The role of αvβ8 on human and in intestinal immune homeostasis

A thesis submitted to The University of Manchester for the degree of Doctor of Philosophy in the Faculty of Biology, Medicine and Health

2018

Elinor Elizabeth Shuttleworth

School of Biological Sciences

TABLE OF CONTENTS

Chapter 1: Introduction ...... 15 1.1. Introduction ...... 15 1.2. Intestinal barrier function ...... 15 1.2.1. Intestinal epithelial layer ...... 17 1.2.2. PRR function in IECs ...... 19 1.2.3. Intestinal mucus layer ...... 20 1.3. Intestinal innate immunity ...... 20 1.3.1. Monocytes and Macrophages ...... 21 1.3.2. Dendritic cells ...... 35 1.4. Intestinal adaptive immunity ...... 41 1.4.1. Intestinal T cells ...... 42 1.4.2. Intestinal B cells ...... 46 1.4.3. Homing and localisation of intestinal T and B cells ...... 47 1.5. Control of TGF-β mediated intestinal immune tolerance ...... 48 1.5.1. The role of αvβ8 mediated TGF-β activation in immune homeostasis ...... 53 Chapter 2 Materials and Methods ...... 58 2.1. Quantifying expression of integrin αvβ8 on peripheral blood mononuclear cells (PBMCs) ...... 58 2.1.1. Peripheral blood collection ...... 58 2.1.2. PBMC isolation ...... 58 2.1.3. PBMC staining for flow cytometry ...... 58 2.1.4. β8 binding antibody-fluorochrome conjugation ...... 60 2.1.5. CD14+ isolation ...... 61 2.1.6. RNA synthesis for ITGB8 qPCR ...... 61 2.1.7. cDNA synthesis for ITGB8 qPCR ...... 62 2.1.8. ITGB8 qPCR ...... 62 2.2. Investigating factors influencing expression of integrin αvβ8 on peripheral blood mononuclear cells (PBMCs) ...... 63 2.2.1. PBMC and monocyte cytokine treatment ...... 63 2.2.2. PBMC and monocyte TLR ligand treatment ...... 63 2.3. Investigating function of αvβ8 on peripheral blood mononuclear cells (PBMCs) ...... 64 2.3.1. TGFβ activation reporter cell co-culture assay ...... 64 2.3.2. PBMC and monocyte treatment with anti-TGF-β antibody or anti-αvβ8 antibody ...... 65 2.3.3. Flow cytometry to assess treated CD14+ monocyte phenotype ...... 65 2.4. Quantifying expression and function of integrin αvβ8 on MDM ...... 65 2.4.1. MDM culture from CD14+ monocytes ...... 65 2.4.2. MDM staining for flow cytometry ...... 66 2.4.3. IL-10 ELISA ...... 66 2.4.4. Phagocytosis assay ...... 66 2.4.5. MDM treatment with neutralising antibodies or TLR ligands ...... 67 2.5. Bioenergetic analysis of MDMs ...... 67 2.5.1. MDM culture for bioenergetic analysis ...... 67 2.5.2. MDM treatment for bioenergetic analysis ...... 68 2.5.3. Bioenergetic analysis using the Seahorse XF96 analyser ...... 68 2.5.3. quantification using the Bicinchoninic Acid (BCA) assay ...... 69 2.5.4. Seahorse data analysis ...... 69 2.6. General data analysis ...... 70 Chapter 3: Integrin αvβ8 mediated TGF-β activation in human monocytes ...... 71

2 3.1. Introduction ...... 71 3.2. Results ...... 71 3.2.1. Flow cytometry gating protocols to identify integrin αvβ8 on human PBMC ...... 71 3.2.2. Expression of integrin αvβ8 on peripheral leucocyte populations ...... 74 3.2.3. Validation of results utilising other integrin β8 staining strategies ...... 77 3.2.4. qPCR analysis of PBMC ITGB8 expression ...... 82 3.2.5. Effects of cytokine treatment on PBMC integrin αvβ8 expression ...... 84 3.2.6. Effects of TLR ligand treatment on PBMC integrin αvβ8 expression ...... 89 3.2.7. Integrin αvβ8-dependent TGF-β activation by monocytes ...... 90 3.2.8. Alterations in CD16 expression in TGF-β, TGF-β blocking and integrin αvβ8 blocking antibody-treated monocytes ...... 94 3.3. Discussion ...... 98 Chapter 4: Integrin αvβ8 mediated TGF-β activation in human macrophages ...... 105 4.1. Introduction ...... 105 4.2. Results ...... 105 4.2.1. Characterisation of human GM-CSF and M-CSF MDM phenotype ...... 105 4.2.2. Expression of integrin αvβ8 on human MDMs ...... 106 4.2.3. Mechanisms governing integrin αvβ8 expression on human MDMs ...... 111 4.2.4. Investigating the role of integrin αvβ8 expression in human MDM phenotype ...... 112 4.3. Discussion ...... 120 Chapter 5: The role of metabolism in determining phenotype ...... 126 5.1. Introduction ...... 126 5.2. Results ...... 127 5.2.1 Establishing a protocol for bioenergetics analysis of human MDMs ...... 127 5.2.2. Establishing the bioenergetics profile of human GM-CSF and M-CSF MDMs ...... 131 5.2.3. Investigating factors which alter the bioenergetics profile of human MDMs ...... 133 5.3. Discussion ...... 145 Chapter 6. Discussion ...... 152 6.1. TGF-β activation in inflammation and intestinal immune homeostasis .. 152 6.2 TGF-β in ...... 154 6.3. TGF-β in cancer ...... 156 6.4. Personalised therapy in IBD ...... 159 References ...... 162

Word Count: 36686 words

3 LIST OF FIGURES

Figure 1.1. Structure of the small intestinal mucosa and 17 submucosa.

Figure 1.2. Human blood monocyte and colonic macrophage 23 phenotype.

Figure 1.3. Murine and human blood monocyte subsets 27

Figure 1.4. Key differences in the TCA cycle between M2 ‘anti- 33 inflammatory’ and M1 ‘pro-inflammatory’ macrophages.

Figure 1.5. Common phenotypes of murine and human circulating 37 and intestinal DC subsets

Figure 1.6. Intestinal T and B lymphocyte subsets within the intestine 43

Figure 1.7. Effects of TGF-β on leucocytes 50

Figure 1.8. Activation of TGF-β via the αv integrin family 51

Figure 3.1. Gating strategy for identification of integrin αvβ8 on 72 peripheral blood lymphocytes, monocytes and NK cells

Figure 3.2. Gating strategy for identification of integrin αvβ8 on 73 peripheral dendritic cells and monocytes

Figure 3.3. Expression of integrin αvβ8 on peripheral dendritic cell 75 subsets as determined by flow cytometry

Figure 3.4. Expression of integrin αvβ8 on peripheral lymphocyte 76 subsets and NK cells as determined by flow cytometry

Figure 3.5. Expression of integrin αvβ8 on peripheral leucocyte 78 subsets as determined by flow cytometry

Figure 3.6. Expression of integrin αvβ8 on peripheral monocyte 79 subsets as determined by flow cytometry

Figure 3.7. Expression of integrin αvβ8 on peripheral leucocyte 81 subsets as determined by flow cytometry using different β8 specific antibodies

Figure 3.8. Expression of integrin αvβ8 on peripheral leucocyte 82 subsets as determined by flow cytometry using different Fc blocking strategies

Figure 3.9. Relative expression of integrin αvβ8 on CD14+ bead 83 separated monocytes and peripheral blood mononuclear cells

4 Figure 3.10. Expression of integrin αvβ8 on peripheral lymphocytes 85 after cytokine treatment

Figure 3.11. Expression of integrin αvβ8 on peripheral monocytes after 86 cytokine treatment

Figure 3.12. Expression of integrin αvβ8 on peripheral monocyte 88 subsets after cytokine treatment

Figure 3.13. Expression of integrin αvβ8 on peripheral monocytes after 89 TLR ligand treatment

Figure 3.14. Expression of integrin αvβ8 on CD14+ bead separated 91 monocytes after TLR ligand treatment

Figure 3.15. Activation of TGF-β by human peripheral blood 92 monocytes

Figure 3.16. Activation of TGF-β by human peripheral blood 93 monocytes in the presence of IL-10 and TNF-α

Figure 3.17. Expression of CD16 on peripheral monocytes after TGF- 96 β, anti-TGF-β antibody or anti-αvβ8 antibody

Figure 3.18. Expression of CD16 on lipopolysaccharide treated 97 peripheral monocytes with or without TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody

Figure 4.1. Characterisation of GM-CSF and M-CSF MDM surface 107 marker phenotype

Figure 4.2. Characterisation of GM-CSF and M-CSF MDM IL-10 108 production and phagocytic capacity

Figure 4.3. Gating strategy for monocyte-derived macrophages 109 (MDMs)

Figure 4.4. Expression of αvβ8 antibody on monocyte-derived 110 macrophage (MDM) models

Figure 4.5. Expression of αvβ8 antibody on monocyte-derived 112 macrophage (MDM) models determined by PCR

Figure 4.6. Expression of integrin αvβ8 on MDMs after TLR ligand 113 treatment

Figure 4.7. Expression of surface markers on MCSF MDMs after 115 treatment with or without TGFβ, anti-TGFβ antibody or anti-αvβ8 antibody

Figure 4.8. IL-10 production of GMCSF and MCSF MDMs after 116 treatment with or without anti-TGFβ antibody or anti-αvβ8

5 antibody

Figure 4.9. Phagocytic capacity of GMCSF and MCSF MDMs after 7 118 day treatment with or without TGFβ, anti-TGFβ antibody or anti-αvβ8 antibody

Figure 4.10. Phagocytic capacity of GMCSF and MCSF MDMs after 119 overnight treatment with or without TGFβ, anti-TGFβ antibody or anti-αvβ8 antibody

Figure 4.11. Integrin β8 expression on human colonic macrophages 123

Figure 5.1 Metabolic profile of human MDMs cultured in standard 12 128 well plates and transferred to XF96 seahorse plates for analysis

Figure 5.2. Metabolic profile of titrated MDMs cultured in XF96 130 seahorse plates

Figure 5.3. Metabolic profile of human GM-CSF and M-CSF MDMs in 132 basal conditions

Figure 5.4. Metabolic profile of human GM-CSF and M-CSF MDMs in 134 maximal conditions

Figure 5.5. Basal metabolic profile of TGF-β treated GM-CSF MDMs 136

Figure 5.6. Basal metabolic profile of TGF-β treated M-CSF MDMs 137

Figure 5.7. Maximal metabolic profile of TGF-β treated GM-CSF 138 MDMs

Figure 5.8. Maximal metabolic profile of TGF-β treated M-CSF MDMs 139

Figure 5.9. Basal metabolic profile of LPS treated GM-CSF and M- 140 CSF MDMs

Figure 5.10. Maximal metabolic profile of LPS treated GM-CSF and M- 141 CSF MDMs

Figure 5.11. Basal metabolic profile of LPS and TGF-β treated GM- 143 CSF and M-CSF MDMs

Figure 5.12. Maximal metabolic profile of LPS and TGF-β treated GM- 144 CSF and M-CSF MDMs

Figure 5.13. Percentage yield of viable GM-CSF and M-CSF treated 148 MDMs after removal from cell culture plates via different methods

6 Figure 5.14. Expression of key surface markers on GM-CSF and M- 149 CSF treated MDMs cultured in Seahorse XF™ cell culture 96-well microplates or standard 96-well cell-culture plates

Figure 5.15. Comparison of metabolic profile of human GMCSF and 150 MCSF MDMs in basal conditions unadjusted or adjusted for protein concentration per well.

LIST OF TABLES

Table 2.1 Antibodies used for PBMC staining to identify integrin αvβ8 expression on peripheral leucocyte populations.

7

ABSTRACT Intestinal immune cells remain tolerant to the trillions of commensal bacteria present in the gut, with perturbations of this process implicated in development of inflammatory bowel disease (IBD). The cytokine TGF-β is a key factor promoting intestinal immune tolerance, but is secreted in a latent state that requires activation to function. Binding of TGF-β to the integrin αvβ8 is a principal mechanism of TGF-β activation, with mouse models demonstrating a crucial role for αvβ8 expression by dendritic cells and regulatory T cells in intestinal immune regulation. Despite this evidence, very little is known regarding the importance of this activating integrin in human intestinal homeostasis.

Utilising flow cytometry here we find that integrin αvβ8 is highly expressed on peripheral blood monocytes with highest levels on intermediate CD14++CD16+ monocytes. Upon monocyte to macrophage differentiation high β8 expression is observed on anti-inflammatory M-CSF differentiated macrophages versus pro- inflammatory GM-CSF macrophages. In monocytes, expression of β8 is upregulated by specific bacterial TLR ligands. Utilising a TGF-β reporter cell line both monocytes and M-CSF MDM display an enhanced ability to activate TGF- β in an αvβ8-dependent manner. Data presented here indicate that macrophage αvβ8-dependent TGF-β activation does not alter expression of surface markers associated with a tolerogenic macrophage phenotype, phagocytosis, or production of the anti-inflammatory cytokine IL-10; nor does TGF-β appear to influence the metabolic profile of macrophages, key differences of which are associated with pro- or anti-inflammatory phenotype. However, the previously undescribed finding of integrin αvβ8 expression on human monocytes and macrophages, which was subsequently confirmed in intestinal populations and found to be downregulated in inflamed IBD mucosa, may highlight an important functional pathway in intestinal immune homeostasis and represent a potential future therapeutic target in IBD.

8

DECLARATION No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

9 COPYRIGHT STATEMENT i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24420), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The University’s policy on Presentation of Theses.

10 ABBREVIATIONS

5-ASA 5-aminosalicylic acid acetyl-CoA acetyl coenzyme A AHR aryl hydrocarbon receptor AKG α-ketoglutarate ALDH aldehyde dehydrogenase AMPK AMP- activated protein kinase APC antigen presenting cells Arg1 arginase-1 ATG16L1 autophagy-related protein 16- ATP adenosine triphosphate BCR B-cell receptor BM bone marrow BMDM bone marrow derived macrophages CCL chemokine (C-C motif) ligand CCR C-C CD Crohn’s disease cDC conventional DC CDP common DC precursor CNS central nervous system CRC colorectal cancer CSC cancer stem-cell DC dendritic cells Dll1 Delta Like Canonical Notch Ligand 1 DSS dextran sulfate sodium EAE experimental autoimmune encephalitis ECAR extracellular acidification rate ECM extracellular matrix EMT epithelial-to-mesenchymal transition ETC electron transport chain FASN fatty acid synthase FcR Fc receptor FMO flow minus one GALT gut associated lymphoid tissue GF germ-free GI gastrointestinal GM-CSF granulocyte-macrophage colony stimulating factor GPR15 G-protein–coupled receptor 15 GVHD graft-versus-host disease GWAS genome-wide association studies HIF hypoxia-inducible factor HMGB1 high-mobility group box 1 HSC hepatic stellate cell

11 hsp72 heat shock protein 72 IBD inflammatory bowel disease IDH isocitrate dehydrogenase IDO indoleamine 2,3-dioxygenase IEC intestinal epithelial cell IEL intraepithelial lymphocytes IFN-γ interferon-γ IgA immunoglobulin A IL interleukin IL-10R IL-10 receptor IL-1Ra IL-1 receptor antagonist ILC innate lymphoid cell iNOS inducible nitric oxide synthase IP intraperitoneal IRG1 immune-responsive 1 protein KO knockout LAP latency associated peptide LCFA long chain fatty acid LLC large latent complex LP lamina propria LPS lipopolysaccharide LTBP latent TGF-β binding protein M cells microfold cells M-CSF macrophage colony stimulating factor MAdCAM mucosal vascular addressin cell-adhesion molecule 1 MDM monocyte-derived macrophages MDP monocyte dendritic cell progenitor MFI median fluorescence intensity MHC major histocompatibility complex mLN mesenteric lymph nodes MMP matrix-metalloproteinase moDC monocyte-derived DC mTOR mammalian target of rapamycin MyD88 myeloid differentiation primary-response protein 88 NF-κB nuclear factor kappa--chain-enhancer of activated B cells NK natural killer NLR NOD- like receptor NO nitric oxide NOD nucleotide-binding oligomerization domain OCR oxygen consumption rate OxPhos oxidative phosphorylation PAI-1 plasminogen activation inhibitor-1 PAMP pathogen associated molecular patterns PBMCs peripheral blood mononuclear cells

12 PC plasma cell pDC plasmacytoid DC PGE2 prostaglandin E2 pIgR polymeric immunoglobulin receptor PP Peyer’s patches PPAR-γ peroxisome proliferator-activated receptor-γ PPP pentose phosphate pathway PRR pattern recognition receptor RA retinoic acid REGIIγ regenerating islet-derived protein IIIγ RGD arginine-glycine-aspartic acid ROS reactive oxygen species SCFA short chain fatty acids SDH succinate dehydrogenase SFB segmented filamentous bacteria SI small intestine SILP small intestinal lamina propria siRNA small interfering RNA SLC small latent complex SPF specific pathogen free TCA tricarboxylic acid TCR T-cell receptor Teff effector T-cell TGF-β transforming growth factor-β TGF-βR TGF-β receptor Th T-helper Th0 Naïve T-helper cell TLR Toll like receptor TLRL TLR ligand TNF-α tumour necrosis factor-α Treg regulatory T cells TSLP thymic stromal lymphopoetin TSP-1 thrombospondin-1 u-PFK2 ubiquitous phosphofructokinase-2 UC ulcerative colitis WT wild-type

13 ACKNOWLEDGEMENTS Firstly thanks to Mark, your excellent guidance and support and considerable forbearance have seen me through my first deep foray into basic science. I hope taking on Team Travis’ first medic has not put you off! Thanks to Aoife, whose admirable skill and dedication as a scientist taught me greatly. Steph thank you for your willingness to share your knowledge, your patience and your huge kindness. Eleanor and Josh, I am so glad we started out together, learning by our many mistakes with lots of humour, and I am so pleased to see what you have both achieved in this time. Thank you to the rest of the Travis lab: Cat, Kevin, Felipe and Abdulelah, and my co-supervisors John and Dave.

Amongst the excellent team within the MCCIR I would like to specifically thank Gareth Howell, Melanie Ward, Fiona Foster, Sheila Brown, Gloria Lopez- Castejon, Aleksander Grabiec, Anu Goenka, Rajia Bahri and Tracy Hussell, for technical and scientific advice, including excellent support in pursuing a research grant, and many fascinating conversations. Special thanks to Cecilia Forss and Muskot, who cheered up many a rainy day. Finally, thanks to Ben Mulhearn, my coffee buddy and fount of wise and compassionate advice.

I would like to thank Dr Scott Levison and Dr Jay Brown, alongside the nurses and administrators at the Manchester Royal Infirmary for their collaboration. I am extremely grateful to them and to the patients who provided samples for this research. I am also profoundly thankful to the MCCIR for funding my first year of research and to the Wellcome Trust for awarding me a Clinical Research Training Fellowship allowing me to continue my PhD. Thank you to the Gastroenterology team at Blackpool Victoria for your understanding and support this past year as I juggled many balls.

Alongside a list of coffee shops in Manchester too numerous to detail, I would like to offer my heartfelt thanks to the Camerons, Channings, Lomases, Plunketts, Cruises, Ant, Caroline, Nichol and the indomitable Shuttle-Knights. Thank you all for you unconditional love and support (and food).

I dedicate this to my Mum, George and Charlie: a beautiful past and future.

14 CHAPTER 1: INTRODUCTION

1.1. Introduction

The human intestinal lumen is home to over 3x1013 bacteria(1) alongside fungi and viruses(2), representing a unique challenge to the immune system in recognition of helpful versus harmful organisms. The human intestinal immune system is uniquely specialised to tolerate these commensal organisms, promoting a beneficial symbiosis by which bacteria assist in digestion of nutrients, promote maturation of the immune system and even modulate immune responses distant from the gut(3). Intestinal leucocytes display specialised features suiting them to such an environment(4). The signals that promote a tolerogenic immune phenotype in both innate and adaptive immunity are a matter of great interest, as inappropriate immune responses within the intestinal tract can result in chronic inflammatory conditions such as those under the umbrella term of inflammatory bowel disease (IBD)(5).

1.2. Intestinal barrier function

Over 2000 different bacterial species of 12 different phyla have been identified within human intestinal microbiota, which is mainly composed of bacteroidetes and firmicutes. An individual will have around 160 species within their unique gut flora(3, 6, 7). The distribution of bacteria through the gut increases from proximal to distal, with around 103–104/ml bacteria in the proximal small intestine (SI), increasing to 1011/ml in the colon(1). The bacterial population within the gut has multiple functions including the release of short chain fatty acids (SCFAs) from fermentation of non-digestible fibres, which provide up to 10% of daily energy intake(3). Intestinal bacteria also produce secondary bile acids, modulate circulating levels of tryptophan, a precursor of the neurotransmitter serotonin, alongside other monoamine neurotransmitters, and provide an important source of B group vitamins and vitamin K(6, 7).

Whilst human intestinal microbiota varies between individuals and can change with time(2), microbial diversity is reduced in IBD, with a lower ratio of firmicutes to proteobacteria and reduced diversity of firmicutes. Similar changes in the

15 microbiota have been described in relatives of those with both patients with both types of IBD, Crohn’s disease (CD) and ulcerative colitis (UC)(8), yet it is unclear whether alterations in the microbiome are as a result of IBD or are in some way causative. Whilst it is uncertain whether the changes seen in the microbiota of IBD patients are cause or consequence, studies have demonstrated an essential role for bacteria in driving inflammation. Multiple genetic models of intestinal inflammation show an absence or attenuation of colitis when mice are reared in germ-free (GF) conditions or treated with antibiotics(9).

The microbiome extends beyond the bacterial species that colonise the intestine. The intestinal virome is coming under increasing scrutiny for its role in intestinal health and disease. Cytomegalovirus and herpesvirus infections causing colitis exacerbations in IBD patients are described(10). Mice with a mutation in the IBD risk allele autophagy-related protein 16-L1 (ATG16L1) infected with norovirus developed more severe inflammation in a chemically induced colitis model, dependent on the presence of intestinal bacteria(11), suggesting a complex interplay between the bacteriome, virome and genetic susceptibility in the aetiology of IBD. Expansion of Caudovirales bacteriophages has been observed in the enteric virome of CD and UC patients compared to non-IBD household controls(12).

The importance of spatial separation of bacteria from the underlying intestinal tissue by the epithelium was demonstrated in the phenotype exhibited by chimeric mice expressing a dominant negative form of the adhesion molecule N-cadherin, resulting in loss of small bowel epithelial integrity at sites expressing the mutant gene. These mice develop focal transmural inflammation at the site of these defects in association with invasion of bacteria into the intestinal tissue(13). Thus, the integrity of the intestinal epithelium is an important factor in preventing inappropriate immune stimulation by these commensal organisms.

16 1.2.1. Intestinal epithelial layer

The intestinal epithelium itself is formed from a single layer of intestinal epithelial cells (IECs) overlying the lamina propria (LP), a layer of connective

Figure 1.1. Structure of the small intestinal mucosa and submucosa. Cross section of the small intestinal wall showing the various cells of the intestinal epithelium forming a structure of intestinal villi and crypts. The epithelium and LP, with their populations of immune cells form the mucosa, which is separated from the submucosa by the muscularis mucosa. tissue containing innate and adaptive leucocytes. Below this is the thin layer of the muscularis mucosa, then the connective tissue submucosa, then a thicker

17 muscle layer, and finally the fibrous, enwrapping serosa(4). The integrity of the IEC layer is maintained by intercellular tight junctions which block paracellular translocation of microbial material(14).

Whilst only the SI contains villi, finger-like projections designed to increase the absorptive capacity of the SI, both SI and colon contain multiple pits, known as the crypts of Lieberkuhn, containing pluripotent stem cells near the basal aspect of the crypts. These stem cells give rise to enterocytes, specialised to absorb nutrients, and, in smaller quantities, mucus-producing goblet cells, neuroendocrine cells and, within the SI, Paneth cells, specialised IECs that produce antimicrobial peptides. In contrast to the other IECs, which migrate apically and are shed into the intestinal lumen in a constant cycle of renewal lasting 4-5 days, Paneth cells migrate basally toward the stem cells, maintaining the microbe-free environment within the crypt(4, 15). More recent work has established a role for a type of cell known as the tuft (or brush) cell, found within the intestinal and respiratory epithelium. Tuft cells have been implicated in anti- helminthic responses through their production of interleukin-25 (IL-25), which then induces IL-13 production by type 2 innate lymphoid cells (ILC2s) which is an important driver of the anti-helminthic immune response(16).

The production of antimicrobial peptides, such as lysozyme and defensins, by Paneth cells, further reinforces the epithelial defence in regions where mucus is sparse(4). One peptide, the lectin regenerating islet-derived protein IIIγ (REGIIIγ), produced by Paneth cells and other IECs was shown to be vital in maintaining the spatial separation of gram positive bacteria and the small intestinal mucosa in mice(17). Expression of certain α-defensins, another class of antimicrobial peptide produced predominantly by Paneth cells, is impaired in patients with IBD affecting the terminal small bowel (ileum)(18). Anti-microbial protein production by IECs can be constitutive, or occurs in response to microbial sensing via pattern recognition receptors (PRRs)(19) that recognise pathogen associated molecular patterns (PAMPs). PAMPs are found on a wide range of microbial organisms, important examples being lipopolysaccharide (LPS), a major component of the cell membrane of gram-negative bacteria, and flagellin, a major component of the bacterial flagellum.

18 1.2.2. PRR function in IECs

In addition to sensing PAMPs, PRRs sense damage associated molecular patterns (DAMPs), substances released by cellular damage, such as the protein high-mobility group box 1 (HMGB1), or ectopic DNA(20, 21). Toll like receptors (TLRs) are a class of PRR that are highly important in mediating innate immunity, and are expressed widely in human epithelial tissues(19). Murine studies indicate that TLR2 is more prominent in the SI, and TLR4 in the colon(4). In addition to TLRs, IECs also express another class of PRR, nucleotide-binding oligomerization domain (NOD)- like receptors (NLRs), which are intracellular(14).

It has been reported that IEC expression of certain TLRs is minimal or absent in epithelial homeostasis, or that some TLRs and NLRs are spatially segregated from contact with intestinal commensals through cytoplasmic or basolateral membrane expression. Such localisation is presumably in order to minimise inappropriate immune responses to the gut microbiome(14), with upregulation of TLR4 observed in active IBD(22). However, loss of TLR signalling in IECs is associated with impairment of intestinal barrier function(21).

The expression of the antimicrobial lectin REGIIIγ in mouse SI was found to be dependent on the IEC-specific expression of myeloid differentiation primary- response protein 88 (MyD88)(17) an adaptor protein involved in downstream signalling for most TLRs(20). Loss of TLR signalling confers increases susceptibility to experimental colitis in association with impaired epithelial integrity: mice lacking MyD88, developed severe morbidity and mortality upon exposure to low doses of the colitis-causing molecule dextran sulfate sodium (DSS) compared to WT mice, in association with dysregulated IEC proliferation and reduced production of epithelial protective factors such as IL-6 and IEC expression of heat shock protein 72 (hsp72)(23).

Despite the ability to elicit PRR signalling in IECs it appears that commensal bacteria such as Lactobacillus and Bacteroides are able to promote intracellular mechanisms that inhibit the activation of nuclear factor kappa-light-chain-

19 enhancer of activated B cells (NF-κB)(14), a major transcription factor promoting pro-inflammatory cytokine production in response to TLR signalling(20). For example, the commensal Bacteriodes thetaiotaomicron has been shown to promote the action of the NF-κB inhibitory factor peroxisome proliferator-activated receptor-γ (PPAR-γ)(24). Interestingly, activation of PPAR- γ may be an important mechanism of action of 5-aminosalicylic acid (5-ASA) derivatives(25), which are a mainstay in the treatment of mild to moderate UC(26).

Various mutations in encoding TLRs and NLRs in humans have been associated with both increased and decreased risk of IBD via diverse mechanisms extending beyond anti-microbial peptide production, consistent with their expression on other cells comprising the innate immune system(21, 27).

1.2.3. Intestinal mucus layer

The intestinal mucus layer reinforces epithelial barrier function. Studies of rat and mouse intestinal mucus demonstrate that the intestinal tract is covered by mucus of variable thickness. Colonic mucus is relatively thick, bi-layered and firmly adherent to the epithelium, whereas that in the SI consists of an aspirable single thinner layer(28), consistent with the denser bacterial population within the colon. Commensal bacteria were found to be absent within the inner colonic mucus layer in wild-type (WT) mice(29). Mice with deletions of muc2, encoding the mucin MUC2, the major protein component of murine intestinal mucus, develop spontaneous colitis and colonic cancer in association with bacterial infiltration of the intestinal crypts(29, 30). This mimics the situation seen in human IBD whereby colitis is associated with an increased risk of colorectal cancer(31). MUC2 is also the predominant mucin in human colonic mucus(32); however MUC2 expression appears to be increased in human IBD(33).

1.3. Intestinal innate immunity

The mononuclear phagocyte population comprising macrophages and dendritic cells (DCs) form a major part of the intestinal innate immune system and will be

20 discussed in more detail below. Also present within the intestine are granulocytes such as eosinophils, mast cells and basophils. Eosinophils have been reported to comprise almost 30% of murine small intestinal lamina propria (SILP) cells(34), with an important role in anti-helminthic responses, through release of cytotoxic substances and key cytokines(35) (alongside basophils)(36), through promotion of B-cell class switching to immunoglobulin A (IgA) in a T cell independent manner (discussed in more detail in section 1.4.2)(37), and potentially as non-conventional antigen presenting cells(35). Eosinophils produce multiple cytokines(35) including transforming growth factor- β (TGF-β)(37, 38), implicated in abnormal tissue remodelling in the condition eosinophilic oesophagitis(39). It is therefore possible that gastrointestinal (GI) eosinophils may play a role in tissue repair in homeostasis, alongside mast cells which express multiple proteases involved in tissue remodelling and vascular permeability(40), influence intestinal motility and interact with the enteric nervous system(4).

ILCs, cells of lymphoid morphology lacking a T-cell receptor (TCR), T-cell, B- cell or myeloid markers are found most abundantly within mucosal sites(41). All three subclasses of ILCs, ILC1 (which includes natural killer [NK] cells), ILC2 and ILC3, which produce a similar cytokine signature to T-helper type 1 (Th1: important in responses to intracellular pathogens), type 2 (Th2: important in helminthic immunity) and type 17 (Th17: important in extracellular pathogen responses) cells respectively(15, 42). These ILC populations are found within the GI tract with varying frequency, and appear to amplify cytokine responses to pathogenic microbes in the intestine(41). IL-22 producing ILC3s appear to have an additional role in maintaining integrity of the intestinal epithelial barrier(43) and preserving a healthy biota through promoting glycosylation of IECs(44).

1.3.1. Monocytes and Macrophages

The intestinal LP contains the most numerous macrophage population in the body, and macrophages comprise one of the largest populations of intestinal leucocytes(45). In both mouse and human intestinal macrophages populations are denser in the colon than the SI, and appear to be scattered through the LP

21 just below the epithelial surface(4, 46). Macrophages have an important role in tissue repair and maintenance, including the clearance of apoptotic and senescent cells(45). They also appear to have an important role in supporting epithelial regeneration, possibly through secretion of factors such as prostaglandins(47). Blood monocytes are an important precursor cell of macrophages, especially in the intestine, as will be discussed in more detail below.

1.3.1.1. Intestinal macrophage phenotype

Intestinal macrophages are highly specialised for the efficient removal of non- pathogenic organisms which manage to traverse the intestinal barrier, displaying superior phagocytic ability to both monocyte and DC populations(48, 49) and ability to rapidly kill ingested bacteria through reactive oxygen species (ROS)-independent mechanisms(48, 50). Intestinal macrophages display specialised characteristics, with studies suggesting that, in contrast to blood monocytes, phagocytosis and microbicide do not result in measurable release of pro-inflammatory cytokines such as tumour necrosis factor-α (TNF-α), IL-1 and IL-12(48). However, more recent data indicate a more heterogeneous picture in that human SI CD14+CD11c+HLA-DRint macrophages, thought to represent recently arrived monocytes, produce constitutively higher levels of pro- and anti-inflammatory cytokines than blood monocytes and are responsive to TLR stimulation (although to a lesser degree than blood monocytes), in contrast to more mature intestinal macrophages which produce far lower levels of cytokines, unaltered by TLR ligation(51).

Intestinal macrophages do not appear to produce large quantities of ROS upon stimulation(52) or inducible nitric oxide synthase (iNOS) in tissue homeostasis(53). However, intestinal macrophages do appear to secrete IL- 10 and small amounts of TNF-α constitutively(54, 55), although conflicting data have been published on whether IL-10 is up- or down-regulated during macrophage maturation(51, 56, 57). TNF-α has been shown to induce the expression of certain matrix-metalloproteinases (MMPs)(58) and may therefore play a role in macrophage-induced tissue remodelling. Although intestinal

22

Figure 1.2. Human blood monocyte and colonic macrophage phenotype. Human classical blood monocytes are believed to be progenitors of the intestinal macrophage population. Upon extravasation into the intestinal LP monocytes undergo a process of maturation toward a tolerogenic intestinal macrophage phenotype, associated with downregulation of CD14, upregulation of CD209 and/or CD163, increased basal production of IL-10, and relative TLR unresponsiveness. Macrophages within the GI tract also play a role in immune defence through phagocytosis of pathogens, and maintenance through phagocytosis of apoptotic cells.

23 macrophages express a range of PRRs, stimulation with different TLR ligands did not result in production of pro-inflammatory cytokines, again, in contrast to blood monocytes(59). Signalling via PRRs in intestinal macrophages may in fact promote immune tolerance. For example, stimulation of the NLR Nod2 with its ligand, the bacterial cell wall component muramyl dipeptide(60) reduced production of pro-inflammatory cytokines upon further stimulation with muramyl dipeptide or LPS in human monocyte-derived macrophages (MDMs)(61), a mechanism reliant on MDM production of the cytokines IL-10, TGF-β and the protein IL-1 receptor antagonist (IL-1Ra)(62). The potential importance of Nod2 function in intestinal immunity is highlighted by genome-wide association studies (GWAS), which have identified mutations in Nod2 as having the strongest association with IBD development(64).

Thus, polymorphisms in the NOD2 gene have been strongly associated with development of CD, although, interestingly, these polymorphisms may be protective in UC(63). Since its discovery as a risk allele for CD in 2001(64) the exact mechanism by which NOD2 mutation confers increased risk has remained unclear. Paneth cell production of defensins appears to be Nod2- dependent(18, 65), and more apparent deficiencies in α-defensin production were noted in patients with ileal CD carrying NOD2 mutations(18). Recent evidence has also established an important role for Nod2 in autophagy(66, 67), the process where formation of an autophagosome (a cytoplasmic structure surrounded by a double membrane) results in engulfment of cytoplasmic material such as organelles, bacteria or bacterial components(15) which has important roles in defence against intracellular bacterial pathogens(66). Nod2 was shown to recruit the autophagy associated protein ATG16L1 to sites of bacterial entry, and murine bone marrow derived macrophages (BMDMs) carrying a Nod2 mutation homologous to the most prevalent human IBD risk allele showed impaired bacteria-mediated autophagy(66). ATG16L1 itself has been demonstrated as a risk allele in IBD(68), and bone marrow (BM) derived cells from IBD patients display impaired autophagy induction despite normal Nod2-ATG16L1 interactions(66).

24 1.3.1.2. Origin of intestinal macrophages

Whilst recent evidence indicates that multiple tissue macrophage populations such as those found in the liver, brain and lung originate from embryonic precursors(69, 70) the intestinal macrophage population appears to be principally derived from circulating monocytes which seed the intestinal LP(71). Evidence for a monocytic origin of the intestinal macrophage population comes from characterisation of murine intestinal monocyte-macrophage populations, demonstrating heterogeneity with variable expression of the chemokine receptor CX3CR1, with increasing CX3CR1 expression correlating with expression of CD163, CD206, and TGF-β receptor 2 (TGF-βR2), markers of a tolerogenic macrophage phenotype(54). Labelled Ly6Chi monocytes (analogous to the human ‘classical’ CD14hiCD16- monocyte population)(72) were shown to populate the colonic LP and upregulate CX3CR1. Adoptive transfer of Ly6Chi monocytes into C-C chemokine receptor type 2 (CCR2) -/- mice lacking circulating Ly6Chi monocytes resulted in population with donor monocytes and their upregulation of CX3CR1 within the colon, but not other tissues(54), indicating the presence of unique conditioning factors within the intestine.

In tandem, studies of resected human ileum demonstrated monocyte- macrophage populations with differential expression of the LPS co-receptor CD14, low expression correlating with HLA-DR and CD209 and/or CD163 expression and absence of CD11c, suggestive of a mature intestinal macrophage phenotype. Bujko et al.(51) identified a population of CD14+CD11c-HLA-DRhi mature intestinal macrophages in human SI and observed replacement of immature and, more slowly, mature macrophage populations with donor cells in pancreas-jejunum transplant recipients, with all macrophage populations being of donor origin by 52 weeks. Data from murine lethally irradiated BM chimeras with abdominal shielding, analysing macrophage turnover utilising the novel macrophage markers Tim4 and CD4, showed persistence of a Tim4+CD4+ population which was dominant in the neonatal intestine and was maintained in monocyte deplete CCR2-/- mice(55), suggesting the existence of an embryonically derived intestinal macrophage population and challenging the idea of complete dependence on circulating

25 monocytes. However, circulating monocytes appear to be the source of most, if not all, of the intestinal macrophage population, and may therefore play an important role in the development of intestinal inflammation.

1.3.1.3. Blood monocytes and intestinal homeostasis

Circulating monocytes have been classified into three major subsets based on differential expression of Ly6C, CCR2, and CX3CR1 in mice and CD14 and CD16 in humans(72, 73). Whilst classical (CD14hiCD16-) monocytes appear to be the most competent at phagocytosis, producing highest levels of ROS, intermediate (CD14hiCD16+) monocytes highly express genes associated with major histocompatibility complex (MHC) II antigen processing and microbial defence and produced the highest levels of the pro-inflammatory cytokines TNF-α, IL-1β and IL-6 upon LPS stimulation(74, 75). Expanded CD16+ monocyte populations have been seen in inflammatory diseases such as , coronary artery disease and Kawasaki’s vasculitis(76). Intermediate monocytes isolated from patients with rheumatoid arthritis were potent inducers of Th17 responses, and peripheral blood levels of intermediate monocytes and Th17 cells in rheumatoid arthritis patients were correlated(77). In IBD, the intermediate monocyte population appears to be expanded in peripheral blood compared to healthy controls(78), but the role that this expanded population plays in IBD is unclear(79). Non-classical (CD14loCD16+) monocytes expressed high levels of genes associated with MHC I processes, motility and transendothelial migration, and demonstrate endothelial ‘crawling’, similar to homologous murine Ly6Clo monocytes(74, 75). Furthermore non- classical monocytes may have a role in antiviral immunity, producing high amounts of TNF-α, IL-1β upon exposure to both measles and herpes simplex virus-1 via TLR7 and TLR8 stimulation(75). Whilst some monocyte- macrophages within the human intestinal LP have been shown to express CD16(80), human CD16+ monocytes migrate poorly through intestinal endothelial monolayers compared to CD16- monocytes(79). Homology of CD16- human monocytes and Ly6Chi monocytes that seed the murine intestinal LP(54, 71) lends further weight to the hypothesis that classical monocytes also

26

Figure 1.3. Murine and human blood monocyte subsets. Murine and human circulating monocytes can be further subdivided into classical, intermediate and non-classical subsets by their differential expression of Ly6C and CX3CR1 in mice, and CD14 and CD16 in humans. Each subset appears to have distinct functions in vivo and in humans an expanded intermediate monocyte population has been observed in multiple inflammatory autoimmune conditions.

27 originate the human intestinal macrophage population. There is evidence to suggest that TGF-β is a major chemoattractant of blood monocytes to the intestine(81).

Gene-expression analysis of monocyte subsets indicates a high level of concordance, particularly between intermediate and non-classical subsets, arguing for a developmental relationship between subsets(74). Furthermore human in vivo studies utilizing deuterium labeling showed sequential labeling of classical, intermediate then non-classical monocytes consistent with transition from classical to non-classical within the circulation(82), similar to the transition from Ly6Chi to Ly6Clo within murine blood(70). Factors that govern acquisition of a ‘mature’ circulating monocyte phenotype are unclear, although it appears that Ly6Chi monocytes negatively regulate the lifespan of the Ly6Clo subset, through as yet undiscovered mechanisms(70). The vascular endothelium may also promote differentiation of monocytes to Ly6Clo through monocyte Notch signalling through expression of the Delta Like Canonical Notch Ligand 1 (Dll1)(83). The cytokines TGF-β and IL-10 have both been reported to upregulate CD16 in human monocytes(84, 85), though the role of these cytokines in regulating monocyte subset differentiation in vivo has not been established.

1.3.1.4. Regulation of intestinal macrophage phenotype

Given the distinct differences between mature intestinal macrophages and their blood monocyte precursors, conditioning signals provided by the intestinal microenvironment are thought to shape macrophage phenotype(48). The pivotal role of macrophage colony stimulating factor (M-CSF) in intestinal macrophage maturation is evidenced by the absence of SI macrophages lacking the M-CSF receptor in a murine BM chimera model(86) and depletion of intestinal macrophages seen in mice with loss of function mutation of M- CSF(87). Interestingly, deficiency of the granulocyte-macrophage colony stimulating factor (GM-CSF) receptor does not seem to influence intestinal macrophage populations in mice(88).

28 IL-10 appears to play a key role in promoting intestinal macrophage tolerance. Intestinal macrophages isolated from IL-10 knockout (KO) mice produce higher levels of pro-inflammatory cytokines in response to LPS stimulation, which was suppressed by addition of exogenous IL-10(89). In WT mice IL-10 was shown to be produced by both colonic macrophages and CD4+ T-cells in response to the intestinal microbiota(89), although it appears to be the ability of macrophages to respond to IL-10 rather than their ability to produce it that is crucial in preventing intestinal inflammation(90, 91). BMDMs lacking a functional IL-10 receptor (IL-10R) and polarised in pro-inflammatory conditions showed increased pro-inflammatory cytokine and IL-10 production(90), a finding also observed in serum and colon explant supernatant of mice lacking IL-10R on their mature intestinal macrophage population(91). These IL-10R-deficient CX3CR1+ intestinal macrophages also upregulated CCR7 and, in contrast to WT cells, were found within the mesenteric lymph nodes (mLN)(91). IL-10R- deficient BMDMs polarised in anti-inflammatory conditions displayed attenuated IL-10 production and reduced differentiation of naïve T cells into regulatory T cells (Treg)(90). Tregs are central in maintaining intestinal immune homeostasis and display the ability to downregulate multiple types of immune response(92). In addition, mutations of the IL-10R in humans are associated with severe, early onset IBD with increased pro-inflammatory cytokine production by peripheral blood mononuclear cells (PBMCs)(93).

Intestinal stroma-derived TGF-β has been shown to reduce pro-inflammatory cytokine production in response to LPS in blood monocytes(48) and TGF-β appears to be associated with development of a gene signature associated with mature intestinal macrophages, promoting expression of CX3CR1 (a function of TGF-β also noted in microglia, which are of yolk sac not monocytic origin), IL- 10, integrin αvβ5, and genes involved in the Notch signalling pathway(56), itself identified as a key pathway in the maintenance of the intestinal macrophage population(94). Comparison of the gene signature of IL-10R and TGF-βR- deficient macrophages indicate complementary and non-overlapping functions of these cytokines in inducing immune tolerance in intestinal macrophages(56, 91).

