T cell transcriptomes: Uncovering the mechanisms for T cell effector
function through gene profiling
Tatyana Chtanova
December 2004
A thesis submitted for the degree of Doctor of Philosophy
Garvan Institute of Medical Research, St Vincent’s Hospital
and the Faculty of Science, University of New South Wales
Sydney, NSW, Australia
Manuscripts
• Chtanova T., R.A. Newton, S. M. Liu, L. Weininger, D. G. Silva, M. Hughes, F. Bertoni, F. Sallusto, M. S. Rolph and C. R. Mackay. Comprehensive leukocyte microarray datasets allow identification of T cell subset restricted genes, and signatures for different types of T cell responses. Manuscript in preparation. • Liu, S. M., T. Chtanova, R. Newton, M. Sisavanh, K. L. Good, D.G. Silva, S. Zimmer, M. J. Frost, S. G. Tangye, M. S. Rolph and C. R. Mackay. Comprehensive microarray datasets of immune cells reveal novel leukocyte subset-specific genes, as well as genes associated with different types of immune response. Submitted to Blood. • Chtanova, T., S. G. Tangye, R. A. Newton, N. Frank, M. R. Hodge, M. S. Rolph, and C. R. Mackay. 2004. T follicular helper cells express a distinctive transcriptional profile, reflecting their role as non-Th1/Th2 effector cells that provide help for B cells. J Immunol 173:68. • Ng, L.G., A.P.R. Sutherland, R. Newton, F. Qian, T. G. Cachero, M. Scott, J. S. Thompson, J. Wheway, T. Chtanova, C. Xin, J. Groom, S. G. Tangye, S. L. Kalled, F. Mackay, and C. R. Mackay. 2004. BAFF-R is the principal BAFF receptor facilitating BAFF co-stimulation of B and T cells. J Immunol 174:807. • Chtanova, T., and C. R. Mackay. 2001. T cell effector subsets: extending the Th1/Th2 paradigm. Adv Immunol 78:233. • Chtanova, T., R. A. Kemp, A. P. Sutherland, F. Ronchese, and C. R. Mackay. 2001. Gene microarrays reveal extensive differential gene expression in both CD4+ and CD8+ type 1 and type 2 T cells. J Immunol 167:3057.
Presentations
• Keystone Symposium. T cell development, 2004, Banff, Canada Chtanova, T., S. G. Tangye, R. A. Newton, M. R. Hodge, M. S. Rolph, and C. R. Mackay. T follicular helper cells (TFH) express a distinctive transcriptional profile, reflecting their role as non-Th1/Th2 effector cells that provide help for B cells. Oral and poster presentations. • The 13th St. Vincent’s campus Research Symposium, 2003, Sydney Australia Chtanova, T., M. J. Frost, S. G. Tangye, R. A. Newton, M. R. Hodge , M. S. Rolph, and C. R. Mackay. T follicular helper cells distinguished from Th1 and Th2 subsets by their expression of certain cytokines, cell surface molecules and the transcription factor BCL-6. Poster presentation. • 32nd Annual Meeting for the Australasian Society for Immunology, 2002, Brisbane, Australia Chtanova, T., R. A. Kemp, M. J. Frost, A. P. R. Sutherland, F. Ronchese†, M. S. Rolph, and C. R. Mackay. Distinguishing genes critical for T cell development and effector function. Poster presentation. • 11th International Congress of Immunology, 2001, Stockholm, Sweden Chtanova, T., R. A. Kemp, A. P. Sutherland, F. Ronchese, and C. R. Mackay.
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Gene microarrays reveal extensive differential gene expression in both CD4(+) and CD8(+) type 1 and type 2 T cells. Oral and poster presentations. • The 10th St. Vincent’s campus Research Symposium, 2000, Sydney Australia Chtanova, T., A. Sutherland, A. Quan and C.R. Mackay. Identification of genes for asthma pathogenesis. Oral presentation.
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Acknowledgements
Firstly, a huge thank you to my supervisor Professor Charles Mackay for his guidance, support and encouragement. Thank you for teaching me a great many things about immunology and what science is all about. I would also like to thank my co- supervisor Dr Michael Rolph for his thoughtful advice and support. I would like to thank many collaborators for generously sharing data and reagents with me. In particular, thank you to Dr Roslyn Kemp (Malaghan Institute, New Zealand) for providing polarized mouse cells; Dr Stuart Tangye (Centenary Institute, Australia) for all the advice and generous sharing of many reagents; Dr Martin Hodge and Nita Frank for kindly providing IL-21R antibody; Dr Diego Silva (ANU Medical School, Australia) for bioinformatics help; Dr David Lowinger for providing tonsil tissue. I am very grateful to the following people for providing their microarray data: Dr Federica Sallusto (Institute for Research in Biomedicine, Switzerland), Dr Rebecca Newton, Sue Liu, Dr Sabine Zimmer and Mary Sisavanh (Garvan Institute, Australia). Thank you also to Lilach Weininger, Melinda Frost and Trina So for their help with the Affymetrix system. I have been very lucky to work with an amazing group of people and I am very grateful to everyone in the Arthritis and Inflammation Research Program for all their help and support over these years. It has been a lot of fun to be a part of this dynamic and exciting group of people. In particular, I had the great pleasure of working with Alison Saunders and Lilach Weininger, both of whom have made my time here so much more enjoyable. Of course, a special mention goes to Dr Frederic Sierro (a.k.a. Swiss Fred) – there is no one I would have rather shared an office with! Thank you for your sense of humour, our many discussions and my lessons in Swiss geography. Many thanks go to my wonderful friends who have kept me sane during these years. A special thank you goes to Jerry Frenkel for being a wonderful friend, colleague and bridesmaid extraordinaire. Throughout my life my parents have always been a huge inspiration to me. I thank you wholeheartedly for your unfailing support through every step of my life. And finally, an enormous thank you to my future husband, James. No words can describe how much your love and support over all these years have meant to me. I could not have done it without you. This thesis is dedicated to you.
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Abstract T cells are at the heart of the adaptive immune response. They mediate many important immunological processes that provide protection against viruses, bacteria and other pathogens. T cell functions include cytotoxicity, cytokine production, immunosuppression, recruitment of other effector cells to the sites of infection, and help for antibody production. The aim of the work described in this thesis was to use gene expression profiling to gain insights into different aspects of T cell biology. In particular we wanted to examine the mechanisms of effector function and identify the genes that underlie the functions of different effector subsets. One of the fundamental divisions in effector T cell responses is based on two distinct patterns of cytokine production by CD4+ T cells. IFN-γ -producing Th1 cells are a major effector subset that protects against intracellular pathogens, while Th2 cells produce IL-4, IL-5 and IL-13 and mediate protection against large extracellular pathogens. We conducted a comprehensive analysis of T cell polarization by profiling gene expression in both mouse and human Th1 and Th2 cells polarized using different experimental protocols, as well as mouse CD8+ type 1 and type 2 T cells. One of the major outcomes of this extensive analysis was the identification of a number of novel markers for Th1 and Th2 cells which may have important roles in Th2 differentiation/function. The other was the delineation of some of the major influences on gene expression profiles during polarization such as the effects of different T cell types, species and differentiation conditions. Providing help to B cells for antibody production is the major function of the + third effector subset of CD4 T cells termed T follicular homing or TFH cells. These cells express the chemokine receptor CXCR5+ and home to B cell follicles where they interact with B cells. However, relatively little is known about the generation of these cells, and the mechanisms of their effector function are yet to be determined. Using oligonucleotide microarrays we identified a TFH-specific gene expression signature. This signature included many novel genes which will undoubtedly enable better identification and characterization of this novel subset. For instance, IL-21 and CD84, which were intimately associated with TFH cells and have already been shown to affect
B cell differentiation and function, provide a possible mechanism for TFH help to B cells. The transcription factor BCL6 which was also preferentially expressed by TFH cells may act as a key regulator of T cell fate.
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A comprehensive study profiling all the major leukocyte subsets revealed distinct gene expression signatures for every leukocyte subset, and enabled identification of numerous leukocyte subset specific genes, and in particular many novel T cell-specific genes. A detailed examination of most major T cell subsets identified distinguishing features of each subset together with gene expression changes associated with T cell activation and exposure to cell culture conditions. In addition, we described a distinctive transcriptional profile for γδ T cells and examined the differences between central and effector memory T cells. We also showed that specific gene expression signatures provide a powerful tool for subset classification. Taken together this work provides important insights into T cell differentiation and effector function, and provides a basis for future work examining numerous novel genes relevant to T cell biology.
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Contributions of collaborators I would like to acknowledge the contributions of the following people to the work presented in this thesis: • Dr Roslyn Kemp (Malaghan Institute, New Zealand) provided polarized Th1 and Th1 and Tc1 and Tc2 mouse cells used for analysis of type 1 and 2 T cell gene expression in Chapter 3 and supplied the data for Figure 3.1;
• The tonsil tissue used as a source of TFH cells was provided by Dr David Lowinger; • Dr Martin Hodge and Nita Frank (Millenium Pharmaceuticals, Cambridge, MA) provided the anti-IL-21R antibody; • The following collaborators provided microarray data: o Activated and resting Th1 and Th2 microarrays - Dr Federica Sallusto (Institute for Research in Biomedicine, Switzerland); o NK, γδ and CD8 T cell microarrays - Dr Rebecca Newton (Garvan Institute, Australia); o Mast cell and basophil microarrays - Sue Liu (Garvan Institute, Australia); o Dendritic cell and macrophage microarrays - Dr Sabine Zimmer (Garvan Institute, Australia); o Eosinophil and neutrophil microarrays - Mary Sisavanh (Garvan Institute, Australia). In addition I would like to thank Dr Stuart Tangye (Centenary Institute, Australia) for helpful advice and generous sharing of many reagents; Dr Diego Silva (ANU Medical School, Australia) for bioinformatics advice; Lilach Weininger, Melinda Frost and Trina So (Garvan Institute, Sydney, Australia) for their help with the Affymetrix system.
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Table of contents
Manuscripts ______i
Presentations ______i
Acknowledgements ______iii
Abstract ______iv
Contributions of collaborators ______vi
Table of contents ______vii
List of figures______xi
List of tables______xiii
Abbreviations ______xiv
1 Introduction ______1
1.1 The innate and adaptive immune responses ______1
1.2 T cell development ______1
1.3 T cell subsets ______5
1.4 The Th1 and Th2 paradigm______7 1.4.1 CD4+ effector T cell subsets: Th1/Th2 cells ______7 1.4.2 Type 1 and type 2 dichotomy in other cells______9 1.4.3 Factors influencing T cell polarization ______9 1.4.4 Molecular basis of T cell polarization ______10 1.4.4.1 Cell surface markers that distinguish T cell subsets______12 1.4.5 Th1 and Th2 subsets in disease ______12
1.5 Follicular B helper T cells ______13 1.5.1 T cell help to B cells ______13 1.5.2 CXCR5+ T cells ______14 1.5.3 CXCR5 expression on B and T lymphocytes ______15 + 1.5.4 Generation of CXCR5 TFH cells______16 1.5.5 CD57+ CXCR5+ T cells ______17
1.5.6 Distinguishing TFH cells and identifying the mechanism of their function ______18
1.6 T cell memory ______18 1.6.1 Life and death of effector T cells______18
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1.6.2 Overview of T cell memory______20 1.6.3 Generation of memory T cells ______21 1.6.4 Memory T cell homeostasis______22 1.6.5 Subsets of memory T cells______23 1.6.6 Heterogeneity of memory T cells ______25
1.7 Gene expression microarrays and the immune system______26
1.8 Experimental objectives ______28
2 Materials and Methods ______30
2.1 Buffers and solutions ______30
2.2 Protocols______30 2.2.1 General cell isolation procedures______30 2.2.1.1 Lymphocyte enrichment using MACS______31 2.2.2 Flow cytometric analysis ______31 2.2.3 Antibody staining and detection ______31 2.2.4 Intracellular staining to assess cytokine production ______32 2.2.5 Cell sorting ______32 2.2.6 Antibodies ______32 2.2.7 Cell culture ______33 2.2.7.1 Generation of human Th1 and Th2 cells ______33 2.2.7.2 Generation of mouse Th1, Th2, Tc1 and Tc2 cultures (performed by Roslyn Kemp, Malaghan Institute, New Zealand) ______34 2.2.7.3 Isolation of CD57+ and CD57- CXCR5+CD4+ lymphocytes from tonsils ______34
2.2.7.4 Isolation of TCM and TEM cells ______35 2.2.8 Ovalbumin model of asthma ______35 2.2.9 Histology ______36 2.2.9.1 Tissue collection ______36 2.2.9.2 Haematoxylin-eosin stain______36 2.2.9.3 Immunofluorescence assays______36
2.3 Gene expression profiling______37 2.3.1.1 RNA isolation ______37 2.3.1.2 Preparation of cRNA and Genechip hybridizations ______37
2.4 Data analysis ______39 2.4.1.1 Mouse microarray data______39 2.4.1.2 Human microarray data ______39 2.4.2 Real-time PCR to monitor gene expression______41
2.5 Brief protocols for microarray experiments conducted by collaborators ____ 42
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2.5.1.1 Generation of resting and activated Th1 and Th2 clones (microarray data provided by Federica Sallusto, Oncology Institute of Southern Switzerland, Switzerland)______43 2.5.1.2 B lymphocyte and plasma cell isolation (Stuart Tangye and Kim Good, Centenary Institute, Sydney, Australia) ______43 2.5.1.3 B cell, NK and T cell subset isolation (Rebecca Newton, Garvan Institute, Sydney, Australia) 44 2.5.1.4 Basophil isolation (Sue Liu, Garvan Institute, Sydney, Australia)______44 2.5.1.5 Mast cell isolation (Sue Liu, Garvan Institute, Sydney, Australia) ______45 2.5.1.6 Macrophage and DC generation (Sabine Zimmer, Garvan Institute, Sydney, Australia) 45 2.5.1.7 Neutrophil and eosinophil isolation (Mary Sisavanh, Garvan Institute, Sydney, Australia) 46
3 The Th1/Th2 paradigm ______47
3.1 Introduction______47
3.2 Results and Discussion ______48 3.2.1 Gene expression in mouse Th1, Th2, Tc1 and Tc2 cells ______48 3.2.1.1 Generation of polarized murine Th1 and Th2 and Tc1 and Tc2 cells ______48 3.2.1.2 Confirmation of polarization at RNA level ______49 3.2.1.3 Microarray analysis of gene expression ______50 3.2.1.4 Differential gene expression between type 1 and type 2 CD4+ and CD8+ T cells _ 51 3.2.2 Gene expression in human Th1 and Th2 cells______57 3.2.2.1 Generation of polarized human Th1 and Th2 cells ______57 3.2.2.2 Microarray analysis of gene expression in human Th1 and Th2 cells ______58 3.2.3 Factors affecting the Th1 and Th2 gene expression profiles ______63 3.2.3.1 Gene expression patterns in murine CD4+ and CD8+ type 1 and 2 T cells ______63 3.2.3.2 Gene expression patterns in murine Th1 and Th2 cells generated using different protocols 64 3.2.3.3 Human Th1 and Th2 gene expression profiles generated in different polarizing conditions 65 3.2.3.4 The Th1/Th2 paradigm in mice and men ______68 3.2.4 Defining the role of Th1 and Th2 genes in T cell biology______68 3.2.4.1 Cytochrome p450 side chain cleavage enzyme 11a1______69 3.2.4.2 GEM______72 3.2.4.3 GPR18 and EBI2 ______72 3.2.4.4 IL-17RB ______76
3.3 Concluding remarks ______79
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4 Identifying a unique transcriptional profile for TFH cells which underlies their effector function ______82
4.1 Introduction______82
4.2 Results ______83
4.2.1 CD57 as a marker of TFH cells in the tonsil ______83 4.2.2 Gene expression profiles of CD57+ and CD57- CXCR5+ CD4+ T cells ______84
4.2.3 Comparison of TFH to other effector subsets ______86
4.2.4 CD84 is expressed with CXCR5 on tonsillar TFH cells but not on blood T cells______90
4.2.5 Expression of CD200 by TFH cells______92
4.2.6 IL-21 mRNA is produced by TFH cells and its receptor is expressed by B and T cells __ 93
4.2.7 BCL-6 as a transcription factor for TFH and B cells______96
4.3 Discussion______97
5 Identification of T cell subset-restricted genes and signatures for different types of T cell responses ______102
5.1 Introduction______102
5.2 Results ______103 5.2.1.1 Comprehensive gene expression dataset for all of the major human leukocyte subsets 103 5.2.1.2 Identification of leukocyte subset signatures, and leukocyte-specific genes ____ 105 5.2.1.3 Comprehensive leucocyte subset profiling allows the identity of T cell selective gene expression signatures ______107 5.2.1.4 Factors influencing gene expression in effector and memory T cells ______109 5.2.1.5 T cell activation induces an extensive transcriptional program ______110 5.2.1.6 The effect of cell culture on T cell gene expression______114 5.2.1.7 Gene expression signatures of T cell subsets, including γδ T cells ______116 5.2.1.8 Identifying T cell subtype predictor genes______121
5.3 Discussion______121
6 General Discussion ______125
Bibliography______130
Appendix 1 ______156
Appendix 2 ______163
Appendix 3 ______168
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List of figures
Figure 1.1 Thymocyte development and selection ______2 Figure 1.2 T cell migration ______4 Figure 1.3 Prevailing model for T cell differentiation ______5 Figure 1.4 Effector T cell subsets T cells can differentiate into several distinct effector subsets with diverse effector functions ______6 Figure 1.5 The Th1 and Th2 paradigm ______8 Figure 1.6 Th2 cells in asthma______13 + Figure 1.7 TFH cells are the third major effector subset of CD4 T lymphocytes ____ 15 Figure 1.8 Subsets of memory T cells______24 Figure 1.9 Generation of effector and central memory T cells ______25 Figure 3.1 Cytokine expression profiles of polarized CD4+ and CD8+ type 1 and type 2 cells ______49 Figure 3.2 Real-time PCR analysis of gene expression in Th1, Th2, Tc1 and Tc2 cells after polarization ______50 Figure 3.3 Comparison of gene expression between type 1 and type 2 T cells ______51 Figure 3.4 Differential gene expression patterns in murine Th1, Th2, Tc1 and Tc2 cells ______53 Figure 3.5 Real-time PCR of select genes identified as differentially expressed by GeneChip analysis ______55 Figure 3.6 Th1 and Th2 polarization at the RNA and protein level______58 Figure 3.7 Differential gene expression patterns in human Th1and Th2 cells ______59 Figure 3.8 Biosynthetic pathway of prostaglandins (PGs) and Thromboxane (TX) A2 62 Figure 3.9 Genes differentially expressed between type 1 and type 2 subsets in both CD4+ and CD8+ T cells ______64 Figure 3.10 Comparison of gene expression in cord blood derived Th1 and Th2 cells with resting and activated peripheral blood derived Th1 and Th2 cells ______67 Figure 3.11 Histology of lung tissue from mice treated with aminoglutethimide and control animals______70 Figure 3.12 FACS assessment of cellular lung infiltrate mice treated with aminoglutethimide and control animals______71 Figure 3.13 GPR18 and EBI2 expression in leukocytes subsets and tissues ______75
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Figure 3.14 Expression of IL-17 and IL-17R family genes in leukocytes and tissues _ 77 Figure 3.15 Expression of IL-17RB ligands in a panel of tissues using SymAtlas (http://symatlas.gnf.org/SymAtlas/) ______78 Figure 4.1 Phenotype of tonsil CD57+ T cells______83 Figure 4.2 CD57+ T cell isolation strategy ______84 Figure 4.3 Genes differentially expressed between CD57+CXCR5+CD4+ and CD57- CXCR5+CD4+ T cells ______86
Figure 4.5 Genes specific to TFH cells ______88 Figure 4.6 Gene expression profiles of a number of genes important for T and B cell biology ______89 Figure 4.7 CD84 expression in tonsil and blood ______91 Figure 4.8 CD200 expression in tonsil and blood ______93 Figure 4.9 Characterization of IL-21R expression on B and T cells ______95 Figure 4.10 Identification of a non-B, non-T cell IL-21R expressing leukocyte subset 96 Figure 4.11 Identification of BCL6-expressing T cells and their localization within the tonsil ______97 Figure 5.1 Distinct gene expression signatures characterize leukocyte subsets ____ 106 Figure 5.2 Identification of T cell selective gene expression using comprehensive gene microarray profiling______108 Figure 5.3 Principal component analysis (PCA) identifies key parameters affecting gene expression patterns in T cell subsets ______110 Figure 5.4 Gene expression profiles of resting and activated T cells ______111 Figure 5.5 Cytokine, chemokines and their receptors regulated during T cell activation ______114 Figure 5.6 Genes that were most significantly regulated during T cell activation __ 113 Figure 5.7 Gene expression profiles of cultured versus isolated T cells ______116 Figure 5.8 Distinct gene expression signatures characterize effector/memory T cell subsets______118 Figure 5.9 Genes differentially expressed by TCM and TEM cells ______120 Figure 5.10 A selection of differentially expressed genes (highly significant) that reliably distinguish T cell subsets______121
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List of tables
Table 2.1 Buffers and solutions ______30 Table 2.2 Treatment groups for asthma mouse model ______35 Table 2.3 Primer sequence. ______41 Table 3.1 20 most differentially expressed ESTs in Th1/Th2 and Tc1/Tc2 cells _____ 56 Table 3.2 EBI2 and GPR18 protein homology search______73 Table 5.1 Microarray experiments used in this study ______104
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Abbreviations
β-ME β-mercatoethanol mAb monoclonal antibody Ab antibody MHC Major histocompatibility complex Ag antigen mRNA messenger RNA ANOVA analysis of variance NFκB Nuclear factor κB APC antigen presenting cell NK natural killer BAFF B cell activating factor from PCA Principal component analysis TNF family bp base pairs PBS Phosphate-buffered saline BSA bovine serum albumin PBMC peripheral blood mononuclear cells cDNA complementary DNA PE R-phycoerythrin COX cyclooxygenase PCR polymerase chain reaction cRNA complementary RNA PG prostaglandin DC dendritic cell RNA Ribonucleid acid DNA deoxyribonucleic acid SAP SLAM-associated protein EBV Epstein Barr virus SLAM Signaling lymphocytic activation molecule ELISA Enzyme linked Tc T cytotoxic immunosorbent assay EST Expressed sequence tag TCM Central memory T EtOH ethanol TEM Effector memory T FACS fluorescence-activated cell TCR T cell receptor sorter FITC fluorescein isothiocyanate TFH T follicular helper GC Germinal centre Tg transgenic i.p. intraperitoneal Th T helper IFN interferon TNF tumour necrosis factor Ig immunoglobulin TRAF TNF receptor associated factor IL interleukin Treg Regulatory T LPS lipopolysaccharide wt Wild type
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1 Introduction
1.1 The innate and adaptive immune responses
The mammalian immune system has evolved to provide multiple levels of protection against invading pathogens. The innate immune system serves as a first line of defense; it provides an immediate but non-specific response using germline-encoded receptors to recognize certain molecular patterns of common pathogens. The adaptive immune response is more complex and usually takes several days to develop. Adaptive immunity, mediated by antigen presenting cells and B and T lymphocytes, involves antigen processing and recognition and provides a specific and sustained response. The effective development of immune responses depends on careful interplay and regulation between innate and adaptive immunity. The cells of the innate immune system, macrophages, neutrophils, dendritic and natural killer cells, are crucial to the initiation and direction of the subsequent adaptive response. Soluble mediators produced by these cells together with cell surface interactions determine whether the adaptive response will favor a cell-mediated type 1 response, which involves immune mechanisms to combat bacteria and viruses, mediated by cytotoxic T lymphocytes, macrophages and NK cells, or a type 2 response involving eosinophils and IgE production by B cells. The adaptive response also gives rise to immunological memory, which is defined as the ability to produce a faster and more robust response when the same pathogen is encountered again. T helper cells, which are the main focus of this thesis, are major regulators of adaptive immune responses. These CD4+ T cells orchestrate the direction of the response and are often critical to the outcome. The aim of this thesis was to create a comprehensive picture of gene expression in key subsets of CD4+ T cells and identify genes important to their function. This should enable a better understanding of fundamental aspects of T cell biology, such as molecular function, and the nature of immunological memory.
1.2 T cell development
T cell precursors originate in the bone marrow, and migrate to the thymus, the principal site for T cell production and maturation in vertebrates. In the thymus, T cell
1 Chapter 1 Introduction
precursors progress through several distinct stages of development, including thymic education through positive and negative selection, as outlined in Figure 1.1.
AB Cortex Subcapsulary zone CD3+pTαβ+4+8+ CD3+αβ+4+8+ Double positive Double positive Medulla DN <5% TCRβ CD4+8- Cortical/medullary selection junction entry site > 95 % Post- selected DP CD3-4-8- SP Double CD4-8+ negative DP γδ+CD3+4-8- Apoptosis Positive selection
Figure 1.1 Thymocyte development and selection. (A) Thymocytes go through sequential stages of development during which they acquire a functional T cell receptor capable of recognizing foreign antigen in the context of self MHC. Adapted from (Janeway and Travers, 1997). (B) Different stages of thymocyte development occur in distinct anatomical locations of the thymus. Adapted from (Witt and Robey, 2004). DN, double negative; DP, double positive; SP, single positive.
Thymocyte development is a tightly controlled process, various stages of which occur in distinct anatomical locations in the thymic lobes which are organized into outer cortical and central medullary areas (Figure 1.1B). T cell progenitors from the bloodstream initially enter the thymus through blood vessels located at the cortical medullary junction. The stages of thymocyte development that follow are defined chiefly through the cell surface expression of CD4 and CD8, the markers for T helper and T cytotoxic lineages respectively. About 20% of these cells will express the more rare γ/δ TCR while most cells will acquire the conventional α/β TCR. T cell progenitors that have not yet rearranged their T cell receptor (TCR) genes are double negative (DN) for CD4 and CD8. These immature thymocytes initially migrate towards the outer edge of the thymus, an area known as the sub-capsulary zone (Lind et al., 2001). They rearrange the TCRβ chain and express it on the cell surface together with a pre-T-cell alpha chain and CD3 molecules. At this stage the cells acquire the expression of CD4 and CD8 to yield double positive (DP) thymocytes. DP thymocytes migrate back into the cortical region, where they rearrange the α chain to acquire a complete α/β TCR and undergo positive selection (Starr et al., 2003). The cells that recognize self MHC receive survival signals (positive selection) but most (>95%) of the cells will die as they fail to recognize self MHC. Following positive selection, surviving thymocytes migrate to the medulla. Here they undergo negative selection, during which the thymocytes with high affinity for self-antigen are deleted by apoptosis. T cells
2 Chapter 1 Introduction
emerge with a functional T cell receptor that can recognize foreign antigen-MHC complexes. In addition they now express either CD8 or CD4 co-receptors alone, which corresponds to their ability to interact with MHC class I or class II molecules respectively (Starr et al., 2003) (Janeway and Travers, 1997). Thymocyte migration to different anatomical compartments is a crucial part of the development process. The mechanisms that control this migration are poorly understood. Observations of thymocyte development in CCR7 transgenic mice showed that this chemokine receptor is involved in medullary migration (Kwan and Killeen, 2004). The studies in mice lacking either the chemokine receptor CCR7 or its ligands show that the interactions between the receptor and its ligands facilitate both the outward migration and differentiation of DN cells and the inward migration of positively selected thymocytes (Misslitz et al., 2004; Ueno et al., 2004). Although these findings provide some clues as to what factors control thymocyte migration, the precise mechanisms of this migration remain to be elucidated. Naïve T cells then leave the thymus and circulate between blood and secondary lymphoid tissues (Butcher and Picker, 1996; Mackay, 1993) (Figure 1.2). They cross high endothelial venules (HEVs) to enter the secondary lymphoid organs. This process is highly specific and takes place when naïve T cells expressing lymph node homing receptors such as CD62L and CCR7 interact with ligands expressed on HEVs, including PNAd and chemokines such as SLC. T cells percolate through the node (Mempel et al., 2004) but eventually move to areas where antigen is displayed by specialized antigen-presenting cells (APCs). If naïve T cells do not encounter a foreign antigen they leave the lymph nodes via efferent lymphatic vessels and return to the bloodstream via the thoracic duct. On the other hand, when a naïve T cell encounters a foreign antigen for which their T cell receptor (TCR) is specific, a complex cascade of events is initiated. T cell receptor engagement together with the appropriate costimulation leads to activation and proliferation accompanied by cytokine synthesis and differentiation. T cells emerge from lymphoid tissues as effector cells with the capacity for cytokine secretion, the ability to help for other cell types (CD4+ T cells), or cytotoxicity (CD8+ T cells). Activated and effector T cells upregulate several receptors that allow them to traffic to new tissues. These effector T cells are diverted from resting lymph nodes through downregulation of lymph node-homing receptors CCR7 and CD62L.
3 Chapter 1 Introduction
Figure 1.2 T cell migration. Mature naïve T cells leave the thymus and circulate between blood and secondary lymphoid organs until they encounter their antigen. Homing to lymph nodes occurs in HEVs, which express traffic molecules for constitutive lymphocyte recruitment. Dendritic cells collect antigenic material in inflamed tissues, and carry Ag to lymph nodes, where they stimulate Ag-specific T cells. Upon stimulation, T cell proliferate and differentiate into effector cells, which express receptors that enable them to migrate to sites of inflammation. While most effector cells are short-lived, a few Ag- experienced cells survive for a long time. These memory cells are subdivided into two populations based on their migratory ability: one subset termed effector memory cells, localize to peripheral tissues, whereas the other subset, called central memory cells, express a similar repertoire of homing molecules as naïve T cells and migrate preferentially to lymphoid tissues. Reproduced from (von Andrian and Mackay, 2000).
The way in which T cells sequentially progress from naïve to different effector and memory subsets is still largely unclear. Once antigen is cleared the majority of antigen-experienced cells die, however a number of primed T cells persist as circulating
4 Chapter 1 Introduction
memory cells. These cells provide long-term protection and produce a rapid and vigorous secondary response upon subsequent encounters with antigen (Figure 1.3). The exact precursor-product relationships between effector and memory T cells are still unresolved.
Effector Antigen T cells presentation Effector cell by APC death Thymus
Memory T cells Naïve T cell Proliferation and differentiation
Immature thymocytes
Figure 1.3 Prevailing model for T cell differentiation. Naïve T cells leave and thymus and circulate between blood and secondary lymphoid organs searching for antigen. If they encounter antigen presented to them by an APC they go through several rounds of proliferation and differentiation to become effector cells. Effector cells then direct the immune response through the production of cytokines and costimulatory molecules. Most of the effector cells die shortly after the antigen has been cleared. A few antigen experienced cells remain to provide long-term protection from re-infection known as immunological memory. Other models of memory cell generation exist and the precise mechanism of memory cell generation is yet to be determined.
1.3 T cell subsets
Mature αβ T cells exist either as CD4+ T cells also known as T helper cells or CD8+ T cells referred to as T cytotoxic cells, although there are also other T cell subsets (e.g. Treg, NKT cells) (Figure 1.4). CD4+ T cells produce cytokines that facilitate antibody production, antiviral responses, defense against large extracellular parasites and many other functions, while CD8+ cytotoxic T cells are responsible for killing of infected cells, although these T cells also produce cytokines. The phenotype of CD4+ and CD8+ effector T cells depends on the environment in which activation occurs. Many different factors including the cytokine environment, density of antigen, the nature of the antigen-presenting cells and TCR affinity influence T cell development. For instance pathogen recognition by different Toll receptors or other innate signals may influence the type of immune response. The direction of the adaptive immune response can be largely determined by the innate system. Based on
5 Chapter 1 Introduction
the patterns of cytokine production, two CD4+ effector T cell subsets have been identified. This dichotomy in effector T cell responses is the essence of the Th1 and Th2 paradigm. More recently the type 1 and type 2 division has been extended to CD8+ T cells and other cell types. It is important to note that T cells producing Th1 and Th2 cytokines simultaneously have also been identified in both humans and mice (Abbas et al., 1996; O'Garra, 1998; Sher and Coffman, 1992). It is still unclear whether these cells, termed Th0, are precursors for Th1 and Th2 cells or a separate stable T cell subset (Kamogawa et al., 1993).
IFN-γ •Cell-mediated immunity Th1 •Defence against intracellular pathogens •Autoimmunity
IFN-γ Th0 IL-4 •Th1/Th2 precursor?
Naïve CD4+ IL-4 •Humoral immunity Th2 IL-5 •Defence against extracellular pathogens Thymus IL-13 •Allergy
•Help for Ab production TFH ?
•Suppression of T cell activation T IL-10 •Tolerance reg TGF-β •Immune homeostasis
•Cytotoxicity Tc1 IFN-γ •Defence against viral pathogens •Autoimmunity Naïve CD8+ •Cytotoxicity Tc2 IL-4 •Defence against viral pathogens IL-5 •Allergy •Non-cognate B cell help •Mucosal immunity γδ IFN-γ •Anti-microbial response T IL-4 •Innate immunity NK IFN-γ •Regulation of adaptive response •Autoimmunity T IL-4
Figure 1.4 Effector T cell subsets T cells can differentiate into several distinct effector subsets with diverse effector functions.
