Signaling Regulation of Inkt Cell Development and Function

Signaling regulation of iNKT Cell Development and Function

Mayra Cruz Tleugabulova

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Immunology University of Toronto

Signaling Regulation of iNKT Cell Development and Function

Mayra Cruz Tleugabulova

Doctor of Philosophy

Department of Immunology University of Toronto

2017 Abstract

Beyond their potential as immunotherapeutic targets, invariant Natural Killer T (iNKT) cells have long interested immunologists because they defy the rules of T cell development through their differential requirement for signalling molecules, such as those downstream of the TCR. In this work, we aimed to define some of the signalling requirements that govern iNKT cells development, homeostasis and function. We first investigate the biophysical properties of TCR- ligand-CD1d interaction using a panel of iNKT TCRs. Using the retrogenic mouse approach, we assessed how TCR properties impacted iNKT cell selection, differentiation, homeostasis and cytokine secretion. We showed that selection and effector differentiation are regulated by different biophysical parameters of TCR-ligand interaction, but that survival, homing and cytokine bias are largely independent of TCR strength. Second, we investigate how deletion of the Src homology domain-containing phosphatase 1 (Shp-1) changed the signal integration and phenotype of iNKT cells. Using mice conditionally lacking Shp-1 on αβ T cells we showed that iNKT cells are biased towards a TH2 phenotype, and hyper-proliferated when cultured in vitro with IL-2, IL-7 or IL-15, suggesting a link between these observations. Taken together, this work highlights how the regulation of the complex signalling resulting from environmental cues, such as T cell receptor engagement and cytokine sensing, impacts the development and effector functions of iNKT cells.

Acknowledgments

I would like to thank my supervisor, Thierry Mallevaey, for his encouragement and understanding throughout my PhD, for giving me the freedom to explore my ideas and for sending me to outstanding conferences to present this work. It’s been a truly unique experience to be one of the first members of the Mallevaey lab. I would also like to thanks all the former and present members of the Mallevaey lab. I would like to especially thank Nichole K. Escalante and Stephanie Fieve for helping to set up and perform many experiments in Chapter 2 of this thesis. Thanks to Thirumahal Selvanantham for teaching me many techniques at the beginning and Irene Lin and Juan Mauricio Umana for their help on the last years of my PhD. Thanks to Dionne White and Joanna Warzyszynska for tremendous help and patience in setting up flow cytometry experiments as well as sorting the small amounts of cells that were needed for experiments. Thanks to Laura Kent for putting so much dedication into keeping a great mouse colony for the lab. It’s been a pleasure to interact with them throughout the last years. I would like to thank all the members of my committee, Juan Carlos Zúñiga Pflücker, Michele Anderson, David Williams and Tania Watts. I have learned a lot from the knowledge, curiosity and enthusiasm they bring to every committee meeting. I owe a lot of my knowledge on T cell development to the labs of JC and Michele, who mentored me and helped me with many techniques. Particular thanks to David for encouraging my interest in photography and inspiring me to practice it more (hopefully one day I can also open my own exhibition!). Thanks to all the collaborators that have helped with the projects. Paul B. Savage and Shenglou Deng (Brigham Young University) for providing lipid antigens for experiments on Chapter 2. Jean-Philippe Julien and June Ereño-Orbea (Hospital for Sick Children) for helping us delve into challenging techniques to assess biophysical properties of receptor interaction on Chapter 2. Dylan Johnson and Benjamin Neel (Princess Margaret Hospital) for providing the Shp-1f/f CD4-cre mice and helping with initial experiments on Chapter 3. Thanks to Manfred Brigl (Brigham and Women's Hospital), Heather Melichar (Maisonneuve-Rosemont Hospital) and Andre Veillette (Institut de Recherches Cliniques de Montreal) for their input on several experiments and helpful conversations. Finally I would like to thank all my friends and colleagues who have made the PhD journey a lot of fun, and to my family for supporting me throughout the entire PhD. It would have been nearly as enjoyable without their support.

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Table of Contents

ACKNOWLEDGMENTS ...... III

TABLE OF CONTENTS ...... IV

LIST OF FIGURES ...... VI

LIST OF TABLES ...... VII

ABBREVIATIONS ...... VIII

LIST OF PUBLICATIONS ...... IX

1 CHAPTER 1: INTRODUCTION ...... 1

1.1 OVERVIEW OF INKT CELLS ...... 1 1.1.1 Definition ...... 1 1.1.2 iNKT Cells in Health and Disease ...... 2

1.2 INKT CELL DEVELOPMENT AND REGULATION ...... 5 1.2.1 Development of iNKT Cells ...... 5 1.2.2 Activation of iNKT Cells ...... 9 1.2.3 Homeostasis and Regulation of iNKT Cells ...... 11 1.2.4 Modulation of the Immune System by iNKT Cells ...... 13 1.2.5 Integration of Signals for iNKT Cell Development and Function ...... 15

1.3 THE T CELL RECEPTOR ...... 16 1.3.1 Antigen Discrimination by Classical TCRs ...... 16 1.3.2 Structure of the iNKT T Cell Receptor ...... 20 1.3.3 iNKT TCR Signaling During Development and Activation ...... 23

1.4 SLAM FAMILY RECEPTORS ...... 26 1.4.1 Overview ...... 26 1.4.2 SLAMF1/CD150 ...... 28 1.4.3 SLAMF6/Ly-108/NTB-A ...... 30 1.4.4 SLAMF3/Ly-9/CD229 ...... 31 1.4.5 Additional SLAM Receptors ...... 32

1.5 CYTOKINES ...... 32 1.5.1 Cytokine signalling in iNKT cell homeostasis ...... 32 1.5.2 Cytokine signalling in iNKT cell activation ...... 34

1.6 THE PROTEIN TYROSINE PHOSPHATASE SHP-1 ...... 34 1.6.1 Shp-1 Classification and Structure ...... 34 1.6.2 Models to Study Shp-1 Function in vivo ...... 35 1.6.3 Regulation of TCR, SLAM and Cytokine Signals by Shp-1 ...... 36

1.7 PURPOSE OF THIS THESIS ...... 39

2 CHAPTER 2: DISCRETE TCR BINDING KINETICS CONTROL INVARIANT NATURAL KILLER T CELL SELECTION AND CENTRAL PRIMING ...... 40

2.1 ABSTRACT ...... 41

2.2 INTRODUCTION ...... 42

2.3 EXPERIMENTAL PROCEDURES ...... 45

2.4 RESULTS ...... 50

2.5 DISCUSSION ...... 74

3 CHAPTER 3: SHP-1 INHIBITS CYTOKINE RECEPTOR SIGNALLING TO MODULATE INKT CELL DIFFERENTIATION ...... 77

3.1 ABSTRACT ...... 78

3.2 INTRODUCTION ...... 79

3.3 EXPERIMENTAL PROCEDURES ...... 81

3.4 RESULTS ...... 85

3.5 DISCUSSION ...... 98

4 DISCUSSION AND FUTURE DIRECTIONS ...... 101

4.1 RECEPTOR SIGNALLING ...... 101 4.1.1 TCR signalling ...... 101 4.1.2 Regulation of signalling by Shp-1 ...... 104

4.2 IMPACT OF RECEPTOR SIGNALLING ON INKT CELL BIOLOGY ...... 106

4.3 FUTURE EXPERIMENTS ...... 108

4.4 CONCLUSIONS ...... 110

REFERENCES ...... 111

List of Figures

Figure 1.1. Signaling pathways and transcription factors important for iNKT cell selection and maturation...... 6 Figure 1.2. Schematic of iNKT cell developmental transitions...... 7 Figure 1.3. Comparison of the docking strategy of an iNKT TCR and classical αβ TCRs...... 21 Figure 1.4. Signaling pathways and transcription factors involved in TCR signaling of iNKT cells...... 25 Figure 1.5. Figure 1.5. Signaling pathways and transcription factors involved in SLAMF1 signaling of iNKT cells...... 29 Figure 1.6. Signaling pathways and transcription factors involved in SLAMF6 signaling of iNKT cells...... 31 Figure 2.1. iNKT TCRs display differential hierarchy of avidity and half-life towards CD1d- lipid complexes...... 52 Figure 2.2. T cell development in TCRβ retrogenic mice...... 54 Figure 2.3. TCR avidity controls iNKT cell selection efficiency in vivo...... 55 Figure 2.4. TCR avidity controls iNKT cell selection efficiency in vivo...... 56 Figure 2.5. Retrogenic mice require CD1d for iNKT cell development and express similar levels of CD1d and SLAMs...... 57 Figure 2.6. Frequency of clonotypic iNKT cells does not depend on survival, proliferation or competition...... 59 Figure 2.7. TCR signal strength is dictated by the binding half-life towards CD1d-ligand complexes...... 61 Figure 2.8. CD1d-lipid-TCR binding half-life impacts iNKT cell effector differentiation...... 63 Figure 2.9. iNKT cell differentiation in TCRβ retrogenic mice...... 65 Figure 2.10. Vβ and Jβ usage by iNKT cell subsets...... 67 Figure 2.11. Stimulation of hybridomas expressing selected iNKT TCRs with lipid antigens presented by plate-bound CD1d or APCs...... 69 Figure 2.12. TCR binding and activation by self-lipids reflects the half-life of CD1d-lipid-TCR interaction...... 71 Figure 2.13. The TCRβ chain does not control the quality of the iNKT cell cytokine response in short term in vivo stimulation...... 73 Figure 3.1. iNKT cells from Shp-1f/f CD4-cre mice lack Ptpn6 and express wild type levels of CD1d, SLAMF1 and SLAMF6...... 86 Figure 3.2. Shp-1-deficient iNKT cells are biased towards iNKT2 and iNKT17 subsets...... 87 Figure 3.3. Shp-1 deficient iNKT cells have a TH2 biased cytokine secretion...... 90 Figure 3.4. Shp-1-deficient iNKT cells drive the expansion of memory Eomes+ CD8 T cells in the thymus but not the spleen...... 91 Figure 3.5. Absence of Shp-1 deficiency does not affect TCR signalling in iNKT cells...... 93 Figure 3.6. Absence of Shp-1 does not affect SLAMF6 mediated upregulation of Egr2 and PLZF...... 95 Figure 3.7. Absence of Shp-1 amplifies cytokine-mediated iNKT cells proliferation...... 97 Figure 4.1. Summary of findings in Chapter 2...... 103 Figure 4.2. Summary of findings in Chapter 3...... 107

List of Tables

Table 2.1. Complementary Determining Regions and J segments from TCRβ chains of selected iNKT TCRs ...... 50

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Abbreviations

AHR – airway hyperreactivity APC – antigen presenting cell BclXL – B cell leukemia 2 like 1 CD – cluster of differentiation CDR – complementary determining region cTEC - cortical thymic epithelial cell DC – dendritic cell DP – double positive thymocyte EC50 – effective concentration of the ligand that produces half the maximal response Egr2 – early growth response 2 GM-CSF – granulocyte macrophage colony stimulating factor Hdac7 – histone deacetylase 7 HEB – Hela E-box binding protein IFN – interferon Ig – immunoglobulin IL – interleukin iNKT – invariant Natural Killer T Cell ITIM – immunoreceptor tyrosine-based inhibitory motif ITSM – immunoreceptor tyrosine-based switch motif Jak – Janus kinase KD – equilibrium constant KOFF – dissociation constant LPS – lipopolysaccharide MAIT – mucosal associated invariant T cell MHC – Major histocompatibility complex NFAT – Nuclear factor of activated T-cells NFκB – nuclear factor kappa-light-chain-enhancer of activated B cells NOD – non obese diabetic PLZF – promyelocytic leukemia zinc finger PMA - asphorbol 12-myristate 13-acetate pMHC – peptide-major histocompatibility complex PTP – protein tyrosine phosphatase RORγt – retinoic acid receptor-related orphan receptor gamma Shp-1 – protein tyrosine phosphatase, non-receptor type 6 Shp-2 – protein tyrosine phosphatase, non-receptor type 11 SLAM – Signaling lymphocytic activation molecule SMACs - supramolecular activation clusters SPR – surface plasmon resonance Stat – signal transducer and activator of transcription T1D – type 1 diabetes TCR – T Cell Receptor TLR – Toll-like Receptor XLP – X-linked lymphoproliferative syndrome αGC – α-galactosyl ceramide

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List of Publications

Cruz Tleugabulova, M., Escalante, N.K., Deng, S., Fiévé S., Ereño Orbea, J., Savage, P.B., Julien, J.P. and T. Mallevaey. 2016. Discrete TCR binding kinetics control invariant Natural Killer T cell selection and central priming. The Journal of Immunology 197: 3959-3969

Escalante, N. K., P. Lemire, M. C. Tleugabulova, D. Prescott, A. Mortha, C. J. Streutker, S. E. Girardin, D. J. Philpott, and T. Mallevaey. 2016. The common mouse protozoa Tritrichomonas muris alters mucosal T cell homeostasis and colitis susceptibility. Journal of Experimental Medicine 213: 2841–2850.

Selvanantham, T., N. K. Escalante, M. Cruz Tleugabulova, S. Fiévé, S. E. Girardin, D. J. Philpott, and T. Mallevaey. 2013. Nod1 and Nod2 Enhance TLR-Mediated Invariant NKT Cell Activation during Bacterial Infection. The Journal of Immunology 191: 5646–5654.

Baglaenko Y., M. Cruz Tleugabulova M., E. Gracey, N. Talaei, K. P. Manion, N. Chang, D. M. Ferri, T. Mallevaey and J. E. Wither. 2016. iNKT cell activation is potentiated by homotypic trans-Ly108 interactions. The Journal of Immunology. In Press

1 Chapter 1: Introduction 1.1 Overview of iNKT Cells

1.1.1 Definition

Invariant Natural Killer T (iNKT) cells are defined as a population of lipid-reactive cluster of differentiation (CD)1d restricted innate-like lymphocytes that express an invariant T

Cell Receptor (TCR) composed of Vα14-Jα18 TCRα chain in mice or Vα24-Jα18 in humans, that pairs mainly with with Vβ8s, Vβ7 or Vβ2 in mice or Vβ11 in humans(1). Their name derives from their original discovery as cells that express both TCR-CD3 complex and the bona fide Natural Killer receptor NK1.1 (CD161, Klrb1c); however subsequent findings made it clear that not all NKT cells expressed NK1.1 and the cells were also present in many strains of mice that seemingly lack NK1.1 expression(1). Their study has been facilitated by the discovery of α- galactosyl ceramide (αGC) as a potent ligand for these cells(2), and the subsequent generation of

CD1d-αGC tetramers that can be used to identify the cells by flow cytometry(3, 4).

Besides iNKT cells, other types of NKT cells exist, and a nomenclature for the cells has been established(5). Type I NKT cells are defined by reactivity to αGC and other α-linked glycosphingolipid species, and include iNKT cells, as well as a much smaller populations of non-

Vα14 (mice) or Vα24 (humans) lymphocytes(6-10). Type II NKT cells are CD1d-restricted but non-reactive to αGC and express a more diverse TCR repertoire(5). Although several reagents have been used to study type II NKT cells(11), none can exclusively and definitively identify the cells, and hence their biology remains much less defined than type I iNKT cells. It is worth noting that while mice only have two CD1d genes (considered Group 2 CD1, based on sequence

1 similarity), humans also contain CD1a, CD1b, CD1c and CD1e (clustered as Group 1 CD1), allowing for a wider presentation of lipids to T cells(12).

iNKT cells are innate-like because of their ability to produce copious amounts of cytokines such as interferon (IFN)-γ, interleukin (IL)-2, IL-4, IL-9, IL-10, IL-13, IL-17, IL-21, and granulocyte macrophage colony stimulating factor (GM-CSF) within minutes to hours after antigen exposure, providing immediate defense against pathogens(13). They also express markers of activated/memory phenotype such as high expression of CD69, CD44, and CD122

(IL-2Rβ-chain) and low expression of CD62L. iNKT cells represent 1-2% of lymphocytes in the thymus and spleen, and 20-30% in the liver of mice. In humans, iNKT cells are mainly enriched in the omentum rather than the liver, where they make up approximately 10% of lymphocytes.

Many of these characteristics are also present in Innate Lymphoid Cells (ILCs) and make iNKT cells part the growing group of “innate-like lymphocytes”, which include γδ T cells, mucosal associated invariant T (MAIT) cells and CD8αα intraepithelial lymphocytes(12, 14). Unlike conventional naïve and central memory T cells, iNKT cells are located mainly within non- lymphoid tissues and have been proposed to function as a bridge between innate and adaptive immunity, amplifying signals that promote rapid antimicrobial response, homeostasis and tissue protection(15).

1.1.2 iNKT Cells in Health and Disease

Due to the wide spectrum of cytokines they release, iNKT cells can have beneficial or deleterious roles in many disease conditions such as infection, cancer, allergy and autoimmunity(13). These have been reviewed extensively (13, 16-21), and I will only provide a snapshot of the literature, to provide context for the work described within this thesis. Two mouse models have been crucial for studies of iNKT cells in disease: 1. CD1d-/- mice that lack

CD1d and hence cannot select iNKT cells during intrathymic development, 2. Jα18-/- mice that lack the Jα18 fragment and hence cannot make a proper rearrangement to produce iNKT cells. It is important to caution the limitations of these models. While CD1d-/- mice lack iNKT cells, they are also devoid of many other CD1d restricted T cells such as Type II NKT cells and some γδ T cells(5, 22). On the other hand, Jα18-/- were thought to only lack iNKT cells, however they were recently found to lack expression of many Jα segments due to epigenetic effects of the neomycin cassette inserted to knock out Jα18, and hence their T cells have a different repertoire of TCRs than wild type mice(23). A new Jα18 deficient mouse devoid of these defects has been recently generated and can be used to corroborate previous findings and initiate more precise studies(24).

Microbial infection(20, 21): iNKT cells have been shown to be essential for clearance of

Streptococcus pneumoniae since Jα18-/- mice died within several days after infection(25). They also provide increased protection against Pseudomonas aeruginosa and Borrelia burgdorferi since CD1d-/- mice show increased bacterial burden(26, 27). Similar protective effects of iNKT cells have been reported for parasites such as Leishmania major and Toxoplasma cruzi as well as viruses such as herpes simplex virus, murine cytomegalovirus, respiratory syncytial virus and influenza virus(20, 21). On the other hand, iNKT cells can be deleterious during infections with

Chlamydia trachomatis, hepatitis C virus and dengue virus(20, 21).

Asthma(18, 28): There is significant evidence that iNKT cells are crucial inducers of allergen, ozone or virus mediated airway hyperreactivity (AHR), a cardinal feature of asthma.

Interestingly, each type of AHR requires a different subset of iNKT cells. Whereas IL-4/IL-13- producing iNKT cells are crucial in allergen-induced AHR, IL-17 producing iNKT cells mediate ozone induced AHR, and CD4- iNKT cells have a role in virus induced AHR(18). Studies of iNKT cells in asthmatic patients have been mainly correlative, and thus it is hard to deduce their

3 particular involvement(18, 28). However iNKT cells represent an attractive target since current corticosteroid treatments create a broad immunosuppressive effect(18).

Autoimmune diseases(16): There is a well-established beneficial role of iNKT cells in Type 1

Diabetes (T1D). iNKT cell frequency is lower in non obese diabetic (NOD) mice, which spontaneously develop diabetes. Increasing iNKT cell numbers in NOD mice by transgenesis or adoptive transfer of iNKT cells rescues mice from diabetes onset. This is thought to occur due to cell-cell mediated inhibition of effector CD4+ T cells by iNKT cells. It has also been shown that

αGC treatment can rescue diabetes onset through recruitment of tolerogenic DCs to the pancreas. iNKT cells have been implicated in many other autoimmune diseases such as multiple sclerosis, lupus erythematosus and atherosclerosis, however studies are conflicting as to whether there is a pathogenic or beneficial role for iNKT cells in these diseases.

Tumor Immunity(17): The anti-tumor effect of iNKT cells is evident from the initial discovery of αGC as a compound that could prolong the life of mice in a B16 melanoma tumor model(29).

This protection is in large part mediated by the copious amount of IFN-γ released upon αGC stimulation. Yet, several trials of αGC or αGC-pulsed DCs showed only minimal efficacy(30).

Antigens that induce even stronger IFN-γ response have been synthesized, such as α-C- galactosylceramide and DB06-1(31-33). With all of these antigens however, iNKT cells switch from producing TH1 cytokines to producing IL-10 after several rounds of activation and therefore lose their anti-tumor properties(32). iNKT cells have also been shown to directly lyse tumor cells through perforin and granzyme B. Importantly, Jα18-/- mice present much higher amounts of spontaneous tumors than wild type mice, suggesting we have yet to discover the best way to use iNKT cells in cancer immunotherapy(17).

1.2 iNKT Cell Development and Regulation

1.2.1 Development of iNKT Cells

iNKT cells derive from the same common lymphoid progenitors as T cells and go through a CD4 CD8 double positive (DP) stage(34-37). Their ontogeny differs in mice and humans(14). Whereas in humans the embryonic thymi contains the highest frequency of iNKT cells, in mice iNKT cells reach their peak of expansion around 3 weeks of age(14). The lineage divergence of iNKT cells from classical T cells is mediated by the stochastic rearrangement of the Vα14-Jα18 TCRα chain at the double positive (DP) stage(14) (Fig. 1.2). Because of the distal position of Jα18 in the Jα region in relation to the Vα and Jα loci, a Vα14-Jα18 TCRα often occurs as the process of secondary rearrangements(38). Therefore, factors that contribute to DP thymocyte survival such as Hela E-box binding protein (HEB, Tcf12)(39), retinoic acid receptor- related orphan receptor gamma (RORγt, Rorc)(36, 37), B cell leukemia 2 like 1 (BclXL,

Bcl2l1)(36, 37), myeloblastosis oncogene (c-Myb, Myb)(40) and histone deacetylase 7 (Hdac7,

Hdac7)(41) are crucial for iNKT cell development(42). After the DP stage, iNKT cell development deviates from that of classical T cells starting with their selection by agonist signals on DP thymocytes rather than on thymic epithelial cells(43). Namely, iNKT cells are selected through TCR engagement of CD1d, as well as homotypic interactions of the signalling lymphocytic activation molecule (SLAM) receptors SLAMF1 and SLAMF6(34, 44, 45). Several pathways such as the nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB)(46-

51) and Calcineurin/Nuclear factor of activated T-cells (NFAT)(52), and transcription factors such as promyelocytic leukemia zinc finger (PLZF, Zbtb16)(53, 54) and early growth response 2

(Egr2, Egr2)(52, 55, 56) are induced at this point and have been shown to be particularly important for development of the iNKT lineage (Fig. 1.1).

Figure 1.1

Specific labels: RasGRP PIP2 PLC-γ (46, 47) Kinase MALT1 CARMA BCL 10 Phosphatase DAG Transcrip,on factor PKCθ (ac,ve form) (46, 48) Ras (46) PA DKG IP3 IKKβ IKKα IKKγ IP3R Erk1/2 2+ (55, 57) CaN Ca NFAT IκBα (52) Ca2+ (46, 48-51) NFκB Let-7 (39) microRNAs Id2 NFAT E2A AP1 Id3 HEB (62) (58, 59) NFκB PLZF Egr2 (53, 54) c-Myc (52, 55, 56) Nucleus IL-2Rβ (60, 61)

Figure 1.1. Signaling pathways and transcription factors important for iNKT cell selection and maturation. The signaling pathways and transcription factors that lead to iNKT cell selection have been derived from gene knockout mice and knowledge from classical T cell signaling pathways. Evidence for the involvement of specific pathways on iNKT cell development is indicated by the reference number.

Two models of iNKT cell maturation have been proposed(63, 64) (Fig. 1.2). The first proposes that once selected, iNKT cells undergo a stepwise linear maturation starting with a stage 0 (CD24hi, CD44-, NK1.1-) right after selection, followed by stage 1 (CD24lo, CD44-,

NK1.1-), stage 2 (CD24lo, CD44+, NK1.1-) and stage 3 (CD24lo, CD44+, NK1.1+)(65-67). The second model states that at the time of or after positive selection, iNKT cells segregate into functionally discrete iNKT1 (T-bet+, PLZFlo, RORγt-), iNKT2 (T-bet-, PLZFhi, RORγt-) and

Figure 1.2

iNKT TCR pI3K AKT iNKT17 mTOR γ CD1d ROR t SLAMF1/F6 IL-7

IL-17RB+ iNKT2 PLZF

IL-15

HEB RORγt IL-17RB- Bcl-x L CD122 c-Myb CaN/NFAT T-bet Hdac7 NFκB Itk/Rlk Egr2 NFκB iNKT1 PLZF mTORC1 T-bet DN DP Stage 0 Stage 1 Stage 2 Stage 3 CD24 CD44 T cell development iNKT lineage development NK1.1

Figure 1.2. Schematic of iNKT cell developmental transitions. iNKT cells develop from DP thymocytes upon rearrangement of the TCRα into a Vα14-Jα18 and selection by other DPs expressing CD1d and SLAMF1/6. Two models of iNKT cell development have been proposed. The first suggests that iNKT cells undergo a stepwise linear progression, starting with a stage 0 right at the point of selection, and transitioning to stages 1 through 3. These stages are characterized by expression of surface markers CD24, CD44 and NK1.1 (bottom boxes). The second model suggests that at the time of, or shortly after selection, iNKT cells segregate into IL- 17RB+ or IL-17RB-, which then lead to subsets (iNKT1, iNKT2 and iNKT17) characterized by the indicated transcription factors. Dotted boxes represent molecules important for the selection (green) particular transitions (red) during the development of iNKT cells.

