Investigation of Events Downstream of T Cell Receptor-Signalling in T Cell Development, Selection, and Differentiation

Investigation of events downstream of T cell receptor-signalling in T cell development, selection, and differentiation

A Dissertation Presented

Paz Prieto Martín

Submitted to the Department of Life Sciences, Faculty of Sciences Imperial College of London for the degree of

DOCTOR OF PHILOSOPHY

2017

IMMUNOLOGY Declaration of Originality

I state that this work is my own and that I have appropriately referenced all external sources in the text. Copyright declaration

The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. Acknowledgements I have to say that completing a PhD is being a very challenging journey. I would not have been able to accomplish it without the support of my supervisors, Masahiro Ono and Tessa Crompton. To both of them, all my gratitude.

Thank you to all colleagues and friends at UCL and Imperial College, specially to David Bending for bringing to our group both his expertise and his friendship. I also deeply appreciate the support of the staff at the ﬂow cytometry facilities and biological services at UCL and Imperial College, always kind and helpful.

It has been a long path from when I ﬁrst ﬁnished my Biology studies at the University of Alcalá, in 2002. I started to do research in immunology through my Masters degree in the same university. From that time, my deepest thanks to Espe Perucha, for being my best research teacher and for her contagious passion for research. Thank you also to María Hernández and the late Eduardo Reyes, for the best recommendation letter ever for doing research in Japan, and for being always positive. Thanks to them and the Ministry of Education of Japan, I went to Kyoto University as a research student.

I was always lucky to have great team members and co-workers to look up to. Thanks to all of them. Thanks a lot to Shimon Sakaguchi for welcoming me into his group. Thanks also to Kajsa Wing, thanks for her patience and her support. All my appreciation as well to Yaguchi-san, Shibata-san and Yoshioka-sensei for their unconditional support inside and outside of the lab.

My gratitude also to my brother Alfredo Prieto, who used to bring me as a child (together with other brothers and sisters) to the lab on week-ends, when he was doing his PhD (those old times). Thank you for being there. And thanks to all members of my big family.

Last but not least, a big hug for Kike Santos, my loving partner, always by my side, helping me to keep my sanity during the hardest times, and sharing every good and bad moment.

Thanks also to the examiners for their helpful discussion during the viva and their comments.

Finally, thanks to all and everyone involved in any way with this project.

Sorry, I almost forgot to say thank you to you, my reader. I hope you ﬁnd this work interesting and helpful.

My best wishes. Abstract

T cell development and differentiation are dependent on T cell receptor (TCR) signalling. This project investigates the events downstream of TCR-signalling in CD4+T cell development and differentiation by studying the transcriptional dynamics of two key genes in individual cells: an immediate early transcribed gene upon TCR engagement (Nr4a3) and a more lately transcribed gene following TCR signalling (Foxp3). Because there were no technologies to study those transcriptional dynamics in vivo in individual cells, a new reporter technology (a ﬂuorescent timer protein called Timer) was used.

The ﬁrst part of this project establishes two new transgenic mouse strains expressing Timer ﬂuorescent protein, Nr4a3Timer and Foxp3Timer. Several independent reporter lines were assessed by their Timer expression, and selected.

The second part studies the dynamics of Timer protein expression in the selected transgenic strains. An analysis framework was established to reveal the dynamics of expression of the reported genes (Nr4a3 and Foxp3) by using the expression and colour maturation of Timer.

The last part of the project was dedicated to addressing biological questions using our new ﬂuorescent reporter strains. First, I used the Timer technology to investigate the temporal dynamics of Nr4a3, CD25 and Foxp3 expression during neonatal thymocyte development upon TCR signalling. This information placed CD25 expression as an early event downstream of TCR signalling, while Foxp3 expression appeared later on, with or without coexpression of CD25. Second, the investigation of the mechanisms downstream of TCR-signalling in peripheral CD4+ T cells in vitro showed the modulatory role of IL-6 and TGFβ on Nr4a3 expression. Finally, investigation of the role of Nr4a3 overexpression in T cell apoptosis showed that Nr4a3 proapoptotic effects are context- dependent, depending on the cytokine environment.

This project constitutes the first application of a fluorescent timer reporter system to the field of T cell immunology. It also provides a better understanding of the role of Nr4a3 in TCR-mediated events in T cell differentiation and activation. Contents

Declaration of Originality 2

Acknowledgements 4

Abstract 5

List of Figures 9

List of Tables 13

Nomenclature 14

Introduction 18 Adaptive immune system and CD4+T cells ...... 18 Nr4a3 ...... 35 Methods for measurement of protein expression dynamics. Timer technology 41 Thesis aims ...... 47

Methods 49 Transfection of HEK 293T cell line ...... 49 Mouse strains ...... 50 Molecular biology techniques ...... 50 Neonatal analysis ...... 53 CD4+T cell differentiation cultures ...... 53 Flow cytometry ...... 54

6 CONTENTS 7

Cell counting ...... 56 Data visualisation ...... 57 Flow cytometric data analysis ...... 57 Statistical analysis ...... 59

1 Establishment of two new transgenic reporter mouse strains 61 1.1 Introduction ...... 61 1.1.1 Generation of BAC transgenic Timer reporter strains ...... 62 1.1.2 Generation of new BAC Tg mice lines for Nr4a3 and Foxp3 ... 64 1.2 Assessing the expression of the Timer protein by ﬂow cytometry . . . . 65 1.3 Establishment of independent lines ...... 66 1.4 Determination of copy number ...... 68 1.5 Determination of homozygosity or heterozygosity ...... 70

2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 73 2.1 Introduction ...... 73 2.2 Timer maturation analysis through neonates data ...... 74

2.2.1 Data transformation: Timer angle (θT imer) and Timer intensity

(IT imer)...... 78 2.2.2 Trajectories of cells through Timer bi-dimensional space (Timer blue-red) ...... 82 2.3 Discussion ...... 87

3 Differentiation of Foxp3+ regulatory T cell in the thymus 89 3.1 Introduction ...... 89

3.2 Results: Investigation of TReg differentiation in the thymus ...... 92 3.2.1 Neonatal CD4SP thymocytes analysis ...... 93

3.2.2 TReg precursors in neonatal thymus ...... 99 3.2.3 CD5 expression during CD4SP development ...... 110

3.2.4 Alternative TReg precursors: CD25-Foxp3+ cells ...... 113 3.2.5 CD4+ T cells in the Spleen during ontogeny ...... 116

3.2.6 Timing of Foxp3 transcription during TReg development in Foxp3Timer:Foxp3GFP reporter mice ...... 123 CONTENTS 8

3.3 Discussion ...... 124 3.3.1 Dynamics of Foxp3 and Nr4a3 expression ...... 129 3.3.2 Foxp3: lineage and feedback control ...... 130

4 The effect of cytokines and costimulation on Nr4a3 transcription 131 4.1 Introduction ...... 131 4.2 In vitro study of T cell differentiation and TCR signalling ...... 133 4.2.1 Effects of IL-6 and TGF-β on Nr4a3 expression ...... 136 4.2.2 Effects of costimulation (CD28) on Nr4a3 expression ...... 141 4.3 Effects of Nr4a3 overexpression on in vitro T cell differentiation . . . . . 145 4.3.1 Tetracycline inducible expression of Nr4a3 (NIGrtTA) ...... 146 4.4 Discussion ...... 155 4.4.1 Regulatory effects of the cytokines IL-6 and TGF-β on Nr4a3 expression ...... 156 4.4.2 Effects of Nr4a3 overexpression on Foxp3 and IFNγ ...... 158 4.4.3 CD28 signals alter the dynamics of expression of Nr4a3 . . . . . 159

5 Investigation of the role of Nr4a3 in apoptosis 161 5.1 Introduction ...... 161 5.2 Nr4a3 overexpression effect on AICD ...... 162 5.3 Investigating the effects of Nr4a3 overexpression on RICD ...... 170 5.4 Discussion ...... 173

Conclusions 174

Future directions 179

Bibliography 181

Appendices 190 A Plasmids ...... 190 B Gating strategy R ...... 191 List of Figures

1 TCR interactions in the cortex and the medulla ...... 20 2 Thymocyte development ...... 25 3 Thymocyte development check points ...... 27 4 Nr4a3 structure ...... 36 5 Dynamics of EGFP maturation scheme ...... 43 6 Timer and EGFP ﬂuorescent properties ...... 45 7 Fluorescent Timer maturation scheme ...... 46

1.1 Establishment of BAC tg lines ...... 63 1.2 Timer plasmid transfected HEK cells scatter plot ...... 66 1.3 Agarose gel of reporter founder mice ...... 67 1.4 Copy number from qPCR ...... 69 1.5 Flow cytometry data of homozygous and heterozygous Nr4a3Timer ... 70 1.6 Mean ﬂuorescence intensity (MFI) of Timer blue and Timer red in homozygous and heterozygous Nr4a3Timer mice ...... 71 1.7 Percentages of Timer blue and Timer red cells in homozygous and heterozygous Nr4a3Timer mice ...... 72

2.1 Patern of expression of Timer blue and Timer red forms in CD4+ cells from thymus and spleen of Nr4a3Timer mice after birth ...... 75 2.2 Patern of expression of Timer blue and Timer red forms in CD4+ cells from thymus and spleen of Foxp3Timer neonates ...... 75 2.3 Gating strategy for Nr4a3Timer ...... 77 2.4 Reference population gating strategy for Foxp3Timer ...... 77 2.5 Timer angle deﬁnition ...... 79

9 LIST OF FIGURES 10

2.6 Geometric explanation of Timer intensity and Timer angle ...... 80 2.7 Screen shot of the Tools menu in FlowJo software showing the Derive Parameters option ...... 81 2.8 FlowJo Derive Parameters Formula for Timer intensity ...... 81 2.9 Nr4a3Timer angle Timer Intensity in thymus and spleen CD4+T cells . . 82 2.10 Modelling of constant transcriptional pattern ...... 85 2.11 Neonatal thymus day 9 Foxp3Timer ...... 85 2.12 Biological data vs. modelling ...... 87

3.1 Current model of TReg differentiation ...... 91 3.2 CD4SP cell counts and percentages in neonates ...... 93 3.3 Gating of CD4SP thymocytes based on CD25 and Foxp3GFP ...... 95 3.4 Cell counts of CD25 and Foxp3 subpopulations in the thymus ...... 96 3.5 Nr4a3Timer+ cells in thymic CD4SP subpopulations ...... 98 3.6 Nr4a3Timer blue and red expression on CD4SP subpopulations . . . . 101 3.7 Population comparisons of Timer angle in CD4SP subpopulations . . . 103 3.8 Population comparisons of Timer intensity in CD4SP subpopulations . . 106 3.9 CD69 in CD4SP subpopulations ...... 109 3.10 CD5 surface levels on CD4SP subpopulations ...... 111 3.11 CD5 surface levels on CD4SP subpopulations depending on Timer . . . 112 3.12 Nr4a3Timer angle comparison of CD4SP (9 subpopulations for CD25 and Foxp3GFP) ...... 115 3.13 Gating strategy of CD4SP splenocytes ...... 116 3.14 Cell counts of CD4+ cells in spleen and thymus ...... 117 3.15 Percentage distribution of CD4+Nr4a3Timer+ cells ...... 118 3.16 Nr4a3Timer angle on CD4+ T cells from neonatal thymus and spleen . 119 3.17 Cell percentages in Nr4a3Timer maturation stages ...... 121 3.18 Nr4a3Timer intensity in CD4SP Nr4a3Timer+ cell throughout neonatal development ...... 122 3.19 Foxp3Timer expression in thymocytes ...... 124

3.20 TReg development timing ...... 126 3.21 Dynamics of gene expression in CD4SP thymocytes ...... 128 LIST OF FIGURES 11

4.1 Signals required for Th cell activation ...... 131 4.2 Staining of intracellular cytokines and Foxp3 in Nr4a3GFP time course . 133 4.3 Expression of Nr4a3GFP in CD4+ T cells in different in vitro culture conditions135 4.4 Effect of IL-6 concentration on the expression of Nr4a3GFP ...... 138 4.5 Effect of IL-6 titration on Foxp3 and Nr4a3 induction ...... 139 4.6 Comparison of IL-6 and TGF-β effects on the expression of Nr4a3GFP 139 4.7 Effect of IL-6 or TGF-β alone on the expression of Nr4a3GFP ...... 140 4.8 Anti-CD28 effects on Nr4a3 expression ...... 143 4.9 Anti-CD3 estimulation is sufﬁcient for Nr4a3 expression ...... 144 4.10 Effect of CD28 costimulation on CD4+ T cell polarization ...... 145 4.11 Tetracycline inducible expression of Nr4a3-GFP ...... 147 4.12 GFP dose response to doxycycline on CD4 and CD8 cells ...... 148 4.13 Conﬁrmation of inducible expression of GFP upon exposure to doxycycline149 4.14 Nr4a3 overexpression effects on Th17 vs. iTreg condition ...... 150 4.15 GFP dose response to doxycycline in Th17, Th1 and Th2 conditions . . 151 4.16 GFP dose response to doxycycline on Th17, iTreg, Th0 and Th1 conditions152 4.17 Comparison of GFP levels measured with different experimental procedures152 4.18 Comparison of IFNγ staining levels at different concentrations of doxycycline ...... 155 4.19 Comparison of IFNγ staining levels at different concentrations of doxycycline ...... 155

5.1 Effect of doxycycline treatment on CD4+ cells survival ...... 163 5.2 Gating strategy for measuring apoptosis ...... 164 5.3 Effect of doxycycline treatment on apoptosis of CD4+ cells in Th17 culture condition ...... 164 5.4 Effect of doxycycline treatment on apoptosis of CD4+ cells in Th17 culture condition ...... 165 5.5 Effect of doxycycline treatment on apoptosis of CD4+ cells in iTreg culture condition ...... 166 5.6 Effect of Nr4a3 overexpression on CD4+ cells apoptosis ...... 167 LIST OF FIGURES 12

5.7 Effect of doxycycline treatment on AICD of CD4+ cells in different polarising conditions ...... 169 5.8 Induction of GFP upon doxycycline treatment on RICD ...... 171 5.9 RICD upon Nr4a3 overexpression ...... 172 10 Gating CD4SP and exclussion of CD69-TCRβ-...... 191 11 Gating strategy of CD4SP thymocytes ...... 192 12 Timer positive threshold at intensity > 6 ...... 193 List of Tables

1 Mice strains used ...... 50 2 Primers ...... 52 3 Standard Taq polymerase PCR program ...... 52 4 Fluorochromes list ...... 55 5 Fluorochrome conjugated antibodies for neonatal T cell development analysis ...... 55 6 FACSAria III conﬁguration ...... 56 7 LSR Fortessa III conﬁguration ...... 56

1.1 Timer founders screened ...... 68 1.2 Copy number estimation of Timer lines ...... 69

3.1 One-way ANOVA for each day after birth ...... 103 3.2 One-way ANOVA for angle ...... 105 3.3 Two-way ANOVA for CD69 ...... 108 3.4 One-way ANOVA for CD69 ...... 108

4.1 In vitro differentiation conditions for CD4+T cells ...... 132 4.2 IL-6 and TGF-β in vitro conditions ...... 137 4.3 Percentages of Foxp3+ cells in Th17 and iTreg conditions at different concentrations of doxycycline ...... 153 4.4 Percentages of IL-17 producing cells in Th17 conditions at different concentrations of doxycycline ...... 154 4.5 Percentages of IFNγ producing cells in Th1 condition at different concentrations of doxycycline ...... 154

13 Nomenclature

AICD activation induced cell death, page 162

Aire autoimmune regulator, page 28

AnnV annexin V, page 164

AP1 activator protein 1, page 22

APC allophycocyanin, page 164

APC antigen presenting cell, page 18

B6 C57BL/6 inbred mouse strain, page 70

BAC bacterial artiﬁcial chromosome, page 46

Bcl-2 named from B-cell lymphoma 2, part of BCL-2 family of anti-apoptotic proteins, page 37

CD25 interleukin-2 receptor α, page 89

CD4SP CD4 single positive thymocytes, page 20

CD8SP CD8 single positive thymocytes, page 24

CDR complementary-determining regions, page 21 cTEC cortical thymic epithelial cells, page 20

CTLA-4 Cytotoxic T-Lymphocyte Antigen 4 (CD152), page 89

14 NOMENCLATURE 15

DC dendritic cell, page 18

DN double negative thymocytes, page 24

DP double positive thymocytes, page 21

EGFP enhanced green ﬂuorescent protein, page 43

Foxp3 forkhead box P3 transcription factor, page 25

GFP green ﬂuorescent protein, page 43

GITR glucocorticoid-induced TNFR-related gene (TNFRSF18), page 89

IFN-γ interferon-γ, page 33

IKK inhibitor of NF-κB kinases, page 141

IL-2 interleukin-2, page 22

IL-2Rα interleukin-2 receptor α, page 89 iNKT Invariant natural killer T cells, page 29

IRES internal ribosomal entry site, page 146

ITAM immunoreceptor tyrosine-based activation motifs, page 22 iTreg induced regulatory cells, page 33

KO knock-out, page 38

LAT linker for activation of T cells, page 22

LTRs long terminal repeat, page 190

MAIT mucosal-associated invariant T cells, page 29

MCA multivariate correspondance analysis, page 41

MFI mean ﬂuorescence intensity, page 71 NOMENCLATURE 16

MHC major histocompatibility complex, page 19 miRNA microRNA, page 23 mTEC medullary thymic epitelial cells, page 20

NF-κB nuclear factor κB, page 22

NFAT nuclear factor of activated T cells, page 22

NIG Nr4a3-IRES-GFP and rtTA mouse strain, page 146

NOR-1 neuron derived orphan receptor, page 38

NR nuclear receptor, page 36

Nr4a orphan nuclear receptor subfamily 4a, page 36

Nr4a3GFP, Nr4a3Timer, Foxp3Timer transgenic mouse strains (italic font), page 47

Nr4a3GFP, Nr4a3Timer, Foxp3Timer genes of the correspondent ﬂuorescent proteins for each transgenic reporter line (italic font), page 65

Nr4a3 nuclear receptor 4a3, page 35

Nr4a3GFP, Nr4a3Timer, Foxp3GFP, Foxp3Timer ﬂuorescent proteins (normal font) cor- responding to each speciﬁc reporter line, page 65

ONR orphan nuclear receptors, page 36

OX40 or TNFSF4 Tumor necrosis factor receptor superfamily member 4, page 91

PBS phosphate buffered saline, page 164

PCA principal components analysis, page 41

PI propidium iodide, page 164

PKC protein kinase C, page 38 NOMENCLATURE 17 pMHC peptide-major histocompatibility complex, page 19

PS phosphatidylserine, page 164 pTreg peripheral regulatory cells, page 33

RAG recombination-activating gene, page 21

Ras-MAPK Ras-mitogen-activated protein kinase pathway, page 141

RICD restimulation induced cell death, page 162

RTE recent thymic emigrants, page 33 rtTA reverse tetracycline-responsive transactivator, page 146

TReg regulatory T cell, page 32

TBP tetracycline binding protein, page 147

TCR T-cell receptor, page 19

Tespa1 thymocyte-expressed positive selection associated-1, page 23

TGF-β transforming growth factor β, page 133

Th helper T cells, page 18

Themis thymocyte expressed molecule involved in selection, page 23

Timer ﬂuorescent timer fast (FTfast), page 45

TNFRSF tumor necrosis factor receptor superfamily, page 89

TRE tetracycline responsive elements, page 147

VGSC voltage-gated Na+ channel, page 23 Introduction

Adaptive immune system and CD4+T cells

The part of the immune system composed of B and T lymphocytes that can speciﬁcally

respond to an immunological threat and create immunological memory afterwards is

referred as the adaptive immune system. As a result, the immune response is enhanced

in subsequent encounters and thus adapts through the life-time of the individual. αβ

CD4+T cells are a main player of the adaptive immune system. They can detect

antigens presented by other cells and become activated. They are called helper T cells

(Th cells) because they are required to help B cell antibody responses (Lederman et al.,

1992).

An adaptive immune response is initiated by the activation of antigen-speciﬁc T cells,

stimulated by professional antigen-presenting cells (APC) (such as dendritic cells (DCs))

(Banchereau et al., 2000). The encounter with their antigen induces antigen-speciﬁc

cells to clonally expand and differentiate into effector T cells. Subsequently, activated

Th cells “help” in the maturation of antigen-speciﬁc B cells (Haan et al., 2014). After

the clearance of the infection, most effector T cells die in a contraction phase, and the

remaining antigen-speciﬁc T cells become memory cells (Haan et al., 2014).

18 Introduction 19

The inappropriate activation of T cells may result in autoimmunity (an unwanted re-

sponse to self antigens), while absence of activation may result in cancer (ineffective

response to quasi-self) or in the absence of response upon infection. On the other

hand, it is useful and important to refrain the immune response to avoid reactions to a

harmless non-self, as in the case of transplantation or allergies. For that reason the

mechanisms of CD4+T cell development and activation have been extensively studied

in immunology.

T-cell receptor

T cells are activated when they recognise antigens presented by other cells. T cells are

able to interact, both in thymus and periphery, with other cells specialized in presenting

antigens (APCs) (such as DCs, B cells and macrophages). APCs can present antigens

to T cells through their major histocompatibility complex (MHC). T cells can recognise

antigens associated with MHC through the antigen-receptor (the T-cell receptor (TCR)), which couples with the peptide-MHC complex (pMHC) on APCs.

The TCR also needs a co-receptor to bind a pMHC complex. T helper cells are called

“CD4+T cells” because they express on their surface the co-receptor CD4 (ﬁg. 1).

There are different MHC classes identiﬁed, but only one of them binds to the complex

TCR-CD4, i.e. “MHC class II”, mainly expressed by APCs (Roche and Furuta, 2015). Introduction 20

FIGURE 1: TCR interactions in the cortex and the medulla. In the thymic cortex, CD4+CD8+ double positive thymocytes (DP) interact with cortical thymic epitelial cells (cTEC), through the interaction of TCRs and CD8 or CD4 co-receptors with peptide-MHC-class-I or pMHC- class-II respectively (left). In the medulla, CD4 single positive cells (CD4SP) interact with pMHC-class II complexes on medullary thymic epitelial cells (mTEC) or DCs. CD4SP cells receive TCR signalling by the integration of TCR and CD4 co-receptor plus interactions with other costimulatory molecules.

TCR allows CD4+T cells to recognize peptides attached to MHC-class II (pMHCII). TCR

interacts with the pMHC complex through the TCRα- and β-chains. The diversity of the TCR repertoire, and thus the diversity of the T cell repertoire, rests on these two

chains. Their genes contain variable sequences which upon processes of recombination

produce speciﬁc TCRα- and β-chains for each T cell. Other six invariable molecules

join to form the TCR complex: CD3δ/, CD3γ/ and CD3ζ/ζ (or ζ/η).

T-cell receptor recombination

TCR genes are susceptible to somatic recombination, producing a different TCR in each

T cell. As a result, T cells are polyclonal for their TCRs, producing an ample repertoire

of T cells with TCRs of different speciﬁcities to recognise antigens with variable binding

afﬁnities.

TCRβ-chain genes are located on mouse chromosome 6 (Chou et al., 1987) or human

chromosome 7. TCR genes are susceptible to allelic exclusion (Uematsu et al., 1988), Introduction 21

producing only one fully rearranged TCRβ-chain in each T cell. The TCRβ-chain sequence is divided in three variable regions: variable (V), diversity (D) and joining

(J) regions. Each region contains numerous segments which are rearranged into one

open-reading frame of V, D and J exons.

The second chain of the TCR, the TCRα-chain, is encoded in chromosome 14 in

mice and humans. It is generated by combinations of variable (V) and joining (J)

gene segments (Evelyne et al., 2013) similarly to the TCRβ-chain. However, unlike

the TCRβ-chain, TCRα-chain is not constrained by allelic exclusion, and so it can be

rearranged in both alleles. By expressing two different rearrangements of their TCRα

gene T cells can express dual TCRs (Padovan et al., 1993).

Each TCR chain contains three loops at the pMHC binding site (complementary-

determining regions (CDR1, CDR2 and CDR3)). TCR diversity relies on the diversity

of these CDRs (Nikolich-Žugich et al., 2004). CDR diversity is achieved by somatic

recombination of gene segments of the TCRα and β-chains. CDR1 and CDR2 are

encoded by V elements, and CDR3 is encoded by V(D)J-recombination segments

(Nemazee, 2006).

TCR gene recombination occurs in the thymus. TCRβ-chain genes are rearranged at

CD4-CD8- double negative stage (DN2 and DN3), and TCRα-chain rearrangement

occurs at the CD4+CD8+ double positive stage (DP) of thymocyte development (see

“T cell development in the thymus”, page 25). The rearrangment is catalysed in both

cases by recombination-activating gene 1 (RAG1) and RAG2 proteins (Ji et al., 2010). Introduction 22

TCR signalling

When TCR and its co-receptor CD4 bind a pMHCII complex, TCR signalling occurs

inside the CD4+T cell. A series of molecular signalling events, such as phosphorylation

and dephosphorylation and recruitment of various enzymes and adaptors, trigger the

transcription of speciﬁc genes. These signalling cascades are responsible for the fate

of thymocytes and T cells (by triggering activation, proliferation, differentiation and

apoptosis.)

As reviewed by Smith-Garvin et al. (2009), Brownlie and Zamoyska (2013) and Fu

et al. (2014), the TCR signal is initiated by the interaction of TCR with pMHC molecules.

The ﬁrst step in the signal transduction network is the activation of the SRC family kinase members Lck and Fyn. Fyn function is dependent on Lck activation, and is involved in TCR-induced interleukin-2 (IL-2) transcription (Filipp et al., 2004). Lck

interacts with the co-receptor CD4 (or CD8) and phosphorylates the immunoreceptor

tyrosine-based activation motifs (ITAMs) of the CD3γ chain, CD3-δ,- and -ζ chains.

This phosphorylation is followed by the recruitment, phosphorylation and activation of ζ

associated protein kinase of 70 kDa (ZAP70). Once activated, ZAP70 phosphorylates

the linker for activation of T cells (LAT). Phosphorylated LAT recruits other signalling

molecules (including SLP76 and PLCγ1 among others) to form a multiprotein complex

(i.e. ‘LAT signalosome’), which propagates the signal into three main pathways leading

to the transcription of NFAT (nuclear factor of activated T cells), NF-κB (nuclear factor

κB) and AP1 (activator protein 1), and the increase in Ca2+ concentration.

TCR signalling occurs during thymic development, providing signals for selection and Introduction 23

development of T cells with TCRs able to recognise self-MHC complexes. TCR sig-

nalling also occurs in the periphery as tonic signalling (Stefanová et al., 2002) or

during immune responses. However, TCR signalling is differentially regulated in the

thymus, during T cell development, as evidenced by thymocytes being more sensitive

to low-afﬁnity ligands than mature T cells (Fu et al., 2014).

Several molecules have been identiﬁed as specialized regulators of TCR signalling

during thymocyte development, such as: THymocyte Expressed Molecule Involved in

Selection (Themis), the thymocyte-expressed positive selection associated-1 (Tespa1),

a voltage-gated Na+ channel (VGSC), and the miRNA miR-181 (Fu et al., 2014).

Themis is temporally expressed during thymocyte development to allow the distinction

between low-afﬁnity and high-afﬁnity TCR signalling. Themis is rapidly phosphorylated

after TCR interaction and reduces the signal strength of low-afﬁnity TCR signalling,

allowing its distinction from high-afﬁnity TCR signals (Fu et al., 2013). This distinction

is crucial for appropriate αβT cell development, as evidenced by the phenotype of the

ThemisKO mouse: in the absence of Themis, TCR signal is increased and more cells

undergo apoptosis at DP stage (Paster et al., 2015). This negative feedback on early

TCR signalling involves the interaction of Themis with the LAT signalosome as recently

described by Paster et al. (2015).

The miRNA miR-181a is another modulator of TCR sensitivity during T cell development expressed during speciﬁc stages of thymocyte development. It increases TCR response

to agonist ligands and also can convert antagonist signalling into agonist by speciﬁcally

repressing the expression of multiple phosphatases of the TCR signalling network (Li

et al., 2007). Introduction 24

T cell development in the thymus

The thymus is the organ where Tcell development occurs. In the thymus, pMHCs are

expressed by different cell types, such as thymic epithelial cells of the cortex (cTEC)

or of the medulla (mTEC), and dendritic cells (DC) and B cells (ﬁg. 1). The duration

and strength of the TCR signal delivered upon its binding to pMHC, are determined by

the afﬁnity of TCR to the pMHC complex (Hogquist and Jameson, 2014). The intensity

of these interactions is translated into signals that control the fate, development and

differentiation of T cells. T cell receptor signalling controls thymocyte development

throughout several selection processes that involve TCR interactions.

Progenitor cells arrive from the bone marrow and give rise to αβT cells. The two major

αβT cells lineages, CD4+ helper T cells and CD8+ cytotoxic T cells, are deﬁned by

their TCR co-receptor molecule expression, CD4 or CD8 respectively (CD4 co-receptor

speciﬁcally interacts with MHC-class II while CD8 co-receptor interacts MHC-class I

molecules). Both T cell populations develop in the thymus from T cell lineage-committed

cells that do not express any of these co-receptors, CD4 or CD8 (i.e. “double negative”

cells (DN)). DN cells differentiate into “double positive” cells (DP), which develop into

“single positive cells” (CD4SP or CD8SP) that emigrate out of the thymus at the end of

their thymic development (as recent thymic emigrants (RTEs)) (ﬁg. 2). Introduction 25

(A)

(B)

FIGURE 2: Thymocyte development: From prethymic progenitor to αβ mature T cells (CD4 or CD8 single positive cells). Cell development stages are shown with coloured spheres at the respective stages. Brieﬂy, T cell progenitors (grey) migrate from the embryonic liver or postnatal bone marrow to the thymus and they develop in the thymic cortex. They are called double negative (DN, green) because they do not express CD4 or CD8 co-receptors. After TCRβ selection, thymocytes upregulate the expression of CD4 and CD8 and are named double positive cells (DP, purple-orange). Positive selection occurs at the DP stage when a TCRα rearrangement is successful, and it induces loss of expression of one of the co-receptors CD8 or CD4, becoming single positive cells: CD4SP or CD8SP (CD4/CD8 commitment phase). These cells migrate to the medulla (right yellow area) where they continue the selection process. Through agonist selection a subset of CD4SP cells become Foxp3+. Finally, thymocytes leave the thymus as recent thymic emigrants (RTEs).

Several selection processes in the thymus are in place to select T cells by their TCR and

guide their differentiation (ﬁg. 2). The ﬁst one occurs at DN stage. DN thymocytes are

classiﬁed by their expression of CD44 and CD25 into four stages: DN1 (CD44+CD25-), Introduction 26

DN2(CD44+CD25+), DN3(CD44-CD25+) and DN4(CD44-CD25-). TCRβ rearrange-

ment occurs during DN2 and DN3 stages. At DN3, once TCRβ is rearranged, it is

expressed together with a pre-α-chain (pTα), forming the pre-TCR. Pre-TCR interaction with a pMHC is a checkpoint to test if a TCRβ chain rearrangement is functional (i.e.

“β-selection”) (Pang et al., 2010). This ﬁrst selection ensures that only thymocytes

expressing a functional TCRβ-chain progress through development. Pre-TCR signal at

β-selection is responsible for allelic exclusion (preventing further rearrangements of the

TCRβ), and it triggers cell proliferation, down-regulation of CD25 (progress into DN4

stage), and activation of expression of CD4 and CD8 co-receptors (transition from DN

to DP thymocytes) (Hoffman et al., 1996).