Natural killer T (NKT) cells constitute another subset of αβ T cells. NK1 T cells coexpress markers associated with the natural killer lineage such as NK1.1 and CD122, as well as with T cells such as the αβ T cell receptor. Some NKT cell subsets do not express NK1.1. Most but not all NKT cells express a semi-invariant αβ TCR that recognizes glycolipids associated with the non-polymorphic MHC-like molecule CD1d (Bendelac et al., 1997). The precise role of NKT cells in immune responses is not clear. They do have the capacity for rapid cytokine production (both Th1 and Th2)
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and may play an important role in manipulating the outcome of an immune response (Chen and Paul, 1997; Kronenberg and Gapin, 2002; Stetson et al., 2003; Yoshimoto and Paul, 1994). Several other functions have been attributed to NKT cells. They can provide help to B cells (Bendelac et al., 1997; Galli et al., 2003; Yoshimoto et al., 1995) and to CD8+ T cells (Denkers et al., 1996; Johnson et al., 2002). CD4+CD25+ T cells are a population of naturally occurring suppressor T cells capable of inhibiting T cell activation in vitro and in vivo (Sakaguchi, 2004; Sakaguchi et al., 1995; Thornton and Shevach, 1998). Regulatory T (Treg) cells can inhibit autoimmune disease in a variety of models and regulate the expansion of other T cell subsets. Although these T cells secrete IL-10 and TGF-β, the mechanism through which they mediate the suppression remains largely unknown (Shevach, 2002). A member of the TNF receptor superfamily, GITR/TNFRSF18 regulates the suppressor function of Treg cells (McHugh et al., 2002). Moreover, transcription factor Foxp3 is expressed exclusively by CD4+CD25+ T cells and not by CD4+CD25- T cells and is required for the development and suppressor function of CD25+ T cells (Fontenot et al., 2003; Hori et al., 2003). CD4+ regulatory T cells play an important role in the maintenance of peripheral tolerance and immune homeostasis.
1.4 The Th1 and Th2 paradigm
1.4.1 CD4+ effector T cell subsets: Th1/Th2 cells The two key cytokines that determine the direction of the adaptive immune response are IL-12 and IL-4. IL-12 production by cells of the innate immune system initiates polarization of CD4+ T helper cells towards the Th1 pathway, while IL-4 production induces CD4+ T cell differentiation towards Th2 pathway (Hsieh et al., 1992; Seder et al., 1992b) (Figure 1.5). Once differentiated, Th1 and Th2 cells secrete distinct arrays of cytokines which then act on other leucocytes and further skew the response towards type 1 or type 2. Two distinct patterns of cytokine production by CD4+ effector T cells were originally described by Mosmann and colleagues (Mosmann et al., 1986). IFN-γ, which is the signature Th1 cytokine, activates macrophages, stimulates class-switching to IgG2a and IgG3 (mouse) and promotes differentiation and activation of CD8+ T cells (Boehm et al., 1997). Th1 responses are invoked to deal with intracellular pathogens, but have also been implicated in
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autoimmune responses. On the other hand, Th2 cells secrete cytokines such as IL-4, IL- 5, and IL-13, function in defense against large extracellular pathogens and have been implicated in allergic responses (Abbas et al., 1996). IL-4, which is considered to be the hallmark Th2 cytokine, induces class switching of B cells to IgE production (Coffman et al., 1986; Paul, 1991; Paul, 1997). Another Th2 cytokine, IL-5, promotes eosinophil production and recruitment from the bone marrow (Kopf et al., 1995). Eosinophils participate in defense against large extracellular parasites and play a crucial role in allergic reactions. For a long time Th2 cells were considered to be the key players in humoral immunity but this perception is changing as the Th1 cytokine IFN-γ induces class switching of B cells to several IgG isotypes and therefore also plays a role in humoral immunity (Finkelman et al., 1988; Snapper and Paul, 1987; Stevens et al., 1988). In addition, IL-4 deficient mice still make antibodies proving that B cell help can occur in the absence of IL-4 (Kopf et al., 1995; Shimoda et al., 1996). Recently a + new effector subset of CD4 T cells, termed TFH cells, has been identified. This subset of CD4+ effector T cells has the capacity to deliver help to B cells, and will be discussed in the next section.
Anti-viral response IFN-γ Intracellular Tc Cytotoxicity activation Th1 IL-12 Phagocytosis by macrophages
Naïve Antigen T cell IgE production presentation by B cells Th2 by APC IL-4 IL-4 Eosinophils Extracellular IL-5 activation Anti-parasite response
Figure 1.5 The Th1 and Th2 paradigm. In the presence of appropriate cytokines produced by the cells of the innate immune system, CD4+ T cells can be polarized into Th1 and Th2 cells. Th1 cells produce IFN-γ and promote responses against intracellular pathogens mediated by CD8+ cytotoxic cells and macrophages. Th2 cell secrete IL-4 and IL-5 and play an important role in responses against large extracellular pathogens.
Th1 and Th2 phenotypes are self-perpetuating in the sense that the cytokines produced by each of the two cell types serve to promote further polarization of cells of the same phenotype and inhibit polarization of the other subset. For instance, IFN-γ induces macrophages to produce IL-12 which leads to Th1 polarization and inhibits
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Th2 function (Fitch et al., 1993). On the other hand, IL-4 directly promotes Th2 polarization and at the same time suppresses Th1 polarization (Sher and Coffman, 1992).
1.4.2 Type 1 and type 2 dichotomy in other cells It is important to note that the type 1 and type 2 dichotomy is not restricted to CD4+ T cells. CD8+ T cells can be differentiated into Tc1 and Tc2 cells which have similar cytokine producing profiles to Th1 and Th2 cells but also retain their cytolytic abilities (Croft et al., 1994; Sad et al., 1995). There is however a strong bias towards IFN-γ production for CD8 cells. In addition, type 1 and type 2 cytokine secreting subsets have been identified for γδ T cells (Ferrick et al., 1995; Hsieh et al., 1996; Subauste et al., 1995), natural killer (NK) cells (Peritt et al., 1998), NKT (Kadowaki et al., 2001) and even dendritic cells (Liu et al., 2001; Rissoan et al., 1999). Distinct dendritic cell subsets can determine the direction of T cell polarization. DC1 cells produce IL-12 and preferentially induce Th1 polarization. The CD4+CD3-CD11c- derived DC2 dendritic cells promote Th2 development even in the absence of IL-4 (Pulendran et al., 1999). Even B cells, when stimulated in an antigen dependent fashion by polarized cytokine-secreting effector T cells can differentiate into polarized effector B cells which produce distinct cytokine profiles after stimulation (Harris et al., 2000).
1.4.3 Factors influencing T cell polarization Due to the vital role that Th1 and Th2 cells play in the outcome of an immune response, identifying factors that control the direction of T cell differentiation is of paramount importance to immunologists. Factors that have been implicated to date include the nature of the pathogen, the dose and route of entry of antigen, the nature of antigen presenting cells and costimulatory molecules, and the genetic background of the host (Bretscher et al., 1992; Constant et al., 1995; Constant and Bottomly, 1997). The ultimate determining factor is the cytokine environment at the time of activation. Th1 polarization is driven by IL-12 produced by phagocytic cells in response to intracellular pathogens (Hsieh et al., 1993a; Hsieh et al., 1993b). In addition to IL-12, other cytokines have also been implicated in inducing Th1 polarization. The effect of IFN-γ, which is produced by NK, CD8 and CD4+ Th1 cells, on T cell differentiation depends to some extent on the mouse strain. As a general rule, IFN-γ favours Th1 development and inhibits Th2 (Szabo et al., 2003). IL-18, a member of the
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IL-1 family, synergizes with IL-12 to induce IFN-γ production by Th1 cells (Micallef et al., 1996). IL-18 knockout mice have reduced NK cell activity and Th1 responses (Takeda et al., 1998). Although IL-12/IL-18 double knockout mice demonstrate a much more severe deficiency in Th1 differentiation and IFN-γ production, Th1 cells are still detectable (Takeda et al., 1998), suggesting that other factors may be involved in Th1 development. For instance, the cytokines IL-23 and IL-27 also promote Th1 responses. IL-23, a heterodimer of the p40 subunit of IL-12 and a novel p19 subunit, induces IFN- γ production and T cell proliferation, similar to IL-12 (Oppmann et al., 2000). IL-27 induces Th1 polarization of both mouse and human CD4 T cells and the proliferation of Th1 cells. It can also synergize with IL-12 to induce IFN-γ production. IL-27 is a heterodimer composed of EBI3 (which shares homology with IL-12 p40) and a novel subunit p28 and may play a role early in the Th1 differentiation process (Pflanz et al., 2002; Szabo et al., 2003). Interestingly, recent evidence suggests that the immunological synapse may also play a role in lineage commitment of CD4+ T cells (Maldonado et al., 2004). The engagement of the TCR on naïve T helper cells causes rapid co-polarization of IFN-γ receptor with the TCR within the developing immunological synapse. This co- polarization is prevented in the presence of IL-4 in a Stat6-dependent manner, providing a possible mechanism for inhibition of Th1 polarization by IL-4 (Maldonado et al., 2004). IL-4 is the most critical factor for Th2 polarization. It is produced mainly by Th2 cells but in addition also by NK T cells, γδ T cells, eosinophils, basophils, and mast cells (Brown et al., 1987; Howard et al., 1982; Moqbel et al., 1995; Seder et al., 1991; Voehringer et al., 2004; Yoshimoto and Paul, 1994).
1.4.4 Molecular basis of T cell polarization Considerable effort has gone into defining the molecular differences between Th1 and Th2 subsets. Transcription factors control type 1 and type 2 differentiation and include the type 2 associated factors GATA-3 (Zheng and Flavell, 1997), c-maf (Ho et al., 1996) and STAT6 (Kaplan et al., 1996a; Shimoda et al., 1996), as well as the type 1 factors T-bet (Szabo et al., 2000) and STAT4 (Kaplan et al., 1996b). T-box transcription factor T-bet has a central role in Th1 development. It controls the expression of the hallmark Th1 cytokine IFN-γ by inducing transcriptional competence of the IFN-γ locus. T-bet also induces IL-12Rβ2 expression (Mullen et al., 2001;
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Mullen et al., 2002). T-bet expression is also sufficient to induce IFN-γ production in both developing and committed Th2 cells (Szabo et al., 2000; Szabo et al., 2002). Another transcription factor important for Th1 development is Stat4. Stat4 is an essential component of the IL-12 signaling pathway and it augments IFN-γ gene transcription (Kaplan et al., 1996b; Thierfelder et al., 1996). GATA-3 is a transcription factor that plays a vital role in Th2 development. Induction of GATA-3 in Th1 cells causes an increase in Th2 cytokine production by these cells; on the other hand, neutralization of this transcription factor in Th2 cells inhibits the expression of Th2 cytokines. GATA-3 suppresses Th1 development by downregulating Stat4 (Usui et al., 2003). Ectopic expression of GATA-3 can mediate Th2 differentiation under Th1 polarizing conditions (Ferber et al., 1999; Ouyang et al., 1998; Zheng and Flavell, 1997). The expression of GATA-3 is induced in response to IL-4 through STAT6 (Ouyang et al., 1998). GATA-3 can also transactivate the IL-5 promoter (Zhang et al., 1997). Interestingly GATA-3 can induce Th2 development in STAT6-deficient T cells, including Th2 cytokine production and c-maf expression which is normally STAT6 dependent (Ouyang et al., 1998). STAT6 is activated in response to IL-4 and plays an important role in Th2 development (Takeda et al., 1996). c-Maf is an oncoprotein that activates the IL-4 promoter, and c-maf deficient mice are defective in IL-4 production (Ho et al., 1996; Kim et al., 1999). The signals responsible for the regulation of transcription factor activity and type 1 or type 2 polarization are unclear, but may relate to the type and dose of antigen (Bretscher et al., 1992; Constant et al., 1995; Hosken et al., 1995) or the nature of the antigen-presenting cell (Maldonado-Lopez et al., 1999; Pulendran et al., 1999; Rissoan et al., 1999). Both in vitro and in vivo models have shown that changing the antigen dose can shift the balance between Th1 and Th2 responses but the direction of the shift can differ depending on the model used (Constant and Bottomly, 1997; Constant et al., 2000). A recent report from Flavell and colleagues highlighted the role of the Notch pathway in T cell differentiation (Amsen et al., 2004). APCs use the Notch pathway to regulate T cell differentiation, with two families of Notch ligands inducing alternative T cell differentiation pathways. Delta induces Th1 responses while Jagged directs naïve T cells towards a Th2 fate. This effect on Th2 cells is mediated by RBPJκ (Amsen et al., 2004).
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Chromatin structure provides another important level of control for T cell differentiation (Grogan et al., 2001; Murphy and Reiner, 2002). Condensed (heterochromatin) is associated with inactive transcriptional activity, whereas decondensed (euchromatin) allows easy accessibility for transcription factors and transcriptional activity. These alterations to chromatin structure persist after cell division. Decondensed chromatin can be identified by its sensitivity to DNase I digestion. The IL-4 locus acquires DNAse hypersensitivity in Th2 cells but not in naïve or Th1 cells (Agarwal and Rao, 1998). The same applies for the IFN-γ locus in Th1 cells (Mullen et al., 2001; Mullen et al., 2002). These events are downstream of GATA- 3 and T-bet expression in Th2 and Th1 cells respectively. DNA methylation is another epigenetic mechanism for gene silencing in mammals. Methyltransferase Dnmt1 and a methyl-CpG-binding protein that recruits repressive complexes, termed MBD2 are important for gene silencing by DNA methylation (Hutchins et al., 2002; Lee et al., 2001b).
1.4.4.1 Cell surface markers that distinguish T cell subsets
The functional program of type 1 and 2 T lymphocytes requires these cells to home to different sites (Sallusto et al., 1998a). Th1 cells preferentially express the chemokine receptors CCR5 and CXCR3, while Th2 cells preferentially express CCR3, CCR4, CCR8 and CRTh2 (reviewed in (Sallusto et al., 1998b; Sallusto et al., 2000)). Other cell surface molecules also distinguish type 1 and type 2 T cells. Molecules preferentially associated with Th1 responses include membrane IFN-γ, IL-18 receptor (Xu et al., 1998) and P- and E-selectin ligand PSGL-1 (Austrup et al., 1997). T1/ST2 has been described as a selective marker of Th2 cell (Lohning et al., 1998). Furthermore, T1/ST2 can be used to manipulate some Th2 responses (Lohning et al., 1998) (reviewed in (Trajkovic et al., 2004)). Recently molecules belonging to the TIM (T-cell immunoglobulin mucin) family have emerged as potential regulators of autoimmune and allergic diseases. TIM3 is expressed only by differentiated Th1 cells whereas TIM1 is expressed by T cells as they develop into Th2 cells but not by Th1 cells (reviewed in (Kuchroo et al., 2003)).
1.4.5 Th1 and Th2 subsets in disease The outcomes of a wide range of diseases are largely dependent on the particular response induced. This is true for both protective and pathological responses.
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This was initially demonstrated by studies of cutaneous leishmaniasis in mice, where a Th1 response led to resistance whereas a Th2 response conferred susceptibility (Heinzel et al., 1989; Sacks and Noben-Trauth, 2002). In general, resistance to intracellular microbes such as bacteria, protozoa, fungi and viruses is linked to IFN-γ and TNF-α production and Th1 responses. Th1 responses have also been implicated in certain organ specific autoimmune diseases such as experimental autoimmune encephalomyelitis and insulin dependent diabetes mellitus (O'Garra, 1998; O'Garra et al., 1997). Th2 responses, on the other hand, are involved in extracellular immunity and are associated with protection against intestinal helminthes. In addition, Th2 cells play an important role in the immunopathology of asthma. Chronic overexpression of Th2 cytokines in the airways results in eosinophilia, mucus production, airway hyperreactivity and airway remodeling (Cohn et al., 2004)(Figure 1.6).
Allergen
Figure 1.6 Th2 cells in asthma. Inhaled allergen stimulates activation of Th2 cells. These cells then produce cytokines IL-4, IL-5, IL-9 and IL-13 which cause recruitment and activation of inflammatory cells including B cells, mast cells and eosinophils. These cells in turn release inflammatory mediators which cause epithelial damage, and release chemoattractants resulting in persistent inflammation and airway remodelling. Reproduced from (Cohn et al., 2004).
1.5 Follicular B helper T cells
1.5.1 T cell help to B cells T cell help for Ab production is an essential component of the immune response. Antigens binding to the B cell receptor (BCR) are internalized and then presented to T cells as short peptides in association with MHC class II together with a costimulatory signal provided by a TNF family molecule CD40L (CD154). In most
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cases B cells rely on T cell help for the generation of an Ab response, although there are some antigens (T independent) that can activate B cells without T cell help. T cell help is required for processes that include germinal center formation, Ab isotype switching and affinity maturation and the development of B cell memory (Hess et al., 1998). Several molecules have been recognized as important in mediating T–B cell interactions: CD70(CD27L), OX40 (CD134) and IL-10 play an important role in promoting the development of plasma cells and their Ig secreting capacity (Agematsu et al., 1998; Choe and Choi, 1998; Jacquot et al., 1997; Jung et al., 2000; Morimoto et al., 2000). The interaction between CD40 and CD40L stimulates B cell proliferation and isotype switching (van Kooten and Banchereau, 2000). Isotype switching is also influenced by cytokines including IL-4 and IFN-γ.
1.5.2 CXCR5+ T cells T cell help to B cells was long thought to be solely attributable to Th2 cells (Abbas et al., 1996; Coffman et al., 1986; O'Garra, 1998), because Th2 clones support Ab production in vitro better than Th1 clones (Abbas et al., 1990) and since IL-4, a Th2 cytokine, was found to stimulate B cell proliferation and class switching and induce up- regulation of co-stimulatory molecules such as CD40. However, B cell help still occurs in the absence of IL-4, since IL-4 and STAT6 deficient mice still make antibodies (Kopf et al., 1995; Shimoda et al., 1996). In addition, IFN-γ, a hallmark Th1 cytokine causes class-switching to some Ig isotypes. Recent evidence suggests that a separate, non-Th1, non-Th2 effector subset acts as a helper T cell for B cells. These T cells, termed follicular B-helper T cells (TFH) are capable of providing help for B cells and are identified by the expression of chemokine receptor CXCR5 (Breitfeld et al., 2000;
Kim et al., 2001c; Schaerli et al., 2000) (Figure 1.7). The expression of CXCR5 by TFH cells allows them to localize to B cell follicles, where they can directly provide help to B cells.
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Th1 Anti-viral IFNγ
Naïve IL-12 T cell IL-4 Anti-parasite Th2 IL-4 CXCR5 IL-5 ? CXCR5 B cell
Help for antibody production TFH
Plasma
Y Y Y Y
+ Figure 1.7 TFH cells are the third major effector subset of CD4 T lymphocytes. TFH cells express chemokine receptor CXCR5 and provide help to B cells for Ab production. The mechanism of their action remains to be identified.
1.5.3 CXCR5 expression on B and T lymphocytes CXCR5 is expressed by most B cells, and is required for the development of B cell follicles in secondary lymphoid tissues. Mice lacking either CXCR5 or its ligand CXCL13 (BLC) display major aberrations in splenic follicular architecture and reduced numbers of lymph nodes and Peyer’s patches (Ansel et al., 2000; Forster et al., 1996).
The expression of CXCR5 by TFH and B cells allows the co-localization of these cells to sites of CXCL13 production (i.e. follicles), thus enabling productive T-B cell interactions. The interaction between effector T cells and B cells occurs first in T cell areas and later in B cell follicles within secondary lymphoid organs (Cyster et al., 1999; MacLennan, 1994). After the first engagement, B cells either migrate to extrafollicular foci where they differentiate into plasma cells that rapidly secret low affinity antibodies, or both B cells and T cells migrate into follicles and form germinal centers where somatic mutation and affinity maturation occur. This process is highly dependent on T cell help. CXCR5 is expressed on antigen-experienced CD4+ T cells but on few CD8+ T cells and not on naive T cells (Moser and Ebert, 2003). A small proportion of circulating CD4+CD45RO+ T cells expresses CXCR5. These cells are in a resting state and are not capable of providing help to B cells for Ab production. They have poor cytokine production abilities and co-express CD62L and CCR7 and are probably a
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subpopulation of central memory T cells. In contrast CXCR5+CD4+ T cells in the tonsil represent a large proportion of the tonsillar memory T cell pool. These cells differ markedly in their activation status from the circulating CXCR5+ T cells and represent the TFH cells capable of providing help for antibody production (Breitfeld et al., 2000; Schaerli et al., 2000). + Several features of CXCR5 TFH cells distinguish this third type of effector T cell. Like other effector T cells, TFH express activation markers such as CD69, low levels of CCR7 and CD62L (similar to Th1 and Th2 cells), and exhibit effector function, namely help for antibody production (Breitfeld et al., 2000; Kim et al., 2001c; Schaerli et al., 2000). Expression of costimulatory molecules and cytokines are important elements of T helper cell functions. CXCR5+ T cells express ICOS, a costimulatory molecule important in T – B cell interactions, as evidenced by the defects in humoral immunity in ICOS or ICOS-L deficient animals. However, Th2 and Th1 cell also express ICOS, although possibly at lower levels. Interestingly CXCR5+ T cells have a very limited cytokine secretion capacity and do not express large amounts of CD70 and CD134 which are important in plasma cell development. This may suggest that further stimulation is needed for CXCR5+ T cells to become effector cells that provide B cell help.
+ 1.5.4 Generation of CXCR5 TFH cells T cells use CCR7 to enter secondary lymphoid organs and move into the T cell zones (Forster et al., 1999). CXCR5 is transiently upregulated on CD4+ T cells following their activation. This upregulation happens prior to proliferation and differentiation and is dependent on costimulatory signals delivered through CD28 and OX-40 (Brocker et al., 1999; Flynn et al., 1998; Walker et al., 1999). CXCR5 expression is transient and fully polarized Th1 and Th2 cells do not express this chemokine receptor (Moser and Ebert, 2003). Several different fates can be envisioned for these CXCR5+ T cells. Some of these cells follow the effector pathway to activation and differentiation, some downregulate CXCR5 and leave the secondary lymphoid tissues to become circulating TCM cells. A fraction of these cells however retain their CXCR5 expression. These cells are antigen-primed, non-polarized effector T cells that are able to home to B cell follicles and provide help to B cells. The downstream events leading to the generation of TFH effector cells are still largely unclear. Possible scenarios for generation of TFH cells include all three T helper subsets Th1, Th2 and
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+ TFH cells arising directly from naive T cells, alternatively CXCR5 T cells could represent an intermediate stage in development and might give rise to all three effector subsets depending on additional stimuli. How cell development fate is decided is unclear at this stage. It is likely that after the initial activation by contact with an antigen loaded APC in the T cell zone, T cells that are destined to become B-helper effector cells must receive an additional signal provided by the cells located in or close to the B cell follicles. Two distinct cell types are excellent candidates for this role. B cells themselves may influence the differentiation of T cells into TFH cells. There is abundant evidence for B cells directing T cell development, polarization and memory (Ebert et al., 2004; Harris et al., 2000; Linton et al., 2000; Stockinger et al., 1996; Tsitoura et al., 2002; van Essen et al., 2000) . They may also be involved in further differentiation of
TFH cells into Th1 or Th2 effector cells since B cells are capable of producing IL-12 and IL-4 which induce T cell polarization. The above evidence suggests that B cells themselves might stimulate T cells to develop into TFH cells. Another cell type that could provide the secondary signals to differentiate + + - CXCR5 T cells into TFH effector cells is CD4 CD3 accessory cells. These cells are of a non-dendritic lineage and interact with antigen-specific CD4+ T cells that have been previously primed by DCs. This interaction may depend on signals through OX40 and CD30. Furthermore they are localized at the sites of T-B collaboration: in B follicles and at the T–B interface (Kim et al., 2003).
1.5.5 CD57+ CXCR5+ T cells
While CXCR5 expression on T cells is important to enable TFH cells to move into proximity of B cells, it may not be sufficient to define the subset of T cells with the capacity to provide help to B cells. Additional markers may be needed to identify true + TFH effector cells. In one study, only a small subset of human CXCR5 T cells that co- expressed CD57 was capable of effector function (Kim et al., 2001c). These T cells were reported as CD57+CD45RO+CXCR5+CD4+ and CCR7-, made up 15-25% of total tonsillar CXCR5+ T cells, and specifically localized to germinal centers (Kim et al., 2001c). CD57+ CXCR5+ T cells supported production of IgG, IgA and IgM by human tonsil B cells, produced IL-10, IL-2, IL-4, IFN-γ and TNF-α following activation, and expressed the co-stimulatory molecules OX40 and CD40L (Kim et al., 2001c).
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However, others have reported that CD57+ GC T cells display a phenotype consistent with anergy (Johansson-Lindbom et al., 2003).
1.5.6 Distinguishing TFH cells and identifying the mechanism of their function
Despite the obvious importance of TFH cells many unanswered questions remain regarding their precise identity, generation and mechanisms of their action. CXCR5+
TFH cells are non-polarized with respect to Th1/Th2 cytokine production. A specific cytokine profile that distinguishes TFH from Th1 and Th2 cells has yet to be described. In addition, it is unclear whether specific transcription factors such as T-bet and GATA3, which determine Th1 and Th2 differentiation (Murphy and Reiner, 2002), exist for TFH cells. In addition, the mechanism for provision of help to B cells remains to be elucidated. Although they have some capacity to produce cytokines after activation, it is also possible that they provide help through costimulatory molecules.
1.6 T cell memory
1.6.1 Life and death of effector T cells A typical primary immune response occurs along the following lines. First, antigen is delivered from the infected tissue site to the draining lymph node via the lymphatics. Innate immune mechanisms contribute to this process, and subsequent naïve T cell activation. Naïve T cells circulate between blood and lymph nodes (Mackay, 1993), and the rare antigen-specific cells are activated by antigen presented by APCs. The next stage is the differentiation and expansion of effector and memory T cells. Effector T cells express receptors for homing to non-lymphoid tissues, where they provide antigen-specific protection. Termination of the primary immune response is followed by wide-scale death of effector cells. This elimination of effector cells prevents over-burdening of the immune system and potentially harmful autoreactive effector T cells causing damage to the host. The process of clearance of activated T cells was first described by Sprent and Miller (Sprent, 1976; Sprent and Miller, 1976). The clearance is accomplished by two key mechanisms: cell death and homing to non- lymphoid tissues. Reinhart et al and Masopust et al described a dramatic redistribution of immune cells from lymphoid to non lymphoid tissues following an antigenic challenge (Mackay and von Andrian, 2001; Masopust et al., 2001; Reinhardt et al.,
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2001). These cells then reside in the non-lymphoid tissues for long periods of time, whereafter they confer protection by rapidly converting to effector cells following re- stimulation. Effector cell death is guided by complex yet poorly understood mechanisms. The available evidence suggests that effector cell death is not the default pathway but a tightly controlled instructional process. Moreover, effector cell death seems to be controlled by different mechanisms in CD4+ and CD8+ T cells. For CD8+ T cells, the deletion of IFN-γ greatly affects elimination of CD8+ effector T cells (Badovinac et al., 2000). This cytokine also has an important role in mediating activation induced cell death in CD4+ T cells (Refaeli et al., 2002). The deletion of CD8+ T cells is largely unaffected in Bcl-2 and BCL-XL transgenic mice and also in animals lacking CTLA-4, and both Fas and TNF receptors (Bachmann et al., 1998; Nguyen et al., 2000; Razvi et al., 1995; Reich et al., 2000). PD-1 and IL-2 may play important roles in elimination of effector T cells, as suggested by reports showing T cell hyperplasia in mice deficient for those genes (reviewed in (Sprent and Surh, 2002)). Similar to CD8+ T cells, Fas/FasL interaction is not critical for CD4+ cell death in normal immune responses but may be important in chronic immune responses to self-antigens (Sytwu et al., 1996; Van Parijs et al., 1998). In conclusion, CD4+ effector cell elimination is likely to be a tightly regulated process that involves cooperation of several cell-death-inducing mechanisms. In addition to active mechanisms for cell death, there is some evidence that cells may undergo passive death through loss of contact with protective cytokines and costimulatory molecules (Blattman et al., 2003). Upregulation of Bcl-2 seems to reverse this effect (Van Parijs et al., 1998). The interplay between Bcl-2 and Bim provides an important mechanism for regulation of activated T cell death (Marrack and Kappler, 2004; Strasser and Pellegrini, 2004). Cytokines such as IL-7 and IL-15 promote T cell survival mainly through increased expression of Bcl-2 and BCL-XL. A pro-apoptotic BH3-only Bcl-2 family member called Bim opposes the effects of Bcl-2 and induces apoptosis in T cells (Hildeman et al., 2002). In healthy T cells Bim is bound to Bcl-2 and BCL-XL on the mitochondria (Zhu et al., 2004). In activated T cells levels of Bim increase and levels of Bcl-2 diminish, resulting in free Bim molecules and leading to initiation of apoptotic signalling. Members of another Bcl-2 related subfamily, Bax and Bak then mediate cell death (Lindsten et al., 2000; Rathmell et al.,
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2002; Wei et al., 2001). In addition, there is some evidence that passive cell death may be promoted by the loss of CD40L interaction with CD40 on APCs (Whitmire et al., 2000).
1.6.2 Overview of T cell memory Our ability to combat pathogens depends to a large extent on the ability of our immune system to remember past infections and mount a rapid and more intense secondary response. This phenomenon is known as immunological memory, and is a hallmark of the adaptive immune system. Although this thesis will focus on T cell memory only, B cells also form memory cells and are an important component of the memory response. Immunological memory is well recognized, and is the principal behind one of the most important manipulations in human and animal health, vaccination, however the development of immunological memory is still poorly understood. Memory T cells differ from naïve T cells in their ability to mount an immune response, in that memory T cells have a lower threshold of activation than naïve cells and are less dependent on costimulation (Demotz et al., 1990; Pihlgren et al., 1996; Sagerstrom et al., 1993). There are marked phenotypic differences between naïve and memory T cells, and a number of markers are used to distinguish memory cells. In general, mouse memory T cells are characterized by high expression of CD44, and for memory CD8+ cells high levels of Ly6C and CD122 (Cho et al., 1999; Pihlgren et al., 1996). Naïve and memory T cells in humans can be distinguished by their expression of CD45 isoforms: naïve T cells express the high molecular weight isoform CD45RA while T cell differentiation into effector/memory cells is marked by a switch to a low molecular weight isoform, CD45RO, generated by the differential splicing of extracellular domains (Beverley et al., 1992; Michie et al., 1992). Usually only activated and memory T cells express low molecular weight isoforms (Lee et al., 1990). Human CD8+ memory T cells also express CD45RO but there is also a subset of antigen experienced CD8+ T cells expressing CD45RA (Hamann et al., 1997). The distinction between effector and memory T cells is somewhat blurred. Effector cells are present during an active immune response and die shortly after the pathogen has been cleared. On the other hand, memory T cells are long-lived and can persist in circulation for many years after the initial antigen encounter. Effector T cells express activation markers such as CD69 and CD25 and possess immediate effector
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function such as cytokine production or CTL activity while memory T cells are in a more resting state and are capable of rapid effector function after re-stimulation.
1.6.3 Generation of memory T cells Memory cell generation appears to be a passive process – effector cells are forced to die by tightly regulated instructional mechanisms, a small number of cells that escape death survive to become memory cells, survival being a default pathway. However, long-term survival appears to involve instructional mechanisms. It is still unclear whether cells need to go through the effector stage to become memory T cells. There is experimental evidence to suggest that some memory cells are derived from typical effector cells whereas other memory cells may be derived from partially differentiated cells (Hu et al., 2001; Manjunath et al., 2001). Recent reports have suggested that T cells can become memory cells bypassing the effector stage altogether (Cho et al., 2000). Alternatively, memory cells could be derived from a subset of precursors developing during the later stages of the immune response (Sprent and Tough, 2001). They might still develop into effector cells but have less contact with the antigen and not be committed to the death pathway. By avoiding death these cells could then become memory cells. This theory helps explain the complete elimination of effector T cells when confronted with high doses of antigen (Sprent and Surh, 2002). The same theory has also been proposed to explain generation of non-polarized central memory cells in humans. This will be discussed in more detail in the next section. Interestingly, memory cells can be generated in response to both foreign and self-antigens. This was recognized with the recent finding that naïve T cells begin to proliferate and differentiate into memory-phenotype T cells when the total numbers of naïve T cells are reduced below a certain threshold (Cho et al., 2000; Goldrath et al., 2000). This response is directed to self-antigens that mediated positive selection in the thymus. However, in normal adults the pool of naïve T cells remains high so the proportion of self-reactive memory cells is low. However, in lymphopenic conditions compensatory homeostatic proliferation occurs. In some instances such as in NOD mice, this process (which is costimulated by IL-21) can result in the creation of an autoreactive pool of T cells that leads to autoimmunity (King et al., 2004). T cell help seems to be a requirement for generating CD8 memory (Bourgeois and Tanchot, 2003). Activated CD8+ T cells express CD40 and receive help from CD4+ T cells via CD40L (Bourgeois et al., 2002). Although CD4+ T cells are not required for
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generation of CD8 effector cells, CD8+ memory T cells generated in the absence of CD4 help have poor proliferative capacity and cytokine production (Sun and Bevan, 2003). In the absence of T cell help, primary CTL responses are normal but there are no secondary responses (Janssen et al., 2003; Shedlock and Shen, 2003; Sun and Bevan, 2003). It is still largely unknown how the cells that will go on to form the memory T cell pool are selected. Recent studies have emphasized the importance of IL-7 for the generation of CD8 T cell memory. IL-7, while not required for CD8+ T cell activation and expansion, is necessary for optimal generation of memory following infection with vesicular stomatitis virus (Schluns et al., 2000). In addition, IL-7Rα, which was expressed on a minority of CD8+ effector cells during peak LCMV response, marked cells that had the potential to develop into long-lived memory cells (Kaech et al., 2003). IL-7 was also important for memory cell survival, although IL-15 could partially compensate for IL-7 deficiency. All IL-7Rαhigh cells expressed granzyme B indicating that memory cells passed through an effector stage. This also suggests that memory is not the default pathway for all effector cells (Masopust et al., 2004).
