Microbe and Host-derived Mechanisms of Protection from Autoimmune Diabetes in Non-obese Diabetic Mice
by
An Qi Xu
A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Immunology University of Toronto
© Copyright by An Qi Xu 2018 Microbe and Host-derived Mechanisms of Protection from Autoimmune Diabetes in
Non-obese Diabetic Mice
An Qi Xu
Master of Science
Department of Immunology
University of Toronto
2018
ABSTRACT
Autoimmune diabetes results from T cell-mediated destruction of insulin-producing pancreatic β cells in both humans and non-obese diabetic (NOD) mice. Our lab previously observed that cecal microbial transfer from male NOD mice into weanling females (MàF) conferred protection in the recipients. The goal of this thesis was to investigate the effects of microbial transfer on autoantigen presentation and islet-reactive CD4+ T cell responses. We found that the proliferation of islet-reactive T cells did not vary in response to autoantigen presentation in MàF recipients compared to controls. However, peripheral antigen presenting cells from MàF recipients promoted a more activated T cell phenotype ex vivo compared to controls. Moreover, CD4+ T cells with major diabetogenic potential were identified in the islets. Since CD4+ T cells are central to immunological events in autoimmune diabetes, analysis of islet-reactive CD4+ T cell responses will shed light in mechanisms of microbe-dependent protection from autoimmune diabetes.
ii ACKNOWLEDGEMENTS
I feel very fortunate to have met the people who I am working with and the people in the field who have provided comments and insights. I received a lot of support and guidance from them and from my family and friends. I would like to hereby acknowledge these bright minds and supportive hands. First and foremost, I would like to thank Dr. Jayne Danska. Thank you for being my supervisor and my mentor. I have always regarded highly of the way you address questions and handle issues. Your analytical skills set standards for me and guided me to develop as a true scientist. I am very grateful for all your advice, as I know you really care about the students and want the best for our good. Thank you also for providing me with the chance to go to the collaborating lab in Denver and learn techniques. I would also like to thank my committee, Dr. Philippe Poussier, Dr. Jennifer Gommerman, and a previous committee member Dr. David Guttman for their insights and support in this project. I would like to thank Dr. Alexandra Paun. Thank you for providing me with lots of useful comments and being always so approachable and helpful. I appreciate all the input and insights you have given and feel very fortunate to work alongside you. I would like to thank Christopher Yau. Thank you for providing lots of valuable input and help. I appreciate that you always give a lot of advice during lab meetings and they were very helpful. I would like to thank Steve Mortin-Toth. Thank you for helping me with the set-up of new procedures and for the management mouse colonies. I really appreciate your meticulous management of lab resources and helpful guidance. I would like to acknowledge Jerry Shen. Thank you for being a very good labmate and for the constructive discussions. It was good to have a peer and a friend like you in the same lab. I would like to acknowledge my other fellow colleagues, Nyrie Israelian, Sabrin Michel, Dr. Alessandra de Paiva Granato, Dr. Shahab Meshkibaf, Frances Simbulan, and other current and past lab members of the Danska/Guidos Lab for all your support and input throughout my project. Special thanks to Dr. John Kappler and Frances Crawford at the Kappler/Marrack Lab at National Jewish Health in Denver, Colorado. Thank you for giving me the chance to visit and
iii teaching me the process of tetramer staining. I received a very warm welcome from the lab and had a great learning experience while visiting. I would also like to thank Philip Pratt at the Barbara Davis Center in University of Colorado for showing me the process of islet isolation. I would like to acknowledge all the funding sources that supported me through this project. I would like to thank Sickkids (including Restracomp), CIHR, Novo-Nordisk Studentship from Banting and Best Diabetes Centre at the University of Toronto, as well as the department of Immunology at the University of Toronto for all the financial support. Finally, I would like to thank my family and friends for all the love and support they have been giving me. To my parents, lots of love. To Dr. Ye Jiang, thank you for being alongside this entire journey. To Doris Zhu, thank you for being an amazing friend. I am truly grateful for having these amazing people in my life.
iv TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...... III TABLE OF CONTENTS ...... V LIST OF FIGURES ...... VIII LIST OF TABLES ...... IX LIST OF ABBREVIATIONS AND NOMENCLATURE ...... X CHAPTER 1: INTRODUCTION ...... 1 OVERVIEW ...... 2 INTRODUCTION ...... 2 1.1 CURRENT TREATMENTS AND MANAGEMENT OF T1D ...... 3 1.1.1 Exogenous insulin replacement therapy ...... 3 1.1.2 Regeneration of pancreatic tissue ...... 3 1.1.3 T1D immunotherapies ...... 4 1.1.4 Identifying T1D risk at pre-clinical stage ...... 5 MODIFIERS OF AUTOIMMUNE DIABETES ...... 6 1.2 GENETIC PREDISPOSITION TO AUTOIMMUNE DIABETES ...... 6 Major histocompatibility complex (MHC) class II haplotypes ...... 7 1.2.1 MHC class II haplotypes at risk in humans ...... 7 1.2.1.1 Single amino acid differences encoded by different HLA alleles contribute to T1D risk ...... 8 1.2.1.2 Combination of risk haplotypes contribute to T1D risk ...... 8 1.2.2 MHC class II molecules in NOD mice ...... 9 1.2.2.1 Peptide binding characteristics of H2-Ag7 in association with autoimmune diabetes risk ...... 10 1.2.2.2 Post-translational modifications of peptides facilitate high-risk MHC-peptide interactions ...... 10 1.2.3 Class I MHC alleles ...... 12 1.2.4 Non-MHC loci associated with T1D that modify T cell function ...... 13 1.3 ENVIRONMENTAL MODIFIERS OF AUTOIMMUNE DIABETES ...... 14 1.3.1 The gut microbiota modulated by the environment in the context of autoimmunity ...... 15 1.3.1.1 Gut microbial composition is modified by method of delivery and source of milk ...... 15 1.3.1.2 Geographical difference in gut microbial composition exists ...... 16 1.3.1.3 Manipulation of gut microbiota in NOD mice ...... 16 1.3.2 The gut microbiota alters the local and systemic immunological environment ...... 16 1.3.2.1 Specific bacterial strains modify host immune responses ...... 17 1.3.2.2 Microbial metabolites modulate host immune responses ...... 18 1.3.3 The gut microbiota as a potential modifier of autoimmune diabetes ...... 19 1.3.3.1 Correlations between abundances of certain bacteria taxa and T1D risk in humans ...... 19 1.3.3.2 Correlations between microbial abundances and autoimmune diabetes risk in NOD mice ...... 20 1.4 UNIQUE PROPERTIES OF NOD MICE IN THE CONTEXT OF AUTOIMMUNE DIABETES STUDY ...... 21 1.4.1 Age of onset and histopathology of insulitis ...... 21 1.4.2 Sex bias in autoimmune diabetes incidence ...... 22 1.4.2.1 Sex-specific protection from the disease in NOD mice is transferrable by microbes ...... 23 1.4.2.2 The link between gut microbes and disease risk in a sex-specific setting ...... 24 1.5 THESIS RATIONALE AND SUMMARY ...... 24 CHAPTER 2: MATERIALS AND METHODS ...... 26 2.1 MICE ...... 27 2.1.1 NOD mice and C57BL/6 mice ...... 27
v 2.1.2 NOD.BDC2.5 transgenic mice ...... 27 2.1.2.1 Genotyping transgenic mice by PCR ...... 27 2.2 MICROBIAL TRANSFER BY ORAL GAVAGE ...... 29 2.3 IN VITRO T CELL PROLIFERATION ASSAYS ...... 29 2.3.1 Allogenic T cell proliferation assay ...... 29 2.3.1.1 Generation of APCs by culturing bone marrow (BM) cells from C57BL/6 (B6) mice ...... 29 2.3.1.2 Isolation of NOD splenic T cells ...... 30 2.3.1.3 Labelling of T cells with carboxyfluorescein succinimidyl ester (CFSE) reporter dye ...... 31 2.3.1.4 C57BL/6 (B6) APCs and NOD T cell co-culture ...... 31 2.3.2 BDC2.5 T cell proliferation assay with BM-derived NOD APCs ...... 31 2.3.2.1 Generation of APCs by culturing bone marrow cells from NOD mice ...... 31 2.3.2.2 Peptide loading onto APCs ...... 32 2.3.2.3 Isolation of NOD.BDC2.5 splenic CD4+ T cells ...... 33 2.3.2.4 Labelling of NOD.BDC2.5 T cells with CFSE ...... 33 2.3.2.5 NOD APCs and NOD.BDC.2.5 T cell co-culture ...... 33 2.3.3 BDC2.5 T cell proliferation assay with splenic APCs ...... 34 2.3.3.1 Isolation of splenic APCs ...... 34 2.3.3.2 Preparation of splenic APCs and peptide loading before co-culture ...... 34 2.3.3.3 splenic APCs and NOD.BDC2.5 T cell co-culture ...... 35 2.4 IN VIVO T CELL ADOPTIVE TRANSFER AND PROLIFERATION ASSAYS ...... 35 2.4.1 NOD.BDC2.5 T cell adoptive transfer ...... 35 2.4.1.1 Isolation and CFSE-labelling of splenic NOD.BDC2.5 CD4+ T cells ...... 35 2.4.2.2 T cell adoptive transfer ...... 35 2.4.2.3 Flow cytometry analysis of T cell proliferation ...... 36 2.4.2 NOD.BDC2.5 T cell and NOD.CD45.2 polyclonal T cell co-transfer ...... 36 2.4.3 NOD.BDC2.5 T cell adoptive transfer into NOD.CD45.2 mice ...... 36 2.5 TETRAMER STAINING FOR INSULIN PEPTIDE-SPECIFIC T CELLS ...... 37 2.5.1 Isolation of pancreatic islets ...... 37 2.5.2 Tetramer staining ...... 38 2.6 ANALYSIS OF MURINE GUT MICROBIAL COMPOSITION BY SEQUENCING THE ENTIRE 16S RRNA GENE ...... 38 2.6.1 Amplification of full 16S rRNA genes with PCR in DNA extracted from gut microbes ...... 38 2.6.2 Purification of PCR products ...... 39 2.6.3 Insertion of PCR amplicons into cloning vectors ...... 40 2.6.4 Transformation of E. coli with vectors containing the insert ...... 40 2.6.5 Picking colonies for growth in liquid culture ...... 41 2.6.6 Plasmid extraction with miniprep ...... 41 2.6.7 Sanger sequencing with M13F and R primers ...... 41 2.6.8 Sequence analysis for phylogenetic relationships ...... 42 CHAPTER 3: RESULTS ...... 44 CHAPTER 3.1 ...... 45 THE IMPACT OF MICROBIAL TRANSFER ON EX VIVO PRESENTATION OF AN ISLET AUTOANTIGEN ...... 45 CHAPTER 3.2 ...... 61 THE IMPACT OF MICROBIAL TRANSFER ON THE ABILITY OF APCS TO PRESENT AUTOANTIGENS IN VIVO ...... 61 CHAPTER 3.3 ...... 71 IDENTIFICATION AND ENUMERATION OF INSULIN PEPTIDE-SPECIFIC T CELLS WITHIN ISLETS AND PERIPHERAL LYMPHOID TISSUES WITH TETRAMER STAINING ...... 71 Development of tetramer staining of islet cell suspensions to quantify insulin-specific T cells ...... 71 CHAPTER 3.4 ...... 79 TAXONOMIC ASSIGNMENTS OF GUT MICROBES FROM NOD WITH OR WITHOUT MICROBIAL TRANSFER ...... 79 BASED ON FULL-LENGTH 16S RRNA GENE SEQUENCING ...... 79
vi 3.4.1 Full-length 16S rRNA gene sequencing of S24-7 rich samples ...... 80 3.4.2 Dissecting the S24-7 family ...... 81 3.4.3 Full-length 16S rRNA gene sequencing of Lachnospiraceae rich samples ...... 82 CHAPTER 4: DISCUSSION ...... 93 4.1 DID MICROBIAL TRANSFER IMPACT AUTOANTIGEN PRESENTING CAPACITY OF APCS EX VIVO? ...... 94 4.1.1 Readiness of antigen presenting cells to present an islet autoantigen ...... 94 4.1.2 Antigen presenting capacities of APCs read by T cell proliferation ...... 97 4.1.3 Antigen presenting capacities of APCs read by T cell activation marker expression ...... 98 4.2 DID MICROBIAL TRANSFER MODULATE THE ACTIVATION OF AUTOANTIGEN-SPECIFIC T CELLS IN VIVO? ...... 99 4.3 ISLET ANTIGEN-SPECIFIC T CELLS AT THE SITE OF ACTION OF AUTOIMMUNE DIABETES ...... 101 4.4 WHAT DOES FULL-LENGTH 16S RRNA GENE SEQUENCING OF GUT MICROBES TELL US? ...... 104 4.4.1 What is Bacteroidetes S24-7 really? ...... 105 4.4.2 Presence of taxa that were sex-specific or linked to T1D risk or protection ...... 107 4.4.3 Limitations and contributions of full-length 16S sequencing to identification of the protective consortium ...... 108 4.5 THE BIG PICTURE ...... 111 REFERENCES ...... 116
vii LIST OF FIGURES Chapter 2 Figure 2.1. Genotyping the hemizygous NOD.BDC2.5 breeders from JAX. Figure 2.2. PCR products of full-length 16S rRNA gene. Figure 2.3. Insertion of full 16S rRNA gene into TOPO vector and cloning reaction.
Chapter 3.1 Figure 3.1.1. BDC2.5 CD4+ T cell harvest from BDC2.5 transgenic TCR-expressing mice. Figure 3.1.2. Alloreactive co-culture. Figure 3.1.3. BDC2.5-specific co-culture using bone marrow-derived APCs. Figure 3.1.4. Peripheral APCs from unmanipulated NOD females and MàF recipients. Figure 3.1.5. Immunopheotypes of enriched APC cell populations prior to co-culture. Figure 3.1.6. BDC2.5-specific co-culture using peripheral APCs.
Chapter 3.2 Figure 3.2.1. Frequencies and proliferation of adoptively transferred BDC2.5 CD4 T cells in control NOD females or MàF recipients. Figure 3.2.2. Proliferation of CFSE-labeled NOD CD45.2 polyclonal T cells and BDC2.5 T cells co-transferred into CD45.1 NOD recipients. Figure 3.2.3. Proliferation of CFSE-labeled BDC2.5 T cells in CD45.2 NOD recipients. Figure 3.2.4. Activation status of adoptively transferred BDC2.5 T cells.
Chapter 3.3 Figure 3.3.1. Identifying insulin peptide B9:23-specific T cells in islets and peripheral lymph nodes. Figure 3.3.2. Expression of CCR9, integrin β7 and CD25 on islet CD4+ T cells. Figure 3.3.3. Expression of CCR9, integrin β7 and CD25 among islet CD4 and CD8 T cells.
Chapter 4.5 Figure 4.5. Schematic diagram summarizing all approaches taken to address the effects of MàF microbial transfer along the entero-insular axis.
viii LIST OF TABLES Chapter 3.3 Table 3.3.1. Enumeration of tetramer-positive cells. Table 3.3.2. Enumeration of cells that expressed either CCR9, Integrin β7, CD25, and/or were tetramer-positive. Chapter 3.4 Table 3.4.1-1. Taxonomic assignments of microbes from the stool sample of a MàF recipient (ID:MF51STL). Table 3.4.1-2. Taxonomic assignments of microbes from the stool sample of an unmanipulated NOD mouse (ID: FP48STL). Table 3.4.2-1. Taxonomic classifications of Firmicutes from MF51STL assigned by SILVA compared to RDP. Table 3.4.2-2. Taxonomic classifications of S24-7 from MF51STL assigned by SILVA compared to RDP. Table 3.4.2-3. Taxonomic classifications of S24-7 from FP48STL assigned by SILVA compared to RDP. Table 3.4.3. Taxonomic assignments of microbes from cecal samples of two MàF recipients (MF51CC, MF52CC) and an unmanipulated NOD mouse (UN24CC).
Chapter 4 Table 4.4. Summary of taxa associated with protected states in female NOD mice from autoimmune diabetes.
ix LIST OF ABBREVIATIONS AND NOMENCLATURE Nomenclature MàF; MF Transfer of NOD male cecal microbiota into NOD females
Abbreviations T1D Type 1 diabetes APC Antigen-presenting cells mAbs Monoclonal antibodies G-CSF Granulocyte colony-stimulating factor GAD-alum L-glutamic acid decarboxylase in aluminum hydroxide GABA Gamma-amino butyric acid NOD Non-obese diabetic (mice) MHC Major histocompatibility complex HLA Human leuokocyte antigen HIP Hybrid insulin peptide ChrA Chromogranin A IAPP Islet amyloid polypeptide tTG transglutaminase NOR Non-obese resistant (mice) CTLA4 Cytotoxic T lymphocyte-associated protein 4 SNPs Single nucleotide polymorphisms GWAS Genome-wide association studies Idd Insulin-dependent diabetes PCR Polymerase chain reaction GF Germ-free mLN Mesenteric lymph node GALT Gut-associated lymphoid tissues SFB Segmented filamentous bacteria EAE Experimental autoimmune encephalopathy Treg Regulatory T cell DC Dendritic cell IDO Indoleamine 2,3-dioxygenase SCFAs Short-chain fatty acids GPCRs G-protein-coupled receptors HDACs Histone deacetylases pLN Pancreatic lymph node SPF Specific pathogen free RA Rheumatoid arthritis MS Multiple sclerosis RT Room temperature TCR T cell receptor SD Standard deviation Tg transgene PMA Phorbol myristate acetate CFSE Carboxyfluorescein succinimidyl ester
x FMO Fluorescence minus one GM-CSF Granulocyte-macrophage colony-stimulating factor IL Interleukin CV Coefficient of variation FRC Fibroblastic reticular cells FDC Follicular dendritic cells MRC Marginal reticular cells LtβR Lymphotoxin-β receptor TNFR Tumor necrosis factor receptor VCAM Vascular cell adhesion protein ICAM Intercellular adhesion molecule cDC Conventional dendritic cell moDC Monocyte-derived dendritic cell LFA Lymphocyte function-associated antigen HFD High-fat diet RNA Ribonucleic acid
xi
CHAPTER 1: Introduction
OVERVIEW The main objective of my thesis was to investigate the mechanisms of protection from autoimmune diabetes conferred in female NOD mice by transfer of male cecal microbes. The Introduction discusses modifiers of autoimmune diabetes in both human and mice known to date, the association between the environment, gut microbial composition and diabetes risk as well as immunological mechanisms underlying autoimmune diabetes pathogenesis. The Results section presents comparison of islet antigen-specific T cells responses in protected recipients of male microbiota versus non-manipulated controls both in vivo and in vitro. In the Discussion, I interpret my observations in the context of microbe-host crosstalk and diabetes protection as previously observed.
INTRODUCTION Type 1 diabetes (T1D) is an autoimmune disease that results in insulin deficiency due to autoimmune destruction of pancreatic β cells. Insulin deficiency leads to hyperglycemia, or abnormally high blood glucose levels, which causes metabolic abnormalities and further complications if left untreated. T1D is diagnosed predominantly in children and adolescents. To manage the disease, these young patients need to exert a substantial effort in self-management1, including obeying dietary restrictions2-4, carbohydrate counting4,5, insulin administration and monitoring blood glucose levels2-4. Often patients are too young to grasp a comprehensive understanding of the importance and the procedures, therefore family support is largely involved in patient care for T1D2,3. Many affected children describe the disease management as being disruptive to their lives2,6-8. T1D poses a substantial burden not only to those who are affected but also to the healthcare system in large due to its prevalence in Western society, particularly Scandinavian countries and North America9-11. It is estimated that over 500,000 children live with type 1 diabetes worldwide as of 20159,10. In the past few decades, rise in T1D incidence has been reported in many epidemiological studies12-14. The most recently published study on T1D incidence trend reports a 1.8% increase per year in T1D incidence among youths younger than 20 years of age in the United States between 2002 and 201215.
2 1.1 Current treatments and management of T1D 1.1.1 Exogenous insulin replacement therapy T1D can be controlled by restoring normal blood glucose levels through exogenous insulin replacement therapy, most commonly through self-administered injections or by computerized insulin pumps16,17. The discovery of insulin in 1921 led to a long search for the most physiological forms of exogenous insulin, and the development of its short-acting and long- acting analogues18 have significantly prolonged life expectancy of T1D patients17. Despite the effort of mimicking endogenous insulin release pattern as a physiological response to blood sugar levels, exogenous insulin administration cannot prevent episodes of hyperglycemia and hypoglycemia from occurring17. Furthermore, exogenous insulin is not the cure for the disease as pancreatic β cells are not restored and life-long exogenous insulin supply is needed.
1.1.2 Regeneration of pancreatic tissue Recent advances in T1D treatments focus on restoring or preserving β cell mass and its function of insulin secretion. One strategy is to regenerate pancreatic tissue in patients, through transplantation of pancreas or islet tissues19 either from human donors or from cellular grafts with bioengineered scaffolds16. Since human donor pancreas and islet tissues are scarce20, current research focuses on expanding insulin-producing cells from humans, either through somatic cell reprogramming21-24 or in vivo organ generation17,25,26. Alternatively, xenograft of porcine islets into T1D patients have been proposed to solve the scarcity issue. This approach is promising given the fact that porcine islets are structurally and physiologically compatible with humans27,28. Bioengineered scaffolds or capsules are being developed to encapsulate grafts from human or porcine sources to protect the grafts from immune attacks from the host29,30. An encapsulated porcine insulin-producing cell product has advanced into clinical trials in Argentina and New Zealand17,31; its interim clinical outcomes showed significant reduction in insulin use and glycated hemoglobin levels, which are indicative of average blood glucose level over a period32,33. Islet or pancreas transplantation still faces several major problems such as lack of oxygenation, blood coagulation on the surfaces of grafts and complement activation16. New strategies have been devised to tackle these hurdles, such as incorporating anti-coagulants34,35 and oxygen carriers36 into surfaces of capsules, as well as co-encapsulating anti-inflammatory drugs to reduce blood-mediated inflammatory responses37.
3 1.1.3 T1D immunotherapies Another strategy to preserve β cell mass and function is to modulate host immune reactivity in the islets. Immune reactivity against islet antigens is initiated when autoreactive T cells recognize islet autoantigens in the context of high-risk major histocompatibility complex (MHC) haplotypes on antigen presenting cells (APCs) in islets and draining pancreatic lymph nodes38. Islet-reactive T cells become activated and localize to islets, where CD8+ T cells provoke β cell death through perforin/granzyme B-mediated cytotoxicity and CD4+ T cells engage with other cell types to create a pro-inflammatory, insulitis-promoting environment39. Early T1D immunotherapies aimed to halt T cell-mediated β cell destruction by directly inhibiting effector T cell function, i.e. with anti-CD3 monoclonal antibodies (mAbs)40-43, which induce T cell tolerance44. C-peptides, a cleavage product generated during insulin release and whose levels positively correlate with β cell function, were preserved in several anti-CD3 mAb trials40-43,45 except for one46. However, the protective effects were limited to delaying but not ablating loss of β cell function, and efficacy depended on the level of residual β cell function in the individuals47-50. Other T cell-targeting strategies, such as blocking T cell co-stimulation and activation with abatacept51 and depleting T effector and effector memory cells with alefacept52 have also achieved similar short-term efficacies in delaying the loss of β cell function51,52. Recent attention on T1D immunotherapy has also been paid to restoring the local milieu in the pancreas from a pro-inflammatory state to a physiological and β cell-promoting steady state47. Treatments with pro-inflammatory cytokine antagonists alone have not yielded protection except with TNF⍺ decoy receptor53,54. However, the use of pro-inflammatory cytokine antagonists or anti-inflammatory agents in combination with lymphocyte-targeting drugs has been proposed47,55. Other promising combination immunotherapies include anti- thymocyte globulin (ATG) in combination with granulocyte colony stimulating factor (G- CSF)56, as well as L-glutamic acid decarboxylase in aluminum hydroxide (GAD-alum)57 in combination with anti-inflammatory agents such as gamma-amino butyric acid (GABA)58. ATG and G-CSF deplete peripheral T cells59 and promote steady state in innate immune cells60, respectively. GAD-alum aims to induce tolerance11 to the common autoantigen GAD61 and steers the Th1-like pro-inflammatory response towards an anti-inflammatory Th2-like response48,62.
4 1.1.4 Identifying T1D risk at pre-clinical stage Identification of populations at high risk of developing T1D helps to improve treatment outcomes, since the higher the level of residual β cell function is at baseline, the better the treatment efficacies47-50. Moreover, the identification of these high-risk individuals presents a window of opportunity for prophylactic measures that halt their progression to disease. Several screening strategies have been employed to identify populations at risk for T1D63. Individuals with first and second-degree relatives who have T1D have approximately 10 to 20-fold increased risk for developing T1D63,64, indicating genetic contribution to T1D risk, however this criterion only captures 10-15% of individuals that later develop T1D63. As studies on genetic predispositions to T1D progress further, at-risk genotypes are identified and are screened for in order to better identify susceptible individuals65. More recent screening strategies take advantage of the common appearance of islet autoantibodies prior to T1D onset63. Islet autoantibodies are antibodies against islet autoantigens such as insulin and GAD 65 (GADA). The appearance of islet autoantibodies, an immunological event also known as seroconversion, reflects the ongoing immunological events leading up to disease onset, where autoreactive B cells receive help from the activated autoreactive CD4+ T cells. The presence of islet autoantibodies against two or more islet autoantigens is strongly predictive of future T1D development66. The time window between seroconversion to multiple islet autoantibodies and clinical onset of T1D varies, with an approximately 11% risk of progression to T1D per year over ten years in individuals with multiple islet autoantibodies63,67. Therefore, the optimal prophylactic measure is to catch this window of opportunity after being identified as at risk but before clinical onset and to intervene or halt disease progression. To date, several prevention trials have been devised for this purpose. Although definitive proof of efficacy has not been obtained, removal of dietary elements such as cow’s milk proteins and bovine insulin in infant formula has been associated with a decrease in the risk for islet autoantibody seroconversion68-70. Moreover, among antigen-specific prevention trials that aim to steer immune responses from pro-inflammatory to tolerogenic, such as the abovementioned GAD-alum approach, oral insulin intake was associated with delayed T1D development in individuals with high insulin autoantibody titres70-72. In order to better understand and identify indicators of disease risk and prophylactic measures, T1D risk factors and modifiers have been extensively explored. The studies in animal models of autoimmune diabetes contributed fundamentally to understanding these factors. One
5 model of interest is the non-obese diabetic (NOD) mouse. NOD mice are an inbred strain derived from the Cataract Shionogi strain73. NOD mice are prone to develop spontaneous autoimmune diabetes as a result of T cell-mediated destruction of pancreatic β cells similar to that in humans. NOD mice share a plethora of similarities in T1D risk and development with those in humans while maintaining several distinct differences, nevertheless making the strain an ideal model for T1D studies.
Modifiers of autoimmune diabetes The next part of Introduction discusses modifiers of autoimmune diabetes common to humans and NOD mice, as well as their implications in disease intervention.
1.2 Genetic predisposition to autoimmune diabetes The genetic contribution to T1D risk in humans is revealed by twin studies. In a primarily genetics-influenced disease, the concordance rate, or the rate of disease development in twins, is higher in monozygotic twins than in dizygotic twins74. Twin studies on T1D to date have consistently shown a higher concordance rate in monozygotic twins than in dizygotic twins, indicating a genetic predisposition to T1D74-76. However, the reported concordance rates in monozygotic twins are far below 100%74-77, indicating that other non-germline factors, ie. somatic genetic recombination and/or environmental factors, modify the risk to develop T1D74. Due to the multi-factorial nature of T1D and a lack of clear inheritance patterns74,75,77,78, multiple genetic approaches have been utilized to identify candidate genes contributing to the disease risk in humans. One approach is to apply linkage analyses that identify genetic regions shared more often than by chance in affected members in the same families77-79. Linkage analyses are effective at identifying infrequent alleles that have a large effect size on disease determination77. Furthermore, linkage studies require the recruitment of families with aggregated T1D incidences. However, T1D is found to be multi-genic and the disease risk for siblings of T1D patients is about 6% compared to 0.4% risk in the general population79, indicating that affected sibling pairs are relatively infrequent77. Therefore, T1D linkage studies in humans have limitations. Another approach is to conduct association studies to identify alleles at loci of interest whose presence correlates with disease status80. Association studies are
6 more powerful at identifying alleles with moderate effect sizes for defining disease risk, but require loci of interest to be determined beforehand, ie. with linkage studies77,81. A more recent genetic approach to find T1D-associated genes in humans has taken association studies genome- wide, an approach known as GWAS, without the requirement for prior knowledge on loci of interest, therefore allowing the discovery of novel T1D-associated gene regions 82-84. As in humans, autoimmune diabetes in NOD mice is multi-genic. The genetic predispositions were mapped with linkage analyses and susceptibility loci were identified in the insulin-dependent diabetes (Idd) regions in the murine genome associated with autoimmune diabetes risk85. The identification of genetic susceptibilities in NOD mice provides corroborating evidence for several causal genetic variants identified in humans and lends insights to gene functions not yet identified in novel T1D-associated loci in humans. I will now examine several genetic susceptibilities discovered in both humans and in NOD mice.
Major histocompatibility complex (MHC) class II haplotypes 1.2.1 MHC class II haplotypes at risk in humans Both linkage and association studies in humans revealed the largest genetic contribution to T1D risk at the human leukocyte antigen (HLA) region on chromosome 6p2179,81,86,87. The HLA region encompasses genes encoding for both class I and class II MHC molecules. Class II MHC molecules are heterodimers composed of ⍺ and β chains encoded by separate loci in the HLA region. Class II MHC molecules interact with the T cell co-receptor CD4, present antigen peptides and activate cognate CD4+ T cells. Class II MHC molecules consist of three types: DR, DP and DQ. The ⍺ chain of DR is monophorphic while the β chain of DR encoded by DRB1 is polymorphic. In addition, the ⍺ and β chains of DP and DQ encoded by DPA1, DPB1, DQA1 and DQB1, respectively, are all polymorphic. Specific combinations of alleles at these loci inherited from a single parent are termed HLA haplotypes. Specific haplotypes, particularly specific combinations of DRB1 and DQB1 alleles, have been associated with significant T1D risk or protection87,88, as quantified by odds ratio (OR). OR associates the presence of the genetic trait with the disease outcome89; an OR greater than 1 associates the genetic trait with T1D risk while an OR less than 1 associates the genetic trait with T1D protection. For instance, among haplotypes that contain the DQA1 allele 03:01 (DQA1*03:01), the combination of DRB1*04:01 and DQB1*03:02 confers T1D risk with an
7 OR of 8.39 whereas the combination of DRB1*04:03 and DQB1*03:02 confers T1D protection with an OR of 0.2787,88, indicating that specific DRB1 alleles confers susceptibility to T1D87. Similarly, among haplotypes that only differ by DQB1 alleles, 03:02 allele confers T1D susceptibility when compared to other DQB1 alleles such as 03:0187.