29 As mentioned some of the evidence for the role of different cytokines in determining macrophage phenotype comes from in vitro models(90). Whilst the limitations of such BMDM or MDM models and the need for clearer nomenclature have been emphasised in recent literature(95, 96), a variety of factors have been described for their ability to polarise BMDM or MDMs toward a pro- inflammatory or an anti-inflammatory, pro-repair macrophage phenotype. Macrophages cultured in the presence of TLR ligands such as LPS or the Th1 associated cytokine interferon-γ (IFN-γ) (which have also been broadly termed as ‘M1’ or ‘classically-activated’ macrophages) express higher levels of pro- inflammatory cytokines (e.g. IL-12, IL-6, TNF-α) and iNOS; whereas, those cultured in the presence of IL-4, IL-10, TGF-β, immune complexes or glucocorticoids (which have been termed ‘M2’ or ‘alternatively-activated’ macrophages) express higher levels of IL-10, arginase-1 (Arg1) and scavenger receptors including CD163, concurrent with anti-inflammatory and repair functions(96, 97).

The maturation of blood monocytes into tolerogenic intestinal macrophages appears to be perturbed in active inflammation. Adoptive transfer of Ly6Chi monocytes into CCR2-/- mice with DSS-mediated colitis resulted in impaired upregulation of CX3CR1 on transferred cells(54). Furthermore analysis of ileal tissue from patients with active CD showed an accumulation of CD14+ cells that were overwhelmingly CD163loCD11chi(54). This was further corroborated by Ogino et al.(57) who demonstrated that human intestinal CD14+CD163lo cells co-expressing the macrophage marker CD64+ express TLR2 and 4 and produce pro-inflammatory cytokines IL1β, IL6 and TNF-α upon TLR stimulation in contrast to CD14+CD163hi myeloid cells, which expressed higher levels of anti-inflammatory IL-10 and TGF-β. This CD14+CD163lo population induced significantly higher levels of Th17 cell differentiation from naïve T cells upon co- culture compared to CD14+CD163hi cells(57). Interestingly CD14+CD163lo cells isolated from areas of active CD inflammation produced significantly higher levels of pro- and anti-inflammatory cytokines compared to those from non- inflamed regions and healthy controls(57).

30 1.3.1.5. The role of metabolism in shaping macrophage phenotype

Whilst alterations in glucose metabolism have long been identified in cancer cells an increasing body of evidence has highlighted similar metabolic changes in immune cells, notably macrophages, often in association with a pro- inflammatory phenotype, with differential expression of genes governing key metabolic pathways between pro- and anti-inflammatory macrophages(98).

In conditions of normal oxygenation (normoxia) cells are able to generate their principal intracellular source of energy adenosine triphosphate (ATP) through enzymatic breakdown of glucose (glycolysis) to pyruvate, metabolism of pyruvate to CO2 in the tricarboxylic acid (TCA) cycle, which produces NADH and FADH which drive oxidative phosphorylation (OxPhos) within the mitochondria, producing even more ATP(98). Whilst glycolysis produces 2 ATP from each molecule of glucose, OxPhos is able to generate up to 34 ATP per glucose molecule(99). In hypoxic conditions cells adapt to favour ATP production by glycolysis(98). Hypoxia-inducible factors (HIFs) are mediators of this response; constitutively expressed, they are protected from proteasomal degradation in low oxygen environments. Fascinatingly, flights or journey at altitudes above 2000m above sea level appear to be associated with subsequent IBD flares(100), and higher levels of HIF-1α and HIF-2 α were expressed in IBD intestinal resection samples, compared to non-IBD controls(101).

In infection glucose is an important source of energy for proliferating bacteria, and glycolysis may play a specific role in bacterial defences against nitric oxide (NO) mediated immune cytotoxicity, not seen with non-glucose carbon sources(102). In this competitive environment, metabolic adaptations are necessary within the immune system, such as the vital upregulation of the glucose transporter Glut1 for full effector T-cell (Teff: e.g. Th1, Th2, Th17) function(103). Preferential glycolysis in normoxia (termed ‘aerobic glycolysis’ or the Warburg effect) has been observed in proliferating cells whereby - although less efficient for ATP generation - glycolysis, shunting of intermediates via the pentose phosphate pathway (PPP), plus production of glucose metabolites in

31 the TCA cycle in association with glutaminolysis provides substrate for amino acid, nucleotide and lipid synthesis(104). However, beyond mere survival and expansion, metabolic alterations within immune cells appear to be related to resultant phenotype.

Accepting the shortcomings of nomenclature(95), for brevity the terms ‘M1’ (principally pro-inflammatory macrophages) and ‘M2’ (principally anti- inflammatory macrophages, as described in section 1.3.1.3.) will be utilised to describe macrophage phenotype , which are similarly associated with notable metabolic differences. ‘M1’ macrophages, unlike ‘M2’, are highly glycolytic and switch to expression of ubiquitous phosphofructokinase-2 (u-PFK2)(105), a more active isoform of the glycolysis enzyme. ‘M2’ macrophages rely on oxidation of fatty acids, potentially from scavenged cell-extrinsic lipids(106), and OxPhos, and inhibition of these metabolic processes in IL-4 polarised ‘M2’ macrophages resulted in increased pro-inflammatory cytokine secretion(107). This dichotomy is not absolute: ‘M2’ macrophages also require glycolysis for polarisation, which was increased upon IL-4 stimulation, possibly to produce fatty acids for oxidation, although they have a higher glycolytic reserve than ‘M1’ cells(108). Human data has also shown that ‘pro-inflammatory’ MDMs increase both glycolysis and OxPhos, but appear proportionally more reliant on glycolysis than ‘anti-inflammatory’ polarised MDMs(109), and CD14+ monocytes increase glycolysis in response to TLR2 and 4 ligands, but only supress OxPhos on TLR4 stimulation(110).

The advantages of metabolic switching in ‘M1’ macrophages may be related to disruption of the TCA cycle: Tannahill et al.(111) noted that glycolysis in ‘M1’ stimulated murine BMDMs was associated with intracellular accumulation of the TCA cycle intermediates fumarate, malate and succinate, alongside citrate and fatty acids, suggesting diversion of intermediates for biosynthesis. This was confirmed by combined metabolic and transcriptional analysis indicating two breakpoints in the TCA cycle; between isocitrate and α- ketoglutarate (AKG) and between succinate and fumarate in ‘M1’ but not ‘M2’ macrophages(112). The first breakpoint may be related to citrate diversion

32 toward production of acetyl coenzyme A (acetyl-CoA), utilised for prostaglandin synthesis, and NADPH, utilised for NO and ROS generation(113). Furthermore,

Figure 1.4. Key differences in the TCA cycle between M2 ‘anti-inflammatory’ and M1 ‘pro- inflammatory’ macrophages. M1 macrophages have been shown to have disturbances of their TCA cycle resulting in intracellular accumulation of the metabolites citrate and succinate. These metabolites appear to contribute to key pro-inflammatory and microbicidal functions

33 within these M1 macrophages, such as prostaglandin and NO synthesis, and increased production of the pro-inflammatory cytokine IL-1β. alongside reduced expression of the enzyme isocitrate dehydrogenase (IDH), which catalyses AKG production, upregulation of immune-responsive gene 1 protein (IRG1) was observed in ‘M1 macrophages’(112). IRG1 is a key enzyme in the production of the antimicrobial metabolite itaconate(114), which can be produced from cis-aconitate diverted from the broken TCA cycle. Glycolysis and succinate accumulation related to the second TCA breakpoint induced a state of ‘pseudohypoxia’ in ‘M1’ macrophages resulting in HIF-1α stabilisation via inhibition of prolyl hydroxylases, and IL-1β production via HIF-1α binding to the IL-1β promoter region despite normoxic conditions(111). Subsequent research indicates that this TCA break between succinate and fumarate is not complete as inhibition of the catalysing enzyme succinate dehydrogenase (SDH) inhibited ROS generation and IL-1β production (115). Of note fumarate has been shown to stabilise HIF-1α(116), which may explain the importance of SDH activity in macrophage generation of IL-1β. In addition to disruption of the TCA cycle, the aspartate-argino-succinate shunt, which links the urea and TCA cycles generating arginine utilised in NO production, was increased in ‘M1’ macrophages and inhibition of this shunt was associated with attenuation of

‘M1’ phenotype(112).

The mechanisms regulating metabolic phenotype in macrophages are complex. IL-4-mediated ‘M2’ metabolic changes appear to be mediated at least in part by the transcription factor IRF4 and mammalian target of rapamycin (mTOR) signalling, which M-CSF promotes in synergy with IL-4. However mTOR has been shown to increase HIF-1α activity and glucose uptake and inhibits lipid metabolism consistent with a ‘M1’ metabolic signature(103, 117, 118). This disparity may in part be explained by integration of mTOR into different signalling complexes(108). There also appears to be inter-regulation of metabolic processes; the role of NO production in inhibiting OxPhos has been described in DCs(119) and macrophages(120), artificial manipulations to increase intracellular succinate levels have also been shown to increase glycolysis, IL-1β and HIF-1α, and decrease IL-10 production(115). IL-10

34 appears to have a regulatory role in promoting OxPhos and reducing glycolysis in an NO independent manner(121) (although other groups have noted increased NO production in IL-10 KO BMDMs)(120) and was shown to protect against the accumulation of unhealthy mitochondria by promoting mitophagy (phagocytosis of mitochondria). These effects, plus inhibition of inflammasome activation were mediated by IL-10 inhibition of mTOR(121). Fascinatingly, IL-10 is increased in response to LPS-induced glycolysis(109) via mTOR in a proposed feedback mechanism(120).

1.3.2. Dendritic cells

DCs are poised between the innate and adaptive immune system; as specialised antigen presenting cells they ingest and present antigen in association with appropriate co-stimulatory signals to promote and shape T-cell responses(122). DCs are able to present antigen to CD4+ Th cells via MHC II , although certain DCs are specialised to prime CD8+ cytotoxic T-cells via presentation of exogenous antigen in association with MHC class I, known as ‘cross presentation’(122). The majority of MHC II-associated antigen is endocytosed exogenous antigen that is subjected to lysosomal degradation prior to MHC II loading. A minority of MHC II associated antigen is derived from the cytosol and nucleus through autophagosome formation and fusion with MHC class II-loading compartments(123).

1.3.2.1. Intestinal dendritic cell phenotype

DCs are found throughout the intestine, both scattered within the LP and in organised lymphoid structures known as gut associated lymphoid tissue (GALT) that are distributed throughout the length of the intestinal tract(4, 14). Peyer’s patches (PPs) are the principle type of GALT in the SI. They comprise multiple B cell follicles, interfollicular areas rich in T-cells, and DC-containing sub- epithelial dome regions. PPs and other lymphoid follicles found within the GI tract are overlaid by a specialised follicle associated epithelium, containing multiple microfold cells (m cells). These cells are able to sample luminal antigen and deliver the contents via the cell through the basement membrane (transcytosis) to specialised antigen presenting cells within the follicle(14). DCs

35 within GALT take up transcytosed live commensals and can present antigen to lymphocytes within the GALT, or transport them to the draining mLNs where they induce B- and T-cell responses(49, 124). It appears that DCs found within the LP outside of GALT are capable of luminal antigen sampling via extension of dendritic processes through epithelial tight junctions, a process dependent on the presence of CX3CR1 in mice(125), prior to antigen transport to mLNs. However these findings have not been fully replicated in more recent studies, and the identity of these cells as bona fide DCs has been questioned given the greater number of CX3CR1+ macrophages within the intestine(126).

Intestinal DCs produce higher levels of IL-10 and stimulate greater production of IL-4 and IL-10 and less IFN-γ from CD4+ Th cells in co-culture, compared to splenic DCs(127), although this ability varies between intestinal DC subsets(128). DC populations within the intestinal mucosa exhibit specific properties including production of retinoic acid (RA), which acts synergistically with TGF-β to induce Treg differentiation(129, 130). RA also promotes gut tropism in effector cells of the adaptive immune system through stimulating their expression of proteins such as integrin α4β7, which binds to mucosal vascular addressin cell-adhesion molecule 1 (MAdCAM-1) found on mucosal endothelial cells, and CCR9, which binds to intestinally expressed chemokine (C-C motif) ligand 25 (CCL25)(131, 132).

The identification of distinct antigen presenting cell populations within the intestinal mucosa has been an area of challenge with the use of traditional markers such as CD11c and CD11b in the murine intestine leading to considerable overlap of macrophage and DC populations(133). Surface expression of the high-affinity IgG receptor FcγR1 (CD64) has been suggested as a reliable differentiator of macrophages (CD64+) and DCs (CD64-) in both mouse and human(126, 134). Murine conventional DC (cDC) populations have been further differentiated by their expression of alpha E integrin (CD103) and CD11b into three major populations: CD103+CD11b-, CD103+CD11b+ and CD103-CD11b+(126). More recent transcriptomic and phenotypic data support a homology between murine intestinal CD103+CD11b- DCs, human intestinal CD103+Sirpα- DCs and human blood CD141+ DCs, which are specialised to

36

Figure 1.5. Common phenotypes of murine and human circulating and intestinal DC subsets Transcriptomic and functional profiling of DC subsets has demonstrated homology between distinct circulating and intestinal DC subsets in both mice and humans with specialised functions proposed for each of these related DC families. cross-present antigen, and appear to rely on the transcription factors BATF3 and IRF8(135, 136); CD103+CD11b+ murine intestinal DCs appear to be homologous to human CD103+Sirpα+ intestinal and CD1c+ blood DCs, and seem to rely on the transcription factor IRF4(86, 135). Finally, murine intestinal CD103-CD11b+ DCs appear analogous to human intestinal CD103-Sirpα+ DCs and human blood monocytes. These CD103-Sirpα+ cells displayed phenotypic

37 features of true DCs, indicating they may be DCs of blood monocyte origin(135).

The ability to migrate to mLNs and induce T-cell expression of gut homing molecules in a RA-dependent manner was thought to be restricted to CD103+ DCs. However, subsequent evidence demonstrated that all three major DC subsets within the murine intestine are able to migrate and induce T-cell gut homing, with each subset producing similar levels of aldehyde dehydrogenase (ALDH; the enzyme required for conversion of dietary vitamin A to RA)(136), although more recent data suggests that the CD103+CD11b- subset may have higher ALDH activity(137). In humans all three equivalent subsets expressed the chemokine receptor CCR7(135), which is essential for DC mLN homing(138). Although murine CD103+ and human CD103+Sirpα+ DCs more efficiently promote differentiation of suppressive Tregs in vitro(126, 135), selective deletion of either CD103+ subset does not appear to alter Treg numbers within the intestinal LP(126). Interestingly, murine and human intestinal DC subsets are also capable of promoting Th1 and Th17 differentiation, although the data here appears slightly conflicting: human CD103+ subsets showed a superior ability to induce Th17 differentiation, whereas CD103- DCs more efficiently promote Th1 differentiation(135) Both murine CD103+ DC subsets appear capable of inducing Th1 differentiation(139), although mice lacking CD103+CD11b- intestinal DCs lacked intestinal Th1 cells, and were unable to mount Th1 immune responses to helminth infection(137). CD103+CD11b+ DCs appear superior in promoting Th17 responses(86, 126). However, murine CD103-CD11b+ from intestinal lymph displayed an enhanced ability to promote both Th1 and Th17 in the absence of overt stimulation(136).

As for macrophages, the importance of Nod2 and the autophagy pathway has been demonstrated in intestinal DCs. Studies utilising immature human monocyte-derived DCs (moDCs) demonstrated an increase in MHC II surface expression after Nod2 stimulation, and inhibition of DC-induced antigen-specific CD4+ Th proliferation after small interfering RNA (siRNA) gene silencing of NOD2 or ATG16L1. This was recapitulated in reduced antigen-specific CD4+ T-

38 cell proliferation induced by DCs derived from CD patients carrying risk alleles of NOD2 or ATG16L1(67). Nod2 may also shape the type of Th response upon antigen presentation. Naïve Th (Th0) cells are stimulated via antigen presentation in concert with delivery of co-stimulatory signals such as ligation of CD28 on the T-cell by CD80 and CD86 on DCs(140). Intraperitoneal (IP) injection of Nod2 ligand was shown to increase the production of Th2 effector cytokines IL-4 and IL-5 from murine splenocytes(141), and lack of Nod2 expression on murine antigen presenting cells was associated with increased production of the Th1 polarising cytokine IL-12, and proliferation of CD4+ T- cells producing the Th1 effector cytokine IFN-γ upon co-culture, in association with increased susceptibility to Th1-driven inflammation in an oral antigen- induced colitis model(142). Altered patterns of cytokine release have been described in IBD(143), which lent support to the belief that CD is predominantly Th1 mediated, and UC predominantly Th2 mediated(144) although subsequent evidence has indicated that alterations in the cytokine milieu are more complex(143).

The combination of DC-mediated uptake and mLN transport of small numbers of commensals and highly efficient phagocytic and microbidal abilities of intestinal macrophages result in controlled exposure of commensals to the intestinal immune system in homeostasis(49), and an increasing body of evidence suggests that IBD may be driven by relative immunodeficiency of the innate immune system leading to persistence of bacteria and chronic immune exposure, rather than overreactivity of the adaptive immune system as the driving process(145).

1.3.2.2. Origin of intestinal dendritic cells

Within the mLNs and GALT cDCs play a pivotal role in directing the immune response (the nomenclature cDC differentiating them from plasmacytoid DC [pDC] which produce large quantities of type I interferon upon viral stimulation)(122). Murine cDCs originate from haematopoietic stem cells within the BM via the macrophage monocyte dendritic cell progenitor (MDP) that gives rise to both DCs and macrophages(122), although there is evidence to suggest

39 that MDPs are not truly pluripotent for both cell types(146). DC committed cells within the MDP fraction differentiate to the common DC precursor (CDP) which then gives rise to M-CSFR- precursors of mainly pDCs and M-CSFR+ precursors of mainly cDCs. In contrast, murine CD103-CD11b+ DCs appear to originate from Ly6Chi monocytes rather than from DC precursors(147), and transcriptomic analysis suggests a monocytic origin of analogous human CD103-Sirpα+, possibly in response to inflammation(135). The analogous processes for other human DC populations are less well defined(122).

Whilst intestinal DCs promote immune tolerance through mediators such as RA and TGF-β, RA itself has been identified as a critical factor in maintenance of CD11b+CD103+ DCs within murine SILP(148). TGF-β has also been shown to promote expression of genes associated with cDC development in precursors, such as Flt3, IRF4, IRF8, and Id2, the latter of which suppresses pDC differentiation and influences development of cDC subsets(149). Within the intestine, TGF-β also appears to be important in development of the CD11b+CD103+ DC subset, as this population was reduced in mice lacking TGF-βR1 on CD11c+ cells, resulting in reduced intestinal Treg and Th17 populations. It was proposed that this reduction in the CD11b+CD103+ DC population was secondary to impaired differentiation from CD11b+CD103- DCs due to their inability to respond to TGF-β(150)

1.3.2.3. Regulation of intestinal dendritic cell phenotype

Despite common BM precursors, intestinal DCs display specialised features suggesting the presence of unique conditioning factors within the intestine. IECs appear to environmentally condition DC phenotype through their production of thymic stromal lymphopoetin (TSLP), which suppressed IL-12 production in human moDC and polarise Th2 responses toward a classically Th1-inducing antigen Salmonella typhimurium(151). TSLP can also promote IgA class switching through increasing DC production of APRIL and BAFF(152).

Furthermore IECs and the intestinal stroma produce TGF-β(48, 153). TGF-β sensing by DCs is critical in its regulation of T cell responses. Mice with a DC

40 specific TGF-βR2 KO (DC-Tgfbr2 KO) developed multiorgan inflammation in association with a more proinflammatory DC phenotype characterised by increased expression of TNF-α, IL-6 and IL-12 and T cell chemoattractants(154). TGF-βR2 KO DCs exhibited impaired Treg induction due to enhanced IFN-γ production, and the autoimmune inflammatory phenotype of DC-Tgfbr2 KO mice was partially abrogated by transfer of Tregs. Interestingly, despite alterations in DC phenotype, DC populations and expression of MHC II and co-stimulatory molecules were preserved in DC- Tgfbr2 KO mice(154). TGF-β autocrine signalling in DCs promotes expression of the enzyme indoleamine 2,3-dioxygenase (IDO), which is associated with a tolerogenic DC phenotype(155), however expression of IDO was not significantly altered in TGF-βR2 KO DCs(154). IEC production of both TSLP and TGF-β appears to be increased by exposure to intestinal bacteria, with gram-negative species inducing higher levels of TSLP(48, 153). With regards the migratory ability of intestinal DCs TGF-β has been shown to upregulate expression of multiple chemokine receptor on moDCs in association with enhanced chemotaxis toward their ligands(156), yet conversely appears to suppress DC expression of CCR7, important in mLN homing(156, 157), so the exact role of TGF-β in DC migration is unclear.

IL-10, which is produced by multiple cell types, notably CD4+ T-cells within the intestine(158) may influence DC migration and Th polarisation, suggested by findings of slightly increased mLN DC numbers and increased IFN-γ and TNF-α producing CD4+ T-lymphocyte numbers within the colonic LP of colitic IL-10 KO mice(159). Both TGF-β and IL-10, alongside prostaglandin E2 (PGE2) have been shown to suppress splenic pDC production of type I IFNs in vitro; PP pDCs were found to produce significantly lower levels of type I IFN than splenic DCs suggesting that the above factors may condition pDCs in the intestine(160).

1.4. Intestinal adaptive immunity

Whilst altered innate immune responses may underpin the aetiology of IBD, this condition is hallmarked by lymphocytic and/or plasmocytic infiltrate of the

41 intestinal LP(161), highlighting the adaptive immune system as the driver of chronic intestinal inflammation, with both T and B cells displaying an abnormally activated phenotype in IBD(15, 162). Understanding the mechanisms that direct adaptive immune function in homeostasis is therefore important in attempting to unravel the sequence of events that lead to dysregulation and disease.

1.4.1. Intestinal T cells

Many T cells reside between IECs, and are generally referred to as intraepithelial lymphocytes (IELs)(4). In contrast to other areas of the body, CD8+ T-cells far outnumber CD4+ T-cells within the intestinal epithelium, and up to 60% of IELs express the γδ TCR, comprising a γ and δ chain instead of The conventional αβ TCR(163). IELs have a role in protection against commensal invasion and tumorigenesis; TCRγδ+ IELs in particular appear specialised toward epithelial maintenance and promoting immune tolerance(163), producing regulatory mediators such as TGF-β, shown to inhibit cytotoxic CD8+TCRαβ+ IELs in co-culture(164).

Within the LP a diverse population of Th1, 2, 17 and Treg CD4+ T-cells predominate, although CD8+ T-cells comprise approximately a third of the LP T-cell population(4). Each of these T-cell phenotypes are characterised by expression of certain transcription factors and cytokines: Th1 cells express the transcription factor T-bet and produce IFN-γ, Th2 cells express GATA-3 and produce IL-4, IL-5 and IL-13, Th17 cells express Rorγt and produce IL-17, broadly, Tregs express Foxp3, and produce IL-10 and TGF-β(15, 143, 165), with the caveats that intestinal Foxp3- Treg populations such as the IL-10 producing Tr1 population are well described(166, 167) and utilisation of Foxp3+ as a Treg marker in humans is problematic due to transient expression of Foxp3 in multiple T cells subtypes during activation(168, 169). The majority of these T- lymphocytes have an effector memory phenotype, consistent with previous priming in the secondary lymphoid tissue(4). The proportion of cells expressing the transcription factor Foxp3, which denotes a regulatory Treg phenotype(92), within the intestine is higher than in other tissues(124), underlining the need for tight immunoregulation due to the presence of the intestinal microbiota.

42 .

Figure 1.6. Intestinal T and B lymphocyte subsets within the intestine. Within the intestinal epithelium and lamina propria multiple T and B lymphocyte subsets are found with unique features and functions in intestinal immunity.

43 1.4.1.1. Factors influencing intestinal T cell phenotype

Both ‘induced’ IELs (expressing αβTCR+ alongside CD8αβ or CD4+) and LP T cells appear to originate from naïve T cells primed in secondary lymphoid tissue that then migrate to the GI tract(4). The origins of CD8β-CD4- ‘natural’ IELs are less clear cut, but thought to be thymic lymphocytes which fully mature within the intestine(163). As described above (section 1.3.2.1) the cytokine environment present at the time of T-cell priming helps determine the resultant T cell phenotype. Cytokines identified in polarisation of T-cell subsets include IL-12 and IFN-γ for Th1, IL-4 for Th2, TGF-β plus IL-6 or IL-21 for Th17 and TGF-β and IL-2 for Tregs(165, 170, 171). Overproduction of certain cytokines has been observed in IBD, such as increased IFN-γ and IL-12 in CD, and IL-5 and IL-13 in UC(172-174). These findings lent support to the belief that CD is predominantly Th1 mediated, and UC predominantly Th2 mediated(144).

A challenge to the pure Th1/Th2 CD/UC paradigm came with evidence emphasizing the role of IL-17 in both UC and CD(175). Higher numbers of IL- 17 producing T-lymphocytes and monocyte/macrophages, and greater IL-17 mRNA expression have been seen in inflamed mucosa of patients with both types of IBD, plus higher serum and faecal IL-17(176, 177). However, IL-17 blockade has given disappointing results in IBD patients with a paradoxical worsening of inflammation in some patients(178). Recipients of IL-17 KO T cells in a murine T cell transfer model of colitis developed more severe disease in association with a Th1 cytokine signature. Additionally, T-cells cultured in vitro in Th1-polarising conditions expressed significantly lower levels of Tbet and Th1 cytokines in the presence of IL-17(179). Neither IFN-γ nor IL-12 blockade in isolation has demonstrated efficacy in the treatment of IBD(144), but Ustekinumab, which blocks the p40 subunit common to IL-12 and 23, induces clinical response in CD(180).

GWAS revealed an association between polymorphisms of the IL-23R gene and CD(181), with IL-23 produced by DCs and macrophages shown to provoke IL-17 production from CD4 lymphocytes(182), which drives inflammation in both IL-10 KO and T-cell transfer models of colitis(183). Whilst, IL-23 is not

44 mandatory for the initial development of the Th17 lineage, it appears to be necessary for Th17 cell maintenance(171). Furthermore evidence suggests that some Th17 cells may have regulatory properties. Thus, myelin-reactive Th17 cells restimulated in vitro in the presence of IL-6 + TGF-β did not induce central nervous system (CNS) inflammation, and were capable of suppressing experimental autoimmune encephalitis (EAE) induced by Th17 cells restimulated in the presence of IL-23, via mechanisms dependent on their enhanced production of IL-10(184). In contrast, IL-23-driven Th17 cells upregulated multiple chemokines resulting in inflammatory cell recruitment to the CNS. Whilst IL-23 or IL-6 in combination with TGF-β induced robust IL-17 production in T-cells(184) these data underline the importance of the local cytokine environment in shaping pro- versus anti-inflammatory phenotype of T- cells, even within the Th17 population.

The presence or absence of IL-6 has been proposed to regulate the Th17 cell/Treg axis(171). Reciprocal regulation of Tregs and Th17 cells is suggested by their inverse populations along the length of the GI tract, whereby Th17 cells are most numerous in the proximal small bowel and decrease toward the colon where Tregs predominate, in contrast to Th1 and Th2 cells which maintain consistent numbers throughout(4). One mechanism of reciprocal control appears to be Foxp3 inhibition of Rorγt-directed transcription of IL-17 and IL- 23R, which is seen with higher concentrations of TGF-β in the absence of IL- 6(185).

Within the SI numbers of Foxp3+ Tregs appear to be influenced by the presence of dietary antigen, whereas in the colon presence of the microbiota is important in maintaining normal Foxp3+ Treg populations(124). In addition to their influence on Tregs, diet and bacteria can also affect proliferation of Teff subsets. Colonisation of GF mice, raised in a sterile environment, with segmented filamentous bacteria (SFB) restored formation of intestinal Th17 cells to levels similar to conventional specific pathogen free (SPF) mice(186). In addition to the importance of dietary RA in intestinal immunity, ligands for the aryl hydrocarbon receptor (AHR), found in cruciferous vegetables are important for the maintenance of IEL populations, and loss of AHR in mice resulted in an

45 outgrowth of SFB, enhanced Th17 responses and increased susceptibility to experimental colitis(187).

Dietary fat composition may also influence intestinal immunity: long-chain fatty acids (LCFAs), found in high quantities in the ‘Western Diet’ have been shown to promote Th1 and Th17 differentiation, whereas SCFAs, which are increased by a diet rich vegetables and legumes due to fermentation of non-digestible fibre by colonic bacteria(188), increased Treg proliferation(189). Interestingly, less than 30% of sibling risk in IBD appears to be associated with genetic variants(68) and second generation immigrants migrating from areas of low to high IBD incidence increase their risk of developing IBD(5), supporting a hypothesis of IBD resulting from environmental factors, such as diet, acting to modulate T cell regulation in a genetically susceptible individual.

1.4.2. Intestinal B cells

Naive B-cells express IgM and IgD, and are activated via binding of their B-cell receptor (BCR) to cognate antigen(190, 191). After activation B-cells can become terminally differentiated Ig secreting plasma cells (PCs) or memory B- cells which can further proliferate upon future encounter of BCR-specific antigen(190). Activated B-cells can undergo class switching dependent on signals delivered at the time of activation. Approximately 80% of the total body PCs are believed to reside in the GI tract, and an estimated 75-80% of SI and 90% of colonic PCs produce IgA(124), with the majority of this IgA appearing specific for pathogens, commensals and self-antigen(192).

After production by PCs IgA binds to the polymeric immunoglobulin receptor (pIgR) expressed on the basolateral surface of IECs, which mediates its transcellular transport and secretion(193). IgA can neutralise toxins produced by pathogenic organisms, protect against enteric viruses, and through binding to certain epitopes can reduce epithelial adhesion and motility of potentially pathogenic luminal bacteria. IgA binding to M-cells promotes uptake of luminal antigen into PPs, and IgA coating increases PP sampling of non-invasive luminal bacteria. Crucially, secretory IgA does not appear to activate the pro-

46 inflammatory complement cascade(191, 194). Interestingly, whilst many pathogen-protective functions of IgA have been demonstrated, mice deficient in IgA are still capable of mounting robust immune responses against enteric virus(195), and IgA may have a key role in maintaining a healthy intestinal microbiome, demonstrated by reduced intestinal microbial diversity in mice with deficient IgA responses(194). IgA has been shown to preferentially coat certain bacterial species, both pathogens and non-pathogens, with increased IgA coating within the SI where bacteria are in closer contact with the epithelium due to a thinner mucus layer. It is possible that this selective IgA coating may preserve bacterial diversity within the intestine(194).

Dietary components have also been shown to influence the function of intestinal B cells. SCFAs were shown to promote IgA production and IgA coating of luminal bacteria through inducing DC production of RA(193). GALT appears to be the principle site of IgA switching(191), with B cells able to undergo class switching through both T-cell dependent and independent mechanisms via interaction with intestinal DCs. In T-cell dependent IgA switching DCs induce expression of TGF-βR2 on B cells(196), TGF-β being a major factor in IgA class switching(197). T-cell independent IgA switching mechanisms rely on DC expression of the cytokines APRIL and BAFF(196). The intestinal biota may influence the predominance of IgA producing PCs within the intestine via TLR and MyD88 signalling through induction of an iNOS producing DC population within GALT(196).

1.4.3. Homing and localisation of intestinal T and B cells

Intestinal adaptive immunity relies on the homing of primed T and B cells to the intestinal mucosa after priming through DC antigen presentation within GALT or mLNs(124). Whilst T-cells primed within the GALT by DCs presenting SI derived cognate antigen upregulate α4β7 and CCR9, it appears that the G- protein–coupled receptor 15 (GPR15) is more important for colonic homing or ‘imprinting’ of T-cells(198). In contrast to mice, in humans GPR15 appears to result in homing of Teffs but not Tregs to the colon, whereas α4β7 results in colonic homing of both Teffs and Tregs(199). α4β7/MAdCAM-1 interactions play

47 an important role in recruitment of T cells to the inflamed intestine in IBD, and antibody mediated blockade of both α4β7 and MAdCAM-1 in a T-cell murine colitis model was shown to significantly abrogate intestinal inflammation(200). The α4β7 specific IgG1 monoclonal antibody Vedolizumab has shown efficacy in the treatment of both UC and CD(201). Given the role of GPR15 in Teff specific colonic homing in humans and the failure of GPR15 KO Teffs to induce colitis in a T cell transfer model, GPR15 could represent a future therapeutic target in IBD(202).

The interaction between epithelially expressed E-cadherin and lymphocyte integrin αEβ7 is important in the maintenance of IEL populations, and may also promote retention of T cells within the intestinal LP(203). The IgG1 monoclonal antibody Etrolizumab specific to the integrin β7 subunit affects both α4β7/MAdCAM-1 and αEβ7/E-cadherin interactions and is being investigated as a therapy for UC(201).

DCs from mLNs and PPs were shown to induce expression of α4β7 and CCR9 on B cells in an RA dependent manner in association with enhanced gut tropism(132). CCR10 is also expressed on intestinal B cells and appears to be important in the homing of B cells and maintenance of memory B cell and PC populations in both SI and colon, whereas CCR9 appears important in B-cell accumulation within the SI only(124).

1.5. Control of TGF-β mediated intestinal immune tolerance

TGF-β is a pleiotropic cytokine exerting diverse immunomodulatory effects in a variety of cell types. Indeed, TGF-β is central in promoting immune tolerance in multiple cell types: it promotes differentiation of Tregs at high concentrations, inhibits IL-2-mediated Th1 and Th2 cell expansion, neutrophil-endothelial adhesion and macrophage activation, antagonises TNF-α function, promotes epithelial integrity and provokes production of secretory IgA(149, 204). Consistent with its role in promoting immune tolerance, mice with a global KO of TGF-β1 (one of three isoforms of TGF-β and the predominant isoform expressed by immune cells)(149, 205) are characterised by wasting and fatal

48 widespread inflammation, with mixed lymphocyte and macrophage infiltration, particularly of heart and lungs(206). T-cell specific KO of TGF-βR2 results in a similarly fatal phenotype secondary to the accelerated development of Th1 CD4+ and cytolytic CD8+ lymphocyte populations(207). TGF-β is also implicated in the development of tissue fibrosis(149), and may have a role in the formation of CD strictures, with higher levels of TGF-β seen in stricture regions of CD intestine(208). Increased is seen in CD strictures and TGF-β is capable of upregulating collagen production in intestinal myofibroblasts, with more marked effects on those isolated from CD strictures(209). The significance of TGF-β in the development of cancer has also been highlighted by the finding of TGF-βR2 mutations in up to 36% of cases of colitis- associated colorectal cancer (CRC), correlating with a more aggressive cancer phenotype(210). TGF- β overexpression occurs at a later stage in the development of other cancers and is again a marker of poor prognosis(211). Yet, despite compelling evidence of the pro-tumour effects, there is also evidence to suggest TGFβ has anti- proliferative properties(212).

Both TGF-β and TGF-βR are widely expressed in different cell types(213, 214). As mentioned, TGF-β occurs as three different isoforms TGF-β1, 2 and 3, which share between 71-79% of their amino acid sequence(215). These isoforms have overlapping but unique patterns of expression, and murine KO models targeting individual isoforms demonstrate unique functions of TGF-β1, 2 and 3(205). Given this widespread expression and the potential deleterious consequences of inappropriate function, the activity of TGF-β is tightly regulated. The TGF-β gene encodes active TGF-β and an N-terminal pro- peptide, the latency associated peptide (LAP)(216). LAP and active TGF-β are then cleaved within the Golgi apparatus by the enzyme furin(217, 218). TGF-β remains non-covalently associated with LAP (which is principally found as a homodimer) to form the small latent complex (SLC)(219). LAP can further bind to a 120-240 kDa known as latent TGF-β binding protein (LTBP) by disulphide bonds to form the large latent complex (LLC)(216). Covalent linkage of the LBTP to the extracellular matrix (ECM) occurs through an isopeptide bond from the N-terminus of LTBP. The hinge domain of LTBP is protease- sensitive allowing LLC to be released from the ECM(213). In order to allow

49 TGF-β/TGF-βR signalling the SLC needs to undergo further modification to reveal the active TGF-β, which is surrounded by LAP in a ‘straightjacket’ conformation, concealing TGF-βR binding domains(220). Multiple mechanisms of TGF-β activation have been observed in vitro, including heat, alterations in pH and ionizing radiation(215). The matrix glycoprotein thrombospondin-1 (TSP-1) has been shown to bind to LAP and activate TGF-β, and has been proposed as a major activating mechanism in vivo(221). However, whilst TSP-1 KO mice display some of the milder features of TGF-β KOs they do not recapitulate the full phenotype(222). More recently interest has focused on the role the integrin family in TGF-β activation.

Figure 1.7. Effects of TGF-β on leucocytes. The cytokine TGF-β is widely expressed, and has been shown to have multiple and diverse effects on both T and B lymphocytes; these include inhibition of Th1 and Th2 differentiation through downregulation of transcription factors, inhibition of Teffs via the action of Tregs, induction of Th17 differentiation and promotion of IgA class switching in B cells. Reproduced with authors’ permission(149).

50

Figure 1.8. Activation of TGF-β via the αv integrin family. The cytokine TGF-β is synthesized as a pro-peptide with LAP [1] that can form a homodimer [2]. This precursor is cleaved in the Golgi apparatus by the enzyme furin, but remains non-covalently associated as the SLC, whereby LAP conceals the active binding sites of TGFβ [3]. The SLC can associate with LTBP to form the LLC, which can bind to ECM components [4]. Cells expressing certain αv are able to bind to the RGD binding site of LAP resulting in release of active TGFβ [5 and 6]. Active TGFβ is able to bind to TGF-βR resulting in an intracellular signaling cascade [7]. Reproduced with authors’ permission(149).

Integrins are widely expressed heterodimeric cell surface receptors which mediate cell-to-cell and cell-ECM interactions(223). The αv-containing integrins αvβ1, αvβ3, αvβ5, αvβ6 and αvβ8 have been shown to bind to the arginine- glycine-aspartic acid (RGD) binding site of LAP(214). The physiological

51 importance of this integrin binding to the RGD binding site on LAP in TGF-β activation is underlined by the fact the mice with a single point mutation at this RGD binding site develop an identical phenotype to TGF-β1 null animals(224) indicating that αv integrin binding to the inactive complex is central in TGF-β activation. With regards the other TGF-β isoforms, TGF-β3 also contains an integrin binding motif suggesting activation by αv integrins, but latent TGF-β2 does not contain an RGD site, indicating an alternate method of activation for this isoform, with both proteolytic and non-proteolytic activity of enzymes such as TSP-1 and MMPs proposed(213, 225). It appears that the mechanism by which αv integrin-LAP interactions result in activation of TGF-β may vary. Activation of TGF-β by αvβ5 and αvβ6 integrin appears to be mediated via integrin-mediated interactions with the actin cytoskeleton and components of the extracellular matrix resulting in conformational changes in the SLC revealing the active TGF-β binding site(226-228). By contrast integrin αvβ8 has been shown to recruit MMP-14 (also known as membrane type 1 MMP [MT1-MMP]) to proteolytically cleave the LAP-TGF-β complex(225).

Whilst both αvβ3 and αvβ5 have been shown to activate TGF-β(229, 230) the phenotype associated with KO of either integrin does not appear to be markedly abnormal(231). Selective deletion of αv integrins on liver hepatic stellate cells (HSCs) or lung or kidney myofibroblasts (cell types believed to drive solid organ fibrosis) was protective in fibrosis models specific to each of these organs. Deletion of β3, β5 and β6 integrins or targeted deletion of β8 integrins on HSC did not confer similar protection, indicating that this protection either required blockade of all αv integrins, or was predominantly reliant on αvβ1 blockade(232). Further work utilising a small molecule inhibitor of integrin αvβ1 has confirmed that αvβ1 mediated TGF-β activation is key in the development of liver and lung fibrosis(233).

With regards the anti-inflammatory role of TGF-β, it has been shown that combined deletion and blockade of αvβ6 and αvβ8 integrins recapitulates the phenotype of TGF-β1 KO mice with lethal autoimmune inflammation and abnormal neurovascular development(234). Mice lacking αvβ6 develop mild pulmonary and dermal inflammation(235), although they appear to be protected

52 against lung fibrosis in association with reduced activation of TGF-β(227). αvβ8 appears to have a central role in normal vasculogenesis: global αvβ8 KO mice succumb to lethal neurovascular developmental defects in utero or early life(236), reduced αvβ8 expression has been noted in humans with arteriovenous malformations(237), and polymorphisms of genes encoding both αv and β8 have been associated with intracerebral haemorrhage(238). However, an important role for αvβ8 in intestinal immune homeostasis was revealed upon finding that leucocyte-specific deletion of αvβ8 resulted in development of an age related, microbiome dependent, T cell-driven colitis(239), which will now be explored further.

1.5.1. The role of αvβ8 mediated TGF-β activation in immune homeostasis

Mice with a conditional KO of β8 integrin on leucocytes appeared healthy until 4 to 5 months of age when they developed progressive wasting and enlarged spleens and mLNs, followed by the development of severe colitis at 10 months of age. These mice had reduced intestinal Tregs, increased numbers of circulating IFN-γ and IL-4 producing T cells, IgE, IgG1 and IgA(239). Complementary evidence from mice lacking αv integrin on endothelial and haematopoietic cells (via crossing of a floxed αv allele to mice expressing Tie2- cre; αv-Tie2 mice) showed these mice also develop severe, and ultimately lethal colitis evident from 12 weeks(240). These αv-Tie2 mice were shown to have marked alterations in intestinal T cell populations with fewer intestinal Tregs, and, interestingly, an even more marked reduction in the Th17 population, alongside an expanded Th1 population, and increased systemic and colonic IL-4 and IFN-γ in association with increased T cell activation (240, 241).

Furthermore, macrophages and DCs derived from αv-tie2 mice displayed impaired phagocytosis of apoptotic cells in vitro, and impaired apoptotic cell clearance was confirmed in vivo in αv-tie2 mice. The role of apoptosis in mediating DC phenotype has been demonstrated by the finding of downregulation of the co-stimulatory receptor CD86 and reduced pro- inflammatory cytokine production by DCs that have ingested apoptotic cells, and increased TGF-β dependent Treg differentiation in DCs that have taken up

53 apoptotic DCs (242, 243). Development of further murine models demonstrated that lack of integrin β8 or αv on myeloid cells was pivotal to development of colitis in both leucocyte specific integrin β8 KO and αv-tie2 mice leucocytes, with no appreciable phenotype in T cell-specific KO mice(239, 240). Certain subsets of intestinal DC have been shown to express high levels of integrin αvβ8, to activate TGF-β in an αvβ8 dependent manner, resulting in an increased ability to differentiate Tregs from naïve T cells, highlighting a pivotal role for intestinal DC expressed integrin αvβ8 in intestinal immune homeostasis(244, 245).

1.5.1.1. The role of αvβ8 mediated TGF-β activation by DCs

Whilst the integrin αv subunit appears to be widely expressed in intestinal and mLN DC populations the β8 subunit, which solely partners with αv, is selectively expressed in CD103+ DCs(244, 245). Given the more recent refinement of CD103+ DCs into CD103+CD11b- and CD103+CD11b+ populations(126), it was found that αvβ8 integrin in mice is limited to the CD103+CD11b- within the intestinal LP, and CD103+CD11b- DCs within the mLN, which were shown to be migratory DCs of intestinal LP origin. Interestingly, β8 integrin expression in CD103+CD11b- DCs found within the mLN, which had migrated from the intestinal LP was consistently higher than expression in CD103+CD11b- DCs within the intestinal LP itself, indicating that the process of migration may somehow further enhance expression levels in this specialised subset(246). In contrast intestinal DC expression of β8 integrin in humans appears to be highest on the CD103+Sirpα+ subset (analogous to murine CD103+CD11b+)(247). This increased β8 expression in both mouse and human DC subsets conferred an enhanced ability to activate TGF-β and induce Treg differentiation(244-247). CD103+CD11b- DC expression of integrin αvβ8 also appears to be important for the differentiation of the CD4+CD8αα+ IEL subset from intestinal CD4+TCRαβ+ T lymphocytes, a process that is TGF-β and RA dependent, (248, 249), as mice lacking αvβ8 on CD11c+ cells (predominantly DCs) had a significantly reduced CD4+CD8αα+ IEL population(137).

