Defining cellular and molecular determinants of CD4+ T helper cell

differentiation during blood-stage Plasmodium infection

Kylie Renee James

Bachelor of Science (Hons)

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2016

School of Medicine

i

Abstract

Malaria is a disease resulting from infection with protozoan parasites of the genus,

Plasmodium. Malaria remains a leading cause of global deaths, particularly of children living in

sub-Saharan Africa. Despite significant research efforts, an effective strategy for inducing robust

Plasmodium-specific immunity remains elusive. CD4+ T helper (Th) cells are critical components

of the adaptive immune response to blood-stage Plasmodium parasites. During experimental

malaria in mice two major Th cell types arise, T helper 1 (Th1) and T follicular helper (Tfh) cells,

both of which are associated with improved infection outcomes. Immune mechanisms controlling

the emergence of Th1 and Tfh cells during Plasmodium infection remain poorly characterised, due

in part to the absence of appropriate in vivo reagents.

To elucidate mechanisms involved in the emergence of parasite-specific Th cells, recently

developed Plasmodium-specific CD4+ T cell receptor (TCR) transgenic T cells (termed PbTII cells)

were assessed for their in vivo response in mouse models of malaria. Adoptively-transferred PbTII

cells proliferated during infection in a manner dependent on MHCII-expressing cells and

conventional dendritic cells. By the peak of infection, PbTII cells had differentiated into Th1 and

Tfh cells, mirroring the endogenous polyclonal CD4+ T cell response, and emphasising the utility

of this reagent for further studies of in vivo Th responses.

Next, a relatively new technology, single-cell RNA sequencing (scRNAseq), was applied to

PbTII cells to explore at a molecular level the process of Th differentiation in vivo. After initial

priming events, emerging Th cells entered a state of high cell cycling, in which they expressed

genes associated with both Th1 and Tfh subsets. This occurred prior to their commitment to a

differentiated, and possibly terminal, Th1 or Tfh effector state. In this pre-Th state, cells remained

'impressionable' to external signals. Using scRNAseq, inflammatory monocytes were identified as

likely providing one such signal. Furthermore, in vivo depletion of monocytes after a period of

initial priming resulted in a reduction in Th1 but not Tfh cell responses.

Although numerous innate immune sensors have been implicated in the detection of blood-

stage Plasmodium infection, whether such molecules influence Th responses remains unclear.

Therefore, PbTII cells were employed to determine T-cell extrinsic signals that might drive Th

responses, and modest roles for individual innate immune sensing molecules were observed.

However, interferon regulatory factor 3 (IRF3), which sits downstream of several innate sensors,

played a strong role in the clonal expansion of PbTII cells during infection. Importantly, using Irf3-/-

mice, IRF3 was shown to support T cell clonal expansion and promote Th1 differentiation. IRF3

was also required for inflammatory monocytes to express MHCII, suggesting a possible

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contributory mechanism. Interestingly, IRF3 exerted a suppressive effect on humoral responses, by

limiting Tfh cell and germinal centre (GC) B cell responses. Notably, IRF3-deficiency improved

GC B cell responses, promoted stronger and longer-lasting Plasmodium-specific serum antibody

levels, and accelerated the resolution of infection. Finally, in mixed bone marrow chimeric mice,

IRF3-deficent B cells responded more effectively than WT counterparts, suggesting a B-cell

intrinsic suppressive role for IRF3.

In summary, PbTII cells have been demonstrated to be an effective tool for studying

Th1/Tfh differentiation in vivo. During experimental blood-stage malaria PbTII cells do not commit

to an effector state until several days after initial priming. Prior to this point, pre-Th cells remain

receptive to external inputs, such as those supplied by myeloid cells including inflammatory

monocytes. Finally, an innate immune transcription factor, IRF3, exhibits roles in sustaining Th1

differentiation, and suppressing Tfh cell and B cell responses. Therefore, IRF3 represents a

potential target for improving immunological protection against blood-stage Plasmodium parasites.

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Declaration by author

This thesis is composed of my original work, and contains no material previously published

or written by another person except where due reference has been made in the text. I have clearly

stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical

assistance, survey design, data analysis, significant technical procedures, professional editorial

advice, and any other original research work used or reported in my thesis. The content of my thesis

is the result of work I have carried out since the commencement of my research higher degree

candidature and does not include a substantial part of work that has been submitted to qualify for

the award of any other degree or diploma in any university or other tertiary institution. I have

clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University

Library and, subject to the policy and procedures of The University of Queensland, the thesis be

made available for research and study in accordance with the Copyright Act 1968 unless a period of

embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the

copyright holder(s) of that material. Where appropriate I have obtained copyright permission from

the copyright holder to reproduce material in this thesis.

iv

Publications during candidature

Peer-reviewed papers

Lonnberg T, Svensson V, James KR, Fernandez-Ruiz D, Sebina I, Montandon R, Soon

MSF, Fogg LG, Nair AS, Liligeto U, Stubbington MJT, Ly LH, Bagger O, Zwiesssele M,

Lawrence ND, Souza-Fonseca-Guimaraes F, Bunn PT, Engerwda CR, Heath WR, Billker O,

Stegle O, Haque A, Teichmann SA, Single-cell RNA-seq and computational analysis using

temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria.Sci Immunol,

(2017)

Sebina I, James KR, Soon SF, Fogg LG, Best SE, de Labastida Rivera F, Montes de Oca

M, Amante FH, Thomas BS, Beattie L, Souza-Fonseca-Guimaraes F, Smyth MJ, Hertzog

PJ, Hill GR, Hutloff A, Engwerda CR, Haque A, IFNAR1-signalling obstructs ICOS-

mediated humoral immunity during non-lethal blood-stage Plasmodium infection, PLoS

Pathog, (2016) 12 1

Proserpio V, Piccolo A, Haim-Vilmovsky L, Kar G, Lonnberg T, Svensson V, Pramanik J,

Natarajan KN, Zhai W, Zhang X, Donati G, Kayikci M, Kotar J, McKenzie AN, Montandon

R, James KR, Fernandez-Ruiz D, Heath WR, Haque A, Billker O, Woodhouse S, Cicuta P,

Nicodemi M, Teichmann SA, Single cell analysis of CD4+ T cell differentiation reveals

three major cell states and progressive acceleration of proliferation. Genome Biol, (2016)

17 1 133

Khoury D, Cromer D, Best S, James KR, Sebina I, Haque A, Davenport M, Reduced

erythrocyte susceptibility and increased host clearance of young parasites slows

Plasmodium growth in a murine model of severe malaria. Sci Rep (2015) 6 5 9412

Edwards C, Zhang V, Werder R, Best S, Sebina I, James KR, Faleiro R, de Labastida

Rivera F, Amante F, Engwerda C, Phipps S, Haque A, Coinfection with blood-stage

Plasmodium promotes systemic type I interferon production during pneumovirus infection

but impairs inflammation and viral control in the lung. Clin Vaccine Immunol (2015) 5 477-

83

Edwards C, Best S, Gun S, Claser C, James KR, de Oca M, Sebina I, de Labastida Rivera

F, Amante F, Hertzog P, Engwerda C, Renia L, Haque A, Spatiotemporal requirements of

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IRF7 in mediating type I IFN-dependent susceptibility to blood-stage Plasmodium infection,

Eur J Immunol (2015) 45 1 130-41

Haque A, Best S, Montes de Oca, M, James KR, Ammerdorffer A, Edwards C, Rivera F,

Amante F, Bunn P, Sheel M, Sebina I, Koyama M, Varelias A, Hertzog P, Kalinke U, Gun

S, Rénia L, Ruedl C, MacDonald K, Hill G, Engwerda C. Type I IFN blockade in CD8- DC

enhances Th1-immunity to malaria. J Clin Invest(2014) 124 6 2483-96

Khoury D, Cromer D, Best S, James KR, Kim P, Engwerda C, Haque A, Davenport M.

Effect of mature blood-stage Plasmodium parasite sequestration on pathogen biomass in

mathematical and in vivo models of malaria. Infection and Immunity. (2014) 82 1 212-20

Bunn P, Stanley A, Rivera F, Mulherin A, Sheel M, Alexander C, Faleiro R, Amante F,

Montes De Oca M, Best S, James KR, Kaye P, Haque A, Engwerda C. Tissue requirements

for establishing long-term CD4+ T cell-mediated immunity following Leishmania donovani

infection. J Immunol. (2014) 192 8 3709-18

Conference abstracts

James KR, Soon SF, Sebina I, Fernandez-Ruiz D, Heath WR, Haque A, IRF3 priorities

cellular immunity over humoral responses during blood-stage malaria, 2015, Australasian

Society for Immunology Annual Meeting, Canberra, Australia (oral presentation).

James KR, Lonnberg T, Fernandez-Ruiz D, Heath WR, Billker O, Teichmann SA, Haque

A, Using single-cell RNAseq to investigate CD4+ T cell differentiation during Plasmodium

infection, 2015, Immunology@Brisbane Conference, Brisbane, Australia (oral presentation).

James KR, Lonnberg T, Fernandez-Ruiz D, Heath WR, Billker O, Teichmann SA, Haque

A, Using Single-cell RNAseq to visualise CD4+ helper cell differentiation in vivo, 2014,

Brisbane Immunology Group Annual Conference, Gold Coast, Australia (poster

presentation).

James KR, Fernandez-Ruiz D, Heath WR, Haque A, Fate “decisions” in antigen-specific

CD4+ T helper cells during experimental blood-stage malaria, 2014, Gordon Research

Conference: 'Immunochemistry and Immunobiology', Newry, Maine, USA (poster

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presentation).

James KJ, Best SE, Montes De Oca M, Engwerda CR, Heath WR, Haque A, Role of IRF3

in controlling CD4+ T cell activation and effector function during Plasmodium

infection,2013, Brisbane Immunology Group Annual Conference, Kingscliff, Australia

(poster presentation).

Publications included in this thesis

No publications included

vii

Contributions by others to the thesis

Dr Ashraful Haque: Conceived the concept of projects, provided input into the design of

experiments, assisted with interpreting the results and edited this thesis.

Dr Tapio Lonnberg; Conceived the concept of scRNAseq analysis of PbTII cells, contributed to

non-routine technical work; analysed scRNAseq of PbTII cells and assisted with interpretation.

Mr Valentine Svensson; Developed the mathematical algorithm for modelling PbTII differentiation

in Chapter 4, contributed to analysis of scRNAseq of PbTII cells and interpretation of the results.

Miss Megan SF Soon; Assisted with routine experiments included in Chapter 5, analysis of results

and generation of ideas.

Mr Ismail Sebina; Assisted with generating ideas and performing the immunofluorescent

microscopy mentioned in section 4.3.8.

Miss Susanna Ng; Created summary graphics included as Figure 4.7 and Figure 4.19

Statement of parts of the thesis submitted to qualify for the award of another

degree

None

viii

Acknowledgements

This thesis would not have been possible without the contributions and support of many

people. Firstly, I would like to express gratitude to my supervisor Dr Ashraful Haque. Thank you

for your patience in mentoring me throughout the years, and for genuinely caring about me, as you

do with all of your students. I would especially like to thank you for pushing me to work hard and

encouraging me to take every opportunity to travel and network, this has really opened my eyes to

the field of science and opened doors for my future career. I would also like to thank my co-

supervisor, Professor Christian Engwerda, for his kind advice and guidance.

To Ismail Sebina and Megan Soon, I could not have hoped to work with cooler people.

Experimental time points were even enjoyable when we were working side-by-side. You are both

incredibly talented and special people. I look forward to catching up with you both in Uganda. To

Arabella Young, Steve Blake, Therese Vu and Teija Frame, thank you for providing me with some

semblance of a social life at QIMRB. When I look back on my candidature in years to come, I will

remember more than just FACS plots and mice; I will remember laughter and shenanigans shared

with you guys. My thanks are also extended to all past and present members of the Malaria

Immunology and Immunology and Infection Laboratories for their support and assistance.

I would like to acknowledge the financial support I received from the Australian

Government in the form of my Australian Postgraduate Award, the Higher Degrees Committee of

QIMRB for my Top-Up Award, and OzEMalaR, Australasian Society for Immunology, QIMRB

and EMBL Australia for travel awards and grants. Thank you to the QIMRB Core Facilities, my

research would not have been possible without you.

On a particularly personal note, I would like to thank and acknowledge my family. Thanks

to my sisters, Megan and Leisa, and more recently my brothers James and Ian, for always having

my back and for providing sibling rivalry; it has been instrumental in motivating me to succeed.

Special thanks to Megan for the many hours of Skype conversation and letters of encouragement

under my car windscreen; I can't tell you what a difference this made during my candidature.

Finally, I would like to thank my parents, Leone and Darryl, for teaching me the value of hard work

and perserverance, for ensuring that I was watered and fed (particularly while writing this thesis),

and most especially for your unwavering love.

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Keywords

t helper cell, malaria, plasmodium, interferon regulatory factor 3, irf3, single-cell rna sequencing,

scrnaseq, germinal centre b cell, innate immunity, monocyte

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 110704 Cellular Immunology, 60%

ANZSRC code: 110803 Medical Parasitology, 20%

ANZSRC code: 060102, Bioinformatics, 20%

Fields of Research (FoR) Classification

FoR code: 1107, Immunology, 70%

FoR code: 0699, Other Biological Sciences, 30%

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Table of contents

Chapter One:Literature review ........................................................................................................ 1

1.2 Malaria ...................................................................................................................................... 2

1.2.1 Progress towards a successful malaria vaccine ................................................................... 4

1.3 Immunity to malaria ................................................................................................................ 5

1.3.1 Experimental models of malaria ......................................................................................... 5

1.3.2 Requirement for antibody-mediated protection .................................................................. 6

1.3.3 Requirement for CD4+ T cell responses ............................................................................. 7

1.3.4 Mechanisms of immune evasion ......................................................................................... 8

1.4 T cell-mediated immunity ....................................................................................................... 9

1.4.1 Development of antigen-specific T cells within the thymus ............................................... 9

1.4.2 Studying antigen specific T cell responses ....................................................................... 10

1.4.3 Antigen presentation to CD4+ T cells during malaria ....................................................... 12

1.4.4 Differentiation of CD4+ T cells ......................................................................................... 13

1.4.5 T helper responses provide protection during Plasmodium infection ............................... 17

1.4.6 CD4+ T cell memory to Plasmodium infection ................................................................. 19

1.4.7 CD8+ T cell during Plasmodium infection ........................................................................ 20

1.5.1 Antibodies ......................................................................................................................... 21

1.5.2 Micro-architecture of the spleen ....................................................................................... 21

1.5.3 Germinal centre reactions ................................................................................................. 22

1.5.4 B cell memory ................................................................................................................... 24

1.6 Innate immunity ..................................................................................................................... 25

1.6.1 Innate immune detection of Plasmodium infection .......................................................... 25

1.6.2 Interferon regulatory factors ............................................................................................. 25

1.6.3 Interferon Regulatory Factor 3 .......................................................................................... 26

1.7 Thesis aims .............................................................................................................................. 28

1.7.1 Thesis outline .................................................................................................................... 28

Chapter Two:Materials and methods ............................................................................................ 30

2.1 Materials ................................................................................................................................. 30

2.1.1 Mice ...................................................................................................................................... 30

2.1.2 Parasites ............................................................................................................................... 33

2.1.3 Infections .............................................................................................................................. 33

2.1.4 Harvesting tissues ................................................................................................................ 34

2.1.5 Processing spleens ............................................................................................................... 34

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2.1.6 Cell transfer ......................................................................................................................... 35

2.1.7 In vivo cell depletions .......................................................................................................... 36

2.1.8 Flow cytometry reagents and materials ............................................................................ 36

2.1.9 Antibodies and dyes ............................................................................................................ 37

2.1.10 Plasmodium-specific ELISA ............................................................................................. 41

2.1.11 Single-cell RNA sequencing (scRNAseq) ........................................................................ 41

2.2 Methods ................................................................................................................................... 43

2.2.1 Mice and ethics .................................................................................................................... 43

2.2.2 Adoptive transfer of Plasmodium-specific CD4+ T (PbTII) cells .................................... 44

2.2.3 CellTrace violet labelling of cells ....................................................................................... 44

2.2.5 In vivo cell depletion ............................................................................................................ 45

2.2.5.1 CD4+ T cells ................................................................................................................... 45

2.2.5.2 CD11c+ dendritic cells ................................................................................................... 45

2.2.5.3 LysM+ monocytes/macrophages .................................................................................... 46

2.2.5.3 B cell depletion .............................................................................................................. 46

2.2.6 Euthanasia ........................................................................................................................... 46

2.2.7 Processing tissues ................................................................................................................ 46

2.2.7.1 Spleens ........................................................................................................................... 46

2.2.7.2 Blood .............................................................................................................................. 47

2.2.7.3 Liver ............................................................................................................................... 47

2.2.7.4 Bone marrow .................................................................................................................. 47

2.2.8 Bone marrow chimeric mice .............................................................................................. 48

2.2.9 Flow cytometry .................................................................................................................... 48

2.2.9.1 Live/Dead stain .............................................................................................................. 48

2.2.9.2 Surface stain ................................................................................................................... 48

2.2.9.3 Secondary antibody stain ............................................................................................... 48

2.2.9.4 Intracellular stain with eBioscience Foxp3 intracellular kit .......................................... 49

2.2.9.5 Detection of apoptosis .................................................................................................... 49

2.2.10 FACS acquisition............................................................................................................... 49

2.2.11 ELISA analysis of Plasmodium-specific immunoglobulins in serum ........................... 49

2.2.12 Single-cell mRNA sequencing .......................................................................................... 50

2.2.13 Processing scRNAseq data and quality control .............................................................. 51

2.2.14 Statistics ............................................................................................................................. 51

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Chapter Three:Assessing the utility of Plasmodium-specific TCR transgenic CD4+ T cells for

studying T helper responses during experimental blood-stage malaria ..................................... 52

3.1 Abstract ................................................................................................................................... 52

3.2 Introduction ............................................................................................................................ 53

3.3 Results ..................................................................................................................................... 55

3.3.1 Generation of Plasmodium-specific TCR transgenic CD4+ T (PbTII) cells ..................... 55

3.3.2 PbTII responses are specific for Plasmodium infection .................................................... 56

3.3.3 Th1 and Tfh are distinct effector subsets .......................................................................... 58

3.3.4 PbTII display the capacity to differentiate into T regulatory 1 cells ................................ 60

3.3.5 PbTII cells migrate to the liver and display a Th1 phenotype .......................................... 62

3.3.6 PbTII cells develop into memory cells after acute PcAS infection .................................. 63

3.3.7 PbTII cells show cross-species reactivity to Plasmodium infection ................................. 64

3.4 Discussion ................................................................................................................................ 66

Chapter Four:Single-cell RNA-sequencing resolves a CD4+ T helper cell fate bifurcation ..... 70

4.1 Abstract ................................................................................................................................... 70

4.2 Introduction ............................................................................................................................ 71

4.3 Results ..................................................................................................................................... 75

4.3.1 Unbiased separation of Th1 and Tfh effector cells ........................................................... 75

4.3.2 Deducing concurrent Th trajectories during a Mixture of Gaussian Processes model ..... 78

4.3.3 Mechanisms underlying the differentiation of Th1 and Tfh cells ..................................... 82

4.3.4 IFNγ and IL-10co-producing PbTII cells emerge from Th1 cells .................................... 84

4.3.5 PbTII cells are receptive to chemokine signals at bifurcation .......................................... 85

4.3.6 B cells support Tfh responses ........................................................................................... 88

4.3.7 ScRNAseq assessment of myeloid cells during PcAS infection ...................................... 89

4.3.8 Ly6Chi monocytes support Th1 fate, but not Tfh fate, commitment ................................. 99

Chapter Five:Interferon Regulatory Factor 3 balances Th1 and Tfh-dependent immunity

during blood-stage Plasmodium infection .................................................................................... 109

5.1 Abstract ................................................................................................................................. 109

5.2 Introduction .......................................................................................................................... 110

5.3 Results ................................................................................................................................... 112

5.3.1 Individual PRRs play only modest roles in controlling Plasmodium-specific CD4+ T cell

responses during blood-stage infection .................................................................................... 112

5.3.2 Haematopoietic cells employ IRF3 to drive Plasmodium-specific Th responses ........... 113

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5.3.3 IRF3-mediated Th1 support correlates with MHCII-expression by inflammatory

monocytes ................................................................................................................................ 117

5.3.4 Conventional dendritic cell activation during Plasmodium infection is independent of

IRF3 expression ....................................................................................................................... 119

5.3.5 IRF3 drives MHCII signalling by inflammatory monocytes to support accumulation of

PbTII cells ................................................................................................................................ 121

5.3.6 IRF3 suppresses humoral immunity during blood-stage Plasmodium infection ............ 123

5.3.7 IRF3 expression by B cells impairs development of humoral immune responses ......... 126

5.3.8 IRF3 contributes to disease severity during lethal experimental malaria ....................... 130

5.4 Discussion .............................................................................................................................. 131

Chapter Six:Final discussion......................................................................................................... 135

6.1 PbTII cells represent an effective tool for studying in vivo CD4+ T cell responses ........ 135

6.2 Modelling of scRNAseq analysis of PbTII cells reveals the emergence of Th fates in vivo

...................................................................................................................................................... 136

6.3 IRF3 balances Th1 and Tfh-mediated immune responses ............................................... 139

6.4 Concluding remarks ............................................................................................................ 140

Chapter Seven: ............................................................................................................................... 141

References ....................................................................................................................................... 141

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Table of figures

Figure 1.1 Life cycle of Plasmodium spp. ……………………………………………………….. 3

Figure 1.2 CD4+ T helper cell differentiation. ………………………………………………….... 16

Figure 1.3 Architecture of the spleen. ……………………………………………………………. 22

Figure 1.4 Germinal centre. ……………………………………………………………………… 23

Figure 1.5 Innate signalling pathways converging on Interferon regulatory factor 3 (IRF3). …… 27

Figure 3.1 Gating strategy for sorted Plasmodium-specific CD4+ TCR transgenic (PbTII) cells. . 55

Figure 3.2 PbTII cells proliferate in an MHCII-dependent manner during PcAS infection. …….. 57

Figure 3.3 Plasmodium-specific TCR transgenic CD4+ T (PbTII) cells bifurcate into Th1 and Tfh

cells during PcAS infection. ……………………………………………………………………… 59

Figure 3.4 Differentiation of PbTII cells during PcAS infection. ………………………………... 60

Figure 3.5 PbTII cells differentiate into regulatory cells during PcAS infection. ………………... 61

Figure 3.6 IFNγ+ PbTII cells migrate to the liver. ………………………………………………... 62

Figure 3.7 PbTII cells develop into memory cells after acute PcAS infection. ………………….. 63

Figure 3.8 PbTII cell expansion and Th1/Tfh differentiation during P. yoelii 17XNL and P. berghei

ANKA blood-stage infection. …………………………………………………………………….. 65

Figure 4.1 Sorting strategy for PbTII cells. ………………………………………………………. 76

Figure 4.2 Single-cell mRNA-sequencing of activated antigen-specific PbTII CD4+ T cells. …... 77

Figure 4.3 Computational delineation of Th1 and Tfh differentiation trajectories. ……………… 79

Figure 4.4 Bifurcation of T cell fates is accompanied by changes in transcription, proliferation and

metabolism. ……………………………………………………………………………………….. 81

Figure 4.5 Unique gene expression signatures corresponding with Th1 and Tfh differentiation. ... 83

Figure 4.6 IL-10/IFNγ co-producing PbTIIs begin to emerge from Th1 cells late in pseudotime. . 84

Figure 4.7 Chemokine signals underlying Th fate commitment. ………………………………… 86

Figure 4.8 Bifurcation of PbTII cells into Th1 and Tfh effector cells. …………………………... 87

Figure 4.9 B cells are essential for Tfh responses in PbTII cells during PcAS infection. ……….. 88

Figure 4.10 Sorting strategy for myeloid cells. …………………………………………………… 90

Figure 4.11 Principal Component Analysis of cDCs from naïve and infected mice. ……………. 91

Figure 4.12 Differential gene expression between single splenic CD8α+ and CD8α- cDCs. ……. 92

Figure 4.13 cDCs influence Th bifurcation in uncommitted PbTII cells. ………………………... 94

Figure 4.14 Differentially-expressed genes between single naïve and day 3 PcAS-infected cDCs. 95

Figure 4.15 Expression of immune-related molecules by myeloid cells. …………………….. 96-98

Figure 4.16 Principal Component Analysis of Ly6Chi monocytes from naïve and infected mice.100

Figure 4.17 Ly6Chi monocytes display similar functional capacity as cDCs. ……………………101

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Figure 4.18 Differentially-expressed genes between single Ly6Chi monocytes from naïve and day 3

PcAS-infected mice. ………………………………………………………………………………102

Figure 4.19 Kinetics of CXCL9 expression by myeloid cell populations. ………………............. 103

Figure 4.20 Figure 4.20 Ly6Chimonocytes support Th1 responses. …………………………….. 104

Figure 4.21 PbTII differentiation is influenced by chemokine-producing cells. ...……………… 105

Figure 5.1 PbTII responses are partially modulated by MyD88- and TRIF-mediated signalling, but

further dependent on IRF3. ………………………………………………………………………. 113

Figure 5.2 IRF3 promotes early protective Th1 responses during blood-stage Plasmodium infection.

……………………………………………………………………………………………………. 115

Figure 5.3 Phenotypic characterisation of Irf3-/->WT bone marrow chimeric mice. …………… 116

Figure 5.4 Requirements for IRF3 in early activation of PbTII cells. …………………………… 118

Figure 5.5 Activation kinetics of conventional dendritic cells during PcAS infection. …………. 120

Figure 5.6 Inflammatory monocytes express MHCII in an IRF3-dependent manner. …………... 122

Figure 5.7 IRF3 limits Plasmodium-specific antibody responses. ………………………………. 124

Figure 5.8 IRF3 limits Plasmodium-specific antibodies GC B cells and Tfh cell differentiation.. 125

Figure 5.9 IRF3 within B cells dampens maturation and GC B cell responses during infection… 127

Figure 5.10 IRF3 limits humoral immune responses during Py17XNL infection. ……………… 129

Figure 5.11 IRF3 impairs resolution of PbA infection in mice. …………………………………. 130

xvi

Abbreviations

AID Activation-Induced Deaminase

APC Antigen-Presenting Cell

bp Base pair

Bcl6 B cell lymphoma 6

BLIMP-1 B-Lymphocyte-Induced Maturation Protein 1

BSA Bovine Serum Albumin

cDC conventional Dendritic Cell

ChIP-seq Chromatin immunoprecipitation followed by sequencing

CRISPR-Cas9 Clustered, Regularly Interspaced, Short Palindromic Repeat-Cas9

CSP Circumsporozoite Protein

CXCL C-X-C motif Chemokine Ligand

CXCR C-X-C motif Chemokine Receptor

CyTOF Mass cytometry

DNA Deoxyribonucleic Acid

DT Diphtheria Toxin

DTR Diphtheria Toxin Receptor

DZ Dark Zone

EAE Experimental Autoimmune Encephalomyelitis

ECM Experimental Cerebral Malaria

ELISPOT Enzyme-Linked ImmunoSPOT

ERCC External RNA Control Consortium

ESC Embryonic Stem Cell

EU European Union

FACS Fluorescence-Activated Cell Sorting

FDC Follicular Dendritic Cell

GC Germinal Centre

GPI Glycosylphosphatidylinositol

GPLVM Gaussian Process Latent Variable Model

HIV Human Immunodeficiency Virus

Hz Haemozoin

ICOS Inducible T-cell Costimulator

ICOS-L Inducible T-cell Costimulator Ligand

xvii

Ig Immunoglobulin

i.p. intraperitoneal

IRF Interferon Regulatory Factor

ISG Interferon Stimulated Genes

i.v. intravenous

L Litre

LAG3 Lymphocyte-Activation Gene 3

LZ Light Zone

MACS Magnetic-Activated Cell Sorting

MAVS Mitochondrial Anti-Viral Signalling protein

MBC Memory B Cell

MHC Major Histocompatibility Complex

MHCI MHC Class I

MHCII MHC Class II

μg Microgram

mg Milligram

μL Microlitre

mL Millilitre

MSP Merozoite Surface Protein

MyD88 Myeloid Differentiation primary response gene 88

NK Natural Killer

OMGP Overlapping Mixture of Gaussian Processes

PALS Periarteriolar Lymphoid Sheath

PbA Plasmodium berghei ANKA

PBMC Peripheral Blood Mononuclear Cell

PC Principal Component

PCA Principal Component Analysis

PcAS Plasmodium chabaudi chabaudi AS

PCR Polymerase Chain Reaction

PD-1 Programmed Death 1

PfEMP1 Plasmodium falciparum Erythrocyte Membrane Protein 1

PfRh5 Plasmodium falciparum Reticulocyte binding protein 5 homologue

PfSPZ Plasmodium falciparum Sporozoite

p.i. post-infection

pMHCII peptide bound Major Histocompatibility Complex II

xviii

pRBC parasitised Red Blood Cell

PRR Pattern Recognition Receptor

p-S6 Phosphorylated ribosomal protein S6

Py17XL Plasmodium yoelii 17X Lethal

Py17XNL Plasmodium yoelii 17X Non-Lethal

RAG Recombination Activation Gene

RBC Red Blood Cell

Rorγt RAR-related orphan receptor gamma thymus-specific

ROS Reactive Oxygen Species

scRNAseq single-cell Ribonucleic Acid sequencing

STING Stimulator of Interferon Genes

T-bet T-box Expressed in T cells

TCR T Cell Receptor

TI IFN Type 1 Interferon

TLR Toll-like Receptor

Treg T regulatory

Th T helper

Tfh T follicular helper

Tr1 T regulator 1

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Chapter One:

Literature review

The human body is constantly at risk of infection from micro-organisms, such as the

protozoan parasite species, Plasmodium, the causative agent of malaria. Whether infection causes

disease depends on a delicate balance between pathogenicity of the organism and defence

mechanisms employed by the body, including those mediated by the immune system (Parkin and

Cohen 2001). Understanding the body’s response to infection is therefore of critical importance to

tip the balance towards a favourable outcome for the host.

The immune system encompasses a vast array of cells with the collective goal of defending

the body against disease. Broadly speaking, the immune system can be divided into two arms based

on the speed and specificity with which it responds. The first arm is the innate immune system,

which is rapid in its response, but displays a limited capacity to distinguish between threats. It

includes physical and chemical barriers, as well as varied cell types, for example phagocytic cells

that engulf particulate matter, and other cells specialised in the rapid production of soluble factors.

The highly conserved nature of this immune response across evolutionarily-distant phyla, including

the simplest of animals, demonstrates its necessity for survival. The second arm of the immune

system is the adaptive immune system, which generally takes longer than the innate immune system

to mount an effective response, and is more specifically targeted towards the immune challenge

presented. The adaptive immune system is also comprised of numerous cell types, the most

important and prevalent of which are T cells and B cells. A key feature of the adaptive response is

that it confers 'immunological memory', so that secondary infections are dealt with more effectively,

via a faster and stronger host adaptive immune response. Although different cell types of the innate

and adaptive immune systems exhibit unique and individual behaviours, in practice, highly complex

interactions between these cells must occur for an effective immune response to infection to be

mounted. Indeed, vaccine design and immune therapies rely on these complex interactions.

Malaria remains a significant global health concern; at least in part due to a limited

understanding of how to induce host immune responses that provide long-lasting protection. This

thesis seeks to determine novel cellular and molecular mechanisms controlling adaptive immune

responses during malaria, principally those mediated by CD4+ T helper cells. This literature review

will provide a detailed exploration of the field of malaria immunology. It will highlight gaps in

knowledge regarding adaptive immune responses to malaria parasites that may be filled by novel

approaches and technologies employed in this thesis.

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1.2 Malaria

Malaria is the disease resulting from infection with the protozoan parasite, Plasmodium. It

remains a leading cause of global mortality, with an estimated 214 million cases and 438,000 deaths

in 2015, 90% of which occurred in sub-Saharan Africa. Children under the age of 5 are particularly

at risk of severe symptoms (WHO 2015). There are five species of Plasmodium known to infect

humans: P. vivax, P. falciparum, P. malariae, P. ovale and P. knowlesi, with P. falciparum causing

the majority of deaths. Symptoms of malaria include fever, fatigue, anaemia, nausea and sweating.

However, more severe complications such as respiratory distress, metabolic acidosis and coma can

also occur during P. falciparum infection. Despite the significant global health burden posed by

malaria, there are still no effective vaccines or immune therapies for generating long-lasting,

Plasmodium-specific immunity.

Part of the difficulty in generating immunity to malaria may be linked to the complex life-

cycle of the Plasmodium parasite. Plasmodium parasites are spread to humans when an infected

female Anopheles mosquito takes a blood meal (Figure 1.1). Transmitted parasites (sporozoites)

migrate through the skin to the liver, where they infect hepatocytes. After a single round of

replication, parasites spread to the bloodstream and infect erythrocytes. Here, they undergo asexual

replication, producing many daughter parasites (merozoites) from every infected red blood cell

(RBC). All of the clinical symptoms associated with malaria occur during the blood-stage of

infection. Furthermore, parasite burden during this stage is currenty the most accurate predictor of

patient prognosis (Dondorp, Desakorn et al. 2005). During P. falciparum infection, parasitised

RBCs (pRBCs) can sequester in soft tissues such as the brain, kidney, liver and lung, which may

perturb blood flow and promote severe symptoms as mentioned above (Carter and Mendis 2002).

Some merozoites give rise to the sexual-stage parasites (gametocytes), which migrate to peripheral

blood vessels of the skin, and await the opportunity to be taken up by a new mosquito taking a

blood meal. If gametocytes are successfully taken up by another mosquito, they undergo sexual

reproduction within the midgut. The resulting sporozoites move to the salivary glands and are

transferred to a new human host when the infected mosquito takes another blood meal.

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Figure 1.1 Life cycle of Plasmodium spp. The parasites (sporozoites) are transmitted to a human

host via the bite of an infected female Anopheles mosquito. Sporozoites migrate through the skin

and take up residence in hepatocytes. Here, they undergo asexual reproduction to produce blood-

stage parasites (merozoites). Merozoites enter the blood stream and infect RBCs. Inside RBCs,

merozoites hijack host machinery to undergo further multiplication, causing rupture of the RBC

when it has reached capacity. The released merozoites infect new RBCs to continue multiplying, or

alternatively, become the sexual-stage parasite (gametocytes). Gametocytes migrate to the dermis

where they may be taken up by mosquitoes, undergo sexual reproduction and continue the life cycle

(Long and Zavala 2016).

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1.2.1 Progress towards a successful malaria vaccine

Large-scale interventions in endemic regions have contributed to the decline of global

mortality due to malaria by 48% between 2000 and 2015 (WHO 2015). These interventions include

the use of insecticide-treated bed nets, indoor residual spraying, chemoprevention in highly-

susceptible groups including pregnant women and young children, and use of rapid diagnostic tests

(Steketee and Campbell 2010, WHO 2015). However, insecticide and antimalarial resistance are an

increasing threat to the control of this disease. Thus, it is generally accepted that inducing long-

lasting immunological protection in at-risk individuals, either naturally or by vaccination, will be

necessary for the eradication of malaria (Moorthy, Good et al. 2004).

Over 100 years ago the German microbiologist Robert Koch was the first to observe

naturally-acquired immunity to symptomatic malaria in individuals living in endemic areas

(Stanisic, Mueller et al. 2010). In Papua New Guinea, where individuals are exposed to hundreds of

infectious mosquito bites each year, young children and transmigrants had higher detectable

parasite burdens than local adults (Stanisic, Mueller et al. 2010). From this he concluded that

protective immunity develops slowly after many years of exposure. Further observations from the

field showed that countries with high transmission rates, such as Kenya and Gambia, have the

lowest incidence of cerebral malaria or severe malaria (Mbogo, Snow et al. 1993, McElroy, Beier et

al. 1994). These observations support the argument that implementing control measures to prevent

the transmission of infection could contribute to poorer outcomes for some individuals infected with

Plasmodium (Snow, Omumbo et al. 1997). Therefore, inducing clinical immunity to curtail the

symptoms of malaria in people living endemic areas is of absolute necessity to reduce the burden of

malaria on world health.

While naturally-acquired immunity is considered 100% effective against severe disease and

death (Doolan, Dobano et al. 2009), it requires multiple infections over a number of years to

develop and is easily lost in the absence of continual re-infection. Thus, considerable effort has

been devoted to the development of a malaria vaccine that induces long-term protective immunity.

In the 1940s, immunity to malaria was induced when ducks and monkeys were vaccinated with

killed P. lophurae and P. knowlesi respectively (Freund, Sommer et al. 1945). In the 1960s, rodents

were also protected after immunization with irradiated sporozoites (Nussenzweig, Vanderberg et al.

1967). Subsequently, protection was demonstrated in humans. However, this was only achieved

after large numbers of bites from irradiated infectious mosquitoes (Clyde 1975). In the 1980s, the

circumsporozoite (CSP) protein was discovered as a major component of the sporozoite coat, which

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demonstrated its promise as an alternative vaccination target (Dame, Williams et al. 1984, Enea,

Ellis et al. 1984).