- int + iNKT17 (T-bet , PLZF , RORγt ) subsets that preferentially produce TH1, TH2 or TH17 cytokines, respectively(68, 69). Furthermore, there are iNKT10 and iNKTFH subsets that arise in the periphery under the proper stimulations conditions(70-72). Recent gene expression studies and epigenetic profiling largely supports the subset model of differentiation as each subtype had a distinct gene expression profile(73-75), yet it is likely that both pathways are interrelated. For example, the iNKT1 subset is equivalent to stage 3 iNKT cells. Furthermore, it was noted that stage 1 and stage 2 iNKT cells are composed of two populations, IL-17RB+ cells that make up iNKT2 and iNKT17 subsets, and IL-17RB- cells that are progenitors of the iNKT1 subset(69). It is also worth noting that the subset model is not completely set in stone. First, although each subset has a tendency to produce particular cytokines, when stimulated with a strong stimulus such asphorbol 12-myristate 13-acetate (PMA)/ionomycin or αGC, a large proportion of these cells will co-produce cytokines of other subsets, particularly IL-4 and IL-13(69, 76), and each subset expressed some cytokines of other subsets(73, 75). Furthermore, Kronenberg and colleagues found that the iNKT2 subset was composed of two subpopulations, in which one expressed a large amount of genes involved in cell cycle progression, indicating that it is likely a progenitor of other iNKT cells(73). Interestingly, each subpopulation expresses particular cytokine receptors: iNKT1 expresses IL-12R, iNKT2 expresses IL-17RB and iNKT17 expresses

IL-23R(69). When stimulated with the corresponding cytokine, each subset produced only the cytokine corresponding to their subset(69). Therefore, it is interesting to speculate that upon cytokine stimulation, each subset will produce specific cytokines, yet under antigen stimulation all iNKT cells have the potential to produce all cytokines.

Many of the genes and pathways involved in each step of iNKT maturation have been characterized(63, 64, 77) (Fig. 1.1, Fig. 1.2). A crucial lineage specifying transcription factor for iNKT cells is PLZF, which is largely controlled by Egr2(53, 54, 56). The absence of PLZF

8 significantly reduces the frequency of iNKT cells, and those that remain show a naïve phenotype(53, 54). On the other hand, transgenic expression of PLZF under the CD4 promoter drives classical T cells to acquire effector functions and travel to non-lymphoid tissues(54). The

NFkB pathway is also involved early during the iNKT cell selection process, since deletion of inhibitor of kappaB kinase β (IKKβ, Ikbkb) at the DP stage leads to a complete absence of iNKT cells in the periphery while sparing conventional T cells(46). Right after selection, iNKT cells undergo large rounds of proliferation as the cells mature from stage 0 to other stages or subsets.

The transcription factor myelocytomatosis oncogene (c-Myc, Myc) is necessary for this progression, although there are conflicting results as to whether it controls the proliferation of the cells(60, 61). Maturation of iNKT cells involves further changes in genetic programs that are particular to the stage or subset. For example, let-7 microRNAs bind to Zbtb16 mRNA to degrade it, thus down-regulating expression of PLZF and inducing an iNKT1 phenotype(62).

The transcription factor kruppel-like factor 2 (KLF2, Klf2) also supports differentiation of iNKT1 supposedly by promoting quiescence of PLZF+ cells, deterring their survival and proliferation(78). The E proteins E2A and HEB bind to the promoter of Zbtb16, and promote development of iNKT2 and iNKT17(39). In turn the E protein inhibitors Id2 and Id3 act synergistically to regulate iNKT cell frequency, and promote an iNKT1 phenotype(58, 59). The growing understanding of the genetic programing of iNKT cells during development has been the subject of excellent reviews(63, 77, 79).

1.2.2 Activation of iNKT Cells

iNKT cells can be activated through TCR-mediated signals induced by foreign or self antigens, and/or cytokine-mediated signals.

Many antigenic glycolipids have been isolated from bacteria, including α-linked glucuronic or galacturonic acids extracted from Sphingomonas spp(80-82) and α-linked glucosyl or galactosyl diacyglycerols obtained from Borrelia burgdorferi(83) and Streptococcus pneumonia(84). These antigens can load on CD1d either directly on the surface or upon processing by antigen presenting cells (APCs) and are potent stimulants for iNKT cells.

Importantly, both inhibitory and stimulatory lipid antigens for iNKT cells have been identified from the symbiotic bacteria Bacteroides fragilis, suggesting that the microbiota can directly modulate activation of iNKT cells and consequently affect diseases such as iNKT cell-mediated oxazolone-induced colitis(85, 86).

On the other hand, many pathogens can induce iNKT cell activation while being seemingly devoid of specific CD1d-restricted lipid ligands(21) and iNKT cells can be activated in non-infectious contexts(87). The need for CD1d in many of these cases indicated that antigen presenting cells must present stimulatory self-antigens to iNKT cells. This is thought to occur through metabolic changes on the APC. Toll-like receptor (TLR) stimulation, for example, turns dentritic cells (DCs) into potent activators of iNKT cells through a mechanism dependent on

CD1d and cytokines(88-90). Thymocytes also present self-antigens for iNKT cell selection and likely have a different lipid repertoire than other CD1d-expressing cells. Searching for iNKT self-antigens has been challenging due to the large amount of background involved with genetic alteration of metabolic pathways and extraction of lipids from CD1d by biochemical assays(13).

Furthermore, it was generally accepted that mammals could not make α-linked glycolipid anomers, which for a while focused the search to β-linked lipids that have less avidity towards the TCR, making it harder to detect a response. Initial candidate self-lipids included isoglobotrihexosylceramide (iGb3)(81, 91) and β-glucosylceramide(92, 93), since they were shown to be upregulated in APCs in response to lipopolysaccharide (LPS). However, Kain et al.

10 recently showed highly purified β-glucosylceramide was not stimulatory for iNKT cells, and antibodies against CD1d-α-GC blocked stimulation by commercially available β- glucosylceramide, suggesting contamination with α-anomers(94). Instead, using sensitive T cell stimulation and enzymatic assays, the authors suggest that α-linked glycolipids can in fact be made by mammalian cells; and α-galactosyl and glucosylceramides represent important self- antigens for iNKT cells during selection and activation(94).

iNKT cells can also be activated by cytokines such as IL-12(88, 95), IL-18(96, 97), IL-

23(98, 99) and IL-25(100, 101). IL-12 in particular appears to be required for a full iNKT cell response during many bacterial infections, even if potent antigens are present(102, 103).

Furthermore, it has been shown that, at least in the context of IL-12 and IL-18, iNKT cells can be activated by cytokines in the absence of TCR signals(96, 104-106). During an immune response, this mechanism would allow for a wider sensing capability by iNKT cells and hence amplification of the innate response to pathogens. One study suggested that iNKT cells are intractable to polarization given that absence of IFN-γR or IL-4R did not bias IFN-γ or IL-4 production by the cells(107). It is clear though that cytokines that can activate and influence iNKT cells, and thus their plasticity in response to cytokine combinations remains to be investigated.

Although not very well studied, iNKT cells can also be activated through their NK receptors, such as NK1.1(108), NKG2D(109) and members of the Ly49 family(110).

1.2.3 Homeostasis and Regulation of iNKT Cells

Unlike naïve T cells which need MHC for their homeostasis, iNKT cells are largely independent of CD1d in the periphery in order to proliferate and survive(111-113). Rather, the cytokines IL-7 and IL-15 are crucial for these processes(112, 113). Interestingly, each iNKT cell

11 subset has different cytokine requirements. Whereas iNKT1 depend on IL-15 for their development and homeostasis(69, 112), iNKT17 cells rely exclusively on IL-7(114). The requirements for iNKT2 cell homeostasis are still undefined, but could involve ICOS/ICOSL interactions given that ICOS-/- iNKT cells have impaired production of IL-4 and IL-13(115). As with classical T cells, the cytokine IL-2 is crucial for the expansion of activated iNKT cells(116).

iNKT cells are mainly tissue-resident cells with limited circulation, which at least in the liver is maintained through the interaction between the integrin lymphocyte function-associated antigen 1 (LFA-1, CD11a) and its ligand intercellular adhesion molecule 1 (Icam-1, CD54)(117).

The major location largely depends on the subtype: iNKT1 are mainly found in the liver and spleen(3, 4, 118), iNKT2 are primarily found in the lung and mesenteric lymph nodes(69, 101,

118), iNKT17 are mainly present in the peripheral lymph nodes and skin(101, 118, 119), iNKTFH can be found within germinal centers(70, 120) and iNKT10 are mainly found in the adipose tissue(71). Using histocytometry, Hogquist and colleagues were able to further dissect the location of iNKT1, iNKT2 and iNKT17 within the thymus, spleen and mesenteric lymph node(118). Whereas iNKT1s are primarily found in the cortex of the thymus and red-pulp of the spleen, iNKT2s are mainly found in the thymic medulla and T cell zones of secondary lymphoid organs. This significantly affects their activation, since intravenous αGC injection mainly targeted iNKT1s in the spleen and liver, inducing IFN-γ and IL-4 responses; whereas oral administration lead to activation of iNKT2 cells in the mesenteric lymph node leading to local

IL-4 secretion(118). Interestingly, so far only iNKT1, iNKT2 and iNKT17 cells have been found in the thymus, indicating that iNKTFH and iNKT10 are either induced in the periphery from other subsets, or that their progenitors are present at very low frequency in the thymus(63). The diverse tissue distribution of iNKT subsets is evident from the chemokine receptors expressed by each subset, which likely instruct their homing upon exit from the thymus(73, 75). iNKT17 are the

only subtype that expresses transcripts for CCR6, a receptor that is also expressed on TH17 cells and could similarly influence iNKT cell migration during inflammatory conditions(121). On the other hand, CCR4, CCR9 and some CCR7 transcripts are mainly present on iNKT2(73, 75).

These receptors primarily instruct homing to the skin, gut and lymphoid tissues, respectively, which coincides with some of the prominent locations for iNKT2s. CXCR3 and CCR5 were predominant on iNKT1, and likely play a role in the recruitment of the cells to sites of inflammation. The chemokine receptor CXCR6 is expressed on all subsets, which could indicate a unifying, although yet undefined, requirement for all iNKT cells(73, 75, 122). Despite this heterogeneous chemokine receptor expression, a study of iNKT cell chemotactic response observed little or no migration of NK1.1+TCRβ+ cells to CCR5 or CXCR6 ligands, suggesting a complex regulation of downstream elements in the chemokine response, and the need to supplement expression data with functional experiments(123). Alternatively, chemokine receptors could play roles other than homing. For example, CXCR6 and CCR5 have been suggested to promote survival or apoptosis of iNKT cells, respectively(122).

1.2.4 Modulation of the Immune System by iNKT Cells

Due to their innate-like properties, iNKT cells influence many other cell types both at steady state and during infection.

iNKT cells, particularly the iNKT2 subset, can produce IL-4 at steady state which stimulates the expansion of memory (CD44hi Eomes+) CD4 and CD8 T cells(124). This is evident in mice genetically deficient for KLF2(125) and Id3(126) that contain increased numbers of iNKT2 cells and memory T cells. Furthermore, certain mouse strains, such as Balb/c and 129, contain much higher frequencies of iNKT2, which correlates with their elevated frequency of

Eomes+ CD8 T cells and elevated levels of IgE(68). Therefore, iNKT cells are at the core of the

T helper bias in different strains of mice(68).

During infection, the bi-directional interaction between dendritic cells (DCs) and iNKT cells has wide implications for the outcome of the immune response. As previously mentioned, activation of DCs through pattern recognition receptors leads to production of cytokines such as

IL-12 and IL-18, and up-regulation of stimulatory self-lipids species. This in turn activates iNKT cells, which produce numerous cytokines and upregulate co-stimulatory molecules. In particular, the CD40-CD40L interaction that ensues between DCs and iNKT cells leads to further production of IL-12 and IL-18 by DCs and transactivation of NK cells, enhanced T cell responses and licensing of DCs(127). Importantly, the structure of the lipid antigen can significantly influence these interactions. Short polyunsaturated glycolipids can load to CD1d at the cell surface and promote an early, yet short-lived, activation of iNKT cells(128-130), which induces IL-4 and IFN-γ secretion by iNKT cells, but does not allow for the prolonged CD40-

CD40L interaction between DCs and iNKT cells required to induce IL-12 mediated activation of

NK cells(131). Because NK cells are a key source of IFN-γ, failure in their activation results in an overall TH2-biased response. In contrast, lipid antigens that bind deep inside the CD1d groove promote sustained interactions with iNKT TCRs and allow polarization towards a TH1 response(132). Although one study suggested that lipid variants bias the immune response through presentation on different APCs(128), a recent study proposed that only CD8α+ DCs are required for this bias(133). When loaded in this APC subset, TH2 biasing lipids induce preferential up-regulation of inhibitory molecules PDL1 and PDL2, whereas TH1 biasing lipids induce activating molecules such as Rae-1, CD70 and CD86(133).

iNKT cells can also influence B cell responses by providing cognate B cell help(134). In particular, the iNKTFH subset has been shown to produce IL-21 and lead to rapid immunoglobulin production, although it does not provide long-term memory(70, 120). In turn, B cells can regulate the frequency and activation of peripheral iNKT cells as demonstrated in an autoimmune model where Fas is specifically deleted on B cells(135).

iNKT cells can influence macrophages by enhancing phagocytosis and bacterial clearance through IFN-γ production during Pseudomonas aeruginosa(26) or Mycobacterium tuberculosis(136) infection. They also induce the differentiation of M2 macrophages during chronic lung disease or at steady state in adipose tissue(137), and kill tumor-associated macrophages(138). In turn, macrophages such as Kupffer cells and stellate cells can present lipids to iNKT cells playing an essential role in tissue homoeostasis(139, 140). iNKT cells can also recruit and influence neutrophils during antitumor or inflammatory responses(13).

1.2.5 Integration of Signals for iNKT Cell Development and Function

It is clear that iNKT cells represent a unique T cell lineage. The microenvironment in which the cells develop plays a crucial role in enforcing their genetic program and activation status. During development, for example, let-7 microRNAs are upregulated by IL-15, Vitamin-D

Receptor and retinoic acid(62), whereas Id3 is thought to be upregulated by the Erk MAPK cascade induced upon TCR ligation(141). It is yet unclear how all these signals get integrated to shape iNKT cell biology. An emerging factor that could regulate these processes is mechanistic target of rapamycin complex (mTOR). Deletion of mTOR completely obliterates iNKT cells, yet deletion of specific members of this complex (Raptor, Rictor) display only partial deficiencies(142-145). Signals from TCR, SLAM family receptors and cytokine receptors are

15 widely recognized for their essential role in iNKT cell development and will be the subject of subsequent sections.

1.3 The T Cell Receptor

1.3.1 Antigen Discrimination by Classical TCRs

The TCR is an intricate molecular structure involving α and β chains that recognize the ligands, and the CD3 complex composed of γ, δ, ε and ζ subunits that are essential for expression at the cell surface, and to transduce signals that activate the cell. The study of classical T cells, which recognize peptide antigens in the context of major histocompatibility complex (MHC) class I or II molecules, has provided most of our insights on how the TCR functions. This has revealed that the TCR has a remarkable selectivity and sensitivity for peptide-MHC complexes(146-148). First, it has been suggested the TCR can recognize down to a single agonist peptide in a pool of self-peptides being presented on MHC on the surface of cells(149-152).

Second, as seen in the context of T cell development, the difference in affinity of agonist versus antagonist peptides is small, yet can lead to widely different outcomes such as positive or negative selection(153). Third, because T cells perform many functions such as cytokine release, proliferation and memory formation, the cell needs to process different antigenic signals into appropriate responses(154).

Importantly, the mechanism by which a TCR gets triggered to start signalling is still unclear(146). Much evidence indicates that a stable TCR-Lck-Zap70 complex needs to be formed in order for productive signalling to occur; yet how TCR engagement induces this remains unknown(147). It has been suggested that triggering can occur by aggregation of TCRs, a conformational change in the TCR or segregation of inhibitory molecules away from the TCR synapse(146). Because support and contradiction for each model can be found in the literature,

16 no clear consensus has been reached(146, 147). The mechanism of antigen discrimination and

TCR triggering are interrelated, and understanding one would significantly facilitate solving the other. For the purpose of this thesis, I will focus on quantitative models of antigen discrimination.

Occupancy model. The occupancy model predicts that the number of receptors engaged by peptide-MHC (pMHC) complexes will determine the strength of signalling that ensues within the cell. Since the number of receptors engaged is determined at equilibrium, this model is based on numerous studies that found the EC50 (effective concentration of the ligand that produces half the maximal response from the T cell) could be predicted by the KD (equilibrium constant), of the

TCR-pMHC interaction(155-158).

Kinetic proofreading model. The kinetic proofreading model proposes that TCR signalling involves a series of reversible biochemical modifications (such as Lck phosphorylation of

Zap70) that can only reach completion if the TCR is engaged to its ligand for long enough. In such a model, the dissociation constant (KOFF) determines whether the T cell gets activated and the strength of such activation(159). Because each step would augment the selectivity of the ligand(160), this model has been thoroughly supported and revisited since first proposed for TCR signalling in 1995 by Timothy McKeithan(161-164).

Serial Triggering Models. The finding that a single agonist pMHC can induce signalling led to the hypothesis that a single pMHC on the presenting cell could serially bind multiple TCRs on the T cell as a method of signal amplification(165). Thus, the overall binding and re-binding events dictates the strength of signalling. As proposed by Lever et al., this system would make sense mathematically if each TCR can only send a limited amount of signalling to the cell. Thus, optimal signalling would occur if multiple TCRs get engaged(148). This was termed the “kinetic

17 proofreading with limited signalling model” and proposes that rather than activation increasing proportional to the KOFF, activation will be optimal at an intermediate KOFF(162, 165-169).

Because several papers have seen an optimal KOFF at low, but not high, ligand concentrations(170, 171), Lever et al also considered a “kinetic proofreading with sustained signalling” model, which posits that each TCR can continue to signal after disengagement of the pMHC from the TCR(148). Thus, at low ligand concentration signalling would increase as more

TCRs are engaged, but at high ligand concentration the number of TCRs engaged would be maximized.

Kinetic Proofreading with amplification through endogenous ligands. Signal amplification could also be explained if one bound TCR can trigger signalling on bystander TCRs that interact weakly to endogenous pMHC complexes(152, 172). Indeed, the presence of self-pMHC is crucial for homeostasis of T cells in the periphery (173, 174) and the ζ chain of the TCR is constantly phosphorylated at steady state(175, 176); indicating that endogenous peptides can transmit some signals to T cells.

Lck come&stay/signal duration An important aspect of classical T cell signalling ignored by many models is the presence of the CD4 and CD8 co-receptors. Indeed most Lck on T cells is constitutively bound to these co-receptors and is crucial in reactivity towards self-antigens(177-

179). Recently, Palmer and colleagues suggested that in addition to the kinetic proofreading model, the rate of co-receptor bound Lck delivery to the TCR is what determines the overall

TCR signal. Hence, T cell negative selection depends on both, the number of TCRs engaged and the time they remain bound to the pMHC after initial triggering(180).

Evidence for and against all of the models can be found in the literature, yet most studies differ on the TCR biophysical properties used and activation readout measured, making it

18 difficult to obtain a complete picture. Moreover, due to the challenging task of analyzing interactions between cells, most studies extrapolate interpretations from suboptimal conditions.

For example, many studies have analyzed T cell activation on cell lines or cultured cells, which do not reflect the steady state ζ chain phosphorylation of T cells in vivo(147). Furthermore, determination of biophysical parameters of TCR-pMHC binding is usually done through surface plasmon resonance (SPR), in which neither of the two molecules is confined to the surface of a lipid bilayer membrane as it is on cells. Two studies found that the dissociation time measured on cells (2D kinetics) is shorter than that measured by SPR (3D kinetics)(181, 182). Furthermore, it is likely that a certain amount of force is exerted during binding of the TCR, which has been suggested to shorten dissociation time of antagonist peptides forming “slip bonds” and extend dissociation of agonists forming “catch bonds”(183). Lastly, interactions at an immune synapse likely obstruct dissociation of molecules after disengagement due to slow mobility of the cells.

Thus, it has been proposed that a single TCR is likely to re-bind the same pMHC molecule several times. In this scenario, overall dissociation time would depend on both the KOFF and the

KON, leading to the proposition that this “aggregated half-life” is more predictive of eventual

TCR signalling(162, 184-186).

Although the various models described attempt to explain a mechanism for the sensitivity and selectivity of TCR signalling, they do not address how it leads to the numerous functions that

T cells achieve. To tackle this, many in vivo studies have tried to correlate the biophysical parameters of TCR binding to different T cell functions(154). For several downstream events, a clear picture is emerging. For example, evidence suggests that low TCR half-life, antigen dose or

TCR signal strength lead to a TH2 response(187-190) whereas higher aggregated half-life lead to a TFH response(191). Additionally, during CD4 T cell memory formation, increased half-life but not affinity is critical for promoting long lived CD4 T cells(192). Understanding how TCR

19 parameters bias T cells towards specific responses could lead to better design of vaccines and immune cell therapies.

Although the biology of iNKT cells differs in numerous ways from that of conventional T cell, the knowledge on T cells has guided much of the studies on this peculiar T cell population.

1.3.2 Structure of the iNKT T Cell Receptor

TCRs interact with pMHCs through 6 distal amino acid loops named complementarity- determining regions (CDRs), in which the CDR1α/β and CDR2α/β are germline-encoded and generally bind to the MHC helices, whereas the CDR3α/β varies tremendously due to junctional diversity and are mainly involved in antigen recognition(193). In a TCR-peptide-MHC interaction, the TCR is positioned perpendicular to the pMHC complex, with a diagonal orientation, and a relatively large footprint typically involving residues from all the 6 CDRs(193)

(Fig. 1.3). In contrast, the iNKT TCR sits at the extreme end of the CD1d molecule, with a parallel orientation relative to CD1d, and a small footprint(194) (Fig. 1.3). Specifically, the invariant TCRα chain dominates the interaction, with the CDR1α contacting the lipid antigen and

CDR3α interacting with the lipid and the two CD1d helices. The TCRβ chain does not interact with the antigen, but the CDR2β makes crucial contacts with the CD1d α1 helix(195, 196). The hypervariable CDR3β loop typically is the furthest away from the CD1d molecule and is not essential for binding. However, it has been shown to make contacts with CD1d in some instances and compensate for low affinity CDR1β and CDR2β(197-200).

Figure 1.3

Figure 1.3. Comparison of the docking strategy of an iNKT TCR and classical αβ TCRs. (a) View of the entire TCR-ligand complexes shows that the iNKT TCR docks to the side on top of the F’ pocket of CD1d, whereas classical TCRs dock centrally on top of MHC. (b) View looking down onto CD1d and MHC shows how the iNKT TCR docks parallel to CD1d α helices, whereas classical TCRs dock diagonal to the MHC α helices. Figure obtained from Rossjohn et al. 2012(206).

iNKT cells do not exhibit antigen discrimination as classical T cells do. It was initially found that the mouse iNKT TCR shows reactivity towards human CD1d and vice versa(201).

Futhermore, CD1d tetramers loaded with certain ligands such as αGC, brightly stain all iNKT cells, whereas other ligands, such as GSL-1, show a spectrum from low to high binding(80).

Numerous mutagenesis and crystallography studies since then have shown that the recognition of

CD1d-lipid complexes by iNKT TCRs is very well conserved regardless of the species(202), lipid antigen(203, 204) or the TCRβ chain used(202, 204). Importantly, all TCRs use the same docking strategy in which only the TCRα mediates contact with the antigen. Variations in the

TCRβ chain shape the strength towards particular antigens through the collaboration between the

CDR2β and CDR3β(204). Thus, the iNKT TCR recognizes ligands in a “one size fits all” fashion, such that TCRs with stronger avidity will bind a wider spectrum of ligands than TCRs with weaker avidity. Although a fraction of iNKT cells express the CD4 co-receptor in mice and

CD4 and/or CD8 in humans, it is thought that they do not bind CD1d and hence antigen discrimination by iNKT cells is likely to be co-receptor independent(205). Nonetheless, these co- receptors have been useful markers to define some iNKT cell subtypes(68, 69).

A significant effort has been made to identify ligands that polarize iNKT cells responses, such that they can be used in therapies for diseases that benefit from a clearly defined cytokine cocktail. Although some ligands can bias the immune response in an iNKT cell dependent fashion, none of the parameters of TCR binding have shown to be important, and it is rather the co-stimulatory properties they induce on APCs that skew the immune system (see previous section). Interestingly, no study so far has compared how individual biophysical parameters of

TCR interaction affect the diverse iNKT cell functions. Although Wun et al. elegantly showed how altered lipid ligands affect the binding and activation of iNKT cells, all the interactions studied had the same hierarchy of KOFF and KD and thus it is not possible to distinguish which parameter is more relevant to signal strength(203). Overall, studies so far show that iNKT TCRs bind ligands in fundamentally different ways than classical T cells. Hence, investigating how they get activated would be instrumental for further understanding of how this immune receptor functions.