1.2.1.1 Single amino acid differences encoded by different HLA alleles contribute to T1D risk Single amino acid differences can be mapped in the products of these protective versus susceptible alleles. A significant amino acid difference found between the DQ β chains encoded by the protective DQB1*03:01 versus the susceptible 03:02 is the presence of an Asp residue versus an Ala residue at codon 57, respectively88,90,91. The residue at codon 57 sits in the HLA- DQ peptide binding groove and makes contact with bound peptides at position 992,93. The negatively charged Asp residue encoded by the protective 03:01 allele forms a salt bridge with an Arg residue at codon 79 of the ⍺ chain92-94. It is thought that both the nature of the residue, which determines the accepted peptides, and its interaction with the ⍺ chain95,96 play a role in conferring T1D protection versus susceptibility97. The peptide groove encoded by the susceptible 03:02 allele prefers an acidic, negatively charged residue at position 9, while the protective Asp57 itself is acidic and repels acidic residues92. Intriguingly, NOD mice also lack the protective Asp57 in their H2-Ag7 MHC class II molecule94,97. This similarity will be explored further later in this section.
1.2.1.2 Combination of risk haplotypes contribute to T1D risk Certain human heterozygous genotypes with DRB1*03 and DRB1*04-containing haplotypes, referred to as DR3 and DR4 haplotypes, respectively, confer significant T1D risk98. The DR3 and DR4 haplotypes each embodies a set of DQA1 and DQB1 alleles. Due to the presence of two different sets of DQA1 and DQB1 alleles, individuals with DR3/DR4 heterozygous genotype are capable of expressing four different DQ heterodimers either encoded in cis, ie. the DQ ⍺ and β chains are encoded on the same haplotype, or in trans, ie. encoded on different haplotypes. All four combinations are associated with T1D susceptibility with the greatest risk conferred by DQA1*05:01 allele on the DR3 haplotype in combination with DQB1*03:02 allele on the DR4 haplotype99. These two high risk alleles were never observed to be encoded in cis99. In other words, the DQ ⍺ chain product encoded by the DR3 haplotype heterodimerizes with the
8 DQ β chain encoded by the DR4 haplotype in an individual with a heterozygous genotype DR3/DR4. This particular trans-complementation confers greater risk than ⍺ and β chains encoded by alleles in cis or with low risk DQA1 and DQB1 alleles in trans87,88,99. Whether the high-risk trans-encoded DQ heterodimer selects for specific diabetogenic peptides remains a hypothesis, however this same heterodimer was found to be more effective at presenting peptides that could also bind cis-encoded DQ molecules in the context of celiac disease100. Studies on autoreactive T cell responses against specific islet autoantigens in both humans and in mice also shed light on peptide selection in the context of high-risk MHC and will be discussed in section 1.2.ii.
1.2.2 MHC class II molecules in NOD mice NOD mice express the high-risk H2-Ag7 MHC class II molecules encoded in the Idd1 region on chromosome 17. The presence of Idd1 is necessary for the development of autoimmune diabetes in NOD mice85,101,102; however, when expressed in another disease-resistant strain, H2-Ag7 by itself is not sufficient to induce insulitis or autoimmune diabetes, suggesting disease-modifying roles played by non-MHC molecules103. The H2-Ag7 molecule is a heterodimer encoded by an Aad allele and a unique Abg7 allele85, the product of the latter is a homolog of that encoded by the human DQB1*03:02. As in humans, NOD mice lack the protective Asp57 residue; in addition, NOD H2-Ag7 bears a His residue at codon 56 rather than a commonly conserved Pro residue 104. Substituting His56 with Pro56 was reported to confer protection in NOD mice105. Moreover, expression of a non-Ag7 molecules confers protection in NOD mice85,105-107. The presence of these uncommon residues in and proximal to the H2-Ag7 binding groove determines its peptide binding capacity94. H2-Ag7 molecules are observed to bind synthetic or phage-display peptides rather promiscuously, ie. by allowing degenerate anchor residues94, and with low affinity108. However, H2-Ag7 MHCs do selectively bind to peptides with acidic residues at the carboxyl end, although still in the µM range (ie. relatively weak), when peptides are naturally processed and the preference was also seen with a human high-risk HLA-DQ109.
9 1.2.2.1 Peptide binding characteristics of H2-Ag7 in association with autoimmune diabetes risk The peptide binding characteristics of H2-Ag7 have implications in presentation of islet autoantigens to autoreactive T cells. First, many epitopes of islet autoantigens such as GAD 65 contain acidic residues at the carboxyl end110. Second, low affinity binding to peptides generates unstable MHC-peptide complexes and is associated with a shorter half-life on the surface of APCs compared to other MHC class II haplotypes108,111. Third, low affinity binding to autogenic peptides may allow conformational flexibility in the binding position, as seen with more than one binding registers in the insulin peptide (B9:23) bound to H2-Ag7 molecule112. These characteristics have been hypothesized to play a role in facilitating islet autoreactive T cells to escape from negative selection, which is a central tolerance mechanism that deletes autoreactive T cell clones upon strong-affinity binding to MHC-autoantigen complexes in the thymus 94,108,113. Furthermore, the explanation to the discrepancy between selectivity for synthetic versus naturally processed peptides may lie within the way peptides are loaded, as proposed in the case of insulin reactivity114. The processing of insulin protein fragments by APCs through the endocytic compartments eliminates unstable peptide-MHC complexes (ie. quality control) and only allows a few complexes with restricted peptide registers to be presented on the surface114. Autoreactive T cell clones recognizing these peptide-MHC complexes are highly negatively selected115. However, soluble insulin peptides engage with MHC in unique, unstable peptide registers and generate a more diverse repertoire of peptide-MHC complexes that would not pass quality control by the endocytic compartments114,116. The insulin-specific T cell clones that escape negative selection reportedly react to these soluble insulin peptides (B9:23) bound to H2- Ag7 ref. 114-116.
1.2.2.2 Post-translational modifications of peptides facilitate high-risk MHC-peptide interactions The H2-Ag7 in NOD mice and a high-risk MHC class II haplotype (DQA1*03:01 and DQB*03:02, also known as the DQ8 haplotype) in humans and have both been reported to bind and present post-translationally modified peptides117-121. Specifically, a hybrid insulin peptide (HIP) recognized by H2-Ag7 was identified as a fragment of the C-peptide covalently joint to a fragment of the cleavage product (known as WE14) of the secretory granule protein chromogranin A(ChrA)117. The C-peptide fragment sits in the N-terminal positions of the H2-
10 Ag7 peptide binding groove while WE14 occupies the rest of the C-terminal positions of the groove117. This HIP-MHC complex is capable of activating NOD-derived autoreactive T cell clones, ie. BDC2.5, that reportedly react to wildtype WE14; in fact, the fusion peptide is required at 10,000-fold less concentration than the wildtype peptide for autoreactive T cell activation117, indicating its potency as an autoantigen. Another HIP that is a conjugate of C- peptide fragment and a fragment of an islet antigen called islet amyloid polypeptide (IAPP) is also capable of activating another autoreactive NOD-derived T cell clone117. More importantly, these HIPs are naturally present in murine β cells and some HIP variants are naturally present in humans with the high-risk haplotype117. In the same study, the cognate autoreactive T cells were shown to be well represented in NOD mice117. The identification of autoreactive T cells responding to these HIPs was achieved by using class II tetramers, which are four HIP-MHC complexes conjugated to a fluorophore, that bind TCRs on cognate T cells and enumerated by flow cytometry. Other post-translational peptide modifications have been observed to be favored by the interaction between the peptide binding groove of the human high-risk DQ haplotype and autoreactive T cells. These include deamidation of glutamine into glutamic acid, particularly at anchor position 9120, which is favored by the peptide binding groove encoded by the high-risk HLA haplotypes and the H2-Ag7 in NOD mice92. The reaction is catalyzed by the enzyme called tissue transglutaminase (tTG) and its role in generating cross-linked autoantigen has been explored in the context of celiac disease122. This post-translational modification was observed in a proinsulin peptide, a prominent islet autoantigen, among several other candidate epitopes that could bind the MHC and be a substrate of tTG120. In addition, formation of a disulfide bridge in an insulin A-chain peptide118 and citrullination of GAD 65 peptides123 have been noted as post- translational modifications that are involved in islet autoantigen modification and recognition. Therefore, post-translational modification of peptides serves as another promising mechanism implicated in breaching tolerance by improving peptide binding to high-risk MHC haplotypes120 and potency to activate autoreactive T cells117,118,120,121.
In this thesis, CD4+ T cell responses against specific islet antigens are studied using T cells expressing transgenic TCR derived from the islet autoreactive T cell clone BDC2.5. The T cells respond to a BDC2.5 mimotope presented on the H2-Ag7 MHC class II molecules on
11 APCs. The mimotope is a strong agonist peptide that can activate BDC2.5 T cell proliferative response with peptide concentrations at nanomolar range124, comparable to the potency of the HIP peptide as previously described117.
1.2.3 Class I MHC alleles Class I MHC molecules comprise of an ⍺ chain, with its two domains forming the peptide- binding groove, and a structural protein called β2-microglobulin. Class I MHC molecules interact with CD8 co-receptor on CD8+ T cells, present antigens to and activate CD8+ T cells. Although often masked by the strong genetic associations between MHC class II haplotypes and autoimmune diabetes risk, class I MHC alleles have been associated both corroboratively and independently with disease risk both in humans and in NOD mice85,88. In humans, class I MHC molecules are HLA-A, B and C. Alleles of both HLA-A and B have been associated with T1D risk, while HLA-C alleles appear to play little role in conferring disease risk or protection88. Among HLA-A alleles, the A*24:02 allele play a significant role in lowering age of T1D onset. Among HLA-B alleles, B*39:06 is significantly correlated with disease risk with an OR of 10.31, while B*57:01 is associated with T1D protection with an OR of 0.19125. The association of Type 1 alleles to disease risk is often modified by the context of class II DR haplotypes but not in the case of the B*39:06 allele, which is associated with disease risk in all DR haplotypes125. In NOD mice, class I MHC molecules are encoded by H2Kd and H2Db. The products of these alleles dimerize with a β2m encoded in another locus to form functional class I molecules. The presence of class I molecules is necessary for the development of autoimmune diabetes in NOD mice, as mice that are null for β2m expression and therefore deficient in class I molecules do not develop diabetes126. Furthermore, the β2m in NOD mice is an “a” type isoform and differs from the “b” type isoform in a closely related mouse strain, the non-obese resistant (NOR) strain, which is resistant to diabetes127. Expression of the b type isoform in NOD mice conferred resistance to diabetes128. The two isoforms differ by only one amino acid128, yet this variation alters the conformation of the class I molecule127, offering a possible explanation to its role in the diabetogenic process.
12 1.2.4 Non-MHC loci associated with T1D that modify T cell function Over 30 susceptibility loci other than those encoding MHC molecules have been identified in humans using the GWAS approach and by linkage analyses in NOD mice85,129. Among these loci several were found to confer autoimmune diabetes risk in both humans and in NOD mice through modifying T cell functions. One of those is the CTLA4 locus encoding the cytotoxic T lymphocyte-associated protein 4 (CTLA4) expressed on the surface of T cells. CTLA-4 is encoded in the region on chromosome 2q33 that has been associated with risk in not only T1D but also Graves’ disease and autoimmune hypothyroidism130. CTLA4 binds to the co-stimulatory molecules CD80/CD86 expressed on APCs and inhibits T cell activation. During T cell activation, CD80/CD86 engage with the co-stimulatory receptor CD28 expressed on T cells. CTLA4 has a higher affinity for CD80/CD86 than CD28 and binds to them with a higher avidity, allowing it to compete with CD28 for binding to CD80/CD86, which contributes to its inhibitory function131. Single nucleotide polymorphisms (SNPs) exist at the 3’ and 5’ ends of the CTLA4 transcript and are weakly associated with T1D risk in humans with an OR of 1.15130. It was found that the levels of soluble CTLA4 (sCTLA4) molecules are lower in individuals with at-risk haplotypes for Graves’ disease130. sCTLA4 molecules are presented in human serum and are implicated in negative regulation of T cell activation, therefore lower levels of sCTLA4 may contribute to autoimmune processes130. Interestingly, CTLA4 encoded in Idd5.1 in NOD mice was also identified as a susceptibility gene; NOD mice also express lower levels of a splice variant of CTLA4 known as ligand-independent CTLA4 (liCTLA4)130. liCTLA4 is also implicated in producing negative signals in T cell activation132. In mice, the levels of liCTLA4 are proposed to be dependent on an SNP in the coding region130. Another category of genes that confer susceptibility to autoimmune diabetes in both humans and in NOD mice is Interleukin 2-associated genes. These include the IL2 gene itself and IL2RA, which encodes CD25, a subunit of the receptor for Interleukin 2 (IL2) important for T cell survival, especially for regulatory T cells133. In humans, IL2RA SNPs were associated with T1D risk134. In NOD mice, IL2 is encoded in Idd3.1. Supplying exogenous IL-2 to NOD mice prevented diabetes development by promoting survival of regulatory T cells135. Other novel genes associated with T1D as discovered by GWAS act as candidate genes for further studies in NOD mice and many more are implicated in modifying T cell activation or function. Autoreactive T cell activation is a collective result of aberrant central tolerance, ie.
13 negative or positive selection through abnormal TCR signaling strength, or peripheral tolerance, as monitored by regulatory T cells and co-stimulation status. In summary, genetics play a profound role in determining the risk to develop autoimmune diabetes. The largest single locus contribution in both humans and in NOD mice comes from the class II MHC haplotypes, while other genes associated with T cell function or antigen presentation by APC contribute to the autoimmune process of islet autoreactive T cells leaking to the periphery and becoming activated. Many cell types other than T cells, particularly APCs, play a significant role in determining the antigenicity of the local milieu and will be examined in a later section. Although genetics accounts for a major contribution to autoimmune diabetes risk, data from twin studies in humans74, the fact that T1D lacks a clear inheritance pattern74,75,77,78, as well as an increase in T1D incidence over a time span of only a few decades12-14, all indicate environmental modifiers of disease risk. A striking example of environmental factors contributing to T1D risk is the difference in T1D incidence on the two sides of the Finland and Russian Karelian border136. The two neighboring populations share similar HLA-DQ haplotypes but differ in socioeconomic conditions; T1D incidence is almost six times higher in Finland than in Russian Karelia136. Environmental factors such as hygiene and diet are influenced by socioeconomic status and were therefore suggested to play a role in the difference in T1D incidence in these two regions136. In NOD mice, the hygiene status also affects autoimmune diabetes incidence. In the next section, I examine environmental modifiers of autoimmune diabetes in humans and in mice.
1.3 Environmental modifiers of autoimmune diabetes Environmental factors such as diet, hygiene and use of antibiotics directly impact the resident population of bacteria, virus and fungi living on the gut mucosal surface within the body, which are collectively termed the gut microbiota. The gut mucosal surface is a major interface where host-microbial interactions occur. This microbial-immune system crosstalk modulates not only the immunological milieu at the gut microbial interface but also influences more distal sites, including the pancreas. I now examine this interaction by breaking it down into three parts: first, how environmental factors change the gut microbial composition; second, how changes in gut
14 microbial composition modulate the immunological milieu; and third, how changes in gut microbial composition may modify risk for autoimmune diabetes.
1.3.1 The gut microbiota modulated by the environment in the context of autoimmunity The gut microbiota is the largest community of microorganisms that reside on or within the human body, consisting of 1014 microbial cells137,138. Gut microbial composition can be analyzed by extracting the DNA from intestinal or fecal material, PCR-amplifying bacterial 16S rRNA gene regions, high-throughput sequencing of the PCR amplicons and sequence analysis by clustering reads with identical taxonomic assignments into operational taxonomic units, or OTUs, thus providing phylogenetic relationships. Such culture-independent, deep sequencing methods allow us to delineate the composition of this complex community and gain insights into differential microbial abundances associated with immune regulation.
1.3.1.1 Gut microbial composition is modified by method of delivery and source of milk In humans, gut microbial composition during infancy is largely influenced by the method of delivery139 and the diet source. Babies delivered vaginally inherit an early gut microbial composition that resembles that of their mothers compared to babies born by Cesarean section139,140. In a study that compared the gut microbiota between the newborns and their mothers, 72% of gut microbial members in the neonates were found in the gut community of their mothers, whereas in C-section delivered babies this percentage dropped to 41%139. Moreover, the gut microbial communities of C-section delivered babies comprise a more heterogenous bacterial taxa that resemble microbes of skin and oral origin and are associated with the delivery environment139. Dietary changes modulate the composition and gene content of the microbiota141,142, as different phyla are specialized at metabolizing different sources of nutrients. The gut microbiota during the first year of life is modulated fundamentally by the source of milk and the cessation breast feeding. Breast-fed babies acquire gut microbial communities enriched in taxa such as Bifidobacterium and Lactobacillus, which are found in breast milk143 and utilize lactic acid139. Their gut microbiota become more mature once breastfeeding stops, with enrichment of taxa such as Bacteroides and Clostridium found in
15 adults139. The juvenile onset of T1D in humans coincides with the period of massive remodeling of the microbiome.
1.3.1.2 Geographical difference in gut microbial composition exists Geographical differences also exist in terms of gut microbial composition. Among neighboring Finnish, Estonian and Russian populations, which share similar HLA haplotypes but differ in T1D incidence, the Finnish and Estonian subjects are consistently colonized by the Bacteroidetes phylum and the Bacteroides genus among other microbes, while the Russian subjects exhibit higher plasticity of their microbial composition140. In the same study, differential association between bacterial taxa and bacterial gene content used for human milk oligosaccharide utilization pathways were observed among Finnish and Russian children140. In Finnish subjects, Bacteriodetes are a major contributor to those genes while in Russian subjects, the major source of such genes is Bifidobacterium140. Although the presence of Bifidobacterium is associated with breastfeeding, the observed contrast in gene utilization was not due to longer breastfeeding among Russian subjects140.
1.3.1.3 Manipulation of gut microbiota in NOD mice Environmental perturbations to the murine gut microbiota can be carried out in a more controlled environment and are useful for studies that examine the relationship between environmental factors, gut microbial strains and certain diseases including autoimmune diabetes. Germ-free (GF) mice and gnotobiotic mice who are ex-GF and are colonized with known bacterial strains provide insights to host-microbial relationships specific for that strain or that microbial community. In NOD mice, delivery by C-section versus vaginal birth gives rise to distinct gut microbiota at weaning144. Moreover, neonatal manipulation of the gut microbiota by administering antibiotics can significantly impact the microbial composition in long term and has been associated with increased145 or decreased146 T1D risk.
1.3.2 The gut microbiota alters the local and systemic immunological environment The gut microbiota both actively educates and receives signals from the host immune system. The presence of specific bacterial strains and microbial metabolites are required for shaping not
16 only gut immunity in the host but also at distal sites147,148. Gut immune tissues include mesenteric lymph nodes (mLN), Peyer’s patches and isolated lymphoid follicles149, where T cells and B cells are activated by the presentation of microbial and dietary antigens on APCs. In addition, small aggregations of lineage-negative progenitor-like150 lymphoid cells known as cryptopatches151 are a member of the gut immune tissues, which are collectively termed gut- associated lymphoid tissues (GALT). Moreover, lymphocytes residing in the lamina propria and intraepithelial cells constitute another arm of gut immunity149,151. The presence of a specific bacterium can modulate intestinal T cell subset fates149.
1.3.2.1 Specific bacterial strains modify host immune responses Segmented filamentous bacteria (SFB), for example, are required for T helper cells differentiating into the Th17 subset152,153, which is important for intestinal resistance to pathogens but is also implicated in autoimmunity154. Specifically, in the context of autoimmune diabetes, the presence of SFB in NOD mice conferred diabetes protection in NOD females, whereas the males were not affected155. The protection was associated with increased populations of Th17 cells in the small intestinal lamina propria in the NOD females155 and was hypothesized to involve the inhibition of Th1 responses as previously reported156. In other autoimmune models, such as experimental autoimmune encephalopathy (EAE) models for multiple sclerosis, gnotobiotic mice that were colonized only with SFB had increased pathogenic Th17 cells not only in the small intestine but also in the spinal cord148,157. Multiple bacterial strains and microbial products have been found to be potent regulatory T cell (Treg) inducers. Specifically, polysaccharide A from Bacteroides fragilis enhances Tregs that produce the anti-inflammatory cytokine IL-10 and confers protection in the EAE mouse model and the experimental colitis model. In EAE, the effect is exerted by enhancing a subset of dendritic cells (DCs)158 that provide instructions for naïve CD4+ T cells to develop into Tregs159. Furthermore, several Clostridia groups have also been demonstrated as Treg inducers160,161. Clostridia are spore-forming Gram-positive bacteria that belong to the Firmicutes phylum and are grouped into clusters due to their heterogeneity162. Clusters IV and XIVa specifically act as major regulators of gut homeostasis160,163,164. Members of Clostridium clusters IV and XIVa isolated from murine160 or human161 fecal material were able to induce Treg differentiation in gnotobiotic mice likely through stimulating intestinal epithelial cells to produce the TGF-β160,161 and other Treg-promoting factors such as indoleamine 2,3-dioxygenase (IDO)160.
17 1.3.2.2 Microbial metabolites modulate host immune responses Diet-derived microbial metabolites such as short-chain fatty acids (SCFAs) are also potent immunomodulators. SCFAs are generated by fermentation and breakdown of dietary fibers in the colon148. SCFAs act as an energy source for colonic epithelial cells165,166. In addition, they are ligands of G-protein-coupled receptors (GPCRs) and exert anti-inflammatory modulations through inhibition of NFkB activation167-169. In addition, SCFAs are largely taken up by colonic epithelial cells through diffusion or through transporters168,170 and modulate gene transcription by inhibiting the activity of histone deacetylases (HDACs)171. SCFAs enhance the functions and number of colonic Tregs through modulation of HDAC expression in a GPCR-dependent manner172. SCFAs can protect mice from T cell-transfer-induced colitis172. Intriguingly, members of Clostridium clusters IV and XIVa produce SCFAs, especially the SCFA butyrate173. In addition to its anti-inflammatory and Treg-inducing properties, butyrate plays a role in maintaining gut integrity by facilitating tight junction assembly174. SCFA levels increased in gnotobiotic mice transferred with Clostridium species and SCFAs alone induced TGF-β expression in intestinal epithelial cells, suggesting a role of SCFA in the ability of Clostridia strains to induce Tregs161. In addition to regulating local immune cell function and the immunogenic state in the gut, SCFAs present in peripheral blood modulate antigen-presenting cells through a GPCR- dependent mechanism and regulate immunological environments at distal sites175. Specifically, propionate-treated mice responded to airway allergens with a dampened inflammatory response: a subset of DCs and also CD4+ T cells in their lung-draining lymph nodes exhibited lower activation states175. Moreover, mice treated with propionate produced more DC precursors and lung DCs that are less capable of inducing Th2 CD4+ T cell responses175. In the context of autoimmune diabetes, treatment with the SCFA acetate or diets enriched for butyrate or acetate release reduced autoimmune diabetes incidence in NOD females, which exhibited expanded Treg populations in the colon and in the spleen169. Mice on the acetate-yielding diet also exhibited decreased antigen presenting capacity of APCs in the pancreatic lymph node (pLN), which was measured by proliferation of labeled and adoptively transferred autoreactive T cells of a single specificity against an islet antigen169. The activation states of the APCs, specifically B cells and DCs, were decreased in mice fed with acetate-enriched diets or in the presence of butyrate, respectively, as measured by levels of co-stimulatory molecule CD80 and CD86
18 expression and MHC molecule expression169. Notably, the diet enriched in acetate altered the resident microbial community composition by enhancing Bacteroides abundance, whose members are mainly acetate and propionate producers169,176,177. Moreover, intraperitoneal administration of butyrate in female NOD mice was shown to increase production of cathelicidin-related antimicrobial peptide in the pancreas, which directly modulates immune infiltrate in the pancreas and reduce incidence of autoimmune diabetes178. Such an effect of diet, microbial composition and microbial metabolites on immunogenic environment in the pLN and diabetes incidence reflects an entero-insular axis149, that is a physical proximity169 or cellular and biochemical connection between the gut and the pancreas and its associated tissues.
1.3.3 The gut microbiota as a potential modifier of autoimmune diabetes The presence of a link between gut microbiota and risk to develop autoimmune diabetes is supported by the hygiene hypothesis, which correlates the observed increased incidence of autoimmune diseases with decreased exposure to microbes as reflected by decreased incidence of infectious diseases179. The comparison of T1D incidences and gut microbial composition between the Russian and Finland subjects136,140 provides a piece of correlational evidence in humans that supports this theory.
1.3.3.1 Correlations between abundances of certain bacteria taxa and T1D risk in humans In attempt to further define characteristics and composition of a microbial community in the human gut that is correlated with T1D susceptibility, the fecal microbial compositions were characterized by 16S rRNA sequencing and were compared between age-matched and HLA haplotype-matched children who seroconverted and those who did not. An increase in the abundance of the Bacteroidetes phylum was observed longitudinally in four children who eventually seroconverted, whereas a decrease in the abundance of the Firmicutes phylum was observed180. The inverse trend was observed in control subjects who did not seroconvert180. Further phylogenetic classification identified bacteria at species level that were differentially abundant in seroconverted versus non-seroconverted children. Notably, several Bacteroides species such as B. ovatus accounted for the increased abundance of the Bacteroidetes phylum in
19 seroconverted children180. Among the species that were more abundant in non-seroconverted children, Facalibacterium prausnitzii180 is a butyrate producer belonging to Clostridium cluster IV with anti-inflammatory properties181. Another study on a different cohort of subjects corroborated the finding that the Bacteroidetes phylum is more abundant in children who seroconverted, compared to age-matched and HLA haplotype-matched children who did not seroconvert165. Moreover, reduced community diversity was observed in the gut microbial composition of seroconverted children in both studies165,180 . Further analysis at the species level revealed an inverse correlation between autoantibody seropositivity and abundances of SCFA- producing species, ie. F. prausnitizii, Bifidobacterium adolescentis and Roseburia faecis165. Bifidobacterium are acetate- and lactate-producing bacteria, whereas R. faecis belongs to Clostridium cluster XIVa and produces butyrate165. Of note, butyrate can be derived from lactate as a fermentation product, therefore Bifidobacterium can also contribute to butyrate production in the gut165,182. The inverse correlation found between SCFA-producing bacteria and seroconversion status, as well as the immunomodulatory properties of SCFAs, together suggest regulatory roles of these bacteria in immunological homeostasis may be associated with T1D progression but further studies are required to demonstrate a causal relationship.
1.3.3.2 Correlations between microbial abundances and autoimmune diabetes risk in NOD mice Studies using the NOD model shed light to a more defined role of gut microbiota in T1D incidence. The interaction between the environment and T1D incidence is evident in the NOD mice by comparing autoimmune diabetes incidences among different housing conditions. Female conventional mice housed in standard lab environment have autoimmune diabetes incidences at around 50%. When the next generation was C-section derived into a SPF environment, autoimmune diabetes incidence increased in each of the two successive generations compared to the parents, indicating the cleaner the facility, the higher the incidence183,184. Manipulation of gut microbial composition by antibiotic treatments produces profound impacts on type 1 diabetes incidence in NOD mice depending on the type of antibiotics and treatment time. Vancomycin, which is an antibiotic targeting mainly Gram-positive bacteria, potentiates autoimmune diabetes incidence when administered prenatally185, while it is reported to decrease the disease incidence when administered from birth until weaning186. Treatment with
20 vancomycin decreases abundances of members of the Clostridales order and members of Lachnospiraceae, Rikenellaceae, Prevotellaceae families, accompanied by increased abundances of Proteobacteria149,185,187. The protection or risk conferred by vancomycin treatment is correlated with an increase or decrease in Th17 cells, respectively146,188. Neomycin targeting Gram-negative bacteria alone or with an antibiotic cocktail is reported to decrease autoimmune diabetes incidence in NOD mice when administered prenatally185,189. Gram-negative-targeting antibiotic cocktail treatment conversely decreased Proteobacteria abundances but increased Lachnospiraceae and Coriobacteriaceae abundances149,189. Moreover, the conferred protection was associated with tolerogenic APC phenotypes that were less able to induce BDC2.5 T cell responses185. Therefore, antibiotic-induced dysbiosis can impact autoimmune diabetes risk in NOD mice.
1.4 Unique properties of NOD mice in the context of autoimmune diabetes study Although humans and the NOD mouse model share many features of T1D, autoimmune diabetes in NOD mice has some unique characteristics. These characteristics may be utilized to provide further insights to the disease rather than being merely dissimilarities that may pose road blocks to translating findings from the animal model to humans.
1.4.1 Age of onset and histopathology of insulitis One unique property related to autoimmune diabetes in NOD mice is the age of disease onset. While T1D in humans is commonly diagnosed in young children and adolescents, NOD mice commonly develop spontaneous autoimmune diabetes in adulthood beyond 10 weeks183. The progression towards disease is initiated when DCs and other innate immune cells begin to infiltrate into the pancreas at 3 weeks of age190-192, followed by infiltration of lymphocytes at 4-7 weeks193. The accumulation of immune cells on the periphery of the islets initiates peri-insulitis at 8-11 weeks of age, although β cell mass usually does not erode194 until beyond 12 weeks193,195 prior to autoimmune diabetes onset. It is worth noticing that although β cell loss is T cell- mediated in both humans and in mice, the histopathology of insulitis is different, as the amount of T cell infiltration193,196 and the percent of islets affected appear to be milder197,198 compared to the mouse mode. Moreover, peri-insulitis and massive accumulation of immune cells in the islet
21 periphery is not apparent in humans even when T1D onset has occurred193. The presence of overt insulitis in NOD mice preceding autoimmune diabetes development helps in studies to grade progression towards disease. Moreover, as the disease onset usually occurs in adulthood in NOD mice, the relatively long pre-diabetic stage enables studies on the immune mechanisms leading up to disease progression or protection.
1.4.2 Sex bias in autoimmune diabetes incidence The NOD model under the SPF condition exhibits a characteristic distinct from humans: there is a 2:1 female-to-male sex bias in autoimmune diabetes incidence184. NOD females under SPF conditions have a disease incidence of around 80%, while the incidence in males is halved183. The sex bias intersects with hygiene status, as NOD mice under a germ-free (GF) condition no longer exhibits the 2:1 female-to-male bias in the disease incidence, which lies at around 60%183. In NOD mice, sex poses an impact on the gut microbiota, as the gut microbial compositions in SPF NOD males and females differ post-puberty (6 weeks)183,199. Our lab previously identified certain male-typic taxa, or taxa identified by 16S rRNA sequencing to be more abundant in males, including Roseburia, Blautia and Coprococcus 1183. In a separate study, male-typic taxa were also identified, although these differentially abundant taxa only slightly overlapped with those identified by our lab183,199. Specifically, Peptococcus was trending towards male-typic in the study by our lab and was significantly male-typic in the other study183,199. Moreover, circulating levels of butyrate and acetate, the two prominent SCFAs, are found to be significantly lower in NOD females than in NOD males169. Therefore, whether an association exists between sex-specific gut microbiome and autoimmune diabetes risk or protection has been of interest to many groups. There is yet another layer to the story: The sex bias in disease incidence is associated with the level of the sex hormone testosterone183, as castrated NOD males exhibit higher disease incidence200 while administering the androgen to NOD females protect them from the disease183,201. GF status, which abrogates the sex bias in disease incidence, is also associated with decreased and increased levels of testosterone in NOD males and females, respectively183. Testosterone levels play a role in defining sex-specific microbial compositions, as microbiota of castrated males clustered more closely to females than to unmanipulated males199. Moreover, colonization of GF males with SPF female cecal microbiota still resulted in a microbiota
22 segregated from that in ex-GF females that similarly received SPF female microbiota, indicating the role of testosterone in defining male-typic microbiota199.
