Medicine, Faculty of
Medicine, Department of
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Wardell, Christine Marie
Doctor of Philosophy - PhD
University of British Columbia
A major barrier to transplantation success is the recipientâs transplant-reactive âalloimmuneâ response which detects and kills non-self graft tissue. Currently, transplant recipients are placed on broad immunosuppressive regimens that increase the risk of infection and cancer. There is a critical need for therapies that specifically target and inhibit transplant recipientsâ alloimmunity while leaving beneficial immune responses intact. Due to their polymorphic nature, human leukocyte antigens (HLA) are often mismatched between transplant donor and recipient, and are major targets of the alloreactive response. In this dissertation, I investigated how we can use T cells to detect and prevent allo-HLA-reactive responses, as well as local autoreactive responses. I developed an assay to quantify and correlate peripheral alloreactive T cells with transplant biopsy results. I designed the assay based on a concept called âtrogocytosisâ, whereby immune cells acquire and re-express proteins following a strong immune synapse. I hypothesized that allo-HLA-reactive T cells trogocytose mismatched allo-HLA molecules, therefore tagging themselves as alloreactive in a way that is detectable by flow cytometry. I tested the assay on blood from kidney transplant recipients that was paired with surveillance biopsy histological scores. I also developed two mouse models of type 1 diabetes and transplantation to assess whether regulatory T cells (Tregs) expressing a chimeric antigen receptor that detects HLA-A2 (A2-CAR Tregs) could protect HLA-A2âº islet transplants against allo- and autoimmunity. I hypothesized that A2-CAR Tregs would reduce the incidence of transplanted HLA-A2âº islet rejection in diabetic HLA-A2negative mice, and in turn, induce tolerance in diabetogenic autoimmune cells that target common islet antigens. I showed that A2-CAR Tregs were effective at preventing hyperglycemia in an aggressive autoimmune diabetes mouse model via linked suppression and infectious tolerance, providing evidence that Tregs can reshape immunity in a persistent manner. Although HLA mismatch between transplant donor and recipient can spur an alloimmune response that quickly destroys graft tissue, donor HLAs can be used as an abundantly-expressed immunotherapeutic target. My Ph.D. studies demonstrated how to harness T cells that detect mismatched HLA molecules to monitor and prevent transplant rejection, mediated by both allo- and autoreactive immune cells.
HARNESSING T CELLS THAT DETECT MISMATCHED HLA TO MONITOR AND PREVENT ALLO- AND AUTOIMMUNITY by Christine Marie Wardell B.Sc., McMaster University, 2017 M.Sc., Western University, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2024 © Christine Marie Wardell, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: HARNESSING T CELLS THAT DETECT MISMATCHED HLA TO MONITOR AND PREVENT ALLO- AND AUTOIMMUNITY submitted by Christine Marie Wardell in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Experimental Medicine Examining Committee: Megan Levings, Professor, Surgery, UBC Supervisor Francis Lynn, Associate Professor, Surgery, UBC Supervisory Committee Member Ramon Klein Geltink, Assistant Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member Gregor Reid, Associate Professor, Pediatrics, UBC University Examiner Kirk Schultz, Professor, Pediatrics, UBC University Examiner Additional Supervisory Committee Members: Robert Holt, Professor, Medical Genetics, UBC Supervisory Committee Member iii Abstract A major barrier to transplantation success is the recipientâs transplant-reactive âalloimmuneâ response which detects and kills non-self graft tissue. Currently, transplant recipients are placed on broad immunosuppressive regimens that increase the risk of infection and cancer. There is a critical need for therapies that specifically target and inhibit transplant recipientsâ alloimmunity while leaving beneficial immune responses intact. Due to their polymorphic nature, human leukocyte antigens (HLA) are often mismatched between transplant donor and recipient, and are major targets of the alloreactive response. In this dissertation, I investigated how we can use T cells to detect and prevent allo-HLA-reactive responses, as well as local autoreactive responses. I developed an assay to quantify and correlate peripheral alloreactive T cells with transplant biopsy results. I designed the assay based on a concept called âtrogocytosisâ, whereby immune cells acquire and re-express proteins following a strong immune synapse. I hypothesized that allo-HLA-reactive T cells trogocytose mismatched allo-HLA molecules, therefore tagging themselves as alloreactive in a way that is detectable by flow cytometry. I tested the assay on blood from kidney transplant recipients that was paired with surveillance biopsy histological scores. I also developed two mouse models of type 1 diabetes and transplantation to assess whether regulatory T cells (Tregs) expressing a chimeric antigen receptor that detects HLA-A2 (A2-CAR Tregs) could protect HLA-A2+ islet transplants against allo- and autoimmunity. I hypothesized that A2-CAR Tregs would reduce the incidence of transplanted HLA-A2+ islet rejection in diabetic HLA-A2negative mice, and in turn, induce tolerance in diabetogenic autoimmune cells that target common islet antigens. I showed that A2-CAR Tregs were effective at preventing hyperglycemia in an aggressive autoimmune diabetes mouse model via linked suppression and infectious tolerance, providing evidence that Tregs can reshape immunity in a persistent manner. Although HLA mismatch between transplant donor and recipient can spur an alloimmune response that quickly destroys graft tissue, donor HLAs can be used as an abundantly-expressed immunotherapeutic target. My Ph.D. studies demonstrated how to harness T cells that detect mismatched HLA molecules to monitor and prevent transplant rejection, mediated by both allo- and autoreactive immune cells. iv Lay summary Transplantation is a life-saving treatment for people with end-stage organ disease. However, the transplant recipientâs immune system will recognize the new organ as foreign and try to destroy it. Therefore, transplant patients must take daily medications to suppress their immune system. Although these medications prolong the life of the transplant, they are broadly suppressive, reducing the bodyâs ability to fight pathogens and cancers. My dissertation discusses the development of an assay to detect the number of immune cells that want to destroy the transplant in blood and correlates those numbers with the transplantâs health, attempting to develop a tool that could inform whether immune-supressing medications could be safely reduced. I also used mouse models to test whether a new cell-based therapy protects transplanted tissue by inhibiting transplant-targeting immune cells. This work lays part of the groundwork for reducing transplant recipientâs reliance on immune-suppressing drugs to enhance patient outcomes. v Preface Dr. Megan Levings and I conceptualized the work presented in this dissertation. I designed all the experiments and analyzed the majority of the data presented in this dissertation. I also completed the majority of the experiments presented here. I had a lot of help experimentally; Vivian Fung helped me to complete many experiments in chapters 2, 3, and 4, either independently following protocols I designed, or in collaboration with me. Eleanor Chen also helped perform experiments presented in chapter 3. Dana Lao performed experiments that are presented in chapter 2. Dr. Paul Orban and Jana Gillies guided many of my cloning efforts. Drs. Qizhi Tang, Brian Fife and Justin Spanier provided DNA and reagents used in this dissertation. Dr. Dan Luciani helped me to image immune interactions in chapter 2. Dr. Tom Blydt-Hansen provided patient samples used in chapter 2. Dr. Caroline Lamarche collected pilot data (not included in this dissertation) supporting the pursuit of chapter 2. Drs. Majid Mojibian and Manjurul Haque, and Derek Dai provided technical training with mice. Dr. Lisa Xu gave support with fluorescent-activated cell sorting. This work builds on much of the data generated by previous and current Levings lab members. The whole Levings lab gave feedback on this work in an on-going manner. Iâm very grateful for the support these projects have received. All experiments presented here were approved by the University of British Columbiaâs animal care committee (A20-0017) and human ethics board (H18-02553, H17-01872). Part of the text in chapter 1.4 is adapted from the submitted review: Wardell CM, Boardman DA, Levings MK. âHarnessing the biology of regulatory T cells to treat diseaseâ. I performed literature review, wrote the majority of the manuscript, and edited the whole manuscript. DAB and MKL both performed literature review and wrote portions of the manuscript. MKL provided supervision and edited the manuscript. I have significantly edited the portions used here to create continuity with the rest of the introduction. Part of the text in chapter 1.4 and the whole of chapter 3 are adapted from the submitted manuscript: Wardell CM, Fung VCW, Chen E, Haque M, Gillies J, Spanier JA, Mojibian M, Fife BT, Levings MK. âCAR Tregs mediate linked suppression and infectious tolerance in islet transplantationâ. I designed all experiments. I performed and analyzed all experiments with support from Fung VCW, Chen E, Haque M, Gillies J and Mojibian M. I wrote and edited the manuscript, receiving editing help from Fung VCW, Spanier JA, Mojibian M, Fife BT and Levings MK. Levings MK provided project supervision. vi Table of contents Abstract .......................................................................................................................................... iii Lay summary ................................................................................................................................. iv Preface............................................................................................................................................. v Table of contents ............................................................................................................................ vi List of tables .................................................................................................................................... x List of figures ................................................................................................................................. xi List of abbreviations ..................................................................................................................... xii Acknowledgements ...................................................................................................................... xiv Chapter 1: Introduction ................................................................................................................... 1 1.1 Type 1 Diabetes ..................................................................................................................... 1 1.1.1 Living with T1D ............................................................................................................ 1 1.1.2 Mechanisms of immune tolerance ................................................................................. 2 1.1.3 Loss of tolerance in T1D ................................................................................................ 3 1.1.4 T1D on the rise ............................................................................................................... 4 1.1.5 T1D etiology .................................................................................................................. 5 1.1.6 Immune-targeting drug interventions for T1D ............................................................... 6 1.2 Barriers to transplantation ..................................................................................................... 7 1.2.1 Barriers facing islet transplantation ............................................................................... 7 1.2.2 The major histocompatibility complex as an alloimmune target ................................... 7 1.2.3 Immunosuppression to prevent transplant rejection ...................................................... 9 1.2.4 Approaches and challenges to monitoring alloimmunity............................................. 10 1.3 Trogocytosis ........................................................................................................................ 13 1.3.1 Trogocytosis by T cells ................................................................................................ 13 1.3.2 Assays leveraging trogocytosis .................................................................................... 14 1.4 Regulatory T cells ............................................................................................................... 15 1.4.1 Treg characterization and development ....................................................................... 15 1.4.2 Treg mechanisms of action .......................................................................................... 17 1.4.3 Treg stability ................................................................................................................ 19 vii 1.4.4 Polyclonal Treg therapy ............................................................................................... 20 1.4.5 Antigen-specific Treg therapy ...................................................................................... 21 1.4.6 A2-CAR Tregs ............................................................................................................. 23 1.5 Summarized research goals and findings ............................................................................ 24 Chapter 2: A trogocytosis-based assay to detect alloreactive T cells ............................................ 26 2.1 Rationale and approach ....................................................................................................... 26 2.2 Methods............................................................................................................................... 26 2.2.1 Ethics............................................................................................................................ 26 2.2.2 Flow cytometry and data analysis ................................................................................ 26 2.2.3 PBMC and CD8 isolation ............................................................................................ 27 2.2.4 Cell transduction .......................................................................................................... 27 2.2.5 Trogocytosis assay ....................................................................................................... 28 2.2.6 Confocal cell imaging .................................................................................................. 29 2.2.7 Statistical analyses and figure preparation ................................................................... 29 2.3 Results ................................................................................................................................. 30 2.3.1 Trogocytosis assay control development ..................................................................... 30 2.3.2 TCRHLA-A2 CD8+ T cells trogocytose HLA-A2 ........................................................... 31 2.3.3 Trogocytosis assay optimization .................................................................................. 33 2.3.4 T cell activation increases trogocytosis background .................................................... 35 2.3.5 Assessing trogocytosis assay sensitivity ...................................................................... 35 2.3.6 The trogocytosis assay lacked the sensitivity required to definitively identify alloreactive immune cells in kidney transplant recipients .................................................... 37 2.4 Discussion ........................................................................................................................... 39 Chapter 3: CAR Tregs mediate linked suppression and infectious tolerance in islet transplantation ............................................................................................................................... 42 3.1 Rationale and approach ....................................................................................................... 42 3.2 Methods............................................................................................................................... 43 3.2.1 Animals and ethics ....................................................................................................... 43 3.2.2 Flow cytometry ............................................................................................................ 43 viii 3.2.3 Treg generation ............................................................................................................ 44 3.2.4 DC suppression assay .................................................................................................. 45 3.2.5 Islet isolation and transplantation ................................................................................ 45 3.2.6 Longitudinal metabolic and immunological monitoring ............................................. 46 3.2.7 Diabetes induction and Treg treatment ........................................................................ 46 3.2.8 Plasma cytokine analysis ............................................................................................. 47 3.2.9 Endpoint phenotyping .................................................................................................. 47 3.2.10 Insulitis scoring .......................................................................................................... 47 3.2.11 Splenocyte intracellular cytokine staining ................................................................. 47 3.2.12 Enucleation ................................................................................................................ 48 3.2.13 Treg specific demethylated region (TSDR) analysis ................................................. 48 3.2.14 Statistical analyses and figure preparation ................................................................. 49 3.3 Results ................................................................................................................................. 49 3.3.1 Generation of A2-CAR Tregs from non obese diabetic mice ...................................... 49 3.3.2 A2-CAR Tregs activated by HLA-A2+ islets protect from autoimmune diabetes ....... 51 3.3.3 A2-CAR Tregs reduce BDC2.5 T cell effector function .............................................. 52 3.3.4 A2-CAR Tregs suppress passenger HLA-A2+ T cells ................................................. 55 3.4.5 A2-CAR Tregs induce infectious tolerance ................................................................. 57 3.4 Discussion ........................................................................................................................... 59 Chapter 4: A2-CAR Treg treatment for diabetic NOD mice ........................................................ 63 4.1 Rationale and approach ....................................................................................................... 63 4.2 Methods............................................................................................................................... 64 4.2.1 Animals and ethics ....................................................................................................... 64 4.2.2 Anti-CD3 treatment and insulin maintenance .............................................................. 65 4.2.3 Islet isolation and transplantation ................................................................................ 65 4.2.4 A2-CAR Tregs ............................................................................................................. 65 4.2.5 Endpoint phenotyping .................................................................................................. 65 4.3 Results ................................................................................................................................. 66 4.3.1 T1D incidence in the NOD colony at BCCHR ............................................................ 66 ix 4.3.2 Anti-CD3 treatment depletes conventional T cells and spares Tregs ........................... 67 4.3.3 A2-CAR Tregs do not prevent islet transplant rejection .............................................. 69 4.3.4 A2-CAR Tregs engraft in HLA-A2+ islet transplanted-mice ....................................... 72 4.3.5 A2-CAR Tregs are found at the site of HLA-A2+ islet transplantation ....................... 73 3.4 Discussion ........................................................................................................................... 75 Chapter 5: Conclusions ................................................................................................................. 79 5.1 Summary and significance of findings ............................................................................... 79 5.2 Limitations and future directions ........................................................................................ 81 References ..................................................................................................................................... 83 x List of tables Table 2.1 Flow cytometry reagents used in chapter 2 ................................................................... 27 Table 3.1 Flow cytometry reagents used in chapter 3 ................................................................... 44 Table 4.1 Flow cytometry reagents used in chapter 4 ................................................................... 64 xi List of figures Figure 1.1: Allorecognition by T cells in mismatched solid organ transplantation ........................ 9 Figure 1.2: Impacts of T cell trogocytosis .................................................................................... 14 Figure 1.3: The 3 phases of Treg-mediated immune suppression ................................................ 19 Figure 1.4: TCR and CAR-mediated T cell activation ................................................................. 23 Figure 2.1: Development of trogocytosis assay controls .............................................................. 31 Figure 2.2: TCRHLA-A2 CD8+ T cells trogocytose HLA-A2 .......................................................... 32 Figure 2.3: Trogocytosis assay optimization ................................................................................ 34 Figure 2.4: Co-stimulation and IL-2 increase trogocytosis background....................................... 35 Figure 2.5: Assessing trogocytosis assay sensitivity .................................................................... 36 Figure 2.6: Testing the trogocytosis assay on kidney transplant recipient blood ......................... 38 Figure 3.1: A2-CAR Treg generation from NOD.FOXP3-GFP mice ........................................... 50 Figure 3.2: A2-CAR Tregs protect mice with an HLA-A2+ islet transplantation from autoimmune diabetes .................................................................................................................... 52 Figure 3.3: A2-CAR Tregs suppress BDC2.5 T cell effector function ......................................... 54 Figure 3.4: A2-CAR Tregs suppress islet-resident, HLA-A2+ passenger T cells ......................... 56 Figure 3.5: A2-CAR Tregs induce stable islet tolerance and FOXP3+ BDC2.5 T cells ............... 58 Figure 4.1: Incidence of diabetes in locally housed NOD colony ................................................ 66 Figure 4.2: Preparing diabetic NOD mice for islet transplantation .............................................. 69 Figure 4.3: A2-CAR Tregs do not prevent HLA-A2+ islet transplant rejection ........................... 71 Figure 4.4: A2-CAR Tregs engraft in diabetic NOD mice ........................................................... 72 Figure 4.5: A2-CAR Tregs are found at the site of transplantation .............................................. 