Mo Atif

Audrey Mohr

Filomena Conti

Olivier Scatton

Guy Gorochov

Dr Makoto Miyara
email: makoto.miyara@aphp.fr

Chapter 1 -Metabolic optimisation of regulatory T-cells in transplantation

Keywords: Regulatory t cells, treg, transplant, cell therapy, metabolism, metabolic, mTOR, hypoxia Regulatory T cell, Treg, analysis, assay, cell isolation, suppression, cytokines, mechanisms

Regulatory T (Treg) cells expressing the FOXP3 transcription factor are presently under investigation by many teams globally as a cellular therapy to induce tolerance in transplantation. This is primarily due to their immunosuppressive and homeostatic functions.

Depending on the type of allograft, Treg cells will need to infiltrate and function in metabolically diverse microenvironments. This means that any resident and circulating Treg cells need to differentially adapt to counter acute or chronic allograft rejection.

However, the links between Treg cell metabolism and function are still not entirely delineated. Current data suggest that Treg cells and their effector counterparts have different metabolite dependencies and metabolic programs. These properties could be exploited to optimise intragraft Treg cell function.

In this review, we discuss the current paradigms regarding Treg cell metabolism and outline critical intracellular axes that link metabolism and function. Finally, we discuss how this knowledge could be clinically translated for the benefit of transplant patients.

Thesis Output

This doctoral thesis comprises of the following original articles and reviews:

Related Publications

The following reviews were also published during the doctoral studies but do not form part of the thesis:

Summary (English)

CD4+ regulatory T (Treg) cells play an important immunosuppressive and homeostatic role in the immune system. These cells are presently being isolated and expanded in vitro as a cellular therapy against chronic inflammatory conditions such as graft rejection, inflammatory bowel disease (IBD) and autoimmune hepatitis. An outstanding issue with Treg cells (pre-and post-expansion) is the role of underlying phenotypic and functional heterogeneity. In this work, we delineated in vivo and in vitro

Treg cell heterogeneity as well as their differentiation trajectories through analysis of their transcriptome and surface proteins at single cell resolution.

A further issue explored is the link between Treg cell heterogeneity and exposure to a fatty acid-rich microenvironment. This is relevant for patients with IBD and gastrointestinal cancers. Treg cells have been found in reduced and increased frequencies respectively in these conditions. Moreover, Treg cell function can be impacted by microbial metabolites such as short-chain fatty acids (SCFAs). Certain SCFAs such as butyrate influence the differentiation of naïve CD4+ T cells into iTregs and even promote the proliferation of tissue Treg cells. The underlying mechanisms for these observations are yet unknown. Furthermore, this effect has been neither demonstrated using thymic-origin human Treg (tTreg) cells nor their subpopulations.

In this work, we studied the effects of butyrate exposure on naïve Treg cells undergoing acute activation. This combined wet-and dry-lab study provided unprecedented insights in the influence of butyrate on Treg cell immunobiology.

Résumé (Français)

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Introduction

The introduction to this thesis consists of the following three chapters:

1. Metabolic optimisation of regulatory T cells in transplantation

Introduction

Novel immunomodulatory approaches are required to induce tolerance in solid organ transplantation (SOT). (1,2) Although spontaneous tolerance has been reported in certain long-term patients (especially post-liver transplant), the majority continue to require ongoing immunosuppression. (3,4) These immunosuppressants have numerous side effects and do not overcome the challenges of delayed allograft dysfunction as well as infectious/neoplastic complications. Hence, there is an urgent clinical need for novel immunomodulatory strategies.

Regulatory T (Treg) cells are a CD4 + T-cell subset that was first identified as having immunosuppressive effects in mice. (5) In SOT, the presence of Treg cells in the periphery and the graft has been associated with allograft tolerance. (6)(7)(8) These cells perform their functions through a range of effector cell contact-dependent andindependent mechanisms. (9)(10)(11)(12)(13) However, in recent years several groups have identified that these functions are tightly linked to Treg cell metabolism and epigenome too. [START_REF] Chapman | mTOR coordinates transcriptional programs and mitochondrial metabolism of activated Treg subsets to protect tissue homeostasis[END_REF][START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Pacella | Fatty acid metabolism complements glycolysis in the selective regulatory T cell expansion during tumor growth[END_REF] (Figure 1-1) These are important links to delineate as Treg cells need to survive and function in the metabolically-demanding microenvironment of a chronically-inflamed allograft. Moreover, novel data demonstrates that metabolites such as acetyl coenzyme A (acetyl CoA) and fatty acids do not just partake in different metabolic programs but can directly modulate the epigenome too. [START_REF] Peng | Aerobic glycolysis promotes T helper 1 cell differentiation through an epigenetic mechanism[END_REF][START_REF] Kespohl | The Microbial Metabolite Butyrate Induces Expression of Th1-Associated Factors in CD4(+) T Cells[END_REF] (Figure 1-2) Through either DNA acetylation or DNA/histone methylation, these metabolites facilitate a complex network involving the epigenome, metabolism, and function of Treg cells. Delineating this network is important to understand Treg cell behaviour in the allograft. In this review, we discuss Treg cell metabolism and interlink it with their diverse functions.

From a clinical perspective, the initial data from Phase I trials in transplant and non-transplant settings has shown Treg cells to be safe. (1,[START_REF] Sanchez-Fueyo | Applicability, safety, and biological activity of regulatory T cell therapy in liver transplantation[END_REF][START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF][START_REF] Oo | Liver homing of clinical grade Tregs after therapeutic infusion in patients with autoimmune hepatitis[END_REF][START_REF] Sawitzki | Regulatory cell therapy in kidney transplantation (The ONE Study): a harmonised design and analysis of seven non-randomised, single-arm, phase 1/2A trials[END_REF] The current Good

Manufacturing Practice (GMP) protocols centre on the hypothesis that the infusion of expanded autologous Treg cells could modulate the inflamed allograft microenvironment in favour of immunoregulation instead. In parallel, Treg cells are also being modified in different ways to augment their potential activity eg. expansion period, antigen-specificity, pharmacological agents. (1,[START_REF] Macdonald | Methods to manufacture regulatory T cells for cell therapy[END_REF][START_REF] Macdonald | Alloantigen-specific regulatory T cells generated with a chimeric antigen receptor[END_REF][START_REF] Miyara | Combination of IL-2, rapamycin, DNA methyltransferase and histone deacetylase inhibitors for the expansion of human regulatory T cells[END_REF] However, metabolic modulation of Treg cells in this context has not been widely performed. [START_REF] Schmidt | Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate[END_REF] In this review, we discuss how metabolic pathways can be exploited to improve the efficacy of Treg cell therapies in transplantation.

Linking Treg cell functions and metabolism

Despite the heterogeneous phenotype of Treg cells, the expression of the Forkhead Box Protein 3 (FOXP3) transcription factor (TF) is considered as a reliable indicator of Treg cells. (1,9,[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] Salomon | B7/CD28 costimulation is essential for the homeostasis of the CD4+CD25+ immunoregulatory T cells that control autoimmune diabetes[END_REF] FOXP3 in combination with T-cell receptor (TCR) activation, IL-2, mammalian transporter of rapamycin (mTOR) complexes, and others play a key role in promoting Treg cell proliferation and function.

However, to sustain these metabolically-demanding processes, Treg cells rely on various stimuli, metabolites, and metabolic pathways. [START_REF] Chapman | mTOR coordinates transcriptional programs and mitochondrial metabolism of activated Treg subsets to protect tissue homeostasis[END_REF][START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Miyara | Combination of IL-2, rapamycin, DNA methyltransferase and histone deacetylase inhibitors for the expansion of human regulatory T cells[END_REF] (Figure 1-1)

These stimuli are dynamic (eg. oxygen gradient, glucose/lipid availability) and vary depending on the type of organ and disease. [START_REF] Lan | Mitochondrial Pathology and Glycolytic Shift during Proximal Tubule Atrophy after Ischemic AKI[END_REF][START_REF] Ma | NAFLD causes selective CD4(+) T lymphocyte loss and promotes hepatocarcinogenesis[END_REF][START_REF] Pouyssegur | Hypoxia signalling in cancer and approaches to enforce tumour regression[END_REF] In recent years, numerous publications have demonstrated how the manipulation of these metabolic factors can in turn modulate Treg cell function. This is important as Treg cells (circulating or resident) need to survive and function in diverse microenvironments. [START_REF] Ma | The imbalance between Tregs, Th17 cells and inflammatory cytokines among renal transplant recipients[END_REF][START_REF] Jeffery | Low-dose interleukin-2 promotes STAT-5 phosphorylation, Treg survival and CTLA-4-dependent function in autoimmune liver diseases[END_REF] During inflammatory diseases (eg. allograft rejection), the microenvironment is infiltrated by other effector cells, who will also start competing for metabolites to survive and function. [START_REF] Kedia-Mehta | Competition for nutrients and its role in controlling immune responses[END_REF] From the perspective of SOT, the aim is that by exploiting cellular metabolism, one could augment Treg cell survival and function in the inflamed allograft microenvironment. (Figure 1-1) This would be an additional strategy to support either tissue-resident Treg cells or infiltrating Treg cells as part of a cellular therapy protocol.

As we discuss in the following sections, many studies into Treg cell immunometabolism have identified drugs/metabolites that mediate their roles through key intracellular axes. As these axes interlink both Treg cell metabolism and function, their modulation is a novel approach with the potential for clinical translation in SOT.

In the following sections, we discuss Treg cell metabolism in-depth and contextualise this through the following three intracellular axes: (Figure 1-1)

1. mTOR 2. Hypoxia

Nuclear Receptors

For clarity, in this review we will describe naturally-occurring Treg cells as thymusderived Treg (tTreg) cells, the induced Treg cells as iTreg cells and peripheral Treg cells as pTreg cells.( 35)

Glycolysis in Treg cells

Upon activation via co-stimulation of the T-cell receptor (TCR) and CD28, the signalling cascades promote glucose uptake (via Glut1 transporters) and glycolysis (Warburg effect) instead. [START_REF] Michalek | Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets[END_REF][START_REF] Gerriets | Foxp3 and Toll-like receptor signaling balance Treg cell anabolic metabolism for suppression[END_REF] (Figure 1-2) This process occurs in the cytoplasm and generates 2 units of adenosine triphosphate (ATP) per mole of glucose converted to CO2. [START_REF] Mookerjee | Quantifying intracellular rates of glycolytic and oxidative ATP production and consumption using extracellular flux measurements[END_REF] In parallel, Treg cells increase FOXP3 expression, cellular proliferation, and immunosuppressive functions. [START_REF] Chapman | mTOR coordinates transcriptional programs and mitochondrial metabolism of activated Treg subsets to protect tissue homeostasis[END_REF][START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Gerriets | Foxp3 and Toll-like receptor signaling balance Treg cell anabolic metabolism for suppression[END_REF][START_REF] Howie | Foxp3 drives oxidative phosphorylation and protection from lipotoxicity[END_REF] It is not yet established why Treg cells switch to this less efficient ATP-generating metabolic program rather than continuing with oxidative phosphorylation (OXPHOS) -especially as cellular activation increases metabolic demands in terms of protein synthesis.

A further process to comprehend is the regulation of the end products of glycolysis (either pyruvate or lactate). [START_REF] Angelin | Foxp3 Reprograms T Cell Metabolism to Function in Low-Glucose, High-Lactate Environments[END_REF] The balance between both of these products depends on the activity of lactate dehydrogenase (LDH) as well as the levels of nicotinamide adenine dinucleotide (reduced form; NADH and oxidised form; NAD + ). [START_REF] Andre | Measurement of cytotoxic activity in experimental cancer[END_REF] This is relevant to Treg cells because FOXP3 can modulate LDH to prevent lactate formation and form pyruvate instead. [START_REF] Angelin | Foxp3 Reprograms T Cell Metabolism to Function in Low-Glucose, High-Lactate Environments[END_REF] Moreover, in a high lactate low glucose environment, Treg cells can convert lactate to pyruvate too. Whilst lactate may negatively impact on T-cell proliferation as a whole, it does not impact Treg cell immunosuppression. This is of particular relevance to tumoural microenvironments which are known to have high levels of local lactate and Treg cell accumulation. [START_REF] Angelin | Foxp3 Reprograms T Cell Metabolism to Function in Low-Glucose, High-Lactate Environments[END_REF] If not converted to lactate, the resulting pyruvate is transported into mitochondria to be converted via pyruvate dehydrogenase into acetyl-CoA and NADH. [START_REF] Hui | Glucose feeds the TCA cycle via circulating lactate[END_REF] This acetyl-CoA molecule subsequently enters the Krebs cycle. ( 42)

Fatty acid metabolism in Treg cells

In addition to glycolysis, Treg cells rely on lipid metabolism to meet their metabolic requirements. In the murine tumour setting, Treg cells were shown to express both genes for glycolysis and as well as the pentose phosphate pathway (PPP). [START_REF] Pacella | Fatty acid metabolism complements glycolysis in the selective regulatory T cell expansion during tumor growth[END_REF] The end products of this pathway could be used for fatty acid synthesis (FASyn) or protein synthesis. Tumoural Treg cells also stored lipids intracellularly and preserved the ability to perform fatty acid oxidation (FAO) too. Overall, this data demonstrated that murine tumoural Tregs were capable of glycolysis and OXPHOS mediated via FASyn/FAO.

However, it is unclear why Treg cells should maintain the FASyn and FAO programs as both would theoretically at least nullify the effects of the other. (Figure 1-2) Indeed, this question has been studied by a few teams in recent years. [START_REF] Michalek | Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets[END_REF][START_REF] Cluxton | Differential Regulation of Human Treg and Th17 Cells by Fatty Acid Synthesis and Glycolysis[END_REF][START_REF] Berod | De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells[END_REF] In one study involving murine T-cells, the inhibition of acetyl-CoA carboxylase (key enzyme for FASyn) in naïve CD4 + T-cells, via either genetic knockout (KO) or pharmacological means, diverted the differentiation process towards FOXP3 + cells instead of IL-17A-producing cells. [START_REF] Berod | De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells[END_REF] These FASyn-inhibited iTregs were just as immunosuppressive in vitro as control iTregs. Moreover, the control iTregs and FASyn-inhibited iTregs had similarly reduced levels of genes for glycolysis and glutaminolysis. Both took up equal amounts of palmitate too. Put together, modulating fatty acid metabolic pathways could be a strategy to polarise iTreg cell differentiation and function.

A further yet important line of inquiry is regarding how FOXP3 can modulate lipid metabolism. (Figure 1-2) FOXP3 + tissue Treg cells take up long-chain fatty acids (lcFAs) into via the CD36 receptor. [START_REF] Wang | CD36-mediated metabolic adaptation supports regulatory T cell survival and function in tumors[END_REF] However, short and medium-chained fatty acids (scFAs and mcFAs respectively) diffuse passively across the cytoplasm and mitochondrial outer/inner membranes to participate in FAO. [START_REF] Kamp | How fatty acids of different chain length enter and leave cells by free diffusion[END_REF] In a series of eloquent experiments using a murine lymphoma cell line (EL4), Howie D et al demonstrated the effects of FOXP3 on lcFAs metabolism. [START_REF] Howie | Foxp3 drives oxidative phosphorylation and protection from lipotoxicity[END_REF] They transfected EL4 cells with a FOXP3-ERT2 construct such that the administration of an oestrogen modulator (4-HT) would translocate this construct to the nucleus. These transfected FOXP3 + cells had an increased oxygen consumption rate (OCR) at baseline than the non-transfected controls. The OCR was further increased after being cultured with palmitate (longchain fatty acid, C16). Interestingly, in EL4-FOXP3 cultures without palmitate, the addition of etomoxir reduced OCR rates. This demonstrated that part of the increased FOXP3-mediated OXPHOS was due to the FAO of endogenous fatty acids. These cells in parallel also increased the expression of genes for mitochondrial ETC complexes. A similar effect was demonstrated in 24hr activated human Treg cells (CD4 + CD25 + FOXP3 + ) as they too augmented genes specific for mitochondria. This further confirmed the role of FOXP3 in promoting mitochondrial-based metabolism.

The same group also studied whether FOXP3 could promote Treg cell survival in a high-fat microenvironment. They found that murine Treg cells were less apoptotic after 18hrs of cultures with lcFAs compared to Teff cells. This was an interesting observation as they found that Treg cells took up more fluorescent-palmitate. This indicated that FOXP3 could indeed be inhibiting the apoptosis-inducing effects of palmitate. In their EL4-FOXP3 cells, they identified the mechanism for this effect as being due to increased FAO of palmitate. Collectively, all these data demonstrate how FOXP3 promotes OXPHOS through increasing FAO of lcFAs and mitochondrial ETS complex synthesis.

However, before Treg cells can engage lcFAs in FAO, the lcFAs need to be transported across the cytoplasm and enter the mitochondria. (Figure 1

Krebs cycle and mitochondrial complexes in Treg cells

The purpose of the above pathways is to generate enough acetyl-CoA to feed into the Krebs cycle and then generate sufficient ATP through the mitochondrial ETC.

This is an important process in Treg cells as links between FOXP3, ETC synthesis, and cellular functions have been described. [START_REF] Howie | Foxp3 drives oxidative phosphorylation and protection from lipotoxicity[END_REF][START_REF] Beier | Essential role of mitochondrial energy metabolism in Foxp3(+) Tregulatory cell function and allograft survival[END_REF][START_REF] Weinberg | Mitochondrial complex III is essential for suppressive function of regulatory T cells[END_REF] (Figure 1-2) Although the mechanism was not uncovered, the induction of FOXP3 in iTreg cells correlated with increased expression of mitochondria-associated genes.( 39) Moreover, a recent manuscript involving mice demonstrated that complex III per se was key to promoting Treg cell suppressive function. [START_REF] Angelin | Foxp3 Reprograms T Cell Metabolism to Function in Low-Glucose, High-Lactate Environments[END_REF][START_REF] Weinberg | Mitochondrial complex III is essential for suppressive function of regulatory T cells[END_REF] The Treg-specific knockout of complex III was associated with reduced immunosuppressive capacity and increased DNA methylation status -without affecting FOXP3 expression, cell frequency, or coinhibitor receptor expression. [START_REF] Weinberg | Mitochondrial complex III is essential for suppressive function of regulatory T cells[END_REF] These mice also developed a general inflammatory condition (similar to that of scurfy mice) and did not live beyond 4 weeks of life. Put together, these data identify the additional role of mitochondrial metabolism alongside FOXP3 in facilitating Treg cell function.

Do tTreg and iTreg cells have different metabolic programs?

Although the majority of FOXP3+ Treg cells are of thymic-origin, a small proportion are induced (iTreg cells in vitro) from effector T (Teff) cells via exposure to different cytokines in the microenvironment. [START_REF] Shevach | tTregs, pTregs, and iTregs: similarities and differences[END_REF] Through studying the conversion of effector T (Teff) cells to iTreg cells, one can identify how their metabolic phenotype changes via de novo FOXP3 induction. This is not possible with tTreg cells which already express FOXP3 upon entering the peripheral circulation.

Unactivated murine tTreg cells (defined as CD4 + FOXP3 + ) have a higher baseline proliferative status (Ki67) and express more Glut1 (glucose transporter) than Teff cells. [START_REF] Gerriets | Foxp3 and Toll-like receptor signaling balance Treg cell anabolic metabolism for suppression[END_REF] Just as FOXP3 expression is a Treg cell-lineage identifier, there is increasing data that OXPHOS is their key lineage metabolic program.( 37) (Figure 1-

2)

This supported by metabolomics data of resting human tTregs (defined as From a functional perspective, the inhibition of either glycolysis or FAO was found not to profoundly affect Treg cell immunosuppressive capacity.( 54) However, the data from this study contrasts with data from other published work that reported

CD4 + CD25 + or CD4 + CD127 lo CD49b -in
Treg cell immunosuppressive capacity to be more significantly reduced when glycolysis, FAO, or lipid/cholesterol synthesis were individually inhibited. ( 53) When reconciling these divergent results, we noted key differences in the experimental design of the suppression assay eg. responder cell type, the dose of metabolism inhibitors, pre-culture period, and the readout dye (thymidine/carboxyfluorescein). It is important to take these differences into account when considering the evidence base.

Collectively, these data demonstrate that the link between tTreg cell metabolism and immunosuppressive function is not fully delineated.

With respect to iTreg cells, the increase in the rate of glycolysis in murine iTreg cells was less pronounced compared to de novo induced Th1/2/17 cells. [START_REF] Michalek | Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets[END_REF] These iTregs also expressed less Glut1. However, similarly to the tTreg cells from the abovediscussed studies, these iTregs demonstrated dependence on lipid metabolism too.

They oxidised significantly more palmitate than their non-Treg counterparts. Moreover, when the FAO inhibitor, etomoxir, was added to the culture system (albeit at a relatively high dose), both the oxidation was inhibited as well as the upregulation of FOXP3. This effect was also identified by another team studying murine iTregs as they demonstrated an association with FOXP3 upregulation and increased OXPHOS rates. [START_REF] Angelin | Foxp3 Reprograms T Cell Metabolism to Function in Low-Glucose, High-Lactate Environments[END_REF] All in all, these data suggest that tTreg and iTreg cells depend on both glycolysis and FAO to meet their metabolic demands. These processes are upregulated during activation and their inhibition affects Treg cell proliferation and function. (Figure 1-2)

Explaining the variation in published experimental literature

When considering the relevant literature on Treg cell metabolism, it is important to take into account a range of factors: (Figure 1-3)

• Culture conditions (in vivo/ex vivo/in vitro)

• Treg cell subtype (thymic, peripheral, induced, FOXP3 + , CD25 + CD127 lo )

• Disease (graft rejection, tumour, autoimmunity) or healthy tissue

• Species (murine, human)

• Microenvironment (competing cells, substrates, oxygen gradient, drugs)

As is evident from our discussions above, a challenge with the current in vitro metabolic assays is ensuring that they reflect in vivo physiology. The concentrations of substrates, their competing cells, and the ongoing disease process are constantly evolving when in vivo. To study glycolysis, certain studies we discuss in this review have used the inhibitor, 2-deoxyglucose (2-DG). [START_REF] Pacella | Fatty acid metabolism complements glycolysis in the selective regulatory T cell expansion during tumor growth[END_REF][START_REF] Tanimine | Differential effects of 2-deoxy-D-glucose on in vitro expanded human regulatory T cell subsets[END_REF] However, there is data demonstrating that this agent can also have off-target effects in terms of triggering kinase pathways (eg. Akt/Erk). [START_REF] Tanimine | Differential effects of 2-deoxy-D-glucose on in vitro expanded human regulatory T cell subsets[END_REF][START_REF] Zhong | The glycolytic inhibitor 2-deoxyglucose activates multiple prosurvival pathways through IGF1R[END_REF] Moreover, to study FAO, different studies have used different doses of palmitate (up to 1000uM). [START_REF] Michalek | Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets[END_REF][START_REF] Howie | Foxp3 drives oxidative phosphorylation and protection from lipotoxicity[END_REF] The challenge here is that palmitate is not an exclusive fatty acid in the microenvironment and the concentration can be affected by albumin levels too. [START_REF] Michalek | Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets[END_REF][START_REF] Abdelmagid | Comprehensive profiling of plasma fatty acid concentrations in young healthy Canadian adults[END_REF][START_REF] Riggio | Total and individual free fatty acid concentrations in liver cirrhosis[END_REF] Finally, certain assays have used etomoxir (mitochondrial lcFA uptake inhibitor) to block CPT1 however, there is data demonstrating that etomoxir acts 'independently of CPT1' instead in T-cells.(59) A further issue of concern is regarding etomoxir dosing as it has off-target effects above the dose of 5uM. [START_REF] O'connor | The CPT1a inhibitor, etomoxir induces severe oxidative stress at commonly used concentrations[END_REF] These are only some of the extraneous variables to consider.

Overall, our key message is that the relevant data must be contextualised within the limitations of the respective assays.

Having discussed Treg cell metabolism in detail, in the next sections we outline the following key internal axes that connect both metabolism and function in Treg cells.

(Figure 1- How is mTOR signalling modulated? From a metabolic perspective, mTORC1 activity can be modulated upstream via essential amino acids and hypoxia (discussed in the next chapter of this review). ( 65) Essential amino acids (eg. arginine, leucine, isoleucine) are known as such as they must be acquired from dietary consumption. Although these play a key role in DNA and protein synthesis, they are also vital in promoting Treg-specific mTORC1 activity. [START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Ikeda | Slc3a2 Mediates Branched-Chain Amino-Acid-Dependent Maintenance of Regulatory T Cells[END_REF] The evolutionary reasons for this relationship remain unknown.

Amino acids are taken up through dedicated receptors such as SLC7A1, SLC7A5, SLC3A2/CD98, ASCT2. [START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Ikeda | Slc3a2 Mediates Branched-Chain Amino-Acid-Dependent Maintenance of Regulatory T Cells[END_REF][START_REF] Nakaya | Inflammatory T cell responses rely on amino acid transporter ASCT2 facilitation of glutamine uptake and mTORC1 kinase activation[END_REF] The expression of these receptors is further increased upon TCR stimulation to optimise amino acid uptake. This is an important mechanism as its inhibition reduces mTORC1 activation. [START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Nakaya | Inflammatory T cell responses rely on amino acid transporter ASCT2 facilitation of glutamine uptake and mTORC1 kinase activation[END_REF] Upon acute cellular activation, the ongoing presence of amino acids such as arginine and leucine sustains activation of mTORC1 as well as of the Treg cell itself (via increased cytotoxic T-cell lymphocyte antigen-4; CTLA4 and inducible T-cell costimulator; ICOS). [START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Do | Nutrient mTORC1 signaling underpins regulatory T cell control of immune tolerance[END_REF] The inhibition of amino acid uptake receptors has been shown in murine models to reduce in vivo Treg cell quantity, cellular proliferation, and suppressive capacity.

Moreover, this effect was specific to Treg cells and not Teff cells -thus further confirming a key role for essential amino acids in these cells. Once inside the Treg cell, amino acids activate the Rag small GTPases (such as RagA/B), which alongside the protein Rheb, recruit mTORC1 to lysosomes. [START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Do | Nutrient mTORC1 signaling underpins regulatory T cell control of immune tolerance[END_REF] These are critical processes as murine KO models of either Rag GTPases or Rheb proteins have demonstrated the mice to have Treg cell metabolism/function and they all developed an autoimmune disease similar to that of scurfy mice.

How does mTORC1 connect metabolism and function?

Resting CD4 + FOXP3 + (Treg cells) have higher levels of constitutive mTORC1 activity than naïve CD4 + T-cells or Teff cells. [START_REF] Do | Nutrient mTORC1 signaling underpins regulatory T cell control of immune tolerance[END_REF][START_REF] Zeng | mTORC1 couples immune signals and metabolic programming to establish T(reg)-cell function[END_REF] Upon activation by anti-CD3, the phosphorylation of S6 and 4E-BP1 (indicators of mTORC1 activity) was increased alongside key functional markers such as CTLA4. [START_REF] Do | Nutrient mTORC1 signaling underpins regulatory T cell control of immune tolerance[END_REF][START_REF] Zeng | mTORC1 couples immune signals and metabolic programming to establish T(reg)-cell function[END_REF] These activated Treg cells were also more immunosuppressive.

From a metabolic perspective, resting non-activated Treg cells (isolated either as CD4 + CD25 + or CD4 + CD127 lo CD49b -) were found to have higher expression levels of genes involved in glucose metabolism (eg. Glut1, Glut3, PKM2) and lipid metabolism (cpt1, fasn, acc1) than Teff cells.( 53

Hypoxia

Understanding the role of hypoxia in Treg cells is especially important with regards to delineating their survival and function in the physiological hypoxia liver. [START_REF] Jungermann | Oxygen: Modulator of metabolic zonation and disease of the liver[END_REF] During hypoxia or a state of high ATP consumption, there is a proportional increase in intracellular AMP as well as hypoxia-inducing-factor-1-alpha (HIF1α) transcription factor (HIF1α) .( 65 In comparison, HIF1α levels increase during hypoxia as it is unable to be degraded via the proteasome-based mechanism. [START_REF] Semenza | Hypoxia-inducible factor 1 (HIF-1) pathway[END_REF] This would normally involve prolyl hydroxylation, subsequent binding to von Hippel-Lindau protein, and ubiquitination. [START_REF] Semenza | Hypoxia-inducible factor 1 (HIF-1) pathway[END_REF] Without this degradation, HIF1α forms a complex with its counterpart HIF1β, which then binds to specific hypoxic response elements (HRE) to influence Treg cell metabolism/function. (Figure 1-2) However, the exact role of HIF1α in Treg cells is not clear as the data we discuss below describe contrasting effects.

FOXP3

Firstly, hypoxia appears to differentially modulate tTreg and iTreg cells.

When murine tTreg cells (CD4 + FOXP3 + ) were cultured for 5 days under acute hypoxia (1% O2) with anti-CD3/28-based activation and IL-2, there was no change in their FOXP3 expression. [START_REF] Clambey | Hypoxia-inducible factor-1 alpha-dependent induction of FoxP3 drives regulatory T-cell abundance and function during inflammatory hypoxia of the mucosa[END_REF] This was also matched by in vivo data demonstrating that tTreg cells from murine models of CD4-specific KO of HIF1α had comparable levels of FOXP3 to control mice. [START_REF] Clambey | Hypoxia-inducible factor-1 alpha-dependent induction of FoxP3 drives regulatory T-cell abundance and function during inflammatory hypoxia of the mucosa[END_REF][START_REF] Shi | HIF1alpha-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of TH17 and Treg cells[END_REF] However, when CD4 + T-cells from control mice were cultured under acute hypoxia with activation, IL-2+/-TGFβ, there was a proportional and significant increase in cells expressing FOXP3. [START_REF] Clambey | Hypoxia-inducible factor-1 alpha-dependent induction of FoxP3 drives regulatory T-cell abundance and function during inflammatory hypoxia of the mucosa[END_REF][START_REF] Ben-Shoshan | Hypoxia controls CD4+CD25+ regulatory T-cell homeostasis via hypoxia-inducible factor-1alpha[END_REF] This increase was also demonstrated in vivo in mice exposed to environmental hypoxia (10% O2 for 24hrs).( 76) Put together, these data suggest that hypoxia does not alter FOXP3 expression on tTregs but induces it in non-Treg cells.

Immunosuppressive Function

In an in vitro suppression assay, the hypoxia-induced iTreg cells discussed above were cultured at different ratios with Teff cells (CD4 + CD25 -) and anti-CD3/28 antibodies for 72 hours. These iTregs were better able to suppress Teff proliferation than their normoxic counterparts. [START_REF] Ben-Shoshan | Hypoxia controls CD4+CD25+ regulatory T-cell homeostasis via hypoxia-inducible factor-1alpha[END_REF] In comparison, a rather different result was achieved using Treg cells from murine models of CD4-specific HIF1α KO. [START_REF] Clambey | Hypoxia-inducible factor-1 alpha-dependent induction of FoxP3 drives regulatory T-cell abundance and function during inflammatory hypoxia of the mucosa[END_REF] In this study, CD4 + CD25 + Treg cells were only slightly less immunosuppressive at the higher ratios of Tregs:Teff cells (1:1, 1:2) compared to control Treg cells. However, there were no differences in immunosuppression between the two groups at the lower ratios. Moreover, adoptive transfer of these HIF1α KO Treg cells into a murine model of T-cellmediated colitis demonstrated that the Treg cells were unable to inhibit weight loss or the development of colitis. Put together, these data suggest that HIF1α also differentially affects the immunosuppressive functions of tTreg/iTreg cells.

(

) 76
A further unknown question is how in vivo Treg cells function in hypoxic inflammatory microenvironments. [START_REF] Nakamura | TCR engagement increases hypoxia-inducible factor-1 alpha protein synthesis via rapamycin-sensitive pathway under hypoxic conditions in human peripheral T cells[END_REF] In one study, involving tTreg cells, their acute activation was associated with an increase in HIF1α was identified compared to normoxic controls. [START_REF] Li | TLR8-Mediated Metabolic Control of Human Treg Function: A Mechanistic Target for Cancer Immunotherapy[END_REF] The PI3K-mTOR pathway was crucial in upregulating HIF1α. [START_REF] Li | TLR8-Mediated Metabolic Control of Human Treg Function: A Mechanistic Target for Cancer Immunotherapy[END_REF][START_REF] Nakamura | TCR engagement increases hypoxia-inducible factor-1 alpha protein synthesis via rapamycin-sensitive pathway under hypoxic conditions in human peripheral T cells[END_REF] In addition, HIF1α played an important role in augmenting tTreg cell function as pre-culturing these cells for 24hrs with a HIF1α-inhibitor reduced their ability to suppress naïve T-cell proliferation. [START_REF] Li | TLR8-Mediated Metabolic Control of Human Treg Function: A Mechanistic Target for Cancer Immunotherapy[END_REF] Put together, these data suggest that tTregs could have augmented HIF1α levels and immunosuppressive function in inflammatory hypoxic microenvironments. This could have positive implications for their utilisation in physiologically hypoxic liver allografts.

Treg cell differentiation and stability

Concerning iTreg cells, an important question is whether hypoxia could influence the differentiation of CD4 + T-cells to Th17/iTreg cells. In one study, murine splenocytes were cultured under hypoxia with anti-CD3 antibody, IL-2, TGFβ for 5 days before staining. [START_REF] Clambey | Hypoxia-inducible factor-1 alpha-dependent induction of FoxP3 drives regulatory T-cell abundance and function during inflammatory hypoxia of the mucosa[END_REF] The authors identified an increase in FOXP3 expression amongst the CD4 + T-cells cultured under hypoxia as opposed to normoxia. Conversely, the CD4 + T-cells did not change their expression levels of RORyt or secretion of IL-17A. Indeed, they found that they had to deliberately culture their splenocytes under Th17-differentiating conditions to induce these changes. This study suggested that HIF1α had an additive effect on differentiation rather than a polarising towards Th17 or iTregs.

However, a different study using a pure naïve CD4 + T-cell population identified that HIF1α was indeed the key factor in influencing differentiation to Th17 cells.( 80) Under Th17-differentiating conditions, they identified that signal transducer and activator of transcription 3 (STAT3)-induced augmentation of HIF1α expression promoted transcription of RORyt. Both HIF1α and RORyt then formed a complex with the histone acetyltransferase, p300, to bind to the IL-17A promoter region. In comparison, when naïve CD4 + T-cells from mice with CD4 + -specific HIF1α KO were cultured under Th17-differentiating conditions, they identified an increase in FOXP3 expression compared to wildtype controls. Moreover, when the same cells were cultured under iTregdifferentiating conditions instead, the HIF1α Overall, considering the data from this study of tTreg cells and studies from the above paragraphs of iTreg cells, it appears that HIF1α differentially modulates their differentiation, stability, and function. This raises additional challenges for understanding how Treg cells survive and function in vivo as environmental hypoxia would equally affect both cell subtypes.

Metabolism

The effect of hypoxia on Treg cells' metabolic pathways and how this influences cell function is not yet established. [START_REF] Semenza | Transcriptional regulation of genes encoding glycolytic enzymes by hypoxiainducible factor 1[END_REF] One would hypothesise that during hypoxia, Treg cells would adopt the non-oxygen-requiring glycolysis pathway to meet their metabolic demands. Indeed, the VHL KO Treg cells from the previous paragraph were found to have upregulated glycolysis-related genes. The glycolytic process also affected their function as the addition of 2-DG (glycolysis inhibitor) to these cells inhibited pro-inflammatory cytokine secretion. Collectively, this suggested that glycolysis was augmented during hypoxia, which in turn induced effector cell function in tTreg cells. [START_REF] Lee | E3 Ubiquitin Ligase VHL Regulates Hypoxia-Inducible Factor-1alpha to Maintain Regulatory T Cell Stability and Suppressive Capacity[END_REF] Regarding iTreg cells, the data suggest that hypoxia and glycolysis independently influence their metabolism. [START_REF] Shi | HIF1alpha-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of TH17 and Treg cells[END_REF] This was based upon several observations. Firstly, newly-differentiated normoxic iTregs demonstrated a significantly lower rate of glycolysis than their newly-differentiated Th1/2/17 counterparts. Secondly, the addition of 2-DG (glycolysis inhibitor) or rapamycin (mTOR inhibitor) to cultures of naïve CD4 + T-cells under Th17-differentiating conditions, prevented the adoption of the Th17-like phenotype. The cells still proliferated however, they adopted a more iTreg-like phenotype instead -as demonstrated by the induction of FOXP3 and reduced secretion of IL-17A.