1.4.2.1 Sex-specific protection from the disease in NOD mice is transferrable by microbes Our lab has previously demonstrated that transfer of adult NOD male cecal microbiota into young NOD females (MàF) confers protection from autoimmune diabetes183. These MàF recipients received the microbial transfer at weanling age that led to a persistent change in the gut microbial composition beyond 10 weeks after the transfer183. Specifically, when comparing MàF recipients with control females at 14 weeks of age, certain taxonomic groups that fall under the lachnospiraceae family, including several male-typic taxa such as Roseburia and a non-male-typic Lachno I. S. were increased due to MàF transfer, whereas several other taxa decreased in abundance183. Alongside this change in gut microbial composition were significantly lower T1D incidences in MàF recipients compared to unmanipulated females and females receiving female microbiota183. This protection exerted effects early on in the time frame: as early as 7 weeks, there was an increase in testosterone levels in the MàF recipients183. The elevated levels of testosterone persisted onto 14 weeks, along with lower insulin auto- antibody titers and lower insulitis severity compared to the controls183. Testosterone played a causal role in this protection, since antagonizing testosterone signaling abrogated these protective phenotypes and failed to decrease T1D incidence183. Further host metabolite analysis by our lab on the MàF recipients revealed correlations between bacterial abundances and specific host serum metabolite levels that were altered due to microbial transfer in a testosterone-dependent manner202. A group of sphingomyelins, which are lipid metabolites enriched in myelin and not produced by microbes203,204, decreased in abundance in protected recipients and were inversely correlated with the abundances of bacterial taxa belonging to families Clostridiaceae, Ruminococcaceae, Rikenellaceae, Prevotellaceae and Verrucomicrobiaceae202. These findings suggest that sex-specific microbial communities were able to exert influence over host metabolite levels and directly or indirectly over host autoimmune actions against β cells. The exactly mechanism by which changes in the microbial composition translate into dampened T cell-mediated autoimmunity and protection from type 1 diabetes is of particular interest to this thesis.
23 1.4.2.2 The link between gut microbes and disease risk in a sex-specific setting Although the sex bias in autoimmune diabetes incidence is unique to NOD mice, studies on sex- specific alterations to autoimmunity progression may provide insights to autoimmunity in humans, which do exhibit sexual dimorphism in many autoimmune diseases such as rheumatoid arthritis (RA) and multiple sclerosis (MS)205. Both RA and MS display higher prevalence in females than in males205. Levels of sex hormones play a role in both diseases, as the diseases ameliorate during later stages of pregnancy but flare post-partum when levels of estrogen and progesterone decline205-207. Although T1D in humans does not exhibit sexual dimorphism in disease prevalence, several sex-specific characteristics do exist. Firstly, sex-specific associations were reported between seroconversion and abundances of specific bacterial taxa in the gut. Particularly, in males, seroconversion is correlated with increased abundances of the Bacteroides genus but this association is not found in females165. Secondly, the disease risk is higher for an individual whose father has T1D compared to one whose mother is affected202,208. Thirdly, the prevalence of autoantibodies against insulin (IAA) in patients with onset at adolescent age is higher in males than in females202,209. Therefore, studies on immune mechanisms behind gut microbial protection from autoimmune diabetes in NOD mice provides a sex-specific context for further research on sex-specific characteristics in T1D presentation and associated gut microbiota, as well as the sexual dimorphism in human autoimmune diseases.
1.5 Thesis Rationale and Summary Autoimmune diabetes risk in humans and in mice is influenced by environmental factors that are capable of producing a profound impact on gut microbiota. The gut microbiota is capable of modulating the host immune system at both local intestinal sites and distal sites. The gut microbiota is therefore an ideal target of intervention that may allow us to halt or reverse progression to autoimmune diabetes in at-risk populations. NOD mice spontaneously develop autoimmune diabetes and recapitulate many characteristics of the human disease, including the presence of environmental modifiers. Furthermore, the disease risk intersects with hygiene status, gut microbial composition and sex. Previous work from our lab has shown protection from T1D by MàF microbial transfer in NOD mice. Such protection exerted effects long before the age of onset, giving rise to lower insulitis severity and lower insulin auto-antibody titers in recipients at 14weeks. This observation points to modulated islet antigen-specific T
24 helper responses. Whether microbial transfer altered the immunogenic capacity of antigen presentation cells in the local milieu or attenuated autoreactive T cell proliferation and T cell responses remains to be elucidated. Therefore, my two aims are to investigate whether MàF microbial transfer leads to: 1: Altered ability of APCs to present autoantigens to CD4+ T cells; 2: Altered frequencies of islet-Ag specific CD4+ T cells at effector sites. Chapter 3.1 describes modulation of the antigen presentation milieu by microbial transfer as measured by islet antigen-specific CD4+ T cell responses. Chapter 3.2 visualizes proliferation and activation of islet antigen-specific T cells at sites proximal to pancreas. Chapter 3.3 presents insulin peptide-specific T cell frequencies within the islets and in peripheral lymphoid tissues identified by tetramer staining. Chapter 3.4 examines taxonomy assignments of gut microbes from MàF recipients and control females based on full-length 16S rRNA sequences. Chapter 4 discusses implications of the observations in microbe-dependent regulation of autoimmune diabetes in NOD mice.
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CHAPTER 2: Materials and Methods
26 2.1 Mice 2.1.1 NOD mice and C57BL/6 mice All NOD/Jsd (NOD) mice and C57BL/6 (B6) mice were maintained in the Barrier site at Peter Gilgan Centre for Research and Learning (Research Institute of The Hospital for Sick Children in Toronto) under Specific Pathogen Free (SPF) condition. The animals received energy-rich diet (Teklad 7904) and autoclaved water. All staff wore protective gears (gowns, caps and crocs) in the Barrier site. Animal welfare is monitored according the animal use protocol (AUP). Husbandry tasks were carried out in the laminar flow hood.
2.1.2 NOD.BDC2.5 transgenic mice NOD.Cg-Tg(TcraBDC2.5, TcrbBDC2.5)1Doi/DoiJ hemizygous breeding pairs were purchased from the Jackson Laboratory (JAX stock #004460)210. The BDC2.5 TCR transgenes consist of rearranged TCR⍺ and TCRβ constructs. The TCR⍺ construct bears V⍺1 while the TCRβ construct bears Vβ4210. Offspring from hemizygous breeding pairs are genotyped by standard PCR and are determined to be either carrying at least one copy of the transgene (Tg+) or none at all (Tg-). In Tg+ mice, 35%-75% V⍺1 transcripts come from the transgene and the expression of Vβ4 is reported to be greater than 95%210. The expression of Vβ4 on T cells was also confirmed by flow cytometry analysis in Tg+ T cell donors.
2.1.2.1 Genotyping transgenic mice by PCR NOD.BDC2.5 mice were maintained as hemizygotes. The breeding strategy was to pair Tg+ mice with Tg- mice. Mice were genotyped for the presence of the transgene by a set of primers that generates a 100base-pair (bp) product found within the transgene. The primers were olMR3844 (5’ CAT GTT TCC CTG CAC ATC AG 3’) and olMR3845 (5’ CCA GAT CCA AAG ATG AGT TGC 3’) (please refer to JAX#004460 protocol: Tg(TcraBDC2.5)1Doi, Tg(TcrbBDC2.5)2Doi). In addition, an internal positive control was employed and is a 200bp product generated in all NOD/LtSz mice (Tg- or +) by a set of primers olMR8744 (5’ CAA ATG TTG CTT GTC TGG TG 3’) and olMR8745 (5’ GTC AGT CGA GTG CAC AGT TT 3’). The control primers were also provided by JAX under the same genotyping protocol.
27 Tails were clipped from dams before weaning. Genomic DNA was extracted using the DNeasy Blood & Tissue kit (Qiagen). DNA concentrations were measured by nanodrop. For each PCR reaction, the mix contained 50ng of DNA, 0.5µM of each primer, 12.5uL of multiplex solution and 3uL (5x Q solution) from the multiplex PCR kit (Qiagen) for a final volume of 25uL. The PCR conditions consist of 94◦C for 3 minutes, 35 cycles of 94◦C for 30 seconds followed by 55◦C for 1 minute and 72◦C for 1 minute, and 72◦C for 2 minutes before 10◦C hold (please refer to JAX#004460 protocol). In addition to samples and positive control, a negative control reaction with water in place of DNA was included. PCR products were visualized by gel electrophoresis using 1.5% agarose gel. 100bp ladder was run simultaneously to measure base- pair size. An example of the genotyping gel is show in Figure 2.1.
Figure 2.1. Genotyping the hemizygous NOD.BDC2.5 breeders from JAX. All four hemizygous breeders were Tg+. PCR generated a 100bp product within the transgene and a 200bp internal control product in the genomic DNA of each breeder. This genotyping was carried out together with another master’s student (Jie Li Shen) in our laboratory, as both of us were using this strain.
28 2.2 Microbial transfer by oral gavage Cecal contents were collected from adult NOD males (8-14 weeks) that were freshly sacrificed. Ten milliliters of RO water were added to the cecal contents, which were then passed through a 70µm cell strainer into a 50mL Falcon tube using the plunger of a syringe. An additional 15mL of RO water was added to the mixture to reach a final volume of 25mL, equivalent to dilution of cecal contents by a factor of 50. Diluted cecal contents were then given to mice freshly weaned using a 1mL slip-tip syringe and gavage needle. All recipients received 200µL of the diluted cecal contents. The gavage process was repeated on the second day.
2.3 In vitro T cell proliferation assays 2.3.1 Allogenic T cell proliferation assay 2.3.1.1 Generation of APCs by culturing bone marrow (BM) cells from C57BL/6 (B6) mice Bone marrow cells were isolated from the femur and tibia of adult B6 mice (8-14 weeks old). Single cell suspensions were made in complete media consisting of RPMI-1640 supplemented with 10% sterile-filtered fetal bovine serum (FBS), 55µM (β)-mercaptoethanol, 2mM L- glutamine, 0.1mM non-essential aminal acids, 1mM sodium pyruvate, 30mM HEPEs (all from Life Technologies; all concentrations listed were final concentrations). Cells were depleted of red blood cells using Gey’s Balanced Salt solution and passed through a 70µm cell strainer. The number of live cells were counted in trypan blue solution (dead cells were distinguished by being stained blue). Bone marrow cells were then plated at a concentration of 1 million cell/mL in each T75 flask (BD Falcon) for a final volume of 20mL in complete media. IL-4 (Cedarlane) and granulocyte-macrophage colony stimulating factor (GM-CSF) (PeproTech) were added to the BM culture both at a final concentration of 20ng/mL. BM cells were cultured for four days before media change and replenishment of IL-4 and GM-CSF at the same concentration. During media change, cells suspended in old media were spun down and re-suspended in new media before being added back into the flask. Lipopolysaccharides (LPS) (Sigma-Aldrich) was added to BM cultures at a final concentration of 100ng/mL on day 5 for stimulation of APCs overnight. On day 6, LPS-stimulated APCs were harvested from the flasks. Cells attached to the flasks were washed with 5mL PBS, then incubated with 5mL PBS+5% EDTA in the 37◦C incubator for 10 minutes before being scraped
29 off and pooled with cells suspended in the supernatant. Cells were counted and analyzed with flow cytometry. For flow cytometry analysis, 2 million cells per staining tube were first blocked with anti-FcR antibodies (clone 2.4G2), then stained with antibodies against CD11b (clone M1/70), CD11c (clone N418), CD80 (clone 16-10A1), CD86(clone GL1) and MHC class II (I-Ab) (clone M5/114.15.2). The channels occupied on the flow cytometer by these antibodies were PE-Cy7, FITC, BV421, APC and PerCPCy5.5, respectively. A fluorescence minus one (FMO) control for each channel was included in the analysis. Cell viability was measured by staining with DAPI right before analysis on the flow cytometry machine.
2.3.1.2 Isolation of NOD splenic T cells Spleens of age-matched NOD mice were harvested and single cell suspensions depleted of red blood cells (RBCs) were made. The pan-T cell isolation kit (Miltenyi Biotec) was used to negatively select for T cells. The kit consists of an antibody cocktail with biotin-conjugated antibodies that bind unwanted cell types (cells expressing one or more of the following markers: CD11b, CD11c, CD19, B220, DX5, CD105, anti-MHC class II, Ter-119)211. Magnetic beads in the kit are coated with anti-biotin antibodies211 and bind biotin-conjugated antibodies which coat the unwanted cell types. In the first step, 10uL of the antibody cocktail and 40uL of staining media (SM; HBSS supplemented with 2% calf serum) per 10 million cells were added. Cells were incubated at 4◦C for 10 minutes. Step 2 follows without washing; 20uL of anti-biotin beads and 30uL SM per 10 million cells were added for another 10-minute incubation at 4◦C211. Before magnetic separation, cells were diluted to a final volume of 3mL and passed through a sterile 70µm cell strainer to prevent aggregates. During magnetic separation, the beads retain antibody-bound cells and allow unbound cells to be negatively selected into an unlabelled fraction. On the Automacs ProSeparator machine, the program selected was “DepleteS”. The purity of T cells in the unlabelled fraction was verified by flow cytometry analysis of CD3 (anti- CD3 conjugated to PE; clone 145-2C11) versus CD19 expression (anti-CD19 conjugated to PE- Cy7; clone 1D3).
30 2.3.1.3 Labelling of T cells with carboxyfluorescein succinimidyl ester (CFSE) reporter dye T cells from the unlabelled fraction from magnetic separation were counted. CFSE stock solution at 5mM was prepared from the CellTrace CFSE Cell Proliferation Kit (ThermoFisher Scientific) by addition of 18uL DMSO into a stock tube. One milliliter (1mL) of CFSE at a final concentration of 10nM is made per 1 million T cells to be stained. The diluent is PBS pre- warmed to 37◦C. T cells were re-suspended in the 10nM CFSE solution and were incubated in the 37◦C incubator for 15 minutes. The staining reaction is stopped with five times the volume of complete media. The T cells were serum-underlaid and washed twice before re-count and plating. CFSE staining was verified by flow cytometry analysis of an aliquot of these cells.
2.3.1.4 C57BL/6 (B6) APCs and NOD T cell co-culture B6 BM-derived APCs were plated into a 96-well round-bottomed plate at 0.25 million/mL, with each well receiving 100µL equivalent of 25,000 BM-derived cells. NOD T cells stained with CFSE were plated at 1 million/mL for 50µL per well. Therefore, each well received 25,000 BM- derived APCs and 50,000 CFSE-labelled T cells. A positive control for T cell proliferation was included by stimulating 50,000 T cells in a well with 5ng/mL PMA and 50ng/mL ionomycin (both from Sigma-Aldrich) for 4 hours, which were then washed with fresh media and supplemented with 20U/well IL-2 (eBioscience-Affymetrix). The plate was cultured in the 37◦C incubator. On Day 3 of the co-culture, each well was harvested into a staining tube for flow cytometry analysis. The cells were blocked with anti-FcR antibodies, then stained with against CD3 (clone 145-2C11), CD45.2 (clone 104) and CD25 (clone PC61.5). The channels occupied were Alexa647, APC-eFluor 780 and PE-Cy7, respectively, while proliferation was measured by CFSE dilution peak and cell viability measured by staining with DAPI.
2.3.2 BDC2.5 T cell proliferation assay with BM-derived NOD APCs 2.3.2.1 Generation of APCs by culturing bone marrow cells from NOD mice Bone marrow cells were harvested from NOD mice using the same method described previously for the harvest of BM cells from B6 mice.
31 NOD BM cells were cultured for four days before media change and replenishment of IL-4 and GM-CSF at the same concentration. During media change, cells suspended in old media were spun down and re-suspended in new media before being added back into the flask. On Day 5, cells were harvested from the flasks. Cells attached to the flasks were washed with 5mL PBS, then incubated with 5mL PBS+5% EDTA in the 37◦C incubator for 10 minutes before being scraped off and pooled with cells suspended in the supernatant. Cells were washed and re-suspended in complete media at 1 million/mL with 100ng/mL LPS, 20ng/mL IL-4 and 20ng/mL GM-CSF. Cells were then plated into a 96-well round-bottomed plate at 0.25 million/mL, with each well receiving 100µL of cells for a total of 25,000 BM-derived APCs. After stimulation with LPS overnight, APCs were analyzed with flow cytometry. For flow cytometry analysis, 2 million cells per staining tube were first blocked with anti-FcR antibodies, then stained with antibodies against CD11b (clone M1/70), CD11c (clone N418), CD80 (clone 16-10A1), CD86 (clone GL1) and MHC class II (H2-Ag7) (clone 10-3.6). The channels occupied were PE-Cy7, PE, BV421, APC and FITC, respectively. This panel was later modified to include anti-F4/80 antibodies (clone BM8) in PE and to replace anti-CD11c in PE with Alexa700 (clone N418). A fluorescence minus one (FMO) control for each channel was included in the analysis. Cell viability was measured by staining with DAPI right before analysis on the flow cytometry machine.
2.3.2.2 Peptide loading onto APCs BDC2.5 mimotope (Anaspec) consists of the sequence (H- RTR PLW VRM E -OH) and constituted a stock solution in DMSO at 1µg/µL. The scrambled version of BDC2.5 mimotope (customized from Anaspec) consists of the sequence (H- RWP RLM RTE V -OH) and also constituted a stock solution in DMSO at 1ug/uL. Further dilutions were made with complete media on the day of the co-culture; diluted peptides of either kind were added to individual wells on Day 5 after the cells were plated into the 96-well plate. The final peptide concentrations were in ng/mL range as indicated in the Results section.
32 2.3.2.3 Isolation of NOD.BDC2.5 splenic CD4+ T cells Spleens of age-matched NOD.BDC2.5 mice were harvested and single cell suspensions depleted of red blood cells (RBCs) were made. The CD4+ T cell isolation kit for mice (Miltenyi Biotec) was used to negatively select for CD4+ T cells. The kit consists of an antibody cocktail with biotin-conjugated antibodies that bind unwanted cell types (cells expressing one or more of the following markers: CD8a, CD11b, CD11c, CD19, B220, DX5, CD105, anti-MHC class II, Ter- 119)212. The steps of applying the kit (staining with the antibody cocktail and anti-biotin beads) and running the automacs ProSeparator remain the same as previously described for pan-T cell isolation kit. The purity of T cells in the negatively selected fraction was verified by flow cytometry analysis with antibodies against CD3 (Alexa647; clone 145-2C11), CD4 (PE; clone RM4-5) and CD19 (PE-Cy7; clone 1D3).
2.3.2.4 Labelling of NOD.BDC2.5 T cells with CFSE The method of labeling NOD.BDC2.5 T cells with CFSE is the same as previously described in 2.3.1.3.
2.3.2.5 NOD APCs and NOD.BDC.2.5 T cell co-culture BM-derived NOD APCs that received peptide loading and LPS-stimulation overnight in the 96- well plate were washed by addition of 150uL of complete media into the plate with a multichannel, spun down at 400g for 5 minutes, then re-suspended in 100uL complete media. NOD T cells stained with CFSE were plated at 1 million/mL for 50uL per well. Therefore, each well received 25,000 BM-derived APCs and 50,000 CFSE-labelled T cells. In addition, a positive control with 4-hour PMA (5 ng/mL) and ionomycin (50 ng/mL) stimulation was again included. The plate was cultured in the 37◦C incubator. On Day 3 of the co-culture, each well was harvested into a staining tube for flow cytometry analysis. The cells were blocked with anti-FcR antibodies, then stained with against CD3 (clone 145-2C11), CD25 (clone PC61.5) and CD69 (clone H1.2F3). The channels occupied were Alexa647, PE-Cy7 and PE-CF594, respectively, while proliferation was measured by CFSE dilution peak and cell viability measured by staining with DAPI.
33 2.3.3 BDC2.5 T cell proliferation assay with splenic APCs 2.3.3.1 Isolation of splenic APCs Spleens of unmanipulated or MàF NOD females at 11-14 weeks of age were harvested. Each spleen was injected with 0.5mL collagenase D solution at 2mg/mL (Sigma-Aldrich) and cut into pieces in a sterile petri dish containing 5mL of the same collagenase D solution. The tissue and the solution were then transferred into a 15mL Falcon tube for 30-minute digestion in the 37◦C incubator. Following incubation, the solution and tissue were passed through a 70µm cell strainer. The residual pieces on the cell strainer were disintegrated using the plunger of a 1mL syringe and were washed through the cell strainer using staining media (SM; HBSS supplemented with 2% calf serum). The single cell suspension was then depleted of RBCs with Gey’s solution and counted. The cells were blocked with anti-FcR antibodies for 30 minutes and stained with biotin-conjugated antibodies against CD3 (clone 145-2C11), CD19 (clone 1D3) and Nkp46 (clone29A1.4) for 30 minutes. The cells were then washed with SM and were resuspended in 20uL of anti-biotin beads and 30uL of SM per 10 million cells. Following a 30- minute incubation at 4◦C, cells were diluted to 3mL, passed through a 70µm cell strainer and run through the Automacs ProSeparator machine using the “DepleteS” program. The unlabelled fraction was analyzed by flow cytometry with two panels. The first panel interrogated the effectiveness of enrichment of CD11b+ and CD11c+ cells. An aliquot of cells from the unlabelled fraction was stained with antibodies specific for CD3 (clone 145-2C11), CD19 (clone 1D3), CD11b (clone M1/70), CD11c (clone N418) and PDCA-1 (clone 927). The channels occupied were PE, BV605, PE-Cy7, Alexa700 and Alexa488, respectively. The second panel examined the readiness for antigen presentation and stained with antibodies specific for CD86 (clone: GL1) and MHC class II (H2-Ag7) (clone: 10-3.6) in addition to CD11b and CD11c. The channels occupied were APC, FITC, PE-Cy7 and Alexa700, respectively. FMO controls were used for all markers except CD3 and CD19. Cell viability was measured by staining with DAPI right before analysis on the flow cytometry machine.
2.3.3.2 Preparation of splenic APCs and peptide loading before co-culture Splenic APCs enriched by negative selection were counted and re-suspended with complete media supplemented with 20ng/mL IL-4 and GM-CSF. The cells were then plated at 0.50 million/mL for 100uL per well and a total of 50,000 cells per well. BDC2.5 mimotopes and
34 scrambled peptides were added to individual wells in the same manner as described in 2.3.2.2. For experiments that examined whether LPS stimulation was necessary for preparation of these APCs, certain wells received LPS at a final concentration of 100 ng/mL, same as in co-culture experiments using BM-derived APCs. The APCs were incubated overnight before being co- cultured with T cells.
2.3.3.3 splenic APCs and NOD.BDC2.5 T cell co-culture Splenic APCs cultured and load with peptides overnight were washed with 150µL complete media and re-suspended in 100µL complete media. Their readiness to present antigens was assessed again with flow cytometry using the second panel described in 2.3.3.1. NOD.BDC2.5 T cells were isolated and stained for CFSE using the same method as described in 2.3 and 2.4. T cells were added at 1 million/mL to the wells containing splenic APCs and each well received 50µL of T cells for a total of 50,000 T cells. Therefore, each well again received 25,000 splenic APCs and 50,000 CFSE-labelled T cells. The plate was cultured in the 37◦C incubator. On Day 4 of the co-culture, each well was harvested into a staining tube for flow cytometry analysis. The cells were blocked with anti-FcR antibodies, then stained with against CD3 (clone 145-2C11), CD25 (clone PC61.5) and CD69 (clone H1.2F3). The channels occupied were Alexa647, PE-Cy7 and PE-CF594, respectively, while proliferation was measured by CFSE dilution peak and cell viability measured by staining with DAPI.
2.4 In vivo T cell adoptive transfer and proliferation assays 2.4.1 NOD.BDC2.5 T cell adoptive transfer 2.4.1.1 Isolation and CFSE-labelling of splenic NOD.BDC2.5 CD4+ T cells The method of isolating and labeling CD4+ NOD.BDC2.5 T cells was the same as previously described in 2.3.2.3 and 2.3.2.4.
2.4.2.2 T cell adoptive transfer CFSE-labelled NOD.BDC2.5 T cells were counted and re-suspended in pre-warmed sterile PBS. Each injection composed of 5 million T cells in 300uL PBS in a 1mL slip-tip Norm-Ject syringe
35 (Fisher Scientific). Mice receiving T cells were put under anesthesia with isofluorane. Tails were warmed up using a tail lamp. Each mouse receives the injection at the tail vein.
2.4.2.3 Flow cytometry analysis of T cell proliferation On Day 6 post-T cell adoptive transfer, mice were sacrificed with isofluorane overdose and dissected right away. The pancreas was harvested first: it was placed into a cryomold covered by OCT (VWR) and frozen right away in 2-butylmethane on dry ice. The cecal contents were harvested from the cecum and frozen on dry ice. The spleen, pLN and mLN were then harvested. Single cell suspensions were made from the spleen and the lymph nodes. Cells were blocked with anti-FcR antibodies and then stained with antibodies specific for CD3 (clone 145- 2C11), CD25 (clone PC61.5) and CD69 (clone H1.2F3). The channels occupied were Alexa647, PE-Cy7 and PE-CF594, respectively, while proliferation was measured by CFSE dilution peak. This panel was later expanded to include antibodies against CD4 (clone RM4-5), TCRVb4 (clone KT4), CD62L (clone MEL-14) and CD44 (clone IM7). The additional channels included BV605, PE, ef450 and Alexa700, respectively.
2.4.2 NOD.BDC2.5 T cell and NOD.CD45.2 polyclonal T cell co-transfer In the co-transfer experiments, polyclonal CD4+ T cells from unmanipulated NOD mice age- matched to NOD.BDC2.5 T cell donors were isolated and stained with CFSE using the same method as described in 2.3 and 2.4. During T cell adoptive transfer, 5 million BDC2.5 T cells and 5 million NOD.CD45.2 T cells were co-transferred in 300uL of PBS per injection. Flow cytometry analysis of T cell proliferation employed the same method as described in 1.3 with the addition of an extra antibody against CD45.2 (clone 104) in channel APC-efluor 780. Cell viability was measured by staining with DAPI right before analysis on the flow cytometry machine.
2.4.3 NOD.BDC2.5 T cell adoptive transfer into NOD.CD45.2 mice The method for this set of experiments is the same as the one described in 1.1-1.3 except that the recipients of the T cell adoptive transfer were NOD.CD45.2 mice. The flow cytometry analysis again included an additional antibody against CD45.1(clone A20) in channel APC-efluor 780.
36
2.5 Tetramer staining for insulin peptide-specific T cells 2.5.1 Isolation of pancreatic islets Mice were anesthetized by isofluorane and sacrificed by terminal bleed with cardiac puncture one at a time. The abdomen of each mouse was opened immediately and the bile duct was exposed by moving the intestines to the right-hand side. A clamp was placed on the ampulla, where the bile duct joins the intestine. The clamp sat right on top of the pouch-like structure of the ampulla, so that the content in the duct do not leak into the intestines but can flow into pancreatic ducts. Three milliliters of Collagenase D (Cedarlane) solution at 1.5mg/mL in HBSS containing calcium chloride and magnesium chloride (+/+) were injected into the bile duct using a 28G butterfly needle, inflating the pancreas. The whole pancreas was then dissected out and placed into 2mL of 1.5 mg/mL Collagenase D solution in a 50mL Falcon tube and placed on ice until all mice were processed. The pancreas immersed in Collagenase D was digested when placed into a 37◦C water bath for 12 minutes. The mixture was then pipetted to further break down surrounding exocrine tissue, followed by another 2 minutes of digestion in the 37◦C water bath. The digestion reaction was stopped by adding 25mL ice-cold HBSS (+/+). Disintegrated exocrine tissue was washed away by spinning the mixture at 290g for 2 minutes at 4◦C, discarding the supernatant, then washed again with 20mL ice-cold HBSS (+/+) followed by centrifugation. The pellet containing islets was re-suspended in 15mL ice-cold HBSS (+/+) and passed through a 70µm cell strainer213. Islets were retained on the cell strainer and were collected by inverting the cell strainer in a Petri dish and passing 15mL ice-cold HBSS (+/+) through the strainer213. This step was repeated to further filter out pieces of exocrine tissue and clean up the resulting islet suspension. Islets were picked from the Petri dish into 500uL of enzyme-free dissociation buffer (Millipore) using a P10 pipettor under a 2.25X magnifying glass. An islet count was obtained while picking. The islets in dissociation buffer were placed on ice until all islets were picked. Once all islets were obtained, they were dissociated in the dissociation buffer when placed into a 37◦C water bath for 5 minutes. The resulting cell suspension were pipetted to breakdown any remaining structure and the cells were passed through a mesh to obtain a single cell suspension.
37
2.5.2 Tetramer staining Tetramers specific for insulin peptides p8G and p8E, both conjugated to PE, were provided by the John Kappler Lab in Denver, Colorado. The diluent for the tetramers, also provided by the Kappler Lab, was complete tumor medium (CTM) containing an anti-FcR antibody 24G2, normal mouse serum (NMS) and sodium azide (NaN3). The Kappler lab also provided an unlabelled HAM57-597 TCR Cβ-specific antibody, which was added to the diluent at 1µg/mL right before tetramer staining. The tetramers were diluted to 0.02mg/mL with the diluent. Cells were spun down at 400g for 5 minutes. 25µL of the diluted tetramer solution was added to each sample. Samples were incubated in a humidified 37◦C incubator with 5% CO2 for 2 hours with gentle agitation by tapping every half hour. Co-stain antibodies included those targeting CD3 (clone 145-2C11), CD4 (clone RM4-5), CD8 (clone 53-6.7), Itgβ7 (clone FIB504), CD25 (clone PC61.5), TCRɣẟ (clone GL3), CD45 (clone 30-F11) and CCR9 (clone CW-1.2), conjugated to Alexa647, ef450, APC-ef780, FITC, PE-Cy7, biotin, Alexa700 and PerCP-ef710, respectively. The antibodies staining for CCR9 were prepared in the tetramer diluent at 3.5 times the working concentration. After two hours of tetramer staining, 10µL of diluted CCR9-targeting antibodies was added to 25µL cells in tetramer stain solution. The cells were stained for 30 minutes in the 37◦C incubator, washed with staining media, then re-suspended in 50µL of co-stain solution containing the remaining co-stain antibodies at working concentration. Cells were stained for 30 minutes at room temperature, washed with staining media, then re-suspended in 50µL of streptavidin conjugated to BV650 at working concentration. After half an hour of secondary staining, cells were washed and re-suspended in DAPI-containing staining media for flow cytometry analysis.
2.6 Analysis of murine gut microbial composition by sequencing the entire 16S rRNA gene 2.6.1 Amplification of full 16S rRNA genes with PCR in DNA extracted from gut microbes Concentrations of the DNA samples were measured by Nanodrop. Fifty nanograms of DNA per sample was added to the PCR mix that consisted of 25µL PCR Master Mix (ThermoScientific #K0171), 0.45µM of forward (X8F) and reverse (X1392R) primers (5uL each at 25ng/µL concentration), as well as water that made the final reaction volume to be 50µL. The PCR
38 Master mix contains a Taq polymerase that adds a deoxyadenosine (A) to the 3’ tail of the PCR products214. The sequences for X8F and X1392R were 5’ AGAGTTTGATYMTGGCTCAG 3’ and 5’ GACGGGCGGTGWGTRCA 3’, respectively. In addition to the samples, a control PCR was performed alongside and yielded a PCR product that controls for cloning and transformation in the next section. The control PCR consisted of 1µL of 100ng control DNA template and 1µL of control primers (0.1µg/µL each), both of which were provided by the TOPO TA Cloning Kit for sequencing (Invitrogen), as well as 23µL of PCR Master Mix214. A negative control with water in place of DNA was also included. The PCR cycle consisted of 65◦C for 10 minutes, 98◦C for 30 seconds, then 35 cycles of 98◦C for 10 seconds followed by 52◦C for 1 minute and 72◦C for 1.5 minutes, and 72◦C for 10 minutes before 4◦C hold. The PCR products were verified on 1% agarose gel. Fifty nanograms of bacterial DNA were enough to generate a clear and prominent band at the expected 1.4kb size (Figure 2.2a). We observed a prominent 1.4kB band of the expected size from PCR amplification of full-length 16S rRNA gene sequences in bacterial DNA extracted from fecal or cecal contents (Figure 2.2b).
