University College London
Identification of novel genetic
mutations leading to rare
monogenic inflammatory diseases
Ciara Maria Mulhern
Thesis submitted for PhD
Infection, Immunity and Inflammation Research and Teaching Department
1
UCL, Great Ormond Street, Institute of Child Health
I, Ciara Maria Mulhern, state that all experimental and analytical work presented in this thesis has been carried out by myself. Where others have contributed to this work, this has been indicated in the thesis.
2
Acknowledgements
Firstly, I would like to say a huge thank you to my supervisors; Dr Despina Eleftheriou,
Dr Ying Hong and Professor Paul Brogan. I cannot say enough words of thanks to
Despina and Ying, two incredible women. Despina has always been so supportive and encouraging. Thank you for always being consistently present throughout my PhD, even when you were on holidays, you still answered my emails. No problem is ever too small for you. Ying, Thank you for always advising me, pushing me and giving me confidence in my work. Thank you for showing me how to work as a proper scientist, how to carry out assays, plan experiments and most importantly, how to get assays to work. You have been a constant support throughout my time at ICH, not just to me but to everyone around you.
Thank you as well to Paul; your constant optimism and positivity was always appreciated.
Thank you for your enthusiasm and words of encouragement. I would also like to thank
Ebun and Dara two mighty postdocs. Ebun, you are a constant delight, always smiling and happy. Thank you for your expertise while gene hunting; you are an absolute pleasure to work with. Dara, thank you for the all the Fr. Ted jokes, and always making me laugh with the constant Irish humour that you provide in the lab .
I would like to say thank you to my parents, without whom this PhD would not have been possible. Thank you for letting me escape London, and sponge off you for 3 months, in
Ireland, while I tackled the writing. Thank you especially to Mum for joining me for walks in the countryside and to Dad for always being inquisitive about my work. Thanks also to my siblings; Colin, Sean, Niamh and Patrick, to my grandparents, and to all my friends who put up with my PhD moaning. You can all rejoice now; it’s over!
3
Abstract
Over the past 20 years, an increasing number of monogenic inflammatory diseases have been described. In particular, monogenic autoinflammatory disorders are characterized by episodic and unprovoked inflammation, without the existence of high-titre autoantibodies or antigen specific T cells. When such diseases occur early in infancy, the cause is often suspected to be genetic. The emergence of nextgeneration genetic sequencing technologies has provided a fast and reliable means upon which to investigate causative genes, leading to the discovery of novel monogenetic autoinflammatory diseases. This technology also aids our understanding of the molecular pathways underpinning these disorders. The aims of this PhD project were to discover novel genetic causes of inflammatory diseases through the use of next generation sequencing and to explore the functional relevance of identified variants in candidate genes. In this thesis, a cohort of children with suspected monogenic inflammation were subject to next- generation sequencing. Several discoveries were made, and various families are discussed herein. For the first family, the index case was an 8 year old girl suffering from severe neuroinflammation manifesting as left sided focal seizures, left sided hemiplegia, granulomatous cerebral inflammation and chorioretinitis. I identified a rare missense p.T647P mutation in the TNFAIP3 gene as the cause of her predominantly neurological presentation. I was then able to show that this variant induces activation of NF-ƙB pathway and enhances NLRP3 inflammasome activity, but also results in significant upregulation of type I interferon signaling pathway. Targeted therapeutic intervention through administration of a Janus kinase inhibitor, resulted in a complete resolution of neuroinflammation and improvement in her clinical presentation.
4
The second family was a consanguineous Pakistani family, where 3 affected individuals were suffering from a systemic inflammatory disorder characterized by fevers, susceptibility to viral infection, rashes, and haemophagocytosis. I identified a homozygous p.M228K mutation in CCR7 in all affected individuals. Functional experiments showed decreased expression of the CCR7 protein in all affected individuals and defective migration of immune cells following chemokine stimulation.
Immunophenotyping also revealed complete absence of central memory T cells and defective IFNγ production in response to viral and bacterial stimuli. Lastly, a homozygous p.D118N mutation in BTNL2 was discovered in the third family I studied, a consanguineous family from Somalia wherein affected individuals were suffering from a familial leukocytoclastic vasculitis.
Therefore, through the use of next generation sequencing, coupled synergistically with pertinent functional readouts, I have identified and characterised three novel monogenic immunological diseases, each associated with varying degrees of autoinflammation and immune deficiency.
5
Impact Statement
This PhD project explored the potential genetic causes of a broad spectrum of children presenting with varying features of immune dysregulation from early in life. The primary beneficiaries of this project, are the patients and families with these rare diseases.
Commonly, these diseases present in infancy and segregate within families, so a genetic cause is often suspected, but not always identified by the current routinely available genetic tests in the UK National Health Service (NHS). Due to the overlapping phenotypes of these disorders, broad (i.e. exome wide), genetic sequencing is required for timely and accurate diagnosis, and (where possible) targeted clinical intervention.
The major way in which patients with this disease benefit from this study are summarised below:
1. The patients involved in this project benefitted from genotype specific targeted
therapy. In the case of A-III-1, this patient had been sick for years with what was
suspected to be an infection or malignant process. Many different treatments had
already been administered, having little to no effect. The identification of the
p.T647P mutation in TNFAIP3, enabled a deeper understanding the
pathophysiology of her clinical condition and an ability to provide the correct
targeted therapy, which resulted in a complete clinical and radiological resolution
of her unusual neuroinflammatory condition. I have elucidated the mechanism by
which aberrations in this gene resulted in the inflammatory manifestations
observed in this patient. My findings, therefore, have the following impact: (i)
Patients with heterozygous TNFAIP3 mutations and autoinflammation should be
investigated for enhanced type I interferon activity, and targeting the IFN pathway
should be considered when deciding upon therapeutic interventions for future
6
patients with mutations in this gene. (ii) Patients with heterozygous TNFAIP3
mutations should also be screened for underlying cerebral inflammation, even in
the absence of overt clinical symptoms.
2. I am the first to describe an immunodysregulatory disorder associated with
homozygous p.M228K in CCR7. Through this work, I have provided key insights
into the cellular and molecular mechanisms underpinning this rare disease
associated with immunodeficiency and autoinflammation. My findings also had
therapeutic implications for this family since the affected individuals were fast-
tracked to allogeneic haematopoietic stem cell transplantation. It is likely that
future studies will expand the phenotype and report more patients with different
genotypes associated with CCR7 deficiency.
3. Patients with autoinflammation, neuroinflammation and immunodeficiency are
now systematically screened for mutations in TNFAIP3 and CCR7 as these
genetic targets are now included in two routinely used in clinical practice genetic
panels: the Vasculitis Inflammation Panel (VIP); and Neuroinflammation Panel
(NIP).
Some of the data generated in this PhD has been published in open access peer- reviewed journals; the remainder is soon to be submitted for peer reviewed publication, hence ultimately contributing to significant scientific knowledge advancement about the genetic causes of rare inflammatory disease for academic beneficiaries in the field. This project also contributes to worldwide academic
7 advancement to address issues of importance such as the pathogenesis of autoinflammatory and immunodysregulatory syndromes.
Lastly, my PhD enabled my training as a highly skilled researcher and facilitated my academic career progress. I have developed expertise and knowledge in this multidisciplinary and collaborative environment. My project also contributes towards the health of the rheumatology/neurology academic discipline, with publications and national/international presentations.
8
Table of Contents
Table of Contents 9
Table of Figures 24
Table of Tables 32
Abbreviations 35
1 Introduction 36
1.1 Monogenic inflammatory Diseases 36
1.1.1 Familial Mediterranean Fever (FMF) 41
1.1.2 TNF receptor-associated periodic syndrome (TRAPS) 43
1.1.3 Cryopyrin associated periodic syndrome (CAPS) 44
1.1.4 Deficiency of Adenosine Deaminase 2 (DADA2) 46
1.1.5 Deficiency of IL-1 Receptor Antagonist (DIRA) 47
1.1.6 Blau Syndrome 49
1.1.7 A20 Haploinsufficiency 50
1.1.8 Hyper IgD Syndrome (HIDS) 51
1.2 Interferonopathies 53
1.2.1 Aicardi Goutières Syndrome (AGS) 53
1.2.2 Proteasome Associated Autoinflammatory Syndromes (PRAAS) 56
1.2.3 STING Associated Vasculitis with onset in Infancy (SAVI) 58
1.3 Autoinflammation secondary to Primary Immunodeficiency 60
1.3.1 Pathophysiological Mechanisms of Autoinflammation within the PID 61
9
1.3.2 Familial haemophagocytic lymphohistiocytosis (FHL) 69
1.4 Finding novel causes of Monogenic Inflammatory Syndromes 72
1.4.1 Next Generation Sequencing 72
1.4.2 Gene Panels 74
1.5 Hypothesis and Aims 77
2 General Materials and Methods 78
2.1 Subjects 78
2.1.1 Patients 78
2.1.2 Healthy controls 79
2.2 Sample Collection 79
2.3 Tissue Culture Media 80
2.4 DNA Extraction Protocols 81
2.4.1 DNA extraction from whole blood 81
2.4.2 DNA extraction from Saliva 82
2.4.3 Nucleic acid quantification 82
2.5 Serum Extraction from Whole Blood 83
2.6 Plasma Extraction from Whole Blood 83
2.7 SNP Genotyping using Homozygosity Mapping 83
2.8 Molecular DNA Techniques 85
2.8.1 Polymerase Chain Reaction 85
2.8.2 PCR Clean-Up 86
10
2.8.3 DNA fragment size quantification 86
2.8.4 Primer design for Sanger Sequencing 87
2.8.5 Sanger Sequencing 89
2.8.6 siRNA silencing of Human Dermal Fibroblast Cells (HDFC) and Jurkat
cells 90
2.9 RNA Protocols 91
2.9.1 RNA extraction from PBMCs 91
2.9.2 RNA extraction from whole blood 91
2.9.3 cDNA Synthesis via Reverse Transcriptase PCR reaction 92
2.9.4 Quantification of Interferon Stimulated Genes (ISGs): 93
2.9.5 Summary of Interferon Stimulated Genes used to quantify IFN activity 95
2.9.6 Calculation of ISG expression 97
2.9.7 qPCR to quantify gene expression, other than IFN stimulated gene
expression 98
2.10 MesoScale Discovery (MSD) for cytokine and chemokine analysis 98
2.11 Tissue Culture Protocols 99
2.11.1 Isolation of peripheral blood mononuclear cells (PBMC) from whole
blood 99
2.11.2 Counting of Cells 99
2.11.3 Maintenance of Human Cell Lines 100
2.11.4 Splitting cells 100
2.11.5 Freezing and Thawing of Cells 101
11
2.12 Protein Protocols 101
2.12.1 Protein Extraction from PBMC and HDFC 101
2.12.2 Protein Quantification 102
2.12.3 Western (Immuno) Blotting 103
2.12.4 Methodology for densitometry 106
2.12.5 Fluorescent Activated Cell Sorting (FACS) 107
2.12.6 FACS analysis 108
2.13 Whole Exome Sequencing 109
2.13.1 Library Preparation and clean up 109
2.13.2 Sequencing Whole Exome Libraries 110
2.14 In Silico Methodologies 111
2.14.1 Analysis of Whole Exome Sequence Data 111
2.14.2 Dataset concatenation 111
2.14.3 Quality Control Analysis 111
2.14.4 Aligning reads 113
2.14.5 Refining the Alignment 113
2.14.6 Variant calling and annotation 114
2.14.7 Filtering variants 115
2.14.8 Annotation of Variants 115
2.15 In Silico Prediction Analysis 116
2.16 Filtering for variants in genes included in targeted gene panels for inflammation 117
12
2.17 Identification of Candidate Genes 117
2.17.1 Statistical Analysis 118
3 Heterozygous TNFAIP3 mutation causes an interferon mediated neuroinflammatory disorder 119
3.1 Summary 119
3.1.1 Background 119
3.1.2 Objectives: 119
3.1.3 Methods 120
3.1.4 Results 120
3.1.5 Conclusions: 121
3.2 Introduction 122
3.2.1 Literature evidence for TNFAIP3 involvement in disease 122
3.2.2 Functional domains of A20 125
3.2.3 A20 is a master immune regulator, restricting the NF-ƙB, interferon and
NLRP3 inflammasome pathways 129
3.2.4 A20 and the NF-ƙB pathway 130
3.2.5 A20 and the Interferon Response 136
3.2.6 A20 and the NLRP3 Inflammasome response pathway 140
3.2.6.2 K+ Efflux: 143
3.2.6.3 Lysosomal Disruption Model: 144
3.2.6.4 Mitochondrial Dysfunction Model: 144
3.2.6.5 Pyroptosis 146
13
3.2.6.6 Inflammasome overactivation in disease: 147
3.3 Pedigree for Family A 149
3.4 Methods 155
3.4.1 Expression of phosphorylated p65 andIRF3 time course assay 155
3.4.2 Flow cytometric gating strategy for phosphorylation assays. 155
3.4.3 Calculation of interferon score 157
3.4.4 Human Dermal Fibroblast Cell (HDFC) culture 157
3.4.5 Co-Immunoprecipitation Assays 158
3.4.6 Antibodies 158
3.4.7 Carboxyfluorescin (FAM) – Fluorochrome Inhibitor of Caspases (FLICA)
Assay 161
3.4.8 Flow cytometric gating strategy for FAM-FLICA assay 162
3.4.9 Z-Score Calculations 163
3.5 Results 164
3.5.1 Suspected mode of Inheritance for A-III-1 164
3.5.2 Whole Exome Sequencing Results 165
3.5.3 Candidate Gene Prioritisation 166
3.5.4 Gene Panel Analysis 167
3.5.5 Confirmation of variants using Integrated Genome Viewer (IGV) and
Sanger Sequencing analysis 169
3.5.6 Choosing a Candidate Gene 177
3.5.7 mRNA Expression of TNFAIP3 180
14
3.5.8 A20 Protein Expression in peripheral blood mononuclear cells (PBMC)
182
3.6 Enhanced NF-ƙB activity in cells of p.T647P TNFAIP3/WT Genotype 186
3.6.1 Increased NF-ƙB signalling in lymphocytes derived from patients with
heterozygous p.T647P mutation in TNFAIP3 186
3.6.2 Enhanced NF-ƙB signalling in human dermal fibroblast cells (HDFC)
derived from proband with heterozygous p.T647P mutation in TNFAIP3 190
3.6.3 Elevated levels of proinflammatory cytokines in serum of patients with
heterozygous p.T647P mutation in TNFAIP3 194
3.6.4 Increased Global Cellular Ubiquitination levels in Human Dermal
Fibroblast Cells (HDFC) derived from A-III-1 200
3.6.5 Increased K63-linked polyubiquitinated NEMO in HDFC derived from A-
III-1. 203
3.7 Heterozygous p.T647P mutation in TNFAIP3 leads to upregulated interferon production 206
3.7.1 Impaired type 1 interferon (IFN) gene expression and signalling in A-III-1
with heterozygous p.T647P mutation in TNFAIP3. 207
3.7.2 Interferon stimulated gene expression of A-III-1 is comparable with that
of other monogenic interferonopathies 210
3.7.3 IFN stimulated gene expression decreases following treatment with
Baricitinib 212
3.7.4 High levels of interferon cytokines in serum of A-III-1. 214
15
3.7.5 Interferon stimulated gene expression in other family members with
p.T647P mutation in TNFAIP3 217
3.7.6 Elevated levels of phosphorylated IRF3 in individuals with the p.T647P
mutation in TNFAIP3 in comparison to healthy controls. 221
3.7.7 siRNA silencing of TNFAIP3 in Human Dermal Fibroblast Cells (HDFC)
results in increased phosphorylation of IRF3 and P65 223
3.7.8 Increased expression of p65, IRF3 phosphorylation in HA20 patient with
heterozygous TNFAIP3 p.N98Tfs25 variant 228
3.8 Increased NLRP3 Inflammasome activation in patient cells 232
3.8.1 Enhanced NLRP3 activation in p.T647P TNFAIP3/WT PBMC 232
3.8.2 Elevated IL-18 and IL-1β cytokine levels in supernatants from PBMC
derived from patients with heterozygous p.T647P TNFAIP3 mutation 235
3.9 Discussion 238
3.10 Conclusion 243
4 Homozygous mutation in CCR7 as a cause of a familial immune dysregulatory disorder 244
4.1 Summary 244
4.1.1 Background 244
4.1.2 Objectives 244
4.1.3 Methods 245
4.1.4 Results 245
4.1.5 Conclusion 245
16
4.2 Introduction 246
4.2.1 G protein Coupled Receptors 246
4.2.2 Lymph Node and Splenic Architecture 248
4.2.3 Development of Secondary Lymphoid Organs (SLOs) 252
4.2.4 Homing to the Lymph Nodes 253
4.2.5 T-Cell Development 255
4.2.6 Regulatory T Cells 260
4.2.7 Dendritic Cells 261
4.2.8 CCR7 in Central and Peripheral Tolerance 264
4.2.9 Autoimmunity 266
4.2.10 Summary 269
4.3 Family Tree of Family B 270
4.4 Clinical presentation of affected individuals 271
4.5 Methods 277
4.5.1 T Cell Stimulation Assay 277
4.5.2 Annexin V Apoptosis Assay 277
4.5.3 FACS Gating Strategy for Annexin V Assay 278
4.5.4 Ki67 Cell Cycle Analysis Assay 279
4.5.5 CFSE Cell Cycle Analysis Assay 280
4.5.6 FACS Gating Strategy for Ki67 and CFSE Assay 281
4.5.7 Migration (Transwell) Assay 283
17
4.5.8 SiRNA CCR7 Knockdown in Jurkat Cell Line 284
4.5.9 Immunophenotyping 285
4.5.10 FACS Gating Strategy used for Immunophenotyping 285
4.6 Suspected mode of inheritance in Family B 287
4.7 Homozygosity mapping in Family B 287
4.7.1 Chromosome 5 288
4.7.2 Chromosome 12 289
4.7.3 Chromosome 17 290
4.8 Results of Whole Exome Sequencing 292
4.9 Integrating Homozygosity Mapping data with Whole Exome Sequencing data
296
4.10 Results of Sanger Sequencing and Integrated Genome Viewer (IGV) analysis:
301
4.10.1 TPSB2 301
4.10.2 TBC1D9B 303
4.10.3 SLFN12 304
4.10.4 CCR7 305
4.11 The Schlafen proteins: Structure and Function 307
4.11.1 Restriction of viral replication by the Schlafen proteins 307
4.11.2 SLFN proteins are differentially expressed during haematopoietic cell
development 308
4.11.3 Role of SLFN proteins in cell cycle regulation and cell proliferation 309
18
4.11.4 Elektra Phenotype in humans 310
4.12 Functional data exploring role of homozygous p.M97T mutation in SLFN12
311
4.12.1 Normal SLFN12 mRNA Expression Levels 311
4.12.2 Normal Cellular Proliferation Levels in B-II-1 and B-II-2 314
4.12.3 Normal Cellular Apoptosis Levels in B-II-1 and B-II-2 317
4.13 Functional relevance of homozygous p.M228K variant in CCR7 321
4.14 Reduced CCR7 protein expression in individuals harbouring the homozygous
p.M228K variant 322
4.15 Immunophenotyping analysis as performed on B-II-1 and B-II-2 328
4.15.1 Inverse CD4:CD8 Ratio in B-II-1 and B-II-2 328
4.15.2 Absence of Memory Cells in B-II-1 and B-II-2 330
4.16 Defective migration of PBMCs with homozygous p.M228K mutation in CCR7
334
4.17 siRNA mediated knockdown of CCR7 in Jurkat cells results in impaired cell
migratory capacity 338
4.18 Discussion 342
4.19 Future Work 346
4.20 Conclusion 347
5 Homozygous mutation in BTNL2 associated with Familial Leucocytoclastic
Vasculitis 348
5.1 Summary 348
19
5.1.1 Background: 348
5.1.2 Aims: 348
5.1.3 Methods: 349
5.1.4 Results: 349
5.1.5 Conclusion: 349
5.2 Introduction: 350
5.3 Family Tree 355
5.4 Clinical Presentation 356
5.5 Methods 358
5.5.1 SYBR Green qPCR 358
5.5.2 Macrophage differentiation assay 358
5.5.3 Anti-CD3/anti-CD28 T cell stimulation protocol 359
5.5.4 THP1 Cell Culture 359
5.6 Suspected mode of inheritance in Family C 360
5.7 Homozygosity Mapping Results 360
5.7.1 Chromosome 6 361
5.7.2 Chromosome 7 362
5.7.3 Chromosome 9 363
5.8 Results from Whole Exome Sequencing 368
5.9 Combining WES data and Homozygosity Mapping 371
5.9.1 FAM65B 374
20
5.9.2 TNXB 375
5.9.3 BTNL2 378
5.10 Sanger Sequencing Confirmation 380
5.10.1 p.W15R mutation in FAM65B 380
5.10.2 p.F1228fs mutation in TNXB 381
5.10.3 p.D118N mutation in BTNL2 382
5.11 Gene Expression Analysis of BTNL2 383
5.11.1 Undetectable BTNL2 expression in PBMC 383
5.11.2 Expression of BTNL2 mRNA is detectable in THP1 cells 386
5.11.3 Detectable expression of BTNL2 mRNA in monocyte-derived
macrophages 389
5.12 Future Investigations 391
5.13 Discussion 393
5.14 Conclusion 395
6 General Discussion 396
6.1 Publications arising from this work 404
7 Bibliography 405
8 Appendix 446
8.1 Primers for Sanger Sequencing for Family A 446
8.2 Primers for Sanger Sequencing for Family B 446
8.3 Primers for Sanger Sequencing for Family C 447
8.4 AGS Sequencing Primers: 447
21
8.4.1 ADAR 447
8.4.2 IFIH1 449
8.4.3 RNASEH2B 451
8.4.4 RNASEH2C 452
8.4.5 SAMHD1 452
8.4.6 TREX1 453
8.5 Gene Panels 454
8.5.1 Neuroinflammation panel (NIP) 454
8.5.2 Vasculitis and inflammation panel (VIP) 455
8.5.3 Paediatric immunodeficiencies panel (PID) 456
8.6 Genes found in ROH – Family B 458
8.6.1 Chromosome 5 458
8.6.2 Chromosome 12 459
8.6.3 Chromosome 17 (a) 460
8.6.4 Chromosome 17 (b) 461
8.6.5 Chromosome 17 (c) 463
8.7 Genes found in ROH – Family C 464
8.7.1 Chromosome 6(a) 464
8.7.2 Chromosome 6(b) 466
8.7.3 Chromosome 7 466
8.7.4 Chromosome 9 467
22
23
Table of Figures
Figure 3-1: Single Nucleotide Polymorphisms (SNPs) and their location along the
TNFAIP3 gene. Image obtained from (Ma and Malynn 2012). 123
Figure 3-2: Diagrammatic representation of the A20 protein 127
Figure 3-3: Proteins involved in the NF-ƙB pathway, as obtained from (Jost and Ruland
2007). 133
Figure 3-4: A20 regulation of RIP1 and NEMO (IKKγ) leading to inhibition of NF-ƙB transcription factor. 135
Figure 3-5: First wave synthesis of interferons. 137
Figure 3-6: Schematic overview of the interferon immune response and regulatory role of the A20 protein. 139
Figure 3-7: Diagrammatic representation of the structure of the NLRP3 inflammasome as obtained from (Lamkanfi and Dixit 2014) 141
Figure 3-8: Pathways which lead to the activation of the NLRP3 inflammasome, as obtained from (Mathur et al. 2018). 145
Figure 3-9: Family tree of Family A. 149
Figure 3-10: Imaging demonstrating neurological inflammation. 152
Figure 3-11: Flow cytometric gating strategy as applied to detect expression of phosphorylated p65 and IRF3. 156
Figure 3-12: Flow Cytometric gating strategy for monocyte FAM-FLICA+ staining. 162
Figure 3-13: Filtering Strategy employed to search for disease causative genes in A-III-
1. 165
Figure 3-14: Detection of heterozygous p.T647P variant in TNFAIP3. 170
Figure 3-15: Detection of heterozygous p.R647T variant in TRAP1. 171
Figure 3-16: Detection of heterozygous p.P853R variant in TYK2. 172
24
Figure 3-17: Detection of heterozygous p.E1163G variant in TYK2. 173
Figure 3-18: Detection of heterozygous p.G524A variant in IRF5. 174
Figure 3-19: Detection of a heterozygous p.N259I variant in PRDM1. 175
Figure 3-20: Gene expression levels of TNFAIP3 in peripheral blood mononuclear cells
(PBMC) from A-III-1. 180
Figure 3-21: Western blot examining expression of A20 protein in PBMC from members of family A and another patient with HA20 that was heterozygote for p.N98Tfs25
TNFAIP3. 182
Figure 3-22: Increased phosphorylation of p65 in lymphocytes derived from A-III-1 compared to healthy control wild type cells. 187
Figure 3-23: Increased expression in NF-ƙB phosphorylated p65 (P-p65) in lymphocytes from individuals harbouring the heterozygous p.T647P TNFAIP3 mutation versus healthy controls. 189
Figure 3-24: Increased expression of P-p65 in HDFC derived from A-III-1 versus healthy control HDFC. 191
Figure 3-26: Increased Lys63 linked Uniquinination in A-III-1 HDFC 201
Figure 3-27: Increased Lys63-ubiquitinated NF-ƙB essential modulator (NEMO) in
HDFC derived from A-III-1 203
Figure 3-28: Increased expression of interferon stimulated genes (ISG) in whole blood from A-III-1 in comparison to healthy controls. 208
Figure 3-29: Interferon stimulated gene expression in A-III-1 and a patient suffering from
STING-associated vasculopathy with onset in infancy (SAVI), a monogenic interferonopathy. 211
Figure 3-30: Decreased expression of IFN stimulated gene expression in whole blood from A-III-1, following treatment with Baricitinib. 213
25
Figure 3-31: IFNα (A), IFNγ (B), IFNβ (C), and IFNλ (D) cytokine expression as measured in A-III-1, (pre and post treatments) and unrelated healthy controls (n=13).
215
Figure 3-32: ISG signature of family members harbouring p.T647P mutation in TNFAIP3 and two pediatric HA20 patients with confirmed heterozygous mutations in TNFAIP3.
218
Figure 3-37: Relative expression of phosphorylated IRF3 expression in lymphocytes from individuals of the p.T647P TNFAIP3/WT genotype versus unrelated healthy controls.
222
Figure 3-33: TNFAIP3 mRNA expression in HDFC transfected with RNAiMAX lipofectamine as measured by qPCR 224
Figure 3-35: Increased expression of activation markers P-IRF3 and P-p65 in HDFC following siRNA silencing of TNFAIP3 and A-III-1 derived HDFC in comparison to scramble siRNA transfected and untransfected healthy control HDFC respectively 226
Figure 3-36: Graphical representations showing the activation of NF-ƙB and interferon inflammatory markers P-p65 (A), P-IRF3 (B) in a patient (p.N98Tfs25 TNFAIP3/WT) with confirmed HA20 231
Figure 3-37: Enhanced Caspase1 activation in p.T647P TNFAIP3/WT CD14+ relative to
WT/WT CD14+ cells. 233
Figure 3-38: Increased IL-18 secretion in p.T647 TNFAIP3/WT versus WT/WT PBMCs upon stimulation with LPS, and LPS and ATP. 235
Figure 3-39: Increased IL-1β secretion in p.T647 TNFAIP3/WT versus WT/WT PBMCs upon stimulation with LPS, and LPS and ATP. 236
Figure 4-1: The structure of the lymph node, as obtained from (Comerford et al. 2013)
249
26
Figure 4-2: Process of cell homing to the lymph node, as obtained from (Förster et al.
2008). 254
Figure 4-3: The role of CCR7 in the thymus, as obtained from (Comerford et al. 2013).
258
Figure 4-4: Downstream CCR7 signalling in Dendritic Cells. 263
Figure 4-5: Family Tree of Family B 270
Figure 4-6: Flow cytometry gating strategy for Annexin V staining. 279
Figure 4-7: Flow cytometry gating strategy to assess the Ki67+ and CFSE+ cell populations on CD3+/CD28+ stimulated and baseline PBMC. 283
Figure 4-8: Gating strategy used to identify subsets of T cells for immunophenotyping analysis. 286
Figure 4-9: Runs of homozygosity on chromosome 5 as identified following homozygosity mapping in family B. 288
Figure 4-10: Runs of homozygosity on Chromosome 12 as identified following homozygosity mapping in family B. 289
Figure 4-11: Runs of homozygosity on chromosome 17 as identified following homozygosity mapping in family B. 290
Figure 4-12: Filtering method used on variants identified by WES for family B. 293
Figure 4-13: Detection of homozygous p.T115I variant in TPSB2. 301
Figure 4-14: Sanger and IGV data for the TBC1D9B gene. 303
Figure 4-15: Sanger sequencing and IGV data for the SLFN12 gene. 304
Figure 4-16: Sanger and IGV data for CCR7 gene. 305
Figure 4-17: Expression analysis of SLFN12 gene expression levels in B-II-1 and B-II-2.
312
27
Figure 4-18: Ki67 expression in B-II-1 and B-II-2 versus healthy control selected T cells.
315
Figure 4-19: CFSE expression in B-II-1 and B-II-2 in comparison to a healthy control individual 317
Figure 4-20: Normal levels of Annexin V staining in B-II-1 at baseline and after anti-
CD3+/anti-CD28+ stimulation, compared to healthy control. 319
Figure 4-21: Reduced CCR7 expression in B-II-1 and B-II-2 harbouring a homozygous mutation in CCR7. 322
Figure 4-22: Reduced CCR7 protein expression in individuals harbouring the p.M228K/p.M228K mutation, as analysed by flow cytometry. 325
Figure 4-23: Skewed CD4:CD8 T cell ratio in B-II-1 and B-II-2. 329
Figure 4-24: Complete absence of CD4+ and CD8+ memory cells in B-II-1 and B-II-2, harbouring a homozygous p.M228K mutation in CCR7. 331
Figure 4-25: Impaired migration of p.M228K/p.M228K CCR7 CD3+ cells in comparison to wild type CD3+ T cells. 335
Figure 4-26: Efficient knockdown of CCR7 mRNA expression in Jurkat cells. 338
Figure 4-27: Impaired migration of CCR7 knockdown Jurkat cells in comparison to wildtype non-transfected Jurkat cells. 340
Figure 5-1: Graphical representation of B7 and Butyrophilin Protein Domains, Obtained from (Arnett and Viney 2014). 351
Figure 5-2: Pedigree of Family C. 355
Figure 5-3: (A) Skin biopsy for C-IV-1 showing neutrophilic infiltrates (B) Vasculitic lesions affecting lower extremities and (C) palpable purpura in the lower limbs for C-IV-
1. 357
Figure 5-4: Runs of homozygosity on Chromosome 6 identified in family C. 361
28
Figure 5-5: Runs of homozygosity on Chromosome 7 identified in family C. 362
Figure 5-6: Runs of homozygosity on Chromosome 9 identified in family C. 363
Figure 5-7: Filtering method applied to WES variants 369
Figure 5-8: Confirmation of p.W15R mutation in FAM65B 380
Figure 5-9: Integrated Genome Viewer and Sanger sequencing confirming p.F1228fs mutation in TNXB. 381
Figure 5-10: Integrated Genome Viewer and Sanger sequencing confirming the p.D118N mutation in BTNL2 382
Figure 5-11: Undetectable BTNL2 expression in baseline resting healthy control PBMCs.
384
Figure 5-12: Successful amplification of the BTNL2 gene expression in THP1 cells. 387
Figure 5-13: Amplification plot showing Ct values for HPRT1 and BTNL2 gene expression from TNFα and LPS stimulated macrophages and from (baseline) macrophages. 389
Figure 6-1: Immunological Disease Continuum 400
29
Table of Tables
Table 1-1: Monogenic autoinflammatory diseases and the biological pathways involved
(non-exhaustive). 37
Table 2-1: Polymerase Chain Reaction (PCR) Components 85
Table 2-2: PCR cycling conditions 86
Table 2-3: Agarose Gel Components 87
Table 2-4: Parameters for primer design 88
Table 2-5: Sanger sequencing PCR master mix. 89
Table 2-6: Thermocycler program for BigDye Termination Reaction. 90
Table 2-7: Reverse Transcriptase PCR reaction components. 92
Table 2-8: Cycling conditions used for generation of cDNA by rtPCR. 93
Table 2-9: qPCR components for Interferon stimulated gene expression 94
Table 2-10: Cycling conditions for SYBR Green qPCR reaction for interferon stimulated gene expression. 94
Table 2-11: List of Interferon Stimulated Genes (ISG) examined in this thesis 95
Table 2-12: Recipe for 1 x 8% and 10% separating gel 103
Table 2-13: Recipe for 5% Stacking Gel 103
Table 2-14: Recipe for 5X Transfer Buffer* 104
Table 2-15: Recipe for 5X Electrophoresis Buffer 104
Table 2-16: Recipe for 10X Tris Buffered Salina (TBS). 104
Table 3-1: Patient A-III-1, routine clinical laboratory investigations 153
Table 3-2: Antibodies used in Immunoblotting analysis 159
Table 3-3: Antibodies used in Flow Cytometric analysis 160
Table 3-4: Variants present in both WES dataset and gene panels from A-III-1 168
30
Table 3-5: Summary of Sanger sequencing results for each of the candidate genes in family A. 176
Table 3-6: Protein expression of A20 relative to actin expression as assessed in cells from each member of family A, a healthy control and a patient heterozygote for p.N98Tfs25
TNFAIP3 with confirmed HA20. 183
Table 3-7: Proinflammatory cytokine measurements in members of family A compared against the median value of 38 unrelated healthy controls. 195
Table 3-8: Z scores of proinflammatory cytokine measurements (listed in Table 3-7) for each family member. 196
Table 3-9: Ubiquitin expression in HDFC relative to actin in both A-III-1 and an unrelated healthy control. 202
Table 3-10: K63-Ubiquitinated NEMO expression relative to actin in HDFCs derived from A-III-1 and an unrelated healthy control. 204
Table 3-11: Interferon Score for A-III-1 following treatment with Baricitinib. 214
Table 3-12: Cytokine measurement in A-III-1, before and after treatment with Baricitinib and compared to thirteen unrelated healthy controls. 216
Table 4-1: Routine clinical laboratory investigations for B-II-1, B-II-2 and B-II-3 273
Table 4-2: Summary of areas of Homozygosity identified in family B. 291
Table 4-3: Candidate genes which remained following WES filtering analysis and are shared amongst the two affected individuals and the unaffected monozygotic twin. 295
Table 4-4: Number of homozygous WES variants, found in ROH and shared by affected individuals and unaffected monozygotic twin. 297
Table 4-5: Homozygous variants identified in family B, frequency in the general population and in silico predictions for likely pathogenicity. 298
31
Table 4-6: Adjusted protein expression of CCR7, relative to Vinculin in cells from members of family B. 324
Table 4-7: Percentage of total CD3+ T cell population 329
Table 4-8: Percentage of total CD4+ or CD8+ T cell population, respectively. 331
Table 5-1: Number of SNPs recovered from each ROH observed only in affected family members for family C. 364
Table 5-2: Genes found in homozygous areas in C-IV-1, C-IV-2 and C-IV-3, which may be of relevance to the disease process. 366
Table 5-3: Homozygous WES variants shared by all three affected siblings in family C after filtering analysis 370
Table 5-4: Number of homozygous WES variants, found in ROH and shared by affected individuals in family C. 372
Table 5-5: Homozygous variants in affected individuals from family C, their frequency in the general population and their in silico prediction of pathogenicity. 373
32
Abbreviations
AAV ANCA associated vasculitis ADA2 Adenosine deaminase 2 ADAR1 Adenosine Deaminase RNA specific AGS Aicardi-Goutières syndrome ANCA Antineutrophil cytoplasmic antibody AID Autoinflammatory Disease ATP Adenosine triphosphate BALT Bronchus Associated Lymphoid Tissue CAPS Cryopyrin Associated Periodic Syndromes CANDLE Chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature CARD Caspase Recruiting Domain CCR7 C-x-C chemokine receptor 7 CCL21 C-C motif chemokine ligand 21 CCL19 C-C motif chemokine ligand 21 CECR1 Cat eye syndrome chromosome region, candidate 1 CGH Comparative genomic hybridization cPAN Cutaneous polyarteritis nodosa CSF Cerebral spinal fluid CT Computed tomography CINCA Chronic infantile neurological cutaneous and articular syndrome CNV Copy Number Variants DADA2 Deficiency of adenosine deaminase 2 DAMP Danger Associated Molecular Pattern DMEM Dulbecco’s modified eagle medium DNA Deoxyribnucleic acid DIRA Deficiency of IL1 receptor Antagonist DMSO Dimethyl Sulfoxide EBV Epstein-Barr Virus EDS Ehlers-Danlos syndrome EGFR Epidermal growth factor receptor EGPA Eosinophilic granulomatosis with polyangiitis EDTA Ethylenediaminetetraacetic acid ELISA Enzyme-linked Immuno Sorbent Assay EULAR European league against rheumatism FACS Fluorescence-activated cell sorting FBS Foetal bovine serum FCAS Familial Cold Autoinflammatory Syndrome FCS Foetal Calf Serum FMF Familial Mediterranean Fever FLICA Caspase 1 Fluorescein gDNA Genomic DNA GRB2 Growth factor receptor-bound protein 2 GPA Granulomatosis with polyangiitis GWAS Genome-Wide Association Study GSDMD Gasdermin D HDFC Human dermal fibroblast cell
33
HSP Henoch-Schönlein purpura HUVEC Human umbilical vein endothelial cell HIDS Hyper IgD Syndrome HSCT Haematopoetic Stem Cell Transplant Ig Immunoglobulin IL Interleukin IKK(α/β/γ) Inhibitor of kappa B kinase (α/β/γ) IRF3 Interferon regulatory factor 3 IFN(α/β/γ/λ) Interferon (alpha, beta, gamma, delta) IFNAR Interferon -alpha/beta receptor IFIH1 Interferon Induced with Helicase C Domain 1 ISRE Interferon stimulated response element JAK-STAT Janus kinase- signal transducer and activator of transcription KD Kawasaki Disease LOH Loss of heterozygosity LPS lipopolysaccharide MEFV Mediterranean Fever MVK Mevalonate Kinase MAPK Mitogen activated protein kinase MHC Major histocompatibility MMD Moyamoya disease MRI Magnetic resonance imaging NAC National Amyloidis Centre NEMO NF-ƙB essential modulator NF-ƙB Nuclear Factor Kappa B NGS Next generation sequencing NIP Neuroinflammatory Panel NLRP3 NOD-, LRR- and pyrin domain containing protein 3 NLRC4 NLR family CARD domain containing protein 4 NNS Nakajo Nishimura Syndrome NOD Nucleotide Oligomerisation Domain NOMID Neonatal Onset Multisystem Inflammatory Disease PAN Polyarteritis nodosa PAX PreAnalytiX PAMP Pathogen Associated Molecular Pattern PBMC Peripheral blood mononuclear cell PLCγ1 Phospholipase Cγ1 PRES Paediatric Rheumatology European Society PAPA pyogenic arthritis, pyoderma gangrenosum and acne PALS Periarteriolar Lymphoid Sheaths PBMC Peripheral Blood Mononuclear Cells PBS(T) Phosphate Buffered Saline (Tween-20) PCR Polymerase Chain Reaction PFAPA Periodic Fever, Aphthous stomatitis, Pharyngitis and Adenitis PID Pediatric inflammatory disease panel PRR Pattern recognition receptor qPCR Quantitative PCR RIPK3 Receptor interacting serine/threonine protein kinase 3 RIPA Radio Immunoprecipitation assay RNASEH2A Ribonuclease H2 Subunit A
34
RTK Receptor tyrosine kinase ROS Reactive Oxygen Species RT Room Temperature SAVI STING-associated vasculitis of infancy SAMHD1 SAM and HD domain containing Deoxynucleoside Triphosphate SNV Single nucleotide variant STING Stimulator of interferon genes SNP Single Nucleotide Polymorphism TAE Tris Acetate EDTA TBK1 TANK binding kinase 1 TAX1BP1 Tax1 binding protein 1 TA Takayasu arteritis TGFβ2 Type II transforming growth factor β TGFβR2 Type II transforming growth factor β receptor TLO Teritary Lymphoid Organs TLR Toll Like Receptor TMEM173 Transmembrane protein 173 TNFα Tumour Necrosis Factor α TNFAIP3 TNF alpha induced protein 3 TNRSF1 TNF Receptor superfamily 1 TRAP1 TNF Receptor Associated Protein 1 TRAPS TNF Receptor Associated Periodic fever Syndrome TRIF TIR domain containing adaptor inducing interferon β TRAF3 TNF Receptor Associated Factor 3 TREX1 Three Prime Repair Exonuclease 1 TYK2 Non receptor tyrosine protein kinase 2 Ub Ubiquitin VIP Vasculitis inflammatory Panel WES Whole exome sequencing WGS Whole genome sequencing WR Working reagent
35
1 Introduction
1.1 Monogenic inflammatory Diseases
In the past 15 years, a growing number of monogenic inflammatory diseases have been described (Almeida de Jesus and Goldbach-Mansky 2013, Martorana et al. 2017). The proteins encoded by mutated genes contributing to these inflammatory conditions are commonly involved in the regulatory pathways of inflammation and are mostly expressed in cells of the innate immune system (Almeida de Jesus and Goldbach-
Mansky 2013). In particular, the monogenic autoinflammatory diseases are genetic disorders characterized by episodic or persistent, seemingly unprovoked inflammation, without evidence of high-titre autoantibodies or antigen-specific T lymphocytes (Russo and Brogan 2014). The concept of autoinflammation was introduced in the late 1990s, when the genetic causes of familial Mediterranean fever (FMF) (The French FMF
Consortium et al. 1997) and the TNF receptor-associated periodic syndrome (TRAPS)
(McDermott et al. 1999) were identified. In contrast to autoimmune diseases, in autoinflammatory conditions, most abnormalities occur from components of the innate immune system (Almeida de Jesus and Goldbach-Mansky 2013). That being said, the arbitrary distinction between dysregulation of the innate and adaptive immune systems and immunodeficiency is increasingly blurred with the discovery of novel monogenic immunological diseases, summarised in Table 1-1, below. Typically these disorders result from dysregulation of the physiological alarm responses to foreign or endogenous danger signals, leading to abnormally increased inflammation, predominantly mediated by cells
36
(neutrophils, monocytes) and molecules (IL-1β, IL-6 and TNF-α) of the innate immune system.
Advancing genetic techniques such as positional cloning, homozygosity mapping and Next Generation Sequencing (NGS) have helped in the discovery of genetic causes of autoinflammatory diseases (Aksentijevich and Kastner 2011a). However, it is clear that autoinflammatory diseases still exist with no genetic explanation, and much work is needed to further understand the genetic, epigenetic, and environmental contribution(s) to these “complex” diseases.
Table 1-1: Monogenic autoinflammatory diseases and the biological pathways involved (non-exhaustive).
Autosomal Dominant (AD), Autosomal Recessive (AR), Mediterranean Fever (MEFV),
TNF Receptor Superfamily Member 1A (TNFRSF1A), Interleukin 1 Receptor
Antagonist (IL1RN), Nucleotide Binding Oligomerisation Domain Containing 2
(NOD2), NACHT LRR And PYD Domains-Containing Protein 3 (NLRP3), Adenosine
Deaminase (ADA2), Cat Eye Syndrome Critical Region Protein 1 (CECR1), Proteasome
Subunit Beta 8 (PSMB8), Transmembrane Protein 173 (TMEM173), TNF Alpha
Induced Protein 3 (TNFAIP3), Mevalonate Kinase (MVK), Proline, Serine, Threonine phosphatase-interacting protein-1 (PSTPIP1, Heme oxidised iron-responsive element binding protein 2 ubiquitin ligase 1 (HOIL-1), Perforin 1 (PRF1), Unc-13 Homolog D
(UNC13D)
37
Disease Gene Mode of Protein
Inheritan function/Pathw
ce ay
Familial Mediterranean Fever (FMF) MEFV AR Encodes Pyrin. AD Pyrin may direct migration of white blood cells to sites of inflammation Tumour Necrosis factor Receptor TNFRSF1A AD Receptor which Associated Periodic Syndrome binds TNFα (TRAPS) Deficiency of IL-1 receptor IL1RN AR Agonist for IL-1 antagonist receptor. Preven ts IL-1α and IL- 1β from binding to this receptor. Blau Syndrome NOD2 AR PRR which leads to upregulation of NF-kB (nuclear factor kappa light chain enhancer of activated B cells) and MAPK (mitogen activated protein kinase) pathways upon stimulation Cryopyrin Associated Periodic Syn NLRP3 AD Encodes dromes: Familial Cold cryopyrin that autoinflammatory Syndrome (FCAS), interacts with Muckle Wells Syndrome (MWS), Chr apoptosis onic Infantile Neurological Cutaneous associated Articular Syndrome (CINCA) speck-like protein ASC, and is a member of NLRP3 inflammasome complex. This complex functions as an important regulator of
38
interleukin-1 and-18 release; and is an upstream activator of NF- ƙB signalling, thus playing a key role in the regulation of inflammation, the innate immune response, and apoptosis. Deficiency of Adenosine Deaminase ADA2 (CEC AR Acts as a growth 2 (DADA2) R1) factor, playing a substantial role in the development of hematopoietic and endothelial cells Proteasome associated PSMB8, and AR Deficiency in autoinflammatory syndromes other Proteasome (PRAAS) – Chronic atypical proteasome pathway, dermatosis with lipodystrophy and genes involved in elevated temperature cellular (CANDLE), Nakajo-Nishimura regulation by syndrome (NNS), Joint contractures, removal of muscular atrophy, microcytic anaemia poly- and panniculitis induced ubiquitinated lipodystrophy (JMP), Japanese proteins in autoinflammatory syndrome with cytoplasm of lipodystrophy (JASL) eukaryotic cells STING associated vasculitis with TMEM173 AD Encodes onset in infancy (SAVI) STING, Type I interferon production A20 Haploinsufficiency TNFAIP3 AD Ubiquitinated protein which acts to repress the NF-ƙB, interferon and NLRP3 inflammasome pathways Hyper IgD Syndrome (HIDS) MVK AR Converts mevalonic acid to mevalonate- 5-phosphate, a
39
step in the cholesterol biosynthesis pathway Pyogenic arthritis, Pyoderma PSTPIP1 AD Interacts with gangrenosum and acne (PAPA) pyrin. Autoinflammation and PLCG2- PLCG2 AD Catalyzes the associated antibody deficiency and conversion of immune dysregulation (APLAID/ phosphatidylino PLAID) sitol 4,5- PLAID is also referred to as familial bisphosphate cold autoinflammatory syndrome 3 (PIP2) to diacylglycerol and inositol- 1,4,5- triphosphate (IP3) Linear Ubiquitin Chain Assembly HOIL-1 AR A component of Complex (LUBAC) Deficiency the LUBAC complex, this E3 Ubiquitin ligase modifies NF-ƙB signalling proteins Autoinflammatory periodic fever, WDR1 AR Regulates actin immunodeficiency, and filament thrombocytopenia (PFIT) assembly, alongside cofilin proteins. Involved in cell migration. Restricts inflammasome assembly. Familial hemophagocytic PRF1, AR These genes lymphohistiocytosis (HLH) UNC13D, play a role in STX11, and endocytic STXBP2 cytotoxic granule release from immune cells.
40
1.1.1 Familial Mediterranean Fever (FMF)
FMF is the most prevalent monogenic autoinflammatory disorder worldwide. People with this disorder suffer with recurrent fever, pleuritic, abdominal and chest pain (from serositis), arthritis, and a variety of cutaneous manifestations. As a result of uncontrolled inflammation, some patients may develop renal amyloidosis type AA (Zaks et al. 2003).
FMF is mainly an autosomal recessive disease; however autosomal dominant forms of transmission have also been described (Booth et al. 2000). Mediterranean fever gene
(MEFV) is the causative gene and encodes pyrin, an intracellular regulator of IL-1 production.
Pyrin is a 781 amino acid protein, expressed in granulocytes, monocytes and synovial and serosal fibroblasts (Chae et al. 2009). Certain ethnicities such as Turks, Arabs
Sephardic and Ashkenazi Jews, and other people of Mediterranean origin have a high carrier frequency of damaging mutations in this gene, ranging from 1/3 to 1/6 of the population (Martorana et al. 2017). Several mutations in pyrin causing FMF have so far been described. These described mutations are summarised in the Infevers website that currently lists 300 different mutations in MEFV (Chae et al. 2009). The mutations associated with a more severe phenotype such as M694V, reside in the B30.2 domain of the protein. This domain is thought to modulate IL-1β production through its interaction with caspase-1 (Chae et al. 2009, Almeida de Jesus and Goldbach-Mansky
2013). Other common disease causing mutations in this gene include M680I, M694I; and
V726A. MEFV E148Q, although previously reported to be disease causing, has highly debated pathogenicity; the current view is that it is not pathogenic (Zaks et al. 2003).
Other mutations include those which have been published by our group such as the homozygous S208T mutation reported which disrupts the 14-3-3 binding domain of pyrin
41
(Hong et al. 2018). People harbouring this mutation were shown to have high levels of
IL-1β and increased caspase-1 activation. Masters et al., 2016 reports another mutation resulting a similar disease termed pyrin associated autoinflammation with neutrophilic dermatosis. This disease is autosomal dominant in nature, and caused by mutations in pyrin, also resulting in the loss of the 14-3-3 domain. (Masters et al. 2016). These patients had high levels of inflammatory cytokines such as IL-1β, IL-6, TNF-α and IL-1Ra and elevated inflammasome activation. Interestingly, however the authors argue that this disease is clinically distinct from FMF; as patients observed here do not suffer from serositis or amyloidosis, but do experience severe recurrent neutrophilic dermatosis and a more severe fever, lasting weeks rather than days.
Colchicine is the first line treatment for FMF, in many cases resulting in complete remission of clinical attacks of FMF, complete suppression of systemic inflammation, and thus protection from the development of reactive AA amyloidosis (Almeida de Jesus and Goldbach-Mansky 2013). AA amyloidosis is one of the more severe long term complications of the disease if not treated adequately and is more prevalent with certain genotypes, specifically homozygosity of the M694V MEFV mutation (Berkun et al. 2007,
Fentoğlu et al. 2017). Whilst colchicine may prevent the development of AA amyloidosis, patients with established reactive AA amyloidosis are commonly non-responsive to colchicine treatment, and require more potent (and historically potentially toxic cytotoxic immunosuppression with chlorambucil), although in the past 15 years IL1 blockade has increasingly been used as an effective and safe treatment for AA amyloidosis. Despite this, a significant proportion of patients may develop organ failure (particularly renal failure) or die (Yabuuchi et al. 2017).
42
1.1.2 TNF receptor-associated periodic syndrome (TRAPS)
TRAPS is an important autoinflammatory disease with a high European prevalence
(Lachmann et al. 2014). It is caused by autosomal dominant mutations in the TNF receptor (TNFR1) encoded by the TNFRSF1A gene. This gene spans 10 exons with disease-causing mutations located in exons 2, 3, 4 and 6. Mutations which affect the N- terminal cysteine residues are known to be highly penetrant, as these mutations affect the final 3D structure of the protein and result in the retention of the protein in the Golgi apparatus (Chan et al. 2000, Shwin et al. 2017). People with TRAPS usually first develop symptoms during childhood or adolescence, and usually experience abdominal pain with prolonged episodes (typically >7 days) of fever. Myalgia, erythematous skin rash and periorbital oedema are common symptoms. Arthralgia, arthritis and pleuritis have also been reported. Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP), haptoglobin, fibrinogen and ferritin are elevated during inflammatory attacks (Shwin et al. 2017). AA amyloidosis is a feature of untreated TRAPS patients, more commonly reported in those patients who have mutations in cysteine residues of the TNFRSF1A gene. Patients with mutations in the cysteine residues have more severe disease, than those with mutations in other residues (Almeida de Jesus and
Goldbach-Mansky 2013). Low penetrant mutations also exist, such as R92Q which are associated with lower risk of amyloidosis, adult-onset disease and much less severe symptoms (Lachmann et al. 2014).
Anti-TNF agents and corticosteroids are used as treatment strategies to suppress inflammatory attacks in TRAPS and control disease severity, i.e. typically greater than 7 days (Shwin et al. 2017). Il-1 blockade is the most used treatment, reducing attack frequency, ameliorating systemic inflammation, and preventing AA amyloidosis.
Anakinra, an IL-1 receptor antagonist has been used in the treatment of TRAPS patients.
43
(Gentileschi et al. 2017) report the disappearance of all TRAPS-related symptoms as well as a decrease in inflammatory markers such as ESR, CRP and SAA in a 59 year old patient with TRAPS, following treatment with anakinra. Likewise, (Obici et al. 2011) show long term efficacy and amelioration of symptoms in seven patients suffering from TRAPS.
(Grimwood et al. 2015) demonstrate response to anakinra treatment in two patients with confirmed TRAPS mutations, resulting in a normalisation of SAA. Canakinumab, a monoclonal antibody which targets IL-1β (Dhimolea 2010), is also beneficial for TRAPS patients with 45% of patients demonstrating a complete response, following 16 weeks of treatment, versus 8% on the placebo (De Benedetti et al. 2018). This drug was also used to treat other autoinflammatory diseases such as FMF and HIDS, having positive results.
At the molecular level, it is thought that there may be several mechanisms responsible for autoinflammation in TRAPS. These include defective TNFR1 trafficking to the cell surface which results in intracellular protein misfolding, protein retention within the
Golgi apparatus, reduced TNFR1 receptor shedding, TNF-independent cell activation leading to increased production of IL-1, IL- 6 production and altered NF-ƙB pathway regulation (John G Ryan 2009, Cantarini et al. 2012). Good response to treatment with
IL-1 blockade with either anakinra or canakinumab has been extensively reported
(Gattorno et al. 2008, Obici et al. 2011, La Torre et al. 2017, De Benedetti et al. 2018).
1.1.3 Cryopyrin associated periodic syndrome (CAPS)
CAPS is rare and affects 1-2 people per 106 of population in America and Europe
(Maksimovic et al., 2008). Familial cold autoinflammatory syndrome (FACS), Muckle
Wells Syndrome (MWS) and Chronic Infantile Neurological Cutaneous Articular
Syndrome (CINCA) all fall under the CAPS umbrella. These three diseases are all caused
44 by autosomal dominant gain of function mutations in the NLRP3 gene (Bousfiha 2018), and this may perhaps explain the overlapping phenotypes observed between these conditions, such as non-pruritic neutrophilic urticarial-like rashes, cold-induced attacks, low-grade fever, arthralgia, and inflammatory eye disease ranging from conjunctivitis to uveitis (Shwin et al. 2017).
NLRP3 binds to PYCARD (PYD and CARD domain containing) and CASP1 (Caspase
1) to form an inflammasome complex. Inflammasomes are important mediators of inflammation, helping to detect pathogenic microorganisms and activate proinflammatory cytokines such as IL-1β and IL-18 (Latz et al.
2013). Approximately 60% of CAPS patients have mutations in the NLRP3 gene resulting in continual overactivation of the inflammasome and dysregulation of IL-1 production (Martorana et al. 2017). Mutations in the NLRP3 gene in CAPS patients are commonly found in exon 3. This encodes the NACHT/NBS domain of the protein, which regulates the oligomerization of the NLRP3 protein (Aksentijevich and Kastner
2011a). Of the ‘mutation-negative’ patients, a proportion have somatic mosaicism for
NLRP3 mutations; others may have mutations in other genes causing clinical features reminiscent of CAPS; and others have truly undefined pathogenesis albeit with some response to IL-1 blockade (Rowczenio et al. 2015).
FCAS is often considered the mildest form of CAPS and is often triggered by exposure to the cold and manifests in low-grade fever, polyarthralgia and urticarial rashes (Almeida de Jesus and Goldbach-Mansky 2013). Muckle and Wells described a more severe condition encompassing amyloidosis, urticarial-like rash, and sensorineural hearing loss
(Martorana et al. 2017). Arthritis, conjunctivitis, uveitis, headache, aseptic meningitis, and fatigue are also common features of the disease. CINCA is the most severe form of
CAPS and usually begins in the first weeks of life. Symptoms include continuous low-
45 grade fever, arthropathy, aseptic meningitis and cutaneous rash, and neutrophilic inflammatory skin lesions (Aubert et al. 2012). Severe neurological involvement is often a diagnostic feature of CINCA, manifesting in chronic irritability, headache, seizures and lower limb spasticity. Arthropathy resulting in joint deformities and cartilage/bony overgrowth are also common features of CINCA.
CAPS syndromes are treated with IL-1blockade, such as anakinra or canakinumab. Patients show a dramatic improvement in inflammatory symptoms with these therapies (Lachmann et al. 2009, Obici et al. 2011, Russo and Brogan 2014).
1.1.4 Deficiency of Adenosine Deaminase 2 (DADA2)
DADA2 is a vasculitic monogenic autoinflammatory disorder which was first described in 2014 (Navon Elkan et al. 2014, Zhou et al. 2014) and is caused by loss of function mutations in the ADA2 gene encoding adenosine deaminase 2 (ADA2). A number of homozygous and compound heterozygous mutations in ADA2 have been reported since the initial description (Nanthapisal et al. 2016, Caorsi et al. 2017). Zhou et al. 2014, were able to show reduced ADA2 protein expression in cell lysates from patients with homozygous mutations in this gene, and reduced enzyme activity in patient serum (Zhou et al. 2014)
Patients suffering from DADA2 usually present with livedo racemosa rashes, recurrent fevers, systemic inflammation, and vasculitic symptoms including vasculitic polyneuropathy and cutaneous vasculitis. Many also present with early-onset lacunar (i.e. small vessel) ischaemic strokes, and varying degrees of B cell immunodeficiency
(Nanthapisal et al. 2016). Disease severity is highly variable even within families, as is age of onset, ranging from 1 year to 59 years (Meyts and Aksentijevich 2018). Various
46 other symptoms such as hepatosplenomegaly, cytopenia, bone marrow failure, or aplasia, and spasticity may also be observed (Martinon and Aksentijevich 2015).
ADA2 is largely homologous to ADA1, a deficiency of which causes severe combined immunodeficiency (SCID) (Martinon and Aksentijevich 2015). These proteins function to deactivate extracellular adenosine and terminate adenosine signalling. However, probably the main role of ADA2 is to act as a growth factor and to play a substantial role in the development of haematopoietic and endothelial cells. It also induces monocyte proliferation and macrophage differentiation (Caorsi et al. 2017). Monocytes in patients suffering from DADA2 show a reduced differentiation to M2 (anti-inflammatory) cells and a surplus of M1 (proinflammatory) cells. Loss of function ADA2 mutations seem to result in an upregulation of inflammatory cytokines and neutrophil expressed genes
(Berkun et al. 2017). Patients with this disease benefit from anti-TNF therapy
(Nanthapisal et al. 2016, Ombrello et al. 2019), although formal randomised controlled trials are lacking. Those with severe immunodeficiency and haematological manifestations may require allogeneic haematopoietic stem cell transplantation (Van
Eyck et al. 2015). Our group is currently exploring gene therapy as an alternative and potentially curative treatment approach for DADA2, although this work is still at a preclinical stage.
1.1.5 Deficiency of IL-1 Receptor Antagonist (DIRA)
In 2009, Aksentijevich and colleagues described a new disorder caused by homozygous mutations in IL1RN, a gene which encodes an antagonist for the IL1 receptor. These mutations were found in nine children from a total of six families with systemic inflammation exhibiting enhanced skin and bone involvement (Aksentijevich et
47 al. 2009). Pustular cutaneous rashes, severe arthritis, oral mucosal lesions and pain during movement, were present early in life. Bony overgrowth, multifocal periostitis and osteomyelitis are common in DIRA. Skin biopsies show extensive neutrophilic infiltrates
(Aksentijevich et al. 2009).
The authors of the original description of DIRA reported the following mutations;
N52KfsX25, E77X and Q54X in IL1RN in Dutch and people of Lebanese origin and one patient with a 175kb deletion on chromosome 2, encompassing the IL1RN gene. This
175kb deletion was also reported in another patient of Puerto Rican origin (Minkis et al.
2012) exhibiting infantile pustulosis. A p.R26X mutation in IL1RN was reported in a girl of Turkish origin with periostitis, sterile osteomyelitis and pustulosis (Ulusoy et al.
2015).
IL1RN functions as an antagonist of the proinflammatory cytokines IL-1α and IL-1β, by binding competitively to the IL1 receptor (Aksentijevich et al. 2009). This thereby prevents the receptor being stimulated by its ligands and prevents further formation of the
IL1 receptor signalling complex (Minkis et al. 2012). Mutations in IL1RN result in a truncated, un-secreted form of the protein (Aksentijevich et al. 2009). As a result, cells become oversensitive to IL-1β stimulation and Aksentijevich et al. 2009, clearly demonstrated that the cells of patients with mutations in IL1RN have an upregulation of proinflammatory cytokines, upon IL-1β stimulation, in comparison to controls. Carriers for these mutations remain asymptomatic.
This disease is treated with anakinra (Minkis et al. 2012) and canakinumab (Ulusoy et al.
2015).
48
1.1.6 Blau Syndrome
Blau syndrome is an early onset autoinflammatory syndrome caused by heterozygous gain of function mutations in the NOD2 gene (Wouters et al. 2014), typically affecting the eye, skin and joints. Manifestations of the disease include recurrent uveitis, polyarthritis, and granulomatous skin eruptions (Arvesen et al. 2017). Blau syndrome may also be referred to as early onset sarcoidosis (EOS). Erythematous, scaly, maculopapular skin rashes are often observed within the first year of life, followed by arthritis and uveitis (Rose et al. 2014). One of the most striking features of Blau syndrome is the presence of multinucleated giant cell granulomas in affected tissues in patients with the disease (Rose et al. 2014, Arvesen et al. 2017). Other less common features include fever, erythema nodosum (Aróstegui et al. 2007, Rosé et al. 2015), lymphadenopathy, transient neuropathy, and pulmonary embolism (Rosé et al.
2015), amongst others. Central nervous system involvement is uncommon in children with this disease (Wouters et al. 2014).
The first report describing mutations in NOD2 for the basis of this disease, listed three different mutations in 4 families (Miceli-Richard et al. 2001): R334Q, R334W and
L469F. Since then, numerous other mutations in NOD2 have been described (Okada and
Cyster 2007, Milman et al. 2009) and are documented on the Infevers website (Anon.
2019a). Many of the mutations in NOD2 so far listed to cause Blau syndrome reside within the NOD/NACHT nucleotide binding domain of the protein. Mutations within this region are postulated to lead to oligomerisation and spontaneous activation of the receptor
(Caso et al. 2014).
NOD2 is a pattern recognition receptor (PRR), which is usually inhibited through interaction between its LRR and CARD domains (Caspase activation and
49 recruitment domains). Binding of a ligand, such as muramyl dipeptide
(MDP) causes cessation of this autoinhibitory state and dimerization of the NOD2 receptor (Caso et al. 2014). It subsequently binds to and activates RIP2, a downstream effector molecule (Wouters et al. 2014). Consequently, the NF-ƙB and
MAPK inflammatory pathways are turned on, resulting in the production of various cytokines such as IL-1β, IL-6, IL-8 and TNFα and the establishment of an innate immunological response (Caruso et al. 2014). Anti-TNF therapy, IL1 blockade and corticosteroids are often used to treat severe disease (Shwin et al. 2017).
1.1.7 A20 Haploinsufficiency
In 2016, Zhou and colleagues described nine patients wherein heterozygous mutations in TNFAIP3 were observed, leading to A20 haploinsufficiency (Zhou et al. 2016). These patients were from 6 unrelated families, mainly exhibiting a phenotype resembling familial Behҫets disease. The reported mutations were frameshift in nature, found in both the OTU domain and fourth zinc finger of the protein. Mutation positive patients presented with recurrent oral and genital ulcers, polyarthritis, and one patient was described as having a systemic lupus erythematosus-like phenotype, with cerebral vasculitis and anterior uveitis (Zhou et al. 2016).
A20 is a protein with dual ubiquitin functions and is a negative regulator of the NF-ƙB pathway. The authors showed, using a series of western blots, how A20 protein expression is downregulated in people harbouring TNFAIP3 mutations (Zhou et al. 2016).
However, the genetic variants described do not have a dominant negative effect as some protein expression is still observed. The authors demonstrate, through transfection experiments, that mutant A20 proteins fail to suppress the NF-ƙB pathway (Zhou et al.
50
2016). Mutant cells show increased phosphorylation of IKKα/β and an over degradation of IkBα in comparison to wild type protein, leading to an upregulation of NF-ƙB activity.
There was also enhanced nuclear translocation of p65 in patient cells in comparison to control cells. NF-ƙB is a big player in immune activation, resulting in mass production of proinflammatory cytokines. The authors also demonstrated an increase in proinflammatory cytokines in the serum of patients versus control serum (Zhou et al.
2016). It is thought that a failure to suppress this pathway, is largely responsible for the increased inflammatory activity observed in these patients, leading to a form of familial
Behҫet’s disease.
Aeschlimann et al., 2018 has also reported a number of patients with A20 haploinsufficiency resulting in symptoms of Behҫet’s disease (Aeschlimann et al. 2018).
The authors stress that clinical phenotypes vary among patients substantially, but symptoms such as oral and genital ulcers, arthralgia, musculoskeletal and gastrointestinal complaints were common, as were fevers, cutaneous lesions and recurrent infections.
Tissue biopsies from multiple sites were indicative of chronic inflammation. However, no functional work was carried out to assess the NF-ƙB activity in these patients.
1.1.8 Hyper IgD Syndrome (HIDS)
As the name suggests, hyper IgD syndrome is characterised by elevated serum levels of
IgD, in addition to lymphadenopathy, abdominal pain, splenomegaly, fever and arthralgia/arthritis. Elevated serum IgA levels are not uncommon with HIDS. People with this disorder also display elevated inflammatory markers such as C-reactive protein and serum amyloid A. This is a rare autosomal dominant disorder which typically manifests during the first year of life. While it has a high prevalence in people of Dutch and French
51 origin (<60%), it has also been reported in numerous countries around the world including the UK, Germany, Czech republic, America, Japan and Italy, amongst others.
HIDS is caused by homozygous or compound heterozygous, loss of function mutations in the mevalonate kinase (MVK) gene. MVK is an enzyme which is localised to the peroxisome and functions in the cholesterol biosynthesis pathway, responsible for the production of cholesterol and other non-sterol products, such as ubiquinone 10, heme A and dolichol, which are involved in the prenylation of proteins (Mulders-Manders and
Simon 2015). It is the second enzyme in this pathway after 3-hydroxy-3-methyl-glutaryl
(HMG)-CoEnzymeA-reductase. MVK catalyses the conversion of mevalonic acid to 5- phosphomevalonate (Mulders-Manders and Simon 2015). Essentially, a deficiency of the
MVK protein leads to a shortage of end products of this pathway.
The V377I mutation in MVK is the most common mutation occurring in 52-90% of people with HIDS (Houten et al. 2000, Cuisset et al. 2001). This mutation prevents appropriate folding of the MVK protein, leading to decreased in vivo enzyme activity. Other mutations such as H20P, I268T, or A334T mutations have also been reported.
Depending on the observed mutations present, the MVK enzyme may have different levels of enzymatic activity. The activity of this protein is responsible for the variety of clinical phenotypes observed, ranging from Hyper IgD syndrome, to mevalonate aciduria, a severe disease manifesting in neurologic development including psychomotor retardation and cerebellar ataxia.
Hyper IgD syndrome results in an overproduction of IL-1β in patient serum. It is therefore unsurprising that this disease is usually treated with IL-1 receptor antagonists such as
Anakinra. This drug has proven successful in reducing the inflammatory episodes in people with this disease (Mulders-Manders and Simon 2015). Canakinumab, an IL-1β
52 monoclonal antibody, has also proven effective in decreasing inflammation in these patients and is thought to have fewer side effects (Galeotti et al. 2012).
1.2 Interferonopathies
This group of monogenic inflammatory diseases are so called because they all exhibit a dysregulation of the interferon (IFN) immune responses. IFNs are produced by plasmacytoid dendritic cells and natural killer cells in response to viruses, bacteria and other invading microorganisms found in the host’s system (Russo and Brogan 2014,
Eleftheriou and Brogan 2017). They “interfere” with viral replication and function to successfully initiate an appropriate immune response so as to combat any pathogenic threats to the host. The pathway leading to type I interferon production is a tightly controlled process, and many proteins help to diminish the interferon response. Mutations in genes leading to an overstimulation of the type I interferon response or a failure to diminish interferon production cause a spectrum of disorders termed the interferonopathies (Aksentijevich and Kastner 2011b). Diseases in this spectrum include
Aicardi Goutières syndrome, STING associated vasculopathy with onset in infancy
(SAVI), Spondyloenchondrodysplasia with immune dysregulation (SPENCDI), and Proteasome Associated Autoinflammatory syndromes (PRAAS). These are all discussed below in more detail.
1.2.1 Aicardi Goutières Syndrome (AGS)
AGS is a rare disease which mainly affects the skin and brain, and is usually diagnosed in early infancy. Most patients present at birth or in very early infancy with similar
53 symptoms to that of a congenital infection and often exhibiting severe neurological involvement. However, we now know that the phenotype of AGS is broader than previously considered in general. Additional symptoms may include poor appetite, irregular movements, irritability and seizures (Rice et al. 2007) as well as hepatosplenomegaly, thrombocytopenia and chilblain lesions. Neuroimaging often shows basal ganglia calcifications, cerebral atrophy and leukodystrophy. Type
1 interferon (IFN) production in the cerebrospinal fluid and serum is consistently upregulated in AGS patients (McEntagart et al. 1998).
Mutations in genes encoding nucleic acid repair enzymes result in AGS (Shwin et al.
2017). The type I interferon production pathway can be initiated when viral DNA binds to nucleic acid sensors in the cytoplasm, such as TLR3, TLR7, 8 and 9. Many of the genes which are known to cause AGS are involved in the removal of exogenous DNA and RNA species left in the cytoplasm after a DNA replication cycle in a cell (Lee-Kirsch et al.
2014). If this DNA is not effectively removed, it may also bind to TLRs leading to inappropriate activation of the type I interferon response.
Loss of function mutations causing AGS in Ribonuclease H2 subunit A
(RNASEH2A), Ribonuclease H2 subunit B (RNASEH2B), Ribonuclease H2 subunit C
(RNASEH2C) and SAM and HD Domain Containing Deoxynucleoside
Triphosphate (SAMHD1) are inherited in an autosomal recessive manner (Rice et al.
2007). Mutations in both adenosine deaminase, RNA specific (ADAR) and Three Prime
Repair Exonuclease 1 (TREX1) have been observed to cause AGS in autosomal dominant or autosomal recessive fashions (Rice et al. 2007), depending on the nature of the particular mutation. Autosomal dominant mutations in Interferon Induced with Helicase
C Domain 1 (IFIHI) also lead to this condition. All types of AGS demonstrate high levels
54 of type 1 IFNs in serum. IFN scores are routinely carried out as a marker of disease severity and are used as a biomarker of the disease (Rice et al. 2013).
AGS is an early onset encephalopathy with basal ganglia calcification and cerebral white matter abnormalities. If neurological symptoms are not present at birth, they will usually appear shortly after culminating in spasticity, seizures, cortical blindness, dystonia and microcephaly (Crow and Rehwinkel 2009). Patients develop fevers and become severely irritable. Patients with TREX1 mutations may also develop other symptoms such as thrombocytopenia, hepatosplenomegaly, transaminitis (Crow and Rehwinkel
2009). TREX1 mutations can also lead to chilblain lesions and more severe skin involvement. Features of systemic lupus erythematosus have also been reported in children with AGS (Crow 2015).
The frequency of AGS is unknown; however mutations in genes associated with AGS can be found in children of all ethnic backgrounds. Mortality rates in this disease are quite high (Rice et al. 2007). It was reported that 81% of patients with mutations in TREX1,
RNASEH2A and RNASEH2B died by the age of 10, while Crow et al., 2015 demonstrate only 15% of all AGS patients surviving past 15 years of life. In particular, patients exhibiting mutations in the TREX1 gene are associated with a poorer prognosis and higher mortality rates compared with those exhibiting mutations in other genes (Crow and Manel
2015), whereas RNASEH2B mutations are associated with a better clinical prognosis and lower mortality rate (Rice et al. 2013). Currently, there is no standard therapy to treat
AGS; however corticosteroids and anti-epileptic medication can be used to treat the symptoms of this disease. Research into the use of immunosuppressive agents such as strategies to block IFN production with the use of Janus Kinase inhibitors (JAK) inhibitors such as Baricitinib are ongoing (Anon. 2019b). Recently there has also been positive data from the use of reverse transcriptase inhibitors (RTIs), which block the
55 reverse transcription of endogenous retroelements, and are currently used in HIV therapy
(Crow and Rehwinkel 2009). Clinical trials show a reduction in the levels of type I interferon production in patients receiving this therapy (Rice et al. 2018).
1.2.2 Proteasome Associated Autoinflammatory Syndromes (PRAAS)
PRAAS describes a spectrum of autoinflammatory phenotypes, all resulting from proteasome dysfunction, and have been described as far back as 1939. Patients experience recurrent fevers with nodular erythema, other skin rashes, anaemia and joint contractures (McDermott et al. 2015). Nowadays, many different manifestations of the disease exist. Chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature (CANDLE), Nakajo- Nishimura syndrome (NNS), Japanese autoinflammatory syndrome with lipodystrophy (JASL), microcytic anaemia and panniculitis-induced lipodystrophy (JMP) are all listed under the PRAAS umbrella. All these diseases have been associated with autosomal recessive loss of function mutations in proteasome genes such as PSMA3, PSMB4, PSMB8, PSMB9 and POMP and as a result, the phenotype of each of these diseases is quite similar.
Mutations in proteasome genes result in substantial reduction in proteasome activity.
This, in turn, leads to an upregulation of the MAPK pathway and IFN production pathway, producing an enhanced inflammatory response (McDermott et al. 2015). The proteasome is an important cellular machine, helping to recycle proteins within the cell which have been marked for degradation by ubiquitin tags. The PSMB8 gene encodes a unit of the proteasome called β5i. β5i joins β1i and β2i to form a catalytically active proteasome (Agarwal et al. 2010, Kitamura et al. 2011). Mutations within the PSMB8 gene cause a decrease in the chymotrypsin-like catalytic activity of the
56 proteasome or stop β5i from being incorporated into the proteasome, and therefore the proteasome machinery cannot function efficiently (Liu et al. 2012). This ultimately leads to an accumulation of ubiquitinated and oxidated proteins within the cell. Two missense mutations in PSMB8; T75M in CANDLE and JMP patients and G201V in NNS and
JASL patients, and one nonsense mutation in this gene; C135X in a patient with CANDLE have so far been reported (Arimochi et al. 2016). Compound heterozygous mutations in proteasome genes have also been documented.
Common features of PRAAS include skin eruptions, fever, lipodystrophy, hepatosplenomegaly, failure to thrive and muscle atrophy (Brehm et al. 2015). While there are subtle differences between the different syndromes that make up PRAAS, it is evident that they represent a spectrum of the same disease.
CANDLE usually presents early in infancy with daily recurrent fevers, delayed physical development, failure to thrive, anaemia, arthritis and arthralgia, progressive lipodystrophy and intracerebral calcification (Torrelo 2017). JMP has been described in patients from Mexico and Portugal. Hepatosplenomegaly, macrosomia, lipodystrophy affecting the face arms and trunk and microcytic anaemia are common features of the disease (Brehm et al. 2015). NNS is found in Japanese populations and is characterised by weakness and wasting of the muscle, early in life. Elongated clubbed fingers, recurrent fevers, nodular erythema, lipo-muscular atrophy, myositis, joint contractures, basal ganglia calcification, hypergammaglobulinemia and hepatosplenomegaly are typical manifestations of NNS (Arima et al. 2011). JASL is another form of PRAAS which is also found in Japanese patients. Its features are very similar to those of NNS. Patients with JASL typically develop lipodystrophy of the upper body, skin eruptions with nodular erythema, hand deformities, recurrent fevers and basal ganglia calcifications (Kitamura
57 et al. 2011). All patients with this disease documented thus far have died of cardiac or respiratory failure.
Oral corticosteroids, IL-1 receptor antagonists and JAK inhibitors such as baricitinib have all been used to treat PRAAS conditions. Mutations in the PSMB8 gene ultimately lead to an over-abundance of defective proteins in the cell. This causes cellular stress and leads to the production of IFNs. In keeping with this, patients with PRAAS exhibit upregulation in the peripheral blood mRNA expression of IFN induced genes (Brehm et al. 2015).
The subsequent release of type 1 IFNs results in the release of several other proinflammatory cytokines and chemokines to recruiting inflammatory cells including myeloid cells and neutrophils. Due to enhanced type 1 IFN release in PRAAS, immature immune cells are immobilised from the bone marrow, and this leads to an inflammatory infiltrate in the skin and other organs (Torrelo 2017).
1.2.3 STING Associated Vasculitis with onset in Infancy (SAVI)
SAVI is a chronic autoinflammatory condition also associated with high type 1 IFN production. SAVI is characterised by severe early onset systemic inflammation, the presence of autoantibodies, cutaneous vasculopathy often resulting in lesions and severe rashes on the fingers, cheeks, toes and ears. There is also extensive pulmonary involvement which may manifest as aggressive fibrotic interstitial lung disease. In fact, pulmonary inflammation is most often the cause of death in patients with SAVI (Liu,
Jesus, et al. 2014).
Patients with SAVI usually present in the neonatal period with severe systemic inflammation, exhibiting a high C- reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Skin lesions are evident on the extremities and develop into painful ulcerative
58 lesions with tissue infarction and eschar formation, often resulting in amputation. Low- grade fevers, livedo reticularis and Raynaud’s phenomenon, are also common (Liu, Jesus, et al. 2014). Pulmonary involvement usually includes interstitial lung disease, adenopathy and lung fibrosis. Biopsies are indicative of neutrophilic inflammatory infiltrates in the blood vessels and in the lymphocytic deposits in the lungs (Liu, Jesus, et al. 2014, Clarke et al. 2016). No brain involvement is observed in patients with SAVI (Shwin et al. 2017).
SAVI is caused by autosomal dominant gain of function mutations in the TMEM173 gene. This gene encodes STING (stimulator of interferon genes) protein.
Upon binding to its ligand, cGAMP, STING dimerises to mediate the production of type
I interferons through a pathway involving the phosphorylation of TBK1 and IRF3 (Clarke et al. 2016). Liu and colleagues identified causative mutations in this gene as p.V147L, p.N154S, p.V155M, all of which were de novo. The mutations seem to cluster around exon 5 of the TMEM173 gene, which is involved in dimerization of the protein (Liu,
Jesus, et al. 2014). Experiments performed on fibroblasts, and peripheral blood mononuclear cells (PBMCs) derived from patients with SAVI show an over-activation of type I interferon stimulated genes, in particular, the IFNβ promoter is constitutionally activated (Liu, Jesus, et al. 2014, Clarke et al. 2016).
59
1.3 Autoinflammation secondary to Primary
Immunodeficiency
Primary immunodeficiency (PID) syndromes are a heterogeneous groups of conditions which results from poor or absent functioning of cells from both the adaptive and innate arms of the immune system (McCusker et al. 2018). People suffering from these conditions often show increased susceptibility to severe and recurrent infections, inflammation and autoimmunity (Engelhardt et al. 2013, McCusker et al. 2018).
Immunodeficiencies resulting from defects of the innate immune system may result from a defect in phagocytosis or a fault in the complement pathway. These diseases include chronic granulomatous disease, C1q, C1r and C3 deficiency (McCusker et al. 2018).
Adaptive immune system associated immunodeficiencies include the T and B cell disorders. Defects in T cell development and differentiation, can result in a lack of functional T cells, with patients often displaying lymphopenia or neutropenia. Faults in
B cell differerentiation or function often result in antibody-mediated deficiencies, such as
IgG, IgA, IgM and IgD (Bousfiha 2018). X-linked agammaglobulinemia, and selective
IgA deficiency are counted among the B cell disorders (McCusker et al. 2018, Picard et al. 2018). As functional T cells are an essential process in providing B cell help, most T cell defects lead to combined immunodeficiencies (CID). Severe combined immunodeficiency (SCID) results in a failure to thrive early in life and severe recurrent infections with opportunistic pathogens. Other less critical combined immunodeficiencies include DiGeorge syndrome, X-linked lymphoproliferative disease and Wiskott-Aldrich syndrome (Picard et al. 2018). Some PIDs may also have features of autoimmunity and autoinflammation. In these instances, lymphocytes although present, may be dysfunctional and often lead to autoimmunity, autoinflammation and self-reactivity
60
(McCusker et al. 2018). Disorders of this type include haemophagocytic lymphohistiocytosis (HLH), autoimmunelymphoproliferative syndrome (ALPS) and autoimmunepolyendocrinopathy candidiasis and ectodermal dystrophy (APECED)
(Picard et al. 2018).
1.3.1 Pathophysiological Mechanisms of Autoinflammation within the PID
Autoinflammation and autoimmunity is a common feature of primary immunodeficiencies (Gathmann et al. 2014). For example, patients with common variable immunodeficiency (CVID) often exhibit low immunoglobulin levels, enteropathy, cytopenias, granulomas and gastrointestinal inflammatory disease along with other inflammatory manifestations complications (Odnoletkova et al. 2018).
Pathophysiological mechanisms such as aberrant regulatory T cell function
(Tregopathies), NF-ƙB pathway defects and abnormalities of the actin cytoskeleton network may contribute to the underlying autoinflammation in the primary immune deficiencies.
1.3.1.1 Tregopathies
Tregopathies comprise a set of monogenic diseases where in the regulatory T cell
(CD4+CD25+FOXP3+) function is defective (Cepika et al. 2018). Loss of function mutations in FOXP3, CTLA4, CD25, STAT3, LRBA and BACH2 genes, which are involved in the proper functioning of Tregs, lead to tregopathies (Cepika et al. 2018).
Regulatory T cells, are key players in regulating immune homeostasis. They secrete cytokines such as IL-10 and TGF-β in order to suppress the function of effector T cells
(Littringer et al. 2018). They also express a number of coinhibitory receptors such as
61
CTLA-4 and PD-1 which bind to T cells in order to suppress proliferation and have a role in supressing the activation of B cells and monocytes (Cepika et al. 2018). They are thus important mediators in controlling overactivation of the adaptive immune system.
Common clinical manifestations of tregopathies include enteropathy, eczema, recurrent infections and lymphoproliferation. These diseases are often treated with immunosuppressive reagents or HSCT therapy.
Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) is a disorder caused by hemizygous mutations in the FOXP3 gene. FOXP3 is a transcription factor which is required for the differentiation into and maintenance of Treg cells (Bacchetta et al. 2018). IPEX is a heterogeneous disorder and over 70 mutations in the FOXP3 gene have been identified, however, it has been reported that the same mutation in FOXP3 can lead to drastically different phenotypes (Gambineri et al. 2008), the c.1150G>A mutation in FOXP3 has been reported in patients who died prematurely and those who survived past 10 years of age (Nieves et al. 2004, Bacchetta et al. 2018).
IPEX most often occurs early in life and can be fatal if not treated appropriately.
Diarrhorea, type 1 diabetes and eczema are typical manifestations with autoimmune enteropathy being a hallmark of the disease (Bacchetta et al. 2018). Other autoimmune symptoms such as thyroid dysfunction, autoimmune cytopenia, autoimmune haemolytic anaemia may also be present. Renal disease resulting in membraneous glomerulous nephritis or interstitial nephritis is prevalent in IPEX patients and can be caused by severe autoimmune manifestations or long term therapy (Barzaghi et al. 2018).
Haematological manifestations include autoimmune haemolytic anaemia, neutropenia and thrombocytopenia (Barzaghi et al. 2018). Manifestations of the disease improve following immunosuppression or haematopoietic stem cell transplantation (HSCT) therapy, supporting the hypothesis that this disease has an underlying inflammatory
62 pathogenesis (Bacchetta et al. 2018). Immunosuppressive drugs used to treat the disease include azathioprine, calcineurin inhibitors, rapamycin, methotrexate, mycophenolate mofetil, and steroids (Barzaghi et al. 2018). Laboratory findings in IPEX patients demonstrate elevated IgE and eosinophil counts and the presence of high titre autoantibodies (Bacchetta et al. 2018).
Cytotoxic T lymphocyte associated protein-4 (CTLA-4) haploinsufficiency, is caused by autosomal dominant mutations in the CTLA-4 gene. CTLA-4 is an inhibitory co- receptor expressed on T cells and Treg cells. It binds to the CD80 and CD86 ligands on antigen presenting cells (APCs), internalizing them via transendocytosis, (Schubert et al. 2014) in order to suppress further activation of T cells, and diminish the immune response (Verma et al. 2017). Ctla-4-/- mice suffer fatal autoimmunity, demonstrating the key importance of this protein in the prevention of autoimmune phenotypes
(Waterhouse et al. 1995, Verma et al. 2017).
When CTLA-4 haploinsufficiency is observed in humans, autoimmune and inflammatory phenotypes also prevail. In a large kindred with 5 affected family members, (Schubert et al. 2014) reports autoimmune cytopenia, hypogammaglobulinemia, autoimmune enteropathy and recurrent respiratory tract infections. Likewise, (Kuehn et al. 2014) reports heterozygous mutations in the CTLA-4 gene in affected members of four unrelated families, leading to similar manifestations as well as; lymphopenia, cytopenias and lymphocytic infiltration in gastrointestinal tract, kidney, CNS, bone marrow and lung (Kuehn et al. 2014, Schubert et al. 2014).
