Dr P Rainer

Dr » Supervisor

defence December P Minoprio

MSc1 Grégoire Bohl

Encarnita P6

Charles Mariotti-Ferrandiz

Guanying Plessy

Ruyji Wang

Osami Iida

Teruhiko Kanagawa

Ri-Ichiro Wakayama

Shohei Hori Manabe

High-Throughput

Valentin Quiniou

Pierre Barennes

Hang-Phuong Pham

Mikhail Shugay

Federica Martina

Vanessa Mhanna

Thomas Dupic

Thierry Mora

Aleksandra Walczak

Adrien Six

Encarnita Mariotti-Ferrandiz

Felipe Leal Valentim

Océane Konza

Roberta Lorenzon

Nidhiben Patel

Laura Maria P3

Gwladys Florez

Pierre Fourcade

Barennes

Karina Gan-Fernandez

Felipe Leal-Valentim

Julien Lion

Djamel Nehar-Belaid

Tristan Courau

Ariadna Gonzalez-Tort

Maria-Grazia Ruocco

Serena Zegninia

Mauro Giacca

Nuala Mooney

David Klatzmann Resident

Vanessa P2

Gwladys Mhanna

Hang P Fourcade

Pierre Pham

Faustine Barennes

Guillaume Brimaud

Bruno Churlaud

Valentin Gouritin

Paul-Gydeon G Quiniou

Adrien Ritvo

Six

David Klatzmann

Pierre P1

Valentin Barennes

Mikhail Quiniou

Evgeniy S Shugay

Alexey N Egorov

Dmitriy M Davydov

Imran Chudakov

Mazlina Uddin

Theres Ismail

Benny Oakes

Anne Chain

Darko Eugster

Amy Sam

Daniel C Ransier

David Douek

Encarnita Klatzmann

Chan Bukhari

Airr Community

Luning Prak

E T^

MANAGEMENT ACTIVITY AND RESEARCH TRAINING 2018-2019

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List of figures

Introduction

One of the fundamental feature of the adaptive immune system is the extraordinary diversity of lymphocyte antigen receptors, immunoglobulins (Ig) and T cell receptors (TCR). This diversity endows the immune system with a potential to recognize, in an unanticipated manner, the diverse antigenic universe of "non-self" including pathogens and autologous tumours. The generation of the Ig and TCR repertoire depends on a somatic recombination machinery which randomly assembles a set of functional Ig or TCR genes from a pool of discontinuous gene segments, called variable (V), diversity (D) and joining (J) segments [START_REF] Chien | Somatic recombination in a murine T-cell receptor gene[END_REF]2]. During the recombination process, which occur in central lymphoid organs (CLO, bone marrow and thymus for B and T cells respectively), nucleotides are added and removed randomly between V, D and J segments and lead to a junctional region, variable in length and sequence. This region, called the CDR3, is the signature of the rearrangement and contributes greatly to the high diversity of the TCR (Figure 1). constitutes the available T cell repertoire (Figure 2). Such diversity will increase the chance to recognize foreign molecules and preclude from self-antigen pathogenic reactivity. For αβ TCR, the potential diversity thus generated has been calculated to be as large as 10 19 or even up to 10 61 as very recently predicted by statistical modelling [START_REF] Bradley | Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition[END_REF][START_REF] Mora | Quantifying lymphocyte receptor diversity[END_REF]. Obviously, only a small fraction of the potential repertoire can actually be used, because of the limitation in the number of cells a human and a mouse can harbour (up to ~10 12 and ~10 8 αβ T cells, respectively), in the body. Furthermore, the actual repertoire is shaped by various selection events that depend on TCR interactions with self-as well as non-self-peptides presented by the major histocompatibility complex (MHC) molecules in the thymus and periphery. However, the actual TCR repertoire 17/67 remains complex enough to have precluded attempts to comprehensively and quantitatively measure it for decades.

TCR repertoire diversity and specificity

The diversity of lymphocyte repertoire appears essential for host defence against "non-self" organisms. Decades of studies highlighted the presence and need for T cells harbouring specific TCR of various peptides derived from pathogens. This has been the ground for the concept of adaptive immune memory, the process by which an individual once exposed to an array of antigens expressed by a pathogen will be able to efficiently respond after a second exposure to the same pathogen [START_REF] Esser | Memory T cells and vaccines[END_REF]. This concept has been crucial in the improvement of health care by the introduction of vaccination as a prevention therapy to control infectious disease spreading and protect individual at the population level.

However, the adaptive immune system play also a major role in the maintenance of immune homeostasis and immunological tolerance to the "self" organism. Autoimmune diseases (ADs) in general have been associated with an alteration of the T cell repertoire, as it has been shown in Type I Diabetes (T1D) mouse model [START_REF] Baker | Restricted islet-cell reactive T cell repertoire of early pancreatic islet infiltrates in NOD mice[END_REF][START_REF] Ferreira | Reduced Regulatory T Cell Diversity in NOD Mice Is Linked to Early Events in the Thymus[END_REF][START_REF] Marrero | High-Throughput Sequencing of Islet-Infiltrating Memory CD4+ T Cells Reveals a Similar Pattern of TCR VÎ 2 Usage in Prediabetic and Diabetic NOD Mice[END_REF][START_REF] Marrero | High-throughput sequencing reveals restricted TCR Vβ usage and public TCRβ clonotypes among pancreatic lymph node memory CD4+ T cells and their involvement in autoimmune diabetes[END_REF]. This holds true also in human, with most advanced results in T1D [START_REF] Eugster | High Diversity in the TCR Repertoire of GAD65 Autoantigen-Specific Human CD4 + T Cells[END_REF][START_REF] Gomez-Tourino | T cell receptor β-chains display abnormal shortening and repertoire sharing in type 1 diabetes[END_REF][START_REF] Jacobsen | T Cell Receptor Profiling in Type 1 Diabetes[END_REF][START_REF] Seay | Tissue distribution and clonal diversity of the T and B cell repertoire in type 1 diabetes[END_REF], Systemic Lupus Erythematosus [START_REF] Costa | Broadened T-cell Repertoire Diversity in ivIg-treated SLE Patients is Also Related to the Individual Status of Regulatory T-cells[END_REF][START_REF] Kolowos | Detection of restricted junctional diversity of peripheral T cells in SLE patients by spectratyping[END_REF][START_REF] Liu | T cell receptor β repertoires as novel diagnostic markers for systemic lupus erythematosus and rheumatoid arthritis[END_REF][START_REF] Sui | Composition and variation analysis of the TCR β-chain CDR3 repertoire in systemic lupus erythematosus using high-throughput sequencing[END_REF][START_REF] Thapa | Longitudinal analysis of peripheral blood T cell receptor diversity in patients with systemic lupus erythematosus by next-generation sequencing[END_REF] and Rheumatoid Arthritis [START_REF] Liu | T cell receptor β repertoires as novel diagnostic markers for systemic lupus erythematosus and rheumatoid arthritis[END_REF][START_REF] Sante | Collagen Specific T-Cell Repertoire and HLA-DR Alleles: Biomarkers of Active Refractory Rheumatoid Arthritis[END_REF][START_REF] Kim | Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients[END_REF][START_REF] Lissina | Divergent roles for antigenic drive in the aetiology of primary versus dasatinib-associated CD8+ TCR-Vβ+ expansions[END_REF][START_REF] Musters | In Rheumatoid Arthritis, Synovitis at Different Inflammatory Sites Is Dominated by Shared but Patient-Specific T Cell Clones[END_REF][START_REF] Rossetti | TCR repertoire sequencing identifies synovial Treg cell clonotypes in the bloodstream during active inflammation in human arthritis[END_REF][START_REF] Schneider-Hohendorf | Sex bias in MHC I-associated shaping of the adaptive immune system[END_REF]. Thus, I truly believe that the description of the T-cell population diversity would help to understand the complex mechanisms underlining the immune response, in particular in ADs.

TCR repertoire and function

Besides the antigen specificity, T cell population is extraordinarily functionally diverse as well.

Since the identification of T4 and T8 in the early 80s [START_REF] Engleman | Antibodies to membrane structures that distinguish suppressor/cytotoxic and helper T lymphocyte subpopulations block the mixed leukocyte reaction in man[END_REF], several subsets, more or less stable, have been characterized based on their location, activation markers, functional properties through cytokines/chemokines production, activation/inhibition surface markers, immune regulation [START_REF] Dupage | Harnessing the plasticity of CD4(+) T cells to treat immune-mediated disease[END_REF][START_REF] Brummelman | Chapter Two -The Single-Cell Phenotypic Identity of Human CD8+ and CD4+ T Cells[END_REF]. Still the link between the function and the antigen-specificity is poorly understood, notably in regards with helper/effector T cells (Teffs) and regulatory T cells (Tregs)

18/67 (i) differentiation, (ii) selection and (iii) their balance, two major cell subsets with opposite function in the regulation of the immune response (Figure 3). From a functional perspectives: How does a skewed immune repertoire lead to the development of pathological autoimmunity? Conversely, is there a minimal repertoire required to efficiently respond to pathogens? How come that at a population level humans are able to, most generally, establish an efficient immune response against pathogens expressing non-self-antigens for which T cells are not positively selected still maintaining the integrity of their organs? Since the beginning of my career, I have been working to address part of these unresolved questions regarding T cell repertoire diversity using systems biology approaches.