After β-selection, proliferation is stopped for the rearrangement of the TCRα chain. The

T cells with a complete TCR (with both α and β chains rearranged) that can recognize

self-peptides presented by MHC-class-I or MHC-class-II (self pMHC) are positively

selected (“positive selection”). This MHC restricted process ensures that T cells can

recognize the endogenous MHCs which present peptides. When T cells fail to recognize

pMHC complexes, and therefore do not receive the positive selection signal, they die

by neglect (more than 90% of DP cells are estimated to die in this way). Additionally, when T cells recognise pMHC complexes and receive too strong TCR signals, they are negatively selected (“negative selection” or “clonal deletion”), and they become

apoptotic and are removed by phagocytes (ﬁg. 3) (reviewed by Klein et al. (2014)). The

TCRα chain is not constrained by allelic exclusion, therefore, increasing the chance of

positive selection (Wang et al., 1998). Introduction 27

FIGURE 3: Thymocyte development check points from DP stage (cortex) to SP (medulla). In the cortex: CD4+CD8+ double positive (DP) thymoctyes interact with cTECs and cortical DCs. Low affinity interactions drive positive selection and progression to single positive stage (SP). Those DP thymocytes receiving strong TCR signals are depleted by negative selection, while those not receiving enough TCR signal die by neglect. In the medulla: CD4SP thymocytes interacting with pMHC with high affinity undergo either negative selection or agonist selection, depending on survival cytokine exposure. Positive selection is achieved by those thymocytes encountering low/intermediate affinity pMHC complexes. The responsiveness to TCR triggering changes dynamically (arrows underneath) through TCR sensitivity, surface TCR levels and motility changes throughout development. (Figure adapted from Moran and Hogquist, 2012; Kurd and Robey, 2016; Hsieh et al., 2012; Wirnsberger et al., 2011).

DP cells are classiﬁed into three developmental stages depending on their TCRβ

and CD5 expression levels: DP1 (TCRβloCD5lo), DP2 (TCRβintCD5hi) and DP3

(TCRβhiCD5int) (Saini et al., 2010). CD5 expression is upregulated during positive

selection at DP stage. CD5 is a surface glycoprotein. Its cytoplasmic domain provides

two different signalling activities that mediate CD5 role in thymic positive selection

and thymocyte survival: a negative regulation of TCR signalling dependent on TCR

signal strength (through the ITIM motives of CD5) (Azzam et al., 2001), and TCR signal

independent negative regulation of ERK activation (Mier-Aguilar, 2016). Introduction 28

Another marker involved in thymocyte positive selection is CD69. CD69 is a membrane

protein which expression is rapidly upregulated upon TCR signalling and it is rapidly

downregulated afterwards. Therefore, CD69 is rapidly induced during positive selection

at the DP stage, and it is downregulated in CD8SP and CD4SP thymocytes in the

medulla, before cells leave the thymus to migrate to the periphery (Sancho et al., 2005).

CD69 serves as a marker to identify thymocytes before positive selection (DP CD69low),

and thymocytes undergoing selection (DP CD69high), and more mature CD4SP or

CD8SP thymocytes (CD4SP or CD8SP thymocytes losing again CD69 expression).

Simultaneously to positive selection, DP cells are selected to become CD4SP or CD8SP

cells, losing the expression of the other co-receptor (reviewed by Singer et al. (2008))

and migrating from the thymic cortex to the thymic medulla. In the medulla, thymocytes

are exposed to tissue restricted antigens (TRA). TRAs are also called peripheral-tissue

antigens (PTAs), which are antigens promiscuously/ectopically expressed and pre-

sented in the medulla, with the involvement of Aire ("autoimmune regulator") (reviewed

by Mathis and Benoist (2009)), or antigens from the periphery brought by peripheral

APC migrating to the thymus (reviewed by Klein et al. (2009)). When thymocytes

receive too strong signals through their TCRs at this time, they are deleted by the

negative selection process. Alternatively, some of those T cells are not deleted and

they differentiate into regulatory T cells or natural Th17 cells (Jenkinson et al., 2015;

Moran and Hogquist, 2012). This process is referred as “agonist selection”, referring

to the agonist high afﬁnity interactions between TCR and pMHCs required for these

differentiation pathways (Hogquist and Jameson, 2014; Moran and Hogquist, 2012;

Baldwin et al., 2004; Stritesky et al., 2012). Introduction 29

αβT cells are the most abundant T cells developing in the thymus. However, other T cell

subsets develop in the thymus as well and do not recognise peptide-MHC complexes.

Some of them express semi-invariant αβTCRs that recognize lipid antigens presented

on CD1d molecules (an MHC-like protein that forms complex with lipid antigens). These

cells are called invariant natural killer T cells (iNKT cells), because of their semi-invariant

TCR rearrangements and because of their expression of natural killer markers such as

CD161 (also known as NK1.1) (Vermijlen and Prinz, 2014). Other semi-invariant αβT

cells are called mucosal-associated invariant T cells (MAIT). MAIT cells semi-invariant

αβTCRs recognize vitamin B metabolites (bacterial and fungal) bound to an antigen

presenting molecule named MR1 (Moody et al., 2015). The development of these two

semi-invariant αβT cell subsets commit to their lineages at DP stage and this lineage

commitment involves interaction with other DP thymocytes expressing MR1 or CD1d

antigen presenting molecules (Wang and Hogquist, 2016).

Another T cell population developing in the thymus but not expressing αβTCR is the

γδT cells. They are a heterogeneous population of T cells with γδ TCR chains. Some

of them recognize antigens through direct binding, in a similar way to antibodies, while

others are CD1d restricted and recognize lipids-CD1d complexes, as iNKT cells do

(Vermijlen and Prinz, 2014). As shown for αβT cells, γδT cells also rearrange their

TCR chains at DN stage. The role of TCR in lineage commitment has been debated,

but the TCR strength model is the prevalent. Brieﬂy, DN T cells receiving weaker TCR

signals would become αβT cells while stronger TCR signals would determine γδT cell

commitment (Fahl et al., 2014; Zarin et al., 2014). Introduction 30

T cell tolerance

Low-afﬁnity interactions between TCR and self-pMHCII result in positive selection of

CD4 thymocytes, and most of the CD4+TCRαβ T cells generated in the thymus express

TCRs with low-afﬁnity to self-pMHC. However, the processes of TCR rearrangement

result in the generation of thymocytes expressing TCRs with a wide range of afﬁnities

for self-pMHC complexes. T cell “tolerance” deals with thymocytes expressing TCRs with high afﬁnity for self-pMHC complexes through different mechanisms depending on

the location of exposure to the self-pMHC complex (Malhotra et al., 2016). Mechanisms

operating on self-reactive CD4+T cells in the thymus are deﬁned as “central tolerance”

mechanisms, and those operating outside of the thymus, as “periferal tolerance” (Xing

and Hogquist, 2012).

A recent study has presented evidence of central tolerance mechanisms for the ﬁrst

time in mice with normal polyclonal CD4+T cells repertoire (Malhotra et al., 2016).

Thymocytes of these mice were exposed to speciﬁc self peptides expressed with

deﬁned patterns. The results show that different mechanisms of tolerance operate

depending on the expression pattern of the self-peptides. Brieﬂy explained, on the

extremes, at one side, a uniform expression of self-antigens by thymic DC or mTEC

triggers near complete deletion of self speciﬁc thymocytes (negative selection). On the

other hand, peptides only expressed outside of the thymus, and with rare presentation

in the periphery because of their cytoplasmic or nuclear location in cells difﬁcult to

reach by DCs, elude negative selection. CD4+T cells speciﬁc for those self-antigens

maintained their tolerance by an immunological ‘ignorance’ mechanism. In fact, upon

immunization with those peptides, the response was similar to the response to a foreign Introduction 31

peptide, resulting in a high number of effector T cells and less TReg cells. In between

those extremes, for tissue-restricted antigens with reduced expression in the thymus,

the thymic deletion was partial and targeted specially thymocytes with high-afﬁnity

TCRs, and there was also higher induction of thymic TReg cells. Upon immunization,

non-deleted cells displayed impaired effector differentiation and enlarged TReg numbers,

and possibly anergy was also induced in the thymus (Malhotra et al., 2016).

Tissue-speciﬁc antigens are presented in the thymus by mTECs to induce both, neg-

ative selection of thymocytes that recognize those antigens with high afﬁnity or their

positive selection and development into thymic Tregs (Su and Anderson, 2016). This

expression of tissue-speciﬁc antigens has been reported to be regulated by AIRE (au-

toimmune regulator gene), but also by AIRE independent mechanisms through FEZF2

transcription factor (Su and Anderson, 2016).

Peripheral tolerance can be achieved through tolerogenic APCs that induce unrespon-

siveness in self-speciﬁc T cells (mechanism known as ‘clonal anergy’) in the absence of

inﬂammation (Mueller, 2010). Other peripheral deletion mechanisms trigger apoptosis

in autoreactive T cells, through Fas and Bim dependent pathways (Mueller, 2010).

Furthermore, the expression of regulatory receptors such as CTLA-4 and PD-1 by au-

toreactive T cells can render them unresponsive to TCR interactions with self-peptides

(Mueller, 2010).

TReg differentiation and selection in the thymus

TReg differentiation and selection in the thymus is therefore part of the central tolerance

mechanisms by which some thymocytes with strong afﬁnity TCRs are not deleted Introduction 32

(Stritesky et al., 2012). This “agonist selection” is driven by stronger “agonist” ligands

that promote the differentiation and development of self-reactive CD4+T cells (Hogquist

and Jameson, 2014; Moran and Hogquist, 2012; Baldwin et al., 2004; Stritesky et al.,

2012). Through this process, instead of being deleted, highly self-reactive T cells

differentiate into alternative cell types such as “regulatory T cell” (TReg) or natural IL-

17-secreting CD4+αβT-cells (nTh17) (Jenkinson et al., 2015; Moran and Hogquist,

2012). These cells develop from the positively selected CD4SP and elude negative

selection (ﬁg. 3). This mechanism of central tolerance is also referred in the literature

as “clonal deviation” (Holler et al., 2007) or “clonal diversion” (Xing and Hogquist, 2012),

to indicate their scape from clonal deletion (i.e. negative selection).

Thymic TReg differentiation has been proposed to occur through two different precursors pathways. Firstly, the two-step model (Hsieh et al., 2012; Lio and Hsieh, 2008) suggests

the initial need of strong TCR signals for CD4 thymocytes to become CD25+ TReg

precursors. This strong TCR signal induces CD25 expression on CD4SP thymocytes

in the ﬁrst place (CD25+Foxp3- TReg precursors), and afterwards Foxp3 expression is

induced in an IL-2 dependent fashion. Another group has reported an intermediate role

of TNFR super family members upon strong TCR signals to become TReg precursors

(Mahmud et al., 2014). Alternatively, other groups have proposed the generation of TReg

through Foxp3+CD25- precursors, which are also dependent on strong TCR signals

and cytokines (IL-2 and IL-15) (Tai et al., 2013; Marshall et al., 2014).

Both, central and peripheral tolerance ensure that strong TCR signalling upon interaction with self-peptide-MHC complexes does not causes autoimmunity to the organism.

Intense research has been carried out on T cell signalling. However, there are many

remaining questions about how the TCR signal is regulated, and how it regulates the Introduction 33

transcriptional activities of the key genes that control the fate of thymocytes or peripheral

T cells.

CD4+T cell differentiation in the periphery

When CD4SP thymocytes leave the thymus to become peripheral CD4+T cells, they

are termed “recent thymic emigrants” (RTEs) CD4+T cells. These RTEs are phenotyp-

ically and functionally different from mature naive T cells (Hogquist et al., 2015) and

progressively complete their maturation in the periphery within the secondary lymphoid

organs (Houston et al., 2008).

TCR signalling mediates T cell activation in the periphery. Depending on the nature of the stimulation (intracellular or extracellular pathogen, bacterial, viral or parasitic

infections, etc.), mature CD4+ T cells differentiate into different T cell subsets such as

Th1, Th2, Th17 and pTreg (peripheral regulatory cells (pTreg) as opposed to thymic

derived Treg cells, and in vitro culture differentiated iTreg cells (Shevach and Thornton,

2014)).

T helper cells differentiate into those four different subsets upon different types of

infection. The immune response to intracellular pathogens (virus or bacteria) is mainly

performed by Th1 cells. Th1 cells are induced by IL-12 and interferon γ (IFN-γ) and

they produce IFN-γ. Responses to fungi and extracellular bacteria are done by Th17

cells, which are induced by IL-6 and TGF-β, and produce IL-17 and IL-22. Responses

to parasites and venoms are supported by Th2 cells, which are induced by IL-4, and

produce IL-4, IL-5 and IL-13 (reviewed by Becattini et al. (2015)). Finally, CD4+T cells Introduction 34

can be induced to differentiate into pTreg cells by the TGF-β and IL-2, playing an

important role in immune suppression.

However, recent reports support highly heterogeneous immune responses, in which

CD4+T cells differentiate into heterogeneous phenotypes upon challenge, and even

after proliferation they maintain the ability to adopt different differentiation fates. This

ﬂexibility to reverse differentiation and redifferentiate into other Th subsets is called

plasticity (Zhu and Paul, 2010; Becattini et al., 2015).

TCR signalling, costimulation, cytokines and transcription factors determine these

plastic differentiation processes of Th cells (reviewed by Zhu and Paul (2010)).

Cytokines involved in Th differentiation

Cytokines are molecules involved in the coordination of immune responses. The

following cytokines direct the differentiation of Th cells into different Th subsets.

IL-12 is expressed by activated DC and macrophages, and it induces the expression of

STAT4 transcription factor on Th cells, which induces T-bet transcription factor expres-

sion and the production of IFN-γ and IL-2. Therefore, involved in Th1 differentiation.

IL-2 is produced by activated DC and T cells, and consumed by activated T cells and

Tregs. As reviewed by Boyman and Sprent (2012), IL-2 induces the transcription of

STAT5, that promotes the differentiation of Th1, Th2 and Treg cells, while it prevents

the differentiation of Th17 cells. Introduction 35

IL-4 is expressed by a small percentage of unstimulated CD4+T cells, and it induces

STAT6 transcription, which promotes GATA3 expression and the production of IL-4. It

supports the differentiation of Th2 cells (reviewed by Silva-Filho et al. (2014)).

IFN-γ is produced by NK cells and Th1 cells. It induces the transcription of STAT1, and

the subsequent expression of T-bet. It ampliﬁes Th1 responses while it prevents the

differentiation into Th2 cells (reviewed by Schroder et al. (2004)).

IL-6 is produced by macrophages. It activates the transcription of STAT3, and together with the presence of TGF-β they promote the differentiation of Th17 cells through the

expression of RORγt transcription factor (reviewed by Hunter and Jones (2015)).

TGF-β inhibits the differentiation into Th1 and Th2 cells. As mentioned above, it induces

the differentiation of Th17 cells in the presence of IL-6, by promoting the expression of

RORγt, and in the presence of IL-2 it promotes iTreg or pTreg differentiation (in vitro or

in vivo, respectively).

Nr4a3

The primary supervisor of this project identiﬁed Nr4a3 (nuclear receptor 4a3) as an

immediate early gene of TCR signals both in the thymus and periphery by investigating

the transcriptomes from thymocytes and peripheral T cell populations (ex vivo data),

together with transcriptomes of activated T cells (in vitro data) (Masahiro Ono, un-

published data) applying a newly adapted multidimensional analysis called Canonical

Correspondance Analysis (CCA) (Ono et al., 2014). Nr4a3 was identiﬁed as a key Introduction 36

molecule accounting for TCR-mediated events in relation to thymic selection and T cell

activation.

As reviewed by Germain et al. (2006), nuclear receptors (NR) belong to a superfamily

of eukaryotic transcription factors. In humans there are 48 NRs, and 25 of those are

considered to be ‘orphan nuclear receptors’ (ONR) for lacking an identiﬁed endogenous

ligand. Nevertheless, ONRs can still act as transcription factors, modulating gene

transcription, by ligand-independent mechanisms. The general structure of nuclear

receptors consists of an N-terminal domain, a DNA-binding domain and a C-terminal

ligand binding domain (Nr4a3 structure shown in ﬁg. 4).

FIGURE 4: Structure and schematic representation of NR4a3 aminoacid sequence. N- terminal region containing the activation function-1 (AF-1); DNA-binding domain (DBD); linker region connecting to C-terminal region; ligand binding domain (LBD); ﬁnal transcriptional domain activation function-2 (AF-2). The percentage of sequence homology with the other family members Nr4a1 and Nr4a2 is shown. (Figure based on Martorell et al. (2008) and Sekine et al. (2011)).

Nr4a3 belongs to the Nr4a subfamily of ONRs. The other two family members are

Nr4a1 and Nr4a2. They are also known by other names such as Nur77 (Nr4a1),

Nurr1 (Nr4a2) and Nor1 (Nr4a3). Nr4a family members are true ONRs, as their ligand

binding domain contains no ligand binding cavity and it lacks a classical binding site for

coactivators or corepressors (assessed for Nr4a2 by Wang et al. (2003)). The Nr4a

family members present extensive amino acid sequence homology in their DNA-binding

domains (∼91–95%) (Cheng et al., 1997), and relative sequence similarity of 58-65% Introduction 37

for their C-terminal ligand binding domains (Kurakula et al., 2014). The N-terminal

domains differ the most, with 26-28% similarity (Kurakula et al., 2014) (ﬁg. 4). Despite

the lack of known ligands and ligand binding cavity, Nr4a family members are tightly

regulated by protein-protein interactions (Kurakula et al., 2014).

Nr4a1 and Nr4a3 are expressed at higher levels in thymocytes than Nr4a2 (Fassett

et al., 2012). They are also expressed by other cells of the immune system: Nr4a1

is highly expressed in Ly6G- monocytes and is involved in their differentiation (Hanna

et al., 2011); Nr4a3 is expressed in different types of DC and involved in their function

(Nagaoka et al., 2017, or their migration to the lymph nodes (Park et al., 2016)), and it

is also expressed by neutrophiles and regulate their number and survival (Prince et al.,

2017). They are also expressed in other tissues including neurons, adipocytes and

muscle.

Apoptosis and the involvement of Nr4a3

Apoptosis is a highly regulated process of programmed cell death characterized by

condensation of nucleus and cytoplasm, cellular fragmentation, and production of

apoptotic bodies, without eliciting inﬂammation (Kerr et al., 1972). This process is

important for deleting immune cells during thymocyte selection and in the contraction

phase of immune responses in the periphery. Apoptotic cells are rapidly recognized

and removed by phagocytic cells.

There are different pathways to apoptosis (reviewed by Youle and Strasser (2008)): the instrinsic or mitochondrial pathway, regulated by Bcl-2 (Chipuk et al., 2010), and

the extrinsic or death-receptor pathway, mediated by tumour necrosis factor receptor Introduction 38

(TNFR) family (such as Fas or TNFR1). Both pathways converge on caspase activation,

that induces the apoptosic processes such as DNA degradation, exposure of phos-

phatidylserine on the cell surface, formation of apoptotic bodies, and cell fragmentation.

Nr4a3 was ﬁrst identiﬁed as a nuclear receptor in rat brain neural cells undergoing

apoptosis, thus it was named neuron derived orphan receptor (NOR-1) (Ohkura et al.,

1994).

Initial studies identiﬁed Nr4a1 as a pro-apoptotic factor, highly expressed in apoptotic

cells following TCR-signalling (T cell hybridomas and thymocytes) by using anti-CD3

stimulation (Woronicz et al., 1994; Liu et al., 1994). Further reports described the role

of Nr4a1 in thymocyte negative selection. The constitutive expression of Nr4a1 leads

to massive cell death, while its inhibition prevents apoptosis during negative selection

(Cainan et al., 1995).

A molecular mechanism for the pro-apoptotic function of Nr4a1 and Nr4a3 in negative

selection of thymocytes has been shown. T cell activation induces the expression of

Nr4a1 and Nr4a3. They have been shown to be a protein kinase C (PKC) substrate (T

cell activation also activates PKC). Upon phosphorylation, Nr4a3 translocates from the

nucleus to the mitochondria where it binds Bcl-2, an anti-apoptotic member of the Bcl-2

family. The interaction between Nr4a1 and Nr4a3 with Bcl-2 changes the conformation

of Bcl-2, converting Bcl-2 to a pro-apoptotic molecule (Thompson and Winoto, 2008;

Kurakula et al., 2014).

In the 90s, analysis of Nr4a1 knock-out (KO) mice indicated they had no phenotype

compared to wt, and this was explained by the redundant function of Nr4a3 in thymo-

cytes (Lee et al., 1995). In fact, Nr4a3 is also highly expressed by TCR stimulated Introduction 39

thymocytes, and shared the role of Nr4a1 in apoptosis, unlike the other family member

Nr4a2 (Cheng et al., 1997).

Additional evidence of the redundant function of Nr4a1 and Nr4a3 comes from the lethal

phenotype in mice with simultaneous depletion of Nr4a3 in all cells and Nr4a1 in CD4+T

cells (Nr4a1/3 double KO). This simultaneous depletion of Nr4a1 and Nr4a3 results in

lethal inﬂammation and severe reduction in TReg numbers (Sekiya et al., 2013), however,

data for DP or other CD4SP cells were not reported. The additional CD4+T cell-speciﬁc

deletion of Nr4a2 (Nr4a1/2/3 triple KO) results in lethal inﬂammation as well, with

shorter lifespan (death within 21 days versus 31 days in the Nr4a1/3 double KO), with

severe reduction of the DP population (from 88.5% to 0.6%) and the absence of TReg

cells. Interestingly, other combinations of Nr4a double KO do not show an autoimmune

phenotype, supporting the requirement for Nr4a1 and Nr4a3 for appropriate T cell

development in the thymus.

New studies of Nr4a1KO mice show a phenotype in different TCR transgenic mice (OT-II

and BDC2.5) and revealed an increase in the percentage and number of CD25+Foxp3-

CD4SP and CD25+Foxp3+ CD4SP thymocytes, suggesting a non redundant role for

Nr4a1 as modulator of negative and agonist selection (Fassett et al., 2012). These

results disagree with the original Nr4a1KO studies performed with H-Y and AND TCR

transgenic mice (Lee et al., 1995), and with the CD4 speciﬁc depletion of Nr4a1, where

no differences were found in negative selection and/or agonist selection of TReg (Sekiya

et al., 2013 supplementary ﬁgure 3).

In a more recent report, Sekiya et al. (2015) generated a conditional KO in which

Nr4a members are deleted speciﬁcally in T cells upon expression of Foxp3 by the Introduction 40

use of Foxp3YFP-Cre system for Nr4a1 and Nr4a2 and ubiquitous deletion of Nr4a3

(Nr4a3-/-) (called Foxp3YFP-Cre-Nr4a-TKO). Brieﬂy, when Foxp3 is expressed at the

SP thymocyte stage, Cre recombinase is expressed and deletes loxP-ﬂanked DNA

sequences in the Nr4a1f/f and Nr4a2f/f genes, preventing their further expression. As

a result, this model avoids the disruption of the DP compartment and the absence

YFP-Cre of TReg cells. However, Foxp3 -Nr4a-TKO mice have reduced Foxp3 expression

levels, and developed fatal multiorgan autoimmunity with a life span of about 22 weeks.

Interestingly, the double conditional KO for Nr4a1 and Nr4a3 also shows autoimmunity

and reduced life span (70% survival at 22 weeks). The heterozygous females, chimeras

of Foxp3YFP-Cre/Foxp3WT cells, were analyzed to avoid the inﬂammatory environment.

As a result, many genes were reduced in TKO TReg cells, such as Foxp3, CD25, and

there was an up-regulation of cytokine genes, and more TReg cells become exFoxp3

cells. These results suggest that Nr4a members are involved in the stability of Foxp3

expression and in the suppressive function of TReg cells.

However, concerns about the Foxp3YFP-Cre system have been reported because of two

limitations: the system leads to hypomorphic expression of Foxp3 (it reduces Foxp3 expression levels) and the leakiness of Cre expression (that deletes ﬂoxed genes by stochastic Cre recombinase activity in cells not expressing Foxp3) (Franckaert et al.,

2015). Therefore, these limitations might also be contributing to the lethal phenotype

observed in Foxp3YFP-Cre-Nr4a conditional KO.

In order to identify cells which received TCR signals, two independent groups have

developed Nr4a1GFP BAC transgenic lines (also denominated Nur77GFP) (Moran et al.

(2011) and Zikherman et al. (2012)). These models are being used to identify cells that Introduction 41

have received strong TCR signals and to measure signal strength by using the GFP

ﬂuorescence intensity (Moran et al., 2011; O’Hagan et al., 2015).

Methods for measurement of protein expression dynamics. Timer technology

Flow cytometry

Flow cytometry is a high-throughput method, that can quickly acquire multiple data

from hundred thousands of cells in suspension. Staining with monoclonal antibodies

provides information about the speciﬁc antigens expressed by each cell, and reporter

proteins allow the study of gene expression.

With the advances in multicolour ﬂow cytometry, the number of parameters obtained is

increasing, and therefore several computational advances had been also introduced,

giving birth to a new ﬁeld at the intersection of immunology and computational biology,

the computational ﬂow cytometry ﬁeld (Saeys et al., 2016). One of the important steps

in the analysis of ﬂow cytometry data is the visualization. Although the traditional visualisation techniques, bidimensional scatterplots or single parameter density plots, were enough for dealing with a small number of parameters, new techniques of data visualization are being introduced to deal with high-dimensional data (Saeys et al.,

2016), such as dimensionality reduction techniques (principal components analysis

(PCA), multivariate correspondance analysis (MCA)...) and data clustering techniques

(Saeys et al., 2016). Introduction 42

Cell development modelling constitutes another interesting advance in the analysis of

ﬂow cytometry data. It consists on new automated approaches developed to ﬁnd the

data gradients between cells to create models of developmental processes.

Regarding ﬂow cytometry data analysis tools, the Flow Cytometry Standard format, FCS

1.0, dates from 1984. FCS 3.1 is currently used by most devices and allows the analysis

of ﬂow cytometry data from any platform (Spidlen et al., 2010). Commercial software

is available for ﬂow cytometry data analysis, such as FlowJo (FlowJo™,LLC.,TreeStar,

USA). In addition, the Bioconductor project started in 2001 as “an open source, open

development software project for the analysis and comprehension of high-throughput

data in genomics and molecular biology”1 (Huber et al., 2015). It contains a collection

of packages mainly in the programming language R. There are several packages

dedicated to ﬂow cytometry data analysis which allow for reproducible analysis.

Fluorescent labelled monoclonal antibodies

Monoclonal antibodies have developed together with ﬂow cytometry to enable the

detection of different molecules present on the cell membrane or inside the cell, in the

cytoplasm or the nucleus. Monoclonal antibodies bind to the antigen they are speciﬁc

for. By conjugating monoclonal antibodies with ﬂuorochromes, they can be detected by

ﬂow cytometry by their ﬂuorescent emission upon interaction with laser light. Nowadays,

conjugated monoclonal antibodies are commercially available and the ﬂuorochromes

industry continues to grow. Flow cytometry devices used in this project had up to ﬁve

different lasers. 1https://www.bioconductor.org/about/ Introduction 43

Fluorescent proteins

Green fluorescent protein (GFP) was purified for the first time in the 1970s from the

jellyﬁsh Aequorea victoria, and its gene was cloned and improved in the form of

enhanced GFP (or EGFP) in the 1990s (reviewed in Stepanenko et al., 2011). Since

then, many knock-in and BAC transgenic GFP reporter systems have been generated.

In this project, Foxp3-IRES-EGFP (Foxp3GFP) reporter (Wang et al., 2008) was used

to identify Foxp3 expressors. Furthermore, the primary supervisor, Masahiro Ono,

generated the BAC transgenic Nr4a3GFP reporter mice which report the transcriptional

activity of the Nr4a3 gene through the expression of EGFP.

The maximum excitation of EGFP occurs at 488nm, with a 509nm emission peak, and

the half-life is estimated to be 54 hours (Sacchetti et al., 2001) (ﬁg. 5). In order to

identify speciﬁcally cells which are transcribing the gene of interest in a closer time-

frame, researchers have been looking for other alternative ﬂuorescent proteins with

shorter half-life.

FIGURE 5: Dynamics of EGFP maturation shown at protein and cellular levels. EGFP ﬂuorescent proteins persist in cells long after the reported gene and EGFP transcriptions stop. Introduction 44

Fluorescent Timer proteins

Fluorescent timer proteins emit ﬂuorescence of different colours during their maturation,

allowing the visualization of different stages of their maturation. In other words, different

colours reﬂect the “age” of each protein from the moment of its expression. Therefore,

the colour pattern informs about the promoter activity.

The ﬁrst report of a ﬂuorescent timer was published in 2000 (Terskikh et al., 2000). It was

obtained through mutagenesis of DsRed (originally called drFP583), a protein obtained

from the nonbioluminescent sea anemone Discosoma striata (Matz et al., 1999). The

speciﬁc mutant changes colour from green to red in a time dependent fashion. Used

as a ﬂuorescent clock, it allows the analysis of the temporal and spatial characteristics

of gene expression, such as activation and down-regulation of gene activity (Terskikh

et al., 2000). Recent gene activation produced green ﬂuorescent regions, yellow-

orange corresponded to continuous activity, and exclusive red ﬂuorescence indicated

the down-regulation of gene expression (as explained in Day and Davidson, 2009).

This ﬂuorescent timer is nowadays commercially available. However, it has two main

disadvantages: its tetrameric form makes it unsuitable for fusion proteins because it can

be disruptive for normal function, and that being green, it is not suitable for combined

experiments with GFP due to spectral overlap.

In 2009 Subach et al. (2009) developed three different ﬂuorescent timers based on

another mutant from DsRed called mCherry (from ‘monomeric’ Cherry) (Shaner et al.,

2004). These derived variants change their ﬂuorescence from blue to red over time with different kinetics for each variant (Subach et al., 2009). They are ﬁrstly expressed

in their blue form and through time they mature into red forms. Their maturation speed Introduction 45

is dependent on temperature, decreasing at lower temperatures, which allows the

maturation process to be virtually stopped by working on ice during sample preparation.

Additionally, the maturation dynamic is highly stable to pH changes (for pH higher than

5.4), and it is also similar in different organisms (bacteria, insects, mammalian cells).

The maturation is irreversible and its speed is independent of the concentration, so that

it can be used as a reference timer (Stepanenko et al., 2011). They are detected by

ﬂow cytometry techniques: the immature blue form is excited by the 405nm laser and

emits blue light with an emission peak of 466nm; and the mature red form is excited

by the 561nm laser, and emits red light with an emission peak of 606nm. These two

colours are compatible with GFP emission peak at 509nm (ﬁg. 6).

FIGURE 6: Excitation and emission spectral overlap of Timer blue, Timer red and EGFP. Excitation spectra (dashed lines), emission spectra (continuous lines), and excitation laser wave- length (vertical straight lines) are shown. Excitation and emission peaks and the range of the filters used for fluorescence detection are specified. (Figure data based on Subach et al. (2009) and BD fluorescence spectrum viewer (http://www.bdbiosciences.com/us/s/spectrumviewer))

The primary supervisor of this project chose one of these variants (FTfast, hereafter

called Timer) to generate reporter mice. Compared with EGFP (with a half life longer

than 54 hours), the blue form presents a shorter half life of about 7.5 hours, which allows

the study of transcriptional changes with a much closer timing scope. Ono generated two Introduction 46

new reporter mice using a bacterial artiﬁcial chromosome (BAC) transgenic approach

(see introduction of Chapter 3, page 63). In this approach, the transcriptional activity

of the endogenous targeted gene is reported by the expression of Timer. When the

reported gene is transcribed, the transcription factors that activate its transcription also

bind to the gene regulatory regions in the BAC transgene, which is integrated in the

genome, and activate the transcription of the Timer gene (see ﬁg. 7). Depending on

the expression pattern of the reported gene, cells may show different patterns of Timer

blue and Timer red ﬂuorescence levels.