1.3.3 iNKT TCR Signaling During Development and Activation

The iNKT TCR is essential for the function of the cells as it is the rearrangement of this receptor that directs the cells into the innate-like lineage(207). iNKT cells receive stronger TCR signals than classical T cells during selection(208) and require a full set of ITAM motifs for their development(209). Accordingly, their development requires TCR pathways distinct from that of classical T cells. Interestingly, these pathways resemble that of naïve T cell priming in the periphery, given iNKT cells differentiate into functionally discrete subsets in the thymus, and mutations in many signalling intermediates required for T cell antigen activation selectively dampen development of iNKT cells. Nonetheless, iNKT cells can also be negatively selected by very strong TCR signals(210-212). Due to the low frequency of iNKT cells, biochemical analysis of TCR signalling has not been possible. Therefore, the generation of knockout mice and knowledge gained from TCR signalling on classical T cells has informed our current understanding on the TCR signals required for iNKT cell development and function. Here I outline key pathways and genes downstream of the TCR that are essential for iNKT cell development (Fig. 1.4).

A key culmination of TCR signals at the earliest point (Stage 0) of iNKT cell selection is the expression of PLZF(53, 54), which is induced by Egr2. As shown by ChIP-seq analysis, Egr2 directly binds to the PLZF promoter and its expression precedes that of PLZF after TCR triggering(56). PLZF- and Egr2-deficient mice have a severe iNKT cell developmental block at stage 0, although a small fraction of cells can proceed to the later stages(52, 56). TCR signals induce Egr2 expression largely through the Calcineurin/NFAT(52), and Ras-Erk1/2-AP1 pathways(55, 57), and inhibition of either leads to a dramatic reduction of iNKT cells.

Interestingly, Egr2 is also required for the transition from stage 2 (NK1.1-) to stage 3 (NK1.1+),

23 or the acquisition of an iNKT1 phenotype(56). It facilitates this transition by inducing IL-2Rβ gene expression, making the cells responsive to IL-15(56).

The TCR induced NFkB pathway is also important for iNKT cell development, and members of this pathway appear to discreetly influence iNKT cell development or function. For example, lack of protein kinase C theta (PKCθ, Prkcq) selectively diminishes thymic iNKTs, B cell leukemia 10 (Bcl10, Bcl10) is required for peripheral iNKT cells and caspase recruitment domain family 11 (CARMA1, Card11) deletion shows no iNKT cell defect(46, 47). Both PKCθ and IκBα degradation are important for stage 2 to stage 3 transition (iNKT1 differentiation)(48,

49).

Similarly, other TCR signalling molecules, IL2 inducible T cell kinase (Itk, Itk) and

TXK tyrosine kinase (Rlk, Txk), act synergistically to promote maturation of iNKT cells to the stage 3 or iNKT1 subset(213, 214). The microRNA miR181 has been proposed to act as a regulator of TCR signalling during iNKT cell development(215), however other studies have not found defects in TCR signalling in miR181 deficient thymocytes(216).

Because many TCR signalling genes affect iNKT cell development, it has been difficult to address their role in iNKT peripheral activation. Using IKKβ inhibitors, it has been shown that the NFκB pathway is critical for iNKT IL-4 and IFN-γ secretion in vivo and in vitro(46). Other studies have stimulated iNKT cells remaining in mice deficient for PKCθ, Itk and Rlk to show that these molecules are crucial for overall cytokine production by iNKT cells(46, 48, 214, 217).

Figure 1.4

Specific labels: Kinase CD1d

Phosphatase Vα Vβ Transcrip,on factor (ac,ve form) Cα Cβ

PIP2 PLC-γ RasGRP Lck (46, 47) MALT1 CARMA LAT BCL 10 DAG Rlk PKCθ Itk ZAP-70 (213, 214, 217) (46, 48) Ras PA DKG IP3 IKKβ (46) IKKα IKKγ IP3R Erk1/2 2+ CaN Ca (55, 57) NFAT IκBα (52) Ca2+ (46, 48-51) NFκB

AP1 Id3 Id2 NFAT (58, 59) NFκB PLZF Egr2 (53, 54) c-Myc Nucleus (52, 55, 56) IL-2Rβ (60, 61) (56)

Figure 1.4. Signaling pathways and transcription factors involved in TCR signaling of iNKT cells. The signaling pathways and transcription factors that lead to iNKT cell selection have been derived from gene knockout mice and knowledge from classical T cell signaling pathways. Evidence for the involvement of specific pathways in TCR signaling of iNKT cell is indicated by the reference number.

1.4 SLAM Family Receptors

1.4.1 Overview

The SLAM family comprises nine type I glycoproteins receptors (SLAMF1-SLAMF9) that participate in various innate and adaptive immune cell functions(218, 219). The SLAM locus is located closely on chromosome 1 and contains all family members, indicating that they arose from gene duplication events. They are also structurally and functionally similar. First, they all contain an extracellular region comprised of two immunoglobulin (Ig)-like domains, with the exception of SLAMF3 that holds four Ig domains. Second, with the exception of SLAMF2 that is

GPI anchored to the membrane, they all contain an intracellular domain with one or more immunoreceptor tyrosine-based switch motif (ITSM) that undergo phosphorylation upon receptor ligation. Lastly, all SLAM receptors engage as self-ligands, with the exception of

SLAMF2 (CD48) that binds to SLAMF4 (2B4). SLAMF1 (CD150), SLAMF6 (Ly108 or NTB-

A) and SLAMF3 (Ly9 or CD229) have been shown to be important for iNKT cell development.

Although there are three known adapters that bind ITSMs on SLAM molecules, only

SLAM-associated adapter (SAP, Sh2d1a) is expressed in iNKT cells(220). Deficiency of SAP in humans causes X-linked lymphoproliferative syndrome (XLP), an immunodeficiency disorder characterized by an inability to mount an effective immune response to Epstein-Barr virus(219).

SLAMs were first implicated in iNKT cell development when it was noticed that SAP deficient mice or XLP patients lack iNKT cells(221-223). Yet, no particular SLAM deficiency showed a complete deletion of iNKT cells, with SLAMF1-/- and SLAMF6-/- showing a slight reduction in the frequency(45). Using mixed bone marrow chimeras to take advantage of the homotypic nature of SLAM interactions, Bendelac and collegues showed that SLAMF1 and SLAMF6 interactions act synergistically and in a SAP dependent manner to induce the iNKT cell lineage at the point of selection(45). On the other hand, SLAMF3 (Ly-9) is thought inhibit iNKT cell

26 development since in its absence there is a remarkable increase in thymic iNKT cells in the

Balb/c strain(224).

SLAM receptor signalling has been implicated in the regulation of cell-cell interactions, cytokine production and modulation of TCR signal strength(218). Two main non-exclusive models of how SLAMs modulate signalling have been proposed(219). One hypothesis is that, by binding to the ITSM of SLAM molecules, SAP prevents the recruitment of phosphatases such as protein tyrosine phosphatase non-receptor type 6 (Shp-1, Ptnp6) and protein tyrosine phosphatase non-receptor type 11 (Shp2, Ptpn11), thus preventing inhibition of signals at immunological synapses. On the other hand, it has been shown that SAP can actively recruit Fyn, or other kinases, which can then mediate phosphorylation of downstream targets. It is likely that both of these mechanisms are at work depending on the cell type, receptor and type of activation.

Interestingly, Fyn-/- mice have a severe deficiency of iNKT cells while sparing T cells, yet the phenotype is not as severe as in SAP-/- mice, indicating compensation by other kinases or Fyn- independent functions mediated by SAP(45). As with TCR signalling, our current knowledge on the signalling downstream of these receptors that is relevant for iNKT cells has only been derived from a combination of biochemical assays on T cells and studies of knockout mice.

Although some studies have looked at the function of SLAMs on peripheral iNKTs, in most cases it is unclear whether the defect involves developmental alteration of the cells or signalling defects at the time of activation. One study addressed this issue by conditionally deleting SAP in mature iNKT cells(220). This showed that contrary to iNKT cells in SAP-/- mice, lack of SAP was critical for TCR induced cytotoxicity of T and B cells, but not for iNKT cell activation and cytokine secretion in vivo(220).

1.4.2 SLAMF1/CD150

SLAMF1 is highly expressed on DPs, but is upregulated on iNKT cells only after activation(45, 225). It is likely that SLAMF1 modulates TCR signalling on iNKT cells given that a small increase in its expression through transgenesis potentiated selection, yet low expression levels diminished iNKT frequency(226). Indeed, in CD4 T cells, SLAMF1 can prolong PKCθ recruitment to the TCR synapse enhancing parts of the NFκB pathway, which is essential for iNKT cell development(46, 227). SLAMF1-mediated enhancement of PKCθ has also been shown to be important for TH2 cytokine secretion, and this pathway is impaired on iNKT cells when SLAMF1 is removed and enhanced when SLAMF1 is overexpressed(225, 226, 228). This suggests that SLAMF1 enhances TCR induced NFκB activation, promoting iNKT cell selection and TH2 cytokine production. It is important to note that other pathways such as

SHIP/Dok1/Dok2, Akt and NFAT have been proposed to be downstream of SLAMF1 on other lymphocytes and their contribution to the phenotype of SLAMF1-/- iNKT cells remains to be explored(219) (Fig. 1.5).

Figure 1.5

Specific labels: Kinase CD1d SLAMF1 Phosphatase Vα Vβ Transcrip,on factor Cα Cβ (ac,ve form)

Fyn PIP2 PLC-γ RasGRP Lck SHIP

DOK1 MALT1 SAP CARMA (227) LAT BCL 10 DOK2 DAG Rlk Itk ZAP-70 Shp-2 PKCθ (229) (227) Ras PA DKG IP3 RasGAP IKKβ IKKα IKKγ IP3R Erk1/2 2+ CaN Ca NFAT MAPK IκBα Ca2+ NFκB

AP1 Id3 Id2 NFAT NFκB PLZF Egr2 (225, 227) IL-4 c-Myc Nucleus IL-2Rβ IL-10 IL-17 (225) (226)

Figure 1.5. Signaling pathways and transcription factors involved in SLAMF1 signaling of iNKT cells. The signaling pathways and transcription factors involved during SLAMF1 signaling have been derived from gene knockout mice and knowledge from classical T cell signaling pathways. Evidence for the involvement of specific pathways in SLAMF1 signaling of iNKT cell is indicated in the reference number. For reference, we also show several signaling pathways downstream of the TCR.

1.4.3 SLAMF6/Ly-108/NTB-A

SLAMF6 is highly expressed on DPs and can also synergize with the TCR to induce Egr2 and PLZF(230), which is crucial for iNKT cell development. SLAMF6 ligation leads to phosphorylation of Vav-1 and Cbl on T cells in a mechanism that requires SAP and Fyn(231).

Given that SLAMF6 is constitutively phosphorylated in thymocytes in a contact dependent fashion(232), it is likely that SLAMF6 engagement leads to similar signalling pathways in T cells and DPs. Interestingly, the low frequency of iNKT cells in SAP-/- mice is increased when

SLAMF6 is also removed (SAP-/-SLAMF6-/- mice)(233). Thus, it has been hypothesized that in the absence of SAP, phosphatases can bind to SLAMF6 and inhibit signals that lead to iNKT cell development(233). Interestingly, Veillette and colleagues recently proposed a role for cis interactions between SLAMF6 molecules during NK cell education(234). Given that some iNKT cells subsets express higher levels of SLAMF6 than others, it would be interesting to explore if this type of interaction plays a role in their differentiation and homeostasis(235) (Fig. 1.6).

Figure 1.6

Specific labels: Kinase CD1d SLAMF6 Phosphatase Vα Vβ

Transcrip,on factor Cα Cβ (ac,ve form)

Fyn PIP2 PLC-γ RasGRP Lck MALT1 SAP CARMA LAT BCL 10 DAG Rlk Itk ZAP-70 Shp-1 PKCθ (233, 236-239) Ras PA DKG IP3 Vav-1 IKKβ c-Cbl IKKγ (231) IKKα IP3R Erk1/2 2+ CaN Ca NFAT Signaling IκBα Ca2+ intermediates NFκB

AP1 Id3 Id2 NFAT

NFκB PLZF Egr2 c-Myc (230) Nucleus IL-2Rβ

Figure 1.6. Signaling pathways and transcription factors involved in SLAMF6 signaling of iNKT cells. The signaling pathways and transcription factors involved during SLAMF6 signaling have been derived from gene knockout mice and knowledge from classical T cell signaling pathways. Evidence for the involvement of specific pathways in SLAMF6 signaling of iNKT cell is indicated in the reference number. For reference, we also show several signaling pathways downstream of the TCR.

1.4.4 SLAMF3/Ly-9/CD229

Unlike SLAMF1 and SLAMF6, SLAMF3 is more highly expressed in CD4 and CD8 double negative and single positive cells than in DPs in the thymus(45). SLAMF3-/- mice present no iNKT cell defects in the C57BL/6 background(240), but have a six fold increase on iNKT

31 cells in the Balb/c background, leading to an increase in innate-like CD8 T cells(224). This indicates that SLAMF3 acts as an inhibitory receptor for iNKT cell development in a strain- specific manner. It has been shown that due to its four Ig domains, SLAMF3 localizes in the periphery of the immunological synapse(218). This led to the hypothesis that it could act by segregating SAP from SLAMF1 and SLAMF3, thus allowing for binding of phosphatases and inhibition of signalling(218). Supporting this idea, ligation of SLAMF3 attenuated TCR signalling on peripheral blood cells and Jurkat cells(241).

1.4.5 Additional SLAM Receptors

So far, no other SLAM molecule has been implicated on iNKT cell development and function. Remarkably, a recent study showed that upon deletion of all SLAM family members iNKT cell development is impaired, but not completely absent as in SAP-/- mice(242). The remaining iNKT cells were skewed towards iNKT2 and iNKT17, with SLAMF1, SLAMF3 and

SLAMF2/SLAMF4 playing a major role in this phenotype(242). Thus, much remains to be known about the regulation of iNKT cells by SLAM family of receptors.

1.5 Cytokines

1.5.1 Cytokine signalling in iNKT cell homeostasis

As with classical T cells, cytokines bind their corresponding multimeric receptors on iNKT cells to modulate homeostasis and activation of the cells. Key homeostatic iNKT cell cytokines include IL-2, IL-7 and IL-15(13). The receptors for all these cytokines are members of the γC family of cytokines, and signal through the Janus kinase (Jak)-signal transducer and activator of transcription (Stat) pathway(243). Several studies have shown that these cytokines are crucial for the survival and proliferation of iNKT cells, yet the signalling they evoke to induce these changes is not fully elucidated.

IL-2 receptor is composed of a high affinity IL-2Rα (CD25), IL-2/IL-15Rβ (CD122) and the γC chain(243). IL-2 signals mainly through Stat5, although some phosphorylation of Stat3 and Stat1 can be found(243). Like T cells, iNKT cells produce IL-2 and induce CD25 expression upon activation, allowing them to respond to their own IL-2 by proliferating(116).

IL-7 receptor is composed of an IL-7Rα (CD127) and γc chain and like IL-2, signals through Stat5 with some Stat3 and Stat1 phosphorylation(243). Initial studies found little or no role for IL-7 on the homeostasis of iNKT cells(112, 113). Yet a recent study showed that the iNKT17 subset expresses much higher levels of CD127 than other subsets, and IL-7, but not IL-

15, is necessary for their survival and proliferation(114). Interestingly, preferential response to

IL-7 did not reflect increased Stat5 and Stat3 phosphorylation, but rather enhanced Akt/mTOR signalling(114). It is likely that IL-7 also contributes to homeostasis of other subsets, given that a range of CD127 expression was seen in RORγt- cells(114).

IL-15 receptor shares the CD122 chain with the IL-2R, and also contains the γc chain(243). In complex, these two molecules have low affinity for IL-15 alone, but high affinity for IL-15 in complex with IL-15Rα chain(244). Interestingly, IL-15 is mainly presented in trans from cells expressing the IL-15Rα to cells expressing CD122 and γc chain, leading to induction of Stat5 phosphorylation(244). CD122 is a marker for stage 3 NKT or iNKT1 cells(68) and IL-

15 is crucial for the differentiation, proliferation and survival of stage 3/iNKT1 subset(112, 113).

During development, Egr2 contributes to the upregulation of CD122, allowing the progression of the cells to stage 3 or iNKT1(56). The requirement for IL-15 has been linked to BclXL and T-bet expression, yet signalling intermediates have not been elucidated(245, 246).

1.5.2 Cytokine signalling in iNKT cell activation

iNKT cells can be activated by a wide variety of cytokines, including IL-12, IL-18, IL-23 and IL-25. Stimulation with IL-12 and IL-18 induce IFN-γ production, but not IL-4, by iNKT cells in the absence of TCR ligation(96, 104-106). IL-12 is a heterodimer composed of IL-12p40 and IL-12p35 subunits, which bind to IL-12Rβ1 and IL-12Rβ2 respectively and signal by inducing Stat4 phosphorylation(243). On the other hand, IL-18 is a members of the IL-1 family, and contains a Toll-IL-1 resistance (TIR) domain that signals through MyD88(247). IL-23 shares the IL-12p40 subunit of IL-12, and contains an IL-23p19 subunit, which bind to IL-12Rβ1 and

IL-23R, respectively. IL-23 signals through Stat4 and Stat3, and induces IL-17 and IL-22 production by iNKT cells(69, 98, 248). Interestingly, a role for IL-1 in the production of IL-17 by iNKT cells has also been reported(99). Finally, IL-25 is a member of the IL-17 cytokine family and binds to the heterodimer of IL-17RA and IL-17RB(249). IL-25 induces production of

TH2 cytokines IL-13, IL-4 and IL-9 on iNKT cells and contributes to their detrimental role in

AHR(69, 100, 101).

1.6 The Protein Tyrosine Phosphatase Shp-1

1.6.1 Shp-1 Classification and Structure

TCR, SLAMs and cytokines signal largely through protein tyrosine phosphorylation; a process initiated and amplified by Protein Tyrosine Kinases (PTKs) such as Lck, SAP and Fyn.

The regulation of this process requires the action of Protein Tyrosine Phosphatases (PTPs), which counteract kinases by dephosphorylating tyrosine residues. Among the strict tyrosine specific phosphatases referred to as classical PTPs (~38 in the human genome), only Shp-1 and

Shp-2 contain two tandem SH2 domains, although two other PTPs contain a single SH2 domain(250). Both Shp-1 and Shp2 are structured such that the SH2 domains are at the N- terminus, followed by a central catalytic domain, and a C terminal tail that has potential tyrosine

34 phosphorylation sites(251). The SH2 domain binds tyrosine-phosphorylated molecules, particularly receptors bearing immunoreceptor tyrosine-based inhibitory motifs (ITIMs), or

ITSMs thus compartmentalizing the phosphatase to a particular location in the cell(251). The

SH2 domain also has a regulatory function since the N-terminal SH2 associates with the catalytic domain at steady state to repress its activity(251). The catalytic domain contains the PTP motif

VHCSAGIGRTG, and its inhibition is released upon binding of the SH2 domains to its corresponding receptor(251). The C terminal tail has been associated with several functions such as modulation of the catalytic activity, recruitment of additional SH2-domain containing proteins

(in the case of Shp-2) and lipid raft localization (in the case of Shp-1). Shp-1 is mainly restricted to hematopoietic cells and generally takes a negative role in signalling(251). On the other hand,

Shp-2 is more ubiquitously expressed and mainly plays a positive role in signalling and activation of the cell(251). Our study focused on Shp-1.

1.6.2 Models to Study Shp-1 Function in vivo

Initial studies of Shp-1 in vivo made use of mice containing a frameshift mutation near the 5’ end Ptpn6, which leads to complete lack of Shp-1 protein(252). These mice were named motheaten (me/me) mice due to their characteristic skin lesions(252) and die at 2-3 weeks due to severe inflammation, myeloid hyperplasia and interstitial pneumonia. Another genetic alteration of the Ptpn6 gene leads to a few amino acid changes in the catalytic domain that reduce the activity of the protein to 20% of the wild type(253). These mice were termed motheaten viable

(meV/meV) and live up to 8-12 weeks. Studies of these mice have made it clear that Shp-1 plays a role in the development, homeostasis or activation of almost every immune cell. Thus, although these models have been essential for much of our understanding of Shp-1, they are unable to resolve cell extrinsic from cell intrinsic effects. To tackle this issue, a floxed Ptpn6 mouse model

(hereafter reffered to as Shp-1f/f) was recently created to induce cre-mediated cell specific deletion of Shp-1(254).

Several Shp-1 inhibitors have been designed such as sodium stibogluconate (SSG) and tyrosine phosphatase inhibitor 1 (TPI-1), however all show some inhibition of Shp-2 or other phosphatases(255). Additionally, use of inhibitors in vivo is challenging as systemic administration leads to toxic effects as demonstrated by phase I dosing trials (255).

1.6.3 Regulation of TCR, SLAM and Cytokine Signals by Shp-1

Initial studies on Shp-1 pointed out that it played a crucial role in TCR signalling. First, the TCR is a great candidate for Shp-1 binding given that the CD3 chains and many of its downstream molecules become heavily phosphorylated, making them ideal docking sites for

SH2 domains(251). Furthermore, thymocytes from motheaten mice showed enhanced negative selection(256) and T cells underwent increased proliferation, IL-2 production and phosphorylation of TCR signalling molecules(257). Furthermore, studies on cell lines showed that Shp-1 expression can inhibit TCR mediated signals by localizing to lipid rafts(258, 259).

Recent studies where Shp-1 was specifically deleted on T cells supported an enhanced ability of

TCR transgenic Shp-1 deficient T cells to proliferate in response to antigen(260); however, a recent study showed that this is due to a higher frequency of memory-like T cells at steady state in the Shp-1 deficient mice given that when sorted, naïve T cells responded equally in the presence or absence of Shp-1(261). Further supporting that Shp-1 does not directly influence

TCR signalling, there is yet no consistent binding target for Shp-1 on this pathway(251).

Although some targets have been proposed based on immunoprecipitation assays, these are not consistent with the phenotype seen in Shp-1 deficient T cells(251). For example, phosphorylation of Shp-1 in complex with Themis was proposed to control positive selection in the thymus(262),

36 yet Shp-1f/f CD4-cre mice exhibit only a mild increase in negative selection at a later developmental stage than observed in Themis deficient mice(263). It has been proposed that other phosphatases, mainly Shp-2, compensate for Shp-1, thus leading to the absence of developmental defects in Shp-1 deficient thymocytes(264). However, this hypothesis is hard to reconcile with the positive role Shp-2 has been shown to play in TCR signals during development(265).

Rather than playing a role in TCR signalling, conditional deletion of Shp-1 on T cells revealed an enhanced IL-4R signalling due to extended Stat6 phosphorylation(261). This supported earlier studies carried out with motheaten mice and cell lines, which proposed the IL-

4R contained ITIMs that lead to the recruitment of Shp-1, contributing to reduction of Stat6 phosphorylation, IL-4R signalling and allergic airway inflammation(266-268). In the absence of

Shp-1, T cells hyperresponsive to IL-4 signals become memory-like, express high levels of

CD44, and low levels of CD25 and CD69(261). Greenberg and colleagues found Shp-1 deficient

T cells produced higher frequency and number of short-lived effector cells during the initial and recall phase of viral immunity without affecting the memory population, making Shp-1 a good target for cancer immunotherapy.

One study has suggested that tonic TCR signals through CD1d-self antigens presented on

DCs regulate Shp-1 expression on iNKT cells, and this in turn prevents hyper-activation of iNKT cells(269). This was shown using iNKT cells heterozygous for the motheaten allele, which presented increased production of IFN-γ and IL-4(269). Given the debated interpretations of

Shp-1 function on TCR signalling from the study of these mice, its function on iNKT cells remains to be established.

As with ITIMs, phosphatases bind to ITSMs of many of the SLAM family receptors, effectively competing with SAP and modulating several processes such as T/B cell interaction and NK or T cell activation(219). Shp-1 has been shown to bind SLAMF4, SLAMF5, SLAMF6 and SLAMF7, out of which SLAMF6 is particularly important for iNKT cell development.

Whereas deletion of SAP leads to complete abrogation of iNKT cells beyond stage 0, co-deletion of SAP and SLAMF6 rescued iNKT cell development to the frequency seen in SLAMF6 deficient mice(233). This led to the hypothesis that the presence of SLAMF6 in the absence of

SAP leads to recruitment of inhibitory signals that are no longer present if SLAMF6 is removed(233). Shp-1 has been proposed as the primary phosphatase involved in this process given previous research associating these two molecules. First, co-immunoprecipitation assays have shown that Shp-1 associates with SLAMF6 on CD4 T cells shortly after antigen specific engagement by B cells(233, 236), and remains constitutively bound in NK cells(237) and activated T cells(238, 239). In activated T cells, higher amounts of Shp-1 are engaged with

SLAMF6 in the absence of SAP(233, 238), or in XLP patients(239), indicating that these two molecules compete for binding to SLAMF6. SLAMF6 has been shown to co-localize with the

TCR and expression levels of SLAMF6 and SAP can influence CD3ζ phosphorylation, T-B conjugate formation and re-stimulation induced apoptosis(238, 239). Shp-1 was linked to this process through studies showing that its localization is restricted to the outer edges of the immune synapse in the presence of SAP, and its inhibition rescues B cell conjugate formation with SAP deficient T cells(233). Interestingly, knockdown of Shp-1, but not Shp-2, rescued induction of apoptosis in re-stimulated SAP deficient T cells, a process important in regulation of immune responses(239). Thus, the overall emerging picture indicates that Shp-1 is the primary phosphatase mediating inhibitory signals downstream of SLAMF6. Nonetheless, studies so far have not directly addressed how conditional deletion of Shp-1 affects iNKT cell development.