74 xii List of abbreviations A2-CAR: HLA-A2-detecting chimeric antigen receptor ACE: anterior chamber of the eye AIM: activation induced marker APC: antigen presenting cell ATG: anti-thymocyte globulin BCCHR: BC Childrenâs Hospital Research Institute CAR: chimeric antigen receptor CD#: cluster of differentiation CFP: cyan fluorescent protein CMMT: Centre for Molecular Medicine and Therapeutics CTLA-4: cytotoxic T-lymphocyte associated protein 4 DC: dendritic cell DNA: deoxyribonucleic acid DSA: donor-specific antibody ELISPOT: enzyme-linked immunospot Fab region: fragment antigen-binding region Fc region: fragment crystallizable region FOXP3: forkhead box P3 GAD: glutamic acid decarboxylase GFP: green fluorescent protein gMFI: geometric mean fluorescent intensity HbA1c: hemoglobin A1C HEK 293: human embryonic kidney 293 cell line HEL: hen egg lysozyme HLA: human leukocyte antigen IGRP: islet-specific glucose-6-phosphatase catalytic subunit-related protein IFN: interferon IL-#: interleukin IPEX: Immune dysregulation, Polyendocrinopathy, Enteropathy, and X-linked syndrome i score: interstitial inflammation score xiii LAP: latency-activated peptide MHC: major histocompatibility complex MIP: mouse insulin 1 promoter MLR: mixed lymphocyte reaction mKO2: monomeric kusabira-orange 2 MMF: mycophenolate mofetil NK cells: natural killer cells NOD: non-obese diabetic mice NRP1: neuropilin-1 NSG: NOD scid gamma mice PBMC: peripheral blood mononuclear cells PD-1: programmed cell death protein 1 PD-L1: programmed cell death protein ligand 1 pMHC: peptide-loaded major histocompatibility complex RORγt: RAR-related orphan receptor γ scFv: single-chain fragment variable T1D: type 1 diabetes Tconv: conventional T cell TCR: T cell receptor Teff: effector T cell TGF-β: transforming growth factor β TNF-α: tumour necrosis factor α tNGFR: truncated nerve growth factor receptor TRAP: T-cell Recognition of APCs by Protein transfer or TRogocytosis Analysis Protocol Treg: regulatory T cell t score: tubulitis score UBC: University of British Columbia ZnT8: zinc transporter 8 xiv Acknowledgements The research presented here and my salary were supported by the BC Childrenâs Hospital, the University of British Columbia, the government of British Columbia, the Canadian Institutes of Health Research, the Transplant Research Foundation of British Columbia, the Canadian Donation and Transplantation Research Program, and the Helmsley Charitable Trust. I am very grateful for those who donate their money for research and the staff that award and monitor its impact. Through interactions with patients, families and health care providers, I have come to appreciate that the burden of type 1 diabetes and transplantation is far greater than I previously understood. In honour of those affected by autoimmune disease and transplantation, I tried to spend our fundersâ money responsibly, to maximize its impact. I gratefully acknowledge the mice that were sacrificed for this project. I am one of the luckiest people to have spent such a wonderful five years in the Levings lab. I felt fully supported by my committee members, Drs. Rob Holt, Ramon Klein Geltink and Francis Lynn, and my collaborators Drs. Brian Fife and Justin Spanier. I am grateful for all of the work they have put into making my work better and the support they have given me in pursuing my next steps. Technicians in our animal facilities (Theresa) and flow cytometry core (Lisa) performed a lot of work that was absolutely essential to supporting my experiments. I feel fully indebted to all members of the Levings lab, past and present, who taught me everything I know and were always eager to discuss next steps. Dana, Eleanor, Majid, Manjurul and Vivian supported/performed many of the experiments presented here. Vivian has been the best technician and support a person could ask for; I am so thankful for her eagerness, competence, curiosity and kindness. This work would not have been completed without her. Megan has been an incredible supervisor. Her intelligence, efficiency, and competency make her an excellent scientist, but her compassion and devotion are what make her a remarkable supervisor. I have always felt that Megan has prioritized my interests and gone out of her way to support my endeavours. Sheâs given me the space to try new things and learn from them. As I pursue a career in academia, Megan is my role model. Finally, I would like to acknowledge Vancouver, where I have found a love for the mountains, many great friends and created a life with Petar that brings me so much joy. As I complete this chapter, I look forward to starting the next one as Mrs. Simidzija. 1 Chapter 1: Introduction 1.1 Type 1 Diabetes 1.1.1 Living with T1D Type 1 Diabetes (T1D) is an autoimmune disease in which a personâs immune system kills the beta cells within pancreatic islets (recently reviewed 1). Beta cells are the bodyâs sole producers of insulin, a hormone that regulates blood glucose levels by triggering uptake by liver, muscle, and adipose cells. Insulin-triggered blood glucose uptake keeps blood glucose levels in a tight range to support other tissues that uptake blood glucose in a passive, insulin-independent manner. When beta cells are killed, the body can no longer release insulin, and blood glucose levels rise. Untreated hyperglycemia is fatal. The discovery of insulin and its purification for use in diabetic patients turned a fatal disease into one that can be managed 2. However, exogenous insulin therapy is not a cure for T1D. Managing T1D is incredibly burdensome and despite treatment advances, Canadians with T1D have an estimated 10.5 year shorter lifespan 3. In 2022, 285,000 Canadians were living with T1D, 11% of which were children 3. T1D is often diagnosed during childhood, adding an incredible workload to their plate that robs them of many childhood freedoms. Following a T1D diagnosis, children and their families embark on a lifelong journey of planning meals, insulin administration, constant blood glucose monitoring and even the most vigilant individuals are likely to have hyper- or hypoglycemic episodes. Most patients are diagnosed with T1D during a hyperglycemic episode, characterized by dehydration, polyuria, polydipsia, weight loss and commonly diabetic ketoacidosis 4-6. Diabetic ketoacidosis develops when the body has insufficient levels of insulin to mediate glucose delivery, resulting in the liver releasing ketones as a source of energy. Hypoglycemia (<3.9 mmol/L) caused by inappropriate insulin administration is a scary risk of T1D that can alter patientsâ physical and cognitive abilities 7,8. Some patients have especially labile diabetes, termed âbrittle diabetesâ 9,10. Due to co-morbid conditions and/or frailty, these patients struggle to maintain euglycemia, having large blood glucose fluctuations, increased incidence of hyper- and hypoglycemic events and therefore, are at a higher risk of disease complications. Long-term complications of T1D include macrovascular/cardiovascular and microvascular (retinopathy/vision loss, nephropathy, neuropathy) conditions. Although the pathophysiology of these diseases is not completely 2 understood, they are driven by hyperglycemia, causing increased fluid load 11 and mitochondrial dysfunction in cells lacking defence mechanisms against high extracellular glucose levels 4. Children with T1D should monitor their blood glucose 6-10 times a day via blood glucose meter or continuous blood glucose monitoring 12. People with T1D are generally managing their insulin needs with automatic continuous subcutaneous insulin infusion pumps, or daily long-acting, basal insulin injections, supplemented throughout the day with multiple rapid-acting, bolus insulin injections to prepare for carbohydrate intake or react to high blood glucose levels 1. Insulin production is generally assessed by blood measurement of c-peptide, the more biostable by-product of insulin production, released in equimolar amounts. Long-term glycemia levels are monitored via hemoglobin A1C (HbA1c) tests. Red blood cell hemoglobin irreversibly binds glucose during periods of hyperglycemia. Therefore, measuring glycosylated HbA1c gives a measure of the amount of time spent in hyperglycemia over the last three months (life span of a red blood cell). Although tuneable based on risk of hypoglycemia, it is recommended for T1D patients to maintain a HbA1c test below 7% or 53 mmol/mol, which is associated with less disease complications 12. Patients with continuous blood glucose monitoring are also recommended to maintain their readings in the 3.9-10 mmol/L range >70% of the time 1. T1D progresses through 3 stages 4. In stage 1, patients have no dysglycemia or symptoms, but have two or more autoantibodies and some degree of beta cell loss. In stage 2, patients still have no overt symptoms but experience some dysglycemia. At stage 3, untreated patients have low (<20%) functional beta cell mass, are hyperglycemic and symptomatic (e.g. polyuria, polydipsia, weight loss). Beta cell loss is primarily driven by autoreactive CD4+ and CD8+ activated âeffectorâ T cells (Teff) (reviewed 13). 1.1.2 Mechanisms of immune tolerance T cells are a component of the adaptive immune system that have the capacity to recognize any protein antigen; they are critical for fighting cancer and infections. However, patients with T1D have a small population of T cells that have lost âtoleranceâ of beta cell antigens, resulting in Teff-mediated destruction of the beta cells. Developing T cells go through a stringent selection process whereby autoreactive clones are depleted; this is called âcentral toleranceâ. T cell progenitors released from the bone marrow travel to the thymus to undergo T cell selection 14. Thymic antigen presenting cells (APCs) 3 present self peptides on major histocompatibility complexes (pMHC) to developing T cells, and so long as a T cell receptor (TCR) has the capacity to bind and recognize antigens presented by MHC, it survives âpositive selectionâ. That T cell then must undergo ânegative selectionâ whereby if it binds to self pMHC too strongly, it will undergo apoptosis. This thymic education system releases T cells from the thymus that are capable of recognizing antigens bound by MHC, preparing the body to detect and defend against non-self intruders, while theoretically inhibiting the release of T cells that recognize and are activated by self antigens to harm tissues. In reality, this system of âcentral toleranceâ induction is highly nuanced and evidence that autoreactive conventional T cells (Tconv) are prominent in everyoneâs body shows that negative selection is not perfect 15,16. Nevertheless, most people do not suffer from autoimmune disease and this is due to âperipheral toleranceâ mechanisms. A main focus of this dissertation is regulatory T cells (Tregs), which are critical to mediating peripheral tolerance (discussed in more detail in section 1.4). Briefly, Tregs are a subset of the CD4+ T cell population that during thymic selection are found to have intermediate TCR affinity for self peptides and as a result, receive signals to gain suppressive functions. So while Tconv receive signals that prime them to kill target cells expressing their cognate antigen, Tregs receive signals that instruct them to suppress inflammatory responses targeting their cognate antigens. In healthy people, autoreactive Tconv that are not culled by central tolerance are constantly being kept at bay by Tregs 15. 1.1.3 Loss of tolerance in T1D In people with autoimmune diseases, such as T1D, central and/or peripheral tolerance mechanisms have failed. Why central tolerance fails to remove so many autoimmune Tconv remains unclear. A recent mathematical model supported the âautoimmune surveillance of hypersecreting mutantsâ hypothesis, which purports that the body permits low-grade autoimmune responses targeting endocrine cells which respond to their input signal by increasing their proliferation and hormone secretion as a protective mechanism to guard against adenomas and cancers 17. In the case of T1D, beta cells respond to glucose, their input signal, by producing insulin. Beta cells are highly active cells that may develop mutations; should a beta cell mutate to become hypersensitive of glucose, it would hyper-secrete insulin, leading to life-threatening hypoglycemia. So, this hypothesis suggests that the immune system permits some degree of autoimmunity against these cells to guard against tumors and hypoglycemia. Endocrine cells 4 whose hormone secretion is not increased by their input signal are at a lower risk of developing autoimmunity 17. If this hypothesis is true, tolerance of beta cell antigens is precarious. Environmental triggers may increase inflammation, tipping a genetically predisposed immune balance in favour of antigen presentation to, and activation of, autoimmune Teff. Indeed, most of the genetic polymorphisms associated with T1D development are related to immune function. Human MHCs, the human leukocyte antigens (HLAs), are highly polymorphic and the class II HLAs -DR3/4 and -DQ2/8 are highly associated with T1D, suggesting that they have a propensity for presenting diabetogenic peptides in a way that permits autoreactive Tconv to develop and evade central tolerance 18. In addition to HLA, another major genetic risk factor is the number of tandem repeats upstream of the proinsulin gene. More repeats, which positively correlate with higher expression of proinsulin peptides in the thymus, are protective, presumably because of more stringent negative selection 19. Other DNA polymorphisms associated with T1D include reduced function of genes involved in Treg function and their ability to maintain peripheral tolerance, including CTLA4 20 and IL2RA 21. A genetic risk score (GRS) that accounts for number and type of autoantibodies, age, ethnicity, sex and single nucleotide polymorphisms in T1D-associated genes is positively correlated with T1D progression rate 18. 1.1.4 T1D on the rise Since 1980, there has been an alarming increase in the incidence of T1D in Canada; from 2000 to 2022, the number of new cases rose 34%, outpacing population growth 3. It is unclear what triggers a break in tolerance and the start of beta cell loss. There are many genetic polymorphisms associated with T1D onset, most of which are linked to immune function 1. However, only 65% of monozygotic twins are concordant for T1D 22. There have been many environmental elements associated with T1D development, including early life diet, vitamin D, gut microbiome species and perhaps most intriguingly, infections (reviewed 23). T1D is most commonly diagnosed in children during months when colds are circulating 24. Moreover, an interesting hallmark of T1D is increased beta cell MHC class I expression 25, which could be increased via interferon (IFN) release in response to viral infection. There is a fair deal of correlative evidence showing that enteroviral infections are associated with T1D, specifically by coxsackieviruses which are capable of infecting and driving inflammation in beta 5 cells 26,27. In populations with a high incidence of enteroviral infections, there is a low incidence of T1D 28. But patients diagnosed with T1D were more likely to have had an enterovirus infection than healthy controls 29. These correlations support the âhygiene hypothesisâ, which suggests that in developed countries where exposure to pathogens is historically lowered, autoimmunity is on the rise because the immune system is not exposed to its co-evolved pathogens that are necessary for shaping its development 30. The lack of infections or late exposure to certain pathogens may cause dysregulation of the immune system. 1.1.5 T1D etiology In the absence of tolerance, naïve diabetogenic Tconv interact with beta cell-derived antigens presented on MHC proteins expressed by APCs, such as dendritic cells (DCs) and macrophages (reviewed 13). These initial interactions are thought to occur in secondary lymphoid structures, namely lymph nodes, that drain from the tissue where those antigens are expressed. CD8+ T cells are activated by pMHC class I which are ubiquitously expressed and present intracellular peptides 31. CD4+ T cells are activated by pMHC class II, which are primarily expressed on APCs and present exogenously acquired peptides. APCs present antigen and co-stimulate CD28 on naive Tconv to activate them to become Teff. Teff then expand and mediate the destruction of any cells expressing their cognate antigen. In people with T1D, Teff have been identified that recognize peptides from proinsulin, preproinsulin, glutamic acid decarboxylase (GAD), islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP), zinc transporter 8 (ZnT8), and more recently hybrid insulin proteins that fuse proinsulin and other beta cell peptide fragments, as well as post-translationally modified peptides (e.g. citrullination) 32,33. CD8+ T cells are highly cytotoxic and recognize peptides presented by MHC class I, which are expressed by almost every cell. To avoid T cell exhaustion induced by continuous antigen exposure, long-lived stem-like CD8+ T cells reside in lymph nodes and give rise to short-lived effector CD8+ T cells 34. These effector CD8+ T cells leave the lymph nodes and travel to the pancreas where they detect beta cell-derived peptides presented by MHC class I on the beta cells and kill them 34. CD4+ T cells play more nuanced roles in immunity, by releasing cytokines that enable CD8+ T and B cell activation 13. B cells produce the autoantibodies that are detectable in T1D patients beginning in stage 1. While T cells, not autoantibodies, are considered the drivers of T1D, mouse models have shown that B 6 cells are potent APCs that preferentially recognize, capture and present beta cell antigens to activate diabetogenic Teff 35. 1.1.6 Immune-targeting drug interventions for T1D Cyclosporin A is a broad immunosuppressive drug that blocks T cell activation. It is not commonly used anymore due to toxicity concerns; however, it was the first immune targeting-drug that showed reduced insulin requirements in patients with T1D 36, paving the way for immunosuppressants in the treatment of T1D. Abatacept is a CTLA-4-Ig antibody that binds to and blocks CD28 to prevent T cells from receiving co-stimulation. Following on the success of a trial that showed that abatacept treatment delayed c-peptide reduction by 9.6 months in recently diagnosed patients 37, a recent phase II study tested the capacity of abatacept to prevent disease progression in T1D patients with stage 1 disease 38. Abatacept-treated patients had significantly higher c-peptide at one year than placebo-treated patients (p = 0.03). During active treatment (one year), there was a transient reduction in the number of circulating Tregs. Given Tregsâ consistent requirement for co-stimulation 39, this was perhaps not surprising, but its correlation with a less impressive clinical outcome than was obtained when treating patients with early onset T1D 37 suggests that the timing of abatacept administration may be critical to abataceptâs ultimate success. Anti-thymocyte globulin (ATG) has long been used to prepare patients for solid organ transplantation. This drug consists of the antibodies produced by a horse or rabbit when vaccinated with Jurkat cells (an immortalized human T cell line) or human thymocytes, respectively. Low doses have been administered to patients with recent-onset T1D and were effective at maintaining higher c-peptide and lower HbA1c levels relative to untreated patients 40. Low dose ATG significantly reduced CD4+ T cells numbers, but increased the frequency of Tregs and exhausted PD-1+ CD4+ T cells 41, indicative of a tolerogenic reset. Recently, Teplizumab has generated a lot of excitement. Teplizumab is an anti-CD3 antibody that lacks a functional Fc region, which would otherwise induce cross-linking with Fc receptor+ cells to induce T cell activation 42. It was originally used to prevent islet and kidney transplant rejection 43,44. Anti-CD3 treatment effectively decreases Tconv burden while preserving Treg numbers 45. The mode of action of anti-CD3 treatment remains to be fully defined, but likely involves a combination of apoptosis, TCR downregulation, and induction of 7 exhaustion/anergy in Teff 45-47. Moreover, it can increase release of TGF-β which promotes Treg function and proliferation 48,49. Teplizumab was approved in 2022 in the United States for the treatment of patients with stage 2 T1D after showing that following a two-week treatment, it remarkably prolonged progression to stage 3 by a median of at least two years 46. 1.2 Barriers to transplantation 1.2.1 Barriers facing islet transplantation For patients with labile/brittle diabetes at high risk of hyper- and hypoglycemic events, beta cell replacement via islet transplantation could be a curative option. Islet transplantation can provide a functional cure for T1D. A recent Canadian study found that 79% of recipients developed insulin independence, with 61% maintaining independence at least one year post-transplant 50. However, this benefit was not long-lived and after 20 years, only 8% were still insulin independent50. Those that had some sustained graft survival had lower insulin requirements than those without 50. During transplant, islets are infused via the hepatic portal vein into the liver. Patients can receive subsequent infusions if the addition is likely to keep their insulin use low. Although now approved in the United States and Canada for treatment of T1D, islet transplantation faces barriers for widespread uptake. Islets are currently isolated from deceased donors and sequential infusions from multiple donors are often needed to treat one patient, severely limiting the number of transplants that can be performed 51. There are many efforts to derive beta cell-like products from stem cells, which would provide a theoretically unlimited source of beta cells 52. However, these cells would still be at risk of autoimmune destruction by the pre-existing diabetogenic response, and if they are derived from a non-syngeneic donor, would also be targeted by a novel alloimmune attack 53. 1.2.2 The major histocompatibility complex as an alloimmune target There are multiple components of the innate immune system, including monocytes and natural killer (NK) cells, which respond instantly to transplanted tissues with alloinflammatory responses (reviewed by 54). However, the focus of this dissertation is the substantial role that T cells play in mediating alloimmunity. HLAs are extremely polymorphic and commonly 8 mismatched between donor and recipient in solid organ transplantation. Transplant recipient T cells constantly engage with HLA throughout the body; when they recognize a non-self HLA protein (i.e. from the transplant donor), they are activated as alloreactive Teff to mount an attack against that tissue. Not surprisingly, the number of HLA mismatches and risk of rejection is positively correlated 55. Following HLA-mismatched solid organ transplantation, alloreactive T cells are activated through three potential pathways (Fig 1.1A) 56. âDirectâ allorecognition refers to (organ) âdonorâ MHC presenting donor-derived peptides on donor cells. âSemi-directâ allorecognition is the same, except that the donor pMHC is presented by the (organ) ârecipientâsâ APCs. âIndirectâ allorecognition is recipient MHC class II on a recipient cell presenting a donor-derived peptide. In cases of haploidentical transplantation (e.g. organ donor is a family donor), half of the HLA proteins expressed by the donor cells are syngeneic to the recipient and can present polymorphic peptides that vary between organ donor and recipient as a form of indirect presentation. Indirect allorecognition is similar to T cell recognition of a pathogenic antigen; the T cell is recognizing non-self antigen that it was not exposed to during thymic selection. However, direct and semi-direct allorecognition are more difficult to understand; there are two models that attempt to explain it (Fig 1.1B) 56. The âpeptide-centricâ model asserts that all TCRs have some ability to dock on common elements on any MHC protein, and upon binding, are activated by the non-self peptide, similar to the indirect pathway/pathogen response. The âMHC-centricâ model suggests that polymorphisms in alloMHC proteins are sufficient to impair TCR docking and induce alloactivation, independent of the allopeptide. Over the years, many experiments to prevent or severely limit peptide presentation, or mutate the presenting MHC proteins, have provided proof-of-concept for both the peptide-centric and MHC-centric models. Ultimately, alloreactive clones have been found that appear to be peptide-restricted and/or peptide-irrelevant, suggesting that both models are needed to understand direct and semi-direct allorecognition 57-59. Following solid organ transplantation, alloreactive T cells are activated in secondary lymphoid organs, to a lesser degree by passenger donor APCs that migrated from the graft (direct pathways) 60, and primarily by host APCs via the semi-direct pathway 61. Activated alloreactive T cells then migrate to the graft where their functions are sustained via semi-direct presentation on host APCs, rather than donor APCs via direct presentation, as these cells are quickly eliminated from the graft 62,63. Alloreactive T cells can also interact with, and give signals to, alloreactive B 9 cells in secondary lymphoid organs to stimulate their production of alloantibodies. Alloantibodies mediate transplant destruction by binding to graft cells and activating the complement pathway or Fc receptor bearing cells (macrophages, NK cells) to kill via antibody-dependent cell cytotoxicity. Figure 1.1: Allorecognition by T cells in mismatched solid organ transplantation (A) Transplant recipient T cells recognize allogeneic pMHC via the direct pathway (any donor cell presenting donor MHC), the indirect pathway (recipient cell presenting recipient MHC loaded with donor peptide), or the semi-direct pathway (recipient APC presenting donor MHC and peptide). (B) Alloreactive TCRs recognize donor pMHC (via direct or semi-direct pathways) as non-self either based on the foreign allopeptide (peptide-centric model) or allogenic structural components of the non-self MHC (MHC-centric model). This figure was created with BioRender. 