FIGURE 7: Fluorescent Timer maturation dynamics shown at individual protein level, cellular level and cellular pool level. Introduction 47

Thesis aims

The main aim of this project is to investigate the temporal dynamics of transcriptional

events downstream of the TCR signal in thymic and peripheral CD4+T cells, and reveal

how they are related to CD4+T cell selection and differentiation.

For this purpose there was the need of new tools to identify the transcriptional activity

and changes throughout time. I used different reporter mice generated by the primary

supervisor, Masahiro Ono: Nr4a3GFP, Nr4a3Timer, Foxp3Timer, and Nr4a3IRES-GFP-tet-on

(a model of controlled induction of expression of Nr4a3). These new tools allow the

investigation of the temporal dynamics of the expression of Foxp3 and Nr4a3 throughout

T cell development, activation and differentiation.

This project is the ﬁrst to study the dynamics of transcription of Nr4a3, in order to

acquire a better understanding of the events downstream of the TCR signal and their

relationship to T cell development and differentiation.

The ﬁrst part of this report (Chapter 1) is dedicated to the establishment of the two new

ﬂuorescent Timer reporter mouse colonies: Nr4a3Timer and Foxp3Timer. The objectives

for this part of the project were:

• To conﬁrm detection of the Timer protein by ﬂow cytometry.

• To analyse several independent lines to assess the quality of expression of the

transgene in each of them.

• To produce colonies (heterozygous, homozygous and double transgenic colonies) to

provide the mice required for experiments. Introduction 48

The second part of the project (Chapter 2) focuses on investigating the dynamics of

Timer protein expression to understand the dynamics of Nr4a3 and Foxp3 transcription

during CD4+T cell differentiation in the thymus of Nr4a3Timer and Foxp3Timer reporter

mice respectively:

• To design an effective approach for the analysis of multidimensional Timer data.

• To study the patterns of Timer in CD4SP cells to infer the transcriptional behaviour of

Nr4a3 and Foxp3 during thymocyte development.

The third part (Chapters 3, 4 and 5) investigates biological questions using the analysis

of the transcriptional activities of the Nr4a3 and Foxp3 genes.

The speciﬁc objectives of this third part are:

• To investigate the dynamics of Nr4a3 expression during CD4SP T cell development,

and use it as a clock for the expression of other known markers such as CD25, Foxp3,

CD69 and CD5, to resolve the order of expression events during CD4+ T cell develop-

ment in the thymus (Chapter 3).

• To test the hypothesis that cytokine signalling controls Nr4a3 expression. Different

cytokines involved in CD4+ Tcell differentiation were tested for their effects on Nr4a3

expression in vitro (Chapter 4).

• To test the hypothesis that Nr4a3 is involved in the apoptosis of CD4+T cells in a context dependent manner. The relation between apoptosis induction and Nr4a3

physiological and enhanced expressions was studied in vitro under several CD4+ Tcell

differentiation conditions (Chapter 5). Methods

Transfection of HEK 293T cell line

Hek 293T were transfected with plasmids containing the ﬂuorescent Timer gene to

assess detection of Timer blue and Timer red ﬂuorescence by ﬂow cytometry.

Cells were cultured with complete RPMI-1640 medium (containing 10%FCS and antibi-

otics).

Fugene 6 transfection reagent (Promega) was used at 3:1 concentration (transfection

reagent:DNA). 2 µg of plasmid DNA were used for each transfection.

Actinomycin D (1 µg/ml) (SIGMA) was used to stop mRNA synthesis 24 hours after

transfection in some samples.

Cells were prepared for ﬂow cytometry 40 hours after transfection. Cells were ﬁrstly washed with 1xPBS and treated with trypsin-EDTA to be dispersed.

49 Methods 50

Mouse strains

Table 1 shows the mouse strains used in this project.

TABLE 1: Mice models used in the project. Mice were maintained at Charles River, University College London and Imperial College London facilities, under the United Kingdom Home Ofﬁce regulations.

Designation Mouse Description Source strain Nr4a3-GFP C57BL/6J Tg(RP23-122N18-∆Nr4a3 EGFP)2 BAC re- Masahiro Ono BAC reporter porter transgenic in which the main exon of transgenic Nr4a3 is replaced by enhanced GFP. EGFP (Nr4a3GFP) expression is controlled by the regulatory sequences of the Nr4a3 gene. Nr4a3-Timer C57BL/6J Tg(RP23-122N18-∆Nr4a3 Timer)2 BAC re- Masahiro Ono BAC reporter porter transgenic in which the main exon of transgenic Nr4a3 is replaced by Timer. Timer expression (Nr4a3Timer) is controlled by the regulatory sequences of the Nr4a3 gene. Foxp3-Timer C57BL/6J Tg(RP23-330D17-∆Foxp3 Timer)2 BAC re- Masahiro Ono BAC reporter porter transgenic in which the ﬁrst exon of transgenic Foxp3 is replaced by Timer. Timer expression (Foxp3Timer) is controlled by the regulatory sequences of the Foxp3 gene. Foxp3- C57BL/6J: Strain name: B6.Cg-Foxp3tm1Mal/J. IRES- The Jackson Laboratory IRES-EGFP 129 mixed3 GFP cassette is inserted in exon 11 of the (Foxp3GFP) Foxp3 gene, without disruption of endogenous gene expression (generated by Malissen group (Wang et al., 2008)) Nr4a3-IRES- BALB/c Double Tg, with 1- Nr4a3-IRES-GFP cassette Masahiro Ono using GFP-tet- under the control of tetracycline responsive ele- rtTA developed by Rose on system ments (TRE) Zamoyska (Legname (NIG:rtTA) et al., 2000) 2- Reverse tetracycline controlled transactivator (rtTA) downstream of hCD2 promoter (Zhumabekov et al., 1995).

2Strain name from Ono, in Risk assessment Form B, Imperial College London, 2015 3Originally derived as 129X1Sv/J, and backcrossed to B6 more than 5 generations (information from The Jackson Laboratory) Methods 51

Molecular biology techniques

Genotyping mice

DNA isolation from tissue

Genomic DNA was isolated from tail or ear biopsies by GenElute™ mammalian genomic

DNA miniprep kit (SIGMA) following the rodent tail preparation protocol4. Alternatively,

biopsies were incubated in 100 µl lysis buffer (100mM Tris HCl pH 8.5, 0.5M EDTA, 10%

SDS, 5M NaCl) with 0.5 µg/ml proteinase K (SIGMA), and incubated on an Eppendorf

thermomixer block at 56◦C and 650rpm for at least 3 hours until tissue was digested.

30 µl of each sample were then boiled at 95◦C for 20 min in a PCR block.

Polymerase chain reaction (PCR) for genotype screening

Genomic DNA samples were used to genotype mice by polymerase chain reaction

(PCR) using oligonucleotide primers speciﬁc for the transgenic sequences (2. Primers were synthesised by Integrated DNA Technologies.

4GenElute Mammalian Genomic DNA Miniprep Kit User guide G1N70. https://www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma/Bulletin/g1n10bul.pdf Methods 52

TABLE 2: Oligonucleotide primers used for genotyping mice and qRT-PCR primers for CD25.

Genotype Sequence 5’-3’ Sense Product Annealing Timer CGCGGAACTAACTTCCCCTC Forward 163bp 60 GTCTTGACCTCAGCGTCGTA Reverse Foxp3Timer ACTCTGCCTTCAGACGAGAC Forward 370bp 57 CCCTGGAGCCGTACATGAAC Reverse Nr4a3Timer CAGGTGGGAGAGGATACCAC Forward 370bp 57 AGTCCTCGTTGTGGGATGTG Reverse Nr4a3GFP GTGCAGGCTGCTAATCCTGT Forward 1000bp 60 GTCCTCCTTGAAGTCGATGC Reverse rtTA CGCTAAAGAAGAAAGGGAAACACC Forward 517bp 63 GCATCGGTAAACATCTGCTCAAAC Reverse NIG ACAGTTCTTCATCACCTCCAGGG Forward 795bp 63 TGTAGTCGGGGTTCATGATTTCT Reverse CD25 CAGGAGTTTCCTAAGCAACG Forward 146bp 53.4 CTGTGTCTGTATGACCCACC Reverse 54.9

PCR reactions were performed using ONETaq® DNA Polymerase or Standard Taq DNA

Polymerase following their respective protocols (NEB).

Standard Taq PCR

Total reaction volume of 25 µl. The volume of genomic DNA added was 1/200 of the

total volume of tail biopsy digestion, diluted 1/20 in the total reaction volume. Reactions

performed as described in New England Biolabs website5. The thermocycling program

used is shown in table 3.

TABLE 3: Thermocycling program for Standard Taq polymerase PCR.

Step Temperature Time Initial denaturation 95◦C 30 seconds 38 ampliﬁcation cycles 95◦C 30 seconds 63◦C 30 seconds 68◦C 1 minute Final extension 68◦C 5 minutes Hold 4◦C ∞

5PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer (M0273). https://www.neb.com/protocols/1/01/01/taq-dna-polymerase-with-standard-taq-buffer-m0273 Methods 53

Gel electrophoresis

PCR products were analysed by agarose gel electrophoresis. Samples were loaded

using 6x loading buffer (NEB) or home made loading buffer (50% glycerol, 0.25% bromophenol blue). 1.5% agarose gels were made with SIGMA agarose, TAE buffer

1x (50x TAE buffer: Trizma base 242g, glacial acetic acid 57.1ml, 0.5M EDTA (pH8)

100ml, up to 1L with water) and GelRed™ nucleic acid gel stain (Biotium) to identify

the ampliﬁed bands. For molecular weight standards, 1kb or 100bp DNA ladder (NEB) were used.

Quantitative PCR (qPCR)

qPCR was used to determine the number of copies of the transgene in the genome or to

distinguish homozygous and heterozygous mice. For quantiﬁcation of the ampliﬁcation

iQ® SYBR™ green supermix (Bio-Rad) was used. Copy number was inferred from the

relative comparison to the CD25 gene ampliﬁcation (comparison of threshold cycles

(Ct) for CD25 and the gene of interest).

Neonatal analysis

Timed matings were set up overnight. Up to three females were placed with one male

at the end of the day, and separated on the following morning around 10 am. The date

of birth was considered the day 0. Methods 54

CD4+T cell differentiation cultures

Splenocytes were cultured at a density of 106 cells per well. Puriﬁed CD4+ naive T cells were cultured at 4 x 104 or 4 x 105 cells per well. When APCs were used for cocultures,

they were added at 1:1 ratio with the CD4+ naive T cells.

Cells were culture in the presence of anti-CD3 (coated plate (5 µg/ml) or soluble

(1 µg/ml)) and anti-CD28 (1 µgg/ml or 0.3 µg/ml) in the presence of different cytokines

cocktails for each differentiation culture: Th0: no cytokines added. Th1: anti-IL-

4 (10 µg/ml), IL-12 (10ng/ml), IL-2 (100ng/ml). Th2: anti-IFNγ (10 µg/ml), anti-IL-12

(10 µg/ml), IL-4 (50ng/ml), IL-2 (100U/ml). iTreg: anti-IL-4 (10 µg/ml), anti-IFNγ (10 µg/ml),

TGFβ (5ng/ml), IL-2 (100U/ml). Th17: anti-IL-4 (10 µg/ml), anti-IFNγ (10 µg/ml), TGFβ

(5ng/ml), IL-6 (2ng/ml).

Flow cytometry

Single cell suspensions were obtained by scratching the thymi on a 70 µm mesh. Cell

suspensions were placed in “v”-bottom 96-well plates and washed with PBS. Fixable

Viability Dye (BD 65-0865-14) was used to exclude dead cells from the analysis,

following the product instructions (1000 times dilution in PBS for 30 minutes at 4

degrees). Finally, the staining of surface antigens using directly-conjugated antibodies was performed at 4 degrees for 25 minutes. The characteristics of ﬂuorochromes used

are shown in table 4 and the panel of antibodies used for neonatal T cell development

are shown in table 5. Methods 55

TABLE 4: Fluorochromes list. Fluorochromes, their excitation and emission properties, excitation laser and ﬁlter for detection.

Fluorochromes Excitation Emission Laser Filter max max 1 BUV395 348 395 355 379/28 2 BUV737 348 737 355 740/35 3 Timer blue (reporter) 403 466 405 450/40 4 FITC (not used with 494 520 488 530/30 GFP reporters) 5 GFP (reporter) 488 509 488 530/30 6 PerCP-Cy5.5 482 695 488 695/40 7 Timer red (reporter) 583 606 561 610/20 8 PE-Cy7 496 785 561 780/60 9 APC 650 660 633 660/20 9 Alexa Fluor 647 650 665 10 eFluor 780 (Viability - 780 633 780/60 Dye) 11 Alexa Fluor 700 696 719 633 730/45

TABLE 5: Fluorochromes used for the neonatal analysis. Fluorochrome, antigen speciﬁcity, clone and ﬂuorescent properties of the different monoclonal antibodies used for neonatal analysis.

Fluorochromes Antigen Clone Fluorophore Antigen Gate of Brand brightness den- interest sity 1 BUV395 CD4 GK1.5 dim high yes BD 2 BUV737 CD8 53-6.7 bright high negative BD 3 Timer blue (reporter) 5 GFP (reporter) 6 PerCP-Cy5.5 CD25 PC61.5 dim spread yes eBioscience Tonbo 7 Timer red (reporter) 8 PE-Cy7 CD5 53-7.3 bright high yes Biolegend 9 APC CD69 H1.2F3 intermediate spread yes eBioscience 10 eFluor 780 Viability bright negative eBioscience Dye 11 Alexa Fluor 700 TCRb H57- dim spread yes Biolegend 597

Data were acquired on a BD Accuri C6 Cytometer in our lab, or on a BD FACSAria III

(required for the Timer excitation and detection) at the Flow cytometry core facility at

ICH (UCL)6. Table 6 shows the FACSAria III conﬁguration at ICH (UCL).

6https://www.ucl.ac.uk/ich/services/lab-services/FCCF/Instruments/facsariaiii Methods 56

TABLE 6: FACSAria III configuration. Lasers, photomultiplier tubes (PMT), long pass (LP) mirrors, bandpass (BP) filters, and fluorochromes. Obscured in grey are the unused channels.

Lasers PMTs LP mirrors BP ﬁlters Fluorochromes Violet Laser (405 nm) G - 450/40 DAPI (Timer blue) Blue Laser (488 nm) A 655LP 695/40 PerCP-Cy5.5 B 502LP 530/30 FITC C - 488/10 SSC Yellow-Green Laser (561 A 735LP 780/60 PE-Cy7 nm) B 685LP 710/50 PE-Cy5-5 PE-AF700 C 630LP 670/14 PE-Cy5 D 600LP 610/20 PE-TxRed (Timer red) E - 582/15 PE YG-582/15 Red Laser (633 nm) A 755LP 780/60 eFluor780 B 690LP 730/45 Alexa Fluor 700 C - 660/20 APC / Alexa Fluor 647

From May 2015 Fortessa III at SAF building in Imperial College was also used. Table 7 shows the conﬁguration of this cytometer.

TABLE 7: LSR Fortessa III configuration. Lasers, photomultiplier tubes (PMT), long pass (LP) mirrors, bandpass (BP) filters, and fluorochromes. Obscured in grey are the unused channels.

Lasers PMTs LP mirrors BP ﬁlters Fluorochromes Ultra Violet Laser (355 nm) A 690LP 740/35 BUV737 B 410LP 379/28 BUV395 Violet Laser (405 nm) F - 450/40 DAPI (Timer blue) Blue Laser (488 nm) A 655LP 695/40 PerCP-Cy5.5 B 505LP 530/30 FITC, AF488, GFP C - 488/10 SSC (Side Scatter) Yellow-Green Laser (561 A 750LP 780/60 PE-Cy7 nm) B 685LP 710/50 PE-Cy5-5, PE-AF700 C 635LP 670/30 PE-Cy5 D 600LP 610/20 PE-TxRed (Timer red) E - 582/15 PE, AF546 Red Laser (633 nm) A 750LP 780/60 APC-Cy7, eFluor780 B 690LP 730/45 Alexa Fluor 700 C - 670/14 APC, Alexa Fluor 647 Methods 57

Cell counting

Manual cell counting with a Neubauer chamber was used to count cells in each sample.

Cell numbers were estimated by multiplying the total cell count of each thymus by

the percentages of cells found in each population, apart from thymi at day 1 after

birth, which were too small and all cells were used for ﬂow cytometry data acquisition.

Consequently, there are no estimates for cell numbers for day 1 neonatal data.

Data visualisation

Flow cytometric data analysis

Software and packages

For ﬂow cytometric analysis, FlowJo (FlowJo™,LLC.,TreeStar, USA), R (R Core Team,

2016) and RStudio (RStudio Team, 2015) softwares were used.

For data analysis and ﬁgures, R sofware was used (R Core Team, 2016) including the

following packages: data.table (Dowle et al., 2015), gridExtra (Auguie, 2016), ggplot2

(Wickham, 2009), ggrepel (Slowikowski, 2016), doBy (Hojsgaard and Halekoh, 2016),

ﬂowCore (Hahne et al., 2009), ggcyto (Jiang, 2015), plyr (Wickham, 2011), PMCMR

(Pohlert, 2014), e1071 (Meyer et al., 2017), and userfriendlyscience (Peters, 2016).

For modelling, R package deSolve was used (Soetaert et al., 2010). Methods 58

Flow cytometric data analysis with R

FCS 3.1 is the flow cytometric data file standard for files obtained from flow cytometric

acquisition. Compensation was set up on the cytometer, so that the compensation

matrix was retrieved from the fcs ﬁle and applied before any data transformation. There

are differences though between .fcs ﬁles depending on the cytometer software (FCS

2.0, FCS 3.0, FCS 3.1) (Spidlen et al., 2010). Speciﬁcally, the compensation settings

deﬁned for the acquisition in the FCS3.0 are stored under the keyword $SPILLOVER when using BD Accuri C6, while other BD cytometers do it under the keyword $SPILL

(Qian et al., 2012). Taking these into account, FCS ﬁles were read in R using ﬂowCore

package (Hahne et al., 2009).

FCS ﬁle names were modiﬁed to include the information of each sample in a convenient

standard way so that it could be extracted and included in a data table directly. For

example, for the data table named “dt”, the ﬁle names were converted into several

columns containing the information of each sample:

filenames<-c( "NFTantiMHC_1_NFTF3G_MLN_antiMHC_021.fcs", "NFTantiMHC_1_NFTF3G_Thy_antiMHC_011.fcs", "NFTantiMHC_2_NFTF3G_MLN_antiMHC_022.fcs", "NFTantiMHC_2_NFTF3G_Thy_antiMHC_012.fcs", "NFTantiMHC_3_NFTF3G_MLN_isotype_023.fcs", "NFTantiMHC_3_NFTF3G_Thy_isotype_013.fcs", "NFTantiMHC_4_NFTF3G_MLN_isotype_024.fcs", "NFTantiMHC_4_NFTF3G_Thy_isotype_014.fcs") dt[,c("exp","mouseID", "genotype", "organ","treatment", "tubeID") := tstrsplit(sample, "_",fixed=TRUE)] #tstrsplit function splits file name information into several columns dt[,"tubeID"]<-gsub(".fcs$","",dt[,tubeID])b #Eliminate the file name extension ".fcs" Methods 59

Once data were organized in a single data table, they were saved in a single csv. Csv

ﬁles can be then read for the analysis: write.csv(dt,"/path/filenamewithallevents.csv",row.names=FALSE) #It creates a csv file with the information in "dt" dt<-fread("/path/filenamewithallevents.csv") #It reads a csv file and creates a data. table object from it

Cells are displayed in linear scale for FSC and SSC. The rest of ﬂuorocromes detectors are displayed in logicle scale (an implementation of the biexponential scale, a hybrid scale that combines linear and log scaling on a single axis to include negative data values) (Parks et al., 2006). Logicle scale was performed by the function ‘scale_x_logicle’ or ‘scale_y_logicle’, from the R package ggcyto (Jiang, 2015). The selection of data subsets allows selection of cells of interest to analyse.

Statistical analysis

R sofware (R Core Team, 2016) was used for data analysis and ﬁgures.

One-way anova with Tukey’s post-hoc test

One-way anova was used to determine the presence of differences between groups.

Moreover, Tukey’s post-hoc test was used to identify the groups that differ.

P-values were adjusted for multiple comparisons by multiplying them by the number of comparisons made.

Statistically signiﬁcant differences are reported as * for p<0.05, ** for p<0.01 and *** for p<0.001. Methods 60

The R package used for these analysis was userfriendlyscience (Peters, 2016),

function oneway with the posthoc method ‘tukey’.

Pearson correlation coefﬁcient

Pearson correlation coefﬁcient (r) was used to measure the linear relationship between

two variables. A value of -1 indicates a negative linear relationship, 0 indicates that

there is no linear relationship, and 1 indicates the strongest positive relationship. The R

function used was cor(), which default method is ‘pearson’.

Text processor

LaTeX7 was used for writing this dissertation, using Sublime Text8 text editor.

Bibliography management

Zotero9 and BibTeX10 were used for managing the bibliography and citations.

7www.latex-project.org/ 8www.sublimetext.com 9www.zotero.org 10www.bibtex.org Chapter 1

Establishment of two new transgenic reporter mouse strains

1.1 Introduction

Fluorescent reporter mice are models in which a fluorescent protein gene is expressed in the mouse genome to monitor the activity of a promoter or the expression and localization of target proteins. There are different techniques to generate fluorescent reporter mice, such as the knock-in approach or the transgenic approach, and both have advantages and disadvantages. Briefly, in the knock-in approach, the reporter gene is inserted into a specific locus in the genome, therefore modifying specifically the endogenous targeted gene by introducing the reporter gene.

In contrast, the bacterial artificial chromosome (BAC) transgenic approach does not modify the endogenous locus: it is based on random integration of a modified BAC containing the fluorescent protein reporter gene under the regulatory sequences of the gene of interest. In other words, whenever the endogenous gene is transcribed, the

ﬂuorescent protein gene is also transcribed from a different location in the genome, being activated by the same regulatory molecules.

61 Chapter 1 Establishment of two new transgenic reporter mouse strains 62

For the purpose of this project of investigating the transcriptional dynamics of Nr4a3,

the BAC transgenic approach was chosen to avoid any alteration in the endogenous

Nr4a3 transcription.

1.1.1 Generation of BAC transgenic Timer reporter strains

The method of generating a new BAC transgenic Timer reporter strain is depicted in

(ﬁg. 1.1). Firstly, a BAC containing the gene of interest is modiﬁed, replacing part

of the sequence with the ﬂuorescent protein gene. Once modiﬁed, the BAC DNA is

puriﬁed and microinjected into the pronucleus of embryos at the 2-cell stage. By random

integration, BAC is integrated into the genome, often as a multi-copy integration in

a tandem manner. Microinjected embryos develop in pseudopregnant females and

the offspring carrying the transgene are selected to be the founders (F0) to establish

independent transgenic mouse lines (Valjent et al., 2009). Chapter 1 Establishment of two new transgenic reporter mouse strains 63

FIGURE 1.1: Establishment of BAC tg lines. The generation of a BAC transgenic new line starts with the design of the construct with homology arms to modify a BAC sequence (containing the gene of interest (blue) and surroundings (grey)). The modiﬁed BAC molecules are microinjected into the pronucleus of fertilized embryos and then transferred into a pseudopregnant female for their development. Tg+ offspring is selected (F0) and mated with wild-type mice to obtain F1 mice to be analysed by ﬂow cytometry. (Over white background are the parts of the process I was involved in).

Due to random integration, each founder presents a different copy number of the

transgene, a different integration site and a different ﬂuorescent protein expression level.

Thus, each founder can produce an independent transgenic line. Lines are established

by mating founders with wild type mice. Their descendants (F1) are screened for the

inheritance of the transgene by PCR of genomic DNA.

The percentages of transmission of the transgene to the offspring give important

information about the founder line (Haruyama et al., 2009). As reviewed by Haruyama,

for example, about 50% of the offspring should inherit the transgene (at Mendelian ratio)

if it is inserted in a non sexual chromosome. In another example, the trangene can be

in the X chromosome of a male founder, producing as a result a 100% of transgene

positive females, and no positive males. In some cases the founder can be a mosaic Chapter 1 Establishment of two new transgenic reporter mouse strains 64

(with the transgene only present in some cells of its organism) and it may not produce

transgenic offspring at all (Haruyama et al., 2009).

Finally, the expression of the reporter ﬂuorescent protein for each line is assessed by

ﬂow cytometry, and lines are selected by the levels of ﬂuorescent protein detected.

1.1.2 Generation of new BAC Tg mice lines for Nr4a3 and Foxp3

The supervisor of this project, Masahiro Ono, chose the BAC transgenic approach in order to avoid any modiﬁcation of the endogenous gene, as the main goal of the project is to investigate the dynamics of physiological transcription of the reported

genes. Dr Ono, generated three new transgenic mice on a C57BL/6 background. The

new strains were named Nr4a3GFP reporter, Nr4a3Timer reporter and Foxp3Timer reporter

(Ono, unpublished).

To generate the Nr4a3 transgenic reporter lines, a BAC containing the Nr4a3 gene

(RP23-122N18) obtained from the BACPAC resource center1 was modiﬁed (Liu et al.,

2003; Warming et al., 2005) by replacing the third exon of the Nr4a3 gene either by

the EGFP or Timer reporter genes. This BAC DNA was used for pronuclear injection

of C57BL/6 embryos at Kyoto University (Ono, unpublished). In the Nr4a3Timer or the

Nr4a3GFP mice, when the Nr4a3 gene is transcribed, Timer or GFP are transcribed,

respectively, enabling the detection of cells by ﬂow cytometry.

The Foxp3Timer line was created using the BAC RP23-330D17 by replacing the third

exon of the Foxp3 gene in the BAC DNA with the Timer gene.

1https://bacpacresources.org/ Chapter 1 Establishment of two new transgenic reporter mouse strains 65

This chapter reports my work on the screening of the founders and the establishment

of the Nr4a3Timer and Foxp3Timer reporter lines.

For clarity purposes, the mouse line names are written in italics and with the reporter

ﬂuorescent gene in superscript (i.e. Nr4a3Timer), the ﬂuorescent protein genes are written in italics and attached to the name of the reported gene (i.e. Nr4a3Timer), their

ﬂuorescent proteins are written in normal fonts (i.e. Nr4a3Timer), and same applies to

the endogenous reported gene (i.e. Nr4a3) and the protein (i.e. Nr4a3)

1.2 Assessing the expression of the Timer protein by ﬂow cytometry

In order to investigate the expression of Timer protein by flow cytometry, the Timer gene was firstly expressed in HEK cells using transient transfection (fig. 1.2). HEK 293T cells

are a human embryonic kidney cell line, widely used for transfection experiments. HEK

cells were trasfected with a pMCs-IRES-Timer plasmid by lipofection as described in

methods section.

Plasmids with mCherry (pMCs-IRES-mCherry) and CFP (pCAG-CFP) were transfected

in order to obtain ﬂow cytometry settings for the two ﬂuorescent forms of Timer (blue,

similar to CFP, and red, mCherry) (ﬁg. 1.2).

Actinomycin D was used at 24 hours to stop all transcription, and allow observation

of mature forms of Timer protein without the presence of newly generated Timer blue

proteins. Chapter 1 Establishment of two new transgenic reporter mouse strains 66

FIGURE 1.2: Scatter plot of HEK cells transfected with differnt plasmids (no plasmid, pCAG-CFP, pMCs-IRES-mCherry and pMCs-IRES-Timer). Axis show Timer blue (CFP) and Timer red (mCherry) ﬂuorescence at 48 hours after transfection. Last panel on the right shows the pMCs-IRES-Timer plasmid transfected HEK cells cultured with Actinomycin D for the last 24 hours of culture.

The excitation/emission peaks are reported to be 403/466 nm for Timer blue and

583/606 nm for Timer red (Subach et al., 2009). In our case, Timer blue was detected

in the DAPI channel upon excitation with the Violet laser (405 nm) and detected after

bandpass ﬁlters of 450/40 or 450/50 (depending on the cytometer, meaning that 430-

470 nm or 425-475 nm wavelenght light went to the detectors respectively). Timer red was detected in the PE-TexasRed channel, upon excitation with the Yellow-Green laser

(561 nm) and detected after ﬁltering with the 610/20 ﬁlter (see Introduction, page 45).

Thus, the excitation efﬁciency of the Yellow-Green laser for Timer red is suboptimal

(64%) (ﬁg. 6 of McIntyre et al., 2010).

1.3 Establishment of independent lines

The transgenic founders (F0) obtained from the microinjected embryos were sent to a

facility of Charles River in UK. Tail biopsies from each of them were digested and their

genomic DNA was tested by PCR to conﬁrm the presence of the transgenes (ﬁg. 1.3). Chapter 1 Establishment of two new transgenic reporter mouse strains 67

FIGURE 1.3: Agarose gel (1.5%) with PCR products of Foxp3Timer and Nr4a3Timer ﬁrst batch of founders. Left ladder marker of 1kb and centre ladder 100bp. Bands around 800bp. Positive control (+ ctrl) from plasmid template, and negative control (- ctrl) from wild type DNA PCR reaction.

A total of 9 founders for Nr4a3Timer and 19 founders for Foxp3Timer were received and mated with C57BL/6 WT mice. Some of the founders failed to produce pups after

months of mating. Successful founder breeders gave rise to the ﬁrst generation (F1). F1

mice carrying the transgene were mated with wild type mice. Some F1 mice were also

analysed by ﬂow cytometry to assess Timer expression.

The lines that showed high levels of Timer expression were selected and colonies

established (table 1.1). Chapter 1 Establishment of two new transgenic reporter mouse strains 68

TABLE 1.1: Timer founders screened.

Trangene and Founder Copy number estima- PCR of F1 Flow cytometry of F1 ID tion

Nr4a3Timer 120 2 Yes Low expression Nr4a3Timer 121 No pups Nr4a3Timer 122 unclear No pups Nr4a3Timer 123 3-4 Yes Good expression Nr4a3Timer 320 No pups Nr4a3Timer 321 No positives Nr4a3Timer 322 Yes Similar expression to 123 (Closed June 2015) Nr4a3Timer 323 5 Yes Similar expression to 123 Nr4a3Timer 303 Negative PCR No positives Foxp3Timer 124 1 Yes Good expression Foxp3Timer 125 1 Yes Mated with Foxp3GFP Foxp3Timer 126 3-5 F1, 1 F2 Yes Foxp3Timer 127 7-8 No pups Foxp3Timer 128 unclear No pups Foxp3Timer 129 1 No pups Foxp3Timer 307 Yes Not good expression Foxp3Timer 308 Yes No Timer detected Foxp3Timer 309 Yes No Timer detected Foxp3Timer 310 No pups Founder sick Foxp3Timer 311 Yes No Timer detected Foxp3Timer 312 Yes No Timer detected Foxp3Timer 313 1 Yes Similar or brighter expression than 124 Foxp3Timer 314 Yes No Timer detected Foxp3Timer 315 No pups Foxp3Timer 316 Yes Low timer detected Foxp3Timer 317 1 Yes Low Timer detected Foxp3Timer 318 No pups Foxp3Timer 319 Yes No Timer detected

1.4 Determination of copy number

The copy number of the transgene in each line was determined by real time quantitative

PCR of genomic DNA. The CD25 gene was used as the reference gene (table 1.2 and

ﬁg. 1.4). Chapter 1 Establishment of two new transgenic reporter mouse strains 69

TABLE 1.2: Copy number of Timer lines estimated by quantitative PCR (qPCR).