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TCR repertoire and systems immunology

Systems immunology is an apparent emerging field of research. Formally termed in 2006 by Ron Germain and Christoph Benoist [29], systems immunology can be naively defined as a systems biology approach applied to the immune system. Yet, the definition of systems biology is probably as diverse as the number of systems biologists as mentioned by Rainer Breitling [START_REF] Breitling | What is systems biology?[END_REF], so it is for systems immunology. If I had to define my vision of systems immunology I would say that it is a global approach aiming at characterizing the immune system (whatever the level of the system considered) through the integration of the diversity of individual components forming that system. Retrospectively, systems immunology (as well as systems biology) is probably the original approach of research, since antiquity when complex systems where observed as a whole and not as the sum of their parts. This phrase, "the whole is greater than the sum of the parts", sometimes attributed to Aristotle and sometimes just as an interpretation of what Aristotle and others tried to express, became viral and somehow the foundation of modern systems biology.

After decades of reductionist approach, critical for the dissection of cellular and molecular mechanisms involved in the immune responses, technological progresses in molecular and cellular biology as well as in computational science now offers the possibility to integrate the parts of a complex system to rebuild the whole and eventually identify how the whole system behave under perturbations [START_REF] Burel | Systems Approaches towards Molecular Profiling of Human Immunity[END_REF][START_REF] Chaussabel | Democratizing systems immunology with modular transcriptional repertoire analyses[END_REF][START_REF] Davis | Systems immunology: just getting started[END_REF][START_REF] Or-Guil | Systems immunology of human humoral immunity[END_REF][START_REF] Shay | Immunological Genome Project and systems immunology[END_REF]. Such approach now provide opportunities to better characterize a system (un-exhaustively) at steady-state [START_REF] Piasecka | Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges[END_REF][START_REF] Thomas-Vaslin | Comprehensive assessment and mathematical modeling of T cell population dynamics and homeostasis[END_REF][START_REF] Thomas-Vaslin | Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers[END_REF][START_REF] Urrutia | Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses[END_REF][START_REF] Vaeth | Tissue resident and follicular Treg cell differentiation is regulated by CRAC channels[END_REF], in vaccinology [START_REF] Blazewicz | GeVaDSs -decision support system for novel Genetic Vaccine development process[END_REF][START_REF] Dérian | Early Transcriptome Signatures from Immunized Mouse Dendritic Cells Predict Late Vaccine-Induced T-Cell Responses[END_REF][START_REF] Fourati | Integrated systems approach defines the antiviral pathways conferring protection by the RV144 HIV vaccine[END_REF][START_REF] Gonçalves | Innate gene signature distinguishes humoral versus cytotoxic responses to influenza vaccination[END_REF][START_REF] Matthijs | Systems Immunology Characterization of Novel Vaccine Formulations for Mycoplasma hyopneumoniae Bacterins[END_REF][START_REF] Mckay | Identification of potential biomarkers of vaccine inflammation in mice[END_REF][START_REF] Six | Systems biology in vaccine design: Systems biology in vaccine design[END_REF][START_REF] Thiébaut | Gene Expression Signatures Associated With Immune and Virological Responses to Therapeutic Vaccination With Dendritic Cells in HIV-Infected Individuals[END_REF], in pregnancy [START_REF] Aghaeepour | An immune clock of human pregnancy[END_REF][START_REF] Blazkova | Multicenter Systems Analysis of Human Blood Reveals Immature Neutrophils in Males and During Pregnancy[END_REF][START_REF] Nehar-Belaid | Regulatory T Cells Orchestrate Similar Immune Evasion of Fetuses and Tumors in Mice[END_REF] and in various pathological conditions, such as autoimmune diseases [START_REF] Banchereau | Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients[END_REF][START_REF] Chaussabel | A Modular Analysis Framework for Blood Genomics Studies: Application to Systemic Lupus Erythematosus[END_REF][START_REF] Jourde-Chiche | Modular Transcriptional Neutrophil Signature As Predictive of Nephritis and of Its Severity in SLE Patients[END_REF][START_REF] Ramilo | Gene expression patterns in blood leukocytes discriminate patients with acute infections[END_REF][START_REF] Rosenzwajg | Low-dose interleukin-2 fosters a dose-dependent regulatory T cell tuned milieu in T1D patients[END_REF][START_REF] Rosenzwajg | Immunological and clinical effects of low-dose interleukin-2 across 11 autoimmune diseases in a single, open clinical trial[END_REF][START_REF] Saadoun | Regulatory T-cell responses to low-dose interleukin-2 in HCV-induced vasculitis[END_REF][START_REF] Terrier | IL-21 modulates Th1 and Th17 responses in giant cell arteritis[END_REF][START_REF] Terrier | Interleukin-21 modulates Th1 and Th17 responses in giant cell arteritis[END_REF][START_REF] Terrier | Serum biomarker signature identifies patients with B-cell non-Hodgkin lymphoma associated with cryoglobulinemia vasculitis in chronic HCV infection[END_REF],

infection [START_REF] Cardona | Can systems immunology lead tuberculosis eradication?[END_REF][START_REF] Kazmin | Systems analysis of protective immune responses to RTS,S malaria vaccination in humans[END_REF][START_REF] Mikkaichi | Identifying determinants of persistent MRSA bacteremia using mathematical modeling[END_REF][START_REF] Querec | Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans[END_REF][START_REF] Singhania | Transcriptional profiling unveils type I and II interferon networks in blood and tissues across diseases[END_REF][START_REF] Tran | Systems immunology of human malaria[END_REF][START_REF] Vallejo | Malaria systems immunology: Plasmodium vivax induces tolerance during primary infection through dysregulation of neutrophils and dendritic cells[END_REF], cancer [START_REF] Nehar-Belaid | Regulatory T Cells Orchestrate Similar Immune Evasion of Fetuses and Tumors in Mice[END_REF][START_REF] Patel | Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma[END_REF][START_REF] Puram | Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer[END_REF][START_REF] Shalek | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells[END_REF][START_REF] Singer | A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells[END_REF][START_REF] Tirosh | Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq[END_REF], … Most of the studies initially focused on methods for which global approaches were introduced and validated more than 15 years ago, such as transcriptomics, genomics, proteomics and cell phenotype…. Adaptive Immune receptor repertoire (AIRR) methodological progresses in the last ten years offered a renewal of the field and is now part of the system immunology approached [START_REF] Bradley | Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition[END_REF][START_REF] Burel | Systems Approaches towards Molecular Profiling of Human Immunity[END_REF][START_REF] Davis | Systems immunology: just getting started[END_REF][START_REF] Or-Guil | Systems immunology of human humoral immunity[END_REF][START_REF] Covacu | System-wide Analysis of the T Cell Response[END_REF][START_REF] Davis | The αβ T Cell Repertoire Comes into Focus[END_REF].

In this manuscript, I will introduce briefly my achievements during the past years and my current research to finish with my perspectives for the next 5 years. Adrien Six, aimed at characterizing brain T lymphocyte diversity in a mouse model of cerebral malaria (CM), one of the severe complications of Plasmodium falciparum infection, in part associated with an uncontrolled immune response. In particular, T lymphocytes play an important role as shown by the protection of T cell-deficient mice infected by the appropriate parasite, Plasmodium berghei ANKA (PbA) [START_REF] Grau | L3T4+ T lymphocytes play a major role in the pathogenesis of murine cerebral malaria[END_REF][START_REF] Sierro | The Ins and Outs of Cerebral Malaria Pathogenesis: Immunopathology, Extracellular Vesicles, Immunometabolism, and Trained Immunity[END_REF]. Moreover, CD8+ T cells reach the brain of CM-developing mice (CM+ mice) [START_REF] Bagot | Comparative Study of Brain CD8+ T Cells Induced by Sporozoites and Those Induced by Blood-Stage Plasmodium berghei ANKA Involved in the Development of Cerebral Malaria[END_REF][START_REF] Belnoue | CCR5 deficiency decreases susceptibility to experimental cerebral malaria[END_REF][START_REF] Boubou | T cell response in malaria pathogenesis: selective increase in T cells carrying the TCR V(beta)8 during experimental cerebral malaria[END_REF]. The complex composition of micro-organisms in general including antigenic, superantigenic and mitogenic activities, alter the diversity, leading to polyclonal proliferations and/or oligoclonal expansions [START_REF] Pied | Evidence for superantigenic activity during murine malaria infection[END_REF][START_REF] Reina-San-Martin | Lymphocyte polyclonal activation: a pitfall for vaccine design against infectious agents[END_REF]. Such modifications can lead to inappropriate responses that, in turn, scramble or divert the protective appropriate response.