Interestingly, both studies also state that the CTLA-4 mutations identified in patients, were also found in healthy family members, insinuating incomplete penetrance of the disease (Kuehn et al. 2014, Schubert et al. 2014). Other manifestations of the disease include hepatosplenomegaly, thrombocytopenia, autoimmune hemolytic anemia,
63 psoriasis and enteropathy. Patients had normal, or in some cases higher percentages of
FOXP3+ T reg cells, in comparison to healthy controls, although their function was reduced (Kuehn et al. 2014, Schubert et al. 2014), probably due to a loss of expression of CTLA-4 on these cells (Schubert et al. 2014). Patients also exhibited a loss of activated B cells and an increase in autoreactive CD21lo B cells, in comparison to healthy controls (Kuehn et al. 2014).
Abatacept (CTLA-4 Ig) , a drug which has been used previously for rheumatoid arthritis
(Ostör 2008, Moots and Naisbett-Groet 2012), is being used to treat CTLA-4 haploinsufficiency, with good response (Lee et al. 2016), although follow up studies are needed to prove the effect. HSCT therapy is also used as a treatment in this disease
(Slatter et al. 2016).
1.3.1.2 Actin cytoskeletal defects
Actin is a ubiquitous protein expressed in all eukaryotic cells (Dominguez and Holmes
2011). Actin monomers form filaments, and are involved in the transport of proteins, vesicles and organelles around the cell. Actin also has an important function in maintaining cell shape, cell division, endocytosis, cell migration, cytokinesis, and numerous other essential processes (Moulding et al. 2013, Tangye et al. 2019).
Specifically, in regards to the immune system, actin has a central role in chemotaxis, phagocytosis and cytotoxic granule release, internalisation and presentation of antigens
(Moulding et al. 2013, Worth and Thrasher 2015). Therefore, it is unsurprising that a mutation which disrupts the efficient functioning of the actin cytoskeleton could have detrimental effects on the immune system and lead to complications such as defects in
64 cell migration, lymphocyte activation and differentiation, apoptosis and cytokine secretion, amongst others (Worth and Thrasher 2015).
Actin monomers are polymerised into filaments which in form the cytoskeletal network, by actin nucleators. Three classes of nucleators exist; the Arp2/3 complex, the formin family and members of the Spire, cordon-bleu and leiomodin family (Moulding et al.
2013).
Wiskott-Aldrich Syndrome is a familial thrombocytopenic disease caused by recurrent infections, eczema and bleeding tendency (Candotti 2018). It was described separately by two clinicians, Wiskott in 1937 and Aldrich in 1954. Dr Aldrich concluded that this disease has an X-linked recessive mode of inheritance (Aldrich et al. 1954), upon examination of a Dutch family carrying the disease.
WAS, an X linked genetic disorder, is caused by mutations in the WAS gene. WASp, the
WAS protein, is an actin nucleation promoting factor (Catucci et al. 2012) and regulates polymerisation by activating the Arp2/3 complex. It exists in an autoinhibited conformation, but is activated upon phosphorylation by Cell Division Control Protein 42 homolog (CDC42), a Rho GTPase (Tangye et al. 2019). WASp is expressed exclusively in hematopoietic cells. This is a large gene containing 12 exons, and approximately 400 causal mutations have been identified, to date (Moratto et al. 2011, Candotti 2018). These mutations result in three distinct conditions; classic WAS, X-linked Thrombocytopenia
(XLT) and X-linked Neutropenia (XLN). Classic WAS is the most severe form od the disease and mutations here often lead to a complete loss of protein expression. XLT has a milder presentation and exhibits a much reduced protein expression. XLN, has a constitutively active form of the protein (Moulding et al. 2013, Candotti 2018).
65
Presentation usually occurs within the first few months of life; however, milder cases may not be diagnosed until adulthood (>16 yo). The presentation of this disease is extremely heterogeneous. Typically, patients suffer from thrombocytopenia and susceptibility to infections, however, complications such as eczema and autoimmunity also occur.
Thrombocytopenia itself can have a varied presentation in these patients and range from petechiae to more severe hematomas and even life threatening intestinal or intracerebral hematomas (Worth and Thrasher 2015, Candotti 2018). WAS patients are immunodeficient and are highly susceptible to bacterial, viral and fungal infections.
Bacterial otitis media, sinusitis and pneumonias are common (Candotti 2018). WAS is often treated with hematopoietic stem cell transplantation (HSCT). Severe forms of the disease have poor prognosis with life expectancy of below 20 years of age (Catucci et al.
2012).
Monocytes and macrophages have a reduced chemotactic function and phagocytic ability in these patients (Worth and Thrasher 2015). Dendritic cells have defective motility and cannot properly form immunological synapses with T cells. NK cells exhibit decreased target cell lysis, decreased integrin mediated migration, and a disorganised immunological synapse (Candotti 2018).
WASp is crucial to the survival and proliferation of fully differentiated T cells. Therefore, in WASp deficient humans and mice, lower numbers of T cell subsets exist, specifically the CD8+ T cell population (Rivers and Thrasher 2017). WASp is essential for the efficient functioning of the immunological synapse, and WASp deficient T cells also have decreased actin polymerisation at the T cell – antigen presenting cell (APC) contact site, resulting in delayed and inefficient recruitment of proteins necessary for TCR activation
(Rivers and Thrasher 2017). WAS patients also exhibit a defect in B cell immunity. This manifests in a disorganisation of peripheral B cell compartments, and an increased
66 number of transitional B cells (Candotti 2018). In addition, Wasp-/- mouse models show hyperproliferation, increased autoantibody production and enhanced differentiation into class-switched plasmablasts (Worth and Thrasher 2015). These cellular abnormalities underlie the pathophysiology of WAS patients and contribute not only towards immunodeficiency but also, towards autoinflammation.
Autoimmune and autoinflammatory complications are common in WAS patients (up to
70%) (Dupuis-Girod et al. 2003, Worth and Thrasher 2015) and may complicate haematopoietic stem cell transplantation (HCST). This can take the form of inflammatory bowel disease, haemolytic anaemia, neutropenia, arthritis, autoimmune thrombocytopenia, IgA nephropathy and or vasculitis (Schurman and Candotti 2003,
Catucci et al. 2012). Autoimmunity and autoinflammation affecting the skin, eye, liver and muscle has also been described (Dupuis-Girod et al. 2003, Schurman and Candotti
2003). Notably, (Lee et al. 2017) show that mutations in the WASp gene lead to autophagy and increased NLRP3 inflammasome formation, classic components of autoinflammatory diseases.
Dedicator of cytokinesis 8 (DOCK8) deficiency is another primary immune deficiency caused by defects of the actin cytoskeleton. DOCK8 is a member of the DOCK180 family of proteins and is a guanine nucleotide exchange factor. Its main function is as an activator of Rho GTPases, including CDC42 (Moulding et al. 2013). As CDC42 is a crucial regulator or the Arp2/3 complex, through its binding to WASp; loss of function mutations in DOCK8 therefore lead to defects in the actin polymerisation pathway.
Homozygous and compound heterozygous mutations in the DOCK8 gene were found to lead to DOCK8 deficiency. Clinically it manifests as asthma, eczema, elevated IgE levels and many allergies. Originally, patients with this phenotype were suspected to have an
67 autosomal recessive form of hyper-IgE syndrome, however two groups then identified loss of function mutation in the DOCK8 gene, present in these patients (Engelhardt et al.
2009, Zhang et al. 2009). DOCK8 deficiency is an immunodeficiency and people with this disorder are highly susceptible to bacterial (Staphlycoccus aureus, Staphlococcus pneumoniae), viral (human papilloma virus (HPV), Epstein Barr Virus (EBV), cytomegalovirus (CMV) ) and fungal infections such as Candida albicans (Tangye et al.
2019). They are also highly susceptible to allergies including food allergies and eczema.
Lymphopenia is a common feature of the disease, as is elevated levels of IgE, IgA, IgG but decreased IgM. These individuals also suffer from impaired antibody responses, and malignancy may also be common (Tangye et al. 2019). Other symptoms may include otitis media, pneumonia, bronchitis and eosinophilia.
DOCK8 plays a substantial role in lymphocyte development, differentiation and effector function. Patients display fewer proportions of naïve cells with sunstantial increases in effector T cells, both in the CD4+ and CD8+ compartments. These cells also show signs of exhaustion, expressing markers such as CD57, CD95 and PD1. They also show a loss of co-stimulatory receptors such as CD27 and CD28 (Randall et al. 2011). CD4+ T cells appear to be biased toward differentiation into Th2 cells, with a reduction in the number of Th1, Th17 cells (Tangye et al. 2017). There is a reduction in high-affinity IgG B cells in those with DOCK8 deficiency (Moulding et al. 2013). DOCK8 dendritic cells exhibit poor migration to the lymph nodes, poor T cell priming and defective cytoskeletal organisation (Harada et al. 2012).
People with loss of function mutations in this gene are highly susceptible to infections. A range of pathogens have been identified in patients cells. It is postulated that this may be due to a defect in cytotoxic T lymphocytes to form conjugates with target cells and a reduced ability of mediators such as cytotoxic granules to travel to the immunological
68 synapse (Tangye et al. 2019). It is also suspected that the increased susceptibility of fungal infections may be due to a decrease in the number of Th17 cells (Ma et al. 2008, Tangye et al. 2019). Exaggerated Th2 responses in these individuals could lead to the eczema, hyper-IgE and severe allergy often observed in these patients. (Wu et al. 2018) showed that administration of IL-21 to Dock8-/- mice had impacts on reducing astma symptoms and IgE production from these mice.
DOCK8 deficiency has a high mortality rate. Immunoglobulin replacement therapy and antimicrobial prophylaxis are often used to treat patients (Tangye et al. 2019). HSCT therapy provides the only cure for the condition, and has proven effective in alleviating many of the symptoms of DOCK8 deficiency with a good survival rate (Aydin et al.
2019).
1.3.2 Familial haemophagocytic lymphohistiocytosis (FHL)
HLH is characterised by an excessive inflammatory response due to hyperactivation of the macrophages and cytotoxic T lymphocytes (CTL’s). This often leads to progressive multi organ failure if left untreated (Gholam et al. 2011). Hallmarks of the disease include a high production of proinflammatory cytokines and tissue infiltration by macrophages and CTL’s (Lovisari et al. 2017). HLH can be classified as secondary HLH, usually as the result of an viral infection, or familial/primary HLH, for which there is an underlying genetic component.
Genetic forms of FHL can also be divided into two subgroups; classic FHL, an autosomal recessive disease wherein HLH is the only manifestation of the disease and FHL in conjunction with other immundeficiency syndromes such as Chediak-Higashi syndrome
69
(CHS), Griscelli syndrome 2 (GS2) and X-linked lymphoproliferative disease (XLP). For the purposes of this review I will focus on classic FHL.
FHL presents early in childhood. It is estimated to have a prevalence of 1/50000 births
(Janka 2012). Symptoms usually present within the first 6 months of life and in some instances may develop in utero (Usmani et al. 2013). Five genetic loci have been identified to date, giving rise to 5 subtypes of autosomal recessive FHL. All genes identified to date encode for proteins that are involved in the granule dependent cytotoxic function of NK and T cells. This mechanism is essential for the elimination of infected target cells and control of the immune response.
The gene leading to FHL1 is currently unknown, but has been identified by linkage studies to reside on 9q21.1 (Ohadi et al. 1999). Perforin is probably the best described gene and leads to FHL2 (An et al. 2013). Perforin is found in lysosomal granules in CTL’s and NK cells. When CTL’s and NK cells come into contact with the target cell, an immunological synapse is formed. Lytic granules traffic from within the immune cell to the immunological synapse, the granules dock and release their contents, into the site between the effector cell and target cell. Perforin is released in this process. It forms pores on the surface of the target cell, allowing the entry of granzymes which then activates a cascade resulting in apoptosis of the target cell (An et al. 2013, Usmani et al. 2013).
FHL3 is caused by mutations in Munc13-4, which is required for vesicle priming
(Feldmann et al. 2003). It colocalises with cytolytic granules near the site of contact between the T cell and the target cell. In FHL3 patients, the docking of lytic granules on the plasma membrane is normal. However the priming of granules, before fusion with the plasma membrane and subsequent release of cytolytic enzymes is impaired
(Feldmann et al. 2003, Boswell et al. 2012). FHL4 is caused by mutations in STX11, is
70 expressed in monocytes, NK cells and cytotoxic T cells and is involved in vesicle priming and membrane fusion (Müller et al. 2014). FHL5 is caused by autosomal recessive mutations in STXBP2, a protein involved in the regulation of intracellular trafficking and control of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex assembly and disassembly (Spessott et al. 2015). STXBP2 contributes to the granule exocytosis machinery. Degranulation of NK and T cells from
STXBP2 deficient patients is impaired as is cytotoxic function of NK cells (Spessott et al. 2015).
As a result of the absent or defective perforin mediated cytotoxicity, there is a decreased removal of target cells from the host. This leads to excessive proliferation of antigen presenting cells and continual stimulation and migration of T cells, resulting in excessive production of cytokines. This cytokine storm underlies the progressive organ dysfunction and eventual death of many affected patients (Usmani et al. 2013). The disease activity in HLH is primarily due to an excessive production of IFNγ, which is produced by an overactivation of T cells and NK cells. This is the main driver behind macrophage activation syndrome and leads to the increased expansion of polyclonal CD8+ T cells.
Macrophages and activated T cells producing numerous cytokines such as TNFα, IL-6,
IL-1 and IL-18 infiltrate organs such as spleen, lymph nodes, brain and bone marrow and continues to exacerbate disease activity (Usmani et al. 2013).
Clinical manifestations of the disease include fever, hepatitis, cytopenias, lymphadenopathy and hepatosplenomegaly, skin rash and variable neurological manifestations(Gholam et al. 2011, Madkaikar et al. 2016). Lymphadenopathy and malignancies such as lymphomas may also present, although these are less common.
Currently, hematopoietic stem cell transplantations provide the only curative medicine for this disease (Lehmberg et al. 2019).
71
1.4 Finding novel causes of Monogenic Inflammatory
Syndromes
In the past 20 to 30 years, many novel genetic discoveries have been made in regards to monogenic inflammatory diseases. This has a huge bearing on our understanding of the pathways involved and the underlying genetic contribution to each disease, which in turn enables the use of better, more targeted therapy for each condition. Many techniques including homozygosity mapping, positional cloning and sequencing strategies, have aided the discovery of novel disease genes. In recent times, there have been many advances in next generation sequencing (NGS) technologies, which has come a long way from the days of sanger sequencing and shotgun sequencing originally developed during the Human Genome Project (HGP) (Lander et al. 2001, Venter et al. 2001). When the first human genome was finally fully sequenced in 2001, it had taken 13 years to complete and had cost a total of $2.7 billion US dollars. Thankfully it is now much easier, faster and cheaper to sequence whole genomes or whole exomes, and this technology is getting better all the time.
1.4.1 Next Generation Sequencing
NGS approaches involve breaking the DNA up into random fragments and then these fragments are ligated to complimentary adaptor sequences. A sequencing-by-synthesis approach is then used to uncover the genetic code (Koboldt et al. 2013). The fragments generated are much shorter than sanger sequencing approaches, usually about 100bp as opposed to 500bp (Zhang et al. 2011), which can make coverage of the particular read an important issue. Regions need to be sufficiently covered in order to definitively say
72 whether a variant is true, or whether it is a false positive. A number of NGS technologies currently exist such as the Roche 454 pyrosequencing technology, Illumina HiSeq,
Illumina MiSeq and ABI SOLiD analyser amongst others (Zhang et al. 2011). The type of sequencer chosen depends on the availability of that system and the individual needs of the project.
Whole genome sequencing (WGS) involves sequencing of the 3 x 109 bases (Rabbani et al. 2012) of the human genome, both coding and non-coding. Whereas whole exome sequencing (WES) refers to only sequencing those parts of the genome which code for proteins. Exons comprise only about 1% of the entire human genome (Rabbani et al. 2012,
2016). We are not yet sure why the genome contains such excessive amounts of intronic regions. Originally it was thought that this was just junk DNA, however we are slowly moving towards the idea that this region could harbour important regulatory sequences, which control when, where and how often a gene is transcribed and translated into protein
(Anderson 2014, FANTOM Consortium and the RIKEN PMI and CLST (DGT) et al.
2014).
Because the exome is just a fraction of the entire genome, it is much faster and much cheaper to sequence. The intronic regions are not well characterised, and much of this region remains a mystery (Maurano et al. 2012, Ward and Kellis 2012), so deciding whether a variant found in this region is pathogenic can be extremely challenging.
Variants found within exonic regions are easier to characterise as much more information is readily available about these regions. It is also more likely that variations found here cause disease, because they may disrupt the formation, production or expression of the protein produced from the gene within which they reside (Kryukov et al. 2007). It is estimated that 85% of all disease causing mutations reside within the protein coding region of the genome (Botstein and Risch 2003). Given this information, the exome
73 appears an ideal source of potential pathogenic variants for as yet uncharacterised rare diseases. However, it is not without its limitations. Because the whole genome is sequenced in WGS, the coverage of each region is quite uniform. However, as WES focuses only on the exome, its capture methodology and therefore its coverage of each region is very often biased, leaving some regions not well covered. In particular, GC rich regions are poorly captured in WES (Kozarewa et al. 2009, Veal et al. 2012). This can lead to misinterpretation of certain mutations, such as copy number variants and we must be aware of this fact when analysing the data. After WES analysis, it is recommended that sanger sequencing, which is viewed as the clinical gold standard approach to sequencing, will then be used to confirm any interesting variants found through the WES data (Reis-Filho 2009). WES and Sanger sequencing methodologies are seen as complementary approaches in this regard.
Indeed deciding which mutations identified in the WES data are of relevance to the disease process can be a tedious and demanding analysis. Numerous variants including indels, synonymous, non-synonymous, frameshift, missense mutations may be identified by WES, and about 12500 variants in an exome produce a change in the protein sequence
(Ng et al. 2008). In order to choose the relevant mutation, these variants are brought through a filtering strategy depending on the estimated mode of inheritance, ethnicity of the patient and other methods such as whether it is predicted damaging by in silico tools.
1.4.2 Gene Panels
One way to combat the vast amount of information gathered from WES data is to use targeted gene panels. Gene panels may be used in both clinical and research settings and are being created to sequence only specific genes in an individual. They may be used
74 when a certain disease is already suspected in a patient, or when a patient displays very similar symptoms to a pre-existing condition and requires a molecular diagnosis.
Gene panels present a more directed and targeted approach to gene sequencing, as they target only specific genes, and as a result, offer a higher depth of coverage on those genes in comparison to WES. Unsurprisingly it is cheaper, and faster to sequence the genes on a gene panel, compared to all the genes in an exome (Omoyinmi et al. 2017).
Prof Brogan/Dr Eleftheriou lab currently has two gene panels up and running. These are the Vasculitis Inflammatory Panel (VIP) (Omoyinmi et al. 2017) and the
Neuroinflammatory Panel (NIP) (McCreary et al. 2019). These panels have a high detection rate, giving the correct molecular diagnosis in 32% of previously undiagnosed patients (Omoyinmi et al. 2017). These panels contain more than 200 genes each, have high depth of coverage for each gene and are also able to detect many different types of mutations such as indels, frameshifts, splice junctions and even somatic mosaicism present within these genes, which may be missed in WES.
Other groups have also adopted the use of gene panels, especially for diagnostic purposes.
In particular, the Mayo Clinic are running AUTOP, an autoinflammatory primary immunodeficiency gene panel (Anon. 2019c). This panel contains genes known to cause monogenic autoinflammatory syndromes such as FMF, TRAPS, CAPS, CANDLE,
PRAAS, PFAPA and Blau syndrome amongst others. Variants found within genes on the panel are then classified based on the American College of Medical Genetics and
Genomics (ACMG) guidelines, which classes variants from 1-5, based on their known pathogenicity, class 5 being highly pathogenic (Richards et al. 2015).
One of the benefits of using a targeted gene panel instead of WES or WGS is that you will not discover any incidental findings in genes which are not related to the phenotype
75 in question, but which may be pathogenic. Reporting incidental findings is of ethical concern as described by the European Society of Human Genetics (Anon. 2019d). The use of gene panels can greatly reduce the time and expense needed to diagnose patients if the clinician suspects a certain disease at play and the presentation is similar to already identified genetic diseases.
Considering the complexity of the autoinflammatory diseases and other immunodysregulatory disorders discovered thus far and given the advent of NGS techniques, the identification of further monogenic diseases belonging to this disease grouping seems likely. At GOSH, there are still a number of familial or sporadic cases with early onset systemic and cerebral inflammation that have no molecular diagnosis identified. My PhD project focused on using NGS to identify the genetic cause of monogenic inflammatory diseases.
76
1.5 Hypothesis and Aims
Therefore I hypothesized, that by using state of art the next generation sequencing (NGS) techniques, namely whole exome sequencing and homozygosity mapping, it is possible to discover novel monogenic diseases resulting in pathological inflammatory syndromes.
The aims of my project were, therefore:
1. To identify the disease-causing mutation(s) for
undefined inflammatory syndromes using NGS technologies.
2. Once potential disease-causing mutations were identified, to begin to elucidate
some of the pathogenic mechanisms in these conditions using a series of
functional experiments.
77
2 General Materials and Methods
This section describes the materials and general methods that were used throughout this thesis. Methods that were applicable to only one chapter will be discussed in detail in the relevant chapter.
2.1 Subjects
2.1.1 Patients
The majority of patients were seen at Great Ormond Street Hospital for Children NHS
Foundation Trust (GOSH) either at the vasculitis clinic (Dr Eleftheriou/Prof Brogan) or the neuroinflammation service (Dr Eleftheriou and Dr Hemingway). Older patients (> 18 years old) were seen at the Royal Free Hospital London, at the National Amyloidosis
Centre (Professor Helen Lachmann). Ethical approval for this study was given by
Bloomsbury ethics committee (ethics number: 08/H0713/82), to which all patients were subsequently enrolled. Informed consent and age appropriate assent were obtained from all participants included in this study.
Index cases with a severe and enhanced neurological and/or systemic inflammatory disease phenotype were recruited. I specifically focussed on patients who developed disease early in life and/or those with a family history (potentially with consanguineous parents). The clinical presentation and relevant family histories are described in detail in each individual chapter.
78
2.1.2 Healthy controls
Healthy adult controls were recruited and consisted of volunteer staff members from within the Infection, Inflammation and Rheumatology Department at the UCL Great
Ormond Street Institute of Child Health, for which full consent was received. Healthy children and adolescent controls (median age 17 years) were recruited through the
Arthritis Research UK Centre for adolescent rheumatology. Informed consent for healthy young people was obtained with local ethics approval (REC 11/LO/0330). Volunteers had no medical history of acute or chronic illnesses and specifically no symptoms of current infection at time of sampling.
2.2 Sample Collection
Depending on the specific experiments planned and samples needed, blood was collected in different tubes containing a variety of preservatives. For DNA extraction, blood was obtained from all patients and their family members, in 100μl of 1%
Ethylenediaminetetraacetic acid (EDTA) tubes. When peripheral blood mononuclear cells (PBMCs) were required, blood was collected in falcon tubes and stabilised using
1µl of preservative free sodium heparin, an anticoagulant (CP Pharmaceuticals) per 1ml of blood collected. When RNA was required, blood was collected in 2.5 ml PAXgene blood RNA tubes (Qiagen). When blood collection was not possible, DNA was instead extracted from saliva. Saliva was provided in DNA Genotek Oragene saliva pots.
Extracted DNA was stored at 40C. Blood was stored at -800C. Saliva was kept at room temperature. RNA PAXgene tubes were kept at room temperature for at least two hours before being transferred to -800C freezer.
79
2.3 Tissue Culture Media
PBMCs were cultured in RPMI media with 10% fetal bovine serum (FBS) and 100 U/ml
Penicillin-Streptomycin (Thermo Fisher Scientific). Jurkat cells were also cultured using
RPMI media with 10% FBS and 100μg/ml Penicillin-Streptomycin (Thermo Fisher
Scientific). Human Dermal Fibroblast Cells (HDFC) were maintained in DMEM/F12
(DMEM/Nutrient Mixture F-12) medium, supplemented with 10% FBS, 100U/mL penicillin, 100U/mL streptomycin (Life Technologies). All cells were incubated at 370C in 5% CO2.
80
2.4 DNA Extraction Protocols
DNA was obtained from patients and their participating family members by extraction through either whole blood or saliva.
2.4.1 DNA extraction from whole blood
DNA was extracted from EDTA tubes using the Qiagen Gentra Puregene blood kit. If frozen, the blood was first allowed to equilibrate to room temperature. Next, 900µl of red blood cell (RBC) lysis solution was added to 300µl of whole blood and inverted 10 times.
This was allowed to incubate for 1 minute at room temperature before being centrifuged at 16,000 x g for 20s. White blood cells appeared as a pellet at the bottom of the Eppendorf tube. The supernatant was discarded, and 300µl of cell lysis solution was then added and the Eppendorf vortexed for 10 seconds. Then, 100µl of protein precipitation solution was added, and again the Eppendorf was vortexed for 20s. This was centrifuged for 1 min at
16,000 x g. Proteins collected at the bottom of the tube. The supernatant was collected into a fresh Eppendorf, and 300µl of isopropanol was added. The supernatant and isopropanol were mixed by inverting the tube 50 times. DNA appeared as white threads.
This was then centrifuged for 1 minute at 16,000 x g. The supernatant was discarded and the Eppendorf inverted on white tissue paper for 2 minutes to remove any residual supernatant. Total of 300µl of 70% ethanol was added to the pellet and inverted several times to wash the DNA. Again this was centrifuged, supernatant removed and dried on tissue paper as before. Next, 100µl of DNA hydration solution was added to resuspend the pellet, and this was vortexed for 10 seconds, to ensure complete resuspension and hydration.
81
2.4.2 DNA extraction from Saliva
The DNA Genotek prepIT L2P kit was used for the extraction of DNA from saliva. Saliva pots were first incubated in a water bath at 500C for at least an hour before beginning the protocol. This step helps inhibit the activation of nucleases present in saliva, which may break down the DNA. To start, 20µl of PT-L2P reagent was then added to 500µL of sample, and vortexed. The sample was then incubated on ice for 10 minutes and centrifuged for 5 minutes at 15,000 x g. Impurities precipitated out and were then collected at the bottom of the tube. Next, 600µL of 95% ethanol was added to the supernatant, and inverted a number of times to mix. The sample was incubated for 10 minutes at room temperature and centrifuged for 2 minutes at 15,000 x g. DNA precipitated at the bottom of the tube. The supernatant was removed and the DNA pellet washed by adding 250µL of 75% ethanol. The ethanol was subsequently removed, and
50µL of Tris-EDTA solution added to resuspend the DNA pellet. This was vortexed a number of times to ensure complete rehydration of the DNA and incubated overnight at room temperature.
2.4.3 Nucleic acid quantification
Nanodrop (Thermo Scientific Nanodrop 1000) measurements were used to quantify
DNA. DNA and RNA have an absorbance maximum of light at 260nm, and proteins have an absorbance maximum at 280nm. Purity of the DNA is therefore calculated using a ratio of A260nm/A280nm. Absorbance at 230nm is used to measure contaminants. The
260/280 ratio for pure DNA is 1.8 and for pure RNA is 2. Pure DNA and RNA have a
260/230 ratio of between 2.0 - 2.2.
82
2.5 Serum Extraction from Whole Blood
Blood was collected in a collecting tube not containing any anti-coagulant. This was incubated at room temperature for 30-45 minutes, to allow the blood to clot. The tube was next centrifuged for 10 minutes at 2000 x g. The supernatant was removed and transferred to a new Eppendorf and centrifuged again, under the same conditions. The supernatant
(serum) was collected and stored at -800C until further use.
2.6 Plasma Extraction from Whole Blood
Blood was collected in tubes containing 1.8mg K2EDTA per ml of blood. This tube was centrifuged at room temperature for 10 minutes at 2000 x g. The supernatant was collected and moved to a new Eppendorf, which was centrifuged again, under the same conditions.
The supernatant, containing plasma, was collected and stored at -800C until further use.
2.7 SNP Genotyping using Homozygosity Mapping
Homozygosity mapping was performed on all immediate family members of family B and family C. The IlluminacytoSNP12-v2.1 was used to genotype SNPs. Mark
Kristiansen processed these samples in the UCL Genomics Centre. Briefly, 200ng of
PCR-free DNA was whole genome amplified overnight and then fragmented enzymatically. This fragmented DNA was then incubated on a BeadChip microarray
(Illumina), containing many bead arrays, which are coated with oligonucleotide probes.
The fragmented DNA hybridizes to complementary probes on the bead arrays. Any
83 unbound and non-specifically hybridised DNA fragments were subsequently washed away. Fluorescently labelled nucleotides were then added to extend the DNA probe by one nucleotide, which is complementary to the bound DNA fragment. This happened via a normal PCR reaction. Lasers then excited the fluorophore of the nucleotide. Illumina iScan was used to read the chip. From the wavelength of light emitted, we can infer the nucleotide added, and hence the genotype of the SNP. High resolution images were recorded for each BeadChip section, and computer algorithms converted the light emitted into raw sequence data, enabling us to infer the particular genotype of each SNP for each person.
Each of these probes represents 1 of 299,149 SNPs. 15,400 of which are located on the X chromosome, 2,972 on the Y chromosome. The mean spacing between SNP probes on this low-density array is 9.7kb. The threshold for calling any particular chromosomal segment homozygous is if it contains 50 or more contiguous homozygous probes.
Homozygosity mapping was analysed using Illumina Genome Studio v2.0.3 platform.
The runs of homozygosity (ROH) algorithm from this program calculates the loss of heterozygosity in the sample. A plot of all the A and B alleles in the sample were generated, creating a plot of B allele frequency against the position of SNP probes on a particular chromosome. “A” being one genotype and B being the alternative genotype. A value of 0 was given to the AA genotype, 1 to BB and 0.5 to the AB genotype. From this plot, large areas of homozygosity, having more than 50 contiguous homozygous SNPs, could be inferred.
84
2.8 Molecular DNA Techniques
2.8.1 Polymerase Chain Reaction
Amplification of DNA was achieved using a FastStart Taq polymerase master mix
(Roche). This master mix contains optimum concentrations of MgCl2, dNTPs, hot-start
Taq polymerase. DNA is first diluted to a concentration of 50ng/µL to be used as a template for the reaction. The following components (listed in Table 2-1) were added to the PCR tube. The reaction mixture was then placed in a thermocycler and subject to the cycling conditions listed in Table 2-2.
Table 2-1: Polymerase Chain Reaction (PCR) Components
Reagent Volume
Fast Start Polymerase 12.5µL
H2O 9µl
Forward Primer 0.75µl
Reverse Primer 0.75µl
Template DNA 2µl
Total 25µl
85
Table 2-2: PCR cycling conditions
Step Temperature (0C) Time(secs)
Initial Denaturation 95 60
Denaturation 95 30
Annealing 60 30
Extension 72 60
Final Extension 72 5 min
Cooling 4 ∞
2.8.2 PCR Clean-Up
PCR products were cleaned up using MinElute 96 UF PCR purification plates (Qiagen).
A total of 80µl of ddH2O was added to the PCR product in one well of the UF PCR purification plate, and this was placed on a vacuum manifold for 20 minutes. Then, 20µl of ddH2O was subsequently added to the well and left to incubate on a shaker for 10 minutes at 150rpm.
2.8.3 DNA fragment size quantification
A 2% agarose gel was made up to determine if the correct PCR product had been obtained.
The following mixture (Table 2-3) was made up and heated at maximum temperature for
90 seconds using a standard microwave oven. This was then poured into a gel dock.
86
Table 2-3: Agarose Gel Components
Reagent Amount
Agarose 2g
1 x Tris-Borate EDTA 100ml
Gel red 10μl
2.8.4 Primer design for Sanger Sequencing
Primers were designed using online tools such as Ensembl, UCSC genome browser,
Primers3, Exon Primer and Oligo EvaluatorTM (Sigma Aldrich). Once primers matching the desired parameters were obtained, they were ordered from Sigma Aldrich. They were resuspended in ddH20, to make a stock solution of 100µM concentration and a working solution of 10µM concentration. Primers were designed using online in silico tools with the parameters as described on Table 2-4 below.
87
Table 2-4: Parameters for primer design
Parameter Reason
Length 20-23bp Ensures specificity
Melting Temperature (Tm) 52-580C Above this induces secondary
annealing
Annealing Temperature (Ta) 600C +/- Ensures specificity
50C
GC content 40-60% Very high GC content primers
are prone to mispairing
GC Clamp Yes Promotes specific binding
Optimum target size 500bp Shorter sequences work better
with Sanger Sequencing
Maximum length of a nucleotide 4 Reduces mispriming
repeat
Minimal distance between primer 50bp Ensures good clear Sanger
and exon/intron boundary sequencing reads
88
2.8.5 Sanger Sequencing
This was achieved using a BigDye Terminator v3.1 kit (Applied Biosystems). Master mix was as described on Table 2-5.
Table 2-5: Sanger sequencing PCR master mix.
Reagent Amount
Ready Reaction Mix 0.5µl
BigDye 5x reaction buffer 2µl
ddH2O 8µl
PCR product 5µl
Primer 0.75µl
Total 16.25µl
The mix was then put in a thermocycler (GeneAmp* PCR control system 9700) and the following cycling conditions, as described in Table 2-6, used.
89
Table 2-6: Thermocycler program for BigDye Termination Reaction.
Step Temperature(0C) Time(secs)
Incubate 96 60
Denaturation 96 10
Annealing 50 5 X2
Extension 60 240 5
Cooling 4 ∞
2.8.6 siRNA silencing of Human Dermal Fibroblast Cells (HDFC) and Jurkat cells
HDFC or Jurkat cells were seeded at 60-80% confluency and incubated in Opti-Mem reduced serum transfection medium (cat no. 31985062, Thermo Fisher Scientific). Next,
10 μM of siRNA targeting TNFAIP3, or scramble control siRNA were incubated with lipofectamine RNAiMax reagent (cat no. 13778-075, Thermo Fisher Scientific) for 20
0 minutes before being added to cells. After 48 hours of incubation at 37 C, 5% CO2 with transfection reagents, time course phosphorylation assays were run as described in section
3.4.1.
RNA was extracted from knockdown cells using TRIzol (cat no. 15596026, Thermo
Fisher Scientific) and reverse transcriptase PCR reaction was performed. Quantitative
PCR was then used to evaluate the knockdown efficiency of the siRNA transfection on the expression of the TNFAIP3 gene. The TNFAIP3 quantitect primer (QT00070301;
Qiagen) was used in this analysis.
90
2.9 RNA Protocols
2.9.1 RNA extraction from PBMCs
Cells were collected using a cell scraper, and the cell suspension media is transferred to an Eppendorf. This is spun at 2000rpm for 3 minutes, the supernatant removed and the cell pellet washed once in 1 ml PBS. After centrifugation, the PBS supernatant was removed, and 1ml of TRIzol was used to resuspend the pellet, for 5 minutes at room temperature. Total of 250μl of chloroform was added and the tube shaken vigorously for
15 seconds. This was again incubated for 5 minutes at room temperature. Next, the
TRIzol-chloroform suspension was spun at 10,000rpm for 5 minutes. At this point, 3 layers existed in the tube. The top aqueous layer was carefully removed from the tube and placed into a fresh Eppendorf. To this, 550μl of isopropanol was added to the aqueous phase and gently mixed by inversion and left to incubate at room temperature for 5 minutes. This was then centrifuged at 14000 rpm for 20 minutes. The samples were kept on ice, and the isopropanol was poured off the pellet. Next, 75% ethanol was added to wash the pellet. The Eppendorfs were re-centrifuged at 9500rpm for 5 minutes. The ethanol was poured off, and the pellets were left to air dry for 15 minutes. Finally, 20μl of TE buffer was added to resuspend the pellet, and they were resolubilised following incubation at 60oC for 5 minutes. RNA concentration was measured using the Nanodrop
2000 spectrophotometer (Thermo Fisher Scientific).
2.9.2 RNA extraction from whole blood
RNA was extracted from whole blood collected in a PAXgene RNA Blood tube, which had been stored at -800C. This was incubated at room temperature for at least two hours
91 before beginning the extraction procedure, carried out using the PAXgene Blood RNA kit IVD (Qiagen) according to the manufacturer’s specifications.
2.9.3 cDNA Synthesis via Reverse Transcriptase PCR reaction
RNA was converted into cDNA using the High Capacity cDNA reverse transcriptase kit
(Applied Biosystems). Total RNA was first quantified using the nanodrop. A total of
400ng RNA was used per reaction, and made up to a volume of 10μl using nuclease free water. Reaction mixture was as follows on Table 2-7, below.
Table 2-7: Reverse Transcriptase PCR reaction components.
rtPCR Mix x1
10x RT Buffer 2μl
25x dNTP Mix 0.8μl
10x Random Primers 2μl
Reverse Transcriptase 1μl
Nuclease Free water 4.2μl
RNA template 10μl
Total 20μl
The mix was then put in a thermocycler (GeneAmp* PCR control system 9700) and the following cycling conditions (described in Table 2-8) used.
92
Table 2-8: Cycling conditions used for generation of cDNA by rtPCR.
Step Temperature Time (min)
Primer Extension 250C 10
cDNA synthesis 370C 120
Reaction Termination 850C 5
40C ∞
2.9.4 Quantification of Interferon Stimulated Genes (ISGs):
Changes in mRNA expression of 11 interferon (IFN) induced genes were assessed using qPCR. Quantitect primers (Qiagen) were ordered for 11 ISGs and one housekeeping gene, in order to measure the expression levels of ISGs in patient and control samples. These genes are listed in Table 2-11. These genes were chosen based on the work of Yannick
Crows lab, investigating the IFN signature in Aicardi Goutieres Syndrome patients (Rice et al. 2013). qPCR reactions were carried out in triplicate using High Profile 96 well PCR plates (Bio-Rad). Components of the reaction are listed below (Table 2-9).
93
Table 2-9: qPCR components for Interferon stimulated gene expression
Master Mix X1
SYBR Green 10μL
ddH2O 6μL
Primers 2μL
cDNA 2μL
Total 20μL
The 96 well plate was then put in a Bio-Rad CFX 96 thermocycler and subjected to the following conditions (Table 2-10).
Table 2-10: Cycling conditions for SYBR Green qPCR reaction for interferon stimulated gene expression.
Temperature (oC) Time (secs)
Initial 95 5 min
Denaturation
Denaturation 94 15
Annealing 55 20 X25
Extension 72 20
Melt curve 65 5
95 5 min
**During the melt curve, the thermocycler will increment the temperature by 0.5oC every 5 seconds until it reaches
95oC, and then remain at 95 oC for 5 minutes.
94
2.9.5 Summary of Interferon Stimulated Genes used to quantify IFN activity
Table 2-11: List of Interferon Stimulated Genes (ISG) examined in this thesis
These genes were used to quantify IFN induced gene expression and genes used for normalisation of interferon expression (house-keeping genes)
95
Gene ID Gene name Type Quantitect Assay Description
Name
IFI27 Interferon alpha inducible Type 1 HS_IFI27_1_SG Mediates Interferon
protein 27 induced apoptosis
IFIT1 interferon-induced protein with Type 1 HS_IFIT1_1_SG Encoded protein inhibits
tetratricopeptide repeats viral replication
IFNB1 Interferon - β -1 Type 1 and HS_IFNB1_1_SG Antiviral Hepatitis C
type 2 activity
IFI44L Interferon induced protein 44 Type 1 HS_IFI44L_1_SG Inhibits viral replication
like
RSAD2 Radical S-adenosyl methionine Type1 HS_RSAD2_1_SG Inhibits virus budding by
domain containing 2 disrupting lipids in the cell
membrane
ISG15 Interferon stimulated gene 25 Type 1 HS_ISG15_1_SG Aids antiviral activity
through the activation of
cell signalling pathways
SIGLEC1 Sialic acid binding Ig like lectin Type 1 HS_SIGLEC1_1_SG Promotes cell signalling
1 between macrophages
CXCL10 C-X-C motif chemokine ligand Type 1 and HS_CXCL10_1_SG Stimulates the production of
10 type 2 monocytes, NK cells and T
cells
CXCL9 C-X-C motif chemokine ligand Type 1 and HS_CXCL9_1_SG Chemoattractant for
9 type 2 lymphocytes
IFNϒ Interferon ϒ Type 2 HS_IFNϒ_1_SG Activates macrophages and
has antiproliferative effects
HPRT1 Hypoxanthine House HS_HPRT1_1_SG Plays a role in protein
phosphoribosyltransferase 1 Keeping salvage pathway and the
Control production of purine
nucleotides
96
2.9.6 Calculation of ISG expression
The expression of each ISG was determined using the Livak method (Livak and
Schmittgen 2001). In brief, as the qPCR was carried out in triplicate, the average Ct value for each gene was calculated. In order to normalise the expression of each gene, the average Ct value for the housekeeping genes was taken away from the average Ct value for the gene in question:
ΔCt = Ct (Gene of interest) – Ct (Housekeeping gene)
For every gene, qPCR of a calibrator or healthy control sample was also carried out.
Expression was then normalised to expression of the calibrator for each gene.
ΔΔCt = ΔCt(patient) – ΔCt (calibrator)
Finally, the expression ratio of each gene was calculated using the formula:
Relative expression = 2(-ΔΔCt)
Graphical representations of these values were then drawn up.
97
2.9.7 qPCR to quantify gene expression, other than IFN stimulated gene
expression qPCR was also used numerous times throughout this project to quantify the levels of other genes, apart from that of the interferon pathway. For this, the SYBR Green enzyme was also used and the same master mix as described in Table 2-9 and the same cycling conditions as described in Table 2-10, were used. Different quantitect primers (Qiagen) were used in each case, and these are described in the relevant chapters. The quantification of these genes was also calculated using the Livak method, described above
(section 2.9.6).
2.10 MesoScale Discovery (MSD) for cytokine and chemokine
analysis
Two MesoScale Discovery (MSD) kits were used throughout this thesis. These are sandwich capture assays where cytokines or chemokines in the serum samples bind to the capture antibodies immobilised on independent spots in the MSD well. Detection antibodies which are conjugated to electrochemiluminescent labels (MSD-SULFO-TAG) are subsequently added. A read buffer is then added and the MSD plate is loaded onto an
MSD instrument. The MSD instrument applies a voltage to the plate electrodes, causing the chemiluminescent labels to emit light. The instrument measures the intensity of the emitted light, and subsequently quantifies each cytokine found in the samples.
To measure levels of TNFα, IL-1β, IL-6, IFNγ, IL-10, IL8, IL-12p70, IL-13, IL-2, and
IL-4 bespoke proinflammatory V-Plex MSD was used; and to measure IL-18 the
MSD human IL-18 kit was used. Briefly, the sample is added to the plate and incubates
98 for 2 hours. The plate is then washed three times with PBS. A solution containing a combination of detection antibodies conjugated with electrochemiluminescent labels is then added to each well and the plate is incubated for a further 2 hours. Read buffer is added to the plate and it is read on the MSD instrument.
2.11 Tissue Culture Protocols
2.11.1 Isolation of peripheral blood mononuclear cells (PBMC) from whole blood
Fresh blood was first diluted with an equal volume of RPMI medium. This was then carefully laid on top of 10ml of LymphoprepTM (Stemcells Technologies) using a Pasteur pipette. The tube was then centrifuged at 800 x g for 20 minutes with no brakes, at room temperature. There was a clear separation in the different blood components following this step and the PBMC laid in a diffuse layer sandwiched between the plasma and
Lymphoprep layers. The PBMC layer was then carefully extracted, using a Pasteur pipette and put into a separate falcon tube. To this, 50mL RPMI-1640 medium is added. This was centrifuged at 250 x g for 10 minutes at room temperature. The supernatant was removed, and the pellet resuspended again in 50mL RPMI-1640 medium. This washing step occurred twice before cells are counted and frozen down.
2.11.2 Counting of Cells
After the final washing step, the supernatant was removed. Ten mL RPMI-1640 medium was added to the cell pellet, for ease of counting. 10μL of this suspension was added to
10μL of 0.4% trypan blue. Cells were counted using a haemocytometer. This was viewed
99 under a light microscope, and unstained live cells were counted within a specified 25 box field. The number of cells calculated according to the formula:
Unstained cells x dilution factor x 104 = viable cells/mL
2.11.3 Maintenance of Human Cell Lines
Jurkat cells were maintained at a concentration of 0.25 x 106 cells/ml, in RPMI medium, supplemented with 10% FCS and 100 U/ml PenStrep. Growth medium was replaced every 3 days.
HDFC from a healthy control individual were gifted from the Wei-Li Di lab, with many thanks. We received these cells at passage 1. HDFC from patients were obtained from skin punch biopsies in the GOSH clinical laboratory. We received patient cells at passage
3. All fibroblasts were maintained in DMEM/F12 medium supplemented with….
(Thermo Fisher Scientific), which was changed every 3-4 days. These cells were split, using trypsin (Sigma Aldrich) once 80% confluency had been reached. HDFC were grown and used for experiments until they reached passage 9.
2.11.4 Splitting cells
When adherent cells (HDFC) reached 80% confluency, they were split. First, the medium was removed from the flask and cells were washed in Dulbeccos phosphate buffered saline (PBS) (Sigma Aldrich), in order to remove any serum from the cells, as this may have been contained in the medium, and interfere with the trypsin reaction. Next, the PBS was removed, and 1ml trypsin (Sigma Aldrich) added per 10cm2 cells. The cells were incubated in the trypsin medium for approximately 3 minutes or until all of the cells have
100 lifted off. Trypsin neutralisation solution (TNS) (Sigma Aldrich) was added in an equal volume to the amount of trypsin added, to stop the reaction, and enough DMEM/F12 added in order to seed a new plate.
2.11.5 Freezing and Thawing of Cells
Cells were centrifuged at 250 x g for 7 minutes and the supernatant removed. Freezing medium (FBS with 10% dimethylsulfoxide (DMSO)) was used to resuspend this pellet.
This suspension was separated into cryogenic vials at 3 x106 cells/mL with 1mL per vial and stored in a Mr Frosty TM Freezing container (Thermo Fisher Scientific) filled with isopropanol, to facilitate slow cooling. This was placed in a -800C freezer for 24 hours before being transferred to a liquid nitrogen tank for long term storage.
When needed, cells were rapidly thawed by incubating the cryovial in a water bath at
370C for 30 seconds. Depending on the cell type, the appropriate medium was then warmed to 370C and added to cells. Cells and medium were spun at 1500 rpm for 7 minutes and the supernatant discarded. The required volume of medium was added to the cell pellet, and cells were then seeded at the required density.
2.12 Protein Protocols
2.12.1 Protein Extraction from PBMC and HDFC
For adherent cells (HDFC) cells were plated in a 6 well plate, and protein extracted when cells reached 80% confluency. All medium was removed. Next, 1ml PBS was then added to each well in order to wash out the FBS, contained in the serum. Once the PBS was removed, 100μl RIPA buffer (Thermo Fisher Scientific) and 1% proteinase inhibitor
101
(Sigma Aldrich) were added to each well. The plate was then incubated on ice for 30 minutes, to ensure complete cell lysis. Each well was then scraped using a cell scraper and pipetted into an Eppendorf. Eppendorf's were spun at 13000 rpm at 40C for 15 minutes, and the supernatant, containing whole protein lysates were collected. Cell debris formed a pellet at the bottom of the Eppendorf and was discarded.
Regarding PBMC, these cells were also kept in a 6 well plate, the wells were scraped using a cell scraper, and the cell suspension was pipetted into Eppendorf's. These were spun at 2000 rpm for 3 minutes. The supernatant was removed, and the pellet was resuspended in 1ml of PBS. These were spun at 2000 rpm for 3 minutes. The supernatant was removed, and 100μl RIPA buffer + 1% proteinase inhibitor was added to each pellet.
This was kept on ice for half an hour, pipetting regularly to resuspend the pellet. The
Eppendorf's were spun at 13000 rpm at 40C for 15 minutes. Cell debris formed a pellet, and the bottom of the Eppendorf and the supernatant, containing proteins was collected.
2.12.2 Protein Quantification
Protein concentration was measured using the Pierce TM bicinchoninic acid (BCA) Kit
(Thermo Fisher Scientific). Standards were made up using a 2mg concentration of bovine serum albumin (BSA) diluted in RIPA buffer, according to the manufacturer’s protocol.
The working reagent (WR) was made up by mixing BCA reagents A and B in a 50:1 ratio.
In a 96 well plate, 10μl of each of the standards and protein samples to be quantified were pipetted into separate wells, and 80μl of WR was added to each of these. The plate was incubated at 370C for 30 minutes before the plate was read on the BMG Labtech
FLUOstar Optima microplate reader at 562nm. Duplicate measurements of each standard was taken.
102
2.12.3 Western (Immuno) Blotting
Table 2-12: Recipe for 1 x 8% and 10% separating gel
Reagents 8% Gel 10% Gel
Volume (mL) Volume (mL)
ddH2O 4.6 3.95
30% Acrylamide mix 2.7 3.35
Tris-Cl (1.5M, pH8.8) 2.5 2.5
SDS (10%) 0.1 0.1
10% ammonium persulfate 0.1 0.1
TEMED 0.006 0.008
Table 2-13: Recipe for 5% Stacking Gel
Reagents Volume (mL)
ddH2O 2.975
0.5M Tris-HCl, pH6.8 1.25
10% SDS 0.05
30% Acrylamide mix 0.670
10% APS 0.05
TEMED 0.005
103
Table 2-14: Recipe for 5X Transfer Buffer*
Reagents Amount
Tris base 15.5g
Glycine 72g
ddH2O 1L
*and adjusted to a pH of 8
Table 2-15: Recipe for 5X Electrophoresis Buffer
Reagents Amount
Tris base 15.5g
Glycine 72g
SDS (0.1% w/v) 5g
ddH2O 1Litre
Table 2-16: Recipe for 10X Tris Buffered Salina (TBS).
Reagent Amount
Tris base 12.11g
NaCl 87.99g
ddH2O 1L
pH 8
104
The separating and stacking gels were made up using the recipes described in Table 2-12 and Table 2-13. Protein concentrations were calculated based on the BCA assay (Thermo fisher ScientificTM).
Lysis buffer, consisting of 2X Laemmli buffer (Bio-Rad) and 10% Β-Mercaptoethanol (a reducing agent), was added to samples in a 3:1 ratio and the samples were denatured by heating at 950C for 5 minutes before being loaded into the gel. 5μl of the Amersham ECL
RainbowTM Marker (RPN800E) was also loaded onto the gel to enable identification of the molecular weight of the proteins. Depending on the concentration of the protein, up to 40μl of sample was added to wells of the gel. A 1x electrophoresis buffer (
Table 2-15) was made up from the 5x stock, and the gel was run at 100V for 2 - 3 hours in this buffer. A 1x transfer buffer solution (Table 2-14) was made from the 5x stock and
200ml of methanol was added to this. This buffer was kept on ice. Transfer components such as the sponges, the nitrocellulose filter paper and the electrophoresis gel were soaked in this buffer before making the transfer sandwich. The nitrocellulose membrane was soaked in methanol. The transfer was ran at 350mA for 50 minutes until the transfer ladder had visibly transferred from the gel to the nitrocellulose membrane. The membrane was then washed in 1X TBS (see Table 2-16) + 0.1% tween for 15 minutes before being blocked in the 1x TBST + 5% milk solution for 1 hour.
At which point, the primary antibody was added. This was made up in 1x TBST + 5% milk and incubated with the membrane, overnight at 40C. The membrane was then washed
3 times in 1x TBS + 0.1% tween for 15 minutes each. The secondary antibody was made up in 1x TBST + 5% milk and was incubated with the membrane for 1 hour at room
105 temperature. Again, the membrane was then washed 3 times in 1x TBS + 0.1% tween for
15 minutes.
Amersham Enhanced Chemiluminescent detection reagents (RPN2109) were mixed together in a 1:1 ratio before being added to the membrane and allowed to incubate for 3 minutes. The gel images were then developed on photographic film in the darkroom.
Membranes were stripped using Restore Western Blot stripping Buffer (Thermo Fisher
Scientific TM) and reblotted for actin which serves as a loading control.
2.12.4 Methodology for densitometry
ImageJ was used to quantify western blot bands. A scan of the gel was converted into a jpeg file and uploaded into the ImageJ software. Before analysis, it was converted into an 8-bit file, so that it could be assessed by the ImageJ software. Using the “rectangular selection” tool, a rectangle was drawn on the background gel, parallel to the lane of bands. This was then dragged across the lane, to create a rectangle around each band on the gel. Rectangles are labelled by the program. The first rectangle, would provide the background control, from which the density of the other bands would be subtracted. The
“Plot lanes” tool, draws a profile plot of each lane. The “straight line” selection tool, is then used to close off the bottom of the plot, and the area of each plot obtained using the
“wand” tool. Using “Label Peaks”, a percentage is given to each plot, representing its size as a percentage of the total size of all peaks.
To normalise the bands, the percentage peak generated for the background band was subtracted from the percentage peak for all bands. Next relative expression values were calculated, by dividing the normalised percentage peak for the gene of interest by that of the housekeeping gene, in the same lane. On each western blot, protein from a healthy
106 control individual was also run. Relative expression values for each band were divided by the relative expression value for the housekeeping control. In this thesis, western blots were carried out once.
2.12.5 Fluorescent Activated Cell Sorting (FACS)
The general methods for FACS are outlined here, with specific antibodies used in each experiment outlined in the relevant chapters. PBMC were thawed and seeded in a 6 well plate overnight and allowed to recover. The following morning, cells which survived were counted, the concentration adjusted and cells were seeded in a 96 well plate. If a stimulation was required, this was added first and incubated with the cells for the required amount of time. After the appropriate incubation period, the plate was spun at 12000 rpm for 2 minutes. The supernatant was either removed and kept for cytokine analysis, or it was discarded. A total of 200μl of Cell Fix Buffer (BD PhosflowTM Biosciences) was added to each well. This was incubated for 10 minutes at 370C. The plate was again spun down at 12,000 rpm for 2 minutes and the supernatant removed. The cell pellets were resuspended in 200μl of Perm buffer III (RUO) (BD PhosFlowTM) and kept on ice for half an hour on a shaker. After which time the cells were again spun at 12,000 rpm for 2 minutes, the supernatant removed and the pellets resuspended in 200μl of BD Staining buffer (BD PhosFlowTM) and were kept on ice for 10 minutes. Cells were again spun and the supernatant discarded. Cells were then incubated in the relevant dilution of the antibody and BD Staining buffer (BD PhosFlowTM) on ice for 1 hour. After which point they were spun, the supernatant discarded and 200μl of staining buffer used to resuspend the pellets. The cells were then analysed by the FACs CaliburTM machine. Cells were gated according to the gate (FL1, FL2) which collects the relevant conjugated antibodies.
107
2.12.6 FACS analysis
Samples were analysed using FlowJo software. For PBMC populations, gating was first carried out to distinguish the lymphocyte population. Compensation was carried out using a non-stain control population, and if multiple colours were used, compensation was carried out for all colours. After gating, the median fluorescent intensity (MFI) values were obtained and were and used to calculate expression values for all samples.
108
2.13 Whole Exome Sequencing
Whole exome sequencing was performed in trios, meaning each proband and his/her parents were sequenced using next generation sequencing technologies. Where there was more than one affected case per family, all affected individuals and both parents were sequenced.
Whole exomes were prepared using SureSelect V6 chemistry kit (Agilent) exome capture kit and sequencing was performed on the HiSeq3000 platform (Illumina). This work was done in collaboration with Deborah Reynolds at the Institute of Neurology (UCL). The
HiSeq3000 Illumina machine has 30X base pair coverage
2.13.1 Library Preparation and clean up
In summary, DNA was first quantified using a Qubit 2.0 fluorometer and normalised to
50ng/μl concentration. Next, the library was prepared. This consisted of first, fragmenting and tagmenting the genomic DNA. This was done using the transpose enzyme Tn5, which cuts the DNA in pieces of about 300bp in size. Adaptor sequences were then ligated to the ends of each fragment. A pair of indices (primer binding sites, which were complimentary to flow cell oligonucleotides) were then added to the tagmented DNA.
The DNA was amplified and then cleaned up using magnetic purification beads and 80% ethanol, producing a PCR amplified library. A library was generated for each sample. All libraries were pooled and hybridised with exon-specific biotinylated capture probes. Any probes which hybridised to the gDNA library were captured using streptavidin beads.
This exon enriched library was again cleaned using magnetic purification beads, and a final PCR amplification step was performed.
109
2.13.2 Sequencing Whole Exome Libraries
Amplified libraries were sequenced on the HiSeq3000 Illumina sequencing machine. The general sequencing methodology was as follows. Sequencing occurs within flow cells, which contain oligonucleotide sequences. These oligonucleotides are complementary to either 3’ or 5’ adaptor sequences, which have been ligated to the fragmented gDNA during the library preparation. In effect, the fragmented sequences of the DNA library, bind the oligonucleotides on the flow cell. Each fragment then undergoes solid-phase bridge amplification, resulting in amplification of single-stranded DNA molecules and millions of DNA clusters across the flow cell.
Sequencing primers were added to the reaction that were complementary to and bind to the adaptor sequence, originally added to the gDNA fragment during the library preparation. Complementary nucleotides were added to extend the sequence and generate a complementary strand. In each cycle, fluorescently tagged terminator nucleotides were also added. When they were incorporated in order to extend the sequence, they were excited by a laser, and a fluorescence signal, corresponding to a particular nucleotide was emitted. This fluorescence was recorded by computer and the particular base identified.
The number of sequencing cycles determines the length of reads generated. In the case of
HiSeq3000 (Illumina), reads were approximately 150bp in length.
Finally, the indices were also sequenced. Specific index primers were added, and the reads were sequenced as above, enabling us to identify specific samples.
110
2.14 In Silico Methodologies
2.14.1 Analysis of Whole Exome Sequence Data
Whole exome sequencing produces millions of reads per sample, each spanning approximately 150 bp. The flow cell contains 8 lanes, generating output of about 300-400 million reads per lane. These reads are then accurately mapped back to the human reference exome using free online software; namely Galaxy (version 17.09.rc1) (Giardine et al. 2005, Goecks et al. 2010, Afgan et al. 2016) and variations in the sequence were inferred. WANNOVAR (Hui Wang Lab) an online functional annotation tool, was then used to annotate these variants. SNPs or INDELS were then further investigated for clinical significance relating to the presenting phenotype in the proband under investigation. Galaxy also carries out a number of other analyses such as quality control analysis.
2.14.2 Dataset concatenation
The first step Galaxy performs is to concatenate the reads. All samples were run across 8 flow cell lanes, generating 8 forward and 8 reverse sequences per sample. For easier subsequent analysis, these sequences were concatenated to produce one forward and one reverse sequence per sample.
2.14.3 Quality Control Analysis
The quality of each concatenated sample needed to be assessed. This was done using the
FastQC tool (Simon Andrews, Babraham Institute) in Galaxy. This tool assesses the
111 following parameters of the concatenated sequences, enabling you to assess the quality of the reads generated.
• Basic Statistics: Provides basic parameters such as number of sequences, length
of sequences and % GC content.
• Per base sequence quality: each base in the sequence is given a quality score, and
this is plotted. Higher scores represent better quality bases.
• Per sequence quality score: The mean quality score of all bases in a particular
sequence are calculated. This is a plot of the distribution of those means. If the
mean quality score of a read is below 27, the FASTQC module will flag a warning
as this relates to a 0.2% error rate, and they should be filtered out.
• Per base sequence content: Calculates the probability of getting a particular base
at a particular position. There should be an equal probability (25%) of getting each
of the bases at each position.
• Per base GC content: Plots the GC content for each base, should be the same
across the read.
• Per sequence GC content: Plots the distribution of GC content across the
sequence, should follow a normal distribution.
• Per base N content: Shows if there are any uncalled bases from the sequence.
• Sequence Duplication Levels: Looks at how unique your sequences are in your
library. Should see most of the sequences in your library only occurring once.
Gives a percentage of non-unique sequences.
• Overrepresented sequences: Looks for individual sequences which are
overrepresented within the set. These sequences must represent more than 0.1%
of the total sequences in the library.
112
• Kmer Content: Looks at whether there is an overrepresentation of Kmers (5mers)
in your sequence.
Probably the most important output from the FastQC tool is the per base sequence quality.
This algorithm calculated the probability that each base has been incorrectly called and assigned a Phred score to this base. Phred scores are integer values representing calling accuracy, i.e. the probability that a particular base is an error (Illumina). Bases with a score >20 have a less than 1% chance that they have been incorrectly called, whereas bases with a score of <20 are thought to have a very poor quality, may be inaccurately called and should be trimmed from the read. If these bases are not removed from further analysis, they may interfere with the alignment of reads to reference sequences.
2.14.4 Aligning reads
Using the Burrows-Wheeler Alignment (BWA) Algorithm for Illumina, these reads were mapped to the Genome Reference Consortium build 37 (GRCh37) human genome, using the Galaxy platform. A Sequence Alignment Map (SAM) file was generated from output of this analysis. This was subsequently converted to a Binary Alignment Map (BAM) file, which is a compressed binary version of a SAM file.
2.14.5 Refining the Alignment
This step provides a more accurate alignment for subsequent analysis. Misalignment of reads often occurs during BWA processing due to the presence of INDELS or duplicated sequences. Therefore I performed local realignment to correct for these errors. The presence of INDELS makes short reads especially difficult to map (Coonrod et al. 2013).
113
Picard tools mark and remove all duplicate reads in the BAM file, which may occur with
PCR during library preparation. These duplicates may have the same start and endpoints, but are in fact different to the read in question and can lead to false-positive or negative calls. The Picard tool will only keep the read with the highest combined base quality.
Realignment then occurs using the GATK (Genome Analysis Tool Kit) tool. INDELS are major sources of error, often causing misalignment of the read, which may result in false positive or false negative variant calls. Finally, a recalibration of base quality scores was computed to gain a more accurate depiction of the sequence quality.
2.14.6 Variant calling and annotation
The process of calling variants was performed in Galaxy by the GATK Unified Genotyper tool. This tool infers the differences in our sample from the reference genome and uses a
Bayesian likelihood model to estimate the most likely genotype at each position.
The output from this analysis is a VCF (variant call file). This is a list of all variants in the sample. Web ANNOVAR (wANNOVAR) is subsequently used to annotate this VCF file. The annotation for each variant includes the base change, zygosity, genomic coordinates, population frequency, and in silico pathogenicity predictions. Variants were also classified based on whether they are missense, synonymous, non-synonymous, frameshift insertion/deletion, non-frameshift insertion/deletion, stoploss and stopgain
(Wang et al. 2010).
114
2.14.7 Filtering variants
The output from wANNOVAR was then taken through various filtering parameters in order to narrow down a causative mutation.
Synonymous variants were first removed. Next, all variants which were present at a frequency of greater than 1% in the general population, according to the 1000 genomes project and 6500 Exome Sequencing Project (6500 ESP) were removed. Pathogenic variants are generally assumed to be rare. As this thesis was an investigation of novel genetic causes of inflammatory conditions, it made sense that I remove any common variants present in the general population from the proband, in each case.
Subsequently, if the variant in the proband was present in a homozygous state in the parents, it was also removed. If it was present in a homozygous state in the parents and they are “healthy”, then it is highly probable that this variant is not disease-causing. I then separated the variants into homozygous and heterozygous variants depending on the expected mode of inheritance and filtered these lists against our in house control exome population. This database consists of 103 individuals. These individuals were former patients and their families, wherein the damaging mutation in each particular family has been removed, and this was used as a further filtering control to narrow down the search of potentially pathogenic variants in probands.
2.14.8 Annotation of Variants
The genes which remained after filtering analysis had taken place, were examined further for clinical relevance and segregation with disease. This involves gathering all the available information from OMIM, Uniprot and GeneCards, in order to assess the
115 functional aspects of the gene within which the variant resided, and different pathways in which it may play a role. Papers which had previously published functional studies of each gene were also obtained from Pubmed and Embase. The information gathered from these sites was carefully considered for each gene, as to whether or not it was relevant to the presenting phenotype.
2.15 In Silico Prediction Analysis
Also of importance, were the pathogenicity scores of each variant. Several algorithms are freely available online, such as Polymorphism Phenotyping v2 (PolyPhen-2), Sorting
Intolerant from Tolerant (SIFT), Mutation taster and likelihood ratio test (LRT). These algorithms calculate the likelihood that a particular variant is disease-causing, based on a number of parameters such as:
• The structure of the protein
• Chemical properties of the amino acid
• Interspecies conservation of the amino acid
• Previously described coincidence with disease (Ghosh et al. 2017).
However, these tools are not always in concordance, as each uses different algorithms and prediction models (Lyon and Wang 2012). They can give false positive and false negative predictions approximately 10.5% (Ghosh et al. 2017, Kerr et al. 2017) of the time and so it is important that this is realised during analysis of the variant. Hence, when
I searched for potential pathogenic variants, these algorithms were considered, but I did not base a final decision solely on the outcome of these algorithms.
116
2.16 Filtering for variants in genes included in targeted gene
panels for inflammation
A candidate gene approach was also considered for the filtering strategy to look for variants in genes which are already known to lead to a certain disease phenotype. Three gene panels namely the NIP (Neuro Inflammation Panel), VIP (Vasculitis inflammation
Panel) and PID (Pediatric Inflammatory Disease) used at GOSH contain lists of genes which are already proven to cause certain monogenic inflammatory disorders. After the filtering steps, the remaining variants were cross referenced against genes on the panels to look for commonalities. Any variants which matched with those contained on the panels were considered further.
2.17 Identification of Candidate Genes
Variants which remained in consideration after filtering and gene panel analysis were subsequently reviewed by experts to discuss the possibility that each individual variant may be causative of the presenting disease phenotype. Before meeting, each expert went through the final list of variants, and chose his/her favourite variants based on his/her own knowledge, literature based evidence linking a certain gene to a disease, whether it is particularly rare, if it was predicted damaging by the in silico online tools and whether the inheritance pattern of the particular variant matches what is described. OMIM,
Genecards and Uniprot were used to investigate the function of the gene, pathways it is involved in and any animal models set up to examine the functional relevance this gene. gNOMAD and ExAC were used to assess the allele frequency of the variant in various
117 populations, and it also gives a summary of in silico pathogenic predictions. Variants of high importance were those which were chosen by all experts at the meeting, which was comprised of Dr Despina Eleftheriou, Professor Paul Brogan, Professor Helen Lachmann,
Dr Kimberly Gilmour, Dr Ebun Omoyinmi and Dr Ying Hong.
Any variants which were chosen were subsequently Sanger sequenced for confirmation and segregation within the family. The functional relevance of the identified variants was then examined.
2.17.1 Statistical Analysis
In vitro experiments were performed in triplicate, and data presented as mean +/- standard error of the mean unless otherwise stated. Statistical differences between groups were calculated using one way ANOVA or repeated-measures ANOVA and the unpaired two- tailed t-test unless otherwise specified. P< 0.05 was regarded as statistically significant.
118
3 Heterozygous TNFAIP3 mutation
causes an interferon mediated
neuroinflammatory disorder
3.1 Summary
3.1.1 Background
Heterozygous loss of function pathogenic variants in Tumour Necrosis Alpha Induced
Protein 3 (TNFAIP3) cause autoinflammation due to haploinsufficiency of the A20 protein (HA20). Central nervous system (CNS) involvement as the primary clinical manifestation of heterozygous TNFAIP3 variants has never been described before. In this chapter, I report a familial case of TNFAIP3 mediated autoinflammation predominantly manifesting as progressive neuroinflammation in the index case and demonstrate an excellent therapeutic response to Janus kinase inhibition.
3.1.2 Objectives:
To identify the genetic cause of a progressive neuroinflammatory disorder in an 8 year old female of non-consanguineous Pakistani-Indian descent and explore the underlying immune mechanisms.
119
3.1.3 Methods
Whole exome sequencing (WES) was carried out in all family members. qPCR assays measured the expression levels of interferon (IFN) stimulated gene expression. IFN dependent signalling was assessed in patient and control derived immune cells by flow cytometry. NF-ƙB, NLRP3 inflammasome activation was also assessed.
3.1.4 Results
WES identified a heterozygous missense p.T647P variant mutation in TNFAIP3 segregating with disease among the family pedigree. Patient derived cells exhibited enhanced phosphorylation of the p65 transcription factor, and increased expression of nuclear factor kappa light chain enhancer of activated B cells (NF-ƙB). NF-ƙB mediated proinflammatory cytokines were elevated in the serum of patients with p.T647P
TNFAIP3 heterozygous variants. There was also enhanced NLRP3 inflammasome activation in patient derived cells. Mutated p.T647P A20 protein failed to control interferon-regulatory-factor-3 (IRF3) activation and interferon (IFN) dependent transcription. Treatment with an oral Janus Kinase (JAK) 1/2 inhibitor resulted in marked clinical and radiological improvement and normalisation of IFN-stimulated gene expression in whole blood. Studying an additional HA20 case, heterozygous for p.N98Tfs25 TNFAIP3 variant but without neurological involvement, I also found impaired IFN mediated immune responses, giving further evidence for dysregulated IFN activity in HA20 patients.
120
3.1.5 Conclusions:
My report expands the spectrum of IFN mediated inflammatory disorders amenable to
Janus kinase inhibition treatment to now include TNFAIP3 associated autoinflammation and neuroinflammation.
121
3.2 Introduction
3.2.1 Literature evidence for TNFAIP3 involvement in disease
Several studies have linked polymorphisms in TNFAIP3 with susceptibility to autoimmune diseases. These include systemic lupus erythematosus (SLE) (Graham et al.
2008, Deng and Tsao 2010), Crohn’s disease (McGovern et al. 2010), rheumatoid arthritis
(RA) (Bowes et al. 2010, McAllister et al. 2011), psoriasis (Stuart et al. 2015, Tsoi et al.
2017), type 1 diabetes (Størling and Pociot 2017) and coeliac disease (Trynka et al. 2009,
Dubois et al. 2010), amongst others (Vereecke et al. 2011, Ma and Malynn 2012, Zhang et al. 2016). It is assumed that these polymorphisms cause a reduction in the function of the A20 protein. Other single nucleotide polymorphisms (SNP) reside in non-coding regions of the gene, and it is suspected that these SNPs cause a reduction in protein expression (Elsby et al. 2010, Ma and Malynn 2012).
Numerous other studies have also made associations between SNPs in TNFAIP3 and SLE
(Adrianto et al. 2011, Musone et al. 2011, Anon. 2019e). (Musone et al. 2011) report a
F127C variant associated with SLE, which reduces A20 expression. (Adrianto et al. 2011) describes a TT>A mutation located in the enhancer region of TNFAIP3 as being a susceptibility locus for SLE. It was then proven that this mutation affects the expression levels of this gene, as it inhibits NF-ƙB from binding to its promoter (Wang et al. 2013), thus providing even more evidence for the correlation between this gene and the development of an autoimmune phenotype.
122
Figure 3-1: Single Nucleotide Polymorphisms (SNPs) and their location along the
TNFAIP3 gene. Image obtained from (Ma and Malynn 2012).
Exons encoding the N terminal OTU domain are shown in orange, exons encoding the C terminal zinc fingers are coloured in green. Non-coding exons are shown in brown. Disease-associated polymorphisms and their position along the chromosome are highlighted. The associated diseases for these polymorphisms are denoted by the coloured keys, above each SNP
As well as association studies, studies looking at monogenic causes of autoinflammation have also been performed regarding the TNFAIP3 gene. Recently (Zhou et al. 2016), showed that heterozygous mutations in TNFAIP3 leading to loss of expression of the A20 protein, result in Behҫets disease. They reported various mutations in A20 across 9 different families, most of which presented with oral and genital ulcers, ocular inflammation, arthralgia, arthritis. Some patients also had symptoms resembling systemic lupus erythematosus and CNS vasculitis (Zhou et al. 2016). All patients had heterozygous mutations in A20 and had decreased expression of this protein relative to that of healthy control individuals, known as A20 haploinsufficiency (HA20). Most mutations were
123 found in the OTU and fourth zinc finger domain of the protein. Authors also show increased NF-ƙB signalling as a result of decreased A20 expression.
(Aeschlimann et al. 2018) report a total of 16 patients with HA20 mutations and associated phenotypes. They report the main symptoms to be oral, genital and gastrointestinal ulcers, cutaneous lesions, fever. The authors do however stress that the clinical phenotypes vary considerably, and relapsing-remitting disease course was common. (Sato et al. 2018) also report a juvenile onset autoinflammatory disease due to another heterozygous frameshift mutation in TNFAIP3, resulting in Behҫets like symptoms. Upon completion of a western blot, they found that this mutation resulted in a loss of A20 protein expression, similar to (Zhou et al. 2016), and classified this mutation as HA20.
Many knockout studies have been performed, which give us an insight into the functioning of the A20 protein. A20 knockout in B cells and dendritic cells causes characteristics similar to SLE. (Kool et al. 2011) demonstrated that mice with A20 dendritic cell knockout exhibited typical SLE features including DNA autoantibodies, glomerulonephritis, arthritis as well as lymphadenopathy and splenomegaly. Their dendritic cells also mature spontaneously and become hyper-reactive towards activating stimuli. (Tavares et al. 2010) produced B cell A20 knockout mice and in doing so, observed a lupus-like autoimmune pathology in these mice. This was characterised by enhanced B cell survival and proliferation, glomerular immunoglobulin deposits and autoantibodies. Both knockout mice strains from Kool et al. 2011 and Tavares et al. 2010 were also more resistant to apoptosis-inducing signals. (Lee et al. 2000), illustrates that mice with A20 deficiency have widespread organ inflammation and are prone to perinatal death. Another study shows that mice which have A20 knockout dendritic cells excrete large amounts of proinflammatory cytokines which results in the activation of both
124 myeloid and lymphoid cells. This, in turn, leads to the over activation of myeloid and lymphoid cells, contributing to a large immunological response (Hammer et al. 2011).
(Guedes et al. 2014) created both heterozygous and A20 knockout mice. These mice suffered microgliosis and astrogliosis and had increases in proinflammatory cytokines, in comparison to WT mice. TNF and LPS induced cytokine production was also significantly higher in A20 deficient mice. They also suggest that A20 deficiency leads to oxidative or nitrosative stress in the brains of these mice. The authors concluded that mice which are partially or fully deficient in the A20 protein are extremely prone to spontaneous neuroinflammation.
Overall, there is significant indication from the literature that mutations in TNFAIP3 affecting A20 protein function lead to autoinflammation and neuroinflammation
3.2.2 Functional domains of A20
A20 is a 90kD protein produced from the tumour necrosis factor alpha-induced protein 3
(TNFAIP3) gene (Catrysse et al. 2014). A20 has global hematopoietic cellular expression in the body and has important activities in numerous physiological processes (De et al.
2014). A20 is a complex protein which is involved in both ubiquitinase and deubiquitinase activities (Heyninck and Beyaert 2005, Coornaert et al. 2009, De et al.
2014). In order to carry out these dual ubiquitin functions, it has several protein domains.
Its C terminal domain is composed of seven zinc fingers which are involved in E3 ubiquitin ligase activities, while its N terminal OTU domain is involved in deubiquitinase
(DUB) activities (De et al. 2014). The OTU domain cleaves K63, K48 and K11 polyubiquitin chains (Boone et al. 2004, Wertz et al. 2004). These dual ubiquitination functions enable A20 to regulate immunoregulatory pathways on multiple levels
125
Ubiquitination is a mechanism by which the cell destroys or gets rid of faulty proteins
(Varshavsky 2017). If proteins are not processed correctly (e.g. incorrect protein folding) and cannot carry out their function, they are ubiquitinated by ubiquitin ligases and targeted to the proteasome for degradation (Varshavsky 2017). Ubiquitin is a 76 amino acid protein and is added to other proteins through the coordinated action of three ubiquitinating enzymes. E1 is a ubiquitin-activating enzyme. E2, a ubiquitin-conjugating enzyme and E3, a ubiquitin ligase enzyme (Amm et al. 2014, Catrysse et al. 2014) and all three are used in order to add ubiquitin groups to proteins.
Ubiquitin contains seven lysine residues which are used during polyubiquitin chain formation. These are K6, K11, K27, K29, K33, K48 ,K63 (Catrysse et al. 2014, Zheng and Shabek 2017). The particular lysine reside which is polyubiquitinated has a distinct impact on what happens to that protein. For instance, polyubiquitination of K48 targets that protein for proteasomal degradation (Sadowski and Sarcevic 2010), usually acting to turn off a particular pathway. However, polyubiquitination of K63 has the opposite effect and links this protein to downstream effector proteins, usually leading to activation of a particular biological pathway. Some proteins function to cleave polyubiquitin chains, thereby ceasing the signal originally created by the polyubiquitin chain (Amm et al.
2014).
126
Figure 3-2: Diagrammatic representation of the A20 protein
The A20 protein is divided into a number of domains including the ovarian tumour (OTU) domain and 7 zinc fingers. The 4th zinc finger and OTU domain are the best characterised and have numerous different functions. The p.T647P mutation lies just beside the 4th zinc finger, as part of the linker region
By adding or cleaving ubiquitin chains from various proteins, A20 can control the activation or degradation of that protein. By this method, it helps to dampen down the immune response and suppress overactivation of the immune system (Coornaert et al.
2009). The heterozygous p.T647P mutation in A-III-1 lies in the linker region, close to the fourth zinc finger (Zf4) domain of the protein. Zf4 itself is a functionally diverse area, responsible for a multitude of biological processes. It is an area involved in E3 ubiquitin ligase processes, K48 polyubiquitination, K63 polyubiquitination and E2 binding. It is also important as it is the region which binds other adaptor proteins, such as TAX1BP1
(Saitoh et al. 2005) and ABIN1 (Mauro et al. 2006, p. 1) which enable A20 to carry out its functions (Wertz et al. 2004). The Zf4 region of A20 is thus extremely important, having implications for a multitude of pathways controlled by the A20 protein. A mutation which disrupts the functioning of the Zf4 region may also be detrimental to the
127 overall functioning of the protein, and therefore could influence the phenotype of our patient.
The linear ubiquitin chain assembly complex (LUBAC), is an E3 ubiquitin ligase complex, specifically involved in building linear polyubiquitin chains (Tokunaga and
Iwai 2012). It is composed 3 proteins, namely; HOIL-1L, HOIP and SHARPIN and was found to have regulatory functions within the NF-ƙB pathway. Upon binding of TNFα to the TNFR1 receptor, TRADD recruits RIP1, which itself becomes ubiquitinated and induces LUBAC recruitment (Tokunaga and Iwai 2012). Next, LUBAC creates linear polyubiquitin chains on NEMO, which acts as a scaffold for the IKK complex. Trans- phosphorylation mediated activation of IKKβ ensues, leading to the phosphorylation of
IkBα, and degradation of this inhibitory protein, enabling p65:p50 to enter the nucleus and transcribe proinflammatory cytokines (Tokunaga and Iwai 2012). Therefore LUBAC acts as a positive regulator of the NF-ƙB pathway.
It was demonstrated that A20 acts in an inhibitory manner on LUBAC, in order to reduce signalling of the NF-ƙB pathway (Verhelst et al. 2012). A20 binds to LUBAC, via its ZF7 domain. In doing so it prevents LUBAC interacting with NEMO. (Verhelst et al. 2012) show that A20 is quickly recruited to LUBAC and NEMO at the TNFR1 receptor, upon stimulation with TNFα. By overexpressing A20 in TNFα induced cells, the authors report that this abrogated the ability of LUBAC to promote NF-ƙB activation and therefore prevent NF-ƙB reporter gene expression. They also found that a mutation in the ZF7 region of A20, which disrupts the function of this domain, was sufficient to prevent A20 binding to LUBAC (Verhelst et al. 2012), and LUBAC-mediated activation of NF-ƙB ensued. Therefore, A20 can act on K48, K63 and linear ubiquitin chains to restrict immune mediated responses.
128
3.2.3 A20 is a master immune regulator, restricting the NF-ƙB, interferon and
NLRP3 inflammasome pathways
The Zf4 region is involved in K48 polyubiquitination, K63 polyubiquitination, E2 binding and E3 ligase activities. It binds to other proteins such as TAX1BP1 and ABIN-
1 (adaptor proteins which enable A20 to exert its inhibitory functions) (Parvatiyar et al.
2010, Gao et al. 2011). In the NF-ƙB pathway, A20 abolishes RIP1 signalling. Zf4 plays a role in recruiting A20 to the polyubiquitinated form of RIP1 (Lu et al. 2013). Zf4 on
A20, ligates K48 polyubiquitin chains to RIP1, hence targeting this molecule for proteasomal degradation (Wertz et al. 2004). RIP1 is one of the central proteins in NF-
ƙB activation, and therefore A20 inhibits the activation of this pathway. Therefore this region is extremely important in enabling A20 to reduce the NF-ƙB transduction signal, giving evidence that a mutation in this domain may be detrimental to the overall functioning of the protein, and therefore may result in the phenotype of A-III-1.
Its Zf4 region is also extremely important in the interferon response system as it is involved in recruiting a protein called TAX1BP1 to A20 (Parvatiyar et al. 2010).
TAX1BP1 aids in A20 binding to a protein complex called the TBK1/IKKε complex.
Once activated, TBK1/IKKε phosphorylate IRF3, leading to transcription of IFNβ, and upregulation of interferons (Zhang et al. 2019, p. 1). A20 binding to TBK1/IKKε prevents phosphorylation of IRF3 and subsequently diminishes the interferon immune response.
A20 cannot bind TBK1/IKKε without TAX1BP1, and Zf4 is crucial for the recruitment of TAX1BP1 (Saitoh et al. 2005, Parvatiyar et al. 2010).
The A20 protein also plays a role in diminishing the NLRP3 inflammasome pathway.
Although the literature does not specify what particular region of the A20 protein is responsible for inflammasome regulation. In general, the A20 protein diminishes
129 inflammasome activation by restricting activation of the NF-ƙB pathway and therefore preventing the transcription of protein components needed for NLRP3 inflammasome assembly. Two cytokines which are secreted from the cell upon NLRP3 inflammasome activation are IL-18 and IL-1β (Schmidt and Lenz 2012). These are first synthesized as inactive cytokines pro-IL-18 and pro-IL-1β. In order to be processed into a functional mature cytokine, pro-IL-1β forms a complex with caspase-1, caspase-8, RIPK1 and
RIPK3 (Duong et al. 2015). This complex is ubiquitinated to aid the processing of pro-
IL-1β. In order to inhibit NLRP3 inflammasome function, A20 binds to this complex and deubiquitinates it. In doing so, it prevents the efficient processing of pro-IL-1β (Duong et al. 2015).
3.2.4 A20 and the NF-ƙB pathway
The NF-ƙB pathway is one of the major immunoregulatory signalling pathways, activated in order to protect the host from both bacterial and viral pathogenic infection (Zhang et al. 2017), and therefore produces a vast array of biological effects, having roles in both innate and adaptive immune responses, inflammation, cell death and survival (Hayden and Ghosh 2011).
The endpoint of the NF-ƙB pathway is the “turning on” or phosphorylation of transcription factors. These transcription factors, once activated, translocate to the nucleus where they can transcribe inflammatory mediators (Hayden and Ghosh 2011) including numerous chemokines. There are five members of the NF-ƙB superfamily of transcription factors, Rel-A (p65), Rel-B, cRel, p105 and p100. The latter two transcription factors are subsequently processed into p50 and p52, respectively. The Rel proteins share a REL homology domain (RHD) and a C terminal transactivating domain
(Oeckinghaus and Ghosh 2009). P105 and p100 contain C terminal domains which are 130 characterised by ankyrin repeats and inhibit the actions of these proteins. These ankyrin repeats are subsequently cleaved, and the p105 and p100 proteins are processed into their active p50 and p52 forms (Gilmore 2006). They subsequently form dimers with members of the Rel subfamily, where they enter the nucleus and activate transcription of inflammatory-related genes (Gilmore 2006). There are two branches of the NF-ƙB pathway, the canonical (classical) and the alternative pathway (Oeckinghaus and Ghosh
2009). For the purposes of this thesis, I will refer only to the canonical pathway. The canonical NF-ƙB pathway has been associated with numerous inflammatory diseases such as rheumatoid arthritis, asthma and inflammatory bowel disease (Park and Hong
2016, Aksentijevich and Zhou 2017). NF-ƙB associated autoinflammatory diseases have recently been referred to as relopathies (Steiner et al. 2018).
The dimer formed by p50 and RelA (p65) is the most common NF-ƙB transcription factor.
It exists in the cytoplasm in an inhibitory formation, bound to IƙB. In order for p50:p65 to enter the nucleus, the IkB kinase must be phosphorylated. The IKK complex is responsible for this. It is composed of three proteins; IKKα, IKKβ and NEMO (IKKϒ).
IKKα and IKKβ have kinase specificities, whereas NEMO is thought to function as a scaffolding protein (50). NEMO becomes polyubiquitinated and is targeted for degradation by the lysosome (Shembade and Harhaj 2012). IKKα and IKKβ are then free to phosphorylate IkB, dissociating it from and the NF-kB transcription factor (p50:p65)
(Liu et al. 2017), as is shown in Figure 3-3. p50:p65 can thus enter the nucleus and regulate the transcription of certain genes.