Trangene ID mouse ID Timer Ct CD25 Ct ∆Ct 2∆Ct Copy number NFT123 94969 21.63 20.43 -1.19 0.43 3.02 NFT123 94492 21.74 20.63 -1.10 0.46 3.21 NFT123 95014 21.20 20.42 -0.77 0.58 4.04 NFT323 94873 20.77 20.28 -0.49 0.71 4.93 NFT323 94874 21.26 20.69 -0.57 0.67 4.67 FT124 94959 23.61 20.92 -2.68 0.15 1.07 FT124 94964 23.08 20.44 -2.63 0.16 1.11 FT124 94757 23.17 20.61 -2.56 0.16 1.17 FT126 95203 23.53 19.66 -3.86 0.06 0.47 FT126 95020 21.45 21.00 -0.45 0.73 5.07 FT126 95104 21.14 20.12 -1.02 0.49 3.42 FT313 94898 23.51 20.72 -2.79 0.14 1.00

FIGURE 1.4: Copy number calculation with qPCR. Copy number for Foxp3Timer lines 124, 126, 313 and Nr4a3Timer lines 123 and 323, was calculated by substracting CD25 Ct from the Timer Ct (∆Ct), and 2∆Ct was normalized to obtain integer values for copy numbers.

Single copy transgenic mouse lines or, in their absence, the mouse lines with a smaller

number of copies were selected to establish the new transgenic reporter strains.

The selected lines were Nr4a3Timer 123 and Foxp3Timer 124 and 313. Chapter 1 Establishment of two new transgenic reporter mouse strains 70

1.5 Determination of homozygosity or heterozygosity

Transgenic mice were ﬁrstly mated with wild type C57BL/6J (B6) for several generations, so that the descendancy from such matings was always heterozygous for the transgene.

However, later on, in order to maximise the production of transgenic mice, homozygous

transgenic mice were produced. We found that the expression level of Timer proteins was different between homozygous and heterozygous Nr4a3Timer mice (ﬁg. 1.5).

(A) Homozygous (B) Heterozygous

FIGURE 1.5: Flow cytometry data from predicted homozygous and heterozygous Nr4a3Timer mice. (Left) Two mice with higher Nr4a3Timer intensities by ﬂow cytometry pattern; (right) two mice with lower Nr4a3Timer intensities. Mesenteric lymph nodes CD4+ cells, mice of 8 weeks of age.

Homozygosity was determined by PCR by Dr David Bending. The estimations of copy

number of each individual mouse were in agreement with the prediction from the ﬂow

cytometry data of Nr4a3Timer ﬂuorescence obtained by ﬂow cytometry. Chapter 1 Establishment of two new transgenic reporter mouse strains 71

Homozygous mice showed higher mean ﬂuorescence intensity (MFI) on Timer blue and

red (ﬁg. 1.6), and a higher percentage of CD4Timer+ cells (ﬁg. 1.7).

(A) Nr4a3Timer blue MFI (B) Nr4a3Timer red MFI

FIGURE 1.6: MFI of Timer blue and Timer red in homozygous and heterozygous Nr4a3Timer mice. Four mice, two predicted homozygous (black) and two predicted heterozygous (grey) for Nr4a3Timer transgene with higher Nr4a3Timer MFI. (A) MFI of Timer blue+ cells; (B) MFI of Timer red+ cells. Mesenteric lymph nodes CD4+ cells, mice of 8 weeks of age. Chapter 1 Establishment of two new transgenic reporter mouse strains 72

FIGURE 1.7: Percentages of Timer+ subpopulations in homozygous and heterozygous Nr4a3Timer mice. Four mice, two predicted homozygous (1 and 2) and two heterozygous (3 and 4) for Nr4a3Timer transgene are shown. CD4+ cells divided into (grey) Timer- cells, (blue) Timer blue+ cells, (purple) Timer blue+red+ cells, and (red) Timer red+ cells. Mesenteric lymph nodes CD4+ cells, mice of 8 weeks of age.

Thus, it was decided to establish and use homozygous Nr4a3Timer mice only, to ensure

the comparability of the results between mice.

In order to obtain homozygous stud mating pairs, SYBR Green-based qPCR was

performed and homozygous mice were identiﬁed and selected. Chapter 2

Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells

2.1 Introduction

As reported by Subach et al. (2009), Timer protein changes its emission spectrum

through time due to ﬂuorochrome maturation (ﬁg. 7 in page 46). First, it acquires the

conformation that emits blue light with an emission peak of 466nm when excited by the

405nm laser. Subsequently, the Timer ﬂuorochrome matures spontaneously to acquire

the different conformation that, when excited by the 561nm laser, emits red light with an

emission peak of 606nm.

In our Nr4a3Timer and Foxp3Timer reporter mice, the expression of the Timer gene is

regulated by the same regulatory and promoter sequences as the endogenous Nr4a3

and Foxp3 gene, respectively. Therefore it is transcribed with the reported gene in a

synchronised manner. Depending on the transcriptional dynamics of the endogenous

73 Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 74

gene, cells present different ﬂuorescence levels of Timer blue and Timer red ﬂuores-

cence. As presented in section 2.2, time course analysis of Timer ﬂuorescence will

reveal the transcriptional dynamics of the reported gene (i.e. Nr4a3 or Foxp3).

This chapter focuses on the analysis of Timer ﬂuorescence in the new Nr4a3Timer and

Foxp3Timer reporter mice, and aims to estimate the dynamics of Nr4a3 and Foxp3

transcription in vivo.

2.2 Timer maturation analysis through neonates data

As mentioned in the introduction, Timer protein presents two states: the immature

blue form (early after the expression) and the mature red form (later after expression)

(see ﬁgure 7 in page 46). The estimated change from blue to red occurs at about 7.1

hours (Subach et al., 2009). As a result, cells contain a mixture of these two forms of

Timer protein. Timer protein expression and maturation processes are captured in the

Timer blue→ Timer red bi-dimensional space (ﬁg. 7 of the introduction, in page 46).

Cells that recently started Timer expression show blue ﬂuorescence but not red; cells

continuously expressing Timer show both blue and red ﬂuorescence; and cells that stop

Timer expression show mainly red ﬂuorescence.

Using Nr4a3Timer and Foxp3Timer reporter mice, the expression of Timer proteins was

analysed by ﬂow cytometry to investigate how Timer expression occurs and changes

across different days during early development (i.e. dynamics of transcription of Nr4a3

and Foxp3 in neonates). Nr4a3Timer and Foxp3Timer have different transcriptional

dynamics, as reﬂected by their different Timer blue and red levels (ﬁg. 2.1 and 2.2). Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 75

FIGURE 2.1: Patern of expression of Timer blue and Timer red forms in CD4+ cells from thymus and spleen of Nr4a3Timer neonates at different days after birth. Representative data from n=3 for each day. Nr4a3Timer+ cells were identiﬁed by the polygonal gate.

FIGURE 2.2: Patern of expression of Timer blue and Timer red forms in CD4+ cells from thymus and spleen of Foxp3Timer neonates. Quadrant gate is shown as Q4 Timer- cells; Q3 Timer blue+red-; Q2 Timer blue+red+; Q1 Timer blue-red+. Representative data from n=3 for each day anlysed after birth.

Firstly, Timer ﬂuorescence was analysed by classifying cells using a quadrant gate (with

four rectangular gates) in the Timer blue (b)- Timer red (r) bi-dimensional space: Timer

negative cells (b-r-), Timer early (b+r-), Timer intermediate (b+r+) and Timer mature

(b-r+) cells (Fig. 2.2). However, this analysis loses some information provided by Timer Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 76

fluorescence due to oversimplification. In order to fully use the power of Timer flow

cytometry data, which are continuous data, rather than binary expression, I generated

two derived variables: the TimerAngle and TimerIntensity, which are deﬁned as (1) the

angle from the axis of Timer blue (θT imer), and (2) the intensity of its expression (IT imer)

respectively.

The data obtained by ﬂow cytometry for Timer blue and Timer red were standardized

for Timer negative populations in order to obtain a symetric distribution of the negative

cells with mean ﬂuorescence intensity of 0 and variance of 1 for both Timer blue and

Timer red. Standardized Timer blue and Timer red values were used to calculate the

Timer angle and Timer intensity. This standardization was required because Timer

blue and Timer red are excitated by two different lasers (405 and 561) and detected by

different detectors (receiving wavelenghts of 430-470nm, and 600-620nm respectively), which provides ﬂuorescence data with different mean and variance for each of them.

The following standard formula was used:

X − µ , (2.1) σ where X is the value to be normalized, and µ and σ are the mean and the standard

deviation of the Timer negative reference population, assuming that the negative data

are normally distributed for both Timer blue and Timer red.

Precisely, a reference population for each group of samples was used to obtain the

mean (µ) and the standard deviation (σ) for each experiment (acquired on the same

day). For the thymus samples from Nr4a3Timer reporter, TCRβ-CD69- cells were used

as reference population (ﬁg. 2.3), because these cells have very few Timer+ cells. Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 77

FIGURE 2.3: Gating strategy for identifying the reference population for Nr4a3Timer. (1) Thymocytes were gated by their FSC-W FSC-A, then (2) by their FSC-A SSC-A, (3) viable cells were selected by their negative staining for the viability dye, and (4) TCRβ- CD69- cells were used as the reference population. Those cells are shown in a scatter plot with Timer blue and Timer red coordinates (5).

FIGURE 2.4: Gating strategy for identifying the reference population for Foxp3Timer using wild type samples. (1) Thymocytes were gated for their FSC-A SSC-A, (2) FSC-A FSC-W gate was used for excluding doublets, then (3) viable cells were selected by their negative staining for the viability dye, and (4) CD3+ cells were used as the reference population. Those cells are shown in a scatter plot with Timer blue and Timer red coordinates (5).

For the Foxp3Timer samples, CD3+ cells from wild type litter mates were used (2.4). Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 78

By deﬁnition, the centre of standardised Timer blue and Timer red data is (0,0) (located

at the origin of both coordinates), and the standard deviation is 1 (for the cells considered

Timer negative).

2.2.1 Data transformation: Timer angle (θT imer) and Timer intensity (IT imer)

Assuming that Timer blue is the x-axis and Timer red is the y-axis, each cell has the

Timer blue value (b) and Timer red value (r). Thus,

x = (b, r). (2.2)

Data were transformed into the two new variables Timer angle (θT imer) and Timer

intensity (IT imer):

x = (b, r) −→ (θT imer,IT imer). (2.3)

The derived variable TimerAngle (θT imer) corresponds to the angle formed by the vector

x and Timer blue axis (ﬁg. 2.5). (θT imer) can be calculated using trigonometry:

b θT imer = arccos(√ ) (2.4) b2 + r2 Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 79

FIGURE 2.5: Coordinates axis x (Timer blue) and y (Timer red) showing the example of one cell and the angle (Timer angle) between its value for Timer blue and Timer red (b,r) and the reference vector (1,0).

The sign of the Timer red value has to be taken into account to indicate if the calculated

(θT imer) is positive or negative. For that purpose the sign function (sgn()) calculates the

sign of Timer red:

  −1 if r < 0    sgn(r) = 0 if r = 0 (2.5)     1 if r > 0,

and is applied to the θT imer value:

b θT imer = sgn(r) arccos(√ ). (2.6) b2 + r2

The formula results in a value of the angle in radians. Therefore, a conversion into

degrees was performed by multiplying the result by 180◦/π (1 rad=180◦/π):

b 180 θT imer = (sgn(r) arccos(√ ))( ). (2.7) b2 + r2 π

The following code shows the deﬁnition of Timer angle variable in R: Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 80

Timer.angle=function(b,r){ Tangle.radians=sign(r)*acos(b/sqrt(b^2+r^2)) Tangle.degrees=Tangle.radians*180/pi return(Tangle.degrees) }

The second transformed variable, the Timer intensity (IT imer), corresponds to the mag-

nitude of the vector x (ﬁg. 2.6). Timer Intensity (IT imer) was generated by calculating

the vector magnitude √ 2 2 IT imer = kxk = b + r (2.8)

FIGURE 2.6: Coordinates axis x (Timer blue) and y (Timer red) showing Timer Intensity and Timer Angle concepts.

The following code shows the deﬁnition of Timer intensity variable in R:

Timer.intensity<- function(b,r){ Tintensity<-sqrt(b^2 + r^2) return(Tintensity) }

Alternatively, FlowJo software contains a derived parameters tool (ﬁg. 2.7) which allows

the creation of derived variables deﬁned by a formula. Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 81

FIGURE 2.7: Screen shot of the Tools menu in FlowJo software showing the Derive Parameters option.

As an example, the Timer intensity could be deﬁned in FlowJo as shown in ﬁgure 2.8.

FIGURE 2.8: Screen shot of the Derive Parameters Formula in FlowJo with the formula for the Timer intensity.

The threshold for Timer intensity was calculated using the reference negative population

and the wild type cells. Under this threshold of intensity (IT imer < 6), cells are

considered negative for Timer, and over that threshold, the value of intensity indicates

the relative amount of Timer protein expressed by a cell (taking into account Timer blue

and Timer red components altogether) (2.9A and 2.9B).

In summary, these mathematical procedures resulted in the two new variables Timer

angle (θT imer) and Timer intensity (IT imer) which contain the information of Timer

ﬂuorescence as the angle (in degrees) from the Timer blue axis (θT imer), and the

distance from the coordinates origin (0,0) (IT imer), respectively (2.9). These variables Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 82 were used to analyse Timer ﬂuorescence in cells and compare it between different cell

subsets.

(A) (B)

FIGURE 2.9: Nr4a3Timer angle Timer Intensity in thymus and spleen CD4+T cells. Scat- terplots of CD4+T cells indicating Timer angle values through the colour of the dots (from blue to red following Timer maturation). A and B, Timer blue and Timer red standardized values visualization, excluding cells with Timer Intensity lower than 6; C and D, Timer Angle and Timer Intensity scatterplot; A and C, Thymus CD4SP cells; B and D, Spleen CD4+ cells. Data includes all CD4+ cells acquired at different days after birth (days 1, 2, 7, 9, 13) and from 3 mice at each time point.

2.2.2 Trajectories of cells through Timer bi-dimensional space (Timer blue-red)

In Subach et al. (2009), a pharmaco-kinetic model was used to understand the dynamics

of chromophore conversion. In this model, Timer protein is ﬁrstly transcribed into mRNA

(N), translated into a nonﬂuorescent form (C), transformed into a ﬂuorescent blue form Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 83

(B, Timer blue), transformed into a nonﬂuorescent form (I), which turns into a ﬂuorescent

red form (R, Timer red), which is degraded into a nonﬂuorescent molecule (D).

k k k k k N c C b B i I r R d D, (2.9)

-1 where kc, kb, ki, kr, kd are kinetic constants (h ), and N, C, B, I, R and D are the kinetic

steps.

The following equations describe the changes in the system, as generation (positive

terms) and transformation (or degradation) (substracted terms) of molecules occur.

  dC = kc × N − kb × C     dB = kb × C − ki × B (2.10)  dI = ki × B − kr × I     dR = kr × I − kd × R

In that model of 6 kinetic steps (N, C, B, I, R and D) and 5 kinetic rate constants, all

reactions are irreversible because the chromophore maturation changes are irreversible.

The ﬁrst equation describes how mRNA (N) translates into colourless protein (C) with a kinetic constant kc. The amount of colourless form (C) in a moment can be determined by the amount of C being produced from mRNA (N) (kc x N) minus the

amount of colourless protein maturating into Timer blue form (-kb x C). Each of the

steps can be understood in the same way, by adding the production and substracting

the transformation/degradation. Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 84

Such a kinetic model was used to understand the biological data obtained from our trans-

genic strain. The model produced different trajectories depending on the transcriptional

dynamics of the targeted gene and the levels of the transcription.

For the kinetic simulation of Timer maturation in CD4+T cells, I started with the kinetic

rate constant values determined by Subach (Subach et al., 2009) and altered them to

ﬁt our biological results better.

  −1 kb = 0.9h     −1 ki = 0.2h (2.11)  −1 kr = 0.8h     −1 kd = 0.1h

In the case of a continuous transcription, the transcription to mRNA and translation

into C form protein are considered constant. Timer protein is continuously produced

(in its Timer blue form) and it will result in an accumulation of cells with high Timer

intensity (due to the accumulation of Timer protein throughout time) with angles around

45 degrees, but never close to 90 degrees because cells continuously produce new

Timer blue proteins (ﬁg. 2.10).

  dC = constant     dB = kb × C − ki × B (2.12)  dI = ki × B − kr × I     dR = kr × I − kd × R Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 85

FIGURE 2.10: Modelling of constant transcriptional pattern. Four trajectories shown corre- sponding to different constant expression rates (the scale of grey colours represents the relative intensity of the expression, the lower the intensity the lighter the colour. C values of 5, 10, 20 and 40). Simulation of cells each hour after initiation of transcription. Axes for Timer blue and Timer red in logicle scale.

Our biological data from the thymus of Foxp3Timer reporter ﬁt with this continuous

pattern (ﬁg. 2.11). However, the Nr4a3Timer data presents a different pattern, with some

cells having stop the transcription of Nr4a3Timer and following trajectories with different

expression intensities. Therefore, I explored the patterns produced by an alternative

transcriptional pattern, a pulsed transcriptional activity.

FIGURE 2.11: Scatter plot for Timer blue and Timer red of CD4 thymocytes from Foxp3Timer reporter mouse at 9 days of age.

As seen in the simulation of the continuous pattern, the progression of a single cell

through the Timer blue-Timer red bi-dimensional space depends on the intensity of

the transcriptional activity. If all cells received similar intensities, all cells would follow

a similar trajectory. However, our Nr4a3Timer data contain a mixture of cells receiving Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 86 different strength of TCR signal, and therefore Timer ﬂuorescence pattern results in different intensities and trajectories. Those with higher intensities progress in an outer trajectory, while the ones with lowest intensities present shorter trajectories (ﬁg. 2.12).

  dN = −a × N     dC = a × N − k × C  b   dB = k × C − k × B (2.13)  b i    dI = ki × B − kr × I     dR = kr × I − kd × R

  −1 a = 0.1h     −1 kc = 0.3h     −1 kb = 0.9h (2.14)  −1 ki = 0.2h     −1 kr = 0.8h     −1 kd = 0.1h Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 87

FIGURE 2.12: Thymus biological data and modelling plots. (A) Scatterplot of thymic CD4SP cells indicating Timer angle values through dots colour (from blue to red following Timer maturation). (B) Trajectories deﬁned by a kinetic model for Nr4a3Timer protein. Four different trajectories result from four different values of initial Timer expression levels (values for N of 50, 100, 300 and 700). In each trajectory, dots are plotted every 6 hours, up to 56 hours. (C) Amount of transcripts produced depending on the initial Timer expression.

2.3 Discussion

In conclusion, Nr4a3Timer and Foxp3Timer reporter mice show that Nr4a3 and Foxp3 genes present different patterns of transcription. Simulated constant transcription of Timer ﬁts with the Foxp3Timer pattern in thymocytes, while a spike transcription corresponds well with the Nr4a3Timer pattern. These patterns provide information about the endogenous gene transcriptional dynamics, Nr4a3 and FoxP3.

Nr4a3 is expressed upon TCR signalling at a rate dependent on the characteristics of the TCR signal. Therefore, we expect to ﬁnd cells expressing Nr4a3 with different intensities in the thymus, as shown by the Nr4a3Timer pattern.

However, in the case of the Nr4a3Timer thymocytes, there is the possibility that the same Timer data can be produced upon subsequent transcriptional spikes for cells with intermediate angle and high intensity. In that case, those cells would not be the result of a high intensity transcription of Nr4a3, but repeated spikes of transcription. This would Chapter 2 Dynamics of Timer expression in Nr4a3Timer and Foxp3Timer CD4+T cells 88

ﬁt with models considering duration and number of TCR contacts as variables of the

TCR signalling strength (Au-Yeung et al., 2014).

On the other hand, from the results, the dynamics of Foxp3 expression dynamics seem

to be similar for all cells expressing it in the thymus during the ﬁrst neonatal weeks, with cells following a similar trajectory from blue to red. This could mean that Foxp3 expression on early days after birth is triggered in an on-off pattern, with continuous

transcription.

Future work should cover different kinds of transcriptional activities (e.g. intermittent, with gradual increase, with gradual decrease...) and use computing simulations to

predict the patterns of Timer expression in each of them. Chapter 3

Differentiation of Foxp3+ regulatory T cell in the thymus

3.1 Introduction

Foxp3-expressing regulatory T cells (TReg) are a CD4+ αβT cell subpopulation involved

in immune tolerance and homeostasis. They are characterized by the expression of

the transcription factor Foxp3, and the IL-2 receptor α (IL-2Rα or CD25) (Stritesky

et al., 2012). Other phenotypical characteristics are high levels of GITR (glucocorticoid-

induced TNFR-related gene) and CTLA-4, which are induced by T cell activation

(Simons and Caton, 2011).

There are three different IL-2R chains, as reviewed by Boyman (Boyman and Sprent

(2012)) and Liao (Liao et al. (2013)). IL2-Rα chain (CD25) is expressed following T

cell activation, while the other two chains (IL-2Rβ and IL-2Rγ) are expressed in resting

lymphocytes (Willerford et al., 1995; Boyman and Sprent, 2012). The IL-2Rβ (CD122)

is part of the IL-15R, and IL-2Rγ (CD132) is the common cytokine receptor γ chain, as it is part of the receptors of IL-2, IL-4, IL-7, IL-9, IL-15 and IL-21 (Leonard, 2001).

-8 CD25 alone binds IL-2 with low afﬁnity (dissociation constant, Kd ∼ 10 M). The dimeric

89 Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 90

-9 IL-2R (formed by IL-2Rβ and IL-2Rγ) has an intermediate afﬁnity for IL-2 (Kd ∼ 10

M). However, CD25 increases the afﬁnity of the trimeric receptor (CD25, IL-2Rβ and

-11 IL-2Rγ) by 10-100 fold (Kd ∼ 10 M) and delivers intracellular signals. Upon IL-2

binding, the complex is internalised and IL-2Rβ and IL-2Rγ are degraded, while CD25

is recycled to the cell surface.

CD25 expression is induced by TCR stimulation. Therefore, it is not a unique character-

istic of TReg but a characteristic of activated T cells (Tact). However, it has been broadly

used for identifying TReg in the past.

TReg mainly differentiate in the thymus, in the thymic medulla, from self-reactive CD4SP

thymocytes through agonist selection (see Introduction, page 27) (Stritesky et al., 2012;

Moran and Hogquist, 2012), escaping negative selection despite receiving strong TCR

signalling (Josefowicz et al., 2012).

Strong TCR signalling (Jordan et al., 2001; Weissler and Caton, 2014), co-stimulation

signalling, γc chain cytokines (such as IL-2, -7 and -15 (Wirnsberger et al., 2011))

or TGFβ (Ouyang et al., 2010) are important for TReg differentiation in the thymus.

Dynamic changes in thymocyte motility have recently also been proposed to regulate

their irresponsiveness to TCR interactions (Kurd and Robey, 2016).

Two developmental pathways have been proposed regarding the order of the upregula-

tion of CD25 or Foxp3 expression: CD4SP thymocytes (CD25-Foxp3-) could develop

into CD25+Foxp3+ (TReg) through two intermediate TReg precursors: CD25+Foxp3- or

CD25-Foxp3+ (ﬁg. 3.1). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 91

FIGURE 3.1: The current understanding of thymic TReg differentiation. In the thymic medulla, CD4SP thymocytes interact with peptide-MHC complexes presented by mTEC, DC and other APCs. High afﬁnity TCR interactions can trigger negative selection or TReg differentiation (agonist selection). Foxp3-CD25- CD4SP thymocytes can differentiate into two TReg precursors: Foxp3-CD25+ and Foxp3+CD25-. Both precursors are reported to differentiate into TReg. (Figure based on Wirnsberger et al., 2011; Moran and Hogquist, 2012; Hsieh et al., 2012; Mahmud et al., 2014; Marshall et al., 2014; Ono and Tanaka, 2015; Kurd and Robey, 2016)

The ﬁrst pathway is described as the two-step model of TReg development (Hsieh

et al., 2012; Lio and Hsieh, 2008). The ﬁrst step is instructive, where strong TCR

signals trigger the upregulation of the IL-2R (identiﬁed by CD25), and therefore the

differentiation of CD4SP into CD25+Foxp3- TReg precursors. Mahmud et al. (2014)

proposed the roles of GITR, OX40 and TNFR2 (TNFRSF members) upon TCR signals

in differentiating into TReg progenitors. The second step, the consolidation phase, is

IL-2 cytokine dependent (TCR independent) for the upregulation of Foxp3 expression,

resulting in CD25+Foxp3+ TReg phenotype (Hsieh et al., 2012; Lio and Hsieh, 2008).

The second pathway has been proposed by Tai et al. (2013), in which TReg develop

from Foxp3+CD25- precursors. Recent results from Marshall et al. (2014) support both Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 92

developmental pathways and provide more insight into the differential requirements of IL-

2 and IL-15 in each of them. Foxp3-CD25+ precursors require both, while Foxp3+CD25-

precursors depend only on IL-15 (Marshall et al., 2014).

However, the dynamics of gene expression regulating the differentiation of CD25+Foxp3+

TReg cells are unclear. Thus, the purpose of this chapter is to investigate the dynamics

of strong TCR signalling by analysing the dynamics of Nr4a3 transcription and the

timing of Foxp3 and CD25 induction during neonatal periods.

3.2 Results: Investigation of TReg differentiation in the thymus

In order to study TReg differentiation in the thymus, postnatal thymi and spleens were

investigated. A double transgenic Nr4a3Timer:Foxp3GFP mice was used to analyse the

dynamics of expression of Nr4a3 and Foxp3.

Foxp3GFP is a knock-in mouse strain in which IRES-EGFP is knocked into the Foxp3 gene in order to report the transcriptional activity of Foxp3 (generated by Wang et

al., 2008). Therefore, lymphocytes from this double transgenic mouse strain express

ﬂuorescent Timer protein when the endogenous Nr4a3 gene is transcribed, and EGFP when the Foxp3 gene is transcribed. Hereafter, the ﬂuorescent proteins produced by

these double transgenic mice are designated as Nr4a3Timer and Foxp3GFP, in order

to clearly state which gene’s transcription is being reported.

In this part of the study, we investigate early thymocyte differentiation pathways using

Nr4a3Timer:Foxp3GFP neonates. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 93

3.2.1 Neonatal CD4SP thymocytes analysis

Ex vivo analysis of the ﬁrst two weeks after birth showed an increase in the size and

cellularity of the thymus. As expected, CD4SP thymocytes were signiﬁcantly increased

from day 2 to day 13 (ﬁg. 3.2A). However, a decrease in cellularity at day 9 was

observed. These results are similar to those reported for Balb/c (Xiao et al., 2003,

ﬁg. 1) or C57BL/Ka (Penit and Vasseur, 1989) neonatal thymus, where the increase

of thymic cellularity was discontinuous, in waves. After birth both reported two main waves. Our data could suggest a similar pattern in C57BL/6 mice.

On the other hand, the percentages of CD4SP cells were not increased from day 2 to

day 13 (ﬁg. 3.2B), but were signiﬁcantly decreased between day 2 and day 9. These

results are also similar to those of Xiao et al. (2003), which reported higher percentages

of CD4SP cells at day 2 and reduced and stable percentages at day 6, 10 and 14.

(A) CD4SP cell counts (B) CD4SP percentages

FIGURE 3.2: (A) CD4SP cell counts and (B) percentages in the thymus at different days after birth. Each individual mouse cell count is shown as independent dots. Error bar indicates the mean ±sd from the three mice analysed at each experimental time point. Statistically signiﬁcant differences between populations were calculated by one-way anova with Tukey’s post-hoc test, and are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001.

CD25 and Foxp3-expressions occur in CD4SP thymocytes after they receive TCR Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 94 signals. In order to determine the relationship between TCR signals and Foxp3/CD25 expression, Nr4a3Timer expression was analysed in the following four populations:

CD25-Foxp3GFP-, CD25+Foxp3GFP- (TReg precursors 1), CD25-Foxp3GFP+ (TReg precursors 2) and CD25+Foxp3GFP+ (TReg) (ﬁg. 3.3). Gating strategies can be found in Appendix section B (page 191). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 95

(A) Thymus CD4SP (B) Thymus CD4SP Timer+

(D) Thymus CD4SP threshold for Nr4a3Timer+

FIGURE 3.3: Gating of CD4SP thymocytes into four CD25 Foxp3GFP deﬁned subpopulations. Quadrant gates deﬁne the populations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3GFP- (orange) in (A), for all CD4SP cells, and (B), for CD4SP Nr4a3Timer+ cells. (C), standardised Nr4a3Timer blue and red expressions in each subpopulation of CD4SP cells. Density lines are used to show the cells distribution. (D), gating for Nr4a3Timer positive at Timer intensity higher than 6 is shown for the four CD4SP subpopulations. Representative data of 3 mice from each day of analysis (day 1, 2, 7, 9 and 13 after birth). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 96

CD4SP subpopulations cell counts

The four populations determined by CD25 and Foxp3GFP were analysed for their cell

counts at different days after birth.

(A) CD4SP (B) CD4SP (including CD25-Foxp3GFP-) (excluding CD25-Foxp3GFP-)

FIGURE 3.4: The number of CD4SP cells (A and B) or CD4SP Nr4a3Timer+ cells (C and D) varies with age for each of the populations: CD25-Foxp3GFP- (grey, only in ﬁgure A and C); CD25-Foxp3GFP+ (purple); CD25+Foxp3GFP- (orange) and CD25+Foxp3GFP+ (green). Error bars represent mean±sd of n=3. Statistically signiﬁcant differences are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001, for differences between population cell counts at each day of analysis.

Most CD4SP cells were CD25-Foxp3GFP- at each time point analysed (ﬁg. 3.4A).

At day 2, the number of CD25+Foxp3GFP- (TReg precursors 1) cells was larger Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 97

than those of the other two populations (CD25-Foxp3GFP+ (TReg precursors 2) and

CD25+Foxp3GFP+) (ﬁg. 3.4B). At day 7, the number of CD25+Foxp3GFP+ and

CD25+Foxp3GFP- cells were similar, while the precursors 2 population (CD25-Foxp3GFP+)

had signiﬁcantly lower numbers. At day 13, no signiﬁcant differences were found and

all three populations had similar cell numbers.

These results could indicate a delay in Foxp3 expression at 2 days after birth (ﬁg. 3.4B).

However, at day 7 and after, the number of CD4SP cells expressing Foxp3 (both with or without concurrent expression of CD25) became similar to the number of CD25+Foxp3-

cells.

Nr4a3Timer+ cells counts in CD4SP subpopulations

The expression of Nr4a3Timer in the Nr4a3Timer mouse allows us to identify Nr4a3

expressing cells and to track the dynamics of Nr4a3 transcription.

Nr4a3Timer+ thymocytes were identiﬁed in all four populations (ﬁg. 3.3D). The largest

number of Nr4a3Timer+ cells was found in the CD25-Foxp3GFP- fraction at each time

point of the analysis (ﬁg. 3.4C). Excluding this population, at day 2, the numbers of

CD25+Foxp3GFP- cells were signiﬁcantly larger than those of CD25-Foxp3GFP+. On

the other hand, differences in the CD25+Foxp3GFP+ cell number were not signiﬁcant

(ﬁg. 3.4D). No signiﬁcant differences were found in the cell numbers of these three

Nr4a3Timer+ populations at day 7 and 9. However, at day 13, CD25+Foxp3GFP+

Nr4a3Timer+ cell numbers were signiﬁcantly higher than numbers of CD25+Foxp3GFP-

and CD25-Foxp3GFP+ (ﬁg. 3.4D). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 98

Nr4a3Timer+ cells percentages in CD4SP subpopulations

Because CD25 and Foxp3 expression are related to TCR signalling and T cell activation,

the expectation was that Nr4a3Timer+ cells would be largely represented by cells

expressing CD25 or Foxp3GFP. Thus, the percentages of cells expressing CD25 and/or

Foxp3 in Nr4a3Timer+ cells were analysed.