54 DC expressed αvβ8 does appear to play an important role during infection: CD11c-specific αvβ8 KO mice were protected against the development of chronic intestinal parasite infection, due to the integrin αvβ8-TGF-β pathway normally suppressing a protective Th2-mediated response(250). Mice lacking the ability to activate TGF-β via αvβ8 on CD11c+ cells due to a CD11c-specific αvβ8 KO were protected against EAE in association with dramatically reduced numbers of Th17 cells in both the CNS and periphery, including the colon(251). These changes occurred independent of the presence of IFN-γ (known to be suppressed by TGF-β), and with no significant differences in latent TGF-β production between DC -specific αvβ8 KO mice and controls. These findings were recapitulated in myeloid-specific integrin αv KO mice that failed to generate pathological Th17 cells and were thus protected from EAE(241). DCs were shown to induce differentiation of Th17 cells via αv-dependent TGF-β activation in the presence of IL-6, which could be blocked by competitive inhibition of the RGD binding site. Furthermore it appears that αv expression by the antigen presenting DC upon cognate T cell interaction is crucial for Th17 differentiation from naïve T cells(241).

Investigation of factors that induce DC expression of β8 integrin has revealed that both TGF-β and RA can enhance β8 expression on CD103+CD11b- DCs in mice(246), of which IECs may be an important source(252). A role for the intestinal microbiota in modulating integrin β8 expression via TLR signalling was also established by the finding of reduced β8 expression on CD103+CD11b- DC in both antibiotic treated and MyD88 KO mice, despite similar representation of this subset within the mLNs in antibiotic treated, MyD88 KO and control mice. Splenic DCs, which express minimal β8, could be induced to express this integrin subunit by treatment with TGF-β, although IL-10, GM-CSF and IFN-γ reduced β8 integrin expression. Whilst RA in isolation was not able to induce β8 integrin expression in splenic DCs, it acted synergistically with TGF- β. Finally treatment with the TLR-9 ligand CpG, and the TLR-5 ligand flagellin also induced β8 integrin expression in splenic DCs. However, experimental models suggest that the increased β8 integrin expression in CD103+CD11b- DC is not due to differential exposure to such factors in vivo, but rather through developmental epigenetic modifications that promote expression of the gene

55 encoding the β8 subunit, Itgb8(246). In humans alternative mechanisms may govern β8 expression as neither TGF-β nor RA appeared to influence moDC expression of this integrin subunit, and β8 was upregulated in these cells by TLR4 and TLR8 ligands only(247). Expression of integrin αvβ8 by intestinal DC is increased in DCs isolated from intestinal tissue from patients with CD, provoking the question as to whether this is related to a causative or compensatory mechanism in IBD(247).

The cellular source of TGF-β that is activated to by integrin αvβ8 expressed by DCs for the modulation of immune responses has been a source of speculation, as mice lacking TGF-β expression by T-cells alone develop spontaneous colitis(253) similar to myeloid cell specific αv or β8 KO models. Further analysis of Tregs has yielded important information on the role of TGF-β expression and αvβ8 mediated activation in T cells.

1.5.1.2. The role of αvβ8 mediated TGF-β activation on Tregs

In murine Foxp3+Tregs the latent TGF-β binding protein GARP is highly expressed upon activation and is important in tethering latent TGF-β to the surface of Tregs. This binding appears important as Tregs lacking expression of GARP displayed an attenuated ability to promote differentiation of both Treg and Th17 differentiation from naive T cells in the presence of splenic DCs(254). Whilst αv integrin expression was confirmed on both Foxp3+ and Foxp3- T cells, integrin β8 is expressed on Tregs but not Th cells(255-257), and was independent of TGF-β and GARP expression(255). Furthermore, αvβ8 was shown to mediate activation of TGF-β from the GARP-latent TGF-β complex on Tregs, and was also important for naïve T cell differentiation to Treg and Th17 upon co-culture with splenic DCs. Addition of both GARP and β8 KO Tregs to this model restored Treg and Th17 numbers, indicating that αvβ8 on one Treg can activate TGF-β from the GARP-latent TGF-β complex on another Treg(255), in contrast to the need for αv integrin expression on cognate antigen presenting DCs for Th17 differentiation(241). Interestingly, Treg expression of integrin β8 appears to be redundant in development of oral tolerance. Thus, whilst deletion of GARP or TGF-β on recipient Tregs resulted in reduced Treg

56 differentiation in donor naïve T cells in an oral tolerance model, with Treg specific TGF-β KO abrogating oral tolerance development, neither DC-derived TGF-β or αvβ8 expression by Tregs were necessary for induction of oral tolerance, indicating that αvβ8 expressing DCs may activate latent TGF-β bound by GARP on Tregs in this setting(258).

In mice, lack of αvβ8 on Tregs does not appear to result in spontaneous inflammation in homeostasis(256), and Tregs lacking integrin β8 were equally suppressive as WT Tregs in vitro(255). However the role of Treg β8 expression in suppressing intestinal inflammation appears to vary with the context of colitis: co-transfer of β8 KO Tregs showed that they were equally effective as WT in conferring protection in a T cell transfer colitis model(255). However, Tregs lacking αvβ8 are unable to rescue ongoing inflammation in established colitis in both chemical and T-cell transfer models(256), indicating a role for Treg integrin αvβ8 expression in colitis resolution but not prevention.

The role of GARP and integrin αvβ8 in the production of active TGF-β in human Tregs was highlighted by co-immunoprecipitation experiments demonstrating formation of integrin αvβ8-GARP-latent TGF-β complexes on Tregs(257). Monoclonal antibodies specific to the human GARP-latent TGF-β complex were shown to inhibit the suppressive abilities of human Tregs in vitro and abrogated the abilities of human Tregs to ameliorate xenogeneic graft-versus-host disease (GVHD) when co transferred with human T cells into mice lacking functional T, B and NK cells(259). Whilst GARP on human Tregs is able to bind latent TGF-β it is not able to activate it(260), however antibody mediated blockade of integrin β8 inhibited Treg production of active TGF-β in a similar manner to blockade of the GARP-latent TGF-β complex; interestingly, this process in human Tregs did not appear to be MMP-14 dependent.

Given the important roles established for αvβ8 expression on DCs and Tregs in intestinal immune homeostasis, and the paucity of data on the role of αvβ8 in other key cell subsets which drive intestinal immunity, namely monocytes and macrophages, the investigation of αvβ8 expression and function on these cell types forms the focus of this thesis.

57

CHAPTER 2 MATERIALS AND METHODS

2.1. Quantifying expression of integrin αvβ8 on peripheral blood mononuclear cells (PBMCs)

2.1.1. Peripheral blood collection

Healthy donor blood was obtained either from leucocyte cones through NHS Blood and Transplant, or via fresh venepuncture obtaining up to 40ml peripheral blood into 10ml Lithium Heparin BD vacutainer tube (Becton Dickinson[BD], Plymouth, UK) from consenting healthy donors, in accordance with the study ethics approval (obtained via the NHS Research Ethics Committee, NRES Committee North West [Greater Manchester South], reference 15/NW/0007).

2.1.2. PBMC isolation

Healthy donor blood was diluted with an equal volume of sterile PBS (Dulbecco’s Phosphate Buffered Saline, Sigma-Aldrich, Saint-Louis, USA) and layered over Ficoll-Paque Plus (GE Healthcare, Uppsala, Sweden), prior to centrifugation for 40 minutes for leucocyte cones and 30 minutes for peripheral blood at 400xg, room temperature, without brake at an acceleration setting of 1. Post centrifugation the plasma layer was removed with a Pasteur pipette and discarded. PBMCs were washed twice with sterile PBS, and manually counted using a haemocytometer.

2.1.3. PBMC staining for flow cytometry

PBMCs at 1 million/ml in PBS were centrifuged and resuspended in 50µl per test of viability stain (Zombie NIR Fixable Viability stain [BioLegend, San Diego, CA, United States] 0.05µl per 50µl PBS). Cells were then incubated at room temperature, shielded from light, for 15 minutes. Cells were washed in fluorescence activated cell sorting (FACS) buffer (PBS with 1% foetal bovine serum [Life Technologies ThermoFisher Scientific, Waltham, MA, United States] and 0.1% sodium azide [Sigma-Aldrich]) and resuspended in FACS

58 buffer per test to a total of 50ul including added antibody volume. Fc receptor blockade was performed with 2% mouse serum (Invitrogen ThermoFisher Scientific) incubated on ice for 10 minutes, or, in stated experiments, with 10% mouse serum on ice for 10 minutes or Human TruStain FcX (BioLegend) 2.5ul/50ul at room temperature, for 10 minutes. Fluorochrome conjugated primary antibodies were added according to experimental protocol as listed in table 2.1.

Surface marker Panel A Panel B

CD1c - BV421

CD3 AF700 FITC

CD11c - BV650

CD14 PerCP-Cy5.5 PerCP-Cy5.5 CD15 - FITC

CD16 BV605 AF700

CD19 BV711 FITC

CD20 - FITC

CD56 FITC FITC

CD141 - PE

HLA-DR PE-Cy7 PE-Cy7

Table 2.1 – Antibodies used for PBMC staining to identify integrin αvβ8 expression on peripheral leucocyte populations (All tabulated antibodies above supplied by BioLegend)

Cells were stained for the β8 integrin subunit using ADWA16 antibody conjugated to APC (see 2.1.4), or in specified experiments with PE conjugated ADWA-7, -10, -16 or -21, 37e1 or 14e5 antibodies (all β8 binding antibodies kind gift of Prof Stephen Nishimura, University of California, San Francisco, USA). Isotype controls were stained with APC or PE IgG1 K control (BioLegend). Upon staining cells were incubated on ice, shielded from light for

59 20 minutes, followed by washing in FACS buffer. Cells were then fixed by incubation in 2% paraformalydehyde (PFA, Sigma-Aldrich) for 15 minutes before washing and resuspension in FACS buffer for flow cytometry. For viability stain compensation, 1 million cells were reserved, and half were heat killed and placed on ice, prior to viability staining as described above. For compensation of other fluorochromes BD CompBeads Set Anti-Mouse Ig, κ, (BD) were used as per manufacturers instructions. Cells were analysed using an LSR II (BD, Oxford, UK) flow cytometer using FACSDiva software (BD). Post-acquisition analysis was performed using Flowjo software (Tree Star, Ashland, US).

2.1.4. β8 binding antibody-fluorochrome conjugation

β8 binding antibodies (ADWA-7, -10, -16 or -21, 37e1 and 14e5) were conjugated to either allophycocyanin (APC) or r-phycoerythrin (RPE) using the LYNX Rapid APC Antibody Conjugation Kit (Bio-Rad, Oxford, UK) or LYNX Rapid RPE Antibody Conjugation Kit respectively.

For conjugation to APC, 15µg of antibody was diluted to a total volume of 10µl with PBS prior to addition to 1µl of the supplied modifier reagent and gentle mixing. The antibody-modifier sample was then added to the LYNX lyophilized mix and gently pipetted twice as per manufacturers instructions. The antibody- modifier mix was incubated in the dark for a minimum of 3 hours and then 1µl of the supplied quencher reagent was added. The conjugated antibody was stored for a minimum of 30 minutes before use. The antibody conjugate was then diluted at 1:10 in FACS buffer to give a final concentration of 125µg/ml. Addition of 2µg diluted antibody per 50µl sample gave a final staining concentration of 5µg/ml antibody.

For conjugation to RPE, 10µg of antibody was diluted to a total volume of 10µl with PBS prior to addition to the supplied modifier reagent, LYNX lyophilized mix and supplied quencher reagent using the method described above. The antibody conjugate was then diluted at 1:10 in FACS buffer to give a final

60 concentration of 100µg/ml. Addition of 2.5µg diluted antibody to 50µl per sample gave a final staining concentration of 5µg/ml antibody.

2.1.5. CD14+ monocyte isolation

PBMCs were resuspended in MACS buffer (sterile PBS containing 2mM EDTA [Sigma Aldrich] and 0.5% foetal bovine serum) with CD14 microbeads (MACS, Miltenyi Biotech GmBH, Bergisch Gladbach, Germany) and incubated at 4°C for 15 minutes. PBMCs were washed and resuspended in MACS buffer. LS separation columns were placed within a QuadraMACS magnet (both Miltenyi Biotech), and rinsed with MACS buffer. The PBMC suspension, up to a maximum of 500x106 per column, was applied to the column followed by 3 rinses of MACS Buffer. Columns were removed from the magnet and cells were flushed with MACS Buffer into a fresh collection tube. Cells were then manually counted using a haemocytometer. Monocyte purity as assessed by percentage of CD14+ cells was >94%.

2.1.6. RNA synthesis for ITGB8 qPCR

Cells of interest were lysed using RLT Buffer (Qiagen, Hilden, Germany), using 350µl for samples of 1 to 3 million cells and stored at -80°C. RNA was later extracted using the RNeasy Mini Kit (Qiagen), according to manufacturer’s instructions. An equal volume of 70% ethanol was added to the thawed cell lysate and mixed by pipetting. The mix was added to an RNeasy mini extraction column and centrifuged at 8000xg for 15 seconds. The flow through was discarded and 700µl RW1 buffer added to the column followed by centrifugation (8000xg, 15 seconds) and discarding of flow through. This process was then repeated using 500µl of RPE buffer, centrifuged for 15 seconds, and then again with a further 500µl RPE buffer, centrifuged for 2 minutes. The spin column was transferred into a fresh collection tube and centrifuged at 17000xg for 1 minute. The spin column was then transferred into a labeled 1.5ml Eppendorf tube (Starlab, Milton Keynes, UK) and 30µl RNase free water (Qiagen) was added to the column prior to centrifugation at 8000xg for 1 minute. The column was then discarded and the eluted RNA placed onto ice. RNA purity was analysed by spectrophotometry (NanoDrop 2000c,

61 ThermoFisher Scientific).

2.1.7. cDNA synthesis for ITGB8 qPCR cDNA was synthesized from RNA using the GoScript Reverse Transcription System (Promega, Madison, USA) or using Superscript III Reverse Transcriptase (Invitrogen ThermoFisher Scientific). For cDNA synthesis with the GoScript Reverse Transcription System RNA was thawed and transferred into a PCR tube, placed within an ice block. Sample RNA quantity was standardized by the addition of RNase free water to a final volume of 4µl. 1ul

(0.5ug) of Oligo(dT)15 primer (Promega) was added and the mix was heated to 70°C for 5 minutes in a thermal cycler (Applied Biosystems 2720 Thermal Cycler, ThermoFisher Scientific). 4ul/test GoScript 5x reaction buffer, 2.4ul/test MgCl2, 1ul/test PCR nucleotide mix, 0.5ul/test Rnasin, 1ul/test GoScript RT and 6.1ul/test nuclease free H20 were added and then heated to 25°C for 5 minutes in a thermal cycler, followed by heating to 42°C for 1 hour, then 70°C for 15 minutes, followed by cooling to 4°C.

For cDNA synthesis using Superscript III sample RNA quantity was standardized by the addition of RNase free water to a final volume of 11µl. 1µl of oligo dT (Invitrogen ThermoFisher Scientific) and 1µl of dNTP mix (Invitrogen ThermoFisher Scientific) per test were added followed by heating to 65°C for 5 min in a thermal cycler. Samples were centrifuged briefly and 4µl/test of 5X first strand buffer, 1µl/test 0.1M DTT, 1µl/test RNase OUT and 1µl/test Superscript III reverse transcriptase (all Invitrogen ThermoFisher Scientific) were added. Samples were incubated at 50°C for 60 min and inactivated by heating to 70°C for 15 min.

2.1.8. ITGB8 qPCR qPCR was performed in a 384 well plate, samples in duplicate, using 10µl of Taq Man Universal Mastermix II with UNG (Life Technologies ThermoFisher Scientific), 1µl of gene specific assay (ITGB8 Hs00174456_m1, with B2M control, Hs00984230_m1, Life Technologies ThermoFisher Scientific) 2ul of

62 cDNA and 7µl of RNAse free water per test. qPCR was performed on an Applied Biosystems QuantStudio 12K Flex (ThermoFisher Scientific).

2.2. Investigating factors influencing expression of integrin αvβ8 on peripheral blood mononuclear cells (PBMCs)

2.2.1. PBMC and monocyte cytokine treatment

PBMCs or isolated CD14+ monocytes were cultured in RPMI plus L-glutamine (Sigma Aldrich) with 10% foetal bovine serum (Life Technologies ThermoFisher Scientific) and 1% penicillin/streptomycin (Sigma Aldrich). Recombinant human TGF-β (Peprotech, Rocky Hill, US) at a final concentration of 20ng/ml, IL-10 (Peprotech) at a final concentration of 100ng/ml, IL-1β (Peprotech) at a final concentration of 25ng/ml or TNF-α (Peprotech) at a final concentration of 40ng/ml were added to specified samples, and samples were cultured either for 4 hours or overnight at 37°C in a 5% CO2 incubator prior to further analysis.

2.2.2. PBMC and monocyte TLR ligand treatment

PBMCs or isolated CD14+ monocytes were cultured in RPMI plus L-glutamine (Sigma Aldrich) with 10% foetal bovine serum and 1% penicillin/streptomycin (Sigma Aldrich). TLR ligands Pam3CysSerLys4 (Pam3CSK4; TLR1/2 agonist) at 0.1µg/ml, heat-killed Listeria monocytogenes (HKLM; TLR2 agonist) at 108 cells/ml, Poly(I:C) high molecular weight (PIC-H; TLR3 agonist) at 1µg/ml and Poly(I:C) low molecular weight (PIC-L; TLR3 agonist) at 1µg/ml, Lipopolysaccharides from Escherichia coli O111:B4 (LPS; TLR4 agonist) at 1µg/ml, S. typhimurium flagellin (TLR5 agonist) at 0.1µg/ml, Imiquimod (TLR7 agonist) at 1µg/ml, R848 (TLR7/8 agonist) at 1µg/ml, ssRNA40 (TLR8 agonist) at 1µg/ml, or ODN2006 (type B; TLR9 agonist) at 1uM/ml were added to specified samples, and all samples were cultured overnight at 37°C in a 5% CO2 incubator prior to further analysis. All TLR ligands were supplied by Invivogen (San Diego, California, US) other than LPS from Sigma Aldrich, and R848 from Invitrogen ThermoFisher.

63 2.3. Investigating function of αvβ8 on peripheral blood mononuclear cells (PBMCs)

2.3.1. TGFβ activation reporter cell co-culture assay

Transformed mink lung (TMLC) cells (kindly provided by Professor Dan Rifkin, NYU, USA) were suspended at 160 000 cells/ml, in Dulbecco's Modified Eagle's Medium high glucose (DMEM3, Sigma Aldrich) with 10% foetal bovine serum and 1% penicillin/streptomycin, in a 96-well plate at 16 000 cells/well and incubated for at least 3 hours. The supernatant was then aspirated and a standard curve of 7.81 to 1000pg/ml of recombinant IL-10 diluted in RPMI plus L-glutamine with 10% foetal bovine serum and 1% penicillin/streptomycin was added to specified wells. Cells of interest were suspended at either 0.5x106 or 1x106 cells/well in RPMI plus L-glutamine with 10% foetal calf serum and 1% penicillin/streptomycin were added to specified wells. U251 cells (kind gift of Prof Stephen Nishimura) were added to specified wells as a positive control. Standard mouse IgG (Sigma Aldrich) at a final concentration of 40µg/ml was added in technical duplicates or triplicates as a control IgG, or monoclonal mouse IgG kappa (MOPC21; BioXcell, West Lebanon, USA) at a final concentration of 40µg/ml. TGF-β 1,2,3 neutralising antibody (1D11, BioXcell) at a final concentration of 40µg/ml and the αvβ8 neutralising antibody ADWA16 at a final concentration of 20µg/ml were added in either technical duplicates or triplicates. LPS at a concentration of 1µg/ml was added to some co-cultures. IL- 10 at a final concentration of 100ng/ml, or TNF-α at a final concentration of 40ng/ml were added to certain CD14+ monocyte/TMLC co-cultures as specified in the text. Cells were co-cultured overnight at 37°C in a 5% CO2 incubator. The supernatant was then discarded and cells were washed in PBS prior to addition of 20µl/well of 1x lysis buffer (made from 5X lysis buffer [Luciferase Assay System, Promega] diluted in water). The plate was then gently vortexed before freezing for a minimum of 1hour at -80oC. The cell lysate was then thawed and transferred into a white 96-well plate (Microlite™ 1+, ThermoFisher Scientific) and then centrifuged at 400xg for 3 minutes. 75ul/well of luciferase assay substrate was added and luminescence was read using a Tecan Infinite M200 Pro Plate reader (Tecan, Mannedorf, Switzerland). Active TGF-β

64 concentration was calculated with Prism 7 (Graphpad, La Jolla, USA) using a sigmoidal 4PL interpolation of log(TGFβ concentration) against relative luminescence units.

2.3.2. PBMC and monocyte treatment with anti-TGF-β antibody or anti- αvβ8 antibody

PBMCs and magnetic bead separated CD14+ monocytes were cultured in RPMI plus L-glutamine with 10% foetal bovine serum and 1% penicillin/streptomycin. TGF-β at 20ng/ml, standard mouse IgG at 40µg/ml, 1D11 at 40µg/ml (unless otherwise specified in the text) or ADWA16 at 20µg/ml (unless otherwise specified in the text) were added and cells were cultured overnight at 37°C in a 5% CO2 incubator.

2.3.3. Flow cytometry to assess treated CD14+ monocyte phenotype

CD14+ monocytes were harvested after overnight culture with control IgG, TGF-β, 1D11 or ADWA16, with or without LPS at a concentration of 1µg/ml, or after overnight treatment with cytokines as detailed in 2.2.1 or TLR ligands as detailed in 2.2.2. Cells were stained utilising the protocol in 2.1.3 with CD16- BV605, CD14-PerCP-Cy5.5, HLA-DR-PE-Cy7, and in some experiments CD163-PE-CF594 (from BD Biosciences, all others BioLegend).

2.4. Quantifying expression and function of integrin αvβ8 on MDM

2.4.1. MDM culture from CD14+ monocytes

Monocytes were resuspended in RPMI plus L-glutamine with 10% foetal bovine serum and 1% penicillin/streptomycin at a concentration of 500 000 cells/ml and seeded at 750 000 cells/well in a flat bottomed 12 well plate (Corning, Corning NY, US). Recombinant human GM-CSF or M-CSF (Peprotech) were added at a concentration of 50ng/ml at days 0 and 3, and cells were cultured for 7 days at 37°C in a 5% CO2 incubator. In stated experiments IFN-γ (Peprotech) at a concentration of 50ng/ml was added at day 6 to GM-CSF differentiated MDMs, and IL-4 (Biolegend) at a concentration of 20ng/ml was added at day 6 to M-CSF differentiated MDMs.

65 2.4.2. MDM staining for flow cytometry

MDMs were harvested from cell culture plates by gentle scraping and then analysed utilising the protocol stated in 2.1.3. Surface protein expression β8 integrin was quantified by staining with ADWA16-APC. For experiments investigating the surface marker phenotype of cultured MDMs the following antibody-fluorophore conjugates were used: CD206-BV421 (BioLegend), CD163-PE-CF594, CD14-PerCP-Cy5.5 and HLA-DR-PE-Cy7.

2.4.3. IL-10 ELISA

Supernatant was removed from MDM cultures on day 7 for ELISA. 96-well Nunc Maxisorp plates (eBioscience ThermoFisher Scientific) were coated with anti-IL-10 capture antibody (eBioscience) in coating buffer (ELISPOT, eBioscience), prior to washing with 0.05% Tween-20 buffer (Sigma Aldrich) in PBS (Sigma Aldrich). ELISA/ELISPOT diluent (eBioscience) was used to block wells and dilute a standard curve of IL-10 (Peprotech). Supernatants were incubated in the plate at room temperature for 2 hours prior to washing and addition of IL-10 capture antibody (eBioscience). Avidin-HRP (eBioscience) was added as per manufacturers instructions, followed by washing and addition of 1x TMB solution (BioLegend) and then TMB stop solution (Biolegend). Plates were read using a Tecan Infinite M200 Pro Plate reader.

2.4.4. Phagocytosis assay

MDMs were seeded as above into standard 12 well plates at 750 000 cells/well. On day 7 supernatants were removed and replaced with 1ml/well of fresh media. Negative wells were treated with 5µg/ml of Cytochalasin D (Sigma Aldrich) and incubated for 30 minutes prior to addition of 100µl of 1mg/ml pHrodo® Red E. Coli Bioparticles® Conjugate for Phagocytosis (Life Technologies) to all wells and incubation for 1 hour. Cells were washed in sterile PBS prior to removal by gentle scraping and fixation in 2% PFA. Flow cytometry was performed on an LSR II flow cytometer using FACSDiva 8 software. Further data analysis was performed using FlowJo software.

66 2.4.5. MDM treatment with neutralising antibodies or TLR ligands

After 7-day culture as described in 2.4.1 GM-CSF, GM-CSF plus IFN-γ, M- CSF, and M-CSF plus IL-4 cultured MDMs were treated for a further 24 hours with either Pam3CSK4, LPS or R848, all at 1ug/ml prior to analysis. For some experiments GM-CSF or M-CSF MDMs were cultured as above and standard mouse IgG at 40µg/ml, 1D11 at 40µg/ml or ADWA16 at 20µg/ml were added at day 7 prior to overnight culture and further analysis. In other experiments, as specified in the text GM-CSF or M-CSF MDMs were cultured as above and standard mouse IgG at 40µg/ml, 1D11 at 40µg/ml, ADWA16 at 20µg/ml, or TGF-β at 20ng/ml were added to the MDM cultures at day 0 and replenished alongside fresh GM-CSF or M-CSF and media at day 3, prior to further analysis at day 7.

2.5. Bioenergetic analysis of MDMs

2.5.1. MDM culture for bioenergetic analysis

In specified experiments MDMs were differentiated in 12 well plates as outlined in section 2.4.1. On day 6 cells were removed by gentle scraping and resuspended in RPMI plus L-glutamine with 10% foetal bovine serum and 1% penicillin/streptomycin. Cells were stained for viability with trypan blue (Sigma Aldrich). Cells were counted using a haemocytometer accounting for both total cells, and trypan blue negative viable cells, as detailed in the main text. Cells were transferred to XF96 V3 PS Cell Culture Microplates (Agilent, Santa Clara, US) at the numbers stated within the text and analysed on day 7.

In other experiments CD14+ bead separated monocytes were seeded directly into XF96 V3 microplates at the numbers stated within the text in 200µl/well of RPMI plus L-glutamine with 10% foetal calf serum and 1% penicillin/streptomycin. GM-CSF or M-CSF were added at 50ng/ml on day 0 and replenished alongside the media at day 3. Cells were analysed at day 7 unless otherwise stated.

67 2.5.2. MDM treatment for bioenergetic analysis

In stated experiments LPS or TGF-β was added to MDMs on day 7 at the concentrations stated in the text and cells were cultured overnight prior to bioenergetic analysis. For TGF-β comparison a vehicle control of a 5mM citric acid in 0.05% BSA, further diluted to 1 in 25 with PBS, was added to control MDMs for the same period. In other experiments LPS, TGF-β or citric acid vehicle control was added for a 4 hour period, as stated in the main text. Seahorse experiments were performed at either 5 or 6 technical replicates per sample and condition.

2.5.3. Bioenergetic analysis using the Seahorse XF96 analyser

The XFe96 FluxPak sensor module (Agilent) was hydrated by pipetting 200µl/well of XF Calibrant (Agilent) into the supplied base plate and gently replacing the sensor module prior to placing in a CO2 free incubator at 37°C overnight. On the day of the assay Seahorse media was prepared by adding 417mg of RPMI medium 1640 powder (ThermoFisher), 90.08mg of 2-Deoxy-D- glucose (Sigma Aldrich) and 5.5mg of sodium pyruvate (Sigma Aldrich) to 50ml of Milli-Q water (Merck, Kenilworth, US). The media was pH tested with an Omega PHH-7011 pH meter (Omega, Manchester, UK) and adjusted with either hydrochloric acid or sodium hydroxide to a pH of 7.35-7.45. The media was then sterile filtered. Old media was removed from the MDMs and replaced with 180µl/well of seahorse media. Cells were then incubated for 1 hour in a CO2 free incubator at 37°C. 10X stressor mix was made by dilution of Oligomycin (≥ 95% Oligomycin A, Sigma Aldrich) and Carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP, Sigma Aldrich) in Seahorse media to a final concentration of 20µM for each. 10X suppressor mix was made by diluting Rotenone (Sigma Aldrich) and Antimycin A (from Streptomyces Sp, Sigma Aldrich) in Seahorse media to a final concentration of 1µM Rotenone and 10µM Antimycin A. The XFe96 FluxPak sensor module was then placed within the XF96 Analyser (Agilent) for calibration as prompted, followed by MDMs in the XF96 V3 microplate. The Seahorse 2.4 Wave software was programmed to measure oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) via the XF96 Analyser at 3 timepoints prior to injection

68 of 20µl/well of the 10X stressor mix (yielding a final in-assay concentration of 2µM Oligomycin and 2µM FCCP). 4 timepoints were then measured prior to injection of 22µl/well of the 10X suppressor mix (yielding a final in-assay concentration of 100nM Rotenone and 1µM Antimycin A), followed by OCR and ECAR measurement at 5 further timepoints.

2.5.3. Protein quantification using the Bicinchoninic Acid (BCA) assay

After removing seahorse plate from analyser the media was removed and the plate was washed 3 times with 200µl/well PBS. Radioimmunoprecipitation assay (RIPA) buffer was made from 150 mM sodium chloride (Sigma Aldrich), 1% Triton X-100 (Sigma Aldrich), 0.5% sodium deoxycholate (Sigma Aldrich), 0.1% sodium dodecyl sulphate (SDS, Sigma Aldrich) and 50mM Tris, pH 8.0 (Sigma Aldrich) in PBS. After aspiration 30µl/well of RIPA buffer was added and the plate was placed on a shaker at 4°C for 30 minutes. An albumin standard curve with a top standard of 1000ug/ml was added in duplicate to spare wells of the plate prior to addition of 200ul BCA reagent/well (50 parts of BCA Reagent A with 1 part of BCA Reagent B from the Pierce™ BCA Protein Assay Kit, ThermoFisher Scientific). The plate was then incubated for 30 minutes on a heater block pre-heated to 60°C. Colorimetry analysis was then performed on a Tecan Infinite M200 Pro Plate reader.

2.5.4. Seahorse data analysis

Technical replicates for each experimental condition were graphed individually using xy plots of OCR/time and ECAR/time using Prism 7. Outlying replicates were then excluded and values for each timepoint were averaged (mean). For OCR, the first 3 timepoints (pre-stressor injection) were averaged, the next 4 timepoints (post-stressor injection) and the final 5 timepoints (post-suppressor injection) were averaged. The mean post-suppressor OCR value (indicating non-mitochondrial respiration) was subtracted from the mean pre-stressor OCR value to give the basal OCR value. The mean post-suppressor OCR value (indicating non-mitochondrial respiration) was subtracted from the mean post- stressor OCR value to give the maximal OCR value. For ECAR, the first 3 timepoints (pre-stressor injection) were averaged (mean) to give the basal

69 ECAR value, the next 4 timepoints (post-stressor injection but pre-suppressor injection) averaged to give the maximal ECAR value.

2.6. General data analysis

Data analysis was performed using Prism 7. Parametric distribution was not assumed. For paired samples with a single variable Wilcoxon’s sign- test was used to test for statistical significance; for paired samples with more than one variable Friedman’s Test with Dunn’s multiple comparisons was used to test for statistical significance; for unpaired samples with more than one variable the Kruskal-Wallis Test was used. Correlation coefficients were calculated by Spearman’s rank-order analysis. Graphical data is displayed as median and inter-quartile range unless otherwise stated in the text.

70

CHAPTER 3: INTEGRIN ΑVΒ8 MEDIATED TGF-Β ACTIVATION IN HUMAN MONOCYTES

3.1. Introduction

Research so far has suggested an important role for the TGF-β activating integrin αvβ8 in maintaining intestinal immune homeostasis, and the established expression of αvβ8 on human and murine intestinal DC subsets and Tregs has been discussed in detail above. However, little is known regarding the expression of integrin αvβ8 within the human immune system, including on other circulating leucocytes. Utilising an antibody specific for the β8 subunit we aimed to elucidate the expression pattern of integrin αvβ8 within circulating human leucocytes, determine the function of this expression and how it is regulated.

3.2. Results

3.2.1. Flow cytometry gating protocols to identify integrin αvβ8 on human PBMC

To determine surface protein expression of integrin αvβ8 on human leucocyte subsets we obtained fresh leucocyte cones or peripheral blood from healthy donors (via the NHS Blood and Transplant Service or healthy volunteers). PBMCs were separated from peripheral blood using a Ficoll gradient centrifugation (see 2.1.2), and analysed for surface marker expression using flow cytometry, utilising the staining protocol described in 2.1.3.

Samples were gated for single cells by FSC-A versus FSC-W, and then SSC-A against SSS-H. Non-viable cells were then excluded via a dead cell stain (Zombie-NIR). Lymphocytes were identified by first gating on their characteristic lower forward and side scatter, followed by gating of CD56+ NK cells, CD3+ T lymphocytes, and CD19+ B lymphocytes (figure 3.1a). Monocytes wer

71 a. Total cells Lymphocytes

Monocytes

Classical Intermediate B cells monocytes monocytes

Non-T non-B T cells Total Non-classical cells monocytes monocytes b. (polygonal gate)

β8-binding Isotype APC FMO antibody control

Figure 3.1: Gating strategy for identification of integrin αvβ8 on peripheral blood lymphocytes, monocytes and NK cells. a. Healthy human donor peripheral blood mononuclear cells (PBMCs) were isolated and stained with antibodies specific for viability, CD56, CD3, CD19, HLA-DR, CD16 and CD14 in addition to integrin β8-binding antibody (ADWA16). b. Representative flow plots of HLA-DR positive monocytes gated on APC positive cells from β8-binding antibody stained, isotype control stained and APC flow minus one (FMO) samples.

72 a.

b.

Figure 3.2: Gating strategy for identification of integrin αvβ8 on peripheral dendritic cells and monocytes. Healthy human donor PBMCs were isolated and stained with antibodies specific for viability, lineage (CD3, CD15, CD19, CD20, CD56), HLA-DR, CD11c, CD1c, CD141, CD16 and CD14, in addition to β8-binding antibody (ADWA16). Viable total cells were gated on lineage- HLA-DR+, CD14-CD16- and then CD11c+ to identify CD141+ and CD1c+ dendritic cell populations. b. Monocytes were identified first on forward and side scatter characteristics (FSC- A and SSC-A), then as HLA-DR+ lineage- and then into classical, intermediate and non- classical subsets by CD14 and CD16 expression. identified by gating on their higher forward and side scatter, then on HLA-DR+ cells, and finally, on CD14+ and/or CD16+ cells (figure 3.1a). Gating on CD14 and CD16 allows division into three subsets as described in the literature, each with distinct functions and associated surface marker profile: CD14hiCD16- ‘classical’ monocytes; CD14hiCD16+ ‘intermediate’ monocytes; and CD14loCD16+ ‘non-classical’ monocytes (figure 3.1a)(74).

73 As the β8 integrin subunit only pairs with αv, staining with an antibody directed to β8 allows identification of αvβ8. Thus, we used the β8-specific antibody ADWA16(247) for identification of αvβ8. An isotype antibody and a fluorescent- minus-one (FMO) control were used to confirm validity of the staining (figure 3.1b), with gates set against the isotype control.

Alternatively samples were stained with for lineage markers (CD3, CD15, CD19, CD20 and CD56), CD1c CD11c, CD14, CD16, HLA-DR and CD141 to identify dendritic cell populations. cDCs were identified by gating on lineage-negative, HLA-DR+ cells from the total cell gate (given the intermediate forward and side scatter characteristics of DCs), followed by gating on CD14-CD16- cells, and then on CD11c+ cells. These CD11c+ DCs were gated into CD141+ DCs and CD1c+ DCs (figure 3.2a). Finally, in this panel, monocytes subsets were identified by expression of CD14 and CD16 (figure 3.2b) as described above. Thus, the described strategies were then used to identify integrin αvβ8 expression on different human blood immune cell populations.

3.2.2. Expression of integrin αvβ8 on peripheral leucocyte populations

3.2.2.1. Expression of integrin αvβ8 on circulating cDCs

Described DC subsets in humans include pDCs and the CD141+ and CD1c+ cDC populations. As previously discussed, murine models have shown the functional importance of integrin αvβ8 on intestinal cDCs(244, 245). Given these findings, expression of αvβ8 on circulating human cDC subsets (figure 3.3a and 3.3b) was studied. Analysis of cDC expression of αvβ8 integrin, was performed both by percentage of total CD141+ or CD1c+ DC expressing the integrin (figure 3.3.c), and by median fluorescence intensity (MFI; figure 3.3d) of the integrin on CD141+ and CD1c+ DCs. Analysis by both of these metrics indicated that circulating CD1c+ DCs express αvβ8 more highly (figure 3.3c and d), although there was inter-donor variation and this difference did not reach statistical significance. Thus, these data suggest that αvβ8 integrin is expressed more highly on circulating CD1c+ DCs, consistent with their higher expression on intestinal CD1c+ DCs(247).

74 3.2.2.2. Expression of integrin αvβ8 on circulating lymphocytes

Integrin αvβ8 expression has been described in murine and human Tregs(256). In contrast, whilst NK cells have been shown to express multiple integrins expression of αvβ8 is not reported(261). Thus, to examine the expression of integrin αvβ8 at the protein level in human lymphocyte populations, the major human peripheral lymphocyte populations (CD3+ T cells, CD19+ B cells and CD56+ NK cells; figures 3.4a and 3.4b) were analysed by flow cytometry. Data a.

b.

b. β8-binding antibody Isotype control

CD11c+ DCs CD141+ DCs CD1c+ DCs

c. d.

20 200

150 15

100

10

APC MFI 50 8 expression β % 5 0

0 -50

CD1c+ DC CD1c+ DC CD141+ DCs CD141+ DCs Figure 3.3: Expression of integrin αvβ8 on peripheral dendritic cell subsets as determined by flow cytometry. Healthy human donor PBMCs were isolated and stained as

75 described in figures 3.1 and 3.2. a. Example flow plot demonstrating staining for CD11c, CD141 and CD1c. b. Example flow plot from a single donor demonstrating staining of integrin αvβ8 versus isotype control on total CD11c+ DCs, CD141+ and CD1c+ DCs. c. Expression was quantified as percentage of β8-binding antibody positive cells within the parent gate and d. median fluorescence intensity (MFI) of specified population. Samples from 6 donors in 3 independent experiments. Graphs show median plus interquartile range. Wilcoxon signed-rank test performed. All comparisons non-significant.

a.

b.

CD3+ CD19+ CD56+ c. d.

20 150

100

15

50

10 0 APC MFI 8 expression β -50 %

5

-100

0 -150

CD3+ CD3+ CD19+ CD56+ CD19+ CD56+ Figure 3.4: Expression of integrin αvβ8 on peripheral lymphocyte subsets and NK cells as determined by flow cytometry. Healthy human donor PBMCs were isolated and stained as described in figures 3.1 and 3.2. a. Example flow plot demonstrating staining for CD3, CD19 and CD56. b. Example flow plot from a single donor demonstrating staining of integrin αvβ8 versus isotype control on CD3+ lymphocytes, CD19+ lymphocytes and CD56+ NK cells. c. Expression was quantified as percentage of β8-binding antibody positive cells within the parent

76 gate and d. MFI of specified population. Graphs show median plus interquartile range. Samples from 13 donors in 8 independent experiments. demonstrated an overall very low surface expression of αvβ8 on T-, B- and NK cells, at both percentage of total CD3+, CD19+ or CD56+ lymphocytes expressing the integrin (figure 3.4c) and by the level of expression by MFI of each lymphocyte population analysed (figure 3.4d). These data show that overall levels of integrin β8 expression on human circulating T- and B- lymphocyte and NK-cells in homeostasis are minimal, comparative to that seen in CD1c+ DCs.

3.2.2.3. Expression of integrin αvβ8 on circulating monocytes

Having found modest expression of integrin αvβ8 on CD1c+ cDCs and very low levels on circulating lymphocytes, peripheral monocyte subsets were next investigated for expression of αvβ8 integrin. Somewhat unexpectedly, given the lack of β8 expression detected on monocytes by proteomics analysis(262), flow cytometry data demonstrated significantly higher expression of β8 by both percentage β8 positive cells (figure 3.5a) and levels of expression (figure 3.5b) compared to all other major circulating leucocyte populations except CD1c+ DCs.

Given the distinct phenotypic properties of each monocyte subset we analysed integrin β8 on individual subsets (figure 3.6a and 3.6b), which revealed high expression of αvβ8 on classical, intermediate and non-classical monocytes. However, expression of αvβ8 was significantly higher on the intermediate monocyte subset both by percentage of monocytes expressing the integrin (figure 3.6c) and levels of expression (figure 3.6d). In summary, in contrast to other circulating leucocytes circulating monocytes appear to express high levels of αvβ8.

3.2.3. Validation of results utilising other integrin β8 staining strategies

Given the finding of significantly higher αvβ8 expression on human peripheral blood monocytes in comparison to other leucocyte populations it was important

77 **** a. *** ** **** n.s. 100

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Figure 3.5: Expression of integrin αvβ8 on peripheral leucocyte subsets as determined by flow cytometry. Healthy human donor PBMCs were isolated and stained as described in figures 3.1 and 3.2. Expression was quantified as a, percentage of β8-binding antibody positive cells within the parent gate and b, MFI of specified population. Samples from 19 donors in 11

78 independent experiments. Graphs show median plus interquartile range. Kruskal-Wallis performed: ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001.

a. Classical Intermediate Total monocytes monocytes monocytes

Non-classical monocytes

b. β8-binding antibody Isotype control

Classical Intermediate Non-classical monocytes monocytes monocytes

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**** ****

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CD14+CD16- CD14+CD16- CD14hiCD16+ CD14loCD16+ CD14hiCD16+ CD14loCD16+ Figure 3.6: Expression of integrin αvβ8 on peripheral monocyte subsets as determined by flow cytometry. a. Healthy human donor PBMCs were isolated and stained as described in figures 3.1 and 3.2. Monocyte subsets were differentiated by CD14 and CD16 expression as CD14+CD16- ‘classical’, CD14hiCD16+ ‘intermediate’, and CD14loCD16+ ‘non-classical’ monocytes. b. Example flow plot from a single donor demonstrating staining of integrin αvβ8 versus isotype control on CD14+CD16-, CD14hiCD16+ and CD14loCD16+ monocytes. c. Expression was quantified as percentage of β8-binding antibody positive cells within the parent

79 gate and d. MFI of specified population. Samples from 19 donors in 11 independent experiments. Graphs show median plus interquartile range. Friedman with Dunn’s multiple comparisons performed. **** p ≤ 0.0001 to confirm that this was not an artefact due to either binding of the ADWA16 antibody clone to a non-αvβ8 monocyte specific surface protein, or due to non- specific binding mediated via Fc receptors (FcRs).

3.2.3.1. Staining with other integrin β8 specific antibody clones

To further investigate integrin β8 expression on monocyte subsets, we conjugated a number of different β8-specific antibody clones to either PE or APC and stained PBMCs from healthy human donors for flow cytometry analysis(247, 263). Co-staining of PBMCs with the β8-binding antibodies ADWA16-APC and ADWA11-PE revealed that the majority of monocytes were positive for both antibodies, whereas lymphocytes were predominantly negative. Single staining with both ADWA11 and ADWA16 revealed the same pattern of high monocyte and low lymphocyte staining (figure 3.7a). Furthermore, staining with a wider variety of antibodies showed a replicable pattern of staining amongst leucocyte populations, with monocytes consistently expressing the highest levels of αvβ8 (figure 3.7b). Comparison of multiple donors indicate that monocytes expressed higher levels of β8 as determined by staining with ADWA7-PE and ADWA21-PE, than lymphocytes which demonstrated levels of staining for the β8 subunit comparable with isotype and FMO samples. This was seen by both percentage β8 positive of parent population (figure 3.7c) although there were insufficient samples to reach significance due to wide inter-individual variability (as observed in figures 3.5a and b).

