sanders LJ. From Thebes to Toronto and the 21st century: an incredible journey. Diabetes Spectr, 15 (1): 56–60. (2002). 17. Mayer J. On diabetes and its connection with heart disease. Br Med J, 1 (1427): 949–951. (1888). 18. Joslin EP. A renaissance of the control of diabetes. J Am Med Assoc, 156 (17): 1584–1585. (1954). 19. Downie E. The clinical approach to the problem of diabetes mellitus. Br Med J, 2 (4944): 863– 867. (1955). 20. Zhang P, Zhang X, Brown J, et al. Global healthcare expenditure on diabetes for 2010 and 2030. Diabetes Res Clin Pract, 87 (3): 293–301. (2010). 21. Lachin JM, Genuth S, Nathan DM, et al. Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial - revisited. Diabetes, 57 (4): 995–1001. (2008). 22. Diabetes Control and Complications Trial (DCCT) Study Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med, 329 (14): 977–986. (1993). 23. Ohkubo Y, Kishikawa H, Araki E, et al. Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study. Diabetes Res Clin Pract, 28 (2): 103–117. (1995). 24. Meinert CL, Knatterud GL, Prout TE, et al. A study of the effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. II. Mortality results. Diabetes, 19 Suppl: S789–S830. (1970). 25. Cornfield J. The University Group Diabetes Program. A further statistical analysis of the mortality findings. JAMA, 217 (12): 1676–1687. (1971). 26. Schor S. The University Group Diabetes Program. A statistician looks at the mortality results. JAMA, 217 (12): 1671–1675. (1971). 27. Seltzer HS. A summary of criticisms of the findings and conclusions of the University Group Diabetes Program (UGDP). Diabetes, 21 (9): 976–979. (1972). 28. O’Sullivan JB & D’Agostino RB. Decisive factors in the tolbutamide controversy. JAMA, 232 (8): 825–829. (1975). 29. Smelo LS. University group diabetes program. JAMA, 215 (13): 2115. (1971).
- 214 -
30. Feinstein AR. Clinical biostatistics. 8. An analytic appraisal of the University Group Diabetes Program (UGDP) study. Clin Pharmacol Ther, 12 (2): 167–191. (1971). 31. The University Group Diabetes Program Study pertaining to phenformin. JAMA, 217 (6): 817. (1971). 32. Leibel B. An analysis of the University Group Diabetes Study Program: data results and conslusions. Can Med Assoc J, 105 (3): 292–294. (1971). 33. Kilo C, Miller JP & Williamson JR. The Achilles heel of the University Group Diabetes Program. JAMA, 243 (5): 450–457. (1980). 34. Kilo C, Williamson JR, Choi SC, et al. Insulin treatment and diabetic vascular complications. JAMA, 241 (1): 26–27. (1979). 35. Kilo C, Miller JP & Williamson JR. The crux of the UGDP. Spurious results and biologically inappropriate data analysis. Diabetologia, 18 (3): 179–185. (1980). 36. Prout TE. A prospective view of the treatment of adult-onset diabetes with special reference to the University Group Diabetes Program and oral hypoglycemic agents. Med Clin North Am, 55 (4): 1065–1075. (1971). 37. Karam JH, Matin SB & Forsham PH. Antidiabetic drugs after the University Group Diabetes Program (UGDP). Annu Rev Pharmacol, 15: 351–366. (1975). 38. Barclay WR. University Group Diabetes Program revisited. JAMA, 240 (4): 377–378. (1978). 39. Goldner MG, Knatterud GL & Prout TE. Effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. 3. Clinical implications of UGDP results. JAMA, 218 (9): 1400–1410. (1971). 40. Knatterud GL, Klimt CR, Levin ME, et al. Effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. VII. Mortality and selected nonfatal events with insulin treatment. JAMA, 240 (1): 37–42. (1978). 41. Ciba-Geigy Corp. v. Mathews. 8 Mar 1977. Fed Suppl, 428: 523–532. (1977). 42. Havas S. The ACCORD trial and control of blood glucose level in type 2 diabetes mellitus: time to challenge conventional wisdom. Arch Intern Med, 169 (2): 150–154. (2009). 43. Ewart RM. The case against aggressive treatment of type 2 diabetes: critique of the UK prospective diabetes study. BMJ, 323 (7317): 854–858. (2001). 44. McCormack J & Greenhalgh T. Seeing what you want to see in randomised controlled trials: versions and perversions of UKPDS data. United Kingdom prospective diabetes study. BMJ, 320 (7251): 1720–1723. (2000). 45. Nathan DM. Some answers, more controversy, from UKPDS. Lancet, 352 (9131): 832–833. (1998). - 215 -
46. UK Prospective Diabetes Study Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet, 352 (9131): 837–853. (1998). 47. Stratton IM, Adler AI, Neil HA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ, 321 (7258): 405–412. (2000). 48. Home PD. Impact of the UKPDS–an overview. Diabet Med, 25 Suppl 2: 2–8. (2008). 49. The Action to Control Cardiovascular Risk in Diabetes Study Group. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med, 358 (24): 2545–2559. (2008). 50. The Action to Control Cardiovascular Risk in Diabetes Study Group. Long-term effects of intensive glucose lowering on cardiovascular outcomes. N Engl J Med, 364 (9): 818–828. (2011). 51. ADVANCE Collaborative Group. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med, 358 (24): 2560–2572. (2008). 52. Duckworth W, Abraira C, Moritz T, et al. Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med, 360 (2): 129–139. (2009). 53. Stein R. Deaths halt part of diabetes study. The Washington Post, Available at: http://www.washingtonpost.com/wp-dyn/content/article/2008/02/06/AR2008020601787.html, accessed April 30, 2010. 54. ScienceDaily. Major international diabetes study does not confirm increased risk of death reported by US trial. ScienceDaily, Available at: http://www.sciencedaily.com/releases/2008/02/080214092156.htm, accessed April 30, 2010. 55. Kolata G. Diabetes study partially halted after deaths. The New York Times, (2008). Available at: http://www.nytimes.com/2008/02/07/health/07diabetes.html?_r=3&scp=1&sq=blood%20glucose %20lowering%20diabetes&st=cse, accessed April 30, 2010. 56. Associated Press. Major diabetes trial halted after deaths. Available at: http://www.msnbc.msn.com/id/23029191/, accessed April 30, 2010. 57. Abbey DM. Earlier treatment may explain diabetes data. Science, 323 (5917): 1009. (2009). 58. Younis N, Soran H & Hassanein M. Cardiovascular disease and intensive glucose lowering in type 2 diabetes. Q J Med, 102 (4): 293–296. (2009). 59. Rydén L, Malmberg K & Mellbin L. Is glucose normalization an evidence-based treatment for patients with type 2 diabetes mellitus? Nat Clin Pract Endocrinol Metab, 5 (1): 8–9. (2009). 60. Tuttle K. More is not always better: intensive glycemic control in type 2 diabetes. Am J Kidney Dis, 53 (1): 12–15. (2009). - 216 -
61. Goodarzi MO & Psaty BM. Glucose lowering to control macrovascular disease in type 2 diabetes: treating the wrong surrogate end point? JAMA, 300 (17): 2051–2053. (2008). 62. Akalin S, Berntorp K, Ceriello A, et al. Intensive glucose therapy and clinical implications of recent data: a consensus statement from the global task force on glycaemic control. Int J Clin Pract, 63 (10): 1421–1425. (2009). 63. Razani B & Semenkovich CF. Getting away from glucose: stop sugarcoating diabetes. Nat Med, 15 (4): 372–373. (2009). 64. Jabbour S. Type 2 diabetes and HbA1c goal: time for individualised therapy. Int J Clin Pract, 63 (10): 1408–1409. (2009). 65. Boyko EJ. ACCORD glycemia results continue to puzzle. Diabetes Care, 33 (5): 1149–1150. (2010). 66. Mazzone T. Intensive glucose lowering and cardiovascular disease prevention in diabetes: reconciling the recent clinical trial data. Circulation, 122 (21): 2201–2211. (2010). 67. Riddle MC. Counterpoint: Intensive glucose control and mortality in ACCORD - still looking for clues. Diabetes Care, 33 (12): 2722–2724. (2010). 68. Lachin JM. Point: Intensive glycemic control and mortality in ACCORD - a chance finding? Diabetes Care, 33 (12): 2719–2721. (2010). 69. Skyler JS, Bergenstal R, Bonow RO, et al. Intensive glycemic control and the prevention of cardiovascular events: implications of the ACCORD, ADVANCE, and VA diabetes trials: a position statement of the American Diabetes Association and a scientific statement of the American College of Cardiology Foundation and the American Heart Association. Diabetes Care, 32 (1): 187–192. (2009). 70. Skyler JS, Bergenstal R, Bonow RO, et al. Intensive glycemic control and the prevention of cardiovascular events: implications of the ACCORD, ADVANCE, and VA Diabetes Trials: a position statement of the American Diabetes Association and a Scientific Statement of the American College of Cardiology Foundation and the American Heart Association. J Am Coll Cardiol, 53 (3): 298–304. (2009). 71. Skyler JS, Bergenstal R, Bonow ROO, et al. Intensive glycemic control and the prevention of cardiovascular events: implications of the ACCORD, ADVANCE, and VA diabetes trials: a position statement of the American Diabetes Association and a scientific statement of the American College of Cardiology Foundation and the American Heart Association. Circulation, 119 (2): 351–357. (2009). 72. Turnbull FM, Abraira C, Anderson RJ, et al. Intensive glucose control and macrovascular outcomes in type 2 diabetes. Diabetologia, 52 (11): 2288–2298. (2009). - 217 -
73. Mannucci E, Monami M, Lamanna C, et al. Prevention of cardiovascular disease through glycemic control in type 2 diabetes: a meta-analysis of randomized clinical trials. Nutr Metab Cardiovasc Dis, 19 (9): 604–612. (2009). 74. Ray KK, Seshasai SRKR, Wijesuriya S, et al. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials. Lancet, 373 (9677): 1765–1772. (2009). 75. Kelly TN, Bazzano LA, Fonseca VA, et al. Systematic review: glucose control and cardiovascular disease in type 2 diabetes. Ann Intern Med, 151 (6): 394–403. (2009). 76. Boussageon R, Bejan-Angoulvant T, Saadatian-Elahi M, et al. Effect of intensive glucose lowering treatment on all cause mortality, cardiovascular death, and microvascular events in type 2 diabetes: meta-analysis of randomised controlled trials. BMJ, 343 (7817): bmj.d4169. (2011). 77. Hemmingsen B, Lund SS, Gluud C, et al. Intensive glycaemic control for patients with type 2 diabetes: systematic review with meta-analysis and trial sequential analysis of randomised clinical trials. BMJ, 343 (7834): d6898–d6898. (2011). 78. Gaede P, Lund-Andersen H, Parving H-HH, et al. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med, 358 (6): 580–591. (2008). 79. Bartnik M & Cosentino F. Dysglycaemia, cardiovascular outcome and treatment. Is the jury still out? Eur Heart J, 30 (11): 1301–1304. (2009). 80. Bianchi C, Miccoli R, Daniele G, et al. Is there evidence that oral hypoglycemic agents reduce cardiovascular morbidity/mortality? Yes. Diabetes Care, 32 Suppl 2: S342–S348. (2009). 81. Del Prato S. Megatrials in type 2 diabetes. From excitement to frustration? Diabetologia, 52 (7): 1219–1226. (2009). 82. Kahn SE. Glucose control in type 2 diabetes: still worthwhile and worth pursuing. JAMA, 301 (15): 1590–1592. (2009). 83. Lerch C & Richter B. Extent of glycemic control in type 2 diabetes mellitus: close, but no cigar. Nutr Metab Cardiovasc Dis, 19 (9): 593–595. (2009). 84. Himsworth HP. The syndrome of diabetes mellitus and its causes. Lancet, 253 (6551): 465–473. (1949). 85. Einstein A. On the method of theoretical physics. Philos Sci, 1 (2): 163–169. (1934). 86. Bagust A, Hopkinson PK, Maslove L, et al. The projected health care burden of type 2 diabetes in the UK from 2000 to 2060. Diabet Med, 19 (Suppl s4): 1–5. (2002). 87. Davis, Knuiman & Hendrie. The obesity-driven rising costs of type 2 diabetes in Australia: projections from the Fremantle Diabetes Study. Intern Med J, 36 (3): 155–161. (2006). - 218 -
88. Wild S, Roglic G, Green A, et al. Global prevalence of diabetes. Diabetes Care, 27 (5): 1047– 1053. (2004). 89. Boyle J, Thompson T, Gregg E, et al. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr, 8 (1): 29. (2010). 90. Stoker B. Dracula. (Oxford University Press, USA, ISBN: 01928338631998). 91. Boulton AJ. Why “diabetes and history”? Diabet Med, 11 (7): 616–617. (1994). 92. Ricketts HT. Does hyperglycemia harm the diabetic patient? Med Clin North Am, 31: 267–277. (1947). 93. Wagener HP, Dry TJS & Wilder RM. Retinitis in diabetes. N Engl J Med, 211 (25): 1131–1137. (1934). 94. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes, 37 (12): 1595–1607. (1988). 95. Reaven GM. The metabolic syndrome: requiescat in pace. Clin Chem, 51 (6): 931–938. (2005). 96. Reaven G. Why a cluster is truly a cluster: insulin resistance and cardiovascular disease. Clin Chem, 54 (5): 785–787. (2008). 97. De Ferranti S & Mozaffarian D. The perfect storm: obesity, adipocyte dysfunction, and metabolic consequences. Clin Chem, 54 (6): 945–955. (2008). 98. Hotamisligil GS. Inflammation and metabolic disorders. Nature, 444 (7121): 860–867. (2006). 99. Shoelson SE & Goldfine AB. Getting away from glucose: fanning the flames of obesity-induced inflammation. Nat Med, 15 (4): 373–374. (2009). 100. Hummasti S & Hotamisligil GS. Endoplasmic reticulum stress and inflammation in obesity and diabetes. Circ Res, 107 (5): 579–591. (2010). 101. Forsythe LK, Wallace JMW & Livingstone MBE. Obesity and inflammation: the effects of weight loss. Nutr Res Rev, 21 (2): 117–133. (2008). 102. Egger G & Dixon J. Non-nutrient causes of low-grade, systemic inflammation: support for a “canary in the mineshaft” view of obesity in chronic disease. Obes Rev, 12 (5): 339–345. (2010). 103. Himsworth HP. The mechanism of diabetes mellitus III. Human diabetes mellitus. Lancet, 234 (6047): 171–176. (1939). 104. World Cancer Research Fund & American Institue for Cancer Research. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. (AICR, Washington DC, ISBN: 978-0-9722522-2-52007). 105. Taubes G. Unraveling the obesity-cancer connection. Science, 335 (6064): 28–32. (2012).
- 219 -
106. Smith U & Gale EAM. Cancer and diabetes: are we ready for prime time? Diabetologia, 53 (8): 1541–1544. (2010). 107. Chowdhury TA. Diabetes and cancer. Q J Med, 103 (12): 905–915. (2010). 108. Gallagher EJ & LeRoith D. Diabetes, cancer, and metformin: connections of metabolism and cell proliferation. Ann N Y Acad Sci, 1243 (1): 54–68. (2011). 109. Lin W-W & Karin M. A cytokine-mediated link between innate immunity, inflammation, and cancer. J Clin Invest, 117 (5): 1175–1183. (2007). 110. Shacter E & Weitzman SA. Chronic inflammation and cancer. Oncology (Williston Park), 16 (2): 217–226, 229; discussion 230–232. (2002). 111. Coussens LM & Werb Z. Inflammation and cancer. Nature, 420 (6917): 860–867. (2002). 112. Gonda TA, Tu S & Wang TC. Chronic inflammation, the tumor microenvironment and carcinogenesis. Cell Cycle, 8 (13): 2005–2013. (2009). 113. Kundu JK & Surh Y-J. Inflammation: gearing the journey to cancer. Mutat Res, 659 (1-2): 15– 30. (2008). 114. Philip M, Rowley DA & Schreiber H. Inflammation as a tumor promoter in cancer induction. Semin Cancer Biol, 14 (6): 433–439. (2004). 115. Biswas SK & Lewis CE. NF-kappaB as a central regulator of macrophage function in tumors. J Leukoc Biol, 88 (5): 877–884. (2010). 116. Chandalia M & Abate N. Metabolic complications of obesity: inflated or inflamed? J Diabetes Complications, 21 (2): 128–136. (2007). 117. Jansson P, Pellmé F, Hammarstedt A, et al. A novel cellular marker of insulin resistance and early atherosclerosis in humans is related to impaired fat cell differentiation and low adiponectin. FASEB J, 17 (11): 1434–1440. (2003). 118. Kim J, Van de Wall E, Laplante M, et al. Obesity-associated improvements in metabolic profile through expansion of adipose tissue. J Clin Invest, 117 (9): 2621–2637. (2007). 119. McLaughlin T, Sherman A, Tsao P, et al. Enhanced proportion of small adipose cells in insulin- resistant vs insulin-sensitive obese individuals implicates impaired adipogenesis. Diabetologia, 50 (8): 1707–1715. (2007). 120. Yang X, Jansson P, Nagaev I, et al. Evidence of impaired adipogenesis in insulin resistance. Biochem Biophys Res Commun, 317 (4): 1045–1051. (2004). 121. Roberts R, Hodson L, Dennis A, et al. Markers of de novo lipogenesis in adipose tissue: associations with small adipocytes and insulin sensitivity in humans. Diabetologia, 52 (5): 882– 890. (2009).
- 220 -
122. Weyer C, Foley JE, Bogardus C, et al. Enlarged subcutaneous abdominal adipocyte size, but not obesity itself, predicts type II diabetes independent of insulin resistance. Diabetologia, 43 (12): 1498–1506. (2000). 123. Ortega Martinez de Victoria E, Xu X, Koska J, et al. Macrophage content in subcutaneous adipose tissue: associations with adiposity, age, inflammatory markers, and whole-body insulin action in healthy Pima Indians. Diabetes, 58 (2): 385–393. (2009). 124. Weisberg SP, McCann D, Desai M, et al. Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest, 112 (12): 1796–1808. (2003). 125. Xu H, Barnes GT, Yang Q, et al. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J Clin Invest, 112 (12): 1821–1830. (2003). 126. Visser M, Bouter LM, McQuillan GM, et al. Elevated C-reactive protein levels in overweight and obese adults. JAMA, 282 (22): 2131–2135. (1999). 127. De Winther M, Kanters E, Kraal G, et al. Nuclear factor kappaB signaling in atherogenesis. Arterioscler Thromb Vasc Biol, 25 (5): 904–914. (2005). 128. Libby P. The molecular mechanisms of the thrombotic complications of atherosclerosis. J Intern Med, 263 (5): 517–527. (2008). 129. Virchow R. Ueber die akute Entzündung der Arterien. Virchows Arch Pathol Anat Physiol Klin Med, 1 (2): 272–378. (1847). 130. Virchow R. The Huxley lecture on recent advances in science and their bearing on medicine and surgery: delivered at the opening of the Charing Cross Hospital Medical School on October 3rd. Br Med J, 2 (1971): 1021–1028. (1898). 131. Kozonis MC & Gurevin I. The value of the C-reative protein determination in coronary artery disease. Ann Intern Med, 46 (1): 79–85. (1957). 132. Levinger EL, Levy H & Elster SK. Study of C-reactive protein in the sera of patients with acute myocardial infarction. Ann Intern Med, 46 (1): 68–78. (1957). 133. Kroop IG & Shackman NH. The C-reactive protein determination as an index of myocardial necrosis in coronary artery disease. Am J Med, 22 (1): 90–98. (1957). 134. Goldner F & Meador C. Transaminase and C-reactive protein levels in acute myocardial infarction: evaluation of indices of necrosis. South Med J, 48 (12): 1339–1346. (1955). 135. Boltax AJ & Fischel EE. Serologic tests for inflammation; serum complement, C-reactive protein and erythrocyte sedimentation rate in myocardial infarction. Am J Med, 20 (3): 418–427. (1956). 136. Elster SK, Braunwald E & Wood HF. A study of C-reactive protein in the serum of patients with congestive heart failure. Am Heart J, 51 (4): 533–541. (1956). - 221 -
137. Kroop IG & Shackman NH. Level of C-reactive protein as a measure of acute myocardial infarction. Proc Soc Exp Biol Med, 86 (1): 95–97. (1954). 138. Ross R. The pathogenesis of atherosclerosis - an update. N Engl J Med, 314 (8): 488–500. (1986). 139. Ross R. Atherosclerosis - an inflammatory disease. N Engl J Med, 340 (2): 115–126. (1999). 140. Pignone M, Alberts MJ, Colwell JA, et al. Aspirin for primary prevention of cardiovascular events in people with diabetes. J Am Coll Cardiol, 55 (25): 2878–2886. (2010). 141. Ogawa H, Nakayama M, Morimoto T, et al. Low-dose aspirin for primary prevention of atherosclerotic events in patients with type 2 diabetes: a randomized controlled trial. JAMA, 300 (18): 2134–2141. (2008). 142. Nicolucci A. Aspirin for primary prevention of cardiovascular events in diabetes: still an open question. JAMA, 300 (18): 2180–2181. (2008). 143. Patel A, Joshi R & De Galan B. Trials of cardiovascular risk factor management in type 2 diabetes. Curr Opin Cardiol, 24 (4): 288–294. (2009). 144. Ong G, Davis TME & Davis WA. Aspirin is associated with reduced cardiovascular and all-cause mortality in type 2 diabetes in a primary prevention setting: the Fremantle diabetes study. Diabetes Care, 33 (2): 317–321. (2010). 145. Zhang C, Sun A, Zhang P, et al. Aspirin for primary prevention of cardiovascular events in patients with diabetes: A meta-analysis. Diabetes Res Clin Pract, 87 (2): 211–218. (2010). 146. Colwell JA. Does aspirin use reduce cardiovascular risk in diabetes? Nat Rev Endocrinol, 5 (4): 188–190. (2009). 147. Ertugrul DT, Tutal E, Yildiz M, et al. Aspirin resistance is associated with glycemic control, the dose of aspirin, and obesity in type 2 diabetes mellitus. J Clin Endocrinol Metab, 95 (6): 2897– 2901. (2010). 148. Kopp E & Ghosh S. Inhibition of NF-kappa B by sodium salicylate and aspirin. Science, 265 (5174): 956–959. (1994). 149. Jasińska M, Owczarek J & Orszulak-Michalak D. Statins: a new insight into their mechanisms of action and consequent pleiotropic effects. Pharmacol Rep, 59 (5): 483–499. (2007). 150. New Zealand Guidelines Group. Management of type 2 diabetes. (New Zealand Guidelines Group, Wellington N.Z., ISBN: 97804760009262003). 151. National Heart Foundation of Australia & Cardiac Society of Australia and New Zealand. Position statement on lipid management - 2005. Heart Lung Circ, 14 (4): 275–291. (2005). 152. Ray KK, Seshasai SRK, Erqou S, et al. Statins and all-cause mortality in high-risk primary prevention: a meta-analysis of 11 randomized controlled trials involving 65,229 participants. Arch Intern Med, 170 (12): 1024–1031. (2010). - 222 -
153. Brugts JJ, Yetgin T, Hoeks SE, et al. The benefits of statins in people without established cardiovascular disease but with cardiovascular risk factors: meta-analysis of randomised controlled trials. BMJ, 338 (7711): b2376+. (2009). 154. Ridker PM, Danielson E, Fonseca FAH, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med, 359 (21): 2195–2207. (2008). 155. Kappagoda CT & Amsterdam EA. Another look at the results of the JUPITER trial. Am J Cardiol, 104 (11): 1603–1605. (2009). 156. Chan PS, Nallamothu BK & Hayward RA. Rosuvastatin in patients with elevated C-reactive protein. N Engl J Med, 360 (10): 1039; author reply 1041–1042. (2009). 157. Green LA. Cholesterol-lowering therapy for primary prevention: still much we don’t know. Arch Intern Med, 170 (12): 1007–1008. (2010). 158. Kaul S, Morrissey RP & Diamond GA. By Jove! What is a clinician to make of JUPITER? Arch Intern Med, 170 (12): 1073–1077. (2010). 159. Mascitelli L, Goldstein MR & Pezzetta F. Earth to JUPITER: how a potent statin may actually be detrimental for lifestyle-associated diseases. Angiology, 61 (1): 119. (2010). 160. Ridker PM & Glynn RJ. JUPITER, rosuvastatin, and the European Medicines Agency. Lancet, 375 (9731): 2071. (2010). 161. Vaccarino V, Bremner JD & Kelley ME. JUPITER: a few words of caution. Circ Cardiovasc Qual Outcomes, 2 (3): 286–288. (2009). 162. De Lorgeril M, Salen P, Abramson J, et al. Cholesterol lowering, cardiovascular diseases, and the rosuvastatin-JUPITER controversy: a critical reappraisal. Arch Intern Med, 170 (12): 1032– 1036. (2010). 163. Sattar N, Preiss D, Murray HM, et al. Statins and risk of incident diabetes: a collaborative meta- analysis of randomised statin trials. Lancet, 375 (9716): 735–742. (2010). 164. Laakso M. Diabetes as a “cardiovascular disease equivalent”: implications for treatment. Nat Clin Pract Cardiovasc Med, 5 (11): 682–683. (2008). 165. Natarajan P & Cannon CP. Could direct inhibition of inflammation be the “next big thing” in treating atherosclerosis? Arterioscler Thromb Vasc Biol, 30 (11): 2081–2083. (2010). 166. Sarov-Blat L, Morgan JM, Fernandez P, et al. Inhibition of p38 mitogen-activated protein kinase reduces inflammation after coronary vascular injury in humans. Arterioscler Thromb Vasc Biol, 30 (11): 2256–2263. (2010). 167. Schieven GL. The biology of p38 kinase: a central role in inflammation. Curr Top Med Chem, 5 (10): 921–928. (2005).
- 223 -
168. Nam JL, Winthrop KL, Van Vollenhoven RF, et al. Current evidence for the management of rheumatoid arthritis with biological disease-modifying antirheumatic drugs: a systematic literature review informing the EULAR recommendations for the management of RA. Ann Rheum Dis, 69 (6): 976–986. (2010). 169. Protogerou AD, Zampeli E, Fragiadaki K, et al. A pilot study of endothelial dysfunction and aortic stiffness after interleukin-6 receptor inhibition in rheumatoid arthritis. Atherosclerosis, 219 (2): 734–736. (2011). 170. Ronald M.A. H. Interleukin 6 as therapeutic target in athero-/arteriosclerosis - small steps towards a new treatment paradigm in cardiovascular disease? Atherosclerosis, 219 (2): 390–391. (2011). 171. Kim C, Cheng Y & Beckles G. Inflammation among women with a history of gestational diabetes mellitus and diagnosed diabetes in the third national health and nutrition examination survey. Diabetes Care, 31 (7): 1386–1388. (2008). 172. Mavridis G, Souliou E, Diza E, et al. Inflammatory cytokines in insulin-treated patients with type 2 diabetes. Nutr Metab Cardiovasc Dis, 18 (7): 471–476. (2008). 173. Kolb H & Mandrup-Poulsen T. An immune origin of type 2 diabetes? Diabetologia, 48 (6): 1038–1050. (2005). 174. Action LADA Study Group, Pham MN, Hawa MI, et al. Pro- and anti-inflammatory cytokines in latent autoimmune diabetes in adults, type 1 and type 2 diabetes patients: Action LADA 4. Diabetologia, 54 (7): 1630–1638. (2011). 175. Esposito K, Nappo F, Giugliano F, et al. Cytokine milieu tends toward inflammation in type 2 diabetes. Diabetes Care, 26 (5): 1647. (2003). 176. Aso Y, Okumura K, Takebayashi K, et al. Relationships of plasma interleukin-18 concentrations to hyperhomocysteinemia and carotid intimal-media wall thickness in patients with type 2 diabetes. Diabetes Care, 26 (9): 2622–2627. (2003). 177. Deepa R, Velmurugan K, Arvind K, et al. Serum levels of interleukin 6, C-reactive protein, vascular cell adhesion molecule 1, and monocyte chemotactic protein 1 in relation to insulin resistance and glucose intolerance - the Chennai Urban Rural Epidemiology Study (CURES). Metabolism, 55 (9): 1232–1238. (2006). 178. Nabipour I, Vahdat K, Jafari SM, et al. Elevated high sensitivity C-reactive protein is associated with type 2 diabetes mellitus: the Persian Gulf Healthy Heart Study. Endocr J, 55 (4): 717–722. (2008).
- 224 -
179. Freeman D, Norrie J, Caslake M, et al. C-reactive protein is an independent predictor of risk for the development of diabetes in the West of Scotland Coronary Prevention Study. Diabetes, 51 (5): 1596–1600. (2002). 180. Hu G, Jousilahti P, Tuomilehto J, et al. Association of serum C-reactive protein level with sex- specific type 2 diabetes risk: a prospective Finnish study. J Clin Endocrinol Metab, 94 (6): 2099– 2105. (2009). 181. Tan K, Wat N, Tam S, et al. C-reactive protein predicts the deterioration of glycemia in chinese subjects with impaired glucose tolerance. Diabetes Care, 26 (8): 2323–2328. (2003). 182. Hu FB, Meigs JB, Li TY, et al. Inflammatory markers and risk of developing type 2 diabetes in women. Diabetes, 53 (3): 693–700. (2004). 183. Dehghan A, Kardys I, De Maat MP, et al. Genetic variation, C-reactive protein levels, and incidence of diabetes. Diabetes, 56 (3): 872–878. (2007). 184. Pradhan A, Manson J, Rifai N, et al. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA, 286 (3): 327–334. (2001). 185. Spranger J, Kroke A, Mohlig M, et al. Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Diabetes, 52 (3): 812–817. (2003). 186. Liu S, Tinker L, Song Y, et al. A prospective study of inflammatory cytokines and diabetes mellitus in a multiethnic cohort of postmenopausal women. Arch Intern Med, 167 (15): 1676– 1685. (2007). 187. Lechleitner M, Herold M, Dzien-Bischinger C, et al. Tumour necrosis factor-alpha plasma levels in elderly patients with type 2 diabetes mellitus - observations over 2 years. Diabet Med, 19 (11): 949–953. (2002). 188. Hivert M, Sun Q, Shrader P, et al. Circulating IL-18 and the risk of type 2 diabetes in women. Diabetologia, 52 (10): 2101–2108. (2009). 189. Thorand B, Kolb H, Baumert J, et al. Elevated levels of interleukin-18 predict the development of type 2 diabetes: results from the MONICA/KORA Augsburg Study, 1984-2002. Diabetes, 54 (10): 2932–2938. (2005). 190. Herder C, Brunner EJ, Rathmann W, et al. Elevated levels of the anti-inflammatory interleukin-1 receptor antagonist precede the onset of type 2 diabetes: the Whitehall II study. Diabetes Care, 32 (3): 421–423. (2009). 191. Carstensen M, Herder C, Kivimäki M, et al. Accelerated increase in serum interleukin-1 receptor antagonist starts 6 years before diagnosis of type 2 diabetes. Diabetes, 59 (5): 1222– 1227. (2010). - 225 -
192. Krakoff J, Funahashi T, Stehouwer C, et al. Inflammatory markers, adiponectin, and risk of type 2 diabetes in the Pima Indian. Diabetes Care, 26 (6): 1745–1751. (2003). 193. Leyton O. The treatment of diabetes mellitus: abstract of three lectures given at the London Hospital. Br Med J, 1 (3252): 707–710. (1923). 194. John HJ. Some interesting and obscure problems in the diagnosis and treatment of diabetes mellitus. Ohio State Med J, 33 (6): 741–748. (1937). 195. American Diabetes Association. Standards of Medical Care in Diabetes - 2012. Diabetes Care, 35 (Suppl 1): S11–S63. (2011). 196. Schnée E. Diabetes: its cause and permanent cure from the standpoint of experience and scientific investigation. (H K Lewis, London, 1889). Available at: http://www.archive.org/details/diabetes00schngoog, accessed January 17, 2012. 197. Agwunobi AO, Reid C, Maycock P, et al. Insulin resistance and substrate utilization in human endotoxemia. Journal of Clinical Endocrinology & Metabolism, 85 (10): 3770 –3778. (2000). 198. Mehta NN, McGillicuddy FC, Anderson PD, et al. Experimental endotoxemia induces adipose inflammation and insulin resistance in humans. Diabetes, 59 (1): 172–181. (2010). 199. Kubaszek A, Pihlajamäki J, Komarovski V, et al. Promoter polymorphisms of the TNF-alpha (G- 308A) and IL-6 (C-174G) genes predict the conversion from impaired glucose tolerance to type 2 diabetes: the Finnish Diabetes Prevention Study. Diabetes, 52 (7): 1872–1876. (2003). 200. Kubaszek A, Pihlajamäki J, Punnonen K, et al. The C-174G promoter polymorphism of the IL-6 gene affects energy expenditure and insulin sensitivity. Diabetes, 52 (2): 558–561. (2003). 201. Koh S, Jang Y, Hyun Y, et al. The interleukin-6 -572C→G promoter polymorphism is associated with type 2 diabetes risk in Koreans. Clin Endocrinol (Oxf), 70 (2): 238–244. (2009). 202. Presta I, Andreozzi F, Succurro E, et al. IL-18 gene polymorphism and metabolic syndrome. Nutr Metab Cardiovasc Dis, 19 (2): e5–e6. (2009). 203. Romzova M, Hohenadel D, Kolostova K, et al. NFkappaB and its inhibitor IkappaB in relation to type 2 diabetes and its microvascular and atherosclerotic complications. Hum Immunol, 67 (9): 706–713. (2006). 204. Luotola K, Pääkkönen R, Alanne M, et al. Association of variation in the interleukin-1 gene family with diabetes and glucose homeostasis. J Clin Endocrinol Metab, 94 (11): 4575–4583. (2009). 205. Keavney B. More evidence against a causal association between C-reactive protein and diabetes. PLoS Medicine, 5 (8): e174. (2008).