The largest subset of Nr4a3Timer+ cells was the CD25-Foxp3GFP- on all days analysed

(ﬁg. 3.5A). However, most CD25-Foxp3GFP- cells were Nr4a3Timer-, as shown by the

small percentages of Nr4a3Timer+ cells in the CD25-Foxp3GFP- population (4.90% ±

2.01 (mean ± sd across all days)) (ﬁg. 3.5B).

(A) Proportion of CD25 and Foxp3 populations(B) Percentages of Nr4a3Timer+ cells in each in Nr4a3Timer+ cells subpopulation

FIGURE 3.5: Bar plot showing ,(A), percentages of Nr4a3Timer+ cells expressing CD25 and/or Foxp3GFP, and (B), percentage of Nr4a3Timer+ cells inside each population: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP- (orange) and CD25+Foxp3GFP+ (green. Data from day 2 to day 13 after birth. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences between populations at each day of analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001.

At day 2, the percentage of CD25+Foxp3GFP- cells in the Nr4a3Timer+ population was signiﬁcantly increased compared to the Foxp3GFP+ subsets (CD25-Foxp3GFP+ Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 99

and CD25+Foxp3GFP+) (ﬁg. 3.5A). From day 7 onwards, the percentage of CD25-

Foxp3GFP+ cells in the Nr4a3Timer+ population was not signiﬁcantly different from the

percentages of CD25+Foxp3GFP+ and CD25-Foxp3GFP+ cells. At day 7 and 13, the

percentage of CD25+Foxp3GFP+ cells in Nr4a3Timer+ population seemed larger than

the percentage of CD25+Foxp3GFP- or CD25-Foxp3GFP+ cells, but these differences were not statistically signiﬁcant (ﬁg. 3.5A).

On the other hand, analysis of the percentage of Nr4a3Timer+ cells in each subpopula-

tion showed that at day 2, Foxp3+ populations (CD25-Foxp3GFP+ and CD25+Foxp3GFP+)

contained higher percentages of Nr4a3Timer+ cells than the CD25+Foxp3GFP- pop-

ulation (ﬁg. 3.5B). At day 7 and 13, the percentage of Nr4a3Timer+ cells in the

CD25+Foxp3GFP+ subset was signiﬁcantly larger than the percentages in the CD25+

Foxp3GFP- population. At day 9, we found no signiﬁcant differences between the

percentages of Nr4a3Timer+ cells in the CD25+Foxp3GFP-, CD25-Foxp3GFP+ and

CD25+Foxp3GFP+ populations. At day 13, the percentage of Nr4a3Timer+ cells in the

CD25+Foxp3GFP+ population was signiﬁcantly larger than in the other three popula-

tions. Therefore, the CD25+Foxp3GFP+ population contains the highest percentage of

Nr4a3Timer+ cells. This indicates that most of these cells express enough Nr4a3 to be

detected by the Timer ﬂuorescence.

3.2.2T Reg precursors in neonatal thymus

Given that TReg arise from cells that receive strong TCR signalling (Lio and Hsieh,

2008), we hypothesised that Nr4a3Timer proteins are less matured in the potential

TReg precursors than in the ﬁnally differentiated Foxp3+CD25+ TReg cells. Therefore, Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 100 we tested if the two proposed precursor populations (CD25+Foxp3GFP- and CD25-

Foxp3GFP+) have more immature (blue) Nr4a3Timer than the Foxp3+CD25+ TReg

cells.

Nr4a3Timer blue levels were signiﬁcantly higher in CD25+Foxp3GFP- and CD25+

Foxp3GFP+, and lower in CD25-Foxp3GFP+ cells (ﬁg. 3.6B). Nr4a3Timer red levels were higher in CD25+Foxp3GFP+ cells. At day 7 and after, CD25-Foxp3GFP+ cells pre-

sented signiﬁcantly lower levels of Nr4a3Timer red compared with CD25+Foxp3GFP+

cells. CD25+Foxp3- cells had the smallest levels of Nr4a3Timer red ﬂuorescence (ﬁg.

3.6). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 101

(A)

(B)

FIGURE 3.6: Nr4a3Timer expression on CD4SP during neonatal development. (A) His- tograms for Nr4a3Timer blue (upper) and red (lower) in CD4SP subpopulations: CD25- Foxp3GFP+ (purple), CD25+Foxp3GFP- (orange) and CD25+Foxp3GFP+ (green). Density lines are shown for each biological replicate (n=3). (B) Median ﬂuorescence intensity of Nr4a3Timer blue (left) and Nr4a3Timer red (right) shown with small circles for each individual mouse. The mean of n=3 medians is represented by a diamond in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001, for differences between populations at each day of analysis. Data analysis from day 2 to day 13 after birth.

In order to clarify the differences in Timer maturation between the different populations, we compared the derived parameters, Timer angle and Timer intensity. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 102

Nr4a3Timer angle, the “age” from Nr4a3 transcription initiation (upon TCR signal)

The Timer angle variable was used to estimate the relative age of individual T cells

since the initiation of Nr4a3 transcription, which is induced after T cells receive strong

TCR signalling. Brieﬂy, cells with low Timer angle values have recently started to

express Nr4a3 and they mostly contain the immature blue form of Timer protein; cells with intermediate angle values are those which have expressed Nr4a3 for a while and

are still expressing it; and cells with higher values have already stopped expressing

Nr4a3 and accumulate the mature red form (see page 82, ﬁg. 2.9).

Samples from double transgenic Nr4a3Timer:Foxp3GFP neonates were analysed by

ﬂow cytometry to determine if CD25+Foxp3GFP- cells (proposed TReg precursors 1)

and CD25-Foxp3GFP+ (proposed TReg precursors 2) precede to the differentiation of

CD25+Foxp3GFP+ cells (TReg cells). For each time point, three biological replicates were analysed. The median Timer angle was calculated for each population, day and

individual mouse. Timer angle values were analysed from day 2 because at day 1 the

number of Nr4a3Timer+ cells was too small.

The relationship between the Timer angle and the populations was analysed by com-

paring the means of Timer angle medians for each population by one-way analysis of variance (one-way anova) for each day after birth. P-values were adjusted by multiplying

them by 4, because the analysis was performed four times, once for each day after

birth (i.e. Bonferroni’s correction). There were statistical differences between means of

Timer angle between the different CD4SP populations at all days after birth (table 3.1). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 103

TABLE 3.1: One-way ANOVA of Timer angle in the different populations at each day analysed. day p-value adjusted p (x4) signiﬁcance 2 1.75×10-6 7.00×10-6 *** 7 7.34×10-7 2.93×10-6 *** 9 8.05×10-6 3.22×10-5 *** 13 4.62×10-7 1.85×10-6 ***

In order to identify which of the populations differed in their Timer angle and at which days after birth, multiple comparisons were performed by Tukey post hoc test (ﬁg. 3.7).

FIGURE 3.7: Timer angle medians in different CD4SP populations during neonatal development. Timing from TCR signalling in CD25/Foxp3GFP populations among CD4SP cells at different days after birth (2, 7, 9 and 13). Nr4a3Timer+ cells in each of the populations: CD25- Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange). Small circles show each individual mouse median of Nr4a3Timer angle values. The mean of n=3 medians is represented by a diamond in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001, for differences between populations at each day of analysis. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 104

Signiﬁcant differences were found for the Timer angle means between CD25+Foxp3GFP+

and CD25+Foxp3GFP- populations at all days analysed. CD25+Foxp3GFP+ (TReg) presented significantly larger Nr4a3Timer angle mean than CD25+Foxp3GFP- cells (precursors 1) (fig. 3.7). At day 2, the mean of Timer angle for CD25-Foxp3GFP+ cells (precursors 2) was significantly larger than those of CD25+Foxp3GFP+ and CD25+Foxp3GFP-

populations. However, Nr4a3Timer angle values for CD25-Foxp3GFP+ were similar to

those of CD25+Foxp3GFP+ (TRegs) from day 7 and after (ﬁg. 3.7).

Here I tested the hypothesis that Nr4a3Timer expression in the precursors populations

represents expression that has more recently started than in the ﬁnally differentiated

CD25+Foxp3GFP+ TReg cells, and therefore has lower Nr4a3Timer angle. These results

indicate that the CD25+Foxp3GFP- precursor population (precursor 1) initiated Nr4a3

transcription earlier than the more differentiated CD25+Foxp3GFP+ TRegs. However,

the precursor 2 population (CD25-Foxp3GFP+) overlapped in its Nr4a3Timer angle with

the CD25+Foxp3+ TRegs cells, suggesting that these two populations have similar “age”

from the initiation of Nr4a3 transcription.

Nr4a3Timer intensity, the intensity of TCR signal

Timer angle analysis allowed us to understand part of the differences in Nr4a3Timer expression between populations. Additionally, Timer intensity was analysed as it

provides an indirect approximation of the intensity of Nr4a3 transcriptional activity (see

page 80).

The hypothesis is that CD25+Foxp3GFP+ TRegs cells will present the highest Nr4a3Timer Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 105

intensity, because they receive the strongest TCR signals. In order to assess this hy-

pothesis, Timer intensity was calculated and compared.

The relationship between Timer intensity and the different CD4SP populations was

analysed by comparing the means of Timer intensity medians for each population by

one-way analysis of variance (one-way anova) for each day after birth. P-values were

adjusted for the number of repetitions of the test, as it was performed once for each

day analysed. Timer intensity was signiﬁcantly different between populations at all days

after birth (table 3.2).

TABLE 3.2: One-way ANOVA of Timer intensity in the different populations at each day analysed.

day p-value adjusted p (x4) signiﬁcance 2 2.08×10-6 8.32×10-6 *** 7 2.49×10-5 9.96×10-5 *** 9 7.26×10-5 2.90×10-4 *** 13 5.47×10-6 2.19×10-5 ***

To identify which of the populations differed in their Timer intensity at each day after

birth, multiple comparisons were performed by Tukey post hoc test for the precursors

(CD25+Foxp3GFP- and CD25-Foxp3GFP+) and CD25+Foxp3GFP+ TRegs populations

(ﬁg. 3.8). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 106

FIGURE 3.8: Intensity of TCR signalling in CD25/Foxp3GFP populations among CD4SP cells at different days after birth (2, 7, 9 and 13). Timer intensity medians of individual mice (small circles) in CD4SP populations during neonatal development from Nr4a3Timer+ cells in each of the populations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange). The mean of n=3 medians is represented by a diamond in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001.

Median Nr4a3Timer intensity was signiﬁcantly higher in CD25+Foxp3GFP+ cells than

in the suggested precursors populations at all days after birth.

We can interpret the results of Nr4a3Timer angle to understand the developmental pathways for CD4SP thymocytes to become CD25+Foxp3+ cells. There is the possibility that some CD25+Foxp3- cells receive further TCR signals and increase their

Nr4a3Timer expression after the initiation of Foxp3 transcription. In support of this model, CD25+Foxp3GFP+ cells harbour high amounts of Nr4a3Timer blue form together with high amounts of red form (ﬁg. 3.6B), indicating they are active for Nr4a3 Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 107

transcription.

On the other hand, CD25-Foxp3GFP+ cells presented low levels of Nr4a3Timer blue

form (ﬁg. 3.6B) and they had mature Nr4a3Timer angle, which indicate that these

cells are not transcribing Nr4a3. Therefore, they are ‘aged’ from their initial Nr4a3 transcription. The weak Nr4a3Timer intensity in these cells may explain their lack of

CD25 expression and the reduced Foxp3 levels.

One possibility is that Nr4a3 transcription was low from the beginning, indicating reduced

TCR signalling. In this sense, they could represent a late phase of CD25+Foxp3- cells

that did not increase their Nr4a3 transcription when they started to express Foxp3, and

subsequently lost CD25 expression.

Alternatively, they could correspond to a later phase of some of the CD25+Foxp3GFP+

cells with brighter Nr4a3Timer intensity. If these cells stopped their Nr4a3 expression,

the Nr4a3Timer red forms would be degraded and the Nr4a3Timer intensity would be

reduced due to that degradation, even if it was high before (see model trajectories of

Nr4a3Timer on page 87).

CD69 downregulation during TReg development

CD4SP cells reduce their CD69 expression levels throughout maturation in the thymic

medulla (McCaughtry et al., 2007). In order to further investigate the dynamics of TReg

differentiation, CD69 expression was analysed.

Two-way anova was used to determine whether there were differences in CD69 expression between the CD4SP subpopulations and on different days after birth (table

3.3). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 108

TABLE 3.3: Two-way ANOVA of CD69 MFI across populations and days analysed. Inten- sity(population+day).

variable p-value signiﬁcance population < 2×10-16 *** day 0.000281 ***

Alternatively, one-way anova test was performed for each day, and there were signiﬁcant

differences in CD69 between populations at all days (table 3.4).

TABLE 3.4: One-way ANOVA of CD69 MFI between populations at each day analysed. CD69(population) (for each day).

day p-value adjusted p (x4) signiﬁcance 2 1.09×10-7 4.36×10-7 *** 7 3.57×10-7 1.43×10-6 *** 9 2.61×10-10 1.04×10-9 *** 13 1×10-10 4×10-10 ***

In order to determine which populations had different CD69 levels, Tukey’s post-hoc

test was used. The CD25+Foxp3GFP- (precursor 1) population expressed signiﬁcantly

higher levels of CD69, while Foxp3GFP+ populations (CD25-Foxp3GFP+ (precursor 2)

and CD25+Foxp3GFP+ (TReg)) expressed lower levels of CD69 (ﬁg. 3.9). This result is consistent with our ﬁndings of differences in the Nr4a3Timer angle between the

CD25+Foxp3GFP- population and TReg, but not between CD25-Foxp3GFP+ and TReg,

serving as further evidence to show that CD25+Foxp3GFP- cells are relatively immature

compared with the more mature CD25-Foxp3GFP+ and CD25+Foxp3GFP+ cells. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 109

(A) CD69 MFI

(B) Nr4a3Timer angle vs. CD69

FIGURE 3.9: CD69 MFI in CD25/Foxp3GFP CD4SP populations at different days after birth (2, 7, 9 and 13). CD69 MFI of individual mice (small circles) from Nr4a3Timer+ cells in each of the CD4SP populations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange). The mean of n=3 medians is represented by a diamond in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001. (A) CD69 levels in each population at different days after birth; (B) Nr4a3Timer angle related to CD69 MFI for each population at different days after birth; (C) Nr4a3Timer intensity related to CD69 MFI for each population at different days after birth. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 110

3.2.3 CD5 expression during CD4SP development

CD5 is a membrane glycoprotein that associates with the TCR complex (see Introduc-

tion, page 27). CD5 surface expression has been reported to correlate with and be

regulated by the TCR signal intensity during positive selection (Azzam et al., 1998;

Azzam et al., 2001).

Strong selecting TCR interactions trigger high surface expression of CD5, and several

groups have reported that thymic TReg cells show higher levels of CD5 (Barra et al.,

2015; Ordoñez-Rueda et al., 2009). Indeed, some of the ﬁrst experimental evidence

for T cells as cause and prevention of autoimmune diseases were performed by using

anti-CD5 antibodies and complement to deplete cells and adoptively transfer CD5high

or CD5low CD8 negative T cell splenocytes into nude mice. Adult spleen CD5low

CD4+T cells were sufﬁcient to cause autoimmune disease in recipient nude mice, while

CD5high showed suppressive activity when co-transferred (Sakaguchi et al., 1985).

However, during neonatal development, we found that in thymic TReg cells and CD25-

Foxp3GFP+ cells expression levels of CD5 were intermediate between those of CD25-

Foxp3GFP- cells (lower CD5 levels) and CD25+Foxp3GFP- cells (higher CD5 levels)

(ﬁg. 3.10). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 111

FIGURE 3.10: CD5 surface levels (MFI) on CD4SP subpopulations at different days after birth (1, 2, 7, 9 and 13) (Nr4a3Timer expression not considered). CD4SP thymocytes subpopulations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange). The mean of n=3 medians is represented by triangles in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001.

Interestingly, the comparison of CD5 expression on Nr4a3Timer+ and Nr4a3Timer-

cells inside each CD4SP population revealed differences in CD25+Foxp3GFP- and

CD25-Foxp3GFP- cells, but not in the Foxp3GFP+ populations. Nr4a3Timer+ CD25-

Foxp3GFP- and CD25+Foxp3GFP- cells presented higher levels of CD5 than their

Nr4a3Timer- counterparts (ﬁg. 3.11A). Indeed, when analysing Nr4a3Timer+ cells,

Foxp3+ subpopulations expressed lower cell surface CD5 compared to CD25-Foxp3-

and CD25+Foxp3- cells (ﬁg. 3.11B).

These ﬁndings indicate that CD5 expression is dynamically regulated during CD4SP

development and maturation. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 112

(A) Nr4a3Timer- and +

(B) Nr4a3Timer+

FIGURE 3.11: CD5 surface levels on CD4SP subpopulations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange). The mean of n=3 medians is represented by diamonds for Nr4a3Timer+ cells, and squares for Nr4a3Timer- cells, in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001.(A) Comparison of Nr4a3Timer+ and Nr4a3Timer- cells at different days after birth (2, 7, 9 and 13). (B) Nr4a3Timer+ CD4SP thymocytes subpopulations. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 113

3.2.4 Alternative TReg precursors: CD25-Foxp3+ cells

Our results indicate that CD25-Foxp3GFP+ cells are mature for both Nr4a3Timer

angle (ﬁg. 3.7) and CD69 expression (ﬁg. 3.9A), similar to CD25+Foxp3GFP+ ma-

ture TReg cells. This is in contradiction with the precursor pathway proposed by Tai

+ et al. (2013), where Foxp3 CD25- precursors differentiate into mature TReg cells.

In that report, CD4SP thymocytes where analysed in ﬁve subsets: (A) Foxp3GFP-

CD25-, (B) Foxp3GFPlowCD25-, (C) Foxp3GFPhighCD25-, (D) Foxp3GFPhighCD25low

and (E) Foxp3GFPhighCD25high, demonstrating in vitro and in vivo differentiation of

Foxp3GFP+CD25- CD4SP thymocytes into Foxp3GFP+CD25+ mature TReg cells.

Therefore, I decided to analyse the data using a similar gating strategy (A, B, C,

D and mature TReg cells) and test whether there was a progression in the Nr4a3Timer

angle in those subpopulations graded for CD25 and Foxp3GFP (ﬁg. 3.12A).

The analysis of Nr4a3Timer angle on subpopulations of the proposed precursors CD25-

Foxp3GFP+ did not result in signiﬁcant differences between cells with higher or lower

Foxp3GFP (B, C) or with CD25lowFoxp3GFPhigh (D) (ﬁg. 3.12B). When those popula-

tions were arranged by their maturity from TCR signalling initiation based on Nr4a3Timer

angle, Foxp3GFP-CD25+ cells (both CD25 high or low) were signiﬁcantly more imma-

ture than the other subpopulations. Next, Foxp3GFPlow cells, both CD25high or CD25low,

(B and Dlow) showed intermediate Nr4a3Timer angles. Last, CD25+Foxp3GFPhigh

high cells (both CD25 or CD25low, mature TReg cells and D), together with CD25-, both

Foxp3high or Foxp3GFPlow, had the largest angle values, being the more mature popula-

tions in terms of Nr4a3 expression (without signiﬁcant differences between them).

Additionally, similar results were obtained from the analysis of CD69 expression in the Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 114

same subpopulations (ﬁg. 3.12C). There were no signiﬁcant differences in CD69 expres-

sion between CD25-Foxp3GFPlow (B), CD25-Foxp3GFPhigh (C), CD25lowFoxp3GFPhigh

high high (D) and CD25 Foxp3GFP cells (mature TReg cells).

These results further support our previous conclusion, that the CD25+Foxp3GFP- sub-

set is the most probable TReg precursor, based on their lower Timer angle values, which indicate that these cells have recently signalled through their TCRs, while CD25-Foxp3+

cells, independently of their levels of Foxp3, and the CD25+Foxp3high, independently

of their CD25 levels, are more mature. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 115

(A) Gating strategy

(B) Timer angle comparison

FIGURE 3.12: Nr4a3Timer angle comparison of CD4SP (9 subpopulations for different levels of CD25 and Foxp3GFP expression). (A) Nr4a3Timer+ CD4SP thymocytes from Nr4a3Timer:Foxp3GFP reporter mice. Nr4a3Timer+ cells from each of the populations: A, CD25-Foxp3GFP-; B, CD25-Foxp3GFPlow; C, CD25-Foxp3GFPhigh; D, CD25lowFoxp3GFPhigh; matTReg, CD25highFoxp3GFPhigh; Dlow, CD25lowFoxp3GFPlog; matTReglow, CD25highFoxp3GFPlow; CD25low, CD25lowFoxp3GFP-; CD25high, CD25highFoxp3GFP-. (B) Timer angle analysis for comparison of the maturity of the nine populations from initial TCR signalling. (C) CD69 MFI analysis. Nr4a3Timer angle medians or CD69 MFI of individual mice are shown as small circles. The mean of n=3 medians is represented by diamonds in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 116

3.2.5 CD4+ T cells in the Spleen during ontogeny

CD4SP thymocytes migrate from the thymus to the periphery as recent thymic emigrants

(RTEs) when they ﬁnish their maturation process in the thymus. In neonates, all

peripheral T cells are RTEs. Therefore, I analysed the neonatal splenic Nr4a3Timer+

CD4+ T cells to investigate if they received TCR signals in the periphery.

I analysed Nr4a3Timer+ CD4+T cells classiﬁed by their expression of Foxp3GFP and

CD25, in a similar way to the thymocytes analysis (3.13).

(A) Spleen CD4+ cells

(B) Spleen CD4+ Nr4a3Timer+ cells

FIGURE 3.13: Gating strategy of CD4SP splenocytes. A, showing all CD4SP cells and B, Nr4a3Timer+ cells. Quadrant gates deﬁne the populations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3GFP- (orange). The plot presents all data from day 1 to day 13 after birth, from three mice for each day of analysis.

Between day 2 and day 7 after birth there was an increase in CD4+ T cells in the spleen

(ﬁg. 3.14B). CD4+ Nr4a3Timer+ cell numbers were increased between day 2 and day

7, but thereafter they showed similar values (ﬁg. 3.14D). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 117

As expected from their immature phenotype, with high CD69 levels and low Nr4a3Timer

angle values in the thymus, CD25+Foxp3GFP- population was reduced in the spleen

(ﬁg. 3.14D).

Nr4a3Timer+ CD25-Foxp3GFP-, CD25-Foxp3GFP+ and CD25+Foxp3GFP+ cells were

present in similar numbers in the spleen (ﬁg. 3.14D).

(A) Thymus CD4SP cell counts (B) Spleen CD4+ T cell counts

FIGURE 3.14: Cell counts of CD4SP thymocytes (A, C) or CD4+ T cell splenocytes (B, D) for each of the four subpopulations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange) at different days after birth (2, 7, 9 and 13). C and D show Nr4a3Timer+ cell counts. Each dot represents the mean of counts, and error bars represent the sd for each day of ontogeny (n=3). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 118

The percentages of Nr4a3+ cells in the Foxp3GFP-CD25- subset were reduced in the spleen compared to the thymus (ﬁg. 3.15), possibly due to negative selection processes. On the other hand, the proportion of Nr4a3+ Foxp3+CD25+ and Nr4a3+

Foxp3+CD25- was increased in the spleen. Foxp3+CD25+ subset was the largest

subset of Nr4a3Timer+ cells in the spleen, while CD25+Foxp3- cells are scarce in the

spleen (ﬁg. 3.15).

(A) Thymus (B) Spleen

FIGURE 3.15: Percentage distribution of CD4+Nr4a3Timer+ cells in the four populations: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange) at different days after birth (1, 2, 7, 9 and 13). Each dot represents the mean of the percentage, and error bars represent the sd for each day of ontogeny (n=3).

Nr4a3Timer angle of CD4+ T cells in the spleen was signiﬁcantly higher compared with

that of CD4SP thymocytes (ﬁg. 3.16A). Furthermore, CD69 MFI was signiﬁcantly lower

in CD4+ T cell splenocyes (ﬁg. 3.16B). These results indicate that CD4+ T cells are

more mature in the spleen than in the thymus. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 119

(A)

(B)

FIGURE 3.16: Comparison of CD4SP thymocytes (diamonds) and CD4+ T cell splenocytes (squares) at different days after birth for each subpopulation: CD25-Foxp3- (grey), CD25-Foxp3+ (purple), CD25+Foxp3GFP- (orange),CD25+Foxp3GFP+ (green). Nr4a3Timer angle medians (A) or CD69 MFI (B) of individual mice are shown as small circles. The mean of n=3 medians is represented by diamonds or squares in the respective population colour. Error bars represent mean±sd of n=3. Statistically signiﬁcant differences from anova Tukey’s post-hoc analysis are shown as * for p<0.05, ** for p<0.01 and *** for p<0.001. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 120

The ﬁrst hypothesis related to the splenocytes is that their Timer angle will be higher

due to the time lag for the migration from the thymus to periphery. Indeed, CD4+ cells

found in the spleen presented higher mean values for Nr4a3Timer angle (ﬁg. 3.17).

A very important question was if we could detect cells receiving strong TCR signals in

the periphery, or if TCR signalling is a process that mainly occurs in the thymus. In order

to answer this, cells were analysed into three angle intervals: cells which had undergone

recent TCR signals (Nr4a3Timer angle < 30), intermediate cells (Nr4a3Timer angle

between 30 - 60), and mature cells where TCR signalling has stopped (Nr4a3Timer

angle > 60). Such analysis provided percentages of cells in each of the TCR signalling

‘age’ intervals (ﬁg. 3.17). In the spleen, the percentages of recent and intermediate cells were very low for CD25-Foxp3GFP-, CD25-Foxp3GFP+ and CD25+Foxp3+, indicating

that most Nr4a3Timer+ cells in the spleen have not recently received TCR signals. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 121

(A) Thymus (B) Spleen

FIGURE 3.17: Percentages of recent, intermediate and mature Nr4a3Timer+ cells in CD4SP Nr4a3Timer+ populations (CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3GFP+ (green), CD25+Foxp3- (orange) at different days after birth (2, 7, 9 and 13). The mean of n=3 is represented by diamonds. Error bars represent mean±sd of n=3 (biological replicates shown with circles).

Another hypothesis would be that thymocytes with the strongest TCR signals would be

depleted in the thymus by negative selection, therefore, being absent from the spleen.

The analysis of Timer intensity values showed no signiﬁcant differences between the

thymus and the spleen (ﬁg. 3.18). However, it seems that in the thymus Timer intensity values for the Foxp3+CD25+ cells were relatively higher than those found in the spleen.

A study with enough statistical power would be required to ﬁnd out if these differences

are statistically signiﬁcant. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 122

FIGURE 3.18: Nr4a3Timer intensity in CD4SP Nr4a3Timer+ populations from the thymus (diamonds) and CD4+ Nr4a3Timer+ T cells from the spleen (squares) at different days after birth (2, 7, 9 and 13). Nr4a3Timer+ cells in each of the populations are shown with colours: CD25-Foxp3GFP- (grey), CD25-Foxp3GFP+ (purple), CD25+Foxp3- (orange) and CD25+Foxp3GFP+ (green). Each small dot represents the median of individual mice. The mean of n=3 medians is represented by diamonds in the respective population colour. Error bars represent mean±sd of n=3. Ther were no statistically signiﬁcant differences from anova Tukey’s post-hoc analysis between thymic and splenic values.

Such a reduction of Timer intensity can be alternatively explained by the progressive

degradation of mature Timer proteins. As explained in Chapter 2 (see page 87, ﬁg.

2.12), cells with the highest values for Timer angle are a mix of cells from higher

intensities that have degraded part of the Timer proteins, and cells from trajectories with lower intensities of Timer. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 123

3.2.6 Timing of Foxp3 transcription during TReg development in Foxp3Timer:Foxp3GFP reporter mice

Next, in order to investigate the dynamics of Foxp3 transcription in neonates, I analysed

the Foxp3Timer:Foxp3GFP reporter mice.

The analysis of Foxp3Timer in neonates showed differences in Foxp3 expression

between Foxp3GFP+CD25+ and Foxp3GFP+CD25- CD4SP subsets. Most Foxp3GFP+

CD25+ cells expressed Foxp3Timer blue and red. In contrast, most CD25-Foxp3GFP+

cells were Foxp3Timer-, probably because they did not express enough Foxp3Timer to

be detected by the cytometer, and Foxp3Timer+ cells showed very low Timer intensity

(ﬁg. 3.19). This indicates that Foxp3 expression in Foxp3GFP+CD25- population is

lower than in Foxp3GFP+CD25+ cells. The results from Nr4a3Timer: Foxp3GFP reporter

mice indicated that Nr4a3Timer angle was more mature in Foxp3GFP+CD25- cells

(their TCR signal events happened earlier than in Foxp3GFP-CD25+ cells), and that

their Nr4a3Timer intensity was low (their TCR signal was probably less strong compared with cells with high Nr4a3Timer intensity).

Summarising the results from both double transgenics, (Foxp3Timer:Foxp3GFP and

Nr4a3Timer:Foxp3GFP reporter mice), we can infer that CD25-Foxp3+ cells receive weak

TCR signals, as supported by the lower Nr4a3Timer intensity values (ﬁg. 3.18), so that

Foxp3 expression is induced later, when cells are already Nr4a3Timer angle high and

once they have downregulated CD69 (ﬁg. 3.9). Furthermore, the pattern of Foxp3Timer

indicates that its expression is probably very low or transient, so that these cells are

mainly Foxp3Timer negative or Foxp3Timer with very low intensity (ﬁg. 3.19). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 124

FIGURE 3.19: Foxp3Timer expression in Foxp3GFP+CD25+ (green) and Foxp3GFP+CD25- (purple). Foxp3 Timer Blue and Foxp3 Timer Red components are shown for 4 different timepoints of development (day 1, 5, 9 and 14 after birth). Data from n=3 for day 1, n=1 for day 4, n=2 for day 9 and n=3 for day 14.

3.3 Discussion

In order to investigate the dynamics of strong TCR signalling during TReg development

in the thymus, we used our Nr4a3Timer: Foxp3GFP reporter. This system allowed us to

investigate thymic and splenic T cells with a polyclonal repertoire and identify cells

receiving strong TCR signalling to follow their development.

Using Nr4a3Timer, we investigated the dynamics of the expression of different markers

(Foxp3, CD25, CD69 and CD5) traditionally used for deﬁning population and stages in

thymocyte differentiation. We also studied the dynamics of the populations deﬁned by

the expression of those markers.

There was already evidence that Foxp3 is expressed quite late downstream of the TCR

signal. However, this important fact is often ignored in TReg development models. The Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 125

currently proposed developmental models, with CD25+Foxp3- and CD25-Foxp3+ as

TReg precursors, lack an understanding of the time variable in the thymocyte develop-

mental process. In this study, by using Timer technology, we examined the relative

timing of appearance of those populations. Our data from the Nr4a3Timer showed that

the Foxp3-CD25+ subset were less mature than Foxp3+CD25- or Foxp3+CD25+ popu-

lations. Interestingly, the Foxp3+CD25- subset was as mature as the Foxp3+CD25+

TReg cells. Thus, it is highly likely that, upon TCR signalling, CD25-Foxp3- CD4SP cells

activate expression of CD25 ﬁrst, followed by Foxp3 expression.