2.6.2 Purification of PCR products PCR products were purified using the QIAquick PCR purification Kit (Qiagen). Briefly, the PCR products were mixed with the high-salt buffer PB in 1:5 ratio, collected on a silica membrane in the QIAquick column, washed with Buffer PE containing ethanol that allow DNA to precipitate and to be retained on the membrane, and then eluted with water215. The concentrations of purified DNA were measured using Nanodrop. Alternatively, all of the PCR products were run on a 1% gel and purified using QIAquick Gel Extraction Kit (Qiagen). Briefly, the band was excised from the gel using a scalpel, weighed, converted to volume (100mg is approximately 100µL) and added to 3 times the volume of buffer QG215. The mixture was heated at 50◦C for 10 minutes, then received 1 gel volume of isopropanol and was transferred into a QIAquick column215. The DNA was precipitated on the column membrane, washed by buffer QG and then by buffer PE before elution with the elution buffer215. The concentrations of purified DNA were measured using Nanodrop.
39
2.6.3 Insertion of PCR amplicons into cloning vectors The goal of the cloning reaction was to insert the purified DNA generated in step 2, which were the full 16S rRNA genes amplified from the gut microbes, into a vector plasmid to be propagated in Escherichia coli. The cloning reaction was done using the TOPO TA cloning Kit for sequencing (Invitrogen). The cloning reaction consisted of purified DNA (to be inserted into the vector), TOPO plasmid vector (Figure 2.3), salt solution and water. As shown in Figure 2.3, the TOPO plasmid vector consists of an overhanging thymidine (T) at the insertion site, where the topoisomerase is attached to214. The 5’OH of the insert attacks the covalent bond between the topoisomerase and the 3’A tail on the complementary strand pairs with the T overhang216. This reaction allows the purified DNA to be inserted214,216. A 3µL cloning reaction requires 0.5µL of the TOPO vector plasmid, which is equivalent to 5ng of the plasmid. A series of molar ratios of plasmid to insert were applied in the cloning experiments, as specified in the Results section. The molar ratio of plasmid to insert was calculated by taking into account the sizes of the plasmid and the insert. Since the plasmid size is about 4 kilobases (kb) while the insert size is about 1.4 kb, the molar concentration (g/mol) of the plasmid is about 3 times the molar concentration of the insert. Therefore, for 5ng plasmid, a 1:20 molar ratio required 33ng insert. The concentration of the insert was adjusted so that each cloning reaction received 2µL of insert. In addition, 0.5µL salt solution was also added to each cloning reaction. A negative control with no insert was also included. The cloning reaction took place at room temperature (RT) for 30 minutes, and the resulting mixture was placed on ice or stored away at -20◦C.
2.6.4 Transformation of E. coli with vectors containing the insert Two microliters of the vector mix following the cloning reaction were added to a vial of 50µL thawed E. coli cells (One Shot TOP10 and DH5⍺-T1 competent cells, provided in the cloning kit; Invitrogen)214. The mix was gently stirred and incubated on ice for 15 minutes, heat-shocked on a 42◦C heat block for 30 seconds and placed on ice214. The bacterial cells then received 250µL sterile SOC medium at RT, shook at 37◦C at 200rpm speed for an hour, then spread onto LB plate containing 50µg/mL carbenicillin214. To ensure single colony formation and prevent
40 growth into a lawn of colonies, two different volumes of the bacterial cells in SOC medium were plated for each cloning reaction (30µL and 70µL). The remaining bacterial cells were stored by adding an equal volume of 40% glycerol and frozen at -80◦C. The plates were incubated at 37◦C overnight. More colonies were generated at 1:16 plasmid to insert ratio than at 1:5. Spreading of 70µL of bacteria transformed with vectors at 1:16 plasmid to insert ratio on the LB agar plate was enough to generate >100 colonies that could be easily distinguished and picked.
2.6.5 Picking colonies for growth in liquid culture Each colony that appears on a plate following overnight culture represents amplification of a single 16S rRNA gene unique for one bacterial taxon. Each plate represents a collection of 16S rRNA genes from the gut microbial community in a given mouse that were incorporated into the vector plasmids and amplified by growth of E. coli. Each colony was picked into 2mL LB broth containing 50µg/mL carbenicillin in a well on a 96-deep-well plate. The plate was shaken at 37◦C overnight to allow further growth of E. coli transformed with vector containing each16S rRNA gene.
2.6.6 Plasmid extraction with miniprep The plasmid vectors from the liquid culture of each bacterial colony were extracted using QIAprep miniprep kit (Qiagen). Briefly, each liquid culture was spun down at 6800g for 3 minutes at RT, resuspended in 250µL buffer P1, followed by addition of 250µL buffer P2 and 350µL Buffer N3 for bacterial lysis217. The mixture was spun down at 17900g for 10 minutes at RT and the supernatant was transferred into the QIAprep column217. The plasmid DNA was precipitated on the membrane, washed with Buffer PB and Buffer PE, then eluted with buffer EB217. The concentrations of plasmid DNA were measured with Nanodrop.
2.6.7 Sanger sequencing with M13F and R primers For each sequencing reaction, 200ng plasmid DNA in 7µL was supplied. The sequencing was performed by TCAG DNA sequencing facility at PGCRL and primers specified were M13F and M13R primers. The insertion site on the TOPO vector is flanked by sequences that bind M13F
41 and M13R primers (Figure 2.3). The sequences of the M13F and M13R primers are 5’ GTAAAACGACGGCCAG 3’ and 5’ CAGGAAACAGCTATGAC 3’, respectively.
2.6.8 Sequence analysis for phylogenetic relationships Sequencing results returned from the facility were sent to RDP Classifier218 for taxonomy assignment. A confidence threshold in percentage was provided at each taxonomic rank.
a) b)
Figure 2.2. PCR products of full-length 16S rRNA gene. A) The amount of bacterial DNA template needed to generate a clean PCR product band were tested. Bacterial DNA extracted from cecal contents of an unmanipulated NOD mouse (ID: UN24CC) were used as templates. Two-fold dilutions down from 100ng of template DNA were applied to each PCR reaction using 8F and 1392R primers that amplify full 16S rRNA genes. B) Fifty nanograms of bacterial DNA template extracted from the stool sample of a MàF recipient (MF51STL) and of an unmanipulated NOD mouse (FP48STL) were applied to PCR reactions using 8F and 1392R primers and generated PCR products of 1.4kb size, equivalent to the full length of the 16S rRNA gene. One kb DNA ladder (Thermo Scientific Gene ruler) shown in the figure219 was used to measure sizes of the PCR products.
42 8F 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 bp V1 V2 V3 V4 V5 V6 V7 V8 V9
1392R
LB plate + carbenicillin
Figure 2.3. Insertion of full 16S rRNA gene into TOPO vector214 and cloning reaction. Full 16S rRNA genes (illustration adapted from Singer et al.220) from murine gut microbes were PCR- amplified using 8F and 1392R primers and purified. The purified DNA acts as insert that is ligated to the TOPO vector by topoisomerase action. The insertion site contains T overhangs that pair with A tails of the insert and is flanked by sequences that bind M13R and M13F primers. These primers are used for Sanger sequencing. Successful insertion disrupts the fusion between lacZ gene to a lethal ccdB gene, thus permitting E. coli that were transformed with vectors containing the insert, but not E. coli transformed with empty vectors, to grow and form colonies. In addition, the ampicillin and kanamycin resistance genes on the TOPO vector select for successful transformation and allow growth of E. coli that were transformed with a vector on an LB plate containing carbenicillin.
43
CHAPTER 3: Results
44 Chapter 3.1 The impact of microbial transfer on ex vivo presentation of an islet autoantigen Our lab previously showed that transfer of cecal microbiota from NOD adult males into young females (MàF) resulted in lower insulin autoantibody levels and lower insulitis severity compared to age-match unmanipulated NOD females and FàF recipients183. Insulin autoantibodies are reflective of the seroconversion process in which insulin peptide-specific CD4+ T cells provide help to autoreactive B cells for autoantibody production. Insulitis severity reflects inflammatory processes within the pancreas that include accumulation of T cells and other immune cell types. Central to both of these pre-clinical markers of disease is activation of islet antigen-specific CD4+ T cells that provide help to B cells and to CD8+ T cells. To determine whether islet antigen-specific CD4+ T cell proliferative responses were altered in MàF recipients, I established a co-culture system to recapitulate the process of CD4+ T cell response to an islet-specific antigen. I employed transgenic NOD mice that express the BDC2.5 T cell receptor (TCR; NOD.BDC2.5) on CD4+ T cells conferring specificity for a chromogranin A epitope presented by MHC class II (H2-Ag7). Splenic BDC2.5 CD4+ T cells were harvested from BDC2.5 TCR transgenic mice and enriched for CD4+ T cells by immunomagnetic separation (Figure 3.1.1a). CD4+ T cells were enriched from an average of 19% among live splenocytes (±9% SD) to an average of 89% (±8% SD) across different CD4+ T cell harvests (Figure 3.1.1b). An average of 88% (±5% SD) of CD4+ BDC2.5 T cells expressed Vβ4, consistent with the inclusion of Vβ4 expression on T cells in Tg+ mice210. BDC2.5 T cell were labeled with the CFSE dye to report on proliferation. T cells treated with phorbol myristate acetate (PMA) and ionomycin were prepared as a positive control for T cell proliferation (Figure 3.1.1c). To validate that co-culture conditions could provoke T cell proliferation, I tested an alloreactive response with APCs from C57BL/6 (H-2b) mice and T cells from NOD (H2-Ag7) mice. The APCs cultured from bone marrow cells produced a heterogeneous population which expressed different levels of CD11b and CD11c (Figure 3.1.2a). The CD11blowCD11c+ subpopulation expressed the highest levels of co-stimulatory molecules CD80 and CD86, as well as MHC class II molecules (Figure 3.1.2 c, d). The CD11b+CD11c+ subpopulation expressed the second highest levels of co-stimulatory and MHC molecules. These two subpopulations responded to overnight LPS stimulation. Specifically, the frequencies of
45 CD80+CD86+ or MHCII+CD86+ cells increased and reached statistical significance for MHCII+CD86+ cells in the CD11b+CD11c+ subpopulation. In contrast, the CD11b+CD11c- and CD11b-CD11c- subpopulations expressed low levels of co-stimulatory and MHC molecules before and after LPS stimulation. The proportions of these four subpopulations did not change with LPS stimulation (Figure 3.1.2b). When co-cultured with CFSE-labeled NOD T cells, T cell proliferation was observed for co-cultures with LPS-stimulated APCs (Figure 3.1.2e, f) but significantly less so for co-cultures with non-stimulated APCs (Figure 3.1.2f). LPS stimulates expression of MHC class II molecules and antigen presenting capacity of DCs and is used in many systems to induce DC maturation221,222. The LPS-stimulated APCs from an allogenic source elicited an alloreactive response from NOD T cells, suggested that the conditions were sufficient for APC to stimulate T cell response. I then examined a BDC2.5-specific response by incubating APCs with BDC2.5 peptide mimotopes. As observed for the B6 cultures, NOD BM-derived APCs were heterogeneous and included the four subpopulations based upon CD11b and CD11c expression (Figure 3.1.3 a, b, c). The CD11blowCD11c+ and CD11b+CD11c+ cells expressed higher levels of co-stimulatory and MHC molecules, and were the most responsive to LPS stimulation, based upon increased expression of CD80+CD86+ or MHCII+CD86+. When these BM-derived APC were incubated with the BDC2.5 mimotope, co-cultured CFSE-labeled BDC2.5 T cell proliferation increased with the concentrations of BDC2.5 mimotope (Figure 3.1.3d). In contrast, APCs loaded with scrambled version of BDC2.5 mimotope did not induce T cell proliferation (Figure 3.1.3d), reflecting the peptide sequence specificity of the T cell response. These results demonstrated that an islet antigen-specific response was recapitulated in this co-culture system. The goal of the co-culture system was to assess whether microbial manipulation in early life altered the ability of APCs to stimulate BDC2.5 specific T cells in a peptide-dependent fashion. APCs were enriched by negative selection of T, B and NK cells from the spleen (Figure 3.1.4a). CD11b and CD11c double negative cells and CD11b single-positive cells constituted the majority of this non-B non-T population (Figure 3.1.4b). The CD11b+CD11c+ and CD11b-CD11c+ subpopulations constituted a minor proportion of the enriched splenocyte population and expressed low levels of MHC class II and CD86 (Figure 3.1.4b). Overnight incubation of these cells with IL-4 and GM-CSF, which increases DC-like properties among these cells223, increased the proportion of CD11b single-positive cells and decreased the
46 proportion of CD11b CD11c double-negative cells and increased the frequencies of MHC class II+ CD86+ cells in both CD11b+CD11c+ and CD11b-CD11c+ subpopulations (Figure 3.1.4b). LPS supplementation did not further increase in the frequencies MHC class II+ CD86+ cells. Therefore, to obtain APC competent to stimulate BDC2.5 T cells without manipulation that might mask in vivo effects of microbe-manipulation in the NOD host, overnight incubation with IL-4 and GM-CSF (without LPS) was selected for the co-culture setting. The expression of MHC class II molecules and CD86 on the APCs freshly enriched from the spleens of unmanipulated or MàF recipients did not differ (Figure 3.1.4c). After the overnight culture, I observed a higher frequency of MHC class II+CD86+ CD11b+CD11c+ cells from MàF recipients compared to control mice (Figure 3.1.4d). To determine whether the CD11b-CD11c- cells might influence the APC-T cell interaction, I further explored their immunophenotypes. Since the cell preparation used collagenase to dissociate the splenic stroma these cells could contribute to the CD11b-CD11c- subpopulation. Indeed, when analyzed for CD45 expression, a marker restricted to cells of hematopoietic origin, most of these cells were identified as non-hematopoietic origin (Figure 3.1.5a). CD11b+Gr-1+ cells were also present and did not express MHC class II or CD86 (Figure 3.1.5c), and displayed higher side scatter (Figure 3.1.5d) suggesting they were a homogenous cell population likely granulocytes. The frequencies of the CD11b-CD11c- subpopulation were less than 10% (Figure 3.1.5b) and did not differ in splenic samples from unmanipulated NOD and MàF recipients. These splenic APC were incubated with the BDC2.5 mimotopes and co-cultured with CFSE-labeled BDC2.5 T cells. The predicted APC (Figure 3.1.5a, blue gate) to T cell ratio for the co-culture was 1:15 (~ 1,330 APC to 20,000 T cells), slightly lower than the 1:10 cell ratio previously reported to support antigen-specific224 or allogenic225 T cell proliferation . BDC2.5 peptide-specific T cell proliferative responses were evaluated across a range of peptide concentrations by quantifying the frequency of T cells with reduced CFSE MFI (CFSElow) indicating these cells had undergone proliferation (Figure 3.1.6a). In comparison, APCs loaded with scrambled peptides did not elicit T cell proliferation (Figure 3.1.6b). Peptide- specific T cell proliferation did not differ between co-cultures with splenic APCs from unmanipulated controls and MàF recipients except at the highest concentration where APCs from unmanipulated controls elicited slightly higher BDC2.5 T cell proliferation.
47 To compare the activation status of BDC2.5 T cells in these co-cultures, I examined the expression of CD25 and CD69 markers on the T cells (Figures 3.1.6c, d). Consistent with a titrated peptide-specific T cell proliferative response, frequencies of CD25-expressing T cells increased with increasing mimotope concentration (Figure 3.1.6c, middle panel). However, the frequencies of CD25+ T cells did not differ between co-cultures with APCs from unmanipulated controls versus MàF recipients. The frequencies of CD25+ cells among proliferated, CFSElow T cells reached around 80% and plateaued at peptide concentrations as low as 30 ng/mL (Figure 3.1.6c, right panel). The frequencies of CD69+ T cells increased with increasing BDC2.5 mimotope concentrations and were slightly higher in co-cultures of APCs from MàF recipients (Figure 3.1.6d, middle panel). The frequencies of CD69+ cells among CFSElow T cells were higher in co-cultures of APCs from MàF recipients compared with those from unmanipulated controls (Figure 3.1.6d, right panel). Thus this in vitro co-culture proliferation of BDC2.5 T cells was peptide-specific, dose- dependent and did not produce different outcomes based upon origin of the APCs. An exception was observed at highest peptide concentration (3000 ng/mL), where APCs from unmanipulated controls stimulated slightly higher T cell proliferative responses compared to APC isolated from MàF recipients. The frequencies of T cells expressing CD25 and CD69 also displayed peptide- specific, dose-dependent increases. CD69+ cells were more frequently observed in co-cultures with APCs from MàF recipients than those from controls at the higher end of the peptide concentration range, while the frequencies of CD69-expressing cells among CFSElow T cells were higher when co-cultured with APCs from MàF recipients. In conclusion, we did not observe an effect of MàF transfer on the capacity of peripheral APCs to provoke the islet antigen ChrA-specific T cell proliferative response ex vivo using this assay. The peripheral APCs from MàF recipients were able to promote a more activated T cell phenotype measured by CD69 expression compared to the controls, although this did not translate into a difference in T cell proliferation.
48 a) Pre-gating strategy
250K 250K 105 200K 200K 96.3 88.0 104 150K 150K
103 84.4 100K 100K
50K 50K 0 0 0 H H - - 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K DAPI 0 50K 100K 150K 200K 250K FSC SSC FSC-A SSC-A FSC-A CD4+ T cell enrichment before after TCRVb4 expression
105 21.0 105 92.8 105
104 104 104 87.1 103 103 103
0 0 0
3 4 5 3 4 5 3 4 5
CD3 0 10 10 10 0 10 10 10 CD3 CD4 0 10 10 10
CD4 CD4 TCRVb4
b) c)
p<0.001
no stimulation +PMA/ionomycin 100 100 100 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 103 104 0 103 104 % CD4+ T cells/ live cells 0 Count CFSE
after before CD4+ T cell enrichment
Figure 3.1.1. BDC2.5 CD4+ T cell harvest from BDC2.5 transgenic TCR-expressing mice. a) An example plot of flow cytometry analysis for BDC2.5 CD4+ T cell harvest. Cells before and after CD4+ T cell enrichment were pre-gated on singlet and live cells and assessed for CD3 and CD4
49 co-expression. TCR Vβ4 expression was assessed among CD4+ T cells. All gates were made based on fluorescence minus one control (FMO). b) The purity of CD4+ T cells was enriched consistently across different CD4+ T cell harvests. The bar graph was generated using data from 28 experiments requiring T cell harvest and plotted with Mean ± SD. Statistical significance was generated by Mann-Whitney test. c) A positive control for T cell proliferation, stimulated by PMA BMDC composition and ionomycin, was measured by CFSE dilution.
CD80+CD86+ freq. a) c)100 stim 80 non-stim 100 5 5 64.4 stim 105 10 6010 80 non-stim 4 104 Q2 10 Q1 5.60 104 5.74 40 60 103 103 (%) Freq. 92.8 40 103 Q4 20 0 0 %CD80+CD86+ 20 0 78.6 6.36 Q3 0 0
CD80 3 4 5 3 4 5 0 10 10 10 Q1 Q2 Q3 Q4 0 50K 100K 150K 200K 250K 0 10 10 10 CD86 BMDC composition MHCII+CD86+ freq. CD11c CD11b+CD11c-CD11b-CD11c- BMDC composition CD3, CD19 dump CD11b+CD11c+ FSC-A CD11b CD11b_low CD11c+ CD11b+CD11c+CD11b+CD11c-CD11b-CD11c- b) CD11b_low CD11c+ p=0.003 d) 5 100 100 10 92.9 stim stim 80 non-stim 80 non-stim104 100 60 stim 60 103 40 80 non-stim 40 Freq. (%) (%) Freq. 0 %MHCII+CD86+ 20 20 60 0 MHCII 0 0 103 104 105 40 CD86 Q1 Q2 Q3 Q4 Freq. (%) (%) Freq. Q1 Q2 Q3 Q4
CD11b-CD11c- 20 CD11b+CD11c+CD11b+CD11c- CD11b+CD11c+CD11b+CD11c-CD11b-CD11c- CD11b_low CD11c+ CD11b_low CD11c+ 0 e) f)
Pre-gating strategy with stim DCs with non-stim DCs p=0.002
CD11b+CD11c+CD11b+CD11c-CD11b-CD11c- 40 CD11b_low CD11c+ 105 40 300 13.8 104 30 30 200 3 10 20 20 27.5 14.0 100 10 0 10 %CFSElow/CD3+ T
0 0 0 3 4 3 4
CD3 0 50K 100K 150K 200K 250K 0 10 10 0 10 10 count SSC-A count CFSE CFSE with Stim DC with non-stim DC
50 Figure 3.1.2. Alloreactive co-culture. Antigen presenting cells (APCs) were cultured from bone marrow cells of C57BL/6 (B6) mice and were either stimulated with LPS overnight or remained non-stimulated. The APCs were analyzed for the expression of co-stimulatory molecules CD80 and CD86, as well as the expression of MHC class II molecules, prior to co-culture. T cells from NOD mice were stained with CFSE and co-cultured with B6 APCs. The proliferation of T cells was analyzed after co-culture. a) APCs were pre-gated to be live singlets and dump (CD3, CD19) negative. Subpopulations were distinguished by CD11b and CD11c expression. b) The proportions of each subpopulation among live singlet dump-negative cells across four different experiments were shown in a bar graph. Black bars represent LPS-stimulated cells and grey bars represent non-stimulated cells. The error bars in the bar graphs represent standard deviation (SD). Proportions of the subpopulations did not differ between LPS-stimulated and non- stimulated APCs according to multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. c) Each subpopulation was analyzed for expression of CD80 versus CD86. Gating was generated based on FMO controls. The frequencies of CD80 and CD86 double- positive APCs were plotted for each subpopulation across four experiments and shown in the bar graph. Frequencies of CD80+CD86+ cells did not differ between LPS-stimulated and non- stimulated APCs according to multiple t tests using the Sidak-Bonferroni correction method with significance set to ⍺ set to 0.05. d) Each subpopulation was also analyzed for expression of MHC class II versus CD86. Gating was generated based on FMO controls. The frequencies of MHC class II and CD86 double-positive APCs were plotted for each subpopulation across four experiments and shown in the bar graph. Statistical significance was generated in the multiple t tests using Sidak-Bonferroni correction method with ⍺ set to 0.05. e) Co-cultured cells were pre-gated for live and singlet cells and T cells were gated by CD3 expression. T cells were analyzed for CFSE expression. CFSElow cells were gated by including all cells that were 2.5 SDs or more to the left of the mean of the non-proliferated population in the co-culture with non-stimulated APCs. f) Proliferation of alloreactive T cells in co-cultures with LPS-stimulated APCs or non-stimulated APCs were shown by plotting the frequencies of CFSElow cells among CD3+ T cells. Statistical significance was generated by Mann-Whitney test.
51 BMDC composition
a) BMDC composition b) CD80+CD86+ freq. 100 100 100 p<0.001 stim stim stim non-stim 80 80 80 non-stim non-stim
60 60 p<0.001 60 40 40 40 Freq. (%) (%) Freq. 20 %CD80+CD86+ 20 freq. among live cells 0 20 0 peptide 3d
0
CD11b-CD11c- CD11b+CD11c+CD11b+CD11c-CD11b-CD11c- CD11b+CD11c+CD11b+CD11c- CD11b_low CD11c+ 100 CD11b_low CD11c+ BDC2.5 mimotope peptide loading o/n w LPS MHCII+CD86+ freq. 80 CD11b+CD11c+CD11b+CD11c-CD11b-CD11c- scrambled peptide c) 100 CD11b_low CD11c+d) 100 stim p<0.001 60 80 non-stim 80
60 p=0.003 40 60
40 %CFSElow/CD3+ 20 40
%MHCII+CD86+ 20 0 %CFSElow/CD3+ 20 10 30 100 300 1000 3000 0 0 10 30 100 300 1000
CD11b+CD11c+CD11b+CD11c-CD11b-CD11c- CD11b_low CD11c+ Figure 3.1.3. BDC2.5-specific co-culture using bone marrow-derived antigen presenting cells (APCs). APCs were cultured from bone marrow cells of NOD mice and were either stimulated with LPS overnight or remained non-stimulated. The APCs were analyzed for the expression of co-stimulatory molecules CD80 and CD86, as well as the expression of MHC class II molecules, prior to co-culture. APCs were loaded with either BDC2.5 mimotope or scrambled peptides. BDC2.5 CD4+ T cells from NOD BDC2.5 mice were stained with CFSE and co- cultured with NOD APCs. The proliferation of T cells was analyzed after co-culture. a) APCs were pre-gated to be live singlets and dump (CD3, CD19) negative. Subpopulations were distinguished by CD11b and CD11c expression. The proportions of each subpopulation among live singlet dump-negative cells across four different experiments were shown in a bar graph. Black bars represent LPS-stimulated cells and grey bars represent non-stimulated cells. The error bars in the bar graphs represent standard deviation (SD). Proportions of the subpopulations did not differ between LPS-stimulated and non-stimulated APCs according to multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. b) Each subpopulation was analyzed for expression of CD80 versus CD86. The frequencies of CD80 and CD86 double-
52 positive APCs were plotted for each subpopulation across three experiments and shown in the bar graph. Statistical significance was generated in the multiple t tests using the Sidak- Bonferroni correction method with ⍺ set to 0.05. c) Each subpopulation was also analyzed for expression of MHC class II versus CD86. The frequencies of MHC class II and CD86 double- positive APCs were plotted for each subpopulation across four experiments and shown in the bar graph. Statistical significance was generated in the multiple t tests using the Sidak- Bonferroni correction method with ⍺ set to 0.05. d) Proliferation of BDC2.5 T cells in co- cultures was shown by plotting the frequencies of CFSElow cells among CD3+ T cells; data plotted from 6 replicates and error bars represent SD. APCs were LPS-stimulated and loaded with either BDC2.5 mimotope (solid line) at indicated peptide concentrations, or with scrambled peptide (dotted line) at 1000 ng/mL, prior to co-culture.
53
a) p=0.04 Gating strategy
100 105 105 40.1 80 0.78 3.40 104 104 60 103 103 40 0 20 0 75.5 19.8 % CD3-CD19- cells/ live cells live cells/ CD3-CD19- % 0 0 103 104 105 0 103 104 105 CD11c all stim status C/MF pooled after MHCII before CD11b CD86 APC enrichment
p<0.001 ns 100 b) all composition C/MF pooled freshall stim status C/MF pooled 80 o/n+IL4/GMCSF o/n+IL4/GMCSF/LPS p=0.003 60 p=0.003 ns p<0.001 ns p<0.001 100 40 100freshp<0.001 fresh 80 o/n+IL4/GMCSF
%MHCII+CD86+ 80 o/n+IL4/GMCSF 20 o/n+IL4/GMCSF/LPS p<0.001 o/n+IL4/GMCSF/LPS 60 p<0.001 ns 60 0 40 40
%MHCII+CD86+ 20 20
CD11b-CD11c- CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+ 0 0
Freq. (%) /live non-B non-T cells composition fresh C vs. MF
CD11b-CD11c- CD11b-CD11c- CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+ CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+ 100 composition fresh C vs. MF c) fresh_C 80 fresh_MFMHCII+CD86+ fresh C vs. MF
100 100 60 Fresh_C fresh_C 80Fresh_MF fresh_MF 40 80 60 60 20 40 40 0 %MHCII+CD86+ Freq. (%) /live non-B non-T cells 20 20
0 0 Freq. (%) /live non-B non-T cells CD11b-CD11c- CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+
CD11b-CD11c- CD11b-CD11c- CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+ CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+ 54 all stim status C/MF separate
all stim status C/MF separate p=0.002 100 all composition C/MF separate o/n+IL4/GMCSF_C d) 80 o/n+IL4/GMCSF_MF
60 p=0.002 100 100 o/n+IL4/GMCSF_C o/n+IL4/GMCSF_C 40 80 80 o/n+IL4/GMCSF_MF o/n+IL4/GMCSF_MF
60%MHCII+CD86+ 20 60
40 40 0 Freq. (%)
%MHCII+CD86+ 20 20
0 0
CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+CD11b-CD11c-
CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+CD11b-CD11c- CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+CD11b-CD11c-
Figure 3.1.4. Peripheral antigen-presenting cells (APCs) from unmanipulated NOD females and MàF recipients. a) APCs from spleens were enriched by negative selection. Cells were pre- gated for singlet and live cells. The frequencies of non-B non-T (CD19- CD3-) cells were compared before and after enrichment. Statistical significance was generated by Mann-Whitney test for ⍺ set to 0.05. The non-T non-B cells were further characterized into four subpopulations based on CD11b and CD11c expression. Each subpopulation was gated for MHC class II and CD86-expressing cells. The gate was determined based on FMO controls. b) Left panel: The frequencies of MHC class II and CD86-expressing cells among each subpopulation were compared between APCs freshly obtained after enrichment (“fresh”, black bars), APCs incubated overnight with IL-4 and GM-CSF (“o/n+IL4/GMCSF”, light grey bars) and APCs incubated overnight with IL-4, GM-CSF and LPS (“o/n+IL4/GMCSF/LPS”, dark grey bars). Statistical significance was generated by multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. Right panel: The proportions of the subpopulations among live non-T non-B cells were compared between APCs from the same three conditions as shown in the left panel. Statistical significance was generated by multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. c) Left panel: The frequencies of MHC class II and CD86- expressing cells among each subpopulation were compared between APCs freshly enriched from unmanipulated NOD females (“o/n+IL4/GMCSF_C”) versus those from MàF recipients (“o/n+IL4/GMCSF_MF”). No statistically significant difference was found using multiple t tests using Sidak-Bonferroni correction method with ⍺ set to 0.05. Right panel: The proportions of
55 the subpopulations among live non-B non-T cells were compared between APCs from the same conditions as shown in the left panel. Proportions of the subpopulations did not differ between APCs from unmanipulated controls and MàF recipients according to multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. Data representative of three independent experiments. d) Left panel: The frequencies of MHC class II and CD86-expressing cells among each subpopulation were compared between APCs incubated overnight with IL-4 and GM-CSF from unmanipulated NOD females (“o/n+IL4/GMCSF_C”) versus those from MàF recipients (“o/n+IL4/GMCSF_MF”). Statistical significance was generated by multiple t tests using Sidak-Bonferroni correction method with ⍺ set to 0.05. Right panel: The proportions of the subpopulations among live non-B non-T cells were compared between APCs from the same conditions as shown in the left panel. Proportions of the subpopulations did not differ between APCs from unmanipulated controls and MàF recipients according to multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. Data representative of three independent experiments.