- 226 -
206. Brunner EJ, Kivimäki M, Witte DR, et al. Inflammation, insulin resistance, and diabetes - mendelian randomization using CRP haplotypes points upstream. PLoS Medicine, 5 (8): e155. (2008). 207. Sheehan NA, Didelez V, Burton PR, et al. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med, 5 (8): e177. (2008). 208. Lawlor DA, Harbord RM, Sterne JAC, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 27 (8): 1133–1163. (2008). 209. Little J & Khoury MJ. Mendelian randomisation: a new spin or real progress? Lancet, 362 (9388): 930–931. (2003). 210. Ebstein W. Invited comment on W. Ebstein: On the therapy of diabetes mellitus, in particular on the application of sodium salicylate. J Mol Med, 80 (10): 618; discussion 619. (2002). 211. Gross M & Greenberg L. The salicylates: a critical bibiographic review. (Hillhouse Press, New Haven, Conneticut, 1948). 212. Bartels K. Uber die Therapeutische Verwertung der Salicylsaure und ihres Natronsalzes in der inneren medicin. Dtsch Med Wschr, 4: 423–5;35–7. (1878). 213. Williamson RT. On the treatment of glycosuria and diabetes mellitus with sodium salicylate. Br Med J, 1 (2100): 760–762. (1901). 214. Williamson RT. On the treatment of diabetes mellitus and glycosuria with aspirin. Br Med J, 2 (2191): 1946–1948. (1902). 215. Paget GE. Reports of societies: Cambridge medical society. Br Med J, 1 (1051): 274–278. (1881). 216. Holden JS. The salicylic treatment of glycosuria. Br Med J, 1 (1322): 816–817. (1886). 217. Reid J, Macdougall AI & Andrews MM. Aspirin and diabetes mellitus. Br Med J, 2 (5053): 1071– 1074. (1957). 218. Gilgore SG. The influence of salicylate on hyperglycemia. Diabetes, 9 (5): 392–393. (1960). 219. Gilgore SG & Rupp JJ. Response of blood glucose to intravenous salicylate. Metabolism, 10 (5): 419–421. (1961). 220. Gilgore SG & Rupp JJ. The long-term response of diabetes mellitus to salicylate therapy: report of a case. JAMA, 180 (1): 65–66. (1962). 221. Hecht A & Goldner MG. Reappraisal of the hypoglycemic action of acetylsalicylate. Metabolism, 8 (4 Pt 1): 418–428. (1959). 222. Reid J. Dinitrophenol and diabetes mellitus: a comparison with salicylate. Br Med J, 2 (5098): 724–727. (1958). 223. Reid J & Lightbody TD. The insulin equivalence of salicylate. Br Med J, 1 (5126): 897–900. (1959). - 227 -
224. Segal S, Blair A & Weinberg A. In vitro effects of salicylate on carbohydrate metabolism. Metabolism, 9 (11): 1033–1046. (1960). 225. Shoelson S, Lee J & Goldfine A. Inflammation and insulin resistance. J Clin Invest, 116 (7): 1793–1801. (2006). 226. Ingle DJ. Effects of aspirin, aminopyrine, and HPC upon glycosuria in the diabetic rat. J Am Pharm Assoc, 42 (4): 247–249. (1953). 227. Ingle DJ & Meeks RC. Suppression of glycosuria during administration of large doses of aspirin to force-fed partially depancreatectomized rats. Am J Physiol, 171 (3): 600–603. (1952). 228. Aspirin and diabetes. Nutr Rev, 16 (6): 167–168. (1958). 229. Bornstein J, Meade BW & Smith MJH. Salicyates and carbohydrate metabolism. Nature, 169 (4290): 115–116. (1952). 230. Lista GA, Nunez OH & Vozzi IR. Tratamiento de la diabetes sacarina con ácido acetilsalicílico. Prensa Med Argent, 45 (35): 2909–2911. (1958). 231. Kabanchik M, Levin H & Tuchszer S. Acción hipoglucemiante del ácido acetilsalicílico en la diabetes de la vejez. Prensa Med Argent, 50 (7): 501–504. (1963). 232. Giugliano D, Torella R, Siniscalchi N, et al. The effect of acetylsalicylic acid on insulin response to glucose and arginine in normal man. Diabetologia, 14 (6): 359–362. (1978). 233. Giugliano D, Torella R, Sgambato S, et al. Acetylsalicyclic acid restores acute insulin response reduced by furosemide in man. Diabetes, 28 (9): 841–845. (1979). 234. Giugliano D, Sacca L, Scognamiglio G, et al. Influence of acetylsalicylic acid on glucose turnover in normal man. Diabete Metab, 8 (4): 279–282. (1982). 235. Giugliano D, Giannetti G, Di Pinto P, et al. Normalization by sodium salicylate of the impaired counterregulatory glucagon response to hypoglycemia in insulin-dependent diabetes. A possible role for endogenous prostaglandins. Diabetes, 34 (6): 521–525. (1985). 236. Giugliano D, Ceriello A, Saccomanno F, et al. Effects of salicylate, tolbutamide, and prostaglandin E2 on insulin responses to glucose in noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab, 61 (1): 160–166. (1985). 237. Giugliano D, Ceriello A, Quatraro A, et al. Interaction between epinephrine, prostaglandin E, and met-enkephalin in the regulation of insulin release in man. Diabete Metab, 11 (5): 295–302. (1985). 238. Giugliano D, Quatraro A, Consoli G, et al. Sodium salicylate restores the impaired insulin response to glucose and improves glucose tolerance in heroin addicts. Acta Diabetol Lat, 24 (3): 205–212. (1987).
- 228 -
239. Giugliano D, Cozzolino D, Ceriello A, et al. Studies on the mechanism of salicylate-induced increase of insulin secretion in man. Diabete Metab, 14 (4): 431–436. (1988). 240. Newman WP & Brodows RG. Aspirin causes tissue insensitivity to insulin in normal man. J Clin Endocrinol Metab, 57 (6): 1102–1106. (1983). 241. Micossi P, Pontiroli AE, Baron SH, et al. Aspirin stimulates insulin and glucagon secretion and increases glucose tolerance in normal and diabetic subjects. Diabetes, 27 (12): 1196–1204. (1978). 242. Hundal RS, Petersen KF, Mayerson AB, et al. Mechanism by which high-dose aspirin improves glucose metabolism in type 2 diabetes. J Clin Invest, 109 (10): 1321–1326. (2002). 243. Möhlig M, Freudenberg M, Bobbert T, et al. Acetylsalicylic acid improves lipid-induced insulin resistance in healthy men. J Clin Endocrinol Metab, 91 (3): 964–967. (2006). 244. Hayashino Y, Hennekens CH & Kurth T. Aspirin use and risk of type 2 diabetes in apparently healthy men. Am J Med, 122 (4): 374–379. (2009). 245. Lastra G & Whaley-Connell A. Diabetes: aspirin and prevention of diabetes still a topic of debate. Nat Rev Endocrinol, 5 (7): 365–366. (2009). 246. Cryer B, Goldschmiedt M, Redfern JS, et al. Comparison of salsalate and aspirin on mucosal injury and gastroduodenal mucosal prostaglandins. Gastroenterology, 99 (6): 1616–1621. (1990). 247. Roth S, Bennett R, Caldron P, et al. Reduced risk of NSAID gastropathy (GI mucosal toxicity) with nonacetylated salicylate (salsalate): an endoscopic study. Semin Arthritis Rheum, 19 (4 Suppl 2): 11–19. (1990). 248. Estes D & Kaplan K. Lack of platelet effect with the aspirin analog, salsalate. Arthritis Rheum, 23 (11): 1303–1307. (1980). 249. Goldfine AB, Fonseca V, Jablonski KA, et al. The effects of salsalate on glycemic control in patients with type 2 diabetes: a randomized trial. Ann Intern Med, 152 (6): 346–357. (2010). 250. Goldfine AB, Silver R, Aldhahi W, et al. Use of salsalate to target inflammation in the treatment of insulin resistance and type 2 diabetes. Clin Transl Sci, 1 (1): 36–43. (2008). 251. Faghihimani E, Aminorroaya A, Rezvanian H, et al. Salsalate improves glycemic control in patients with newly diagnosed type 2 diabetes. Acta Diabetologica, (2011). doi:10.1007/s00592- 011-0329-2. 252. Fleischman A, Shoelson SE, Bernier R, et al. Salsalate improves glycemia and inflammatory parameters in obese young adults. Diabetes Care, 31 (2): 289–294. (2007). 253. Fernandez-Real J-M, Lopez-Bermejo A, Ropero A-B, et al. Salicylates increase insulin secretion in healthy obese subjects. J Clin Endocrinol Metab, 93 (7): 2523–2530. (2008).
- 229 -
254. Koska J, Ortega E, Bunt JC, et al. The effect of salsalate on insulin action and glucose tolerance in obese non-diabetic patients: results of a randomised double-blind placebo-controlled study. Diabetologia, 52 (3): 385–393. (2008). 255. Meex RCR, Phielix E, Moonen-Kornips E, et al. Stimulation of human whole-body energy expenditure by salsalate is fueled by higher lipid oxidation under fasting conditions and by higher oxidative glucose disposal under insulin-stimulated conditions. J Clin Endocrinol Metab, 96 (5): 1415–1423. (2011). 256. Shoelson S. Targeting INflammation using SALsalate for Type 2 Diabetes-stage II (TINSAL- T2D), NCT00799643. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00799643?term=NCT00799643&rank=1, accessed May 5, 2010. 257. Shoelson S. Targeting INflammation Using SALsalate in Type 2 Diabetes (TINSAL-T2D), NCT00392678. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00392678?term=NCT00392678&rank=1, accessed May 5, 2010. 258. Reaven P. Salsalate therapy to reduce insulin resistance and cardiovascular risk, NCT00330733. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00330733?term=NCT00330733&rank=1, accessed May 5, 2010. 259. Goldfine A. The study of inflammation on blood glucose levels in obese people, NCT00258115. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00258115?term=NCT00258115&rank=1, accessed May 5, 2010. 260. Welty F. Targeting inflammation using salsalate in cardiovascular disease, NCT00624923. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00624923?term=NCT00624923&rank=1, accessed May 5, 2010. 261. Vita J. Effect of salsalate on bed rest-induced vascular dysfunction, NCT00553995. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00553995?term=NCT00553995&rank=1, accessed May 5, 2010. 262. Mather K. Vascular function, endothelin, and inflammation in pre-diabetic obesity versus Lean healthy controls, NCT00837590. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00837590?term=NCT00837590&rank=1, accessed May 5, 2010. - 230 -
263. Beckman J. The impact of reducing inflammation on vascular function in the metabolic syndrome, NCT00762827. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00762827?term=NCT00762827&rank=1, accessed May 5, 2010. 264. Beckman J. Inflammation and vascular function in atherosclerosis, NCT00760019. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00760019?term=NCT00760019&rank=1, accessed May 5, 2010. 265. Umpierrez G. FFA hypertension and inflammation in lean and obese subjects, NCT00721617. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00721617?term=NCT00721617&rank=1, accessed May 5, 2010. 266. McComsey G. Safety and efficacy of salsalate to treat endothelial dysfunction in HIV-infected adults, NCT01046682. Available at: http://www.clinicaltrials.gov/ct2/show/NCT01046682?term=NCT01046682&rank=1, accessed May 5, 2010. 267. Raghavan RP & Cummings MH. Effects of different doses of aspirin on pathophysiological markers in type 2 diabetes. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00898950?term=raghavan&rank=37, accessed April 30, 2010. 268. Chyka PA, Erdman AR, Christianson G, et al. Salicylate poisoning: An evidence-based consensus guideline for out-of-hospital management. Clin Toxicol, 45 (2): 95–131. (2007). 269. Rosen CJ. The rosiglitazone story – lessons from an FDA advisory committee meeting. N Engl J Med, 357 (9): 844–846. (2007). 270. European Medicines Agency. Press Release: The European Medicines Agency recommends suspension of the marketing authorisation of Acomplia. (European Medicines Agency, London, 2008). Available at: http://www.ema.europa.eu/humandocs/PDFs/EPAR/acomplia/53777708en.pdf, accessed April 30, 2010. 271. Kazi D. Rosiglitazone and implications for pharmacovigilance. BMJ, 334 (7606): 1233–1234. (2007). 272. Horder J, Cowen PJ, Di Simplicio M, et al. Acute administration of the cannabinoid CB1 antagonist rimonabant impairs positive affective memory in healthy volunteers. Psychopharmacology (Berl), 205 (1): 85–91. (2009).
- 231 -
273. McPartland JM. Obesity, the endocannabinoid system, and bias arising from pharmaceutical sponsorship. PLoS ONE, 4 (3): e5092. (2009). 274. Rumsfeld JS & Nallamothu BK. The hope and fear of rimonabant. JAMA, 299 (13): 1601–1602. (2008). 275. Davies SL, Moral MA & Bozzo J. Salicylates targeting insulin resistance. Drugs Future, 32 (4): 361–365. (2007). 276. Jayet P, Thalmann S, Sartori C, et al. Nitro-aspirin improves insulin sensitivity in obese insulin- resistant men. American Heart Association Scientific Sessions 2004. 277. Loriga G, Shgirlanzoni C, Bruno S, et al. Nitric-oxide releasing aspirin improves insulin activity and systemic hemodynamics in subjects with type 2 diabetes and early nephropathy. American Heart Association Scientific Sessions 2005. 278. Lazzarato L, Cena C, Rolando B, et al. Searching for new NO-donor aspirin-like molecules: Furoxanylacyl derivatives of salicylic acid and related furazans. Bioorg Med Chem, 19 (19): 5852–5860. (2011). 279. Van Vliet T. Effect of reduction of inflammatory status on glucose metabolism in overweight men, NCT00221052. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00221052?term=NCT00221052&rank=1, accessed May 5, 2010. 280. Zmijowskyj T. Study of CRx-401 on glucose levels in subjects with type II diabetes, NCT00506298. Available at: http://www.clinicaltrials.gov/ct2/show/NCT00506298?term=NCT00506298&rank=1, accessed May 5, 2010. 281. Larsen CM, Faulenbach M, Vaag A, et al. Interleukin-1-receptor antagonist in type 2 diabetes mellitus. N Engl J Med, 356 (15): 1517–1526. (2007). 282. Larsen CM, Faulenbach M, Vaag A, et al. Sustained effects of interleukin-1 receptor antagonist treatment in type 2 diabetes. Diabetes Care, 32 (9): 1663–1668. (2009). 283. Selvin E, Paynter NP & Erlinger TP. The effect of weight loss on C-reactive protein: a systematic review. Arch Intern Med, 167 (1): 31–39. (2007). 284. Esposito K, Pontillo A, Di Palo C, et al. Effect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: a randomized trial. JAMA, 289 (14): 1799–1804. (2003). 285. Madsen EL, Bruun JM, Skogstrand K, et al. Long-term weight loss decreases the nontraditional cardiovascular risk factors interleukin-18 and matrix metalloproteinase-9 in obese subjects. Metabolism, 58 (7): 946–953. (2009).
- 232 -
286. Esposito K, Marfella R, Ciotola M, et al. Effect of a mediterranean-style diet on endothelial dysfunction and markers of vascular inflammation in the metabolic syndrome: a randomized trial. JAMA, 292 (12): 1440–1446. (2004). 287. Trøseid M, Arnesen H, Hjerkinn EM, et al. Serum levels of interleukin-18 are reduced by diet and n-3 fatty acid intervention in elderly high-risk men. Metabolism, 58 (11): 1543–1549. (2009). 288. Herder C, Peltonen M, Koenig W, et al. Anti-inflammatory effect of lifestyle changes in the Finnish Diabetes Prevention Study. Diabetologia, 52 (3): 433–442. (2009). 289. Bruun J, Stallknecht B, Helge J, et al. Interleukin-18 in plasma and adipose tissue: effects of obesity, insulin resistance, and weight loss. Eur J Endocrinol, 157 (4): 465–471. (2007). 290. Belza A, Toubro S, Stender S, et al. Effect of diet-induced energy deficit and body fat reduction on high-sensitive CRP and other inflammatory markers in obese subjects. Int J Obes (Lond), 33 (4): 456–464. (2009). 291. Belalcazar LM, Reboussin DM, Haffner SM, et al. A 1-year lifestyle intervention for weight loss in individuals with type 2 diabetes reduces high C-reactive protein levels and identifies metabolic predictors of change: from the Look AHEAD (Action for Health in Diabetes) study. Diabetes Care, 33 (11): 2297–2303. (2010). 292. Hofsø D, Nordstrand N, Johnson LK, et al. Obesity-related cardiovascular risk factors after weight loss: a clinical trial comparing gastric bypass surgery and intensive lifestyle intervention. Eur J Endocrinol, 163 (5): 735–745. (2010). 293. Snel M, Van Diepen JA, Stijnen T, et al. Immediate and long-term effects of addition of exercise to a 16-week very low calorie diet on low-grade inflammation in obese, insulin-dependent type 2 diabetic patients. Food Chem Toxicol, 49 (12): 3104–3111. (2011). 294. De Mello VDF, Kolehmainen M, Pulkkinen L, et al. Downregulation of genes involved in NFkappaB activation in peripheral blood mononuclear cells after weight loss is associated with the improvement of insulin sensitivity in individuals with the metabolic syndrome: the GENOBIN study. Diabetologia, 51 (11): 2060–2067. (2008). 295. Bikman BT, Zheng D, Pories WJ, et al. Mechanism for improved insulin sensitivity after gastric bypass surgery. J Clin Endocrinol Metab, 93 (12): 4656–4663. (2008). 296. Basu A, Devaraj S & Jialal I. Dietary factors that promote or retard inflammation. Arterioscler Thromb Vasc Biol, 26 (5): 995–1001. (2006). 297. Dietrich M & Jialal I. The effect of weight loss on a stable biomarker of inflammation, C- reactive protein. Nutr Rev, 63 (1): 22–28. (2005).
- 233 -
298. Bosutti A, Malaponte G, Zanetti M, et al. Calorie restriction modulates inactivity-induced changes in the inflammatory markers C-reactive protein and pentraxin-3. J Clin Endocrinol Metab, 93 (8): 3226–3229. (2008). 299. Tam CS, Viardot A, Clément K, et al. Short-term overfeeding may induce peripheral insulin resistance without altering subcutaneous adipose tissue macrophages in humans. Diabetes, 59 (9): 2164–2170. (2010). 300. Zhang X, Zhang G, Zhang H, et al. Hypothalamic IKKβ/NF-κB and ER stress link overnutrition to energy imbalance and obesity. Cell, 135 (1): 61–73. (2008). 301. Cani PD, Amar J, Iglesias MA, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes, 56 (7): 1761–1772. (2007). 302. Calder PC, Ahluwalia N, Brouns F, et al. Dietary factors and low-grade inflammation in relation to overweight and obesity. Br J Nutr, 106 (Suppl. S3): S1–S78. (2011). 303. Calder PC, Albers R, Antoine J-M, et al. Inflammatory disease processes and interactions with nutrition. Br J Nutr, 101 (Suppl. S1): 1–45. (2009). 304. Hamer M & Steptoe A. Influence of specific nutrients on progression of atherosclerosis, vascular function, haemostasis and inflammation in coronary heart disease patients: a systematic review. Br J Nutr, 95 (5): 849–859. (2006). 305. Calder PC. n−3 polyunsaturated fatty acids, inflammation, and inflammatory diseases. Am J Clin Nutr, 83 (6): S1505–1519S. (2006). 306. Oliver E, McGillicuddy F, Phillips C, et al. The role of inflammation and macrophage accumulation in the development of obesity-induced type 2 diabetes mellitus and the possible therapeutic effects of long-chain n-3 PUFA. Proc Nutr Soc, 69 (2): 232–243. (2010). 307. Kabir M, Skurnik G, Naour N, et al. Treatment for 2 mo with n 3 polyunsaturated fatty acids reduces adiposity and some atherogenic factors but does not improve insulin sensitivity in women with type 2 diabetes: a randomized controlled study. Am J Clin Nutr, 86 (6): 1670–1679. (2007). 308. Malekshahi Moghadam A, Saedisomeolia A, Djalali M, et al. Efficacy of omega-3 fatty acid supplementation on serum levels of tumour necrosis factor-alpha, C-reactive protein and interleukin-2 in type 2 diabetes mellitus patients. Singapore Med J, 53 (9): 615–619. (2012). 309. Wong C-Y, Yiu K-H, Li S-W, et al. Fish-oil supplement has neutral effects on vascular and metabolic function but improves renal function in patients with type 2 diabetes mellitus. Diabet Med, 27 (1): 54–60. (2010).
- 234 -
310. Mori TA, Woodman RJ, Burke V, et al. Effect of eicosapentaenoic acid and docosahexaenoic acid on oxidative stress and inflammatory markers in treated-hypertensive type 2 diabetic subjects. Free Radic Biol Med, 35 (7): 772–781. (2003). 311. Chan DC, Watts GF, Barrett PHR, et al. Effect of atorvastatin and fish oil on plasma high- sensitivity C-reactive protein concentrations in individuals with visceral obesity. Clin Chem, 48 (6 Pt 1): 877–883. (2002). 312. Jellema A, Plat J & Mensink RP. Weight reduction, but not a moderate intake of fish oil, lowers concentrations of inflammatory markers and PAI-1 antigen in obese men during the fasting and postprandial state. Eur J Clin Invest, 34 (11): 766–773. (2004). 313. Johnson GH & Fritsche K. Effect of dietary linoleic acid on markers of inflammation in healthy persons: a systematic review of randomized controlled trials. J Acad Nutr Diet, 112 (7): 1029– 1041, 1041.e1–15. (2012). 314. Bjermo H, Iggman D, Kullberg J, et al. Effects of n-6 PUFAs compared with SFAs on liver fat, lipoproteins, and inflammation in abdominal obesity: a randomized controlled trial. Am J Clin Nutr, 95 (5): 1003–1012. (2012). 315. King DE EB. Effect of a high-fiber diet vs a fiber-supplemented diet on C-reactive protein level. Arch Intern Med, 167 (5): 502–506. (2007). 316. Zemel MB, Sun X, Sobhani T, et al. Effects of dairy compared with soy on oxidative and inflammatory stress in overweight and obese subjects. Am J Clin Nutr, 91 (1): 16–22. (2010). 317. Greany KA, Nettleton JA, Wangen KE, et al. Consumption of isoflavone-rich soy protein does not alter homocysteine or markers of inflammation in postmenopausal women. Eur J Clin Nutr, 62 (12): 1419–1425. (2008). 318. Törmälä R, Appt S, Clarkson TB, et al. Impact of soy supplementation on sex steroids and vascular inflammation markers in postmenopausal women using tibolone: role of equol production capability. Climacteric, 11 (5): 409–415. (2008). 319. Blum A, Lang N, Peleg A, et al. Effects of oral soy protein on markers of inflammation in postmenopausal women with mild hypercholesterolemia. Am Heart J, 145 (2): E7. (2003). 320. Beavers KM, Serra MC, Beavers DP, et al. Soymilk supplementation does not alter plasma markers of inflammation and oxidative stress in postmenopausal women. Nutr Res, 29 (9): 616– 622. (2009). 321. Nasca MM, Zhou J-R & Welty FK. Effect of soy nuts on adhesion molecules and markers of inflammation in hypertensive and normotensive postmenopausal women. Am J Cardiol, 102 (1): 84–86. (2008).
- 235 -
322. Charles C, Yuskavage J, Carlson O, et al. Effects of high-dose isoflavones on metabolic and inflammatory markers in healthy postmenopausal women. Menopause, 16 (2): 395–400. (2009). 323. Beavers KM, Serra MC, Beavers DP, et al. Soy and the exercise-induced inflammatory response in postmenopausal women. Appl Physiol Nutr Metab, 35 (3): 261–269. (2010). 324. Jenkins DJA, Kendall CWC, Connelly PW, et al. Effects of high- and low-isoflavone (phytoestrogen) soy foods on inflammatory biomarkers and proinflammatory cytokines in middle-aged men and women. Metabolism, 51 (7): 919–924. (2002). 325. Ma L, Grann K, Li M, et al. A pilot study to evaluate the effect of soy isolate protein on the serum lipid profile and other potential cardiovascular risk markers in moderately hypercholesterolemic Chinese adults. Ecol Food Nutr, 50 (6): 473–485. (2011). 326. Azadbakht L, Atabak S & Esmaillzadeh A. Soy protein intake, cardiorenal indices, and C- reactive protein in type 2 diabetes with nephropathy: a longitudinal randomized clinical trial. Diabetes Care, 31 (4): 648–654. (2008). 327. Liu J-F, Liu Y-H, Chen C-M, et al. The effect of almonds on inflammation and oxidative stress in Chinese patients with type 2 diabetes mellitus: a randomized crossover controlled feeding trial. Eur J Nutr, (2012). doi:10.1007/s00394-012-0400-y. 328. Mukuddem-Petersen J, Stonehouse (Oosthuizen) W, Jerling JC, et al. Effects of a high walnut and high cashew nut diet on selected markers of the metabolic syndrome: a controlled feeding trial. Br J Nutr, 97 (06): 1144–1153. (2007). 329. Casas-Agustench P, López-Uriarte P, Bulló M, et al. Effects of one serving of mixed nuts on serum lipids, insulin resistance and inflammatory markers in patients with the metabolic syndrome. Nutr Metab Cardiovasc Dis, 21 (2): 126–135. (2011). 330. Zhao G, Etherton TD, Martin KR, et al. Dietary alpha-linolenic acid reduces inflammatory and lipid cardiovascular risk factors in hypercholesterolemic men and women. J Nutr, 134 (11): 2991–2997. (2004). 331. Zhao G, Etherton TD, Martin KR, et al. Dietary alpha-linolenic acid inhibits proinflammatory cytokine production by peripheral blood mononuclear cells in hypercholesterolemic subjects. Am J Clin Nutr, 85 (2): 385–391. (2007). 332. Ros E, Núñez I, Pérez-Heras A, et al. A walnut diet improves endothelial function in hypercholesterolemic subjects: a randomized crossover trial. Circulation, 109 (13): 1609–1614. (2004). 333. Sari I, Baltaci Y, Bagci C, et al. Effect of pistachio diet on lipid parameters, endothelial function, inflammation, and oxidative status: A prospective study. Nutrition, 26 (4): 399–404. (2010).
- 236 -
334. Vlassara H, Cai W, Crandall J, et al. Inflammatory mediators are induced by dietary glycotoxins, a major risk factor for diabetic angiopathy. Proc Natl Acad Sci U S A, 99 (24): 15596–15601. (2002). 335. Negrean M, Stirban A, Stratmann B, et al. Effects of low- and high-advanced glycation endproduct meals on macro- and microvascular endothelial function and oxidative stress in patients with type 2 diabetes mellitus. Am J Clin Nutr, 85 (5): 1236–1243. (2007). 336. Camhi SM, Stefanick ML, Ridker PM, et al. Changes in C-reactive protein from low-fat diet and/or physical activity in men and women with and without metabolic syndrome. Metabolism, 59 (1): 54–61. (2010). 337. Khoo J, Piantadosi C, Duncan R, et al. Comparing effects of a low-energy diet and a high- protein low-fat diet on sexual and endothelial function, urinary tract symptoms, and inflammation in obese diabetic men. J Sex Med, 8 (10): 2868–2875. (2011). 338. Davis NJ, Crandall JP, Gajavelli S, et al. Differential effects of low-carbohydrate and low-fat diets on inflammation and endothelial function in diabetes. J Diabetes Complications, 25 (6): 371–376. (2011). 339. Marfella R, Cacciapuoti F, Siniscalchi M, et al. Effect of moderate red wine intake on cardiac prognosis after recent acute myocardial infarction of subjects with type 2 diabetes mellitus. Diabet Med, 23 (9): 974–981. (2006). 340. Sacanella E, Vázquez-Agell M, Mena MP, et al. Down-regulation of adhesion molecules and other inflammatory biomarkers after moderate wine consumption in healthy women: a randomized trial. Am J Clin Nutr, 86 (5): 1463–1469. (2007). 341. Vázquez-Agell M, Sacanella E, Tobias E, et al. Inflammatory markers of atherosclerosis are decreased after moderate consumption of cava (sparkling wine) in men with low cardiovascular risk. J Nutr, 137 (10): 2279–2284. (2007). 342. Avellone G, Di Garbo V, Campisi D, et al. Effects of moderate Sicilian red wine consumption on inflammatory biomarkers of atherosclerosis. Eur J Clin Nutr, 60 (1): 41–47. (2006). 343. Retterstol L, Berge KE, Braaten Ø, et al. A daily glass of red wine: does it affect markers of inflammation? Alcohol Alcohol, 40 (2): 102–105. (2005). 344. Estruch R, Sacanella E, Badia E, et al. Different effects of red wine and gin consumption on inflammatory biomarkers of atherosclerosis: a prospective randomized crossover trial. Effects of wine on inflammatory markers. Atherosclerosis, 175 (1): 117–123. (2004). 345. Weseler AR, Ruijters EJB, Drittij-Reijnders M-J, et al. Pleiotropic benefit of monomeric and oligomeric flavanols on vascular health - a randomized controlled clinical pilot study. PLoS ONE, 6 (12): e28460. (2011). - 237 -
346. Fukino Y, Shimbo M, Aoki N, et al. Randomized controlled trial for an effect of green tea consumption on insulin resistance and inflammation markers. J Nutr Sci Vitaminol, 51 (5): 335– 342. (2005). 347. Ryu OH, Lee J, Lee KW, et al. Effects of green tea consumption on inflammation, insulin resistance and pulse wave velocity in type 2 diabetes patients. Diabetes Res Clin Pract, 71 (3): 356–358. (2006). 348. Basu A, Du M, Sanchez K, et al. Green tea minimally affects biomarkers of inflammation in obese subjects with metabolic syndrome. Nutrition, 27 (2): 206–213. (2011). 349. Bogdanski P, Suliburska J, Szulinska M, et al. Green tea extract reduces blood pressure, inflammatory biomarkers, and oxidative stress and improves parameters associated with insulin resistance in obese, hypertensive patients. Nutr Res, 32 (6): 421–427. (2012). 350. Eichenberger P, Colombani PC & Mettler S. Effects of 3-week consumption of green tea extracts on whole-body metabolism during cycling exercise in endurance-trained men. Int J Vitam Nutr Res, 79 (1): 24–33. (2009). 351. Steptoe A, Gibson EL, Vuononvirta R, et al. The effects of chronic tea intake on platelet activation and inflammation: a double-blind placebo controlled trial. Atherosclerosis, 193 (2): 277–282. (2007). 352. Kempf K, Herder C, Erlund I, et al. Effects of coffee consumption on subclinical inflammation and other risk factors for type 2 diabetes: a clinical trial. Am J Clin Nutr, 91 (4): 950–957. (2010). 353. Shechter M, Shalmon G, Scheinowitz M, et al. Impact of acute caffeine ingestion on endothelial function in subjects with and without coronary artery disease. Am J Cardiol, 107 (9): 1255– 1261. (2011). 354. Gavrieli A, Yannakoulia M, Fragopoulou E, et al. Caffeinated coffee does not acutely affect energy intake, appetite, or inflammation but prevents serum cortisol concentrations from falling in healthy men. J Nutr, 141 (4): 703–707. (2011). 355. Chuang S-C, Vermeulen R, Sharabiani MTA, et al. The intake of grain fibers modulates cytokine levels in blood. Biomarkers, 16 (6): 504–510. (2011). 356. King DE, Egan BM & Geesey ME. Relation of dietary fat and fiber to elevation of C-reactive protein. Am J Cardiol, 92 (11): 1335–1339. (2003). 357. Ajani UA, Ford ES & Mokdad AH. Dietary fiber and C-reactive protein: findings from national health and nutrition examination survey data. J Nutr, 134 (5): 1181–1185. (2004). 358. Ma Y, Griffith JA, Chasan-Taber L, et al. Association between dietary fiber and serum C- reactive protein. Am J Clin Nutr, 83 (4): 760–766. (2006). - 238 -
359. Ma Y, Hébert JR, Li W, et al. Association between dietary fiber and markers of systemic inflammation in the Women’s Health Initiative Observational Study. Nutrition, 24 (10): 941– 949. (2008). 360. North CJ, Venter CS & Jerling JC. The effects of dietary fibre on C-reactive protein, an inflammation marker predicting cardiovascular disease. Eur J Clin Nutr, 63 (8): 921–933. (2009). 361. Urpi-Sarda M, Casas R, Chiva-Blanch G, et al. The Mediterranean diet pattern and its main components are associated with lower plasma concentrations of tumor necrosis factor receptor 60 in patients at high risk for cardiovascular disease. J Nutr, 142 (6): 1019–1025. (2012). 362. Mena M-P, Sacanella E, Vazquez-Agell M, et al. Inhibition of circulating immune cell activation: a molecular antiinflammatory effect of the Mediterranean diet. Am J Clin Nutr, 89 (1): 248–256. (2009). 363. Athyros VG, Kakafika AI, Papageorgiou AA, et al. Effect of a plant stanol ester-containing spread, placebo spread, or Mediterranean diet on estimated cardiovascular risk and lipid, inflammatory and haemostatic factors. Nutr Metab Cardiovasc Dis, 21 (3): 213–221. (2011). 364. Yubero-Serrano EM, Gonzalez-Guardia L, Rangel-Zuñiga O, et al. Mediterranean diet supplemented with coenzyme Q10 modifies the expression of proinflammatory and endoplasmic eeticulum stress–related genes in elderly men and women. J Gerontol A Biol Sci Med Sci, 67A (1): 3–10. (2012). 365. Camargo A, Delgado-Lista J, Garcia-Rios A, et al. Expression of proinflammatory, proatherogenic genes is reduced by the Mediterranean diet in elderly people. Br J Nutr, 108 (03): 500–508. (2012). 366. Michalsen A, Lehmann N, Pithan C, et al. Mediterranean diet has no effect on markers of inflammation and metabolic risk factors in patients with coronary artery disease. Eur J Clin Nutr, 60 (4): 478. (2006). 367. Thomazella MCD, Góes MFS, Andrade CR, et al. Effects of high adherence to mediterranean or low-fat diets in medicated secondary prevention patients. Am J Cardiol, 108 (11): 1523–1529. (2011). 368. Blum A, Monir M, Khazim K, et al. Tomato-rich (Mediterranean) diet does not modify inflammatory markers. Clin Invest Med, 30 (2): E70–E74. (2007). 369. De Mello VDF, Schwab U, Kolehmainen M, et al. A diet high in fatty fish, bilberries and wholegrain products improves markers of endothelial function and inflammation in individuals with impaired glucose metabolism in a randomised controlled trial: the Sysdimet study. Diabetologia, 54 (11): 2755–2767. (2011).
- 239 -
370. Jenkins DJA, Kendall CWC, Marchie A, et al. Effects of a dietary portfolio of cholesterol- lowering foods vs lovastatin on serum lipids and C-reactive protein. JAMA, 290 (4): 502–510. (2003). 371. Azadbakht L, Surkan PJ, Esmaillzadeh A, et al. The Dietary Approaches to Stop Hypertension eating plan affects C-reactive protein, coagulation abnormalities, and hepatic function tests among type 2 diabetic patients. J Nutr, 141 (6): 1083–1088. (2011). 372. Wolever T, Gibbs A, Mehling C, et al. The Canadian trial of Carbohydrates in Diabetes (CCD), a 1-y controlled trial of low-glycemic-index dietary carbohydrate in type 2 diabetes: no effect on glycated hemoglobin but reduction in C-reactive protein. Am J Clin Nutr, 87 (1): 114–125. (2008). 373. Bakker GC, Erk MJ van, Pellis L, et al. An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach. Am J Clin Nutr, 91 (4): 1044–1059. (2010). 374. Thomas NE & Williams DRR. Inflammatory factors, physical activity, and physical fitness in young people. Scand J Med Sci Sports, 18 (5): 543–556. (2008). 375. Ford ES. Does exercise reduce inflammation? Physical activity and C-reactive protein among U.S. adults. Epidemiology, 13 (5): 561–568. (2002). 376. Pitsavos C, Panagiotakos DB, Chrysohoou C, et al. The associations between physical activity, inflammation, and coagulation markers, in people with metabolic syndrome: the ATTICA study. Eur J Cardiovasc Prev Rehabil, 12 (2): 151–158. (2005). 377. Lin C-Y, Chen P-C, Kuo H-K, et al. Effects of obesity, physical activity, and cardiorespiratory fitness on blood pressure, inflammation, and insulin resistance in the National Health and Nutrition Survey 1999-2002. Nutr Metab Cardiovasc Dis, 20 (10): 713–719. (2010). 378. Mora S, Lee I-M, Buring JE, et al. Association of physical activity and body mass index with novel and traditional cardiovascular biomarkers in women. JAMA, 295 (12): 1412–1419. (2006). 379. Oberbach A, Lehmann S, Kirsch K, et al. Long-term exercise training decreases interleukin-6 (IL-6) serum levels in subjects with impaired glucose tolerance: effect of the -174G/C variant in IL-6 gene. Eur J Endocrinol, 159 (2): 129–136. (2008). 380. Neumayr G, Ludwiczek O, Hoertnagl H, et al. The impact of prolonged strenuous endurance exercise on interleukin 18 and interleukin 18 binding protein in recreational cyclists. Int J Sports Med, 26 (10): 836–840. (2005). 381. Kadoglou N, Perrea D, Iliadis F, et al. Exercise reduces resistin and inflammatory cytokines in patients with type 2 diabetes. Diabetes Care, 30 (3): 719–721. (2007).