Moreover, the 2-DG inhibited glycolysis directly and did not modulate glycolysisrelated genes. Concerning hypoxia, the iTreg cells originating from a CD4specific HIF1α-KO murine model expressed more FOXP3 and CTLA4 than their controls. Collectively, these data demonstrated that the induction of hypoxia and glycolysis were key to influencing CD4 + T-cell differentiation towards iTreg cells.

Overall, the roles of hypoxia in modulating Treg cell phenotype, function, and metabolism still need to be defined. We do not know if Treg cells still perform FAO despite hypoxia. We also do not know if by adopting glycolysis, whether they upregulate the PPP program for amino acid synthesis. Furthermore, a limitation of many studies is that they elucidated the effects of acute hypoxia only through in vitro experiments. (Figure 1-3) In parallel, the KO models are unable to account for any tissue-specific effects of hypoxia and do not exclude the possibility of redundancy mechanisms to compensate for KO of HIF1/VHL. This issues are important for the liver allograft as its physiological hypoxia means that resident/circulating cells would have to adapt to survive/function in response to the chronically hypoxic microenvironment.

Nuclear receptors

The nuclear receptors are a unique family that interconnects lipophilic steroidal and nonsteroidal ligands directly with DNA modulation.( 83) (Figure 1-1) A key difference in their mechanism is that steroidal receptors bind to DNA as homodimers whereas the nonsteroidal receptors bind to DNA as heterodimers attached to the retinoid X receptor (RXR). In doing so, they collectively modulate genes responsible for cell differentiation, proliferation, function, and metabolism.( 83) (Figure 1-2) Whilst an in-depth discussion of these receptors is beyond the scope of this review, the ones implicated in promoting Treg cells include:

• Peroxisome proliferator-activator receptors (PPAR⍺, β, !)

• Liver X receptors (LXR⍺, β)

• Farsenoid X receptors (FXR)

• Vitamin D receptors (VDR)

• Retinoic acid receptor (RAR⍺, β, !)

PPAR

The PPAR receptors are activated via fatty acids or pharmacological agonists. [START_REF] Angela | Fatty acid metabolic reprogramming via mTOR-mediated inductions of PPARgamma directs early activation of T cells[END_REF] As previously discussed, acute activation of Treg cells upregulates mTORC1 activity, increases glycolysis and fatty acid catabolism. However, recent work in CD4 + T-cells has demonstrated that the mechanism for fatty acid catabolism is dependent on mTORC1 inducing PPAR! as well as the sterol regulatory elementbinding protein 1 (SREBP1). [START_REF] Angela | Fatty acid metabolic reprogramming via mTOR-mediated inductions of PPARgamma directs early activation of T cells[END_REF] The PPAR! gene promoted the expression of genes to take-up lipids, perform fatty acid synthesis, and lipolysis too. Moreover, the induction of PPAR! was also important in promoting the proliferation and activation of CD4 + Tcells. [START_REF] Angela | Fatty acid metabolic reprogramming via mTOR-mediated inductions of PPARgamma directs early activation of T cells[END_REF] The role of PPAR! in Treg cells has been particularly investigated in visceral adipose tissue (VAT). For example, the expression of both PPAR! and FOXP3 was identified as crucial to inducing the genetic signature of VAT-specific Treg cells. [START_REF] Cipolletta | PPAR-gamma is a major driver of the accumulation and phenotype of adipose tissue Treg cells[END_REF] PPARy agonism via thiazolidinediones upregulated genes crucial to both Treg cell metabolism and function. For example, genes for lcFA metabolism such as CD36, CPT1 as well as fatty acid synthesis were upregulated. In parallel, there was also an increase in the expression of FOXP3 and Gata3 (2) In comparison, the omega-muricholic and 3β-hydroxydeoxycholic (isoDCA) acids were found to significantly increase FOXP3 expression on naïve murine CD4 + T-cells when co-cultured with DCs. [START_REF] Campbell | Bacterial metabolism of bile acids promotes generation of peripheral regulatory T cells[END_REF] This effect was not possible when the biliary acids were cultured with naïve CD4 T-cells onlythus, indicating an indirect DC-based effect. Indeed, the mechanism involved isoDCA having an antagonistic effect upon binding to the FXR receptor on DCs and downregulating a range of pro-inflammatory genes.

Through a range of innovative murine models and engineered microbes, they confirmed that these effects by demonstrating how these biliary acids could induce colonic pTreg cells. However, the exact nature of the interaction between the DCs and naïve CD4 + T-cells was not identified.

Moreover, the effects of these biliary acids on the metabolism of the iTreg/pTreg cells were not explored either.

VDR

The VDR receptor in T-cells is activated by the active form of vitamin D, 1,25dihydroxyvitamin D3. [START_REF] Chawla | Nuclear receptors and lipid physiology: opening the X-files[END_REF] The receptor then forms a heterodimeric complex with RXR, which can bind to specific sections of DNA (called vitamin D response elements; VDRE). [START_REF] Haussler | Molecular mechanisms of vitamin D action[END_REF] This propagates the transcription of a range of protein complexes responsible for enacting T-cell functions.

The addition of active vitamin D into cultures of activated human CD4 + T-cells or CD4 + CD25 -T-cells has been shown to significantly increase FOXP3 expression compared to controls. [START_REF] Jeffery | 1,25-Dihydroxyvitamin D3 and IL-2 combine to inhibit T cell production of inflammatory cytokines and promote development of regulatory T cells expressing CTLA-4 and FoxP3[END_REF][START_REF] Urry | The role of 1alpha,25-dihydroxyvitamin D3 and cytokines in the promotion of distinct Foxp3+ and IL-10+ CD4+ T cells[END_REF] In parallel, it also increased expression of CTLA4 and reduced secretion of IFN-!, IL-17A, and IL-2. These iTregs were better able to suppress the proliferation of Teff cells during in vitro assays and they secreted slightly more IL-10 too. However, these iTregs were unstable as FOXP3

Translational potential of Treg cell metabolism

The aim of targeting Treg cell metabolism for cellular therapy applications is to induce specific metabolic pathways that augment cellular survival/function. (Figure 1 This combined immunometabolic modulation could be performed either in vivo or in vitro. (Figure 1-4) Concerning the in vivo approach, this would involve either dietary supplementation of substrates (eg. fatty acids, amino acids) or systemic administration of substrates/drugs. This approach is clinically feasible and could be periodically delivered in response to post-transplant protocol biopsies. However, to ensure its efficacy, there will need to be additional pharmacokinetic studies and even utilisation of novel drug delivery approaches for Treg-specific targeting. In comparison, the in vitro approach would involve culture media supplementation with the relevant substrates/drugs during the GMP process. In this way, the Treg cells could be specifically targeted and the substrates/drugs leftover could be washed out at the end of the culture process.

Metabolite supplementation

Treg cells utilise glycolysis, FAO, and OXPHOS as their constitutive metabolic programs. (Figure 1-2) During activation, Treg (and Teff) cells upregulate the expression of Glut1 transporters and their rate of glycolysis (ECAR). Hence, to allow Treg cells to outcompete their effector counterparts, supporting FAO could be a viable therapeutic approach instead. This would involve either direct supplementation of fatty acids, or inhibiting FASyn (via C75, soraphen). [START_REF] Schmidt | Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate[END_REF][START_REF] Cluxton | Differential Regulation of Human Treg and Th17 Cells by Fatty Acid Synthesis and Glycolysis[END_REF][START_REF] Berod | De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells[END_REF] In the past, direct supplementation with scFAs has been attempted, however perhaps using lcFAs or polyunsaturated FAs would be more efficient in terms of ATP generation.( 26) (Figure 1-2) An opposite approach would involve targeting Teff cells by inhibiting glucose uptake (via Glut transporters), glycolysis (eg. 2-DG), or inhibiting fatty acid uptake (CD36), fatty acid transport (a) or mitochondrial uptake (CPT1). [START_REF] O'connor | The CPT1a inhibitor, etomoxir induces severe oxidative stress at commonly used concentrations[END_REF](103)(104)(105)(106) However, we do not know yet which metabolic pathways are utilised or favourable to a graft experiencing acute or chronic rejection. It is also not known whether over usage of particular metabolic pathways would increase oxidative stress and propagate early apoptosis.(39) (Figure 1-2) From a pharmaceutical perspective, although systemic infusion of dietary metabolites would be a route of administration, one could also utilise the oral route for improved bioavailability in intestinal and hepatic allografts.(107) However, further pharmacokinetic studies are needed to assess these approaches in-depth.

Finally, a novel point of intervention could be through in vitro organ machine perfusion technologies.(108-110) (Figure 1-4) These are increasingly being used for conditioning to improve organ quality and subsequent patient outcomes. Upon explantation, the organ is first connected to a circulatory circuit within the perfusion machine. During the hours that follow (depending on the platform), the organ physiology can be monitored using a range of quality-control criteria such as colour, pH, lactate levels, and even bile production. This period of perfusion would be optimal for metabolic-based immunomodulation.

Pharmacological modulation

In this manuscript, we have discussed the internal immunometabolic axes of Treg cells that could be modulated to optimise both metabolic/functional pathways.

a. mTOR

mTOR inhibition via rapamycin is a part of current immunosuppression protocols as well as in vitro GMP Treg cell expansion protocols. (1,[START_REF] Sanchez-Fueyo | Applicability, safety, and biological activity of regulatory T cell therapy in liver transplantation[END_REF][START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF]111) This is because rapamycin improves Treg cell expansion, FOXP3 expression, and immunosuppressive function. [START_REF] Miyara | Combination of IL-2, rapamycin, DNA methyltransferase and histone deacetylase inhibitors for the expansion of human regulatory T cells[END_REF] Another way of inhibiting mTOR involves metformin, which is an AMPK kinase activator. [START_REF] Blagih | The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo[END_REF]112) In Treg cells, it can inhibit FASyn by targeting ACC.(112) This could augment FAO activity to outcompete the Teff cells.

However, the mTOR inhibitory approach is complicated by data discussed in previous sections demonstrating divergent roles of mTOR signalling in Treg cells. [START_REF] Chapman | mTOR coordinates transcriptional programs and mitochondrial metabolism of activated Treg subsets to protect tissue homeostasis[END_REF][START_REF] Li | TLR8-Mediated Metabolic Control of Human Treg Function: A Mechanistic Target for Cancer Immunotherapy[END_REF] (Figure 2) mTOR signalling improves Treg cell proliferation, glycolysis, lipid metabolism, and OXPHOS. Furthermore, essential amino acids also promote mTORC1 activity in Treg cells. Indeed, the ongoing presence of amino acids is necessary for Treg cells to sustain mTORC1 activity and Treg cell function. [START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF][START_REF] Do | Nutrient mTORC1 signaling underpins regulatory T cell control of immune tolerance[END_REF] To reconcile the differences in published studies, although the idea of an mTOR 'oscillatory switch' has been hypothesised, it does mean that any mTOR-based modulation needs to be carefully refined for clinical benefit.( 113 In comparison, although LXR agonists are being studied preexperimentally in the settings of atherosclerosis/dyslipidaemia, their noted side effect of inducing hepatic steatosis is a safety concern.(119) Concerning the FXR receptor, indirect modulation via biliary acids is likely to be more challenging. There will need to be a range of pharmacokinetic studies to study their composition as well as intestinal/hepatic bioavailability. Furthermore, ensuring the specificity of their action on Treg cells only will require the use of more novel drug delivery approaches. Hence, LXR and FXR modulation is not a currently feasible Treg cell modulation strategy in transplantation.

Future Challenges

In summary, this review has outlined numerous ways in which Treg cell metabolism could be exploited for therapeutic benefit in transplantation. Through our discussions, we have also highlighted crucial ongoing knowledge gaps. The following themes need to be addressed in detail if we are going to move forward with translational Treg cell immunometabolism:

• When should Treg cells be administered post-transplant?

• Do antigen-specific or genetically engineered Treg cells metabolise differently?

• How can drugs and metabolites be delivered specifically to in vivo Tregs cells?

• At which time point post-infusion will Treg-boosting metabolites need to be administered?

• Can in vivo Treg cell metabolic programs be switched on/off as per clinical need?

• How can Treg cell metabolic activity be monitored in the graft?

Although the focus of this review has been on Treg cell metabolism, there is also a global cohort of teams actively researching other novel approaches such as genetic engineering, improved donor antigen-specificity, and epigenetics to optimise Treg cells. In light of the acceptable safety profile of Treg cells demonstrated in recent clinical trials, we can look forward to these novel approaches being exploited to optimise Treg cell efficacy. The coming years are indeed going to be exciting for Treg cell therapy -both for us and for our patients. Chapter 1) Activation and mTOR: Upon acute activation, the mTORC1 complex is activated.

Figures (

This process is also promoted (and maintained) via amino acids.

Glucose: Treg cells upregulate glycolysis and glucose uptake upon acute activation.

Lipids/Fatty acids: Short-and medium-chained fatty acids are taken up into cells via passive diffusion whereas long-chain fatty acids depend on transporters. Fatty acids affect cellular survival, metabolism and epigenetics.

Hypoxia: Hypoxia is associated with increased levels of adenosine monophosphate (AMP) and HIF1 complex. These modulate mTORC1 activity, fatty acid synthesis and the transcriptome.

Nuclear receptors: Nuclear receptors are a key link between the steroidal and nonsteroidal ligands and DNA modulation. During this expansion process, the function and metabolism of Treg cells can be modified through drugs or change the substrate composition of metabolites. After the expansion process, the Treg cells could be returned to the patient or infused into an organ reperfusion circuit.

Organ reperfusion:

The recent introduction of organ reperfusion machines into clinical practice is changing the modus operandi of transplantation. Extended-criteria organs can be re-conditioned using different oxygen-delivery molecules, cytokines, drugs and metabolites to reduce their intrinsic ischaemia/inflammatory burden as well as support the function of resident Treg cells. In parallel, recipient-expanded Treg cells could be infused during the reperfusion process to further optimise the reconditioning process. The recovery of an organ can then be measured through objective criteria (eg. pH, bile acid, lactate) and a decision made to implant or discard the organ.

Chapter 2 -Regulatory T cells in solid organ transplantation

Muhammad Atif 1,2,3,5 , Filomena Conti 2 , Guy Gorochov 1 , Ye Htun Oo

Keywords

Regulatory t cells, treg, transplant, clinical trial, safety, foxp3

Running Head: Regulatory T cells in solid organ transplantation

Abstract

The induction of graft tolerance remains the holy grail of transplantation. This is important as chronic allograft dysfunction and the side effects of immunosuppression regimens place a major burden on the lives of transplant patients and their healthcare systems. This has mandated the need to understand the immunobiology of graft rejection and identify novel therapeutics. Regulatory T (Treg) cells play an important role in modulating pro-inflammatory microenvironments and maintaining tissue homeostasis. However, there are fundamental unanswered questions regarding Treg cell immunobiology. These cells are a heterogeneous entity with functionally diverse roles. Moreover, the adoption of novel deeper immunophenotyping and genomic sequencing technologies has identified this phenotype and function to be more complex than expected. Hence, a comprehensive understanding of Treg cell heterogeneity is needed to safely and effectively exploit their therapeutic potential.

From a clinical perspective, the recent decade has seen different clinical teams commence and complete first-in-man clinical trials utilising Treg cells as an adoptive cellular therapy. In this review, we discuss these trials from a translational perspective with an important focus on safety. Finally, we identify crucial knowledge gaps for future study.

Introduction

The achievement of graft tolerance remains the holy grail of transplantation. This is clinically driven by the need to prevent chronic allograft dysfunction and minimise the long-term side effects of immunosuppression.(120) In this regard, the increased presence of regulatory T-cells (Treg cells) within the peripheral circulation and graft microenvironment has been identified as being important in inducing graft tolerance. (7,8,121) These cells are a part of the adaptive immune system and are considered to have key roles in immunosuppression and homeostasis in different disease settings. (9,122) Treg cells in the peripheral circulation are characterised as CD4 + with high levels of IL-2 receptor alpha chain (CD25 high ) and low levels of CD127 and intracellularly, expressing the Forkhead Box P3 transcription factor (FOXP3 + ). (9,122) However, the adoption of novel deeper immunophenotyping technologies has identified this phenotype to be more heterogeneous than initially considered. (9,(123)(124)(125)) (Figure 2-1) These data differ depending on the species, type of Treg cells, differentiation state and microenvironment. (9,[START_REF] Jeffery | Low-dose interleukin-2 promotes STAT-5 phosphorylation, Treg survival and CTLA-4-dependent function in autoimmune liver diseases[END_REF][START_REF] Shevach | tTregs, pTregs, and iTregs: similarities and differences[END_REF]126)

Heterogeneity of Treg cells

Treg Classification

Polyclonal murine and human Treg cells have been classically classified into 3 groups; thymic Treg (tTreg), peripheral Treg (pTreg) and induced Treg (iTreg) cells. [START_REF] Shevach | tTregs, pTregs, and iTregs: similarities and differences[END_REF]126,134) Several authors controversially differentiate between tTreg cells and pTreg cells by the higher expression levels of Helios and Neuropilin-1 (Nrp-1) on tTreg cells.( 35 However, in humans, although both cells were identified in distinct clusters using dimensionality reduction analysis, up to 55% of human Treg cells were found overlapping with effector T cells. These overlapping Treg cells (described as 'furtive'

Treg cells by the study authors) had lower levels of FOXP3 expression than bona fide

Treg cells in humans. Whilst the mechanism for this was not delineated, this indicates that the link between FOXP3 expression and the CD4 + CD25 + CD127 lo phenotype is not as direct as previously described.(159) This is important from a clinical perspective as current in-human trial protocols are expanding Treg cells with differing phenotypes;

CD4 + CD25 + (eg. TRACT trial) or CD4 + CD25 + CD127 lo expression (eg. LITTMUS/TASK trials). Although FOXP3 expression may form part of the product release criteria, it is uncertain as to how stable FOXP3 expression will be in vivo and which other transcription factors may be necessary to maintain in vivo Treg function.

How important is FOXP3 expression in Treg cells?

In spite of the adoption of novel approaches, the common theme through all immunomonitoring studies is the use of FOXP3 as a reliable signature of Treg lineage.

Rationale for Clinical Trials

In spite of the pre-clinical interest in delineating the cross-talk between FOXP3 and Treg cell immunosuppressive function, it is possible to incorporate FOXP3 measurements as part of GMP-expansion protocols and use these as surrogate indicators of Treg cell product quality and potency. (2,[START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF] This is important as Treg cell therapy in transplantation could more accurately target the anti-rejection response whilst having minimal off-target effects and chronic allograft toxicity. This is of major interest to our patients who currently adhere to differing long-term immunosuppression regimens to prevent graft rejection. These decisions regarding immunosuppression as made in consideration of the harmful consequences of long-term drug toxicity as well as aiming to maintain a functional immune system against pathogens/tumours. In addition, our transplant patients are often multi-morbid so it is important to ensure that any transplant-related pharmacotherapy does not interfere with other drugs or impact on their quality of life.( 181) Hence, the successful translation of Treg cell therapy could benefit our patients in many ways.

Clinical Trials: Today and Tomorrow

The recent decade has seen the delivery of first-in-man clinical trials of Treg cellular therapies for a range of transplant-related and non-transplant-related indications. [START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF]182,183) Although these trials have focussed on safety, a core theme across all is the heterogeneity in GMP protocols with regards to; cell isolation, manipulation, expansion, dosing, specificity and post-administration cell tracking.

Whilst some consensus towards standardisation was developed in the multi-centre ONE study consortium (184) (Treg cells cellular therapy in kidney transplantation), the reality is that the novelty of Treg cells mandates numerous in-human studies focussing on optimisation and standardisation of GMP conditions. In Table 2-1, we outline these current trials involving Treg cells cell therapy in SOT and BMT.

Liver

The first published trial using these cells in SOT was by [START_REF] Todo | The ONE Study compares cell therapy products in organ transplantation: introduction to a review series on suppressive monocyte-derived cells[END_REF] from Hokkaido (Japan), who generated a donor-specific cell product with a view to tolerance induction after liver transplantation.(183) 10 patients with end-stage liver disease received a graft from a living donor. The recipients also underwent a splenectomy on the day of transplant and were subsequently on 1 month of mycophenolate (MMF) and long-term tacrolimus/cyclosporine. They were weaned off from 6 months onwards if graft function was stable and all immunosuppression was withdrawn at 18 months. Before the transplant, the team collected 4-5x10 9 cells each from the donor and recipient. The recipient and irradiated donor cells were co-cultured with antagonist anti-CD80/86

antibodies for 2 weeks. After 2 weeks, the remaining cells had to be more than 80% viable with >1x10 6 /kg being CD4 + CD25 + FOXP3 + to be eligible for recipient administration on postoperative day 13. The final doses administered were 8.92-37.7x10 6 /kg cells of which, 0.23-6.37x10 6 /kg were Treg cells. In terms of Treg percentage, this would equate to a range of between 2.6-16.9% Treg cells out of all infused cells. Importantly, there was no reported significant adverse event (SAE) and seven patients were weaned off immunosuppression by 18 It is noteworthy that the three patients who developed acute cellular rejection during the immunosuppression weaning process, all underwent transplantation for autoimmune liver disease. This could imply that the immunoregulatory threshold for these patients is much higher and thus achieving long-term control of effector responses could be more challenging.

Overall, this study has demonstrated the feasibility of their approach in producing a potent anti-donor cellular product with the potential for tolerance induction. The long-term outcomes of this study are awaited.

Trials in Progress

In contrast to the study above, other transplant centres have focussed on manufacturing a 'purer' Treg cell product with/without alloantigen-specificity instead. (See Table 2-

Kidney

In parallel, similarly, novel work has also been undertaken by multiple centres globally in kidney transplantation. A key advantage of the kidney is that there is a larger scope logistically for living donors, which provides continuous access to fresh donor antigens and thus, donor-specific Treg cells.( 20)

TRACT Trial

The TRACT trial from Northwestern University (Chicago, USA) utilised in vitro expanded polyclonal Treg cells infused in a dose-escalation manner (500-5000x10 6 cells) into 9 patients who had received renal allografts from livingdonors. [START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF] Patients received alemtuzumab on the day of transplant and postoperative day 2 and were maintained on MMF and tacrolimus (switched to sirolimus on day 30). The Treg cells were subsequently infused on day 60.

There were no cases of opportunistic infections resulting from cytomegalovirus or polyoma virus nor any cases of rejection for the 2 years reported.

Their Treg cells were isolated using the CliniMACS system and expanded in vitro for 3 weeks using agonist anti-CD3/anti-CD28 beads, IL-2,

Transforming Growth Factor Beta (TGF-b) and sirolimus. The product purity was >98% CD4 + CD25 + and >80% FOXP3 + with similarly high demethylation status (though not 100%). These latter facts are important as demethylation is However, a critical limitation from the current trial was that the deuterated signal was undetectable by three months -compared to being detectable over twelve months in their previous trial. This negates the long-term use of this technique.

There was no reported data identifying these Treg cells at the 2 weeks and 6 months' post-infusion renal biopsies. Therefore, it is unclear whether their deuterated Treg cells product was able to migrate into the graft. This is supplemented by the fact that no patient demonstrated an increase in FOXP3 + % expression in the renal biopsy at 2 weeks post-infusion and only 1 did so at 6 months. Moreover, although this technique facilitates short-term tracking, it does not provide knowledge regarding in vivo Treg function. All in all, no SAEs occurred after the infusion of FACS-isolated and polyclonally-expanded deuterated Treg cells in the context of kidney transplantation. This work is being built upon currently through the TASK trial (NCT02711826), which will compare the efficacy of polyclonal and alloantigen-specific Treg cells.

Trials in Progress

The ONE Study consortium aimed to assess the feasibility and safety of novel cellular therapies in kidney transplantation.( 196

Long-term Safety Issues

Due to the recent nature of Treg cell therapy trials in transplant, only short-term issues can be initially identified. However, it is the long-term safety issues classically associated with mainstream immunosuppressive drugs such as opportunistic infections, malignancies and chronic allograft dysfunction that need to be closely monitored for too. In addition, there may also be other Tregspecific safety issues uncovered of which we are currently unaware. These issues require the implementation of regular clinical follow-up, immunomonitoring as well as broader pharmacovigilance (potentially via a cell therapy registry). In the meantime, considering the pre-experimental data involving Treg cells, there may be particular Treg-specific safety issues that we can at least pre-empt.

Treg cell Plasticity

The potential for Treg cells to adopt a Th17-like effector cell phenotype and function in an inflammatory microenvironment is a critical safety concern. This is because Th17 cells are elevated in patients with acute and chronic allograft rejection and thus, may contribute towards an exacerbation of both conditions. [START_REF] Ma | The imbalance between Tregs, Th17 cells and inflammatory cytokines among renal transplant recipients[END_REF]199)

Future Perspectives

In the end, the discussions concerning Treg heterogeneity are to aid the development of clinically safe protocols in which, Treg cells can be manipulated and manufactured. To this day, two main approaches could facilitate this in humans; in vivo expansion of Treg cells and in vitro Treg expansion under GMP conditions. (2,[START_REF] Miyara | Combination of IL-2, rapamycin, DNA methyltransferase and histone deacetylase inhibitors for the expansion of human regulatory T cells[END_REF]126,184) The former approach has been the subject of clinical trials involving low dose IL-2 in the transplant setting (eg. NCT02739412, NCT02949492, NCT02417870). However, the majority of centres are focussing on in vitro Treg expansion instead. Comparing data from both these approaches will be critical in identifying the optimal method of exploiting Tregs clinically.

A further critical question going forward will be immunosuppression management in Treg cell therapy trials. Depending on the transplant centre protocol, different patients currently are subject to different immunosuppression regimes (eg.

steroid-sparing, deferring the introduction of calcineurin inhibitors) with differing doses.

It is important to take these immunosuppressive agents into account when designing trials as concurrent immunosuppression can modulate Treg cell survival and function.

This aspect has been comprehensively discussed previously by Furukawa A et al With all this in mind, there are numerous questions regarding cell product composition and transplant protocols (Table 2-2) that require addressing if the field of Treg cell therapy in SOT is to progress.

The data we glean from first-in-man clinical trials and pre-clinical studies will help answer the critical known unknowns (Table 2-2). (133) However, we recognise that the novel nature of Treg cells means that there are numerous other unknown unknowns that we will only begin to realise in the coming years. Moreover, it will only be through multi-centre collaborations that we can address a range of these new aspects of translational Treg cell biology. Indeed, there has never been a more exciting time to be involved in the field of Treg cell therapies in transplantation. The successful translation of Treg cell therapies could transform the lives of our transplant patients.

Tables (Chapter 2)

Identification and Selection

• What are the ideal Treg cell phenotypic identifiers?

• What level of specificity (polyclonal, TCR Tg or CAR) is required?

• Is there a need to select Treg cells with transplanted graft-specific homing potential?

Pharmacology

• What is the optimal Treg infusion dose?

• When should Treg cells be administered in a transplant protocol?

• Will one or multiple Treg cell infusions be required to reach the trial end-point?

• How can in vivo Treg cell pharmacokinetics and pharmacodynamics be monitored/traced?

Maintaining Treg Cell Function

• What are the ideal tissue sites (lymph nodes, thymus, graft) for immunoregulatory activity?

• Which is the optimal immunosuppression regimen to maintain in vivo Treg cell function?

• What is the role of the microenvironment in maintaining in vivo Treg cell function?

• What is the optimal Treg epigenetic program to maintain their in vivo survival and function?

Pharmacovigilance

• What are the long-term consequences of Treg cell therapies?

• Is an international patient registry feasible? Those SCFAs not metabolised or stored are released into the peripheral circulation via the hepatic vein (HV).( 216) The basis for these data was derived from studies conducted in the 1980s on recently deceased humans. In these 6 individuals, the pH from the jejunum to the rectum increased from 5.7 to 6.6 and in parallel, so did the levels of SCFAs (from being undetectable proximally).( 216) Acetate was found in the highest concentrations in the colon (43.5-69.1mmol/kg); HPV (108-404µM), HV (32-239µM) and then in peripheral blood (PB; 19-146µM). Propionate followed a similar pattern; colon (14.2-26.7mmol/kg), HPV (18-194µM), HV(2-69µM) and PB (1-13µM). Finally, butyrate also followed the same compartment-based reduction; colon (14.7-26.1mmol/kg), HPV (4-64µM), HV (2-32µM) and PB (1-12µM). This work was built upon by another group who measured SCFA concentrations in the hepatic artery (HA) in addition to the other measurements above. ( 217) In this study, the liver was identified as the main metaboliser of SCFAs. Outgoing concentrations of acetate, propionate, and butyrate were in similar ranges to what had been previously reported.

Figures (Chapter 2)

Put together, these data demonstrate that circulatory cells are exposed to much lower levels of SCFAs than intestinal/intrahepatic cells.

Which type of Treg cells do SCFAs promote?

Thymic-origin

In (not their functions) through FFAR2/GPR43 and inhibiting certain HDAC isoforms.

Peripheral or induced

In comparison, another team demonstrated a similar effect through the high fibre diet (HFD) with pTreg cells instead.( 211) Although these mice had increased colonic levels of SCFAs (acetate/butyrate), the colonic Treg cells in this study were identified as being both Helios and neuropilin-1 negative suggesting that they were peripherally-induced. This was also shown via in vitro cultures of naïve CD4+ T cells with high dose anti-CD3 (10µg/ml), anti-CD28 (1µg/ml), TGFβ (0.2ng/ml), IL-2 (10ng/ml) and 100µM of acetate, propionate or butyrate. After 5 days of culture (additional TGFβ and IL-2 on day 3), the propionate-and butyrate-cultured CD4+ T cells significantly upregulated FOXP3 expression compared to control (till 40-50%).

Importantly, this effect was specific to iTreg cells as butyrate did not upregulate transcription factors associated with other CD4+ T cell subsets such as Th1/2/17.

From a mechanistic perspective, butyrate very mildly promoted histone H3 acetylation of the FOXP3 promoter and the conserved non-coding sequence region 3 (CNS3).

However, this observation alone is unlikely to explain the FOXP3 upregulation and i/pTreg cell induction.

Undefined origin

The studies in this section are discussed here as there was no phenotypic data in their manuscripts that helped clarify the origin of the Treg cells implicated.

In From a mechanistic perspective, the CD4+ T cells expressed very low levels of the GPR41/43 receptor genes and the effects were unchanged in the GPR41/43 KO models. However, the SCFAs significantly reduced HDAC activity levels in a dosedependent manner (0.1-10mM) and in parallel, further increased secretion of all 3 cytokines. This work also found that acetate and propionate increased phosphorylation of ribosomal protein S6 and signal transducer and activator of transcription 3 (STAT3).

Going forward, delineating these dichotomous mechanisms further involved in promoting either of the inflammatory or regulatory cell subpopulations would be an important step in precisely tuning CD4+ T cell differentiation and functions.

SCFAs in clinical trials

SCFAs have already undergone Phase I/II trials as a therapeutic against solid tumours, haematological cancers, inflammatory bowel disease (IBD; Crohns and ulcerative colitis; UC), metabolic syndrome, and autoimmunity (atopic dermatitis, vasculitis).(226-229) Whilst a full discussion is beyond the scope of this manuscript, those involving intestinal conditions are outlined below.

In a Phase I study involving cancer patients (colorectal, lung, breast etc), the daily intravenous administration of a butyrate prodrug (0.047-3.3g/m 2 /day) over 15 days was deemed safe after not being associated with serious adverse events. ( 229)

All patients had previously been on one/two courses of chemoradiotherapy already.

11-14% of patients developed nausea, fevers, or fatigue and 6% developed anaemia.

One patient with lung cancer even demonstrated a halving of the size of their cancer initially and then a 90% reduction after 8 courses of the butyrate pro-drug. Overall, considering that butyrate has a half-life of 6 minutes, maximising its delivery to the site of disease activity is critical.

In patients with metabolic syndrome (versus lean), consuming 4g of butyrate daily for 4 weeks did not lead to different similar faecal and serological levels (54-57.6µM/g and 3-4µM respectively). ( 230) In another study involving patients with relapsing mild/moderate ileocolic Crohns on oral mesalazine, 4g of daily butyrate for 8 weeks (versus placebo) led to remission in >50% as per the Crohns Disease Activity Index (CDAI).( 227) These patients also demonstrated a reduction in serological IL-1β, NFκB however, levels of IFN! and TNF⍺ were unchanged. Similar data was also demonstrated in patients in another study of ulcerative colitis patients on the same dose and with the incorporation of endoscopic and histological endpoints as well.(228)

In the future, it will be important to build upon these clinical trials by incorporating periodic immunological assays for mechanistic insights.

What are the unknowns?

In light of the data discussed above, there are several outstanding questions regarding the effects of SCFAs on human Treg cells:

1. Do SCFAs promote the proliferation and expansion of tTreg cells? transforming growth factor-beta (TGF-B) and butyrate (short-chain fatty acid). [START_REF] Schmidt | Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate[END_REF][START_REF] Shevach | tTregs, pTregs, and iTregs: similarities and differences[END_REF]100,126,208,211,241) However, the iTreg cells are less stable in pro-inflammatory

CD4+ T cell isolation

All PBMCs underwent CD4+ T cell enrichment using the negative CD4+ T cell isolation kit and LS columns (all from Miltenyi, France).

Flow cytometry (sorting and immunophenotyping)

For the flow-sorted CD4+ T cell experiment, enriched CD4+ T cells were stained using

In vitro expansion cell cultures

All naïve Treg/Teff cells were separately cultured in serum-free TexsMACS medium (Miltenyi). In the control condition, we cultured 1x10 5 cells per well with 300IU/ml of interleukin-2 (IL-2), 2µl of anti-CD3/28 activator beads (Thermofisher, France) in 96well flat-bottom plates. For methylprednisolone, we added 100µg/ml and for rapamycin, we added 100nM. The plates were incubated for 2 weeks with further 300IU/ml IL-2 supplementation every 3 days. After one week, the cells were washed and re-plated in 24-or 96-well flat-bottom plates with fresh media, activator beads, IL-2, and the corresponding small molecule. At the end of the 2-week culture period, the cells were washed, some stained for phenotyping, suppression assay, and the rest for single-cell sequencing.

Treg cell suppression assay

After the 2-week culture period, the Treg cells were washed and counted. The frozen naïve Teff cells were thawed, counted, and labelled with 1µM CFSE dye. For the 1:1

Treg:Teff cell ratio condition, the cells were plated at 50,000 of each cell type with anti-CD3/28 activator beads (1:10 bead:cell ratio). We progressively reduced the Treg cell quantity depending on the ratio of the relevant condition. All cell cultures were performed in serum-free TexsMACS medium (1% penicillin/streptomycin) in 96-well round-bottom plates. The plates were cultured for 5 days at 37℃/5%CO2. At the end of day 5, the cells were acquired on the BD FACS Canto II (BD Biosciences, France).

BD Rhapsody single-cell capture and library preparation

All cells (whether freshly enriched CD4+ T cells, flow-sorted or post 2-week expansion)

were resuspended in Stain Buffer (BD Biosciences, France) and the viability of all was verified using Calcein AM (Thermofisher, France) and DRAQ7 (BD Biosciences, France). The cellular viability was >93%. The individual conditions were labelled with a Sample Tag (SMK) to permit downstream demultiplexing and then pooled. Once pooled, the cells were labelled with the 12-28 Abseq surface proteins.

(Supplementary Table 56789).

Next, the cells were captured using magnetic cell capture beads on the BD Rhapsody Express. In effect, after preparing the cell loading cartridge using the BD WTA Cartridge Kit, loading the cell capture beads, we loaded the cells to initially lyse them.

The identified multiplet rate was 10.1-10.4%. The cell capture beads were then recuperated and underwent reverse transcription within 30 minutes. They were also treated with exonuclease before proceeding with library preparation.

Library preparation was performed using the BD Rhapsody cDNA kit. Firstly, the capture beads were denatured to remove the Abseq surface protein and Sample Tag products. These products were amplified via polymerase chain reaction (PCR). The cell capture beads were initially hybridised using random primers and then extended using an extension enzyme mix. These were then purified using AMPure XP magnetic beads (Beckman Coulter, France) and amplified via PCR before undergoing a further round of purification. We performed quality control checks via the Promega Quantus fluorometer and the Agilent Bioanalyser before proceeding with the second round of PCRs.