The two other SLAM members involved in iNKT cell development, SLAMF1 and SLAMF3, also recruit phosphatases such as Shp-2; however there is no evidence of association with Shp-

1(229, 270, 271).

1.7 Purpose of this Thesis

Overall, iNKT cells are a unique regulatory T cell population that could be targeted for immunotherapy in several diseases. Furthermore, their differential developmental requirements compared to classical T cells make them a great tool to study the factors and pathways that define lineage decisions. In this thesis, I aimed to understand how various signalling modules regulate iNKT cell development and function. First, I investigated the biophysical parameters of

TCR interaction with CD1d-lipid complexes that define selection, differentiation and function of iNKT cells (Chapter 2). Then, I examined whether these processes could be modulated by the protein tyrosine phosphatase Shp-1 (Chapter 3).

2 Chapter 2: Discrete TCR binding kinetics control invariant Natural Killer T cell selection and central priming

Mayra Cruz Tleugabulova1, Nichole K. Escalante1, Shenglou Deng2, Stephanie Fieve1, June Ereño-Orbea3, Paul B. Savage2, Jean-Philippe Julien1, 3, 4 and Thierry Mallevaey1

1Department of Immunology, University of Toronto, 1 King’s College Circle, Toronto, ON, M5S 1A8, Canada, 2Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, 3The Hospital for Sick Children Research Institute, Toronto, Canada, 4Department of Biochemistry, University of Toronto, 1 King’s College Circle, Toronto, ON, M5S 1A8, Canada

M.C.T. designed and performed experiments, analyzed the data and wrote the manuscript. T.M. designed and performed experiments and wrote the manuscript. N.K.E., J.P.J. and J.E.O. performed experiments and reviewed the manuscript. S.F. performed experiments. S.D. and P.B.S. synthesized the glycolipid ligands.

Adapted from:

Cruz Tleugabulova, M., N. K. Escalante, S. Deng, S. Fiévé, J. Ereño-Orbea, P. B. Savage, J.-P. Julien, and T. Mallevaey. 2016. Discrete TCR Binding Kinetics Control Invariant NKT Cell Selection and Central Priming. The Journal of Immunology 197: 3959–3969.

2.1 Abstract

Invariant Natural Killer T (iNKT) cells develop and differentiate in the thymus, segregating into

+ iNKT1/2/17 subsets akin to TH1/2/17 classical CD4 T cells; yet iNKT TCRs recognize antigens in a fundamentally different way. How the biophysical parameters of iNKT TCRs influence signal strength in vivo and how such signals affect the development and differentiation of these cells is unknown. Here, we manipulated TCRs in vivo to generate clonotypic iNKT cells using

TCR retrogenic chimaeras. We report that the biophysical properties of CD1d-lipid-TCR interactions differentially impacted the development and effector differentiation of iNKT cells.

Whereas selection efficiency strongly correlated with TCR avidity, TCR signalling, cell-cell conjugate formation and iNKT effector differentiation correlated with the half-life of CD1d- lipid-TCR interactions. TCR binding properties however did not modulate antigen induced iNKT cytokine production. Our work establishes that discrete TCR interaction kinetics influence iNKT cell development and central priming.

2.2 Introduction

Invariant Natural Killer T (iNKT) cells are innate lymphocytes that express semi- invariant TCRs that recognize (glyco)lipid antigens presented by the MHC class Ib molecule

CD1d(13). Their ability to secrete large amounts of cytokines and chemokines within minutes following stimulation allows them to boost innate and adaptive responses. Consequently, iNKT cells play protective or deleterious functions in diseases such as microbial infection, autoimmunity, allergy and cancer(13). iNKT cells develop in the thymus where they are selected on double positive (DP) thymocytes through agonist TCR signals and SLAM/SAP-mediated signals, which lead them to acquire a memory-like phenotype and fast/potent effector functions(63). Upon or after positive selection, iNKT cells are “primed” and differentiate into

+ iNKT1, iNKT2 and iNKT17 subsets akin to TH1, TH2 and TH17 classical CD4 T cells subsets, respectively(68), which may contribute to the seemingly opposite functions iNKT cells play in disease. Although molecules such as mechanistic target of rapamycin (mTOR), lymphoid enhancer factor 1 (LEF1) and inhibitor of DNA binding (Id) proteins have been shown to influence the frequency of these subsets, the mechanisms of thymic effector differentiation remain largely unknown(63, 77).

iNKT cells potently release both TH1 and TH2 cytokines independently of IL-4R and

IFN-γR expression(107), yet certain stimuli such as IL-12 can induce IFN-γ production by iNKT cells independently of the TCR(103). Therefore, whether iNKT cells get polarized and the mechanisms for this remain unclear. Several glycolipid ligands can skew the overall immune response towards TH1 or TH2. Yet these ligands do not polarize the cytokines released by iNKT cells per se, and none of the biophysical properties of the TCR-ligand interaction correlate with this bias(33, 130, 131, 133, 203).

iNKT cell development and function is highly dependent on signals through their T cell receptors (TCR), which are composed of an invariant Vα chain (Vα14-Jα18 in mice or Vα24-

Jα18 in humans), paired with a diverse set of Vβ chains (Vβ8s, Vβ7, Vβ2 in mice or Vβ11 in humans). Unlike classical αβ TCRs, iNKT TCRs dock parallel and above the C terminal side of the CD1dα helices such that only the invariant Vα chain makes contact with the lipid antigen(205, 206). The Vβ chain only makes contact with CD1d, mainly through the complementarity-determining region (CDR) 1β and CDR2β. The hypervariable CDR3β loop is not essential for binding but it has been shown to make contacts with CD1d in some instances and compensate for low affinity CDR1/2β(205, 206). This mode of interaction is very well conserved among different species, and likely contributes to the promiscuous ligand recognition described for iNKT cells(272).

The kinetics of T cell receptor (TCR)-ligand interactions play an important role in T cell selection(153, 164, 273), activation(154), effector differentiation(191), and memory formation(192, 274); yet the mechanisms of such processes are not fully understood. Several models have been proposed, with the fundamental ones being the occupancy and kinetic proofreading models. The occupancy model predicts that signal strength will reflect the number of TCRs that are engaged, and therefore will correlate with the affinity (KD) of TCR-ligand interactions(148). On the other hand, the kinetic proofreading model predicts that TCR signalling correlates with the dissociation rate (KOFF), such that the amount of signalling will reflect the time a TCR is engaged to its ligand, only reaching downstream events if it remains bound for a long enough time(148, 159).

Many studies have modeled T cell function based on their TCR kinetics, yet whether and how properties of CD1d-lipid-TCR interactions lead to differential signal strength in vivo and

43 their functional implications for iNKT cell development and effector differentiation remain elusive. A link between TCR signal strength and iNKT cell development has only been suggested following the observations that the avidity conferred by certain Vβ frameworks correlated with their prevalence within the iNKT repertoire(204), and that iNKT2s express higher levels of PLZF, Nur77 and Vβ7(44, 68, 275). Here, we directly addressed how CD1d- lipid-TCR interactions influence iNKT cell fate in vivo. We manipulated iNKT TCRs in vitro and in vivo, through the generation of TCRβ retrogenic chimaeras. We show that iNKT cell selection is governed by the avidity of CD1d-lipid-TCR interactions, and is not restricted by the thymic niche. On the other hand, TCR signalling, the ability to form cell-cell conjugates and iNKT cell effector differentiation are dictated by CD1d-lipid-TCR half-life. Interestingly, despite the modulation of effector fate, TCR binding parameters did not influence the quality of the cytokine response in short term in vivo stimulation. Our results show that distinct biophysical parameters of TCR-ligand interactions control the selection and effector differentiation of iNKT cells.

2.3 Experimental Procedures

Mice and Reagents

Mice were used between 5-8 weeks of age. C57BL/6 (B6) wild-type (WT), TCRβ-/- and Nr4a1- eGFP/Cre mice were purchased from Jackson Laboratories. CD1d-/- mice were generated and generously provided by Dr. Chyung-Ru Wang (Northwestern University, USA)(276). Jα18- deficient mice were kindly provided by Dr. Laurent Gapin (University of Colorado Denver,

USA). All strains were housed at the Division of Comparative Medicine, University of Toronto animal facility under specific-pathogen free conditions, and animal procedures were approved by the Faculty of Medicine and Pharmacy Animal Care Committee at the University of Toronto

(Animal use protocols 9003, 9571, 10135, 10715 and 11113).

α-galactosylceramide (KRN7000, αGC) was purchased from Diagnocine. Antibodies were purchased from eBiosciences, Biolegend or BD Biosciences. PBS57-loaded and unloaded biotinylated CD1d monomers were obtained from the NIH Tetramer Core Facility. PBS44,

PBS218 and PBS221 tetramers were made as previously described(94). Unloaded monomers were loaded with lipids (PBS44, PBS218, PBS221) in 0.2M malonate, 0.2% Tween20 overnight at 37ºC in the presence of 0.2mg/ml of SaposinB (generously provided by Dr. Luc Teyton).

Monomers were tetramerized by addition of fluorochrome-conjugated streptavidin. For stimulation assays, mCD1d monomers were purified from the culture supernatant of transduced

HEK293 cells lines obtained from the NIH Tetramer Core facility, using affinity chromatography.

Glycolipid Synthesis

PBS44 was synthesized as described previously(277). For PBS218 and PBS221, TMS- protected donors 1 and 2 were prepared in quantitative yields by treating D-glucose and D-

45 galactose with chlorotrimethylsilane in the presence of imidazole, respectively (Supplementary

Fig. S6). Glycosylation of acceptor 3 or 4 was performed according to the reported procedure(278). Crude products were sequentially treated with acidic hydrogen resin, and sodium methoxide. Silica gel chromatography afforded desired products PBS218, PBS221, and

PBS44. Structures and purities were verified using proton and carbon NMR and high resolution

MS.

Cloning, cell lines and retroviral infection

TCR constructs were generated by overlapping PCR according to published methods(279, 280) and cloned into mouse stem cell virus-based plasmids with an internal ribosome entry site (IRES) plus sequence encoding for green fluorescent protein (GFP) (MIGR1) or ametrine (pMIAII)(a gift from Dr. Dario Vignali). CD3 multicistronic vectors were obtained from Dr. Dario Vignali(280). The TCRαβ-/- 5KC-78.3.20 hybridoma was used to generate a CD4- and CD1dlow 6KC hybridoma. 6KC hybridomas and mouse WT3 fibroblasts(281) were transduced with different retroviral constructs by spinfection at 4500 x g, 37oC for 90 min.

Transduced cells were sorted for similar levels of TCR expression. Mouse Bcl2 and human BclXL templates were obtained from Dr. Cynthia Guidos (The Hospital for Sick Children, University of

Toronto) and cloned downstream of a cleavable 2A peptide DNA fragment by overlapping PCR.

Retroviruses were generated using Jet Prime transfection reagent and used to spin-infect cells at

4500 x g for 90 min at 37ºC in retrovirus-containing supernatants supplemented with polybrene

(8µg/ml). Cells were sorted on a FacsAria II (BD Biosciences) based on expression of TCR and the reporter gene.

Tetramer equilibrium and decay measurements

These assays were done as previously described(282). For equilibrium staining, 1 x 105 cells were stained with anti-TCRβ antibodies and different concentrations of CD1d-lipid tetramers at 22ºC for 3 hrs in PBS containing 0.5% FBS, 2mM EDTA and 0.1% sodium azide.

Cells were then washed and fixed using cytofix/cytoperm (BD Biosciences) for 30 min at 4ºC and analyzed by flow cytometry. For tetramer decay assay, 0.5 x 105 WT3 cells were stained with CD1d-lipid tetramers for 45 min at 22ºC. Cells were then washed and re-suspended in 100µl of 100µg/ml anti-CD1d purified antibody 1B1 (Biolegend) for various amounts of time. Cells were then fixed and analyzed by flow cytometry on a Fortessa X-20 (BD Biosciences)

Cell-Cell conjugation assay

CD1d-sufficient and deficient thymocytes were stained with either PKH26 red fluorescent cell linker (SIGMA) or e450 proliferation dye (eBiosciences) in PBS for 20 min at

22ºC. Thymocytes were washed twice with complete RPMI medium and mixed in a 1:1 ratio. 2 x

106 total thymocytes were incubated with 0.1 x106 hybridomas expressing a specific TCR in complete RPMI supplemented with 10% fetal bovine serum, centrifuged for 1 min and incubated at 37ºC for 30 min. Cells were immediately fixed with addition of 2 volumes of cytofix/cytoperm and incubated at room temperature for 30 min, washed and analyzed by flow cytometry on a

Fortessa X-20.

Hybridoma Stimulation assays

Flat-bottom 96 well plates were coated with 10 µg/ml of purified mCD1d in PBS at 37ºC for 1 hr, washed with PBS and the lipid ligands were then added at different concentrations in

0.1M malonate and 5 µg/ml SaposinB and incubated overnight at 37ºC. Bone marrow-derived dendritic cells (BMDCs) were differentiated for 8 days in GM-CSF-conditioned medium. For

47 cell stimulation assays, lipids antigens were added to the medium for 4 hrs and cells were washed prior to stimulation. 0.1 x 106 hybridomas were stimulated with different concentrations of B6

WT thymocytes, BMDCs, 0.1 million sorted thymocytes, or immobilized CD1d-lipid complexes for 2 hrs at 37ºC. Stimulation was stopped by addition of cold buffer and the cells were stained for TCRβ, fixed using FoxP3 staining kit (eBiosciences), stained for Nur77 and analyzed by flow cytometry.

Retrogenic mice

Protocol was based on previously described methods(279). TCRβ-/-, TCRβ-/- Nr4a1- eGFP/Cre or TCRβ-/- CD1d1/d2-/- mice were injected with 5-fluorouracil (0.15 mg per g of weight). Bone marrow was collected 4 days later from femur and tibia and cultured overnight in complete IMDM supplemented with SCF, IL-6, IL-3 and Flt-3L. Cells were transduced with retrovirus-containing supernatants supplemented with polybrene (8µg/ml) by two successive spin-infections at 650 x g for 90 min at 34ºC, with a 30 min incubation at 37oC between spin- infections. The following day, transduction efficiency was assessed by flow cytometry, and a minimum of 0.3 x 106 transduced cells were injected intravenously into lethally irradiated (2 x

550 rad) Jα18-/- recipient mice. Mice were analyzed six to eight weeks post reconstitution.

In vivo stimulations

Retrogenic mice were injected with 0.5µg of αGalCer and spleen and liver cells were collected after 90 min. Cells were stained for extracellular proteins, fixed and permeabilized using Cytofix/Cytoperm buffer, stained for cytokines and analyzed by flow cytometry on a

Fortessa.

Data Analysis

Flow cytometry data was analyzed using FlowJo (Tree Star). Statistical analysis was performed using Prism (GraphPad). Statistical tests are indicated for each figure and were selected based on the normality test for each data set. TCRβ sequencing results were analyzed using ImmunoSEQ analyzer (Adaptive Biotechnologies).

2.4 Results

Interaction kinetics of iNKT TCRs towards CD1d-lipid complexes display differential hierarchy of avidity and half-life

To address the role of CD1d-antigen-TCR interaction in iNKT cell development and function, we selected six TCRs that have been described to interact with CD1d-α- galactosylceramide (αGalCer) complexes with a wide range of avidities(197, 204). Four TCRs,

Vβ8.2 DO-11.10 (hereafter referred to as 8-DO)(195), 2C12 (8-2C)(283), DN32.D3 (8-DN)(284) and 24.8.A (8-24)(285) contain the TCR Vβ8.2 framework and differed only in the CDR3β-Jβ region (Table 2.1). The additional two TCRs contain the same CDR3β-Jβ region as 8-DO, but in the context of Vβ7 or Vβ2, and are referred to as 7-DO and 2-DO respectively (Table 2.1). Of note, the 8-DN, 8-2C and 8-24 TCRs were originally identified from the natural iNKT cell repertoire, whereas 8-DO, 7-DO and 2-DO were engineered(197, 204). We reasoned that this selection would allow us to perform an in depth analysis of the effect of the TCRβ chains on iNKT cell development and function.

Table 2.1 Complementary Determining Regions and J segments from TCRβ chains of selected iNKT TCRs

CDR1β CDR2β CDR3β Jβ Reference

8-DO NNHNN SYGAGS GSGTTN NTEVFFGKGTRLTVV Vβ8.2 DO-11.10 (195)

8-2C NNHNN SYGAGS GDEGYTQ TQYFGPGTRLLVL 2C12 (283)

8-DN NNHNN SYGAGS GDPDIQNTL QNTLYFGAGTRLSVL DN32.D3 (284)

8-24 NNHNN SYGAGS GDWGQDTQ QDTQYFGPGTRLLVL 24.8.A (285)

7-DO MSHET SYDVDS GSGTTN NTEVFFGKGTRLTVV Vβ7 DO-11.10 (204)

2-DO NSQYPW LRSPGD GSGTTN NTEVFFGKGTRLTVV Vβ2 DO-11.10 (204)

To characterize the kinetics of CD1d-lipid-TCR interaction, the TCRs were expressed in a TCRα-β- hybridoma or WT3 fibroblasts expressing the CD3 complex and sorted for similar

TCR expression. Using CD1d tetramers loaded with the prototypical ligand PBS57, we calculated the avidity as the 1/EC50 of equilibrium staining (Fig. 2.1.a left panels) and the half- life as the t1/2 of tetramer binding (Fig. 2.1.a right panels). These assays have been shown to reflect the KD and KOFF of TCR-ligand interactions respectively(282). As expected, we found that the TCRβ chain greatly modulated the strength of interaction between the TCRs (Fig. 2.1.a).

However, the avidity of the TCRs was not a straight reflection of their half-life for PBS57 tetramer binding (Fig. 2.1.a). For example, 8-DN showed relatively high avidity but dissociated much faster than most other TCRs (Fig. 2.1.a). On the other hand, 8-24 had one of the lowest avidity, but intermediate half-life (Fig. 2.1.a). Of note, 8-2C, but none of the other TCRs, was autoreactive as it bound to CD1d tetramers without addition of exogenous antigens (data not shown).

Previous work has proposed that the variability in the CDR2β and CDR3β loops broadly modulates the strength of CD1d-lipid-TCR interaction(204). A recent study however has challenged this idea and proposed that pairing of specific Vβ-Jβ segments allows the TCR to discriminate between ligands rather than fine-tuning the overall strength of recognition(286). To address this, we determined the avidity and half-life of the TCRs for CD1d tetramers loaded with additional ligands (Fig. 2.1.b). We selected one α-galactosylceramide (PBS44) and 2 α- glucosylceramides (PBS218 and PBS221), which have recently been identified as the major endogenous α-linked glycolipids in mammals(87, 94) (Fig. 2.1.b). PBS218 and PBS221, which have the same glucose headgroup but different lipid tails, had very similar binding parameters, indicating that the stability of the ligands within CD1d did not play a major role (Fig. 2.1.b). In contrast, lipids with galactose headgroups generally had higher avidity and longer half-life than

51 those with glucose headgroup (Fig. 2.1.b). Importantly, the hierarchy of binding of the TCRs was the same regardless of the ligand indicating that the TCRs did not exhibit preferential ligand recognition (Fig. 2.1.b). Overall, this shows that the panel of TCRs recognizes each ligand in the same relative order of strength, but the hierarchy of avidity (8-2C > 8-DO ≥ 8-DN ≥ 7-DO > 8-

24 ≥ 2-DO) differs from that of dissociation (8-2C > 8-DO > 8-24 ≥ 7-DO > 8-DN > 2-DO) (Fig.

2.1).

Figure 2.1

Figure 2.1. iNKT TCRs display differential hierarchy of avidity and half-life towards CD1d-lipid complexes. (a) PBS57 loaded CD1d tetramers were used to measure avidity (left panels) and half-life (right panels) of selected TCRs. Avidity was measured by flow cytometry analysis of tetramer binding at equilibrium and the data was fit to a 4-parameter logistic model.

The relative avidity was calculated as the 1/EC50. Half-life was measured by flow cytometry analysis of tetramer decay. Linear plots were obtained by taking the natural logarithm of the fluorescence normalized to t=0 and used to determine the t1/2 of binding. (b) CD1d tetramers loaded with ligands PBS44, PBS218 and PBS221 were used to measure avidity and half-life of selected TCRs as described in (a). Hierarchical order of TCR avidity (left) and half-life (right)

52 towards all CD1d-ligand complexes measured. Data represents mean ± s.e.m from 2 to 3 experiments.

TCR avidity controls iNKT cell selection efficiency in vivo

To assess the impact of the TCR on iNKT cell development, we expressed the TCRβ chain of each TCR in vivo using the retrogenic chimaera approach(279, 280) (Fig. 2.2.a). We used TCRβ-

/- bone marrow (BM) cells as donors and Jα18-/- recipient mice, which allowed natural rearrangement of the TCRα chain and ensured iNKT cells arise only from the grafted BM (Fig.

2.2.a). After reconstitution, retrogenic mice had robust production of DP and single positive (SP) thymocytes indicating normal T cell development (Fig. 2.3.a, Fig. 2.2.1b-h).

All chimaeras contained iNKT cells, albeit in various amounts (Fig. 2.3.a-c). Classical T cell selection has been proposed to follow a general bell shaped distribution with a sharp

threshold for negative selection(153). Some studies attributed the affinity/KD as the defining parameter for selection(287), whereas others suggested that the half-life/KOFF dictates selection(164). Both positive and negative selection have been shown to occur for iNKT cells(204, 210). Given that all ligands tested induced the same hierarchy of binding, we used the binding parameters of PBS57-CD1d tetramers as representative of all ligands. For each TCR we plotted the frequency of thymic and splenic iNKT cells against the avidity or the half-life of

CD1d-PBS57-TCR interaction (from Fig. 2.1.a) and fit the data to a Log-Gaussian distribution

(Fig. 2.3.d,e, Fig. 2.4.a,b). This revealed that iNKT cell selection associated better with avidity than half-life, suggesting that selection is governed by the overall number of TCRs that get engaged rather than the duration of TCR engagement.

Figure 2.2

Figure 2.2. T cell development in TCRβ retrogenic mice. (a) Schematic protocol of the generation of retrogenic mice. Donor bone marrow was collected and transduced with selected TCRβ chains (Table 1) and cultured overnight. After determining transduction efficiency, bone marrow was i.v injected into Jα18-/- mice. (b) Number of thymocytes on retrogenic mice expressing the indicated TCRβ chain. (c-e) Frequency (left panels) and absolute number (right

54 panels) of T cells (c), CD8+ T cells (d) and CD4+ T cells (e) in the thymus of retrogenic chimaeras. (f-h) Frequency (left panels) and absolute number (right panels) of T cells (f), CD8+ T cells (g) and CD4+ T cells (h) in the spleen of retrogenic chimaeras. Data shows the mean ± s.e.m (b-h) (n = 9 to 30 from 8 experiments).

Figure 2.3 a 8-DO 8-2C 8-DN 8-24 7-DO 2-DO d Thymus 5.48 84.5 4.96 84.4 7.19 82.6 7.85 83.9 2.25 86 5.74 80.6 0.6 SDresiduals=0.1437

0.4

7.75 1.5 8.4 1.25 8.3 1.06 5.28 1.58 8.2 2.36 11.2 1.64 CD4 CD8 0.2 cells Thymus (%) 0.398 0.247 0.344 0.058 0.270 0.016 T 0.0 iNK 10-4 10-3 10-2

1/EC50 PBS57 Tetramer etramer T TCRβ b 1.2 Thymus 1.2 Thymus c 1.6 Spleen 0.8 Spleen e Thymus p<0.0001 p<0.0001 p<0.0001 p<0.0001 0.8 SDresiduals=0.1949 ) ) 0.9 6 0.9 1.2 6 0.6 0.6

0.6 0.6 0.8 0.4 0.4 cells (%) cells (%) T T cells (x10 cells (x10 0.3 T 0.3 0.4 T 0.2 0.2 cells Thymus (%) iNK iNK T iNK iNK

0 0 0 0 iNK 0.0 100 103 106

8-24 8-24 8-24 8-24 t PBS57 Tetramer

8-2C 8-2C 8-2C 8-2C 1/2 8-DN 8-DN 8-DN 8-DN 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO

Figure 2.3. TCR avidity controls iNKT cell selection efficiency in vivo. (a) Representative flow cytometry plots of T cells (top panel) and iNKT cells (bottom panel) gated on GFP+ thymocytes from retrogenic mice. (b, c) Frequency and absolute number of clonotypic iNKT cells in thymus (b) and spleen (c) of retrogenic mice. Data represents individual mice and the mean ± s.e.m (n=6 to 19 from 8 experiments). p-value was obtained by Kruskal-Wallis test. (d, e) Log-Gaussian fit between frequency of thymic iNKT cells in retrogenic mice and avidity (d) or half-life (e) of TCR binding to CD1d-PBS57 tetramers. Shown are the line of best fit (thick line), and 95% confidence intervals (thin lines).