- 240 -
382. Kohut M, Mccann D, Russell D, et al. Aerobic exercise, but not flexibility/resistance exercise, reduces serum IL-18, CRP, and IL-6 independent of beta-blockers, BMI, and psychosocial factors in older adults. Brain Behav Immun, 20 (3): 201–209. (2006). 383. Lambert C, Wright N, Finck B, et al. Exercise but not diet-induced weight loss decreases skeletal muscle inflammatory gene expression in frail obese elderly persons. J Appl Physiol, 105 (2): 473–478. (2008). 384. Ribeiro F, Alves AJ, Duarte JA, et al. Is exercise training an effective therapy targeting endothelial dysfunction and vascular wall inflammation? Int J Cardiol, 141 (3): 214–221. (2010). 385. Kadoglou NPE, Iliadis F, Sailer N, et al. Exercise training ameliorates the effects of rosiglitazone on traditional and novel cardiovascular risk factors in patients with type 2 diabetes mellitus. Metabolism, 59 (4): 599–607. (2009). 386. Thompson D, Markovitch D, Betts JA, et al. Time course of changes in inflammatory markers during a 6-mo exercise intervention in sedentary middle-aged men: a randomized-controlled trial. J Appl Physiol, 108 (4): 769–779. (2010). 387. Thomas TR, Warner SO, Dellsperger KC, et al. Exercise and the metabolic syndrome with weight regain. J Appl Physiol, 109 (1): 3–10. (2010). 388. Yates T, Davies MJ, Gorely T, et al. The effect of increased ambulatory activity on markers of chronic low-grade inflammation: evidence from the PREPARE programme randomized controlled trial. Diabet Med, 27 (11): 1256–1263. (2010). 389. Marcell TJ, McAuley KA, Traustadóttir T, et al. Exercise training is not associated with improved levels of C-reactive protein or adiponectin. Metabolism, 54 (4): 533–541. (2005). 390. Handschin C & Spiegelman BM. The role of exercise and PGC1alpha in inflammation and chronic disease. Nature, 454 (7203): 463–469. (2008). 391. Gleeson M. Immune function in sport and exercise. J Appl Physiol, 103 (2): 693–699. (2007). 392. Balducci S, Zanuso S, Nicolucci A, et al. Anti-inflammatory effect of exercise training in subjects with type 2 diabetes and the metabolic syndrome is dependent on exercise modalities and independent of weight loss. Nutr Metab Cardiovasc Dis, 20 (8): 608–617. (2010). 393. Galassetti PR. Yes, yes, IL-6: what else? J Appl Physiol, 108 (4): 767–768. (2010). 394. Shen J & Ordovas J. Impact of genetic and environmental factors on hsCRP concentrations and response to therapeutic agents. Clin Chem, 55 (2): 256–264. (2009). 395. Mora S, Cook N, Buring J, et al. Physical activity and reduced risk of cardiovascular events: potential mediating mechanisms. Circulation, 116 (19): 2110–2118. (2007).
- 241 -
396. Aas AM, Seljeflot I, Torjesen PA, et al. Blood glucose lowering by means of lifestyle intervention has different effects on adipokines as compared with insulin treatment in subjects with type 2 diabetes. Diabetologia, 49 (5): 872–880. (2006). 397. Giannopoulou I, Fernhall B, Carhart R, et al. Effects of diet and/or exercise on the adipocytokine and inflammatory cytokine levels of postmenopausal women with type 2 diabetes. Metabolism, 54 (7): 866–875. (2005). 398. Bozzetto L, De Natale C, Di Capua L, et al. The association of hs-CRP with fasting and postprandial plasma lipids in patients with type 2 diabetes is disrupted by dietary monounsaturated fatty acids. Acta Diabetol, (2011). doi:10.1007/s00592-011-0318-5. 399. Bastard J-P, Jardel C, Bruckert E, et al. Elevated levels of interleukin 6 are reduced in serum and subcutaneous adipose tissue of obese women after weight loss. J Clin Endocrinol Metab, 85 (9): 3338–3342. (2000). 400. Monzillo LU, Hamdy O, Horton ES, et al. Effect of lifestyle modification on adipokine levels in obese subjects with insulin resistance. Obes Res, 11 (9): 1048–1054. (2003). 401. Yamamoto Y & Gaynor RB. IkappaB kinases: key regulators of the NF-kappaB pathway. Trends Biochem Sci, 29 (2): 72–79. (2004). 402. Hayden MS & Ghosh S. Signaling to NF-κB. Genes Dev, 18 (18): 2195–2224. (2004). 403. Tamura Y, Ogihara T, Uchida T, et al. Amelioration of glucose tolerance by hepatic inhibition of nuclear factor κB in db/db mice. Diabetologia, 50 (1): 131–141. (2006). 404. Wunderlich FT, Luedde T, Singer S, et al. Hepatic NF- B essential modulator deficiency prevents obesity-induced insulin resistance but synergizes with high-fat feeding in tumorigenesis. Proc Natl Acad Sci U S A, 105 (4): 1297–1302. (2008). 405. Chiang S-HH, Bazuine M, Lumeng CN, et al. The protein kinase IKKepsilon regulates energy balance in obese mice. Cell, 138 (5): 961–975. (2009). 406. Olefsky JM. IKKε: a bridge between obesity and inflammation. Cell, 138 (5): 834–836. (2009). 407. Arkan MC, Hevener AL, Greten FR, et al. IKK-β links inflammation to obesity-induced insulin resistance. Nat Med, 11 (2): 191–198. (2005). 408. Cai D, Yuan M, Frantz DF, et al. Local and systemic insulin resistance resulting from hepatic activation of IKK-β and NF-κB. Nat Med, 11 (2): 183–190. (2005). 409. Kamon J, Yamauchi T, Muto S, et al. A novel IKKbeta inhibitor stimulates adiponectin levels and ameliorates obesity-linked insulin resistance. Biochem Biophys Res Commun, 323 (1): 242– 248. (2004). 410. Cai D. NFkappaB-mediated metabolic inflammation in peripheral tissues versus central nervous system. Cell Cycle, 8 (16): 2542–2548. (2009). - 242 -
411. Videla LA, Tapia G, Rodrigo R, et al. Liver NF-[kappa]B and AP-1 DNA binding in obese patients. Obesity (Silver Spring), 17 (5): 973–979. (2009). 412. Sinha S. Fatty acid-induced insulin resistance in L6 myotubes is prevented by inhibition of activation and nuclear localization of nuclear factor kappa B. J Biol Chem, 279 (40): 41294– 41301. (2004). 413. Dietze D, Ramrath S, Ritzeler O, et al. Inhibitor kappaB kinase is involved in the paracrine crosstalk between human fat and muscle cells. Int J Obes Relat Metab Disord, 28 (8): 985–992. (2004). 414. Chen F. Is NF-kappaB a culprit in type 2 diabetes? Biochem Biophys Res Commun, 332 (1): 1–3. (2005). 415. Shoelson SE, Lee J & Yuan M. Inflammation and the IKKβ/IκB/NF-κB axis in obesity- and diet- induced insulin resistance. Int J Obes Relat Metab Disord, 27: S49–S52. (2003). 416. Zeender E, Maedler K, Bosco D, et al. Pioglitazone and sodium salicylate protect human beta- cells against apoptosis and impaired function induced by glucose and interleukin-1 beta. J Clin Endocrinol Metab, 89 (10): 5059–5066. (2004). 417. Yang J, Park Y, Zhang H, et al. Feed-forward signaling of TNF-alpha and NF-kappaB via IKK- beta pathway contributes to insulin resistance and coronary arteriolar dysfunction in type 2 diabetic mice. Am J Physiol Heart Circ Physiol, 296 (6): H1850–1858. (2009). 418. Huang N-L, Chiang S-H, Hsueh C-H, et al. Metformin inhibits TNF-α-induced IκB kinase phosphorylation, IκB-α degradation and IL-6 production in endothelial cells through PI3K- dependent AMPK phosphorylation. Int J Cardiol, 134 (2): 169–175. (2009). 419. Milanski M, Degasperi G, Coope A, et al. Saturated fatty acids produce an inflammatory response predominantly through the activation of TLR4 signaling in hypothalamus: implications for the pathogenesis of obesity. J Neurosci, 29 (2): 359–370. (2009). 420. Schaeffler A, Gross P, Buettner R, et al. Fatty acid-induced induction of toll-like receptor- 4/nuclear factor-κB pathway in adipocytes links nutritional signalling with innate immunity. Immunology, 126 (2): 233–245. (2009). 421. Shi H, Kokoeva MV, Inouye K, et al. TLR4 links innate immunity and fatty acid-induced insulin resistance. J Clin Invest, 116 (11): 3015–3025. (2006). 422. Basak S & Hoffmann A. Crosstalk via the NF-kappaB signaling system. Cytokine Growth Factor Rev, 19 (3-4): 187–197. (2008). 423. Dinarello CA. Interleukin 1 and interleukin 18 as mediators of inflammation and the aging process. Am J Clin Nutr, 83 (2): 447S–455S. (2006).
- 243 -
424. Moriwaki Y, Yamamoto T, Shibutani Y, et al. Elevated levels of interleukin-18 and tumor necrosis factor-alpha in serum of patients with type 2 diabetes mellitus: relationship with diabetic nephropathy. Metabolism, 52 (5): 605–608. (2003). 425. Suchanek H, Myśliwska J, Siebert J, et al. High serum interleukin-18 concentrations in patients with coronary artery disease and type 2 diabetes mellitus. Eur Cytokine Netw, 16 (3): 177–185. (2005). 426. Escobar-Morreale H, Botella-Carretero J, Villuendas G, et al. Serum interleukin-18 concentrations are increased in the polycystic ovary syndrome: relationship to insulin resistance and to obesity. J Clin Endocrinol Metab, 89 (2): 806–811. (2004). 427. Murdolo G, Herder C, Wang Z, et al. In situ profiling of adipokines in subcutaneous microdialysates from lean and obese individuals. Am J Physiol Endocrinol Metab, 295 (5): E1095–E1105. (2008). 428. Esposito K, Pontillo A, Ciotola M, et al. Weight loss reduces interleukin-18 levels in obese women. J Clin Endocrinol Metab, 87 (8): 3864–3866. (2002). 429. Esposito K, Marfella R & Giugliano D. Plasma interleukin-18 concentrations are elevated in type 2 diabetes. Diabetes Care, 27 (1): 272. (2004). 430. Kuzmicki M, Telejko B, Zonenberg A, et al. Circulating pro- and anti-inflammatory cytokines in polish women with gestational diabetes. Horm Metab Res, 40 (8): 556–560. (2008). 431. Leick L, Lindegaard B, Stensvold D, et al. Adipose tissue interleukin-18 mRNA and plasma interleukin-18: effect of obesity and exercise. Obesity (Silver Spring), 15 (2): 356–363. (2007). 432. Skurk T, Kolb H, Müller-Scholze S, et al. The proatherogenic cytokine interleukin-18 is secreted by human adipocytes. Eur J Endocrinol, 152 (6): 863–868. (2005). 433. Chalikias G, Tziakas D, Kaski J, et al. Interleukin-18/interleukin-10 ratio is an independent predictor of recurrent coronary events during a 1-year follow-up in patients with acute coronary syndrome. Int J Cardiol, 117 (3): 333–339. (2007). 434. Blankenberg S, Luc G, Ducimetière P, et al. Interleukin-18 and the risk of coronary heart disease in European men: the PRospective epIdemiological study of Myocardial Infarction (PRIME). Circulation, 108 (20): 2453–2459. (2003). 435. Chandrasekar B, Mummidi S, Claycomb W, et al. Interleukin-18 is a pro-hypertrophic cytokine that acts through a phosphatidylinositol 3-kinase-phosphoinositide-dependent kinase-1-Akt- GATA4 signaling pathway in cardiomyocytes. J Biol Chem, 280 (6): 4553–4567. (2005). 436. Chandrasekar B, Valente A, Freeman G, et al. Interleukin-18 induces human cardiac endothelial cell death via a novel signaling pathway involving NF-kappaB-dependent PTEN activation. Biochem Biophys Res Commun, 339 (3): 956–963. (2006). - 244 -
437. Chen M, Chen C, Yang C, et al. Interleukin-18: a strong predictor of the extent of coronary artery disease in patients with unstable angina. Heart Vessels, 22 (6): 371–375. (2007). 438. Leon MLA & Zuckerman SH. Gamma interferon: a central mediator in atherosclerosis. Inflamm Res, 54 (10): 395–411. (2005). 439. McLaren JE & Ramji DP. Interferon gamma: A master regulator of atherosclerosis. Cytokine Growth Factor Rev, 20 (2): 125–135. (2009). 440. Sugama S & Conti B. Interleukin-18 and stress. Brain Res Rev, 58 (1): 85–95. (2008). 441. Gerdes N, Sukhova G, Libby P, et al. Expression of interleukin (IL)-18 and functional IL-18 receptor on human vascular endothelial cells, smooth muscle cells, and macrophages: implications for atherogenesis. J Exp Med, 195 (2): 245–257. (2002). 442. Mallat Z, Corbaz A, Scoazec A, et al. Expression of interleukin-18 in human atherosclerotic plaques and relation to plaque instability. Circulation, 104 (14): 1598–1603. (2001). 443. De Nooijer R, Von der Thüsen J, Verkleij C, et al. Overexpression of IL-18 decreases intimal collagen content and promotes a vulnerable plaque phenotype in apolipoprotein-E-deficient mice. Arterioscler Thromb Vasc Biol, 24 (12): 2313–2319. (2004). 444. Elhage R, Jawien J, Rudling M, et al. Reduced atherosclerosis in interleukin-18 deficient apolipoprotein E-knockout mice. Cardiovasc Res, 59 (1): 234–240. (2003). 445. Whitman S, Ravisankar P & Daugherty A. Interleukin-18 enhances atherosclerosis in apolipoprotein E(-/-) mice through release of interferon-gamma. Circ Res, 90 (2): E34–8. (2002). 446. Tenger C, Sundborger A, Jawien J, et al. IL-18 accelerates atherosclerosis accompanied by elevation of IFN-gamma and CXCL16 expression independently of T cells. Arterioscler Thromb Vasc Biol, 25 (4): 791–796. (2005). 447. Mallat Z, Corbaz A, Scoazec A, et al. Interleukin-18/interleukin-18 binding protein signaling modulates atherosclerotic lesion development and stability. Circ Res, 89 (7): E41–45. (2001). 448. Wang M, Tan J, Wang Y, et al. IL-18 binding protein-expressing mesenchymal stem cells improve myocardial protection after ischemia or infarction. Proc Natl Acad Sci U S A, 106 (41): 17499–17504. (2009). 449. Tan H-W, Liu X, Bi X-P, et al. IL-18 overexpression promotes vascular inflammation and remodeling in a rat model of metabolic syndrome. Atherosclerosis, 208 (2): 350–357. (2010). 450. Mallat Z, Heymes C, Corbaz A, et al. Evidence for altered interleukin 18 (IL)-18 pathway in human heart failure. FASEB J, 18 (14): 1752–1754. (2004). 451. Naito Y, Tsujino T, Wakabayashi K, et al. Increased interleukin-18 expression in nonrheumatic aortic valve stenosis. Int J Cardiol, 144 (2): 260–263. (2010).
- 245 -
452. Youssef A, Chang L, Hang C, et al. Level and value of interleukin-18 in patients with acute myocardial infarction undergoing primary coronary angioplasty. Circ J, 71 (5): 703–708. (2007). 453. Gao Y, Tong G-XX, Zhang X-WW, et al. Interleukin-18 levels on admission are associated with mid-term adverse clinical events in patients with ST-segment elevation acute myocardial infarction undergoing percutaneous coronary intervention. Int Heart J, 51 (2): 75–81. (2010). 454. Narins C, Lin D, Burton P, et al. Interleukin-18 and interleukin-18 binding protein levels before and after percutaneous coronary intervention in patients with and without recent myocardial infarction. Am J Cardiol, 94 (10): 1285–1287. (2004). 455. Mallat Z, Henry P, Fressonnet R, et al. Increased plasma concentrations of interleukin-18 in acute coronary syndromes. Heart, 88 (5): 467–469. (2002). 456. Koenig W, Khuseyinova N, Baumert J, et al. Increased concentrations of C-reactive protein and IL-6 but not IL-18 are independently associated with incident coronary events in middle-aged men and women: results from the MONICA/KORA Augsburg case-cohort study, 1984-2002. Arterioscler Thromb Vasc Biol, 26 (12): 2745–2751. (2006). 457. Thompson S, Novick D, Stock C, et al. Free interleukin (IL)-18 levels, and the impact of IL18 and IL18BP genetic variation, in CHD patients and healthy men. Arterioscler Thromb Vasc Biol, 27 (12): 2743–2749. (2007). 458. Everett B, Bansal S, Rifai N, et al. Interleukin-18 and the risk of future cardiovascular disease among initially healthy women. Atherosclerosis, 202 (1): 282–288. (2009). 459. Welsh P, Woodward M, Rumley A, et al. Does interleukin-18 or tumour necrosis factor-α have an independent association with the risk of coronary heart disease? Results from a prospective study in New Zealand. Cytokine, 50 (1): 94–98. (2010). 460. Blankenberg S, Tiret L, Bickel C, et al. Interleukin-18 is a strong predictor of cardiovascular death in stable and unstable angina. Circulation, 106 (1): 24–30. (2002). 461. Espinola-Klein C, Rupprecht H, Bickel C, et al. Inflammation, atherosclerotic burden and cardiovascular prognosis. Atherosclerosis, 195 (2): e126–e134. (2007). 462. Trøseid M, Seljeflot I, Hjerkinn EM, et al. Interleukin-18 is a strong predictor of cardiovascular events in elderly men with the metabolic syndrome: synergistic effect of inflammation and hyperglycemia. Diabetes Care, 32 (3): 486–492. (2008). 463. Hung J, Knuiman M, Divitini M, et al. C-reactive protein and interleukin-18 levels in relation to coronary heart disease: prospective cohort study from Busselton Western Australia. Heart Lung Circ, 17 (2): 90–95. (2008). 464. Jefferis BJ, Papacosta O, Owen CG, et al. Interleukin 18 and coronary heart disease: prospective study and systematic review. Atherosclerosis, 217 (1): 227–233. (2011). - 246 -
465. Hartford M, Wiklund O, Hulten LM, et al. Interleukin-18 as a predictor of future events in patients with acute coronary syndromes. Arterioscler Thromb Vasc Biol, 30 (10): 2039–2046. (2010). 466. Chalikias GK, Tziakas DN, Kaski JC, et al. Interleukin-18: interleukin-10 ratio and in-hospital adverse events in patients with acute coronary syndrome. Atherosclerosis, 182 (1): 135–143. (2005). 467. Yamagami H, Kitagawa K, Hoshi T, et al. Associations of serum IL-18 levels with carotid intima-media thickness. Arterioscler Thromb Vasc Biol, 25 (7): 1458–1462. (2005). 468. Chapman C, McQuillan B, Beilby J, et al. Interleukin-18 levels are not associated with subclinical carotid atherosclerosis in a community population. The Perth Carotid Ultrasound Disease Assessment Study (CUDAS). Atherosclerosis, 189 (2): 414–419. (2006). 469. Zirlik A, Abdullah SM, Gerdes N, et al. Interleukin-18, the metabolic syndrome, and subclinical atherosclerosis: results from the Dallas heart study. Arterioscler Thromb Vasc Biol, 27 (9): 2043–2049. (2007). 470. Trøseid M, Seljeflot I, Weiss TW, et al. Arterial stiffness is independently associated with interleukin-18 and components of the metabolic syndrome. Atherosclerosis, 209 (2): 337–339. (2010). 471. Li Q-X, Fu Q-Q, Shi S-W, et al. Relationship between plasma inflammatory markers and plaque fibrous cap thickness determined by intravascular optical coherence tomography. Heart, 96 (3): 196–201. (2010). 472. Hulthe J, McPheat W, Samnegård A, et al. Plasma interleukin (IL)-18 concentrations is elevated in patients with previous myocardial infarction and related to severity of coronary atherosclerosis independently of C-reactive protein and IL-6. Atherosclerosis, 188 (2): 450–454. (2006). 473. Hernesniemi J, Karhunen P, Rontu R, et al. Interleukin-18 promoter polymorphism associates with the occurrence of sudden cardiac death among Caucasian males: the Helsinki Sudden Death Study. Atherosclerosis, 196 (2): 643–649. (2008). 474. Hernesniemi JA, Karhunen PJ, Oksala N, et al. Interleukin 18 gene promoter polymorphism: a link between hypertension and pre-hospital sudden cardiac death: the Helsinki Sudden Death Study. Eur Heart J, 30 (23): 2939–2946. (2009). 475. Liu W, Tang Q, Jiang H, et al. Promoter polymorphism of interleukin-18 in angiographically proven coronary artery disease. Angiology, 60 (2): 180–185. (2009). 476. Koch W, Wolferstetter H, Schatke A, et al. Interleukin 18 gene variation and risk of acute myocardial infarction. Cytokine, 56 (3): 786–791. (2011). - 247 -
477. Tiret L, Godefroy T, Lubos E, et al. Genetic analysis of the interleukin-18 system highlights the role of the interleukin-18 gene in cardiovascular disease. Circulation, 112 (5): 643–650. (2005). 478. Szeto C-C, Chow K-M, Poon PY-K, et al. Association of interleukin-18 promoter polymorphism and atherosclerotic diseases in Chinese patients with diabetic nephropathy. Nephrology (Carlton), 14 (6): 606–612. (2009). 479. Grisoni M-L, Proust C, Alanne M, et al. Haplotypic analysis of tag SNPs of the interleukin-18 gene in relation to cardiovascular disease events: the MORGAM Project. Eur J Hum Genet, 16 (12): 1512–1520. (2008). 480. Smart MC, Dedoussis G, Yiannakouris N, et al. Genetic variation within IL18 is associated with insulin levels, insulin resistance and postprandial measures. Nutr Metab Cardiovasc Dis, 21 (7): 476–484. (2011). 481. Yang Y, Qiao J & Li M-Z. Association of polymorphisms of interleukin-18 gene promoter region with polycystic ovary syndrome in Chinese population. Reprod Biol Endocrinol, 8 (1): 125. (2010). 482. Shaw DM, Sutherland AM, Russell JA, et al. Novel polymorphism of interleukin-18 associated with greater inflammation after cardiac surgery. Crit Care, 13 (1): R9. (2009). 483. Thompson S, Sanders J, Stephens J, et al. A common interleukin 18 haplotype is associated with higher body mass index in subjects with diabetes and coronary heart disease. Metabolism, 56 (5): 662–669. (2007). 484. Grisoni M-L, Proust C, Alanne M, et al. Lack of association between polymorphisms of the IL18R1 and IL18RAP genes and cardiovascular risk: the MORGAM Project. BMC Medical Genetics, 10: 44+. (2009). 485. Olusi SO, Al-Awadhi A & Abraham M. Relations of serum interleukin 18 levels to serum lipid and glucose concentrations in an apparently healthy adult population. Horm Res, 60 (1): 29–33. (2003). 486. Straczkowski M, Kowalska I, Nikolajuk A, et al. Increased serum interleukin-18 concentration is associated with hypoadiponectinemia in obesity, independently of insulin resistance. Int J Obes (Lond), 31 (2): 221–225. (2007). 487. Tso T, Huang W, Huang H, et al. Elevation of plasma interleukin-18 concentration is associated with insulin levels in patients with systemic lupus erythematosus. Lupus, 15 (4): 207–212. (2006). 488. Tso T, Huang W, Huang H, et al. Relationship of plasma interleukin-18 concentrations to traditional and non-traditional cardiovascular risk factors in patients with systemic lupus erythematosus. Rheumatology (Oxford), 45 (9): 1148–1153. (2006). - 248 -
489. Fischer C, Perstrup L, Berntsen A, et al. Elevated plasma interleukin-18 is a marker of insulin- resistance in type 2 diabetic and non-diabetic humans. Clin Immunol, 117 (2): 152–160. (2005). 490. Evans J, Collins M, Jennings C, et al. The association of interleukin-18 genotype and serum levels with metabolic risk factors for cardiovascular disease. Eur J Endocrinol, 157 (5): 633–640. (2007). 491. Zhang Y, Yang Y, Hong J, et al. Elevated serum levels of interleukin-18 are associated with insulin resistance in women with polycystic ovary syndrome. Endocrine, 29 (3): 419–423. (2006). 492. Hung J, McQuillan B, Chapman C, et al. Elevated interleukin-18 levels are associated with the metabolic syndrome independent of obesity and insulin resistance. Arterioscler Thromb Vasc Biol, 25 (6): 1268–1273. (2005). 493. Kowalska I, Straczkowski M, Nikolajuk A, et al. Insulin resistance, serum adiponectin, and proinflammatory markers in young subjects with the metabolic syndrome. Metabolism, 57 (11): 1539–1544. (2008). 494. Kim, Jin H, Kang, et al. Effects of rosiglitazone and metformin on inflammatory markers and adipokines: decrease in interleukin-18 is an independent factor for the improvement of homeostasis model assessment-beta in type 2 diabetes mellitus. Clin Endocrinol (Oxf), 66 (2): 282–289. (2007). 495. Nicoletti F, Di Marco R, Papaccio G, et al. Essential pathogenic role of endogenous IL-18 in murine diabetes induced by multiple low doses of streptozotocin. Prevention of hyperglycemia and insulitis by a recombinant IL-18-binding protein: Fc construct. Eur J Immunol, 33 (8): 2278–2286. (2003). 496. Smyth DJ, Plagnol V, Walker NM, et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. N Engl J Med, 359 (26): 2767–2777. (2008). 497. Wong E, Freiberg M, Tracy R, et al. Epidemiology of cytokines: the women on the move through activity and nutrition (WOMAN) study. Am J Epidemiol, 168 (4): 443–453. (2008). 498. Vilarrasa N, Vendrell J, Sánchez-Santos R, et al. Effect of weight loss induced by gastric bypass on proinflammatory interleukin-18, soluble tumour necrosis factor-α receptors, C-reactive protein and adiponectin in morbidly obese patients. Clin Endocrinol (Oxf), 67 (5): 679–686. (2007). 499. Botella-Carretero J, Alvarez-Blasco F, Martinez-García M, et al. The decrease in serum IL-18 levels after bariatric surgery in morbidly obese women is a time-dependent event. Obes Surg, 17 (9): 1199–1208. (2007).
- 249 -
500. Esposito K, Nappo F, Marfella R, et al. Inflammatory cytokine concentrations are acutely increased by hyperglycemia in humans: role of oxidative stress. Circulation, 106 (16): 2067– 2072. (2002). 501. Esposito K, Nappo F, Giugliano F, et al. Meal modulation of circulating interleukin 18 and adiponectin concentrations in healthy subjects and in patients with type 2 diabetes mellitus. Am J Clin Nutr, 78 (6): 1135–1140. (2003). 502. Yang Y-S, Li X-Y, Hong J, et al. Interleukin-18 enhances glucose uptake in 3T3-L1 adipocytes. Endocrine, 32 (3): 297–302. (2007). 503. Netea M, Joosten L, Lewis E, et al. Deficiency of interleukin-18 in mice leads to hyperphagia, obesity and insulin resistance. Nat Med, 12 (6): 650–656. (2006). 504. Myers MG, Heymsfield SB, Haft C, et al. Challenges and opportunities of defining clinical leptin resistance. Cell Metabolism, 15 (2): 150–156. (2012). 505. Zilverschoon G, Tack C, Joosten L, et al. Interleukin-18 resistance in patients with obesity and type 2 diabetes mellitus. Int J Obes (Lond), 32 (9): 1407–1414. (2008). 506. Zorrilla E, Sanchez-Alavez M, Sugama S, et al. Interleukin-18 controls energy homeostasis by suppressing appetite and feed efficiency. Proc Natl Acad Sci U S A, 104 (26): 11097–11102. (2007). 507. Jounai N, Kobiyama K & Takeshita F. Intracellular inflammatory sensors for foreign invaders and substances of self-origin. Adv Exp Med Biol, 738: 60–78. (2012). 508. Jacobs SR & Damania B. NLRs, inflammasomes, and viral infection. J Leukoc Biol, 92 (3): 469– 477. (2012). 509. Wack A, Openshaw P & O’Garra A. Contribution of cytokines to pathology and protection in virus infection. Curr Opin Virol, 1 (3): 184–195. (2011). 510. Rathinam VAK, Vanaja SK & Fitzgerald KA. Regulation of inflammasome signaling. Nat Immunol, 13 (4): 333–332. (2012). 511. Rose WE, Eickhoff JC, Shukla SK, et al. Elevated serum interleukin-10 at time of hospital admission is predictive of mortality in patients with Staphylococcus aureus bacteremia. J Infect Dis, 206 (10): 1604–1611. (2012). 512. Lee N, Wong CK, Chan PKS, et al. Cytokine response patterns in severe pandemic 2009 H1N1 and seasonal influenza among hospitalized adults. PLoS ONE, 6 (10): e26050. (2011). 513. Damjanovic D, Small C-L, Jeyananthan M, et al. Immunopathology in influenza virus infection: uncoupling the friend from foe. Clin Immunol, 144 (1): 57–69. (2012). 514. Poole L, Dickens C & Steptoe A. The puzzle of depression and acute coronary syndrome: Reviewing the role of acute inflammation. J Psychosom Res, 71 (2): 61–68. (2011). - 250 -
515. Heo JM, Park JH, Kim JH, et al. Comparison of inflammatory markers between diabetic and nondiabetic ST segment elevation myocardial infarction. J Cardiol, 60 (3): 204–209. (2012). 516. Liu C, Ying Z, Harkema J, et al. Epidemiological and experimental links between air pollution and type 2 diabetes. Toxicol Pathol, (2012). doi:10.1177/0192623312464531. 517. Van den Boogaard M, Ramakers BP, Van Alfen N, et al. Endotoxemia-induced inflammation and the effect on the human brain. Crit Care, 14 (3): R81. (2010). 518. Dandona P, Ghanim H, Bandyopadhyay A, et al. Insulin suppresses endotoxin-induced oxidative, nitrosative, and inflammatory stress in humans. Diabetes Care, 33 (11): 2416 –2423. (2010). 519. Pedersen BK & Febbraio MA. Muscle as an endocrine organ: focus on muscle-derived interleukin-6. Physiol Rev, 88 (4): 1379–1406. (2008). 520. Pedersen BK. Muscular interleukin-6 and its role as an energy sensor. Med Sci Sports Exerc, 44 (3): 392–396. (2012). 521. Fisman EZ & Tenenbaum A. The ubiquitous interleukin-6: a time for reappraisal. Cardiovasc Diabetol, 9: 62. (2010). 522. Mann JI, De Leeuw I, Hermansen K, et al. Evidence-based nutritional approaches to the treatment and prevention of diabetes mellitus. Nutr Metab Cardiovasc Dis, 14 (6): 373–394. (2004). 523. American Diabetes Association. Nutrition recommendations and interventions for diabetes: a position statement of the American Diabetes Association. Diabetes Care, 31 Suppl 1: S61–S78. (2008). 524. New Zealand Guidelines Group. Guidance on the management of type 2 diabetes 2011. (New Zealand Guidelines Group, Wellington N.Z., ISBN: 978-1-877509-49-02011). Available at: http://www.nzgg.org.nz/library_resources/16_diabetes_guidance_document, accessed September 19, 2011. 525. Edgar National Centre for Diabetes Research (ENCDR), University of Otago, NZ. Available at: http://www.otago.ac.nz/diabetes/, accessed September 19, 2011. 526. Hunninghake DB, Stein EA, Dujovne CA, et al. The efficacy of intensive dietary therapy alone or combined with lovastatin in outpatients with hypercholesterolemia. N Engl J Med, 328 (17): 1213–1219. (1993). 527. Bak AA, Huizer J, Leijten PA, et al. Diet and pravastatin in moderate hypercholesterolaemia: a randomized trial in 215 middle-aged men free from cardiovascular disease. J Intern Med, 244 (5): 371–378. (1998).
- 251 -
528. Chisholm A, Mann J, Sutherland W, et al. Dietary management of patients with familial hypercholesterolaemia treated with simvastatin. Q J Med, 85 (307-308): 825–831. (1992). 529. Coppell KJ, Anderson K, Williams S, et al. Evaluation of diabetes care in the Otago region using a diabetes register, 1998-2003. Diabetes Res Clin Pract, 71 (3): 345–352. (2006). 530. Huber C, Batchelor JR, Fuchs D, et al. Immune response-associated production of neopterin. Release from macrophages primarily under control of interferon-gamma. J Exp Med, 160 (1): 310–316. (1984). 531. Grammer TBB, Fuchs D, Boehm BO, et al. Neopterin as a predictor of total and cardiovascular mortality in individuals undergoing angiography in the Ludwigshafen risk and cardiovascular health study. Clin Chem, 55 (6): 1135+. (2009). 532. Avanzas P, Arroyo-Espliguero R, Cosin-Sales J, et al. Prognostic value of neopterin levels in treated patients with hypertension and chest pain but without obstructive coronary artery disease. Am J Cardiol, 93 (5): 627–629. (2004). 533. Avanzas P, Arroyo-Espliguero R, Quiles J, et al. Elevated serum neopterin predicts future adverse cardiac events in patients with chronic stable angina pectoris. Eur Heart J, 26 (5): 457– 463. (2005). 534. Ray KK, Morrow DA, Sabatine MS, et al. Long-term prognostic value of neopterin: a novel marker of monocyte activation in patients with acute coronary syndrome. Circulation, 115 (24): 3071–3078. (2007). 535. Sasaki T, Takeishi Y, Suzuki S, et al. High serum level of neopterin is a risk factor of patients with heart failure. Int J Cardiol, 145 (2): 318. (2010). 536. Vengen IT, Dale AC, Wiseth R, et al. Neopterin predicts the risk for fatal ischemic heart disease in type 2 diabetes mellitus. Long-term follow-up of the HUNT 1 study. Atherosclerosis, 207 (1): 239–244. (2009). 537. Spencer ME, Jain A, Matteini A, et al. Serum levels of the immune activation marker neopterin change with age and gender and are modified by race, BMI, and percentage of body fat. J Gerontol A Biol Sci Med Sci, 65 (8): 858–865. (2010). 538. Lucas RM, Ponsonby A-L & Dear K. Mid-life stress is associated with both up- and down- regulation of markers of humoral and cellular immunity. Stress, 10 (4): 351–361. (2007). 539. Maes M. Depression is an inflammatory disease, but cell-mediated immune activation is the key component of depression. Prog Neuropsychopharmacol Biol Psychiatry, 35 (3): 664–675. (2011). 540. Sugioka K, Naruko T, Hozumi T, et al. Elevated levels of neopterin are associated with carotid plaques with complex morphology in patients with stable angina pectoris. Atherosclerosis, 208 (2): 524–530. (2010). - 252 -
541. Alber HF, Duftner C, Wanitschek M, et al. Neopterin, CD4+CD28- lymphocytes and the extent and severity of coronary artery disease. Int J Cardiol, 135 (1): 27–35. (2009). 542. Arora S, Gunther A, Wennerblom B, et al. Systemic markers of inflammation are associated with cardiac allograft vasculopathy and an increased intimal inflammatory component. Am J Transplant, 10 (6): 1428–1436. (2010). 543. Dominguez-Rodriguez A, Abreu-Gonzalez P, Samimi-Fard S, et al. Neopterin predicts left ventricular remodeling in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. J Am Coll Cardiol, 55 (10): A123.E1155. (2010). 544. Dominguez-Rodriguez A, Abreu-Gonzalez P, Avanzas P, et al. Neopterin predicts left ventricular remodeling in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Atherosclerosis, 211 (2): 574–578. (2010). 545. Estévez-Loureiro R, Recio-Mayoral A, Sieira-Rodríguez-Moret JA, et al. Neopterin levels and left ventricular dysfunction in patients with chronic stable angina pectoris. Atherosclerosis, 207 (2): 514–518. (2009). 546. Cirillo P, Pacileo M, Rosa SD, et al. Neopterin induces pro-atherothrombotic phenotype in human coronary endothelial cells. J Thromb Haemost, 4 (10): 2248–2255. (2006). 547. Soos J. Identification of natural antibodies to interleukin-18 in the sera of normal humans and three nonhuman primate species. Clin Immunol, 109 (2): 188–196. (2003). 548. Herzyk DJ, Soos JM, Maier CC, et al. Immunopharmacology of recombinant human interleukin-18 in non-human primates. Cytokine, 20 (1): 38–48. (2002). 549. Andersson N, Strandberg L, Nilsson S, et al. Variants of the interleukin-1 receptor antagonist gene are associated with fat mass in men. Int J Obes (Lond), 33 (5): 525–533. (2009). 550. Carter K, Hung J, Powell B, et al. Association of interleukin-1 gene polymorphisms with central obesity and metabolic syndrome in a coronary heart disease population. Hum Genet, 124 (3): 199–206. (2008). 551. De Roos B, Rungapamestry V, Ross K, et al. Attenuation of inflammation and cellular stress- related pathways maintains insulin sensitivity in obese type I interleukin-1 receptor knockout mice on a high-fat diet. Proteomics, 9 (12): 3244–3256. (2009). 552. Chamberlain J, Francis S, Brookes Z, et al. Interleukin-1 regulates multiple atherogenic mechanisms in response to fat feeding. PLoS ONE, 4 (4): e5073. (2009). 553. Owyang AM, Maedler K, Gross L, et al. XOMA 052, an anti-IL-1beta monoclonal antibody, improves glucose control and beta-cell function in the diet-induced obesity mouse model. Endocrinology, 151 (6): 2515–2527. (2010).