We hypothesized that during Plasmodium infection, the diversity of T repertoire is highly modified. At that time, we made used of Immunoscope technique [START_REF] Pannetier | The sizes of the CDR3 hypervariable regions of the murine T-cell receptor beta chains vary as a function of the recombined germ-line segments[END_REF] (also known as CDR3

spectratyping [START_REF] Gorski | Circulating T cell repertoire complexity in normal individuals and bone marrow recipients analyzed by CDR3 size spectratyping. Correlation with immune status[END_REF]).

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Method: From qualitative to quantitative TCR profiling by Immunoscope Immunoscope technique developed at the Pasteur Institute in early 90's [START_REF] Pannetier | The sizes of the CDR3 hypervariable regions of the murine T-cell receptor beta chains vary as a function of the recombined germ-line segments[END_REF] generates CDR3 length profiles, at that time mostly analysed qualitatively. Although such approach was promising and efficient for the study of pathological conditions associated with major expansions of clones, it reveals lack of quantitative reliability [START_REF] Fozza | Study of the T-cell receptor repertoire by CDR3 spectratyping[END_REF]. To overcome such issue, Adrien Six and his former PhD student developed a software able to quantitatively manage the information output from Immunoscope, namely ISEApeaks [START_REF] Collette | New methods and software tools for high throughput CDR3 spectratyping. Application to T lymphocyte repertoire modifications during experimental malaria[END_REF]. The principle was simple: rearrangements were labelled during amplification by PCR and separated at the molecular level using sequencing methods. The labelling allowed to identify and measure the intensity of each rearrangement. The separation based on the size, the output corresponded to the CDR3 length of each rearrangement. The data from the CDR3 length profiles obtained from the sequencer could therefore be retrieved and gathered in a peak database. CDR3 spectratypes could then be quantitatively analysed by applying different measures such as perturbation indexes, translating the modifications between samples but also reflecting the emergence of recurrent oligoclonal expansions. Associated to multivariate statistical methods implemented in ISEApeaks, this strategy allowed a rigorous analysis of the repertoire and can be seen as the grounds of TCR database integration.

Results

In a first study, we showed a perturbation of blood TCRß repertoire in CM+ mice, compared to healthy mice and also to infected mice without cerebral symptoms, partly due to recurrently expanded T cell clones [START_REF] Collette | A profound alteration of blood TCRB repertoire allows prediction of cerebral malaria[END_REF]. Moreover, the cerebral damages associated to the infiltration of CD8+ T lymphocytes in the brain of CM+ mice suggested that these modifications were directly associated with the outcome of CM. In order to evidence the dynamics of this alteration, we performed a kinetic analysis of blood, spleen and brain TCRß repertoires, from day 3 postinfection (p i) until the death following the development of neurological signs of PbA-infected mice. The repertoire of total blood, spleen and brain lymphocytes was analysed by comparison between infected and non-infected mice. We observed a global alteration of the repertoire of infected mice from day 5 in the spleen and day 6 in the blood. The perturbation increases for all

Vß in the blood and the spleen. Interestingly, perturbation of most of the Vß is peculiar to each compartment analysed suggesting a compartmentalization of the diversity during the infection.

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Strikingly, the comparison of brain repertoires between healthy and CM+ mice revealed a perturbation limited to 5 Vß out of 23, and recurrent oligloclonal expansions among 2 Vß families.

These 5 Vß are perturbed in the spleen and the blood, following a similar kinetic. These results confirmed our previous observations of a huge perturbation of the repertoire associated to the development of cerebral malaria. Moreover, they indicate a possible targeted alteration of the brain TCRß diversity, suggesting an oriented recognition, of parasitic or self-antigens, in the brain.

We therefore applied a strategy for TCR signature detection based on an approach developed by Adrien Six team for transcriptome analysis [START_REF] Pham | A novel strategy for molecular signature discovery based on independent component analysis[END_REF]. We could identify a TCR signature in the blood discriminating with > 90% accuracy the development of CM. In addition, this blood signature was also found in the spleen and more importantly in the brain, strongly suggesting a pathogenic role of T cells bearing those TCR in the development of CM in mice [START_REF] Mariotti-Ferrandiz | A TCRβ Repertoire Signature Can Predict Experimental Cerebral Malaria[END_REF].

Conclusion

TCR repertoire is modified at early stage of T1D onset in mouse

In parallel, I was involved in a collaborative study of the T cell repertoire in young NOD mice.

Initiation of the T cell response that cause autoimmune diabetes in NOD mice occurs in the pancreatic lymph nodes (PLN) around two weeks of age. To identify T cells that were activated early in response to beta cell antigens at this site, we investigated the TCR diversity in the pancreatic lymph nodes (PLN) in comparison with inguinal lymph nodes (ILN) as controls, of 10, 14, 18 and 22¬ day-old NOD females. An analysis of all V chains by Immunoscope and ISEApeaks (see methods) revealed intensive perturbations of the repertoire through time in the PLN as well as in the ILN, suggesting that the overall TCR repertoire is subject to major changes between 10 and 22 days of age. In contrast, when a selected set of V chains was analysed at the V-J level, several unique TCR perturbations and oligoclonal expansions were observed in the PLN compared to the ILN. Different oligoclonal expansions were found at different time points, 23/67 suggesting that the initiation of diabetes in NOD mice may involve several T cell specificities that are activated in a sequential manner [START_REF] Petrovic Berglund | TCR repertoire dynamics in the pancreatic lymph nodes of non-obese diabetic (NOD) mice at the time of disease initiation[END_REF].

Altogether

TCR repertoire and specificity of regulatory T cells

Soon after my PhD defence, I joined Dr Hori's laboratory at Riken Institute in Japan with the aim to link TCR repertoire diversity with T cell subset functions. This post-doctoral internship was supported by a Japan Society for Promotion of Science (JSPS) post-doctoral fellowship that I obtained soon after joining Dr Hori's laboratory.

Seminal work from Shimon Sakaguchi [START_REF] Itoh | Thymus and autoimmunity: production of CD25+CD4+ naturally anergic and suppressive T cells as a key function of the thymus in maintaining immunologic self-tolerance[END_REF][START_REF] Nishimura | Induction of antigen-specific immunologic tolerance by in vivo and in vitro antigen-specific expansion of naturally arising Foxp3+CD25+CD4+ regulatory T cells[END_REF][START_REF] Sakaguchi | Immunologic self-tolerance maintained by activated T cells expressing IL-2 receptor alpha-chains (CD25). Breakdown of a single mechanism of self-tolerance causes various autoimmune diseases[END_REF], further confirmed by others [START_REF] Shevach | Biological Functions of Regulatory T Cells[END_REF], established that the normal αβ T cell repertoire consists of not only "conventional/effector" T (Teff) cells that exhibit ridding or aggressive functions but also regulatory T (Treg) cells that display protective or suppressive functions over aggressive immune activities to ensure immunological tolerance to "self" and innocuous environmental antigens including commensal bacteria [START_REF] Cebula | Thymus-derived regulatory T cells contribute to tolerance to commensal microbiota[END_REF][START_REF] Russler-Germain | Antigen-specific regulatory T-cell responses to intestinal microbiota[END_REF]. This notion

Conclusion

Those results also supported previous observations by others [109][110][111][112] that Tregs and Teffs may differentiate independently of their TCR and therefore may share part of their diversity.

Indeed, as Foxp3+ cells have been found to function in a wide range of immunological contexts, their specificity has been questioned, and especially the overlap with conventional T cells 26/67 (Foxp3 -). While some groups showed distinct Foxp3+ and Foxp3-repertoires and revealed selfreactivity among Foxp3 + TCRs [110,113,114] others conclude to a high overlap between both populations arguing that Foxp3 + have a non-self-reactive repertoire [START_REF] Cebula | Thymus-derived regulatory T cells contribute to tolerance to commensal microbiota[END_REF]112]. Therefore, from this project we moved to the characterization of TCR repertoire diversity of the 1D2 mouse.

Treg TCR repertoire diversity

Method

As for the next-generation sequencing platform, we have used the 454 FLX Titanium from Roche, which could generate up to 1.6x10 6 individual reads per run with a read length of up to 400-bp, a length sufficient to determine the nucleotide sequence of the CDR3 and the V2 and J gene segment usage.