In the second round, the Abseq/SMK products were split using different AMPure bead ratios and individually underwent a further round of PCR using specific Abseq/SMKspecific primers. The second group that had previously undergone hybridisation also underwent a further round of PCR. All three libraries were then purified and subject to further quality control checks as done previously. Before sequencing, all libraries were reconcentrated at 4nM and pooled. The libraries were sequenced on the Illumina NovaSeq 6000. Overall, we aimed for 13500-27000 reads/Abseq/cell, 1700-2500 reads/SMK/cell and 50000-70000 reads/transcript/cell. We assigned 60 cycles for read 1 and 8 cycles for index read 1 (i7).

Pre-processing sequenced data

We aligned the FASTQ files using the online SevenBridges (www.sevenbridges.com) cloud platform which has the Rhapsody WTA pre-processing pipeline. This was done using the GRCh38-PhiX reference genome and Abseq panel (Supplementary Table 56789). The paired R1/R2 reads were filtered, annotated, and combined into matrix files for subsequent bioinformatics. All downstream analysis was done with RStudio (v1.41106), R (v4) and, Seurat (v4). (248-250)

Firstly, we read in the distribution-based error correction (DBEC) matrix containing all WTA and Abseq data. This was then combined with the Sample Tag matrix. We removed all multiplets or undetermined cells and split the DBEC matrix into its constituent transcriptome (WTA) and surface protein (Abseq) modalities. These modalities were read into Seurat with the Sample Tag information to connect the two.

We removed all low-quality cells expressing <200 or more than 4000 genes as well as those with >37.5% mitochondrial genes. To reduce the risk of type 1 error based on the cell cycle, we scored the cells based on these genes and them regressed them out.

Dimensionality reduction and differential gene expression

All conditions were normalised, scaled, and underwent dimensionality reduction using PCA (principal components analysis) as well as UMAP (uniform manifold approximation and projection). ( 251) The Abseq modality was separately normalised using the CLR method. We set 1.2 as the optimal resolution for clustering before performing differential expression (DE) analysis using the FindAllMarkers function.

Visualisation of the data was performed using DimPlot, FeaturePlot in Seurat, and the Nebulosa package. ( 252

Signalling pathways and regulons

The PROGENy (Pathway Responses Genes) package was used for signalling pathways analysis and the DoRothEA package was used for regulon identification.

(255-258) Both packages have been manually curated using data from multiple publically available datasets. These packages individually score the cellular transcriptome, re-scaled the dataset in a separate assay, and plot them on a heatmap.

The DoRothEA transcription factor analysis is based on both published works and computational inferences (these were excluded from our analysis).

Pseudotime

To construct the differentiation trajectories, we used Monocle 3. (259, 260) These unsupervised constructions were based on the transcriptomic similarity between individual cells from naïve to activated and memory effectors. Considering that we had thymic CD3+ cells as well as peripheral circulatory naïve cells, this gave us a biologically relevant starting point for all trajectories.

Data integration

Integration analyses were performed using Seurat (v4). (250) This package identifies common cell states and features between datasets. The common cell states are used as cross-database anchors that undergo modification for batch effects as well.

Results

Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses

Although To study this, we utilised two approaches. In the former, we negatively enriched CD4+ T cells from a healthy donor and then flow-sorted them into 5 distinct CD4+ T cell subpopulations (Fraction I, II, III as well as effector memory cells (Fraction IV/V;

Fr IV/V) and naïve Teffs (Fraction VI; Fr VI). In the latter approach, we only negatively enriched for CD4+ T cells from the peripheral blood of two different healthy donors.

These cells were separately put through BD Rhapsody WTA/Abseq workflows using a 12 and 28 surface protein panel respectively. We also added Sample Tags to each Overall, this analysis demonstrated that Fraction III cells could be the intermediate state between Fraction I and II Treg cells. We also inferred the regulons driving the diverse signalling processes in these CD4+ T cell subpopulations. Consistent with our expectations, rapamycin propagated a FOXP3++ cluster of Treg cells. However, this did not result in superior immunosuppressive capacity.

Treg cells post-expansion consisted of distinct subsets with heterogeneous gene regulatory networks

(Supplementary Figure 5-3B) After single-cell capture, library preparation, sequencing, and pre-processing, we had a total of 8539 cells from the 3 conditions.

To avoid false positives based on cellular proliferation states, we egressed out genes associated with the cellular cycle. All cells then underwent normalisation, scaling, and clustering based on the PCA and UMAP algorithms.

The first observation was that expanded Treg cells formed 9 heterogeneous clusters (Figure 5-4A, Table 5- The second observation was that clusters 0, 1, 3, and 8 were commonly present throughout the 3 conditions. (Figure 5-4C, Table 5- This correlated with increased expression of IGHM too. Notably. these genes were not similarly upregulated in all of the new cell clusters. All these new cell clusters differentially expressed CD3, CD4, CD45, CD5, CD11a. and CD162 on their surface.

(Table 56Finally, in terms of gene regulatory networks (GRNs), the common clusters Collectively, our data demonstrated that Treg cells post-expansion were heterogeneous in their proteo-transcriptomic profiles and GRNs. This heterogeneity was present even with bead-based activation alongside IL-2 or either methylprednisolone or rapamycin. However, this heterogeneity did not translate into differences in Treg cell immunosuppressive functions.

Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells

A further role of small molecules is to either induce iTregs from effector cells or promote the apoptosis of effector impurities in Treg expansion. To study this, we cultured naïve Teffs (CD4+CD45RA+CD25-) for 2 weeks with either anti-CD3/28

activator beads and IL-2 (control) as well as with methylprednisolone or rapamycin.

The cells then underwent single-cell analysis similar to the Treg cells described above.

After pre-processing, we had 8385 cells from the 3 different conditions. (Figure 5-5A)

These also underwent the same analysis workflow as the Treg cells.

Similarly, to the Treg cells, the pseudo-bulk analysis of the Teff surface proteins demonstrated low expression of chemotaxis markers (CXCR5, CCR4).

(Supplementary Figure 5-4) However, all cells expressed high levels of CD25, HLA.ABC, LFA-1 (CD11a/CD18) and CD46. Both the small molecules downregulated TNFRSF4 (OX40) and HLA-DR whereas rapamycin also downregulated PD1.

Moreover, the following markers were expressed in a bimodal fashion regardless of the conditions; CD69, CD161, TNFRSF9 (4-1BB), CD49b, and CD59. In terms of their lineage factors, all the cells highly expressed GATA3 and but very low Despite the broad surface protein homogeneity across conditions, this was not the case when clustering cells based on their transcriptome. (Table 567) Control Teff cells were collectively dominated by genes involved in antigen-presentation (HLA-DPA/B1, -DQA1, -DRA/B, and CD74). These also expressed genes implicated in chemotaxis (CCL3-4 and CXCL8) and IFN! secretion (IFNG). However, both our immunomodulators inhibited the upregulation of MHC class II-restricted genes.

Moreover, methylprednisolone disproportionally induced cluster 4, which consisted of genes implicated in Treg differentiation (AREG, MAL, MAF), chemotaxis (CXCR4), glycolysis (SLC2A3), and G-protein-coupled receptor signalling (GPR171). (306,307) This cluster was also identified by differential expression of surface CD3 and CD162.

(Table 5- These subpopulations differentially expressed surface markers such as CD45RA, CD31, CD62L, CD27, CCR4, CD28, HLA-DR, ICOS, and CD49d amongst others.

However, in comparison to the other studies discussed above, this work did not identify functional heterogeneity between their subsets. A key advantage of our work versus the studies mentioned above is that we included all unselected enriched CD4+ T cells and flow-sorted CD4+ T cell subpopulations. As many studies discussed above studied a particular type of Treg (CD4+CD25 hi CD127 lo ), our approach facilitated an unbiased overview of all subpopulations (Treg or not, naïve or mature).

Although not covered by our study, the benefit of studying murine models is the capacity to delineate tissue Treg cell heterogeneity. Tissue Treg cells are increasingly implicated in local and systemic diseases. [START_REF] Cipolletta | PPAR-gamma is a major driver of the accumulation and phenotype of adipose tissue Treg cells[END_REF](320)(321)(322) The splenic Treg

Delineating differentiation trajectories

The influence of Treg cell differentiation trajectories on subsequent cellular heterogeneity is still being defined. Whether Treg cells exist in discrete states or remain susceptible to cell fate alterations throughout their cell cycle is not established. Finally, a broader issue is regarding inter-centre comparability of our expanded Treg cells. Across the Treg cell field, there exist differences in Treg cell definitions, length of GMP expansion, and culture conditions. (1,2,[START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF][START_REF] Sawitzki | Regulatory cell therapy in kidney transplantation (The ONE Study): a harmonised design and analysis of seven non-randomised, single-arm, phase 1/2A trials[END_REF]182,195) Certain teams have also expanded Treg cells from the thymus or performed genetic modulation beforehand. [START_REF] Macdonald | Alloantigen-specific regulatory T cells generated with a chimeric antigen receptor[END_REF]332) In this regard, the development of a multi-centre

Fraction I cells can differentiate into

Treg cell transcriptome reference atlas would be useful to identify differences in the expanded Treg cell products.

In conclusion, our work demonstrated the transcriptomic homogeneity of activated Treg cells across the intrathymic re-circulatory and peripheral circulatory compartments. We identified Treg cells post-expansion to consist of 9 distinct subpopulations. Through combining the in vivo and in vitro transcriptome, we identified a model of CD4+ T cell differentiation trajectories under Treg-expansion conditions.

Future work should focus on the TCR repertoire and regulatory network manipulation to better delineate these Treg cell fates. As such, the extrapolation of this data to human CD4+ T cells has been limited. [START_REF] Schmidt | Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate[END_REF]224) These findings were then biologically validated through phosphorylation and metabolite uptake studies. Butyrate acutely inhibited the phosphorylation of both signal transducer and activator of transcription 5 (STAT5) and p38. Finally, we confirmed that the effects of butyrate were independent of fatty acid oxidation (FAO).

Figures (Chapter 5)

(ThermoFisher, France), albumin, BODIPY 1-10μM before/after 2 hours of activation, and BODIPY-10μM with 100μM palmitate. activator beads as well as with and without butyrate. To study the durability of the effects of butyrate as well as have enough cell numbers for downstream analysis, we cultured for 14 days. This involved another dose of butyrate and new beads on day 7.

In vitro

At the end of the culture period, all cells were spun and counted using Calcein AM (Thermofisher) and DRAQ7 (BD Biosciences) on the BD Rhapsody scanner before being resuspended in BD Stain Buffer. The cellular viability was 93-99%. Each condition (control and butyrate) was then labelled with a separate Sample Tag (SMK) before multiplexing. Next, the pooled sample was labelled with the Abseq surface antibody mix. (Table 6-1) The Rhapsody cartridge was prepared using the Whole Transcriptome Analysis (WTA) kit on the BD Rhapsody Express instrument. We then loaded the cell capture beads, lysed the cells using the Express magnets and retrieved the cell capture beads. The experiment passed all quality control checks on the Rhapsody scanner. Our final multiplet rate was 10.4%. Next, the cell capture beads underwent reverse transcription before treatment with exonuclease.

Preparation of libraries and sequencing

The library preparation involved using the cDNA kit to perform random priming extension, two rounds of PCR as well as regular quality controls. Firstly, the Abseq/SMK products were separated from the capture beads through denaturation.

The remaining capture beads then underwent hybridisation using the random primer mix before being extended using the extension enzyme mix. After AMPure XP magnetic bead-based purification, these random priming/extension (RPE) products underwent 11 cycles of PCR. In parallel, the Abseq/SMK products underwent PCR amplification in 10 cycles instead. After purifying the PCR products of each library using the AMPure XP beads, quality control checks were performed using the Promega Quantus fluorometer and the Agilent Bioanalyser.

Next, the Abseq/SMK products were divided into two to prepare the individual Abseq and SMK libraries separately. The first group underwent 12 cycles of PCR amplification using SMK primers. After purification and further DNA concentration check, we performed a further 6 cycles of index PCR. In parallel, the second group underwent 10 PCR cycles to form the final Abseq Index library. We also performed a new round of PCR on the purified RPE library (11 cycles) to form the WTA index library. All three libraries (Abseq, SMK and WTA) were purified as above and

Results

Higher sensitivity of naïve Treg cells to the antiproliferative effect of high dose butyrate

The direct effects of butyrate on human naïve Tregs and Teffs are not fully delineated. We first asked how these cells would differentiate upon acute exposure to different doses of butyrate. This was studied using the Fr I cells (defined as

Butyrate reduces FOXP3 expression in naïve Treg cells but induces it in naïve Teffs

Next, we asked whether butyrate could modulate FOXP3 expression in activated Treg or Teff cells. We cultured naïve Treg cells and Teffs as described above for 6 days. We found that butyrate significantly downregulated FOXP3 expression in

Palmitate has no discernible effect on Treg or Teff cells

To delineate the responsible mechanism for these differential effects, we considered whether it involved increased engagement of fatty acid oxidation (FAO). (207,344) The increased utilisation of FAO or via inhibition of fatty acid synthesis (FAS) promotes mitochondrial metabolism and the differentiation of naïve CD4+ T cells to FOXP3+ cells. [START_REF] Howie | Foxp3 drives oxidative phosphorylation and protection from lipotoxicity[END_REF][START_REF] Berod | De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells[END_REF] To do this, we used palmitate, a long-chain fatty acid (LCFA) that undergoes 7 cycles of FAO (rather than only 1 with butyrate).

(344)

In terms of viability, we found that both Treg and Teff cells remained viable even at the highest dose of palmitate. (Supplementary Figure 6 We also performed a cluster-by-cluster analysis to study the subpopulations in greater detail. (Table 6-2) Both the control and butyrate conditions were dominated by clusters 0, 1, and 3. We identified that cluster 0 highly expressed the CASP8, OGT, NKTR, MIAT, and SP100 genes. (361) Overall, these Treg-related genes were differentially expressed across the 4 additional clusters (2, 4, 5, and 7) promoted by butyrate. (Figure 6-4A, Table 6-2) Cluster 2 cells upregulated TCF7, MAL, PTGER2, SYNE2, and NCR3 genes. Overall, these data demonstrated the capacity of butyrate to promote Treg cell populations with divergent regulatory and effector functions.

Homogenous surface protein profiles did not permit precise differentiation of divergent cells

The transcriptomic data allowed us to identify cell clusters with both regulatory and effector cell profiles. As we had multimodal data on these cells, we wanted to see which of the 28 protein markers could best discriminate between these cells. To reduce technical noise, we increased the log fold change threshold to 0.9 for this modality. Although clusters 0, 1, 3, and 5 had RNA profiles most reflective of Treg-like cells, they did not have surface proteins sufficiently up or down-regulated to distinguish these clusters via that modality. This homogenous population was collectively CD4+CD25+DR+PD1-CD161-, however none of the other 20 protein markers appropriately discriminated between these subpopulations. (Supplementary Figure 6-8B) In comparison, cluster 5 was defined as CD45+CD44+CD2+CD162+

As for the other clusters, we identified differentially expressed surface markers for all. (Table 6-3A) Notably, cluster 4 and cluster 5 (identified as Treg cells above) had identical surface protein profiles. This would make it practically implausible to differentiate them through flow cytometry-based means. In comparison, we identified a unique comprehensive profile for the other 3 Teff-like subpopulations. These subpopulations also expressed a unique marker not upregulated in any other cell. Put together, our data demonstrated that butyrate promoted the proliferation of both CD5+ and CD11a+ Teff-like subpopulations. In parallel, it also promoted the proliferation of a CD45+CD4+CD44+CD2+CD162+ population with divergent transcriptomic profiles.

Integrated transcriptomes confirmed butyrate induced FOXP3 low state in Treg cells

Having studied butyrate-modulated Treg cells, we hypothesised whether a similar effect would occur in Teff cells. To do this, we performed the same co-culture as above using naïve Teff cells instead. These cells were also put through the Rhapsody WTA pipeline and processed bioinformatically exactly as their Treg cell counterparts were. (Table 6- To delineate which aspects of Signal 1 and 2 were being interfered with by butyrate, we swapped the activator beads for plate-coated anti-CD3 (0.1 and 1µg/ml) and soluble CD28 (2.5µg/ml) instead. We found that pSTAT5 was similarly downregulated in all conditions. (Supplementary Figure 6-7A) Hence, butyrate was inhibiting STAT5 phosphorylation downstream of both CD3, CD28, as well as JAK1/3 signalling cascades.

We also studied the role of p38 MAPK as its phosphorylation has been differentially implicated both in the induction of Treg cells and Th17 differentiation. In our system, we found that p38 phosphorylation was downregulated by butyrate in the cells stimulated with a stronger anti-CD3 signal. (Supplementary Figure 6-7B)

In our previous analysis, we identified the increased expression of PTGER2 gene in butyrate-cultured Treg cells. As this gene leads to increased Akt signalling, we decided to biologically validate this. ( 274) Indeed, we found butyrate induced significantly more pAkt compared to the beads-only control. (Figure 6-6C) However, this effect was not reproducible using anti-CD3/28 antibodies and neither was it butyrate dose-dependent. Moreover, as this effect was identified after 24hrs of activation rather than the 2hrs with pSTAT5/p38, it is difficult to exclude the role of any intranuclear mechanism in mediating it. We also studied the phosphorylation of mTORC1 however, the quality of the antibody staining was too suboptimal to be conclusive. (Supplementary Figure 6-7C)

In conclusion, our data demonstrated that butyrate inhibited both phosphorylation of STAT5 and p38 during early CD4+ T cell activation. Although the exact biochemical interaction was not identified, our data demonstrated the novel capacity of SCFAs to perturb Signal 3 early via an intracytoplasmic mechanism. Regarding Teff cells, butyrate did not modulate their cytokine secretion profile.

Butyrate induces a pro-inflammatory profile in immunosuppressive

(data not shown) These cells continued to secrete IFN!, IL-

Discussion

Treg cells are at the crossroads of intestinal inflammation as they are immunosuppressive, homeostatic, and are predisposed to catabolise FFAs. [START_REF] Michalek | Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets[END_REF][START_REF] Berod | De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells[END_REF]207,208,278,333,337,338) Treg cell heterogeneity has also been investigated by many teams previously. (9,[START_REF] Shevach | tTregs, pTregs, and iTregs: similarities and differences[END_REF]124,125,155) Based on FOXP3 and CD45RA expression, we had identified 3

Treg subpopulations in the past. (9) This present study identified that Fraction III A key reason for the differences between our data and that of our colleagues is perhaps due to technical depth. We used 2000 variable features to cluster our cells based on 30,234 genes. In addition, we also had a surface protein modality to draw equivalence with flow cytometry data.

Previous work on SCFAs has demonstrated the induction of iTregs from naïve CD4+ T cells cultured at similar SCFA concentrations. [START_REF] Schmidt | Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate[END_REF](208)(209)(210)(211)224) Although these experiments had a comparable set-up to ours with anti-CD3/28 activator beads (or coated antibodies) and IL-2, they all utilised TGFβ too. In one study, the iTreg effect was further enhanced with pre-treatment of dendritic cells with butyrate instead. Going forward, our work makes an important contribution in untangling the complex link between dietary fats and inflammation. Butyrate is an organic FFA naturally found in the diet that polarises immunoregulatory cells towards novel proinflammatory effector cells. We also identified novel mechanisms utilised by butyrate to drive this process. These pathways could be therapeutically targeted to modulate the effects of SCFAs on T cell-based immune homeostasis. Moreover, single-cell sequencing confirmed these observations as butyrate was found to induce de novo effector cells. We defined the proteo-transcriptome of these de novo cells as well. Their induction was driven by a broad range of transcription factors implicated in cell growth and effector cell functions. In line with the reduction in CD25, FOXP3, and proliferating cells, we also identified the capacity of butyrate to inhibit the phosphorylation of STAT5 and p38. The butyrate-induced FOXP3 lo Treg cells also produced more IL-2 and IL-17A. Mechanistically, all these effects were independent of fatty acid oxidation and TGFβ. Remarkably, this heterogeneity did not affect their overall immunosuppressive capacity.

Figures (Chapter 6)

Scientific contribution of this work

In order to safely use in vitro expanded Treg cells for therapeutic purposes, it is important to delineate their underlying heterogeneity. This is vital as phenotypically and functionally heterogeneous subpopulations have been described in the past.

Understanding the basis for these observations could also reveal new targets for perturbation through genetic or pharmacological means to optimise their functions. In this regard, our work makes an important contribution.

Although the Treg cell signature of foetal thymic Treg cells has been reported upon, those works were based on the transcriptome only. In our work, we delineated the proteo-transcriptome of intrathymic Treg cells and after comparing with peripheral circulatory Treg cells, identified that these Treg cells were homogeneous. The similarity of our findings suggests that the activated Treg cell signature is defined early in life and remains consistent to adulthood.

Our work also further dissected the CD4+FOXP3+ T cell subpopulations to identify the basis for underlying heterogeneity and subsequent differentiation trajectories. We found that pro-inflammatory FOXP3 lo effector non-Treg cells were heterogeneous; consisting of naive, activated Tregs as well as memory effector cells.

This was important because in comparison to previous works, our study identified the FOXP3 lo cells as an intermediary state before cells acquired an activated Treg celllike profile. Indeed, these latter cells were proteo-transcriptomically homogeneous. We also identified new targets in these subpopulations for future perturbation studies.

Future Perspectives

A number of aspects regarding currently open questions and future studies have already been discussed throughout this manuscript. Hence, we discuss two broader themes below.

Multi-level Treg cell heterogeneity

In this work, we linked the Treg cell transcriptome with surface protein levels to in vitro assays only. (9,398,399) These data will need to be validated through the course of the multiple ongoing clinical trials involving Treg cell therapies and low-dose interleukin-2 (IL-2). (195,234,334,400,401) Defining the phenotype of Treg cell subpopulations

Due to the heterogeneous nature of Treg cells, different teams may use varying phenotypic markers to define their Treg cells. (9,124,321,398) It is important to take these into account when comparing data between studies. For example, Treg cells can be classified into three subtypes depending on their origin; thymic (tTreg), induced from effector T cells in vitro (iTreg) and induced from effector T cells in vivo (peripheral, pTreg) cells. [START_REF] Shevach | tTregs, pTregs, and iTregs: similarities and differences[END_REF]126) Prepare a single cell suspension of fresh/thawed peripheral blood mononuclear cells (PBMCs).

2.

Pass the PBMCs through a 30 μM filter to remove any clumped cells (especially important for thawed samples)

3.

Count cells using a haemocytometer (check viability with trypan blue dye).

4.

Re-suspend cells up to 20 x 10 6 /ml in sterile cell culture media. Put approximately 1-2

x 10 6 cells aside for compensation controls later if flow cytometric sorting is being used.

5.

Considering that Treg cells make up <5% of all PBMCs, ensure that you start with sufficient PBMCs upstream to deliver enough Treg cell yield for subsequent assays. iii.Flow cytometric sorting 1.

Resuspend cells in FACS buffer or culture media (any from Common Reagents section) at a concentration of 10 x 10 6 cells/50 µL.

2.

Depending on the Treg cell classification system, we recommend either of the following antibody panels:

a. FITC anti-CD4, APC anti-CD8, PE anti-CD25, BV421 anti-CD127 b. FITC anti-CD4, APC anti-CD8, PE anti-CD25, PE-Cy7 anti-CD45RA

• The BD versions of these antibodies can be added at a 1:10 dilution (e.g., 5 ml antibody and 45 ml cells.

• Prepare single-channel compensation controls with compensation beads or PBMCs.

• Avoid antibody in FL1 channel if CFSE staining is planned via the suppression assay later.

• We recommend titrating with newer dyes especially if using other flow cytometers.

3.

After incubation at 4°C for 30 min, wash with FACS buffer.

4.

Resuspend in cell culture media (10 x 10 6 cells/ml maximum to prevent clogging in the flow cytometer sheath).

5.

Prepare sterile FACS tubes for collecting the different isolated cell subpopulations by adding 500 µl of culture media (any from Common Reagents section) media to each tube beforehand as a diluent for newly isolated cells.

6.

Set up panel on flow cytometer having passed the compensation tubes. This process will differ depending on the brand of the cytometer.

7.

Run the tubes through the cytometer.

8.

Note down the individual cell counts per subpopulation isolated.

9.

Purity check: take 10 µl from each of flow sorted subpopulation and add it to 90 µl of PBS/FACS buffer in a new tube. Analyse by flow cytometer to check purity of sorted cells.

10.

Centrifuge isolated cells at 650g for 10 minutes at room temperature. Resuspend them in an appropriate volume of sterile culture media, and re-count cells.

iv.(Alternative protocol) Hybrid or magnetic beads only

The magnetic bead-based approach can be used in two ways (Figure A-2)

1. Hybrid: CD4 + or CD25 + T cell bead enrichment before flow cytometric sorting 2. Treg cell isolation directly using beads from PBMCs

We discuss the concepts underlying these different approaches below rather than rewriting the individual protocols in detail. These protocols are well-described in the datasheets for the various magnetic bead products on the websites of the major vendors. In our experience, we have not needed to deviate from these protocols. A second approach is to utilise CD25 positive selection beads instead. The enriched CD25 + fraction can then be marked with fluorescent antibodies to undergo quicker flow cytometric sorting just using a CD4 + T cell isolation kit. In parallel, the CD25 -fraction can then undergo CD4 + bead-based selection as described above to isolate the control CD4 + Teff cells.

These are then also marked with fluorescent antibodies to isolate the naïve Teff subpopulation.

Treg cell isolation

There are several commercial Treg cell isolation kits available from major suppliers (e.g., Miltenyi, STEMCELL Technologies). The process of performing the Treg cell isolation is similar between the different companies. The key differences remain in their separation technologies as Miltenyi utilise their proprietary MACS columns whereas STEMCELL Technologies utilise their EasySep magnetic block.

Expanding Treg cells

Due to limitations in Treg cell yield, different teams developed protocols to stimulate the proliferation of these cells and expand them.(413-416) Treg cells rapidly expand within a week and can continue doing so upon regular stimulation and culture media replenishment.( 413) We describe our own protocols below. Please refer to (182,195,(414)(415)(416)(417) for protocols developed by other research groups. In recent years, these protocols have been modified in line with Good Manufacturing Practice (GMP) regulations for future cell therapy use. (182,195,400,408) 1.

Re-suspend isolated Treg cells (and control Teffs) in separate polypropylene tubes containing cell expansion media at no more than 10 5 cells/100ul. This allows for an additional 100µl of additional media, stimulators, cytokines or any other treatment reagents to be added later to make up a total volume of 200ul.

2.

The cells can be directly plated in 96-well plates (flat/U-bottom). (Figure A-3) We advise no more than 10 5 cells per well to facilitate sufficient contact with activator beads, space for expansion and prevent clogging.

3.

Add anti-CD3/CD28 activator beads (2 µl/well, Dynabeads 40 x 10 6 beads/ml).

4.

Add 300 IU/ml IL-2.

5.

Add any additional treatment reagent, e.g., 1 μg/ml Rapamycin 6.

Top up the volumes in each well to a maximum of 200 µl with culture media. We added additional IL-2 (300 IU/ml) to the culture every 3 days. After 7 days of stimulation, cells are recounted and re-plated with fresh culture media. Depending on the cellular confluence, half the cells would either be put in a different well or all the cells would be transferred to a 24-well plate to facilitate ongoing growth. This entire expansion process was terminated at 21 days.

At each restimulation period, we also remove approximately 3-5 x 10 (See Table A-1 for plate layout) 8.

To equilibrate the total volumes in each well, further culture media is added to ensure each well's volume is 200 µl altogether.

9.

The plate is spun at 500g (RCF) for 3 minutes (with brake) to sink all cells to the bottom and then placed in an incubator for 4-5 days (37ºC/5% CO2).

10.

At the end of the incubation period, the cells are spun, resuspended in FACS buffer/media, and passed through the flow cytometer. Additional labelling is not required as the Teffs were pre-labelled with CFSE.

Calculate the suppressive capacity of Treg cells as follows:

!" $

Cytokine Secretion Assay

The cytokine secretion assay performed on newly isolated and expanded Treg cells is another tool in the armoury to assess cellular function. This is important to comprehensively exclude the effects of Treg cell plasticity as well as any FOXP3 expressing subpopulations that have effector functions.

Our regular laboratory assay assesses for the secretion of IFNγ, IL-2, and IL-17A. However, it is also possible to change the antibody panel to study other cytokines such as IL-10, TGFβ, TNFα, and IL-22.

1.

Two wells are needed per T cell condition (unstimulated and stimulated). Add half of the 3-5 x 10 4 T cells into each well.

2.

Centrifuge cells at 650g for 10 min at room temperature. Remove cell culture media by inversing plates.

3.

Add 180 µl cell culture media to the unstimulated wells. i.

Ensure that no overlap with viability dyes (e.g., 7AAD clashes with PerCP-Cy5.5)

14.

Wash off after 45 minutes with FACS staining buffer twice 15.

Transfer to FACS tubes.

Table Legends (Appendix 1)

Table A-

FIGURES (CHAPTER 5 )

 5 FIGURES (CHAPTER 5) .................................................................................................................................

FIGURE 5 -

 5 FIGURE 5-1 ...................................................................................................................................................... FIGURE 5-2 ...................................................................................................................................................... FIGURE 5-3 ...................................................................................................................................................... FIGURE 5-4 ...................................................................................................................................................... FIGURE 5-5 ...................................................................................................................................................... FIGURE 5-6 ...................................................................................................................................................... FIGURE 5-7 ...................................................................................................................................................... SUPPLEMENTARY FIGURE 5-1 .......................................................................................................................... SUPPLEMENTARY FIGURE 5-2 .......................................................................................................................... SUPPLEMENTARY FIGURE 5-3 .......................................................................................................................... SUPPLEMENTARY FIGURE 5-4 .......................................................................................................................... TABLES (CHAPTER 5) ...................................................................................................................................TABLE 5-1 ........................................................................................................................................................ Abseq surface profiles of each cluster ....................................................................................................... RNA profiles of each cluster ...................................................................................................................... TABLE 5-2 ........................................................................................................................................................ Abseq surface profiles of each cluster ....................................................................................................... RNA profiles of each cluster ...................................................................................................................... TABLE 5-3 ........................................................................................................................................................ Abseq profiles of each cell type ................................................................................................................. RNA profiles of each cell type ................................................................................................................... TABLE 5-4 ........................................................................................................................................................ Abseq profiles of each cell type ................................................................................................................. RNA profiles of each cell type ................................................................................................................... TABLE 5-5 ........................................................................................................................................................ RNA profiles of each cluster ...................................................................................................................... Abseq profiles of each cell type ................................................................................................................. TABLE 5-6 ........................................................................................................................................................ Abseq profiles of each cluster .................................................................................................................... TABLE 5-7 ........................................................................................................................................................ Abseq profiles of each cluster .................................................................................................................... RNA profiles of each cluster ...................................................................................................................... TABLE 5-8 ........................................................................................................................................................ RNA profiles of each cluster ...................................................................................................................... TABLE 5-9 ........................................................................................................................................................

FIGURE

  FIGURE AND TABLE LEGENDS (CHAPTER 5) .......................................................................................

Figure 5 - 1 :

 51 Figure 5-1: Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses .....................................................................................................................................................Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells .......................................................................................................................................Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells .............................................................................................Figure 5-4: Treg cells post-expansion are heterogeneous in their proteo-transcriptomic profiles and gene regulatory networks ....................................................................................................................................Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells ....................................................................................................................................................................Figure 5-6: Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells ..Figure 5-7: Inferring gene regulatory networks of expanding naïve CD4+ T cells ................................... SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................

Figure 5 - 2 :

 52 Figure 5-1: Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses .....................................................................................................................................................Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells .......................................................................................................................................Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells .............................................................................................Figure 5-4: Treg cells post-expansion are heterogeneous in their proteo-transcriptomic profiles and gene regulatory networks ....................................................................................................................................Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells ....................................................................................................................................................................Figure 5-6: Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells ..Figure 5-7: Inferring gene regulatory networks of expanding naïve CD4+ T cells ................................... SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................

Figure 5 - 3 :

 53 Figure 5-1: Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses .....................................................................................................................................................Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells .......................................................................................................................................Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells .............................................................................................Figure 5-4: Treg cells post-expansion are heterogeneous in their proteo-transcriptomic profiles and gene regulatory networks ....................................................................................................................................Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells ....................................................................................................................................................................Figure 5-6: Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells ..Figure 5-7: Inferring gene regulatory networks of expanding naïve CD4+ T cells ................................... SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................

Figure 5 - 4 :

 54 Figure 5-1: Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses .....................................................................................................................................................Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells .......................................................................................................................................Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells .............................................................................................Figure 5-4: Treg cells post-expansion are heterogeneous in their proteo-transcriptomic profiles and gene regulatory networks ....................................................................................................................................Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells ....................................................................................................................................................................Figure 5-6: Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells ..Figure 5-7: Inferring gene regulatory networks of expanding naïve CD4+ T cells ................................... SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................

Figure 5 - 5 :

 55 Figure 5-1: Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses .....................................................................................................................................................Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells .......................................................................................................................................Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells .............................................................................................Figure 5-4: Treg cells post-expansion are heterogeneous in their proteo-transcriptomic profiles and gene regulatory networks ....................................................................................................................................Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells ....................................................................................................................................................................Figure 5-6: Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells ..Figure 5-7: Inferring gene regulatory networks of expanding naïve CD4+ T cells ................................... SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................

Figure 5 - 6 :

 56 Figure 5-1: Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses .....................................................................................................................................................Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells .......................................................................................................................................Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells .............................................................................................Figure 5-4: Treg cells post-expansion are heterogeneous in their proteo-transcriptomic profiles and gene regulatory networks ....................................................................................................................................Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells ....................................................................................................................................................................Figure 5-6: Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells ..Figure 5-7: Inferring gene regulatory networks of expanding naïve CD4+ T cells ................................... SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................

Figure 5 - 7 :

 57 Figure 5-1: Parallel analysis of surface protein and transcriptome delineated distinct T cell subsets in adult thymuses .....................................................................................................................................................Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells .......................................................................................................................................Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells .............................................................................................Figure 5-4: Treg cells post-expansion are heterogeneous in their proteo-transcriptomic profiles and gene regulatory networks ....................................................................................................................................Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells ....................................................................................................................................................................Figure 5-6: Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells ..Figure 5-7: Inferring gene regulatory networks of expanding naïve CD4+ T cells ................................... SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................

Figure 6 - 3 :Figure 6 - 4 :Figure 6 - 5 :Figure 6 - 6 :Figure 6 - 7 :

 6364656667 FIGURES ...........................................................................................................................................................Figure 6-1: Butyrate did not modulate the differentiation of naïve Tregs into activated cells. ................. Figure 6-2: Butyrate converged FOXP3 expression in naïve Treg and naïve Teff cells towards a FOXP3lo state. ........................................................................................................................................................... Figure 6-3: High dose butyrate negatively impacted expression of Treg-associated markers whereas low dose did not. ............................................................................................................................................... Figure 6-4: Single cell transcriptomics revealed 8 distinct Treg cell subpopulations. .............................. Figure 6-5: Integrated transcriptomic confirmed butyrate induced a FOXP3 lo state in both Treg and Teff cells. ...........................................................................................................................................................Figure 6-6: Butyrate inhibitions phosphorylation of STAT5 during early CD4+ T cell activation. ......... Figure 6-7: Butyrate induced a pro-inflammatory profile in immunosuppressive Treg cells. .................. SUPPLEMENTARY FIGURE LEGENDS ................................................................................................................ Supplementary Figure 6-1: ......................................................................................................................... Supplementary Figure 6-2: ......................................................................................................................... Supplementary Figure 6-3: ......................................................................................................................... Supplementary Figure 6-4: ......................................................................................................................... Supplementary Figure 6-5: ......................................................................................................................... Supplementary Figure 6-6: ......................................................................................................................... Supplementary Figure 6-7: ......................................................................................................................... Supplementary Figure 6-8: ......................................................................................................................... TABLE LEGENDS ..............................................................................................................................................

1 . 3 . 5 .