It is possible that CD25 expression precedes Foxp3 expression in both populations

(CD25-Foxp3+ and CD25+Foxp3+) depending on the interactions with different p-MHC

complexes, the expression levels of Nr4a3 and other factors, or alternatively, that Foxp3

expression can happen without previous expression of CD25. From our results, Foxp3

expressing cells are more mature for their Nr4a3Timer proteins, irrespective of their

CD25 expression, indicating that Foxp3+ cells have been transcribing Nr4a3 for a longer

period of time than cells not expressing Foxp3. These timing results are supported

by the expression of other activation markers, such as CD69 (high in CD25+Foxp3-

and low in CD25-Foxp3+ cells and CD25+Foxp3+). CD69 is upregulated upon positive

selection and only downregulated at the end of CD4SP cell maturation (ﬁg. 3.20).

Therefore, Foxp3 expression is delayed in time (independently of CD25 expression

levels) in thymocytes. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 126

FIGURE 3.20: TReg development timing. High afﬁnity TCR interactions trigger negative selection or TReg differentiation (agonist selection). Foxp3 expression in CD4SP thymocytes is delayed in time: Foxp3-CD25+ are CD69high, Nr4a3Timer anglelow, while Foxp3+ thymocytes (independently of their CD25 expression) are CD69low and Nr4a3Timer angle high.

Tai’s paper provides data on CD24, Qa-2 and GITR expression to support the idea that

the Foxp3+CD25- population progressively gives rise to Foxp3+CD25+ cells. Potentially,

some CD25-Foxp3+ cells could also give rise to CD25+Foxp3+ cells. Alternatively, and

most probable because of the Timer maturation data, some CD25+Foxp3+ could develop into CD25-Foxp3+ and change their expression of several immunological markers

together with the reduction of CD25. Nevertheless, CD25-Foxp3+ cells constitute a

mature population when compared with other cells with lower Nr4a3Timer intensity,

such as CD25+Foxp3-. The biological relevance of this CD25-Foxp3+ population is

uncertain. They could represent a stage of negative selection of thymocytes, and be

abortive cells. However, in our results they are present in the spleen as recent thymic

emigrants. On the other hand, they could express or re-express CD25 and become

CD25+Foxp3+ cells. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 127

In addition, Nr4a3Timer intensity of the Foxp3+CD25- population was reduced, showing

that these cells have received weaker TCR signals. This would agree with the Hogquist

Nr4a1 reporter (Moran et al., 2011) expression, where Foxp3+CD25- cells present

low-intermediate Nr4a1GFP (Marshall et al., 2014). However, Marshall et al. (2014)

showed using Nr4a1GFP, that expression of Nr4a1 is high in CD25-Foxp3+, and low

in CD25+Foxp3- and CD25+Foxp3+ populations. Summarising, Nr4a3 and Nr4a1 in

CD4SP thymocyte subpopulations would be differentially expressed: CD25-Foxp3+

(Nr4a1GFP high, Nr4a3Timer low), CD25+Foxp3- (Nr4a1GFP low, Nr4a3Timer low)

and CD25+Foxp3+ (Nr4a1GFP low, Nr4a3Timer high).

Alternatively, there is another Nr4a1GFP BAC transgenic reporter (Zikherman et al.,

2012), in which Nr4a1 expression in thymocytes is very similar to our Nr4a3Timer

expression (O’Hagan et al., 2015, ﬁgure 7). Moreover, data from Nr4a1 protein levels

on similar subpopulations have been reported (Feng et al., 2015, extended data ﬁgure

4d), which were also in agreement with our results: Foxp3+ CD4SP cells presented the

highest Nr4a1 protein MFI when compared with CD25+Foxp3- precursors.

From the gene expression perspective, our results provide the timing of gene expression

(ﬁg. 3.21), instead of focusing on the population layer. This timing information allows to

generate new hypothesis about speciﬁc gene regulation and interactions. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 128

FIGURE 3.21: Gene expression temporal dynamics upon TCR signals in CD4SP thymocytes. Our model proposes that intermediate-affinity TCR-pMHCII interactions trigger Nr4a3 expression at intermediate levels, and subsequent expression of CD25 and later Foxp3. How- ever, CD25 expression is not sustained in these cells. On the other hand, higher-affinity TCR-pMHCII interactions trigger high and sustained expression of Nr4a3, subsequent CD25 expression and later Foxp3 expression. Colours depict the cell population classification over the gene expression dynamics diagrams (orange for CD25+Foxp3-, purple for CD25-Foxp3+ and green for CD25+Foxp3+ CD4SP thymocytes). Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 129

3.3.1 Dynamics of Foxp3 and Nr4a3 expression

The Nr4a3Timer data allows us to understand the timing of the development of thymus-

derived TReg. Our study also suggests that there are differences in the dynamics of

CD25 and Foxp3 expression in early neonates. It has been reported that the medulla,

speciﬁcally the mTEC at neonatal stages, presents different antigen processing and

presentation machinery in comparison with adult mice (Yang et al., 2015). Unlike

that study, which compares 0-10 days of age and 35-45, our data present differences

between day 2 and day 7 after birth.

As revealed by our Nr4a3Timer data, we found differences in the timing of Foxp3

expression in the thymus between day 2 and day 7 after birth. Most CD4SP CD25+

cells became Foxp3+ earlier after TCR signalling at day 2 than on day 7 and afterwards.

On day 2 the CD25+ cells showed downregulation of CD69 earlier, and some kept

high levels of CD69 through time without expressing Foxp3. This could be related to

differences in the medullary niche, still in develpment at day 2 after birth, with different

IL-2 availability at day 2 and day 7, among other factors. However, at day 2 the intensity

of Nr4a3 is lower, so we cannot discard the possibility that cells progress quicker through

their Timer trajectory and that this is the reason why they seem to become Foxp3+

earlier after TCR signal than when they have higher intensities of Nr4a3. Nevertheless,

CD69 expression pattern would not ﬁt with this latter explanation. The issue could be

experimentally solved by monitoring different TCR signalling intensities and measuring

the timing of the progression from Timer blue to Timer red. Chapter 3 Differentiation of Foxp3+ regulatory T cell in the thymus 130

3.3.2 Foxp3: lineage and feedback control

The feedback control perspective proposes that Foxp3 is a component of a general system that controls T cell activation, as an alternative to the lineage perspective

(Ono and Tanaka, 2015). One of the hypotheses proposed states that in the thymus

(and in the periphery similarly) upon interacting with the cognate antigen, T cells are

activated to become CD25+CD4SP, and that these activated T cells generate Foxp3+

T cells (TReg) and Foxp3- memory-like T cells (Ono and Tanaka, 2015). Our early

neonatal results identiﬁed Foxp3GFP- Nr4a3 Timer angle high cells in the thymus and

in the spleen. This indicates that these CD25+Foxp3-Nr4a3Timer+ cells have received

strong enough TCR signalling to activate transcription of Nr4a3Timer, that they have

survived, and have migrated to the spleen. These cells could correspond to the Foxp3-

memory-like T cells proposed by the feedback control perspective model. Chapter 4

The effect of cytokines and costimulation signals on Nr4a3 transcription

4.1 Introduction

T cell activation and differentiation is controlled by TCR signalling but also by costimula-

tion and cytokines signalling (Kalinski´ et al., 1999) (ﬁg. 4.1).

FIGURE 4.1: Signals involved in CD4+ T cell activation and differentiation. 1, antigen recognition by the interaction of TCR and CD4 molecules with the p-MHCII complex; 2, costimulatory signals, such as the delivered by CD28 interaction with B7 molecules (CD80 OR CD86); 3, cytokine receptor signalling upon recognition of their cytokine ligands.

131 Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 132

It has been reported that TCR signalling can alter cytokine-mediated signalling (reviewed in Huang and August (2015)). However, it is not fully understood how the

cytokine milieu affects TCR signalling events. Because Nr4a3 expression is an early

downstream TCR signalling event, I investigated the expression of Nr4a3 under differ-

ent cytokine signals and thereby analysed how cytokine signalling can affect Nr4a3

expression.

Splenocytes or CD4 naïve T cells were cultured in vitro under several well-established

T cell differentiation conditions (Th1, Th2, Th17, Treg and Th0 control (table 4.1)) and

the expression of Nr4a3 was analysed using the Nr4a3GFP mouse strain.

TABLE 4.1: In vitro differentiation conditions for CD4+T cells. Description of the cytokines included in each helper T cell differentiation condition. In addition, anti-CD3 and anti-CD28 antibodies were given for TCR stimulation.

Conditions Cytokines Th0 No cytokines added Th1 IL-2, IL-12, anti-IL-4 Th2 IL-2, IL-4, anti-IFNγ, anti-IL-12 iTreg IL-2, TGF-β, anti-IFNγ, anti-IL-4 Th17 IL-6, TGF-β, anti-IL-4, anti-IFNγ

Intracellular staining for cytokines conﬁrmed the production of IFNγ and IL-17 in the

Th1 and Th17 conditions respectively, and Foxp3 intranuclear staining was used to

conﬁrm iTreg skewed conditions (ﬁg. 4.2). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 133

FIGURE 4.2: Expression of IFNγ, IL-4 and IL-17 and Foxp3 in CD4+ cells in different skewed conditions (Th1, Th2, Th17 and iTreg). Representative scatterplot from splenocytes cultures. Analysis performed at day 6 or 7 of culture for each culture experiment.

4.2 In vitro study of T cell differentiation and TCR signalling

On TCR ligation and costimulation (anti-CD3 and anti-CD28), different cytokines can

skew CD4+T cell differentiation into different helper T cells subsets. When splenocytes were cultured in the absence of added cytokines (Th0 conditions) and compared with

splenocytes cultured in the presence of IL-2, IL-12 and anti-IL-4 (Th1 conditions) or in

the presence of IL-2, TGF-β, anti-IFNγ, anti-IL-4 (iTreg conditions), Nr4a3 expression was similar, as reported by the Nr4a3GFP ﬂuorescence intensity (ﬁg. 4.3A). Cells

gradually increased their Nr4a3GFP expression through time in all three conditions,

reaching maximum levels at 44 hours of culture. Therefore, we found no evidence that Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 134

presence or absence of IL-2 or IL-12 affected Nr4a3 expression on CD4+ T cells using

these experimental conditions.

However, in the presence of IL-2 together with IL-4 (Th2 conditions) or in the conditions with IL-6 and TGF-β (Th17) (table 4.1), the levels of Nr4a3GFP were reduced compared with those in the other conditions at 20 hours of culture and thereafter. (ﬁg. 4.3A). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 135

(A) Splenocytes (B) naive CD4+ with APCs

FIGURE 4.3: Expression of Nr4a3GFP on CD4+ T cells in different in vitro culture conditions. (A) Splenocytes culture, (B) naive CD4+ cells cocultured with splenic APCs, and (C) naive CD4+ cells cultured in anti-CD3-coated wells, in different skewing conditions: Th0 (cyan), Th1 (purple), iTreg (pink), Th2 (green), Th17 (blue). Each experiment was performed once, with two biological replicates shown for each time point. The x-axis represents hours from culture set up.

Another experiment was performed with bead-puriﬁed naïve CD4+ T cells in the pres-

ence of APCs (splenocytes depleted of T cells and inactivated by Mitomycin-C treat-

ment) (ﬁg. 4.3B). In these cultures, Nr4a3GFP levels peaked at 16 hours for Th17 and

Th2 conditions, and at 40 hours for Th0, Th1 and iTreg conditions. At 140 hours levels Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 136

of Nr4a3GFP were reduced for all conditions, and iTreg was the condition with highest

levels.

In order to investigate further the effects of cytokine signals in the absence of APCs,

CD4+ naïve T cells were cultured alone in anti-CD3 antibody-coated wells with soluble

anti-CD28 antibody (ﬁg. 4.3C). Differences for Th17 conditions where observed at 16,

40 and 112 hours of culture.

Interestingly, in the absence of APCs, puriﬁed CD4+ naïve T cells in Th2 conditions

had higher levels of Nr4a3GFP at 40 hours (ﬁg. 4.3C) than in the presence of APCs

(4.3C), reaching similar levels to those of Th0 or Th1 conditions. However, at 112 hours,

Nr4a3GFP levels in Th2 conditions were similar to those in Th17 conditions. In other words, in Th2 conditions in the presence of APCs Nr4a3 expression was similar to that

of Th17 conditions, while in the absence of APCs, CD4+ naive T cells expressed Nr4a3

at higher levels at 40 hours. Therefore, it is possible that APCs produce some soluble

factors in the Th2 condition which repressed Nr4a3 expression.

4.2.1 Effects of IL-6 and TGF-β on Nr4a3 expression

Th17-differentiating conditions consistently resulted in reduced expression of Nr4a3GFP.

Therefore, we hypothesised that either or both IL-6 or TGF-β may have a suppressive

effect on Nr4a3 transcription. In vitro time course experiments were performed to

measure and compare the effects of IL-6 and TGF-β on Nr4a3 expression using

Nr4a3GFP (table 4.2). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 137

TABLE 4.2: Concentrations of IL-6 and TGF-β added to CD4+ naive T cells stimulated by plate-coated anti-CD3 and soluble anti-CD28 antibodies. Time course analysis was performed and the expression of Nr4a3GFP was measured by ﬂow cytometry at 15, 38 and 134 hours.

IL-6 titration experiment TGF-β titration experiment IL-6 (ng/ml) TGF-β (ng/ml) IL-6 (ng/ml) TGF-β (ng/ml) 0 5 20 0 0.31 5 20 0.02 1.25 5 20 0.1 5 5 20 0.5 20 5 20 2.5 40 5 20 5

Effects of IL-6 on Nr4a3 expression

Time course analysis with titrated doses of IL-6 showed that IL-6 delayed and sup-

pressed the expression of Nr4a3GFP in a dose-dependent manner as early as at 15

hours, and that this effect became more pronounced later on, as shown at 134 hours

of culture (ﬁg. 4.4). It seems that at 134 hours of culture, Nr4a3GFP ﬂuorescence was saturated in cells with lower concentrations of IL-6 in the presence of TGF-β, and

this resulted in a change in the shape of the dose-response curve to a sigmoid one.

The suppression of Nr4a3GFP expression was only maintained with 5ng/ml of IL-6 or

higher. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 138

FIGURE 4.4: Effect of IL-6 concentration on the expression of Nr4a3GFP. CD4+ naive T cells were cultured in anti-CD3-coated plates and Th17 differentiation conditions with titrated doses of IL-6. Time course analysis for 15, 38 and 134 hours after culture was set up. Plot shows the MFI of Nr4a3GFP from two biological replicates. Colour indicates the time point (15h (red), 38h (purple) and 134h (green)). Data from one experiment with two biological replicates (n=2).

The reduction of IL-6 concentration in the culture (in the presence of TGF-β) resulted in

higher percentages of Foxp3 expressing cells (ﬁg. 4.5A). This was expected because

IL-6 is known to inhibit the generation of iTreg (Bettelli et al., 2006). Even though it was not technically possible to detect GFP simultaneously with the intranuclear staining

of Foxp3 to analyse if Foxp3+ cells expressed more Nr4a3GFP, we compared both

independent results. Interestingly, both Nr4a3GFP ﬂuorescence intensity and the

percentage of Foxp3+ cells showed a negative correlation with IL-6 concentration in the

culture (ﬁg. 4.5). However, even if at 5ng/ml of IL-6 the percentages of Foxp3+ cells were high, this fact did not result in high Nr4a3GFP expression levels. In other words,

the percentage of Foxp3+ cells cannot explain the reduction of Nr4a3GFP at 5ng/ml of

IL-6. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 139

(A) Percentage of Foxp3+ cells (B) Nr4a3GFP MFI

FIGURE 4.5: Effect of IL-6 titration on Foxp3 and Nr4a3 induction. A, Percentage of Foxp3+ cells and B, Nr4a3 mean ﬂuorescence intensity (MFI) at each concentration of IL-6 at 134 hours of culture. Data from one experiment with two biological replicates (n=2).

Effects of TGF-β on Nr4a3 expression

In Th17 differentiation conditions, TGF-β reduced Nr4a3GFP expression in a dose

dependent manner (ﬁg. 4.6B).

(A) IL-6 serial dilution (B) TGF-β serial dilution

FIGURE 4.6: Comparison of IL-6 and TGF-β effects on the expression of Nr4a3GFP. CD4+ naive T cells were cultured in anti-CD3-coated plates (5 µg/ml) and Th17 differentiation conditions with titrated doses of (A) IL-6 or (B) TGF-β. Time course analysis was performed at 15, 38 and 134 hours after culture. Plot showing the MFI of Nr4a3GFP from two biological replicates. Colour indicates the time point: 15h (red), 38h (purple) and 134h (green). Data from one experiment with two biological replicates (n=2). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 140

The results indicate that both IL-6 and TGF-β are required for the suppression of Nr4a3

(ﬁg. 4.6B). In the titration experiments for IL-6 and TGF-β, when the concentration of

either of them is 0, the levels of Nr4a3GFP are increased in comparison to the Th17

conditions, where the IL-6 and TGF-β are both present. Speciﬁcally, the presence of

20ng/ml of IL-6 (the standard concentration for the Th17 differentiation culture), without

TGF-β, resulted in levels of Nr4a3GFP as high as those of Th0 conditions (without

cytokines) at 134 hours culture (ﬁg. 4.6B and 4.7). In a similar fashion, the presence of

5ng/ml of TGF-β in the absence of IL-6 (or reduced amounts) resulted in higher levels

of Nr4a3GFP than the Th17 conditions, but lower than Th0 conditions at 38 and 134

hours (ﬁg. 4.6A and 4.7). Therefore, both TGF-β and IL-6 seem to be responsible for

the Nr4a3 downregulation observed in the Th17 conditions.

(A) Nr4a3GFP density plot (B) Nr4a3GFP MFI

FIGURE 4.7: Effect of IL-6 or TGF-β alone on the expression of Nr4a3GFP. CD4+ naive T cells cultured in antiCD3-coated plates in the presence of IL-6 (cyan), TGF-β (pink), both (blue) or none (grey). Time course data for 15, 38 and 134 hours of culture. Plots showing: (A) density plot of GFP Fluorescence intensity of Nr4a3GFP from two biological replicates represented as independent lines; (B) Nr4a3GFP MFI of the two biological replicates (dots), and mean value for each conditions throughout time (lines). Data from one experiment with two biological replicates (n=2). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 141

Interestingly, in the absence of TGF-β, IL-6 downregulated Nr4a3 at 15 and 38 hours.

However, at 134 hours of culture, levels of Nr4a3GFP were high, similar to those of

the Th0 condition (ﬁg. 4.6). Hence, IL-6 alone failed to maintain the suppression

of Nr4a3GFP at 134 hours. On the other hand, TGF-β alone at 15 hours resulted in similar Nr4a3GFP expression as the Th0 conditions, but maintained the same intermediate levels of Nr4a3GFP throughout the 6 days of culture, preventing the upregulation. Therefore, TGF-β and IL-6 interfere with TCR downstream signals with different dynamics. TGF-β failed to suppress at 15 hours but maintained Nr4a3

expression at intermediate levels up to 134 hours, while IL-6 showed a stronger early

downregulation, that did not last.

4.2.2 Effects of costimulation (CD28) on Nr4a3 expression

Th1, Th0 and iTreg conditions resulted in increased levels of Nr4a3GFP. This result led

to the hypothesis that Th1, Th0 and iTreg conditions are providing different downstream

regulation of TCR signal, which results in increased Nr4a3 expression.

By altering the magnitude of the costimulatory signal CD28, we can modulate the

activation of some TCR downstream signalling molecules such as IKK (inhibitor of

NF-κB kinases), PKC (protein kinase C) or Ras-MAPK pathway (Ras-mitogen-activated

protein kinase pathway). Changes in the amount of CD28 signalling should result in

changes in the activation of those TCR downstream molecules, and I hypothesised that

this will result in a reduction of Nr4a3GFP,especially in Th0, Th1 and iTreg differentiation

conditions, because these conditions showed the highest levels of Nr4a3GFP. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 142

This hypothesis was tested by using titrated concentrations of anti-CD28 antibody in the cultures. T cell cultures with low anti-CD28 concentrations had lower Nr4a3GFP

expression at 16 and 40 hours of culture (ﬁg. 4.8). On the other hand, at 112 hours of

culture, all conditions with different concentrations of anti-CD28 expressed similar levels

of Nr4a3GFP. Therefore, the CD28 costimulatory signal accelerates the upregulation

of Nr4a3, and probably also the expression of other genes downstream of the TCR

signal, but it does not increase the plateau level of Nr4a3 expression under continuous

stimulation. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 143

(A) Time course

(B) 40 hours dose-response curves

FIGURE 4.8: Anti-CD28 effects on Nr4a3GFP expression on CD4+ naive T cells cultured in antiCD3-coated plates. Each in vitro culture condition is shown in independent graphs (from left to right): Th0, Th17, Th2, iTreg. (A)Nr4a3GFP MFI of two biological replicates (dots) are shown at 16, 40 and 112 hours of culture. Mean values for each concentration and time point are shown with lines. The gradient of blue indicates the concentration of anti-CD28 (0.03µl/ml (lighter blue) 0.1, 0.3, 1µl/ml (darker blue)). (B) 40 hours dose response graphs and Pearson correlation coefﬁcient (r) for each T cell differentiation conditions. Data from one experiment with two biological replicates (n=2).

Importantly, T cell cultures without anti-CD28 (anti-CD3 coated plates), also upregulated

Nr4a3GFP expression (ﬁg. 4.9). This means that TCR engagements by anti-CD3

antibody are sufﬁcient to induce Nr4a3 expression. It seemed that anti-CD3 alone

induced lower Nr4a3GFP levels. However, more data would be required to determine

if anti-CD3 stimulation changes the plateau level of Nr4a3 expression, in comparison with the presence of CD28 costimulatory signals. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 144

FIGURE 4.9: Expression of Nr4a3GFP on CD4+ naive T cells cultured in antiCD3-coated plates in the presence of anti-CD28 (blue gradient dots) or without anti-CD28 (black dots). Data from Th17, Th2 conditions. Data from one experiment with two biological replicates (n=2).

Furthermore, CD28 dose titration changed the percentages of IL-17 and IFN-γ producers in Th1 and Th17 conditions respectively (ﬁg. 4.10A). Anti-CD28 stimulation led to a dose dependent reduction of IFN-γ+ cells in Th1 and Th17 polarizing conditions. On the other hand, in Th17 differentiation conditions the increased costimulation through

CD28 resulted in an increase of IL-17+ cells at lower doses of anti-CD28 (from 0.03 to 0.3 µg/ml), but at the highest dose of anti-CD28 (1 µg/ml) a reduction of IL-17+ percentages was observed (ﬁg. 4.10B). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 145

(A) IFNγ+ cells (B) IL-17+ cells

FIGURE 4.10: Effect of CD28 costimulation on CD4+ T cell polarization. Percentages of IFNγ (A) or IL-17 producers (B) in different polarization conditions at different concentrations of anti-CD28 antibodies. Two biological replicates shown for each concentration. Intracellular cytokines staining performed at day 7 of culture. Data from one experiment with two biological replicates (n=2).

In summary, CD28 had an effect in the dynamics of expression of Nr4a3, accelerating

its expression up to an expression threshold. Additionally, CD28 costimulation also

resulted in changes in CD4+ T cell polarization, as shown by the percentages of IFN-γ

and IL-17 producers.

4.3 Effects of Nr4a3 overexpression on in vitro T cell differentiation

Given the reduced expression of Nr4a3 in CD4+ cells under Th17 conditions, we

hypothesised that increased levels of Nr4a3 inhibit Th17 differentiation but do not affect

differentiation of other populations (Th1, Th2 or iTreg). In order to investigate the role of Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 146

Nr4a3 in T cell differentiation and function, the primary supervisor developed a novel

Nr4a3-inducible Tet-on transgenic mouse strain (Nr4a3-IRES-EGFP (NIG)) (ﬁg. 4.11).

4.3.1 Tetracycline inducible expression of Nr4a3 (NIGrtTA)

In these mice, doxycycline treatment activates the transcription of the inducible trans-

gene, Nr4a3-IRES-EGFP, in which the IRES (internal ribosomal entry site) (Kim et al.,

1992) allows a synchronized expression of transgenic Nr4a3 and EGFP. Therefore,

cells overexpressing Nr4a3 are detected by their GFP ﬂuorescence by ﬂow cytometry.

This model is based on Tet-On system (Kistner et al., 1996), which uses two transgenes.

The ﬁrst transgene is continuously expressed in T cells, producing a protein which binds tetracyclines. The second transgene contains the genes to be conditionally

expressed under the control of sequences activated by the binding of the tetracycline-

protein complex. Our double transgenic line was obtained by crossing the following

two transgenic strains: ﬁrst, the rtTA strain carrying the reverse tetracycline-responsive

TransActivator domain (rtTA) (Legname et al., 2000) under the control of the human-

CD2 promoter (resulting in constitutive expression in T cells) (Zhumabekov et al., 1995),

and second, the Nr4a3-IRES-GFP (NIG) construct, under the control of tetracycline

responsive elements (Masahiro Ono, unpublished) (Fig. 4.11). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 147

FIGURE 4.11: Tetracycline inducible expression of Nr4a3-GFP. This mouse strain contains two transgenes: (1) the sequence for the “Tetracycline binding protei” (TBP under the promoter of human-CD2, and (2) the Nr4a3-IRES-GFP sequence under the control of “tetracycline responsive elements” (TRE). Doxycycline (Doxy), a tetracycline, forms a complex with the TBP and binds the “tetracycline responsive elements” (TRE) activating the transcription of Nr4a3-IRES-GFP, producing Nr4a3 and GFP.

Therefore, these NIGrtTA mice have normal endogenous expression of Nr4a3 when

not exposed to tetracyclines. However, upon tetracycline (doxycycline) exposure, the

tetracycline binds to the tetracycline binding protein (TBP) and this complex activates

the transcription of the NIG construct that is under the control of tetracycline responsive

elements (TRE). As a result, Nr4a3 endogenous expression is topped up by the

overexpression of the transgenic Nr4a3. In other words, GFP in this system reports the

amount of transcription of the Nr4a3 from the NIG construct, not the total amount of

Nr4a3 expressed.

Splenocytes cultured with serial dilutions of doxycycline resulted in increased expression

of GFP in CD4 and CD8 cells (median ﬂuorescence shown in ﬁg. 4.12 and CD4+ cells

GFP expression detailed in 4.13A). Interestingly, the CD8 population showed a stronger

effect on GFP levels. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 148

FIGURE 4.12: GFP dose response to doxycycline on CD4 (black) and CD8 (grey) splenic cells by NIG+rtTA+ Tet-on mice. Splenocytes from NIG+rtTA+ mouse (n=1) were cultured with anti-CD3 plate-bound and soluble anti-CD28 for 18h. Data from one experiment. Median ﬂuorescent intensity (MFI) of GFP is shown. At 1µg/ml doxycycline two controls are depicted: absence of stimulation (red) and cells without rtTA element (NIG+rtTA-)(pink).

The system was validated as the expression of GFP required doxycycline (no GFP was

observed in the absence of doxycycline) and the rtTA transgene (no GFP was observed

in cells from mice lacking the rtTA sequence (NIG+rtTA-)) (ﬁg. 4.12 and 4.13).

Furthermore, it was conﬁrmed that this sytem required TCR stimulation in addition

to doxycycline and rtTA expression, as GFP was not expressed in the absence of

stimulation with anti-CD3 (ﬁg. 4.13). In previous reports using the tet-on system

(Legname et al., 2000) Concavalin A was used for the stimulation of splenocytes in vitro.

Therefore, I speculate that T cell activation is required for the constitutive expression of

rtTA. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 149

(B) 1µg/ml doxycycline with (A) GFP dose response to doxycycline controls

FIGURE 4.13: Conﬁrmation of inducible expression of GFP upon exposure to doxycycline. (A) Splenocytes from NIG+rtTA+ mouse (n=1) were cultured with anti-CD3 plate-bound and soluble anti-CD28 for 18h. Violin plots show the density distribution of the ﬂuorescence, and dots represent the MFI. (B) Comparison of GFP levels in CD4+ T cells from NIG+rtTA+ mouse in the absence of anti-CD3 (red) or with anti-CD3 stimulation (black), and cells from a NIG+rtTA- mouse (magenta), all three with 1µg/ml doxycycline. Data from one experiment.

To determine the effects of Nr4a3 overexpression, naive CD4+ T cells from the spleen were cultured in various differentiation conditions (Th1, iTreg, Th2 and Th17, as de-

scribed in table 4.1) under different concentrations of doxycycline. At day 5 (after 116

hours of culture) Th17 and iTreg conditions were analysed for their levels of GFP. GFP

MFI was similar in Th17 condition compared with iTreg condition cells with the same

amount of doxycycline (ﬁg. 4.14). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 150

FIGURE 4.14: GFP dose response to doxycycline on CD4+ naive T cells under Th17 condition (blue) or iTreg condition (pink) with anti-CD3 plate-bound and soluble anti- CD28 for 116h. GFP MFI is shown for NIG+rtTA+ cells (left) and NIG+rtTA- cells (right) (n=1 of each). Data from one experiment.

In the same experiment, the levels of GFP for Th1, Th2 and Th17 conditions were analysed 5 hours after culture with PMA and ionomycin. All three conditions showed

GFP increase in the presence of doxycycline. However, Th17 conditions showed higher levels of GFP than Th1 or Th2 conditions upon Nr4a3 overexpression (ﬁg. 4.15). Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 151

(A) NIG+rtTA+ and NIG+rtTA- cells (B) NIG+rtTA+ cells

FIGURE 4.15: GFP dose response to doxycycline on CD4+ naive T cells under Th17 (blue), Th1 (purple) or Th2 differentiation conditons (green). NIG+rtTA+ cells (left) and NIG+rtTA- cells (right) (n=1 of each) were cultured with anti-CD3 plate-bound, soluble anti-CD28 for 116h. (A) MFI of GFP is shown. (B) Density plots of the same data for NIG+rtTA+ cells. Data from one experiment.

This result could be affected by a higher propensity to cell death upon PMA/ionomycin

strong stimulation conditions. Therefore, I performed a time course experiment measur-

ing the levels of GFP without PMA/ionomycin incubation at day 1, 3 and 6 of culture

(ﬁg. 4.16) with Th0, Th1, Th17 and iTreg conditions. GFP levels peaked at day 3 for all

conditions. The overexpression of Nr4a3 was similar for all conditions. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 152

FIGURE 4.16: GFP dose response to doxycycline on CD4+ naive T cells under Th17 (blue), iTreg (pink), Th0 (cyan) or Th1 (purple) conditions. Cells were cultured with anti- CD3 plate-bound, soluble anti-CD28 for 1, 3 or 6 days. NIG+rtTA+ cells shown (n=1). MFI of GFP shown with points. Data from one experiment.

Furthermore, to assess the effect of the intracellular staining procedures (including the

PMA-ionomycin culture) on the GFP analysis, values of GFP were compared for the same cultured conditions with and without the treatments (ﬁg. 4.17). Both analysis produced similar results (ﬁg. 4.17).

FIGURE 4.17: Comparison of GFP MFI measured with different experimental procedures: CD4+ naive T cells under Th17 (blue), iTreg (pink), Th0 (cyan) or Th1 (purple) differentiation conditions, were cultured with anti-CD3 plate-bound, soluble anti-CD28 for 6 days with 1µ/ml doxycycline and whether directly stained after culture or incubated 5 h with PMA/Ionomycin (as indicated in x-axis. NIG+rtTA+ cells shown (n=1). Data from one experiment. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 153

Next, the differentiation of cells in the different conditions was assessed by analysing

the percentage of cells expressing Foxp3, IL-17, IFNγ and IL-4 after 116 hours of culture (5 days). In iTreg differentiation conditions, 97.0±0.5% of cells were Foxp3+

irrespectively of doxycycline concentrations and mouse genotype (rtTA+ or rtTA-). In

Th17 conditions, when rtTA+ cells where exposed to doxycycline they produced higher

percentage of Foxp3+ cells (table 4.3). This can be (1) the result of a direct effect of

Nr4a3 overexpression on Foxp3 trancription or (2) the effect of Nr4a3 overexpression

on T cell differentiation and survival in Th17 condition, by positively selecting Foxp3 ex-

pressors and depleting other cells. The reduction of GFP positive cells (cells expressing

physiological Nr4a3 in the Nr4a3GFP model) supports the second option.