We used the 1D2 mice harbouring the Foxp3 hCD2 reporter allele and one TCRC-null allele

[120] and sorted peripheral CD4 + T cells into Foxp3 + Treg and Foxp3 -Tconv cells by flow cytometry 27/67 based on the hCD2 reporter expression from the thymus of 5 weeks old mice and peripheral secondary lymphoid organs from 5 and 8 weeks old mice.

Because there are numerous V gene segments, we have restricted our analyses to cells expressing the TRAV14 (V2) family (which is one of the most major family). I designed a specific primer that covers all the 6 TRAV14 family members and a reverse primer specific for the constant region so that we can amplify TRAV14 transcripts.

Results

This collaboration allowed me to access to the state-of-the-art sequencers and to develop in tight collaboration with a bioinformatician a tool, clonotypeR, for TCR data obtained from next generation sequencing (NGS), unavailable at that time [121]. clonotypeR does include functions for automatic TCR sequences analysis as well as statistical modelling of repertoire diversity, by computing diversity indexes (richness, Morisita-Horn, Species Abundance Distribution).

Our quantitative comparisons of V and J segments frequency, CDR3 length and clonotype distribution between samples, showed that Foxp3+ and Foxp3-T cells can be discriminated by these different parameters from the TCRα in a fixed TCRβ background, in line with the works of Hsieh et al [113] and Pacholczyk et al. [112]. In our study, as we sequenced millions of CDR3, we could show that the differences between Foxp3+ and Foxp3-repertoires are quan ta ve rather than qualitative, and that a frequent CDR3 in one population will tend to be rare but not completely absent from the other population. This challenges the concept of private clonotype in such high-resolution studies. Strikingly, at the level of TRAV14 and TRAJ segments, we found differential usage between Foxp3+ and Foxp3-in 1D2β. Similar observations were also reported earlier with a different fixed β chain [113], although no statistical comparisons could be achieved.

Given that the Vα and Jα nucleotides contributing to the CDR3 region are the same in each TRAV14 segments respectively, those results suggest a contribution of the conserved regions from the V segments to T cells function. Structure analysis of self-reactive human and murine TCRs showed that TCRs specific for self-antigens display an unconventional structure of V regions compared to non-self-reactive TCR [122]. In addition, this is a crucial point for fundamental analysis of TCR repertoire since most of studies focused the immune diversity analysis to the 28/67

CDR3 region, arguing that peptide/MHC recognition is limited to CDR3 region. However, if we consider the contribution of TRAV segments as well as TRAJ, this may revise upwards the extent of the TCR diversity.

As in previous studies, we found a certain overlap between regulatory T cells and conventional T cells, in periphery and in the thymus. To try to distinguish thymic versus peripheral shaping of the repertoire, we analysed also recent thymic-emigrants by using Rag1-GFP model, as it is known that RAG-expression, required for the rearrangement, is abolished prior to thymus exit. The reporter gene GFP allowing to track those cells which recently expressed RAG, presumably recently emigrating from the thymus.

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Statistically significant fold changes are indicated by a red dot. With each statistical comparison defining three groups (up, no difference, down), the stacked bar charts represent the contribution of these three groups to the thymic, RTE and peripheral Foxp3+ or Foxp3-popula ons. (D) Same as C, except that the comparison is RTE vs. periphery.

First of all, by measuring the Morisita-Horn similarity distance, we found that RTE repertoire are highly similar with that of thymic cells, for both Treg and Teff (Figure 4 A&B). Interestingly, Foxp3-clonotypes enriched in the thymus compared to RTE were found to be abundant in thymic and RTE Foxp3+ population, suggesting that these clonotypes were auto-reactive, either expressed by both thymic Foxp3 + and Foxp3 -and then deleted from the Foxp3-subset, or expressed by Foxp3 -and redirected toward the Foxp3 + stage upon thymic maturation (Figure 4 C&D). This result was apparently in contradiction with previous results showing that TCR engagement is required to upregulate the IL2R chain (CD25) expression and the corresponding signalling pathways which in turn led to the transcription of Foxp3 [110,123] arguing for the existence of Tregs precursors among the CD4SP cells CD25 + . However, since we separated the cells based on the expression or the absence of expression of Foxp3, our Foxp3-samples may contain precursors of Tregs. It is reasonable to think that, Foxp3-clonotypes found in the Foxp3 + compartment may correspond or at least be enriched in Foxp3+ precursors which did not upregulate CD25 yet. If so, they must be found preferentially in the peripheral Foxp3 + compartment.

To cope with this point, we carefully analysed the peripheral distribution of thymic clonotypes.

Interestingly, while 16% and 20% of exclusive-thymic Foxp3 + and exclusive-thymic Foxp3clonotypes respectively are found in the periphery, 60% of clonotypes shared between thymic Foxp3 + and Foxp3 -are selected to the periphery and 78% are found in peripheral Foxp3 + compartment (among which half are also shared with Foxp3 -cells) supporting indirectly the two step model. However, the direct evidence requires the comparison of Foxp3 -CD25 -cells [124]. Towards a systems immunology approach for biomarker discovery (since 2012)

Conclusion

PUBLICATION ACHIEVEMENTS B : POST-DOC

In 2012, I joined Prof David Klatzmann laboratory as a post-doctoral fellow to work on two recently granted projects obtained by David Klatzmann:

-A European Research Council Advanced Grant project aiming at "Deciphering the regulatory T cell repertoire: towards biomarkers and biotherapies for autoimmune diseases" (TRiPoD project).

-A Laboratory of Excellence project funded by the local National Agency for Research (Agence Nationale pour la Recherche, ANR) aiming at revisiting the nosography of autoimmune and inflammatory diseases through a systems immunology approach (Transimmunom project).

In those two project, there was a major focus on Treg biology as well as on their TCR repertoire composition and specificity, in human and/or mouse physiology and pathology.

I therefore had two major missions to fulfil the expectations on those projects.

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Implementation and coordination of the Treg TCR repertoire studies in mouse and human physiological and pathological conditions

Development and implementation of TCR repertoire diversity analysis approaches by NGS Background Systematic sequencing of "all" Ig/TCR transcripts expressed in a lymphocyte population of interest can be achieved by NGS technologies which become available after the seminal work of Weinstein et al. in 2009 [125]. NGS allows to produce several tens of thousands of sequences in a short amount of time. A benefit of this approach is clearly that several angles of analysis can be looked at such as clonotypes frequency, Ig/TCR V-C or V-J usage, CDR3 diversity, CDR3 sequence analysis, V gene allele identification… However, while several studies at that time, including mine at Riken, have highlighted the feasibility of using deep sequencing for the analysis of Ig/TCR repertoire diversity [126], now called AIRRSeq (for Adaptive Immune Receptor Repertoire

Sequencing) [127,128], the ability to process the complexity of the information provided by such amounts of data remained limited and specific software developments for automatic annotation of TCR sequences and statistical modelling of repertoire diversity were needed, as we summarized in a Frontiers in Immunology review [129].

In addition, besides the computational needs, the molecular approaches used and proposed by different laboratories and/or companies, such as Adaptive Biotechnologies and iRepertoire in the US, were at their early birth and there was no consensus nor grounded experience, as it was a newly born field. Such absence of consensus was raised by the AIRR community, launched in 2015 by Felix Breden and Jamie Scott and is currently tackled by different working groups, including two I belong to.

In tight collaboration with Prof. Adrien Six Integrative Immunology team (i2) in the laboratory, I have been involved in the development and implementation of (i) TCR repertoire modelling strategy and tools and (ii) setup and validation of a robust experimental design for TCR library preparation and sequencing. iReceptor gateway for AIRR-seq data integration and query [142] and was willing to expand its tool to other laboratories. I have been supervising, with the help of the IT manager of the laboratory and the iReceptor team, the setting up of an iReceptor repository in the laboratory, now functional and waiting for data loading.

In addition to the tool development, in a co-supervised work with Adrien Six, we revealed that over sequencing small samples may lead to artificial diversity that can eventually be overcome by using Shannon entropy as a normalization method [132] as it is well known to identify what is called the true diversity [143]. These results led us to realize the necessity in establishing a robust method for TCR repertoire analysis by NGS.

Conclusions

In tight collaboration with statisticians, computer scientists and bioinformaticians, I implemented a framework for advanced still user friendly TCR repertoire modelling that can now be applied for (i) diversity exploration and (ii) TCR signature identification in mice and humans.

Portraying TCR repertoire diversity of Treg subsets in mouse

Experimental design

To answer that question, I coordinated the experimental design for Treg subset sampling in mouse by FACS sorting as well as the TCR repertoire analysis.