The canonical pathway is initially stimulated through the activation of TLR, TNFR, BCL and TCR receptors, amongst others (Hayden and Ghosh 2011). In the case of TNFR1,
TNFα binds causing trimerization of TNFR1. TRADD, an adaptor protein, is subsequently recruited and creates a binding site for TRAF2 (Oeckinghaus and Ghosh
131
2009). TRAF2, an E3 ubiquitin ligase adds K63 polyubiquitin chains to RIP1. The K63 polyubiquitin scaffold attracts the TAB1:TAB2:TAK1 complex (Oeckinghaus and
Ghosh 2009, Shembade and Harhaj 2010). This enables RIP1 to phosphorylate TAK1.
Subsequently, TAK1 phosphorylates and thus activates IKKβ, (Shembade and Harhaj
2010) ultimately leading to p50:RelA NF-ƙB activation.
132
Figure 3-3: Proteins involved in the NF-ƙB pathway, as obtained from (Jost and
Ruland 2007).
133
A20 regulates the NF-kB pathway via two main mechanisms:
1. A20 modifies the NF-ƙB pathway through its interactions with RIP1. RIP1 has
K63 polyubiquitin chains. The fourth zinc finger of A20 is involved in cleaving
these chains on RIP1, thereby preventing it from interacting with the
TAB1:TAB2:TAK1 complex, and diminishing the activation of NF-ƙB (Wertz et
al. 2004, Chen 2005). A20 also exerts its inhibitory function by targeting RIP1 for
proteasomal degradation through the addition of K48 ubiquitin groups to the
protein (Wertz et al. 2004).
2. A20 acts on NEMO to prevent NF-ƙB (p65) phosphorylation. A20 binds to the C
terminal tail of NEMO, resulting in the deubiquitination of this protein (Zhou et
al. 2016). NEMO subsequently remains bound to the IKK complex, preventing
further stimulation of the NF-ƙB pathway. In a clever study carried out by
Zilberman- Rudenko and colleagues, cells expressing ΔCT-NEMO (NEMO with
C terminal deletion), showed an overactivation of the NF-ƙB pathway in
comparison to wildtype cells (Zilberman-Rudenko et al. 2016). It seems that when
A20 fails to bind NEMO, its recruitment to TNFR1 is also impaired. In wildtype
cells, A20 binds to TNFR1 which results in the degradation of RIP1 (Zilberman-
Rudenko et al. 2016) ΔCT-NEMO cells showed an abundance of K63
polyubiquitinated RIP1, culminating in an overstimulation of the NF-ƙB pathway.
134
Figure 3-4: A20 regulation of RIP1 and NEMO (IKKγ) leading to inhibition of NF-
ƙB transcription factor.
Taken from Nature Immunology 12,709-714(2011) doi:10.1038/ni.2055
135
3.2.5 A20 and the Interferon Response
Interferons are cytokines which play a vital role in the human immune system and are primarily produced by cells in response to viral and bacterial infections (Perry et al. 2005).
Three types of interferon exist; type 1 (IFNα, IFNβ), type 2 (IFNγ) and type 3 (IFNλ)
(González-Navajas et al. 2012). All signal through different receptors in order to activate the JAK-STAT pathway. The type 1 IFNs signal through a heterodimeric receptor complex comprising IFNAR1 and IFNAR2 (Weerd et al. 2007) on the cell surface. JAK1 and TYK2, tyrosine kinase molecules which are bound to the IFNAR receptors, trans- phosphorylate to bring about their sustained activation (Rawlings 2004). These tyrosine kinase molecules consequently activate STAT proteins. STAT proteins become phosphorylated and activate IRF9. The interferons are transcribed when STAT1/STAT2 dimers bind to IRF9, forming the ISG factor 3 (ISGF3) complex (Au-Yeung et al. 2013,
Blaszczyk et al. 2016). This complex enters the nucleus and binds to Interferon stimulated response element (ISRE) to transcribe the interferon stimulated genes (Au-Yeung et al.
2013, p.). This is known as the second wave synthesis of interferons.
IRF3 is an important transcription factor of IFNα/IFNβ. When IRF3 becomes phosphorylated, it dimerises, translocates from the cytoplasm to the nucleus and binds to the IFNβ promoter, in the first wave synthesis of IFNs (Haller and Weber 2009). IFNβ is secreted from the cell and binds to an IFNAR receptor on a neighbouring cell, which leads to the activation of the JAK-STAT pathway. The first wave synthesis of interferons culminates in the phosphorylation of both IRF3 and IRF7 and second wave synthesis of
IFNs (Ivashkiv and Donlin 2014).
IRF3 is activitated following the recognition of viral dsDNA or RNA in the cell. Viral dsDNA, which is produced as a by-product of a viruses replication cycle, serves as a
136 pathogen associated molecular pattern (PAMP) and is sensed by pattern recognition receptors (PRRs) and Toll like receptors (TLRs) on the hosts cells (Thompson et al.
2011). Conventional dendritic cells (cDCs) activate the IFN pathway via the TLR3 PRR, which detects viral DNA in the endosomal membrane (Ng and Gommerman 2013). An adaptor molecule TRIF binds to activated TLR3, and in turn, activates TRAF3. TRAF3 next polyubiquitinates the TBK1/IKKε complex, activating it (Kawasaki and Kawai
2014). Phosphorylated TBK1/IKKε then phosphorylates IRF3, which dimerises and moves into the nucleus to transcribe IFNβ (Kawasaki and Kawai 2014).
Figure 3-5: First wave synthesis of interferons.
This pathway culminates in the dimerization and phosphorylation of IRF3, which transcribes
IFNβ. Diagram obtained from (Verstrepen et al. 2011)
137
In particular, TBK1/IKKε phosphorylate IRF3 on Ser386 (Nakatsu et al. 2014). Of note,
(Saitoh et al. 2005) showed that this phosphorylation is impaired by A20. The authors demonstrate that A20 is co-immunoprecipitated with TBK1/IKKε. It was concluded that the interaction between A20 and TBK1/IKKε inhibits upstream IRF3 phosphorylation.
TRAF3 polyubiquitinates TBK1:IKKε complex on K63, thus leading to their activation and subsequent phosphorylation of IRF3 (Kawasaki and Kawai 2014). A20 binding to
TBK1/IKKε antagonises the interaction between TRAF3 and TBK1/IKKε. Therefore, when A20 is bound, TBK1 and IKKε cannot be polyubiquitinated and therefore cannot phosphorylate IRF3. TAX1BP1 acts as an adaptor molecule for A20 (Parvatiyar et al.
2010). A20 is unable to bind TBK1/IKKε without first binding TAX1BP1, an important adaptor protein (Saitoh et al. 2005, Verstrepen et al. 2011). As we know, it is the zinc finger domains of the A20 protein which are responsible for A20 binding to TAX1BP1
(Saitoh et al. 2005, Parvatiyar et al. 2010, Onizawa et al. 2015) as A20 proteins lacking their C terminal zinc finger domains are simply unable to bind to TBK1/IKKε.
138
First Wave Second Wave
Synthesis Synthesis
Figure 3-6: Schematic overview of the interferon immune response and regulatory role of the A20 protein.
TNFα (Tumour necrosis factor alpha), TLR3/4 (Toll like receptor 3/4), TRIF (TIR-domain containing adaptor inducing interferon-β), TRAF3 (TNF receptor associated factor 3), TBK1
(TANK-binding kinase 1), IKKi (Inhibitor of Nuclear Factor Kappa B Kinase subunit epsilon),
TAX1BP1 (Tax1 binding protein 1), IRF3 (Interferon regulatory factor 3), IFNβ (Interferon
Beta), JAK (Janus Kinase), STAT (Signal Transducer Activator of Transcription)
139
3.2.6 A20 and the NLRP3 Inflammasome response pathway
Inflammasomes are part of the innate immune system and form in response to signals from pathogen-associated molecular pathogens (PAMPs) and danger associated molecular pathogens (DAMPs), in order to eliminate foreign threats (Martinon et al.
2002, Lamkanfi and Dixit 2014). They are multimeric protein complexes and typically comprise of a sensor, and adaptor and procaspase-1 (Malik and Kanneganti 2017). The sensor is typically a receptor such as NOD, NLR and ALR receptors found in the cytoplasm. The adaptor protein links the receptor to the procaspase-1 and is an apoptosis associated speck-like protein containing a CARD domain (ASC). This adaptor protein oligomerises and forms a SPECK structure. ASC recruits procaspase-1 which is eventually converted to active caspase-1 by proximity induced self-cleavage (Malik and
Kanneganti 2017). Active caspase1 has two major functions; to convert pro-IL-18 and pro-IL-1β into biologically active cytokines, and to induce pyroptosis.
3.2.6.1 Receptors/sensors:
Many receptors lead to inflammasome formation. However, the NOD-like receptors are probably the best characterised. The human genome encodes for 22 NOD receptors
(Schroder and Tschopp 2010); however the mouse genome encodes many more.
However, only NLRP1, NLRP3 and NLRC4 are capable of inducing caspase-1 activation via inflammasome formation (Malik and Kanneganti 2017). All receptors have a generalised structure which includes a central nucleotide binding domain (NACHT), a C terminal Leucine rich repeat (LRR) domain and an N terminal domain, either containing pyrin (NLRP) or caspase (NLRC) (Schroder and Tschopp 2010). LRRs function in ligand sensing (Schroder and Tschopp 2010). Homotypic CARD-CARD or PYD-PYD
140 interactions are crucial to inflammasome assembly. The NACHT domain facilitates oligomerisation and activation of the complex via ATP binding (Schroder and Tschopp
2010). NLRP1 and NLRC4 can directly bind to procaspase-1, NLRP3 must recruit ASC, in order to induce inflammasome activation (Malik and Kanneganti 2017). For the purposes of this thesis I will only discuss the NLRP3 inflammasome further, as this is the inflammasome complex upon which A20 has a direct effect.
Figure 3-7: Diagrammatic representation of the structure of the NLRP3 inflammasome as obtained from (Lamkanfi and Dixit 2014)
In general, the cytosolic receptors (NOD, NLR) are in an inhibitory state. Upon ligand binding, they become activated and oligomerise. They then recruit ASC through PYD-
PYD interactions. This nucleates to produce an ASC speck (Cai et al. 2014, Lu et al. 2014,
Kuri et al. 2017), which is a helical fibrillary assembly of ASC molecules (Mangan et al.
2018). ASC next binds procaspase-1 through interaction between the CARD domain in
ASC and that in Caspase-1. The close proximity of multiple procaspase-1 proteins induces its self-cleavage to form biologically active caspase-1 (Guo et al. 2015). The
141 resultant inflammasome complex contains the sensor, adaptor and enzyme at increasing concentrations (Malik and Kanneganti 2017). Caspase-1 converts pro-IL-1β and pro-IL-
18 into active cytokines. These are important inflammatory mediators and coordinate myeloid cell recruitment to the site of immunological injury or infection (Schroder and
Tschopp 2010). The inflammasome also functions by inducing pyroptosis, a form of inflammatory cell death (Bergsbaken et al. 2009).
NLRP3 is probably the best characterised inflammasome sensor. It can form inflammasomes in response to a vast array of infectious PAMPs and endogenous DAMPs.
These include microbial cell wall components, crystalline agents, nucleic acids, pore- forming toxins and endogenous molecules, including ATP and uric acid crystals
(Schroder and Tschopp 2010, Malik and Kanneganti 2017). It is responsive to cellular injury and can be activated in response to ATP (Mariathasan et al. 2006) and hyaluronan
(Feng et al. 2012). It is also activated by metabolites of neurodegenerative diseases such as β amyloid peptide produced as a result of Alzheimer’s disease (Halle et al. 2008). It can detect signs of metabolic stress such as high levels of extracellular glucose (Zhou et al. 2010), as in type 2 diabetes, and monosodium urate (MSU) crystals which form during gout (Martinon et al. 2002). Additionally, the NLRP3 inflammasome can become activated in response to environmental stimulants such as silica, asbestos (Cassel et al.
2008) and UV irradiation (Feldmeyer et al. 2007).
As it is activated by such a diverse range of PAMPs and DAMPs, it has been proposed that it likely senses a common cellular distress signal, instead of directly interacting with each of these triggers (Mathur et al. 2018). These common distress signals have been identified as changes in cell volume, rupture of lysosomes, formation of reactive oxygen species(ROS) (Piippo et al. 2018), potassium (K+) efflux , increase in cellular ATP.
142
Essentially, two steps are required for NLRP3 inflammasome activation;
1. Priming: signalling through TLRs, or other transmembrane receptors induces the
activation of innate inflammatory signalling pathways such as NF-ƙB, AP1 and
MyD88 pathways. These pathways are crucial to inflammasome activation as they
induce the transcription and translation of the NLRP3 and Caspase1 gene, and
also produce pro-IL-18 and pro-IL-1β. This ensures there is sufficient sensor and
pro cytokines in the cell for inflammasome activation.
2. Activation: This occurs when a primed cell is subjected to an activating stimulus,
such as those mentioned above. The sensor oligomerises and nucleates ASC.
Procaspase-1 is converted into bioactive caspase-1. NLRP3 deubiquitination was
also shown to promote formation of the inflammasome (Juliana et al. 2012). It
was shown that BRCC3, a K63 specific deubiquitinase removes ubiquitin chains
from the LRR domain of NLRP3, in order to promote activation of the NLRP3
inflammasome (Py et al. 2013).
The NLRP3 inflammasome has many proposed methods of activation summarised below.
3.2.6.2 K+ Efflux:
NLRP3 ionophores create pores or channels which may permeabilize the cell towards potassium efflux (Anon. 2019f, p. 3). Ionophores are microbial pathogenesis factors, and
NLRP3 may react to this potassium efflux as a way of sensing pathogens in the cell
(Mangan et al. 2018, Yang et al. 2019). High levels of ATP within the cell also enables
K+ ion to leave the cell, through opening the P2X7 channel (Próchnicki et al. 2016).
143
3.2.6.3 Lysosomal Disruption Model:
Lysosomal disruption may result from phagocytosis of proteinaceous aggregates such as amyloid β or crystalline material such as uric acid or cholesterol crystals (Hornung et al.
2008). These crystals and protein aggregates may form in the cell as a result of a metabolic or neurological disease. Upon lysosomal rupture, cathepsins are released and appear to be crucial in the activation of the inflammasome, although it is not yet fully understood how(Mangan et al. 2018). ATP is also released upon lysosomal rupture and amplifies the reaction further.
3.2.6.4 Mitochondrial Dysfunction Model:
Mitochondrial reactive oxygen species (mtROS) are also crucial for NLRP3 activation.
Many NLRP3 triggers lead to an increased production of mtROS (Mangan et al. 2018).
Disruption of mitophagy, followed by a priming stimulus, increases the activation of the
NLRP3 inflammasome in response to all K+ dependent triggers (Greten et al. 2007). In support of this, patients with disrupted mitophagy spontaneously secrete IL-1β (van der
Burgh et al. 2014). These data suggest that disruption of mitophagy or the accumulation of damaged mitochondria within a cell, leading to an increased production of mtROS, is sufficient to induce NLRP3 inflammasome activation.
144
Figure 3-8: Pathways which lead to the activation of the NLRP3 inflammasome, as obtained from (Mathur et al. 2018).
145
3.2.6.5 Pyroptosis:
Pyroptosis is an inflammatory form of cell death that is distinct from apoptosis
(Bergsbaken et al. 2009) and is caspase-1 dependent (Bergsbaken et al. 2009). Pores form in the cellular membrane, inducing the influx of extracellular fluid. The cell then swells and eventually lyses to release its intracellular contents (Aachoui et al. 2013, Man et al.
2017). Pyroptosis occurs most frequently upon infection with intracellular pathogens and halts replication of intracellular pathogens in macrophages, an important defence mechanism of the innate immune system (Jorgensen and Miao 2015). By eradicating infected immune cells, and exposing any surviving bacteria to circulating phagocytes, pyroptosis promotes destruction of the infecting foreign entity (Schroder and Tschopp
2010, Lamkanfi and Dixit 2014). It may also stimulate the adaptive immune system, through releasing numerous antigens into the circulation (Bergsbaken et al. 2009). When cell lysis occurs, the active cytokines IL-1β and IL-18 produced via caspase-1 activation, will also be released into the extracellular milieu and help stimulate the adaptive immune system (Jorgensen and Miao 2015). IL-1β specifically promotes the activation of Th1 and
Th17 cells, while IL-18 is important for IL-17 expression by Th17 cells (van de Veerdonk et al. 2011, Dinarello et al. 2013). Active caspase-1 induces pyroptosis through its interactions with Gasdermin D (GSDMD) (He et al. 2015). GSDMD is held in an inactive form by its C terminal domain within the cytoplasm of the cell (Ding et al. 2016). It is cleaved by bioactive caspase-1 and is thus relieved from its inhibitory state (He et al.
2015). Gasdermin D oligomerises and forms pores in the cell membrane (Ding et al.
2016). This pore formation disrupts the osmotic potential of the cell leading to swelling and eventually, cell lysis (Malik and Kanneganti 2017).
146
3.2.6.6 Inflammasome overactivation in disease:
HA20 patients have also recently been shown to exhibit high inflammasome activation
(Zhou et al. 2016). It is also well documented that A20 works to reduce inflammasome assembly. Many mouse models have shown that Tnfaip3-/- knockout cells display enhanced secretion of IL-1β and IL-18 and increased NLRP3 inflammasome formation and pyroptosis (Vande Walle et al. 2014, Duong et al. 2015, Rajamäki et al. 2018).
In a series of experiments by (Vande Walle et al. 2014), the authors suggest that A20 negatively regulates the inflammasome pathway at the level of the priming signal (Vande
Walle et al. 2014). The TLR receptors are first engaged in order to turn on the NF-ƙB pathway, through LPS stimulation. This is imperative to the assembly of the inflammasome as it results in the transcription of caspase-1, NLRP3, pro-IL-1β and pro-
IL-18. As we know, A20 acts to restrict the NF-ƙB pathway, via its interactions with
NEMO and RIPK1 (Aksentijevich and Zhou 2017). Vande Walle suggest therefore that as A20 restricts the activation of NF-ƙB, it decreases the expression levels of caspase-1,
NLRP3 and inactive cytokines in the cytoplasm(Vande Walle et al. 2014). Rajamaki et al. 2018, also proposes the hypothesis that A20 restricts the formation of the inflammasome complex, by reducing the level of crucial components needed for inflammasome assembly (Rajamäki et al. 2018).
Duong et al. 2014 suggests the A20 also plays a role at the level of the activation signal
(usually ATP). The authors discovered the formation of a pro-IL-1β complex, through a series of co-immunoprecipitation experiments. This pro-IL-1β complex consisted of pro-
IL-1β, Caspase1, Caspase8, RIPK3 and RIPK1, and it appears that more Caspase1,
Caspase8, RIPK3 and RIPK1 are associated with pro-IL-1β in A20 deficient cells (Duong et al. 2015). It is thought that this complex forms in order to synthesize fully mature IL-
1β, which is secreted from the cell.
147
A20 binds to this pro-IL-1β complex and in doing so, reduces the amount of IL-1β secreted from these cells. The authors demonstrate that A20 inhibits the association of
RIPK1 with the pro-IL-1β complex. The catalytic activity of RIPK1 and RIPK3 drive the processing of pro-IL-1β into functional mature IL-1β.
Unsurprisingly, this pro-IL-1β complex displays increased ubiquitination in A20 deficient cells. RIPK3 and Casp1 appear to be responsible for ubiquitinating the pro-IL-
1β complex. A20, a deubiquitinase enzyme functions to restrict this ubiquitination and therefore inhibit the processing of pro-IL-1β.
148
3.3 Pedigree for Family A
Figure 3-9: Family tree of Family A.
The proband in this family is A-III-1.
A-III-1 is an 8 year old female patient of non-consanguineous Pakistani-Indian descent
(family tree shown in Figure 3-9) referred to the neuroinflammation service at GOSH
(lead clinicians Dr Eleftheriou/Dr Hemingway) with left-sided focal seizures and acute
149 uveitis. She had a long-standing past medical history of chorioretinitis that was diagnosed age 6 months and was considered secondary to congenital infection; no clear pathogen had been identified, however. There was no history of skin rashes, pyrexia, gastrointestinal or respiratory symptoms of note; she had occasional mouth ulcers but no genital ulcers. Magnetic resonance imaging (MRI) of her brain during the acute presentation with seizures revealed a contrast enhancing intracranial mass lesion affecting predominantly the white matter (Figure 3-10 (A)). Extensive screening tests for infectious, rheumatological (including antibody testing for ANCA, ANA, complement studies, NOD2 genetic testing), immunological (NBT test) and metabolic causes were negative. She underwent a brain biopsy that showed diffuse granulomatous inflammation with no evidence of vasculitis or malignancy (Figure 3-10 (D)). Remaining investigations are summarised in Table 3-1. TB was considered in the differential, and empirical treatment started. Of note, however, brain tissue culture, PCR testing for mycobacteria
(typical and atypical) and Quantiferon testing were all negative. Despite anti-TB treatment over the next 6 months, there was further radiologically evident increase in size of the mass lesion with the patient developing fixed left-sided hemiplegia and new onset ataxia. At that point, she was thought to have an unclassified neuroinflammatory disorder with a prominent granulomatous inflammatory component (“neurosarcoid-like” disease) and therapy with corticosteroids and mycophenolate mofetil was initiated. There was no response to this treatment, with slow increase in size of the mass lesion, and ongoing contrast enhancement suggestive of persistent inflammation. Intravenous cyclophosphamide (6 monthly pulses at 500-750 mg/m2) was then given, mycophenolate mofetil stopped, and steroid treatment continued at even higher doses (2 mg/kg per day).
There was poor response to these treatments, and significant steroid related side effects were noted. A second brain biopsy was then undertaken, that again revealed
150 granulomatous inflammation, and no other pathology to account for this. Of note, extensive deep sequencing for over 1000 pathogens was negative. A PET-CT scan whole body was also performed that showed no evidence of malignancy. CT brain scan showed intracerebral calcification (Figure 3-10 (C)) suggestive of an interferonopathy. For this case, interferon score testing to explore mRNA expression of 6 ISGs was undertaken.
This revealed significant type I interferon-induced gene upregulation. The results of the
IFN transcriptomic signature provided an important diagnostic clue for this case to guide my gene hunting approach for suspected monogenic inflammatory disease, but also provided an important clue as to the best targeted treatment. The patient was subsequently started on baricitinib, an oral JAK-STAT signalling blocker via a compassionate use programme facilitated by Pharma (Eli Lilly). Twelve months later she remains stable with no further seizures, improvement of ataxia and marked resolution of the intracerebral inflammatory lesion (Figure 3-10 (B)). Corticosteroid therapy has been successfully weaned off for the first time in 2 years.
Interestingly, the youngest sibling (A-III-2) has also developed facial butterfly skin rashes and arthralgia, now aged 14 months old. She did not display any fever, orgenital ulceration, gastrointestinal, respiratory symptoms, or uveitis. Her ESR was 25 mm/h,
CRP 15 mg/L, SAA 3 mg/L. ANA/ANCA was negative, normal C3 and C4 levels and normal IgG/IgA/IgM. There was also family history, as A-II-4 died of systemic lupus erythematosus at the age of 17. A-II-2, the mother of the proband had oral ulceration, no genital ulceration, no gastrointestinal or respiratory symptoms, no rashes uveitis or arthritis. Her ESR was 5 mm/h, CRP 5 mg/L, SAA 5 mg/L. ANA/ANCA was negative, normal C3 and C4 levels and normal IgG/IgA/IgM.
151
Figure 3-10: Imaging demonstrating neurological inflammation.
(A) magnetic resonance imaging (MRI) scan of A-III-1 showing solid inflammatory white matter lesion surrounded by vasogenic oedema before treatment (B) Resolution of that lesion after treatment with Baricitinib (janus kinase inhibitor). (C) Computed tomography brain scan demonstrating foci of calcifications (arrow) in the thalamic lesion and the periventricular white matter within the area of vasogenic edema. (D) Brain biopsy showed necrotising granulomatous inflammation with p65/RELA nuclear stain.
152
Table 3-1: Patient A-III-1, routine clinical laboratory investigations
1. Autoantibodies tested: antinuclear antibodies, anti-neutrophil cytoplasm antibodies, rheumatoid factor, anti-tissue transglutaminase antibodies, anti-thyroid peroxidase antibodies, anti-myelin oligodendrocyte antibodies, anti-yo, anti-hu, anti-ri antibodies, NMDAR antibodies, rheumatoid factor antibodies, coeliac screen antibodies, b2 glycoprotein and anticardiolipin antibodies.
Laboratory Investigations Patient A-III-1
Autoantibodies persistent >3 months Absent1
Haemoglobin 10g/L (120-160)
Platelet count 182x109/L (150-450)
White blood cell count 6.25x109/L (4.0-11)
Lymphocyte subsets Normal
Immunoglobulin G 19.9g/L (3.1-13.8)
Immunoglobulin A 1.94g/L (0.4-0.7)
Immunoglobulin M 2.29g/L (0.5-2.2)
Immunoglobulin D 7 kU/L (2-100)
Adenovirus, CMV, EBV, HSV,VZV Negative
Parechovirus PCR
Toxoplasma PCR Negative
Mycoplasma antibodies Negative
Quantiferon Negative
Nitroblue tetrazolium test Normal
Brucella serology Negative
153
Toxocara serology Normal
16s PCR and 18s PCR CSF Negative
JC and BK virus Negative
Complement C3 1.77g/L (0.75-1.65)
Complement C4 0.24g/L (0.14-0.54)
Liver Enzymes ALT 15 U/L (10-25)
ALP 96 U/L (150-380)
CSF white cell count < 1x106
CSF cytospin Negative
CSF oligoclonal bands Negative
ESR (median) 35 mm/H (range 5-75mm/H)
CRP (median) 15 mg/L (range 5- 35 mg/L)
SAA (median) 5 mg/L (range 3-15 mg/L)
In order to find a genetic cause for this condition, whole exome sequencing was carried out on A-III-1 and her parents.
154
3.4 Methods
This section describes the specific methods used in this chapter. General methods are described in chapter 2.
3.4.1 Expression of phosphorylated p65 andIRF3 time course assay
PBMCs were incubated with 100ng/ml TNFα for up to 60 minutes. Cells were fixed using
Cell Fix Buffer (BD PhosflowTM Biosciences) for 10 minutes at 370C. Cells were then permeabilised using Perm buffer III (BD PhosFlowTM) and kept on ice for half an hour.
Cells were then stained using BD Staining buffer (BD PhosFlowTM), and are kept on ice for 10 minutes. Cells were stained for 1 hour, making up a 1 in 50 dilution of PE-anti-
NF-ƙB-p65 antibody, (cat no. S565447, BD Biosciences) and PE-anti-IRF3 (cat no.
612564, BD biosciences) and staining buffer. Flow cytometric analysis was performed on a BD Calibur flow cytometer. Results were analysed with FlowJo software (version
10.4.2, TreeStar, Ashland, Ore).
3.4.2 Flow cytometric gating strategy for phosphorylation assays.
The flow cytometric gating strategy for assessment of expression of phosphorylated p65, and IRF3 are described in Figure 3-11, below.
155
Figure 3-11: Flow cytometric gating strategy as applied to detect expression of phosphorylated p65 and IRF3.
(A) Forward (x-axis) and side (y-axis) scatter properties were used to gate the lymphocyte population. (B) Gating on the PE+ population was achieved using an unstained control population. The PE fluorochrome was detected using the FL2 channel on FACS Calibur Becton
Dickinson.
156
3.4.3 Calculation of interferon score
An interferon score was computed using gene expression data from qPCR assays and following this equation as outlined in (Feng et al. 2006).
퐺푒푛푒 (푖)푝푎푡푖푒푛푡 − 푚푒푎푛 퐺푒푛푒 (푖)푐푡푟푙 ∑ 푆퐷(퐺푒푛푒 (푖)푐푡푟푙)
Where (i) is each ISG, Gene (i) patient is the gene expression of that ISG in the patient,
Gene (i) ctrl is the mean relative expression of the same ISG in the control samples and
SD(Gene (i) ctrl) is the standard deviation of the control population expression of this
ISG. This was calculated for each gene and the median of these values was taken as the overall combined interferon score for each individual. If this combined interferon score was larger than the threshold value for the control population, then it was said to be a positive IFN score.
Scores calculated from 13 control samples were used to determine threshold values.
Thresholds were established which were two standard deviations above the mean of the average control scores. Patient scores which fell above this threshold were considered positive.
3.4.4 Human Dermal Fibroblast Cell (HDFC) culture
Dermal fibroblasts were obtained from A-III-1 and from unrelated healthy control subjects. Cells were maintained in DMEM/F12 (DMEM/Nutrient Mixture F-12) and supplemented with 10% FBS, 100U/mL penicillin, 100U/mL streptomycin (Life
0 Technologies). Cells were incubated at 37 C in 5% CO2.
157
3.4.5 Co-Immunoprecipitation Assays
Protein lysates were prepared from dermal fibroblast cells using Pierce IP lysis buffer (cat no. 87787, Thermo Fisher Scientific), and quantified using the Pierce BCA Protein Assay
Kit (Thermo Fisher Scientific). Protein lysates were then normalised to a standard concentration and incubated with antibodies (1:100 dilution) at 40C overnight. Protein A
Dynabeads (cat no. 10006D, Thermo Fisher Scientific) were washed in PBS and then added to the protein lysate-antibody mixture at 40C overnight. The lysate-antibody- dynabeads mixture was then placed on a magnet, and any unbound material removed and kept as the supernatant. The dynabeads were then washed in PBS, and incubated at 950C for 10 minutes in 30μL 2x Laemmli buffer (S3401, Sigma Aldrich) with 10% β- mercaptoethanol (CAS No. 60-24-2, Sigma Aldrich), before being loaded onto an SDS-
PAGE gel, and an immunoblot carried out.
3.4.6 Antibodies
The antibodies used for immunoblotting are summarised in Table 3-2. Antibodies used for flow cytometry are summarised in Table 3-3.
158
Table 3-2: Antibodies used in Immunoblotting analysis
Cat. No Clone Type Company
Anti-A20 #5630 Rabbit Primary Cell signalling
monoclonal technology
Anti-TBK1 #3504 Rabbit Primary Cell signalling
monoclonal technology
Anti- Sc-56919 Mouse Primary Santa Cruz
NEMO monoclonal
Anti-Ub Sc-271289 Mouse Primary Santa Cruz
monoclonal
Anti-K63- 05-1308 Rabbit Primary Merck
Ub monoclonal
Anti-Actin MAB1501 Mouse Primary Merck
monoclonal
HRP P0447 Goat anti- Secondary Agilent Dako
conjugated Mouse
polyclonal
HRP P0399 Swine anti- Secondary Agilent Dako
conjugated Rabbit
polyclonal
159
Table 3-3: Antibodies used in Flow Cytometric analysis
Fluorochr Clone Cat. No Phosphorylatio Isotype Company
ome n Site
P-p65 PE K10- 558423 Ser529 Mouse BD
895.12. IgG Bioscience
50 s
P-IRF3 PE D6O1M #83611 Ser396 Rabbit Cell
IgG Signaling
160
3.4.7 Carboxyfluorescin (FAM) – Fluorochrome Inhibitor of Caspases (FLICA)
Assay
A FAM-FLICA assay was carried out in order to measure Caspase-1 activation in patient cells as a marker of NLRP3 inflammasome activation. This assay contains a reagent which irreversibly binds to active caspase-1 in the cells, and emits a green fluorescent dye upon binding. The intensity of this dye is then used as a measure of caspase-1 activation in these cells. The kit uses a non-toxic and cell-permeable reagent called Fluorochrome
Inhibitor of Caspases (FLICA), to enter live cells. Carboxyflourescin (FAM) serves as a green fluorescent probe and is bound to YVAD, the target sequence which binds caspase-
1, and fluoro methyl ketone (FMK) a caspase-1 inhibitor.
PBMC were counted seeded at a concentration of 2 x 106 cells/ml. 200μL of this cell suspension was then added to each well of a 96 well round bottom plate. Cells were pre- primed with 100ng/ml LPS for 4 hours. Certain wells were also incubated with 3.75μM
ATP for 30 minutes. Cells were then incubated for 1 hour in the FAM-FLICA (#20279,
Bio-Rad) reagent and stained with Per CP-anti-CD14 (BioLegend) for 1 hour @ 370C.
Flow cytometric analysis was performed on a BD Calibur flow cytometer to identify
FLICA+ monocytes. Results were analysed with FlowJo software (version 10.4.2,
TreeStar, Ashland, Ore).
161
3.4.8 Flow cytometric gating strategy for FAM-FLICA assay
The gating strategy for the FAM-FLICA assay to detect caspase 1 activation as surrogate marker of NLRPR3 inflammasome activation is shown in Figure 3-12.
Figure 3-12: Flow Cytometric gating strategy for monocyte FAM-FLICA+ staining.
(A) Forward (x-axis) and side (y-axis) scatter properties were used to gate the lymphocyte population. (B) Gating on the CD14+ population was achieved using an unstained control population (shaded in grey). CD14 (PerCP fluorochrome) was detected using the FL3 channel on FACS Calibur Becton Dickinson. (C) FAM-FLICA+ cells were identified using an unstained control population. Gating on the FAM-FLICA+ population was detected in the FL1 channel.
162
3.4.9 Z-Score Calculations
This score, computes how many standard deviations away from the control population
푥− 휇 mean, our data point lies. Z-Scores were calculated using the formula, 푧 = , where 휎
ꭓ is the cytokine measurement from each family member, μ is the control population mean, and σ is the control population standard deviation. Control populations must be normally distributed for this equation and this was first validated using the Shapiro Wilks test. Z scores close to zero represent the data point is the same as control population mean.
Negative Z scores, suggest the cytokine measurement is to the left of or below the control mean, Positive Z scores suggest the measurement is to the right of or above the population mean.
163
3.5 Results
3.5.1 Suspected mode of Inheritance for A-III-1
To summarise, A-III-1 was presenting with granulomatous inflammatory brain lesions, intracerebral calcification, chorioretinitis and uveitis. A-III-2, the sister of the proband is just two years old, but at present is exhibiting a butterfly malar rash on the face and arthralgia. A-II-2, the mother of the proband, also has episodes of oral ulceration. The maternal uncle of the proband died of SLE when he was just 17 years old. Therefore I postulated that there is an autosomal dominant genetic basis behind this disease and that the mutation is most likely rare but not de novo. It is unlikely that the presenting phenotype was due to heterozygous de novo variants given both siblings seemed to be affected.
As the presenting phenotype of A-III-1 was similar to Aicardi Goutières Syndrome
(AGS), before whole exome sequencing was undertaken, Sanger sequencing was carried out for AGS related genes. The genes sequenced were ADAR1, SAMHD1, IFIH1, TREX1,
RNASEH2A, RNASEH2B and RNASEH2C. Please refer to appendix (section 8.4) for primers used in this analysis.
On analysis, no pathogenic mutations were found in any AGS related genes, and so whole exome sequencing was then performed.
164
3.5.2 Whole Exome Sequencing Results
A total of 23279 variants were identified by WES in A-III-1. After removing synonymous variants and variants that were present at less than 1% in the general population, according to the 1000 Genomes project and the ESP6500 database, 2347 variants remained. Next, I removed any variants which were present in a homozygous state in the parents, leaving
1573 variants. Variants present in a homozygous state in the parents are presumed non- pathogenic. A total of 696 variants remained after this analysis. The filtering approach is shown in Figure 3-13 below.
Figure 3-13: Filtering Strategy employed to search for disease causative genes in A-
III-1.
165
3.5.3 Candidate Gene Prioritisation
In order to identify a candidate gene, pertaining to the phenotype at hand, two separate approaches were undertaken. In the first approach, the variants in 696 genes which remained after filtering analysis were annotated and prioritised based on their function and evidence from the literature. As a second analysis, I cross referenced the variants in
696 genes after filtering, with candidate genes which were contained on gene panels, to see if any matches occurred. Our lab has three gene panels: The neuroinflammatory panel
(NIP), vasculitis inflammatory panel (VIP) and paediatric inflammatory disease panel
(PID) gene panels. Any variant in any genes included in the above gene panels, and functionally matching the phenotype of A-III-1, were prioritised.
The first approach, provides an unbiased methodology of looking at the variants and also ensures that no potential disease causing variants are missed. However, 696 genes is quite substantial and investigating each in detail is time consuming. Therefore, by using the second approach, I gained a quick overview as to whether any variants in A-III-1, are contained within genes already known to cause autoinflammatory and neuroinflammatory diseases. This would help to focus my search for potential candidate genes. However, using this approach by itself is not recommended as there is a possibility of not finding anything of relevance on the gene panels, and potential other important variants, not contained on gene panels may be overlooked. Both approaches were used in parallel in this thesis.
166
3.5.4 Gene Panel Analysis
The 696 genes that remained after the filtering analysis in A-III-1, were cross-referenced with genes included in the NIP, VIP and PID panels (see appendix section 8.5). The results of this analysis were as follows:
167
Table 3-4: Variants present in both WES dataset and gene panels from A-III-1
Variants which remained after WES filtering analysis were cross referenced against list of genes included in targeted gene panels used at Great Ormond Street Hospital including the
Neuroinflammation Panel (NIP), Vasculitis Inflammation Panel (VIP) and Primary immunodeficiencies panel (PID).
Gene Transcript ID Exon Nucleotide Amino Frequency
change Acid
Change
LYST NM_000081 Exon 6 c.C3025A p.Q1009K 0
MYLK NM_053027 Exon 10 c.A794G p.K265R 0
C6 NM_001115131 Exon 7 c.G823T p.G275W 0.0016
TNFAIP3 NM_001270507 Exon 8 c.A1939C p.T647P 0.0008
ATP7B NM_000053 Exon 4 c.C1706T p.T569I 0
TRAP1 NM_016292 Exon 16 c.G1940C p.R647T 0
DOCK8 NM_203447 Exon 5 c.G452A p.R151Q 0.001
TRAP1 NM_016292 Exon 16 c.G1940C p.R647T 0.095
IL17RC NM_153461 Exon 3 c.C398G p.A133G 0.0002
TYK2 NM_003331 Exon 25 c.A3488G p.E1163G 0.0066
TYK2 NM_003331 Exon 18 c.C2558G p.P853R 0
HFE NM_000410 Exon 4 c.G829A p.E277K 0.0018
CBS NM_000071 Exon 10 c.T833C p.I278T 0.0002
168
Two lists of candidate genes were created, one from the first approach, (discussed in section 3.5.3) containing functionally relevant genes which remained after filtering analysis and one from the second approach, containing a list of genes which were present on the gene panels (Table 3-4). A meeting which consisted of experts in the field was then held to discuss each variant and the possibility that it may be causing the disease (see section 2.17).
A final gene list was devised during this meeting. Genes were chosen based on their function, the frequency of the mutation, existing familial studies on each specific gene and in silico model predictions. The resulting candidate genes chosen from this discussion are as follows; TNFAIP3, TRAP1, TYK2, IRF5, PRDM1 and DIAPH1. Primers were then designed for each of these genes, in order to confirm the mutation in A-III-1 and other family members.
3.5.5 Confirmation of variants using Integrated Genome Viewer (IGV) and
Sanger Sequencing analysis
Sanger sequencing of the genetic variants identified by WES in the chosen candidate genes were carried out. These results were compared to the coverage of each region from the WES data, as viewed on IGV (Integrated genome viewer). Where true variants are present, they should be well covered by WES, and this should correspond with what is observed by the Sanger sequencing. Whole exome sequencing was carried out in trios (A-
III-1, A-II-2 and A-II-1). However, all members of the family (including A-III-2) were
Sanger sequenced (see Figure 3-14 to Figure 3-19)
169
TNFAIP3
A B
Figure 3-14: Detection of heterozygous p.T647P variant in TNFAIP3.
(A) Review of whole exome sequencing data (IGV) revealed heterozygous p.T647P variant
in TNFAIP3 present on both forward (red) and reverse (blue) reads for A-III-1 and A-II-2,
but absent in A-II-1. (B) Sanger sequencing for this variant reveals that A-III-1, A-III-2,
A-II-2 are all heterozygous for this variant, whereas A-II-1 is wild type, corresponding
with the WES data.
170
TRAP1
AA B
Figure 3-15: Detection of heterozygous p.R647T variant in TRAP1.
(A)Review of whole exome sequencing data (IGV) revealed a heterozygous p.R647T variant in
TRAP1 only in A-III-1 and absent in all other family members. (B) Sanger sequencing for this variant demonstrates that this variant is only detected in A-III-1 (proband). All other family members are wildtype at this position.
171
TYK2 exon 18
A B
Figure 3-16: Detection of heterozygous p.P853R variant in TYK2.
(A) Review of whole exome sequencing data (IGV) revealed a heterozygous p.P853R variant in
TYK2 present in all family members sequenced (A-III-1 and A-II-2 and A-II-1). (B) Sanger sequencing of this variant demonstrates that A-III-1, A-II-1 and A-II-2 are all heterozygotes for this variant, whereas A-III-2 was wild type for this variant. This corresponds with the WES data.
172
TYK2 exon 25
A B
Figure 3-17: Detection of heterozygous p.E1163G variant in TYK2.
(A) Review of whole exome sequencing data (IGV) revealed a heterozygous p.E1163G variant in
TYK2 present in all family members sequenced (A-III-1, A-II-2 and A-II-1). (B) Sanger sequencing for this variant shows that A-III-1, A-II-2 and A-II-1 are all heterozygous for this variant, whereas A-III-2 was wild type at this position.
173
IRF5
A B
Figure 3-18: Detection of heterozygous p.G524A variant in IRF5.
(A) Review of whole exome sequencing data revealed a heterozygous p.G524A variant in IRF5 present in all family members sequenced (A-III-1, A-II-2, and A-II-1). (B) Sanger sequencing reveals that this variant is present in all family members.
The p.G524A variant in IRF5, is located next to a 30bp deletion (this deletion is present in every member of the family, and is a common deletion in this gene (Graham et al.
2007), because of this the Sanger sequencing failed to cover the exact nucleotide site of the mutation, and so we cannot say this is a true variant. Here, we can only rely on the
WES data. As the IGV data shows that this variant is present in a heterozygous manner, in all family members sequenced, it is unlikely to be the causal mutation in this case.
174
PRDM1
A B
Figure 3-19: Detection of a heterozygous p.N259I variant in PRDM1.
(A) Review of whole exome sequencing data (IGV) revealed a heterozygous p.N259I variant in
PRDM1 present only in the proband (A-III-1). (B) Sanger sequencing for this variant reveals that
A-III-1 is heterozygous for this variant; all other family members are wildtype at this position; this corresponds with what we see in the WES data.
175
Table 3-5: Summary of Sanger sequencing results for each of the candidate genes in family A.
Gene AA A- A-III-2 A-II-2 A-II-1 Population
Change III-1 Frequency
(gnomAD)
TNFAIP3 p.T647P Het Het Het Homo 0.0018
wt
TRAP1 p.R647T Het Homo Homo Homo 0.000037
wt wt wt
TYK2ex25 p.E1163G Het Homo Het Het 0.005518
wt
TYK2ex18 p.P853R Het Homo Het Het 0.0002
wt
IRF5 p.G524A Het No Het Het 0
data
PRDM1 p.N259I Het Homo Homo Homo 0
wt wt wt
176
3.5.6 Choosing a Candidate Gene
The main choices from this analysis were:
• TNFAIP3: This is a gene previously implicated in numerous autoimmune
diseases, such as rheumatoid arthritis, Beҫhets, and systemic lupus erythematosus
(Zhou et al. 2016, Aeschlimann et al. 2018). Animal models with dysfunctional
copies of this gene have severe brain inflammation (Guedes et al. 2014). This
mutation segregates appropriately with the disease phenotype as A-II-2 and A-
III-2 also have the same genotype as A-III-1 for this variant.
• TRAP1: This is a mitochondrial chaperone protein involved in mitochondrial
respiration. Variants in this gene are associated with mitochondrial dysfunction
(Fitzgerald et al. 2017). This variant in TRAP1 is only found in A-III-1, and so it
also does not show the proper pattern of familial segregation with the disease
phenotype, as it appears here as if it is a de novo mutation. We felt it likely that
this was not causative of the disease in question and decided to exclude this from
further analysis.
• TYK2: There are two variants in this gene, one in exon 18 and one in exon 25,
neither of which have been described before. Whether these are on the same allele
or different alleles is unknown, so it is possible that the proband is compound
heterozygous for TYK2. Homozygous recessive variants have been found before
in this gene, causing TYK2 deficiency and manifesting in severe mycobacterial
and viral infections (Kreins et al. 2015). A-III-1 had been extensively investigated
for bacterial and viral cultures and had been negative on multiple occasions. Also,
the given pattern of segregation of both these mutations is not appropriate for this
disease as these mutations are also seen in every member of the family and is
177
therefore unlikely to be disease-causing. I felt it necessary to discount this from
further investigation.
• IRF5: The mutation which presented here was a likely candidate as the protein
produced is one of the transcription factors of the interferon system (Yanai et al.
2007). Interferon upregulation is something which is widely associated with
AGS, and so it was suspected that interferons may play a role in the pathogenesis
of this disease. The Sanger sequencing failed to cover this exact nucleotide, as it
was located just two base pairs away from a common deletion. However, from
the WES data, we see that both parents, A-II-1 and A-II-2 appear to have the same
genotype as the proband at this locus. Therefore, it seemed unlikely that the
variant in this gene would be causative of the observed phenotype.
• PRDM1: The protein encoded from this gene is a repressor of IFN-β gene
expression (Elias et al. 2018, p. 1), and so it also proved an interesting candidate.
However, from the sequencing analysis, it appears that this mutation is de novo,
as it is not present in any of the parents. This segregation of this disease is not de
novo, as the maternal uncle died quite young of a lupus-like disease, and both
mother and sister of the proband are presenting with inflammatory symptoms.
Therefore this mutation was unlikely to be the cause of disease in family A.
Given the suspected mode of inheritance of the disease and the results from the Sanger sequencing and IGV analysis of whole exome sequencing data and its functional relevance to the phenotype, I decided to investigate the heterozygous missense variant in
TNFAIP3 further.
As shown in Table 3-5, the frequency of this variant in the general population (0.0018) is higher than you might expect given its pattern of dominant inheritance. This family is of
Pakistani ethnicity and the population frequency for the variant in the South Asian
178 population is 0.00075. Given its low prevalence here, it is likely to be more damaging to people of South Asian ethnicity. Considering its general population frequency, it is still below 1% and is therefore considered rare. (Xue et al. 2012) in an assessment of 1000
Genomes data, found that each healthy individual carried between 45-80 different mutations, which were classified by the human gene mutation database (HGMD) as damaging, and were previously associated with disease phenotypes. The authors postulate that healthy individuals can carry damaging variants and not manifest any phenotypes associated with these variants because (1) the disorder in question is recessive, or (2) in the case of dominant phenotypes, the disease may have late onset or may require additional environmental or epigenetic factors for expression. In regards to the p.T647P variant in TNFAIP3, the latter option may provide an answer. Therefore, even though this variant has a high population prevalence, it should not be ruled out as the causative mutation in this individual.
Given the results of Sanger sequencing, evidence from the literature in relation to functional relevance of the phenotype, I decided to proceed with my investigation into the variant identified in TNFAIP3, as it appears that this was the most likely cause of disease, in this case.
179
3.5.7 mRNA Expression of TNFAIP3
QPCR experiments were used to determine the expression levels of the TNFAIP3 mRNA relative to control in order to assess whether the p.T647P mutation had an effect on gene expression. Figure 3-20, below shows mRNA expression for TNFAIP3 in A-III-1 relative to a healthy control. This analysis demonstrates that the gene expression levels of
TNFAIP3 are similar in A-III-1 and the healthy control individual.
2 .0
H C
) q
C A - III- 1
-
( 2
1 .5
=
n
o
i
t
a
c i
f 1 .0
i
t
n
a
u
Q
e 0 .5
v
i
t
a
l
e R
0 .0
C -1 H II -I A
T N F A IP 3 m R N A e x p r e s s io n in P B M C s
Figure 3-20: Gene expression levels of TNFAIP3 in peripheral blood mononuclear cells (PBMC) from A-III-1.
A TRIzol extraction was used to obtain mRNA from PBMCs from both A-III-1 and a healthy control. The mRNA was then converted into cDNA using an reverse transcriptase PCR reaction and expression of TNFAIP3 (QT00041853) measured using qPCR. The expression of this gene was normalised to a housekeeping control gene, HPRT1 (QT00059066), and the expression
180 calculated using the Livak (2-(ΔΔCq)) method (Livak and Schmittgen 2001). Experiments were carried out in triplicate, and error bars represent the standard error from the mean of those triplicates. This analysis demonstrated that the gene expression levels of TNFAIP3 are similar in
A-III-1 and the healthy control individual, a t-test shows p = 0.245.
The data generated here is representative of a single blood draw. This was a preliminary investigation into the effects of the variant on gene expression. Limited information can be gathered here as qPCR data is highly variable (Taylor et al. 2019). From this analysis, it appears as though the expression of mutated TNFAIP3 is slightly higher, in comparison to control. However, there is no significant difference between groups. (p = 0.245).
181
3.5.8 A20 Protein Expression in peripheral blood mononuclear cells (PBMC)
In order to examine the levels of A20 protein expression, a western blot was carried out on whole PBMC, in all family members, an unrelated healthy control and a paediatric patient with a confirmed heterozygous p.N98Tfs25 mutation in TNFAIP3.
Figure 3-21: Western blot examining expression of A20 protein in PBMC from members of family A and another patient with HA20 that was heterozygote for p.N98Tfs25 TNFAIP3.
Whole protein was extracted from PBMC of all family members, an unrelated healthy control and a patient with a confirmed heterozygous mutation (p.N98Tfs25 TNFAIP3/WT) in A20 leading to
A20 haploinsufficiency (Zhou et al. 2016). An immunoblot was carried out to assess protein expression using anti-A20 (#5630, cell signalling). This blot was then stripped and re-blotted using anti-Actin (MAB1501, Merck). There was no reduction in protein expression in PBMC of members of family A unlike the cells derived from the heterozygote for p.N98Tfs25 TNFAIP3 variant that showed reduced A20 protein expression. This experiment was carried out once.
182
Table 3-6: Protein expression of A20 relative to actin expression as assessed in cells from each member of family A, a healthy control and a patient heterozygote for p.N98Tfs25 TNFAIP3 with confirmed HA20.
This data was carried according to the methodology as outlined in section 2.12.4.
Area Under % Peak Size Relative
the Curve ** Density
A20 HC 17994.688 15.162 1.018267
HA20 9425.217 7.941 0.448087
A-III-1 25980.342 21.890 1.135904
A-III-2 23608.271 19.891 1.080093
A-II-1 19868.517 16.740 1.094332
A-II-2 21809.241 18.376 1.275845
Actin HC 37276.584 14.890
HA20 44365.848 17.722
A-III-1 48242.626 19.271
A-III-2 46103.576 18.416
A-II-1 38295.262 15.297
A-II-2 36055.262 14.403
** This column is generated upon analysis using ImageJ. It represents the peak size expressed as a percentage, relative to the total size of all the peaks
Here we observe A20 protein expression in all family members, a HA20 patient with a heterozygous p.N98Tfs25 mutation in TNFAIP3 and a healthy control. As is obvious from the western blot (Figure 3-21), there is no loss of A20 protein expression, in those individuals with the p.T647P TNFAIP3 mutation versus wild type individuals. Given the
183 densities as analysed using ImageJ (Table 3-6), there is very little difference in A20 protein expression between all family members and the unrelated WT healthy control.
The HA20 patient harbouring the heterozygous p.N98Tfs25 mutation, however, has significantly less protein expression, approximately 44% that of the healthy control.
Zhou et al. 2016, demonstrated that people with heterozygous frameshift mutations in
A20, resulting in A20 haploinsufficiency (HA20), have decreased A20 protein expression
(Zhou et al. 2016). This is not the case with A-III-1. A-III-2 and A-II-3, who also harbour the p.T647P mutation in TNFAIP3, have similar levels of protein expression as the healthy control and A-II-4, who does not have the TNFAIP3 mutation. We can, therefore, conclude, that this missense mutation has little to no effect on A20 protein expression and that if the disease process is due to this mutation in TNFAIP3, that this mutation must result in another aspect of protein function.
Very often, proteins may be expressed in adequate amounts but may still not be functional. Many processes take place in order for mRNA to evolve into a fully functional protein. It goes through many rounds of folding, in order to obtain the correct primary, secondary, tertiary and quaternary protein structure. This 3D structure is extremely important in protein functioning, especially in the binding capacity of a particular protein.
It is well documented that improper folding hinders the efficiency and accuracy of protein:protein binding (Dill et al. 2008, Tuncbag et al. 2012). Western blot analysis cannot tell us anything about the 3D structure of a protein, as the protein is both denatured and reduced before being loaded onto the gel.
Therefore, in order to investigate whether the heterozygous p.T647P mutation has any effect of the function of A20. I decided to assess biological events which occur downstream of the A20 protein, in patient-derived cells. The following sections first
184 describe the known function of A20 protein and the downstream pathways regulated by
A20.
185
3.6 Enhanced NF-ƙB activity in cells of p.T647P
TNFAIP3/WT Genotype
As it is well known that heterozygous mutations in TNFAIP3 lead to excessive NF-ƙB pathway activity (Zhou et al. 2016), I investigated whether the cells of A-III-1 and other family members also had increased activation of this pathway.
3.6.1 Increased NF-ƙB signalling in lymphocytes derived from patients with
heterozygous p.T647P mutation in TNFAIP3
As A20 is a known negative regulator the NF-ƙB pathway, I sought to measure the activity of this pathway in lymphocytes derived from A-III-1. A preliminary investigation was set up whereby, I measured the phosphorylation status of p65 (P-p65), the prominent
NF-ƙB transcription factor, in lymphocytes of A-III-1 and an unrelated healthy control.
A time course assay was set up, wherein I stimulated PBMCs with 100ng/ml TNFα at specified time points (refer to section 3.4.1), in order to induce NF-ƙB pathway activation, the endpoint of which is the phosphorylation of p65.
186
2 0 0
s C o n tro l
e t
y A - III- 1
c
o
h
p
B m
1 5 0
y
-
l
F
n
i
N
,
)
n
i
9
2
e
5
s S
a 1 0 0
e
p
(
r
c
5
n
6
i
p
e
d
v
i
e
t
t
a
a l
l 5 0
e
y
r
R
o
h
p
s
o
h p
0 0 1 0 2 0 3 0 4 0 5 0 6 0 T im e (m in s )
Figure 3-22: Increased phosphorylation of p65 in lymphocytes derived from A-III-
1 compared to healthy control wild type cells.
Peripheral blood mononuclear cells (PBMC) were stimulated with 100ng/ml for up to 60 minutes. Cells were then fixed, permeabilised and stained using PE conjugated anti-NF-ƙB p65
(pS529) and gated to collect P-p65+ lymphocytes. Mean expression of P-p65-PE is plotted against the amount of time cells were incubated in TNFα. There was increased expression of p65 in lymphocytes derived from A-III-1 (heterozygous for p.T647P mutation in TNFAIP3) compared to control cells (p=0.014).This experiment was carried out once.
187
The phosphorylation status of p65, at various time points in A-III-1 and in an unrelated healthy control, is demonstrated in the graph above (
2 0 0
s C o n tro l
e t
y A - III- 1
c
o
h
p
B m
1 5 0
y
-
l
F
n
i
N
,
)
n
i
9
2
e
5
s S
a 1 0 0
e
p
(
r
c
5
n
6
i
p
e
d
v
i
e
t
t
a
a l
l 5 0
e
y
r
R
o
h
p
s
o
h p
0 0 1 0 2 0 3 0 4 0 5 0 6 0 T im e (m in s )
Figure 3-22). Using a PE conjugated anti-NF-ƙB p65 (pS529) antibody, the median fluorescent intensity (MFI) was used as a measure of p65 phosphorylation. Although this is a preliminary experiment, it does appear that there is somewhat increased NF-ƙB activity in patient cells. Based on this initial evidence I then sought to conclusively prove this hypothesis using increased N numbers from other family members who also carry the heterozygous p.T647P TNFAIP3 variant and different cell types to measure p65 phosphorylation, upon TNFα stimulation.
188
2 0 0
s W T /W T (n = 3 ) e
t p .T 6 4 7 P /W T (n = 3 ) y
c p = 0 .0 1 2 4
o
h
p B
m 1 5 0
y
-
l
F
n
i
N
,
)
n
i
9
2
e
5
s S
a 1 0 0
e
p
(
r
c
5
n
6
i
p
e
d
v
i
e
t
t
a
a
l
l e
y 5 0
r
R
o
h
p
s
o
h
p
0
0 0 0 0 0 1 2 3 6
T im e (m in s )
Figure 3-23: Increased expression in NF-ƙB phosphorylated p65 (P-p65) in lymphocytes from individuals harbouring the heterozygous p.T647P TNFAIP3 mutation versus healthy controls.
Peripheral blood mononuclear cells (PBMC) were stimulated with 100ng/ml TNFα for up to 60 minutes. Cells were then fixed, permeabilised and stained using PE conjugated anti-NF-ƙB p65
(pS529). Expression values were obtained using the MFI of P-p65+ lymphocytes. Data is expressed as the fold change relative to the mean of the baseline for each respective group. The mean of triplicates is plotted, and error bars represent standard error from the mean (SEM) of those triplicates. Analysis was performed on a FACS Calibur Becton Dickson. There was increased expression of NF-ƙB phosphorylated p65 (P-p65) in lymphocytes from all individuals harbouring the heterozygous p.T647P TNFAIP3 mutation versus healthy controls; p=0.0124.
Overall p-value was calculated using repeated measures ANOVA.
189
There was a statistically significant difference between the relative P-p65 expression in subjects with TNFAIP3 p.T647P mutation (n=3) compared to control (n=3) when all time points were considered (p=0.0124).
3.6.2 Enhanced NF-ƙB signalling in human dermal fibroblast cells (HDFC)
derived from proband with heterozygous p.T647P mutation in TNFAIP3
A skin biopsy was taken from A-III-1, and human dermal fibroblast cells (HDFC) were saved, for further experimentation. Using these HDFC, the phosphorylated p65 assay was performed to examine whether a similar result to that from lymphocytes (section 3.6.1) would be observed.
190
W T /W T (n = 3 )
p .T 6 4 7 P T N F A IP 3 /W T (n = 3 )
B 1 6 0
p = 0 .0 2 6 1
C
-
F
F
D
N
H
f
n
o i
1 4 0
,
n
)
o
9
i
2
s
5
s
e
S
r
p
p
(
x 1 2 0
5
e
6
n
p
i
d
e
e
s
t
a
a
l
e r
y 1 0 0
r
c
o
n
i
h
p
e
s
v
i
o
t
a
h
l
p e
8 0 R 0 5 0 0 0 1 3 6
T im e (m in s )
Figure 3-24: Increased expression of P-p65 in HDFC derived from A-III-1 versus healthy control HDFC.
Human dermal fibroblast cells (HDFC) were grown until the were 60-80% confluent, at which time they were stimulated with 100ng/ml TNFα for up to 60 minutes. Cells were then fixed, permeabilised and stained using PE conjugated anti-NF-ƙB p65 (pS529). Expression values were obtained using the MFI of P-p65+ HDFCs. Data is expressed as the fold change relative to the baseline for each respective group. Experiments were carried out in triplicate and the mean of triplicates is plotted, and error bars represent standard error from the mean (SEM) of those triplicates. Analysis was performed on a FACS Calibur Becton Dickson. There was increased expression of NF-ƙB phosphorylated p65 (P-p65) in HDFC derived from A-III-1, harbouring the heterozygous p.T647P TNFAIP3 mutation versus healthy control HDFC; p=0.0216. P-value was calculated using repeated measures ANOVA.
191
There was a statistically significant difference between the relative P-p65 expression in
A-III-1 HDFC carrying the heterozygous p.T647P TNFAIP3 mutation (n=3) compared to control (n=3) when all time points were considered (p=0.0261).
Based on the data presented in Figure 3-23 and Figure 3-24, there were differences in baseline p65 phosphorylation levels between those harbouring the heterozygous mutation in TNFAIP3 and healthy controls. Individuals harbouring the mutation tended to have a higher baseline level of phosphorylation, and this was most likely due to an underlying inflammatory process, causing a general NF-ƙB upregulation. This was corrected for by normalisation of MFI values. By denoting the baseline levels in each individual as 100, I could then calculate the relative change in phosphorylation following TNFα stimulation.
Visualising the data in this way also enables easy comparison between healthy controls and heterozygous individuals, at each time point.
Of course, the NF-ƙB pathway is a central player in numerous different aspects of the immune system, and the response observed here may be due to general underlying inflammation in patient cells. This is important to bear in mind when interpreting these results.
The graph demonstrates a more substantial increase in the level of p65 phosphorylation upon TNFα stimulation in individuals of the p.T647P/WT genotype than healthy controls.
This increase is observed in both lymphocyte (figure 3-23) and HDFC (figure 3-24) data presented. Differences in phosphorylation were significant in each case; repeated measures ANOVA were p= 0.0124 and p= 0.0261 respectively. In the lymphocyte population, the biggest differences in phosphorylation occur at 20 and 30 minutes post
TNFα stimulation. In the HDFC population, the biggest differences occur much earlier
192 and can be observed 5 minutes post TNFα stimulation. It is unclear what accounts for the time differences between different cell populations.
In figure 3-23, the shape of the plot does not follow the same trend for both groups
(healthy and patient). The difference between subsequent time points is more substantial in the disease population, with the healthy control population display little differences between time points. The phosphorylation status of p65 is slightly raised from baseline in the first instance, in healthy cells and then declines following a longer exposure to the stimulus. This is likely due to tightly regulated mechanisms controlling NF-ƙB pathway activation working effectively in these cells. Referring to (Zhou et al 2016), figure 2(d), where immunoblots assessing p65 are displayed, differences between timepoints in control population appear minimal. P65 phosphorylation is increased in cells of p.T647P/WT genotype as inherent mechanisms which control this process are defective.
In figure 3-24, both groups follow a more similar trend. In WT/WT cells there is an slight increase in p65 phosphorylation over multiple time points before this phosphorylation level returns to more normal levels, nicely displaying the cyclical phosphorylation cycle.
For p.T647P/WT cells, although phosphorylation levels of p65 do come down following a longer exposure to the stimulus, it is consistently high and does not return to normal, as these cells struggle to regulate the NF-ƙB pathway.
193
3.6.3 Elevated levels of proinflammatory cytokines in serum of patients with
heterozygous p.T647P mutation in TNFAIP3
A mesoscale discovery (MSD) assay was performed as a further analysis of the NF-ƙB pathway in family A, by measuring cytokines in serum produced as a result of activation of this pathway. Median cytokine measurements of 38 healthy controls were used as a comparison.
It appears that the expression of all cytokines is much higher in A-III-1 compared to the median of the control population (Table 3-7). We also see that cytokine measurements in serum of A-III-2 and A-II-2, other family members harbouring the p.T647P
TNFAIP3/WT mutation are also elevated in comparison to the healthy controls.
The results suggest that individuals with this heterozygous mutation in TNFAIP3 have higher NF-ƙB pathway related cytokine release, compared to wildtype individuals.
Interestingly, cytokine measurements from A-II-1, having the wildtype genotype, were at similar levels to those of the control group
Notably, the patient (A-III-1), had treatment with Baricitinib, a JAK1/2 inhibitor. I also measured cytokine levels in serum, collected after this treatment had been administered,
We can observe that there is a downregulation in the expression of all cytokines following administration of Baricitinib, and in many cases, it is comparable to healthy control levels.
194
Table 3-7: Proinflammatory cytokine measurements in members of family A compared against the median value of 38 unrelated healthy controls.
Cytoki A-III-1 A-III-1 A-III-2 A-II-2 A-II-1 Healthy Controls
nes Pre Post (median) pg/ml pg/ml (median range
treatment Treatment pg/ml pg/ml)
(median) pg/ml
pg/ml
IL-18 738.62 617.6 800.98 655.28 449.32 362.7 (228.9-
966)
IL-6 2.47 0.59 0.74 0.94 0.8 0.21 (0.03-0.71)
IL-8 14.57 6.25 7.43 5.39 3.25 5.37 (2.09-
17.18)
TNFα 1.42 0.83 3.3 1.60 1.55 1.49 (0.66-3.87)
IL-1β 0.17 0.08 0.14 0.08 0.05 0.05 (0.0013-
0.29)
195
Table 3-8: Z scores of proinflammatory cytokine measurements (listed in Table 3-7) for each family member.
Control population data has been validated to represent a normal distribution in each case using the shapiro wilks test. The area under the curve was found using the Z-Score tables, freely available online. See section 3.4.9 for methodology on calculations.
Cytokine Family Replicates Z-Scores Area under member curve IL-18 A-III-1 1 2.334201 0.9901 2 1.72007 0.9573 3 1.264265 0.8962 A-III-1 1 1.719729 0.9564 (post treatment) A-III-2 1 1.987769 0.9761 2 2.650776 0.996 A-II-2 1 0.865346 0.8051 A-II-1 1 1.911054 0.9719 IL-6 A-III-1 1 10.97562 2 4.052452 3 2.349647 0.9904 A-III-1 1 1.519558 0.9345 (post treatment) A-III-2 1 1.575163 0.9418 2 2.270136 0.9984 A-II-2 1 3.30492 0.9995 A-II-1 1 2.827029 0.9976 IL-8 A-III-1 1 2.038674 0.9788 2 -0.16495 0.4364 3 1.954427 0.9744 A-III-1 1 2.039114 0.978 (post treatment) A-III-2 1 0.149198 0.5557 2 -0.56431 0.2877 A-II-2 1 -0.30849 0.0606
196
A-II-1 1 -0.30849 0.0606 TNFα A-III-1 1 -0.0789 0.4721 2 2.47735 0.9932 3 1.581135 0.9429 A-III-1 1 -0.07057 0.4721 (post treatment) A-III-2 1 3.359858 0.9996 2 2.474388 0.9932 A-II-2 1 0.267385 0.6026 A-II-1 1 0.171678 0.5675 IL-1β A-III-1 1 0.912277 0.8159 2 -0.14767 0.4443 3 0.813036 0.791 A-III-1 0.917401 0.8186 (post treatment) A-III-2 1 0.531084 0.7019 2 0.293453 0.6141 A-II-2 1 -0.5114 0.2776 A-II-1 1 -0.09304 0.4641
Z scores represent the number of standard deviations away from the population mean you data lies. A Z score of 0, demonstates that your data point perfectly fits the population mean. Looking at the replicates in this data we can see just how variable cytokine measurements are. For example, looking at IL-18, 3 replicates were taken ranging between 1.26 and 2.33 standard deviations away from the control population mean.
Nonetheless, these measurements overall show that our patient has a higher production of most cytokines that the control population mean and that this cytokine production diminishes following treatment with Baricitinib.
Another way to interpret this information is by using the area under the curve. This area multiplied by 100, translates as the percentage of values which lie below this data point in the normal population. We can see that for all three IL-18 measurements of A-III-1,
197 between 89-99% of values in the control population lie below the measurement taken for
A-III-1. This insinuates that A-III-1 produces much higher quantites of IL-18 than the control population average. Two area under the curve values are missing for IL-6, as these
Z-scores (10.97 and 4.05) were not found on the table, as they lie above the normal distribution. A-III-1 produces extremely high quantites of this cytokine. Some variability is observed in the IL-8 and TNFα data, with two out of three measurements suggesting high production of these cytokines and one measurement in each suggesting a lower production (43.64% and 47%, respectively). However, for each measurement, it is still produced at a higher quantity than the population mean.
The high Z-scores and resultant areas under the curve for the measurements listed in this table indicate higher than would be expected cytokine production, in our proband. This corresponds to an active inflammatory process in this individual.
198
IL -1 8 IL -6
1 5 0 0 3
1 0 0 0 2
l
l
m
m
/
/
g
g
p p 5 0 0 1
0 0
C 2 2 1 2 2 1 1 I- I- I- C 1 - - - H - I I I H I- II II II II -I - - I I - - I A A -I - - A A A A A A
IL -8 T N F
2 0 5
4
1 5 l
l 3
m
m /
1 0 /
g
g p p 2
5 1
0 0
C 1 2 2 1 C 2 2 1 - I- I- I- -1 I- I- I- H II II I I H I I I I I - - - II -I - - - A A - A A A A A A
IL -1
0 .4
0 .3
l m
/ 0 .2
g p
0 .1
0 .0
C 1 -2 -2 -1 H I- II II II II -I - - - A A A A
Figure 3-25: Dot plots representing pro-inflammatory measurements across all family members, and a healthy control population consisting of 38 individuals.
A-III-1 data presented here is before treatment.
199
Although, the cytokine data produced here is indicative of an active inflammatory process in A-III-1, it is not without its limitations. Firstly, only one measurement was obtained from each of the parents in this case. Given the variability of serum cytokine readings, one reading is not sufficient to generate an accurate view of cytokine production in these people. N = 3 would be a more optimal number of readings to accurately access cytokine production. Also of note, the readings obtained for all family members harbouring the heterozygous mutation in TNFAIP3 is quite variable for each cytokine. We know that the phenotype for each family member is also very variable and this mutation has an incomplete penetrance. Therefore, whether or not these individuals had symptoms or an acute phase response at the time of serum collection has a large bearing on these cytokine measurements.
3.6.4 Increased Global Cellular Ubiquitination levels in Human Dermal Fibroblast
Cells (HDFC) derived from A-III-1
A co-immunoprecipitation experiment (see section 3.4.5) was carried out in order to investigate the levels of ubiquitin, and in particular, K63 linked ubiquitin, in fibroblast cells of A-III-1 versus to fibroblasts from a healthy control individual.
200
Figure 3-26: Increased Lys63 linked Uniquinination in A-III-1 HDFC
HDFCs were stimulated with 20ng/ml TNFα and total protein extracted. The K65-Ub antibody
(Merck, 05-1308) was added to 100μg of total extracted protein overnight. This mixture was then incubated with protein A dynabeads overnight (Thermo Fisher Scientific, Cat:10001D). The bead-antibody-protein complex was run on an SDS-PAGE gel and blotted with a general ubiquitin antibody, Ub (sc-271289). A β-actin immunobot was used as a control from lysate samples pre-IP. There was increased abundance and molecular weight of Lys63-ubiquitin in fibroblast cells derived from A-III-1 compared to healthy control cells.
201
Table 3-9: Ubiquitin expression in HDFC relative to actin in both A-III-1 and an unrelated healthy control.
This data was carried according to the methodology as outlined in section 2.12.4.
Area under % Peak Size Relative
the Curve ** Density
Ub A-III-1 Baseline 16521.49 9.311 0.395489
TNFα 81534.33 26.209 0.955417
HC Baseline 48971.76 11.742 0.486735
TNFα 57548.43 18.499 0.70815
Actin A-III-1 Baseline 12123.12 23.543
TNFα 15430.12 27.432
HC Baseline 13123.52 24.124
TNFα 14436.12 26.123
** This column is generated upon analysis using ImageJ. It represents the peak size expressed as a percentage, relative to the total size of all the peaks
As is obvious from this blot (Figure 3-26), upon stimulation with TNFα, there is a large increase in the amount of K63-Ub in patient HDFCs in comparison to that of the healthy control cells. A20 is a deubiquitinase enzyme. This enzyme binds to multiple protein targets such as RIP1 and NEMO, in order to deubiquitinate K63 chains on these proteins, turning off the NF-ƙB pathway and diminishes the immune response. High ubiquitin levels are indicative of increased NF-ƙB activation in these cells. A similar result was observed in the original description of HA20 (Zhou et al. 2016).
202
3.6.5 Increased K63-linked polyubiquitinated NEMO in HDFC derived from A-
III-1.
A co-immunoprecipitation experiment was performed to examine NEMO specific K63-
Ub chains, in patient HDFCs versus healthy control HDFCs, following the protocol as outlined in section 3.4.5.
Figure 3-27: Increased Lys63-ubiquitinated NF-ƙB essential modulator (NEMO) in
HDFC derived from A-III-1
HDFCs were stimulated with 20ng/ml TNFα and total protein extracted. The K65-Ub antibody
(Merck, 05-1308) was added to 100μg of total extracted protein overnight. This mixture was then incubated with protein A dynabeads overnight (Thermo Fisher Scientific, Cat:10001D). The bead-antibody-protein complex was run on an SDS-PAGE gel and blotted with a general ubiquitin antibody, Ub (sc-271289). ). A β-actin immunobot was used as a control from lysate
203 samples pre-IP. There was increased abundance and molecular weight of Lys63-ubiquitinated
NF-ƙB essential modulator in HDFC from A-III-1 compared to control cells.
Table 3-10: K63-Ubiquitinated NEMO expression relative to actin in HDFCs derived from A-III-1 and an unrelated healthy control.
Area under % Peak Size Relative
the Curve ** Density
NEMO HC Baseline 16924.72 17.124 0.759615
TNFα 66575.41 23.124 0.909248
III.1 Baseline 23462.87 24.629 0.942773
TNFα 99589.24 35.123 1.294953
Actin HC Baseline 11123.12 22.543
TNFα 13430.12 25.432
III.1 Baseline 12123.52 26.124
TNFα 14436.12 27.123
** This column is generated upon analysis using ImageJ. It represents the peak size expressed as a percentage, relative to the total size of all the peaks.
As is obvious from this blot (Figure 3-27), after 60 minutes of TNFα stimulation there is a large increase in NEMO specific K63 polyubiquitin chains, in cells derived from A-
III-1 in comparison to control cells. As we understand it, A20 binds to the C terminal tail of NEMO in order to deubiquitinate this protein. The effect of which leaves NEMO bound to IKKα and IKKβ (Mauro et al. 2006, Solt et al. 2009). The maintenance of this IKK complex makes it impossible for p65 to become phosphorylated and so diminishes the
NF-ƙB immune response. However, in our patient, the opposite is true. We observe high
204
NEMO polyubiquitination. This infers that A20 is unable to carry out its deubiquitinase activity on NEMO. In this scenario, NEMO dissociates from the IKK complex, and continual phosphorylation of p65 occurs. Therefore the NF-ƙB inflammatory response pathway is constitutively activated in this patient.
205
3.7 Heterozygous p.T647P mutation in TNFAIP3 leads to
upregulated interferon production
As mentioned previously, this patient had a neurological presentation which was extremely similar to Aicardi Goutières syndrome (AGS). AGS is an inherited encephalopathy which manifests in neurological and liver abnormalities, weak muscles, cognitive and developmental delay (Crow 1993). When AGS occurs early in life, symptoms often appear similar to that of a congenital viral infection. Before WES was carried out, it was assumed that AGS may provide a diagnosis for this patient. However, as demonstrated by the Sanger sequencing, A-III-1 was wild type at all positions for all
11 AGS genes sequenced. Strikingly, AGS is an interferonopathy, and the high levels of interferon activity in these patients, lead to their inflammatory presentation and enhanced neurological symptoms and neuroinflammatory lesions. As there was a large overlap in the clinical presentation of A-III-1 and AGS, this prompted me to investigate whether the interferon pathway was also activated in A-III-1.
206
3.7.1 Impaired type 1 interferon (IFN) gene expression and signalling in A-III-1
with heterozygous p.T647P mutation in TNFAIP3.
In order to investigate the interferon response, I examined the mRNA expression levels of 11 IFN stimulated genes (ISG) by qPCR. Genes produced as a result of stimulation by type 1 IFNs include IFI27, IFI44L, IFIT1, ISG15, RSAD2 and SIGLEC1. Type 2 IFNs produce IFNγ. Genes stimulated by both type 1 and type 2 include CXCL10, CXCL9, IL-
18 and IFNβ1.
In te rfe ro n S ig n a tu re in A -III-1
8 0
H C
A - III- 1
6 0
)
q
C
-
(
2
=
n
o
i t
a 4 0
c
i
f
i
t
n
a
u
Q
e
v
i
t
a
l
e R 2 0
0
7 L 1 5 2 0 9 1 8 2 1 G 1 I 4 IT 1 D C 1 L B N - F I4 G A L C I IF E N IF IL IF IS S L C X IF R G X C I C S In te r fe r o n S tim u la te d G e n e s
207
Figure 3-28: Increased expression of interferon stimulated genes (ISG) in whole blood from A-III-1 in comparison to healthy controls.
A PAX gene extraction was carried out to obtain RNA from whole blood. RNA was then converted into cDNA via a reverse transcriptase reaction. SYBR Green qPCR experiments were used to measure the mRNA expression of each gene. The values shown are derived from three replicate experiments, and error bars represent the standard error from the mean of these triplicates. The expression level of each gene was normalised to that of a housekeeping gene, HPRT1. The results are displayed as relative to the expression levels of the same genes in a healthy control. There was significant upregulation of ISG expression in whole blood from A-III-1 compared to healthy controls.
The Livak method (explained in section 2.9.6), enables us to determine the relative quantity of each gene by setting the expression of the control sample for each gene to 1.
For some genes, such as ISG15 we can see that the expression is 60 times higher than the control. We can clearly see here that A-III-1 exhibited a high type 1 IFN signature. There was little difference between the control and A-III-1 for genes upregulated by type 2 IFNs.
Suggesting that this disease is purely mediated by type 1 IFNs and that type 2 IFNs are unlikely to play a role.
An interferon score was calculated according the the methodology as outlined in section
3.4.3. Before treatment, A-III-2 had an IFN score of 5.277032. As this was larger than the threshold value, as calculated from 13 healthy controls (1.655), we can say that this is a positive IFN score.
208
While interferon activity in this case has been assessed using qPCR data by measuring the expression of ISGs, found in whole blood, another useful method that could be used to assess this activity is RNASeq. Using the premise that RNA is a measure of gene expression, this technology sequences cDNA which is reverse transcribed from RNA in the cell. This technology is useful in measuring abundances of RNA transcripts and thereby, delineating the relative expression levels of particular transcripts. It would provide a alternative mechanism by which to measure IFN activity in patient cells.
RNASeq can also provide information on alternatively spliced transcripts, post- transcriptional modifications and therefore is a useful tool in molecular biology.
209
3.7.2 Interferon stimulated gene expression of A-III-1 is comparable with that of
other monogenic interferonopathies
I next wanted to compare of the expression levels of ISGs observed in A-III-1 were comparable to those observed in other defined monogenic interferonopathies. To this end, the interferon qPCR assay was carried out on a patient with STING-associated vasculopathy with onset in infancy (SAVI), as characterised by the heterozygous p.V155M mutation in the TMEM173 gene along with 13 healthy controls.
As is obvious from Figure 3-29 below, the expression levels of ISGs in A-III-1 are comparable with the expression levels in the SAVI patient. The expression level for some genes is even higher in A-III-1 (e.g. IFI44L, ISG15). In one particular gene, IFIT1, the expression level is much higher in the SAVI patient (SAVI relative expression = 65.3),
A-III-1 relative expression = 4.54). However, overall, the levels of ISGs appear to be comparable.
210
6 0 0 A - III- 1
5 0 0 W T /W T
4 0 0 W T /p .V 1 5 5 M T M E M 1 7 3 (S A V I)
3 0 0
)
q
C
- (
6 0
2
=
n
o
i
t
a
c
i
f
i t
n 4 0
a
u
Q
e
v
i
t
a
l
e R 2 0
0
7 L 1 5 2 2 1 I 4 IT 1 D C F 4 G I I IF A E IF IS S L R IG S
In te r fe r o n S tim u la te d G e n e s
Figure 3-29: Interferon stimulated gene expression in A-III-1 and a patient suffering from STING-associated vasculopathy with onset in infancy (SAVI), a monogenic interferonopathy.
A PAX gene extraction was carried out to obtain RNA from whole blood. RNA was then converted into cDNA via a reverse transcriptase reaction. SYBR Green qPCR experiments were used to measure the mRNA expression of each gene. The values shown are derived from three replicate experiments, and error bars represent the standard error from the mean of these triplicates. The expression level of each gene was normalised to that of a housekeeping gene, HPRT1. The Livak method (expression = 2^(-ΔΔCq)) was used to obtain the expression value for each gene.
Expression levels of ISGs from a healthy control population (n=13) are also graphed, and error bars represent standard error from those means. Levels of ISG in A-III-1 were comparable to those detected in a patient with heterozygous p.V155M TMEM13 variant causing SAVI.
211
3.7.3 IFN stimulated gene expression decreases following treatment with
Baricitinib
As it was proven that A-III-1 had an extremely high expression of IFN stimulated gene expression and that this may be contributing to the disease pathogenesis, it was decided that she would receive Baricitinib. This drug is a JAK1/2 inhibitor, and so it works by blocking the second wave synthesis of IFNs.
IFN stimulated gene (ISG) expression was reassessed in A-III-1 in subsequent months following treatment with Baricitinib. The ISG expression in whole blood from A-III-1 decreased significantly following treatment with Baricitinib (Figure 3-30). There was a significant decline in the expression levels of ISG’s even after two months of treatment.
At 6 months following the administration of Baricitinib, we see that the expression levels of most ISGs is now comparable to healthy control levels. Some ISGs exhibit an almost absent expression.
212
7 0 B a s e lin e
M o n th 2
) q
C M o n th 6
6 0
- H e a lth y C o n tro l
(
2
=
5 0
n
o
i
t
a c
i 4 0 f
i 1 5
t
n
a
u Q
1 0
e
v
i
t a
l 5
e R
0
7 L 1 5 2 1 I2 4 IT 1 D C 4 G E IF I IF A IF IS S L R IG S
In te rfe ro n S tim u la te d G e n e s
Figure 3-30: Decreased expression of IFN stimulated gene expression in whole blood from A-III-1, following treatment with Baricitinib.
A PAX gene extraction was carried out to obtain RNA from whole blood. RNA was then converted into cDNA via a reverse transcriptase reaction. SYBR Green qPCR experiments were then used to calculate the level of mRNA expression for each gene. The expression level of each gene was normalised to that of a housekeeping gene, HPRT1. The results are displayed as relative to the expression levels of the same genes in a healthy control. The values shown are derived from three replicate experiments, and error bars represent the standard error from the mean of these triplicates. IFN stimulated gene expression significantly decreased following treatment with baricitinib.
213
Table 3-11: Interferon Score for A-III-1 following treatment with Baricitinib.
Blood sample taken IFN Score Control Result Threshold Baseline 5.277032 1.655 Positive (Pre-treatment) Month 2 3.052309 1.655 Positive
Month 6 0.809769 1.655 Negative
As is evident from Figure 3-30 and Table 3-11, there is a significant reduction in the levels of interferon stimulated genes produced following treatment with Baricitinib. The interferon score diminishes substantially and by month six following treatment, it is below the threshold value and classed as negative. Baricitinib is a Jak inhibitor and therefore prevents activation of the Jak-Stat pathway. As outlined in section 3.2.5, the second wave synthesis of interferons occurs through the Jak-Stat pathway, culminating in the transcription of interferons and interferon stimulated genes. Therefore, by blocking this pathway, and preventing the transcription of type 1 interferons, the interferon signature in this patient dramatically declines.
3.7.4 High levels of interferon cytokines in serum of A-III-1.
In order to further quantify the levels of interferon elevation in the proband, a number of cytokines were measured in the serum of A-III-1 taken before and after treatment with baricitinib (Figure 3-31). Again we can see that the levels of all interferons is significantly higher than the control population before treatment, but the levels of interferons is reduced following treatment.
214
IF N IF N -
1 .5 3
2
1 .0
l
l
m
m /
/ 1
g
g
p p 0 .5 0
0 .0 -1
t t t t C n n C n n H e e H e e tm tm tm tm a a a a e e e e r r r r T T T T e t e t r s r s o o P P 1 P 1 P I- 1 I- 1 II I- II I- - II - II A - A - A A
IF N IF N
8 0 0 1 5
6 0 0
1 0
l
l
m
m /
/ 4 0 0
g
g
p p 5 2 0 0
0 0
t t t t C n n C n n H e e H e e m t tm tm tm a a a a e e e e r r r r T T T T e t e t r s r s o o P P 1 P 1 P I- 1 - I - II -1 I II I I - I - II A - A - A A
Figure 3-31: IFNα (A), IFNγ (B), IFNβ (C), and IFNλ (D) cytokine expression as measured in A-III-1, (pre and post treatments) and unrelated healthy controls
(n=13).
For all cytokines measured, A-III-1 has a higher expression than the mean of the control population. This level decreased significantly following treatment with Baricitinib. In each case, the level of each cytokine post treatment in A-III-1, is comparable to that of the control
215 population. These measurements were carried out using Meso Scale Discovery (MSD) Assay as outlined in section 2.10.
Table 3-12: Cytokine measurement in A-III-1, before and after treatment with
Baricitinib and compared to thirteen unrelated healthy controls.
Cytokines A-III-1 A-III-1 Healthy Controls
Pre-treatment (pg/ml) Post-treatment (pg/ml) Median pg/ml
IFNγ 663.3974 47.43308 22.62
(6.9-38.59)
IFNλ 12.1766 0.00 0.91
(0.21-33.34)
IFNα 1.3924 0.5511 0.2187
(0.1644-0.7927)
IFNβ 2.728 0.00 0.2427
(0.02-0.7927)
As we can see from this analysis (Figure 3-31 and Table 3-12), all IFN cytokines measured were elevated in comparison to the median of the 13 healthy controls. This compliments the qPCR interferon assay data. We can also observe from this data that all cytokines measured, exhibit a decrease in their expression following treatment with
Baricitinib. By decreasing the levels of interferon mediated cytokines in the blood, this drug helps to decrease immune system activation and control the inflammatory process.
216
3.7.5 Interferon stimulated gene expression in other family members with p.T647P
mutation in TNFAIP3
217
Figure 3-32: ISG signature of family members harbouring p.T647P mutation in
TNFAIP3 and two pediatric HA20 patients with confirmed heterozygous mutations in TNFAIP3.