1.2.3 Immunosuppression to prevent transplant rejection Alloreactivity is a reality of non-syngeneic transplantation, so solid organ transplant recipients are maintained on intense immunosuppressive regimens to diminish alloimmunity and preserve allograft function. Transplant recipients receive two types of immunosuppression; prior to transplantation, patients receive âinductionâ medications with the goal of debulking T cells. Following transplantation, patients receive âmaintenanceâ immunosuppression to longitudinally restrain alloimmunity. Preferred immunosuppressive regimens vary between hospitals and organs, with the past decade seeing an increased interest in personalizing each patientâs maintenance medications based on their circumstances 64. 10 Some of the drugs that target T cells used to treat T1D are also used in solid organ transplant; ATG is commonly administered as an induction therapy for solid organ transplant 64,65. Some centers also induce with anti-CD25 antibodies (e.g. Basiliximab, Daclizumab), which block IL-2 signaling and subsequent activation of Teff 66. Maintenance therapy typically consists of tacrolimus, mycophenolate mofetil (MMF) and corticosteroids (e.g. prednisone) 64. Tacrolimus binds to and inhibits FK506 binding protein 12, blocking the phosphatase activity of calcineurin, and ultimately limiting IL-2 production and T cell survival 67. Mycophenolate mofetil inhibits inosine monophosphate dehydrogenase, blocking de novo purine synthesis and therefore inhibiting T cell proliferation 68. Although advancements in immunosuppression have greatly extended the life of transplanted organs, these drugs have a variety of serious side effects. Long-term tacrolimus use is nephrotoxic 67 and corticosteroid use increases risk of diabetes and cardiovascular complications 69. But perhaps even more importantly, all broad immunosuppressants suppress anti-cancer and anti-pathogen immunity, making cancer and infections leading causes of death in transplant recipients 70. 1.2.4 Approaches and challenges to monitoring alloimmunity Given the toxicities and severe consequences of broad immunosuppression, there is a current focus on determining ways to evaluate alloimmunity in an on-going basis to inform immunosuppression modulation so as to balance allograft health and immune competency. As it pertains to my studies described in chapter 2, below I discuss approaches used in kidney transplantation Kidney allograft health can be monitored non-invasively by looking for signs of organ distress; increased serum creatinine levels and albuminuria suggest that the kidney allograft is experiencing rejection. Although immunosuppressants can be adjusted to try to reduce the on-going attack, this information is essentially too late because rejection has already occurred. Urine and blood are easily accessible samples for biomarker surveillance. Transcriptome panels in blood 71 and urine 72 correlate well with biopsy results. Donor-derived, cell-free DNA levels in blood have a short half-life (30 minutes), and these cell-free DNA levels correlate better with rejection than serum creatinine measurements 73. However, all these assays need further validation and rely on expensive technologies that are not accessible at every hospital. 11 One great advance in alloimmune monitoring has been the development of cell-based (e.g. complement-dependent cytotoxicity test) and solid-phase (e.g. Luminex, ELISA) assays to detect donor-specific antibodies (DSA) 74. Each version of this DSA assay mixes recipient serum (containing antibodies) with donor HLA proteins and then detects recipient antibodies complexed with donor HLAs, indicative of DSAs in the transplant recipient. Patients waiting for an organ transplant that have high levels of pre-existing DSAs (e.g. due to blood transfusion or pregnancy) are at a higher risk of subsequent rejection unless DSA levels can be tapered down with rituximab (anti-CD20) and intravenous immune globulin 75,76. Development of de novo antibodies post-transplantation is also associated with deteriorating transplant health and eventual graft loss 77. While methods to detect DSAs are now routine and reliable, there is still a lack of assays to detect alloreactive T cells. B cells require CD4+ T cell help for antibody production; mice that do not have CD4+ T cells do not produce alloantibodies following transplantation 78,79. Moreover, histological indications of T cell-mediated rejection (discussed below) tend to occur earlier following transplantation and are less associated with graft loss than indications of antibody-mediated rejection 80. Alloreactive T cell presence is therefore a requirement for DSA-mediated graft loss and the formerâs appearance may occur prior to DSAs, indicating an earlier window to prevent rejection. The development of methods to detect, quantify and correlate alloreactive T cells with transplant outcomes is certainly warranted. To date, there have been many attempts to develop assays that reliably detect alloreactive T cells, but none have achieved the ease and reproducibility of assays that detect DSAs. The current gold standard for defining subclinical T cell-mediated rejection episodes is through graft biopsies, either for cause or regularly planned surveillance. Typically, one or two biopsies are retrieved and then assessed histologically. As a part of the Banff conventions, lesion interstitial inflammation (i score) and tubulitis (t score) are scored 0-3, with a score >0 suggestive of T cell-mediated rejection 81. However, histological analysis lacks absolute predictive power 82. Biopsy transcriptomic panels have been designed to predict fibrosis and rejection, but are not widely used 83,84. Moreover, biopsies themselves have sampling errors and are invasive. Accounting for preparation and recovery, patients devote at least a day to the procedure and up to 17% experience minor complications (typically related to bleeding), and 1.9% have major complications, including hospitalization 85. Due to all these factors, biopsies are not performed frequently (1-2 times per year). The need for non-invasive diagnostics is clear. 12 Classically, mixed lymphocyte reactions (MLRs) are inexpensive assays that combine donor and recipient immune cells and look multiple days later for recipient T cell proliferation as an indication of alloreactivity. This assay is lengthy, has high background and is a challenge to quantify. Refinements have attempted to reduce variability driven by the donor cell source and use activation markers rather than proliferation as a readout. For example, a protocol was recently developed where B cells in donor-derived peripheral blood mononuclear cells (PBMCs) were activated for two days to enhance HLA expression, irradiated and labelled to differentiate them from co-cultured responder (i.e. recipient) PBMCs previously rested for one day 86. Alloreactive T cells were defined based on expression of two activation-induced markers (AIMs): CD69+CD137+ CD8+ T cells and CD69+CD154+ CD4+ T cells, both of which were identifiable beyond background levels by 18-24 hours 86. Similarly, enzyme-linked immunospot (ELISPOT) assays plate T cell-depleted donor cells (PBMCs or splenic) with recipient PBMCs and single IFN-γ-producing cells are visualized by microscopy. ELISPOT assays have been trialed at multiple sites to evaluate their utility as a prognostic marker for kidney graft rejection. A recent meta-analysis evaluated more than 1,000 kidney recipients from 12 studies and found that a positive pre-transplant ELISPOT test has a sensitivity and specificity of 65% and a negative predictive value of 70-95% 87. While pre-transplant ELISPOTs may be useful for identifying patients at a low risk of rejection, its positive predictive value was only 16-60% 87. Another meta-analysis found similar predictive values for pre-transplant ELISPOTs, and these values remained similar for post-transplant ELISPOTs as well 88. A recent study showed that when patients lacking pre-transplant T cells and donor specific antibodies were treated with tacrolimus monotherapy, instead of standard of care (tacrolimus, mycophenolate mofetil and prednisone), there was a non-significant trend for increased incidence of biopsy-proven acute rejection but decreased risk of viral infection 89. These assays need further validation before potentially entering clinical use. However, one of the biggest problems with these assays is that they rely on donor cells as stimulators, which are often difficult, if not impossible, to obtain since these cells must be collected and cryopreserved at a high standard at the time of organ donation. They are therefore also limited in numbers. Later in chapter 2, I discuss a blood-based assay I developed to monitor alloimmunity that overcomes the barriers currently associated with tracking alloreactive T cells, based on a concept called trogocytosis. 13 1.3 Trogocytosis 1.3.1 Trogocytosis by T cells Trogocytosis is a process whereby after a cell creates a strong bond with another cell, it removes a protein expressed on the cell membrane of its partner and re-expresses it on its own membrane (Fig 1.2A). This is a biological process, not restricted to immune cells, that has experienced a recent burst of interest in the immunology field. It remains unclear how trogocytosis is mediated, and when trogocytosis occurs instead of phagocytosis or endocytosis. T cells have been shown to trogocytose MHC proteins in an antigen-specific manner, following formation of a strong immune synapse 90-92. Inhibition of actin polymerization or several kinases downstream of TCR signaling, or incubation at 4° C, inhibits trogocytosis in T cells 90-92. Therefore, blocking antigen-specific T cell conjugate formation and signaling with the MHC-bearing cell blocks trogocytosis. T cells that have trogocytosed their cognate pMHC have been shown to have a multitude of functions (Fig 1.2B). They can receive signals in cis that sustain their survival and influence cytokine production and function 93-95. They also act in trans as APCs for other T cells, activating them and influencing their function, and often governing negative feedback loops 95-97. T cells may also trogocytose so much pMHC, that they effectively deplete target cell pMHC, limiting downstream antigen-recognition and activation of other T cells 98,99. In extreme cases, T cells can also express so much trogocytosed pMHC that they become targets of fracticide by other cognate T cells 100. Most data show the implications of T cell-mediated trogocytosis of pMHC complexes; however, T cells can also trogocytose proteins in the same lipid rafts as their cognate pMHC, including CD80, other pMHCs and PD-L1 92,101,102. Trogocytosed PD-L1 is functional, allowing CD8+ PD-L1-trogocytosed cells to induce apoptosis of PD-1+ T cells 102. It is important to keep in mind that most of these data are generated in vitro; it is still unclear how large of a role T cell trogocytosis plays in disease in vivo, as it is difficult to assess. However, there have been multiple in vitro assays optimized to leverage the process of trogocytosis to identify antigen-specific T cells and targets. 14 Figure 1.2: Impacts of T cell trogocytosis (A) T cells that create a strong, antigen-dependent immune synapse with their cognate pMHC remove pMHC, as well as neighbouring proteins, from the APC and re-integrate those proteins into their own membrane. (B) T cell-mediated trogocytosis leaves APCs with low levels of pMHC while giving the trogocytosis+ T cell antigen presentation capabilities that act in cis or trans. This figure was created with BioRender. 1.3.2 Assays leveraging trogocytosis Multiple assays that rely on T cell pMHC trogocytosis have been developed to identify antigen-specific T cells and their antigens. TRAP assays (T-cell Recognition of APCs by Protein transfer or TRogocytosis Analysis Protocols) co-incubate donor cells pulsed with a peptide of interest with T cells of mixed specificities 103-105. T cells that trogocytose the pMHC can then be identified as antigen-specific, to similar levels of sensitivity as obtained with tetramers, cytokine staining, or AIM assays 103-105. Trogocytosis+ cells are viable following the assay, allowing for further characterization if desired. The reverse assay, to identify a target antigen, has also been 15 developed 106. Trogocytosis is bidirectional, with pMHC donor cells trogocytosing TCRs from T cells as well. One group of researchers looking to leverage this concept created a library of donor cells that each expressed a different single-chain trimer (MHC proteins translated conjugated to a defined peptide). They then showed that co-incubation with T cells transduced to express orphan TCRs (cognate antigen is unknown) resulted in trogocytosis of the TCR onto the donor cell bearing its cognate peptide; TCR+ donor cells were sorted and next-generation sequencing used to identify the originally transduced single-chain trimer/peptide 106. Most trogocytosis assays biotinylate the cells that bear the protein that will be trogocytosed and then stain the acceptor cells with streptavidin to identify trogocytosis+ cells. Alternatively, other groups have developed similar assays using cell lines that have been engineered to express fluorescently-tagged MHC proteins, negating the need for biotin-labelling and streptavidin staining 100,107. Importantly, each of these studies highlighted that non-antigen specific membrane sharing is a reality of any co-culture experiment, necessitating equivalently labelled control donor cells for setting background thresholds. These assays have largely focused on identifying Teff, but could also be leveraged for identifying antigen-specific Tregs, which have a higher propensity for trogocytosis and utilize trogocytosis to mediate suppression 108,109. 1.4 Regulatory T cells 1.4.1 Treg characterization and development Tregs constitute 2-5% of the adult human CD4+ T cell repertoire 110. They are defined based on their function; while the majority of immune system cells cause inflammation to fight pathogens and cancers, Tregs are balancers that keep inflammation in check to prevent pathology. Treg dysregulation is therefore linked with many diseases. When Tregs underperform, Teff that recognize autoantigens are unrestrained and can cause autoimmune disease 15,16. When a patient receives transplanted tissue, invigorating a Treg response to restrain alloreactive Teff is a critical component of allograft acceptance 111,112. Establishing a balance between Tregs and Teff is therefore critical to human health. Manipulating this balance in favour of Tregs is the purpose of Treg therapy. Tregs are characterized by their high expression of FOXP3 and CD25. Their lineage-defining transcription factor, FOXP3, is essential to functional Treg development; patients with 16 genetic mutations in FOXP3 have Immune dysregulation, Polyendocrinopathy, Enteropathy, and X-linked (IPEX) syndrome, characterized by widespread autoimmunity 113,114. Distinct from Tconv, Tregs acquire interleukin-2 (IL-2), a cytokine required by all T cells for growth and function, via constitutive expression of CD25 115. While most T cells produce and use a portion of their own IL-2, FOXP3 represses IL2 transcription 116-119. CD25 is the IL-2 receptor α subunit that makes a trimeric IL-2 receptor with 100 times higher affinity for IL-2 than the dimeric IL-2 receptor expressed by Tconv 120, allowing Tregs to easily acquire IL-2 from their environment. Recently, the transcription factor HELIOS has also been proposed to be expressed by stable Tregs 121,122. Tregs arise via two major developmental pathways: selection in the thymus to generate thymic (t)Treg cells or differentiation in the periphery from Tconv to generate peripheral (p)Treg cells. In mice, surface expression of neuropillin-1 (NRP1) is often used to define tTreg cells as it is not expressed by pTreg cells, but in humans it does not effectively identify Treg origin 123. In the thymus, Treg differentiation is driven by the pattern and strength of TCR and cytokine signaling. Tregs express TCRs that detect self antigens, with hydrophobic pMHC binding regions, endowing them with âintermediateâ affinity TCRs 124. CD4+ T cells that express high affinity TCRs for self antigens are deleted in the early stages of thymic selection; however, CD4+ T cells with intermediate autoantigen TCR affinities persist to further selection stages 125. That population of âintermediateâ affinity TCR-bearing T cells bifurcates into two populations. The first has slightly higher affinity TCRs that receive continuous antigen signaling to become autoreactive, IL-2 producing effector T cells. The second has lower antigen affinities that maintain less continuous TCR signaling, interrupted by the presence of the immunosuppressive cytokine TGF-β, that ultimately drives their expression of FOXP3 125. FOXP3+ Tregs then thrive, somewhat paradoxically, in the IL-2 produced by their autoreactive counterparts 125,126. Development of pTregs is best characterized in intestinal tissues where these cells differentiate to induce tolerance of dietary antigens and commensal microbiota. Recently, a trio of papers reported the role of a new type of intestinal APC termed RORγt+ APCs, which share features with both dendritic and epithelial cells 127-129. They express integrins αvβ8 and αvβ3, which convert latent TGF-β to its active form, which in turn converts Tconv into FOXP3+ Tregs. 17 1.4.2 Treg mechanisms of action Tregs are activated to exert their suppressive function when they recognize their cognate pMHC on an APC. Upon activation, Tregs suppress inflammation in three main phases (Fig 1.3). First, they interact with APCs to reduce the latterâs capacity to activate Tconv. Second, they release cytokines and metabolites that signal surrounding cells to reduce their inflammatory activity. Finally, they establish long term tolerance by promoting the expansion of Treg populations that recognize antigens of interest. Tregs inhibit Tconv activation by physically blocking their access to cognate antigens and by depleting co-stimulatory proteins from APCs, which primarily reside in secondary lymphoid organs including lymph nodes (Fig 1.3, left panel). Live microscopy reveals that Tregs form tight bonds with APCs presenting their cognate antigen in a way that blocks Tconv that recognize the same antigen from accessing the APC to be activated 108. Furthermore, when Tregs pull away from APCs, they remove their cognate pMHC by trogocytosis, further preventing subsequent Tconv activation 108. In addition to presenting antigen, APCs also express the costimulatory proteins CD80 and CD86 which ligate CD28 on Tconv to induce the latterâs full activation. Tregs express high levels of the surface protein CTLA-4, which outcompetes CD28 for binding to CD80 and CD86, and further depletes CD80 and CD86 from the surface of the APC 109,130. Tregs produce multiple anti-inflammatory cytokines and metabolites, most notably TGF-β and IL-10 (Fig 1.3, middle panel). TGF-β is produced by many T cells, but only in its latent form and tethered to the cell membrane, complexed with other proteins, including the latency-activated peptide (LAP), that impede its functionality. However, Tregs are the prominent T cell population that express the αvβ8 integrin, which releases TGF-β from its inhibitory complex as its active form 131. The active form of TGF-β maintains and promotes FOXP3 expression 132,133. Tregs produce some IL-10, which suppresses production of innate, pro-inflammatory cytokines (e.g. TNF-α, IL-6 and IL-1β) and APC function 134,135. Tregs can also produce IL-35, (Ebi3âp35 heterodimer) which diminishes inflammation and promotes Treg function 136. Finally, Tregs express surface ectonucleotidases CD39 and CD73, which work together to convert extracellular ATP into adenosine 137,138. While high extracellular ATP promotes inflammation, adenosine inhibits effector T cells and stabilizes Tregs. Not only do Tregs produce anti-inflammatory mediators, a popular hypothesis is that they behave as an âIL-2 sinkâ; since CD25 expression 18 gives Tregs an IL-2 receptor with 100 times higher affinity for IL-2 than Tconv 120, Tregs deprive Tconv of the IL-2 in their shared environment 139,140. A fundamental premise of Treg therapy is its potential to induce long-lasting tolerance via bystander or linked suppression, as well as infectious tolerance (Fig 1.3, right panel), concepts introduced by Dr. H. Waldmann through studies in transplantation 141,142. Linked suppression was first introduced by Dr. Herman Waldmann, who performed experiments to tolerize mice to allogeneic skin graft âAâ with anti-CD4 and anti-CD8 antibodies 141. Subsequent transplantation with skin from a donor that was generated from a cross between donor âAâ and a new allogeneic donor âBâ (skin âAxBâ) was tolerated in these mice. Finally, mice that received another transplant that was exclusively from donor âBâ tolerated that transplant as well. During the first transplant âAâ, the conditioning regimen generated Tregs that were protective of âAâ antigens. During the second âAxBâ transplant, the Tregs that initially protected âAâ skin established tolerance of the âBâ antigens as well so that the third âBâ skin transplantation was tolerated, via linking âAâsâ suppression to âBâ. This provided the first evidence of linked suppression 141. More recently, a less-defined version of this concept has come to be known as bystander suppression, which refers to Treg-mediated suppression of Teff that recognize a distinct antigen when both Treg and Teff antigens are presented by the same cell and/or tissue. Infectious tolerance refers to a Treg-mediated, long-lived increase in the ratio of Tregs to Teff 142. Although the exact mechanisms of infectious tolerance remain unclear, the process likely depends on a combination of Treg functions already discussed here, including contact-dependent Teff and antigen presenting cell (APC) inhibition, TGF-β-driven FOXP3 expression and environmental metabolic disruption 108,141,143-147. 19 Figure 1.3: The 3 phases of Treg-mediated immune suppression In phase 1, Tregs prevent Teff activation by physically blocking Teffâs access to APCs and stripping APCs of co-stimulatory and MHC proteins. Treg-APC interactions are thought to occur predominantly in secondary lymphoid organs, but may also occur in tissues. In phase 2, Tregs release a variety of cytokines into their microenvironment, generally suppressing inflammation. Tregs express the ectonucleases CD39 and CD73, which work together to convert ATP into adenosine, which has similar anti-inflammatory properties. Tregs also preferentially uptake IL-2 from their environment, limiting the amount available to Teff. In phase 3, Tregs establish long-lasting immune tolerance by inducing the expansion of existing Tregs and/or converting Teff into Tregs, potentially via TGF-β mobilization. This figure was created with BioRender. 1.4.3 Treg stability Understanding Treg stability is key for defining risk of conversion into pathogenic Teff, as well as longevity of therapeutic effects. The best marker of Treg stability is the level of CpG DNA methylation at the Treg-specific de-methylated region (TSDR), a non-coding DNA sequence element located in the first intron of FOXP3 148. This locus is stably demethylated in Tregs, but not in Teff 148. Of note, since FOXP3 is on the X chromosome, in females one allele is methylated due to X-inactivation, making it important to account for sex during the analysis. In the context of inflammation, some studies in mice show stable FOXP3 expression and lineage stability 149, whereas others report loss of FOXP3 and gain of an effector T cell phenotype 150,151. These contrasting results likely arise from differences in the lineage-tracing systems used, as well as the fact that FOXP3 is transiently expressed in activated Teff that may be present in putative Treg populations 118. In humans, Tregs are resistant to lineage instability driven by 20 inflammatory cytokines including IL-6, TNF-a, or IL-12 152,153 and multiple pre-clinical models of adoptive Treg therapy did not reveal instability 154-156. Helios (gene IKZF2) expression is also associated with lineage stability, potentially via its potent ability to suppress IL2 transcription, as demonstrated in a series of patients with biallelic or dominant negative IKZF2 mutations 121,122. Indeed, ex vivo culture of human FOXP3+ Helios+ Tregs in pro-inflammatory cytokines does not destabilize their phenotype, and actually enhanced their proliferation 152,153. However, knock out of IKZF2 in human Tregs does not change their phenotype or function, suggesting that Helios may be a Treg stability marker, but that its ongoing expression may not be required for fully functional cells 157. Overall, the conflicting reports regarding Treg stability are likely attributed to different Treg enrichment and tracking protocols used between studies, as well as to differences between mice and humans. To date, there have been no reports of a Treg cell product losing its suppressive capacity in patients, suggesting that current clinical Treg isolation protocols derive functionally-stable products. 