In human, a similar approach has been implemented by Valentin Quiniou (PhD Student) under the supervision of David Klatzmann to obtain LN, thymus, spleen and blood from organ donors enrolled in an approved protocol by the local ethical committee at APHP. I am not directly involved in the supervision of this study, so I will not further detail it [150].

In mice, we selected two mouse background: (i) the commonly used C57BL/6 mouse background, Foxp3-, CD4SPFoxp3+ and CD8SP as thymocytes positively selected and committed to their respective peripheral subsets (CD4 Teffs, CD4 Tregs and CD8 Teffs). Importantly, those later subsets may include cells that did not undergo negative selection at the time of sampling. This protocol has been applied to young (8-10 weeks old), old (20-26 weeks old) and very old (60 weeks old) mice, female and male separately. Since some Treg subset where very rare in some LN (especially renal and para-aortic), we decided to pool the cells from 6 to 8 age, sex and genetic background matched animals. This protocol was validated by the local ethical committee. All the 36/67 sorted cells were systematically stored in lysis buffer at -80°C. Since the beginning of the study in 2013, we collected more than 4000 samples across more than 100 experiments.

Results

During sample collection, we started the analysis of the TCR repertoire of the different T cell subset sorted from the spleen. I supervised two master students, among which one continued as a PhD student in the lab under my co-supervision together with Adrien Six, and granted with a 3 years doctoral fellowship from the French Ministry of Education. A manuscript focusing on splenic CD4 subsets spleen is currently in preparation [155] where we show that amTregs in NOD mice are more diverse than in C57BL/6 probably as a results of interleukine-2 (IL2) production impairment in NOD mouse. We therefore applied the same strategy of cell sorting in NOD mice treated with AAV-IL2, a vector restoring IL2 production in mice [156]. This time, cells were sorted from individual mice to 37/67 appreciate inter-individual variation. Our results show that IL-2 treatment induces amTregs repertoire expansions (as well as nTregs to a less extend) but not Teffs. In addition, we inferred the specificity of the most expanded clonotypes in each subset and condition (C57BL/6, NOD, NOD+IL2) by comparing them with T1D associated TCR published elsewhere. We found that the expanded clonotypes in amTregs and Teffs (but not in nTregs) were enriched in T1D specific clonotypes in NOD treated by IL2, as well as in C57BL/6 but not in NOD non treated mice. Altogether, those results suggest (i) that pancreas self-antigen TCRs are naturally arising in mice and enriched in amTregs, (ii) that amTregs are exhausted in NOD background due to impaired IL2 production leading to a poor control of autoreactive Teffs that escape negative selection in the thymus. Further characterization of the overlap between amTregs and Teffs will be required. In addition, since non treated mice repertoires were generated from cells pooled from several mice while NOD treated with IL2 repertoires were analysed from individual mice, we cannot exclude 38/67 that the differences, especially in terms of specificity are due to private response of individual mice that might be diluted in NOD pooled repertoires.

In two collaborative studies within the laboratory and with the team of Luis Graca (Instituto Gulbenkian, Portugal), I have been involved in the precise characterization of regulatory follicular T cells (Tfr) and their TCR repertoire in NOD and C57BL/6 mice [133,134,157].

Conclusions

This study has been setting up the ground for our TCR repertoire modelling strategy and already provide insights into fundamental questions regarding T cell differentiation and TCR repertoire diversity importance to better understand pathological conditions.

Coordination of Systems Immunology Activity for the Study of AIDs

Background

Autoimmune and inflammatory diseases (AIDs) have been shown to form a continuum of diseases from pure autoimmune to pure inflammatory diseases [158] mainly based on genetic studies. It is also known that most of the patient diagnosed for one AID, may progressively develop other AIDs. The most representative situation is that of T1D patients, who may develop by time renal and cardiac autoimmune disorders. Since (i) the immune system play a major role in such diseases, (ii) most of the treatments associated with each of the AIDs are not fully beneficial for all the patients and (iii) treatment for one disease may be detrimental in case of a secondary AID occur. The Transimmunom project aims at revisiting the nosography of AIDs through a systems immunology approach consisting in the cross-analysis of multi-parametric data, including blood transcriptome, serum cytokines and auto-antibodies, sorted blood Treg and Teffs TCR repertoire as well as gut microbiome and clinical data.

In the framework of this project, I participated to the overall experimental design of sample collection, benchmarking of data integration tools and molecular biology methods [159] and led the clinical data integration in a harmonized database [160] as well as the validation of molecular biology approaches for TCR repertoire sequencing by NGS [161].

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AID patient clinical data integration

In tight collaboration with Roberta Lorenzon, the medical doctor of the Transimmunom trial, we established a workflow (Figure 9).

Results

To tackle the challenge, we assembled a clinical expert consortium (CEC) to select relevant clinicbiological features to be collected for all patients and a cohort management team comprising biologists, clinicians and computer scientists to design an electronic case report form (eCRF).

After more than a year and a half (100h of meetings), we (i) choose as an eCRF platform an opensource CFR-part 11 compliant electronic data capture system, OpenClinica, (ii) selected with the CEC 865 clinical and biological parameters covering all the diseases and to be (for more than 80%) collected for all the patient and healthy volunteers recruited in the trial, (iii) coded the parameters using CDISC standards into 5835 coded values and (iv) designed 28 structured eCRFs Establishment of a robust experimental method for TCR library preparation and sequencing

Background

Since the advance of NGS, analysis of the TCR repertoire is increasingly being used to understand disease pathogenesis and lymphocyte dynamics in health as well as in various pathological contexts such as autoimmune diseases, infections or cancers [START_REF] Davis | The αβ T Cell Repertoire Comes into Focus[END_REF][162][163][164][165][166][167]. Indeed, with the high resolution provided by NGS should contribute to (1) deciphering the specificity or the breadth of a given adaptive immune response under pathological conditions, (2) identifying TCRs as biomarkers of diseases or clinical response to a treatment and (3) stratifying patients according to their TCR, therefore fostering new personalized therapeutic development.

However, because of the broad immunological situations where TCR-Seq can apply with the aim either at characterizing subtle modifications of the repertoire [START_REF] Eugster | High Diversity in the TCR Repertoire of GAD65 Autoantigen-Specific Human CD4 + T Cells[END_REF][START_REF] Gomez-Tourino | T cell receptor β-chains display abnormal shortening and repertoire sharing in type 1 diabetes[END_REF][166][167][168][169][170][171][172][173][174] or at identifying 41/67 and tracking major clonal lymphomas [175][176][177], the requirement in terms of specificity and sensitivity of the method is different. Technically speaking, there are a number of caveats for which there is a lack of consensus in the field. Indeed, each step from sample preparation to analysis is critical and may have a profound impact on the actual data generated and subsequent interpretation of the results [178]. Still, there is no precise answer on the impact of methodology on TCR diversity measures, such as gene usage, rearrangement insertion and deletion, clonotype distribution, and quantification of rare versus abundant sequences. Two major approaches are used: 5'RACE-PCR (exclusively on RNA) and multiplex-PCR (either on RNA or DNA). To solve those issue and identify a robust and reproducible method that we could use in our different ongoing projects, I launched a broad comparative study and succeeded to mobilize key leaders in the field.

Results

We compared 8 different methods, including three from commercial entities, using as starting material a standardized set of input samples (Figure 10). Our aims were for each method to evaluate (i) their robustness in identifying and quantifying clonotypes of different sizes and (ii) their sensitivity to the input material quantity. Using the same analysis strategy on all the data generated, we observed differences between methods for (i) intra-and inter-method In addition to the molecular method validation, in collaboration with the LIGAN EquipEx to finetune the sequencing method, with the team of Michelle Rosenzwajg to set an efficient and handy method for Treg vs. Teff cell separation from blood samples and recently with a French biotechcompany (Primadiag), commercializing automatization systems for NGS library preparation, together with Pierre Barennes we established a complete workflow for TCR library preparation and sequencing from patient blood samples.

General Conclusions

During my second post-doc and my starting position as an assistant professor in i3 lab, I (i) develop a framework for TCR repertoire modelling in mouse and humans including wet and dry lab developments, (ii) characterize several Treg cell subset TCR peculiarities and (iii) set-up a systems immunology approach for AIDs study. Illumina MiSeq V2 300x300

Multipex-1

RACE-3

Illumina MiSeq V2 250x250

Multiplex-2 integration tool to combine clinical and multi-OMICS data on a patient-oriented database, using tranSMART platform [188][189][190], and coordinate the design the of framework to make tranSMART interoperable with iReceptor federated repository. This work will consist mainly in (i) establishing the AIRR variables to be integrated in tranSMART for multi-OMICS cross-analysis, (ii) establishing a prototype for minimal standard systems immunology data and (iii) designing a tranSMART-API for multi-OMICS including AIRR data.