A PAX gene extraction was carried out to obtain RNA from whole blood. RNA was then converted into cDNA via a reverse transcriptase reaction. SYBR Green qPCR experiments were used to measure the mRNA expression of each gene. The values shown are derived from three replicate experiments, and error bars represent the standard error from the mean of those triplicates. The expression level of each gene was normalised to that of a housekeeping gene, HPRT1. The Livak method (expression = 2-(ΔΔCq)) was used to obtain the expression value for each gene.
Expression levels of ISGs from a healthy control population (n=13) are also graphed, and error bars represent standard error from those means. (A) ISG levels of A-III-2 are comparable to that of a helathy control and much less than that of the SAVI patient. (B) ISG levels of A-II-2 are comparable to that of a helathy control and much less than that of the SAVI patient. (C) ISG levels of a HA20 patient with the p.R27X/ wt TNFAIP3 genotype are higher than that of the control and are comparable to that of the SAVI patient. (D) ISG levels of a HA20 patient with the p.N98Tfs25/ wt TNFAIP3 genotype are comparable to that of the control and are much less that of the SAVI patient.
The interferon assay was performed on (A) A-III-2, a sister of A-III-1, who also harbours the p.T647P TNFAIP3 heterozygous mutation. It appears from this analysis that this individual is producing interferons at a level that is comparable to the healthy control population (n=13). The interferon stimulated gene (ISG) expression of A-III-2 is much lower as the same genes expressed in the SAVI individual. This individual was quite young and experienced episodes of inflammation, such as a malar type rash on her face and some signs of arthritis. This qPCR experiment was performed on unstimulated blood.
At the time of blood collection, this person was not experiencing an inflammatory
218 episode, suggested by her normal interferon signature (B) A-II-2 . IFN stimulated gene expression is comparable to that of the healthy control population and much lower than that of the SAVI individual. A-II-2 was not symptomatic at the time of sampling.
This assay is performed on whole blood and so whether or not I detected upregulation of type I interferon gene signature is much dependant on the symtpoms and acute phase response or treatment of individual was at the time of sampling. If they are not experiencing any inflammation at the time when blood was collected then the IFN signature could be normal. This assay then could be more reasonably viewed as a general overview of interferon activity, it is not dependent on mutations in TNFAIP3, as many different biological factors may contribute to an upregulated interferon response.
However, this is the first time A20 has been implicated in the interferon response.
Even though A-III-2 and A-II-2 were not inflammatory and therefore not producing a high level of interferons at the time of blood collection, I hypothesized that cells from these individuals may respond similarly to A-III-1, when they are placed under the same inflammatory stress (see section 3.7.6) in vitro.
This assay was also performed on two patients with confirmed HA20 mutations. I was also able to gain access to PBMC derived from two patients with confirmed HA20 genotypes (p.R27X/WT and p.N98Tfs25/WT). (Zhou et al. 2016), demonstrated that the
NF-ƙB pathway was upregulated in these patients. However, I thought it would be interesting to assess whether or not the interferon pathway was also aberrantly regulated in these patients. To this end, the interferon qPCR assay was performed (C and D), on whole blood from both of these patients.
As is evident from this graph (C), this individual has a modestly increased IFN stimulated gene expression. Most of the genes assayed are approximately 10 times higher than the
219 control, with some genes, such as IFI44L being expressed at 20 times higher than control population. Interestingly it is not quite as high as the SAVI patient, who was displaying a more severe inflammatory presentation. Nonetheless, we can say that this person has an upregulated expression of interferon stimulated genes. This was an interesting find and gives further evidence for the fact that A20 is involved regulation of the interferon immune response. (D) displays the interferon assay performed on whole blood from an individual with the p.N98Tfs25/WT genotype. Interestingly, in this patient, the level of expression of IFN stimulated genes is not significantly different from the control.
At the time of blood collection, the first HA20 patient (p.R271X/WT) (Figure 3-32, C) was inflammatory, reflected in the enhanced IFN stimulated gene expression. However, the second HA20 patient (p.N98Tfs25 TNFAIP3/WT) was not presenting with inflammatory symptoms, at time of sampling. As a result, normal ISG levels were observed. However, this patient suffers from autoinflammation manifesting in Behҫets disease, and the p.N98Tfs25 TNFAIP3/WT mutation is well documented (Papadopolou et al. 2019, Zhou et al. 2019) and known to lead to this disorder. My suspicions remained that upon stimulation with an inflammatory stress, cells with this genotype would behave similar to those of A-III-1. Indeed, it was proven that when subjected to an inflammatory stimulus, cells of the p.N98Tfs25 TNFAIP3/WT genotype are unable to regulate the interferon pathway, leading to increased IRF3 phosphorylation (see section 3.7.8)
220
3.7.6 Elevated levels of phosphorylated IRF3 in individuals with the p.T647P
mutation in TNFAIP3 in comparison to healthy controls.
As it appeared that the second wave synthesis of IFNs is upregulated in A-III-1, I then sought to assess whether the first wave synthesis was also aberrantly regulated. IRF3 is an important protein in the regulation of interferon expression. A20 helps prevent excess interferon activation by binding to TBK1 and thus preventing the phosphorylation of
IRF3. Given this information, I thought it necessary to assess the phosphorylation status and therefore activation status of IRF3, in A-III-1, and other members of the family carrying this mutation.
The graph below (Figure 3-33) represents the increase in IRF3 phosphorylation from baseline over time, following stimulation with 100ng/ml TNFα (p = 0.017). At 5 minutes post TNFα stimulation, the heterozygous p.T647P TNFAIP3 group (mean = 124.849 ,
SEM = 9.9) had higher P-IRF3 than the wildtype control cells (mean = 87.93, SEM =
1.48). At 30 minutes expression of P-IRF3 was higher in individuals of the p.T647P
TNFAIP3/WT genotype mean = 115.5, SEM = 2.4 compared to healthy control mean of
85.179, SEM = 10.7. At 60 minutes expression of P-IRF3 was also higher in cells from patients with p.T647P TNFAIP3/WT genotype (mean = 112.27, SEM = 2.2) compared to
WT/WT cells (mean = 83.69, SEM = 4.081).
I can conclude, therefore, that individuals with the p.T647P in TNFAIP3 have elevated levels of IRF3 phosphorylation, upon stimulation with TNFα, indicative of activation of the IFN pathway in people with this genotype.
221
2 0 0 W T /W T (n = 3 )
) 6
9 p .T 6 4 7 P /W T (n = 3 )
3
r I
e p = 0 .0 1 7
F
S
(
M
3
e F
n 1 5 0
i
l
R
I
e
s
d
a
e
t
b
a
l
o
t
y
r
e
o
v i
h 1 0 0
t
p
a
l
s
e
o
r
h
,
p
n
o
n
i
i
s
e
s g
e 5 0
r
n
a
p
x
h
e
c
d
l
o F
0
0 5 0 0 0 0 1 2 3 6 T im e (m in s )
Figure 3-33: Relative expression of phosphorylated IRF3 expression in lymphocytes from individuals of the p.T647P TNFAIP3/WT genotype versus unrelated healthy controls.
PBMCs were stimulated with 100ng/ml TNFα for up to 60 minutes. Cells were then fixed, permeabilised and stained using anti-P-IRF3-PE. Expression values were obtained using the MFI of lymphocytes. Data is expressed as the fold change relative to the mean of the baseline for each respective group. The mean of triplicates is plotted, and error bars represent standard error from the mean of those triplicates. A one way ANOVA test was used to compute overall P values. There was enhanced expression of p-IRF3 in lymphocytes from heterozygotes for p.T647P TNFAIP3 compared to control cells (p=0.0007).
222
3.7.7 siRNA silencing of TNFAIP3 in Human Dermal Fibroblast Cells (HDFC)
results in increased phosphorylation of IRF3 and P65
As a further measure of the importance of the A20 protein in regulating the immune response, I queried whether silencing the expression of TNFAIP3 would also impair IFN responses. Human dermal fibroblasts cells were obtained from A-III-1 and cultured.
Wildtype HDFCs were obtained from a healthy control individual and used to perform siRNA transfection experiments in order to reduce the expression of TNFAIP3. Cells were then subjected to an inflammatory stimulus, TNFα for up to 60 minutes and the phosphorylation of inflammatory markers, namely IRF3 and p65, were measured. Two tandem experiments were set up
1. HDFCs transfected with siRNA targeting the TNFAIP3 gene, using cells
transfected with scrambled siRNA were used as a control.
2. Untransfected patient HDFCs (p.T647P TNFAIP3/WT genotype), and
untransfected healthy control HDFCs (WT/WT genotype) as a control.
I hypothesized that there should be a similar trend in both sets of experiments. In each case, patient HDFCs and TNFAIP3 siRNA transfected cells should express higher activation of the inflammatory markers p65-P, IRF3-P.
First, a qPCR experiment was set up in order to quantify to what extent the TNFAIP3 gene had been knocked down. HDFC were incubated for 3 days with the transfection reagents in Opti-Mem transfection medium before a TRIzol RNA extraction took place.
This was then converted into cDNA using reverse transcriptase PCR, and the levels of
A20 quantified using qPCR.
223
1 .5
3
P
I
)
A
q F
C p = 0 .0 2 5 5
W T /W T
N
- T
( T N F A IP 3 s iR N A
f
2
o
1 .0
= S c ra m b le d s iR N A
n
n
o
i
o
t
i
a
s
c
s
i
f
e
i
r
t
p
n x
a 0 .5
e
u
Q
A
e
N
v
R
i
t
m
a
l e
R 0 .0
T A A W N N / R T iR i s W s 3 d e IP l A b F m a N r T c S
Figure 3-34: TNFAIP3 mRNA expression in HDFC transfected with RNAiMAX lipofectamine as measured by qPCR
RNA was extracted from HDFC via a TRIzol extraction. RNA was reverse transcribed into cDNA and SYBR Green qPCR experiments used to assess expression of TNFAIP3. Experiments were carried out in triplicate, and error bars represent standard error from the mean of those triplicates. The Livak method was used to calculate the relative expression of TNFAIP3, using the housekeeping gene ACTN-β. HDFCs transfected with TNFAIP3 siRNA, expressed TNFAIP3 at 58% to that of healthy control HDFCs. Experiments was carried out in triplicate, and the mean of each triplicate is plotted. Error bars represent standard error from the mean of triplicates.
Unpaired t-tests were used to calculate p values.
224
A qPCR experiment was carried out in order to quantify the levels of TNFAIP3 produced in siRNA transfected cells, in comparison to healthy control cells and scramble siRNA transfected cells. From this analysis, it was observed that the HDFCs which were transfected with TNFAIP3 siRNA, expressed TNFAIP3 at 58% (p = 0.0255) to that of healthy control HDFCs. The scramble expressed TNFAIP3 at 65% to that of healthy control HDFCs. This is a good replicate of the situation in our proband as A-III-1 has a heterozygote mutation in TNFAIP3.
Once I had confirmed effective silencing of the TNFAIP3 gene in HDFCs, I explored the activation status of inflammatory markers P-IRF3 and P-p65 in the knockdown cells. As we can see from the graphs (A and C) in Figure 3-35 below, the TNFAIP3 knockdown cells display higher expression of the inflammatory markers, compared to the scrambled control cells. The patient HDFC also demonstrate a higher activation of all markers tested, in comparison to un-transfected healthy control HDFC (Figure 3-35:
B and D). Both experiments portray a similar trend. This gives further evidence to suggest that it is indeed a defect in the A20 protein, which leads to high NF-ƙB and interferon pathway stimulation.
.
225
A B
In c re a s e d P -p 6 5 in T N F A IP 3 k n o c k d o w n H D F C s In c re a s e d P -p 6 5 in p .T 6 4 7 P T N F A IP 3 /W T
2 5 0 H D F C s c o m p a re d to W T /W T H D F C s
d
e d
t siR N A T N F A IP 3 2 5 0
e
a
)
l
t
I y
S c ra m b le d s iR N A a
F
) l
r p .T 6 4 7 P T N F A IP 3 /W T
I
y o
M p = 0 .0 4 7 5
F
r (
h 2 0 0
W T /W T
o
M
p
e
(
h
s
n p = 0 .0 2
i 2 0 0
p
o
e
l
s
h
n
e
i
o
p
s
l
h
a
e
B
b
p
s
1 5 0
a
-
o
B
t
b
F
1 5 0
e
-
N
o
t
v
F
f
i
t
o
e
N
a
v
l
f
n
i
e t
1 0 0 o
o
r
i
a
l
,
s
n )
e 1 0 0
s
o
9
r
i
e
2
,
r
s
)
5
p
s
9
S
x
e
2
p
r
e
( 5
5 0
p
e
S
5
x
v 6
p 5 0
i
e
(
t
p
a
e
5
l
v
6
e
i
t
p
R a
0 l e 0 5 0 0 0
1 3 6 0 R 0 5 0 0 0 T im e (m in s ) 1 3 6
T im e (m in s )
C D
In c re a s e d P -IR F 3 in T N F A IP 3 k n o c k d o w n H D F C s In c re a s e d P -IR F 3 in p .T 6 4 7 P T N F A IP 3 /W T H D F C s c o m p a re d to W T /W T H D F C s
T N F A IP 3 s iR N A 1 5 0
S c ra m b le d s iR N A )
I p .T 6 4 7 P T N F A IP 3 /W T
d p = 0 .0 3
F e
) 2 0 0
t I
d W T /W T
M
a
(
F
l
e
t
y e
M p = 0 .0 0 7
a
r
(
l
n
i
o
y l
e 1 8 0
r
h
e
n
i
o
p
s
l
s
h a
1 0 0 e
o
p
b
s
s
h
a
o o
p 1 6 0
b
t
h
f
e
o
p
o
t
v
f
i
n
t
e
o
a
v
o
l i
i 1 4 0
n
t
e
s
a
o
r
s
l
i
,
e
e
s
)
r
r
s 6
5 0
,
p
e
9
) x
r 1 2 0
3
6
e
p
r
9
x
e
e
3
e
r
S
v
(
i
e
t
e
3
S v
a 1 0 0
(
l
i
F
t
e
3
a
R
l
I
R
F
e
R
I R 0 8 0
0 5 0 0 0 0 5 0 0 0 1 3 6 1 3 6
T im e (m in s ) T im e (m in s )
Figure 3-35: Increased expression of activation markers P-IRF3 and P-p65 in HDFC following siRNA silencing of TNFAIP3 and A-III-1 derived HDFC in comparison to scramble siRNA transfected and untransfected healthy control HDFC respectively
Cells were stimulated with 100ng/ml TNFα for up to 60 minutes. Cells were then fixed, permeabilised and stained using the relative antibodies. Expression values were obtained using the MFI of fibroblasts and data expressed as the fold change relative to the mean of the baseline for each respective group. Experiments were carried out in triplicate, and the mean of triplicates
226 plotted. Error bars represent standard error from the mean of those triplicates. In all TNFAIP3 knockdown siRNA experiments above (A,C,E and G) there is a higher activation of inflammatory markers in knockdown cells. In all untransfected HDFC experiments above, using HDFC derived from A-III-1 and a healthy control (B,D,F and H) there is a higher activation status of inflammatory markers observed in A-III-1 cells.
In each experiment; the knockout cells display enhanced expression of activation markers in comparison to the scramble control. Likewise, patient fibroblasts demonstrate enhanced expression of all inflammatory activation markers in comparison to untransfected healthy control fibroblasts. Therefore, It seems reasonable to infer that a loss of A20 expression or, simply a loss of A20 functionality, as is observed in A-III-1’s cells, is responsible for the enhanced inflammation observed in this patient, as measured by the inflammatory markers, P-IRF3 and P-p65.
227
3.7.8 Increased expression of p65, IRF3 phosphorylation in HA20 patient with
heterozygous TNFAIP3 p.N98Tfs25 variant
The interferon qPCR assay was carried out on two patients with previously described
HA20 mutations, with conflicting results. While one of these patients was shown to have somewhat upregulated interferon activity, the other had relatively normal levels of interferon stimulated gene expression. As this assay is performed on baseline unstimulated blood, I predicted that this may have been due to whether or not these individuals were experiencing inflammatory symptoms at the time of blood collection.
To test this hypothesis, I performed a number of assays, measuring the levels of a number of inflammatory markers, namely: P-p65, P-IRF3, on stimulated PBMCs.
From this analysis (Figure 3-36), we can conclude that the heterozygous p.N98Tfs25 mutation in TNFAIP3 hinders the ability of the A20 protein to prevent activation of the interferon and NF-ƙB pathways, producing excessive P65 and IRF3 phosphorylation in cells derived from the HA20 patient (p.N98Tfs25 TNFAIP3/WT genotype). Interestingly, a similar result was observed for both the p.T647P TNFAIP3/WT and p.N98Tfs25
TNFAIP3/WT genotypes, in these assays. So we can infer that heterozygous dominant mutations in the TNFAIP3 gene produce high activation of the inflammatory pathways.
Of note, however, these mutations most likely work in very different ways. We know from Zhou et al. 2016, and from section 3.5.8 (Figure 3-21) that the p.N98Tfs25 mutation in TNFAIP3 leads to haploinsufficiency of this gene. The p.N98Tfs25/WT A20 product is expressed at approximately half the level of a WT/WT A20 protein. This mutation produces a frameshift in the reading frame, which consequently leads to mRNA
228 degradation of the mutant allele. Only the wild type allele produces a fully functional protein product, and so expression of this protein is severely reduced.
We know that there is no reduced expression of the p.T647P TNFAIP3/WT genotype in comparison to wild type controls. Yet, our assays measuring activation of inflammatory markers such as p65 and IRF3 in cells of the p.T647P TNFAIP3/WT genotype produce a very similar result to that of cells with the p.N98Tfs25 TNFAIP3/WT genotype.
Therefore, even though there are different molecular mechanisms for each of the p.T647P
TNFAIP3/WT and p.N98Tfs25 TNFAIP3/WT genotypes, both produce a similar result, leading to excessive activation of inflammatory pathways, as measured by our assays.
229
A
2 0 0 W T /W T
p .N 9 8 T fs 2 5 T N F A IP 3 /W T
)
5
I 6
F p = 0 .0 1 1
p
M
(
d
e
e
t
n
a
i l
l 1 5 0
y
e
r
s
o
a
h
b
p
o
s
t
o
e
h
v
p
i
t 1 0 0
a
B
l
e
r
-
,
F
n
o
N
i
s
n
i
s
e
e r
g 5 0
p
n
x
a
e
h
)
c
9
2
d
l
5
o
S
F
p
(
0
0 5 0 0 0 0 1 2 3 6
T im e (m in s )
B
W T /W T
p .N 9 8 T fs 2 5 T N F A IP 3 /W T
2 0 0 p = 0 .0 1
)
I
F
M
(
3
e
F
n
i
R
l
I
e 1 5 0
d
s
e
a
t
b
a
l
o
y
t
r
o
e
h
v
i
p
t s
a 1 0 0
l
o
e
h
r
,
p
n
n
i
o
i
e
s
s
g
e
n r
a 5 0
p
h
x
c
e
d
)
l
6
o
9
F
3
r
e S
( 0
0 5 0 0 0 0 1 2 3 6
T im e (m in s )
230
Figure 3-36: Graphical representations showing the activation of NF-ƙB and interferon inflammatory markers P-p65 (A), P-IRF3 (B) in a patient (p.N98Tfs25
TNFAIP3/WT) with confirmed HA20
Cells were stimulated with 100ng/ml TNFα for up to 60 minutes. Cells were then fixed, permeabilised and stained using the relative antibodies. Expression values were obtained using the MFI of fibroblasts and data expressed as the fold change relative to the mean of the baseline for each respective group. Experiments were carried out in triplicate, and the mean of triplicates plotted. Error bars represent standard error from the mean. In all experiments (A and B) there is a higher activation status (phosphorylation) of inflammatory markers in patient cells (p.N98Tfs25 TNFAIP3/WT) in comparison to healthy control cells.
231
3.8 Increased NLRP3 Inflammasome activation in patient
cells
As it is documented that loss of A20 protein leads to excessive NLRP3 activation, contributing to the inflammatory phenotype of HA20 patients (Zhou et al. 2016), I investigated whether this pathway was over-activated in A-III-1.
3.8.1 Enhanced NLRP3 activation in p.T647P TNFAIP3/WT PBMC
Cells were stimulated with LPS or LPS and ATP to turn on caspase 1 activation. LPS binds the TLR on the cell surface, providing the priming signal (i.e. NF-ƙB activation).
The addition of ATP provides the activation signal, turning on canonical NLRP3 inflammasome activation. There should be little, to no inflammasome activation observed in cells which are only stimulated with LPS. Healthy wildtype cells should only exhibit
NLRP3 inflammasome activation, and therefore caspase-1 expression, in the presence of
LPS and ATP.
232
In c re a s e d C a s p a s e -1 a c tiv a tio n in p .T 6 4 7 P T N F A IP 3 /W T c e lls in c o m p a ris o n to W T /W T c e lls
4 0
p = 0 .0 3
3 0
s
l
l
e C
p = 0 .0 0 9
+ 4
1 W T /W T
D 2 0 C
p .T 6 4 7 P T N F A IP 3 /W T
n
i
%
+
M A
F 1 0
0
g P n S T ti P s L A e + R S P L
Figure 3-37: Enhanced Caspase1 activation in p.T647P TNFAIP3/WT CD14+ relative to WT/WT CD14+ cells.
PBMCs were stimulated with 100ng/ml LPS for 4 hours and 3.75mM ATP for 30 minutes. Cells were then incubated in the FAM-FLICA reagent and anti-CD14-PE for 1 hour. MFI values from
FAM+ CD14+ cells were graphed and used to assess the activation of caspase 1. Experiments were carried out in triplicate, and error bars represent standard error from the mean of those triplicates. Unpaired T-tests were carried out to assess differences in caspase 1 expression between p.T647P TNFAIP3/WT cells and WT/WT cells. There was enhanced caspase 1 activation in p.T647P TNFAIP3/WT CD14+ cells relative to WT/WT CD14+ cells (p=0,009).
In the healthy control monocytes, we observe very little increase in caspase-1 activation between baseline and LPS stimulated cells (Figure 3-37). However, once ATP is introduced the level of caspase-1 activation in these cells is increased. Alternatively,
CD14+ cells in A-III-1 play out a different scenario. These display increased caspase-1
233 activation even upon stimulation with LPS alone and this difference is significantly different from control cells (p = 0.009). Caspase-1 activation is higher still once ATP is introduced and again is upregulated relative to the control population (p = 0.03). A similar pattern of caspase-1 activation was observed in HA20 patients, as shown by (Zhou et al.
2016).
234
3.8.2 Elevated IL-18 and IL-1β cytokine levels in supernatants from PBMC
derived from patients with heterozygous p.T647P TNFAIP3 mutation
As a further measure of NLRP3 inflammasome activity, I decided to measure the cytokines produced as a result of this pathway; namely IL-18 and IL-1β. Supernatants were collected from cells stimulated during the FAM-FLICA assay, and a Meso Scale
Discovery (MSD) assay was used to assess the levels of NLRP3 associated cytokines in these supernatants.
p = 0 .0 2 2 0 0 p .T 6 4 7 P T N F A IP 3 /W T p = 0 .0 1 1 5 0 W T /W T
1 0 0
)
l m
/ 5 0
g p
( 4 0
8 1
- 3 0
L I 2 0
1 0
0
e n S P li P T e L A s a + B S P L
Figure 3-38: Increased IL-18 secretion in p.T647 TNFAIP3/WT versus WT/WT
PBMCs upon stimulation with LPS, and LPS and ATP.
Supernatants from stimulated PBMCs were collected, and an MSD assay performed in order to quantify the levels of IL-18 secreted from these cells. Experiments were carried out in triplicate.
Horizontal lines represent the mean of those triplicates. Unpaired T-tests were performed to
235 assess statistical significance between the two groups. In both the supernatants from LPS and
LPS + ATP stimulations, the level of IL-18 is significantly higher from patient (p.T647P
TNFAIP3/WT) cells in comparison to healthy control cells.
p = 0 .0 0 7
8 0 0 W T /W T 6 0 0 p .T 6 4 7 P T N F A IP 3 /W T
4 0 0 )
l p = 0 .0 7 m
/ 2 0 0
g
p ( 6 0
5 0 1
- 4 0
L I 3 0 2 0 1 0 0
S e P n L P li T e A s a + B S P L
Figure 3-39: Increased IL-1β secretion in p.T647 TNFAIP3/WT versus WT/WT
PBMCs upon stimulation with LPS, and LPS and ATP.
Supernatants from stimulated PBMCs were collected, and an MSD assay performed in order to quantify the levels of IL-1β secreted from these cells. Experiments were carried out in triplicate.
Horizontal lines represent the mean of those triplicates. Unpaired T-tests were performed to assess statistical significance between the two groups. In both the supernatants from LPS and
236
LPS + ATP stimulations, the level of IL-1β is significantly higher from patient (p.T647P
TNFAIP3/WT) cells in comparison to healthy control cells.
As shown in Figure 3-38 and Figure 3-39, significantly more IL-18 and IL-1β is secreted from cells of individuals harbouring the p.T647P TNFAIP3/WT mutation than unrelated healthy control cells, upon stimulation.
In the case of IL-18, WT/WT cells had a mean of 4.52 pg/ml (SEM = 2.9) upon stimulation with LPS and a mean of 54.32 pg/ml (SEM = 3.9) upon stimulation with LPS and ATP, while p.T647P TNFAIP3/WT cells had a mean of 61.18 pg/ml (SEM = 13.9) and 139.56 pg/ml (SEM =23.34) respectively. An unpaired T test proved these means were statistically different in each case (p = 0.01 and p = 0.02 respectively). A similar trend is observed for IL-1β. WT/WT cells had a mean of 7.69pg/ml (SEM = 2.2) upon stimulation with LPS and a mean of 41.81pg/ml (SEM = 10.2) upon stimulation with LPS and ATP, while p.T647P TNFAIP3/WT cells had a mean of 26.57pg/ml (SEM = 4.17) and 469.12pg/ml (SEM = 145.3) respectively. An unpaired T test proved these means were statistically different in each case (p = 0.07 and p = 0.007 respectively).
I thus concluded that the heterozygous p.T647P mutation in TNFAIP3 prevents A20 exerting its inhibitory function on the NLRP3 inflammasome pathway. In wildtype cells, when the priming signal (LPS) is first initiated, A20 regulates the NF-ƙB pathway and prevents overexpression of inflammasome components. When the activation signal
(ATP) is thus provided, it binds to the ubiquitinated pro-IL-1β complex and prevents spontaneous processing of this complex into mature IL-1β, for secretion. However, this was not observed in heterozygous p.T647P TNFAIP3 cells, as these cells demonstrate both high caspase-1 activation, as demonstrated by the FLICA assay and high IL-18 and
IL-1β secretion as measured by the MSD assay.
237
3.9 Discussion
I have identified a heterozygous dominant mutation in the TNFAIP3 gene, which is present in three members of family A and adds TNFAIP3 mediated neuroinflammation to the ever expanding spectrum of monogenic interferonopathies. The proband, in this case, displayed enhanced neuroinflammatory symptoms manifesting in left-sided focal seizures, chorioretinitis and acute uveitis. Autoinflammation resulting from loss of A20 function has been reported many times (Guedes et al. 2014, Zhou et al. 2016,
Aeschlimann et al. 2018, Rajamäki et al. 2018), and in particular, has been previously described to specifically lead to spontaneous cerebral inflammation (Guedes et al. 2014).
My study has advanced the field in three ways. Firstly, I have identified a rare heterozygous dominant mutation that has never before been reported in the literature in regards to autoinflammation. Secondly, I have elucidated the role of this gene within the
IFN pathway, a novel investigation as regards the function of mutated A20 and its impact on autoinflammation. Thirdly, I am also the first to show that by targeting this pathway directly with the JAK inhibitor (Baricitinib), the inflammatory symptoms observed in this patient were revoked.
After whole exome sequencing was performed, in order to narrow the search for a candidate gene, I decided to look for variants which were of autosomal dominant inheritance in this family. As demonstrated by (Zhou et al. 2016) through a series of transfection experiments the authors show that HA20 mutations does not have a dominant negative effect. They transfected HEK293T cells with vectors overexpressing mutant forms of TNFAIP3, and measured the NF-ƙB response by method of luciferase reporter assays. Interestingly, they observed that when wildtype and mutant TNFAIP3 constructs were overexpressed together in the same cell, NF-ƙB activity was normal. Therefore,
238 heterozygous mutations in TNFAIP3, do not prevent the wildtype allele from performing its function, and HA20 mutations are not dominant negative. It is proposed that the HA20 mutations described in (Zhou et al. 2016), are haploinsufficient. Haploinsufficiency, is defined as a situation wherein the total level of protein produced is about half the normal level, and is insufficient to maintain the effective cellular functioning of that protein
(Huang et al. 2010, Anon. 2020i). While (Zhou et al. 2016) observed a substantial loss of
A20 protein product, and therefore in the case of the specific mutations described in that paper, haploinsufficiency of A20 is most likely the mechanism for autosomal domainance in that case. However, that is not what was observed in this thesis. There was no loss of
A20 protein expression from p.T647P/WT individuals. However, despite this, pathways which are regulated by A20 are overactivated in patient cells. Moreover, when provoked with an inflammatory stimulus, these pathways present enhanced NF-ƙB and IRF3 pathway activity in other family members with the mutation. Therefore, I believe the function of this protein to be defective, even if produced at levels comparable to control cells. It is therefore likely that the method of autosomal dominance in this family is functional insufficiency.
Three members of this family harboured the mutation in TNFAIP3. Despite this there was a large degree of phenotypic variability observed, between family members. While the proband, A-III-1, unquestionably had the most severe presentation, manifesting in neurological lesions and A-III-2 and A-II-2, had milder phenotypes. A-III-2 presented with a malar type facial rash and A-II-2 had occasional oral ulcers. Phenotypic variability in relation to this gene has been widely documented (Guedes et al. 2014, Zhou et al. 2016,
Aeschlimann et al. 2018). A20 is a promiscuous protein which is involved in a multitude of inflammatory pathways, increasing the phenotypic spectrum observed amongst HA20 patients, from mouth ulcers to severe cerebral inflammation. Although it has been
239 reported that cerebral inflammation is quite rare in patients with HA20 (2/16, 13%)
(Aeschlimann et al. 2018), the true frequency of cerebral involvement in this disease is perhaps under-represented. Clinicians should, therefore, consider screening for CNS involvement in patients with suspected HA20 and exhibiting other autoinflammatory symptoms. Notably, enhanced CNS involvement has been documented in knockout mouse studies (Guedes et al. 2014), and the authors state that heterozygote variants cause much milder neuroinflammatory symptoms, compared to the enhanced CNS inflammation observed in homozygote knockout mice. Environmental triggers, namely infections, may also play a role in the severity of the phenotype observed in HA20 patients.
In particular, I examined the role of this mutation in the NF-ƙB, interferon and NLRP3 inflammasome pathways. Patient PBMCs harbouring the p.T647P mutation in TNFAIP3 and A20 siRNA knockdown fibroblasts were unable to suppress P-p65 and P-IRF3 expression upon inflammatory stimulation, providing evidence for aberrant regulation of the NF-ƙB and interferon pathways. Proband cells also display increased expression of
IFN stimulated genes and increased activation of caspase-1.
This thesis relies on data obtained from phosflow experiments and cytokine measurements by MSD in order to assess the activation of the NF-ƙB pathway. There are many other ways in which to measure the NF-ƙB pathway which were not performed here, and is one of the limitations of this thesis. Firstly, there are many other proteins of
NF-ƙB pathway which are also phosphorylated upon upregulation of this pathway. These include IƙBα, IKKα/IKKβ, P38 and JNK amongst others. The phosphorylation status of these markers could have also been assessed using phosflow measurements or, in the case of (Zhou et al. 2016) this could be assessed using immunoblot, monitoring the activation of these markers following exposure to TNFα stimulation over time.
240
It would also have been interesting to perform fractional separation of nuclear and cytoplasmic cell components and look at p65 in these compartments separately. Inactive p65 is bound in the cytoplasm to an inhibitory protein, but upon activation, translocates to the nucleus. Therefore looking at the localisation of p65, would also provide substantial information as to the activation of this protein (Zhou et al. 2016).
Another way to look at this pathway would be to use a luciferase reporter assay (Zhou et al. 2016). This could be achieved by overexpressing the mutant protein in a cell line such as HEK 293T cells or Jurkat cells, and measuring the ability of TNFα stimulated transfected cells to diminish the NF-ƙB response. To carry out this assay, cells are transfected with 3 plasimds, an NF-ƙB reporter plasmid, a luciferse control plasmid, and a plasmid containing GFP tagged p.T647P A20. Luciferase activity viewed in these cells, is normalised using control plasmid and is a direct result of NF-ƙB activity. (Zhou et al.
2016) performed these assays and found a higher luciferse activity in cells transfected with mutant A20, than cells overexpressing wildtype A20. Interestingly, the authors found that when both mutant A20 and wildtype A20 were expressed together in the same cell, luciferase activity was comparable to the luciferase activity observed from cells overexpressing wildtype A20 alone. They concluded that in this instance, mutant A20 does not have a dominant negative effect.
As A20 is largely involved in ubiquitinase functions, a number of immunoprecipitation experiments were carried out to assess ubiquitin levels of specific proteins. Patient cells showed defective removal of ubiquitin from NEMO, and overall increased level of cellular ubiquitin in mutant cells, demonstrating that A20 cannot sufficiently carry out its deubiquitinase activities. NF-ƙB, interferon and NLRP3 inflammasome related cytokines are largely upregulated in proband cells.
241
Therefore, I concluded that while HA20 can result from a decreased expression of the
A20 protein, in the case of the heterozygous p.T647P mutation, inflammatory episodes were more likely caused by a molecular dysfunction of this protein. As we know, the C terminal zinc finger domain of the protein is heavily involved in binding to other protein targets. This binding enables A20 to deubiquitinate these protein targets in order to regulate inflammatory pathways. The position of this mutation makes it difficult for A20 to associate with its protein partners, making it impossible for A20 to perform its regulatory functions, and turn off inflammation. As a result, increased NF-ƙB, interferon and NLRP3 inflammasome activity is observed in the cells of the proband.
A-III-1 received Baricitinib treatment, a JAK inhibitor, to control the interferon immune response. Recently a report was published where the authors use a JAK inhibitor,
Toficitinib, to control the interferon immune response (Schwartz et al. 2019). A clinical trial of 12 patients with confirmed HA20, 2 of which had mutations in the Zf4 domain of the protein, report good clinical and immunological responses to this drug, with a clear reduction in interferon activity, post treatment. (Aeschlimann et al. 2018) also report the use of Tofacitinib, in treatment of one of the HA20 patients described in that report.
Although this data shows conclusive evidence to support the role of this heterozygous mutation in the enhanced neuroinflammatory symptoms observed in this patient, there are several limitations to my work. Firstly, another immunoprecipitation experiment would be complementary to this work in order to investigate the binding of RIP1 to A20. As we know that Zf4 plays a distinct role in enabling A20 to abolish RIP1 signalling (Lu et al.
2013), having important implications for the NF-ƙB pathway. However, this is not completely essential as I have already shown the impact of mutated A20 on NEMO, a protein which works in concert with RIP1 to also drive NF-ƙB inflammation. Secondly, there are a number of cellular avenues, where A20 plays a role, which were not
242 investigated in this study. A20 appears to be a master regulator of cellular processes and is found to be implicated in apoptosis (Grey et al. 1999, He and Ting 2002) and cancer progression (da Silva et al. 2014, Yang et al. 2018, p. 3). I have not carried out any assays to assess the impact of the p.T647P mutation in apoptosis or cell cycling, but it would be interesting to investigate whether or not A20 plays a role here. Thirdly, my investigations have largely been limited to the innate immune system. Recent reports also link A20 to the adaptive immune system, suggesting that it is a negative regulator of T cell activation
(Coornaert et al. 2009, Düwel et al. 2009, Rodriguez et al. 2014).
3.10 Conclusion
In summary, I have identified a new mutation in TNFAIP3, leading to autoinflammation predominantly manifesting as cerebral inflammation. I have elucidated the mechanism by which aberrations in this gene resulted in the inflammatory manifestations observed in this patient. My findings have the following implications:
1. Patients with heterozygous TNFAIP3 mutations and autoinflammation should be
investigated for enhanced interferon activity and targeting the IFN pathway
should be considered when deciding upon therapeutic interventions.
2. Patients with heterozygous TNFAIP3 mutations should also be investigated for
underlying cerebral inflammation even in the absence of overt clinical symptoms.
243
4 Homozygous mutation in CCR7
as a cause of a familial immune
dysregulatory disorder
4.1 Summary
4.1.1 Background
Here, I present a consanguineous family of Saudi Arabian origin, with three affected children, suffering from an immune dysregulatory disorder. Whole exome sequencing identified a homozygous variant in G protein-coupled receptor, C-C chemokine receptor
7 (CCR7) as the cause of this disease. CCR7, a lymph node homing molecule, is imperative to the efficient functioning of the adaptive immune response, and I show here how a homozygous mutation in this gene leads to delayed and insufficient T cell responses, leaving individuals bearing this mutation, susceptible to viral infections.
4.1.2 Objectives
To identify the pathogenic variant in this family as the cause of a disease mimicking systemic juvenile idiopathic arthritis, with macrophage activation syndrome and susceptibility to infection.
244
4.1.3 Methods
Whole exome sequencing was carried out on all family members. Western blot analysis was used to assess protein expression of the chosen candidate gene. Immunophenotyping was used to assess populations of T cells within individuals bearing the identified homozygous mutation. A transwell assay was set up to measure migration efficiency of mutant cells.
4.1.4 Results
WES identified a homozygous p.M228K mutation in CCR7 in all affected family members and an unaffected healthy monozygotic twin sister. Western blot and qPCR experiments show decreased expression of CCR7 in individuals with the mutation.
Immunophenotyping shows an inverted CD4:CD8 T cell ratio in individuals of the p.M228K/p.M228K CCR7 genotype as well as a complete absence of central memory T cells. The transwell assay demonstrated a poor migration capacity of mutated cells, towards the CCL21 chemokine. Mutant cells show decreased expression of IFNγ, when subjected to a viral stimulus.
4.1.5 Conclusion
I propose that homozygous mutation in CCR7 results in poor lymph node homing and as a result severely delayed adaptive immune responses, leaving individuals of this genotype, susceptible to viral infection.
245
4.2 Introduction
C-x-C chemokine receptor 7 (CCR7) is a G protein-coupled receptor. It has a primary role in the homing of naïve T cells, B cells and activated dendritic cells to lymph nodes, where they may mount an efficient adaptive immune response. Indeed this has implications for many other aspects of the immune system, and lack of a fully functional
CCR7 protein can have detrimental effects on the organism.
4.2.1 G protein Coupled Receptors
G protein-coupled receptors are a diverse family of membrane proteins which have a defined structure. They are characterised by the presence of seven transmembrane alpha helices, an extracellular N terminus and an intracellular C terminus (Kroeze et al. 2003,
Rosenbaum et al. 2009). It was recently determined that vertebrates have 6 classes of
GPCRs, which are classed based on their functional and structural similarity. CCR7 is part of the largest family of GPCRs, the rhodopsin family (Attwood and Findlay 1994).
The main function of GPCRs is to relay messages from the extracellular environment to the intracellular environment of the cell. This happens through the binding of ligands, which change the structural conformation of the receptor leading to the initiation of a particular signalling cascade within that cell (Rosenbaum et al. 2009). The binding of different ligands may induce different stable conformations of the receptor activating diverse cellular pathways. This enables fine-tuning of the physiological response
(Galandrin et al. 2007, Weis and Kobilka 2018).
Another factor contributing to the diversity of GPCRs is the G protein with which they are associated. Each G protein has three subunits, α, β and γ. In humans alone, there are
246
20 different types of Gα subunit, 5 types of Gβ subunit and 12 types of Gγ subunit
(Robishaw and Berlot 2004, Oldham and Hamm 2006) creating immense biological diversity among these receptors. When the GPCR is in its inactive state, the Gα, Gβ and
Gγ subunits form a heterotrimeric complex. The α and γ subunits have lipid membrane anchors and so are also attached to the membrane (Campbell and Smrcka 2018). In this instance, Gα is also bound to GDP. Once the GPCR becomes activated, by binding to its respective ligand, it undergoes a conformational change. This enables the GPCR to act as a guanine exchange factor (GEF). The Gα subunit to which it is attached swaps its GDP for a GTP molecule. The Gα protein then becomes activated and dissociates from its Gβ and Gγ partners (Mahoney and Sunahara 2016).
The independent Gα subunit then binds to other membrane-associated proteins and downstream effector molecules to initiate signalling cascades. The Gβ and Gγ subunits, whilst remaining bound to one another, also bind downstream molecules to initiate signalling responses. It is believed that they mainly bind to ion channels such as N, P and
Q type voltage-gated calcium channels (McCudden et al. 2005, Betke et al. 2012), whilst
Gα proteins mainly function to switch on second messenger signalling including adenylyl cyclase, phospholipase C, guanylyl cyclase, amongst others.
Of course, the GPCR may be bound by other proteins such as β-arrestins (Jean-Charles et al. 2017). The C terminus of the GPCR contains serine and threonine residues that may become phosphorylated and lead to the recruitment of β-arrestins (Jean-Charles et al.
2017). When β-arrestins are bound, they prevent the binding of G proteins. β arrestin recruitment leads to the activation of the extracellular signal regulated kinase (ERK) pathway and can also lead to receptor internalization (Smith and Rajagopal 2016), a process which leads to desensitization of the ligand and dampening of the GPCR signal.
247
So depending on the type of G protein or β arrestin bound to the CCR7 receptor, a whole plethora of different biological pathways may be evoked, each producing second messengers and having distinct downstream effects, enabling fine-tuning of cellular responses. The ligands (CCL21 or CCL19) which bind the CCR7 receptor change the receptor conformation and therefore influence which G protein or β arrestin which binds the CCR7 receptor; thereby influencing the downstream events of receptor activation.
These many possible targets of CCR7 mean that it can influence a large variety of pathways, and hence has numerous important non-redundant functions in the immune system.
4.2.2 Lymph Node and Splenic Architecture
The lymphatic system is a series of vessels and nodes which collects interstitial fluid leaking from the capillaries and returns it to the blood (Moore and Bertram 2018). The lymphatic system also has an important function in helping to fight invading pathogens by providing an appropriate environment to mount an efficient adaptive immune response.
Although T cells mature in the thymus and B cells mature in the bone marrow, which are the primary lymphoid organs, they cannot become activated here (Alberts et al. 2002).
Naïve T cells leave the thymus and enter the lymph node or spleen, the secondary lymphoid organs (SLO) (Alberts et al. 2002). It is here that they encounter antigen- presenting dendritic cells (APCs), become activated, undergo clonal expansion and eventually leave the secondary lymphoid organs, to re-enter the circulation and help fight the invading pathogen (Alberts et al. 2002). This is also true of B cells, that receive help from activated T cells, enabling the B cells to divide, expand and re-enter the circulation.
248
Lymph nodes are therefore highly organised structures which are specialised specifically for this purpose. They have a unique microarchitecture which help to facilitate of this process, and animals with disturbed lymph node architecture often have delayed or even absent adaptive immune responses (Thomas et al. 2016).
Figure 4-1: The structure of the lymph node, as obtained from (Comerford et al.
2013)
249
Naïve T cells enter the lymph node through the high endothelial venules (HEVs) and position themselves within the T cell zone, the paracortex. This positioning is controlled by CCR7, as fibroblastic reticular cells (FRCs) within the paracortex, abundantly express
CCL19 and CCL21, and recruit T cells to this site (Link et al. 2007). B cells also enter the lymph node through the HEVs, but migrate towards the B cell follicle, where C-x-C chemokine ligand 13 (CXCL13), a ligand for C-x-C chemokine receptor 5 (CXCR5) is present (Comerford et al. 2013). It appears that CCR7 delivers signals that direct cells to the T cell areas, whereas CXCR5 guides cells to the B cell follicles. Following activation,
B cells downregulate CXCR5 and upregulate CCR7 which enables them to migrate towards the T cell zone of the lymph node, here activated B cells receive help for CD4+
T helper cells. Dendritic cells and macrophages enter through the afferent lymphatic vessels (Liao and von der Weid 2015). Activated antigen-presenting dendritic cells, which enter the lymph node, also express CCR7. Thus, they are directed toward the T cell zone, where they come into contact with T cells. There they can activate T cells expressing the correct receptor for the presenting antigen (Alberts et al. 2002). This T cell will undergo clonal expansion and differentiate into central memory T cells, effector T cells and helper T cells (Smith-Garvin et al. 2009, Kim and Williams 2010), before leaving the lymph node to re-enter the circulation. These activated T cells will then migrate toward the site of infection in order to kill the invading pathogen (Smith-Garvin et al. 2009).
CCR7-/- animals have severely delayed T cell responses. Interestingly, however, the B cell responses appear normal. CXCR5 is essential to establish a B cell area in the secondary lymphoid organs, and so a functional B cell area is still observed in the absence of CCR7 (Ohl et al. 2003) .
250
The spleen is another important SLO which partakes in the generation of the adaptive immune response. In CCR7-/- mice, the organisation of the spleen is disturbed.
Lymphocytes enter the spleen from the blood at the marginal zone (MZ) and migrate into the periarteriolar lymphoid sheath (PALS), also known as the T cell zone. Studies using
CCR7 knockout mice have shown a highly disorganised splenic architecture, with a complete lack of an organised T cell area in the spleen, resulting from an impaired migration of T cells into the periarteriolar lymphoid sheaths (PALS) (Gunn et al. 1999,
Comerford et al. 2013). The PALS is located within the white pulp of the spleen, which is organised as lymphoid sheaths, having distinct B and T cell areas (Mebius and Kraal
2005). Again this organisation of B and T cell areas is controlled by chemokines and their receptors, namely CCR7 and CXCR5. Within the PALS, T cells interact with dendritic cells and provide help to B cells, much like in the lymph nodes (Mebius and Kraal 2005).
The red pulp of the spleen is an area which is used for filtering the blood, and usually has little to do with lymphocytes. Instead, macrophages commonly reside within the red pulp of the spleen (Kurotaki et al. 2015), which clear away old red blood cells and phagocytose pathogenic micro-organisms (Zhao et al. 2015). However, in plt/plt mice, it was observed that there was a severe lack of T cells in the PALS of the spleen (Mori et al. 2001). It was also noted that instead of the white pulp, T cells are often observed in the red pulp and in the marginal zone between the red and white pulp (Mori et al. 2001). This disorganisation of lymphocytes is thought to contribute to the delayed adaptive immune responses of these mice, and leave them highly susceptible to viral infections (Gunn et al. 1999).
In addition, CCR7-/- mice also exhibit the spontaneous development of tertiary lymphoid organs (TLO) (Höpken et al. 2007), such as bronchial associated lymphoid tissue (BALT)
(Kocks et al. 2007, Fleige et al. 2018), which is not observed in wildtype animals.
251
4.2.3 Development of Secondary Lymphoid Organs (SLOs)
As well as directing the traffic of lymphocytes into the correct locations within SLO’s,
CCR7 and its chemokines, has also been shown to have a role in the formation of secondary lymphoid organs. Other cytokines which help in the development of these organs are CXCR5 and CXCL13. CCR7 KO mice still develop most SLOs including
Peyers Patches, spleen and most LN; however, it has been documented that these mice often have absent popliteal, inguinal and parathymic lymph nodes (Ohl et al. 2003).
Although these mice still contain secondary lymphoid organs, these organs display significantly disrupted organisation, including severe defects in lymphocyte compartmentalisation.
It was shown that CCR7 plays somewhat redundant roles with CXCR5 when it comes to secondary lymphoid organogenesis. Two separate studies show that mice who are deficient for both these proteins lack facial, bronchial and cervical lymph nodes (Luther et al. 2002, Ohl et al. 2003). The authors also found that these structures were still present when the mice were only deficient in one protein.
In addition, in diseases featuring chronic inflammation, such as rheumatoid arthritis and
Sjörgens syndrome, tertiary lymphoid structures (TLOs) spontaneously develop within other organs such as the lungs, It was also reported that Ccr7-/- mice develop TLOs at mucosal sites including the stomach, colon and lung (Davalos‐Misslitz et al. 2007, Kocks et al. 2007) including the formation of bronchial associated lymphoid tissue (BALT)
(Fleige et al. 2018). It was shown that ectopic expression of CCL21 into the thymus, pancreas and liver induced TLO formation within these tissues, strongly suggesting that
CCR7 has a role to play in their development (Grant et al. 2002, Marinkovic 2006).
CCR7’s role here is hard to define. On the one hand, lack of CCR7 leads to spontaneous
252 lymphoid neogenesis (Kocks et al. 2007), indicating that CCR7 is not needed for this process, and on the other hand, induction of CCL21 leads to TLO formation (Grant et al.
2002), insinuating that it is needed for this process.
4.2.4 Homing to the Lymph Nodes
Naïve T cells enter the lymph node through the high endothelial venules (HEV), which are located in the cortex of the lymph node. Two important mechanisms control lymphocyte trafficking in the HEVs.
1. The adhesion of lymphocytes to the endothelial wall
2. The transmigration of the lymphocyte through the endothelial wall and into the
surrounding tissue.
253
Figure 4-2: Process of cell homing to the lymph node, as obtained from (Förster et al. 2008).
CCR7 is largely implicated in this process. First, the process of tethering and rolling begins as CD62L, a lymphocyte L-selectin binds its ligands, peripheral node addressins
(PNAd), present on the HEVs. This results in a transient attachment of T lymphocytes to the endothelial wall, and because of the force of the blood flow, a rolling motion of T cells is observed along the HEV. Next, these rolling T cells, which express CCR7, bind
CCL21, which is immobilised on glycosaminoglycans (GAG) on the surface of HEVs
(Ley et al. 2007). These signals, lead to conformational changes in integrins, present on the surface of lymphocytes, enabling the firm adhesion of the lymphocyte to intracellular adhesion molecule 1 (ICAM1) or ICAM2, present on the endothelial cell surface (Ley
1996). Finally, the T cells perform a lateral locomotion and transmigrate through the endothelial cell surface of the HEV (Ley et al. 2007). This process is also mediated in part by CCR7, as CCR7 ligands are presented on the luminal surfaces of the HEVs.
254
This homing behaviour applies to naïve T cells, regulatory T cells (Tregs) and central memory T cells (TCM). TCM express CCR7 and L-selectin and continuously re-circulate through the lymph nodes, where they respond to secondary antigen encounter and rapidly give rise to effector and memory T cells (Förster et al. 2008). Effector T cells express different surface molecules which enable them to translocate to peripheral sites.
4.2.5 T-Cell Development
T cells are pivotal in the control of an appropriate adaptive immune response. It is therefore unsurprising that CCR7 is expressed at various stages of thymocyte development. CCR7 is needed during the many different stages of thymocyte development, to enable thymocytes to migrate to different areas of the thymus, in order to progress to the next stage of development. It is also required for the process of positive and negative selection, which occurs in the thymus, in order to rid the body of self- reactive T cells (Davalos-Misslitz et al. 2007).
Lymphocyte precursors develop in the bone marrow from haematopoietic stem cells
(Famili et al. 2017). Precursors which are destined to become thymocytes, enter the thymus, where they will mature. They express low levels of CCR7, enabling them to migrate across the Cortico Medullary Junction (CMJ) and enter the thymus (Comerford et al. 2013). When progenitor cells first arrive at the thymus, they lack most of the surface receptors required for functionally mature T cells, and their receptor genes have not yet undergone rearrangement. This maturation process begins in the thymus, where the stromal cells provide the appropriate microenvironment to enable differentiation to occur
(Bunting et al. 2011). When they first arrive here, they undergo an initial expansion and
255 differentiation phase and exhibit some T cell specific receptors. They are now known as double negative (DN) cells.
DN1 cells express neither CD4 nor CD8 receptors, common of fully differentiated αβ T cells (Misslitz et al. 2004). During this phase of differentiation, many changes and recombination events occur in order to mature and gain a unique T cell receptor, which will recognise a specific antigen. DN1 cells migrate from the medulla, outward toward the cortex where they are termed DN2 cells. Rearrangement of the T cell receptor β chain begins at this stage Rearrangement continues and is most prominent in the DN3 stage of development when the developing thymocytes move outwards toward the outer cortex and subcapsular zone, mediated by expression of CCR7. Assembly of a complete pre-T cell receptor takes place at this stage, and cells that fail to make successful β chain rearrangements will die (Charles A Janeway et al. 2001). Upon assembly of a successful
β chain, the cells then progress to the DN4 stage of development. Here the CD4 vs CD8 lineage decision occurs, and the DN4 stage thymocytes migrate from the subcapsular zone towards the cortex (Charles A Janeway et al. 2001, Kurobe et al. 2006). They then form double positive cells (DP) where they undergo rearrangements of the α chain locus and express both CD4 and CD8 receptors. Once in the cortex, these double positive (DP) cells undergo positive selection (Kurd and Robey 2016). The purpose of positive selection is to ensure that the TCR can bind to MHC class I (CD8+ T cells) and class II (CD4+ T cells) molecules. Those which recognise and bind self-peptide: self-MHC complexes will survive and proliferate. They then loose expression of either CD4 or CD8 receptors, becoming single positive (SP) cells. Cells which are incapable to recognising and binding to MHC molecules will die. SP cells re-express the CCR7 receptor in order to migrate back towards the medulla (Kwan and Killeen 2004) where both CCL21 and CCL19 are expressed by the medullary thymic epithelial cells (mTECs) (Ueno et al. 2004). Negative
256 selection of SP T cells takes place here (Klein et al. 2014). Negative selection occurs in order to rid the body of T cells which recognise self-antigens, presented by the MHC molecules. During positive selection, T cells should bind the self-peptide: self-MHC complex for a short period of time. Developing T cells which bind the self-peptide: self-
MHC molecules too strongly, will, therefore, have a TCR which recognises self-antigens.
Negative selection takes care of these dangerous T cells and they undergo apoptosis and are eradicated from the body. SP cells which survive negative selection express CCR7 and emigrate from the thymus into the peripheral circulation (Nitta et al. 2009). Positive and negative selection of thymocytes is important for the generation of self-tolerance in an organism, and in helping to prevent autoimmune diseases.
It is imperative that T cell precursors developing in the thymus, move to different parts of the thymus in order to differentiate. This is because different areas of the thymus provide the specific microenvironments and chemokine cues needed for a specific stage of T cell development. CCR7 plays a somewhat nonredundant role in enabling the immature thymocytes to migrate to different areas of the thymus in order to progress to the next stage of differentiation.
257
Figure 4-3: The role of CCR7 in the thymus, as obtained from (Comerford et al.
2013).
(1) Thymocyte precursors expressing low levels of CCR7 enter the thymus by crossing the CMJ,
CCR9 and CXCR4 may also be involved in this process. (2) Double negative 1 (DN1) and DN2 cells use the CCR7 receptor to migrate to the subcapsular zone of the thymus. DN3 and DN4 cells lose their CCR7 expression, (3) but is re-expressed at the double positive (DP) stage of positive selection. Single positive (SP) cells migrate back towards the medulla, an area rich in CCL19 and CCL21 expression. Negative selection takes place here before mature naïve T cells migrate out of the thymus into the peripheral circulation.
Plt/plt mice (which do not express CCL19 and CCL21, ligands for CCR7) or Ccr7-/- mice have an accumulation of DN2 cells at the CMJ and exhibit a decreased number of DN3
258 cells. CCR7 deficient mice display impaired negative selection of thymocytes (Davalos-
Misslitz et al. 2007). It was also found that as SP thymocytes in Ccr7-/- mice cannot undergo negative selection. These mice have an increased level of spontaneous autoimmunity (Kurobe et al. 2006).
It is also interesting that Ccr7-/- mice and plt/plt mice have a disturbed thymic architecture
(Misslitz et al. 2004). The authors carried out histological analysis of CCR7 deficient and plt/plt mice and found that the medulla regions in mutant mice was much smaller and more numerous than in wildtype mice. They noted that medullary areas were also misplaced to the outer rim of the thymus (Misslitz et al. 2004). CCR7 deficient mice had reduced thymic cellularity compared to age matched controls. They had increased numbers of DN1 and DN2 cells but diminished proportions of DN3 and DN4 cells, implying that transition from DN1 to DN2 stage may be delayed. CCR7 deficient animals seem to still be capable of generating normal T cells, but at significantly reduced numbers
(Misslitz et al. 2004). Adding to this information, the architecture of secondary lymphoid organs (SLO) is also severely hampered in Ccr7-/- mice (Förster et al. 1999).
In contrast to T cells, CCR7 deficiency does not appear to affect B cell development
(Förster et al. 1999). B cell development is instead controlled by a different chemokine receptor; CXCR5 (Pereira et al. 2010).
259
4.2.6 Regulatory T Cells
CCR7 deficiency has a profound effect on the function of regulatory T cells (Schneider et al. 2007). The authors show, through use of a contact hypersensitivity assay, that there is an exacerbated immune response in CCR7 KO animals (Schneider et al. 2007). This is thought to be mediated in part, through a lack of regulatory T (Treg) cell function, as this response is mitigated by the adoptive transfer of wildtype FoxP3+ CD4+CD25+ Tregs.
Regulatory T cells in CCR7 KO mice exhibited a skewed tissue distribution, with few migrating to the lymph nodes but significantly increased numbers of FOXP3+ Tregs cells in the spleen, an opposite distribution to wildtype mice (Schneider et al. 2007). In addition, they also display a disrupted localisation in lymph nodes of CCR7 KO mice.
The authors also demonstrate a disorganised thymic distribution of Tregs, explaining that they exist in both the cortex and medulla. In wild type mice, these cells only exist in the medulla of the thymus (Schneider et al. 2007).
While it has been shown that the suppressive activity of CCR7 deficient regulatory T cells is equipotent to that of wildtype Tregs in vitro (Kocks et al. 2007, Menning et al. 2007), the in vivo model presents a different story. Tregs in CCR7 KO mice were unable to reduce numbers of helper T cells in these animals, upon inflammatory stimulation (Schneider et al. 2007), leading to dramatic CD4+ lymphocytic infiltrates in numerous organs (Förster et al. 2008). Regulatory T cells in wild type animals suppress antigen-induced proliferation of TH cells. As a result, these CCR7 KO animals displayed enhanced susceptibility to inflammatory bowel disease (IBD) (Schneider et al. 2007), an autoimmune disease.
260
4.2.7 Dendritic Cells
Dendritic cells (DC), which function as antigen-presenting cells (APCs) exist in a naïve state in the periphery. There are two types of dendritic cells; plasmacytoid dendritic cells
(pDCs) and myeloid dendritic cells (mDCs). Both types constantly sample their environment and acquire antigen via receptor-mediated endocytosis, phagocytosis and macropinocytosis (Sanchez-Sanchez et al. 2006). Upon acquisition of antigen from a foreign invading particle, DCs begin to differentiate. They upregulate the expression of
MHC and costimulatory molecules along with CCR7 and rapidly enter the lymphatics. pDCs home to the lymph nodes via high endothelial venules (HEV). mDCs enter via the afferent lymphatics (Seth et al. 2011). Within the lymph nodes, DCs migrate to the T cell zone, where they interact with numerous T cells. T cells displaying the correct T cell receptor for the antigen displayed on the dendritic cell, will undergo clonal expansion, differentiate and migrate out to the peripheral organs in order to halt infection of the invading pathogen.
It was found that CCR7, implicates the cytoarchitecture of dendritic cells, the migratory speed, and chemotaxis in order to bring dendritic cells towards high concentrations of chemokine compounds and home efficiently towards the lymph node. These events occur through diverse cellular mechanisms, downstream of the activated G protein coupled receptor.
In particular, it has been observed that CCR7 regulates the cytoarchitecture of DCs, by regulation of the actin cytoskeleton (Müller et al. 2001, Riol-Blanco et al. 2005), as dendritic protrusions were observed upon stimulation of the CCR7 receptor (Yanagawa and Onoé 2002). This aids in the DCs migration toward the lymph node, but also in
“capturing” T cells whilst in the lymph node, by increasing the surface area of the DC
261
(Yanagawa and Onoé 2002, Sanchez-Sanchez et al. 2006). CCR7’s impact on the organisation of the actin cytoskeleton of the cell is mediated through its actions on the small GTPase; Rho (Riol-Blanco et al. 2005).
Some groups have also demonstrated that expression of CCR7 may protect DCs from apoptosis (Sánchez-Sánchez et al. 2004, Riol-Blanco et al. 2005). DCs normally die in the lymph nodes, and so they have a limited time span upon which to encounter the appropriate T cell and induce an effective immune response. Therefore, preventing apoptosis increases the amount of time they have to encounter and activate the specific T cell, harbouring the correct TCR (Riol-Blanco et al. 2005, Sanchez-Sanchez et al. 2006).
This survival function of CCR7 is thought to be mediated through the PI3K pathway (Kim et al. 2005, Riol-Blanco et al. 2005).
Expression of CCR7 on DCs is also shown to regulate the migratory speed of dendritic cells (Sanchez-Sanchez et al. 2006). By inducing efficient binding between the activated dendritic cell and its substrate, the endothelial wall of the lymph node, a rolling and tethering motion occurs, enabling quick migration towards the lymph node (Förster et al.
2008). Riol-Blanco and colleagues concluded that the migratory speed of dendritic cells is controlled via the GTPase Rho and Pyk2, as the migratory speed of dendritic cells was drastically reduced in cells subjected to a Rho inhibitor or a dominant-negative Pyk2 construct which was overexpressed in transfected cells. Cells still moved in the right direction (i.e. towards higher concentrations of the chemokine) albeit at a much slower rate. Hence, Rho and Pyk2 have no effect on chemotaxis. They solely influence the speed at which migration takes place (Riol-Blanco et al. 2005).
The cell translocates towards high concentration gradients of CCL19 and CCL21, two ligands for CCR7. The regulation of chemotaxis by CCR7 appears to be mediated through
262 members of the mitogen activated protein kinase (MAPK) pathway, namely ERK1/2,
JNK and p38 (Sanchez-Sanchez et al. 2006, Liu, Safdar, et al. 2014). By method of inhibition experiments, Riol-Blanco and colleagues determined that ERK1/2 and p38 independently control JNK signalling to regulate chemotaxis in dendritic cells stimulated with CCL19 and CCL21 (Riol-Blanco et al. 2005). After stimulation with CCL19 and
CCL21, they observed a consistent phosphorylation of ERK1/2, JNK and p38. Using the
PTX inhibitor, which completely uncouples Gi protein signalling, these MAP kinases no longer exhibited phosphorylation. Thus the authors concluded that migratory speed is mediated by Gi signalling through its downstream effector molecules, members of the
MAPK family (Riol-Blanco et al. 2005). Interestingly, they also found that ERK1/2 and p38 independently regulate JNK phosphorylation.
Figure 4-4: Downstream CCR7 signalling in Dendritic Cells.
263
Indeed it was also observed that a small proportion of naïve pDCs also express CCR7
(Riol-Blanco et al. 2005, Seth et al. 2011). Seth and colleagues demonstrated the expression of CCR7 on naïve pDCs and that these cells migrate to the lymph node even under non-inflammatory conditions. They used knockout mouse models to show that there is a reduced number of pDCs in the lymph node under steady state as well as under inflammatory conditions, in comparison to wildtype mice (Seth et al. 2011). In fact, Ohl et al. found that this population of DCs (CD11c+ MHC class IIhi DCs) was completely absent in CCR7 knockout mice (Ohl et al. 2004). Naïve pDCs are constantly sampling their environment, even in the absence of an infection and continually migrating to the lymph node. These dendritic cells are important for the presentation of self-antigens to T cells in order to establish peripheral tolerance and prevent autoimmune diseases (Worbs and Förster 2007).
4.2.8 CCR7 in Central and Peripheral Tolerance
As mentioned previously, CCR7 deficient and plt/plt mice are prone to developing autoimmune phenotypes (Kurobe et al. 2006, Davalos-Misslitz et al. 2007, Worbs and
Förster 2007). This is largely thought to be due to an inability of these animals to distinguish self from non-self. CCR7 is detrimental in guiding developing thymocytes to specific areas of the thymus is order to proceed towards the next stage of differentiation.
Central tolerance is defined as the deletion of self-reactive B and T lymphocytes during their development in the bone marrow and thymus, respectively. Many studies found that
CCR7 and CCL21Ser deficient mice have a similar phenotype to that of Aire deficient mice. Aire is a nuclear protein, expressed by mTECs, which is essential for the organ specific self-antigen presentation by mTECs (Zuklys et al. 2000). The expression of Aire
264 is essential for establishing organ specific self-tolerance, and mice missing this protein are prone to autoimmune polyendocrinopathy candidiasis ectodermal dystrophy
(APECED) (Nagamine et al. 1997, Anderson et al. 2002). Studies have shown that developing thymocytes in CCL21 deficient animals cannot migrate to the medulla of the thymus, an area where wild type thymocytes are exposed to Aire, undergo negative selection and develop self-tolerance (Kurobe et al. 2006, Kozai et al. 2017). Instead,
CCL21 deficient thymocytes remain in the cortex and are released into the circulation from there (Kurobe et al. 2006). Therefore they never undergo the process of negative selection and so are spontaneously self-reactive. These animals also develop autoimmune dacryoadenitis, and exhibit lymphocytic infiltrates into the lacrimal and salivary glands
(Kurobe et al. 2006, Kozai et al. 2017, p. 21).
Likewise, peripheral tolerance takes place in the secondary lymphoid tissues, usually the lymphoid organs and spleen and functions to prevent the activation of self-reactive lymphocytes which escaped positive and negative deletion in the thymus. Not all self- antigens exist in the thymus. Many T cells only encounter their self-antigens after they leave the thymus. It is important that these lymphocytes are deleted in order to prevent autoimmune diseases. Dendritic cells are important mediators of peripheral tolerance
(Sanchez-Sanchez et al. 2006). The consensus is that DCs exist in an immature state in the epithelium and interstitial space of organs where they are constantly sampling their environment by antigen uptake through phagocytosis, endocytosis and macropinocytosis
(Sanchez-Sanchez et al. 2006). Once they encounter an invading foreign entity, they mature rapidly, express MHC and co-stimulatory molecules and CCR7 and immediately relocate to the lymph nodes, where they alert the T cells (Sallusto and Lanzavecchia
2002). However, it is also important to be aware that CCR7 is also expressed on semi- mature dendritic cells (Ohl et al. 2004, Jang et al. 2006). Such DCs carrying self-antigens
265 are also transported to the lymph nodes where they come into contact with self-reactive
T cells. T cells which respond to signals from immature DCs can be eliminated in the thymus, an important step in the prevention of autoimmunity.
This has been described in numerous studies where the authors show that CCR7 deficient mice lack contact sensitivity (Förster et al. 1999), delayed type hypersensitivity (Worbs et al. 2006) and have a severely delayed response to antigen. When wild type mice are fed using OVA, a non-responsive state towards OVA is observed upon re-exposure to this antigen. A clever study was performed by Worbs et al. 2006, where they attempted to induce oral tolerance in CCR7 deficient mice by subjecting them to OVA. They demonstrate an impaired migration of OVA presenting Dendritic cells towards the lymph node, and as a result, these mice elicit an immune response upon readministration to
OVA, a response which is absent in wildtype mice (Worbs et al. 2006). A similar study was carried out by Hintzen et al. 2006, where it was demonstrated that only wildtype mice enter a state of non-responsiveness following OVA administration via the respiratory route, unlike CCR7 deficient mice who exhibit a pronounced response to OVA administration (Hintzen et al. 2006). This data suggests that CCR7 is of detrimental importance to the induction of peripheral tolerance and the prevention of autoimmune diseases through its transportation of semi-mature dendritic cells residing at mucosal sites and constantly sampling self-antigens, to the lymph nodes.
4.2.9 Autoimmunity
Given the lack of central and peripheral tolerance in CCR7 deficient and plt/plt mice, it is perhaps unsurprising then that these animals develop spontaneous autoimmunity. This autoimmunity presented itself in many ways. Firstly, mutant animals displayed severely
266 reduced numbers of lymphocytes in their lymphoid organs (Worbs and Förster 2007) but increased lymphocytes in the periphery. Second, these animals also display a disturbed lymphoid architecture. Lymph nodes in CCR7-/- and plt/plt animals lack characteristic segregation of B cell follicles and T cell zones, which is observed in wildtype animals.
Third, it is also well documented that these animals display a generalised organ autoimmunity (Anderson et al. 2002, Kurobe et al. 2006, Davalos‐Misslitz et al. 2007).
This is characterised by high titres of circulating autoantibodies, IgG depositions on renal glomeruli (Förster et al. 2008) leading to chronic autoimmune renal disease and the occurrence of lymphocytic infiltrations in several organs (Kurobe et al. 2006, Davalos‐
Misslitz et al. 2007). Davalos-Misslitz claim that large numbers of lymphocytes were observed in the salivary and lachrymal glands (Kurobe et al. 2006, Davalos‐Misslitz et al. 2007) . It is known that T and B cell infiltrates in the exocrine glands is a characteristic of Sjogren syndrome, an autoimmune disease commonly resulting in the destruction of these glands (Tucci et al. 2005). Kozai et al. generated mice deficient in CCL21Ser chemokine. The authors found that these animals were highly susceptible to autoimmune dacryoadenitis, a disorder which also results in lymphocytic infiltrates into the lacrimal and salivary glands (Kozai et al. 2017).
Plt/plt mice exhibit delayed but ultimately enhanced T cell responses (Gunn et al. 1999,
Mori et al. 2001). The T cell responses within these mice persist for long periods of time.
This insinuated a failure of these mice to effectively clear antigen-specific T cells after their activation. Interestingly, the authors discovered that there was a maintenance of antigen reactive T cells in plt/plt mice (Mori et al. 2001, Yasuda et al. 2007). They found that upon stimulation with OVA, wt mice maintained the T cell response until day 16, and rapidly decreased thereafter. However, in plt/plt mice, CD4+ T cell proliferation was still observed 16 months after immunisation (Yasuda et al. 2007). They also found that
267 the total LN CD4+ T cell number was much lower in plt/plt mice than in wt mice. They found a decreased apoptosis of CD4+ T cells in plt/plt mice compared to wildtype mice
(Yasuda et al. 2007). More specifically, during the clonal expansion phase, there was little difference in the rate of apoptosis between wildtype and plt/plt mice. However, during the clonal contraction phase, there was an increase in apoptosis observed in wildtype mice, that did not occur in plt/plt mice (Yasuda et al. 2007). This aberrant apoptosis of
CD4+ T cells in response to an inflammatory stimulus results in a prolonged inflammatory response in these mice. These striking results have been replicated in other studies, namely; (Mori et al. 2001, Grinnan et al. 2006) who also subjected plt/plt mice to OVA stimulation to measure immune responses.
Also of note, is the generation of tertiary lymphoid structures in the organs of CCR7 mutant mice. Tertiary lymphoid organs are commonly observed in other autoimmune disorders such as Sjogren syndrome, autoimmune thyroiditis, rheumatoid arthritis and multiple sclerosis. The tertiary structures are commonly found in the gastric mucosa and submucosa (Davalos‐Misslitz et al. 2007) and are highly organised organoids displaying distinct T and B cell areas. Of note, bronchus-associated lymphoid tissue (BALT) has also been reported in CCR7 deficient animals (Fleige et al. 2018). BALT, similar to other tertiary lymphoid organs is highly organised, displaying B cell follicles complete with germinal centres and T cell areas and high endothelial venules. BALT spontaneously generates in CCR7 deficient mice (Kocks et al. 2007), and this was demonstrated that a defect in Treg cell homing was the cause of BALT formation in these mice. Impairment of DC egress from the lung has also been demonstrated to contribute to BALT formation
(Fleige et al. 2018). It is also important to note that BALT can be induced in CCR7-/- mice by administration of viruses such as influenza virus, herpes virus 68 and modified vaccinia virus Ankara (MVA), (Moyron-Quiroz et al. 2004, Kocks et al. 2007, Halle et
268 al. 2009) and simply by the addition of LPS in new born CCR7 deficient mice (Rangel-
Moreno et al. 2011).
4.2.10 Summary
CCR7 is a G protein-coupled receptor which binds 2 chemokines: CCL19 and CCL21.
It’s main functions include the homing of naïve T cells and B cells and activated dendritic cells to the lymph node, where these cells may interact in order to mount an efficient immune response. The microarchitecture of the lymph node and other secondary lymphoid organs is very important to enable these interactions to take place. Mice studies have been crucial in our understanding of the CCR7 protein, and two mouse models have been described: CCR7-/- knockout mice; and plt/plt mice. Both mouse models display a disrupted SLO architecture, have reduced numbers of lymphocytes in the lymphoid organs, mount ineffective T cell responses, are highly susceptible to viral infections and are unable to establish self-tolerance. As a result, these animals are prone to autoimmune diseases, such as autoimmune dacryoadenitis.
269
4.3 Family Tree of Family B
The index case in this family is B-II-2. At 23 years old, she presented with systemic inflammation and macrophage activation syndrome. The younger sister (B-II-3) died at the age of 12 while presenting with a persistent cytomegalovirus (CMV) infection. This sibling had a working diagnosis of sJIA, and had also developed a macrophage activation syndrome triggered (it was proposed) by CMV.
Figure 4-5: Family Tree of Family B
This family tree represents a consanguineous union, wherein parents are first cousins. Affected family members are shaded in black. Deceased relatives are depicted by a black diagonal line through the centre.
270
4.4 Clinical presentation of affected individuals
B-II-3 presented age 7 years old with fevers, erythematous skin rashes, arthralgia and hepatosplenomegaly. Extensive investigations for an infectious cause of her initial presentation showed low titre EBV positive PCR at 6516 copies/ml of blood while CMV, adenovirus PCRs were negative at the time; blood cultures were negative, and no other infection was identified (see table Table 4-1). She had elevated ferritin at 1457 IU/ml (RR
< 200 IU/ml), normal coagulation screen and elevated LDH at 2560 IU/ml (RR 380-770
IU/ml), raised ESR (200 mm/h; RR< 20 mm/H) and CRP (>270 mg/L; RR< 5 mg/L); cytopaenias (platelet count 63x109/L; RR150-450 and white cell count 1.34 x109/L; RR
4.5-13.5). CD107a degranulation assay and perforin expression on both NK cells and T cells were normal. Bone marrow aspirate showed reactive marrow, mild haemophagocytosis, and no evidence of malignant infiltrate. Histology of skin biopsy suggested leucocytoclastic vasculitis, negative for immune complexes and IgA on immunofluorescence. She was considered to have “atypical” systemic juvenile idiopathic arthritis and was started on corticosteroids and methotrexate. In view of recurrent flares of her systemic symptoms with recurrent fevers and rashes, often triggered by viral illnesses, she was started on anakinra within 12 months of diagnosis (dose titred up from
2 to 4 mg/kg/day) and corticosteroids were continued at relatively high dose
(prednisolone 1.5mg to 2mg/kg/day). Of note, EBV viral loads remained persistently positive over the years (median 6794, range 1341-608,000 copies/ml).
Age 11 years old she was readmitted with ongoing fevers and rising ESR and CRP, ferritin and acute myocardial inflammation. Ciclosporin was added to her therapy for suspected macrophage activation syndrome (dose 5 mg/kg/day). Blood cultures undertaken at the time were positive for Listeria monocytogenes, and treatment with
271 ampicillin and gentamycin started. Acute phase reactants remained elevated; however, she developed ileac perforation requiring surgical intervention and then deteriorated further cardiovascular. She died on intensive care; histological examination of gastrointestinal biopsy showed from CMV colitis (see histology, Figure 4-6 (E)).
Her sister B-II-2 developed similar symptoms at a later age when she was 20 years old.
Her initial presentation with fevers, skin rashes and arthritis was triggered by a presumed infectious episode of pharyngitis, but no organism was identified. She was treated with prednisolone (2 mg/kg/day) and methotrexate (15 mg/m2/sc weekly). As she was unable to tolerate methotrexate, she was started on tocilizumab but had an anaphylactic reaction to the fourth dose precluding her from having any further doses. Anakinra (3-4 mg/kg/day sc) was started, but she had what was described as a localised “skin allergic reaction”, so this was stopped 6 months after starting. She is currently on ciclosporin (3 mg/kg/day) and high dose daily prednisolone (1 mg/kg/day), with improvement in significant flares of her symptoms, but has mild ongoing pericardial effusion, with normal myocardial function, and elevated ESR, CRP< SAA. All other immunological and microbiological investigations are summarised in Table 4-1.
B-II-1 remains currently asymptomatic, age 22 years old. As B-II-1 is a monozygotic twin of B-II-2, I assumed that she is presymptomatic and may develop symptoms later in life.
In this regard, I treated her as an affected individual throughout this thesis. DNA from this individual was also sent for whole exome sequencing, and her exome was included when filtering variants. However, perhaps this was not necessary as the same candidate gene would have been obtained from sequencing of the two affected individuals (B-II-2 and B-II-3). It would have been equally appropriate to sequence only affected individuals and then confirm the presence of pathogenic variant in B-II-1 by sanger sequencing.
272
Table 4-1: Routine clinical laboratory investigations for B-II-1, B-II-2 and B-II-3
1 Autoantibodies tested: antinuclear antibodies, anti-neutrophil cytoplasm antibodies, rheumatoid factor, anti-tissue transglutaminase antibodies, anti-thyroid peroxidase antibodies, anti myelin oligodendrocyte antibodies, anti-yo, anti-hu, anti-ri antibodies, NMDAR antibodies, rheumatoid factor antibodies, coeliac screen antibodies, b2 glycoprotein and anticardiolipin antibodies.
Laboratory Patient B-II-3 (Reference Patient B-II- Patient B-II-2
investigations range) 1 (Reference (Reference
range) range)
Autoantibodies Absent[1] Absent[1] Absent
persistent >3
months
Haemoglobin 11.1g/L [2] (11.5-15.5) 138g/L 137 g/L
Platelet count 63x109/L [3] (150-450) 278x109/L 323x109/L
White blood cell 1.34 x109/L [4] (14.5-13.5) 15.3x109/L 9.6 x109/L
count
Ferritin 725 IU/L (0-150) 40 ug/L 49951ug/L
Immunoglobulin 12.90 G/L ( 4.9 - 16.1) 8.8 G/L 12.4 G/L
G
273
Immunoglobulin 0.97 G/L ( 0.4 - 2.0 ) 1.0 G/L 1.2g G/L
A
Immunoglobulin 1.42 G/L ( 0.5 - 2.0) 2.3 G/L 0.9g G/L
M
Perforin Normal Normal Normal
expression
CD107 Normal Normal Normal
degranulation
assay
Mycoplasma Negative Negative Negative
antibodies
Quantiferon Negative Negative
Negative
Nitroblue Normal Not done Not done
tetrazolium test
EBV PCR Positive Negative 12000copies/ml
CMV PCR Positive Negative not detected
Complement C3 1.85H G/L ( 0.75 - 1.65 ) 1.22 G/L(0.75 1.3 G/L(0.75 -
- 1.65 ) 1.65 )
Complement C4 0.28 g/L (0.14-0.54) 0.23 G/L 0.24 G/L (0.14-
(0.14-0.54) 0.54)
Liver enzymes ALT 15 U/L (10 – 25) 16 U/L (10 – 47 U/L (10 –
ALP 96 U/L (10 – 25) 25) 25)
71 U/L (10 – 161 U/L (10 –
25) 25)
274
LDH 2454 (380-770) IU/l 168 (380- 350(380-770)
770) IU/l IU/l
Serum amyloid 549 (0-9) U/L 8.1 U/L 578 U/L
A
CRP 210 (<10) mg/L 10 mg/L 118mg/L
ESR 110 (0-10) mm/H 5 mm/H 75 mm/H
275
Figure 4-6: Photomicrographs demonstrating a focal area of mucosal ulceration with associated underlying inflammation.
(A)H&E stain at x20 magnification, demonstrating mucosal ulceration and disruption to lining of the gut. (B) H&E stain at x40 magnification. (C) H&E stain at x200 magnification demonstrating numerous, scattered cells with intranuclear inclusions, indicative of CMV. (D) Immunohistochemical staining for Cytomegalovirus confirms positivity in the cells (CMV immunostain with Haematoxylin counterstain)
276
4.5 Methods
Specific methods used to determine the genetic cause of the disease in this family and cell culture analyses in order to investigate that cause further are outlined here. General methods used in this chapter are outlined in Chapter 2.
4.5.1 T Cell Stimulation Assay
Using a 48 well plate (Grenier, 677180), wells were coated with 2μg/ml anti-CD3 (Merck,
OKT3) and 2μg/ml anti-CD28 (BD Pharmingen, 556620) diluted in PBS. This was
0 incubated overnight at 37 C and 5% CO2. The following day this anti-CD3/anti-CD28 mixture was removed, and each of the wells were washed with PBS. PBMCs were thawed, using the protocol as described in section 2.11.5. They were then counted using a haemocytometer and were resuspended in RPMI medium at a concentration of 1x106 cells/ml. 300μl of this cell suspension was added to the pre-coated anti-CD3/anti-CD28.
0 This was incubated for 72 hours at 37 C and 5% CO2.
4.5.2 Annexin V Apoptosis Assay
Once a cell has decided it is undergoing programmed cell death, a protein called phosphatidlyserine (PS) is translocated from the inner side of the plasma membrane to the outside. Annexin V, a calcium dependent phospholipid binding protein, binds PS.
Annexin V fluorescence is then detected via flow cytometry. Therefore, the more
Annexin V is present on cells, the more these cells are subject to apoptosis.
277
Following the T cell stimulation assay, cells were removed for the plate and transferred to labelled Eppendorf's. Eppendorf's were spun at 5000rpm for 3 minutes. Cell pellets were resuspended in 50μL Annexin binding buffer (Abcam, ab14085), 1μL Annexin V
(FITC) antibody (Abcam, ab14085), and 1μL CD3 (PE) antibody. Eppendorf's were covered in tinfoil and incubated at room temperature for 10 minutes. Cells were analysed by
FACS.
4.5.3 FACS Gating Strategy for Annexin V Assay
For Annexin V staining, cells were first gated to collect lymphocytes, next the CD3+
(PE+) population was isolated, and from this Annexin V (FITC+) cells were collected.
278
Figure 4-6: Flow cytometry gating strategy for Annexin V staining.
(A) Forward (x-axis) and side (y-axis) scatter properties were used to gate the lymphocyte population. (B) Gating on the PE+ population was achieved using the FL2 channel and forward scatter properties of the cells. (C) The Annexin V population was isolated from the CD3+ cells using the FL1 channel.
4.5.4 Ki67 Cell Cycle Analysis Assay
The Ki67 protein is a marker for cellular proliferation (Sobecki et al. n.d.). This protein is abundant when the cell is actively dividing and is absent during interphase. Therefore if the cell is undergoing proliferation and is in any stage of the cell cycle (G1, S, G2 and mitosis), Ki67 will be present but will be undetectable in sleeping cells (Bruno and
Darzynkiewicz 1992).
Following the T cell stimulation assay (see section 4.5.1), cells were transferred to labelled Eppendorf and centrifuged at 5000rpm for 3 minutes. The supernatant was discarded, and 200μl of staining buffer added to resuspend the cell pellet. This was again spun at 5000rpm for 3 minutes. 50μl of staining buffer and 1μl of CD3+ (PE) antibody was added to each cell pellet. Each Eppendorf was covered in tinfoil and incubated for
15 minutes at room temperature. 150μl of staining buffer was again added to each
Eppendorf, and this was centrifuged at 5000rpm for 3 minutes, and the supernatant discarded. 200μl of Cytoperm solution (Permeabilisation solution, BD Biosciences,
554722) was added to each Eppendorf, and these were incubated on ice for 30 minutes.
Pellets were washed twice with 1x perm wash (BD Biosciences, 554723), and the supernatant discarded. To each of the cell pellets, 50μl perm wash and 1μl Ki67 (Alexa
Fluor 647) antibody was added and incubated on ice for 30 minutes. Cells were washed
279 in 1x perm wash and analysed by flow cytometry. Alexa Fluor has a maximum emission of 668nm. It can be excited between 633-635nm (red laser).
4.5.5 CFSE Cell Cycle Analysis Assay
CFSE (carboxy-fluorescein-succinimidyl ester) staining is another mechanism by which cellular proliferation can be measured. CFSE is cell-permeable, and covalently couples to intracellular molecules, such as lysine residues (Parish 1999). It remains within the cell for very long periods of time. When that particular cell divides, the daughter cells will have approximately half the concentration of the CFSE dye, intracellularly (Lyons and
Parish 1994). Using non-toxic concentrations of CFSE dye, approximately 7-8 cell divisions may be identified by the CFSE marker, before the dye will become too low to be detected by flow cytometry (Lyons and Parish 1994). Therefore cells with low concentrations of the CFSE dye, have undergone multiple cell divisions and are highly proliferative, whereas those with high concentrations of the CFSE dye are resting.
In order to stimulate T cell activation, the anti-CD3/anti-CD28 mix was made up and added to wells of a 48 well plate, as described in section 4.5.1. 24 hours later, this mixture was removed, and wells were washed with PBS. Pre-labelled CFSE cells were then added to wells which contained the anti-CD3/anti-CD28 stimulation and incubated for 3 days at
o 37 C, 5% CO2.
In order to pre-label cells with CFSE dye, the staining solution was first made up. To the
CFSE staining solution, 18μl of DMSO was added. This stock solution was diluted in
20ml warmed PBS, to make a final Cell Trace CFSE staining solution concentration of
5μM. Cells were thawed as per the protocol described in section 2.11.5. Cells were counted and adjusted to a concentration to 1x106 cells/ml. 1ml of cells, per sample, were
280 centrifuged at 300xg for 5 minutes, and the supernatant discarded. Cells are resuspended in 1ml 5μM Cell Trace CFSE staining solution and incubated for 20 minutes at 370C.