The current most advanced vaccine candidate, RTS,S, is based on the CSP protein combined

with the hepatitis B surface antigen to target the liver stage of disease. RTS,S is given alongside the

adjuvant, AS01, a combination of mono-phosphoryl lipid A (MPL, a toll-like receptor 4 agonist),

QS21 (a derivative of Quill A) and lipids (Stoute, Slaoui et al. 1997, Kester, Cummings et al. 2009).

Results from phase III clinical trials of RTS,S were modest, with an efficacy of approximately 50%

over 12 months in children aged 5-17 months (Olotu, Fegan et al. 2013). Furthermore, efficacy

waned during subsequent years, reaching only 0.4% by the fourth year and declining most rapidly

in children with high exposure to infection. However, since the application of this vaccine is

considered useful in conjunction with current vector control measures, the European Medicines

Agency’s Committee for Medicinal Products for Human Use recently adopted a positive scientific

opinion for its use outside the European Union (EU) (Kaslow and Biernaux 2015).

A combination vaccine approach, in which a range of Plasmodium antigens expressed at

different stages of infection are targeted, could offer a way forward. Hence, the discovery of other

candidate vaccines is still an area of intense research (Seder, Chang et al. 2013, Reddy, Pandey et

al. 2014, Ishizuka, Lyke et al. 2016). Progress in this venture is slow, since it is still unclear what

characteristics new vaccines should have and how their effectiveness should be evaluated. This

necessitates continued studies into the immune mechanisms mediating protection to Plasmodium

infection (Riley and Stewart 2013).

1.3 Immunity to malaria

1.3.1 Experimental models of malaria

Studies of the immune response to Plasmodium infection have benefitted from field

observations, clinical trials and experimental models. Although human studies are desirable, they

have many limitations, including lack of access to relevant tissues and the difficulty in

demonstrating causation (Langhorne, Buffet et al. 2011). For these reasons animal models can be

useful. Four mouse models of Plasmodium infection are routinely used for modeling the various

manifestations of human malaria. Plasmodium berghei ANKA (PbA) is typically used to mimic

severe and cerebral malaria as seen during P. falciparum infection of humans (Renia, Howland et

al. 2012). C57BL6/J mice infected with PbA experience cerebral complications such as seizures,

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paralysis, and coma, and typically succumb to the infection after approximately 6-10 days (Hunt,

Golenser et al. 2006). P. chabaudi chabaudi AS (PcAS) infection is non-lethal, similar to human P.

vivax and P. falciparum infections (Stephens, Culleton et al. 2012), and establishes a recrudescent

infection (Achtman, Stephens et al. 2007) as seen in human P. vivax and P. ovale infections

(Imwong, Snounou et al. 2007). Finally, two strains of P. yoelii, a lethal P. yoelii 17 YM (Py17XL)

strain that causes hyperparasitemia and subsequent anaemia, and P. yoelii 17X nonlethal

(Py17XNL) strain that models slow-resolving infection (Amante and Good 1997).

1.3.2 Requirement for antibody-mediated protection

Antibodies have a well-established role in protection against natural Plasmodium infection.

Cohen and MacGregor first observed this in 1960 when they transferred purified antibodies from

naturally-immune adults of West Africa into partially immune children, resulting in rapidly

decreased parasitemia and alleviated symptoms (Cohen, McGregor et al. 1961). More recent

support for the association between Plasmodium-specific antibodies and naturally-acquired

immunity was provided by a prospective study in which antibody levels were measured in the

serum of young Malian children each year until young adulthood, when clinical immunity is

typically observed (Crompton, Kayala et al. 2010). Antibody levels rose each subsequent year of

exposure to infection up until young adulthood, when it plateaued at a high level (Crompton,

Kayala et al. 2010). However, in the absence of infection, naturally-acquired antibody titres are

easily lost, particularly in young children (Akpogheneta, Duah et al. 2008). During experimental

malaria, antibody mediated protection is most convincingly demonstrated in scenarios where mice

lack B cells, such as JHD mice, which fail to control Py17XNL infection (van der Heyde, Huszar et

al. 1994). Similar results were seen in Aicda-/- mice with mature B cells that cannot undergo

antibody class-switching (Butler, Moebius et al. 2012).

Protective antibody responses are likely generated against antigens expressed on the surface

of merozoite or parasite-derived antigens on the surface of pRBCs. A meta-analysis of pool data

from three studies on merozoite proteins determined a dose-associated response of antibodies

against merozoite surface protein 1-19 (MSP-119), by showing a 15% reduction in symptomatic P.

falciparum infections per doubling of the antibody levels in individuals (Fowkes, Richards et al.

2010). Similarly, risk of symptomatic infection dropped 54% in people with measurable IgG to

MSP-3-Ct (Fowkes, Richards et al. 2010). Parasite antigens expressed on the surface of RBC (also

known as variant surface antigens; VSAs) that could be targets for antibodies include P.

falciparum erythrocyte membrane protein 1 (PfEMP1) (Leech, Barnwell et al. 1984), repetitive

interspersed family (RIFIN) proteins (Cheng, Cloonan et al. 1998, Fernandez, Hommel et al. 1999),

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sub-telomeric variable open reading frame (STEVOR) proteins (Kaviratne, Khan et al. 2002,

Blythe, Yam et al. 2008, Niang, Yan Yam et al. 2009), surface-associated interspersed gene family

(SURFIN) proteins (Winter, Kawai et al. 2005) and P. falciparum Maurer’s cleft two

transmembrane (PfMC-2TM) proteins (Sam-Yellowe, Florens et al. 2004, Lavazec, Sanyal et al.

2006). The best studied, PfEMP1, was first identified in the sera of infected aotus monkeys and is

now known to be involved in the adherence of pRBCs to endothelial cells. Due to the high

variability of PfEMP1 and the many other antigens expressed on iRBCs, it is difficult to show

PfEMP1-specific antibody responses in humans. However, using recombinant PfEMP1, a study in

Papua New Guinea demonstrated that the magnitude of anti-PfEMP1 responses was limited and

variant specific in young children, but in adulthood, serum antibodies were capable of recognising

at least 20 different variants, indicating that the PfEMP1-specific antibody repertoire increases with

age (Barry, Trieu et al. 2011). Antibodies against merozoite surface antigens or parasite-derived

antigens on the RBCs could provide protection by blocking the invasion of free blood-stage

parasites into new RBCs, opsonising merozoites for phagocytosis, and antibody-dependent cellular

inhibition (Blackman, Heidrich et al. 1990, Perraut, Mercereau-Puijalon et al. 1995, Bull, Lowe et

al. 1998), however this remains to be determined.

1.3.3 Requirement for CD4+ T cell responses

T cells are another key component of the adaptive immune response, and have also been

associated with improved outcomes during Plasmodium infection. Adults co-infected with

Plasmodium and Human Immunodeficiency Virus (HIV), which infects and destroys CD4+ T cells,

were hospitalised with clinical malaria twice as often than those with malaria alone (Whitworth,

Morgan et al. 2000). Additionally, individuals receiving multiple subclinical injections of P.

falciparum acquired a level of immunity that was attributed to cellular immunity on the basis of

increased production of a prototypical CD4+ cytokine, IFNγ, and absence of detectable

Plasmodium-specific antibodies throughout the study (Pombo, Lawrence et al. 2002). Serum levels

of IFNγ have also been positively associated with protection against re-infection in field studies

(Deloron, Chougnet et al. 1991). Furthermore, IFNγ production as determined by Enzyme-linked

ImmunoSpot (ELISPOT) assay of ex vivo cultures of lymphocytes was associated with improved

protection against malaria during clinical trials for RTS,S (Sun, Schwenk et al. 2003, Kester,

Cummings et al. 2009).

In mouse models of infection, the involvement of CD4+ T cells in protection against blood-

stage Plasmodium infection is most clearly evidenced by increased parasite burden and mortality in

T cell-depleted mice, a phenotype that can be rescued by adoptive transfer of unfractioned or

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purified immune splenic cells (Weiss, Sedegah et al. 1993). Mice genetically deficient in IFNγ

(Amani, Vigario et al. 2000, Su and Stevenson 2000) or the IFNγ receptor (Ifnγr-/-) (Favre, Ryffel et

al. 1997) also fail to control infection and succumb to hyperparasitemia. In animal vaccination

studies conducted by Pinzon-Charry et al., a killed blood-stage parasite vaccine with the adjuvant,

CpG-oligodeoxynucleotides induced protective CD4+ T cells in mice (Pinzon-Charry, McPhun et

al. 2010). Even at very low doses (103 pRBCs), this vaccine induced strong proliferation of parasite-

specific CD4+ T cells within the spleen and increased serum IFNγ, IL-12, IL-10 and TNF-α levels.

At the time of rechallenge, vaccinated mice were completely protected from the high mortality and

severe disease experienced by control mice that received killed parasites alone. Cell-depletion

studies ruled out the involvement of CD8+ T cells and NK cells that can also produce IFNγ.

Importantly, the role of antibodies was also discounted due to low titres induced by vaccination and

the failure of passive transfer of immune sera to protect naive mice from infection (Pinzon-Charry,

McPhun et al. 2010). Experiments performed in SCID mice, demonstrated that transfer of enriched

splenic CD4+ T cells controlled primary PcAS infections, although this was insufficient to

completely eliminate parasites, indicating that humoral immunity is likely required for final

resolution of infection (Meding and Langhorne 1991).

1.3.4 Mechanisms of immune evasion

The adaptive immune system clearly has the capacity to mount protective immune responses

against Plasmodium infection. However, protective immunity requires many intermittent infections

to acquire and is easily lost. The difficulty in maintaining clinical protection is hypothesised to be

partly due to parasite evasion mechanisms. One means by which Plasmodium parasites can evade

antigen-specific adaptive immune responses is via antigen diversity. For example, as previously

mentioned, PfEMP1, a parasite-derived adhesion molecule expressed on the surface of pRBCs, is

involved in sequestration of pRBCs and immune evasion. Therefore, PfEMP1 is targeted by host

antigen-specific antibodies to block sequestration. However, PfEMP1 is encoded by the var gene,

which has over 60 antigenically and functionally distinct variants within a single parasite genome

(Smith, Chitnis et al. 1995, Su, Heatwole et al. 1995). Therefore, switching var gene expression can

prevent binding of antigen-specific antibodies and enable continued sequestration.

Numerous studies have also reported immunomodulation by Plasmodium infection.

Reduced numbers of circulating innate immune cells have been observed in human patients infected

with P. falciparum and P. vivax, at least partially due to cellular apoptosis (Woodberry, Minigo et

al. 2012, Pinzon-Charry, Woodberry et al. 2013). Innate cells are critical for priming adaptive

responses. Therefore, their clearance could have knock-on effects on long-lived immune responses.

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Apoptosis of antigen-specific T cells has also been observed during experimental Plasmodium

infection (Xu, Wipasa et al. 2002). Additionally, inappropriate activation of T cell inhibitory

pathways, such as those mediated by high expression of PD1 and LAG3, may also contribute to

functional exhaustion of T cells as observed in children exposed to P. falciparum infection in Mali

(Butler, Moebius et al. 2012). Furthermore, PD1+ LAG3+ CD4+ T cells have been associated with

poorer antibody and memory B cell responses during infection (Illingworth, Butler et al. 2013).

Blocking these inhibitory receptors can result in better control of parasitemia in mice (Butler,

Moebius et al. 2012, Hafalla, Claser et al. 2012, Horne-Debets, Faleiro et al. 2013). These potential

immune evasion mechanisms highlight the dynamic relationship between host immune cells and

parasites, thus adding another layer of complexity to the induction of protective immune responses

to malaria.

1.4 T cell-mediated immunity

1.4.1 Development of antigen-specific T cells within the thymus

The adaptive immune system is adept at recognising different pathogens and responding

accordingly. Antigen specificity is provided through the expression of unique surface-bound

receptors on adaptive immune cells, as postulated in the clonal selection theory first described by

Burnet in 1957 (Burnet 1957). CD4+ and CD8+ T cells express unique T cell receptor (TCR)

complexes composed of variable disulphide-linked α and β TCR chains non-covalently associated

with a number of invariant CD3 polypeptides. Generation of TCRs begins in the thymus with the

CD4-CD8- (DN) thymocyte derived from a common hematopoietic progenitor cells. The DN

thymocyte undergoes rearrangement at the Vβ gene locus leading to expression of a functional Vβ

chain paired with a non-variant pre-Vα chain (Groettrup, Ungewiss et al. 1993). This drives

proliferation of the thymocyte, up-regulation of both CD4 and CD8 (DP) and cessation of

rearrangement at the Vβ locus. The end of Vβ rearrangement is termed allelic exclusion and results

in almost all T cells expressing a single Vβ variant (Casanova, Romero et al. 1991). The DP T cell

then undergoes rearrangement at the α locus until a TCR is created that is competent for positive

selection through recognition of major histocompatibility complex (MHC) molecules expressed by

local antigen presenting cells (APCs). If the DP T cell expresses a Vα chain that does not contribute

to recognition, it may still be expressed alongside the next iteration (Borgulya, Kishi et al. 1992).

For this reason, almost 30% of all T cells express two Vα variants (Padovan, Casorati et al. 1993).

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90% of DP cells will only generate 'useless' TCRs that cannot successfully bind MHC molecules,

and undergo cell death (Klein, Kyewski et al. 2014).

In the event of a TCR successfully recognising an MHC molecule, the affinity of the TCR

for 'self-peptide' bound to the MHC molecule determines the fate of the T cell. If affinity is high,

the T cell is deemed 'self-reactive' and promptly deleted. T cells with moderate affinity may

progress to become natural T regulatory cells. Lastly, T cells with low affinity for self antigen

migrate out of the thymus and join the naïve T cell repertoire. In a study by Liu and Bosselut,

longer interactions between the peptide-MHC (p-MHC) complex and the TCR of these low affinity-

binders was shown to be constitutive for CD8+ T cell development, regardless of the MHC class

type (Liu and Bosselut 2004). The significance of positive selection by recognition of self-peptide

was evident from a study by Mandl et al. who showed that the affinity of CD4+ T cells for self-

peptide bound to MCH molecules was positively associated with its affinity for foreign bound

antigen (Mandl, Monteiro et al. 2013).

The resulting naïve T cell repertoire of the human body has the capacity to recognise an

estimated 109 different antigens, making them one of the most diverse populations of cells in the

body (Davis and Bjorkman 1988). However, this high diversity within the naive T cell repertoire

comes at a cost to the number of each clone that can be accommodated. In fact, the frequency of

naïve CD4+ T cells specific to any given antigen is estimated to be only 20 to 200 cells per mouse

(Moon, Chu et al. 2007) or 1 to 10 per million CD4+ T cells in humans (Jenkins, Chu et al. 2010).

Clonal expansion of these rare clones occurs in secondary lymphoid organs when a TCR recognises

a peptide bound to a MHC molecule, of which there are two classes. MHC class I (MHCI)

molecules are expressed by all nucleated cells and are recognised by CD8+ T cells. On the other

hand, MHC class II (MHCII) molecules are expressed by specialised APCs, including DCs,

macrophages and B cells, and are recognised by CD4+ T cells. Upon recognition of cognate

antigens, expansion of T cells must be vigorous to provide a protective response to acute disease

(Buchholz, Schumacher et al. 2016). Studying the development of antigen-specific T cell responses

in vivo is of high interest, but is confounded by the low frequency with which clones exist within

the entire T cell pool.

1.4.2 Studying antigen specific T cell responses

The ‘gold standard’ for circumventing the problem of low antigen-specific T cell

frequencies is the use of peptide-bound MHC multimers or TCR transgenic mice expressing clonal

populations of T cells with proven antigen specificity. Peptide-bound MHC multimers are a soluble

form of MHC molecules with bound antigen (Nepom 2012). The MHC multimer commonly

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consists of a streptavidin core bound to four biotin-conjugated MHC molecules and tagged with a

fluorochrome for easy flow cytometry-based detection of bound T cells. Peptide-bound MHCI

tetramers were first applied to study CD8+ T cells 20 years ago. One example of their early

application was in determining the frequencies and functions of antigen-specific CD8+ T cells

among peripheral blood mononuclear cells (PBMCs) of HIV patients (Altman, Moss et al. 1996).

Since then, the system has been applied to MHCII tetramers for studying CD4+ T cell responses to a

wide variety of diseases, including type 1 diabetes, coeliac disease, pemphigus vulgaris, rheumatoid

arthritis, multiple sclerosis, and uveitis (Danke, Koelle et al. 2004, Danke, Yang et al. 2005,

Mallone, Kochik et al. 2005, Oling, Marttila et al. 2005). However, MHC tetramer design requires

prior knowledge of peptide sequences, which in conjunction with MHC molecules, bind to T cell

clones of interest (Nepom 2012).

The second major approach for studying antigen-specific T cell responses in vivo is the use

of TCR transgenic T cells. TCR transgenic T cell-expressing mice are created by culturing

immortalised T cells with antigen-loaded APCs. Responding cells are selected for and their α and β

TCR chains sequenced. These sequences are used to create cDNA, which is then cloned into

expression vectors and injected into blastocysts to generate transgenic founder mice (Mueller,

Heath et al. 2002). The first TCR transgenic mouse was developed in the late 1980's towards the

minor histocompatibility HY complex and was used to study negative selection of T cells

(Kisielow, Bluthmann et al. 1988). Subsequent transgenic mice were used to study positive

selection (Hogquist, Jameson et al. 1994). TCR transgenic cells have been instrumental in observing

CD8+ T cell exhaustion in mice with persistent LCMV infection (Moskophidis, Lechner et al.

1993), and in showing that initial encounters with APCs trigger memory programming (Kaech and

Ahmed 2001). Similarly, LCMV-specific CD4+ T cells have been used to demonstrate the

requirement for TCR signal strength in CD4+ T cell differentiation (Williams, Ravkov et al. 2008)

and a role for CD4+ T cells in rescuing exhausted CD8+ T cells (Aubert, Kamphorst et al. 2011).

TCR transgenic T cells have not only informed T cell biology, but also their interactions

with other immune cells and contribution to immune responses. Proliferation of MHCI-restricted

TCR transgenic CD8+ T cells were used to measure antigen presentation during acute and

recrudescent HSV-1 infection, leading to the identification of CD103+ dermal DCs as dominant

APCs in this model (Mueller, Heath et al. 2002, Bedoui, Whitney et al. 2009). PbA parasites

transgenically modified to express the model protein OVA, recognized by OVA-specific TCR

transgenic CD4+ (OTII) or CD8+ (OTI) cells have revealed a requirement for DCs in inducing CTL

cells (Lundie, de Koning-Ward et al. 2008) and protective CD4+ T cells during Plasmodium

infection (Haque, Best et al. 2014) respectively. A more biologically relevant MHCI-restricted TCR

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transgenic T cell-expressing mouse that harbors CD8+ T (termed PbTI) cells responsive to a

Plasmodium derived antigen has been generated and was able to protect recipient mice against liver

stage Plasmodium infection (Lau, Fernandez-Ruiz et al. 2014).

Only one TCR transgenic system to date has been used for studying Plasmodium-specific

CD4+ T cells in vivo (Stephens, Albano et al. 2005). The authors of this paper generated CD4+ T

cells reactive to Plasmodium MSP-1 protein (Stephens, Albano et al. 2005). These cells were useful

in determining the role for DC subsets in antigen-presentation during malaria (Sponaas, Cadman et

al. 2006). However, a significant caveat of this reagent was that a second Vα chain was transcribed

alongside the transgenic Vα chain, which may have confounded results. Furthermore, the genetic

background of these transgenic mice (BALB/c) has few available knock-out strains, thus limiting its

application for immunological studies. Currently, no other reagent exists for studying Plasmodium-

specific CD4+ T cells in vivo. This lack of appropriate tools has hampered efforts to understand

many aspects of CD4+ T cell responses during malaria.

1.4.3 Antigen presentation to CD4+ T cells during malaria

DCs are generally regarded as the most proficient APC, and as suggested above, have been

linked to priming T cells during Plasmodium infection. DCs can be divided into two main subsets:

plasmacytoid dendritic cells (pDCs), which express low levels of MHCII and the co-stimulatory

integrin CD11c in steady state, and classical dendritic cells (cDCs), which express high MHCII and

CD11c. pDCs have a key role in the production of type I interferons (TI IFN), which are

particularly important for protection against viral infection. On the other hand, cDCs are superior at

interacting with T cells due to their capacity to phagocytose antigen, upregulate MHCII, express co-

stimulatory molecules such as CD86, CD40 and CD80, as well as produce chemo-attractant

molecules (Villadangos and Schnorrer 2007, Segura and Villadangos 2009, Reizis, Bunin et al.

2011, Joffre, Segura et al. 2012). In secondary lymphoid tissues, in which T cell priming occurs,

cDCs can be subdivided into two main groups based on their differential expression of CD8α and

CD11b (Dudziak, Kamphorst et al. 2007). CD8α+CD11b- cDCs play a unique role in resistance to

certain viral infections due to their ability to process non-replicating antigens for presentation by

MHCI (termed cross-presentation), particularly to CD8+ T cells (den Haan and Bevan 2002, Iyoda,

Shimoyama et al. 2002, Allan, Smith et al. 2003). In contrast, CD8α-CD11b+ cDCs, which also

express Sirpα and Clec4a4 (also known as DCIR2), are more adept at presenting antigen to and

activating CD4+ T cells, with limited capacity for eliciting CD8+ T cell activation (Dudziak,

Kamphorst et al. 2007)

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The requirement for cDCs in initiating T cell responses during Plasmodium infection has

been demonstrated in a number of studies. During liver-stage infection, depletion of CD11c-

expressing cells resulted in reduced activation of CD8+ T cells (Jung, Unutmaz et al. 2002), which

are required for killing infected hepatocytes expressing Plasmodium antigen in a contact-dependent

manner (Cockburn, Amino et al. 2013, Cockburn, Tse et al. 2014). During blood-stage infection,

the spleen is a major site of both parasite clearance and developing immune responses (Grun, Long

et al. 1985, Chotivanich, Udomsangpetch et al. 2002). The number of cDCs within the spleen

increases considerably during blood-stage infection, as does expression of MHCII, CD40 and CD80

(Perry, Rush et al. 2004). Here, cDCs act as APCs for CD8+ T cells as well, with CD8α+ DCs the

most prominent APC (Lundie, de Koning-Ward et al. 2008). In addition, co-culture of naïve CD4+

T cells with cDCs harvested at day 6 of infection with Py17XNL has been shown to induce T cell

activation and expression of IFNγ, TNFα and IL-2 (Perry, Rush et al. 2004). Moreover, depletion of

cDCs during PbA infection perturbs CD4+ T cell responses, resulting in alleviated immune

pathology within the brain (deWalick, Amante et al. 2007). While both cDC populations are

capable of priming CD4+ T cells during in vivo PcAS infection, ex vivo experiments indicate that

CD8α- cDCs may be more effective than their CD8α+ counterparts (Sponaas, Cadman et al. 2006).

With respect to the involvement of other APCs in T cell responses to blood-stage malaria, a study

by Sponaas et al showed a population of Ly6C+CD11bhi monocytes were generated in the bone

marrow during infection, migrated to the spleen and upregulated MHCII. However, monocytes

sorted from the spleens of day 8 PcAS infected mice displayed limited capacity to induce

proliferation or cytokine production from naïve CD4+ T cells in vitro, suggesting these cells do not

play a significant role in priming naïve CD4+ T cells (Sponaas, Freitas do Rosario et al. 2009).

1.4.4 Differentiation of CD4+ T cells

Activated CD4+ T cells differentiate into a spectrum of T helper cells that provide

specialised responses to different immune challenges. Diversity in CD4+ T cell responses was first

reported in the early 1970’s, but it was not until a seminal study by Mosmann and Coffman in 1986

that CD4+ T cells were subdivided into helper populations (Mosmann, Cherwinski et al. 1986). In

this study, T cell clones were categorised into two subsets termed T helper 1 (Th1) and T helper 2

(Th2), based on patterns of cytokine production. Other Th subsets have since been defined (Figure

1.2). It is generally believed that the differentiation programs of these subsets are predominantly

governed by signals derived from antigen-presenting cells (APC) and the microenvironment at the

time of activation (Coquet, Rausch et al. 2015). However, inherent factors such as the balance of

transcription factor expression or TCR affinity may also contribute to fate decision (Tubo and

Jenkins 2014, Weinmann 2014).

14

Th1 cells are characterised by secretion of IFNγ and TNFα and are associated with

inflammatory responses to intracellular pathogens. Their differentiation is driven by IFNγ and

expression of the master transcription factor T-bet (encoded by Tbx21) (Szabo, Kim et al. 2000,

Szabo, Sullivan et al. 2003). Th1 cells also contribute to the pathogenicity of organ-specific

autoimmune diseases, such as autoimmune type 1 diabetes and multiple sclerosis (MS), as

demonstrated in a mouse model of MS, experimental autoimmune encephalomyelitis (EAE) (Szabo,

Sullivan et al. 2003, Christen and von Herrath 2004, Sospedra and Martin 2005). Th17 cells express

IL-17A, IL-22, IL-21 and the transcription factor RORγt. Like Th1 cells, Th17 cells contribute to

autoimmune diseases including EAE (Sospedra and Martin 2005, Kang, Wang et al. 2013), but can

also provide protection against extracellular pathogens (Curtis and Way 2009). Another population

of Th cells that produce IL-22, but not IL-21 or IL-17A, are termed Th22 cells (Duhen, Geiger et al.

2009). Th22 cells have been isolated from the skin of patients with psoriasis, atopic eczema (AE)

and allergic contact dermatitis (ACD) (Eyerich, Eyerich et al. 2009). Th2 cells are defined by

secretion of IL-4, IL-5 and IL-13 and expression of the transcription factor GATA3. Th2 responses

are induced during helminth infection and provoke production of IL-10 and IL-13, which have a

passive anti-inflammatory effect (Chen, Liu et al. 2012). Th2 cytokines have also been

demonstrated in allergic inflammation such as asthma (Robinson, Hamid et al. 1992). Th9 cells

produce the canonical cytokine IL-9, which promotes production of mucus during airway

inflammation (Kaplan, Hufford et al. 2015). T follicular helper cells (Tfh) were first identified in

human tonsils in the early 2000's and were noted for high expression of the B cell zone-homing

marker (CXCR5) (Breitfeld, Ohl et al. 2000, Kim, Rott et al. 2001, Chtanova, Tangye et al. 2004).

They are now further distinguished by expression of Programmed cell Death Protein-1 (PD1),

Inducible T cell Co-Stimulator (ICOS) and the master transcription factor Bcl-6, and secretion of

IL-21 (Crotty 2011). Differentiation of Tfh cells is promoted by IL-6 and IL-21 (Eto, Lao et al.

2011). Tfh cells are critical for germinal centre reactions, including the differentiation of antigen-

specific B cells into memory or plasma cells and antibody production during T cell dependent B cell

responses (Crotty 2011).

T regulatory cells (Tregs), as previously mentioned, are essential in maintaining peripheral

tolerance to self-antigens and limiting chronic inflammation by regulating the response of other Th

cell subsets. Tregs are characterised by expression of the key transcription factor Forkhead box P3

(FOXP3), CD25, and production of IL-10 and TGF-β (Wing and Sakaguchi 2014). Their

development is driven by recognition of self-antigens, and IL-2 and TGF-β. The importance of

Tregs in central tolerance is demonstrated in mice and humans deficient in Foxp3, who exhibit

hyperactive T helper responses and overproduction of inflammatory cytokines (Brunkow, Jeffery et

15

al. 2001, Ochs, Ziegler et al. 2005). A subset of Tregs differentiates within the thymus, and is

therefore named thymus-derived Tregs (tTregs). However, the differentiation of Tregs can also be

induced in the periphery (pTregs) (Abbas, Benoist et al. 2013). A third, Th1-like, regulatory subset

that produces IL-10, but does not express CD25 or FOXP3 has been classified as T regulatory 1

(Tr1) cells (Groux, O'Garra et al. 1997).

Despite categorisation of T helper cells into subsets there is likely plasticity between them

(Zhu and Paul 2010). Some defining surface markers and cytokines are shared between subsets,

such as IL-22 expression by both Th-17 and Th-22 cells. This crossover has led to scepticism in the

field about whether these are unique bona fide T helper subsets (Ahlfors, Morrison et al. 2014).

Furthermore, individual cells differentiating toward one subset may display patterns of expression

resembling another subset. It has been proposed in a study by Nakayamada et al that differentiating

Th1 cells first transition through a Tfh-like phenotype, by expressing Bcl6 and IL-12 (Nakayamada,

Kanno et al. 2011). Early expression of Tfh markers by pre-Th1 cells is likely initiated by the

activation of STAT4, but this induces transcription of Tbx21 to commit the cells to the Th1 fate

(Nakayamada, Kanno et al. 2011).

16

Figure 1.2 CD4+ T helper cell differentiation. CD4+ T cells are primed through recognition of

antigen bound to MHCII molecules presented by APCs. Under the guidance of external cues,

including cytokines, as well as internal signals, primed CD4+ T cells differentiate into a spectrum of

T helper cells, including T helper 1 (Th1), Th2, Th17, T regulatory (Treg), Th9 and T follicular

helper (Tfh) cells (Russ, Prier et al. 2013).

17

1.4.5 T helper responses provide protection during Plasmodium infection

Th1 responses have long been known to contribute to protective immune responses to

blood-stage Plasmodium infection. Transfer of IFNγ-secreting CD4+ T cells into athymic

Py17XNL-infected mice conferred protection, as manifested by suppressed growth parasite number

and improved resolution of the infection (Amante and Good 1997). Similarly, Plasmodium-specific

IFNγ+ Th1 cells, but not control OVA-specific CD4+ cells, allowed for resolution of PcAS infection

in CD4+-depleted mice (Taylor-Robinson and Phillips 1994). During PbA infection, enhanced

IFNγ+ T-bet+ CD4+ T cells in mice lacking Type I IFN signalling were associated with better control

of infections, including lower morbidity and mortality (Haque, Best et al. 2011, Haque, Best et al.

2014). In human infection, expansion of IFNγ-producing CD4+ T cells was observed during drug-

induced clearance of P. falciparum (Mewono, Agnandji et al. 2009). Additionally, a positive

correlation between serum IFNγ levels and the outcome of infection has fuelled confidence in an

association between Th1 cells and protective immunity (Deloron, Chougnet et al. 1991, Sun,

Schwenk et al. 2003, Kester, Cummings et al. 2009). However, since IFNγ is also produced by

other immune cells, including CD8+ T cells, natural killer (NK) cells, NK T cells and γδ T cells

(Schlub, Sun et al. 2011, Villegas-Mendez, Greig et al. 2012), it is not clear whether IFNγ-secreting

CD4+ T (Th1) cells play the major protective role in humans. A possible mechanism by which Th1

cells provide protection is by IFNγ- and TNFα- mediated activation of innate immune cells, such as

macrophages and natural killer (NK) cells (Taylor-Robinson and Phillips 1998). These cells could

respond by secreting reactive oxygen species (ROS) that directly kill infected erythrocytes (Clark,

Hunt et al. 1987, Taylor-Robinson and Phillips 1998). However, a prolonged proinflammatory Th1

response can also contribute to immunopathology, as demonstrated by resistance to experimental

cerebral malaria (ECM) of PbA infected Tbx21-/-mice (Oakley, Sahu et al. 2013).

An important aspect of clinical immunity is the ability to suppress potentially harmful

inflammatory responses once the infection has been cleared. The number of CD4+ FOXP3+ CD127lo

T cells was higher in healthy Gambian individuals living in rural regions experiencing high seasonal

malaria transmission, compared to individuals living in urban areas with low transmission (Finney,

Nwakanma et al. 2009). These Tregs expressed an effector memory phenotype, and suppressed

cytokine production by CD4+ T cells exposed to Plasmodium antigen (Finney, Nwakanma et al.

2009). Of particular interest was the constant ratio between T-bet+ CD4+ T cells and Treg cells in

individuals during the transmission and non-transmission seasons, suggesting that increased Th1

responses to Plasmodium infection was balanced with a proportionate Treg response. The balance

of inflammation and immunosuppression can also be observed in the outcomes of the two strains of

P. yoelii infection. Depletion of CD25+ Treg cells in BALB/c mice with a monoclonal antibody

18

enabled complete resolution of an otherwise lethal Py17XL infection (Hisaeda, Maekawa et al.

2004). However, depletion of CD25+ Treg cells during Py17XNL infection did not affect either the

parasitemia or the outcome of infection (Hisaeda, Maekawa et al. 2004).

Regulatory mechanisms provided by IL-10-producing Tr1 cells are also present during

Plasmodium infection. IL-10 and IFNγ co-producing cells were observed in stimulated PBMCs

from acutely infected children from The Gambia, Malia and Uganda (Walther, Jeffries et al. 2009,

Jagannathan, Eccles-James et al. 2014, Portugal, Moebius et al. 2014). To demonstrate the function

of Tr1 cells, Couper et al. transferred Il10-/- or wild-type (WT) CD4+ T cells into Rag1-/- mice,

which lack T or B cells, and infected mice with P. yoelii (Couper, Blount et al. 2008). While all

mice eventually succumbed to infection, mice receiving Il10-/- T cells experienced lower

parasitemia, but more severe pathology associated with greater numbers of circulating IFNγ+ CD4+

T cells (Couper, Blount et al. 2008). This data suggests that Tr1 cells have a role in balancing

immune-mediated clearance of parasites with immunopathology.

The role of Tfh cells in the class switching of B cells and production of antibodies make

them another obvious target for enhancing the immune response to Plasmodium (Perez-Mazliah and

Langhorne 2014). Before the characterisation of Tfh cells, PcAS, a model for chronic infection was

observed to have biphasic Th responses. While Th1 cells were observed early during blood-stage

infection, a population of IFNγ- CD4+ T cells were shown to be necessary for B cell responses

(Langhorne, Gillard et al. 1989). This observation coincided with the appearance of an IL-4-

producing CD4+ T-cell population at this later time of infection in both humans and mice

(Langhorne, Gillard et al. 1989, Troye-Blomberg, Riley et al. 1990). These data were interpreted as

an early activation of Th1 cells that controlled parasitemia, followed by a Th2 response that assisted

in activating B cell responses to clear infection (Langhorne, Meding et al. 1989, von der Weid and

Langhorne 1993). However, the frequency of CD4+ T cells able to help B cells to produce

Plasmodium-specific antibodies was much higher than the frequency of IL-4-producing CD4+ T

cells (Langhorne, Gillard et al. 1989). Furthermore, control of PcAS infection and production of

specific IgG responses was possible even in the complete absence of IL-4 (von der Weid, Kopf et

al. 1994). The identification of Tfh cells, specialising in B cell help, provided the answer to this

puzzle. In recent murine studies, therapeutic blockade of PD1 and LAG3 boosted the CD4+ T cell

response and improved parasite clearance in Py17XNL-infected mice (Butler, Moebius et al. 2012).

Further assessment showed that the blockade resulted in a 7-fold enhancement in Tfh numbers and

50-fold enhancement in plasmablast B cells (Butler, Moebius et al. 2012). IL-21-producing cells

have also been identified in people living in malaria-endemic areas, and these have been associated

with increased levels of IgG (Mewono, Matondo Maya et al. 2008, Mewono, Agnandji et al. 2009).

19

Tfh cells exhibit heterogeneity in their phenotype and behaviour during Plasmodium

infection. In Malain malaria-exposed children, a population of circulating memory PD-1+ CXCR5+

Tfh cells possessed phenotypic and functional characteristics of GC Tfh cells (Obeng-Adjei,

Portugal et al. 2015). These cells had a marked downregulation of Bcl6 permitting their exit from

secondary lymphoid organs. Furthermore, longitudinal studies in the same children showed that

expression of CXCR3 was preferentially induced during acute infection and skewed the response of

these cells to more of a Th1 phenotype (Obeng-Adjei, Portugal et al. 2015). These circulating

CXCR3+ Tfh cells had reduced ability to support antibody production by B cells compared to their

CXCR3- counterparts. This could explain the absence of correlation between Tfh responses and

plasma cell numbers and antibody titres or breadth in these children (Obeng-Adjei, Portugal et al.

2015).

1.4.6 CD4+ T cell memory to Plasmodium infection

After the initial wave of antigen-specific proliferation in response to antigen and subsequent

clearance of infection, the number of reactive T cells drops back to levels approximately 100-1000

fold higher than the initial frequency (Buchholz, Schumacher et al. 2016). The remaining cells can

last for the life-time of the host and provide a minimum 1 day advantage in responses to re-infection

(Schlub, Sun et al. 2011). CD4+ T memory cells can be divided into central memory cells, which

are positive for the cell adhesion molecule CD44 and the secondary lymphoid homing molecule

CD62L, and effector memory cells, which are positive for CD44 but negative for CD62L.