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1 : Somatic recombination of the TCR and TCR locus in thymocytes .......................................Figure 2 : Repertoire level according to the differentiation stage of T and B cells .................................. Figure 3: Schematic view of the link between TCR specificity and differentiation, selection and balance of Tregs and Teffs .......................................................................................................................................Figure 4 : Recent thymic emigrants reveal TCR repertoire maturation dynamics ...................................Figure 5 : TCR repertoire exploration modelling ....................................................................................... Figure 6 : Screen shot of DiversiTR, a web-app for TCR repertoire analysis ............................................Figure 7 : B6 but not NOD amTregs show lower diversity and more expansions than nTregs and Teffs. ....................................................................................................................................................................Figure 8 : amTregs and nTregs are are expanded in NOD upon low-dose IL-2 treatment. ......................Figure 9 : eCRF design and implementation workflow. ............................................................................Figure 10 : TCR library method comparison -Experimental design. ........................................................Figure 11 : Probability distribution reveals clinical condition and cell-subset CDR3aa signatures .........Figure 12 : Schematic representative of my research perspectives .........................................................

Figure 4 :

 4 Figure 1 : Somatic recombination of the TCR and TCR locus in thymocytes .......................................Figure 2 : Repertoire level according to the differentiation stage of T and B cells .................................. Figure 3: Schematic view of the link between TCR specificity and differentiation, selection and balance of Tregs and Teffs .......................................................................................................................................Figure 4 : Recent thymic emigrants reveal TCR repertoire maturation dynamics ...................................Figure 5 : TCR repertoire exploration modelling ....................................................................................... Figure 6 : Screen shot of DiversiTR, a web-app for TCR repertoire analysis ............................................Figure 7 : B6 but not NOD amTregs show lower diversity and more expansions than nTregs and Teffs. ....................................................................................................................................................................Figure 8 : amTregs and nTregs are are expanded in NOD upon low-dose IL-2 treatment. ......................Figure 9 : eCRF design and implementation workflow. ............................................................................Figure 10 : TCR library method comparison -Experimental design. ........................................................Figure 11 : Probability distribution reveals clinical condition and cell-subset CDR3aa signatures .........Figure 12 : Schematic representative of my research perspectives .........................................................

Figure 5 :Figure 6 :Figure 7 :Figure 8 :Figure 9 :Figure 11 :Figure 12 : 67 -

 56789111267 Figure 1 : Somatic recombination of the TCR and TCR locus in thymocytes .......................................Figure 2 : Repertoire level according to the differentiation stage of T and B cells .................................. Figure 3: Schematic view of the link between TCR specificity and differentiation, selection and balance of Tregs and Teffs .......................................................................................................................................Figure 4 : Recent thymic emigrants reveal TCR repertoire maturation dynamics ...................................Figure 5 : TCR repertoire exploration modelling ....................................................................................... Figure 6 : Screen shot of DiversiTR, a web-app for TCR repertoire analysis ............................................Figure 7 : B6 but not NOD amTregs show lower diversity and more expansions than nTregs and Teffs. ....................................................................................................................................................................Figure 8 : amTregs and nTregs are are expanded in NOD upon low-dose IL-2 treatment. ......................Figure 9 : eCRF design and implementation workflow. ............................................................................Figure 10 : TCR library method comparison -Experimental design. ........................................................Figure 11 : Probability distribution reveals clinical condition and cell-subset CDR3aa signatures .........Figure 12 : Schematic representative of my research perspectives .........................................................

Figure 1 : 67 TCR

 167 Figure 1 : Somatic recombination of the TCR and TCR locus in thymocytes

Figure 2 :

 2 Figure 2 : Repertoire level according to the differentiation stage of T and B cells

Figure 3 :

 3 Figure 3: Schematic view of the link between TCR specificity and differentiation, selection and balance of Tregs and Teffs

20 / 67 T

 2067 cell receptor diversity inception (2004-2012) During my early career, I had the chance to join two major laboratories in the field of T cell biology, including TCR repertoire diversity and regulatory T cells differentiation and characterization: -From my MSc degree internship, I joined Prof. Adrien Six team in Prof. Pierre-André Cazenave laboratory at Pasteur Institute (Paris, France) to study the TCR repertoire alteration during the course of Plasmodium berghei ANKA infection in mouse. -Right after my PhD, I joined Dr. Hori's laboratory at Riken Institute (Yokohama, Japan) to work on Treg specificity and repertoire in mouse physiological condition. Identification of a blood TCR signature predicting Cerebral Malaria Background During my MSc degree and my PhD internships, the later supported by a French Ministery of Education and Research Doctoral fellowship (MNERT), I worked on the characterization of the T lymphocytes diversity in different mouse models. My main research project, supervised by Prof.

  With this work we brought a dynamic dimension to the repertoire description, showing for the first time (1) the evolution of TCRß diversity during the course of the infection, (2) the description of the global brain TCRß repertoire in mouse and (3) the biomarker potential of TCR signatures. This work was awarded in 2006 by the "FOCIS poster of distinction".

  has been firmly established by the findings by Hori's lab and others that the development of functional Treg cells is controlled by the transcription factor Foxp3 and that their defective generation underlies the catastrophic autoimmune, inflammatory and allergic disease that develops in Foxp3-mutant mice and humans suffering from the IPEX (immune dysregulation, polyendocrinopathy, enteropathy, X-linked) syndrome[START_REF] Brunkow | Disruption of a new forkhead/winged-helix protein, scurfin, results in the fatal lymphoproliferative disorder of the scurfy mouse[END_REF][START_REF] Fontenot | Foxp3 programs the development and function of CD4+CD25+ regulatory T cells[END_REF][START_REF] Hori | Foxp3: a critical regulator of the development and function of regulatory T cells[END_REF][100][101]. Still, Treg deficiency or insufficiency was not clearly addressed at that time. Two main hypothesis were reasonably 24/67 stated: The first hypothesis would suggest that AIDs are due to a quantitative defect of Tregs explained by a decrease number of cells leading to an imbalance between the Treg and the Teff compartment. This has been supported by the description of an IL-2 insufficiency in T1D patients (as well as the NOD mouse model) for instance. The second hypothesis would rather explain AIDs development due to a limited/altered Treg cell repertoire diversity that could lead to the development of various autoimmune or allergic diseases by creating "holes" in the antigenic universe recognized by Treg cells. For my post-doctoral internship, I proposed to tackle the second hypothesis by first characterizing Treg TCR repertoire diversity in physiology. In addition, Treg specificity was (and is still) unknown. I therefore established a mouse model to try to resolve it. Treg TCR specificity Background Studies on TCR specificity often relied on transgenic mice expressing only one single TCRβ chain and characterized the diversity of their TCRα repertoire, in order to make the analysis and data interpretation straightforward by eliminating the diversity of TCRβ chains and the diversity caused by pairing of TCRα and β chains. It remained unclear, however, whether the picture obtained from such transgenic systems can be generalized to the fully diverse T cell repertoire in normal, non-transgenic individuals. In addition, since the TCRα repertoire and the consequent antigenic recognition by the resulting αβ TCR have been shown to be somehow skewed in such TCRβ "fixed" mice compared to normal non-transgenic mice, it is possible that different pictures may be obtained from different transgenic systems. My first project aimed at establishing a more physiological mouse model to address the TCR repertoire and antigen specificity of Treg cells. In collaboration with Dr. Teruhiko Wakayama (Riken Institute), the lab established Treg cell-derived nuclear-transferred ES (nt-ES) cell lines. Briefly, they sorted Treg cells based on CD25Hi expression by FACS sorting, retrieve the respective nucleus and transferred them onto ES denucleated cell line using an established protocol [102]. The efficiency of such nuclear transfer with Treg cell derived nuclei was very low, leading to only 6 established nt-ES cell line. Each of those were characterized for their TCR by Sanger 25/67 sequencing. By germ-line transmission, they obtained one functional TCRβ gene from one of the nt-ES lines, called 1D2. Although they failed to obtain the functional 1D2 TCR gene by germline transmission, we decided to take advantage of the 1D2 allele and utilized this unique Tregderived TCR fixed mouse model for characterization of TCR repertoire and specificity of Treg cells. Results First, I determined whether the original 1D2 nt-ES cells are indeed derived from Treg cells by "retrospective" sequencing. We introduced the Foxp3 hCD2 reporter allele that was generated in the by Herman Waldmann lab in UK [103], as a more reliable reporter-gene of Foxp3 expression [104], and one TCRC-null allele to prevent expression of two TCR chains on the surface [105] into the 1D2 homozygous mice, sorted Foxp3 hCD2+ Treg and Foxp3 hCD2-Tconv cells, and performed conventional sequencing of the original V44/J44 rearrangements. As a result, we found that the 1D2 clonotype was expressed preferentially in Treg cells, although it was also expressed in Tconv cells albeit at a lower frequency, indicating that this 1D2  TCR was indeed derived from Treg cells. Second, to reconstitute the original 1D2 TCR in vivo and to address its specificity, I cloned both 1D2 and  cDNA into a retroviral vector, infected RAG -/-BM cells with the retrovirus, and transferred the 1D2-transduced BM cells into irradiated host mice, using a published protocol for retrogenic mice generation [106,107]. We found that both Foxp3 + and Foxp3 -T cells expressing the 1D2 TCR did develop in such retrogenic mice, further confirming that the 1D2  TCR can instruct developing T cells to differentiate into Treg cells. Importantly, such differentiation could be achieved only in BM chimeras reconstituted with 1D2transduced BM and wild type BM cells, establishing Hsieh's observations that Treg differentiation is highly dependent on intraclonal competition [108].