1.6.4 Memory T cell homeostasis A lot of effort has gone to the identification of factors governing long term survival of memory T cells, which is of paramount importance for vaccine development. The memory cell pool is maintained though the long-term survival and division of memory cells. Memory cells divide more slowly than antigen-stimulated T cells during an immune response, but faster than naïve T cells. The factors affecting the survival of memory CD8+ T cells are better understood than those responsible for the survival of CD4+ memory T cells. IL-15 is important both for the survival and turnover of CD8+ memory T cells (Goldrath et al., 2002; Kennedy et al., 2000; Lodolce et al., 1998). Increased levels of IL-15 cause selective expansion of CD44highCD8+ cells in IL- 15 transgenic mice (Zhang et al., 1998). The turnover of these cells is inhibited by IL-2 which has an opposing effect to IL-15 on their homeostasis (Ku et al., 2000). IL-7 is another important survival factor for CD8+ T cells (Goldrath et al., 2002; Schluns et al., 2000), and it is likely that IL-15 and IL-7 act in concert to maintain the CD8+ memory T cell pool (Schluns and Lefrancois, 2003). The survival of CD4+ long-lived memory T cells may also be cytokine dependent (Li et al., 2003). However, they can be generated
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independently of γ-chain cytokines (IL-2, -4, -7, -9, -15) so the identity of the cytokine is still to be determined (Lantz et al., 2000; Sprent and Surh, 2002). There is also evidence that costimulation through OX-40/OX-40L may be important for CD4+ memory T cell survival (Rogers et al., 2001). Constant exposure to antigen does not appear to be a prerequisite to memory cell homeostasis. Memory cells survive for prolonged periods of time in MHC-/- hosts showing that long-term survival is independent of TCR ligation (Murali-Krishna et al., 1999; Swain et al., 1999; Tanchot et al., 1997). It is noteworthy though that recent studies have shown that the functionality of these cells may be affected (Kassiotis et al., 2002). Total numbers of memory cells remain relatively constant throughout life. This implies that the rate of expansion of memory T cells is equal to the rate of cell death. This presumably reflects a balance between life-sustaining signals and pro-apoptotic signals (Schluns and Lefrancois, 2003). It is possible that life-sustaining cytokines act through upregulation of anti-apoptotic molecules such as Bcl-2 (Akashi et al., 1997; Maraskovsky et al., 1997; Yajima et al., 2002).
1.6.5 Subsets of memory T cells Immunological memory can be roughly divided into two functional categories: immediate protection in the periphery and initiation of a recall response in the secondary lymphoid organs. Immediate protection is mediated by a subset of memory cells termed effector memory T (TEM) cells, while reactive memory is mediated by central memory T (TCM) cells (Lanzavecchia and Sallusto, 2000; Sallusto et al., 2004;
Sallusto et al., 1999). TEM cells are in an overtly activated state and resemble effector cells. They have a rapid turnover, direct CTL (for CD8+ T cells) and express activation markers such as CD69 and CD25, high levels of integrins and tissue-homing chemokine receptors. TCM cells express receptors for homing to lymph nodes and are in a quiescent state lacking immediate effector functions. These cells however still retain many of the functional attributes of effector cells and can efficiently differentiate into an effector phenotype after secondary stimulation. The expression of homing receptors for migration to secondary lymphoid organs has been one of the most useful markers to discriminate between TCM and TEM cells (Figure 1.8). TCM cells express the chemokine receptor CCR7 and CD62L (L-
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selectin), which are required for entry through HEV and migration to the T cell area of the secondary lymphoid organs (Campbell et al., 1998; Forster et al., 1999). TEM cells do not express CCR7 and are heterogeneous with regard to CD62L expression. They express chemokine receptors and molecules required for homing into inflamed tissue and can be found in blood, spleen, liver, lungs and gut but are generally excluded from lymph nodes due to the loss of CD62L and CCR7 (Campbell et al., 2001). In addition to the above subsets, CD8 memory T cells have an additional subset of TEM cells that expresses CD45RA (TEMRA). Two populations of memory T cells have also been identified in mice: one found primarily in the lymph nodes and producing IL-2, while non-lymphoid tissues have populations producing IFN-γ (Reinhardt et al., 2001). CD4+ CD8+
TEM TCM TEM TCM CD45RO CD45RO TNAIVE TEMRA TNAIVE
CCR7 CCR7
Figure 1.8 Subsets of memory T cells. Circulating T cells can be divided into distinct populations based on their expression of the chemokine receptor CCR7 and CD45RO.
The varying strength of antigenic stimulation during an immune response could be responsible for the generation of different memory T cell subsets (Lanzavecchia and
Sallusto, 2000). TEM cells may arise from effector cells generated early in the immune response when DCs are loaded with large amounts of antigen and producing large quantities of polarizing cytokines (Sallusto et al., 2004). During the later stages of the immune response APCs exhaust their capacity to produce polarizing cytokines such as IL-12 and express low concentrations of antigen (Langenkamp et al., 2000). The T cells that are recruited at this stage may therefore avoid polarization and retain CCR7 expression and become long-lived TCM cells.
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Activation induced cell High level of death stimulation TEM CCR7- Antigen Naïve effector presentation T cell by APC ? CCR7+CD62L+ Low level of non-effector stimulation
Death by TCM neglect
Figure 1.9 Generation of effector and central memory T cells. A model to explain how different subsets of memory T cells can be generated during different stages of the immune response. Adapted from (Sallusto et al., 2004).
The relationship between the two subsets of memory T cells is unclear. As mentioned above, TCM and TEM cells might arise from two different precursors. Another possibility is that TEM cells arise from TCM cells after stimulation. Interestingly, a recent finding by Ahmed and colleagues suggests that this may not be the case, at least + for CD8 T cells (Wherry et al., 2003). Their findings suggest that TCM cells are more effective in mediating protective immunity and it is the TEM cells that convert to TCM cells in the absence of antigen and not vice versa. Their results suggest the following progression: naïve –> effector –> TEM –> TCM. This is in contrast to the situation in humans where both TEM and TCM subsets show persistence and stability (Baron et al.,
2003) and there is no apparent conversion of TEM to TCM cells (Langenkamp et al., 2003). Another recent study examining T cell responses against Leishmania major + demonstrated that CD4 TCM cells mediate long term protection and readily convert to tissue-homing effector cells upon secondary infection (Zaph et al., 2004). This suggests that significant differences exist between mouse and human memory T cell subsets.
1.6.6 Heterogeneity of memory T cells
Both TEM and TCM subsets are heterogeneous for expression of chemokine receptors, cytokine profiles, adhesion and costimulatory molecules (Kim et al., 2001b).
TEM cells express Th1 and Th2 chemokine receptors and also skin and gut homing molecules CLA and α4β7. When TEM cells are stimulated under neutral conditions they
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retain their Th1 or Th2 phenotype but can also be induced to produce the alternative cytokine under the opposite polarizing conditions, while still producing the original cytokines (Messi et al., 2003). However, mouse TEM cells become rapidly committed after stimulation (Grogan et al., 2001). Similarly, TCM cells can be divided into three functional subsets based on the expression of chemokine receptors CXCR3, CCR4 and + CXCR5. CXCR3 TCM cells secrete some IFN-γ and represent pre-Th1 cells, while + CCR4 TCM cells produced IL-4 and can be converted to Th2 cells following + stimulation. CXCR5 TCM cells did not express Th1 or Th2-associated receptors and remained non-polarized (Rivino et al., 2004).
1.7 Gene expression microarrays and the immune system
Monoclonal antibodies (mAbs) have for a long time been the tool of choice for immunologists seeking to identify new molecules and determine their function. With the completion of the human genome, a new approach involving homology searches to identify molecules belonging to immunologically important families became available. One of the best examples of this approach was the identification of a new member of the TNF family termed BAFF (Mackay et al., 2003; Schneider et al., 1999). Recent advances in genomics technologies have led to the development of microarrays, which provide enormous information on gene expression in cells or tissues (Shaffer et al., 2001; van der Pouw Kraan et al., 2004). The main advantage of microarrays as tools for gene expression analysis is the global scope of such experiments. Microarray technology allows the study of gene expression of an entire human or mouse genome in one experiment. Two different types of microarray technology are widely used: spotted cDNA microarrays and oligonucleotide arrays. Both rely on hybridization of labeled nucleic acid probes to oligonucleotides representing thousands of genes. The data to be presented in this thesis was derived using Affymetrix microarrays. These arrays rely on a Perfect Match/Mismatch probe strategy, where genes are represented by 11-16 oligonucleotide (25mers) probe sets and each includes the perfect match probe and a mismatch probe which has a single base mismatch. Quantification is achieved by comparison of the signals of the mismatch and the perfect match oligonucleotides. Using this comparison the genes can be defined as present or absent, and comparison of the same probe sets for different samples allows one to determine whether transcript
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expression is increased or decreased. The linear amplification of mRNA to cRNA using in vitro transcription can be used for amplification and labeling of mRNA in total RNA preparations in the nanogram range, thus allowing analysis of rare cell populations. The latest Affymetrix microarrays interrogate entire mouse and human genomes and contain tens of thousands of probe sets. Analysis of huge data sets can be challenging and several different approaches can provide useful information. The simplest form of analysis involves looking at the levels of expression of individual genes. If a baseline and sample microarrays are available then a straight forward comparison can be made using Affymetrix analysis algorithms producing fold change and an increasing or decreasing call. The drawbacks of this approach are that fold change can be misleading when the probe is expressed at a very low level in one of the samples. In addition it does not carry a statistical component and is difficult to apply to multigroup samples. Differential gene expression can be assessed using statistical methods such as ANOVA. The advantage of this approach is that introduces a statistical component and that it is not limited to Affymetrix arrays and analysis algorithms. However, high degree of confidence in requires a large number of replicates. In addition, ANOVA makes a number of assumptions (Materials and Methods section) about the data which could introduce errors into the analysis. Several different clustering techniques can be used to reduce data complexity and identify patterns in gene expression data. One of the most widely used approaches to study gene expression patterns in large datasets is hierarchical clustering. Hierarchical clustering based on Pearson correlation is commonly used for grouping genes and samples with similar expression patterns (Staudt and Brown, 2000). Data points are arranged in a hierarchy and displayed in a phylogenetic tree of clusters of genes in a hierarchically ordered relationship. Branch lengths represent the degree of similarity between sets. Gene expression profiles that are similar across the experimental samples are clustered together. Genes involved in similar biochemical or functional pathways are coordinately regulated. Genes coordinately expressed in a particular cell type or during a biological response have been termed gene expression ‘signatures’. While hierarchical clustering provides an easy way for analyzing complex data sets it does suffer from lack of robustness. Some other clustering methods used for gene expression data analysis include self-organising maps and k-means (Wang et al., 2002). Self-organizing maps can be used to identify gene expression patterns by
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reducing the dimensionality of data. This method is suited to analyzing experiments where no previous information about the distribution of data is available, however the expected number of clusters needs to be predefined. K-means clusters data into k groups of genes with similar expression patterns. The drawback of this approach is that it requires prior knowledge of the data analyzed and can give different output depending on the starting conditions used. In the few years since the introduction of microarray technology researchers have used large-scale gene expression analysis to broaden our understanding of vital immunological processes, including normal and pathogenic responses, tolerance, and immunological memory (Shaffer et al., 2001; Staudt and Brown, 2000; van der Pouw Kraan et al., 2004). Transcriptional profiles of leukocytes involved in the innate and adaptive immune responses have been reported (Boldrick et al., 2002; McHugh et al., 2002; Nakajima et al., 2001; Shen et al., 2004). In addition microarrays have been used successfully to study critical immunological processes such as leukocyte activation (Feske et al., 2001; Nau et al., 2002; Teague et al., 1999), differentiation (Lu et al., 2004; Rogge et al., 2000) and many other processes.
1.8 Experimental objectives
An understanding of T cell biology has been a central topic of contemporary immunology. Important advances have been made in delineating the factors that contribute to T cell development and differentiation, tolerance and effector function. However, many key questions remain. Although the Th1 and Th2 paradigm has been the focus of intense scrutiny in recent years, there are still few markers which reliably distinguish Th1 and Th2 cells. Little is known about the third effector subset of CD4+ T cells, TFH cells. All of the mechanisms for T cell help to B cells (or T cells) are yet to be identified. Even more questions remain as to the nature of memory T cells. There is no conclusive evidence to date regarding the precursor-product relationship between
TEM and TCM cells. The exact mechanism of survival and homeostasis of memory T cells is still not known. Microarrays have already been used to address some fundamental questions in immunology and to identify many leukocyte-specific transcriptional profiles. Here we used Affymetrix microarrays to conduct a detailed study of gene expression patterns in T cells in general and in specific T cell subsets. The aim of this study was to identify
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genes that are specific to particular effector T cell subsets, that might underlie their function. In addition, using our extensive database of leukocyte gene expression profiles, we wanted to identify genes that distinguish T cell subsets from each other, and T cells from other leukocytes. To approach some of these questions we have conducted a broad analysis of key effector and memory T cell subsets, including mouse and human Th1 and Th2 cells and the more recently identified TFH cells, central and effector memory T cells, γδ T cells and others. This comprehensive dataset enabled us to not only establish which genes were expressed by all T cells, but also which genes were expressed uniquely in each subset and are important to the function of these subsets.
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2 Materials and Methods
2.1 Buffers and solutions
The buffers and solutions used in the experiments are summarized in Table 2.1.
Table 2.1 Buffers and solutions.
BUFFER COMPONENTS MANUFACTURER FACS buffer 1xPBS 0.5% BSA GibcoBRL 0.05 mM NaN3 Amersham Fragmentation 40 mM Tris-Acetate, pH 8.1 Sigma buffer 100 mM Potassium acetate (KOAc) Sigma 30 mM Magnesium acetate (MgOAc) Merck 10xPBS 2.6% Disodiumhydrogen orthophosphate Merck (Na2HPO4) 0.2% Potassium Chloride (KCl) Merck 0.24% Potassium dihydrogen Merck orthophosphate (KH2PO4) 8% Sodium chloride (NaCl) Ajax Finechem MACS buffer 1xPBS Gibco BRL 0.2% fetal calf serum 2 mM EDTA RBC lysis 0.156 M Ammonium chloride (NH4Cl) Merck buffer 0.01 M Sodium hydrogen carbonate Merck (NaHCO2) Ajax Finechem 1 mM Ethylenediamine Tetraacetic acid (EDTA) 10x TBS 1.2% Tris Base 10 mM Promega 8.7% Sodium chloride (NaCl) Ajax Finechem TBS-T 1xTBS 0.05% Triton X-100 TE 10 mM Tris-Cl, pH 7.5 1 mM EDTA Sigma
2.2 Protocols
2.2.1 General cell isolation procedures PBMCs from human peripheral blood were isolated by density gradient centrifugation using Ficoll-PaqueTM (Amersham Biosciences, Uppsala, Sweden). Blood from healthy donors was diluted 1:2 with PBS. Typically 30 ml of diluted blood was overlayed on 15 ml of Ficoll-PaqueTM in a 50 ml conical tube without disturbing the
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blood-Ficoll interface. Gradient centrifugation was performed at 850 x g for 20 min at room temperature without brake. The PBMC layer was collected and washed twice with PBS to remove platelets and Ficoll. Tonsils were obtained from young patients (1-15 years) undergoing tonsillectomies to relieve tonsillitis with informed consent from the patients and in accordance with Institutional Ethics Approval. Tonsillar mononuclear cells were isolated by mechanical disruption, followed by Ficoll-PaqueTM density gradient centrifugation.
2.2.1.1 Lymphocyte enrichment using MACS
Magnetic activated cell sorting (MACS) was used to isolate CD4+ T cells or CD45RO+ cells for in vitro culture and for enrichment prior to sorting for lymphocyte subsets. Antibody-magnetic bead labeling was conducted according to the manufacturer’s protocol for the particular kit used (Miltenyi Biotec, Auburn, CA). All incubations were performed at 6-12oC and followed by a wash in at least 10 vol of cold MACS buffer. The separation was carried out using VarioMACS magnet and an appropriate MACS column: MS+ column for up to 1 x 107 cells from 2 x 108 total cells or an LS+ column for up to 1 x 108 cells from 2 x 109 total cells. The column was first washed with MACS buffer. The cell suspension was then passed through the column twice and washed 3 x with MACS buffer. The flow through containing non-selected cells was collected as a negative fraction, while positive fraction was eluted using a plunger once the column was removed from the magnetic field. Flow cytometric analysis was conducted on the fraction to evaluate cell purity which was typically >90% for negative selection, and greater than 95% for positive selection.
2.2.2 Flow cytometric analysis
2.2.3 Antibody staining and detection Leukocytes were incubated for 15-30 min on ice in the dark with fluorochrome- or biotin-conjugated primary monoclonal antibodies diluted in 100 µl of FACS buffer. The cells were then pelleted by centrifugation at 200 x g for 2 min and washed twice. A second 10 min incubation with a secondary fluorescently conjugated antibody was performed where necessary and the washing steps were repeated. Prior to analysis the cells were resuspended in 200 µl of FACS buffer. Routine flow cytometry was
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performed using a FACSCalibur flow cytometer (Becton Dickinson) with CellquestTM software used for data analysis.
2.2.4 Intracellular staining to assess cytokine production Prior to intracellular staining for cytokine production cells were stimulated with 1 µg/ml plate-bound anti-CD3 (clone TR66) and soluble anti-CD28 (BD Pharmingen) for 18-24 hours. Golgi Plug reagent (Becton Dickinson) was added at 1 µl/ml of culture medium for the last four hours of stimulation. The cells were then harvested and washed in PBS. Extracellular staining was performed as described above prior to permeabilization. The cells were then incubated for 10-20 min on ice in 100 µl of BD Cytofix/Cytoperm solution (Becton Dickinson) which fixed and permeabilized the cells. The cells were then washed twice in 150 µl of 1 x Perm/Wash solution (Becton Dickinson) and incubated for 30 min on ice in the dark with antibodies for intracellular staining diluted to an appropriate concentration in 100 µl of 1 x Per/Wash solution. Cells were then washed twice in 1x Perm/Wash and resuspended in FACS buffer for flow cytometric analysis.
2.2.5 Cell sorting Prior to sorting the cells were stained with fluorescently conjugated antibodies using the protocol described. The cells were then sorted using FACS Star sorter (Becton Dickinson) at the Centenary Institute, Sydney and using FACS Vantage SE with the FACSDiVa option (Becton Dickinson) at the Garvan Institute, Sydney.
2.2.6 Antibodies Antibodies were obtained from Becton Dickinson (San Jose, CA), unless otherwise indicated: CD4 (RPA-T4), CD45RO(UCHL1), CD57(NK-1), CD28(CD28.2), CD84 (2G7), CCR7(2H4), CD19(HIB19), IgM(G20-127), CD69(FN50), CD95(DX2), CD27(M-T271), CD134/OX40(ACT35), CD62L(DREG- 56), CD38(HIT2), IgD(IA6-2), CD24(ML5), CD25(2A3), CXCR5-biotin (RF8B2). The following antibodies were obtained from R&D Systems (Minneapolis, MN): CCR7-FITC (150503), CXCR5-PE/biotin (51505.111), CD4-unlabelled (goat polyclonal IgG). Anti-CD200 mAbs were obtained from Serotec (Oxford, UK) and BCL6 (N-3, polyclonal rabbit IgG) from Santa Cruz Biotechnology (Santa Cruz, CA), anti-CD84 mAb (Neomarkers, Freemont, CA), anti-CD3 (polyclonal rabbit anti-human
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Ab, Dako Cytomation, Denmark), anti-SLAM (clone A12), was kindly provided by DNAX Research Institute, Palo Alto, CA. The following secondary antibodies were used: streptavidin Cy-Chrome and streptavidin-PerCP (BD), PE-, Cy5TM-conjugated streptavidin and Cy3TM-conjugated donkey anti-mouse IgG (Jackson ImmunoResearch, West Grove, PA), goat anti-rabbit FITC (Santa Cruz Biotechnology, Santa Cruz, CA), donkey anti-goat Alexa Fluor 488 (Molecular Probes, Eugene, Oregon). Mouse anti-human IL-21R monoclonal antibody was kindly provided by Martin Hodge and Nita Frank (Millenium Pharmaceuticals, Cambridge, MA). Briefly, a mAb against human IL-21R was generated by immunizing BALB/c mice initially using epidermal gene gun bombardment with DNA from a pCDNA3.1 expression plasmid containing full length IL-21 receptor cDNA, and subsequently with an Fc-fusion protein of human IL-21R. This fusion protein was purified from the culture medium of COS cells transiently transfected with a plasmid encoding the extracellular domain of the IL-21R linked in frame to the Fc portion of human IgG1. Mice were immunized 5 times, and cell fusion performed using standard procedures, as described (Ponath et al., 1996). Positive hybridomas were selected by ELISA, using IL-21R Fc-fusion protein to coat wells, and also by specific staining of IL-21R transfected Baf/3 cells but not untransfected cells.
2.2.7 Cell culture Unless otherwise specified human cell cultures were maintained in RPMI 1640 (Invitrogen), supplemented with 10% heat inactivated FCS (Thermotrace), 100U/ml penicillin/streptomycin and 2mM L-Glutamine (Invitrogen Life Technologies, Carlsbad, CA, USA).
2.2.7.1 Generation of human Th1 and Th2 cells
Human neonatal leukocytes were isolated from heparinized cord blood by Ficoll-PaqueTM density gradient centrifugation. CD4+ T cells were obtained using a CD4 negative selection kit (Miltenyi Biotec). Polarized Th1 and Th2 cells were generated as previously described (Sornasse et al., 1996) by stimulation with immobilized anti-CD3 at 1-5 µg/ml (clone TR66), soluble anti-CD28 (1µg/ml; BD Pharmingen) and IL-2 (100U/ml). Th1 cultures also received IL-12 (5ng/ml; BD Pharmingen) and neutralizing anti-IL-4 (1µg/ml; BD Pharmingen), and Th2 cultures received IL-4 (10ng/ml; BD Pharmingen) and neutralizing anti-IFN-γ (1µg/ml; BD
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Pharmingen). The cells were cultured for 3-4 days, harvested and restimulated under the same conditions for another 3-4 days. At this stage cells were harvested for RNA isolation. Cytokine production was evaluated by intracellular staining of cells stimulated overnight with anti-CD3/CD28 and incubated with Golgi Plug (BD Pharmingen) for 4 h prior to staining. The most highly polarized cultures were selected for RNA isolation and Genechip analysis.
2.2.7.2 Generation of mouse Th1, Th2, Tc1 and Tc2 cultures (performed by Roslyn Kemp, Malaghan Institute, New Zealand)
Single cell suspensions from spleen and lymph nodes of C57BL/6J mice (The Jackson Laboratory) were washed in IMDM (Life Technologies, New Zealand) containing 2mM glutamine, 1% penicillin-streptomycin, 5x10-5M 2-ME (all from Sigma) without FCS, resuspended at 2x106cells/mL and incubated for 2 h at 37oC to deplete macrophages. Anti-CD4 (5µg/mL, GK1.5) or anti-CD8 (5µg/mL, 2.43) was added to the suspension (1x106 cells/mL in incomplete medium) and cells were incubated for 30 min at 4oC under gentle rotation. This was followed by an identical incubation with streptavidin-conjugated Dynabeads (Dynal) added at 5 beads/cell. After magnetic enrichment, cells were 90-100% CD4+ or CD8+ by FACS staining. Purified T cells (1x106/mL) were grown in 6-well plates (Falcon, Becton-Dickinson) coated with Armenian (5µg/mL) and Syrian (2µg/mL) anti-hamster antibodies (both from BD Pharmingen) and recoated with anti-CD3 (2C11). 10U/mL IL-2 (human recombinant IL2L6), 10 ng/mL IL-6 and soluble anti-CD28 (1:50 final concentration of 37.51 hybridoma supernatant) were added to Tc1 cultures. For Th1, Th2, Tc2 cell generation, conditions were identical to those for Tc1 generation, however 10 ng/mL IL-12 (BD Pharmingen) was added to Th1 cultures and IL-4 (produced in stationary cultures of a mIL-4-producing cell line) was added to Th2 and Tc2 cultures at 2000 U/mL. Cultures were maintained for 5 days, with replacement anti-CD28 and cytokines on days 2, 3 and 4. On day 5, cells were removed from the plates and transferred to uncoated 6-well plates with 100 U/mL IL-2 for 48 hours (IL-2 was replaced after 24 hours). The cells were restimulated with anti-CD3 without the cytokines for 24 hours and harvested. The cells were then pelleted and shipped on dry ice to the Garvan Institute.
2.2.7.3 Isolation of CD57+ and CD57- CXCR5+CD4+ lymphocytes from tonsils
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Tonsillar mononuclear cells were isolated by mechanical disruption, followed by Ficoll-PaqueTM density gradient centrifugation. Prior to sorting, tonsil cells were first enriched by selecting CD45RO+ cells using a magnetic bead positive selection method (Miltenyi Biotec). This enrichment resulted in the isolation of 80% pure memory CD4+ T cells. These cells were then stained with anti-CD57, CD4 and CXCR5 antibodies and sorted into CD57+CXCR5+CD4+ and CD57-CXCR5+CD4+ populations using a FACS-Star flow cytometer (Beckton Dickinson). Typical purities achieved were 95% or higher.
2.2.7.4 Isolation of TCM and TEM cells Memory T cell populations were isolated by sorting PBMCs enriched for CD4+ + + - cells using CD4 MACS beads (Miltenyi Biotec) into CD4 CD45RO CCR7 (TEM) and + + + CD4 CD45RO CCR7 (TCM) populations using the FACS-Star. Typical purities achieved were 96% or higher.
2.2.8 Ovalbumin model of asthma The mice used in the asthma model were 8 week old female BALB/c mice obtained from Animal Resource Centre (ARC) (Perth, WA, Australia). To induce allergic inflammation, mice received an intraperitoneal (IP) injection of ovalbumin (OVA) (Sigma) with alum (Pierce) on day 0 administered at 100 µg/mouse in a volume of 200 µl. On days 15-21, mice were exposed to aerosolized OVA generated by a nebulizer that generates 90% of particles 5 microns in diameter for 20 minutes. The nebulizer is connected to a Perspex box, 300 mm x 200 mm x 150 mm in size, where the mice are placed for the exposure to the aerosol. Mice were sacrificed on day 21 for cardiac puncture, bronchoalveolar lavage fluid (BALF), and lung tissue for flow cytometric analysis and histology. The different treatment groups are summarized in Table 2.2 (3 mice were used for each group).
Table 2.2 Treatment groups for asthma mouse model.
# Group Treatment I Control Injected IP with PBS in alum alone (200µl) and exposed to PBS aerosol. II Asthma, non- Injected IP with 100 µg OVA/PBS in alum (200 µl) and treated exposed to OVA aerosol. III Asthma, control- Injected IP with 100 µg OVA/PBS in alum and exposed treated to OVA aerosol. The mice were injected IP with sterile
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water without the drug (control treatment) on days 2, 4, 6, 8, 10, 12, 14. IV Asthma, Injected IP with 100 µg OVA/ PBS in alum and exposed aminoglutethimide to OVA aerosol. The mice were treated with IP injection treated of aminoglutethimide at 10mg/kg in sterile water. The treatment was administered on days 2, 4, 6, 8, 10, 12, and 14.
2.2.9 Histology
2.2.9.1 Tissue collection
Tissues were frozen in Tissue-Tek OCT compound (Sakura International) or fixed in 10% buffered formalin and embedded in paraffin by the Histology Department at the University of New South Wales (Sydney Australia). Frozen tissue sections (6µm) were made using a Cryostat (Leica, Wetzlar, Germany), paraffin embedded tissues were cut using a Microtome (Leica).
2.2.9.2 Haematoxylin-eosin stain
Sections were de-waxed and re-hydrated for 10 min in Xylene (twice; Merck), 100% EtOH (twice), 70% EtOH and water. The re-hydrated tissue was stained with haematoxylin (Merck) for 3 min, washed for 2 min in running water, stained with Scots blueing solution for 2 min and then rinsed in 70% EtOH and stained with eosin (Sigma, diluted to 30% in 70% EtOH) for 30sec then rapidly dehydrated in 100% EtOH followed by Xylene. Slides were dried and mounted using Eukitt (Calibrated Instruments Inc., Hawthorne, NY, USA).
2.2.9.3 Immunofluorescence assays
Frozen sections of tonsils were fixed in 1% paraformaldehyde/sodium phosphate for 20 min, blocked with 10% normal goat or horse serum for 30 min in a humidified chamber. Stained with primary Abs to CD84, CD57, BCL6, CD3 and CD4 diluted in PBS/1% BSA for 90 min, washed 3 times in PBS, followed by an incubation with fluorochrome-conjugated secondary antibodies. This incubation was followed by another three rounds of washing, then the slides were coverslipped using VectashieldTM mounting medium (Vector laboratories Inc., Burlingame, CA, USA). The images were
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obtained using Zeiss fluorescent microscope (AxioVision Software) or Leica TCS SP2 RS confocal microscope (Leica).
2.3 Gene expression profiling
2.3.1.1 RNA isolation
Two RNA isolation protocols were used in this study depending on the amount of starting material. For less than 1 x 106 cells, total RNA was isolated using TRIzol Reagent (Invitrogen, CA). Briefly, cells were pelleted by centrifugation and lyzed by pipetting in 300µl TRIzol reagent. 5 µg of GenEluteTM linear polyacrylamide (Sigma) was added as a carrier and the mixture was vortexed with 60 µl of chloroform (Ajax Finechem, Sydney, Australia). The phases were then separated by centrifugation for 5 min at 14,000 x g. The upper aqueous phase was collected and mixed with 0.8 vol. of isopropanol (Sigma) and allowed to precipitate at -20oC overnight. RNA was pelleted by centrifugation for 30 min at 4oC at 14,000 x g and washed in 500 µl of 75% EtOH, air-dried and resuspended in RNAse–free water. When more than 1 x 106 cells were used, RNA isolation was carried out using the RNeasy Total RNA Isolation Kit (Qiagen, Chatsworth, CA) as per manufacturer’s instructions. RNA was quantified by spectrophotometric analysis where A260 of 1 was equivalent to 40 µg/ml RNA. Alternatively RNA was quantified against RNA standards by electrophoresis on 1% agarose gel stained in SYBR Gold (1 in 30,000 dilution, NeoMarkers).
2.3.1.2 Preparation of cRNA and Genechip hybridizations
cRNA was prepared as described (Baugh et al., 2001). Briefly, 500ng of RNA in 3.5 µl of water was mixed with 1µl of 5 µM HPLC-purified T7-(T)24 primer (Geneworks, Adelaide, Australia) and denatured for 4 min at 70oC. The mixture was then rapidly cooled on compacted ice. The remaining reagents for first strand synthesis were added to the following final concentrations in a total volume of 10 µl, 1 x first strand buffer (GibcoBRL), 10 mM fresh DTT (GibcoBRL), 1mM each dNTPs (Promega, Madison, WI), 2.4 µg T4 single strand nucleic acid binding protein (T4gp32; Ambion, Austin, Texas, USA), 20 U RNase Inhibitor (Roche Molecular Biochemicals,
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Sydney, Australia) and 100 U Superscript II (Roche). The reaction was carried out for 1 hour at 42oC. This followed by an inactivation step carried out at 65oC for 15 min. The following reagents were then added to first strand tube to carry out second strand synthesis: 1 x 2nd strand buffer (Invitrogen), 10 mM dNTPs (Promega), 20 U DNA Polymerase I (Roche), 5 U E.coli DNA Ligase (Invitrogen), 1 U RNAse H (Roche) and RNase free water to make up to 65 µl. Second strand synthesis was carried out at 16oC for 2 hours followed by addition of 10U T4 DNA polymerase (Roche) for a further 15 min. The reaction was heat inactivated by incubating at 70oC for 10 minutes and placed on compacted ice to cool. The samples were cleaned up by mixing with one vol of phenol/chloroform (Sigma), transferred to Phase-Lock tubes (Eppendorf) and centrifuged at 14,000 x g for 5 min at 4oC. The upper phase was then transferred to the Biogel P-6 tubes (Bio Rad) and centrifuged for 4 min 4000 x g. The double stranded cDNA was precipitated by the addition of 2.5 vol of 100% EtOH and 20µg of glycogen (Roche) and incubation at - 20oC for 2 hours. DNA was pelleted by centrifugation by centrifugation at 15,000 x g for 20 min. The pellet was washed once with 70% EtOH, air dried and resuspended in 22 µl of RNase-free water. The double stranded cDNA was then in vitro transcribed and biotinylated using the Bioarray High Yield Transcript labeling kit (Enzo, Farmingdale, NY, USA). The kit components (HY reaction buffer, biotin-labeled ribonucleotides, DTT, RNase inhibitor mix and T7 DNA polymerase) were combined with double stranded cDNA to 1 x concentration in a total volume of 40 µl and incubated at 37oC overnight. The cRNA was then cleaned up using the RNeasy Mini Kit (Qiagen). The sample volume was adjusted to 200 µl with RNase-free water, 700 µl of RLT buffer (Qiagen) and 500 µl of 100% EtOH were added. The mix was transferred to two separate spin columns and centrifuged for 30 sec at 15,000 x g. The columns were washed twice with 500 µl of RPE buffer (Qiagen) and eluted in 50µl of water twice using the first elution as to re-elute the column the second time. The biotin- labeled antisense target cRNA was quantitated by spectrophotometry and fragmented in fragmentation buffer for 35 min at 95oC. Fifteen µg of fragmented cRNA was used to prepare a hybridization cocktail, which included probe array controls and blocking agents. Hybridization was carried out for 16 hours at 45oC and 60 rpm. Washing and staining of the hybridized probe array was performed by an automated fluidics station, according to the manufacturer’s protocols. The stained probe array was scanned using
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the Agilent GeneArray Laser Scanner and the resultant image captured as a data image file, which was then analyzed using Microarray Analysis Suite software (Affymetrix, Santa Clara, CA). Hybridization of labeled cRNA to Affymetrix gene chips, washing and staining of the hybridized probe arrays were carried out by the staff at the Affymetrix facility at the Garvan Institute, Sydney. Mouse Th1, Th2, Tc1, and Tc2 cRNA was hybridized to Mu11K (11,000 murine genes and EST clusters) arrays for expression analysis. All human gene expression experiments used in this study were performed using U133A+B arrays (44, 000 probe sets).