56 a)
17,3 250K 250K 96,2 5 5 250K 5 10 10 17.3 85.685,5 250K 96.2 10510 200K200K 200K200K 4 4 4 10 10 10410 150K 150K 150K 150K 3 FSC-H SSC-H 10 62,562.5 3 3 3 10 10 100K 100K 10 100K 100K
0 Comp-U-515_30-A :: DAPI 2 0 50K 50K 010 17,3 50K250K 250K 96,2
Comp-R-720_40-A :: CD45_Alexa700 5 5 10 85,5 50K 100 3 -10 all stim status C/MF separate 2 0 0 -10 200K 200K 0 50K 100K 150K 200K 250K 0 0 50K 100K 150K 200K 250K 0 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K 4 04 50k 100k 150k 200k 250k 0 50k 100k 150k 200k 250k 10 0 50k 100k 150k 200k 250k 10 SSC-A FSC-A 0 50k 100kSSC-A 150k 200k 250k SSC-A 150K 150K H
H 3 FSC-H SSC-H 62,5
- 10 3 samples_MF4_004.fcs samples_MF4_004.fcs - samples_MF4_004.fcs samples_MF4_004.fcs 10 Ungated 100K hematopoietic cells 100K Singlets Singlets DAPI 71064 12292 SC 10514 10112 CD45 FSC S 0 FSC-A Comp-U-515_30-A :: DAPI 2 SSC-A SSC-A 50K 50K SSC-A 10
Comp-R-720_40-A :: CD45_Alexa700 0 3 -10 2 0 0 8,40 56,9 -10 5 5 5 10 10 10 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200Kp=0.002250K 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K
SSC-A 1004 FSC-A SSC-A SSC-A 10 4 4 10 10 o/n+IL4/GMCSF_C samples_MF4_004.fcs98,2 3 samples_MF4_004.fcs samples_MF4_004.fcs samples_MF4_004.fcs 10 Ungated 80hematopoietic cells Singlets 3,76 Singletsb) 71064 12292 3 10514 10112 3 10 o/n+IL4/GMCSF_MF 10 0
0
Comp-V-431_28-A :: Gr1_ef450 0 Comp-R-660_20-A :: Ter119_APC 560 8,40 56.956,9 5 10 35 Comp-G-780_60-A :: CD11b_PECY7 5 10 -1010 40,7 105 8.40 5 3 10 10-10 26,8 80
4 3 3 4 5 3 3 4 5 0 50K 100K 150K 200K 250K 104 -10 0 10 10 10 -10 0 10 10 10 4 10 44 104 40 1010 10 FSC-A Comp-V-660_20-A :: Nkp46_BV650 Comp-G-575_26-A :: CD11c_PE 98,2 3 98.2 10 60 103 3,76 3 33 3.76 10 3 1010 10 %MHCII+CD86+ 20 o/n+IL4/GMCSF_C 0 0 40 o/n+IL4/GMCSF_MF 00
Comp-V-431_28-A :: Gr1_ef450 00 Comp-R-660_20-A :: Ter119_APC
3 Comp-G-780_60-A :: CD11b_PECY7 -10 40,7 3 40.7 -10 0 26.826,8
0 50K 100K 150K 200K 250K 3 3 4 5 3 3 4 5 Gr1-Nkp46- cells 20 0 50k 100k 150k 200k 250k -10 00 1010 3 10104 10105 -10 00 10103 10104 10105 FSC-A Comp-V-660_20-A :: Nkp46_BV650 Comp-G-575_26-A :: CD11c_PE Freq. (%) /live (%) Freq. CD45+Ter119-
1 0 - 119 - Gr CD11b
Ter FSC-A NKp46 CD11c CD11b+CD11c-CD11b+CD11c+CD11b-CD11c+ CD11b-CD11c-
CD11b+CD11c- CD11b-CD11c+CD11b-CD11c- CD11b+CD11c+ d)
96,9 96,9 2,00 2,00 5 5 510 10 Sample Name Subset Name Count 10 2.00 5 5 1010 samples_MF4_004.fcs CD11b-CD11c+ 676 samples_MF4_004.fcs Gr1- 2524 samples_MF4_004.fcs Gr1+ 3652 4 4 4 10 4 1010 10104 c) Sample Name Subset Name Count 5 96.9 0 105 3 samples_MF4_004.fcs CD11b-CD11c+ 676 0 10103 3 3 3 10 samples_MF4_004.fcs Gr1- 2524 1010 10 samples_MF4_004.fcs Gr1+ 3652 4 104 2 1010
0 0 0 0 3 Comp-G-780_60-A :: CD11b_PECY7 Comp-G-780_60-A :: CD11b_PECY7 0 103
Comp-V-431_28-A :: Gr1_ef450 2 10-10 0 0 5 5 5 10 1058,910 58.958,9 3 3 3 4 3 5 4 5 2 -10 0 -10 103 0 104 10 10 5 10 10 10 4 0 10 10 10 0 50K 100K 150K 200K 250K 4 1 10 10 Comp-G-575_26-AComp-G-575_26-A :: CD11c_PE :: CD11c_PE 0 4 - 0 0 50k 100k 150k 200k 250k Gr1+ 10 SSC-A Comp-V-431_28-A :: Gr1_ef450 2 -10 Gr CD11b 3 3 CD11c 10 10103 samples_MF4_004.fcssamples_MF4_004.fcs SSC-A Gr1- Gr1+ Gr1+ Gr1+ 3652 3652 0 50K 100K 150K 200K 250K 1 Sample Name Subset Name Count - 100 0 50k 100k 150k 200k 250k Gr1+Gr1-CD11b-CD11c+ 0 00 100 SSC-A samples_MF4_004.fcsGr1- CD11b-CD11c+ 676 samples_MF4_004.fcs Gr1- 2524 Gr samples_MF4_004.fcs Gr1+ 3652 Comp-V-431_28-A :: Gr1_ef450 Comp-V-431_28-A :: Gr1_ef450 SSC80 -A Gr1- 3 80 3 -10 0,52 0,52 0,055 0,055 Gr1-CD11b-CD11c+ -10 5 5 10510 0.52 10 0.055 Sample Name Subset Name Count 100 60 Gr1-CD11b-CD11c+ 3 3 4 5 100 samples_MF4_004.fcs CD11b-CD11c+ 676 3 -103 0 410 5 10 10 60 -10 0 10 10 3 10 4 5 samples_MF4_004.fcs Gr1- 2524 0 10 10 10 4 4 Comp-V-660_20-A :: Nkp46_BV650 410 10 samples_MF4_004.fcs Gr1+ 3652 Comp-V-660_20-A1 :: Nkp46_BV650 10 80 - 804040 Normalized To Mode Gr
samples_MF4_004.fcssamples_MF4_004.fcs 3 3 3 60 NKp46 1010 10 6020 non-erythrocytes non-erythrocytes 20 6204 6204 400400 Normalized To Mode Comp-B-515_30-A :: MHCII_FITC
Comp-B-515_30-A :: MHCII_FITC 0 50K 100K 150K 200K 250K 00 0 0 50k 100k 150k 200k 250k 99,3 99,3 0.140,14 0,14 2020 SSC-A Norm. Norm. count 3 3 3 4 3 5 4 5 -10 0 -1010 0 10 10 10 10 10 SSC-A 0 103 104 105 0 Comp-R-780_60-AComp-R-780_60-A :: CD86_APCCY7 :: CD86_APCCY7 0 0 50K 100K 150K 200K 250K
MHCII 0 50k 100k 150k 200k 250k CD86 SSC-A
samples_MF4_004.fcssamples_MF4_004.fcs Norm. count Gr1+ Gr1+ SSC-A 3652 3652 57 Figure 3.1.5. Immunophenotypes of enriched APC cell populations prior to co-culture. APCs were enriched from splenocytes the day before and were cultured in IL4 and GMCSF overnight. An example plot of gating strategy used to determine the composition of the cell population enriched for APCs from an MàF recipient. a) CD45+live singlet hematopoietic cells were further analyzed for the presence of erythrocytes (Ter119+), granulocytes (Gr-1+), NK cells (NKp46+). The remaining cell population were analyzed for CD11b and CD11c that identify them as monocyte-derived DCs and macrophages. Gates were created based on FMO controls. c) Gr-1+ cells were further analyzed for CD11b and CD11c expression, as well as the expression of MHC class II molecules and co-stimulatory molecule CD86. c) The proportions of the subpopulations among live CD45+Ter119-Gr1-Nkp46- cells were compared between APCs incubated overnight with IL-4 and GM-CSF from unmanipulated NOD females (“o/n+IL4/GMCSF_C”) versus those from MàF recipients (“o/n+IL4/GMCSF_MF”). Proportions of the subpopulations did not differ between APCs from unmanipulated controls and MàF recipients according to multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. Data representative of seven mice (n=7). d) Gr-1+ cells (gated in red), Gr-1- cells (gated in blue) and Gr-1-CD11b-CD11c+ cells (gated in green) were compared for granularity by SSC-A.
58 a)
[BDC2.5 mimotope] ng/mL
0 10 30 50 100 300 1000 3000
120 150 60 200 200 150 100 80 60 150150 80 150 60 150 90 60 150 80 60 150 100100 60 4040 100 34.4 50.1 55.7 100 100 17.8 34,4 50,1 40 53.2 60 55,7 53,2 1.24 6,666.66 10.9 17,8 1,24 10,9 Count 40 Count
40 Count 100 100 Count Count
Count 60 100Count Count 40 50 20 50 50 20 30 20 50 20 5050 50 50 30 20 20
0 0 0 0 0 0 0 0 0 3 3 4 5 0 3 3 4 5 0 0 3 3 4 5 -10 0 10 10 10 3 3 4 5 3 3 4 5 -10 0 10 10 10 0 3 3 4 5 0 3 3 4 5 0 3 3 4 5 -10 0 10 10 10 0 -10 0 10 10 10 -10 0 10 10 10 -10 0 10 10 10 -10 0 10 10 10 -10 0 10 10 10 4 5 4 5 4 5 4 5 4 5 4 5 4 5 4 5 0 10Comp-B-515_30-A :: CFSE 10 Comp-B-515_30-A :: CFSE Comp-B-515_30-A :: CFSE Comp-B-515_30-A :: CFSE Comp-B-515_30-A :: CFSE 0 10Comp-B-515_30-A :: CFSE 10 0 10 10 0 10 10 0 10 10 0 10Comp-B-515_30-A :: CFSE 10 0 10 10 0 10Comp-B-515_30-A :: CFSE 10splenic DC Ctrl
MF3_100_D8_D08_025.fcs MF3_50_E8_E08_034.fcs MF3_300_C8_C08_016.fcs MF3_1000_B8_B08_007.fcs MF3_3000_B9_B09_008.fcs UN1_0_G11_G11_060.fcs MF3_10_G8_G08_052.fcs MF3_30_F8_F08_043.fcs live T cells live T cells MF live T cells live T cells live T cells count live T cells live T cells live T cells 2157 2302 splenic DC 2153 1977 2231 2503 2313 2237 CFSE 250K 250K 82,0 76,3 Scrambled Ctrl 5 10 Scrambled MF 200K 80 200K p<0.001 b) 4 80 10 * 150K 150K 60 FSC-H SSC-H 3 48,8 60 10 100K 100K splenic DC Ctrl
Ctrl 50K 40 50K Comp-U-515_30-A :: DAPI 2 MF 10 40 MF 0 Scrambled Ctrl 0 0 2 Scrambled Ctrl -10 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K Scrambled MF %CFSElow/CD3+ 20 3 3 4 5 %CFSElow/CD3+ 20 -10 0 10 10 Scrambled10 MF FSC-A SSC-A 80 Comp-R-660_20-A :: CD3_Alexa647
MF2_1000_B6_B06_005.fcs 0 MF2_1000_B6_B06_005.fcs0 10 30 50 100 10300 30 100050 100 3000300 1000MF2_1000_B6_B06_005.fcs3000 Ungated Singlets Singlets 6624 5433 4146
250K 250K CD25+/CFSElow 82,0 76,3 60 CD25+/live T cells 5 [peptide] ng/mL 10 [peptide] ng/mL
200K 200K CD25+/live T cells CD25+/live T cells 4 10 150K c) 150K 58,2 16,3 5 580 60 100 1010 5 40 58.2 10 Ctrl Ctrl FSC-H SSC-H 3 80 48,8 80 10 MF MF 100K 100K Ctrl 80 Ctrl 4 460 1010 10 MF MF 60 40 60
50K 50K Comp-U-515_30-A :: DAPI 2 60 10 %CFSElow/CD3+ 20 40 59,7 0 40 Count 40 3 3 0 10100 102 40 -10 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K 3 3 4 5 20 -10 0 10 10 10 %CD25/T cells %CD25/T cells 0 %CD25+/T cells 0 20 20 20 FSC-A 0 SSC-A 20 Comp-R-660_20-A :: CD3_Alexa647 %CD25+/CFSE_low Comp-R-660_20-A :: CD3_Alexa647 Comp-R-660_20-A :: CD3_Alexa647 3 0 3 CD25+/CFSElow -10 -10 0 0 10 30 0 5010 10030 100 300300 1000 10003000 3000 0 0 10 30 100 300 1000 3000 MF2_1000_B6_B06_005.fcs MF2_1000_B6_B06_005.fcs 0 0 10 30 50 100 300 1000 3000 3 4 5 MF2_1000_B6_B06_005.fcs0 10 3 30 50 4100 3005 1000 3000 Ungated Singlets 0 10 3 10 4 105 0 10 10 10 3 3 4 5 CD3 0 10 10 10 Singlets -10 0 10 10 10 6624 5433 Comp-G-780_60-A :: CD25_PECY7 4146 Comp-G-610_20-A :: CD69_PECF594 CD25 [peptide] ng/mL Comp-B-515_30-A :: CFSE [peptide] ng/mL 100 CD69+/CFSElow[peptide] ng/mL CD69+/live[peptide] ng/mL T cells Ctrl MF2_1000_B6_B06_005.fcs MF2_1000_B6_B06_005.fcs[peptide] ng/mL MF2_1000_B6_B06_005.fcs live T cells live T cells MF CD25+/live T cells live T cells 80 CD25+/live T cells 2022 2022 2022 d) 60 58,2 16,3 5 5 60 40 60 10 10 16.3 p<0.001 p<0.001 p=0.002 p<0.001 p<0.001 p<0.001 Ctrl 105 p=0.002 p<0.001 Ctrl 80 80 MF * * Ctrl40MF * * * * * * Ctrl 4 44 30 MF MF 10 1010 60 40 60 40 20 %CD25+/CFSE_low 59,7 20 40 40 3 33 Count 10 1010 0 10 20 30 100 300 1000 3000 20 %CD25/T cells 0 %CD25/T cells 20 20 0 %CD69+/T cells 10 %CD69+/CFSE_low Comp-R-660_20-A :: CD3_Alexa647 Comp-R-660_20-A :: CD3_Alexa647 3 3 [peptide] ng/mL -10 -10 0 0 0 10 30 100 300 1000 3000 0 10 30 100 300 1000 3000 0 0 0 3 4 5 3 4 5 0 10 10 10 0 103 104 105 30 10 3 30 504 100 5300 1000 3000 10 30 50 100 300 1000 3000 CD3 0 10 10 10 -10 0 10 10 10 Comp-G-780_60-A :: CD25_PECY7 Comp-G-610_20-A :: CD69_PECF594 CD69 Comp-B-515_30-A[peptide] :: CFSE ng/mL [peptide] ng/mL [peptide] ng/mL [peptide] ng/mL MF2_1000_B6_B06_005.fcs MF2_1000_B6_B06_005.fcs MF2_1000_B6_B06_005.fcs live T cells live T cells live T cells 2022 2022 Figure 3.1.6. BDC2.5-specific co2022-culture using peripheral APCs. Co-culture consisted of peripheral APCs loaded with peptides and CFSE-labeled BDC2.5 T cells and were analyzed by flow cytometry on Day 4. a) A titrated BDC2.5 T cell proliferative response is shown. Cells were pregated for live CD3+ cells. The gate defining CFSElow cells was drawn 1.5 SD to the left of the major peak in co-culture with scrambled peptides. b) The frequencies of CFSElow cells among live T cells were plotted across the peptide concentrations. No difference in BDC2.5-
59 specific T cell proliferative responses were detected between co-cultures using peripheral APCs from unmanipulated controls versus those from MF recipients across the peptide concentrations except at 3000 ng/mL. Statistical significance was generated by multiple t tests using the Sidak- Bonferroni correction method with ⍺ set to 0.05. Data represents of three independent experiments using nine mice. c) The expression of CD25 among live T cells were analyzed. Gate was drawn based on the FMO control. Frequencies of CD25+ cells among live T cells (middle panel) and among CFSElow cells (right panel) were plotted across the peptide concentrations. No differences in CD25 expression were found between co-cultures using peripheral APCs from unmanipulated controls versus those from MF recipients. d) The expression of CD69 among live T cells were analyzed. Gate was drawn based on the FMO control. Frequencies of CD25+ cells among live T cells (middle panel) and among CFSElow cells (right panel) were plotted across the peptide concentrations. No differences in CD25 expression among live T cells were found between co-cultures using peripheral APCs from unmanipulated controls versus those from MF recipients across the peptide concentrations except at 300 and 1000 ng/mL. Among CFSElow cells, the frequencies of CD69+ cells were higher in co-cultures using peripheral APCs from MF recipients than those from unmanipulated controls across the peptide concentrations except at 50 ng/mL. Statistical significance was generated by multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05.
60 Chapter 3.2 The impact of microbial transfer on the ability of APCs to present autoantigens in vivo To further investigate whether transfer of male microbes in young NOD females influenced an islet antigen-specific T cell response, I employed a T cell adoptive transfer approach. CFSE-labeled BDC2.5 CD4 T cells were injected in control or MàF recipient NOD females and their proliferation and activation responses were examined. CFSE+ BDC2.5 CD4 T cells were readily detected in pancreatic lymph nodes (pLN), mesenteric lymph nodes (mLN) and spleen of all recipients of adoptive transfer (Figure 3.2.1a, b), and were most numerous in pLNs (Figure 3.2.1d). Moreover, no significant difference was observed in frequencies (Figure 3.2.1e) or absolute numbers (Figure 3.2.1f) of CFSE+ T cells in each tissue between control NOD females and MàF recipients. CFSE division peaks resulting in CFSElow cells represented BDC2.5 CD4 T cells that had undergone cell division in vivo (Figure 3.2.1g). Their frequencies were greatest in pLNs compared to other tissues in MàF recipients (Figure 3.2.1h). In control females, CFSElow cells were more frequent in pLNs than in mLNs but not significantly different compared to the spleen (Figure 3.2.1h). No significant difference in frequencies or numbers of CFSElow cells were observed in any tissue between control NOD females and MàF recipients (Figure 3.2.1i, j). To validate that the CFSEhi cells population were non-divided cells, I co-transferred CFSE-labeled BDC2.5 T cells (CD45.1), together with CFSE-labeled polyclonal CD45.2 congenic NOD T cells into control and MàF CD45.1 NOD female recipients. The vast majority (>90%) of CD45.2+ polyclonal T cells in pLNs were CFSEhi compared to only 30% of the BDC2.5 T cells validating the assumption that high CFSE intensity represented non-divided cells (Figure 3.2.2). To identify all transferred T cells, independent of their CFSE intensity, I repeated the adoptive transfer studies using the CD45 allotypic marker to =discriminate donor and host cells. CFSE-labeled BDC2.5 T cells (CD45.1) were transferred into control or MàF NOD recipients congenic forCD45.2. CFSElow T cells accounted for ~50% of CD45.1+ BDC2.5 T cells identified in the pLNs of recipient mice (Figure 3.2.3a) and a lower proportion in mLNs and spleen (Figure 3.2.3d). These results validated my previous evidence that a 6-day adoptive transfer period was optimal to capture in vivo T cell proliferation by CFSE dilution. As previously observed (Fig. 3.2.1 e, f, i, j) the frequencies and absolute cell numbers of CFSE+
61 cells and CFSElow cells in pLN, mLN or spleen did not differ between non-manipulated and MàF recipient females (Figure 3.2.3b-e). To examine the activation status of adoptively transferred T cells, I characterized CD25 and CD69 expression (Figure 3.2.4a, c). The CD25 CD69 double negative subpopulation (Q1) comprised the largest proportion of CFSE+ BDC2.5 T cells, followed by the CD69+CD25- subpopulation (Q2) (Figure 3.2.4c). No significant difference in the proportions of these two subpopulations, or of CFSE dilution within each subpopulation, were detected between control and MàF recipients (Figure 3.2.4c, e). The CD25 and CD69 double positive cells (Q3) as well as CD69-CD25+ cells (Q4) contributed minor proportions of transferred CFSE+ BDC2.5 T cells (Figure 3.2.4c). The proportions of Q1-Q4 populations did not differ between control and MàF female recipients (Figure 3.2.4c). Antigen-experienced T cells are also distinguished by high CD44 expression226. For effector T cells, high CD44 expression is accompanied by low CD62L expression226. Analysis of CD44 and CD62L expression on CFSE+ cells was used to enhance my prior evidence of activation status (CD25, CD69) on transferred BDC2.5 T cells. CD62LhiCD44lo naïve T cells and CD62LloCD44hi antigen-experienced T cells were both present among CFSE+ BDC2.5 T cells in the pLNs (Figure 3.2.4b, d). There was no difference in the frequencies of these subpopulations among CFSE+ cells in control NOD and MàF recipients. T cell proliferation (frequencies of CFSElow cells) (Figure 3.2.4f) was more prominent among CD62LloCD44hi antigen-experienced T cells than CD62LhiCD44lo naïve T cells in the pLNs of both control and MàF recipients (Figure 3.2.4g). No differences were found in the frequencies of CFSElow CD62LloCD44hi cells between control and MàF recipients (Figure 3.2.4f). In summary, the frequencies and proliferation of BDC2.5 T cells did not differ in recipients who had or had not previously received male cecal microbiota in early life. The transferred BDC2.5 T cells were most numerous, and contained highest frequencies of CFSElow cells in pLNs compared to other tissues, consistent with their islet antigen-reactivity. Furthermore, BDC2.5 T cells in the pLNs expressed markers consistent with antigen induced activation although the levels did not differ between control females and MàF recipients. Thus, MàF microbial transfer did not affect the activation or proliferation of islet antigen ChrA- specific T cells following adoptive transfer.
62 a) Pre-gating strategy
51,3 5 5 1010 51.3
4 10104
3 10103
2 10 Comp-Alexa 647-A :: CD3 00 2 -10 3 4 5 Absl number 0 100 103 1010 4 1010 5 Comp-PE-A :: CD4 CD3 Freq CFSE+ CD4pLN_C tube1_017.fcs Live cells 255572
BDC2.5 T cell proliferation b) in vivo: pLN p<0.001 c) d) p<0.001 p=0.001 p<0.001 p<0.001 BDC2.5 T cell proliferation pLN Unmanipulated NODin vivo: pLN0.6MàF recipient 2000 p<0.001 5 0.500.51 5 0.21 10 10 1500 pLN Unmanipulated NOD0.4 MàF recipient 104 104 105 0.500.51 105 0.21 1000 103 103 Control female 0.2 4 104 Cell number M->F recipients 10 500 CD3 0 3 0 3 10 662 cells T % CFSE+/CD4+ 10 528 CFSE 0.0 0 Freq CFSE+ 0 103 104 0 103 104
CD3 0 pLN0 mLN pLN mLN pLN mLN pLN mLN 662 spleen 528 spleen spleen spleen 30 CFSE 3030 0 103 104 unmanipulated0 F 103 M->F104 unmanipulated F M->F 0.6 20 2020 Freq CFSE+ 30 3030 Absl number e) f) 0.4 51.2 33.1 10 1010 2000 20 0.6 2020
count Unmanipulated F51.2 00 33.1 1500 CFSE 0 10 1010 0.2 BDC2.5 T cell proliferation M->Fin vivo: 0 103pLN104 0.4 0 103 104 1000
count 0 00
% CFSE+/CD4+ T cells Unmanipulated F CFSE 3 4 3 4 Control female BDC2.5 T cell proliferation in vivo: 0 10pLN100.2 0 10 10 0.0 à Cell number pLN Unmanipulated NOD M F recipient 500M->F M->F recipients
5 pLN 105 0.500.51 10 0.21 mLN % CFSE+/CD4+ T cells spleen pLN Unmanipulated NOD MàF recipient 0.0 % CFSE low 0 104 104 105 0.500.51 105 0.21 pLN pLN mLN 3 mLN 103 10 spleen spleen 4 104 10 3 103
CD3 10 0 662 0 528 CFSE g) h) p=0.02 0 103 104 0 103 104 CD3 0 662 0 528p=0.002 CFSE p=0.02 30 3030 80 0 103 104 0 103 104
20 30 2020 60 3030
2051.2 40 202033.1 10 1010 Control female M->F recipients 20 33.1 51.2 10
count 10 0 00 10 CFSE 3 4 3 4 0 10 10 cells low/CFSE+ %CFSE 0 10 10 0 count 0 00 CFSE 3 4 3 4 0 10 10 pLN mLN0 10 pLN10 mLN spleen spleen Control F M->F
63
i) % CFSE low j) Absl number CFSE low
1000 80 800 60 600 Control female 40 Control400 female M->F recipients
Cell number M->F recipients 20 200
%CFSE low/CFSE+ cells 0 0
pLN pLN mLN mLN spleen spleen
Figure 3.2.1. Frequencies and proliferation of adoptively transferred BDC2.5 CD4 T cells in control NOD females or MàF recipients. a) Cells harvested from pLN, mLN or spleen were pre-gated on live singlets and CD4 T cells. The plot shown is an example of pre-gating on cells collected from the pLN of a control female. b) CFSE+ cells, whose CFSE intensities were 5 standard deviations or more to the right of the mean of CFSE- cells, were gated from CD4 T cells. The plot shown is an example of the gating for CFSE+ cells from CD4 T cells from the pLN of an control female. The number at the top of the gate represents the frequency of CFSE+ cells out of CD4 T cells. The number at the bottom represents the absolute cell number in the CFSE+ gate. The frequencies (c, e) and absolute cell numbers (d, f) of CFSE+ cells among CD4 T cells were plotted for 7 control NOD females and 12 MàF recipients that were adoptively transferred with CFSE-labelled BDC2.5 CD4 T cells. Significant differences were found in CFSE+ cell frequencies (c) and cell numbers (d) between pLN and other tissues with multiple t tests using the Sidak-Bonferroni correction method for ⍺ set to 0.05. No significant difference was found with multiple t tests using the Sidak-Bonferroni correction method for ⍺ set to 0.05 between control and MàF samples in the frequencies (e) or cell numbers (f) of CFSE+ cells in any of the three tissues. g) CFSE intensities of CFSE+ CD4 T cells were shown on a histogram. CFSElow cells were gated by including all cells that were 2.5 standard deviations or more to the left of the mean of the brightest peak. The frequencies (h, i) of CFSElow cells out of CFSE+ CD4 T cells and the absolute cell numbers (j) were plotted for 7 control NOD females and 12 MàF recipients that were adoptively transferred with CFSE-labelled BDC2.5 CD4 T cells. Significant differences in CFSElow cell frequencies (h) were found between pLN and other tissues with multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. No significant
64 difference was found with multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05 between control and MàF samples in the frequencies (i) or cell numbers (j) of CFSE+ cells in any of the three tissues.
60
40 Pre-gating strategy 6.87 20 105 105 52.4 104 104 0 0 103 104 count 103 103 CFSE 99.9 0 0 20 105 0.074 15 0 103 104 105 0 103 104 105 104
CD3 10
CD4 CD45.2 CFSE 103 70.3 5 0
0 0 103 104 0 103 104 CD3 CFSE count CFSE
Figure 3.2.2. Proliferation of CFSE-labeled NOD CD45.2 polyclonal T cells and BDC2.5 T cells co-transferred into CD45.1 NOD recipients. Cells harvested from pLN, mLN or spleen were pre-gated on live singlets and CD4 T cells. The plots shown were cells collected from the pLN of a MàF recipient. CD45.2-expressing cells (top arrow) were gated for CFSE expression. Few division peaks measured by CFSE dilution were observed in the CD45.2-expressing cells. CFSElow cells were gated by including all cells that were 2.5 standard deviations or more to the left of the mean of the major peak. CD45.2- CFSE+ cells, whose CFSE intensities were 5 standard deviations or more to the right of the mean of CFSE- cells, were gated from CD45.2- CD4 T cells (bottom arrow). CFSElow cells were gated using the same gate created for CD45.2+ polyclonal T cells.