 135 Atif M, Cherai M, Cretet C, Saichi M, Aoudjehane L, Balderas B, Conti F, Scatton O, Gorochov G, Oo YH, Miyara M. 2021. Butyrate inhibits STAT and induces effector subsets in human Treg cells. (original article under reviewjournal confidential) 2. Atif M, Chera M, Miyara M. Phenotypic and Functional Studies of Treg cell subpopulations. Methods in Molecular Biology. (submitted; book chapter) Atif M, Mohr A, Conti F, Scatton O, Gorochov G, Miyara M. Metabolic Optimisation of Regulatory T cells in Transplantation. 2020. Frontiers in Immunology. 2 nd September. (published; review) 4. Atif M, Conti F, Scatton O, Gorochov G, Miyara M. Regulatory T cells in Solid Organ Transplantation. 2020. Clinical and Translational Immunology. 23 rd February. (published; review) Atif M, Cherai M, Cretet C, Saichi M, Balderas B, Conti F, Scatton O, Gorochov G, Oo YH, Miyara M. 2021. Single-cell multimodal transcriptomics defines the heterogeneity and differentiation trajectories of regulatory T cells. (original article not presently submitted)

1 . 2 .

 12 Giganti G, Atif M, Mohsen Y, Mastronicola D, Grageda N, Povoleri G, Miyara M, Scotta C. 2020. Treg cell therapy: How cell heterogeneity can make the difference. European Journal of Immunology. Jan;51(1):39-55. Atif M*, Mohr A*, Balderas B, Gorochov G, Miyara M. 2019. Role of FOXP3+ regulatory T cells in human autoimmune and inflammatory diseases. Clinical and Experimental Immunology. Jul;197(1):24-35 (*joint first authors)

  cellules T régulatrices CD4+ (Treg) jouent un rôle immunosuppresseur et homéostatique dans le système immunitaire. Actuellement, ces cellules font objet des plusieurs essais cliniques après avoir été isolées et expandues dans le cadre d'une thérapie cellulaire contre le rejet de greffe, la maladie intestinale inflammatoire (MICI) et l'hépatite auto-immune. Un défi majeur lié aux cellules Treg (pré-et post-expansion) est leur l'hétérogénéité. Dans ce travail, nous avons élucidé l'hétérogénéité des cellules Treg in vivo et in vitro ainsi que leurs trajectoires de différenciation grâce à l'analyse de leur transcriptome et des protéines de surface à une résolution cellulaire unique. D'ailleurs, un autre problème étudié dans cette thèse est le lien entre l'hétérogénéité des cellules Treg et l'exposition à un microenvironnement riche en acides gras. Ceci est pertinent pour les patients atteints de MICI et de cancers gastrointestinaux. Les cellules Treg ont été trouvées respectivement à des fréquences réduites et augmentées dans ces conditions. De plus, la fonction des cellules Treg peut être affectée par des métabolites microbiens tels que les acides gras à chaîne courte (AGCC). Certains comme le butyrate influencent la différenciation des cellules T CD4+ naïves vers un état des Tregs induits et favorisent même la prolifération des cellules Treg tissulaires. Néanmoins, cet effet n'a été démontré ni en utilisant des cellules Treg humaines d'origine thymique (tTreg) ni leurs sous-populations. Dans ce travail, nous avons étudié les effets de l'exposition au butyrate sur les cellules Treg naïves subissant une activation aiguë. Cette étude combinée des expériences immunologiques et bio-informatiques a mieux approfondi notre compréhension sur les effets du butyrate sur l'immunobiologie des cellules Treg.

2 .

 2 fatty acids and Treg cells: What's known? What isn't? The first and second chapters are published: 1. Atif M, Mohr A, Conti F, Scatton O, Gorochov G, Miyara M. 2020. Metabolic Optimisation of Regulatory T cells in Transplantation. Frontiers in Immunology. 2 nd September. Atif M, Conti F, Scatton O, Gorochov G, Miyara M. Regulatory T cells in Solid Organ Transplantation. 2020. Clinical and Translational Immunology. 23 rdFebruary.

- 2 )

 2 These two processes are facilitated by the fatty acid-binding proteins (FABP) and the carnitine palmitoyl-transferase transporters (CPT1/2) respectively.[START_REF] Raud | Fatty acid metabolism in CD8(+) T cell memory: Challenging current concepts[END_REF] Treg cells predominantly express the FABP5 transporter although other isoforms have been described.[START_REF] Field | Mitochondrial Integrity Regulated by Lipid Metabolism Is a Cell-Intrinsic Checkpoint for Treg Suppressive Function[END_REF][START_REF] Frizzell | Organ-specific isoform selection of fatty acidbinding proteins in tissue-resident lymphocytes[END_REF] Recent work by Field C et al demonstrated that pharmacological inhibition of FABP5 in newly-differentiated iTregs switched their metabolic program from OXPHOS to glycolysis (as evidence by the extracellular acidification rates; ECAR).[START_REF] Field | Mitochondrial Integrity Regulated by Lipid Metabolism Is a Cell-Intrinsic Checkpoint for Treg Suppressive Function[END_REF] These cells also developed an altered mitochondrial structure and synthesised fewer proteins specific for the mitochondrial ETCs. As a consequence, lcFAs were unable to engage in FAO and the Krebs cycle. However, in an interesting demonstration of the roles of lcFA metabolism in modulating Treg cell function, they also identified that FABP5 inhibition in iTregs and human Treg cells led to increased in vitro suppression via IL-10 secretion. The mechanism for this effect involved the release of mitochondrial DNA and subsequent increase in interferon signalling via the innate pattern recognition pathway, cycle GMP-AMP synthase (cGAS) and Stimulator of Interferon Genes (STING). Collectively, these data suggest that inhibiting lcFA-FAO metabolic pathway may be more favourable as an approach to increasing Treg cell suppressive function. They also suggest that the overall effects of FAO on Treg cells are broader than just supplementing the Krebs cycle. It is plausible that various intermediates produced during FAO such as acetyl-CoA and reduced flavin/nicotinamide adenine dinucleotides (FADH/NADH) could be interfering with Treg cell function through yet unknown mechanisms. The actual FAO process occurs in the mitochondria and involves the formation of one acetyl-CoA molecule per cycle.(50) The acylated fatty acids keep entering the FAO cycle until a 2-carbon unit can no longer be formed. Each cycle also produces an NADH and FADH2 molecule that donate additional electrons to the ETCs.(50) With regards to producing ATP, this is a very efficient process as the full metabolism of a 16-chain fatty acid (palmitate) leads to 106 molecules of ATP -much more than via glycolysis or glucose substrate-only OXPHOS.[START_REF] Lochner | Fatty acid metabolism in the regulation of T cell function[END_REF] This may explain why in a glucosedeprived tumoural microenvironment, Treg cells utilise CD36 to maximise fat uptake as a fuel to meet their metabolic demands.[START_REF] Wang | CD36-mediated metabolic adaptation supports regulatory T cell survival and function in tumors[END_REF]

  the referenced study), which found Treg cells to produce increased glycolysis-and OXPHOS-related metabolites such as lactate, αketoglutarate, and succinate in comparison to Teff cells or naïve CD4 + T-cells.[START_REF] Li | TLR8-Mediated Metabolic Control of Human Treg Function: A Mechanistic Target for Cancer Immunotherapy[END_REF]. In comparison, another study utilising a proteomics approach, demonstrate slightly different results.[START_REF] Procaccini | The Proteomic Landscape of Human Ex Vivo Regulatory and Conventional T Cells Reveals Specific Metabolic Requirements[END_REF] Although resting human Treg cells (CD4 + CD25 + CD127 lo ) did indeed express a greater quantity of glycolysis-related proteins than Teff cells (CD4 + CD25 -), the Teff cells expressed a greater quantity of proteins related to the Krebs cycle and the mitochondrial electron transport chain (ETC) instead. Moreover, these proteomic differences did not translate into differing metabolic programs as the Treg cells consistently had a higher baseline rate of ECAR and OCR. Collectively, these datasets suggest that both glycolysis and oxidative phosphorylation are a fundamental part of the baseline tTreg cell metabolic program.In comparison, acutely activated human tTreg cells in the in vitro setting initially reduce their rate of ECAR and OCR in comparison to Teff cells.(43) However, after a ~1-week stimulation with anti-CD3 antibody, IL-2, and antigen-presenting cells (APCs), these Treg cells had significantly higher rates of ECAR and OCR than corresponding Teff cells. The activated Treg cells also took up more of the fluorescent glucose dye than Teff cells despite having similar levels of Glut1 expression.Moreover, the importance of FAO was demonstrated in these Treg cells too as the addition of palmitate to cultures further increased the OCR. Put together, these data suggested that tTreg cells were better equipped than Teff cells to meet their additional metabolic requirements through upregulating both glycolysis and FAO upon acute activation. (Figure1-2)

Figure 1 - 1 )

 11 The mTOR complexes play a central role in Treg cell metabolism and function.[START_REF] Chapman | mTOR coordinates transcriptional programs and mitochondrial metabolism of activated Treg subsets to protect tissue homeostasis[END_REF][START_REF] Pollizzi | Regulation of T cells by mTOR: the known knowns and the known unknowns[END_REF] (Their ability to sense upstream changes in the microenvironment and subsequently modulate Treg cell metabolism/function that makes them the highway of immunometabolic modulation.[START_REF] Pollizzi | Regulation of T cells by mTOR: the known knowns and the known unknowns[END_REF] mTOR signalling is facilitated through mTOR being linked with other adapter proteins in the form of mTORC1 and mTORC2 complexes.[START_REF] Pollizzi | Regulation of T cells by mTOR: the known knowns and the known unknowns[END_REF] The phosphorylation signalling cascade upstream of mTOR starts with stimulation of either the TCR complex or CD28.(62) (Figure1-2) This triggers sequential phosphorylation of phosphoinositide 3-kinase (PI3K), phosphoinositide-dependent kinase 1 (PDK1) and then protein kinase B (Akt). Akt subsequently inhibits the heterodimeric tuberous sclerosis complex (TSC1/2) to maintain Rheb protein activity.[START_REF] Huang | A complex interplay between Akt, TSC2 and the two mTOR complexes[END_REF] Finally, Rheb can directly and indirectly increase mTORC1 activation.[START_REF] Colombetti | Prolonged TCR/CD28 engagement drives IL-2-independent T cell clonal expansion through signaling mediated by the mammalian target of rapamycin[END_REF][START_REF] Manning | Rheb fills a GAP between TSC and TOR[END_REF] This promotes Treg cell immunosuppressive function, prevents the onset of autoimmunity and maintains tissue homeostasis.[START_REF] Chapman | mTOR coordinates transcriptional programs and mitochondrial metabolism of activated Treg subsets to protect tissue homeostasis[END_REF]

Figure 1 - 2 ) 2 )

 122 ) Upon Treg cell activation, the increase in mTOR signalling upregulated interferon regulatory factor 4 (IRF4) which further promoted genes for cellular growth, glycolysis, OXPHOS, fatty acid metabolism amongst others.(14) Moreover, transfecting tTreg cells with Rheb to upregulate mTOR signalling further increased glucose uptake and glycolysis.[START_REF] Li | TLR8-Mediated Metabolic Control of Human Treg Function: A Mechanistic Target for Cancer Immunotherapy[END_REF] All this data collectively indicated that promoting mTORC1 activity could also promote Treg cell activation, function, and support both the glycolysis and OXPHOS metabolic pathways.However, numerous others have demonstrated often divergent effects of mTOR signalling on Treg cells. For example, in a Treg-specific Raptor KO murine model (to inhibit mTORC1 activity), the mice demonstrated an increase in CD4 + FOXP3 + (Treg cells).(69) These Treg cells were still immunosuppressive during in vitro assays however they were unable to inhibit colitis development or the scurfy phenotype of in vivo murine models.Furthermore, from a metabolic perspective, the Raptor KO Treg cells had lower levels of ECAR and OCR. They also downregulated genes for cholesterol and lipid biosynthesis. In particular, cholesterol biosynthesis was demonstrated mechanistically as being important in promoting Treg cell activation, proliferation, and function. Collectively, these data demonstrated that constitutive in vivo mTORC1 signalling was important and that mTORC1 played a critical role in promoting lipogenic metabolism and Treg cell function.These findings were taken further by another group who developed two different murine models to delineate mTORC1 activity; Treg-specific KO of RagA/B GTPases (amino acid sensors) and Treg-specific KO of Rheb1/2.[START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF] As expected, mTORC1 activity was relatively reduced (not completely inhibited) upon TCR-stimulation in both models compared to wild-type mice. From a functional perspective, the cells demonstrated reduced in vitro immunosuppressive capacity -although FOXP3 expression was unaffected.(15) Put together, these data suggest that both a combination of mTORC1-related and non-mTORC1-related effects of Rag/Rheb protein signalling could be involved in Treg cell function.From a metabolic perspective, Treg cells from both models also had reduced rates of glycolysis and oxidative phosphorylation.(15) Indeed, the RagA/B KO mice also had fewer mitochondria, reduced mitochondrial function, and superoxide levels. The analysis of the transcriptomes of activated Treg cells of both KO models compared to wild-type Treg cells demonstrated upregulation in pro-inflammatory genes (eg. interferon-gamma, tumour necrosis factor-alpha) as well as a downregulation in genes for cellular proliferation (eg. myc, G2M checkpoint) and oxidative phosphorylation.[START_REF] Shi | Amino Acids License Kinase mTORC1 Activity and Treg Cell Function via Small G Proteins Rag and Rheb[END_REF] However, within these transcriptomic alterations, there was also a divergence in terms of the effects of metabolism. The RagA/B KO Treg cells upregulated genes involved with lysosomes and lipid metabolism and downregulated those specific for mitochondrial complexes. In comparison, the Rheb1/2 KO Treg cells downregulated genes for metabolising fatty acids and cholesterol without any changes in mitochondrial biosynthesis/function. Collectively, these data demonstrate that mTORC1 activity plays an important role in Treg cell activation, function, and increased metabolic demands (via glycolysis and OXPHOS). However, they also demonstrate that the RagA/B and Rheb1/2 proteins differentially modulate lipid metabolism and OXPHOS. Exactly how these divergent metabolic pathways are reconciled during Treg cell activation and increased mTORC1 activity is yet unknown.How does mTORC2 connect Treg cell metabolism and function?Concerning mTORC2, its role in Treg cells has been relatively less welldefined compared to mTORC1. mTORC2 activates Akt via phosphorylation at the serine residue (position 473).(70) (In response, Akt phosphorylates mTORC1 and the FOXO transcription factors.(70) Phosphorylation of the FOXO TFs propagates their subsequent degradation via ubiquitination. Hence, any experiments involving mTORC2 inhibition need to take into account that the downstream effects could involve reduced mTORC1 activity as well as increased FOXO levels. (Figure 1-Studies into the effects of mTORC2 modulation on Treg cells have generally demonstrated mixed effects on their phenotype and function.[START_REF] Zeng | mTORC1 couples immune signals and metabolic programming to establish T(reg)-cell function[END_REF][START_REF] Delgoffe | The kinase mTOR regulates the differentiation of helper T cells through the selective activation of signaling by mTORC1 and mTORC2[END_REF][START_REF] Charbonnier | Functional reprogramming of regulatory T cells in the absence of Foxp3[END_REF] In one study, the murine model of Treg-specific mTORC2 KO (Rictor-/-) demonstrated reduced frequencies of Treg cells in all peripheral tissue (except the thymus).(69) However, the phenotype of the Treg cells was unaltered (CTLA4, ICOS levels). They also remained immunosuppressive during in vitro assays. From a metabolic perspective, their mitochondrial function was also unaffected.These findings are in contrast to those of another group who developed murine models with a Treg-specific loss-of-function modification of FOXP3 with and without an additional Rictor KO (mTORC2 inhibition).(71) Through these single-and dual-mutated mice, they were able to delineate the relationship between functional FOXP3 + Treg cells and those without a functioning mTORC2 component.(71) The phenotypic analysis of Treg cells from the dualmutated mice demonstrated increased expression of markers such as glucocorticoid-induced tumour necrosis factor receptor (GITR) and ICOS. These cells had the capacity to secrete more IL-4, IL-10, and less IFN! compared to the FOXP3-mutation mice (IL-17A was unchanged). They were also more immunosuppressive during in vitro suppression assays compared to Tregs from the FOXP3-mutation mice. However, importantly, the suppressive capacity of the FOXP3-only mutated Treg cells improved with rapamycin pretreatment. Collectively, these data suggested that mTORC2 inhibition promoted Treg cell activation status, Th2-like differentiation, and immunosuppressive function. From a metabolic perspective, the Treg cells of the FOXP3-mutation mice upregulated glycolysis and OXPHOS (as demonstrated via increased ECAR and OCR respectively).(71) This was reflected by upregulation of enzymes and metabolites involved in glycolysis and the Krebs cycle. Furthermore, the inhibition of glycolysis in these cells reduced their secretion of IFN!, IL-4, and improved their in vitro immunosuppressive function. However, this metabolic reprogramming was attenuated in Treg cells from mice with the additional Rictor-KO (mTORC2 inhibition). Put together, these data suggest that FOXP3 and mTORC2 have opposing effects on Treg cell phenotype, metabolism and function.(71) However, it is yet unknown how this relationship is affected by other metabolites such as amino acids (which sustain mTORC1 activity) or fatty acids.

  ) Both of these cofactors utilise different signalling pathways to modulate Treg cell functions. The proportional increase in AMP leads to adenosine monophosphate kinase (AMPK) phosphorylation and activation by liver kinase B1 (Lkb1). This Lkb1 enzyme is crucial for Treg cell metabolism and function.(65) The activated AMPK then inhibits Rheb and phosphorylates Raptor (mTOR adapter protein) to inhibit mTORC1 activity.(73) Interestingly, activated AMPK also in parallel, inhibits acetyl-CoA carboxylase (ACC) to prevent fatty acid synthesis.(74) Although this latter mechanism has not been demonstrated in Treg cells, it may be a potential metabolic adaptation during a low ATP state to divert available intracellular lipids towards acetyl-CoAgenerating FAO instead.

A

  further question is whether HIF1α can modulate Treg cell stability. This was demonstrated using a Treg-specific model of von Hippel-Lindau (VHL) KO to study the effects of HIF1α overexpression in Treg cells only.(81) These mice did not survive beyond 6-11 weeks and had increased Th1 infiltrates in all tissues. Interestingly, the Treg cells from this model had no alterations in their baseline activation or functional phenotypes (eg. CD25, CTLA4, CD69). However, after 48 hours of anti-CD3/28-based activation, the Treg cells secreted significantly more IFN!, IL-4, IL-10, and other chemokines than controls (crucially no IL-2). From a functional perspective, the adoptive transfer of these cells into a RagKO murine colitis model found that the Treg cells lost FOXP3 expression after 8 weeks, there was an accumulation of IFN!-producing Th1 cells and thus, the development of colitis was not prevented. Considering the data discussed in the previous paragraph, it is plausible that VHL KO-Treg cells were more susceptible to degradation of FOXP3 and the promotion of the effector Th1 program through binding to the HRE regions of the IFN! gene.

- 1 )

 1 These optimised Treg cells could outcompete Teffs metabolically in the allograft and thus, inhibit their survival and suppress their effector activity.(34) Treg cells also have the potential for Th17 plasticity so combined immunometabolic optimisation could prevent Treg cell interconversion in an inflammatory allograft.(32, 101) A further advantage of optimising Treg cell activity is to exploit their bystander suppressor functions and thereby reduce the effector functions of other pro-inflammatory cells.(102)

2 )Figure 1 - 4 )

 214 the Hypoxia section, the overall effects of hypoxia signalling in Treg cells are unclear. In tTreg cells, HIF1α seems to reduce (or not affect) FOXP3 expression, augments glycolysis, and induce effector activity. In comparison, HIF1α induces FOXP3 in iTregs. (Figure 1-In terms of manipulating tTreg cells during cell therapy or targeting allograft resident tTregs, increasing either local oxygen delivery or reducing HIF1α levels could be therapeutic approaches. (Indeed, non-bloodbased oxygen carriers have already been investigated in normothermic machine perfusion of the liver.(114) The livers were more efficient at taking up the stored oxygen from these carrier molecules than haemoglobin. With regard to HIF1α levels, a range of HIF1α-targeting agonists/antagonists are either in trials or clinically-available.(115-117) These could either be systemically administered to patients or utilised in GMP culture protocols or machine perfusion technologies. c. Nuclear Receptors Nuclear receptor agonism could more precisely target Treg cell metabolism than oxygen-based or mTOR modulation. For example, PPARβ/! agonism has been shown to jointly promote FOXP3 expression, FAO, FASyn, and mitochondrial function in Treg cells. Indeed, PPAR! agonists such as thiazolidinediones are clinically available and have been used for many years in diabetes already. (118) Many patients with kidney allografts will be used to taking them too.

Figure 1 - 1 Figure 1 - 2 Figure 1 - 3 Figure 1 - 4 FigureFigure 1 - 1 :

 1112131411 Figure 1-1

Figure 1 - 2 :

 12 Figure 1-2: Illustrating the complex and multiple links between Treg

Figure 1 - 3 :

 13 Figure 1-3: Illustrating the range of variables that could explain

Figure 1 - 4 :

 14 Figure 1-4: Demonstrating how regulatory T-cells can be

  Hence, a comprehensive understanding of Treg cell heterogeneity is needed to safely and effectively exploit their therapeutic potential. As such, we consider it timely in this review to outline established and novel data regarding Treg heterogeneity and discuss future lines of inquiry. In solid organ and bone marrow transplantation (SOT and BMT, respectively), Treg cells have been identified as modulators of both T-cell-mediated and antibodymediated rejection.(127, 128) However, our understanding of the underlying mechanisms is complicated as effector T cells (Teffs) can adopt the Treg-like phenotype and functions. In reverse, Treg cells can alter their phenotype and functions to adopt a Th17-like effector cell profile too. It is important to understand these alterations as they can impact the regulatory balance in the graft. (129) A further limitation is that much of our understanding to date originates from in vitro experiments and in vivo murine (or non-human primate; NHP or swine) models. (130-132) It is only in recent years through clinical trials can the in vivo relevance of these mechanisms to humans undergoing SOT be deciphered. These trials mainly involve in vitro expansion of autologous Treg cells under Good Manufacturing Practice (GMP) conditions utilising various pharmacological agents that promote their differentiation, expansion, stability and, function. (133) Considering this recent progress, we consider it timely to outline the recent clinical trials in SOT with a focus on safety.

Figure 2 - 1 )Figure 2 - 2 )

 2122 ) Helios is a redundant transcription factor part of the Ikaros family in Treg cells whereas Nrp-1 is a receptor for class III semaphorins and modulates Treg interactions with dendritic cells(135, 136) and attenuates inflammatory colitis and promotes antitumour immunity[START_REF] Shevach | tTregs, pTregs, and iTregs: similarities and differences[END_REF] 137). However, Helios/Nrp-1 on their own cannot categorise tTregs and pTreg cells in humans.(138, 139) A further way of identifying Treg cells is by classifying all CD4 + T-cells on the basis of CD45RA and FOXP3 expression into 3 phenotypically and functionally distinct subpopulations.(9) (These subpopulations include naïve/resting Treg cells (CD45RA + FOXP3 + ), activated/effector Treg cells (CD45RA -FOXP3 +++ ) and FOXP3 + effector non-Treg cells (CD45RA -FOXP3 + ). The activated/effector Treg cells are more proliferative and functional as evidenced by higher expression of Ki67 and CTLA4 respectively.The FOXP3 + non-Treg effector cells are not immunosuppressive and produce cytokines such as IL-2, Interferon-Gamma (IFN-y) and IL-17. The functional role of FOXP3 in the naïve and activated Treg cells is further reinforced by the finding that their FOXP3 regions are mostly demethylated in comparison to that of the non-suppressive FOXP3 + effector cells. However, both FOXP3 and the Treg-specific demethylated region (TSDR) are intracellular entities so challenging to adapt to cellular therapy applications in patients. Hence, identification of novel cell-surface markers such as sialyl Lewis x (CD15s) whose expression is strongly correlated with a highly suppressive effector Treg are necessary.[START_REF] Miyara | Combination of IL-2, rapamycin, DNA methyltransferase and histone deacetylase inhibitors for the expansion of human regulatory T cells[END_REF] 140) This can also facilitate the exclusion of non-suppressor FOXP3 + cells, which have the potential for effector function.(9) A further subdivision has been through identifying chemokine receptors to split effector Treg cells (CD25 hi CD127 lo CD45RO + ) into co-existing T-helper (Th)-like Treg cells; Th1 (CXCR3 + ) , Th2 (CCR4 + ), Th17 (CCR4 + CCR6 + ) and Th22 (CCR4 + CCR6 + CCR10 + ) Treg cells. (125) (This was matched by their cytokine release profile as although both Th1-like and Th17-like Treg cells produced IL-10, each subset respectively produced IFN-γ and IL-17 only. Interestingly, their Th2like and Th22-like counterparts did not produce any IL-10 or IL-4 or IL-22, the lack of the latter two would be consistent with the anti-inflammatory function of Treg cells. These findings are important for two key reasons; (1) homing potential and (2) like-forlike interaction. In terms of homing potential, the ability to isolate, modulate and infuse Treg cells with specific chemokine markers would optimise their ability to home towards specific sites of alloreactive cell contact (eg. allograft, lymph nodes). (141, 142) In terms of like-for-like interaction, the selection of similar Th-subtype Treg cells could optimise their potential to modulate similar Th-subtype effector responses. (142, 143) In parallel with the above classifications, other authors have adopted similar approaches to investigate Treg cell subpopulations using markers such as; CD161, CD45RO, CD39, CD49d, Programmed Death-1 (PD1), T-cell immunoreceptor with Ig and ITIM domain (TIGIT) and T-cell immunoglobulin and mucin domain 3 (TIM-3) to elucidate subpopulation-level heterogeneity. (125, 144-148) (Figure 2-2) Establishing mechanistically-relevant links between these subpopulations, their markers, and FOXP3 (or other transcription factors) is further complicated by the fact that non-FOXP3 expressing T cells can be induced or genetically-modified to express FOXP3 and adopt Treg-like functions too. (149-152) This is important clinically as increased FOXP3 transcripts and Treg cells have been identified in patients developing operational tolerance or reduced rejection after SOT. (153, 154) Putting all this together, there is a need to delineate Treg heterogeneity using novel immunophenotyping approaches.Understanding Treg biology via novel technologiesThe adoption of novel technologies such as Cytometry by Time of Flight (CyTOF) and Single Cell RNA Sequencing (scRNAseq) has uncovered further Treg heterogeneity.(123, 124,(155)(156)(157) In one study investigating Treg cells (CD4 + CD25 + CD127 lo ) from peripheral blood, 22 novel subpopulations were identified using a range of markers such as HLA-DR, CD62L, CD27, ICOS, etc (in addition to the markers discussed above).(155) This has been studied further by other teams in a range of transplant-related (renal/liver) and non-transplant-related settings respectively. (123, 156-158) In the case of liver transplantation, a 22 marker panel identified the presence of significantly more Treg cells (CD4 + CD25 + FOXP3 + ) in their 'tolerant' cohort post-transplant compared to controls on immunosuppression. (156) However, it was a novel non-Treg T-cell subset (CD4 + CD25 + CD5 + CD38 -/lo CD45RA + ) that correlated specifically with tolerance in these children post-transplant. Using a different technology, Zemmour D et al. (2018) performed scRNAseq on human and murine Treg and effector T cells and demonstrated distinct transcriptomic profiles between both cell types. (124) In this study, human Treg and effector cellswere sorted as CD4 + CD25 + CD127 lo and CD4+CD25-CD127 hi respectively whereas their murine counterparts from FOXP3 gfp mice were sorted as CD4 + TCRβ + GFP + or CD4 + TCRβ + GFP -instead. With their murine cells, they could reliably differentiate between Treg cells and effector T cells by identifying transcripts specific to both -such as their origin (Helios), function (FOXP3, IL-2, CTLA4, OX40, GITR), and others which may be related to metabolic processes but require further study (eg. folate receptor, GPR43 and a mitochondrial crista protein). Importantly, all the genes for these transcripts are targets of FOXP3 transcription factor; thus further demonstrating its critical role in Treg function.

(Figure 2 - 3 )Figure 2 - 3 )

 2323 126, 134, 149) This is a consistent finding based on data demonstrating that either genetic or pharmacological induction or inhibition of FOXP3 (in humans and mice) led to critical alterations in the function of the manipulated cell -no matter whether this was initially a bona fide Treg or an effector T-cell. (149) However, although the Treg signature is defined mostly in the thymus (160), this begs the questions; what contributes to the remainder of the tTreg transcriptomic signature and how are both tTreg transcriptomic signature and function maintained in the graft/lymphoidal microenvironment? (This is important as FOXP3 expression alone is insufficient to maintain a stable Treg transcriptomic signature and function. (161) FOXP3 interacts with numerous transcription factors such as Nuclear factor of activated T cells (NFAT), Activator protein-1 (AP-1) and Forkhead box (FOXO) amongst others. (162-164) These complexes bind to the conserved noncoding sequences (CNS1-3) on the FOXP3 locus to promote particular transcripts such as il2ra, ctla4, etc indicative of the 'Treg program'. (149, 161, 165) (Figure 2-3) This locus of interest has been further expanded due to the discovery of the CNS0 region and recent work on super-enhancers. (166) This was mainly based on Satb1 (chromatin organising protein), whose deficiency was linked to suboptimal Treg-specific super-enhancer activation and thereby negatively altered the initial development of tTregs. (166) Taking all this data into account, the focus on FOXP3 is likely rather reductionist as experiments involving transfection of FOXP3 in naïve T-cells have demonstrated that only a portion of the bona fide Treg signature was induced (eg. ctla4, il2ra, nrp1, tnfrsf18). (160, 167) Some of these genes are FOXP3-dependent and some not. (167, 168) They can also be influenced by TCR-, IL-2-and TGF-b-based signalling cascades. (160) (Figure 2-3) These data indicate that the overall Treg signature is a complex interplay between FOXP3 coregulatory genes, regulators upstream of FOXP3 and parallel pathways (TCR, IL-2, TGF-b). (160) This may explain why FOXP3 -Treg cells continue expressing mainly Treg-like transcriptional signatures in spite of the lack of FOXP3 as there could be underlying additional mechanisms either intrinsic to Treg cells or the microenvironment. From a transplant perspective, it is important to understand how these regulatory elements can mechanistically influence Treg cell and effector T cell function in the graft/lymphoidal tissue. One of the mechanisms in promoting FOXP3 expression may involve epigenetic regulation. (165, 169) Stable FOXP3 expression is dependent on FOXP3 protein and histones acetylation and the ability of FOXP3 and its coregulatory transcription factors to bind to the Treg-specific demethylated region (TSDR) -found on CNS2 of the FOXP3 locus. (165, 170) (This region is partially demethylated in iTreg cells (thus potentially explaining their instability) and fully methylated in effector T cells. (35, 134, 135) However, although the continual demethylation status may be necessary to maintain FOXP3 expression, the function of Treg cells is in parallel also dependent on CpG demethylated patterns on other genes such as il2ra, ikzf4, and ctla4 amongst others. (161) These patterns are optimally modulated in the periphery, which suggests that recently-emigrated tTreg cells still need to maturate -again due to a pre-set Treg-intrinsic program or tissuespecific immunoregulation. Unsurprisingly, the loss of demethylated patterns and FOXP3 expression negatively impacts on Treg function. (171) Considering the role of FOXP3, its stability and expression is a key aspect during the manufacturing of the Treg cell product. FOXP3 expression can be maintained by utilising DNA methyltransferase inhibitors (DNMT; azacytidine) and histone deacetylase inhibitors (HDAC; voronistat) during in vitro Treg expansion.(25)DNMT inhibition aims to maintain the demethylated patterns as discussed above whereas HDAC inhibition aims to maintain the acetylation status of histones and perhaps of FOXP3 and thereby optimise its expression.[START_REF] Miyara | Combination of IL-2, rapamycin, DNA methyltransferase and histone deacetylase inhibitors for the expansion of human regulatory T cells[END_REF] 172) Using both DNMT/HDAC inhibitors (azacytidine and voronistat), Treg function was enhanced in vitro and in vivo within a xeno-graft versus host disease (xeno-GvHD) model.[START_REF] Miyara | Combination of IL-2, rapamycin, DNA methyltransferase and histone deacetylase inhibitors for the expansion of human regulatory T cells[END_REF] Azacytidine targets the ten-eleven translocation enzyme (TET) family (specifically TET2/3) to optimise the stability of FOXP3 expression.(173, 174) The importance of this approach was also illustrated byTao R et a.. (2007) who induced tolerance to cardiac and islet allografts in murine models through administering a combination of rapamycin and a pan-HDAC inhibitor (trichostatin-A). (175) However, these data involved pan-inhibition of their relevant enzymes so did not reflect the effects of modulating their numerous isoforms. This is important as divergence in the inhibition of different isoforms and their effects on Treg function is known.(176, 177) The authors also used an HDAC9 KO murine model to demonstrate a boost to the quantity of Treg cells and their function. Similar effects have also been demonstrated studies focussing on inhibition of HDAC3/10/11 either pharmacologically or via enzyme KO or Tregspecific enzyme KO. (178-180) Putting this together, although these studies aim to elicit the effects of targeted enzyme isoform modulation, it is just as important to delineate the mechanisms underlying interactions between these isoforms and FOXP3 (or other transcription factors) and how this influences Treg phenotype/function. (179) The specificity of any epigenetic modulation is important to ensure that only the intended effect on Treg phenotype/function is achieved and any off-target effect is minimised.

  to contextualise this weaning period with regards to other published data. In this study, weaning started at 6 months post-transplant and thus, took place over the next 12 months. In another adult cohort undergoing a liver transplant, 77 patients underwent immunosuppression weaning over 15 months.(185) 10 of them successfully remained off immunosuppression for 1 year without clinical evidence of rejection and 9 continued as such for 2 years altogether. In comparison, in another adult cohort, 102 patients who were already at least 3-years post-liver transplant were weaned off immunosuppression over 6-9 months.(186) The 33 patients who successfully remained off immunosuppression for 1 year after withdrawal had been on immunosuppressants for longer, avoided receiving calcineurin inhibitors and were older. A further study involving 20 paediatric liver transplant recipients was able to successfully withdraw immunosuppression in 12 patients over a period of at least 9 months.(187) These 12 patients remained off immunosuppression for on average 3 years (median 35.7 months). All these data indicate that the weaning period employed by Todo S et al (2016) is not in an unfamiliar range to that employed by other studies. Most importantly, we must emphasise that there is a lack of a robust evidence base regarding optimum weaning times for patients on immunosuppression regimens. This is being complicated further by Treg-based cellular therapies. Five patients demonstrated in vitro donor-specific nonresponsiveness and two were hyporesponsive, thus implying a degree of donor-antigen specificity of their co-cultured cell product. However, from a mechanistic perspective, not only did the co-cultured cells inhibit donor-specific responses, but at more concentrated doses, third-party tolerogenic responses were also elicited during mixed lymphocyte reaction (MLR) experiments. It is also important to note that the specific role of Treg cells in mediating the reported hypo-or nonresponsiveness cannot be concluded as patients received an infusion containing all cultured cells rather than Treg cells only. Hence, the possibility of other mechanisms such as indirect Treg-based immunoregulation (eg. via regulatory dendritic cells or macrophages) or multi-cellular immunoregulation (eg. anergy induction, infectious tolerance, bystander suppression) cannot be excluded. (188-190) Nevertheless, these data suggest this co-cultured cell product was more potent towards donor-specific antigens than third-party cells. The underlying mechanisms of operational tolerance induced by this cell therapy are yet undiscovered.