- 253 -
554. Osborn O, Brownell SE, Sanchez-Alavez M, et al. Treatment with an interleukin 1 beta antibody improves glycemic control in diet-induced obesity. Cytokine, 44 (1): 141–148. (2008). 555. Stienstra R, Van Diepen JA, Tack CJ, et al. Inflammasome is a central player in the induction of obesity and insulin resistance. Proc Natl Acad Sci U S A, 108 (37): 15324–15329. (2011). 556. Ehses JA, Lacraz G, Giroix MH, et al. IL-1 antagonism reduces hyperglycemia and tissue inflammation in the type 2 diabetic GK rat. Proc Natl Acad Sci U S A, 106 (33): 13998–14003. (2009). 557. Luotola K, Pietilä A, Zeller T, et al. Associations between interleukin-1 (IL-1) gene variations or IL-1 receptor antagonist levels and the development of type 2 diabetes. J Intern Med, 269 (3): 322–332. (2011). 558. Hoene, M, Weigert, et al. The role of interleukin-6 in insulin resistance, body fat distribution and energy balance. Obes Rev, 9 (1): 20–29. (2008). 559. Heinrich PC, Castell JV & Andus T. Interleukin-6 and the acute phase response. Biochem J, 265 (3): 621–636. (1990). 560. Pedersen BK, Steensberg A, Fischer C, et al. The metabolic role of IL-6 produced during exercise: is IL-6 an exercise factor? Proc Nutr Soc, 63 (2): 263–267. (2004). 561. Steensberg A, Keller C, Starkie RL, et al. IL-6 and TNF-alpha expression in, and release from, contracting human skeletal muscle. Am J Physiol Endocrinol Metab, 283 (6): E1272–8. (2002). 562. Marfella R, Esposito K, Siniscalchi M, et al. Effect of weight loss on cardiac synchronization and proinflammatory cytokines in premenopausal obese women. Diabetes Care, 27 (1): 47–52. (2004). 563. Mohamed-Ali V, Goodrick S, Rawesh A, et al. Subcutaneous adipose tissue releases interleukin- 6, but not tumor necrosis factor-alpha, in vivo. J Clin Endocrinol Metab, 82 (12): 4196–4200. (1997). 564. Kim H-JJ, Higashimori T, Park S-YY, et al. Differential effects of interleukin-6 and -10 on skeletal muscle and liver insulin action in vivo. Diabetes, 53 (4): 1060–1067. (2004). 565. O’Garra A & Murphy KM. From IL-10 to IL-12: how pathogens and their products stimulate APCs to induce Th1 development. Nat Immunol, 10 (9): 929–932. (2009). 566. Blüher M, Fasshauer M, Tönjes A, et al. Association of interleukin-6, C-reactive protein, interleukin-10 and adiponectin plasma concentrations with measures of obesity, insulin sensitivity and glucose metabolism. Exp Clin Endocrinol Diabetes, 113 (9): 534–537. (2005). 567. Jung SHH, Park HSS, Kim K-SS, et al. Effect of weight loss on some serum cytokines in human obesity: increase in IL-10 after weight loss. J Nutr Biochem, 19 (6): 371–375. (2008).
- 254 -
568. Van Exel E, Gussekloo J, De Craen AJ, et al. Low production capacity of interleukin-10 associates with the metabolic syndrome and type 2 diabetes : the Leiden 85-plus study. Diabetes, 51 (4): 1088–1092. (2002). 569. Scarpelli D, Cardellini M, Andreozzi F, et al. Variants of the interleukin-10 promoter gene are associated with obesity and insulin resistance but not type 2 diabetes in Caucasian Italian subjects. Diabetes, 55 (5): 1529–1533. (2006). 570. Chang Y-HH, Huang C-NN, Wu C-YY, et al. Association of interleukin-10 A-592C and T-819C polymorphisms with type 2 diabetes mellitus. Hum Immunol, 66 (12): 1258–1263. (2005). 571. Hong E-GG, Ko HJJ, Cho Y-RR, et al. Interleukin-10 prevents diet-induced insulin resistance by attenuating macrophage and cytokine response in skeletal muscle. Diabetes, 58 (11): 2525– 2535. (2009). 572. Cintra DE, Pauli JR, Araújo EP, et al. Interleukin-10 is a protective factor against diet-induced insulin resistance in liver. J Hepatol, 48 (4): 628–637. (2008). 573. Li H & Lin X. Positive and negative signaling components involved in TNFalpha-induced NF- kappaB activation. Cytokine, 41 (1): 1–8. (2008). 574. Ishizuka K, Usui I, Kanatani Y, et al. Chronic tumor necrosis factor-alpha treatment causes insulin resistance via insulin receptor substrate-1 serine phosphorylation and suppressor of cytokine signaling-3 induction in 3T3-L1 adipocytes. Endocrinology, 148 (6): 2994–3003. (2007). 575. Aderka D, Engelmann H, Maor Y, et al. Stabilization of the bioactivity of tumor necrosis factor by its soluble receptors. J Exp Med, 175 (2): 323–329. (1992). 576. Diez-Ruiz A, Tilz GP, Zangerle R, et al. Soluble receptors for tumour necrosis factor in clinical laboratory diagnosis. Eur J Haematol, 54 (1): 1–8. (1995). 577. Gonzalez-Gay MA, De Matias JM, Gonzalez-Juanatey C, et al. Anti-tumor necrosis factor-alpha blockade improves insulin resistance in patients with rheumatoid arthritis. Clin Exp Rheumatol, 24 (1): 83–86. (2006). 578. Huvers FC, Popa C, Netea MG, et al. Improved insulin sensitivity by anti-TNFalpha antibody treatment in patients with rheumatic diseases. Ann Rheum Dis, 66 (4): 558–559. (2007). 579. Kiortsis DN, Mavridis AK, Vasakos S, et al. Effects of infliximab treatment on insulin resistance in patients with rheumatoid arthritis and ankylosing spondylitis. Ann Rheum Dis, 64 (5): 765– 766. (2005). 580. Oguz FM, Oguz A & Uzunlulu M. The effect of infliximab treatment on insulin resistance in patients with rheumatoid arthritis. Acta Clin Belg, 62 (4): 218–222. (2007).
- 255 -
581. Tam, Lai-Shan, Tomlinson, et al. Impact of TNF inhibition on insulin resistance and lipids levels in patients with rheumatoid arthritis. Clin Rheumatol, 26 (9): 1495–1498. (2007). 582. Yazdani-Biuki B, Stelzl H, Brezinschek HP, et al. Improvement of insulin sensitivity in insulin resistant subjects during prolonged treatment with the anti-TNF-alpha antibody infliximab. Eur J Clin Invest, 34 (9): 641–642. (2004). 583. Ursini F, Succurro E, Grembiale A, et al. Sudden progression from impaired glucose tolerance to type 2 diabetes after discontinuation of administration of anti-tumor necrosis factor-alpha antibody infliximab. Int J Immunopathol Pharmacol, 23 (3): 961–963. (2010). 584. Ashall L, Horton CA, Nelson DE, et al. Pulsatile stimulation determines timing and specificity of NF-kappaB-dependent transcription. Science, 324 (5924): 242–246. (2009). 585. Shai I, Schulze MB, Manson JE, et al. A prospective study of soluble tumor necrosis factor-alpha receptor II (sTNF-RII) and risk of coronary heart disease among women with type 2 diabetes. Diabetes Care, 28 (6): 1376–1382. (2005). 586. Schulz R & Heusch G. Tumor necrosis factor-alpha and its receptors 1 and 2: yin and yang in myocardial infarction? Circulation, 119 (10): 1355–1357. (2009). 587. Hamid T, Gu Y, Ortines RV, et al. Divergent tumor necrosis factor receptor-related remodeling responses in heart failure: role of nuclear factor-kappaB and inflammatory activation. Circulation, 119 (10): 1386–1397. (2009). 588. Dunlay S, Weston S, Redfield M, et al. Tumor necrosis factor-α and mortality in heart failure. A community study. Circulation, 118 (6): 625–631. (2008). 589. Jiang G, Dallas-Yang Q, Liu F, et al. Salicylic acid reverses phorbol 12-myristate-13-acetate (PMA)- and tumor necrosis factor alpha (TNFalpha)-induced insulin receptor substrate 1 (IRS1) serine 307 phosphorylation and insulin resistance in human embryonic kidney 293 (HEK293) cells. J Biol Chem, 278 (1): 180–186. (2003). 590. Diamond GA, Bax L & Kaul S. Uncertain effects of rosiglitazone on the risk for myocardial infarction and cardiovascular death. Ann Intern Med, 147 (8): 578–581. (2007). 591. Enriori PJ, Evans AE, Sinnayah P, et al. Diet-induced obesity causes severe but reversible leptin resistance in arcuate melanocortin neurons. Cell Metab, 5 (3): 181–194. (2007). 592. Somoza B, Guzmán R, Cano V, et al. Induction of cardiac uncoupling protein-2 expression and adenosine 5’-monophosphate-activated protein kinase phosphorylation during early states of diet-induced obesity in mice. Endocrinology, 148 (3): 924–931. (2007). 593. Lin C-Y, Higginbotham DA, Judd RL, et al. Central leptin increases insulin sensitivity in streptozotocin-induced diabetic rats. Am J Physiol Endocrinol Metab, 282 (5): E1084–1091. (2002). - 256 -
594. Park S, Hong SM, Sung SR, et al. Long-term effects of central leptin and resistin on body weight, insulin resistance, and beta-cell function and mass by the modulation of hypothalamic leptin and insulin signaling. Endocrinology, 149 (2): 445–454. (2008). 595. Pocai A, Morgan K, Buettner C, et al. Central leptin acutely reverses diet-induced hepatic insulin resistance. Diabetes, 54 (11): 3182–3189. (2005). 596. Yu X, Park B-H, Wang M-Y, et al. Making insulin-deficient type 1 diabetic rodents thrive without insulin. Proc Natl Acad Sci U S A, 105 (37): 14070–14075. (2008). 597. Wang M-YY, Chen L, Clark GO, et al. Leptin therapy in insulin-deficient type I diabetes. Proc Natl Acad Sci U S A, 107 (11): 4813–4819. (2010). 598. Yang R & Barouch LA. Leptin signaling and obesity: cardiovascular consequences. Circ Res, 101 (6): 545–559. (2007). 599. Shan J, Nguyen TB, Totary-Jain H, et al. Leptin-enhanced neointimal hyperplasia is reduced by mTOR and PI3K inhibitors. Proc Natl Acad Sci U S A, 105 (48): 19006–19011. (2008). 600. Sattar N, Wannamethee G, Sarwar N, et al. Leptin and coronary heart disease prospective study and systematic review. J Am Coll Cardiol, 53 (2): 167–175. (2009). 601. Coppell KJ, Kataoka M, Williams SM, et al. Nutritional intervention in patients with type 2 diabetes who are hyperglycaemic despite optimised drug treatment - Lifestyle Over and Above Drugs in Diabetes (LOADD) study: randomised controlled trial. BMJ, 341 (7766): c3337. (2010). 602. Blankenberg S, Zeller T, Saarela O, et al. Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: The MONICA, Risk, Genetics, Archiving, and Monograph (MORGAM) biomarker project. Circulation, 121 (22): 2388–2397. (2010). 603. Salomaa V, Havulinna A, Saarela O, et al. Thirty-one novel biomarkers as predictors for clinically incident diabetes. PLoS ONE, 5 (4): e10100+. (2010). 604. Araki, Haneda, Koya, et al. Predictive impact of elevated serum level of IL-18 for early renal dysfunction in type 2 diabetes: an observational follow-up study. Diabetologia, 50 (4): 867–873. (2007). 605. Kim S, Eisenstein M, Reznikov L, et al. Structural requirements of six naturally occurring isoforms of the IL-18 binding protein to inhibit IL-18. Proc Natl Acad Sci U S A, 97 (3): 1190– 1195. (2000). 606. Ludwiczek O, Kaser A, Novick D, et al. Elevated systemic levels of free interleukin-18 (IL-18) in patients with Crohn’s disease. Eur Cytokine Netw, 16 (1): 27–33. (2005). 607. Novick D, Schwartsburd B, Pinkus R, et al. A novel IL-18BP ELISA shows elevated serum IL- 18BP in sepsis and extensive decrease of free IL-18. Cytokine, 14 (6): 334–342. (2001). - 257 -
608. Dinarello C. Targeting interleukin 18 with interleukin 18 binding protein. Ann Rheum Dis, 59 (Suppl 1): i17–i20. (2000). 609. Paulukat J, Bosmann M, Nold M, et al. Expression and release of IL-18 binding protein in response to IFN-gamma. J Immunol, 167 (12): 7038–7043. (2001). 610. Majak P, Bjørnstad J, Vengen OA, et al. Plasma IL-18 and IL-18BP are altered differently in reverse remodeling following aortic valve replacement. Scand Cardiovasc J, 44 (2): 113–118. (2009). 611. Reznikov LL, Kim SH, Westcott JY, et al. IL-18 binding protein increases spontaneous and IL-1- induced prostaglandin production via inhibition of IFN-gamma. Proc Natl Acad Sci U S A, 97 (5): 2174–2179. (2000). 612. Fantuzzi G, Banda N, Guthridge C, et al. Generation and characterization of mice transgenic for human IL-18-binding protein isoform a. J Leukoc Biol, 74 (5): 889–896. (2003). 613. He Z, Lu L, Altmann C, et al. Interleukin-18 (IL-18) binding protein transgenic mice are protected against ischemic AKI. Am J Physiol Renal Physiol, 295 (5): F1414–F1421. (2008). 614. Curran-Everett D. Multiple comparisons: philosophies and illustrations. Am J Physiol Regul Integr Comp Physiol, 279 (1): R1–R8. (2000). 615. Perneger TV. What’s wrong with Bonferroni adjustments. BMJ, 316 (7139): 1236–1238. (1998). 616. Giugliano G, Nicoletti G, Grella E, et al. Effect of liposuction on insulin resistance and vascular inflammatory markers in obese women. Br J Plast Surg, 57 (3): 190–194. (2004). 617. Vilarrasa N, Gómez JM, Masdevall C, et al. Estudio de la relación entre las concentraciones plasmáticas de adiponectina, interleucina 18 y ghrelina y la densidad mineral ósea en mujeres con obesidad mórbida tras bypass gástrico. Endocrinol Nutr, 56 (7): 355–360. (2009). 618. Schernthaner G-H, Kopp H-P, Kriwanek S, et al. Effect of massive weight loss induced by bariatric surgery on serum levels of interleukin-18 and monocyte-chemoattractant-protein-1 in morbid obesity. Obes Surg, 16 (6): 709–715. (2006). 619. Yamaoka-Tojo M, Tojo T, Wakaume K, et al. Circulating interleukin-18: a specific biomarker for atherosclerosis-prone patients with metabolic syndrome. Nutr Metab, 8 (1): 3. (2011). 620. Christiansen T, Paulsen SK, Bruun JM, et al. Exercise training versus diet-induced weight-loss on metabolic risk factors and inflammatory markers in obese subjects: a 12-week randomized intervention study. Am J Physiol Endocrinol Metab, 298 (4): E824–831. (2010). 621. Siegmund B. Interleukin-18 in intestinal inflammation: friend and foe? Immunity, 32 (3): 300– 302. (2010). 622. Zaki MH, Lamkanfi M & Kanneganti T-D. The Nlrp3 inflammasome: contributions to intestinal homeostasis. Trends Immunol, 32 (4): 171–179. (2011). - 258 -
623. Henao-Mejia J, Elinav E, Jin C, et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature, 482 (7384): 179–185. (2012). 624. Harris K, Kassis A, Major G, et al. Is the gut microbiota a new factor contributing to obesity and its metabolic disorders? J Obes, 2012: Article ID 879151. (2012). 625. Maslanik T, Tannura K, Mahaffey L, et al. Commensal bacteria and MAMPs are necessary for stress-induced increases in IL-1β and IL-18 but not IL-6, IL-10 or MCP-1. PLoS ONE, 7 (12): e50636. (2012). 626. Sugama S, Wang N, Shimokawa N, et al. The adrenal gland is a source of stress-induced circulating IL-18. J Neuroimmunol, 172 (1-2): 59–65. (2006). 627. Matsunaga H, Hokari R, Ueda T, et al. Physiological stress exacerbates murine colitis by enhancing proinflammatory cytokine expression that is dependent on IL-18. Am J Physiol Gastrointest Liver Physiol, 301 (3): G555–64. (2011). 628. Sekiyama A, Ueda H, Kashiwamura S, et al. A stress-induced, superoxide-mediated caspase-1 activation pathway causes plasma IL-18 upregulation. Immunity, 22 (6): 669–677. (2005). 629. Sekiyama A, Ueda H, Kashiwamura S, et al. IL-18; a cytokine translates a stress into medical science. J Med Invest, 52 Suppl: 236–239. (2005). 630. Muneta Y, Minagawa Y, Nakane T, et al. Interleukin-18 expression in pig salivary glands and salivary content changes during acute immobilization stress. Stress, 14 (5): 549–556. (2011). 631. Sekiyama A, Ueda H, Kashiwamura S, et al. A role of the adrenal gland in stress-induced up- regulation of cytokines in plasma. J Neuroimmunol, 171 (1-2): 38–44. (2006). 632. Seino H, Ueda H, Kokai M, et al. IL-18 mediates the formation of stress-induced, histamine- dependent gastric lesions. Am J Physiol Gastrointest Liver Physiol, 292 (1): G262–267. (2007). 633. Reseland JE, Mundal HH, Hollung K, et al. Cigarette smoking may reduce plasma leptin concentration via catecholamines. Prostaglandins Leukot Essent Fatty Acids, 73 (1): 43–49. (2005). 634. Ruhl CE & Everhart JE. Leptin concentrations in the United States: Relations with demographic and anthropometric measures. Am J Clin Nutr, 74 (3): 295–301. (2001). 635. Saad MF, Damani S, Gingerich RL, et al. Sexual dimorphism in plasma leptin concentration. J Clin Endocrinol Metab, 82 (2): 579–584. (1997). 636. Rosenbaum M, Nicolson M, Hirsch J, et al. Effects of gender, body composition, and menopause on plasma concentrations of leptin. J Clin Endocrinol Metab, 81 (9): 3424–3427. (1996). 637. Sun L, Hu FB, Yu Z, et al. Lean body mass, interleukin 18, and metabolic syndrome in apparently healthy Chinese. PLoS ONE, 6 (3): e18104. (2011).
- 259 -
638. Vilarrasa N, Vendrell J, Maravall J, et al. IL-18: relationship with anthropometry, body composition parameters, leptin and arterial hypertension. Horm Metab Res, 38 (8): 507–512. (2006). 639. Wood IS, Wang B, Jenkins JR, et al. The pro-inflammatory cytokine IL-18 is expressed in human adipose tissue and strongly upregulated by TNF[alpha] in human adipocytes. Biochem Biophys Res Commun, 337 (2): 422–429. (2005). 640. Membrez M, Ammon-Zufferey C, Philippe D, et al. Interleukin-18 protein level is upregulated in adipose tissue of obese mice. Obesity (Silver Spring), 17: 393+. (2009). 641. Ledochowski M, Murr C, Widner B, et al. Association between insulin resistance, body mass and neopterin concentrations. Clinica Chimica Acta, 282 (1-2): 115–123. (1999). 642. Bertz L, Barani J, Gottsäter A, et al. Are there differences of inflammatory bio-markers between diabetic and non-diabetic patients with critical limb ischemia? Int Angiol, 25 (4): 370–377. (2006). 643. Weitgasser R, Lechleitner M, Koch T, et al. Antibodies to heat-shock protein 65 and neopterin levels in patients with type 1 diabetes mellitus. Exp Clin Endocrinol Diabetes, 111 (3): 127–131. (2003). 644. Asci A, Baydar T, Cetinkaya R, et al. Evaluation of neopterin levels in patients undergoing hemodialysis. Hemodial Int, 14 (2): 240–246. (2010). 645. Bariş N, Erdoğan M, Sezer E, et al. Alterations in L-arginine and inflammatory markers in type 2 diabetic patients with and without microalbuminuria. Acta Diabetol, 46 (4): 309–316. (2009). 646. Avanzas P, Arroyo-Espliguero R & Kaski JCC. Papel de la neopterina en la medicina cardiovascular. Rev Esp Cardiol, 62 (11): 1341–1342. (2009). 647. Fuchs D, Avanzas P, Arroyo-Espliguero R, et al. The role of neopterin in atherogenesis and cardiovascular risk assessment. Curr Med Chem, 16 (35): 4644–4653. (2009). 648. Avanzas P, Arroyo-Espliguero R & Juan. Neopterin and cardiovascular disease: growing evidence for a role in patient risk stratification. Clin Chem, 55 (6): 1056+. (2009). 649. Alber HF, Wanitschek M, Duftner C, et al. Neopterin: marker of coronary artery disease activity, severity and/or extent in patients with clinically stable angina? Int J Cardiol, 144 (1): 75–76. (2010). 650. De Rosa S, Cirillo P, Pacileo M, et al. Neopterin: from forgotten biomarker to leading actor in cardiovascular pathophysiology. Curr Vasc Pharmacol, 9 (2): 188–199. (2011). 651. Nazer B, Ray KK, Sloan S, et al. Prognostic utility of neopterin and risk of heart failure hospitalization after an acute coronary syndrome. Eur Heart J, 32 (11): 1390–1397. (2011).
- 260 -
652. Pacileo M, Cirillo P, De Rosa S, et al. The role of neopterin in cardiovascular disease. Monaldi Arch Chest Dis, 68 (2): 68–73. (2007). 653. Sugioka K, Naruko T, Matsumura Y, et al. Neopterin and atherosclerotic plaque instability in coronary and carotid arteries. J Atheroscler Thromb, 17 (11): 1115–1121. (2010). 654. Varol E, Gülcan M, Aylak F, et al. Increased neopterin levels and its association with angiographic variables in patients with slow coronary flow: an observational study. Anadolu Kardiyol Derg, 11 (8): 692–697. (2011). 655. Avanzas P, Arroyoespliguero R & Kaski J. Neopterin: marker of coronary artery disease activity or extension in patients with chronic stable angina? Int J Cardiol, 144 (1): 74–75. (2010). 656. Mizutani K, Naruko T, Komatsu R, et al. Enhanced expression of neopterin in accumulated macrophages at aortic valve stenosis in patients with hemodialysis. J Am Coll Cardiol, 57 (14, Suppl 1): E1303. (2011). 657. Pedersen ER, Midttun O, Ueland PM, et al. Systemic Markers of Interferon-{gamma}-Mediated Immune Activation and Long-Term Prognosis in Patients With Stable Coronary Artery Disease. Arterioscler Thromb Vasc Biol, 31 (3): 698–704. (2011). 658. Schnabel RB, Schulz A, Messow CM, et al. Multiple marker approach to risk stratification in patients with stable coronary artery disease. Eur Heart J, 31 (24): 3024–3031. (2010). 659. Wolsk E, Mygind H, Grøndahl TS, et al. The role of leptin in human lipid and glucose metabolism: the effects of acute recombinant human leptin infusion in young healthy males. Am J Clin Nutr, 94 (6): 1533–1544. (2011). 660. Muoio DM, Dohm GL, Fiedorek FT, et al. Leptin directly alters lipid partitioning in skeletal muscle. Diabetes, 46 (8): 1360–1363. (1997). 661. Frühbeck G, Aguado M, Gómez-Ambrosi J, et al. Lipolytic effect of in vivo leptin administration on adipocytes of lean and ob/ob mice, but not db/db mice. Biochem Biophys Res Commun, 250 (1): 99–102. (1998). 662. Diéguez C, Vazquez MJ, Romero A, et al. Hypothalamic control of lipid metabolism: focus on leptin, ghrelin and melanocortins. Neuroendocrinology, 94 (1): 1–11. (2011). 663. Levi J, Gray SL, Speck M, et al. Acute disruption of leptin signaling in vivo leads to increased insulin levels and insulin resistance. Endocrinology, 152 (9): 3385–3395. (2011). 664. Paz‐Filho G, Wong M & Licinio J. Ten years of leptin replacement therapy. Obes Rev, 12 (5): e315–e323. (2011). 665. Licinio J, Caglayan S, Ozata M, et al. Phenotypic effects of leptin replacement on morbid obesity, diabetes mellitus, hypogonadism, and behavior in leptin-deficient adults. Proc Natl Acad Sci U S A, 101 (13): 4531–4536. (2004). - 261 -
666. Mantzoros CS, Magkos F, Brinkoetter M, et al. Leptin in human physiology and pathophysiology. Am J Physiol Endocrinol Metab, 301 (4): E567–E584. (2011). 667. Magkos F, Brennan A, Sweeney L, et al. Leptin replacement improves postprandial glycemia and insulin sensitivity in human immunodeficiency virus-infected lipoatrophic men treated with pioglitazone: a pilot study. Metab Clin Exp, 60 (7): 1045–1049. (2011). 668. Moon H-S, Matarese G, Brennan AM, et al. Efficacy of metreleptin in obese patients with type 2 diabetes: cellular and molecular pathways underlying leptin tolerance. Diabetes, 60 (6): 1647– 1656. (2011). 669. Dossus L, Becker S, Achaintre D, et al. Validity of multiplex-based assays for cytokine measurements in serum and plasma from “non-diseased” subjects: comparison with ELISA. J Immunol Methods, 350 (1-2): 125–132. (2009). 670. Breen EC, Reynolds SM, Cox C, et al. Multisite comparison of high-sensitivity multiplex cytokine assays. Clin Vaccine Immunol, 18 (8): 1229–1242. (2011). 671. Loo B-M, Marniemi J & Jula A. Evaluation of multiplex immunoassays, used for determination of adiponectin, resistin, leptin, and ghrelin from human blood samples, in comparison to ELISA assays. Scand J Clin Lab Invest, 71 (3): 221–226. (2011). 672. Martos-Moreno GA, Burgos-Ramos E, Canelles S, et al. Evaluation of a multiplex assay for adipokine concentrations in obese children. Clin Chem Lab Med, 48 (10): 1439–1446. (2010). 673. Chowdhury F, Williams A & Johnson P. Validation and comparison of two multiplex technologies, Luminex® and Mesoscale Discovery, for human cytokine profiling. J Immunol Methods, 340 (1): 55–64. (2009). 674. Wong H-L, Pfeiffer RM, Fears TR, et al. Reproducibility and correlations of multiplex cytokine levels in asymptomatic persons. Cancer Epidemiol Biomarkers Prev, 17 (12): 3450–3456. (2008). 675. Elshal MF & McCoy JP. Multiplex bead array assays: Performance evaluation and comparison of sensitivity to ELISA. Methods, 38 (4): 317–323. (2006). 676. Liu MY, Xydakis AM, Hoogeveen RC, et al. Multiplexed analysis of biomarkers related to obesity and the metabolic syndrome in human plasma, using the Luminex-100 system. Clin Chem, 51 (7): 1102–1109. (2005). 677. Kellar KL, Kalwar RR, Dubois KA, et al. Multiplexed fluorescent bead‐based immunoassays for quantitation of human cytokines in serum and culture supernatants. Cytometry, 45 (1): 27–36. (2001). 678. Djoba Siawaya JF, Roberts T, Babb C, et al. An evaluation of commercial fluorescent bead-based Luminex cytokine assays. PLoS ONE, 3 (7): e2535. (2008).