65 250K 250K 86,5 86,5 250K 250K 98,6 98,6 5 5 5 5 10 10 10 10
200K 200K 200K 200K 53,4 53,4
4 4 4 4 10 10 10 10
150K 150K 150K 150K
FSC-H 3
FSC-H 3 SSC-H SSC-H 10 10 3 3 10 10 100K 100K 100K 100K 79,5 79,5 Comp-U-450_50-A :: DAPI Comp-U-450_50-A :: DAPI 2 2 50K 50K 50K 50K 10 10 0 0 Comp-R-660_20-A :: CD3 Alexa647 Comp-R-660_20-A :: CD3 Alexa647 0 0
0 0 0 0
0 50K0 100K50K 150K100K 200K150K 250K200K 250K 0 50K0 100K50K 150K100K 200K150K 250K200K 250K 0 50K0 100K50K 150K100K 200K150K 250K200K 250K 3 3 4 45 5 0 0 10 1010 1010 10
FSC-A FSC-A SSC-A SSC-A FSC-A FSC-A Comp-V-610_20-AComp-V-610_20-A :: CD4 BV605 :: CD4 BV605
pLN_MF1_003.fcspLN_MF1_003.fcs pLN_MF1_003.fcspLN_MF1_003.fcs pLN_MF1_003.fcspLN_MF1_003.fcs pLN_MF1_003.fcspLN_MF1_003.fcs Ungated Ungated Singlets Singlets Singlets Singlets Live Live a) 318036 318036 275110 275110 271300 271300 215642 215642 Pre-gating strategy
250K 86,5 250K 98,6 0,86 0,86 5 55 5 5 5 10 1010 1010 10 0.86 25 2525
200K 200K 53.453,4
4 44 4 4 4 20 20 10 1010 1010 10 20
150K 150K 15 1515
FSC-H 3 SSC-H 10 33 3 3 3 10 1010 10
10 Paramter Y 100K 100K Paramter Y 79,5 10 1010 47,2 47.247,2 Comp-U-450_50-A :: DAPI 2 50K 50K 10 00 00 0 5.0 55.0 Comp-R-660_20-A :: CD3 Alexa647 Comp-R-660_20-A :: CD3 Alexa647 Comp-R-660_20-A :: CD3 Alexa647 0 0 0 0 00 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K 3 4 5 3 3 3 3 4 4 5 5 3 3 4 4 0 0 10103 10104 10105 -10 0 0-10 1010 03 10 1010 4 10105 10 0 00 10 1010 3 10 1010 4 FSC-A SSC-A FSC-A Comp-V-610_20-A :: CD4 BV605 Comp-R-780_60-AComp-R-780_60-A :: CD45_1 ::APCef780 CD45_1 APCef780 CD3 CD3 Comp-B-515_20-AComp-B-515_20-A :: CFSE :: CFSE last batch ofcounts FreqCFSE CFSE+ CD4 CD45.1 last batch Absl number CFSE+ pLN_MF1_003.fcs pLN_MF1_003.fcs pLN_MF1_003.fcs pLN_MF1_003.fcslast batch of Freq CFSE+pLN_MF1_003.fcspLN_MF1_003.fcs pLN_MF1_003.fcspLN_MF1_003.fcs Ungated Singlets Singlets Live CD4+ T CD4+ T CD45_1+ CD45_1+ 318036 275110 271300 b) 215642 115239 115239 c) 987 987
0.6 5 5 10 10 0.8 1500 15 Control15 female Control female Control female 4 4 0,86 10 5 10 M->F recipients 10 22,4 22,4 M->F recipients 25 M->F recipients 10 10 0.6 3 3 0.4 10 1000 10 4 10 20 Paramter Y Paramter Y
2 2 10 10 75,9 75,9 5.0 5.0 4,07 4,07 15 0.4 0 0 3 10
Paramter Y 2 2 -10
Comp-V-431_28-A :: CD62L ef450 -10 0.2 Comp-V-431_28-A :: CD62L ef450 10 500 Cell number 47,2 0 0 0.2 3 4 0 0 10 3 10 4 5.0 4 45 5 0 10 10 Comp-R-660_20-A :: CD3 Alexa647 0 0 10 1010 10
% CFSE+/CD4+ T cells Comp-B-515_20-AComp-B-515_20-A :: CFSE :: CFSE
% CFSE+/CD4+ T cells Comp-R-720_40-AComp-R-720_40-A :: CD44 Alexa700 :: CD44 Alexa700 0 3 3 4 5 3 4 0.0 0 -10 0 10 10 10 0 10 10 0.0 last batch % CFSE low last batch Absl number CFSE low Sample NameSample NameSubset NameSubsetCount Name Count Comp-R-780_60-A :: CD45_1 APCef780 Comp-B-515_20-A :: CFSE pLN_MF1_003.fcspLN_MF1_003.fcs pLN_MF1_003.fcspLN_MF1_003.fcs CD62Llo CD44hi CD62Llo CD44hi749 749 pLN_MF1_003.fcs CD62Lhi CD44lo 221 pLN pLN pLNmLN CD45_1+ CD45_1+mLN pLN_MF1_003.fcs CD62Lhi CD44lo 221 mLN 987 987 spleen spleen spleen pLN_MF1_003.fcs pLN_MF1_003.fcs CD4+ T CD45_1+ d) e) 115239 987 5 10 5 100 600 10 1,62 1,62 5 1,11 1,11 15 15 10 Control female Control female 15 4 4 10 10 M->F recipients 4 M->F recipients 10 80 22,4 10 10 10 400 3 10 3 3 10 10 60 Paramter Y Paramter Y Paramter Y 5.0 52,7 2 0 0 5.0 52,7 10 75,9 5.0 4,07 Comp-G-780_60-A :: CD25 PECy7 40 Comp-G-780_60-A :: CD25 PECy7 0 200 3 3 60,9 60,934,0 34,0 -10 -10
2 Cell number -10 Comp-V-431_28-A :: CD62L ef450 0 0 3 3 4 5 -10 0 3 10 3 10 104 5 0 20 -10 0 10 10 10 3 3 4 4 0 0 10 10 10 10 3 4 Comp-G-610_20-A :: CD69 PECF594 4 5 0 10 10 Comp-G-610_20-A :: CD69 PECF594 0 10 10 Comp-B-515_20-AComp-B-515_20-A :: CFSE :: CFSE Comp-B-515_20-A :: CFSE %CFSE low/CFSE+ cells Comp-R-720_40-A :: CD44 Alexa700 0 0 pLN_MF1_003.fcspLN_MF1_003.fcs Sample NameSampleSubset Name NameSubsetCount Name Count Sample Name Subset Name Count CD45_1+ CD45_1+ pLN_MF1_003.fcspLN_MF1_003.fcs CD69+CD25- CD69+CD25- 336 336 987 pLN_MF1_003.fcs CD69-CD25- 601 pLN_MF1_003.fcs pLN_MF1_003.fcs CD62Llo CD44hi 749 pLN mLN pLN 987 mLN pLN_MF1_003.fcs CD69-CD25- 601 CD45_1+ pLN_MF1_003.fcs CD62Lhi CD44lo 221 spleen spleen 987
5 10 Figure 3.2.3. Proliferation of CFSE-labeled BDC2.5 T cells in CD45.2 NOD recipients. Cells 1,11 1,62 15
4 10 harvested from pLN, mLN or spleen were pre-gated on live singlets and CD4 T cells. Shown in
10
3 (a) is a representative plot of cells collected from the pLN of a CD45.2 control NOD female. 10 Paramter Y
0 5.0 52,7 BDC2.5 T cells were gated by CD45.1 expression, gating of which was determined based on an Comp-G-780_60-A :: CD25 PECy7
3 60,9 34,0 -10 low 0 3 3 4 5 FMO control. CFSE cells were gated by including all cells that were 2.5 standard deviations -10 0 10 10 10 3 4 0 10 10 Comp-G-610_20-A :: CD69 PECF594 Comp-B-515_20-A :: CFSE
pLN_MF1_003.fcs Sample Name Subset Name Count CD45_1+ pLN_MF1_003.fcs CD69+CD25- 336 987 pLN_MF1_003.fcs CD69-CD25- 601 66 or more to the left of the mean of the brightest peak. Frequencies (b) and absolute cell numbers (c) of CFSE+ cells among CD4 T cells were plotted as bar graphs for five control females and three MàF recipients and the error bars represent standard deviation. Frequencies (d) and absolute cell numbers (e) of CFSElow cells among CFSE+ cells were also plotted as bar graphs and the error bars represent standard deviation. No significant difference between control females and MàF recipients were found in b-e by multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05.
67
Pre-gating strategy 5 a) 5 10 b) 10 6.67 2.22 5 35.8 105 0.16 6.67 Q4 2.22Q3 105 35.8 10 Q5 4 4 10 10 4 104 104 10 55.6 55.6 3 103 10 3 103 103 10 0 0 0 0 0 Q6 Q165.6 Q221.7 65.6 21.7
3 4 4 5 0 104 105 3 4 50 103 104 105 0 10 10 0 CD25 10 10 0 10 10 10 CD25 CD3 CD62L CD69 CD62L CFSE CD69 CD44 CD44 10 10 8 8 23.7 23.7 6 6
4 4
2 2 0 0 freq. CD25 CD69 exp/CFSE+ 0 103 104 0 103 104 count count freq. CD25 CD69 exp/CFSE+ CFSE CFSE freq. CD44 CD62L exp/CFSE+ c) d)
80 100 100 Ctrl Ctrl Ctrl M->F 80 MF60 80 MF
(%) 60 (%) 40 60 40
Freq Freq 20 40 20 Freq. (%) among CFSE+ among (%) Freq. CFSE+ among (%) Freq. 0 0 20 CFSElo withinQ CD251 CD69Q2 CFSE+Q3 MINUSQ4 TWO SUBPOP Q5 Q6 Freq. (%) among CFSE+ among (%) Freq. 0 CFSElo within CD62 CD44 CFSE+ CD69-CD25- CD69+CD25- CD69+CD25+ CD69-CD25+ e) f) CD62Lhi CD44lo CD62Llo CD44hi
100 CD69-CD25- CD69+CD25- CD69+CD25+ CD69-CD25+ 100 Ctrl Ctrl 80 M->F 80 MF
(%) 60 (%) 60
40 40 Freq Freq 20 20 Freq. (%) of CFSElo of (%) Freq. CFSElo of (%) Freq. 0 0 CFSElo/Q1 CFSElo/Q2 CFSElo/Q5 CFSElo/Q6
%CFSElo/CD69-CD25- CFSE+%CFSElo/CD69+CD25- CFSE+ %CFSElo/CD62Lhi CD44lo CFSE+%CFSElo/CD62Llo CD44hi CFSE+
68 p=0.001p=0.001 p<0.001 ** * 100100 p=0.001 p<0.001 g) * * 8080 100
80 6060 60 CFSElo%CFSElo/CD62Lhi/Q5 CD44lo CFSE+ 4040 %CFSElo/CD62Lhi CD44lo CFSE+ 40 CFSElo%CFSElo/CD62Llo/Q6 CD44hi CFSE+ %CFSElo/CD62Llo CD44hi CFSE+ 2020 Freq. (%) of CFSElo of (%) Freq. CFSElo of (%) Freq. 20 Freq. (%) of CFSElo of (%) Freq.
00 0
Ctrl CtrlCtrl M->F M->F
Figure 3.2.4. Activation status of adoptively transferred BDC2.5 T cells. (a) Singlet live CD4 T cells harvested from the pLNs were gated for CFSE expression. CFSE+ gating was determined using the same criteria as described previously in Fig 3.2.1b. CFSE+ cells were characterized by CD25 and CD69 expression. CD25 and CD69 gatings were based on FMO controls. Each subpopulation was further gated for CFSElow cells. The CFSElow gating was determined as described in Fig 3.2.1g. The plot shown is an example of pre-gating on cells collected from the pLN of a MàF recipient. (b) Based on the same pre-gating strategy, CFSE+ cells were also gated for CD62L and CD44 expression. CD62L and CD44 gatings were based on FMO controls. Each subpopulation was also gated for CFSElow cells. (c) The frequencies of CD69- CD25-(Q1), CD69+CD25-(Q2), CD69+CD25+(Q3) and CD69-CD25+(Q4) subpopulations among CFSE+ BDC2.5 T cells were plotted for pLN cells collected from 6 control NOD females and 6 MàF recipients. No significant difference was detected among frequencies of each subpopulation between control and MàF recipients by multiple t tests using the Sidak- Bonferroni correction method with ⍺ set to 0.05. d) The frequencies of CD62LhiCD44lo (Q5) and CD62loCD44hi (Q6) subpopulations among CFSE+ BDC2.5 T cells were plotted for pLN cells collected from the 6 control NOD females and 6 MàF recipients. No significant difference was detected among frequencies of each subpopulation between control and MàF recipients by multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. e) The frequencies of CFSElo cells among CD69-CD25- (CFSElo/Q1) and CD69+CD25- (CFSElo/Q2) CFSE+ cells were plotted for pLN cells collected from the 6 control NOD females and 6 MàF recipients. No significant difference was detected among frequencies of the subpopulations
69 between control and MàF recipients by multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05. The CD69+CD25+ and CD69-CD25+ subpopulations contained too few cells for meaningful characterization by the CFSElo gate. f) The frequencies of CFSElo cells among CD62LhiCD44lo (CFSElo/Q5) and CD62loCD44hi (CFSElo/Q6) CFSE+ cells were plotted for pLN cells collected from the 6 control NOD females and 6 MàF recipients. No significant difference was detected among frequencies of the subpopulations between control and MàF recipients by multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05.
70 Chapter 3.3 Identification and enumeration of insulin peptide-specific T cells within islets and peripheral lymphoid tissues with tetramer staining The pathogenesis of autoimmune diabetes involves the activation of islet antigen- specific CD4+ T cells and their infiltration into the islets. The B9:23 epitope on insulin β chain is a major islet autoantigen against which cognate CD4+ T cell responses have been strongly implicated in disease in NOD mice112,227,228. The identification and enumeration of B9:23- reactive CD4 T cells at effector sites reports directly on islet antigen-specific CD4+ T cell responses in NOD mice. One of my aims was to investigate whether the MàF microbial transfer associated with protection from disease alters islet-antigen specific CD4+ T cell frequencies at effector sites. Chapter 3.1 and 3.2 examined the well-characterized CD4+ T cell response against synthetic peptides and naturally occurring epitopes on chromogranin A presented by APCs. Here I analyzed insulin B9:23-specific T cell frequencies in the islets and peripheral lymph nodes. Particularly, I used flow cytometry to ask whether the frequencies and immunophenotypes of these B9:23-specific T cells in the islets and LN differ between unmanipulated NOD females and MàF recipients.
Development of tetramer staining of islet cell suspensions to quantify insulin-specific T cells To identify B9:23-specific T cells, I employed labeled tetramers developed in the Kappler Lab (Denver, Colorado)228 as reagents in flow cytometry analysis. Kapplers’ group has identified two versions of peptide B9:23 that places the amino acid glutamic acid (E) or without its side chain (glycine, G) at position 8 in the peptide-binding groove of the H2-Ag7 MHC molecule. Tetramers consisting of four H2-Ag7 MHC class II molecules bound to these two versions of peptides, p8E and p8G, identified B9-23-specific CD4+ T cells in the islets of NOD mice at 8 and 12 weeks of age228. Using these same tetramers, we enumerated insulin peptide- specific CD4+ T cells in the islets of unmanipulated control females and MàF recipients at 11- 14 weeks of age. Since the tetramers are built with MHC class II molecules complexed to insulin peptides, CD8+ T cells were not expected to bind to either of the tetramers providing a negative control.
71 First, I needed to develop the technique of islet isolation and preparation of islet cells suspensions suitable for flow analysis. The frequencies and absolute numbers of CD4+ B9:23 tetramer+ T cells were quantified in islets, pLNs and mLNs isolated from from pre-diabetic unmanipulated NOD, MàF recipient NOD, as well as from other NOD mice used to pilot this complex series of methods (Figure 3.3.1a, b). Due to the low number of intra-islet T cells and limited number of islets that can be isolated from each mouse, islet cells were pooled from 3 to 4 mice of the same type for these studies. LN cell suspensions were from individual mice. As a proportion of all CD4+ T cells, the frequency of tetramer-positive CD4+ T cells was approximately 1% in the islets, and less than 0.1% in the LN tissues (Figure 3.3.1c). Given these low frequencies, it was essential to consider the sensitivity of the assay and minimal number of events required to quantify these rare cells. One critical parameter to evaluate the precision of absolute cell count measurements is the coefficient of variation (CV) with a formula of: �� = ×100%, # where SD represents standard deviation and �� = #��������� ������ �� �������� ref 229,230. For example, when 60 tetramer-positive cells are counted from an islet sample, the CV obtained was 13%. Given the 95% confidence interval (CI) two SD away from the mean, the true count was -45 - 75 (60±15). The CVs and 95% CI for the tetramer-positive cells at other tissue locations are summarized (Table 3.3.1). Eighteen of the twenty samples evaluated from control and MàF recipients displayed a < 20% CV. This observation indicates that most samples contained sufficient cell numbers that frequencies of tetramer-positive cells relatively could be reliably measured. Preliminary results (presented here) measured three pooled islet samples (each containing islets from 3-4 mice) from each of the control and MàF conditions. Additional points are being collected to increase the replication of analysis of tetramer-reactive cells in the islets. Due to the rarity of tetramer-positive cells among total CD4+ T cells, I asked whether additional cell surface markers could help to refine the frequencies of tetramer-positive cells. Integrin β7 (Itgβ7) and CCR9 have been observed on infiltrating lymphocytes in the islets of prediabetic NOD mice231-233. I evaluated the frequency of tetramer+ CD4+ T cells that co- expressed β7, CCR9 and CD25 in islets isolated from 11 to 14-week-old control and MàF
72 NOD mice (Figure 3.3.2). Among total CD4+ T cells from the islets, an average of 1.04% were tetramer-positive (Figures 3.3.2b, d, f). In comparison, 3.9% of CD4+ T cells were Itgβ7+ (Figure 3.3.2b), 0.8% expressed CCR9 (Figure 3.3.2d) and 12.2% expressed CD25 (Figure 3.3.2f). Absolute cell numbers within the gates used to define the expression of each of these markers (purple gates in Figures 3.3.2 a, c, e) from each data point are listed in Table 3.3.2 along with the CVs and 95% CI. All of these gated populations contained sufficient cell numbers to yield CVs < 20%. This means that the frequencies of integrin β7, CCR9, or CD25- expressing islet CD4+ T cells were measured with relatively reliable cell counts. Itgβ7+ tetramer+ cells comprised 0.165% of total islet CD4+ T cells (Figure 3.3.2 b). Since tetramer-positive cells were 1.04% of the total CD4+ T cells, integrin β7 expression was not enriched among B9:23-specific CD4+ T cells. Similarly, CCR9- and CD25-expressing tetramer-positive CD4+ cells were 0.068% and 0.323%, respectively (Figures 3.3.2 d, f), however the CVs for these cell counts were > 20% (Table 3.3.2) suggesting that the low frequencies of integrin β7, CCR9, CD25 co-expression by tetramer-positive islet T cells cannot be reliably measured under the conditions tested. The frequencies CCR9, integrin β7 or CD25-expressing cells among CD4+ T cells as well as CD8+ T cells were also plotted for the islet samples from 11 to 14-week-old NOD mice that were either unmanipulated or MàF recipients. The frequencies of integrin β7-positive cells and CCR9-positive cells were higher in CD8+ T cells than CD4+ T cells (Figure 3.3.3). In summary, tetramer staining using insulin peptide B9:23-specific tetramers identified cognate CD4+ T cells particularly in islets of control and MàF recipients, as well as other NOD mice. Sufficient cell numbers were collected to reliably determine the frequencies of tetramer-positive CD4+ T cells, as well as the frequencies of integrin β7, CCR9, or CD25- expressing CD4+ T cells with less than 20% coefficient of variation in absolute cell numbers for most data points. Neither integrin β7, CCR9 nor CD25 acted as an enrichment marker for identifying these insulin peptide B9:23-specific CD4 T cells, although an interesting finding was that islet CD8 T cells consisted of higher frequencies of CCR9-expressing and integrin β7- expressing cells than did islet CD4 T cells. Additional experiments now underway will be able to determine whether manipulation of the female microbiome by oral gavage with male-sourced microbes, impacts the frequency of B9:23-specific T cells within islets.
73 a) Pre-gating strategy
CD45 250K Singlets 250K250K Singlets 5 5 55 250K 1010 37.737.7 1010 90.090.0 98.6
200K200K 200K200K 4 4 104 10104 1010 150K150K 150K150K CD45 3 Live 250K Singlets 250K Singlets 5 10 5 Live
3 3 SSC-H FSC-H 103 37.7 10 53.0 SSC-H 90.0 FSC-H 98.6 10103 10 53.053.0 100K100K 100K100K 200K 200K 24 4 10100 010 50K 50K Comp-U-515_30-A :: DAPI 50K 0 Comp-U-515_30-A :: DAPI 50K 0 150K 150K 3 Live 0 0 Comp-R-720_40-A :: CD45_Alexa700 10 0 0 Comp-R-720_40-A :: CD45_Alexa700
3 SSC-H FSC-H 0 10 53.0 0 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K 100K 0 50K 100K 150K 200K 250K 100K 0 50K 100K 150K 200K 250K 0 50k 100k 150k 200k 250k 0 50k 100k 150k 200k 250k 0 50k 100k 150k 200k 250k 0 50k 100k 150k 200k 250k SSC-A 2 FSC-A SSC-A FSC-A SSC-A 10 FSC-A SSC-A FSC-A
50K 50KH
0 Comp-U-515_30-A :: DAPI - H
0 - islets_MF1_002.fcsislets_MF1_002.fcs islets_MF1_002.fcsislets_MF1_002.fcs islets_MF1_002.fcsislets_MF1_002.fcs islets_MF1_002.fcsislets_MF1_002.fcs Ungated CD45 0 Live 0 Singlets Comp-R-720_40-A :: CD45_Alexa700 FSC 117933 DAPI 44510 23602 21252 SSC CD45 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K 0 50KFSC100K-A 150K 200K 250K SSC-A SSC-A SSC-A SSC-A FSC-A SSC-A FSC-A 5 5 10 islets_MF1_002.fcs 10 islets_MF1_002.fcs islets_MF1_002.fcs islets_MF1_002.fcs Ungated CD45 Live Singlets 117933 44510 23602 21252 non-CD4 CD4+ b) 4 non-gd T 4 10 non-gd T 10 18.6 81.3 10 20.7 10
5 55 1053 103 1010 1010
4 4 non-CD4 CD4+ 4 non-gd T 4 18.618.6 81.381.3 1010 20.720.7 1010 0 0 10 Sample Name Subset Name
Comp-R-660_20-A :: CD3_Alexa647 Comp-R-660_20-A :: CD3_Alexa647 islets_MF1_002.fcs CD4+ Comp-R-660_20-A :: CD3_Alexa647 3 Comp-R-660_20-A :: CD3_Alexa647 3 islets_MF1_002.fcs CD4+ 103 103 10 3 4 5 10 3 4 5 8.0 islets_MF1_002.fcsislets_MF1_002.fcs CD8+ 0 10 10 10 0 10 10 10 Comp-V-660_20-A :: TCRgd_biotin Comp-V-431_28-A :: CD4_ef450
islets_MF1_002.fcs islets_MF1_002.fcs 6.0 0 islets_MF1_002.fcs 0 islets_MF1_002.fcs 0 Singlets non-gd T 1010 Sample Name Subset Name 20948 4342 Count Count
Comp-R-660_20-A :: CD3_Alexa647 Comp-R-660_20-A :: CD3_Alexa647 islets_MF1_002.fcs CD4+ 4.0 3 4 5 3 4 5 88.0 islets_MF1_002.fcs CD8+ 0 103 104 105 0 103 104 105 0 10 10 10 0 10 10 10 tet+tet+ Comp-V-660_20-A :: TCRgd_biotin Comp-V-431_28-A :: CD4_ef450 0.37 2.0 islets_MF1_002.fcs islets_MF1_002.fcs 66.0
CD3 5 Singlets 10 non-gd T 20948 CD3 4342 0 TCRgd Count CD4 CD4 44.0 3 4 5 CD8+ 0 10 10 10 4 CD8+ 10 67.6 Comp-G-575_26-A :: tetramer_PEtet+ CD8 0.37 22.0 5 1053 1010 0 0 3 4 5 0 103 104 105 4 CD8+ 0 10 10 10 10104 67.667.6 Comp-G-575_26-A :: tetramer_PE 0 Comp-R-660_20-A :: CD3_Alexa647 Comp-R-660_20-A :: CD3_Alexa647 3
103 count 10 3 3 4 5 -10 0 10 10 10 tetramers Comp-R-780_60-A :: CD8_APCef780 0 islets_MF1_002.fcsislets_MF1_002.fcs Comp-R-660_20-A :: CD3_Alexa647 non-CD4 809 3 3 4 5 -10 00 103 1010 4 10105absl # Comp-R-780_60-A :: CD8_APCef780
CD3 islets_MF1_002.fcs non-CD4 809 CD8 freq 175 Ctrl 150 MF c) 125 d) absl # Nlrp1b-KO 603A 8 100 Ctrl PP null 6 75 MF
counts 50 175 4 Nlrp1b-KO 603A Ctrl 25 150PP null 2 MF 0 125 -25 Nlrp1b-KO 603A 0.06 100 PP null pLN islets mLN 75 0.04 counts %tetramer+/CD4T 50 0.02 25 0.00 0
pLN mLN pLN islets islets mLN
74 Figure 3.3.1. Identifying insulin peptide B9:23-specific T cells in islets and peripheral lymph nodes. a) Pre-gating strategy showing an islet sample from 3 pooled MàF recipients. Hematopoietic (CD45+) live singlet cells were identified, from which b) non-ɣẟ T cells where identified. Of these T cells, CD4+ T cells and CD8+ T cells were identified and examined for staining with the tetramers (p8G and p8E). The gate defining tetramer-positive cells were drawn by excluding 99.5% of all CD8+ T cells, where no tetramer-positive cells were expected. Frequencies c) and absolute cell numbers d) of tetramer-positive T cells among all CD4+ T cells were plotted for islets samples from 11-14 weeks old unmanipulated NOD females (Ctrl) and MàF recipients, as well as other NOD mice at 230 days of age being used as practice: NOD mice that were deficient in peyer’s patches (PP null; generated by Dr. Alexandra Paun) and Nlrp1b-knockout mice from line 603A (Nlrp1b-KO 603A; generated by Christopher Y. Yau). Multiple t tests using the Sidak-Bonferroni correction method with ⍺ set to 0.05 were performed for comparison between controls and MàF recipients. No difference in either frequencies and absolute cell numbers were found.
Control M->F recipients Tissue Data point Count CV (%) 95% CI Count CV (%) 95% CI islets 1 60 13 60±15 56 13 56±15 2 45 15 45±13 38 16 38±12 3 175 8 175±26 9 33 9±6 pLN 1 56 13 56±15 34 17 34±12 2 40 16 40±13 73 12 73±17 3 39 16 39±12 60 13 60±15 4 66 12 66±16 5 30 18 30±11 mLN 1 53 14 53±15 35 17 35±12 2 19 23 19±9 24 20 24±10 3 33 17 33±11 27 19 27±10
Table 3.3.1. Enumeration of tetramer-positive cells. Individual absolute cell counts of tetramer- positive cells at the three tissue locations from the controls and MàF recipients were listed,
75 along with coefficient of variation (CV) and 95% confidence intervals (CI) of the enumeration. CV (%) that was greater than 20% are shown in bold.
a) b) 100 Pre-gated on CD4+ T cells
55 Itgb7+ Itgb7+tet+ 1010 3,11Itgβ7+ Itgβ7+tet+0,25 10
4 3.907 10104 1 1.040 3 10103 0.1 0.165 00 Comp-B-515_30-A :: Itgb7_FITC 0.01 3 4 5 7 00 10103 10104 10105 Itgb7+ Tet+ Itgb7+ Tet+ β Comp-G-575_26-A :: tetramer_PE tg
I tetramers Freq(%)/CD4 CD4 islets_MF1_002.fcs CD4+ 3537
100 Pre-gated on CD4+ T cells c) Pre-gated on CD4+ T cells d)
CCR9+ CCR9+Tet+ 5 5 1010 1010 CCR9+0,76 CCR9+tet+0,11 10
88.0 4 10104 1 66.0 0.833 1.040 3 10103 Count 44.0 0.1 tet+ tet1,07+ 0 0.088 22.0 0
Comp-B-710_50-A :: CCR9_PerCP710 0.01 0 3 4 5 0 0 103 104 105 3 4 5 0 10 10 10 CCR9+ Tet+ CCR9+ Tet+ 0 0 10103 10104 10105 Comp-G-575_26-A :: tetramer_PE Comp-G-575_26-A :: tetramer_PE CCR9 Freq(%)/CD4 CD4 tetramersislets_MF1_002.fcs tetramersislets_MF1_002.fcs CD4+ Cell counts Cell CD4+ 3537 3537
100 e) Pre-gated on CD4+ T cells f)
5 CD25+ CD25+tet+ 10105 12,0CD25+ CD25+tet+0,25 10 12.167
4 10104 1 1.040 3 10103 0.1 0.323 2 10 0 2 Comp-G-780_60-A :: CD25_PECY7 -10 0.01 3 4 5 00 10103 10104 10105 CD25+ Tet+ CD25+Tet+ Comp-G-575_26-A :: tetramer_PE
CD25 Freq(%)/CD4 CD4 tetramersislets_MF1_002.fcs CD4+ 3537
Figure 3.3.2. Expression of CCR9, integrin β7 and CD25 on islet CD4+ T cells. Cells were pre- gated for CD45+ live singlet cells that were CD3+ TCRɣẟ-CD4+. Tetramer-positive cells (tet+) were determined the same way as described in 3.3.1. Intergin β7-expressing cells (Itgβ7+;
76 purple gate) and Intergin β7-expressing tetramer-positive cells (Itgβ7+tet+; red gate) were also identified. Gates were determined based on FMO controls. The frequencies of tet+, Itgβ7+ and Itgβ7+tet+ cells among CD4+ T cells were plotted (orange stacked column graph). CCR9 expression (blue column graph) and CD25 expression (green column graph) among CD4+ T cells were analyzed in the same manner. Data are representative of three islet pools (isolated from 10 mice).
marker+ marker+Tet+ Marker Data point Count CV(%) 95% CI Count CV(%) 95% CI 1 27 19 27±10 4 50 4±4 CCR9 2 36 17 36±12 6 41 6±5 3 47 15 47±14 2 71 2±3 1 110 10 110±21 9 33 9±6 Itgb7 2 200 7 200±28 5 45 5±5 3 219 7 219±30 7 38 7±5 1 426 5 426±41 9 33 9±6 CD25 2 559 4 559±47 20 22 20±9 3 636 4 636±50 19 23 19±9 1 38 16 38±12 Tetramer 2 56 13 56±15 (Tet) 3 45 15 45±13
Table 3.3.2. Enumeration of cells that expressed either CCR9, Integrin β7, CD25, and/or were tetramer-positive. Absolute cell counts from each data point were listed along with coefficient of variation (CV) and 95% confidence intervals (CI).
77 CCR9 Itgb7 CD25 100 CD4 90CCR9 Itgb7 CD25 100 80 CD4 90 Freq (%)/CD4 70 80 60 Freq (%)/CD4 70CCR9 Itgb7 CD25 100 50 60 CD4 90CCR9 Itgb7 CD25 CCR9 Itgb7 CD25 40 100100 100 CD4 50 9090 CD8 80 CD4 30 8080 90 Freq (%)/CD4 40 Freq (%)/CD4 7070 70 20 CD8 80 6060 30 p<0.001 Freq (%)/CD8 (%) 5050 60 Freq (%)/CD4 10 70 Freq 4040 20 CD8 50 3030 Freq (%)/CD8 0 p<0.001 60 2020 10 CCR9 Itgb7 CD25 Freq (%)/CD8 40 1010 50 00 CD8 0 CCR9 Itgb7 CD25 CCR9 Itgb7 CD25 30 CCR9 Itgb7 CD25 40 Figure 3.3.3. Expression of CCR9, integrin β7 and CD25 among islet CD4 and CD8CD8 T cells. 20 Cells were pre-gated for CD45+ live singlet cells that were CD3+ TCRɣẟ-CD4+ and analyzed 30 Freq (%)/CD8 using the same purple gates as defined in Figure 3.3.2. Frequencies of CCR9+, integrin β7 10 (Itgβ7+) and CD25+ cells among CD4+ and CD8+ T cells were plotted. Data representative of 20 three islet data points (n=10 mice). Multiple t tests using the Sidak-Bonferroni correctionFreq (%)/CD8 0 method with ⍺ set to 0.05 were performed for comparisons of frequencies of CCR9, Itgβ7 and 10 CCR9 Itgb7 CD25 CD25-expressing cells among CD4 vs CD8 T cells.
0 CCR9 Itgb7 CD25
78 Chapter 3.4 Taxonomic assignments of gut microbes from NOD with or without microbial transfer based on full-length 16S rRNA gene sequencing
The protection from autoimmune diabetes in MàF recipients depended on microbial transfer, which produced a durable change in the gut microbial composition183. In the published study, 16S rRNA gene sequencing of the gut microbiota of unmanipulated NOD females and MàF recipients was performed with high-throughput sequencing that relied on amplicons of V1 and V3 regions234, or V4 regions alone235, of the 16S rRNA gene183. The lengths of variable regions in the 16S rRNA gene vary between 100bp to 300bp (Figure 2.2). Sequences of such length allow taxonomic classification but with limited classification accuracy and/or ability218. Poorly characterized bacterial taxa such as the S24-7 family frequently appear in high-throughput sequencing data of murine microbiota and do not provide useful taxonomic interpretation. To better identify bacteria taxa that were present in mice protected from autoimmune diabetes due to microbial transfer, we asked whether extending the length of the sequencing region in the 16S rRNA gene will help to deconstruct these poorly characterized taxonomic groups and identify taxa that previously fell under these groups due to short sequence reads. The16S rRNA gene contains nine variable regions and is about 1.4kb long. A full-length 16S rRNA gene sequence allows classification down to the genus level at an overall confidence threshold of 91.4% using the RDP classifer system218. In comparison, high-throughput sequencing using one or two variable regions allow sequence classification to the genus level at an overall confidence threshold of 71.1 to 83.2%218. Therefore, full-length sequences of the 16S rRNA gene may provide more taxonomic information and allow further classification of previously poorly classified taxa. However, high-throughput sequencing methods do not permit generation of sequence reads of such length236. Therefore, to sequence the full-length 16S rRNA gene, the Sanger sequencing method was employed and required templates from each individual bacterial taxon. To generate enough templates from individual bacterial taxa, we cloned the 16S rRNA genes amplified from murine cecal or fecal contents into plasmid vectors for propagation in E. coli. Each E. coli colony represented amplification of a full-length 16S rRNA gene from one bacterial taxon. Although the number of bacterial taxa that were sequenced in the end was very limited compared to the overall complexity of murine gut microbiota, taxa that were
79 abundant in the sequenced pool may reflect its abundance in the overall gut microbial community. This is due to the fact that more 16S rRNA gene amplicons were generated from taxa that were more abundant in the cecal or fecal contents, and more of those amplicons were cloned and propagated to reach the sequencing step.