Currently, the kinetics of CD4+ memory T cell responses to Plasmodium infection in humans is

unknown. However, in a follow-up study to the Madagascan malaria epidemic of 1987, memory

CD4+ T cell responses to simple peptide epitopes from the Pf155/RESA antigen persisted in the

absence of reinfection for at least 5 years, although both the frequency and magnitude of the

responses were lower than those detected in the same individuals 3 years previously (Migot,

Chougnet et al. 1993).

In experimental studies, MSP-1-specific CD4+ T cells differentiated into effector and central

memory (TEM and TCM) cells during PcAS infection (Stephens and Langhorne 2010). Furthermore,

transfer of CD4+ T cells from immunized mice provided protection against PbA and PcAS infection

even in the absence of B cells, suggesting that memory CD4+ T cells can provide a degree of

protective immunity via a mechanism independent of the humoral response (Grun and Weidanz

1981, Tsuji, Romero et al. 1990, Langhorne 1994). In contrast and as previously mentioned, several

reports from experimental infections suggested that Plasmodium infection may lead to functional

deletion or modulation of activated CD4+ T cells, leading to defective memory responses

20

(Hirunpetcharat and Good 1998, Xu, Wipasa et al. 2002). This has also been hypothesised to be due

to normal contraction of effector cells, which is necessary to regulate inflammatory responses and

thus prevent pathology (Langhorne, Ndungu et al. 2008). The exact reason why T cell memory

responses are not consistently induced in mice and humans remains to be elucidated.

1.4.7 CD8+ T cell during Plasmodium infection

Plasmodium-specific CD8+ T cells contribute to both protection and immunopathogy during

Plasmodium infection. The first scenario occurs during the pre-erythrocytic stage of infection. Upon

infection with sporozoites, CD8+ T cells are primed by CD8α+cDCs in the skin draining lymph

nodes (Chakravarty, Cockburn et al. 2007, Radtke, Kastenmuller et al. 2015). They then migrate to

the liver and destroy infected hepatocytes expressing parasite-derived antigens bound to MHCI

molecules (Chakravarty, Cockburn et al. 2007, Cockburn, Amino et al. 2013). The significance of

CD8+ T cells in generating protection is evident from β2-microglobulin knock-out mice (β2m-/-;

deficient of MHCI expression) that fail to generated protection after attenuated P. berghei

sporozoite challenge (White, Snyder et al. 1996). This protection is likely mediated by a

combination of effector mechanisms including perforin-associated direct killing and pathways

involving IFNγ and TNFα (Butler, Schmidt et al. 2010, Cockburn, Amino et al. 2013).

CD8+ T cells take on a more sinister role during the erythrocytic-stage of severe P.

falciparum infection. Parasite-infected RBCs can sequester in brain, resulting in cerebral malaria.

Studies of the mouse model of cerebral malaria, PbA, have demonstrated that antigen-specific

CD8+ T cells migrate to brain in response to the production of chemokines such as CXCL10 by

local cells (Nitcheu, Bonduelle et al. 2003). Here they target endothelia cells expressing parasite

antigens with secreted IFNγ, perforin and granzyme B, with the latter two being essential for CD8+

T cell pathogenicity (Nitcheu, Bonduelle et al. 2003, Haque, Best et al. 2011). In support of the

involvement of CD8+ T cells in this pathogenicity, mice depleted of CD8+ T cells are immune to

cerebral symptoms of PbA infection, but susceptibility is restored upon adoptive transfer of PbTI

cells (Lau, Fernandez-Ruiz et al. 2014). Given the ethical considerations, it is impossible to explore

the development and mechanism of human CM. However, post-mortem studies of the brains of

Malawian children with CM, have shown that, just as in ECM, small numbers of CD8+T cells can

be found in brain capillaries (Dorovini-Zis, Schmidt et al. 2011). Likewise, a positive association

between CXCL10 expression and CM has been found in the brains of Ghanaian children (Armah,

Wilson et al. 2007). Further studies into the development of ECM, which can be supported by

clinical observations, are still required for developing CM interventions.

21

1.5 Humoral immunity

1.5.1 Antibodies

Antibodies, Y-shaped effector proteins of B cells, are protective against many infections,

including malaria (Chapter 1.3.2). Antibodies can be divided into five classes or isotypes- IgG,

IgM, IgA, IgD, and IgE, with IgG further divided into IgG1, IgG2a/c, IgG2b and IgG3. The base of

the “Y”, the Fc (crystallisable fragment) region, is common within each isotype and determines the

function of the antibody by binding to specific Fc receptors. The arms of the “Y” are called

fragment antigen-binding (Fab) regions and confer antigen specificity. Isotype expression changes

during maturation and response of B cells. For instance, naïve B cells express only surface bound

IgD and IgM, while activated B cells secrete IgG, IgE and IgA, each with more defined roles.

Maturation of B cells occurs in lymphoid tissues such as the spleen.

1.5.2 Micro-architecture of the spleen

Under steady-state conditions, the spleen consists of white pulp and red pulp that can be

visualised macroscopically. Closer examination reveals that white pulp is further divided into B cell

follicles and adjacent T cell zones (Figure 1.3). The B cell follicle contains follicular B cells

(IgMmedIgDhiCD21medCD23hi) and follicular dendritic cells (FDCs). FDCs express the CXCR5

ligand, CXCL13, which acts as a major chemoattractant for B cells and Tfh cells, and may facilitate

the movement of these cells into the follicle (Pereira, Kelly et al. 2010, Victora and Nussenzweig

2012). The T cell zone, also known as the periarteriolar sheath (PALS), contains mostly T cells,

which interact with passing APCs and B cells (Cesta 2006). Blood enters the white pulp through a

central arteriole and disperses via a conduit system through which only small antigenic molecules

can pass(Nolte, Belien et al. 2003). The white pulp is surrounded by the red pulp, which is highly

vascular and consists of various APCs and other immune cells. Red pulp filters the blood for dead

cells and larger foreign material (Nolte, Belien et al. 2003). The area between the red and white

pulps is termed the marginal zone. Different subsets of macrophages, DCs and marginal zone B

cells (IgMhiIgDlowCD21hiCD23low) reside here (Kumararatne, Bazin et al. 1981, Martin and Kearney

2002).

22

Figure 1.3 Architecture of the spleen. The spleen is divided into red and white pulps. Red pulp

contains an array of immune cells and specialises in filtering the blood. The white pulp areas are

subdivided into B cell follicles and neighbouring T cell zones. These are supplied with blood

through a central arteriole and conduit network, and are the foci of antibody production during

infection (Batista and Harwood 2009).

1.5.3 Germinal centre reactions

In the follicle, B cells are activated when they recognise either soluble or bound antigens

presented by APCs via surface immunoglobulins (Ig) that serve as antigen-specific B cell receptors

(BCRs) (Batista and Harwood 2009). This initiates proliferation and differentiation of B cells.

Activated B cells can become extrafollicular plasmablasts, which move out of the follicle and

provide rapid antibody production for early control of infection, or they can remain in the follicle

and initiate germinal centre (GC) reactions. GC B cells are identified by their expression of Fas, η-

glycolylneuraminic acid (ligand of the antibody GL-7), the transcription factor Bcl-6, reduced

expression of CD38 and affinity for peanut agglutinin (Rose, Birbeck et al. 1980, Oliver, Martin et

al. 1997, Naito, Takematsu et al. 2007). GCs begin to develop within follicles approximately 6 days

after antigen exposure (Jacob, Kelsoe et al. 1991). A GC is divided into two distinct regions, the

Dark Zone (DZ) and Light Zone (LZ) (Figure 1.4). The DZ is proximal to the T cell zone and

23

consists almost exclusively of GC B cells. GC B cells in the DZ receive signals from Tfh cells that

migrate to the border of T cell zones and B cell follicles under the direction of CXCR5 (Arnold,

Campbell et al. 2007). In the DZ, GC B cells proliferate and undergo BCR diversification, in which

the enzyme activation-induced deaminase (AID) deaminates cytidine residues in the variable

regions (Batista and Harwood 2009, Dominguez-Sola, Victora et al. 2012, Meyer-Hermann, Mohr

et al. 2012). GC B cells then migrate to the LZ, which is proximal to the marginal zone. Here they

experience clonal selection, whereby B cells with varying BCR affinities compete for antigen

presented by FDCs. Higher-affinity cells are selectively expanded, while lower-affinity cells are

eliminated by apoptosis (Aguzzi, Kranich et al. 2014). Approximately 30% of positively selected

GC B cells return to the DZ to repeat the cycle of somatic hypermutation and selection in a process

termed 'cyclic re-entry', resulting in the progressive improvement of antigen specificity within the

GC B cell pool (Victora, Schwickert et al. 2010). Alternatively, high-affinity GC B cells

differentiate into plasma cells under the control of B-lymphocyte-induced maturation protein 1

(BLIMP-1) and subsequently increase their synthesis and secretion of antibodies (Shaffer, Lin et al.

2002).

Figure 1.4 Germinal centre. The germinal centre is divided into two areas, the light and dark

zones. Frozen spleen section stained with CD35 (Follicular DCs (FDCs), IgD (B cell mantel), and

GFP-expressing germinal centre (GC) B cells. T cell zone is unstained and sits below the germinal

centre. White dashed line shows the division between zones (Victora and Nussenzweig 2012).

24

1.5.4 B cell memory

GC B cells typically undergo apoptosis due to high expression of the death receptor Fas and loss of

expression of the anti-apoptotic molecule Bcl-2 (Liu, Joshua et al. 1989). However, GC B cells can

also differentiate into long-lived antigen-producing plasma cells and quiescent memory B cells

(MBCs) to contribute to immunological memory. There are no common markers for MBCs in mice

other than class switching from IgD. In a study by Tomayako et al, higher expression of CD80,

PDL2 and CD73 was observed on MBCs relative to naïve cells (Tomayko, Steinel et al. 2010).

However, there was extensive heterogeneity within the expression of these markers (Tomayko,

Steinel et al. 2010). In humans, CD27 marks a population of antigen-experienced B cells that are

regarded as MBCs (Sanz, Wei et al. 2008). Longitudinal studies have identified the presence of

long-term MBCs and antibodies specific to Plasmodium in individuals from Thailand and

Madagascar years after infection with P. falciparum (Migot, Chougnet et al. 1995, Wipasa,

Suphavilai et al. 2010). In a prospective study, MBCs specific to two P. falciparum antigens,

AMA1 and MSP1, increased in frequency in responses to acute infection, but then decrease to just

above pre-infection levels following a 6-month period of reduced exposure. The MBC compartment

expanded in a gradual, stepwise fashion after multiple infections in subsequent malaria seasons.

This was compared to the stable antigen-specific MBC population generated in response to tetanus

immunisation to conclude that the Plasmodium-specific MBC compartments does not simply occur

with age, but requires repeated antigen exposure. In line with this, the proportions of individuals

with memory B cells induced after natural exposure or vaccination against various viruses is

typically 60-100% (Nanan, Heinrich et al. 2001, Amanna, Carlson et al. 2007), the range of

individuals in low to moderate endemic regions with antibodies to a given Plasmodium antigen only

reaches 30-60% (Dorfman, Bejon et al. 2005, Weiss, Traore et al. 2010, Wipasa, Suphavilai et al.

2010, Nogaro, Hafalla et al. 2011). Furthermore, in fatal malaria cases, there are significant changes

in splenic architecture, including breakdown in the marginal zone, loss of B cells and reduced

formation of germinal centres (Urban, Hien et al. 2005). This suggests that B cell responses in

malaria patients are compromised and may lead to short-lived clinical protection to malaria

observed in the field.

25

1.6 Innate immunity

1.6.1 Innate immune detection of Plasmodium infection

Malaria is a highly inflammatory disease, known to induce cyclic bursts of fever in infected

individuals (Prakash, Fesel et al. 2006). This inflammation is clear evidence of the visibility of

Plasmodium parasites by the innate immune system. Foreign antigen is identified by innate immune

cells through pattern-recognition receptors (PRRs) expressed on the cell surface or within the

cytoplasm. Among the known Plasmodium antigens recognised by innate cells, are

Glycosylphosphatidylinositols (GPIs) that can be detected by macrophages through surface-bound

Toll-like receptor (TLR) 2 and TLR4 (Krishnegowda, Hajjar et al. 2005). Similarly, malarial

pigment, hemozoin (Hz) has been reported to trigger TLR9-signalling (Coban, Ishii et al. 2005).

When cultured in vitro with human peripheral blood monocytes, Hz purified from the blood of P.

falciparum infected humans resulted in the production of proinflammatory cytokines, chemokines

and nitric oxide (Sherry, Alava et al. 1995, Coban, Ishii et al. 2005). In addition, Hz has been

reported to activate the NALP3 inflammasome of macrophages (Griffith, Sun et al. 2009, Shio,

Eisenbarth et al. 2009). However, the immunogenicity of hemozoin is debatable given observations

by Parroche et al. that hemozoin itself is immunologically inert, but is coated with Plasmodium

DNA that can be recognised through TLR9 (Parroche, Lauw et al. 2007). Recently, Sharma et al.

reported that AT-rich Plasmodium DNA was detected through the intracellular Stimulator of

Interferon Genes (STING) signalling pathway in humans (Sharma, DeOliveira et al. 2011).

Furthermore, using PbA-infected mice, the downstream effector molecules TBK1, interferon

regulatory factor 3 (IRF3) and IRF7 were also implicated in Plasmodium detection. Given that the

innate immune system can clearly detect Plasmodium infection, it seems likely that innate-

signalling pathways may play a role in controlling, and possibly distorting adaptive immune

responses during malaria. However, only one report has provided evidence of this, with MyD88, an

signalling adaptor molecule for surface bound TLRs, promoting IFNγ and TNFα expressing CD4+

T cells during Plasmodium infection(Franklin, Rodrigues et al. 2007).

1.6.2 Interferon regulatory factors

IRFs are a family of nine (IRF1-9) transcription regulators, highly conserved in mice and

humans. IRFs were first identified as transcriptional regulators of type 1 interferons (TI IFNs) and

interferon-stimulated genes (ISGs), but have now been linked with many facets of innate and

adaptive immunity. For example, IRF1, IRF4, IRF5 and IRF8 induce the expression of pro-

inflammatory cytokines in response to PRR activation (Tsujimura, Tamura et al. 2004, Negishi,

26

Ohba et al. 2005, Takaoka, Yanai et al. 2005, Negishi, Fujita et al. 2006). These cytokines can, in

turn, skew the adaptive immune response. For example, IRF1 biases the response towards Th1

differentiation, as demonstrated by defective IFN-γ production and increased IL-4 production in

Irf1-/- mice compared to IRF1 sufficient controls (Taki, Sato et al. 1997). Similarly, Leishmania

infection of Irf4-/- mice elicited decreased differentiation into IFNγ-producing Th1 cells compared

to heterozygous controls (Lohoff, Mittrucker et al. 2002). On the other hand, IRF1, IRF2, IRF4 and

IRF8 have been implicated in the development of hematopoietic cells, including DCs, NK, CD8+ T

and B cells (Matsuyama, Kimura et al. 1993, Ogasawara, Hida et al. 1998, Lu, Medina et al. 2003,

Suzuki, Honma et al. 2004, Tamura, Tailor et al. 2005).

With regards to the role of IRFs in malaria, a whole genome transcriptional array conducted

on splenic CD4+ T cells from PbA-infected mice showed prominent up- and down-regulation of

genes encoding components of type 1 (IFN-α/β) and type 2 (IFN-γ) interferon pathways (Haque,

Best et al. 2011). While the importance of type 2 interferons during malaria is established, the

significance of TI IFNs required further investigation. Mice unresponsive to TI IFN signalling

(Ifnar1-/-) had 10-fold higher levels of serum IFN-γ and significantly more IFN-γ-positive Th cells

during PbA infection compared to mice capable of TI IFN signalling (Haque, Best et al. 2011). This

suggested a role for TI IFNs in suppressing the development of the Th1 response. IRF7 and IRF3

are key transcription factors regulating the expression of TI IFNs, thus their involvement in immune

responses to Plasmodium infection was also investigated. Indeed, IRF7 was demonstrated to be

essential of the suppressive role of TI IFNs (Edwards, Best et al. 2015). However, the same was not

seen of IRF3 (Edwards, Best et al. 2015), highlighting a disconnection between the function of

IRF3 and IRF7 during Plasmodium infection.

1.6.3 Interferon Regulatory Factor 3

IRF3 is typically associated with production of TI IFNs in response to viruses. IRF3 is

constitutively expressed in the cytosol of most cells and activated downstream of a number of cell

membrane or cytosolic nucleic acid detection receptors (Figure 1.5) (Cavlar, Ablasser et al. 2012).

Phosphorylation of IRF3 by kinases such as TBK1 results in its translocation to the nucleus where it

facilitates the expression of TI IFNs and ISGs (Fitzgerald, Rowe et al. 2003, Tarassishin, Bauman et

al. 2013). IRF3, in association with TBK1, has been shown to be important in B cell responses to

CpG (Oganesyan, Saha et al. 2008). Additionally, activated IRF3 mediates apoptosis of virus-

infected cells in an IFN- and p53-independent way (Weaver, Ando et al. 2001).

27

Figure 1.5 Innate signalling pathways converging on Interferon regulatory factor 3 (IRF3).

Pathogen products such as DNA or dsRNA act as ligands for receptors expressed on the cell

surface, in the cytosol and in endosomes, notably TLRs, mitochondrial antiviral-signalling protein

(MAVS/IPS-1) and the DNA sensors, DDX41 and DAI. These activate specific adaptor molecules,

which in turn interact with kinases. The kinase TBK1 phosphorylates cytosolic IRF3 resulting in its

translocation to the nucleus, thereby facilitating its action as a transcription factor for type 1

interferons and other interferon-stimulated cytokines.

Since IRF3 appears to be a convergence point for several PRR-signalling pathways, it is

ideally situated to influence adaptive immune responses. IRF3 has been reported to suppress Th1

responses during viral and bacterial co-infection (Negishi, Yanai et al. 2012), but drive Th1 and

Th17 responses in the central nervous system during EAE (Fitzgerald, O'Brien et al. 2014). During

liver-stage PbA infection of mice, IRF3 has been reported by two independent studies to be a driver

of TI IFN responses (Liehl, Zuzarte-Luis et al. 2014, Miller, Sack et al. 2014). Whether IRF3 is also

involved in shaping adaptive immune responses to blood-stage malaria is currently unknown.

28

1.7 Thesis aims

This thesis aims to explore cellular and molecular mechanisms that modulate Plasmodium-

specific CD4+ T helper responses during experimental blood-stage malaria.

Specifically it aims to:

1. Determine whether a newly developed Plasmodium-specific CD4+ T cell can be effectively

used to track T helper responses.

2. Resolve the process of Th1 and Tfh cell differentiation to gain molecular insight into

mechanisms controlling Th fate decision-making.

3. Investigate whether innate immune signalling pathways can influence Th1 and Tfh-

dependent immune responses during infection.

1.7.1 Thesis outline

The materials and methods used throughout this thesis are described in Chapter 2.

The importance of CD4+ T helper cells for protection against malaria is well established.

However, the molecular and cellular mechanisms underlying their emergence in vivo remain poorly

delineated. A novel Plasmodium-specific TCR transgenic mouse was recently generated by

Professor Bill Heath and Dr Daniel Fernandez Ruiz (Doherty Institute, University of Melbourne),

but its response during in vivo Plasmodium infection was uncharacterised. In Chapter 3, CD4+ T

cells from this transgenic mouse (termed PbTII cells) are tested for their utility in tracking Th

responses in vivo. PbTII cells are shown to proliferate during experimental malaria in a cDC-

dependent manner. In addition, PbTII cells are demonstrated to differentiate into Th1 and Tfh

effector cells, and also regulatory and memory populations.

The bifurcation of PbTII cells into two Th subpopulations offered a unique opportunity to

study the process of fate 'decision-making' of a clonal population during in vivo infection. To this

end, in Chapter 4, single-cell RNA sequencing (scRNAseq) is applied to PbTII cells during the

first week of PcAS infection. By applying a combination of modelling algorithms to the

transcriptomic data, the process of Th1 and Tfh differentiation is resolved. While Cxcr5 is the only

chemokine receptor associating with Tfh fate decision, a number of chemokine receptors, including

Cxcr3, correlate with the Th1 fate decision. ScRNAseq analysis of myeloid populations at the time

of PbTII bifurcation, revealed expression of CXCR3 ligands not only by cDCs, but also

29

Ly6Chiinflammatory monocytes. For the first time, the requirement for inflammatory monocytes in

Th1 responses is confirmed on a functional level.

Given that PRR can detect Plasmodium infection and induce innate immune responses, it is

possible that these influence Th fate decision. In Chapter 5, the first side-by-side comparison of

endosomal and cytosolic PRRs is performed using the PbTII cells. Minor opposing roles for TLR

adaptor molecules, MyD88 and Trif are observed. However, a critical role for the downstream

transcription factor IRF3 in promoting PbTII accumulation, and furthermore, in their differentiation

towards Th1 cells is demonstrated. This requirement corresponded with a role for IRF3 in MHCII

expression by Ly6Chi inflammatory monocytes. Conversely, IRF3 is observed to suppress Tfh

differentiation, GC B cell responses and resulting Plasmodium-specific antibody production in a B

cell-intrinsic mechanism.

Chapter 6 summarises the findings outline in each of the results chapters, their relationship

with each other and how they contribute to a wider scientific understanding of CD4+ T cell

responses during malaria. Finally, future directions of this project will be discussed.

30

Chapter Two:

Materials and methods

2.1 Materials

2.1.1 Mice

Strain Description Reference

C57BL/6J Inbred animal expressing MHC class II (MHCII)

IA(b)

Australian Resource

Centre (Canning

Vale, Western

Australia)

B6.SJL-Ptprca These congenic mice express the leukocyte

surface antigen CD45.1 (also referred to as

Ly5.1) Ptprca (protein-tyrosine phosphatase,

receptor type c, a allele). This maker is used to

distinguish between cells from these mice and

those from WT C57BL/6J mice, which express

CD45.1/Ly5.2.

Australian Resource

Centre (Canning

Vale, Western

Australia)

B6.129S7-

Rag1tm1Mom(Rag1

-/-)

Mice lack mature T and B cell populations due

to a mutation in the recombination activation

gene (RAG) 1. The original mutation was made

in the 129-derived AB1 ES cell line. The

C57BL/6J strain was generated by backcrossing

mice carrying the mutation to C57BL/6J inbred

mice 10 times.

(Mombaerts,

Iacomini et al. 1992)

LysMCre x iDTR F1 generation of LysMCre and iDTR breeding

generating a mouse in which a diphtheria toxin

receptor (DTR) is expressed off the lysozyme M

promoter, such that lysozyme M expressing

Generated in-house

31

myeloid cells can be depleted upon diphtheria

toxin (DT) administration.

CD11c-DOG Mice in which DTR, ovalbumin (OVA) and GFP

are expressed off the CD11c promoter allowing

for depletion of CD11c expressing cells upon

administration of DT.

(Hochweller,

Striegler et al. 2008)

Irf7-/- This mouse was generated by homologous

recombination of an Irf7 gene-targeting construct

in E14-1S cells, and their microinjection into

C57BL/6 blastocysts. A homozygous line was

generated by intercrossing heterozygous mice.

(Honda, Yanai et al.

2005)

Irf3-/- An Irf3 gene-targeting vector was isolated from

the C57BL/6N genomic library and

electroporated into CCE embryonic stem cells

(ESCs). Positive cells were injected into

C57BL/6J blastocysts. Irf3-/- mice were

generated by intercrossing heterozygous mice.

(Sato, Suemori et al.

2000)

Myd88-/- The Myd88 gene was disrupted by homologous

recombination in E14.1 embryonic stem cells

with a targeting vector containing neomycin

resistance gene. Clones with homologous

recombination were microinjected into C57BL/6

blastocysts. Homozygous knock-out mice were

born at Mendelian ratio.

(Adachi, Kawai et al.

1998)

Trif-/- A targeting vector was generated by replacing

the Trif-encoding gene with a neomycin-

resistance gene cassette. This was transfected

into ESCs. Homologous recombinants were

micro-injected into C57BL/6 mice and

heterozygous progenies were intercrossed in

order to obtain a homozygous line.

(Yamamoto, Sato et

al. 2003)

Casp1−/−Casp111 Mice were obtained by backcrossing (Kayagaki, Warming

32

29mt/129mt (Casp1/1

1-/-)

NOD/ShiLtJ Casp1/11 double knockout to

C57BL/6 for more than 10 generations.

et al. 2011)

Mavs-/-(Ips1-/-) A targeting vector was designed to disrupt two

exons harboring the CARD-like domain of IPS-

1. Homozygous knock-out mice were born at

expected Mendelian ratio.

(Kumar, Kawai et al.

2006)

B6(Cg)-

Tmem173tm1.2Cam

b/J

A targeting vector covering the Mpys locus was

transfected into J<8A3.N1 ESCs. Positive clones

were injected into C57BL/6J blastocysts.

Heterozygous mice were intercrossed to achieve

a homozygous line.

(Jin, Hill et al. 2011)

Tlr3-/- To generate these mice, the first exon of the Tlr3

gene was replaced with a neomycin-resistance

cassette flanked by two loxP sites. Chimeric

mice were generated by microinjecting

embryonic stem cells from three positive clones

into C57BL/6 blastocysts. Male chimeric mice

were mated with female and mutation was

confirmed by southern blot.

(Alexopoulou, Holt

et al. 2001)

OTII

MHC class II-restricted, αβTCR transgenic mice,

which express CD4+ T cells that are specific for

the OVA323–339 determinant. This mouse is on

a Rag1-/- background eliminative the expression

of endogenous TCRs.

(Barnden, Allison et

al. 1998)

μMT KO These mice were generated by targeted

disruption of the gene encoding the

immunoglobulin (Ig) u-chain constant region of

IgM. Since IgM is the first immunoglobulin

expressed by pre-immature B cells, homozygous

mutation of this gene results in B cell deficiency.

(Kitamura, Roes et

al. 1991)

PbTII x CD45.1

(PbTII)

MHCII-restricted T cell receptor (TCR)

transgenic PbTII mice were generated using

TCR genes isolated from a IA(b)-restricted

(Fernandez-Ruiz

DL, Ghazanfari et al.

2016)

33

hybridoma originally derived from a T cell line

isolated from a C57BL/6 mouse infected with

blood-stage P. berghei ANKA. Female PbTII

mice were crossed with male B6.SJL-Ptprca.

Mice were maintained as a heterozygous line and

phenotyped by flow cytometry assessment of

Vα2 and Vβ12 transgenes by CD4+ T cells.

2.1.2 Parasites

(Stabilates stored at -80◦C)

Parasite Description Reference

P. berghei ANKA

(PbA)

A transgenic PbA line (231c1l) expressing

luciferase and GFP under the control of the

ef1-α promoter. Lethal experimental model.

C57BL/6 mice succumb to cerebral malaria

after approximately 1 week of infection.

(Franke-Fayard, Janse et al.

2005)

P. chabaudi

chabaudi AS

(PcAS)

A model of resolving infection. C57Bl/6J

mice resolve infection with a standard dose

within 15 days.

(Stephens, Culleton et al.

2012)

P. yoelii 17X non-

lethal (Py17XNL)

Model of resolving infection. C57Bl/6J mice

resolve infection with a standard dose by day

30.

(Amante and Good 1997)

2.1.3 Infections

Consumable/Reagent Source

5ml tubes Greiner Labortechnik (Frickenhausen,

34

Germany)

30mL tubes Sarstedt (Pookara, Australia)

Haemocytometer Pacific Laboratory Product (Blackburn, VIC,

Aus)

Immersion oil Merck (Darmatadt, Germany)

CliniPure Haem Kwik Fixative Grale Scientific (Ringwood, VIC, Aus)

CliniPure Haem Kwik Stain No 1 Grale Scientific (Ringwood, VIC, Aus)

CliniPure Haem Kwik Stain No 2 Grale Scientific (Ringwood, VIC, Aus)

Heparin 5000 IU in 5mL Pfizer (New York, NY, USA)

Trypan Blue 0.4% Thermo Fischer Scientific

Sodium chloride 0.9% Baxter (Toongabbie, NSW, Aus)

1mL Syringe Terumo (Tokyo, Japan)

26 G needle Terumo (Tokyo, Japan)

2.1.4 Harvesting tissues

Consumable/Reagent Source

1mL Insulin syringe and 18 gauge needle Terumo (Tokyo, Japan)

10mL tubes Sarstedt(Pookara, Aus)

1.5mL eppendorf tube VWR (Radnor, PA, USA)

2.1.5 Processing spleens

Consumable/Reagent Source

Petri dishes Greiner Labortechnik (Frickenhausen,

35

Germany)

Falcon Yellow 100uM Nylon Strainer Fischer Scientific (Waltham, MA, USA)

Deoxyribonuclease 1 100mg Worthington Biochemical

Corporation(Lakewood, NJ, USA)

5ml syringe Terumo (Tokyo, Japan)

Collagenase Type 4 100mg Worthington Biochemical Corporation

(Lakewood, NJ, USA)

2.1.6 Cell transfer

Consumable/Reagent Source

MS columns Miltenyi Biotec (Bergisch Gladbach,

Germany)

LS columns Miltenyi Biotec (Bergisch Gladbach,

Germany)

Octomagnant Miltenyi Biotec (Bergisch Gladbach,

Germany)

CD4 (L3T4) Microbeads Miltenyi Biotec (Bergisch Gladbach,

Germany)

CD11b Microbeads Miltenyi Biotec (Bergisch Gladbach,

Germany)

MACS stand Miltenyi Biotec (Bergisch Gladbach,

Germany)

Vacuum-driven filter 500mL Merck Millipore (Darmstadt , Germany)

36

2.1.7 In vivo cell depletions

Consumable/Reagent Source

Diptheria toxin from Corynebacterium

diptheriae

Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

anti-CD4 depleting monoclonal antibody

(clone GK5.1)

BioXCell (West Lebanon, NH, USA)

anti-CD20 depleting (clone 5D2) Genentech (South San Francisco, CA, USA)

2.1.8 Flow cytometry reagents and materials

Consumable/Reagent Source

CELLSTAR Falcon tube (50mL) Greiner Labortechnik (Frickenhausen,

Germany)

18 G needle Terumo (Tokyo, Japan)

Falcon Centrifuge tubes (5mL) Corning (Corning, NY, USA)

RBC Lysis Buffer Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

Pipette 5mL Sarstedt (Pookara, Aus)

Pipette 10mL Sarstedt (Pookara, Aus)

Pipette 25mL Sarstedt (Pookara, Aus)

Corning Costar U bottom 96-well plates Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

BD FACS lysing solution BD Biosciences (Franklin Lakes, NJ, USA)

Foxp3 Staining Buffer Set eBioscience (San Diego, CA, USA)

Sucrose Chem-supply (Gillman, SA, Aus)

37

Ionomycin calcium salt from Streptomyces

conglobatus powder

Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

Phorbol 12-myristate 13-acetate (PMA) Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

Brefeldin A (BFA) Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

Dulbecco's Phosphate buffered saline (dPBS) Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

Percoll Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

2.1.9 Antibodies and dyes

Antibody/Dye Clone Source

Rat α mouse CD4-BV605 RM4-5 BD Biosciences (Franklin

Lakes, NJ, USA)

Hamster α mouse TCRb-APCCy7 H57-597 Biolegend (San Diego, CA,

USA)

Hamster α mouse CD69- PE H1.2F3 Biolegend (San Diego, CA,

USA)

Biotin Rat α mouse CXCR5 2G8 BD Biosciences (Franklin

Lakes, NJ, USA)

Rat α mouse Vα2-FITC B20.1 Biolegend (San Diego, CA,

USA)

Rat α mouse CXCR6- PE SA051D1 Biolegend (San Diego, CA,

USA)

Rat α mouse Vβ12-TCR PerCP-

eFluor710

MR11-1 eBioscience (San Diego, CA,

USA)

Hamster α mouse CD279 (PD-1)- APC-

eFluor 780

J43 eBioscience (San Diego, CA,

USA)

38

Rat α mouse CD279 (PD-1)-APC 29F.1A12 Biolegend (San Diego, CA,

USA)

Rat α mouse ICOS-PE 7E.17G9 eBioscience (San Diego, CA,

USA)

Mouse α mouse CD45.1-FITC A20 Biolegend (San Diego, CA,

USA)

Mouse α mouse CD45.1-Alexa Fluor

700

A20 Biolegend (San Diego, CA,

USA)

Streptavidin- BV605 Biolegend (San Diego, CA,

USA)

Streptavidin-PercpCy5.5 Biolegend (San Diego, CA,

USA)

Streptavidin-PeCy7 Biolegend (San Diego, CA,

USA)

Mouse α-mouse CD45.2-Alexa Fluor

700

104 Biolegend (San Diego, CA,

USA)

Hamster α mouse CD11c-APC N418 Biolegend (San Diego, CA,

USA)

Hamster α mouse CD11c- PercpCy5.5 N418 eBioscience (San Diego, CA,

USA)

Rat α mouse MHCII(I-A/I-E)-Pacific

Blue

M5/114-15.3 Biolegend (San Diego, CA,

USA)

Rat α mouse MHCII(I-A/I-E)-APC M5/114-15.3 Biolegend (San Diego, CA,

USA)

Rat α mouse Ly6C-FITC HK1.4 Biolegend (San Diego, CA,

USA)

Rat α mouse Ly6G-PE 1A8 Biolegend (San Diego, CA,

USA)

Rat α mouse B220-APCCy7 RA3-6B2 Biolegend (San Diego, CA,

USA)

Hamster α mouse TCRβ-BV605 H57-597 BD Biosciences (Franklin

Lakes, NJ, USA)

Hamster α mouse TCRβ-APCCy7 H57-597 Biolegend (San Diego, CA,

USA)

39

Rat α mouse CD8a-PECy7 53-6.7 Biolegend (San Diego, CA,

USA)

Rat α mouse CD172a (Sirpα)-FITC P84 Biolegend (San Diego, CA,

USA)

Rat α mouse CD11b-PercpCy5.5 M1/70 Biolegend (San Diego, CA,

USA)

Rat α mouse CD11b-BV421 M1/70 Biolegend (San Diego, CA,

USA)

Rat α mouse CD86-PE GL1 Biolegend (San Diego, CA,

USA)

Hamster α mouse CD80-PE 16-10A1 Biolegend (San Diego, CA,

USA)

Rat α mouse CD40-PE 1C10 eBioscience (San Diego, CA,

USA)

Rat α mouse CD38- Pacific Blue 90 Biolegend (San Diego, CA,

USA)

Rat α mouse B220-Alexa Fluor 700 RA3-6B2 Biolegend (San Diego, CA,

USA)

Rat α mouse CD138-BV605 281-2 Biolegend (San Diego, CA,

USA)

Rat α mouse CD80-PercpCy5.5 16-10A1 Biolegend (San Diego, CA,

USA)

Rat α mouse CD73-PE TY/23 BD Biosciences (Franklin

Lakes, NJ, USA)

Rat α mouse CD19-FITC 6D5 Biolegend (San Diego, CA,

USA)

Rat α mouse GL7-eFluor660 GL-7 eBioscience (San Diego, CA,

USA)

Hamster α mouse CD95 (FAS)- PeCy7 Jo2 BD Biosciences (Franklin

Lakes, NJ, USA)

Rat α mouse CD23-APC B3B4 Biolegend (San Diego, CA,

USA)

Rat α mouse CD21/CD35-PercpCy5.5 7E9 Biolegend (San Diego, CA,

USA)

40

Rat α mouseIFNγ-BV421 XMG1.2 Biolegend (San Diego, CA,

USA)

Rat α mouseIFNγ-PeCy7 XMG1.2 Biolegend (San Diego, CA,

USA)

Mouse α human/mouse Tbet-eFluor660 eBio4B10 eBioscience (San Diego, CA,

USA)

Mouse α mouse Bcl2-Alexa Fluor 647 BCL/10C4 Biolegend (San Diego, CA,

USA)

Rat α human/mouse RoRγT-APC AFKJS-9 eBioscience (San Diego, CA,

USA)

Rat α mouse FOXP3-Alexa Fluor 647 MF-14 Biolegend (San Diego, CA,

USA)

Rat α mouse IL-17A- BV605 TC11-18H10.1 Biolegend (San Diego, CA,

USA)

Rat α human/mouse GATA3-PerCP-

eFluor 710

TWAJ eBioscience (San Diego, CA,

USA)

Rat α mouse IL-10-BV421 JES5-16E3 Biolegend (San Diego, CA,

USA)

Rat α mouse IL-4-PE 11B11 BD Biosciences (Franklin

Lakes, NJ, USA)

Rabbit α human/mouse TCF-1- PE CD63D9 Cell Signaling Technology

(Danvers, MA, USA)

Hamster α mouse CXCL9-PE MIG-2F5.5 Biolegend (San Diego, CA,

USA)

Rat α rat/mouse Ki67-PE SolA15 eBioscience (San Diego, CA,

USA)

Mouse α human/mouse Bcl6-

PercpCy5.5

K112-91 BD Biosciences (Franklin

Lakes, NJ, USA)

Mouse α human/mouse Bcl6-PE K112-91 BD Biosciences (Franklin

Lakes, NJ, USA)

Hamster α mouse CD183 (CXCR3)- PE CXCR3-173 Biolegend (San Diego, CA,

USA)

Syto-84 Life Technologies (Carlsbad,

CA, USA)

41

Hoechst 33342 Sigma Aldrich Pty Ltd (St Louis

Missouri, USA)

CellTrace Violet ThermoFisher Scientific

(Massachusetts, USA)

Zombie Aqua Dye Biolegend (San Diego, CA,

USA)

2.1.10 Plasmodium-specific ELISA

Reagent Source

Corning Costar Flat bottom 96-well plates Greiner Labortechnik (Frickenhausen,

Germany)

OPD Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

Tween 20

Sigma Aldrich Pty Ltd (St Louis Missouri,

USA)

Streptavidin-HRP BD Biosciences (Franklin Lakes, NJ, USA)

Anti-IgM, total IgG, IgG1, IgG2b and IgG3 Jackson ImmunoResearch (West Grove, PA,

USA)

2.1.11 Single-cell RNA sequencing (scRNAseq)

Reagent Source

C1™ IFC for mRNA seq (5-10 μm) Fluidigm (San Fransisco, CA, USA)

C1™ Single-Cell Auto Prep Reagent Kit for

mRNASeq

Fluidigm (Fransisco, CA, USA)

42

Clontech SMARTer® Kit Designed for the

C1™ System, 10 IFCs

Takara Bio USA (formally Clontech)

(Mountain View, CA, USA)

Nextera XT DNA Sample Preparation Kit Illumina, (San Diego, CA, USA)

Nextera XT DNA Sample Preparation Index

Kit (96 indices, 384 samples)

Illumina (San Diego, CA, USA)

ArrayControl™ ERRC RNA Spikes Life Technologies (Carlsbad, CA, USA)

AMPure XP beads Beckman Coulter (Brea, CA, USA)

2.1.12 Media and stock solutions

RPMI/PS:- 10mL penicillin; 1L RPMI prepared in-house by QIMRB core services

PBS:- Prepared in-house by QIMRB core services

FACS Buffer:- Bovine serum albumin (BSA) (Life Technology) 100g/L; 10%w/v Sodium Azide

1ml/L; 0.5M EDTA in PBS pH8 (Sigma)10ml/L; 1L PBS; filtered and de-gassed through 0.2μm

bottle-top filter at 4oC for 20 minutes.