1.4.4 Polyclonal Treg therapy Given Tregsâ inherent ability to suppress undesirable immune responses, they have become an agent of immune therapy to treat autoimmune disease and promote transplant acceptance. There have been attempts to expand Tregs in vivo, primarily by targeting CD25 with IL-2 or IL-2-based compounds (reviewed 158). However, here Iâll focus on adoptive Treg transfer. The purpose of these therapies is to re-shift the balance of Treg to Teff to induce long-lasting tolerance of autoantigens and/or alloantigens, simply by increasing Treg abundance. Clinical trials of Treg therapy to date have primarily tested autologous polyclonal, ex vivo-expanded Tregs, demonstrating excellent safety and tolerability 159,160. Of particular interest are data from âthe ONE studyâ, an international, multi-centre study which tested several cell therapies in living donor kidney transplantation with a focus on the ability to reduce immunosuppression. Patients who received Treg therapy had no increase in rejection despite lower immunosuppression, and moreover, had lower rates of viral infections 161. Within the ONE study, Reinke et al. showed that most (8 of 11) Treg-treated patients achieved stable monotherapy immunosuppression whereas a reference group remained on standard dual or triple drug immunosuppression 162. The ongoing TWO Study (ISRCTN: 11038572) aims to confirm the benefit of polyclonal Treg therapy in 21 transplantation by testing whether immunosuppression can be tapered to a low-dose tacrolimus monotherapy following polyclonal Treg infusion in kidney transplant recipients 163. There have also been a couple of safety/dosing trials using autologous, polyclonal ex vivo-expanded Tregs to treat T1D. Fourteen adults with recent onset T1D received a single infusion of 5 million to 2.6 trillion Tregs in a dose escalation study and were followed for one year 164. Stable ex vivo Treg expansion was feasible and Tregs appeared to remain stable in vivo, with blood abundance continuously dropping post-injection until they were undetectable about three months later. There were no Treg-associated adverse events recorded. There was no apparent benefit or harm on metabolic markers of disease, with too few patients enrolled to draw any conclusions on efficacy 164. In a follow-up study, Tregs were injected to adults with recent onset T1D in conjunction with a short course of low dose IL-2, with the latter given to hopefully enhance adoptive Treg engraftment 165. While IL-2 administration did enhance Treg engraftment and expanded endogenous Tregs, it also inadvertently expanded cytotoxic T cells and natural killer (NK) cells. Despite no associated adverse events, it was determined that it is unlikely wise to combine wildtype IL-2 with adoptive Treg therapy 165. 1.4.5 Antigen-specific Treg therapy While these first in-human trials using polyclonal Treg therapy have shown excellent safety profiles, the next phase of Treg therapy will be antigen-specific. Tregs suppress best when they recognize their cognate antigen. When administering a large polyclonal repertoire of Tregs, the hope is that a few of those Tregs will recognize the auto- or alloantigens of interest, expand and induce protection. However, pre-clinical studies show that administration of Tregs enriched or engineered for disease-relevant antigen specificity are far more therapeutic than polyclonal Tregs and can be administered in much lower, feasible numbers to obtain efficacy 166-168. Approaches to enrich or engineer Tregs for antigen specificity include stimulation with relevant antigens during expansion 169-171, or introduction of antigen-specific TCRs or chimeric antigen receptors (CARs) (Fig 1.4) 166,167,168 . There have been quite a few successful pre-clinical approaches introducing T1D-relevant TCRs into Tregs (reviewed 172). The initial evidence that antigen-specific Tregs were more effective than their polyclonal counterparts came when Drs. Qizhi Tang and Jeffrey Bluestone showed that Tregs isolated from the BDC2.5 TCR-transgenic mouse (recognizes a beta cell 22 hybrid peptide) were far more effective than polyclonal Tregs at preventing T1D and protecting islet transplants 173. Since then, there have been many approaches to gene editing Tregs to express a relevant TCR. Notably, TCR delivery and FOXP3 editing 174 have been combined to generate islet-specific Tregs from Tconv 175. Mouse TCR-transgenic FOXP3-edited Tregs were shown to home to the pancreas, stably persist and prevent disease development in an adoptive transfer model of T1D. This multi-editing approach is now being pursued commercially for the treatment of T1D by GentiBio (GNTI-122) 176. Recently, our lab, amongst others, have leveraged the concept of CARs from the cancer world 177. CARs contain the intracellular signaling domains required for TCR signaling, but their antigen recognition domain is that of the variable region of an antibody (Fig 1.4). Therefore, unlike TCRs that can only see their antigens presented in the context of MHC, CARs can theoretically detect any antigen so long as there is an antibody that can bind it. Generally, this has been restricted to membrane-bound targets, but it is possible to use CARs to re-direct Treg specificity to non-membrane bound proteins. For example, patients with Haemophilia A have a deficit in blood coagulation factor VIII production and often mount an anti-factor VIII immune response against the therapeutic protein administered as a treatment to overcome the deficit. CAR Tregs that recognize factor VIII suppress this anti-therapeutic protein response in mouse and humanized models of Haemophilia A 178,179. Moreover, in some cases ideal antigens may be intracellular, making them difficult CAR targets. This limitation was recently overcome by making âTCR-likeâ CARs specific for an insulin peptide presented by MHC class II. In two parallel studies, Tregs expressing CARs that recognized an insulin peptide presented by MHC suppressed pathogenic T cell proliferation in vitro and significantly delayed or prevented T1D in vivo 180,181. However, the first and most characterized CAR Treg product to date recognizes HLA-A2. 23 Figure 1.4: TCR and CAR-mediated T cell activation When TCRs and CARs bind to their cognate antigen, ligation results in downstream signaling that ultimately leads to T cell activation. (A) TCRs (purple) recognize peptides presented by MHC proteins (green). The TCR is expressed with multiple proteins critical for signal transduction, including the CD3 complex (grey and CD3ζ highlighted in blue) and the co-stimulatory protein CD28 (red). (B) The antigen-binding domain of a CAR is the variable Fab region of an antibody (a scFv; purple); CARs recognize extracellular antigens (green). Second generation CARs (used in this dissertation) have a co-stimulatory signaling domain (from CD28 in this case; red) and a CD3ζ signaling domain (blue). This figure was created with BioRender. 1.4.6 A2-CAR Tregs We and others developed CARs that recognize HLA-A2 (A2-CAR), a commonly mismatched protein in organ transplantation. Class I MHC proteins are highly expressed by most cells, making it a strong target of the alloreactive response (section 1.2.2), but also an ideal candidate antigen to instruct Treg protection. Moreover, up to 27.1% of people express HLA-A2 (HLA-A*02:01), which is high for a such a polymorphic protein 182. There are multiple mouse strains available that have been engineered to express the human HLA-A2 protein, making way for many pre-clinical models to demonstrate that A2-CAR Tregs suppress anti-HLA-A2-directed 24 alloimmunity significantly better than polyclonal Tregs in multiple humanized and mouse transplant models 154,183-189. A2-CAR Tregs are easily generated and expanded ex vivo from human and mouse Tregs, and maintain their suppressive phenotype. They home to and prolong HLA-A2+ skin graft survival 183,185,188,189, protect heart transplants 186, and prevent xenogeneic GVHD 154,187,190. A2-CAR Tregs have been shown to home to HLA-A2+ islet grafts 187, but whether they can protect islet grafts had not been reported. All of this pre-clinical work led to rapid clinical translation and two ongoing clinical trials of A2-CAR Tregs in kidney and liver transplantation (NCT04817774 for kidney 191, NCT05234190 for liver). 1.5 Summarized research goals and findings The work presented in this dissertation sought to capitalize on mismatched HLA in the context of transplantation. In chapter 2, I sought to develop a blood-based assay to detect alloreactive T cells in HLA-A2negative patients who received an HLA-A2+ kidney transplant. I hypothesized that alloreactive T cells would trogocytose donor HLAs fused to a fluorescent probe from an immortalized cell line, thus tagging them as HLA-A2-alloreactive. I also predicted that the quantity of trogocytosis+ cells would negatively correlate with kidney health, as determined by protocol biopsy scores. The hope was that this assay would overcome many of the current barriers for assessing transplant health (section 1.2.4). The assay was optimized based on an engineered HLA-A2-reactive CD8+ T cell clone and able to identify trogocytosis+, alloreactive T cells when they made up >1% of the tested PBMCs. However, in these preliminary trials on patient samples, the trogocytosis assay was not sensitive enough to detect alloreactive lymphocytes in transplant recipients. In chapter 3, I tested the hypothesis that A2-CAR Tregs could effectively suppress diabetogenic T cells from destroying HLA-A2+ islet transplants via bystander or linked suppression. Immunocompromised mice were transplanted with HLA-A2+ islets and then adoptively transferred diabetogenic CD4+ T cells with or without A2-CAR Tregs. A2-CAR Tregs limited diabetogenic T cell engraftment and proliferation to protect mice against hyperglycemia. Remarkably, despite the A2-CAR Treg antigen being restricted to the islet transplant, A2-CAR Treg treatment mediated protection of the endogenous HLA-A2negative pancreas by linked suppression and via infectious tolerance, by inducing expansion of the Treg compartment within the originally diabetogenic T cell population. 25 Building on the success of chapter 3, in chapter 4, I hypothesized that in an immunocompetent mouse model of spontaneous T1D, A2-CAR Treg treatment would prolong survival of HLA-A2+ islet transplants. To promote A2-CAR Treg engraftment, mice were pretreated with an anti-CD3 antibody. I found that A2-CAR Tregs engrafted, remained Foxp3+ and trafficked to the site of transplantation. However, in this pilot study, A2-CAR Treg treatment did not extend transplant survival beyond what was observed with anti-CD3 treatment alone. 26 Chapter 2: A trogocytosis-based assay to detect alloreactive T cells 2.1 Rationale and approach Kidney transplant recipients rely on surveillance graft biopsies to give their health care teams information on whether their transplant is healthy and the immunosuppressive regimen is appropriate. These biopsies are invasive, infrequent and only provide a small glimpse of the whole organ, potentially missing signs of rejection. There have been attempts to develop blood-based assays for monitoring T cell-mediated alloimmunity in transplant recipients (discussed in section 1.2.4); however, these methods tend to lack reproducibility and rely on donor tissue availability, severely limiting their broad utility. I proposed to create a new trogocytosis-based assay to detect alloreactive T cells. I hypothesized that alloreactive immune cells (with a focus on CD8+ T cells) would trogocytose donor HLA. To test this hypothesis, I created cell lines that were engineered to express candidate HLA class I proteins fused to fluorescent proteins. Following co-culture of these cells with CD8+ T cells or PBMCs, I predicted that alloreactive T cells would become trogocytosis+ and express the donor HLA-fluorescent probe, making them quantifiable by flow cytometry. I designed and optimized this assay using candidate HLAs and a peptide-independent, HLA-A2-reactive CD8+ T cell clone, and then tested it on blood samples collected from HLA-mismatched kidney transplant recipients to determine whether trogocytosis+ cell numbers correlated with graft health. 2.2 Methods 2.2.1 Ethics Control PBMCs from healthy volunteers were collected and used in accordance with UBC Human Ethics Application H18-02553. Kidney transplant recipient samples were collected and used in accordance with UBC Human Ethics Application H17-01872. 2.2.2 Flow cytometry and data analysis Dead cells were identified and excluded based on extracellular staining with fixable viability dye. Assays that used PBMCs were Fc blocked prior to cell staining (Invitrogen). Cells 27 that received an intracellular stain were fixed and permeabilized using the FOXP3/Transcription Factor staining buffer set (Thermo Fisher Scientific). Table 2.1 lists information about the antibodies used for this study. Flow cytometry data were collected on the LSRFortessa X-20 or A5 Symphony (BD Biosciences). Data were analyzed with FlowJo version 10. Target Fluorophore Clone Company Dilution CD19 BV711 SJ25C1 BD Biosciences 1:100 CD3 BUV737 UCHT1 BD Biosciences 1:200 CD4 PerCP-eF710 SK3 Invitrogen 1:200 CD8 BV605 RPA-T8 Biolegend 1:150 CD14 PE-Cy5 61D3 Invitrogen 1:100 CD25 BUV496 2A3 BD Biosciences 1:200 CD25 BV421 BC96 Biolegend 1:200 CD69 BV786 FN50 BD Biosciences 1:200 CD127 BV650 A019D5 Biolegend 1:50 CD137 (4-1BB) APC 4B4-1 Biolegend 1:200 Fixable Viability Dye eF780 eBioscience 1:1500 Granzyme B AF647 GB11 Biolegend 1:50 HLA-A2 BV421 BB7.2 BD Biosciences 1:150 HLA-A2: IGRP265â273 PE Monomer: NIH tetramer facility Streptavidin: Agilent Technologies 1:200 HLA-A3 FITC GAP.A3 Invitrogen 1:150 IFN-γ BV421 4S.B3 Biolegend 1:100 Mouse TCRβ BV510 H57-597 Biolegend 1:100 Perforin BB700 δG9 BD Biosciences 1:50 Table 2.1 Flow cytometry reagents used in chapter 2 All antibodies bind human antigens, with the exception of mouse TCRβ. 2.2.3 PBMC and CD8 isolation Whole PBMCs were isolated from HLA-A2/3negative buffy coats using density centrifugation on Lymphoprep (STEMCELL Technologies). CD8+ T cells were subsequently magnetically purified using a positive selection kit (STEMCELL Technologies). PBMCs and CD8+ T cells were cryopreserved in 90% ImmunoCult-XF T cell expansion medium (STEMCELL Technologies) with 10% DMSO (Fisher Scientific). 2.2.4 Cell transduction K562s (ATCC), with or without CD86, were transduced with lentivirus encoding green fluorescent protein (GFP). Sequences for HLA-A*02:01 and HLA-A*03:01 were retrieved from 28 the IMGT database, synthesized, and cloned as fusion proteins between the HLA intracellular terminus and the monomeric kusabira-orange 2 (mKO2) fluorescent protein. mKO2-HLA fusion DNA was cloned into a lentiviral expression plasmid under the control of the EF1-α promoter. K562s were transduced with lentivirus encoding the mKO2-HLA genes. K562s were purified by fluorescent cell sorting based on GFP+ and mKO2+ expression. K562s were maintained in culture in RPMI (Gibco), supplemented with 10% fetal bovine serum (Gibco, Seradigm, VWR), penicillin-streptomycin (Gibco) and glutamax (Gibco); K562s were passaged every two to three days. DNA for TCRHLA-A2 was received from Dr. Qizhi Tangâs lab (University of California San Fransisco; originally described as TCR clone 32 in 192). The human constant regions were replaced via restriction enzyme digestion (New England Biolabs) and ligation (Invitrogen) with mouse constant regions 193. The new DNA segment was cloned into a pCCL backbone containing the truncated nerve growth factor receptor (tNGFR) transduction marker. Previously isolated CD8+ T cells were thawed and stimulated with 1:3 T-expander CD3/CD28 Dynabeads (Thermo Fisher) overnight in ImmunoCult-XF T cell expansion medium supplemented with penicillin/streptomycin and 200 U/mL IL-2 (Proleukin). The following day, lentivirus encoding the TCRHLA-A2 cDNA was added to the T cells. Six days later, beads were magnetically removed from the CD8+ T cells before purifying tNGFR+ T cells using a magnetic positive selection kit (STEMCELL Technologies). Purified CD8+ T cells were phenotyped, rested for three days and then cryopreserved for later use. 2.2.5 Trogocytosis assay The trogocytosis assay evolved as it was optimized, as explained in this chapter. Unless otherwise stated, the assay was performed as it is described in this section. For optimization experiments, the experiment was scaled down in size (i.e. less cells were plated), but maintained the same seeding density and media volume normalized to cm2. Numbers reported in this section were those used for patient samples. All washes were performed at room temperature for 5 minutes at 450 xg. CD86negative mKO2-HLA-A2/3+ K562s were collected from continuous culture and washed in PBS twice. Cryopreserved PBMCs were thawed in prewarmed Immunocult-XF T cell expansion media, pelleted, then washed once more in PBS. K562s and PBMCs were re-29 suspended in ImmunoCult-XF T cell expansion medium supplemented with penicillin-streptomycin (no IL-2). Cells were counted using the automated CellDrop cell counter (DeNovix). K562s were adjusted to a concentration of 1.591 million/mL. PBMCs were adjusted to a concentration of 1 million/mL. PBMCs were plated at a 1:1 ratio with K562s, at a cell density of 200,000 cells/cm2; in a 6-well plate (= 9.57 cm2), 950,000 of each K562s (= 0.597 mL) and PBMCs (= 0.95 mL) were added to each well. Six mL of media was added to each well to bring the total volume to 7.547 mL (=0.788 mL per cm2). The plate was placed in the incubator (37°C, 5% CO2) for 24 hours. At assay completion, the media was carefully aspirated from the top of the wells without disturbing the cells on the bottom of the well. The cells were then collected into microcentrifuge tubes and pelleted. The wells were washed with PBS, which was added to the cell pellet and pelleted once more. Cells were surface stained and kept on ice where possible to deter further trogocytosis. In between reading sample sets on the flow cytometer, two tubes of water were run to reduce the background event rate to <10 events/second. 2.2.6 Confocal cell imaging TCRHLA-A2 CD8+ T cells were thawed and stained in 0.05 μg/mL Hoechst 33342 (Thermo Fisher Scientific). CD8+ T cells were plated at a 1:1 ratio with mKO2-HLA-A2+ K562s in μ-slide wells (ibidi). Cells were imaged in a Takai Hit INUBTFP-WSKM stage-top incubator maintained at 37°C and 5% CO2 using a Leica SP8 laser scanning confocal microscope (Concord, Canada). Images were prepared for publication with Leica Application Suite X (LAS X). 2.2.7 Statistical analyses and figure preparation Figure schematics were generated in BioRender. Graphs were generated with GraphPad Prism 10. Statistical testing was also completed in GraphPad Prism 10; the statistical test used for each panel is described in the figure legend and p values are annotated where appropriate (significance indicated with asterisks; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001). 30 2.3 Results 2.3.1 Trogocytosis assay control development K562s are a non-adherent erythroleukemic cell line that lacks HLA and is easy to grow and transduce, making this cell line the ideal candidate to present a chosen HLA 194. The plan was to create multiple K562 cell lines that each expressed an HLA protein fused to a fluorescent probe 100. Then, PBMCs from transplant recipients would be cultured with K562s expressing one of their donorâs HLA proteins. I hypothesized that following coculture, alloreactive immune cells would trogocytose the relevant donor HLA from the K562s and therefore be identifiable by flow cytometry based on expression of the HLA-fused fluorescent probe (Fig 2.1A). This assay was developed to look for class I HLA trogocytosis, given that theyâre prominent targets of the alloimmune response (Fig 2.1B). K562s were also transduced to express GFP, so that they could be easily distinguished from immune cells during flow analysis. Next, I sought a positive control for assay optimization. CD8+ T cells are most likely to recognize allo-HLA class I targets, since CD8 binds the α3 region of class I HLA 195. I obtained the sequence of a TCR that was isolated from the blood of a T1D patient that was activated by HLA-A2 (clone TCR32 192). This clone was initially isolated because it was thought to be specific for a diabetes-relevant peptide (IGRP265â273) presented by HLA-A2, but it was later found to recognize HLA-A2 independently from the peptide presented 192. I obtained the sequence of this TCR clone and replaced the human constant regions with mouse constant regions to prevent mispairing with the endogenous TCR when expressed in primary human CD8+ T cells 193. Primary human HLA-A2negative CD8+ T cells were transduced using lentivirus and TCR expression was confirmed (described in section 2.2.4; Fig 2.1C). From here onwards, these cells are referred to as TCRHLA-A2 CD8+ T cells. TCRHLA-A2 CD8+ T cells proliferated and expressed pro-inflammatory proteins when cocultured either with HLA-A2+ K562s alone or loaded with IGRP peptide, but not when cultured with HLAnegative K562s (Fig 2.1D and E). Thus, I confirmed that TCRHLA-A2 CD8+ T cells are activated by HLA-A2, irrelevant of the presented peptide, and were an ideal âalloreactiveâ T cell control for trogocytosis assay design and optimization. 31 Figure 2.1: Development of trogocytosis assay controls (A) The trogocytosis assay was based on the hypothesis that alloreactive T cells within transplant recipient PBMCs cocultured with a cell line that expresses the donor HLA fused to a fluorescent probe would trogocytose the donor HLA, lighting themselves up with the attached fluorescent probe. (B) HLA-A2/A3+ K562s express mKO2 and stain with the matched HLA antibody. (C) Human CD8+ T cells were transduced with TCRHLA-A2 encoding the mouse constant regions using lentivirus (orange). Transduction marker+ (tNGFR) T cells were magnetically enriched and T cells were stained for the mouse TCRβ chain and with the HLA-A2:IGRP tetramer (UT=untransduced CD8+ T cells, grey). Untransduced (grey) or TCRHLA-A2 CD8+ T cells were co-cultured with HLA-A2+ K562s alone (orange) or loaded with IGRP peptide (pink). Three days later, dilution of cell proliferation dye (eBioscience) on the T cells was measured (D; representative of n=3). After 24 hours, markers of CD8+ T cell activation were quantified. In the last four hours, Brefeldin A (Sigma-Aldrich, 10 μg/mL) and monensin (1x, Biolegend) was added to cultures to maintain intracellular IFN-γ, perforin and granzyme B (E; n=3, mean with SD; dotted line at y=1 indicates no change in gMFI following indicated treatment relative to co-culture with blank K562s). 2.3.2 TCRHLA-A2 CD8+ T cells trogocytose HLA-A2 Having confirmed that TCRHLA-A2 CD8+ T cells proliferated and produced pro-inflammatory proteins in response to HLA-A2 stimulation, I next sought to determine whether they could also trogocytose HLA-A2. TCRHLA-A2 CD8+ T cells were cocultured with mKO2-HLA-A2+ K562s and imaged using a confocal microscope. TCRHLA-A2 CD8+ T cells interacted with the mKO2-HLA-A2+ K562s and after 30 minutes, were seen expressing mKO2 on their own membranes, suggestive that they were trogocytosing HLA-A2 (described in 2.2.6; Fig 2.2A). TCRHLA-A2 CD8+ T cells were mixed at a 1:1 ratio with autologous untransduced CD8+ T cells and cocultured with mKO2-HLA-A2+ K562s. After 24 hours, mKO2 expression on the CD3+ CD8+ T cells was evaluated by flow cytometry. While pure untransduced CD3+ CD8+ T 32 cells were not mKO2+, 56% of the 1:1 mix did express mKO2 (Fig 2.2B, top left). Trogocytosis correlated with multiple known AIMs, with 50-70% of trogocytosis+ T cells also expressing CD69, CD137 (4-1BB) and CD25 86 (Fig 2.2B). To rule out that TCRHLA-A2 CD8+ T were obtaining HLA-A2 membrane fragments in the form of endosomes, a transwell assay was set up. TCRHLA-A2 CD8+ T expressed high levels of mKO2 following coculture with HLA-A2+ K562s in the same well, but not HLA-A3+ K562s, or when separated from HLA-A2+ K562s by the transwell (Fig 2.2C). This indicated that HLA-A2 trogocytosis to TCRHLA-A2 CD8+ T cells was specific and contact-dependent. Interestingly, although there are many reports of T cells trogocytosing MHC proteins onto their own membranes, the reverse has also been shown 106. Following co-culture with high amounts of TCRHLA-A2 CD8+ T cells, HLA-A2+, but not HLA-A3+, K562s expressed the mouse TCRβ, indicative of âreverseâ trogocytosis (Fig 2.2D). These data support that TCRHLA-A2 CD8+ T cells specifically trogocytose HLA-A2 proteins. Figure 2.2: TCRHLA-A2 CD8+ T cells trogocytose HLA-A2 (A) TCRHLA-A2 CD8+ T cells were labelled with Hoechst nuclear stain and then co-incubated with mKO2-HLA-A2+ K562s. Images were captured at the start of coculture (top) and in the same spot 30 minutes later (bottom). (B) 33 Untransduced CD8+ T cells or TCRHLA-A2 CD8+ T cells mixed 1:1 with untransduced CD8+ T cells were co-cultured with mKO2-HLA-A2+ K562s. After 24 hours, CD3+ CD8+ T cell expression of mKO2 and AIM markers were evaluated by flow cytometry. (C) In a 0.4μm transwell insert (100 million pores/cm2, polyethylene terephthalate, tissue-culture treated, Falcon) mKO2-HLA-A3+ (grey) or -A2+ (blue) K562s were co-cultured with TCRHLA-A2 CD8+ T cells. The bottom well held untransduced (UT) or TCRHLA-A2 CD8+ T cells. After 24 hours the gMFI of mKO2 on the CD3+CD8+ T cells in both the insert and bottom wells were quantified by flow cytometry (n=2, bars at means). (D) TCRHLA-A2 CD8+ T cells were serially diluted in untransduced CD8+ T cells and cocultured with mKO2-HLA-A3+ (black) or -A2+ (red) K562s. After 24 hours, the frequencies of mouse TCRβ+ K562s were determined (n=3, mean with SD; 2-way ANOVA with Sidakâs multiple comparisons tests; x axis is on a log10 scale). 