- 262 -
679. Thompson DK, Huffman KM, Kraus WE, et al. Critical appraisal of four IL-6 immunoassays. PLoS ONE, 7 (2): e30659. (2012). 680. Khan SS, Smith MS, Reda D, et al. Multiplex bead array assays for detection of soluble cytokines: comparisons of sensitivity and quantitative values among kits from multiple manufacturers. Cytometry B Clin Cytom, 61B (1): 35–39. (2004). 681. Lee NK, Sowa H, Hinoi E, et al. Endocrine regulation of energy metabolism by the skeleton. Cell, 130 (3): 456–469. (2007). 682. Antuna-Puente B, Feve B, Fellahi S, et al. Adipokines: The missing link between insulin resistance and obesity. Diabetes Metab, 34 (1): 2–11. (2008). 683. Suhre K, Meisinger C, Döring A, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE, 5 (11): e13953. (2010). 684. Lucio M, Fekete A, Weigert C, et al. Insulin sensitivity is reflected by characteristic metabolic fingerprints - a fourier transform mass spectrometric non-targeted metabolomics approach. PLoS ONE, 5 (10): e13317. (2010). 685. Cao H, Gerhold K, Mayers JR, et al. Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism. Cell, 134 (6): 933–944. (2008). 686. Birlouez-Aragon I, Saavedra G, Tessier FJ, et al. A diet based on high-heat-treated foods promotes risk factors for diabetes mellitus and cardiovascular diseases. Am J Clin Nutr, 91 (5): 1220–1226. (2010). 687. Patel CJ, Bhattacharya J & Butte AJ. An environment-wide association study (EWAS) on type 2 diabetes mellitus. PLoS ONE, 5 (5): e10746. (2010). 688. Lang IA, Galloway TS, Scarlett A, et al. Association of urinary bisphenol A concentration with medical disorders and laboratory abnormalities in adults. JAMA, 300 (11): 1303–1310. (2008). 689. Vom Saal FS & Myers JP. Bisphenol A and risk of metabolic disorders. JAMA, 300 (11): 1353– 1355. (2008). 690. Magendie M. Memoires et comunications des membres et des correspondants de l’academie. C R Hebd Seances Acad Sci, t28 (13): 393–394. (1849). 691. Sherwin R. Bringing light to the dark side of insulin: a journey across the blood-brain barrier. Diabetes, 57 (9): 2259–2268. (2008). 692. Ranson SW. Some functions of the hypothalamus. Bull N Y Acad Med, 13 (5): 241–271. (1937). 693. Brooks CM. The history of thought concerning the hypothalamus and its functions. Brain Res Bull, 20 (6): 657–667. (1988). 694. Davis L. The relation of the hypophysis, hypothalamus, and the autonomic nervous system to carbohydrate metabolism. Ann Surg, 100 (4): 654–666. (1934). - 263 -
695. Maickel RP & Zabik JE. The pharmacology of anorexigenesis. Life Sci, 21 (2): 173–179. (1977). 696. Mayer J & Thomas DW. Regulation of food intake and obesity. Science, 156 (773): 328–337. (1967). 697. Houssay BA & Biasotti A. The hypophysis, carbohydrate metabolism and diabetes. Endocrinology, 15 (6): 511–523. (1931). 698. Hummel KP, Dickie MM & Coleman DL. Diabetes, a new mutation in the mouse. Science, 153 (3740): 1127–1128. (1966). 699. Ingalls AM, Dickie MM & Snell GD. Obese, a new mutation in the house mouse. J Hered, 41 (12): 317–318. (1950). 700. Flier JS & Maratos-Flier E. Lasker lauds leptin. Cell, 143 (1): 9–12. (2010). 701. Coleman DL. A historical perspective on leptin. Nat Med, 16 (10): 1097–1099. (2010). 702. Neill US. Leaping for leptin: the 2010 Albert Lasker Basic Medical Research Award goes to Douglas Coleman and Jeffrey M. Friedman. J Clin Invest, 120 (10): 3413–3418. (2010). 703. Nishizawa Y & Bray GA. Evidence for a circulating ergostatic factor: studies on parabiotic rats. Am J Physiol, 239 (3): R344–351. (1980). 704. Fleming DG. V. Humoral and metabolic factors in the regulation of food and water intake. Food intake studies in parabiotic rats. Ann N Y Acad Sci, 157 (2): 985–1003. (1969). 705. Han PW, Mu JY & Lepkovsky S. Food intake of parabiotic rats. Am J Physiol, 205 (6): 1139– 1143. (1963). 706. Harris RB & Martin RJ. Specific depletion of body fat in parabiotic partners of tube-fed obese rats. Am J Physiol, 247 (2 Pt 2): R380–386. (1984). 707. Parameswaran SV, Steffens AB, Hervey GR, et al. Involvement of a humoral factor in regulation of body weight in parabiotic rats. Am J Physiol, 232 (5): R150–157. (1977). 708. Coleman DL & Hummel KP. Effects of parabiosis of normal with genetically diabetic mice. Am J Physiol, 217 (5): 1298–1304. (1969). 709. Coleman DL. Effects of parabiosis of obese with diabetes and normal mice. Diabetologia, 9 (4): 294–298. (1973). 710. Hervey GR. The effects of lesions in the hypothalamus in parabiotic rats. J Physiol (Lond.), 145 (2): 336–352. (1959). 711. Hervey GR, Parameswaran SV & Steffens AB. The effects of lateral hypothalamic stimulation in parabiotic rats [proceedings]. J Physiol (Lond.), 266 (1): 64P–65P. (1977). 712. German J, Kim F, Schwartz GJ, et al. Hypothalamic leptin signaling regulates hepatic insulin sensitivity via a neurocircuit involving the vagus nerve. Endocrinology, 150 (10): 4502–4511. (2009). - 264 -
713. Morton GJ, Gelling RW, Niswender KD, et al. Leptin regulates insulin sensitivity via phosphatidylinositol-3-OH kinase signaling in mediobasal hypothalamic neurons. Cell Metab, 2 (6): 411–420. (2005). 714. Kubota N, Yano W, Kubota T, et al. Adiponectin stimulates AMP-activated protein kinase in the hypothalamus and increases food intake. Cell Metab, 6 (1): 55–68. (2007). 715. Sandoval DA, Bagnol D, Woods SC, et al. Arcuate glucagon-like peptide 1 receptors regulate glucose homeostasis but not food intake. Diabetes, 57 (8): 2046–2054. (2008). 716. Toda C, Shiuchi T, Lee S, et al. Distinct effects of leptin and a melanocortin receptor agonist injected into medial hypothalamic nuclei on glucose uptake in peripheral tissues. Diabetes, 58 (12): 2757–2765. (2009). 717. Park S, Ahn IS & Kim DS. Central infusion of leptin improves insulin resistance and suppresses beta-cell function, but not beta-cell mass, primarily through the sympathetic nervous system in a type 2 diabetic rat model. Life Sci, 86 (23-24): 854–862. (2010). 718. Coomans CP, Biermasz NR, Geerling JJ, et al. Stimulatory effect of insulin on glucose uptake by muscle involves the central nervous system in insulin-sensitive mice. Diabetes, 60 (12): 3132– 3140. (2011). 719. Cha SH, Wolfgang M, Tokutake Y, et al. Differential effects of central fructose and glucose on hypothalamic malonyl-CoA and food intake. Proc Natl Acad Sci U S A, 105 (44): 16871–16875. (2008). 720. Blouet C, Jo Y-H, Li X, et al. Mediobasal hypothalamic leucine sensing regulates food intake through activation of a hypothalamus-brainstem circuit. J Neurosci, 29 (26): 8302–8311. (2009). 721. Cota D, Proulx K, Smith KAB, et al. Hypothalamic mTOR signaling regulates food intake. Science, 312 (5775): 927–930. (2006). 722. Lam CKL, Chari M, Rutter GA, et al. Hypothalamic nutrient sensing activates a forebrain- hindbrain neuronal circuit to regulate glucose production in vivo. Diabetes, 60 (1): 107–113. (2011). 723. Lam TKT, Pocai A, Gutierrez-Juarez R, et al. Hypothalamic sensing of circulating fatty acids is required for glucose homeostasis. Nat Med, 11 (3): 320–327. (2005). 724. Su Y, Lam TKT, He W, et al. Hypothalamic leucine metabolism regulates liver glucose production. Diabetes, 61 (1): 85–93. (2012). 725. Ramadori G, Gautron L, Fujikawa T, et al. Central administration of resveratrol improves diet- induced diabetes. Endocrinology, 150 (12): 5326–5333. (2009). 726. Cintra DE, Ropelle ER, Moraes JC, et al. Unsaturated fatty acids revert diet-induced hypothalamic inflammation in obesity. PLoS ONE, 7 (1): e30571. (2012). - 265 -
727. Fujikawa T, Chuang J-C, Sakata I, et al. Leptin therapy improves insulin-deficient type 1 diabetes by CNS-dependent mechanisms in mice. Proc Natl Acad Sci U S A, 107 (40): 17391– 17396. (2010). 728. German JP, Thaler JP, Wisse BE, et al. Leptin activates a novel CNS mechanism for insulin- independent normalization of severe diabetic hyperglycemia. Endocrinology, 152 (2): 394–303. (2011). 729. Schwartz MW & Porte D. Diabetes, obesity, and the brain. Science, 307 (5708): 375–379. (2005). 730. Elmquist JK & Marcus JN. Rethinking the central causes of diabetes. Nat Med, 9 (6): 645–647. (2003). 731. Ahima RS, Qi Y, Singhal NS, et al. Brain adipocytokine action and metabolic regulation. Diabetes, 55 (Suppl 2): S145–154. (2006). 732. Münzberg H. Differential leptin access into the brain - a hierarchical organization of hypothalamic leptin target sites? Physiol Behav, 94 (5): 664–669. (2008). 733. Faouzi M, Leshan R, Björnholm M, et al. Differential accessibility of circulating leptin to individual hypothalamic sites. Endocrinology, 148 (11): 5414–5423. (2007). 734. Mullier A, Bouret SG, Prevot V, et al. Differential distribution of tight junction proteins suggests a role for tanycytes in blood-hypothalamus barrier regulation in the adult mouse brain. J Comp Neurol, 518 (7): 943–962. (2010). 735. Rodríguez EM, Blázquez JL & Guerra M. The design of barriers in the hypothalamus allows the median eminence and the arcuate nucleus to enjoy private milieus: the former opens to the portal blood and the latter to the cerebrospinal fluid. Peptides, 31 (4): 757–776. (2010). 736. Ciofi P, Garret M, Lapirot O, et al. Brain-endocrine interactions: a microvascular route in the mediobasal hypothalamus. Endocrinology, 150 (12): 5509–5519. (2009). 737. Münzberg H, Flier JS & Bjørbaek C. Region-specific leptin resistance within the hypothalamus of diet-induced obese mice. Endocrinology, 145 (11): 4880–4889. (2004). 738. Chun SK & Jo Y-H. Loss of leptin receptors on hypothalamic POMC neurons alters synaptic inhibition. J Neurophysiol, 104 (5): 2321–2328. (2010). 739. Horvath TL. Synaptic plasticity mediating leptin’s effect on metabolism. Prog Brain Res, 153: 47–55. (2006). 740. Pinto S, Roseberry AG, Liu H, et al. Rapid rewiring of arcuate nucleus feeding circuits by leptin. Science, 304 (5667): 110–115. (2004). 741. Elmquist JK & Flier JS. Neuroscience. The fat-brain axis enters a new dimension. Science, 304 (5667): 63–64. (2004). - 266 -
742. Gelez H, Poirier S, Facchinetti P, et al. Neuroanatomical distribution of the melanocortin-4 receptors in male and female rodent brain. J Chem Neuroanat, 40 (4): 310–324. (2010). 743. Mountjoy KG. Functions for pro-opiomelanocortin-derived peptides in obesity and diabetes. Biochem J, 428 (3): 305–324. (2010). 744. Santini F, Maffei M, Pelosini C, et al. Melanocortin-4 receptor mutations in obesity. Adv Clin Chem, 48: 95–109. (2009). 745. Tups A. Physiological models of leptin resistance. J Neuroendocrinol, 21 (11): 961–971. (2009). 746. Król E, Tups A, Archer ZA, et al. Altered expression of SOCS3 in the hypothalamic arcuate nucleus during seasonal body mass changes in the field vole, Microtus agrestis. J Neuroendocrinol, 19 (2): 83–94. (2007). 747. Tups A, Barrett P, Ross AW, et al. The suppressor of cytokine signalling 3, SOCS3, may be one critical modulator of seasonal body weight changes in the Siberian hamster, Phodopus sungorus. J Neuroendocrinol, 18 (2): 139–145. (2006). 748. Anukulkitch C, Rao A, Pereira A, et al. Expression of genes for appetite-regulating peptides in the hypothalamus of genetically selected lean and fat sheep. Neuroendocrinology, 91 (3): 223– 238. (2010). 749. Adam CL & Findlay PA. Decreased blood-brain leptin transfer in an ovine model of obesity and weight loss: resolving the cause of leptin resistance. Int J Obes (Lond), 34 (6): 980–988. (2010). 750. Qi Y, Henry BA, Oldfield BJ, et al. The action of leptin on appetite-regulating cells in the ovine hypothalamus: demonstration of direct action in the absence of the arcuate nucleus. Endocrinology, 151 (5): 2106–2116. (2010). 751. Szczesna M, Zieba DA, Klocek-Gorka BA, et al. Seasonal effects of central leptin infusion and prolactin treatment on pituitary SOCS-3 gene expression in ewes. J Endocrinol, 208 (1): 81–88. (2011). 752. De Souza CT, Araujo EP, Bordin S, et al. Consumption of a fat-rich diet activates a proinflammatory response and induces insulin resistance in the hypothalamus. Endocrinology, 146 (10): 4192–4199. (2005). 753. Erridge C & Samani NJ. Saturated fatty acids do not directly stimulate toll-like receptor signaling. Arterioscler Thromb Vasc Biol, 29 (11): 1944–1949. (2009). 754. Amar J, Burcelin R, Ruidavets JB, et al. Energy intake is associated with endotoxemia in apparently healthy men. Am J Clin Nutr, 87 (5): 1219–1223. (2008). 755. Cani PD, Bibiloni R, Knauf C, et al. Changes in gut microbiota control metabolic endotoxemia- induced inflammation in high-fat diet-induced obesity and diabetes in mice. Diabetes, 57 (6): 1470–1481. (2008). - 267 -
756. Ding S, Chi MM, Scull BP, et al. High-fat diet: bacteria interactions promote intestinal inflammation which precedes and correlates with obesity and insulin resistance in mouse. PLoS ONE, 5 (8): e12191. (2010). 757. Manco M, Putignani L & Bottazzo GF. Gut microbiota, lipopolysaccharides, and innate immunity in the pathogenesis of obesity and cardiovascular risk. Endocr Rev, 31 (6): 817–844. (2010). 758. Kleinridders A, Schenten D, Könner AC, et al. MyD88 signaling in the CNS is required for development of fatty acid-induced leptin resistance and diet-induced obesity. Cell Metab, 10 (4): 249–259. (2009). 759. Jang P-GG, Namkoong C, Kang GMM, et al. NF-kappaB activation in hypothalamic pro- opiomelanocortin neurons is essential in illness- and leptin-induced anorexia. J Biol Chem, 285 (13): 9706–9715. (2010). 760. Ropelle ER, Flores MB, Cintra DE, et al. IL-6 and IL-10 anti-inflammatory activity links exercise to hypothalamic insulin and leptin sensitivity through IKKbeta and ER stress inhibition. PLoS Biol, 8 (8): e1000465. (2010). 761. Buchanan JB & Johnson RW. Regulation of food intake by inflammatory cytokines in the brain. Neuroendocrinology, 86 (3): 183–190. (2007). 762. García MC, Wernstedt I, Berndtsson A, et al. Mature-onset obesity in interleukin-1 receptor I knockout mice. Diabetes, 55 (5): 1205–1213. (2006). 763. Somm E, Henrichot E, Pernin A, et al. Decreased fat mass in interleukin-1 receptor antagonist- deficient mice: impact on adipogenesis, food intake, and energy expenditure. Diabetes, 54 (12): 3503–3509. (2005). 764. Scarlett JM, Jobst EE, Enriori PJ, et al. Regulation of central melanocortin signaling by interleukin-1beta. Endocrinology, 148 (9): 4217–4225. (2007). 765. Scarlett JM, Zhu X, Enriori PJ, et al. Regulation of AgRP mRNA transcription and peptide secretion by acute and chronic inflammation. Endocrinology, 149 (10): 4837–4845. (2008). 766. Lee J-K, Kim S-H, Lewis EC, et al. Differences in signaling pathways by IL-1beta and IL-18. Proc Natl Acad Sci U S A, 101 (23): 8815–8820. (2004). 767. Paxinos G & Watson C. The rat brain in stereotaxic coordinates, sixth edition. (Academic Press, ISBN: 01254761242007). 768. Invitrogen. Invitrogen OligoPerfectTM Designer. Available at: http://tools.invitrogen.com/content.cfm?pageid=9716, accessed December 2, 2010. 769. Rozen S & Skaletsky H. Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol, 132: 365–386. (2000). - 268 -
770. National Center for Biotechnology Information. Primer-BLAST primer designing tool. Available at: http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi, accessed December 2, 2010. 771. Benson DA, Karsch-Mizrachi I, Lipman DJ, et al. GenBank. Nucleic Acids Res, 36 (Database issue): D25–30. (2008). 772. Hubbard TJP, Aken BL, Ayling S, et al. Ensembl 2009. Nucleic Acids Res, 37 (Suppl 1): D690– 697. (2009). 773. National Center for Biotechnology Information. BLAST: Basic Local Alignment Search Tool. Available at: http://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed July 30, 2010. 774. Premier Biosoft International. NetPrimer. Available at: http://www.premierbiosoft.com/netprimer/index.html, accessed December 2, 2010. 775. Andre R, Wheeler R, Collins P, et al. Identification of a truncated IL-18R beta mRNA: a putative regulator of IL-18 expressed in rat brain. J Neuroimmunol, 145 (1-2): 40–45. (2003). 776. NCBI GenBank Database. Available at: http://www.ncbi.nlm.nih.gov/genbank/, accessed July 28, 2010. 777. Sugama S, Cho B, Baker H, et al. Neurons of the superior nucleus of the medial habenula and ependymal cells express IL-18 in rat CNS. Brain Res, 958 (1): 1–9. (2002). 778. Wood GS & Warnke R. Suppression of endogenous avidin-binding activity in tissues and its relevance to biotin-avidin detection systems. J Histochem Cytochem, 29 (10): 1196–1204. (1981). 779. McKay BE, Molineux ML & Turner RW. Endogenous biotin in rat brain: implications for false- positive results with avidin-biotin and streptavidin-biotin techniques. Methods Mol Biol, 418: 111–128. (2008). 780. Hedtjärn M, Leverin A-L, Eriksson K, et al. Interleukin-18 involvement in hypoxic-ischemic brain injury. J Neurosci, 22 (14): 5910–5919. (2002). 781. Yatsiv I, Morganti-Kossmann M, Perez D, et al. Elevated intracranial IL-18 in humans and mice after traumatic brain injury and evidence of neuroprotective effects of IL-18-binding protein after experimental closed head injury. J Cereb Blood Flow Metab, 22 (8): 971–978. (2002). 782. Schmidt OI, Morganti-Kossmann MC, Heyde CE, et al. Tumor necrosis factor-mediated inhibition of interleukin-18 in the brain: a clinical and experimental study in head-injured patients and in a murine model of closed head injury. J Neuroinflammation, 1 (1): 13. (2004). 783. Jeon GSS, Park SKK, Park SWW, et al. Glial expression of interleukin-18 and its receptor after excitotoxic damage in the mouse hippocampus. Neurochem Res, 33 (1): 179–184. (2008).
- 269 -
784. Culhane AC, Hall MD, Rothwell NJ, et al. Cloning of rat brain interleukin-18 cDNA. Mol Psychiatry, 3 (4): 362–366. (1998). 785. Gregg C, Zhang J, Butler JE, et al. Sex-specific parent-of-origin allelic expression in the mouse brain. Science, 329 (5992): 682–685. (2010). 786. Wheeler R, Culhane A, Hall M, et al. Detection of the interleukin 18 family in rat brain by RT- PCR. Brain Res Mol Brain Res, 77 (2): 290–293. (2000). 787. Wang Y, Zhang X, Zhang Y, et al. Expression and localization of IL-18 in the hypothalamic- pituitary-ovarian axis of non-pregnant, pregnant, and abortive rats. J Reprod Immunol, 92 (1-2): 45–53. 788. Yamamoto Y, Tanahashi T, Katsuura S, et al. Interleukin-18 deficiency reduces neuropeptide gene expressions in the mouse amygdala related with behavioral change. J Neuroimmunol, 299 (1-2): 129–139. (2010). 789. Wang N, Sugama S, Conti B, et al. Interleukin-18 mRNA expression in the rat pituitary gland. J Neuroimmunol, 173 (1-2): 117–125. (2006). 790. Andoh T, Kishi H, Motoki K, et al. Protective effect of IL-18 on kainate- and IL-1β-induced cerebellar ataxia in mice. J Immunol, 180 (4): 2322–2328. (2008). 791. Conti B, Park L, Calingasan N, et al. Cultures of astrocytes and microglia express interleukin 18. Brain Res Mol Brain Res, 67 (1): 46–52. (1999). 792. Ojala J, Alafuzoff I, Herukka S-K, et al. Expression of interleukin-18 is increased in the brains of Alzheimer’s disease patients. Neurobiol Aging, 30 (2): 198–209. (2009). 793. Sugama S, Wirz SA, Barr AM, et al. Interleukin-18 null mice show diminished microglial activation and reduced dopaminergic neuron loss following acute 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine treatment. Neuroscience, 128 (2): 451–458. (2004). 794. Sugama S, Fujita M, Hashimoto M, et al. Stress induced morphological microglial activation in the rodent brain: Involvement of interleukin-18. Neuroscience, 146 (3): 1388–1399. (2007). 795. Prinz M & Hanisch UK. Murine microglial cells produce and respond to interleukin-18. J Neurochem, 72 (5): 2215–2218. (1999). 796. Allen Institute for Brain Science. Experiment Detail, Allen Brain Atlas, Mouse Brain, IL-18, experiment 68499255. Available at: http://mouse.brain-map.org/experiment/show/68499255, accessed December 23, 2011. 797. Eurexpress Project. Eurexpress, Il18 : interleukin 18 (Il18). Available at: http://www.eurexpress.org/ee/databases/assay.jsp?assayID=euxassay_000487&image=01, accessed December 23, 2011.
- 270 -
798. Bianco IH & Wilson SW. The habenular nuclei: a conserved asymmetric relay station in the vertebrate brain. Philos Trans R Soc Lond B Biol Sci, 364 (1519): 1005–1020. (2009). 799. Groenewegen HJ, Ahlenius S, Haber SN, et al. Cytoarchitecture, fiber connections, and some histochemical aspects of the interpeduncular nucleus in the rat. J Comp Neurol, 249 (1): 65–102. (1986). 800. Sutherland RJ. The dorsal diencephalic conduction system: a review of the anatomy and functions of the habenular complex. Neurosci Biobehav Rev, 6 (1): 1–13. (1982). 801. Conti B, Jahng JW, Tinti C, et al. Induction of interferon-gamma inducing factor in the adrenal cortex. J Biol Chem, 272 (4): 2035–2037. (1997). 802. Yang Y-JJ, Wang Z-YY, Chen S-HH, et al. Cloning and characterization of a new isoform of mouse interleukin-18. Acta Biochim Biophys Sin (Shanghai), 37 (12): 826–834. (2005). 803. Andre R, Lerouet D, Kimber I, et al. Regulation of expression of the novel IL-1 receptor family members in the mouse brain. J Neurochem, 95 (2): 324–330. (2005). 804. Alboni S, Cervia D, Ross B, et al. Mapping of the full length and the truncated interleukin-18 receptor alpha in the mouse brain. J Neuroimmunol, 214 (1-2): 43–54. (2009). 805. Alboni S, Montanari C, Benatti C, et al. Constitutive and LPS-regulated expression of interleukin-18 receptor beta variants in the mouse brain. Brain Behav Immun, 25 (3): 483–493. (2011). 806. Fiszer D, Rozwadowska N, Rychlewski L, et al. Identification of IL-18RAP mRNA truncated splice variants in human testis and the other human tissues. Cytokine, 39 (3): 178–183. (2007). 807. Allen Institute for Brain Science. Allen Mouse Brain Atlas. (2009). Available at: http://mouse.brain-map.org, accessed September 21, 2011. 808. Eurexpress Project. Eurexpress: Il18r1 : interleukin 18 receptor 1. Available at: http://www.eurexpress.org/ee/databases/assay.jsp?assayID=euxassay_012379&image=01, accessed November 29, 2011. 809. Eurexpress Project. Eurexpress: Il18rap : interleukin 18 receptor accessory protein (Il18rap). Available at: http://www.eurexpress.org/ee/databases/assay.jsp?assayID=euxassay_002235&image=01, accessed November 29, 2011. 810. Diez-Roux G, Banfi S, Sultan M, et al. A high-resolution anatomical atlas of the transcriptome in the mouse embryo. PLoS Biol, 9 (1): e1000582. (2011). 811. Aizawa H, Kobayashi M, Tanaka S, et al. Molecular characterization of the subnuclei in rat habenula. The Journal of Comparative Neurology, 520 (18): 4051–4066. (2012).
- 271 -
812. Dinarello C, Arend W, Sims J, et al. IL-1 family nomenclature. Nat Immunol, 11 (11): 973. (2010). 813. Dinarello CA. Immunological and inflammatory functions of the interleukin-1 family. Annu Rev Immunol, 27: 519–550. (2009). 814. Nold MF, Nold-Petry CA, Zepp JA, et al. CS05-3. Interleukin 37 exerts its anti-inflammatory functions by associating with IL-18R alpha and SIGIRR. Cytokine, 56 (1): 12. (2011). 815. Gutcher I, Urich E, Wolter K, et al. Interleukin 18–independent engagement of interleukin 18 receptor-α is required for autoimmune inflammation. Nat Immunol, 7 (9): 946–953. (2006). 816. Lewis E & Dinarello C. Responses of IL-18- and IL-18 receptor-deficient pancreatic islets with convergence of positive and negative signals for the IL-18 receptor. Proc Natl Acad Sci U S A, 103 (45): 16852–16857. (2006). 817. Eisener-Dorman AF, Lawrence DA & Bolivar VJ. Behavioral and genetic investigations of low exploratory behavior in Il18r1-/- mice: we can’t always blame it on the targeted gene. Brain Behav Immun, 24 (7): 1116–1125. (2010). 818. Valnegri P, Montrasio C, Brambilla D, et al. The X-linked intellectual disability protein IL1RAPL1 regulates excitatory synapse formation by binding PTP delta and RhoGAP2. Hum Mol Genet, 20 (24): 4797–4809. (2011). 819. Yoshida T, Yasumura M, Uemura T, et al. IL-1 Receptor Accessory Protein-Like 1 associated with mental retardation and autism mediates synapse formation by trans-synaptic interaction with protein tyrosine phosphatase delta. J Neurosci, 31 (38): 13485–13499. (2011). 820. Skuse DH. X-linked genes and mental functioning. Hum Mol Genet, 14: R27–R32. (2005). 821. Delbridge ML, McMillan DA, Doherty RJ, et al. Origin and evolution of candidate mental retardation genes on the human X chromosome (MRX). BMC Genomics, 9: 65. (2008). 822. Zechner U, Wilda M, Kehrer-Sawatzki H, et al. A high density of X-linked genes for general cognitive ability: a run-away process shaping human evolution? Trends Genet, 17 (12): 697– 701. (2001). 823. Ferrante MI, Ghiani M, Bulfone A, et al. IL1RAPL2 maps to Xq22 and is specifically expressed in the central nervous system. Gene, 275 (2): 217–221. (2001). 824. Carrié A, Jun L, Bienvenu T, et al. A new member of the IL-1 receptor family highly expressed in hippocampus and involved in X-linked mental retardation. Nat Genet, 23 (1): 25–31. (1999). 825. Sana TR, Debets R, Timans JC, et al. Computational identification, cloning, and characterization of IL-1R9, a novel interleukin-1 receptor-like gene encoded over an unusually large interval of human chromosome Xq22.2-q22.3. Genomics, 69 (2): 252–262. (2000).
- 272 -
826. Meng X, Leman M & Xiang Y. Variola virus IL-18 binding protein interacts with three human IL-18 residues that are part of a binding site for human IL-18 receptor alpha subunit. Virology, 358 (1): 211–220. (2007). 827. Krumm B, Meng X, Li Y, et al. Structural basis for antagonism of human interleukin 18 by poxvirus interleukin 18-binding protein. Proc Natl Acad Sci U S A, 105 (52): 20711–20715. (2008). 828. Xiang Y & Moss B. Correspondence of the functional epitopes of poxvirus and human interleukin-18-binding proteins. J Virol, 75 (20): 9947–9954. (2001). 829. European Bioinformatics Institute. MAFFT: fast multiple alignment tools for large batches of sequences. Available at: http://www.ebi.ac.uk/Tools/msa/mafft/, accessed September 28, 2011. 830. European Bioinformatics Institute. MView - a multiple alignment viewer. Available at: http://www.ebi.ac.uk/Tools/msa/mview/, accessed September 28, 2011. 831. Taylor WR. The classification of amino acid conservation. J Theor Biol, 119 (2): 205–218. (1986). 832. Bork P, Brown NP, Hegyi H, et al. The protein phosphatase 2C (PP2C) superfamily: detection of bacterial homologues. Protein Sci, 5 (7): 1421–1425. (1996). 833. Esteban DJ & Buller RML. Identification of residues in an orthopoxvirus interleukin-18 binding protein involved in ligand binding and species specificity. Virology, 323 (2): 197–207. (2004). 834. Kato Z, Jee J, Shikano H, et al. The structure and binding mode of interleukin-18. Nat Struct Biol, 10 (11): 966–971. (2003). 835. Ramón y Cajal S. Histology of the nervous system of man and vertebrates; translated from the French by Neely Swanson and Larry W Swanson. (Oxford University Press, New York, ISBN: 01950740171995). 836. Santa Cruz Biotechnology Inc. IL-1R8 (P-15) antibody, sc-161736 datasheet. Available at: http://datasheets.scbt.com/sc-161736.pdf, accessed September 22, 2011. 837. Santa Cruz Biotechnology Inc. IL-1R9 (T-14) antibody, sc-131571 datasheet. Available at: http://datasheets.scbt.com/sc-131571.pdf, accessed October 20, 2011. 838. Saper CB. An open letter to our readers on the use of antibodies. J. Comp. Neurol., 493 (4): 477– 478. (2005). 839. True LD. Quality control in molecular immunohistochemistry. Histochem. Cell Biol., 130 (3): 473–480. (2008). 840. Gambino F, Pavlowsky A, Béglé A, et al. IL1-receptor accessory protein-like 1 (IL1RAPL1), a protein involved in cognitive functions, regulates N-type Ca2+-channel and neurite elongation. Proc Natl Acad Sci U S A, 104 (21): 9063–9068. (2007). - 273 -
841. Pavlowsky A, Gianfelice A, Pallotto M, et al. A postsynaptic signaling pathway that may account for the cognitive defect due to IL1RAPL1 mutation. Curr Biol, 20 (2): 103–115. (2010). 842. Paxinos G & Franklin KBJ. The mouse brain in stereotaxic coordinates. (Academic Press, ISBN: 012547637X2001). 843. Allen Institute for Brain Science. Experiment Detail, Allen Brain Atlas, Mouse Brain, IL1RAPL1, experiment 70278045. Available at: http://mouse.brain- map.org/experiment/show/70278045, accessed January 10, 2012. 844. Eurexpress Project. Eurexpress: Il1rapl1 : interleukin 1 receptor accessory protein-like 1. Available at: http://www.eurexpress.org/ee/databases/assay.jsp?assayID=euxassay_016091&image=01, accessed November 29, 2011. 845. Pavlowsky A, Zanchi A, Pallotto M, et al. Neuronal JNK pathway activation by IL-1 is mediated through IL1RAPL1, a protein required for development of cognitive functions. Commun Integr Biol, 3 (3): 245–247. (2010). 846. Gambino F, Kneib M, Pavlowsky A, et al. IL1RAPL1 controls inhibitory networks during cerebellar development in mice. Eur J Neurosci, 30 (8): 1476–1486. (2009). 847. Bahi N, Friocourt G, Carrié A, et al. IL1 receptor accessory protein like, a protein involved in X- linked mental retardation, interacts with Neuronal Calcium Sensor-1 and regulates exocytosis. Hum Mol Genet, 12 (12): 1415–1425. (2003). 848. Born TL, Smith DE, Garka KE, et al. Identification and characterization of two members of a novel class of the interleukin-1 receptor (IL-1R) family. Delineation of a new class of IL-1R- related proteins based on signaling. J Biol Chem, 275 (39): 29946–29954. (2000). 849. Eurexpress Project. Eurexpress: Il1rapl2 : interleukin 1 receptor accessory protein-like 2. Available at: http://www.eurexpress.org/ee/databases/assay.jsp?assayID=euxassay_009596&image=01, accessed November 29, 2011. 850. Jin H, Gardner RJ, Viswesvaraiah R, et al. Two novel members of the interleukin-1 receptor gene family, one deleted in Xp22.1-Xp21.3 mental retardation. Eur J Hum Genet, 8 (2): 87–94. (2000). 851. Mizuno K, Hasegawa K, Katagiri T, et al. MPTP delta, a putative murine homolog of HPTP delta, is expressed in specialized regions of the brain and in the B-cell lineage. Mol Cell Biol, 13 (9): 5513–5523. (1993). 852. Paul S & Lombroso PJ. Receptor and nonreceptor protein tyrosine phosphatases in the nervous system. Cell Mol Life Sci, 60 (11): 2465–2482. (2003). - 274 -
853. Khan JA, Brint EK, O’Neill LAJ, et al. Crystal structure of the Toll/interleukin-1 receptor domain of human IL-1RAPL. J Biol Chem, 279 (30): 31664–31670. (2004). 854. Lindblad-Toh K, Garber M, Zuk O, et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature, 478 (7370): 476–482. (2011). 855. Yoshida T, Shiroshima T, Lee S-J, et al. Interleukin-1 receptor accessory protein organizes neuronal synaptogenesis as a cell adhesion molecule. J Neurosci, 32 (8): 2588–2600. (2012). 856. Huang Y, Smith DE, Ibáñez-Sandoval O, et al. Neuron-specific effects of interleukin-1β are mediated by a novel isoform of the IL-1 receptor accessory protein. J Neurosci, 31 (49): 18048– 18059. (2011). 857. Taishi P, Davis CJ, Bayomy O, et al. Brain-specific interleukin-1 receptor accessory protein in sleep regulation. J Appl Physiol, 112 (6): 1015–1022. (2012). 858. Smith DE, Lipsky BP, Russell C, et al. A central nervous system-restricted isoform of the interleukin-1 receptor accessory protein modulates neuronal responses to interleukin-1. Immunity, 30 (6): 817–831. (2009). 859. Boutin H, Kimber I, Rothwell NJ, et al. The expanding interleukin-1 family and its receptors: do alternative IL-1 receptor/signaling pathways exist in the brain? Mol Neurobiol, 27 (3): 239–248. (2003). 860. Jackson Laboratories. JAX Mice Database - 004130 B6.129P2-Il18-tm1Aki/J. Available at: http://jaxmice.jax.org/strain/004130.html, accessed October 20, 2011. 861. Ross MT, Grafham DV, Coffey AJ, et al. The DNA sequence of the human X chromosome. Nature, 434 (7031): 325–337. (2005). 862. Kohn M, Kehrer-Sawatzki H, Steinbach P, et al. Recruitment of old genes to new functions: evidences obtained by comparing the orthologues of human XLMR genes in mouse and chicken. Cytogenet Genome Res, 116 (3): 173–180. (2007). 863. Volin MV & Koch AE. Interleukin-18: a mediator of inflammation and angiogenesis in rheumatoid arthritis. J Interferon Cytokine Res, 31 (10): 745–751. (2011). 864. Ma Y, Zhang B, Tang R-K, et al. Interleukin-18 promoter polymorphism and asthma risk: a meta-analysis. Mol Biol Rep, 39 (2): 1371–1376. (2012). 865. Birrell MA & Eltom S. The role of the NLRP3 inflammasome in the pathogenesis of airway disease. Pharmacol Ther, 130 (3): 364–370. (2011). 866. Concha ML & Wilson SW. Asymmetry in the epithalamus of vertebrates. J Anat, 199 (Pt 1-2): 63–84. (2001). 867. Eakin RM. The third eye. (University of California Press, Berkeley, ISBN: 05200241331973).
- 275 -
868. Kemali M, Guglielmotti V & Fiorino L. The asymmetry of the habenular nuclei of female and male frogs in spring and in winter. Brain Res, 517 (1-2): 251–255. (1990). 869. Hattar S, Kumar M, Park A, et al. Central projections of melanopsin-expressing retinal ganglion cells in the mouse. J Comp Neurol, 497 (3): 326–349. (2006). 870. Zhao H & Rusak B. Circadian firing-rate rhythms and light responses of rat habenular nucleus neurons in vivo and in vitro. Neuroscience, 132 (2): 519–528. (2005). 871. Tavakoli-Nezhad M & Schwartz WJ. Hamsters running on time: is the lateral habenula a part of the clock? Chronobiol Int, 23 (1-2): 217–224. (2006). 872. Chastrette N, Pfaff DW & Gibbs RB. Effects of daytime and nighttime stress on Fos-like immunoreactivity in the paraventricular nucleus of the hypothalamus, the habenula, and the posterior paraventricular nucleus of the thalamus. Brain Res, 563 (1-2): 339–344. (1991). 873. Rønnekleiv OK & Møller M. Brain-pineal nervous connections in the rat: an ultrastructure study following habenular lesion. Exp Brain Res, 37 (3): 551–562. (1979). 874. Dibner C, Schibler U & Albrecht U. The mammalian circadian timing system: organization and coordination of central and peripheral clocks. Annu Rev Physiol, 72: 517–549. (2010). 875. Kubota T, Fang J, Brown RA, et al. Interleukin-18 promotes sleep in rabbits and rats. Am J Physiol Regul Integr Comp Physiol, 281 (3): R828–838. (2001). 876. Andres KH, Von Düring M & Veh RW. Subnuclear organization of the rat habenular complexes. J Comp Neurol, 407 (1): 130–150. (1999). 877. Cupédo RN. The surface ultractructure of the habenular complex of the rat. Anat Embryol, 152 (1): 43–64. (1977). 878. Cupédo RNJ. The surface ultrastructure of the habenular complex. Acta Morphol Neerl Scand, 19 (1): 74–75. (1981). 879. Welsh MG. Intraventricular blood vessels associated with the deep pineal gland of the Mongolian gerbil, Meriones unguiculatus. Cell Tissue Res, 249 (3): 587–592. (1987). 880. Cupédo RN & De Weerd H. Tanycytes in the medial habenular nucleus of the rat. Anat Embryol, 172 (1): 7–10. (1985). 881. Cupédo RN, De Weerd H & Dijk F. Membrane specializations and their relation to HRP transport in the medial habenular nucleus of the rat. Anat Embryol, 171 (1): 21–30. (1985). 882. Veening JG & Barendregt HP. The regulation of brain states by neuroactive substances distributed via the cerebrospinal fluid; a review. Cerebrospinal Fluid Res, 7: 1. (2010). 883. Sutherland RJ & Nakajima S. Self-stimulation of the habenular complex in the rat. J Comp Physiol Psychol, 95 (5): 781–791. (1981).