MACS Buffer:- BSA 100g/L; 0.5M EDTA 10ml/L; 1L PBS; filtered and de-gassed through 0.2um

bottle-top filter at 4oC for 20 minutes.

Fetal Calf Serum (FCS):- Fetal calf serum (Bovogene) was heat inactivated at 56oC for 1 hour.

FACS Block:- Generated in-house by harvesting supernatant of 120/G8 hybridoma to block against

Fc receptor.

4% PFA:- Paraformaldehyde powder (MP Biomedicals, USA) 4g/100mL; PBS 100mL; added

~20μL NaOH while gently heating and stirring until powder dissolves. Add HCL until pH is 7.

Aliquots of this were stored at -20oC until use. 1-2% PFA was made with the addition of PBS just

prior to use.

1.6% saline:- 0.16g saline; 100mL sterile milli-Q water

12% saline:- 1.2g saline; 100mL sterile milli-Q water

33% Percoll:- 1:3 v:v of Percoll in PBS

43

ELISA coating buffer:-1 L PBS; 1.59g Na2CO3; 2.93g NaHCO3; adjust pH to 9.6; filtered

ELISA blocking buffer:- 1 L PBS; 1% BSA; 0.05% Tween 20; filtered

ELISA wash buffer:- 05mL Tween20; 1L PBS.

PcAS and Py17XNL antigens:- prepared in house

2.2 Methods

2.2.1 Mice and ethics

Mice used for single-cell RNAseq experiments were maintained under specific pathogen-

free conditions at the Wellcome Trust Genome Campus Research Support Facility (Cambridge,

UK). These animal facilities are approved by and registered with the UK Home Office. All

procedures were in accordance with the Animals (Scientific Procedures) Act 1986. The protocols

were approved by the Animal Welfare and Ethical Review Body of the Wellcome Trust Genome

Campus.

All other C57Bl/6, Rag1-/-, Irf7-/-, Irf3-/-, Myd88-/-, Trif-/-, Casp1/11-/-, Mavs-/-, Tmem173-/-,

Tlr3-/-, CD11c-Cre, LysMCre x iDTRand μMT mice (all on a C57BL/6J background) were

maintained in-house at QIMR Berghofer Medical Research Institute. PbTII mice were maintained

as crosses with B6.SJL-Ptprca mice at QIMR Berghofer Medical Research Institute, and typed for

the presence of the PbTII TCR by flow cytometric assessment of peripheral blood CD4+ T cells for

co-expression of Vα2 and Vβ12.Animal procedures here were approved and monitored by the

QIMR Berghofer Medical Research Institute Animal Ethics Committee (approval no. A02-633M)

in accordance with Australian National Health and Medical Research Council guidelines.

All mice were female unless otherwise stated and used at 8-12 weeks of age.

44

2.2.2 Adoptive transfer of Plasmodium-specific CD4+ T (PbTII) cells

Spleens from PbTII mice were aseptically removed and collected into a 10mL

polypropylene tube containing 10mL of RPMI/PS. Spleens were mechanically homogenised

through a 100μm strainer with a 5mL syringe and transferred back into original 10mL tube. Cells

were pelleted by centrifuge at 1200rpm for 7 minutes at room temperature. The supernatant was

discarding before lysis of erythrocytes with 5mL of RBC lysis buffer for 5 minutes at room

temperature. The lysis buffer was quenched by topping up the tube with freshly made ice-cold

MACS buffer, and cells were pelleted as before, but at 4oC. The supernatant was discarded. Cells

were resuspended in 400μL of MACS buffer and 100μL of CD4+ microbeads and incubated for 15

minutes on ice and in the dark. In this time, the octomagnet was attached to the MACS stand and an

MS column was attached to the magnet. The column was equilibrated with 1mL of MACS buffer.

Cells were washed with MACS buffer, centrifuged as before and the supernatant discarded. Cells

were resuspended in 1mL MACS buffer. Using a 1mL pipette, cells were carefully passed through a

100μM cell strainer suspended above the MS column. The column was washed three times with

0.5mL of MACS buffer, allowing each wash to completely flow through before the addition of the

next. After the final wash, the column was removed from the magnet and placed in a new 10mL

tube. 1mL of MACS buffer was added to the column then, using force, cells were eluted from the

column with a plunger. Cells were washed with RPMI/PS, centrifuged and the supernatant

removed. Enriched cells were resuspended in 1mL RPMI/PS and counted with a haemocytometer.

10μL of cell suspension was stained for expression of CD4, TCRβ, Vα2 and Vβ12 and assessed by

flow cytometric methods for purity of enriched cells (purity typically >80% CD4+TCRβ+). Cells

were diluted to the desired concentration in RPMI/PS and injected into mice via a lateral vein using

a 1mL syringe and 18-gauge needle.

2.2.3 CellTrace violet labelling of cells

Cells were washed twice in PBS and resuspended in 1mL of PBS per 107 cells. 1μL of 1mM

CellTrace™ Violet dye in PBS was added for each 1mL of cell suspension. Cells were incubated at

37oC in the dark for 15 minutes. Cells were washed twice in RPMI/PS before being counted with a

45

haemocytometer and purity assessed by flow cytometry as above. Cells were resuspended in

RPMI/PS and injected via a lateral tail vein.

2.2.4 Infections

Plasmodium chabaudi chabaudi AS (PcAS), Plasmodium yoelii 17XNL (Py17XNL) and

Plasmodium berghei ANKA (PbA) infected red blood cells (obtained from frozen stocks) were

passaged through a C57Bl/6J mice prior to being used. Mice were infected intravenously via a

lateral tail vein with 1x104 (Py17XNL) or 1x105 (PcAS and PbA) freshly prepared parasitised RBC

(pRBC) in 200μL RPMI. Blood parasite burden (parasitemia) was measured from thin blood smears

obtained from tail bleeds. Alternatively, parasitemia was measure by taking one drop of blood from

the tail into 250μL of RPMI containing 5U/mL heparin sulphate and stained with 5μM Syto84 to

detect RNA, and 10ug/mL Hoechst33342 to detect DNA, for 30 minutes in the dark and at room

temperature. Staining was quenched with 10x initial volume of RPMI and samples were analysed

immediately by flow cytometry, using a BD FACS Canto II Cell Analyser (BD Biosciences) and

FlowJo software (Treestar, CA, USA).

2.2.5 In vivo cell depletion

2.2.5.1 CD4+ T cells

Depletion of CD4+ T cells was achieved by administering mice with 0.1mg of anti-CD4 depleting

monoclonal antibody (clone GK5.1) or an isotype control in 200μL 0.9% saline (Baxter) by

intravenous (i.v.) injection, one day prior to infection.

2.2.5.2 CD11c+ dendritic cells

Depletion of conventional CD11c+ dendritic cells was achieved using CD11c-DOG mice. Mice

were administered 160ng of DT (Sigma Aldrich) in 200μL of 0.9% saline (Baxter).

46

2.2.5.3 LysM+ monocytes/macrophages

Cellular depletion in LysMCre x iDTR mice was performed by intraperitoneal (i.p.) injection of

10ng/g DT (Sigma-Aldrich) in 200μL 0.9% saline (Baxter) per mouse at 3 day post-infection.

Control mice were given saline only.

2.2.5.3 B cell depletion

For B cell depletion, anti-CD20 (Genentech) or isotype control antibody was administered in a

single 0.25mg dose via i.p. injection in 200μL 0.9% saline (Baxter), 7 days prior to infection.

2.2.6 Euthanasia

Mice were sacrificed by asphyxiation with 80:20 mix of CO2:O2 and doused with 80% ethanol.

2.2.7 Processing tissues

2.2.7.1 Spleens

Spleens were collected into 10mL of RPMI/PS. When assessing myeloid cell populations,

spleens were incubated in Deoxyribosnuclease and collagenase for 25 minutes with constant

agitation. A cell suspension was created by homogenising each spleen through a 70μm cell strainer

with a 5mL syringe. RBCs were lysed with RBC lysing buffer (Sigma) for 5 minutes at room

temperature and splenocytes were washed once in RPMI/PS. Cell numbers were calculated from a

haemocytometer with Trypan Blue exclusion (Sigma-Aldrich). Following processing of tissue,

approximately 1/20th of total splenocytes diluted in 200μL of RPMI was transferred into a U-

bottomed 96-well plate.

47

2.2.7.2 Blood

Whole blood was harvested via cardiac bleed using a 26-gauge insulin syringe with needle

into a lithium/heparin tube for plasma or into Eppendorf’s containing 250μL of 1000ug heparin in

RPMI/PS for flow cytometry. Each blood sample was pelleted at 1200rpm for 5 minutes in a bench-

top centrifuge. The supernatant of was removed with a 1mL pipette. Samples were stained as

described below and then washed twice with FACS buffer. Red blood cells were lysed with BD

FACS Lysing solution for 7 minutes at room temperature, while mechanically disrupting the pellet

by pipetting up and down. Samples were washed twice as before.

2.2.7.3 Liver

Mice were perfused by injecting d-PBS through the heart. Livers were collected into 5mL of

PBS. Livers were digested in deoxyribosnuclease and collagenase for 25 minutes with constant

agitation before being homogenised through mesh. Liver cells were transferred into a new 50mL

FALCON tube, topped up to 50mL with cold PBS/2% FCS and pelleted for 7 minutes at 1200 rpm.

The supernatant was removed and the cells were washed again in 40mL of PBS/2% FCS. After

removing the supernatant, cells were resuspended in 25mL of 33% Percoll in PBS. Cells were spun

at room temperature for 12 minutes at 1700 rpm and with the brake off. Carefully, the top layer and

supernatant were removed from the pellet with a Pasteur pipette. RBCs were lysed with RBC lysing

buffer for 5 minutes at room temperature and cells were washed once in RPMI/PS. Cell were made

up to 200μL in RPMI/PS and a fraction taken for cell count by haemocytometer with Trypan Blue

exclusion. The remaining liver cells were transferred to a 96-well plate for staining.

2.2.7.4 Bone marrow

Hind limbs were removed from mice and collected into 25mL of RPMI/PS. Working

aseptically, tissue was removed from limbs to leave only femurs. The ends of bones were removed

with a scalpel. Using an 18-gauge needle and 10mL syringe filled with RPMI/PS, bone marrow was

flushed from the femurs in a new 50mL FALCON tube containing 10mL of RPMI/PS. Cells were

pelleted at 1200rpm for 10 minutes. Red blood cells were lysed with 5mL of lysis buffer for 5

minutes. Cells were washed twice with RPMI before being counted with a haemocytometer and

diluted to the appropriate concentration in RPMI/PS.

48

2.2.8 Bone marrow chimeric mice

Bone marrow (BM) chimeric mice were generated by injecting 2x106 syngenic BM cells

freshly harvested from the femurs of Ptprca (CD45.1) Irf3-/-, or μMT mice (described in section

2.2.7.4) into lethally irradiated (11Gy (137Cs source)) WT or Irf3-/- recipient mice via a lateral tail

vein. Recipient mice were treated with Baytril (100μg/mL enrofloxacin) in drinking water 7 days

prior to and 4 weeks following irradiation. At six weeks post-transplantation, engraftment was

assessed by flow cytometry. BM was allowed to reconstitute for 8 weeks following transplantation

before mice were used for experiments.

2.2.9 Flow cytometry

2.2.9.1 Live/Dead stain

Samples were washed twice by topping up wells to 200μL with PBS, centrifuging at

1200rpm for 4 minutes at room temperature, removing supernatant by quickly flicking the plate and

resuspending cells by gently vortexing. Cells were stained with 50μL of zombie dye diluted at

1:400 in PBS for 15 minutes in the dark and at room temperature. Samples were washed twice with

cold FACS buffer as before but at 4oC.

2.2.9.2 Surface stain

Fc receptors were blocked by incubating cells in 100μL of FACS block for 10 minutes on

ice. The plate was centrifuged, the block removed by quickly flicking and cells resuspended by

gently vortexing. The samples were stained 50μL of surface monoclonal antibodies made up in

FACS buffer for 20 minutes on ice. Cells were washed twice with cold FACS buffer.

2.2.9.3 Secondary antibody stain

Secondary antibody diluted in FACS buffer was added to each sample and incubated on ice

for 15 minutes. Cells were washed twice further.

49

2.2.9.4 Intracellular stain with eBioscience Foxp3 intracellular kit

Approximately 200μL of a 5mL preparation of homogenised spenocytes were transferred

into a 96 well U-bottom plate. Cells were pelleted by centrifuge for 4 minutes at 1200rpm. To each

sample, 200μL of 10μg/mL BFA, 10% FSC in RPMI/PS was added. For IL-10/IFNγ restimulation,

25ng/ml of PMA and 500ng/ml Ionomycin was also added to BFA cocktail. Cells were incubated

for 3 hours at 37oC. Cells were pelleted and washed twice with RPMI/PS, and surface stain was

performed as above. Following surface staining, cells were fixed and permeabilised by adding

100μL of freshly made Fix/perm solution and incubated on ice for 30 minutes, according to the

eBioscience Foxp3 intracellular staining protocol. Cells were washed twice with Perm buffer.

Intracellular antibodies diluted in Perm buffer were added and incubated for 1 hour on ice. Cells

were washed twice as before.

2.2.9.5 Detection of apoptosis

Following surface staining of samples, cell surface display of phosphatidylserine was

detected using the Annexin V-PE apoptosis Detection Kit I (BD Biosciences).

2.2.10 FACS acquisition

Samples were fixed in 2% paraformaldehyde and transferred into 5ml polypropelene FACS

tubes for acquisition on a BD LSR Fortessa Cell Analyser (BD Biosciences), MoFlo XDP

(Beckman Coulter) or a FACS Aria II (Becton Dickinson) instrument. Data were analysed using

FlowJo software (Tree Star).

2.2.11 ELISA analysis of Plasmodium-specific immunoglobulins in serum

Costar EIA/RIA 96-well flat bottom plates were coated overnight at 4oC with100μL/well of

2.5μg of soluble whole parasite lysate/ml in bicarbonate coating buffer. Wells were washed three

times (all washes in 0.005% Tween in PBS) and then blocked for 1hr at 37oC with 1% BSA in PBS

(Blocking buffer). Wells were washed three times and 100μL of sera diluted to 1/400, 1/800, 1/1600

50

or 1/3200 was added before being incubated for one hour at 37oC. Following six washes, wells were

incubated in the dark with biotinylated anti-IgM, total IgG, IgG1, IgG2b and IgG3 for one hour at

room temperature. Unbound antibodies were washed off (six times) prior to incubation with

streptavidin HRP in the dark for 30 minutes at room temperature. Wells were washed six times and

developed with 100μL of OPD for 3-5 minutes in the dark before termination with an equal volume

of 1M HCl. Absorbance was determined at 492nm using a Biotek synergy H4 ELISA plate reader

(Biotek, USA). Data were analysed using Gen5 software (version 2) and GraphPad Prism (version

6).

2.2.12 Single-cell mRNA sequencing

Activated PbTII T cells were sorted as CD4+ TCRβ+ CD4+ and CD69+ and/or divided at least

once as measured using the CellTrace proliferation dye. Dendritic cells were sorted as lineage

negative, CD11chi and MHCIIhi. Naive dendritic cells were further sorted as CD8α+ and CD11b- or

CD8α- and CD11b+. Inflammatory monocytes were identified as lineage negative, CD11bhi, Ly6Chi

and Ly6Glo.

Single cell capture and processing was performed using the Fluidigm C1 system as in

(Mahata, Zhang et al. 2014). The cell suspension obtained from sorting (made to 5000 cells/3μL)

was loaded on to the Fluidigm C1 platform using small–sized capture chips (5-10μm cells). 1 μL of

a 1:4000 dilution of External RNA Control Consortium (ERCC) spike-ins (Ambion, Life

Technologies) was included in the lysis buffer. Reverse transcription and preamplification of cDNA

were performed using the SMARTer Ultra Low RNA kit (Clontech). The sequencing libraries were

prepared using Nextera XT DNA Sample Preparation Kit (Illumina), according to the protocol

supplied by Fluidigm (PN 100-5950 B1). cDNA from each sample were barcoded (Illumina).

Libraries from up to 96 single cells were pooled and purified using AMPure XP beads (Beckman

Coulter). Pooled samples were sequenced on an Illumina HiSeq 2500 instrument, using paired-end

100-base pair reads. Depth was 1-6 million reads per cell.

51

2.2.13 Processing scRNAseq data and quality control

Reads were mapped to the Mus musculus genome (Ensembl version 38.75) concatenated

with the ERCC sequences, using GSNAP (version 2014-05-15_v2) with default parameters. Gene

expressions were quantified from the samples using Salmon (version 0.4.0) (Patro 2015). For

quality control of the single-cell data the number of input read pairs, and the amount of

mitochondrial gene content were assessed. Samples with fewer than 100,000 reads or more than

35% mitochondrial gene content, as well as cells where the number of genes detected was less than

100 + 0.008 * (mitochondrial content) were considered as failed. These were removed from further

analysis, and the remainder of the samples were taken as individual single cells. For expression

values, the Transcripts Per Millions (TPM's) estimated by Salmon (Patro 2015) included ERCC

spike-ins. Thus, for analysis of the cells, ERCC's were removed from the expression table and

TPM's scaled so they again summed to a million. This generated endogenous TPM values,

representing the relative abundance of a given gene within a cell. Also, genes were filtered for

expression in at least three cells at minimum level of 1 TPM prior to analysis, unless stated

otherwise.

2.2.14 Statistics

Single-cell RNAseq reads were analysed using R and RStudio software. Comparison

between two groups was performed using non-parametric Mann-Whitney tests. Comparisons

between multiple groups were performed using a one-way ANOVA or Two-way ANOVA with

Tukey's test for multiple comparisons. Graphs with individual points show median value, and bar

graphs display mean values ± SEM. p<0.05 was considered significant (p<0.05=*, p<0.01=**,

p<0.001=***, p<0.0001=****). All statistical analyse was performed using GraphPad Prism 6

software (San Diego, CA, USA).

52

Chapter Three:

Assessing the utility of Plasmodium-specific TCR transgenic CD4+ T

cells for studying T helper responses during experimental blood-stage

malaria

3.1 Abstract

Antigen-specific T helper cell responses have well-established roles in immunological

protection against malaria in humans and experimental mice. However, studying these responses in

vivo has proven difficult, partly due to an absence of appropriate Plasmodium-specific reagents.

Here, a newly generated MHCII-restricted TCR transgenic CD4+ T cell line was tested for its utility

in studying Plasmodium-specific T helper cell responses during blood-stage infection. CD4+ T cells

from PbTII TCR transgenic mice (PbTII cells) proliferated in response to multiple Plasmodium

strains, in a manner dependent on cDCs and MHCII. Furthermore, PbTII cells differentiated into

both Th1 and Tfh cells, but not other Th subsets, within the same animals during the first week of

infection. Moreover, the PbTII Th response closely resembled the endogenous polyclonal response.

Finally, PbTII cells also displayed Tr1, and effector and central memory phenotypes during the later

stages of infection. Therefore, PbTII cells represent a unique in vivo tool, with extensive

applications for studying Plasmodium-specific CD4+ T cell responses.

53

3.2 Introduction

Epidemiological studies and in vivo modelling have implicated CD4+ Th cell responses and

high-affinity Plasmodium-specific antibodies in the control of pRBCs (Weiss, Sedegah et al. 1993,

Whitworth, Morgan et al. 2000, Pombo, Lawrence et al. 2002). Th1 cells, defined by expression of

the transcription factor, T-bet, and secretion of the cytokine, IFNγ (Luckheeram, Zhou et al. 2012),

are generated during blood-stage Plasmodium infection (Suss, Eichmann et al. 1988), and can

control pRBC numbers (Pinzon-Charry, McPhun et al. 2010). More recently, Plasmodium infection

of mice has been reported to elicit anti-parasitic Tfh cells (Butler, Moebius et al. 2012, Perez-

Mazliah, Ng et al. 2015), which are defined by expression of the transcription factor Bcl6, the

cytokine IL-21, and cell-surface receptors CXCR5, ICOS and PD1 (Schaerli, Willimann et al. 2000,

Nurieva, Chung et al. 2009, Crotty 2011). In other experimental systems, Tfh cells are known to

migrate into B cell follicles to instruct the formation and maintenance of germinal centres, and

thereby to drive high-affinity antibody production (Kerfoot, Yaari et al. 2011). A recent study by

Ryg-Cornejo et al, demonstrated that in a model of severe malaria, pro-inflammatory cytokines

perturbed Tfh responses resulting in weak GC reactions (Ryg-Cornejo, Ioannidis et al. 2015). The

critical importance of humoral immunity for controlling pRBC is demonstrated by the inability of

mice lacking B cells or the cellular machinery for immunoglobulin-class-switching to resolve

infection (van der Heyde, Huszar et al. 1994, Butler, Moebius et al. 2012). Furthermore, passive

transfer of Plasmodium-specific antibodies in humans can provide protection against severe disease

(Cohen, McGregor et al. 1961). However, there remains a substantial gap in our current

understanding of how Th cell responses are generated during Plasmodium infection.

Genetically-marked TCR transgenic T cells allow for in vivo tracking of antigen-specific T

cell clones in modified immune environments. A Plasmodium-specific TCR transgenic mouse line

that expressed CD4+ T cells reactive against the blood-stage protein MSP-1 was used to examine

requirements for cDC subsets in initiating CD4+ T cell responses in vitro co-culture experiments

during PcAS infection (Sponaas, Cadman et al. 2006). One caveat of this reagent, however, was

that it was developed on the BALB/c genetic background, in which few immunologically-related

gene knock-out mice are currently available. This limits opportunities for identifying T-cell

extrinsic mechanisms that control CD4+ T cell responses. For this reason, Professor Bill Heath, at

The Peter Doherty Institute, University of Melbourne, Australia, has worked towards generating

reagents for studying T cell responses in mouse models of Plasmodium infection. His laboratory

firstly developed transgenic PbA parasites that expressed peptides derived from the model antigen

chicken egg ovalbumin, for which OTI (CD8+) and OTII (CD4+) T cells could be used to study

54

antigen-specific T cell responses. While these reagents have been useful (Langhorne 1989, Lundie,

de Koning-Ward et al. 2008, Haque, Best et al. 2011), it was preferable to develop T cell lines that

respond to wild-type Plasmodium antigens. Thus, a CD8+ TCR transgenic line (termed PbTI) was

developed and recently used to determine roles for CD8+ T cells during liver-stage and blood-stage

infections (Lau, Fernandez-Ruiz et al. 2014). A similar CD4+ TCR transgenic reagent, termed

PbTII, had been generated by the Heath laboratory, but had yet to be tested in vivo.

In this chapter, PbTII cells were tested for their utility as reporters of Plasmodium-specific

CD4+ T cell responses during in vivo blood-stage infection. PbTII cells proliferated upon infection

in a manner dependent on MHCII-expressing cells and cDCs. At the peak of infection towards the

end of the first week, PbTII cells had simultaneously differentiated in the same animals into Th1

and Tfh effector cells. Their response resembled the endogenous polyclonal response, thus

highlighting their usefulness for studying CD4+ T cell responses in vivo. Furthermore, PbTII cells

exhibited regulatory and memory phenotypes during later stages of infection.

55

3.3 Results

3.3.1 Generation of Plasmodium-specific TCR transgenic CD4+ T (PbTII) cells

To address the lack of immunological tools for assessing CD4+ T cell responses in vivo, our

collaborators Prof. Bill Heath and Dr Daniel Fernandez-Ruiz at The Peter Doherty Institute

(University of Melbourne) generated a novel MHCII-restricted TCR-transgenic mouse line (termed

PbTII). This mouse was developed by first generating an IAb-restricted hybridoma (termed D73) by

fusing an immortalised cell line with T cells taken from the spleen of a PbA-infected mouse. Vα2

and Vβ12 transgenes cloned from D73 were then used to generate the MHCII-restricted PbTII mice.

More than 90% of splenic CD4+ T cells in PbTII mice co-expressed Vα2 and Vβ12 TCRs (Figure

3.1A). In order to track these cells once transferred into recipient C57Bl/6J (CD45.2) mice, the

PbTII mice were crossed with congenic CD45.1+ (Ptprca) mice (Figure 3.1A).

Figure 3.1 Gating strategy for sorted Plasmodium-specific CD4+ TCR transgenic (PbTII) cells.

(A) Representative plot of PbTII cells (Vα2+ Vβ12+ CD45.1+ CD4+ TCRβ+) MACS enriched from the

spleens of PbTII-expressing mice based on CD4+ expression. Flow-through of the MACS

enrichment is shown for comparison.

CD

45

.1

CTV

81.2 71.5

88.2 94.1

0.2530.0

CD

4

TCRβ

Vα

2

Vβ12

FS

C-H

FSC-A

SS

C-A

FSC-A

A

56

3.3.2 PbTII responses are specific for Plasmodium infection

Firstly, it was important to determine whether PbTII cells responded during Plasmodium

infection. To test this, increasing numbers of CellTrace™ Violet (CTV)-labelled PbTII cells, or 105

CTV-labelled OTII cells as a negative control, were transferred into wild-type C57BL/6J mice,

which were infected with PcAS or left naive. By 7 days post-infection (p.i.), which approximates to

the peak of parasitemia in this model, PbTII cells had proliferated extensively, as determined by

CTV dilution (Figure 3.2A). The absolute numbers of splenic PbTII cells retrieved at 7 days p.i.

were approximately 10-fold higher than the initial transfer dose of 103 or 104 PbTII cells (Figure

3.2A). At higher transfer numbers of 105 and 106 cells, the absolute number of splenic PbTII cells

was approximately equivalent to the number transferred (Figure 3.2A). In contrast, non-

Plasmodium-specific OTII cells were below the threshold of detection by 7 days p.i., suggesting

they had failed to respond to the infection (Figure 3.2A). Thus, PbTII cells proliferated during in

vivo PcAS infection, with lower numbers of transferred cells eliciting stronger proliferative

responses.

To determine if PbTII proliferative responses required cDCs in vivo, splenic PbTII cells

were enumerated in PcAS-infected CD11c-DTR recipient mice, which had received 104 PbTII cells

and were treated with diphtheria toxin (DT) to deplete CD11chi cells, or with control saline (Figure

3.2B). In addition, PbTII responses were examined in PcAS-infected, CD11cCre IAbf/f recipient mice

(104 PbTII cells transferred), in which CD11c+ cells lacked expression of the MHCII molecule, IAb,

compared to control IAbf/f littermates (Figure 3.2B). In both experimental systems, lack of cDCs or

MHCII-expression by CD11chi cells resulted in substantial reductions in splenic PbTII numbers

(Figure 3.2B). Together, these data strongly suggested that in vivo proliferation of PbTII cells

during PcAS infection was dependent upon cDC-mediated MHCII-restricted antigen presentation.

Finally, to explore the influence of PbTII cells on parasitemia, increasing numbers of PbTII

cells were transferred into PcAS-infected WT mice, and peripheral parasitemia was monitored until

the first peak of infection was controlled. No change in the kinetics of PcAS infection was observed

upon transfer of PbTII cells compared with infected mice receiving no PbTII cells, demonstrating

that PbTII cells did not influence the outcome of PcAS infection in WT mice (Figure 3.2C).

57

Figure 3.2 PbTII cells proliferate in an MHCII-dependent manner during PcAS infection. CTV-

labelled PbTII cells or non-specific control OTII cells were transferred at the indicated number into

C57Bl6/J mice that were left naïve or infected with PcAS. (A) Representative plots of total CD4+ T

cell population showing expansion of transferred cells and graphs depicting total numbers of

transferred cells in the spleen of mice at 7 days p.i.. (B)Numbers of PbTII cells (104 transferred) in

the spleens of DT treated CD11c-DTR or CD11cCreIAbfl/fl or appropriate control mice at 7 days p.i.

(n=5-6). (C) Kinetics of infection determined by proportion of parasitised red blood cells (pRBC).

(A) Data are a single experiment, or (B &C) representative of two independent experiments.

Graphs show mean with SEM. * p<0.05, **p<0.01.

D a y s p o s t - in fe c t io n

Pa

ras

ite

mia

(%

iR

BC

)

0 5 1 0 1 5

0

2 0

4 0

6 0

10

1 02

1 04

1 06

B

No

. P

bT

II (

x1

05)

WT

: D

T

CD

11c -D

TR

: D

T

IAbf /

f

CD

11cC

re I

Ab

f /f

0

5

10

C

Pb

TII

ce

lls

0

10

^5

10

^3

10

^4

10

^5

10

^6

10

^5

1 0 - 1

1 0 0

1 0 1

1 0 2

1 0 3

1 0 4

1 0 5

1 0 6

1 0 7

+ - + + + + +

- + + + + + -

- - - - - - +

P c A S

P b T II

O T II

No transfer

100,000PbTII cells

100,000 OTII cells

CD

45

.1

CTV

AF

SC

H

Liv

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ea

d

SS

CA

CD

4FSCA FSCA

FSCA TCRβ

58

3.3.3 Th1 and Tfh are distinct effector subsets

CD4+ T cell clones have been reported to display preferential skewing towards certain Th

subsets (Tubo, Pagan et al. 2013). OTII cells, for example, preferentially differentiate into Tfh cells

over Th1 cells in response to LCMV infection (Tubo, Pagan et al. 2013). Thus, it was important to

determine whether PbTII cells differentiated into T helper cells, and the kinetics of this

differentiation. To enable tracking of PbTII cells at earlier time points, 106 CTV-labelled PbTII

cells were transferred into mice and splenic PbTII cell responses assessed at 1, 4 and 7 days p.i.

with PcAS (Figure 3.3A). PbTII cells proliferated extensively by 4 days p.i. as determined by the

loss of CTV and total numbers of splenic PbTII cells. Additionally, PbTII cells that had progressed

through more than 5 divisions up-regulated CXCR5 expression, a marker of Tfh differentiation, and

exhibited modest, direct ex vivo expression of IFNγ, a Th1 marker (Figure 3.3A). PbTII expansion,

as well as IFNγ and CXCR5 expression increased up to day 7 p.i. (Figure 3.3A).

Intra-clonal competition has previously been reported at transfer of higher numbers that can

affect the magnitude of the T cell responses (Foulds, Rotte et al. 2006, Badovinac, Haring et al.

2007, Bautista, Lio et al. 2009). Therefore, to characterise Th1 and Tfh responses further at peak

infection, 104 PbTII cells were transferred. Importantly, most PbTII cells expressed either IFNγ

(Th1) or CXCR5 (Tfh) at 7days p.i. (Figure 3.3B), in a manner similar to the polyclonal Th

response (Figure 3.3B). Of particular note, bifurcated PbTII cells, expressing either IFNγ or

CXCR5, also expressed canonical transcription factors T-bet or Bcl6 respectively (Figure 3.3C). To

determine the effect of intra-clonal competition on PbTII cell responses, PbTII cells were

transferred at numbers of 102, 104 or 106 into WT mice, where transfer 102 represents the most

physiologically relevant frequency in naïve mice (Moon, Chu et al. 2007). Proportions of Th1 or

Tfh cells within the PbTII population were equivalent for transfers of 102 or 104 cells, while transfer

of 106 PbTII cells elicited lower proportions of Th1 cells and Tfh cells (Figure 3.4A). The absolute

number of both effector populations was equally high upon transfer of 104 or 106 compared to 102

cells (Figure 3.4A) Thus, use of 104 PbTII cells was appropriated for assessing PbTII responses at

peak infection. Together, these data suggests that PbTII cells bifurcate into either Th1 or Tfh

effector cells, similar to the polyclonal response.

59

Figure 3.3 Plasmodium-specific TCR transgenic CD4+ T (PbTII) cells bifurcate into Th1 and Tfh

cells during PcAS infection. (A) CellTrace™ Violet (CTV)-labelled, CD45.1+ PbTII cells (106)

were transferred into WT mice (n=4) prior to infection with PcAS. Representative FACS plots of

CTV loss and IFNγ or CXCR5 expression, and numbers of splenic PbTIIs at indicated days p.i.. (B)

Representative FACS plots and summary graphs showing proportions of PbTII cells (CD45.1+

CD4+ TCRβ+ live singlets) and endogenous polyclonal CD4+ T cells (CD45.1- CD4+ TCRβ+ live

singlets) expressing IFNγ or CXCR5 in PcAS-infected mice 7 days p.i. (receiving 104 PbTII cells)

compared to naïve controls (106 PbTII cells). (C) Left: representative FACS plots (gated on

CD45.1+ CD4+ TCRβ+ live singlets) and a summary graph showing T-bet and Bcl6 expression by

splenic PbTII cells (CD45.1+) from naive and 7-day PcAS-infected mice (n=5). Right:

representative histograms of IFNγ and CXCR5 expression by T-bethi Bcl6lo (red), Bcl6hi T-betlo

(blue) PbTII cells compared to those from naive mice (grey). Data are representative of two

independent experiments. Statistics: Mann-Whitney U test; *** p<0.001; ****p<0.0001.

CXCR5

IFNγ

Naiv

eP

cA

SPbTIIB

0.4 0.3

2.16

0.4 0.2

3.0

7.3 1.5

17.6

25.8 2.9

27.6

Polyclonal

2.21

12.7

49.5

40.1

Bcl6

Tb

et

CXCR5

IFNγ

C

Na

ive

PcA

S

0 2 4 6 8

1 0 4

1 0 5

1 0 6

1 0 7

D a y s p o s t - in fe c t io n

Pb

TII

ce

lls

/sp

lee

n0

5

1 0

1 5

% o

f C

D4

+ T

ce

lls

***

****

P c A S - -+ +

IFN

+

CX

CR

5+

0

1 0

2 0

3 0

% o

f P

bT

II

***

P c A S - -+ +

IFN

+

CX

CR

5+

***

CXCR5

IFNγ

Naiv

eP

cA

S

0

2 0

4 0

6 0

% o

f P

bT

II

P c A S - -+ +

Tb

et+

Bc

l6+

**

Da

y 1

Da

y 4

CD

45

.1 0.3

1.4

7.0IF

Nγ

CX

CR

5

Day 7

CTV

A

60

Figure 3.4 Differentiation of PbTII cells during PcAS infection. PbTII cells were transferred at

the indicated doses into PcAS-infected WT mice. Proportions and absolute numbers of T-bet+ IFNγ+

(Th1) or CXCR5+ Bcl6+ (Tfh) splenic PbTII cells at day 7 p.i..

3.3.4 PbTII display the capacity to differentiate into T regulatory 1 cells

Regulatory responses by Tregs and Tr1 cells have been observed in human and experimental

malaria studies (Langhorne, Gillard et al. 1989, Amante, Stanley et al. 2007, Couper, Blount et al.