2.3.3 Trogocytosis assay optimization Next, the coculture conditions were optimized to maximize the difference between true trogocytosis and background. In these optimization experiments, I report the difference in the HLA-mKO2 signal (geometric mean fluorescence intensity (gMFI) of mKO2) of TCRHLA-A2 CD8+ T cells when cocultured with control HLA-A3+ K562s vs. HLA-A2+ K562s. I chose to report differences in fluorescent intensity rather than frequencies of mKO2+ T cells because different conditions created different levels of trogocytosis background, making it challenging to set defined gates. Although trogocytosis occurred in 30 minutes or less (Fig 2.2A), 24 hours of coculture gave the largest difference in mKO2 expression by TCRHLA-A2 CD8+ T cells between coculture with HLA-A2+ and -A3+ K562s (Fig 2.3A). Co-culture beyond 24 hours did not increase this difference (data not shown). TCRHLA-A2 CD8+ T cells were cocultured with varying ratios of K562s. Below a 1:1 K562:T cell ratio, trogocytosis was suboptimal; however at a ratio of 1:1 and higher, trogocytosis was comparable (Fig 2.3B). Since the K562s and T cells settled at the bottom of the plate, I considered that cell density may play a role in trogocytosis efficiency. To determine which seeding density facilitated the largest trogocytosis+ signal over background, the assay was plated with variable total cell densities (sum of T cells and K562s). Multiple experiments were run, using different size wells. Trogocytosis was optimal at 100,000 to 200,000 cells/cm2, regardless of the well size (Fig 2.3C). At high cell densities over 500,000 cells/cm2, trogocytosis was severely inhibited (data not shown). Finally, I wanted to test whether resting cells post-thaw and pre-trogocytosis assay would impact trogocytosis outcome. Specifically, I wanted to test if resting cells following thawing would increase their viability or antigen-dependent T cell activation 196. However, I found that trogocytosis efficacy was similar when T cells were thawed and plated immediately or when they 34 were thawed and allowed to rest in culture for 24 hours before being cocultured with K562s (Fig 2.3D). Taken together, my data show that when using TCRHLA-A2 CD8+ T cells, the trogocytosis assay achieved maximal signal sensitivity when cells were seeded immediately after thawing, and co-cultured with K562s for 24 hours, at a 1:1 ratio, at 100,000 - 200,000 cells/cm2. Figure 2.3: Trogocytosis assay optimization (A) The trogocytosis assay was performed for varying lengths of time with untransduced (grey) or TCRHLA-A2 CD8+ (purple) T cells and the fold changes in CD3+CD8+ mKO2 gMFI following co-culture with HLA-A2+ K562s over HLA-A3+ K562s are plotted (n=4, mean with SD, 2-way ANOVA with Tukeyâs multiple comparisons correction). (B) TCRHLA-A2 CD8+ T cells were plated at varying ratios with K562s while maintaining a constant total cell seeding density (n=5, median with range, ANOVA with Tukeyâs multiple comparisons correction). (C) TCRHLA-A2 CD8+ T cells were plated with HLA-A2+ (solid lines) or HLA-A3+ (dashed lines) K562s at different total cell densities per cm2 in different sizes of wells (96 in black, 48 in blue and 12 in orange). The raw compensated gMFIs of mKO2-HLA on CD8+ T cells are plotted; experiments were performed over different days and flow cytometer voltages were not standardized (n=1-3, mean with SD when n>1). (D) TCRHLA-A2 CD8+ T cells were placed into the trogocytosis assay immediately post-thaw (pink), or rested for 24 hours before K562s were added and the assay read out a following 24 hours later (orange; n=5, pairs indicated with bars at means, paired t-test = non-significant). 35 2.3.4 T cell activation increases trogocytosis background Next, the impact of T cell co-stimulation and/or exogenous IL-2 on trogocytosis assay sensitivity were analyzed, since these signals are required for T cell activation and function. CD86+ K562s were transduced to express mKO2-HLA-A2 or -A3 and they were used to stimulate TCRHLA-A2 CD8+ T cells in the presence or absence of 100 U/mL of IL-2. Both CD86 co-stimulation and IL-2 decreased the sensitivity of the assay because they increased the background level of irrelevant mKO2-HLA-A3 trogocytosis by TCRHLA-A2 CD8+ T cells (Fig 2.4A and B). Therefore, K562s lacking co-stimulatory molecules were used for all trogocytosis assays, in the absence of exogenous IL-2 addition. Figure 2.4: Co-stimulation and IL-2 increase trogocytosis background The trogocytosis assay was performed with K562s in the presence or absence of the co-stimulatory molecule CD86 on the K562s and/or 100 U/mL of IL-2. (A) Histograms show trogocytosis by TCRHLA-A2 CD8+ T cells co-cultured with HLA-A3+ (grey line) or HLA-A2+ (blue line) K562s in the presence or absence of CD86 and IL-2. (B) Box plots quantify this assay, showing the fold change of mKO2 gMFI (HLA-A2 over HLA-A3) on TCRHLA-A2 CD8+ T cells. Untransduced cells are plotted in grey and TCRHLA-A2 cells in purple (n=4, median with range, 2-way ANOVA with Tukeyâs multiple comparisons correction). 2.3.5 Assessing trogocytosis assay sensitivity Having optimized the trogocytosis assay parameters, I next assessed the assayâs sensitivity. Although the assay was optimized based on a CD8+ T cell clone, other immune cells, including B cells and other types of T cells, could theoretically detect and trogocytose class I HLAs. Therefore, a flow cytometry panel was optimized to be able to capture these populations (Fig 2.5A). To test the assayâs sensitivity, TCRHLA-A2 CD8+ T cells were titrated into autologous PBMCs (frozen in aliquots at the time of the initial CD8+ T cell selection), and combinations of PBMCs and TCRHLA-A2 CD8+ T cells were tested in the trogocytosis assay (Fig 2.5B). I found 36 that the assay background could be reduced when trogocytosis+ CD3+CD8+ T cells were identified by a combination an AIM and mKO2. Although there was variation between experiments, each repeat showed that the assay could detect trogocytosis events over background when TCRHLA-A2 CD8+ T cells were present at frequencies of >1% of PBMCs. Figure 2.5: Assessing trogocytosis assay sensitivity (A) An abbreviated version of the gating protocol used to identify PBMC subsets that may participate in donor HLA trogocytosis. (B) TCRHLA-A2 CD8+ T cells were spiked into autologous PBMCs at the indicated frequencies (x axis; on a log10 scale with values for 0% TCRHLA-A2 entered at x = 0.05 and indicated by 0). The number of mKO2+ (Â± AIM marker) events per 10,000 CD3+CD8+ T cells are plotted on a log10 y scale. Each colour represents a separate donor/experiment; due to inconsistencies in trialed TCRHLA-A2 CD8+ T cell dilutions between experiments, statistics could not be calculated. The dashed lines indicate co-incubation with HLA-A3+ K562s and the solid lines with HLA-A2+ K562s. 37 2.3.6 The trogocytosis assay lacked the sensitivity required to definitively identify alloreactive immune cells in kidney transplant recipients Having optimized the trogocytosis assay and a flow cytometry panel to identify various immune populations, I obtained cryopreserved PBMCs from kidney transplant recipients to assess whether I could identify alloreactive immune cells in their blood with the trogocytosis assay. PBMCs from HLA-A2negative kidney recipients that received an HLA-A2+ kidney transplant had blood drawn and PBMCs cryopreserved at the time of protocol graft biopsies. I obtained 3 sets of patient PBMC samples, each set containing one sample drawn when no kidney transplant rejection was detected (Banff t and i scores = 0), and one when the biopsy did identify graft rejection (Banff t + i score >0; Fig 2.6A) 81. The patient samples, along with one or two control PBMC samples from healthy donors, were run in the optimized trogocytosis assay and accompanying flow panel as shown in Fig 2.5A (Fig 2.6B). Trogocytosis+ (mKO2+) cells were identified on B cells and multiple type of T cells (Fig 2.6C). The fold change (frequency of trogocytosis+ events with HLA-A2+ over HLA-A3+) were plotted for each immune population; a score of 1 indicates comparable trogocytosis+ events to background levels. Only with patient 3, and when mKO2 was combined with CD69 co-expression, was a positive fold change detected in the CD8+ T cell population during the patientâs rejection period and not in the sample collected in the absence of rejection. However, the results are difficult to interpret as some control samples reported positive fold changes as well. Moreover, there were many instances where fold change values were negative, indicating that âbackgroundâ levels of trogocytosis were higher with HLA-A3+ K562s than with HLA-A2+ K562s. These data show that there is still a need to further optimize this assay for use with patient samples, with a specific focus on reducing background and minimizing variability. 38 Figure 2.6: Testing the trogocytosis assay on kidney transplant recipient blood (A) PBMCs were previously isolated and frozen from HLA-A2negative patients that received an HLA-A2+ kidney transplant. Where applicable, blood draws were performed on the day of transplant surveillance biopsy. The table indicates time from transplant and the histologically-determined Banff t (tubulitis) and interstitial inflammation (i) biopsy scores associated with each blood draw. For each patient, PBMCs isolated in the absence or presence of biopsy-confirmed rejection were used in the trogocytosis assay along with control PBMCs previously isolated and cryopreserved. (B) Representative contour plots show HLA-mKO2 and CD69 expression on CD3+CD8+ T cells following co-culture with HLA-A3+ (top) or -A2+ K562s (bottom) within PBMCs from a healthy control (left), a transplant recipient in the absence of rejection (middle) and the same transplant recipient during rejection (right). (C) The fold chance of the number of mKO2+ (Â± CD69+) events within the indicated immune populations on the y axis for culture with HLA-A2+ over -A3+ are shown for each set of patient samples. Bar(s) in grey are control PBMCs from healthy donors (for patients 2 and 3, two sets of PBMC controls were run), blue are samples drawn in the absence of rejection, and red are samples drawn at the time of biopsy-confirmed transplant rejection. Given the small n and high variability, no statistical tests were performed. 39 2.4 Discussion I successfully developed a trogocytosis-based assay that can detect an alloreactive CD8+ T cell clone when it makes up >1% of PBMCs. However, this assay was not sensitive enough to detect trogocytosis+ alloreactive immune cells in the blood of patients previously transplanted with an HLA-mismatched kidney and experiencing biopsy-confirmed rejection. There are many possible reasons why this assay was not sufficiently sensitive. To date, the most successful assays to detect alloreactive T cells in the blood of kidney transplant recipients used IFN-γ ELISPOTS (section 1.2.4), showing that patients experiencing rejection had more IFN-γ+ cells in their blood than those who were not 197,198. The frequency of IFN-γ+ cells amongst PBMCs in these studies was â¤0.05%; i.e. a number of cells which falls below the limit of detection of the trogocytosis assay. Of note, since not all antigen-specific T cells produce IFN-γ upon antigen recognition 199, it is possible that the trogocytosis assay would capture more alloreactive lymphocytes than the ELISPOT assay. A major issue with the trogocytosis assay is that there were relatively high levels of background mKO2 transfer to T cells in a non-antigen-specific manner. This background was reduced when alloreactive trogocytosis+ T cells were co-identified with an AIM. Since T cells are continually scanning APCs for their cognate antigen, it seems likely that some degree of membrane sharing could occur even with a brief and ultimately unproductive immune synapse 200. However, even with the addition of an AIM to detect TCR-triggered T cell activation, the background was not sufficiently reduced. It is possible that using HLA-A3 as an irrelevant control was not the best choice and/or that the K562 cell system was suboptimal. In addition, K562s expressing a recipient HLA fused to mKO2 may serve as a better negative control for the trogocytosis assay (and IFN-γ ELISPOT assays); a recently described AIM assay showed very low background levels when using autologous PBMCs as stimulator cells 86. The utility of IFN-γ ELISPOT assays has been evaluated primarily using pre-transplant blood as an indicator of risk of rejection (to complement what is standard practice with donor-specific antibodies), indeed finding a higher response to be predictive of rejection 87,88. These data suggest that the alloimmune T cell response is to some extent pre-formed. I hypothesized that with signs of T cell-mediated rejection, the alloreactive T cells would expand and increase in frequency in the blood. However, a recent IFN-γ ELISPOT assay that looked at pre- and post-transplant reactions with both donor and third-party stimulator cells reported similar mean 40 responses between (1) pre- and post- transplant samples and (2) donor and third party stimulators, when stratified by rejection status 197. The authors did not report paired statistical tests, so it is possible that there are differences that a comparison of means failed to identify. However, these data support the ideas that: (1) third party stimulation can elicit a comparably sized response to relevant stimulation, suggesting some people are predisposed to strong alloimmunity; and (2), that alloreactive T cells may not expand in the blood during rejection. Another consideration is that if K562s presenting a recipient HLA were used as controls, that would still not control for indirect presentation. Functional HLA class I proteins require peptide loading to reach the cell surface 201, so transduced HLAs are being loaded with endogenous K562 peptides that may be identified as non-self by co-cultured T cells. To reduce this potential background, a large amount of exogenous, recipient-syngeneic peptide could be pre-incubated with the K562s to control for peptide presentation as well. K562s that express one donor HLA provide the target for only a fraction of the alloreactive response. To account for indirect allorecognition, perhaps a kidney cell line could be used in place of K562s; human embryonic kidney (HEK) 293 cells are easy to grow and engineer 202. Their endogenous HLA proteins would have to be knocked out and replaced by HLA-fluorescent probe proteins, but then these cells would theoretically present kidney peptides that would not be identical to those found in the transplant, but would likely elicit a stronger indirect alloresponse than without. In that same vein, the recently published TreaT (Transplant reactive T-cells)-assay collected renal tubular epithelial cells from transplanted patientsâ urine, cultured them to enhance HLA expression and found that when co-cultured in an AIM assay with PBMCs, more alloreactive T cells were quantified in patients with early acute rejection 203. Although this is a more complicated approach than relying on immortalized cell lines, this is a novel way to obtain donor tissue in a non-invasive manner. To increase the chances of detecting alloimmune cells, recipient PBMCs could be co-cultured with multiple donor HLA-bearing cell lines, including class IIs, which would have a higher likelihood of capturing alloreactive CD4+ T cells. The K562s that were generated for this set of experiments were sorted in bulk for mKO2 expression; in hindsight, it would have been preferable to sort single cell clones and grow cell lines with similar levels of mKO2 expression. It is possible that the heterogeneity of HLA/mKO2 expression in the cell lines might have 41 resulted in HLA expression level variation between wells and experiments, thus changing the probability of T cell interactions and/or amount of mKO2 trogocytosed with each interaction. The trogocytosis assay was optimized based on CD8+ T cells that were expanded ex vivo and transduced to express a chosen TCR; the PBMCs plated in this assay were not previously activated and expanded, which may have put them at a disadvantage for detecting donor HLAs, due to lack of prior activation/priming. Moreover, it remains unknown if the assay conditions are optimized for detecting alloreactive B cells or CD4+ T cell subsets. For example, CD4+ T cells proliferate and produce IL-2 on different time scales following CD28 stimulation relative to CD8+ T cells 204. CD28 stimulation is also essential for Treg activation and suppression, and is thus perhaps necessary for Treg trogocytosis 39. The majority of alloimmune cells are expected to be in the kidneyâs draining lymph node or in the kidney itself (section 1.2.2); these cells may therefore be too scarce to detect in the blood. In the context of kidney transplantation, the urine may be a readily available location for quantifying alloreactive T cells. Immune and kidney cells are detectable in urine from transplant patients, and patients experiencing rejection have higher numbers of T cells 205. However, T cell number was not correlative with worsening graft function in the absence of rejection. T cell numbers in the urine are low (0 to a few hundred per mL) which would make running a trogocytosis assay on these cells difficult; however, given their recent proximity with the kidney, these cells could have recently trogocytosed donor HLA from the graft itself and may be detectable by flow cytometry. Clearly, thereâs many aspects of this assay that could be further explored. Chief among them I believe is to test HLA class II proteins for detection of CD4+ T cells. Since Tregs have a higher propensity for trogocytosis than Tconv 108, I would be interested to see whether Treg levels change over time in transplant recipients and whether an increase in the number is protective. I believe the assay should also be trialed on a larger number of pre-transplant samples to determine if the assay is useful for predicting transplant rejection, as most studies using IFN-γ ELISPOT assays showed. Moreover, once patients are transplanted, their immunosuppressive drugs restrict T cellsâ capacity for activation ex vivo 206, potentially limiting the utility of this assay post-transplant. Although careful measures must be taken to account for non-specific membrane sharing, trogocytosis may be considered a new AIM for alloreactivity assays. 42 Chapter 3: CAR Tregs mediate linked suppression and infectious tolerance in islet transplantation 3.1 Rationale and approach As discussed in section 1.4.6, A2-CAR Tregs have proven effective at suppressing anti-HLA-A2 alloimmunity. However, it was unknown whether A2-CAR Tregs were able to suppress detrimental immune responses targeting antigens distinct from HLA-A2. The potential for therapeutic Tregs to control immunity to multiple antigens via bystander and/or linked suppression, and/or have a long-lived tolerogenic effect via expansion of endogenous Tregs (infectious tolerance) is highly desirable in the cell therapy context. Through these mechanisms, a single, potentially transient, dose of Tregs could have a long-lasting effect. However, evidence supporting the ability of CAR-engineered Tregs to induce linked/bystander suppression and infectious tolerance was lacking. Here I tested the hypothesis that HLA-A2-detecting CAR Tregs could induce long-lasting tolerance within a CD4+ T cell population that recognized a distinct, diabetogenic antigen to protect against hyperglycemia using a mouse model of islet transplantation. I developed an in vivo model of islet transplantation with autoimmune diabetes challenge to test whether allospecific A2-CAR Tregs were able to suppress autoantigen-specific CD4+ T cell-induced T1D when both target antigens were expressed by the same tissue (the islet transplant). NSG mice were transplanted with HLA-A2+ islets and then administered diabetogenic CD4+ BDC2.5 Teff to induce T1D. BDC2.5 T cells are isolated from the BDC2.5 transgenic TCR mouse and recognize a hybrid peptide between the proinsulin C chain and chromogranin A presented by the NOD MHC class II protein, I-Ag7 207. Mice received BDC2.5 Teff alone or were co-administered with polyclonal or A2-CAR Tregs (3:1 BDC2.5 Teff). Blood glucose was regularly monitored for hyperglycemia (â¥20 mM glucose). Immune populations were monitored weekly in the blood and in the spleen and pancreas at endpoint. Mice were euthanized once hyperglycemic, 7 days post-T cell injection or 4-6 weeks post-last intervention. 43 3.2 Methods 3.2.1 Animals and ethics All in vivo experiments were approved by the University of British Columbia Animal Care Committee (A20-0017). NOD scid gamma mice (NSG; JAX 005557) and non-obese diabetic (NOD). FOXP3-GFP (JAX 008694) were purchased from The Jackson laboratory and bred in-house under specific pathogen-free conditions. NOD-HLA-A2 mice were generated by crossing the HLA-A2 from NSG-HLA-A2 (JAX 014570) onto the NOD/ShiLtJ (JAX 001976) background, resulting in mice with wildtype Prkdc, wildtype IL2rg, and homozygous HLA-A2. To generate NOD.Rag1KO-HLA-A2 mice, NOD.Rag1KO-MIP.CFP mice received from Dr. Brain Fife (University of Minnesota) were crossed with NOD-HLA-A2 mice until progeny were homozygous for Rag1-KO, and transgenic positive for MIP-CFP (cyan fluorescent protein expressed under the mouse insulin 1 promoter) and HLA-A2. NOD.FOXP3-GFP x BDC2.5 mice were generated by crossing NOD.BDC2.5 (JAX 004460) and NOD.FOXP3-GFP mice and subsequently genotyped for FOXP3-GFP and BDC2.5 TCR co-expression. 3.2.2 Flow cytometry Dead cells were identified and excluded based on extracellular staining with fixable viability dye. Cells that received an intracellular stain were fixed and permeabilized using the FOXP3/Transcription Factor staining buffer set (Thermo Fisher Scientific). Cells were Fc blocked prior to staining (clone 2.4G2, BD Biosciences). Table 3.1 lists the information of all antibodies used for this study. Flow cytometry data were collected on the LSRFortessa X-20, A5 Symphony (BD Biosciences) or Cytoflex (Beckman Coulter). Data were analyzed with FlowJo version 10. Target Fluorophore Clone Company Dilution CD4 BV785 GK1.5 BD Biosciences 1:200 CD4 BV605 RM4-5 BD Biosciences 1:200 CD8 BUV805 or BV605 53-6.7 BD Biosciences 1:150 CD11c SuperBright 780 N418 BD Biosciences 1:100 CD45 BUV395 30-F11 BD Biosciences 1:400 CD80 BUV395 16-10A1 BD Biosciences 1:100 CD86 BV650 GL1 BD Biosciences 1:100 Fixable Viability Dye eFluor780 eBioscience 1:1500 FOXP3 PeCy7 FJK-16s Thermo Fisher Scientific 1:75 44 HELIOS PE-Dazzle594 22F6 Biolegend 1:75 HLA-A2 APC, BV421 or PE BB7.2 Biolegend, BD Biosciences or Invitrogen 1:150 I-Ag7 Pacific Blue or Alexa Fluor 488 10.2-16 Provided by Dr. Brian Fife (University of Minnesota) 1:200 IFN-γ PE-Cy7 XMG1.2 eBioscience 1:100 Ki-67 Alexa Fluor 700 or eF450 SolA15 Invitrogen 1:200 myc Alexa Fluor 647 9E10 UBC abLab 1:200 P63 tetramer PE Provided by Dr. Brian Fife (University of Minnesota) 1:200 Thy1.1 BV711 OX-7 BD Biosciences 1:300 Thy1.2 BV510 53-2.1 Biolegend 1:300 TNF-α PE MP6-XT22 BD Pharmingen 1:100 Vβ4 FITC KT4 BD Biosciences 1:150 Table 3.1 Flow cytometry reagents used in chapter 3 All antibodies bind mouse antigens, with the exception of HLA-A2. 3.2.3 Treg generation Treg generation from NOD.FOXP3-GFP mice was previously described 180. Splenocytes and lymph nodes were collected from adult (age 8-16 weeks) female or male NOD.FOXP3-GFP mice. Lymphoid organs were homogenized in 15 mL dounce tissue grinders (VWR) and CD4+ T cells were enriched via magnetic negative selection (STEMCELL Technologies). Tregs were then sorted on a BD FACS Aria Fusion based on viability dyenegative CD4+ GFP-FOXP3+. Tregs were seeded at an approximate density of 1.2 million cells/cm2 in ImmunoCult-XF T cell expansion medium (STEMCELL Technologies) supplemented with 50 μM β-mercaptoethanol (Sigma-Aldrich) and penicillin-streptomycin (Gibco), with 1000 U/mL IL-2 (Proleukin), 50 nM rapamycin (Sigma-Aldrich) and a 3:1 CD3/CD28 mouse T-activator Dynabead to Treg ratio (Thermo Fisher Scientific). On day 3 of expansion, Tregs were transduced with retrovirus, 2 μg/ml Lipofectamine 2000 (Thermo Fisher Scientific) and 1.6 μg/mL Polybrene (MilliporeSigma) by centrifuging at 32°C and 805 xg for 90 minutes. Polyclonal Tregs were transduced with a Thy1.1 vector. A2-CAR Tregs were transduced with a vector carrying Thy1.1 and the A2-CAR which has been previously described 183. Briefly, the A2-CARâs single chain fragment variable (scFv) region was derived from the anti-HLA-A2 antibody clone BB7.2. The CAR has an extracellular myc tag for identification, CD28 transmembrane domain, and CD28 and CD3ζ intracellular signalling domains. On day 7, Dynabeads were magnetically removed and Thy1.1+ cells were positively selected (STEMCELL Technologies). Treg expansion varied 45 from batch to batch, but was typically around 4-fold from sort to post-Thy1.1 selection. Tregs rested overnight until day 8, at which point they were phenotyped and frozen for future use. 3.2.4 DC suppression assay This assay has been previously described 189. Spleens were collected from adult, sex-matched NOD/ShiLtJ or NOD-HLA-A2 mice and enzymatically digested (STEMCELL Technologies). CD11c+ DCs were positively magnetically isolated (STEMCELL Technologies). DCs were plated at a 1:2 ratio with Tregs and left to co-incubate for 2 days, at which point CD11c+ DC expression of CD80, CD86 and I-Ag7 were measured by flow cytometry. 