1 )

 1 This line of research is based on data demonstrating that donor-reactive Treg cells better suppress the alloreactive effector T-cell response than polyclonal Treg cells.(191) It is important to elucidate and exploit this mechanism in humans as the extent of product purity and alloantigen specificity could have implications for therapeutic potency and infused cell numbers.[START_REF] Macdonald | Alloantigen-specific regulatory T cells generated with a chimeric antigen receptor[END_REF] 192, 193) The teams from Nanjing (China) and Kings College Hospital (United Kingdom) are focussing on polyclonal Treg cells in their trials (NCT01624077 and NCT02166177). In comparison, one arm of the LITTMUS trial (NCT03577431, NCT03654040) at Massachusetts General Hospital (MGH) will infuse alloantigen-specific Treg cells (CD4 + CD25 + CD127 lo ) with exposure to costimulatory blockade at doses of 2.5-500x10 6 cells. Whereas, the second arm of the LITTMUS trial at University of California: San Francisco (UCSF) will infuse alloantigen-specific Treg cells (CD4 + CD25 + CD127 lo ) without exposure to costimulatory blockade at doses of 100-500x10 6 cells instead. In comparison, two other trials from UCSF (ARTEMIS and dELTA trials) are also producing donor alloantigen-specific Treg cells however their Treg cells will be administered in patients who are 2-6 years or 12 weeks' post-transplant respectively. By comparing these latter two trials one could generate critical hypotheses regarding the optimal timepoints for Treg infusion. All these data collectively would also improve our current understanding of post-transplant immunoregulatory and tolerance induction mechanisms.

  crucial for Treg cell stability and an incompletely demethylated FOXP3 gene can lead to the conversion of Treg cells into effector cells.(126, 149, 160, 165) However, as the demethylation status of Treg cells in this trial was high, one would expect their efficiency close to those of bona fide Treg cells. This is based on our discussion earlier regarding Treg subpopulations of which, the naive Fr I and activated Fr II subpopulations have been shown to be 85-100% demethylated.(9) Other mechanistic experiments in this trial also focussed on the expression of Treg cells markers of function (CTLA4, CD62L), homing (CXCR4) and differentiation status (CD45RO). All functional markers were significantly increased post-expansion and more than 80% CD4 + cells were CD45RO + , thus indicating their Treg cells were mature at the end of the expansion period. All these data are further enhanced by the data from in vitro suppression assays confirming superior Treg suppressive capacity at the end of the expansion period. Putting all this together, this trial reported no cases of opportunistic infections or rejection after the infusion of polyclonal Treg cells in patients undergoing de novo kidney transplantation. The next steps will be to demonstrate the in vivo survival and function of these Treg cells.TASK TrialIn comparison, the preliminary TASK trial from UCSF focussed on utilising polyclonal Treg cells to modulate subclinical inflammation in the renal allograft. (182) This is an important study question as subclinical inflammation due to its chronic low-grade nature is not promptly diagnosed and can facilitate delayed allograft dysfunction. The authors recruited 3 patients who demonstrated such inflammation (Banff i-and t-<2) (194) on their 6-months post-transplant biopsy. Patients were already on MMF, tacrolimus and, prednisolone when they had their Treg cells acquired. Importantly, from a safety perspective, the authors reported no SAEs as a direct result of their Treg product and there were no reported infections or graft dysfunction during the 1year follow-up period. The patients received in vitro expanded autologous Treg cells at an average dose of 320x10 6 cells. These cells were isolated as CD4 + CD25 + CD127 lo via flow-activated cell sorting (FACS). They were expanded for 14 days using anti-CD3/anti-CD28 activation beads, IL-2 and using deuterated glucose. Their expanded Treg cells were >97% CD4 + and >93% FOXP3 + with extremely low (<0.37%) of contaminating CD8 + cells. It is already clear that there are numerous differences between this trial and the TRACT trial above in regards to pathology, Treg dosing, isolation and expansion protocols.[START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF] 182) A novel development in this trial was the use of deuterated glucose to facilitate in vivo infused Treg tracking. This approach was previously used by the group in a Treg-cell therapy trial in type 1 diabetes.(195)

1 )

 1 ) A key aspect of this consortium was that different centres adopted a common immunosuppression protocol (tacrolimus, mycophenolate and 3 months of steroids) with the critical difference being the type of cell therapy product utilised. The centres at Charité (Germany), Oxford & Kings College London (UK), UCSF and MGH (USA) focussed on developing different forms of Treg cell therapies. (See Table2-The process of conducting this study also highlighted the novel opportunities and challenges associated with manufacturing and monitoring the use of these different cell therapies. These include aspects such as getting regulatory authority approvals, setting up a GMP facility, patient recruitment, immunomonitoring and analysing large-scale datasets. Although these trials were not powered to test efficacy, it is noteworthy that the 12 patients treated in the UK with polyclonal Treg cells did not experience acute rejection as per biopsy.(196) The recent study reports also suggest that patients were able to reduce their immunosuppression and most importantly, that 'cell therapy…is safe'.(196) We await the publication of these datasets. Initial immunomonitoring analysis was presented at the recent American Transplant Congress 2019.(197) All of these data will be crucial in enhancing our early understanding of Treg-based cell therapy in clinical kidney transplantation. A positive advance already is that the Oxford group is undertaking the Phase II stage (TWO Study), which will aim to test the efficacy of Treg cells in preventing graft rejection.(198) The results of this trial and those of other centres are awaited with great interest.(196)

  This is supported by murine data demonstrating Th17 cells as key players in the promotion of allograft rejection.(101, 200) Treg cells when stimulated in the presence of pro-inflammatory cytokines such as IL-1, IL-2, IL-15, IL-21, IL-23 alongside allogeneic monocytes, can upregulate the Th17 lineage transcription factor, retinoic-acid-receptor related orphan receptor gamma (RORγt) and secrete IL-17.(201) Another pathway involving Treg-Th17 cross-talk lies in the fact that both originate from naïve CD4 + cells in the first place.(202) However, their subsequent differentiation pathway is determined by the presence of TGF-β without IL-6 (for Tregs) or with IL-6 (for Th17) in the microenvironment respectively.(202) This potential of the microenvironment to convert Treg cells into rejection-propagating Th17 cells could dictate the time point at which to infuse Treg cells. In theory, Treg cells therapy could be provided proactively (i.e. before patients develop rejection) or reactively (i.e. given to patients with diagnosed acute or chronic rejection). However, it is unknown as to whether Treg cells can induce tolerance in a microenvironment already infiltrated by effector T-cells. In one murine study, it was shown that Treg cells can prevent the initial activation of only resting effector T-cells.(203) However, in a different study involving RAG knockout mice, the adoptive transfer of Treg cells was unable to prevent the onset of autoimmunity in those mice that had previously been administered effector T-cells.(5) Moreover, effector T-cells can be resistant to the direct effects of Treg cells.(5) If these findings were directly translated into the clinic, it would mean providing Treg cell therapy proactively to prevent the onset of rejection in the first place. However, even then the question would be how to stratify patients. Bystander Suppression A potential problem with Treg cell therapy could arise as Treg cells once activated can perform their suppressive function in a non-antigen specific manner.(102) This effect known as 'bystander suppression' was identified in transgenic murine models whereby antigen-specific CD4 + CD25 + T-cells initially required contact with a complementary epitope for TCR-mediated activation.(102) However, upon being activated these cells were able to suppress effector CD4 + T-cells specific for third-party antigens too. This effect has also been demonstrated in an in vivo murine model of allogenic skin transplant.(204) If indeed this phenomenon is replicated in our patients too, there could be implications with regards to modulating antigen-specific rejection whilst maintaining immunity towards pathogens and potential tumoural neoepitopes.

(

  2016).(205)Another unaddressed issue is whether genetic modification of Treg cells could augment their in vivo modulation of graft rejection. A full discussion of genetic modification has been beyond the scope of this review however, we reference here some key articles from teams that have developed FOXP3-overexpressing Tregs, T-cell-receptor-transgenic; TCR-Tg Treg and chimeric-antigen-receptor-specific; CAR-Tregs.[START_REF] Macdonald | Alloantigen-specific regulatory T cells generated with a chimeric antigen receptor[END_REF] 152, 206) With regards to Treg heterogeneity, although novel technologies may facilitate deeper comprehension of Treg cells and their subsets, the adoption of these technologies for routine clinical use is impractical and financially unviable. Hence the key question is, how deep do we need to go? It may be that we only utilise novel and expensive technologies for pre-clinical experiments to identify and validate the Treg program for optimal in vivo activity. This data could then be reversed engineered and adapted to clinically-available assays to facilitate use in larger multi-centre trials.

Figure 2 - 1 Figure 2 - 2 FigureFigure 2 - 1 :

 212221 Figure 2-1

Figure 2 - 2 :

 22 Figure 2-2: Different ways of dissecting Treg cell subpopulations

Figure 2 - 3 :

 23 Figure 2-3: Key elements involved in Treg cell behaviours

  a murine model of dietary SCFA supplementation (heterogeneous SCFAs or uniquely acetate, propionate or butyrate), an expansion of colonic Treg cells was identified.(209) This was regardless of mice being germ-free (GF) or specific pathogen-free (SPF). These cells were Helios+ and secreted more IL-10 too. These observations were also replicated in vitro as exposing colonic Treg cells to 100µM propionate upregulated the genes for FOXP3, TGFβ and IL-10 within 24 hours.Although these cells were reported in the original manuscript as being more immunosuppressive against responder effector T cells in an in vitro assay, an important experimental caveat was that the SCFAs were added with both Tregs and Teffs in situ. Hence, although SCFAs promoted the local expansion of thymicoriginating Treg cells, it was not proven that they were of superior immunosuppressive capacity (despite of increased IL-10 secretion). From a mechanistic perspective, these effects were reduced in mice with free fatty acid receptor 2 (FFAR2; GPR43) KO model. FFAR2 expression levels were already highest in the colon in the wild-type mice thus, further indicating its role in mediating intracellular signalling triggered by SCFAs. Finally, culturing 100µM of propionate with colonic Treg cells of FFAR2+/+ mice (vs KO) was also associated with reduced colonic expression of histone deacetylases 6 and 9 (HDAC6/9). HDAC9 KO specifically in Tregs/Teffs has been shown in two separate studies to promote FOXP3+ Treg cells with increased CTLA4 expression and immunosuppressive capacity.(220, 221) Collectively, these data demonstrated the capacity of propionate to promote colonic expansion of tTreg cells

  a murine model of dietary SCFA supplementation (200mM acetate/propionate/butyrate over 6 weeks), another group identified the induction of renal fibrosis and hydronephrosis. (214) The histological data demonstrated increased infiltration of CD4+ T cells with Th1, Th17, and Treg cell (FOXP3+ and IL-10+) profiles. This phenomenon was unaltered in GPR41/43 KO mice. However, it was inhibited in mice with TCR KO as well as when IFN! and IL-17A neutralising antibodies were added. Collectively, these data demonstrated that SCFAs could modulate T-cells (directly or indirectly) in a receptor-independent manner to promote Th1/17/Treg-like cytokines, mTORC1 phosphorylation, and develop renal fibrosis.A recent study elucidated the in vitro effects of butyrate (0.00625-1mM) on intestinal CD4+ T cells (as opposed to splenic/lymphoidal or circulatory cells in most studies).(213) This was important as gut-resident cells are less likely to be antigeninexperienced and more likely to be of effector memory type. In this work, they found butyrate (up to 0.5mM) reduced the proliferation, IL-2 responsiveness (CD25), and activation status (HLA-DR) of acutely stimulated gut-resident CD4+ T cells. FOXP3 expression increased to 0.5% amongst the CD4+CD25+ T cells. These cells also demonstrated a dose-dependent reduction in IFN! and IL-17A secretion. Put together, these data suggested that butyrate did not induce a Treg-like phenotype in gutresident CD4+ T cells.Immunosuppressive IFNy+IL-10+IL-17A+ CD4+ T cellsCompared to the Treg-induction work above, another group showed how SCFAs combined with cytokines (Signal 3) promoted Th1 and Th17-like cells from naïve CD4+ T cells. (224) In Th1-promoting conditions 4, 12) and Th17promoting conditions TNF⍺,, the addition of acetate(1-20mM), propionate (0.1-1mM), or butyrate (0.125-0.5mM) significantly increased the secretion of IFN!, IL-10 and IL-17A in a dose-dependent manner.Intriguingly, when injected into RAG KO mice, these cells did not induce as much colitis as the control cells. Histological assessment identified upregulation of both IL-17+ and FOXP3+ cells in the colon. Moreover, in an in vitro suppression assay, the acetatecultured CD4+ T cells highly inhibited the proliferation of responder T cells. This effect was IL-10-dependent. With regards to FOXP3, it was only increased in the Th17 condition with reduced anti-CD3 stimulation (1µg/ml instead of 5). Both acetate and propionate led to significant upregulation of ROR!t-FOXP3+ and ROR!t+FOXP3+ cells. A similar effect was also shown in a separate study involving mucosal/lymphoidal T cells with propionate/butyrate that upregulated FOXP3+ and increased IL-17A secretion.(225) Collectively, these data demonstrated the capacity of SCFAs to promote CD4+ T cells with both inflammatory and regulatory profiles.

  a. If so, what is their phenotype? b. Do they secrete IL-2? c. Are they immunosuppressive? 2. Do SCFAs induce iTreg cells without TGFβ in situ or pre-exposed APCs? 3. How do SCFAs polarise the differentiation of naïve CD4+ T cells to cells with both regulatory and effector phenotypes? 4. Apart from HDACi and mTOR, do SCFAs act via any other cellular surface or intracytoplasmic mechanisms on CD4+ T cells? a. Metabolism: Is FAO implicated? b. Intracytoplasmic signalling: Are STAT3/STAT5 implicated? Akt? c. How do SCFAs perturb the transcriptome of Treg/Teff cells? d. What are the gene regulatory networks? PPAR!-led? Chapter 4 -Aims & Objectives This thesis will address the following aims and corresponding objectives: 1. How do the transcriptional programs of acutely activated naïve Treg cells perturb their differentiation trajectories in an in vitro expansion protocol? a) Define the proteo-transcriptomic landscape and gene regulatory networks of peripheral circulatory CD4+ T cell subpopulations b) Dissect the pro-inflammatory effector non-Treg FOXP3 lo cell population c) Delineate the differentiation trajectories and the regulons of CD4+ T cell subpopulations starting from their naïve subsets d) Define the proteo-transcriptomic landscape of in vitro expanded Treg/Teff cells and their subpopulations e) Delineate the effects of methylprednisolone and rapamycin on the transcriptomes and gene regulatory networks of expanded Treg/Teff cell subpopulations f) Integrate the transcriptomes of unexpanded and expanded CD4+ T cells from the same donors to delineate the differentiation trajectories of naïve CD4+ T cells and their regulons.

2 .

 2 How does butyrate modulate the differentiation, proliferation and functions of naïve Treg and Teff cells? a. Identify the optimal butyrate dose in cultures of naïve Treg/Teff cells b. Study the phenotypic and functional changes in acutely activated Treg/Teff cells with butyrate c. Clarify the role of FAO in promoting or inhibiting the above perturbations d. Delineate the capacity of butyrate to modulate Treg/Teff differentiation by perturbing Signal 1 and/or 2 pathways in CD4+ T cells e. Delineate the proteo-transcriptome and gene regulatory networks of butyrate-cultured Treg/Teff cellsOriginal Article 1Single-cell proteo-transcriptomics defines the heterogeneity and differentiation trajectories of regulatory T cellsIntroductionRegulatory T (Treg) cells play an important role in preventing autoimmunity and promoting allograft tolerance.(1, 5, 12) Several teams are presently implicated in activating and expanding these cells in vivo (via low-dose interleukin-2; IL-2) or in vitro via expansion protocols.[START_REF] Sanchez-Fueyo | Applicability, safety, and biological activity of regulatory T cell therapy in liver transplantation[END_REF][START_REF] Mathew | A Phase I Clinical Trial with Ex Vivo Expanded Recipient Regulatory T cells in Living Donor Kidney Transplants[END_REF][START_REF] Sawitzki | Regulatory cell therapy in kidney transplantation (The ONE Study): a harmonised design and analysis of seven non-randomised, single-arm, phase 1/2A trials[END_REF](231)(232)(233)(234) To exploit Treg cells clinically, it is important to delineate their underlying heterogeneity and how this influences their differentiation trajectories. Current in vitro expansion protocols provided a unique setting to study these phenomena. CD4+ Treg cells express CD25 (IL-2 receptor alpha chain) and the Forkhead Box Protein 3 (FOXP3) transcription factor. (133, 149, 235, 236) Due to their underlying heterogeneity, certain research groups focus on CD45RA+ Tregs (naïve variety), CD25++ type, and/or excluding the CD127hi (IL-7 receptor) cells. (1, 20, 182, 233, 237) In the past, the CD4+CD25++CD45RA+ Treg cells were found to consistently express high levels of FOXP3, cytotoxic T-lymphocyte antigen-4 (CTLA4), secrete very low levels of pro-inflammatory cytokines, and be the most immunosuppressive. (233) This work was taken further by the identification of 3 functionally heterogenous Treg cell subpopulations that were described based on CD45RA, CD25 and FOXP3. (9, 140) (Supplementary Figure 5-1A) The naïve Treg cells (FOXP3 lo CD45RA+CD25+; Fraction I; Fr I) differentiated into both activated Treg cells (FOXP3 hi CD45RA-CD25++; Fraction II; Fr II) and/or effector non-Treg cells (FOXP3 lo CD45RA-CD25+; Fraction III; Fr III). The activated Treg cells were the most immunosuppressive however the effector non-Treg cells were nonimmunosuppressive and secreted pro-inflammatory cytokines such as IFN!, IL-2, and IL-17A. However, as outlined below, recent data suggest that Treg cells exist in several diverse states. Through single-cell RNA sequencing, one team identified that human Treg cells in healthy patients consisted of 5 populations (naives, FOXP3 hi , HLA-DR hi , MKI67 hi , and LIMS hi ) that collectively converged into two distinct trajectories (FOXP3 hi and MKI67 hi ). (238) The FOXP3 hi cells were more immunosuppressive than the MKI67 hi cells. They also expressed more CD25, (cytotoxic T lymphocyte antigen-4) CTLA4 proteins as well as the genes for FOXP3 stabilisation, Helios, antigen recognition and IL-10 secretion. In parallel, another team found that murine splenic Treg cells consisted of 6 subpopulations with profiles involving differing responsiveness to IL-2, tumour necrosis factor (TNF), costimulation and capacity to secrete granzymes. (124) Treg cells also change their phenotype when transitioning from lymphoid to nonlymphoid tissue or the peripheral circulation to tissue residency. (239, 240) It is presently unknown whether infused Treg cells for therapy may undergo similar adaptations too. Thus, it is important to delineate the key drivers in the early differentiation and proliferation of Treg cells. This could identify new therapeutic targets to ensure the maintenance of Treg cell stability in an inflammatory allograft or lymph node. A similar question concerns the induced Treg (iTreg) cells too. The naïve effector T (Teff) cells upregulate FOXP3 to adopt an iTreg-like profile upon activation alongside IL-2 and small molecules such as costimulation blockade (CTLA4-Ig),

  settings and downregulate FOXP3 to re-adopt an effector profile. (242-244) The transcriptomic programs driving initial FOXP3 upregulation are not yet established. (134, 245) Recent work demonstrated that naïve CD4+ T cells influenced by the cytokine milieu differentiated towards central memory cells, which then took up either a terminal Treg-like trajectory or adopted an effector memory profile before becoming terminal effectors. (246) In parallel, two studies showed that a key influencer of naïve murine CD4+ T cell differentiation trajectories was the TCR structure instead. (124, 247) Considering that naïve CD4+ T cells are found in higher frequencies in the peripheral circulation, delineating the mechanisms required to induce stable and functional iTregs would be logistically and financially beneficial. In this study, we deciphered the proteo-transcriptomic profiles and differentiation trajectories of in vivo and in vitro circulatory Treg and Teff cells. We found that circulatory naïve cells underwent transcriptomic refinement upon thymic egression to form heterogeneous subsets. However, intrathymic re-circulatory and peripheral circulatory Treg cells broadly shared the same signature as activated Treg cells. The differentiation trajectory of naïve Treg cells towards activated Treg cells involved passing via an intermediate CD25+FOXP3 lo state. Furthermore, our 2-week Treg cell expansion protocol demonstrated 9 distinct Treg cell subpopulations defined by their transcriptome and surface protein profiles. Finally, we performed cell fate analysis of cells under Treg-expansion conditions across two biological timepoints to identify two distinct differentiation trajectories of naive CD4+ T cells. Their distinct gene regulatory networks were also delineated.MethodsStudy approval and sample processingBlood samples underwent Ficoll-based density-gradient centrifugation (Lymphoprep, STEMCELL Technologies, France). The isolated lymphocytes were resuspended in serum-free TexsMACS medium (Miltenyi, France) with 1% penicillin/streptomycin (Thermofisher, France).The thymic explants were acquired from donors who had undergone brain death (DBD at the Foch Hospital, Paris, France (approval by the Biomedicine Agency; PFS13-007). Tissues were washed in 10% foetal calf serum (FCS; Thermofisher, France) and RPMI 1640 (Thermofisher, France) with 1% penicillin/streptomycin (Thermofisher, France). They were then manually dissected, filtered, and underwent Ficoll-based density-gradient centrifugation (Lymphoprep, STEMCELL Technologies, France). The lymphocytes were isolated and frozen in 10% DMSO/90% FCS.All healthy peripheral blood donors gave informed written consent before donation.This study was performed in line with the Declaration of Helsinki with approval from the local hospital ethics committee (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale of Pitié-Salpétrière Hospital, Paris).

  CD4-FITC (clone RPA-T4), CD25-PE (clone 2A3), CD127-BV421 (clone HIL-7R-M21), and CD45RA-PeCy7 (clone L48) (all BD Biosciences). The cells then underwent flow-based sorting using the FACS Aria III (BD Biosciences, France). The purity was >95%. The naïve Tregs (Fraction I) were identified as CD4+CD25++CD127-CD45RA+, the activated Tregs (Fraction II) as CD4+CD25++CD127-CD45RA-and the effector non-Tregs (Fraction III) as CD4+CD25+CD127-CD45RA-and the memory effector cells (Fraction IV/V) as CD4+CD25-CD127+CD45RA-. For the expanded Treg and Teff experiments, the enriched CD4+ T cells were stained with CD4-FITC, CD25-PE, and CD45RA-PeCy7 (all BD Biosciences). The cells then underwent flow-based sorting using the Biorad S3e cell sorter (Biorad, France). The naïve Treg cells were identified as CD4+CD25++CD45RA+ and the naïve Teff cells were identified as CD4+CD25-CD45RA+. Cell purity was routinely >90%. For the downstream suppression assay, the naïve Teff cells were frozen in 10% DMSO/FCS. The thymic lymphocytes were stained with CD3-BV510 (clone SK7), CD4-PerCP-Cy5.5 (clone RPA-T4), CD8-PE-Cy7 (clone SK1), CD25-PE, CD127-FITC (clone HIL-7R-M21), CD45RA-APC-H7 (clone HI100), and FOXP3-APC (clone 259D/C7) (all from BD Biosciences, France). The post-expansion Treg and Teff cells were stained with Live/Dead dye-APC-Cy7 (Thermofisher, France), CD4-PerCP-Cy5.5, CD25-FITC (clone M-A251), FOXP3-PE (clone 259D/C7), TIGIT-PE-Cy7 (clone MBSA43), and CD15s-APC (clone CSLEX1) (all BD Biosciences, France). Data were acquired using the FACS Canto II (BD Biosciences, France) and analysed with FlowJo version 9 (BD Biosciences, France).

)

  For the CD4+ T cell experiment, we identified cell clusters initially in an unsupervised manner based on their combined proteo-transcriptomic profiles. These were crosschecked with the Azimuth reference atlas (PBMC) database. (253) We removed any impurities, added the labels as metadata and reclustered the cells based on both modalities. For the flow-sorted CD4+ T cell subpopulations, each subpopulation had already been tagged using a Sample Tag. This permitted comparison of cell clusters across the tags as well as delineation of any common/different cells. The proteotranscriptomic profiles improved our capacity to resolve subpopulation differences that had for many years previously been based upon flow cytometry profiles. For the expanded Treg/Teff cells, the individual conditions were tagged using Sample Tags to permit downstream demultiplexing. When comparing the signatures of the Treg cells identified in the CD4+ T cell experiment against those of the expanded Treg cells, we used the UCell package.(254)

(Figure 5 -

 5 the thymus is the source of almost all naturally occurring (not induced)Treg cells, they do undergo further refinement of their epigenetic signatures in the periphery.(261, 262) In order to study circulatory Treg cells, it was important to define their intrathymic profile. (263) We processed two adult thymuses to isolate their constituent cells and put them through the BD Rhapsody Whole Transcriptome Assay (WTA) workflow with surface protein (Abseq) profiling. For comparability, we also stained the cells for flow cytometry-based (FACS) analysis.After alignment and annotation using the SevenBridges WTA pipeline, we removed any doublets or unviable cells. Due to lymphocytes expressing a greater quantity of protein than RNA, we split the protein and RNA datasets to avoid skewing towards one modality. These data were individually and simultaneously scaled, normalised, and clustered using PCA and UMAP. To facilitate reproducible cellular identification, we used published datasets for reference to classify the thymocytes; thymic epithelial cells (cortical or medullary; c/mTECs), macrophages, B-lymphocytes as well as CD3+ lymphocytes. (264) (Figure5-1A, Table5-1)The TECs consisted of 3 cell clusters expressing genes involved in antigen-recognition (FCN1, JCHAIN, CD74, HLA-DP/Q/R), IL1 signalling (IL1B, IL1RN), type 1 interferon signalling (IRF7/8), lysis (LYZ, GZMB), cellular differentiation (TCF4), tumour inhibition (SERPINF1) as well as chemotaxis (CXCL8, CCL3). These cells could also be identified via surface markers such as CD31, CD45RA, HLA-ABC, HLA-DR as well as CD25, CCR4, CD44, CD11a, CD18, and CD162.Dual interrogation of surface protein and transcriptomic profiles permitted better delineation of all the diverse CD3+, CD4/8 double-positive (DP) and, CD4 or CD8 single-positive (SP) T cells. (Figure5-1B, Table5-2 and 5-3) The CD3+ lymphocytes expressed genes involved in antigen recognition (TRAC, TRBC1, CD3D, CD3G), T cell activation, and differentiation (ITM2A, LEF1, SOX4), and chromatin accessibility (SATB1, TOX2). (166, 265-268) (Table 5-3) In the next stage of development, the CD4/8 DP cells expressed genes involved in T cell signalling (CD1A, CD1B, CD1E) and T cell differentiation (HDAC7, SOX4) as well as genes implicated in migration/trafficking (AQP3), anti-apoptosis (MDM4) and cell-cell adhesion (CHD1). (267, 269-271) Upon acquiring a CD4 SP profile, the cells expressed genes involved in adhesion/migration (SELL), cell differentiation, (RGCC, ID2), Treg/Th17 cell development (JUNB), and promoting IFN! secretion over IL-10 (PTGER), cytokine receptor signalling (IL6ST, IL7R) and autophagy (TNFAIP3). (272-275) Of this population, the CD4+ Treg cells expressed genes involved in immune/tissue homeostasis (FOXP3), co-inhibition (TIGIT, CTLA4, ICOS), cytokine signalling (IL2RA, IL32, GBP5), cell differentiation (BATF), proliferation (PIM2) and IL-10 synthesis (MAF). (188, 273, 276, 277) Further re-clustering on the Treg cells did not provide us with sufficient resolution to identify their subpopulations. Regarding Treg cells, the FACS-based staining already demonstrated their presence via a range of definitions currently used in the Treg field; CD4+CD25+CD127lo, CD4+CD45RA+CD25++, or CD4+FOXP3+. (9, 159, 278) 1C) From a developmental perspective, the CD4/8+ DP cells upregulated CD25 with low levels of FOXP3 as well. Both of these elements were further upregulated in the CD4+ SP cells. This correlated with published work from us and others on two-stage intrathymic Treg cell development. (140, 262) The second type of intrathymic Treg cell (FOXP3 hi CD25-) has also been described however, it was not definitively demonstrated in our data. (279) This may be explained by the fact that the second type is prone to apoptosis as it does not express the low-affinity CD25 receptor. Using the 28 surface protein Abseq panel, we further interrogated the surface profile of the identified Treg cells. (Table 5-3) Using the CD69/MHC-I classification, our Treg cells were CD69-MHC-I+ indicating late-state maturity. (280) (Figure 5-1D) However, the added value of our work was the parallel study of surface markers as we also identified CD44 and CD46 as being differentially expressed on Treg cells. Moreover, both the expression of CD25, as well as the corresponding IL2RA gene, was found only in Treg cells. This suggested that the intrathymic Treg cells were already utilising the JAK-STAT signalling axis to support their functions. We confirmed this by analysing the signalling pathways via the PROGENy package in these cells. (257, 258) (Figure 5-1E) Collectively, the combined surface protein (FACS and Abseq) and transcriptomic signatures of the Treg cells suggested that they were responsive to IL-2 and capable of immunosuppressive functions. Put together, multimodal single-cell profiling permitted the delineation of adult thymic stromal and immune cell subsets. To better understand human Treg and Teff development, it was necessary to compare these proteo-transcriptomic signatures with the peripheral blood. Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells In the past, 6 distinct subpopulations were described within the CD4+ T cell compartment of which, 3 were FOXP3+. (9, 140) (Supplementary Figure 5-1A) The proteo-transcriptomic differences between these peripheral and intrathymic Treg cells are yet unknown. Establishing the drivers of naïve Treg cell differentiation towards activated Treg or effector cells is important to optimise the functions of expanded Treg cells.

5 ) 4 ) 5 )(Table 5 - 5 ) 5 ) 5 )(Figure 5 -2C, Table 5 - 5 )

 5455555555 patients' cells to allow downstream demultiplexing. Cell labelling was performed through a systemic cluster-by-cluster analysis based on established biological knowledge and an unbiased study of all differentially expressed transcripts/proteins. To maximise reproducibility, all cell labels were cross-checked against the Azimuth reference atlas and other published works.(240, 246, 250, 253) Whilst most CD4+ T cell lineages were identified within the enriched CD4+ dataset, we found two groups of cells expressing genes related to cell-cell adhesion, migration (ITGB1, LIMS1, ANXA2, LGALS1/3) as well as apoptosis induction (ANXA1, LGALS1/3). (281-284) (Table5-Both cell groups collectively expressed surface CD44 however, the latter also expressed HLA-DR.Hence, without a specific lineage factor, we labelled these cells as 'infiltrating' and 'activated' respectively. To further increase the resolution for unsupervised trajectory discovery, we then integrated the three datasets (thymic, flow-sorted CD4+ T cells, and enriched-only CD4+ T cells). (Figure5-2A) Using the Seurat package, we established common anchors between the 34991 cells and reduced batch effects to identify 2000 commonly variable features. Finally, all cells underwent re-scaling and re-clustering via PCA and UMAP. (Figure 5-2B) Our first finding was that thymic CD4SP (non-Tregs) and circulatory naïve (Tregs or Teffs) formed heterogenous cell clusters with partial overlap only. This suggested that circulatory naïve CD4+ T cells underwent peripheral refinement of their signature upon thymic egression. Cluster 1 was a common cluster across thymic CD3+, CD4+ SP, and circulatory naïve cells. It consisted of 3 differentially expressed genes with presently unestablished functions in T cells (LYZ, RASGEF1B, and IGKC). (Table 5-5) This cluster was not present in the FACS-sorted naïve Treg or Teff cells indicating that our FACS-sorting strategy was excluding this population. A further observation was that the naïve Treg/Teff cells had virtually identical transcriptomes (despite having been FACS-sorted based on their CD25 expression). This suggested that the RNA and surface protein profiles were insufficient in explaining their different differentiation trajectories. (9) The other cell clusters comprising the circulatory naïve CD4+ cells were clusters 0, 5, 7, and 8. Apart from the lower quality ribosomal and mitochondrial genes, many differentially expressed genes in these clusters were involved in cell development (LTB), TCR signalling (FYB1), T cell activation (CDK5R1, TPT1), thioredoxin regulation (TXNIP) and cytokine signalling (IL7R). (285, 286) The cluster 8 genes are discussed in the next paragraph due to their Treg relevance. Overall, these data demonstrated that the naïve CD4+ T cells (Tregs and Teffs) consisted of distinct heterogeneous effector and regulatory subsets. Strikingly, our analysis demonstrated that the intrathymic Treg cells, peripheral circulatory Tregs (from enriched-only and flow-sorted Fraction II activated cells) were broadly transcriptomically homogeneous. (Figure 5-2B, C) These cells clustered together across cellular compartments. (Figure 5-2B) As demonstrated in Figure 5-2C, they shared cluster 2, which consisted of genes such as FOXP3 (lineage factor) and others involved in antigen recognition (B2M, HLA.A, TRBC, CD74), immunosuppressive function (FOXP3, DUSP4), costimulation (CD52), FOXP3 upregulation (KLF2), cell growth (PIK3R1, S100A4, SLFN5), migration (ITGB1), cytokine signalling (IL32), inhibitor of chemokine signalling (RGS1) and reducing oxidative stress (UCP2). (287-289) (Table 5-The circulatory Treg cells (unsorted) also comprised of cluster 8. This cluster was only cross-identified in the naïve Teff cell cohort suggesting that these cells also encompass naïve/early-activated peripherallyinduced Treg (pTreg) cells. This was confirmed by their expression of genes such as FOXP3, IKZF2 (Helios), TIGIT, FCRL3, PIK3R1, RGS1 and SELL. Overall, the specific differentially expressed markers of activated Treg (Fraction II) cells were CD25+HLA-DR+. (Table 5-These correlated well with previous work demonstrating activated Treg cells to be the most immunosuppressive Treg cell subpopulation. Finally, the cross-compartment overlap of the Fraction II Treg cells may indicate that some circulating Treg cells could return to the thymus.In parallel, the effector non-Treg cells (Fraction III) were a heterogenous subset. They consisted of naïve cells (clusters 0, 5, 7. and 8), Treg cells (clusters 2 and 8) as well as newer clusters(3, 4, 6, and 9) belonging to the memory Teffs (Fr IV/V). Clusters 3 and 9 had genes implicated in antigen recognition (HLA.A, B2M, CD74), activation (CD69, RUNX3), chemotaxis (CCL5, CXCR3, CXCR4), cytolysis (GNLY, GZMA, GZMH, GZMK), cytokine signalling (IRF1, IL32) and effector functions (FGFBP2, DUSP2/4). Interestingly, certain genes that were found amongst these Fraction III cells (cluster 4; KLRB1, GATA3, ITGA4, ITGB1, S100A4, AQP3), also overlapped with the Th17 transcriptomic signature identified in the enriched-only CD4+ T cell dataset. (Table5-4) Finally, cluster 6 consisted of genes such as; JUNB/D, FOS, GPR183, IL7R, KLF6, NR4A2, PIK3R1. and TXNIP. We dissected these cells within their original dataset (Supplementary Figure5-1C) and identified their sole differentially expressed surface marker of memory-like effector cells as Programmed Death-1 (PD1). All of these data are concordant with past studies demonstrating the suboptimal immunosuppressive function of Fraction III cells and their secretion of pro-inflammatory cytokines such as IFN!, IL-2. and IL-17A.(9, 140) Importantly, the heterogeneous nature of these cells suggests that they may still be capable of immunosuppressive activity as they still consist of naïve Treg/Teff cells and activated Treg cell clusters.Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cellsUsing the integrated dataset of CD4+ T cells in different cell states, we next characterised their differentiation trajectories. A fundamental advantage of our integrated data was the ability to use thymic-sourced CD3+ cells as the starting point. Based on the Monocle 3 trajectory construction algorithms, our data demonstrated various transition states between thymic-sourced and circulatory naïve CD4+ T cells (Tregs/Teffs). (Figure 5-3A) This was concomitant with our finding previously that circulatory naïve CD4+ T cells underwent peripheral refinement of their circuitry upon thymic egression. (Figure 5-2B) Subsequently, the naïve CD4+ T cells differentiated either into Fraction III (FOXP3 lo effector non-Treg cells) or activated/infiltrating effector cells. (Figure 5-3A; right UMAP) The former then upregulated FOXP3 to become Fraction II (FOXP3 hi activated Treg cells). Although in the past it had been demonstrated that Fraction I cells differentiated into Fraction II cells, this new integrated analysis suggested that Fraction III cells can be an intermediary stage between these two cell states. (9) As previously mentioned, the activated/infiltrating cells were defined by the differential expression of surface CD44, HLA-DR, and CD5 as well as genes implicated in cell-cell adhesion, migration (ITGB1, LIMS1, ANXA2, LGALS1/3), and apoptosis induction (ANXA1, LGALS1/3). (Table 5-4 for surface protein (Abseq) and RNA profiles) The pseudotime gene expression analysis confirmed these observations through upregulation of FOXP3 being first demonstrated in Fraction III cells as well as other Treg-associated markers such as IKZF2, IL2RA, CTLA4, and TIGIT. (Figure 5-3B) Furthermore, the connecting branch between activated/infiltrating effector cells and Fraction II cells in Figure 5-3A suggested the capacity for differentiation from one state to another without demonstrating the direction. As biologically expected, the activated/infiltrating cells then differentiated to adopt a memory effector profile. (Figure 5-3A) Importantly, this analysis did not delineate whether the Fraction II cells subsequently re-adopted a Fraction III-like or an activated/infiltrating cell-like profile. We also did not have the resolution to dissect any plasticity between the Fraction III cells or the activated/infiltrating effector cells. We next studied the signalling pathways and regulons driving these processes via the PROGENy and DoRothEA packages. The latter contains a database of drug modulation-based experiments and transcription factor motifs/interactions based on the published literature and computational predictions. (256-258) Amongst the flowsorted CD4+ T cell subpopulations, there was insufficient resolution to infer the upregulated signalling pathways and regulons in the naïve cells. (Figure 5-3C) This analysis demonstrated that the differentiation of Fraction III to Fraction II cells implicated upregulated EGFR, JAK-STAT, MAPK, NF-κB, and TNF⍺ signalling. This was reflected by a progressive upregulation of regulons such as E2F1, NFYA, NFATC2, LYL1, and STAT4. Although the EGFR and JAK-STAT pathways were downregulated in the memory Teffs, they upregulated genes implicated in hypoxia and PI3K signalling instead. This was driven by regulons such as IRF1 and JUND.