2008, Finney, Nwakanma et al. 2009, Walther, Jeffries et al. 2009, Berretta, St-Pierre et al. 2011).

Similarly, IL-4 positive Th-2 cells have been implicated in immune responses to malaria

(Langhorne, Gillard et al. 1989). Therefore, PbTII cells were assessed for their capacity to

differentiate into regulatory T cells and other Th subsets during PcAS infection. While

approximately 4% of the recipient polyclonal CD4+ T cells expressed FOXP3 at peak infection

(Figure 3.5A), no such FOXP3 expression was observed from the PbTII cells (Figure 3.5A).

Notably, however, after peak infection, ex vivo restimulation of PbTII cells resulted in co-

expression of IL-10 and IFNγ by approximately 5% of PbTII cells (Figure 3.5B), evidence of Tr1

effector function as seen from polyclonal CD4+ T cells in this infection (Montes de Oca, Kumar et

al. 2016). Finally, neither endogenous polyclonal CD4+ T cells nor PbTII cells expressed Th2

markers, IL-4+ and GATA3+, or Th17 markers, IL-17A and RORγT at peak infection (Figure 3.5C).

PcAS

Tb

et+

IF

N

+ (

%)

106

102

104

106

0

1 0

2 0

3 0**

N S

Bc

l6+C

XC

R5

+(%

)

106

102

104

106

0

2 0

4 0

6 0

8 0*

N S

PcAS

106

102

104

106

0 .0

0 .1

0 .2

1

2

3

4

Tb

et+

IF

N

+ (

x1

05)

*

*

N S

PcAS

106

102

104

106

0 .0

0 .5

1 .0

5

1 0

1 5

Bc

l6+ C

XC

R5

+(x

10

5)

*

*

N S

PcAS

61

Together, this demonstrates that during PcAS infection, PbTII cells have the capacity to

differentiate into Tr1 cells, but not Treg, Th2 and Th17 cell subsets.

Figure 3.5 PbTII cells differentiate into regulatory cells during PcAS infection. PbTII cells were

transferred into C57Bl/6J mice infected with PcAS (104/mouse) or left uninfected (106/mouse).

(A)Representative FACS plots (gated on CD4+ TCRβ+ live singlets) and proportion of Treg

(FOXP3+) cells within the PbTII (CD45.1+) or polyclonal CD4+ T cell population (CD45.1-) at 7

days p.i.. (B) Representative FACS plots (gated on CD45.1+ CD4+ TCRβ+ live singlets) and

summary graph of ex vivo restimulated IL-10 and IFNγ co-expression by splenic PbTII cells (106

transferred) in naive (n=2) or day 9-infected mice (n=6). (C) Representative FACS plots (gated on

CD45.1+ CD4+ TCRβ+ live singlets) of IL-4+ GATA3+ (Th2) and IL-17+ RORγt+ (Th17) splenic

PbTII cells at 7 days p.i. (n=6). Data show mean and SEM and are representative of two

independent experiments. Statistics: Mann-Whitney U test; *p<0.05.

CD45.1

FO

XP

3

GATA3

IL-4

0.010

RORγt

IL-1

7

0.030

Naive PcAS

0.323.54

Po

lyc

lon

al

Pb

TII

0

1

2

3

4

5

% F

OX

P3

+

*

A

C

Naive PcAS

IL-10

IFNγ

B1.484.78 6.8114.0

Na

ive

Pc

AS

0

2

4

6

8

% I

L-1

0+ I

FN

+

62

3.3.5 PbTII cells migrate to the liver and display a Th1 phenotype

Next, it was of interest whether effector PbTII cells could migrate to peripheral tissues such

as the liver, in which CD4+ T cell responses have previously been identified during blood-stage

malaria (Mastelic, do Rosario et al. 2012). Indeed, PbTII cells were observed in the liver at peak

infection, although roughly 100-fold fewer compared to the number within the spleen (Figure 3.6A

and Figure 3.3A). Of the PbTII cells within the liver, approximately 50% expressed IFNγ (Figure

3.6B). Conversely, no CXCR5 expression was detected (Figure 3.6B). Together, these data show

that PbTII cells can migrate to the liver, and display a Th1, but not Tfh effector phenotype.

Figure 3.6 IFNγ+ PbTII cells migrate to the liver. PbTII cells (106) were transferred into mice that

were infected with PcAS (n=4) or left naive (n=1). (A) Absolute numbers of PbTII cells in the liver

at day 7 p.i.. (B) Representative FACS plots (gated on CD45.1+ CD4+ TCRβ+ live singlets) and

proportions of IFNγ and CXCR5 expression by PbTII cells in the liver at day 7 p.i.. Numbers on

plots show proportion of PbTII population within respective quadrants. Data show mean with SEM

and are a single experiment.

Naiv

e

PcA

S

0

2

4

6

Pb

TII

(x

10

5)

IFNγ

CXCR5

PcA

SN

aiv

e

IFN

+

CX

CR

5+

0

2 0

4 0

6 0

% o

f P

bT

II

1.8 0.4

0.4

52.5 0.5

0.7

B

A

63

3.3.6 PbTII cells develop into memory cells after acute PcAS infection

Memory T cells have been associated with long-lasting protection against Plasmodium

infection in mice and humans (Grun and Weidanz 1981, Tsuji, Romero et al. 1990, Migot,

Chougnet et al. 1993, Langhorne 1994, Stephens and Langhorne 2010). However, it has been

challenging to definitively track memory T cell responses. To determine if PbTII cells survived

beyond the first week of infection, and could convert into memory phenotypes, PbTII cells were

assessed after peak parasitemia had been controlled. PbTII cells were detected in the spleens of WT

recipient mice by 37 days p.i. with PcAS (Figure 3.7A), and depending on the number of cells

transferred, exhibited both effector memory (CD62Llo CD44hi) and central memory (CD62Lhi

CD44hi) phenotypes (Figure 3.7B). Thus, PbTII cells exhibited the potential to form splenic

memory responses after acute PcAS infection.

Figure 3.7 PbTII cells develop into memory cells after acute PcAS infection. 104 or 106 PbTII

cells were adoptively transferred to C57Bl/6J mice (n=3-4) infected with PcAS. (A) Enumerated

splenic PbTII cells at 37 days p.i.. (B) Representative FACS plots of CD62L and CD44 expression

by recipient polyclonal CD4+ T cells (gated on CD45.1- CD4+ TCRβ+ live singlets) or PbTII cells

(gated on CD45.1+ CD4+ TCRβ+ live singlets) and summary graphs showing proportions of central

memory (CD62L+ CD44+) (TCM) and effector memory (CD62L-CD44+) (TEM) splenic PbTII cells.

Numbers in plots are proportion of population within respective quadrants. Data are a single

experiment.

Recipient CD4+

CD44

CD

62

L

104 106

14.6 29.2

53.6

0 18.9

81.1

0.73 33.3

65.3

B TCM TEM

A

Pb

TII

ce

lls

104

106

1 0 0

1 0 2

1 0 4

1 0 6

% C

D6

2L

+ C

D4

4+

104

106

0

2 0

4 0

6 0

% C

D6

2L

+ C

D4

4-

104

106

0

2 0

4 0

6 0

8 0

1 0 0

64

3.3.7 PbTII cells show cross-species reactivity to Plasmodium infection

Lastly, PbTII cells were assessed for their ability to respond to two other commonly used

rodent Plasmodium strains- Py17XNL and PbA. During Py17XNL infection, transfer of fewer than

105 cells did not give rise to a reliable PbTII response. Thus, 105 cells were determined to be the

optimal dose in this model. Similar to PbTII responses during PcAS infection, PbTII cells expanded

and bifurcated into IFNγ+ T-bet+ Th1 cells or CXCR5+ Bcl6+ Tfh cells in WT mice by day 11 of

Py17XNL infection, which was also seen in the endogenous polyclonal response (Figure 3.8A).

Finally, transfer of 106 PbTII cells was found to be the optimal transfer dose during PbA infection.

PbTII cells proliferated, and began to differentiate into Th1 cells, but not Tfh cells by day 4 p.i.

during PbA infection (Figure 3.8B), in accordance with polyclonal CD4+ T cell responses and

previous reports (Ryg-Cornejo, Ioannidis et al. 2015). These results together demonstrate the

reactivity of PbTII cells in several murine models of malaria, and furthermore highlight possible

differences in magnitude of Th responses mounted during infection with these three strains.

65

Figure 3.8 PbTII cell expansion and Th1/Tfh differentiation during P. yoelii 17XNL and P.

berghei ANKA blood-stage infection. (A) Representative FACS plots of polyclonal CD4+ T cells

(gated on CD45.1- CD4+ TCRβ+ live singlets) and PbTII cells (105 transferred) (gated on CD45.1+

CD4+ TCRβ+ live singlets), showing proportions expressing IFNγ or CXCR5, as well as absolute

numbers of splenic PbTII cells 11 days p.i. with Py17XNL, compared to uninfected WT mice. (B)

Representative FACS plots of polyclonal CD4+ T cells (gated on CD45.1- CD4+ TCRβ+ live

singlets) and PbTII cells (106 transferred) (gated on CD45.1+ CD4+ TCRβ+ live singlets), showing

proportions expressing IFNγ or CXCR5, as well as absolute numbers of splenic PbTIIs 4 days p.i.

with PbA. Data pooled from two independent experiments. Statistics: Mann-Whitney U test;

*p<0.05; **p<0.01; ****p<0.0001.

Py1

7X

NL

Na

ive

Polyclonal PbTII

IFNγ

CXCR5

0.29 0

0.9

0.1 0.1

1.9

0.9 0.1

10.1

3.28 0.1

19.3

0

2

4

1 0

2 0

% o

f p

op

ula

tio

n

**

*

P y 1 7 X N L - - -+ + + - +

IFN

+

CX

CR

5+

IFN

+

CX

CR

5+

*

Polyclonal PbTII

0 .0 1

0 .1

1

1 0

1 0 0

% I

FN

+**

P b A - -+ +

**

IFNγP

bA

NK

AN

aiv

e

CXCR5

Polyclonal PbTII0.31 0.3

0.2

0.1 0

0.3

0.85 0

0.8

7.2 0.1

0.6

Polyclonal PbTII

B

Naiv

e

Py17X

NL

0

1 0

2 0

3 0

Pb

TII

(x

10

5)

****

Naiv

e

PbA

NK

A

0

1 0

2 0

3 0

Pb

TII

(x

10

5)

**

A

66

3.4 Discussion

Antigen-specific CD4+ T helper responses protect against blood-stage Plasmodium infection

in humans and experimental mice. However, studying the emergence of these responses in vivo is

difficult due to the low frequency of antigen-specific T cells in the naive repertoire, and that T cell

priming occurs in the spleen, which is not easily visualised in humans. Murine TCR transgenic T

cells are therefore a powerful reagent, as they allow for in vivo tracking of T cell priming and

differentiation. Moreover, their responses to manipulation of the external immune environment can

be assessed via transfer into genetically altered recipient mice. Unfortunately, few TCR transgenic

reagents have been available for assessing CD4+ T cell responses during Plasmodium infection.

Here, the utility of a recently developed Plasmodium-specific TCR transgenic CD4+ T cell line for

following T helper responses during in vivo blood-stage malaria was investigated.

Adoptively transferred PbTII cells proliferated extensively, and differentiated into two T

helper subsets- Th1 and Tfh cells, during PcAS infection. The response of PbTII cells was critically

dependent on MHCII-expressing CD11c+ cells. This is consistent with previous in vivo and ex vivo

studies showing important roles for cDCs in priming CD4+ T cells during Plasmodium infection

(deWalick, Amante et al. 2007, Sponaas, Freitas do Rosario et al. 2009, Haque, Best et al. 2014).

While IL-17A and IL-17F-producing Th17 cells and IL-4-producing Th2 have been identified

during experimental blood-stage Plasmodium infection (Langhorne, Gillard et al. 1989, Keswani

and Bhattacharyya 2014), whether these subsets have a significant role in protective immunity has

not been demonstrated. Here, no evidence of Th2 of Th17 differentiation was observed from PbTII

cells or endogenous polyclonal CD4+ T cells at peak infection. Thus, in the spleen during PcAS

infection at least, these subsets do not appear to contribute to immune responses.

Although experimental malaria and field studies have reported the involvement of Treg cells

in immune responses to infection (Hisaeda, Maekawa et al. 2004, Finney, Nwakanma et al. 2009),

PbTII cells did not express FOXP3 at peak infection. Since a subset of FOXP3+ Tregs develop in

the thymus from moderate affinity of their TCR for self-antigen, it is not surprising that PbTII cells,

with forced expression of a Plasmodium reactive TCR, display limited capacity to become

regulatory cells. Interesting, however, PbTII cells did have the capacity to co-produced the

regulatory cytokine IL-10 and the proinflammatory cytokine IFNγ+, a phenotype that has been

termed 'Tr1' and has also previously been observed in human and experimental malaria (Couper,

Blount et al. 2008, Walther, Jeffries et al. 2009). This population appeared to emerge from the

IFNγ+ populations at a time point after the emergence of the Th1 response, suggesting that it could

represent a continuation from the Th1 phenotype. A study by Cardone et al. has proposed that CD46

67

expression mediates the switch from the Th1 phenotype to a Tr1 phenotype in purified human CD4+

T cells (Cardone, Le Friec et al. 2010). However the relationship between Th1 and Tr1 cells has not

been demonstrated within a clonal population or in vivo. Thus, these observations from PbTII cells

offer the prospect of exploring whether Tr1 cells do emerge from Th1 cells during in vivo infection.

After the acute infection, PbTII cells displayed both TCM and TEM phenotypes within the

spleen. Memory T cells contribute to long-term immunity to malaria (Grun and Weidanz 1981,

Tsuji, Romero et al. 1990, Langhorne 1994), but are also hypothesised to be functionally deleted or

modulated during Plasmodium infection (Hirunpetcharat and Good 1998, Xu, Wipasa et al. 2002).

PbTII cells would be useful for examining how these populations of memory T cells develop, and

also how their development can be boosted in the future to improve long-term protection by

vaccines or other immune-interventions. In addition to circulating TEM and lymphoid-resident TCM

populations (Sallusto, Geginat et al. 2004), another tissue resident memory T cells (TRMs) have been

shown to reside in peripheral tissues. The best described TRMs are CD8+ and have been shown to

provide local immune responses in the skin (Mackay, Rahimpour et al. 2013), ganglia (Gebhardt,

Wakim et al. 2009), intestinal mucosa (Kim, Reed et al. 1997), brain (Wakim, Woodward-Davis et

al. 2010) and thymus (Hofmann, Oschowitzer et al. 2013). CD4+ TRMs have also been described in

the skin (Glennie, Yeramilli et al. 2015), genital tract (Iijima and Iwasaki 2014) and lungs (Teijaro,

Turner et al. 2011). In the liver, CD44hi antigen-specific CD8+ TRMs were identified in mice at 45-

days after immunization with irradiated P. yoelii sporozoites (Tse, Cockburn et al. 2013). These

memory cells displayed differential gene expression compared with antigen-specific memory CD8+

T cells in the spleen, suggesting they represented a distinct population from central memory T cells.

Whether an equivalent tissue-resident CD4+ T cell population exists within the liver is unknown.

Here, PbTII cells are also shown to exist within the liver during blood stage-infection. A large

proportion exhibited a Th1 phenotype, but none differentiated into Tfh cells, in alignment with the

role of Tfh cells in germinal centre formation within secondary lymphoid organs (Crotty 2011).

Given the ideal localisation of liver-resident TRMs to contribute to memory responses against liver-

stage infection (Tse, Cockburn et al. 2013), assessing PbTII memory responses in the liver would be

of high interest, specifically for vaccine development targeting liver-stage of infection.

A particularly interesting observation from these data was that adoptive transfer of PbTII

cells did not modulate the kinetics of the infection. A possible explanation is that PbTII cells are not

involved in the clearance of pRBCs. However, this does not seem likely given the strong Th1

response of PbTII cells at peak infection and the demonstrated contribution of Th1 responses in

clearance of pRBCs (Taylor-Robinson and Phillips 1994, Amante and Good 1997, Haque, Best et

al. 2011, Haque, Best et al. 2014). Another explanation could be that endogenous CD4+ T cells in

68

WT mice infected with PcAS are already adequate for the number of macrophages to phagocytose

the pRBCs (Clark, Hunt et al. 1987, Taylor-Robinson and Phillips 1998)and for the number of B

cells that contribute to resolution of infection (Butler, Moebius et al. 2012). In order to test this

hypothesis, further experiments could include transfer of PbTII cells into Rag1-/- mice that do not

have endogenous T or B cell responses. PbTII cells would not be expected to differentiate into Tfh

cells in the absence of B cells within this system (Kerfoot, Yaari et al. 2011), but it would allow for

functional assessment of Th1 PbTII cells. Alternatively, PbTII cells could be transferred into CD40

ligand-deficient mice, in which endogenous T cells lack the ability to receive co-stimulatory signals

and can therefore not undergo activation (Grewal, Foellmer et al. 1996).

Here, increasing doses of PbTII cells were tested for their responses compared with an

approximately physiologically relevant number of 102 PbTII cells (Moon, Chu et al. 2007).

Compared to 102 transferred cells, 104 resulted in similar PbTII cell responses by peak PcAS

infection; however transfer of 106 PbTII cells resulted in reduced expansion and proportions of T

helper populations. This suggests that transfer of 104 cells is a practical method for studying

physiologically relevant responses during PcAS infection. At transfer doses in the order of 106 cells,

however, PbTII cells experienced intra-clonal competition, possibly due to processes such as

competition for contact with APCs or for physical room within the spleen. This phenomenon of

intra-clonal competition has been observed within multiple TCR transgenic cells (Langhorne,

Gillard et al. 1989, Langhorne and Simon 1989). It was particularly interesting that the Th1

response was impaired to a greater extent at higher PbTII transfer doses than the Tfh response,

suggesting that Tfh cells are more impartial to clonal competition. This observation is consistent

with the concept that Tfh differentiation is the default pathway for activated CD4+ T cells(Crotty

2014). A study by Eto et al. provides support for this idea by showing that unlike other

differentiation of Th subsets, which require the presence of a key cytokine, Tfh differentiation can

be induced by multiple redundant signals during viral challenge (Eto, Lao et al. 2011). A highly

reliable Tfh response is biologically conceivable given that subsequent GC development and

affinity maturation are central processes of the adaptive immune response.

By using a single T cell clone, these data have provided the evidence that the magnitude of

Th responses for a given TCR is different between models. The optimal responses were observed

upon the transfer of 106, 104 and 105 cells respectively for PbA, PcAS and Py17XNL infections.

However, a direct comparison between the Th responses induced by these different strains cannot

be definitively performed since the parasite antigen triggering PbTII cells, and whether this antigen

differs between Plasmodium strains, has not yet been determined. Nevertheless, relative to

endogenous CD4+ T cell responses, it can be inferred that the greater the endogenous Th response,

69

the fewer PbTII cells required. TCR transgenic cells can exhibit some preference in their

differentiation toward a particular Th subset (Tubo, Pagan et al. 2013). This was not the case with

PbTII cells, since they could bifurcate into both Th1 and Tfh cells during in vivo Py17XNL and

PcAS infections, in a manner that closely resembled the endogenous polyclonal responses. During

PbA infection, PbTII cells gave rise to Th1 cells before the animals succumbed to disease 7-9 days

after infection. As with the endogenous polyclonal response, CXCR5-expressing PbTII cells failed

to develop in this model. The absence of a polyclonal Tfh response during PbA infection has been

reported recently (Ryg-Cornejo, Ioannidis et al. 2015). Although the antigen to which PbTII cells

respond is yet to be determined, the cross-reactivity of this line suggests that the target epitope is

expressed by at least three common rodent infective strains. Whether PbTII cells also respond to

human infective Plasmodium species such as P. falciparum, or to other life stages of the parasite,

such as the liver-infective sporozoite stage, will be of future interest. Further studies will be

required to determine the exact antigen recognized by PbTII cells.

In conclusion, the utility of a novel TCR transgenic tool for studying Plasmodium-specific

CD4+ T cell responses during experimental blood-stage malaria has been demonstrated. PbTII cells

proliferated in a cDC- and MHCII-dependent manner. They developed into Th1 and Tfh cells, Tr1

cells and later TEM and TCM cells. Therefore, this reagent provides an opportunity for examining

multiple aspects of CD4+ T cell biology during experimental models of malaria in mice.

70

Chapter Four:

Single-cell RNA-sequencing resolves a CD4+ T helper cell fate

bifurcation

4.1 Abstract

CD4+ T helper cells are critical for immunity to blood-stage Plasmodium infection. During

experimental malaria in mice T helper 1 (Th1) and T follicular helper (Tfh) subsets emerge, which

have both been associated with better infection outcomes. In vitro experimentation and

mathematical modelling have proposed asymmetric cell division and internal stochastic events as

possible mechanisms controlling T cell differentiation fates. However, molecular mechanisms

controlling this process in vivo remain unclear. In Chapter 3, TCR transgenic PbTII cells were

shown to concurrently differentiate, or bifurcate, into Th1 or Tfh effector cells during infection.

Here, to study this process at molecular resolution, a single-cell RNAseq (scRNAseq) approach was

adopted. Emerging Th cells entered a state of high cell-cycling after initial priming, in which they

expressed markers of both Th1 and Tfh subsets. This occurred prior to a commitment to a

differentiated, and possibly terminal, Th1 or Tfh effector state. Furthermore, expression of

chemokine receptors prior to bifurcation suggested that CD4+ T cells were receptive to chemokine-

expressing cells at this point. While B cells were shown to sustain PbTII Tfh responses, additional

scRNAseq analysis of myeloid populations supported roles for cDCs and Ly6Chi inflammatory

monocytes in interacting with un-committed, activated PbTII cells. Depletion of Ly6Chi monocytes

after PbTII activation, and prior to bifurcation, demonstrated their support of Th1 but not Tfh

responses. In summary, Plasmodium-specific CD4+ T cells do not commit to an effector state until

several days after initial activation, before which they are indistinguishable at the transcriptional

level. In particular, activated PbTII cells required support from B cells and myeloid populations to

sustain commitment to Tfh and Th1 effector states respectively.

71

4.2 Introduction

CD4+ T cells exhibit substantial functional diversity in response to many immune

challenges, including blood-stage Plasmodium infection. Naive CD4+ T cells are activated upon

recognition of antigenic peptides presented by MHCII molecules on the surface of APCs. They

proceed to undergo clonal proliferation and differentiate into a range of T helper (Th) cell subsets,

classified according to their expression of cytokines, transcription factors and surface molecules. T

cells typically display skewed effector phenotypes in response to an immune challenge. For

example, intracellular infections induce differentiation of TNFα- and IFNγ- producing Th1 cells,

whereas helminth infections and allergy are characterised by the emergence of IL-4-, IL-5- and IL-

13- secreting Th2 cells. As discussed in Chapter 3, two subsets concurrently emerge during

Plasmodium infection: Th1 cells, which are considered important for macrophage-mediated

clearance of infected red blood cells (Clark, Hunt et al. 1987, Taylor-Robinson and Phillips 1998),

and Tfh cells, which assist in the production of high-affinity antibodies (Perez-Mazliah, Ng et al.

2015). While Th1 and Tfh cells are established to be protective during malaria, the molecular and

cellular mechanisms underlying their development are not fully understood.

Recent in vivo data suggested that the unique TCR of a single naïve CD4+ T cell imparts a

genetically programmed preference towards a particular Th fate (Tubo, Pagan et al. 2013). This

study performed limiting dilution-mediated single-cell adoptive transfer of naïve transgenic T cells

into mice, and showed that clonal cells gave rise to a limited range of effector subsets in response to

Listeria monocytogenes infection. However, extracellular factors such as exposure to cytokine

signals also profoundly influence the magnitude of the response, and skew differentiation towards

particular Th fates. For example, extracellular IFNγ can promote a Th1 response, while IL-4

promotes Th2 development. Additionally, the stochastic balance of several master transcription

factors known to be expressed in CD4+ T cells, such as Blimp1, T-bet and Bcl6, drive and stabilize

particularly Th fates (Szabo, Kim et al. 2000, Liu, Yan et al. 2012, Tubo and Jenkins 2014). This

supports a view of Th development as a choice between clearly distinct states. However, the

molecular relationship between Th subsets, particularly between Tfh and other Th fates remains

unclear in vivo.

Intra-vital microscopy has demonstrated that naïve CD4+ T cells make stable engagements

with cDCs during initial priming events in secondary lymphoid tissue (Celli, Lemaitre et al. 2007).

Thereafter, further pMHCII contact is required to sustain expansion of the responding T cell

population (Obst, van Santen et al. 2005). A recent study by Groom et al. suggested that

chemokines CXCL9 and CXCL10 facilitated interaction between adoptively-transferred antigen-

72

loaded cDCs and activated CXCR3+ CD4+ T cells in lymph nodes, and furthermore, were

associated with Th1 differentiation (Groom, Richmond et al. 2012). The generation of Tfh cells is

also a multi-step process. After the initial priming events, internal upregulation of Bcl6 drives the

expression of Tfh-associated genes, including CXCR5 to promote migration of pre-GC Tfh cells to

the T-B cell border of the follicle. Here, Tfh cells are critical for supporting GC formation and B

cells are imperative for sustaining Tfh effector cells, for example via ICOS:ICOSL signalling (Choi,

Kageyama et al. 2011). Whether interactions with different endogenous myeloid cells modulate the

decision of a primed T cell towards either Th1 or Tfh fates in vivo is yet to be explored.

Flow cytometry has been instrumental in studying CD4+ Th cells and their relationship with

disease. However, even the most advanced machines only allow for the simultaneous assessment of

15-20 parameters. Furthermore, this methodology depends almost completely on high-quality

antibodies, a reliance that tends to bias analyses towards better-known molecules. More recently, a

mass spectrometry-based flow cytometry method, CyTOF, has extended the number of assessable

parameters to ~50 (Newell and Davis 2014). CyTOF allowed for dissection of the relationship

between the CD8+ T cell phenotype, function and antigen-specificity by simultaneously assessing

several markers for each quality (Newell, Sigal et al. 2012). Similarly, this technique was applied to

murine myeloid cells taken from various tissues (Becher, Schlitzer et al. 2014). In this study,

machine-learning dimensional reduction of all parameters was used to cluster the cells in a semi-

unbiased way according to their similarity. Nevertheless, CyTOF still requires the generation of

antibodies against target molecules and is therefore poorly suited for the discovery of novel

molecules that control Th biology.

Single-cell genomics is a relatively new technique that possesses extraordinary potential for

studying cellular processes in many fields including immunology. The origins of single-cell

genomics stem from gene expression profiling of single cells using microarrays (Tang, Lao et al.

2011). However, the advent of next-generation sequencing has taken this field even further by

allowing for sequencing of mRNA from individual cells, a process termed single-cell RNA

sequencing (scRNAseq) (Tang, Barbacioru et al. 2009, Islam, Kjallquist et al. 2011, Ramskold, Luo

et al. 2012). In initial studies, technical and economical limitations restricted sequencing to only a

handful of cells (Lao, Tang et al. 2009, Tang, Barbacioru et al. 2009). However, continual

improvements in the efficient physical isolation of individual cells, amplification of the genome of

single cells to acquire sufficient material for downstream analyses, cost-effectiveness of this process

and development of computational and analytical approaches to make sure the data is fully

exploited and correctly interpreted, continue to drive more elaborate studies. ScRNAseq has begun

73

to provide novel information into the biology and response of immune cells that would never have

been possible with population-level methods.

A key area in which scRNAseq has provided new information is in cellular heterogeneity.

This was done to explore heterogeneity within CD11c+ mouse myeloid cells (Jaitin, Kenigsberg et

al. 2014), and within cells engaged in the tumour microenvironment (Kim, Lee et al. 2016).

Furthermore, scRNAseq allows for the identification of rare and potentially biologically important

cells, as demonstrated in a paper by Shalek et al., which showed that rare DCs respond to immune

stimuli early and drive the response of the core DC population via paracrine signalling (Shalek,

Satija et al. 2014). Another significant advantage of scRNAseq is the assessment of covariance of

genes. This means that if genes share similar or even inverse patterns of expression, it can be

reasoned that they are involved in related-biological processes, possibly leading to the identification

of novel gene interactions. For example, a subset of in vitro-induced Th2 cells were shown to

activate genes in the steroid synthesis pathway, and inhibit Th differentiation in a Cyp11a1 enzyme-

dependent manner (Mahata, Zhang et al. 2014). Due to the in-depth profile of cells provided by

scRNAseq, the description of other transcriptional features, such as splice variants (Shalek, Satija et

al. 2013) and allele-specific expression (Tang, Barbacioru et al. 2011) is also possible.

Studying immune processes, such as the response kinetics of T cells, has previously proven

difficult due to the extensive heterogeneity within even a clonal population of cells. Population-

level transcriptomic approaches such as RNAseq on pooled cell samples tend to mask heterogeneity

amongst responding cells. Instead, flow cytometry-based methods are commonly adopted to take

discrete 'snapshots' of a response through time. However, given the heterogeneity in the response

kinetics of individual cells in any snapshot, studying intermediate states is challenging. This issue

can be solved with single-cell RNAseq combined with computational analysis. In a study by

Gaublomme et al. scRNAseq data was used to model the transition of IL-17A+ Th17 cells through a

pre-Th1 effector state during acute EAE (Gaublomme, Yosef et al. 2015). To separate transitional

states and identify novel transcription factors mediating their development, the authors combined

dimensional reduction and unbiased clustering by Principal Component Analysis (PCA) with

annotation with known gene lists describing Th states. The model was then mined for novel genes

corresponding to different states. However, prior cell-sorting according to expression of the

canonical Th17 cytokine, IL-17A, limited this analysis to changes within an already differentiated

Th17-like cell population. This approach has not yet been applied to study the early processed of Th

differentiation in vivo.

74

Our collaborator, Dr Sarah Teichmann leads the single-cell genomics department at the

Wellcome Trust Sanger Institute, Hinxton, UK. Her team features computational biologists and

immunologists, who use single-cell genomic approaches to study aspects of T cell biology. An

example of the strength of this integrated approach was demonstrated by the development of

TraCeR, a computational method that determines full-length TCR sequences from sequencing data

for single T cells, thus allowing for assessment of clonality and paving the way for following the

development of effector responses from single clones (Stubbington MJT 2015). In another study, Dr

Teichmann's team integrated in-depth transcriptome profiling, bioinformatic data analysis and

functional validation via flow-cytometry and live-imaging of single-cells to demonstrate the

relationship between cell-cycling and differentiation of Th2 cells (Fujii, Kumazaki et al. 1996).

In this chapter, the single-cell genomics expertise of Dr Sarah Teichmann and Dr Tapio

Lonnberg, and computational knowledge of Valentine Svennson (Wellcome Trust Sanger Institute,

UK) were employed to resolve the concurrent emergence of Th1 and Tfh PbTII cells during

experimental malaria. PbTII cells entered a state of high cell-cycling and metabolic activity prior to

commitment to differentiation. In this state, they expressed a range of chemokine receptors,

suggesting that they were impressionable to signals from chemokine-expressing cells. ScRNAseq

analysis of myeloid cells predicted a role for inflammatory monocytes in "coaching" PbTII cells

towards a Th1 fate, likely via the CXCR3-signalling axis. This association was confirmed in vivo by

timed depletion of inflammatory monocytes.

75

4.3 Results

4.3.1 Unbiased separation of Th1 and Tfh effector cells

To study the concurrent differentiation of antigen-specific CD4+ T cell during in vivo blood-

stage Plasmodium infection, scRNAseq was performed on PbTII cells during PcAS infection. To

achieve this, CD4+ T cells were enriched from the spleens of PbTII mice, labelled with the

proliferation dye CTV (Figure 3.1A) and adoptively transferred into C57BL/6J mice before

infection with PcAS. A transfer dose of 106 cells per mouse was adopted to enable ease of

retrieving the cells at early time points of infection. At days 2, 3, 4 and 7 p.i., activated PbTII cells,

defined by expression of CD69 or dilution of CTV, were sorted by flow cytometry cell sorting

(FACS) (Figure 4.1A). Single cells were processed using the Fluidigm C1 platform, which allows

for automated cell lysis, reverse transcription of mRNA and pre-amplification of cDNA, thus

limiting technical variance. For quality control, External RNA Control Consortium (ERCC) spike-

in RNAs were added at a known concentration to each sample at the time of cell lysis. The cDNA

for each cell was harvested and further amplified via polymerase chain reaction (PCR), during

which unique barcodes were added. The cDNA libraries were pooled and cleaned for mRNA of a

minimum length of 100 base pairs (bp). The pooled cDNA libraries were subjected to 100 bp

paired-end Illumina Next-Generation sequencing. Unlike single-end sequencing, paired-end

sequencing allows for both ends of the reference gene to be sequenced, thus improving the quality

of the reads. Additionally, paired-end sequencing allows for detection of genomic rearrangement

and repetitive sequence elements, as well as gene fusions and novel transcripts (Fullwood, Wei et

al. 2009). Transcripts were mapped to the Mus musculus genome and normalised to the ERCC

spike-in measurements. Finally, cell filtering was performed to attain transcriptional data from only

high quality cells (Figure 4.2A).

Firstly, to determine whether this scRNAseq could characterise responding PbTII cells,

dimensionality reduction was performed using PCA on all reads for cells from day 7 p.i.. PCA uses

weighted combinations of each of the gene expression measurements to create composite

parameters that maximally represent the relationship of the cells with a minimal number of new

parameters. This analysis showed clear separation of two populations of PbTII cells along PC2 (Fig

4.2B). Using established makers of either effector fate, these two populations were shown to

correspond to Th1 or Tfh phenotypes (Fig 4.2C) as observed by flow cytometric assessments in

Chapter 3 (Fig 3.4B).

76

Figure 4.1 Sorting strategy for PbTII cells. (A) PbTII cells (CD45.1+ CD4+ TCRβ+) were

adoptively transferred into WT congenic (CD45.2+) recipient mice. At indicated days, early

activated (CD69+) and/or proliferated (CTVlo) PbTII cells were cell-sorted from spleens of PcAS

infected mice, or naïve PbTII cells (CD69loCTVhi) at the indicated days p.i..

FS

C-H

FSC-A

SS

C-A

FSC-A

CD

4

TCRβ

CD

69

CTV

CD

45.1

TCRβ

Day 2

Day 3

Day 4

Day 7

Uninfected day 7

A

77

Figure 4.2 Single-cell mRNA-sequencing of activated antigen-specific PbTII CD4+ T cells. (A)

Experimental setup. PbTII cells were transferred from a single donor to multiple recipients. n is the

number of recipient mice per time point. Also shown are the numbers of single cells from which

high-quality mRNAseq data was successfully recorded. (B) PCA of single PbTII cells at 7 days

post-infection with PcAS. The arrows represent the Pearson correlation with PC1 and PC2. Cell

size refers to the number of detected genes. The size of the data points also represents cell size.

“Th1 signature” and “Tfh signature” refer to cumulative expression of genes associated with Th1

or Tfh phenotypes (Hale, Youngblood et al. 2013). PC, Principal Component. (C) Expression of top

50 genes with largest PC2 loadings of day 7 cells (D). The genes were annotated as Th1- or Tfh-

associated based on public datasets (Marshall, Chandele et al. 2011, Liu, Yan et al. 2012, Hale,

Youngblood et al. 2013, Stubbington MJT 2015). PC, Principal Component. (B) and (D) were

generated by T. Lonnberg.

B C

A

76 78

78

4.3.2 Deducing concurrent Th trajectories during a Mixture of Gaussian Processes model

To assess overall heterogeneity within PbTII cells, PCA was then performed on the entire

PbTII cell dataset across all time points (Fig 4.3A). Although timepoint information was not used in

this analysis, cells clustered largely in accordance with the days at which cells were sorted (Fig

4.3A). This suggested that the gene expression variation between single PbTII cells could be used to

recreate the progression from a state of naivety through to terminal differentiation. To model the

dynamics of the differentiation of these cells, a temporal mixture model that builds on two

modelling approaches, a Gaussian Process Latent Variable Model (GPLVM) and an Overlapping

Mixtures of Gaussian Processes (OMGP) model, was developed and applied (Lawrence 2010).

GPLVM first reconstructed a progression trajectory (termed “psuedotime”) from the observed data,

thereby establishing an order for the cells (Figure 4.3B). Although this model used the sample time

as prior information, the inferred temporal ordering does not strictly adhere to these experimental

time points. For this reason, cells sorted at day 4 p.i. were mixed with some of the cells from day 3

and day 7 at either end of the day 4 pseudotime distribution (Fig 4.3B). This was consistent with the

idea that bulk assessments of cells at specific time points fail to take into account the heterogeneity

and differential kinetics of responses made by individual cells.