Figure 2.4

Figure 2.4. TCR avidity controls iNKT cell selection efficiency in vivo. (a, b) Log-Gaussian fit between mean frequency of splenic iNKT cells in retrogenic mice and avidity (a) or half-life (b) of TCR binding to PBS57-loaded CD1d tetramers. Shown are the line of best fit (thick line), and 95% confidence intervals (thin lines). (c) iNKT cell frequency in the liver (left panel), inguinal lymph node (middle panel) and mesenteric lymph node (right panel) of retrogenic mice. Data represents mean ± s.e.m (n=3 to 14 from 3 experiments).

Of note, the relative frequency of iNKT cells with a given TCR was similar in the liver as well as inguinal and mesenteric lymph nodes, indicating that the TCR did not affect the migration of the cells in the periphery (Fig. 2.4.c). Importantly, no iNKT cells developed in any of the TCRβ chimaeras that used TCRβ-/-CD1d-/- BM (Fig. 2.5.a) and DPs in all chimeras had similar expression of CD1d and SLAMF1/F6 (Fig. 2.5.b). Because forced expression of an autoreactive TCRβ chain can lead to the development of ‘naïve’ iNKT cells with Vα14-Jα18

TCRα variants(210), we sequenced the Vα14-Jα18 joining region from single sorted splenic

TCRβ+ Tetramer+ cells from all chimaeras, and found that all retrogenic iNKT cells contained the canonical Vα14-Jα18 chain (data not shown).

Overall, these experiments confirmed that bona fide iNKT cells develop in retrogenic mice, and their selection is governed by the avidity, and not the half-life, of the TCR for CD1d- lipid complexes.

Figure 2.5

Figure 2.5. Retrogenic mice require CD1d for iNKT cell development and express similar levels of CD1d and SLAMs. (a) Representative flow cytometry plots of frequency of TCRβ+ CD1d tetramer+ cells among GFP+ thymocytes from retrogenic mice generated using TCRβ-/- CD1d-/- BM (n=2 mice per group). (b) Expression of CD1d, CD150 and Ly108 on DPs from retrogenic mice expressing the specified TCRβ chain and measured as mean fluorescence intensity by flow cytometry. Data represents individual mice and the mean ± s.e.m (n = 6 to 18 from 3 experiments). p-value was obtained by Kruskal-Wallis test.

Frequency of clonotypic iNKT cells does not depend on survival, proliferation or competition

Selection events during T cell development are closely linked with survival(288). Hence, we investigated whether low iNKT cell output was a consequence of increased apoptosis by staining ex vivo cells with Annexin V. iNKT cells in all chimaeras showed similar levels of

Annexin V staining regardless of their TCR (Fig. 2.6.a). In parallel, we generated retrogenics in which the anti-apoptotic factors Bcl-2 or BclXL were overexpressed together with the low avidity

TCRs 8-24 or 2-DO. Although Bcl-2- and BclXL-containing constructs increased the frequency of classical T cells in these chimaeras, they did not improve iNKT cell selection efficiency (Fig.

2.6.b). Together, these results suggest that the lower iNKT cell thymic output observed in some chimaeras is not due to increased cell death.

Newly selected iNKT cells have been shown to undergo a rapid wave of expansion(66).

Thus we assessed whether TCR avidity influenced proliferation by staining for Ki-67 a marker of cells in the active phase of the cell cycle(289). iNKT cells in all chimaeras had comparable Ki-67 expression, with the exception of 8-DN relative to 8-24 chimaeras (Fig. 2.6.c). However the 8-

DN TCR selects iNKT cells efficiently (Fig. 2.6.b). Therefore, we conclude that the lower iNKT cell output in our chimaeras is not mediated by impaired proliferation.

Similar to iNKT cells, regulatory T cells develop through agonist selection. It has been shown that their selection is instructed by the TCR and limited by a small thymic niche(290,

291). We considered the possibility that iNKT cells with low avidity TCRs only developed due to absence of competition. We tested this by creating dual retrogenic mice through transfer of

TCRβ-/- BM expressing the 8-24 (low avidity) or 8-DO (high avidity) TCRs and Ametrine or

GFP as reporter, respectively, into Jα18-/- recipient mice. We found that 8-DO iNKT cells did not outcompete 8-24 iNKT cells and the frequency of iNKT cells relative to Ametrine or GFP

58 positive cells was similar to the non-competitive setting (Fig. 2.6.d). From this, we conclude that iNKT cell clones expressing TCRs of varying avidity do not compete for a limited thymic niche.

Figure 2.6

8-24 8-24/Bcl-2 8-24/BclXL a b 0.08 8-24 25 p=0.267 0.041 0.047 0.056 0.06 20 0.04 cells (%) T 15 11.6 25.5 26.3 0.02 iNK 0 Tetramer iNKT cells (%) iNKT + 10 TCRβ XL Bcl Bcl-2 5 None 2-DO 2-DO/Bcl-2 2-DO/BclXL 0.02 2-DO 0 Annexin V 0.008 0.007 0.004 0.015 8-24 8-2C 8-DN 8-DO 7-DO 2-DO 0.01 cells (%) T 2.83 15.7 12.5 0.005 iNK

Tetramer 0

TCRβ XL Bcl Bcl-2 c d None p=0.024 100 39 0.056 Thymus Spleen 0.8 1.6 *** * * 75 51.4 0.6 1.2 50 Ametrine 0.4 0.8 GFP iNKT cells (%) iNKT + 25 0.177 0.2 0.4 iNKT cells (%) iNKT cells (%) iNKT Ki67 0 0 0 8-DO 8-24 8-DO 8-24 8-24 Tetramer 8-2C 8-DN 8-DO 7-DO 2-DO TCRβ

Figure 2.6. Frequency of clonotypic iNKT cells does not depend on survival, proliferation or competition. (a) Frequency of thymic Annexin V+ iNKT cells in retrogenic mice analyzed by flow cytometry (n=11 to 22 from 7 experiments). (b) Representative flow cytometry plots and frequency of 8-24 (top panel) or 2-DO (bottom panel) thymic iNKT cells out of GFP+ + thymocytes overexpressing Bcl2 or BclXL (n=6 from 2 experiments). (c) Frequency of Ki-67 iNKT cells on retrogenic thymic iNKT cells (n=4 to 17 from 6 experiments). (d) Representative flow cytometry plots and frequency of iNKT cells expressing the 8-24 (Ametrine+) and 8-DO (GFP+) TCRβ chains generated in competitive retrogenic setting (n=4 to 5 from 3 experiments). Data represents individual mice and the mean ± s.e.m (a-d). p-values were obtained by Kruskal- Wallis test (a-c) with Dunn’s post test (c) or by paired t-test (d).

TCR half-life governs signal strength of iNKT TCR

We next analyzed how TCR interactions influence signalling on iNKT cells in vivo. For this, we assessed expression of CD5 and Nur77, both of which reflect TCR signal strength(208,

292). To measure expression of Nur77, we generated TCRβ-/-Nur77-GFP mice and used them as donors for retrogenic chimaeras. Both CD5 and Nur77 expression showed differences among the

TCRs (Fig. 2.7.a-d left panels). In contrast to the association between selection efficiency and

TCR avidity, CD5 expression correlated with the TCR half-life but not with TCR avidity (Fig.

2.7.a,b, middle and right panels). Nur77 expression correlated better with half-life than with avidity of tetramer binding, yet it did not reach significance with either (Fig. 2.7.c,d, middle and right panels). Because TCR expression level can affect the signalling outcome, we analyzed

TCRβ expression in retrogenic mice. This showed that only 8-24 thymic iNKT cells had elevated levels of TCR (Fig. 2.7.e). To correct for this, we re-analyzed the correlations in the thymus excluding this TCR (Fig. 2.7.f,g). In this scenario, although CD5 levels correlated with binding avidity, the correlation with half-life was stronger (Fig. 2.7.f). Nur77 expression only correlated significantly with half-life (Fig. 2.7.g). Taken together our results indicate that the half-life of

CD1d-lipid-TCR interactions contributes to downstream signalling more prominently than avidity.

Figure 2.7

a Thymus Thymus Thymus b Spleen 25 20 20 20 15 Spleen 15 Spleen ) ) ) )

p<0.0001 p<0.0001 ) ) 3 3 3 3 3 3 20 15 15 15 10 10 15 10 10 10 10 5 5 5 5 r2=0.539 5 r2=0.870 5 r2=0.586 r2=0.677 CD5 MFI (x 10 CD5 MFI (x 10 CD5 MFI (x 10 CD5 MFI (x 10 CD5 MFI (x 10 p=0.0972 p=0.007 p=0.0759 CD5 MFI (x 10 p=0.044 0 0 0 0 0 0 10-4 10-3 10-2 100 103 106 10-4 10-3 10-2 100 103 106 8-24 8-24 8-2C 8-2C 1/EC PBS57 Tetramer t PBS57 Tetramer 1/EC PBS57 Tetramer t PBS57 Tetramer 8-DN 8-DN 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO 50 1/2 50 1/2

c Thymus Thymus Thymus d Spleen Spleen Spleen ) ) ) 1.5 1.5 ) 1.5 1.5 1.5 ) 1.5 ) 3 3 3 3 3

p=0.009 3 p=0.034

1 1 1 1 1 1

0.5 0.5 0.5 0.5 0.5 0.5

2 2 2 2 Nur77 MFI (x 10

Nur77 MFI (x 10 Nur77 MFI (x 10 r =0.047 p=0.679 r =0.529 p=0.101 r =0.001 p=0.942 r =0.187 p=0.392 Nur77 MFI (x 10 Nur77 MFI (x 10 0 0 0 Nur77 MFI (x 10 0 0 0 10-4 10-3 10-2 100 103 106 10-4 10-3 10-2 100 103 106 8-24 8-24

8-2C 1/EC PBS57 Tetramer t PBS57 Tetramer 8-2C 1/EC PBS57 Tetramer t PBS57 Tetramer 8-DN 8-DN 8-DO 7-DO 2-DO 50 1/2 8-DO 7-DO 2-DO 50 1/2

Thymus Spleen e p<0.0001 p=0.260 f Thymus Thymus g Thymus Thymus

2 20 20 ) 1.5 ) 1.5 3 3 ) ) ) *** * 3 3 3 ** *** 1.6 ** 15 15 1 1.2 1 10 10 0.8 0.5 0.5 0.4 5 r2=0.816 5 r2=0.915 r2=0.367 r2=0.831 CD5 MFI (x 10 CD5 MFI (x 10 TCR MFI (x 10 Nur77 MFI (x 10 p=0.036 p=0.011 Nur77 MFI (x 10 p=0.279 p=0.031 0 0 0 0 0 10-4 10-3 10-2 100 103 106 10-4 10-3 10-2 100 103 106 8-24 8-24 8-2C 8-2C 1/EC PBS57 Tetramer t PBS57 Tetramer 1/EC PBS57 Tetramer t PBS57 Tetramer 8-DN 8-DN 8-DO 8-DO 7-DO 2-DO 7-DO 2-DO 50 1/2 50 1/2

Figure 2.7. TCR signal strength is dictated by the binding half-life towards CD1d-ligand complexes. (a-b) Mean fluorescence intensity (MFI) of CD5 in thymic (a) and splenic (b) retrogenic iNKT cells measured by flow cytometry (left panels) and correlation with TCR binding avidity (middle panels) or half-life (right panels) for PBS57 tetramers (n=6 to 14 from 5 experiments). (c, d) MFI of Nur77-GFP in thymic (c) and splenic (d) retrogenic iNKT cells measured by flow cytometry (left panels) and correlation with TCR binding avidity (middle panels) or half-life (right panels) for PBS57 tetramers (n=6 from 2 experiments). (e) MFI of TCRβ on iNKT cells from thymus and spleen of retrogenic mice measured by flow cytometry (n=11 to 25 from 8 experiments). (f, g) Correlations between CD5 (f) or Nur77 (g) expression and TCR binding avidity (left panels) or half-life (right panels) for PBS57-CD1d tetramers corrected for TCR expression (8-24 removed). Data represents individual mice and the mean ± s.e.m analyzed by Kruskal-Wallis test (a-d left panels, e) with Dunn’s post test (e). Correlations of mean values were analyzed by linear regression (a-d middle and right panels, f, g). Shown are the line of best fit (thick line) and 95% confidence intervals (thin lines).

TCR half-life governs effector differentiation of iNKT cells

To assess how iNKT cell differentiation is influenced by each TCR, we stained for PLZF and RORγt, defining transcription factors for iNKT1 (PLZFlo, RORγt-), iNKT2 (PLZFhi, RORγt-) and iNKT17 (PLZFint, RORγt+) subsets (68). Although the three subsets were found in all chimaeras, we observed clear shifts in their distribution (Fig. 2.8.a-c). In line with previous studies(68, 208), we found that the frequency of iNKT2 correlated with CD5 and Nur77 expression levels in the thymus (Fig. 2.8.d). In the spleen, CD5 but not Nur77 expression correlated with the frequency of iNKT2 cells (Fig. 2.8.e).

We then correlated both the avidity and half-life of tetramer binding with the frequency of iNKT1 and iNKT2 cells in the thymus and spleen (Fig. 2.8.c-i). As with CD5 and Nur77, t1/2 of tetramer binding showed a stronger positive and negative correlation for iNKT2 and iNKT1, respectively, than 1/EC50 (Fig. 2.8.f-i), although in the thymus this correlation was only significant when we corrected for unequal TCR expression by excluding 8-24 retrogenic cells

(Fig. 2.8.j,k). Although, iNKT17 frequency did not correlate with avidity or half-life, the 8-DN and 2-DO TCRs, which have the shortest half-life, contained the most prevalent iNKT17 populations (Fig. 2.8.b,c, Fig. 2.9.a).

Figure 2.8 a 8-DO 8-2C 8-DN 8-24 7-DO 2-DO NKT2 NKT2 NKT2 NKT2 NKT2 NKT2 24.352 66.625 21.053 57.331 53.684 5.882

NKT1 NKT1 NKT1 NKT1 NKT1 NKT1 72.539 NKT17 28.801 NKT17 68.879 NKT17 37.007 NKT17 44.000 NKT17 87.330 NKT17 1.554 2.472 8.924 3.337 2.105 4.977 PLZF RORγt Thymus iNKT1 Thymus iNKT2 Thymus iNKT17 b d ) Thymus 100 p<0.0001 100 p<0.0001 24 p=0.005 Thymus 3 20 1.5 ) 18 3 75 75 15 1 50 50 12 10 0.5 iNKT1 (%) iNKT2 (%) 25 6 2 2 25 iNKT17 (%) 5 r =0.674 r =0.684

CD5 MFI (x 10 p=0.045 p=0.042 0 0 0 0 0 0 20 40 60 80 MFI (x 10 Nur77-GFP 0 20 40 60 80 8-24 8-24 8-24

8-2C 8-2C 8-2C iNKT2 (%) iNKT2 (%) 8-DN 8-DN 8-DN 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO

Spleen iNKT1 Spleen iNKT2 Spleen iNKT17 Spleen Spleen c e ) 100 p=0.0013 100 p<0.0001 24 p=0.003 20 3 1.5 ) 3 75 75 18 15 1 50 50 12 10 0.5 iNKT1 (%) iNKT2 (%) 5 2 2 25 25 iNKT17 (%) 6 r =0.707 r =0.417

CD5 MFI (x 10 p=0.036 p=0.166 0 0 0 0 0 0 20 40 60 MFI (x 10 Nur77-GFP 0 20 40 60

8-24 8-24 8-24 iNKT2 (%) iNKT2 (%) 8-2C 8-2C 8-2C 8-DN 8-DN 8-DN 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO f h j 100 Thymus 100 Spleen 80 Thymus 60 Spleen 80 Thymus

75 75 60 60 40 50 50 40 40 20 iNKT1 (%) iNKT2 (%) iNKT2 (%) iNKT1 (%) 25 25 r2=0.223 iNKT2 (%) 20 r2=0.082 r2=0.297 20 r2=0.676 r2=0.047 p=0.679 p=0.345 p=0.581 p=0.263 p=0.087 0 0 0 0 0 10-4 10-3 10-2 10-4 10-3 10-2 10-4 10-3 10-2 10-4 10-3 10-2 10-4 10-3 10-2

1/EC50 PBS57 Tetramer 1/EC50 PBS57 Tetramer 1/EC50 PBS57 Tetramer 1/EC50 PBS57 Tetramer 1/EC50 PBS57 Tetramer g Thymus Spleen i Thymus Spleen k Thymus 80 100 80 60 80

60 75 60 60 40 40 50 40 40 20 iNKT1 (%) iNKT2 (%) iNKT2 (%) iNKT1 (%) 20 r2=0.412 25 r2=0.728 iNKT2 (%) 20 r2=0.465 r2=0.729 20 r2=0.885 p=0.169 p=0.031 p=0.139 p=0.030 p=0.017 0 0 0 0 0 100 103 106 100 103 106 100 103 106 100 103 106 100 103 106 t PBS57 Tetramer t PBS57 Tetramer t PBS57 Tetramer t PBS57 Tetramer t PBS57 Tetramer 1/2 1/2 1/2 1/2 1/2

Figure 2.8. CD1d-lipid-TCR binding half-life impacts iNKT cell effector differentiation. (a) Representative flow cytometry plots of PLZF and RORγt expression by thymic retrogenic iNKT cells. (b, c) Frequency of iNKT cell subsets in the thymus (b) and spleen (c) of retrogenic chimaeras. Data represents individual mice and the mean ± s.e.m (n=6 to 11 from 6 experiments). (d, e) Correlations between expression of CD5 (left panels) or Nur77 (right panels) by iNKT cells and the frequency of iNKT2 from the thymus (d) or spleen (e). (f-i) Correlations

63 between frequency of thymic and splenic iNKT1 (f, g) or iNKT2 (h, i) and TCR binding avidity (f, h) or half-life (g, h) for PBS57 tetramers. Correlations were analyzed by linear regression (d- i). Shown are the line of best fit (thick line) and 95% confidence intervals (thin line).

A former classification of iNKT cells defined a linear progression from stage 1 (CD44-

NK1.1-) to stage 2 (CD44+NK1.1-) and stage 3 (CD44+NK1.1+)(293), where stage 3 is equivalent to iNKT1 and stages 1 and 2 are a mixture of iNKT2, iNKT17 and progenitors(68). As expected,

8-2C and 8-24 iNKT cells had higher frequency of stage 1 and 2 whereas 8-DO, 8-DN and 2-DO had higher frequency of stage 3 (Fig. 2.9.b). Importantly, the relative proportion of stage 3 between 8-DO and 8-24 was similar in single and dual (competitive) chimaeras (Fig. 2.9.c), suggesting that intrinsic factors drive iNKT cell effector differentiation. Expression of CD69,

CD122 and NKG2D, which are expressed at higher levels on iNKT1 cells (stage 3), were elevated in the 8-DO, 8-DN, 7-DO and 2-DO chimaeras (Fig. 2.9.d-f). The fact that expression of iNKT stages and other markers agrees with the distribution of subsets in retrogenic mice substantiates an important, cell intrinsic role for the TCR in the differentiation of iNKT cells.

Figure 2.9

Figure 2.9. iNKT cell differentiation in TCRβ retrogenic mice. (a) Correlations between mean frequency of thymic and splenic iNKT17 cells and TCR binding avidity or half-life for CD1d-PBS57 tetramers. Correlations were analyzed by linear regression. Shown are the lines of best fit (thick line) and 95% confidence intervals (thin lines). (b) Frequency of iNKT cell stages in retrogenic mice expressing the indicated TCRβ chain. Data represents individual mice and the mean ± s.e.m (n=14 to 25 from 8 experiments). p-value was obtained by Kruskal-Wallis test with Dunn’s post test. (c) Frequency of iNKT cells at stage 3 of development in competitive retrogenic mice engrafted with equal amounts of TCRβ-/- BM transduced with 8-DO (GFP+) or 8- 24 (Ametrine+). Data represents individual mice and the mean ± s.e.m (n=5 from 3 experiments). Data was analyzed by paired t-test. (d-f) Expression of CD69, CD122 and NKG2D in TCRβ retrogenic mice. Data represents individual mice and the mean ± s.e.m (n=4 to 16 mice per group). p-value was obtained by Kruskal-Wallis test with Dunn’s post test.

Because the 8-2C and 8-24 TCRs shared the Jβ2.5 gene segment, we considered the possibility that sequence elements of some Jβ segments could bias differentiation towards iNKT2 cells. To explore this, we sorted iNKT cells subsets from pooled wild-type thymi based on differential expression of CD4, CD27 and NK1.1, surrogate markers for iNKT1 (NK1.1+), iNKT2 (NK1.1-CD4+CD27+) and iNKT17 (NK1.1-CD4-CD27-), and sequenced their TCRβ repertoire (Fig. 2.10.a). We observed increased Vβ7 (TRBV29) usage by iNKT2 cells, as previously reported(68), yet there was no particular bias in Jβ2.5 (Fig. 2.10.b). Interestingly, the

TCR containing the Vβ7 framework in this study did not have the highest iNKT2 cell frequency, reinforcing the importance of the CDR3β in the selection of Vβ7+ TCRs with elevated TCR half- life in the wild type repertoire.

Overall, these data shows that iNKT cell effector differentiation depends on the amount of time their TCRs are engaged.

Figure 2.10. Vβ and Jβ usage by iNKT cell subsets. (a) Gating strategy for sorting iNKT cell subsets. iNKT cells were enriched from thymi of 8 B6 mice and sorted based on surface expression of NK1.1, CD4 and CD27. Frozen cells were shipped to Adaptive Biotechnologies (Seattle, WA) for DNA-extraction and sequencing of the TCRβ chain. (b) Vβ family usage by iNKT1, iNKT2 and iNKT17 subsets. (c) Jβ family usage by iNKT1, iNKT2 and iNKT17 subsets.

Figure 2.10

a 1) Thymocytes 2) Singlets 3) Live/CD8- Aqua Life/Dead

CD8

3) PBS57 tetramer+ 4) NKT1.1- TCRβ+ (NKT) 5) NKT1.1+ (NKT1) PBS57 tetramer

TCRβ NK1.1 4.1) CD27+CD4+(NKT2) 5.1) CD4+(NKT1 CD4+) 4.2) CD27-CD4- (NKT17) 5.2) CD4-(NKT1 CD4-) CD4 CD4

CD27 NK1.1 b 50 NKT1 40 NKT2 NKT17 30

Percent (reads) 10

0 1 1 1 1 1 1 1 1 1 1 2 1 2 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 TCRBV01-0 TCRBV02-0 TCRBV03-0 TCRBV04-0 TCRBV05-0 TCRBV06-0 TCRBV07-0 TCRBV10-0 TCRBV11-0 TCRBV12-0 TCRBV12-0 TCRBV13-0 TCRBV13-0 TCRBV13-0 TCRBV14-0 TCRBV15-0 TCRBV16-0 TCRBV17-0 TCRBV18-0 TCRBV19-0 TCRBV20-0 TCRBV21-0 TCRBV22-0 TCRBV23-0 TCRBV24-0 TCRBV25-0 TCRBV26-0 TCRBV27-0 TCRBV28-0 TCRBV29-0 TCRBV30-0 TCRBV31-0

25 c NKT1

20 NKT2 NKT17 15

5 Percent (reads)

0 1 2 3 4 5 6 7 1 2 3 4 5 6 7

TCRBJ01-0 TCRBJ01-0 TCRBJ01-0 TCRBJ01-0 TCRBJ01-0 TCRBJ01-0 TCRBJ01-0 TCRBJ02-0 TCRBJ02-0 TCRBJ02-0 TCRBJ02-0 TCRBJ02-0 TCRBJ02-0 TCRBJ02-0

2.4.1 TCR activation in vitro is influenced by the context of lipid presentation

We hypothesized that two non-mutually exclusive mechanisms could explain the different TCR biophysical parameters involved in selection and differentiation. First, TCR sensitivity for self-antigens could change from pre- to post-selected stages. Second, selection and differentiation could be mediated by different self-ligands in the thymus. Because of the overall low frequency of iNKT cells, it was not feasible to test our first hypothesis on retrogenic iNKT cells. However, we explored the second hypothesis with the TCR hybridomas used for kinetic binding assays. Although these cells may not entirely reflect physiological signalling of iNKT cells, they can be used to understand how changes in stimuli affect activation outcomes.

We first measured Nur77 up-regulation following stimulation with PBS44, PBS218 and

PBS221 loaded on plate-bound CD1d, ligands for which we had calculated the avidity and half- life (Fig. 2.11.a). Only 8-2C (high avidity and long half-life) and 2-DO (low avidity and short half-life) TCRs showed significantly higher and lower 1/EC50 respectively (Fig. 2.11.a). All

TCRs with intermediate avidity and half-life up-regulated Nur77 to the same extent, indicating that upon stimulation with immobilized CD1d-glycolipid complexes, high avidity can compensate for short half-life and vice versa (Fig. 2.11.a). Importantly, as with avidity and half- life measurements (Fig. 2.1), the hierarchy of TCR potency was the same for all ligands used, supporting that none of the TCRs preferentially recognized a particular ligand.