- 276 -
884. Glick SD, Ramirez RL, Livi JM, et al. 18-methoxycoronaridine acts in the medial habenula and/or interpeduncular nucleus to decrease morphine self-administration in rats. Eur J Pharmacol, 537 (1-3): 94–98. (2006). 885. Hashimoto K, Amano T, Sakai N, et al. Cell-dependent physiological synaptic action of morphine in the rat habenular nucleus: morphine both inhibits and facilitates excitatory synaptic transmission. Neurosci Lett, 451 (3): 270–273. (2009). 886. Zhang F, Zhou W, Liu H, et al. Increased c-Fos expression in the medial part of the lateral habenula during cue-evoked heroin-seeking in rats. Neurosci Lett, 386 (2): 133–137. (2005). 887. Martin TJ, Miller M, Dworkin SI, et al. Alteration of local cerebral glucose utilization following intravenous administration of heroin in Fischer 344 rats. Brain Res, 755 (2): 313–318. (1997). 888. Zahm DS, Becker ML, Freiman AJ, et al. Fos after single and repeated self-administration of cocaine and saline in the rat: emphasis on the basal forebrain and recalibration of expression. Neuropsychopharmacology, 35 (2): 445–463. (2010). 889. Friedman A, Lax E, Dikshtein Y, et al. Electrical stimulation of the lateral habenula produces an inhibitory effect on sucrose self-administration. Neuropharmacology, 60 (2-3): 381–387. (2011). 890. Knapp CM, Printseva B, Cottam N, et al. Effects of cue exposure on brain glucose utilization 8 days after repeated cocaine administration. Brain Res, 950 (1-2): 119–126. (2002). 891. Ryabinin AE, Wang YM, Bachtell RK, et al. Cocaine- and alcohol-mediated expression of inducible transcription factors is blocked by pentobarbital anesthesia. Brain Res, 877 (2): 251– 261. (2000). 892. Franklin TR & Druhan JP. Expression of Fos-related antigens in the nucleus accumbens and associated regions following exposure to a cocaine-paired environment. Eur J Neurosci, 12 (6): 2097–2106. (2000). 893. Fonck C, Nashmi R, Salas R, et al. Demonstration of functional alpha4-containing nicotinic receptors in the medial habenula. Neuropharmacology, 56 (1): 247–253. (2009). 894. Salas R, Sturm R, Boulter J, et al. Nicotinic receptors in the habenulo-interpeduncular system are necessary for nicotine withdrawal in mice. J Neurosci, 29 (10): 3014–3018. (2009). 895. De Biasi M & Salas R. Influence of neuronal nicotinic receptors over nicotine addiction and withdrawal. Exp Biol Med (Maywood), 233 (8): 917–929. (2008). 896. Carlson J, Armstrong B, Switzer RC, et al. Selective neurotoxic effects of nicotine on axons in fasciculus retroflexus further support evidence that this a weak link in brain across multiple drugs of abuse. Neuropharmacology, 39 (13): 2792–2798. (2000). 897. Fowler CD, Lu Q, Johnson PM, et al. Habenular alpha-5 nicotinic receptor subunit signalling controls nicotine intake. Nature, 471 (7340): 597–601. (2011). - 277 -
898. Chen F, Rezvani A, Jarrott B, et al. Distribution of GABA(A) receptors in the limbic system of alcohol-preferring and non-preferring rats: in situ hybridisation histochemistry and receptor autoradiography. Neurochem Int, 32 (2): 143–151. (1998). 899. Hwang BH, Suzuki R, Lumeng L, et al. Innate differences in neuropeptide Y (NPY) mRNA expression in discrete brain regions between alcohol-preferring (P) and -nonpreferring (NP) rats: a significantly low level of NPY mRNA in dentate gyrus of the hippocampus and absence of NPY mRNA in the medial habenular nucleus of P rats. Neuropeptides, 38 (6): 359–368. (2004). 900. Williams-Hemby L, Grant KA, Gatto GJ, et al. Metabolic mapping of the effects of chronic voluntary ethanol consumption in rats. Pharmacol Biochem Behav, 54 (2): 415–423. (1996). 901. Deutch AY, Holliday J, Roth RH, et al. Immunohistochemical localization of a neuronal nicotinic acetylcholine receptor in mammalian brain. Proc Natl Acad Sci U S A, 84 (23): 8697– 8701. (1987). 902. London ED, Waller SB & Wamsley JK. Autoradiographic localization of [3H]nicotine binding sites in the rat brain. Neurosci Lett, 53 (2): 179–184. (1985). 903. Gallego X, Molas S, Amador-Arjona A, et al. Overexpression of the CHRNA5/A3/B4 genomic cluster in mice increases the sensitivity to nicotine and modifies its reinforcing effects. Amino Acids, 43 (2): 897–909. (2012). 904. Carlson J, Noguchi K & Ellison G. Nicotine produces selective degeneration in the medial habenula and fasciculus retroflexus. Brain Res, 906 (1-2): 127–134. (2001). 905. Ciani E, Severi S, Bartesaghi R, et al. Neurochemical correlates of nicotine neurotoxicity on rat habenulo-interpeduncular cholinergic neurons. Neurotoxicology, 26 (3): 467–474. (2005). 906. Ellison G. Neural degeneration following chronic stimulant abuse reveals a weak link in brain, fasciculus retroflexus, implying the loss of forebrain control circuitry. Eur Neuropsychopharmacol, 12 (4): 287–297. (2002). 907. Ji H & Shepard PD. Lateral habenula stimulation inhibits rat midbrain dopamine neurons through a GABA(A) receptor-mediated mechanism. J Neurosci, 27 (26): 6923–6930. (2007). 908. Omelchenko N, Bell R & Sesack SR. Lateral habenula projections to dopamine and GABA neurons in the rat ventral tegmental area. Eur J Neurosci, 30 (7): 1239–1250. (2009). 909. Brinschwitz K, Dittgen A, Madai VI, et al. Glutamatergic axons from the lateral habenula mainly terminate on GABAergic neurons of the ventral midbrain. Neuroscience, 168 (2): 463– 476. (2010). 910. Kim U. Topographic commissural and descending projections of the habenula in the rat. J Comp Neurol, 513 (2): 173–187. (2009). - 278 -
911. Nishikawa T, Fage D & Scatton B. Evidence for, and nature of, the tonic inhibitory influence of habenulointerpeduncular pathways upon cerebral dopaminergic transmission in the rat. Brain Res, 373 (1-2): 324–336. (1986). 912. Lecourtier L & Kelly PH. A conductor hidden in the orchestra? Role of the habenular complex in monoamine transmission and cognition. Neurosci Biobehav Rev, 31 (5): 658–672. (2007). 913. Phillipson OT & Pycock CJ. Dopamine neurones of the ventral tegmentum project to both medial and lateral habenula. Some implications for habenular function. Exp Brain Res, 45 (1-2): 89–94. (1982). 914. Gruber C, Kahl A, Lebenheim L, et al. Dopaminergic projections from the VTA substantially contribute to the mesohabenular pathway in the rat. Neurosci Lett, 427 (3): 165–170. (2007). 915. Aghajanian GK & Wang RY. Habenular and other midbrain raphe afferents demonstrated by a modified retrograde tracing technique. Brain Res, 122 (2): 229–242. (1977). 916. Contestabile A, Villani L, Fasolo A, et al. Topography of cholinergic and substance P pathways in the habenulo-interpeduncular system of the rat. An immunocytochemical and microchemical approach. Neuroscience, 21 (1): 253–270. (1987). 917. Rønnekleiv OK & Kelly MJ. Distribution of substance P neurons in the epithalamus of the rat: an immunohistochemical investigation. J Pineal Res, 1 (4): 355–370. (1984). 918. Wirtshafter D. The control of ingestive behavior by the median raphe nucleus. Appetite, 36 (1): 99–105. (2001). 919. Trinko R, Sears RM, Guarnieri DJ, et al. Neural mechanisms underlying obesity and drug addiction. Physiol Behav, 91 (5): 499–505. (2007). 920. Narayanan NS, Guarnieri DJ & DiLeone RJ. Metabolic hormones, dopamine circuits, and feeding. Front Neuroendocrinol, 31 (1): 104–112. (2010). 921. Lisoprawski A, Herve D, Blanc G, et al. Selective activation of the mesocortico-frontal dopaminergic neurons induced by lesion of the habenula in the rat. Brain Res, 183 (1): 229–234. (1980). 922. Davis JF, Choi DL & Benoit SC. Insulin, leptin and reward. Trends Endocrinol Metab, 21 (2): 68–74. (2010). 923. Vucetic Z & Reyes TM. Central dopaminergic circuitry controlling food intake and reward: implications for the regulation of obesity. Wiley Interdiscip Rev Syst Biol Med, 2 (5): 577–593. (2010). 924. Johnson PM & Kenny PJ. Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat Neurosci, 13 (5): 635–641. (2010).
- 279 -
925. Fulton S, Pissios P, Manchon RPP, et al. Leptin regulation of the mesoaccumbens dopamine pathway. Neuron, 51 (6): 811–822. (2006). 926. Hommel JD, Trinko R, Sears RM, et al. Leptin receptor signaling in midbrain dopamine neurons regulates feeding. Neuron, 51 (6): 801–810. (2006). 927. Leinninger GM, Jo Y-H, Leshan RL, et al. Leptin acts via leptin receptor-expressing lateral hypothalamic neurons to modulate the mesolimbic dopamine system and suppress feeding. Cell Metab, 10 (2): 89–98. (2009). 928. Figlewicz DP, Szot P, Chavez M, et al. Intraventricular insulin increases dopamine transporter mRNA in rat VTA/substantia nigra. Brain Res, 644 (2): 331–334. (1994). 929. Lin S & Huang XF. Fasting increases leptin receptor mRNA expression in lean but not obese (ob/ob) mouse brain. Neuroreport, 8 (16): 3625–3629. (1997). 930. Unger J, McNeill TH, Moxley RT, et al. Distribution of insulin receptor-like immunoreactivity in the rat forebrain. Neuroscience, 31 (1): 143–157. (1989). 931. Merchenthaler I, Lane M & Shughrue P. Distribution of pre-pro-glucagon and glucagon-like peptide-1 receptor messenger RNAs in the rat central nervous system. J Comp Neurol, 403 (2): 261–280. (1999). 932. Ren X. Sweet taste signaling functions as a hypothalamic glucose sensor. Front Integr Neurosci, 3: 12. (2009). 933. Kumar KG, Trevaskis JL, Lam DD, et al. Identification of adropin as a secreted factor linking dietary macronutrient intake with energy homeostasis and lipid metabolism. Cell Metab, 8 (6): 468–481. (2008). 934. Smith SL, Harrold JA & Williams G. Diet-induced obesity increases mu opioid receptor binding in specific regions of the rat brain. Brain Res, 953 (1-2): 215–222. (2002). 935. Wolinsky TD, Carr KD, Hiller JM, et al. Effects of chronic food restriction on mu and kappa opioid binding in rat forebrain: a quantitative autoradiographic study. Brain Res, 656 (2): 274– 280. (1994). 936. Park TH & Carr KD. Neuroanatomical patterns of fos-like immunoreactivity induced by a palatable meal and meal-paired environment in saline- and naltrexone-treated rats. Brain Res, 805 (1-2): 169–180. (1998). 937. Donovick PJ, Burright RG & Zuromski E. Localization of quinine aversion within the septum, habenula, and interpeduncular nucleus of the rat. J Comp Physiol Psychol, 71 (3): 376–383. (1970). 938. Herkenham M & Nauta WJH. Efferent connections of the habenular nuclei in the rat. J Comp Neurol, 187 (1): 19–47. (1979). - 280 -
939. Omoto Y, Tokime K, Yamanaka K, et al. Human mast cell chymase cleaves pro-IL-18 and generates a novel and biologically active IL-18 fragment. J Immunol, 177 (12): 8315–8319. (2006). 940. Mizutani H, Schechter N, Lazarus G, et al. Rapid and specific conversion of precursor interleukin 1 beta (IL-1 beta) to an active IL-1 species by human mast cell chymase. J Exp Med, 174 (4): 821–825. (1991). 941. Sugawara S, Uehara A, Nochi T, et al. Neutrophil proteinase 3-mediated induction of bioactive IL-18 secretion by human oral epithelial cells. J Immunol, 167 (11): 6568–6575. (2001). 942. Omoto Y, Yamanaka K, Tokime K, et al. Granzyme B is a novel interleukin-18 converting enzyme. J Dermatol Sci, 59 (2): 129–135. (2010). 943. Akita K, Ohtsuki T, Nukada Y, et al. Involvement of caspase-1 and caspase-3 in the production and processing of mature human interleukin 18 in monocytic THP.1 cells. J Biol Chem, 272 (42): 26595–26603. (1997). 944. Allen Institute for Brain Science. Allen Brain Atlas, Mouse Brain. Experiment 69752137: Mast cell chymase-1. Available at: http://mouse.brain-map.org/experiment/show/69752137, accessed December 18, 2011. 945. Allen Institute for Brain Science. Experiment Detail, Allen Brain Atlas, Mouse Brain, Caspase-3, experiment 70431217. Available at: http://mouse.brain-map.org/experiment/show/70431217, accessed April 26, 2012. 946. Dinarello CA. Interleukin-1 beta, interleukin-18, and the interleukin-1 beta converting enzyme. Ann N Y Acad Sci, 856: 1–11. (1998). 947. Bromberg-Martin ES, Matsumoto M, Hong S, et al. A pallidus-habenula-dopamine pathway signals inferred stimulus values. J Neurophysiol, 104 (2): 1068–1076. (2010). 948. Bromberg-Martin ES, Matsumoto M & Hikosaka O. Distinct tonic and phasic anticipatory activity in lateral habenula and dopamine neurons. Neuron, 67 (1): 144–155. (2010). 949. Bromberg-Martin ES, Matsumoto M, Nakahara H, et al. Multiple timescales of memory in lateral habenula and dopamine neurons. Neuron, 67 (3): 499–510. (2010). 950. Shumake J, Ilango A, Scheich H, et al. Differential neuromodulation of acquisition and retrieval of avoidance learning by the lateral habenula and ventral tegmental area. J Neurosci, 30 (17): 5876–5883. (2010). 951. Matsumoto M & Hikosaka O. Lateral habenula as a source of negative reward signals in dopamine neurons. Nature, 447 (7148): 1111–1115. (2007).
- 281 -
952. Kim U & Chang S-Y. Dendritic morphology, local circuitry, and intrinsic electrophysiology of neurons in the rat medial and lateral habenular nuclei of the epithalamus. J Comp Neurol, 483 (2): 236–250. (2005). 953. Qin C & Luo M. Neurochemical phenotypes of the afferent and efferent projections of the mouse medial habenula. Neuroscience, 161 (3): 827–837. (2009). 954. Oades RD & Halliday GM. Ventral tegmental (A10) system: neurobiology. 1. Anatomy and connectivity. Brain Res Rev, 12 (2): 117–165. (1987). 955. Cupédo RN & De Weerd H. Serotonergic intraventricular axons in the habenular region. Phagocytosis after induced degeneration. Anat Embryol, 158 (2): 213–226. (1980). 956. Dockray GJ & Burdyga G. Plasticity in vagal afferent neurones during feeding and fasting: mechanisms and significance. Acta Physiol (Oxf), 201 (3): 313–321. (2011). 957. Hayes MR, De Jonghe BC & Kanoski SE. Role of the glucagon-like-peptide-1 receptor in the control of energy balance. Physiol Behav, 100 (5): 503–510. (2010). 958. Yudkin JS, Richter B & Gale EAM. Intensified glucose lowering in type 2 diabetes: time for a reappraisal. Diabetologia, 53 (10): 2079–2085. (2010). 959. Mann DM, Woodward M & Muntner P. Preventing diabetes complications: are we too glucocentric? Int J Clin Pract, 64 (8): 1024–1027. (2010). 960. Hirsch IB & Brownlee M. Beyond hemoglobin A1c - need for additional markers of risk for diabetic microvascular complications. JAMA, 303 (22): 2291–2292. (2010). 961. Stirban A, Tschoepe D & Stratmann B. Shifting the disease management paradigm from glucose. What are the pros? Diabetes Care, 32 (Suppl 2): S349–S352. (2009). 962. Bernard C. Leçons sur le diabète et la glycogenèse animale. (J-B Baillière et fils (Paris), 1877). Available at: http://www.archive.org/details/leonssurlediabt00berngoog, accessed January 19, 2012. 963. Goldstein J. Keep that 1876 journal handy, it just may help treat diabetes. Wall Street Journal, (2009). Available at: http://online.wsj.com/article/SB123240913513395859.html, accessed November 23, 2010. 964. Vig J. Diabetes: Salicylates increase insulin levels rather than insulin actions. Nat Rev Endocrinol, 5 (4): 183. (2009). 965. Astrup A. Is cardiometabolic risk improved by weight-loss drugs? Lancet, 376 (9741): 567–568. (2010). 966. Boekholdt SM & Peters RJG. Rimonabant: obituary for a wonder drug. Lancet, 376 (9740): 489– 490. (2010). 967. Ledford H. Slim spoils for obesity drugs. Nature, 468 (7326): 878a. (2010). - 282 -
968. Road rules for drugs: a survey of signals. Nat Med, 16 (12): 1362–1363. (2010). 969. Editorial. New obesity pill: new hopes, old fears. Lancet, 376 (9758): 2042. (2010). 970. Endocrine Society. Endocrine Daily Briefing. (2011). Available at: http://mailview.custombriefings.com/mailview.aspx?m=2011020201endo&r=4836903-88c7, accessed February 2, 2011. 971. Pollack A. F.D.A. fails to approve Contrave, a new diet pill. The New York Times, (2011). Available at: http://www.nytimes.com/2011/02/02/business/02drug.html?_r=1&partner=rss&emc=rss, accessed February 2, 2011. 972. Pour OR & Dagogo-Jack S. Prediabetes as a therapeutic target. Clin Chem, 57 (2): 215–220. (2011). 973. Kirkman MS & Kendall DM. Hemoglobin A1c to diagnose diabetes: why the controversy over adding a new tool? Clin Chem, 57 (2): 255–257. (2011). 974. Newburgh L & Conn J. A new interpretation of hyperglycemia in obese middle-aged persons. J Am Med Assoc, 112 (1): 7–11. (1939). 975. Cochrane AL. Effectiveness and efficiency: random reflections on health services. (RSM Press, ISBN: 97818531539451999). 976. Jiao P, Ma J, Feng B, et al. FFA-induced adipocyte inflammation and insulin resistance: involvement of ER stress and IKK[beta] pathways. Obesity, 19 (3): 483–491. (2011). 977. Scheuner D & Kaufman RJ. The unfolded protein response: a pathway that links insulin demand with beta-cell failure and diabetes. Endocr Rev, 29 (3): 317–333. (2008). 978. Minamino T, Komuro I & Kitakaze M. Endoplasmic reticulum stress as a therapeutic target in cardiovascular disease. Circ Res, 107 (9): 1071–1082. (2010). 979. Thomas SE, Dalton LE, Daly M-L, et al. Diabetes as a disease of endoplasmic reticulum stress. Diabetes Metab Res Rev, 26 (8): 611–621. (2010). 980. Martínez de Morentin PB & López M. “Mens sana in corpore sano”: exercise and hypothalamic ER stress. PLoS Biol, 8 (8): e1000464. (2010). 981. Boden G. Endoplasmic reticulum stress: another link between obesity and insulin resistance/inflammation? Diabetes, 58 (3): 518–519. (2009). 982. Gregor M, Yang L, Fabbrini E, et al. Endoplasmic reticulum stress is reduced in tissues of obese subjects after weight loss. Diabetes, 58 (3): 693–700. (2009). 983. Han MS, Chung KW, Cheon HG, et al. Imatinib mesylate reduces endoplasmic reticulum stress and induces remission of diabetes in db/db mice. Diabetes, 58 (2): 329–336. (2009).
- 283 -
984. Eizirik D, Cardozo A & Cnop M. The role for endoplasmic reticulum stress in diabetes mellitus. Endocr Rev, 29 (1): 42–61. (2008). 985. Song B, Scheuner D, Ron D, et al. Chop deletion reduces oxidative stress, improves beta cell function, and promotes cell survival in multiple mouse models of diabetes. J Clin Invest, 118 (10): 3378–3389. (2008). 986. Hosoi T, Sasaki M, Miyahara T, et al. Endoplasmic reticulum stress induces leptin resistance. Mol Pharmacol, 74 (6): 1610–1619. (2008). 987. Sharma N, Das S, Mondal A, et al. Endoplasmic reticulum stress markers are associated with obesity in non-diabetic subjects. J Clin Endocrinol Metab, 93 (11): 4532–4541. (2008). 988. Won JC, Jang P-G, Namkoong C, et al. Central administration of an endoplasmic reticulum stress inducer inhibits the anorexigenic effects of leptin and insulin. Obesity (Silver Spring), 17 (10): 1861–1865. (2009). 989. Tong Y, Lin Y, Zhang Y, et al. Association between TCF7L2 gene polymorphisms and susceptibility to type 2 diabetes mellitus: a large Human Genome Epidemiology (HuGE) review and meta-analysis. BMC Med Genet, 10: 15. (2009). 990. Lotta LA. Genome-wide association studies in atherothrombosis. Eur J Intern Med, 21 (2): 74– 78. (2010). 991. Harismendy O, Notani D, Song X, et al. 9p21 DNA variants associated with coronary artery disease impair interferon-γ signalling response. Nature, 470 (7333): 264–268. (2011). 992. Lanza IR, Zhang S, Ward LE, et al. Quantitative metabolomics by 1H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes. PLoS ONE, 5 (5): e10538. (2010). 993. Ha CY, Kim JY, Paik JK, et al. The association of specific metabolites of lipid metabolism with markers of oxidative stress, inflammation and arterial stiffness in men with newly diagnosed type 2 diabetes. Clinical Endocrinology, 76 (5): 674–682. (2012). 994. Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med, 17 (4): 448–453. (2011). 995. Glass DJ & Hall N. A brief history of the hypothesis. Cell, 134 (3): 378–381. (2008). 996. Glass DJ. A critique of the hypothesis, and a defense of the question, as a framework for experimentation. Clin Chem, 56 (7): 1080–1085. (2010). 997. Food and Drug Administration. Avandia (rosiglitazone maleate) tablets. Drug approval package. Company: SmithKline Beecham Pharmaceuticals. Application No: 21-071. Approval Date: 5/25/1999. Available at: http://www.accessdata.fda.gov/drugsatfda_docs/nda/99/21071_Avandia.cfm, accessed April 23, 2012. - 284 -
998. Baucus M & Grassley C. Staff report on GlaxoSmithKline and the diabetes drug Avandia. (U.S. Government Printing Office, Washington, 2010). 999. Houtkooper RH & Auwerx J. Obesity: new life for antidiabetic drugs. Nature, 466 (7305): 443– 444. (2010). 1000. Choi JH, Banks AS, Estall JL, et al. Anti-diabetic drugs inhibit obesity-linked phosphorylation of PPAR-gamma by Cdk5. Nature, 466 (7305): 451–456. (2010). 1001. Choi JH, Banks AS, Kamenecka TM, et al. Antidiabetic actions of a non-agonist PPAR-γ ligand blocking Cdk5-mediated phosphorylation. Nature, 477 (7365): 477–481. (2011). 1002. Ryan KK, Li B, Grayson BE, et al. A role for central nervous system PPAR-γ in the regulation of energy balance. Nat Med, 17 (5): 623–626. (2011). 1003. Lu M, Sarruf DA, Talukdar S, et al. Brain PPAR-γ promotes obesity and is required for the insulin-sensitizing effect of thiazolidinediones. Nat Med, 17 (5): 618–622. (2011). 1004. International Diabetes Federation. Diabetes and impaired glucose tolerance. Diabetes Atlas. Available at: http://www.diabetesatlas.org/content/diabetes-and-impaired-glucose-tolerance, accessed March 29, 2011. 1005. Bennett WL, Maruthur NM, Singh S, et al. Comparative effectiveness and safety of medications for type 2 diabetes: an update including new drugs and 2-drug combinations. Ann Intern Med, 154 (9): 602–613. (2011).
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- 286 - Section 5:
Appendices
- 287 - - 288 - Appendix 1:
Chapter 3, LOADD study supplementary information
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- 290 - Appendix 1, Table 1: Baseline characteristics of LOADD study participants. All measures report n (%) with the exception of age and duration of diabetes. As previously published in Coppell KJ, Kataoka M, Williams SM, et al. Nutritional intervention in patients with type 2 diabetes who are hyperglycaemic despite optimised drug treatment - Lifestyle Over and Above Drugs in Diabetes (LOADD) study: randomised controlled trial. BMJ, 341 (7766): c3337. (2010).
Intervention Control n 45 48 Age (yrs) – mean (SD) 56.6 (8.8) 58.4 (8.8) Women 28 (62) 27 (56) Current smoker 4 (9) 3 (6) Duration of diabetes (yrs) – mean (SD) 8.7 (6.4) 9.0 (5.8) Ethnicity: New Zealand European 34 (76) 42 (88) Mäori 1 (2) 2 (4) Pacific Island 3 (7) 0 (0) Other 7 (16) 4 (8) Medication: Oral hypoglycaemic agents 31 (69) 34 (71) Insulin 1 (2) 0 (0) Insulin and oral hypoglycaemic agents 13 (29) 14 (29) Lipid modifying drugs 26 (58) 31 (65) ACE inhibitors, A2 receptor blockers 24 (53) 30 (63) Other antihypertensive agents 14 (31) 23 (48) Aspirin 17 (38) 31 (65)
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Appendix 1, Table 2: Dietary intake across intervention and control groups. Difference (95 % CI) and p values adjusted for age, sex and baseline measures. As previously published in Coppell KJ, Kataoka M, Williams SM, et al. Nutritional intervention in patients with type 2 diabetes who are hyperglycaemic despite optimised drug treatment - Lifestyle Over and Above Drugs in Diabetes (LOADD) study: randomised controlled trial. BMJ, 341 (7766): c3337. (2010).
Intervention Control Baseline 6 months Baseline 6 months Difference Dietary component (n = 39) (n = 39) (n = 39) (n = 39) (95 % CI) P value Energy (kJ) 8020 (1899) 6855 (1770) 7845 (2085) 7171 (2087) - 334 (- 1082, 414) 0.376 Protein (% TE) 19.7 (3.7) 22.1 (3.9) 19.2 (3.5) 20.4 (4.1) 1.6 (0.04, 3.1) 0.045 Total fat (% TE) 30.9 (6.1) 28.7 (5.3) 29.8 (6.1) 29.9 (6.6) - 1.7 (- 4.4, 1.0) 0.211 Saturated fatty acids (% TE) 11.2 (3.2) 9.7 (2.5) 11.3 (3.5) 11.7 (3.4) - 1.9 (- 3.3, - 0.6) 0.006 Polyunsaturated fatty acids (% TE) 5.2 (1.6) 5.6 (1.8) 5.1 (2.0) 4.7 (1.9) 1.2 (- 0.7, 3.2) 0.211 Carbohydrate (% TE) 47.5 (7.1) 48.0 (5.3) 50.1 (7.5) 48.5 (7.2) 0.5 (- 2.3, 3.2) 0.731 Dietary fibre (g) 25.7 (6.3) 26.3 (7.3) 26.4 (5.6) 23.5 (7.6) 3.0 (- 0.2, 6.1) 0.064
Appendix 1, Table 3: Pairwise correlation coefficients of the change in BMI, HbA1c, IL-18 and neopterin for participants from both control and intervention groups.
∆ BMI ∆ HbA1c ∆ IL-18
r p n r p n r p n
∆ HbA1c 0.302 0.003 92 1 - - - - -
∆ IL-18 -0.038 0.724 90 0.137 0.199 90 1 - -
∆ neopterin -0.112 0.336 76 -0.024 0.835 76 0.299 0.009 76
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- Appendix 1, Figure 1: Scatter graphs showing the correlation of change in neopterin levels with various dietary components, as follows: A – dietary fat; B – polyunsaturated fat
(PUFA) intake; C – monounsaturated fat (MUFA) intake; D – saturated fat (SFA) intake; E – SFA intake for participants in control group only; F – SFA intake for participants in intervention group only; G – protein intake; H – carbohydrate (CHO) intake; I – dietary fibre intake. Note that the two outlying Δ neopterin values shown in Figure 2, Chapter 3 have been excluded.
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- Appendix 1, Figure 2: Scatter graphs showing the correlation of change in IL-18 levels with various dietary components, as follows: A – dietary fat; B – polyunsaturated fat
(PUFA) intake; C – monounsaturated fat (MUFA) intake; D – saturated fat (SFA) intake; E – carbohydrate (CHO) intake; F – protein intake; G – dietary fibre intake.
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- Appendix 1, Figure 3: Distribution of baseline measures of adipokines and inflammatory markers in the LOADD study, used for the generation of Pearson pairwise correlation
coefficients shown in Chapter 3, Table 6. Outliers described in Chapter 2, Sections 2.4 & 2.5 have been excluded.
Appendix 1, Figure 3 cont:
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300 300
- Appendix 1, Table 4, Panels A - G: Pearson correlation coefficients, p-values, and number of observations for correlations shown in Chapter 3, Table 6. r = Pearson pairwise correlation coefficient; p = p-value; n = number of observations. Units for measurements are as shown in Table 6, Chapter 3.
A
HbA1c waist weight t. chol. HDL LDL
r p n r p n r p n r p n r p n r p n
waist 0.19 0.050 104 ------
weight 0.27 0.007 104 0.88 0.000 104 ------
t. chol. 0.28 0.004 104 -0.01 0.903 104 0.00 0.966 104 ------
HDL -0.06 0.513 104 -0.16 0.098 104 -0.18 0.072 104 0.09 0.361 104 ------
LDL 0.29 0.003 104 -0.03 0.734 104 0.00 0.966 104 0.96 0.000 104 0.01 0.898 104 - - -
TAG 0.11 0.272 104 0.17 0.098 104 0.12 0.237 104 0.35 0.000 104 -0.41 0.000 104 0.13 0.182 104
glucose 0.80 0.000 104 0.12 0.209 104 0.21 0.032 104 0.20 0.037 104 -0.10 0.337 104 0.20 0.040 104
SBP 0.07 0.492 104 0.19 0.048 104 0.15 0.130 104 0.05 0.606 104 -0.06 0.543 104 0.07 0.504 104
DBP 0.17 0.085 104 0.26 0.008 104 0.24 0.016 104 -0.04 0.682 104 -0.15 0.128 104 -0.03 0.778 104
BMI 0.32 0.001 104 0.81 0.000 104 0.84 0.000 104 0.06 0.568 104 -0.07 0.486 104 0.04 0.656 104
uric acid -0.03 0.730 104 0.26 0.007 104 0.25 0.012 104 0.03 0.762 104 -0.05 0.614 104 0.01 0.893 104
leptin 0.30 0.006 83 0.30 0.006 83 0.27 0.013 83 0.29 0.008 83 0.24 0.029 83 0.26 0.017 83
neopterin 0.05 0.660 79 0.14 0.217 79 0.06 0.623 79 -0.14 0.222 79 -0.01 0.962 79 -0.16 0.156 79
sTNF-R1 -0.05 0.626 90 0.22 0.040 90 0.14 0.189 90 0.00 0.984 90 -0.04 0.727 90 -0.06 0.582 90
sTNF-R2 -0.16 0.128 90 0.12 0.279 90 0.04 0.690 90 -0.06 0.588 90 0.01 0.923 90 -0.07 0.534 90
B
HbA1c waist weight t. chol. HDL LDL
r p n r p n r p n r p n r p n r p n
IL-1β 0.01 0.941 63 -0.05 0.686 63 -0.02 0.904 63 0.16 0.211 63 0.17 0.179 63 0.21 0.098 63
IL-1ra 0.03 0.855 34 -0.04 0.808 34 -0.12 0.511 34 0.16 0.381 34 0.30 0.081 34 0.10 0.575 34
IL-6 -0.10 0.422 68 -0.11 0.351 68 -0.15 0.211 68 -0.01 0.915 68 0.19 0.127 68 -0.03 0.833 68
IL-10 -0.05 0.708 65 -0.07 0.589 65 -0.01 0.946 65 0.03 0.832 65 0.29 0.018 65 0.05 0.708 65
TNFα 0.03 0.777 90 0.10 0.367 90 0.18 0.091 90 0.03 0.770 90 -0.17 0.111 90 0.00 0.982 90
IL-18 0.13 0.221 91 0.31 0.002 91 0.28 0.007 91 -0.08 0.441 91 -0.23 0.026 91 -0.15 0.885 91
IL-18BP 0.11 0.348 76 0.12 0.320 76 0.13 0.279 76 0.00 0.985 76 0.00 0.985 76 0.00 0.985 76
age -0.23 0.017 104 0.05 0.593 104 -0.04 0.682 104 -0.15 0.123 104 0.13 0.194 104 -0.17 0.078 104
male sex -0.12 0.237 104 0.20 0.044 104 0.21 0.044 104 -0.09 0.353 104 -0.22 0.027 104 -0.06 0.577 104
duration 0.15 0.134 104 0.17 0.079 104 0.23 0.017 104 -0.20 0.044 104 0.09 0.385 104 -0.20 0.040 104
smoker -0.07 0.467 104 -0.22 0.022 104 -0.15 0.136 104 0.05 0.588 104 -0.17 0.085 104 0.08 0.415 104
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301 301
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302 302 C
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TAG glucose SBP DBP BMI uric acid
r p n r p n r p n r p n r p n r p n
glucose 0.13 0.194 104 ------
SBP 0.01 0.922 104 0.05 0.613 104 ------
DBP 0.05 0.605 104 0.22 0.028 104 0.61 0.000 104 ------
BMI 0.10 0.319 104 0.23 0.018 104 0.12 0.224 104 0.06 0.529 104 ------
uric acid 0.09 0.362 104 -0.15 0.128 104 0.21 0.034 104 0.10 0.327 104 0.18 0.070 104 - - -
leptin 0.00 0.989 79 0.25 0.024 83 -0.09 0.394 83 -0.14 0.208 83 0.48 0.000 83 -0.04 0.741 83
neopterin 0.01 0.956 79 -0.03 0.761 79 0.09 0.448 79 -0.04 0.706 79 0.13 0.237 79 0.02 0.861 79
sTNF-R1 0.17 0.120 90 -0.07 0.489 90 0.06 0.591 90 -0.15 0.169 90 0.17 0.105 90 0.07 0.529 90
sTNF-R2 -0.01 0.947 90 -0.27 0.010 90 0.09 0.386 90 -0.18 0.097 90 0.08 0.448 90 0.19 0.080 90
IL-1β -0.16 0.200 63 -0.04 0.740 63 0.09 0.507 63 0.20 0.121 63 0.05 0.708 63 0.16 0.218 63
IL-1ra -0.01 0.965 34 -0.09 0.629 34 0.16 0.376 34 0.07 0.684 34 -0.14 0.442 34 -0.12 0.500 34
IL-6 -0.10 0.430 68 -0.10 0.420 68 -0.09 0.463 68 -0.07 0.550 68 -0.10 0.401 68 -0.17 0.174 68
IL-10 -0.24 0.054 65 -0.12 0.350 65 -0.07 0.600 65 0.05 0.709 65 -0.11 0.387 65 -0.06 0.631 65
TNFα 0.20 0.058 90 -0.04 0.687 90 -0.18 0.099 90 -0.08 0.427 90 0.14 0.181 90 -0.04 0.743 90
IL-18 -0.03 0.780 91 -0.04 0.720 91 0.03 0.808 91 0.03 0.767 91 0.26 0.012 91 0.14 0.202 91
IL-18BP 0.00 0.989 76 0.09 0.435 76 0.19 0.099 76 0.23 0.047 76 0.10 0.403 76 -0.03 0.784 76
age -0.06 0.524 104 -0.22 0.023 104 0.30 0.002 104 -0.19 0.054 104 -0.02 0.846 104 0.15 0.117 104
D
TAG glucose SBP DBP BMI uric acid
r p n r p n r p n r p n r p n r p n male sex 0.01 0.921 104 -0.15 0.120 104 0.04 0.667 104 0.22 0.024 104 -0.19 0.054 104 0.28 0.004 104 duration -0.10 0.302 104 -0.01 0.881 104 0.15 0.134 104 -0.10 0.293 104 0.26 0.008 104 0.10 0.314 104 smoker 0.05 0.630 104 -0.06 0.558 104 -0.12 0.234 104 0.02 0.854 104 -0.15 0.120 104 -0.13 0.189 104
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30
3
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E
leptin neopterin sTNF-R1 sTNF-R2 IL-1β IL-1ra
r p n r p n r p n r p n r p n r p n
neopterin 0.02 0.839 72 ------
sTNF-R1 0.16 0.145 82 0.22 0.053 79 ------
sTNF-R2 0.12 0.297 82 0.25 0.029 79 0.53 0.000 90 ------
IL-1β -0.01 0.945 59 0.00 0.994 51 -0.03 0.821 62 -0.16 0.201 62 ------
IL-1ra -0.20 0.318 28 0.50 0.004 31 0.01 0.950 34 -0.10 0.556 34 -0.04 0.849 26 - - -
IL-6 -0.05 0.677 61 0.00 0.973 58 0.11 0.388 67 0.03 0.800 67 0.05 0.749 49 0.12 0.498 32
IL-10 -0.02 0.877 58 0.06 0.685 53 -0.01 0.917 64 -0.14 0.281 64 0.01 0.963 53 0.58 0.001 29
TNFα 0.00 0.971 82 -0.06 0.590 78 0.13 0.219 89 -0.09 0.405 89 0.13 0.295 63 -0.06 0.736 34
IL-18 0.03 0.764 83 0.18 0.111 80 0.18 0.093 91 0.15 0.147 91 0.07 0.600 63 0.30 0.088 34
IL-18BP 0.00 0.995 70 0.17 0.154 68 0.15 0.206 75 0.07 0.572 75 -0.06 0.675 52 0.11 0.562 31
age -0.04 0.720 83 0.32 0.004 79 0.22 0.041 90 0.24 0.021 90 -0.25 0.046 63 0.04 0.815 34
male sex -0.38 0.000 83 -0.02 0.843 79 0.03 0.744 90 -0.11 0.325 90 -0.10 0.423 63 0.16 0.358 34
duration 0.14 0.202 83 0.22 0.055 79 0.03 0.784 90 0.13 0.217 90 -0.12 0.365 63 0.26 0.139 34
smoker -0.24 0.030 83 0.07 0.552 79 0.11 0.309 90 0.22 0.042 90 0.38 0.002 63 -0.15 0.406 34
F
IL-6 IL-10 TNFα IL-18 IL-18 BP age
r p n r p n r p n r p n r p n r p n
IL-10 0.08 0.578 54 ------
TNFα 0.28 0.019 68 0.28 0.023 65 ------
IL-18 -0.11 0.383 68 0.16 0.207 65 0.05 0.628 90 ------
IL-18 BP -0.05 0.687 58 0.24 0.083 53 0.09 0.444 75 0.12 0.313 76 ------
age 0.02 0.898 68 0.01 0.963 65 -0.28 0.008 90 -0.10 0.354 91 0.12 0.294 76 - - -
male sex -0.16 0.194 68 0.16 0.213 65 0.10 0.359 90 0.18 0.093 91 0.09 0.417 76 0.06 0.483 121
duration -0.19 0.127 68 -0.06 0.619 65 -0.08 0.432 90 0.14 0.181 91 0.07 0.550 76 0.25 0.005 120
smoker -0.01 0.918 68 -0.10 0.441 65 0.30 0.004 90 0.05 0.605 91 -0.02 0.874 76 -0.31 0.001 121
G
male sex duration
r p n r p n
duration 0.03 0.779 120 - - -
smoker 0.05 0.617 121 -0.20 0.031 120
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305 305
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306 306 Appendix 1, Figure 4: Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines (y-axes) and age & sex (x axes) identified in
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Table 6. For each panel (e.g. A, B) a linear regression line plotted with GraphPad Prism (red dashed line) and the corresponding r, p, and n values are shown below the graph. Panels marked A’, B’, etc. show the same scatter as the corresponding upper panel but with outliers suspected of influencing the correlation (marked in red) removed.