Next, OMGP was used to determine whether multiple independent trends could be occurring

(Lawrence 2010). Using this approach, two co-emerging trends or trajectories were identified

(Figure 4.3C). Cells following these alternate trajectories transcribed previously described Th1/Tfh

signature genes (Hale, Youngblood et al. 2013), indicating they corresponded to the emergence of

Th1 and Tfh cells (Fig 4.2C). To determine the time point at which these Th1 and Tfh trajectories

separated or 'bifurcated', they were followed backwards from the ends of the Th1 and Tfh trends,

where trend assignment was clear, to a point where trend assignments became completely

ambiguous. The point at which bifurcation initiated was amongst late day 4 p.i. cells relative to

pseudotime (Fig 4.3C). In a separate repeat experiment performed by Dr Tapio Lonnberg,

scRNAseq was performed on PbTII cells at day 0, 4 and 7 during PcAS infection. Consistent with

the original dataset, PbTII cells at day 7, but not day 4 p.i. were easily clustered into two

populations that corresponded with Th1 and Tfh gene signatures (data not shown). Furthermore,

bifurcating genes in the original dataset were also expressed by day 7 p.i. cells and were able to

independently separate Th1 and Tfh populations (data not shown). Together, this data demonstrates

that the process of pseudotime inference coupled with time series mixture modelling is effective at

modelling the co-development of two Th effector populations during infection and could likely be

applicable more generally for studying cellular processes in scRNAseq data.

79

Figure 4.3 Computational delineation of Th1 and Tfh differentiation trajectories. (A) Principal

component analysis of normalised mRNA expression data of single PbTII cells. (B) Unbiased

ordering of single PbTII cells using a Bayesian Gaussian Process Latent Variable Model and

assuming a 1-dimentional latent space (“pseudotime”). The blue line depicts the progress of

pseudotime. The text labels illustrate features of typical cells on that region of the pseudotime, and

are proved purely as a visual aid. (C) Inference of two simultaneous trends through pseudotime

using Overlapping Mixtures of Gaussian Processes (OMGP). The red and blue lines depict the

distinct trends, corresponding with characteristics of Th1 and Tfh differentiation, respectively. The

black dots correspond to the first instance when the two trajectories can be separated with

confidence. The colours of data points represent their relationship with the Th trends. (B) and (C)

were generated by V. Svensson and T. Lonnberg.

80

Next, PbTII cells at the point of bifurcation were characterised. Bifurcating PbTII cells

expressed the highest number of genes, indicative of high transcriptional activity, compared to those

at all other points in pseudotime (Fig 4.4A). These cells also expressed high levels of the cell-

cycling gene Mki67 (encoding Ki-67) (Fig 4.4B). High expression of Ki-67 before the emergence of

Th effector markers was confirmed on protein level by flow cytometry (Fig 4.4C). Cell cycle

activity was further supported by computational allocation of cells into cell cycle stages (Scialdone,

Natarajan et al. 2015), and flow cytometric confirmation of DNA content and cell size (Figure

4.4D-E). Bifurcating PbTII cells also had increased expression of genes associated with aerobic

glycolysis (data not shown), an indication of increased metabolic requirements being met by

glucose metabolism and increased mTORC1 activity. Consistent with this was the observed

elevated levels of ribosomal protein S6 phosphorylation (p-S6) at day 4 p.i. (Figure 4.4F). These

data suggested that PbTII cells entered an uncommitted state of high metabolic activity and cell-

cycling prior to a commitment to either Th1 or Tfh differentiation.

81

Figure 4.4 Bifurcation of T cell fates is accompanied by changes in transcription, proliferation

and metabolism. (A) The relationship of Th1-Tfh bifurcation point and the number of detected

genes per cell. (B) The expression level of the proliferative marker Ki-67 (encoded by the Mki67

gene) across pseudotime. (C) Representative FACS plots showing kinetics of CellTrace™ Violet

(CTV) dilution and Ki-67, IFNγ or CXCR5 expression, with summary graphs showing % of PbTII

cells expressing these markers (after 106 PbTII cells transferred) in uninfected (Day 0) and PcAS-

infected mice at indicated days post-infection (n=4 mice/time point, with individual mouse data

A B

C

D E F

Day 0 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

IFNγ

0 1 2 3 4 5 6 7

0

2 0

4 0

% I

FN

+

0 1 2 3 4 5 6 7

0

5 0

1 0 0

% K

i-6

7+

CTV

CTV

0 1 2 3 4 5 6 7

0

1 0

2 0

3 0

4 0

5 0

% C

XC

R5

+

CTV

CX

CR

5K

i-6

7

82

shown in summary graphs; solid line in summary graphs indicates results from third order

polynominal regression analysis). Data are representative of two independent experiments. (D)

Experimental and computational analysis of cell cycle speed of PbTII CD4+ T cells activated in

response to PcAS. The allocation of cells to cell cycle phases was performed by flow cytometry

using Hoechst staining and computationally using the Cyclone algorithm (Luscher 2012). The

relative cell-cycle speed was determined by measuring the fraction of cells in S, G2, or M phases.

(E) Cell size estimation using FSC (Forward Scatter) measurements of PbTII cells. (F) Cellular

metabolic activity of PbTII cells in naive mice (n=3) and at days 4 and 7 p.i. (n=6) as determined

by flow cytometric assessment of ribosomal protein S6 phosphorylation (p-S6). Flow cytometric

data are representative of two independent experiments. Statistics are one-way ANOVA and Tukey's

multiple comparisons tests ***p<0.001. NS, not significant. (A), (B) and (D) were generated by T.

Lonnberg.

4.3.3 Mechanisms underlying the differentiation of Th1 and Tfh cells

Molecular mechanisms possibly controlling Th1/Tfh fate decision were next explored. To

this end, the concordance of each gene with bifurcation was calculated as a 'bifurcation statistic'.

The relationship of genes with the direction of bifurcation was determined from the correlation

between their expression and trend assignment (Figure 4.5A). The overall pattern in gene

correlation was an up-regulation of gene expression during progression towards a Th1 fate (Figure

4.5A-B). Among the top genes contributing to the Th1 trend were established genes including Ifng

and Il18ra. In accordance with a recently published report by Shaw et al., Id2 was also among the

top genes associating Th1 commitment (Shaw, Belanger et al. 2016). However, novel genes were

also present including Gna15 and Mxd1, representing genes of interest for Th1 fate commitment.

Likewise, Tfh associated genes included established Tfh genes Cxcr5, Bcl6, IL21 and Tcf1 (encodes

TCF7) as previously reported (Choi, Gullicksrud et al. 2015, Xu, Cao et al. 2015). Novel genes

were also associated with the Tfh fate (Figure 4.5A-B). Assessment of transcription factor Id2

revealed that it continued to increase in expression until differentiation, suggesting that it could be

involved in stabilising fate commitment (Figure 4.5C). Interestingly, Tcf1 expression was high in

the naive state as expected, and was retained only in cells heading towards Tfh commitment. Using

flow-cytometry, the association between TCF7 protein expression and Tfh (CXCR5+) and naive

states, but not the Th1 state, was confirmed (Figure 4.5D).

83

Figure 4.5 Unique gene expression signatures corresponding with Th1 and Tfh differentiation.

(A)Identification of genes associated with the differentiation of Th1 or Tfh cells. For every gene, the

correlation of its expression with pseudotime (x-axis) and Tfh trend assignment (y-axis) are shown.

Statistical significance was determined using the bifurcating score. Genes satisfying the

significance threshold of FDR<0.002 are represented in colours according to the functional

classification of the genes. FDR, False Discovery Rate, estimated by performing the same analysis

with permuted data. (B) The genes with strongest association with Th1 (left) or Tfh (right)

differentiation. The genes were filtered using the bifurcation score as in (B). The genes were then

ranked in descending order of association with either Th1 or Tfh trend. (C) The expression of Id2

(upper panel) and Tcf7 (lower panel) across pseudotime. The curves represent the Th1 (red) and

Tfh (blue) trends when weighing the information from data points according to trend assignment.

The colour of the data points represents the strength of the relationship with the Th1 trend. (D)

Representative FACS plots (gated on CD45.1+ CD4+ TCRβ+ live singlets) showing TCF-1 (gene

product of Tcf7) expression in CXCR5+ (blue gate) and IFNγ+ (red gate) PbTIIs at 7 days post-

11.8 2.8

46.4

0 0

6.4

TCF-1

IFNγ+ CXCR5-

IFNγ- CXCR5+

IFNγ- CXCR5-

Isotype

CXCR5

Naive PcAS

IFNγ

IFN+

CX

CR

5+

0

5 0

1 0 0

1 5 0

2 0 0

TC

F-1

ex

pre

ss

ion

(Ge

om

Me

an

)

*

% o

f M

ax

D

A

C

B

84

infection (n=4) (isotype control shown in black in FACS histogram), compared to naïve PbTIIs

(grey gate) (n=1). Summary graph shows mean & standard deviations for geometric mean

fluorescence intensity of TCF-1 expression in gated PbTII populations. Statistics: Mann-Whitney U

test, *p<0.05. (A), (B) and (D) were generated by T. Lonnberg.

4.3.4 IFNγ and IL-10co-producing PbTII cells emerge from Th1 cells

Expression of the regulatory cytokine, Il-10, emerged after bifurcation and exhibited similar

kinetics and Th1 fate association as Ifnγ (Figure 4.6). Assessment of Ifnγ andIl-10 expression by

PbTII cells at day 7 p.i. revealed a population of co-producing cells, commonly referred to as Tr1

cells, emerging from Ifnγ+ cells (Figure 4.6).

Figure 4.6 IL-10/IFNγ co-producing PbTIIs begin to emerge from Th1 cells late in pseudotime. The expression of IFNγ (upper panel) and Il-10 (lower panel) across pseudotime. The curves

represent the Th1 (red) and Tfh (blue) trends when weighing the information from individual PbTII

cells according to trend assignment. Dot plot of Ifnγ and Il-10gene expression PbTIIs from day 7

p.i., with green box showing Il-10 producers as co-expressors of Ifnγ.Figure generated by T.

Lonnberg.

IFN

IL-10

IFN log(TPM+1)

IL-1

0 log

(TP

M+

1)

Pseudotime

Exp

ressio

n (

Log

10

TP

M)

85

4.3.5 PbTII cells are receptive to chemokine signals at bifurcation

Included in the top genes corresponding to either Th1 or Tfh trajectories were a number of

chemokine receptors. Among the highest ranked genes for the Th1 fate were Cxcr6, Ccr2, Ccr5,

Ccr4 and Cxcr3 (Figure 4.5A). In contrast, the only bifurcating chemokine receptor associated with

Tfh fate development was Cxcr5 (Figure 4.5A). When investigating the kinetics of chemokine

expression, two waves could be identified (Figure 4.7A). Cxcr6, Ccr2, and Ccr5 were among a late

wave, whose expression began after bifurcation and continued to increase with differentiation

(Figure 4.7A). The kinetics of CXCR6 expression and association with the Th1 phenotype was

confirmed on the protein level (Figure 4.7B-C). In contrast, Cxcr5, Cxcr3 and Ccr4 were among an

early wave, whose transcription and translation into protein peaked around the time of bifurcation

(Fig 4.7A and 4.7D). The difference in the timing of peak expression of these chemokine receptors

was reasoned to be due to differences in their function. For instance, while the late stage bifurcating

genes Cxcr6, Ccr2 and Ccr5 might be important for trafficking and effector function of CD4+ T

cells, earlier expression of Cxcr5, Cxcr3 and Ccr4 might be involved in cell-to-cell interactions that

promote differentiation of activated PbTII cells prior to bifurcation (Figure 4.8).

86

A

B

C

D 15.7 11.9

10.9

CXCR5

CX

CR

3

2.8 0.4

0.8

Naïve PcAS

Naiv

e

PcA

S

0

5

1 0

1 5

% C

XC

R3

+C

XC

R5

+

*

CTV 0 1 2 3 4 5 6 7

0

1 0

2 0

3 0

4 0

% C

XC

R6

+Day 0 Day 4 Day 5 Day 6 Day 7

CX

CR

6

T-bet+ Bcl6-

T-bet- Bcl6+

T-bet- Bcl6-

Day 0 Day 7

T-b

et

+B

cl6

+

0

5 0

1 0 0

1 5 0

2 0 0

CX

CR

6 e

xp

re

ss

ion

(Ge

om

Me

an

)

*

CXCR6 Bcl6

T-b

et

2.5

9.9

72.7

39.9

47.2

3.6

% o

f M

ax

87

Figure 4.7 Chemokine signals underlying Th fate commitment. (A) The expression kinetics of the

chemokine receptor genes Cxcr5, Cxcr3, Ccr4, Ccr2, Ccr5, and Cxcr6 across pseudotime. The

curves represent the expression patterns associated with the Th1 (red) and the Tfh (blue) trends. (B)

Representative FACS plots (gated on CD45.1+ CD4+ TCRβ+ live singlets) showing kinetics of

CellTraceTM Violet (CTV) dilution and CXCR6 expression, with summary graphs showing

proportion of PbTII cells expressing CXCR6 (after 106 PbTII cells transferred) in un-infected (Day

0) and PcAS-infected mice at indicated days p.i. (n=4 mice/time point, with individual mouse data

shown in summary graphs; solid line in summary graphs indicates results from third order

polynominal regression analysis). (C) Representative FACS plots (gated on CD45.1+ CD4+ TCRβ+

live singlets) showing CXCR6 expression in T-bethi (red gate) or Bcl6hi (blue gate) PbTII cells,

compared to naïve PbTIIs (grey) at 7 days p.i.. Summary graph shows mean & standard deviations

for geometric mean fluorescence intensity of CXCR6 expression in gated PbTII populations (n=4

mice). Data are representative of two independent experiments. Statistics: Mann-Whitney U test

*p<0.05. (A) was generated by T. Lonnberg.

Figure 4.8 Bifurcation of PbTII cells into Th1 and Tfh effector cells. A model summarizing the

expression patterns of Id2, Tcf7, and chemokine receptors during Th1-Tfh cell fate determination.

The size of the cell represents proliferative capacity and the colour of receptors and transcription

factors represent differences in expression level.

88

4.3.6 B cells support Tfh responses

Since CXCR5 drives migration and retention of Tfh cells in B cell areas (Breitfeld, Ohl et al.

2000, Kim, Rott et al. 2001, Chtanova, Tangye et al. 2004), the role of B cells in supporting Tfh fate

commitment in PbTII cells was explored. To this end, an anti-CD20 depleting antibody was used to

deplete B cells from mice prior to infection with PcAS. Tfh responses were significantly reduced in

B cell-depleted mice compared to control-treated mice (Figure 4.9). In contrast, Th1 cell

proportions were comparatively improved in B cell depleted mice (Figure 4.9). These data revealed

that B cells were crucial for Tfh differentiation, but not Th1 differentiation of PbTII cells.

Figure 4.9 B cells are essential for Tfh responses in PbTII cells during PcAS

infection.Representative FACS plots (gated on CD4+ TCRβ+ CD45.1+ live singlets) and summary

graphs of splenic PbTII cells, showing absolute and relative numbers exhibiting Tfh (Bcl6+

CXCR5+) and Th1 (T-bet+ IFNγ+) phenotypes in WT mice (receiving 104 PbTII cells) treated with

anti-CD20 monoclonal antibodies to deplete B-cells, or control IgG, and infected for 7 days with

PcAS. Naïve mice received 106 PbTII cells. Individual mice data are (n=5) shown in summary

graphs. Results are representative of two independent experiments. Mann-Whitney U test *p<0.05;

**p<0.01.

Bcl6

CX

CR

5

IgG αCD20 Naive

5.1 22.0 36.5

T-bet

IFNγ

4.6 17.4 3.3

0

20

40

60

% T

-bet+

IF

Ng+

*

IgG

αCD20

0

2

4

6

8

PbT

II c

ells

(x10

5)

0

5

10

15

20

% B

cl6

+ C

XC

R5

+

**

IgG

αCD20

0

2

4

6

8

10

Bcl6

+ C

XC

R5

+ (x 1

04)

**

IgG

αCD20

0

10

20

30

T-b

et+

IF

Ng+

cells

(x 1

04)

89

4.3.7 ScRNAseq assessment of myeloid cells during PcAS infection

Given that B cells were essential for Tfh commitment, it was hypothesised that myeloid cell

might provide alternative, competing support for Th1 commitment. Therefore, scRNAseq was

performed on splenic CD8α+ CD11b- cDCs, CD8α- CD11b+ cDCs, and Ly6Chi inflammatory

monocytes prior to the bifurcation of PbTII cells and from the same PcAS infected mice (Fig 4.1A

and Fig 4.10). This approach was particularly appropriate given the possibility that subsets of the

myeloid cell-types were providing unique signals to uncommitted PbTII cells.

Firstly, PCA of all cDCs separated naïve CD8α+ cells from their CD8α- counterparts along

PC2 (Fig 4.11). Differential gene expression analysis of these two naïve subsets, using the

algorithm Monocle (Trapnell, Cacchiarelli et al. 2014), showed generally higher expression of

genes within the CD8α- subset (Fig 4.12A-C). Among these were genes known to associate with

either subset, including Cd8α, Irf8, Clec4a4 and Sirpα. However, new associations were identified

including co-stimulatory molecule Cd80 with CD8α- cells and Cxcl9 with CD8α+ cells, suggesting

differences in the steady-state condition of these two subsets. While naïve CD8α+ and CD8α- cDCs

were easily discernible on the transcriptional level, no obvious clusters were evident amongst day 3

p.i. cDCs (Fig 4.11) suggesting a convergence of these cells during infection. Nevertheless, using

only differentially expressed genes between naïve CD8α+ and CD8α- cDCs, day 3 p.i. cDCs were

clustered into two approximately equal clusters, as per the mixing ratio of CD8α+ to CD8α- prior to

processing and sequencing (Fig 4.12D). These results demonstrate the un-biased separation of naïve

cDCs by single-cell transcriptomics, and a general convergence at a transcriptomic level upon

activation, as previously reported (Jaitin, Kenigsberg et al. 2014).

90

Figure 4.10 Sorting strategy for myeloid cells.Representative FACS plots showing sorting strategy

for CD8α+ and CD11b+ cDCs, and Ly6Chi inflammatory monocytes from the spleens of naive and 3-

day PcAS-infected mice.

FS

C-H

FSC-A

SS

C-A

FSC-A

B220

FSC-A

TC

Rβ

FSC-A

Ly6

C

CD11b

Ly6G

Ly6C

CD

11c

MHCII

CD

8α

CD11b

6.62

2.56

7.55

15.7

59.6

25.9

84.2

78.9

Day 3

Naive

Day 3

Naive

CD8α+/CD8α-

cDC

Ly6Chi

monocyte

91

Figure 4.11 Principal Component Analysis of cDCs from naïve and infected mice. Principal

Component Analysis (PCA) of mRNA reads (filtered by minimum expression of 100 TPM in at least

2 cells) from 131 single splenic naïve CD8α+ and CD8α- and mixed day 3 PcAS-infected cDCs.

PC1-PC6 shown. Axis labels show proportional contribution of respective PC.

92

Figure 4.12 Differential gene expression between single splenic CD8α+ and CD8α- cDCs. (A)

Results of differential gene expression analysis between naïve splenic CD8α+ and CD8α- cDCs, for

all genes expressed in greater than 2 cells. (B) Complete list of differentially-expressed genes

between naïve CD8α+ and CD8α- cDCs, which were expressed in >10 cells of either subset with a

qval <0.2 as determined in (A). (C) Heatmap of naïve cDCs ordered by PC2 (Figure 4.10) and

expression of genes from (B) ordered by PC2 loading in (Figure 4.10). (D) Heatmap examining

hierarchical clustering of mixed CD8α+ CD11b- and CD8α- CD11b+ day 3-infected cDCs (cell-

sorted and mixed at a ratio of 50:50 prior to scRNAseq) using differentially expressed genes from

(B) ordered by PC2 loading shown in (Figure 4.10).

93

PCA conducted on all genes in cDCs also separated naïve cells from day 3 p.i. cells along

PC6 (Figure 4.11 and 4.13A). To explore genes contributing to changes in these cDC states,

differential expression analysis was performed on naïve versus day 3 p.i. cDCs. This identified 30

differentially expressed genes, 29 of which were upregulated during infection (Figure 4.13B and

4.14). Among these upregulated genes were interferon-related transcription factors Irf1 and Stat1,

and CXCR3 ligands Cxcl9 and Cxcl10. Notably, expression patterns varied for individual genes.

For example Stat1 and Irf1 were both expressed in naïve cells, but further upregulated during

infection (Figure 4.13C and 4.15). Cxcl9 was expressed in naïve CD8α+ cells and also upregulated

during infection. On the other hand, Cxcl10 was not expressed in naïve cells, but was expressed

upon infection (Fig 4.13C and 4.15). These results showed that cDCs experienced up-regulation of

several genes during infection but little down-regulation, and furthermore, suggested that cDCs

were capable of interacting with CXCR3+ T cells via expression of Cxcl9/10 as previously reported

(Groom, Richmond et al. 2012).

94

Figure 4.13 cDCs influence Th bifurcation in uncommitted PbTII cells. (A) Principal Component

Analyses of mRNA reads (filtered by minimum expression of 100 TPM in at least 2 cells) of 131

single splenic CD8α+ and CD11b+ CD8α- cDCs from a naïve mouse, and mixed cDCs from a day

3-infected mouse, showing PC combinations best separating populations cells. (B) Volcano plots

showing fold-change and confidence for differentially expressed genes between naïve and infected

cDCs- genes filtered on expression in >10 cells; genes satisfying qval<0.05 are represented in

colours according to functional classification displayed (Full list is included as Figure 4.13). (C)

Expression heatmaps for significantly (qval<0.05) differentially expressed genes in (B): cells and

genes are ordered according to PC6 score and loading respectively as in (A).

95

Figure 4.14 Differentially-expressed genes between single naïve and day 3 PcAS-infected cDCs.

List of differentially-expressed genes, expressed in >10 cells (qval<0.05) between naïve and day 3-

infected cDCs. Mean TPM fold-change in gene expression is relative to naïve levels.

Gene name Log2(Fold Change) qval

Pianp -7.262625898 1.14E-37

Tgtp1 4.359998594 4.84E-11

Tgtp2 2.78069932 1.68E-10

Ifi47 3.557446068 4.23E-10

Kdm6b 4.254218426 1.22E-09

Actb 1.621695106 1.01E-07

Igtp 4.003078626 1.43E-07

AC124762.1 3.062289012 3.69E-07

Gm15427 -0.977819831 1.51E-06

Stat1 2.491431703 1.92E-06

U6 -3.307885729 8.27E-06

Snora31 0.926874728 9.92E-06

Nlrc5 2.091145817 1.43E-05

Gm12250 5.431395825 1.61E-05

Zbp1 4.871392677 5.59E-05

Gbp4 3.405248926 0.000181786

R3hdm4 2.1863913 0.000251891

Slc39a1 3.199473697 0.000708528

Gm10800 0.641967923 0.00083472

Cxcl10 3.425848442 0.001808979

Alkbh5 3.19927987 0.001977138

Cxcl9 2.422667011 0.002384972

Dtx3l 1.744309287 0.004686724

Wtap 2.846340387 0.004866485

AC131780.3 1.105647313 0.005073109

Gbp3 1.396086788 0.005888858

Wac 2.867605317 0.008312287

Pml 2.963358709 0.013544203

Arf4 1.885252908 0.015554393

Irf1 1.437877607 0.022724835

Gbp2 2.124613484 0.045504232

96

97

98

Figure 4.15 Expression of immune-related molecules by myeloid cells. Heatmaps showing

normalised mRNA expression of select (A) chemokines, (B) costimulatory molecules and (C)

cytokines and respective receptors (rows) by single splenic cDCs and Ly6Chi monocytes (columns)

from naïve or 3-day PcAS-infected mice.

99

4.3.8 Ly6Chi monocytes support Th1 fate, but not Tfh fate, commitment

Ly6Chi inflammatory monocytes have been shown to migrate from the bone marrow to the

spleen during blood-stage Plasmodium infection, and furthermore, up-regulate MHCII expression

(Sponaas, Freitas do Rosario et al. 2009). Therefore, it was hypothesised that they also played a role

in mediating Th cell responses. Firstly, PCA of all monocyte scRNAseq data allowed for unbiased

separation of naïve and day 3 p.i. cells along PC2 (Figure 4.16 and 4.17A). Differential gene

expression analysis of naïve versus day 3 p.i. cells identified approximately 100 genes, which

unlike cDCs, were both up- and down-regulated during infection (Figure 4.17B and 4.18). This

highlights a significant difference in the directionality of gene expression during infection between

cDCs and monocytes. Among the top upregulated genes were chemokines including Cxcl2, Ccl2

and Ccl3. Of particular interest, ~40% of up-regulated genes by monocytes were in common with

those up-regulated in cDCs (Figures 4.13C and 4.17C), including Stat1, Irf1 and Cxcl10. This

suggested possible similarities in the functions of monocytes and cDCs during infection.

Furthermore, monocytes up-regulated an array of immune-interaction related genes including Tnf,

Cd40, Pdl1, Ccl4, Ccl5, Cxcl16, Cxcl9, and Cxcl11, providing evidence of complex interactions and

multiple roles for these cells during infection (Figure 4.15). Assessment of the kinetics of one of

these chemokines, Cxcl9, a ligand for CXCR3 that was highly expressed by non-bifurcated PbTII

cells (Figure 4.6), showed similar expression in both cDCs and Ly6Chi monocytes (Figure 4.19).

Expression of CXCL9 in both myeloid populations was evident by day 4 p.i., which corresponded

to the start of PbTII bifurcation (Figure 4.3C and 4.19). Thus, these results indicated that, like

cDCs, Ly6Chi monocytes possessed the ability to signal to CXCR3+ uncommitted PbTII cells.

100

Figure 4.16 Principal Component Analysis of Ly6Chi monocytes from naïve and infected mice. Results of Principal Component Analysis, using scRNAseq mRNA reads (filtered by minimum

expression of 100 TPM in at least 2 cells), from 154 single splenic Ly6Chi monocytes from naïve

and infected mice. PC1-PC6 shown. Axis labels show proportional contribution of respective PC.

Arrows demonstrate the correlation between number of expressed genes and PCs.

101

Figure 4.17 Ly6Chi monocytes display similar functional capacity as cDCs. (A) Principal

Component Analysis of mRNA reads, filtered by minimum expression of 100 TPM in at least 2 cells,

of 154 single Ly6Chi monocytes from naïve and infected mice, showing PC combinations best

separating populations of Ly6Chi monocytes from naïve and infected mice. (B) Volcano plots

showing fold-change and confidence for differentially expressed genes between Ly6Chi monocytes in

infected versus naïve mice - genes filtered on expression in >10 cells; genes satisfying qval<0.05

are represented in colours according to functional classification displayed. (C) Expression

heatmaps for significantly (qval<0.05) differentially expressed genes in Ly6Chi monocytes, between

naive and infected mice: cells and genes are ordered according to PC2 as in (A).

102

Gene names Log2 (Fold Change) qval

Gbp2 5.343418344 2.14E-33

Egr1 5.025006039 1.11E-25

Ifi47 3.25317286 6.85E-20

Cxcl10 9.85360446 8.78E-20

Irf1 3.970316816 3.93E-17

Tgtp1 12.50667977 9.12E-17

Tgtp2 3.857804903 7.32E-15

Stat1 2.746907486 2.36E-10

Gbp4 5.769038177 1.34E-09

Igtp 2.698204505 1.46E-09

Fam26f 4.706888987 1.56E-08

Ifi205 2.558201091 6.71E-08

Gbp7 2.716164761 9.43E-07

U2 3.961441106 9.47E-07

U2 3.961231475 9.48E-07

U2 3.961976492 9.48E-07

U2 3.961489939 9.48E-07

Gbp6 7.596693594 1.30E-06

Irgm1 2.554582038 2.23E-06

Gbp3 3.654656366 3.14E-06

Serpina3g 10.47471346 4.62E-06

Nfkbiz 3.217801159 7.52E-06

Gm17334 3.707732087 8.59E-06

Gbp5 5.768713305 9.75E-06

D4Wsu53e -1.288040437 1.86E-05

Ceacam1 -1.486635161 1.94E-05

Gpcpd1 -3.576733856 1.98E-05

Hpgd -2.92213678 2.11E-05

Pim1 2.213701378 2.51E-05

Cd244 -2.007720552 3.26E-05

Cd40 1.698069689 7.05E-05

Cd274 5.404622826 8.55E-05

Cd300lb -1.80189338 0.000103172

Kdm6b 2.709769407 0.000130236

CT572998.1 4.259284122 0.000138539

Nfkbia 2.509585516 0.000147245

Cxcl2 5.033164709 0.000187925

Ccl3 5.803174704 0.00021635

Pira2 -2.108050548 0.000224494

Susd3 -1.732317859 0.000287997

Nfic -4.21991054 0.000325979

Ogt -1.844048384 0.000337242

Mll3 -2.637985297 0.000371892

Mast3 -9.017849562 0.000378042

Ptpre -1.633894765 0.000403653

Ccrl2 4.814048545 0.000416755

Lilra6 -2.054712791 0.000437711

Atp2b1 -1.48633724 0.000471865

Ptplad2 -1.122982025 0.00083638

Dnm1l -2.684877387 0.000891606

Stk4 -1.035914887 0.001069216

Hmgb2 -0.207565085 0.001163912

Mar-01 -1.991700596 0.001322837

Ifit2 3.957433264 0.00143919

Klf4 -2.630782711 0.001586378

Calm2 -0.875337793 0.001609773

Itfg3 -1.448044803 0.002167052

Dnase1l3 3.164154469 0.002213986

Nrp2 3.684929824 0.002518771

Zbp1 1.922148464 0.003888078

Gbp9 1.57174886 0.003914237

Tsc22d3 -1.148850425 0.00396859

Rell1 -2.692054659 0.004025406

Srsf2 -1.228632903 0.004077318

Jun 2.221736196 0.005316553

Hnrnph1 -2.226101504 0.005488455

Itgb2 -1.023709661 0.005808116

Ifit3 2.666797336 0.005902332

Eif4a2 -1.392997137 0.006046446

Rps6ka1 -1.7229663 0.006594323

Rtp4 2.192950173 0.006616606

Sfr1 -0.700341246 0.006695782

Fam107b -1.630418866 0.007788727

Tmem164 -1.105130173 0.007980954

Mnda 1.139858542 0.009659075

Glg1 -0.769603966 0.010631512

Irgm2 3.514642515 0.011006151

Emilin2 -1.779653439 0.011571272

Tlk1 -2.156357298 0.011857332

Tmem126b -6.422066301 0.014667174

Lrp1 -1.509844249 0.01681349

Zzef1 -4.023332903 0.017752319

Pik3cg -1.615162871 0.01892715

Camk1d -0.381536785 0.019708425

Cd84 -1.748595178 0.019823417

Vps13c -1.655458694 0.020838227

Cd300ld -4.309443608 0.021491441

Atf3 2.585532022 0.021661981

Emr4 -1.483345739 0.022678297

Cx3cr1 -2.061113213 0.02338043

Nedd8 -0.453894943 0.024468547

Tubb6 -1.63027126 0.025148487

Ndrg1 -1.902455843 0.028273704

Mknk2 -1.739508433 0.0286694

Pqlc3 -1.400220797 0.029203455

Nfam1 -1.189024309 0.030856908

Fam89b -1.853036401 0.031955789

U2 8.954028155 0.032731379

U2 8.953370271 0.032731379

Foxj2 -4.788634353 0.033617561

H2-Q10 2.322965089 0.036776651

Ccl2 5.211788278 0.038421772

Idh1 -1.68740312 0.040729371

Gm12250 2.676296613 0.043565984

Fam46a -0.638461732 0.043914316

Ankrd44 -0.778489063 0.045666952

Pdcd4 -0.894788719 0.047438862

Figure 4.18 Differentially-expressed genes between single Ly6Chi monocytes from naïve and day

3 PcAS-infected mice. List of differentially-expressed genes, expressed in >10 cells (qval<0.05)

between Ly6Chi monocytes from naïve and day 3-infected mice. Mean TPM fold-change in gene

expression relative to naïve levels.

103

Figure 4.19 Kinetics of CXCL9 expression by myeloid cell populations. Representative FACS

histograms and proportions of splenic CD8α+ cDCs, CD8α- cDCs and Ly6Chi monocytes expressing

CXCL9 in naive and infected mice between 2-7 days p.i. with PcAS- individual mouse data plotted

with line at mean; data representative of two independent experiments (n=4 mice/time

point/experiment).

To test the requirement for cDCs and Ly6Chi monocytes in supporting Th1 differentiation,

CD11c-DOG and LysMCre x iDTR systems were employed. In these respective systems CD11c-

expressing cDCs and Ly6Chi monocytes were depleted upon administration of DT at day 3 p.i., a

time when adoptively-transferred PbTII cells had been primed, but were yet to differentiate (Figure

4.3C and 4.20A). Of note, other CD11c-expressing cells including B cells are also depleted from

DT-treated CD11c-DOG mice (Hochweller, Striegler et al. 2008). In addition to robust monocyte

depletion from the LysMCre x iDTR mice, in an experiment performed by Ismail Sebina, ~30% of

CD68+ macrophages, but not marginal zone macrophages, were also depleted in these mice (data

not shown). No effect was observed for cDCs, T cells or B cells in DT-treated LysMCre x iDTR mice

during infection (data not shown).

At day 7 p.i., PbTII cells recovered from DT-treated LysMCre x iDTR mice had significantly

lower proportions and a trend towards reduced absolute numbers of Th1 PbTII cells compared to

saline-treated controls (Figure 4.20). Tfh proportions were comparably improved (Figure 4.20).

Similarly, depletion of cDCs resulted in a slight, but not significant decrease in Th1 proportions and

total numbers, and an increased proportion of Tfh PbTII cells (Figure 4.20). Together, these data

supported a model in which progression of activated, uncommitted PbTII cells towards a Tfh fate

N 2 3 4 5 6 7

0

2 0

4 0

6 0

8 0

1 0 0

N 2 3 4 5 6 7

0

2 0

4 0

6 0

8 0

1 0 0

% C

XC

L9

+

N 2 3 4 5 6 7

0

2 0

4 0

6 0

CD8α+ DC CD8α- DC Ly6chi Monocytes

Days post-infection

0

2

3

4

5

6

7

FMOCXCL9

% o

f M

ax

104

was dependent upon B cells, and a Th1 fate was enforced by DCs and more substantially by

chemokine-expressing Ly6Chi inflammatory monocytes (Figure 4.21).

Figure 4.20 Ly6Chi monocytes support Th1 responses. (A) Scheme depicting experimental design:

PbTII cells were transferred into WT, CD11c-DOGmice, LysMCre x iDTR mice 1 day prior to

infection. At 3 days p.i., mice were treated with diphtheria toxin (DT) or control saline, with PbTII

Th responses assessed at 7 days p.i.. (B) Representative FACS plots (gated on splenic PbTII

cells)showing proportions, and (C) absolute numbers of Th1 (T-bethi IFNγ+) and Tfh (CXCR5+)

PbTII cells in DT or saline-treated LysMCre x iDTR mice and DT treated WT and CD11c-DOG

mice. LyMCre-iDTR data are representative of three independent experiments, and CD11c-DOG

data represents a single experiment. Numbers depict proportions within respective gates. Statistics:

Mann-Whitney U Test. ****p<0.0001. NS, not significant.

PcAS infection

Day 0 Day -1 Day 3 Day 7

Monocyte or cDC depletion

(Diphtheria toxin

(DT))

Transfer of PbTII cells

into LysMCre x iDTR or

CD11cCre x iDTR mice

Analysis of PbTII response

Time

4.8

Naive LysMCre x iDTR :

Saline LysMCre x iDTR :

DT

T-bet

IFNγ

CX

CR

5

PD1

0.6

20.8 16.2 19.2 11.6

18.2 25.2 20.3 15.3

WT: DT CD11c-DOG:

DT 0

10

20

30

% T

-bet+

IF

Ng+

NS*

0

20

40

60

80

100

T-b

et+

IF

Ng+

cells

(x 1

04)

NS

NS

WT:DT

CD11

cCre x

iDTR

:DT

LysM

Cre x

iDTR

:Sal

ine

LysM

Cre x

iDTR

:DT

0

10

20

30

40

% C

XC

R5

+

**

WT:

DT

CD11

c-DOG:D

T

LysM

Cre x

iDTR

:Saline

LysM

Cre x

iDTR

:DT

0

20

40

60

80

100

CX

CR

5+ cells

(x 1

04)

NS

NS

105

Figure 4.21 PbTII differentiation is influenced by chemokine-producing cells. Summary model

proposing chemokine interactions between non-bifurcated PbTII cells and myeloid cells support a

Th1 fate, while the Tfh fate is sustained by B cells.

106

4.4 Discussion

Th cells control immune responses during infection, cancer and auto-immunity. Therefore,

understanding the process of Th differentiation is of paramount importance. Previous techniques

have struggled to study this process at a genomic level in vivo due to the extent of heterogeneity

within the responding T cell pool. Here, scRNAseq was applied to PbTII cells during in vivo blood-

stage PcAS infection. Using a newly developed computational approach, the process of

differentiation was modelled from this data, revealing two alternate trajectories corresponding to

Th1 and Tfh differentiation.