3.2.3.2. Utilisation of alternative Fc Receptor blocking strategies

Non-specific staining due to antibody binding to Fc receptors (FcRs) has been reported in human monocytes, and multiple FcR blocking strategies using both human and mouse serum, human and mouse IgG and commercially available human FcR blocking agents have been described(264). In order to investigate whether the higher staining for αvβ8 seen in monocytes is due to non-specific

80 FcR mediated binding PBMCs were left without FcR block, blocked with 2% mouse serum,10% mouse serum, or with Human TruStain FcXTM block, and then stained with ADWA7-PE (figure 3.8a), ADWA16-PE (figure 3.8b) a.

Total monocytes

Total lymphocytes

ADWA16 APC/ ADWA11 PE ADWA16 APC ADWA 11 PE

b. c.

3000 ADWA 7 100 ADWA 10 D1 ADWA 7 ADWA 11 D1 ADWA 21 ADWA 21 80 D1 PE Iso 2000 D1 PE FMO PE iso PE FMO 60

1000 PE MFI

8 expression 40 β

0 %

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-1000 0 Total cells

T lymphocytes B lymphocytes

Classical monocytes Monocytes Monocytes Monocytes Monocytes Intermediate monocytesNon classical monocytes Lymphocytes Lymphocytes Lymphocytes Lymphocytes Figure 3.7: Expression of integrin αvβ8 on peripheral leucocyte subsets as determined by flow cytometry using different β8 specific antibodies. a. Healthy human donor PBMCs were isolated and stained as described in figure 3.1. Representative flow plots of total monocytes and total lymphocytes stained with the β8-binding antibodies ADWA16 APC or ADWA11 PE, or co-stained with ADWA16 APC and ADWA11 PE. b. MFI of PE staining on different cell types in PBMC samples from a single donor stained with different β8 specific antibodies. c. PE positive total lymphocytes and total monocytes as a percentage of parent gate from PBMCs stained with the β8 specific antibodies ADWA7 and ADWA21, PE IgG1 or PE FMO. PBMCs from 3 donors in 2 independent experiments. Graphs show median value.

81 or ADWA21-PE (figure 3.8c). Interestingly blocking with TruStain FcXTM yielded similar results to no block, whereas staining was attenuated with 10% mouse serum; 2% mouse serum giving intermediate results. However, the pattern of increased staining for αvβ8 on monocyte populations was maintained with all blocking strategies. It was therefore decided to proceed with 2% mouse serum FcR block for future flow cytometry staining. a b 100 ADWA 7 FcX 100 ADWA 16 FcX . ADWA 7 NB . ADWA 16 NB ADWA 7 2% MS ADWA 16 2% MS 80 ADWA 7 10% MS 80 ADWA 16 10% MS PE FMO PE FMO PE iso PE iso 60 60

40 40 8 expression 8 expression β β % %

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60 PE iso

40 8 expression β %

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Total Classical Intermediate lymphocytes monocytes Non-classical monocytes monocytes Figure 3.8: Expression of integrin αvβ8 on peripheral leucocyte subsets as determined by flow cytometry using different Fc receptor blocking strategies. PBMCs were isolated from a healthy donor and then treated with either 2% mouse serum (2% MS), 10% mouse serum (10% MS), FcX Human FcR block (FcX) or no block (NB) prior to staining with the β8- binding antibodies a. ADWA7-PE. b. ADWA16-PE. c. ADWA21-PE, with isotype (iso) and FMO controls.

3.2.4. qPCR analysis of PBMC ITGB8 expression

To determine whether high expression levels of integrin αvβ8 observed at the protein level by flow cytometry were also observed at the level we used qPCR to compare the expression of the ITGB8 gene on CD14+ bead separated monocytes versus unseparated PBMCs. As monocytes comprise

82 only 10-20%(256) of circulating peripheral blood leucocytes, the CD14+ enriched fraction might be presumed to express higher levels of ITGB8 in line with their significantly higher surface expression. Surprisingly, in

a 4 .

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ITGB8 ΔCT (PCR)

Figure 3.9: Relative expression of integrin αvβ8 on CD14+ bead separated monocytes and peripheral blood mononuclear cells. RNA was isolated from healthy human donor PBMCs and CD14+ magnetic bead separated monocytes. PCR for the ITGB8 gene encoding the human integrin β8 subunit was performed. a. Expression of ITGB8 on CD14+ monocytes

83 relative to unseparated PBMCs, samples normalised to B2M (encoding β2-microglobulin). Graph shows median plus interquartile range. b. Integrin β8 protein expression as determined via percentage positive CD14+ monocytes via flow cytometry versus same sample gene expression of ITGB8 on PCR, expressed as ΔCT value. the unstimulated state CD14+ monocytes expressed lower levels of the ITGB8 gene compared to whole PBMCs (figure 3.9a). Furthermore there was no inverse correlation between ITGB8 expression (expressed as ΔCT value) on CD14+ monocytes, and αvβ8 protein expression (expressed as percentage αvβ8 positive cells within the parent population; figure 3.9b). Therefore this data indicates that in human blood monocytes there is no clear correlation between gene and protein expression of integrin β8.

3.2.5. Effects of cytokine treatment on PBMC integrin αvβ8 expression

Given previous work suggesting that β8 expression can be regulated by pro- inflammatory signals in other cell types(246, 247) we tested whether various cytokines regulated expression of integrin β8 in human immune cells. PBMCs were treated overnight with TGF-β, IL-10, IL-1β or TNF-α, and expression was analysed by flow cytometry compared to untreated cells. Overall, expression levels remained low on T lymphocytes, by both percentage αvβ8 positive T lymphocytes (figure 3.10a) and MFI (figure 3.10b); on B lymphocytes, by both percentage αvβ8 positive B lymphocytes (figure 3.10c) and MFI (figure 3.10d); and on NK cells, by both percentage αvβ8 positive NK cells (figure 3.10e) and MFI (figure 3.10f) irrespective of cytokine treatment. Thus, these results indicate that β8 expression on lymphocytes is consistently low irrespective of cytokine environment.

Next, cytokine modulation of integrin αvβ8 expression on monocytes was investigated. IL-10 treatment of PBMCs resulted in a significant increase in αvβ8 expression in the gated monocyte population, both by percentage αvβ8 positive total monocytes (figure 3.11a) and MFI (figure 3.11b), whereas no other treatments caused a significant change in αvβ8 expression (figure 3.11a and b). To examine whether induction of integrin αvβ8 by IL-10 was due to a direct effect upon monocytes, or whether it was mediated indirectly via effects of IL-10

84 on other PBMC populations and resultant monocyte interactions, CD14+ bead separated monocytes were cultured in isolation with the same cytokine treatments. On flow cytometry analysis IL-10 appeared to increase

CD3+ T- lymphocytes a. b.

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0 0 β β α β β α TGF IL-10 IL-1 TNF TGF IL-10 IL-1 TNF Untreated Untreated Figure 3.10: Expression of integrin αvβ8 on peripheral lymphocytes after cytokine treatment. Healthy human donor PBMCs were isolated and cultured overnight untreated or with TGF-β, IL-10, IL-1β or TNF-α prior to analysis by flow cytometry. T lymphocyte, B lymphocytes

85 and NK cells were gated as described in figure 3.1. Expression on T lymphocytes was quantified as a. percentage of β8-binding antibody positive cells within the T lymphocyte gate b. MFI of gated T lymphocytes; on B lymphocytes as c. percentage of β8-binding antibody positive cells within the B lymphocyte gate d. MFI of gated B lymphocytes; and on NK cells as e. percentage of β8-binding antibody positive cells within the NK cell gate f. MFI of gated NK cells. 6 donors in 3 independent experiments. Graphs show median plus interquartile range Friedman with Dunn’s multiple comparisons performed; all comparisons non-significant.

a. * b. *

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0 -2000 β β α β β α TGF IL-10 IL-1 TNF TGF IL-10 IL-1 TNF Untreated Untreated Figure 3.11: Expression of integrin αvβ8 on peripheral monocytes after cytokine treatment. Healthy human donor PBMCs were isolated and cultured overnight untreated or with TGF-β, IL-10, IL-1β or TNF-α prior to analysis by flow cytometry. Total monocytes were gated as described in figures 3.1 and 3.2. αvβ8 expression on total monocyte was quantified as a. percentage of β8-binding antibody positive cells within the monocyte gate b. MFI of gated monocytes. 11 donors in 4 independent experiments. Kruskal-Wallis performed: * p ≤0.05. CD14+ monocytes were magnetic bead separated from PBMCs and cultured in isolation with or without cytokines as figures a and b. αvβ8 expression from bead separated monocytes and

86 gated total monocytes from paired whole PBMCs is expressed as c. percentage of β8-binding antibody positive cells within the total monocyte gate d. MFI of gated total monocytes. 4 donors in 2 independent experiments. Graphs show median plus interquartile range. Multiple Wilcoxon’s tests performed; all comparisons non-significant.

αvβ8 expression on isolated monocytes, although this did not reach statistical significance (figure 3.11c and 3.11d). On comparison of gated total monocytes from whole PBMC cultures, and CD14+ monocytes cultured in isolation from the same donors, there was a similar pattern of expression upon cytokine treatment. Furthermore, there were no statistically significant differences in αvβ8 expression, either as percentage αvβ8 positive cells within the parent population (figure 3.11c), or MFI (figure 3.11d), between gated monocytes from PBMCs, and isolated monocytes upon cytokine treatment. Thus, these data suggest that upregulation of integrin αvβ8 is due to a direct effect of IL-10 on blood monocytes.

Given the significantly higher levels of αvβ8 at baseline on the intermediate monocytes, and the functional differences described between monocyte subsets(74) we examined expression of αvβ8 on different subsets of monocyte cultured within whole PBMCs and treated with cytokines for 4 hours. IL-10 increased αvβ8 levels on all three monocyte subsets by both percentage of αvβ8+ cells (figure 3.12a, c and e), and MFI (figure 3.12b, d and f), similar to results obtained with total monocytes. Intriguingly IL-1β increased αvβ8 levels on the non-classical monocyte subset, but not the classical or intermediate population (Figure 3.12e and f). No effects on integrin αvβ8 expression were observed in any of the monocyte subsets in the presence of TGF-β or TNF-α (Figure 3.12a-f). Thus, these results indicate that IL-10 can upregulate integrin αvβ8 expression in all monocyte subsets, whereas IL-1β causes a subset- specific upregulation of the integrin in non-classical monocytes.

87 Classical monocytes

a. * b.

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% 20

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% 20 500

0 0 β β α β β α TGF IL-10 IL-1 TNF TGF IL-10 IL-1 TNF Untreated Untreated Figure 3.12: Expression of integrin αvβ8 on peripheral monocyte subsets after cytokine treatment. Healthy human donor PBMCs were isolated and cultured for 4 hours untreated or with TGF-β, IL-10, IL-1β or TNF-α prior to analysis by flow cytometry. Monocytes subsets were gated as described in figure 3.1. αvβ8 expression on CD14+CD16- ‘classical’ monocytes was

88 quantified as a. percentage of β8-binding antibody positive cells within the CD14+CD16- monocyte gate b. MFI of gated CD14+CD16- monocytes. αvβ8 expression on CD14hiCD16+ ‘intermediate’ monocytes was quantified as c. percentage of β8-binding antibody positive cells within the CD14hiCD16+ monocyte gate d. MFI of gated CD14hiCD16+ monocytes. αvβ8 expression on CD14loCD16+ ‘non-classical’ monocytes was quantified as e. percentage of β8- binding antibody positive cells within the CD14loCD16+ monocyte gate b. MFI of gated CD14loCD16+ monocytes. 4 donors in 2 independent experiments. Graphs show median plus interquartile range. Friedman with Dunn’s multiple comparisons performed; * p ≤ 0.05, ** p ≤ 0.01.

3.2.6. Effects of TLR ligand treatment on PBMC integrin αvβ8 expression

TLR ligands (TLRLs) have also been shown to be important factors influencing αvβ8 expression on DCs(246, 247). Thus, we examined the effect of a range of TLRLs on expression levels in human blood monocytes. When PBMCs a. b. * * 100 3000

80

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β 1000 %

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Untx Untx TLR 4L TLR 4L TLR 1/2L TLR 7/8L TLR 1/2L TLR 7/8L Figure 3.13: Expression of integrin αvβ8 on peripheral monocytes after TLR ligand treatment. Healthy human donor PBMCs were isolated and cultured overnight untreated or with TLR1/2 ligand (Pam3CSK4), TLR 4 ligand (LPS) or TLR 7/8 ligand (R848) prior to analysis by flow cytometry. Total monocytes were gated as described in figure 3.1. Expression on total monocytes was quantified as a. percentage of β8-binding antibody positive cells within the total monocyte gate b. MFI of gated total monocytes. 3 donors in 2 independent experiments. Graphs show median and interquartile range. Friedman with Dunn’s multiple comparisons performed; * p ≤ 0.05. were treated with the TLR4L LPS, a significant increase in αvβ8 expression was detected on monocytes, both at the level of percentage αvβ8+ cells (figure 3.13a) and MFI (figure 3.13b). No difference in integrin αvβ8 expression was

89 observed by stimulation with ligands to TLR1/2, with a trend of an increase in expression after treatment with TLR7/8L (figure 3.13a and b).

To assess if this observed increase in monocyte αvβ8 expression was due to a direct effect of TLR4L on monocytes, or mediated via another leucocyte population, we cultured isolated monocytes overnight with the same TLRLs. TLR4L significantly increased the expression (% cells and MFI) of αvβ8 (figure 3.14a and b), which was not significantly different from expression on monocytes gated from PBMCs from the same donors (figure 3.14c and 3.14d), in support of a direct role of TLR4L engagement by monocytes in promoting αvβ8 surface expression. Although treatment of total PMBCs with TLR7/8L caused a trend of an increase in integrin αvβ8 expression levels in gated monocytes (figure 3.13a and b), this increase became significant upon treatment of isolated monocytes (figure 3.14a and b). Thus, these results indicate that selected TLR ligands upregulate integrin αvβ8 expression by direct action on circulating human blood monocytes.

3.2.7. Integrin αvβ8-dependent TGF-β activation by monocytes

As previously discussed, one of the key functions of integrin αvβ8 is activation of the cytokine TGF-β. Having identified that circulating human monocytes express high levels of integrin αvβ8, and that this expression is further increased by the cytokine IL-10 and the TLR-4 ligand LPS, it was important to determine if monocytes are able to activate TGF-β in an αvβ8-dependant manner. To this end, we co-cultured CD14+ bead separated monocytes overnight with an active TGF-β reporter cell line that expresses luciferase under the control of the TGF-β responsive plasminogen activation inhibitor-1 (PAI-1) promoter(265). Control mouse IgG, the TGF-β -1, -2 and -3 blocking antibody 1D11, or the αvβ8 blocking antibody ADWA16 were added to cultures to determine levels of integrin αvβ8-dependent TGF-β activation. Utilisation of this TGF-β activation assay indicated that human CD14+ monocytes were able to activate TGF-β; furthermore, this signal appeared to be abrogated by addition of an αvβ8 blocking antibody (figure 3.15a), suggesting that TGF-β activation is an αvβ8-dependent process in human CD14+ blood monocytes. On addition of

90 a. * b. * 100 *** 3000 **

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Figure 3.14: Expression of integrin αvβ8 on CD14+ bead separated monocytes after TLR ligand treatment. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured in isolation overnight untreated or with TLR1/2 ligand (Pam3CSK4), TLR 4 ligand (LPS) or TLR 7/8 ligand (R848) prior to analysis by flow cytometry. Expression of integrin αvβ8 on bead-separated CD14+ monocytes was quantified as a. percentage of β8-binding antibody positive cells within the total monocyte gate b. MFI of gated total monocytes. 7 donors in 5 independent experiments. Friedman with Dunn’s multiple comparisons performed; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001. Expression of αvβ8 on TLR ligand treated CD14+ bead-separated monocytes cultured in isolation was compared with gated monocytes (as figure 1) from TLR ligand treated whole PBMCs expressed as c. percentage of β8-binding antibody positive cells within the total monocyte, or d. MFI of gated total monocyte. Monocytes cultured in isolation and in whole PBMCs were compared for each condition using multiple Wilcoxon tests; all analyses non-significant. Graphs show median and interquartile range.

91 a. 25

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Figure 3.15: Activation of TGF-β by human peripheral blood monocytes. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and then cultured overnight with the TGF-β reporter cell line, in the presence of IgG control antibody, anti- TGF-β antibody (1D11) or anti-integrin αvβ8 antibody (ADWA16) prior to lysis and measurement of luminescence. Relative luminescence units were converted to active TGF-β through use of a standard curve obtained by addition of known concentrations of active TGF-β to the reporter cell line. Baseline luminescence seen after addition of anti-TGF-β antibody was subtracted. Cell co-cultured were performed a. without lipopolysaccharide or b. with

92 lipopolysaccharide at a concentration of 1µg/ml. 2 donors in 2 independent experiments. Graphs show median with range.

30

20 pg/ml (-1D11) β 10 TGF

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8 blocking 8 blocking 8 blocking IgG controlβ IgG controlβ IgG controlβ v v v α antibody α antibody α antibody

Untx IL10 TNFα

Figure 3.16: Activation of TGF-β by human peripheral blood monocytes in the presence of IL-10 and TNF-α. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and then cultured overnight with the TGF-β reporter cell line with or without the cytokines IL-10 or TNF-α. Replicates for each condition were cultured in the presence of IgG control antibody, anti-TGF-β antibody or anti-integrin αvβ8 antibody) prior to lysis and measurement of luminescence. Relative luminescence units were converted to active TGF-β through use of a standard curve obtained by addition of known concentrations of active TGF-β to the reporter cell line. Baseline luminescence seen after addition of anti-TGF-β antibody was subtracted. 2 donors in 2 independent experiments. Graphs show median with range.

93 LPS to the assay this effect was no longer seen (figure 3.15b), which is surprising given the finding of αvβ8 upregulation on monocytes in the presence of LPS.

Given the significant increase seen on monocyte αvβ8 expression after treatment with IL-10, the assay was repeated with the above conditions in the presence of IL-10. In the presence of IL-10, monocyte activation of TGF-β appeared markedly reduced (figure 3.16), despite a paradoxical increase in overall monocytes αvβ8 expression upon IL-10 treatment seen above (figure 3.11-3.12). Given this finding in the presence of an anti-inflammatory cytokine, a pro-inflammatory cytokine (TNF-α) was added to see if an opposite effect would be observed. However, no effect was observed on the ability of monocytes to activate TGF-β via αvβ8 upon addition of TNF-α (figure 3.16). Thus, these data may suggest that whilst human blood monocytes are able to activate TGF-β in an αvβ8-dependent manner, factors which increase expression of αvβ8 on monocytes, namely LPS and IL-10, do not appear to confer an enhanced ability to activate TGF-β upon these cells.

3.2.8. Alterations in CD16 expression in TGF-β, TGF-β blocking and integrin αvβ8 blocking antibody-treated monocytes

Having demonstrated that human blood monocytes both express high levels of integrin αvβ8 and appear to activate TGF-β in an αvβ8-dependent manner, we then wished to examine the functional consequences of this TGF-β activation in regulating monocyte phenotype. As previously discussed, human peripheral monocytes can be further divided into functionally distinct subsets, with an expanded pro-inflammatory CD14hiCD16+ ‘intermediate’ monocyte subset seen in the peripheral circulation in association with multiple auto- immune inflammatory conditions including IBD. Factors known to induce expression of CD16 on CD14+CD16- ‘classical’ monocytes include M-CSF, IL-10 and TGF- β(266). To interrogate if this observed increase in CD16 expression on TGF-β- treated monocytes required integrin αvβ8-mediated TGF-β activation, bead separated CD14+ monocytes were cultured overnight either alone, with exogenous TGF-β, with TGF-β blocking antibody, or with αvβ8 blocking

94 antibody. Whilst TGF-β did significantly increase overall CD16+ expression on cultured CD14+ monocytes by both percentage CD16+ monocytes (figure 3.17a), and MFI (figure 3.17b), treatment with TGF-β or αvβ8 antibody in the unstimulated state did not alter CD16+ on isolated CD14+ monocytes compared to untreated cells by either parameter (figure 3.17a and b). To ensure the lack of alteration in CD16+ seen the above experiments was not due to a suboptimal concentration of αvβ8 blocking antibody, the experiment was repeated, comparing untreated CD14+ monocytes, monocytes treated with αvβ8 blocking antibody at 20µg/ml, and those treated with αvβ8 blocking antibody at 40µg/ml. Again there was no significant difference between the CD16 expression of untreated monocytes and those treated with either concentration of blocking antibody by percentage CD16+ monocytes (figure 3.17c), or MFI (figure 3.17d).

Finally given the aforementioned expansion of CD14hiCD16+ ‘intermediate’ monocyte subset in chronic inflammatory conditions, the effect of LPS, both alone and in concert with TGF-β, TGF-β blocking antibody, or with αvβ8 blocking antibody, on CD16+ expression was also assessed. CD14+ monocytes were treated overnight with a range of LPS doses. Treatment with the higher doses (100ng/ml and 1000ng/ml) of LPS resulted in a significant reduction in the expression of CD16+ by percentage of CD16+ monocytes (figure 3.18a) but not by MFI (figure 3.18b). However, addition of TGF-β, TGF-β blocking antibody, or αvβ8 blocking antibody alongside LPS did not further alter CD16+ expression by either parameter (figure 3.18c and d).

Thus, together, data indicate that integrin αvβ8 is highly expressed on human peripheral blood monocytes, and at a lower level on circulating CD1c+ DCs, but expression is minimal on other circulating leucocyte populations. Furthermore, this αvβ8 expression on blood monocytes appears to result in TGF-β activation. IL-10, TLR-4 and TLR-7/8 ligands were shown to upregulate αvβ8 expression yet this does not appear to translate into enhanced TGF-β activation; conversely there appeared to be a trend toward suppression of αvβ8-dependent TGF-β activation with addition of LPS or IL-10. Finally, although addition of exogenous TGF-β to monocyte cultures has been shown to induce CD16

95 expression(84), αvβ8 mediated TGF-β activation does not appear to play a role in this process. a. b.

* * 100 6000

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0 0 8 8 8 8 β β β β v v v v Untx α α Untx α α 20 40 20 40 anti- anti- anti- anti-

Figure 3.17: Expression of CD16 on peripheral monocytes after TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured in isolation overnight untreated or with TGF- β, anti-TGF-β antibody or anti-αvβ8 antibody. Expression of CD16 was characterised by a. percentage CD16 positive of total monocytes and b. MFI of gated monocytes. 8 donors in 5 independent experiments. CD14+ monocytes were cultured overnight untreated, with 20µg/ml or with 40µg/ml anti-αvβ8 antibody. Expression of CD16 was characterised by c. percentage CD16 positive of total monocytes and d. MFI of gated monocytes. 4 donors in 2 independent experiments. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. * p ≤ 0.05

96 a. b. * 100 800 *

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0 0 β 8 β 8 β β v v LPS LPS LPS LPS α LPS LPS LPS LPS α TGFB TGFB anti- TGF anti- anti- TGF anti- antibody antibody antibody antibody

Figure 3.18: Expression of CD16 on lipopolysaccharide treated peripheral monocytes with or without TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured in isolation overnight untreated or with 10ng/ml, 100ng/ml or 1000ng/ml lipopolysaccharide. Expression of CD16 was characterised by a. percentage CD16 positive of total monocytes and b. MFI of gated monocytes. 4 donors in 2 independent experiments. CD14+ monocytes were cultured overnight with 100ng/ml LPS alone or with TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. Expression of CD16 was characterised by c. percentage CD16 positive of total monocytes and d. MFI of gated monocytes. 9 donors in 5 independent experiments. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. * p ≤ 0.05.

97 3.3. Discussion

The αv integrins have been shown to have a key role in the suppression of widespread autoimmunity through their activation of TGF-β, and amongst these integrins αvβ8 plays a principal role. In humans αvβ8 expression has been observed on intestinal dendritic cells(247) and on Tregs(256), where it appears to be necessary for their immunosuppressive properties(257), yet little is known regarding its expression on other immune cell types. Through flow cytometry analysis we demonstrate that integrin αvβ8 is highly expressed on circulating human monocytes, highest on the intermediate subset, and that human blood monocytes activate TGF-β in an αvβ8-dependent manner. Interestingly both ‘anti-inflammatory’ (IL-10) and ‘pro-inflammatory’ (LPS) cues are able to increase αvβ8 expression on monocytes, although this did not appear to translate into an enhanced ability to activate TGF-β.

Regarding the lack of correlation between monocyte surface expression of integrin αvβ8 as determined by flow cytometry, and expression of the ITGB8 gene, it is true that comparison of CD14+ PBMCs with matched donor PBMCs is less accurate than gene expression analysis of cytometer sorted cell populations. Although circulating monocytes represent 10-20% of the PBMC population(266), so enrichment to over 94% could be reasonably presumed to be sufficient to demonstrate a change in ITGB8 expression relative to un- enriched PBMCs, gene expression analysis of sorted subsets remains an important avenue of future investigation. The interrelationship between integrin subunit gene transcription and cell surface integrin heterodimer expression is complex: α and β subunits appear to be synthesised at different rates, certain integrins, such as αv and β1 appear to be synthesised in excess, and the rate of production of integrin subunits may be influenced by the relative abundance of their binding partner. Precursor forms of integrin subunits appear to be retained within the cell, possibly within the endoplasmic reticulum, and at least some precursors are degraded rather than proceeding to further processing required for maturation(267, 268). Furthermore, the process of endocytosis and recycling of mature integrin heterodimers has been well described(269). Therefore, recycling of transcribed integrin precursors, presence of an intracellular

98 reservoir of precursor integrins, and recycling of the mature integrin are all potential explanations for the lack of correlation between ITGB8 and β8 protein expression in the steady state. Interestingly preliminary data indicated an increase in ITGB8 gene expression in unsorted human PBMCs upon treatment with TLR 4 and 8 ligands (data not shown), which requires further corroboration. In light of the lack of correlation between human integrin β8 gene and protein expression in unstimulated cells it should be noted that outside of xenogeneic models all murine data regarding β8 expression has been based on itgb8 gene expression due to lack of a reliable β8-binding antibody for flow cytometry.

As described above, circulating monocytes can be divided further into three subsets. Classical CD14hiCD16- monocytes comprise around 90% of circulating monocytes(72), are effective phagocytes and produce high amounts of ROS; intermediate CD14hiCD16+ monocytes produce high amounts of pro- inflammatory cytokines upon TLR stimulation; non-classical CD14loCD16+ monocytes appear to have unique properties in patrolling the vascular endothelium and viral immune response(75, 270). Whilst the exact ontogeny of these subsets is unknown, there is evidence that is highly suggestive of a developmental relationship whereby CD14loCD16+ non-classical monocytes are a more mature phenotype derived from the classical CD14hiCD16- population, with the CD14hiCD16+ representing an intermediate developmental step(70, 74). The intermediate monocyte population is expanded in multiple inflammatory conditions including Crohn’s disease, rheumatoid arthritis and major trauma(76). The functional consequences of this expanded intermediate population and whether they are a cause or consequence of these inflammatory disorders is unclear, although it has been shown that they are highly effective in inducing Th17 expansion in vitro(77). In a study of trauma patients, increased circulating TGF-β was observed in tandem with this intermediate monocyte expansion, and shown to have an essential role in promoting this upregulation of CD16+ on monocytes from healthy donors when incubated with trauma patient serum(271). TGF-β has been shown to promote CD16+ expression in blood monocytes (84), as seen in our hands. It is therefore intriguing that the intermediate monocyte subset expressed the highest levels of the TGF-β activating integrin αvβ8 leading to questions as to whether this is functionally

99 significant by inducing CD16+ expression. However, our data suggests that CD14+ monocyte expression of integrin αvβ8 does not appear to play an important role in modulating monocyte CD16+ expression, and one possible explanation for this may be a discrepancy between αvβ8 expression and TGF-β activation in monocyte subsets.

Whilst results presented here demonstrating integrin αvβ8-dependent TGF-β activation were obtained utilising CD14+ magnetic bead selection, and therefore encompass both classical and intermediate monocytes but not the CD14lo non- classical population, more extensive data utilising sorted monocyte subsets showed that CD14hiCD16- classical monocytes activated the highest levels of TGF-β and CD14loCD16+ non-classical monocytes the lowest, with intermediate levels of activation by the CD14hiCD16+ population(272). As these data did not accord with the pattern of surface αvβ8 expression on the monocyte populations, other factors were investigated that would influence TGF-β activation. αvβ8 has been shown to co-localise with MMP-14, as described in the introduction; MMP-14 localisation with αvβ8 and subsequent LAP cleavage is thought to be the mechanism of αvβ8-mediated TGF-β expression. It was confirmed that classical monocytes express the highest levels of MMP-14 and non-classical the lowest, corresponding with their relative abilities to activate TGF-β. Furthermore, addition of MMP-14 blocking antibody to the reporter cell co-culture inhibited the TGF-β-activating ability of classical monocytes(272).

The observed trend toward increased expression of αvβ8 on circulating CD1c+ DCs compared to CD141+ DCs is particularly intriguing given the proposed functional homology between human intestinal CD103+ SIRPα- DCs and blood CD141+ DCs, and human intestinal CD103+SIRPα+ DCs and blood CD1c+ DCs(135). CD103+SIRPα+ DCs appear to be specialised in promoting Treg differentiation(135); as this is a TGF-β dependant process, the finding of increased αvβ8 on the homologous blood subset would suggest a role for αvβ8 mediated TGF-β activation on these cells. Indeed isolation of human intestinal CD1c+ DCs demonstrated that these cells express higher levels of αvβ8, which corresponded to increased expression in both CD103+SIRPα+ and CD103-

100 SIRPα+ DCs(247). Less certain is the parallel between expression of αvβ8 on human CD1c+ and the established role of αvβ8 mediated TGF-β activation in promoting Treg differentiation on murine CD103+ DCs(244). CD103+ intestinal DCs in mice are a heterologous population containing the CD103+CD11b+ and CD103+CD11b- DC populations believed to be analogous to human CD1c+ and CD141+ DCs respectively; further dissection as to which of these two populations preferentially activate TGF-β via αvβ8 to promote Tregs is required.

The expression and function of integrin αvβ8 has been best characterised on DCs and Tregs in mice and humans: αvβ8 mediated TGF-β activation appears to be important for Treg differentiation by certain DC subsets; αvβ8 mediated TGF-β activation by murine and human Tregs appears to be essential in ameliorating both established colitis and xenogeneic GVHD respectively via suppression of pathogenic T cells(256, 257). Furthermore β8 expression has been described on human CD4+ utilising proteomics technology(262). Therefore the finding of uniformly low expression levels amongst lymphocyte subsets may seem unexpected given published data. It should be noted that Tregs comprise only 1-2% of total lymphocytes and 5-10% of CD3+CD4+ T lymphocytes(273). Use of a gating strategy designed to interrogate broad differences in expression on major leucocyte populations may not highlight markedly higher expression levels on minority subsets within a given population.

Interestingly, a wide range of αvβ8 expression levels was observed in untreated monocytes (13.9-93.6% αvβ8 positive). PBMCs were obtained from healthy donors (i.e. those who meet NHS blood and transplant criteria for blood donation), but within this heterogeneous population it is possible that donors may have altered inflammatory states from subclinical intercurrent or recent infection, resulting in a wide range of αvβ8 expression in untreated monocytes. In vitro experiments should lend valuable clues regarding the mechanisms controlling β8 expression, helping to unravel the mystery of the observed variation in human leucocyte αvβ8 expression. Evidence regarding the effects of TGF-β itself on monocyte cytokine release is contradictory: TGF-β has been shown to provoke and inhibit release of both TNF-α from monocytes cultured in

101 vitro(274-276). Data regarding mechanisms controlling TGF-β indicates that LPS has been shown to decrease TGF-β production(277) and appears to modulate signalling downstream of the TGF-βR, but whether TGF-β plays a pro- or anti-inflammatory role in this setting is unclear(277, 278).

On review of factors known to influence αvβ8 the data is also inconclusive. Studies utilising murine DCs intestinal CD103+CD11b- DCs from MyD88 knockout mice, a pathway involved in IL-1β and TLR signalling, showed significantly reduced expression of itgb8; however, treatment of non-αvβ8 expressing splenic DCs with exogenous IL-1β showed no effect(267, 268). Conversely, treatment of human lung with IL-1β promoted an increase in αvβ8 expression(279). TGF-β itself has been shown to promote expression of multiple integrin subunits(267), and was shown to induce a small but significant increase in itgb8 expression on murine splenic DCs, which was further increased by RA(246). Furthermore CD103+CD11b- intestinal DCs from mice lacking a functional TGF-β type II receptor on CD11c+ cells (predominantly DCs) expressed significantly lower levels of itgb8 compared to WT(246). Interestingly neither RA nor TGF-β were found to upregulate αvβ8 on human MoDCs(247), consistent with the findings here in TGF-β treated human monocytes. Boucard et al. also demonstrated that DCs isolated from mouse spleen decreased expression of itgb8 after IL-10 treatment(246). The finding of a significant increase in αvβ8 expression by monocytes after IL-10 treatment of PBMCs appears to be in direct contrast to that seen in mouse DCs. However data from the Travis laboratory has also shown an IL-10-induced increase on αvβ8 expression in human moDCs(280). The observed increase in monocyte αvβ8 expression upon treatment with LPS and TLR 7/8L is in agreement with published data on human moDC, which shows a significant increase in αvβ8 expression after treatment with both TLR4L and TLR8L, although not TLR7L(247). Again, this is in contrast to murine DC data that show TLR5L and TLR9L, but not TLR4L or TLR7/8L, induced αvβ8 upregulation(246). The disparate findings between human and mouse and different cell types may be related to the method of analysis (i.e. PCR or flow cytometry), although the upregulation in αvβ8 on IL-10 treated moDCs observed within the Travis group was significant both by flow cytometry and qPCR(280). Alternatively it may

102 indicate lack of homology between human and murine expression of this integrin and its regulation (which would be consistent with the higher expression of αvβ8 on different intestinal cDC homologs), and/or significantly different mechanisms controlling αvβ8 on alternate cell types.

The finding of increased αvβ8 expression by circulating monocytes upon IL-10 treatment is particularly interesting given the proposed function of monocytes as the precursor of intestinal macrophages. These macrophages are conditioned in an IL-10 and TGF-β-rich environment, with both cytokines believed to contribute to the tolerant phenotype of typical intestinal macrophages(45). The inter-relationship between the two cytokines is incompletely defined; whilst TGF-β has been shown to promote IL-10 expression in T cells differentiated in Th1 polarising conditions, TGF-βR expression is shown to be upregulated by IL- 10(281), and it is possible that IL-10-induced αvβ8 expression may represent a further reciprocal control mechanism, whereby IL-10 enhances monocyte- macrophage capacity to activate TGF-β. Given the increased monocyte expression of αvβ8 induced by IL-10, it was important to ascertain if this translated into an enhanced ability to activate TGF-β. The finding of similar levels of αvβ8 mediated TGF-β activation in the presence of TNF-α to untreated cells, but suppression of monocyte TGF-β activation in the presence of IL-10 was contrary to expected findings. Studies of the role of TGF-β and IL-10 in osteogenesis, showed that IL-10 reduced TGF-β1 mRNA from murine bone marrow cultures. Within the culture supernatant a partial reduction in latent TGF-β was detected upon IL-10 treatment, however active TGF-β was almost undetectable, in contrast to untreated bone marrow cells(282). Therefore, in addition to affecting TGF-β expression, IL-10 may affect mechanisms governing TGF-β activation, so pre-treatment of monocytes with IL-10 rather than direct addition of IL-10 to the TMLC assay may yield more accurate data regarding effects of IL-10 mediated αvβ8 upregulation on TGF-β activation. Furthermore, analysis of the effects of IL-10 on monocyte MMP-14 expression should be investigated in this setting.

The finding of significantly higher levels of integrin protein expression on monocytes is of particular interest given the roles of monocyte-macrophages in

103 intestinal immunity(133), and the related functions of TGF-β(149). As described, circulating monocytes are believed to be the source of the continually replenished intestinal macrophage population(71). Whilst it has previously been suggested that CD16+ monocytes extravasate to populate the intestinal LP(283), multiple lines of evidence now suggest that it is the classical CD14hiCD16- monocytes that are recruited to the intestine where they undergo further maturation. TGF-β itself is a monocyte chemo-attractant and experimental work has suggested an important role for TGF-β in recruiting monocytes to the intestinal macrophage pool(81). It is therefore possible that the αvβ8-dependent TGF-β activation observed on blood monocytes may play an important role in monocyte chemotaxis toward intestinal tissues, and this represents an important avenue for future study.

Summary of findings: • Human peripheral blood monocytes express high levels of integrin αvβ8. • Both IL-10 and TLR4L appear to upregulate integrin αvβ8 on human peripheral blood monocytes. • CD14+ monocytes are able to activate TGF-β in an αvβ8-dependent manner. • The functional significance of monocyte αvβ8-dependent TGF-β activation remains unclear.

104 CHAPTER 4: INTEGRIN ΑVΒ8 MEDIATED TGF-Β ACTIVATION IN HUMAN MACROPHAGES

4.1. Introduction

The many roles of human monocytes include that of continuously replenishing the intestinal macrophage population. Intestinal macrophages have unique characteristics, such as attenuated pro-inflammatory cytokine production upon TLR stimulation(48), which allow them to effectively clear pathogens without promoting widespread inflammatory reactions in the face of a high microbial load within the intestine. These characteristics appear to be gradually acquired after recruitment of monocytes to the intestinal LP(54), and an expanded population of immature, pro-inflammatory macrophages has been described in the LP of patients with IBD(57, 284). It has been suggested that established inflammation results in a state of ‘arrested development’ in intestinal macrophages, possibly through alterations in the cytokine milieu(143). Evidence suggests that both IL-10 and TGF-β(56, 89) are signals that promote macrophage maturation in homeostasis. Given the finding of high levels of the TGF-β-activating integrin αvβ8 on human blood monocytes, we wished to see if this integrin played a role in promoting an anti-inflammatory macrophage phenotype through the use of in vitro MDM models.

4.2. Results

4.2.1. Characterisation of human GM-CSF and M-CSF MDM phenotype

In order to investigate expression of αvβ8 on human MDMs we obtained fresh leucocyte cones (via the NHS Blood and Transplant Service) or peripheral blood from healthy donors. PBMCs were separated from peripheral blood using a Ficoll gradient centrifugation (see 2.1.2). Monocytes were isolated utilising CD14+ magnetic bead separation and were then cultured for 7 days in the presence of either GM-CSF, which is reported to promote a more pro- inflammatory MDM phenotype, or M-CSF, which is reported to promote a more anti-inflammatory MDM phenotype(285, 286).

105

In order to assess the resultant macrophage phenotype after differentiation with GM-CSF and M-CSF we analysed surface marker expression, phagocytosis and cytokine production. In studies of human intestinal macrophages, certain surface markers have been described in association with pro- or anti- inflammatory phenotype, such as high expression of the haemoglobin- scavenger receptor CD163 and HLA-DR in anti-inflammatory intestinal macrophages(57, 284). We found that M-CSF macrophages more highly expressed CD163 and CD14, both in terms of percentage positive cells (figure 4.1a and 4.1c) and levels of expression by MFI (figure 4.1b and 4.1d). HLA-DR was expressed on both GM-CSF and M-CSF MDMs (figure 4.1e), but at significantly higher levels on M-CSF MDMs by MFI (figure 4.1f). Further characterisation of the phenotype of GM-CSF and M-CSF MDMs was performed by analysing IL-10 levels in supernatants from GM-CSF and M-CSF cultured MDMs. M-CSF MDMs produced significantly higher levels of IL-10 in culture than GM-CSF MDMs (figure 4.2a). Analysis of phagocytic ability utilising fluorophore labelled bacteria revealed a trend toward increased phagocytosis in M-CSF MDMs (figure 4.2b and c).

In summary human M-CSF MDMs express higher levels of CD163, CD14 and HLA-DR, produce higher levels of IL-10 and display a trend toward higher levels of phagocytosis than GM-CSF MDMs.

4.2.2. Expression of integrin αvβ8 on human MDMs

GM-CSF and M-CSF MDMs were harvested and analysed for surface marker expression by flow cytometry, utilising the staining protocol described in 2.4.2. (figure 4.3).

Having successfully cultured MDMs in both GM-CSF and M-CSF, we further polarised cells toward pro- or anti-inflammatory phenotype by the addition of IFN-γ to GM-CSF or IL-4 to M-CSF MDMs at day 6, IFN-γ having been shown to polarise macrophages toward a pro-inflammatory and IL-4 toward an anti- inflammatory state(97, 287). Expression of αvβ8 was quantified by flow

106 cytometry as described, on GM-CSF, GM-CSF + IFN-γ, M-CSF and M-CSF + IL-4 MDMs. Notably αvβ8 expression was significantly higher on MDMs differentiated utilising M-CSF, than those differentiated in GM-CSF with or

a. *** b. * 100 100000

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M-CSF M-CSF GM-CSF GM-CSF Figure 4.1: Characterisation of GM-CSF and M-CSF MDM surface marker phenotype CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of either GM-CSF or M-CSF. Expression of CD163 was quantified as a, percentage of CD163 positive cells within the gated MDMs and b, MFI of gated

107 MDMs. Expression of CD14 was quantified as c, percentage of CD14 positive cells within the gated MDMs and d, MFI of gated MDMs. Expression of HLA-DR was quantified as e, percentage of HLA-DR positive cells within the gated MDMs and f, MFI of gated MDMs. 6 donors in 3 independent experiments. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.

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M-CSF M-CSF GM-CSF GM-CSF Figure 4.2: Characterisation of GM-CSF and M-CSF MDM IL-10 production and phagocytic capacity CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of either GM-CSF or M-CSF. a. Supernatants were harvested at day 7 and IL-10 concentration was determined by ELISA. 6 donors in 3 independent experiments. Wilcoxon test performed; * p ≤ 0.05. To determine phagocytic capacity of MDMs cells were co-cultured for 1 hour with PE-positive bead- conjugated E coli, prior to washing and fixation. Cells from each condition were pre-treated with cytochalasin D for 30 minutes prior as a negative control. Phagocytic capacity was expressed as b. percentage PE positive gated MDMs or c. MFI of gated positive MDMs minus MFI of matched negative control MDMs. 5 donors in 3 independent experiments. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. * p ≤ 0.05.

108 a. GM-CSF

β8-binding Isotype APC FMO antibody control b. M-CSF

β8-binding Isotype APC FMO control antibody Figure 4.3: Gating strategy for monocyte-derived macrophages (MDMs). CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of either GM-CSF or M-CSF. Cells were stained with a viability stain, and β8-binding antibody. Cells were gated on single, live cells and then on forward and side scatter characteristics, specific for a. GM-CSF MDMs and b. M-CSF MDMs. Cells were gated for integrin β8 against isotype controls for a. GM-CSF and b. M-CSF MDMs.

109 a. b. ** * ** * 100 80000

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Figure 4.4: Expression of αvβ8 antibody on monocyte-derived macrophage (MDM) models. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of either GM-CSF or M-CSF, with addition of interferon gamma (IFN-γ) or interleukin 4 (IL-4) to some cultures in the last 24 hours. Expression of integrin αvβ8 was quantified as a, percentage of β8-binding antibody positive cells within the gated MDMs and b, MFI of gated MDMs. 11 donors in 7 independent experiments. Graphs show median and interquartile range. Kruskal-Wallis performed; * p ≤ 0.05, ** p ≤ 0.01. c. Example flow plots of integrin αvβ8 expression on GM-CSF, GM-CSF/IFNγ, M-CSF and M-CSF/IL-4 MDMs. Plots show single, live MDMs demonstrating αvβ8 positive gating, and histograms of β8-binding antibody stained single, live MDMs (coloured line) against equivalent isotype controls (grey shaded).