2.4 Data analysis
2.4.1.1 Mouse microarray data
From data image files, gene transcript levels were determined using MAS4.0 software (Affymetrix). The expression levels of all genes on the Mu11K array set were compared between type 1 and type 2 cells, with differences of 2-fold or larger likely to reflect significant changes in gene expression. Genes that showed a change of 2-fold or greater in at least two separate experiments were considered as differentially expressed. Each probe was assigned a call of present (expressed) or absent (not expressed) using Affymetrix decision matrix. A small percentage of probes on Mu11K array set were found by Affymetrix to be made using mouse sequences that were input into the databases with ambiguous directionality assignments. The results presented in this study are only minimally affected by these ambiguities. The microarrays used for analysis of mouse RNA were superceded by new generation arrays therefore different analysis software was used to for human microarray data. Higher sample number and more complicted data set for human samples meant that more complex analysis techniques could be applied to the analysis of human data.
2.4.1.2 Human microarray data
Fluorescence intensities were measured on U133A and B arrays using the Agilent GeneArray Laser Scanner and absolute expression of genes were determined and scaled to 150 using algorithms in MicroArray Analysis Suite 5.0 (MAS5) Software (Affymetrix). Signal value represents the level of expression of a transcript. Signal log ratio is the change in expression level of a transcript expressed as the log2 ratio (a signal log2 ratio of 1 is equal to a fold change of 2). Genes that showed a change of 2 fold or
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greater were considered differentially expressed. Data was imported as a Microsoft Excel file into Spotfire (Spotfire, Somerville, MA) for graphical representation of gene expression patterns. Spotfire software was used to map GeneChip analysis results where fluorescence intensity detected on the GeneChips is represented by a colour scale. Annotations were extracted using NetAffx analysis tool (www.affymetrix.com). In depth analysis and clustering of data was carried out using GeneSpring software (Silicon Genetics, Redwood City, CA). After data transformation (to convert any negative value to 0.01), normalization was performed by using a per-chip 50th percentile normalization and per-gene median normalization method. Genes that were consistently absent or below noise level were excluded from analysis. To identify genes with statistically significant differences between T cell subsets or cell types, one-way ANOVA (variances assumed equal) with a p-value cut-off of 0.05 and Benjamini and Hochberg false discovery rate as multiple testing correction were performed. The Student-Newman-Keuls post hoc test was used to identify the specific groups in which significant differential expression occurred. It is important to note that ANOVA makes a number of assumptions such as the homogeneity of variance among the samples, normal distribution and equal sample size. Violation of two of these assumptions can lead to type I errors. A significant limitation of our analysis is low replicate number of samples. Where it was not possible to keep the sample size equal used the results of the ANOVA tests very conservatively (i.e. by setting a low p-value threshold). Benjamini and Hochberg false discovery rate as multiple testing correction were also used where possible. Real time PCR to test selected genes is highly recommended as a starting point for downstream analysis. Hierarchical clustering was performed, on both genes and individual experiments, with Pearson correlation as a measure of similarity to group genes and samples with similar expression patterns. Data points were arranged in a hierarchy and displayed in a phylogenetic tree of clusters of genes in a hierarchically ordered relationship. Branch lengths represent the degree of similarity between sets. Gene expression profiles that were similar across the experimental samples were clustered together. Principal components analysis on conditions was preformed using all genes that were expressed in at least two samples to identify the components responsible for the highest variability in the data in GeneSpring software.
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2.4.2 Real-time PCR to monitor gene expression Total RNA was isolated from cells as described. Two different protocols were used for cDNA synthesis. Large scale protocol was used to make cDNA from mouse samples where abundant RNA was available. Briefly, RNA (2 µg per reaction) was reverse transcribed using AMV Reverse Transcriptase (Promega) at 42oC for 70 minutes in the presence of 250µM dNTPs, AMV RT 5x Reaction Buffer (Promega,
250mM Tris-HCl, 250mM KCl, 50mM MgCl2, 50mM DTT, 2.5mM spermidine) and
1µM oligo-p(dT)15 primer (Roche Molecular Biochemicals). Most human cDNA samples were synthesized using small scale protocol. cDNA was made using Reverse- IT RTase Blend Kit (ABgene, UK) according to manufacturers instructions. Following cDNA synthesis, 2 µl of cDNA template were used for each PCR reaction. Real-time PCR reactions were conducted using a Roche Light CyclerTM system with FastStart DNA Master SYBR Green I kit (Roche Molecular Biochemicals) according to manufacturer’s specifications using 3mM MgCl2 and 1µM primers. After an initial denaturation for 10 min at 95°C, the reaction mixes were run for 40 cycles at 95°C (15 s), 63°C (5 s), and 72°C (10 s). The following mouse primers are from Overbergh et al. (Overbergh et al., 1999): β-actin, IFN-γ, IL-2, IL-4, IL-5, IL-10 and IL-13. Primers for human IL-5 and IL-13 are from Storduer et al. (Stordeur et al., 2002). The remaining primers were designed using MacVector Software (version 6.5.3, Oxford Molecular Group) or Primer3 software (Rozen and Skaletsky, 2000). Each gene was normalized to a housekeeping gene (β-actin for mouse samples and GAPDH for human) before fold change was calculated (using crossing point values) to account for variations between different samples.
Table 2.3 Primer sequences.
GENE SEQUENCE (5’-3’) PRODUCT NAME SIZE (BP) Mouse primers β-actin AGAGGGAAATCGTGCGTGAC 148 CAATAGTGATGACCTGGCCGT IFN-γ TCAAGTGGCATAGATGTGGAAGAA 92 TGGCTCTGCAGGATTTTCATG IL-2 CCTGAGCAGGATGGAGAATTACA 141 TCCAGAACATGCCGCAGAG IL-4 ACAGGAGAAGGGACGCCAT 95
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GAAGCCCTACAGACGAGCTCA IL-5 AGCACAGTGGTGAAAGAGACCTT 116 TCCAATGCATAGCTGGTGATTT IL-10 GGTTGCCAAGCCTTATCGGA 190 ACCTGCTCCACTGCCTTGCT IL-13 AGACCAGACTCCCCTGTGCA 123 TGGGTCCTGTAGATGGCATTG Eta-1 GATGATGACGATGATGATGACGATGG 143 GATTGGAGTGAAAGTGTCTGCTTGTG amphiregulin AGTGCTGTTGCTGCTGGTCTTAG 151 CGCTTATGGTGGAAACCTCTCTTC gelsolin ATGGTGGTGGAGCACCCCGAA 431 GCGTCCTTTGACCTGGAAGAG SDF3 CCTGACATCCACAGCACCTACAAG 133 TTCTCCAGAGGGGCAACAAAGC CCR1 TTTTAAGGCCCAGTGGGAGTT 450 TGGTATAGCCACATGCCTTT TRAF4 TCTGGAAGATTGGAAGTTATGGGCG 172 ACGGATGTAGATGGAGAGGTGTGTGC CD37 AGAGAGTTGCCTCAGCCTCATCAAG 141 GAAGGACAAACCCACAAAGGACACG HLF ACATCCCCCTGGACAGCAAGACTTTC 138 TGGTAGAACTCATAGGCAGAGCGTC c-maf ATCCGACTGAAGCAGAAGAGGC 180 TCTCCTTGTAGGCGTCCCTTTC Human primers GAPDH GACATCAAGAAGGTGGTGAA 180 TGTCATACCAGGAAATGAGC IFN-γ TCCCATGGGTTGTGTGTTTA 198 AAGCACCAGGCATGAAATCT IL-4 TGTCACTGCAAATCGACACC 208 CTTGGAGGCAGCAAAGATGT IL-5 AGCTGCCTACGTGTATGCCA 71 GCAGTGCCAAGGTCTCTTTCA IL-13 TGAGGAGCTGGTCAACATCA 76 CAGGTTGATGCTCCATACCAT
2.5 Brief protocols for microarray experiments conducted by collaborators
Comprehensive analysis of gene expression patterns in T cell subsets and other leukocytes involved the use of a number of microarray profiles kindly provided by many collaborators. Brief protocols describing the isolation/generation of these cells are included for clarity.
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2.5.1.1 Generation of resting and activated Th1 and Th2 clones (microarray data provided by Federica Sallusto, Institute for Research in Biomedicine, Switzerland)
Adult CD4+ naive T cells were isolated from PBMC using a combination of MACS and FACS sorting. Briefly, CD4+ T cells were positively selected with anti- CD4-coated MACS microbeads (Miltenyi Biotec). Cells were then stained with anti- CD45RA antibodies and naive CD45RA+ T cells were sorted on a FACS Vantage (BD Bioscience). The cells were cultured in RPMI 1640 medium supplemented with 1% glutamax, 1% Na-pyruvate, 1% non-essential amino acids, 50 µg/ml streptomycin/penicillin (Gibco-BRL), 5x10-5 M 2-mercaptoethanol (Merk) and 5% human serum (Swiss Blood Center). Single naive T cells were distributed in 96-well plates by FACS Vantage sorting and stimulated with 105 allogeneic irradiated (40Gy) PBMC and 1 µg/ml PHA (Murex Biotech Limited) in IL-2 containing medium in the presence of 2 ng/ml recombinant IL-4 (R&D Systems) and 1 µg/ml neutralizing anti- IL-12 antibodies (R&D Systems). Clones were expanded in IL-2-containing medium. Cytokine production assay was performed after ~2 weeks. After 8 weeks selected T cell clones were restimulated under the same Th2-condition or under opposite Th1- condition (0.5 ng/ml recombinant IL-12 (R&D Systems) and 0.5 µg/ml neutralizing anti-IL-4 antibodies (R&D Systems)). Clones were expanded in IL-2 containing medium. Cytokine production assay was performed after 2 weeks. RNA was extracted from resting and activated T cell clones after 4 weeks using Trizol. For cytokine production assays T cells were stimulated with 2x10e-7 M PMA and 1 µg/ml ionomycin (Sigma) for 4 hours. Brefeldin A (10 µg/ml) was added for the last 2 hours. Fluorochrome-labeled anti-IFN-γ and anti-IL-4 antibodies (Pharmingen) were used after fixation and permeabilization performed using the Cytofix/Cytoperm kit (Pharmingen). T cells (10e7/condition) were left untreated (NA) or activated (Act) for 4 hours with 1 µg/ml anti-CD3 antibody (clone TR66) and 50 ng/ml PDBu (Sigma).
2.5.1.2 B lymphocyte and plasma cell isolation (Stuart Tangye and Kim Good, Centenary Institute, Sydney, Australia)
Normal human spleens were obtained from organ donors (Australian Red Cross Blood Service, Sydney, Australia). Mononuclear cells were prepared by slicing splenic tissue into small pieces and disrupting the capsule by forcing the tissue through a filter
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mesh. Red blood cells were lyzed and the remaining cells washed twice and cryopreserved in liquid nitrogen until required. Naïve and memory B cells were isolated by sorting on a FACStar (Becton Dickinson, San Jose, CA) following labeling with FITC-anti-CD20 and PE-anti-CD27 mAb and collecting CD27-CD20+ and CD27+CD20+ B cells, respectively (Tangye et al., 2003a; Tangye et al., 1998). To isolate IgM-expressing (non-switched) and isotype-switched memory B cells, splenic MNC were labeled with anti-CD20 mAb, anti-CD27 mAb and a cocktail of mAb specific for IgG, IgA and IgE (IgG/A/E), or IgM and IgD (IgM/D), followed by Streptavidin-TriColour, and CD27+IgG/A/E- (IgM-expressing, non-switched) and CD27+IgM/D- (isotype-switched) memory B cells were collected (Avery et al., 2003; Tangye et al., 2003a). Plasma cells were isolated by sorting CD38++CD20± cells on a FACStarPlus or FACSVantage (BD Biosciences) after labeling MNC with FITC-anti- CD20 and PE-anti-CD38 mAb (Ellyard et al., 2004). Plasma cell chips were run using pooled cRNA from up to five donors.
2.5.1.3 B cell, NK and T cell subset isolation (Rebecca Newton, Garvan Institute, Sydney, Australia)
CD19+ B cells and CD16+CD56+ NK cells were sorted from PBMC using a FACSVantage SE DiVa® (BD Biosciences, San Jose, CA). Doublets and aggregated cells were excluded to minimize contaminating cells. Only >96% pure B cells and NK cells, as determined by FACS analysis, were used for microarray analyses.
2.5.1.4 Basophil isolation (Sue Liu, Garvan Institute, Sydney, Australia)
For basophil isolation, 200 ml of human blood was collected from healthy donors. CD3+ cells and CD14+ cells were depleted from PBMC by MACS (Miltenyi Biotec) according to the manufacturer’s instructions. Resultant cells were stained with 5E8 anti-CCR3 antibody (generated in our laboratory) and labeled with Cy5-conjugated donkey anti-mouse IgG (Jackson Laboratories, Bar Harbor, Maine). Basophils were sorted by collecting CCR3hi, forward- and side-scatter low cells. RNA was extracted from >96% pure basophils where purity was determined by FACS analysis and Wright giemsa (Sigma) stained cytospins. RNA was pooled from two independent donors of microarray analysis.
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2.5.1.5 Mast cell isolation (Sue Liu, Garvan Institute, Sydney, Australia)
Neonatal mononuclear leukocytes were isolated using Ficoll-Paque as described. Contaminating red blood cells were removed using Whole Blood Erythrocyte Lyzing Kit (R & D Systems). Mononuclear cells were then seeded at 2 x 106 cells/ml in complete media containing 100 µM non-essential amino acids (Invitrogen Life Technologies) and 0.2 µM β-mercaptoethanol (ICN Biomedicals, Aurora, Ohio) supplemented with 100 ng/ml stem cell factor (SCF) (a gift from Amgen), 10 ng/ml interleukin (IL)-10 (BD Biosciences), and 5 ng/ml IL-6 (BD Biosciences). Non-adherent cells were transferred weekly to new flasks and resuspended with fresh media at a concentration of 106 cells/ml. Cell purity and maturity was determined by metachromatic staining with 1%Toluidine blue (Sigma) in 0.5 M HCl. Mast cells were used after 7 - 10 weeks in culture when cells were ~ 95% pure and mature. Mast cells were activated by FcεR1 cross-linking. First, the cells were primed with 2 µg/ml human IgE anti-NP (Serotec, Oxford, UK) in the presence of 100 ng/ml SCF for 18 hours and then washed and activated with 5 µg/ml mouse anti-human IgE (Serotec) with 100 ng/ml SCF for two hours. Control cells were incubated with media containing SCF for the same duration. Activated and control mast cells were harvested by centrifugation and washed with PBS before RNA extraction.
2.5.1.6 Macrophage and DC generation (Sabine Zimmer, Garvan Institute, Sydney, Australia)
Macrophages were differentiated from peripheral blood monocytes isolated using CD14 positive selection by MACS (Miltenyi Biotec) in the presence of GM-CSF and then activated for 4 h with 100 ng/mL LPS (Sigma). Purified monocytes were resuspended at 106 cells/ml in complete medium supplemented with 500 U/ml GM- CSF (BD Biosciences) and cultured for 11 days. Adherent cells showed the typical morphology of macrophages with a purity of >99% by Wright Giemsa staining. Dendritic cells were differentiated from purified monocytes resuspended at 106 cells/ml in complete medium supplemented with 800 U/ml IL-4 and 1500 U/ml GM-CSF (both from BD Biosciences) and cultured for 5 days. DC cultures were fed after 4 days with fresh media containing IL-4 and GM-CSF. Non-adherent immature dendritic cells were harvested after day 5 either for RNA isolation or activated by adding 100 ng/ml LPS (Sigma) for 48 hours.
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2.5.1.7 Neutrophil and eosinophil isolation (Mary Sisavanh, Garvan Institute, Sydney, Australia)
Isolation of neutrophils was performed entirely at room temperature. The plasma was depleted of platelets by centrifugation. Red blood cells were then removed by 6% dextran T500 (Pharmacia, Uppsala, Sweden) sedimentation. Granulocyte enrichment was achieved by centrifugation of leukocytes through a discontinuous 70% and 80% isotonic Percoll (Amersham) gradient. Granulocytes were harvested from the 70%/80% interface, washed with PBS and contaminating erythrocytes were eliminated using the Whole Blood Erythrocyte Lyzing Kit (R&D Systems) according to the manufacturer’s instructions. Contaminating eosinophils were removed by incubation of granulocytes with 7B11 anti-CCR3 followed by depletion using anti-mouse secondary antibody conjugated to MACS beads. Neutrophil preparations were >97% pure as determined by Wright giemsa staining of cytospins. RNA was extracted from resting neutrophils and neutrophils stimulated for 1 h with 100 ng/ml LPS (Sigma) for microarray analysis. For eosinophil isolation, neutrophils were depleted from the granulocyte population using anti-CD16 MACS beads (Miltenyi Biotec). Purity of eosinophils was >99% as determined by staining of cytospins using Wright Giemsa. Eosinophils were then resuspended at 2.5 × 106 cells/ml and incubated with phorbol-12-myristate-13- acetate (PMA) (50 ng/ml) for 2 hours, or with complete media only as a control. RNA was extracted and equal amounts of RNA from the three independent donors were then pooled for microarray analysis.
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3 The Th1/Th2 paradigm
3.1 Introduction
CD4+ T cells differentiate into distinct effector subsets to orchestrate immune responses to diverse pathogens. Th1 cells produce IFN-γ, which activates phagocytes and promotes protection against intracellular pathogens. Th2 cells produce IL-4, IL-5, IL-13 and IL-9 which together with IgE, eosinophils and basophils promote clearance of large extracellular parasites. In addition to their protective roles, Th1 and Th2 cells are also involved in pathological responses. For instance, many autoimmune diseases involve type 1 responses while Th2 cells are thought to be the key mediators of allergic diseases such as asthma (Abbas et al., 1996; Mosmann et al., 1986; O'Garra et al., 1997). The type 1 and 2 dichotomy also extends to CD8+ T cells. They can be polarized to effector subsets with distinct cytokine production profiles, similar to those found in CD4+ T cells (Croft et al., 1994; Seder et al., 1992a). This polarization has been observed both in vitro and in vivo (Cerwenka et al., 1998; Maggi et al., 1994; Sad et al., 1995). Interestingly, both type 1 and type 2 CD8+ cells retain cytolytic activity (Sad et al., 1995). The innate immune response and the nature of interactions of APCs with T cells help shape the direction of the subsequent adaptive immune response (Dabbagh and Lewis, 2003). In particular, factors which may influence the direction of the immune response include the type and dose of antigen (Bretscher et al., 1992; Constant et al., 1995; Hosken et al., 1995) the avidity of the interaction between the TCR and antigen- MHC complex (Ho and Glimcher, 2002; Mowen and Glimcher, 2004), and the nature of the antigen presenting cell (Maldonado-Lopez et al., 1999; Pulendran et al., 1999; Rissoan et al., 1999). For instance, recognition of microbial molecules by Toll-like receptors (TLR) on the surface of APCs leads to their activation and maturation followed by the expression of cytokines and costimulatory molecules required for the initiation of the adaptive immune response (Akira et al., 2001; Barton and Medzhitov, 2002; Schnare et al., 2001). TLR ligands generally induce APCs to produce IL-12, IL- 23 and IL-27. On the other hand response against large extracellular pathogens such as helminthes involves production of cytokines such as IL-4. The cytokines (together with an array of costimulatory molecules) produced by APCs during the initiation of the
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adaptive immune response are important in determining the direction of T cell polarization. IL-4 drives T cell differentiation to type 2 cells. IL-12 is the principal cytokine for type 1 development, but other cytokines including IFN-γ, IL-18, IL-23 and IL-27 can also contribute to various stages of Th1 polarization (Szabo et al., 2003). Distinct transcription factors such as T-bet and Stat4 for Th1 cells, and GATA-3, Stat6 and c-maf for Th2 cells, control T cell polarization (Murphy and Reiner, 2002). Because of their distinct roles in immune responses, Th1 and Th2 cells home to different sites. This homing is achieved via the differential expression of chemokine receptors. Th1 cells preferentially express the chemokine receptors CCR5 and CXCR3, while Th2 cells preferentially express CCR3, CCR4, CCR8 and CRTh2 (Chtanova and Mackay, 2001; Sallusto et al., 1998a; Sallusto et al., 2000; von Andrian and Mackay, 2000). Moreover, examination of T cells in situ, particularly in pathologic conditions, confirms the association of certain chemoattractant receptors with Th1 and Th2 responses (Moser and Loetscher, 2001; Qin et al., 1998). Th1 and Th2 polarization is an area of active research by immunologists, however many key questions remain to be addressed. In particular, few reliable markers exist that distinguish Th1 and Th2 cells, especially since chemokine receptor expression is subject to changes following activation. We decided to study the gene profiles of Th1 and Th2 cells in order to identify novel genes specific to each subset, that might be important to their function. Although other studies have used microarrays to study gene expression in Th1 and Th2 cells (Lu et al., 2004; Rogge et al., 2000) here we present a comprehensive study spanning both murine and human expression profiles. The broader scope of our study allowed us to address several questions not previously examined in other studies, including the differences between murine and human type 1 and type 2 cells and the effect of different polarization protocols of Th1 and Th2 gene expression profiles. In addition, the pattern of gene expression in CD8+ Tc1 and Tc2 cells provided detailed information on the distinction between polarized CD4+ and CD8+ T cells.
3.2 Results and Discussion
3.2.1 Gene expression in mouse Th1, Th2, Tc1 and Tc2 cells
3.2.1.1 Generation of polarized murine Th1 and Th2 and Tc1 and Tc2 cells
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Polarized Th1 and Th2 and Tc1 and Tc2 cells were generated by Roslyn Kemp (Malaghan Institute of Medical Research, Wellington School of Medicine, Wellington, New Zealand) using the protocol described in Chapter 2. Although a Th1-like cytokine profile appears to be the default pathway for CD8+ T cells, the type 2 profile can be generated by the addition of type 2 cytokines to in vitro cultures. Type 2 CD8+ T (Tc2) cells produce typical Th2 cytokines IL-4 and IL-5, although they still produce IFN-γ but at lower levels than type 1 (Tc1) cells. Representative cytokine profiles as determined by ELISA are shown in Figure 3.1. For instance, Tc2 cells produced greater than 35-fold more IL-4 and IL-5 protein than Tc1 cells. As expected Th2 cells produced more IL-4 and IL-5 while Th1 cells showed a higher expression of IFNγ. After polarization was completed, the cells were then harvested, washed and pelleted.
A Th1 vs Th2 cytokine profile B Tc1 vs Tc2 cytokine profile 6000 27000
5000 8000
4000 6000 3000 Th1 Tc1 Th2 4000 Tc2 2000 2000 1000 cytokine concetration (ng/mL) concetration cytokine cytokine concetration (ng/mL) 0 0 IL-4 IL-5 IFN-gamma IL-4 IL-5 IFN-gamma
Figure 3.1 Cytokine expression profiles of polarized CD4+ and CD8+ type 1 and type 2 cells. The cells were polarized as described in Chapter 2. Their cytokine production was determined by ELISA. The data for this figure was provided by Roslyn Kemp.
3.2.1.2 Confirmation of polarization at RNA level
We obtained RNA from polarized Th1, Th2, Tc1 and Tc2 cells and used real time PCR to assess polarization at the RNA level by comparing the levels of expression of key Th1 and Th2 cytokines. Representative cytokine profiles measured at RNA level are shown in Figure 3.2. Th2 cells showed a much higher expression of RNA transcripts for IL-4, IL-5 and IL-13, while Th1 cells expressed higher levels of transcripts for IL-2 and IFN-γ. This pattern of gene expression was consistent with published reports, indicating that our polarization protocol was yielding well- differentiated Th1 and Th2 cells. Similar to CD4+ T cells, IFN-γ transcripts were expressed at a higher level in Tc1 cells, while expression of IL-4, IL-5, and IL-13 was much higher in Tc2 cells as was expected for polarized CD8+ T cells (Breitfeld et al.,
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2000; Sad et al., 1995). For CD8+ cells, IFN-γ has been reported to be expressed abundantly in type 1 cells, but also to some extent in type 2 cells (Cerwenka et al., 1998; Fowler et al., 1996). Interestingly, in our system the expression of type 2 cytokines was particularly strong, for both CD4+ and CD8+ cells. One possibility is that our experimental protocol may favor high type 2 cytokine expression compared to type 1 cytokine expression, another is that the murine immune system is programmed for stronger Th2 responses, at least for CD57BL/6 mice. Taken together these results indicate that we have successfully generated highly polarized CD4+ and CD8+ T cells and that this polarization can be successfully detected at both the RNA and protein level.
A IL-4 67 B IL-4 925 IL-5 22 IL-5 59 IL-10 17 IL-10 96 IL-13 3 IL-13 28 IFN- γ -4 IFN-γ -6 IL-2 -9 IL-2 2 Fold change (Th2 relative to Th1) Fold change (Tc2 relative to Tc1)
Figure 3.2 Real-time PCR analysis of gene expression in Th1, Th2, Tc1 and Tc2 cells after polarization. The changes in mRNA levels of several cytokines important in differentiation of type 1 and 2 T cell subsets were assessed using real-time PCR. Total RNA was extracted from polarized cells and transcribed to cDNA. Cytokine-specific primers were used to amplify the cDNA template and the increase in product was monitored for each amplification cycle. The fold-change values obtained after normalizing each sample to a housekeeping gene (β-actin) represent the change in mRNA level of a particular gene when comparing Th2 to Th1 (A) and Tc2 to Tc1 (B) cells. Positive fold change values indicate that the transcript is present at a higher level in type 2 T cells, while negative values indicate that the transcript is more abundant in type 1 T cells.
3.2.1.3 Microarray analysis of gene expression
We next analyzed gene expression in Th1, Th2, Tc1 and Tc2 cells using the Affymetrix Mu11K array set which contains 11,000 full-length genes and ESTs. For an overall picture of gene expression in the different T cell subsets, log average difference values (which are reflective of the level of expression of the gene) were plotted for each T cell subset for biologically relevant comparisons (Figure 3.3). We found that approximately 60% of all probes for both CD4+ and CD8+ T cells were expressed (according to a call made for each probe using an Affymetrix decision matrix) in either type 1 or type 2 cells (Figure 3.3 inset). This high degree of gene expression is consistent with an earlier study of gene expression in naïve and activated T cells
50 Chapter 3 The Th1/Th2 Paradigm
(Teague et al., 1999). However, the percentage of genes expressed in T cells is probably significantly overestimated in this experiment, since it is likely that genes involved in T cell biology are over-represented on these early incomplete arrays. Not surprisingly, the majority of genes expressed by T cells did not show biased expression under type 1 or type 2 polarizing conditions. This suggests that most gene products perform house-keeping processes or functions common to both type 1 and type 2 T cells, and to all effector cells. Nevertheless we found a number of genes expressed preferentially in either type 1 or type 2 T cells.
5 A. Th1 vs Th2 B. Tc1 vs Tc2 5 11,000 full-length genes and ESTs 11,000 full-length genes and ESTs
4 360 differentially expressed e 1100 differentially expressed 200 differ by at least 3-fold 4 500 differ by at least 3-fold
3 3
2 2 Th2 (Log Average Difference) Average (Log Th2 1 Differenc Average (Log Tc2 1 6,900 present in at 6,400 present in at least one sample least one sample 0 0 012345012345 Th1 (Log Average Difference) Tc1 (Log Average Difference)
Figure 3.3 Comparison of gene expression between type 1 and type 2 T cells. cRNA probes were prepared from polarized cells, hybridized to Mu11K array set (containing 11,000 full-length genes and ESTs), stained and scanned as described in the Materials and Methods. The levels of expression of transcripts were calculated using algorithms in the GeneChip Analysis software and expressed as average differences. 2-fold (dotted lines) and 3-fold (dashed lines) changes in expression are indicated. Each gene transcript was defined as present or absent based on the level of expression according to an Affymetrix software algorithm. The inset in each figure shows all the probes that were defined as present in at least one T cell subset. The results are representative of two separate experiments.
3.2.1.4 Differential gene expression between type 1 and type 2 CD4+ and CD8+ T cells
Genes that were differentially expressed (a difference of two-fold or greater is likely to reflect a real change in gene expression) in at least two separate experiments are listed in Fig. 3.4. To better understand the processes involved in T cell differentiation, the differentially expressed genes were organized into a number of functional categories. As expected, a large number of cytokines and growth factors were differentially expressed by either type 1 or type 2 cells (Figure 3.4i). Many cytokines including IL-4, IL-5, IL-6, IL-3, IL-13, GM-CSF, IL-2, lymphotoxin-α and
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IFN-γ were expressed as expected for polarized type 1 and type 2 cells providing us with a high degree of confidence in our array results. In addition, several factors not previously associated with T cell polarization were differentially regulated. Type 2 cells expressed higher levels of stromal cell derived factor, SDF3 (also known as pigment epithelial-derived factor, PEDF), as well as the EGF-like growth factor, amphiregulin. Both of these factors have not been previously associated with T cell differentiation. Interestingly, a recent report highlighted the role of PEDF in the regulation of NFATc2 in endothelial cells (Zaichuk et al., 2004). NFATc2 and other members of the NFAT family play a complex yet important role in regulating T cell cytokine production and differentiation (reviewed in (Ho and Glimcher, 2002)). It is noteworthy that the cytokine profiles of Tc2 and Th2 cells were similar to a large extent. In addition to cytokines we also noted the differential expression of several cytokine receptors. In particular, IL-1 receptor-related protein 1, which is a component of the IL-18 receptor (Born et al., 1998; Hoshino et al., 1999; Torigoe et al., 1997) was preferentially expressed in both Th1 and Tc1 cells. Preferential expression of this molecule by Th1 cells was first shown by Xu and colleagues (Xu et al., 1998). Another member of the IL-1 receptor like family, IL-1 receptor type II (IL-1RII) was expressed at a higher level in Tc2 cells.