TABLE 4.3: Percentages of Foxp3+ cells in Th17 and iTreg conditions at different concentrations of doxycycline after 5 days of culture.

Doxycycline (µg/ml) % Foxp3+ in Th17 % Foxp3+ in iTreg rtTA+ rtTA- rtTA+ rtTA- 0 12.8 17.6 97.2 97.4 0.3 29.0 No data 97.1 No data 1 33.3 12.1 97.0 96.1

The percentage of IL-17 producing cells in Th17 condition was assessed in two experi-

ments, both in experimental cells (NIG+rtTA+ cells) and controls (NIG+rtTA- cells or

NIG-rtTA+ cells) (table 4.4). The levels of IL-17 expression were also measured but

results from two experiments were not consistent. Nr4a3 overexpression resulted in no

consistent effect on IL-17 production or Th17 differentiation. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 154

TABLE 4.4: Percentages of IL-17 producing cells in Th17 conditions at different concentrations of doxycycline.

Experiment 1 Doxycycline (µg/ml) % of IL-17+ cells NIG+rtTA+ NIG+rtTA- 0 8.0 9.0 0.3 3.8 No data 1 3.7 5.1 Experiment 2 Doxycycline (µg/ml) % of IL-17+ cells NIG+rtTA+ NIG+rtTA- NIG-rtTA+ 0 25.1 25.5 20.2 0.1 27.3 No data No data 1 30.7 22.8 29.3

In Th1 conditions, where T cells differentiate to express IFNγ, the percentage of IFNγ

positive cell was also variable between experiments. Therefore, we did not ﬁnd a direct

effect of Nr4a3 overexpression on the induction of IFNγ expression in Th1 conditions

(table 4.5).

TABLE 4.5: Percentages of IFNγ producing cells in Th1 conditions at different concentrations of doxycycline. Two independent experiments results shown (Left and right).

Doxycycline (µg/ml) % of IFNγ+ cells in Th1 NIG+rtTA+ NIG+rtTA- 0 48.2 49.04 0.3 36.8 No data 1 31.8 46.9 Doxycycline (µg/ml) % of IFNγ+ cells in Th1 NIG+rtTA+ 0 77.7 0.1 84.0 1 73.6

However, the intensity of IFNγ expression was decreased upon doxycycline treatment

in both experiments (ﬁg. 4.18 and ﬁg. 4.19). Thus, in Th1 condition overexpression of

Nr4a3 reduced the intensity of IFNγ expression on IFNγ producing cells. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 155

(A) Experiment 1 (B) Experiment 2

FIGURE 4.18: Comparison of IFNγ staining levels at different concentrations of doxycycline under Th1 differentiation conditions. CD4+ naive T cells were cultured with anti-CD3 plate-bound, soluble anti-CD28 for 5 or 6 days with raising concentrations of doxycycline (light purple, 0µg/ml; purple, 0.3µg/ml; dark purple, 1µg/ml). Density plots show IFNγ intracellular staining of two independent experiments. The negative control for ﬂuorescence is shown as (grey) lines from cells in Th17 differentiation conditions. Data from two independent experiments.

FIGURE 4.19: Scatterplots of experiment 2. Comparison of IFNγ staining levels at different concentrations of doxycycline. CD4+ naive T cells under Th0 (left), Th1 (right three graphs) conditions, cultured with anti-CD3 plate-bound, soluble anti-CD28 for 6 days with raising concentrations of doxycycline. Scatterplots shows CD4 surface staining and IFNγ intracellular staining. Data from one experiment.

4.4 Discussion

CD4+ T cell activation and differentiation are controlled by antigen recognition signals,

costimulatory signals and cytokine induced signals. In this chapter, I investigated the

effects of cytokine signalling on Nr4a3 expression, and obtained three main ﬁndings.

First, IL-6 and TGF-β had a suppressive effect on Nr4a3 expression. Second, Nr4a3 Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 156

overexpression increased the percentage of Foxp3 expressors in the Th17 differen-

tiation conditions. Third, Nr4a3 overexpression reduced IFNγ expression in the Th1

differentiation conditions. In addition, I investigated CD28 costimulatory signals. The

results suggest a modulatory effect of anti-CD28 ligation on the temporal dynamics

of Nr4a3 expression in the different polarizing conditions, increasing the number of

IL-17+ cells in the Th17 conditions, and decreasing the number of IFNγ+ cells in Th1

conditions.

4.4.1 Regulatory effects of the cytokines IL-6 and TGF-β on Nr4a3 expression

Using naive CD4+ T cells from Nr4a3GFP mice, we analysed the expression of Nr4a3GFP

during T cell differentiation, and showed that Th17 differentiation cytokines (IL-6 and

TGF-β) decreased Nr4a3 expression compared with Th1, Th2 and iTreg cytokines.

Titration experiments of IL-6 and TGF-β revealed a dose dependent reduction of Nr4a3

expression. The presence of IL-6 or TGF-β alone in the polarizing cultures resulted in

partial reduction of Nr4a3 expression, suggesting a synergistic effect of both cytokines.

IL-6 is a proinﬂammatory cytokine immediately and transiently produced by APCs, and

also by non-hematopoietic cells. It is released upon infection or tissue injuries, and it

triggers warning signals that activate defence mechanisms (Tanaka et al., 2014). IL-6

misregulation is involved in chronic inﬂammation, autoimmunity and cancer (Nishimoto

et al., 2008).

On the other hand, TGF-β is a morphogen produced by many immune-cell types, such

as macrophages, DCs and T cells (Rubtsov and Rudensky, 2007). It is a regulator Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 157

of immune responses by promoting or preventing the differentiation, survival and

proliferation of many different cell types (Chen and Dijke, 2016). TGF-β misregulation

is involved in cancer, autoimmunity and inﬂammation (Akhurst and Hata, 2012).

Therefore, it is relevant in these contexts that IL-6 and TGF-β have a regulatory effect

on Nr4a3 expression, potentially suppressing other downstream TCR-signal activities

as well. These results imply that such IL-6 and TGF-β mediated mechanisms reduce

TCR downstream activities or increase the death of cells receiving higher TCR signals,

and thereby prevent the excessive activation of high-afﬁnity T cells to antigens in tissues

upon danger signals or infection.

In the case of misregulation, if IL-6 and TGF-β levels in a microenvironment are in

excess, they may excessively suppress the TCR signal downstream activities, and

contribute to cancer progression. On the other hand, lack of or reduced levels of

IL-6 and TGF-β may enhance the activity of high-afﬁnity T cells, which can lead to

autoimmune reactions.

IL-6 signals are mediated through the transcription factor STAT3. A recent report has

identiﬁed STAT3 as a transcriptional repressor of Nr4a3 in cells overexpressing a consti-

tutively activated STAT3 mutant. STAT3 silences Nr4a3 by promoter hypermethylation

(Yeh et al., 2016). Therefore, STAT3 is a reasonable candidate for the reduction of

Nr4a3 expression observed in our IL-6 dose titration experiments.

On the other hand, TGF-β signalling is mediated through Smad2 and Smad3 signal

transducers. Both TGF-β and IL-6 signal cascades have been reported to interact in an

antagonistic manner through Smad3-STAT3 interaction (Wang et al., 2016). In contrast, we have observed a synergistic mechanism in the suppression of Nr4a3 expression. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 158

Furthermore, our results suggest that the effects of TGF-β and IL-6 on Nr4a3 expression

have different dynamics. We hypothesize that IL-6 has a more immediate effect, while

TGF-β consolidates and maintains Nr4a3 repression in Th17 conditions.

4.4.2 Effects of Nr4a3 overexpression on Foxp3 and IFNγ

The expression levels of Nr4a3 induced by Tet-on were similar in the different T cell

differentiation conditions, but we have no data on the endogenous expression of Nr4a3

in the Tet-on system.

We found, using the NIG system, that the percentage of Foxp3 cells was increased in

Th17 conditions in the presence of doxycycline. This result may be attributable to the

increased amount of Nr4a3, which can directly induce Foxp3 expression. Alternatively,

Foxp3+ cells may survive better when the levels of Nr4a3 are increased. The effects

of Nr4a3 on survival and apoptosis are studied and presented in the following chapter

(Chapter 5, page 161).

Another important ﬁnding is that Nr4a3 overexpression reduces IFNγ expression levels

in Th1 conditions. IFNγ is a regulator of the Th1-type immune response, and it is involved in the response against infections and tumors, but also associated to autoimmune diseases and inﬂammation (Zaidi and Merlino, 2011). The suppression of IFNγ by Nr4a3 suggests that Nr4a3 is a negative feedback mechanism for the

Th1-type response, and that Nr4a3 overexpression may convert Th1-type responses

into Th2-type. Therefore, it would be of interest to investigate Nr4a3 expression in Th2

diseases. Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 159

A limitation in the Tet-on system is that it needs T cell activation to induce the transgenic

expression of Nr4a3. This means that the transgenic Nr4a3 expression is induced as

a top-up to the endogenous expression of Nr4a3. In addition, it is possible that the

transgenic Nr4a3 changes the endogenous regulation of the Nr4a3 gene, dampening

the effect of Tet-induced Nr4a3 expression on T cell activities.

4.4.3 CD28 signals alter the dynamics of expression of Nr4a3

Finally, costimulatory signals through CD28 molecules were investigated using titrated

doses of anti-CD28 antibody. Costimulation through CD28 accelerated Nr4a3 endoge-

nous expression in a dose dependent manner in the different polarizing conditions

tested (Th0, Th1, Th2, Th17 and iTreg) at early time points (16 and 40 hours), although

the saturated expression levels were not changed by CD28 (ﬁg. 4.8). These data

indicate that CD28 costimulation changes the temporal dynamics of Nr4a3 expression, while the effect does not last upon continuous stimulation. This regulation of temporal

dynamics could be further investigated using the Nr4a3Timer model.

Thus, CD28 signals may regulate Nr4a3 transcription. It is of interest to investigate which gene regulatory regions have CD28-response elements, and also to identify the

signalling molecule(s) that activate Nr4a3 expression. One candidate is Bhlhe40 (also

referred as DEC1), which is known to be activated by CD28 signals (Martínez-Llordella

et al., 2013). This is the ﬁrst report relating the dose dependent regulation of Nr4a3

expression dynamics through CD28 costimulation.

Another interesting implication of these results is related to the Th17 differentiation

conditions. CD28 signal has been reported to be required for Th17 differentiation Chapter 4 Cytokines and costimulation signals on Nr4a3 transcription 160 and CTLA-4 blockade to increase Th17 differentiation and IL-17 production in vivo and in vitro (Ying et al., 2010). In agreement, we observed increased percentages of IL-17 producers when increasing the concentration of anti-CD28 antibodies (up to

0.3µmg/ml). Chapter 5

Investigation of the role of Nr4a3 in apoptosis

5.1 Introduction

Nr4a3 was ﬁrstly described as an orphan nuclear receptor involved in immature thymo-

cytes and T cell hybridomas TCR-induced apoptosis (Cheng et al., 1997). Kurakula

reported in 2014 that Nr4a3 could translocate from the nucleus to the mitochondria where it binds to and thereby changes Bcl-2 into a pro-apoptotic protein (Kurakula et al.,

2014).

The results in the previous chapter (page 135) indicated differences in Nr4a3 expression under different cytokines differentiation conditions. This leads to the question of whether

T cells show different susceptibilities to apoptosis in these different cytokine conditions.

The observed reduction of Nr4a3 levels in Th17 polarising conditions could be due to the depletion of Nr4a3high cells in the presence of IL-6 and TGF-β, while in other

conditions Nr4a3high cells survive better. In other words, Nr4a3-induced apoptosis could

be context dependent and vary between conditions. Thus, in this chapter I test the

161 Chapter 5 Investigation of the role of Nr4a3 in apoptosis 162

hypothesis that Nr4a3 promotes apoptosis effectively in Th17 polarisation conditions

but not in other differentiation conditions (Th1, and iTreg).

Activation induced cell death (AICD) and restimulation induced cell death (RICD) deﬁne

two different processes involving apoptosis. AICD refers to the induction of apoptosis

upon primary TCR stimulation in resting cells, while RICD refers to the induction of

apoptosis upon restimulation of TCR in activated cells (Zheng et al., 2017). In order

to test the differential susceptibility to apoptosis in different polarisation conditions, I

studied both AICD and RICD in CD4+ naive T cells upon transgenic overexpression of

Nr4a3 in Th1, Th17 and iTreg polarising conditions.

As described in the previous chapter (page 147), the tetracycline inducible Nr4a3-IRES-

GFP transgenic mouse (NIG; Ono, unpublished) was used to investigate the effect of

Nr4a3 overexpression on CD4+ naive T cells in different in vitro differentiation conditions.

Brieﬂy, in this model T cells express Nr4a3 and GFP in response to doxycycline and

anti-CD3 stimulation.

5.2 Nr4a3 overexpression effect on AICD

NIG+rtTA+ cells were cultured with titrated doses of doxycycline, and were analysed for

their survival and apoptosis. Controls include NIG+rtTA- cells (which do not carry rtTA

and therefore do not respond to doxycycline treatment), rtTA+NIG- cells (which do not

carry the NIG transgene and therefore do not respond to doxycyclen either), and the

condition without doxycycline. Chapter 5 Investigation of the role of Nr4a3 in apoptosis 163

First, the percentage of live cells was measured after 116 hours of culture. In the

Th17 conditions, survival was reduced by exposure to doxycycline in NIG+rtTA+ cells,

reporting an effect of Nr4a3 overexpression on reducing the survival in this condition

(ﬁg. 5.1). Interestingly, Th1 conditions showed the opposite effect: increased survival

correlated with the amount of doxycycline. This indicates that the overexpression of

Nr4a3 in Th1 and Th17 conditions affects survival in an opposite manner.

In Th2 conditions survival was moderately reduced in the presence of 1µg/ml doxycy-

cline in both rtTA+ and rtTA- cells.

FIGURE 5.1: Effect of doxycycline treatment on CD4+ cells survival. Cells from rtTA- mouse were used as controls (right panel). After 116h of culture in different conditions (purple, Th1; blue, Th17; green, Th2) cells were stimulated for 5 hours with PMA/IM before the intracellular staining protocol. Alive cells were discriminated by their low levels of permeable viability dye staining. Percentages of alive cells for two experimental replicates are shown. Lines connecting mean values at each concentration of doxycycline and polarising condition are depicted. Data from one experiment with two biological replicates (n=2).

To explore the differences in activation and apoptosis further, I analysed apoptosis in

T cells under different T cell differentiation conditions in the presence or absence of

doxycycline. Annexin V and propidium iodide (PI) staining are commonly used together

to assess apoptosis by ﬂow cytometry. Therefore, I tested in time-course experiments Chapter 5 Investigation of the role of Nr4a3 in apoptosis 164 whether Nr4a3 overexpression induces apoptosis in a culture condition-dependent

manner (ﬁg. 5.2).

(A) (B) Negative control for annexin V (EDTA treated cells)

FIGURE 5.2: Representative scatterplot to illustrate the analysis strategy for measuring apoptosis. Allophycocyanin (APC) conjugated annexin V (AnnV) and propidium iodide (PI) were used to classify cells into four quadrant gates: viable (AnnV+PI-), early apoptotic (AnnV+PI+), late apoptotic (AnnV+PI+)and necrotic cells (AnnV-PI+) as described in the ﬁgure. (B) To determine the threshold of annexin V staining, cells were treated with tris-EDTA PBS to disrupt the binding of annexin V (annexin V interaction with phosphatidylserine (PS) is dependent on calcium).

Cells from NIG+rtTA- and rtTA+NIG- mice do not overexpress Nr4a3 by doxycycline

and served as controls (ﬁg. 5.3).

(A) NIG+rtTA- cells in Th17 condition (B) rtTA+NIG- cells in Th17 condition

FIGURE 5.3: Effect of doxycycline treatment on apoptosis of CD4+ cells in Th17 culture condition. Percentages of viable cells (AnnexinV-PI- (darker grey)), early apoptotic cells (AnnV+PI- (medium dark grey)), late apoptotic cells (AnnV+PI+ (medium light grey)) and necrotic cells (AnnV-PI+ (lighter grey)) are shown for each day of analysis (day 1, 3 and 6). Results from one experiment. Chapter 5 Investigation of the role of Nr4a3 in apoptosis 165

The experiment with NIG+rtTA+ cells in Th17 differentiation conditions showed that

doxycycline reduced the percentages of viable cells and increased the percentages of

late apoptotic and necrotic cells in a dose-dependent manner (ﬁg. 5.4). In other words,

overexpression of Nr4a3 had a detrimental effect on CD4+ T cell survival in the Th17

culture conditions. These results are in agreement with our previous cell survival data.

Data from two independent experiments revealed that cells in the Th17 conditions

showed the strongest effect of doxycycline treatment on AICD (ﬁg. 5.6). In the Th17

conditions, doxycycline decreased the percentages of viable cells, while increasing

those of early and late apoptotic cells.

FIGURE 5.4: Effect of doxycycline treatment on apoptosis of CD4+ cells in Th17 culture condition. Percentages of viable cells (AnnexinV-PI- (darker blue)), early apoptotic cells (AnnV+PI- (medium dark blue)), late apoptotic cells (AnnV+PI+ (medium light blue)) and necrotic cells (AnnV-PI+ (lighter blue)) are shown for each day of analysis (day 1, 3 and 6). Results from one experiment.

The viability of CD4+ cells in iTreg conditions was also reduced by doxycycline treatment

(ﬁg. 5.5 and 5.6). Chapter 5 Investigation of the role of Nr4a3 in apoptosis 166

FIGURE 5.5: Effect of doxycycline treatment on apoptosis of CD4+ cells in iTreg culture condition. Percentages of viable cells (AnnexinV-PI- (darker pink)), early apoptotic cells (AnnV+PI- (medium dark pink)), late apoptotic cells (AnnV+PI+ (medium light pink)) and necrotic cells (AnnV-PI+ lighter pink)) are shown for each day of analysis (day 1, 3 and 6). Results from one experiment.

However, in comparison with cells in the Th17 conditions, iTreg cells had higher per-

centages of viable cells at day 6 in the presence of doxycycline. Both Th17 and iTreg

conditions showed some reduction in viability, in contrast with cells in Th1 conditions, which showed increased viability (5.6). Chapter 5 Investigation of the role of Nr4a3 in apoptosis 167

FIGURE 5.6: Effect of Nr4a3 overexpression (upon doxycycline treatment) on CD4+T cells under different culture conditions: (purple), Th1; (blue) Th17; and (pink) iTreg. Per- centages of viable cells (AnnV-PI-), early apoptotic cells (AnnV+PI-) and late apoptotic cells (AnnV+PI+) are shown for each day of analysis (day 1, 3 and 6) and doxycycline concentration (0 and 1µg/ml). The data are from two independent experiments. Lines represent the mean of both experiments. Cells from NIG+rtTA- (x-shape) and NIG-rtTA+ (cross shape) mice under Th17 conditions were used as controls in one of the experiments (these cells do not overexpress Nr4a3 upon doxycycline treatment). Results from one experiment.

Cells in Th1 conditions showed reduced percentages of viable cells at days 3 and 6

of culture irrespective of doxycycline concentration. This reduction in viable cells was

consistent with the increase in the percentage of late apoptotic cells at day 3, both

in the presence or absence of doxycycline (ﬁg. 5.6). However, doxycycline seems

to moderately increase the percentage of viable cells compared with Th1 conditions without doxycycline (ﬁg. 5.6).

In contrast, Nr4a3 overexpression in Th17 conditions resulted in decreased percentages

of viable cells in AICD (ﬁg. 5.6). Thus, Nr4a3-overexpressing cells showed the opposite

effects between Th17 and Th1 conditions.

In order to determine whether the rate of proliferation is changed by Nr4a3 expression,

cell numbers were also measured. Th1 cells showed an increase in cell numbers with Chapter 5 Investigation of the role of Nr4a3 in apoptosis 168

Nr4a3 overexpression. In contrast, Th17 and iTreg conditions showed decreased cell numbers (ﬁg. 5.7). Chapter 5 Investigation of the role of Nr4a3 in apoptosis 169

(A) Th1 cell numbers

(B) Th17 cell numbers

FIGURE 5.7: Effect of doxycycline treatment on apoptosis of CD4+ T cells in Th1, Th17 and iTreg polarising conditions. Percentages of viable cells (AnnexinV-PI- (darker colour)), early apoptotic cells (AnnV+PI- (medium dark colour)) and late apoptotic cells (AnnV+PI+ (medium light colour)) and necrotic cells (lighter colour) are shown for each day of analysis (day 1, 3 and 6). Results from one experiment. Chapter 5 Investigation of the role of Nr4a3 in apoptosis 170

In summary, these results indicate that Th1 conditions supported stronger proliferation

in the presence of doxycycline. High percentages of cells in Th1 conditions were

becoming apoptotic, but doxycycline diminished the percentage of apoptotic cells. In

contrast, Th17 conditions and iTreg conditions showed proapoptotic effects of Nr4a3

overexpression, with reduction of cell numbers and percentage of viable cells. Th17

conditions showed the strongest proapoptotic effect of Nr4a3 overexpression.

5.3 Investigating the effects of Nr4a3 overexpression on RICD

Nr4a3 overexpression resulted in higher activation induced cell death in some differ-

entiation conditions than in others, i.e. in a context dependent manner. To further

investigate whether or not Nr4a3 has pro-apoptotic effects in different T cell differentia-

tion conditions, I investigated the effects of Nr4a3 overexpression during restimulation.

T cells were cultured in Th1, Th17 and iTreg conditions, and subsequently restimulated

to measure RICD in each condition. Doxycycline was added at the time of restimulation,

and as a result we could observe the effect of Nr4a3 overexpression when activated T

cells were restimulated.

The experiment was performed with CD4 naive T cells from splenocytes of NIG mice, in which Nr4a3 is overexpressed upon exposure to doxycycline. Cells were cultured in Th1,

Th17 and iTreg conditions for 3 days, then changed to a culture with the same polarising

conditions but without stimulation (without anti-CD3) for 2 days, and ﬁnally transferred

to an anti-CD3 coated plate and IL-2, in the presence or absence of doxycycline. Chapter 5 Investigation of the role of Nr4a3 in apoptosis 171

Addition of doxycycline during the 22 hours of restimulation induced the transcription of

GFP in all three conditions (ﬁg. 5.8). Unexpectedly, we observed some GFP+ cells in

the absence of restimulation in Th1 and Treg conditions (ﬁg. 5.8). This means that a

small percentage of cells (2-6%, as shown in ﬁg. 5.8) in those cultures responded to

doxycycline. Since NIG+rtTA+ T cells express GFP and Nr4a3 only by the combined

stimulation by anti-CD3 and doxycycline, this result suggests that the mechanisms

downstream of the TCR signal were still operating in a small percentage of cells in Th1

and Treg conditions, even 3 days after culture washes.

FIGURE 5.8: GFP expression induced by doxycycline in the different differentiation conditions (Th1, Th17, iTreg) upon RICD or not restimulated controls. CD4 naïve cells were cultured in differentiating conditions for 3 days, then anti-CD3 stimulation was removed for 2 days and ﬁnally cells were restimulated with anti-CD3 (or not, in the unstimulated controls) for 22 hours in the presence or absence of doxycycline. Doxycycline treatment shown as black (treated, 1/mug/ml) and grey (not treated). Experiment was performed with cells from two NIG mice (two biological replicates), shown as triangle and circular shapes. Results from one experiment.

The percentage of viable cells was greatly reduced upon restimulation. However, Nr4a3

overexpression did not drastically change survival upon restimulation (ﬁg. 5.9). Chapter 5 Investigation of the role of Nr4a3 in apoptosis 172

FIGURE 5.9: Effect of doxycycline on apoptosis in the different differentiation conditions (Th1 (purple), Th17 (blue), iTreg(pink)) upon RICD (ﬁlled circles) or non-restimulated controls (empty triangles and dashed lines). CD4 naïve cells were cultured in differentiating conditions for 3 days, then anti-CD3 stimulation was removed for 2 days and ﬁnally cells were restimulated with anti-CD3 or not (in the unstimulated controls ) for 22 hours in the absence or presence of doxycycline (1/mug/ml). Results from one experiment performed with cells from two NIG mice (two biological replicates).

Cultures without restimulation served as negative control for the RICD and showed better survival in all three conditions (ﬁg. 5.9).

Upon restimulation, Nr4a3-overexpression did not significantly change cell survival and apoptosis in activated T cells in any of the conditions. The long culture and the effects of restimulation on apoptosis were very strong already (see viable cells at fig. 5.9), making it difficult to investigate further apoptotic effects depending on Nr4a3 overexpression. Chapter 5 Investigation of the role of Nr4a3 in apoptosis 173

5.4 Discussion

In chapter 4, doxycycline treatment in the Th17 condition resulted in an increase in

Foxp3+ cells at 116 hours of culture (table 4.3 at page 153). This increase could

be due to Foxp3+ cells surviving better to higher levels of Nr4a3 in Th17 polarising

conditions, in comparison with the Foxp3- cells. Therefore, cell survival was analysed in

this chapter to determine if the transgenic expression of Nr4a3 increases apoptosis in

Th17 differentiation conditions upon Nr4a3 overexpression. In fact, our results indicate

an increase in apoptosis in Th17 conditions upon Nr4a3 overexpression, suggesting

that the increased Foxp3+ cells percentages were due to a reduction of Foxp3- cell

numbers.

The Tet-on overexpression of Nr4a3, which is not regulated by IL-6 or TGF-β, resulted

in higher percentages of apoptotic cells in Th17 polarising conditions, and to a lesser

extent in iTreg conditions. IL-6 is induced by danger signals in innate immune cells

during inﬂammation by various situations including infection and autoimmunity. On

the other hand, TGF-β is abundant in tissues, especially in cancer microenvironments.

Therefore, given that Nr4a3 is induced by strong TCR signals (see Chapter 4, page

143), these results suggest that T cells are prone to die when they receive strong TCR

signals in the presence of IL-6 and TGF-β. Thus, T cell responses may be regulated by

removing such T cells with high afﬁnities to antigens in inﬂamed tissues, which include

TReg (Moran et al., 2011).

Nr4a3 was ﬁrst described as an apoptotic factor in T cell hybridomas and immature

thymocytes (Cheng et al., 1997), and is still considered as such. However, we showed

that Nr4a3 overexpression usually does not actively induce apoptosis. Indeed, it Conclusions 174 may induce apoptosis under speciﬁc conditions. Therefore, we describe Nr4a3 as a context-dependent proapoptotic factor. Conclusions

It is broadly accepted that TReg cells develop from the thymus by agonist selection,

express Foxp3 transcription factor and are involved in immune tolerance (Hsieh et al.,

2012). Their importance is of no doubt. However, there are still two main “schools

of thought about TReg cells”, as stated by Basten and Fazekas in their review of T- cell dependent suppression (Basten and Fazekas de St Groth (2008)): the lineage

perspective, which considers TReg cells as a unique and specialized subtype of T cell,

and the feedback control perspective, which considers Foxp3 and other T cell activation

markers as components of a general system that controls T cell activation (Ono and

Tanaka, 2015).

As suggested by Ono and Tanaka (2015), to accomplish a better understanding of T

cell development and activation processes we need a broader systemic approach to

study the T cell development and differentiation. Therefore, rather than focusing on

how TReg cells develop in the thymus, I focused on the dynamics of the expression of

different markers triggered by TCR signals during development and differentiation.

However, to contextualize this study in the current literature, the results were presented

following the conventions of deﬁning populations. In other words, this project uses the

language of the current TReg lineage perspective, but its aim is to focus on the dynamic

175 Conclusions 176

regulation of genes upon TCR signals. The main goal was to analyse that dynamism in

different CD4+T cell activation situations (such as neonatal thymic development in vivo,

or in adult cells stimulated in vitro).

In order to do that, this project applied, for the ﬁrst time in the immunology ﬁeld,

ﬂuorescent Timer technology. The unstable blue form of Timer protein allows us to

distinguish recently translated proteins by their blue ﬂuorescence, from the matured

ones, which emit red ﬂuorescence. As a result, our new Timer transgenic lines allow

us to analyse the dynamics of the expression of genes and proteins triggered by TCR

signalling.

Understanding the usefulness of the Timer approach and how to better analyse the

Timer ﬂow cytometry data has been one of the challenges of this project. This study

provides a framework useful for further projects using ﬂuorescent timer proteins with

ﬂow cytometry analysis. Timer angle and intensity supply integrated information of the

ﬂuorescent timer expression dynamics, allowing to understand the temporal dynamics

of the data. This Timer data analysis allows to analyse Timer ﬂuorescence data as

continuous data rather than the usually categorised ﬂow cytometry analysis. Thus, the

Timer ﬂuorescence technology will be compatible with single cell sequencing, a very

relevant technique suggested by Ono and Tanaka (2015).

The timing of Nr4a3 expression, an immediately expressed gene upon TCR engage-

ment with p-MHCs, is reported by Timer expression, providing us with a “time-mapping”

that contains the temporal sequence of dynamic events (chapter 3, ﬁg. 3.21). From the

lineage perspective, this time-mapping reveals the TReg development timing. However,

from a broader perspective, it allows us to investigate dynamic expression of key genes Conclusions 177

during T cell differentiation without invoking the idea of distinct T cell populations.

Using Nr4a3 expression as a measure of time from TCR signalling, we showed that

upon TCR signals, CD4SP cells ﬁrstly express CD25 and later Foxp3 is expressed.

Therefore, as shown in chapter 3 (page 128), CD25+Foxp3- phenotype identiﬁes cells which are relatively new in the medulla, having started to receive TCR signal very

recently, as opposed to Foxp3 expressing cells, independently of their CD25 expression, which are cells that have been receiving TCR signals for a certain time already.

The TCR afﬁnity of each cell and the frequency of encounters with antigens compatible with their speciﬁcity, will determine the strength of TCR-pMHCII signals. In our model,

this strength is reﬂected in Nr4a3Timer transcriptional dynamics. Lower-afﬁnity TCR

interactions will trigger low transcription of Nr4a3, and probably reduced levels of

CD25. In some cells, this signalling is enough for triggering Foxp3 expression, but not

enough to sustain CD25 expression. However, stronger TCR signalling or continuous

interactions produce higher amounts of Nr4a3 and sustained CD25 expression, and

later on they sustained the expression of Foxp3.

Another important ﬁnding of this project is the regulatory effect of IL-6 and TGF-β on

TCR signalling. We propose two non exclusive possibilities for the downregulation of

Nr4a3 expression observed in the presence of IL-6 and TGF-β in a dose dependent

manner: that IL-6 and TGF-β increase the susceptibility of cells to apoptosis induced

by high levels of Nr4a3, depleting high-afﬁnity cells; and that Nr4a3 transcription is

downregulated by IL-6 and TGF-β.

This investigation contributes to the research community, both by providing a framework

for using the Timer reporter technology, and by providing insight into the temporal Conclusions 178 dynamics of some events downstream of T cell receptor-signalling in T cell development, selection, and differentiation. Future directions

This thesis initiates the ﬁeld of Timer technology application to immunology. I present a

quantitative analysis framework for studying gene expression dynamics in individual

cells that can be used not only in the immunology ﬁeld but also in any research involving

cellular biology.

First, our Nr4a3Timer reporter mice could be used for studying the temporal dynamics

of gene expression in other immune cell types, such as DC, NK cells, neutrophiles or

macrophages. Additionally, it could be used for studying the Nr4a3 expression timing

and frequency in other various non immune cell types during development, such as

neurons or muscle cells.