110 without the addition of IFN-γ by both percentage cells expressing the integrin (figure 4.4a and c) and the quantity of integrin expressed (figure 4.4b and c). M- CSF plus IL-4 MDMs displayed similarly high levels of β8 expression to M-CSF MDMs (figure 4.4 a, b and c). In summary, MDMs differentiated with GM-CSF +/- IFN-γ, thought to represent a more pro-inflammatory phenotype, express low levels of integrin αvβ8, in contrast to those differentiated with M-CSF +/- IL-4, thought to represent a more anti-inflammatory phenotype, that express high levels of integrin αvβ8.

We then wished to ascertain if this differential expression of the β8 protein at the cellular surface was reflected at the gene expression level by PCR. To this end, expression levels of ITGB8 were measured over different time-points during MDM differentiation. Given the striking contrast observed in protein expression between GM-CSF and M-CSF MDMs we focused on these MDM models. Despite a suggestion of an initial increase in ITGB8 after stimulation with both GM-CSF or M-CSF (figure 4.5a) this was not replicated in larger samples where there appeared to be no significant difference in gene expression over time (figure 4.5b) or between GM-CSF and M-CSF MDMs after full differentiation (figure 4.5c).

4.2.3. Mechanisms governing integrin αvβ8 expression on human MDMs

Next, we investigated potential mechanisms controlling expression of integrin αvβ8 on macrophage populations. Given the finding of increased integrin αvβ8 expression on TLR-4 ligand and TLR-7/8 ligand treated monocytes, both GM- CSF and M-CSF differentiated MDMs were treated with a range of TLR ligands. Interestingly, contrary to the findings in human monocytes, treatment with TLR- 4 and TLR-7/8 ligands did not result in any significant changes in β8 integrin protein expression in GM-CSF (figure 4.6a and b), or M-CSF MDMs (figure 4.6c and d); nor did treatment with TLR 1/2 ligand.

111 4.2.4. Investigating the role of integrin αvβ8 expression in human MDM phenotype

Next, given that significantly higher expression of integrin αvβ8 was found in M- CSF compared to GM-CSF-derived MDMs, the potential functional implications of this higher expression was investigated. Data from the Travis group demonstrated that M-CSF MDMs display an enhanced ability to activate TGF-β in an αvβ8-dependent manner, at similar levels to CD14+

a. b.

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M-CSF GM-CSF Figure 4.5: Expression of αvβ8 antibody on monocyte-derived macrophage (MDM) models determined by PCR. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of either GM-CSF or M- CSF. Cytokines were replenished on day 3. a. Monocyte- MDMs from one donor were harvested at 0, 24, 48, 72, 96 and 144 hours, and integrin β8 encoding ITGB8 expression was

112 determined using PCR, quantified as fold change relative to expression on same donor day 0 monocytes. b. GM-CSF and M-CSF MDMs were harvested at day 3 and day 7 and ITGB8 expression was determined using qPCR, quantified as fold change relative to expression on same donor day 3 GM-CSF MDMs. 5 donors in 2 independent experiments. Friedman with Dunn’s multiple comparisons performed. c. GM-CSF and M-CSF MDMs were harvested at day 7 and ITGB8 expression of M-CSF MDMs was determined using qPCR, quantified as fold change relative to expression on same donor GM-CSF. 8 donors in 4 individual experiments. Wilcoxon test performed. ITGB8 expression was normalised to same sample expression of the B2M housekeeping gene (encoding β2-microglobulin).

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Untx Untx TLR4L TLR4L TLR1/2L TLR7/8L TLR1/2L TLR7/8L Figure 4.6: Expression of integrin αvβ8 on MDMs after TLR ligand treatment. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of either GM-CSF or M-CSF. Expression of integrin αvβ8 on

113 GM-CSF MDMs was quantified as a, percentage of β8-binding antibody positive cells within the gated MDMs and b, MFI of gated MDMs; and on M-CSF MDMs as c, percentage of β8-binding antibody positive cells within the gated MDMs and d, MFI of gated MDMs; 4 donors in 4 independent experiments. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non significant. monocytes(272). In order to investigate the potential functional role of this αvβ8 integrin expression and resultant TGF-β activation on M-CSF MDMs, expression of CD163, CD14 and HLA-DR, which were differentially expressed on GM-CSF and M-CSF MDMs and which in intestinal monocyte-macrophages were shown to be associated with anti- or pro-inflammatory phenotype(54, 57, 284), were analysed after M-CSF MDMs were cultured alone or with the addition of exogenous TGF-β, TGF-β blocking antibody, αvβ8-blocking antibody or control antibody. Neither addition of exogenous TGF-β, or TGF-β-blocking or αvβ8-blocking antibody appeared to alter expression of CD163 or HLA-DR in M- CSF MDMs (figure 4.7a, b, e and f). Whilst M-CSF MDM widely expressed CD14 (figure 4.7c) there was a trend toward altered levels of expression as determined by MFI after TGF-β and TGF-β-blocking treatments (figure 4.7d); thus M-CSF MDMs treated with exogenous TGF-β showed a trend toward decreased levels of CD14+ expression, and blocking TGF-β appeared to increase levels of CD14+ expression (figure 4.7d). However, treatment with αvβ8-blocking antibody did not appear to influence CD14+ expression (figure 4.7d). These data indicate that autocrine integrin αvβ8-mediated TGFβ activation is not important in regulation of CD14 expression on M-CSF MDMs. Treatment of GM-CSF MDMs with TGF-β or blocking antibodies did not alter expression of CD163, CD14 or HLA-DR (data not shown).

Given the higher production of IL-10 in M-CSF MDMs we also sought to see if αvβ8-mediated TGF-β activation plays a role in this process. IL-10 levels were measured in supernatants of MDMs cultured in GM-CSF or M-CSF alone or with TGF-β blocking or αvβ8-blocking antibody. Neither TGF-β nor αvβ8 blockade in GM-CSF (figure 4.8a) or M-CSF (figure 4.8b) MDMs resulted in any alterations in IL-10 production. We then assessed whether blocking TGF-β or

114

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block block β 8 block β 8 block treated β treated β β Untreated v β Untreated v α IgG control α IgG controlTGF- TGF- TGF- TGF- Figure 4.7: Expression of surface markers on M-CSF MDMs after treatment with or without TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of M-CSF, alone or with control IgG, TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. Expression of CD206 on MCSF MDMs was quantified as a, percentage of CD163 positive cells within the gated MDMs and b, MFI of gated MDMs; expression of CD14 was

115 quantified as c, percentage of CD14 positive cells within the gated MDMs and d, median MFI of gated MDMs; expression of HLA-DR was quantified as e, percentage of HLA-DR positive cells within the gated MDMs and f, MFI of gated MDMs. 6 donors in 3 independent experiments. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant. GM-CSF M-CSF a. b.

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block block β 8 block β 8 block β β Untreated v Untreated v α α TGF- TGF- Figure 4.8: IL-10 production of GM-CSF and M-CSF MDMs after treatment with or without anti-TGF-β antibody or anti-αvβ8 antibody. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of GM-CSF or M-CSF alone or with anti-TGF-β antibody or anti-αvβ8 antibody. Supernatants were harvested at 7 days to determine concentration of IL-10 using ELISA against a standard curve of IL-10, for a. GM-CSF and b. M-CSF MDMs. Human MDMs were cultured as above and then

116 treated with LPS on day 6 for 24 hours. Supernatants were harvested at 7 days to determine concentration of IL-10 using ELISA against a standard curve of IL-10, for c. GM-CSF and d. M- CSF MDMs. 5 donors in 4 independent experiments. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant.

αvβ8 altered MDM IL-10 production in response to LPS stimulation, and found that again antibody mediated TGF-β or αvβ8-blockade did not result in any differences in GM-CSF (figure 4.8c) or M-CSF (figure 4.8d) MDMs compared to non antibody-treated controls.

A possible role for αvβ8 in phagocytosis was also investigated by utilising GM- CSF and M-CSF MDMs cultured alone or with TGF-β, TGF-β-blocking, αvβ8- blocking or control antibody throughout the culture period. Phagocytic ability of MDMs was analysed utilising fluorochrome-labelled bacteria at day 7 post- differentiation (figure 4.9a and b). As with surface marker expression and IL-10 production, enhancing or blocking TGF-β activity during MDM differentiation had no effect on GM-CSF (figure 4.9c and d) or M-CSF (figure 4.9e and f) phagocytosis. Finally, the effects of TGF-β blocking or αvβ8-blocking antibodies on MDM phagocytosis over a shorter time period were assessed in fully differentiated GM-CSF and M-CSF MDMs by overnight treatment prior to incubation with labelled bacteria. This showed, consistent with the longer treatment, that blocking TGF-β or αvβ8-mediated TGF-β activation does not influence phagocytosis in human in vitro GM-CSF (figure 4.10a and b) or M- CSF differentiated MDMs (figure 4.10c and d).

In summary, human in vitro-derived M-CSF MDMs, which express high levels of CD163 and HLA-DR and produce constitutively higher levels of IL-10 than GM- CSF MDMs, also express significantly higher levels of integrin αvβ8. Whilst M- CSF MDMs have been shown to activate TGF-β in an αvβ8-dependent manner, similar to CD14+ monocytes and in contrast to GM-CSF MDMs, this macrophage αvβ8-mediated TGF-β activation has a hitherto unknown functional role, with no evidence thus far supporting a function of this pathway in expression of tolerance-associated surface markers, IL-10 production and phagocytosis.

117 a. Bead-incubated Control

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Figure 4.9: Phagocytic capacity of GM-CSF and M-CSF MDMs after 7 day treatment with or without TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of GM-CSF or M-CSF alone or with control IgG, TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. MDMs were co-cultured for 1 hour with 100µlPE-positive bead-conjugated E

118 coli per ml of media, prior to washing and fixation. Cells from each condition were pre-treated with cytochalasin D for 30 minutes prior as a negative control. Representative flow plots of a. GM-CSF and b. M-CSF MDMs showing gating of single MDMs and PE- positive cells against equivalent negative control samples. Phagocytic capacity of treated GM-CSF MDMs was expressed as c. percentage PE positive gated MDMs or d. MFI of gated positive MDMs minus MFI of matched negative control MDMs. Phagocytic capacity of treated M-CSF MDMs was expressed as e. percentage PE positive gated MDMs or f. MFI of gated positive MDMs minus MFI of matched negative control MDMs. 5 donors in 3 independent experiments. Graphs show median with interquartile range. Kruskal-Wallis performed. All comparisons non-significant. GM-CSF

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block β 8 block block β β 8 block Untreated v β α Untreated v IgG Control TGF- α IgG Control TGF- Figure 4.10: Phagocytic capacity of GM-CSF and M-CSF MDMs after overnight treatment with or without TGF-β, anti-TGF-β antibody or anti-αvβ8 antibody. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of GM-CSF or M-CSF and then either untreated or treated with control IgG, TGF- β, anti-TGF-β antibody or anti-αvβ8 antibody overnight. The following day MDMs were co-

119 cultured for 1 hour with PE-positive bead-conjugated E coli, prior to washing and fixation. Cells from each condition were pre-treated with cytochalasin D for 30 minutes prior as a negative control. Representative flow plots of a. GM-CSF and b. M-CSF MDMs showing gating of single MDMs and PE- positive cells against equivalent negative control samples. Phagocytic capacity of treated GM-CSF MDMs was expressed as c. percentage PE positive gated MDMs or d. MFI of gated positive MDMs minus MFI of matched negative control MDMs. Phagocytic capacity of treated M-CSF MDMs was expressed as e. percentage PE positive gated MDMs or f. MFI of gated positive MDMs minus MFI of matched negative control MDMs. 5 donors in 3 independent experiments. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant.

4.3. Discussion

The finding here of high levels of αvβ8 integrin expression on M-CSF macrophages in association with an enhanced ability to activate TGF-β via αvβ8(272) indicates that ‘anti-inflammatory’ M-CSF MDMs provide a useful in vitro model to investigate the role of αvβ8-mediated TGF-β activation in macrophages. In contrast, GM-CSF MDMs express low levels of integrin αvβ8 and activate significantly lower levels of TGF-β via αvβ8 providing a contrasting model in which this pathway is downregulated.

The use of different MDM models, their nomenclature and their utility has been hotly debated(95, 97). However, they provide a useful adjunct to in vivo murine models, where, although parallels with human models are frequently described, key differences such as the differential expression of αvβ8 on intestinal DC subsets between mice and humans are also observed(247). Furthermore, isolation of sufficient numbers of viable human tissue macrophages for functional experiments can present a challenge(288) and in vitro-derived MDMs may yield complementary data to guide experiments utilising the more precious resource of tissue macrophages. M-CSF derived MDMs are believed to be more representative of tissue macrophages, with M-CSF being produced widely and found within the circulation, whereas GM-CSF production appears to occur in the presence of inflammatory stimuli(289). Murine intestinal macrophages in homeostasis appear to be M-CSF but not GM-CSF dependent(86-88). GM-CSF MDM cultures have been described to contain CD11c+ cells with antigen- presenting properties more akin to DCs(97); however, genomics analysis

120 reveals that GM-CSF and M-CSF differentiated MDMs are distinct from GM- CSF and IL-4 differentiated MoDCs(96), and furthermore GM-CSF monocyte derived cells have been shown to cluster with monocytes and M-CSF MDMs rather than lymphoid tissue DCs(290, 291).

Gene expression profiling of human GM-CSF and M-CSF MDMs revealed that almost 90% of genes were similarly expressed(285). However, key phenotypic differences between these MDM subtypes have been identified. IL-10 gene expression appears to be increased in M-CSF MDMs, alongside constitutive secretion of IL-10, which is not seen in GM-CSF MDMs. Upon LPS simulation both M-CSF and GM-CSF MDMs can produce IL-10, more so in M-CSF MDMs(285), with the same findings in terms of basal gene expression and LPS stimulated IL-10 production reported in murine M-CSF and GM-CSF BMDMs(292). Conversely, GM-CSF MDMs can produce significantly more TNF-α and IL12p40 constitutively and more TNF-α upon LPS stimulation(285, 286), and murine GM-CSF BMDMs expressed higher TNF-α, IL-12p70, and IL- 23 levels than M-CSF differentiated BMDMs(292). These published findings are consistent with the data reported here demonstrating significantly higher constitutive production of IL-10 by M-CSF MDMs with very low levels detected in GM-CSF culture supernatant. M-CSF MDMs appear to be more phagocytic than GM-CSF MDMs for both latex beads and a variety of pathogens(293), again reflected in the data presented here showing a trend toward increased phagocytosis of fluorochrome bead-labelled E. Coli by M-CSF MDMs. Finally, the surface marker expression profile we observed, with significantly higher expression of CD163 and CD14 on M-CSF MDMs compared to GM-CSF MDMs. has also been reported in the literature(286, 293).

It is therefore interesting that cells which produce anti-inflammatory cytokines and are more highly phagocytic – a key function for tissue maintenance and repair- express higher levels of the TGF-β activating integrin αvβ8 and have subsequently been shown to be specialised to activate TGF-β via αvβ8(272), whereas low levels of integrin αvβ8 are seen on the more pro-inflammatory GM- CSF MDMs. TGF-β itself can inhibit monocyte production of GM-CSF in response to pathogenic stimuli(294) and GM-CSF can inhibit the ability of TGF-

121 β to induce CD16 expression in monocytes(295). Both intestinal macrophages and MDMs have been shown to express a range of TLRs(59, 296, 297), although intestinal macrophages appear to become less TLR responsive during maturation(51, 59). However, in contrast to that seen in DCs(247) and monocyte data presented here, TLR treatment does not appear to affect expression of integrin αvβ8 on MDMs.

Again a discrepancy was seen in surface protein expression of αvβ8 and gene expression of ITGB8, discussed in greater detail in chapter 3, and intracellular staining and imaging techniques may pinpoint intracellular mechanisms that regulate αvβ8. Interestingly this finding of high levels of integrin αvβ8 in M-CSF MDMs contrasts with the findings from Boucard-Jourdin et al.(246) whereby the murine CD103-CD11b+ population within the LP, which encompasses intestinal macrophages, express very low levels of itgb8. However, integrin αvβ8 has been identified on human intestinal macrophages. Work from the Travis group has shown that αvβ8 is highly expressed in cells isolated from human colonic mucosa expressing the markers CD64 and CD14 (figure 4.11a)(272), identifying human intestinal macrophages(134) which have also been shown to express the surface markers CD163 and CD206 (figure 4.11b), proposed as markers of an anti-inflammatory intestinal macrophage phenotype(54). Thiesen et al.(284) observed a significantly expanded population of CD14+ monocyte- macrophages in the inflamed intestine of patients with CD versus controls, and in inflamed versus non-inflamed tissue from CD patients. This expanded population was CD14hiHLA-DRlo and expressed high levels of CD62L (L- , which is crucial for inflammatory monocyte recruitment)(298) whereas the CD14hiHLA-DRhi population expressed the highest levels of CD16, CD163, HLA-DR, and CX3CR1. Further to this Ogino et al.(57) described a CD14+CD163hi colonic macrophage population in humans that express TLR-5 and several anti-inflammatory markers, including IL-10, HMOX1, and TGFB and show constitutive high production of IL-10, which were proposed to correspond to the mature HLA-DRhiCD163hiCD209hi macrophage subset described by Bain et al.(54). However, an expanded CD14+CD163lo population was observed in the inflamed colon of IBD patients. These cells expressed TLR-2,-4 and -5, and high production of IL-6, IL-1β, and TNF-α observed in these cells, even in the

122 absence of stimulation, which was further increased in response to LPS, but not in response to TLR-2L or -5L. Upon co-culture with naïve T-cells this CD14+CD163lo population induced the highest number of IL-17+ cells, which was reliant on the production of IL-6, IL-1, IL-23, and TGF-β. a.

b.

Figure 4.11: Integrin β8 expression on human colonic macrophages. a. Integrin β8 levels were analysed by flow cytometry on human colonic monocyte/macrophage populations, gated as single, live, CD45+, lineage (CD3, CD15, CD19, CD20, CD56)-, HLA-DR+ CD14+ CD64+ cells. Expression of integrin αvβ8 was compared to equivalent CD14-CD64- cells (n=9). b. Integrin β8+ monocytes/macrophages were analysed for expression of the tissue macrophage markers CD163 and CD206 by flow cytometry (representative image from n=9 donors). Reproduced with authors’ permission(272).

The confirmation of high levels of integrin αvβ8 in both in vitro and tissue CD163hi macrophage populations associated with an anti-inflammatory phenotype characterised by IL-10 production opens intriguing possibilities for a

123 potential anti-inflammatory role for αvβ8 in human macrophages. However, attempts to establish the role of αvβ8 mediated TGF-β activation in determining macrophage phenotype indicate that this process does not affect expression of the key surface marker CD163, or HLA-DR. Interestingly other authors have shown that treatment of both monocytes and MDMs with TGF-β supressed CD163 expression, in contrast to the increase seen with IL-10(299, 300), further highlighting the complexity of TGF-β function. However in data presented here blocking antibodies to TGF-β and integrin αvβ8 were added in the presence of either GM-CSF or M-CSF, whereas published data utilised either monocytes or fully differentiated MDMs that were then treated with TGF-β. It is therefore possible that the actions of exogenous TGF-β or TGF-β blockade were masked by the presence of growth factors which alter CD163 expression, and repeating these experiments with TGF-β and integrin αvβ8 blocking treatments on fully differentiated MDMs in the absence of growth factors may yield different results. Interestingly, TGF-β treatment of M-CSF MDMs revealed a trend toward reduced CD14 expression, with a trend toward increased expression when TGF-β was blocked. Transient reduction in CD14 expression in human monocytes after TGF-β treatment has been reported(301), and TGF-β appears to mediate blockade of LPS induced CD14 expression in murine peritoneal macrophages, resulting in reduced expression in TNF-α and IL-1β expression in response to LPS(302). Curiously antibody blockade of integrin αvβ8 did not seem to alter CD14 expression.

Regarding the role of TGF-β in IL-10 production, IL-10 production by both Foxp3+ and Foxp3- murine T-cells appears to be TGF-β dependent(166), and the TGF-β signalling molecule Smad4 has been shown to bind to the IL-10 promoter region, resulting in IL-10 production from Th1 but not Th2 cells, which abrogated bleomycin induced lung fibrosis in WT but not IL-10 KO mice(303). In contrast to murine T-cell data, our data thus far has not demonstrated a role for TGF-β in human macrophage production of IL-10. TGF-β treated human MDMs showed a trend toward increased phagocytosis according to data published by Porcheray et al.(300), in contrast to data presented here showing that blockade of TGF-β signalling during MDM differentiation in the presence of growth factors did not alter phagocytosis, nor did TGF-β blocking of fully differentiated MDMs.

124

Further experiments assessing the role of TGF-β and αvβ8 blockade on a wider profile of cytokines and surface markers in fully differentiated MDMs may reveal a role for αvβ8 mediated TGF-β activation in these processes. It is also possible that αvβ8 mediated TGF-β activation is important in other macrophage functions. Although macrophages are less efficient than DCs in terms of antigen-presentation they can still effectively promote T-cell differentiation(57) and co-culture experiments utilising MDMs and sorted tissue macrophages in the presence of TGF-β or αvβ8 blocking antibodies should help in delineating the importance of macrophage αvβ8 expression in antigen presentation and T- cell polarisation. Furthermore intestinal macrophages play an important role in tissue maintenance and repair(45), and the importance of an intact epithelial barrier in maintaining intestinal homeostasis is shown by both MUC2 KO mice(30) and mice with mutations of N-cadherin and defective epithelial cell adhesion(13), which develop spontaneous intestinal inflammation. Investigation of the role of macrophage αvβ8 mediated TGF-β activation in healing by utilising scratch assays, either of treated macrophages in isolation(304), or in combination with intestinal epithelial cells(305), may also provide vital clues as to the role of the pathway, although the cell line selected should take into account the downregulation of TGF-βR observed in multiple intestinal epithelial cell lines(306).

Summary of findings:

• Integrin αvβ8 is more highly expressed on human in vitro-derived M-CSF MDMs than GM-CSF differentiated MDMs

• M-CSF MDMs express high levels of CD163 and HLA-DR and produce constitutively higher levels of IL-10 than GM-CSF MDMs,

• M-CSF but not GM-CSF MDMs activate TGF-β in an αvβ8-dependent manner, similar to CD14+ monocytes.

• Analysis of expression of tolerance-associated surface markers, IL-10 production and phagocytosis utilising human M-CSF and GM-CSF MDMs has not revealed a role thus far for macrophage αvβ8-mediated

TGF-β activation.

125 CHAPTER 5: THE ROLE OF METABOLISM IN DETERMINING MACROPHAGE PHENOTYPE

5.1. Introduction

Work thus far has not demonstrated a clear functional role for the increased expression of integrin αvβ8 and enhanced αvβ8-dependent TGF-β activation of M-CSF MDMs, which display an IL-10 producing anti-inflammatory phenotype, in contrast to low αvβ8 expressing GM-CSF MDMs, reported to display a more pro-inflammatory phenotype(285, 286). Recent evidence suggests that metabolism is a key regulator of immune cell function especially in macrophages(98). A range of macrophage activation states have been described, which have previously been characterised as ‘M1’ for macrophages polarised with stimuli such as LPS and IFN-γ, which produce more ROS and pro-inflammatory cytokines, and ‘M2’ for macrophages polarised with stimuli such as IL-4 or IL-13, which express high levels of arginase and scavenger receptors, and are considered to have more anti-inflammatory and wound healing function(307). Whilst these broad categories encompass heterogeneous groups of MDMs, differences in metabolism between so-called pro-inflammatory ‘M1’ and anti-inflammatory ‘M2’ polarised MDMs have been noted in mice. Namely, ‘M1’ MDMs show increased glycolysis and conversion of pyruvate to lactate, displaying a lower rate of OxPhos in association with characteristic ‘breaks’ in the TCA cycle. These ‘breaks’ allow for diversion of metabolites for the synthesis of pro-inflammatory compounds such as precursors for NO and prostaglandins(308). By contrast ‘M2’ MDMs display an intact TCA cycle and higher levels of OxPhos, a more efficient source of ATP, which may be advantageous in prolonged anti-helminthic or tissue repair responses(98).

In vitro experiments have demonstrated that pro-inflammatory cytokines and TLRLs trigger observed metabolic changes in ‘M1’ macrophages via regulation of intracellular signalling pathways such as mTOR(105, 309). In contrast to LPS and IFN-γ, addition of Th2 and anti-inflammatory cytokines such as IL-4 and IL- 10 does not appear to significantly alter the metabolic signature of

126 macrophages compared to untreated controls(105), with more recent evidence suggesting that IL-10, a key cytokine promoting acquisition of immune tolerance in intestinal macrophages, is able to oppose LPS-induced metabolic changes in macrophages(121). Given evidence suggesting a similar role for TGF-β in promoting macrophage tolerance(56) and our finding of an enhanced ability to activate TGF-β on human M-CSF MDMs secondary to high expression of integrin αvβ8, we investigated whether TGF-β was also important in macrophage metabolic switching.

5.2. Results

5.2.1 Establishing a protocol for bioenergetics analysis of human MDMs

In order to establish the relative commitment of a cell to OxPhos (which is the metabolic signature associated with anti-inflammatory, pro-repair macrophages) or glycolysis (which in the presence of normoxia is associated with pro- inflammatory macrophages) bioenergetics analysis, using a Seahorse™ XF analyser (Agilent) provides useful proxy measures. The oxygen consumption rate (OCR) of cells provides a relative measure of the rate of OxPhos within a cell (higher OCR meaning more OxPhos occurring within the cell culture). Similarly, as glycolysis results in generation of lactic acid, the extracellular acidification rate (ECAR - the rate of change of pH within the cellular culture medium) measures the amount of glycolysis within cells (higher ECAR meaning more glycolysis occurring within the cell culture).

Cells can then be stimulated with oligomycin, which inhibits complex V of the electron transport chain (ETC) and increases glycolysis, and FCCP which depolarises the mitochondrial membrane and increases oxygen consumption(310). These treatments therefore give a ‘stressed’ or ‘maximal’ measurement for glycolysis (oligomycin treatment) and cellular OxPhos (FCCP treatment) (figure 5.1a and b). Furthermore any signal for non-mitochondrial oxygen consumption can be determine and adjusted for by adding rotenone and antimycin A, which inhibit complexes I and III of the ETC respectively(310). Measurements obtained after rotenone/antimycin A treatment can be

127 subtracted from values obtained before and after oligomycin/FCCP treatment to give basal and maximal values for OCR (figure 5.1a). a. b. Maximal OCR (minus Maximal ECAR non-mitochondrial respiration) GMCSF Rotenone & MCSF Rotenone & Basal OCR 500 antimycin A Basal ECAR antimycin A MCSF (minus non- 80 injection injection mitochondrial GMCSF respiration) Oligomycin & FCCP 400 injection Oligomycin 60 & FCCP injection 300

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50000 total 25000 total 50000 total 25000 total 50000 viable 25000 viable 50000 viable 25000 viable Cells/well Cells/well Figure 5.1: Metabolic profile of human MDMs cultured in standard 12 well plates and transferred to XF96 seahorse plates for analysis. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 6 days in the presence of GM-CSF or M-CSF prior to harvest, counted with or without tryphan blue viability stain and transfer to XF96 plates for 24 hour prior to metabolic profile analysis. a. Representative plot of oxygen consumption rate (OCR) and b. extracellular acidification rate (ECAR) of GM-CSF and

128 M-CSF MDMs before and after sequential injection of oligomycin and FCCP, and then rotenone and antimycin c. Pooled data for basal and maximal OCR and d. ECAR of GM-CSF MDMs transferred at 50000 tryphan blue negative (viable) cells/well, 25000 viable cells/well, 50000 total cells (not tryphan blue stained)/well or 25000 total cells/well. e. Basal and maximal OCR and f. ECAR of M-CSF MDMs transferred at 50000 viable cells/well, 25000 viable cells/well, 50000 total cells/well or 25000 total cells/well. Pooled data represents 2 donors in 1 experiment with 4-6 technical replicates per donor/condition. Graphs show median with range.

Whilst protocols for bioenergetics analysis (i.e. proxy measurement of OxPhos and glycolysis) utilising murine MDMs have been published widely in the literature(106, 121), the literature using human macrophages is relatively sparse. Thus, to be able to test the potential role of TGFβ in regulation of metabolism in human macrophages, we first had to establish protocols for measurement of glycolysis and OxPhos in human macrophages.

Published protocols for murine MDMs describe the differentiation of MDMs in standard culture plates prior to transfer to specialised plates for bioenergetics analysis (so-called Seahorse™ plates). Thus, here GM-CSF and M-CSF MDMs were first cultured in standard 12-well tissue culture plates as described in section 2.4.1. prior to removal by gentle scraping. Cells were either counted without viability stain and directly seeded into Seahorse™ plates at a range of cell numbers per well, or stained for viability, counted and transferred into plates at a range of viable cells per well. As can be seen, a trend toward lower OCR (figure 5.1c and e) and ECAR (figure 5.1d and f) values were seen in both GM- CSF and M-CSF MDMs when not adjusted for viability.

The discrepancy in the bioenergetic values from MDMs plated without viability staining and those plated after staining for viability and counting viable cells only indicates a large reduction in viability of harvested MDMs. This was confirmed by comparison of unstained and viability stained cell counts which show that an average 17% (range 12 to 28%) of harvested GM-CSF MDMs were viable, and an average of 37% (range 20 to 46%) of harvested M-CSF MDMs were viable. This loss of viability may occur during the course of culture or may be provoked by the process of cell scraping and transfer. Given the latter possibility, in order to exclude possible cell loss during the process o

129

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Seeded in plate Transferred Seeded in plate Transferred Figure 5.2: Metabolic profile of titrated MDMs cultured in XF96 seahorse plates CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in the presence of GM-CSF or M-CSF in XF96 plates for 24 hours prior to metabolic profile analysis. OCR plots showing basal and maximal OCR rates of 50000, 25000 or 12500 viable a. GM-CSF or b. M-CSF MDMs per well. c. Pooled data displaying basal and maximal OCR values of 75000, 50000 and 25000 GM-CSF MDMs seeded from day 0 in XF96 plates, and values from GM-CSF MDMs cultured in standard plates until day 6, harvested, tryphan blue stained and transferred at 50000 and 25000 cells/well. d. Basal and maximal

130 values from 75000, 50000 and 25000 M-CSF MDMs seeded in XF96 plate, and values from 25000 and 50000 tryphan blue negative cells transferred at day 6. e. Basal and maximal ECAR of GM-CSF MDMs as in c. f. Basal and maximal ECAR of M-CSF MDMs as in d. 7 donors in 4 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with range. replating we wished to see if monocyte to MDM differentiation within Seahorse™ plates would represent a viable alternative. We therefore compared the OCR and ECAR values of MDMs differentiated from monocytes in Seahorse™ plates and analysed in-situ with those cultured in standard plates then viability stained, counted and transferred into Seahorse™ plates. As results obtained from MDMs differentiated in Seahorse™ plates at 75 000 cells/well were similar to those transferred to at 50 000 viable cells/well (figure 5.2), and given the low yield of viable MDMs after removal from standard culture plates, future experiments utilised MDMs differentiated in Seahorse™ plates at 75 000 cells/well.

5.2.2. Establishing the bioenergetics profile of human GM-CSF and M-CSF MDMs

Whilst GM-CSF and M-CSF differentiated MDMs do not fit a pure ‘M1’ versus ‘M2’ paradigm, cytokine and gene expression profiles point to a more pro- inflammatory phenotype in GM-CSF macrophages(285), proposed to be akin to tissue macrophages within a pro-inflammatory environment, versus M-CSF MDMs representing tissue macrophages in homeostasis(289). Given the significantly different expression of the TGF-β-activating integrin αvβ8 between macrophages differentiated in GM-CSF versus M-CSF, and in light of the hypothesis that TGF-β may alter the metabolic activity of macrophages, the bioenergetics profile of GM-CSF and M-CSF MDMs was analysed by OCR and ECAR in basal conditions.

Surprisingly GM-CSF MDMs exhibited higher levels of OxPhos than M-CSF MDMS (figure 5.3a and c). At rest GM-CSF MDMs showed a trend toward increased glycolysis compared to M-CSF MDMs (figure 5.3b and d) in agreement with previous experiments with murine pro- and anti-inflammatory

131 a. b. 800 150 Rotenone & GMCSF Oligomycin Rotenone & antimycin A MCSF Basal OCR GMCSF & FCCP antimycin A injection (minus non- MCSF injection injection mitochondrial 600 respiration)

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M-CSF GM-CSF Figure 5.3: Metabolic profile of human GM-CSF and M-CSF MDMs in basal conditions CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured in XF96 plates for 7 days in the presence of GM-CSF or M-CSF prior to analysis. a. and b. Example plots of (a) OCR and (b) ECAR measurements of GM-CSF and M-CSF MDMs. c. Pooled data for basal OCR values of GM-CSF and M-CSF MDMs, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non- mitochondrial respiration in a.) from average pre- oligomycin and FCCP injection values. d. Pooled data for basal ECAR values of GM-CSF and M-CSF MDMs, obtained from average of

132 pre-oligomycin and FCCP injection values. e. Basal OCR/ECAR ratios of GM-CSF and M-CSF MDMs, calculated by dividing basal OCR values by basal ECAR values. 11 donors in 6 independent experiment, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Wilcoxon test performed; ** p ≤ 0.01. macrophages(105, 311). Division of OCR by ECAR in order to give the OCR/ECAR ratio, a relative measure of commitment to OxPhos over glycolysis did not reveal any significant differences between GM-CSF and M-CSF MDMs in basal conditions (figure 5.3e).

On analysis of maximal OCR and ECAR after stimulation with oligomycin/FCCP (figure 5.4a and b) GM-CSF MDMs showed a trend toward higher rates of OxPhos (figure 5.4c) and significantly higher rates of glycolysis (figure 5.4d) than M-CSF MDMs. However, analysis of OCR/ECAR ratios suggested a trend toward increased commitment to glycolysis by GM-CSF MDMs than M-CSF MDMs (figure 5.4e). Thus, together, these data suggest that GM-CSF MDMs are more metabolically active, both in terms of OxPhos and glycolysis, although upon maximal stimulation GM-CSF MDMs divert proportionally more glucose toward glycolysis than M-CSF MDMs.

5.2.3. Investigating factors which alter the bioenergetics profile of human MDMs

Although shown to be a crucial cytokine in regulation of immunity(149), the role of TGF-β in regulating metabolic activity of immune cells is completely unknown. To assess the effect of TGF-β treatment on the metabolic behaviour of human MDMs, GM-CSF MDMs were treated overnight with TGF-β at a range of concentrations, or with vehicle control prior to assessing bioenergetics profile (figure 5.5a and b). Data demonstrated that there was no significant effect of TGF-β treatment on the OCR (figure 5.5c), ECAR (figure 5.5d) or OCR/ECAR ratio (figure 5.5e) of GM-CSF MDMs at baseline. Similarly, TGF-β treatment of M-CSF MDMs showed no alterations to the basal bioenergetics profile (figure 5.6). Analysis of the effect of TGF-β on maximal metabolic profile (post

133 Maximal a. b. ECAR 800 Maximal OCR (minus 150 GM-CSF non-mitochondrial respiration) Rotenone & GM-CSF Oligomycin M-CSF antimycin A Oligomycin Rotenone & & FCCP M-CSF injection & FCCP antimycin A injection 600 injection injection

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M-CSF GM-CSF Figure 5.4: Metabolic profile of human GM-CSF and M-CSF MDMs in maximal conditions CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured in XF96 plates for 7 days in the presence of GM-CSF or M-CSF prior to analysis. a. and b. Example plots of (a) OCR and (b) ECAR measurements of GM-CSF and M-CSF MDMs. c. Pooled data for maximal OCR values of GM-CSF and M-CSF MDMs, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non- mitochondrial respiration in a.) from average post- oligomycin and FCCP injection values. d. Pooled data for maximal ECAR values of GM-CSF and M-CSF MDMs, obtained from average

134 of post-oligomycin and FCCP injection values. e. Maximal OCR/ECAR ratios of GM-CSF and M-CSF MDMs, calculated by dividing maximal OCR values by maximal ECAR values. 4 donors in 2 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Wilcoxon tests performed; * p ≤ 0.05. oligomycin/FCCP) of GM-CSF (figure 5.7) and M-CSF (figure 5.8) MDMs also revealed no significant differences in either OCR or ECAR over a range of TGF-β concentrations compared to vehicle control treated samples. Thus, together these data indicate that neither glycolysis nor OxPhos pathways in human macrophages, at least at basal levels, are altered by TGF-β.

Differences observed in the metabolic profile of IL-10 KO macrophages became apparent after treatment with LPS(121). Therefore, it is possible that any changes to metabolic phenotype in TGF-β-treated MDMs might only become apparent after their activation. To establish the optimum concentration of LPS that induced changes in the bioenergetics profile of human MDMs, GM-CSF (figure 5.9a, c and e) and M-CSF MDMs (figure 5.9b, d and f) were treated with a range of LPS concentrations and basal bioenergetic profile values were obtained. Although LPS did not alter GM-CSF basal OCR values (figure 5.9a), a trend toward increased ECAR was noted with increasing concentrations of LPS, which reached significance at 100ng/ml compared to vehicle control (figure 5.9c) with an observed trend in decreasing OCR/ECAR ratio (figure 5.9e). By contrast LPS treatment did not appear to alter the bioenergetic profile of M-CSF MDMs across all concentrations (figure 5.9b, d and f), unlike the reported increase in ECAR observed in murine BMDMs differentiated in M-CSF containing medium after LPS treatment(121) and decreased OCR/ECAR ratio observed in murine peritoneal macrophages after LPS treatment(312). Interestingly, LPS treatment did not appear to alter the maximum rate of OxPhos or glycolysis in GM-CSF- or M-CSF-treated MDMs at any concentration of LPS used (figure 5.10).

Finally, to assess if TGF-β did abrogate the observed LPS-mediated increase in glycolysis in GM-CSF MDMs, MDMs were incubated with TGF-β or vehicle

135 a. b. 800 Oligomycin Rotenone & 150 & FCCP antimycin A Oligomycin Rotenone & injection injection & FCCP antimycin A injection injection

600 GM-CSF vehicle GM-CSF vehicle control control 100 Basal OCR GM-CSF TGF-β (minus non- GM-CSF TGF-β 10ng/ml mitochondrial 10ng/ml Basal 400 respiration) ECAR

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Vehicle TGF- TGF- TGF- TGF- 1ng/ml control 0.01ng/ml 0.1ng/ml 10ng/ml

Figure 5.5: Basal metabolic profile of TGF-β treated GM-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of GM-CSF. They were treated overnight with vehicle control (dilute citric acid in PBS) or 0.01, 0.1, 1 or 10ng/ml of TGF-β prior to determination of metabolic profile. a. and b. Example plots of (a) OCR and (b) ECAR of same donor GM-CSF MDMs treated with vehicle control or 10ng/ml TGF-β c. Pooled data for basal OCR values of GM-CSF MDMs treated with vehicle control or TGF-β, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.) from average pre- oligomycin and FCCP injection values d. Pooled data for basal ECAR values

136 of GM-CSF MDMs treated with vehicle control or TGF-β, obtained from average of pre- oligomycin and FCCP injection values. e. Basal OCR/ECAR ratios of GM-CSF MDMs treated with vehicle control or TGF-β, calculated by dividing basal OCR values by basal ECAR values. 5 donors in 3 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant.

a. OxPhos b. Glycolysis

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Vehicle TGF- TGF- TGF- TGF- 1ng/ml control 0.01ng/ml 0.1ng/ml 10ng/ml

Figure 5.6: Basal metabolic profile of TGF-β treated M-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of M-CSF. They were treated overnight with vehicle control (dilute citric acid in PBS) or 0.01, 0.1, 1 or 10ng/ml of TGF-β prior to determination of metabolic profile. a. Pooled data for basal OCR values of M-CSF MDMs treated with vehicle control or TGF-β, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.) from average pre- oligomycin and FCCP injection values b. Pooled data for basal ECAR values of M-CSF MDMs treated with vehicle control or TGF-β, obtained from average of pre-oligomycin and FCCP injection values. c. Basal OCR/ECAR ratios of M-CSF MDMs treated with vehicle control or TGF-β, calculated by dividing basal OCR values by basal ECAR values. 5 donors in 3 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant.

137 a. b. Maximal OCR (minus Maximal 800 non-mitochondrial respiration) 150 ECAR

Oligomycin Rotenone & Oligomycin Rotenone & & FCCP antimycin A & FCCP antimycin A injection injection injection injection 600 GM-CSF vehicle control GM-CSF vehicle 100 control GM-CSF TGF-β 10ng/ml GM-CSF TGF-β 400 10ng/ml

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Vehicle TGF- TGF- TGF- TGF- 1ng/ml control 0.01ng/ml 0.1ng/ml 10ng/ml

Figure 5.7: Maximal metabolic profile of TGF-β treated GM-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of GM-CSF. They were treated overnight with vehicle control (dilute citric acid in PBS) or 0.01, 0.1, 1 or 10ng/ml of TGF-β prior to determination of metabolic profile. a. and b. Example plots of (a) OCR and (b) ECAR of same donor GM-CSF MDMs treated with vehicle control or 10ng/ml TGF-β c. Pooled data for maximal OCR values of GM-CSF MDMs treated with vehicle control or TGF-β, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.)

138 from average post- oligomycin and FCCP injection values d. Pooled data for maximal ECAR values of GM-CSF MDMs treated with vehicle control or TGF-β, obtained from average of post- oligomycin and FCCP injection values. e. Maximal OCR/ECAR ratios of GM-CSF MDMs treated with vehicle control or TGF-β, calculated by dividing maximal OCR values by maximal ECAR values. 4 donors in 2 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant.

a. OxPhos b. Glycolysis

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maximal 5

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Vehicle TGF- TGF- TGF- TGF- 1ng/ml control 0.01ng/ml 0.1ng/ml 10ng/ml

Figure 5.8: Maximal metabolic profile of TGF-β treated M-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of M-CSF. They were treated overnight with vehicle control (dilute citric acid in PBS) or 0.01, 0.1, 1 or 10ng/ml of TGF-β prior to determination of metabolic profile. a. Pooled data for maximal OCR values of M-CSF MDMs treated with vehicle control or TGF-β, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.) from average post- oligomycin and FCCP injection values b. Pooled data for maximal ECAR values of M-CSF MDMs treated with vehicle control or TGF-β, obtained from average of post-oligomycin and FCCP injection values. c. Maximal OCR/ECAR ratios of M-CSF MDMs treated with vehicle control or TGF-β, calculated by dividing maximal OCR values by maximal ECAR values. 5 donors in 3 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with

139 interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant. GM-CSF M-CSF a. basal b. basal

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LPS LPS LPS LPS LPS LPS Vehicle Vehicle 1ng/ml 10ng/ml 1ng/ml 10ng/ml control 100ng/ml control 100ng/ml

Figure 5.9: Basal metabolic profile of LPS treated GM-CSF and M-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of GM-CSF or M-CSF. They were then left overnight untreated or treated with 1, 10 or 100ng/ml of LPS prior to determination of metabolic profile. Pooled data for basal OCR values of a. GM-CSF and b. M-CSF MDMs untreated or LPS treated, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.) from average pre- oligomycin and FCCP injection

140 values. Pooled data for basal ECAR values of c. GM-CSF and d. M-CSF MDMs untreated or LPS treated, obtained from average of pre-oligomycin and FCCP injection values. Basal OCR/ECAR ratios of e. GM-CSF and f. M-CSF MDMs untreated or LPS treated, calculated by dividing basal OCR values by basal ECAR values. 5 donors in 2 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed; * p ≤ 0.05.