IL-4* M25892 25.3 ajuba* U79776 3.7 A IL-5* X06271 20.9 zyxin* Y07711 2.6 19 . 7 IL-10* M37897 mbh-1* X54511 2.2 SDF-3* D50460 5.4 gelsolin* J04953 2.1 IL-3* X02732 4.3 GM-CSF* X03019 3.1 -2 U64450 nucleophosmin/nucleoplasmin-3* IL-17 (CTLA-8) U43088 2.4 -2.7 M12301 granzyme C IL-6* X54542 2.1 -4 L76150 p16ink4a IL-7R* M29697 2 iv -4.1 M32057 MOK2* 2 amphiregulin* L41352 3.2 -2 U64199 IL-12R beta2 Cacna1c* L01776 -2.3 K00083 IFN-gamma* -2 U17297 Epb72 -2.4 M29464 PDGFA -2.6 L42463 Rho-GDI3* -7 L05439 IGFBP-2 -3.3 U38196 palmytoylated protein p55* -10.7 M16762 IL-2 -3.9 U20372 voltage-dep. Ca channel beta-3 subunit i -12.4 U43673 IL-18R* v -4.5 M14757 multidrug resistant protein* c-maf* S74567 10 . 5 Gem GTPase (gem)* U10551 5.2 6.3 mHLF* D89787 TRAF5* D78141 2.3 c-fos* V00727 5.1 4.2 ZAP-70* U04379 2.1 PPAR-gamma2* U09138 vi mIRF7* U73037 3.6 -2.3 X92346 TRAF4* cornichon-like protein* AB006191 2.9 argininosuccinate synthetase M31690 2.4 2.8 GATA-3* X55123 Edn1 gene for preproendothelin-1* D70842 2 TIS 11* M58566 2.5 adenylosuccinate synthase* M74495 2 Rpt-1r* J03776 2.4 GC binding protein* Z36270 2.3 liver carnitine palmitoyltransferase I* AF017175 2 2.2 Blimp-1 U08185 -2.2 U28016 parathion hydrolase-related protein* NFIL3/E4BP4* U83148 2 -2.9 U06923 Stat4 -2.3 L02333 UDP glucuronosyltransferase ii -4.8 L12147 Early B-cell factor* vii -2.6 M21285 stearoyl-CoA desaturase gene I-309* M23501 7.7 interferon-induced 15-KDa protein X56602 13 . 5 CCR1* U28404 5.5 mouse 19.5 mRNA, complete cds M32486 11. 1 ctla-2 alpha* X15591 4.7 sFRP-1 U88566 8.1 ctla-2 beta* X15592 3.7 Selenium-binding liver protein M31419 6.6 Lama5 U37501 3.7 204 interferon-activatable protein M32032 6.5 Eta-1* X16151 3.4 neurophilin* D50086 3.4 CD24a* X53825 3.4 Ifi203 AF022371 3.3 prostacyclin receptor* D26157 2.3 ecotropic viral integration site 2* M34896 2.9 CD27* L24495 2.3 GBP3 U44731 2.4 integrin beta7 M95632 2.1 tumor-associated membrane protein U25633 2.3 CXCR4* AB000803 2.1 M.musculus (Balb/C) Tx01* Z31362 2.3 CCR5 D83648 2 GARG-16 U43084 2.1 -2.6 D13695 ST2L* -2.1 U92437 MMAC1 -2.9 J03535 embigin -2.3 AF020313 proline-rich protein 48 iii -4.3 X53176 integrin alpha4* viii -12.6 AF013262 lumican*
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IL-13 M23504 12 3 . 2 MyD116 X51829 16 . 9 B M-CSF M21952 71.9 granzyme F X14092 12 . 3 amphiregulin* L41352 13 . 5 preprogranzyme D U66472 8.7 GM-CSF* X03019 11. 8 granzyme E X12821 7.6 IL-4* M25892 8.4 gelsolin* J04953 6.7 IL-10* M37897 8.2 H1 histone subtype H1(0) X13171 5.7 IL-5* X06271 7.8 zyxin Y07711 4.2 IL-1RII X59769 6.7 TDAG51 U44088 4.2 IL-6* X54542 6.2 Gadd45 L28177 4.1 IL-3* X02732 5.2 mbh-1* X54511 4 IL-7R* M29697 5 ajuba* U79776 3.3 SDF3* D50460 2.4 bcl-3 M90397 3.2 IL-12Rbeta U23922 2.2 Tnfrsf18 U82534 2 IL-12Rbeta2 U64199 2 -3.9 L75822 mac 25 SDF2 D50646 2 -6.5 M32057 zinc finger protein (MOK2)* -3.7 K00083 IFN-gamma* iv -9.6 U64450 nucleophosmin/nucleoplasmin-3* -10.9 U43673 IL-18R* 3.5 i VDLVLDL receptor receptor U06670 -16.3 M17015 LT-alpha Cacna1c* L01776 3.4 c-fos* V00727 27.3 -2.6 M14757 multidrug resistant protein* GATA-3* X55123 23.7 -5.9 U38196 palmytoylated protein p55* c-maf* S74567 15 . 9 v -12.4 L42463 Rho-GDI3* C/EBP beta X62600 8.6 Ptpre U35368 9.3 PPAR-gamma2 U09138 8.6 MARCKS M60474 6.4 8.1 mHLF D89787 Gem GTPase* U10551 5.2 cornichon-like protein AB006191 7.9 4.8 7.4 SLAP U29056 jumonji protein D31967 ZAP-70* U04379 3.1 6.3 GC Binding Protein* Z36270 Jak2 L16956 2.7 TIS11* M58566 5.6 Pim-2 L41495 2.6 zinc finger protein A20 U19463 4.4 Erk-1 Z14249 2.5 Grg1 U61362 4.4 TRAF5* D78141 2.1 B-ATF AF017021 3.8 vi X92346 TRAF4* ets-1 X53953 3.8 -2.8 mIRF7 U73037 adenylosuccinate synthetase* M74495 6 3.6 3.7 NFIL3/E4BP4* U83148 3.5 mGSTT2 U48420 CREM M60285 2.7 cathepsin A J05261 3 Rpt-1r* J03776 2.3 liver carnitine palmitoyltransferase I* AF017175 2.7 IRF-1 M21065 2.3 schwannoma-associated protein AF026124 2.4 -2.2 U13262 myelin gene expression factor 2 glucocerebrosidase M24119 2.1 -2.8 Edn1 gene for preproendothelin-1* D70842 2 ii L12147 early B-cell factor* -4.5 U63648 p160 myb-binding protein sialyltransferase 4 X73523 2 CCR1* U28404 7.1 -2.3 L26316 dihydrofolate reductase CD24a* X53825 5.5 -3.7 U28016 parathion hydrolase* prostacyclin receptor* D26157 5.2 vii -4.3 M21285 stearoyl-CoA desaturase* (CD1) endoglin X77952 3.9 11. 2 LAG-3 X98113 3.7 TDD5 U52073 7.5 gp49 M65027 3.6 LIM protein 3 U77040 gp49B U05265 3.6 ecotropic viral integration site 2* M34896 4.8 OTS-8 M73748 3.3 complement component C5S M35525 4.8 ctla-2-beta* X15592 3.1 inhibin beta-A subunit X69619 4.1 PRECAM-1 L06039 3 (Balb/C) Tx01* Z31362 3.4 Eta-1* X16151 2.9 neuropilin* D50086 3.3 CD28 M34563 2.9 CXCR4* AB000803 2.7 calcyclin M37761 3 CD53 X97227 2.5 furin L26489 2.8 CD27* L24495 2.4 annexin XI U65986 2.6 I-309* M23501 2.3 pYMT2/B X05260 2.6 ctla-2-alpha* X15591 2.3 flotillin U90435 2.3 -2.3 L31580 CCR7 CRABP-II M35523 2.2 X53176 integrin alpha4* -2.4 intracisternal A-particle IAP-IL3 X04120 -3.4 D13695 ST2L* 2 iii -7.5 L23108 CD36 viii -4.7 AF013262 lumican*
Figure 3.4 Differential gene expression patterns in murine Th1, Th2, Tc1 and Tc2 cells. Gene expression profiles of murine Th1, Th2 (A), Tc1 and Tc2 (B) were generated using Affymetrix Mu11K set and analysed using GeneChip software. Genes were considered as differentially expressed if a change of at least two-fold or greater was observed in at least 2 separate experiments. The x-axis in this figure represents fold change Th2 relative to Th1 (A) and Tc2 relative to Tc1 (B). Positive fold change values indicate that the transcript is present at a higher level in type 2 cells, while negative values indicate that the transcript is more abundant in type 1 cells. An asterisk next to the gene name signifies that the gene is differentially expressed in a similar fashion in both CD4+ and CD8+ T cells. The level of expression of a gene (arbitrary fluorescence units) is indicated by colour: high level of expression (>5000) red for type 1 genes and navy blue for type 2; medium level (1500-5000) orange for type 1 and blue for type 2; low level (<1500) yellow for type 1 and light blue for type 2 genes. As a comparison, the level of expression of β-actin is approximately 25450 for CD4+ T cells and 44000 for CD8+ T cells (arbitrary fluorescence units). GenBank accession numbers are indicated next to the gene name. Differentially expressed genes were grouped into several broad categories based on known functions of genes: i Cytokines, cytokine receptors and growth factors; ii Transcriptional regulation; iii Adhesion, migration and cell surface molecules; iv Apoptosis, proteolysis, cell cycle and nuclear proteins; v Transport proteins; vi Proteins involved in signaling; vii Other enzymes; viii Miscellaneous.
Not surprisingly, a number of differentially expressed genes between type 1 and type 2 subsets are involved in transcriptional regulation (Figure 3.4ii). The role of some
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transcription factors, including GATA3, c-maf and Stat4, in T cell differentiation has been well characterized (Ho et al., 1996; Ho et al., 1998; Kaplan et al., 1996b; Zhang et al., 1997; Zheng and Flavell, 1997). Several transcription factors were found that have not previously been associated with T cell polarization, including c-fos and mHLF for type 2 cells, and early B cell factor for type 1 T cells. The precise role of these transcription factors in T cell differentiation remains to be determined. One possibility is that they are involved in the regulation of individual cytokine gene expression, similarly to how c-maf regulates IL-4 production (Ho et al., 1996; Ho et al., 1998), while T-bet has been shown control IFN-γ production (Szabo et al., 2000). Two members of the Tumor Necrosis Factor Receptor-associated Factor (TRAF) family showed differential expression between type 1 and type 2 cells. TRAF4 was expressed at a higher level in type 1 cells while TRAF5 was preferentially expressed in type 2 cells (Figure 3.4vi). Members of this family serve as adapter proteins that mediate cytokine signaling, in particular they seem to play a role in signal transduction from TNFR and Toll/IL-1 receptor molecules resulting in activation of transcription factors NF-κB and AP-1 (Inoue et al., 2000). A recent report suggested a role for TRAF5 in modulating the Th1/Th2 balance by limiting the induction of Th2 responses (So et al., 2004). Because of their distinct roles in immune responses, type 1 and type 2 cells are required to home to different sites around the body. This selective homing is achieved by differential expression of chemokine receptors and adhesion molecules by Th1 and Th2 cells (Bonecchi et al., 1998; Sallusto et al., 1998a). We have observed higher levels of transcripts for CCR1, β7 integrin and CXCR4 in Th2 cells, while Th1 cells expressed higher levels of α4 integrin (Figure 3.4iii). The expression of CCR1 on polarized T cells has been the subject of some debate as it was previously considered to be Th1 specific but recently there has been emerging evidence to suggest otherwise (Bonecchi et al., 1998). Higher levels of CCR1 transcript were expressed by both CD4+ and CD8+ type 2 T cells. A recent study showed that CCR1-deficient mice have significantly lower levels of the Th2 cytokines IL-4 and IL-13 in an allergic airway model (41). Th2 cells also produced chemokines such as TCA-3 (I-309) (the ligand of a Th2 associated receptor CCR8 (D'Ambrosio et al., 1998; Goya et al., 1998)). Our Genechip experiments failed to confirm preferential expression of several other chemokine receptors. In particular, type 2 chemokine receptor CCR3 was not
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differentially expressed, nevertheless we noted preferential expression of CCR4 by type 2 cells in one of the experiments. Type 1 chemokine receptor CCR5 was not differentially expressed in CD8+ cells but was slightly (2-fold) overexpressed by Th2 cells. This deviation from the expected chemokine receptor profile could be due to the fact that the expression of some chemokine receptors varies depending on cytokine stimulation and other factors (Sallusto et al., 1998b). Probe sets for the receptors CXCR3 and CRTh2 were not included in the array set. Interestingly, our GeneChip experiments identified differential expression of several granzymes. For instance, Th1 cells expressed higher levels of granzyme C compared to Th2 cells (Figure 3.4Aiv), while Tc2 cells expressed elevated levels of granzymes D, E, and F (Figure 3.4Biv). Granzymes are a family of serine proteases which are found in cytotoxic lymphocyte granules. Although granzymes A and B have been shown to be involved in cytolysis, the biological functions of granzymes C-F are yet to be identified (Kam et al., 2000). The expression of several granzymes has been noted in CD4+ T cells, especially after activation (Grossman et al., 2003; Grossman et al., 2004; Sedelies et al., 2004; Zaunders et al., 2004). Several studies have compared cytolytic abilities of Tc1 and Tc2 cells but the results have been inconclusive (Cerwenka et al., 1999; Dobrzanski et al., 2000). Tc1 cells may be more efficient at clearing some viral infections than Tc2 cells, although this could relate to different migration properties of the two subsets (Cerwenka et al., 1999; Wirth et al., 2000). We selected several differentially expressed genes and confirmed their expression by real-time PCR (Figure 3.5). Although there were differences in the fold change values detected by the two methods, real-time PCR results correlated well with the differential gene expression data produced using Affymetrix microarrays. This gave us confidence that the gene expression data derived from the gene arrays was reliable.
10.5 c-maf 10.4 A 6.3 HLF 8 amphiregulin 13.5 3.4 23 Eta-1 7.1 B 7.1 CCR1 13.5 5.5 CCR1 2.9 6.3 Eta-1 8.3 5.4 gelsolin 6.7 SDF3 3.6 7 2 SDF3 2.4 amphiregulin 2.7 2 Gene Chip -7.5 1.3 CD36 -14.3 Real-time PCR CD37 1.4 Gene Chip FoldFold change change (Tc2 (Tc2 relative relative to Tc1) -2.3 TRAF4 -2.2 Real-time PCR FoldFold change change (Th2 (Th2 relativerelative to Th1)to Th1)
Figure 3.5 Real-time PCR of select genes identified as differentially expressed by GeneChip analysis. The fold-change values of select genes were obtained using real-time PCR as described above.
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Positive fold-change values (shown next to the bars) indicate that the transcript is present at a higher level in type 2 cells, while negative values indicate that the transcript is more abundant in type 1 cells. Corresponding fold-change values for CD4+ (A) and CD8+ (B) T cells determined using Affymetrix MAS4 GeneChip analysis software (solid bars) are shown next to fold-change values as determined by real-time PCR (open bars).
In addition to the differentially expressed full-length genes, we also identified a number of differentially expressed ESTs (Table 3.1). For some of the ESTs, full-length genes have now been identified. Among the differentially expressed ESTs for which the full-length genes are already known, we noted preferential expression by Th2 and Tc2 cells of cytochrome p450 side-chain cleavage enzyme 11a1 and aldo-ketoreductase a, while both Th1 and Tc1 cells expressed higher levels of transcripts homologous to Jun dimerization protein gene. The significance of these genes in T cell polarization and function needs to be addressed in further studies.
Table 3.1 20 most differentially expressed ESTs in Th1/Th2 and Tc1/Tc2 cells. ESTs listed were identified using Affymetrix GeneChip analysis software MAS 4.0. ESTs were considered as differentially expressed if a change of two-fold or greater was observed in at least 2 separate experiments. Positive fold change values indicate that the transcript is present at a higher level in type 2 cells, while negative values indicate that the transcript is more abundant in type 1 cells.
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Identifier Fold Description and/or putative function if known Change ESTs differentially expressed in Th1/Th2 cells AA389018 109.3 Cytochrome P450 side chain cleavage enzyme 11a1 AA185666 26.3 clone IMAGE:637071 AA175784 11 viral hemorrhagic septicemia virus(VHSV) induced gene 1, viperin AA208866 11 aldo-keto reductase a C80153 7.8 clone J0077C07 AA153032 7.6 similar to Ifi16 protein W08269 6.2 SDF3 or pigment epithelium-derived factor AA462409 5.9 clone IMAGE:850998 AA175660 5.8 clone IMAGE:619526 AA174777 5.5 clone IMAGE:617199 AA182125 5.2 clone IMAGE:635924 AA066782 4.9 osteopontin/eta-1 AA592828 4.6 aldo-keto reductase Akr1c13 AA117223 4.5 aldo-keto reductase Akr1c13 AA614971 4.5 NF-κ light polypeptide gene enhancer in B-cells inhibitor ζ(Nfkbiz) W36620 3 G protein-coupled receptor kinase 5 AA110812 -3.1 phosphoribosylpyrophosphate synthetase subunit I AA616664 -3.3 membrane-associated protein 17 AA711915 -3.3 Homologous to Rattus norvegicus Jun dimerization protein 1 gene C77346 -3.8 clone J0029F01 ESTs differentially expressed in Tc1/Tc2 cells AA274626 36.4 CD37 AA462409 35.3 clone IMAGE:850998 AA389018 29.6 Cytochrome P450 side chain cleavage enzyme 11a1 W20873 28 interferon induced transmembrane protein 2 (Ifitm2) AA270796 22.9 activating transcription factor 5 (Atf5) AA711151 19.3 clone IMAGE:1167110 AA104818 18.9 oxidized low density lipoprotein (lectin-like) receptor 1 (Olr1), AA208866 17.9 Mus musculus aldo-keto reductase a mRNA C76863 15.4 tribbles homolog 3 AA289002 12.1 clone IMAGE:752142 AA711915 -3.8 Homologous to Rattus norvegicus Jun dimerization protein 1 gene AA110812 -3.8 phosphoribosylpyrophosphate synthetase subunit I (PRS I) AA475660 -3.9 FtsJ homolog 3 AA137436 -4 stearoyl-CoA desaturase gene, exon 6 AA574814 -4.2 clone IMAGE:991743 AA144734 -4.4 small nuclear ribonucleoprotein polypeptide A' W57316 -4.4 tubulin, alpha 2 AA203807 -4.8 clone IMAGE:643880 C78047 -4.8 Ran-interacting protein MOG1 W47725 -7.3 farnesyl pyrophosphate synthase
3.2.2 Gene expression in human Th1 and Th2 cells
3.2.2.1 Generation of polarized human Th1 and Th2 cells
We sought to complement our study of murine type 1 and type 2 cells by analyzing gene expression in human Th1 and Th2 cells. U133A+B microarrays interrogate many more genes than those assessed in earlier studies (Rogge et al., 2000), encompassing most of the human genome, which allowed us to conduct a more comprehensive study than any of the earlier analyses.
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Using human cord blood as a source of naïve CD4+ T cells we generated polarized Th1 and Th2 cells. The success of our polarization method was assessed both at the protein level by intracellular staining of cells for signature Th1 and Th2 cytokines, IFN-γ and IL-4, and at the RNA level by real time PCR for IFN-γ, IL-4, IL-5 and IL-13. The most highly polarized cultures were selected for microarray gene expression analysis (Figure 3.6).
1000 316
10 0
10 10 7
1 IFN-g IL-4 IL-5 IL-13
0.1
0.01 Foldchange (Th2 vs Th1)
1/ 4 5 1 0.001 IL-4
IFN-γ
Figure 3.6 Th1 and Th2 polarization at the RNA and protein level. The changes in mRNA levels of several cytokines important in differentiation of Th1 and Th2 cells were assessed using real-time PCR (A). Total RNA was extracted from polarized cells and transcribed to cDNA. Cytokine-specific primers were used to amplify the cDNA template and the increase in product was monitored for each amplification cycle. The fold-change values obtained after normalizing each sample to a housekeeping gene (GAPDH) represent the change in mRNA level of a particular gene when comparing Th2 to Th1 cells. Intracellular staining for IFN-γ and IL-4 was used to assess cytokine production in restimulated Th1 and Th2 cell (B). Th1 cells are shown in red and Th2 are in green for (A) and (B).
3.2.2.2 Microarray analysis of gene expression in human Th1 and Th2 cells
We analyzed gene expression in our in vitro polarized Th1 and Th2 cells using Affymetrix U133A and B arrays, which contain almost 45,000 probes and thus interrogate most of the human genome. The genes that were reproducibly highly differentially expressed in duplicate experiments are listed in Figure 3.7.
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Th1 SLR Accession Gene name -7.6 AF228422 normal mucosa of esophagus specific 1 -6.2 U62325 amyloid beta precursor protein-binding family B member -5.5 M63310 annexin A3 -5.5 NM_002462 myxovirus resistance 1, interferon-inducible protein p78 -4.8 AL121985 SLAM family member 7/CRACC -4.7 AF043337 IL-8 -4.7 X56210 H factor (complement)-like 1 -4.7 X04697 H factor 1 (complement) -4.7 AW189843 viperin Th1 Th2 -4.6 M29383 interferon-gamma -4.6 AA142842 XIAP associated factor-1 -4.5 BG149557 Homo sapiens cDNA FLJ31407 fis clone NT2NE2000137 -4.5 AI075407 IFN-induced protein with tetratricopeptide repeats 4 -4.3 NM_012449 six transmembrane epithelial antigen of the prostate -4.3 BC005858 fibronectin 1 -4.2 AW276078 Homo sapiens cDNA clone IMAGE:6272440, partial cds -4.2 AL157473 hypothetical protein DKFZp761L0424 Expression -4.2 NM_006433 granulysin -4.1 U04897 RAR-related orphan receptor A 0 400 800 -4.0 NM_000595 lymphotoxin alpha (TNF superfamily, member 1) -4.0 AW166711 KIAA0403 protein -3.8 AV734646 chromosome 6 open reading frame 187 -3.7 AI742057 hypothetical protein LOC129607 -3.5 AW006185 Homo sapiens hypothetical protein LOC283666 -3.4 AK025298 autism susceptibility candidate 2 -3.4 AW664964 Homo sapiens cDNA clone IMAGE:2980348 3' -3.4 NM_021803 interleukin 21 -3.3 NM_001953 endothelial cell growth factor 1 (platelet-derived) -3.3 AF264784 trichorhinophalangeal syndrome I -3.3 BF115739 hypothetical protein FLJ14001 -3.3 AY029180 plasminogen activator, urokinase receptor -3.1 NM_002534 2',5'-oligoadenylate synthetase 1, 40/46kDa -3.1 AI693178 Homo sapiens mRNA; cDNA DKFZp761L1121 -3.1 NM_001559 interleukin 12 receptor, beta 2 -3.1 BE217880 interleukin 7 receptor -3.1 NM_022168 melanoma differentiation associated protein-5 Th2 -3.1 L35594 ectonucleotide pyrophosphatase/phosphodiesterase 2 -3.1 NM_022873 interferon, alpha-inducible protein (clone IFI-6-16) SLR Accession Gene name -3.1 AU144882 SAM and SH3 domain containing 1 2.1 BE549540 cysteinyl leukotriene receptor 1 -3.0 AL050069 docking protein 5 2.1 NM_004430 early growth response 3 -3.0 AW014593 guanylate binding protein 1, interferon-inducible, 67kDa 2.1 AI377271 nucleobindin 2 -3.0 NM_005195 KIAA0146 protein -2.9 U16996 dual specificity phosphatase 5 2.1 AF237813 4-aminobutyrate aminotransferase -2.9 AI420319 Homo sapiens cDNA FLJ41218 fis clone BRALZ2018492 2.1 NM_004772 chromosome 5 open reading frame 13 -2.9 BC001872 immunoglobulin heavy constant mu 2.1 NM_005368 myoglobin -2.8 NM_016323 cyclin-E binding protein 1 2.2 AI969697 Homo sapiens cDNA clone IMAGE:2563432 3' -2.8 NM_005525 hydroxysteroid (11-beta) dehydrogenase 1 2.2 AI796169 GATA3 -2.7 N74607 aquaporin 3 2.2 AI927919 nm23-phosphorylated unknown substrate -2.7 NM_005101 interferon, alpha-inducible protein (clone IFI-15K) 2.3 NM_004938 death-associated protein kinase 1 -2.7 AI675354 MLLT4 2.3 NM_016008 dynein 2 light intermediate chain -2.7 AI335263 neuropilin and tolloid-like 2 2.3 AW629527 FLJ41238 protein -2.6 BE966604 Homo sapiens, clone IMAGE:3616574, mRNA 2.3 AI699465 Homo sapiens cDNA clone IMAGE:2301545 3' -2.6 AA633203 epithelial stromal interaction 1 (breast) 2.3 AW204712 hypothetical protein LOC170371 -2.6 AW451197 Homo sapiens, clone IMAGE:5278089, mRNA -2.6 W27419 hypothetical protein FLJ90005 2.3 NM_014485 prostaglandin D2 synthase, hematopoietic -2.6 NM_000594 TNF (TNF superfamily, member 2) 2.3 AF027205 serine protease inhibitor, Kunitz type, 2 -2.5 AI421071 CCR1 2.4 AI342246 Homo sapiens cDNA clone IMAGE:1949152 3' -2.5 AA211909 chromosome 20 open reading frame 100 2.4 D43968 runt-related transcription factor 1 -2.5 AL031602 hypothetical protein FLJ90005 2.5 AF052094 endothelial PAS domain protein 1 -2.5 BG287862 hypothetical protein MGC5395 2.5 BF037662 Homo sapiens cDNA clone IMAGE:3865344 5' -2.3 NM_018641 carbohydrate (chondroitin 4) sulfotransferase 12 2.5 NM_013332 hypoxia-inducible protein 2 -2.3 AI073984 interferon consensus sequence binding protein 1 2.5 BE883841 sestrin 3 -2.3 AK023113 KIAA1618 protein 2.5 AB003476 A kinase (PRKA) anchor protein (gravin) 12 -2.3 AI825926 phospholipid scramblase 1 2.6 NM_004369 collagen, type VI, alpha 3 -2.3 U49396 purinergic receptor P2X, ligand-gated ion channel, 5 -2.3 N21426 synaptotagmin-like 2 2.7 BF114870 small nuclear ribonucleoprotein polypeptide N -2.3 AA195074 TRAF2 binding protein 2.7 U73531 CXCR6 -2.3 AU152178 anthrax toxin receptor 2 2.8 AL031230 aldehyde dehydrogenase 5 family, member A1 -2.3 AI479176 integrin, alpha 9 2.8 NM_004951 Epstein-Barr virus induced gene 2 (EBI2) -2.2 AI659800 hypothetical protein FLJ38725 2.9 BC001247 epithelial protein lost in neoplasm beta -2.2 NM_004030 interferon regulatory factor 7 2.9 AI638433 phosphodiesterase 7B -2.2 AB021123 Ksp37 protein 3.0 AI670947 connector enhancer of KSR2 -2.2 AW341649 tumor protein p53 inducible nuclear protein 1 3.0 R15072 hypothetical protein FLJ30794 -2.2 NM_020987 ankyrin 3, node of Ranvier (ankyrin G) 3.1 AF261135 G protein-coupled receptor 18 (GPR18) -2.2 NM_000647 CCR2 3.1 BE616825 NIMA (never in mitosis gene a)-related kinase 6 -2.2 M60278 diphtheria toxin receptor 3.1 BC005047 dual specificity phosphatase 6 -2.2 NM_004106 Fc epsilon receptor I gamma chain -2.2 BE669858 hypothetical protein FLJ39885 3.2 Y19026 homer homolog 2 (Drosophila) -2.2 BG286973 hypothetical protein LOC340061 3.2 BE672260 beta3Gn-T -2.2 AI949827 nuclear factor (erythroid-derived 2)-like 3 3.3 U12767 nuclear receptor subfamily 4, group A, member 3 -2.2 NM_013351 T-bet (T-box 21) 3.4 AI962891 Abelson helper integration site -2.1 AU156189 Homo sapiens cDNA FLJ13432 fis clone PLACE1002537 3.4 AF008915 ecotropic viral integration site 5 -2.1 AF251061 neurocalcin delta 3.4 NM_003866 inositol polyphosphate-4-phosphatase, type II, 105kDa -2.1 W58365 polycystic kidney disease 1-like 3.5 AA456099 Homo sapiens cDNA clone IMAGE:813528 3' -2.1 BE326919 spermidine/spermine N1-acetyltransferase 3.5 NM_000963 prostaglandin-endoperoxide synthase 2 -2.1 AI064690 Homo sapiens transcribed sequences 3.5 AI342543 hypothetical protein FLJ33069 -2.1 NM_014824 KIAA0769 gene product 3.6 AV696234 xenobiotic/medium-chain fatty acid:CoA ligase -2.1 AW294022 KIAA1718 protein 3.8 M68874 phospholipase A2, group IVA -2.0 NM_003851 cellular repressor of E1A-stimulated genes -2.0 M21121 CCL5 4.0 AL048542 adhesion molecule AMICA -2.0 AA788711 collagen, type I, alpha 2 4.2 AA923354 monoamine oxidase A -2.0 AW182860 EH-domain containing 1 4.8 AF250309 interleukin 17 receptor B -2.0 BG545653 guanylate binding protein 5 4.9 NM_002674 pro-melanin-concentrating hormone -2.0 BF115739 hypothetical protein FLJ14001 5.1 NM_000961 prostaglandin I2 (prostacyclin) synthase -2.0 NM_005567 lectin, galactoside-binding, soluble, 3 binding protein 5.2 AW129783 Homo sapiens cDNA clone IMAGE:2607995 3' -2.0 NM_015714 putative lymphocyte G0/G1 switch gene
Figure 3.7 Differential gene expression patterns in human Th1and Th2 cells. Gene expression profiles of human Th1 and Th2 cells were generated using Affymetrix U133 set and analysed using MAS5.0 software. Genes that had a fold-change of 4 or greater (i.e. Signal Log Ration (SLR) of greater or equal to 2) in replicate experiments and whose expression was greater than 100 units in either Th1 or Th2 cells are shown. Positive SLR values signify that the transcript is expressed at a higher level in Th2
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cells, negative SLR values signify that the transcript expression is higher in Th1 cells. The colour reflects the level of expression. The heat map was generated using Spotfire software.
As expected, signature cytokines and transcription factors, such as IFN-γ and T- bet and IL-4 and GATA-3, were preferentially expressed by Th1 and Th2 cells respectively, although IL-4 expression was detected in only one of the replicate experiments. A number of other cytokines and cytokine receptors were differentially regulated between the two cell types. Cytokines IL-21, IL-8, IL-1α, IL-15 and IL-22 and lymphotoxin-α, and receptors IL-12Rβ2 and IL-7R were expressed at a higher level in Th1 cells. IL-9 and IL-26 and also cytokine receptor IL-17RB were higher in Th2 cells, a member of the TNF family of molecules, LIGHT, was also higher in Th2 cells. While the differential expression of some cytokines and cytokine receptors such as IL-21, lymphotoxin-α, IL-12Rβ2 by Th1 cells and IL-9 by Th2 cells has been described before, many other genes have never been associated with Th1/Th2 polarization. As was observed for the murine type 1 and type 2 gene expression profiles, many molecules associated with cell migration were differentially expressed by the two cell types. For instance, chemokine receptor CCR5 was preferentially expressed by Th1 cells, as reported previously (Qin et al., 1998; Sallusto et al., 2000). The expression of CXCR3 was also slightly higher in Th1 cells but by less than 2-fold in the second experiment (data not shown). Interestingly, CCR2 and CCR1 were also expressed at a higher level by Th1 cells. The expression of CCR1 by Th1 cells is in contrast with our observations in polarized mouse T cells. This discrepancy could reflect a difference between polarization in mouse and human T cells. The expression of CCR2 has previously been described in the context of Th1 responses and seems to be important for IFN-γ production by Th1 cells in some models (Boring et al., 1997; Sato et al., 1999; Traynor et al., 2002). Chemokine receptor CCR3 which is traditionally associated with Th2 responses was absent in both cell types. However, another Th2- associated chemokine receptor CCR4 was expressed in Th2 cells only (although at very low levels), and was increased compared to Th1 in one of the experiments. Interestingly, chemokine receptor CXCR6 showed a consistently higher expression in Th2 cells. This finding is in contrast to previous reports that CXCR6 is a Th-1- associated receptor (Kim et al., 2001a). Another receptor traditionally associated with Th2 responses, CRTh2, was absent in both subsets. In addition to chemokine receptors,
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Th1 cells also expressed higher levels of ligands for chemokine receptors CCR1 and CCR5, CCL3 (MIP-1α), CCL4 (MIP-1β), CCL5 (RANTES) and also the ligand for Th1-associated chemokine receptor CXCR3, CXCL10 (IP10). These inflammatory chemokines play important roles in Th1-mediated immune responses (Dorner et al., 2002; Schrum et al., 1996). Interestingly several genes from the prostaglandin metabolism pathway were regulated between Th1 and Th2 cells (Figure 3.8). Three synthases including prostaglandin D2 synthase, prostaglandin I2 synthase and prostaglandin-endoperoxide synthase 2 (COX-2) were preferentially expressed by Th2 cells. Prostaglandins are important mediators of immune responses and allergic disease in particular (Nagata and
Hirai, 2003). PGD2, produced mostly by allergen-provoked mast cells (Lewis et al., 1982), has multiple effects on the immune system including enhancement of mediator release and induction of chemotaxis by eosinophils, basophils and Th2 cells, inhibition of superoxide in neutrophils, and suppression of T cell mitogenesis (Giles and Leff, 1988; Hirai et al., 2001). The expression of prostaglandin D synthase by Th2 cells has also been documented by Tanaka et al., who also noted the induction of coordinated expression of COX-2 and PGD2 production upon stimulation of Th2 lines (Tanaka et al., 2000). Cyclooxygenases COX-1 and COX-2 catalyze the first rate-limiting step in the conversion of arachidonic acid to prostaglandins. While the expression of COX-1 is constitutive, COX-2 is inducible and is the target for most non-steroidal anti- inflammatory drugs. In T cells COX-2 is induced shortly after activation (Iniguez et al., 1999). We also noted preferential expression of prostaglandin I2 synthase, the enzyme which catalyses the conversion of PGH2 to PGI2 (prostacyclin). Jaffar and colleagues demonstrated that PGI2, which is a potent vasodilator and inhibitor of platelet aggregation, can downmodulate Th2 responses by promoting IL-10 production at the sites of allergic inflammation (Jaffar et al., 2002). Surprisingly we did not see preferential expression of CRTh2 (GPR44) by Th2 cells, in fact, the message for this gene was absent form both Th1 and Th2 cells at the RNA level. It has been observed previously that re-stimulation of CRTh2-expressing Th2 cells causes rapid but transient down-regulation of the expression of CRTh2 at the RNA and protein level, and this expression is restored several days after the withdrawal of stimulation: CRTh2 is also downmodulated by PGD2 (Nagata and Hirai, 2003). In fact, analysis of microarray data provided by Federica Sallusto (discussed in more detail in the next section) did show
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that CRTh2 was preferentially expressed by resting Th2 cells, but was downregulated after activation in both Th1 and Th2 cells.
Arachidonic acid COX-1 COX-2
PGG2 COX-1 COX-2 TP TXA2 IP PGI PGIS receptor 2 PGH TxS receptor (prostacyclin) 2 PGFS PGDS PGES
PGF2 FP PGD2 α PGE2 receptor
DP1 and DP2 (CRTH2) EP1, EP2, EP3 receptors and EP4 receptors
Figure 3.8 Biosynthetic pathway of prostaglandins (PGs) and Thromboxane (TX) A2. PGDS, PGD synthase. Genes expressed at higher level in Th1 cells are shown in red while genes whose expression was higher in Th2 cells are boxed in green.
In addition to these Th2-expressed genes, we also noted that the expression of prostaglandin E receptor 2 was higher in Th1 cells. This is somewhat surprising since
PGE2 is generally considered to suppress Th1 responses and favor Th2 responses (Betz and Fox, 1991; Katamura et al., 1995). PGE2 has inhibitory and protective effects in autoimmune disease and modulates mostly suppressive or inhibitory functions on T cells. PGE2 antagonizes the actions of calcineurin phosphatase to stimulate the transcription of cytokine genes such as IL-2 and IFN-γ (Paliogianni et al., 1993) and suppresses T cell proliferation (Minakuchi et al., 1990). Biological actions of this prostaglandin are mediated by four G-protein coupled receptors EP1-4, which have unique expression patterns and couple to distinct signaling pathways. EP1, EP2 and EP4 receptors are detected in T cells at the RNA level, but the EP2 receptor seems to be the main mediator of PGE2 effects on T cells (Nataraj et al., 2001). Taken together the differential expression of several genes involved in prostaglandin metabolism, both confirm the role of prostaglandins as regulators of Th1 and Th2 balance and suggest that prostaglandin production may comprise another effector mechanism of T helper cells.
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It is also noteworthy that similarly to murine type 1 and type 2 cells, we found differential expression of granzymes. For instance, granzyme K was expressed at a higher level in Th1 cells while the level of granzyme A was higher in Th2 cells. To the best of our knowledge there are no reports so far of preferential expression of granzymes by Th2 cells although an earlier study of gene expression in polarized Th1 and Th2 cells noted preferential expression of granzyme B by Th1 cells (Rogge et al., 2000). It is yet unclear what the functional significance of granzyme expression by Th1 and Th2 cells may have. Many more genes were differentially expressed by Th1 and Th2 cells and may therefore be important in T cells polarization and effector function. It is beyond the scope of this thesis to discuss all of these genes but we selected several genes Th1 and Th2 genes which are highlighted in the next section.