Significant findings of this study are novel genes corresponding to either Th differentiation

trajectory. Top hits included previously established genes such as Ifng, Id2, Tcf1 and Cxcr5,

supporting the biological accuracy of this model. In addition, novel genes, such as Mxd1 and Asap1,

associating with Th1 and Tfh differentiation were identified and could represent possible targets for

tailoring Th differentiation. A possible direction for future work is the exploration of the roles

played by these genes in Th differentiation by PbTII cells.

Several studies have followed the cell fate decision to determine contributing factors.

Limiting dilution studies conducted in the laboratory of Marc Jenkins showed that single transferred

T cells gave rise to both Th1 and Tfh effector cells, as seen from PbTII cells. In addition, their study

showed that clonal cells tended to generate a similar pattern of effector cells, consistent with the

idea the unique TCRs predispose T cells toward a particular fate (Tubo, Pagan et al. 2013).

Marchingo et al. employed in vitro culture of CD8+ T cells with different combinations of stimuli

and in vivo validation to further explore the role of the TCR in cell fate (Marchingo, Kan et al.

2014). They showed that early TCR signalling and costimulatory signals imprinted the T cell with a

defined division fate. In vitro work by Pham et al. further showed that interactions with stromal

cells and chemokine receptor signalling resulted in the assymetric cell division of T cell and

differential inheritance of fate determinants by daughter cells (Pham, Shimoni et al. 2015). In this

way, the microenvironment influenced fate decision of developing cells. The current study has

added to this body of work by showing that PbTII cells express many genes prior to fate

commitment, including chemokine receptors that can facilitate either Th1 or Tfh differentiation.

This suggests that uncommitted CD4+ T cells require external signals, such as those provided

through chemokine receptors, to skew gene expression towards one of the effector fates. Without

knowing the true identity of the cells tested here, it is difficult to know exactly when this

commitment occurs. However, the modelling of the expression data suggests that bifurcation occurs

several days after priming and at approximately the same time as peak gene expression, suggesting

that as the cell fate solidifies, gene expression becomes more specific.

107

Among the chemokine receptors that peaked in expression around bifurcation, only Cxcr5

associated with Tfh trend assignment. Since CXCR5 permits T cells to enter the B cell follicle, this

association supports the requirement of B cells in Tfh responses (Kerfoot, Yaari et al. 2011).

Indeed, Tfh responses, but not Th1 responses, were significantly impaired upon B cells depletion.

The initial differentiation of Tfh cells has been reported to occur independently of B cells (Kerfoot,

Yaari et al. 2011). In a separatestudy, differentiation of Tfh cells was demonstrated to rely on

CD8α- cDCs within the interfollicular zone via enhanced ICOSL and OX40L expression in

response to targeted Yersinia pestia and OVA antigens (Shin, Han et al. 2015). Here, scRNAseq of

cDCs at the time of PbTII bifurcation revealed expression of ligands for CXCR3, but not CXCR5,

suggesting they preferentially support Th1 differentiation (Groom, Richmond et al. 2012).

Furthermore, timed depletion of CD11c-expressing DCs resulted in a trend towards decreased Th1

responses, but no change in Tfh responses. T, this suggests that B cells in the follicle are the

primary support for Tfh responses during Plasmodium infection, however other cells may be

involved at different stages of Tfh differentiation.

For the first time, inflammatory monocytes were shown to adopt a similar transcriptional

profile to cDCs, suggesting similar roles during in vivo Plasmodium infection. Previous ex vivo

studies dismissed the role of inflammatory monocytes in priming CD4+ T cell responses during

malaria (Sponaas, Cadman et al. 2006), but did not determine whether they provided support for Th

differentiation. By using a timed-depletion system in vivo, inflammatory monocytes were

demonstrated to provide support to activated PbTII cells. While Th1 responses were impaired in

these monocyte-depleted mice, the absolute number of Tfh PbTII cells was unchanged, suggesting

that monocytes have a role in enforcing rather than driving Th1 commitment at the expense of Tfh

differentiation. An important caveat was the partial depletion of splenic CD68+ macrophages in the

LysMCre x iDTR transgenic system. Therefore, a role for these macrophages in supporting Th1 fate

cannot be discounted. Nevertheless, inflammatory monocyte-mediated support for Th1 responses is

consistent with viral and fungal infection studies (Hohl, Rivera et al. 2009, Nakano, Lin et al. 2009,

Wuthrich, Ersland et al. 2012).

Together, these data support a model in which cDCs prime naïve antigen-specific T cells,

driving them to proliferate and reach a state of high cell-cycling and stochastic gene expression.

PbTII cells then become receptive to chemoattractant signals from multiple cell types in different

zones of the spleen. For example, B cells support Tfh commitment, while cDCs and inflammatory

monocytes enforce Th1 fate commitment. These signals are not necessarily competing. While this

study has focused on intercellular singling as a central mediator of Th fate commitment, upstream

intracellular balances of gene expression likely also contribute. Follow up studies into the spatial

108

positioning of activated T cells within secondary lymphoid tissue and their direct interaction with

chemokine-expressing cells may further solidify this model.

109

Chapter Five:

Interferon Regulatory Factor 3 balances Th1 and Tfh-dependent

immunity during blood-stage Plasmodium infection

5.1 Abstract

Innate immune signalling pathways are activated during blood-stage Plasmodium infection

and serve to elicit an inflammatory response. Given that innate immune cells can modulate parasite-

specific CD4+ T helper cell differentiation, as described in Chapter 4, it was hypothesized that

molecules associated with innate immunity were also involved. To date, however, this has been

largely unstudied during Plasmodium infection. Using non-lethal, resolving models of blood-stage

malaria, endosomal and cytosolic signalling pathways were compared for their role in mediating

PbTII cell responses. While no major roles were identified for individual pathways, IRF3, which

sits at the convergence point for multiple pathways, was shown to influence PbTII responses. In

particular, IRF3 promoted proliferation and differentiation of Th1 cells, which was associated with

MHCII expression by Ly6Chi inflammatory monocytes and early parasite control. Conversely, IRF3

also acted within B cells to suppress anti-parasitic humoral immune responses, such as the

development of germinal centre B cells, Ig-class switching and Tfh cell responses. Importantly,

IRF3-mediated suppression of humoral immunity was associated with impaired resolution of

infection. Thus, a novel role for an innate immune transcription factor, IRF3, was delineated in

governing the balance between Th1 and Tfh-dependent immunity during experimental blood-stage

malaria

110

5.2 Introduction

Pattern Recognition Receptor (PRR) pathways have been implicated in innate immune

recognition of pRBC-derived molecules such as haemozoin and Plasmodium DNA, via TLRs,

NOD-like receptors (NLRs), and cytosolic DNA detection via STING (encoded by

Tmem173)(Krishnegowda, Hajjar et al. 2005, Parroche, Lauw et al. 2007, Kalantari, DeOliveira et

al. 2014). In these studies, the biological importance of PRR-related molecules was inferred in vivo

based on systemic inflammatory cytokine production. However, few studies have explored in vivo

roles for PRR-related molecules in driving adaptive immune responses during Plasmodium

infection (Franklin, Rodrigues et al. 2007).

IRFs were originally discovered, and are best known for roles in driving production of Type

I IFN, and expression of Interferon-stimulated genes (ISGs) (Tamura, Yanai et al. 2008). However,

it is becoming increasingly clear that IRFs perform many distinct roles in vivo. For instance, while

IRF7 is a master controller of Type I IFN responses (Honda, Yanai et al. 2005), IRF1, 2, 4 and 8

play crucial roles in the development and immunological responses of haematopoietically-derived

immune cells. IRF3 has been reported to perform several functions, including the production of

IFNβ and the suppression of tumour growth via apoptosis in response to DNA-damage.

Furthermore, an in vitro report described a role for IRF3 in B cells responding to CpG (Oganesyan,

Saha et al. 2008), while TBK1, a primary activator of IRF3, was reported to restrain B cells from

immunoglobulin class-switching and IgA-mediated pathology in mice (Jin, Xiao et al. 2012).

Finally, IRF3 is positioned at a convergence point for multiple PRR-signalling pathways, including

those mediated by TRIF-dependent TLRs, MAVS, and STING (Servant, Grandvaux et al. 2002),

which highlights its capacity to integrate and convey innate immune signals to the adaptive immune

system. In support of this, IRF3 has been reported to play context-dependent roles in controlling Th

responses. During viral and bacterial co-infection, IRF3 suppressed Th1 responses by competing for

binding to the IL12β promoter in macrophages (Negishi, Yanai et al. 2012). Conversely, IRF3

drove Th1 and Th17 responses in the central nervous system during experimental autoimmune

encephalomyelitis (Fitzgerald, O'Brien et al. 2014). Therefore, IRF3 displays significant potential to

influence Th and B cell responses, and indeed is likely activated in response to pRBCs (Sharma,

DeOliveira et al. 2011); yet the consequences of this for adaptive immunity to malaria remain

unexplored.

Here, the role of PRR pathways in modulating PbTII responses to in vivo infection is first

tested. Minor roles for the TLR adaptor molecules, MyD88 and Trif are identified. However, most

strikingly, a critical role for IRF3 for the expansion of Plasmodium-specific CD4+ T cells and for

111

promoting their differentiation towards the Th1 fate is observed. Additionally, IRF3 suppresses Tfh-

mediated humoral responses, in a likely secondary mechanism, leading to poorer Plasmodium-

specific antibody production and ultimately, poorer resolution of infection.

112

5.3 Results

5.3.1 Individual PRRs play only modest roles in controlling Plasmodium-specific CD4+ T cell

responses during blood-stage infection

Numerous PRRs have been reported to participate in early innate immune responses to

blood-stage Plasmodium parasites in vivo. Nevertheless, their role in generating antigen-specific

CD4+ T cell responses remains unclear. Therefore, PbTII cells were employed to explore roles for

TLRs, cytosolic DNA/RNA detection, and inflammasomes in mediating CD4+ T cell responses.

PbTII cells were transferred into WT control mice, or those deficient in TLR-signalling (Trif-/-, Tlr3-

/-, and Myd88-/-), or cytosolic DNA/RNA detection (Mavs-/- and Tmem173-/-). After infection with

PcAS, it was noted that PbTII cells expanded equivalently in mice lacking either MAVS or STING

compared to WT controls (Figure 5.1A), suggesting that cytosolic nucleic acid detection was not

essential for the activation of Plasmodium-specific CD4+ T cell responses. Similarly, PbTII

expansion occurred at wild-type levels in mice deficient in Caspase1/11, suggesting that

inflammasome function is dispensable for this response (Figure 5.1A). No requirements were

observed for TLR3, a detector of endosomally located nucleic acids, nor its signalling adaptor,

TRIF, in mediating PbTII proliferative responses (Figure 5.1A). Instead, TRIF suppressed PbTII

responses, while MyD88 played a modest supportive role (Figure 5.1A). Thus, while this approach

revealed modest opposing roles for the TLR adaptors, TRIF and MyD88, it did not identify a

specific PRR-pathway that was critical for driving parasite-specific CD4+ T cell responses. Instead,

IRF3, a convergence point for multiple PRRs, was identified as an important mediator of this

process (Figure 5.1A). To excluded the possibility of non-specific expansion of PbTII cells upon

transfer, physiologically-relevant numbers were transferred into naive C57Bl6/J, Irf3-/-, Myd88-/-,

Mavs-/-, Trif-/-andTmem173-/-, and PcAS-infected WT or Irf3-/-mice. PbTII numbers remained at

approximately one-tenth of the transferred number in all of the naive mice compared to significantly

increased numbers in infected mice by day 8 post-transfer (Figure 5.1B), thus confirming the

Plasmodium-specific expansion of PbTII cells even in immunologically perturbed environments.

113

Figure 5.1 PbTII responses are partially modulated by MyD88- and TRIF-mediated signalling,

but further dependent on IRF3. (A) Enumeration of PbTII cells transferred into naïve mice (106)

or WT, Mavs-/-, Tmem173-/-, Casp-1/11-/-, Tlr3-/-, Trif-/-, Myd88-/-and Irf3-/- mice (104) at 7 days p.i.

with PcAS. (B) Enumeration of PbTII cells (104, indicated by the horizontal dotted line) transferred

into male or female naive C57Bl/6, Irf3-/-,Myd88-/-, Mavs-/-, Trif-/-and Tmem173-/- mice at day 8 post-

transfer, or WT and Irf3-/- at day 7 p.i. with PcAS. (A) Data are pooled from two independent

experiments. (B) Data are from a single experiment of male and female mice. Mann-Whitney

test.*p<0.05, **p<0.01, ****p<0.0001.

5.3.2 Haematopoietic cells employ IRF3 to drive Plasmodium-specific Th responses

Next, the effect of IRF3 on Plasmodium-specific Th differentiation was explored. PbTII

cells were transferred into Irf3-/-or WT mice, and T-bet/IFNγ co-expression (Th1) or Bcl6/CXCR5

0

5

1 0

Pb

TII

(x

10

5)

N a iv e (n = 3 )

W T (n = 1 0 )

C a s p 1 /1 1- /-

( n = 9 )

Pb

TII

(x

10

5)

0

2 0

4 0

** N a iv e (n = 2 )

W T (n = 6 )

T r i f- /-

( n = 7 )

T lr 3- /-

( n = 6 )

Pb

TII

(x

10

5)

0

5

1 0

M a v s- /-

( n = 7 )

N a iv e (n = 3 )

W T (n = 5 )

T m e m 1 7 3- /-

( n = 9 )

0

5

1 0

1 5

Pb

TII

(x

10

5)

********

N a iv e (n = 8 )

W T (n = 1 6 )

M y d 8 8- /-

( n = 2 4 )

I r f3- /-

( n = 1 7 )**

A

B

WT

I rf3- /

-

Myd88- /

-

Mavs- /

-

Tr if

- /-

Tm

em

173- /

-

WT

I rf3- /

-

1 0 1

1 0 2

1 0 3

1 0 4

1 0 5

1 0 6

1 0 7

Pb

TII

ce

lls

/sp

lee

n

*

P c A S

114

co-expression (Tfh) by splenic PbTII cells was assessed at peak PcAS infection. In addition to

reduced numbers of splenic PbTII cells observed in Irf3-/- mice compared to WT controls (Figure

5.1A), there was a substantial qualitative defect in the proportion of PbTII cells developing into Th1

cells (Figure 5.2A). Conversely, Irf3-/- mice had greater proportions of Tfh cells amongst the PbTII

populations (Figure 5.2A). Importantly, IRF3 also supported polyclonal Th1 responses and

dampened Tfh responses during PcAS infection (Figure 5.2B).

To explore which host cells might employ IRF3 to drive PbTII responses, bone marrow

(BM) chimeric mice were generated, in which haematopoietically-derived cells lacked IRF3 (via

reconstitution of lethally irradiated WT mice with Irf3-/-or control WT BM cells; Irf3-/->WT, or

WT>WT controls). In generating these chimeras, flow cytometric assessment of pooled bone

marrow cells was first performed. There were no major differences in the presence of long-term or

short-term haematopoietic stem cells (HSC), or multipotent progenitor cells within the Kit+Sca-1+

HSC compartment between WT and Irf3-/- BM, suggesting no major HSC defect caused by IRF3-

deficiency (Figure 5.3A). After generating the chimeras, no differences were observed in bulk

splenic B-cells, or in modest plasmablast responses between un-infected Irf3-/->WT chimeras and

WT>WT controls (Figure 5.3B). Slight increased numbers of GC B cells were noted in the Irf3-/-

>WT chimeric mice. However, there were no significant differences in absolute numbers of splenic

neutrophils, monocytes, cDCs or T cells in Irf3-/->WT chimeras compared to WT>WT controls

(Figure 5.3C). These data suggested that lethally-irradiated WT mice given Irf3-/- HSC reconstituted

the spleen as effectively as with WT HSC, with only minor perturbations in the B-cell

compartment. Assessment of PbTII responses in PcAS-infected Irf3-/->WT mice and WT>WT

controls revealed that haematopoietic cells required IRF3 to fully support PbTII expansion and Th1

differentiation, while promoting Tfh differentiation (Figure 5.2C). Therefore, our data indicated that

haematopoietic cells, but not CD4+ T cells themselves, employed IRF3 to drive Plasmodium-

specificTh1 responses and suppress Tfh responses. To confirm a requirement for IRF3 within

haematopoietic cells in supporting polyclonal Th1 responses, as shown earlier for PbTII cells

(Figure 5.2C), Irf3-/->WT BM chimeric mice, and WT>WT controls were similarly employed. We

observed that PcAS-infected Irf3-/->WT BM chimeric mice displayed a 50% reduction in splenic

Th1 responses (Figure 5.2D), but a 60% increase in Tfh responses (Figure 5.2D), and increased

parasitemias (Figure 5.2E) compared to WT>WT BM chimeric controls. Together, our results and

previous data demonstrate that while IRF7 and Type I IFN-signalling suppress Th1-immunity

(Haque, Best et al. 2011, Haque, Best et al. 2014, Edwards, Best et al. 2015), haematopoietically-

derived, non-CD4+ T cells require IRF3 to fully support early Th1 responses and anti-parasitic

immunity during non-lethal blood-stage Plasmodium infection.

115

Figure 5.2 IRF3 promotes early protective Th1 responses during blood-stage Plasmodium

infection. (A) Representative FACS plots (gated on CD45.1+ CD4+ TCRβ+ live singlets),

proportions and absolute numbers of splenic PbTII cells exhibiting Th1 (T-bet+ IFNγ+) and Tfh

(CXCR5+ Bcl6+) phenotypes in WT and Irf3-/- mice at 7 days p.i. with PcAS compared to un-

infected WT mice. (B) Representative FACS plots (gated on CD4+ TCRβ+ live singlets), proportions

and absolute numbers of splenic polyclonal CD4+ T cells exhibiting Th1 and Tfh phenotypes in WT

and Irf3-/- mice at 7 days p.i. with PcAS. (C) Numbers of splenic PbTII cells in Irf3-/->WT BM

chimeric mice or WT>WT chimeric controls (receiving 104 PbTII cells) 7 days p.i. with PcAS, as

well as proportions and absolute numbers exhibiting Th1 and Tfh phenotypes. (D) Proportions of

splenic polyclonal CD4+ T cells exhibiting Th1 and Tfh phenotypes at 7 days p.i. with PcAS. Data

are representative of two independent experiments. (E) Parasitemia in Irf3-/->WT BM chimeric

mice or WT>WT chimeric controls at 7 days p.i. with PcAS (data pooled from two independent

experiments). Statistics: Mann-Whitney U test; *p<0.05, **p<0.01, ****p<0.0001.

Irf3-/-WTNaive

Tbet

IFNγ

A0.5 14.223.5

CX

CR

5

Bcl6

10.3 41.9 57.9

FMO (Bcl6)

0

1.1

% T

be

t+IF

N

+

0

1 0

2 0

3 0 **Isotype (Tbet)

FMO (IFNγ)

Bc

l6+ C

XC

R5

+ (

x1

04)

Naiv

e

WT

I rf3- /

-

0

1 0

2 0

3 0 *

Tb

et+

IFN

+ (

x1

04)

0

1 0

2 0

**

% B

cl6

+ C

XC

R5

+

Naiv

e

WT

I rf3- /

-

0

2 0

4 0

6 0

*

Irf3-/-WTNaive

Tbet

IFNγ

0.1 6.6 3.6

B

2.9 22.0 27.2

CX

CR

5

Bcl6 Bc

l6+ C

XC

R5

+(x

10

5)

Naiv

e

WT

I rf3- /

-

0

2 0

4 0

6 0

8 0

1 0 0 *

0

0.4

Tb

et+

IF

N

+(x

10

5)

0

1 0

2 0

3 0

4 0

5 0

****

% T

be

t+ I

FN

+

0

2

4

6

8 ****Isotype (Tbet)

FMO (IFNγ)

FMO (Bcl6)

% B

cl6

+C

XC

R5

+

Naiv

e

WT

I rf3- /

-

0

1 0

2 0

3 0

4 0**

WT

>W

T

I rf3- /

- >W

T

0

5

1 0

1 5

2 0

Pb

TII

(x

10

5)

**

% T

be

t+ I

FN

+

WT

>W

T

I rf3- /

- >W

T

0

2

4

6

8

1 0 ****

E

WT

>W

T

I rf3- /

- >W

T

0

2

4

6

8

Tb

et+

IFN

+ (

x1

05) **

WT

>W

T

I rf3- /

- >W

T

0

2

4

6

Bc

l6+ C

XC

R5

+ (

x1

05)

C

0

1 0

2 0

3 0

4 0

5 0

% T

be

t+ I

FN

+

**

0

2 0

4 0

6 0

% B

cl6

+ C

XC

R5

+

**

% B

cl6

+ C

XC

R5

+

WT

>W

T

I rf3- /

- >W

T

0

1 0

2 0

3 0

**

D

Pa

ras

ite

mia

(%

pR

BC

)

WT

>W

T

I rf3- /

- >W

T

0

2 0

4 0

6 0

*

116

Figure 5.3 Phenotypic characterisation of Irf3-/->WT bone marrow chimeric mice. (A) FACS

gating strategy for long-term haematopoietic stem cells (LT-HSC; Lin- Kit+ Sca1+ CD150+ CD48-),

short-term HSCs (ST-HSC; Lin- Kit+ Sca1+ CD150+ CD48+) and multipotent progenitors (MPP;

Lin-Kit+ Sca1+ CD150- CD48+) in BM cells from WT and Irf3-/- mice (pooled from n=6/group). (BD)

WT or Irf3-/- BM cells were transferred into lethally-irradiated WT mice, and after 8 weeks, splenic

WT Irf3-/-

6.6 9.0

71.1

6.6 7.2

74.3FS

CH

SS

CA

FSCA FSCA

Lin

Kit

FSCA Sca1 CD

15

0

CD48LT-HSC ST-HSC

MPP

A

0.22 0.22

0.27 0.40

GL

7

Fas

IgD

CD

13

8

60.2 63.3

B2

20

CD19

WT>WT Irf3-/->WT

0

2 0

4 0

6 0

% B

22

0+C

D1

9+

0 .0

0 .1

0 .2

0 .3

0 .4

0 .5%

CD

13

8+Ig

D+

0 .0

0 .2

0 .4

0 .6

0 .8

%F

as

+G

L-7

+

B

0

1

2

3

4

5

B2

20

+C

D1

9+

(x1

07)

0

5

1 0

1 5

CD

13

8+Ig

D+

(x1

04)

0

1 0

2 0

3 0

Fa

s+G

L-7

+

(x1

04)

*

8.11 7.4722.4 20.1

TCRβ

CD4

0.65 0.50

Ly6

C

Ly6G

0.46 0.51

CD

11

b

CD11b

0.89 0.52

CD

11

c

MHCII

0

1 0

2 0

%C

D4

+T

CR

+

0

2

4

6

8

%C

D4

-T

CR

+

0 .0

0 .5

1 .0

1 .5

%L

y6

G+C

D1

1b

+

0 .0

0 .2

0 .4

0 .6

0 .8

%L

y6

Ch

i CD

11

b+ *

0 .0

0 .5

1 .0

1 .5

CD

11

ch

i MH

CII

hi

**

0

1

2

3

4

5

CD

4-T

CR

+

(x1

06)

0

5

1 0

1 5

CD

4+T

CR

+

(x1

06)

0

2

4

6

Ly

6G

+C

D1

1b

+

(x1

05)

0

1

2

3

4

Ly

6C

hi C

D1

1b

+

(x1

05)

0

1

2

3

4

5

CD

11

ch

i MH

CII

hi

(x1

05)

WT>WT Irf3-/->WTC

117

immune populations were assessed (n=6). (B) Representative plots (gated on live singlets) showing

proportions and absolute numbers of splenic B cells, plasmablasts (B220+ CD19+ IgDlo CD138+)

and GC B cells (B220+ CD19+ Fas+ GL-7+). (C) Representative FACS plots of various myeloid cell

populations, including proportions and absolute numbers in the spleens of un-infected Irf3-/->WT

and WT>WT chimeric mice; Ly6G+ CD11b+ neutrophils (Ly6Ghi Ly6Clo B220- TCRβ- live singlets),

inflammatory monocytes (Ly6Chi Ly6Glo B220- TCRβ- live singlets), cDCs (CD11chi MHCIIhi B220-

TCRβ- live singlets) and CD4+ or CD8+ T cells (CD4+ TCRβ+ or CD4- TCRβ+ live singlets).

Experiment performed once. Statistics: Mann-Whitney U test, *p<0.05.

5.3.3 IRF3-mediated Th1 support correlates with MHCII-expression by inflammatory

monocytes

The observation of poorer accumulation of PbTII cells in the absence of IRF3 (Fig 5.1A) led

to the consideration of a defect in early activation. However, flow cytometric assessment of

expression of the early activation marker CD69 showed that CTV-labelled PbTII cells were

comparably activated at day 2 p.i. in WT or IRF3 deficient mice (Figure 5.4A). In further

experiments in which 106 PbTII cells were transferred for ease of detection, it was noted that a

greater proportion of PbTII cells in WT mice had divided at least once by day 4 p.i. as evident by

the loss of CTV compared to PbTII cells transferred into Irf3-/- mice (Figure 5.4B). When more

physiologically relevant numbers were transferred and the possibility for intraclonal competition

minimised, a trend towards reduced Th1 responses was observed by day 4 p.i., which reached

statistical significance by day 5 p.i. (Figure 5.4C). IRF3 did not support accumulation of PbTII cells

by preventing apoptosis, since no differences in PbTII cells from infected WT and Irf3-/-recipients

was evident, either in expression of the anti-apoptotic protein Bcl-2 (Figure 5.4D), or cell-surface

display of phosphatidylserine (Figure 5.4E). Therefore, this data suggested that IRF3 supports

splenic PbTII responses by sustaining their proliferation, rather than mediating early T cell

activation or preventing T cell apoptosis.

118

Figure 5.4 Requirements for IRF3 in early activation of PbTII cells. (A) CTV labelled PbTII cells

(106) were transferred into WT and Irf3-/- mice one day prior to infection with PcAS. (B)

Representative FACS plots (gated on CD45.1+ CD4+ TCRβ+ live singlets) of CTV positivity and

CD69 expression, and proportion of unproliferated PbTII cells (CTVhi) that are CD69+ at 1, 2 and

3 days p.i. with PcAS. (C) Absolute numbers and proportions exhibiting a Th1 (IFNγ+ T-bet+) or

Tfh (CXCR5+ Bcl6+) phenotype. (D) Representative histograms (gated on CD45.1+ CD4+ TCRβ+

live singlets), and expression of Bcl2 expression by PbTII cells in WT or Ir3-/- mice at 2, 3 and 4

days p.i. with PcAS relative to naive PbTII cells (n=4). (E) Representative plots (gated on CD45.1+

CD4+ TCRβ+ live singlets) of 7AAD and Annexin V-positivity by PbTII cells in WT or Irf3-/- mice at

7 days p.i. with PcAS and plot showing staining of FSCAlo dead/dying cells for comparison. (A)

Data are representative of replicate experiments. (B-E) Data from experiments performed once

(n=4). Geom Mean: Geometric mean. Statistics: Mann-Whitney test. *p<0.05, **p<0.01. NS, not

significant.

0 1 2 3 4 5

-2 0 0 0

0

2 0 0 0

4 0 0 0

D a y s p o s t - in fe c t io n

BC

L-2

le

ve

ls r

ela

tiv

e t

o u

nin

fec

ted

Annexin V

7A

AD

WT

Irf3-/-

WT

I rf3- /

-

1 5

2 0

2 5

3 0%

An

ne

xin

+ 7

AA

D-

ED

FSCAlo

control

0 1.2

27.6

0.81 31.4

39.4

0 1.90

19.3

Naive

WT

Irf3-/-

FMO

Bcl2

A

CTV

CD

69

48.6

65.7Irf3-/-

WT

Naive 7.9

Irf3-/-

WT

Naive

Day 3 Day 4

CTV

B

C

4 5

0 .1

1

1 0

Pb

TII

ce

lls

(x

10

5)

**

N S

4 5

0

1 0

2 0

3 0

4 0

% I

FN

+ T

be

t+

4 5

0

1 0

2 0

3 0

4 0

5 0

% C

XC

R5

+ B

cl6

+

*

3 4

0

2 0

4 0

6 0

8 0

% P

roli

fera

ted

ce

lls

*

Naiv

e

WT

I rf3- /

-

0

1

2

3

4

CD

69

+ (

x 1

04)

Naiv

e

WT

I rf3- /

-

0

2 0

4 0

6 0

8 0

%C

D6

9+

*

Days post-infection

Days post-infection

119

5.3.4 Conventional dendritic cell activation during Plasmodium infection is independent of

IRF3 expression

Next, it was hypothesized that haematopoietic myeloid cells employed IRF3 to support

proliferation and Th1-differentiation of activated CD4+ T cells. CD4+ T cells require continual

antigen presentation for each new round of division (Obst, van Santen et al. 2005). Moreover,

prolonged MHCII-mediated signals have been demonstrated to encourage Th1-differentiation in

vivo (Celli, Lemaitre et al. 2007). Thus, the activation and signalling capacity of conventional

dendritic cells, which are essential for CD4+ T cells response (Fig 3.2B), was assessed during the

course of PcAS infection (Haque, Best et al. 2014). In WT mice, cDCs experienced peak expression

of cell surface MHCII, or co-stimulatory molecules, CD86 or CD40 followed by a sharp decrease in

expression (Figure 5.5A and Figure 5.5B). Splenic cDCs in Irf3-/- mice also demonstrated

comparable expression of these markers suggesting an equivalent capacity to support CD4+ T cell

responses (Figure 5.5B). These results did not support a role for IRF3 in cDC-mediated PbTII

activation during PcAS infection.

120

Figure 5.5 Activation kinetics of conventional dendritic cells during PcAS infection. (A) Gating

strategy for CD11c+ MHCII+ dendritic cells and Ly6Chi CD11b+ inflammatory monocytes. (B)

Expression of MHCII, CD86 and CD40 by CD8α+ and Sirpα+ CD11c+ MHCII+ dendritic cells from

WT or Irf3-/-mice at 1-4 and 7 days p.i. with PcAS. Data are representative of two independent

experiments. Geom Mean: geometric Mean fluorescence intensity.

FS

CH

Liv

e/D

ea

d

SS

CA

FSCA

FSCA

FSCA

B2

20

TC

Rβ

CD

11

c

FSCA

FSCA

MHCII

Ly6

C

CD11b

Ly6

G

Ly6C

CD8α

Sirpα

A

0

2 0 0 0

4 0 0 0

6 0 0 0

1 2 3 4 7

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

0

2 0 0

4 0 0

6 0 0

8 0 0

W T

I r f 3- /-

1 2 3 4 7

0

2 0 0

4 0 0

6 0 0

CD

8α

-cD

CC

D8α

+ c

DC

Ge

o m

ea

n F

L

CD86 CD40

Ge

om

me

an

FL

Days post-infection

B

121

5.3.5 IRF3 drives MHCII signalling by inflammatory monocytes to support accumulation of

PbTII cells

Inflammatory monocytes migrate from the bone marrow to the spleen during blood-stage

infection and are reported to be involved in the clearance of pRBC (Sponaas, Freitas do Rosario et

al. 2009). In other in vivo infection systems, inflammatory monocytes are also reported to support

Th1 responses (Leon, Lopez-Bravo et al. 2007, Flores-Langarica, Marshall et al. 2011).

Furthermore, CD4+ T cell proliferation after experimental vaccination depended on both

inflammatory monocytes in association with IRF3 (Marichal, Ohata et al. 2011). Therefore, Ly6Chi

monocytic responses were assessed first in WT mice infected with PcAS (Figure 5.5A). Strikingly,

splenic Ly6Chi CD11b+ inflammatory monocytes dramatically up-regulated cell surface MHCII

expression up until peak PcAS infection, to a level close to that seen on cDCs from naive mice

(Figure 5.6A and 5.6B). Ly6Chi inflammatory monocytes in Irf3-/-mice, however, displayed a

profound defect in MHCII upregulation compared to WT controls (Figure 5.6B). This defect in

MHCII expression and total numbers of MHCII expression cells in Irf3-/- mice was evident at the

same time PbTII cells displayed reduced expansion (Figure 5.4C and 5.6C). Finally, Irf3-/->WT BM

chimeras confirmed the haematopoetic requirement for IRF3 in driving MHCII expression by

inflammatory monocytes (Figure 5.6D). Taken together, these data suggested that IRF3 did not

influence initial priming of CD4+ T cells, but instead promoted inflammatory monocytic responses

that might sustain Th1 responses during infection.

122

Figure 5.6 Inflammatory monocytes express MHCII in an IRF3-dependent manner. (A)

Expression kinetics and numbers of MHCII+ by inflammatory monocytes during PcAS infection in

WT mice. (B) Representative histograms, proportion of MHCII+ and mean expression of MHCII by

Ly6Chi CD11b+ Ly6G- inflammatory monocytes from naïve or WT and Irf3-/- monocytes at 7 days p.i.

with PcAS. Histogram also shows MHCII expression by cDCs from naïve WT mice as a

comparison. (C) Proportions, geometric mean fluorescence and absolute numbers of MHCII+

splenic Ly6Chi monocytes at days 5 and 7 p.i. with PcAS. (D) MHCII expression and proportions of

MHCII+ Ly6Chi CD11b+ Ly6G- monocytes from lethally irradiated WT BM chimeric mice

reconstituted with WT or Irf3-/- bone marrow. Data are representative of two independent

experiments. Geom Mean: geometric mean fluorescence intensity. Mann-Whitney test. *p<0.05,

**p<0.01. NS, not significant.

Na

ive

WT

Irf3

-/-

cD

C

MHCII

5 7

0

2 0

4 0

6 0

% M

HC

II+

**

**

A

0 2 4 6 8

0

1 0

2 0

3 0

4 0

5 0

6 0

D a y s p o s t - in fe c t io n

% M

HC

II+

0 2 4 6 8

0

1

2

3

4

5

6

7

D a y s p o s t - in fe c t io n

MH

CII

+

CD

11

bh

i Ly

6C

hi

(x

10

5)

5 7

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

MH

CII

Ge

o M

ea

n F

L

**

**

5 7

0

2 0

4 0

6 0

% M

HC

II+

**

**

5 7

0

5

1 0

MH

CII

+(x

10

5)

**

**

Days post-infection

C

Naiv

e

WT

>W

T

I rf3- /

- >W

T

0

5

1 0

1 5

2 0

MH

CII

+ (

x1

05)

N S

Naiv

e

WT

>W

T

I rf3- /

- >W

T

0

2 0

4 0

6 0

% M

HC

II+

**D

Naiv

e

WT

>W

T

I rf3- /

- >W

T

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

MH

CII

Ge

o M

ea

n F

L **

B

123

5.3.6 IRF3 suppresses humoral immunity during blood-stage Plasmodium infection

Although IRF3 supported splenic PbTII numbers (Figures 5.3A), of those recovered from

Irf3-/- mice, a greater proportion had become Tfh cells compared with WT controls (Figure 5.2A).

This data indicated that IRF3 was not required for Tfh differentiation, and in fact regulated humoral

responses. Since Tfh responses are known to support affinity maturation of B cells and the

production of antibodies (Arnold, Campbell et al. 2007), it was next hypothesized that IRF3 played

another role during the later stages of infection in suppressing humoral immunity. In support of this,

Irf3-/-mice exhibited improved resolution of PcAS infection compared to their WT counterparts (Fig

5.7A). Since Plasmodium-specific antibodies have been shown to contribute to the clearance of

Plasmodium infection, antibody titres were assessed in WT and Irf3-/- mice throughout the course of

PcAS infection. Higher levels of total IgG, IgM, IgG1, IgG2a, IgG2b and IgG3 were seen in Irf3-/-

mice throughout infection (Fig 5.7B). Furthermore, increased GC B cell responses were observed in

Irf3-/- mice compared to WT controls at 7 and 14 days p.i. with PcAS (Fig 5.8A and 5.8B). Using

Irf3-/->WT BM chimeric mice, the observed suppression of GC B cells was shown to be due to a

hematopoietic role for IRF3 (Figure 5.8C). Finally, memory B cell responses were significantly

stronger at day 40 p.i. in Irf3-/- mice, determined by the co-expression of CD38 and CD80 (Figure

5.8D) (Tomayko, Steinel et al. 2010). Together, these results support a role for IRF3 in suppressing

Tfh-mediated GC B cell, antibody and memory responses during non-lethal blood-stage

Plasmodium infection.