GM-CSF M-CSF a. maximal b. maximal

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OxPhos/Glycolysis LPS LPS LPS LPS LPS LPS Vehicle Vehicle 1ng/ml 10ng/ml 1ng/ml 10ng/ml control 100ng/ml control 100ng/ml

Figure 5.10: Maximal metabolic profile of LPS treated GM-CSF and M-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and

141 cultured for 7 days in XF96 plates in the presence of GM-CSF or M-CSF. They were then left overnight untreated or treated with 1, 10 or 100ng/ml of LPS prior to determination of metabolic profile. Pooled data for maximal OCR values of a. GM-CSF and b. M-CSF MDMs untreated or LPS treated, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.) from average post- oligomycin and FCCP injection values. Pooled data for maximal ECAR values of c. GM-CSF and d. M-CSF MDMs untreated or LPS treated obtained from average of post-oligomycin and FCCP injection values. Maximal OCR/ECAR ratios of e. GM-CSF and f. M-CSF MDMs untreated or LPS treated, calculated by dividing maximal OCR values by maximal ECAR values. 5 donors in 2 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed, all comparisons non-significant. control and then either left unstimulated or stimulated with LPS. As observed previously, none of the treatments affected OCR in GM-CSF or M-CSF MDMs (figure 5.11a and b), whereas LPS-stimulated GM-CSF MDMs exhibited increased ECAR (figure 5.11c) and a trend toward decrease OCR/ECAR ratios (figure 5.11e). However, GM-CSF MDMs treated with TGF-β and then stimulated with LPS showed no differences in ECAR compared to those treated with LPS alone (figure 5.11c and e), indicating that TGF-β does not play a similar role to IL-10 in abrogating a LPS-induced glycolytic switch in MDMs. Similarly, combined TGF-β and LPS stimulation did not alter the basal bioenergetic profile of treated M-CSF MDMs (figure 5.11b, d and f). Additionally, analysis of maximal OCR and ECAR in GM-CSF and M-CSF MDMs showed no effects of TGF-β treatment in the absence or presence of LPS stimulation (figure 5.12). Together, these data suggest that the bioenergetic profile of human MDMs differentiated toward a pro-inflammatory (GM-CSF) or anti- inflammatory (M-CSF) phenotype does not fully approximate the paradigm of glycolytic ‘inflammatory’ and OxPhos dependent ‘anti-inflammatory’ murine macrophages. Additionally, experiments thus far have not yet established a role for TGF-β in altering macrophage metabolism in MDMs at rest or after LPS stimulation.

142 GM-CSF basal M-CSF basal a. b.

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Figure 5.11: Basal metabolic profile of LPS and TGF-β treated GM-CSF and M-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of GM-CSF or M-CSF. They were then treated with vehicle control (dilute citric acid in PBS), 10ng/ml TGF-β, 10ng/ml LPS or 10ng/ml TGF-β plus 10ng/ml LPS for 4 hours prior to determination of metabolic profile. Pooled data for basal OCR values of a. GM-CSF and b. M-CSF MDMs untreated or treated with LPS, TGF-β or both, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.) from average pre- oligomycin and FCCP injection values. Pooled data for basal ECAR values of c. GM-CSF and d. M-CSF MDMs untreated or treated with LPS, TGF-β or both, obtained from average of pre-oligomycin and FCCP injection

143 values. Basal OCR/ECAR ratios of e. GM-CSF and f. M-CSF MDMs untreated or treated with LPS, TGF-β or both, calculated by dividing basal OCR values by basal ECAR values. 5 donors in 2 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed; * p ≤ 0.05. GMCSF maximal MCSF maximal a. b. 500 400

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Figure 5.12: Maximal metabolic profile of LPS and TGF-β treated GM-CSF and M-CSF MDMs CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of GM-CSF or M-CSF. They were then treated with vehicle control (dilute citric acid in PBS), 10ng/ml TGF-β, 10ng/ml LPS or

144 10ng/ml TGF-β plus 10ng/ml LPS for 4 hours prior to determination of metabolic profile. Pooled data for maximal OCR values of a. GM-CSF and b. M-CSF MDMs untreated or treated with LPS, TGF-β or both, calculated by subtraction of average measurements post- rotenone and antimycin A treatment (non-mitochondrial respiration in a.) from average post- oligomycin and FCCP injection values. Pooled data for maximal ECAR values of c. GM-CSF and d. M-CSF MDMs untreated or treated with LPS, TGF-β or both, obtained from average of post-oligomycin and FCCP injection values. Maximal OCR/ECAR ratios of e. GM-CSF and f. M-CSF MDMs untreated or treated with LPS, TGF-β or both, calculated by dividing maximal OCR values by maximal ECAR values. 5 donors in 2 independent experiments, with 4-6 technical replicates per donor/condition. Graphs show median with interquartile range. Friedman with Dunn’s multiple comparisons performed, * p ≤ 0.05.

5.3. Discussion

To study the potential role of TGF-β in determining the bioenergetic profile of cells we had to first establish a protocol for analysis of human MDMs. Published experiments analysing murine BMDMs describe detaching cells from culture plates utilising citrate saline prior to seeding cells in a 96-well Seahorse XF™ cell culture microplates at 50 000 cells per well(313). Experiments utilising human MDMs describe seeding fully differentiated MDMs at 200 000 cells per well in 24-well Seahorse XF™ cell culture microplates. As the surface area at the base of a well in the 96-well plate is 40% that of a well in a 24-well Seahorse XF™ cell culture microplate(314), the final number of cells utilised by us (75 000 cells/well) is broadly equivalent.

In contrast to other published protocols, we chose to differentiate MDMs within the 96-well Seahorse XF™ cell culture microplate rather than differentiating in a standard flat-bottomed plate and then transferring. This was due to the sharp decrease in viable cells noted upon removal from the standard culture plate even with gentle scraping. Alternative methods of detaching cells from the culture plate were tried, namely removal with Accutase™ and TrypLE™ cell detachment solutions. However, all removal methods resulted in very low yield of viable cells compared to initial monocyte numbers as seen in figure 5.13. The viability of cells removed by Accutase™ and TrypLE™ appeared higher for GM- CSF MDMs (average viability 57% with Accutase™ and 63% with TrypLE™) and similar with M-CSF MDMs (average viability 45% with Accutase™ and 40%

145 with TrypLE™) to removal by scraping, but this was offset by a lower total cell harvest with cell detachment solutions. Furthermore, monocyte differentiation to MDMs in 96-well Seahorse XF™ plates did not seem to affect cell surface marker expression compared to MDMs seeded in standard 96-well cell culture plates after removal with Accutase™, as seen in figure 5.14, hence our decision to utilise cells differentiated within the Seahorse XF™ plates.

Prior to bioenergetics analysis, the media was changed and therefore non- viable non-adherent cells would be removed prior to analysis. To control for cell loss we utilised a bicinchoninic acid (BCA) assay in an attempt to normalise results for total protein content in the well (as a surrogate marker for total cell number). However, despite adding an extra step to analysis, this did not significantly alter the results obtained, as summarised in figure 5.15. Therefore results presented within section 5.2 are from bioenergetics analysis without BCA assay adjustment. Other authors have also questioned the accuracy of BCA for protein estimation in this setting, instead utilising a cell proliferation assay (313). Furthermore, to increase the validity of results six replicates were plated for each cell type and treatment and any outlying technical replicates were excluded from analysis.

Macrophages were first described as ‘M1’ and ‘M2’ based on the observation that these cells from C57BL/6 mice, which preferentially mount Th1 responses, produce higher quantities of NO when stimulated with either LPS or IFN-γ, whereas macrophages from BALB/c mice, which preferentially mount Th2 responses, increased arginase activity but not NO production in response to LPS(315). This definition was further expanded to incorporate a variety of ‘M2’ stimuli such as IL-3 plus IL-14, or IL-10 which promotes the differentiation of macrophages specialised for immunoregulation and tissue remodelling(287), although more recent evidence suggests significant plasticity of polarised macrophages(285, 300).

GM-CSF differentiated MDMs have phenotypic characteristics in common with macrophages stimulated with LPS and IFN-γ such as IL-23 and TNF-α production and the ability to preferentially promote Th1 responses(285, 287,

146 316), whereas M-CSF MDMs produce IL-10 constitutively, similar to ‘M2’ MDMs. Furthermore M-CSF MDMs polarised to ‘M1’ with LPS and IFN-γ secrete more IL-10 and less TNF-α than GM-CSF differentiated ‘M1’ MDMs(286). Therefore it might be expected that GM-CSF MDMs display a more glycolytic profile in common with ‘M1’ macrophages, than M-CSF MDMs. A published study on the bioenergetic profile of human GM-CSF and M-CSF MDMs suggested that, in contrast to the pro-glycolytic profile observed in murine ‘M1’ MDMs, and pro-OxPhos profile of murine ‘M2’ MDMs(309) human GM-CSF MDMs are both more glycolytic and have higher levels of OxPhos in basal and maximal conditions than human M-CSF MDMs(109), similar to results presented here. However, upon calculating OCR/ECAR ratio, Izquierdo et al.(109) did observe that human M-CSF MDMs in resting conditions had a significantly higher OCR/ECAR ratio than GM-CSF MDMs. By contrast we did not observe this in resting conditions, but analysis of values after stimulation with oligomycin/FCCP showed a trend toward higher OCR/ECAR ratio in M-

CSF MDMs.

Treatment of murine peritoneal macrophages with IL-4 plus IL-13 or IL-10 did not induce any observable differences in glucose consumption and lactate production compared to untreated, in contrast to LPS, which induced markedly increased levels of glycolysis(105). These findings correspond with data presented here, showing a dose-dependent increase in ECAR in LPS-treated GM-CSF MDMs in basal conditions. A similar increase was not seen in M-CSF- differentiated MDMs, which may represent a similar finding to the observation of an attenuated ‘M1’ phenotype in such cells after LPS and IFN-γ polarisation (286, 316).

TGF-β has been shown to have complementary effects to IL-10 in promoting a tolerogenic macrophage phenotype(56, 317) so it is not unsurprising that, similar to IL-10 alone(105), TGF-β treatment did not result in alterations in the bioenergetics profile of MDMs. However, macrophages from IL-10 KO mice displayed a more highly glycolytic profile than WT BMDMs upon LPS treatment, which was abrogated by addition of exogenous IL-10(121). Here, we find that treatment of human MDMs with LPS and TGF-β did not similarly abrogate the

147 increased ECAR noted in LPS-treated GM-CSF MDMs in basal conditions. Data do suggest that TGF-β can have diverse roles in influencing metabolism in other non-immune cell types. Whilst TGF-β was shown to increase both glycolysis and oxygen consumption in rabbit renal cells(318), in cancer cells TGF-β inhibited lipogenesis and increased OxPhos, resulting in decreased cell adhesion molecule expression and increased motility, both of which are thought to be significant in promoting cancer metastasis(319). Thus, although it appears that metabolism in macrophages is regulated in a TGFβ—independent fashion, we cannot rule out that, in some conditions or contexts, TGFβ may regulate aspects of metabolic behaviour of macrophages not elucidated here.

60 Scraping Accutase (45 min) TrypLE (45 min)

40

20 Percentage viable cell yield

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Figure 5.13: Percentage yield of viable GM-CSF and M-CSF treated MDMs after removal from cell culture plates via different methods. Human CD14+ monocytes were cultured for 7 days in standard 12-well cell-culture plates in the presence of GM-CSF or M-CSF. Cells were harvested at day 7 by either gentle scraping or a 45 minute treatment with Accutase™ or TrypLE™ cell detachment solutions. Cells were then viability stained with Tryphan blue and counted. The percentage yield was calculated by dividing the final number of viable cells per well by 750 000 (the number of seeded monocytes). Graph shows median and interquartile range.

148

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c. GM-CSF M-CSF GM-CSF M-CSF

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GM-CSF M-CSF Figure 5.14: Expression of key surface markers on GM-CSF and M-CSF treated MDMs cultured in Seahorse XF™ cell culture 96-well microplates or standard 96-well cell- culture plates. CD14+ monocytes were plated at 50 000 or 75 000 cells/well in Seahorse XF™ cell culture 96-well microplates, 100 000 cells/well in standard 96-well cell-culture plates and differentiated for 7 days in the presence of either GM-CSF or M-CSF, and then harvested by 45 minute treatment with Accutase™ prior to staining and analysis for expression of a. CD163, b. CD14 and c. HLA-DR. Graphs shown median and interquartile range. Friedman with Dunn’s multiple comparisons performed. All comparisons non-significant.

149 Unadjusted BCA adjusted a. b. ** * 200 500

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8 8

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0 0 OxPhos/Glycolysis M-CSF M-CSF GM-CSF GM-CSF Figure 5.15: Comparison of metabolic profile of human GM-CSF and M-CSF MDMs in basal conditions unadjusted or adjusted for protein concentration per well. CD14+ monocytes were separated from healthy human donor PBMCs using magnetic beads and cultured for 7 days in XF96 plates in the presence of GM-CSF or M-CSF. After metabolic analysis MDMs were lysed in-plate prior to determination of protein levels per well using a bicinchoninic acid (BCA) assay. Protein levels were calculated against a standard curve and OCR and ECAR values were adjusted to give values per mg of protein. Basal OCR values of

150 GM-CSF and M-CSF MDMs a. unadjusted and b. adjusted for protein concentration per well. Basal ECAR values of GM-CSF and M-CSF MDMs c. unadjusted and d. adjusted for protein concentration per well. Basal OCR/ECAR ratios of GM-CSF and M-CSF MDMs e. unadjusted and f. adjusted for protein concentration per well. Basal values: 8 donors in 4 independent experiments. Wilcoxon tests performed; * p ≤ 0.05.

Summary of findings: • Human in vitro-derived 'pro-inflammatory’ GM-CSF MDMs display higher rates of OxPhos in basal conditions than 'anti-inflammatory’ M-CSF differentiated MDMs. • Human GM-CSF MDMs display higher rates of glycolysis in maximal conditions that M-CSF differentiated MDMs. • These findings do not fully approximate those seen in murine ‘pro- inflammatory/M1’ macrophages, which are more glycolytic, or ‘anti- inflammatory/M2’ macrophage models, which are more OxPhos dependent. • LPS treatment was shown to increase glycolysis in GM-CSF MDMs in basal but not maximal conditions, and did not alter the metabolic profile of M-CSF MDMs.

• Pre treatment with TGF-β did not alter the metabolic changes observed in GM-CSF MDMs upon LPS treatment. • TGF-β treatment alone did not alter rates of glycolysis or OxPhos in GM- CSF or M-CSF MDMs.

151 CHAPTER 6. DISCUSSION

6.1. TGF-β activation in inflammation and intestinal immune homeostasis

The importance of integrin αvβ8 expression and TGF-β in maintaining intestinal immune homeostasis in mice was highlighted by development of spontaneous colitis in mice with CD11c+ cell-specific integrin β8 KO(239). Although CD11c+ cells in mice may also include a subset of macrophages(133), further work has indicated that integrin β8 expression in mice is restricted to the CD103+ DC population (at least in the intestine)(244, 272). The finding here of high expression of integrin αvβ8 on human circulating monocytes, MDMs and intestinal macrophage populations(272) suggests there are important differences in expression patterns of human αvβ8 in mouse versus human.

The functional importance of αvβ8 expression in murine intestinal immune homeostasis appears to principally relate to DC activation of the multi-functional cytokine TGF-β resulting in preferential Treg induction(244, 245), although Treg expression of αvβ8 is necessary for their suppression of established inflammation(256). It appears that human monocytes and MDMs also highly express αvβ8 are similarly specialised to activate TGF-β(272). Classical CD14+CD16- monocytes, although expressing less integrin αvβ8 than the intermediate CD14hiCD16+ population, appear to activate more TGF-β than other monocyte populations, despite similar expression of latent TGF-β. This enhanced ability to activate TGF-β has been suggested to be due to the higher expression of the transmembrane protease MMP-14 in classical monocytes versus other monocyte subsets (4), which has been shown to co-localise with αvβ8 to cleave TGF-β(225). Published data suggest that TGF-β can both promote and inhibit pro-inflammatory cytokine release from monocytes and macrophages(320-322), although αvβ8-dependent TGF-β activation in human monocytes appears to inhibit their production of the pro-inflammatory cytokine TNF-α upon LPS stimulation(272). Despite similar expression of both latent TGF-β and MMP-14 compared to GM-CSF differentiated MDMs, M-CSF MDMs expressed significantly higher levels of integrin αvβ8 and were shown to activate higher amounts of TGF-β(272). However, the functional consequences

152 of MDM expression of αvβ8 are not yet fully apparent. Focusing here on tolerogenic macrophage functions, blockade of TGF-β or αvβ8 did not appear to alter expression of surface markers associated with an anti-inflammatory macrophage phenotype such as CD163(54, 57), phagocytic capacity or secretion of the anti-inflammatory cytokine IL-10.

Multiple lines of evidence suggest that intestinal macrophage populations are continually replenished from circulating monocytes(51, 54, 71) and TGF-β is has been suggested to be a major chemoattractant of monocytes(275). Furthermore, TGF-β is able to facilitate passage to sites of inflammation by promoting expression of multiple integrin subunits involved in cell adhesion(267), alongside MMP-9(323), thus mediating ECM binding and breakdown. Analysis of inflamed sections of IBD mucosa demonstrate an expanded population of pro-inflammatory macrophages thought to represent recently recruited blood classical monocytes, which then continue to drive inflammation and further inflammatory cell recruitment(54, 57, 284, 324). It is possible that αvβ8-dependent TGF-β activation by monocytes may be involved in recruitment to the inflamed intestine, and monocyte migration models utilising TGF-β and αvβ8 blocking antibodies represent important future experiments. However, it should be noted that whilst both intestinal macrophages and recently arrived monocytes isolated from the intestinal LP of healthy subjects appear to express high levels of αvβ8, monocytes and macrophages isolated from the intestinal LP of IBD patients with active inflammation expressed significantly lower levels of αvβ8(272). This finding appears more consistent with the observed lack of TLRL-induced αvβ8 upregulation on MDMs in this thesis, although why monocytes in vitro appear to upregulate αvβ8 in response to TLR4L and 7/8L but do not appear to do so in the inflamed intestine where high levels of PRR signalling would be expected is not clear.

A further complication in the analysis of mechanisms controlling αvβ8 expression and TGF-β activation is the vast diversity in genetic makeup and exposome of human subjects, likely reflected in the wide range in variation observed in percentage of αvβ8 positive total monocytes in healthy donors. Furthermore, similar to the finding that certain polymorphisms of the TGFB1

153 gene can predispose to asthma severity(111), a genetic variant of the ITGB8 gene has been identified as a risk allele for IBD, with a predicted effect of increased expression, potentially on monocytes(325). However, these data were based on gene-mapping analyses rather than functional experiments, and the ITGB8 risk allele was found to be carried by almost 90% of the population. Furthermore data presented here indicates that integrin protein and gene expression do not necessarily correlate. Comparison of carriers and non- carriers of the ITGB8 variant in question in terms of monocyte expression of αvβ8 and αvβ8-dependent TGF-β activation should be key in elucidation of the function of this allele.

It is also possible that the hypothesised anti-inflammatory effects of αvβ8- dependent TGF-β activation in human monocyte-macrophages may vary between early or established inflammation. Similar to the finding that efficacy of TGF-β in attenuating pro-inflammatory cytokine treatment in PBMC was timing- dependent relative to LPS stimulation(326) the importance of timing in integrin modulation is underlined by the finding that early administration of anti-αvβ6/8 antibody inhibited transformation to cancer-associated myofibroblasts in a cell co-culture, but after three days only blockade of TGF-β itself was effective(327). Despite these caveats, data from murine models highlighting the importance of intestinal DC expression of integrin αvβ8 in intestinal homeostasis(239, 244), and the finding of significantly reduced αvβ8 expression on monocytes and macrophages from inflamed IBD compared to healthy intestinal tissue(272) are highly suggestive that human intestinal monocyte-macrophage expression of αvβ8 promotes intestinal immune tolerance.

6.2 TGF-β in fibrosis

TGF-β signalling can result in seemingly paradoxical effects, as observed by its ability to promote both pro-inflammatory Th17 and anti-inflammatory Treg differentiation(149). As a further example of the beneficial and deleterious effects of TGF-β, pro-repair macrophages, important in resolution of injury and inflammation, may become aberrantly activated, instigating the onset of fibrosis

154 of which TGF-β is a major driver(328). Transgenic mice with increased circulating levels of active TGF-β developed renal failure secondary to progressive interstitial fibrosis within the kidneys and liver fibrosis(329). Epidermal over-expression of TGF-β in mice resulted in spontaneous psoriasis- like skin lesions(330), whereas inhibition of TGF-β signalling via various mechanisms has been shown to protect against fibrosis in kidney, skin, lung, liver and the cardiovascular system(331-339). Indeed pirfenidone, which inhibits pro-fibrotic actions of TGF-β(340) has been approved for the treatment of idiopathic pulmonary fibrosis(341).

The increasing stiffness of the ECM in fibrosis induces further release of active TGF-β, possibly through integrin-mediated activation(342) suggesting that cytoskeleton-binding integrins may amplify TGF-β signalling to further promote fibrosis. Furthermore TGF-β upregulates multiple αv integrins on various cell types(343), although its role in regulating αvβ8 may vary(246, 247), with data here showing no significant effect of TGF-β on expression of αvβ8 by monocytes. Pulmonary fibroblast expression of integrin αvβ8 and αvβ8- mediated TGF-β activation correlated with severity of airway disease in human COPD(344). Anti-integrin therapies have shown therapeutic promise in fibrosis: both RGD and αvβ6-blockade appears protective in pulmonary, hepatic and renal fibrosis models(227, 232, 345, 346). More recently, anti-integrin αvβ8 antibodies have been shown to block IL-1β and TGF-β driven inflammation and fibrosis in mice in a cigarette smoke induced exacerbation model of COPD(347).

Whilst our data suggest that αvβ8-dependent TGF-β activation has a protective role in maintaining intestinal immune homeostasis it is important to note that IBD can be associated with development of fibrotic strictures(31, 161). Whilst a minority of patients with IBD develop strictures(348) high levels of TGF-β have been found within IBD strictures and enhanced TGF-β-induced collagen production observed in CD stricture myofibroblasts(208, 209). Fibrosis appears to be an exaggerated response of normal tissue healing and repair(328), so it is possible that enhancement of αvβ8-dependent TGF-β activation at an early

155 disease stage may be protective against the chronic inflammation that drives fibrosis(349). Conversely it is possible that increasing αvβ8-dependent TGF-β activation may exacerbate fibrosis. Precise actions of TGF-β have been shown to differ between cell type, location, developmental stage of the organism, surrounding cytokine milieu, timing of administration, local TGF-β concentration, and even genetic variants of TGF-β itself(171, 330, 350-355). Therefore understanding the factors that influence αvβ8-mediated TGF-β activation and the effects of this TGF-β activation in context are vital when considering manipulation of this pathway for therapeutic benefit in IBD.

6.3. TGF-β in cancer

Another recent area of major research focus is the role of TGF-β in cancer, where TGF-β has been shown to both protect against and promote neoplasia(356). TGF-β appears to inhibit tumorgenesis in the early stages(356), via suppression of the proto-oncogene c-Myc(357), and anti-proliferative and pro-apoptotic properties. Indeed, loss of responsiveness to TGF-β regulation characterises many cancers including those of the GI tract(358, 359). The anti- proliferative effect of TGF-β has been shown to prevent establishment of distant metastases(360, 361). However, increased TGF-β expression has been observed in multiple cancer tissues(359) including colorectal cancer (CRC)(362), alongside increased circulating levels of TGF-β, which correlates with cancer stage. At later stages in tumour progression, TGF-β signalling can promote cancer growth and spread via multiple mechanisms including suppression of anti-tumour immunity by inhibition of T-cell proliferation(363), promotion of B-cell apoptosis(364), inhibition of DC differentiation and chemotaxis(356), suppression of IFN-γ induced NK generation, and promotion of a tolerogenic macrophage and neutrophil phenotype(359). TGF-β also promotes epithelial-to-mesenchymal transition (EMT), which contributes to cancer invasion and metastasis through actions such as downregulation of the cell-cell adhesion molecule E-cadherin resulting in increased motility(365, 366). EMT is associated with the presence cancer stem-cells (CSCs) that display self- renewing, tumour initiating and sustaining properties(359, 367), and are notoriously treatment resistant(368-370). The TGF-β signalling pathway

156 appears to be highly activated in these CSCs(371) although whether TGF-β blockade reduces or promotes survival of this population is controversial(369, 371-373).

Whilst conventional chemo- and radiotherapy have been shown to upregulate TGF-β signalling resulting in treatment resistance by various mechanisms(370, 374-377), a combination of TGF-β inhibition with chemo- or radiotherapy increased treatment response in multiple animal models of cancer (370, 374- 376, 378-380). However, TGF-β blockade in human trials thus far have suggested modest efficacy (381-383). Immunotherapies in cancer act by promoting anti-tumour immunity but are less efficacious in some larger tumours due to inhibition of the cytotoxicity of anti-tumour lymphocytes by tumour- produced TGF-β(384). Anti-TGF-β therapies have therefore been successfully combined with immunotherapies in mouse cancer models(384-386).

Given the multi-functional role of TGF-β, it is not surprising that studies have suggested potential adverse effects of inhibiting TGF-β activity. Blockade of TGF-β with the anti-TGF-β-1,-2 and -3 antibody was associated with the skin lesions, notably keratoacanthomas and squamous cell carcinoma, which regressed with cessation of therapy(381). These lesions resembled those seen in Ferguson-Smith disease, caused by loss-of-function mutations in the gene encoding TGF-βR1(387). High-dose TGF-β inhibition in rodents was associated with cardiac valvular lesions, skeletal abnormalities and increased susceptibility to renal cancers(388-390), although extensive evaluation of cardiac safety in 79 cancer patients treated with an anti-TGF-βR small molecule inhibitor was reassuring(391). However, given the potential side-effects of widespread TGF-β blockade, and the observation that whilst increased circulating TGF-β has been reported in association with cancer progression and metastasis(379), local TGF-β production at sites of distant spread inhibits establishment of metastases(360), it has been suggested that utilisation of therapies aimed at blocking TGF-β activating integrins may help fine-tune response to TGF-β which may be anti- or pro-neoplastic if inhibited systemically(392).

157 Increased expression of integrin αvβ6 has been observed in multiple malignancies including CRC, lung, cervical and breast carcinoma, where αvβ6 expression was associated with shorter survival(393-396). Yet anti-integrin therapies have shown paradoxical responses in the treatment of cancer. Inhibition of integrin αvβ6 in pharyngeal and breast cancer models (396, 397) was shown to be efficacious, and improved survival was observed with anti-αv integrin antibody plus chemotherapy in high αvβ6-expressing CRC(398). However acceleration of disease progression in both late and early stages of a model with αvβ6 blockade has been reported(399), consistent with the finding of significantly increased cancer development in αvβ6 KO mice(400).

Some human breast and prostate carcinomas express αvβ8 in association with a more invasive phenotype(401, 402), and it should be noted that the anti-αvβ6 antibody utilised in some studies(396, 397) also bound integrin αvβ8. Comparison with an αvβ6-specific antibody or β6 siRNA knockdown yielded no significant differences in breast cancer cell-line invasion leading authors to conclude that the observed responses were due to blockade of αvβ6 but not αvβ8 integrin(396). However, these findings may be biased by the lack of an effective antibody specific to murine β8. Mice with double KO of αvβ6 and αvβ8 phenocopy TGF-β1 and -3 KO mice(234), indicating that therapies which inhibit integrin αvβ8 may have a similar effect in cancer therapy to those inhibiting αvβ6. Certainly, tumour-associated production of TGF-β has been shown to promote differentiation of Tregs, which neutralise cytotoxic anti-tumour lymphocytes, thus allowing the tumour to evade immune responses(392). αvβ8- dependent TGF-β activation by murine DCs results in preferential induction of Tregs(244), suggesting that inhibition of αvβ8-dependent TGF-β activation may represent a future therapeutic strategy in cancer through enhancing anti-tumour immunity. Notably, colitis and rectal abscess were observed in patients treated with anti-αv integrin antibody(398) consistent with the role of integrin αvβ8 in murine intestinal immune homeostasis(239, 240), further highlighting the delicate balance of TGF-β activation. Adding to the complexity is our finding of monocyte-macrophage αvβ8 expression and αvβ8-dependent TGF-β activation, not seen in mice(272), with an as yet unknown function in cancer immunity.

158

The possible pro-tumourigenic properties of increased integrin αvβ8-dependent TGF-β activation highlighted above underline the need for caution in potentially modifying this pathway in IBD therapy. IBD is associated with the development of colitis associated CRC(403). TGFβ-RII mutations have been identified in up to 36% of colitis associated CRC, associated with a more aggressive cancer phenotype(210). CRC also appears to develop more frequently in areas of colonic fibrosis(404), where TGF-β is known to be upregulated(208). Again, however, timely treatment to enhance αvβ8-dependent TGF-β activation could effectively terminate the chronic inflammation that leads to fibrosis and cancer in the longer-term(349).

6.4. Personalised therapy in IBD

Disease location, associated complications, and histopathological features differentiate the two clinical entities of CD and UC. Even within these entities there is a diversity of phenotype which can include development of fistulae (abnormal connections between two epithelialized surfaces) that can join intestinal loops, the skin, bladder, vagina and perianal area(161) and, as mentioned above, stricturing resulting from active inflammation and/or established scarring and fibrosis(31, 161, 405). The diversity of phenotype seen in IBD is mirrored by the varying responses to anti-TNF-α monoclonal antibodies which have revolutionized the treatment of IBD since introduction into clinical practice(406). Approximately two-thirds of patients with CD or UC will have a response to anti-TNF-α therapy(26, 407, 408), which has been shown to dampen inflammation by diverse mechanisms (409-411). However, the lack of a primary response in a third of patients(412) indicates that cytokine profiles and inflammatory processes may differ between IBD patients. Furthermore, a wide variation of clinical course is seen in IBD(413), and whilst an aggressive treatment approach is associated with better disease outcomes(414, 415) this could result in unnecessary exposure to powerful immunosuppressives in those with a more benign disease course. All of the above highlight the need for strategies to better define individual disease phenotypes and deliver personalised therapy.

159

Attempts to predict disease course and non-response to anti-TNF-α antibodies have mainly relied on patient characteristics and basic clinical parameters(416). More recent approached have included genetic signatures, which whilst predictive of non-response to anti-TNFs in UC and Crohn’s colitis, appear less accurate in Crohn’s ileitis(417). More recently Atreya et al.(418) utilised fluorescent-labelled TNF-α antibodies either in vivo with confocal laser endomicroscopy or ex vivo on intestinal biopsy samples, and showed that high levels of staining predicted response to anti-TNF- α therapy. It was also shown that more patients expressing high CD103 on baseline colon biopsy achieved clinical remission with anti-β7 integrin therapy than patients expressing low levels of CD103, suggesting potential utility in predicting treatment response(419).

The potentially diverse actions of αvβ8-dependent TGF-β activation mean that a personalised approach to IBD therapy utilising agents that target this pathway is highly desirable. Furthermore development of more sophisticated strategies to predict disease behaviour and underlying mechanisms may reveal groups of patients in which enhancing TGF-β signalling will be therapeutically effective. Both TGF-β and the protein Smad7, a protein which inhibits TGF-β downstream signalling, are upregulated in inflamed IBD mucosa(420, 421). An orally administered Smad7 inhibitor showed great promise in inducing clinical remission in CD in a phase 2 trial(422), but a phase 3 trial was later terminated due to lack of efficacy(423). The finding of high levels of monocyte and macrophage expression of αvβ8 and αvβ8-mediated TGF-β activation, with differential expression of the integrin in healthy and inflamed intestine(272), suggest that the αvβ8-TGF-β pathway may offer a more cell-specific target to enhance TGF-β signalling. Enhancing expression of αvβ8 represents a therapeutic challenge, although advances in gene therapy for a variety of conditions have yielded encouraging results in phase 1/2 trials(142). Ex vivo targeting of immune cells of interest such as monocytes followed by re- introduction to the patient rather than in vivo delivery of gene therapy may well be preferable to avoid the potential severe adverse effects of global TGF-β overactivation(28).

160

The finding of high expression of integrin αvβ8 on human monocytes and anti- inflammatory macrophages is not previously described, and in contrast to the known expression of integrin αvβ8 on DCs and Tregs does not reflect the murine system, where αvβ8 appears to be absent on monocytes and macrophages. Human monocytes and macrophages appear specialised to activate TGF-β in an αvβ8-dependent manner, and αvβ8 expression is downregulated on these populations in the actively inflamed intestine of IBD patients(272). Taken together these data indicate that integrin αvβ8-dependent TGF-β activation by monocyte-macrophages may play an important role in intestinal immune homeostasis, particularly in light of the discovery of ITGB8 as an IBD risk allele. Given recent interest in integrin-modulating therapies for a variety of conditions the αvβ8-TGF-β pathway may represent an attractive therapeutic target in IBD, fibrosis and cancer.

161 REFERENCES

1. Sender R, Fuchs S, Milo R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol. 2016;14(8):e1002533. 2. Ivanov, II, Honda K. Intestinal commensal microbes as immune modulators. Cell Host Microbe. 2012;12(4):496-508. 3. Marchesi JR, Adams DH, Fava F, Hermes GD, Hirschfield GM, Hold G, et al. The gut microbiota and host health: a new clinical frontier. Gut. 2016;65(2):330-9. 4. Mowat AM, Agace WW. Regional specialization within the intestinal immune system. Nat Rev Immunol. 2014;14(10):667-85. 5. Ananthakrishnan AN. Epidemiology and risk factors for IBD. Nat Rev Gastroenterol Hepatol. 2015;12(4):205-17. 6. Clarke G, Stilling RM, Kennedy PJ, Stanton C, Cryan JF, Dinan TG. Minireview: Gut microbiota: the neglected endocrine organ. Mol Endocrinol. 2014;28(8):1221-38. 7. Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474(11):1823-36. 8. Matsuoka K, Kanai T. The gut microbiota and inflammatory bowel disease. Semin Immunopathol. 2015;37(1):47-55. 9. Kamada N, Seo SU, Chen GY, Nunez G. Role of the gut microbiota in immunity and inflammatory disease. Nat Rev Immunol. 2013;13(5):321-35. 10. Wang W, Jovel J, Halloran B, Wine E, Patterson J, Ford G, et al. Metagenomic analysis of microbiome in colon tissue from subjects with inflammatory bowel diseases reveals interplay of viruses and bacteria. Inflamm Bowel Dis. 2015;21(6):1419-27. 11. Cadwell K, Patel KK, Maloney NS, Liu TC, Ng AC, Storer CE, et al. Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine. Cell. 2010;141(7):1135-45. 12. Norman JM, Handley SA, Baldridge MT, Droit L, Liu CY, Keller BC, et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell. 2015;160(3):447-60. 13. Hermiston ML, Gordon JI. Inflammatory Bowel Disease and Adenomas in Mice Expressing a Dominant Negative N-Cadherin. Science. 1995;270:1203- 7. 14. Artis D. Epithelial-cell recognition of commensal bacteria and maintenance of immune homeostasis in the gut. Nature Reviews Immunology. 2008;8(6):411-20. 15. Geremia A, Biancheri P, Allan P, Corazza GR, Di Sabatino A. Innate and adaptive immunity in inflammatory bowel disease. Autoimmun Rev. 2014;13(1):3-10. 16. Grencis RK, Worthington JJ. Tuft Cells: A New Flavor in Innate Epithelial Immunity. Trends Parasitol. 2016;32(8):583-5. 17. Vaishnava S, Yamamoto M, Severson KM, Ruhn KA, Yu X, Koren O, et al. The Antibacterial Lectin RegIIIg Promotes the Spatial Segregation of Microbiota and Host in the Intestine. Science. 2011;334:255-8. 18. Wehkamp J, Salzman NH, Porte E, Nuding S, Weichenthal M, Petras RE, et al. Reduced Paneth cell α-defensins in ileal Crohn’s disease. Proc Natl Acad Sci U S A. 2005;102:18129–34.

162 19. Uehara A, Fujimoto Y, Fukase K, Takada H. Various human epithelial cells express functional Toll-like receptors, NOD1 and NOD2 to produce anti- microbial peptides, but not proinflammatory cytokines. Mol Immunol. 2007;44(12):3100-11. 20. O'Neill LA, Golenbock D, Bowie AG. The history of Toll-like receptors - redefining innate immunity. Nat Rev Immunol. 2013;13(6):453-60. 21. Cario E. Toll-like receptors in inflammatory bowel diseases: a decade later. Inflamm Bowel Dis. 2010;16(9):1583-97. 22. Cario E, Podolsky DK. Differential Alteration in Intestinal Epithelial Cell Expression of Toll-Like Receptor 3 (TLR3) and TLR4 in Inflammatory Bowel Disease. Infect Immun. 2000;68(12):7010–7. 23. Rakoff-Nahoum S, Paglino J, Eslami-Varzaneh F, Edberg S, Medzhitov R. Recognition of commensal microflora by toll-like receptors is required for intestinal homeostasis. Cell. 2004;118(2):229-41. 24. Kelly D, Campbell JI, King TP, Grant G, Jansson EA, Coutts AG, et al. Commensal anaerobic gut bacteria attenuate inflammation by regulating nuclear-cytoplasmic shuttling of PPAR-gamma and RelA. Nat Immunol. 2004;5(1):104-12. 25. Desreumaux P, Ghosh S. Review article: mode of action and delivery of 5-aminosalicylic acid – new evidence. Aliment Pharmacol Ther. 2006;24:2-9. 26. Harbord M, Eliakim R, Bettenworth D, Karmiris K, Katsanos K, Kopylov U, et al. Third European Evidence-based Consensus on Diagnosis and Management of Ulcerative Colitis. Part 2: Current Management. J Crohns Colitis. 2017. 27. Maloy KJ, Powrie F. Intestinal homeostasis and its breakdown in inflammatory bowel disease. Nature. 2011;474(7351):298-306. 28. Atuma C, Strugala V, Allen A, Holm L. The adherent gastrointestinal mucus gel layer: thickness and physical state in vivo. Am J Physiol Gastrointest Liver Physiol. 2001;280:G922–G9. 29. Johansson MEV, Phillipson M, Petersson J, Velcich A, Holm L, Hansson GC. The inner of the two Muc2 mucin-dependent mucus layers in colon is devoid of bacteria. Proc Natl Acad Sci U S A. 2008;105:15064–9. 30. Van der Sluis M, De Koning BA, De Bruijn AC, Velcich A, Meijerink JP, Van Goudoever JB, et al. Muc2-deficient mice spontaneously develop colitis, indicating that MUC2 is critical for colonic protection. Gastroenterology. 2006;131(1):117-29. 31. Magro F, Gionchetti P, Eliakim R, Ardizzone S, Armuzzi A, Barreiro-de Acosta M, et al. Third European Evidence-based Consensus on Diagnosis and Management of Ulcerative Colitis. Part 1: Definitions, Diagnosis, Extra-intestinal Manifestations, Pregnancy, Cancer Surveillance, Surgery, and Ileo-anal Pouch Disorders. J Crohns Colitis. 2017;11(6):649-70. 32. Tytgat KMAJ, Buller HA, Opdam FJM, Kim YS, Einerhand AWC, Dekker J. Biosynthesis of Human Colonic Mucin: Muc2 Is the Prominent Secretory Mucin. Gastroenterology. 1994;107:1352-63. 33. Niv Y. Mucin gene expression in the intestine of ulcerative colitis patients: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol. 2016;28(11):1241-5. 34. Carlens J, Wahl B, Ballmaier M, Bulfone-Paus S, Forster R, Pabst O. Common gamma-chain-dependent signals confer selective survival of eosinophils in the murine small intestine. J Immunol. 2009;183(9):5600-7.

163 35. Jung Y, Rothenberg ME. Roles and regulation of gastrointestinal eosinophils in immunity and disease. J Immunol. 2014;193(3):999-1005. 36. Schwartz C, Turqueti-Neves A, Hartmann S, Yu P, Nimmerjahn F, Voehringer D. Basophil-mediated protection against gastrointestinal helminths requires IgE-induced cytokine secretion. Proc Natl Acad Sci U S A. 2014;111(48):E5169-77. 37. Chu VT, Beller A, Rausch S, Strandmark J, Zanker M, Arbach O, et al. Eosinophils promote generation and maintenance of immunoglobulin-A- expressing plasma cells and contribute to gut immune homeostasis. Immunity. 2014;40(4):582-93. 38. Minshall EM, Leung DYM, Martin RJ, Song YL, Cameron L, Ernst P, et al. Eosinophil-associated TGF- β1 mRNA Expression and Airways Fibrosis in Bronchial Asthma. Am J Respir Cell Mol Biol. 1997;17:326–33. 39. Blanchard C, Mingler MK, McBride M, Putnam PE, Collins MH, Chang G, et al. Periostin facilitates eosinophil tissue infiltration in allergic lung and esophageal responses. Mucosal Immunol. 2008;1(4):289-96. 40. Miller HRP, Pemberton AD. Tissue-specific expression of mast cell granule serine proteinases and their role in inflammation in the lung and gut. Immunology. 2002;105:375–90. 41. Geremia A, Arancibia-Cárcamo CV. Innate Lymphoid Cells in Intestinal Inflammation. Frontiers in Immunology. 2017;8:1296. 42. Spits H, Artis D, Colonna M, Diefenbach A, Di Santo JP, Eberl G, et al. Innate lymphoid cells--a proposal for uniform nomenclature. Nat Rev Immunol. 2013;13(2):145-9. 43. Mizuno S, Mikami Y, Kamada N, Handa T, Hayashi A, Sato T, et al. Cross-talk between RORgammat+ innate lymphoid cells and intestinal macrophages induces mucosal IL-22 production in Crohn's disease. Inflamm Bowel Dis. 2014;20(8):1426-34. 44. Pickard JM, Maurice CF, Kinnebrew MA, Abt MC, Schenten D, Golovkina TV, et al. Rapid fucosylation of intestinal epithelium sustains host-commensal symbiosis in sickness. Nature. 2014;514(7524):638-41. 45. Bain CC, Mowat AM. Macrophages in intestinal homeostasis and inflammation. Immunological Reviews. 2014;260:102–17. 46. Nagashima R, Maeda K, Imai Y, Takahashi T. Lamina Propria Macrophages in the Human Gastrointestinal Mucosa: Their Distribution, Immunohistological Phenotype, and Function. The Journal of Histochemistry and Cytochemistry. 1996;44:721-31,. 47. Pull SL, Doherty JM, Mills JC, Gordon JI, Stappenbeck TS. Activated macrophages are an adaptive element of the colonic epithelial progenitor niche necessary for regenerative responses to injury. Proc Natl Acad Sci U S A. 2005;102:99–104. 48. Smythies LE, Sellers M, Clements RH, Mosteller-Barnum M, Meng G, Benjamin WH, et al. Human intestinal macrophages display profound inflammatory anergy despite avid phagocytic and bacteriocidal activity. J Clin Invest. 2005;115(1):66-75. 49. Macpherson AJ, Uhr T. Induction of Protective IgA by Intestinal Dendritic Cells Carrying Commensal Bacteria. Science. 2004;303:1662-5. 50. Smith PD, Smythies LE, Shen R, Greenwell-Wild T, Gliozzi M, Wahl SM. Intestinal macrophages and response to microbial encroachment. Mucosal Immunol. 2011;4(1):31-42.