3.2.3 Factors affecting the Th1 and Th2 gene expression profiles We sought to identify the most significant influences on the gene expression profiles induced during Th1 and Th2 polarization. We examined gene expression in Th1 and Th2 cells under various polarization protocols, cell types and even species.
3.2.3.1 Gene expression patterns in murine CD4+ and CD8+ type 1 and 2 T cells
We first asked whether the broad pattern of gene expression for type 1 and type 2 polarization was similar for CD4+ and CD8+ T cells polarized in similar conditions. Most of the genes differentially expressed by CD4+ and CD8+ T cells followed the same pattern of expression in both T helper and T cytotoxic cells, with only a handful of genes showing the opposite pattern of expression in the two cell types (Figure 3.9). This indicates a degree of similarity in the genetic program leading to T cell polarization. This was expected since similar factors induce polarization of both T helper and T cytotoxic cells into type 1 and type 2 subsets, and roughly similar cytokine producing phenotypes are created. Despite some similarity in gene expression there were also some differences as many of the genes that were differentially expressed in CD8+ T cells were not in CD4+ T cells and vice versa (Figure 3.4 A and B). These genes could be responsible for the distinct properties of the T helper and T cytotoxic subsets. In general, polarizing CD4+ and CD8+ T cells in similar conditions leads to
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extensive similarities in the resulting gene expression profiles although many noteworthy differences reflect the distinct functions of CD4+ and CD8+ T cells.
1000 Higher expression in Tc1 Higher expression in Tc2
100 EST
IL-4
IL-5 10 EST
EST Higher expression Th2Higher in 1 0.01 0.1 1 10 100 Fold change relative to Th2 to relative change Fold IL-1 Rrp 0.1
0.01 Higher in expression Th1 Fold change relative to Tc2
Figure 3.9 Genes differentially expressed between type 1 and type 2 subsets in both CD4+ and CD8+ T cells. Fold changes for all the genes that were differentially expressed between type 1 and type 2 subsets for both CD4+ and CD8+ T cells were plotted. A fold change of less than 0.5 indicates that the gene is expressed at a higher level in type 1 cells, while a fold change of greater than 2 indicates higher expression in type 2 cells.
3.2.3.2 Gene expression patterns in murine Th1 and Th2 cells generated using different protocols
We next sought to compare our mouse Th1 and Th2 polarization profiles with those generated using different polarization protocols. Ours was the first study to identify the differences in gene expression between murine Th1 and Th2 cells. A recent study by Flavell and colleagues examined gene expression in mouse resting (four days after stimulation) and activated (re-stimulated) Th1 and Th2 cells (Lu et al., 2004). A direct gene by gene comparison of gene expression between the two studies is impossible since different microarrays were used and only partial data are available from the Lu et al. study. Nevertheless, we examined available data to determine general trends between the two studies. Despite the different polarization protocols and microarrays used in the two studies, a number of genes were differentially expressed in
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a similar way in both. In particular, the expression of cytokines and receptors (IFN-γ, IL-4, IL5, IL-12Rβ2, IL-18R1) and certain transcription factors critical for Th1 and Th2 polarization (Stat4 and GATA-3) was similar between the two studies. In contrast, differential expression in other functional categories including molecules involved in adhesion and migration, signal transduction and apoptosis were a lot less consistent between the two experiments. Several genes not previously associated with T cell polarization were similarly regulated in the two studies. These genes include CD24a antigen, NFIL3 and HLF/EPAS1 (which was also preferentially expressed by human Th2 cells in our study). Enzymes cytochrome p450 cholesterol side chain cleavage enzyme 11a1 and aldo-ketoreductase family 1, member c13 were highly preferentially expressed in Th2 cells in both studies. There were also several genes whose expression was regulated in the opposite manner between the two studies including CCR5, CTLA- 2β and interferon activated gene 204. Analysis of gene expression profiles generated in different polarizing conditions showed that many genes, including key transcription factors and cytokines involved in Th1 and Th2 differentiation and effector function, were similarly differentially expressed. However, different polarization protocols introduced large differences in the gene expression patterns. Interestingly a number of genes, whose significance in T cell polarization remains to be addressed, were also consistently differentially expressed suggesting an important role in either differentiation or function of polarized T cells. Genes that were uniquely differentially expressed in either study may be specific to particular polarization conditions or may be upregulated transiently and may play important roles at specific polarization stages.
3.2.3.3 Human Th1 and Th2 gene expression profiles generated in different polarizing conditions
Several studies have examined polarization in human Th1 and Th2 cells at the level of gene expression. The first study to report Th1/Th2 gene profiling was Rogge et al (Rogge et al., 2000) and was performed using earlier generation HuGeneFL arrays which contain only 6,000 genes. Few genes in addition to the well-established markers of T cell polarization were consistently differentially expressed by Th1 and Th2 cells both in the Rogge et al. study and in ours. This is most likely due to the fact that Rogge et al. study used cells at a very early stage of polarization.
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Data describing Th1 and Th2 polarization in human T cells before and after activation assessed using Affymetrix U133A+B arrays (kindly provided by Federica Sallusto, Oncology Institute of Southern Switzerland), allowed us to perform a more comprehensive comparison between cells polarized in different conditions. In the Sallusto study, Th2 clones were generated over several weeks and then split into two populations, one of which was cultured in Th2 conditions while the other was polarized in Th1 conditions. In addition, a proportion of cells were restimulated for 4 h prior to harvesting. We compared genes differentially expressed in the two studies (Figure 3.10). We found that while some genes were shared between cord blood derived Th1 and Th2 cells and both resting and activated peripheral blood derived Th1 and Th2 cells, few genes were similarly expressed between all three. This is not surprising considering that vastly different polarization protocols were used in the two studies. Interestingly, there was also little overlap between genes that were differentially expressed by resting and activated Th1 and Th2 cells polarized in the same conditions. A more comprehensive assessment of gene expression changes after activation is presented in Chapter 5. Overall, the comparison of human Th1 and Th2 gene expression data polarized by different protocols shows that gene expression profiles vary significantly depending on the polarization protocols and the stage of differentiation. While each of these studies provides valuable insight into the differential gene expression profiles of Th1 and Th2 cells at various stages of development, it would be of considerable interest to track changes in Th1/Th2 gene expression over a period of time. This time-course study would better identify the changes induced at different stages of polarization. Another important direction for investigation is to look at the gene expression profiles of Th1 and Th2 cells generated in vivo. The absence of reliable markers for Th1 and Th2 cells makes their isolation difficult, however a possible avenue for future research would be to analyze gene expression in effector T cells generated during infections that lead to a predominantly Th1 or Th2 responses such as Leishmania major (C57BL/6 strain) and Nippostrongylus brasiliensis respectively.
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Preferentially expressed in cbTh1 A and Th1rest
Accession Gene name Preferentially expressed in cbTh1 AU152178 anthrax toxin receptor 2 and Th1act M21121 CCL5 NM_000579 CCR5 Accession Gene name NM_002104 granzyme K NM_000876 insulin-like growth factor 2 receptor AI692645cDNA FLJ26063 fis, clone PRS04788 NM_002984 CCL4 AW006185 hypothetical protein LOC283666 NM_002983 CCL3 NM_024841 hypothetical protein FLJ14213 M29383 IFN-gamma cbTh1 AL049265 interleukin 6 signal transducer/gp130 AL564683 C/EBP beta NM_005204 MAP3K8 W46388 superoxide dismutase 2 AF228422 normal mucosa of esophagus specific NM_021181 SLAM family member 7 NM_006540 nuclear receptor coactivator 2 NM_021803 IL-21 373 U04897 RAR-related orphan receptor A AV734646 clone RP1-93H18 chromosome 6q21 NM_004180 TANK NM_003595tyrosylprotein sulfotransferase 2 11 15 0 Th1act Th1rest 34 5 162
Preferentially expressed in Th1act and Th1rest Accession Gene name AB059408 homeodomain-only protein NM_000632 integrin, alpha M (CD11b) NM_003853 interleukin 18 receptor accessory protein AL096776 ras homolog gene family, member U B NM_013437 suppression of tumorigenicity
Preferentially expressed in cbTh2 and Th2act
Accession Gene name AL136179 SRY (sex determining region Y)-box 4 NM_002844 protein tyrosine phosphatase, receptor type, K NM_000963 prostaglandin-endoperoxide synthase 2 NM_004951 Epstein-Barr virus induced gene 2 NM_014485 prostaglandin D2 synthase, hematopoietic Preferentially expressed in cbTh2 NM_002674 pro-melanin-concentrating hormone and Th1rest NM_000590 interleukin 9 AI796169 GATA binding protein 3 cbTh2 Accession Gene name M68874 phospholipase A2, group IVA NM_002844 protein tyrosine phosphatase, receptor type, K NM_018725 interleukin 17 receptor B NM_014485prostaglandin D2 synthase, hematopoietic BC001247 epithelial protein lost in neoplasm beta NM_020379 mannosidase, alpha, class 1C, member 1 AI638433 phosphodiesterase 7B 247 NM_018725 interleukin 17 receptor B AL353944 cDNA DKFZp761J1112 AI458439 cDNA DKFZp586O0724 AK000141 cDNA FLJ20134 fis, clone COL06604 AL353944 cDNA DKFZp761J1112 BF685808 sestrin 3 20 8 Preferentially expressed in cbTh2, 6 Th2act and Th2rest Accession Gene name NM_002844 protein tyrosine phosphatase, receptor type, K Th2act NM_014485prostaglandin D2 synthase, hematopoietic Th2rest NM_018725 interleukin 17 receptor B AL353944 cDNA DKFZp761J1112 45 10 69
Preferentially expressed in Th2act and Th2rest
Accession Gene name NM_002844 protein tyrosine phosphatase receptor type K NM_014485 prostaglandin D2 synthase, hematopoietic M73069 androgen receptor NM_018725 interleukin 17 receptor B T68445clone RP11-383C12 chromosome Xq11.1-13.1 AI025415choline dehydrogenase AL353944 cDNA DKFZp761J1112
Figure 3.10 Comparison of gene expression in cord blood derived Th1 and Th2 cells with resting and activated peripheral blood derived Th1 and Th2 cells. Genes were defined as preferentially expressed in Th1 cells SLR≤-1 and decreasing (A) and in Th2 cells if SLR ≥1 and increasing (B) as assigned using MAS5.0 in all replicate experiments.
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3.2.3.4 The Th1/Th2 paradigm in mice and men
Since mice are often used as model organisms to study mammalian immune responses, we decided to compare gene expression profiles of mouse and human Th1 and Th2 cells. Although many factors involved in T cell differentiation play a similar role in the polarization of both mouse and human T cells, many differences exist between the mouse and human immune responses (reviewed in (Mestas and Hughes, 2004)). For instance, in humans IFN-α acts through STAT4 to induce Th1 development, whereas this effect is not observed in mice (Farrar and Murphy, 2000; Farrar et al., 2000). Furthermore, effective responses to some pathogens are sometimes achieved by different effector T cells in humans and mice. A Th2 response is required to combat schistosomiasis infection in humans, while IFN-γ and a Th1 response are key requirements for parasite clearance in mice (Hagan, 1993; Pearce et al., 1991; Pearce and Sher, 1991). Some genes, including well characterized markers of T cell polarization (IFN-γ, GATA-3 and IL-12Rβ2) were expressed in a similar manner in both murine and human Th1 and Th2 cells. However, most novel genes that were differentially expressed by murine Th1 and Th2 cells were not altered in human Th1 and Th2 cells and vice versa. In some cases, genes were regulated in the opposite manner. For instance, chemokine receptors CCR1 and CCR5 and cytokine receptor IL-7R were expressed at higher levels in human Th1 but mouse Th2 cells. In general, murine Th1/Th2 gene expression profiles showed a bias towards Th2 where as human gene expression profiles were slightly biased towards Th1. It is still unclear whether this bias is a reflection of differences between species or differences specific to the particular polarization protocols used.
3.2.4 Defining the role of Th1 and Th2 genes in T cell biology One of the main aims of Th1/Th2 gene expression analysis was to identify novel genes not previously described in the context of T cell polarization. These genes could provide important clues about the mechanisms of differentiation and could also be used to mark Th1 and Th2 T cell subsets. This section describes downstream analysis of several genes identified as differentially expressed in Th1 and Th2 cells using gene expression profiling.
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3.2.4.1 Cytochrome p450 side chain cleavage enzyme 11a1
One of the most highly regulated genes in both murine Th2 and Tc2 cells was cytochrome P450 side chain cleavage enzyme 11a1 (CYP11A1). CYP11A1 is mainly expressed in steroidogenic tissues where it catalyses the rate-limiting step of the steroid synthetic pathway - side chain cleavage of cholesterol to pregnenolone:
Cholesterol + reduced adrenal ferredoxin + O2 = pregnenolone +
4-methylpentanal + oxidized adrenal ferredoxin + H2O. This reaction is the first step in the synthesis of steroid hormones which include mineralocorticoids, glucocorticoids, and sex hormones (Hanukoglu and Jefcoate, 1980; Hanukoglu et al., 1981a; Hanukoglu et al., 1981b). Steroid hormones regulate many vital physiological processes, including metabolism of proteins, lipids and carbohydrates, ion balance and sexual characteristics (Miller, 1988). In addition they play an important role in inflammation and modulation of immune responses. Glucocorticoids, in particular, have a variety of immunomodulatory activities. Dexamethasone can induce Th2 production in vitro and suppresses the production of Th1 cytokines IFN-γ and TNF-α (Ramierz et al., 1996). Corticosterone suppresses IL- 12 production in monocytes and enhances their capacity to stimulate IL-4 production by CD4+ T cells (Blotta et al., 1997). The expression of CYP11a1 in steroidogenic cells is controlled by the pituitary (Guo et al., 2003). Tissue specific trophic hormones, glucocorticoids and cytokines can affect the levels of CYP11A1 in various tissues. TNF, IFN-γ and IL-1 inhibited the expression of this enzyme in Leydig cells, while epidermal growth factor (EGF) increased its expression in granulosa and placental cells (reviewed in (Herrmann et al., 2002)). Aminoglutethimide suppresses steroidogenesis by inhibiting CYP11A1. Interestingly, a previous study has found an association between Th1/Th2 balance and CYP11a1 expression in CD4+ T cells. Treatment of normal mice inoculated with highly metastatic B1610 melanoma cells with aminoglutethimide shifted the T cell response from Th2 to Th1 (Oka et al., 2000). We hypothesized that inhibiting CYP11A1 may have a protective effect in an animal model of asthma due to a reduction in Th2 cells and related mediators. We tested the effect of inhibiting CYP11a1 using amnioglutethimide in an allergic asthma model. BALB/c mice were sensitized by IP injections of OVA. The animals in the treatment group were injected with aminoglutethimide (10 mg/kg) on days 2, 4, 6, 8,
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10, 12, and 14. Allergic inflammation was then induced by treating mice with aerosolized OVA. The mice were then sacrificed and allergic inflammation was evaluated by lung histology and flow analysis of lung cells.
Figure 3.11 Histology of lung tissue from mice treated with aminoglutethimide and control animals. Mice were sensitized by IP injections of OVA, challenged with OVA aerosols and treated as described in Chapter 2. Materials and Methods. Lung sections were stained with hematoxylin and eosin.
We examined lung histology to assess the degree of inflammation in each group of mice (Figure 3.11). As expected, mice treated with OVA had cellular infiltrates in
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the lung characteristic of an inflammatory response. This was also confirmed by FACS analysis of lung cells (Figure 3.12) which showed an obvious increase in granulocyte numbers in animals treated with OVA compared to controls (PBS treated animals). 4 25
Asthma, PBSR2 only non-treatedR2 16 19
Asthma, aminoglute R2 Asthma,R2 thimide-
SCS PBS-treated treated
FCS
Figure 3.12 FACS assessment of cellular lung infiltrate mice treated with aminoglutethimide and control animals. Following the completion of the model, mice were sacrificed and one the lung lobes was disrupted by mechanical disintegration followed by collagenase digestion. Cells were harvested and analysed by FACS. The percentages of cells in the granulocyte gate based on FCS and SCS are indicated on the plots.
Aminoglutethimide treatment did not produce a reduction in lung cellular infiltrate as would be expected if a Th2 response was inhibited. On the contrary, lung histology (Figure 3.11) suggests that more cells infiltrated the lungs of animals treated with aminoglutethimide than those of mice in the control group, although FACS analysis did not show a significant difference between the two groups. This is contrary to our hypothesis which suggested a possible inhibition of the Th2 response via the suppression of CYP11A1 activity. The increase in the inflammation in the lungs of aminoglutethimide-treated animals suggests that effect of inhibiting CYP11A1 activity is not restricted to the shift in the Th1/Th2 equilibrium. This is not unexpected considering the broad range of action of steroid hormones. These results suggest that CYP11A1 is not a suitable target for treating asthma as the potential range of side effects is too broad. Interestingly this gene was also strongly preferentially expressed by Th2 cells in a study by Lu and colleagues which involved microarray gene
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expression analysis of murine T cells polarized under different conditions (Lu et al., 2004). This preferential expression was apparent both in 96 hour polarized and restimulated cells. This suggests that CYP11A1 may be an important regulator of mouse Th2 responses. This is in contrast to what is seen in human polarized T cells. We did not detect the transcript for CYP11A1 in any of our human T cell gene expression profiles. This result highlights one of the many confounding differences between the mouse and human Th1 and Th2 responses. It is possible that the difference observed here could be due to the sequence/probe-specifc differences between mouse and human CYP11A1. However, despite extensive research in the area, no study so far has observed CYP11A1 expression in human Th1 and Th2 cells. Quantitative PCR could be used to test this further.
3.2.4.2 GEM
Another gene preferentially expressed in mouse Th2 and Tc2 cells was GEM (GTP-binding mitogen-induced T-cell protein or RAS-like protein KIR). It belongs to the RAD/GEM family of GTP-binding proteins and has guanine nucleotide-binding activity but undetectable intrinsic GTPase activity. Several possible functions for GEM have been suggested with the major function yet to be determined. Reports indicate that GEM may have a role in the inhibition of voltage-gated calcium channel activity (Beguin et al., 2001) and a negative regulation of Rho-Rho kinase pathway for cytoskeletal reorganization (Ward et al., 2004; Ward et al., 2002). In addition to being preferentially expressed by murine type 2 T cells, GEM transcript is also upregulated in mast cells after activation. Immunofluorescence assays and real time PCR showed that GEM is a membrane associated protein, strongly upregulated after T cell activation (data not shown). However, analysis of gene expression in human cells using Affymetrix microarrays and real-time PCR showed no preferential expression by Th2 cells when compared to Th1. The role of GEM in leukocyte activation and immune responses is being currently investigated by another member of our laboratory.
3.2.4.3 GPR18 and EBI2
We selected several additional candidates from the human Th1/Th2 gene profiling analysis for downstream analysis. Since few cell surface molecules which reliably identify Th1 and especially Th2 cells have been identified, we decided to focus on two orphan G-protein coupled receptors, GPR18 and EBI2 and the cytokine receptor
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IL-17RB. All three genes were preferentially expressed by human cord blood derived Th2 cells. Both IL-17RB and EBI2 were preferentially expressed by long-term polarized Th2 cells, although GPR18 was actually Th1-biased in the long-term culture. Interestingly, GPR18 and EBI2 are in close proximity to each other on chromosome 13q32 and also share some homology at the protein level (Table 3.1). Both proteins are also homologous to cysteinyl leukotriene receptor 1 which was also preferentially expressed by Th2 cells (Figure 3.7).
Table 3.2 EBI2 and GPR18 protein homology search. The most homologous proteins for EBI2 (A) and GPR18 (B) were identified using UCSC human gene sorter (http://genome.ucsc.edu/cgi-bin/hgNear).
E- A Name Value Genome Position Description 1 EBI2 0 chr13 97,651,226 EBI2 2 P2RY5 9.8e-40 chr13 46,784,262 purinergic receptor P2Y, G-protein coupled, 5 3 CYSLTR1 1.7e-39 chrX 76,284,610 cysteinyl leukotriene receptor 1 4 GPR17 4.9e-39 chr2 128,500,253 G protein-coupled receptor 17. 5 GPR23 5.4e-38 chrX 76,767,318 G protein-coupled receptor 23 6 CYSLTR2 9.1e-38 chr13 47,080,095 cysteinyl leukotriene receptor 2 7 CCR1 1.1e-35 chr3 46,207,366 chemokine (C-C motif) receptor 1 8 F2R 4.3e-35 chr5 76,105,695 coagulation factor II (thrombin) receptor 9 P2RY8 1e-33 chrY 1,264,189 Hypothetical protein. 10 P2RY8 1e-33 chrX 1,264,189 Hypothetical protein. 11 AGTR1 2.3e-33 chr3 149,759,143 angiotensin II receptor, type 1 12 P2RY2 2.3e-33 chr11 72,664,676 purinergic receptor P2Y, G-protein coupled, 2 13 P2RY1 2.3e-33 chr3 153,875,158 purinergic receptor P2Y, G-protein coupled, 1 14 F2RL2 4e-33 chr5 75,999,357 Hypothetical protein DKFZp686N1782. 15 GPR34 3.4e-32 chrX 40,583,330 G protein-coupled receptor 34 16 CCR8 7.5e-32 chr3 39,333,802 chemokine (C-C motif) receptor 8 17 FKSG79 9.8e-32 chrX 77,182,971 Putative P2Y purinoceptor FKSG79. 18 P2RY6 9.8e-32 chr11 72,722,754 pyrimidinergic receptor P2Y, G-protein coupled, 6 19 F2RL1 2.2e-31 chr5 76,207,069 coagulation factor II (thrombin) receptor-like 1 20 P2RY4 3.7e-31 chrX 68,345,731 pyrimidinergic receptor P2Y4
E- B # Name Value Genome Position Description 1 GPR18 0 chr13 97,606,822 G protein-coupled receptor 18 2 P2RY5 9.6e-31 chr13 46,784,262 purinergic receptor P2Y, G-protein coupled, 5 3 GPR17 6.2e-30 chr2 128,500,253 G protein-coupled receptor 17. 4 GPR23 8.2e-30 chrX 76,767,318 G protein-coupled receptor 23 5 GPR34 2e-28 chrX 40,583,330 G protein-coupled receptor 34 6 GPR80 6.5e-27 chr13 95,337,399 G protein-coupled receptor 80 7 EBI2 5.5e-26 chr13 97,651,226 EBI2 8 CCR6 1.6e-25 chr6 167,453,625 chemokine (C-C motif) receptor 6 9 P2RY1 2.7e-25 chr3 153,875,158 purinergic receptor P2Y, G-protein coupled, 1 10 CYSLTR2 7.9e-25 chr13 47,080,095 cysteinyl leukotriene receptor 2 11 OPRK1 1.8e-24 chr8 54,202,832 opioid receptor, kappa 1 12 GPR92 8.7e-24 chr12 6,606,959 G protein-coupled receptor 92 13 CCR7 1.1e-23 chr17 39,089,026 chemokine (C-C motif) receptor 7 14 CCR8 1.5e-23 chr3 39,333,802 chemokine (C-C motif) receptor 8 15 AGTRL1 2.5e-23 chr11 56,778,241 angiotensin II receptor-like 1 16 OPRM1 3.3e-23 chr6 154,418,633 Mu opioid receptor variant MOR-1R. 17 FKSG79 4.3e-23 chrX 77,182,971 Putative P2Y purinoceptor FKSG79. 18 CYSLTR1 5.7e-23 chrX 76,284,610 cysteinyl leukotriene receptor 1 19 F2RL1 7.4e-23 chr5 76,207,069 coagulation factor II (thrombin) receptor-like 1 20 GPR1 9.7e-23 chr2 207,261,863 G protein-coupled receptor 1.
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GPR18 is a highly conserved gene with 89% identity and 92% amino acid similarity between human and canine GPR18, and 86% amino acid. Similarity between human and mouse. Interestingly it has less than 34% amino acid similarity to other GPCRs (Gantz et al., 1997). EBI2 or Epstein-Barr virus induced protein 2 is an orphan G-protein coupled receptor which was first identified as one of the proteins induced following Epstein-Barr virus infection of B lymphocytes (Birkenbach et al., 1993). Both GPR18 and EBI2 are orphan G-protein coupled receptors and appear to be seven transmembrane domain spanning proteins. Transcripts for both proteins were detected in T and B cell subsets (Figure 3.13). Additionally EBI2 expression was observed in eosinophils and basophils and also in LPS-stimulated DCs and macrophages. In general, the expression of both genes was restricted to leukocytes. The highly leukocyte-restricted expression of these genes makes them an attractive target for future investigation to determine their roles in immune responses. mAbs to these proteins, as well as gene-deficient mice, are currently underway within the laboratory.
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A EBI2 GPR18 EM CM FH Th1 T T Th2 T - T cells B cells B FH Tcells Th2act Th1act + Th1rest Th2rest + T Naïve T NK cellsNK γδ Basophils CD57 Eosinophils CD8 Neutrophils Naïve B cells B Naïve Immature DC LPS stim macrophages CD19 Macrophages Plasma B cells 6 hr LPS stim LPS hr 6 DC 48 hr LPS stim LPS hr 48 DC Resting mast cellsResting mast IgE stim cells mast IgM memory B cells B IgM memory IgGAE cells B memory B EBI2
GPR18
Figure 3.13 GPR18 and EBI2 expression in leukocytes subsets and tissues. The expression of EBI2 and GPR18 was analysed in different cell types (profiled by members pf Arthritis and Inflammation Research program) using Affymetrix Data Mining Tool and visualized using Spotfire (A). The expression of EBI2 and GPR18in a panel of tissues was assessed using SymAtlas (B) (http://symatlas.gnf.org/SymAtlas/).
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3.2.4.4 IL-17RB
IL-17RB (also known as IL-17RH1 and Evi27) belongs to the IL-17R family of cytokine receptors (Kolls and Linden, 2004; Moseley et al., 2003). The receptor is well conserved with 82% similarity between the mouse and human homologs at the protein level and maps to chromosome 3p21.1 (Shi et al., 2000). Alternative splicing of IL- 17RB results in a secreted soluble form of the protein, which can act as a decoy receptor (Haudenschild et al., 2002; Tian et al., 2000). Here we demonstrated the preferential expression of IL-17RB at the mRNA level in Th2 cells polarized using different experimental protocols. To the best of our knowledge we are the first to describe a Th2 expression profile for this cytokine receptor, however there is an increasing body of evidence linking one of its ligands, IL-17E, to Th2 inflammatory responses. IL-17RB is induced in antigen-presenting cells by Th2 cytokines (Gratchev et al., 2004). IL-17E is produced by Th2 cells and IgE stimulated mast cells (Ikeda et al., 2003), and induces eosinophilia, IgE, IL-4, IL-5 and IL-13 production and multi- organ inflammation (Fort et al., 2001; Hurst et al., 2002; Kim et al., 2002; Pan et al., 2001). In our hands, IL-17 and IL-17Rb are the only two receptors of the IL-17R family that were expressed to a significant extent in leucocytes (Figure 3.14a). The expression of IL-17RB in particular was mostly confined to T cells with the highest expression in Th2 cells. Using SymAtlas, the highest IL-17RB transcript expression was observed in a B lymphoblasts lymphoma and kidneys, with significant expression in the central nervous system (Figure 3.14b).
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Probe set A IL-17 208402_at 216876_s_at IL-17B 220273_at IL-17C 224079_at 227401_at IL-17D 228977_at IL-17E 220971_at IL-17R 205707_at 224156_x_at 235531_at IL-17RB 236897_at 224361_s_at 219255_x_at 221926_s_at 221947_at IL-17RC 64440_at 224514_x_at IL-17RD 229263_at 229401_at IL-17RE 236186_x_at EM CM FH Th1 T T Th2 T - Tcells B cells FH T cells Th2act Th1act + Th1rest T + Th2rest Naïve T NK cells γδ Basophils CD57 CD8 Eosinophils Neutrophils Naïve B cells Immature DC LPS stim macrophages CD19 Macrophages Plasma B cells Plasma B 6 hr LPS stim LPS hr 6 DC 48 hr LPS stim48 hr DC Resting mast cells mast Resting IgE stim mast cells IgM memory B cells B memory IgM IgGAE memory B cells
B IL-17RB 219255_x_at
Figure 3.14 Expression of IL-17 and IL-17R family genes in leukocytes and tissues. The expression of all known family members was analysed in leukocyte subsets (A) and across various tissues (B) (http://symatlas.gnf.org/SymAtlas/).
The ligands for IL-17RB are IL-17B, and IL-17E which shows the most avid binding (Lee et al., 2001a; Shi et al., 2000). IL-17B is expressed as a non-covalent dimer in the pancreas, small intestine, stomach and the spinal cord (Li et al., 2000). IL- 17E (also known as IL-25) is the most divergent known member of the IL-17 family and is expressed in the brain, lung, testis and prostate (Lee et al., 2001a). We also looked at the expression of these two cytokines at the RNA level in a panel of tissues interrogated in SymAtlas (Figure 3.15). While the expression of IL-17B was confined to non-hematopoietic tissues, IL-17E was highly expressed in the bone marrow and in the thymus to some extent. Remarkably the highest transcript expression was detected in bronchial epithelial cells, which may provide IL-17E to Th2 cells during asthmatic responses.
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IL-17B
IL-17E
Figure 3.15 Expression of IL-17RB ligands in a panel of tissues using SymAtlas (http://symatlas.gnf.org/SymAtlas/).
Taken together these data suggest that IL-17RB and its ligands play an important role in inflammatory immune responses, and their function in Th2 biology deserves in depth investigation.
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3.3 Concluding remarks
Here we used Affymetrix microarrays to produce comprehensive gene expression profiles of mouse and human Th1 and Th2 cells and also mouse Tc1 and Tc2 cells. Using these profiles we identified numerous genes preferentially expressed by either Th1 or Th2 cells. The genes include transcription factors, enzymes involved in metabolic functions and signaling and also a number of cell surface molecules. The identity of numerous type 1 and type 2 expressed genes should lead to studies confirming a functional role for protein products, using either in vitro or in vivo models. The broad extent of this study also allowed us to look at the differences introduced into gene expression programs by such factors as the host species, different T cell types and also polarization protocols. We found that similar polarizing conditions produce largely similar gene expression profiles in different T cell types, CD4+ and CD8+, although some genes were differentially expressed by either T helper or T cytotoxic cells. Comparing our mouse Th1/Th2 gene expression data with the Th1/Th2 gene expression profiles generated in a study by Lu et al (Lu et al., 2004), showed that the similarities were not that extensive. However, transcription factors important for the establishment of Th1 and Th2 phenotypes and also cytokines and cytokine receptors characteristic of Th1 and Th2 cells were expressed similarly in the two studies. Several molecules whose role in T cell polarization remains to be determined also followed a similar pattern of expression. A comparison of gene expression profiles of several human Th1/Th2 cells polarized in different conditions showed that although some genes showed a similar pattern of expression across the different experiments, generally widely different differential gene expression programs were evoked in response to different polarization conditions. Finally, we found that few genes apart from those already described as essential to T cell polarization (including IFN-γ and GATA-3) showed similar trends in expression in mouse and human Th1/Th2 cells. Interestingly our mouse gene expression pattern showed a strong Th2 bias, whereas in our human study Th1 over-expressed genes predominated. This may relate to the differences between mouse and human Th1 and Th2 cells. It is possible that in mouse (or at least in some strains) it is easier to generate highly polarized type 2 cells where as in human T cells it is harder to generate stable Th2 cells producing type 2 cytokines and expressing most of the Th2 polarization markers in vitro. In the three studies of gene expression in human Th1 and Th2 cells that we presented here, the
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increased expression of IL-4 by Th2 cells was only detected in one of the two replicate polarization cultures conducted by us. Other typically type 2 cytokines such as IL-5 and IL-13 were equally fickle. This may indicate that the expression of Th2 cytokines at the RNA level peaks shortly after TCR stimulation. Alternatively, Th1 and Th2 cells may not be generated within the same time frame, i.e. polarized Th1 cells arise much earlier than Th2 cells. Another experimental difference which could be contributing to the lack of concordance between gene expression patterns in mouse and human type 1 and type 2 cells is the difference in microarrays used to analyze mouse and human RNA. Earlier generation arrays were used for gene expression analysis in murine T cells. These arrays interrogate less genes and use probe set design technology different to that of the later generation arrays. The development of new arrays which interrogate the entire mouse and human genomes will hopefully put an end to these technical problems. It would be beneficial to re-analyze gene expression in murine T cells using new Affymetrix arrays which interrogate the entire mouse genome. It is unlikely however that the differences in arrays could account for the extensive differences in gene expression patterns between mouse and human Th1 and Th2 cells observed in experiments conducted by ours and other groups. Several recent reviews have focused on the issues of accuracy and reproducibility of microarray data (Miklos and Maleszka, 2004; Stears et al., 2003; Van Bakel and Holstege, 2004). Since isolation of Th1 and Th2 cells from peripheral blood or tissues is technically difficult, most studies of Th1 and Th2 biology have used cells that have been polarized in vitro for varying periods of time. This introduces another level of variability to the resultant gene expression result. However, this does not invalidate the results of any of the above studies. The studies are valuable assessments of differential gene expression in Th1 and Th2 cells at different stages during polarization. The variability in gene expression profiles of Th1 and Th2 cells may also imply that the precise phenotypes of Th1 and Th2 cells in vivo may differ significantly depending on the particular pathogen. However, our study does highlight the need for reliable markers to identify and discriminate Th1 and Th2 cells ex vivo. Although chemokine receptors such as CCR5 and CXCR3 for Th1 cells and CCR4 and CCR3 and also CRTh2 for Th2 cells are strongly associated with polarized cells, their expression is often downregulated after activation. We have identified two G-protein
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coupled receptors, EBI2 and GPR18 and also a novel cytokine receptor IL-17RB that were intimately associated with Th2 cells. Downstream studies will evaluate their utility as markers of the Th2 effector subset. In conclusion, here we performed a broad ranging analysis of type and type 2 differentiation. In addition to identifying numerous differentially expressed genes, we also highlighted the most significant influences on gene expression profiles in Th1 and Th2 cells. In our study we found noteworthy differences that may reflect either species differences, or differences in experimental design or polarizing conditions. These differences provide important considerations for further investigation of Th1 and Th2 polarization and immune responses.