  Our next question was to identify heterogeneity amongst expanded Treg cells using different small molecules. Past work based on flow-sorted CD4+CD25++ Tregs demonstrated the CD45RA+ subset to consistently express high levels of FOXP3, CTLA4 and be more immunosuppressive than their CD45RA-counterparts. (233) Whilst other groups may prefer to exclude CD127hi cells, this does not guarantee the highest levels of FOXP3 expression post-expansion. (290) Hence, in our in vitro system we sorted CD4+CD25++CD45RA+ cells from two healthy donors and cultured them for 2-weeks in 4 different conditions; control (using anti-CD3/28 activator beads with IL-2 only) as well as methylprednisolone (100µg/ml) and rapamycin (100nM). Rapamycin-expanded Treg cells have been well-defined and it is often part of current Good Manufacturing Practice (GMP)-like expansion protocols. (20, 25, 291) Methylprednisolone has been shown to increase circulatory Treg cell proportions amongst CD4+ T cells when administered during active systemic lupus erythematosus. (292) It also upregulates DR+ expression on Treg cells and increases their immunosuppressive capacity. (293) Hence, we wanted to both study these phenomena at the single-cell level and identify their underlying mechanisms. After the 2-week culture period, the cells of each condition were washed, some taken for FACS-based staining and the rest analysed via the BD Rhapsody technology. The FACS staining demonstrated similar upregulation of CD25, FOXP3, and TIGIT expression across all 3 conditions. (Supplementary Figure 5-3A)

6 )Figure 5

 65 Figure 5-2)

6 )(Figure 5 -(Figure 5 -(Figure 5 -

 6555 These consisted of genes implicated in Treg cell differentiation (SATB1), maintaining FOXP3 stability (FOXP1), IL-2 inhibition (IKZF2), and the cytoskeleton (TUBA1B, TUBB, STMN). (240, 294) Interestingly, these exact cytoskeletal genes were upregulated in lung and bone marrow resident T cells (compared to circulatory T cells) in a separate study. (240) We also identified newer genes involved in IL-10 secretion (HMGB1/2), supporting tumoural growth (MIAT, MALAT1) as well as cellular lysis (NKTR) and bystander cell apoptosis (CASP8). (295-298) Put together, these Treg cells demonstrated capacity for potential tissue residency. We captured more of the clusters 2, 4, 5, and 7 in the rapamycin condition. 4C) Interestingly, FOXP3 expression was increased in these new subsets. 4D) This correlated with the FACS data showing rapamycin inducing a FOXP3++ subpopulation. (Supplementary Figure 5-3A) When compared to the in vivo homogeneous Treg cell clusters identified in (clusters 2 and 8 in Tregs UMAP of Figure 5-2C), these new clusters expressed at least 20-40% of identical genes. 4E) These clusters collectively consisted of genes implicated in antigenrecognition (TRAC), Treg/Th17 differentiation (MAF, AQP3, TXNIP, and TCF7), and Th1 differentiation (IL7R). (Table 5-6) In terms of immunosuppressive mechanisms, they expressed genes implicated in FOXP3 stability (DUSP4), co-inhibition (CTLA4, TIGIT/CD226, ICOS, TNFRSF1B, CD96), APC maturation (CD40LG), migration (ITGB7) as well as B-T cell crosstalk (FCMR). (299-302) Although the roles of TIGIT and CD226 are still being delineated, there is early evidence demonstrating CD226+ as a marker of immunosuppressive IFN!+IL-10+ Treg cells. (299) The FCMR receptor incorporates immunoglobulin M (IgM) uptake into T cells, which alongside low-level TCR/CD28 stimulation promotes T-cell proliferation and IL-2 responsiveness. (302)

  (0,1,3 and 8 were dominated by PI3K, EGFR. and Trail signalling. (Figure 5-4F) The drivers of these processes were FOXP1, NR5A1, MXI1. and TFAP2C. (Figure 5-4G) FOXP1 is a FOXP3 stabiliser, maintains CTLA4 expression and thus immunosuppressive function. (294) However, the roles of the other three regulons are presently unestablished in Treg cell immunobiology. In comparison, the new cells were collectively dominated by hypoxia, JAK-STAT, NF-κB, p53, TNF⍺, TGFβ, VEGF and, Wnt signalling pathways. The regulons involved in promoting these cells were ETS1, IRF4, LYL1, TBX21, REST, ELF1, NR2C2, and MYC. ETS1 and ELF1 are implicated in FOXP3 regulation, Treg cell development, and immunosuppressive functions. (303) IRF4 is involved in activated Treg differentiation and function. (235, 304) The rest of the identified regulons are involved in the cell cycle, T cell activation, or differentiation.

Figure 5 -

 5 5B) Collectively, many of these markers are implicated in IL-2 responsiveness, co-inhibition, T-cell activation, Th17 differentiation, and trafficking. (144, 281, 305) Finally, apart from IL2RA and CD74, no Treg-associated genes were highly expressed in any cluster. (Figure 5-5C) Overall, these Teff expansion protocols did not sufficiently promote iTreg cells.

7 ) 8 ) 6 ) 7 )(Figure 5 - 3 )

 786753 Rapamycin induced cluster 1 cells expressing genes for pro-inflammatory cytokine signalling (TNF, IL5, IL13), mitochondrial electron decoupling (UCP2), and anti-apoptosis (BCL2L1). Interestingly the UCP2 gene is known to inhibit the differentiation of CD8+ T cells and promote their clonal expansion. (289) This cluster was identified by differential expression of surface CD44 and HLA.ABC. Overall, these data suggested that neither methylprednisolone nor rapamycin polarised naïve Teff cells to iTreg cells. In terms of GRNs, the control Teffs were dominated by diverse EGFR, JAK-STAT, NF-κB, TNF⍺, TGFβ, and MAPK signalling pathways. (Figure 5-5D) These were driven by regulons involved in cell cycle regulation (E2F1, 2, 4), NFAT signalling (FOS/JUNB), type 1 interferon signalling (IRF1-3, STAT2), and T-cell activation (NKFB1) amongst many others. IRF1 in particular is known to inhibit FOXP3 upregulation. (308) In comparison, methylprednisolone promoted hypoxia, p53, and the TRAIL pathways, which were driven by regulons that inhibit Th2-like cytokines Integration analysis revealed differentiation trajectories of expanding naïve CD4+ T cells We next investigated the Treg and Teff differentiation trajectories from their originating naïve CD4+ T cells. To do this, we integrated all datasets of unexpanded enriched CD4+ T cells as well as expanded Tregs and Teffs. The unexpanded CD4+ T cells were a fairer control than the intrathymic CD4+ SPs or flow-sorted CD4+ T cells due to identical donors, inter-experimental technical similarities, and that they contained CD4+ T cells at all differentiation states (naïve, Th1, Treg, Th17, etc). Overall, we identified 19 cell clusters from approximately 27,000 cells. (Figure 5-6A) Our first observation was that the Treg cells originating from the enriched CD4+ T cell dataset were split across two cell clusters (clusters 2 and 8). (Figure 5-6B; see UMAP of Fresh Treg cells) We also observed that the expanded Treg cells (control, methylprednisolone, and rapamycin) broadly localised around or overlapped with both of these cell clusters. (Figure 5-6B; P1 and P2 groups outlined in dotted lines) The first population of expanded Treg cells (henceforth called Treg-P1) comprising of clusters 0, 2, 3, 5, and 6 was close to or overlapped with the unexpanded naïve cells and cluster 2 of the fresh Treg cells. (Figure 5-6B; P1 group outlined in dotted lines) A second population (called Treg-P2) comprising of clusters 4, 8, and 11 was close to or overlapped with cluster 8 of the fresh Treg cells. (Figure 5-6B; P2 group outlined in dotted lines) There was no overlap of the expanded Treg cells with the Th17 or other cytotoxic cells indicating the commitment of expanding Treg cells to the Treg cell lineage. The Treg-P1 cells (see clusters 0, 2, 3, 5, and 6 in Table 5-8) expressed Treg-associated genes such as FOXP3, IKZF2, IL2RA, CTLA4, TIGIT, PRDM1 (Blimp-1), BACH2 as well as genes implicated in antigen-recognition (TRAC, TRBC1), TNF signalling (TNFRSF1B), Treg/Th17 differentiation (TXNIP) and the cytoskeleton (TUBA1B, TUBB, STMN1). In comparison, the Treg-P2 cells (see clusters 4expressed FOXP3, IKZF2, CTLA4, TIGIT, CD74, AREG, and additionally, genes implicated in antigen-recognition (TRBC1), cell-cell adhesion/migration (ITGB1, LIMS1, ANXA2), chemotaxis (CCR6), chemokine signalling inhibitor (RGS1), late differentiation (TOX), and cytokine signalling (IL7R, IL32) amongst others. We also identified LAYN, which has been previously found as upregulated in tumoural Treg cells. (309) As the integration analysis was based on the transcriptome only, we crosschecked the Treg-P1/2 cells in their original datasets with their original clustering to identify their corresponding surface protein markers. (Supplementary Fig 5-3C) Within the expanded Treg cell dataset, most of these cells belonged to clusters 0Clusters 0,1 and 3 did not have any differentially expressed surface markers. However, clusters 4 and 5 differentially expressed; CD4, CD25, CD44, CD45, CD11a/CD18, CD2, CD5, HLA.ABC, and CD162. Furthermore, within the expanded Teff cell dataset, most of the Treg-P1/P2 cells belonged to clusters 2-4 and 8. (Figure 5-5A, Table 5-Of these only cluster 4 differentially expressed any surface markers (CD3+CD162+). Interestingly, this exact cluster was induced by methylprednisolone. Finally, in the enriched CD4+ T cell dataset, these cells differentially expressed (logFC≧0.6) in clusters 0/3 (CD45RA+) and cluster 7 (CD25+DR+). These clusters almost exclusively belonged to the naïve or Treg cell compartments. (Supplementary Figure 5-1B, Table 5-4) Furthermore, the expanded Teff cells consisted of two broad populations. 6B) The first population overlapped with both Treg-P1/P2 cells whereas the second population was previously unidentified in the unexpanded CD4+ control (clusters 7, 13-19 in Figure 5-6A and -B). The overlap with Treg-P1/P2 cells corresponded with published data showing that certain proliferating Teffs upregulate FOXP3 to adopt a Treg-like profile. (243, 246, 310) This also linked up with our data from the flow-sorted CD4+ T cell dataset showing the transcriptomic overlap between naïve Teffs and naïve Treg cells. (Figure 5-2A; middle UMAP) Considering that the second group of cells was only found in the in vitro cultures, they likely reflect terminal activated Teff cells related to the cell culture process. These were found in variable proportions across the three experimental conditions. Having CD4+ T cells from matched donors across two biologically relevant timepoints and different differentiation states, provided us with better resolution to delineate their differentiation trajectories. Indeed, this cell fate analysis showed that starting from the naïve (N) cells, there was one differentiation trajectory towards the (P1) cells and another towards the activated/infiltrating (A) cells. (Figure 5-6C; both UMAPs) The (A) cells (labelled as cluster 1 on Figure 5-6A) were an intermediate stage before the expanding cells differentiated towards the Treg-P2 (P2) cells or became in vitro terminal activated effector (X) cells. The connecting branch between the Treg-P1 (P1) cells and (A) cells suggested the capacity of (P1) cells to subsequently differentiate into (A) cells. This analysis did not elucidate whether Treg-P2 (P2) cells re-adopted an (A) cell-like profile. To delineate the drivers of these differentiation trajectories, we analysed the GRNs and inferred the transcription factors. The naïve cells and Treg-P1 cells (clusters 0, 2, 3, 5, and 6) were supported by low-level signalling in the p53, Trail, and VEGF pathways. (Figure 5-7A) In comparison, the activated/infiltrating cells (cluster 1) initially upregulated the hypoxia, NFκB, and TNF⍺ pathways before adopting a Treg-P2-like profile (clusters 4, 8, and 11) that implicated JAK-STAT, EGFR, and MAPK signalling as well. In terms of regulons, those implicated in the signalling pathways of naïve and activated/infiltrating cells were inferred as; ETS1/2, CEBPB, NFATC2, and ZNF263. (Figure 5-7B) All of these regulons were then downregulated upon the cells acquiring a Treg-P1 or -P2-like or terminal activated effector cell transcriptome. The regulons driving differentiation towards the Treg-P1/P2 cells were NR4A1, ESRRA, LYL1, STAT2, and TCF12. Interestingly, NR4A1 can bind to ESRRA and has been implicated in early T-cell differentiation, inhibition of IFN! and IL-17A secretion, and promotion of anergic CD4+ T cells. (124, 311) STAT2 plays an important role in carcinogenesis as well as promotion of murine tumoural Treg cells and inhibition of Th17 cells. (312, 313) The critical regulons implicated in acquiring a terminal activated effector cell profile were STAT2 and the terminal differentiation factor, E2F4. (314) Overall, these data demonstrate that naive CD4+ T cells during acute activation as well as with in situ IL-2 differentiated towards two transcriptomically distinct Treg cell subsets. The first Treg cell subset (Treg-P1) closer to the naive cells had the capacity to adopt an activated non-Treg state upon upregulating diverse signalling pathways such as hypoxia, NFκB, and TNF⍺. Cells in that activated non-Treg state could then upregulate a different set of signalling pathways to either adopt a Treg-P2 profile or become terminal activated effector cells. Discussion In chronic inflammatory environments, Treg cells downregulate FOXP3 to adopt pathogenic effector functions. (129, 242, 244) Similarly, certain Treg cells postexpansion can downregulate FOXP3 too. (290) Hence, delineating the single-cell transcriptome of expanded Treg cells is important to optimise their efficacy when administered to patients and to also eliminate effector impurities. An understanding of their regulatory networks could identify newer targets for therapeutic modulation. In this study, we found that the intrathymic re-circulatory and peripheral circulatory Treg cells had broadly overlapping proteo-transcriptomic profiles with Fraction II activated Treg cells. The pseudotime differentiation trajectory of naïve Treg cells towards activated Treg cells involved passing via an intermediate state (CD25+FOXP3 lo ). Furthermore, the expanded Treg cells in our 2-week protocol comprised of 9 distinct subpopulations defined by their transcriptome and surface protein markers. Using two biologically pertinent timepoints, the cell fate analysis of expanding cells showed that naive CD4+ T cells under Treg-expansion conditions differentiated along two paths to become either Treg-P1 or -P2 cells. The Treg-P1 cells could then differentiate into activated non-Treg cells, which were a key decisionmaking point before the cells either adopted a Treg-P2 profile or became terminal activated effector cells. Distinct gene regulatory networks were implicated in these cells.Defining intrathymic Treg cellsIn the past, seminal work identified CD4+CD8-CD25+ thymocytes from adult mice as expressing significantly higher surface CD5, CD44, CD54, CD62L, and CD122 (IL-2 receptor β chain) than their CD25-counterparts. (315) These thymocytes were only immunosuppressive upon cognate TCR stimulation. However, delineating the phenotype and functions of intrathymic resident Treg cells is challenging as adult murine thymuses produce fewer Treg cells with age and up to 90% of intrathymic Treg cells in such mice can be recirculatory. (316, 317) In work by Thiault N et al, such recirculatory cells had an activated phenotype as demonstrated via upregulation of surface CD44, CD103, TIGIT, and Nrp-1. (317) Bulk transcriptomic analyses of same cells demonstrated increased expression (compared to the minority remaining resident Treg cells) of IL2RA and genes involved in co-inhibition (CTLA4, LAG3, PDligand-1, TIGIT), cytokine secretion (IL10, TGFB1/2, IFNG), and chemotaxis (CCR3-6, 8, CXCR5-6). (317) This work was built upon by a recent single-cell transcriptomic analysis of thymuses originating from human foetal tissue early post-conception (7-17 weeks). (263) In comparison with the previous study, these foetal Treg cells expressed the following genes in common (FOXP3, IL2RA, CTLA4, and TIGIT) but in addition, others involved in Treg differentiation (BATF), FOXP3 stability (PRDM1), coinhibition (ICOS), cytokine signalling (IL32) and TNF responsiveness (TNFRSF4, TNFRSF18. (see Table S4 in reference (263)) In common with these studies, our work involving adult thymuses identified intrathymic re-circulatory Treg cells expressing virtually identical signatures. (Table 5-The added value of our data was the delineation of surface protein markers of the Treg cells at single-cell resolution (↑CD25, CD44, CD46, and HLA.ABC). (Table 5-3) Overall, these data suggested that intrathymic foetal Treg cells and adult re-circulatory Treg cells expressed the activated Treg cell transcriptome. (Figure 5-2C and cluster 2 in Table 5-5) Finally, we could not verify without paired peripheral blood, if the intrathymic re-circulatory Treg cells underwent reprogramming upon thymic re-entry or whether they maintained an activated phenotype in the periphery. In terms of murine thymic Treg development, there are two types of Treg cells; CD25+FOXP3+ (in the medulla) and FOXP3 lo (in the medulla/cortex). (262) The former mature in two steps; (1) early strong TCR stimulation of the CD4 SPs leading to an upregulation of CD25 and costimulatory/inhibitory receptors such as OX40 (TNFRSF9), TNF receptor 2 (TNFR2), and glucocorticoid-induced TNF-related receptor (GITR), and (2) IL-2 signalling, which upregulates FOXP3 via STAT5. CD4+ SPs can also upregulate FOXP3 via CD28 costimulation to become CD4+CD25-FOXP3+ cells. There are contrasting data as to whether this latter population is more or less susceptible to apoptosis than the CD25+FOXP3+ cells. (262, 279) It has been demonstrated that the CD25-FOXP3+ cells apoptose due to suboptimal !-chain signalling. (279) Without IL-2 these FOXP3+ cells do not upregulate CD25 and do not survive in vitro. In the study on human foetal thymuses discussed above (263), their Treg cells originated from an early subset (labelled as differentiating Treg cells in original study) expressing relatively lower levels of FOXP3, CTLA4 but more naivety genes such as CCR7, CD27, LEF1, SELL, and inhibitor of chemokine signalling (RGS1). (263) This early subset was not identified in our adult thymic Treg cells. In comparison, our intrathymic Treg cells expressed CD25 (surface and RNA), FOXP3, coinhibitory receptors (CTLA4, TIGIT, ICOS) as well as BATF. As per the CD69/MHC-I classification, these Treg cells were of late-stage maturity. (280) One hypothesis to explain these findings is that intrathymic recently differentiated Treg cells may egress from the thymus early on, which may explain why we found so few naïve Treg-like cells. Going forward, a more comprehensive delineation of the developmental pathways of human intrathymic Treg cells will require simultaneous analyses of the transcriptomes and TCR repertoires of paired thymic and peripheral blood CD4+ T cells. (238)Resolving Treg cell heterogeneityIn the past, different CD4+FOXP3+ T cells were described with phenotypic and functional heterogeneity.(9, 140, 233, 278) Of these, the Fraction I/II (FOXP3+ naïve, FOXP3++ activated Treg) cells were immunosuppressive whereas the Fraction III cell (FOXP3 lo ; effector non-Treg) cells were not. Through multimodal single-cell sequencing, our study demonstrated that Fraction II Treg cells were transcriptomically homogeneous whereas the Fraction I and III cells were heterogeneous. (Figure 5-2C) The Fraction I and VI (naïve Teff cells) had virtually identical transcriptomes even though the former had been sorted based on CD25 hi expression. This suggests that other elements of cellular anatomy (eg. epigenome) may explain their subsequently different differentiation trajectories. (166, 245) Moreover, the Fraction III cells comprised of Fraction I/II cells and Fraction IV/V (effector memory) cells. The effector memory cells differentially expressed surface Programmed Death-1 (PD1), which is a marker of activated, exhausted, and even dysfunctional Treg cells (depending on the disease model). (318, 319) The role of PD1 modulation in these Fraction III cells is not yet known. We also identified that Treg cells post-expansion consisted of 9 subpopulations with distinct transcriptomes and surface markers. This is despite commencing with CD25++CD45RA+ cells that were previously shown to consistently express high levels of FOXP3, CTLA4, and be immunosuppressive. (233, 290) FOXP3 levels were variable in our cells post-expansion as well. (Figure 5-4D) In comparison to us, other groups identified other Treg subsets. One team split CD4+CD25 hi CD127 lo CD45RO+ Treg cells on the basis of chemokine receptors into; Th1 (CXCR3+), Th2 (CCR4+), Th17 (CCR4+CCR6+) and Th22 (CCR4+CCR6+CCR10+) subpopulations. (125) These subpopulations had high expression levels of FOXP3 (90%) and Helios (70-90%) indicating that the majority were of thymic origin. Although they were all equally immunosuppressive in the in vitro immunosuppression assays, they demonstrated a level of specialisation in terms of cytokine secretion. The Th1 Tregs secreted IFN!/IL-10, Th17 Tregs secreted IL-10/17 whereas the Th2/22 Tregs secreted neither of IL-4/10/22. Using Mass Cytometry by Time of Flight (CyTOF), another team demonstrated 22 subpopulations amongst CD4+CD25 hi CD127 lo Treg cells. (155)

(

  CD4+FOXP3+) cells broadly express FOXP3, IL2RA/B, IKZF2, NR4A1 and TNF signalling receptors. (124) However, further clustering on this population has identified 6 subsets; naïve cells (CCR7/SELL), early activated cells (NR4A1, EGR1/2, MYC), and another cluster with trafficking/migration genes (CCR2, ITGB1, CXCR3) and ICOS. Moreover, recent work on lymphoid and nonlymphoid Treg cells uncovered two new subsets; central (SELL, TCF7, and BCL1) and effector (IKZF2, TNFRSF9, PDCD1, Relb). (239) In the colon, 3 subsets were identified; GATA3 hi (NRP1, AREG, IL1RL1, IKZF2), lymphoid-like (SELL, CCR7, TCF7), and an immunosuppressive type (LAG3, IL10, GZMB, and CXCR3). Interestingly, from a human tissue perspective, recent data suggests that certain tissue Treg cells have a similar set of genes regardless of their compartment (FOXP3, CTLA4, TNFRSF4, BATF, IRF4/5, PRDM1, IKZF4, etc). (240) Overall, whilst a full discussion of tissue Treg cells is beyond the scope of our work, these data do suggest the need to better define compartmentspecific Treg heterogeneity in humans.

  Fraction II cells, which can then downregulate FOXP3 to become Fraction III before becoming Fraction IV/V cells. (9) In comparison, our snapshot pseudotime analysis of integrated intrathymic, enriched peripheral CD4+ T cells and flow-sorted CD4+ T cell subpopulations suggested that naïve cells (whole or Fractions I/VI) differentiated along two trajectories. The first trajectory involved adopting a FOXP3 lo (Fraction III) profile before further upregulating FOXP3 to become Fraction II cells. The second trajectory involved adopting an activated/infiltrating (CD44+HLA-DR+/CD5+) profile with increased expression of genes involved in trafficking/apoptosis before either upregulating FOXP3 to become activated Treg cells or differentiating into Fraction IV/V cells. Interestingly, the same conclusion was also reached in two other studies (one involving Treg cells and the other CD4+ T cells only). (238, 246) In the first study (238), the naive Treg cells differentiated along two paths; Path 1 consisting of Treg-associated genes (IKZF2, RORA, CTLA4, SLC2A3, HLA-A1/B, IL10RA, FOXP1, etc) or Path 2 consisting of effector cell-associated genes (HLA-DPA1/B1, HLA-DRA/B1/B5, CD74, IL32, LGALS1, etc). The Path 2 cells consisted mainly of either MKI67 hi or HLA-DR hi Treg cells. In the second study (246), naïve CD4+ T cells first adopted a central memory-like profile and then either an effector memory or branched off to become Treg cells. All these profiles were very similar to those uncovered by us. The key advantages of our data were integration based on unbiased enriched CD4+ T cells, clustering based on 2000 variable featuresas well as the capacity to identify cell surface proteins. As we discuss below, we also inferred novel GRNs and regulons.A limitation of the pseudotime analyses above was the lack of distinct biologically relevant time points. This question could be answered in two ways; transcriptomic similarity across two time points and/or TCR repertoire analysis. We answered this question through the former by integrating unexpanded enriched whole CD4+ T cells with Treg and Teff cells 2-weeks post-expansion. This analysis showed that naïve CD4+ T cells differentiated along two trajectories to Treg-P1 and -P2 cells.Both the P1/P2 cell types expressed Treg-associated genes however the Treg-P2 cells also expressed genes involved in late differentiation, migration, chemotaxis and cytokine signalling. To differentiate into Treg-P2 cells, the naïve cells firstly adopted an activated/infiltrating cell profile from which certain cells became Treg-P2 and the rest became effector memory cells. In comparison, a different approach to study differentiation trajectories is via single-cell TCR repertoire analysis. (238) This could be linked up with the single-cell transcriptome to identify the profiles of any dominant T cell clones. In the study from the previous paragraph involving Path 1/2 Treg cells, the authors analysed the TCR repertoires of Treg cells and found that most TCR overlaps were between naïve and Path 2 cells and then between the Path 2 and Path 1 cells. The Path 2 cells also expressed less FOXP3 protein than Path 1 cells. Collectively, their data also suggested that the naïve Treg cells initially adopted an activated and proliferative FOXP3 lo profile before either upregulating FOXP3 and continuing along Path 1 or remaining on Path 2 to become effector memory cells. The Path 1 cells expressed more CD25 and CTLA4 proteins than the Path 2 cells and were more immunosuppressive too. Overall, although this latter study was based on pseudotime, the TCR repertoire permitted a clearer delineation of lineage fates than our analysis based on transcriptomic similarity. We demonstrated in two separate integration analyses that Treg cell differentiation involved broadly two trajectories with one involving an intermediate FOXP3 lo state. To prove this, in future work we will delineate the single-cell TCR repertoire of unexpanded and expanded Treg cells. This will be linked up with the single-cell transcriptome to better understand the basis for lineage cell fates and clonal expansions. Inferred regulatory networks A further ensemble of findings from our work was the inferred GRNs and regulons in the Treg cell subpopulations. In the flow-sorted CD4+ T cells, the activated Treg cells and memory cells collectively upregulated signalling in the NF-κB and TNF⍺ pathways however, the activated Treg cells were also dominated by EGFR and JAK-STAT signalling. (Figure 5-3C) In comparison, the memory cells were dominated by hypoxia and PI3K signalling instead. One of the key regulons in the memory cells (IRF1) has already been shown to downregulate FOXP3 expression. (308) Interestingly, both the activated Treg cells and the expanded Treg-P1/P2 cells shared similar signalling pathways (EGFR, JAK-STAT, NFκB, p53, TNF⍺, and VEGF) as well as the LYL1 regulon. (Figures 5-3C vs 5-7A/B) Most of these pathways have been previously demonstrated to increase Treg cell immunosuppression.[START_REF] Jeffery | Low-dose interleukin-2 promotes STAT-5 phosphorylation, Treg survival and CTLA-4-dependent function in autoimmune liver diseases[END_REF] 236, 301, 323, 324) The role of TNF⍺ remains controversial as although it is upregulated in proinflammatory settings it can also increase Treg cell suppressive capacities via the TNF receptor 2 (TNFR2). (301) The roles of p53 and VEGF in Treg cells are less wellstudied. (325, 326) Regarding the regulons in expanded Treg-P1 and P2 cells, acquisition of their transcriptome was driven by the NR4A1, ESRRA, STAT2, LYL1, and TCF12. Interestingly, NR4A1 has already been shown to promote anergy in CD4+ T cells and inhibit IFN! and IL-17A secretion. (311) STAT2 is also important as its knockdown was shown to reduce FOXP3+IL-10+ Treg cells in MC38 colonic tumour mice. (313) The combination of interferon alpha-beta receptor-1 (IFNAR1), STAT1, and STAT2 mediated type 1 interferon signalling in that murine model to promote IL-10-secreting tumoural FOXP3+ Treg cells and inhibit Th17 cells. Finally, the roles of LYL1 and TCF12 in Treg cells are not yet delineated. Going forward, these identified GRNs and their inferred regulons need to be biologically validated in Treg cells via targeted perturbations such as gene knock-out experiments. In this regard, certain clustered regularly interspaced short palindromic repeat (CRISPR)-based knock-out protocols adapted for Tregs have already been published. (327-329) These studies focused mainly on elucidating the effects of knocking out FOXP3, IL2RA, IL6RA, and ubiquitinspecific peptidase 22 (USP22). Limitations Our study has certain limitations. Although challenging to acquire, studying thymic and peripheral circulatory Treg cells from paediatric and young adult donors would be very insightful in understanding early Treg cell ontogeny. Tregs form 5-10% of all CD4+ T cells in children and their proliferative status (Ki67) is higher at 5 years of age compared to their first year of life. (330) Thus, a simultaneous study of their transcriptomes and TCR repertoires could delineate their differentiation trajectories and identify any dominant T cell clones. This will need to be integrated with the foetal thymic data for a broader overview of Treg cell development from early conception to early years of life. (263) A further limitation in our study is the source of Treg cells in the expansion cultures and the variety of immunomodulatory agents tested. Although the Treg cells originated from healthy donors, in clinical practice they will likely originate from patients with late-stage organ failure. Current data suggests that Treg cells from such patients remain immunosuppressive post-expansion but it is yet unknown whether they will maintain such functionality when injected in vivo. (241, 331) Long-term immunosuppression can differentially perturb or augment the functions of Treg cells. (205) Moreover, Treg cells have also been shown to downregulate FOXP3 in inflammatory settings and adopt pathogenic effector profiles. (129, 244) Thus, to improve the clinical applicability and patient safety of our work, it will be important to delineate the transcriptomic profiles and GRNs of expanded Treg cells from patients with late-stage organ disease. Parallel conditions could also include different immunosuppressive agents (clinically-approved and novel pre-clinical targets) to identify patient-specific Treg-optimising regimes.

Figure 5 - 1

 51 Figure 5-1

Figure 5

 5 Figure 5-1

Figure 5

 5 Figure 5-2

Figure 5

 5 Figure 5-3

  Figure 4

Figure 5

 5 Figure 5-5

  Figure 6

  Figure 7

FigureFigure 5 - 1 :

 51 Figure and Table Legends (Chapter 5)

Figure 5 - 2 :

 52 Figure 5-2: Treg cells were transcriptomically homogenous in the integrated analysis of thymic and circulatory T cells

Figure 5 - 3 :

 53 Figure 5-3: Pseudotime and regulon analysis demonstrated effector non-Tregs as the intermediate state between naïve cells and activated Treg cells

Figure 5 - 4 :

 54 Figure 5-4: Treg cells post-expansion are heterogeneous in their proteotranscriptomic profiles and gene regulatory networks

Figure 5 - 5 : 2

 552 Figure 5-5: Methylprednisolone and rapamycin alone did not induce Treg cells from naïve effector cells

  Suppression assay (Beads)Previously cryopreserved naïve Teff cells were thawed, counted, and labelled with 1µM CFSE. For the 1:1 Treg to Teff cell ratio conditions, the Treg cells were washed, counted, and plated at 5x10 4 per well with 5x10 4 CFSE-labelled Teffs with anti-CD3/CD28 activator beads (1:10 bead:cell ratio). The Treg cell quantity was reduced in proportion to the different conditions tested(1:2-8). All cultures involved serum-free TexsMACS medium with the cells in round-bottom plates. The plate was cultured for 5 days at 37℃/5% CO2. At the end of 5 days, the cells were washed and acquired.Performing single-cell multiplexing, AbSeq labelling, and captureAs previously, we flow-sorted naïve Treg and Teff cells and cultured them separately in serum-free TexsMACS medium with 300IU/ml of IL-2 and 2µl of anti-CD3/28

(Figure 6 -Figure 6 -

 66 Figure 6-2B) Collectively, this suggested that naïve Treg cells were more profoundly

Treg cells. (Figure 6 -

 6 2A/B) In comparison, it induced FOXP3 in naïve Teffs in all doses till 400µM (p = 0.01; 2-way ANOVA) but this was always at levels inferior to those of the naïve Tregs exposed to the identical butyrate concentration. This suggested that butyrate differentially modulated FOXP3 expression amongst naïve CD4+ T cell subpopulations.We also considered whether this effect was associated with modulation of key phenotypic markers of activated Treg cells (eg. CD25, cytotoxic T-lymphocyteassociated protein 4; CTLA4, lymphocyte-activation gene 3; LAG3, inducible T-cell costimulator; ICOS).(9,[START_REF] Jeffery | Low-dose interleukin-2 promotes STAT-5 phosphorylation, Treg survival and CTLA-4-dependent function in autoimmune liver diseases[END_REF] 343) We found that butyrate significantly downregulated CD25 expression in naïve Treg cells that had been activated. (Figure6-3A/B) A trend for a similar effect was seen in these cells with CD69 expression (p = 0.07). (Figure 6-3A) The Teff cells not upregulating FOXP3 maintained CD69 levels. This suggested that butyrate inhibited Treg activation as well as reduced their IL-2 responsiveness. In comparison, butyrate induced CD25 expression in activated Teff cells. (Figure 6-3A) Coupled with the induction of FOXP3, these findings suggested that butyrate promoted the IL-2 responsiveness of FOXP3+ Teff cells. CD4+ T-cells express key immune checkpoints whose modulation affects both the cells per se and others in the microenvironment. CTLA4 is an important mediator of Treg cell-mediated immunosuppression. (33) Similarly, ICOS is highly expressed on activated T cells. (343) We found that butyrate downregulated both CTLA4 and ICOS expression in naïve Treg cells upon activation. (Figure 6-3C/D) In comparison, CTLA4 expression remained low in FOXP3+ Teffs but ICOS expression was upregulated. LAG3 expression remained consistent in both cell types till the highest dose of butyrate in naive Treg cells. (Supplementary Figure 6-2A) Collectively, these data suggest that butyrate differentially modulated naïve Treg and Teff cells. It had a deleterious effect on the co-inhibitory capacity of naive Tregs post-activation whilst inducing a regulatory phenotype in naïve Teff cells.

  -3A) Strikingly, palmitate neither modulated the proliferation nor FOXP3 expression in either of the Treg or Teff cells. (Supplementary Figure 6-4A) IL-2 responsiveness and PD1 expression were ,also unaffected. (Supplementary Figure 6-3B, 4B) Palmitate also did not perturb the capacity of our cells to secrete IFN!, IL-2 and IL-17A. (Supplementary Figure 6-3C) Due to the lack of any effect, we wondered if palmitate was even being incorporated by our cells. Being an LCFA, it may require a dedicated transporter such as CD36. (45) A search of the single-cell atlas of T cells online also demonstrated no expression of CD36 on peripheral or intrahepatic CD4+ T cells. (http://hcc.cancerpku.cn/) We confirmed this via FACS staining too. (Supplementary Figure 6-5A) All of this suggested that passive diffusion was the only logical route for palmitate incorporation. To study this, we used BODIPY-palmitate, which is a fluorescent dye attached to palmitate. When incorporated into cells, its fluorescence could be quantified via flow cytometry. Indeed, we found BODIPY-palmitate to be taken up by both circulatory and intrahepatic CD4+ T cells in a dose-dependent manner. (Supplementary Figure 6-5C) This was further increased upon acute activation (2hrs) of our cells. (Supplementary Figure 6-5B) In conclusion, CD4+ T cells incorporated palmitate via passive diffusion however this did not modulate their phenotype nor function. Hence, the observed effects of butyrate on naïve Treg and Teff cells were independent of FAO.