164 51. Bujko A, Atlasy N, Landsverk OJB, Richter L, Yaqub S, Horneland R, et al. Transcriptional and functional profiling defines human small intestinal macrophage subsets. J Exp Med. 2018;215(2):441-58. 52. Rugtveit J, Haraldsen G, Hogasen AK, Bakka A, Brandtzaeg P, Scott H. Respiratory burst of intestinal macrophages in inflammatory bowel disease is mainly caused by CD14+L1+ monocyte derived cells. Gut. 1995;37:367-73. 53. Roberts PJ, Riley GP, Morgan K, Miller R, Hunter JO, Middleton SJ. The physiological expression of inducible nitric oxide synthase (iNOS) in the human colon. J Clin Pathol. 2001;54:293–7. 54. Bain CC, Scott CL, Uronen-Hansson H, Gudjonsson S, Jansson O, Grip O, et al. Resident and pro-inflammatory macrophages in the colon represent alternative context-dependent fates of the same Ly6Chi monocyte precursors. Mucosal Immunol. 2013;6(3):498-510. 55. Shaw TN, Houston SA, Wemyss K, Bridgeman HM, Barbera TA, Zangerle-Murray T, et al. Tissue-resident macrophages in the intestine are long lived and defined by Tim-4 and CD4 expression. J Exp Med. 2018;215(6):1507- 18. 56. Schridde A, Bain CC, Mayer JU, Montgomery J, Pollet E, Denecke B, et al. Tissue-specific differentiation of colonic macrophages requires TGFbeta receptor-mediated signaling. Mucosal Immunol. 2017;10(6):1387-99. 57. Ogino T, Nishimura J, Barman S, Kayama H, Uematsu S, Okuzaki D, et al. Increased Th17-inducing activity of CD14+ CD163 low myeloid cells in intestinal lamina propria of patients with Crohn's disease. Gastroenterology. 2013;145(6):1380-91 e1. 58. Pender SLF, Quinn JJ, Sanderson IR, Macdonald TT. Butyrate upregulates stromelysin-1 production by intestinal mesenchymal cells. Am J Physiol Gastrointest Liver Physiol. 2000;279:G918–G24. 59. Smythies LE, Shen R, Bimczok D, Novak L, Clements RH, Eckhoff DE, et al. Inflammation anergy in human intestinal macrophages is due to Smad- induced IkappaBalpha expression and NF-kappaB inactivation. J Biol Chem. 2010;285(25):19593-604. 60. Caruso R, Warner N, Inohara N, Nunez G. NOD1 and NOD2: signaling, host defense, and inflammatory disease. Immunity. 2014;41(6):898-908. 61. Hedl M, Li J, Cho JH, Abraham C. Chronic stimulation of Nod2 mediates tolerance to bacterial products. Proc Natl Acad Sci U S A. 2007;104:19440–5. 62. Hedl M, Abraham C. Secretory mediators regulate Nod2-induced tolerance in human macrophages. Gastroenterology. 2011;140(1):231-41. 63. Ek WE, D’Amato M, Halfvarson J. The history of genetics in inflammatory bowel disease. Annals of Gastroenterology. 2014 27:294-303. 64. Hugot J-P, Chamaillard M, Zouali H, Lesage S, Cezard J-P, Belaiche J, et al. Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease. Nature. 2001;411:599-603. 65. Voss E, Wehkamp J, Wehkamp K, Stange EF, Schroder JM, Harder J. NOD2/CARD15 mediates induction of the antimicrobial peptide human beta- defensin-2. J Biol Chem. 2006;281(4):2005-11. 66. Travassos LH, Carneiro LA, Ramjeet M, Hussey S, Kim YG, Magalhaes JG, et al. Nod1 and Nod2 direct autophagy by recruiting ATG16L1 to the plasma membrane at the site of bacterial entry. Nat Immunol. 2010;11(1):55-62.

165 67. Cooney R, Baker J, Brain O, Danis B, Pichulik T, Allan P, et al. NOD2 stimulation induces autophagy in dendritic cells influencing bacterial handling and antigen presentation. Nat Med. 2010;16(1):90-7. 68. Hampe J, Franke A, Rosenstiel P, Till A, Teuber M, Huse K, et al. A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1. Nat Genet. 2007;39(2):207-11. 69. Ginhoux F, Guilliams M. Tissue-Resident Macrophage Ontogeny and Homeostasis. Immunity. 2016;44(3):439-49. 70. Yona S, Kim KW, Wolf Y, Mildner A, Varol D, Breker M, et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity. 2013;38(1):79-91. 71. Bain CC, Bravo-Blas A, Scott CL, Gomez Perdiguero E, Geissmann F, Henri S, et al. Constant replenishment from circulating monocytes maintains the macrophage pool in the intestine of adult mice. Nat Immunol. 2014;15(10):929- 37. 72. Ziegler-Heitbrock L, Ancuta P, Crowe S, Dalod M, Grau V, Hart DN, et al. Nomenclature of monocytes and dendritic cells in blood. Blood. 2010;116(16):e74-80. 73. Mildner A, Marinkovic G, Jung S. Murine Monocytes: Origins, Subsets, Fates, and Functions. Microbiol Spectr. 2016;4(5). 74. Zawada AM, Rogacev KS, Rotter B, Winter P, Marell RR, Fliser D, et al. SuperSAGE evidence for CD14++CD16+ monocytes as a third monocyte subset. Blood. 2011;118(12):e50-61. 75. Cros J, Cagnard N, Woollard K, Patey N, Zhang SY, Senechal B, et al. Human CD14dim monocytes patrol and sense nucleic acids and viruses via TLR7 and TLR8 receptors. Immunity. 2010;33(3):375-86. 76. Ziegler-Heitbrock L. The CD14+ CD16+ blood monocytes: their role in infection and inflammation. J Leukoc Biol. 2007;81(3):584-92. 77. Rossol M, Kraus S, Pierer M, Baerwald C, Wagner U. The CD14(bright) CD16+ monocyte subset is expanded in rheumatoid arthritis and promotes expansion of the Th17 cell population. Arthritis Rheum. 2012;64(3):671-7. 78. Grip O, Bredberg A, Lindgren S, Henriksson G. Increased subpopulations of CD16(+) and CD56(+) blood monocytes in patients with active Crohn's disease. Inflamm Bowel Dis. 2007;13(5):566-72. 79. Koch S, Kucharzik T, Heidemann J, Nusrat A, Luegering A. Investigating the role of proinflammatory CD16+ monocytes in the pathogenesis of inflammatory bowel disease. Clin Exp Immunol. 2010;161(2):332-41. 80. Rogler G, Hausmann M, Vogl D, Aschenbrenner E, Andus T, Falk W, et al. Isolation and phenotypic characterization of colonic macrophages. Clin Exp Immunol 1998;112:205–15. 81. Smythies LE, Maheshwari A, Clements R, Eckhoff D, Novak L, Vu HL, et al. Mucosal IL-8 and TGF-beta recruit blood monocytes: evidence for cross-talk between the lamina propria stroma and myeloid cells. J Leukoc Biol. 2006;80(3):492-9. 82. Patel AA, Zhang Y, Fullerton JN, Boelen L, Rongvaux A, Maini AA, et al. The fate and lifespan of human monocyte subsets in steady state and systemic inflammation. J Exp Med. 2017;214(7):1913-23.

166 83. Gamrekelashvili J, Giagnorio R, Jussofie J, Soehnlein O, Duchene J, Briseno CG, et al. Regulation of monocyte cell fate by blood vessels mediated by Notch signalling. Nat Commun. 2016;7:12597. 84. Welch GR, Wong HL, Wahl SM. Selective Induction of FcyRIII on Human Monocytes by Transforming Growth Factor-β. The Journal of Immunology. 1990;144:3444-8. 85. Calzada-Wack JC, Frankenberger M, Ziegler-Heitbrock HWL. Interleukin- 10 Drives Human Monocytes to CD16 Positive Macrophages. Journal of Inflammation. 1996;46:78-85. 86. Schlitzer A, McGovern N, Teo P, Zelante T, Atarashi K, Low D, et al. IRF4 transcription factor-dependent CD11b+ dendritic cells in human and mouse control mucosal IL-17 cytokine responses. Immunity. 2013;38(5):970-83. 87. Ryan GR, Dai X-M, Dominguez MG, Tong W, Chuan F, Chisholm O, et al. Rescue of the colony-stimulating factor 1 (CSF-1)–nullizygous mouse (Csf1op/Csf1op) phenotype with a CSF-1 transgene and identification of sites of local CSF-1 synthesis. Blood. 2001;98:74-84. 88. Greter M, Helft J, Chow A, Hashimoto D, Mortha A, Agudo-Cantero J, et al. GM-CSF controls nonlymphoid tissue dendritic cell homeostasis but is dispensable for the differentiation of inflammatory dendritic cells. Immunity. 2012;36(6):1031-46. 89. Ueda Y, Kayama H, Jeon SG, Kusu T, Isaka Y, Rakugi H, et al. Commensal microbiota induce LPS hyporesponsiveness in colonic macrophages via the production of IL-10. Int Immunol. 2010;22(12):953-62. 90. Shouval DS, Biswas A, Goettel JA, McCann K, Conaway E, Redhu NS, et al. Interleukin-10 receptor signaling in innate immune cells regulates mucosal immune tolerance and anti-inflammatory macrophage function. Immunity. 2014;40(5):706-19. 91. Zigmond E, Bernshtein B, Friedlander G, Walker CR, Yona S, Kim KW, et al. Macrophage-restricted interleukin-10 receptor deficiency, but not IL-10 deficiency, causes severe spontaneous colitis. Immunity. 2014;40(5):720-33. 92. Josefowicz SZ, Lu LF, Rudensky AY. Regulatory T cells: mechanisms of differentiation and function. Annu Rev Immunol. 2012;30:531-64. 93. Gertz M, Schäffer AA, Noyan F, Mario Perro, Diestelhorst J, Allroth A, et al. Inflammatory Bowel Disease and Mutations Affecting the Interleukin-10 Receptor. N Engl J Med. 2009;361:2033-45. 94. Ishifune C, Maruyama S, Sasaki Y, Yagita H, Hozumi K, Tomita T, et al. Differentiation of CD11c+ CX3CR1+ cells in the small intestine requires Notch signaling. Proc Natl Acad Sci U S A. 2014;111(16):5986-91. 95. Martinez FO, Gordon S. The M1 and M2 paradigm of macrophage activation: time for reassessment. F1000Prime Rep. 2014;6:13. 96. Xue J, Schmidt SV, Sander J, Draffehn A, Krebs W, Quester I, et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity. 2014;40(2):274-88. 97. Murray PJ, Allen JE, Biswas SK, Fisher EA, Gilroy DW, Goerdt S, et al. Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity. 2014;41(1):14-20. 98. O'Neill LA, Pearce EJ. Immunometabolism governs dendritic cell and macrophage function. J Exp Med. 2016;213(1):15-23. 99. Loftus RM, Finlay DK. Immunometabolism: Cellular Metabolism Turns Immune Regulator. J Biol Chem. 2016;291(1):1-10.

167 100. Vavricka SR, Rogler G, Maetzler S, Misselwitz B, Safroneeva E, Frei P, et al. High altitude journeys and flights are associated with an increased risk of flares in inflammatory bowel disease patients. Journal of Crohn's and Colitis. 2014;8(3):191-9. 101. Giatromanolaki A, Sivridis E, Maltezos E, Papazoglou D, Simopoulos C, Gatter KC, et al. Hypoxia inducible factor 1α and 2α overexpression in inflammatory bowel disease. J Clin Pathol. 2003;56:209–13. 102. Vitko NP, Spahich NA, Richardson AR. Glycolytic dependency of high- level nitric oxide resistance and virulence in Staphylococcus aureus. MBio. 2015;6(2). 103. Macintyre AN, Gerriets VA, Nichols AG, Michalek RD, Rudolph MC, Deoliveira D, et al. The glucose transporter Glut1 is selectively essential for CD4 T cell activation and effector function. Cell Metab. 2014;20(1):61-72. 104. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation. Science. 2009;324:1029-33. 105. Rodriguez-Prados JC, Traves PG, Cuenca J, Rico D, Aragones J, Martin-Sanz P, et al. Substrate fate in activated macrophages: a comparison between innate, classic, and alternative activation. J Immunol. 2010;185(1):605- 14. 106. Huang SC-C, Everts B, Ivanova Y, O'Sullivan D, Nascimento M, Smith AM, et al. Cell-intrinsic lysosomal lipolysis is essential for alternative activation of macrophages. Nature Immunology. 2014;15(9):846-55. 107. Vats D, Mukundan L, Odegaard JI, Zhang L, Smith KL, Morel CR, et al. Oxidative metabolism and PGC-1beta attenuate macrophage-mediated inflammation. Cell Metab. 2006;4(1):13-24. 108. Huang SC, Smith AM, Everts B, Colonna M, Pearce EL, Schilling JD, et al. Metabolic Reprogramming Mediated by the mTORC2-IRF4 Signaling Axis Is Essential for Macrophage Alternative Activation. Immunity. 2016;45(4):817-30. 109. Izquierdo E, Cuevas VD, Fernandez-Arroyo S, Riera-Borrull M, Orta- Zavalza E, Joven J, et al. Reshaping of Human Macrophage Polarization through Modulation of Glucose Catabolic Pathways. J Immunol. 2015;195(5):2442-51. 110. Lachmandas E, Boutens L, Ratter JM, Hijmans A, Hooiveld GJ, Joosten LA, et al. Microbial stimulation of different Toll-like receptor signalling pathways induces diverse metabolic programmes in human monocytes. Nat Microbiol. 2016;2:16246. 111. Tannahill GM, Curtis AM, Adamik J, Palsson-McDermott EM, McGettrick AF, Goel G, et al. Succinate is an inflammatory signal that induces IL-1beta through HIF-1alpha. Nature. 2013;496(7444):238-42. 112. Jha AK, Huang SC, Sergushichev A, Lampropoulou V, Ivanova Y, Loginicheva E, et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity. 2015;42(3):419-30. 113. Infantino V, Convertini P, Cucci L, Panaro MA, Di Noia MA, Calvello R, et al. The mitochondrial citrate carrier: a new player in inflammation. Biochem J. 2011;438(3):433-6. 114. Michelucci A, Cordes T, Ghelfi J, Pailot A, Reiling N, Goldmann O, et al. Immune-responsive gene 1 protein links metabolism to immunity by catalyzing itaconic acid production. Proc Natl Acad Sci U S A. 2013;110(19):7820-5.

168 115. Mills EL, Kelly B, Logan A, Costa AS, Varma M, Bryant CE, et al. Succinate Dehydrogenase Supports Metabolic Repurposing of Mitochondria to Drive Inflammatory Macrophages. Cell. 2016;167(2):457-70 e13. 116. Isaacs JS, Jung YJ, Mole DR, Lee S, Torres-Cabala C, Chung YL, et al. HIF overexpression correlates with biallelic loss of fumarate hydratase in renal cancer: novel role of fumarate in regulation of HIF stability. Cancer Cell. 2005;8(2):143-53. 117. Land SC, Tee AR. Hypoxia-inducible factor 1alpha is regulated by the mammalian target of rapamycin (mTOR) via an mTOR signaling motif. J Biol Chem. 2007;282(28):20534-43. 118. Wieman HL, Wofford JA, Rathmell JC. Cytokine Stimulation Promotes Glucose Uptake via Phosphatidylinositol-3 Kinase/Akt Regulation of Glut1 Activity and Trafficking. Molecular Biology of the Cell 2007;18 1437–46. 119. Everts B, Amiel E, van der Windt GJ, Freitas TC, Chott R, Yarasheski KE, et al. Commitment to glycolysis sustains survival of NO-producing inflammatory dendritic cells. Blood. 2012;120(7):1422-31. 120. Baseler WA, Davies LC, Quigley L, Ridnour LA, Weiss JM, Hussain SP, et al. Autocrine IL-10 functions as a rheostat for M1 macrophage glycolytic commitment by tuning nitric oxide production. Redox Biol. 2016;10:12-23. 121. Ip WKE, Hoshi N, Shouval DS, Snapper S, Medzhitov R. Anti- inflammatory effect of IL-10 mediated by metabolic reprogramming of macrophages. Science. 2017;356(6337):513-9. 122. Schlitzer A, McGovern N, Ginhoux F. Dendritic cells and monocyte- derived cells: Two complementary and integrated functional systems. Semin Cell Dev Biol. 2015;41:9-22. 123. Schmid D, Pypaert M, Munz C. Antigen-loading compartments for major histocompatibility complex class II molecules continuously receive input from autophagosomes. Immunity. 2007;26(1):79-92. 124. Agace WW, McCoy KD. Regionalized Development and Maintenance of the Intestinal Adaptive Immune Landscape. Immunity. 2017;46(4):532-48. 125. Niess JH, Brand S, Gu X, Landsman L, Jung S, McCormick BA, et al. CX3CR1-Mediated Dendritic Cell Access to the Intestinal Lumen and Bacterial Clearance. Science. 2005;307:254-8. 126. Persson EK, Scott CL, Mowat AM, Agace WW. Dendritic cell subsets in the intestinal lamina propria: Ontogeny and function. European Journal of Immunology. 2013;43:3098–107. 127. Iwasaki A, Kelsall BL. Freshly Isolated Peyer’s Patch, But Not Spleen, Dendritic Cells Produce Interleukin 10 and Induce the Differentiation of T Helper Type 2 Cells. The Journal of Experimental Medicine. 1999;190:229–39. 128. Coombes JL, Powrie F. Dendritic cells in intestinal immune regulation. Nat Rev Immunol. 2008;8(6):435-46. 129. Coombes JL, Siddiqui KR, Arancibia-Carcamo CV, Hall J, Sun CM, Belkaid Y, et al. A functionally specialized population of mucosal CD103+ DCs induces Foxp3+ regulatory T cells via a TGF-beta and retinoic acid-dependent mechanism. J Exp Med. 2007;204(8):1757-64. 130. Sun CM, Hall JA, Blank RB, Bouladoux N, Oukka M, Mora JR, et al. Small intestine lamina propria dendritic cells promote de novo generation of Foxp3 T reg cells via retinoic acid. J Exp Med. 2007;204(8):1775-85.

169 131. Mora JR, Bono MR, Manjunath N, Weninger W, Cavanagh LL, Rosemblatt M, et al. Selective imprinting of gut-homing T cells by Peyer’s patch dendritic cells. Nature. 2003;424(6944):88-98. 132. Mora JR, Iwata M, Eksteen B, Song S-Y, Junt T, Senman B, et al. Generation of Gut-Homing IgA-Secreting B Cells by Intestinal Dendritic Cells. Science. 2006;314:1157-60. 133. Bain CC, Mowat AM. The monocyte-macrophage axis in the intestine. Cell Immunol. 2014;291(1-2):41-8. 134. Tamoutounour S, Henri S, Lelouard H, de Bovis B, de Haar C, van der Woude CJ, et al. CD64 distinguishes macrophages from dendritic cells in the gut and reveals the Th1-inducing role of mesenteric lymph node macrophages during colitis. Eur J Immunol. 2012;42(12):3150-66. 135. Watchmaker PB, Lahl K, Lee M, Baumjohann D, Morton J, Kim SJ, et al. Comparative transcriptional and functional profiling defines conserved programs of intestinal DC differentiation in humans and mice. Nat Immunol. 2014;15(1):98-108. 136. Cerovic V, Houston SA, Scott CL, Aumeunier A, Yrlid U, Mowat AM, et al. Intestinal CD103(-) dendritic cells migrate in lymph and prime effector T cells. Mucosal Immunol. 2013;6(1):104-13. 137. Luda KM, Joeris T, Persson EK, Rivollier A, Demiri M, Sitnik KM, et al. IRF8 Transcription-Factor-Dependent Classical Dendritic Cells Are Essential for Intestinal T Cell Homeostasis. Immunity. 2016;44(4):860-74. 138. Jang MH, Sougawa N, Tanaka T, Hirata T, Hiroi T, Tohya K, et al. CCR7 Is Critically Important for Migration of Dendritic Cells in Intestinal Lamina Propria to Mesenteric Lymph Nodes. The Journal of Immunology. 2006;176(2):803-10. 139. Fujimoto K, Karuppuchamy T, Takemura N, Shimohigoshi M, Machida T, Haseda Y, et al. A new subset of CD103+CD8alpha+ dendritic cells in the small intestine expresses TLR3, TLR7, and TLR9 and induces Th1 response and CTL activity. J Immunol. 2011;186(11):6287-95. 140. Kapsenberg ML. Dendritic-cell control of pathogen-driven T-cell polarization. Nat Rev Immunol. 2003;3(12):984-93. 141. Magalhaes JG, Fritz JH, Le Bourhis L, Sellge G, Travassos LH, Selvanantham T, et al. Nod2-Dependent Th2 Polarization of Antigen-Specific Immunity. The Journal of Immunology. 2008;181(11):7925-35. 142. Watanabe T, Kitani A, Murray PJ, Wakatsuki Y, Fuss IJ, Strober W. Nucleotide binding oligomerization domain 2 deficiency leads to dysregulated TLR2 signaling and induction of antigen-specific colitis. Immunity. 2006;25(3):473-85. 143. Neurath MF. Cytokines in inflammatory bowel disease. Nat Rev Immunol. 2014;14(5):329-42. 144. Monteleone G, Caruso R, Pallone F. Targets for new immunomodulation strategies in inflammatory bowel disease. Autoimmun Rev. 2014;13(1):11-4. 145. Steinbach EC, Plevy SE. The role of macrophages and dendritic cells in the initiation of inflammation in IBD. Inflamm Bowel Dis. 2014;20(1):166-75. 146. Sathe P, Metcalf D, Vremec D, Naik SH, Langdon WY, Huntington ND, et al. Lymphoid tissue and plasmacytoid dendritic cells and macrophages do not share a common macrophage-dendritic cell-restricted progenitor. Immunity. 2014;41(1):104-15. 147. Bogunovic M, Ginhoux F, Helft J, Shang L, Hashimoto D, Greter M, et al. Origin of the lamina propria dendritic cell network. Immunity. 2009;31(3):513-25.

170 148. Klebanoff CA, Spencer SP, Torabi-Parizi P, Grainger JR, Roychoudhuri R, Ji Y, et al. Retinoic acid controls the homeostasis of pre-cDC-derived splenic and intestinal dendritic cells. J Exp Med. 2013;210(10):1961-76. 149. Kelly A, Houston SA, Sherwood E, Casulli J, Travis MA. Regulation of Innate and Adaptive Immunity by TGFbeta. Adv Immunol. 2017;134:137-233. 150. Bain CC, Montgomery J, Scott CL, Kel JM, Girard-Madoux MJH, Martens L, et al. TGFbetaR signalling controls CD103(+)CD11b(+) dendritic cell development in the intestine. Nat Commun. 2017;8(1):620. 151. Rimoldi M, Chieppa M, Salucci V, Avogadri F, Sonzogni A, Sampietro GM, et al. Intestinal immune homeostasis is regulated by the crosstalk between epithelial cells and dendritic cells. Nat Immunol. 2005;6(5):507-14. 152. Rescigno M, Di Sabatino A. Dendritic cells in intestinal homeostasis and disease. J Clin Invest. 2009;119(9):2441-50. 153. Zeuthen LH, Fink LN, Frokiaer H. Epithelial cells prime the immune response to an array of gut-derived commensals towards a tolerogenic phenotype through distinct actions of thymic stromal lymphopoietin and transforming growth factor-beta. Immunology. 2008;123(2):197-208. 154. Ramalingam R, Larmonier CB, Thurston RD, Midura-Kiela MT, Zheng SG, Ghishan FK, et al. Dendritic cell-specific disruption of TGF-beta receptor II leads to altered regulatory T cell phenotype and spontaneous multiorgan autoimmunity. J Immunol. 2012;189(8):3878-93. 155. Belladonna ML, Volpi C, Bianchi R, Vacca C, Orabona C, Pallotta MT, et al. Cutting Edge: Autocrine TGF- Sustains Default Tolerogenesis by IDO- Competent Dendritic Cells. The Journal of Immunology. 2008;181(8):5194-8. 156. Sato K, Kawasaki H, Nagayama H, Enomoto M, Morimoto C, Tadokoro K, et al. TGF- 1 Reciprocally Controls Chemotaxis of Human Peripheral Blood Monocyte-Derived Dendritic Cells Via Chemokine Receptors. The Journal of Immunology. 2000;164(5):2285-95. 157. Seeger P, Musso T, Sozzani S. The TGF-beta superfamily in dendritic cell biology. Cytokine Growth Factor Rev. 2015;26(6):647-57. 158. Saraiva M, O'Garra A. The regulation of IL-10 production by immune cells. Nat Rev Immunol. 2010;10(3):170-81. 159. Rakoff-Nahoum S, Hao L, Medzhitov R. Role of toll-like receptors in spontaneous commensal-dependent colitis. Immunity. 2006;25(2):319-29. 160. Contractor N, Louten J, Kim L, Biron CA, Kelsall BL. Cutting Edge: Peyer's Patch Plasmacytoid Dendritic Cells (pDCs) Produce Low Levels of Type I Interferons: Possible Role for IL-10, TGF , and Prostaglandin E2 in Conditioning a Unique Mucosal pDC Phenotype. The Journal of Immunology. 2007;179(5):2690-4. 161. Gomollon F, Dignass A, Annese V, Tilg H, Van Assche G, Lindsay JO, et al. 3rd European Evidence-based Consensus on the Diagnosis and Management of Crohn's Disease 2016: Part 1: Diagnosis and Medical Management. J Crohns Colitis. 2017;11(1):3-25. 162. Cupi ML, Sarra M, Marafini I, Monteleone I, Franze E, Ortenzi A, et al. Plasma cells in the mucosa of patients with inflammatory bowel disease produce granzyme B and possess cytotoxic activities. J Immunol. 2014;192(12):6083-91. 163. Cheroutre H, Lambolez F, Mucida D. The light and dark sides of intestinal intraepithelial lymphocytes. Nat Rev Immunol. 2011;11(7):445-56.

171 164. Bhagat G, Naiyer AJ, Shah JG, Harper J, Jabri B, Wang TC, et al. Small intestinal CD8+TCRgammadelta+NKG2A+ intraepithelial lymphocytes have attributes of regulatory cells in patients with celiac disease. J Clin Invest. 2008;118(1):281-93. 165. Jiang S, Dong C. A complex issue on CD4+ T-cell subsets. Immunological Reviews. 2013;252:5–11. 166. Maynard CL, Harrington LE, Janowski KM, Oliver JR, Zindl CL, Rudensky AY, et al. Regulatory T cells expressing interleukin 10 develop from Foxp3+ and Foxp3- precursor cells in the absence of interleukin 10. Nat Immunol. 2007;8(9):931-41. 167. Izcue A, Coombes JL, Powrie F. Regulatory lymphocytes and intestinal inflammation. Annu Rev Immunol. 2009;27:313-38. 168. Tran DQ, Ramsey H, Shevach EM. Induction of FOXP3 expression in naive human CD4+FOXP3 T cells by T-cell receptor stimulation is transforming growth factor-beta dependent but does not confer a regulatory phenotype. Blood. 2007;110(8):2983-90. 169. Wang J, Ioan-Facsinay A, Voort EIHvd, Huizinga TWJ, Toes REM. Transient expression of FOXP3 in human activated nonregulatory CD4+ T cells. Eur J Immunol. 2007;37(1):129–38. 170. Shale M, Schiering C, Powrie F. CD4+ T-cell subsets in intestinal inflammation. Immunological Reviews. 2013;252:164–82. 171. Korn T, Bettelli E, Oukka M, Kuchroo VK. IL-17 and Th17 Cells. Annu Rev Immunol. 2009;27:485-517. 172. Fuss IJ, Neurath M, Boirivant M, Klein JS, Motte Cdl, A.Strong S, et al. Disparate CD4+ Lamina Propria (LP)Lymphokine Secretion Profiles in Inflammatory Bowel Disease. J Immunol. 1996;157:1261-70. 173. Monteleone G, Biancone L, Marasco R, Morrone G, Marasco O, Luzza F, et al. Interleukin 12 is expressed and actively released by Crohn's disease intestinal lamina propria mononuclear cells. Gastroenterology. 1997;112:1169– 78. 174. Heller F, Florian P, Bojarski C, Richter J, Christ M, Hillenbrand B, et al. Interleukin-13 is the key effector Th2 cytokine in ulcerative colitis that affects epithelial tight junctions, apoptosis, and cell restitution. Gastroenterology. 2005;129(2):550-64. 175. Galvez J. Role of Th17 Cells in the Pathogenesis of Human IBD. ISRN Inflamm. 2014;2014:928461. 176. Fujino S, Andoh A, Bamba S, Ogawa A, Hata K, Araki Y, et al. Increased expression of in inflammatory bowel disease. Gut. 2003;52:65– 70. 177. Holtta V, Klemetti P, Sipponen T, Westerholm-Ormio M, Kociubinski G, Salo H, et al. IL-23/IL-17 immunity as a hallmark of Crohn's disease. Inflamm Bowel Dis. 2008;14(9):1175-84. 178. Hueber W, Sands BE, Lewitzky S, Vandemeulebroecke M, Reinisch W, Higgins PD, et al. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn's disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut. 2012;61(12):1693-700. 179. O'Connor W, Jr., Kamanaka M, Booth CJ, Town T, Nakae S, Iwakura Y, et al. A protective function for interleukin 17A in T cell-mediated intestinal inflammation. Nat Immunol. 2009;10(6):603-9.

172 180. Feagan BG, Sandborn WJ, Gasink C, Jacobstein D, Lang Y, Friedman JR, et al. Ustekinumab as Induction and Maintenance Therapy for Crohn's Disease. N Engl J Med. 2016;375(20):1946-60. 181. Duerr RH, Taylor KD, Brant SR, Rioux JD, Silverberg MS, Daly MJ, et al. A Genome-Wide Association Study Identifies IL23R as an Inflammatory Bowel Disease Gene. Science 2006;314:1461-3 182. Aggarwal S, Ghilardi N, Xie MH, de Sauvage FJ, Gurney AL. Interleukin- 23 promotes a distinct CD4 T cell activation state characterized by the production of interleukin-17. J Biol Chem. 2003;278(3):1910-4. 183. Yen D, Cheung J, Scheerens H, Poulet F, McClanahan T, McKenzie B, et al. IL-23 is essential for T cell-mediated colitis and promotes inflammation via IL-17 and IL-6. J Clin Invest. 2006;116(5):1310-6. 184. McGeachy MJ, Bak-Jensen KS, Chen Y, Tato CM, Blumenschein W, McClanahan T, et al. TGF-beta and IL-6 drive the production of IL-17 and IL-10 by T cells and restrain T(H)-17 cell-mediated pathology. Nat Immunol. 2007;8(12):1390-7. 185. Zhou L, Lopes JE, Chong MM, Ivanov, II, Min R, Victora GD, et al. TGF- beta-induced Foxp3 inhibits T(H)17 cell differentiation by antagonizing RORgammat function. Nature. 2008;453(7192):236-40. 186. Ivanov, II, Atarashi K, Manel N, Brodie EL, Shima T, Karaoz U, et al. Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell. 2009;139(3):485-98. 187. Qiu J, Guo X, Chen ZM, He L, Sonnenberg GF, Artis D, et al. Group 3 innate lymphoid cells inhibit T-cell-mediated intestinal inflammation through aryl hydrocarbon receptor signaling and regulation of microflora. Immunity. 2013;39(2):386-99. 188. Maslowski KM, Mackay CR. Diet, gut microbiota and immune responses. Nat Immunol. 2011;12(1):5-9. 189. Haghikia A, Jorg S, Duscha A, Berg J, Manzel A, Waschbisch A, et al. Dietary Fatty Acids Directly Impact Central Nervous System Autoimmunity via the Small Intestine. Immunity. 2015;43(4):817-29. 190. Mora JR, von Andrian UH. Differentiation and homing of IgA-secreting cells. Mucosal Immunol. 2008;1(2):96-109. 191. Gutzeit C, Magri G, Cerutti A. Intestinal IgA production and its role in host-microbe interaction. Immunological Reviews. 2014;260:76–85. 192. Benckert J, Schmolka N, Kreschel C, Zoller MJ, Sturm A, Wiedenmann B, et al. The majority of intestinal IgA+ and IgG+ plasmablasts in the human gut are antigen-specific. J Clin Invest. 2011;121(5):1946-55. 193. Wu W, Sun M, Chen F, Cao AT, Liu H, Zhao Y, et al. Microbiota metabolite short-chain fatty acid acetate promotes intestinal IgA response to microbiota which is mediated by GPR43. Mucosal Immunol. 2017;10(4):946-56. 194. Pabst O, Cerovic V, Hornef M. Secretory IgA in the Coordination of Establishment and Maintenance of the Microbiota. Trends Immunol. 2016;37(5):287-96. 195. Kuklin NA, Rott L, Feng N, Conner ME, Wagner N, Muller W, et al. Protective Intestinal Anti-Rotavirus B Cell Immunity Is Dependent on α4β7 Integrin Expression But Does Not Require IgA Antibody Production. J Immunol. 2001;166(3):1894-902.

173 196. Tezuka H, Abe Y, Iwata M, Takeuchi H, Ishikawa H, Matsushita M, et al. Regulation of IgA production by naturally occurring TNF/iNOS-producing dendritic cells. Nature. 2007;448(7156):929-33. 197. Cazac BB, Roes J. TGF-β Receptor Controls B Cell Responsiveness and Induction of IgA In Vivo. Immunity. 2000;13:443–51. 198. Habtezion A, Nguyen LP, Hadeiba H, Butcher EC. Leukocyte Trafficking to the Small Intestine and Colon. Gastroenterology. 2016;150(2):340-54. 199. Fischer A, Zundler S, Atreya R, Rath T, Voskens C, Hirschmann S, et al. Differential effects of alpha4beta7 and GPR15 on homing of effector and regulatory T cells from patients with UC to the inflamed gut in vivo. Gut. 2016;65(10):1642-64. 200. Picarella D, Hurlbut P, Rottman J, Shi X, Butcher E, Ringler DJ. Monoclonal antibodies specific for beta 7 integrin and mucosal addressin cell adhesion molecule-1 (MAdCAM-1) reduce inflammation in the colon of scid mice reconstituted with CD45RBhigh CD4+ T cells. The Journal of Immunology. 1997;158:2099-106. 201. Lobaton T, Vermeire S, Van Assche G, Rutgeerts P. Review article: anti- adhesion therapies for inflammatory bowel disease. Aliment Pharmacol Ther. 2014;39(6):579-94. 202. Nguyen LP, Pan J, Dinh TT, Hadeiba H, O'Hara E, 3rd, Ebtikar A, et al. Role and species-specific expression of colon T cell homing receptor GPR15 in colitis. Nat Immunol. 2015;16(2):207-13. 203. Schön MP, Arya A, Murphy EA, Adams CM, Strauch UG, Agace WW, et al. Mucosal T Lymphocyte Numbers Are Selectively Reduced in Integrin αE (CD103)-Deficient Mice. J Immunol. 1999;162:6641-9. 204. Biancheri P, Giuffrida P, Docena GH, MacDonald TT, Corazza GR, Di Sabatino A. The role of transforming growth factor (TGF)-beta in modulating the immune response and fibrogenesis in the gut. Cytokine Growth Factor Rev. 2014;25(1):45-55. 205. Letterio JJ, Böttinger EP. TGF-ß Knockout and Dominant-Negative Receptor Transgenic Mice. Miner Electrolyte Metab 1998;24:161–7. 206. Kulkarni AB, Huh C-G, Becker D, Geiser A, Lyght M, Flanders KC, et al. Transforming growth factor β1 null mutation in mice causes excessive inflammatory response and early death. Proc Natl Acad Sci U S A. 1993;90:770-4. 207. Marie JC, Liggitt D, Rudensky AY. Cellular mechanisms of fatal early- onset autoimmunity in mice with the T cell-specific targeting of transforming growth factor-beta receptor. Immunity. 2006;25(3):441-54. 208. Di Sabatino A, Jackson CL, Pickard KM, Buckley M, Rovedatti L, Leakey NA, et al. Transforming growth factor beta signalling and matrix metalloproteinases in the mucosa overlying Crohn's disease strictures. Gut. 2009;58(6):777-89. 209. Stallmach A, Schuppan D, Riese HH, Matthes H, Riecken EO. Increased Collagen Type III Synthesis by Fibroblasts Isolated From Strictures of Patients With Crohn’s Disease. Gastroenterology. 1992;102:1920-9. 210. Fujiwara I, Yashiro M, Kubo N, Maeda K, Hirakawa K. Ulcerative colitis- associated colorectal cancer is frequently associated with the microsatellite instability pathway. Dis Colon Rectum. 2008;51(9):1387-94. 211. Elliott RL, Blobe GC. Role of transforming growth factor Beta in human cancer. J Clin Oncol. 2005;23(9):2078-93.

174 212. Becker C, Fantini MC, Schramm C, Lehr HA, Wirtz S, Nikolaev A, et al. TGF-beta suppresses tumor progression in colon cancer by inhibition of IL-6 trans-signaling. Immunity. 2004;21(4):491-501. 213. Annes JP. Making sense of latent TGFbeta activation. Journal of Cell Science. 2003;116(2):217-24. 214. Worthington JJ, Klementowicz JE, Travis MA. TGFbeta: a sleeping giant awoken by integrins. Trends Biochem Sci. 2011;36(1):47-54. 215. Travis MA, Sheppard D. TGF-beta activation and function in immunity. Annu Rev Immunol. 2014;32:51-82. 216. Gleizes P-E, Munger JS, Nunes I, Harpel JG, Mazzieri R, Noguera I, et al. TGF-β Latency: Biological Significance and Mechanisms of Activation. Stem Cells. 1997;15:190-7. 217. Pesu M, Watford WT, Wei L, Xu L, Fuss I, Strober W, et al. T-cell- expressed proprotein convertase furin is essential for maintenance of peripheral immune tolerance. Nature. 2008;455(7210):246-50. 218. Dubois CM, Laprise MH, Blanchette F, Gentry LE, Leduc R. Processing of transforming growth factor beta 1 precursor by human furin convertase. The Journal of Biological. The Journal of Biological Chemistry. 1995;270:10618-24. 219. Munger JS, Harpel JG, Gleizes P-E, Mazzieri R, Nunes I, Rifkin DB. Latent transforming growth factor-β: Structural features and mechanisms of activation. Kidney International. 1997;51(5):1376-82. 220. Shi M, Zhu J, Wang R, Chen X, Mi L, Walz T, et al. Latent TGF-β structure and activation. Nature. 2011;474(7351):343-9. 221. Ribeiro SMF, Poczatek M, Schultz-Cherry S, Villain M, Murphy-Ullrich JE. The Activation Sequence of Thrombospondin-1 Interacts with the Latency- associated Peptide to Regulate Activation of Latent Transforming Growth Factor-β. The Journal of Biological Chemistry. 1999;274:13586–93. 222. Crawford SE, Stellmach V, Murphy-Ullrich JE, Ribeiro SMF, Lawler J, Hynes RO, et al. Thrombospondin-1 Is a Major Activator of TGF- β1 In Vivo. Cell,. 1998;93:1159–70. 223. Anderson LR, Owens TW, Naylor MJ. Structural and mechanical functions of integrins. Biophys Rev. 2014;6(2):203-13. 224. Yang Z, Mu Z, Dabovic B, Jurukovski V, Yu D, Sung J, et al. Absence of integrin-mediated TGFbeta1 activation in vivo recapitulates the phenotype of TGFbeta1-null mice. J Cell Biol. 2007;176(6):787-93. 225. Mu D, Cambier S, Fjellbirkeland L, Baron JL, Munger JS, Kawakatsu H, et al. The integrin alpha(v)beta8 mediates epithelial homeostasis through MT1- MMP-dependent activation of TGF-beta1. J Cell Biol. 2002;157(3):493-507. 226. Giacomini MM, Travis MA, Kudo M, Sheppard D. Epithelial cells utilize cortical actin/myosin to activate latent TGF-β through integrin αvβ6-dependent physical force. Experimental Cell Research. 2012;318(6):716-22. 227. Munger JS, Huang X, Kawakatsu H, Griffiths MJD, SL, Wu J, et al. The Integrin αvβ6 Binds and Activates Latent TGFβ1: A Mechanism for Regulating Pulmonary Inflammation and Fibrosis. Cell. 1999;96:319–28. 228. Wipff PJ, Rifkin DB, Meister JJ, Hinz B. Myofibroblast contraction activates latent TGF-beta1 from the extracellular matrix. J Cell Biol. 2007;179(6):1311-23. 229. Asano Y, Ihn H, Yamane K, Jinnin M, Mimura Y, Tamaki K. Increased Expression of Integrin αvβ3 Contributes to the Establishment of Autocrine TGF-

175 β Signaling in Scleroderma Fibroblasts. The Journal of Immunology. 2005;175(11):7708-18. 230. Asano Y, Ihn H, Yamane K, Jinnin M, Mimura Y, Tamaki K. Involvement of alphavbeta5 integrin-mediated activation of latent transforming growth factor beta1 in autocrine transforming growth factor beta signaling in systemic sclerosis fibroblasts. Arthritis Rheum. 2005;52(9):2897-905. 231. Reynolds LE, Wyder L, Lively JC, Taverna D, Robinson SD, Huang X, et al. Enhanced pathological in mice lacking β3 integrin or β3 and β5 integrins. Nature Medicine. 2002;8:27-34. 232. Henderson NC, Arnold TD, Katamura Y, Giacomini MM, Rodriguez JD, McCarty JH, et al. Targeting of αv integrin identifies a core molecular pathway that regulates fibrosis in several organs. Nature Medicine. 2013;19(12):1617- 24. 233. Reed NI, Jo H, Chen C, Tsujino K, Arnold TD, DeGrado WF, et al. The αvβ1 integrin plays a critical in vivo role in tissue fibrosis. Science Translational Medicine. 2015;7:288ra79. 234. Aluwihare P, Mu Z, Zhao Z, Yu D, Weinreb PH, Horan GS, et al. Mice that lack activity of v 6- and v 8-integrins reproduce the abnormalities of Tgfb1- and Tgfb3-null mice. Journal of Cell Science. 2008;122(2):227-32. 235. Huang XZ, Wu JF, Cass D, Erle DJ, Corry D, Young SG, et al. Inactivation of the subunit gene reveals a role of epithelial integrins in regulating inflammation in the lung and skin. The Journal of Cell Biology. 1996;133(4):921-8. 236. Zhu J, Motejlek K, Wang D, Zang K, Schmidt A, Reichardt LF. β8 integrins are required for vascular morphogenesis in mouse embryos. Development. 2002;129:2891-903. 237. Su H, Kim H, Pawlikowska L, Kitamura H, Shen F, Cambier S, et al. Reduced expression of integrin alphavbeta8 is associated with brain arteriovenous malformation pathogenesis. Am J Pathol. 2010;176(2):1018-27. 238. Dardiotis E, Siokas V, Zafeiridis T, Paterakis K, Tsivgoulis G, Dardioti M, et al. Integrins AV and B8 Gene Polymorphisms and Risk for Intracerebral Hemorrhage in Greek and Polish Populations. Neuromolecular Med. 2017;19(1):69-80. 239. Travis MA, Reizis B, Melton AC, Masteller E, Tang Q, Proctor JM, et al. Loss of integrin alpha(v)beta8 on dendritic cells causes autoimmunity and colitis in mice. Nature. 2007;449(7160):361-5. 240. Lacy-Hulbert A, Smith AM, Tissire H, Barry M, Crowley D, Bronson RT, et al. Ulcerative colitis and autoimmunity induced by loss of myeloid αv integrins. Proc Natl Acad Sci U S A. 2007;104:15823-8. 241. Acharya M, Mukhopadhyay S, Païdassi H, Jamil T, Chow C, Kissler S, et al. αv Integrin expression by DCs is required for Th17 cell differentiation and development of experimental autoimmune encephalomyelitis in mice. Journal of Clinical Investigation. 2010;120(12):4445-52. 242. Stuart LM, Lucas M, Simpson C, Lamb J, Savill J, Lacy-Hulbert A. Inhibitory Effects of Apoptotic Cell Ingestion upon Endotoxin-Driven Myeloid Dendritic Cell Maturation. The Journal of Immunology. 2002;168(4):1627-35. 243. Kushwah R, Wu J, Oliver JR, Jiang G, Zhang J, Siminovitch KA, et al. Uptake of apoptotic DC converts immature DC into tolerogenic DC that induce differentiation of Foxp3+ Treg. Eur J Immunol. 2010;40(4):1022-35.