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Appendix 1, Figure 5: Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and smoking iden tified in Table 6. For each panel (e.g. A, B) a linear regression line plotted with GraphPad Prism (red dashed line) and the corresponding r, p, and n values are shown below the graph. Panels marked A’, B’, etc. show the same scatter as the corresponding upper panel but with outliers suspected of influencing the correlation (marked in red) removed.
Appendix 1, Figure 6: Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and anthropometric measures identified in Table 6. For each panel (e.g. A, B) a linear regression line plotted with GraphPad Prism (red dashed line) and the correspond ing r, p, and n values are shown below the graph.
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Appendix 1, Figure 7: Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and markers of c ardiovascular risk identified in Table 6. For each panel (e.g. A, B) a linear regression line plotted with GraphPad Prism (red dashed line) and the corresponding r, p, and n values are shown below the graph. Panels marked A’, B’, etc. show the same scatter as the corresponding upper panel but with outliers suspected of influencing the correlation (marked in red) removed.
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Appendix 1, Figure 8: Scatter plots of statistically significant pairwise correlations between inflammatory markers & adipokines and markers of glycaemia identified in Table 6. For each panel (e.g. A, B) a linear regression line plotted with GraphPad Prism (red dashed line) and the corresponding r, p, and n values are shown below the graph. Panels marked A’, B’, etc. show the same scatter as the corresponding upper panel but with outliers suspected of influencing the correlation (marked in red) removed
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Appendix 1, Figure 9: Scatter plots of statistically significant pairwise correlations between inflammatory markers and adipokines identified in Table 6. For each panel (e.g. A, B) a linear regression line plotted with GraphPad Prism (red dashed line) and the corresponding r, p, and n values are shown below the graph. In order to examine whether associations were robust or unduly influenced by possible outliers influencing the strength of association, suspected outlier s (marked in upper graphs with red circles) were excluded in corresponding panels below (e.g. A’, B’), which show the same analysis with circled points removed.
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Appendix 2:
Section 3 supplemental information, detecting IL-18 system splice variants by RT-PCR
- 317 - Appendix 2.1: Establishing methodology to examine expression of an IL-18 splice variant
Appendix 2.1.1 Designing primers to amplify an IL-18 splice variant by RT-PCR:
Primers were designed to bind to either the 57 bp segment present only in the full-length IL-18 transcript, or to span the nucleotides either side of this segment to amplify the IL-18s transcript, as shown in Appendix 3, Figure 1 and Appendix 3, Table 1. A trial PCR was set up to test these primers, using RT products extracted from male Sprague-Dawley spleen and adrenal tissue. PCR tubes were prepared as stated in the General Laboratory Methods section, Chapter 6, Section 6.2, using PCR settings of 94 °C for 3 min as an initial denaturing step, followed by 35 cycles of 94 °C for 30 seconds for denaturing, 57 °C for 30 seconds for annealing, and 72 °C for 45 seconds for extension steps. A final extension step of 72 °C for 5 minutes concluded the PCR. Gel electrophoresis of reaction products is shown in Appendix 3, Figure 2.
Appendix 2.1.2 Performance and testing of IL-18 splice variant primers:
While the primer combination F and the ‘common reverse’ primer should theoretically amplify both transcripts, this is difficult to visualise; a lighter band corresponding to the IL18s transcript could be present in the adrenal RT+ lane (3rd left from ladder) although the intensity of the full-length IL18 band is such that it is difficult to know if the lighter IL18s band is truly present. The primer combination F and ‘IL-18s’ reverse primer, designed to amplify the IL-18s transcript, produced a clear band the expected size in the spleen, and in the adrenal RT+ product apparently shows some ability to amplify the full-length IL-18 transcript as well, with a heavier band corresponding to the expected size of the full-length IL-18 transcript visible (expected size shown in Appendix 2, Table 1). This could potentially arise due to the first 13 nucleotides of the ‘IL-18s’ reverse primer providing sufficient adhesion to the full-length IL-18 transcript to allow some amplification, despite the 3’ instability introduced by the remaining nucleotides of the primer not matching the reference sequence. The primer combination F and ‘IL-18 full only’ reverse produced clear single bands in both spleen and adrenal samples showing amplification of the full-length IL-18 transcript.
- 318 -
Appendix 2, Table 1: Primer details for establishing methodology for amplifying an IL-18 splice variant and examining its expression in the habenula (Chapter 7, Section 7.5).
Transcript(s) Primer pair Primer sequences Primers designed to bind Expected product
IL18 transcripts
IL18: both Forward (F) CTGTGTTCGAGGACATGCCTG ENSRNOT00000013093, 191-211; full IL18: 353 bp transcripts IL18 reference sequence within exon 3 IL18s: 296 bp
Common reverse ATCCCCATTTTCATCCTTCC ENSRNOT00000013093, binding 524-543; IL18 reference sequence within exon 5
IL18s * Forward (F) CTGTGTTCGAGGACATGCCTG ENSRNOT00000013093, 191-211; IL18s: 183 bp IL18 reference sequence within exon 3 (full IL18: 240 bp)
IL18s reverse CTGGCACACGTTT-CTCAAAG ENSRNOT00000013093 354-360/418-430; IL18 reference sequence within exons 4 - 5
IL18 full Forward (F) CTGTGTTCGAGGACATGCCTG ENSRNOT00000013093, 191-211; IL18 full IL18: 232 bp transcript only † reference sequence within exon 3
IL18 full reverse CGTTTCTGAAAGAATATGAGATCAC ENSRNOT00000013093, binding 398-422; IL18 reference sequence within exon 5
* Reverse primer was designed to cross the exon sequences either side of the 57 bp segment which is missing in the IL18s transcript. The point of division between exons 4 and 5 is shown as a dash in the primer sequence.
† Underlined bases show those binding with the 57bp segment missing in the IL18s transcript.
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Appendix 2, Figure 1: Schematic diagram showing location of primers designed to amplify rat IL-18 splice variants, shown against reference sequence NM_019165.1/ENSROT00000013093. One common forward primer ‘F’ was combined with three different reverse primers spanning the 57 bp segment which is present only in the full-length IL-18 transcript. ‘Common reverse primer’, binding nucleotides 524-543 of the reference sequence, should theoretically amplify both transcripts; ‘IL-18 full only’ primer, binding nucleotides 398-422, is almost entirely within the 57 bp segment and thus designed to amplify only the full-length IL-18 transcript; ‘IL-18s’ reverse primer corresponds to the nucleotides either side of the 57 bp segment, nucleotides 418-430 and 354- 360, and theoretically should only amplify the the IL-18s transcript.
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Appendix 2, Figure 2: RT-PCR testing the amplification of IL-18 transcripts. Primer combinations are shown above. Samples from left-to-right are: 100 bp ladder, spl = spleen, adr = adrenal, H20 = control lane with water only. Positive and negative symbols indicate RT+ and RT- lanes. Expected product sizes are shown in Chapter 6, Table 7. The darker band towards the middle of the DNA ladder is 600 bp. Expected product sizes - IL-18: 352 bp, IL-18s: 182 bp (239 bp for amplification of full-length IL-18), full IL-18 only: 231 bp.
- 321 - Appendix 2.2: Establishing methodology to examine expression of an IL-18r1 splice variant
A splice variant of IL-18r1 has been reported by Alboni et al. 1, which contains an inserted intron sequence downstream of exon 9. The inserted nucleotides form a stop codon shortly into the previously intronic sequence (Appendix 2, Figure 3), such that the receptor is thought to form a truncated version of the IL-18r1 reference sequence, encoding the transmembrane region but lacking much of the intracellular amino acid sequence. The corresponding intron in the rat was compared to that in the mouse (Appendix 2, Figure 3), showing that a similar sequence could be encoded by the equivalent intron in the rat. A reverse primer was therefore designed to bind the equivalent nucleotides in the rat as those targeted by Alboni et al. in the mouse. A PCR reaction was carried out using cDNA reverse transcribed from rat spleen total RNA, using primers and reaction conditions as described in Chapter 7, Tables 9 and 10. Gel electrophoresis showed a band of the expected size, and samples were purified by column purification (as described in the General Laboratory Methods section, Chapter 5) and sequenced by the Genetics Analysis Services, Department of Anatomy, University of Otago. Resulting reaction sequences are shown aligned to the predicted rat IL-18r1 type II sequence in Appendix 2, Figure 4. Sequences showed the amplification of the desired transcript.
- 322 - Mouse GGTAATATGCTGCTGTGACAGGGGCACCCTAGCTCATGTTTTTGTGCCTGCACCCAGCAAA |||||| ||| |||||||||||| | |||||||||||||| |||||||||| || ||| Rat GGTAATCTGCCGCTGTGACAGGGACGTCCTAGCTCATGTTTTCATGCCTGCACCAAGTAAA
Mouse GGAAAGAAACACTGGTCTTCCTTTTATTTGAATGATCCCACTAATGATCTCTGAGCCTCG ||| | ||||||||||||||||||||| |||| | |||| |||| || Rat GGA-CG---CACTGGTCTTCCTTTTATTTGGATGACGCTGCTAACAATCTGAGACTGC--
Mouse TTCCCAGCCTGGAGTCAGGGGTTACTCTGTGTGGGTTACACTGAAACAGGCGACATGCAA ||||||||||||||||||| ||||| |||||||| ||||||||| | ||| | ||||| Rat TTCCCAGCCTGGAGTCAGGAGTTACACTGTGTGGCTTACACTGAGGCGGGCTATGTGCAA
Mouse CCCTATACTTGTTTGTGAGAAAACAGATGTGTTTCTTCTGATGTCTCATGTCTGTGAACT || | |||||| |||| | ||| ||||||||||||| || ||||||||||||||| Rat GTCTGTGCTTGTTAGTGACTACTCAGGTGTGTTTCTTCTGGTGCTTCATGTCTGTGAACT
Mouse GTCTCCTCACACGTCTTGTGCTGTTAGTCCGAGTGTGTTTACACACACA ||||||||||| ||| ||||||| ||| |||||| ||||||||| Rat GTCTCCTCACATGTCCCGTGCTGTCCGTCTACGTGTGT--ACACACACA
Appendix 2, Figure 3. Alignment of rat intronic sequence with the corresponding mouse intronic sequence transcribed in the IL18r1 type II splice variant described in Alboni et al. 1 (nucleotides 25812 – 26100 of ENSMUSG00000026070) and rat sequence (nucleotides 19354 – 19634 of ENSRNOG00000015027; transcript codes refer to the Ensembl database entries 2). The initial codons in the sequence are shown by alternating grey highlighting; the final nucleotide in exon 9, upstream of the shown intron, forms the first base of the first codon in both species and is shown underlined. Underlined and bold nucleotides show common stop codon. Nucleotides in bold and double-underlined show position of reverse primer used to amplify the type II transcript by Alboni et al. in the mouse 1 and the equivalent bases in the rat sequence used as the basis of the reverse primer used here, as shown in Chapter 7, Table 9. Vertical lines indicate matching nucleotides across the species.
- 323 -
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318 318 - Appendix 2, Figure 4: Sequencing results for rat IL-18r1 type II. Shown is the manual alignment of reaction product from 'Forward C' and 'IL-18r1 type II reverse' primers as shown in Chapter 7, Table 9 ('Product') from sequencing reactions with expected sequence (above, 'Ref') derived from reference sequence ENSRNOG00000015027 from the Ensembl database. The previously intronic region which is inserted in IL-18r1 type II downstream of exon 9 is shown in grey highlighting in the reference sequence. A: product from sequencing reaction using the forward primer 'Forward C', B: product from sequencing reaction using the reverse primer 'IL-18r1 type II reverse'.
A.
Ref: CCAACGAAGAAGCCACAGACACCAAAAGCTTCATCTTGGTGAGAAAAGAAACACCTGACATCCAAGGCCATGTCTTTATGAGAGGAATAACCATGGTTGTTCTCACCTCTGTGGCA ||| | | | || ||||| |||| |||| ||||||||||| ||||||| || || | ||||| ||||||||||| Product: CTTTGGSTGMKWAAGAAAMCCTGAG-TCCAGGGCCCTGTCTTTATGASAGGAATAGCCMYGGGTTTTCTCMCCTCTGTGGCA
Ref: GCAGTGTGCGTGGTGATTTTGTGCATCATTTATAAAGTTGATTTGGTTTTATTCTATAGACGTGTAGCAGAAAGAGACGAGACACTAACAGGTAATCTGCCGCTGTGACAGGGACG |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: GCAGTGTGCGTGGTGATTTTGTGCATCATTTATAAAGTTGATTTGGTTTTATTCTATAGACGTGTAGCAGAAAGAGACGAGACACTAACAGGTAATCTGCCGCTGTGACAGGGACG
Ref: TCCTAGCTCATGTTTTCATGCCTGCACCAAGTAAAGGACGCACTGGTCTTCCTTTTATTTGGATGACGCTGCTAACAATCTGAGACTGCTTCCCAGCCTGGAGTCAGGAGTTACAC ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||||||||||||||| |||||||||||||||||||||| Product: TCCTAGCTCATGTTTTCATGCCTGCACCAAGTAAAGGACGCACTGGTCTTCCTTTTATTTGGATGACRCTGCTAACAATCTGAGACTGCTTCCYAGCCTGGAGTCAGGAGTTACAC
Ref: TGTGTGGCTTACACTGAGGCGGGCTATGTGCAAGTCTGTGCTTGTTAGTGACTACTCAGGTGTGTTTCTTCTGGTGCTTCATGTCTGTGAACTGTCTCCTCACATGTCCCGTGCT ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: TGTGTGGCTTACACTGAGGCGGGCTATGTGCAAGTCTGTGCTTGTTAGTGACTACTCAGGTGTGTTTCTTCTGGTGCTTCATGTCTGTGAACTGTCTCCTCACATGTCCCGTGCT
B.
Ref: CCAACGAAGAAGCCACAGACACCAAAAGCTTCATCTTGGTGAGAAAAGAAACACCTGACATCCAAGGCCATGTCTTTATGAGAGGAATAACCATGGTTGTTCTCACCTCTGTGGC ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: TCCAACGAAGAAGCCACAGACACCAAAAGCTTCATCTTGGTGAGAAAAGAAACACCTGACATCCAAGGCCATGTCTTTATGAGAGGAATAACCATGGTTGTTCTCACCTCTGTGGC
Ref: AGCAGTGTGCGTGGTGATTTTGTGCATCATTTATAAAGTTGATTTGGTTTTATTCTATAGACGTGTAGCAGAAAGAGACGAGACACTAACAGGTAATCTGCCGCTGTGACAGGGAC |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: AGCAGTGTGCGTGGTGATTTTGTGCATCATTTATAAAGTTGATTTGGTTTTATTCTATAGACGTGTAGCAGAAAGAGACGAGACACTAACAGGTAATCTGCCGCTGTGACAGGGAC
Ref: GTCCTAGCTCATGTTTTCATGCCTGCACCAAGTAAAGGACGCACTGGTCTTCCTTTTATTTGGATGACGCTGCTAACAATCTGAGACTGCTTCCCAGCCTGGAGTCAGGAGTTACA |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||||||||||| Product: GTCCTAGCTCATGTTTTCATGCCTGCACCAAGTAAAGGACGCACTGGTCTTCCTTTTATTTGGATGACGCTGCTAACAATCTGAGACTGCTTCC-AGCCTGGAGTCAGGAGTTACA
Ref: CTGTGTGGCTTACACTGAGGCGGGCTATGTGCAAGTCTGTGCTTGTTAGTGACTACTCA-GGTGTGTTTCTTCTGGT--GCTTCATGTCTGTGAACTGTCTCCTCACATGTCCCGT |||||||||||||||||||||||| ||| ||||| ||| ||| ||||| |||||| | |||| |||| | Product: CTGTGTGGCTTACACTGAGGCGGGG-ATGCCCCAGTCTCTGCTCTAGAGT--CTACTAGCGGTGTGATGACTCTGTCAAGCTTACCG
Ref: GCT
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Appendix 2.3: Establishing methodology to examine expression of sIL-18rap
An IL-18rap splice variant had been reported in the rat 3 and human 4 at the time of undertaking the experiments described in this thesis. Subsequently the equivalent splice variant has been described in the mouse 5. This has been referred to as sIL-18Rβ by Andre et al. in the rat 3, as IL-18RAPshort by Fiszer et al. in the human 4, and as short IL-18Rβ by Alboni et al. in the mouse 5. In this thesis, this splice variant will be referred to as sIL-18rap. Primers were designed to amplify this transcript in the rat, as described in Chapter 7, Tables 9 and 10, using a semi-nested PCR protocol. These primers were tested on cDNA obtained from reverse transcription of rat spleen samples, using the PCR settings described in Chapter 7, Section 7.2. Gel electrophoresis showed a band of the expected size, and the remaining PCR product was purified by column purification, as described in the General Laboratory Methods chapter, Chapter 5, and sequenced by the Genetics Analysis Service, Department of Anatomy, University of Otago. Sequencing results are shown below in Appendix 2, Figure 5 manually aligned against the expected reference sequence. Results demonstrated that the desired transcript had been amplified.
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Appendix 2, Figure 5: Sequencing results for rat sIL-18rap. Shown is the manual alignment of reaction product (386 bp; 'Product') from sequencing reactions with expected sequence (above, 'Ref') derived from reference sequence ENSRNOT00000020194 from Ensembl database. The previously intronic region which is inserted in sIL-18rap is shown in grey highlighting in the reference sequence. A: product from sequencing reaction using the forward primer, B: product from sequencing reaction using the reverse primer.
A.
Ref: CAAATCATCCCAGTCTGAAAATATTCTCAAATGTTCTTTTATCTAGACAAGCGCTGACAGAACTTTCTCTGTCTCTCAGGAGGCCCCAGGATACAGCTTGTTGTGTGAAGACTGTC ||||||| |||| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: CYMKGATCTTTTT-WYAGAC-AGCGCTGACAGAACTTTCTCTGTCTCTCAGGAGGCCCCAGGATACAGCTTGTTGTGTGAAGACTGTC
Ref: TTGGAAGTTAAGCCTCAGAGAAATATATCCTGTGGGAGCTCAGCACAAGATGAGCAAGTCCTACTACTTGGCAGCACTGGCTCCATTCACTGTCCCAGCCTCAGCTGCCAAAGTGA |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: TTGGAAGTTAAGCCTCAGAGAAATATATCCTGTGGGAGCTCAGCACAAGATGAGCAAGTCCTACTACTTGGCAGCACTGGCTCCATTCACTGTCCCAGCCTCAGCTGCCAAAGTGA
Ref: TGTACGGAGTCCAGAGATGACCTGGTACAAGGATGGAAGACTGCTGCCCGAGTATAAGAAAAATCCAATTGAGATGGAGGATATTTATGATTTTCACCAAGGCTTGTACGTGT GCG |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: TGTACGGAGTCCAGAGATGACCTGGTACAAGGATGGAAGACTGCTGCCCGAGTATAAGAAAAATCCAATTGAGATGGAGGATATTTATGATTTTCACCAAGGCTTGTACGTGT GCG
Ref: ATTACACTCAGTCAGATAATGTGAGTTCCTGG--ACCGTC |||||||||||||||||||||||||||||||| |||||| Product: ATTACACTCAGTCAGATAATGTGAGTTCCTGGGGACCGTCA
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B.
Ref: CAAATCATCCCAGTCTGAAAATATTCTCAAATGTTCTTTTATCTAGACAAGCGCTGACAGAACTTTCTCTGTCTCTCAGGAGGCCCCAGGATACAGCTTGTTGTGTGAAGACTGTC |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: TCAAATCATCCCAGTCTGAAAATATTCTCAAATGTTCTTTTATCTAGACAAGCGCTGACAGAACTTTCTCTGTCTCTCAGGAGGCCCCAGGATACAGCTTGTTGTGTGAAGACTGTC
Ref: TTGGAAGTTAAGCCTCAGAGAAATATATCCTGTGGGAGCTCAGCACAAGATGAGCAAGTCCTACTACTTGGCAGCACTGGCTCCATTCACTGTCCCAGCCTCAGCTGCCAAAGTGAT ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Product: TTGGAAGTTAAGCCTCAGAGAAATATATCCTGTGGGAGCTCAGCACAAGATGAGCAAGTCCTACTACTTGGCAGCACTGGCTCCATTCACTGTCCCAGCCTCAGCTGCCAAAGTGAT
Ref: GTACGGAGTCCAGAGATGACCTGGTACAAGGATGGAAGACTGCTGCCCGAGTATAAGAAAAATCCAATTGAGATGGAGGATATTTATGATTTTCACCAAGGCTTGTACGTGTGCGAT |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||| ||||| |||| Product: GTACGGAGTCCAGAGATGACCTGGTACAAGGATGGAAGACTG-TGCCCGAGTATAAGAAAAATCCAATTGAGATGGAGGATATTTATGATTT-CACCAAGGCT-GTACG----CGAT
Ref: TACACTCAGTCAGATAATGTGAGTTCCTGGACCGTC | | Product: AATTGTA
References, Appendix 2:
1. Alboni S, Cervia D, Ross B, et al. Mapping of the full length and the truncated interleukin-18 receptor alpha in the mouse brain. J Neuroimmunol, 214 (1-2): 43-54. (2009). 2. Hubbard TJP, Aken BL, Ayling S, et al. Ensembl 2009. Nucleic Acids Res, 37 (Suppl 1): D690- 697. (2009). 3. Andre R, Wheeler R, Collins P, et al. Identification of a truncated IL-18R beta mRNA: a putative regulator of IL-18 expressed in rat brain. J Neuroimmunol, 145 (1-2): 40-45. (2003). 4. Fiszer D, Rozwadowska N, Rychlewski L, et al. Identification of IL-18RAP mRNA truncated splice variants in human testis and the other human tissues. Cytokine, 39 (3): 178-183. (2007). 5. Alboni S, Montanari C, Benatti C, et al. Constitutive and LPS-regulated expression of interleukin-18 receptor beta variants in the mouse brain. Brain Behav Immun, 25 (3): 483-493. (2011).
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- 330 - Appendix 3:
Section 3 supplementary information, images of micropunch procedure
- 331 -
Appendix 3, Figure 1: Micropunches of rat brain habenula and arcuate nuclei. A - frozen slide-mounted section; B - the same section after micropunching of the habenula and arcuate nuclei; C - approximate position shown in Nissl-stained coronal section, adapted from http://brainmaps.org, showing the position of the habenula and arcuate nucleus of the hypothalamus.
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Appendix 3, Figure 2: Micropunches of rat brain habenula and ventromedial hypothalamic nuclei. A - frozen slide-mounted section; B - the same section after micropunching of the habenula and ventromedial hypothalamic nuclei; C - approximate position shown in Nissl-stained coronal section, in the rat brain, adapted from http://brainmaps.org, showing the position of the habenula and ventromedial hypothalamic nucleus (VMH).
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Appendix 3, Figure 3: Micropunches of rat brain paraventricular hypothalamic nuclei. A - frozen slide- mounted section; B - the same section after micropunching of the paraventricular hypothalamic nuclei; C - approximate position in Nissl-stained coronal brain section, adapted from http://brainmaps.org, indicating the position of the paraventricular hypothalamic nucleus (PVN).
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Appendix 3, Figure 4: Micropunches of rat brain interpeduncular nuclei and ventral tegmental area. A - frozen slide-mounted section; B - the same section after micropunching of the interpeduncular nuclei and ventral tegmental area; C - approximate position shown in Nissl-stained coronal section, adapted from http://brainmaps.org, indicating the position of the interpeduncular nucleus (IPN) and ventral tegmental area (VTA).
- 335 -
- 336 - Appendix 4:
Chapter 9 supplementary information: IL1RAPL1 and IL1RAPL2 bioinformatics comparisons
- 337 - Appendix 4, Table 1: Consensus amino acid residue codes
Symbol Description Members o alcohol S, T l aliphatic I, L, V a aromatic F, H, W, Y c charged D, E, H, K, R h hydrophobic A, C, F, G, H, I, K, L, M, R, T, V, W, Y - negative D, E p polar C, D, E, H, K, N, Q, R, S, T + positive H, K, R s small A, C, D, G, N, P, S, T, V u tiny A, G, S t turnlike A, C, D, E, G, H, K, N, Q, R, S, T
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Appendix 4, Tables 2a and 2b: Reference protein and nucleotide sequences used for multiple sequence alignment in Figures 19 and 20 (Chapter 9).
Appendix 4, Table 2a: Sequences aligned at protein level only
NCBI reference code Description Species Code used in Figure xc NP_003846.1 interleukin-18 receptor 1 precursor Homo sapiens IL18r1 NP_000868.1 interleukin-1 receptor type 1 precursor Homo sapiens ILr1 NP_057316.3 interleukin-1 receptor-like 1 isoform 1 precursor Homo sapiens IL1rl1 NP_002173.1 interleukin-1 receptor accessory protein isoform 1 Homo sapiens IL1rap AAI06765.1 IL18RAP protein Homo sapiens IL18rap NP_003845.2 interleukin-1 receptor-like 2 precursor Homo sapiens IL1rl2 NP_004624.1 interleukin-1 receptor type 2 precursor Homo sapiens IL1r2 NP_001128525.1 single Ig IL-1-related receptor Homo sapiens SIGIRR
Appendix 4, Table 2b: Sequences aligned at protein and nucleotide level
Protein Nucleotide Species Code used in NCBI reference Description NCBI reference Description Figure xc code code NP_055086.1 interleukin-1 receptor accessory NM_014271.3 interleukin 1 receptor accessory Homo sapiens IL1RAPL1 protein-like 1 precursor protein-like 1 NP_059112.1 X-linked interleukin-1 receptor NM_017416.1 interleukin 1 receptor accessory Homo sapiens IL1RAPL2 accessory protein-like 2 protein-like 2 NP_001138529.1 interleukin-18-binding protein NM_001145057.1 interleukin 18 binding protein, Homo sapiens IL18BPa isoform a precursor transcript variant G NP_671531.1 EVM013 Gene ID: 951634 EVM013 IL-18 binding protein Ectromelia virus EVM013 NP_044005.1 MC054L Gene ID: 1487073 MC054L Molluscum contagiosum MC054L virus subtype 1
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339 339 NP_042049.1 IL-18 binding protein Gene ID: 1486400 D5L Variola virus VV IL18BP NP_619813.1 CPXV024 protein Gene ID: 1485899 CPXV024 Cowpox virus CPXV024
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340 340 Appendix 4, Table 3: Sources of amino acid sequences for 23 species used in multiple sequence alignment of IL1RAPL, IL-18BP and IL-18 proteins. Protein codes refer to
- either entries in the NCBI or Ensembl databases. For the 23 species, 3 were lacking available sequences for IL1RAPL2.
Species Protein Label Binomial name IL1RAPL1 IL1RAPL2 IL-18BP IL-18 (common name) Mouse Mus musculus NP_001153875.1 NP_109613.1 NP_034661.1 NP_032386.1 Rat Rattus norvegicus NP_808796.1 NP_001159814.1 NP_445826.1 NP_062038.1 Cow Bos taurus DAA12672.1 DAA13148.1 XP_002693478.1 NP_776516.1 Gorrilla Gorilla gorilla ENSGGOP00000002965 ENSGGOP00000002732 ENSGGOP00000016552 ENSGGOP00000015475 Pig Sus scrofa ENSSSCP00000012983 XP_001925189.4 XP_003129665.1 NP_999162.1 Elephant Loxodonta africana ENSLAFP00000003644 XP_003414922.1 XP_003420153.1 ENSLAFP00000022754 Microbat Myotis lucifugus ENSMLUP00000010583 ENSMLUP00000000522 ENSMLUP00000016750 ENSMLUP00000019457 Rhesus monkey Macaca mulatta XP_001094959.1 XP_001092265.1 ACB56928.1 NP_001028006.1 Panda Ailuropoda melanoleuca XP_002917099.1 - ENSAMEP00000011457 ENSAMEP00000004279 Human Homo sapiens NP_055086.1 NP_059112.1 NP_001034749.1 NP_001553.1 Gibbon Nomascus leucogenys XP_003261220.1 XP_003262188.1 XP_003254823.1 ENSNLEP00000008896 Chimpanzee Pan troglodytes NP_001009038.1 XP_001139071.2 XP_001174598.1 ENSPTRP00000007359 Marmoset Callithrix jacchus XP_002762793.1 XP_002763187.1 XP_002754923.1 ENSCJAP00000022914 Dog Canis lupus familiaris XP_548919.3 XP_549165.3 NP_001041483.1 NP_001003169.1 Mouse Lemur Microcebus murinus ENSMICP00000008882 ENSMICP00000013067 ENSMICP00000013509 ENSMICP00000007862 Orangutan Pongo abelii ENSPPYP00000022613 XP_002831995.1 XP_002822232.1 ENSPPYP00000004441 Alpaca Vicugna pacos ENSVPAP00000006150 ENSVPAP00000006126 ENSVPAP00000007461 ENSVPAP00000000052 Pika Ochotona princeps ENSOPRP00000012356 - ENSOPRP00000013041 ENSOPRP00000011595 Hedgehog Erinaceus europaeus ENSEEUP00000003652 ENSEEUP00000001342 ENSEEUP00000003443 ENSEEUP00000003610 Tasmanian Devil Sarcophilus harrisii ENSSHAP00000009976 - ENSSHAP00000006411 ENSSHAP00000013657 Horse Equus caballus ENSECAP00000013884 XP_001492643.3 XP_001499501.1 NP_001075981.1 Platypus Ornithorhynchus anatinus ENSOANP00000010794 ENSOANP00000018456 ENSOANP00000026761 ENSOANP00000022580 Hydrax Procavia capensis ENSPCAP00000003578 ENSPCAP00000008566 ENSPCAP00000002011 ENSPCAP00000008651
Appendix 4, Figure 1: (Following pages) Multiple species alignment of IL-18BP and the third IgG domain of the IL1RAPL proteins in 23 species (for IL-18BP and IL1RAPL1), and 20 of these species for which IL1RAPL2 sequences are available. A: alignment of third Ig domain of IL1RAPL1. B: third Ig domain of IL1RAPL2. C: IL-18BP. D: Alignment
of IL-18 from the same species. Underlined residues on the human IL-18 sequence are those identified across the studies of Kato et al. 1, Meng et al. 2, and Kim et al. 3,4 as necessary for binding IL-18 BP or IL-18r1.
-
341 341
-
-
342 342
- A.
1 Mouse 100.0% PKLLYPMESKLTVQETQLGGSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 2 Rat 99.1% PKLLYPMESKLTIQETQLGGSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 3 Cow 93.9% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 4 Gorrilla 98.9% ------DSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 5 Pig 98.9% ------GDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 6 Elephant 98.9% ------GDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 7 Microbat 98.2% ------GDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVQEGDLGNYSCYVENGNGRRHASILLH 8 Rhesus monkey 97.1% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 9 Panda 97.5% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 10 Human 98.6% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 11 Gibbon 98.6% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 12 Chimpanzee 98.6% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 13 Marmoset 98.4% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 14 Dog 98.4% PKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 15 Mouse Lemur 95.5% PKLLYPVESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 16 Orangutan 98.3% PKLLYPVESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCY-ENGNGRRHASVLLH 17 Alpaca 97.4% PKLLYPVESKLTIQEIQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEADLGNYSCYVENGNGRRHASVLLH 18 Pika 71.8% XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 19 Hedgehog 97.1% PKLLYPVESKMTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 20 Tasmanian Devil 97.5% ------GNLANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 21 Horse 88.1% ------GDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH 22 Platypus 87.0% PKLLHPLESKLTVQEIQLGNSANLTCRAFFGYSGEVSPLIYWMKGEKFIEDLDENRVWESDIRVLKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRQASVLLH 23 Hydrax 73.5% PKLLYPLESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRISKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASILLH consensus/100% ...... l.KEHLGEQEVSISLIVDSVpEuDLGNYSCY.ENGNGRRpASlLLH consensus/90% ...... GsSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH consensus/80% ...... GsSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLH
B.