3.4.1 Full-length 16S rRNA gene sequencing of S24-7 rich samples A stool sample from a MàF recipient (MF51STL) was included in the analysis. The fecal microbial composition of this MàF recipient was previously characterized by high-throughput sequencing to be rich in the S24-7 family, which belongs to Bacteriodetes phylum. Cloning of the full-length 16S rRNA gene amplicons from bacterial taxa present in MF51STL generated 95 sequence reads that were fed into RDP classifier. All 95 sequence reads were of good quality and were assigned taxonomical classifications. Of the 95 sequence reads from MF51STL shown in Table 3.4.1-1, 80 were classified as Bacteroidetes, 13 were Firmicutes and 2 were Proteobacteria. Among Bacteroidetes, Porphyromonadaceae was the most abundant family. Among Porphyromonadaceae, Barnesiella was the most abundant genus. The next abundant family among Bacteroidetes was Prevotellaceae, of which the Prevotella genus was the most abundant. The 13 Firmicutes were overrepresented by the Lachnospiraceae family and included taxa that were identified at the genus level as Clostridium XIVa. Ruminococcaceae was also spotted in the Fimicutes. The majority of the sequence reads (87/95) were assigned to families at a confidence threshold above 80%. The assignment to the genus level was more ambiguous, with only 24 out of the 95 reads passed 80% confidence threshold at the genus level. Cloning and sequencing of the full-length 16S rRNA gene amplicons from bacterial taxa present in a stool sample of an unmanipulated NOD female (FP48STL) also generated 95 sequence reads that were fed into RDP classifier. The fecal microbial composition of this unmanipulated mouse was also previously characterized by high-throughput sequencing to be rich in the S24-7 family. Of the 95 sequence reads, 88 were of good quality and were assigned taxonomic classification as shown in Table 3.4.1-2. Among these 88 taxa, Bacteroidetes was again the dominant phylum and Porphyromonadaceae was the most abundant family, among which Barnesiella was the most represented genus, similar to that observed in MF51STL. Among Bacteroidetes, families that were seen in the MF51STL sample such as Rikenellaceae and
80 Prevotellaceae were again present in the FP48STL sample. Among the Firmicutes, Lachnospiraceae and Lachnobacillaceae accounted for the most abundant families. Clostridium XIVa was again spotted among Lachnospiraceae. Of the 88 sequence reads, 80 were assigned families at a confidence threshold above 80%. However, assignments at the genus level was again less defined, with 25 out of the 88 reads passed 80% confidence threshold with the assigned genera.
3.4.2 Dissecting the S24-7 family Since RDP classifier does not, or no longer uses S24-7 as a taxonomic unit, I resorted to another taxonomic classification tool to identify which and how many of the sequence reads from MF51STL and FP48STL belonged to S24-7 but were assigned more specific taxa in RDP. While RDP runs phylogenetic classification based on Bergey’s outline237, which is a comprehensive manual of taxonomic roadmaps238, with minor modifications taken from the NCBI database237, the SINA Alignment Service on the SILVA taxonomy database uses manual curation based on the ribosomal small subunit (SSU) rRNA guide tree (ARB 2004 release by Wolfgang Ludwig) for phylogenetic classification237. By feeding the 95 sequence reads from MF51STL into SILVA and rejecting sequences below 70% identity, I found 35 out of the 95 sequence reads from MF51STL that are classified as Bacteroidetes S24-7 by SILVA. The taxonomic assignments by SILVA were compared to RDP assignments for each sequence read. Sequences that were identified as Firmicutes in both databases were listed in Table 3.4.2-1. The family assignments were identical between the two databases for all sequence reads that were classified as Firmicutes in both databases except one sequence read (ID:94). Half of the genus assignments for the Firmicutes from the two databases agree completely with each other while the other half contained variations, specifically Lachnospiraceae NK4A136 versus Eisenbergiella, as well as Oscillospira versus Flavonifractor. Therefore, valid comparisons at the family level can be made between classifications by the two taxonomies, while comparisons at the genus level should be exercised with caution. All 35 sequences that were identified as Bacteroidetes S24-7 by SILVA were classified into the Porphyromonadaceae family by RDP at high confidence threshold (>90% with exception of two sequences at 89% and 82%) (Table 3.4.2-2). At the genus level, Barnesiella accounted for 31 out the 35 sequences while Coprobacter made up the remaining four (colored in blue). The confidence thresholds that identified the sequences as Barnesiella in
81 RDP were largely below 80% (27/31, with the 4 exceptions colored in yellow). Similarly, the confidence thresholds that identified the four sequences as Coprobacter were all below 80%. Among the 88 sequences reads from FP48STL, 36 were identified as Bacteroidetes S24- 7 by SILVA. Similar to what we observed in MF51STL, these 36 sequence reads were again all classified into the family Porphyromonadaceae by RDP at confidence thresholds above 80%, except for the two highlighted in green in Table 3.2.2-3. Barnesiella accounted for 30 out of the 36 reads at the genus level and Coprobacter accounted for the rest (colored in blue). The assignments at the genus level was again less defined, with 5/30 of Barnesiella (colored in yellow) and only 1/6 of Coprobacter passed 80% confidence threshold (colored in dark blue).
3.4.3 Full-length 16S rRNA gene sequencing of Lachnospiraceae rich samples E. coli transformed with full-length 16S rRNA genes from three Lachnospiraceae-rich cecal samples were previously generated and stored frozen in glycerol. Two of these cecal samples (MF51CC and MF52CC) were from MàF recipients and one came from an unmanipulated mouse (UN24CC). I re-derived the frozen E. coli in LB culture containing carbenicillin and performed sequencing on the plasmids extracted from each culture. MF51CC, MF52CC and UN24CC each gave rise to 10, 12 and 24 quality sequence reads, respectively. As shown in Table 3.4.3, Firmicutes was the dominant phylum among the sequenced taxa in all three cecal samples. Among the Firmicutes in all three samples, Lachnospiraceae was the dominant family. Among the Lachnospiraceae, genera that frequently appeared in previously analyzed samples and in literature were present in one or more of these samples: Coprococcus was present in the two MàF cecal samples, while Clostridium XIVa appeared in MF52CC and UN24CC. In addition, Blautia appeared in UN24CC. Aside of Lachnospiraceae, Ruminococcaceae appeared in MF51CC and UN24CC. Members of the phylum Bacteriodetes were present in MF52CC and UN24CC, among which were Prevotellaceae in MF52CC, as well as Rikenellaceae and Porphyromonadaceae in UN24CC. Most reads from all three samples were assigned families at confidence threshold above 80%, except for 4 reads in UN24CC. Assignments at the genus level were again more ambiguous; only 1, 2 and 5 reads from MF51CC, MF52CC and UN24CC, respectively, passed 80% confidence threshold at the genus level.
82 Taken together, the analysis of the samples rich in S24-7 or Lachnospiraceae revealed the differentially abundant presence of taxa that are members of those two groups even at small sample sizes. The S24-7 family was not used as a taxonomic unit in RDP analysis and its members in the S24-7-rich samples were classified into the family Porphyromonadaceae at high confidence threshold, most of which were identified as Barnesiella at the genus level but at confidence thresholds below 80%. Among the Firmicutes, genera such as Clostridium XIVa appeared among the samples analyzed.
83 Phylum Abundance Family genus(#) [conf. level%] Bacteroidetes 80/95 Rikenellaceae (1) [100%] Alistipes [100%] Bacteroidaceae (3) [>80%] Bacteroides [100%] Bacteroides [96%] (2) Prevotellaceae (8) [>80%] Prevotella (6) [>80%] Prevotella [70%] Alloprevotella [99%] Prevotellaceae (3) [<80%] Paraprevotella (2) [<80%] Alloprevotella [49%] Porphyromonadaceae (62) [>80%] Barnesiella (8) [>80%] Barnesiella (41) [<80%] Tannerella (2) [<80%] Coprobacter [81%] Coprobacter (9) [<80%] Odoribacter [98%] Porphyromonadaceae (3) [<80%] Tannerella [30%] Microbacter (2) [<80%] Firmicutes 13/95 Lactobacillaceae (2) [100%] Lactobacillus (2) [100%] Eubacteriaceae (1) [64%] Eubacterium[64%] Ruminococcaceae (1) [100%] Flavonifractor [48%] Lachnospiraceae (7) [>80%] Murimonas [47%] Robinsoniella (2) [<80%] Eisenbergiella (3) [<80%] Clostridium XIVa [69%] Lachnospiraceae (1) [43%] Eisenbergiella [18%] Carnobacteriaceae (1) [99%] Isobaculum [32%] Helicobacteraceae (1) Proteobacteria 2/95 [100%] Helicobacter [97%] Desulfovibrionaceae (1) [98%] Bilophila [50%]
Table 3.4.1-1. Taxonomic assignments of microbes from the stool sample of a MàF recipient (ID:MF51STL). Full-length bacterial 16S rRNA gene sequences from the DNA extract of the stool sample were amplified and cloned into plasmid vectors, which permitted growth of 95 colonies of transformed E. coli. Each E. coli colony amplified vectors containing a single full-
84 length 16S rRNA gene. Plasmid vectors were extracted and sequenced for the 16S rRNA gene insert. Sequences were read and assigned taxonomies by RDP classifier218. The number in round parentheses indicate the number of bacterial taxa, out of the 95 that were sequenced, that falls under the same family or genus. The percentage in the square parentheses indicate the confidence threshold when a particular sequence was assigned to a taxon. Taxa highlighted in pink are those that were previously associated with a protective state from autoimmune diabetes or with regulatory roles in gut homeostasis183,202.
Phylum Abundance Family genus(#) [conf. level%] Bacteriodetes 70/88 Rikenellaceae (2) [100%] Alistipes (2) [100%] Prevotellaceae (7) [>80%] Alloprevotella (6) [>80%] Prevotella (1) [89%] Porphyromonadaceae (56) [>80%] Coprobacter (1) [85%] Coprobacter (17) [<80%] Barnesiella (6) [>80%] Barnesiella (31) [<80%] Tannerella (1) [50%] Porphyromonadaceae (5) [<80%] Barnesiella (2) [<80%] Paludibacter (2) [<80%] Coprobacter (1) [34%] Firmicutes 18/88 Ruminococcaceae (1) [86%] Oscillibacter [41%] Clostridium XlVa (1) Lachnospiraceae (4) [>80%] [57%] Ruminococcus2 (1) [86%] Fusicatenibacter (2) [<80%] Lactobacillaceae (7) [>80%] Lactobacillus (7) [>80%] Clostridiaceae1 (3) [>80%] Anaerobacter (1) [93%] Anaerobacter (2) [<80%] Clostridiaceae1 (2) [<80%] Anaerobacter (2) [<80%] Carnobacteriaceae (1) [64%] Catellicoccus [41%]
Table3.4.1-2. Taxonomic assignments of microbes from the stool sample of an unmanipulated NOD mouse (ID: FP48STL). Full-length bacterial 16S rRNA gene sequences from the DNA extract of the stool sample were amplified and cloned into plasmid vectors, which permitted
85 growth of 95 colonies of transformed E. coli. Each E. coli colony amplified vectors containing a single full-length 16S rRNA gene. Plasmid vectors were extracted and sequenced for the 16S rRNA gene insert. Sequences were read and assigned taxonomies by RDP classifier218. The number in round parentheses indicate the number of bacterial taxa, out of the 88 sequences that were of good quality, that falls under the same family or genus. The percentage in the square parentheses indicate the confidence threshold when a particular sequence was assigned to a taxon. Taxa highlighted in pink are those that were previously associated with a protective state from autoimmune diabetes or with regulatory roles in gut homeostasis183,202.
SILVA RDP ID Phylum Family Genus Phylum Family Genus 9 Firmicutes Lactobacillaceae Lactobacillus Firmicutes Lactobacillaceae Lactobacillus [100%] 12 Firmicutes Lachnospiraceae Lachnospiraceae Firmicutes Lachnospiraceae Eisenbergiella NK4A136 group [58%]
21 Firmicutes Lachnospiraceae uncultured Firmicutes Lachnospiraceae Clostridium XIVa [69%] 25 Firmicutes Lactobacillaceae Lactobacillus Firmicutes Lactobacillaceae Lactobacillus [100%] 53 Firmicutes Ruminococcaceae Oscillospira Firmicutes Ruminococcaeceae Flavonifractor [48%] 85 Firmicutes Lachnospiraceae Clostridium Firmicutes Lachnospiraceae Clostridium XIVa XIVa[69%] 90 Firmicutes Lachnospiraceae Lachnospiraceae Firmicutes Lachnospiraceae Eisenbergiella NK4A136 group [57%]
94 Firmicutes Ruminococcaceae Eubacterium Bacteroidetes Eubacteriaceae Eubacterium coprostanoligenes [64%] group
Table3.4.2-1. Taxonomic classifications of Firmicutes from MF51STL assigned by SILVA compared to RDP. Each ID represents the number assigned to a specific sequence read. Therefore, each row represents one single sequence read and its taxonomic assignment in SILVA versus in RDP. Sequence identity assigned by SILVA were all above 70%. The percentage in the square parentheses indicate the confidence threshold when a particular sequence was assigned to a taxon in RDP.
86 SILVA RDP ID Phylum Family Phylum Family Genus 6 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [95%] 8 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [73%] 11 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [89%] 13 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [97%] Barnesiella [53%] 15 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [97%] Barnesiella [44%] 17 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [61%] 22 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [66%] 30 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [46%] 33 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [48%] 34 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [71%] 37 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [82%] Barnesiella [49%] 38 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [49%] 39 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [96%] Barnesiella [34%] 40 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [70%] 41 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [80%] 43 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [46%] 44 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [61%] 45 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [94%] Barnesiella [34%] 47 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [97%] Barnesiella [41%] 48 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [91%] 49 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [55%] 52 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [89%] Barnesiella [51%] 54 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [43%] 57 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [96%] Barnesiella [57%] 58 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [65%] 61 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [95%] Barnesiella [52%] 66 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [97%] Barnesiella [46%] 67 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [73%] 74 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [98%] Barnesiella [51%] 75 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [34%] 77 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [57%] 78 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [98%] Barnesiella [66%] 86 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [61%] 93 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [73%] 95 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [45%]
87 Table 3.4.2-2. Taxonomic classifications of S24-7 from MF51STL assigned by SILVA compared to RDP. Each ID represents the number assigned to a specific sequence read. Therefore, each row represents one single sequence read and its taxonomic assignment in SILVA versus in RDP. The phylum assignments were identical between the two databases for all sequence reads that were identified as S24-7 in SILVA. No further classifications were made within the S24-7 family. Sequence identity assigned by SILVA were all above 70%. The percentage in the square parentheses indicate the confidence threshold when a particular sequence was assigned to a taxon in RDP. Cells in yellow represent reads assigned to Barnesiella at a confidence threshold above 80%. Cells in blue represent reads assigned to Coprobacter.
88 SILVA RDP ID Phylum Family Phylum Family Genus 1 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [47%] 2 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [59%] 8 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [78%] Barnesiella [55%] 11 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [60%] 12 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [95%] Barnesiella [41%] 15 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [77%] 17 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [97%] Barnesiella [47%] 18 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [93%] Coprobacter [32%] 19 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [99%] 21 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [74%] 23 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [71%] 26 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [67%] 27 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [59%] 29 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [50%] 31 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [70%] 34 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [71%] 41 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [59%] 43 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [97%] Barnesiella [75%] 45 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [37%] 46 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [94%] Barnesiella [37%] 47 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [98%] Barnesiella [41%] 51 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [60%] 52 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [96%] Barnesiella [59%] 59 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [85%] 61 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [51%] 63 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [92%] 64 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [87%] Coprobacter [48%] 66 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Coprobacter [29%] 68 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [72%] Barnesiella [25%] 77 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [93%] Barnesiella [66%] 78 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [88%] 81 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [99%] Barnesiella [85%] 86 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [42%] 90 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [85%] 93 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Coprobacter [34%] 94 Bacteroidetes S24-7 Bacteroidetes Porphyromonadaceae [100%] Barnesiella [74%]
89 Table 3.4.2-3. Taxonomic classifications of S24-7 from FP48STL assigned by SILVA compared to RDP. Each ID represents the number assigned to a specific sequence read. Therefore, each row represents one single sequence read and its taxonomic assignment in SILVA versus in RDP. The phylum assignments were identical between the two databases for all sequence reads that were identified as S24-7 in SILVA. No further classifications were made within the S24-7 family. Sequence identity assigned by SILVA were all above 70%. The percentage in the square parentheses indicate the confidence threshold when a particular sequence was assigned to a taxon in RDP. Cells in yellow represent reads assigned to Barnesiella at confidence thresholds above 80%. Cells in blue represent reads assigned to Coprobacter, while a darker shade represents an assignment with over 80% confidence threshold. Cells in green represent reads assigned to Porphyromonadaceae at confidence thresholds below 80%.
90 sample Phylum Abundance Family genus(#) [conf. level%] MF51CC Firmicutes 10/10 Lachnospiraceae (8) [>95%] Coprococcus (1) [67%] Eisenbergiella (4) [<80%] Ruminococcus2 (1) [63%] Robinsoniella (1) [30%] Fusicatenibacter (1) [44%] Ruminococcaceae (2) [100%] Oscillibacter (1) [100%] Intestinimonas (1) [83%] MF52CC Firmicutes 9/12 Lachnospiraceae (9) [>90%] Clostridium XIVa (3) [<80%] Acetitomaculum (1) [<80%] Coprococcus (2) [<80%] Hungatella (1) [19%] Acetatifactor (1) [64%] Mobilitalea (1) [12%] Bacteroidetes 3/12 Prevotellaceae (3) [100%] Prevotella (1) [100%] Alloprevotella (1) [95%] Alloprevotella (1) [73%] UN24CC Firmicutes 17/24 Lachnospiraceae (12) [>95%] Marvinbryantia (2) [<80%] Clostridium XlVa (1) [66%] Acetatifactor (2) [<80%] Blautia (1) [48%] Eisenbergiella (5) [<80%] Murimonas (1) [25%] Lactobacillaceae (1) [45%] Lactobacillus [45%] Ruminococcaceae (1) [100%] Butyricicoccus [100%] Ruminococcaceae (1) [68%] Intestinimonas [34%] Clostridiaceae1 (1) [80%] Anaerobacter [35%] Christensenellaceae (1) [79%] Christensenella [79%] Actinobacteria 1/24 Coriobacteriaceae (1) [95%] Paraeggerthella [29%] Bacteriodetes 6/24 Rikenellaceae (2) [100%] Alistipes (2) [100%] Porphyromonadaceae (3) [100%] Barnesiella (1) [59%] Barnesiella (2) [>80%] Porphyromonadaceae (1) [64%] Coprobactor [46%]
Table 3.4.3. Taxonomic assignments of microbes from cecal samples of two MàF recipients (MF51CC, MF52CC) and an unmanipulated NOD mouse (UN24CC). Full-length bacterial 16S rRNA gene sequences from the DNA extracts of the samples were amplified and cloned into plasmid vectors, which permitted growth of transformed E. coli colonies. Each E. coli colony
91 amplified vectors containing a single full-length 16S rRNA gene. Plasmid vectors were extracted and sequenced for the 16S rRNA gene insert. Sequences were read and assigned taxonomies by RDP classifier218. The number in round parentheses indicate the number of bacterial taxa, out of the total number of sequences that were of good quality, that falls under the same family or genus. The percentage in the square parentheses indicate the confidence threshold when a particular sequence was assigned to a taxon. Taxa highlighted in pink are those that were previously associated with a protective state from autoimmune diabetes or with regulatory roles in gut homeostasis.
92
CHAPTER 4: Discussion
93 4.1 Did microbial transfer impact autoantigen presenting capacity of APCs ex vivo? Previous lab members showed that the transfer of adult cecal microbial contents into female NOD mice early in life led to markedly different islet-specific immunological responses at 14 weeks of age compared to unmanipulated NOD females183. These differences were exemplified by lower insulin autoantibodies and lower levels of insulitis in MàF recipients. Since CD4+ T cells play crucial roles in providing help to both autoreactive B cells and CD8+ T cells, which mediate autoantibody production and islet inflammation, respectively, I hypothesized that the protective effects were dependent on altered antigen presentation to and activation of islet antigen-specific CD4+ T cells. To explore the effects of microbial transfer on the autoantigen presentation and autoreactive T cell activation, I employed a co-culture system consisting of peripheral APCs either from unmanipulated NOD females or MàF recipients, as well as BDC2.5 T cells with a single specificity to an islet antigen chromogranin A presented on the NOD MHC class II. By examination of BDC2.5 T cell activation and proliferation, I compared the ability of peripheral APCs isolated from the control and MàF mice to present BDC2.5 peptides. The comparison between unmanipulated and MàF recipients was made across three parameters: 1) frequencies of APCs ready for antigen presentation; 2) BDC2.5 T cell proliferation across peptide concentrations; 3) frequencies of BDC2.5 T cells expressing activation markers.
4.1.1 Readiness of antigen presenting cells to present an islet autoantigen The ability of APCs in the co-culture system to present antigens is intrinsically dependent on the composition of the APCs and the expression of molecules necessary for T cell activation, particularly the MHC class II molecules and co-stimulatory molecules such as CD86. In the experimental setting of the co-cultures, the APCs came from the periphery of mice that were either unmanipulated or MàF recipients. Following the depletion of erythrocytes, B cells, T cells and NK cells, the resulting cell population consisted of a major subpopulation of CD11b-CD11c- cells along with CD11b and/or CD11c-expressing cells. Further immunophenotyping of the resulting cell population identified a prominent CD45- cell population (Figure 3.1.5). In the spleen, cells of non-hematopoietic origin comprise of stromal cells including fibroblastic reticular cells (FRC), follicular dendritic cells (FDC) and marginal reticular cells (MRC)239,240. These stromal cells share expression of a combination of markers
94 such as lymphotoxin-β receptor (LTβR), tumor necrosis factor receptor 1 (TNFR1), as well as adhesion molecules such as VCAM1 and ICAM1239. Future immunophenotyping including a combination of these markers could better identify the stromal cell populations. Although stromal cells do not present antigens in the context of MHC molecules239,241, the presence of these stromal cells in the co-culture system could potentially affect the response by secreting cytokines such as IL6239,240,242, which has anti-apoptotic effects on CD4 T cells243 and can bias effector T cell responses towards Th2244. In future experiments, technical modifications of value would be positive selection for CD45+ cells245 prior to negative selection to remove specific populations of hematopoietic origin. One other cell population that was present in the co-culture system was Gr-1-expressing cells. Further characterization of these Gr-1+ cells revealed that they expressed CD11b but not CD11c, and that they did not possess characteristics as APCs by the lack of expression of either MHC class II molecules or CD86 (Figure 3.1.5c). In the spleen, Gr-1+ CD11b+CD11c- MHC class II- cells include mainly neutrophils and a subset of monocytes246,247. They can be distinguished as monocytes are SSClo and neutrophils are SSCint, 246, although a better approach would be to distinguish Ly6C from Ly6G to identify monocytes that are Ly6C+Ly6G-247. A comparison of side scatter revealed the Gr-1-expressing cells as a homogenous population that was more granular than the CD11c-expressing DCs (Figure 3.1.5d). These phenotypes were consistent with those of neutrophils, which were reported to be identified by the anti-Gr-1 antibody clone RB6-8C5248. Although they did not directly interfere with antigen presentation in the co-culture, the granulocytes could potentially impact T cell response by secreting cytokines such as IL10, TGFβ and IL12249-251. Therefore, in future follow-up experiments, the negative selection process should also include the depletion of Gr-1+ granulocytes by using Ly6G- targeting antibodies. Antigen presenting cells enriched from the spleens of unmanipulated NOD females versus MàF recipients were compared for composition and markers associated with APC competence. When freshly obtained from the spleens, APCs of control vs. MàF mouse origin did not differ in expression of MHC class II and CD86 or CD11b and CD11c (Figure 3.1.4c). However, after overnight incubation with IL4 and GM-CSF and prior to co-culture, the CD11b and CD11c double-expressing subpopulation among cells obtained from MàF recipients had higher frequencies of MHC class II and CD86 double-expressing cells than that from
95 unmanipulated NOD females (Figure 3.1.4d). The CD11b-CD11c+ cells displayed the same trend but did not reach statistical significance. No significant difference in APC composition was found between mice from the two conditions (Figure 3.1.4c, 3.1.5b). These results indicate that APCs, particularly CD11b+CD11c+ cells, from MàF recipients seemed to be more responsive to cues that instruct them to become more APC-like than those from unmanipulated NOD females. These results do not support a hypothesis that the microbial transfer that mediated protection from autoimmune diabetes dampened the antigen presenting capacity of APCs. CD11b+CD11c+ cells include two subsets: conventional DCs 2 (cDC2) that express DC inhibitory receptor 2 (DCIR2) and the lack of CD8⍺ expression252,253, as well as monocyte- derived DCs that lack DCIR2 and express SIGN/CD209252-256. cDC2s are a major resident cDC population in the spleen257, while monocyte-derived DCs (moDCs) arise under inflammatory conditions driven by GM-CSF256. cDC2s have tolerogenic roles in the pathogenesis of autoimmune diabetes254. In particular, BDC2.5 T cells transferred into NOD.SCID mice displayed limited expansion in the spleen followed by clonal contraction after BDC2.5 mimotopes were delivered specifically to DCIR2+ cDC2s using chimeric antibodies253,258. The cDC2-specific islet antigen delivery also delayed autoimmune diabetes onset compared to PBS or antigen delivery to CD8+ cDC1s, which instead induced sustained BDC2.5 T cell responses in vivo253. The expression of the co-stimulatory molecule CD40 has been positively correlated with breaking tolerance and sustaining antigen-specific T cell responses252,253. CD11b+CD11c+ cells have immunogenic properties, as NOD mice given CD11b and CD11c co-expressing cells after reconstitution with CD11c-depleted NOD.scid bone marrow developed diabetes after BDC2.5 T cell transfer259. Notably, cDC2s and moDCs were not examined in this study. moDCs in the spleen arise from blood monocytes in response to inflammatory signals260 and in vitro cultures supplemented with GM-CSF256. These moDCs are MHC class IIhi, express co-stimulatory molecules and are good T cell activators256. Therefore, the observation that CD11b and CD11c co-expressing cells from MàF recipients contained higher frequencies of MHC class II and CD86 co-expressing cells than those from control NOD females after overnight incubation with IL4 and GM-CSF may reflect increased frequencies and/or responsiveness of cDC2s or moDCs, or both, to GM-CSF. It is likely that both cDC2s and moDCs were present in the CD11b+CD11c+ subpopulation. cDCs responding to inflammatory cues like GM-CSF have
96 been reported261. One possibility is that compared to control females cDC2s from MàF recipients were more responsive to inflammatory cues. Since co-stimulation status was associated with breaking tolerance252,253, my observation would be inconsistent with the protective phenotype in MàF recipients. If the higher frequencies of cells ready to act as APCs were attributed solely to moDCs, it would be interesting to determine whether differences in the frequencies of moDCs among CD11b+CD11c+ cells between controls and MàF recipients result in distinct IL4 and GM-CSF responsiveness. Since the immediately ex vivo frequencies of freshly enriched CD11b+CD11c+ cells were comparable between the two conditions, I would ask whether the frequencies of cDC2s among ex vivo splenic CD11b+CD11c+ cells differ between controls and MàF recipients, considering the reported tolerogenic roles of cDC2s254. CD11b-CD11c+ cells also possess the same trend that higher frequencies of MHC class II and CD86 co-expressing cells were found in APCs from MàF recipients after overnight incubation with IL4 and GM-CSF, although not reaching statistical significance. It is worth mentioning that a subset of CD11b-CD11c+ cells in the spleen that do not express CD8 were previously identified as merocytic DCs by other studies and play exacerbating roles in autoimmune diabetes onset in NOD mice262. Further delineation of the CD11b-CD11c+ subpopulation is required to answer whether the CD11b-CD11c+ cells in the co-culture contained CD8⍺- DCs. In this thesis, co-cultures using ex vivo APCs enriched from the periphery allowed the analysis of the antigen presenting capacities of the APCs from both the APC and the T cell response perspective. However, the co-cultures required more manipulations done on the APCs and the use of synthetic islet antigen peptides that may not represent physiological conditions. Therefore, to compliment the co-culture studies, I used in vivo adoptive transfer of islet antigen- specific T cells to assess the environment across different tissues. These results will be discussed in 4.2.
4.1.2 Antigen presenting capacities of APCs read by T cell proliferation T cell proliferation was observed in both co-culture settings that used bone marrow (BM)-derived APCs during the optimization process. In the alloreactive co-culture setting, the frequencies of proliferated T cells were between 20 and 30% among all T cells in the co-cultures (Figure 3.1.2 f), which are consistent with reported responder frequencies in alloreactive
97 responses263. In the alloreactive co-culture setting, no specific peptides were added exogenously, as alloreactive responses can be responses towards non-self MHC class II molecules bound to peptides generated from cellular proteins264. In the antigen-specific setting using bone marrow- derived APCs, the T cell response was peptide specific, as co-cultures with scrambled peptides at the highest concentration within the peptide range failed to induce BDC2.5 T cell proliferation. In the experimental co-culture setting where APCs were enriched and prepared from the periphery of unmanipulated NOD controls or MàF recipients, BDC2.5 T cells responded to the APCs loaded with increasing peptide concentrations in a titrated manner (Figure 3.1.6a, b). The observed T cell proliferation was again antigen specific, as APCs from either origin loaded with scrambled peptides failed to induce BDC2.5 T cell response. Compared to the BDC2.5 T cell proliferation in co-cultures with BM-derived APCs, the BDC2.5 T cell proliferation induced by peripheral APCs was titrated over a broader range. This is consistent with the observation that the enriched population from the spleen contained higher proportions of cells that were double- negative for CD11b and CD11c than BM-derived APCs. BDC2.5 T cell proliferation showed no difference between co-cultures with control or MàF peripheral APCs except at 3000 ng/mL peptide concentration, where peripheral APCs from unmanipulated controls induced slightly higher T cell proliferation. When taken into consideration that MàF peripheral APCs had higher frequencies of MHC class II and CD86 co- expressing cells among CD11b+CD11c+ cells going into the co-culture, two possible explanations arise: 1) the observed difference in the readiness to act as APCs among subpopulations between the two conditions was not sufficient to result in a difference in eliciting T cell response; 2) the higher frequencies of CD11b+CD11c+ cells, particularly cDC2s, that were ready to act as APCs, actually resulted in higher T cell activation but not in proliferation. These two possibilities are discussed further in the next section by taking into account T cell activation marker expression.