Background

  At that time, and since the discovery of the TCR loci[START_REF] Chien | Somatic recombination in a murine T-cell receptor gene[END_REF], several attempts have been made to estimate the repertoire diversity of various T cell subsets including Treg cells in humans and in mice[112][113][114][115][116][117][118] by using conventional sequencing approaches, i.e., RT-PCR amplification of rearranged TCRα or β transcripts followed by cloning and sequencing of individual clones by the Sanger method. However, the information obtained from such studies has been limitedalthough informative -and sometimes contradictory, primarily because of technical limitations of the conventional approach (labour-and cost-intensive, inherently "low-throughput"). As a result, one could only infer the diversity of the whole repertoire from such small samples based on some statistical assumptions on the underlying clonal size distributions [119]. To further develop our ongoing research activity, I decided to establish a novel strategy for comprehensive and quantitative measurement of the TCR repertoire diversity of Treg and Tconv cells by taking advantage of the next-generation sequencers that have been fully functional at the Riken Institute. To this end, I established a collaboration with Drs. Plessy (Bioinformatician) and Manabe (Head of the sequencing facility at Riken Institute) and obtained a Riken President Fund for feasibility studies in 2009.

Figure 4 :

 4 Figure 4 : Recent thymic emigrants reveal TCR repertoire maturation dynamics (A) Hierarchical clustering of the 1D2b libraries calculated based on the Morisita-Horn similarity. (B) Morisita-Horn index calculated between thymus and RTE repertoires (left) or peripheral and RTE repertoires (right) using 6000 sequences sub-sampled libraries from Foxp3+ (red) and Foxp3-(black) samples. In graphs, the bar represent the median and statistical significant tested with t-test. (C) Differential representation of clonotypes in thymus and RTE Foxp3+ or Foxp3-cells. The dot plots represent each clonotype, with on the ver cal axis the fold change between the compared populations (up, thymus; down, RTE), and on the horizontal axis the average expression level.

During those 5

 5 years of post-doc in Japan, I expanded my skills in cellular and molecular immunology and developed new skills in bioinformatics in particular for TCR repertoire tools and strategies development. With the pioneer works of TCR repertoire studies by NGS, I could show that (i) TCR repertoire diversity from thymus and periphery discriminate Tregs from Teffs, (ii) total Treg repertoire is highly diverse and (iii) that the clonal size distribution of Treg and Teffs reflects 30/67 central and peripheral selection events. This in turn suggests that, by tracking changes of the clonal size distributions during T cell development or during immune responses, we will be able to delineate how each selection event impinges on the shaping of the actual Treg and Tconv cell repertoires. Altogether, 2 research articles were published and one is (still) in preparation out of my first postdoc work (PUBLICATION ACHIEVEMENTS B).

32 / 67 Results

 3267 Bringing my experience and developments from my first post-doctoral internship, I have been working on the establishment of a workflow to systematically compute various metrics and analysis strategies, allowing to model the TCR repertoire diversity, composition and variation in human and mouse samples [130-132].

Figure 5 :

 5 Figure 5 : TCR repertoire exploration modelling

Figure 6 :

 6 Figure 6 : Screen shot of DiversiTR, a web-app for TCR repertoire analysis

Background

  Treg have been shown as previously introduced as an heterogeneous population, based either on their differentiation origin(thymus vs. periphery [144] but also on their activation status. Indeed, the i3 laboratory showed that in the periphery, activated/memory Tregs (amTregs), characterized as CD4+Foxp3+CD44hiCD62L, are enriched in self-antigen specific Tregs, in comparison with their naïve counterpart (nTregs, CD4+Foxp3+CD44hiCD62L)[145][146][147] and that they are enriched in deep lymph nodes (dLN) draining deep organs[130]. In addition, a first TCR repertoire study, focusing on only one TCRV by NGS in physiological mouse background (C57BL/6), revealed that (i) amTregs and nTregs were characterized by more expansions, compared with Teffs, (ii) expansions of Treg subsets are observed in deep LN compared with their superficial LN counterparts and (iii) amTregs and Teffs display a higher overlap than nTregs and Teffs as well as nTregs and amTregs. Overall, the results suggested an instructive model for mice Treg selection in the thymus as well as a self-antigen specificity of amTregs enriched in deep LN, in line with other studies performed in TCRb transgenic mice[148,149]. This first study prompted35/67 us to further characterize Tregs subset repertoires in different tissue LN as well as in the thymus in mice but also in humans, where indeed seminal studies suggested a global Treg repertoire as diverse as Teff cells[117].

  as a reference model for physiological Treg differentiation and function and (ii) the Non-Obese-Diabetic (NOD) mouse background as an autoimmune disease model where Treg function is impaired notably due to a defect in IL2 [151-153] and specific tissue (the pancreas) is attacked by effector CD4 and CD8 cells [154]. In both backgrounds, a Green Fluorescent Protein (GFP) reporter gene for Foxp3 expression was added by mouse mating in order to unambiguously identify Foxp3 + Tregs. amTregs, nTregs, Teffs (CD4+Foxp3-) and CD8+ T cells were sorted from 6 LN (3 deep LN and 3 superficial LN) as well as from the spleen. In addition, thymic subsets where sorted in order to reproduce the major steps of thymocytes differentiation, including Tregs: double positive (DP) CD3-thymocytes, as representative of cells that did not undergo TCR selection, DPCD3+ as representative of TCR selected cells, and CD4 Single Positive (CD4SP)

Figure 7 :

 7 Figure 7 : B6 but not NOD amTregs show lower diversity and more expansions than nTregs and Teffs. (A) Cumulative frequency of the 1% most predominant clonotypes in each metamouse within the three cell populations, with each purple line representing a unique clonotype (grey lines indicate rare clonotypes) in B6 (upper plot) and in NOD (lower plot) backgrounds. (C) Shannon entropy index was calculated for amTregs (in black), nTregs (in yellow) and Teffs (in green). Statistical analyses were performed using the two-way ANOVA test to compare the different cell subsets (*: p<0.05). (D) Renyi profiles display the diversity as a function of the parameter α for amTregs, nTregs and Teffs in B6 (upper plot) and NOD (lower plot). An analysis of similarities (ANOSIM) revealed a statistical difference between the groups in B6 (***: p<0.001) but not in NOD (ns: p≥0.05).

Figure 8 :

 8 Figure 8 : amTregs and nTregs are are expanded in NOD upon low-dose IL-2 treatment. (A) Experimental design. (B) The abundance (in %) of the expanded clonotypes are plotted for each sample (n=6) within the three populations and mouse models. Red circles represent sequences that lie 3*IQR or more above the third quartile calculated across all 18 samples within each population.

Figure 9 :

 9 Figure 9 : eCRF design and implementation workflow. The figure represents the 3-steps workflow adopted for the eCRF design and implementation: (1) Protocol design, (2) eCRF design and (3) eCRF validation. In each box are listed the actions, its aim and the person in charge of it. Colour code: Blue: clinical team, purple: biological team, green: computer scientist, and orange: the trial monitor team and brown the Core management team (see methods for description).