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4 Identifying a unique transcriptional profile for TFH cells which underlies their effector function
4.1 Introduction
+ CXCR5 T helper cells, termed TFH, interact with B cells in the secondary lymphoid organs to provide help for antibody production and class switching (Cyster et al., 1999; MacLennan, 1994). The expression of CXCR5 by TFH and B cells allows their co-localization to sites of CXCL13 production (i.e. follicles), thus enabling productive T-B cell interactions. Since the majority of memory T cells in the secondary lymphoid organs express CXCR5, it is possible that not all CXCR5+ T cells are equally efficient at providing help to B cells and additional markers are required to identify TFH cells within the CXCR5+ population. Kim and colleagues proposed that CD57 may be one such marker since CD57+CXCR5+ T cells localize specifically to germinal centers and are efficient at providing help to B cells (Kim et al., 2001c). However, this conclusion has been questioned in a recent report where CD57+ T cells displayed an anergic phenotype (Johansson-Lindbom et al., 2003).
It is likely that TFH cells mediate their function through a soluble factor like a cytokine or through cell surface interactions. However, TFH cells do not express large quantities of cytokines usually associated with Th1 or Th2 cells and a TFH specific cytokine profile is yet to be described. TFH cells can interact with B cells via direct cell to cell contact mediated through costimulatory molecules. Several molecules, including
ICOS and CD40L, have been described as a potential means for TFH cells to interact with B cells (Breitfeld et al., 2000; Schaerli et al., 2000). However, neither of these molecules is expressed exclusively by TFH cells and it is likely that additional molecules are involved in mediating the interaction between B and T cells. Thus the precise mechanisms for TFH effector function remain to be defined. In addition, transcription factors that lead to TFH differentiation, similar to T-bet and GATA3 for Th1 and Th2 cells, are yet to be identified. We sought to understand the function, identity and molecular interactions of this third subset of human effector T cells, TFH, particularly since the discovery of transcription factors, cytokines and homing molecules on Th1 and Th2 cells has
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provided considerable insight into the biological role of these effector subsets. Using
Affymetrix microarrays to define a transcriptional program that distinguished TFH cells from the other effector (Th1 and Th2) and memory (TCM and TEM), we identified numerous new factors that are likely to play an important role in mediating T cell help to B cells, several of which we have characterized in depth.
4.2 Results
4.2.1 CD57 as a marker of TFH cells in the tonsil While CXCR5+ T cells make up the majority of memory T cells in the tonsils, it is likely that only a subset of these T cells is efficient at providing help to B cells.
CD57 (together with CXCR5) has been described as a marker of the TFH subset (Kim et al., 2001c). The expression of activation markers CD69, CD95, CD27 and low expression of CCR7 is consistent with CD57+ T cells serving as an effector subset (Figure 4.1). CCR7 CD62L CD45RO CD95 CD69 CD27
CD57
Figure 4.1 Phenotype of tonsil CD57+ T cells. Flow analysis of marker expression on CD57+CXCR5+CD4+ cells in the tonsil.
We sought to isolate CD57+ and CD57- CXCR5+ T cells from human tonsils to identify genes specific to the CD57+ subset. Although CXCR5+ T cells are abundant in the human tonsil, CD57+CXCR5+ T cells represent a minor subset of total tonsillar
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lymphocytes. Therefore we devised a strategy to isolate CD57+ and CD57- CXCR5+ CD4+ T cells by first enriching for CD45RO cells using MACS beads followed by staining with antibodies against CD57, CD4, CXCR5 and sorting into CD57+ and CD57- populations that are also CXCR5+ and CD4+ (Figure 4.2). This strategy enabled us to obtain sufficient cells for microarray analysis.
CD57+
4
D
C
CXCR5 CD57
CD45RO CD57
MACS 5 enrichment Sorting R
C
X
C CXCR5
5
R
C CD4
X
CD57 C CD57-
CD4
4
D
C
CD57
Figure 4.2 CD57+ T cell isolation strategy. A strategy involving MACS enrichment for CD45RO+ memory T cells from the tonsils followed by FACS isolation of antibody stained populations.
4.2.2 Gene expression profiles of CD57+ and CD57- CXCR5+ CD4+ T cells We used Affymetrix U133A and B microarrays, which incorporate almost 45,000 probe sets, to identify genes that are differentially expressed between CD57+ and CD57- T cells (Figure 4.3).
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Signal Log Signal Ratio
Gene name Accession 0 600 1200 -30 3 HEC1 NM 006101.1 guanylate binding protein 1, interferon-inducible NM 002053.1 asp -like, microcephaly associated gene NM 018123.1 centromere protein A (17kD) NM 001809.2 EBI2 NM 004951.1 phosphodiesterase 1, alpha D45421.1 cell division cycle 2, G1 to S and G2 to M gene AL524035 histidine ammonia-lyase NM 002108.2 similar to Metallothionein-IE U43148.1 lipoma HMGIC fusion partner NM 005780.1 hairy/enhancer-of-split related with YRPW motif 1 NM 012258.1 hypothetical protein FLJ14299 BG290193 purinergic receptor P2Y, G-protein coupled, 8 AI436587 secretoglobin, family 3A, member 1 AA742697 chromogranin B (secretogranin 1) NM 001819.1 protein tyrosine phosphatase non-receptor type 14 BE617483 guanine nucleotide binding protein 4 NM 004485.1 microtubule-associated protein 7 T62571 caveolin 1 NM 001753.2 KIAA1389 protein AB037810.1 weakly similar to hypothetical protein FLJ20378 AI467947 hypothetical protein HSPC195 BC002490.1 hypothetical protein HSPC195 BC006428.1 cDNA FLJ32401 fis, clone SKMUS2000339 AW129783 MAFB NM 005461.1 amphoterin induced gene 2 AC004010 hypothetical protein HSPC195 AK001782.1 + - + - Exp1 Exp2 CD57 CD57 CD57 CD57 Exp2 CD57+ Exp1 vs CD57-
Figure 4.3 Genes differentially expressed between CD57+CXCR5+CD4+ and CD57-CXCR5+CD4+ T cells. CD57+CXCR5+CD4+ and CD57-CXCR5+CD4+ T cells were isolated using FACS. RNA extracted from these cells was converted to a labelled cRNA probe and gene expression was analysed using U133A and U133B Affymetrix microarrays. Genes that were considered present and more than 2- fold different (i.e. signal log ratio of less than or equal to –1, or greater than or equal to 1 using Affymetrix MAS 5.0 software) in 2 replicate experiments are listed. Signal values, depicted as a heat map using Spotfire software, indicate the level of gene expression.
Interestingly, we found that the overall gene expression was overwhelmingly similar between the two T cell populations, and the genes that were reproducibly differentially expressed did not represent likely candidates to account for helper T-cell function. Recently, CD57 has also been suggested as a marker for a separate T cell population with a possible regulatory role (Johansson-Lindbom et al., 2003). However, few of the genes shown in Figure 4.3 appeared to be relevant to this function either.
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This suggested that other markers were needed to define the effector T helper subset within CXCR5+ T cells.
4.2.3 Comparison of TFH to other effector subsets To identify the genes involved in T cell helper function of CXCR5+ T cells, we compared gene expression by CD57+CXCR5+CD4+ T cells to other effector (Th1 and
Th2) and memory (TEM and TCM) T cell subsets. We first performed a generalized comparison of gene expression between three effector cell types to determine whether there was a bias in TFH gene expression towards a Th1 or Th2 expression profile. This comparison showed that the vast majority of effector T cell transcripts were in fact common to all three effector subsets (>10,000 transcripts) (Figure 4.4).
TFH TFH P Th1 A Th2 A
T P FH 429 T P Th1 P FH Th2 A Th1 A 58 Th2 P TFH P 136 Th1 P Th2 P 10891 Th1 140 TFH A Th1 P 188 Th2 A 786 Th2 TFH A TFH A Th1 A Th1 P Th2 P Th2 P
Figure 4.4 Comparison of gene expression in the three major effector subsets TFH, Th1 and Th2. The calls of A (absent) or P (present) were assigned using Affymetrix MAS 5.0 software based on the levels of transcript expression.
Nevertheless there were certain genes that were uniquely associated with each of the effector subsets analyzed. More genes were similarly expressed between TFH and Th2 cells (136), than between TFH and Th1 cells (58), which might relate to the origin of TFH cells or possibly shared roles in providing help for B cells. This
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difference, however is not very substantial compared to the numbers of genes that are common to all cell types.
We next looked at the genes that were preferentially expressed by TFH cells compared to Th1, Th2, TCM and TEM cells. The genes that showed the highest preferential expression in TFH cells (according to mean fold-change) are shown in Figure 4.5.
Molecules already associated with TFH were readily identified, including
(expectedly) CXCR5, but also ICOS which was recently described as a TFH molecule (Breitfeld et al., 2000). Remarkably, we observed several molecules preferentially expressed in TFH cells, that are also known to contribute to B cell development and function. These included IL-6R (Burdin et al., 1995), BCL6 (Shaffer et al., 2000; Ye et al., 1997), CD30L (Cerutti et al., 2000), CD27 (Agematsu et al., 1995; Kobata et al., 1995), CXCL13 (Legler et al., 1998) and others (Figure 4.5). In addition, numerous other genes showed a strong preferential expression in TFH cells, including various transcription factors, signal transduction molecules, cytokine receptors and other cell surface molecules.
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Signal 0 200 400 Signal Log Ratio -1 6.5 12 Accession number Gene name 1 NM 012258.1 hairy/enhancer-of-split related with YRPW motif 1 2 L32867.1 sialyltransferase 8A 3 NM 022969.1 fibroblast growth factor receptor 2 4 AL567411 cyclin-dependent kinase 5, regulatory subunit 1 5 X68829.1 CXCR5 6 NM 005461.1 v-maf 7 NM 006419.1 CXCL13 8 BC004490.1 v-fos 9 AV700030 IL6R 10 AB018580.1 aldo-keto reductase family 1, member C3 11 L21181.1 islet cell autoantigen 1 (69kD) 12 AA742697 secretoglobin, family 3A, member 1 13 BE740743 thyroid stimulating hormone receptor 14 NM 000260.1 myosin VIIA 15 AI393930 achaete-scute complex homolog-like 2 16 U58515.1 chitinase 3-like 2 17 NM 001706.1 BCL6 18 NM 004364.1 CCAAT/enhancer binding protein (C/EBP), alpha 19 AW207777 dopamine beta-hydroxylase 20 NM 023929.1 zinc finger protein RINZF 21 NM 002854.1 parvalbumin 22 AU146891 MAD, mothers against decapentaplegic homolog 1 23 AK024964.1 nuclear factor I/A 24 NM 004999.1 myosin VI 25 BF059748 glucosaminyl transferase 2, I-branching enzyme 26 AI524095 lymphocyte antigen 9 27 NM 001819.1 chromogranin B (secretogranin 1) 28 NM 021785.2 retinoic acid induced 2 29 NM 017965.1 solute carrier family 7 30 NM 021643.1 tribbles homolog 2 (TRB2) 31 AI871641 rap2 interacting protein x (RIPX) 32 AF313468.1 dendritic cell-associated C-type lectin-1 33 NM 002167.1 inhibitor of DNA binding 3 34 D50925.1 PAS domain containing serine/threonine kinase 35 NM 002108.2 histidine ammonia-lyase 36 AU147399 caveolin 1 37 NM 004665.1 vanin 2 38 NM 006235.1 POU domain, class 2, associating factor 1 39 AB014520 plexin D1 40 AA058770 glucocorticoid induced transcript 1 41 AA330389 carbonic anhydrase VIII 42 NM 005231.1 amplaxin/ems1 43 W65310 TNFRSF10A/TRAILR1 44 NM 004417.2 dual specificity phosphatase 1 45 BE617483 protein tyrosine phosphatase, non-receptor type 14 46 AI676095 phosphatidylinositol transfer protein, cytoplasmic 1 47 AL049265.1 interleukin 6 signal transducer 48 AF057557.1 regulator of Fas-induced apoptosis 49 AF261135.1 GPR18 50 AI961231 thymus high mobility group box protein TOX 51 AK024964.1 nuclear factor I/A 52 AF063591.1 CD200/OX2 53 NM 018651.1 zinc finger protein 54 AW166711 phosphoinositide-binding protein PIP3-E 55 M90391.1 interleukin 16 56 AI873273 solute carrier family 16, member 6 57 NM 005018.1 programmed cell death 1 58 NM 020179.1 FN5 protein 59 NM 013450.1 bromodomain adjacent to zinc finger domain, 2B 60 AW294080 B and T lymphocyte attenuator 61 NM 003919.1 sarcoglycan, epsilon 62 NM 018214.1 LAP and no PDZ protein 63 AA084273 zinc finger protein 404 64 NM 002221.1 inositol 1,4,5-trisphosphate 3-kinase B 65 NM 014832.1 TBC1 domain family, member 4 66 AA576959 sphingosine-1-phosphate phosphotase 2 67 NM 002923.1 regulator of G-protein signalling 2, 24kD 68 AL040198 hairy/enhancer-of-split related with YRPW motif-like 69 M87789.1 immunoglobulin heavy constant gamma 3 70 W03103 development and differentiation enhancing factor 1 71 AI934556 nuclear receptor subfamily 3, group C, member 1 72 NM 002736.1 protein kinase, cAMP-dependent, regulatory, type II 73 AL044018 LIM domain-containing preferred translocation partner 74 NM 006264.1 APO-1CD95 (Fas)-associated phosphatase 75 AL390144.1 nerve injury gene 283 76 NM 014061.1 APR-1 protein 77 NM 003740.1 potassium channel, subfamily K, member 5 (TASK-2) 78 U10485 lymphoid-restricted membrane protein 79 NM 012417.1 retinal degeneration B beta 80 AV735100 p300/CBP-associated factor 81 AL117515.1 phospholipase C-like 2 82 AF162428.1 sarcosine dehydrogenase 83 NM 030952.1 likely ortholog of rat SNF1/AMP-activated protein kinase 84 BF516337 xylosyltransferase I 85 BG491844 v-jun 86 NM 001353.2 aldo-keto reductase family 1, member C1 87 AF045451.1 NGFI-A binding protein 1 (EGR1 binding protein 1) 88 BE883841 sestrin 3 89 NM 021070.1 latent TGF beta binding protein 90 AF027205.1 serine protease inhibitor, Kunitz type, 2 91 X17115.1 immunoglobulin heavy constant mu 92 BE672557 musashi homolog 2 93 AF036906.1 linker for activation of T cells 94 NM 014112.1 trichorhinophalangeal syndrome I 95 AK027071.1 TGF beta-stimulated protein TSC-22 96 M33376.1 aldo-keto reductase family 1, member C2 97 AW772123 protein phosphatase 2 regulatory subunit B gamma isoform 98 AA041298 evolutionarily related interleukin-1beta converting enzyme 108 NM 021803.1 interleukin 21 130 NM 003874.1 CD84 CM EM CM EM a b FH FH CM EM T T T T Th1a Th1b Th2a Th2b avsT bvsT avsT bvsT bvsTh1a bvsTh2a avsTh1a avsTh1b avsTh1b avsTh2a avsTh2b bvsTh2b FH FH FH FH FH FH FH FH FH FH FH FH T T T T T T T T T T T T
Figure 4.5 Genes specific to TFH cells. The list of genes preferentially expressed in TFH cells compared to all other subsets was generated from genes upregulated in at least 9 out of 12 comparisons to the other T cell subsets. The genes were then ranked in descending order starting with the genes with the highest composite signal log ratio value. ESTs and hypothetical proteins were excluded from gene lists. Signal values (which represent the level of transcript expression) and signal log ratio values (which represent the change in expression level of a transcript expressed as the log2 ratio) are shown. Genes that showed
88 Chapter 4 TFH cells
a change of 2 fold or greater (i.e. a signal log ratio of 1 or greater) were considered differentially expressed.
We examined several genes of interest across multiple genechip experiments performed in our laboratory. These included DC and macrophage subsets, B cells, eosinophils and neutrophils, and various other leukocyte subsets. We selected genes from the entire set of TFH preferentially expressed genes, as well as genes known to be important for T cell costimulation, migration and T-B interactions in different cell types
(Figure 4.6). This analysis showed that most TFH expressed genes were also expressed by other leukocyte types. In many cases these genes were shared with other T cell subsets, and in a number of instances TFH genes were also expressed by B cells.
Signal 0200 400 Accession Gene name AF063591.1 CD200 NM 003874.1 CD84 AF054816.1 CD84 AF244129.1 CD229/Ly9 AF100539.1 SAP S67779.1 BCL6 NM 021803.1 IL-21 AF269133.1 IL-21R AB023135.1 ICOS AW294080 BTLA BG536887 CTLA-4 NM_005018.1 PDCD1 AF222341.1 CD28 NM_003037.1 SLAM NM 013351.1 T-bet AI796169 GATA3 M29383.1 IFN-gamma NM_000586.1 IL-2 NM_000589.1 IL-4 NM_000572.1 IL-10 NM_002188.1 IL-13 NM 006419.1 CXCL13 NM 001504.1 CXCR3 AF348491.1 CXCR4 NM_001716.1 CXCR5 NM_006564.1 CXCR6 NM_000655.2 L-selectin NM_001250.1 CD40 NM 000074.1 CD40L NM 001242.1 CD27 NM 001252.1 CD70 AW518486 CD30L NM_001243.1 CD30 BC006196.1 CD137, 4-1BB NM_003811.1 4-1BBL NM_003326.1 OX40L AJ277151 OX40 NM 003807.1 LIGHT BC002794.1 HVEM + - B T + CM EM NK Th1 T T Th2 γδ CD57 CD57 CD19 Resting Resting FH FH mast cell Resting Resting T T mast cell 6 hr LPS 6 hr neutrophils Resting eos Resting Immature DC Immature IgE activated Macrophage s PMA act eos activatedDC LPS act neut act LPS macrophages LPS activated 48 hr LPS48 hr act DC
Figure 4.6 Gene expression profiles of a number of genes important for T and B cell biology. The expression of genes relevant to T and B cell biology was analysed in different cell types using Affymetrix Data Mining Tool. Where several Affymetrix probes exist for a particular gene representative probes are shown. For CD84 more than one pattern existed and two representative probes are shown.
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We selected molecules of interest, for a more detailed analysis using flow cytometry and immunohistochemistry. In particular, the cell surface molecules CD84 and CD200, the cytokine IL-21 and the transcription factor BCL6 were intimately associated with the TFH subset, and represented interesting molecules for further study.
4.2.4 CD84 is expressed with CXCR5 on tonsillar TFH cells but not on blood T cells
The expression of several preferentially expressed TFH genes was assessed at the protein level, using both flow cytometry and immunohistochemistry. CD84, a member of the CD2 subset of the Ig superfamily of cell surface receptors (de la Fuente et al.,
1997), was preferentially expressed on TFH cells, both at the RNA level (Figure 4.5), and at the protein level (Figure 4.7a). CD84 is expressed on some B cells and T cells and contributes to their activation (Martin et al., 2001; Tangye et al., 2003b; Tangye et al., 2002). Most tonsillar CXCR5+ cells expressed CD84 (Figure 4.7a). This applied to both T cells and B cells and was in striking contrast to the expression pattern of CD84 in peripheral blood. Most of the CD84+ T cells in blood did not express CXCR5, although all CXCR5+ T cells expressed CD84 (Figure 4.7a). We next examined the phenotype of CXCR5+CD4+CD84+ cells in the tonsil (Figure 4.7b). These cells expressed the activation marker CD69, were CCR7low and heterogeneous with regard to CD57 expression (Figure 4.7b). On tonsil tissue sections CD84 was present in germinal centers, but was also highly expressed immediately outside the germinal centers in the B cell follicle (Figure 4.7c). Interestingly, mRNA for another member of the CD2 subgroup of Ig-superfamily receptors, CD229 (Ly9), was also preferentially expressed + in TFH cells (Figure 4.5). CD229 is expressed on both B and T cells (some CD69 ) (de la Fuente et al., 2001). However, the lack of commercially available reagents precluded detailed analysis of expression of CD229 on TFH cells.
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a Lymphocyte gatedCD4+ gated CD19+ gated CD4+ gated
Tonsil
SLAM
Blood CXCR5 CD84 b
Tonsil CD84 CD57 CD69 CD25 CCR7 c CD84 BCL6 CD84 CD4
Figure 4.7 CD84 expression in tonsil and blood. (a) Flow analysis of CD84 expression on CXCR5+ cells in tonsil and blood, the plots are gated on lymphocytes by FSC and SSC. (b) Phenotype of CD84+ T cells in the tonsil, the plots are gated to show CXCR5+CD4+ cells only. (c) Tonsil cryostat sections were stained with antibodies to BCL6 (green) and CD84 (red), and CD4 (green) and CD84 (red), as indicated on the individual panels. The results are representative of at least three different experiments.
The cytoplasmic domains of CD84 and CD229 bind the Src homology 2 domain-containing protein SH2D1A (also known as signaling lymphocytic activation molecule-associated protein [SAP]) (Lewis et al., 2001; Sayos et al., 2001; Tangye et al., 2003b; Tangye et al., 2002). SAP was also preferentially expressed in TFH cells
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(Figure 4.5). Studies in SAP-deficient mice have shown SAP to be essential for the generation of long-term humoral immunity (Crotty et al., 2003). Apart from CD84 and CD229, SAP also interacts with human SLAM (Sayos et al., 1998), 2B4 (Tangye et al., 1999), and NTB-A (Bottino et al., 2001), as well as the mouse homologue of human CS-1/CRACC (mouse novel Ly9 (Tovar et al., 2002)). These molecules are expressed in NK, T, B cells and monocytes (Bottino et al., 2001; Bouchon et al., 2001; Castro et al., 1999; Lewis et al., 2001; Sayos et al., 2001; Sayos et al., 1998; Tangye et al., 1999; Tangye et al., 2002). While CD229 and especially CD84 (due to its co-expression with CXCR5) can recruit SAP and may be involved in mediating interactions between T and B cells, other SAP-associating receptors may also play a role in mediating B cell helper function. SLAM, in particular, is highly expressed on both activated human B and T cells (Cocks et al., 1995; Crotty et al., 2003; Punnonen et al., 1997). Our gene expression analysis showed SLAM was expressed by CXCR5+ T cells at levels similar to that seen in Th1 or Th2 cells. Furthermore, T and B cells from both tonsil and peripheral blood expressed SLAM protein (Figure 4.7), and there was no correlation between SLAM and CXCR5 expression on CD4+ tonsillar T cells. Another partner for SAP, 2B4, was not expressed by CXCR5+ T cells (data not shown), consistent with its absence from peripheral blood CD4+ T cells (Tangye et al., 2000a; Tangye et al., 2000b), and is therefore unlikely to be involved in T-B cell collaboration. Although SAP mediated B cell help could possibly occur through NTB- A, which was not represented on Affymetrix U133 arrays, we suggest that CD84, and possibly CD229, are the most likely facilitators of SAP-mediated signaling for the maintenance of humoral immunity.
4.2.5 Expression of CD200 by TFH cells
Another cell surface molecule whose expression was higher in TFH cells compared to other T cell subsets was CD200. Almost all CXCR5+CD4+ T cells in the tonsil expressed CD200 (Figure 4.8a). Similar to CD84, the expression pattern of CD200 in the tonsil differed markedly from that in peripheral blood (Figure 4.8b), where there was no preferential pattern of expression displayed by T cells. In fact, CD200+ T cells were almost completely absent from the periphery but represented a major fraction of the tonsillar T cell population. The phenotype of CD200+ T cells in tonsil was consistent with activated effector cells, based on staining with CD69, CD27, CD95, CD62L and CCR7 (but heterogeneous with respect to CD57 expression).
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CD200 is a member of the Ig superfamily (Hoek et al., 2000; Wright et al., 2000), and its receptor is reported to be a myeloid-specific glycoprotein CD200R (Wright et al., 2000). CD200 is involved in regulating cells of the myeloid lineage, however its expression by tonsillar T cells also suggests a role in humoral immunity.
Lymphocyte gated CD4+ gated CD19+ gated a
Tonsil
Peripheral blood CXCR5 CD200 b
Tonsil CD57 CD69 CD25 OX40 CD200 CCR7 CD62L CD27 CD95
Figure 4.8 CD200 expression in tonsil and blood. (a) Flow analysis of CD200 expression on CXCR5+ cells in tonsil and blood, the plots are gated on lymphocytes by FSC and SSC. (b) Phenotype of CD200+ T cells in the tonsil, the plots are gated to show CXCR5+CD4+ cells only. The results are representative of at least three different experiments.
4.2.6 IL-21 mRNA is produced by TFH cells and its receptor is expressed by B and T cells T cell help for B cells or other leukocytes may proceed through cell-cell interactions, however secreted cytokines may also provide a helper signal. Another gene preferentially expressed in CXCR5+ T cells that may function as a cytokine for B
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cell help was IL-21. Interestingly, another cell type that showed a consistently high level of IL-21 expression was Th1 cells. IL-21 has been reported to have a number of effects on T, B and NK cells including activation, Ig class-switching, proliferation and apoptosis (Mehta et al., 2004). To identify the cells that express the IL-21R, a mAb to the human IL-21R was obtained from Millenium Pharmaceuticals (Chtanova et al., 2004) and we performed a comprehensive flow cytometric analysis to identify IL-21R+ cells. The main cell type expressing IL-21R in peripheral blood was CD19+ B cells; few if any T cells in blood expressed IL-21R (Figure 4.9a). Similar to the blood, tonsillar CD19+ B cells also expressed IL-21R. However, there was a small but distinct population of CD4+ T cells in tonsil that also expressed IL-21R (Figure 4.9b). It should be noted that this population was not present in all donors, probably reflecting variations in the degree of inflammation or cellular activation in each donor (data not shown). In addition, IL-21R expression was detected on both Th1 and Th2 cells (data not shown). We examined IL-21R expression on tonsillar and blood B cells in more detail (Figure 4.9c and d). Although IL-21R was expressed on the majority of B cells both in the tonsil and the periphery, fewer memory B cells (IgD-, CD38dull) expressed this receptor compared to naïve B cells. Since IL-21R was more abundantly expressed by naïve and GC B cells compared to memory B cells, provision of IL-21 by TFH cells within the germinal centre microenvironment may contribute to the initial expansion of CD40L-stimulated naïve B cells, or the sustained growth of proliferating centroblasts.
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Tonsil Blood a IL-21R CD19CD4 CD19 CD4 b Tonsil CD19+ gated IL-21R
IgM CD24 CD27 CD19+ CD19+IL-21R+ CD19+IL-21R- c 36.6 8.8 42.3 12.6 28.8 4 Tonsil
19.8 29.7 10 32.6 32.8 25.3
2.7 2.9 0.9 2.3 5.4 4.2
CD38 CD19+ CD19+IL-21R+ CD19+IL-21R-
0.9 8.2 0.9 7 0.9 0.6 Blood 12.1 50.9 10.7 75.4 16.1 6
21.5 6.7 2.6 3.6 64.8 11.8
IgD
Figure 4.9 Characterization of IL-21R expression on B and T cells. (a) Flow analysis of IL-21R expression on PBMC and tonsil cells, the plots are gated on lymphocytes by FSC and SSC. (b) Phenotype of B cells in the tonsil, the plots are gated to show CD19+ cells. (c) Differential expression of IL-21R in B cell subsets, the plots are gated to show CD19+ cells. The results are representative of at least three different experiments.
We have also noted that a non-B subset of cells expressed IL-21R in peripheral blood. Further phenotypic analysis showed that these cells were also negative for T, NK and basophil markers (Figure 4.10). The cells expressed CD16 together with CD11c, HLA-DR and CD86, which suggests that the non-B cell population of cells
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expressing IL-12R in peripheral blood is a CD16+ subset of dendritic cells. Interestingly, this subset of cells is absent from tonsils.
CD3 CD19 CD56 CCR3 Gated on CD16+ Gated on CD19- Gated on CD16+ Gated on CD11c+ IL-21R lymphocytes lymphocytes lymphocytes lymphocytes
CD86 CD16 CD11c HLA-DR
Figure 4.10 Identification of a non-B, non-T cell IL-21R expressing leukocyte subset. Flow analysis of IL-21R expression on non-B, non-T cells. The plots in the top panel are gated on lymphocytes by forward and side scatter. The plots in the bottom panel are gated on lymphocytes and as indicated on each plot.
4.2.7 BCL-6 as a transcription factor for TFH and B cells
A number of transcription factors were preferentially expressed by TFH cells. The transcriptional repressor BCL6 (Chang et al., 1996), which is a major regulator of B cell differentiation (Shaffer et al., 2000) was preferentially expressed in CXCR5+ T cells from tonsils, compared to Th1 or Th2 effector subsets (Figure 4.5). In the tonsil, BCL6 was expressed by GC B cells, and was also high on T cells (as shown previously by others (Cattoretti et al., 1995)) within the GCs (Figure 4.11). BCL6 was expressed by both CD57+ and CD57- T cells within germinal centers (Figure 4.11).
Many other genes were intimately associated with the TFH subset and could be important for its function. It is beyond the scope of this thesis to discuss all of these genes. In particular, two members of the hairy/enhancer-of-split related with YRPW motif family (HEY1 and HEYL), and the highly conserved PAS domain containing serine/threonine kinase (PASK) will be of interest for future studies.
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a BCL6 CD3 b BCL6 CD57
cdBCL6 CD3 BCL6 CD3
Figure 4.11 Identification of BCL6-expressing T cells and their localization within the tonsil. Human tonsil was stained with antibodies to BCL6 (red, a, b, c, and d), CD3 (green, a, c and d) and CD57 (green, b), as indicated on the individual panels. Yellow arrows indicate CD57+BCL6+ cells, white arrows indicate CD57+BCL6- cells. The results are representative of at least three different experiments.
4.3 Discussion
Identification of a CD4+ T cell subset capable of providing help to B cells for Ab production expands to at least three the number of major effector T cell subsets (Breitfeld et al., 2000; Kim et al., 2001c; Schaerli et al., 2000). Despite the importance of TFH cells and their abundance in secondary lymphoid tissues, relatively little is known about how these T cells are generated and how they mediate their function. Here, we have used gene microarrays as well as mAbs to identify molecules associated with TFH cells, that distinguish these cells from the Th1 or Th2 effector subsets.
The gene expression profile of TFH cells showed that they expressed a number of molecules traditionally associated with B cell maturation and function, such as CXCR5, IL-6R, the transcription factor BCL6, CD84 and others. This suggests that common factors are involved in the differentiation, migration, survival and function of
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B and TFH cells. Molecules such as CXCR5 promote humoral immunity, by facilitating the migration and co-localization of the cell types essential for productive T-dependent antibody responses – TFH and B cells. Likewise, IL-6 is a cytokine that promotes B cell survival and antibody responses. Thus, certain molecules can be considered as closely associated with the process of humoral immunity, and in most cases perform similar functions on B and TFH cells. It is also noteworthy that the ligand for CXCR5, CXCL13 was also preferentially expressed by TFH cells. Kim and colleagues also noted the expression of this chemokine by TFH cells, which was upregulated after TCR activation
(Kim et al., 2004). CXCL13 produced by TFH cells could attract CXCR5 bearing B cells, thus facilitating contact between TFH cells and B cells. CD84 was one of the cell surface molecules most closely associated with the
TFH phenotype. CD84 is a member of the CD2 subgroup of Ig-superfamily receptors (de la Fuente et al., 1997; Sidorenko and Clark, 2003; Tangye et al., 2000b) and is expressed on B cells and T cells in human spleen and peripheral blood (Martin et al., 2001; Tangye et al., 2003b; Tangye et al., 2002), and serves as a self-ligand (Martin et al., 2001). The preferential expression of CD84 on TFH and B cells suggests that CD84 may be important in facilitating T cell help for B cells, presumably at sites of CXCL13 expression such as the follicles. Further evidence supporting the importance of CD84 expression by TFH cells comes from a study showing that the expression of SAP (through which CD84 signals) is essential in T cells for late B cell help (Crotty et al., 2003). Following BCA-1 (CXCL13) directed migration of T cells to B cell follicles, CD84 on B cells could interact with CD84 on T cells, thus initiating a SAP-dependent signaling cascade. However SAP also interacts with other molecules including CD229, which was also preferentially expressed by TFH cells, SLAM, 2B4, and NTB-A which are expressed by NK, T cells, B cells and monocytes (Bottino et al., 2001; Castro et al., 1999; Lewis et al., 2001; Sayos et al., 2001; Sayos et al., 1998; Tangye et al., 1999; Tangye et al., 2002). We found no preferential association between SLAM and CXCR5 expression in tonsil T cells, suggesting that CD84 (and CD229), rather than SLAM are the likely signaling partners for SAP in CXCR5+CD4+ T cells. Notably, we also found that SAP was preferentially expressed by TFH cells. Signaling through SAP-associating receptors expressed by T cells presumably leads to expression of cytokines and cell surface molecules that are important for the effector function of TFH cells, and which enable these cells to provide help to B cells.
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CD200 was another cell surface molecule preferentially expressed by TFH cells. CD200 is a member of the Ig superfamily and its receptor is myeloid-specific glycoprotein CD200R (Barclay et al., 2002; Gorczynski et al., 2004; Wright et al., 2003b). Although much of what is known about CD200 function suggests that it is involved in regulation of cells of the myeloid lineage, its expression by TFH cells suggests an additional role in adaptive immune responses. CD200R is expressed by DCs and macrophages but recent studies also show expression by T cell subsets and even B cells (Wright et al., 2003b). A possible role for CD200 on TFH cells would be to facilitate the interactions between TFH cells and dendritic cells which may provide an additional signal for TFH differentiation.