124

Figure 5.7 IRF3 limits Plasmodium-specific antibody responses. (A-B) Time-course analysis of

(A) parasitemia and (B) parasite-specific IgM, total IgG, IgG1, IgG2b, IgG2c and IgG3 in sera

(diluted 1 in 400) in WT and Irf3-/- mice (n=6/group) infected with PcAS. Data are pooled from

two independent experiments. Mann-Whitney test. *p<0.05, **p<0.01, ***p<0.001,

****p<0.0001.

A Total IgGIgM IgG1

IgG2b IgG2c IgG3

B

Days post-infection

0 1 0 2 0 3 0 4 0

0

1

2

3

*

* * * *

* * * *

0 1 0 2 0 3 0 4 0

0 .0

0 .5

1 .0

1 .5

2 .0

OD

(4

92

nm

)

* * * **

* ** * * *

0 1 0 2 0 3 0 4 0

0 .0

0 .5

1 .0

1 .5

2 .0

* * *

* * *

*

0 1 0 2 0 3 0 4 0

0 .0

0 .5

1 .0

1 .5

* * *

* * *

* * * *

* * *

0 1 0 2 0 3 0 4 0

0 .0

0 .5

1 .0

1 .5

2 .0

* * *

* * * *

* *

0 1 0 2 0 3 0 4 0

0 .0

0 .5

1 .0

1 .5

2 .0

2 .5

* * *

* * * *

0 1 0 2 0 3 0 4 0 5 0

0 .1

1

1 0

D a y s p o s t - in fe c t io n

Pa

ras

ite

mia

(%

pR

BC

s)

W T

I r f3- /-

* *

* ** *

* *

* ** *

* *

* ** *

* *

* *

**

* *

*

* * *

125

Figure 5.8 IRF3 limits Plasmodium-specific antibodies GC B cells and Tfh cell differentiation.

(A) FACS gating strategy for splenic B-cells. (B) Representative FACS plots (gated on B220+

CD19+ live singlets) showing proportions and absolute numbers of splenic GC B cells (Fas+ GL7+)

in WT and Irf3-/- mice, at indicated days p.i. with PcAS. (C) Absolute numbers of splenic GC B cells

(Fas+ GL7+) in Irf3-/->WT BM chimeric mice and WT>WT chimeric controls at 7 days p.i. with

PcAS. (D) Representative FACS plots (gated on IgDlo B-cells) showing proportions and absolute

numbers of splenic B cells exhibiting a CD38hi CD80hi memory phenotype in WT and Irf3-/- mice at

73 days p.i. with PcAS, compared with naïve WT mice. Data (B) & (C) are representative and (D)

pooled from two independent experiments. Statistics: Mann-Whitney U test; *p<0.05, **p<0.01,

***p<0.001.

Fa

s+ G

l7+ (

x1

06)

WT

I rf3- /

-

WT

I rf3- /

-

WT

I rf3- /

-

0

1 0

2 0

3 0

**

**

*

% F

as

+ G

L7

+

0

1 0

2 0

3 0

****

Day 15

Irf3-/-

WT 1.7

3.9

19.5

24.0

Day 7

Fas

GL

7

Day 0A

FS

CH

Liv

e/D

ea

d

SS

CA

B2

20

FSCA FSCA

FSCA CD19

Day 7 Day 15Day 0

0.5

0.2

C

9.39 18.2 28.1

Irf3-/-Naive

CD38

CD

80

WTD

Naiv

e

WT

I rf3- /

-

0

1 0

2 0

3 0

4 0%

CD

80

+C

D3

8+

***

Naiv

eW

T

I rf3- /

-

0

2

4

6

8

1 0

CD

80

+ C

D3

8+ (

x1

06)

IgD

CD

13

8

IgDlo gating

WT

>W

T

I rf3- /

- >W

T

0

2

4

6

8

1 0

Fa

s+

GL

7+ (

x1

06) **

B

126

5.3.7 IRF3 expression by B cells impairs development of humoral immune responses

Given that IRF3 and its upstream kinase TBK1 were previously reported to operate within B

cells (Oganesyan, Saha et al. 2008, Jin, Xiao et al. 2012); a possible B cell-intrinsic function for

IRF3 during blood-stage Plasmodium infection was next explored. To this end, mixed BM chimeric

mice were generated in which only B cells were IRF3-deficient. This was achieved by

reconstituting lethally-irradiated WT mice with a mixture of BM cells (80% from B-cell deficient,

μMT, mice and 20%either from Irf3-/-or WT control mice) (Figure 5.9A). B cell-specific IRF3-

deficient chimeras displayed no defect in the production of Plasmodium-specific serum IgG, by 15

days p.i with PcAS, compared to WT control mixed BM chimeras, suggesting that B cells did not

depend on IRF3 to suppress high-affinity IgG production (Figure 5.9B). However, it was observed

that when B cells lacked IRF3, polyclonal Tfh responses were modestly increased by 15 days p.i.

(Figure 5.9C). Together, these data pointed towards a subtle role for IRF3 in B cells, not in

suppressing bulk GC B cell responses, but in modulating interactions between B cells and Tfh cells.

To expose subtle roles for IRF3 in B cells, a competitive chimeric approach was adopted, in

which mixtures of WT and Irf3-/- BM were used to reconstitute lethally-irradiated WT mice (Figure

5.9D). In preliminary experiments, 50:50 ratios of BM cells yielded expected ratios within T cell

and myeloid cell compartments (data not shown), but the B cell compartment was skewed heavily

towards Irf3-/- B cells (data not shown). In subsequent experiments we employed 70:30 mixtures of

WT and Irf3-/-BM, and again yielded marked skewing only of B cells (not T cells or myeloid cells)

towards an Irf3-/-genotype (Figure 5.9E and data not shown). These data suggested that in the

context of BM transplantation, IRF3 specifically suppressed B cell development from

haematopoietic stem cells (HSC). Nevertheless, WT and Irf3-/-B cells in mixed BM chimeric mice

displayed similar capacities to populate follicular, extra-follicular and marginal zone compartments

in the spleen, as assessed by expression of CD23 and CD21 (Figure 5.9F). Next, mixed BM

chimeric mice were infected with PcAS, and early GC B cell formation was examined (Figure

5.9G). Firstly, it was noted that uninfected controls exhibited background GC B cell responses,

although this had not generated Plasmodium-specific IgG (Figure 5.9B), and was likely the result of

total body irradiation and BM engraftment. More importantly, in this competitive environment, Irf3-

/-B cells possessed an advantage in generating early GC B cell responses compared to WT B cells in

the same animals (Figure 5.9G). Taken together, this approach revealed that IRF3 acted in B cells,

not only to restrain B cell development from HSC, but also to suppress GC B cell responses during

blood-stage Plasmodium infection.

127

Figure 5.9 IRF3 within B cells dampens maturation and GC B cell responses during infection.

(A) Schematic of lethally-irradiated WT mice reconstituted with 80% μMT and 20% WT or Irf3-/-

bone marrow. (B) PcAS-specific total IgG and (C) proportion of CXCR5+ ICOS+ CD4+ TCRβ+ cells

in chimeras described in (A) at 15 days p.i. (pooled from two independent experiments). (D)

Schematic of lethally-irradiated WT mice administered 70% WT (CD45.1) and 30% Irf3-/- (CD45.2)

bone marrow-derived cells. (E) Representative plots (gated on B220+ CD19+ live singlets) and

pooled proportions of WT and Irf3-/- B220+CD19+ cells, and (F) their localisation to the extra-

follicular, follicular and marginal zone within the spleens of naïve chimeric mice described in (D)

at 12 weeks post-transplant. (G) Representative plots (gated on CD45.1+/CD45.1- B220+ CD19+

live singlets) and enumeration of Gl7 and Fas co-expression by B splenocytes in chimeric mice

described in (D) at 15 days p.i. with PcAS. (F-G) Data are representative data of two independent

experiments. Statistical analyses are Mann-Whitney test except (D), which is a one-way ANOVA

corrected with Tukey’s multiple comparison test. *p<0.05, ****p<0.0001. NS, not significant.

D E

A

WT

I rf3- /

-

0

5

1 0

1 5

2 0

% C

D2

3-C

D2

1-

N S

E x tra F o l l ic u la r

30% Irf3-/-

(CD45.2+) WT

(CD45.1+)

70% WT

(CD45.1+)

GWTIrf3-/-

Gl7

Fas

Naive PcAS

C

F

4.75 14.31.3 2.80

B

Irf3-/- (CD45.2+)

WT

(C

D4

5.1

+)

8.1

83.3

WT

80% µMT

20% Irf3-/-

or WT

or

WT B cells

Irf3-/- B cells

WT

Irf3

-/-

WT

Irf3

-/-

0

1 0

2 0

3 0

4 0

% C

XC

R5

+ I

CO

S+

N a ive P c A S

*

N S

WT

Irf3

-/-

WT

Irf3

-/-

0

5

1 0

1 5

% F

as

+ G

L7

+

****

N a ive P c A S

N S

8-12 weeks

reconstitution

8-10 weeks

reconstitution

WT

I rf3- /

-

0

2 0

4 0

6 0

8 0

% C

D2

3+ C

D2

1- *

F o ll ic u la r

Irf3-/-WT

1:4

00

1:8

00

1:1

600

1:3

200

0 .0

0 .5

1 .0

1 .5

2 .0

OD

(4

92

nm

)

N a iv e W T

N a ive I r f 3- /-

P c A S W T

P c A S I r f 3- /-

WT

I rf3- /

-

1

1 0

1 0 0

% o

f s

ple

nic

B-c

ell

s

WT

I rf3- /

-

0

5

1 0

1 5

% C

D2

3-C

D2

1+

N S

M a rg in a l Z o n e

128

To further explore the role of IRF3 in suppressing humoral immunity during experimental

malaria, a second model of resolving infection, Py17XNL, was employed. While WT mice

recovered from Py17XNL infection after 30 days, as expected, mice deficient of T and B cells

(Rag1-/-) succumbed to hyperparasitemia, highlighting the importance of adaptive immune

responses in controlling this infection. Irf7-/- mice experienced significantly lower burdens of

infection and faster resolution compared to WT mice, consistent with recent reports in other murine

malaria models (Haque, Best et al. 2014, Edwards, Best et al. 2015). IRF3 deficient mice, however,

showed reduced control of the initial ascent in parasite burden, but improved resolution of infection

compared to WT mice (Figure 5.10A). As in the PcAS system, improved resolution of Py17XNL

infection by Irf3-/- mice was associated with higher titres of Plasmodium-specific total IgG, IgM,

IgG1, IgG2b, IgG2c and IgG3 levels, which were maintained up to day 81 p.i. compared to WT

levels (Figure 5.10B). Furthermore, GC B cell proportions and total numbers were significantly

improved in the absence of IRF3 (Figure 5.10C). Lastly, memory B cell responses as determined by

proportions and total numbers of CD80+ and CD38+memory B cells were markedly improved in

Irf3-/- mice compared to their WT counterparts at day 81 p.i. (Figure 5.10D). Thus, IRF3 dampened

GC B cell responses, antibody titres and resolution of infection in a second model of slow resolving

Plasmodium infection.

129

Figure 5.10 IRF3 limits humoral immune responses during Py17XNL infection. (A) Time-course

analysis of parasitemia in Irf3-/-, Irf7-/, Rag1-/- and C57Bl/6 mice (n=5-6) infected with Py17XNL;

cross indicates ethically-motivated euthanasia of heavily infected mice. (B) Time-course analysis of

parasite-specific IgM, total IgG, IgG1, IgG2b, IgG2c and IgG3 in sera (1/400 dilution) in Irf3-/- and

WT mice (n=6/group) infected with Py17XNL. (C) Representative FACS plots (gated on B220+

CD19+ live singlets) and graphs showing proportions and absolute numbers of splenic B cells

exhibiting a GC B cell phenotype (Fas+ GL7+) in infected Irf3-/- and WT mice, at 7 and 14 dpi with

Py17XNL, compared to un-infected WT controls. (D) Representative FACS plots (gated on IgDlo

B220+ CD19+ live singlets) and graphs showing proportions and absolute numbers of splenic B

cells exhibiting a CD38hi CD80+ memory phenotype in naïve WT or infected WT and Irf3-/- mice at

81 dpi with Py17XNL. Data representative of (A, C & D) or pooled (B) from two independent

experiments. Statistics: Mann-Whitney U test, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

A

IgG2c IgG3

B

0 2 0 4 0 6 0 8 0

0

1

2

3

****

****

****** ********

0 2 0 4 0 6 0 8 0

0 .0

0 .5

1 .0

1 .5

OD

(4

92

nm

)

****

0 2 0 4 0 6 0 8 0

0 .0

0 .5

1 .0

1 .5

2 .0

****

****

****

****

********

0 2 0 4 0 6 0 8 0

0 .0

0 .5

1 .0

1 .5

2 .0

*

****

****

******

**

0 2 0 4 0 6 0 8 0

0

1

2

3

*

****

****

********

********

0 2 0 4 0 6 0 8 0

0

1

2

3

OD

(4

92

nm

)

***

****

****

****

********

IgG1

IgG2b

Total IgGIgM

Days post-infection

D14D7

D14

Naive WT Irf3-/-

Fas

Gl7

C0.9 1.80.2

16.7 37.6

D7 D14D7

0.2

Naive WT Irf3-/-

CD38

CD

80

9.51 19.4 30.6

D

D a y s p o s t - in fe c t io n

Pa

ras

ite

mia

(%

pR

BC

)

0 1 0 2 0 3 0

0

2 0

4 0

6 0

8 0 W T

I r f 3- /-

R a g 1- /-

I r f 7- /-

* *

* *

* ** *

* ** *

*

* *

* ** ** * * * * *

* *

†

IgD

CD

13

8

Naiv

e

WT

I rf3- /

-

Naiv

e

WT

I rf3- /

-

0 .0

0 .5

2

4

6

8

1 0

1 2

Fa

s+

Gl7

+ (

x1

06

)

**

**

Naiv

e

WT

I rf3- /

-

Naiv

e

WT

I rf3- /

-

0

2

1 0

2 0

3 0

4 0

5 0

%F

as

+ G

L7

+

*

*

IgDlo gating

Naiv

e

WT

I rf3- /

-

0

1 0

2 0

3 0

4 0

% C

D8

0+ C

D3

8+

**

Naiv

e

WT

I rf3- /

-

0

2

4

6

8

1 0

CD

80

+ C

D3

8+ (

x1

06) **

130

5.3.8 IRF3 contributes to disease severity during lethal experimental malaria

Since IRF3 suppressed Tfh-mediated humoral responses during non-lethal Plasmodium

infections, whether IRF3 could also modulate the outcome of lethal infection was explored. To this

end, PbA, a model of severe and cerebral malaria was employed. WT mice infected with PbA

succumbed to cerebral symptoms between 7 and 10 days p.i. (Figure 5.11). Mice deficient of

granzyme B experienced no cerebral symptoms as expected (Haque, Best et al. 2011), confirming

the role of cytotoxic cytokines in these ECM, but succumbed to the infection at approximately day

15 p.i. (Figure 5.11). As expected, Rag1-/- mice were also immune to cerebral malaria (Amante,

Haque et al. 2010), but were unable to resolve the infection. Strikingly, in the absence of IRF3,

mice experienced no cerebral symptoms and furthermore, were able to completely clear infection

after approximately 30 days (Figure 5.11). These results further support a role for IRF3, in

suppressing the antibody-mediated resolution of blood-stage infection.

Figure 5.11 IRF3 impairs resolution of PbA infection in mice. Time-course analysis of PbA

parasitemia in C57Bl/6, Irf3-/-, Rag1-/- and Gzmb-/- mice (n=6). Grey box denotes time-frame of

cerebral symptoms experienced by WT mice.

D a y s p o s t - in fe c t io n

Pa

ras

ite

mia

(%

iR

BC

)

0 5 1 0 1 5 2 0 2 5 3 0 3 5

0 .1

1

1 0

1 0 0W T

I r f 3- /-

R a g 1- /-

G z m B- /-

A

131

5.4 Discussion

In this study, whether PRR-signalling pathways direct Plasmodium-specific CD4+ T cell

responses during non-lethal, experimental malaria have been assessed. Modest opposing roles for

MyD88 and TRIF-dependent signalling were identified, but more importantly, a complex, biphasic

role for the innate immune transcription factor, IRF3, which is ubiquitously expressed by immune

cells and sits at the convergence point of multiple PRRs, was discovered. Over the course of

infection, IRF3 was initially critical for driving Th1 responses, activating monocytes, and

controlling pRBC, but later suppressed B cell responses, limited Plasmodium-specific antibody

production, and delayed resolution of infection.

Naturally-acquired immunity to malaria requires multiple infections over many months to

years to fully develop. Nonetheless, protective immunity against blood-stage Plasmodium parasites

can be generated in humans, and thus efforts to accelerate its onset will likely be beneficial. The

innate immune system plays a crucial role in sculpting adaptive immune responses. However, few if

any molecular mechanisms have been reported via which innate immune responses drive adaptive

immunity to malaria. Several endosomal and cell-surface PRRs, and their associated signalling

adaptors, have been implicated in Plasmodium sensing in vivo. Parasite RNA activated the

cytoplasmic receptor, MDA5, during the liver stage of infection (Liehl, Zuzarte-Luis et al. 2014).

TLR2 and TLR4 signalled in response to Plasmodium glycosylphosphatidylinositol (GPI) moieties

(Krishnegowda, Hajjar et al. 2005). TLR9 detected malaria haemozoin, possibly in complex with

parasite DNA (Coban, Ishii et al. 2005). Finally, the STING/TBK1/IRF3 axis was activated,

predominantly in vitro, by AT-rich Plasmodium DNA motifs (Sharma, DeOliveira et al. 2011).

Despite these reports, only a single study has suggested that a PRR-signalling adaptor, in this case

MyD88, could elicit Th cell responses during Plasmodium infection (Franklin, Rodrigues et al.

2007). For the first time, PbTII cells have allowed for the delineation of a modest contributory role

for MyD88, and a similarly modest suppressive role for TRIF-signalling. However, these data did

not support roles for caspase 1/11-dependent inflammasomes or the STING pathway in driving

CD4+ T cell responses. Given reports that these pathways are activated in vivo, it is speculated that

myeloid cells other than those involved in T cell responses, for example certain macrophage

subsets, employ STING and inflammasome pathways to trigger inflammatory cytokine production

during infection.

Another possibility is that functional redundancy exists between these PRR pathways, such

that in any given innate immune cell, the absence of one pathway is readily compensated for by the

activation of another. Such functional redundancy could ensure robust innate immune defense

132

against a spectrum of microbial challenges in a genetically diverse population. The concept of

functional redundancy of PRR signalling pathways in response to pRBCs is indirectly supported by

the fact that IRF3, which can integrate multiple PRR signals, was crucial for monocyte activation

and Th1 responses. Thus, IRF3 constitutes one of the first described innate immune targets for

driving CD4+ T cell responses during malaria, and as such could be investigated as a molecular

target for novel adjuvants in candidate malaria vaccines.

The finding that IRF3 drives Th1 responses contrasts with previous reports that Type I IFN

signalling via IFNAR1 and the transcription factor IRF7 suppressed Th1-immunity to malaria.

Indeed, while IFNAR1-signalling suppressed monocytic responses in this previous work (Haque,

Best et al. 2014, Edwards, Best et al. 2015), IRF3 was essential here for driving activation of

inflammatory monocytes. Therefore, the findings here adds to an increasing body of work showing

that IRF3 acts very differently to its related IRF family member, IRF7, and re-enforces the view that

IRF3 does not simply induce Type I IFN responses, but plays a number of other roles in multiple

immune cells.

In order for CD4+ T cells to differentiate into Th1 cells, they are reported to require

sustained MHCII signalling, after an initial period of cDC-activation (Celli, Lemaitre et al. 2007).

Therefore, one possible mechanism by which IRF3 supports Th1 responses is by driving

inflammatory monocytes to provide MHCII-mediated signals to activated CD4+ T cells. Indeed,

data reported in Chapter 4 indicate that monocyte/macrophages are specifically required to support

Th1 responses during Plasmodium infection. Moreover, a link between IRF3 and inflammatory

monocyte-mediated Th support was previously demonstrated in the observation by Marichal et al

that the adjuvanticity of alum during experimental vaccination was strongly dependent on IRF3 and

inflammatory monocytes (Marichal, Ohata et al. 2011). Therefore, it is proposed that IRF3 allows

inflammatory monocytes in the spleen to support Th1 differentiation in activated Plasmodium-

specificCD4+ T cells.

The involvement of IRFs in eliciting Type I IFN responses to pathogens and controlling

development of haematopoietically-derived immune cells is well documented (reviewed by

(Battistini 2009) and (Tamura, Yanai et al. 2008)). While IRF4 and IRF8 are well established to

control many aspects of B lymphocyte biology, whether IRF3 acts in B cells is less clear. IRF3

activation occurs in CpG-treated B cells in vitro (Oganesyan, Saha et al. 2008). Furthermore, the

IRF3-related kinase, TBK1 acts within B cells to suppress pathogenic IgA production in mice (Jin,

Xiao et al. 2012). Results discussed here demonstrated a B cell-intrinsic role for IRF3, which

suppresses B cell development from HSC after lethal irradiation and bone marrow transplantation.

133

Furthermore, IRF3 acted in B cells to suppress GC B cell responses and Tfh cells during infection.

Thus, this work provides the first direct in vivo evidence that IRF3 can act within B cells to

suppress their function. One caveat of the BM chimeric approach was the significant amount of

non-Plasmodium-specific GC B cell activity, likely generated in response to irradiation and bone

marrow transplantation. Thus, in the future, transgenic tools such as inducible B cell-specific IRF3-

deficient mice will enable more stringent testing of how IRF3 controls B cell function in vivo.

Nevertheless, in this study, improved Plasmodium-specific IgG production, GC B cell and Tfh

responses were seen when IRF3-mediated suppression of humoral immunity was lifted. Thus, we

propose IRF3 as a B cell-intrinsic regulator of humoral responses during Plasmodium infection,

which acts to limit production of parasite-specific antibodies.

Here, for the first time, PbA-infected Irf3-/- mice were shown to be resistant to ECM, but

more importantly, completely resolved infection. PbA infection in C57Bl/6J mice is characterised

by the sequestration of pRBCs into soft tissues leading to the fatal cerebral pathology. The Th1

cytokine, IFNγ, contributes to sequestration by increasing the expression of leukocyte adhesion

molecules on the cerebral microvascular endothelium (Hunt and Grau 2003). Additionally, previous

studies have shown CXCR3-mediated Th1 traffic to the brain correlated with the onset of cerebral

symptoms (Hansen, Bernard et al. 2007). A role for IRF3 in promoting Th1 responses during PbA

infection, as evident in non-lethal infections, could explain the absence of ECM in Irf3-/-mice.

However, this mechanism was not tested, and the possibility of IRF3 having a T cell-independent

role in mediating ECM cannot be ruled out. In addition to resistance to ECM, Irf3-/- mice may be

the first knock-out line to clear PbA infection. Given the observed role of IRF3 in suppressing

Plasmodium-specific antibodies during PcAS and Py17XNL infections and the reported importance

of antibodies in resolving infection, it is speculated that IRF3 also plays a role in suppressing the

development of Plasmodium-specific antibodies during PbA infection. It will be important in

future experiments to determine the precise molecular and cellular mechanisms by which IRF3

limits humoral immunity. Such information may reveal a novel immune-target to boost humoral

immunity to malaria.

In summary, a biphasic role for IRF3 was revealed during non-lethal experimental malaria.

Firstly, IRF3 acted to drive Th1 responses, likely via up-regulation of MHCII on inflammatory

monocytes, which resulted in better control of pRBC numbers during the early phase of infection.

During later stages of infection, IRF3 performed a separate function within B cells to suppress GC

B cell and Tfh responses, which was associated with limited production of Plasmodium-specific

antibodies and delayed resolution of infection. Given its unique position downstream of several

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PRR pathways, and its potential role in B cells, IRF3 represents a unique molecular target for

modulating cellular and humoral immunity to malaria.

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Chapter Six:

Final discussion

Malaria remains a leading cause of global deaths due to infection. While extensive research

efforts support roles for CD4+ T cells and antibodies in providing immunity to human and

experimental malaria, how these responses can be induced in vivo is poorly understood. To address

this gap in knowledge, this thesis aimed to determine novel cellular and molecular determinants

mediating adaptive immune responses during malaria, with particular focus on Th responses. The

three objectives of this thesis were to:

1. Determine whether a newly developed Plasmodium-specific CD4+ T cell can be effectively

used to track T helper responses.

2. Resolve the process of Th1 and Tfh cell differentiation to gain molecular insight into

mechanisms controlling Th fate decision-making.

3. Investigate whether innate immune signalling pathways can influence Th1- and Tfh-

dependent immune responses during infection.

6.1 PbTII cells represent an effective tool for studying in vivo CD4+ T cell

responses

CD4+ T cell responses during Plasmodium infection have been difficult to study partly due

to the absence of appropriate antigen-specific tools (Stephens, Albano et al. 2005). In Chapter 3 of

this thesis, a malaria-specific TCR transgenic mouse, which contains CD4+ T (PbTII) cells specific

for Plasmodium, was tested in vivo for its responsiveness. PbTII cells proliferated and differentiated

into Th1 and Tfh cells, which resembled the endogenous polyclonal response.

In addition to Th1 and Tfh effector subsets, PbTII cells were also observed to differentiate

into IL-10 and IFNγ co-producing cells, referred to as Tr1 cells. Tr1 may be important for

controlling immunopathology caused by potent inflammatory responses during both experimental

and human infections(Couper, Blount et al. 2008, Walther, Jeffries et al. 2009, Freitas do Rosario,

Lamb et al. 2012, Jagannathan, Eccles-James et al. 2014, Portugal, Moebius et al. 2014, Montes de

Oca, Kumar et al. 2016). Modelling of PbTII scRNAseq data revealed that Tr1 cells emerged from

the end of the Th1 differentiation branch, supporting the idea that Tr1 cells are direct descendants of

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Th1 cells (Cardone, Le Friec et al. 2010). Further scRNAseq and computational analysis of PbTII

cells could elucidate the relationship betweenTh1 and Tr1 cells, with the objective of identifying

novel approaches for balancing parasite control and immunopathology during infection.

Immunological memory relies on long-term memory cells that are induced by previous

infections. Memory responses during Plasmodium infection are difficult to induce and,

unfortunately, difficult to maintain, as evidenced by the difficulty in developing naturally-acquired

immunity and in the modest results from leading vaccine strategies(Stanisic, Mueller et al. 2010,

Olotu, Fegan et al. 2013). Here, PbTII cells developed into both effector and central memory (TCM

and TEM) phenotypes late in infection. Further studies determining the longevity of these responses

in vivo will demonstrate the utility of PbTII cells for examining memory responses. In addition, the

transition between effector cells and memory cells during in vivo infection could be studied through

modelling scRNAseq data of PbTII cells. Possible findings could include identification of unique

subsets of effector cells that are inclined to become memory cells,or novel molecular mechanisms

associating with this transition. Such information will be imperative to improving strategies to boost

immunological memory in individuals at risk of infection.

6.2 Modelling of scRNAseq analysis of PbTII cells reveals the emergence of Th

fates in vivo

Studying the emergence of Th responses in vivo has previously been confounded by

extensive heterogeneity within cell populations at any given time, and different response kinetics. In

Chapter 4, computational modelling of PbTII scRNAseq data was used to resolve the co-emergence

of Th responses during PcAS infection. This allowed for the identification of gene expression

profiles corresponding with either Th1 or Tfh differentiation, including expression of a range of

transcription factors, receptors and genes with unclassified function. A critical next step in this

research is to confirm with functional studies the relationship between these genes and

differentiation. As a proof of concept, preliminary studies have focussed on CXCR3, which

associated with Th1 differentiation.Blockade of CXCR3 during infection resulted in skewed

differentiation of PbTII cells towards a Tfh state. Ongoing work has applied CRISPR-Cas9

technology to delete Cxcr3 from PbTII cells to further demonstrate the requirement of CXCR3

signalling for Th1 differentiation.

An initial hypothesis of this thesis was that innate immune cells shape Th responses during

Plasmodium infection. The first evidence to support this hypothesis was the identification of two

waves of Th-associate chemokine receptor expression - one that peaked late in differentiation and

137

an earlier wave that peaked around bifurcation. This suggests that while receptors expressed after

bifurcation might mediate migration and effector function of PbTII cells, those peaking at

bifurcation could allow T cells to sense chemokine-expressing cells. Of thechemokines expressed

early, only Cxcr5 associated with commitment to Tfh fate decision. CXCR5 promotes the migration

of Tfh cells to the B- and T-cell bordered where they provide ‘help’ to B cells for production of

high-affinity antibodies(Crotty 2011). As demonstrated here and previously, B cells are also

necessary to sustain the Tfh phenotype(Kerfoot, Yaari et al. 2011). On the other hand, Th1 fate

commitment was associated with expression of several chemokine receptors that peaked at

bifurcation. Among these wasCxcr3, which has previously been demonstrated facilitate cDC - T

cell interactions and promote Th1 responses(Groom, Richmond et al. 2012).

Ly6Chiinflammatory monocytes are known to migrate to the spleen during Plasmodium

infection(Sponaas, Cadman et al. 2006). They were inferior primers of naive CD4+ T cells

compared to cDCs (Sponaas, Cadman et al. 2006), however their interaction with activated T cells

had not been explored. Here, scRNAseq analysis supported a novel role for Ly6Chi monocytes in

sustaining Th1 responses throughCXCR3:CXCL9/10 chemokine axis. Using the LysMCre x iDTR

system to deplete monocytes/macrophages after PbTII cells had been primed but were yet to

differentiate, strong evidence has been provided for their involvement. Comparison of scRNAseq

analysis of monocytes with cDCs revealed an overlap of approximately 40% of genes differentially

unregulated during infection. This suggests that monocytes have a similar function to cDCs during

PcAS infection. To explore whether monocytes can act as APCs, preliminary studies have

investigated PbTII responses in LysMcre x IAbf/fmice, in which monocytes do not express MHCII.

While early activation of PbTII cells to infection was not perturbed in these mice, total number of

Th1 cells at peak infection was significantly reduced. These data supports a model in which naive

CD4+ T cells are primed by cDCs, and require additional antigen stimulation thereafter to sustain

their response(Obst, van Santen et al. 2005). Since Th1 cells provide 'help' to another immune cells,

including macrophages and NK cells (Taylor-Robinson and Phillips 1998), it seems reasonable that

their responses would be tightly regulated by a requirement for continued antigen presentation, such

that other response are indirectly limited.

Activated PbTII cells expressed an assortment of chemokine receptors prior to Th

differentiation. This indicates that they are receptive to competing chemokine signals, which

increase the likelihood of interacting with different cells in a heterogeneous tissue environment. In

this way, PbTII differentiation could result from chance events, rather than preprogramming during

priming. To explore the relationship between PbTII differentiation and spatial intercellular

interactions, intravital imaging of PbTII cells, myeloid cells and B cells within the spleen could be

138

performed during PcAS infection. Furthermore, sorting PbTII cells from microdissected T cell

zones, marginal zones and follicles could allow for more in-depth analysis of their phenotype by

scRNAseq. Results from this research would provide clues for accurate delivery of antigen to the

right APCs and in the right location to induce appropriate T cell responses to infections or vaccine

challenges.

As demonstrated here and in other studies, scRNAseq can be applied to explore

heterogeneity between cells and to model biological processes(Jaitin, Kenigsberg et al. 2014,

Gaublomme, Yosef et al. 2015). Another feature of scRNAseq is that it allows for extensive

information to be gained from as few as a handful of cells. This is particularly attractive for human

immunological studies, which are often complicated by ethical constraints limiting the number of

cells or range of tissues attainable for assessment. Although many studies use peripheral blood

mononuclear cells (PBMCs) as a surrogate for immune response in peripheral tissues, comparison

of cellular responses from both sites has revealed striking differences(Strauss, Bergmann et al.

2007, Czarnowicki, Gonzalez et al. 2015). ScRNAseq has already proven useful in the area of

human cancer research(Patel, Tirosh et al. 2014, Navin 2015, Kim, Lee et al. 2016) and in studying

human embryogenesis (Yan, Yang et al. 2013). Future human disease studies could apply

scRNAseq on cells from biopsies taken during diagnosis and disease management assessments,

such as for Crohn's disease, to model how cells contribute to changes in disease state.

An important caveat of scRNAseq is that transcriptional activity does not necessarily

correspond to translation into protein, or indeed cellular function. Extensive research into the

relationship between RNA translation and protein levels has shown only a 50% overlap in the loci

controlling the expression of these elements, suggesting distinct regulator mechanisms(Wu,

Candille et al. 2013). Additionally, factors such as microRNAs that regulate translation, as well as

mRNA synthesis and decay rates modulate the level of protein expressed(Baek, Villen et al. 2008,

Schwanhausser, Busse et al. 2011).Therefore, the field is moving towards a multi-omics approach,

in which genomics is combined with proteomics to gain a more holistic picture of the cellular

activity. To demonstrate how this might be achieved, in a hypothetical study of T cell

differentiation, phosphoproteomics could be used to monitor phosphorylation signalling of

transcription factor localisation of such regulatory factors. Transcriptomics would elucidate the

cohort of genes up or downregulated by activated transcription factors. Finally, proteomics would

determine which transcripts become proteins at which time.

139

6.3 IRF3 balances Th1 and Tfh-mediated immune responses

Following from the observation that monocytes support Th1 differentiation, in Chapter 6

innate immune signaling pathways were compared for their contribution to Th responses. Although

STING, TLRs and NLRs have all been implicated in the detection of Plasmodium products

(Krishnegowda, Hajjar et al. 2005, Parroche, Lauw et al. 2007, Sharma, DeOliveira et al. 2011,

Kalantari, DeOliveira et al. 2014), no role for isolated PRRs was observed in eliciting an immune

response. This highlights a caveat of a reductionist approach to immunological studies, since the

removal of one element from a system does not take into account possible compensation by other

elements. Thus, it is possible that functional redundancy exists between PRRs in driving Th

responses, as suggested by the literature for ensuring robustness in innate responses towards

continually evolving pathogens (Nish and Medzhitov 2011). To circumvent this issue, PbTII

responses to infection could be followed in mice deficient in two or even three key molecules in

different signaling pathways.

Here, a critical role for IRF3, a regulator of TI IFNs that sits downstream of a number of

PRR signaling pathways, was identified. IRF3 promoted expansion of antigen-specific CD4+ T cell

responses and their differentiation into Th1 cells. This contrasts with a recently reported role for TI

IFN and the master transcription factor IRF7 in impairing Th1 responses during Plasmodium

infection (Haque, Best et al. 2014, Edwards, Best et al. 2015). Therefore, this data suggests that

IRF3 displays a diverse array of functions in immune cells. The role for IRF3 in supporting Th1

responses after their initial activation was associated with MHCII expression by Ly6Chi

inflammatory monocytes. Together with scRNAseq data reported in Chapter 4, this suggests a

model in which monocytes upregulate MHCII expression under direction of IRF3 and facilitate

encounters with CXCR3+ T cells through production of CXCL9/10. Monocytes support Th1

responses, which assist in clearance of pRBCs(Amante and Good 1997). An obvious question

arising from this data is whether IRF3 is activated within monocytes. Future work in this area could

implement phosphoproteomics, to assess the phosphorylation of IRF3 within sorted Ly6Chi

monocytes as a measure of its activation. Furthermore, chromatin immunoprecipitation followed by

sequencing (ChIP-seq) analysis could be used to determine where IRF3 is binding to the genome to

provide mechanistic insight into its function. Advancements in this technology could include single-

cell ChIP-seq (Rotem, Ram et al. 2015) in a single-cell multi-omics analysis as suggested above.

B cell-intrinsic IRF3 was shown to suppress the development of GC B cell responses and

subsequent Plasmodium-specific antibody titres during blood-stage infection. This is consistent

with previous reports and suggestions of IRF3 activation with B cells (Oganesyan, Saha et al. 2008,

140

Jin, Xiao et al. 2012). Tfh differentiation was also dampened, likely as a direct consequence of

suppressed GC B cell responses. This suggests that IRF3 acts as a negative regulator of B cells to

prevent excessive humoral responses. Indeed, IRF3, independently of interferons, has been shown

to be induced by DNA-damaging agents, and was involved in cell cycle checkpoint control in

response to these signals (Kim, Kim et al. 1999, Kim, Kim et al. 2000, Weaver, Ando et al. 2001).

Ectopic expression of IRF3 significantly attenuated cell growth, while expression of mutated IRF3

facilitated oncogenic transformation of human cell lines (Kim, Lee et al. 2003). Importantly, the

data presented here showed that in the absence of IRF3 mice were able to clear the infection sooner

and exhibited greater numbers of memory B cells. Intriguingly, mice deficient in IRF3 cleared a

lethal infection of PbA, possibly due to improved humoral responses. The mechanism behind how

IRF3-deficient mice survive cerebral symptoms of PbA infection would be an interesting avenue to

pursue.

6.4 Concluding remarks

Using a new antigen-specific TCR transgenic tool and cutting-edge scRNAseq technology

with computational modelling, this thesis has demonstrated novel cellular and molecular

mechanisms for modulating protective immunity during malaria. These findings have profound

significance in identifying novel strategies to boost immunity to malaria, for example by using

suitable adjuvants to induce monocytic responses or carefully alleviating the effects of IRF3 in B

cells to improve humoral immunity. Future studies utilising the PbTII system and scRNAseq will

continue to provide a more in-depth picture of the complex immune mechanisms that currently

hinder the eradication of malaria.

141

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