Cells are then spun at 300xg for 5 minutes. 1 ml RPMI-1640 medium was added to resuspend the cell pellet and 300μl of resuspended cells were added to the anti-CD3/anti-
CD28 wells of a 48 well plate and incubated for 3 days. 300μl of resuspended cells were also added to unstimulated wells, as a control, and incubated for 3 days. At which point, cells were analysed by flow cytometry. The CFSE dye has an emission spectrum of between 492-517nm, and so can be analysed using the same laser as per Alexa Fluor 488.
4.5.6 FACS Gating Strategy for Ki67 and CFSE Assay
For both Ki67 and CFSE assays, cells were first gated to collect live cells. Next, the CD3+ population was isolated, and from that, the CFSE (emission max = 517nm) and Ki67
(FITC+) populations were assessed.
281
282
282
Figure 4-7: Flow cytometry gating strategy to assess the Ki67+ and CFSE+ cell populations on CD3+/CD28+ stimulated and baseline PBMC.
(A) Forward (x-axis) and side (y-axis) scatter properties were used to gate the live cell population. (B) Gating on the CD3+ population was achieved using the FL2 channel (y axis) and forward scatter (x axis) properties. (C) CD3+ Ki67+ (FITC) cells were distinguished by FL1 channel. (D) CD3+ CFSE+ (emission max = 517nm) cells were distinguished by FL1 channel.
4.5.7 Migration (Transwell) Assay
Cells were thawed using the protocol as outlined in section 2.11.5. They were seeded at
6 o a concentration of 1x10 cells/ml in a 6 well plate and incubated at 37 C, 5% CO2 overnight, to enable recovery after freezing.
The following day, cells were counted in order to ensure accurate results, as cells may have died overnight. To this end, wells were scraped using a cell scraper and transferred to a 15 ml falcon tube, and 5 ml RPMI-1640 (serum-free) medium added. Falcon tubes were spun at 1500 rpm for 5 minutes. The supernatant was discarded. The cell pellet was reconstituted in 10 ml, and cells were again counted using a haemocytometer. The falcon tube was spun and RPMI-1640 (serum-free) medium added to the cell pellet, in order to adjust the concentration to 1x106 cells/ml.
A 1μg/ml CCL21 solution was made up using serum-free RPMI-1640 medium. In a 48 well plate, 650μl of this solution was added to the bottom of some wells. To others, 650μl
RPMI-1640 medium (serum-free) was added, as a control. Inserts containing polycarbonate filter of 5μm size were placed on top of the medium. 100μl of cell suspension was added to each insert.
283
The transwell assay was left to incubate for 6 hours (PBMC) or 24 hours (Jurkat cells) at
o 37 C, 5% CO2. At which point, the insert was removed and 200μl aliquot of cells which had migrated through the insert, were collected from the bottom of each well. Cells were stained with anti-CD3 (FITC) and counted using flow cytometry (cytoflex). Cells were acquired for a fixed time period of 60 seconds.
4.5.8 SiRNA CCR7 Knockdown in Jurkat Cell Line
Jurkat cells were maintained in RPMI-1640 + 10%FCS medium. When they had reached near 100% confluency, they were split and reconstituted with Opti-MEM reduced serum transfection medium (cat no. 31985062, Thermo Fisher Scientific) and seeded at a concentration of 0.5x106 cells/ml in a 6 well plate, adding 1ml per well.
9μl of the Lipofectamine RNAiMAX reagent was added to 150μl of Opti-MEM medium.
In a separate Eppendorf, 3μl of CCR7 siRNA (10μM) (Life Technologies Ltd, AM16708) or control scrambled siRNA (10μM) (Santa Cruz Biotechnology, sc-37007) was added to
150μl of Opti-MEM medium. These were vortexed and incubated at room temperature for 10 minutes. These were then mixed and incubated for a further 20 minutes at room temperature. 300μl of this mixture was then added to each well. Cells were incubated with transfection reagents for 48 hours. At which point a TRIzol extraction was carried out
(see section 2.9.1), and RNA quantified and converted into cDNA (see section 2.9.3). A qPCR reaction was then used to assess the expression levels of CCR7 mRNA, in transfected and untransfected Jurkat cells.
284
4.5.9 Immunophenotyping
Five μl of all single fluorochrome antibodies used in the analysis was pipetted to the bottom of a 15ml falcon tube. Blood samples were mixed, by inverting several times.
Total of 100μl of whole blood was added to the bottom of the falcon tube. This was mixed using a vortex and incubated at room temperature, in the dark for 10 minutes. 1ml of
FACS Lyse buffer (BD Biosciences, cat no.349202), was added to the tube, mixed and incubated at room temperature, in the dark for a further 10 minutes. Tubes were then spun using the Hettich EBA 21 centrifuge, spinning at 1000 RCF for 45 seconds. The supernatant was then discarded and the pellet resuspended in 1ml CellWASH (BD
Biosciences, cat no.349524) and the spin repeated. To the pellet, 250μl of CellWASH was added before being run on FACS.
4.5.10 FACS Gating Strategy used for Immunophenotyping
The basic gating strategy for this work is outlined in Figure 4-8, below.
285
A B
C
Figure 4-8: Gating strategy used to identify subsets of T cells for immunophenotyping analysis.
(A) Forward (x-axis) and side (y-axis) scatter properties were used to gate the lymphocyte population. (B) Gating on the CD3+ (Brilliant Violet 510) population was achieved using the
FL1 channel (x-axis), and side scatter (y-axis) properties. (C) From the CD3+ positive population, CD4+ and CD8+ cells were distinguished. The CD4+ population was positive for
PerCP-Cy5 staining. The CD8+ population was positive for the APC-Cy7 stain.
286
4.6 Suspected mode of inheritance in Family B
To summarise, this family is consanguineous in origin. The parents of this family are first cousins, and two children are affected. They presented with an immune dysregulatory disorder characterised by systemic inflammation, macrophage activation syndrome and death from viral infection in one case. B-II-3 first presented at the age of eight and unfortunately passed away at the age of 12, from CMV colitis in the context of uncontrolled inflammation. Her older sister, B-II-2, who is now 23, has been unwell for the past two years with similar symptoms. Interestingly this sister is a monozygotic twin.
Her twin, however, is currently healthy, but as has been the case for her sister, it may be the case she may develop symptoms later in life. Due to the consanguineous nature of this family, I suspect the mode of inheritance of the causative gene to be homozygous recessive in nature.
4.7 Homozygosity mapping in Family B
In order to narrow down the search for candidate genes, homozygosity mapping was carried out. Using this technique, I was able to identify homozygous areas in all family members. I looked for areas of homozygosity which were shared amongst the two affected siblings and the unaffected twin, and not present in the healthy brother or parents.
Only runs of homozygosity (ROH) greater than 1Mb were considered significant. Only three chromosomes had homozygous regions which matched this criteria; chromosome
5, chromosome 12 and three regions on chromosome 17.
287
Each of the graphs below represents one whole chromosome. The base position of each
SNP along the chromosome is on the x-axis, and the y-axis demonstrates the B allele frequency at each position. B allele frequency is given a score from 0 to 1, wherein AA genotypes are denoted 0, AB genotypes are denoted 0.5 and BB genotypes are given a value of 1. Each of the red boxes outlined in the graph represent a homozygous area which is shared by affected siblings and not seen in any unaffected family members.
4.7.1 Chromosome 5
Figure 4-9: Runs of homozygosity on chromosome 5 as identified following homozygosity mapping in family B.
The x-axis shows the base position along chromosome 5. The y-axis represents the B allele frequency of each base. Homozygous regions are shaded in green. Affected family members are
B-II-1, B-II-2 and B-II-3. Outlined by a vertical red box is a ROH of 4.5Mb in length. The ROH does not appear in healthy family members.
288
4.7.2 Chromosome 12
Figure 4-10: Runs of homozygosity on Chromosome 12 as identified following homozygosity mapping in family B.
The x- axis shows the base positions along chromosome 12. The y-axis represents the B allele frequency of each base. Affected family members are B-II-1, B-II-2 and B-II-3. Outlined by a red box is a ROH of 9.7Mb in length. The ROH does not appear in any healthy family members.
289
4.7.3 Chromosome 17
Figure 4-11: Runs of homozygosity on chromosome 17 as identified following homozygosity mapping in family B.
The x-axis shows the base positions along chromosome 17. The y-axis represents the B allele frequency of each base. Affected family members are B-II-1, B-II-2 and B-II-3. There are three
ROH, on this chromosome outlined by red boxes, which are present in the affected individuals and not present in any unaffected individuals. The first ROH (A) is 6Mb in length. (B) is 23Mb in length and (C) is 1Mb in length.
290
Table 4-2: Summary of areas of Homozygosity identified in family B.
This table outlines each homozygous area, larger than 1Mb and shared by the B-IV-1 and all affected family members. It shows the start, and end position of each ROH and the total no. of
SNPs found in this area. This information was gathered from Genome Studio.
Chromosome Starting End Length No. of SNP No. of
position position probes genes
5 173,608,806 178,165,521 4,556,715 624 82
bp
12 18,282,343 27,983,485 9,701,142 1460 78
bp
17 (a) 12,375,660 18,612,626 6,236,966 963 118
bp
17 (b) 25,402,163 48,464,975 23,062,812 3139 727
bp
17 (c) 77,981,978 79,018,596 1,036,618 178 28
bp
As is evident from the graphs above and Table 4-2, 17(B) is the largest ROH in this family. Therefore it is expected that the culprit gene will be contained within this area
(Szpiech et al. 2013).
This information, while useful, only informed me about the areas of the chromosome that are homozygous in nature. In order to identify the particular culprit gene, I needed more information, about the genes residing within these areas, and the function of those genes.
291
From this information, I could then decide whether or not variants in these genes may be involved in the disease pathogenesis. To this end, I first compiled a list of all the genes which were contained inside these ROH (see appendix section 8.6), this information was obtained from the Genome Studio software. The list was then annotated using Gene a la
Carte, to look for potential variants which may be involved in the presenting phenotype of the affected individuals.
Next whole exome sequencing was also carried out. The whole exome sequencing data was filtered, according to the following diagrams (Figure 4-12 (a), (b) and (c)) for each of the affected individuals.
4.8 Results of Whole Exome Sequencing
In order to gain more information about the variants contained within the homozygous regions, whole exome sequencing was undertaken for all family members. WES also enabled us to gain information about variants outside homozygous regions which may also be of relevance.
The whole exome fastq files were put through the Galaxy pipeline and annotation files obtained from wANNOVAR. This annotated list from each affected family member was then filtered using the following filtering strategy.
292
293 293
Figure 4-12: Filtering method used on variants identified by
WES for family B.
Filtering strategy as applied to B-II-1 (A), B-II-2 (B) and B-II-
3 (C).
Based on the above analysis (Figure 4-12), we can see that B-II-1 had 56 possible candidate genes remaining after this filtering process. B-II-2 and B-II-3 had 38 and 53 genes, respectively. I then found variants in genes which remained after filtering analysis and were common among these individuals. The results are tabulated below in Table 4-3.
294
Table 4-3: Candidate genes which remained following WES filtering analysis and are shared amongst the two affected individuals and the unaffected monozygotic twin.
Chromosome Position Gene Nucleotide Amino Frequenc PolyPhen
Change Acid y Prediction
Change
4 1554114 DCHS2 c.C1061A p.A354D 0 T
47
5 1769521 FAM193B c.A1304C p.E435A 0.0026 D
78
5 1793203 TBC1D9B c.A725G p.E242G 0 D
20
16 1279347 TPSB2 c.C344T p.T115I 0 .
17 1802443 MYO15A c.C2324T p.S775L 0 D
8
17 1804689 MYO15A c.G5925A p.W1975 0.0048 .
4 X
17 2079921 CCDC144 c.G124A p.G42R 0.0016 D
0 NL
17 2838470 EFCAB5 c.C2375T p.S792L 0.01 T
3
17 3035175 LRRC37B c.C1708A p.L570I 0 T
8
17 3053398 RHOT1 c.T1472C p.V491A 0.0014 T
4
295
17 3374975 SLFN12 c.T290C p.M97T 0.0016 D
8
17 3871144 CCR7 c.T683A p.M228 0 D
8 K
Interestingly, most of the shared genes are contained on chromosome 17. Referring to the homozygosity data, region B on chromosome 17 was a ROH reaching 23 Mb in length and spanning from 25,402,163 to 48,464,975. Most of the genes on (Table 4-3) that are located on chromosome 17 are found within this area. I, therefore, suspect that this region more than likely contains the culprit gene.
4.9 Integrating Homozygosity Mapping data with Whole
Exome Sequencing data
Next, I examined both the homozygosity mapping data and the WES data together, in order to narrow down the search further. I specifically focused on homozygous WES variants which were contained within ROH, identified by the homozygosity mapping data. I then further identified those homozygous WES variants which were shared among each of the affected individuals.
296
Table 4-4: Number of homozygous WES variants, found in ROH and shared by affected individuals and unaffected monozygotic twin.
Chromosome No. of genes in Total no. of No. of Shared
overlapping WES homozygous Homozygous
ROH variants in WES variants WES variants in
ROH in ROH ROH
5 81 48 39 3
12 77 34 34 1
17 (a) 117 63 52 1
17 (b) 726 388 310 8
17 (c) 28 28 27 0
Total 1,029 561 462 13
In order to find the particular causative variant, I focused on the last column of Table 4-4.
A gene list, containing homozygous variants from the WES data, contained within the overlapping ROH, which were shared between the two affected siblings and the, as yet unaffected, monozygotic twin sister, was compiled. This is listed below.
297
Table 4-5: Homozygous variants identified in family B, frequency in the general population and in silico predictions for likely pathogenicity.
Chromosome Position Gene Nucleotide Amino Frequency PolyPhen
Change Acid Predictio
Change n
5 1769521 FAM193 c.A1304C p.E435A 0.0026 D
78 B
5 1793203 TBC1D9 c.A725G p.E242G 0 D
20 B
17 1802443 MYO15A c.C2324T p.S775L 0 D
8
17 1804689 MYO15A c.G5925A p.W197 0.0048 .
4 5X
17 2079921 CCDC14 c.G124A p.G42R 0.0016 D
0 4NL
17 2838470 EFCAB5 c.C2375T p.S792L 0.01 T
3
17 3035175 LRRC37 c.C1708A p.L570I 0 T
8 B
17 3053398 RHOT1 c.T1472C p.V491 0.0014 T
4 A
17 3374975 SLFN12 c.T290C p.M97T 0.0016 D
8
17 3871144 CCR7 c.T683A p.M228 0 D
8 K
298
Interestingly, Table 4-5 is quite similar to Table 4-3. It is slightly shorter, only by about
3 genes. These three genes were not contained within the ROH that were shared among the affected siblings and unaffected twin sister.
The genes in Table 4-5 were annotated using Gene a la Carte, Uniprot and OMIM in order to find out more information about each gene. A multidisciplinary meeting identified a priority of genes for investigation. Variantsin 4 genes were chosen including CCR7,
SLFN12, TPSBP2 and TBC19DB, and sanger sequencing was carried out for all four genes.
Ccr7-/- mice are highly susceptible to viral infection, and have a propensity to develop autoimmune diseases. This gene is also highly involved in T cell development and in the establishment of central and peripheral tolerance. Due to its chemotactic function in the migration of T cells and dendritic cells towards the lymph node it is also of vital importance for the establishment of the adaptive immune system. Based on the literature evidence surrounding CCR7, I felt this provided the answer in this family.
CCR7: C-x-C Chemokine Receptor 7, is a G protein-coupled receptor, induced by Epstein
Barr Virus (EBV). It is expressed on numerous haemopoietic cells. This gene is a mediator of EBV effects on B cells; however, its main function is in the homing of naïve
T cells, B cells and activated dendritic cells to the lymph nodes.
SLFN12: This gene belongs to the Schlafen family of proteins. These proteins are expressed at different times during hematopoietic cell development, involved in T cell quiescence and regulation of the cell cycle. They are upregulated in response to interferons and so are thought to play a role in viral defence.
TPSB2: Tryptase Beta 2. Tryptase is a protease present on mast cells and is secreted upon the coupled activation-degranulation response of mast cells. The disease presentation of
299
these individuals is similar to that of people with HLH, as they presented with macrophage activation syndrome and had high levels of proinflammatory cytokines.
Genes which are known to cause HLH are involved in the granule dependent cytotoxic function of NK and T cells. This mechanism is essential for the elimination of infected target cells and control of the immune response. Autosomal recessive mutations in genes such as PRF1, UNC13D, STX11 and STXBP2 are known to cause HLH. Therefore, when investigating this family, I decided to also look for genes which are also involved in these processes. As TPSB2 is involved in degranulation, it was decided that this should also be sanger sequenced
TBC1D9B: TBC1 Domain Family Member 9B, Acts as a GTPase-activating protein for the Rab family members. Rab GTPases are a large family of proteins with important cellular functions. They are involved in the regulation of membrane trafficking, vesicle formation, and vesicle movement along actin. Diseases associated with dysfunctional Rab proteins include choroideremia, cancer and Parkinson’s disease.
300
4.10 Results of Sanger Sequencing and Integrated Genome
Viewer (IGV) analysis:
All four of the shortlisted genes were Sanger sequenced to check if the variant was a true variant and whether or not it segregated correctly with the inheritance of the disease. The
IGV database was also used to analyse each variant. This database enables us to visualise the coverage of each variant following whole exome sequencing. This data should correspond to the Sanger sequencing data.
4.10.1 TPSB2
Figure 4-13: Detection of homozygous p.T115I variant in TPSB2.
(A)Sanger sequencing for this variant reveals that all family members are all homozygous wildtype for this variant. (B) Review of whole exome sequencing data (IGV) for homozygous p.T115I variant in TPSB2. Faimtly coloured bands represent poor mapping quality. When reads have been accurately mapped to the correct position in the genome the forward reads are 301
coloured red and reverse reads are blue. The algorithm in Galaxy has little confidence that these reads have been mapped back to the correct position. TPBS2 is not a true variant, as it is wildtype in all family members (Sanger sequencing).
IGV displays the depth of coverage of a particular region, i.e. how many reads map back to that particular region of the genome. If an area is well covered and has numerous reads that align to it, we can be quite confident that this area has been sequenced correctly, and that any variants found in this area are very likely to be true. Reads orientated in the forwards direction, are coloured in red. Those orientated in the backwards direction are coloured in blue.
Many of the reads in this area are faintly coloured. Only two of the bands are coloured properly. This is quite unusual. When the algorithm (in Galaxy) is sure that a particular read has aligned to the correct sequence, the band in IGV is coloured in. Faint bands like the ones seen here (Figure 4-13) have a mapping quality of zero, meaning that the algorithm has no confidence that they have been mapped to the correct position, and that they are equally likely to map to another position in the genome.
Given the fact that the Sanger sequencing showed that every family member is wildtype at this position and that the bands in the IGV window are faintly coloured and therefore equally likely to map to another position in the genome, I believe that this is not a true variant. Therefore the variant in TSBP2 was not further examined.
302
4.10.2 TBC1D9B
Figure 4-14: Sanger and IGV data for the TBC1D9B gene.
(A) Sanger sequencing of exon 5 of the TBC1D9B gene. A black line highlights the p.E242G
mutation. (B) Screenshot of the IGV window showing the p.E242G mutation in TBC1D9B in
B-II-2. The sequencing reads which mapped to this region are shown by the blue (forward
reads) and pink (reverse reads) bars. The wild type sequence is shown below the bars.
303
4.10.3 SLFN12
Figure 4-15: Sanger sequencing and IGV data for the SLFN12 gene.
(A) Sanger sequencing of exon 2 of the SLFN12 gene. A black line highlights the p.M97T mutation. (B) Screenshot of the IGV window showing the p.M97T mutation in SLFN12 in B-II-2.
The sequencing reads which mapped to this region are shown by the blue and pink bars. The wild type sequence is shown below the bars. Parents are heterozygous at this position, B-II-1 is wildtype, while B-II-1, B-II-2 and B-II-3 are homozygous at this position.
304
4.10.4 CCR7
Figure 4-16: Sanger and IGV data for CCR7 gene.
(A) Sanger sequencing of exon 3 of the CCR7 gene. A black line highlights the p.M228K mutation.
(B) Screenshot of the IGV window showing the p.M228K mutation in CCR7 in B-II-2. The sequencing reads which mapped to this region are shown by the blue (positive strand) and red
(negative strand) bars. The wild type sequence is shown below the bars. Parents are heterozygous at this position, B-II-1 is wildtype, while B-II-1, B-II-2 and B-II-3 are homozygous at this position.
From this analysis, it is obvious that the mutations picked up by the WES data for
TBC1D9B, SLFN12 and CCR7, are all true variants. They have been well covered by the
WES, as demonstrated by IGV. In addition to this, we can also see that these variants have been picked up by the Sanger sequencing and segregate with the pattern of disease within the family. Each of the variants in the TBC1D9B, SLFN12 and CCR7 genes were present in a homozygous state in the affected individuals and unaffected monozygotic
305
twin, and present in a heterozygous state in both parents and absent in the unaffected brother.
TSBP2 is unlikely to be a true variant, and its expression does not segregate with disease inheritance. Given what we know about TBC1D9B from the literature, the pathways that it is involved in, its biological function, and predictions by the in silico models, it is unlikely that this gene is the cause of disease. Given this information, we decided to discount the variants in TSPB2 and TBC1D9B. Therefore, for subsequent investigation into the genetic variants involved in this disease, I decided to focus my functional experiments on two genes, namely: CCR7 and SLFN12. The homozygous variants in both of these genes is located within the largest ROH found in the family, on chromosome 17
(B).
306
4.11 The Schlafen proteins: Structure and Function
Schlafen family member 12 (SLFN12) is a member of the Schlafen gene family, first identified by (Schwarz et al. 1998). These genes are differentially expressed during thymocyte maturation and T cell activation (Schwarz et al. 1998). These genes have been divided into three groups (groups 1, 2 and 3) depending on their size and subcellular location. Group 1 schlafens are the smallest, ranging from 37-42 kDa. Group 2 have molecular masses of between 58-68 kDa and group 3, the largest group are between 100-
104 kDa. Group 1 and 2 proteins localise in the cytoplasm, while group 3 schlafens contain a nuclear localisation signal and remain in the nucleus (Neumann et al. 2008).
Schlafen proteins which reside in the cytoplasm are known to modulate cell growth arrest, and function in T cell quiescence. Those which reside in the nucleus are thought to regulate the cell cycle (Neumann et al. 2008). There is only one cytoplasmic human schlafen protein, h-slfn12, which belongs to the group two category (Mavrommatis et al.
2013).
4.11.1 Restriction of viral replication by the Schlafen proteins
(Katsoulidis et al. 2010) cleverly demonstrated through the use of knockdown experiments that these genes are inducible by interferon alpha (IFNα), as their mRNA expression was increased upon IFNα induction, and that this occurs through the JAK-
STAT and MAPK pathway. When STAT1 and p38 were knocked out in the cell, there was no increase in expression of the SLFN proteins, upon stimulation with IFNα. This was also true in monocytes and monocyte-derived macrophages and T cells (Puck et al.
307
2015). The authors show, slfn5, slfn12 and slfn12L are downregulated upon T cell activation, but that this downregulation is reversible upon addition of TNFα.
As they are upregulated in response to interferon stimulation, it is unsurprising then they have a part to play in viral defence. For instance, SLFN11 functions to block the production of retroviruses such as HIV-1 (Li et al. 2012, ABDEL-MOHSEN et al. 2015).
SLFN11 restricts HIV-1 by inhibiting the function of HIV tRNA, subsequently blocks
HIV replication.
A homozygous mutation in m-slfn2, resulting in a dysfunctional m-slfn2 protein, known as the Elektra phenotype (Berger et al. 2010), left mice susceptible to viral and bacterial infections. These mice died 6 days post-infection with murine cytomegalovirus (MCMV), while all wildtype mice survived this infection (Berger et al. 2010). However,
(Katsoulidis et al. 2010) has conflicting opinions on the SLFN role in viral replication.
These authors state that although induced by interferons, SLFN proteins have no effect on the generation of antiviral responses of the cell. SLFN2 knockout cells and wildtype cells were subjected to encephalomyocarditis virus (EMCV); however both groups of cells responded equally and had equal protection from the virus (Katsoulidis et al. 2010).
4.11.2 SLFN proteins are differentially expressed during haematopoietic cell
development
The SLFN proteins are expressed at different stages of T cell, monocyte and dendritic cell development and activation (Puck et al. 2015) and their expression changes throughout the life cycle of these cells. For instance, SLFN12L and SLFN13 are relatively lowly expressed in monocytes; however, their expression is upregulated during differentiation in monocyte-derived dendritic cells. Expression of slfn genes inhibits T cell proliferation
308
and maintains T cell quiescence (Goldshtein et al. 2016). Mouse models have been imperative in explaining how the SLFN protein regulate T cell development. For example, m-slfn1 and m-slfn2 are upregulated during T cell growth when m-slfn4 is downregulated (Liu et al. 2018). It was also found that when m-slfn2 is knocked out, T cells are subject to spontaneous apoptosis (Berger et al. 2010). Similarly, m-slfn4 is heavily involved in macrophage differentiation. M-slfn4 is downregulated during macrophage differentiation and upregulated during macrophage activation (van Zuylen et al. 2011).
4.11.3 Role of SLFN proteins in cell cycle regulation and cell proliferation
Numerous studies report that the SLFN proteins are involved in cell cycle regulation.
Schwarz et al., 1998, showed that overexpression of m-slfn1 results in growth suppression by preventing entry into the cell cycle (Schwarz et al. 1998). SLFN proteins are involved in the maintenance of cell quiescence. Katsoulidis et al., 2010 demonstrate that m-slfn2 knocked down T cells, show enhanced proliferation in relation to their control counterparts (MTT method) (Katsoulidis et al. 2010). They also illustrate that there is much higher cyclin D1 levels in m-slfn2 knockdown cells than in control cells, insinuating that expression of m-slfn2 prevents entry into the cell cycle and inhibits cell growth. M-slfn1 was also found to control cell growth by regulating the expression of cyclin D1 (Brady et al. 2005).
CD8+ and CD4+ T cells failed to expand normally in mice of the Elektra phenotype (m- slfn2 knockout). Annexin V staining revealed an increase in apoptosis in these cells in comparison to wildtype cells. It is believed that this mutation leads to cell death upon T cell activation signals (Berger et al. 2010). Elektra mice also had severely diminished
309
numbers of T cells, in response to infection with lymphocytic choriomeningitis virus
(LCMV). Elektra T cells fail to maintain cellular quiescence. As a result, they enter a post-mitotic phase, similar to that of recently activated T cells. They lose their proliferation potential and undergo cell death in response to proliferation/activation signals. This leads to diminished numbers of T cells in Elektra mutant mice (Goldshtein et al. 2016).
4.11.4 Elektra Phenotype in humans
A study published by Recher et al., 2014, describes a patient who harbours a large heterozygous deletion in SLFN11, SLFN12 and SLFN13 genes (Recher et al. 2014). These genes are situated next to each other on chromosome 17. This study found that the patient exhibited profound defects in T cell proliferation and cell cycle regulation. This patient displayed both enhanced hematopoietic cell proliferation and apoptosis signals, as shown by CSFE and Annexin V staining (Recher et al. 2014). Interestingly, the patient was susceptible to cancer as she displayed a Merkel cell carcinoma on her upper thigh, a carcinoma associated with viral infection, such as Merkel cell polyomavirus (MCPyV).
She was also diagnosed with a T cell lymphoma (Recher et al. 2014). She appeared vulnerable to viral infections, as large amounts EBV and Torque Teno virus (TTV) DNA were detected in peripheral blood and plasma, respectively (Recher et al. 2014).
Immunophenotyping was performed but was unremarkable. The patient displayed a normal CD4+/CD8+ ratio and a normal distribution of naïve (CCR7+CD45RA+) and memory cells (CCR7+ CD45RA-) (Recher et al. 2014). Upon antiCD3/CD28 stimulation, the patient did, however, display abnormal T cell proliferation, as demonstrated by CFSE
310
staining and excessive T cell apoptosis as demonstrated by annexin V staining (Recher et al. 2014).
4.12 Functional data exploring role of homozygous p.M97T
mutation in SLFN12
This p.M97T homozygous mutation found in SLFN12 was predicted damaging by the in silico models Polyphen and Mutation taster. It is a rare mutation and has a population frequency of 0.0016, in the general population (Karczewski et al. 2019). Mouse Slfn2 is a paralog of human SLFN12. Therefore we would expect the phenotype of the proband and other family members with the homozygous mutation in SLFN12 to be similar to
Elektra mouse model, with a defective m-slfn2 protein. Indeed, the sibling of the proband died of a CMV infection. We know that Elektra mice were largely susceptible to viral infections such as MCMV and lymphocytic choriomeningitis virus (LCMV). Hence, this phenotype fits well with our proband. However, it is still important to investigate other aspects of an aberrant SLFN12 protein. Based on studies carried out on the Elektra phenotype (Berger et al. 2010), and the study involving the patient with a homozygous deletion in SLFN11-13 (Recher et al. 2014), I decided to investigate the T cell proliferation and apoptotic properties of our proband (B-II-2) and her twin sister (B-II-1).
4.12.1 Normal SLFN12 mRNA Expression Levels
First and foremost, I decided to investigate whether or not the homozygous p.M97T variant in this gene caused aberrant SLFN12 protein expression in these individuals in
311
comparison to healthy controls. This was investigated via qPCR. PBMCs from both B-
II-2 and B-II-1 and an unrelated healthy control individual were stimulated with IFNα, as it is highly reported that the schlafen proteins are upregulated in response to interferons
(Katsoulidis et al. 2010, Mavrommatis et al. 2013).
A B p = 0 .1 5 4
2 .5 2 .5 C tr l
) C trl + IF N
B -II-1 q
)
C q
B -II-2 B -II-1 + IF N
C
- 2 .0
(
2 .0 B -II-2 + IF N
-
(
2
=
2
n 1 .5
=
o i
n 1 .5
t
o
a
i
c
t
i
a
f
i
c
t i
f 1 .0
n
i
t a
n 1 .0
u
a
Q
u
e
Q
v
i e
t 0 .5
v
a
i l
t 0 .5
e
a
l
R
e R 0 .0
0 .0 N N N l 1 2 IF F F r I- I- I I t I I + + + C - - B B l -1 -2 tr I I C -I -I B B
Figure 4-17: Expression analysis of SLFN12 gene expression levels in B-II-1 and B-
II-2.
The RNA from PBMC were collected using a TRIzol extraction. This RNA was then converted to cDNA using a reverse transcriptase assay, and the expression of this gene was elucidated via qPCR. (A) Baseline mRNA expression of SLFN12. (B) mRNA expression of SLFN12 after PBMCs were stimulated with 50ng/ml IFNα for four hours. Gene expression was calculated using the
Livak Method =2-(ΔΔCq), and values were normalised to that of the level of a housekeeping control gene (HPRT1). Values were expressed as relative to gene expression in the healthy control individual. A quantitect primer assay was used to detect expression of SLFN12 (NM_018042).
Experiments were carried out in triplicate, and error bars represent standard error from the mean
312
of these triplicates. There were no significant differences in SLFN12 mRNA expression levels between patients and controls.
From this analysis, I found that there was little difference in expression between the control and both patients. There seemed to be slightly increased expression of SLFN12 mRNA upon IFNα stimulation, although this increase was not significantly different from the control (Unpaired T test, p = 0.154)
As was mentioned previously, the Schlafen genes are differentially expressed during T cell development and appear to be involved in cell cycle regulation of T cells, monocytes and dendritic cells. Specifically, they have been shown to inhibit T cell proliferation and promote T cell quiescence. Interestingly when m-slfn2 (the paralog for h-slfn12) was knocked out, spontaneous T cell apoptosis occurred. A patient was described, who had a large heterozygous deletion encompassing slfn11, slfn12 and slfn13. This patient was said to mimic the Elektra phenotype (m-slfn2 knockout) observed in mice (Recher et al. 2014).
This patient displayed aberrant cellular proliferation and apoptosis. Patient PBMCs were stimulated in vitro with anti-CD3/anti-CD28, and the (carboxy-fluorescein-succinimidyl ester) CFSE dilution pattern, as well as Annexin V staining, was observed (Recher et al.
2014). The patients T cells failed to proliferate, as demonstrated by the CFSE staining, and there was excessive T cell apoptosis, as indicated by Annexin V staining. In light of this information, I decided to investigate the T cell proliferation and apoptosis properties of B-II-1 and B-II-2.
313
4.12.2 Normal Cellular Proliferation Levels in B-II-1 and B-II-2
Cell proliferation in these patients was investigated via two separate mechanisms, Ki67 and CFSE staining.
In order to measure cellular proliferation of T cells in healthy controls in comparison to our patient cells, PBMCs were subjected to anti-CD3+/anti-CD28+ stimulation for 3 days
(see section 4.5.1). Ki67 and CFSE antibodies were added to cells and the relative fluorescent intensity of these antibodies measured by flow cytometry (see sections 4.5.4 and 4.5.5, respectively).
The graph below (Figure 4-18) depicts Ki67+ staining in B-II-1 (A and B) and B-II-2 (C and D) at baseline and following stimulation with anti CD3+/anti CD28+. In both conditions, baseline and stimulated, there is very little difference between the two affected individuals (B-II-1 and B-II-2) and healthy control individuals. At baseline, the MFI of the healthy control cells is 14.9, whereas in B-II-1 it is 13.11. After anti-CD3+/CD28+ stimulation, the MFI of healthy control cells is 33.8 while in B-II-1 it is 28.3. Similar levels were observed in B-II-2. B-II-1 and B-II-2, mirror the levels of Ki67+ staining as seen in the control population.
314
Figure 4-18: Ki67 expression in B-II-1 and B-II-2 versus healthy control selected T cells.
PBMCs were stimulated with anti CD3+/CD28+ for 72 hours as described in section 4.5.1. Cells were fixed, stained with anti-CD3+, permeabilised and labelled with anti-Ki67 as described in section 4.5.4. Red lines represent the patient. Black dotted lines represent the healthy control and grey shaded histogram represents the non-stain control in each case. (A) The histogram represents the Ki67 staining in B-II-1 and a healthy control at baseline. (B) Ki67 staining in B-
II-1 and a healthy control after antiCD3/antiCD28 stimulation. (C) This histogram represents
Ki67 staining in B-II-2 versus a healthy control at baseline. (D) Ki67 staining in B-II-2 versus a healthy control after antiCD3/antiCD28 stimulation. Cells were analysed on a FACS Calibur
Becton Dickson.
315
As a further measure of cell cycle regulation, CFSE staining was also carried out in B-II-
1, B-II-2 and a healthy control individual. Following stimulation with anti-CD3+/anti-
CD28+ beads, the MFI of the healthy control was 45.9 , while in B-II-1 and B-II-2 it was
44.2 and 48.2 respectively. Again, as is evident from the Figure 4-19, there is little difference between B-II-1, B-II-2 and control cells in terms of CFSE staining in PBMC stimulated for 3 days with antiCD3+/antiCD28+. I concluded that B-II-1 and B-II-2 do not show signs of aberrant T cell proliferation.
316
Figure 4-19: CFSE expression in B-II-1 and B-II-2 in comparison to a healthy control individual
PBMCs were prelabelled with CFSE dye before being stimulated with anti CD3+/CD28+ for 72 hours as described in section 4.5.5. Cells were fixed, stained with anti-CD3+ and analysed on a
FACS Calibur Becton Dickson.. Red lines represent the patient (either B-II-1 or B-II-2); black dotted lines represent the healthy control and grey shaded histogram represents baseline CFSE staining. CFSE staining can be viewed in the FL-1 gate. (A) The histogram represents the CFSE fluorescence in B-II-1 and a healthy control after antiCD3/antiCD28 stimulation. (B) CFSE fluorescence in B-II-2 and a healthy control after antiCD3/antiCD28 stimulation. B-II-1 and B-
II-2 display similar CFSE profiles to the healthy control.
If the homozygous p.M97T variant in SLFN12 caused this protein to be dysfunctional, then I would expect to see, results similar to the patient as described by (Recher et al.
2014). This patient displayed aberrant CFSE staining, indicating that her cells were proliferating significantly more than that of a healthy control individual (Recher et al.
2014). However, in this case, there is very little difference in the CFSE profile of B-II-1 and B-II-2, in comparison to the healthy control. I can conclude that B-II-1 and B-II-2 do not have aberrant T cell proliferation.
4.12.3 Normal Cellular Apoptosis Levels in B-II-1 and B-II-2
It is known that members of the schlafen gene family promote T cell quiescence
(Goldshtein et al. 2016) and that patients with aberrantly regulated schlafen proteins have abnormal apoptosis levels (Recher et al. 2014). In addition, the T cells of elektra mice
(m-slfn2 KO) exhibit spontaneous apoptosis. Therefore, I decided to also measure the
317
apoptotic properties of T cells in B-II-1 and B-II-2. This was achieved via Annexin V staining.
PBMC from healthy control individuals and from B-II-1 and B-II-2 were subjected to antiCD3+/antiCD28+ stimulation for 3 days and stained with both CD3+ (PE) and
Annexin V (FITC) antibodies. At baseline, the MFI of B-II-1 is 3.87, while the healthy control is 4.32. Subsequently, after anti-CD3+/anti-CD28+ stimulation, the MFI of B-II-
1 is 6.99 while the healthy control is 8.2. As is evident from Figure 4-20, there is little difference between B-II-1 and healthy control. In conclusion, I can infer that the T cells of these patients display normal apoptosis levels. In comparison to the paper, wherein the authors demonstrated that a patient displaying the Elektra phenotype had enhanced apoptosis levels in comparison to a healthy control individual (Recher et al. 2014).
318
Figure 4-20: Normal levels of Annexin V staining in B-II-1 at baseline and after anti-
CD3+/anti-CD28+ stimulation, compared to healthy control.
PBMCs were stimulated with anti CD3+/CD28+ for 72 hours as described in section 4.5.1. Cells were spun, incubated in Annexin V binding buffer and stained with anti-Annexin V and anti
CD3+, before being analysed on a FACS Calibur Becton Dickson. CD3+ cells were analysed for
Annexin V staining (FITC). (A) Annexin V staining at baseline. (B) Annexin V staining after anti-
CD3+/anti-CD28+ stimulation. There is little difference between the Annexin V staining between
B-II-1 and healthy control cells, both at baseline and after anti-CD3+/anti-CD28+ stimulation.
319
Given that B-II-1 and B-II-2 have similar levels of SLFN12 mRNA expression, similar
Ki67, CFSE and Annexin V staining in comparison to an unrelated healthy control, I believed it was unlikely that the variant in this gene was causative of the disease in question (Berger et al. 2010, Recher et al. 2014, Goldshtein et al. 2016). It is also worth noting that the p.M97T variant identified in this family does not have the correct zygosity normally observed in people with Schlafen mutations. The patient identified by Recher et al., 2014, had a heterozygous deletion. Whereas, p.M97T is homozygous in the affected members of this family.
I, therefore, explored other variants found in this family and their contribution to the disease, namely the homozygous variant identified in CCR7.
320
4.13 Functional relevance of homozygous p.M228K variant in
CCR7
I next proceeded with my investigation of the impact of the homozygous p.M228K variant in CCR7, on the disease phenotype of B-II-1 and B-II-2. CCR7 is a G protein-coupled receptor (GPCR) which is present on the surface of numerous lymphoid cells including naïve T cells, central memory T cells, regulatory T cells, B cells, dendritic cells (DC) and natural killer (NK) cells (Comerford et al. 2013). It has two chemokine ligands, CCL19 and CCL21 (Förster et al. 2008). Because of its ubiquitous expression, it has important functions in controlling the adaptive immune response, and an abrogation of this protein expression or function could have catastrophic effects on the organism. In fact, animal studies using knockout CCR7 or (paucity of lymph node) plt/plt mice which do not express the CCL19 and CCL21 ligands show severe immune defects, often resulting in autoimmune phenotypes (Davalos-Misslitz et al. 2007). Having implications on the development of new thymocytes, central and peripheral tolerance, the homing of activated T cells and dendritic cells to the lymph nodes, as well as their egress from the lymph node into peripheral sites, I think it is reasonable to propose that the homozygous variant found in this gene may be an excellent candidate to explain the phenotype in this kindred.
321
4.14 Reduced CCR7 protein expression in individuals
harbouring the homozygous p.M228K variant
A western blot was performed in order to assess the impact of the homozygous p.M228K variant on the expression of the CCR7 protein. This was carried out using the protocol described in section 2.12.3, on resting PBMCs. Whole protein was extracted from PBMCs from B-II-1 and B-II-2; monozygotic twins harbouring the homozygous variant, parents
(heterozygotes) and an unrelated healthy control individual.
Figure 4-21: Reduced CCR7 expression in B-II-1 and B-II-2 harbouring a homozygous mutation in CCR7.
Whole proteins were harvested from PBMCs and loaded onto an SDS-Page gel and a western blot performed. After transfer, the membrane was raised against anti-CCR7. The blot was then stripped and raised against anti-Vinculin, a housekeeping gene. B-II-2 and
B-II-1 who harbour the homozygous mutation in CCR7 displayed reduced expression of
322
this protein in comparison to healthy controls cells and members of family B who are wild type.
As is clear from Figure 4-21, the two individuals B-II-2 and B-II-1 who harbour the homozygous variant in CCR7 displayed reduced expression of this protein in comparison to an unrelated healthy control individual and other members of the family. Interestingly, the two members of the family who are heterozygous for this variant, do not display any lack of protein expression.
323
Table 4-6: Adjusted protein expression of CCR7, relative to Vinculin in cells from members of family B.
Densitometry was assessed according to the protocol as outlined in 2.12.4.
Area Under Percentage Relative Adjusted
the Curve Expression Expression
CCR7 HC 80280.95 15.034 0.69195 1
B-II-2 51585.65 8.018 0.430104 0.621583
B-I-1 89650.31 16.672 0.876966 1.267383
B-II-1 65648.4 9.476 0.44136 0.63785
B-I-2 75128.17 14.133 0.738016 1.066574
Vinculin HC 35725.59 21.727
B-II-2 25886.05 18.642
B-I-1 26399.32 19.011
B-II-1 29813.79 21.47
B-I-2 21037.19 19.15
**Relative protein expression was obtained by taking the Vinculin percentage from the
CCR7 percentage for every individual. Adjusted protein expression was obtained by normalising the relative expression to that of the unrelated healthy control individual.
Using the adjusted protein expression, as calculated from ImageJ (Table 4-6), it appears that individuals harbouring the homozygous mutation in CCR7, are expressing the protein at approximately 62% that of normal individuals.
As another method of measuring CCR7 protein expression, flow cytometry analysis was performed on cryopreserved PBMCs from B-II-1 and B-II-2 and an unrelated healthy
324
control. Cells were gated on CD4+ and CD8+ T cells and then the CCR7+ cells within these populations were analysed. In CD4+ T cells, the MFI for B-II-1 is 761, while in healthy control cells it is 1310. In CD8+ T cells the MFI for B-II-1 is 822, while in healthy control cells it is 1426. Similar values were observed for B-II-2. There is reduced CCR7 expression in patient cells compared to healthy control cells.
Figure 4-22: Reduced CCR7 protein expression in individuals harbouring the p.M228K/p.M228K mutation, as analysed by flow cytometry.
CCR7 expression is reduced in B-II-1 and B-II-2 PBMC in both CD4+ and CD8+ cells, relative to healthy control cells at baseline. PBMCs were thawed using the protocol as described in section 2.11.5 and incubated at 370C overnight to allow recovery. They were fixed and stained with anti-CD4, anti-CD8 and anti-CCR7, and analysed on a FACS Calibur Becton Dickson. This experiment was performed only once.
325
As is evident from the graphs above, histograms have two peaks, representing different cell populations. This is most likely due to the fact that CD4+ and CD8+ are general T cell markers and indeed numerous subpopulations exist within CD4+ and CD8+ lineages.
These include naïve T cells, effector T cells, and memory cells. Given this information, it would have been appropriate to stain for other markers such as CD45RA (naïve),
CD45R0 (memory) and CD38 or CD25 (effector) and to assess the expression of CCR7 within each of these subpopulations, rather than looking at general CD4+ and CD8+ populations as a whole. It is my estimation that these numerous subpopulation are the reasons behind the multiple peaks as seen in figure 4-22.
It is also important to note that this experiment was performed on PBMCs which had been subject to cryopreservation. It would of course, have been desireable to perform the assay on fresh cells, however, this was tricky given the fact that blood collection was performed in a different hospital (Royal Free, London), and occurred during afternoon clinics.
However, cryopreservation of samples can introduce certain bias. It is documented that freezing can affect the expression of certain genes. (Yang et al. 2016) report a total of
1,367 genes whose expression was 3 fold difference to fresh PBMCs, following 14 months of cryopreservation at -1500C. Cryopreservation can also induce delayed onset cell death, which reduces the viability and recovery of PBMCs post thaw. The PBMCs used in this analysis were cryopreserved from between 2-6 months, so it is difficult to say if the same effect would be seen as in (Yang et al. 2016). Nonetheless, the freezing of these samples may have had an impact on expression of CCR7 and therefore it would be more beneficial to perform this analysis using fresh PBMC.
CCR7 is a cell surface receptor. After transcription and translation have taken place, the correctly folded protein needs to migrate to the cell surface in order to carry out its
326
appropriate function. It is therefore possible that the p.M228K homozygous mutation found in CCR7 may implicate this trafficking to the cell surface. This possibility could be tested using in vitro recombinant models. It would be possible to explore this hypothesis using a vector wherein the mutated CCR7 is tagged to green fluorescent protein (GFP), and this was transfected into a cell line, such as Jurkat cells or HEK293 T cell. By overexpressing the mutated protein in this way and coupling it with GFP, it would be possible to view its localisation within the cell or on its surface. A positive control using a vector which expresses wildtype CCR7, also tagged to a fluorescent marker, should be used for comparative purposes.
327
4.15 Immunophenotyping analysis as performed on B-II-1 and
B-II-2
It is well documented that CCR7 is an important gene during T cell development, enabling thymocytes to migrate to distinct areas of the thymus in order to differentiate. Mouse models have also demonstrated the importance of this gene in lymphoid homing.
Therefore, I decided to carry out immunophenotyping in order to analyse distinct T cell populations, in B-II-1 and B-II-2, who have decreased CCR7 protein expression (see section 4.14). All immunophenotyping work was carried out on resting PBMC, in collaboration with the clinical immunology laboratory under the supervision of Dr
Kimberly Gilmour.
4.15.1 Inverse CD4:CD8 Ratio in B-II-1 and B-II-2
The first observation made from the immunophenotyping analysis was that individuals with a homozygous mutation in CCR7 have an abnormal CD4:CD8 T cell ratio. Although this ratio can vary greatly depending on age, sex and ethnicity, healthy individuals generally display a much higher percentage of CD4+ T cells than CD8+ T cells, with a ratio anywhere between 1.5-2.5 (McBride and Striker 2017) considered healthy.
For this analysis, lymphocytes were first gated from the rest of the PBMCs, and from here, the T cell population (CD3+) was isolated. This T cell population was then assessed for CD4+ and CD8+ markers. The gating strategy was explained in section 4.5.10.
328
Figure 4-23: Skewed CD4:CD8 T cell ratio in B-II-1 and B-II-2.
This analysis was run on whole PBMCs, previously gated on CD3+ cells (see gating strategy section 4.5.10). The CD3+ population is then separated into CD4+ cells (x-axis) and CD8+ cells
(y-axis), and the ratio obtained, as a percentage of the total CD3+ population.
Table 4-7: Percentage of total CD3+ T cell population
CD4+ CD8+ CD4:CD8 ratio
Control 67.5 26.8 2.5
B-II-2 24.9 68.2 0.36
B-II-1 42.5 44.6 0.97
From this analysis, it was observed that B-II-2 has much more CD8+ T cells than CD4+
T cells, representing an inverse CD4:CD8 T cell ratio. A similar story is presented in B-
II-1.
329
4.15.2 Absence of Memory Cells in B-II-1 and B-II-2
CD8+ memory T cells are central to combating viral infection and is particularly important during CMV infection where memory T cell inflation is observed. Also of note,
B-II-3 previously passed away due to a CMV infection. In light of this, a memory cell immunophenotyping panel was performed on B-II-2, B-II-1 and an unrelated healthy
control, in order to assess this population of cells in individuals with the homozygous p.M228K CCR7 genotype.
330
Figure 4-24: Complete absence of CD4+ and CD8+ memory cells in B-II-1 and B-II-
2, harbouring a homozygous p.M228K mutation in CCR7.
The analysis was carried out on whole PBMCs, previously separated into CD3+ cells (see gating strategy section 4.5.10), and subsequently divided into their respective CD4+ and CD8+ cell populations (see Figure 4-23). The CD4+ and CD8+ population was then separated into naïve and memory cells in each individual. CD45RA (naïve) marker is on the x-axis and the CD45R0
(memory) marker on the y-axis. Cells positive for CD45R0 (memory cells) are contained in gate
P3. Cells positive for the CD45RA marker (naïve cells) are contained in gate P4. Numbers underneath the graph represent the percentage of total CD4+ or CD8+ T cells, contained in gate
P3 or P4.
Table 4-8: Percentage of total CD4+ or CD8+ T cell population, respectively.
CD4+ CD4+ CD8+ CD8+
CD45R0 CD45RA CD45R0 CD45RA
Control 45.5 20.2 32.0 37.6
B-II-2 0.6 23.1 0.0 88.3
B-II-1 3.3 66.1 0.2 86.6
As we can see (Figure 4-24), there are almost no memory cells found in individuals harbouring the homozygous p.M228K mutation in CCR7. As observed from the percentages, 42.5% of cells in the healthy control have the CD45R0 phenotype and are therefore classified as memory cells. This is compared with 0.6% in B-II-2 and 3.3% in
B-II-1. There is a clear deficiency in the CD4+ memory T cell population in these
331
individuals. Likewise, in the CD8+ population, a similar trend is observed. The healthy control individual has 32% total memory cells, compared with 0% in B-II-2 and B-II-1.
It is also interesting to note that there appears to be a much higher percentage of naïve
CD8+ T cells in individuals displaying the p.M228K/p.M228K CCR7 genotype, relative to the control individual. In the healthy control, 37.6% of cells are naïve, whilst 88.3% and 86.6% of all CD8+ T cells are naïve in B-II-2 and B-II-1, respectively. Controls used in this analysis were age matched. This is interesting and fits with that reported in the literature. Forster et al., 1999, demonstrated that CCR7 deficient mice display reduced numbers of naïve T cells in the lymph node but in contrast, high numbers of naïve cells in the periphery (Förster et al. 1999). Many studies since then have also reported this, namely; (Debes et al. 2005, Höpken et al. 2010, Eisenbarth 2019). It appears that the reason for this is simply because naïve cells have great difficulty getting to the lymph nodes in the absence of CCR7. Thus, they reside in the periphery.
In both the CD4+ and CD8+ populations above there is an intermediate population that is present in a much lower capacity in healthy control cells. It appears as though this population has markers for both naïve (CD45RA) and memory cells (CD45R0). These cells can be observed following stimulation of naïve cells, as these cells begin to lose their
CD45RA isoform expression and gain CD45R0 expression (Summers et al. 1994). These dual positive transitional cells display functional characteristics of both naïve and memory T cells. They suppress immunoglobulin secretion by B cells, similar to naïve T lymphocytes, but they also have capacity to produce IFNγ similar to memory T cells
(Summers et al. 1994).
Although this is a rather unusual phenotype, it has been reported to be an expanded population in autoimmune and autoinflammatory diseases, such as rheumatoid arthritis
332
(Summers et al. 1994), familial erythrophagocytic lymphohistiocytosis (FEL) (Peakman et al. 1994) and diabetes mellitus (Smerdon et al. 1993). Interestingly, it has been reported that this population is also expanded in healthy first degree relative of patients with FEL and diabetes mellitus (Peakman et al. 1994), and so is thought to have genetic causation.
The authors suggest that the increase in the CD45RA+ CD45R0+ cells observed in patients with autoimmune disease could be due to abnormal T cell differentiation, and an inability to control the balance between the two CD45 isoforms (Peakman et al. 1994).
Recent literature describing these populations is limited. Further immunophenotyping to assess other markers that these cells express would be interesting to investigate. For instance do they express any activation markers such as CD25 and CD69. It would also be interesting to speculate whether these cells express CCR7.
As we know, memory cells, specifically CD8+ memory T cells, are important for mounting protection against viral infection. This is especially true of CMV infection.
Upon first exposure to the pathogen, memory cell inflation is observed, with the pool of memory cells expanding even more upon reactivation of the latent virus. It appears that memory cells are particularly important for combating infection of this particular virus, as memory cell inflation is not observed upon exposure to EBV, vaccinia virus, or others.
Therefore, a lack of memory cells could be detrimental upon exposure to CMV. B-II-3, who also bore the homozygous p.M228K mutation in CCR7, died at the age of 11 from
CMV infection. It is plausible that this individual also displayed a complete lack of memory T cells, but this couldn’t be assessed in the context of this project as this patient was deceased.
333
4.16 Defective migration of PBMCs with homozygous
p.M228K mutation in CCR7
Probably the most well known and most important function of CCR7 is in the homing of haematopoietic cells to the lymph node. When the CCR7 receptor is expressed on cells, it enables them to chemotax towards its two ligands, namely CCL19 and CCL21. To this end, cells migrate to the lymph node where they interact with other cells. This migration to the lymph node is imperative for a systems ability to mount a rapid and efficient adaptive immune response. In order to investigate the migratory capacity of cells of the p.M228K/p.M228K CCR7 genotype, a transwell assay was carried out.
As CCL21 is a ligand for CCR7, cells which have a fully functional copy of CCR7 should migrate towards the chemokine. Cells which have defective CCR7 should show defective migration towards this chemokine. By this reasoning, it is expected that we should see significantly less numbers of migrated cells, in individuals harbouring the homozygous p.M228K mutation in CCR7.
334
A p .M 2 2 8 K /p .M 2 2 8 K
W T /W T
l l
e p .M 2 2 8 K /W T
w
/
s
l l
e 4 0
c
,
3
.
n
3
o
i
5
t
1 a
p = 0 .0 1 8
r
=
g
i n
a 3 0
m
e
l
l
m
e
c
f
o
+ n
3 p = 0 .0 0 0 2
o
D
i
t
C a
2 0
r
n
g
i
i
e
m
s
a
s
e
u
r
o
c
e
n
n i
1 0
a
e
t
v
n
i
t
o
a
p
l
s
e
a
R
n o
0 d
e T s K T 8 a 2 W /W 2 / b T K M 8 . W 2 /p 2 K .M 8 2 p 2 .M B p
6 0 0 0
t p .M 2 2 8 K /W T
n u
o p .M 2 2 8 K /p .M 2 2 8 K
c
l
l W T /W T
e
c
d 4 0 0 0
e
t
a
r
g
i
m
+ 3
D 2 0 0 0
C
e
t
u
l
o
s
b A 0
e 1 n 2 li e L s C a C B +
Figure 4-25: Impaired migration of p.M228K/p.M228K CCR7 CD3+ cells in comparison to wild type CD3+ T cells.
200μl from the bottom of each well was collected, and CD3+ cells present were counted by FACS.
Experiments were carried out in triplicate, and individuals of the same genotype grouped
335
together. Error bars represent standard error from the mean of those triplicates. Two unrelated healthy control individuals were used in the analysis. There was significantly impaired migration of CD3+ T cells observed in individuals with p.M228K/p.M228K CCR7 compared to healthy controls (p=0.0002). Unpaired T-tests were used to calculate statistical differences between groups.
By removing the level of spontaneous migration (migration observed at baseline) in each group of cells, I calculated the relative increase in migration for each genotype. The results are presented on the graph above (Figure 4-25). Heterozygotes display the largest relative increase in migration across the polycarbonate membrane in response to CCL21
(mean = 16.8, SEM = 11.57) compared to wildtype cells that displayed a moderate increase in migration (mean = 9.73 , SEM = 2.6; p=0.018) while the p.M228K CCR7 homozygous cells demonstrate an extremely low migratory capacity compared to control cells (mean = 1.23, SEM = 0.77; p=0.0002). From these analyses, we can infer that cells harbouring the homozygous p.M228K mutation in CCR7, have defective migration in response to the CCL21 chemokine.
Non-migrated cells were not assessed for viability. One could hypothesise that observed differences in migration between groups could be caused by an increased susceptibility of p.M228K/p.M228K CCR7 cells to cell death. However, I feel that this is very unlikely to be the case as cellular proliferation and apoptosis levels of cells harbouring the homozygous mutation in CCR7 were assessed in section 4.12, and was found to be comparable to control cells.
It would be additive to this assay if it could also be performed in the presence of a second chemokine. CXCL12 could be used in this case as it is a potent T cell chemoattractant
(Borge et al. 2010, Munk et al. 2011). If p.M228K homozygote cells displayed normal
336
cell migration in response to CXCL12, this would give further support to the hypothesis that a homozygous mutation in CCR7, is responsible for poor chemotaxis towards
CCL21.
337
4.17 siRNA mediated knockdown of CCR7 in Jurkat cells
results in impaired cell migratory capacity
I decided to replicate the findings of the migration assay by repeating the transwell assay using Jurkat cells and knocking down the CCR7 gene. Jurkat cells, a T cell line, were transfected using lipofectamine RNAiMAX and incubated in OptiMEM medium for 2.5 days, as described in section 2.8.6. A qPCR was performed in order to examine the efficiency of the knockdown.
C C R 7 E x p re s s io n
n o
i 1 .5 s
s C C R 7 K n o c k d o w n
e r
p S c ra m b le
x E
1 .0 C o n tro l
A
N
R
m
7
R 0 .5
C
C
e
v
i t
a 0 .0 l
e n le
R l w b o o m tr d a n k r c c o o S C n K 7 R C C
Figure 4-26: Efficient knockdown of CCR7 mRNA expression in Jurkat cells.
Relative CCR7 mRNA expression in siRNA transfected Jurkat cells, scramble siRNA transfected
Jurkat cells and untransfected Jurkat cells, was assessed using the quantitect CCR7 primer
338
(QT01666686, Qiagen). Experiments were performed in triplicate. Error bars represent standard error from the mean of those triplicates. β-actin (QT00095431, Qiagen) was used as a housekeeping control gene, and the Livak method (2^(-ΔΔCq)) used to determine the relative mRNA expression of CCR7 in each group of cells.
Knockdown cells expressed CCR7 at 56.7% to that of the untransfected control Jurkat cells. This was thought to be sufficient, as the affected individuals (B-II-1 and B-II-2) are expressing the CCR7 protein at about 62% that of healthy control individuals (see western blot, section 4.14).
Following the successful knockdown of CCR7 in these cells, the transwell assay was then performed on the knockout Jurkat cells, in order to assess the migration capacity of the
CCR7 knockout cells. This assay was carried out in collaboration with Dr Ying Hong.
Unsurprisingly, a similar result was obtained as with the patient PBMCs. The knockout
Jurkat cell line displays a very poor migratory capacity in comparison to their un- transfected counterparts (Figure 4-27). Wildtype Jurkat cells display the largest relative increase in migration across the polycarbonate membrane in response to CCL21 (mean =
3335.667, SEM = 424.1). Scramble control cells display a moderate increase in migration
(mean = 739, SEM = 203.8) while the p.M228K homozygous cells demonstrate an extremely low increase in migration (mean = 214, SEM = 18.02). The difference in the relative increase in migration, between the wildtype and CCR7 knockdown cells is statistically significant (p=0.0192). This is again very strong evidence that CCR7 is the culprit gene in this family. While this is compelling evidence for the fact that CCR7 is the culprit gene in this family, we cannot ignore the scramble control cells. It should be noted that scramble cells also have a low migration capacity, and it is comparable to that of CCR7 knockdown cells. It should be considered that the lack of migration capacity of
339
CCR7 knockdown cells is due to the transfection process itself and not due to lack of
CCR7 protein. It is therefore difficult to conclude from this experiment whether CCR7 has an impact on the migration of these cells through the transwell pores.
Figure 4-27: Impaired migration of CCR7 knockdown Jurkat cells in comparison to wildtype non-transfected Jurkat cells.
200μl from the bottom of each well was collected, and Jurkat cells present were counted by FACS.
Experiments were carried out in triplicate, error bars represent standard error from the mean of those triplicates. Cells of the same genotype were grouped together. This graph represents the increase from baseline migration for each cell type, i.e. migration which occurred in the absence of CCL21. There were significantly less cell migration in response to CCL21 when CCR7 was silenced using siRNA compared to control cells (p = 0.0192). Unpaired T-tests were used to calculate statistical differences between groups.
340
Using mRNA extracted from CCR7 siRNA transfected Jurkat cells, I have shown that these cells display a modest knockdown in CCR7 expression in comparison to wildtype untransfected Jurkat cells. However, it would be beneficial to confirm this knowndown in protein expression, as well as mRNA expression. As such a western blot should be performed, to assess the level of CCR7 protein knockdown in siRNA transfected cells.
Figure 4-26 shows that transfected cells, express CCR7 at 56.7% that of wildtype cells, and as such show a large reduction in their migration capacity. Interestingly, CCR7 heterozygotes who presumably only have one functional CCR7 allele actually display increased migration across the polycarbonate membrane. These results are surprising as we would expect knockdown cells to display similar results to that of the heterozygotes. It is difficult to pinpoint the reason for such poor migration capacity in CCR7 knockdown cells. It is interesting to speculate that perhaps this is a technical fault, and may be due to the transfection reaction. Transfected cells sometimes behave unusually, although, the scramble control cells while able to migrate, could not achieve this as efficiently as wildtype cells. It would be beneficial in this case to also include a positive control to ensure that transfected Jurkat cells are capable of migrating efficiently. In this case, another cytokine such as CXCL12 could be used to ensure the migration assay is working appropriately.
341
4.18 Discussion
I have identified a homozygous variant in the CCR7 gene in a family presenting with an immune dysregulatory disorder. CCR7 is a multifarious protein having implications in peripheral and central tolerance, lymph node and splenic architecture and in homing haemopoietic cells to the lymph nodes. The variant I have described is extremely rare and causes a downregulation of CCR7 protein expression, loss of IFNγ production in T cells, and a failed migration towards CCL21 chemokine for haematopoietic cells carrying this mutation. This project has advanced the field in two ways, firstly although there is ample literature surrounding CCR7 knockout mouse models and plt/plt mice, never before has a homozygous mutation in the CCR7 gene been described in humans. This particular variant is novel and extremely rare. Secondly, I have shown through experimentation, the mechanisms which underpin this disease, having importance for clinicians treating this disease.
I found a novel homozygous nonsynonymous mutation in CCR7. This mutation is not listed in any human population frequency databases, ClinVar, gNOMAD or ExAC databases. It was predicted damaging, unanimously by all in silico tools. It has also not been described before in the literature. Likewise, no mutation, heterozygous or homozygous in CCR7 has ever been described before, in a patient, in the literature. This is the first monogenic heritability study to be carried out in CCR7.
Nonetheless, a GWAS study was conducted examining 160 patients suffering from
Sjogren syndrome, systemic lupus erythematosus and systemic sclerosis (Kahlmann et al.
2007). The group report 1 homozygous mutation at position -60 C/T, located in the promoter region of CCR7. The authors transfected a T cell line (HUT78) with a luciferase plasmid, containing the mutation in the promoter region of CCR7. The authors
342
demonstrated that this mutation corresponded with a loss of protein expression which resulted in a reduced luciferase activity (Kahlmann et al. 2007) in these cells (Kahlmann
2007). The authors concluded that this variant could lead to increased susceptibility to autoimmunity. Although this is a susceptibility and not a familial study, it is interesting that the homozygous mutation described in family B, suffering from an autoimmune disease, also results in a loss of protein expression.
Immunophenotyping analyses in this study revealed some very interesting insights.
Firstly, these individuals had an inversed CD4:CD8 ratio. This is perhaps unsurprising given what we know about this ratio and its relationship with viral infection. To my knowledge, this has not been reported in mouse models used to investigate the effects of
CCR7 deficiency. An absence of memory T cells was also described in both twins, harbouring the homozygous p.M228K mutation.
CCR7 is best known for its role as a homing molecule, in guiding naïve T cells, B cells and activated dendritic cells towards to the lymph node. In order to measure the impact of the homozygous p.M228K mutation on the migratory function of CCR7, a transwell assay was carried out. In vivo, I have conclusively demonstrated that this mutation greatly disrupts the migratory capacity of cells harbouring the mutated CCR7 protein. This infers that mutant cells cannot efficiently translocate into the lymphoid organs, resulting in a delayed adaptive immune response. Numerous studies have also carried out migration assays using CCR7 knockout cell lines (Otero et al. 2008), CCR7 transfected cell lines
(Kobayashi et al. 2017) and moreover Ccr7-/- mice display reduced numbers of lymphocytes and dendritic cells in their SLO’s (Seth et al. 2011), resulting from defective migration.
343
Evidence from the literature suggests that the immune response in Ccr7-/- or plt/plt mice is delayed (Mori et al. 2001, Förster et al. 2008, Kocks et al. 2009, Comerford et al. 2013,
Fleige et al. 2018). Interestingly, however, an immune response does still occur. This implies that the haematopoietic cells must still migrate to the lymph node and secondary lymphoid organs, albeit at a much reduced rate. CD62L, (a.k.a L-selectin) is another receptor expressed on the cell membrane, often in concert with CCR7 (see section 4.2.4).
It is a cell adhesion molecule and encourages homing to the lymph node (Ivetic et al.
2019). Interestingly, CD62L has a similar cellular pattern of expression as CCR7 and is often used in immunophenotyping assays to identify naïve T cells and distinguish between different types of memory T cells (Hengel et al. 2003, Unsoeld and Pircher
2005). CD34, expressed on endothelial cells, is a ligand for CD62L. The binding of CD34 to CD62L facilitates endothelial-lymphocyte adhesion (Ivetic et al. 2019), and encourages their migration across the endothelial barrier and into the secondary lymphoid organs
(Ivetic et al. 2019). Although it has not been tested in this study, I am assuming that
CD62L is still functioning and has optimal protein expression in affected family members. This would still enable adhesion of immune cells to the endothelium and eventual migration toward the lymph node, albeit at a much slower rate. Other studies have also come to this consensus, for example, in a study assessing BALT formation in
Ccr7-/- mice, results were replicated in mice who had received anti-L-selectin treatment, and therefore did not express functioning CD62L (Fleige et al. 2018). Likewise, L- selectin deficient mice have been generated, and studies examining L-selectin and found lack on contact sensitization in mutant mouse strains (Arbonés et al. 1994, Steeber et al.
1996, Mori et al. 2001). This profound defect was attributed to a lack of migration of naïve T cells in these mice into the lymph nodes (Arbonés et al. 1994, Steeber et al. 1996).
Therefore, it appears that CCR7 and CD62L work together to drive migration to the
344
secondary lymphoid organs. CD62L is the first step in the initiation of this process, enabling T cell adhesion to the endothelial wall with CCR7 providing more stable and directional cues (see section 4.2.4). Therefore, in the absence of CCR7, a functional
CD62L may still provide some migratory help, and guide lymphocytes to the lymph node, mounting a delayed, but not absent, immune response (Mori et al. 2001).
Although the data generated in this project provides preliminary evidence for the contributions of CCR7 in the disease phenotype of these individuals, there are several limitations to my work. Firstly, during this project, I discovered that patients carrying the homozygous mutation in CCR7 have little to no memory T cells. While this is an interesting discovery, it has not been reported previously in relation to CCR7 and therefore may be a novel observation requiring further study and validation. I would therefore ideally like to carry out more immunophenotyping analyses in order to analyse the memory T cell population further. My analyses simply looked at the general memory
T cell population, using the CD45R0 marker. However, different memory subtypes exist, expressing different variations of markers. I would like to investigate these subtypes further, namely; central and effector memory T cell subtypes. Central memory T cells express CCR7, and therefore, it makes sense that these individuals have low or absent central memory cells, due to loss of CCR7 expression. However, it is interesting to consider whether the use of different markers, such as CD62L or CD27, were used to assess these populations of cells, perhaps we would find very different results.
Undoubtedly, more immunophenotyping analysis is needed to investigate these subtypes further.
Secondly, I have not assessed the expression or function of CD62L. As previously stated, this receptor is another important homing molecule, which works alongside CCR7, to traffic lymphocytes and dendritic cells to the lymph nodes. In this study, I have presumed
345
that individuals of family B have a fully functional copy of CD62L. However, a western blot or FACS analysis examining CD62L protein expression would confirm my suspicions.
Thirdly, for the functional experiments examining the SLFN12 gene, some of these experiments had only been carried out once. While the results of these experiments did give substantial evidence that there is little difference between the patients and control, in regards to the functioning of this gene; I would ideally like to repeat these experiments, in order to gain more data points and compute a p-value, to conclusively prove that the homozygous p.M97T variant found in SLFN12 has little impact on this protein function.
The identification of this homozygous p.M228K mutation in CCR7, a major player in the control of the immune system and a major contributor to the development of self- tolerance and therefore the prevention of autoimmune diseases, has been shown to result in the severe phenotypes as demonstrated by family B.
4.19 Future Work
There are numerous aspects of this investigation which have yet to be carried out. Firstly, it is heavily documented in the literature that this receptor is important for the homing of dendritic cells, to the lymph node. Given more time, I would like to investigate the impact of this mutation on dendritic cells. This would primarily involve immunophenotyping of activated and naïve dendritic cells. It has previously been determined that a group of naïve dendritic cells, constantly sampling their environment readily migrate to the lymph node where they contribute to the generation of self-tolerance in the organism (Seth et al.
2011). In fact, Ohl et al. found that this population of DCs (CD11c+ MHC class IIhi DCs)
346
was completely absent in CCR7 knockout mice (Ohl et al. 2004). It would be interesting to investigate whether this population of dendritic cells is also absent in individuals with the homozygous p.M228K mutation in CCR7. Other investigations may include; an examination of actin cytoskeleton polarisation, as mediated by CCR7 via Rho GTPases, and/or an investigation into the chemotaxis or migratory properties of dendritic cells as mediated by CCR7 through members of the MAP kinase pathway (outlined in section
4.2.7).
4.20 Conclusion
In summary, I have identified a pathogenic mutation in CCR7 which is present in several members of Family B. I have investigated how this mutation disrupts the normal functioning of CCR7 and delineated a mechanism by which this mutation contributes to the disease phenotype of this family. My findings have important clinical implications, in that people suffering from severe viral infections and/or autoinflammation should be screened for homozygous mutations in CCR7. For this specific family, my findings led to the patient being fast-tracked for haematopoietic stem cell transplantation.
347
5 Homozygous mutation in BTNL2
associated with Familial
Leucocytoclastic Vasculitis
5.1 Summary
5.1.1 Background:
Here I present a consanguineous family originally from Somalia, wherein three out of four siblings experience episodes of leukocytoclastic vasculitis. This manifests in purple purpuric rashes on the lower extremities, abdominal pain and swelling in the joints. All affected children also experience joint hypermobility, skin laxity, hearing difficulties and mild developmental delay.
5.1.2 Aims:
I aimed to use a combination of homozygosity mapping and whole exome sequencing to determine the genetic cause of the leucocytoclastic vasculitis as observed in these individuals.
348
5.1.3 Methods:
Homozygosity mapping was undertaken in order to identify large homozygous areas in the genome, shared by all three affected family members, and absent in unaffected family members, which may harbour the causative mutation. Whole exome sequencing was also undertaken to identify the homozygous variants within these areas. Upon identification of the likely candidate gene(s), qPCR analyses were performed to measure the mRNA expression of that gene(s).
5.1.4 Results:
The combining of homozygosity mapping and WES data lead to the identification of a causative homozygous variant in BTNL2. This gene could be detected by qPCR in THP1 cells and macrophages.
5.1.5 Conclusion:
Through the use of homozygosity mapping and WES, it is possible to identify putatively causative mutations leading to leucocytoclastic vasculitis in consanguineous families.
Further experiments are required to confirm the mechanism of pathogenesis of vasculitis in this family.
349
5.2 Introduction:
Butyrophilin-like 2, BTNL2 is a B7-like molecule, which is part of the butyrophilin gene family (Arnett et al. 2007). This gene is located on chromosome 6p21.3 between the major histocompatibility complex HLA regions II and III (Tong et al. 2016). B7 molecules are expressed on the surface of antigen-presenting cells and act as costimulatory molecules for T cell activation, by binding to the CD28 receptor on the surface of T cells (Arnett and Viney 2014). They are also involved in the inhibition of T cells by engaging CTLA4 receptors of the T cell surface (Swanson et al.
2013). Butyrophilin molecules, originally found in milk droplets, share homology with the B7 family of proteins. They have a role in the secretion and stabilization of milk fat globules but recently were shown to have immunomodulatory functions. They are involved in T cell selection and cell fate determination (Arnett and Viney 2014). In particular, it was demonstrated that the butyrophilins can induce the differentiation of naïve CD4+T cells to regulatory T cells (Swanson et al. 2013). There are 13 members of the butyrophilin family, including BTNL2.
The butyrophilins comprise a signal peptide, and IgV like domain, an IgC like domain, a cytoplasmic and transmembrane domain as well as a B30.2 region, thought to bind TRIM proteins (Nguyen et al. 2006). The BTNL2 protein contains two IgV and IgC regions.
However, unlike most butyrophilins, it does not contain the B30.2 domain (Figure 5-1).
350
Figure 5-1: Graphical representation of B7 and Butyrophilin Protein Domains,
Obtained from (Arnett and Viney 2014).
Because of its structural similarity to B7 molecules, such as CD80/CD86 molecules, it was originally suspected that this protein was also a T cell co-stimulator, however (Arnett et al. 2007) used murine cytokine profiling and T cell activation experiments to show that it is, in fact, a T cell repressor. This is supported by the fact that the IgV region of CD80, which is used to bind CTLA4, and inhibit T cell production, is conserved in BTNL2
(Simmonds et al. 2006, Arnett and Viney 2014).
Northern blot and Taqman analysis show in mice, that this protein is expressed in both lymphoid and non-lymphoid organs such as the spleen, lung, large intestine, small intestine (specifically the Peyers patches and cecum), thymus and lymph nodes and in particular it localised to the endothelial cells within those tissues (Nguyen et al. 2006,
Arnett and Viney 2014). As mouse and human BTNL2 proteins are 63%
351
identical (Nguyen et al. 2006), we would expect a similar expression pattern in humans, although this kind of analysis has yet to be carried out.
This gene has been implicated as a susceptibility locus in a number of diseases such as sarcoidosis, ulcerative colitis (Pathan et al. 2009), type 1 diabetes (Orozco et al.
2005), Grave’s disease (Simmonds et al. 2006), SLE (Orozco et al. 2005), rheumatoid arthritis (Mitsunaga et al. 2013), IBD, inclusion body myositis (Price et al. 2004) and
Kawasaki disease (Hsueh et al. 2010).
A number of studies have made an association between BTNL2 and sarcoidosis.
Valentonyte et al., 2005 used a 3-point SNP scan to find a susceptibility locus for the disease. A 15kb segment on chromosome 6 within the MHC locus, containing 4 SNPs showed the strongest association. In particular, they found the SNP rs2076530 in BTNL2 to be most strongly associated with the disease (p=0.04). The authors concluded that this mutation disrupts a splice site in the BTNL2 gene, resulting in truncation of the protein and thus disrupting the subcellular localization of BTNL2. Tong et al., 2016 carried out a meta-analysis study and found a statistically significant relationship (p=0.005) between the same mutation in BTNL2 and granulomatous disease susceptibility, such as sarcoidosis, tuberculosis and Crohns disease (Tong et al.
2016). While Simmonds claims that this same homozygous variant is associated with
Graves’ disease (Simmonds et al. 2006). This study used linkage disequilibrium analysis in a large cohort of Caucasian patients to investigate the association of this variant in
BTNL2 with Graves’ disease patients. While there was a significant association, the effect of this mutation was thought to be secondary to another variant also found in these patients, DRB1 β74. It was suggested that the disease may be due to the contributory effects of both these variants. Another study also claims that this variant in is Hardy
Weinberg equilibrium with patients exhibiting rheumatoid arthritis, systemic lupus
352
erythematosus and type 1 diabetes (Orozco et al. 2005). Rs2076530 has been extensively studied and has associations with numerous autoimmune diseases. This particular SNP is located at the 3’ end of the protein. It leads to an alternative splice site in the gene, producing a frameshift, and results in a premature stop codon. Cellular localization studies, in Hela and HEK cells, show that membrane localization of the protein was disrupted and the mutated form of the protein resided instead in the cytoplasm
(Valentonyte et al. 2005).
Given the vast array of association studies correlating the rs2076530 mutation in BTNL2 with immune disorders, it is perhaps unsurprising that a familial case describing this mutation also exists. Coudurier et al., 2009, reported the same homozygous mutation in a familial case of sarcoidosis and suggests, similar to Valentonyte et al., 2005 that this particular variant causes a mislocalisation of the protein (Coudurier et al. 2009).
Unfortunately, however, no functional work was carried out in this study.
Apart from the rs2076530 SNP, other mutations within BTNL2 have also been associated with autoimmune disorders. Mitsunaga et al., 2013 identified BTNL2 as a rheumatoid arthritis susceptibility disease gene. In a small study of 432 cases and controls, they report
3 SNPs, namely rs28362678, rs28362677 and rs1521946 in exon 6 of BTNL2 which were shown to be in strong linkage disequilibrium with the disease (Mitsunaga et al. 2013).
Using logistic regression analysis, they show that these SNPs are significantly associated with rheumatoid arthritis independently of NOTCH4 and HLA-DRB1. Another SNP in BTNL2, rs2294881, was also identified in a GWAS analysis (Pathan et al. 2009). They found a significant association (p = 5.69 x 10-4) between the SNP in BTNL2 and ulcerative colitis patients.
353
Attempts have been made to understand the biological pathway that BTNL2 is involved in. (Nguyen et al. 2006) reports that this protein binds to an unidentified receptor on T cells and B cells, in order to inhibit their proliferation. B7 molecules bind to receptors on
T cells such as CD28, CTLA4, ICOS and PD1. However, it has been demonstrated that BTNL2 does not bind any of these receptors (Arnett et al. 2007). It has been demonstrated that the BTNL2 protein binds a receptor on T cells, distinct from the well- known CD28, CTLA4, ICOS and PD1 receptors, and this receptor becomes upregulated upon T cell activation (Nguyen et al. 2006). Even though BTNL2 also binds a receptor also on B cells, it does not seem to inhibit B cell proliferation.
Luciferase reporter assays show that pathways such as AP-1, NFAT and NF-ƙB are significantly downregulated upon overexpression of plate-bound BTNL2-Ig (Nguyen et al. 2006). The authors concluded that BTNL2 antagonises the proliferation of T cells and inhibits TCR signalling events. Interestingly it has also been reported that cytokines produced by T cells, such as IL6, IL2, IL10, TNFα, GM-CSF and IFNγ, are downregulated when cells overexpress BTNL2 (Nguyen et al. 2006). There is also evidence to suggest that BTNL2, while having T cell inhibitory activities, may induce the differentiation of CD4+ T cells into regulatory T cells, which express FOXP3 (Swanson et al. 2013). T-regs are important cells in maintaining immune homeostasis and balance.
This provides further evidence that BTNL2 is an immune response repressor.
354
5.3 Family Tree
Family C, shown in Figure 5-2 below, is a consanguineous kindred, wherein affected individuals suffer from persistent episodes of leucocytoclastic vasculitis.
Figure 5-2: Pedigree of Family C.
Affected family members are shaded in black. The proband, in this case, is C-IV-1. C-IV-2 and
C-IV-3 are also affected.
355
5.4 Clinical Presentation
All 3 affected siblings (C-IV-1, C-IV-2, C-IV-3) presented at the age of 4, 5 and 7 years old respectively with recurrent episodes of vasculitic rashes affecting their arms, legs, and lower abdomen with palpable purpura (Figure 5-3). These episodes were associated with abdominal pain and arthralgia of the knees suggestive of a diagnosis of chronic IgA vasculitis (previously referred to as Henoch Schönlein purpura). Renal function and blood pressure were normal, and there was no evidence of proteinuria for any of the children.
Skin biopsy for C-IV-1 revealed an inflammatory cell infiltrate composed predominantly of neutrophils and nuclear dust (Figure 5-3 (A)).
Direct immunofluorescence for IgG, IgA, IgM, and C3 was negative. There was no confirmed family history of autoimmunity or immunodeficiency. On review of systems, there was no current history of prolonged or periodic fevers, arthralgia, or other systemic features. All 3 siblings had a background of some mild learning difficulties and developmental delay; C-IV-1 also had a history of recurrent episode of purulent otitis media requiring antibiotics and leading to mild conductive hearing loss. Physical examination demonstrated an injected left middle ear with effusion for C-IV-1; marked skin elasticity and hypermobility for all siblings and vasculitic rashes affecting the lower limbs. Blood pressure was normal, and there was no significant proteinuria or other organ- specific involvement with vasculitis.
Laboratory investigations revealed elevated inflammatory markers (median ESR 85 mm/h, range 20-120 mm/h and median CRP 110 mg/L; range 35-170 mg/L) during the acute episodes. All full blood count parameters were within normal limits. Other autoantibodies (antinuclear antibody; ANCA including anti-proteinase 3 and anti- myeloperoxidase; anti-double stranded DNA; anticardiolipin; and against extractable
356
nuclear antigens) were all negative. Notably, they all had normal levels of C3 and C4, normal function of alternative and classical complement pathways, normal levels of C1q and complement factor I and H.
They were considered to have a familial form of leucocytoclastic vasculitis and were started on colchicine therapy with partial response (less frequent episodes of skin rashes and improvement in acute phase reactants).
Figure 5-3: (A) Skin biopsy for C-IV-1 showing neutrophilic infiltrates (B) Vasculitic lesions affecting lower extremities and (C) palpable purpura in the lower limbs for
C-IV-1.
357
5.5 Methods
5.5.1 SYBR Green qPCR
This dsDNA intercalating dye based qPCR protocol was performed on RNA extracted from patient PBMCs. RNA was first converted into cDNA, using 400ng of RNA per reaction (see section 2.9.3). A master mix was made up using the components as outlined in section 2.9.7. The quantitect BTNL2 primer (Qiagen, QT00090671) was used to measure the expression of BTNL2 mRNA. The quantitect HPRT1 primer (Qiagen,
QT00059066) was used as a housekeeping control gene. Experiments were carried out in triplicate with the mean of three experiments used to calculate mRNA expression.
Expression of BTNL2 was calculated using the Livak method, as described in section
2.9.6.
5.5.2 Macrophage differentiation assay
Patient PBMCs were thawed, using the protocol as described in section 2.11.5. They were seeded into a 6 well plate at a concentration of 1 million cells/ml, in RPMI-1640 medium.
This was incubated at 370C for 3 hours until the monocytes had adhered to the bottom of the well. A total of 1.5mL of medium was then drawn up from the top of each well, using a P1000 pipette, to collect all the non-adherent cells. Only monocytes should remain in the bottom of the well. 1.5mL of fresh RPMI-1640 medium was added to each well, along
0 with 50ng/ml M-CSF. Cells were incubated at 37 C, 5% CO2 for 6 days. On day 6 of the stimulation, 100ng/ml TNFα and 100ng/ml LPS were added to the wells for 4 hours to stimulate BTNL2 expression. All medium was then removed, and a TRIzol RNA extraction was performed on differentiated macrophages (see section 2.9.1). RNA
358
obtained from this protocol was quantified using a nanodrop and converted into cDNA using an RTPCR protocol (see section 2.9.3). qPCR analysis was then undertaken using
SYBR Green to quantify BTNL2 mRNA expression in patient cells
5.5.3 Anti-CD3/anti-CD28 T cell stimulation protocol
Using a 48 well plate (Grenier, 677180), wells were coated with 2μg/ml anti-CD3 (Merck,
OKT3) and 2μg/ml anti-CD28 (BD Pharmingen, 556620) diluted in PBS. This was
0 incubated overnight at 37 C and 5% CO2. The following day this anti-CD3/anti-CD28 mixture was removed, and each of the wells were washed with PBS. PBMCs were thawed, using the protocol as described in section 2.11.5. They were then counted using a haemocytometer and were resuspended in RPMI medium at a concentration of 1x106 cells/ml. 300μl of this cell suspension was added to the pre-coated anti-CD3/anti-CD28.
0 This was incubated for 72 hours at 37 C and 5% CO2.
5.5.4 THP1 Cell Culture
THP1 cells were maintained in RPM1-1640 (10% FBS) medium. They were seeded at a centration of 5x105 cells/ml and kept in T25 flasks until confluent, approximately 8x105 cells/ml. To split cells, an equal volume of medium was added to the flask. Half the total volume of the flask was then removed and transferred to another T25. RPMI-1640 medium was renewed every 3 days.
359
5.6 Suspected mode of inheritance in Family C
To summarise, this is a consanguineous family wherein three out of four children presented with leukocytoclastic vasculitis, suffering from rashes and severe skin lesions.
Both parents were healthy. I, therefore, suspected that mode of inheritance of this disease is homozygous recessive in nature, wherein both parents are asymptomatic heterozygous carriers of the mutation.