Figure 6 -

 6 Figure 6-2A) The PTGER2 gene promotes Akt signalling, IFNy secretion and IL-10

  Interestingly, TCF7 is known to polarise Treg cell differentiation towards Teff cells instead. (362) In parallel, the PTGER2 gene promotes IFN! secretion and IL-10 inhibition. (274) SYNE2 and NCR3 genes produce proteins for the cytoskeleton and cell membrane. (348, 363) Furthermore, the signalling pathway analysis of these cells demonstrated that most genes were implicated in JAK-STAT, hypoxia and WNT signalling. (Figure 6-4C) Hence, Cluster 2 cells represent effector non-Treg cells. Cluster 7 cells upregulated ID2, IL17RB, CD40LG, CYP1B1, and IFITM1/2 genes. The ID2 gene is implicated in reducing FOXP3 expression and promoting the conversion of Treg cells into a Th17 phenotype. (364) To complement this, the IL17RB gene codes for the IL-17RB receptor involved in IL-17RB cytokine-based stimulation. (365) Although the IL17 family consists of 6 different proteins, they are all relatively homologous and similarly upregulate NF-kB and MAPK signalling pathways. (365) The CD40LG gene promotes the expression of the TNF superfamily protein implicated in the maturation of antigen-presenting cells. (366, 367) The CYP1B1 gene is increased upon kyurenine-based stimulation and arylhydrocarbon signalling to promote Treg cell differentiation and immunosuppressive function. (368) Finally, the IFITM1/2 genes are implicated in promoting Th2-like phenotype amongst CD4+ T cells. (369) Overall, the genes expressed by these cells were dominated by TGFβ and hypoxia signalling pathways as well NF-kB, TNF⍺, and JAK-STAT. Collectively, this suggests that cluster 7 cells have an IL-17-secreting transcriptomic profile. Cluster 4 cells upregulated AQP3, CSF2RB, LAG3, MAF, and JUN genes. The AQP3 is elevated in Th17 cells as well as those actively trafficking. (270, 370) The CSF2RB gene partially forms the receptor complex for IL-3/5 and colony-stimulating factor (CSF) signalling. (371) LAG3 is involved in promoting immunoregulation and self-tolerance. (360) The MAF gene is highly expressed in both Treg cells and Th17 cells. (277) It encodes for the c-Maf transcription factor which alongside STAT3 and other co-factors promotes the synthesis of cytokines such as IL-10, -21, and -23. (277) JUN codes for c-Jun, a member of the AP-1 family, involved in T cell development and resistance to exhaustion. (372, 373) Collectively, these cells were supported by similar signalling pathways to those of cluster 7; NF-κB, TNF⍺, JAK-STAT however there was less dominance of TGFβ and hypoxia signalling. This suggests that these cells are similarly effector non-Treg cells albeit with distinct pathway adaptations. In contrast, cluster 5 cells upregulated TIGIT, LAG3, SAT1, TNFRSF1B, ICOS, IL2RB, and CTLA4 genes. Most of these genes are well-established in promoting Treg cells differentiation and immunosuppressive functions through their respective surface co-inhibitory receptors. (333, 334, 374) IL2RB forms part of the IL-2 signalling complex alongside CD25 (IL2RA) and IL2RG to promote IL-2 responsiveness amongst Treg cells. (236) We also identified TNFRSF1B that promotes expression of the TNFR2 receptor. (301) This receptor is highly expressed on Treg cells with superior immunosuppressive functions. (375) This suggested that butyrate could increase TNF secretion from Treg cells as well as their capacity to respond via TNFR2.

4 )

 4 These two datasets (Treg and Teff cells) were then integrated using the Seurat v4 package. (Figure 6-5A) This pipeline aims to connect similar cell states, genes and reduce batch effects. Overall, 5563 Treg and 8663 Teff cells were anchored and clustered using their 2000 most variable features. (Figure 6-5B, Table 6-5) Firstly, the data confirmed Treg cells as forming clusters 4-7 and their butyrate counterparts additionally forming clusters 8-9 and 11. (Figure 6-5C, Table 6-5) These new butyrate-induced Treg cell clusters were not identified in any Teff condition indicating a specific role for butyrate in promoting these clusters. In comparison, the Teff cells mainly formed clusters 0, 2-3, 10, and 12. Certain activated Teff cells are known to upregulate FOXP3 to adopt an iTreg-like profile when cultured with IL-2. This exact phenomenon was noted with the induction of clusters 1, 4-7 in the control/butyrate Teff cells. Cluster 1 was unique to these Treg cluster-overlapping Teff cells. The cells in this cluster expressed genes involved in cytolysis (GNLY, GZMA), antigen recognition (TRBC2) and pro-inflammatory functions (FOS/JUN). In comparison, the other Treg-like clusters (4-7) demonstrated high expression of many genes involved in immunosuppression/homeostasis (FOXP3), co-inhibition (CTLA4, LAG3, PRDM1), Treg differentiation (IRF4), bystander T cell apoptosis (LGALS3), cytokine signalling (MAF) and tumoural growth (MALAT1). We also verified the origins of clusters 1, 4-7 in their pre-integrated Treg and Teff datasets to identify their locations on their original UMAPs as well as surface markers. (Supplementary Figure 6-8C) In the Treg cell dataset, these cells were found almost exclusively in clusters 0, 1, 3, and 6. (Figure 6-5A) Of these, only cluster 6 differentially expressed surface markers; CD25, HLA.DR, CD2, and HLA.ABC. In the Teff cell dataset, these cells mainly belonged to clusters 1, 2, 7, and some to 3 and 9 too. (Figure 6-5A, Supplementary Figure 6-8C) Of these, clusters 1 and 9 differentially expressed CD4 and CD11a/CD18 whereas cluster 3 expressed CD162 only. (Table 6-3B) Furthermore, all the Treg-like cluster genes from the integration analysis were expressed at relatively lower levels in the Teff clusters (0, 2-3, 10, 12) consistent with their expected phenotype. (Figure 6-5D) Interestingly, we identified the new butyrateinduced Treg cells (clusters 8-9, 11) expressing these genes at an intermediate level between the control Treg and Teff cell clusters. This is consistent with our phenotypic data demonstrating the deleterious effects of butyrate on naïve Treg cells. Next, we identified the signalling pathways and regulons driving these cell clusters. (Figure 6-5E/F) The Treg cell clusters (4-7) were driven by p53 and TRAIL signalling. The Teff cell clusters (0, 3, 10, 12) were driven by diverse signalling pathways (eg. JAK-STAT, NFκB, hypoxia, PI3K, TGFβ) instead. Interestingly, certain Treg cells(clusters 8, 9, 11) were also driven by these same diverse signalling pathways (as well as p53). This was further confirmed when we studied their regulons as certain Teff cells and Treg cells upregulated the same regulons (eg. GATA3, FOS/JUN, NFKB1, RELA, and STAT3). These regulons are implicated in pro-that stabilise FOXP3 expression, promote Treg cell proliferation, and modulate IL-2/17A secretion.(376)(377)(378) In the activated CD4+ T cell control, we found STAT5 phosphorylated at 2hrs with a reduction at 24hrs. (Figure6-6A) However, in the activated condition with butyrate, there was a reduction of pSTAT5 within 2hrs, which was sustained at 24hrs too. This effect was also butyrate dose-dependent. Importantly, this effect was neither specific to Treg nor Teff cells. (Figure 6-6B) We also verified whether this was specific to STAT5 or related to upstream JAK1/3 blockade. Interestingly, JAK1 phosphorylation was increased in Treg cells independent of the butyrate dose. (Supplementary Figure 6-6A) The is likely related to the increased baseline IL-2 responsiveness of Treg cells via the combined expression of CD25 (IL-2⍺) and IL-2 β/! chain receptors. In parallel, JAK3 phosphorylation was increased in Teff cells with high dose butyrate. (Supplementary Figure 6-6B) Put together, butyrate alongside acute CD4+ T cell activation was associated with increased upstream JAK1/3 phosphorylation but reduced phosphorylation of downstream STAT5.

(Figure 6 -

 6 Treg cellsTreg cells enact their immunosuppressive functions through multiple mechanisms.(333) The data thus far demonstrated that butyrate downregulated FOXP3, co-inhibitory receptors as well as pSTAT5 and p38 in Treg cells. The singlecell analyses also suggested the de novo induction of FOXP3 lo cells. All these changes suggested a reduction in Treg cell immunosuppressive function. We verified this hypothesis in 2 ways: in vitro immunosuppression and cytokine secretion assays.For the former, we firstly pre-cultured naïve Tregs for 6 days as above in 100µM butyrate. These Tregs were then co-cultured at different ratios with autologous naïve Teffs (CFSE-stained) as well as anti-CD3/28 activator beads for 4 days. Our data demonstrated that low-dose butyrate maintained Treg immunosuppressive capacity.(Figure6-7A, Supplementary Figure6-4C)In parallel, at the end of the 6-day culture above, we further stimulated our Treg cells with PMA/ionomycin and BD Golgistop for 5 hours. They were then fixed, permeabilised, and stained with the cytokine antibodies. Strikingly, our data showed that low dose butyrate induced IL-2 and IL-17A, secretion from the Treg cells postactivation. (Figure6-7B-D) This correlated with previous data demonstrating promotion of the STAT3 regulon and inhibition of STAT5 phosphorylation by butyrate. 5F and 6A) Moreover, as butyrate upregulated the TNF-signalling pathway, we checked for TNF⍺ secretion too. (Figure 6-4C) Indeed, this was biologically confirmed via increased expression of the TNF⍺ gene amongst butyrate-induced cell clusters as well as TNF⍺ secretion. (Figure 6-7D) Considering that the Treg cells continued to express FOXP3, this suggested that low dose butyrate was promoting pro-inflammatory properties via a FOXP3-independent mechanism whilst maintaining immunosuppressive functions.(378)

  pathways such as TNF⍺, JAK-STAT driven by regulons such as GATA3, JUN/FOS and STAT3. Indeed, these findings were biologically validated as we demonstrated the increased secretion of TNF⍺, IL-2, and IL-17A from the activated Treg cells measured via flow cytometry. Interestingly, a similar phenomenon had already been described by us and others in the past. (144, 379-381) All Treg cells in these studies (and ours) demonstrated immunosuppressive and homeostatic functions in different model systems despite their pro-inflammatory capabilities. It is plausible that the Treg cells implicated in these model systems consisted of similarly distinct regulatory and effector subpopulations. Nevertheless, by moving beyond multicolour flow cytometry to multimodal single-cell profiling, we gained novel insights into the entire cellular transcriptome with their matched surface protein expression.

FOXP3

  lo Treg/Teff cells were being commonly induced by butyrate. In another study, using Cytometry by Time of Flight (CyTOF), 22 novel Treg cell subsets were identified using more than 30 surface markers. (155) Using a different single-cell profiling technology, another group also studied the Treg cell transcriptome with different results. (124) They identified 6 splenic Treg cell clusters in mice based upon 100 variable features. 3 clusters had 'resting' state signatures and the other 3 had more 'activated' state Treg signatures. Similar to us, they too found diversity in FOXP3 expression levels as well as effector cell signatures. Our study had additional resolution to delineate the regulons driving the differentiation of pro-inflammatory butyrate-induced Treg cell subpopulations; GATA3, FOS/JUN, NFKB1, RELA and STAT3 .

Figure 6 Figure 6 - 4 Figure 6

 6646 Figure 6-1

Figure 6 Figure 6 - 6 Figure 6

 6666 Figure 6-5

FigureFiguresFigure 6 - 1 :

 61 Figure and Table Legends (Chapter 6)

Figure 6 - 2 :

 62 Figure 6-2: Butyrate converged FOXP3 expression in naïve Treg and naïve Teff cells towards a FOXP3lo state.

Figure 6 - 3 :

 63 Figure 6-3: High dose butyrate negatively impacted expression of Tregassociated markers whereas low dose did not.

Figure 6 - 4 :

 64 Figure 6-4: Single cell transcriptomics revealed 8 distinct Treg cell subpopulations.

Figure 6 - 5 :

 65 Figure 6-5: Integrated transcriptomic confirmed butyrate induced a FOXP3 lo state in both Treg and Teff cells.

Figure 6 - 6 :

 66 Figure 6-6: Butyrate inhibitions phosphorylation of STAT5 during early CD4+ T cell activation.

Figure 6 - 7 :

 67 Figure 6-7: Butyrate induced a pro-inflammatory profile in immunosuppressive Treg cells.

Table 6 - 3 :Table 6 - 4 :Table 6 - 5 :

 636465 (A) Corresponding surface protein profile of 5 Treg cell clusters based on Wilcoxon Rank Sum Test and Bonferroni correction (p_val_adj <0.05). To improve specificity, the minimum fold change per cluster was increased to 0.9. The final column shows the unique surface marker that characterised each population. (B) Differentially expressed surface markers of the Teff cells based on Wilcoxon Rank Sum Test and Bonferroni correction (p_val_adj <0.05). Parallel demonstration of the transcriptome (4A) and surface protein profile (4B) of the Teff cell split by cluster. This was based on Wilcoxon Rank Sum Test and Bonferroni correction (p_val_adj<0.05) amongst minimum 25% of cells per cluster. DE gene expression analysis of the integrated Treg and Teff transcriptomes based on Wilcoxon Rank Sum Test and Bonferroni correction (p_val_adj <0.05). The data is shown cluster-by-cluster.

Finally

  , in terms of Treg cell expansions, our work found distinct heterogeneity within these Treg cells. This is therapeutically important as the heterogenous subpopulations were also dominated by different signalling pathways. It is thus plausible that not all Treg cells post-expansion function similarly. In terms of differentiation trajectories, past work has been based upon pseudotime analyes of cell subsets at a single timepoint. We took this further by performing similar analyses of Treg cells pre-and post-expansion. These biologically relevant endpoints provided us with better resolution to define Treg cell states and trajectories in the in vitro expansion setting. We also identified the regulons implicated in these cell states for future genetic modulation. This is important to promote the development of functionally homogeneous Treg cells post-expansion. With regard to the second part of this work, we have better delineated the influence of dietary metabolites on peripheral immunoregulatory mechanisms. Dietary metabolites such as free fatty acids, amino acids, and glucose are increasingly implicated in the functions of Treg cells. These are important observations because circulating and tissue-resident Treg cells are exposed to diverse microenvironments in health and disease. The perturbation of immunometabolic mechanisms could be an important arm in modulating Treg cells to prevent autoimmunity, promote allograft tolerance or even antitumour immunity. Our work also elucidated new mechanisms by which butyrate could perturb Treg cells. Published works had already demonstrated the capacity of butyrate to inhibit histone deacetylase, signal via the FFAR receptors, promote the phosphorylation of mTORC1 and STAT3. In comparison, we found that butyrate inhibited the phosphorylation of STAT5 and p38. It also upregulated a range of regulons such as GATA3, JUN/FOS, NFKB1, STAT3, RELA and ETS1. Most importantly, these effects were independent of fatty acid oxidation and TGFβ. All of these mechanisms could be targeted directly via butyrate or through other pharmacological or genetic means to finely tune Treg cell functions depending on the disease setting. Collectively, this work further confirmed that dietary metabolites have effects on the adaptive immune system that go beyond just engaging in classic metabolic pathways.

  measured via single cell (Rhapsody) and bulk means (flow cytometry). However, this did not explain how transcriptomically homogenous naïve Treg and Teff cells could have such distinct differentiation trajectories. In this regard, future work needs to integrate other layers of cellular anatomy too. For example, the role of the methylome in modulating FOXP3 stability and Treg cell functions is well-established.(126, 170, 245, 303) The Treg cell proteome has also been linked with particular signalling pathways (eg. STAT4) that promote Treg cells with IFN! secreting capacity. (147) In that work CD49d and CCR4 were found as markers of the Fraction III Treg cell subsets with pro-inflammatory functions. Furthermore, a relatively unexplored area is the metabolome, which has been linked with development of diverse tissue pathology.(391-393) The metabolome consists of multiple nutrients (eg. lipids, fatty acids, amino acids). As alluded to in Chapter 1 of this work, a number of these metabolites can perturb Treg cell functions. In this thesis, we only elucidated the effects of butyrate and palmitate on Treg cells. Large-scale high throughput studies are needed to study a wider range of fatty acids -especially the polyunsaturated type. Delineating these aspects via combined immune assays and single cell mass spectrometry could Introduction CD4 + regulatory T (Treg) cells play critical roles in promoting and maintaining immune homeostasis.(5, 334, 395) Although the functions of Treg were first characterised in mice, subsequent studies have also demonstrated similar roles in humans. (395-397) Due to ethical restrictions, studies on the mechanisms of actions of human Treg cells have been restricted

  tTreg cells are identified by the expression of the transcription factor Helios and in mice, Neuropilin-1 as well.(126, 136) Despite these differences, the expression of the transcription factor FOXP3 was once considered a reliable lineage marker of Treg cells.(149) Subsequent data showed that using FOXP3 and the expression of CD45RA (naïve cell state), human CD4 + T cells from the peripheral blood could be split into different subpopulations.(9) (Figure A-1) Within these subpopulations, one is FOXP3 hi (fraction II) and the other two FOXP3 lo (Fraction I that is CD45RA + and Fraction III that is CD45RA -). (Figure A-1B) However, even within the FOXP3 expressing populations, only two (Fraction I/II) were identified as having immunosuppressive properties.(9) The third, FOXP3 hi CD45RA -population (Fraction III) had effector non-Treg cell functions as evidenced by the secretion of IFN-γ, IL-2, and IL-17A. Both Fractions II/III were the result of activation and differentiation of Fraction I FOXP3 hi cells. However, the cell fateMethodsIsolating Treg cellsThe three approaches for isolating Treg cells from lymphocytes are either through magnetic beads or fluorescence based (FACS) sorting or a hybrid approach.

6 .Figure

 6 Isolate Treg cells by one of the following approaches: a. Flow cytometric sorting only (Figure A-2.1) b. Hybrid: T cell magnetic bead-based enrichment (using CD4 + or CD25 + T cell enrichment beads) before flow cytometric sorting (Figure A-2.3) c. Magnetic beads only: Depending on the isolation kit used this involves a two-or threestep procedure to isolate CD4 + CD25 + T cells or CD4 + CD127 lo CD25 + Treg (

3. 1 . 2 . 1 .

 121 Hybrid: CD4 + /CD25 + T cell enrichment If CD4 + T cell enrichment is initially desired, then this can performed by either positive or negative selection of CD4 + T cells. During positive selection, these cells are specifically bound to magnetic particles whereas, in negative selection, these cells are untouched. The negative selection approach is usually preferred especially as it may be necessary to use other beads for downstream immunoassays. These cells can then be marked with fluorescent antibodies for subsequent FACS-based sorting.

7 .

 7 Put the plate in the incubator at 37°C/5% CO2. (Figure A-3)

4 3 )

 43 cells for realtime immunophenotyping to check the expression of key markers such as CD25, FOXP3, and Ki67 (a proliferation marker). (Figure A-If sufficient cells are available, use 5 x 10 4 to check the Treg cell cytokine secretion profiles via PMA/ionomycin-based stimulation.GMP Modifications (optional)The appropriate protocol modifications to produce clinical-grade Treg cells will require close coordination with the national medical regulator and supplier. The local platform for processing the cells will need dedicated technical staff too. At our Cell Therapy Unit, we have the Miltenyi CliniMACS Prodigy system for the sterile enrichment, isolation, and culture of clinical-grade Treg cells. We envisage that the Treg isolation process could be sped up in the future with newer platforms such as the BD Influx or MACSQuant Tyto Cell Sorters. Furthermore, only GMP-compliant products must be used from the very start of processing the patient sample. For example, Miltenyi have developed the following compliant antibodies for sorting; CD4 VioBlue, CD25 PE, CD127 FITC, CD45RA FITC. In terms of cell expansion, the following GMP-grade products are available to replace the laboratory protocol 7.Treg cells are added at different volumes depending on the Treg:Teff ratio under study.

Figure A- 1 FigureFigure A- 1 FACS

 11 Figure A-1

Figure A- 2

 2 Figure A-2

Figure A- 3

 3 Figure A-3

1

 1 Proposed plate layout of the in vitro Treg cell suppression assay using Teff cells and irradiated APCs. Different Treg:Teff ratios are used to test the Treg cell suppression function. As a control, it is important to also have unstimulated Teffs as a separate condition. After the respective cellular components have been added to their wells, the volumes can be topped up with media to ensure 200µl per well. If soluble anti-CD28 or anti-CD3/28 activator beads are used instead then more volume may need to be added to each well to have 200µl altogether.

Table 6 -

 6 5: Integrated Treg/Teff clusters ....................................................................................................

FIGURE AND

TABLE LEGENDS (CHAPTER 6) ....................................................................................... FIGURES ........................................................................................................................................................... Figure 6-1: Butyrate did not modulate the differentiation of naïve Tregs into activated cells. ................. Figure 6-2: Butyrate converged FOXP3 expression in naïve Treg and naïve Teff cells towards a FOXP3lo state. ...........................................................................................................................................................

Table 2 -1: Registered trials on Clinicaltrials.gov involving Regulatory T-cell Therapy in transplantation Checked: 28 th April 2019

 2

	Organ/Tissue		Trial ID/Name	Lead		Dosing	Nature of Cell Product
	Transplanted			Institution(s)
	Liver			Todo/Okumura (183)	Hokkaido, Japan	8.92-37.7x10 6 /kg cells but	Donor-specific and with
							0.23-6.37x10 6 /kg Treg cells	costimulation blockade
	Liver	(2-6yrs	post-	ARTEMIS	UCSF, USA	300-500x10 6 cells	Donor-specific
	transplant)		(NCT02474199)
	Liver (12 weeks' post-	dELTA	UCSF, USA	25-960x10 6 cells	Donor-specific
	transplant)		(NCT02188719)
	Liver			LITTMUS-UCSF	UCSF, USA	Target: 100-500x10 6 cells	Donor-specific
				(NCT03654040)
	Liver			LITTMUS-MGH	MGH, USA		Target: 2.5-500x10 6 cells	Donor-specific and with
				(NCT03577431)				costimulation blockade
	Liver			ThRIL (NCT02166177) King's	College	0.5-6.5x10 6 /kg	Polyclonal
					Hospital, UK

Table 2 -2: Key known unknowns in the field of Treg-based cell therapy for transplant-related applications Known Unknowns

 2

  when naïve CD4+ T cells were cultured with TGFβ. This effect was CNS1-dependent too (compared to CNS3 in the previous study). From a mechanistic perspective, culturing naïve CD4+ T cells with butyrate pre-cultured DCs as well as anti-CD3 and TGFβ significantly increased the cell numbers of CD4+FOXP3+ cells.

	nTreg cells too. In terms of the phenotype, expression of CD25/CTLA4 proteins and to support epigenetic mechanisms and polarise CD4+ T cells towards a regulatory
	IKZF4/SATB1 mRNA was upregulated in all conditions with TGFβ (but excluding phenotype.
	rapamycin). The condition with butyrate was associated with the highest % of IFNy
	secretion	(~5%).	However,	despite	these	phenotypic	data,	the
	Butyrate acetylated histone 3 (H3) in DCs and pre-cultures up till 2mM were shown to TGFβ/ATRA/Rapamycin conditioned iTreg cells were the most immunosuppressive
	increase FOXP3 induction in CD4+ T cells. This latter observation has been against responder CD4+ and CD8+ T cells. This latter observation was unfortunately
	independently confirmed by other groups as well although their mechanisms not replicated in the in vivo xeno-graft versus host disease (xenoGvHD) model.
	implicated the SLC5A8 high-affinity SCFA transporter, DC upregulation of the Overall, this work demonstrated the potential for SCFAs to reprogram naïve human
	indoleamine 2,3-dioxgenase-1 and Ald1A2 (retinoic acid producer) enzymes, or
	downregulation of co-stimulation modules (CD80/86 and HLA-DR). (222, 223)
	cultured for 6 days with plate-coated anti-CD3 (5µg/ml), soluble anti-CD28 (1µg/ml),
	IL-2 (100U/ml), TGFβ (5ng/ml) and in separate conditions; all-trans retinoic acid
	(ATRA; 10nM), with rapamycin (100nm) and butyrate (100µM). A parallel condition Finally, although pentanoate and butyrate both increased IL-10, only butyrate
	also involved CD4+CD25++ cells (labelled as naturally-occurring Treg; nTreg cells in increased FOXP3 after 4 weeks of dietary supplementation of either SCFA (150mM).
	original manuscript). Interestingly, the conditions with IL-2/TGFβ/ATRA and IL-This effect was replicated in vitro with 1mM of butyrate too. Overall, these data
	2/TGFβ/butyrate induced the highest expression of FOXP3 (60-80%)amongst all demonstrated the capacity of pentanoate to modulate intracellular metabolic pathways
	CD4+ (and CD4+CD25+) T cells. This was on par with %FOXP3 in the unstimulated

In comparison to the above-discussed work, this study neither demonstrated a direct in vivo role for butyrate in promoting Treg cells nor delineated the mechanism for their observations. Using the SPF model, another group also suggested that butyrate and propionate promoted extrathymic pTreg cells in the colon. (208) Although not statistically confirmed, butyrate was shown to promote more FOXP3 compared to other SCFAs Collectively, these studies provided evidence for direct and indirect (via DCs) effects of butyrate on expanding CD4+FOXP3+ p/iTreg cells through diverse mechanisms. An important knowledge gap in these studies was the lack of comprehensive phenotypic profiling of the SCFA-induced Treg cells and demonstration of their immunosuppressive capacity. Taking these data forward, another group integrated SCFAs into their in vitro Treg cell expansion protocol.(26) In this work, human naïve CD4+ T cells were CD4+ T cells towards an iTreg-like phenotype in 6 days however different additional modulatory agents are needed to boost immunosuppressive capacity in their protocol. Although most work continues to focus on C2-C4 SCFAs, a recent study also demonstrated similarly multimodal effects via pentanoate (C5) instead. (212) Adding pentanoate (1-5mM) to naive CD4+ T cells cultured under Th17-inducing conditions (IL-1β, IL-6, IL-23) led to a significant reduction in IL-17A secretion as well as genes such as IL17A, IL21, STAT3, RORC and TGFB3. This was also shown in vivo through amelioration of experimental autoimmune encephalitis (EAE). In parallel, pentanoate promoted glycolysis and increased IL-10 secretion. The underlying mechanism was postulated as involving increased glycolysis, increased acetyl-coenzyme A levels (acetyl-CoA), which increased intracellular capacity to acetylate histone proteins.

Table 5 -1 Abseq surface profiles of each cluster

 5

	avg_log2FC p_val_adj cluster	gene
	0.51738654	7.00E-103	0 CD3.UCHT1.CD3E.AHS0231.pAbO
	0.368428144	9.23E-64	0 CD5.UCHT2.CD5.AHS0047.pAbO
	0.289251868	1.88E-55	0 CD2.CD2.AHS0029.pAbO
	0.254261564	5.13E-44	0 CD69.CD69.AHS0010.pAbO
	0.566264648	9.84E-98	1 CD3.UCHT1.CD3E.AHS0231.pAbO
	0.415471584	9.68E-66	1 CD5.UCHT2.CD5.AHS0047.pAbO
	0.290049318	6.12E-41	1 CD69.CD69.AHS0010.pAbO
	0.60730184	1.48E-127	2 CD5.UCHT2.CD5.AHS0047.pAbO
	0.574418673	3.66E-105	2 CD3.UCHT1.CD3E.AHS0231.pAbO
	0.333842927	1.59E-33	2 CD69.CD69.AHS0010.pAbO
	0.338975902	1.36E-17	2 CD4.SK3.CD4.AHS0032.pAbO
	2.61955403	0	3 HLA.DR.CD74.AHS0035.pAbO
	1.992800854	0	3 HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO
	1.230326865	0	3 CD45RA.HI100.PTPRC.AHS0009.pAbO
	1.243503243	2.51E-248	3 CD44.515.CD44.AHS0140.pAbO
	0.292893996	1.44E-195	3 CD46.CD46.AHS0071.pAbO
	0.97519668	2.73E-96	4 CD11a.ITGAL.AHS0081.pAbO
	0.504099215	4.10E-87	4 CD18.ITGB2.AHS0091.pAbO
	0.252507971	4.88E-48	4 CD162.SELPLG.AHS0139.pAbO
	0.371186772	1.94E-22	4 CD44.515.CD44.AHS0140.pAbO
	0.252664346	3.46E-15	4 CD279.EH12.1.PDCD1.AHS0014.pAbO
	0.340306522 0.000359831	4 CD8.SK1.CD8A.AHS0228.pAbO
	0.720374568	1.49E-55	5 CD279.EH12.1.PDCD1.AHS0014.pAbO
	0.337687029	2.45E-30	5 CD18.ITGB2.AHS0091.pAbO
	0.528034195	1.51E-19	5 CD11a.ITGAL.AHS0081.pAbO
	0.345653469	1.10E-14	5 CD2.CD2.AHS0029.pAbO
	1.433541306	2.74E-53	6 CD18.ITGB2.AHS0091.pAbO
	0.649798922	2.65E-48	6 CD162.SELPLG.AHS0139.pAbO
	1.265373844	5.06E-40	6 CD31.WM59.PECAM1.AHS0170.pAbO
	1.317325161	6.78E-39	6 HLA.DR.CD74.AHS0035.pAbO
	0.718764138	1.55E-23	6 CD11a.ITGAL.AHS0081.pAbO
	0.679584543	8.51E-19	6 CD44.515.CD44.AHS0140.pAbO
	1.123515294	2.94E-58	7 CD45RA.HI100.PTPRC.AHS0009.pAbO
	1.949347975	2.87E-53	7 HLA.DR.CD74.AHS0035.pAbO
	0.705609051	1.42E-24	7 HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO
	0.547351644	1.69E-13	7 CD44.515.CD44.AHS0140.pAbO

Table 5 -2 Abseq surface profiles of each cluster avg_log2FC p_val_adj cluster gene

 5

	0.359966809	1.72E-63	0 CD5.UCHT2.CD5.AHS0047.pAbO
	0.389831365	1.64E-56	0 CD3.UCHT1.CD3E.AHS0231.pAbO
	0.263810488	3.38E-48	0 CD31.WM59.PECAM1.AHS0170.pAbO
	0.272025865	6.80E-21	0 CD69.CD69.AHS0010.pAbO
	1.010085162	9.57E-96	2 CD11a.ITGAL.AHS0081.pAbO
	0.586742387	1.34E-90	2 CD18.ITGB2.AHS0091.pAbO
	0.883947052	1.35E-62	2 CD44.515.CD44.AHS0140.pAbO
	0.438804307	2.15E-60	2 CD162.SELPLG.AHS0139.pAbO
	0.370298904	1.86E-33	2 HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO
	0.498953595	6.21E-05	2 CD45RA.HI100.PTPRC.AHS0009.pAbO
	1.793401019	6.96E-59	3 HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO
	0.287288613	1.27E-33	3 CD46.CD46.AHS0071.pAbO
	0.354056223	8.74E-27	3 CD25.M.A251.IL2RA.AHS0166.pAbO
	0.279148177	3.02E-20	3 CD162.SELPLG.AHS0139.pAbO
	0.5308884	3.17E-15	3 CD4.SK3.CD4.AHS0032.pAbO
	0.431037738	7.55E-14	3 CD44.515.CD44.AHS0140.pAbO
	0.31372159	2.13E-11	4 CD59.CD59.AHS0224.pAbO
	0.646573122	1.42E-09	4 CD8.SK1.CD8A.AHS0228.pAbO
	0.378247022	1.08E-06	4 CD2.CD2.AHS0029.pAbO
	0.551350583	3.57E-06	4 CD4.SK3.CD4.AHS0032.pAbO
	RNA profiles of each cluster
	avg_log2FC	p_val_adj	cluster gene
	0.886566537	4.02E-96	0 RPS8
	0.810068882	2.41E-92	0 RPL34
	0.852773262	4.54E-88	0 RPS6
	0.826695617	2.08E-81	0 RPL32
	0.835646171	2.72E-73	0 RPL23
	0.897261088	1.58E-71	0 RPS24
	0.782893221	2.36E-68	0 RPL37A
	0.895007865	2.04E-67	0 RPS18
	0.770839722	2.70E-62	0 RPL31
	0.840746745	2.35E-60	0 TNFAIP3
	0.751138422	2.38E-60	0 RPL9

Table 5 -3 Abseq profiles of each cell type

 5

	avg_log2FC p_val_adj	cluster	gene
	1.010499962	8.51E-99 CD8SP	CD11a.ITGAL.AHS0081.pAbO
	0.593677496	4.59E-96 CD8SP	CD18.ITGB2.AHS0091.pAbO
	0.865650995	5.89E-61 CD8SP	CD44.515.CD44.AHS0140.pAbO
	0.426129181	3.33E-58 CD8SP	CD162.SELPLG.AHS0139.pAbO
	0.369920511	1.43E-34 CD8SP	HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO
	0.250538432	7.45E-10 CD8SP	CD279.EH12.1.PDCD1.AHS0014.pAbO
	0.466037858 0.001434201 CD8SP	CD45RA.HI100.PTPRC.AHS0009.pAbO
			CD4SP
	1.748005091	2.00E-65	Tregs	HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO
			CD4SP
	0.2715671	1.13E-34	Tregs	CD46.CD46.AHS0071.pAbO
			CD4SP
	0.323408329	2.23E-23	Tregs	CD25.M.A251.IL2RA.AHS0166.pAbO
			CD4SP
	0.527154645	2.68E-17	Tregs	CD4.SK3.CD4.AHS0032.pAbO
			CD4SP
	0.391459961	7.35E-13	Tregs	CD44.515.CD44.AHS0140.pAbO
	0.351241173	4.95E-49 CD4SP	CD5.UCHT2.CD5.AHS0047.pAbO
	0.304661659	4.50E-31 CD4SP	CD3.UCHT1.CD3E.AHS0231.pAbO
	0.30461034	2.40E-12 CD4/8 (DP) CD2.CD2.AHS0029.pAbO
	0.332234382	1.13E-08 CD4/8 (DP) CD8.SK1.CD8A.AHS0228.pAbO
	RNA profiles of each cell type
	avg_log2FC p_val_adj	cluster	gene
	4.424297791	0 CD8SP	CCL5
	2.679980862	2.93E-183 CD8SP	GZMA
	2.97047388	3.33E-170 CD8SP	NKG7
	2.153487626	6.98E-141 CD8SP	GZMK
	2.018488295	5.12E-132 CD8SP	CST7
	2.098419573	4.59E-126 CD8SP	CTSW
	2.279501553	5.40E-110 CD8SP	DNAJB1
	1.783477655	3.56E-79 CD8SP	KLF6
	1.766046631	6.06E-74 CD8SP	RUNX3
	1.814772132	9.84E-67 CD8SP	NFKBIA
	1.777879397	4.12E-66 CD8SP	ZFP36
	2.202416889	1.45E-64 CD8SP	HSPA1B

Table 5 -4 Abseq profiles of each cell type

 5

	avg_log2FC p_val_adj	cluster	gene
	0.328792432	4.10E-137 Infiltrating CD44.515.CD44.AHS0140.pAbO
	0.286122031	2.50E-20 Infiltrating CD4.SK3.CD4.AHS0032.pAbO
	0.491419143	9.11E-236 Th17	CD2.CD2.AHS0029.pAbO
	0.471144024	1.93E-172 Th17	CD44.515.CD44.AHS0140.pAbO
	0.347767182	7.73E-122 Th17	CD11a.ITGAL.AHS0081.pAbO
	0.414508146	4.05E-82 Th17	CD25.M.A251.IL2RA.AHS0166.pAbO
	0.298408033	7.00E-55 Th17	CD162.SELPLG.AHS0139.pAbO
	0.465649592	7.03E-27 Th17	CD4.SK3.CD4.AHS0032.pAbO
	1.163271269	9.81E-191 Th1 CTL CD11a.ITGAL.AHS0081.pAbO
	0.460710369	7.76E-128 Th1 CTL CD18.ITGB2.AHS0091.pAbO
	0.631866006	3.40E-90 Th1 CTL CD162.SELPLG.AHS0139.pAbO
	0.4342331	1.08E-83 Th1 CTL CD279.EH12.1.PDCD1.AHS0014.pAbO
	0.362062143	2.87E-32 Th1 CTL HLA.DR.CD74.AHS0035.pAbO
	0.840813311	1.74E-151 Tregs	CD25.M.A251.IL2RA.AHS0166.pAbO
	1.503292041	1.61E-38 Tregs	HLA.DR.CD74.AHS0035.pAbO
	0.415484035	1.97E-35 Tregs	CD162.SELPLG.AHS0139.pAbO
	1.747579009		0 Naive	CD45RA.HI100.PTPRC.AHS0009.pAbO
	0.846317366		0 Naive	CD31.WM59.PECAM1.AHS0170.pAbO
	0.710367137		0 Naive	CD3.UCHT1.CD3E.AHS0231.pAbO
	0.828391907	3.69E-136 Th0 CTL CD11a.ITGAL.AHS0081.pAbO
	0.32828817	1.35E-79 Th0 CTL CD18.ITGB2.AHS0091.pAbO
	0.386967597	4.58E-55 Th0 CTL CD2.CD2.AHS0029.pAbO
	0.46124738	1.31E-53 Th0 CTL CD279.EH12.1.PDCD1.AHS0014.pAbO
	0.255329937	2.93E-24 Th0 CTL

HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO

	RNA profiles of each cell type 0.82527906 0.000136203 Tregs	HMGB2
	0.845905256	1.88E-137	Naive	CCR7
	avg_log2FC 0.821293623	p_val_adj 1.28E-125	cluster Naive	gene TRABD2A
	0.728626122 0.686461036	8.92E-142 Infiltrating 1.59E-118 Naive LINC00861 ITGB1
	0.50454624 0.684625517	8.53E-105 Infiltrating 1.02E-115 Naive	AHNAK LEF1
	0.599768229 0.760626092	1.33E-90 Infiltrating 4.93E-89 Naive	CD84 IL6ST
	0.561051266 0.720859904	1.21E-87 Infiltrating 2.00E-85 Naive CHRM3.AS2 KLF6
	0.711960433 0.619064017	2.84E-83 Infiltrating 3.25E-69 Naive	MAF SATB1
	0.513123279 0.657259826	1.47E-70 Infiltrating 4.98E-65 Naive	ANXA1 ACTN1
	0.545728428 0.600775919	3.37E-67 Infiltrating 2.84E-59 Naive	GPR183 LRRN3
	0.481217826 0.574666399	2.60E-61 Infiltrating 1.27E-41 Naive	PLP2 NUCB2
	0.586354812 2.441598995	1.44E-57 Infiltrating 0 Th0 CTL	LIMS1 GZMK
	0.494921572 1.891098043	1.40E-54 Infiltrating 2.35E-186 Th0 CTL	GLIPR1 GZMA
	0.981396464 1.323782225	6.13E-193 2.57E-151	Th17 Th0 CTL	CCR6 DUSP2
	1.268626405 1.145266188	2.01E-192 1.25E-121	Th17 Th0 CTL	KLRB1 EOMES
	0.787475643 1.666150533	7.93E-173 1.00E-106	Th17 Th0 CTL	S100A4 CCL5
	0.78048997 1.08442113	1.41E-152 4.46E-66	Th17 Th0 CTL	IL7R CST7
	0.833491527 0.93622022	3.22E-112 3.24E-41	Th17 Th0 CTL	PRDM1 PLEK
	0.592823131 0.874266926	1.80E-102 7.44E-40	Th17 TNFSF13B Th0 CTL LYAR
	0.609283163 1.074610119	6.19E-90 1.55E-36	Th17 Th0 CTL	AHNAK GBP5
	0.600315442 0.882837817	1.67E-64 7.29E-25	Th17 Th0 CTL	ANXA1 KLRB1
	0.645955221 1.370133891	5.72E-63 3.16E-64 Activated Th17	ERN1 ITGB1
	0.621276514 1.159664875	3.29E-55 3.86E-58 Activated Th17	TNFAIP3 S100A4
	2.861720287 1.233372424	0 8.97E-58 Activated Th1 CTL	GZMH FAM129A
	2.642990236 0.92304864	0 5.42E-45 Activated Th1 CTL	FGFBP2 sept-11
	2.429438554 1.466314442	0 4.52E-43 Activated Th1 CTL	GZMA LGALS1
	2.388597434 1.104810191	0 2.53E-34 Activated Th1 CTL	CX3CR1 ANXA2
	2.357109905 0.901337703	3.63E-302 3.02E-30 Activated Th1 CTL	PLEK LGALS3
	3.026090242 0.878662727	7.75E-279 8.84E-29 Activated Th1 CTL	NKG7 ALOX5AP
	1.409953668 2.148841205 0.85212184 0.421919322 1.955938236 0.281255257 0.000136859 Activated CD5.UCHT2.CD5.AHS0047.pAbO 8.90E-257 Th1 CTL 2.47E-26 Activated ACTN4 PRF1 2.43E-62 Activated HLA.DR.CD74.AHS0035.pAbO 1.11E-222 Th1 CTL CST7 2.810325898 5.42E-239 Th1 CTL CCL5 5.01E-31 Activated CD44.515.CD44.AHS0140.pAbO 3.751907263 6.73E-245 Th1 CTL GNLY 0.874926884 3.01E-21 Activated GLIPR1
	2.087716155	0	Tregs	FOXP3
	1.976637889	0	Tregs	IKZF2
	1.381430137	9.93E-216	Tregs	FCRL3
	1.571427203	4.50E-208	Tregs	TIGIT
	1.651961006	1.00E-168	Tregs	RTKN2
	1.263797804	5.19E-92	Tregs	IL2RA
	1.12271226	9.91E-46	Tregs	GBP5
	0.865616983	1.11E-27	Tregs	GBP2
	0.810883856	3.51E-21	Tregs	FAM129A

Table 5 -5 RNA profiles of each cluster

 5

	avg_log2FC	p_val_adj	cluster gene
	0.597951873	0	0 RPS8
	0.558589356	0	0 RPS6
	0.552716318	0	0 RPS18
	0.548906064	0	0 RPL34
	0.434016554	2.37E-297	0 RPS29
	0.579134015	4.27E-153	0 RPS24
	0.831231259	2.75E-110	0 TXNIP
	0.484676331	2.48E-19	0 RPS26
	0.526149612	2.20E-07	0 FYB1
	0.398741593	7.41E-259	1 IGKC
	0.603635013	1.04E-172	1 LYZ
	0.251941463	5.15E-126	1 RASGEF1B
	1.906641356	0	2 IL32
	1.367379744	6.44E-160	2 CD52
	2.909453815	5.83E-142	2 DUSP4
	1.678392389	1.88E-138	2 S100A4
	2.099184727	4.65E-131	2 CD74
	1.931931976	3.07E-96	2 FOXP3
	1.275611134	3.42E-67	2 ITGB1
	1.212632956	3.09E-51	2 SLFN5
	1.91391217	5.99E-34	2 RGS1
	1.538527574 0.001493184	2 TENT5C
	3.909231988	0	3 NKG7
	3.781907389	0	3 CCL5
	3.722693439	0	3 GNLY
	3.171242841	0	3 FGFBP2
	2.944898224	0	3 GZMH
	2.813885238	0	3 GZMA
	1.537378552	4.95E-279	3 SH3BGRL3
	2.454878826	9.44E-255	3 CST7
	1.766504962	1.94E-138	3 RUNX3
	1.422383353	8.89E-13	3 DUSP2
	0.487452651	0	4 ITGB1
	0.4339784	0	4 AHNAK
	0.281387617	0	4 S100A4
	1.211182697	3.94E-63	4 MAF

Table 5 -7

 5

		Abseq profiles of each cluster
	avg_log2FC p_val_adj cluster gene
	1.577082467	0	0 HLA.DR.CD74.AHS0035.pAbO
	0.807836494	0	0 CD134.ACT35.TNFRSF4.AHS0013.pAbO
	0.815898613 1.37E-208	0 CD44.515.CD44.AHS0140.pAbO
	0.742056952 6.37E-108	1 HLA.A-B-C.HLA.A-B-C.AHS0066.pAbO
	0.647669804 9.62E-56	1 CD44.515.CD44.AHS0140.pAbO
	1.183783472 2.29E-233	4 CD162.SELPLG.AHS0139.pAbO
	0.970607689 2.86E-207	4 CD3.UCHT1.CD3E.AHS0231.pAbO
	0.729052512 2.16E-133	5 CD18.ITGB2.AHS0091.pAbO
	0.748686323 2.58E-120	5 CD11a.ITGAL.AHS0081.pAbO
	0.608565744 4.01E-106	5 CD69.CD69.AHS0010.pAbO
	1.324163695 7.74E-160	7 HLA.DR.CD74.AHS0035.pAbO
	0.803456906 3.44E-99	7 CD162.SELPLG.AHS0139.pAbO
	RNA profiles of each cluster
	avg_log2FC p_val_adj cluster gene
	0.811384037 4.97E-260	0 HLA.DRA
	0.594446457 1.47E-245	0 TM9SF2
	0.719551077 9.21E-224	0 HLA.DPA1
	0.571118471 1.15E-220	0 CD74
	0.571020675 5.59E-219	0 RPL22L1
	0.635216153 3.01E-204	0 HLA.DPB1
	0.568094196 8.08E-201	0 HSP90AA1
	0.641461252 1.41E-198	0 HLA.DRB5
	0.598482745 6.91E-182	0 HLA.DRB1
	0.578053446 3.97E-178	0 HSPD1
	0.494523193	1.55E-72	1 MT2A
	0.445048084	2.47E-61	1 AC007952.4
	0.331900841	3.87E-42	1 ACTN1
	0.306703198	6.93E-39	1 CYP1B1
	0.302678752	2.06E-37	1 UCP2
	0.52937445	4.53E-37	1 IL5
	0.389530001	3.94E-30	1 LTA
	0.325553307	5.76E-30	1 BCL2L1
	0.408486824	4.97E-29	1 IL13
	0.318898287	4.09E-24	1 TNF
	0.281489066 0.00311298	2 AC007952.4

Table 5 -8 RNA profiles of each cluster avg_log2FC p_val_adj cluster gene

 5

	0.902959746	0	0 TCF7
	0.762043341	0	0 SELL
	0.336272824	0	0 ZMAT1
	0.250043299	0	0 EPHB6
	0.472815953	1.80E-229	0 FHIT
	0.41823896	5.18E-172	0 FYB1
	0.427246024	1.35E-168	0 SCML4
	0.278920883	1.14E-165	0 ADA2
	0.408253669	9.18E-164	0 CEP85L
	0.439283366	2.41E-155	0 ITGA6
	0.516036929	3.13E-142	0 BACH2
	0.390114797	2.79E-131	0 GABPB1.AS1
	0.283518474	2.11E-128	0 ANKRD55
	0.250629591	2.80E-112	0 AKAP9
	0.877020807	4.31E-108	0 CCR7
	0.26410301	2.14E-92	0 RB1CC1
	0.502537384	6.39E-86	0 IPCEF1
	0.639266299	1.49E-82	0 TSHZ2
	0.476638185	5.17E-80	0 TNRC6B
	0.370606665	1.06E-74	0 IL6R
	0.495823867	1.49E-64	0 FCMR
	0.892061162	1.55E-24	0 IL6ST
	0.541469631	3.18E-23	0 AIF1
	0.476882142	9.99E-14	0 PLAC8
	0.322422785	1.37E-12	0 MAL
	0.48743889	1.60E-08	0 LRRN3
	0.291162767	2.35E-06	0 SESN3
	1.089047113	0	1 ANXA1
	0.893986341	0	1 IL7R
	0.883266064	0	1 AHNAK
	0.330678564	0	1 EPHA4
	0.922892845	1.58E-237	1 ITGB1
	0.338508896	2.27E-230	1 TSPAN18
	0.521167721	2.16E-214	1 CCR6
	0.631031911	1.97E-174	1 KLF6
	0.414403758	2.08E-167	1 DUSP1
	0.657033773	3.84E-131	1 PLP2

Table 5 -9 28-Abseq antibody panel 12-Abseq antibody panel

 5

	CD45	CD3
	CD3	CD4
	CD4	CD8
	CD8	CD31
	CD31	CD45RA
	CD45RA	CD25
	CD25	HLA-DR
	HLA-ABC	CD279 (PD1)
	HLA-DR	CCR4
	CD279 (PD1)	CD49b
	CD154	CD278
	CD134	CD161
	CD137
	CXCR5
	CCR4
	CD161
	CD59
	CD69
	CD44
	CD278
	CD162
	CD46
	CD49b
	CD59
	CD18
	CD2
	CD5
	CD11a

  The Fr I cells are predisposed to become activated Treg cells. However, it is not known how SCFAs such as butyrate modulate these pathways.It is also not established whether butyrate requires the G-protein coupled receptors 41/43 (or any other FFA receptor; FFAR) to mediate its effects on human CD4+ T cells. (209) Although butyrate is an inhibitor of histone deacetylation, it is known that it can mediate its effects via other mechanisms of action too.(208, 209, 224) In this work, we deliver a novel perspective on how butyrate modulates Treg cells. Low dose butyrate polarised the differentiation trajectory of naïve Tregs cells into cells with both immunosuppressive and pro-inflammatory functions. On the contrary, butyrate induced FOXP3 in Teff cells without inducing a regulatory profile. Through multimodal single-cell profiling, we dissected the transcriptome, gene regulatory networks, and surface protein profiles of these regulatory and effector subpopulations.

	Human naïve CD4+ T cells consist of naïve Treg cells (Fraction I; Fr I) as
	well as naïve Teff cells (Fraction VI; Fr VI). These cells undergo different thymic
	developmental pathways. (340) Upon activation, these cells have different
	differentiation trajectories too.

  2, IL-17A, and TNF⍺ independently of the butyrate levels or de novo FOXP3 induction. This suggested that the demonstrated butyrate-based de novo FOXP3 induction was not associated with the adoption of an immunosuppressive profile. Hence, further indicating that the appropriate term for these cells is FOXP3+ Teffs and not induced Treg (iTreg) cells.

Collectively, these data suggested that butyrate neither perturbed nor promoted immunosuppressive mechanisms in naïve Treg and Teff cells. Butyrate had the unique capacity to induce effector cytokine-secreting subsets amongst immunosuppressive Treg cells.

  However, FFAs are heterogenous entities that mediate non-metabolic effects too.(208)(209)(210)(211) Although the roles of SCFAs on Treg cells are characterised in the murine setting, there is a need to extrapolate this to humans as well as delineate the underlying mechanisms.In this work, we used multimodal single-cell sequencing to deliver a novel and global perspective on how butyrate induced a FOXP3 lo state in both naïve Treg and Teff cells. Strikingly, butyrate-induced new subpopulations amongst naive Treg cells that were not identified in cultures with naïve Teff cells. With the proteo-transcriptomic profile of these cells, we also delineated that butyrate promoted diverse signalling

  To specifically elucidate the effects of FFAs only, we neither utilised TGFβ nor an intermediary cell. Although our Teffs upregulated FOXP3, they did not adopt an immunosuppressive profile and thus, cannot be technically labelled as iTreg cells.(382) We confirmed this observation through unsupervised clustering of the Teff cells at single-cell level. Collectively, this data suggested that butyrate suboptimally promoted regulatory functions in Teff cells.[START_REF] Schmidt | Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate[END_REF] Our work also focussed on identifying novel mechanisms of action of butyrate. To model this better, in vitro experiments using surgically resected adipose and liver tissue would be useful. Although we used physiologically relevant doses of FFAs, the in vivo setting implicates a more heterogenous range of FFAs and lipids. A parallel study of the tissue lipid metabolome and cellular transcriptome could uncover novel insights into in vivo Treg biology.

	inhibited both pSTAT5 and p38 downstream of CD3/CD28 or JAK signalling and
	independent of FFAR expression. Although the exact biochemical interaction between
	butyrate and these entities was not delineated, this data demonstrates the capacity of
	butyrate to perturb acute cytoplasmic phosphorylation dynamics.
	Our study has certain limitations. The acute activation of Treg/Teff cells in high
	This builds upon previous murine work in which, colonic Treg cells cultured with FFA conditions does not reflect the chronic nature of obesity-related inflammation.
	butyrate were rendered more immunosuppressive through butyrate acting on the (390)
	GPR43 (FFAR2) receptor and enhancing histone acetylation.(209) However, our
	analysis as well as that of others suggested that human CD4+ T cells do not express
	the FFAR receptors thus, excluding this as a mechanism. (383) Furthermore, we
	demonstrated that butyrate acutely inhibited the phosphorylation of STAT5 and p38.
	This is important as STAT5 plays a nonredundant role in mediating FOXP3 expression
	and Treg immunosuppression. (384, 385) This finding completed data demonstrating
	a reduction in CD25 expression with butyrate. (Fig 3A) This receptor is dependent on
	STAT5 signalling for ongoing expression. (386, 387) Considering that butyrate induced
	FOXP3 in Teffs, this suggests that activation of IL-2-STAT5 signalling is not necessary
	for FOXP3 upregulation in Teff cells. Moreover, the increase in JAK1 phosphorylation
	in Treg cells indicates an important role for pJAK1 in the recruitment of STAT5 per se
	or other STAT proteins. Other reported studies found pJAK3 to be key to STAT5
	recruitment in Treg cells instead. (386, 388) Whilst both of these JAK proteins are
	associated with IL2Rβ/! respectively, they all form part of the IL-2 receptor signalling
	complex and are involved in recruiting STAT5 via its SH2 domain. (376, 377) This
	does not exclude the possibility that there are also non-STAT targets of JAK kinases
	in CD4+ T cells such as ERK. (389) Collectively, our data demonstrates that butyrate

Table 6 - 4

 64

Table 6 -

 6

		4A (RNA)
	avg_log2FC p_val_adj	cluster gene
	0.708266198	2.53E-243	0 HLA.DRA
	0.592012656	6.22E-240	0 TM9SF2
	0.491140385	6.72E-239	0 PPID
	0.519442556	2.62E-233	0 BATF3
	0.6290573	4.94E-210	0 HLA.DPA1
	0.489308393	2.41E-203	0 CD74
	0.551517853	4.48E-192	0 HLA.DPB1
	0.555972846	8.04E-183	0 HLA.DRB5
	0.495451117	8.81E-180	0 HSP90AA1
	0.529188192	3.37E-176	0 HLA.DRB1
	1.181279813	1.35E-125	1 IFITM2
	0.963051482	2.49E-96	1 MALAT1
	1.497414867	3.49E-69	1 FOS
	1.526067913	3.52E-53	1 GNLY
	0.958907118	5.97E-25	1 PGGHG
	1.25748508	8.03E-25	1 EGR1
	0.952280303	3.35E-24	1 JUN
	0.954388586	4.03E-21	1 TRBC1
	1.01123555	1.17E-19	1 CD69
	0.998411339	1.94E-17	1 SAT1
	0.394173469	3.52E-51	2 HIST1H4C
	0.437825385	2.21E-29	2 JUN
	0.336773936	1.02E-26	2 GZMA
	0.426686789	2.36E-26	2 AC007952.4
	0.440429151	6.31E-26	2 CLEC2D
	0.420870301	1.92E-25	2 TXNIP
	0.42273727	1.69E-22	2 CCNL1
	0.387604454	5.00E-22	2 IFITM2
	0.37670886	1.51E-18	2 SOX4
	0.730512523	6.04E-07	2 MALAT1
	0.46776344	6.31E-29	3 SH3BGRL3
	1.314005929	2.71E-24	3 LTB
	0.680291882	6.61E-14	3 ARL4C
	0.670331574	1.18E-11	3 CD52
	0.766480957 0.002080201	3 IL17RB
	0.54136271 0.002354943	3 GPR171
	0.582626073	1.39E-54	4 KLF2
	0.397961775	1.30E-31	4 VIM

Table 6 - 5

 65

Table 6 -5: Integrated Treg/Teff clusters

 6

	avg_log2FC	p_val_adj	cluster gene
	1.250095264	0	0 HLA.DRA
	1.108794645	0	0 HLA.DQA1
	1.106583595	0	0 HLA.DPB1
	1.084747157	0	0 HLA.DRB5
	1.072941654	0	0 HLA.DPA1
	1.048763099	0	0 HLA.DRB1
	0.796683089	0	0 HLA.DQB1
	0.640395992	0	0 TUBA1C
	0.597099627	0	0 PAICS
	0.573790466	0	0 FABP5
	0.532510269	7.46E-298	0 CYCS
	0.561860577	5.82E-289	0 HSPH1
	0.631468361	4.49E-288	0 RPL22L1
	0.500675064	2.80E-276	0 NOP56
	0.656247839	3.99E-264	0 HDGFL3
	0.776127364	1.22E-253	0 BATF3
	0.616319073	2.27E-158	0 PSAT1
	0.53103298	1.17E-138	0 PPID
	0.52829085	4.16E-77	0 PBK
	0.671722145	4.71E-19	0 GJB2
	0.723034845	1.11E-92	1 SRGN
	1.218381336	4.60E-80	1 IFITM2
	0.823718276	3.21E-69	1 MALAT1
	0.656688079	1.47E-56	1 TRBC2
	0.911657444	1.50E-31	1 GZMA
	1.556146419	2.65E-23	1 GNLY
	0.718907387	2.37E-17	1 FOSB
	0.639579993	2.50E-14	1 GZMB
	0.854646265	3.54E-14	1 LAYN
	0.592604461	1.70E-12	1 PDE4D
	0.77609823	4.90E-08	1 SOX4
	0.684271013	3.64E-06	1 C12orf75
	1.206172242	5.79E-05	1 JUN
	1.578625645	7.38E-05	1 FOS
	0.719633756 0.001008528	1 MAL
	0.771860399 0.003697367	1 PHLDA1
	0.354505058	1.52E-53	2 IL13

Table Legends

 Legends

Table 6 -

 6 1: 28 surface protein Abseq panel

Table 6 - 2 :

 62 DE gene expression analysis of Treg cell clusters using Wilcoxon Rank Sum Test and Bonferroni correction (p_val_adj<0.05). All genes with minimum fold change 0.25 are shown amongst minimum 25% of cells per cluster.

Tables(Chapter 5)

Tables(Chapter 6)

Acknowledgements I would like to start by thanking all current and former members of the laboratory at the Centre for Immunology and Infectious Diseases, Sorbonne Université. There are of course many of you but briefly; Dr Mustapha Cherai, Dr Baptiste Fouquet, Dr Karim Dorgham, Dr Christophe Parizot, Ms Clara Cretet, Dr Paul Breillat, Drs Elyes & Acknowledgements MA is funded by the EASL Juan Rodes PhD Fellowship. YHO is supported by the Sir Jules Thorn Biomedical Research Charity and NIHR BRC. The study was supported by the Programme Hospitalier de Recherche Clinique (PHRC: AOR17082, PHRCN 190321), DIM Thérapie Génique Ile de France, Association ASPERIM (Association pour l'enseignement et la recherche en immunologie) and AFPCA (Association Française de la PolyChondrite Atrophiante).

Code Availability

Pending intellectual property clearance, all code will be uploaded to: https://github.com/drmoatif/ The R software packages used are cited in the original articles and described in the methods sections.

Figure 2-3

There is a complex interaction between FOXP3 and other transcription factors (eg.

NFAT, AP-1) and these interact with the FOXP3 gene across the different loci.

Although several intracellular mechanisms are triggered, they all centre on the crucial cross-talk between the FOXP3 gene and others to transcribe an optimal 'Treg program'. This 'Treg program' is then put into action via protein translation and ultimately facilitates Treg function via the numerous mechanisms illustrated.

Chapter 5 -Single-cell proteo-transcriptomics defines the heterogeneity and differentiation trajectories of regulatory T cells

Mo Atif 1,2,4 , Mustapha Cherai 1 , Clara Cretet 1 , Melissa Saichi 3 , Bob Balderas 6 , Filomena Conti 2 , Olivier Scatton 2 , Ye Htun Oo 4,5 , Guy Gorochov 1 , Makoto Miyara

Statistics

The FindAllMarkers function used for DE analysis of the transcriptome was based upon the non-parametric Wilcoxon rank-sum test that measured log-fold changes in gene expression levels between different cells as well as the Bonferroni correction. All data was significant if the adjusted p-value was <0.05. We analysed differences only if they were significant in 25% of cells per cluster/condition and involved at least a 0.25-fold change. For the surface protein modality, we increased the latter threshold to 0.6 to improve specificity.

Supplementary

Supplementary Table Legends

Supplementary Table 5-1: Proteo-transcriptome of intrathymic cells

Showing the initial surface protein (Abseq) and RNA profiles used to identify each cell type in Figure 5-1A.

Supplementary Table 5-2: Proteo-transcriptome of intrathymic T cells

Showing the surface protein and RNA profiles after reclustering only on the T cell clusters from Figure 5-1A. The output of this clustering is shown on Figure 5-1B.

Supplementary Table 5-3: Proteo-transcriptome of intrathymic T cells (pseudobulk)

Showing the identified surface protein and RNA profiles of the different T cell subsets identified in Figure 5-1B.

Supplementary Table 5-4: Proteo-transcriptome of peripheral circulating CD4+ T cells (pseudobulk)

Showing the identified surface protein and RNA profiles of the different enriched CD4+

T cell subsets identified in Supplementary Figure 5-1B.

Supplementary Table 5-

Supplementary Table 5-7: Proteo-transcriptome of expanded Teff cells

Showing the surface protein (Abseq) and transcriptome profile of Teff cells postexpansion clustered on Figure 5-5A. This is shown cluster-by-cluster and pseudobulk.

Supplementary Table 5-

Methods

Naïve Treg and Teff cell isolation

Initially, CD4+ T cells were separated from PBMCs via CD4 microbeads and LS columns (Miltenyi, France). These cells underwent FACS-based sorting (Biorad S3e, France) using CD4-FITC (clone RPA-T4), CD25-PE (clone 2A3) and CD45RA-PeCy7

(clone L48) (all from BD, France). The naïve Treg cells were identified as CD4+CD25++CD45RA+ and naïve Teff cells as CD4+CD25-CD45RA+.

FFAs stock solutions

Butyrate (Sigma, France) was diluted in phosphate-buffered saline to form a stock of 100mM solution. Palmitate (Sigma, France) was diluted in ethanol and human albumin (Sigma, France) to form a stock of 100mM palmitate-albumin solution.

Cultures with FFAs

All cells were suspended in serum-free TexsMACS medium (Miltenyi). In a 96-well flatbottomed plate, we added 10 5 Treg or Teff cells, 300 IU/ml of IL-2, 100-800µM FFAs, and 2μl of anti-CD3/28 activator beads (ThermoFisher, France). The plates were incubated (37°C/5% CO2) for 6 days with 300 IU/ml IL-2 supplementation on day 3.

After the 6 days period, the cells were stained with the viability dye (BD Horizon 780 or 7AAD depending on the panel) before undergoing being stained with the following

BODIPY-Palmitate Uptake

BODIPY FL C16 dye (ThermoFisher, France) was used to quantify palmitate uptake into CD4+ T-cells before and after activation. In a 96-well plate, we added 0.5-1x10 5

CD4+ cells/well. We used the following control conditions; media, dimethyl sulfoxide underwent a final round of quality control checks. The libraries were reconcentrated and pooled at 4nM before undergoing sequencing.

The pooled libraries were sequenced on the Illumina NovaSeq 6000 aiming for 13500 reads per cell of the Abseq, 1700 reads/cell of the SMK and 50000 reads/cells of the WTA. We assigned 60 cycles for read 1 and 8 cycles for index read 1 (i7).

Single-cell data processing and filtering

The FASTQ files were aligned using the BD Rhapsody WTA pipeline on SevenBridges (www.sevenbridges.com) against the GRCh38-PhiX reference genome and Abseq panel reference. (Table 6-1) The R1 and R2 reads first underwent filtering for quality control, annotation and then combined into different matrix files. We used the distribution-based error correction (DBEC) output matrix for downstream analysis as this was corrected for any PCR-related artefacts or UMI errors.

Single-cell data analysis pipeline: pre-processing and dimensionality reduction All downstream analysis was performed using R (v4), Seurat (v4) in RStudio (v1.41106). (248,249,253) We read in the DBEC matrix (containing all WTA and Abseq data) as well as the SMK matrix (containing Sample Tag information). Firstly, we filtered out all cells that were multiplets or not assigned a Sample Tag. We then split the DBEC matrix into two separate Abseq and WTA matrices. These matrices were read into Seurat using the SMK metadata to link the RNA and surface protein modalities.

To set the thresholds for low-quality cells, we visualised the number of unique features against the cell counts and mitochondrial genes. Hence, we excluded all low-quality cells with less than 200 or more than 4000 features as well as those with more than 37.5% mitochondrial genes. We further scored the cells based on their cell cycles and then regressed these genes.

Next, both the control and butyrate conditions underwent normalisation, centralisation, scaling, and dimensionality reduction using PCA (Principal Components Analysis) and UMAP (uniform-manifold approximation and project) for clustering. The UMAP projections gave a better visualisation of the spread of our clusters. We clustered at a resolution of 1.2 as it was the most optimal balance between identifying heterogeneous cell clusters as well as differentially expressed (DE) genes. The Abseq surface protein (ADT) data also underwent normalisation separately using the CLR method. The EnhancedVolcano package was used for the volcano plot whereas the Nebulosa package was used to improve visualisation of particular features over the Seurat FeaturePlot function. (252, 341)

Inferring signalling pathways and regulons

For the signalling pathways analysis, we used the PROGENy (Pathway Responses Genes) package. (255,257,258) PROGENy is based upon a large cohort of public datasets involving perturbation experiments. It also accounts for post-translational modifications, which standard Reactome/KEGG pathway analyses do not. Using the RNA modality, we constructed PROGENy pathways activity scores. These scores were integrated into a separate assay in the Seurat object. We then re-scaled our data based on these scores. These scores were then aligned with the previously identified cell clusters before being visualised on the heatmap.

To identify the regulons differing between the control and butyrate, we used the DoRothEA package. (256-258) The regulon network was constructed using the RNA expression levels of all genes compared with a manually curated database. This database contains a range of transcription factors (TF) and target interactions based upon published literature and theoretical inferences based on complementary motifs.

We included all interactions with a confidence grade from A (highest) to C (intermediate), which excluded the theoretical inferences. These interactions underwent VIPER-based statistical scoring. The top 20 most variable TFs were then selected and visualised on the heatmap.

Statistics (Wet-lab experiments)

To compare differences between Tregs/Teffs and the different FFA doses simultaneously, we performed the two-way ANOVA with Tukey's multiple comparisons test. In experiments with a single population (CD4+/Treg/Teff), we used one-way ANOVA. Differences were significant only with a p<0.05. All tests were done using GraphPad Prism 8 (California, USA).

Statistics (Bioinformatics)

The DE analysis of the RNA modality was performed using the FindAllMarkers function in Seurat with parameters of 0.25 (minimum fraction of cells with detected genes) and 0.25 (minimum log fold chance threshold; logFC). In parallel, for the DE analysis of the surface protein modality, we increased the logFC threshold to 0.9 to improve the specificity of our readout and remove background noise. All this was performed using the non-parametric Wilcoxon Rank Sum Test with Bonferroni correction based on all genes in the dataset. Only the genes/ADT with an adjusted p-value of less than 5%

were deemed statistically significant.

Study Approval

Healthy donors gave written informed consent at the EFS (Etablissement français du sang) at Hôpital Sainte-Antoine, Paris, France. All research was conducted in line with the Paris Hospitals Networks research ethics guidelines.

Single-cell transcriptome sequencing reveals 8 distinct subpopulations within expanding Treg cells

In light of the different mechanisms exploited by butyrate, we wanted a global overview of its modulatory functions. We also wanted to delineate if and how butyrate was modulating Treg cell heterogeneity. To study this, at the end of the Treg cell cultures period (with or without butyrate), we put these cells through the BD Rhapsody single-cell transcriptomics workflow to study simultaneously cell surface proteins (BD Abseq antibodies) and the whole transcriptome (WTA). We analysed 5563 Treg cells each with data on their corresponding 28 surface proteins and 30,234 genes.

Butyrate inhibits phosphorylation of STAT5 and p38 during early CD4+ T cell activation

We firstly investigated whether butyrate upregulated JAK-STAT and PI3K

signalling in CD4+ T cells. Their activation is dependent on triggering TCR/CD3 (Signal 1), CD28 (Signal 2), and the microenvironment (Signal 3). Although butyrate can passive diffuse into cells, it was plausible that it could modulate intracellular activity via surface receptors too. ( 209) To delineate this, we considered whether butyrate could modulate Signal 3 via surface FFA receptors (FFAR) such as FFAR1-4. These are G-protein coupled receptors (GPCR) also known as GPR40, GPR43, GPR41 and GPR120 respectively. In the past, it has both been suggested that T cells may and may not express these receptors. [START_REF] Kespohl | The Microbial Metabolite Butyrate Induces Expression of Th1-Associated Factors in CD4(+) T Cells[END_REF]209,224) Considering the role of the liver in lipid metabolism, we decided to interrogate published single cell transcriptomic data on liver-resident and circulatory T cells from matched donors. These receptors were not expressed on either of the cells. (http://hcc.cancer-pku.cn/).

We hypothesised whether butyrate could modulate the early downstream TCR signalling pathways through cytoplasmic mechanisms. To study this, we performed the phosphoflow assay to monitor multiple signalling molecules simultaneously within our CD4+ T cells. In particular, we focused on the phosphorylation of Janus kinases 1 4,5,6,7) in their original pre-integrated Treg and Teff datasets respectively. These can be compared with their original UMAPs in Figure 6-7A to identify their location. Table 6-3B shows the surface markers of the Teff cells.

Supplementary Figure Legends

Chapter 7 -Summary, Scientific Contribution & Future

Perspectives

Summary of Key Results

In the first part of this work, we delineated the proteo-transcriptomic landscape of peripheral circulatory and intrathymic re-circulatory Treg cells. Firstly, we found that Treg cells were homogenous across both the thymic and circulatory compartments.

Considering that our thymuses originated from adult patients, these Treg cells likely reflected the re-circulating cohort rather than bona fide thymocytes. Furthermore, through a pseudotime analysis of the Treg cell subpopulations, we found that naïve

Materials

Common Reagents

Cell culture media (either of the following):

• RPMI 1640 with 10% foetal calf serum (FCS), 1% penicillin/streptomycin, and 1% glutamine.

• X-Vivo 15 (Lonza) with 5% human serum, 2 mM glutamine, 1 mM pyruvate, 1% nonessential amino acids, 100 U/ml penicillin, 100 ug/ml streptomycin, and amphotericin B.

• TexsMACS (Miltenyi Biotec) serum free, 1% penicillin/streptomycin.

• Filter all media before first use.

Isolating Treg cells

Cell preparation

Suppression Assay

The immunosuppressive function of freshly isolated and expanded Treg cells can be examined by in vitro suppression assay. This assay is regularly used by many teams (including ours)

although results are often difficult to compare due to technical differences in assay methods. (9,12,418,419) If this assay is planned, it is important to keep and freeze autologous Teffs and APCs from the same PBMCs used for isolating Treg cells. Teffs can be sorted in parallel using the Treg isolation protocol. Meanwhile, APCs can be positively selected using CD14 + and CD19 + beads.

1.

The evening before, a 96-well plate is coated with 0.5 μg/ml anti-CD3 in PBS. This plate is left in the fridge overnight and washed with PBS twice before adding the cells. Another approach is to coat the plate the morning of the experiment, cover with film and leave it in the incubator (37ºC/5% CO2). This is not required if the plan is to use anti-CD3/CD28 activator beads instead.

2.

Freshly isolated or expanded Tregs cells are resuspended in cell culture media at a concentration of 10 4 cells/100μl.

Additional mechanistic studies

Due to the mixture of three different cell types per well, it is not possible to validate whether the effects of Treg cells on Teffs are direct or indirect via APCs (cell-contact) or cytokine based. This is why single cell control wells are important. In addition, various techniques are also available to investigate these mechanisms further.

Transwells

To exclude the role of cell contact, the Treg cells can be plated in a different layer to that containing the Teffs/APCs. This allows one to study the role of the cytokines (e.g., IL-2, IL-10, IL-35, TGF-β) in facilitating Treg suppression. In addition, blocking antibodies can be added to the culture conditions to delineate the role of individual cytokines.

Checkpoint inhibitors

There are numerous points of contact between Treg cells and Teffs due to complementary checkpoint ligands (e.g., PD1/PD-L1, CTLA4/CD80/CD86). The roles of these checkpoints in mediating the immunosuppressive effects of Treg cells can be elucidated through adding the relevant checkpoint inhibitors into the culture conditions -either at the time of the assay or pre-culturing only.

Hypoxia

An ongoing issue with in vitro assays is ensuring their physiological relevance. To this end, it is known that oxygen partial pressures differ in different body compartments. However, exactly how this influences Treg cell behaviour is not yet fully established. Our protocol involves placing the cells in an incubator (37ºC/5% CO2) but this can be modified by placing the cells in a hypoxic chamber that allows close control of local oxygen partial pressures.