  865 parameters. Examples of coding are check boxes for clinical investigation, numerical values with units, disease scores as a result of an automated calculations, and coding of possible treatment formulas, doses and dosage regimens per disease. Technical adjustment have been implemented to allow data entry and extraction of this amount of data, rarely achieved in classical eCRFs designs. Conclusions This task was one of my first mission when I joined the i3 laboratory. Although it can appear far from my initial background, I have been proposed this task based on the experienced I acquired in interdisciplinary projects since the beginning of my PhD. On top of this, I learned a lot regarding translational research and clinical trials, which offered me an additional knowledge to undertake new multidisciplinary studies, including translational and clinical trial research programs. This work will be valorised through upcoming collaboration within the new Medical & University Department of Immunology and Biotherapies at Pitié-Salpêtrière as well as the Hu-Precimed consortium, a project structuring the Precision Medicine sector in France to which I am participating, together with several members of the Transimmunom project, with regards with clinical data integration.

  reproducibility and (ii) the accuracy of TCR determination. Notably, our study revealed that starting from RNA, RACE-PCR based methods performed better than multiplex-PCR. While DNA based methods provided high quality data, the absence of commercialized methods for TCR repertoire limits the interest of such approaches. This study highlighted the advantages and limitations of different TCR-seq methods, which may be used to guide approaches to the study of human diseases in which the TR plays a fundamental role. This work has been handle by Pierre Barennes, PhD student under my co-supervision and the manuscript is now under revision[161].42/67

Figure 10 :

 10 Figure 10 : TCR library method comparison -Experimental design.DNA and RNA of FACS-sorted effector T cells (CD4+CD25-CD127+) from one donor and cultured Jurkat leukemic Tcells were extracted. Jurkat RNA has been then added to effector T cells RNA at the following ratios: 1:1000, 1:100, 1:10 to a final quantity of 100ng. Same dilutions have been performed on DNA samples with DNA from Jurkat cells. Triplicate of each ratio condition were processed by 7 TR library preparation methods for RNA and by one TR library preparation for DNA. All the libraries were sequenced on Illumina MiSeq as indicated in the figure panel.

[ 67 Conclusions

 67 Project iReceptor + (SC1-BHC-05-2018 (H2020), 1 research engineer recruited in June 2019, 1 doctoral fellow (recruitments in progress); funded until 2022].Collaborative research projectsIntegration and analysis of multi-OMICS data from patients with AIDs (Transimmunom Project [Transimmunom Project, ANR-11-LABX-0069] and SLE patients under ILT-101 treatment [iMAP Project (ANR -16-RHUS-0001)]; Role of regulatory T lymphocytes in a model of autism induced by "maternal Immune Activation" (Hôpital Debré, Paris, France); Participation in the structuring of a precision medicine sector, Hu-PreciMED; Immune related adverse events in cancer patients under Immune check-point inhibitors treatment: TCR as a biomarker of IrAEs outcome, Departement Medical Universitaire, Pitie-Salpêtrière Hospital (consortium establishment and fund raising on going).49/With time, I acquired an expertise on the TCR repertoire field, conceptually, scientifically and technically as well as in high-throughput data analysis. In the last years, I further expanded my interest towards systems immunology with the aim to integrate function, cell-cell interaction and T cell repertoire information together. My future research will aim at bridging the gap between TCR repertoire diversity and immune cell function towards translational systems immunology in tight collaboration with clinicians, bioinformaticians and community networks within the framework of the four main projects I am involved in (Figure12).

Figure 12 :

 12 Figure 12 : Schematic representative of my research perspectives

  , 5 research articles, one review and one book chapter were published out of my PhD work (PUBLICATION ACHIEVEMENTS A).

	PUBLICATION ACHIEVEMENTS A : PHD
	Main research projects:
	Mariotti-Ferrandiz E, et al PlosOne. PMID: 26844551 (T8)
	Petrovic-Berglund J.

*, Mariotti-Ferrandiz M.E*, et al. Molecular Immunology. PMID: 18471883 (T3) Collette A, et al.. Journal of Immunology. PMID: 15383590 (T1) Collaborative research projects Montaudouin C et al. PlosOne. PMID: 20454452 (T5) Guillot-Delost et al. J. Gene Med. PMID: 19514009 (T2) Reviews & book chapter: Boudinot P et al. Molecular Immunology. PMID: 18279958 (R1) Mariotti-Ferrandiz M.E. and Adrien Six. Arnette edition, ISBN: 9782718411972 (B1)

WB-2018-10690 (Fondation de France) -Coordinator Dr. P. Ellul (2018 "New research collaboration" program (Franco-Canadian Fund for Research) -Coordinator Prof. F. Breden (Simon Fraser Partner -unfunded ): ImSysT project Co-coordinator with Prof. D. Klatzmann -unfunded 10/67 2015 RHU (ANR) 2nd call -iMAP project -Coordinator Pr. D. Klatzmann Workpackage leader -Integrating and analyzing MultiOMICS data from patients with Systemic Lupus Erythematosus under Low-dose IL-2 Therapy -Funded until 2021 RHU (TRIADE) 1st call: TRiADE -Coordinator Pr. D. Klatzmann Collaborator -Integration and analysis of multiOMICS data -pre-selected but not funded FELLOWSHIP AND AWARDS 2018 Teaching and research premium Sorbonne Université (2018-2022) 2017 FOCIS Center of Excellence Travel Award 2008-2010 Japan Society for Promotion of Science (JSPS) post-doctoral fellowship 2007 Pasteur Institute PhD fellowship 2006 Fondation de France PhD fellowship FOCIS 2006 Poster of Distinction 2003-2006 French government doctoral fellowship (MNERT) 2002-2003 University of Paris Merit fellowship SCIENTIFIC COMMUNICATIONS (Selection) d'Immunologie (SFI) -member

List of Publication achievements

Curriculum Vitae

Encarnita MARIOTTI-FERRANDIZ 8 rue Sainte Foy phone : + 33 (0)1 42 17 74 68 75002 Paris, France encarnita.mariotti@sorbonne-universite.fr Birth: March 22 1979, Le Blanc-Mesnil (France). Married.

Assistant-Professor, UFR927, Immunology department, Sorbonne Université Perspectives (2019Perspectives ( -2023) ) In the next four years, I will continue my research work on (i) the analysis of the TCR repertoire of T lymphocytes in physiological and pathological conditions with the integration of single-cell approaches and (ii) the integration of multi-OMICS data for the implementation of systems immunology approaches in translational research. Teff sorted from peripheral blood, the TCR repertoire data will be obtained by high-throughput sequencing according to the protocol validated under my direction at the laboratory [161]. I will coordinate the implementation of the statistical modelling of the data using the tools available in the laboratory [130,[132][133][134]. Our preliminary results comparing blood Treg and Teff repertoires from healthy donors and T1D patients and using probabilistic model revealed disease and cell-subset signatures (Figure 11). [Transimmunom Project,funded until (b) in response to immunomodulatory therapy and to identify its predictive value by comparing the repertoire before and after treatment of responder vs. non-responder patients.

The TCR, a potential biomarker of the physiopathological state in humans

Main research areas

This project will be based on the interventional clinical trial LUPIL-2 (NCT02955615) for which I coordinate the activity "multi-omics integration" (see below) and the analysis of the TCR repertoire. Recent work have shown the clinical potential of ILT-101 immunotherapy (IL-2 low dose) in patients with different AIDs, including SLE [START_REF] Rosenzwajg | Low-dose interleukin-2 fosters a dose-dependent regulatory T cell tuned milieu in T1D patients[END_REF][START_REF] Rosenzwajg | Immunological and clinical effects of low-dose interleukin-2 across 11 autoimmune diseases in a single, open clinical trial[END_REF]151,179]. This activation can lead to modifications of the Treg and Teff repertoire, as we found in mice, which can serve as a "predictive biomarker of response to treatment". The TCR repertoire data will be produced according to the same experimental design as in the Transimmunom project using longitudinal samples (n = 5) obtained from 50 placebo patients and 50 patients treated with ILT-101.

In both contexts, I will coordinate the implementation of statistical data modelling using the tools available in the laboratory as described earlier.

Systems Immunology: a Holistic Approach to the Study of Autoimmune and Inflammatory Diseases (AIDs)

The complexity of cellular interactions and regulation during immune responses requires comprehensive approaches aimed at not only studying the individual components involved, but also their complex interactions [START_REF] Benoist | A plaidoyer for 'systems immunology[END_REF][START_REF] Burel | Systems Approaches towards Molecular Profiling of Human Immunity[END_REF]182]. Systems biology approaches are increasingly applied to the study of immune diseases. The i3 laboratory therefore introduced a systems immunology approach in 2008 by studying the immune system [159] as tissue and combining cytomic, genetic, transcriptomic and proteomic studies [START_REF] Nehar-Belaid | Regulatory T Cells Orchestrate Similar Immune Evasion of Fetuses and Tumors in Mice[END_REF][START_REF] Saadoun | Regulatory T-cell responses to low-dose interleukin-2 in HCV-induced vasculitis[END_REF][START_REF] Terrier | Serum biomarker signature identifies patients with B-cell non-Hodgkin lymphoma associated with cryoglobulinemia vasculitis in chronic HCV infection[END_REF][START_REF] Pham | A novel strategy for molecular signature discovery based on independent component analysis[END_REF]132,134,157,160,183,184]. These approaches are at the heart of the Transimmunom and iMAP projects. database to foster data analysis. The third aim, which I am responsible for, is to build a framework for the integration of multi-OMICS and clinical data together with AIRR data from iReceptor. To this end, I will take advantage of the implementation within Transimmunom project of an

Main research area