3.2.5 Islet isolation and transplantation Islets were isolated from male or female NOD-HLA-A2 (age 6-11 weeks) or NOD-Rag1KO-HLA-A2 mice (age â¥8 weeks). The pancreas was exposed in euthanized mice and the common bile ductâs entry to the duodenum clamped. The common bile duct was cannulated with a 27G needle and the pancreas inflated with 1000 U/mL of collagenase XI (Sigma-Alrich) in Hankâs Balanced Salt Solution (Gibco). The pancreas was excised and incubated in a 39°C water bath for 15 minutes. The pancreas was gently shaken to dissociate islets from the exocrine pancreas. The islets were washed and caught on a 100 μm filter, and then rinsed into a non-tissue culture treated petri dish in RPMI (Gibco), supplemented with 10% fetal bovine serum (Gibco, Seradigm, VWR), penicillin-streptomycin and glutamax (Gibco). Islets were hand-picked and transplanted on the same or following day. NSG mice (age â¥8 weeks) were transplanted with 50-150 islets into the anterior chamber of the eye (ACE) as previously described 208. Given that the NSG mice were incapable of raising an allogeneic immune response targeting the islets, sex-matching was preferred but not required. We did not observe any impact of sex on study results. Once under anesthesia, a 27G needle was used to create a small hole entering into the ACE of the left eye. This hole was slightly enlarged using a pair of fine scissors and the islets, collected in a stripper (Origio MidAtlantic Devices) connected to a micromanipulator, were carefully placed into the ACE. 46 3.2.6 Longitudinal metabolic and immunological monitoring Non-fasted morning blood glucose was measured via saphenous bleed using the OneTouch Ultra 2 machine and glucose strips. Blood glucose was measured starting seven days post-BDC2.5 Teff injection, and then 1-2 times a week until endpoint. Mice were deemed diabetic when their blood glucose measured 20 mM or above. Blood was collected from the saphenous vein for flow analysis once a week. Blood was centrifuged for 10 minutes at 450 xg and plasma volume measured and frozen at -20°C for later analysis. The remaining cellular pellet volume was measured and the red blood cells lysed with ammonium chloride (STEMCELL Technologies). Cells were then stained to identify viability dyenegative CD45+ leukocytes. Adoptively transferred Tregs were further identified as Thy1.2+ CD4+ Thy1.1+. BDC2.5 Teff were identified as Thy1.2+ Thy1.1negative CD4+ Vβ4+ P63 tetramer+. Passenger T cells were identified as CD4+ or CD8+ and HLA-A2+. Cells were stained intracellularly for FOXP3, HELIOS and Ki-67. Total cell counts were calculated based on total blood volume collected and 123count eBead flow cytometry controls (Thermo Fisher Scientific). 3.2.7 Diabetes induction and Treg treatment Spleens and lymph nodes were collected from male and female NOD.FOXP3-GFP x BDC2.5 mice, homogenized to single cell suspensions and magnetically enriched for CD4+ T cells as already described. BDC Teff were then sorted as viability dyenegative CD4+ FOXP3-GFPnegative. Cells were immediately frozen for later use. At least two weeks following islet transplantation, the blood glucose of transplant recipients was measured at baseline to exclude hyperglycemic (â¥20 mM) mice (occasionally induced by passenger T cells). BDC2.5 Teff and Tregs were thawed, counted and injected via tail vein in a volume of 150µL PBS. Mice transplanted with NOD-HLA-A2 islets were injected with 50,000 BDC Teff ± 150,000 Tregs in the same injection. This 3 Tregs to 1 BDC Teff dose was chosen based on the lowest protective Treg administration ratio demonstrated in a recent study that showed that antigen-specific Tregs prevented hyperglycemia in mice that received BDC Teff 180. BDC-induced diabetes was less effective in the absence of passenger T cells, so mice transplanted with NOD-Rag1KO-HLA-A2 islets were injected with 150,000 BDC Teff ± 450,000 Tregs, maintaining the same Teff:Treg ratio. 47 3.2.8 Plasma cytokine analysis Plasma samples isolated from blood collected at day 7 and previously frozen at -20°C were thawed and samples from multiple mice that received the same experimental treatment were pooled to reach the minimum sample volume required for cytokine analysis (20 µL) using the Th1/Th2/Th17 cytokine kit (BD Biosciences). Data were analyzed using the FCAP array software, version 3.0.1 (Soft Flow). 3.2.9 Endpoint phenotyping On day 7, splenocytes were harvested as already described. Islets were collected as described, and flow through from islet isolation was kept and analyzed as exocrine pancreas. Absolute number of cells are reported for whole spleen and all exocrine tissue analyzed. Islets were counted and hand picked before gently trypsinizing at 37°C to achieve single cell suspensions. Absolute counts for islets are reported per islet (total number of cells/number of hand-picked islets). 3.2.10 Insulitis scoring On day 7, pancreases were harvested, gently fixed at 4°C for 2-4 hours in Cytofix (diluted 1:4 in PBS; BD biosciences), then embedded in O.C.T. embedding compound (Fisher Scientific or Sakura) and frozen at -80°C. Four 12 μm sections spaced 50 μm apart were fixed in 95% Ethanol and then stained with Hematoxylin and Eosin. A blinded insulitis score was applied to each islet; â0â represented no immune cells surrounding the islet, â1â represented peri-insulitis (mononuclear cells surrounding the islet periphery along with periductal insulitis), â2â represented moderate insulitis (mononuclear cells infiltrating <50% of islet area), â3â represented severe insulitis (mononuclear cells infiltrating >50% of islet area), and '4' represented severe structural derangement of the islet (mononuclear cells infiltrating center of the islet with few remaining cells). 3.2.11 Splenocyte intracellular cytokine staining At day 7, spleens were harvested and homogenized into single cell suspensions. Red blood cells were lysed using ammonium chloride solution (STEMCELL Technologies). Five hundred thousand splenocytes were plated with 500 nM of an irrelevant peptide (hen egg 48 lysozyme HEL11-25: AMKRHGLDNYRGYSL) or the BDC2.5 mimetic peptide (P63: RTRPLWVRME) (GenScript). After 20 hours of co-incubation, 10 μg/mL of Brefledin A (Sigma-Aldrich) was gently added. After a total of 24 hours, cells were stained intracellularly for IFN-γ and TNF-α. 3.2.12 Enucleation To test whether the endogenous pancreas was functional four weeks post BDC2.5 and A2-CAR Treg injection, either the islet graft-containing eye (left) or the non-transplanted control eye (right) was enucleated under gaseous isoflurane. Once in the surgical level of anesthesia, the eye was gently lifted out of the eye socket and two sutures were tied around the optical nerve. The eye was then cut above the suture ties and the eyelids glued shut with Vetbond tissue adhesive (3M). 3.2.13 Treg specific demethylated region (TSDR) analysis Four to six weeks post-enucleation, spleens were harvested and homogenized into single cell suspensions. Thy1.1+ Tregs were sorted on the BD FACS Aria Fusion based on viability dyenegative CD4+ Thy1.1+. Spare BDC2.5 Teff and A2-CAR Tregs thawed for T cell injection were used as controls. Genomic DNA was isolated from previously frozen cell pellets and bisulfite converted using the EZ DNA methylation-direct kit (Zymo Research). Two PCRs were performed with the PyroMark PCR kit (Qiagen) in order to report the average methylation of 8 representative CpG sites in the regulatory T cellâspecific demethylated region (TSDR) on the X chromosome. Pyrosequencing was performed on a PyroMark Q96 ID (Qiagen) with PyroMark Gold Q96 reagents (Qiagen) and Streptavidin Sepharose (GE Healthcare). Results for male and female T cells are represented separately and unadjusted; i.e. reported female methylation has not been adjusted to account for X inactivation. PCR primers: PCR1 FWD â TGGGTTTTGTATGGTAGTTAGATGG, PCR1 REV-Biotin â CCCTATTATCACAACCTAAACTTAACC, SEQ1 FWD â TTGTATGGTAGTTAGATGGA, PCR2 FWD-Biotin â GGGTTTTGTATGGTAGTTAGATGG, PCR2 REV â AAACCCTATTATCACAACCTAAACTT, and SEQ2 REV â CCTAAACTTAACCAAATTTTTCT. 49 3.2.14 Statistical analyses and figure preparation Figure schematics were generated in BioRender. Graphs were generated with GraphPad Prism 10. Statistical testing was also completed in GraphPad Prism 10; the statistical test used for each panel is described in the figure legend. Generally, reports of cell numbers are presented as medians with interquartile range and assumed non-parametric due to wide variance in engraftment, while frequencies of marker expression are presented as means with standard deviations (SD) and assumed parametric. Plots that show paired values have bars at the means. 3.3 Results 3.3.1 Generation of A2-CAR Tregs from non obese diabetic mice Islet transplantation can provide a functional cure for type 1 diabetes (T1D) 50, but success is limited by rejection mediated by both alloreactive and re-current autoreactive T cells 53. To test if A2-CAR Tregs mediate linked and/or bystander suppression to control autoimmunity, I established a model of HLA-A2+ islet transplantation in a diabetogenic T cell adoptive transfer model of T1D. I first generated polyclonal and antigen-specific A2-CAR Tregs from non obese diabetic (NOD) mice by sorting and expanding CD4+GFP+ cells from the spleen and lymph nodes of NOD.FOXP3-GFP (Thy1.2+) mice 180 (described in section 3.2.3). Three days after activation, Tregs were transduced with a retrovirus encoding either the transduction marker Thy1.1 alone (referred to as polyclonal Tregs hereafter), or both the A2-CAR and Thy1.1 (referred to as A2-CAR Tregs hereafter) 183 (Fig 3.1A). The resulting expanded Tregs expressed high levels of both lineage-defining transcription factors, FOXP3 and HELIOS (mean double positive = 82±6% for polyclonal and = 86±6% for A2-CAR; Fig 3.1B), as well as Thy1.1 (mean = 88±7% for polyclonal and = 100±0% for A2-CAR; Fig 3.1C). For the A2-CAR Tregs, expression of Thy1.1 and the CAR (myc tag) were directly correlated (Fig 3.1C, left flow plot). Linked and bystander suppression are thought to be mediated at least in part through Treg-mediated induction of tolerogenic APCs, via removal of co-stimulatory molecules and MHC molecules 209. To test if NOD A2-CAR Tregs influenced APC activity, the former were co-cultured with HLA-A2negative or HLA-A2+ NOD splenic dendritic cells (DCs) 189. After 48 hours, DCs were analyzed for expression of the co-stimulatory molecules CD80 and CD86, and the NOD MHC class II molecule, I-Ag7 (described in section 3.2.4). Whereas polyclonal Tregs had a 50 similar effect on HLA-A2negative and HLA-A2+ DCs, A2-CAR Tregs significantly reduced the expression of CD80 and CD86 on HLA-A2+ relative to HLA-A2negative DCs, and showed a similar trend for I-Ag7 (Fig 3.1D). Thus, NOD A2-CAR Tregs have a heightened, antigen-specific ability to decrease the potential for APCs to activate Teff. Figure 3.1: A2-CAR Treg generation from NOD.FOXP3-GFP mice (A) FOXP3-GFP+ Tregs were sorted from reporter NOD mice and stimulated in the presence of CD3/CD28 beads, IL-2 and rapamycin. Tregs were retrovirally transduced on day three to express the transduction marker Thy1.1 and the A2-CAR. Beads were removed on day 7, and transduced cells were magnetically selected and rested overnight before being frozen on day 8. Markers of Treg stability, FOXP3 and HELIOS (B), and Thy1.1 expression (C), were quantified on the day of freezing (n=7; mean and SD). (D) Expanded Tregs were cultured with HLA-A2negative or HLA-A2+ NOD splenic DCs for two days; representative histograms of Treg effect on DC expression of co-stimulatory CD80 (top) and CD86 (bottom) are shown on the left and quantified on the right, along with I-Ag7 (n=3-4; bar at mean with individual pairs shown; paired t-test). 51 3.3.2 A2-CAR Tregs activated by HLA-A2+ islets protect from autoimmune diabetes To test if A2-CAR Tregs could suppress autoreactive Teff, I next established a model of islet transplantation and autoimmune diabetes (Fig 3.2A). 50-150 HLA-A2+ NOD islets were hand-picked from pre-diabetic, 6-11 week old mice, transplanted into the left anterior chamber of the eye (ACE) of NSG mice, and allowed to engraft for at least 2 weeks (described in section 3.2.5). The ACE is a validated location to study islet-directed auto- and alloimmunity 208,210-213: in NOD mice, ACE islet transplants are subject to autoimmune attack at the same rate as islets in the native pancreas 208. Moreover, islet transplants into the ACE are quicker, less invasive, less painful and require less islet mass for euglycemia maintenance than transplants into the kidney capsule (the traditional location of mouse islet transplantation), therefore reducing animal numbers and pain. Diabetogenic CD4+ BDC2.5 Teff were sorted as CD4+FOXP3-GFPnegative cells from NOD.FOXP3-GFP x BDC2.5 mice to obtain BDC2.5 Teff that were FOXP3negative (Fig 3.2A). As expected, within a median of 7 days, injection of 50,000 BDC2.5 Teff induced hyperglycemia in HLA-A2+ islet recipient mice (blood glucose â¥20 mmol; Fig 3.2B). Co-injection with 150,000 polyclonal Tregs protected 33% of recipient mice against hyperglycemia; however, co-injection of the same number of A2-CAR Tregs protected most (82%) of the treated mice until endpoint (maximum duration tested = 63 days; described in section 3.2.7). T cell engraftment was tracked weekly in blood using flow cytometry. One- and two-weeks post-T cell injection, A2-CAR Treg-treated mice had a significantly lower number of circulating BDC2.5 Teff compared to untreated mice (Fig 3.2C). A2-CAR Tregs engrafted in the blood at high levels relative to their polyclonal counterparts (Fig 3.2D), and experienced a proliferative, presumably antigen-stimulated, burst by day 7 (Fig 3.2E). These data suggest that expression of the CAR antigen on the transplanted islets activated A2-CAR Tregs to reduce engraftment of the co-expressed diabetogenic peptide-recognizing BDC2.5 Teff population. 52 Figure 3.2: A2-CAR Tregs protect mice with an HLA-A2+ islet transplantation from autoimmune diabetes (A) NSG mice were transplanted with 50-150 islets from HLA-A2+ NOD mice in the left anterior chamber of the eye (ACE). At least two weeks post-transplant, mice were injected with FOXP3negative BDC Teffs sorted from NOD.FOXP3-GFP x BDC2.5 mice; representative and average FOXP3 and HELIOS expression is shown (mean ± SD). Some mice were also treated with polyclonal or A2-CAR Tregs (1 BDC2.5 Teff:3 Treg ratio). (B) Diabetes-free survival of each treatment group, with â¥20 mM blood glucose reading defining diabetes onset (n=8-11; Mantel-Cox Logrank test; ticks indicate censored mice). (C) FOXP3negative BDC2.5 Teff engraftment in blood at days 7 and 14 post-cell injection (n=5-11; mean ± SD; mixed effects analysis with Tukeyâs multiple comparisons). (D) Absolute number of Thy1.1+ Tregs engrafted in blood over time (n=9-11; individual pairs shown with bars at means; mixed effects analysis with uncorrected Fisherâs LSD). (E) Frequency of Ki-67-expressing cells within adoptively transferred FOXP3+Thy1.1+ Tregs in the blood (n=4-11; bar at median; mixed effects analysis with uncorrected Fisherâs LSD). 3.3.3 A2-CAR Tregs reduce BDC2.5 T cell effector function In our model, the endogenous HLA-A2negative pancreas was unmanipulated. This allowed us to evaluate whether A2-CAR Treg treatment reduced the ability of BDC2.5 Teffs to be activated by their cognate antigen even in the absence of co-expressed A2-CAR Treg antigen, which would be suggestive of linked suppression. Seven days post-T cell injection, flow cytometric analysis of the endogenous pancreas revealed that mice which received A2-CAR Tregs had diminished BDC2.5 T cell infiltration in the exocrine pancreas (Fig 3.3A), and that the BDC Teff were less proliferative (Fig 3.3B). Moreover, the endogenous pancreas of A2-CAR Treg treated mice had less severe insulitis compared to mice which did not receive Tregs (Fig 53 3.3C). This suppressive effect of A2-CAR Treg on BDC2.5 Teffs was systemic as there were also fewer and less proliferative BDC2.5 Teff in the spleens of A2-CAR Treg treated mice (Fig 3.3D & E). To assess whether A2-CAR Treg treatment had a systemic impact on BDC2.5 T cell effector function, splenocytes were harvested on day 7 and stimulated with their mimetope P63 peptide (described in section 3.2.11). We found that A2-CAR Treg treated mice had a significant reduction in the proportion of BDC2.5 T cells producing IFN-γ (Fig 3.3F) and nearly undetectable levels of plasma IFN-γ, with a significant reduction compared to non-Treg treated mice (Fig 3.3G). Taken together, these data show that A2-CAR Treg treatment suppresses BDC2.5 T cell engraftment, proliferation and inflammatory potential in the endogenous blood, spleen and pancreas. 54 Figure 3.3: A2-CAR Tregs suppress BDC2.5 T cell effector function (A) The number of BDC2.5 T cells found in the endogenous pancreas per islet (left) and in the exocrine pancreas (right) 7 days post-T cell injection (n=4-5; median with interquartile range; Mann-Whitney test). (B) Representative and averaged Ki-67 expression on BDC2.5 T cells in the endogenous islets (top) and exocrine pancreas (bottom) at day 7 (n=4-7; mean with SD; Welchâs t-test). (C) Pancreas sections taken on day 7 were assessed for insulitis (0 no infiltration â 4 high infiltration; n=2-3 mice per group; means graphed; 2-way ANOVA with Å ídákâs multiple comparisons). (D) The number of BDC2.5 Teff per spleen 7 days post-T cell injection (n=6-7; median with interquartile range; Mann-Whitney test). (E) Ki-67 expression on BDC2.5 Teff in the spleens at day 7 (n=6-7; mean with SD; Welchâs t-test). (F) 7 days post-T cell injection, splenocytes were stimulated with the BDC2.5 mimetope peptide (P63) for 24 hours and intracellular IFN-γ and TNF-α were determined (n=9-11; mean ± SD; 2-way ANOVA with Å ídákâs multiple comparisons). (G) IFN-γ in plasma collected on day 7 and pooled from multiple mice was measured. (n=13-14 mice; median with interquartile range; Mann-Whitney test). 55 3.3.4 A2-CAR Tregs suppress passenger HLA-A2+ T cells A hallmark of autoimmune diabetes in NOD mice is islet infiltration of immune cells, especially autoimmune CD4+ and CD8+ T cells 214. We therefore considered the possibility that the transplanted islets from pre-diabetic, immunocompetent HLA-A2+ NOD mice may contain passenger T cells. Indeed, I found that all islet transplant recipients had detectable circulating HLA-A2+ CD4+ and CD8+ T cells prior to injection of BDC2.5 T cells, although their absolute numbers were highly variable (Fig 3.4A). Some transplanted mice were euthanized prior to adoptive T cell transfer due when passenger T cell engraftment alone (without addition of BDC2.5 T cells) was sufficient to induce hyperglycemia (data not shown). Remarkably, A2-CAR Treg treatment almost entirely eliminated these passenger T cells from the mouse. Seven days after T cell injection, HLA-A2+ T cells were almost undetectable in spleens (Fig 3.4B), and regardless of their pre-CAR-Treg treatment numbers in the blood, HLA-A2+ CD8+ and CD4+ T cells were significantly reduced in the circulation of A2-CAR Treg-treated, but not untreated or polyclonal Treg-treated, mice (Fig 3.4C). Similar to other tissues, there was a trend to fewer HLA-A2+ T cells in the endogenous islets of A2-CAR Treg-treated mice (Fig 3.4D). These data suggest that in this model, autoimmunity was likely caused by a combination of natural autoreactive T cells with multiple specificities and BDC2.5 T cells. Thus, A2-CAR Tregs mediated bystander and/or linked suppression to control multiple autoreactive specificities. The systemic presence of HLA-A2+ T cells raised the possibility that the effects of A2-CAR Treg treatment on BDC2.5 T cells were not mediated through linked suppression, but rather bystander suppression: e.g. potentially A2-CAR Tregs were stimulated by HLA-A2+ passenger T cells near BDC2.5 T cells, causing bystander suppression of the BDC2.5 T cells that was continuously dependent on A2-CAR Treg local presence and activation. To determine if the systemic tolerogenic effects of A2-CAR Tregs depended on the presence of passenger HLA-A2+ T cells, transplants were repeated using islets from A2+ NOD.Rag1-/- mice, which do not have any T or B cells. The absence of HLA-A2+ passenger T cells resulted in a significant reduction the number of A2-CAR Tregs in the endogenous pancreas (Fig 3.4E), but the accumulation of BDC2.5 T cells in the endogenous pancreas was nevertheless significantly reduced relative to untreated mice (Fig 3.4F). Moreover, A2-CAR Treg therapy completely protected these mice from diabetes (Fig 3.4G). These data show that the systemic protective effect of A2-CAR Tregs does not depend on HLA-A2+ T cell engraftment and presence of the CAR antigen in the 56 endogenous islets. Thus A2-CAR Tregs may mediate linked suppression by modulating BDC2.5 T cell activity such that the suppressed effect no longer depends on the local presence of HLA-A2. Figure 3.4: A2-CAR Tregs suppress islet-resident, HLA-A2+ passenger T cells (A) Representative CD4+ and CD8+ T cells detected in blood in mice without or with an HLA-A2+ islet transplant. The adjacent violin plot shows the absolute number of HLA-A2+ T cells post-HLA-A2+ islet transplant prior to T cell injection (n=28; median with interquartile range). The number of HLA-A2+ CD4+ and CD8+ T cells in (B) spleens at day 7 (n=4-7; median with interquartile range; Kruskal-Wallis tests), and (C) blood on days 0 and 7 (n=7-8; individual pairs shown with bars at means; 2-way repeated measures ANOVA with Tukeyâs multiple comparisons). (D) The number of HLA-A2+ T cells in the endogenous HLA-A2negative islets 7 days post-T cell injection (n=4-7; median with interquartile range; Kruskal-Wallis tests). (E-G) Islets from HLA-A2+ NOD.Rag1-/- mice were transplanted into NSG mice, then injected with BDC2.5 T cells alone or with A2-CAR Tregs. (E) The number of A2-CAR Tregs found per endogenous islet and in the exocrine pancreas in mice transplanted with either HLA-A2+ NOD (NOD) or HLA-A2+ NOD.Rag1-/- (RAG1-KO) islets at day 7 (n=3-5; median with interquartile range; Mann-Whitney test). (F) BDC2.5 T cell accumulation in endogenous islets (left) and exocrine pancreas (right) at day 7 (n=3; median with interquartile range; Mann-Whitney test). (G) Diabetes-free survival of each treatment group transplanted with HLA-A2+ Rag1-KO islets (n=8-11; Mantel-Cox Logrank test). 57 3.4.5 A2-CAR Tregs induce infectious tolerance To further explore whether A2-CAR Tregs had stably re-shaped BCD2.5 Teff activity, 4 weeks after treatment with A2-CAR Tregs we removed the cellular source of co-expressed A2-CAR and BDC2.5 T cell antigens by enucleating the eye previously transplanted with HLA-A2+ islets (described in section 3.2.12). As a control, some mice that received A2-CAR Tregs had their non-transplanted eye removed. I found that all enucleated mice maintained euglycemia for at least three weeks post-removal (Fig 3.5A). Removal of the HLA-A2+ graft did not result in a significant change in BDC2.5 Teff engraftment (Fig 3.5B). In the presence or absence of CAR antigen, the number (Fig 3.5C) and proportion (Fig 3.5D) of circulating FOXP3-expressing A2-CAR Tregs reached homeostatic levels and retained a level of de-methylation at the Treg-specific demethylation region (TSDR) consistent with stable Treg lineage (Fig 3.5E). Building upon evidence that A2-CAR Treg treatment significantly diminished BDC2.5 T cell effector function, I next sought evidence for infectious tolerance, which is a Treg-mediated and long-lived increase in the bodyâs ratio of Tregs to Teff 142. I first assessed the fold change in the number of circulating FOXP3+ BDC2.5 T cells over time. Whereas untreated mice had no significant increase in circulating FOXP3+ BDC2.5 T cells from day 7 to 14, in A2-CAR Treg treated mice they significantly increased each week following day 7 (Fig 3.5F, top). Ultimately, this increase in FOXP3+ BDC2.5 T cells contributed to the emergence of a long-lasting, tolerogenic balance of BDC2.5 Teff to total Tregs (A2-CAR plus BDC2.5 Tregs) in A2 CAR Treg-treated mice that was never achieved in untreated mice (Fig 3.5F, bottom). Moreover, there was evidence that FOXP3+ BDC2.5 T cells expanded in endogenous islets over time. Seven days following A2-CAR Treg injection, the proportion of FOXP3+ BDC2.5 T cells in endogenous islets was low (mean FOXP3+ =5.4%, Fig 3.5G), but overtime this population expanded ~five-fold such that 27 days post-enucleation, about one third of BDC2.5 T cells resident in islets expressed FOXP3 (mean FOXP3+ = 27.7%, Fig 3.5G), indicating infectious tolerance. Importantly, we found a similar trend in A2-CAR Treg-treated mice transplanted with A2+ NOD.Rag1-/- islets; these mice also had an increase in circulating BDC2.5 Tregs (Fig 3.5H, left) and maintained euglycemia post HLA-A2+ graft removal (Fig 3.5H, right). Thus, even in the absence of the A2-CAR antigen, BDC2.5 T cells were rendered tolerant to the endogenous pancreas by A2-CAR Treg treatment. 58 Figure 3.5: A2-CAR Tregs induce stable islet tolerance and FOXP3+ BDC2.5 T cells (A) Twenty-eight days post-A2-CAR Treg injection, mice had either their non-transplanted (black) or islet-transplanted eye (pink) removed and blood glucose was monitored for another 28 days. Light lines show individual mice and the heavy lines show mean blood glucose readings for each group. (B) BDC2.5 Teff and Thy1.1+ A2-CAR Treg (C) engraftment and (D) FOXP3 expression in the blood over time leading up to and following enucleation, indicated by vertical dashed line (n=5-7; median +/- SD). (E) Four to six weeks following enucleation, splenic Thy1.1+ cells were sorted for TSDR analysis. BDC2.5 Teff and A2-CAR Treg methylation prior to cell injection are shown in the top two rows as controls (mean of n=1-4). (F) Top graph: fold change in numbers of FOXP3+ BDC2.5 T cells in blood at the indicated times. For mice with 0 Tregs on day 7, day 14 was considered the baseline (n=6-21; 59 bars at mean fold-changes; Wilcoxon signed rank test where theoretical median = 1). Bottom graph: ratio of FOXP3negative BDC2.5 Teff to the sum of FOXP3+ cell (BDC + Thy1.1) (n=6-21; mean ± SD). (G) Representative flow plots and averaged data of FOXP3 expression of islet-infiltrating BDC2.5 T cells in A2-CAR Treg-treated mice 7 days post-T cell injection or 27 days post-enucleation (n=4; median with interquartile range; Mann-Whitney test). (H) In mice transplanted with HLA-A2+ NOD.Rag1-/- islets and treated with A2-CAR-Tregs: left, the fold change of FOXP3+ BDC2.5 T cells in blood (n=4; paired samples with bars at mean fold-changes) and right, blood glucose levels before and after removal of the transplanted eye at day 42 (n=3-4; Mantel-Cox Logrank test). 