Figure 2.11

a Plate-bound b Thymocytes c BMDC

100 ) 5.0 100 15 100 0.4 -2 PBS44 PBS44 PBS44 2.0 75 1.0 75 75 0.3 10 50 50 50 0.2 0.5 5 25 25 25 0.1 1/EC50 Nur77 Nur77+ cells (%) 1/EC50 Nur77 Nur77+ cells (%) Nur77+ cells (%)

0 1/EC50 Nur77 (x 10 0.0 0 0 0 0.0 100 102 104 10-3 10-1 101 100 101 102 103 8-24 8-24 8-24 8-2C PBS44 (lng/ml) 8-2C Thymocytes (lng/ml) BMDCs (lng/ml) 8-2C 8-DN 8-DN 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO 8-DN 8-DO 7-DO 2-DO

100 ) 8.0 100 20 PBS218 100 0.5 PBS218 -2 PBS218 0.5 0.4 75 0.4 75 15 75 0.3 50 50 10 50 0.2 0.2

25 1/EC50 Nur77 25 5 25 0.1 1/EC50 Nur77 Nur77+ cells (%) Nur77+ cells (%) Nur77+ cells (%) ND 0 0.0 ND 0 0 0.0 1/EC50 Nur77 (x 10 0 100 102 104 10-3 10-1 101 100 101 102 103 8-24 8-24 PBS218 (lng/ml) Thymocytes (ng/ml) 8-24 8-2C 8-2C 8-2C 8-DN 8-DN 8-DN 8-DO 7-DO 2-DO

BMDCs (lng/ml) 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO

100 ) 4.0 100 8 100 0.3 -2 PBS221 PBS221 PBS221 6 3.0 75 75 75 0.3 0.2 2 50 0.2 50 50 0.1 25 0.1 25 1 25 1/EC50 Nur77 1/EC50 Nur77 Nur77+ cells (%) Nur77+ cells (%) Nur77+ cells (%) ND ND

0 1/EC50 Nur77 (x 10 0.0 0 0 0 0.0 100 102 104 10-3 10-1 101 100 101 102 103 8-24

Thymocytes ( ng/ml) 8-24 8-24 8-2C

PBS221 ( ng/ml) 8-2C BMDCs (lng/ml) 8-2C 8-DN 8-DO 7-DO 2-DO 8-DN 8-DN 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO

Figure 2.11. Stimulation of hybridomas expressing selected iNKT TCRs with lipid antigens presented by plate-bound CD1d or APCs. (a-c) Expression of Nur77 on hybridomas containing the indicated iNKT TCRs following stimulation with PBS44 (top row), PBS218 (middle row) or PBS221 (bottom row) presented by plate-bound CD1d (a), thymocytes (b) or BMDCs (c) for 2 hrs and analyzed by flow cytometry. The data was fit to a 4-parameter logistic non-linear model and the relative avidity was calculated as the 1/EC50. Data represents the mean ± s.e.m. of 3-5 individual experiments.

The potency of ligands is influenced by their kinetics of trafficking within antigen presenting cells (APCs). We loaded either wild-type thymocytes or bone marrow derived dendritic cells (BMDCs) with a high concentration (5µg/ml) of PBS44, PBS218 or PBS221, and used increasing APC:hybridoma ratios to stimulate TCR-expressing hybridomas (Fig. 2.11.b,c).

In contrast to the plate-bound setting, the hierarchy of activation was not the same for every

69 ligand. PBS44 was much more potent than either PBS218 or PBS221, with all TCRs except 8-24 reaching a similarly high 1/EC50 (Fig. 2.11.b,c). Because PBS44 shares the same lipid tail with

PBS221, this difference is likely mediated by the interaction with the TCR rather than stability of the lipid in CD1d. In support of this, TCR activation displayed essentially the same pattern for both α-glucosylceramides (PBS218 and PBS221), with the exception of increased 7-DO activation towards PBS221-loaded thymocytes (Fig. 2.11.b,c). This suggests that presentation in the context of cells could have dramatic effects on iNKT cell activation by directly affecting the

CD1d-lipid-TCR interface.

TCR binding and activation by self-lipids reflects the half-life of CD1d- lipid-TCR interaction

Based on the above results, we assessed the TCR binding strength and potency towards

CD1d-self-antigen complexes presented by developmentally relevant cells, and tested whether it recapitulated the hierarchy of avidity or half-life seen in vivo. First, we measured the ability of each TCR-expressing hybridoma to form conjugates with fluorescently labeled CD1d+/+ and

CD1d-/- thymocytes (Fig. 2.12.a). The extent of conjugate formation correlated well with the half-life but not the avidity of the TCR for PBS57-CD1d complexes (Fig. 2.12.a). Second, we used thymocytes as APCs without addition of exogenous antigens as an unbiased approach to measure signal strength towards self-antigens (Fig. 2.12.b). As with the conjugate formation, the potency of the TCR towards thymocytes correlated better with TCR half-life than avidity for

CD1d-PBS57 (Fig. 2.12.b). This indicates that half-life better predicts TCR mediated cell-cell interactions and TCR signal strength.

Figure 2.12

a 20 p=0.0077 15 r2=0.48 15 r2=0.960 10 p=0.127 p=0.0006 4 10 10

2 5 5 Ratio of bound Ratio of bound CD1dwt:CD1dko CD1dwt:CD1dko Ratio of bound

CD1dwt : CD1dko 0 0 0 10-4 10-3 10-2 100 102 104 106 8-24 8-2C 8-DN 8-DO 7-DO 2-DO 1/EC50 PBS57 Tetramer t1/2 PBS57 Tetramer

b 80 3 r2=0.459 3 r2=0.889 p=0.139 p=0.005

60 cells cells + 2 + 2 40 cells (%) + Nur77 1 Nur77 1 20 50 50 Nur77 1/EC 1/EC 0 0 (unloaded thymocytes) 0 (unloaded thymocytes) 10-2 100 102 10-4 10-3 10-2 100 102 104 106 Thymocytes (x106) 1/EC50 PBS57 Tetramer t1/2 PBS57 Tetramer

c 80 TCRβ-/lo 80 TCRβhi 80 mDCs p=0.0292 p=0.0083 p=0.0292 60 60 60

40 40 40 cells (%) cells (%) cells (%) + + + 20 20 20 Nur77 Nur77 Nur77 0 0 0 8-24 8-24 8-24 8-2C 8-2C 8-2C 8-DN 8-DN 8-DN 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO 8-DO 7-DO 2-DO

d 80 TCRβ-/lo 80 TCRβhi 50 mDCs 2 2 2 r =0.511 r =0.445 40 r =0.351 60 p=0.111 60 p=0.148 p=0.215 30 cells (%) cells (%)

cells (%) 40 40 + + + 20 20 20 10 Nur77 Nur77 Nur77 0 0 0 10-4 10-3 10-2 10-4 10-3 10-2 10-4 10-3 10-2

1/EC50 PBS57 Tetramer 1/EC50 PBS57 Tetramer 1/EC50 PBS57 Tetramer e 80 TCRβ-/lo 80 TCRβhi 50 mDCs 2 2 2 r =0.973 r =0.827 40 r =0.858 60 p=0.0003 60 p=0.012 p=0.008 30

cells (%) 40 cells (%) 40 + + cells (%)

+ 20 20 20 10 Nur77 Nur77 Nur77 0 0 0 100 103 106 100 103 106 100 103 106 t PBS57 Tetramer t PBS57 Tetramer t PBS57 Tetramer 1/2 1/2 1/2

Figure 2.12. TCR binding and activation by self-lipids reflects the half-life of CD1d-lipid- TCR interaction (a) Conjugate formation between hybridomas expressing the indicated iNKT TCR and CD1d-expressing thymocytes (left panel). Data shows the ratio of fluorescently labeled CD1d+/+ or CD1d-/- thymocytes bound to hybridomas analyzed by flow cytometry (4 experiments). Correlations between mean conjugate formation and TCR binding avidity (middle panel) or half-life (right panel) for PBS57 tetramers. (b) Expression of Nur77 on hybridomas containing the indicated iNKT TCR following stimulation with thymocytes for 2 hrs and

analyzed by flow cytometry (left panel) (5 experiments). Correlation between mean 1/EC50 of Nur77 upregulation and TCR binding avidity (middle panel) or half-life (right panel) for PBS57 tetramers. (c) Expression of Nur77 on hybridomas containing the indicated iNKT TCRs following stimulation with TCRβ-/lo, TCRβ+ or mDCs for 2 hrs. Data represents the mean ± s.e.m. of 2 individual experiments. (d, e) Correlations between mean Nur77 expression on hybridomas after incubation with sorted thymocytes and TCR binding avidity (d) or half-life (e) for PBS57 tetramers. Data represents the mean ± s.e.m. (a, b left panels, c). Correlations were analyzed by linear regression. Shown are the line of best fit (thick line) and 95% confidence intervals (thin lines).

Given that most iNKT2 cells are present in the medulla of the thymus(118), we entertained the possibility that selection and differentiation are compartmentalized events subjected to unique sets of ligands. When bulk thymocytes are used for antigen presentation, these signals might be diluted out. Therefore, we sorted TCRβ-/lo and TCRβhi thymocytes as well as medullary DCs, which are found in the cortex, inner-cortex/medulla, and medulla respectively. Each cell type induced different levels of activation, with TCRβ+ cells being the most potent stimulators (Fig. 2.12.c). In each case, Nur77 up-regulation correlated better with

TCR half-life than avidity (Fig. 2.12.d,e). This suggests that the discrepancy between selection efficiency and effector differentiation of iNKT cells cannot be explained by activation through sets of ligands expressed in different thymic compartments.

The iNKT TCR does not control the quality of the response in vivo

Although iNKT cell subsets preferentially secrete TH1, TH2 or TH17 cytokines, this response is not absolute given that many iNKT cells produce both IL-4 and IFN-γ and the extent of their activation is influenced by their location within lymphoid organs(118). Given that our set of TCRs induced different frequency of iNKT cell subsets, we predicted that they would also polarize the iNKT cytokine response upon antigen stimulation. To test this, we challenged the 8-

DO, 8-DN, 8-24 and 7-DO retrogenic chimaeras, which express TCRs with intermediate avidity and half-life, with α-GalCer or PBS221 and analyzed production of IL-4 and IFN-γ after 90 minutes. All splenic (Fig. 2.13) and hepatic (not shown) iNKT cells produced high amounts of cytokines, with a prominent population of IFN-γ and IL-4 double positive cells. However, we did not observe any bias towards either IL-4 or IFN-γ with either ligand, indicating that although the

TCR can influence the differentiation of the cells, it does not skew them towards a particular cytokine in short-term stimulation (Fig. 2.13).

Figure 2.13 a αGalCer b PBS221 8-DO 8-DN 8-24 7-DO 8-DO 8-DN 8-24 7-DO 14.4 28.4 18.8 27.4 13.7 19.9 11.7 26.4 10.2 29.59.15 30.610.8 32.111.9 27.7 IFN-γ 44.3 12.9 44.3 9.6 55.7 10.7 51.2 10.7 IFN-γ 49 11.345 15.250.4 6.6753.6 6.8 IL-4 IL-4 60 60 3 60 60 1.5 + +

45 45 45 45 2 1.0 /IFN-γ /IFN-γ + + 30 30 30 30 0.5 iNKT cells (%) iNKT

iNKT cells (%) iNKT 1 + + iNKT cells (%) iNKT 15 iNKT cells (%) iNKT

15 15 + 15 + Ratio IL-4 Ratio IL-4 IL-4 IL-4 IFN-γ 0 0.0 0 IFN-γ 0 0 0 8-24 8-24 8-24 8-24 8-24 8-24 8-DN 7-DO 8-DO 8-DN 8-DO 8-DN 8-DN 8-DN 8-DN 7-DO 8-DO 7-DO 8-DO 7-DO 8-DO 8-DO 7-DO

Figure 2.13. The TCRβ chain does not control the quality of the iNKT cell cytokine response in short term in vivo stimulation. (a) Representative flow cytometry plots of splenic iNKT cells stimulated with αGC (0.5µg per mouse) for 90 minutes and stained for IFN-γ and IL- 4 as well as the corresponding frequency and ratio of IL-4 to IFN-γ. (b) Representative flow cytometry plots of splenic iNKT cells stimulated with PBS221 (2µg per mouse) for 90 minutes and stained for IFN-γ and IL-4 as well as the corresponding frequency and ratio of IL-4 to IFN-γ. Data shows individual mice and the mean ± s.e.m. (n=13 to 17 from 6 experiments).

2.5 Discussion

To the best of our knowledge, this study provides the first comprehensive in vivo characterization of how TCR binding parameters influence iNKT cell development and effector differentiation. iNKT cell development is constrained by positive and negative selection events(204, 210). Our work shows that within this developmental window, the TCR binding avidity and half-life differently control these biological outcomes. In line with previous observations(33, 130, 131, 133, 203), the TCR did not induce preferential IL-4 or IFN-γ production by iNKT cells, indicating that the TCR controls iNKT cell central priming but not antigen mediated polarization in the periphery.

Although some studies investigated the impact of differential TCR Vβ gene usage(68,

203, 204, 275), they were confounded by the repertoire variability found within the CDR3β region, making it difficult to draw conclusions. We accounted for this by using a selection of

TCR variants in both the CDR2β and CDR3β loops, and displaying a wide range of avidities and half-lives for CD1d-lipid. We found that both the CDR2β and CDR3β regions influence iNKT cell selection efficiency as a result of differential TCR avidity for CD1d-lipid complexes, supporting previous findings(204). Although one study found that the CDR3β was dispensable for positive selection, the binding parameters of the TCRs used had not been assessed(35).

Our study supports the view that iNKT TCRs exhibit extensive ligand promiscuity(195,

204). Indeed, the comparison of ligands with differences in both the acyl chain and head-group revealed that regardless of the ligand, the relative TCR avidity, half-life and hybridoma activation was the same for the 6 TCRs tested. Noteworthy, the activation hierarchy of these

TCRs was different between cell-free and cell-mediated stimulation. This suggests that properties of cell-cell interactions have an important role in iNKT cell activation. In accordance

74 with this idea, a recent study showed that actin-dependent formation of large CD1d nanoclusters on APCs is crucial for iNKT cell autoreactivity(294). This may also reconcile the identification of some ligand-specific iNKT TCRs in a recent study(286), in which lipid loaded-APCs were used for TCR identification. In our study, binding strength and potency of TCRs towards self- antigens presented by developmentally relevant cells were reflective of the half-life of interaction. Therefore, although presentation of antigens by cells can alter the hierarchy of TCR potency, half-life was a good predictor of activation by self-lipids.

Although support for both the occupancy model and the kinetic proofreading models is found in the literature, a concrete consensus is still missing and many other models have emerged to explain experimental discrepancies(148). Surprisingly, our experiments show that whereas selection is reflective of the avidity of the TCR, effector differentiation correlated better with half-life. Therefore, using iNKT cells we demonstrated that TCR signalling is not universally dependent on one kinetic parameter. Modeling TCR signalling for classical T cells has been challenging due to the inability to directly compare unique TCR-pMHC interactions with structural differences in docking modes, involvement of the CD4/8 co-receptors, as well as the use of ligands or TCRs where the KOFF directly correlates with the KD. Indeed, Palmer and colleagues recently suggested that the rate-limiting step during TCR triggering involves the recruitment of active Lck-coupled CD4/8 co-receptors(180). In contrast to classical T cells, iNKT TCRs are promiscuous, the TCR docking strategy onto CD1d-lipid complexes is strikingly conserved and iNKT cells are considered co-receptor independent(205, 206). Therefore, alternative models are needed to explain effects of TCR signal strength on iNKT cells. It has been proposed that extensive re-binding takes place at the TCR synapse and hence a model where the dwell time of interaction takes into account the KON is more appropriate(184, 186). It is unlikely that this model is suitable for both selection and differentiation of iNKT cells given

75 that the hierarchy of TCRs is different for each process. Additionally, selection and differentiation are likely not dependent on discrete sets of ligands since the hierarchy of TCR strength was the same towards different populations of thymic cells. Rather, we propose that iNKT cell selection and effector differentiation are subjected to differential TCR sensitivity.

Conventional T cell positive selection signals involve slow Ca2+ accumulation over time from transient interactions of developing thymocytes with cortical thymic epithelial cells, whereas negative selection results from sustained interactions with mDCs accompanied with strong/fast

Ca2+ burst(153, 295). Furthermore, changes in TCR sensitivity have been reported as a result of changes in surface levels and clustering of the TCR(296, 297) as well as expression of phosphatases(298) and other components of the TCR signalling pathway(299, 300). It is conceivable that pre-selected iNKT cells experience short-lived interactions with DPs and these signalling events rely more on the avidity of CD1d-lipid-TCR complexes, which ultimately reflects the number of receptors engaged. On the other hand, differentiation likely relies on long cell-cell interactions and is more sensitive to differences in TCR half-life. Our work contributes to the understanding of how TCR signals induce fate decisions, which is crucial for the rational design of T cell therapies.

3 Chapter 3: Shp-1 inhibits cytokine receptor signalling to modulate iNKT cell differentiation

Mayra Cruz Tleugabulova1, Juan Mauricio Umana1, Irene Lin1, Thierry Mallevaey1

1Department of Immunology, University of Toronto, 1 King’s College Circle, Toronto, ON, M5S

1A8, Canada,

M.C.T. designed and performed experiments, analyzed the data and wrote the manuscript. JMU and IL performed experiments. T.M. designed experiments and wrote the manuscript.

This manuscript is being prepared for publication.

3.1 Abstract

Invariant Natural Killer T (iNKT) cells are lipid reactive cells that acquire a memory-like phenotype during development through signalling programs that differ from those of classical T cells. Phosphatases play a crucial role in regulation of signal transduction. We investigated how the Src homology domain-containing phosphatase 1 (Shp-1) regulates iNKT cell biology using mice with conditional deletion of Shp-1 on αβ T cells. We show that Shp-1 deficiency does not influence selection of iNKT cells; but rather biases iNKT cells towards an iNKT2 and iNKT17 phenotype in the thymus and promotes IL-4 over IFN-γ secretion upon antigen stimulation.

TCR- and SLAMF6-mediated signals, which have been suggested to promote iNKT2/17 differentiation, were not enhanced in Shp-1 deficient iNKT cells. Instead, Shp-1 regulated iNKT cell in vitro proliferation upon exposure to IL-2, IL-7 or IL-15. Our findings suggest that Shp-1 controls iNKT cell effector differentiation after positive selection through the modulation of cytokine responsiveness.

3.2 Introduction

The remarkable capacity of the immune system to fight disease relies on the ability of immune cells to sense, transmit and process signals from their environment. A central mechanism of signal transduction involves phosphorylation of proteins by protein tyrosine kinases upon ligation of receptors on the surface of cells(301). The newly formed phosphotyrosine groups then recruit additional proteins by binding to specific phosphotyrosine binding motifs, allowing for signal amplification. Essential for the regulation of these pathways are protein tyrosine phosphatases, which counteract kinase activity by de-phosphorylating tyrosine residues.

The Src homology region 2 domain-containing phosphatases (Shp-1 and Shp-2), are the only phosphatases known to contain two tandem Src homology 2 (SH2) domains(250, 251). Shp-

1 (encoded by the Ptpn6 gene) is almost exclusively expressed on haematopoietic cells, whereas

Shp-2 is more ubiquitously expressed. The importance of Shp-1 in controlling immune cell signalling is exemplified in motheaten mice, in which a null frameshift mutation in the Ptpn6 locus leads to severe defects in all hematopoietic cells resulting in systemic inflammation, autoimmunity, interstitial pneumonia and death at 2-3 weeks(302). A less severe mutation in

Ptpn6 that particularly alters the catalytic domain, results in 20% activity of the wild type Shp-1 and leads to a milder phenotype in “motheaten viable” mice, extending the life span to 8-10 weeks(303). Studies using these mice have placed Shp-1 as a key negative regulator of immune cell activation(304, 305), downstream of receptors such as the T cell receptor(306), B cell receptor(307), natural killer (NK) cell receptors(308, 309), chemokine and cytokine receptors(310-313), SLAM receptors(233, 234), the death receptor FAS as well as integrins(314,

315). However, most of these studies are confounded by cell extrinsic factors resulting from the systemic inflammation in motheaten mice, and may not reflect bona fide modulation of receptor

79 signalling. To address this issue, a mouse model containing a floxed Ptpn6 locus (here named

Shp-1f/f) was created and is now shedding light on the role of Shp-1 in the regulation of signalling cascades in various immune cells. For example, knocking out Shp-1 specifically in B cells or dendritic cells results in autoimmunity whereas in Shp-1 ablation in neutrophils leads to cutaneous inflammation(254, 316). Surprisingly, deletion of Shp-1 in T cells did not result in either the autoimmune or inflammatory phenotypes observed in motheaten mice, but the mice accumulate memory CD4 and CD8 T cells(260, 261). The number of potential binding candidates of Shp-1 make it challenging to uncover its function in T cells(261). An initial study revealed increased proliferation of Shp-1 deficient T cells in response to CD3 ligation(260). It was later shown that this was due to the increased memory T cell compartment, and when analyzed in isolation, Shp-1-deficient naïve T cells do not display increased TCR signalling(261). Challenging this view, others suggest that Shp-1f/f CD4-cre mice have a mildly enhanced TCR signalling during negative selection(263). On the other hand, Ishikawa et al propose a positive role of Shp-1 in TCR signalling during T cell development through phosphorylation of Shp-1 serine 557(317). These recent studies clearly emphasized that there is still much to learn about the regulation of the phospho-proteome in lymphocytes.

Invariant Natural Killer T (iNKT) cells are CD1d-restricted, lipid-reactive, innate-like lymphocytes that play protective or deleterious functions in many diseases due to their capacity to secrete large amounts of a wide array of cytokines within minutes to hours after antigen exposure(13). iNKT cells develop in the thymus where they are selected and primed on double positive (DP) thymocytes. During or shortly after selection they differentiate into subsets that preferentially secrete TH1 (NKT1), TH2 (NKT2) and TH17 (NKT17) cytokines, and can be identified by differential expression of the transcription factors T-bet, PLZF and RORγt respectively(63). Their particular function and development requires numerous signals distinct

80 from classical T cells. Specifically, iNKT cell development relies on strong TCR stimulation, as well as SLAMF1- and SLAMF6-mediated signals(45, 208). In the periphery, CD1d-lipid complexes and/or cytokine signals induce iNKT cell activation without the need of primary stimulation(13), and homeostasis of the subsets iNKT1 and iNKT17 rely on the cytokines IL-15 and IL-7 respectively. Shp-1 has been proposed to regulate several of these signalling pathways on classical T cells, yet its function in iNKT cell development and function remains to be determined. Using mice heterozygous for the motheaten locus, Casorati and colleagues suggested that Shp-1 expression on iNKT cells prevents hyperactivation of the cells(269). Yet the contrasting reports on Shp-1 function on T cells from use of these mice convey the need to assess its function under non-inflammatory conditions. In this study, we used the Shp-1f/f CD4-cre mouse model to specifically ablate Shp-1 expression from αβ T cells at the CD4 CD8 double positive (DP) stage of thymocyte development. Although these mice have normal numbers of iNKT cells in all the tissues tested, they have increased frequency of iNKT2s and iNKT17s and a concomitant reduction in iNKT1s in the thymus, as well as a functional bias towards a TH2 response. Lack of Shp-1 did not enhance signals downstream of the TCR or SLAMF6 receptors as previously proposed. Rather, Shp-1 regulates iNKT cell proliferation in response to multiple cytokines. We propose a model whereby Shp-1 regulates cytokine receptor signalling in iNKT cells to regulate their TH1/2/17 effector differentiation and functional responses.

3.3 Experimental Procedures

Mice and reagents

Mice were used between 5-8 weeks of age, unless otherwise specified. C57BL/6 (B6) wild-type (WT) and RAG1-/- mice were purchased from Jackson Laboratories. Shp-1f/f and Shp-

1f/f CD4-cre mice were generously provided by Dr. Benjamin Neel (Princess Margaret Cancer

Center, Canada). CD1d-/- mice were generated and generously provided by Dr. Chyung-Ru

Wang (Northwestern University, USA)(276). SAP-/- mice were generously provided by Andre

Veillette (Institut de Recherches Cliniques de Montréal, Canada). All strains were housed at the

Division of Comparative Medicine, University of Toronto animal facility under specific- pathogen free conditions, and animal procedures were approved by the Faculty of Medicine and

Pharmacy Animal Care Committee at the University of Toronto (Animal use protocols 9003,

9571, 10135, 10715 and 11113). α-galactosylceramide (KRN7000, αGC) was purchased from

Diagnocine. Antibodies used were purchased from eBiosciences, Biolegend or BD Biosciences.

PBS57-loaded and unloaded biotinylated CD1d monomers were obtained from the NIH Tetramer

Core Facility, and were tetramerized by addition of fluorochrome-conjugated streptavidin. For stimulation assays, mouse CD1d monomers were purified from the culture supernatant of transduced HEK293 cells lines obtained from the NIH Tetramer Core facility, using affinity chromatography.