4.1.3 Antigen presenting capacities of APCs read by T cell activation marker expression The expression of activation markers CD25 and CD69 were analyzed on BDC2.5 T cells in co- culture with peripheral APCs. The frequencies of cells that expressed either activation marker increased with increasing peptide concentrations. No difference was detected in frequencies of
98 CD25+ T cells in co-cultures with APCs from unmanipulated controls versus MàF recipients. However, T cells cultured with APCs from MàF recipients contained higher frequencies of CD69-expressing cells (Figure 3.1.6d). Moreover, among T cells that proliferated, CD69+ cells were more frequent in co-cultures with APCs from MàF recipients. In summary, compared to control mice, APCs from MàF recipients contained more CD11b+CD11c+ cells that co- expressed MHC class II and CD86, elicited comparable T cell proliferative responses but produced higher frequencies of CFSEloCD69+ T cells. Based on these observations, a plausible explanation is that cDC2s from MàF recipients induced increased T cell activation (reflected in CD69 expression), due to enhanced co- stimulation capacity. Further investigation would be needed to 1) confirm the identity of the CD11b+CD11c+ cells that co-express MHC class II and CD86 as cDC; 2) isolation of cDCs by cell sorting and testing by co-culture with BDC2.5 T cells. As previously mentioned, cDC2s have been ascribed immunoregulatory properties253, as has CD69 expression in regulation of T helper cell differentiation265. Any possible link between the two could be further examined by prolonging the co-culture of cDC2s and T cells where T cell clonal contraction may be observed253, as well as by characterizing T helper cell differentiation through analysis of cytokine and transcriptional factor expression.
4.2 Did microbial transfer modulate the activation of autoantigen-specific T cells in vivo? An approach to explore the effects of microbial transfer on the autoantigen presentation and autoreactive T cell activation was the adoptive transfer of BDC2.5 T cells into unmanipulated NOD females or MàF recipients. Autoantigen presentation was read by the BDC2.5 T cell proliferation and the expression of activation markers. Since BDC2.5 T cells are reactive to naturally occurring antigens in the islets266, their location, activation and proliferation reflect physiological islet autoantigen presentation. Adoptively transferred T cells were identified by CFSE staining and by use of donor and hosts that differed by the congenic marker CD45.1. These T cells were the most abundant in pancreatic lymph nodes (pLNs) compared to mesenteric lymph nodes and spleen (Figure 3.2.1c, d), consistent with the location of islet antigen-specific T cell activation262,267,268. There were no differences in either frequencies or numbers of these BDC2.5 T cells between control females and MàF recipients (Figures 3.2.1e, f; 3.2.3b, c). When proliferation was quantified by the
99 numbers of CFSElow cells among transferred T cells, no difference in frequencies or cell numbers were detected between control or MàF recipients (Figures 3.2.1i, j; 3.2.3d, e). Therefore, the transfer of diabetes-protective microbial communities did not appear to alter BDC2.5 T cell responses to the naturally occurring antigen presented in the peripheral tissues. Of future interest is to investigate the retention and egress of BDC2.5 T cells in the islets and islet-associated tissues. It is likely that the T cells that proliferated in the pLNs went on to infiltrate the islets267. In future experiments BDC2.5 T cells could be identified in the islets using tetramers such as tetAg7 loaded with peptides p79 or p17269. In this thesis, tetramers that identified insulin-specific T cells were used to explore intraislet autoreactive T cell frequencies. These results will be discussed in Chapter 4.3. The activation status of BDC2.5 T cells identified in the pLN, where islet antigen- specific antigen presentation occurs, was also investigated. The expression of CD25 and CD69 revealed that the T cells negative for both markers were most frequent among all BDC2.5 T cells in the pLNs (Figure 3.2.4c). Among this subpopulation, comparable proliferation was observed between T cells transferred into control females versus MàF recipients (Figure 3.2.4e). The second numerous subpopulation were CD69+CD25- and were also observed in comparable frequencies between controls and MàF mice. Comparable frequencies of CSFElo BDC2.5 T cells that were negative for CD25 and CD69 and positive for CD69 were observed suggesting that both of these subpopulations contained cells that had responded to the antigen. Since the expression of both activation markers is temporally proximal to T cell activation, so future experiments could include evaluation of earlier time points post-transfer. The expression of CD44 versus CD62L was also investigated to better differentiate antigen-experienced from naïve T cells in the pLNs. Inclusion of CD44 and CD62L markers did not reveal any differences between controls and MàF recipients (Figure 3.2.4d). As expected, CD62LloCD44hi antigen- experienced T cells had higher frequencies of CFSElo cells than CD62LhiCD44lo naïve T cells (Figure 3.2.4g), consistent with a previous study268. The proportion of CFSElo cells among naïve or activated T cells did not differ between controls and MàF recipients (Figure 3.2.4f). These observations point to comparable activation phenotypes of BDC2.5 T cells that were transferred into control NOD females or MàF recipients. Taken together, these results suggest that presentation of islet antigens recognized by BDC2.5 T cells occurred in the pLN, and was not altered by the diabetes-protective microbial transfer in early life.
100 The presence of islet autoantigens in the pLNs can be a result of apoptotic islet fragments being captured by APCs within the pancreas and then transferred into pLN for the activation of islet antigen-specific T cells268. This process can start as early as 2 weeks of age during a wave of physiological apoptosis in β cells due to tissue remodeling270, but this early event does not translate into autoimmunity given that other diabetes-resistant strains around the same age when transferred with BDC2.5 T cells were also observed with BDC2.5 T cell proliferation268. This group identified CD11b+CD11c+CD8⍺- DCs as the APCs responsible for the early presentation of islet autoantigens to autoreactive T cells in the pLNs268. A previous report showed that in the setting of limited artificially-induced β cell apoptosis, which the authors argued protected against diabetes in NOD mice via tolerance induction, CD11b+CD11c+ DC in the pLNs could induce proliferation of adoptively transferred BDC2.5 T cells. These T cells expressed lower levels of IFNɣ compared to the control condition and inhibited proliferation of naïve BDC2.5 T cells in vitro271. These observations suggest a potential link between CD11b+CD11c+ APCs in the pancreas-pLN axis that present antigens derived from apoptotic islet cells and tolerogenic T cell responses. Although my observations were that proliferation of adoptively transferred BDC2.5 T cells in MàF recipients did not exhibit difference compared to controls, further investigation could examine possible effects on islet-reactive T cell cytokine expression profiles and the functional phenotypes of pLN/Islet associated APCs.
4.3 Islet antigen-specific T cells at the site of action of autoimmune diabetes Islet-reactive T cells play a major role in initiating lymphocyte infiltration and inflammation in the islets272,273. Entry of islet antigen-specific CD4+ T cells into the islets is reported to be dependent on TCR recognition of MHC class II molecules on intra-islet APCs presenting cognate antigens, as well as engagement of adhesion molecules, such as LFA1 on T cells with ICAM-1 expressed on islet vessels273,274. The presence of islet-reactive CD4+ T cells within islets enhances adhesion molecule expression on islet vessels, particularly ICAM-1 and VCAM- 1, which facilitates entry of non-specific CD4+ T cells and augmenting lymphocyte infiltration275. Interestingly, it was reported that while the entry of islet-reactive CD4+ T cells do not require chemokine receptor-mediated chemotaxis, non-specific effector CD4+ T cells do275. Infiltrated CD4+ T cells interact with other cell types and together they create a local
101 environment that promotes inflammation, as well as lymphocyte proliferation and survival231,273. Therefore, the identification of islet-reactive CD4+ T cells within the islets is an important parameter for characterization of diabetogenic process at the site of action. In this thesis, I identified insulin peptide-reactive T cells within the islets and peripheral lymph nodes. Insulin is produced solely by pancreatic β cells and is a major islet autoantigen in both humans and in mice. In humans, insulin autoantibodies are among the ones tested for seroconversion276,277. In NOD mice, insulin-reactive CD4+ T cells were identified to be reactive to an insulin peptide B9-23 derived from the insulin β chain115. B9:23 peptides can engage with the H2-Ag7 MHC class II molecules in an unstable, low-affinity register. Autoreactive T cells recognizing the low-affinity register escape thymic deletion and are diabetogenic in NOD mice116,273,278. As a major player in the diabetogenic process, insulin-reactive CD4+ T cells and their phenotypes are an important parameter for assessing islet antigen-specific T cell responses in the disease-prone and disease-protective states. In order to investigate whether the MàF microbial transfer altered the frequencies of islet-reactive CD4+ T cells at effector sites, we employed insulin peptide B9-23-bound tetramers generated by the Kappler lab. The frequencies of these tetramer-positive T cells were higher in the islets than in the pancreatic lymph nodes (pLN) and mesenteric lymph nodes (mLN) (Figure 3.3.1), consistent with other studies228 and with the local abundance of islet antigens in the islets. Preliminary results suggest no significant difference between the frequencies of these B9-23 specific CD4+ T cells in the islets, pLN or mLN. If this conclusion holds true with future expansion of the data pool, this would suggest that the MàF transfer did not alter the appearance of insulin-reactive CD4+ T cells in the islets. Currently, all absolute cell counts for tetramer-positive cells resulted in coefficients of variation (CV) below 20% for all except two measurements among all 20 data points taken. More statistical confidence for drawing such a conclusion can be achieved by pooling more islets and obtaining at least a hundred tetramer-positive cells at each measurement to achieve a coefficient of variation (CV) of no more than 10%229,230. Since the tetramer-bound insulin-reactive T cells are a relatively rare population and constituted very low frequencies among total CD4 T cells, we sought for markers based on which insulin B9:23-reactive CD4+ T cell frequencies can be enriched. Two cell trafficking markers, CCR9 and integrin β7, were considered. CCR9 enables response to the chemokine
102 CCL25 that is produced in the thymus and small intestines279 and also found in islet preparations from NOD mice231. A population of CCR9-expressing CD4+ T cells migrate into the islets and their accumulation in the islets is dependent on the CCR9:CCL25 interaction231. Interestingly, this cell population resembles T follicular helper cells (Tfh) and produces IL-21231, a cytokine that promotes lymphocyte survival in islets and sustain autoimmune processes231,273. Preliminary results for CCR9 expression on the CD4+ T cells from the islets in this study identified the presence of CCR9+CD4+ T cells. However, only a small proportion of B9-23 specific CD4+ T cells expressed CCR9 (Figure 3.3.2), rendering CCR9 an inappropriate marker for identification of B9-23 specific CD4+ T cells. Intriguingly, it was previously reported that early infiltration of autoreactive T cells did not depend on the chemokine receptor-mediated chemotaxis while recruitment of nonspecific CD4+ T cell did, since pertussis toxin that blocks chemokine receptor signalling, including that of CCR9, did not affect the localization of diabetogenic T cells but affected localization of nonspecific CD4+ T cells to the islets272. Therefore, it is likely that CCR9 plays a role in recruiting effector T cells, including the IL-21 producing Tfh-like cells, and enhances localization of CCR9-expressing autoreactive T cells to the islets. Since CCL25 is also expressed in the small intestines279, we wondered whether there exists a shuttling of lymphocytes on the enteroinsular axis. Therefore, the expression of integrin β7, which heterodimerize with ⍺4 and mediates lymphocyte trafficking through binding to MadCAM-1 expressed on mucosal surfaces, was also evaluated in this study. The gut-homing ⍺4β7 integrin has been implied in autoimmune diabetes, as its expression was found on islet- infiltrating lymphocytes, and the blocade of its interaction with MadCAM-1 protected NOD mice from developing autoimmune diabetes231-233,280. In addition to intestinal endothelial cells, MadCAM-1 is also expressed on vessels next to inflamed islets and mediates integrin-dependent lymphocyte infiltration233,281. I detected β7 expression on islet CD4+ T cells. However, the expression of integrin β7 was sparse among insulin tetramer-positive cells, indicating that integrin β7 was not specifically enriched on these T cells. However, the frequencies of β7 +CD8+ T cells in the islets were substantially higher than β7 + CD4+ T cells (Figure 3.3.3). Of future interest is to examine whether autoreactive CD8+ T cells may traffick along the entero- insular axis using islet-reactive CD8-specific tetramers. In addition to lymphocyte trafficking, the activation marker CD25 was also examined for their ability to enrich for frequencies of tetramer-bound T cells and may corroborative results in
103 Chapters 3.1 and 3.2. My preliminary results identified a small proportion of B9:23-specific T cells that expressed CD25 (Figure 3.3.2). In sum, analysis on CCR9, integrin β7 and CD25 may assist in analysis of homing and activation characteristics of total infiltrating lymphocytes between conditions of mice. These markers did not discriminate additional features of tetramer- bound B9:23-specific CD4 T cells, due at least in part, to paucity of cells expressing these markers in this population of interest.
4.4 What does full-length 16S rRNA gene sequencing of gut microbes tell us? Microbial transfer conferred changes in the recipients’ gut microbiota even at ten weeks post- transfer183. Differential abundances of certain taxa in cecal microbiota were observed in MàF recipients versus unmanipulated female NOD mice and were associated with changes in abundances of certain serum metabolites183,202. Therefore, the analysis of gut microbial composition in MàF recipients and unmanipulated controls becomes crucial in order to shed light on which groups of microbes in the transferred microbes may have exerted protection from autoimmune diabetes in the MàF recipients. High-throughput sequencing using one or two variable regions of the 16S rRNA gene provides an overall structure of the microbial community and may generate confident taxonomic classifications from broad taxonomic ranks, such as the phylum level, down to narrower ranks such as the family or even genus level218. With high-throughput sequencing, our lab and others have identified taxa whose abundances were associated with environmental perturbations that modified autoimmune diabetes risk149,183,185,187,189,202. However, thorough delineation of taxonomic classification down to the genus level for murine gut microbes is often impeded by the ill- characterized nature of the murine microbiota, as well as the lengths and quality of the sequence reads. One example of poorly characterized murine microbes is the Bacteroidetes S24-7 family, whose presence is substantial in the murine gut microbiota282 and was consistently observed in the previous high-throughput sequencing data on NOD gut microbiota generated by our lab183. To obtain more phylogenetic information on gut microbes and better understand the taxa that were present in high-throughput sequencing data on NOD MàF or unmanipulated females, I employed full-length 16S rRNA gene sequencing on gut microbes isolated from NOD MàF or unmanipulated females and compared phylogenetic analyses generated by two different taxonomy databases. The discussion of these full-length sequencing data as shown in Results
104 Chapter 3.4 will take three approaches: 1) To better understand taxa that are characterized as S24-7; 2) to spot taxa that previously stood out in phylogenetic analyses of high-throughput sequencing data; 3) to explore the validity and limitations of using full-length 16S rRNA sequencing as a corroborating strategy to high-throughput sequencing in understanding murine gut microbial composition.
4.4.1 What is Bacteroidetes S24-7 really? The S24-7 family was first reported in 2002 as an uncultured group belonging to Bacteroidetes that constitutes a large proportion of sequences derived from murine gut microbiota283. The name S24-7 was adopted by the Greengenes and SILVA taxonomies after an environmental clone that falls under the family282,283. The presence of this family of microbes was noted in studies that characterized murine gut microbiota and differential abundances were noted with environmental modifiers282. Specifically, in a type II diabetes model, the S24-7 family tripled in proportion among Bacteroidetes, in addition to an increased Bacteroidetes to Firmicutes ratio, within the gut of C57BL/6 mice fed a diabetogenic/non-obesogenic high-fat diet (HFD) that then became diabetic versus those that did not become diabetic284. Moreover, the same study showed that supplementation of the HFD with dietary fibers, ie. gluco-oligosaccharides, further increased the abundance of S24-7 family, suggesting a direct modulation of the abundance of this family by dietary factors284. However, supplementation with dietary fibers decreased diabetogenicity of the diet in mice284, suggesting that S24-7 could act as a microbial signature of a specific intervention284 but its abundance did not directly correlate with the disease risk. The presence of S24-7 was also spotted in the analyses of cecal microbiota of unmanipulated NOD males and females at 14 weeks of age, as well as MàF and FàF recipients in the study by Dr. Janet Markle from our lab that investigated sex-specific microbial- dependent protection from autoimmune diabetes183. However, the fold changes of the abundances of this family among all comparisons, ie. unmanipulated NOD males versus females, MàF recipients versus unmanipulated females, as well as MàF versus FàF recipients, did not reach statistical significance. Despite the recurrent appearance of S24-7 in analyses on murine gut microbiota, further classifications at the genus level is not available for the family and its phylogenetic relationships
105 to other taxa within the order were not clear at the time when the sequencing data were generated (November 2015). S24-7 caught our attention in a separate set of high-throughput sequencing data on gut microbes from MàF recipients and unmanipulated NOD females. The family was enriched in a group of samples coming from both MàF recipients and unmanipulated females, while another group of samples, again consisted of both conditions, were overrepresented by the family Lachnospiraceae. Full-length 16S rRNA gene sequencing and phylogenetic analysis of two S24-7-rich samples, MF51STL and FP48STL, revealed that 36.8% (35/95) and 40.9% (36/88) of the sequences interrogated from the two samples respectively were characterized as S24-7 by SILVA taxonomy. The comparison between taxonomic assignments by SILVA versus RDP taxonomies revealed the closest members within the phylum Bacteroidetes to S24-7, in particular the family Porphyromonadaceae and its genera Barnesiella and Coprobacter (Tables 3.4.2-2 and 3.4.2-3). At the family level, the percentages of sequences that were classified by SILVA as S24-7 passing an 80% confidence threshold to be Porphyromonadaceae in RDP taxonomy were 100% (35/35) and 94.4% (34/36) among the two samples. Those percentages drop to 94.2% (33/35) and 91.7% (33/36), respectively, if the confidence threshold at query raises to 90%. This reflects high proximity between the two families if not overlapping. Among Porphyromoadaceae, the only two genera that were associated with S24-7 sequences in RDP analysis were Barnesiella and Coprobacter. It is worth noticing that the confidence thresholds at the genus level were mostly below 80%, with only 11.4% (4/35) and 16.7% (6/36) among the sequences interrogated in the two samples surpassing an 80% confidence threshold. This supports the idea of S24-7 being phylogenetically proximal to the two genera but distinct on its own. The phylogenetic proximity with Barnesiella and Coprobacter is consistent with a study published in 2016 by Ormerod et. al. that were among the first to perform genomics analysis on S24-7282. The study compared near full-size S24-7 population genomes to reference genomes belonging to the order Bacteroidales from NCBI at 120 single-copy marker genes282. The group showed that the S24-7 population genomes clustered most proximally with Barnesiella and Coprobacter282. Furthermore, these two genera appeared to be more distant from other genera within Porphyromonadaceae and Ormerod et. al. argued that Barnesiella and Coprobacter should form a novel family “Barnesiellaceae” distinct from Porphyromonadaceae282. Taken together, the full-length 16S rRNA sequencing of two S24-7-rich samples exercised the
106 maximal power of single bacterial identification using 16S rRNA gene sequencing and the resulting taxonomic assignments agreed with the current phylogenetic understanding of the family S24-7. The metabolic capacities of members of this family were investigated by Ormerod et. al along with its phylogenetic relationships with others282. In the same study, they reported that the interrogated population genomes of the family S24-7 could be subdivided into three groups based on the preference of the bacteria-encoded enzyme glycoside hydrolase for its carbohydrate source282. In addition to the fermentative capacity, the majority of S24-7 genomes were reported to encode components of the electron transport chain and proteins that relieve oxidative stress282. Therefore, the S24-7 family were characterized to be fermentative and possibly microaerobic microbes, also termed as nanaerobes (growth at nanomolar concentrations of oxygen)282,285. Relating back to its differential abundances associated with environmental modifiers such as dietary factors as previously discussed, the increase in S24-7 abundances in mice fed a HFD supplemented with dietary fibers284 is likely a direct result of carbohydrate utilization. Furthermore, S24-7 members are not highly coated by IgA compared to families such as Prevotellaceae that were associated with inflammation in murine colitis model282,286, but the majority do possess peptidases282 whose homologs can degrade complement proteins282,287. Therefore, the interaction between S24-7 with dietary factors and immunological niche in the gut in association with diabetes status may have collectively played a role in S24-7 as a component of the microbial signature of different interventions or disease status, as previously observed in HFD-induced diabetes284.
4.4.2 Presence of taxa that were sex-specific or linked to T1D risk or protection One main purpose of characterizing gut microbes and the microbial community structure is to deconstruct the complex gut microbiota associated with protection from autoimmune diabetes and identify bacterial taxa that exerted the protective effects. Past studies by our lab have associated the abundances of several taxa with the disease status or with microbial interventions that conferred protection from autoimmune diabetes in NOD mice. A summary of those taxa is presented in Table 4.4. Several among these taxa appeared as taxonomic assignments for some of the sequence-reads in the full-length 16S rRNA analysis of the cecal or fecal samples; they are highlighted in pink in Table 4.4. It is worth noticing that the confidence thresholds for the
107 assignments of Blautia and Coprococcus to the respective sequence reads were under 80%, while the assignments of Alistipes, Prevotella and Bacteroides exceeded 80% confidence threshold (highlighted in pink in Tables 3.4.1-1, 3.4.1-2, 3.4.3). Therefore, some of the sequences in the full-length 16S rRNA gene analysis closely related to or overlapped with taxa that were identified in NOD mice to be associated with a protected state from autoimmune diabetes. Moreover, at least one sequence from 4 out the 5 gut microbial samples were closely related to Clostridium XIVa, which are producers of the SCFA butyrate and regulators of gut homeostasis through induction of Treg and modulation of tight junction. Taken together, these results indicate the presence of taxa closely related to or overlapping with those previously associated with a protected state and/or with regulation of gut homeostasis in the cecal or fecal samples of MàF recipients or unmanipulated NOD females.
4.4.3 Limitations and contributions of full-length 16S sequencing to identification of the protective consortium The advent of high-throughput 16S rRNA gene sequencing has tremendously improved efficiency and breadth of surveying microbial communities, although it does come at a cost of generating short sequence reads. Murine microbial communities have been less studied than those in humans and a lot of taxa remain uncultured and undefined. Short sequence reads therefore may add to the challenge of strain identification for murine microbes. Accurate identification of microbes that were differentially abundant in MàF protected recipients is the first step for determining members of a protective consortium. Bacterial identification by full-length 16S rRNA gene sequencing eliminates the possibility that the lengths of the partial 16S sequence reads might have hindered proper classification. By interrogating sequence similarity to the reference strain at over 1000 bp length, full-length sequencing maximizes the power of bacterial identification using the 16S rRNA gene. In fact, full-length 16S sequencing, which was suggested as a reference method for bacterial identification in 2000288, was recommended for application whenever possible by several review articles up to 2008289,290. In this study, one purpose of using full-length 16S sequencing was to better understand poorly-defined taxa such as the abundant S24-7 family. Some insights were gained into its phylogenetic relationships to closely related taxa without the confounder of sequence lengths. Moreover, several taxa associated with a diabetes-protected or
108 hemeostatic state in mice were classified at the maximal power of bacterial identification using 16S rRNA gene sequencing. A major limitation of the full-length sequencing is the sampling size. The full-length gene sequencing could only sample a very small portion of the microbial community in a sample, due to the lengthy and laborious nature of the method. Therefore, no meaningful comparisons could be drawn to abundances of taxa between MàF versus unmanipulated samples. However, the abundance of a specific taxon among all sequences interrogated within each sample may reflect its abundance in the entire microbial community specific to that sample. In other words, the more abundant that taxon was in the microbial community, the more abundant its 16S gene amplicons were present during the cloning step. Consequently, more plasmid vectors received its 16S gene amplicons and more E. coli colonies would carry the plasmid vectors that contained its 16S gene sequence. This seems to hold true for abundant taxa such as S24-7 and Lachnospiraceae. Among sequences interrogated for the two S24-7-rich samples, 36.8% and 40.9% were identified as S24-7 by SILVA. Among sequences interrogated for the three Lachnospiraceae-rich samples, 80%, 75% and 54.2% were identified as Lachnospiraceae. However, the number of sequences inquired for each sample is likely way below what is needed to capture a fair representation of the microbial diversity, therefore further conclusions about intra-sample bacterial abundances should be refrained. High-throughput sequencing is definitely a more logical approach for delineating microbial community structures and differentiating abundances between taxa.
109
Phylum Family Genus Association with the protected state Firmicutes Lachnospiraceae Blautia Lachnospiraceae Roseburia ⬆ abundance in MàF vs. control F183 Lachnospiraceae Coprococcus 1 Lachnospiraceae Coprococcus ⬆ abundance in MàF vs. control F; correlated with ⬇ abundance of sphingolipids202 Bacteroidetes Rikenellaceae Alistipes ⬆ abundance in MàF vs. control F; correlated with ⬇ abundance of sphingolipids202; Rikenellaceae Rikenella Prevotellaceae Prevotella ⬆ abundance in MàF vs. control F; correlated with ⬇ abundance of sphingolipids202;
Table 4.4. Summary of taxa associated with protected states in female NOD mice from autoimmune diabetes. Data was compiled from the cecal microbial analyses by Markle et. al. that demonstrated protection in MàF recipients from autoimmune diabetes incidence. ⬆ indicates increased abundance or being more abundant than the comparator. Highlighted taxa appeared in the full-length 16S rRNA analysis in Results Chapter 3.4.
110 4.5 the Big Picture Our lab has established links between the composition of gut microbes and islet autoimmunity in the NOD model. My previous lab members demonstrated that transfer of NOD male cecal microbiota into NOD females (MàF) at weanling age exerted prolonged effects on the cecal microbial composition of the recipients and protected them from autoimmune diabetes onset183. Such link between the gut and autoimmune activities in the pancreas can be explored further along the enteroinsular axis291, which describes the cellular, hormonal and neuronal crosstalk between the two anatomical locations149,291. While this thesis focuses on the “insular” side of the axis and the effects of MàF transfer on islet-reactive T cell responses, we can take a step back first and take the “entero” side into the big picture. Differential abundances of certain taxa in the gut have been associated with the protective state149,185,187-189. Previous high-throughput sequencing of cecal contents of the MàF recipients identified taxa among the Firmicutes that were differentially abundant in the recipients compared to control females183 and those that were correlated with differential abundances of serum metabolites202. Some of these taxa associated with protection were also identified in the full-length 16S rRNA gene sequencing in this thesis (summarized in Figure 4.5). When we reflect upon how differential microbial abundances might translate into protection from autoimmune diabetes, several possible routes arise for the protective effects: 1) direct modulation of gut associated immune compartments by microbes in the protective state, ie. biased towards an anti-inflammatory and immunoregulatory environment160,161,163,164, communicated through immune cell trafficking to distant sites including the pancreas231-233,280,281; 2) indirect modulation of the proximal167-169,172-174 and distant169,175-177 immune responses by microbial metabolites; 3) microbial metabolite modulation of host hormonal183 and neuronal292 responses which then impact autoreactive activities in the pancreas. These three possible routes may not be mutually exclusive. The protective effects of MàF transfer, specifically those that relayed from the gut to autoimmune responses in the pancreas remain incompletely understood. The majority of the work in this thesis focused on the effector side and explored the effects of protective microbial transfer on islet-reactive CD4 T cell responses in NOD mice. Islet autoreactive CD4 T cells play crucial roles in activating islet-reactive B cells for islet autoantibody production, as well as providing help to islet-reactive CD8 T cells that mediate cytotoxic events leading to insulitis. Both insulin autoantibody levels and degree of insulitis
111 were reported to be dampened in MàF recipients183. Therefore, we hypothesized that the protective effects of MàF transfer led to decreased autoantigen presentation to and activation of islet-reactive CD4 T cell responses. These approaches taken are visually summarized in Figure 4.5. Two main anatomical sites where islet autoreactive T cell activation takes place are the pancreas itself and pancreatic lymph nodes (pLN). In the pancreas, islet autoreactive T cells were examined using insulin peptide-bound tetramers. Highest frequencies of cognate CD4 T cells were found within the islets compared to other sites including the pLN. Current observations of these intra-islet insulin-specific T cell frequencies and absolute cell numbers showed no difference between age-matched unmanipulated controls and MàF recipients. In the pLN, islet-reactive T cell responses to autoantigen presentation in the local milieu were examined with adoptively transferred BDC2.5 T cells with a single specificity against the islet antigen chromogranin A. The adoptively transferred cells were retained preferentially in the pLN compared to other sites that are more distant from the pancreas, such as the spleen, indicating antigen-specific interactions with other cellular compartments in the pLN. While proliferation and antigen experience among the BDC2.5 T cells were both detected, neither differed between age-matched unmanipulated NOD controls and MàF recipients. While the pancreas and the pLN are the effector sites of autoimmune actions leading to autoimmune diabetes onset and likely affected by the protective effects of MàF transfer, we wondered whether the microbial perturbation exerted a more global effect on the presentation of islet autoantigens. Therefore, we examined ex vivo presentation of islet autoantigen by peripheral antigen presenting cells (APCs) and their capacities to activate and induce proliferation in BDC2.5 T cells. Nuances in readiness for antigen presentation among APCs were detected. Particularly, a subpopulation of APCs from MàF recipients induced a higher activation status among proliferated BDC2.5 T cells. This may reflect a difference in the tolerogenic versus stimulatory capacities of these APCs, although this difference did not affect the ability of the APCs to induce islet antigen-specific T cell proliferation. Thus, under current experimental conditions, we did not see an effect of MàF transfer on antigen presentation to and the proliferation of two autoantigen-specific CD4 T cell responses. These results indicate that the CD4+ T cells from MàF recipients that are specific for the two islet autoantigens ChrA and insulin B9:23 peptide retained their diabetogenic
112 potential. Our group has previously shown that transfer of T cells from MàF recipients into lymphocyte-deficient, diabetes-resistant NOD.SCID mice was able to induce autoimmune diabetes, although with a slower kinetics compared to those that received T cells from control mice182. Therefore, while the diabetogenic potential of the T cells remains after the MàF microbial manipulation, other factors such as precursor frequencies and cell trafficking may convey the protective effects from MàF transfer. Moreover, CD4 T helper cell differentiation following activation by autoantigen presentation remains to be determined. Identifying how the microbe-dependent protection impacted effector cell compartments such as the autoreactive CD4 T responses will add insights to identification of 1) how microbial signals reach the effector site and 2) sources of such microbial signals. Understanding the host and microbial mechanisms of protection from autoimmune diabetes in the mouse model may be the first step in the identification of novel therapeutic targets or compounds that delay or reverse progression to type 1 diabetes in humans.
113
spleen
CD86 CFSE+ activation CD25 H2-Ag7 APC BDC2.5 T cell CD69 CD11b APC-like properties CD11c co-culture proliferation
Ex vivo In vivo
CFSE+ BDC2.5 T cell APC
proliferation pLN activation
H2-Ag7 CD69 pancreas CD44
Small intestine
Insulin peptide B9-23 variants: Cecum p8E and p8G MàF Insulin-reactive T cells
8F
Firmicutes Bacteroides 1392R Lachnospiraceae Rikenellaceae Blautia Alistipes Lachnospiraceae Prevotellaceae Coprococcus Prevotella
114 Figure 4.5. Schematic diagram summarizing all approaches taken to address the effects of MàF microbial transfer along the entero-insular axis. The cecal microbiota of MàF recipients were sequenced with full-length 16S rRNA gene sequencing and identified presence of taxa previously associated with diabetes risk or protection183,202. In vivo autoantigen presentation was assessed by proliferation and activation of adoptively transferred BDC2.5 T cells in pLN. Ex vivo autoantigen presentation by peripheral APCs were assessed by co-culturing with BDC2.5 T cells. Three readouts for the co-cultures were the readiness for antigen presentation (APC-like properties) of the peripheral APCs, the proliferation of BDC2.5 T cells and their activation status. Furthermore, insulin peptide-reactive T cells were enumerated in the islets using tetramers.
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