3.4 Discussion Taken together, these data suggest that A2-CAR Tregs suppress islet-autoreactive T cells, mediating both linked suppression and infectious tolerance. These findings have significant implications for the field of Treg therapy because they provide evidence that an engineered Treg product has the potential to permanently re-shape immune responses such that long-term tolerance is maintained by cells other than the therapeutic Tregs. To the best of my knowledge, these are the first data to formally show that an antigen-engineered Tregs can induce long-lasting tolerance to a distinct antigen in vivo via linked suppression (discussed in section 1.4.2). Although widely cited as a key Treg mechanism of action, there have been few in vivo studies showing evidence of linked suppression. Anti-CD4 and anti-CD8 antibodies can establish linked tolerance of allogenic skin grafts 141, and there is a report that autoantigen-detecting Tregs isolated from TCR-trangenic mice reduced symptom severity in a mouse model of multiple sclerosis elicited by a distinct autoantigen 215. In terms of bystander suppression, multiple reports show engineered Tregs suppress diabetogenic T cells with distinct antigen specificities in vitro 175,180,216. In vivo, MHC:insulin-specific CAR Tregs limited BDC2.5 T cell engraftment 180 and MHC class I-specific CAR Tregs protected multiple transplanted tissues from alloreactive immunity 217. Although these studies demonstrated bystander suppression, they did not show long-lived tolerance of distinct antigens present in tissues where the CAR antigen was absent. Our model uniquely allowed me to evaluate the capacity of A2-CAR Tregs to inhibit BDC2.5 effector function in the endogenous pancreas, which lacked A2-CAR antigen expression, thereby allowing us to probe whether A2-CAR Tregs mediated linked suppression. I found that in the endogenous HLA-A2negative pancreas, in the absence or presence of HLA-A2+ passenger T cells, BDC2.5 T cells had suppressed engraftment, proliferation and inflammatory potential even if there were little to no A2-CAR Tregs in the tissue. Moreover, A2-CAR Treg treatment not only suppressed BDC2.5 effector function, it resulted in a re-balanced, tolerant immune state. 60 I found that the endogenous HLA-A2negative pancreas was protected and functional even after HLA-A2+ graft removal, an outcome correlated with a significant increase in FOXP3+ BDC2.5 T cells residing in endogenous islets. These data suggest that A2-CAR Tregs tolerized BDC2.5 T cells in a long-lasting way, demonstrating infectious tolerance. This finding has significant implications for the engineered Treg therapy field because if these cells can permanently induce tolerance, their continued presence may not be required. A limitation is that we were unable to completely eliminate all FOXP3+ BDC2.5 T cells prior to injection so we cannot determine whether the increased population of FOXP3+ BDC2.5 Tregs arose from preferential expansion of a small number of transferred BDC2.5 Tregs and/or via de novo conversion into new Tregs. This question is complicated to address. We trialled various approaches to depleting Foxp3+ Tregs from the BDC2.5 population and were never able to fully deplete them prior to transfer. Perhaps an in vivo cell lineage-tracing system 218 could be developed to search for BDC2.5 Tregs that at some point expressed a non-Treg transcription factor (e.g. T-bet 219); the identification of these cells multiple weeks post-injection would support de novo conversion of Teff into Tregs. Nevertheless, no matter the mechanism, the ability of A2-CAR Tregs to expand FOXP3+ BDC2.5 T cells is a significant advancement in our understanding of how Treg therapy can re-shape tolerance. Importantly though, we must consider which antigens are susceptible to linked/bystander and infectious tolerance. Experiments testing whether A2-CAR Tregs can induce tolerance of pathogens is an important future direction of the lab that will help us understand the boundaries, if any, of Treg suppression and address important safety questions. A2-CAR Tregs reduced engraftment of diabetogenic BDC2.5 T cells and islet-resident passenger T cells. Mechanistically, Tregs are thought to mediate suppression via APC modulation, tolerogenic cytokine release and metabolic microenvironment manipulation. There is also some evidence that Tregs may engage in cytolysis via granzyme/perforin and/or Fas/FasL pathways 220. Our findings raise the question of where A2-CAR Tregs mediated their effects. Although A2-CAR Tregs suppress DC function 189, NSG mice have dysfunctional DCs and small, hypocellular lymphoid organs 221, so in this model, A2-CAR Tregs most likely suppress BDC2.5 T cells at the site of transplantation, resulting in a systemic effect. This possibility is supported by intravital imaging studies of mice with ACE islet transplants and injected with 61 BDC Teff and Tregs which showed that Tregs create long-lasting bonds with BDC2.5 T cells, more so than with DCs, in a partially CTLA-4-dependent manner 212. A limitation of this study was that at the time, I had not yet established methodologies to interrogate A2-CAR Treg phenotype in the ACE and further validate that A2-CAR Tregs were activated in antigen-dependent manner in vivo. To confirm that A2-CAR Tregs were specifically activated in vivo by HLA-A2, I transplanted a small number of NSG mice with HLA-A2negative NOD islets and injected them with A2-CAR Tregs and BDC2.5 T cells (data not shown). A2-CAR Tregs engrafted and protected against hyperglycemia at rates similar to those seen with polyclonal Treg treatment (data not shown; requires repetition). A further limitation of these studies was that at the time, I had not yet found a way to evaluate the immune infiltrate in ACE islet grafts. Attempts to perform intravital imaging of the grafts were challenging due to mouse respiratory-induced movement. For future experiments, our team has now identified ways to homogenize enucleated eyes and use immunohistochemistry to evaluate graft infiltrates by flow cytometry and confocal imaging, respectively. These new methods will inform whether A2-CAR Tregs are enriched and proliferative in the HLA-A2+ islet grafts (I would hypothesize that this is true). I am also interested in future studies to evaluate the Treg and BDC2.5 T cell phenotypes in the graft. Throughout my studies, I stained for many activation and functional markers, but never saw expression trends that informed how A2-CAR Tregs mediate BDC2.5 suppression. I hypothesize that Tregs are tolerizing BDC2.5 T cells in the ACE, so I expect that future evaluation of this compartment using histological and flow cytometry-based approaches will help to shed light on how CAR Tregs induce tolerance. Although the BDC2.5 transfer model of autoimmune diabetes has caveats, it uniquely allowed me to probe the questions of bystander/linked suppression and infectious tolerance. The BDC transfer model allowed us to evaluate the impact Treg therapy on autoimmune CD4+ T cells in the absence of a confounding anti-HLA-A2 alloimmune response which may be difficult to parse from the autoimmune response. However, it is important for our future studies to assess whether A2-CAR Tregs maintain tolerance of autoimmune targets when faced with an additional alloimmune response, which may trigger more inflammation than can be managed by the Tregs. Another advantage of the BDC transfer model was that the lymphopenic NSG recipients already had âspaceâ for Treg engraftment, to a degree mimicking the situation in people undergoing islet 62 transplantation receiving aggressive conditioning regimens in preparation 50. However, T1D is an autoimmune disorder in which CD4+ T cells, CD8+ T cells and B cells all have pathogenic roles 13, and my inability to evaluate the impacts of A2-CAR Tregs on these cells is a limitation of this study. A future direction is to test A2-CAR Tregs in an immunocompetent NOD model of T1D. However, given the small number and polyclonal repertoire of autoimmune T cells in NOD mice, precisely tracking the impact of Tregs on these naturally developed cells is challenging. In this model, I introduced Tregs at the same time as the diabetogenic T cells. In this context, there are multiple theoretical windows for Treg treatment; prior to transplant and the development of an alloreactive immune response and/or during an early stage of T1D as a preventative measure, or once an autoimmune response has eliminated enough beta cell mass for a patient to rely on exogenous insulin. These data suggest that A2-CAR Tregs are effective at suppressing an autoimmune response in its early stages, as a preventative measure. Our lab has previously shown that while effective at prolonging skin graft survival when administered at the time of skin transplant, A2-CAR Tregs are unable to protect skin grafts in mice previously sensitized to HLA-A2 183. Only in the absence of prior sensitization do A2-CAR Tregs reduce donor-specific antibodies and plasma cells, and modestly, but statistically insignificantly, reduce the frequency of donor-specific IFN-γ-producing T cells. The therapeutic window for A2-CAR Tregs in suppression of alloimmunity and linked autoimmunity should continue to be investigated. Ideally, Tregs could be used to treat patients with established allo- or autoimmunity; however, if that is not the case, we must determine how to screen patients in the early, potentially preclinical stages of disease, for maximum efficacy. Overall, the results of this study show that antigen-specific CAR Tregs can be activated in vivo to induce tolerance of autoantigens via linked suppression. The first in-human trials of A2-CAR Tregs in transplantation are underway (NCT04817774, NCT05234190) and will provide important safety and efficacy data. Upon further validation of A2-CAR Treg efficacy and infectious tolerance induction in immunocompetent preclinical models of autoimmunity, a natural next step for A2-CAR Treg therapy in humans will be use in transplantation with the intent to control both allo- and autoimmunity. 63 Chapter 4: A2-CAR Treg treatment for diabetic NOD mice 4.1 Rationale and approach The results presented in chapter 3 suggest that A2-CAR Tregs may be an effective concurrent therapy option to protect islet transplants in patients with T1D. However as discussed in section 3.4, there are a few caveats to the BDC2.5 transfer model of T1D. To overcome these study limitations, I designed an experiment to treat diabetic NOD mice with A2-CAR Tregs around the time of islet transplantation. NOD mice are an immunocompetent mouse strain in which the majority of female mice spontaneously develop T1D due to genetic predispositions that mirror those found in humans that develop T1D 222. This strain serves as an excellent model for assessment of A2-CAR Treg treatment impact on immune cells beyond just diabetogenic CD4+ T cells, including the additional alloimmune response targeting HLA-A2. Additionally, diabetic mice (â¥20 mM blood glucose) were used in this set of experiments, allowing determination of whether A2-CAR Treg treatment could suppress an established diabetogenic immune response. After hyperglycemia onset and in preparation for islet transplantation with A2-CAR Treg treatment, mice were treated with an anti-CD3 treatment that is analogous with Teplizumab in humans (section 1.1.6). This was done to (1) condition mice to create immune âspaceâ for Treg engraftment, and (2) to incompletely suppress the diabetogenic and alloimmune responses to allow islets and Tregs time to engraft. Given Teplizumabâs recent approval for patients with stage 2 diabetes in the United States as well as its apparent Treg-sparing properties 45, it appeared to be an ideal immunosuppressive drug to trial with A2-CAR Tregs in a diabetic environment. Here, diabetic female NOD mice were treated with a low dose of anti-CD3 antibody (2.5 μg for five days), then transplanted with HLA-A2+ islets. Some mice were then treated with A2-CAR Tregs. The animalsâ blood glucose was monitored and immune cell populations in the blood and in the transplanted tissue were assessed using flow cytometry. I hypothesized that transplanted mice co-treated with anti-CD3 and A2-CAR Tregs would have extended non-diabetic survival than transplanted mice treated with anti-CD3 alone. 64 4.2 Methods Flow cytometry was performed as outlined in section 3.2.2. A table of the antibodies used in this study are listed in Table 4.1. Blood glucose was measured and blood immunophenotyping was performed as outlined in section 3.2.6. Figures were generated and analyzed as outlined in section 3.2.14. Target Fluorophore Clone Company Dilution CD4 BV605 RM4-5 BD Biosciences 1:200 CD8 BUV805 53-6.7 BD Biosciences 1:150 CD45 BUV395 30-F11 BD Biosciences 1:400 Fixable viability dye eFluor780 eBioscience 1:1500 FOXP3 FITC FJK-16s Invitrogen 1:75 HELIOS PE-Dazzle594 22F6 Biolegend 1:75 HLA-A2 tetramer PE Monomer: NIH tetramer facility Streptavidin: Agilent Technologies 1:200 I-Ag7:IGRP tetramer (peptide: VYLKTNVFL) BV650 Monomer: NIH tetramer facility Streptavidin: BD Biosciences 1:100 Ki-67 Alexa Fluor 700 SolA15 Invitrogen 1:200 LAP PE TW7-16B4 Invitrogen 1:100 Thy1.1 BV711 OX-7 BD Biosciences 1:300 Thy1.2 BV510 53-2.1 Biolegend 1:300 Table 4.1 Flow cytometry reagents used in chapter 4 All antibodies bind mouse antigens, with the exception of the HLA-A2 tetramer. 4.2.1 Animals and ethics All in vivo experiments were approved by the University of British Columbia Animal Care Committee (A20-0017). Female NOD/ShiLtJ (JAX 001976) mice aged 4-6 weeks old were ordered from the Jackson Laboratory and housed in the Centre for Molecular Medicine and Therapeutics (CMMT) until they were 12-14 weeks old, at which time they were transferred the BC Childrenâs Hospital Research Institute (BCCHR) to start weekly blood glucose monitoring. Mice were maintained in the CMMT in early life because it is considered a âcleanerâ facility than BCCHR and cleaner facilities tend to be associated with increased T1D onset 223. Female NOD mice were used given their higher incidence of T1D over male mice. NOD.Rag1KO-HLA-A2 and NOD.FOXP3-GFP mice were bred in-house and obtained as described in section 3.2.1. 65 The former were used as islet donors and the latter which were genotyped GFPnegative were used as Treg donors. 4.2.2 Anti-CD3 treatment and insulin maintenance Mouse blood glucose was measured once a week. As soon as a mouse reached â¥20 mM blood glucose (= day -4), she started to receive daily treatment until transplanted with islets (= day 0): mice were maintained on one daily slow release insulin dose (subcutaneous, 1.5 U, Lantus) and received a daily injection of CD3-F(abâ)2 (intraperitoneal, 2.5 μg, clone 145-2C11, Bio X cell). CD3-F(abâ)2 is made up primarily by the Fab binding domains of the 145-2C11 antibody clone only, as the majority of the Fc fragment has been removed with pepsin digestion. Anti-CD3 antibodies that keep the Fc region intact induce cross-linking with Fc receptor+ cells to induce T cell activation 42. Insulin was not administered on the day of transplantation in expectation that beta cell damage-induced insulin would be released during transplantation. 4.2.3 Islet isolation and transplantation Islet isolations and transplants were performed as outlined in section 3.2.5. NOD.Rag1KO-HLA-A2 mice were used as islet donors. 100-150 islets were transplanted in each eye of the NOD mouse (>200 islets per mouse). 4.2.4 A2-CAR Tregs A2-CAR Tregs were generated as described in section 3.2.3. Additional steps were taken to reduce A2-CAR Treg immunogenicity, as we believe they are a target of the alloreactive response to some degree in immunocompetent hosts 189. Genotyped GFPnegative NOD.FOXP3-GFP mice (equivalent to wildtype NOD mice) were used as Treg donors to eliminate GFP which may spur anti-CAR Treg immunity in recipient mice 189. Further, the A2-CAR construct was updated to eliminate cloning âscarsâ and the myc tag. 0.5-1.5 million A2-CAR Tregs were administered via tail vein injection 3 days post-transplantation (= day 10). 4.2.5 Endpoint phenotyping Mice were euthanized by cervical dislocation under gaseous isoflurane. Islet-transplanted eyes were enucleated and homogenized in a 15 mL dounce tissue grinder (VWR). This was 66 sufficient to create single cell suspensions for flow cytometry. CD45+ cells were identified and phenotyped by flow cytometry. Total cell counts per transplanted eyes were calculated using 123count eBead flow cytometry controls (Thermo Fisher Scientific). 4.3 Results 4.3.1 T1D incidence in the NOD colony at BCCHR Median age at onset and incidence of T1D in NOD colonies varies widely between facilities, with breeding approaches, diet, and housing conditions all thought to contribute to the variation 223. It is believed that NOD mice are slower to develop T1D in âdirtierâ facilities. This notion further supports the hygiene hypothesisâ explanation for increasing rates of autoimmune disease (discussed in section 1.1.4), as researchers have suggested that certain microbial species are required for a tolerogenic, mature immune system to develop 224,225. NOD mice were ordered from the Jackson Laboratory at 4-6 weeks old and housed at the CMMT until 12-14 weeks of age, at which point they were transferred to BCCHR for weekly blood glucose monitoring. 50% of mice were hyperglycemic by 27 weeks. Ultimately two thirds (64%) developed T1D by week 35 (Fig 4.1). Figure 4.1: Incidence of diabetes in locally housed NOD colony Female NOD mice (n=30) were ordered from the Jackson laboratory to the CMMT animal care facility. Mice were transferred the BCCHR housing unit at 12-14 weeks of age. Blood glucose was measured once per week before noon. Mice that had a reading of or over 20 mM were deemed diabetic. Ticks indicate censored data (last available reading). 67 4.3.2 Anti-CD3 treatment depletes conventional T cells and spares Tregs Prior to commencing A2-CAR Treg treatment, I determined what supportive care was required to promote survival of diabetic mice through islet transplantation (section 4.2.3). From hyperglycemia onset to time of transplant (~ one week), NOD mice maintained their weight, healthy appearance and activity. However, without any supportive care, these mice failed to fully recover post-islet transplantation; they had persistent weight loss and their blood glucose was not lowered by transplantation, leading to their euthanasia shortly after transplantation (Fig 4.2A, dashed lines). We hypothesized that in the short time that they were hyperglycemic, although appearing healthy, their organs were suffering hyperglycemia-induced damage that was then exacerbated by the stress of anaesthesia (isoflurane) and transplantation. Therefore, I decided to administer one daily low dose of slow-release insulin to diabetic mice until transplantation to mitigate potential organ damage. This procedural modification allowed mice to recover well from surgery, showing minimal weight loss post-transplant and even restoration of euglycemia for a short period prior to transplant rejection (Fig 4.2A, solid lines). Once I established that maintenance insulin prepared mice for successful transplantation, I began to treat mice with anti-CD3, which was hypothesized to extend graft survival by reducing autoreactive Teff (section 1.1.6). Anti-CD3 treatment is very effective in NOD mice and can significantly prolong islet transplant survival at high doses (e.g. 50 μg/day for five days) 210,226. Therefore, a low dose of CD3-F(abâ)2 (2.5 μg/day for five days) was chosen based on its ability to only partially reduce T cell burden relative to curative high doses 226. The goal of my CD3-F(abâ)2 treatment was to diminish the number/activity of diabetogenic and alloreactive T cells, thus delaying rejection of the transplanted islets and creating immune space to promote A2-CAR Treg engraftment. Given how low this CD3-F(abâ)2 dose was, I did not expect it to fully protect islet transplants. I hypothesized that A2-CAR Treg co-administration would synergize to elongate transplant survival. On day -4, at the first appearance of hyperglycemia, mice were treated once daily with insulin and CD3-F(abâ)2 until day 0, when mice received a final dose of CD3-F(abâ)2 and were transplanted with >200 HLA-A2+ islets split between both ACEs (described in section 4.2.2 and 4.2.3; Fig 4.2B). Blood was analyzed on day -4 and 0 in three groups of CD3-F(abâ)2-treated diabetic NOD mice: NOD mice that developed diabetes early and were not treated immediately (hyperglycemic >one week), mice that never achieved euglycemia following islet 68 transplantation, and mice that did achieve euglycemia following transplantation. In mice that were hyperglycemic >one week prior to treatment initiation or that did not achieve euglycemia post-islet transplant, CD3-F(abâ)2 treatment did not lower blood glucose from day -4 to day 0, while mice that did achieve euglycemia post-transplant all had lower blood glucose readings on day 0 (Fig 4.2C). These data suggest that in diabetic NOD mice that had a successful islet transplant, there was likely some remaining beta cell mass that transiently recovered upon CD3-F(abâ)2 treatment. As previously reported and discussed in section 1.1.6 45,48,49, Treg numbers were not appreciably changed by CD3-F(abâ)2 treatment in most treated mice (Fig 4.2D, left). With the exception of mice that were already diabetic, CD3-F(abâ)2 treatment reduced the number of circulating FOXP3negative CD4+ and CD8+ T cells (Fig 4.2D, middle). In all cases, CD3-F(abâ)2 treatment reduced the number of detectable IGRP-specific CD8+ T cells (Fig 4.2D, right). IGRP-specific CD8+ Teff make up the largest fraction of islet-infiltrating diabetogenic CD8+ T cells in NOD mice 227 and are identifiable in the blood of NOD mice, making them the easiest diabetogenic T cell clone to track longitudinally. However, in this study, IGRP-specific cells were detected via tetramer, so it is unknown whether CD3-F(abâ)2 treatment induced temporary TCR downregulation or whether these cells were eliminated/displaced from the blood. Taken together, these data show that low dose CD3-F(abâ)2 treatment conditions the immune system to prepare mice for successful islet transplantation. 69 Figure 4.2: Preparing diabetic NOD mice for islet transplantation (A) Once mice were deemed diabetic, they were left without intervention until the day of transplant (vertical line indicates day of transplant; dashed lines indicate no treatment, n=2) or treated with 1.5 U of insulin (subcutaneous) once per day until transplantation (solid lines, n=1). Blood glucose readings are plotted in black on the left y axis (horizontal line at 20 mM indicates diabetic threshold) and % body weight loss (horizontal line at 80% indicates humane endpoint) is plotted in red on the right y axis. (B) The schematic shows that once a NOD mouse was deemed diabetic, it entered a CD3-F(abâ)2 and insulin treatment plan until transplantation. Mice were treated with 1.5 U of insulin (subcutaneous) and 2.5 μg of CD3-F(abâ)2 (intraperitoneal) daily from day -4 to -1. On day 0, mice received one last 2.5 μg CD3-F(abâ)2 dose in the morning and then were transplanted with >200 HLA-A2+ islets spread between both eyes. (C) Paired blood glucose readings are shown for mice that underwent CD3-F(abâ)2 treatment, before the first dose and just before the last dose. Data are plotted for mice that were diabetic >one week prior to treatment initiation (left), that never achieved euglycemia following transplantation (middle) and that did achieve euglycemia following transplantation (right) (n=2-7, bar at mean; Wilcoxon test). (D) The percent fold change of the number of FOXP3+ Treg, CD4+ Teff, CD8+ and IGRP-specific CD8+ T cells found in the blood (adjusted per uL) on day 0 relative to day -4 are plotted for mice that were diabetic >one week prior to treatment initiation (yellow), that never achieved euglycemia following transplantation (blue) and that did achieve euglycemia following transplantation (green). The line at 100% indicates no change from day -4 to 0 (n=1-7, bars at medians; Wilcoxon signed rank test where median=100). 