Flow cytometry

Cells were stained with Aqua Live/Dead (Life Technologies) in PBS for 30 minutes at room temperature (RT), and stained with antibodies and tetramers for 45 minutes in MACS buffer (PBS containing 0.5% fecal calf serum and 2mM EDTA). For transcription factor staining, cells were fixed and permeabilized using the FoxP3/transcription factor buffer set

(eBiosciences). For cytokine staining, cells were fixed and permeabilized using the

Cytofix/Cytoperm buffer set (BD Biosciences). Cells were analyzed using the LSR Fortessa (BD

Biosciences).

Cell sorting

Thymocytes and splenocytes were stained with APC conjugated PBS57 tetramers. iNKT cells were enriched from these preparations using an APC positive selection kit according to the

82 manufacturer’s protocol (STEM CELL Technologies). For in vitro polarization or adoptive transfer experiments, enriched iNKT cells were used directly and identified by PBS57 tetramer staining at the time of analysis by flow cytometry using LSR Fortessa (BD Biosciences). For in vitro proliferation experiments, cells were further stained with Aqua Live Dead marker (Life

Technologies) and TCRβ antibody (eBiosciences), and sorted using the FACSAria (BD

Biosciences). Cells were washed with PBS, incubated with 5ng/ml CFSE, washed with cRPMI and seeded in 96 well plates with 10ng/ml of IL-2, IL-7 or IL-15.

TH polarization assay

Enriched or sorted cells were incubated for 4 days in 96 well plates with specific cytokine conditions: TREG (2.5ng/mL TGFβ, 2µg/ml anti-CD28, 100U IL-2); TH17 (2ng/ml TGFβ,

25ng/ml IL-6, 2µg/ml anti-CD28, 10µg/ml anti-IL-4, 10µg/ml anti-IFN-γ); TH1 (15ng/ml IL-12,

10µg/ml anti-IL-4, 2µg/ml anti-CD28, 100U IL-2); TH2 (20ng/ml IL-4, 10µg/ml anti-IFN-γ,

2µg/ml anti-CD28, 100U IL-2). Cells were then stimulated in vitro with anti-CD3 for 4 hours with Golgi stop.

Mixed bone marrow chimera

Bone marrow was collected from femur and tibia of C57BL/6 and Shp-1f/f CD4-cre mice, and transferred either separately or in a 1:1 ratio into lethally irradiated (2 x 450cGy)

RAG1-/- recipient mice. Mice were analyzed at 7 weeks post-transfer.

In vivo stimulations

Mice were injected with 0.5µg of αGC, and spleen and liver cells were collected after 90 min. Cells were stained for extracellular proteins, fixed and permeabilized using

Cytofix/Cytoperm buffer (DB Biosciences), stained for cytokines and analyzed by flow cytometry.

In vitro stimulations

To measure cytokine secretion, 96 well flat bottom plates were coated with 10µg/ml of mCD1d monomers for 1 hour at 37°C, washed and incubated with 5µg/ml αGalCer in PBS containing 0.05% tyloxypol overnight, or wells were coated with 1µg/ml of anti-CD3 antibody

(clone 2C11) overnight. Wells were then washed, seeded with 0.5 million thymocytes or splenocytes, and incubated at 37°C for 6 hours, with addition of Golgi stop after 2 hours. Cells were then analyzed by flow cytometry. To measure upregulation of Nur77 and Egr2 upon TCR signalling, 96 well flat bottom plates were coated with different concentrations of anti-CD3 antibody (clone 2C11) for 2 hours at 37°C. 0.5 million thymocytes and splenocytes were then added to each well and incubated for 2 hours at 37°C. Cells were then analyzed by flow cytometry.

PLZF upregulation in double positive thymocytes

Pre-selection double positive thymocytes (PSDPs) were enriched and stimulated as previously described(230). Flat bottom 96 well plates were coated with 1µg/ml anti-CD3 and

5µg/ml of either anti-SLAMF6, anti-CD28 or isotype control antibodies overnight. Plates were washed and seeded with 0.5 million enriched PSDPs. PSDPs were stimulated for 30 minutes to measure Egr2 upregulation or 48 hours to measure PLZF upregulation, and analyzed by flow cytometry. To measure downregulation of Egr2 expression, PSDPs were left in media at 37°C after 30 minutes stimulation with anti-CD3 and anti-SLAMF6.

Data Analysis

3.4 Results

Cell intrinsic Shp-1 deletion biases iNKT cell effector differentiation

To investigate how Shp-1 affects iNKT cell development and function, we took advantage of Shp-1f/f CD4-cre mice in which Shp-1 is deleted in αβ T cells at the DP stage of thymic development. Because iNKT cells are selected at the DP stage, the Ptpn6 locus is excised upon CD4 expression. We confirmed that the Ptpn6 gene was deleted in splenic iNKT cells from

Shp-1f/f CD4-cre mice (Fig. 3.1.a). Importantly, absence of Shp-1 at the DP stage did not affect expression of CD1d or the SLAM family receptors SLAMF1 and SLAMF6, which are all essential for iNKT cell development and function (Fig. 1.b). Furthermore, CD1d expression levels on splenic CD11b+ and CD11c+ antigen-presenting cells were not affected in Shp-1f/f

CD4cre mice (Fig 3.1.a). We then analyzed the frequency of iNKT cells in different organs in

Shp-1f/f and Shp-1f/f CD4-cre mice (Fig. 3.2.a). Interestingly, we did not observe any difference in their frequency and absolute numbers (not shown) in the thymus, spleen or liver, and only a modest increase in frequency in the inguinal lymph nodes, indicating that iNKT cell selection and localization was largely unaltered (Fig. 3.2.a). Upon or after positive selection, iNKT cells differentiate into IFN-γ-producing (iNKT1), IL-4-producing (iNKT2) or IL-17-producing

(iNKT17) cells that can significantly alter the function of surrounding cells(68). To assess the frequency of these subsets, we analyzed the expression of PLZF and RORγt in iNKT cells from

Shp-1f/f and Shp-1f/f CD4-cre mice, which define the different subsets as PLZFlo, RORγt-

(iNKT1), PLZFhi, RORγt- (iNKT2) and PLZFint, RORγt+ (iNKT17) (Fig. 3.2.b). We observed a stark increase in iNKT2s and iNKT17s, and a concomitant decrease in iNKT1s, in the thymus of

Shp-1f/f CD4cre mice (Fig. 3.2.b). However, iNKT cell subsets remained unaffected in the spleen of these mice (Fig. 3.2.b).

To test whether this phenotype was cell intrinsic, we performed single and mixed bone marrow chimeras using CD45.1 wild type and CD45.2 Shp-1f/f CD4-cre mice as donors and

RAG1-/- mice as recipients (Fig. 3.2c). In both single and mixed bone marrow chimeras, thymic

Shp-1-deficient iNKT cells still contained higher frequency of iNKT2 cells than wild type iNKT cells, indicating that Shp-1 deficiency skews thymic iNKT cell effector differentiation towards iNKT2s in a cell-intrinsic fashion (Fig. 3.2.c). Concurrent with the increase in PLZF, we observed increased expression of its upstream regulator, Egr2, in iNKT cells from Shp-1 deficient mice (Fig. 3.2.d). To test whether this phenotype was a transient developmental defect, we analyzed older 16-week old mice for iNKT cell subsets. Although the overall frequency of iNKT2s was much lower compared to 6-week-old mice, we still observed an increase in iNKT2s and iNKT17s in the thymus, but not the spleen, of Shp-1f/f CD4-cre mice compared with Shp-

1f/f mice (Fig. 3.2.e). Overall, these results show that cell intrinsic Shp-1 deficiency leads to increased frequency of iNKT2s in the thymus but not the spleen.

Figure 3.1

PCR for Shp-1 PCR for Cre a b Gated on DPs Gated on DPs Gated on DPs Gated on CD11b+ Gated on CD11c+ 1.4 ns 1.4 ns 3.2 ns 10 splenocytes 1.5 splenocytes

) ns ns ) ) 3 ) ) 3 3 3 3 1.2 1.2 7.5 2.8 1.0 (x 10 (x 10 1.0 1.0 5.0 MFI (x 10 MFI x 10 MFI (x 10 2.4 0.5 0.8 2.5

0.8 CD1d MFI CD1d MFI Ly108 CD1d CD150 Shp-1f/f Shp-1f/f Shp-1f/f Shp-1f/f water 0.6 0.6 2.0 0 0 CD4cre CD4cre wt ko wt ko wt ko wt ko wt ko

Figure 3.1. iNKT cells from Shp-1f/f CD4-cre mice lack Ptpn6 and express wild type levels of CD1d, SLAMF1 and SLAMF6. (a) PCR of Ptpn6 or Cre using DNA extracted from sorted iNKT cells. (b) Expression of CD1d, SLAMF1 and SLAMF6 on thymic double positive thymocytes and expression of CD1d on CD11b+ or CD11c+ splenocytes. Data represents individual mice and mean +/- s.e.m.

Figure 3.2

Figure 3.2. Shp-1-deficient iNKT cells are biased towards iNKT2 and iNKT17 subsets (a) Frequency of iNKT cells in thymus, spleen, liver and inguinal lymph node of Shp-1f/f and Shp- 1f/f CD4-cre mice. (b) Representative flow cytometry plots of PLZF and RORγt expression, and derived frequency iNKT cell subsets in thymic and splenic iNKT cells from Shp-1f/f and Shp- 1f/f CD4-cre. (c) Representative diagram of mixed bone marrow chimera experiment set up and frequency of iNKT subsets from wild type CD45.1 or Shp-1f/f CD4-cre CD45.2 donor cells. (d)

Expression of Egr2 on iNKT cells from Shp-1f/f and Shp-1f/f CD4-cre mice. (e) Frequency of iNKT2 and iNKT17 subsets in thymus and spleen of 16 week old Shp-1f/f and Shp-1f/f CD4-cre mice. Data represents individual mice and mean +/- s.e.m. p-values were obtained by unpaired t- test.

Shp-1 deficient iNKT cells have an increased TH2 response

Previous work has shown that iNKT cells from motheaten mice display hyperproduction of cytokines(269). We assessed whether Shp-1f/f CD4-cre iNKT cells that develop in the absence of systemic inflammation were similarly functionally affected. For this, we first performed an in vivo αGC challenge and measured iNKT cell production of IL-4 and IFN-γ by flow cytometry (Fig. 3.3.a). Surprisingly, iNKT cells from Shp-1f/f CD4-cre mice were not hyper-responsive; instead they produced lower amounts of IFN-γ, overall biasing the cells towards a TH2 phenotype (Fig. 3.3.a). Interestingly, the reduction in IFN-γ producing cells was mainly a result of a decrease in IL-4+ IFN-γ+ double producer cells, and not in IL-4 and IFN-γ single producers (Fig. 3.3.a). We next stimulated both thymic and splenic iNKT cells from Shp-

1f/f and Shp-1f/f CD4cre mice in vitro with immobilized CD1d-αGC complexes, anti-CD3 antibodies, or PMA/ionomycin. Surprisingly, thymic iNKT cells from Shp-1-deficient mice were overall less responsive and produced less IL-4 and less IFN-γ (Fig. 3.3.b). On the other hand, splenic iNKT cells from Shp-1 knockout mice produced slightly more IL-4 than wild type iNKT cells (Fig. 3.3.b). The IL-4/IFN-γ ratio demonstrated that both thymic and splenic Shp-1- deficient iNKT cells were skewed towards a TH2 phenotype upon stimulation with CD1d-αGC or

PMA/ionomycin, (Fig. 3.3.b). To further test this, we magnetically enriched iNKT cells from

Shp-1f/f and Shp-1f/f CD4-cre mice and stimulated them for 4 days under either TH1 (CD3,

CD28, IL-2, IL-12, anti-IL-4), or TH2 (CD3, CD28, IL-2, IL-4, anti-IFN-γ) polarizing conditions,

and the cells were then re-stimulated with CD3. As expected, under TH1 polarizing conditions the cells produced mainly IFN-γ, and very little IL-4 (Fig. 3.3.c). Shp-1-deficient iNKT cells however produced less IFN-γ than their Shp-1-sufficient counterpart (Fig. 3.3.c). Under TH2 polarizing conditions, iNKT cells had a severely reduced IFN-γ response and Shp-1-deficient iNKT cells produced less IFN-γ than Shp-1-sufficient iNKT cells (Fig. 3.3.c). Of note, although only the thymus displayed increased frequency of iNKT2s (Fig. 3.2.b), iNKT cells from both thymus and spleen showed a similar bias towards TH2 in this assay. Together, these results suggest that Shp-1 deficiency promotes the differentiation of iNKT2s and a TH2-biased cytokine profile, regardless of the initial iNKT subset composition.

Shp-1 deficient iNKT cells induce memory Eomes+ CD8 T cell expansion in the thymus but not the spleen

Shp-1f/f CD4-cre mice have an expansion of memory CD4 and CD8 T cells defined by the high expression of CD44(261). Several studies have implicated iNKT cells as a source of IL-

4 that drives the expansion of these Eomes+ CD8 T cells, which are typically also CD44high (68).

We investigated the role of iNKT cells in the expansion of memory CD44high cells found on Shp-

1f/f CD4-cre mice. We first confirmed that, consistent with expansion of CD44high T cells, there was a higher frequency of Eomes+ CD8 T cells in the spleen of Shp-1f/f CD4-cre mice (Fig.

3.4.a). Surprisingly, the thymus of Shp-1f/f CD4-cre mice also showed a 3-fold increase in the frequency of Eomes+ CD8 T cells (Fig. 3.4.a), despite previous studies reporting no difference in

CD44high cells in the thymus(261). We then crossed the Shp-1f/f CD4-cre mice with CD1d-/- mice to ablate iNKT cells and other CD1d restricted cells. Interestingly, deletion of CD1d reduced the frequency of Eomes+ CD8T cells back to wild type levels in the thymus, but not the spleen (Fig. 3.4.b). This indicates that a different source of IL-4 drives the expansion of Eomes+

CD8 T cells in the periphery. Coincidently, the thymus, but not the spleen, has an expansion of

89 iNKT2s, suggesting that perhaps iNKT2 cells make more IL-4 at steady state, however when stimulated, all iNKT cells will produce IL-4 regardless of their phenotype.

Figure 3.3 a Shp-1 f/f Shp-1 f/f CD4cre ns 3.25 47.04 1.78 24.37 100 ns 80 ** 60 ** 20 ns 40 (%) (%) (%) - + 75 60 + 15 30 40 IL-4 IL-4 IL-4 20 total (%)

50 40 + 10 - + γ γ γ γ 20 γ 25 20 5 10 IL-4 total (%) IFN- IFN- IFN- IFN- IFN- 19.90 29.80 36.22 37.59 0 0 0 0 0 IL-4 wt ko wt ko wt ko wt ko wt ko b Shp-1 f/f Shp-1 f/f CD4cre 3 *** ns * 4.7 19 2.9 6.3 ratio

γ 2

Thymus 1 IL-4 / IFN- 0 63 13 83 8.2 CD1d-αGC CD3/CD28 PMA/iono 10 5.7 11 12 6 ** ns *** ratio

γ 4 Spleen

γ 2 IL-4 / IFN- IFN- 72 11 63 14 0 IL-4 CD1d-αGC CD3/CD28 PMA/iono

T 1 polarizing conditions T 2 polarizing conditions c H H Shp-1 f/f Shp-1 f/f CD4cre Shp-1 f/f Shp-1 f/f CD4cre 26 2.8 5.8 1.4 3.8 0.34 0.61 0.11

Thymus

66 4.7 86 6.5 91 4.5 95 4.5 35 4.2 19 3.8 16 1.9 8.1 2.5

Spleen γ

IFN- 59 1.7 72 4.2 77 5.2 80 9.8 IL-4

Figure 3.3. Shp-1 deficient iNKT cells have a TH2 biased cytokine secretion. Representative flow cytometry plots of IL-4 and IFN-γ expression on iNKT cells from Shp-1f/f and Shp-1f/f CD4-cre mice, as well as the corresponding frequency of IL-4+ and/or IFN-γ+ cells or ratio of IL- 4+ to IFN-γ+ cells, after stimulation with different conditions. (a) In vivo stimulation of iNKT cells by injection of 0.5µg of αGC per mouse (b) In vitro stimulation with plate-bound CD1d-

αGC, plate-bound CD3 or PMA/ionomycin. (c) In vitro stimulation of iNKT cells with plate- bound CD3 after 4 days of culture in either TH1 or TH2 polarizing condition. Data represents individual mice +/- s.e.m. p-values were obtained by unpaired t-test.

Figure 3.4 a Thymus Spleen b Thymus Spleen ns s s *** s * 60 * * ll

s *** 20

25 80 ll ll ll e e e e c c c

T T

20 T

15 T

+ 60 + +

+ 40 8 8 8 8 15 CD

CD 10 CD CD 40 / / / + / + +

+ 20

10 s s s s e e 5 e e m 20 m m m o

5 o o o E E

E 0 0

% % wt ko wt ko wt ko wt ko % 0 % 0 wt ko wt ko CD1d+/- CD1d-/- CD1d+/- CD1d-/-

Figure 3.4. Shp-1-deficient iNKT cells drive the expansion of memory Eomes+ CD8 T cells in the thymus but not the spleen. (a) Frequency of Eomes+ CD8 T cells in the thymus and spleen of Shp-1f/f and Shp-1f/f CD4-cre. (b) Frequency of Eomes+ CD8 T cells in the thymus and spleen of Shp-1f/f CD1d+/-, Shp-1f/f CD1d-/-, Shp-1f/f CD1d+/- CD4cre, Shp-1f/f CD1d-/- CD4-cre. Data represents individual experiments and mean +/- s.e.m of 2-3 independent experiments. p-values were obtained by unpaired t-test.

Shp-1 deficiency does not affect iNKT TCR signalling

Although a large body of work has shown that Shp-1 negatively regulates the TCR signalling cascade(306), Neel and colleagues recently suggested that this was not the case(261).

As pointed out earlier, we did not find any difference in iNKT cell frequency (Fig. 3.2.a), indicating that TCR signals important for positive and negative selection were not significantly affected by the absence of Shp-1. Nonetheless, the iNKT2 cells that are more prominent on Shp-

1f/f CD4-cre mice have been reported to receive stronger TCR signals(68, 318). Most iNKT cells express either a Vβ8, Vβ7 or Vβ2 TCR Vβ chain. It was suggested that Vβ7-containing iNKT

TCRs preferentially recognize self-antigens(275), and that iNKT2s are enriched in Vβ7- containing TCRs(68). We assessed whether Shp-1 deficiency affected the TCRβ repertoire of iNKT cells, but found no difference in the frequency of Vβ8+ or Vβ7+ iNKT cells (Fig. 3.5.a).

We then measured the strength of TCR signal in thymic and splenic iNKT cells by mean of

Nur77 and Egr2 upregulation upon stimulation with different concentrations of immobilized anti-

CD3 antibodies. Both Shp-1f/f and Shp-1f/f CD4cre iNKT cells upregulated these molecules to the same extent (Fig. 3.5.b). We also measured expression of CD5 as a marker generally associated with increased TCR signal strength. Thymic iNKT cells did not show a significant difference in the expression of CD5 (Fig. 3.5.c). However, splenic iNKT cells from the Shp-1f/f

CD4-cre mice showed approximately a 50% increase in expression of CD5 (Fig. 3.5.c).

Interestingly, CD5 expression by CD4 and CD8 T cells followed the same trend (Fig. 3.5.c).

Because the increased CD5 expression is restricted to the spleen, we hypothesize this is due to an expansion of CD5hi cells which have been shown to preferentially contribute to the memory T cells(319). Overall, these data suggest that Shp-1 does not modulate TCR signals within iNKT cells, although further experiments are needed to show CD5 expression is not due to tonic TCR signals.

Figure 3.5 a Thymus b Thymus Thymus c Thymus Thymus Thymus ns ns 80 80 12 iNKT 24 CD4SP 8 CD8SP 60 ) ns ) ns ) wt 3 3 3 ns ko 60 60 22 10 7 40 20 NKT cells NKT NKT cells NKT + 40

40 + 18 20 8 6 20 % iNKT cells % iNKT 20 16 CD5 MFI (x 10 CD5 MFI (x 10 CD5 MFI (x 10

0 % EGR2 0 6 14 5

0 % NUR77 Vβ8 Vβ7 10-2 100 102 10-2 100 102 wt ko wt ko wt ko [CD3] ng/ml [CD3] ng/ml

Spleen Spleen Spleen Spleen Spleen Spleen 60 ns ns 80 48 iNKT 20 CD4SP 11 CD8SP ) 8 ) ) 3 3 *** **** 3 10 * 60 36 7 18 40 9 16 NKT cells NKT + 40 cells NKT 24 + 6 8 14 20 7

% iNKT cells % iNKT 20 12 5 12 CD5 MFI (x 10 CD5 MFI (x 10 6 CD5 MFI (x 10 % EGR2

0 % NUR77 0 0 10 -2 0 2 -2 0 2 4 5 Vβ8 Vβ7 10 10 10 10 10 10 wt ko wt ko wt ko [CD3] ng/ml [CD3] ng/ml

Figure 3.5. Absence of Shp-1 deficiency does not affect TCR signalling in iNKT cells. (a) Frequency of Vβ8+ and Vβ7+ iNKT cells in the thymus and spleen of Shp-1f/f and Shp-1f/f CD4- cre mice. (b) Frequency of Nur77+ and Egr2+ thymic or splenic iNKT cells upon stimulation with different concentrations of CD3. (c) Expression of CD5 on thymic and splenic iNKT cells, CD4 and CD8 T cells. Data represents individual mice and mean+/- s.e.m of 2-3 independent experiments. p-values were obtained using unpaired t-test.

Shp-1 deficiency does not affect SLAMF6 mediated upregulation of Egr2 and PLZF

Several studies have suggested that Shp-1 attenuates signalling of SLAM family receptors in NK and T cells(233, 234). Shp-1 has been proposed to compete with SAP for binding to the SLAMF6 receptor to modulate the development of iNKT cells and follicular helper T cells (233). Specifically, removal of SLAMF6 in addition to its signalling adaptor molecule SAP, partially restored iNKT cell frequency of SAP-/- mice to levels equivalent to that of SLAM6-/-(233). With this in mind, we crossed the Shp-1f/f CD4-cre mice with Sh2d1a-/- mice; however, this did not rescue the selection of iNKT cells in the SAP-/- mice, indicating that Shp-1 does not compete with SAP for binding to SLAMF6, or that other phosphatases can compensate

93 for the absence of Shp-1 (Fig. 3.6.a). We then measured SLAMF6 signalling by measuring upregulation of PLZF in pre-selection DP thymocytes (PSDPs) upon crosslinking of CD3 and

SLAMF6 (Fig. 3.6.b). As previously described, we found that co-stimulation with anti-CD3 and anti-SLAMF6 antibodies significantly increased the frequency of PLZF positive PSDPs after 48 hours of stimulation, whereas stimulation with CD3, CD3 with CD28 or SLAMF6 did not (Fig.

3.6.b). However, in this assay, both Shp-1-sufficient and Shp-1-deficient PSDPs upregulated

PLZF to the same extent (Fig. 3.6.b), suggesting that Shp-1 does not regulate SLAMF6 signalling. Egr2 is also upregulated upon co-ligation of CD3 and SLAMF6, albeit with a much faster kinetics, reaching a peak at 1 hour after engagement(230). We tested whether Shp-1 controls proximal signalling downstream of SLAMF6 by looking at the kinetics of Egr2 downregulation for several hours after removal of CD3 and SLAMF6 stimuli (Fig. 3.6.c). Again, we did not see any difference between Shp-1f/f and Shp-1f/f CD4-cre PSDPs (Fig. 3.6.c). Taken together this data shows that Shp-1 does not negatively regulate the SLAMF6-mediated enhancement of PLZF expression.

Figure 3.6

a SAP-/- SAP-/- SAP+/- b 25 Shp-1f/f c 50 s

Shp-1f/f Shp-1f/f CD4-cre Shp-1f/f CD4-cre ll Shp-1f/f CD4-cre 40

20 + 2 ce 0.00 0.00 0.14 + 15 30 GR E

LZF 10 20 % P

% 5 10 0 0 PBS57 tetramer CD3+ CD3+ CD3+ 0 2 4 6 8 10 - SLAMF6 TCRβ IgG2a CD28 SLAMF6 Hours after CD3+SLAMF6 withdrawal

Figure 3.6. Absence of Shp-1 does not affect SLAMF6 mediated upregulation of Egr2 and PLZF. (a) Representative flow cytometry plots of PBS57 tetramer staining on thymocytes from Sh2d1a-/- Shp-1f/f, Sh2d1a-/- Shp-1f/f CD4-cre and Sh2d1a+/- Shp-1f/f CD4-cre mice. Plots were gated on live cells. (b) Frequency of live PLZF+ cells in PSDPs from Shp-1f/f and Shp-1f/f CD4- cre mice after 48 hours of stimulation with the indicated immobilized antibodies. (c) Frequency of Egr2+ PSDPs upon 30-minute stimulation with plate-bound CD3 and SLAMF6 antibodies (0 min), and at different time points after removal of stimulus. Data represents individual mice and mean +/- s.e.m of 2-3 independent experiments. P-values were obtained by unpaired t-test.