The analysis framework can also be used for the analysis of other genes, by creating

other new Timer reporter models. Interesting target genes would be those which expres-

sion dynamics can be used as timers for developmental, activation, or differentiation

processes.

Another interesting follow up from this project would be to identify Nr4a3 interacting

factors by using NIG mice, through the overexpression of Nr4a3 induced by doxycycline

and subsequent chromatin immunoprecipitation. This line of research could lead to the

179 Future directions 180 identiﬁcation of new key molecules involved in gene expression regulation under TCR signalling. Bibliography

Akhurst, Rosemary J. and Akiko Hata (2012). ‘Targeting the TGFbeta signalling pathway in disease’. Nat Rev Drug Discov 11.10, 790–811. Auguie, Baptiste (2016). gridExtra: Miscellaneous Functions for "Grid" Graphics. R package version 2.2.1. Azzam, Hala S. et al. (1998). ‘CD5 Expression Is Developmentally Regulated By T Cell Receptor (TCR) Signals and TCR Avidity’. J Exp Med 188.12, 2301–2311. Azzam, Hala S. et al. (2001). ‘Fine Tuning of TCR Signaling by CD5’. J Immunol 166.9, 5464–5472. Baldwin, Troy A., Kristin A. Hogquist, and Stephen C. Jameson (2004). ‘The Fourth Way? Harnessing Aggressive Tendencies in the Thymus’. J Immunol 173.11, 6515–6520. Banchereau, Jacques et al. (2000). ‘Immunobiology of Dendritic Cells’. Annual Review of Immunology 18, 767–811. Barra, Melanie M. et al. (2015). ‘Transcription Factor 7 Limits Regulatory T Cell Generation in the Thymus’. J Immunol 195.7, 3058–3070. Basten, Antony and Barbara Fazekas de St Groth (2008). ‘Special regulatory T-cell review: T-cell dependent suppression revisited’. Immunology 123.1, 33–39. Becattini, Simone et al. (2015). ‘Functional heterogeneity of human memory CD4+ T cell clones primed by pathogens or vaccines’. Science 347.6220, 400–406. Bettelli, Estelle et al. (2006). ‘Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells’. Nature 441.7090, 235–238. Boyman, Onur and Jonathan Sprent (2012). ‘The role of interleukin-2 during homeostasis and activation of the immune system’. Nature Reviews Immunology 12.3, 180. Brownlie, Rebecca J. and Rose Zamoyska (2013). ‘T cell receptor signalling networks: branched, diversiﬁed and bounded’. Nat Rev Immunol 13.4, 257–269. Cainan, Barbara J. et al. (1995). ‘A role for the orphan steroid receptor Nur77 in apoptosis accompanying antigen-induced negative selection’. Immunity 3.3, 273–282. Chen, WanJun and Peter ten Dijke (2016). ‘Immunoregulation by members of the TGFbeta superfamily’. Nat Rev Immunol 16.12, 723–740. Cheng, Laurence Eng-Chee et al. (1997). ‘Functional redundancy of the Nur77 and Nor-1 orphan steroid receptors in T-cell apoptosis’. The EMBO Journal 16.8, 1865–1875.

181 BIBLIOGRAPHY 182

Chipuk, Jerry E. et al. (2010). ‘The BCL-2 Family Reunion’. Molecular Cell 37.3, 299–310. Chou, H. S. et al. (1987). ‘Tandem linkage and unusual RNA splicing of the T-cell receptor beta-chain variable-region genes’. PNAS 84.7, 1992–1996. Day, Richard N. and Michael W. Davidson (2009). ‘The fluorescent protein palette: tools for cellular imaging’. Chem Soc Rev 38.10, 2887–2921. Dowle, M. et al. (2015). data.table: Extension of Data.frame. R package version 1.9.6. Evelyne, Jouvin-Marche, Patrizia Fuschiotti, and Patrice Noël Marche (2013). Dynamic Aspects of TCRalpha Gene Recombination: Qualitative and Quantitative Assessments of the TCRalpha Chain Repertoire in Man and Mouse. Landes Bioscience. Fahl, Shawn P., Francis Coffey, and David L. Wiest (2014). ‘Origins of gammadelta T Cell Effector Subsets: A Riddle Wrapped in an Enigma’. The Journal of Immunology 193.9, 4289–4294. Fassett, Marlys S. et al. (2012). ‘Nuclear receptor Nr4a1 modulates both regulatory T-cell (Treg) differentiation and clonal deletion’. PNAS 109.10, 3891–3896. Feng, Yongqiang et al. (2015). ‘A mechanism for expansion of regulatory T-cell repertoire and its role in self-tolerance’. Nature 528.7580, 132–136. Filipp, Dominik et al. (2004). ‘Enrichment of Lck in Lipid Rafts Regulates Colocalized Fyn Activation and the Initiation of Proximal Signals through TCRalphabeta’. The Journal of Immunology 172.7, 4266–4274. Franckaert, Dean et al. (2015). ‘Promiscuous Foxp3-cre activity reveals a differential requirement for CD28 in Foxp3+ and Foxp3- T cells’. Immunol Cell Biol 93.4, 417–423. Fu, Guo et al. (2013). ‘Themis sets the signal threshold for positive and negative selection in T-cell development’. Nature 504.7480, 441–445. Fu, Guo et al. (2014). ‘Fine-tuning T cell receptor signaling to control T cell development’. Trends in Immunology 35.7, 311–318. Germain, Pierre et al. (2006). ‘Overview of Nomenclature of Nuclear Receptors’. Pharmacol Rev 58.4, 685–704. Haan, Joke M. M. den, Ramon Arens, and Menno C. van Zelm (2014). ‘The activation of the adaptive immune system: Cross-talk between antigen-presenting cells, T cells and B cells’. Immunology Letters. Dutch Society for Immunology 1964 – 2014Special issue on the occasion of the 50th anniversary of the Dutch Society for Immunology (NVVI) 162.2, 103–112. Hahne, Florian et al. (2009). ‘flowCore: a Bioconductor package for high throughput flow cytometry’. BMC Bioinformatics 10, 106. Hanna, Richard N. et al. (2011). ‘The transcription factor NR4A1 (Nur77) controls bone marrow differentiation and the survival of Ly6C- monocytes’. Nat Immunol 12.8, 778–785. Haruyama, Naoto, Andrew Cho, and Ashok B Kulkarni (2009). ‘Overview: Engineering transgenic constructs and mice’. Curr Protoc Cell Biol chapter: Unit-19.10. Hoffman, E. S. et al. (1996). ‘Productive T-cell receptor beta-chain gene rearrangement: coincident regulation of cell cycle and clonality during development in vivo.’ Genes Dev. 10.8, 948–962. BIBLIOGRAPHY 183

Hogquist, Kristin A. and Stephen C. Jameson (2014). ‘The self-obsession of T cells: how TCR signaling thresholds affect fate ’decisions’ and effector function’. Nat Immunol 15.9, 815–823. Hogquist, Kristin A. et al. (2015). ‘T Cell Adolescence: Maturation Events Beyond Positive Selection’. J. Immunol. 195.4, 1351–1357. Hojsgaard, Soren and Ulrich Halekoh (2016). doBy: Groupwise Statistics, LSmeans, Linear Contrasts, Utilities. R package version 4.5-15. Holler, Phillip D. et al. (2007). ‘The same genomic region conditions clonal deletion and clonal deviation to the CD8alphaalpha and regulatory T cell lineages in NOD versus C57BL/6 mice’. Proc Natl Acad Sci U S A 104.17, 7187–7192. Houston, Evan G., Robert Nechanitzky, and Pamela J. Fink (2008). ‘Contact with secondary lymphoid organs drives post-thymic T cell maturation’. J Immunol 181.8, 5213–5217. Hsieh, Chyi-Song, Hyang-Mi Lee, and Chan-Wang J. Lio (2012). ‘Selection of regulatory T cells in the thymus’. Nat Rev Immunol 12.3, 157–167. Huang, Weishan and Avery August (2015). ‘The signaling symphony: T cell receptor tunes cytokine- mediated T cell differentiation’. J Leukoc Biol 97.3, 477–485. Huber, Wolfgang et al. (2015). ‘Orchestrating high-throughput genomic analysis with Bioconductor’. Nat Meth 12.2, 115–121. Hunter, Christopher A. and Simon A. Jones (2015). ‘IL-6 as a keystone cytokine in health and disease’. Nature Immunology 16.5, 448. Jenkinson, William E. et al. (2015). ‘Natural Th17 cells are critically regulated by functional medullary thymic microenvironments’. Journal of Autoimmunity 63, 13–22. Ji, Yanhong et al. (2010). ‘The In Vivo Pattern of Binding of RAG1 and RAG2 to Antigen Receptor Loci’. Cell 141.3, 419–431. Jiang, Mike (2015). ggcyto: Visualize Cytometry data with ggplot. R package version 1.0.6. Jordan, Martha S. et al. (2001). ‘Thymic selection of CD4+CD25+ regulatory T cells induced by an agonist self-peptide’. Nat Immunol 2.4, 301–306. Josefowicz, Steven Z., Li-Fan Lu, and Alexander Y. Rudensky (2012). ‘Regulatory T Cells: Mechanisms of Differentiation and Function’. Annual Review of Immunology 30.1, 531–564. Kalinski,´ Pawel et al. (1999). ‘T-cell priming by type-1and type-2 polarized dendritic cells: the concept of a third signal’. Immunology Today 20.12, 561–567. Kerr, J. F. R., A. H. Wyllie, and A. R. Currie (1972). ‘Apoptosis: A Basic Biological Phenomenon with Wideranging Implications in Tissue Kinetics’. Br J Cancer 26.4, 239–257. Kim, D. G. et al. (1992). ‘Construction of a bifunctional mRNA in the mouse by using the internal ribosomal entry site of the encephalomyocarditis virus.’ Mol. Cell. Biol. 12.8, 3636–3643. Kistner, A et al. (1996). ‘Doxycycline-mediated quantitative and tissue-speciﬁc control of gene expression in transgenic mice.’ Proc Natl Acad Sci U S A 93.20, 10933–10938. Kitamura, Toshio et al. (2003). ‘Retrovirus-mediated gene transfer and expression cloning: powerful tools in functional genomics’. Experimental Hematology 31.11, 1007–1014. BIBLIOGRAPHY 184

Klein, Ludger et al. (2009). ‘Antigen presentation in the thymus for positive selection and central tolerance induction’. Nat Rev Immunol 9.12, 833–844. Klein, Ludger et al. (2014). ‘Positive and negative selection of the T cell repertoire: what thymocytes see (and don’t see)’. Nat Rev Immunol 14.6, 377–391. Kurakula, Kondababu et al. (2014). ‘NR4A nuclear receptors are orphans but not lonesome’. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1843.11, 2543–2555. Kurd, Nadia and Ellen A. Robey (2016). ‘T-cell selection in the thymus: a spatial and temporal perspective’. Immunol Rev 271.1, 114–126. Lederman, S. et al. (1992). ‘Identification of a novel surface protein on activated CD4+ T cells that induces contact-dependent B cell differentiation (help).’ Journal of Experimental Medicine 175.4, 1091–1101. Lee, S. L. et al. (1995). ‘Unimpaired thymic and peripheral T cell death in mice lacking the nuclear receptor NGFI-B (Nur77)’. Science 269.5223, 532–535. Legname, Giuseppe et al. (2000). ‘Inducible Expression of a p56Lck Transgene Reveals a Central Role for Lck in the Differentiation of CD4 SP Thymocytes’. Immunity 12.5, 537–546. Leonard, Warren J. (2001). ‘Cytokines and immunodeficiency diseases’. Nat Rev Immunol 1.3, 200–208. Li, Qi-Jing et al. (2007). ‘miR-181a Is an Intrinsic Modulator of T Cell Sensitivity and Selection’. Cell 129.1, 147–161. Liao, Wei, Jian-Xin Lin, and Warren J. Leonard (2013). ‘Interleukin-2 at the Crossroads of Effector Responses, Tolerance, and Immunotherapy’. Immunity 38.1, 13–25. Lio, Chan-Wang Joaquim and Chyi-Song Hsieh (2008). ‘A Two-Step Process for Thymic Regulatory T Cell Development’. Immunity 28.1, 100–111. Liu, Pentao, Nancy A. Jenkins, and Neal G. Copeland (2003). ‘A Highly Efficient Recombineering-Based Method for Generating Conditional Knockout Mutations’. Genome Res 13.3, 476–484. Liu, Zheng-Gang et al. (1994). ‘Apoptotic signals delivered through the T-cell receptor of a T-cell hybrid require the immediate–early gene nur77’. Nature 367.6460, 281–284. Mahmud, Shawn A. et al. (2014). ‘Costimulation via the tumor-necrosis factor receptor superfamily couples TCR signal strength to the thymic differentiation of regulatory T cells’. Nat Immunol 15.5, 473–481. Malhotra, Deepali et al. (2016). ‘Tolerance is established in polyclonal CD4+ T cells by distinct mechanisms, according to self-peptide expression patterns’. Nat Immunol 17.2, 187–195. Marshall, Daniel et al. (2014). ‘Differential requirement for IL-2 and IL-15 during bifurcated development of thymic regulatory T cells’. J Immunol 193.11, 5525–5533. Martínez-Llordella, Marc et al. (2013). ‘CD28-inducible transcription factor DEC1 is required for efficient autoreactive CD4+ T cell response’. J Exp Med 210.8, 1603–1619. Martorell, Lluis et al. (2008). ‘Vascular effects of thrombin: involvement of NOR-1 in thrombin-induced mitogenic stimulus in vascular cells’. Front. Biosci. 13, 2909–2915. Mathis, Diane and Christophe Benoist (2009). ‘Aire’. Annu. Rev. Immunol. 27, 287–312. BIBLIOGRAPHY 185

Matz, Mikhail V. et al. (1999). ‘Fluorescent proteins from nonbioluminescent Anthozoa species’. Nat Biotech 17.10, 969–973. McCaughtry, Tom M., Matthew S. Wilken, and Kristin A. Hogquist (2007). ‘Thymic emigration revisited’. J Exp Med 204.11, 2513–2520. McIntyre, Catherine et al. (2010). ‘Advantages of the 561-nm (Yellow-Green) Laser on the BD FACSAria III’. biocompare,(31 May 2010). Meyer, David et al. (2017). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. Mier-Aguilar, Carlos A. (2016). ‘CD5-CK2 Signaling Modulates Erk Activation and Thymocyte Survival’. PLoS One. 2016; 11(12): e0168155. Moody, D. Branch et al. (2015). ‘Lipid and small-molecule display by CD1 and MR1’. Nature Reviews Immunology 15.10, 643. Moran, Amy E. and Kristin A. Hogquist (2012). ‘T-cell receptor affinity in thymic development’. Immunology 135.4, 261–267. Moran, Amy E. et al. (2011). ‘T cell receptor signal strength in Treg and iNKT cell development demon- strated by a novel fluorescent reporter mouse’. J Exp Med 208.6, 1279–1289. Mueller, Daniel L. (2010). ‘Mechanisms maintaining peripheral tolerance’. Nat Immunol 11.1, 21–27. Nagaoka, Masanori et al. (2017). ‘The Orphan Nuclear Receptor NR4A3 Is Involved in the Function of Dendritic Cells’. The Journal of Immunology, ji1601911. Nemazee, David (2006). ‘Receptor editing in lymphocyte development and central tolerance’. Nat Rev Immunol 6.10, 728–740. Nikolich-Žugich, Janko, Mark K. Slifka, and Ilhem Messaoudi (2004). ‘The many important facets of T-cell repertoire diversity’. Nat Rev Immunol 4.2, 123–132. Nishimoto, Norihiro et al. (2008). ‘Mechanisms and pathologic significances in increase in serum interleukin-6 (IL-6) and soluble IL-6 receptor after administration of an anti–IL-6 receptor antibody, tocilizumab, in patients with rheumatoid arthritis and Castleman disease’. Blood 112.10, 3959–3964. O’Hagan, Kyle Leonard et al. (2015). ‘Pak2 Links TCR Signaling Strength to the Development of Regulatory T Cells and Maintains Peripheral Tolerance’. The Journal of Immunology 195.4, 1564– 1577. Ohkura, N. et al. (1994). ‘Molecular Cloning of a Novel Thyroid/Steroid Receptor Superfamily Gene from Cultured Rat Neuronal Cells’. Biochemical and Biophysical Research Communications 205.3, 1959–1965. Ono, Masahiro and Reiko J. Tanaka (2015). ‘Controversies concerning thymus-derived regulatory T cells: fundamental issues and a new perspective’. Immunol Cell Biol. Ono, Masahiro, Reiko J. Tanaka, and Manabu Kano (2014). ‘Visualisation of the T cell differentiation programme by Canonical Correspondence Analysis of transcriptomes’. BMC Genomics 15.1, 1028. Ordoñez-Rueda, Diana et al. (2009). ‘Increased numbers of thymic and peripheral CD4+ CD25+Foxp3+ cells in the absence of CD5 signaling’. Eur. J. Immunol. 39.8, 2233–2247. BIBLIOGRAPHY 186

Ouyang, Weiming et al. (2010). ‘Transforming Growth Factor-beta Signaling Curbs Thymic Negative Selection Promoting Regulatory T Cell Development’. Immunity 32.5, 642–653. Padovan, E. et al. (1993). ‘Expression of two T cell receptor alpha chains: dual receptor T cells’. Science 262.5132, 422–424. Pang, Siew Siew et al. (2010). ‘The structural basis for autonomous dimerization of the pre-T-cell antigen receptor’. Nature 467.7317, 844–848. Park, Kiwon et al. (2016). ‘The transcription factor NR4A3 controls CD103+ dendritic cell migration’. J Clin Invest 126.12, 4603–4615. Parks, David R., Mario Roederer, and Wayne A. Moore (2006). ‘A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data’. Cytometry 69A.6, 541–551. Paster, Wolfgang et al. (2015). ‘A THEMIS:SHP1 complex promotes T-cell survival’. The EMBO Journal 34.3, 393–409. Penit, C. and F. Vasseur (1989). ‘Cell proliferation and differentiation in the fetal and early postnatal mouse thymus.’ J Immunol 142.10, 3369–3377. Peters, Gjalt-Jorn Ygram (2016). userfriendlyscience: Quantitative analysis made accessible. Pohlert, Thorsten (2014). The Pairwise Multiple Comparison of Mean Ranks Package (PMCMR). Prince, Lynne R. et al. (2017). ‘NR4A orphan nuclear receptor family members, NR4A2 and NR4A3, regulate neutrophil number and survival’. Blood, blood–2017–03–770164. Qian, Yu et al. (2012). ‘FCSTrans: An open source software system for FCS file conversion and data transformation’. Cytometry 81A.5, 353–356. R Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Roche, Paul A. and Kazuyuki Furuta (2015). ‘The ins and outs of MHC class II-mediated antigen processing and presentation’. Nat Rev Immunol 15.4, 203–216. RStudio Team (2015). RStudio: Integrated Development Environment for R. Boston, MA: RStudio, Inc. Rubtsov, Yuri P. and Alexander Y. Rudensky (2007). ‘TGFbeta signalling in control of T-cell-mediated self-reactivity’. Nat Rev Immunol 7.6, 443–453. Sacchetti, Andrea et al. (2001). ‘Efficient GFP mutations profoundly affect mRNA transcription and translation rates’. FEBS Letters 492.1, 151–155. Saeys, Yvan, Sofie Van Gassen, and Bart N. Lambrecht (2016). ‘Computational flow cytometry: helping to make sense of high-dimensional immunology data’. Nat Rev Immunol 16.7, 449–462. Saini, Manoj et al. (2010). ‘Regulation of Zap70 Expression During Thymocyte Development Enables Temporal Separation of CD4 and CD8 Repertoire Selection at Different Signaling Thresholds’. Sci. Signal. 3.114, ra23–ra23. Sakaguchi, S. et al. (1985). ‘Organ-specific autoimmune diseases induced in mice by elimination of T cell subset. I. Evidence for the active participation of T cells in natural self-tolerance; deficit of a T cell subset as a possible cause of autoimmune disease’. J. Exp. Med. 161.1, 72–87. BIBLIOGRAPHY 187

Sancho, David, Manuel Gómez, and Francisco Sánchez-Madrid (2005). ‘CD69 is an immunoregulatory molecule induced following activation’. Trends Immunol. 26.3, 136–140. Schroder, Kate et al. (2004). ‘Interferon-gamma: an overview of signals, mechanisms and functions’. J Leukoc Biol 75.2, 163–189. Sekine, Yusuke et al. (2011). ‘p38 MAPKs regulate the expression of genes in the dopamine synthesis pathway through phosphorylation of NR4A nuclear receptors’. J Cell Sci 124.17, 3006–3016. Sekiya, Takashi et al. (2013). ‘Nr4a receptors are essential for thymic regulatory T cell development and immune homeostasis’. Nat Immunol 14.3, 230–237. Sekiya, Takashi et al. (2015). ‘Suppression of Th2 and Tfh immune reactions by Nr4a receptors in mature T reg cells’. J Exp Med 212.10, 1623–1640. Shaner, Nathan C. et al. (2004). ‘Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein’. Nat Biotech 22.12, 1567–1572. Shevach, Ethan M and Angela M Thornton (2014). ‘tTregs, pTregs, and iTregs: Similarities and Differ- ences’. Immunol Rev 259.1, 88–102. Silva-Filho, J. L., C. Caruso-Neves, and A. a. S. Pinheiro (2014). ‘IL-4: an important cytokine in determining the fate of T cells’. Biophys Rev 6.1, 111–118. Simons, Donald M and Andrew J Caton (2011). ‘Flow cytometric profiling of mature and developing regulatory T cells in the thymus’. Methods Mol Biol 707, 55–69. Singer, Alfred, Stanley Adoro, and Jung-Hyun Park (2008). ‘Lineage fate and intense debate: myths, models and mechanisms of CD4- versus CD8-lineage choice’. Nat Rev Immunol 8.10, 788–801. Slowikowski, Kamil (2016). ggrepel: Repulsive Text and Label Geoms for ’ggplot2’. Smith-Garvin, Jennifer E., Gary A. Koretzky, and Martha S. Jordan (2009). ‘T Cell Activation’. Annu Rev Immunol 27, 591–619. Soetaert, Karline, Thomas Petzoldt, and R. Woodrow Setzer (2010). ‘Solving Differential Equations in R: Package deSolve’. Journal of Statistical Software 33.9, 1–25. Spidlen, Josef et al. (2010). ‘Data File Standard for Flow Cytometry, Version FCS 3.1’. Cytometry A 77.1, 97–100. Stefanová, Irena, Jeffrey R. Dorfman, and Ronald N. Germain (2002). ‘Self-recognition promotes the foreign antigen sensitivity of naive T lymphocytes’. Nature 420.6914, 429–434. Stepanenko, Olesya V. et al. (2011). ‘Modern fluorescent proteins: from chromophore formation to novel intracellular applications’. Biotechniques 51.5, 313–passim. Stritesky, Gretta L., Stephen C. Jameson, and Kristin A. Hogquist (2012). ‘Selection of self-reactive T cells in the thymus’. Annu Rev Immunol 30, 95–114. Su, Maureen A. and Mark S. Anderson (2016). ‘Aire expands: new roles in immune tolerance and beyond’. Nat Rev Immunol 16.4, 247–258. Subach, Fedor V et al. (2009). ‘Monomeric fluorescent timers that change color from blue to red report on cellular trafficking’. Nat Chem Biol 5.2, 118–126. BIBLIOGRAPHY 188

Tai, Xuguang et al. (2013). ‘Foxp3 transcription factor is pro-apoptotic and lethal to developing regulatory T cells unless counterbalanced by cytokine survival signals’. Immunity 38.6, 1116–1128. Tanaka, Toshio, Masashi Narazaki, and Tadamitsu Kishimoto (2014). ‘IL-6 in Inflammation, Immunity, and Disease’. Cold Spring Harb Perspect Biol 6.10, a016295. Terskikh, Alexey et al. (2000). ‘"Fluorescent Timer": Protein That Changes Color with Time’. Science 290.5496, 1585–1588. Thompson, Jennifer and Astar Winoto (2008). ‘During negative selection, Nur77 family proteins translocate to mitochondria where they associate with Bcl-2 and expose its proapoptotic BH3 domain’. J Exp Med 205.5, 1029–1036. Uematsu, Yasushi et al. (1988). ‘In transgenic mice the introduced functional T cell receptor beta gene prevents expression of endogenous beta genes’. Cell 52.6, 831–841. Valjent, Emmanuel et al. (2009). ‘Looking BAC at striatal signaling: cell-specific analysis in new transgenic mice’. Trends in Neurosciences 32.10, 538–547. Vermijlen, David and Immo Prinz (2014). ‘Ontogeny of Innate T Lymphocytes – Some Innate Lymphocytes are More Innate than Others’. Front Immunol 5. Wang, Fanping, Ching-Yu Huang, and Osami Kanagawa (1998). ‘Rapid deletion of rearranged T cell antigen receptor (TCR) Valpha-Jalpha segment by secondary rearrangement in the thymus: Role of continuous rearrangement of TCR alpha chain gene and positive selection in the T cell repertoire formation’. PNAS 95.20, 11834–11839. Wang, Gaohang et al. (2016). ‘STAT3 selectively interacts with Smad3 to antagonize TGF-beta signaling’. Oncogene 35.33, 4388–4398. Wang, Haiguang and Kristin A. Hogquist (2016). ‘How MAIT cells get their start’. Nature Immunology 17.11, 1238. Wang, Ying et al. (2008). ‘Th2 Lymphoproliferative Disorder of LatY136F Mutant Mice Unfolds Indepen- dently of TCR-MHC Engagement and Is Insensitive to the Action of Foxp3+ Regulatory T Cells’. J Immunol 180.3, 1565–1575. Wang, Zhulun et al. (2003). ‘Structure and function of Nurr1 identifies a class of ligand-independent nuclear receptors’. Nature 423.6939, 555–560. Warming, Søren et al. (2005). ‘Simple and highly efficient BAC recombineering using galK selection’. Nucleic Acids Res 33.4, e36. Weissler, Katherine A. and Andrew J. Caton (2014). ‘The role of T-cell receptor recognition of peptide:MHC complexes in the formation and activity of Foxp3+ regulatory T cells’. Immunol Rev 259.1, 11–22. Wickham, Hadley (2009). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. Wickham, Hadley (2011). ‘The Split-Apply-Combine Strategy for Data Analysis’. Journal of Statistical Software 40.1, 1–29. Willerford, Dennis M. et al. (1995). ‘Interleukin-2 receptor alpha chain regulates the size and content of the peripheral lymphoid compartment’. Immunity 3.4, 521–530. BIBLIOGRAPHY 189

Wirnsberger, Gerald, Maria Hinterberger, and Ludger Klein (2011). ‘Regulatory T-cell differentiation versus clonal deletion of autoreactive thymocytes’. Immunol Cell Biol 89.1, 45–53. Woronicz, John D. et al. (1994). ‘Requirement for the orphan steroid receptor Nur77 in apoptosis of T-cell hybridomas’. Nature 367.6460, 277–281. Xiao, Shi Yun, Yan Li, and Wei Feng Chen (2003). ‘Kinetics of thymocyte developmental process in fetal and neonatal mice’. Cell Res 13.4, 265–273. Xing, Yan and Kristin A. Hogquist (2012). ‘T-Cell Tolerance: Central and Peripheral’. Cold Spring Harb Perspect Biol 4.6, a006957. Yang, Siyoung et al. (2015). ‘Regulatory T cells generated early in life play a distinct role in maintaining self-tolerance’. Science 348.6234, 589–594. Yeh, Chung-Min et al. (2016). ‘Epigenetic silencing of the NR4A3 tumor suppressor, by aberrant JAK/STAT signaling, predicts prognosis in gastric cancer’. Scientiﬁc Reports 6, 31690. Au-Yeung, Byron B. et al. (2014). ‘Quantitative and temporal requirements revealed for Zap70 catalytic activity during T cell development’. Nat Immunol 15.7, 687–694. Ying, Haiyan et al. (2010). ‘Cutting Edge: CTLA-4–B7 Interaction Suppresses Th17 Cell Differentiation’. J Immunol 185.3, 1375–1378. Youle, Richard J. and Andreas Strasser (2008). ‘The BCL-2 protein family: opposing activities that mediate cell death’. Nat Rev Mol Cell Biol 9.1, 47–59. Zaidi, M. Raza and Glenn Merlino (2011). ‘The Two Faces of Interferon-gamma in cancer’. Clin Cancer Res 17.19, 6118–6124. Zarin, Payam et al. (2014). ‘Enforcement of gammadelta-lineage commitment by the pre–T-cell receptor in precursors with weak gammadelta-TCR signals’. PNAS 111.15, 5658–5663. Zheng, Lixin, Jian Li, and Michael Lenardo (2017). ‘Restimulation-induced cell death: new medical and research perspectives’. Immunol Rev 277.1, 44–60. Zhu, Jinfang and William E. Paul (2010). ‘Peripheral CD4+ T-cell differentiation regulated by networks of cytokines and transcription factors’. Immunological Reviews 238.1, 247–262. Zhumabekov, Talgat et al. (1995). ‘Improved version of a human CD2 minigene based vector for T cell-speciﬁc expression in transgenic mice’. Journal of Immunological Methods 185.1, 133–140. Zikherman, Julie, Ramya Parameswaran, and Arthur Weiss (2012). ‘Endogenous antigen tunes the responsiveness of naive B cells but not T cells’. Nature 489.7414, 160–164. Appendices

A Plasmids

We used pMCs vector developed by Kitamura et al. (2003), which contains modiﬁed 3’ and 5’ LTRs (long terminal repeat. This vector is highly efﬁcient in transient transfection and produces high amounts of proteins. We used for GFP the pMCs-USER-IRES-GFP and for mCherry we used the pMCs-IRES-mCherry.

For expression of CFP, we used the mammalian vector pCAG-CFP.

190 BIBLIOGRAPHY 191

B Gating strategy R

CD4SP gating and exclussion of CD69-TCRβ-

(A) All thymocytes

(B) CD4SP

FIGURE 10: Gating CD4SP and exclussion of CD69-TCRβ-.A, CD4 CD8 scatterplot for all thymocytes showing the gating of CD4SP cells (CD4>275 and CD8>4000). B, exclussion of CD69-TGFβ- in CD4SP cells. Cells with TCRβ lower than 300 and CD69 lower than 400 (grey rectangle) were excluded from the analysis of CD4 SP cells. BIBLIOGRAPHY 192

CD4 and CD8 expression in CD4SP (excluding CD69-TCRβ-) for each CD25 and Foxp3GFP subpopulation

(A) Thymus CD25-Foxp3GFP-

(B) Thymus CD25-Foxp3GFP+

(D) Thymus CD25+Foxp3GFP+

FIGURE 11: Gating strategy of CD4SP thymocytes, depicting CD25 and Foxp3 expressions for all thymocytes. (A), CD25-Foxp3GFP- (grey;(B), CD25-Foxp3GFP+ (orange); (C), CD25-Foxp3GFP+ (purple); (D) CD25+Foxp3GFP+ (green). Plots present all data from day 1 to day 13 after birth. BIBLIOGRAPHY 193

CD4SP Timer negative threshold

(A) Day 1 (B) Day 2 (C) Day 7 (D) Day 9 (E) Day 13

FIGURE 12: Timer positive threshold on CD69-TCRβ- thymocytes at intensity higher than 6 (grey, Timer negative cells; black, Timer positive cells). Representative scatterplot of one mouse from each time point (day 1, 2, 7, 9, 13). Cells with TCRβ lower than 300 and CD69 lower than 400 shown because they were used as the reference population for the normalization of Timer data. More than 99.9% of these cells have Timer intensity under 6.