176 244. Worthington JJ, Czajkowska BI, Melton AC, Travis MA. Intestinal dendritic cells specialize to activate transforming growth factor-beta and induce Foxp3+ regulatory T cells via integrin alphavbeta8. Gastroenterology. 2011;141(5):1802-12. 245. Païdassi H, Acharya M, Zhang A, Mukhopadhyay S, Kwon M, Chow C, et al. Preferential Expression of Integrin αvβ8 Promotes Generation of Regulatory T Cells by Mouse CD103+ Dendritic Cells. Gastroenterology. 2011;141(5):1813-20. 246. Boucard-Jourdin M, Kugler D, Endale Ahanda ML, This S, De Calisto J, Zhang A, et al. beta8 Integrin Expression and Activation of TGF-beta by Intestinal Dendritic Cells Are Determined by Both Tissue Microenvironment and Cell Lineage. J Immunol. 2016;197(5):1968-78. 247. Fenton TM, Kelly A, Shuttleworth EE, Smedley C, Atakilit A, Powrie F, et al. Inflammatory cues enhance TGFbeta activation by distinct subsets of human intestinal dendritic cells via integrin alphavbeta8. Mucosal Immunol. 2017;10(3):624-34. 248. Reis BS, Rogoz A, Costa-Pinto FA, Taniuchi I, Mucida D. Mutual expression of the transcription factors Runx3 and ThPOK regulates intestinal CD4+ T cell immunity. Nature Immunology. 2013;14(3):271-80. 249. Grueter B, Petter M, Egawa T, Laule-Kilian K, Aldrian CJ, Wuerch A, et al. Runx3 Regulates Integrin E/CD103 and CD4 Expression during Development of CD4-/CD8+ T Cells. The Journal of Immunology. 2005;175(3):1694-705. 250. Worthington JJ, Klementowicz JE, Rahman S, Czajkowska BI, Smedley C, Waldmann H, et al. Loss of the TGFbeta-activating integrin alphavbeta8 on dendritic cells protects mice from chronic intestinal parasitic infection via control of type 2 immunity. PLoS Pathog. 2013;9(10):e1003675. 251. Melton AC, Bailey-Bucktrout SL, Travis MA, Fife BT, Bluestone JA, Sheppard D. Expression of alphavbeta8 integrin on dendritic cells regulates Th17 cell development and experimental autoimmune encephalomyelitis in mice. J Clin Invest. 2010;120(12):4436-44. 252. Iliev ID, Mileti E, Matteoli G, Chieppa M, Rescigno M. Intestinal epithelial cells promote colitis-protective regulatory T-cell differentiation through dendritic cell conditioning. Mucosal Immunol. 2009;2(4):340-50. 253. Li MO, Wan YY, Flavell RA. T cell-produced transforming growth factor- beta1 controls T cell tolerance and regulates Th1- and Th17-cell differentiation. Immunity. 2007;26(5):579-91. 254. Edwards JP, Fujii H, Zhou AX, Creemers J, Unutmaz D, Shevach EM. Regulation of the expression of GARP/latent TGF-beta1 complexes on mouse T cells and their role in regulatory T cell and Th17 differentiation. J Immunol. 2013;190(11):5506-15. 255. Edwards JP, Thornton AM, Shevach EM. Release of active TGF-beta1 from the latent TGF-beta1/GARP complex on T regulatory cells is mediated by integrin beta8. J Immunol. 2014;193(6):2843-9. 256. Worthington JJ, Kelly A, Smedley C, Bauche D, Campbell S, Marie JC, et al. Integrin alphavbeta8-Mediated TGF-beta Activation by Effector Regulatory T Cells Is Essential for Suppression of T-Cell-Mediated Inflammation. Immunity. 2015;42(5):903-15. 257. Stockis J, Lienart S, Colau D, Collignon A, Nishimura SL, Sheppard D, et al. Blocking immunosuppression by human Tregs in vivo with antibodies

177 targeting integrin alphaVbeta8. Proc Natl Acad Sci U S A. 2017;114(47):E10161-E8. 258. Edwards JP, Hand TW, Morais da Fonseca D, Glass DD, Belkaid Y, Shevach EM. The GARP/Latent TGF-β1 complex on Treg cells modulates the induction of peripherally derived Treg cells during oral tolerance. European Journal of Immunology. 2016;46(6):1480-9. 259. Cuende J, Liénart Sp, Dedobbeleer O, van der Woning B, De Boeck G, Stockis J, et al. Monoclonal antibodies against GARP/TGF-β1 complexes inhibit the immunosuppressive activity of human regulatory T cells in vivo. Science Translational Medicine. 2015;7:284ra56. 260. Stockis J, Colau D, Coulie PG, Lucas S. Membrane protein GARP is a receptor for latent TGF-beta on the surface of activated human Treg. Eur J Immunol. 2009;39(12):3315-22. 261. Maenpaa A, Jaaskeleinen J, Carpen O, Pattaroyo M, Timonen T. Expression of integrins and other adhesion molecules on NK cells: impact of IL- 2 on short- and long-term cultures. International Journal of Cancer. 1993;53:850-5. 262. Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al. A draft map of the human proteome. Nature. 2014;509(7502):575-81. 263. Reboldi A, Arnon TI, Rodda LB, Atakilit A, Sheppard D, Cyster JG. IgA production requires B cell interaction with subepithelial dendritic cells in Peyer's patches. Science. 2016;352(6287):aaf4822. 264. Andersen MN, Al-Karradi SN, Kragstrup TW, Hokland M. Elimination of erroneous results in flow cytometry caused by antibody binding to Fc receptors on human monocytes and macrophages. Cytometry A. 2016;89(11):1001-9. 265. Abe M, Harpel J, Metz C, Nunes I, Loskutoff D, DB R. An Assay for Transforming Growth Factor-Beta Using Cells Transfected with a Plasminogen Activator Inhibitor-1 Promoter- Luciferase Construct. Analytical Biochemistry. 1994;216:276-84. 266. Haniffa M, Bigley V, Collin M. Human mononuclear phagocyte system reunited. Semin Cell Dev Biol. 2015;41:59-69. 267. Heino J, Ignotzp RA, Hemlerll ME, Crousen C, Massague J. Regulation of Cell Adhesion Receptors by Transforming Growth Factor-Beta: Concomitant Regulation of Integrins that Share a Common Beta1 Subunit. The Journal of Biological Chemistry. 1989;264:380-8. 268. Sheppard D, Cohen DS, Wang A, Busk M. Transforming Growth Factor Beta Differentially Regulates Expression of Integrin Subunits in Guinea Pig Airway Epithelial Cells. The Journal of Biological Chemistry. 1992;267:17409- 14,. 269. Bridgewater RE, Norman JC, Caswell PT. Integrin trafficking at a glance. J Cell Sci. 2012;125(Pt 16):3695-701. 270. Dutertre CA, Amraoui S, DeRosa A, Jourdain JP, Vimeux L, Goguet M, et al. Pivotal role of M-DC8(+) monocytes from viremic HIV-infected patients in TNFalpha overproduction in response to microbial products. Blood. 2012;120(11):2259-68. 271. West SD, Goldberg D, Ziegler A, Krencicki M, Du Clos TW, Mold C. Transforming Growth Factor-β, Macrophage Colony- Stimulating Factor and C- Reactive Protein Levels Correlate with CD14highCD16+ Monocyte Induction and Activation in Trauma Patients. PLoS ONE 2012;7:e52406.

178 272. Kelly A, Gunaltay S, McEntee C, Shuttleworth EE, Smedley C, Houston SA, et al. Human monocytes and macrophages regulate immune tolerance via integrin αvβ8- mediated TGFβ activation. In press. 273. Frequencies of Cell Types in Human Peripheral Blood. Version 4.0.0 ed: Stemcell Technologies. 274. Wiseman DM, Polverini PJ, Kamp DW, Leibovich SJ. Transforming Growth Factor-Beta (TGFβ) is Chemotactic For Human Monocytes and Induces Their Expression of Angiogenic Activity. Biochemical and Biophysical Research Communications. 1988;157:793-800. 275. Wahl SM, Hunt DA, Wakefield LM, McCartney-Francis N, Wahl LM, Roberts AB, et al. Transforming growth factor type β induces monocyte chemotaxis and growth factor production. Proc Natl Acad Sci U S A. 1987;84:5788-92. 276. Pilette C, Ouadrhiri Y, Van Snick J, Renauld JC, Staquet P, Vaerman JP, et al. IL-9 Inhibits Oxidative Burst and TNF- Release in Lipopolysaccharide- Stimulated Human Monocytes Through TGF- The Journal of Immunology. 2002;168(8):4103-11. 277. Chen Y, Kam CS, Liu FQ, Liu Y, Lui VC, Lamb JR, et al. LPS-induced up-regulation of TGF-beta receptor 1 is associated with TNF-alpha expression in human monocyte-derived macrophages. J Leukoc Biol. 2008;83(5):1165-73. 278. Kim EY, Kim BC. Lipopolysaccharide inhibits transforming growth factor- beta1-stimulated Smad6 expression by inducing phosphorylation of the linker region of Smad3 through a TLR4-IRAK1-ERK1/2 pathway. FEBS Lett. 2011;585(5):779-85. 279. Kitamura H, Cambier S, Somanath S, Barker T, Minagawa S, Markovics J, et al. Mouse and human lung fibroblasts regulate dendritic cell trafficking, airway inflammation, and fibrosis through integrin alphavbeta8-mediated activation of TGF-beta. J Clin Invest. 2011;121(7):2863-75. 280. Fenton TM. Integrin αvβ8 on human dendritic cells: a role in intestinal immune homeostasis. Manchester: University of Manchester; 2015. 281. Cottrez F, Groux H. Regulation of TGF-β Response During T Cell Activation Is Modulated by IL-10. J Immunol. 2001;167:773-8. 282. Van Vlasselaer P, Borremans B, van Gorp U, Dasch JR, De Waal- Malefyt R. Interleukin 10 Inhibits Transforming Growth Factor-B (TGF-β) Synthesis Required for Osteogenic Commitment of Mouse Bone Marrow Cells. The Journal of Cell Biology. 1994;124:569-77. 283. Ziegler-Heitbrock HWL, Fingerle G, Strobel M, Schraut W, Stelter F, Schutt C, et al. The novel subset of CD14+/CD16+blood monocytes exhibits features of tissue macrophages. European Journal of Immunology. 1993;23 2053-8. 284. Thiesen S, Janciauskiene S, Uronen-Hansson H, Agace W, Hogerkorp CM, Spee P, et al. CD14(hi)HLA-DR(dim) macrophages, with a resemblance to classical blood monocytes, dominate inflamed mucosa in Crohn's disease. J Leukoc Biol. 2014;95(3):531-41. 285. Lacey DC, Achuthan A, Fleetwood AJ, Dinh H, Roiniotis J, Scholz GM, et al. Defining GM-CSF- and macrophage-CSF-dependent macrophage responses by in vitro models. J Immunol. 2012;188(11):5752-65. 286. Rey-Giraud F, Hafner M, Ries CH. In vitro generation of monocyte- derived macrophages under serum-free conditions improves their tumor promoting functions. PLoS One. 2012;7(8):e42656.

179 287. Mantovani A, Sica A, Sozzani S, Allavena P, Vecchi A, Locati M. The chemokine system in diverse forms of macrophage activation and polarization. Trends Immunol. 2004;25(12):677-86. 288. Cassetta L, Noy R, Swierczak A, Sugano G, Smith H, Wiechmann L, et al. Isolation of Mouse and Human Tumor-Associated Macrophages. Tumor Microenvironment. Advances in Experimental Medicine and Biology2016. p. 211-29. 289. Hamilton JA. Colony-stimulating factors in inflammation and autoimmunity. Nat Rev Immunol. 2008;8(7):533-44. 290. Robbins SH, Walzer T, Dembele D, Thibault C, Defays A, Bessou G, et al. Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling. Genome Biol. 2008;9(1):R17. 291. Crozat K, Guiton R, Guilliams M, Henri S, Baranek T, Schwartz-Cornil I, et al. Comparative genomics as a tool to reveal functional equivalences between human and mouse dendritic cell subsets. Immunological Reviews. 2010;234:177–98. 292. Fleetwood AJ, Dinh H, Cook AD, Hertzog PJ, Hamilton JA. GM-CSF- and M-CSF-dependent macrophage phenotypes display differential dependence on type I interferon signaling. J Leukoc Biol. 2009;86(2):411-21. 293. Neu C, Sedlag A, Bayer C, Forster S, Crauwels P, Niess JH, et al. CD14- dependent monocyte isolation enhances phagocytosis of listeria monocytogenes by proinflammatory, GM-CSF-derived macrophages. PLoS One. 2013;8(6):e66898. 294. Høgåsen AKM, Hestdal K, Høgåsen K, Abrahamsen TG. Transforming growth factor β modulates C3 and factor B biosynthesis and complement receptor 3 expression in cultured human monocytes. Journal of Leukocyte Biology. 1995;57:287–96. 295. Kruger M, Coorevits L, M. DWTP, Casteels-Van Daele M, Van De Winkel JGJ, Ceuppens JL. Granulocyte-macrophage colony-stimulating factor antagonizes the transforming growth factor-β-induced expression of FcyRIII(CD16) on human monocytes. Immunology. 1996 87:162-7. 296. Henning LN, Azad AK, Parsa KVL, Crowther JE, Tridandapani S, Schlesinger LS. Pulmonary Surfactant Protein A Regulates TLR Expression and Activity in Human Macrophages. The Journal of Immunology. 2008;180(12):7847-58. 297. Schlaepfer E, Rochat MA, Duo L, Speck RF. Triggering TLR2, -3, -4, -5, and -8 reinforces the restrictive nature of M1- and M2-polarized macrophages to HIV. J Virol. 2014;88(17):9769-81. 298. Shi C, Pamer EG. Monocyte recruitment during infection and inflammation. Nat Rev Immunol. 2011;11(11):762-74. 299. Pioli P, Goonan K, Wardwell K, Guyre P. TGF-β regulation of human macrophage scavenger receptor CD163 is Smad3-dependent. Journal of Leukocyte Biology. 2004;76:500–8. 300. Porcheray F, Viaud S, Rimaniol AC, Leone C, Samah B, Dereuddre- Bosquet N, et al. Macrophage activation switching: an asset for the resolution of inflammation. Clin Exp Immunol. 2005;142(3):481-9. 301. Hamon G, Mulloy RH, Chen G, Chow R, Birkenmaier C, Horn JK. Transforming Growth Factor-β1 Lowers the CD14 Content of Monocytes. Journal of Surgical Research. 1994;57:574-8.

180 302. Imai K, Takeshita A, Hanazawa S. Transforming Growth Factor-β Inhibits Lipopolysaccharide-Stimulated Expression of Inflammatory Cytokines in Mouse Macrophages through Downregulation of Activation Protein 1 and CD14 Receptor Expression. Infection and Immunity. 2000;68:2418–23. 303. Kitani A, Fuss I, Nakamura K, Kumaki F, Usui T, Strober W. Transforming growth factor (TGF)-beta1-producing regulatory T cells induce Smad-mediated interleukin 10 secretion that facilitates coordinated immunoregulatory activity and amelioration of TGF-beta1-mediated fibrosis. J Exp Med. 2003;198(8):1179-88. 304. Roney KE, O'Connor BP, Wen H, Holl EK, Guthrie EH, Davis BK, et al. Plexin-B2 Negatively Regulates Macrophage Motility, Rac, and Cdc42 Activation. PLoS ONE. 2011;6(9). 305. Vos A, Wildenberg M, Arijs I, Duijvestein M, Verhaar A, de Hertogh G, et al. Regulatory macrophages induced by infliximab are involved in healing in vivo and in vitro. Inflammatory Bowel Diseases 2012;18:401-8. 306. Winesett MP, Ramse GW, Barnard JA. Type II TGFβ receptor expression in intestinal cell lines and in the intestinal tract. Carcinogenesis. 1996;17 989-95. 307. Martinez FO, Gordon S, Locati M, Mantovani A. Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression. The Journal of Immunology. 2006;177(10):7303-11. 308. O’Neill LAJ, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nature Reviews Immunology. 2016;16:553-65. 309. Kelly B, O'Neill LA. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. 2015;25(7):771-84. 310. Cheng SC, Quintin J, Cramer RA, Shepardson KM, Saeed S, Kumar V, et al. mTOR- and HIF-1 -mediated aerobic glycolysis as metabolic basis for trained immunity. Science. 2014;345(6204):1250684-. 311. Van den Bossche J, Baardman J, Otto NA, van der Velden S, Neele AE, van den Berg SM, et al. Mitochondrial Dysfunction Prevents Repolarization of Inflammatory Macrophages. Cell Rep. 2016;17(3):684-96. 312. Tavakoli S, Zamora D, Ullevig S, Asmis R. Bioenergetic profiles diverge during macrophage polarization: implications for the interpretation of 18F-FDG PET imaging of atherosclerosis. J Nucl Med. 2013;54(9):1661-7. 313. Van den Bossche J, Baardman J, de Winther MP. Metabolic Characterization of Polarized M1 and M2 Bone Marrow-derived Macrophages Using Real-time Extracellular Flux Analysis. J Vis Exp. 2015(105). 314. Agilent. Agilent Seahorse XF Sensor Cartridges and Cell Culture Microplates Product Brochure: Agilent; 2018 [Available from: https://www.agilent.com/cs/library/brochures/5991- 8657EN_seahorse_plastics_brochure.pdf. 315. Mills CD, Kincaid K, Alt JM, Heilman MJ, Hill AM. M-1/M-2 Macrophages and the Th1/Th2 Paradigm. The Journal of Immunology. 2000;164(12):6166-73. 316. Verreck FAW, de Boer T, Langenberg DML, Hoeve MA, Kramer M, Vaisberg E, et al. Human IL-23-producing type 1 macrophages promote but IL- 10-producing type 2 macrophages subvert immunity to (myco)bacteria. Proc Natl Acad Sci U S A. 2004;101:4560–5. 317. Mia S, Warnecke A, Zhang XM, Malmstrom V, Harris RA. An optimized protocol for human M2 macrophages using M-CSF and IL-4/IL-10/TGF-beta

181 yields a dominant immunosuppressive phenotype. Scand J Immunol. 2014;79(5):305-14. 318. Nowak G, Schnellmann RG. Autocrine production and TGF-β1-mediated effects on metabolism and viability in renal cells. Am J Physiol . 1996;271: F689-F97. 319. Jiang L, Xiao L, Sugiura H, Huang X, Ali A, Kuro-o M, et al. Metabolic reprogramming during TGFbeta1-induced epithelial-to-mesenchymal transition. Oncogene. 2015;34(30):3908-16. 320. Allen JB, Manthey CL, Hand AR, Ohura K, Ellingsworth L, Wahl SM. Rapid onset synovial inflammation and hyperplasia induced by transforming growth factor beta. Journal of Experimental Medicine. 1990;171(1):231-47. 321. Wahl SM, McCartney-Francis N, Allen JB, Dougherty EB, F DS. Macrophage production of TGF-beta and regulation by TGF-beta. Ann N Y Acad Sci. 1990;593:188-96. 322. Turner M, Chantry D, Feldmann M. Transforming Growth Factor β Induces the Production of Interleukin 6 by Human Peripheral Blood Mononuclear Cells. Cytokine. 1990;2:211-6. 323. Wahl SM, Allen JB, Weeks BS, Wong HL, Klotman PE. Transforming growth factor β enhances integrin expression and type IV collagenase secretion in human monocytes. Proc Natl Acad Sci U S A. 1993;90:4577-81. 324. Schenk M, Bouchon A, Seibold F, Mueller C. TREM-1--expressing intestinal macrophages crucially amplify chronic inflammation in experimental colitis and inflammatory bowel diseases. J Clin Invest. 2007;117(10):3097-106. 325. de Lange KM, Moutsianas L, Lee JC, Lamb CA, Luo Y, Kennedy NA, et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat Genet. 2017;49(2):256-61. 326. Brennan FM, Chantry D, Turner M, Foxwell B, Maini R, Feldmann M. Detection of transforming growth factor-beta in rheumatoid arthritis synovial tissue: lack of effect on spontaneous cytokine production in joint cell cultures. Clin Exp Immunol. 1990;81:278-85. 327. Eberlein C, Rooney C, Ross SJ, Farren M, Weir HM, Barry ST. E- Cadherin and EpCAM expression by NSCLC tumour cells associate with normal fibroblast activation through a pathway initiated by integrin alphavbeta6 and maintained through TGFbeta signalling. Oncogene. 2015;34(6):704-16. 328. Wynn TA, Vannella KM. Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity. 2016;44(3):450-62. 329. Kopp JB, Factor VM, Mozes M, Nagy P, Sanderson N, Böttinger EP, et al. Transgenic mice with increased plasma levels of TGF-beta 1 develop progressive renal disease. Lab Invest. 1996 74 991-1003. 330. Li AG, Wang D, Feng X-H, Wang X-J. Latent TGFβ1 overexpression in keratinocytes results in a severe psoriasis-like skin disorder. EMBO J. 2004;23:1770–81. 331. Li JH. Smad7 Inhibits Fibrotic Effect of TGF-β on Renal Tubular Epithelial Cells by Blocking Smad2 Activation. Journal of the American Society of Nephrology. 2002;13(6):1464-72. 332. Shah M, Foreman DM, Ferguson MW. Control of scarring in adult wounds by neutralising antibody to transforming growth factor β. The Lancet. 1992;339:213-4.

182 333. Dadrich M, Nicolay NH, Flechsig P, Bickelhaupt S, Hoeltgen L, Roeder F, et al. Combined inhibition of TGFβ and PDGF signaling attenuates radiation- induced pulmonary fibrosis. OncoImmunology. 2015;5(5). 334. Border WA, Okuda S, Languino LR, Sporn MB, Ruoslahti E. Suppression of experimental glomerulonephritis by antiserum against transforming growth factor beta 1. Nature. 1990;346 371-4. 335. Sato M, Muragaki Y, Saika S, Roberts AB, Ooshima A. Targeted disruption of TGF-beta1/Smad3 signaling protects against renal tubulointerstitial fibrosis induced by unilateral ureteral obstruction. J Clin Invest. 2003;112(10):1486-94. 336. Kim JY, An HJ, Kim WH, Gwon MG, Gu H, Park YY, et al. Anti-fibrotic Effects of Synthetic Oligodeoxynucleotide for TGF-beta1 and Smad in an Animal Model of Liver . Mol Ther Nucleic Acids. 2017;8:250-63. 337. Yao EH, Fukuda N, Ueno T, Matsuda H, Nagase H, Matsumoto Y, et al. A pyrrole-imidazole polyamide targeting transforming growth factor-beta1 inhibits restenosis and preserves endothelialization in the injured artery. Cardiovasc Res. 2009;81(4):797-804. 338. Vincenti F, Fervenza FC, Campbell KN, Diaz M, Gesualdo L, Nelson P, et al. A Phase 2, Double-Blind, Placebo-Controlled, Randomized Study of Fresolimumab in Patients With Steroid-Resistant Primary Focal Segmental Glomerulosclerosis. Kidney Int Rep. 2017;2(5):800-10. 339. Rice LM, Padilla CM, McLaughlin SR, Mathes A, Ziemek J, Goummih S, et al. Fresolimumab treatment decreases biomarkers and improves clinical symptoms in systemic sclerosis patients. J Clin Invest. 2015;125(7):2795-807. 340. Margaritopoulos GA, Vasarmidi E, Antoniou KM. Pirfenidone in the treatment of idiopathic pulmonary fibrosis: an evidence-based review of its place in therapy. Core Evid. 2016;11:11-22. 341. Pirfenidone for treating idiopathic pulmonary fibrosis: Technology appraisal guidance [TA504]. National Institute for Health and Care Excellence (NICE); 2018. 342. Klingberg F, Chow ML, Koehler A, Boo S, Buscemi L, Quinn TM, et al. Prestress in the extracellular matrix sensitizes latent TGF-beta1 for activation. J Cell Biol. 2014;207(2):283-97. 343. Margadant C, Sonnenberg A. Integrin-TGF-beta crosstalk in fibrosis, cancer and wound healing. EMBO Rep. 2010;11(2):97-105. 344. Araya J, Cambier S, Markovics JA, Wolters P, Jablons D, Hill A, et al. Squamous metaplasia amplifies pathologic epithelial-mesenchymal interactions in COPD patients. J Clin Invest. 2007;117(11):3551-62. 345. Patsenker E, Popov Y, Stickel F, Jonczyk A, Goodman SL, Schuppan D. Inhibition of integrin alphavbeta6 on cholangiocytes blocks transforming growth factor-beta activation and retards biliary fibrosis progression. Gastroenterology. 2008;135(2):660-70. 346. Hahm K, Lukashev ME, Luo Y, Yang WJ, Dolinski BM, Weinreb PH, et al. Alphav beta6 integrin regulates renal fibrosis and inflammation in Alport mouse. Am J Pathol. 2007;170(1):110-25. 347. Minagawa S, Lou J, Seed RI, Cormier A, Wu S, Cheng Y, et al. Selective targeting of TGF-beta activation to treat fibroinflammatory airway disease. Sci Transl Med. 2014;6(241):241ra79.

183 348. Cosnes J, Cattan Sp, Blain A, Beaugerie L, Carbonnel F, Parc R, et al. Long-Term Evolution of Disease Behavior of Crohn’s Disease. Inflammatory Bowel Diseases. 2002;8:244–50. 349. Rybinski B, Franco-Barraza J, Cukierman E. The wound healing, chronic fibrosis, and cancer progression triad. Physiological Genomics. 2014;46(7):223- 44. 350. Gamble JR, Khew-Goodall Y, Vadas MA. Transforming growth factor- beta inhibits E-selectin expression on human endothelial cells. J Immunol. 1993:4494-450. 351. Kitamura M. Identification of an inhibitor targeting macrophage production of monocyte chemoattractant protein-1 as TGF-beta 1. J Immunol. 1997;159:1404-11. 352. Sherry B, Espinoza M, Manogue KR, Cerami A. Induction of the Chemokine β Peptides, MIP-lα and MIP-1β, by Lipopolysaccharide Is Differentially Regulated by Immunomodulatory Cytokines γ-IFN, IL-10, IL-4, and TGF-β. Molecular Medicine. 1998;4:648-57. 353. Lee EY, Chung CH, Khoury CC, Yeo TK, Pyagay PE, Wang A, et al. The monocyte chemoattractant protein-1/CCR2 loop, inducible by TGF-beta, increases podocyte motility and albumin permeability. Am J Physiol Renal Physiol. 2009;297(1):F85-94. 354. Takeshita A, Chen Y, Watanabe A, Kitano S, Hanazawa S. TGF-beta induces expression of monocyte chemoattractant JE/monocyte chemoattractant protein 1 via transcriptional factor AP-1 induced by protein kinase in osteoblastic cells. J Immunol. 1995;155:419-26. 355. Pulleyn LJ, Newton R, Adcock IM, Barnes PJ. TGFbeta1 allele association with asthma severity. Hum Genet. 2001;109(6):623-7. 356. Akhurst RJ, Hata A. Targeting the TGFβ signalling pathway in disease. Nature Reviews Drug Discovery. 2012;11:790–811. 357. Alexandrow MG, Moses HL. Transforming Growth Factor β and Cell Cycle Regulation'. Cancer Res 1995;55:1452-7. 358. Heldin CH, Landstrom M, Moustakas A. Mechanism of TGF-beta signaling to growth arrest, apoptosis, and epithelial-mesenchymal transition. Curr Opin Cell Biol. 2009;21(2):166-76. 359. Akhurst RJ. Targeting TGF-beta Signaling for Therapeutic Gain. Cold Spring Harb Perspect Biol. 2017;9(10). 360. Giampieri S, Pinner S, Sahai E. Intravital imaging illuminates transforming growth factor beta signaling switches during metastasis. Cancer Res. 2010;70(9):3435-9. 361. Bragado P, Estrada Y, Parikh F, Krause S, Capobianco C, Farina HG, et al. TGF-beta2 dictates disseminated tumour cell fate in target organs through TGF-beta-RIII and p38alpha/beta signalling. Nat Cell Biol. 2013;15(11):1351- 61. 362. Bellone G, Carbone A, Tibaudi D, Mauri F, Ferrero I, Smirne C, et al. Differential expression of transforming growth factors-β1, -β2 and -β3 in human colon carcinoma. European Journal of Cancer. 2001 37:224-33. 363. Rubtsov YP, Rudensky AY. TGFβ signalling in control of T-cell-mediated self-reactivity. Nature Reviews Immunology. 2007;7(6):443-53. 364. Ramesh S, Wildey GM, Howe PH. Transforming growth factor β (TGFβ)- induced apoptosis: The rise & fall of Bim. Cell Cycle. 2009;8:11–7.

184 365. Vogelmann R, Nguyen-tat MD, Giehl K, Adler G, Wedlich D, Menke A. TGFβ-induced downregulation of E-cadherin-based cell-cell adhesion depends on PI3-kinase and PTEN. Journal of Cell Science. 2005;118(20):4901-12. 366. Qiang L, Shah P, Barcellos-Hoff MH, He YY. TGF-beta signaling links E- cadherin loss to suppression of nucleotide excision repair. Oncogene. 2016;35(25):3293-302. 367. Qureshi-Baig K, Ullmann P, Haan S, Letellier E. Tumor-Initiating Cells: a criTICal review of isolation approaches and new challenges in targeting strategies. Mol Cancer. 2017;16(1):40. 368. Li X, Lewis MT, Huang J, Gutierrez C, Osborne CK, Wu MF, et al. Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy. J Natl Cancer Inst. 2008;100(9):672-9. 369. Anido J, Saez-Borderias A, Gonzalez-Junca A, Rodon L, Folch G, Carmona MA, et al. TGF-beta Receptor Inhibitors Target the CD44(high)/Id1(high) Glioma-Initiating Cell Population in Human Glioblastoma. Cancer Cell. 2010;18(6):655-68. 370. Zhang M, Kleber S, Rohrich M, Timke C, Han N, Tuettenberg J, et al. Blockade of TGF-beta signaling by the TGFbetaR-I kinase inhibitor LY2109761 enhances radiation response and prolongs survival in glioblastoma. Cancer Res. 2011;71(23):7155-67. 371. Shipitsin M, Campbell LL, Argani P, Weremowicz S, Bloushtain-Qimron N, Yao J, et al. Molecular definition of breast tumor heterogeneity. Cancer Cell. 2007;11(3):259-73. 372. Ehata S, Johansson E, Katayama R, Koike S, Watanabe A, Hoshino Y, et al. Transforming growth factor-beta decreases the cancer-initiating cell population within diffuse-type gastric carcinoma cells. Oncogene. 2011;30(14):1693-705. 373. Tang B, Yoo N, Vu M, Mamura M, Nam JS, Ooshima A, et al. Transforming growth factor-beta can suppress tumorigenesis through effects on the putative cancer stem or early progenitor cell and committed progeny in a breast cancer xenograft model. Cancer Res. 2007;67(18):8643-52. 374. Bhola NE, Balko JM, Dugger TC, Kuba MG, Sánchez V, Sanders M, et al. TGF-β inhibition enhances chemotherapy action against triple-negative breast cancer. Journal of Clinical Investigation. 2013;123(3):1348-58. 375. Bouquet F, Pal A, Pilones KA, Demaria S, Hann B, Akhurst RJ, et al. TGFbeta1 inhibition increases the radiosensitivity of breast cancer cells in vitro and promotes tumor control by radiation in vivo. Clin Cancer Res. 2011;17(21):6754-65. 376. Bandyopadhyay A, Wang L, Agyin J, Tang Y, Lin S, Yeh IT, et al. Doxorubicin in combination with a small TGFbeta inhibitor: a potential novel therapy for metastatic breast cancer in mouse models. PLoS One. 2010;5(4):e10365. 377. Ohmori T, Yang J-L, Price JO, Arteaga CL. Blockade of Tumor Cell Transforming Growth Factor-β Enhances Cell Cycle Progression and Sensitizes Human Breast Carcinoma Cells to Cytotoxic Chemotherapy. Experimental Cell Research. 1998;245:350–9. 378. Huang S, Hölzel M, Knijnenburg T, Schlicker A, Roepman P, McDermott U, et al. MED12 Controls the Response to Multiple Cancer Drugs through Regulation of TGF-β Receptor Signaling. Cell. 2012;151(5):937-50.

185 379. Biswas S, Guix M, Rinehart C, Dugger TC, Chytil A, Moses HL, et al. Inhibition of TGF-beta with neutralizing antibodies prevents radiation-induced acceleration of metastatic cancer progression. J Clin Invest. 2007;117(5):1305- 13. 380. Kano MR, Bae Y, Iwata C, Morishita Y, Yashiro M, Oka M, et al. Improvement of cancer-targeting therapy, using nanocarriers for intractable solid tumors by inhibition of TGF-β signaling. Proc Natl Acad Sci U S A. 2007;104:3460–5 381. Morris JC, Tan JC, Olencki TE, Shapiro GI, Dezube BJ, Reiss M, et al. Phase I Study of GC1008 (Fresolimumab): A Human Anti- Transforming Growth Factor-Beta (TGFβ) Monoclonal Antibody in Patients with Advanced Malignant Melanoma or Renal Cell Carcinoma. PLoS One. 2014;9:e90353. 382. den Hollander MW, Bensch F, Glaudemans AW, Oude Munnink TH, Enting RH, den Dunnen WF, et al. TGF-beta Antibody Uptake in Recurrent High-Grade Glioma Imaged with 89Zr-Fresolimumab PET. J Nucl Med. 2015;56(9):1310-4. 383. Brandes AA, Carpentier AF, Kesari S, Sepulveda-Sanchez JM, Wheeler HR, Chinot O, et al. A Phase II randomized study of galunisertib monotherapy or galunisertib plus lomustine compared with lomustine monotherapy in patients with recurrent glioblastoma. Neuro Oncol. 2016;18(8):1146-56. 384. Triplett TA, Tucker CG, Triplett KC, Alderman Z, Sun L, Ling LE, et al. STAT3 Signaling Is Required for Optimal Regression of Large Established Tumors in Mice Treated with Anti-OX40 and TGFbeta Receptor Blockade. Cancer Immunol Res. 2015;3(5):526-35. 385. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544-8. 386. Tauriello DVF, Palomo-Ponce S, Stork D, Berenguer-Llergo A, Badia- Ramentol J, Iglesias M, et al. TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis. Nature. 2018;554(7693):538-43. 387. Goudie DR, D'Alessandro M, Merriman B, Lee H, Szeverenyi I, Avery S, et al. Multiple self-healing squamous epithelioma is caused by a disease- specific spectrum of mutations in TGFBR1. Nat Genet. 2011;43(4):365-9. 388. Anderton MJ, Mellor HR, Bell A, Sadler C, Pass M, Powell S, et al. Induction of heart valve lesions by small-molecule ALK5 inhibitors. Toxicol Pathol. 2011;39(6):916-24. 389. Frazier K, Thomas R, Scicchitano M, Mirabile R, Boyce R, Zimmerman D, et al. Inhibition of ALK5 signaling induces physeal dysplasia in rats. Toxicol Pathol. 2007;35(2):284-95. 390. Laping NJ, Everitt JI, Frazier KS, Burgert M, Portis MJ, Cadacio C, et al. Tumor-specific efficacy of transforming growth factor-beta RI inhibition in Eker rats. Clin Cancer Res. 2007;13(10):3087-99. 391. Kovacs RJ, Maldonado G, Azaro A, Fernandez MS, Romero FL, Sepulveda-Sanchez JM, et al. Cardiac Safety of TGF-beta Receptor I Kinase Inhibitor LY2157299 Monohydrate in Cancer Patients in a First-in-Human Dose Study. Cardiovasc Toxicol. 2015;15(4):309-23. 392. Khan Z, Marshall JF. The role of integrins in TGFbeta activation in the tumour stroma. Cell Tissue Res. 2016;365(3):657-73. 393. Bates RC, Bellovin DI, Brown C, Maynard E, Wu B, Kawakatsu H, et al. Transcriptional activation of integrin beta6 during the epithelial-mesenchymal

186 transition defines a novel prognostic indicator of aggressive colon carcinoma. J Clin Invest. 2005;115(2):339-47. 394. Elayadi AN, Samli KN, Prudkin L, Liu YH, Bian A, Xie XJ, et al. A peptide selected by biopanning identifies the integrin alphavbeta6 as a prognostic biomarker for nonsmall cell lung cancer. Cancer Res. 2007;67(12):5889-95. 395. Hazelbag S, Kenter GG, Gorter A, Dreef EJ, Koopman LA, Violette SM, et al. Overexpression of the alpha v beta 6 integrin in cervical squamous cell carcinoma is a prognostic factor for decreased survival. J Pathol. 2007;212(3):316-24. 396. Moore KM, Thomas GJ, Duffy SW, Warwick J, Gabe R, Chou P, et al. Therapeutic targeting of integrin alphavbeta6 in breast cancer. J Natl Cancer Inst. 2014;106(8). 397. Eberlein C, Kendrew J, McDaid K, Alfred A, Kang JS, Jacobs VN, et al. A human monoclonal antibody 264RAD targeting alphavbeta6 integrin reduces tumour growth and metastasis, and modulates key biomarkers in vivo. Oncogene. 2013;32(37):4406-16. 398. Elez E, Kocakova I, Hohler T, Martens UM, Bokemeyer C, Van Cutsem E, et al. Abituzumab combined with cetuximab plus irinotecan versus cetuximab plus irinotecan alone for patients with KRAS wild-type metastatic colorectal cancer: the randomised phase I/II POSEIDON trial. Ann Oncol. 2015;26(1):132- 40. 399. Hezel AF, Deshpande V, Zimmerman SM, Contino G, Alagesan B, O'Dell MR, et al. TGF-beta and alphavbeta6 integrin act in a common pathway to suppress pancreatic cancer progression. Cancer Res. 2012;72(18):4840-5. 400. Ludlow A, Yee KO, Lipman R, Bronson R, Weinreb P, Huang X, et al. Characterization of integrin β6 and thrombospondin-1 double–null mice. J Cell Mol Med. 2005;9:421-37. 401. Mertens-Walker I, Fernandini BC, Maharaj MS, Rockstroh A, Nelson CC, Herington AC, et al. The tumour-promoting , EphB4, regulates expression of integrin-beta8 in prostate cancer cells. BMC Cancer. 2015;15:164. 402. Landemaine T, Jackson A, Bellahcene A, Rucci N, Sin S, Abad BM, et al. A six-gene signature predicting breast cancer lung metastasis. Cancer Res. 2008;68(15):6092-9. 403. Sebastian S, Hernandez V, Myrelid P, Kariv R, Tsianos E, Toruner M, et al. Colorectal cancer in inflammatory bowel disease: results of the 3rd ECCO pathogenesis scientific workshop (I). J Crohns Colitis. 2014;8(1):5-18. 404. Sonnenberg A, Genta RM. Epithelial Dysplasia and Cancer in IBD Strictures. J Crohns Colitis. 2015;9(9):769-75. 405. Langholz E. Current trends in inflammatory bowel disease: the natural history. Therap Adv Gastroenterol. 2010;3(2):77-86. 406. van Dullemen HM, van Deventer SJ, Hommes DW, Bijl HA, Jansen J, Tytgat GNJ, et al. Treatment of Crohn's Disease With Anti-Tumor Necrosis Factor Chimeric Monoclonal Antibody (cA2). Gastroenterology. 1995;109:129- 35. 407. Peyrin-Biroulet L, Deltenre P, de Suray N, Branche J, Sandborn WJ, Colombel JF. Efficacy and safety of tumor necrosis factor antagonists in Crohn's disease: meta-analysis of placebo-controlled trials. Clin Gastroenterol Hepatol. 2008;6(6):644-53.

187 408. Ben-Horin S, Kopylov U, Chowers Y. Optimizing anti-TNF treatments in inflammatory bowel disease. Autoimmun Rev. 2014;13(1):24-30. 409. Bourne T, Fossati G, Nesbitt A. A PEGylated Fab′ Fragment against Tumor Necrosis Factor for the Treatment of Crohn Disease: Exploring a New Mechanism of Action. Biodrugs. 2008;22:331-7. 410. Nesbitt A, Fossati G, Bergin M, Stephens P, Stephens S, Foulkes R, et al. Mechanism of action of certolizumab pegol (CDP870): in vitro comparison with other anti-tumor necrosis factor alpha agents. Inflamm Bowel Dis. 2007;13(11):1323-32. 411. Mitoma H, Horiuchi T, Hatta N, Tsukamoto H, Harashima S-I, Kikuchi Y, et al. Infliximab induces potent anti-inflammatory responses by outside-to-inside signals through transmembrane TNF-α. Gastroenterology. 2005;128(2):376-92. 412. Allez M, Karmiris K, Louis E, Van Assche G, Ben-Horin S, Klein A, et al. Report of the ECCO pathogenesis workshop on anti-TNF therapy failures in inflammatory bowel diseases: definitions, frequency and pharmacological aspects. J Crohns Colitis. 2010;4(4):355-66. 413. Wintjens D, F. B, Saccenti E, Jeuring S, Romberg-Camps M, Oostenbrug L, et al. Assessment of disease activity patterns during the first 10 years after diagnosis in a population-based Crohn’s disease cohort shows a quiescent disease course for a substantial proportion of the population. Journal of Crohn's and Colitis. 2018:S001-S3. 414. D'Haens G, Baert F, van Assche G, Caenepeel P, Vergauwe P, Tuynman H, et al. Early combined immunosuppression or conventional management in patients with newly diagnosed Crohn's disease: an open randomised trial. The Lancet. 2008;371(9613):660-7. 415. Baert F, Moortgat L, Van Assche G, Caenepeel P, Vergauwe P, De Vos M, et al. Mucosal healing predicts sustained clinical remission in patients with early-stage Crohn's disease. Gastroenterology. 2010;138(2):463-8; quiz e10-1. 416. Flamant M, Roblin X. Inflammatory bowel disease: towards a personalized medicine. Therap Adv Gastroenterol. 2018;11:1756283X17745029. 417. Arijs I, Quintens R, Van Lommel L, Van Steen K, De Hertogh G, Lemaire K, et al. Predictive value of epithelial gene expression profiles for response to infliximab in Crohnʼs disease‡. Inflammatory Bowel Diseases. 2010;16(12):2090-8. 418. Atreya R, Neumann H, Neufert C, Waldner MJ, Billmeier U, Zopf Y, et al. In vivo imaging using fluorescent antibodies to tumor necrosis factor predicts therapeutic response in Crohn's disease. Nat Med. 2014;20(3):313-8. 419. Vermeire S, O'Byrne S, Keir M, Williams M, Lu TT, Mansfield JC, et al. Etrolizumab as induction therapy for ulcerative colitis: a randomised, controlled, phase 2 trial. The Lancet. 2014;384(9940):309-18. 420. Babyatsky MW, Rossiter G, Podolsky DK. Expression of Transforming Growth Factors α and β in Colonic Mucosa in Inflammatory Bowel Disease. Gastroenterology. 1996;110:975–84. 421. Monteleone G, Kumberova A, Croft NM, McKenzie C, Steer HW, MacDonald TT. Blocking Smad7 restores TGF-β1 signaling in chronic inflammatory bowel disease. Journal of Clinical Investigation. 2001;108(4):601- 9.

188 422. Monteleone G, Neurath MF, Ardizzone S, Di Sabatino A, Fantini MC, Castiglione F, et al. Mongersen, an oral SMAD7 antisense oligonucleotide, and Crohn's disease. N Engl J Med. 2015;372(12):1104-13. 423. A Long-term Active Treatment Study of Mongersen (GED-0301) in Subjects With Crohn's Disease 2018 [Available from: https://clinicaltrials.gov/ct2/show/NCT02641392.

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