5.7 Homozygosity Mapping Results
Consanguineous families often exhibit large areas of homozygosity throughout their genome (Woods et al. 2006). Therefore, homozygosity mapping was the first employed to look for large homozygous areas across the genome of each individual in the family. It was expected that the affected family members would share this homozygous region and that the culprit gene would be contained within.
Therefore I specifically looked for homozygous regions, across the entire genome, which were shared among the affected members of the family and not observed in other unaffected “healthy” family members. Only ROH larger than 1Mb were considered significant (Ceballos et al. 2018). Although each chromosome harboured ROH’s, only three chromosomes contained homozygous regions (> 1Mb) which were shared among the affected siblings. These were chromosome 6 (containing two ROH), chromosome 7 and chromosome 9.
360
5.7.1 Chromosome 6
Figure 5-4: Runs of homozygosity on Chromosome 6 identified in family C.
The x-axis shows the base positions along chromosome 6. The y-axis represents the B allele frequency of each base. Homozygous regions are shaded in green. Affected family members are
C-IV-1, C-IV-2 and C-IV-3, highlighted by a red horizontal rectangle. Outlined by a vertical red box is a ROH of 8Mb and a smaller one of 1Mb length, which are shared by affected siblings.
These ROH’s do not appear in any other family members.
361
5.7.2 Chromosome 7
Figure 5-5: Runs of homozygosity on Chromosome 7 identified in family C.
The x-axis shows the base positions along chromosome 7. The y-axis represents the B allele frequency of each base. Homozygous regions are shaded in green. Affected family members are
C-IV-1, C-IV-2 and C-IV-3, highlighted by a red rectangle. Outlined by a vertical red box is a
ROH of 8Mb length, shared by affected siblings. This ROH does not appear in healthy family members.
362
5.7.3 Chromosome 9
Figure 5-6: Runs of homozygosity on Chromosome 9 identified in family C.
The x-axis shows the base positions along chromosome 9. The y-axis represents the B allele frequency of each base. Homozygous regions are shaded in green. Affected family members are
C-IV-1, C-IV-2 and C-IV-3. Outlined by a vertical red box is a ROH of 6.9Mb length, shared by affected siblings. This ROH does not appear in healthy family members.
363
Table 5-1: Number of SNPs recovered from each ROH observed only in affected family members for family C.
This table outlines each homozygous area, larger than 1Mb and shared by the C-IV-1, C-IV-2 and C-IV-3, the start and end positions of each ROH as well as the number of SNPs in this area.
This information was gathered from Genome Studio.
Chromosome Start End Length No. of No. of
Position Position SNPs genes
6 (a) 24,758,824 32,943,151 8,184,327 729 389
(8Mb)
6 (b) 45,783,884 47,658,627 1,874,743 59 20
(1.8Mb)
7 40,910,751 48,944,233 8,033,552 518 92
(8Mb)
9 91,535,461 98,444,303 6,908,842 397 74
(6.9Mb)
Total 1703 549
By obtaining the genomic coordinates of the ROH found in each of the affected siblings,
I could compile a list of all the genes (see appendix section 8.7) contained within the homozygous regions. One of the genes contained within the ROH, shared by all affected siblings, should be the candidate gene.
364
I then carried out a subsequent analysis, based on the 549 genes found within these ROH.
These genes were annotated using the same freely available online tools and rated based on their functional relevance to the presenting phenotype. Table 5-2 outlines the genes within these ROH, which may be functionally relevant (for the complete gene list refer to appendix, section 8.7). We can see from Table 5-1, there are two ROH that are at least
8Mb in length. However, the 8Mb region on chromosome 6, is much more densely covered by the SNP probes. In addition, it contains a higher number of genes than the
8Mb region on chromosome 7. By default, the ROH on chromosome 6, therefore, contains most of the relevant genes (Szpiech et al. 2013). I, therefore, suspect that this region more than likely contains the culprit gene
365
Table 5-2: Genes found in homozygous areas in C-IV-1, C-IV-2 and C-IV-3, which may be of relevance to the disease process.
Information in this table regarding the gene function was obtained from www.genecards.org and www.omim.org
Chromosome Gene Function 6 PSORS1C1 Associated with susceptibility to psoriasis and systemic sclerosis 6 CCHCR1 Regulates keratinocyte proliferation. Mutations in this gene are associated with psoriasis 6 TCF19 Trans-activating factor important in transcription of genes involved in stages of cell cycle progression 6 MICA Stress induced self-antigen recognised by T cells. Susceptibility to psoriasis 6 LTA Member of the tumor necrosis factor family. Susceptibility to myocardial infarction, non- Hodgkin's lymphoma, and psoriatic arthritis 6 FAM65B Associated with autosomal recessive deafness. RNAi knockdown of this gene in mouse myofibroblasts caused a reduction in myotube formation. 6 NCR3 The protein encoded by this gene is a natural cytotoxicity receptor (NCR) that may aid NK cells in the lysis of tumor cells. Malaria susceptibility 6 BAG6 A nuclear protein implicated in the control of apoptosis 6 TNXB Appears to mediate interactions between cells and the extracellular matrix. Substrate-adhesion molecule that appears to inhibit cell migration. Accelerates collagen fibril formation.
366
6 NOTCH4 The NOTCH pathway is evolutionarily conserved and important in cellular differentiation, proliferation and apoptosis 6 BTNL2 Involved in immune surveillance, serving as a negative T-cell regulator by decreasing T-cell proliferation and cytokine release. 6 HLA-DRA 6 HLA- DQA1 6 TAP1 Involved in the transport of antigens from the cytoplasm to the endoplasmic reticulum for association with MHC class I molecules 6 TAP2 As above 6 PLA2G7 Involved in the hydrolysis of phospholipids into fatty acids and other lipophilic molecules 7 SPDYE1 Williams Beuren syndrome 7 PGAM2 Mutations in this gene cause muscle phosphoglycerate mutase efficiency 7 CCM2 The protein is required for normal cytoskeletal structure, cell-cell interactions, and lumen formation in endothelial cells. Involved in MAP3K signalling 7 NACAD Prevents inappropriate targeting of non-secretory polypeptides to the endoplasmic reticulum 9 S1PR3 Receptor for sphingosine 1-phosphate and contributes to the regulation of angiogenesis and vascular endothelial cell function 9 SUSD3 Plays a role in breast tumorigenesis by promoting oestrogen-dependent cell proliferation, cell-cell interactions and migration
367
While this analysis does give us a lot of information, as it is possible to identify the genes contained within the homozygous areas, more information is needed. The microarray used to carry out homozygosity mapping uses SNP probes containing SNPs which are common in the general population. It does not measure every position along the genome.
In order to find rare variants, within these homozygous areas or elsewhere throughout the genome, which may be leading to the disease pathogenesis, whole exome sequencing was carried out.
5.8 Results from Whole Exome Sequencing
It was decided that whole exome sequencing would be carried out, encase there were any damaging mutations outside the homozygous regions which may have been missed by the homozygosity mapping. The proband, two more affected siblings and parents, were whole exome sequenced.
The whole exome fastq files were put through the Galaxy platform and annotation files obtained from wANNOVAR. This annotated list from each affected family member was then filtered using the following filtering strategy.
368
B A
370
369 C
Figure 5-7: Filtering method applied to WES variants
Variant filtering method as applied to (A) C-IV-1, (B) C-IV-2 and (C) C-IV-3
Variants which remained after this filtering analysis from each of the affected family members were cross-referenced to search for common variants shared by all affected individuals. A total of 13 mutations remained, described in Table 5-3, below.
Table 5-3: Homozygous WES variants shared by all three affected siblings in family
C after filtering analysis
Chromosome Gene Effect Variant Frequency; In gnomAD silico predict ions 12 KLRB1 Nonsynonymo p.E103K 0.00405 T/P/B us
15 GOLGA Nonsynonymo p.E760G 0.1225 . 6L2 us 6 FAM65 Nonsynonymo p.W15R 0.0094 T/D/D B us
6 SLC17A Stopgain p.Q379X 0 T/./. 4 6 OR12D Nonsynonymo p.C70F 0.0063 D/B/N 2 us 6 TNXB Frameshift p.F1228f 0 . s
6 BTNL2 Nonsynonymo p.D118N 0.01204 D/B/N us 8 GINS4 Nonsynonymo p.E132K 0 T/P/N us
8 KAT6A Nonsynonymo p.R972H 0.00001 T/B/N us 7 SPDYE Nonsynonymo p.R297Q 0.0024 D/D/N 1 us 7 NACAD Nonsynonymo p.P409L 0.00019 D/P/D us
9 CD2AP Nonsynonymo p.T551A 0.00031 T/B/N us
370
Interestingly, most of the shared relevant genes are located on chromosome 6. Referring to the homozygosity data on Table 5-1, chromosome 6 harbours one of the largest homozygous regions and the largest number of SNPs. Region A on chromosome 6 is a
ROH reaching 8 Mb in length and spanning from 24,758,824 to 32,943,151. Most of the variants on Table 5-3, that are located on chromosome 6 are found within this area. I, therefore, suspected that this region could contain the culprit gene.
5.9 Combining WES data and Homozygosity Mapping
In order to narrow down the search for the candidate gene further, I examined both the homozygosity mapping data and the WES data together. Specifically, I focused on homozygous WES variants which were contained within ROH, identified by the homozygosity mapping data. To further this analysis, I then identified those homozygous
WES variants, which were shared among each of the affected individuals.
371
Table 5-4: Number of homozygous WES variants, found in ROH and shared by affected individuals in family C.
Chromosome No. of genes in Total no. of No. of Shared
overlapping WES homozygous homozygous
ROH variants in WES variants in WES variants in
ROH ROH ROH
6 (a) 389 426 379 7
6 (b) 20 21 14 0
7 92 53 26 2
9 74 23 20 0
Total 575 523 439 9
In order to find the particular causative variant, I focused on the last column of Table 5-4.
A gene list, containing homozygous variants from the WES data, contained within the overlapping ROH, which were shared between the affected siblings, was compiled ( Table
5-5). This analysis proved very effective. By compiling data from both homozygosity mapping and WES, I narrowed the number of candidate variants down to 9.
372
Table 5-5: Homozygous variants in affected individuals from family C, their frequency in the general population and their in silico prediction of pathogenicity.
Chromos Position Gene Nucleotide Amino Frequenc Polyphen
ome Change Acid y (1000G) In silico
Change Predictio
n
6 25042112 FAM65B c.T43C p.W15R 0.0034 D
6 25778182 SLC17A4 c.C1135T p.Q379X 0.0048 B
6 29364685 OR12D2 c.G209T p.C70F 0.0048 B
6 31238053 HLA-C c.C829G p.Q277E 0 B
6 32049864 TNXB c.3682_3685d p.F1228f 0 .
el s
6 32372791 BTNL2 c.G352A p.D118N 0.0044 D
6 32632700 HLADQB c.A254T p.Q85L 0 B
1
7 44047124 SPDYE1 c.G890A p.R297Q 0.0048 D
7 45124553 NACAD c.C1226T p.P409L 0 D
The function of these genes was further delineated using the available online software such as Gene a la Carte, Uniprot and OMIM. Candidate genes were chosen based on the strategy as outlined in 2.17.
373
Based on the phenotype observed in this family, it was agreed that the most relevant genes were FAM65B, TNXB and BTNL2. FAM65B has been previously associated with hearing loss (Diaz-Horta et al. 2014). TNXB is known to cause skin laxity (Kaufman and Butler
2016), and BTNL2 is a negative regulator of T cells (Arnett et al. 2007), likely to be responsible for the vasculitic lesions seen in this family.
I then sought to find where these genes were located in the genome. In particular, I used the homozygosity mapping data to pinpoint the location of these genes to chromosome 6.
The variant in FAM65B lies at 25042112bp of the chromosome. The variant in TNXB can be found at the 32049864 bp and in BTNL2, the variant is at position 32372791bp.
Referring to Table 5-1, we can see that all these mutations lie within the same homozygous segment on chromosome 6 (24,758,824bp – 32,943,151bp). This ROH is present only in affected individuals. All other members of the family are heterozygous at these positions. We can, therefore, assume that the disease segregates with this segment of chromosome 6.
5.9.1 FAM65B
FAM65B is also known as RIPOR2, is an inhibitor of RhoA, a small G protein. Inhibition of RhoA activity can lead to the polarisation of T cells and neutrophils, myoblast fusion and the differentiation of hair cells (Anon. 2019g, p. 2). The protein encoded by this gene is expressed on the plasma membrane and is an important component of the hair cell stereocilia, which is thought to be essential for hearing. In particular, it is required for the normal development of hair cell stereocilia within the cochlea of the inner ear (Anon.
2019g, p. 2). It is also involved in mechanosensory hair cell function and maintains the structural organization of the basal domain of stereocilia.
374
A splice site mutation (c.102-1G>A) in this gene has been implicated in hearing loss
(Diaz-Horta et al. 2014). The mutation in this gene segregated with autosomal recessive deafness within 6 affected members of a consanguineous family. This mutation lead to exon skipping and deletion of 52 amino acid residues, encoding the PX membrane localisation domain of the protein. The authors showed that this mutation was abnormally overexpressed in the cytoplasm but could not reach the plasma membrane. The authors also show that this protein is essential for normal hearing using an animal model.
Knockdown of this gene in zebrafish caused a reduction in neuromasts and saccular hair cells, resulting in hearing loss (Diaz-Horta et al. 2014). The authors conclude that this mutation is essential for normal hearing and that a defect in this protein results in autosomal recessive deafness.
The variant identified in family C is not the same as was identified by Diaz Horta et al.
(Diaz-Horta et al. 2014). However, it has been predicted damaging and deleterious by both SIFT and Polyphen in silico models and is quite rare. According to gnomAD, it occurs in 1562 alleles, including 9 homozygotes. However, it is causative of profound sensorineural hearing loss in humans and mice (Diaz-Horta et al. 2014). Under these circumstances, as none of the affected individuals in this family suffer from sensorineural hearing loss, it is unlikely therefore that this variant is pathogenic.
5.9.2 TNXB
TNXB encodes a protein called Tenascin XB, which belongs to the Tenascin family of proteins. These proteins are a family of extracellular membrane (ECM) proteins. Tenascin
XB is localised on the outer reticular lamina of the basement membrane of the cell
(Pénisson-Besnier et al. 2013). TNXB is a substrate adhesion molecule which accelerates
375
collagen fibril formation. This protein has previously been associated with Ehlers Danlos syndrome (Kaufman and Butler 2016), a connective tissue disorder. TNXB is thought to function during wound healing and to mature the extracellular matrix (Egging et al.
2007).
Numerous studies have made the link between TNXB deficiency and Ehlers Danlos syndrome, resulting in hyperextensible skin and joints, easy bruising, vascular fragility and skin laxity. Burch and colleagues report a male suffering from congenital adrenal hyperplasia and associated with Ehlers Danlos syndrome (Burch et al. 1997). The authors report a homozygous 30kb deletion in CYP21B, responsible for the adrenal hyperplasia, interestingly the coding region of CYP21B overlaps with that of TNXB. Therein, they also found the creation of a hybrid gene between TNXB and TNXA, with early termination of this hybrid TNX translation, resulting in a loss of TNXB protein expression. The patient demonstrated abnormal elastin bodies beneath the dermal-epidermal junction, a diffuse increase in perivascular matrix, and uneven packing of myelin sheath of peripheral nerves
(Burch et al. 1997). Numerous other reports have also reported the link between Ehlers
Danlos syndrome and deficiency of TNXB (Schalkwijk et al. 2001, Lindor and Bristow
2005). Interestingly, there have also been reports of compound heterozygous mutations in TNXB, leading to muscle weakness, primarily manifesting in myopathy (Pénisson-
Besnier et al. 2013). Indeed, using mouse models Mao and colleagues inactivated the
TNXB gene, resulting in mice with progressive skin hyperextensibility (Mao et al. 2002), mimicking Ehlers Danlos Syndrome.
Although the p.F1228fs mutation in TNXB has not been described before, frameshift mutations are often detrimental to the translation of the protein and often results in early termination (Okamura et al. 2006). Given the nature of the mutation observed and the fact that it is homozygous in nature, I suspect that this leads to a loss of TNXB protein
376
expression, leading to Ehlers Danlos syndrome in these patients. This mutation has also been predicted damaging by the in silico models and is extremely rare and is not listed in either the 1000G, ExAC or gnomAD databases. Hence, we can assume it is completely novel. Therefore, based on evidence from the literature, and the nature of the mutation present in this gene (i.e. frameshift) I feel it is reasonable to suggest that genetic variants in this gene may provide an explanation for the skin laxity observed in the affected members of this family.
Indeed, vascular phenotypes of EDS have also been described previously. However these have been described in relation to the COL1A1 gene (Hoffman et al 1991). Another report by (Kapferer-Seebacher et al. 2016) also examined EDS and found vascular manifestations such as pretibial hyperpigmentation, easy bruising, vascular fragility in individuals who had mutations in the C1R and C1S genes. Therefore, as vascular phenotypes had been documented before in different genes leading to EDS, it is possible that the vasculitic presentations observed in this family may be attributable to the homozygous mutation observed in TNXB.
377
5.9.3 BTNL2
The BTNL2 gene encodes for Butyrophilin-like 2, a transmembrane protein belonging to the butyrophilin-like B7 family of receptors (Stammers et al. 2000). This family typically function as immunoregulators and have homology to the butyrophilin proteins. This particular protein is thought to act as a T cell repressor, by suppressing T cell function and cytokine release. This gene has been associated with susceptibility to sarcoidosis (Lin et al. 2015), but also a number of other immune-associated conditions such as rheumatoid arthritis, SLE, ulcerative colitis, IBD and type 1 diabetes (Orozco et al. 2005).
According to mouse studies, this gene is heavily expressed in the small and large intestines, colon and appendix as well as lymphoid tissues. PCR has also enabled the detection of this gene on T cells, B cells and macrophages (Nguyen et al. 2006). Stammers and colleagues mapped BTNL2 to the border to the major histocompatibility complex class II (MHCII) (Stammers et al. 2000), it was therefore suspected to have implications in the immune system. It was observed in mice, that BTNL2 binds a receptor on T cells and B cells that is distinct from Cd28 and Ctla4 (Nguyen et al. 2006). This receptor, however, has not yet been identified.
Valentonyte and colleagues found a homozygous splice site mutation in BTNL2 in a major study examining 947 cases of both sporadic and familial sarcoidosis (Valentonyte et al.
2005), which had high association with the disease. This mutation resulted in a premature stop codon and mislocalisation of the resulting protein (Valentonyte et al. 2005). Other studies found high association with the rs2076530 SNP in BTNL2 and sarcoidosis in an
Iranian population (Vazifehmand et al. 2017) an American population (Rybicki et al.
2001) and a German population (Li et al. 2006). The A allele of this SNP was seen to be significantly increased in individuals suffering from sarcoidosis as opposed to healthy
378
controls, in all studies (Rybicki et al. 2001, Li et al. 2006, Vazifehmand et al. 2017).
Sarcoidosis is an inflammatory granulomatous disorder, affecting the lungs and lymph nodes (Li et al. 2006). It is characterised by an over exaggerated immune response and T cell infiltration. One familial study has also been carried out identifying the same rs2076530 homozygous SNP in a family affected with sarcoidosis. The authors also concluded that this was due to a mislocalisation of the protein (Coudurier et al. 2009).
BTNL2, based on evidence from the literature, is an interesting gene and could be responsible for the purple purpuric rashes and severe vasculitic lesions observed on these individuals. The mutation identified in family C documented in 3181 alleles in gnomAD, including 119 homozygotes. Given the high number of homozygotes, it is likely that this mutation in a polymorphism. The family in question are originally from Somalia. Africa has 0 homozygotes and the allele frequency for this population is 0.00096. Given the rarity of this variant in the African population, we cannot rule out the possibility that it may be damaging for people of this ethnicity. Also of note, it has been predicted damaging by the in silico models. Therefore, given its segregation with the disease phenotype, its zygosity and functional relevance as evidenced from the literature, I, therefore, felt that this was a good candidate to study further in relation to the vasculitic lesions observed on affected individuals.
379
5.10 Sanger Sequencing Confirmation
Confirmation of p.F1228fs mutation in TNXB, p.W15R in FAM65B and p.D118N in
BTNL2 by Sanger sequencing and Integrated Genome Viewer (IGV) analysis.
5.10.1 p.W15R mutation in FAM65B
Figure 5-8: Confirmation of p.W15R mutation in FAM65B
(A)Integrated genome viewer, of this homozygous variant in FAM65B in three affected family members. This variant is present in a heterozygous state in C-III-1 and C-III-2. (B) Sanger sequencing analysis, which has been aligned to reference sequence exon 1 of FAM65B
(NM_001286446). The blue vertical line indicates the position of the homozygous variant, present in all affected siblings. The parents and unaffected sibling are heterozygous at this position.
380
5.10.2 p.F1228fs mutation in TNXB
Figure 5-9: Integrated Genome Viewer and Sanger sequencing confirming p.F1228fs mutation in TNXB.
(A)Integrated genome viewer, of this homozygous variant in TNXB in three affected family members (C-IV-1, C-IV-2 and C-IV-3). This variant is present in a heterozygous state in both parents (C-III-1 and C-III-2). (B) Sanger sequencing analysis which has been aligned to reference sequence exon 9 of TNXB (NM_032470). The blue vertical line is located beside where the frameshift resides. In unaffected individuals, the base in question is the “T” located 1bp to the right of the blue vertical line. In affected individuals, this base does not exist, due to the frameshift mutation. The parents and unaffected sibling are heterozygous at this position
381
5.10.3 p.D118N mutation in BTNL2
Figure 5-10: Integrated Genome Viewer and Sanger sequencing confirming the p.D118N mutation in BTNL2
(A)Integrated genome viewer, of this homozygous variant in BTNL2 in three affected family members. C-III-1 and C-III-2 are heterozygous at this position. (B) Sanger sequencing analysis of this variant which has been aligned to reference sequence exon 2 of BTNL2 (NM_001304561).
The blue vertical line indicates the position of the homozygous variant, present in all affected siblings (C-IV-1, C-IV-2, C-IV-3). The parents and unaffected sibling are heterozygous at this position.
According to the Sanger sequencing analysis. The variants in TNXB, FAM65B and
BTNL2 are true variants. In addition, they segregate appropriately with the disease phenotype. As the mutation in BTNL2 is most likely responsible for the vasculitic lesions, and this aspect of the phenotype is most urgent and most problematic to this family, it was decided that the BTNL2 gene should be investigated further.
382
5.11 Gene Expression Analysis of BTNL2
As a first line of investigation into the impact of this mutation on the BTNL2 gene, I decided to examine whether this mutation caused a change in the gene expression of
BTNL2. I first examined the expression of this gene through a series of qPCR experiments.
Several groups have previously investigated the expression pattern of BTNL2. In mice, it is expressed in the spleen, lung, large and small intestine (Arnett and Viney 2014). The tissue and cellular expression of BTNL2 in humans can be found in protein atlas and has a similar distribution to mice. Nguyen and colleagues, also show that BTNL2 is expressed on T cells, B cells and macrophages (Nguyen et al. 2006). As PBMCs contain a varied population of immune cells, and as they are easily accessible to my lab, detecting expression of BTNL2 in PBMCs provided a good starting point.
5.11.1 Undetectable BTNL2 expression in PBMC
RNA was extracted from healthy control PBMC and converted into cDNA using rtPCR.
A SYBR Green qPCR was then used to measure the expression of BTNL2.
The plot below (Figure 5-11) is an amplification plot from the qPCR analysis.
Intercalating DNA dyes or probes are used in the reaction. Therefore, the exponential rise in DNA molecules corresponds to an increasing fluorescence in the PCR tube. When this dye reaches a high enough fluorescence that it can be detected by the computer, an amplification plot is generated, by plotting relative fluorescence against the number of amplification cycles (Wagner 2013). This amplification plot represents the accumulation of a specific product over the entire PCR reaction (~40 cycles) (Anon. 2019h). A
383
threshold (green horizontal line) is used to get rid of any background fluorescence. The
Ct (threshold cycle) value is determined by the cycle number at which the fluorescence signal crosses the threshold (Anon. 2019h). The Ct value is inversely related to the starting quantity of the target and can be used to calculate the initial DNA/RNA copy number
(Anon. 2019h). Therefore, the earlier (least number of cycles) the fluorescence is detected, the higher the expression of the gene/DNA molecule.
Figure 5-11: Undetectable BTNL2 expression in baseline resting healthy control
PBMCs.
RNA was extracted from unstimulated healthy control PBMC using the TRIzol extraction method.
This was converted into cDNA and used to detect BTNL2 expression. HPRT1 is used as a housekeeping control gene in this analysis. Experiments were performed in triplicate. The y-axis represents the relative fluorescence unit (RFU); the x-axis represents the number of amplification cycles. This showed undetectable expression of BTNL2 in resting healthy control cells. 384
As is evident from the graph above, HPRT1 is highly expressed in PBMCs with a mean
Ct value of 21. However, BTNL2 is expressed much later, having a mean Ct value of 39.
Therefore, I cannot say for sure that I am detecting the true BTNL2 mRNA, as, at 35-40 cycles, the reaction is no longer reliable. It is thought to be primer dimers that have been amplified (Anon. 2019h). Thus, this represents a negative result. BTNL2 has not been detected in baseline healthy control PBMCs.
In light of this result, other attempts were made to try to detect the BTNL2 gene by qPCR.
Firstly, I reasoned that, as it is documented that this gene binds to a receptor on T cells
(Nguyen et al. 2006), that perhaps by performing a anti-CD3/anti-CD28 stimulation on
PBMC, I may be able to detect its expression in these induced T cells. This, however, was again unsuccessful. From these cells, a Ct value of 37 was obtained, with the control gene
HPRT1 having a Ct value of 22. While this is an improvement on the above experiment
(Figure 5-11), it is still not considered reliable, and so the results of this experiment were deemed negative.
Next, I thought perhaps the methodology I was using was flawed. Perhaps the primers were not specific, or the SYBR Green qPCR mix wasn’t appropriate for this gene.
(Lebrero-Fernandez et al. 2016) claimed to detect BTNL2 expression via qPCR using
GoTaq qPCR master mix in colon cancer samples and using their own designed primers.
Therefore, I tried to replicate their findings using GoTaq, and the primers provided by this paper, on anti-CD3/anti-CD28 stimulated PBMC (Lebrero-Fernandez et al. 2016).
Again, however, this experiment failed. A Ct value of 39 was obtained which is extremely low and not a viable result. HPRT1 was again used as a housekeeping control gene here and had a Ct value in this instance of 25.
385
As the same was result was obtained for both qPCR master mixes (GoTaq and SYBR
Green), I concluded that BTNL2 expression cannot be detected in these cells, and so I sought to find other cell types where this gene may be expressed.
5.11.2 Expression of BTNL2 mRNA is detectable in THP1 cells
Valentonyte and colleagues report that they could detect the expression of this protein in
THP1 cells (Valentonyte et al. 2005), so I investigated whether I could replicate their findings.
Given the use of standards in this experiment, the computer program (Bio-Rad) generated a standard curve. The R2 value represents how close the observed data is to the fitted regression line (Editor 2019). As we know, the relative fluorescence of the sample is directly proportional to the amount of cDNA in the sample. The program automatically plots the line that should be observed (green line slanting downwards) and then plots where the cDNA standards lie in relation to this line. Therefore, if the R2 value is 1, the data fits the model perfectly, and we can be confident that the primers are working efficiently and the standards were diluted properly. Low R2 values (<0.5) are not considered reliable.
386
Figure 5-12: Successful amplification of the BTNL2 gene expression in THP1 cells.
THP1 cells were cultured according to the protocol, as described in section 5.5.4. They were stimulated with 10ng/ml TNFα for 4 hours. RNA was obtained from a TRIzol extraction of stimulated THP1 cells and converted into cDNA. Expression of the gene was measured using
387
the SYBR Green master mix. HPRT1 was used as a housekeeping gene. Standards of cDNA were made up consisting of 1 in 5 dilutions. A qPCR experiment using SYBR Green was used to quantify BTNL2 expression in each of the standards. Experiments were performed in triplicate.
The mean of the triplicates were taken as the Ct value for a particular standard.
The first standard, representing the highest concentration of cDNA, has a Ct value of
30.27, implying a positive result and amplification of the correct BTNL2 target mRNA. It is also apparent from the amplification plot in Figure 5-12 above that there is a good separation out of the standards, and the less concentrated the cDNA, the lower the Ct value. In addition, the standard curve in Figure 5-12, shows that the R2 of the standards is 0.775. This is a positive result and means that BTNL2 is being amplified efficiently.
This implies that the quantitect primers used in this analysis are specific. I can, therefore, conclude that successful amplification of BTNL2 has been achieved. BTNL2 is detectable on TNFα stimulated THP1 cells.
Although this was a good result, THP1 cells are a cell line. It would, therefore, be impossible to measure differences in expression of BTNL2 in patient cells using the THP1 cell line. As patient PBMCs are easily accessible to my lab, I questioned whether there is any other cell type that is inducible from PBMCs that would be possible to detect BTNL2 on. Apart from T cells and B cells, the literature also reports that BTNL2 is detectable on macrophages (Nguyen et al. 2006).
388
5.11.3 Detectable expression of BTNL2 mRNA in monocyte-derived macrophages
In brief, I obtained monocyte-derived macrophages from healthy control PBMC using the differentiation method as described in section 5.5.2. Upon successful differentiation of macrophages, RNA was extracted via a TRIzol extraction protocol and converted to cDNA using a reverse transcriptase PCR. Using SYBR Green qPCR master mix and the quantitect BTNL2 primer, I attempted to measure the expression of BTNL2 mRNA using
HPRT1 as a housekeeping control. Experiments were performed in triplicate.
Figure 5-13: Amplification plot showing Ct values for HPRT1 and BTNL2 gene expression from TNFα and LPS stimulated macrophages and from (baseline) macrophages.
RNA was extracted from 100ng/ml TNFα and 100ng/ml LPS stimulated monocyte-derived macrophages, from a healthy control individual and converted into cDNA. A qPCR experiment
389
using SYBR Green was used to quantify BTNL2 expression. BTNL2 expression in unstimulated
(baseline) monocyte-derived macrophages was also assessed. Experiments were performed in triplicate. HPRT1 was used as a housekeeping control gene in this analysis. The y-axis represents the relative fluorescence unit (RFU), the x-axis represents the number of amplification cycles
Macrophages were stimulated with 100ng/ml TNFα and 100ng/ml LPS for 4 hours.
Stimulated macrophages had a mean Ct value of 28.23. Unstimulated macrophages had a mean Ct value of 32. In both instances, it is considered a positive result. However, the
TNFα and LPS stimulated macrophages represent the most efficient amplification cycle and therefore highest expression of BTNL2. HPRT1 was used as a housekeeping control gene in this analysis and had a mean Ct value of 22.58.
This amplification plot indicates a positive result and detection of BTNL2 mRNA. I attempted to repeat this experiment by stimulating macrophages from patient PBMC.
Unfortunately, in this particular case, it proved difficult to obtain PBMCs from this family. Therefore I could not carry out the final step and measure the expression of pattern this gene in patient cells.
390
5.12 Future Investigations
There are a number of future investigations which could be undertaken here in order to investigate whether BTNL2 could lead to leukocytoclastic vasculitis in this family.
1. First and foremost, I would like to obtain patient PBMCs and repeat the monocyte
derived macrophage experiment on these cells in order to assess the expression of
BTNL2, and investigate whether the homozygous p.D118N mutation has any
impact on the expression pattern of this gene.
2. BTNL2 is a negative regulator of T cell proliferation (Nguyen et al. 2006). Given
this information, it would be interesting to assess whether the individuals have
excessive numbers of T cells. T cell immunophenotyping could be carried out in
order to assess this. Perhaps in-vitro work could also be carried out using Ki67 or
CFSE staining on CD3/CD28 stimulated PBMCs. Then analysing the
fluorescence of this dye on CD4+ T cells, by method of flow cytometry, as a
measure of T cell proliferation.
3. BTNL2 expression induces the production of FOXP3 regulatory T cells (Swanson
et al. 2013). Therefore we would expect affected individuals, to have reduced
numbers of Tregs. Indeed this could also be assessed via T cell
immunophenotyping assays.
4. Using luciferase reporter assays, Nguyen and colleagues demonstrated that
BTNL2 has implications in the AP-1, NFAT and NF-ƙB pathways. Specifically,
an increase in BTNL2 expression causes a reduction in AP-1, NFAT and NF-ƙB
pathway activation (Nguyen et al. 2006). Perhaps, I could perform an in vitro
assay, wherein I stimulate the activity of these pathways via TNFα, or using pate
391
bound anti-CD3+ (Nguyen et al. 2006) and measure the expression of the AP-1,
NFAT and NF-ƙB transcription factors, by flow cytometry.
5. Nguyen and colleagues also reported a reduction on IL-2 production upon BTNL2
expression, when CD4+ T cells were activated with anti-CD3/CD28 beads
(Nguyen et al. 2006). This experiment could easily be repeated, and the expression
of IL-2 measured by ELISA or flow cytometry.
These experiments would give a good indication as to whether or not the homozygous p.D118N mutation identified in BTNL2 has an effect on the protein function. Given positive results from these experiments, we may gain a clearer insight into physiological processes which underpin the disease phenotype in this family.
392
5.13 Discussion
In this consanguineous family presenting with leukocytoclastic vasculitis, skin laxity and bruising, I have identified homozygous mutations in 2 genes which segregate appropriately with the disease phenotypes, through the combined use of homozygosity mapping and WES analysis. Namely; p.F1228fs in TNXB likely responsible for the skin laxity in affected individuals; and p.D118N in BTNL2 possibly leading to familial leukocytoclastic vasculitis, were identified in all affected members of this family. In this study, I focused my efforts on the mutation identified in BTNL2, as vasculitis was the most threatening condition to the health of these individuals.
Numerous groups have provided sufficient evidence through the use of GWAS and familial studies that there is a substantial correlation between genetic variants in the
BTNL2 gene and risk of developing autoimmune disorders. Of interest, a familial study carried out by Coudrier et al., also suggested that monogenic inflammation manifesting as sarcoidosis may be driven by homozygous mutations in BTNL2 (Coudurier et al.
2009). The rs2076530 homozygous mutation was found to be extremely rare and predicted damaging by all in silico tools. Therefore, I decided to investigate the effect of the homozygous p.D118N mutation on this gene, and whether or not this may be linked to leukocytoclastic vasculitis.
Several attempts were made to quantify its expression by qPCR experiments, using cDNA obtained from PBMCs, THP1 cells and monocyte-derived macrophages. Indeed, despite the extremely low global expression of this gene, I have shown it is detectable in TNFα and LPS stimulated macrophages.
393
Although this research gives some preliminary data as to how BTNL2 may lead to vasculitic lesions and purpuric rashes as observed on affected members of Family C, there are several limitations to this investigation. Firstly, substantially more work needs to be done on this topic in order to conclusively say whether or not BTNL2 leads to vasculitis in these individuals. As a starting point, experiments such as those listed in section 5.12 should be carried out. Secondly, while the qPCR experiments would be nice to carry out in order to get an indication of BTNL2 expression in these individuals, this experiment only measures mRNA expression. Ideally, a western blot should be carried out in order to measure protein expression. However, as it took so long to find a cell type which expressed detectable amounts of this protein, I had insufficient time during this project to carry out a western blot.
BTNL2 is not the only culprit gene in Family C. Affected individuals demonstrate a complex clinical presentation resulting in a blending of phenotypes and in fact, 2 genetic syndromes might account for the complex phenotype observed here (features of Ehlers-
Danlos, and familial leukocytoclastic vasculitis). The third limitation to this project is that given the time constraints, no investigation had been made into the other mutations identified. This is a shame as these were true mutations, confirmed by sanger sequencing, are rare, predicted damaging and p.F1228fs in TNXB is also novel.
The three mutations identified in this family were found in the same region, on the same arm of chromosome 6. Children of this family who inherited both copies of this segment of the genome, presented with three distinct phenotypes. This blending of phenotypes, however, is not uncommon. In fact, in a meta-analysis carried out by (Posey et al. 2017), they found that approximately 5% of patients had more than one clinical genetic diagnosis, responsible for the complex presenting phenotypes.
394
My data suggests that sequencing of BTNL2 should be on clinicians’ radar when looking for genetic causes of leukocytoclastic vasculitis and other autoimmune diseases, and patients presenting with this condition should be screened for homozygous mutations in this gene in future.
5.14 Conclusion
I have identified a homozygous mutation in BTNL2 in this kindred, thought to be responsible for the purple purpuric rashes and severe vasculitic lesions observed here.
Given the low global expression of this gene, I have found, it is possible to detect BTNL2 mRNA expression in TNFα and LPS stimulated macrophages. The p.D118N homozygous mutation in BTNL2 is possibly responsible for the leukocytoclastic vasculitis observed in this family. However, further investigations need to be carried out in order to definitively prove this.
395
6 General Discussion
This study was undertaken with an aim to identify genetic causes of rare inflammatory diseases and to investigate the biological pathways at hand, in each case. My investigation had some interesting findings, and as outlined by the previous chapters, the results of this investigation have been:
1. The heterozygous p.T647P mutation in TNFAIP3 results in overactivation of the
NF-ƙB, interferon and NLRP3 immune response pathways. This predominantly
manifested in severe enhanced granulomatous cerebral inflammation in a family
of Pakistani heritage. Upon administration of a JAK 1/2 inhibitor, symptoms in
the proband of family A improved significantly.
2. The homozygous p.M228K mutation in CCR7 as observed in a consanguineous
family of Saudi-Arabian descent results in a reduced migratory capacity of
immune cells bearing this mutation. As a result, individuals of this genotype have
delayed T cell responses, low IFNγ production and are susceptible to viral
infections.
3. The homozygous p.D118N mutation in BTNL2 is possibly responsible for the
leukocytoclastic vasculitis as presented by a consanguineous family of Somalian
descent; studies in this family, however, also suggest genetic complexity, and a
potential (and speculative) blending of phenotypes derived from variants in two
candidate genes.
Whole exome sequencing with illumia HiSeq, which employs a sequencing by synthesis approach was used in this thesis to identify candidate genes. This is a powerful technique and output from this technology is generally short reads of 70-100bp in length. Short read
396
sequences such as this are generally preferred as they ensure accuracy and and supported by a wide range of analysis pipelines (Heather and Chain 2016). However, short read sequences can be tricky to map back to the reference genome/exome and therefore this may introduce potential sources or error in the data. The identification of large deletions/insertions is poor using this technology, and it generally works best for identifying SNPs and other small sources of variance.
Other sequencing platforms 454 pyrosequencing, Oxford Nanopore sequencing technology and Abi SOLID Sequencing (a sequencing by ligation approach) also exist and are widely used to perform whole exome/genome sequencing. 454 pyrosequencing amplifies DNA using oligonucleotides attached to beads which are contained within water droplets. It performs an emulsion PCR in order to amplify and sequence the DNA.
This method produces reads of up to 700bp. Of course there can be many advantages of producing longer reads. These include increased mapping certainty, detection of structural variants, detection of base modifications and decreased amplification bias which is common in shorter sequencing reads. Oxford Nanopore technology, sequences
DNA by passing it through a pore. The current changes as A, T, C and G pass through the pore and this enables identification of specific bases. Nanopore technology produces extremely long read lengths, ranging anywhere from 500bp to 2.3Mb long (Jain et al.
2018). This ultra-long read sequencing technology is increasing becoming more and more common. The type of sequencing technology used largely depends on the individual needs of the scientist, what they intend to find and the availability of different types of sequencing machines within their lab.
Indeed, once sequencing has been performed and candidate genes identified, the causal effect needs to be verified. Complimentary technologies such as various transcriptomic and proteomic approachs may be used. RNASeq technology can be extremely useful and
397
essentially enables the sequencing of RNA of a cell. It therefore can tell us much about post-transcriptional modifications, differently spliced transcripts and expression of certain transcripts, and therefore can be useful in proving the impact of deleterious variants(Marguerat and Bähler 2010). High throughput proteomic approaches may involve mass spectrometry techniques. Mass spectrometry enables characterisation of the proteome, through the identification of numerous protein or peptides in a sample. The sample is first ionized by bombarding it with electrons and then separated according to their mass-to-charge ratio. This technique may be used to determine protein expression, define protein interactions and may also identify sites of protein modification (Han et al.
2008). These high throughput techniques may be used to analyse downstream effects of genetic variants and determine their pathogenicity.
Of course, the sequencing approaches outlined above may be used for whole genome sequencing as well as whole exome sequencing. Whole genome sequencing enables sequencing of the intronic regions of DNA. This region is vastly uncharacterised and is thought to yield numerous transcriptional and regulatory elements for genes (Rose 2019).
This may provide answers to may diseases, which appear genetic but for which no exonic mutation has yet been identified. Understanding more about the intronic sequences may also provide more information on splicing regulation, promoter sequences and genomic sites and sequences optimised to bind protein partners (Rose 2019). The intronic region is also an important site for the regulation of nonsense mediated decay (NMD) (Jo and
Choi 2015). This is a mechanism important for the removal of deleterious mRNA sequences, some of which may contain premature termination codons (PTC). Introns play a role in recognition of mRNA sequences containing PTC’s (Jo and Choi 2015) and therefore variants which disrupt this function could be extremely harmful to the cell and
398
may cause disease. Therefore, sequencing of intronic regions in order to learn more about their function, could be very beneficial.
This thesis has examined the impact of genes involved in a variety of different immune disorders in paediatric patients presenting with very different phenotypes, encompassing both the adaptive and innate immune responses. Recently, the lines between both branches of the immune system are becoming increasingly blurred. (Peckham et al. 2017) instead, suggest that the immune system represents a continuum. At present, diseases are classified depending on whether the cells contributing to the disease are of the myeloid lineage (innate) or the lymphoid lineage (adaptive). As previously described, autoinflammatory conditions are defined by cells of the innate immune system, whereas autoimmune conditions are classified as an over responsiveness of the adaptive immune system (Arakelyan et al. 2017).
However, for certain diseases, considerable overlap between the cells responsible for the disease pathogenesis exist. For example, sJIA, rheumatoid arthritis, coeliac disease and psoriasis are not easily classified, and have complicated mechanisms at play, manifesting from both arms of the immune system. These diseases, while genetic, are also largely induced by environmental triggers.
399
Figure 6-1: Immunological Disease Continuum
Adopted from (Peckham et al. 2017)
In recent years, many monogenic disorders have been discovered which predispose an individual to autoinflammation, autoimmunity or indeed, various combinations of both
(Peckham et al. 2017). Classifications are not as clear cut, as it was once assumed they were. Interestingly, (McGonagle and McDermott 2006) also propose an autoimmune- autoinflammatory disease spectrum and suggest that many diseases classified as autoinflammatory, also display signs of autoimmunity. They recommend that diseases of the immune system can be thought of as being purely autoinflammatory, purely autoimmune or combinations of both which variably interact during disease presentation, resulting in very varied phenotypes (McGonagle and McDermott 2006). They also
400
speculate that some autoinflammatory diseases such as Behҫets disease, may experience secondary autoimmune manifestations, and likewise, diseases that are predominantly autoimmune in nature, such as rheumatoid arthritis, may experience some secondary autoinflammatory manifestations (McGonagle and McDermott 2006). This is an interesting concept and should be taken into account when deciding upon treatments for these diseases, and the authors suggest that both anti-cytokine and anti-lymphocyte therapies can be useful for diseases which display signs of both autoimmunity and autoinflammation (McGonagle and McDermott 2006).
Interestingly, the haze between different branches of the immune system was also brought to light in this project in a different way, when a homozygous defect in CCR7, was shown to contribute to an immune deficiency, but where signs of autoinflammation also existed.
This is highly complex and paradoxical in nature, and extremely difficult to treat clinically. The identification of genetic mutations in this gene and indeed, the understanding behind the molecular pathology of this disease would not have been possible without the power of whole exome sequencing.
My project has generated some important data and doing so is a prime example of how
WES data can be used efficiently to identify candidate genes leading to rare monogenic diseases. Although effective, the filtering and ranking of variants after WES analysis is a lengthy and laborious process, and very often can take years to narrow down the correct mutation. Since completing my PhD, a new tool has come to light, called Exomiser
(Robinson et al. 2014). This tool reduces the work required to find and prioritise candidate genes. Exomiser takes a whole exome dataset in the form of a variant call file (VCF) and filters this dataset by removing synonymous, common and off-target variants. It then ranks the remaining variants based on their predicted pathogenicity. An assumed mode of inheritance can also be given if desired, and this will also be used to prioritise variants.
401
Users also input a human phenotype ontology (HPO) list of terms which relate to the presenting phenotype which the program will use to prioritise genes. The program then also looks at mouse and zebrafish models having mutations in orthologous genes. Finally, the genes which remain are assigned a phenotypic interpretation of variants in exomes
(PHIVE) score, which will prioritise variants based on the likelihood that each gene is responsible for the presenting phenotype.
This program is beginning to be used by members of my lab group and already having promising results. It has shown to be very useful for denoting which variants may be promising to look at and could also vanquish weeks of work that goes into filtering and narrowing down causative genes. Indeed, other tools such as Alamut
(https://www.interactive-biosoftware.com/) are also now being used by my lab and greatly speed up the process of prioritising candidate genes. Alamut is a useful tool, if you have already filtered the variants, as it has all the information from animal models, familial and GWAS studies together in one place. It also has a pathogenicity score and splice site prediction algorithm. This makes it easy to quickly compare variants to one another and reduces the need to look at a variety of websites such as OMIM, GeneCards,
ExAC etc., as all the information is contained on Alamut. Again, this goes a long way to reducing the work that is needed to identify candidate genes.
When first, the human genome was sequenced in 2001, it took 13 years to complete
(Lander et al. 2001). Now, this can be accomplished in just 3 days, for a fraction of the price. The analysis subsequently took much longer and was quite labour intensive. Now, the process of looking for candidate genes is becoming more and more automated and will continue along this trajectory in future.
402
It is clear that there are still as yet undiscovered cases of rare genetic inflammatory disorders. Although they may be rare, the economic, physical and emotional burden of these diseases is substantial. The identification of the correct candidate genes provides information behind the physiological and molecular causes underpinning such diseases.
This, in turn, informs clinical practice and could provide patients with more targeted and effective treatments, as demonstrated in chapter 3. It may also highlight certain biological targets for potential drug intervention. Targeted treatment is much more beneficial to the patient, resulting in a faster recovery rate, fewer side effects and fewer overall complications (Wertheimer et al. 2001, Alessandrini et al. 2016). Due to the automation of candidate gene identification and the benefits of providing targeted treatments, I believe the field of pharmacogenetics and the practice of providing medication based on the genotype will be commonplace in future.
403
6.1 Publications arising from this work
Keylock, A., Hong, Y., Saunders, D., Omoyinmi, E., Mulhern, C., Roebuck, D., Brogan,
P., Ganesan, V. and Eleftheriou, D., 2018. Moyamoya-like cerebrovascular disease in a child with a novel mutation in myosin heavy chain 11. Neurology, 90(3), pp.136–138
Mulhern, C.M., Hong, Y., Omoyinmi, E., Jacques, T., D’Arco, F., Hemingway, C.,
Brogan, P.A. and Eleftheriou, D., 2019. Janus kinase 1/2 inhibition for the treatment of autoinflammation associated with heterozygous TNFAIP3 mutation. Journal of Allergy and Clinical Immunology, p.S0091674919307456
McCreary D, Omoyinmi E, Hong Y, Mulhern C, Papadopoulou C, Casimir M, et al.
Development and Validation of a Targeted Next-Generation Sequencing Gene Panel for
Children With Neuroinflammation. JAMA Netw Open. 2019 Oct 30;2(10):e1914274– e1914274
404
7 Bibliography
Aachoui, Y., Sagulenko, V., Miao, E. A., and Stacey, K. J., 2013. Inflammasome- mediated pyroptotic and apoptotic cell death, and defense against infection. Current Opinion in Microbiology, 16 (3), 319–326. ABDEL-MOHSEN, M., WANG, C., STRAIN, M. C., LADA, S. M., DENG, X., COCKERHAM, L. R., PILCHER, C. D., HECHT, F. M., LIEGLER, T., RICHMAN, D. D., DEEKS, S. G., and PILLAI, S. K., 2015. Select Host Restriction Factors Are Associated with HIV Persistence During Antiretroviral Therapy. AIDS (London, England), 29 (4), 411–420. Adrianto, I., Wen, F., Templeton, A., Wiley, G., King, J. B., Lessard, C. J., Bates, J. S., Hu, Y., Kelly, J. A., Kaufman, K. M., Guthridge, J. M., Alarcon-Riquelme, M. E., Anaya, J. M., Bae, S. C., Bang, S. Y., Boackle, S. A., Brown, E. E., Petri, M. A., Gallant, C., Ramsey-Goldman, R., Reveille, J. D., Vila, L. M., Criswell, L. A., Edberg, J. C., Freedman, B. I., Gregersen, P. K., Gilkeson, G. S., Jacob, C. O., James, J. A., Kamen, D. L., Kimberly, R. P., Martin, J., Merrill, J. T., Niewold, T. B., Park, S. Y., Pons-Estel, B. A., Scofield, R. H., Stevens, A. M., Tsao, B. P., Vyse, T. J., Langefeld, C. D., Harley, J. B., Moser, K. L., Webb, C. F., Humphrey, M. B., Montgomery, C. G., and Gaffney, P. M., 2011. Association of a functional variant downstream of TNFAIP3 with systemic lupus erythematosus. Nat Genet, 43 (3), 253–8. Aeschlimann, F. A., Batu, E. D., Canna, S. W., Go, E., Gul, A., Hoffmann, P., Leavis, H. L., Ozen, S., Schwartz, D. M., Stone, D. L., van Royen-Kerkof, A., Kastner, D. L., Aksentijevich, I., and Laxer, R. M., 2018. A20 haploinsufficiency (HA20): clinical phenotypes and disease course of patients with a newly recognised NF-kB-mediated autoinflammatory disease. Ann Rheum Dis. Afgan, E., Baker, D., van den Beek, M., Blankenberg, D., Bouvier, D., Čech, M., Chilton, J., Clements, D., Coraor, N., Eberhard, C., Grüning, B., Guerler, A., Hillman-Jackson, J., Von Kuster, G., Rasche, E., Soranzo, N., Turaga, N., Taylor, J., Nekrutenko, A., and Goecks, J., 2016. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Research, 44 (W1), W3–W10. Agarwal, A. K., Xing, C., DeMartino, G. N., Mizrachi, D., Hernandez, M. D., Sousa, A. B., Martinez de Villarreal, L., dos Santos, H. G., and Garg, A., 2010. PSMB8 encoding the beta5i proteasome subunit is mutated in joint contractures, muscle atrophy, microcytic anemia, and panniculitis-induced lipodystrophy syndrome. Am J Hum Genet, 87 (6), 866–72. Aksentijevich, I. and Kastner, D. L., 2011a. Genetics of monogenic autoinflammatory diseases: past successes, future challenges. Nature Reviews. Rheumatology, 7 (8), 469–478. Aksentijevich, I. and Kastner, D. L., 2011b. Genetics of monogenic autoinflammatory diseases: past successes, future challenges. Nat Rev Rheumatol, 7 (8), 469–78. Aksentijevich, I., Masters, S. L., Ferguson, P. J., Dancey, P., Frenkel, J., van Royen- Kerkhoff, A., Laxer, R., Tedgård, U., Cowen, E. W., Pham, T.-H., Booty, M., Estes, J. D., Sandler, N. G., Plass, N., Stone, D. L., Turner, M. L., Hill, S., Butman, J. A., Schneider, R., Babyn, P., El-Shanti, H. I., Pope, E., Barron, K., Bing, X., Laurence, A., Lee, C.-C. R., Chapelle, D., Clarke, G. I., Ohson, K.,
405
Nicholson, M., Gadina, M., Yang, B., Korman, B. D., Gregersen, P. K., van Hagen, P. M., Hak, A. E., Huizing, M., Rahman, P., Douek, D. C., Remmers, E. F., Kastner, D. L., and Goldbach-Mansky, R., 2009. An Autoinflammatory Disease with Deficiency of the Interleukin-1–Receptor Antagonist. New England Journal of Medicine, 360 (23), 2426–2437. Aksentijevich, I. and Zhou, Q., 2017. NF-kappaB Pathway in Autoinflammatory Diseases: Dysregulation of Protein Modifications by Ubiquitin Defines a New Category of Autoinflammatory Diseases. Front Immunol, 8, 399. Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., and Walter, P., 2002. Lymphocytes and the Cellular Basis of Adaptive Immunity. Molecular Biology of the Cell. 4th edition [online]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK26921/ [Accessed 12 Aug 2019]. Aldrich, R. A., Steinberg, A. G., and Campbell, D. C., 1954. Pedigree demonstrating a sex-linked recessive condition characterized by draining ears, eczematoid dermatitis and bloody diarrhea. Pediatrics, 13 (2), 133–139. Alessandrini, M., Chaudhry, M., Dodgen, T. M., and Pepper, M. S., 2016. Pharmacogenomics and Global Precision Medicine in the Context of Adverse Drug Reactions: Top 10 Opportunities and Challenges for the Next Decade. OMICS: A Journal of Integrative Biology, 20 (10), 593–603. Almeida de Jesus, A. and Goldbach-Mansky, R., 2013. Monogenic autoinflammatory diseases: concept and clinical manifestations. Clin Immunol, 147 (3), 155–74. Amm, I., Sommer, T., and Wolf, D. H., 2014. Protein quality control and elimination of protein waste: the role of the ubiquitin-proteasome system. Biochimica Et Biophysica Acta, 1843 (1), 182–196. An, O., Gursoy, A., Gurgey, A., and Keskin, O., 2013. Structural and functional analysis of perforin mutations in association with clinical data of familial hemophagocytic lymphohistiocytosis type 2 (FHL2) patients. Protein Science : A Publication of the Protein Society, 22 (6), 823–839. Anderson, M. S., Venanzi, E. S., Klein, L., Chen, Z., Berzins, S. P., Turley, S. J., Boehmer, H. von, Bronson, R., Dierich, A., Benoist, C., and Mathis, D., 2002. Projection of an Immunological Self Shadow Within the Thymus by the Aire Protein. Science, 298 (5597), 1395–1401. Anderson, S. K., 2014. Probabilistic Bidirectional Promoter Switches: Noncoding RNA Takes Control. Molecular Therapy. Nucleic Acids, 3 (9), e191. Anon., 2019a. Infevers [online]. Available from: https://infevers.umai- montpellier.fr/web/ [Accessed 25 Nov 2019]. Anon., 2019b. JAK Inhibitor Treatment in AGS - Full Text View - ClinicalTrials.gov [online]. Available from: https://clinicaltrials.gov/ct2/show/NCT03921554 [Accessed 26 Nov 2019]. Anon., 2019c. AUTOP - Clinical: Autoinflammatory Primary Immunodeficiency (PID) Gene Panel, Varies [online]. Available from: https://www.mayocliniclabs.com/test-catalog/Clinical+and+Interpretive/65666 [Accessed 11 Oct 2019]. Anon., 2019d. European Society of Human Genetics: ESHG Home [online]. Available from: https://www.eshg.org/index.php?id=home [Accessed 11 Oct 2019]. Anon., 2019e. The association of a nonsynonymous single‐nucleotide polymorphism in TNFAIP3 with systemic lupus erythematosus and rheumatoid arthritis in the Japanese population - Shimane - 2010 - Arthritis & Rheumatism - Wiley Online Library [online]. Available from:
406
https://onlinelibrary.wiley.com/doi/full/10.1002/art.27190 [Accessed 15 Jul 2019]. Anon., 2019f. Activation of the NALP3 inflammasome is triggered by low intracellular potassium concentration | Cell Death & Differentiation [online]. Available from: https://www.nature.com/articles/4402195 [Accessed 3 Jun 2019]. Anon., 2019g. RIPOR2 - Rho family-interacting cell polarization regulator 2 - Homo sapiens (Human) - RIPOR2 gene & protein [online]. Available from: https://www.uniprot.org/uniprot/Q9Y4F9 [Accessed 28 Aug 2019]. Anon., 2019h. Real-Time PCR: Understanding Ct - IE [online]. Available from: https://www.thermofisher.com/tr/en/home/life-science/pcr/real-time-pcr/real- time-pcr-learning-center/real-time-pcr-basics/real-time-pcr-understanding- ct.html [Accessed 2 Sep 2019]. Anon., 2020i. Definition of Haploinsufficiency [online]. MedicineNet. Available from: https://www.medicinenet.com/script/main/art.asp?articlekey=18474 [Accessed 7 Apr 2020]. Arakelyan, A., Nersisyan, L., Poghosyan, D., Khondkaryan, L., Hakobyan, A., Löffler- Wirth, H., Melanitou, E., and Binder, H., 2017. Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles. PLOS ONE, 12 (11), e0187572. Arbonés, M. L., Ord, D. C., Ley, K., Ratech, H., Maynard-Curry, C., Otten, G., Capon, D. J., and Tedder, T. F., 1994. Lymphocyte homing and leukocyte rolling and migration are impaired in L-selectin-deficient mice. Immunity, 1 (4), 247–260. Arima, K., Kinoshita, A., Mishima, H., Kanazawa, N., Kaneko, T., Mizushima, T., Ichinose, K., Nakamura, H., Tsujino, A., Kawakami, A., Matsunaka, M., Kasagi, S., Kawano, S., Kumagai, S., Ohmura, K., Mimori, T., Hirano, M., Ueno, S., Tanaka, K., Tanaka, M., Toyoshima, I., Sugino, H., Yamakawa, A., Tanaka, K., Niikawa, N., Furukawa, F., Murata, S., Eguchi, K., Ida, H., and Yoshiura, K., 2011. Proteasome assembly defect due to a proteasome subunit beta type 8 (PSMB8) mutation causes the autoinflammatory disorder, Nakajo-Nishimura syndrome. Proceedings of the National Academy of Sciences of the United States of America, 108 (36), 14914–14919. Arimochi, H., Sasaki, Y., Kitamura, A., and Yasutomo, K., 2016. Dysfunctional immunoproteasomes in autoinflammatory diseases. Inflammation and Regeneration, 36 (1), 13. Arnett, H. A., Escobar, S. S., Gonzalez-Suarez, E., Budelsky, A. L., Steffen, L. A., Boiani, N., Zhang, M., Siu, G., Brewer, A. W., and Viney, J. L., 2007. BTNL2, a butyrophilin/B7-like molecule, is a negative costimulatory molecule modulated in intestinal inflammation. J Immunol, 178 (3), 1523–33. Arnett, H. A. and Viney, J. L., 2014. Immune modulation by butyrophilins. Nat Rev Immunol, 14 (8), 559–69. Aróstegui, J. I., Arnal, C., Merino, R., Modesto, C., Antonia Carballo, M., Moreno, P., García-Consuegra, J., Naranjo, A., Ramos, E., de Paz, P., Rius, J., Plaza, S., and Yagüe, J., 2007. NOD2 gene-associated pediatric granulomatous arthritis: clinical diversity, novel and recurrent mutations, and evidence of clinical improvement with interleukin-1 blockade in a Spanish cohort. Arthritis and Rheumatism, 56 (11), 3805–3813. Arvesen, K. B., Herlin, T., Larsen, D. A., Koppelhus, U., Ramsing, M., Skytte, A.-B., and Sommerlund, M., 2017. Diagnosis and Treatment of Blau Syndrome/Early- onset Sarcoidosis, an Autoinflammatory Granulomatous Disease, in an Infant. Acta Dermato-Venereologica, 97 (1), 126–127.
407
Attwood, T. K. and Findlay, J. B. C., 1994. Fingerprinting G-protein-coupled receptors. Protein Engineering, Design and Selection, 7 (2), 195–203. Aubert, P., Suárez-Fariñas, M., Mitsui, H., Johnson-Huang, L. M., Harden, J. L., Pierson, K. C., Dolan, J. G., Novitskaya, I., Coats, I., Estes, J., Cowen, E. W., Plass, N., Lee, C.-C. R., Sun, H.-W., Lowes, M. A., and Goldbach-Mansky, R., 2012. Homeostatic tissue responses in skin biopsies from NOMID patients with constitutive overproduction of IL-1β. PloS One, 7 (11), e49408. Au-Yeung, N., Mandhana, R., and Horvath, C. M., 2013. Transcriptional regulation by STAT1 and STAT2 in the interferon JAK-STAT pathway. JAK-STAT [online], 2 (3). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3772101/ [Accessed 8 Jan 2019]. Aydin, S. E., Freeman, A. F., Al-Herz, W., Al-Mousa, H. A., Arnaout, R. K., Aydin, R. C., Barlogis, V., Belohradsky, B. H., Bonfim, C., Bredius, R. G., Chu, J. I., Ciocarlie, O. C., Doğu, F., Gaspar, H. B., Geha, R. S., Gennery, A. R., Hauck, F., Hawwari, A., Hickstein, D. D., Hoenig, M., Ikinciogullari, A., Klein, C., Kumar, A., Ifversen, M. R. S., Matthes, S., Metin, A., Neven, B., Pai, S.-Y., Parikh, S. H., Picard, C., Renner, E. D., Sanal, Ö., Schulz, A. S., Schuster, F., Shah, N. N., Shereck, E. B., Slatter, M. A., Su, H. C., van Montfrans, J., Woessmann, W., Ziegler, J. B., Albert, M. H., and Inborn Errors Working Party of the European Group for Blood and Marrow Transplantation and the European Society for Primary Immunodeficiencies, 2019. Hematopoietic Stem Cell Transplantation as Treatment for Patients with DOCK8 Deficiency. The Journal of Allergy and Clinical Immunology. In Practice, 7 (3), 848–855. Bacchetta, R., Barzaghi, F., and Roncarolo, M.-G., 2018. From IPEX syndrome to FOXP3 mutation: a lesson on immune dysregulation. Annals of the New York Academy of Sciences, 1417 (1), 5–22. Barzaghi, F., Amaya Hernandez, L. C., Neven, B., Ricci, S., Kucuk, Z. Y., Bleesing, J. J., Nademi, Z., Slatter, M. A., Ulloa, E. R., Shcherbina, A., Roppelt, A., Worth, A., Silva, J., Aiuti, A., Murguia-Favela, L., Speckmann, C., Carneiro-Sampaio, M., Fernandes, J. F., Baris, S., Ozen, A., Karakoc-Aydiner, E., Kiykim, A., Schulz, A., Steinmann, S., Notarangelo, L. D., Gambineri, E., Lionetti, P., Shearer, W. T., Forbes, L. R., Martinez, C., Moshous, D., Blanche, S., Fisher, A., Ruemmele, F. M., Tissandier, C., Ouachee-Chardin, M., Rieux-Laucat, F., Cavazzana, M., Qasim, W., Lucarelli, B., Albert, M. H., Kobayashi, I., Alonso, L., Diaz De Heredia, C., Kanegane, H., Lawitschka, A., Seo, J. J., Gonzalez- Vicent, M., Diaz, M. A., Goyal, R. K., Sauer, M. G., Yesilipek, A., Kim, M., Yilmaz-Demirdag, Y., Bhatia, M., Khlevner, J., Richmond Padilla, E. J., Martino, S., Montin, D., Neth, O., Molinos-Quintana, A., Valverde-Fernandez, J., Broides, A., Pinsk, V., Ballauf, A., Haerynck, F., Bordon, V., Dhooge, C., Garcia-Lloret, M. L., Bredius, R. G., Kałwak, K., Haddad, E., Seidel, M. G., Duckers, G., Pai, S.-Y., Dvorak, C. C., Ehl, S., Locatelli, F., Goldman, F., Gennery, A. R., Cowan, M. J., Roncarolo, M.-G., and Bacchetta, R., 2018. Long-term follow-up of IPEX syndrome patients after different therapeutic strategies: An international multicenter retrospective study. Journal of Allergy and Clinical Immunology, 141 (3), 1036-1049.e5. Berger, M., Krebs, P., Crozat, K., Li, X., Croker, B. A., Siggs, O. M., Popkin, D., Du, X., Lawson, B. R., Theofilopoulos, A. N., Xia, Y., Khovananth, K., Moresco, E. M. Y., Satoh, T., Takeuchi, O., Akira, S., and Beutler, B., 2010. A Slfn2 mutation causes lymphoid and myeloid immunodeficiency due to loss of immune cell quiescence. Nature immunology, 11 (4), 335–343.
408
Bergsbaken, T., Fink, S. L., and Cookson, B. T., 2009. Pyroptosis: host cell death and inflammation. Nature reviews. Microbiology, 7 (2), 99–109. Berkun, Y., Padeh, S., Reichman, B., Zaks, N., Rabinovich, E., Lidar, M., Shainberg, B., and Livneh, A., 2007. A single testing of serum amyloid a levels as a tool for diagnosis and treatment dilemmas in familial Mediterranean fever. Seminars in Arthritis and Rheumatism, 37 (3), 182–188. Berkun, Y., Segel, R., and Navon-Elkan, P., 2017. Adenosine Deaminase 2 Deficiency: More Than Monogenic Vasculitis. The Israel Medical Association journal: IMAJ, 19 (7), 435–437. Betke, K. M., Wells, C. A., and Hamm, H. E., 2012. GPCR Mediated Regulation of Synaptic Transmission. Progress in Neurobiology, 96 (3), 304–321. Blaszczyk, K., Nowicka, H., Kostyrko, K., Antonczyk, A., Wesoly, J., and Bluyssen, H. A. R., 2016. The unique role of STAT2 in constitutive and IFN-induced transcription and antiviral responses. Cytokine & Growth Factor Reviews, 29, 71–81. Boone, D. L., Turer, E. E., Lee, E. G., Ahmad, R.-C., Wheeler, M. T., Tsui, C., Hurley, P., Chien, M., Chai, S., Hitotsumatsu, O., McNally, E., Pickart, C., and Ma, A., 2004. The ubiquitin-modifying enzyme A20 is required for termination of Toll- like receptor responses. Nature Immunology, 5 (10), 1052–1060. Booth, D. R., Gillmore, J. D., Lachmann, H. J., Booth, S. E., Bybee, A., Soytürk, M., Akar, S., Pepys, M. B., Tunca, M., and Hawkins, P. N., 2000. The genetic basis of autosomal dominant familial Mediterranean fever. QJM: monthly journal of the Association of Physicians, 93 (4), 217–221. Borge, M., Nannini, P. R., Galletti, J. G., Morande, P. E., Ávalos, J. S., Bezares, R. F., Giordano, M., and Gamberale, R., 2010. CXCL12-induced chemotaxis is impaired in T cells from patients with ZAP-70-negative chronic lymphocytic leukemia. Haematologica, 95 (5), 768–775. Boswell, K. L., James, D. J., Esquibel, J. M., Bruinsma, S., Shirakawa, R., Horiuchi, H., and Martin, T. F. J., 2012. Munc13-4 reconstitutes calcium-dependent SNARE- mediated membrane fusion. The Journal of Cell Biology, 197 (2), 301–312. Botstein, D. and Risch, N., 2003. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nature Genetics, 33 Suppl, 228–237. Bousfiha, A., Jeddane, L. ,. Picard, C. et al, 2018. The 2017 IUIS Phenotypic Classification for Primary Immunodeficiencies. Journal of Clinical immunlogy [online], (J Clin Immunol (2018) 38: 129.). Available from: https://doi.org/10.1007/s10875-017-0465-8. Bowes, J., Lawrence, R., Eyre, S., Panoutsopoulou, K., Orozco, G., Elliott, K. S., Ke, X., Morris, A. P., UKRAG, Thomson, W., Worthington, J., Barton, A., and Zeggini, E., 2010. Rare variation at the TNFAIP3 locus and susceptibility to rheumatoid arthritis. Human Genetics, 128 (6), 627–633. Brady, G., Boggan, L., Bowie, A., and O’Neill, L. A. J., 2005. Schlafen-1 causes a cell cycle arrest by inhibiting induction of cyclin D1. The Journal of Biological Chemistry, 280 (35), 30723–30734. Brehm, A., Liu, Y., Sheikh, A., Marrero, B., Omoyinmi, E., Zhou, Q., Montealegre, G., Biancotto, A., Reinhardt, A., Almeida de Jesus, A., Pelletier, M., Tsai, W. L., Remmers, E. F., Kardava, L., Hill, S., Kim, H., Lachmann, H. J., Megarbane, A., Chae, J. J., Brady, J., Castillo, R. D., Brown, D., Casano, A. V., Gao, L., Chapelle, D., Huang, Y., Stone, D., Chen, Y., Sotzny, F., Lee, C. C., Kastner, D. L., Torrelo, A., Zlotogorski, A., Moir, S., Gadina, M., McCoy, P., Wesley, R.,
409
Rother, K. I., Hildebrand, P. W., Brogan, P., Kruger, E., Aksentijevich, I., and Goldbach-Mansky, R., 2015. Additive loss-of-function proteasome subunit mutations in CANDLE/PRAAS patients promote type I IFN production. J Clin Invest, 125 (11), 4196–211. Bruno, S. and Darzynkiewicz, Z., 1992. Cell cycle dependent expression and stability of the nuclear protein detected by Ki-67 antibody in HL-60 cells. Cell Proliferation, 25 (1), 31–40. Bunting, M. D., Comerford, I., and McColl, S. R., 2011. Finding their niche: chemokines directing cell migration in the thymus. Immunology and Cell Biology, 89 (2), 185–196. Burch, G. H., Gong, Y., Liu, W., Dettman, R. W., Curry, C. J., Smith, L., Miller, W. L., and Bristow, J., 1997. Tenascin-X deficiency is associated with Ehlers-Danlos syndrome. Nature Genetics, 17 (1), 104–108. van der Burgh, R., Nijhuis, L., Pervolaraki, K., Compeer, E. B., Jongeneel, L. H., van Gijn, M., Coffer, P. J., Murphy, M. P., Mastroberardino, P. G., Frenkel, J., and Boes, M., 2014. Defects in mitochondrial clearance predispose human monocytes to interleukin-1β hypersecretion. The Journal of Biological Chemistry, 289 (8), 5000–5012. Cai, X., Chen, J., Xu, H., Liu, S., Jiang, Q.-X., Halfmann, R., and Chen, Z. J., 2014. Prion-like polymerization underlies signal transduction in antiviral immune defense and inflammasome activation. Cell, 156 (6), 1207–1222. Campbell, A. P. and Smrcka, A. V., 2018. Targeting G protein-coupled receptor signalling by blocking G proteins. Nature Reviews. Drug Discovery, 17 (11), 789–803. Candotti, F., 2018. Clinical Manifestations and Pathophysiological Mechanisms of the Wiskott-Aldrich Syndrome. Journal of Clinical Immunology, 38 (1), 13–27. Cantarini, L., Lucherini, O. M., Cimaz, R., Rigante, D., Baldari, C. T., Laghi Pasini, F., and Galeazzi, M., 2012. Typical and severe tumor necrosis factor receptor- associated periodic syndrome in the absence of mutations in the TNFRSF1A gene: a case series. Rheumatology International, 32 (12), 4015–4018. Caorsi, R., Penco, F., Grossi, A., Insalaco, A., Omenetti, A., Alessio, M., Conti, G., Marchetti, F., Picco, P., Tommasini, A., Martino, S., Malattia, C., Gallizi, R., Podda, R. A., Salis, A., Falcini, F., Schena, F., Garbarino, F., Morreale, A., Pardeo, M., Ventrici, C., Passarelli, C., Zhou, Q., Severino, M., Gandolfo, C., Damonte, G., Martini, A., Ravelli, A., Aksentijevich, I., Ceccherini, I., and Gattorno, M., 2017. ADA2 deficiency (DADA2) as an unrecognised cause of early onset polyarteritis nodosa and stroke: a multicentre national study. Ann Rheum Dis, 76 (10), 1648–1656. Caruso, R., Warner, N., Inohara, N., and Núñez, G., 2014. NOD1 and NOD2: signaling, host defense, and inflammatory disease. Immunity, 41 (6), 898–908. Caso, F., Wouters, C. H., Rose, C. D., Costa, L., Tognon, S., Sfriso, P., Cantarini, L., Rigante, D., and Punzi, L., 2014. Blau syndrome and latent tubercular infection: an unresolved partnership. International Journal of Rheumatic Diseases, 17 (5), 586–587. Cassel, S. L., Eisenbarth, S. C., Iyer, S. S., Sadler, J. J., Colegio, O. R., Tephly, L. A., Carter, A. B., Rothman, P. B., Flavell, R. A., and Sutterwala, F. S., 2008. The Nalp3 inflammasome is essential for the development of silicosis. Proceedings of the National Academy of Sciences of the United States of America, 105 (26), 9035–9040.
410
Catrysse, L., Vereecke, L., Beyaert, R., and van Loo, G., 2014. A20 in inflammation and autoimmunity. Trends Immunol, 35 (1), 22–31. Catucci, M., Castiello, M. C., Pala, F., Bosticardo, M., and Villa, A., 2012. Autoimmunity in wiskott-Aldrich syndrome: an unsolved enigma. Frontiers in Immunology, 3, 209. Ceballos, F. C., Hazelhurst, S., and Ramsay, M., 2018. Assessing runs of Homozygosity: a comparison of SNP Array and whole genome sequence low coverage data. BMC Genomics, 19 (1), 106. Cepika, A.-M., Sato, Y., Liu, J. M.-H., Uyeda, M. J., Bacchetta, R., and Roncarolo, M. G., 2018. Tregopathies: Monogenic diseases resulting in regulatory T-cell deficiency. Journal of Allergy and Clinical Immunology, 142 (6), 1679–1695. Chae, J. J., Aksentijevich, I., and Kastner, D. L., 2009. Advances in the understanding of familial Mediterranean fever and possibilities for targeted therapy. Br J Haematol, 146 (5), 467–78. Chan, F. K., Chun, H. J., Zheng, L., Siegel, R. M., Bui, K. L., and Lenardo, M. J., 2000. A domain in TNF receptors that mediates ligand-independent receptor assembly and signaling. Science (New York, N.Y.), 288 (5475), 2351–2354. Charles A Janeway, J., Travers, P., Walport, M., and Shlomchik, M. J., 2001. Generation of lymphocytes in bone marrow and thymus. Immunobiology: The Immune System in Health and Disease. 5th edition [online]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK27123/ [Accessed 23 Apr 2019]. Chen, Z. J., 2005. Ubiquitin Signaling in the NF-κB Pathway. Nature cell biology, 7 (8), 758–765. Clarke, S. L. N., Pellowe, E. J., de Jesus, A. A., Goldbach-Mansky, R., Hilliard, T. N., and Ramanan, A. V., 2016. Interstitial Lung Disease Caused by STING- associated Vasculopathy with Onset in Infancy. American Journal of Respiratory and Critical Care Medicine, 194 (5), 639–642. Comerford, I., Harata-Lee, Y., Bunting, M. D., Gregor, C., Kara, E. E., and McColl, S. R., 2013. A myriad of functions and complex regulation of the CCR7/CCL19/CCL21 chemokine axis in the adaptive immune system. Cytokine & Growth Factor Reviews, 24 (3), 269–283. Coonrod, E. M., Margraf, R. L., Russell, A., Voelkerding, K. V., and Reese, M. G., 2013. Clinical analysis of genome next-generation sequencing data using the Omicia platform. Expert review of molecular diagnostics [online], 13 (6). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3828661/ [Accessed 19 Jul 2019]. Coornaert, B., Carpentier, I., and Beyaert, R., 2009. A20: central gatekeeper in inflammation and immunity. J Biol Chem, 284 (13), 8217–21. Coudurier, M., Freymond, N., Aissaoui, S., Calender, A., Pacheco, Y., and Devouassoux, G., 2009. Homozygous variant rs2076530 of BTNL2 and familial sarcoidosis. Sarcoidosis Vasc Diffuse Lung Dis, 26 (2), 162–6. Crow, Y. J., 1993. Aicardi-Goutieres Syndrome. In: Adam, M. P., Ardinger, H. H., Pagon, R. A., Wallace, S. E., Bean, L. J. H., Stephens, K., and Amemiya, A., eds. GeneReviews((R)). Seattle (WA): University of Washington, Seattle University of Washington, Seattle. GeneReviews is a registered trademark of the University of Washington, Seattle. All rights reserved. Crow, Y. J., 2015. Type I interferonopathies: mendelian type I interferon up-regulation. Curr Opin Immunol, 32, 7–12. Crow, Y. J. and Manel, N., 2015. Aicardi-Goutieres syndrome and the type I interferonopathies. Nat Rev Immunol, 15 (7), 429–40.
411
Crow, Y. J. and Rehwinkel, J., 2009. Aicardi-Goutieres syndrome and related phenotypes: linking nucleic acid metabolism with autoimmunity. Hum Mol Genet, 18 (R2), R130-6. Cuisset, L., Drenth, J. P., Simon, A., Vincent, M. F., van der Velde Visser, S., van der Meer, J. W., Grateau, G., and Delpech, M., 2001. Molecular analysis of MVK mutations and enzymatic activity in hyper-IgD and periodic fever syndrome. European Journal of Human Genetics, 9 (4), 260–266. Davalos‐Misslitz, A. C. M., Rieckenberg, J., Willenzon, S., Worbs, T., Kremmer, E., Bernhardt, G., and Förster, R., 2007. Generalized multi-organ autoimmunity in CCR7-deficient mice. European Journal of Immunology, 37 (3), 613–622. Davalos-Misslitz, A. C. M., Worbs, T., Willenzon, S., Bernhardt, G., and Förster, R., 2007. Impaired responsiveness to T-cell receptor stimulation and defective negative selection of thymocytes in CCR7-deficient mice. Blood, 110 (13), 4351–4359. De, A., Dainichi, T., Rathinam, C. V., and Ghosh, S., 2014. The deubiquitinase activity of A20 is dispensable for NF-κB signaling. EMBO Reports, 15 (7), 775–783. De Benedetti, F., Gattorno, M., Anton, J., Ben-Chetrit, E., Frenkel, J., Hoffman, H. M., Koné-Paut, I., Lachmann, H. J., Ozen, S., Simon, A., Zeft, A., Calvo Penades, I., Moutschen, M., Quartier, P., Kasapcopur, O., Shcherbina, A., Hofer, M., Hashkes, P. J., Van der Hilst, J., Hara, R., Bujan-Rivas, S., Constantin, T., Gul, A., Livneh, A., Brogan, P., Cattalini, M., Obici, L., Lheritier, K., Speziale, A., and Junge, G., 2018. Canakinumab for the Treatment of Autoinflammatory Recurrent Fever Syndromes. New England Journal of Medicine, 378 (20), 1908– 1919. Debes, G. F., Arnold, C. N., Young, A. J., Krautwald, S., Lipp, M., Hay, J. B., and Butcher, E. C., 2005. Chemokine receptor CCR7 required for T lymphocyte exit from peripheral tissues. Nature Immunology, 6 (9), 889–894. Deng, Y. and Tsao, B. P., 2010. Genetic susceptibility to systemic lupus erythematosus in the genomic era. Nature reviews. Rheumatology, 6 (12), 683–692. Dhimolea, E., 2010. Canakinumab. MAbs, 2 (1), 3–13. Diaz-Horta, O., Subasioglu-Uzak, A., Grati, M., DeSmidt, A., Foster, J., Cao, L., Bademci, G., Tokgoz-Yilmaz, S., Duman, D., Cengiz, F. B., Abad, C., Mittal, R., Blanton, S., Liu, X. Z., Farooq, A., Walz, K., Lu, Z., and Tekin, M., 2014. FAM65B is a membrane-associated protein of hair cell stereocilia required for hearing. Proceedings of the National Academy of Sciences, 111 (27), 9864– 9868. Dill, K. A., Ozkan, S. B., Shell, M. S., and Weikl, T. R., 2008. The Protein Folding Problem, 32. Dinarello, C., Novick, D., Kim, S., and Kaplanski, G., 2013. Interleukin-18 and IL-18 Binding Protein. Frontiers in Immunology [online], 4. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2013.00289/full [Accessed 4 Jun 2019]. Ding, J., Wang, K., Liu, W., She, Y., Sun, Q., Shi, J., Sun, H., Wang, D.-C., and Shao, F., 2016. Pore-forming activity and structural autoinhibition of the gasdermin family. Nature, 535 (7610), 111–116. Dominguez, R. and Holmes, K. C., 2011. Actin Structure and Function. Annual review of biophysics, 40, 169–186. Dubois, P. C., Trynka, G., Franke, L., Hunt, K. A., Romanos, J., Curtotti, A., Zhernakova, A., Heap, G. A., Ádány, R., Aromaa, A., Bardella, M. T., van den Berg, L. H., Bockett, N. A., de la Concha, E. G., Dema, B., Fehrmann, R. S.,
412
Fernández-Arquero, M., Fiatal, S., Grandone, E., Green, P. M., Groen, H. J., Gwilliam, R., Houwen, R. H., Hunt, S. E., Kaukinen, K., Kelleher, D., Korponay-Szabo, I., Kurppa, K., MacMathuna, P., Mäki, M., Mazzilli, M. C., McCann, O. T., Mearin, M. L., Mein, C. A., Mirza, M. M., Mistry, V., Mora, B., Morley, K. I., Mulder, C. J., Murray, J. A., Núñez, C., Oosterom, E., Ophoff, R. A., Polanco, I., Peltonen, L., Platteel, M., Rybak, A., Salomaa, V., Schweizer, J. J., Sperandeo, M. P., Tack, G. J., Turner, G., Veldink, J. H., Verbeek, W. H., Weersma, R. K., Wolters, V. M., Urcelay, E., Cukrowska, B., Greco, L., Neuhausen, S. L., McManus, R., Barisani, D., Deloukas, P., Barrett, J. C., Saavalainen, P., Wijmenga, C., and van Heel, D. A., 2010. Multiple common variants for celiac disease influencing immune gene expression. Nature genetics, 42 (4), 295–302. Duong, B. H., Onizawa, M., Oses-Prieto, J. A., Advincula, R., Burlingame, A., Malynn, B. A., and Ma, A., 2015. A20 restricts ubiquitination of pro-interleukin-1beta protein complexes and suppresses NLRP3 inflammasome activity. Immunity, 42 (1), 55–67. Dupuis-Girod, S., Medioni, J., Haddad, E., Quartier, P., Cavazzana-Calvo, M., Le Deist, F., de Saint Basile, G., Delaunay, J., Schwarz, K., Casanova, J.-L., Blanche, S., and Fischer, A., 2003. Autoimmunity in Wiskott-Aldrich syndrome: risk factors, clinical features, and outcome in a single-center cohort of 55 patients. Pediatrics, 111 (5 Pt 1), e622-627. Düwel, M., Welteke, V., Oeckinghaus, A., Baens, M., Kloo, B., Ferch, U., Darnay, B. G., Ruland, J., Marynen, P., and Krappmann, D., 2009. A20 Negatively Regulates T Cell Receptor Signaling to NF-κB by Cleaving Malt1 Ubiquitin Chains. The Journal of Immunology, 182 (12), 7718–7728. Editor, M. B., 2019. Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit? [online]. Available from: https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis- how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit [Accessed 2 Sep 2019]. Egging, D., van Vlijmen-Willems, I., van Tongeren, T., Schalkwijk, J., and Peeters, A., 2007. Wound healing in tenascin-X deficient mice suggests that tenascin-X is involved in matrix maturation rather than matrix deposition. Connective Tissue Research, 48 (2), 93–98. Eisenbarth, S. C., 2019. Dendritic cell subsets in T cell programming: location dictates function. Nature Reviews Immunology, 19 (2), 89–103. Eleftheriou, D. and Brogan, P. A., 2017. Genetic interferonopathies: An overview. Best Practice & Research. Clinical Rheumatology, 31 (4), 441–459. Elias, S., Robertson, E. J., Bikoff, E. K., and Mould, A. W., 2018. Blimp-1/PRDM1 is a critical regulator of Type III Interferon responses in mammary epithelial cells. Scientific Reports, 8 (1), 237. Elsby, L. M., Orozco, G., Denton, J., Worthington, J., Ray, D. W., and Donn, R. P., 2010. Functional evaluation of TNFAIP3 (A20) in rheumatoid arthritis. Clinical and Experimental Rheumatology, 28 (5), 708–714. Engelhardt, K. R., Grimbacher, B., and Niehues, T., 2013. Angeborene Immundefekte. Zeitschrift für Rheumatologie, 72 (7), 643–652. Engelhardt, K. R., McGhee, S., Winkler, S., Sassi, A., Woellner, C., Lopez-Herrera, G., Chen, A., Kim, H. S., Lloret, M. G., Schulze, I., Ehl, S., Thiel, J., Pfeifer, D., Veelken, H., Niehues, T., Siepermann, K., Weinspach, S., Reisli, I., Keles, S., Genel, F., Kütükçüler, N., Camcioğlu, Y., Somer, A., Aydiner, E. K., Barlan, I.,
413
Gennery, A., Metin, A., Degerliyurt, A., Pietrogrande, M. C., Yeganeh, M., Baz, Z., Al-Tamemi, S., Klein, C., Puck, J. M., Holland, S. M., McCabe, E. R. B., Grimbacher, B., and Chatila, T., 2009. Large Deletions and Point Mutations Involving DOCK8 in the Autosomal Recessive Form of the Hyper-IgE Syndrome. The Journal of allergy and clinical immunology, 124 (6), 1289. Famili, F., Wiekmeijer, A.-S., and Staal, F. J., 2017. The development of T cells from stem cells in mice and humans. Future Science OA [online], 3 (3). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583695/ [Accessed 23 Apr 2019]. FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest, A. R. R., Kawaji, H., Rehli, M., Baillie, J. K., de Hoon, M. J. L., Haberle, V., Lassmann, T., Kulakovskiy, I. V., Lizio, M., Itoh, M., Andersson, R., Mungall, C. J., Meehan, T. F., Schmeier, S., Bertin, N., Jørgensen, M., Dimont, E., Arner, E., Schmidl, C., Schaefer, U., Medvedeva, Y. A., Plessy, C., Vitezic, M., Severin, J., Semple, C. A., Ishizu, Y., Young, R. S., Francescatto, M., Alam, I., Albanese, D., Altschuler, G. M., Arakawa, T., Archer, J. A. C., Arner, P., Babina, M., Rennie, S., Balwierz, P. J., Beckhouse, A. G., Pradhan-Bhatt, S., Blake, J. A., Blumenthal, A., Bodega, B., Bonetti, A., Briggs, J., Brombacher, F., Burroughs, A. M., Califano, A., Cannistraci, C. V., Carbajo, D., Chen, Y., Chierici, M., Ciani, Y., Clevers, H. C., Dalla, E., Davis, C. A., Detmar, M., Diehl, A. D., Dohi, T., Drabløs, F., Edge, A. S. B., Edinger, M., Ekwall, K., Endoh, M., Enomoto, H., Fagiolini, M., Fairbairn, L., Fang, H., Farach-Carson, M. C., Faulkner, G. J., Favorov, A. V., Fisher, M. E., Frith, M. C., Fujita, R., Fukuda, S., Furlanello, C., Furino, M., Furusawa, J., Geijtenbeek, T. B., Gibson, A. P., Gingeras, T., Goldowitz, D., Gough, J., Guhl, S., Guler, R., Gustincich, S., Ha, T. J., Hamaguchi, M., Hara, M., Harbers, M., Harshbarger, J., Hasegawa, A., Hasegawa, Y., Hashimoto, T., Herlyn, M., Hitchens, K. J., Ho Sui, S. J., Hofmann, O. M., Hoof, I., Hori, F., Huminiecki, L., Iida, K., Ikawa, T., Jankovic, B. R., Jia, H., Joshi, A., Jurman, G., Kaczkowski, B., Kai, C., Kaida, K., Kaiho, A., Kajiyama, K., Kanamori-Katayama, M., Kasianov, A. S., Kasukawa, T., Katayama, S., Kato, S., Kawaguchi, S., Kawamoto, H., Kawamura, Y. I., Kawashima, T., Kempfle, J. S., Kenna, T. J., Kere, J., Khachigian, L. M., Kitamura, T., Klinken, S. P., Knox, A. J., Kojima, M., Kojima, S., Kondo, N., Koseki, H., Koyasu, S., Krampitz, S., Kubosaki, A., Kwon, A. T., Laros, J. F. J., Lee, W., Lennartsson, A., Li, K., Lilje, B., Lipovich, L., Mackay-Sim, A., Manabe, R., Mar, J. C., Marchand, B., Mathelier, A., Mejhert, N., Meynert, A., Mizuno, Y., de Lima Morais, D. A., Morikawa, H., Morimoto, M., Moro, K., Motakis, E., Motohashi, H., Mummery, C. L., Murata, M., Nagao-Sato, S., Nakachi, Y., Nakahara, F., Nakamura, T., Nakamura, Y., Nakazato, K., van Nimwegen, E., Ninomiya, N., Nishiyori, H., Noma, S., Noma, S., Noazaki, T., Ogishima, S., Ohkura, N., Ohimiya, H., Ohno, H., Ohshima, M., Okada-Hatakeyama, M., Okazaki, Y., Orlando, V., Ovchinnikov, D. A., Pain, A., Passier, R., Patrikakis, M., Persson, H., Piazza, S., Prendergast, J. G. D., Rackham, O. J. L., Ramilowski, J. A., Rashid, M., Ravasi, T., Rizzu, P., Roncador, M., Roy, S., Rye, M. B., Saijyo, E., Sajantila, A., Saka, A., Sakaguchi, S., Sakai, M., Sato, H., Savvi, S., Saxena, A., Schneider, C., Schultes, E. A., Schulze-Tanzil, G. G., Schwegmann, A., Sengstag, T., Sheng, G., Shimoji, H., Shimoni, Y., Shin, J. W., Simon, C., Sugiyama, D., Sugiyama, T., Suzuki, M., Suzuki, N., Swoboda, R. K., ’t Hoen, P. A. C., Tagami, M., Takahashi, N., Takai, J., Tanaka, H., Tatsukawa, H., Tatum, Z., Thompson, M.,
414
Toyodo, H., Toyoda, T., Valen, E., van de Wetering, M., van den Berg, L. M., Verado, R., Vijayan, D., Vorontsov, I. E., Wasserman, W. W., Watanabe, S., Wells, C. A., Winteringham, L. N., Wolvetang, E., Wood, E. J., Yamaguchi, Y., Yamamoto, M., Yoneda, M., Yonekura, Y., Yoshida, S., Zabierowski, S. E., Zhang, P. G., Zhao, X., Zucchelli, S., Summers, K. M., Suzuki, H., Daub, C. O., Kawai, J., Heutink, P., Hide, W., Freeman, T. C., Lenhard, B., Bajic, V. B., Taylor, M. S., Makeev, V. J., Sandelin, A., Hume, D. A., Carninci, P., and Hayashizaki, Y., 2014. A promoter-level mammalian expression atlas. Nature, 507 (7493), 462–470. Feldmann, J., Callebaut, I., Raposo, G., Certain, S., Bacq, D., Dumont, C., Lambert, N., Ouachée-Chardin, M., Chedeville, G., Tamary, H., Minard-Colin, V., Vilmer, E., Blanche, S., Le Deist, F., Fischer, A., and de Saint Basile, G., 2003. Munc13- 4 is essential for cytolytic granules fusion and is mutated in a form of familial hemophagocytic lymphohistiocytosis (FHL3). Cell, 115 (4), 461–473. Feldmeyer, L., Keller, M., Niklaus, G., Hohl, D., Werner, S., and Beer, H.-D., 2007. The inflammasome mediates UVB-induced activation and secretion of interleukin-1beta by keratinocytes. Current biology: CB, 17 (13), 1140–1145. Feng, F., Li, Z., Potts-Kant, E. N., Wu, Y., Foster, W. M., Williams, K. L., and Hollingsworth, J. W., 2012. Hyaluronan activation of the Nlrp3 inflammasome contributes to the development of airway hyperresponsiveness. Environmental Health Perspectives, 120 (12), 1692–1698. Feng, X., Wu, H., Grossman, J. M., Hanvivadhanakul, P., FitzGerald, J. D., Park, G. S., Dong, X., Chen, W., Kim, M. H., Weng, H. H., Furst, D. E., Gorn, A., McMahon, M., Taylor, M., Brahn, E., Hahn, B. H., and Tsao, B. P., 2006. Association of increased interferon-inducible gene expression with disease activity and lupus nephritis in patients with systemic lupus erythematosus. Arthritis & Rheumatism, 54 (9), 2951–2962. Fentoğlu, Ö., Dinç, G., Bağcı, Ö., Doğru, A., İlhan, I., Kırzıoğlu, F. Y., and Orhan, H., 2017. R202Q/M694V as novel MEFV gene mutations in chronic periodontitis and familial Mediterranean fever. Journal of Periodontal Research, 52 (6), 994– 1003. Fitzgerald, J. C., Zimprich, A., Carvajal Berrio, D. A., Schindler, K. M., Maurer, B., Schulte, C., Bus, C., Hauser, A.-K., Kübler, M., Lewin, R., Bobbili, D. R., Schwarz, L. M., Vartholomaiou, E., Brockmann, K., Wüst, R., Madlung, J., Nordheim, A., Riess, O., Martins, L. M., Glaab, E., May, P., Schenke-Layland, K., Picard, D., Sharma, M., Gasser, T., and Krüger, R., 2017. Metformin reverses TRAP1 mutation-associated alterations in mitochondrial function in Parkinson’s disease. Brain, 140 (9), 2444–2459. Fleige, H., Bosnjak, B., Permanyer, M., Ristenpart, J., Bubke, A., Willenzon, S., Sutter, G., Luther, S. A., and Förster, R., 2018. Manifold Roles of CCR7 and Its Ligands in the Induction and Maintenance of Bronchus-Associated Lymphoid Tissue. Cell Reports, 23 (3), 783–795. Förster, R., Davalos-Misslitz, A. C., and Rot, A., 2008. CCR7 and its ligands: balancing immunity and tolerance. Nature Reviews Immunology, 8 (5), 362–371. Förster, R., Schubel, A., Breitfeld, D., Kremmer, E., Renner-Müller, I., Wolf, E., and Lipp, M., 1999. CCR7 Coordinates the Primary Immune Response by Establishing Functional Microenvironments in Secondary Lymphoid Organs. Cell, 99 (1), 23–33.