1 Cow 100.0% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 2 Human 92.9% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 3 Orangutan 93.6% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 4 Gibbon 93.3% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 5 Rhesus 93.0% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 6 Chimpanzee 95.0% ------MENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 7 Gorrilla 94.9% ------KPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 8 Marmoset 92.4% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 9 Pig 97.1% ------MENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 10 Alpaca 85.3% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 11 Mouse 86.9% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 12 Horse 93.0% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 13 Dog 92.9% PPKPLFPMENQPSVIDVQLGKPLNIACKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 14 Elephant 92.4% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 15 Hydrax 65.9% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKYIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 16 Mouse Lemur 90.8% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELTLIFDSVVEADLANYTCHVENRNGRKHASVLLR 17 Rat 91.0% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELTLIFDSVVEADLANYTCHVENRNGRKHASVLLR 18 Hedgehog 83.2% PPMPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIKEGEIRLLKEHLGEKEVELTLIFDSVLEADLANYTCHVENRNGRKHASVLLR 19 Microbat 92.5% ------GKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR 20 Platypus 89.4% PPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELEGHIREGEIRLLKEHLGEKEVELTLTFDAVQEADLANYTCHVENRNGRKHASVLLR consensus/100% ...... KPLNIsCKAFFGFSGESGPMIYWMKGEKaIEELtGHI+EGEIRLLKEHLGEKEVELsLhFDuV.EADLANYTCHVENRNGRKHASVLLR consensus/90% ...... MENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELsLIFDSVVEADLANYTCHVENRNGRKHASVLLR consensus/80% PPhPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVLLR
-
343 343
-
-
344 344
- C.
1 Marmoset 100.0% FPAAKQCPALEVTWPEVELPLNGTLTLSCTACSHFPHFSILYWLGNGSFIEHLPG-LWEGSTRWERRSTGTQLSRTLVLEQLTPALHSTNFSCVLVDPEQVVQRYVVLA 2 Rhesus monkey 78.8% FPAAKQCPALEVTWPEVEMPLNGTLTLSCTACSRFPNFSMLYWLGNGSFIEHLPGQLWEGSTSREHGSTGTRLYKALVLEQLTPALHSTNFSCVLMDPAQVVQRHVILA 3 Orangutan 70.7% FPAAKQCPALEVTWPEVEVPLNGTLTLSCVACSRFPNFSILYWLGNGSFIEHLPGRLWEGSTSREHGSTGTQLCKALVLEQLTPALHSTNFSCVLVDPEQVVQRHVVLA 4 Chimpanzee 80.6% FPAAKQCPALEVTWPEVEVPLNGTLSLSCVACSRFPNFSILYWLGNGSFIEHLPGQLWEGSTSRERGSTGTQLCKALVLEQLTPALHSTNFSCVLVDPEQVVQRHVVLA 5 Human 81.1% FPAAKQCPALEVTWPEVEVPLNGTLSLSCVACSRFPNFSILYWLGNGSFIEHLPGRLWEGSTSRERGSTGTQLCKALVLEQLTPALHSTNFSCVLVDPEQVVQRHVVLA 6 Gorilla 78.1% FPAAKQCPALEVTWPEVEVPLNGTLSLSCVACSRFPNFSILYWLGNGSFIEHLPGRLWEGSTSRERGSTGTQLCKALVLEQLTPALHSTNFSCVLMDPEQVVQRHVVLA 7 Gibbon 79.6% FPAAKQCPALEVTWPEVEVPLNGTLTLSCMACSRFPNFSILYWLGNGSFIEHLPGRLREGRTSREHGSTGTQLCKALVLEQLTPALHSTNFSCVLVDPGQVVQHHVVLA 8 Horse 64.5% LLTAKRCPALEVTLPEVEVPLNGTLTLSCTACSRFHHFSILYWLGNGSFIEHLPGRLREGSMSREHRGRSTQLWRALVLEELSPALRSTSFSCVFTDPEQTVQRHVVLA 9 Microbat 66.7% LPTTKQCPALEVTWPEDGLSLNGTLTLSCTACSRFHHFSILYWLGNGSFIEHLPDRLQEGDTSWEHRGASTQLKKALVLEELSPVLRNTNFSCVFTDPEYTVQRHVVLA 10 Cow 64.0% LPAAKQCPALTVTWPAEEVSLNGTLTLSCTACSRFPHFSILYWLGNGSFIEHLPGRLWEGSTRREYRGKWTQLWRPLVLEELSPTLQDTNFSCVFMDPGQTVQRHLVLA 11 Alpaca 74.8% PPTAQQCPALEVTWPEVEVPLNGTLTLSCTACSRFPHFSILYWLGNGSFIERLPGRLWEGSTRE--RRTRTQLWRALVLEELSPALRSTNFS------12 Mouse 65.8% HSAAERCPALEVTWPEVEVPLNGTLTLSCTACSRFPHFSILYWLGNGSFIEHLSGRLREGSTRREHGSMSTRLWKALVLEELSPALRSSNFSCVFVDPDQVARHHIVLA 13 Elephant 58.3% LPTAKQCPALEMTWPEVEVPPNGTLTLSCTGCSRFP-FSILYWLGNGSFIKHLPGRLREGSTSRVHGSTSTWLRGALVVEELSPALRSTNFSCVLIDLVQTAQHHVVLA 14 Pig 61.5% LLRAKGCPGLEVTWPEEEVPLNGTLTLSCTACSRFPHFSILYWLGNGSFIERLPGRLREGSIRRERRGAGTQLRRALVLEQLSPGLRHANFSCVFSDPGQTAQRHLVLA 15 Dog 60.5% ------MLTLSCTACSRSP-FNILFWMGNGSFIEHLPGRLREGSTSRESRGEHTQLRRTLVLEELSPTLRNTDFSCVFADPSHTTQQHVVLA 16 Panda 60.1% LPVTMWCPALEVTWPEVQGPLNGTLTLSCTACSRFP-FSILYWLGNGSFIEHLPGRLREGSTSREYRGARTHLRRALVLEELSPTLRNTNFSCIFADPEQTAQHHVVLA 17 Hedgehog 61.7% ---AKQCPALAVTWPAVDDPLNGTLTLSCTACSHFPHFSILYWLGNGSFIEHLPGRLYEVDTSWTQCSTHTQLQKALVLEELSPALRSTNFSCVFADPAHTAQYHVL-- 18 Pika 63.8% FPVAKRCPAMEVTWPEVEVPLNGTLALSCAACSHIPAFHALYWLGNGSFIEHLPGPLWEGSTSRHHRHTGTWLQRPLVLEELTPILRNTNFTCVSVDTEQVALHHVIPA 19 Mouse Lemur 59.8% VP-TKQYPALDVIWPEKEVPLNGTLTLSCTACSRFPYFSILYWLGNGSFIEHLPGRLKEGHTSREHRNTSTWLHRALVLEELSPTLRSTNFSCLFVDPGQVAQYHIILA 20 Rat 58.6% VP-TKQYPTLDVIWPEKEVPLNGTLTLSCTACSRFPNFSILYWLGNGSFIEHLPGRLREGHTSREQRNASTWLHRALVLEELSPSLLSTNFSCLFVDPGQVAQYHVILA 21 Hyrax 42.6% ---VKQCPALEVTWPEVEVPPXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXGTWLQRPLVLEELS-ALRSTNFSCVLMDPAQTL-RHAVLV 22 Tasmanian Devil 54.1% ------MSMNETLTLSCKGCSRFAHSSFLYWLGNDTFIENLPGKLWEAKTRRLRHSGVTLLQRDLVLEELSPDLQNTQFSCVLVDPVQVLQRAVLLA 23 Platypus 30.1% FPSTVKCPGLQVHGPKLEVPYGVALNRSCSGCSLNREFSVMYWLGNGLLIEDLPGAVSEGYIRLE--ALRGQGWRPL---NLSPSPKCSVLHSAENRPSFVR------consensus/100% ...... s.h.tsL...pLo....ps.hp...... consensus/90% ...s..hPshthhhPt.ths.s.sLsLSChuCS+...FshLYWLGNGoFIEcLPG.L.Euphph..tthtT.L.+sLVLEpLoPsLpsosFSCl..Ds..sh..hhl.. consensus/80% ...sppCPuLpVpWPc.-hPhNGTLoLSCsACS+Fs.FSlLYWLGNGSFIE+LPGpLhEGpTphp.tsttT.L.+sLVLEpLoPsL+sTNFSClhhDPtpssphHllLA
D.
1 Marmoset 100.0% MAAE------PVEDNCINFVEMKFTGDALYFRAEDDENLETDHFAKL-KS-KLAIIRNVDDQVLFLGQGN-HPLFEDMTDSDCEANASRTIFIIYMYKDSEPRGLAV 2 Rhesus monkey 79.8% MAAE------PAEDNCINFVAMKFIDSTLYFIAEDDENLESDYFGKL-ES-KLSIIRNLNDQVLFIDQGN-RPLFEDMTDSDCRDNAPRTIFIINMYKDSQPRGMAV 3 Human 80.8% MAAE------PVEDNCINFVAMKFIDNTLYFIAEDDENLESDYFGKL-ES-KLSVIRNLNDQVLFIDQGN-RPLFEDMTDSDCRDNAPRTIFIISMYKDSQPRGMAV 4 Chimpanzee 78.2% MAAE------PVEDNCINFVAMKFIDNTLYFIVNKEKNLESDYFGKL-ES-KLSVIRNLNDQVLFIDQGN-RPLFEDMTDSDCRDNAPRTIFIISMYKDSQPRGMAV 5 Orangutan 78.2% MAAE------PVEDNCINFVAMKFIDNTLYFIVNKEKNLESDYFGKL-ES-KFSIIRNLNDQVLFIDQGN-QPLFEDMTDSDCRDNAPRTIFIISMYKDSQPRGMAV 6 Gorilla 78.8% MAAE------PVEDNCINFVAMKFIDNTLYFIGKANENLESDYFGKL-ES-KLSVIRNLNDQVLFIDQGN-RPLFEDMTDSDCRDNAPRTIFIISMYKDSQPRGMAV 7 Gibbon 77.7% MAAE------PVEDNCVNFVAMKFIDNTLYFIGKANENLESDYFHKM-ES-KLSIIRNLNDQVLFIDQGN-RPLFEDMTDSDCRDNAPRTIFIISMYKDSKPRGMAV 8 Pig 73.1% MAAE------P-EDNCISFVEMKFINNTLYFVAENDEDLESDYFGKL-EP-KLSIIRNLNDQVLFINQGH-QAVFEDMPDSDCSDNAPQTVFIIYMYKDSLTRGLAV 9 Horse 73.6% MAAG------PVEDNCISLVEMKFIDNTLYFVAENDENLESDYFGRL-EP-KLSIIRNLNDQVLFINQGN-QPVFEDMPDSDCTDNAPQTVFIIYMYKDSLTRGLAV 10 Panda 71.5% MAAN------T-EDNCINFVEMKLIDNTLYFIAENDDSLESDYFGKL-EP-KLSIIRNLNDQVLFVNQGN-QPVFEDMPDSDCTDNAPHTVFIIYMYKDSLTRGLAV 11 Cow 73.1% MAAE------QVEDYCISFVEMKFINNTLYFVAENDEDLESDHFGKL-EP-KLSIIRNLNDQVLFINQGN-QPVFEDMPDSDCSDNAPQTIFIIYMYKDSLTRGLAV 12 Dog 70.5% MAAN------LIEDNCINLVKMKFVNNTLYFKAESDEGLESDYFGKL-EP-KLSIIRNLNDQVLFVNEGN-QPVFEDMPDSDCTDNAPHTIFIIYMYKDSLTRGLAV 13 Alpaca 64.8% MAVQ------PIEDNCMSFVEMRFINNTLYFVVR-EKGLEADYFGKL-EP-KLSVIRNLNDQVLFINQGN-LPVFEDMPDSDCSDNAPQTLFIINVYRDSLIRGVAV 14 Mouse Lemur 68.9% ------NLESDYFGRL-EP-KFSIIRNLNNQVLFMD--G-ERIFEDMPDKDCKDNALQTRFIINIYKDSCARGMAV 15 Elephant 63.6% MAAE------LVEDSCINFVEMKFIGNTLYFIPENDGSLESDHFGKL-EPTKFAIIRNLNNQVLFIDQKN-KPVFEDMPDSDCNDNAAQTVFIITMYKDSQPRRLAV 16 Hyrax 45.3% MAAE------PAEDSYINFVGMKFIGNTLYFILNSVGSLESDHFGKL-EPAKYTIIRNLNNQVLFIDQKN-QPVFEDMPDSDCR-XXXXXXXXXXXXXXXXXXXXXX 17 Mouse 58.5% MAAM------SED-SCVNFKEMMFIDNTLYFIPEENGDLESDNFGRL-HC-TTAVIRNINDQVLFVDKRQ--PVFEDMTDIDQSASEPQTRLIIYMYKDSEVRGLAV 18 Rat 60.5% MAAM------SEEGSCVNFKEMMFIDNTLYLIPEDNGDLESDHFGRL-HC-TTAVIRSINDQVLFVDKRN-PPVFEDMPDIDRTANESQTRLIIYMYKDSEVRGLAV 19 Pika 61.7% RILE------PTEEDCINFVAMKFTDNTLYFIEEN--NLESDYFGKL-EP-RISIIRNLNDQVLFVDQEK-KPLFEDMPDSDCTDNGPQTTFIIHIYKDSYARGMAV 20 Microbat 63.1% ------LESDYFGRI-EP-RLSIIRNVNNQVLFVYQGS-HAVFEDMTDSECEENASQTKIIIYTYKDSYPRGMAV 21 Hedgehog 42.8% MAAE------PVEDIFIDLVDMEFINDTLYFIDELLXXXXXXXXXXX-XX-XXXXXXXXXXXXXXXXXXX-XXXXXXXXXXXXXXNKPQIRFIMYLYKDSNVRGLAV 22 Platypus 32.2% ------VRLWDSTPCCLQENINGFESDAFMKHGNP-KFQVFQDSSTKVLVMKQDSCTPRFEAMSEQEIKDNAPQTKFIIQFYKNTKPGCYPV 23 Tasmanian Devil 28.7% MAAAKDCQEVACSPEENCK--LNPNFIDNMEPEFREDECCLNCEAILKYSVP-MCINLKNSNEKISFFNQEEEVVIFEEMMNNEKRENAPWTQFMLQRYYCSGPRSL-V consensus/100% ...... consensus/90% ...... hhssh...h.p...sLEsDhFh+h ps phtll+s.sspVLFhtptp .slFE-Ms-.-pptNtspThhIl.hY+sS.sRshsV consensus/80% MAs. ..Et.hhshhtMpFhssTLYhh.pt.tsLESDaFu+l cs +hulIRNlNsQVLFlsptp .slFEDMsDpDppsNuspThFII.hYKDS.sRGhAV
-
345 345
-
-
346 346
- D cont.
1 Marmoset 100.0% TISVKC-GKISTLSCEN-KIISFKEISPPDNITDTKSDIIFFQRSVPGHDDKMQFESSSYEGYFLACKKEED--LFK-LILKRKDELGD-KSLLFTV------QS--KD 2 Rhesus 79.8% AISVKC-EKISTLSCEN-RIISFKEMNPPDNIKDTKSDIIFFQRSVPGHDNKMQFESSSYEGYFLACEKERD--LYK-LILKKKDELGD-RSIMFTV------QN--ED 3 Human 80.8% TISVKC-EKISTLSCEN-KIISFKEMNPPDNIKDTKSDIIFFQRSVPGHDNKMQFESSSYEGYFLACEKERD--LFK-LILKKEDELGD-RSIMFTV------QN--ED 4 Chimpanzee 78.2% TISVKC-EKISTLSCEN-KIISFKEMNPPDNIKDTKSDIIFFQRSVPGHDNKMQFESSSYEGYFLACEKERD--LFK-LILKKEDELGD-RSIMFTV------QN--ED 5 Orangutan 78.2% TISVKC-EKISTLSCEN-KIISFKEMNPPDNIKDTKSDIIFFQRSVPGHDNKMQFESSSYEGYFLACEKERD--LFK-LILKKEDELGD-RSIMFTV------QN--ED 6 Gorilla 78.8% TISVKC-EKISTLSCEN-KIISFKEMDPPDNIKDTKSDIIFFQRSVPGHDNKMQFESSSYEGYFLACEKERD--LFK-LILKKEDELGD-RSIMFTV------QN--ED 7 Gibbon 77.7% TISVKC-EKISTLSCEN-KIISFKEMNPPDNIKDTKSDIIFFQRSVPGHDNKMQFESSSYEGYFLACEKERD--LFK-LILKKKDELGD-RAIMFTV------QN--EA 8 Pig 73.1% TISVQC-KKMSTLSCKN-KTLSFKEMSPPDNIDDEGNDIIFFQRSVPGHDDKIQFESSLYKGYFLACKKEND--LFK-LILKEKDECGD-KSIMFTV------QN--KN 9 Horse 73.6% TISVKC-EKTSTLSCKN-KIISFKEMSPPENINDEGNDIIFFQRSVPGHDDKIQFESSLYKGYFLACEKEND--LFK-LILKEKDENGD-KSVMFTV------QN--QN 10 Panda 71.5% TISVKC-KKMSTLSCKN-KTISFKEMCPPNSINDEGNDIIFFQRSVPGHDDKIQFESSLYQGYFLACQKEKD--LFK-LILKKKDESGD-KSIMFTV------QN--KS 11 Cow 73.1% TISVQC-KKMSTLSCEN-KIVSFKEMNPPDNIDNEESDIIFFQRSVPGHDDKIQFESSLYKGYFLACKKEND--LFK-LILKKQDDNRD-KSVMFTV------QN--QN 12 Dog 70.5% TISVKY-KTMSTLSCKN-KTISFQKMSPPDSINDEGNDIIFFQRSVPGHDDKIQFESSLYKGHFLACKKEND--LFK-LILKDKDENGD-KSIMFTV------QN--KS 13 Alpaca 64.8% TISVRC-RKTYTLSCKD-KIVSFKEMSPPDNIDDEGNDIIFFQRSVPGHDDKIQFESSFYKGYFLACKKEND--FFK-LILKEKEDFGD-KAIMFTV------QD--KN 14 Mouse 68.9% AISVKY-GEMTTLSCKN-KIISFKEISPPDSINDTKSDIIFFQRSVPGHDDKMQFESSLYEGYFLACERERD--LYK-LILKKKDENGD-KSVMFTV------ES--Q- 15 Elephant 63.6% TVSVKCSKRILTLSCEN-KTANFKEMSPPEDINDTQSDIIFFMKGVEGHDEKTQFESSSYPGYFLASERDAD--LYK-LILKEKNQIGD-NSVMFTI------DL--QE 16 Hyrax 45.3% XXXXXXXXXXXXXXXXX-XXXXXXEITPPNYINDTKSDIIFYMKDVEGHHEKAQFESSLYPGYFLASEKDAD--LYK-LILKEKNQIRD-NSVMFT------17 Mouse 58.5% TLSVKD-SKMSTLSCKN-KIISFEEMDPPENIDDIQSDLIFFQKRVPGH-NKMEFESSLYEGHFLACQKEDD--AFK-LILKKKDENGD-KSVMFTL------TNLHQS 18 Rat 60.5% TLSVKD-GRMSTLSCKN-KIISFEEMNPPENIDDIKSDLIFFQKRVPGH-NKMEFESSLYEGHFLACQKEDD--AFK-LVLKRKDENGD-KSVMFTL------TNLHQS 19 Pika 61.7% TLSVKN-KKILTLSCEN-KIISFK------20 Microbat 63.1% TLSVKC-PEMFTLSCMN-KN------21 Hedgehog 42.8% SFSVYS-KKMYTLSCKD-KIISFKEMNPPDNINEDKSDIIFFQKLVKGHNKKMKFESSLYKGCFLACKREGD--RFK-LVLKDPNEELPIDSVMFIL------ED---E 22 Platypus 32.2% TISVKSGDRTFHLSCQG-NILHFEKGEVPQKIDEDQSDIIFFQKGIVGFKDRYQFQPSSCPGSYLACEKKQNDSYFT-MILKKVSGDEN-DSTHMTL------KP--FP 23 Tasmanian Devil 28.7% AFSVKTENENYILSCKNHQELHFKYGDFPYEIEDKDEDILFWQSKVIGY-EKVEFESFLYPEHYLAWEKIQD--KFEMLVLKEKLDGVE-MSTIFTCILCEKNENYEKCEK consensus/100% ...... consensus/90% shSVp..tch.pLSCps p.hpFp.hp.P..Ipp.tpDllFa.p.l.Ga.p+hpFps..h.t.aLA.p+.ts hap hlLKc..t... .uhhhh...... consensus/80% olSV+s tch.TLSCcN KhlsFcchsPPppIp-ptsDIIFFQ+tV.GH.pKhpFESS.Y.GaFLAsp+-tD haK LlLKcpsp.t- pSlhFTl p. ..
Appendix 4, Figure 2: Alignment of rat (NP_808796.1) vs human (NP_055086.1) IL1RAPL1 amino acid sequences. Note only four amino acids differ between species across the entire protein. Identities = 688/696 (99%), Positives = 692/696 (99%), Gaps = 0/696 (0%).
Rat 1 MKAPIPHLILLYATFTQSLKVVTKRGSADGCTDWSVDIKKYQVLVGEPVRIKCALFYGYIRTNYTLAQSAGLSLMWYKSSGPGDFEEPIAFDGSRMSKEEDSIWFRPTLLQDSGL 115 MKAPIPHLILLYATFTQSLKVVTKRGSADGCTDWS+DIKKYQVLVGEPVRIKCALFYGYIRTNY+LAQSAGLSLMWYKSSGPGDFEEPIAFDGSRMSKEEDSIWFRPTLLQDSGL Human 1 MKAPIPHLILLYATFTQSLKVVTKRGSADGCTDWSIDIKKYQVLVGEPVRIKCALFYGYIRTNYSLAQSAGLSLMWYKSSGPGDFEEPIAFDGSRMSKEEDSIWFRPTLLQDSGL 115
Rat 116 YACVIRNSTYCMKVSISLTVGENDTGLCYNSKMKYFEKAELSKSKEISCRDIEDFLLPTREPEILWYKECRTKTWRPSIVFKRDTLLIKEVKEDDIGNYTCELKYGGFVVRRTTE 230 YACVIRNSTYCMKVSISLTVGENDTGLCYNSKMKYFEKAELSKSKEISCRDIEDFLLPTREPEILWYKECRTKTWRPSIVFKRDTLLI+EV+EDDIGNYTCELKYGGFVVRRTTE Human 116 YACVIRNSTYCMKVSISLTVGENDTGLCYNSKMKYFEKAELSKSKEISCRDIEDFLLPTREPEILWYKECRTKTWRPSIVFKRDTLLIREVREDDIGNYTCELKYGGFVVRRTTE 230
Rat 231 LTVTAPLTDKPPKLLYPMESKLTIQETQLGGSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHA 345 LTVTAPLTDKPPKLLYPMESKLTIQETQLG SANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHA Human 231 LTVTAPLTDKPPKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHA 345
Rat 346 SVLLHKRELMYTVELAGGLGAILLLLVCSVTIYKCYKIEIMLFYRNHFGAEELDGDNKDYDAYLSYTKVDPDQWNQETGEEERFALEILPDMLEKHYGYKLFIPDRDLIPTGTYI 460 SVLLHKRELMYTVELAGGLGAILLLLVC VTIYKCYKIEIMLFYRNHFGAEELDGDNKDYDAYLSYTKVDPDQWNQETGEEERFALEILPDMLEKHYGYKLFIPDRDLIPTGTYI Human 346 SVLLHKRELMYTVELAGGLGAILLLLVCLVTIYKCYKIEIMLFYRNHFGAEELDGDNKDYDAYLSYTKVDPDQWNQETGEEERFALEILPDMLEKHYGYKLFIPDRDLIPTGTYI 460
Rat 461 EDVARCVDQSKRLIIVMTPNYVVRRGWSIFELETRLRNMLVTGEIKVILIECSELRGIMNYQEVEALKHTIKLLTVIKWHGPKCNKLNSKFWKRLQYEMPFKRIEPITHEQALDV 575 EDVARCVDQSKRLIIVMTPNYVVRRGWSIFELETRLRNMLVTGEIKVILIECSELRGIMNYQEVEALKHTIKLLTVIKWHGPKCNKLNSKFWKRLQYEMPFKRIEPITHEQALDV Human 461 EDVARCVDQSKRLIIVMTPNYVVRRGWSIFELETRLRNMLVTGEIKVILIECSELRGIMNYQEVEALKHTIKLLTVIKWHGPKCNKLNSKFWKRLQYEMPFKRIEPITHEQALDV 575
Rat 576 SEQGPFGELQTVSAISMAAATSTALATAHPDLRSTFHNTYHSQMRQKHYYRSYEYDVPPTGTLPLTSIGNQHTYCNIPMTLINGQRPQTKSSREPNPDEAHTNSAILPLLARETS 690 SEQGPFGELQTVSAISMAAATSTALATAHPDLRSTFHNTYHSQMRQKHYYRSYEYDVPPTGTLPLTSIGNQHTYCNIPMTLINGQRPQTKSSRE NPDEAHTNSAILPLL RETS Human 576 SEQGPFGELQTVSAISMAAATSTALATAHPDLRSTFHNTYHSQMRQKHYYRSYEYDVPPTGTLPLTSIGNQHTYCNIPMTLINGQRPQTKSSREQNPDEAHTNSAILPLLPRETS 690
Rat 691 ISSVIW 696
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347 347 ISSVIW
- Human 691 ISSVIW 696
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348 348 Appendix 4, Figure 3: Alignment of rat (NP_001159814.1) vs human (NP_059112.1) IL1RAPL2 amino acid sequences. Note that 37 amino acids differ between species across
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the entire protein. Identities = 649/686 (95%), Positives = 664/686 (97%), Gaps = 0/686 (0%).
Query 1 MKLPLLLALVVCSAVSTNLKMVSKRNSVDGCIDWSVDLKTYMALAGEPVRVKCALFYSYIRTNYSMAQSTGLRLMWYRNKGDLEEPIIFSEVRMSKEEDAIWFHSAEAQDSGFYT 115 MK P LLALVVCS VSTNLKMVSKRNSVDGCIDWSVDLKTYMALAGEPVRVKCALFYSYIRTNYS AQSTGLRLMWY+NKGDLEEPIIFSEVRMSKEED+IWFHSAEAQDSGFYT Sbjct 1 MKPPFLLALVVCSVVSTNLKMVSKRNSVDGCIDWSVDLKTYMALAGEPVRVKCALFYSYIRTNYSTAQSTGLRLMWYKNKGDLEEPIIFSEVRMSKEEDSIWFHSAEAQDSGFYT 115
Query 116 CVLRNSTYCMKVSMSLTVAENESGLCYNSRIRYLEKSEVTKRKEISCPDMDDFKKSDQEPDVVWYKECKPKMWRSIIIQKGNALLIQEVQEEDGGNYTCELKYEGKLVRRTTELK 230 CVLRNSTYCMKVSMSLTVAENESGLCYNSRIRYLEKSEVTKRKEISCPDMDDFKKSDQEPDVVWYKECKPKMWRSIIIQKGNALLIQEVQEEDGGNYTCELKYEGKLVRRTTELK Sbjct 116 CVLRNSTYCMKVSMSLTVAENESGLCYNSRIRYLEKSEVTKRKEISCPDMDDFKKSDQEPDVVWYKECKPKMWRSIIIQKGNALLIQEVQEEDGGNYTCELKYEGKLVRRTTELK 230
Query 231 VTALLTDKPPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELTLIFDSVVEADLANYTCHVENRNGRKHASVL 345 VTALLTDKPPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVEL LIFDSVVEADLANYTCHVENRNGRKHASVL Sbjct 231 VTALLTDKPPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEELAGHIREGEIRLLKEHLGEKEVELALIFDSVVEADLANYTCHVENRNGRKHASVL 345
Query 346 LRKKDLIYKIELAGGLGAIFLLLILLLVVYKCYNIELMLFYRQRFGGDETTDDNKEYDAYLSYTKVDQDTLDCDNTEEEQFALEILPDVLEKHYGYKLFIPERDLIPSGTYIEDL 460 LRKKDLIYKIELAGGLGAIFLLL+LL+V+YKCYNIELMLFYRQ FG DET DDNKEYDAYLSYTKVDQDTLDCDN EEEQFALE+LPDVLEKHYGYKLFIPERDLIPSGTY+EDL Sbjct 346 LRKKDLIYKIELAGGLGAIFLLLVLLVVIYKCYNIELMLFYRQHFGADETNDDNKEYDAYLSYTKVDQDTLDCDNPEEEQFALEVLPDVLEKHYGYKLFIPERDLIPSGTYMEDL 460
Query 461 TRCVEQSRRLIIVLTPDYILRRGWSIFELESRLHNMLVSGEIKVILIECTELKGKVNCPEVESLKHNIKLLSLIKWKGPKSSKLNSKFWKHLVYEMPIKKKEMLSHCHVLDSAEQ 575 TR VEQSRRLIIVLTPDYILRRGWSIFELESRLHNMLVSGEIKVILIECTELKGKVNC EVESLK +IKLLSLIKWKG KSSKLNSKFWKHLVYEMPIKKKEML CHVLDSAEQ Sbjct 461 TRYVEQSRRLIIVLTPDYILRRGWSIFELESRLHNMLVSGEIKVILIECTELKGKVNCQEVESLKRSIKLLSLIKWKGSKSSKLNSKFWKHLVYEMPIKKKEMLPRCHVLDSAEQ 575
Query 576 GLFGELQPIPSIAMTSTSATMVPSQADLPEFHHSDSMQMRHCCRGYKHEMPANTLSVPSLGNHHTYCNLPLTLLNGQLPLNNSLKETEELSRNNPLLPLSSKELSFTSDIW 686 GLFGELQPIPSIAMTSTSAT+V SQADLPEFH SDSMQ+RHCCRGYKHE+PA TL VPSLGNHHTYCNLPLTLLNGQLPLNN+LK+T+E RN+ LLPLSSKELSFTSDIW Sbjct 576 GLFGELQPIPSIAMTSTSATLVSSQADLPEFHPSDSMQIRHCCRGYKHEIPATTLPVPSLGNHHTYCNLPLTLLNGQLPLNNTLKDTQEFHRNSSLLPLSSKELSFTSDIW 686
Appendix 4, Figure 4: Rat IL1RAPL1 (NP_808796.1) aligned with rat IL1RAPL2 (NP_001159814.1). Identities = 421/696 (60%), Positives = 541/696 (78%), Gaps = 10/696 (1%).
IL1RAPL1 1 MKAPIPHLILLYATFTQSLKVVTKRGSADGCTDWSVDIKKYQVLVGEPVRIKCALFYGYIRTNYTLAQSAGLSLMWYKSSGPGDFEEPIAFDGSRMSKEEDSIWFRPTLLQD 112 MK P+ +++ + + +LK+V+KR S DGC DWSVD+K Y L GEPVR+KCALFY YIRTNY++AQS GL LMWY++ G D EEPI F RMSKEED+IWF QD IL1RAPL2 1 MKLPLLLALVVCSAVSTNLKMVSKRNSVDGCIDWSVDLKTYMALAGEPVRVKCALFYSYIRTNYSMAQSTGLRLMWYRNKG--DLEEPIIFSEVRMSKEEDAIWFHSAEAQD 110
IL1RAPL1 113 SGLYACVIRNSTYCMKVSISLTVGENDTGLCYNSKMKYFEKAELSKSKEISCRDIEDFLLPTREPEILWYKECRTKTWRPSIVFKRDTLLIKEVKEDDIGNYTCELKYGGFV 224 SG Y CV+RNSTYCMKVS+SLTV EN++GLCYNS+++Y EK+E++K KEISC D++DF +EP+++WYKEC+ K WR I+ K + LLI+EV+E+D GNYTCELKY G + IL1RAPL2 111 SGFYTCVLRNSTYCMKVSMSLTVAENESGLCYNSRIRYLEKSEVTKRKEISCPDMDDFKKSDQEPDVVWYKECKPKMWRSIIIQKGNALLIQEVQEEDGGNYTCELKYEGKL 222
IL1RAPL1 225 VRRTTELTVTAPLTDKPPKLLYPMESKLTIQETQLGGSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYV 336 VRRTTEL VTA LTDKPPK L+PME++ ++ + QLG N+ C+AFFG+SG+ P+IYWMKGEKFIE+L + E +IR+LKEHLGE+EV ++LI DSV E DL NY+C+V IL1RAPL2 223 VRRTTELKVTALLTDKPPKPLFPMENQPSVIDVQLGKPLNIPCKAFFGFSGESGPMIYWMKGEKFIEEL-AGHIREGEIRLLKEHLGEKEVELTLIFDSVVEADLANYTCHV 333
IL1RAPL1 337 ENGNGRRHASVLLHKRELMYTVELAGGLGAILLLLVCSVTIYKCYKIEIMLFYRNHFGAEELDGDNKDYDAYLSYTKVDPDQWNQETGEEERFALEILPDMLEKHYGYKLFI 448 EN NGR+HASVLL K++L+Y +ELAGGLGAI LLL+ + +YKCY IE+MLFYR FG +E DNK+YDAYLSYTKVD D + + EEE+FALEILPD+LEKHYGYKLFI IL1RAPL2 334 ENRNGRKHASVLLRKKDLIYKIELAGGLGAIFLLLILLLVVYKCYNIELMLFYRQRFGGDETTDDNKEYDAYLSYTKVDQDTLDCDNTEEEQFALEILPDVLEKHYGYKLFI 445
IL1RAPL1 449 PDRDLIPTGTYIEDVARCVDQSKRLIIVMTPNYVVRRGWSIFELETRLRNMLVTGEIKVILIECSELRGIMNYQEVEALKHTIKLLTVIKWHGPKCNKLNSKFWKRLQYEMP 560 P+RDLIP+GTYIED+ RCV+QS+RLIIV+TP+Y++RRGWSIFELE+RL NMLV+GEIKVILIEC+EL+G +N EVE+LKH IKLL++IKW GPK +KLNSKFWK L YEMP IL1RAPL2 446 PERDLIPSGTYIEDLTRCVEQSRRLIIVLTPDYILRRGWSIFELESRLHNMLVSGEIKVILIECTELKGKVNCPEVESLKHNIKLLSLIKWKGPKSSKLNSKFWKHLVYEMP 557
IL1RAPL1 561 FKRIEPITHEQALDVSEQGPFGELQTVSAISMAAATSTALATAHPDLRSTFHNTYHSQMRQKHYYRSYEYDVPPTGTLPLTSIGNQHTYCNIPMTLINGQRPQTKSSREPNP 672 K+ E ++H LD +EQG FGELQ + +I+M +TS + + DL FH++ QMR H R Y++++ P TL + S+GN HTYCN+P+TL+NGQ P S +E IL1RAPL2 558 IKKKEMLSHCHVLDSAEQGLFGELQPIPSIAM-TSTSATMVPSQADL-PEFHHSDSMQMR--HCCRGYKHEM-PANTLSVPSLGNHHTYCNLPLTLLNGQLPLNNSLKE--T 662
IL1RAPL1 673 DEAHTNSAILPLLARETSISSVIW 696 +E N+ +LPL ++E S +S IW IL1RAPL2 663 EELSRNNPLLPLSSKELSFTSDIW 686
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349 349
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References to Appendix 4:
1. Kato Z, Jee J, Shikano H, et al. The structure and binding mode of interleukin-18. Nat Struct Biol, 10, 966–971. (2003). 2. Meng X, Leman M & Xiang Y. Variola virus IL-18 binding protein interacts with three human IL-18 residues that are part of a binding site for human IL-18 receptor alpha subunit. Virology, 358, 211–220. (2007). 3. Kim S, Azam T, Yoon D, et al. Site-specific mutations in the mature form of human IL-18 with enhanced biological activity and decreased neutralization by IL-18 binding protein. Proc Natl Acad Sci U S A, 98, 3304–3309. (2001). 4. Kim S-H, Azam T, Novick D, et al. Identification of amino acid residues critical for biological activity in human interleukin-18. J Biol Chem, 277, 10998–11003. (2002).
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