Absence of Shp-1 amplifies proliferation of iNKT cells in different cytokine conditions

Several studies have suggested that Shp-1 regulates cytokine and chemokine receptor signalling(261, 310-313). iNKT cells require IL-7 and IL-15 for survival and proliferation under homeostatic conditions, and expand in the presence of IL-2(112-114, 116). We tested if this proliferation was modulated by Shp-1 by sorting Shp-1f/f and Shp-1f/f CD4-cre iNKT cells and culturing them for 4 days with each of these cytokines. Surprisingly, Shp-1-deficient iNKT cells proliferated to a much greater extent than Shp-1-sufficient iNKT cells under all cytokine conditions (Fig. 3.7.a), except IL-4, which did not induce iNKT cell proliferation (not shown).

This indicates that Shp-1 is crucial to maintain cytokine signals in check, and hence likely contributes to iNKT cell homeostasis in vivo.

We then looked for the phosphotyrosine residues that might be the target for Shp-1 downstream of these cytokine receptors. Surprisingly, there was no difference in expression of

Stat5 phosphorylation between Shp-1f/f and Shp-1f/f CD4-cre iNKT cells upon IL-7 or IL-15 stimulation (Fig. 3.7.b). Given that Shp-1f/f CD4-cre CD4 and CD8 T cells display prolonged

Stat6 signals downstream of IL-4(261), we also analyzed Stat6 phosphorylation on iNKT cells.

Again, there was no difference in Stat6 phosphorylation between Shp-1f/f and Shp-1f/f CD4-cre iNKT cells upon IL-4 stimulation. Given that all three homeostatic cytokines induced increased proliferation, we thought that Shp-1 might regulate a central pathway downstream of all of these receptors. Mechanistic target of rapamycin (mTOR) pathway has emerged as a central link between environmental signals and iNKT cell fate decisions(142). Particularly, IL-7 receptor signalling is dependent on mTOR for iNKT17 differentiation(114). We checked for targets of both mTORC1 (phospho-S6) and mTORC2 (phospho-Akt473) upon stimulation with IL-7 (Fig.

3.7.c), IL-15 (not shown) or IL-4 (not shown), yet none of these targets showed enhanced expression in the absence of Shp-1 (Fig. 3.7.c). The phosphorylation of extracellular signal- regulated kinase (ERK), which also connects many signalling pathways, was also not affected in

Shp-1f/f CD4-cre mice. Thus, Stats, ERK or proteins in the mTOR pathway are not targets for

Shp-1 downstream of IL-2, IL-7 or IL-15 cytokine receptor signals on iNKT cells.

Figure 3.7

Thymus iNKT Spleen iNKT ) 3 ) 3 a b 3 6 c IL-7 IL-7 4 2

IL-2 2 1

pSTAT5 MFI (x 10 pSTAT5 0 pSTAT5 - IL-7 pAKT473 pAKT473 MFI (x 10 - IL-7 )

3 6 15 )

IL-15 IL-7 2 4 1 IL-7 2 5 pS6 MFI (x 10

pSTAT5 MFI (x 10 pSTAT5 0 0 pSTAT5 - IL-15 pS6 - IL-7

15 ) 8 IL-4 IL-7 3 6 10 IL-15 4 5 2 pERK MFI (x 10 pSTAT6 MFI (x 10) pSTAT6 0 0 pSTAT6 - IL-4 pERK - IL-7 CSFE wt + medium ko + medium wt + medium ko + medium wt ko wt + cytokine ko + cytokine wt + cytokine ko + cytokine

Figure 3.7. Absence of Shp-1 amplifies cytokine-mediated iNKT cell proliferation. (a) Representative flow cytometry plots of CFSE dilution profile of sorted thymic or splenic iNKT cells incubated for 4 days with 10ng/ml of IL-2, IL-7 or IL-15. (b) Representative flow cytometry plots and corresponding MFI of phospho-Stat5 or phospho-Stat6 on iNKT cells upon 30-minute incubation with indicated cytokine. (c) Representative flow cytometry plots and corresponding MFI of phospho-Akt473, phospho-S6 and phospho-ERK on iNKT cells upon incubation overnight with the indicated cytokine. Data represents individual mice and mean +/- s.e.m of 2 independent experiments. p-values were obtained using unpaired t-test.

3.5 Discussion

The role of Shp-1 in T cell development and function has been much debated. Although several studies initially identified it as a negative regulator of TCR signalling, these findings were much compromised by cell extrinsic effects resulting from the systemic inflammation in the motheaten mice used in these initial studies. Although conditional T cell-specific deletion of

Shp-1 does not result in autoimmunity or inflammation, the role of Shp-1 in T cell biology remains ill defined. Some studies indicated that Shp-1 plays a negative role in TCR signalling during T development(262, 263), or proliferation(260), whereas others propose that Shp-1 regulates IL-4 but not TCR signals(261), and others even suggest its involvement in enhancing

TCR signals(317). In this study, we found no evidence that Shp-1 negatively or positively modulates TCR signalling. In addition, our data suggests that Shp-1 does not compete with the adapter molecule SAP to regulate SLAMF6-mediated signalling. However, the absence of Shp-1 enhanced iNKT cells proliferation upon in vitro exposure to IL-2, IL-7 and IL-15. These cytokines are all part of the γc family of receptors, and hence it remains to be investigated whether Shp-1 regulates signalling of other cytokine members in this family, or even outside of this family. Additionally, we still have to confirm that cytokine receptor levels are similar in

Shp-1-deficient and -sufficient iNKT cells. To decipher whether Shp-1 functions in proximal receptor signalling, we measured phosphorylation of Stat proteins upon engagement of respective cytokine receptors. Nonetheless, we did not find any difference in Stat phosphorylation between Shp-1-sufficient and -deficient iNKT cells. A tempting hypothesis was that Shp-1 inhibits a central pathway such as mTOR or ERK, thus acting as a central regulator during cytokine receptor signalling. However, we did not find increased phosphorylation of mTOR targets S6 and Akt473, nor increased phospho-ERK. Thus, these pathways are likely not the targets of Shp-1. It remains to be tested whether Shp-1 regulates the duration of these

98 signalling cascades rather than their initiation. Indeed, Johnson et al. observed a prolonged phosphorylation of Stat6 in Shp-1f/f CD4-cre T cells following IL-4 exposure and withdrawal(261).

In vivo, the absence of Shp-1 led to a bias towards the iNKT2 subset in a cell intrinsic manner during differentiation in the thymus. Interestingly, although this subset bias was only evident in the thymus, both thymic and splenic iNKT cells were functionally biased towards production of IL-4, indicating that perhaps the same pathway through which Shp-1 controls iNKT cell central priming in the thymus also regulates preferential cytokine production.

Although both TCR and SLAMF6 signalling have been proposed to enhance expression of the iNKT2 defining marker PLZF, we did not find enhanced signalling downstream of either receptor in Shp-1 deficient iNKT cells. Therefore, it is likely that biased differentiation of iNKT cells in Shp-1 deficient mice occurs as a result of enhanced cytokine signals. Because we find enhanced signalling downstream of multiple cytokines, it is likely that the synergy between these signals result in the final iNKT cell outcome. Certain cytokines, such as IL-25 and IL-33, can induce IL-13 and IL-4 cytokine production whereas others such as IL-12 and IL-18 promote

IFN-γ production(69). It would thus be interesting to test whether engagement of these cytokine receptors induce higher production of cytokines on iNKT cells.

In line with the phenotype of other mouse strains containing an expansion of iNKT2s, we found that the higher prevalence of iNKT2s in Shp-1f/f CD4-cre mice was responsible for the expansion of memory Eomes+ CD8 T cells in the thymus. This expansion of Eomes+ CD8 T cells in the thymus had gone unnoticed in previous studies, likely due to the use of the less specific

CD44 marker, rather than Eomes, to identify the cells(261). Surprisingly, the much larger expansion of Eomes+ CD8 T cells in the periphery was iNKT cell independent, suggesting that

99 other sources, such as CD4-dependent γδ T cells, are likely providing excess IL-4 in this scenario, or the increased sensitivity of the IL-4R in CD8 T cells is sufficient to promote a memory phenotype even with the limited amount of IL-4 produced.

Although SLAMF6 is generally considered an activating receptor, recent studies have suggested that this receptor can also act as a negative regulator of signal transduction. This has been unravelled by the combined SLAMF6 and SAP deficiency, which largely restored iNKT cell development found in SAP-/- mice. It was suggested that the combined deficiency prevented the recruitment of phosphatases such as Shp-1 by SLAMF6(233). Given that absence of Shp-1 does not increase SLAMF6 signals, it is likely that other phosphatases act downstream of these pathways or fully compensate for the absence of Shp-1. This also indicates that either Shp-1 has a very specific receptor and target specificity, or that a small fraction of signals are exclusively regulated by this phosphatase. Indeed, neither iNKT cells nor T cells displayed uncontrollable cytokine production or expansion in vivo. This emphasizes the complex and synergistic nature of signal transduction regulation in lymphocytes and the need to better understand how various signalling cascades regulate iNKT cell biology in order to target these cells for immunotherapies.

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4 Discussion and Future Directions 4.1 Receptor signalling

4.1.1 TCR signalling

Although the TCR has been the subject of rigorous studies for decades, the mechanisms of signal initiation and antigen discrimination remain enigmatic. The discovery of unconventional γδ and αβ TCRs, that bind their ligands in very different ways than classical αβ

TCRs(193), begged the question of whether the mechanisms of signal initiation are universal, or unique for each type of TCRs. Because the ligands for many of the unconventional TCRs remain unknown, the mechanisms of antigen discrimination and triggering are still elusive. iNKT TCRs are the best characterized among the unconventional TCRs, and several antigens have been discovered. Several studies have suggested that unlike conventional TCRs, iNKT TCRs recognize antigens in a “one size fits all” fashion, thus resembling innate pattern recognition receptors. In Chapter 2, we measured the KD (avidity) and the KOFF (half-life) of 6 different iNKT TCRs towards 4 different ligands presented on CD1d. Because the hierarchy of avidity or half-life of the TCRs was the same for each ligand tested, the present work supports the view that the iNKT TCR does not display discrete antigen specificity. Rather, each TCR has an intrinsic strength of binding to the CD1d-lipid complexes, with stronger TCRs binding a wider spectrum of ligands than weaker TCRs. Conversely, certain ligands such as PBS57 bind to TCRs more strongly than others such as PBS218, and this will reflect on the spectrum of TCRs they can bind, and consequently the number of iNKT cell clones they can activate. Furthermore, the strength of binding influences activation of hybridomas upon plate-bound CD1d-antigen complexes. Overall, these data support the view that the iNKT TCR recognizes ligands in a very different fashion than classical T cell TCRs. Nonetheless, iNKT cells are one of many lipid reactive T cells, and characterization of other unconventional TCRs indicate variation in this

101 docking strategy and specificity towards certain lipids. For example, Type II NKT TCRs dock above the A’ pocket of CD1d, as opposed to the F’ pocket for type I NKT TCRs, and do not recognize α-linked lipid antigens, exemplifying a different antigen repertoire from iNKT cells.

Another example, germline-encoded mycolyl-lipid reactive (GEM) T cells TCRs, that recognize glucose monomycolate presented by CD1b, dock centrally on top of CD1b and the TCR surrounds the glucose moiety creating a very specific binding to the antigen(320). Thus, although we describe iNKT cells as promiscuous, these cells are only a small representation of the larger group of lipid reactive T cells that we can describe as exhibiting antigen discrimination.

How does the strength of TCR interaction impact iNKT cell development and instruct the myriad of functions they perform? We further showed that the TCRb chain could seemingly independently modulate the avidity and half-life of interaction with CD1d-lipid complexes. In simpler terms, some TCRs that bound the ligand for brief periods of time could bind a high number of ligands at equilibrium, and some TCRs that bound the ligand for prolonged periods of time only bound a small proportion of ligands at equilibrium, likely due to differences in association constant (Fig. 4.1). During stimulation with immobilized CD1d-lipid complexes, avidity and half-life seemed to cooperate such that all TCRs with intermediate avidity and/or half-life signaled similarly. When stimulation was transmitted through the same lipids presented on cells rather than plate-bound, the hierarchy of activation differed markedly, and did not reflect either avidity or half-life, indicating involvement of other parameters. Nonetheless, cell-cell interaction in the absence of exogenous lipids was strictly dependent on the half-life of interaction as was the effector differentiation of iNKT cells. On the other hand, thymic selection was more reflective of the avidity of the TCRs. As mentioned, this shows that in vivo TCR activation is not an inflexible all-or-nothing response, but rather that various TCR interaction parameters get decoded through signalling to instruct diverse fates. This could also involve a

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“binding strength maturation” mechanism, where different developmental stages of iNKT cells perceive signals differently. Such a mechanism has been proposed to instruct the higher sensitivity of memory T cells to low doses of ligands through larger oligomerization of receptors at the surface(296, 297). Similarly, TCR sensitivity changes throughout T cell maturation to allow proper selection with self-antigens and activation in the periphery(298-300, 321) (Fig. 4.1).

Figure 4.1

Thymus Avidity Thymus 0.6 SDresiduals=0.1437

0.4

Selec%on 0.2 cells Thymus (%) T 0.0 • Changes in TCR iNK 10-4 10-3 10-2 1/EC PBS57 Tetramer expression/clustering 50

Half-life • Expression of Thymus 80 phosphatases and/or other molecules Diﬀeren%a%on 60 40

iNKT2 (%) 20 r2=0.885 p=0.017 0 100 103 106 t PBS57 Tetramer 1/2

Figure 4.1. Summary of findings in Chapter 2. Selection is governed by the number of TCRs that get engaged, which is measured by the avidity (1/EC50) of tetramer binding. We hypothesize this to be a reflection of transient interactions as developing thymocytes travel through the thymus. On the other hand, differentiation into different effector subsets is governed by the duration of interaction, measured by the half-life (t1/2) of tetramer binding. Long interaction half- life coincides with the ability of each TCR to form cell-cell conjugates, and thus we propose that in vivo differentiation is a reflection of prolonged cell-cell interactions. The differences in outcomes from these interactions could be the result of varying involvement of intracellular signalling molecules such as phosphatases, or a “binding strength maturation” mechanism whereby once selected iNKT cells change the expression or clustering of TCRs leading to a change in susceptibility to CD1d-lipid complexes.

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Several signalling molecules have been proposed to regulate TCR sensitivity to ligands, which includes phosphatases. The phosphatase Shp-1 has been coined as a regulator of TCR signalling(251). Thus, in Chapter 3, we explored how it regulates iNKT cell selection and differentiation by conditionally deleting Shp-1 on all αβ T cells. Shp-1 was not involved in selection of iNKT cells, but rather on effector differentiation. Although this could indicate that

Shp-1 is a regulator of half-life of TCR interaction, we found no evidence supporting this hypothesis. First, although the TCR Vβ repertoire has been shown to shift towards greater Vβ7 usage in the context of limited or lower affinity TCR-CD1d interactions, the Vβ repertoire was similar in iNKT cells sufficient or deficient for Shp-1. Furthermore, CD3 stimulation did not result in overt activation of iNKT cells lacking Shp-1. This indicated that Shp-1 is not an inhibitor of TCR signals or that other phosphatases can completely compensate for it. The latter hypothesis would explain several studies that report co-immunoprecipitation and localization of

Shp-1 with molecules on the TCR signalling pathway(262, 322, 323). Other phosphatases shown to be involved in inhibition of TCR signals include protein tyrosine phosphatase non-receptor type 22 (Ptpn22)(324) and dual specificity phosphatase 5 (Dusp5/6)(325). Deletion of multiple phosphatases would be essential to deciphering their synergy versus individual contribution to

TCR signals.

4.1.2 Regulation of signalling by Shp-1

In Chapter 3 we also explored regulation of SLAMF6 signalling by the Shp-1 phosphatase. Our findings support the view that SLAMF6 synergizes with the TCR to induce

Egr2 and PLZF expression on developing thymocytes at the time of selection(230). Nonetheless, despite multiple studies linking Shp-1 as an inhibitor of SLAMF6 signalling(233, 236, 238), the

SLAMF6-mediated PLZF upregulation was not enhanced in the absence of Shp-1. Furthermore, the combined deletion of Shp-1 and SAP did not rescue iNKT cell selection as observed in mice

104 with deficiencies in both SLAMF6 and SAP(233). Therefore, Shp-1 does not appear to regulate

SLAMF6 signalling on developing thymocytes, or other phosphatases completely compensate for it. It is also possible that Shp-1 only exhibits inhibition downstream of the TCR and

SLAMF6 at a very specific point during iNKT cell selection. It is worth pointing out that

SLAMF6 is expressed on mature iNKT cells, particularly the iNKT2 subset (data not shown), although it is still unclear whether SLAMF6 plays an active role in iNKT2 differentiation. Thus, it is possible that Shp-1 plays a role downstream of SLAMF6 at this more mature stage of iNKT cells.

iNKT cells have been generally viewed as intractable to cytokine mediated polarization(107), despite several studies reporting cytokine secretion in response to cytokine stimulation(69, 96, 97, 104). In contrast with the previous view(107), our results show that incubation in TH1 or TH2 polarizing conditions can significantly influence the production of IFN-

γ by iNKT cells, indicating that cytokines can influence activation of the cells. Surprisingly, Shp-

1 deficient iNKT cells proliferated to a much larger extent than Shp-1 sufficient iNKT cells when cultured in the presence of IL-2, IL-7 and IL-15 cytokines. This shows that Shp-1 is required for the regulation of cytokine signalling, yet what protein or pathways it dephosphorylates is still unclear. Given that that IL-2, IL-7 and IL-15 are all γc cytokines, Shp-1 could be acting at a very early stage in signalling, inducing greater or prolonged Stat phosphorylation. Surprisingly, after

30 minutes of culture with the respective cytokines, we did not observe increased on Stat5 or

Stat6 phosphorylation on thymic iNKT cells. Whether absence of Shp-1 leads to prolonged Stat phosphorylation and therefore sustained signalling remains to be investigated. Future experiments should also address whether Shp-1 regulates signalling downstream of other cytokines such as IL-12, IL-18 and IL-25, all of which have been shown to promote activation of iNKT cells and their release of specific cytokines.

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4.2 Impact of receptor signalling on iNKT cell biology

iNKT cells are wired in a fashion that enables their innate-like functions and differentiates them from classical T cells. Although it is largely believed that thymic selection

“imprints” their peculiar functional programs, the mechanisms and signalling pathways involved are elusive. In Chapter 2, we showed that TCR avidity and half-life regulate selection and differentiation of iNKT cells, respectively. Surprisingly, neither of these parameters influenced the survival, proliferation, homing or cytokine secretion of these cells. This work suggests that although TCR properties influence iNKT cell ontogeny and central priming, the homeostasis of iNKT cells is largely TCR independent. Hayday and colleagues have suggested that some γδ T such as CD27- γδ and dendritic epidermal T cells (DETCs) are selected through agonist TCR signals, yet shortly after attenuate TCR signals and are largely independent of the TCR for activation and cytokine production properties(321). Given our current results, it is likely that iNKT cells use a similar mechanism for their homeostasis, which would also explain their hypo- responsiveness to self-ligands in the periphery. Furthermore, iNKT cells function independently of the TCR during viral and several bacterial infections, as well as in many disease conditions.

Nonetheless, TCR ligation with potent lipids induces potent cytokine secretion on iNKT cells.

Although there was no cytokine bias and most TCRs lead to equal production of cytokines by iNKT cells, some TCRs induced less IL-4 and IFN-γ upon specific stimuli such as αGalCer.

Given that iNKT cells can be activated through TCR-dependent and independent pathways(96,

103, 104), it would be interesting to explore how each monoclonal population of iNKT cells is activated in the context of bacteria that express or lack lipids that can be presented by CD1d.

Results from Chapter 3 show that Shp-1 can also modulate effector differentiation of iNKT cells (Fig. 4.2). Because we only see an increase in effector differentiation in the thymus, this is likely a developmental effect. Given that IL-2, IL-7 and IL-15 cytokine signals, but not

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TCR or SLAMF6 signals, are augmented in Shp-1 deficient mice; the increased iNKT2 and iNKT17 could be a result of increased proliferation or survival within the thymus. Nonetheless, splenic iNKT cells are biased towards an iNKT2 phenotype when stimulated with αGalCer, suggesting an intrinsic central mechanism of TH2 bias. Importantly, the frequency of iNKT cells did not differ from Shp-1f/f vs Shp-1f/f CD4-cre, indicating that although homeostatic cytokines induce proliferation of iNKT cells in vitro, iNKT cells are restricted by a niche in vivo.

Figure 4.2

WT iNKT Shp1-/- iNKT IL-2, IL-7, IL-15 IL-2, IL-7, IL-15

NKT2 IFNγ NKT2 IFNγ

NKT1 NKT1

NKT17 NKT17 IL-4 IL-4

Lymph Lymph node node

Figure 4.2. Summary of findings in Chapter 3. Compared to wild type iNKT cells, Shp-1 deficient iNKT cells are more susceptible to proliferation upon incubation with IL-2, IL-7 and IL-15. We hypothesize that the enhanced synergistic signalling through multiple cytokine receptors on iNKT cells induces an increased frequency of iNKT2s and iNKT17s in the thymus of and reduced IFN-γ production by iNKT cells in the periphery, leading to an overall TH2 bias in the mice. Further experiments are needed to understand the link between cytokine signals and effector subset differentiation.

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4.3 Future experiments

Although big strides have been made in resolving what we know about iNKT cells, much remains to be discovered. The biggest difficulty lies in addressing aspects that require large amounts of cells or proteins since the frequency of iNKT cells is quite small. New technology using single cells such as single-cell RNA sequencing and single cell mass cytometry are now enabling some of these experiments. Equally difficult is the measurement of transient interactions such as Shp-1 binding to specific receptors or T cell receptor binding to its cognate ligand. Voldman and colleagues have created microfluidic devices that allow single T cell pairing with antigen presenting cells and measurement of various activation parameters such as calcium flux and cytokine secretion(326). This approach would be useful in deciphering further how TCR strength alters early activation events on iNKT cells. Another emerging method is the use of optogenetics, in which proteins are genetically modified to be responsive to light in order to obtain exquisite control over the initiation and termination of particular signalling pathways(327). Creating photoactivatable TCRs or signalling molecules, would enable one to further dissect the effect of location and timing of signalling on cell activation and fate.

A particular aspect of signalling that remains to be explored is the formation of the immunological synapse upon engagement of unconventional TCRs. The synapse is arranged as supramolecular activation clusters (SMACs), which contain a central region enriched with TCRs, and an outer ring containing co-stimulatory and adhesion molecules(328). Given the radically different docking strategies between classical and iNKT TCRs, it is likely that differences also extend to TCR aggregation and SMAC formation, thus resulting in different signals and effector outcome. Furthermore, the DP-DP interactions that select iNKT cells are likely more transient than DP-cortical thymic epithelial cell (cTEC) interactions due to trafficking of the cells through the thymic cortex. Furthermore, DPs are much smaller than cTECs, and have different

108 expression and surface arrangement of co-stimulatory molecules. These differences are likely to influence the outcome of activation and hence contribute to the different fates of iNKT cells compared to classical T cells. A study by Melichar et al. used thymic slices where pre-labeled developing thymocytes could be added and their calcium flux could be visualized as they interacted with selecting cells(295). Such a system could be applied to iNKT cells, using Vα14 transgenic DPs added to thymic slices from either wild type or CD1d-/- mice, although the mobile nature of the presenting DPs could impose some challenges to the visualization. In a bioengineering approach, nanoparticles coated with receptors that mimic the immune synapse could be used to assess the effect of shape, and particular co-receptor engagement, on the activation particular pathways on iNKT cells during development (by adding the nanoparticles to fetal thymic organ cultures or injecting them intrathymically) or during an immune response (by administering the nanoparticles in vivo)(329).

Still highly under-researched are the differential responses obtained from synergy of different pathways, such as TCR signalling and SLAMF6 or other co-stimulatory molecules.

Similarly, studying how redundancy of different kinases and phosphatases affects functional outcomes would be crucial for therapy using inhibitors for such processes. Obtaining a good picture of these processes would require dual deletion of several proteins, as well as gene and protein profiling of the cells. Of particular interest for use in therapy would be to understand what makes iNKT cells secrete multiple cytokines at once, and how to restrict this secretion to particular cytokines of interest. From previous studies and our current work, stimulation through the TCR induces production of a myriad of different cytokines regardless of the strength of the interaction. Interestingly, stimulation with cytokines such as IL-12, IL-23 and IL-25 induces a much more narrow cytokine response. Given the advent of chimeric receptors, one could envision exploiting the cytoplasmic regions of these receptors to induce those signalling cascades

109 with a desired extracellular sensor. Alternatively, one could exploit these pathways by activating or inhibiting downstream molecules. These approaches could prove useful in diseases such as asthma, which have shown to involve specific subsets of iNKT cells.

4.4 Conclusions

Overall, this thesis aims to broaden the knowledge of how iNKT cells integrate multiple signalling pathways to induce their particular lineage fate and function. First, we propose that individual iNKT cell processes can be governed by different kinetics parameters of TCR-ligand-

CD1d interaction. Second, Shp-1 regulates the frequency of iNKT cell effector differentiation and cytokine secretion upon stimulation, likely through inhibition of cytokine signalling.

Broadly, our work highlights the complex nature of signalling modules that characterize iNKT cell development and functional responses.

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