415
Galandrin, S., Oligny-Longpré, G., and Bouvier, M., 2007. The evasive nature of drug efficacy: implications for drug discovery. Trends in Pharmacological Sciences, 28 (8), 423–430. Galeotti, C., Meinzer, U., Quartier, P., Rossi-Semerano, L., Bader-Meunier, B., Pillet, P., and Kone-Paut, I., 2012. Efficacy of interleukin-1-targeting drugs in mevalonate kinase deficiency. Rheumatology, 51 (10), 1855–1859. Gambineri, E., Perroni, L., Passerini, L., Bianchi, L., Doglioni, C., Meschi, F., Bonfanti, R., Sznajer, Y., Tommasini, A., Lawitschka, A., Junker, A., Dunstheimer, D., Heidemann, P. H., Cazzola, G., Cipolli, M., Friedrich, W., Janic, D., Azzi, N., Richmond, E., Vignola, S., Barabino, A., Chiumello, G., Azzari, C., Roncarolo, M.-G., and Bacchetta, R., 2008. Clinical and molecular profile of a new series of patients with immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome: inconsistent correlation between forkhead box protein 3 expression and disease severity. The Journal of Allergy and Clinical Immunology, 122 (6), 1105-1112.e1. Gao, L., Coope, H., Grant, S., Ma, A., Ley, S. C., and Harhaj, E. W., 2011. ABIN1 protein cooperates with TAX1BP1 and A20 proteins to inhibit antiviral signaling. J Biol Chem, 286 (42), 36592–602. Gathmann, B., Mahlaoui, N., CEREDIH, Gérard, L., Oksenhendler, E., Warnatz, K., Schulze, I., Kindle, G., Kuijpers, T. W., Dutch WID, van Beem, R. T., Guzman, D., Workman, S., Soler-Palacín, P., De Gracia, J., Witte, T., Schmidt, R. E., Litzman, J., Hlavackova, E., Thon, V., Borte, M., Borte, S., Kumararatne, D., Feighery, C., Longhurst, H., Helbert, M., Szaflarska, A., Sediva, A., Belohradsky, B. H., Jones, A., Baumann, U., Meyts, I., Kutukculer, N., Wågström, P., Galal, N. M., Roesler, J., Farmaki, E., Zinovieva, N., Ciznar, P., Papadopoulou-Alataki, E., Bienemann, K., Velbri, S., Panahloo, Z., Grimbacher, B., and European Society for Immunodeficiencies Registry Working Party, 2014. Clinical picture and treatment of 2212 patients with common variable immunodeficiency. The Journal of Allergy and Clinical Immunology, 134 (1), 116–126. Gattorno, M., Pelagatti, M. A., Meini, A., Obici, L., Barcellona, R., Federici, S., Buoncompagni, A., Plebani, A., Merlini, G., and Martini, A., 2008. Persistent efficacy of anakinra in patients with tumor necrosis factor receptor-associated periodic syndrome. Arthritis and Rheumatism, 58 (5), 1516–1520. Gentileschi, S., Rigante, D., Vitale, A., Sota, J., Frediani, B., Galeazzi, M., and Cantarini, L., 2017. Efficacy and safety of anakinra in tumor necrosis factor receptor-associated periodic syndrome (TRAPS) complicated by severe renal failure: a report after long-term follow-up and review of the literature. Clinical Rheumatology, 36 (7), 1687–1690. Gholam, C., Grigoriadou, S., Gilmour, K. C., and Gaspar, H. B., 2011. Familial haemophagocytic lymphohistiocytosis: advances in the genetic basis, diagnosis and management. Clinical and Experimental Immunology, 163 (3), 271–283. Ghosh, R., Oak, N., and Plon, S. E., 2017. Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines. Genome Biology, 18 (1), 225. Giardine, B., Riemer, C., Hardison, R. C., Burhans, R., Elnitski, L., Shah, P., Zhang, Y., Blankenberg, D., Albert, I., Taylor, J., Miller, W., Kent, W. J., and Nekrutenko, A., 2005. Galaxy: a platform for interactive large-scale genome analysis. Genome Research, 15 (10), 1451–1455.
416
Gilmore, T. D., 2006. Introduction to NF-kappaB: players, pathways, perspectives. Oncogene, 25 (51), 6680–6684. Goecks, J., Nekrutenko, A., and Taylor, J., 2010. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biology, 11 (8), R86. Goldshtein, A., Zerbib, S. M., Omar, I., Cohen-Daniel, L., Popkin, D., and Berger, M., 2016. Loss of T-cell quiescence by targeting Slfn2 prevents the development and progression of T-ALL. Oncotarget, 7 (30), 46835–46847. González-Navajas, J. M., Lee, J., David, M., and Raz, E., 2012. Immunomodulatory functions of type I interferons. Nature reviews. Immunology, 12 (2), 125–135. Graham, R. R., Cotsapas, C., Davies, L., Hackett, R., Lessard, C. J., Leon, J. M., Burtt, N. P., Guiducci, C., Parkin, M., Gates, C., Plenge, R. M., Behrens, T. W., Wither, J. E., Rioux, J. D., Fortin, P. R., Graham, D. C., Wong, A. K., Vyse, T. J., Daly, M. J., Altshuler, D., Moser, K. L., and Gaffney, P. M., 2008. Genetic Variants Near TNFAIP3 on 6q23 are Associated with Systemic Lupus Erythematosus (SLE). Nature genetics, 40 (9), 1059–1061. Graham, R. R., Kyogoku, C., Sigurdsson, S., Vlasova, I. A., Davies, L. R. L., Baechler, E. C., Plenge, R. M., Koeuth, T., Ortmann, W. A., Hom, G., Bauer, J. W., Gillett, C., Burtt, N., Graham, D. S. C., Onofrio, R., Petri, M., Gunnarsson, I., Svenungsson, E., Rönnblom, L., Nordmark, G., Gregersen, P. K., Moser, K., Gaffney, P. M., Criswell, L. A., Vyse, T. J., Syvänen, A.-C., Bohjanen, P. R., Daly, M. J., Behrens, T. W., and Altshuler, D., 2007. Three functional variants of IFN regulatory factor 5 (IRF5) define risk and protective haplotypes for human lupus. Proceedings of the National Academy of Sciences, 104 (16), 6758–6763. Grant, A. J., Goddard, S., Ahmed-Choudhury, J., Reynolds, G., Jackson, D. G., Briskin, M., Wu, L., Hübscher, S. G., and Adams, D. H., 2002. Hepatic Expression of Secondary Lymphoid Chemokine (CCL21) Promotes the Development of Portal-Associated Lymphoid Tissue in Chronic Inflammatory Liver Disease. The American Journal of Pathology, 160 (4), 1445–1455. Greten, F. R., Arkan, M. C., Bollrath, J., Hsu, L.-C., Goode, J., Miething, C., Göktuna, S. I., Neuenhahn, M., Fierer, J., Paxian, S., Van Rooijen, N., Xu, Y., O’Cain, T., Jaffee, B. B., Busch, D. H., Duyster, J., Schmid, R. M., Eckmann, L., and Karin, M., 2007. NF-κB Is a Negative Regulator of IL-1β Secretion as Revealed by Genetic and Pharmacological Inhibition of IKKβ. Cell, 130 (5), 918–931. Grey, S. T., Arvelo, M. B., Hasenkamp, W., Bach, F. H., and Ferran, C., 1999. A20 Inhibits Cytokine-Induced Apoptosis and Nuclear Factor κB–Dependent Gene Activation in Islets. The Journal of Experimental Medicine, 190 (8), 1135–1146. Grimwood, C., Despert, V., Jeru, I., and Hentgen, V., 2015. On-demand treatment with anakinra: a treatment option for selected TRAPS patients. Rheumatology, 54 (9), 1749–1751. Grinnan, D., Sung, S.-S., Dougherty, J. A., Knowles, A. R., Allen, M. B., Rose, C. E., Nakano, H., Gunn, M. D., Fu, S. M., and Rose, C. E., 2006. Enhanced allergen- induced airway inflammation in paucity of lymph node T cell (plt) mutant mice. Journal of Allergy and Clinical Immunology, 118 (6), 1234–1241. Guedes, R. P., Csizmadia, E., Moll, H. P., Ma, A., Ferran, C., and da Silva, C. G., 2014. A20 deficiency causes spontaneous neuroinflammation in mice. Journal of Neuroinflammation, 11, 122. Gunn, M. D., Kyuwa, S., Tam, C., Kakiuchi, T., Matsuzawa, A., Williams, L. T., and Nakano, H., 1999. Mice lacking expression of secondary lymphoid organ
417
chemokine have defects in lymphocyte homing and dendritic cell localization. The Journal of Experimental Medicine, 189 (3), 451–460. Guo, H., Callaway, J. B., and Ting, J. P.-Y., 2015. Inflammasomes: mechanism of action, role in disease, and therapeutics. Nature Medicine, 21 (7), 677–687. Halle, A., Hornung, V., Petzold, G. C., Stewart, C. R., Monks, B. G., Reinheckel, T., Fitzgerald, K. A., Latz, E., Moore, K. J., and Golenbock, D. T., 2008. The NALP3 inflammasome is involved in the innate immune response to amyloid- beta. Nature Immunology, 9 (8), 857–865. Halle, S., Dujardin, H. C., Bakocevic, N., Fleige, H., Danzer, H., Willenzon, S., Suezer, Y., Hämmerling, G., Garbi, N., Sutter, G., Worbs, T., and Förster, R., 2009. Induced bronchus-associated lymphoid tissue serves as a general priming site for T cells and is maintained by dendritic cells. The Journal of Experimental Medicine, 206 (12), 2593–2601. Haller, O. and Weber, F., 2009. The interferon response circuit in antiviral host defense. Verh K Acad Geneeskd Belg, 71 (1–2), 73–86. Hammer, G. E., Turer, E. E., Taylor, K. E., Fang, C. J., Advincula, R., Oshima, S., Barrera, J., Huang, E. J., Hou, B., Malynn, B. A., Reizis, B., DeFranco, A., Criswell, L. A., Nakamura, M. C., and Ma, A., 2011. Dendritic cell expression of A20 preserves immune homeostasis and prevents colitis and spondyloarthritis. Nature immunology, 12 (12), 1184–1193. Han, X., Aslanian, A., and Yates, J. R., 2008. Mass Spectrometry for Proteomics. Current opinion in chemical biology, 12 (5), 483–490. Harada, Y., Tanaka, Y., Terasawa, M., Pieczyk, M., Habiro, K., Katakai, T., Hanawa- Suetsugu, K., Kukimoto-Niino, M., Nishizaki, T., Shirouzu, M., Duan, X., Uruno, T., Nishikimi, A., Sanematsu, F., Yokoyama, S., Stein, J. V., Kinashi, T., and Fukui, Y., 2012. DOCK8 is a Cdc42 activator critical for interstitial dendritic cell migration during immune responses. Blood, 119 (19), 4451–4461. Hayden, M. S. and Ghosh, S., 2011. NF-κB in immunobiology. Cell Research, 21 (2), 223–244. He, K.-L. and Ting, A. T., 2002. A20 Inhibits Tumor Necrosis Factor (TNF) Alpha- Induced Apoptosis by Disrupting Recruitment of TRADD and RIP to the TNF Receptor 1 Complex in Jurkat T Cells. Molecular and Cellular Biology, 22 (17), 6034–6045. He, W., Wan, H., Hu, L., Chen, P., Wang, X., Huang, Z., Yang, Z.-H., Zhong, C.-Q., and Han, J., 2015. Gasdermin D is an executor of pyroptosis and required for interleukin-1β secretion. Cell Research, 25 (12), 1285–1298. Heather, J. M. and Chain, B., 2016. The sequence of sequencers: The history of sequencing DNA. Genomics, 107 (1), 1–8. Hengel, R. L., Thaker, V., Pavlick, M. V., Metcalf, J. A., Dennis, G., Yang, J., Lempicki, R. A., Sereti, I., and Lane, H. C., 2003. Cutting edge: L-selectin (CD62L) expression distinguishes small resting memory CD4+ T cells that preferentially respond to recall antigen. Journal of Immunology (Baltimore, Md.: 1950), 170 (1), 28–32. Heyninck, K. and Beyaert, R., 2005. A20 inhibits NF-kappaB activation by dual ubiquitin-editing functions. Trends Biochem Sci, 30 (1), 1–4. Hintzen, G., Ohl, L., del Rio, M.-L., Rodriguez-Barbosa, J.-I., Pabst, O., Kocks, J. R., Krege, J., Hardtke, S., and Forster, R., 2006. Induction of Tolerance to Innocuous Inhaled Antigen Relies on a CCR7-Dependent Dendritic Cell- Mediated Antigen Transport to the Bronchial Lymph Node. The Journal of Immunology, 177 (10), 7346–7354.
418
Hong, Y., Standing, A., Nanthapisal, S., Sebire, N., Jolles, S., Omoyinmi, E., Verstegen, R., Brogan, P. A., and Eleftheriou, D., 2018. Autoinflammation due to homozygous S208 MEFV mutation | Annals of the Rheumatic Diseases. [online]. Available from: https://ard.bmj.com/content/78/4/571 [Accessed 25 Nov 2019]. Höpken, U. E., Wengner, A. M., Loddenkemper, C., Stein, H., Heimesaat, M. M., Rehm, A., and Lipp, M., 2007. CCR7 deficiency causes ectopic lymphoid neogenesis and disturbed mucosal tissue integrity. Blood, 109 (3), 886–895. Höpken, U. E., Winter, S., Achtman, A. H., Krüger, K., and Lipp, M., 2010. CCR7 regulates lymphocyte egress and recirculation through body cavities. Journal of Leukocyte Biology, 87 (4), 671–682. Hornung, V., Bauernfeind, F., Halle, A., Samstad, E. O., Kono, H., Rock, K. L., Fitzgerald, K. A., and Latz, E., 2008. Silica crystals and aluminum salts mediate NALP-3 inflammasome activation via phagosomal destabilization. Nature immunology, 9 (8), 847–856. Houten, S. M., Frenkel, J., Kuis, W., Wanders, R. J. A., Poll-The, B. T., and Waterham, H. R., 2000. Molecular basis of classical mevalonic aciduria and the hyperimmunoglobulinaemia D and periodic fever syndrome: High frequency of 3 mutations in the mevalonate kinase gene. Journal of Inherited Metabolic Disease, 23 (4), 367–370. Hsueh, K. C., Lin, Y. J., Chang, J. S., Wan, L., and Tsai, F. J., 2010. BTNL2 gene polymorphisms may be associated with susceptibility to Kawasaki disease and formation of coronary artery lesions in Taiwanese children. Eur J Pediatr, 169 (6), 713–9. Huang, N., Lee, I., Marcotte, E. M., and Hurles, M. E., 2010. Characterising and Predicting Haploinsufficiency in the Human Genome. PLOS Genetics, 6 (10), e1001154. Ivashkiv, L. B. and Donlin, L. T., 2014. Regulation of type I interferon responses. Nature Reviews Immunology, 14 (1), 36–49. Ivetic, A., Hoskins Green, H. L., and Hart, S. J., 2019. L-selectin: A Major Regulator of Leukocyte Adhesion, Migration and Signaling. Frontiers in Immunology [online], 10. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2019.01068/full [Accessed 19 Aug 2019]. Jain, M., Koren, S., Miga, K. H., Quick, J., Rand, A. C., Sasani, T. A., Tyson, J. R., Beggs, A. D., Dilthey, A. T., Fiddes, I. T., Malla, S., Marriott, H., Nieto, T., O’Grady, J., Olsen, H. E., Pedersen, B. S., Rhie, A., Richardson, H., Quinlan, A. R., Snutch, T. P., Tee, L., Paten, B., Phillippy, A. M., Simpson, J. T., Loman, N. J., and Loose, M., 2018. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nature Biotechnology, 36 (4), 338–345. Jang, M. H., Sougawa, N., Tanaka, T., Hirata, T., Hiroi, T., Tohya, K., Guo, Z., Umemoto, E., Ebisuno, Y., Yang, B.-G., Seoh, J.-Y., Lipp, M., Kiyono, H., and Miyasaka, M., 2006. CCR7 Is Critically Important for Migration of Dendritic Cells in Intestinal Lamina Propria to Mesenteric Lymph Nodes. The Journal of Immunology, 176 (2), 803–810. Janka, G. E., 2012. Familial and acquired hemophagocytic lymphohistiocytosis. Annual Review of Medicine, 63, 233–246. Jean-Charles, P.-Y., Kaur, S., and Shenoy, S. K., 2017. GPCR signaling via β-arrestin- dependent mechanisms. Journal of cardiovascular pharmacology, 70 (3), 142– 158.
419
Jo, B.-S. and Choi, S. S., 2015. Introns: The Functional Benefits of Introns in Genomes. Genomics & Informatics, 13 (4), 112–118. John G Ryan, I. A., 2009. TNF Receptor-Associated Periodic Syndrome (TRAPS): Towardsa Molecular Understanding of the Systemic AutoinflammatoryDiseases. Arthritis Rheumatology. Jorgensen, I. and Miao, E. A., 2015. Pyroptotic cell death defends against intracellular pathogens. Immunological Reviews, 265 (1), 130–142. Jost, P. J. and Ruland, J., 2007. Aberrant NF-κB signaling in lymphoma: mechanisms, consequences, and therapeutic implications. Blood, 109 (7), 2700–2707. Juliana, C., Fernandes-Alnemri, T., Kang, S., Farias, A., Qin, F., and Alnemri, E. S., 2012. Non-transcriptional priming and deubiquitination regulate NLRP3 inflammasome activation. The Journal of Biological Chemistry, 287 (43), 36617–36622. Kahlmann, D., Davalos-Misslitz, A. C. M., Ohl, L., Stanke, F., Witte, T., and Förster, R., 2007. Genetic variants of chemokine receptor CCR7 in patients with systemic lupus erythematosus, Sjogren’s syndrome and systemic sclerosis. BMC Genetics, 8, 33. Kapferer-Seebacher, I., Pepin, M., Werner, R., Aitman, T. J., Nordgren, A., Stoiber, H., Thielens, N., Gaboriaud, C., Amberger, A., Schossig, A., Gruber, R., Giunta, C., Bamshad, M., Björck, E., Chen, C., Chitayat, D., Dorschner, M., Schmitt- Egenolf, M., Hale, C. J., Hanna, D., Hennies, H. C., Heiss-Kisielewsky, I., Lindstrand, A., Lundberg, P., Mitchell, A. L., Nickerson, D. A., Reinstein, E., Rohrbach, M., Romani, N., Schmuth, M., Silver, R., Taylan, F., Vandersteen, A., Vandrovcova, J., Weerakkody, R., Yang, M., Pope, F. M., Aleck, K., Banki, Z., Dudas, J., Dumfahrt, H., Haririan, H., Hartsfield, J. K., Kagen, C. N., Lindert, U., Meitinger, T., Posch, W., Pritz, C., Ross, D., Schroer, R. J., Wick, G., Wildin, R., Wilflingseder, D., Byers, P. H., and Zschocke, J., 2016. Periodontal Ehlers-Danlos Syndrome Is Caused by Mutations in C1R and C1S, which Encode Subcomponents C1r and C1s of Complement. The American Journal of Human Genetics, 99 (5), 1005–1014. Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., Gauthier, L. D., Brand, H., Solomonson, M., Watts, N. A., Rhodes, D., Singer-Berk, M., England, E. M., Seaby, E. G., Kosmicki, J. A., Walters, R. K., Tashman, K., Farjoun, Y., Banks, E., Poterba, T., Wang, A., Seed, C., Whiffin, N., Chong, J. X., Samocha, K. E., Pierce-Hoffman, E., Zappala, Z., O’Donnell-Luria, A. H., Minikel, E. V., Weisburd, B., Lek, M., Ware, J. S., Vittal, C., Armean, I. M., Bergelson, L., Cibulskis, K., Connolly, K. M., Covarrubias, M., Donnelly, S., Ferriera, S., Gabriel, S., Gentry, J., Gupta, N., Jeandet, T., Kaplan, D., Llanwarne, C., Munshi, R., Novod, S., Petrillo, N., Roazen, D., Ruano-Rubio, V., Saltzman, A., Schleicher, M., Soto, J., Tibbetts, K., Tolonen, C., Wade, G., Talkowski, M. E., Consortium, T. G. A. D., Neale, B. M., Daly, M. J., and MacArthur, D. G., 2019. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein- coding genes. bioRxiv, 531210. Katsoulidis, E., Mavrommatis, E., Woodard, J., Shields, M. A., Sassano, A., Carayol, N., Sawicki, K. T., Munshi, H. G., and Platanias, L. C., 2010. Role of Interferon α (IFNα)-inducible Schlafen-5 in Regulation of Anchorage-independent Growth and Invasion of Malignant Melanoma Cells. Journal of Biological Chemistry, 285 (51), 40333–40341.
420
Kaufman, C. S. and Butler, M. G., 2016. Mutation in TNXB gene causes moderate to severe Ehlers-Danlos syndrome. World Journal of Medical Genetics, 6 (2), 17– 21. Kawasaki, T. and Kawai, T., 2014. Toll-Like Receptor Signaling Pathways. Frontiers in Immunology [online], 5. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4174766/ [Accessed 8 Jan 2019]. Kerr, I. D., Cox, H. C., Moyes, K., Evans, B., Burdett, B. C., van Kan, A., McElroy, H., Vail, P. J., Brown, K. L., Sumampong, D. B., Monteferrante, N. J., Hardman, K. L., Theisen, A., Mundt, E., Wenstrup, R. J., and Eggington, J. M., 2017. Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes. Journal of Community Genetics, 8 (2), 87–95. Kim, C. and Williams, M. A., 2010. Nature and nurture: T-cell receptor-dependent and T-cell receptor-independent differentiation cues in the selection of the memory T-cell pool. Immunology, 131 (3), 310–317. Kim, J.-W., Ferris, R. L., and Whiteside, T. L., 2005. Chemokine C receptor 7 (CCR7) expression on circulating CD8+ T cells enhances their resistance to apoptosis. Cancer Research, 65 (9 Supplement), 1416–1416. Kitamura, A., Maekawa, Y., Uehara, H., Izumi, K., Kawachi, I., Nishizawa, M., Toyoshima, Y., Takahashi, H., Standley, D. M., Tanaka, K., Hamazaki, J., Murata, S., Obara, K., Toyoshima, I., and Yasutomo, K., 2011. A mutation in the immunoproteasome subunit PSMB8 causes autoinflammation and lipodystrophy in humans. J Clin Invest, 121 (10), 4150–60. Klein, L., Kyewski, B., Allen, P. M., and Hogquist, K. A., 2014. Positive and negative selection of the T cell repertoire: what thymocytes see and don’t see. Nature reviews. Immunology, 14 (6), 377–391. Kobayashi, D., Endo, M., Ochi, H., Hojo, H., Miyasaka, M., and Hayasaka, H., 2017. Regulation of CCR7-dependent cell migration through CCR7 homodimer formation. Scientific Reports [online], 7 (1). Available from: http://www.nature.com/articles/s41598-017-09113-4 [Accessed 15 Feb 2019]. Koboldt, D. C., Steinberg, K. M., Larson, D. E., Wilson, R. K., and Mardis, E. R., 2013. The next-generation sequencing revolution and its impact on genomics. Cell, 155 (1), 27–38. Kocks, J. R., Adler, H., Danzer, H., Hoffmann, K., Jonigk, D., Lehmann, U., and Förster, R., 2009. Chemokine Receptor CCR7 Contributes to a Rapid and Efficient Clearance of Lytic Murine γ-Herpes Virus 68 from the Lung, Whereas Bronchus-Associated Lymphoid Tissue Harbors Virus during Latency. The Journal of Immunology, 182 (11), 6861–6869. Kocks, J. R., Davalos-Misslitz, A. C. M., Hintzen, G., Ohl, L., and Förster, R., 2007. Regulatory T cells interfere with the development of bronchus-associated lymphoid tissue. The Journal of Experimental Medicine, 204 (4), 723–734. Kool, M., van Loo, G., Waelput, W., De Prijck, S., Muskens, F., Sze, M., van Praet, J., Branco-Madeira, F., Janssens, S., Reizis, B., Elewaut, D., Beyaert, R., Hammad, H., and Lambrecht, B. N., 2011. The ubiquitin-editing protein A20 prevents dendritic cell activation, recognition of apoptotic cells, and systemic autoimmunity. Immunity, 35 (1), 82–96. Kozai, M., Kubo, Y., Katakai, T., Kondo, H., Kiyonari, H., Schaeuble, K., Luther, S. A., Ishimaru, N., Ohigashi, I., and Takahama, Y., 2017. Essential role of CCL21 in establishment of central self-tolerance in T cells. The Journal of Experimental Medicine, 214 (7), 1925–1935.
421
Kozarewa, I., Ning, Z., Quail, M. A., Sanders, M. J., Berriman, M., and Turner, D. J., 2009. Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes. Nature Methods, 6 (4), 291–295. Kreins, A. Y., Ciancanelli, M. J., Okada, S., Kong, X.-F., Ramírez-Alejo, N., Kilic, S. S., Baghdadi, J. E., Nonoyama, S., Mahdaviani, S. A., Ailal, F., Bousfiha, A., Mansouri, D., Nievas, E., Ma, C. S., Rao, G., Bernasconi, A., Kuehn, H. S., Niemela, J., Stoddard, J., Deveau, P., Cobat, A., Azbaoui, S. E., Sabri, A., Lim, C. K., Sundin, M., Avery, D. T., Halwani, R., Grant, A. V., Boisson, B., Bogunovic, D., Itan, Y., Moncada-Velez, M., Martinez-Barricarte, R., Migaud, M., Deswarte, C., Alsina, L., Kotlarz, D., Klein, C., Muller-Fleckenstein, I., Fleckenstein, B., Cormier-Daire, V., Rose-John, S., Picard, C., Hammarstrom, L., Puel, A., Al-Muhsen, S., Abel, L., Chaussabel, D., Rosenzweig, S. D., Minegishi, Y., Tangye, S. G., Bustamante, J., Casanova, J.-L., and Boisson- Dupuis, S., 2015. Human TYK2 deficiency: Mycobacterial and viral infections without hyper-IgE syndrome. Journal of Experimental Medicine, 212 (10), 1641–1662. Kroeze, W. K., Sheffler, D. J., and Roth, B. L., 2003. G-protein-coupled receptors at a glance. Journal of Cell Science, 116 (24), 4867–4869. Kryukov, G. V., Pennacchio, L. A., and Sunyaev, S. R., 2007. Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. American Journal of Human Genetics, 80 (4), 727–739. Kuehn, H. S., Ouyang, W., Lo, B., Deenick, E. K., Niemela, J. E., Avery, D. T., Schickel, J.-N., Tran, D. Q., Stoddard, J., Zhang, Y., Frucht, D. M., Dumitriu, B., Scheinberg, P., Folio, L. R., Frein, C. A., Price, S., Koh, C., Heller, T., Seroogy, C. M., Huttenlocher, A., Rao, V. K., Su, H. C., Kleiner, D., Notarangelo, L. D., Rampertaap, Y., Olivier, K. N., McElwee, J., Hughes, J., Pittaluga, S., Oliveira, J. B., Meffre, E., Fleisher, T. A., Holland, S. M., Lenardo, M. J., Tangye, S. G., and Uzel, G., 2014. Immune dysregulation in human subjects with heterozygous germline mutations in CTLA4. Science (New York, N.Y.), 345 (6204), 1623–1627. Kurd, N. and Robey, E. A., 2016. T cell selection in the thymus: a spatial and temporal perspective. Immunological reviews, 271 (1), 114–126. Kuri, P., Schieber, N. L., Thumberger, T., Wittbrodt, J., Schwab, Y., and Leptin, M., 2017. Dynamics of in vivo ASC speck formation. The Journal of Cell Biology, 216 (9), 2891–2909. Kurobe, H., Liu, C., Ueno, T., Saito, F., Ohigashi, I., Seach, N., Arakaki, R., Hayashi, Y., Kitagawa, T., Lipp, M., Boyd, R. L., and Takahama, Y., 2006. CCR7- dependent cortex-to-medulla migration of positively selected thymocytes is essential for establishing central tolerance. Immunity, 24 (2), 165–177. Kurotaki, D., Uede, T., and Tamura, T., 2015. Functions and development of red pulp macrophages. Microbiology and Immunology, 59 (2), 55–62. Kwan, J. and Killeen, N., 2004. CCR7 directs the migration of thymocytes into the thymic medulla. Journal of Immunology (Baltimore, Md.: 1950), 172 (7), 3999– 4007. La Torre, F., Caparello, M. C., and Cimaz, R., 2017. Canakinumab for the treatment of TNF-receptor associated periodic syndrome. Expert Review of Clinical Immunology, 13 (6), 513–523.
422
Lachmann, H. J., Kuemmerle-Deschner, J. B., Hachulla, E., Widmer, A., and Patel, N., 2009. Use of Canakinumab in the Cryopyrin-Associated Periodic Syndrome. n engl j med, 10. Lachmann, H. J., Papa, R., Gerhold, K., Obici, L., Touitou, I., Cantarini, L., Frenkel, J., Anton, J., Kone-Paut, I., Cattalini, M., Bader-Meunier, B., Insalaco, A., Hentgen, V., Merino, R., Modesto, C., Toplak, N., Berendes, R., Ozen, S., Cimaz, R., Jansson, A., Brogan, P. A., Hawkins, P. N., Ruperto, N., Martini, A., Woo, P., Gattorno, M., and Paediatric Rheumatology International Trials Organisation (PRINTO), the EUROTRAPS and the Eurofever Project, 2014. The phenotype of TNF receptor-associated autoinflammatory syndrome (TRAPS) at presentation: a series of 158 cases from the Eurofever/EUROTRAPS international registry. Annals of the Rheumatic Diseases, 73 (12), 2160–2167. Lamkanfi, M. and Dixit, V. M., 2014. Mechanisms and Functions of Inflammasomes. Cell, 157 (5), 1013–1022. Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., Funke, R., Gage, D., Harris, K., Heaford, A., Howland, J., Kann, L., Lehoczky, J., LeVine, R., McEwan, P., McKernan, K., Meldrim, J., Mesirov, J. P., Miranda, C., Morris, W., Naylor, J., Raymond, C., Rosetti, M., Santos, R., Sheridan, A., Sougnez, C., Stange- Thomann, Y., Stojanovic, N., Subramanian, A., Wyman, D., Rogers, J., Sulston, J., Ainscough, R., Beck, S., Bentley, D., Burton, J., Clee, C., Carter, N., Coulson, A., Deadman, R., Deloukas, P., Dunham, A., Dunham, I., Durbin, R., French, L., Grafham, D., Gregory, S., Hubbard, T., Humphray, S., Hunt, A., Jones, M., Lloyd, C., McMurray, A., Matthews, L., Mercer, S., Milne, S., Mullikin, J. C., Mungall, A., Plumb, R., Ross, M., Shownkeen, R., Sims, S., Waterston, R. H., Wilson, R. K., Hillier, L. W., McPherson, J. D., Marra, M. A., Mardis, E. R., Fulton, L. A., Chinwalla, A. T., Pepin, K. H., Gish, W. R., Chissoe, S. L., Wendl, M. C., Delehaunty, K. D., Miner, T. L., Delehaunty, A., Kramer, J. B., Cook, L. L., Fulton, R. S., Johnson, D. L., Minx, P. J., Clifton, S. W., Hawkins, T., Branscomb, E., Predki, P., Richardson, P., Wenning, S., Slezak, T., Doggett, N., Cheng, J. F., Olsen, A., Lucas, S., Elkin, C., Uberbacher, E., Frazier, M., Gibbs, R. A., Muzny, D. M., Scherer, S. E., Bouck, J. B., Sodergren, E. J., Worley, K. C., Rives, C. M., Gorrell, J. H., Metzker, M. L., Naylor, S. L., Kucherlapati, R. S., Nelson, D. L., Weinstock, G. M., Sakaki, Y., Fujiyama, A., Hattori, M., Yada, T., Toyoda, A., Itoh, T., Kawagoe, C., Watanabe, H., Totoki, Y., Taylor, T., Weissenbach, J., Heilig, R., Saurin, W., Artiguenave, F., Brottier, P., Bruls, T., Pelletier, E., Robert, C., Wincker, P., Smith, D. R., Doucette-Stamm, L., Rubenfield, M., Weinstock, K., Lee, H. M., Dubois, J., Rosenthal, A., Platzer, M., Nyakatura, G., Taudien, S., Rump, A., Yang, H., Yu, J., Wang, J., Huang, G., Gu, J., Hood, L., Rowen, L., Madan, A., Qin, S., Davis, R. W., Federspiel, N. A., Abola, A. P., Proctor, M. J., Myers, R. M., Schmutz, J., Dickson, M., Grimwood, J., Cox, D. R., Olson, M. V., Kaul, R., Raymond, C., Shimizu, N., Kawasaki, K., Minoshima, S., Evans, G. A., Athanasiou, M., Schultz, R., Roe, B. A., Chen, F., Pan, H., Ramser, J., Lehrach, H., Reinhardt, R., McCombie, W. R., de la Bastide, M., Dedhia, N., Blöcker, H., Hornischer, K., Nordsiek, G., Agarwala, R., Aravind, L., Bailey, J. A., Bateman, A., Batzoglou, S., Birney, E., Bork, P., Brown, D. G., Burge, C. B., Cerutti, L., Chen, H. C., Church, D., Clamp, M., Copley, R. R., Doerks, T., Eddy, S. R., Eichler, E. E., Furey, T. S., Galagan, J., Gilbert, J. G., Harmon, C., Hayashizaki,
423
Y., Haussler, D., Hermjakob, H., Hokamp, K., Jang, W., Johnson, L. S., Jones, T. A., Kasif, S., Kaspryzk, A., Kennedy, S., Kent, W. J., Kitts, P., Koonin, E. V., Korf, I., Kulp, D., Lancet, D., Lowe, T. M., McLysaght, A., Mikkelsen, T., Moran, J. V., Mulder, N., Pollara, V. J., Ponting, C. P., Schuler, G., Schultz, J., Slater, G., Smit, A. F., Stupka, E., Szustakowki, J., Thierry-Mieg, D., Thierry- Mieg, J., Wagner, L., Wallis, J., Wheeler, R., Williams, A., Wolf, Y. I., Wolfe, K. H., Yang, S. P., Yeh, R. F., Collins, F., Guyer, M. S., Peterson, J., Felsenfeld, A., Wetterstrand, K. A., Patrinos, A., Morgan, M. J., de Jong, P., Catanese, J. J., Osoegawa, K., Shizuya, H., Choi, S., Chen, Y. J., Szustakowki, J., and International Human Genome Sequencing Consortium, 2001. Initial sequencing and analysis of the human genome. Nature, 409 (6822), 860–921. Latz, E., Xiao, T. S., and Stutz, A., 2013. Activation and regulation of the inflammasomes. Nature reviews. Immunology [online], 13 (6). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3807999/ [Accessed 18 Sep 2019]. Lebrero-Fernandez, C., Wenzel, U. A., Akeus, P., Wang, Y., Strid, H., Simren, M., Gustavsson, B., Borjesson, L. G., Cardell, S. L., Ohman, L., Quiding-Jarbrink, M., and Bas-Forsberg, A., 2016. Altered expression of Butyrophilin (BTN) and BTN-like (BTNL) genes in intestinal inflammation and colon cancer. Immun Inflamm Dis, 4 (2), 191–200. Lee, E. G., Boone, D. L., Chai, S., Libby, S. L., Chien, M., Lodolce, J. P., and Ma, A., 2000. Failure to Regulate TNF-Induced NF-κB and Cell Death Responses in A20-Deficient Mice. Science, 289 (5488), 2350–2354. Lee, P. P., Lobato-Márquez, D., Pramanik, N., Sirianni, A., Daza-Cajigal, V., Rivers, E., Cavazza, A., Bouma, G., Moulding, D., Hultenby, K., Westerberg, L. S., Hollinshead, M., Lau, Y.-L., Burns, S. O., Mostowy, S., Bajaj-Elliott, M., and Thrasher, A. J., 2017. Wiskott-Aldrich syndrome protein regulates autophagy and inflammasome activity in innate immune cells. Nature Communications, 8 (1), 1576. Lee, S., Moon, J. S., Lee, C.-R., Kim, H.-E., Baek, S.-M., Hwang, S., Kang, G. H., Seo, J. K., Shin, C. H., Kang, H. J., Ko, J. S., Park, S. G., and Choi, M., 2016. Abatacept alleviates severe autoimmune symptoms in a patient carrying a de novo variant in CTLA-4. The Journal of Allergy and Clinical Immunology, 137 (1), 327–330. Lee-Kirsch, M. A., Wolf, C., and Gunther, C., 2014. Aicardi-Goutieres syndrome: a model disease for systemic autoimmunity. Clin Exp Immunol, 175 (1), 17–24. Lehmberg, K., Moshous, D., and Booth, C., 2019. Haematopoietic Stem Cell Transplantation for Primary Haemophagocytic Lymphohistiocytosis. Frontiers in Pediatrics [online], 7. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823612/ [Accessed 1 Apr 2020]. Ley, K., 1996. Molecular mechanisms of leukocyte recruitment in the inflammatory process. Cardiovascular Research, 32 (4), 733–742. Ley, K., Laudanna, C., Cybulsky, M. I., and Nourshargh, S., 2007. Getting to the site of inflammation: the leukocyte adhesion cascade updated. Nature Reviews. Immunology, 7 (9), 678–689. Li, M., Kao, E., Gao, X., Sandig, H., Limmer, K., Pavon-Eternod, M., Jones, T. E., Landry, S., Pan, T., Weitzman, M. D., and David, M., 2012. Codon-usage-based inhibition of HIV protein synthesis by human schlafen 11. Nature, 491 (7422), 125–128.
424
Li, Y., Wollnik, B., Pabst, S., Lennarz, M., Rohmann, E., Gillissen, A., Vetter, H., and Grohe, C., 2006. BTNL2 gene variant and sarcoidosis. Thorax, 61 (3), 273–4. Liao, S. and von der Weid, P.-Y., 2015. Lymphatic System: An Active Pathway for Immune Protection. Seminars in cell & developmental biology, 38, 83–89. Lin, Y., Wei, J., Fan, L., and Cheng, D., 2015. BTNL2 gene polymorphism and sarcoidosis susceptibility: a meta-analysis. PloS One, 10 (4), e0122639. Lindor, N. M. and Bristow, J., 2005. Tenascin-X deficiency in autosomal recessive Ehlers–Danlos syndrome. American Journal of Medical Genetics Part A, 135A (1), 75–80. Link, A., Vogt, T. K., Favre, S., Britschgi, M. R., Acha-Orbea, H., Hinz, B., Cyster, J. G., and Luther, S. A., 2007. Fibroblastic reticular cells in lymph nodes regulate the homeostasis of naive T cells. Nature Immunology, 8 (11), 1255–1265. Littringer, K., Moresi, C., Rakebrandt, N., Zhou, X., Schorer, M., Dolowschiak, T., Kirchner, F., Rost, F., Keller, C. W., McHugh, D., LeibundGut-Landmann, S., Robinson, M. D., and Joller, N., 2018. Common Features of Regulatory T Cell Specialization During Th1 Responses. Frontiers in Immunology [online], 9. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2018.01344/full [Accessed 28 Mar 2020]. Liu, F., Zhou, P., Wang, Q., Zhang, M., and Li, D., 2018. The Schlafen family: complex roles in different cell types and virus replication. Cell Biology International [online], 42. Available from: https://www.readcube.com/articles/10.1002/cbin.10778 [Accessed 28 Jan 2019]. Liu, F.-Y., Safdar, J., Li, Z.-N., Fang, Q.-G., Zhang, X., Xu, Z.-F., and Sun, C.-F., 2014. CCR7 regulates cell migration and invasion through MAPKs in metastatic squamous cell carcinoma of head and neck. International Journal of Oncology, 45 (6), 2502–2510. Liu, T., Zhang, L., Joo, D., and Sun, S.-C., 2017. NF-κB signaling in inflammation. Signal Transduction and Targeted Therapy, 2, 17023. Liu, Y., Jesus, A. A., Marrero, B., Yang, D., Ramsey, S. E., Sanchez, G. A. M., Tenbrock, K., Wittkowski, H., Jones, O. Y., Kuehn, H. S., Lee, C. R., DiMattia, M. A., Cowen, E. W., Gonzalez, B., Palmer, I., DiGiovanna, J. J., Biancotto, A., Kim, H., Tsai, W. L., Trier, A. M., Huang, Y., Stone, D. L., Hill, S., Kim, H. J., Hilaire, C. S., Gurprasad, S., Plass, N., Chapelle, D., Horkayne-Szakaly, I., Foell, D., Barysenka, A., Candotti, F., Holland, S. M., Hughes, J. D., Mehmet, H., Issekutz, A. C., Raffeld, M., McElwee, J., Fontana, J. R., Minniti, C. P., Moir, S., Kastner, D. L., Gadina, M., Steven, A. C., Wingfield, P. T., Brooks, S. R., Rosenzweig, S. D., Fleisher, T. A., Deng, Z., Boehm, M., Paller, A. S., and Goldbach-Mansky, R., 2014. Activated STING in a vascular and pulmonary syndrome. N Engl J Med, 371 (6), 507–518. Liu, Y., Ramot, Y., Torrelo, A., Paller, A. S., Si, N., Babay, S., Kim, P. W., Sheikh, A., Lee, C. C., Chen, Y., Vera, A., Zhang, X., Goldbach-Mansky, R., and Zlotogorski, A., 2012. Mutations in proteasome subunit beta type 8 cause chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature with evidence of genetic and phenotypic heterogeneity. Arthritis Rheum, 64 (3), 895–907. Livak, K. J. and Schmittgen, T. D., 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods (San Diego, Calif.), 25 (4), 402–408.
425
Lovisari, F., Terzi, V., Lippi, M. G., Brioschi, P. R., and Fumagalli, R., 2017. Hemophagocytic lymphohistiocytosis complicated by multiorgan failure. Medicine [online], 96 (50). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815748/ [Accessed 1 Apr 2020]. Lu, A., Magupalli, V., Ruan, J., Yin, Q., Atianand, M. K., Vos, M., Schröder, G. F., Fitzgerald, K. A., Wu, H., and Egelman, E. H., 2014. Unified Polymerization Mechanism for the Assembly of ASC-dependent Inflammasomes. Cell, 156 (6), 1193–1206. Lu, T. T., Onizawa, M., Hammer, G. E., Turer, E. E., Yin, Q., Damko, E., Agelidis, A., Shifrin, N., Advincula, R., Barrera, J., Malynn, B. A., Wu, H., and Ma, A., 2013. Dimerization and ubiquitin mediated recruitment of A20, a complex deubiquitinating enzyme. Immunity, 38 (5), 896–905. Luther, S. A., Bidgol, A., Hargreaves, D. C., Schmidt, A., Xu, Y., Paniyadi, J., Matloubian, M., and Cyster, J. G., 2002. Differing activities of homeostatic chemokines CCL19, CCL21, and CXCL12 in lymphocyte and dendritic cell recruitment and lymphoid neogenesis. Journal of Immunology (Baltimore, Md.: 1950), 169 (1), 424–433. Lyon, G. J. and Wang, K., 2012. Identifying disease mutations in genomic medicine settings: current challenges and how to accelerate progress. Genome Medicine, 4 (7), 58. Lyons, A. B. and Parish, C. R., 1994. Determination of lymphocyte division by flow cytometry. Journal of Immunological Methods, 171 (1), 131–137. Ma, A. and Malynn, B. A., 2012. A20: linking a complex regulator of ubiquitylation to immunity and human disease. Nat Rev Immunol, 12 (11), 774–85. Ma, C. S., Chew, G. Y. J., Simpson, N., Priyadarshi, A., Wong, M., Grimbacher, B., Fulcher, D. A., Tangye, S. G., and Cook, M. C., 2008. Deficiency of Th17 cells in hyper IgE syndrome due to mutations in STAT3. Journal of Experimental Medicine, 205 (7), 1551–1557. Madkaikar, M., Shabrish, S., and Desai, M., 2016. Current Updates on Classification, Diagnosis and Treatment of Hemophagocytic Lymphohistiocytosis (HLH). Indian Journal of Pediatrics, 83 (5), 434–443. Mahoney, J. P. and Sunahara, R. K., 2016. Mechanistic insights into GPCR-G protein interactions. Current opinion in structural biology, 41, 247–254. Malik, A. and Kanneganti, T.-D., 2017. Inflammasome activation and assembly at a glance. Journal of Cell Science, 130 (23), 3955–3963. Man, S. M., Karki, R., and Kanneganti, T.-D., 2017. Molecular mechanisms and functions of pyroptosis, inflammatory caspases and inflammasomes in infectious diseases. Immunological reviews, 277 (1), 61–75. Mangan, M. S. J., Olhava, E. J., Roush, W. R., Seidel, H. M., Glick, G. D., and Latz, E., 2018. Targeting the NLRP3 inflammasome in inflammatory diseases. Nature Reviews Drug Discovery, 17 (8), 588–606. Mao, J. R., Taylor, G., Dean, W. B., Wagner, D. R., Afzal, V., Lotz, J. C., Rubin, E. M., and Bristow, J., 2002. Tenascin-X deficiency mimics Ehlers-Danlos syndrome in mice through alteration of collagen deposition. Nature Genetics, 30 (4), 421– 425. Marguerat, S. and Bähler, J., 2010. RNA-seq: from technology to biology. Cellular and Molecular Life Sciences, 67 (4), 569–579. Mariathasan, S., Weiss, D. S., Newton, K., McBride, J., O’Rourke, K., Roose-Girma, M., Lee, W. P., Weinrauch, Y., Monack, D. M., and Dixit, V. M., 2006.
426
Cryopyrin activates the inflammasome in response to toxins and ATP. Nature, 440 (7081), 228–232. Marinkovic, T., 2006. Interaction of mature CD3+CD4+ T cells with dendritic cells triggers the development of tertiary lymphoid structures in the thyroid. Journal of Clinical Investigation, 116 (10), 2622–2632. Martinon, F. and Aksentijevich, I., 2015. New players driving inflammation in monogenic autoinflammatory diseases. Nature Reviews. Rheumatology, 11 (1), 11–20. Martinon, F., Burns, K., and Tschopp, J., 2002. The inflammasome: a molecular platform triggering activation of inflammatory caspases and processing of proIL-beta. Molecular Cell, 10 (2), 417–426. Martorana, D., Bonatti, F., Mozzoni, P., Vaglio, A., and Percesepe, A., 2017. Monogenic Autoinflammatory Diseases with Mendelian Inheritance: Genes, Mutations, and Genotype/Phenotype Correlations. Front Immunol, 8, 344. Masters, S. L., Lagou, V., Jéru, I., Baker, P. J., Van Eyck, L., Parry, D. A., Lawless, D., De Nardo, D., Garcia-Perez, J. E., Dagley, L. F., Holley, C. L., Dooley, J., Moghaddas, F., Pasciuto, E., Jeandel, P.-Y., Sciot, R., Lyras, D., Webb, A. I., Nicholson, S. E., De Somer, L., van Nieuwenhove, E., Ruuth-Praz, J., Copin, B., Cochet, E., Medlej-Hashim, M., Megarbane, A., Schroder, K., Savic, S., Goris, A., Amselem, S., Wouters, C., and Liston, A., 2016. Familial autoinflammation with neutrophilic dermatosis reveals a regulatory mechanism of pyrin activation. Science Translational Medicine, 8 (332), 332ra45-332ra45. Mathur, A., Hayward, J. A., and Man, S. M., 2018. Molecular mechanisms of inflammasome signaling. Journal of Leukocyte Biology, 103 (2), 233–257. Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., Shafer, A., Neri, F., Lee, K., Kutyavin, T., Stehling-Sun, S., Johnson, A. K., Canfield, T. K., Giste, E., Diegel, M., Bates, D., Hansen, R. S., Neph, S., Sabo, P. J., Heimfeld, S., Raubitschek, A., Ziegler, S., Cotsapas, C., Sotoodehnia, N., Glass, I., Sunyaev, S. R., Kaul, R., and Stamatoyannopoulos, J. A., 2012. Systematic localization of common disease-associated variation in regulatory DNA. Science (New York, N.Y.), 337 (6099), 1190–1195. Mauro, C., Pacifico, F., Lavorgna, A., Mellone, S., Iannetti, A., Acquaviva, R., Formisano, S., Vito, P., and Leonardi, A., 2006. ABIN-1 binds to NEMO/IKKgamma and co-operates with A20 in inhibiting NF-kappaB. The Journal of Biological Chemistry, 281 (27), 18482–18488. Mavrommatis, E., Fish, E. N., and Platanias, L. C., 2013. The Schlafen Family of Proteins and Their Regulation by Interferons. Journal of Interferon & Cytokine Research, 33 (4), 206–210. McAllister, K., Eyre, S., and Orozco, G., 2011. Genetics of rheumatoid arthritis: GWAS and beyond. Open Access Rheumatology : Research and Reviews, 3, 31–46. McBride, J. A. and Striker, R., 2017. Imbalance in the game of T cells: What can the CD4/CD8 T-cell ratio tell us about HIV and health? PLOS Pathogens, 13 (11), e1006624. McCreary, D., Omoyinmi, E., Hong, Y., Mulhern, C., Papadopoulou, C., Casimir, M., Hacohen, Y., Nyanhete, R., Ahlfors, H., Cullup, T., Lim, M., Gilmour, K., Mankad, K., Wassmer, E., Berg, S., Hemingway, C., Brogan, P., and Eleftheriou, D., 2019. Development and Validation of a Targeted Next- Generation Sequencing Gene Panel for Children With Neuroinflammation. JAMA Network Open, 2 (10), e1914274–e1914274.
427
McCudden, C. R., Hains, M. D., Kimple, R. J., Siderovski, D. P., and Willard, F. S., 2005. G-protein signaling: back to the future. Cellular and Molecular Life Sciences, 62 (5), 551–577. McCusker, C., Upton, J., and Warrington, R., 2018. Primary immunodeficiency. Allergy, Asthma & Clinical Immunology, 14 (S2), 61. McDermott, A., Jacks, J., Kessler, M., Emanuel, P. D., and Gao, L., 2015. Proteasome- associated autoinflammatory syndromes: advances in pathogeneses, clinical presentations, diagnosis, and management. Int J Dermatol, 54 (2), 121–9. McDermott, M. F., Aksentijevich, I., Galone, J., McDermott, E., Ogunkolade, B. W., Centola, M., Mansfield, E., Gadina, M., Karenko, L., Pettersson, T., McCarthy, J., Frucht, D., Aringer, M., Torosyan, Y., Teppo, A. M., Wilson, M., Karaarslan, H. M., Wan, Y., and Kastner, D. L., 1999. Germline Mutations in the Extracellular Domains of the 55 kDa TNF Receptor, TNFR1, Define a Family of Dominantly Inherited Autoinflammatory Syndromes. Cell, 97 (1), 133–144. McEntagart, M., Kamel, H., Lebon, P., and King, M. D., 1998. Aicardi-Goutières syndrome: an expanding phenotype. Neuropediatrics, 29 (3), 163–167. McGonagle, D. and McDermott, M. F., 2006. A Proposed Classification of the Immunological Diseases. PLoS Medicine, 3 (8), 7. McGovern, D. P. B., Gardet, A., Törkvist, L., Goyette, P., Essers, J., Taylor, K. D., Neale, B. M., Ong, R. T. H., Lagacé, C., Li, C., Green, T., Stevens, C. R., Beauchamp, C., Fleshner, P. R., Carlson, M., D’Amato, M., Halfvarson, J., Hibberd, M. L., Lördal, M., Padyukov, L., Andriulli, A., Colombo, E., Latiano, A., Palmieri, O., Bernard, E.-J., Deslandres, C., Hommes, D. W., de Jong, D. J., Stokkers, P. C., Weersma, R. K., Sharma, Y., Silverberg, M., Cho, J. H., Wu, J., Roeder, K., Brant, S. R., Schumm, L. P., Duerr, R. H., Dubinsky, M. C., Glazer, N. L., Haritunians, T., Ippoliti, A., Melmed, G. Y., Siscovick, D. S., Vasiliauskas, E. A., Targan, S. R., Annese, V., Wijmenga, C., Pettersson, S., Rotter, J. I., Xavier, R. J., Daly, M. J., Rioux, J. D., and Seielstad, M., 2010. Genome-wide association identifies multiple ulcerative colitis susceptibility loci. Nature genetics, 42 (4), 332–337. Mebius, R. E. and Kraal, G., 2005. Structure and function of the spleen. Nature Reviews Immunology, 5 (8), 606–616. Menning, A., Höpken, U. E., Siegmund, K., Lipp, M., Hamann, A., and Huehn, J., 2007. Distinctive role of CCR7 in migration and functional activity of naive- and effector/memory-like Treg subsets. European Journal of Immunology, 37 (6), 1575–1583. Meyts, I. and Aksentijevich, I., 2018. Deficiency of Adenosine Deaminase 2 (DADA2): Updates on the Phenotype, Genetics, Pathogenesis, and Treatment. Journal of Clinical Immunology, 38 (5), 569–578. Miceli-Richard, C., Lesage, S., Rybojad, M., Prieur, A. M., Manouvrier-Hanu, S., Häfner, R., Chamaillard, M., Zouali, H., Thomas, G., and Hugot, J. P., 2001. CARD15 mutations in Blau syndrome. Nature Genetics, 29 (1), 19–20. Milman, N., Ursin, K., Rødevand, E., Nielsen, F. C., and Hansen, T. V. O., 2009. A novel mutation in the NOD2 gene associated with Blau syndrome: a Norwegian family with four affected members. Scandinavian Journal of Rheumatology, 38 (3), 190–197. Minkis, K., Aksentijevich, I., Goldbach-Mansky, R., Magro, C., Scott, R., Davis, J. G., Sardana, N., and Herzog, R., 2012. Interleukin 1 receptor antagonist deficiency presenting as infantile pustulosis mimicking infantile pustular psoriasis. Archives of Dermatology, 148 (6), 747–752.
428
Misslitz, A., Pabst, O., Hintzen, G., Ohl, L., Kremmer, E., Petrie, H. T., and Förster, R., 2004. Thymic T cell development and progenitor localization depend on CCR7. The Journal of Experimental Medicine, 200 (4), 481–491. Mitsunaga, S., Hosomichi, K., Okudaira, Y., Nakaoka, H., Kunii, N., Suzuki, Y., Kuwana, M., Sato, S., Kaneko, Y., Homma, Y., Kashiwase, K., Azuma, F., Kulski, J. K., Inoue, I., and Inoko, H., 2013. Exome sequencing identifies novel rheumatoid arthritis-susceptible variants in the BTNL2. J Hum Genet, 58 (4), 210–5. Moore, J. E. and Bertram, C. D., 2018. Lymphatic System Flows. Annual review of fluid mechanics, 50, 459–482. Moots, R. J. and Naisbett-Groet, B., 2012. The efficacy of biologic agents in patients with rheumatoid arthritis and an inadequate response to tumour necrosis factor inhibitors: a systematic review. Rheumatology, 51 (12), 2252–2261. Moratto, D., Giliani, S., Bonfim, C., Mazzolari, E., Fischer, A., Ochs, H. D., Cant, A. J., Thrasher, A. J., Cowan, M. J., Albert, M. H., Small, T., Pai, S.-Y., Haddad, E., Lisa, A., Hambleton, S., Slatter, M., Cavazzana-Calvo, M., Mahlaoui, N., Picard, C., Torgerson, T. R., Burroughs, L., Koliski, A., Neto, J. Z., Porta, F., Qasim, W., Veys, P., Kavanau, K., Hönig, M., Schulz, A., Friedrich, W., and Notarangelo, L. D., 2011. Long-term outcome and lineage-specific chimerism in 194 patients with Wiskott-Aldrich syndrome treated by hematopoietic cell transplantation in the period 1980-2009: an international collaborative study. Blood, 118 (6), 1675–1684. Mori, S., Nakano, H., Aritomi, K., Wang, C. R., Gunn, M. D., and Kakiuchi, T., 2001. Mice lacking expression of the chemokines CCL21-ser and CCL19 (plt mice) demonstrate delayed but enhanced T cell immune responses. The Journal of Experimental Medicine, 193 (2), 207–218. Moulding, D. A., Record, J., Malinova, D., and Thrasher, A. J., 2013. Actin cytoskeletal defects in immunodeficiency. Immunological Reviews, 256 (1), 282–299. Moyron-Quiroz, J. E., Rangel-Moreno, J., Kusser, K., Hartson, L., Sprague, F., Goodrich, S., Woodland, D. L., Lund, F. E., and Randall, T. D., 2004. Role of inducible bronchus associated lymphoid tissue (iBALT) in respiratory immunity. Nature Medicine, 10 (9), 927–934. Mulders-Manders, C. M. and Simon, A., 2015. Hyper-IgD syndrome/mevalonate kinase deficiency: what is new? Seminars in Immunopathology, 37 (4), 371–376. Müller, A., Homey, B., Soto, H., Ge, N., Catron, D., Buchanan, M. E., McClanahan, T., Murphy, E., Yuan, W., Wagner, S. N., Barrera, J. L., Mohar, A., Verástegui, E., and Zlotnik, A., 2001. Involvement of chemokine receptors in breast cancer metastasis. Nature, 410 (6824), 50–56. Müller, M.-L., Chiang, S. C. C., Meeths, M., Tesi, B., Entesarian, M., Nilsson, D., Wood, S. M., Nordenskjöld, M., Henter, J.-I., Naqvi, A., and Bryceson, Y. T., 2014. An N-Terminal Missense Mutation in STX11 Causative of FHL4 Abrogates Syntaxin-11 Binding to Munc18-2. Frontiers in Immunology [online], 4. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3890652/ [Accessed 1 Apr 2020]. Munk, R., Ghosh, P., Ghosh, M. C., Saito, T., Xu, M., Carter, A., Indig, F., Taub, D. D., and Longo, D. L., 2011. Involvement of mTOR in CXCL12 Mediated T Cell Signaling and Migration. PLOS ONE, 6 (9), e24667. Musone, S. L., Taylor, K. E., Nititham, J., Chu, C., Poon, A., Liao, W., Lam, E. T., Ma, A., Kwok, P.-Y., and Criswell, L. A., 2011. Sequencing of TNFAIP3 and
429
Association of Variants with Multiple Autoimmune Diseases. Genes and immunity, 12 (3), 176–182. Nagamine, K., Peterson, P., Scott, H. S., Kudoh, J., Minoshima, S., Heino, M., Krohn, K. J. E., Lalioti, M. D., Mullis, P. E., Antonarakis, S. E., Kawasaki, K., Asakawa, S., Ito, F., and Shimizu, N., 1997. Positional cloning of the APECED gene. Nature Genetics, 17 (4), 393. Nakatsu, Y., Matsuoka, M., Chang, T.-H., Otsuki, N., Noda, M., Kimura, H., Sakai, K., Kato, H., Takeda, M., and Kubota, T., 2014. Functionally Distinct Effects of the C-Terminal Regions of IKKε and TBK1 on Type I IFN Production. PLoS ONE [online], 9 (4). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983252/ [Accessed 8 Jan 2019]. Nanthapisal, S., Murphy, C., Omoyinmi, E., Hong, Y., Standing, A., Berg, S., Ekelund, M., Jolles, S., Harper, L., Youngstein, T., Gilmour, K., Klein, N. J., Eleftheriou, D., and Brogan, P. A., 2016. Deficiency of Adenosine Deaminase Type 2: A Description of Phenotype and Genotype in Fifteen Cases. Arthritis Rheumatol, 68 (9), 2314–22. Navon Elkan, P., Pierce, S. B., Segel, R., Walsh, T., Barash, J., Padeh, S., Zlotogorski, A., Berkun, Y., Press, J. J., Mukamel, M., Voth, I., Hashkes, P. J., Harel, L., Hoffer, V., Ling, E., Yalcinkaya, F., Kasapcopur, O., Lee, M. K., Klevit, R. E., Renbaum, P., Weinberg-Shukron, A., Sener, E. F., Schormair, B., Zeligson, S., Marek-Yagel, D., Strom, T. M., Shohat, M., Singer, A., Rubinow, A., Pras, E., Winkelmann, J., Tekin, M., Anikster, Y., King, M.-C., and Levy-Lahad, E., 2014. Mutant adenosine deaminase 2 in a polyarteritis nodosa vasculopathy. The New England Journal of Medicine, 370 (10), 921–931. Neumann, B., Zhao, L., Murphy, K., and Gonda, T. J., 2008. Subcellular localization of the Schlafen protein family. Biochemical and Biophysical Research Communications, 370 (1), 62–66. Ng, D. and Gommerman, J. L., 2013. The Regulation of Immune Responses by DC Derived Type I IFN. Frontiers in Immunology [online], 4. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3631742/ [Accessed 8 Jan 2019]. Ng, P. C., Levy, S., Huang, J., Stockwell, T. B., Walenz, B. P., Li, K., Axelrod, N., Busam, D. A., Strausberg, R. L., and Venter, J. C., 2008. Genetic variation in an individual human exome. PLoS genetics, 4 (8), e1000160. Nguyen, T., Liu, X. K., Zhang, Y., and Dong, C., 2006. BTNL2, a butyrophilin-like molecule that functions to inhibit T cell activation. J Immunol, 176 (12), 7354– 60. Nieves, D. S., Phipps, R. P., Pollock, S. J., Ochs, H. D., Zhu, Q., Scott, G. A., Ryan, C. K., Kobayashi, I., Rossi, T. M., and Goldsmith, L. A., 2004. Dermatologic and immunologic findings in the immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome. Archives of Dermatology, 140 (4), 466–472. Nitta, T., Nitta, S., Lei, Y., Lipp, M., and Takahama, Y., 2009. CCR7-mediated migration of developing thymocytes to the medulla is essential for negative selection to tissue-restricted antigens. Proceedings of the National Academy of Sciences of the United States of America, 106 (40), 17129–17133. Obici, L., Meini, A., Cattalini, M., Chicca, S., Galliani, M., Donadei, S., Plebani, A., and Merlini, G., 2011. Favourable and sustained response to anakinra in tumour necrosis factor receptor-associated periodic syndrome (TRAPS) with or without AA amyloidosis. Annals of the Rheumatic Diseases, 70 (8), 1511–1512.
430
Odnoletkova, I., Kindle, G., Quinti, I., Grimbacher, B., Knerr, V., Gathmann, B., Ehl, S., Mahlaoui, N., Van Wilder, P., Bogaerts, K., de Vries, E., Farrugia, A., Krishnarajah, S., Mendivil, J., Prior, M., Rübesam, T., Runken, M., and in collaboration with the Plasma Protein Therapeutics Association (PPTA) Taskforce, 2018. The burden of common variable immunodeficiency disorders: a retrospective analysis of the European Society for Immunodeficiency (ESID) registry data. Orphanet Journal of Rare Diseases, 13 (1), 201. Oeckinghaus, A. and Ghosh, S., 2009. The NF-κB Family of Transcription Factors and Its Regulation. Cold Spring Harbor Perspectives in Biology [online], 1 (4). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773619/ [Accessed 3 Jul 2019]. Ohadi, M., Lalloz, M. R., Sham, P., Zhao, J., Dearlove, A. M., Shiach, C., Kinsey, S., Rhodes, M., and Layton, D. M., 1999. Localization of a gene for familial hemophagocytic lymphohistiocytosis at chromosome 9q21.3-22 by homozygosity mapping. American Journal of Human Genetics, 64 (1), 165–171. Ohl, L., Henning, G., Krautwald, S., Lipp, M., Hardtke, S., Bernhardt, G., Pabst, O., and Förster, R., 2003. Cooperating Mechanisms of CXCR5 and CCR7 in Development and Organization of Secondary Lymphoid Organs. The Journal of Experimental Medicine, 197 (9), 1199–1204. Ohl, L., Mohaupt, M., Czeloth, N., Hintzen, G., Kiafard, Z., Zwirner, J., Blankenstein, T., Henning, G., and Förster, R., 2004. CCR7 governs skin dendritic cell migration under inflammatory and steady-state conditions. Immunity, 21 (2), 279–288. Okada, T. and Cyster, J. G., 2007. CC Chemokine Receptor 7 Contributes to Gi- Dependent T Cell Motility in the Lymph Node. The Journal of Immunology, 178 (5), 2973–2978. Okamura, K., Feuk, L., Marquès-Bonet, T., Navarro, A., and Scherer, S. W., 2006. Frequent appearance of novel protein-coding sequences by frameshift translation. Genomics, 88 (6), 690–697. Oldham, W. M. and Hamm, H. E., 2006. Structural basis of function in heterotrimeric G proteins. Quarterly Reviews of Biophysics, 39 (2), 117–166. Ombrello, A. K., Qin, J., Hoffmann, P. M., Kumar, P., Stone, D., Jones, A., Romeo, T., Barham, B., Pinto-Patarroyo, G., Toro, C., Soldatos, A., Zhou, Q., Deuitch, N., Aksentijevich, I., Sheldon, S. L., Kelly, S., Man, A., Barron, K., Hershfield, M., Flegel, W. A., and Kastner, D. L., 2019. Treatment Strategies for Deficiency of Adenosine Deaminase 2. New England Journal of Medicine, 380 (16), 1582– 1584. Omoyinmi, E., Standing, A., Keylock, A., Price-Kuehne, F., Melo Gomes, S., Rowczenio, D., Nanthapisal, S., Cullup, T., Nyanhete, R., Ashton, E., Murphy, C., Clarke, M., Ahlfors, H., Jenkins, L., Gilmour, K., Eleftheriou, D., Lachmann, H. J., Hawkins, P. N., Klein, N., and Brogan, P. A., 2017. Clinical impact of a targeted next-generation sequencing gene panel for autoinflammation and vasculitis. PLoS One, 12 (7), e0181874. Onizawa, M., Oshima, S., Schulze-Topphoff, U., Oses-Prieto, J. A., Lu, T., Tavares, R., Prodhomme, T., Duong, B., Whang, M. I., Advincula, R., Agelidis, A., Barrera, J., Wu, H., Burlingame, A., Malynn, B. A., Zamvil, S. S., and Ma, A., 2015. The ubiquitin-modifying enzyme A20 restricts ubiquitination of the kinase RIPK3 and protects cells from necroptosis. Nat Immunol, 16 (6), 618–27. Orozco, G., Eerligh, P., Sanchez, E., Zhernakova, S., Roep, B. O., Gonzalez-Gay, M. A., Lopez-Nevot, M. A., Callejas, J. L., Hidalgo, C., Pascual-Salcedo, D., Balsa,
431
A., Gonzalez-Escribano, M. F., Koeleman, B. P., and Martin, J., 2005. Analysis of a functional BTNL2 polymorphism in type 1 diabetes, rheumatoid arthritis, and systemic lupus erythematosus. Hum Immunol, 66 (12), 1235–41. Ostör, A. J. K., 2008. Abatacept: a T-cell co-stimulation modulator for the treatment of rheumatoid arthritis. Clinical Rheumatology, 27 (11), 1343–1353. Otero, C., Eisele, P. S., Schaeuble, K., Groettrup, M., and Legler, D. F., 2008. Distinct motifs in the chemokine receptor CCR7 regulate signal transduction, receptor trafficking and chemotaxis. Journal of Cell Science, 121 (Pt 16), 2759–2767. Parish, C. R., 1999. Fluorescent dyes for lymphocyte migration and proliferation studies. Immunology & Cell Biology, 77 (6), 499–508. Park, M. H. and Hong, J. T., 2016. Roles of NF-κB in Cancer and Inflammatory Diseases and Their Therapeutic Approaches. Cells [online], 5 (2). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4931664/ [Accessed 8 Jan 2019]. Parvatiyar, K., Barber, G. N., and Harhaj, E. W., 2010. TAX1BP1 and A20 inhibit antiviral signaling by targeting TBK1-IKKi kinases. J Biol Chem, 285 (20), 14999–5009. Pathan, S., Gowdy, R. E., Cooney, R., Beckly, J. B., Hancock, L., Guo, C., Barrett, J. C., Morris, A., and Jewell, D. P., 2009. Confirmation of the novel association at the BTNL2 locus with ulcerative colitis. Tissue Antigens, 74 (4), 322–9. Peakman, M., Mahalingam, M., Leslie, R. D., and Vergani, D., 1994. Co-expression of CD45RA (naive) and CD45R0 (memory) T-cell markers. Lancet (London, England), 343 (8894), 424. Peckham, D., Scambler, T., Savic, S., and McDermott, M. F., 2017. The burgeoning field of innate immune-mediated disease and autoinflammation. The Journal of Pathology, 241 (2), 123–139. Pénisson-Besnier, I., Allamand, V., Beurrier, P., Martin, L., Schalkwijk, J., van Vlijmen-Willems, I., Gartioux, C., Malfait, F., Syx, D., Macchi, L., Marcorelles, P., Arbeille, B., Croué, A., De Paepe, A., and Dubas, F., 2013. Compound heterozygous mutations of the TNXB gene cause primary myopathy. Neuromuscular disorders: NMD, 23 (8), 664–669. Pereira, J. P., Kelly, L. M., and Cyster, J. G., 2010. Finding the right niche: B-cell migration in the early phases of T-dependent antibody responses. International Immunology, 22 (6), 413–419. Perry, A. K., Chen, G., Zheng, D., Tang, H., and Cheng, G., 2005. The host type I interferon response to viral and bacterial infections. Cell Research, 15 (6), 407– 422. Picard, C., Bobby Gaspar, H., Al-Herz, W., Bousfiha, A., Casanova, J.-L., Chatila, T., Crow, Y. J., Cunningham-Rundles, C., Etzioni, A., Franco, J. L., Holland, S. M., Klein, C., Morio, T., Ochs, H. D., Oksenhendler, E., Puck, J., Tang, M. L. K., Tangye, S. G., Torgerson, T. R., and Sullivan, K. E., 2018. International Union of Immunological Societies: 2017 Primary Immunodeficiency Diseases Committee Report on Inborn Errors of Immunity. Journal of Clinical Immunology, 38 (1), 96–128. Piippo, N., Korhonen, E., Hytti, M., Kinnunen, K., Kaarniranta, K., and Kauppinen, A., 2018. Oxidative Stress is the Principal Contributor to Inflammasome Activation in Retinal Pigment Epithelium Cells with Defunct Proteasomes and Autophagy. Cellular Physiology and Biochemistry: International Journal of Experimental Cellular Physiology, Biochemistry, and Pharmacology, 49 (1), 359–367.