4.3.3 A2-CAR Tregs do not prevent islet transplant rejection Having established a protocol for identifying, treating and successfully transplanting diabetic NOD mice with HLA-A2+ islets, I next sought to determine whether A2-CAR Treg treatment would synergize with anti-CD3 treatment to prolong islet allograft survival. A2-CAR Tregs were generated using syngeneic GFPnegative Tregs and a CAR sequence that omitted the myc tag, to reduce CAR Treg immunogenicity (described in section 4.2.4). A2-CAR Tregs 70 expressed high levels of the transduction marker Thy1.1, which directly correlated with CAR expression, detected based on binding to HLA-A2 tetramer (Fig 4.3A). Three days post-transplantation (day 3), one group of CD3-F(abâ)2 -treated mice was given 0.5-1.5 million A2-CAR Tregs, while the others received CD3-F(abâ)2 treatment alone. CD3-F(abâ)2 plus A2-CAR Treg treatment did not impact time to hyperglycemia differently relative to mice treated with CD3-F(abâ)2 alone; all mice were hyperglycemic by day 17 post-transplantation (Fig 4.3B). Additionally, A2-CAR Treg treatment did not appear to impact immune populations in the blood beyond what was observed with CD3-F(abâ)2 treatment alone (Fig 4.3C-J). CD8+ and IGRP-specific CD8+ T cell numbers did not recover to pre-CD3-F(abâ)2 treatment levels by day 17 post-transplant (Fig 4.3C and D); however, CD8+ T cells did appear to transiently be more proliferative at the end of CD3-F(abâ)2 treatment (Fig 4.3E left). Similarly, FOXP3negative CD4+ T cell numbers did not fully recover (Fig 4.3F) and treatment did not appear to have any impact on proliferation (Fig 4.3G left). Treg numbers were unaltered by CD3-F(abâ)2 treatment (Fig 4.3H) and transiently made up a larger proportion of the CD4+ T cell population (Fig 4.3I). CD3-F(abâ)2 treatment had no impact on their expression of Ki-67 (Fig 4.3J left). I stained for a multitude of T cell activation/exhaustion markers, and the only one consistently expressed by T cells in the blood was LAP. LAP is translated attached with TGF-β, maintaining the latter on the cell membrane in its latent state. It is expressed by activated T cells, traditionally Tregs, and plays a role in immune regulation 228. Although expressed by all T cell populations in the blood, its expression was not influenced by CD3-F(abâ)2 or A2-CAR Treg treatment (Fig 4.3E, G, J right). 71 Figure 4.3: A2-CAR Tregs do not prevent HLA-A2+ islet transplant rejection (A) Flow plot shows HLA-A2 tetramer binding and Thy1.1 expression of untransduced (black) and A2-CAR transduced Tregs (red) isolated and expanded from NOD mice. (B) Three days following islet transplantation, some mice received 0.5-1.5 million A2-CAR Tregs (pink) via tail vein injection. Blood glucose readings over time are graphed (left; horizontal line at 20 mM indicates diabetic threshold) for CD3-F(Abâ)2 alone (black; n=4) and CD3-F(Abâ)2 plus A2-CAR Treg-treated mice (n=5). Vertical line at day 0 indicates islet transplantation and line at day 3 indicates A2-CAR Treg treatment for relative mice (mean Â± standard deviation). The survival curve for these mice is shown on the right. The number of CD8+ T cells (C), IGRP-specific CD8+ T cells (D), CD4+ FOXP3negative Teff cells (F) and CD4+ Foxp3+ Tregs (H) found in the blood are plotted over time (median with range). The percentage of CD8+ T cells (E), CD4+ Teff (G) and Tregs (J) expressing Ki-67 (left) and LAP (right) are plotted in grey for CD3-F(Abâ)2 alone and in pink for mice treated with CD3-F(Abâ)2 plus A2-CAR Tregs. Data collected from day 7 or 10 are plotted together (mean Â± standard deviation). (I) The frequencies of FOXP3 and HELIOS expression within the CD4+ T cell population over time are plotted for CD3-F(Abâ)2 on the left and CD3-F(Abâ)2 with A2-CAR Tregs on the right (mean with standard deviation; 2-way ANOVA with Tukeyâs multiple comparisons. Stars indicate *p<0.05, **p<0.01, ****p<0.0001 for day 0 compared to day -4). 72 4.3.4 A2-CAR Tregs engraft in HLA-A2+ islet transplanted-mice A2-CAR Treg engraftment in the blood was assessed one and two weeks post-tail vein injection. Importantly, A2-CAR Tregs were identified based on the transduction marker Thy1.1; therefore I was only able to detect Tregs with the capacity to express the A2-CAR and missing adoptively transferred, transduction markernegative Tregs, a population we often see emerge in immunocompetent mouse models 183. Whether this population of untransduced Tregs are an expansion of Tregs that were never truly transduced, or previously transduced Tregs that no longer express the transduction marker remains unclear. Moreover, in the experiments described in chapter 3, CAR (myc) and Thy1.1 expression always positively correlated. In this model, I did not validate that correlation due to the lack of a myc tag in the CAR and so it is possible that Thy1.1+ Tregs were no longer expressing the A2-CAR. A2-CAR Tregs had low engraftment one week post-injection, and this continued to drop a week later (median <1 cell/μL blood; Fig 4.4A). 83% of A2-CAR Tregs expressed FOXP3 at one week, which dropped to 60% at week two (Fig 4.4B). In mice that survived to week two, A2-CAR Tregs had a trend for increased proliferation (Fig 4.4C) and activation (Fig 4.4D), although these expression patterns were similar to what was observed in the endogenous Treg population (Fig 4.3J). These data suggest that A2-CAR Tregs failed to thrive in CD3-F(abâ)2-treated mice and were not preferentially activated in the blood by HLA-A2+ islet transplantation. Figure 4.4: A2-CAR Tregs engraft in diabetic NOD mice (A) Thy1.1+ A2-CAR Tregs were quantified in the blood on days 10 and 17, one and two weeks after injection, respectively (n=5; bar at mean). (B) The frequency of FOXP3 and HELIOS expression within the Thy1.1+ CD4+ T cell population are plotted over time (mean with standard deviation). Thy1.1+ expression of Ki-67 (C) and LAP (D) are plotted for days 10 and 17 (n=5; bar at mean). 73 4.3.5 A2-CAR Tregs are found at the site of HLA-A2+ islet transplantation Although A2-CAR Treg engraftment in the blood was low, I wanted to know whether A2-CAR Tregs successfully trafficked to the HLA-A2+ islet graft and whether they impacted immune responses in the eye. Eyes containing the islet grafts were collected for immune phenotyping from one A2-CAR Treg-treated mouse diabetic by day 10, another diabetic by day 17, and one mouse not treated with A2-CAR Tregs (CD3-F(abâ)2 only), diabetic by day 18. A2-CAR Tregs were found in the islet graft at day 10, but were barely detectable at day 17 (Fig 4.5A). Despite graft loss of function, A2-CAR Tregs were remarkably stable at each time point, containing less than 15% FOXP3negative cells (Fig 4.5B). One week after transfer, A2-CAR Tregs were very proliferative in the graft, but this was not observed two weeks post-transfer (Fig 4.5C). CD8+ T cell engraftment was lowest in the A2-CAR Treg-treated mouse at day 10, despite this being the time of highest CD8+ T cell proliferation (Fig 4.5D). This was also true when looking specifically at IGRP-specific CD8+ T cells (Fig 4.5E) and CD4+ Teff (Fig 4.5F). There were more endogenous Tregs in the grafts that did not receive A2-CAR Treg treatment at day 18 than in mice treated with A2-CAR Tregs, which each had comparable levels at both time points (Fig 4.5G, left). These cells were slightly more proliferative at the earlier time point in A2-CAR Treg treated mice, but all endogenous Treg populations maintained some level of proliferation regardless of time or treatment (Fig 4.5G, right). No true conclusions can be drawn until more data are collected; however, these preliminary data suggest that A2-CAR Tregs did not impact the islet graftâs immune infiltrate aside from perhaps decreasing the number of infiltrating endogenous Tregs. 74 Figure 4.5: A2-CAR Tregs are found at the site of transplantation Transplanted eyes were harvested and homogenized for flow analysis from diabetic mice at endpoint (n=1 for all groups shown). Two mice were treated with A2-CAR Tregs (pink) and one was not (grey). The percentage of Thy1.1+ CD4+ T cells (A), CD8+ T cells (D), IGRP-specific CD8+ T cells (E), CD4+ Teff cells (F) and Thy1.1negative Tregs (G) within the CD45 population of the grafts are plotted. The frequency of these cells expressing Ki-67 is also plotted (C, right panel of D-G). (B) The frequency of FOXP3 and HELIOS expression within the Thy1.1+ CD4+ T cell population is plotted. 75 3.4 Discussion The pilot experiments described here were ambitious and early data did not show that A2-CAR Tregs protected HLA-A2+ islet transplants in diabetic NOD mice. This work did, however, (1) establish a treatment regimen that prepares NOD mice for successful islet transplantation, (2) confirm that low doses of CD3-F(abâ)2 reduce Tconv numbers, spare Tregs, and support the function of remaining beta cells, and (3) show that A2-CAR Tregs stably engraft in diabetic NOD mice. Anti-CD3 treatment has previously been shown to stably reverse hyperglycemia in some diabetic NOD mice 229 and prolong survival of islet transplants 210. A study in recent-onset diabetic NOD mice showed that high dose anti-CD3 treatment (complete antibody, including Fc region, 5 days of 50 μg) followed one week later with adoptive transfer of BDC2.5 Tregs induced long-lasting remission in all treated mice, with less remission observed with anti-CD3 treatment alone or in combination with polyclonal Treg transfer, and no durable remission induced with BDC2.5 Treg transfer alone 230. BDC2.5 Treg islet engraftment in treated mice was strong and persistent. Moreover, Tregs were administered at a time when blood Treg abundance had recovered following anti-CD3 treatment, suggesting that endogenous Treg depletion was not required for successful transferred Treg engraftment 230. These findings suggest that anti-CD3 treatment can be synergistic with Treg treatment to induce tolerance in an established diabetogenic response. In our study, in remains unclear why A2-CAR Treg treatment did not extend allograft survival. It is possible that in an immunocompetent model where a preexisting autoimmune response exists at the time of transplant, A2-CAR Tregs are unable to induce bystander/linked tolerance. If they can, perhaps combination with a higher dose of anti-CD3 would be sufficient to tolerize the autoimmune response, maybe through higher A2-CAR Treg engraftment. Or alternatively, anti-CD3 treatment may have reduced Treg engraftment or function; knocking out CD3/TCR in A2-CAR Tregs so that there is no chance of interaction with anti-CD3 treatment could increase engraftment 231. Another consideration is that it is not known if the islet allografts were lost due to allo- and/or autoimmune attack. In the future, we will assess the grafts by histology to determine whether both alpha and beta cells were killed, suggesting that A2-CAR Tregs failed to suppress the anti-HLA-A2 alloimmune response, or whether beta cells were selectively destroyed, which would suggest that A2-CAR Tregs were unable to quell the 76 diabetogenic autoimmune response. Further investigation is required to determine if A2-CAR Tregs are able to induce tolerance in the context of the simultaneous allo- and autoimmune responses to be efficacious in islet transplantation. There are multiple approaches we plan to take in the future to better assess how A2-CAR Tregs interact with and impact the host immune system. In addition to optimizing a histological read-out of graft survival, we froze plasma collected from treated mice. In future studies this material will be used to measure if A2-CAR Tregs impacted cytokine or auto- and alloantibody production. It is also important that future experiments evaluate relevant tissue immune populations. I was only able to look at a few islet grafts, and only once the transplant was rejected. The immune infiltrate of the graft must be evaluated prior to islet rejection, either by endpoint flow cytometry, histology or intravital imaging 210, to assess how treatment influences the kinetics of insulitis. It is still debated whether Tregs mediate the majority of their effects at the site of transplantation, and/or in the draining lymph node, making the latter another important tissue for future endpoint analysis. Finally, in this study one of the biggest challenges was assessing treatment impact on diabetogenic immune cells. While the IGRP tetramer was able to reliably detect one diabetogenic CD8+ T cell clone, the autoimmune population is polyclonal and use of islet peptide-stimulated assays to capture antibody-producing B cells 183, IFN-γ-producing T cells 183, or proliferating T cells 180 would help determine whether A2-CAR Treg treatment impacts the diabetogenic immune cells in a way that may not be detectable when looking at the entire polyclonal immune repertoire. Similar assays could be created to monitor alloimmune responses. The goal of this study was to protect islet transplants in mice with overt T1D. T1D patients that receive islet transplantations have established autoimmune responses; however, itâs unknown whether therapeutic Tregs can be efficacious so late in clinical progression. Although we will continue this investigation, it is possible that A2-CAR Tregs could also be useful for prevention of T1D. Teplizumab is approved and effective for patients with stage 2 T1D (autoantibodies and dysglycemia) in delaying progression to stage 3 (symptomatic) 46. Perhaps A2-CAR Tregs could be useful is controlling autoimmunity at an early stage as well, with or without anti-CD3 co-treatment. If A2-CAR Tregs can induce linked tolerance as supported by chapter 3, we could envision HLA-A2negative patients with stage two T1D receiving a small HLA-A2+ islet transplant (perhaps even in the ACE, NCT04198350) and A2-CAR Tregs. The A2-CAR 77 Tregs, via linked suppression and infectious tolerance, would then theoretically tolerize the autoimmune response, protecting and preserving endogenous beta cell mass. It may seem counterintuitive to use an A2-CAR Treg, rather than skipping the islet transplant and simply using an islet-specific Treg; however, it is difficult to identify (1) surface antigens that are specific for beta cells and (2) TCR or CARs that have high affinity or specificity, respectively. Naturally-occurring diabetogenic TCRs by nature of central tolerance are relatively weak 232. Despite these challenges, there are multiple genetic engineering approaches to develop diabetic autoantigen-specific Treg products (summarized by 166), with some preclinical success observed using CAR- 180 and TCR-engineered Tregs 175. While these products hold promise and should continue to be studied, A2-CAR Tregs have been shown by multiple groups to be very efficacious and specific (section 1.4.6). Moreover, they are already in clinical trials (NCT04817774, NCT05234190). If they are capable of inducing linked suppression and infectious tolerance, then they could theoretically be used not only to control alloimmunity, but also autoimmunity targeting any tissue. Therefore, our future studies will assess whether A2-CAR Tregs can be used in conjunction with an HLA-A2+ islet transplant to prevent T1D in pre-diabetic NOD mice. One important consideration for Treg therapy efficacy in patients with T1D is the source of Tregs itself. Ideally patients are treated with their own Tregs, expanded and engineered ex vivo prior to re-infusion. This should introduce the least immunogenic Treg product and have the best chance at persistence. However, patients with T1D have T1D in part because their Tregs have abnormally low suppressive function and have failed to maintain beta cell tolerance (reviewed 233). In my animal studies, I used Tregs sorted from pre-diabetic NOD mice that have genetic alterations in Treg genes 234. There have been many preclinical studies showing that Tregs from NOD or NOD BDC2.5 mice can prevent and treat T1D in NOD mice 173,175,180, suggesting that the genetic alterations in these Tregs can be overcome. But in a genetically diverse human T1D population, itâs possible that some patients have Tregs that cannot be rescued and leveraged as therapy. To date, only polyclonal Tregs have been trialled in patients with early T1D, so it is difficult to know whether their lack of efficacy is due to lack of antigen specificity or due to Treg dysfunction 164,165. One way to overcome this potential issue is to use third party, allogenic Tregs from blood 235, umbilical cord blood 236,237 or thymus 238. These Tregs may only require a brief residence in the recipient if they can induce infectious tolerance. Otherwise, in order to avoid 78 alloimmune-mediated depletion, these Tregs should either be HLA-matched to the recipient, or genetically modified to lack the highly polymorphic HLA proteins. As interest in Treg therapy continues to grow, these alternative sources of Tregs will be put to the test and may ultimately be the best way to make Treg therapy more broadly accessible. The data presented in section 4.3 raised more questions than answers; however, they provided the first step of many in testing A2-CAR Tregs for the treatment of T1D in an immunocompetent pre-clinical model. Future focus will be on therapy optimization, identifying and tracking impacts on antigen-specific lymphocytes and finding the optimal timing for treatment. 79 Chapter 5: Conclusions 5.1 Summary and significance of findings Transplant donor HLA proteins that do not match the transplant recipientâs HLA proteins are a major target of the alloimmune response; their expression on transplanted tissue is what leads to their demise. However, because they are so abundantly expressed, they also make excellent therapeutic targets. In chapter 2, I discussed the development of a trogocytosis-based assay to identify alloreactive T cells, based on their high affinity for donor HLA. In chapters 3 and 4, I showed that A2-CAR Tregs are drawn to and activated by donor HLA-A2 to mediate suppression of a damaging autoimmune response. In its current form, the trogocytosis assay did not function as hypothesized; I failed to convincingly detect alloreactive lymphocytes in the blood of transplant recipients during a biopsy-confirmed rejection episode. As discussed in 2.4, there are many ways the assay could be further optimized to increase its sensitivity. However, thereâs also a chance that alloreactive T cells are not detectable in the blood during rejection; they may be sequestered in the relevant tissues. The leading candidate assay for quantifying peripheral alloreactive T cells, the IFN-γ ELISPOT, has been tested largely as a predictor of rejection 87,88, and has so far failed to provide data showing that alloreactive T cell quantities increase post-transplant 197. Nonetheless, the trogocytosis assay was able to detect alloreactive T cells if they were >1% of PBMCs. A key beneficial feature of the trogocytosis assay is that it functioned using a cell line presenting donor HLA. Use of a cell line overcomes the limitation of relying on donor splenocytes (deceased donor) or PMBCs (living donor), which is a major barrier of MLR-based assays. Since K562s are easy to grow and transduce, a library of different HLA+ K562s might be a future approach to overcome the barrier associated with donor stimulator cells for any MLR-based assay. So while the trogocytosis assay has room for optimization, I showed that K562s expressing donor HLAs are an accessible replacement for allogeneic stimulation, and that the trogocytosis assay detects alloreactive CD8+ T cell clones when >1% of PBMCs. In contrast to chapter 2, the results in chapter 3 were more exciting than I had expected. Mice were transplanted with HLA-A2+ islets and their endogenous pancreas was left intact. When administered diabetogenic T cells, all mice became hyperglycemic; both their graft and endogenous pancreas were clearly non-functional. However, mice treated with A2-CAR Tregs 80 did not develop hyperglycemia. This could have been because A2-CAR Tregs were trafficking to the islet graft where HLA-A2 was expressed, and inducing local tolerance of the graft only. However, the endogenous pancreas was also protected in A2-CAR Treg-treated mice, even when the A2-CAR Tregs were not in the pancreas itself. A2-CAR Treg treatment limited BDC engraftment, proliferation and inflammation in the pancreas, providing evidence that an engineered Treg can induce linked suppression. Moreover, the frequency of Tregs within the BDC population increased over time providing the first evidence of engineered Treg infectious tolerance induction. These results are significant for a couple of reasons. Perhaps the biggest barrier to all immunotherapy is specific antigen identification. Itâs very difficult to find one well-expressed surface antigen that is restricted to one tissue, making it difficult to find therapeutic targets for autoimmune disease. Many transplant recipients receive grafts to replace tissue theyâve lost to autoimmune disease. These allografts have distinct, allorestricted antigens â class I HLAs. Although the donor HLA is not the direct autoimmune target, my work showed that it may be sufficient to instruct CAR Tregs to protect the entire tissue. In certain conditions, such as T1D, it may be plausible to administer a small, relatively non-invasive transplant to a non-end stage recipient in conjunction with alloreactive CAR Treg therapy to induce tolerance towards autoantigens in a systemic way to stabilize remaining tissue function. Infectious tolerance is the holy grail of Treg therapy (introduced in section 1.4.2); itâs critical to the feasibility and success of Treg therapy because it is unlikely that one administration of Tregs would survive indefinitely in the body 164. The goal of Treg therapy is to reestablish tolerance in the body, likely by permanently increasing the abundance of Tregs to counteract the effector T cells. Data in support of this concept have been difficult to acquire since it is likely to occur on an antigen-specific scale, and itâs difficult to identify changes in a polyclonal immune repertoire. So despite the limitations of using the BDC2.5 transfer model of T1D discussed in 3.4, the oligoclonal populations in these mice allowed us to directly address this question. These data support the concept that Tregs can mediate infectious tolerance and will support further investigation into its mechanisms and ways to tune it for therapeutic use. Given the promising results presented in chapter 3, I set up a more challenging and clinically relevant model of islet transplantation and A2-CAR Treg treatment to cure hyperglycemic NOD mice, rather than just try to prevent T1D onset (as was the goal of chapter 3). The additional challenges of this model included an alloimmune response, a well-underway 81 diabetogenic immune response, a polyclonal T cell repertoire and a mouse that needed a transplant to function immediately to restore its endogenous insulin production. While pre-treatment with anti-CD3 prepared mice for successful transplantation, A2-CAR Treg treatment did not prolong survival of the allograft. A2-CAR Treg engraftment was weak, but they were found at the site of the graft with a stable phenotype. This was the first step in testing A2-CAR Tregs for the treatment of T1D, in the most challenging preclinical model that I could think of. Thereâs plenty more to do. 5.2 Limitations and future directions For the trogocytosis assay, I outlined many next steps for assay optimization before trying the assay with patient samples again (section 2.4). My preference would be to remove the endogenous HLA proteins from HEK 293 cells and engineer them to express HLA class I and class II proteins fused to fluorescent probes. I would control for background trogocytosis with a recipient HLA-matched cell line, pulsed with a self-peptide (and perhaps a viral peptide, to serve as a positive control). A limitation of the data presented in chapter 2 was that only three sets of patient samples were trialed in the assay. Upon further increasing assay sensitivity, I would trial the assay on a higher number of pre-transplant/immunosuppression samples to see if they are predictive of allograft rejection. A2-CAR Tregs are now in clinical trials for the protection of solid organ transplants; we need to know how they work. Although mice are imperfect preclinical models that fail to capture many aspects and diversity observed in the human population 239, they uniquely allow us to track immune cell behaviour over time and in different tissues in a way that cannot be performed with humans. Therefore, our future plans will continue to rely on mouse models. The exciting results obtained in chapter 3 were collected in an immunocompromised mouse model with multiple limitations discussed in section 3.4. Moving forward, we will continue the work started in chapter 4 by using NOD mice, which are a more clinically relevant mouse model. We will begin by treating prediabetic NOD mice with islet transplantation and A2-CAR Treg treatment. Many immune interventions for T1D occur in stage 1 or 2, so I would like to start at a similar stage in mice to align with clinical approaches. Data from these prevention studies will inform us whether A2-CAR Tregs impact (1) the anti-HLA-A2 alloimmune response, (2) the local intragraft autoimmune response, and (3) the diabetogenic response in the endogenous pancreas. I 82 also want to assess how and where tolerance is happening. One of my favourite papers to be published in recent years showed that in the NOD mouse, stem-like progenitors of autoimmune CD8+ T cells reside in the pancreatic draining lymph node and give rise to short-lived effector T cells that mediate damage in the pancreas 34. Itâs important to determine if and whether Tregs can abrogate these cells at the source in the lymph node, or whether theyâre needed in the tissue to constantly quell each new wave of effector T cells. Our next studies will look at diabetogenic T cells in all of these tissues; they will provide clues to A2-CAR Treg mechanism and guide their application as a cure for T1D. 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Thesis/Dissertation
eng
Experimental Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Levings, Megan K.
Harnessing T cells that detect mismatched HLA to monitor and prevent allo- and autoimmunity
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