432
Posey, J. E., Harel, T., Liu, P., Rosenfeld, J. A., James, R. A., Coban Akdemir, Z. H., Walkiewicz, M., Bi, W., Xiao, R., Ding, Y., Xia, F., Beaudet, A. L., Muzny, D. M., Gibbs, R. A., Boerwinkle, E., Eng, C. M., Sutton, V. R., Shaw, C. A., Plon, S. E., Yang, Y., and Lupski, J. R., 2017. Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation. The New England Journal of Medicine, 376 (1), 21–31. Price, P., Santoso, L., Mastaglia, F., Garlepp, M., Kok, C. C., Allcock, R., and Laing, N., 2004. Two major histocompatibility complex haplotypes influence susceptibility to sporadic inclusion body myositis: critical evaluation of an association with HLA-DR3. Tissue Antigens, 64 (5), 575–80. Próchnicki, T., Mangan, M. S., and Latz, E., 2016. Recent insights into the molecular mechanisms of the NLRP3 inflammasome activation. F1000Research, 5, 1469. Puck, A., Aigner, R., Modak, M., Cejka, P., Blaas, D., and Stöckl, J., 2015. Expression and regulation of Schlafen (SLFN) family members in primary human monocytes, monocyte-derived dendritic cells and T cells. Results in Immunology, 5, 23–32. Py, B. F., Kim, M.-S., Vakifahmetoglu-Norberg, H., and Yuan, J., 2013. Deubiquitination of NLRP3 by BRCC3 Critically Regulates Inflammasome Activity. Molecular Cell, 49 (2), 331–338. Rabbani, B., Mahdieh, N., Hosomichi, K., Nakaoka, H., and Inoue, I., 2012. Next- generation sequencing: impact of exome sequencing in characterizing Mendelian disorders. J Hum Genet, 57 (10), 621–32. Rabbani, B., Nakaoka, H., Akhondzadeh, S., Tekin, M., and Mahdieh, N., 2016. Next generation sequencing: implications in personalized medicine and pharmacogenomics. Mol Biosyst, 12 (6), 1818–30. Rajamäki, K., Keskitalo, S., Seppänen, M., Kuismin, O., Vähäsalo, P., Trotta, L., Väänänen, A., Glumoff, V., Keskitalo, P., Kaarteenaho, R., Jartti, A., Hautala, N., Jackson, P., Nordström, D. C., Saarela, J., Hautala, T., Eklund, K. K., and Varjosalo, M., 2018. Haploinsufficiency of A20 impairs protein-protein interactome and leads into caspase-8-dependent enhancement of NLRP3 inflammasome activation. RMD open, 4 (2), e000740. Randall, K. L., Chan, S. S.-Y., Ma, C. S., Fung, I., Mei, Y., Yabas, M., Tan, A., Arkwright, P. D., Al Suwairi, W., Lugo Reyes, S. O., Yamazaki-Nakashimada, M. A., de la Luz Garcia-Cruz, M., Smart, J. M., Picard, C., Okada, S., Jouanguy, E., Casanova, J.-L., Lambe, T., Cornall, R. J., Russell, S., Oliaro, J., Tangye, S. G., Bertram, E. M., and Goodnow, C. C., 2011. DOCK8 deficiency impairs CD8 T cell survival and function in humans and mice. The Journal of Experimental Medicine, 208 (11), 2305–2320. Rangel-Moreno, J., Carragher, D. M., de la Luz Garcia-Hernandez, M., Hwang, J. Y., Kusser, K., Hartson, L., Kolls, J. K., Khader, S. A., and Randall, T. D., 2011. The development of inducible bronchus-associated lymphoid tissue depends on IL-17. Nature Immunology, 12 (7), 639–646. Rawlings, J. S., 2004. The JAK/STAT signaling pathway. Journal of Cell Science, 117 (8), 1281–1283. Recher, M., Karjalainen-Lindsberg, M.-L., Lindlöf, M., Söderlund-Venermo, M., Lanzi, G., Väisänen, E., Kumar, A., Sadeghi, M., Berger, C. T., Alitalo, T., Anttila, P., Kolehmainen, M., Franssila, R., Chen, T., Siitonen, S., Delmonte, O. M., Walter, J. E., Pessach, I., Hess, C., Simpson, M. A., Navarini, A. A., Giliani, S., Hedman, K., Seppänen, M., and Notarangelo, L. D., 2014. Genetic variation in schlafen genes in a patient with a recapitulation of the murine Elektra
433
phenotype. The Journal of Allergy and Clinical Immunology, 133 (5), 1462– 1465, 1465.e1–5. Reis-Filho, J. S., 2009. Next-generation sequencing. Breast Cancer Res, 11 Suppl 3, S12. Rice, G. I., Forte, G. M. A., Szynkiewicz, M., Chase, D. S., Aeby, A., Abdel-Hamid, M. S., Ackroyd, S., Allcock, R., Bailey, K. M., Balottin, U., Barnerias, C., Bernard, G., Bodemer, C., Botella, M. P., Cereda, C., Chandler, K. E., Dabydeen, L., Dale, R. C., De Laet, C., De Goede, C. G. E. L., Del Toro, M., Effat, L., Enamorado, N. N., Fazzi, E., Gener, B., Haldre, M., Lin, J.-P. S.-M., Livingston, J. H., Lourenco, C. M., Marques, W., Oades, P., Peterson, P., Rasmussen, M., Roubertie, A., Schmidt, J. L., Shalev, S. A., Simon, R., Spiegel, R., Swoboda, K. J., Temtamy, S. A., Vassallo, G., Vilain, C. N., Vogt, J., Wermenbol, V., Whitehouse, W. P., Soler, D., Olivieri, I., Orcesi, S., Aglan, M. S., Zaki, M. S., Abdel-Salam, G. M. H., Vanderver, A., Kisand, K., Rozenberg, F., Lebon, P., and Crow, Y. J., 2013. Assessment of interferon-related biomarkers in Aicardi- Goutières syndrome associated with mutations in TREX1, RNASEH2A, RNASEH2B, RNASEH2C, SAMHD1, and ADAR: a case-control study. The Lancet. Neurology, 12 (12), 1159–1169. Rice, G., Meyzer, C., Bouazza, N., Hully, M., Boddaert, N., Semeraro, M., Zeef, L., Vincent, B., Duffy, D., Llibre, A., Baek, J., Sambe, M., Henry, E., Jolaine, V., Barnerias, C., Barth, M., Belot, A., Cances, C., and Crow, Y., 2018. Reverse- Transcriptase Inhibitors in the Aicardi–Goutières Syndrome. New England Journal of Medicine, 379, 2275–2277. Rice, G., Patrick, T., Parmar, R., Taylor, C. F., Aeby, A., Aicardi, J., Artuch, R., Montalto, S. A., Bacino, C. A., Barroso, B., Baxter, P., Benko, W. S., Bergmann, C., Bertini, E., Biancheri, R., Blair, E. M., Blau, N., Bonthron, D. T., Briggs, T., Brueton, L. A., Brunner, H. G., Burke, C. J., Carr, I. M., Carvalho, D. R., Chandler, K. E., Christen, H. J., Corry, P. C., Cowan, F. M., Cox, H., D’Arrigo, S., Dean, J., De Laet, C., De Praeter, C., Dery, C., Ferrie, C. D., Flintoff, K., Frints, S. G., Garcia-Cazorla, A., Gener, B., Goizet, C., Goutieres, F., Green, A. J., Guet, A., Hamel, B. C., Hayward, B. E., Heiberg, A., Hennekam, R. C., Husson, M., Jackson, A. P., Jayatunga, R., Jiang, Y. H., Kant, S. G., Kao, A., King, M. D., Kingston, H. M., Klepper, J., van der Knaap, M. S., Kornberg, A. J., Kotzot, D., Kratzer, W., Lacombe, D., Lagae, L., Landrieu, P. G., Lanzi, G., Leitch, A., Lim, M. J., Livingston, J. H., Lourenco, C. M., Lyall, E. G., Lynch, S. A., Lyons, M. J., Marom, D., McClure, J. P., McWilliam, R., Melancon, S. B., Mewasingh, L. D., Moutard, M. L., Nischal, K. K., Ostergaard, J. R., Prendiville, J., Rasmussen, M., Rogers, R. C., Roland, D., Rosser, E. M., Rostasy, K., Roubertie, A., Sanchis, A., Schiffmann, R., Scholl-Burgi, S., Seal, S., Shalev, S. A., Corcoles, C. S., Sinha, G. P., Soler, D., Spiegel, R., Stephenson, J. B., Tacke, U., Tan, T. Y., Till, M., Tolmie, J. L., Tomlin, P., Vagnarelli, F., Valente, E. M., Van Coster, R. N., Van der Aa, N., Vanderver, A., Vles, J. S., Voit, T., Wassmer, E., Weschke, B., Whiteford, M. L., Willemsen, M. A., Zankl, A., Zuberi, S. M., Orcesi, S., Fazzi, E., Lebon, P., and Crow, Y. J., 2007. Clinical and molecular phenotype of Aicardi-Goutieres syndrome. Am J Hum Genet, 81 (4), 713–25. Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., Voelkerding, K., and Rehm, H. L., 2015. Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and
434
Genomics and the Association for Molecular Pathology. Genetics in medicine : official journal of the American College of Medical Genetics, 17 (5), 405–424. Riol-Blanco, L., Sánchez-Sánchez, N., Torres, A., Tejedor, A., Narumiya, S., Corbí, A. L., Sánchez-Mateos, P., and Rodríguez-Fernández, J. L., 2005. The Chemokine Receptor CCR7 Activates in Dendritic Cells Two Signaling Modules That Independently Regulate Chemotaxis and Migratory Speed. The Journal of Immunology, 174 (7), 4070–4080. Rivers, E. and Thrasher, A. J., 2017. Wiskott-Aldrich syndrome protein: Emerging mechanisms in immunity. European Journal of Immunology, 47 (11), 1857– 1866. Robinson, P. N., Köhler, S., Oellrich, A., Sanger Mouse Genetics Project, Wang, K., Mungall, C. J., Lewis, S. E., Washington, N., Bauer, S., Seelow, D., Krawitz, P., Gilissen, C., Haendel, M., and Smedley, D., 2014. Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Research, 24 (2), 340–348. Robishaw, J. D. and Berlot, C. H., 2004. Translating G protein subunit diversity into functional specificity. Current Opinion in Cell Biology, 16 (2), 206–209. Rodriguez, M. S., Egaña, I., Lopitz-Otsoa, F., Aillet, F., Lopez-Mato, M. P., Dorronsoro, A., Lobato-Gil, S., Sutherland, J. D., Barrio, R., Trigueros, C., and Lang, V., 2014. The RING ubiquitin E3 RNF114 interacts with A20 and modulates NF-κB activity and T-cell activation. Cell Death & Disease, 5 (8), e1399–e1399. Rose, A. B., 2019. Introns as Gene Regulators: A Brick on the Accelerator. Frontiers in Genetics [online], 9. Available from: https://www.frontiersin.org/articles/10.3389/fgene.2018.00672/full [Accessed 7 Apr 2020]. Rose, C. D., Neven, B., and Wouters, C., 2014. Granulomatous inflammation: The overlap of immune deficiency and inflammation. Best Practice & Research. Clinical Rheumatology, 28 (2), 191–212. Rosé, C. D., Pans, S., Casteels, I., Anton, J., Bader-Meunier, B., Brissaud, P., Cimaz, R., Espada, G., Fernandez-Martin, J., Hachulla, E., Harjacek, M., Khubchandani, R., Mackensen, F., Merino, R., Naranjo, A., Oliveira-Knupp, S., Pajot, C., Russo, R., Thomée, C., Vastert, S., Wulffraat, N., Arostegui, J. I., Foley, K. P., Bertin, J., and Wouters, C. H., 2015. Blau syndrome: cross- sectional data from a multicentre study of clinical, radiological and functional outcomes. Rheumatology (Oxford, England), 54 (6), 1008–1016. Rosenbaum, D. M., Rasmussen, S. G. F., and Kobilka, B. K., 2009. The structure and function of G-protein-coupled receptors. Nature, 459 (7245), 356–363. Rowczenio, D., Omoyinmi, E., Trojer, H., Lane, T., Brogan, P., Hawkins, P., and Lachmann, H., 2015. First case of somatic mosaicism in TRAPS caused by a novel 24 nucleotides deletion in the TNFRSF1A gene. Pediatric Rheumatology Online Journal, 13 (Suppl 1), O60. Russo, R. A. G. and Brogan, P. A., 2014. Monogenic autoinflammatory diseases. Rheumatology, 53 (11), 1927–1939. Rybicki, B. A., Iannuzzi, M. C., Frederick, M. M., Thompson, B. W., Rossman, M. D., Bresnitz, E. A., Terrin, M. L., Moller, D. R., Barnard, J., Baughman, R. P., DePalo, L., Hunninghake, G., Johns, C., Judson, M. A., Knatterud, G. L., McLennan, G., Newman, L. S., Rabin, D. L., Rose, C., Teirstein, A. S., Weinberger, S. E., Yeager, H., Cherniack, R., and ACCESS Research Group, 2001. Familial aggregation of sarcoidosis. A case-control etiologic study of
435
sarcoidosis (ACCESS). American Journal of Respiratory and Critical Care Medicine, 164 (11), 2085–2091. Sadowski, M. and Sarcevic, B., 2010. Mechanisms of mono- and poly-ubiquitination: Ubiquitination specificity depends on compatibility between the E2 catalytic core and amino acid residues proximal to the lysine. Cell Division, 5, 19. Saitoh, T., Yamamoto, M., Miyagishi, M., Taira, K., Nakanishi, M., Fujita, T., Akira, S., Yamamoto, N., and Yamaoka, S., 2005. A20 is a negative regulator of IFN regulatory factor 3 signaling. J Immunol, 174 (3), 1507–12. Sallusto, F. and Lanzavecchia, A., 2002. The instructive role of dendritic cells on T-cell responses. Arthritis Research, 4 (Suppl 3), S127–S132. Sanchez-Sanchez, N., Riol-Blanco, L., and Rodriguez-Fernandez, J. L., 2006. The Multiple Personalities of the Chemokine Receptor CCR7 in Dendritic Cells. The Journal of Immunology, 176 (9), 5153–5159. Sánchez-Sánchez, N., Riol-Blanco, L., de la Rosa, G., Puig-Kröger, A., García-Bordas, J., Martín, D., Longo, N., Cuadrado, A., Cabañas, C., Corbí, A. L., Sánchez- Mateos, P., and Rodríguez-Fernández, J. L., 2004. Chemokine receptor CCR7 induces intracellular signaling that inhibits apoptosis of mature dendritic cells. Blood, 104 (3), 619–625. Sato, S., Fujita, Y., Shigemura, T., Matoba, H., Agematsu, K., Sumichika, Y., Yashiro, M., Ono, A., Kawasaki, Y., Kobayashi, H., Watanabe, H., Koga, T., Kawakami, A., and Migita, K., 2018. Juvenile onset autoinflammatory disease due to a novel mutation in TNFAIP3 (A20). Arthritis Research & Therapy, 20 (1), 274. Schalkwijk, J., Zweers, M. C., Steijlen, P. M., Dean, W. B., Taylor, G., Van Vlijmen, I. M., Van Haren, B., Miller, W. L., and Bristow, J., 2001. A recessive form of the Ehlers-Danlos syndrome caused by tenascin-X deficiency. New England Journal of Medicine, 345 (16), 1167–1175. Schmidt, R. L. and Lenz, L. L., 2012. Distinct Licensing of IL-18 and IL-1β Secretion in Response to NLRP3 Inflammasome Activation. PLOS ONE, 7 (9), e45186. Schneider, M. A., Meingassner, J. G., Lipp, M., Moore, H. D., and Rot, A., 2007. CCR7 is required for the in vivo function of CD4+ CD25+ regulatory T cells. The Journal of Experimental Medicine, 204 (4), 735–745. Schroder, K. and Tschopp, J., 2010. The Inflammasomes. Cell, 140 (6), 821–832. Schubert, D., Bode, C., Kenefeck, R., Hou, T. Z., Wing, J. B., Kennedy, A., Bulashevska, A., Petersen, B.-S., Schäffer, A. A., Grüning, B. A., Unger, S., Frede, N., Baumann, U., Witte, T., Schmidt, R. E., Dueckers, G., Niehues, T., Seneviratne, S., Kanariou, M., Speckmann, C., Ehl, S., Rensing-Ehl, A., Warnatz, K., Rakhmanov, M., Thimme, R., Hasselblatt, P., Emmerich, F., Cathomen, T., Backofen, R., Fisch, P., Seidl, M., May, A., Schmitt-Graeff, A., Ikemizu, S., Salzer, U., Franke, A., Sakaguchi, S., Walker, L. S. K., Sansom, D. M., and Grimbacher, B., 2014. Autosomal dominant immune dysregulation syndrome in humans with CTLA4 mutations. Nature Medicine, 20 (12), 1410– 1416. Schurman, S. H. and Candotti, F., 2003. Autoimmunity in Wiskott-Aldrich syndrome. Current Opinion in Rheumatology, 15 (4), 446–453. Schwartz, D. M., Blackstone, S. A., Sampaio-Moura, N., Rosenzweig, S., Burma, A. M., Stone, D., Hoffmann, P., Jones, A., Romeo, T., Barron, K. S., Waldman, M. A., Aksentijevich, I., Kastner, D. L., Milner, J. D., and Ombrello, A. K., 2019. Type I interferon signature predicts response to JAK inhibition in haploinsufficiency of A20. Annals of the Rheumatic Diseases [online]. Available
436
from: https://ard.bmj.com/content/early/2019/11/25/annrheumdis-2019-215918 [Accessed 29 Nov 2019]. Schwarz, D. A., Katayama, C. D., and Hedrick, S. M., 1998. Schlafen, a New Family of Growth Regulatory Genes that Affect Thymocyte Development. Immunity, 9 (5), 657–668. Seth, S., Oberdörfer, L., Hyde, R., Hoff, K., Thies, V., Worbs, T., Schmitz, S., and Förster, R., 2011. CCR7 essentially contributes to the homing of plasmacytoid dendritic cells to lymph nodes under steady-state as well as inflammatory conditions. Journal of Immunology (Baltimore, Md.: 1950), 186 (6), 3364–3372. Shembade, N. and Harhaj, E., 2010. A20 inhibition of NFkappaB and inflammation: targeting E2:E3 ubiquitin enzyme complexes. Cell Cycle, 9 (13), 2481–2. Shembade, N. and Harhaj, E. W., 2012. Regulation of NF-kappaB signaling by the A20 deubiquitinase. Cell Mol Immunol, 9 (2), 123–30. Shwin, K. W., Lee, C. R., and Goldbach-Mansky, R., 2017. Dermatologic Manifestations of Monogenic Autoinflammatory Diseases. Dermatol Clin, 35 (1), 21–38. da Silva, C. G., Minussi, D. C., Ferran, C., and Bredel, M., 2014. A20 expressing tumors and anticancer drug resistance. Advances in Experimental Medicine and Biology, 809, 65–81. Simmonds, M. J., Heward, J. M., Barrett, J. C., Franklyn, J. A., and Gough, S. C., 2006. Association of the BTNL2 rs2076530 single nucleotide polymorphism with Graves’ disease appears to be secondary to DRB1 exon 2 position beta74. Clin Endocrinol (Oxf), 65 (4), 429–32. Slatter, M. A., Engelhardt, K. R., Burroughs, L. M., Arkwright, P. D., Nademi, Z., Skoda-Smith, S., Hagin, D., Kennedy, A., Barge, D., Flood, T., Abinun, M., Wynn, R. F., Gennery, A. R., Cant, A. J., Sansom, D., Hambleton, S., and Torgerson, T. R., 2016. Hematopoietic stem cell transplantation for CTLA4 deficiency. The Journal of Allergy and Clinical Immunology, 138 (2), 615- 619.e1. Smerdon, R. A., Peakman, M., Hussain, M. J., Alviggi, L., Watkins, P. J., Leslie, R. D. G., and Vergani, D., 1993. Increase in Simultaneous Coexpression of Naive and Memory Lymphocyte Markers at Diagnosis of IDDM, 42, 7. Smith, J. S. and Rajagopal, S., 2016. The β-Arrestins: Multifunctional Regulators of G Protein-coupled Receptors. Journal of Biological Chemistry, 291 (17), 8969– 8977. Smith-Garvin, J. E., Koretzky, G. A., and Jordan, M. S., 2009. T Cell Activation. Annual review of immunology, 27, 591–619. Sobecki, M., Mrouj, K., Camasses, A., Parisis, N., Nicolas, E., Llères, D., Gerbe, F., Prieto, S., Krasinska, L., David, A., Eguren, M., Birling, M.-C., Urbach, S., Hem, S., Déjardin, J., Malumbres, M., Jay, P., Dulic, V., Lafontaine, D. L., Feil, R., and Fisher, D., n.d. The cell proliferation antigen Ki-67 organises heterochromatin. eLife [online], 5. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841783/ [Accessed 8 Mar 2019]. Solt, L. A., Madge, L. A., and May, M. J., 2009. NEMO-binding Domains of Both IKKα and IKKβ Regulate IκB Kinase Complex Assembly and Classical NF-κB Activation. The Journal of Biological Chemistry, 284 (40), 27596–27608. Spessott, W. A., Sanmillan, M. L., McCormick, M. E., Patel, N., Villanueva, J., Zhang, K., Nichols, K. E., and Giraudo, C. G., 2015. Hemophagocytic
437
lymphohistiocytosis caused by dominant-negative mutations in STXBP2 that inhibit SNARE-mediated membrane fusion. Blood, 125 (10), 1566–1577. Stammers, M., Rowen, L., Rhodes, D., Trowsdale, J., and Beck, S., 2000. BTL-II: a polymorphic locus with homology to the butyrophilin gene family, located at the border of the major histocompatibility complex class II and class III regions in human and mouse. Immunogenetics, 51 (4–5), 373–382. Steeber, D. A., Green, N. E., Sato, S., and Tedder, T. F., 1996. Lyphocyte migration in L-selectin-deficient mice. Altered subset migration and aging of the immune system. Journal of Immunology (Baltimore, Md.: 1950), 157 (3), 1096–1106. Steiner, A., Harapas, C. R., Masters, S. L., and Davidson, S., 2018. An Update on Autoinflammatory Diseases: Relopathies. Current Rheumatology Reports [online], 20 (7). Available from: http://link.springer.com/10.1007/s11926-018- 0749-x [Accessed 5 Jun 2018]. Størling, J. and Pociot, F., 2017. Type 1 Diabetes Candidate Genes Linked to Pancreatic Islet Cell Inflammation and Beta-Cell Apoptosis. Genes [online], 8 (2). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333061/ [Accessed 8 Jan 2019]. Stuart, P. E., Nair, R. P., Tsoi, L. C., Tejasvi, T., Das, S., Kang, H. M., Ellinghaus, E., Chandran, V., Callis-Duffin, K., Ike, R., Li, Y., Wen, X., Enerbäck, C., Gudjonsson, J. E., Kõks, S., Kingo, K., Esko, T., Mrowietz, U., Reis, A., Wichmann, H. E., Gieger, C., Hoffmann, P., Nöthen, M. M., Winkelmann, J., Kunz, M., Moreta, E. G., Mease, P. J., Ritchlin, C. T., Bowcock, A. M., Krueger, G. G., Lim, H. W., Weidinger, S., Weichenthal, M., Voorhees, J. J., Rahman, P., Gregersen, P. K., Franke, A., Gladman, D. D., Abecasis, G. R., and Elder, J. T., 2015. Genome-wide Association Analysis of Psoriatic Arthritis and Cutaneous Psoriasis Reveals Differences in Their Genetic Architecture. American Journal of Human Genetics, 97 (6), 816–836. Summers, K. L., O’Donnell, J. L., and Hart, D. N., 1994. Co-expression of the CD45RA and CD45RO antigens on T lymphocytes in chronic arthritis. Clinical and Experimental Immunology, 97 (1), 39–44. Swanson, R. M., Gavin, M. A., Escobar, S. S., Rottman, J. B., Lipsky, B. P., Dube, S., Li, L., Bigler, J., Wolfson, M., Arnett, H. A., and Viney, J. L., 2013. Butyrophilin-like 2 modulates B7 costimulation to induce Foxp3 expression and regulatory T cell development in mature T cells. J Immunol, 190 (5), 2027–35. Szpiech, Z. A., Xu, J., Pemberton, T. J., Peng, W., Zöllner, S., Rosenberg, N. A., and Li, J. Z., 2013. Long runs of homozygosity are enriched for deleterious variation. American Journal of Human Genetics, 93 (1), 90–102. Tangye, S. G., Bucciol, G., Casas‐Martin, J., Pillay, B., Ma, C. S., Moens, L., and Meyts, I., 2019. Human inborn errors of the actin cytoskeleton affecting immunity: way beyond WAS and WIP. Immunology & Cell Biology, 97 (4), 389–402. Tangye, S. G., Pillay, B., Randall, K. L., Avery, D. T., Phan, T. G., Gray, P., Ziegler, J. B., Smart, J. M., Peake, J., Arkwright, P. D., Hambleton, S., Orange, J., Goodnow, C. C., Uzel, G., Casanova, J.-L., Lugo Reyes, S. O., Freeman, A. F., Su, H. C., and Ma, C. S., 2017. Dedicator of cytokinesis 8-deficient CD4+ T cells are biased to a TH2 effector fate at the expense of TH1 and TH17 cells. The Journal of Allergy and Clinical Immunology, 139 (3), 933–949. Tavares, R. M., Turer, E. E., Liu, C. L., Advincula, R., Scapini, P., Rhee, L., Barrera, J., Lowell, C. A., Utz, P. J., Malynn, B. A., and Ma, A., 2010. The ubiquitin
438
modifying enzyme A20 restricts B cell survival and prevents autoimmunity. Immunity, 33 (2), 181–91. Taylor, S. C., Nadeau, K., Abbasi, M., Lachance, C., Nguyen, M., and Fenrich, J., 2019. The Ultimate qPCR Experiment: Producing Publication Quality, Reproducible Data the First Time. Trends in Biotechnology, 37 (7), 761–774. The French FMF Consortium, Bernot, A., Clepet, C., Dasilva, C., Devaud, C., Petit, J.- L., Caloustian, C., Cruaud, C., Samson, D., Pulcini, F., Weissenbach, J., Heilig, R., Notanicola, C., Domingo, C., Rozenbaum, M., Benchetrit, E., Topaloglu, R., Dewalle, M., Dross, C., Hadjari, P., Dupont, M., Demaille, J., Touitou, I., Smaoui, N., Nedelec, B., Méry, J.-P., Chaabouni, H., Delpech, M., and Grateau, G., 1997. A candidate gene for familial Mediterranean fever. Nature Genetics, 17 (1), 25–31. Thomas, S. N., Rohner, N. A., and Edwards, E. E., 2016. Implications of Lymphatic Transport to Lymph Nodes in Immunity and Immunotherapy. Annual review of biomedical engineering, 18, 207–233. Thompson, M. R., Kaminski, J. J., Kurt-Jones, E. A., and Fitzgerald, K. A., 2011. Pattern Recognition Receptors and the Innate Immune Response to Viral Infection. Viruses, 3 (6), 920–940. Tokunaga, F. and Iwai, K., 2012. LUBAC, a novel ubiquitin ligase for linear ubiquitination, is crucial for inflammation and immune responses. Microbes and Infection, 14 (7), 563–572. Tong, X., Ma, Y., Niu, X., Yan, Z., Liu, S., Peng, B., Peng, S., and Fan, H., 2016. The BTNL2 G16071A gene polymorphism increases granulomatous disease susceptibility: A meta-analysis including FPRP test of 8710 participants. Medicine (Baltimore), 95 (30), e4325. Torrelo, A., 2017. CANDLE Syndrome As a Paradigm of Proteasome-Related Autoinflammation. Front Immunol, 8, 927. Trynka, G., Zhernakova, A., Romanos, J., Franke, L., Hunt, K. A., Turner, G., Bruinenberg, M., Heap, G. A., Platteel, M., Ryan, A. W., de Kovel, C., Holmes, G. K. T., Howdle, P. D., Walters, J. R. F., Sanders, D. S., Mulder, C. J. J., Mearin, M. L., Verbeek, W. H. M., Trimble, V., Stevens, F. M., Kelleher, D., Barisani, D., Bardella, M. T., McManus, R., van Heel, D. A., and Wijmenga, C., 2009. Coeliac disease-associated risk variants in TNFAIP3 and REL implicate altered NF-kappaB signalling. Gut, 58 (8), 1078–1083. Tsoi, L. C., Stuart, P. E., Tian, C., Gudjonsson, J. E., Das, S., Zawistowski, M., Ellinghaus, E., Barker, J. N., Chandran, V., Dand, N., Duffin, K. C., Enerbäck, C., Esko, T., Franke, A., Gladman, D. D., Hoffmann, P., Kingo, K., Kõks, S., Krueger, G. G., Lim, H. W., Metspalu, A., Mrowietz, U., Mucha, S., Rahman, P., Reis, A., Tejasvi, T., Trembath, R., Voorhees, J. J., Weidinger, S., Weichenthal, M., Wen, X., Eriksson, N., Kang, H. M., Hinds, D. A., Nair, R. P., Abecasis, G. R., and Elder, J. T., 2017. Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants. Nature Communications [online], 8. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5458077/ [Accessed 8 Jan 2019]. Tucci, M., Quatraro, C., and Silvestris, F., 2005. Sjögren’s syndrome: an autoimmune disorder with otolaryngological involvement. Acta Otorhinolaryngologica Italica, 25 (3), 139–144. Tuncbag, N., Keskin, O., Nussinov, R., and Gursoy, A., 2012. Fast and accurate modeling of protein-protein interactions by combining template-interface-based
439
docking with flexible refinement. Proteins: Structure, Function, and Bioinformatics, 80 (4), 1239–1249. Ueno, T., Saito, F., Gray, D. H. D., Kuse, S., Hieshima, K., Nakano, H., Kakiuchi, T., Lipp, M., Boyd, R. L., and Takahama, Y., 2004. CCR7 signals are essential for cortex-medulla migration of developing thymocytes. The Journal of Experimental Medicine, 200 (4), 493–505. Ulusoy, E., Karaca, N. E., El-Shanti, H., Kilicoglu, E., Aksu, G., and Kutukculer, N., 2015. Interleukin-1 receptor antagonist deficiency with a novel mutation; late onset and successful treatment with canakinumab: a case report. Journal of Medical Case Reports [online], 9. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495801/ [Accessed 18 Sep 2019]. Unsoeld, H. and Pircher, H., 2005. Complex Memory T-Cell Phenotypes Revealed by Coexpression of CD62L and CCR7. Journal of Virology, 79 (7), 4510–4513. Usmani, G. N., Woda, B. A., and Newburger, P. E., 2013. Advances in understanding the pathogenesis of HLH. British Journal of Haematology, 161 (5), 609–622. Valentonyte, R., Hampe, J., Huse, K., Rosenstiel, P., Albrecht, M., Stenzel, A., Nagy, M., Gaede, K. I., Franke, A., Haesler, R., Koch, A., Lengauer, T., Seegert, D., Reiling, N., Ehlers, S., Schwinger, E., Platzer, M., Krawczak, M., Muller- Quernheim, J., Schurmann, M., and Schreiber, S., 2005. Sarcoidosis is associated with a truncating splice site mutation in BTNL2. Nat Genet, 37 (4), 357–64. Van Eyck, L., Hershfield, M. S., Pombal, D., Kelly, S. J., Ganson, N. J., Moens, L., Frans, G., Schaballie, H., De Hertogh, G., Dooley, J., Bossuyt, X., Wouters, C., Liston, A., and Meyts, I., 2015. Hematopoietic stem cell transplantation rescues the immunologic phenotype and prevents vasculopathy in patients with adenosine deaminase 2 deficiency. The Journal of Allergy and Clinical Immunology, 135 (1), 283-287.e5. Vande Walle, L., Van Opdenbosch, N., Jacques, P., Fossoul, A., Verheugen, E., Vogel, P., Beyaert, R., Elewaut, D., Kanneganti, T.-D., van Loo, G., and Lamkanfi, M., 2014. Negative regulation of the NLRP3 inflammasome by A20 protects against arthritis. Nature, 512 (7512), 69–73. Varshavsky, A., 2017. The Ubiquitin System, Autophagy, and Regulated Protein Degradation. Annual Review of Biochemistry, 86, 123–128. Vazifehmand, R., Gheytuli, K., Ruhani, M., Moghaddasi, A., Saber, T., Khorshid, H. R. K., and Saber, S., 2017. Detection of BTNL 2 Gene Mutation ( rs 2076530 Allele ) in Iranian Sarcoidosis Patients : A clinical and Genetic Study. In: . Veal, C. D., Freeman, P. J., Jacobs, K., Lancaster, O., Jamain, S., Leboyer, M., Albanes, D., Vaghela, R. R., Gut, I., Chanock, S. J., and Brookes, A. J., 2012. A mechanistic basis for amplification differences between samples and between genome regions. BMC Genomics, 13 (1), 455. van de Veerdonk, F. L., Joosten, L. A. B., Shaw, P. J., Smeekens, S. P., Malireddi, S., van der Meer, J. W. M., Kullberg, B.-J., Netea, M. G., and Kanneganti, T.-D., 2011. The inflammasome drives protective Th1 and Th17 cellular responses in disseminated candidiasis. European journal of immunology, 41 (8), 2260–2268. Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., Gocayne, J. D., Amanatides, P., Ballew, R. M., Huson, D. H., Wortman, J. R., Zhang, Q., Kodira, C. D., Zheng, X. H., Chen, L., Skupski, M., Subramanian, G., Thomas, P. D., Zhang, J., Gabor Miklos, G. L., Nelson, C., Broder, S., Clark, A. G., Nadeau, J., McKusick, V.
440
A., Zinder, N., Levine, A. J., Roberts, R. J., Simon, M., Slayman, C., Hunkapiller, M., Bolanos, R., Delcher, A., Dew, I., Fasulo, D., Flanigan, M., Florea, L., Halpern, A., Hannenhalli, S., Kravitz, S., Levy, S., Mobarry, C., Reinert, K., Remington, K., Abu-Threideh, J., Beasley, E., Biddick, K., Bonazzi, V., Brandon, R., Cargill, M., Chandramouliswaran, I., Charlab, R., Chaturvedi, K., Deng, Z., Di Francesco, V., Dunn, P., Eilbeck, K., Evangelista, C., Gabrielian, A. E., Gan, W., Ge, W., Gong, F., Gu, Z., Guan, P., Heiman, T. J., Higgins, M. E., Ji, R. R., Ke, Z., Ketchum, K. A., Lai, Z., Lei, Y., Li, Z., Li, J., Liang, Y., Lin, X., Lu, F., Merkulov, G. V., Milshina, N., Moore, H. M., Naik, A. K., Narayan, V. A., Neelam, B., Nusskern, D., Rusch, D. B., Salzberg, S., Shao, W., Shue, B., Sun, J., Wang, Z., Wang, A., Wang, X., Wang, J., Wei, M., Wides, R., Xiao, C., Yan, C., Yao, A., Ye, J., Zhan, M., Zhang, W., Zhang, H., Zhao, Q., Zheng, L., Zhong, F., Zhong, W., Zhu, S., Zhao, S., Gilbert, D., Baumhueter, S., Spier, G., Carter, C., Cravchik, A., Woodage, T., Ali, F., An, H., Awe, A., Baldwin, D., Baden, H., Barnstead, M., Barrow, I., Beeson, K., Busam, D., Carver, A., Center, A., Cheng, M. L., Curry, L., Danaher, S., Davenport, L., Desilets, R., Dietz, S., Dodson, K., Doup, L., Ferriera, S., Garg, N., Gluecksmann, A., Hart, B., Haynes, J., Haynes, C., Heiner, C., Hladun, S., Hostin, D., Houck, J., Howland, T., Ibegwam, C., Johnson, J., Kalush, F., Kline, L., Koduru, S., Love, A., Mann, F., May, D., McCawley, S., McIntosh, T., McMullen, I., Moy, M., Moy, L., Murphy, B., Nelson, K., Pfannkoch, C., Pratts, E., Puri, V., Qureshi, H., Reardon, M., Rodriguez, R., Rogers, Y. H., Romblad, D., Ruhfel, B., Scott, R., Sitter, C., Smallwood, M., Stewart, E., Strong, R., Suh, E., Thomas, R., Tint, N. N., Tse, S., Vech, C., Wang, G., Wetter, J., Williams, S., Williams, M., Windsor, S., Winn-Deen, E., Wolfe, K., Zaveri, J., Zaveri, K., Abril, J. F., Guigó, R., Campbell, M. J., Sjolander, K. V., Karlak, B., Kejariwal, A., Mi, H., Lazareva, B., Hatton, T., Narechania, A., Diemer, K., Muruganujan, A., Guo, N., Sato, S., Bafna, V., Istrail, S., Lippert, R., Schwartz, R., Walenz, B., Yooseph, S., Allen, D., Basu, A., Baxendale, J., Blick, L., Caminha, M., Carnes-Stine, J., Caulk, P., Chiang, Y. H., Coyne, M., Dahlke, C., Mays, A., Dombroski, M., Donnelly, M., Ely, D., Esparham, S., Fosler, C., Gire, H., Glanowski, S., Glasser, K., Glodek, A., Gorokhov, M., Graham, K., Gropman, B., Harris, M., Heil, J., Henderson, S., Hoover, J., Jennings, D., Jordan, C., Jordan, J., Kasha, J., Kagan, L., Kraft, C., Levitsky, A., Lewis, M., Liu, X., Lopez, J., Ma, D., Majoros, W., McDaniel, J., Murphy, S., Newman, M., Nguyen, T., Nguyen, N., Nodell, M., Pan, S., Peck, J., Peterson, M., Rowe, W., Sanders, R., Scott, J., Simpson, M., Smith, T., Sprague, A., Stockwell, T., Turner, R., Venter, E., Wang, M., Wen, M., Wu, D., Wu, M., Xia, A., Zandieh, A., and Zhu, X., 2001. The sequence of the human genome. Science (New York, N.Y.), 291 (5507), 1304–1351. Vereecke, L., Beyaert, R., and van Loo, G., 2011. Genetic relationships between A20/TNFAIP3, chronic inflammation and autoimmune disease. Biochem Soc Trans, 39 (4), 1086–91. Verhelst, K., Carpentier, I., Kreike, M., Meloni, L., Verstrepen, L., Kensche, T., Dikic, I., and Beyaert, R., 2012. A20 inhibits LUBAC-mediated NF-kappaB activation by binding linear polyubiquitin chains via its zinc finger 7. Embo j, 31 (19), 3845–55. Verma, N., Burns, S. O., Walker, L. S. K., and Sansom, D. M., 2017. Immune deficiency and autoimmunity in patients with CTLA‐4 (CD152) mutations. Clinical and Experimental Immunology, 190 (1), 1–7.
441
Verstrepen, L., Verhelst, K., Carpentier, I., and Beyaert, R., 2011. TAX1BP1, a ubiquitin-binding adaptor protein in innate immunity and beyond. Trends Biochem Sci, 36 (7), 347–54. Wagner, E. M., 2013. Monitoring gene expression: quantitative real-time rt-PCR. Methods in Molecular Biology (Clifton, N.J.), 1027, 19–45. Wang, C., Ahlford, A., Laxman, N., Nordmark, G., Eloranta, M. L., Gunnarsson, I., Svenungsson, E., Padyukov, L., Sturfelt, G., Jonsen, A., Bengtsson, A. A., Truedsson, L., Rantapaa-Dahlqvist, S., Sjowall, C., Sandling, J. K., Ronnblom, L., and Syvanen, A. C., 2013. Contribution of IKBKE and IFIH1 gene variants to SLE susceptibility. Genes Immun, 14 (4), 217–22. Wang, K., Li, M., and Hakonarson, H., 2010. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38 (16), e164. Ward, L. D. and Kellis, M., 2012. Interpreting noncoding genetic variation in complex traits and human disease. Nature Biotechnology, 30 (11), 1095–1106. Waterhouse, P., Penninger, J. M., Timms, E., Wakeham, A., Shahinian, A., Lee, K. P., Thompson, C. B., Griesser, H., and Mak, T. W., 1995. Lymphoproliferative disorders with early lethality in mice deficient in Ctla-4. Science (New York, N.Y.), 270 (5238), 985–988. Weerd, N. A. de, Samarajiwa, S. A., and Hertzog, P. J., 2007. Type I Interferon Receptors: Biochemistry and Biological Functions. Journal of Biological Chemistry, 282 (28), 20053–20057. Weis, W. I. and Kobilka, B. K., 2018. The Molecular Basis of G Protein-Coupled Receptor Activation. Annual Review of Biochemistry, 87, 897–919. Wertheimer, A., Levy, R., and O’Connor, T., 2001. Too many drugs? The clinical and economic value of incremental innovations. In: Research in Human Capital and Development [online]. Bingley: Emerald (MCB UP ), 77–118. Available from: https://www.emeraldinsight.com/10.1016/S0194-3960(01)14005-9 [Accessed 18 Sep 2019]. Wertz, I. E., O’Rourke, K. M., Zhou, H., Eby, M., Aravind, L., Seshagiri, S., Wu, P., Wiesmann, C., Baker, R., Boone, D. L., Ma, A., Koonin, E. V., and Dixit, V. M., 2004. De-ubiquitination and ubiquitin ligase domains of A20 downregulate NF-κB signalling. Nature, 430 (7000), 694–699. Woods, C. G., Cox, J., Springell, K., Hampshire, D. J., Mohamed, M. D., McKibbin, M., Stern, R., Raymond, F. L., Sandford, R., Malik Sharif, S., Karbani, G., Ahmed, M., Bond, J., Clayton, D., and Inglehearn, C. F., 2006. Quantification of Homozygosity in Consanguineous Individuals with Autosomal Recessive Disease. American Journal of Human Genetics, 78 (5), 889–896. Worbs, T., Bode, U., Yan, S., Hoffmann, M. W., Hintzen, G., Bernhardt, G., Förster, R., and Pabst, O., 2006. Oral tolerance originates in the intestinal immune system and relies on antigen carriage by dendritic cells. The Journal of Experimental Medicine, 203 (3), 519–527. Worbs, T. and Förster, R., 2007. A key role for CCR7 in establishing central and peripheral tolerance. Trends in Immunology, 28 (6), 274–280. Worth, A. J. J. and Thrasher, A. J., 2015. Current and emerging treatment options for Wiskott-Aldrich syndrome. Expert Review of Clinical Immunology, 11 (9), 1015–1032. Wouters, C. H., Maes, A., Foley, K. P., Bertin, J., and Rose, C. D., 2014. Blau Syndrome, the prototypic auto-inflammatory granulomatous disease. Pediatric Rheumatology Online Journal, 12, 33.
442
Wu, J., Zhang, S., Qin, T., Jiang, J., Liu, Q., Zhang, L., Zhao, X., and Dai, J., 2018. IL- 21 alleviates allergic asthma in DOCK8-knockout mice. Biochemical and Biophysical Research Communications, 501 (1), 92–99. Xue, Y., Chen, Y., Ayub, Q., Huang, N., Ball, E. V., Mort, M., Phillips, A. D., Shaw, K., Stenson, P. D., Cooper, D. N., and Tyler-Smith, C., 2012. Deleterious- and Disease-Allele Prevalence in Healthy Individuals: Insights from Current Predictions, Mutation Databases, and Population-Scale Resequencing. American Journal of Human Genetics, 91 (6), 1022–1032. Yabuuchi, J., Hayami, N., Hoshino, J., Sumida, K., Suwabe, T., Ueno, T., Sekine, A., Kawada, M., Yamanouchi, M., Hiramatsu, R., Hasegawa, E., Sawa, N., Takaichi, K., Fujii, T., Ohashi, K., Migita, K., Masaki, T., and Ubara, Y., 2017. AA Amyloidosis and Atypical Familial Mediterranean Fever with Exon 2 and 3 Mutations. Case Reports in Nephrology and Dialysis, 7 (2), 102–107. Yanagawa, Y. and Onoé, K., 2002. CCL19 induces rapid dendritic extension of murine dendritic cells. Blood, 100 (6), 1948–1956. Yanai, H., Chen, H., Inuzuka, T., Kondo, S., Mak, T. W., Takaoka, A., Honda, K., and Taniguchi, T., 2007. Role of IFN regulatory factor 5 transcription factor in antiviral immunity and tumor suppression. Proceedings of the National Academy of Sciences of the United States of America, 104 (9), 3402–3407. Yang, C., Zang, W., Tang, Z., Ji, Y., Xu, R., Yang, Y., Luo, A., Hu, B., Zhang, Z., Liu, Z., and Zheng, X., 2018. A20/TNFAIP3 Regulates the DNA Damage Response and Mediates Tumor Cell Resistance to DNA-Damaging Therapy. Cancer Research, 78 (4), 1069–1082. Yang, J., Diaz, N., Adelsberger, J., Zhou, X., Stevens, R., Rupert, A., Metcalf, J. A., Baseler, M., Barbon, C., Imamichi, T., Lempicki, R., and Cosentino, L. M., 2016. The effects of storage temperature on PBMC gene expression. BMC Immunology [online], 17. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4791795/ [Accessed 8 Apr 2020]. Yang, Y., Wang, H., Kouadir, M., Song, H., and Shi, F., 2019. Recent advances in the mechanisms of NLRP3 inflammasome activation and its inhibitors. Cell Death & Disease [online], 10 (2). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372664/ [Accessed 9 Jul 2019]. Yasuda, T., Kuwabara, T., Nakano, H., Aritomi, K., Onodera, T., Lipp, M., Takahama, Y., and Kakiuchi, T., 2007. Chemokines CCL19 and CCL21 promote activation- induced cell death of antigen-responding T cells. Blood, 109 (2), 449–456. Zaks, N., Shinar, Y., Padeh, S., Lidar, M., Mor, A., Tokov, I., Pras, M., Langevitz, P., Pras, E., and Livneh, A., 2003. Analysis of the three most common MEFV mutations in 412 patients with familial Mediterranean fever. Isr Med Assoc J, 5 (8), 585–8. Zhang, J., Chiodini, R., Badr, A., and Zhang, G., 2011. The impact of next-generation sequencing on genomics. J Genet Genomics, 38 (3), 95–109. Zhang, K., Zhang, Y., Xue, J., Meng, Q., Liu, H., Bi, C., Li, C., Hu, L., Yu, H., Xiong, T., Yang, Y., Cui, S., Bu, Z., He, X., Li, J., Huang, L., and Weng, C., 2019. DDX19 Inhibits Type I Interferon Production by Disrupting TBK1-IKKε-IRF3 Interactions and Promoting TBK1 and IKKε Degradation. Cell Reports, 26 (5), 1258-1272.e4.
443
Zhang, M., Peng, L.-L., Wang, Y., Wang, J.-S., Liu, J., Liu, M.-M., Hu, J., Song, B., and Yang, H.-B., 2016. Roles of A20 in autoimmune diseases. Immunologic Research, 64 (2), 337–344. Zhang, Q., Davis, J. C., Lamborn, I. T., Freeman, A. F., Jing, H., Favreau, A. J., Matthews, H. F., Davis, J., Turner, M. L., Uzel, G., Holland, S. M., and Su, H. C., 2009. Combined immunodeficiency associated with DOCK8 mutations. The New England Journal of Medicine, 361 (21), 2046–2055. Zhang, Q., Lenardo, M. J., and Baltimore, D., 2017. 30 Years of NF-κB: A Blossoming of Relevance to Human Pathobiology. Cell, 168 (1–2), 37–57. Zhao, L., Liu, L., Guo, B., and Zhu, B., 2015. Regulation of adaptive immune responses by guiding cell movements in the spleen. Frontiers in Microbiology [online], 6. Available from: https://www.frontiersin.org/articles/10.3389/fmicb.2015.00645/full [Accessed 12 Aug 2019]. Zheng, N. and Shabek, N., 2017. Ubiquitin Ligases: Structure, Function, and Regulation. Annual Review of Biochemistry, 86 (1), 129–157. Zhou, Q., Wang, H., Schwartz, D. M., Stoffels, M., Park, Y. H., Zhang, Y., Yang, D., Demirkaya, E., Takeuchi, M., Tsai, W. L., Lyons, J. J., Yu, X., Ouyang, C., Chen, C., Chin, D. T., Zaal, K., Chandrasekharappa, S. C., E, P. H., Yu, Z., Mullikin, J. C., Hasni, S. A., Wertz, I. E., Ombrello, A. K., Stone, D. L., Hoffmann, P., Jones, A., Barham, B. K., Leavis, H. L., van Royen-Kerkof, A., Sibley, C., Batu, E. D., Gul, A., Siegel, R. M., Boehm, M., Milner, J. D., Ozen, S., Gadina, M., Chae, J., Laxer, R. M., Kastner, D. L., and Aksentijevich, I., 2016. Loss-of-function mutations in TNFAIP3 leading to A20 haploinsufficiency cause an early-onset autoinflammatory disease. Nat Genet, 48 (1), 67–73. Zhou, Q., Yang, D., Ombrello, A. K., Zavialov, A. V., Toro, C., Zavialov, A. V., Stone, D. L., Chae, J. J., Rosenzweig, S. D., Bishop, K., Barron, K. S., Kuehn, H. S., Hoffmann, P., Negro, A., Tsai, W. L., Cowen, E. W., Pei, W., Milner, J. D., Silvin, C., Heller, T., Chin, D. T., Patronas, N. J., Barber, J. S., Lee, C. C., Wood, G. M., Ling, A., Kelly, S. J., Kleiner, D. E., Mullikin, J. C., Ganson, N. J., Kong, H. H., Hambleton, S., Candotti, F., Quezado, M. M., Calvo, K. R., Alao, H., Barham, B. K., Jones, A., Meschia, J. F., Worrall, B. B., Kasner, S. E., Rich, S. S., Goldbach-Mansky, R., Abinun, M., Chalom, E., Gotte, A. C., Punaro, M., Pascual, V., Verbsky, J. W., Torgerson, T. R., Singer, N. G., Gershon, T. R., Ozen, S., Karadag, O., Fleisher, T. A., Remmers, E. F., Burgess, S. M., Moir, S. L., Gadina, M., Sood, R., Hershfield, M. S., Boehm, M., Kastner, D. L., and Aksentijevich, I., 2014. Early-onset stroke and vasculopathy associated with mutations in ADA2. N Engl J Med, 370 (10), 911–20. Zhou, R., Tardivel, A., Thorens, B., Choi, I., and Tschopp, J., 2010. Thioredoxin- interacting protein links oxidative stress to inflammasome activation. Nature Immunology, 11 (2), 136–140. Zilberman-Rudenko, J., Shawver, L. M., Wessel, A. W., Luo, Y., Pelletier, M., Tsai, W. L., Lee, Y., Vonortas, S., Cheng, L., Ashwell, J. D., Orange, J. S., Siegel, R. M., and Hanson, E. P., 2016. Recruitment of A20 by the C-terminal domain of NEMO suppresses NF-κB activation and autoinflammatory disease. Proceedings of the National Academy of Sciences of the United States of America, 113 (6), 1612–1617. Zuklys, S., Balciunaite, G., Agarwal, A., Fasler-Kan, E., Palmer, E., and Holländer, G. A., 2000. Normal Thymic Architecture and Negative Selection Are Associated
444
with Aire Expression, the Gene Defective in the Autoimmune- Polyendocrinopathy-Candidiasis-Ectodermal Dystrophy (APECED). The Journal of Immunology, 165 (4), 1976–1983. van Zuylen, W. J., Garceau, V., Idris, A., Schroder, K., Irvine, K. M., Lattin, J. E., Ovchinnikov, D. A., Perkins, A. C., Cook, A. D., Hamilton, J. A., Hertzog, P. J., Stacey, K. J., Kellie, S., Hume, D. A., and Sweet, M. J., 2011. Macrophage Activation and Differentiation Signals Regulate Schlafen-4 Gene Expression: Evidence for Schlafen-4 as a Modulator of Myelopoiesis. PLoS ONE [online], 6 (1). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3017543/ [Accessed 29 Jan 2019].
445
8 Appendix
8.1 Primers for Sanger Sequencing for Family A
Gene Exo Forward Primer Reverse Primer Transcri n pt Length TNFAI 8 ATCTCTGTATCGGTGGG TTGTCACTGTCGGTAGAA 321bp P3 GTG AACG
TRAP1 16 GTAAGATCCTGCCAGCC ACAAAAGAACACCACAC 282bp CTC ACAGG
TYK2 25 GTCTTTCCCTGACCCCAC CTTGGTTTCATCCTGGAG 292bp TATC CAG
TYK2 18 CTCTGGGGACTTGACTCT CGCTATAGGCATACAGCA 308bp GC GG
IRF5 6 CTTCAGCTGCAGAGGAT ACCCTCCTTGCCAATCCT 586bp GTTG AC
PRDM 5 CAACTTTGGCAGTTTTGC CACAGGGGACACCGTATT 494bp 1 TTC C
DIAPH 24 GTGTTGAGGCTGGGATT ACAGGAAGTTTTCTTTCC 255bp 1 ATTAG CTTC
8.2 Primers for Sanger Sequencing for Family B
Gen Exo Forward Primer Reverse Primer Transc e n ript Length
446
IFI 3 CAATCTCTATTAGGAACCTC CCTCTGATTAATAGGTT 340bp H1 TAGTTTG CTGCCC
8.3 Primers for Sanger Sequencing for Family C
Gene Exo Forward Primer Reverse Primer Transcr n ipt Length TNXB 9 GTTTTGGGCTGAAGGGA CCTATGTGGGATTTGGCT 450bp AGTG TCC FAM6 1 GGAAGTTAAGGGAAGG GAGGGCAAGAAATGAGT 370bp 5B AAAGGC GTGTT BTNL 2 TAGTTGCCTCCATTTTG TCAATCAGGGTAGAGGA 577bp 2 ACAGG CTAAGC
8.4 AGS Sequencing Primers:
8.4.1 ADAR
Exon Left Primer Right Primer Transcri pt Length
1 CACTTCCAGTGCGGAGTAG AAAGCCTGTGAGGTTGTAAA 193bp C CG
2.1 AATTGCCTTCTCAGCCCTTC CTCGATTGATTTCTTTCTTCGG 622bp
447
2.2 GTGTTGATTGCCTTTCCTCA ACTCAAGAGGATCTTCCAAG 513bp C GC
2.3 AGCCTTTTATTGCAGTCTCA TCAGTCTTGCTGGTTCTGGTC 570bp GC
2.4 AGAAACGCAGAGTTCCTCA CAGGAGCAAAAGCACCTGAC 505bp CC
3 AGCAGAAAATTCCAGGTTG TTGCCTCAAGGGAGTCAGTTA 349bp AAG C
4 TGTGATGGACTAACCAGTG GGCAAGGAAGAAAATGTGTC 282bp TTTTC TC
5 - 6 CAGAGGCTAGGTCAGGCTC TTGTTTTCCCTGGGTTACAGA 599bp C C
7 CTAACCCTTCCTTGGAACA CACCTCCACTTAGGAGTTAGG 360bp ATG AG
8 AAGTAGAGCTTGATCTCCC TTCTCCCTGCCTTGGACTTAC 360bp TGC
9 CAGAACAAATAGCCTTCAT GGAACTGGAGCTCTCCACAG 225bp CCTG
10 TAGAAACAACTGCTTTTCC TTTGGATACCCAATTCTAACA 248bp CAG GC
11 GAAAACATCCCTTGCTTCT tggtaaaatcctagcagccTTG 324bp GTC
12 TTAAGGAGGATAGAAACCA GTTTGGGATCTGGGCACAAG 321bp CGC
13 CACATGCTTCTGCCTCTTAA TGTTTACATGACTGCTTGAAA 246bp C AGG
14 CCCTGCTTCAGAATCTTATT CAGAGGTATGATGCACCCTTG 263bp CC
15 TCCACTGTGAGCTCCTTATC ATGAGGAATGCTACGACCTA 373bp TTAC CC
448
8.4.2 IFIH1
Exo Left Primer Right Primer Transcri n pt Length
1 GCAGACAACAGCACCATCTG GAGGTCAAGCACATTTGGAAA 603bp G
2 TTCTAAAGGGTATTTCCTGTTT TGTTTAGCATTGTGTCTTTCTG 332bp AAGG C
3 CAATCTCTATTAGGAACCTCTA CCTCTGATTAATAGGTTCTGCC 340bp GTTTG C
4 CTGTGTGCTGTAGAGGTGTGC ATTAGGGAGGGTATGAACAGC 280bp C
5 AGAGGCCTACGTTCAGTTTCA AGTCAATGACACAAATGCCAT 389bp G C
6 GAGTGATGGGAACGTTGTTAT GTTTCCTCCAGGAAGTAGAAG 504bp G G
7 CTGAAAGTGGACTGCTCTTGT CTCAACTTCCCATTAATTATAT 593bp G CACTC
8 CTACGTTGAATAAAGTGAAAG TTTGCCATCTTTCTACTGAATG 244bp GG
9 ATTCCAGCAGAGGTAACAGGG AAATTGGATAGCATTGGAATC 547bp TC
10 GAACTCAAGCAGCAAGTCTAG CTGAATGACCAAATGACTGAC 669bp G TG
11 tgtacattgtggagtggctaaatc aggtcaagggtgcagtgaatc 476bp
12 GGAAATCAAATGTTGTCTCAC TGACAGCAATATGAAAGGCAA 511bp C C
13 GCTGAACAACCCAATATCATT CAGAGATATCAATGGCAACCA 347bp TC C
449
14 AAGACCTCCAAATTTCAGGAG TTTACAATGCAACCTGCTTCAC 343bp AC
15 acccatgaactacacactggta acaaaagagagagcaagaggaa 250bp
16 ATGTGGCCAGgtgagtaaattg TGATTCTTATGTCAGTTCTGTA 386bp GCA
450
8.4.3 RNASEH2B
Exon Left Primer Right Primer Transcri pt Length 1 GAAACGAAATTCGGTCCCTG CTGTCCCAGAGAGAATCCCAT 355bp C 2 GTAAGGTGAGCAACAAAACA CAGAAAGAGCAAGGACACAAA 223bp GC G 3 CTGGATAGTCTTTTGGGGTGT GAATGGAAAAGCTGGAGTATG 246bp G TG 4 ATAGCCACATTGTCTTTCCAA GGCCCATATGATTCATGTTTTC 282bp G 5 CACTAAGTTTAAAGGCCCAG TTCAGTGTGTCTGTAAGCAGCC 301bp CC 6 ATGTTCTCAGGTTTGTAAATT ACACAATTTTCCTCTTTTCACT 342bp AAGC G 7 CTTAAATGGTCTGAAGGCCA CCTAAGATAGGCATTTACACAT 268bp CC AAGC 8 CCCTTTATCCAGATAGGGTCA ATAAGCCATAAGGAAACCCCA 273bp G C 9 TCTTAAGTTGGCCCTGTCTTT TTAGAACCAACTAAACTCATGT 200bp C GG 10 TGGATTGATGTTGTGTCAAAG GTATTCATGCACATCACGCAC 200bp 11 ACCTTCCTTAATGAAACTTGA AACAGTCAGGAAGGACAAACT 290bp GAC G
451
8.4.4 RNASEH2C
Exon Left Primer Right Primer Fragme nt Length 1 CAGCTTCAGTGTCAGCTC CGATGAGAAGCGCGCAG 283bp G 2 ctgcgcgcttctcatcg gctcagcatcgggactaca 359bp 3 GACTTCgtgagcaaagggatag tacccagtagaatcctccagg 293bp 4.1 gttattctgaacagctgggtcc TGTGTACAGTGAGAAGGGA 467bp TCC 4.2 GGGGCAATAGGGGTAAG ATGTCATCTCCACTCTGCAG 671bp AGAAG TA 4.3 CAGAGGAGATGGGTTCAG GAGAAGTGGAGGGCATAGA 592bp CA ACT 4.4 AGGAGAGGGTGAGTAAG GAGTTGGAGGGAAAGAGCC 576bp AGGT AT 4.5 GTTGTGAGGATGCAAGGA CACATCCTCAGAACTTCTCA 385bp GAAA GC
8.4.5 SAMHD1
Exon Left Primer Right Primer Fragme nt Length 1 AATAGGCTGCCAATACTCCTT cctcgggtcttcctttcctc 493bp G 2 TCTGGGTAAATGTTGGTGTAT TCCCTGAAAGATGGATAAAG 212bp CC TG 3 TTTGAATTATTGATTGTTCCTT TCACTGAGAAGCAGATTTCC 313bp CC TC 4 tgcacacaaatttcagttggac ggcctaagataactatttcatgagaca 299bp
452
5 CCAGAACTATCACTCCTCTTG CAATTTCCATATTCTCTTGGT 276bp C TG 6 tgttgagccaagattgtaccac atatgtgaatgaaagcaccctg 354bp 7 TAATTTTGTTAGTCAGGGCTCC CAGAAGGAATCATGAAAAG 295bp GC 8-9 TCTAGTTTATCTCATATTCCC TTATTTCCAAATTTCAGACC 519bp AGTCAG AGG 10 TTGTCTGCCACTATCCCTTTTC GGAAACCATTTTCAATTTAA 325bp TCAC 11 ACCAAGCACAAATTTAGTAC gaagcctgggcaatatagtgag 435bp GG 12 TTTGCGAACTGCCTGTTAAG GGTCTCCTCTTGGAGGACAG 303bp AG 13 CTGTTTGTGGCTCAAAGACTT TGGGTGCTTTATCTTTAAAA 214bp G CG 14 ATGCTCCTACAGCCCTGAGTT GATATGCCTTAAAACCTAAT 271bp C TTGC 15 ACCAGCTGATATCTCCAATGT TTCAGCAGATAGACTTACTT 278bp G TTGG 16 GAAATAAGATGATGGAAACTGGC GAAGCCTCTAAATGAATTGT 254bp GC
8.4.6 TREX1
Exon Left Primer Right Primer Transcrip t Length 2.1 taacactgggcactcacaca AAGTCGTAGCGGTCACCATT 460bp 2.2 CACACAATGGTGACCGCT AATTGACCACTCAGTGCTATG 640bp AC G 2.3 CCTAGGCAGCATCTACACT caagaagggtaggagccaattg 484bp CG
453
8.5 Gene Panels
8.5.1 Neuroinflammation panel (NIP)
AARS2 COQ9 GATA2 NDUFAF1 PMP22 SKI TYMP ABCD1 CORO1A GBE1 NDUFS1 POLG SKIV2L UNC13D ACOX1 COX10 GFAP NDUFS2 POLG2 SLC16A2 USB1 ACP5 COX15 GFM1 NDUFS3 POLR1C SLC17A5 USP18 ACTA2 CPT2 GJB1 NDUFS4 POLR3A SLC1A4 WDR1 ADA2 CSF1R GJC2 NDUFS5 POLR3B SLC25A12 XIAP ADAR CST3 GLA NDUFS6 POMP SLC29A3 YY1AP1 ADCK3 CTC1 GTF2H5 NDUFS7 PRF1 SLC2A10 CHR17 AIFM1 CTLA4 GUCY1A3 NDUFS8 PRKCD SMAD2 CHR1 AIMP1 CTP HEPACAM NDUFV1 PRKG1 SMAD3 CHR17 ALDH3A2 CYP27A1 HFE NEFL PRX SMAD4 ARSA DARS HMBS NF1 PSAP SNORD118 ARX DARS2 HSD17B4 NFU1 PSMA3 SOX10 ASPA DDX58 HTRA1 NLRC4 PSMB3 SPTAN1 ATP7B DGUOK IBA57 NLRP12 PSMB4 STAT2 ATPAF2 DNASE1 IFIH1 NLRP3 PSMB8 STAT3 BCS1L DNASE1L3 IL1RN NOD2 PSMB9 STAT4 BMPR2 DNASE2 IRF5 NOTCH1 PSTPIP1 STX11 BOLA3 EARS2 IRF8 NOTCH3 PTPN22 STXBP2 BTD EGR2 ISCA2 NUBPL PYCARD SUCLA2 C1QA EIF2B1 ISG15 OTULIN PYCR2 SUMF1 C1QB EIF2B2 ITPR3 PARN RAB27A SURF1 C1QC EIF2B3 LMNA PDHA1 RANBP2 TACO1 C1R EIF2B4 LMNB1 PEX1 RARS TBK1 C2 EIF2B5 LONP1 PEX10 RASGRP1 TGFB2 C3 ELN LOX PEX11A RBCK1 TGFB3 C5 ERCC2 LPIN2 PEX11B RHOD TGFBI C6 ERCC3 LYST PEX12 RNASEH2A TGFBR1 C7 ERCC6 MASP2 PEX13 RNASEH2B TGFBR2 C8A ERCC8 MAT2A PEX14 RNASEH2C TMEM173 C8B ETFDH MEFV PEX16 RNASET2 TNFAIP3 CBL EXOSC8 MFAP5 PEX2 RNF213 TNFRSF1A CBS FAM126A MFN2 PEX26 RRM2B TRAP1 CFH FAS MLC1 PEX3 SAMD3 TREX1 CFHR5 FBN1 MPLKIP PEX5 SAMHD1 TRIM28
454
CFI FBN2 MPZ PEX6 SCN9A TRNT1 CLCN2 FOLR1 MTTL1 PEX7 SCO1 TSC1 CLEC16A FOXE3 MRPS16 PGM3 SCO2 TSC2 COL3A1 FOXP3 MVK PHYH SCP2 TTC37 COL4A1 FUCA1 MYH11 PLCG2 SDHAF1 TUBB4A COPA FXN MYLK PLOD1 SDHB TUFM COQ2 GALC MYO5A PLP1 SH2D1A TWNK
8.5.2 Vasculitis and inflammation panel (VIP)
ACTA2 IKBKG IL1RN C6 COL4A1 BMPR2 IL2RA IL36RN C7 CST3 FBN1 ITGB2 LPIN2 C8A GLA SLC2A10 LRBA LYN C8B HTRA1 TGFBR1 NCF1 MEFV C9 NOTCH3 TGFBR2 NCF2 MVK CFH ACP5 MYH11 NCF4 NLRP12 CFHR5 ADAR RNF213 PIK3R1 NLRP3 CFI DNASE1 GUCY1A3 PTEN NOD2 CFP DNASE1L3 MYLK RET PLCG2 MASP2 PRKCD PRKG1 SKIV2L PSMB8 MBL2 RNASEH2A SMAD3 SLC37A4 PSTPIP1 SERPING1 RNASEH2B SMAD4 TTC37 RBCK1 COL3A1 RNASEH2C TGFB2 WAS TMEM173 COL5A1 SAMHD1 HFE AP3B1 TNFRSF1A COL5A2 TREX1 RHOD CBL TRNT1 PLOD1 CECR1 ELN CORO1A AP1S3 PRF1 TRAP1 FBN2 CTPS1 CARD14 SLC29A3 WDR1 NOTCH1 IFNGR1 NLRC4 STX11 SKI IFNGR2 NLRP7 STXBP2 ADAM17 MAGT1 POMP UNC13D AICDA PIK3CD PSMA3 DNASE2 BTK VPS13B PSMB4 LYST CD40LG NF1 PSMB9 RAB27A COL7A1 B2M SH3BP2 APOA1 CYBA CTC1 TNFAIP3 APOA2 CYBB STK4 TNFRSF11A FGA DCLRE1C CASP10 PYCARD GSN DOCK8 CASP8 NLRP6 LYZ FERMT1 FAS C1QA TTR FOXP3 FASLG C1QB APOE
455
G6PC3 NRAS C1QC SAA1 GUCY2C SH2D1A C1R SAA2 HPS1 XIAP C2 SAA4 HPS4 IL10 C3 APOC3 HPS6 IL10RA C4A APOA4 ICOS IL10RB C5 CBS
8.5.3 Paediatric immunodeficiencies panel (PID)
WAS UNC119 IGLL1 IL10RB IKBKG GATA1 CARD11 CD79A TREX1 IKBKG VPS33B OX40 CD79B RNASEH2B IKBKG VIPAS39 WIPF1 BLNK RNASEH2C IKBKG IL2RG ATM PIK3R1 RNASEH2A IKBKG JAK3 MRE11 TCF3 SAMHD1 NFKBIA IL7R NBN ICOS ADAR IRAK4 PTPRC BLM CD19 ACP5 MYD88 CD3D DNMT3B CD81 ELANE RBCK1 CD3E ZBTB24 MS4A1 GFI1 CXCR4 CD3G PMS2 CR2 HAX1 TMC6 CORO1A RNF168 TNFRSF13B G6PC3 TMC8 RAG1 MCM4 LRBA VPS45 STAT2 RAG2 TBX1 TNFRSF13C SLC37A4 TLR3 DCLRE1C CHD7 TNFSF12 LAMTOR2 UNC93B1 PRKDC SEMA3E NFKB2 ITGB2 TRAF3 AK2 SMARCAL1 AICDA SLC35C1 TICAM1 ADA STAT3 UNG FERMT3 CARD9 CD40LG STAT3 IGKC RAC2 IL17RA CD40 TYK2 PRKCD ACTB IL17F PNP DKC1 PIK3CD FPR1 STAT1 CD8A NHP2 PRF1 CTSC TRAF3IP2 ZAP70 NOP10 UNC13D CEBPE MEFV TAP1 RTEL1 STX11 SBDS MEFV TAP2 RTEL1 STXBP2 TAZ MVK TAPBP TERC LYST VPS13B NLRP3 CIITA TERT RAB27A USB1 NLRP12 RFX5 TERT AP3B1 CYBB NLRP3 RFXAP TINF2 XIAP CYBA TNFRSF1A RFXANK SPINK5 CD27 NCF1 IL10 ITK FOXN1 FOXP3 NCF2 PSTPIP1 SH2D1A ORAI1 IL2RA NCF4 NOD2
456
RMRP STIM1 AIRE IL12RB1 LPIN2 MAGT1 STAT5A ITCH IL12B IL1RN DOCK8 STAT5B FAS IFNGR1 IL36RN RHOH SP110 FASLG IFNGR1 SLC29A3 STK4 IKZF1 CASP10 IFNGR2 CARD14 TRAC POLE CASP8 STAT1 SH3BP2 LCK TTC7A FADD IRF8 PSMB8 MALT1 BTK XRCC4 SLC11A1 IL21 IL21R IGHM CCBE1 CARD11 IL17RC C1QB RORC NFKB1 IL10RA IRF3 C1QC IL12RB2 CD28 GATA2 JAGN1 C1R TMEM173 MKL1 CSF2RA ISG15 C1S IRF7 NHEJ1 PLCG2 BCL10 C4A TRNT1 NRAS C1QA CTLA4 C4B CD46 C9 CFHR1 C2 CD59 SERPING1 CFHR3 C3 FCN3 CFB THBD C5 CD247 CFD MASP1 C6 LIG4 CFP MASP2 C7 C8B CFI COLEC11 C8A CFH
457
8.6 Genes found in ROH – Family B
8.6.1 Chromosome 5
MSX2 RGS14 MXD3 DRD1 SLC34A1 MXD3 SFXN1 SLC34A1 LMAN2 HRH2 PFN3 HRH2 F12 CPLX2 GRK6 CPLX2 GRK6 THOC3 GRK6 FAM153B PRR7 C5orf25 DBN1 KIAA1191 DBN1 KIAA1191 PDLIM7 KIAA1191 PDLIM7 ARL10 PDLIM7 NOP16 DOK3 HIGD2A DOK3 CLTB DOK3 CLTB DDX41 FAF2 KIAA1931 RNF44 FAM193B PCDH24 TMED9 GPRIN1 B4GALT7 SNCB LOC202181 SNCB FAM153A EIF4E1B THOC3 TSPAN17 PROP1 TSPAN17 FAM153C TSPAN17 N4BP3 UNC5A RMND5B HK3 NHP2 UIMC1 NHP2 ZNF346 FLJ13057 FGFR4 HNRNPAB FGFR4 HNRNPAB FGFR4 AGXT2L2 NSD1 COL23A1 NSD1 CLK4 RAB24 ZNF354A RAB24 PRELID1
458
8.6.2 Chromosome 12
PIK3C2G CASC1 PLCZ1 CASC1 CAPZA3 LYRM5 PLEKHA5 KRAS PLEKHA5 KRAS PLEKHA5 IFLTD1 AEBP2 IFLTD1 AEBP2 IFLTD1 PDE3A IFLTD1 SLCO1C1 IFLTD1 SLCO1C1 RASSF8 SLCO1C1 RASSF8 SLCO1C1 RASSF8 SLCO1B3 RASSF8 LST-3TM12 BHLHE41 SLCO1B1 SSPN SLCO1A2 SSPN SLCO1A2 ITPR2 IAPP C12orf11 PYROXD1 FGFR1OP2 RECQL TM7SF3 RECQL MED21 GOLT1B C12orf71 C12orf39 STK38L GYS2 ARNTL2 LDHB C12orf70 KCNJ8 PPFIBP1 ABCC9 PPFIBP1 ABCC9 REP15 ABCC9 MRPS35 CMAS LOC100133893 ST8SIA1 KLHDC5 KIAA0528 CASC1 ETNK1 ETNK1 SOX5 SOX5 SOX5 MIR920 C12orf67 BCAT1 DAD1L C12orf77 LRMP 459
8.6.3 Chromosome 17 (a)
MYOCD C17orf45 LRRC48 MYOCD C17orf45 LRRC48 MYOCD NCRNA00188 LRRC48 RICH2 C17orf45 ATPAF2 ELAC2 C17orf45 C17orf39 ELAC2 NCRNA00188 DRG2 ELAC2 C17orf45 MYO15A HS3ST3A1 C17orf45 ALKBH5 CDRT15P NCRNA00188 LLGL1 COX10 C17orf45 FLII CDRT15 C17orf45 SMCR7 HS3ST3B1 C17orf45 SMCR7 MGC12916 NCRNA00188 SMCR7 PMP22 C17orf45 TOP3A PMP22 SNORD49B SMCR8 PMP22 SNORD49A SHMT1 TEKT3 SNORD65 SHMT1 CDRT4 C17orf76 EVPLL FAM18B2 C17orf76 LOC339240 FAM18B2 ZNF287 LGALS9C CDRT1 ZNF624 tl132 TRIM16 CCDC144A FAM106A ZNF286A FLJ36492 CCDC144B ZNF286A FAM106A TBC1D28 TBC1D26 LOC400578 ZNF286B MEIS3 TNFRSF13B FKHRL1P2 ADORA2B MPRIP ZSWIM7 MPRIP ZSWIM7 PLD6 TTC19 FLCN NCOR1 FLCN PIGL COPS3 MIR1288 NT5M CENPV MED9 UBB RASD1 TRPV2 PEMT C17orf45 PEMT NCRNA00188 PEMT C17orf45 RAI1 C17orf45 SMCR5 C17orf45 SREBF1 C17orf45 SREBF1 NCRNA00188 MIR33B C17orf45 TOM1L2 460
8.6.4 Chromosome 17 (b)
WSB1 RPL23A TBC1D29 CCL1 RDM1 C17orf78 WSB1 SNORD42B DKFZp667M C17orf102 LYZL6 TADA2A KSR1 SNORD4A SUZ12P TMEM132E CCL16 TADA2A HUAT SNORD42A CRLF3 CCT6B CCL15 TADA2A LGALS9 SNORD4B ATAD5 ZNF830 CCL15 DUSP14 LGALS9 TLCD1 C17orf42 LIG3 CCL14 SYNRG NOS2 TLCD1 ADAP2 LIG3 CCL14 SYNRG C17orf108 NEK8 RNF135 RFFL CCL15 SYNRG NLK TRAF4 RNF135 RFFL CCL23 SYNRG PYY2 C17orf63 DPRXP4 RAD51L3 CCL23 SYNRG PPY2 C17orf63 NF1 RAD51L3 CCL18 SYNRG FLJ40504 ERAL1 NF1 RAD51L3 CCL3 SYNRG TMEM97 MIR451 NF1 FNDC8 CCL4 DDX52 IFT20 MIR144 OMG NLE1 TBC1D3B DDX52 TNFAIP1 FLOT2 EVI2B NLE1 CCL3L1 HNF1B POLDIP2 DHRS13 EVI2A UNC45B CCL3L3 HNF1B TMEM199 PHF12 EVI2A UNC45B CCL4L1 LOC284100 SEBOX PHF12 RAB11FIP4 AMAC1 CCL4L2 TBC1D3F SEBOX SEZ6 MIR193A SLFN5 TBC1D3C TBC1D3 VTN SEZ6 MIR365-2 SLFN11 CCL3L1 TBC1D3 SARM1 PIPOX C17orf79 SLFN11 CCL3L3 TBC1D3F SLC46A1 MYO18A UTP6 SLFN11 CCL4L2 MRPL45 SLC13A2 MYO18A SUZ12 SLFN11 CCL4L1 GPR179 SLC13A2 TIAF1 LRRC37B SLFN11 TBC1D3H SOCS7 SLC13A2 CRYBA1 RHOT1 SLFN12 TBC1D3C ARHGAP23 SLC13A2 NUFIP2 RHOT1 SLFN13 TBC1D3G SNIP FOXN1 TAOK1 RHOT1 SLFN12L ZNHIT3 C17orf96 UNC119 ABHD15 ARGFXP2 SLFN14 MYO19 MLLT6 UNC119 TP53I13 RHBDL3 SNORD7 MYO19 CISD3 PIGS GIT1 C17orf75 PEX12 MYO19 PCGF2 ALDOC GIT1 MIR632 AP2B1 PIGW PSMB3 SPAG5 ANKRD13B ZNF207 AP2B1 GGNBP2 PIP4K2B FLJ25006 CORO6 ZNF207 RASL10B DHRS11 CCDC49 LOC645851 SSH2 ZNF207 GAS2L2 MRM1 C17orf98 KIAA0100 EFCAB5 PSMD11 C17orf50 LHX1 RPL23 SDF2 EFCAB5 CDK5R1 MMP28 AATF SNORA21 SUPT6H EFCAB5 MYO1D MMP28 ACACA LASP1 PROCA1 CCDC55 TMEM98 TAF15 ACACA FBXO47 RAB34 MIR423 TMEM98 TAF15 ACACA PLXDC1 RAB34 SLC6A4 SPACA3 C17orf66 CASC3 ARL5C RAB34 BLMH ACCN1 CCL5 CACNB1 RAB34 TMIGD1 ACCN1 RDM1 CACNB1 RAB34 CPD CCL2 WNK4 CACNB1 RAB34 GOSR1 CCL7 KRTAP4-1 RPL19 461
RAB34 GOSR1 CCL11 CBX1 GJC1 MPP3 STAC2 RAPGEFL1 NKIRAS2 CCDC56 CD300LG MAPT LOC90110 WIPF2 DHX58 CNTD1 CD300LG MAPT FBXL20 CDC6 KAT2A BECN1 CD300LG MAPT MED1 RARA HSPB9 PSME3 CD300LG LOC10048 CRKRS RARA RAB5C PSME3 MPP2 STH CRKRS RARA RAB5C AOC2 Apr-02 KIAA1267 NEUROD2 RARA KCNH4 AOC2 PPY LRRC37A PPP1R1B GJD3 HCRT AOC3 PYY ARL17P1 PPP1R1B TOP2A GHDC LOC90586 NAGS ARL17B STARD3 IGFBP4 GHDC LOC388387 TMEM101 ARL17P1 STARD3 TNS4 GHDC G6PC LSM12 ARL17 STARD3 CCR7 GHDC AARSD1 G6PC3 LRRC37A2 TCAP SMARCE1 STAT5B AARSD1 G6PC3 ARL17P1 PNMT KRT222 STAT5A AARSD1 G6PC3 ARL17P1 PGAP3 KRT24 STAT3 AARSD1 HDAC5 NSF ERBB2 KRT25 STAT3 RUNDC1 HDAC5 WNT3 ERBB2 KRT26 STAT3 RPL27 C17orf53 WNT9B C17orf37 KRT27 PTRF IFI35 C17orf53 GOSR2 GRB7 KRT28 ATP6V0A1 VAT1 ASB16 GOSR2 GRB7 KRT10 ATP6V0A1 RND2 C17orf65 GOSR2 IKZF3 TMEM99 ATP6V0A1 BRCA1 TMUB2 RPRML IKZF3 KRT12 NAGLU BRCA1 TMUB2 CDC27 IKZF3 KRT20 HSD17B1 BRCA1 TMUB2 CDC27 IKZF3 KRT23 COASY BRCA1 ATXN7L3 MYL4 IKZF3 KRT39 COASY BRCA1 ATXN7L3 MYL4 IKZF3 KRT40 COASY BRCA1 UBTF ITGB3 ZPBP2 KRTAP3-3 COASY NBR2 UBTF C17orf57 ZPBP2 KRTAP3-2 COASY NBR1 UBTF LOC1002 GSDMB KRTAP3-1 MLX NBR1 SLC4A1 NPEPPS GSDMB KRTAP1-5 MLX NBR1 RUNDC3A KPNB1 GSDMB KRTAP1-3 MLX TMEM106A RUNDC3A TBKBP1 GSDMB KRTAP1-1 PSMC3IP LOC100130 RUNDC3A TBX21 ORMDL3 KRTAP2-1 PSMC3IP LOC100130 SLC25A39 OSBPL7 GSDMA KRTAP2-2 LOC162427 ARL4D SLC25A39 MRPL10 PSMD3 LOC730 FAM134C MIR2117 GRN LRRC46 CSF3 KRTAP2-4 TUBG1 DHX8 FAM171A2 SCRN2 CSF3 KRTAP4-7 TUBG2 ETV4 ITGA2B SCRN2 CSF3 KRTAP4-8 PLEKHH3 ETV4 GPATCH8 SP6 MED24 KRTAP4-9 CCR10 MEOX1 FZD2 SP2 MED24 KRTAP4-11 CNTNAP1 MEOX1 C17orf104 PNPO SNORD124 KRTAP4-12 EZH1 MEOX1 CCDC43 ATAD4 THRA KRTAP4-5 LOC100190938 SOST CCDC43 CDK5RAP3 THRA KRTAP4-4 LOC100190938 DUSP3 DBF4B COPZ2 NR1D1 KRTAP4-3 RAMP2 C17orf105 DBF4B MIR152 MSL1 KRTAP4-2 VPS25 MPP3 ADAM11 NFE2L1
462
8.6.5 Chromosome 17 (c)
TBC1D16 CCDC40 GAA GAA GAA EIF4A3 CARD14 CARD14 SGSH SLC26A11 SLC26A11 SLC26A11 SLC26A11 RNF213 KIAA1618 LOC100294362 FLJ35220 FLJ35220 FLJ35220 NPTX1 RPTOR RPTOR CHMP6 FLJ90757 BAIAP2 BAIAP2
463
8.7 Genes found in ROH – Family C
8.7.1 Chromosome 6(a)
GMNN HIST1H2AD HIST1H2BL ZNF311 SFTA2 LST1 FAM65B HIST1H2BF HIST1H2AI OR2W1 DPCR1 LST1 FAM65B HIST1H4E HIST1H3H OR2B3 MUC21 LST1 DKFZp686H12 HIST1H2BG HIST1H2AJ OR2J3 HCG22 SFTA2 DKFZp686H12 HIST1H2AE HIST1H2B OR2J2 C6orf15 DPCR1 LRRC16A HIST1H3E HIST1H4J OR14J1 PSORS1C1 MUC21 SCGN HIST1H1D HIST1H4K OR5V1 CDSN HCG22 HIST1H2AA HIST1H4F HIST1H2A OR12D3 PSORS1C2 C6orf15 HIST1H2BA HIST1H4G HIST1H2B OR12D2 CCHCR1 PSORS1C1 SLC17A4 HIST1H3F HIST1H2A OR11A1 CCHCR1 CDSN SLC17A1 HIST1H2BH HIST1H1B OR10C1 CCHCR1 PSORS1C2 SLC17A3 HIST1H3G HIST1H3I OR2H1 TCF19 CCHCR1 SLC17A3 HIST1H2BI HIST1H4L MAS1L TCF19 CCHCR1 SLC17A2 HIST1H4H HIST1H3J UBD POU5F1 CCHCR1 TRIM38 BTN3A2 HIST1H2A SNORD32B POU5F1 TCF19 HIST1H1A BTN2A2 HIST1H2B OR2H2 PSORS1C3 TCF19 HIST1H3A BTN2A2 OR2B2 GABBR1 HCG27 POU5F1 HIST1H4A BTN3A1 OR2B6 GABBR1 HLA-C POU5F1 HIST1H4B BTN3A1 ZNF165 GABBR1 HLA-B PSORS1C3 HIST1H3B BTN3A1 ZSCAN12L MOG MICA HCG27 HIST1H2AB BTN3A1 ZSCAN16 MOG HCP5 HLA-C HIST1H2BB BTN2A3 ZNF192 MOG HCG26 HLA-B HIST1H3C BTN3A3 ZNF389 MOG MICB MICA HIST1H1C BTN3A3 pp14762 MOG MCCD1 HCP5 HFE BTN2A1 ZNF193 MOG BAT1 HCG26 GPX5 BTN2A1 ZKSCAN4 MOG BAT1 MICB SCAND3 BTN1A1 NKAPL MOG SNORD11 MCCD1 TRIM27 HCG11 ZNF187 MOG SNORD84 BAT1 HCG9 HMGN4 ZNF187 MOG ATP6V1G2 BAT1 HTEX4 ABT1 ZNF187 MOG ATP6V1G2 SNORD1 HLA-A ZNF322A PGBD1 ZFP57 NFKBIL1 SNORD8 ZNF204P SMA4 ZNF323 HLA-F NFKBIL1 ATP6V ZNF391 C6orf41 ZNF323 HLA-F NFKBIL1 ATP6V1G2 HIST1H4C LOC100270746 ZNF323 HLA-F NFKBIL1 NFKBIL1 HIST1H1T HIST1H2BJ ZNF323 DKFZp762B162 LTA NFKBIL1 HIST1H2BC HIST1H2AG ZNF323 DKFZp762B162 LTA NFKBIL1 HIST1H2AC HIST1H2BK ZNF323 IFITM4P TNF NFKBIL1 HIST1H1E HIST1H4I ZKSCAN3 HCG4 LTB LTA HIST1H2BD HIST1H2AH ZSCAN12 HLA-G LTB LTA HIST1H2BD PRSS16 ZSCAN12 HLA-H LST1 TNF
464
HIST1H2BE POM121L2 ZSCAN23 HCG2P7 LST1 LTB HIST1H4D FKSG83 GPX6 HCG4P6 LST1 LTB HIST1H3D C2H2 GPX5 HLA-A LST1 LST1 ZNRD1 LST1 LST1 SNORD48 LST1 NCR3 ZNRD1 LST1 LST1 SNORD52 LST1 NCR3 PPP1R11 LST1 LST1 NEU1 LST1 NCR3 RNF39 SFTA2 SFTA2 SLC44A4 SFTA2 AIF1 RNF39 DPCR1 DPCR1 EHMT2 DPCR1 AIF1 TRIM31 MUC21 MUC21 EHMT2 MUC21 AIF1 TRIM40 HCG22 HCG22 ZBTB12 HCG22 BAT2 TRIM10 C6orf15 C6orf15 C2 C6orf15 SNORA38 TRIM10 PSORS1C1 PSORS1C1 C2 PSORS1C1 BAT3 TRIM15 CDSN CDSN CFB CDSN BAT3 TRIM26 PSORS1C2 PSORS1C2 RDBP PSORS1C2 BAT3 HLA-B CCHCR1 CCHCR1 MIR1236 CCHCR1 BAT3 HCG18 CCHCR1 CCHCR1 SKIV2L CCHCR1 APOM HCG18 CCHCR1 CCHCR1 DOM3Z CCHCR1 C6orf47 TRIM39 TCF19 TCF19 STK19 TCF19 BAT4 TRIM39 TCF19 TCF19 STK19 TCF19 CSNK2B RPP21 POU5F1 POU5F1 STK19 POU5F1 LY6G5B HLA-E POU5F1 POU5F1 C4B POU5F1 LY6G5C GNL1 PSORS1C3 PSORS1C3 C4A PSORS1C3 BAT5 PRR3 HCG27 HCG27 CYP21A2 HCG27 LY6G6F PRR3 HLA-C HLA-C CYP21A2 HLA-C G6e ABCF1 HLA-B HLA-B TNXB HLA-B G6E ABCF1 MICA MICA TNXA MICA LY6G6D MIR877 HCP5 HCP5 STK19 HCP5 LY6G6C PPP1R10 HCG26 NOTCH4 STK19 HCG26 C6orf25 MRPS18B MICB C6orf10 C4A MICB C6orf25 C6orf134 MCCD1 BTNL2 C4B MCCD1 C6orf25 C6orf136 BAT1 HLA-DRA CYP21A2 BAT1 C6orf25 C6orf136 BAT1 HLA-DRB5 CYP21A2 BAT1 C6orf25 C6orf136 SNORD117 HLA DRB4 TNXB SNORD117 C6orf25 DHX16 SNORD84 HLA-DRB1 TNXB SNORD84 DDAH2 DHX16 ATP6V1G2 HLA-DQA1 ATF6B ATP6V1G2 CLIC1 KIAA1949 ATP6V1G2 HLA-DQB1 ATF6B ATP6V1G2 MSH5 KIAA1949 NFKBIL1 HLA-DQA2 FKBPL NFKBIL1 MSH5 NRM NFKBIL1 HLA-DQB2 PRRT1 NFKBIL1 MSH5 MDC1 NFKBIL1 HLA-DOB PPT2 NFKBIL1 MSH5 TUBB NFKBIL1 TAP2 PPT2 NFKBIL1 C6orf26 FLOT1 LTA TAP2 EGFL8 LTA C6orf27 IER3 LTA PSMB8 AGPAT1 LTA VARS DDR1 TNF PSMB8 AGPAT1 TNF LSM2 DDR1 LTB TAP1 RNF5 LTB HSPA1L DDR1 LTB PSMB9 RNF5 LTB HSPA1A GTF2H4 LST1 PSMB9 AGER LST1 HSPA1B
465
8.7.2 Chromosome 6(b)
CLIC5 CLIC5 ENPP4 ENPP5 RCAN2 CYP39A1 SLC25A27 TDRD6 TDRD6 PLA2G7 PLA2G7 LOC100287718 MEP1A GPR116 GPR116 GPR110 GPR110 TNFRSF21 CD2AP GPR111
8.7.3 Chromosome 7
INHBA SPDYE1 NUDCD3 CCM2 IGFBP3 UBE2D4 LOC285954 RASA4P NPC1L1 CCM2 IGFBP3 POLR2J4 LOC285954 FLJ35390 NPC1L1 CCM2 TNS3 YKT6 GLI3 FLJ35390 DDX56 CCM2 C7orf65 CAMK2B C7orf25 DBNL TMED4 NACAD PKD1L1 SNORA9 C7orf25 DBNL OGDH TBRG4 C7orf69 CCM2 PSMA2 DBNL OGDH TBRG4 HUS1 MRPL32 PGAM2 OGDH TBRG4 SUNC1 HECW1 POLM ZMIZ2 SNORA5A SUNC1 STK17A AEBP1 ZMIZ2 SNORA5C C7orf57 C7orf44 POLD2 PPIA SNORA5B UPP1 BLVRA POLD2 H2AFV RAMP3 UPP1 MRPS24 MYL7 H2AFV ADCY1 ABCA13 URG4 GCK PURB IGFBP1
466
URG4 GCK MYO1G URG4 GCK C7orf40
8.7.4 Chromosome 9
C9orf47 C9orf129 ANKRD19 C9orf47 FAM120AOS ZNF484 S1PR3 FAM120A ZNF484 SHC3 PHF2 CKS2 BARX1 SECISBP2 PTPDC1 SEMA4D PTPDC1 SEMA4D MIRLET7A1 GADD45G MIRLET7F1 UNQ6494 MIRLET7D DIRAS2 ZNF169 SYK FAM22F SYK HIATL1 SYK FBP2 SYK FBP1 AUH FBP1 NFIL3 C9orf3 ROR2 MIR2278 SPTLC1 MIR23B SPTLC1 MIR27B PTPRV MIR24-1 C9orf44 FANCC IARS PTCH1 IARS PTCH1 SNORA84 PTCH1 NOL8 PTCH1 NOL8 PTCH1 CENPP PTCH1 OGN PTCH1
OGN FGD3
OMD FGD3
ASPN SUSD3
ECM2 C9orf89
IPPK NINJ1
BICD2 WNK2 BICD2
467