TFH cells preferentially expressed cytokines and chemokines such as IL-21, IL-16 and CXCL13. IL-21 is closely related to IL-2 and IL-15 and receptors for these cytokines share the common γ chain (Asao et al., 2001; Parrish-Novak et al., 2000). The gene encoding the common γ chain is mutated in humans with X-linked severe combined immunodeficiency disease (XSCID) which is characterized by the absence of T and NK cells and non-functional B cells (Leonard, 2001; Noguchi et al., 1993). IL-21 was particularly interesting since it is produced by activated T cells, while B cells, T cells and NK cells express the receptor for this cytokine (IL-21R) ((Parrish-Novak et al., 2000) and this report). IL-21R was more abundantly expressed by naïve and GC B cells compared to memory B cells. Thus, provision of IL-21 by TFH cells within the germinal centre microenvironment may contribute to the initial expansion of CD40L- stimulated naïve B cells, or the sustained growth of proliferating centroblasts. The effects of IL-21 on B cells, which include apoptosis, growth arrest and also costimulation, vary depending on the B cell development stage and costimulatory signals (Parrish-Novak et al., 2000; Parrish-Novak et al., 2002; Sivakumar et al., 2004). While the precise effect of IL-21 seems to differ between mouse strains, IL-21 seems to favor a B cell response in the context of BCR activation and cognate T cell help, while activation through TLRs alone promotes the apoptotic effects of IL-21 (Jin et al., 2004). In addition, IL-21 induces plasma cell differentiation, promotes production of IgG1 and IgG3 by human B cells and also induces Blimp-1 and BCL-6 (Pene et al., 2004) (Ozaki et al., 2004). The preferential expression of this cytokine by TFH cells and the expression of IL-21R by B cells in the tonsil makes IL-21 a likely soluble transducer of T cell signals to B cells in the follicles. Interestingly, Th1 cells also expressed high
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levels of IL-21 mRNA. IL-21 promotes Th1 responses, by enhancing IFN-γ production (Kasaian et al., 2002; Strengell et al., 2003). However IL-21 has also been described as a Th2 cytokine (Wurster et al., 2002). Interestingly, IL-21R has been identified on many actively proliferating T cells in NOD mice, and increased expression of IL-21 in NOD mice likely contributes to autoreactive T cell expansion and disease pathogenesis (King et al., 2004). We also noted the expression of IL-21R by non-B, non-T, non-NK subsets of cells in peripheral blood. Flow cytometric analysis of cell surface markers of these cells identified them as a CD16+ subset of DCs. This is the first study to report IL-21R protein expression by DCs, although an earlier study reported the expression of IL-21R mRNA by DCs and macrophages, and the inhibitory effect of IL-21 on DC activation and maturation (Brandt et al., 2003). Transcription factors such as T-bet and GATA-3 have been shown to direct polarization to Th1 and Th2 cells, respectively, however no such factors have yet been identified for TFH cells. Our results show that the transcription factor BCL6 is preferentially expressed by TFH cells compared to other effector T cell subsets. Expression of BCL6 is restricted to GC B and T cells (Allman et al., 1996; Cattoretti et al., 1995; Onizuka et al., 1995). Over-expression studies and microarray analysis have revealed that BCL6 arrests development of B cells into Ig-secreting effector cells by regulating expression of genes involved in B cell migration, activation, proliferation and differentiation (Reljic et al., 2000; Shaffer et al., 2000). BCL6 has emerged as a major regulator of B cell differentiation, by skewing B cells toward a GC fate rather than a plasma cell fate (Shaffer et al., 2000). BCL6-deficient mice display normal B cell, T cell and lymphoid organ development, but show defective T-dependent Ab responses, including a complete lack of affinity maturation due to the inability of follicular B cells to proliferate and form GCs. In addition, BCL6-deficient mice develop a Th2-like inflammatory response in multiple organs characterized by infiltrates of IgE-bearing B cells, and eosinophils (Ye et al., 1997). Remarkably, BCL-6 deficient mice develop Th2-type inflammation even in the absence of STAT6 and IL-4 (Dent et al., 1998). BCL6 represses GATA-3 expression (Kusam et al., 2003) and is downregulated in T cells following exposure to IL-4 (Lund et al., 2003). Together with our findings, these data suggest that BCL-6 is a transcription factor that directs T cells towards a TFH phenotype, and may in fact act as a switch that determines Th2 or TFH cell fate. The possible connection between TFH and Th2 is evidenced by a recent
100 Chapter 4 TFH cells
finding that a subset of CXCR5high T cells expresses the Th2 marker CRTh2 and likely represents a Th2 precursor population (Johansson-Lindbom et al., 2003). In conclusion, we have used oligonucleotide gene microarrays to study gene expression in key effector subsets of CD4+ T cells. Our analysis identified numerous candidate genes that were preferentially expressed in TFH cells, and may mediate B-cell helper function. mAb staining confirmed the expression pattern of several of these markers. Thus, the identification of particular molecules preferentially expressed by
TFH cells may be useful for the isolation and subsequent investigation of this important subset of effector T cells. The association of certain molecules with T-dependent antibody responses, through their expression on both B cells and TFH cells, also indicates that molecules traditionally associated with B cell responses are in fact more closely associated with T-dependent humoral immunity and serve important roles on
TFH cells. The large number of genes strongly associated with TFH cells provides numerous opportunities for further study, to understand all of the molecular mechanisms for T-dependent antibody responses.
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5 Identification of T cell subset-restricted genes and signatures for different types of T cell responses
5.1 Introduction
As discussed extensively in previous chapters, specialized subsets of effector T cells participate in different types of immune responses. Th1 cells produce IFN-γ and protect against intracellular pathogens, whereas Th2 cells produce cytokines such as IL- 4 and IL-5 and protect against large extracellular parasites (Mosmann et al., 1986). The recently identified T follicular homing (TFH) subset provides help to B cells in B cell follicles (Breitfeld et al., 2000; Schaerli et al., 2000). All of these effector T cell subsets interact with other leukocyte types including B cells, dendritic cells and macrophages, as part of a coordinated response to pathogenic challenge. In addition, other subsets of T cells provide immune protection at a different level. γδ T cells and NK T cells contribute to specific aspects of the cellular immune response, particularly innate immunity. Effector and central memory T cells have distinct roles in the recall response to antigenic challenge (Sallusto et al., 2004; Sallusto et al., 1999). The specialized functions of leukocyte subsets are reflected in the differing patterns of molecules and genes they express. These molecules often underlie the unique function of the subsets they mark. For instance, CD3 defines T cells since it is an essential component of the T cell receptor complex; CD4 is associated with MHC class II recognition, mostly for T helper responses, while CD8 is associated with MHC class I recognition and cytotoxic responses; B cells are defined by immunoglobulin expression and production. Immunological research during much of the 80s and 90s was spent identifying and characterizing leukocyte markers, first through mAb production, and then through gene sequencing. This led to the discovery of most of the CD molecules which are used today to mark leukocyte subsets, and define subset function. Recently, chemokine receptors and other molecules involved in cell migration have proved useful for defining leukocyte subsets, particularly stages of T cell
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differentiation and function. CXCR5 marks TFH cells, and facilitates their migration to the B cells follicles where they provide help to B cells (Breitfeld et al., 2000; Schaerli et al., 2000). Th1 cells preferentially express CCR5 and CXCR3, while Th2 cells preferentially express CCR3, CCR4 and CRTh2 (Qin et al., 1998; Sallusto et al., 1998a; Sallusto et al., 1998b). CCR7 facilitates naïve T cell homing to secondary lymphoid organs (Forster et al., 1999), but also distinguishes TCM from TEM cells (Sallusto et al., 2004; Sallusto et al., 1999). More recently, gene microarrays have proven to be a powerful approach for identifying novel gene expression patterns for various subsets of leucocytes (Shaffer et al., 2001; van der Pouw Kraan et al., 2004). We and others have successfully used microarray technology to create gene expression profiles of effector and memory T cell subsets as well as other leukocyte subsets (Chtanova et al., 2001; Chtanova et al., 2004; McHugh et al., 2002; Rogge et al., 2000) (see also chapters 3 and 4). This chapter presents a comprehensive analysis of most of the major subsets of human T cells, and identifies at the level of gene expression the distinguishing features associated with T cell differentiation to effector subsets, T cell activation, and memory T cell development. In addition, we identify T cell subset-specific gene expression signatures and describe a unique transcriptional profile for γδ T cells. Finally, the use of a comprehensive collection of genechip datasets, representing gene expression profiles for all of the major human leukocyte subsets, has allowed us to identify many novel T cell-specific genes.
5.2 Results
5.2.1.1 Comprehensive gene expression dataset for all of the major human
leukocyte subsets
Using Affymetrix U133-A and -B chips, we screened >44,000 probe sets representing ~39,000 transcripts to develop transcript profiles for all of the major immune cell types, including neutrophils, macrophages, DCs, NK cells, various B cell subsets, mast cells, eosinophils, basophils and most of the major T cell subsets (see Table 5.1).
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Table 5.1 Microarray experiments used in this study.
The following gene expression data was contributed by collaborators: naïve cord blood T cells, + + γδ T cells, CD8 TEM and TEMRA, NK cells and CD19 B cells (R. Newton), macrophages and dendritic cells (S. Zimmer), mast cells and basophils (S. Liu), eosinophils and neutrophils (M. Sisavanh), Garvan Institute, Sydney, Australia. Splenic B cell subset gene profiling data was provided by S. G. Tangye and K. Good, Centenary Institute, Sydney, Australia. Resting and activated Th1 and Th2 gene expression data was provided by F. Sallusto, Oncology Institute of Southern Switzerland, Bellinzona, Switzerland. Leukocyte Cell type Source U133A+B subset arrays T cells Naïve cord blood CD4+CD45RA+ T cells isolated from cord blood 1 + + + TCM CCR7 CD4 CD45RO T cells isolated from PBMC 2 - + + TEM CCR7 CD4 CD45RO T cells isolated from PBMC 2 + + + TFH CD57 CXCR5 CD4 T cells isolated from tonsil 2 cbTh1 Cord blood CD4+ T cells polarized under Th1 2 conditions cbTh2 Cord blood CD4+ T cells polarized under Th2 2 conditions pbTh1NA T cell clones polarized under Th1 conditions 2 pbTh2NA T cell clones polarized under Th2 conditions 3 pbTh1ACT T cell clones polarized under Th1 conditions, activated 3 pbTh2ACT T cell clones polarized under Th2 conditions, activated 3 γδ T cells γδ T cells isolated from PBMC by FACS 2 + + - + CD8 TEM CD8 CCR7 CD45RO T cells isolated from PBMC 1 + + - - CD8 TEMRA CD8 CCR7 CD45RO T cells isolated from PBMC 1 NK CD16+CD56+ CD16+CD56+ cells isolated from PBMC by FACS 2 B cells Naïve Isolated from spleen by FACS 2 IgG IgA IgE Isolated from spleen by FACS 2 memory IgM memory Isolated from spleen by FACS 2 Plasma Isolated from spleen by FACS 1 CD19 CD19+ B cells isolated by FACS from PBMC 2 Dendritic Immature Monocyte derived DC 2 cells 6 hr LPS Immature DC activated in the presence of 100ng/ml 2 stimulated LPS for 6 hr 48 hr LPS Immature DCs activated in the presence of 100ng/ml 2 stimulated LPS for 48 hr Macrophages Resting Monocyte derived 2 4 hr LPS treated Macrophages activated for 4hr in the presence of 2 100ng/ml LPS Eosinophils Resting Eosinophils isolated from peripheral blood by MACS 1 Activated Eosinophils activated for 2hr with PMA 1 Neutrophils Resting Neutrophils isolated from peripheral blood by MACS 2 Activated Neutrophils activated for 1hr with LPS 1 Mast cells Resting Cord blood derived mast cells 2 Activated Mast cells activated through IgE receptor 2 Basophils Resting Basophils isolated from PBMC by FACS 1
Hierarchical clustering of all genes (excluding those that were absent or below the noise level in all leukocytes) was used to assess the relationship between leukocyte subsets (Figure 5.1a). As expected, lymphoid (B, T and NK cells) and myeloid
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(basophil, eosinophil, neutrophil, mast cell and monocyte-derived DCs and macrophages) subsets clustered separately. Furthermore, leukocyte subsets that diverged from a common progenitor relatively recently had more similar patterns of gene expression. This was especially obvious for DCs and macrophages which were both derived from monocytes. Importantly, leukocytes differentiated in vitro showed large differences in gene expression profiles compared with ex vivo cells isolated from blood or tissues. For instance, cultured T cells clustered away not only from isolated ex vivo T cells but also from all other leukocytes (including activated leukocytes).
5.2.1.2 Identification of leukocyte subset signatures, and leukocyte-specific genes
We used a one-way ANOVA to identify the genes with significant differential expression between leukocyte types. Using this approach, specific gene expression signatures could be distinguished for all of the major human leukocyte types (Figure 5.1b). As expected, closely related cell types (e.g. DC and macrophages) showed similar expression patterns and few genes had significant differences in expression between these cell types. Other leukocytes, of which neutrophils were the most striking example, showed extensive differences in gene expression when compared to all other cell types profiled. Discrete clusters for each cell type could easily be distinguished after the genes and individual arrays were arranged using unsupervised hierarchical clustering (Figure 5.1b). These clusters represent specific signatures for each leukocyte type. Each signature contains both genes previously described as specific to that leukocyte subset and also numerous novel genes. Selected leukocyte signature genes are shown in Appendix 1.
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(p)
A
Normalized expression 0 1 5
Naïve+ Memory T B cell DC + Mac MC Neut Baso Th1 and γδT+NK cells Eos Th2 cells
B i) Mast cell signature iv) B cell signature C-kit CD19 tryptase PAX5 cathepsin G CD79 chymase TLR10 myeloperoxidase BCL11a i) DUSP10 EBF ii) BLNK iii) CD22 TCL1 ii) Eosinophil signature CCR6 iv) Ig molecules CCR3 IL-5Ra histamine receptor H4 v) T cell signature OSR1 CD3 TCR LCK iii) Neutrophil signature CD40L GATA3 IL-8R aplha ICOS TLR5 CD28 TLR6 CD6 Caspase 4 CD5 Caspase 5 DUSP1 FLIP vi) γδ T and NK cell signature Killer cell Ig-like receptors EDG8 granulysin v) serine proteases vi)
T cell subsets DC + Mac Neut Baso B cell MC Eos γδT+NK
Figure 5.1 Distinct gene expression signatures characterize leukocyte subsets. (A) Hierarchical clustering of all genes (expressed in at least 2 samples) was performed using Pearson correlation. (B) Genes that differ significantly between the different leukocyte subsets were identified using one-way ANOVA. Selected signature genes are shown. Gene expression was normalized around 1 for all experiments and the scale shown in this figure is the same for all following figures unless otherwise indicated. The colour scale indicates normalized expression levels, where 1 is normal expression, 0 - low expression, and values above 1 indicate high expression. Mac, macrophages; MC, mast cells; Neut, neutrophils; Eos, eosinophils; Baso, basophils; NK, NK cells.
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5.2.1.3 Comprehensive leucocyte subset profiling allows the identity of T cell selective gene expression signatures
To identify novel genes that are selectively expressed by T cells, we applied one-way ANOVA to find genes that most reliably discriminate between T cells and other leucocytes (Figure 5.2a). We also used SymAtlas database (http://symatlas.gnf.org/SymAtlas/) (Su et al., 2004) to examine the expression of these gene transcripts in a range of lymphoid and non-lymphoid tissues. We identified a large number of genes that were preferentially expressed by T cells compared to non-T cell leukocytes (greater than 1,200 probe sets on the Affymetrix U133 A+B arrays were preferentially expressed by T cells with p<0.0001). Due to space limitations we are only able to list ~100 genes most intimately associated with T cells (Figure 5.2a). Approximately 400 additional genes selectively expressed by T cells (p<0.000001) are provided in Appendix 2. As expected, some T cell-restricted genes were expressed outside of the hematopoietic lineage, while others were highly specific to T cells (Figure 5.2a). We found that many genes were shared between T cells and NK cells, indicating that the origin and functional relevance of these two leukocytes are closely related. Many of the genes identified through this approach (Figure 5.2a), such as CD3 chains, CD28, ICOS, CD40L, TCR, lck, ZAP70 and GATA3, have well-characterized roles in T cell biology, and confirmed that our approach successfully identified T cell- specific genes. However we also identified numerous additional genes, such as GPR171/platelet activating receptor homolog, golgin-67 and PBX4, whose expression was specific to T cells, but with no known function in T cells. GPR171 or platelet activating receptor homolog is a 7-transmembrane receptor expressed at the single positive stage of thymocyte development (GEO Profiles, NCBI), and a protein BLAST search showed it to be most similar to purinergic receptor p2Y. Golgin-67 was originally identified by screening a T cell line; it has the potential Cdc2 and Src kinase motifs and localizes to the Golgi complex (Jakymiw et al., 2000). These are several examples of the many candidate T cell-specific genes identified in this study.
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Accession Gene name Highest expression AU155661 cullin 4A Bone marrow, B lymhoblasts, NK and T cells AI688640 natural killer-tumor recognition sequence T cells and bone marrow cells AI692879 discs, large homolog 1 NA AU146285 CDNA FLJ10224 fis, clone HEMBB1000025 NA AK026008 WD-repeat protein Bone marrow, B lymhoblasts, NK and T cells BG250498 TAF3 widely expressed, high levels of transcript in T cells AA868729 Hypothetical protein BC008217 NA A Normalized expression M74777 CD26 smooth muscle, cardiac myocytes, T cells J04132 CD3 zeta NK and T cells 015 NM_000878 interleukin 2 receptor, beta NK and T cells D13720 IL2-inducible T-cell kinase T and NK cells NM_001767 CD2 T and NK cells NM_000073 CD3 gamma T and NK cells NM_000732 CD3 delta T cells g BC003070 GATA3 Placenta, T, NK and B cells AI912976 RASGRF2 widely expressed NM_005816 CD96 NK and T cells AW193600 Clone CDABP0095 NA AF100542 SH2D1A/SAP T and NK cells AJ240085 T-cell receptor interacting molecule CD4+ and CD8+ T cells NM_006139 CD28 CD4+ and CD8+ T cells AI745230 Transcribed sequence NA AJ300182 pre-B-cell leukemia transcription factor 4 T cells and thyroid NM_000074 TNFSF5/CD40L/CD154 CD4+ and CD8+ T cells NM_018961 ubiquitin associated and SH3 domain containing, A CD4+ and CD8+ T cells AB023135 ICOS B lymphoblasts and T cells AL529104 calcium/calmodulin-dependent protein kinase IV widely expressed, high in T cells NM_014207 CD5 widely expressed, high in T cells AA858297 immune associated nucleotide T and Nk cells, monocytes AI435089 immune associated nucleotide 4 like 1 T and NK cells and heart AL137145 protein kinase C, theta widely expressed, high in T cells NM_006255 protein kinase C, eta NK and T cells AI817942 ZAP70 NK and T cells AA918317 B-cell CLL/lymphoma 11B (zinc finger protein) T cells and NK cells AF288571 lymphoid enhancer-binding factor 1 T cells AA456099 Transcribed sequences NA NM_003866 inositol polyphosphate-4-phosphatase, type II T cells, B lymhpoblasts and bronchial epithelial cells NM_002371 mal, T-cell differentiation protein tongue, MOLT4 and thymus AL117477 PHD finger protein 19 NA BF131791 WW domain-containing protein 1 T cells and NK cells NM_005739 RAS guanyl releasing protein 1 T cells and NK cells AB039327 calcium/calmodulin-dependent serine protein kinase widely expressed, high in T cells BF796940 chromosome 19 open reading frame 2 widely expressed, high in T cells NM_006482 DYRK2 mostly Tand NK cells NM_022775 DiGeorge syndrome critical region gene 8 highest in K562 line AA279958 golgi associated PDZ and coiled-coil motif containing NA NM_006144 granzyme A NK and T cells NM_005442 eomesodermin homolog (Xenopus laevis) bone marrow, ovary, NK and T cells NM_005317 granzyme M NK and T cells AI829961 CD7 NK and T cells NM_000733 CD3 epsilon T cells NM_004221 natural killer cell transcript 4 T and Nk cells, B lymphoblasts NM_013308 platelet activating receptor homolog, GPR171 CD4+ and CD8+ T cells NM_002971 special AT-rich sequence binding protein 1 T cells, bone marrow CD34+ and NK cells NM_017831 ring finger protein 125 T and NK cells, B lymphoblasts NM_003726 src family associated phosphoprotein 1 NK and T cells, B lymphoblasts AA305476 Src-like-adaptor 2 NK and DCs, T cells NM_004867 integral membrane protein 2A MOLT4 and thymus AA702930 ETS1 NA AI082004 Transcribed sequence NA AW275132 membrane targeting C2 domain containing 1NA AL559122 T cell receptor beta chain BV20S1 BJ1-5 BC1 NA U07236 lymphocyte-specific protein tyrosine kinase Thymus, T, NK cells AW134823 CD6 T cells M15565 T cell receptor alpha chain T cells NM_024600 hypothetical protein FLJ20898 Lung, heart and T cells AI952009 SPOCK2 Brain and T cells AF012074 phosphodiesterase 4D, cAMP-specific widely expressed, high in T cells R54042 hypothetical protein LOC150271 NA AB011114 KIAA0542 gene product/SFI1 highest in T cells, some expression in other tissues AI928764 hypothetical protein LOC154761 NA NM_015392 neural proliferation, differentiation and control, 1 Prostate, brain AF065389 transmembrane 4 superfamily member 9 bone marrow CD71+early erythroid AI299467 Transcribed sequences NA NM_018556 signal-regulatory protein beta 2 CD4+ and CD8+ T cells AW024095 T cell receptor, clone IGRB40 NA AI694536 syntrophin, beta 2 widely expressed AF129166 acyl-CoA synthetase long-chain family member 6 bone marrow CD71+early erythroid AI052447 BACH2 NA AB050468 leucine-rich repeats and Ig-like domains 1 brain, Daudi, T cells U19969 transcription factor 8 (represses IL-2 expression) T cells, B cells, Dcs NM_007054 kinesin family member 3A widely expressed NM_013269 lectin-like NK cell receptor T cells and B cells AF163441 golgin-67 T cells, bone marrow and B lymphoblasts BG180003 CDNA FLJ39663 fis, clone SMINT2007187 NA BE044440 KIAA1201 protein Adrenal cortex and gland AB038160 transmembrane protease, serine 3 widely expressed γδ T + NK S74774 FYN oncogene related to SRC, FGR, YES NK, T cells and monocytes T cell Non-T cell NM_003790 TNFRSF25/APO3 T cells and Cerebellum peduncles cell R61322 hypothetical protein LOC92558 widely expressed AW572279 DNA (cytosine-5-)-methyltransferase 3 alpha widely expressed BF115793 ZNF83 NA BE872563 kinesin heavy chain member 2 widely expressed AW274756 cyclin-dependent kinase 6 widely expressed B W87688 putative dimethyladenosine transferase widely expressed, high in T cells AY029179 cell division cycle associated 7 MOLT4 and bone marrow CD34+
TCR alpha TCR beta CD3 epsilon CD3 delta CD3 zeta CD3 gamma LCK FYN ZAP70 LAT SHC1 * RAS GAP PLC-gamma 1 SOS *
PIK3-alpha
PKC SLP-76 * Calmodulin 1 * * *PKC beta * * Calmodulin 3
NFAT PTPN7 *NF-kappa-B p65delta Nucleus ELK1
Figure 5.2 Identification of T cell-selective gene expression using comprehensive gene microarray profiling. (A) cRNA was generated from numerous leukocyte types (Table 5.1) and microarray gene expression profiles were generated using Affymetrix U133A+B genechips and analyzed using GeneSpring software. 1-way ANOVA was used to identify the genes that were significantly different between the numerous T cell subsets and non-T cell leukocyte subsets. Approximately 100 genes most significantly (lowest p-value) preferentially expressed in T cells are listed. SymAtlas (Su et al., 2004) was used to assess the expression of these genes in a range of hematopoietic and non-hematopoietic
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tissues, and a summary is provided in the right hand column. (B) Genes involved in TCR complex signaling and T cell costimulation were selected from the genes preferentially expressed by T cells. Genes that were preferentially expressed by T cells compared to all other leukocytes, are shown in yellow boxes. Downward blue arrows signify that the gene was downregulated after activation, upward red arrows indicate upregulation. An asterisk next to the gene name indicates that multiple probes showed variable expression patterns. T cell signalling map was kindly provided by Diego Silva (ANU Medical School, Canberra, Australia).
Figure 5.2a shows that in addition to genes specifically expressed by T cells, there were also many genes that were specifically absent from T cells, compared to other leukocyte subsets. Not surprisingly these include several genes associated with the innate immune system including TLR4, CXCL2, CD32 but also effector molecules such as granulin and some cathepsins. Although we found that many genes strongly associated with T cells have already been identified as important in T cell biology, we also identified a number of ESTs and hypothetical proteins that were equally strongly associated with T cells. These novel proteins will most likely provide important insights into T cell development and function. T cells mediate their effector function following TCR engagement. We examined the expression of genes that comprise part of the TCR complex, or are involved in costimulation and signaling, and found more than 150 genes that were selectively expressed by T cells (Appendix 3). Selected genes involved in the immunological synapse and downstream signaling in T cells are shown in Figure 5.2b. Although the majority of molecules involved in T cell signaling cascades were selectively expressed by T cells, some were also shared by other leucocytes. Interestingly, many of the T cell signaling molecules were expressed at a higher level in resting rather than activated T cells. This downregulation in expression of signaling molecules following T cell activation has been noted in earlier studies (Diehn et al., 2002; Teague et al., 1999) and could be linked to TCR downmodulation which occurs following stimulation (Salio et al., 1997; Valitutti et al., 1997). It is also possible that transient upregulation of signaling molecules occurred at an earlier time-point.
5.2.1.4 Factors influencing gene expression in effector and memory T cells
After examining the genes that distinguish T cells from other leukocytes, we focused on the differences between the T cell subsets. Our dataset of gene chip profiles contains a diverse array of T cell subsets including resting and activated Th1 and Th2
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cells, naïve, central and effector memory CD4+ T cells, CD8+ T cells and αβ and γδ T cells (Table 5.1). We used principal component analysis to characterize the most abundant themes in our T cell data. This analysis identified the predominant patterns or parameters responsible for most of the variability in the T cell gene expression profiles. The first three principal components, which accounted for over half the variability in gene expression, clearly separated T cells based on the isolation method and activation state. This suggests that T cell activation and culture were the two major influences on gene expression patterns in the T cell subsets in this study (Figure 5.3). This is an important consideration, because the study of T cell function has traditionally relied on culturing and in vitro activation.
Y: [PCA component 2 (13.28% variance)] 1
Cultured T cells Act Th1/Th2 0 Cb Th1/Th2
Rest γδ T cells Th1/Th2
0 0 X: [PCA component 1 (30.03% variance) Memory αβ 1 T cells
1Z: [PCA component 3 (9.835% variance)] Isolated T cells
Figure 5.3 Principal component analysis (PCA) identifies key parameters affecting gene expression patterns in T cell subsets. PCA was performed on all T cell arrays using GeneSpring software. Only probe sets that were expressed in at least two T cell arrays were used in the analysis.
5.2.1.5 T cell activation induces an extensive transcriptional program
To identify the changes induced by T cell activation, we analyzed gene expression in Th1 and Th2 cells before and after in vitro activation. TCR engagement (anti-CD3 and Phorbol 12,13-dibutyrate treatment) was accompanied by a vast change
110 Chapter 5 T cell gene expression signature
in gene expression in Th1 and Th2 cells. The majority of genes affected by activation were regulated in a similar manner in both Th1 and Th2 cells (Figure 5.4a). Activation induced an extensive change in gene expression, with more than 7,000 genes significantly altered following activation (p<0.05). The most significantly regulated genes (lowest p-value) genes are shown in Figure 5.5.
A Act vs Act vs rest rest Th1 Th2 4956
357 589
protein biosynthesis 9.22E-16 protein-mitochondrial targeting 3.82E-13 regulation of cell cycle 2.40E-12 B rRNA processing 9.68E-12 regulation of translational initiation 1.38E-11 nuclear mRNA splicing, via spliceosome 2.07E-09 positive regulation of I-kappaB kinase or NF- 3.59E-09 immune response 1.83E-08 NLS-bearing substrate-nucleus import 2.76E-08 RNA splicing 1.48E-07 translational initiation 2.42E-06 induction of apoptosis by extracellular signals 4.98E-06 positive regulation of cell proliferation 5.90E-06 anti-apoptosis 8.73E-06 apoptosis 1. 16 E - 0 5 vitamin metabolism 1.70E-05 cell surface receptor linked signal 1.95E-05 regulation of DNA recombination 5.19E-05 regulation of translation 0.000102 pyrimidine nucleotide biosynthesis 0.000111 tRNA processing 0.000158 STAT protein dimerization 0.00023 mRNA-nucleus export 0.00028 transcription from Pol II promoter 0.000383 de novo' pyrimidine base biosynthesis 0.000509 RNA processing 0.000516 regulation of apoptosis 0.000654 tyrosine phosphorylation of STAT protein 0.000966 STAT protein nuclear translocation 0.000966 intracellular protein transport 0.00111 transcription 0.00133 JAK-STAT cascade 0.00164 signal transduction 0.00202 calcium-mediated signaling 0.00262 cell proliferation 0.00305 phospholipid biosynthesis 0.00345 transcription from Pol III promoter 0.00352 0.00389 regulation of transcription, DNA-dependent mRNA processing 0.00411 DNA metabolism 0.0043 leukocyte cell adhesion 0.00555 protein folding 0.00593 response to pest or pathogen or parasite 0.00643 Higher after activation natural killer cell activation 0.00761 B-cell differentiation 0.00761 I-kappaB kinase or NF-kappaB cascade 0.0101 chemotaxis 0.0102 cell growth and or or maintenance 0.0107 neurotransmitter transport 0.0115 inactivation of MAPK 0.0115 cell-cell adhesion 0.0122 protein targeting 0.0166 negative regulation of cell proliferation 0.0188 transcription initiation 0.0206 MAPKKK cascade 0.0206 protein transport 0.0212 negative regulation of transcription 0.0212 DNA repair 0.0221 amino acid transport 0.0259 response to stress 0.0338 exocytosis 0.0406 glycolysis 0.0411 translational elongation 0.0423 anion transport 0.0423 one-carbon compound metabolism 0.046 cell-cell signaling 0.0467 0 20406080100120 Number of genes
intracellular protein transport cell cycle 3.84E-06 3.19E-09 catecholamine metabolism 3.87E-05 carbohydrate metabolism 5.09E-05 protein modification 5.38E-05 me t a b o l i sm 0.000137 transcription 0.000236 negative regulation of cell cycle 0.000366 mitotic anaphase 0.000394 actin polymerization and or or depolymerization 0.000394 mi t o si s 0.000628 ubiquitin-dependent protein catabolism 0.000797 steroid metabolism 0.00084 protein kinase cascade 0.00103 embryonic limb morphogenesis 0.00137 mitochondrial electron transport, NADH to ubiquinone 0.00183 GPI anchor biosynthesis 0.00235 DNA replication initiation 0.00261 positive regulation of I-kappaB kinase or NF-kappaB cascade 0.00318 tricarboxylic acid cycle 0.00351
Lower after activation after Lower nucleotide metabolism 0.00383 beta-tubulin folding 0.00435 small GTPase mediated signal transduction 0.00512 endocytosis 0.00709 mitotic chromosome condensation 0.00723 cytokinesis 0.00855 actin cytoskeleton organization and biogenesis 0.0108 induction of apoptosis 0.0136 fatty acid beta-oxidation 0.0145 DNA replication 0.016 regulation of apoptosis 0.0175 phospholipid biosynthesis 0.0232 response to stress 0.0292 actin filament-based movement 0.0303 fatty acid metabolism 0.0315 lipid transport 0.033 biological_process unknown 0.034 electron transport 0.0411 protein folding 0.0468 0 204060 Number of genes Activated Resting T cells T cells
Figure 5.4 Gene expression profiles of resting and activated T cells. Th2 clones generated from naïve CD4+ T cells were cultured in Th1 and Th2 polarizing conditions to produce Th1 and Th2 cells. RNA
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was extracted from resting and activated T cell clones after 4 weeks. T cells (107/condition) were left untreated or activated for 4 hours with 1 µg/ml anti-CD3 antibody and 50 ng/ml Phorbol 12, 13- dibutyrate. Gene expression of resting and activated T cells was analysed using U133A+B Affymetrix microarrays. (A) Genes that showed significant changes in expression between resting and activated Th1 and Th2 cells were identified using 1-way ANOVA. The numbers of genes regulated by activation in Th1 but not Th2, or Th2 but not Th1 and in both are shown by Venn diagram. (B) Gene Ontology for biological processes was created using GeneSpring software and processes that showed a significant overlap with genes that increased or decreased after T cell activation are listed.
We used gene ontology in the GeneSpring software program to identify the biological processes that were upregulated in either resting or activated T cells (Figure 5.4b). A p-value of less than 0.05 (calculated using GeneSpring software) identifies the processes that were over-represented in our gene list compared to what would be expected by chance. As expected, activated T cells upregulated a number of processes associated with higher metabolic and effector activity including genes involved in transcription and translation, and also cell-cell signaling and cell-cell adhesion. Not surprisingly, genes involved in immune effector function were significantly regulated, for instance a large number of cytokines and chemokines showed major changes in expression. Most of the cytokines, chemokines and their receptors showed increased levels of transcript expression following activation (Figure 5.6). IL-10R was one of the few cytokine receptors downregulated after activation, suggesting that T cells are less susceptible to suppression by this cytokine following TCR engagement.
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Accession Gene name Activated T cells Resting T cells BC005975 Siah-interacting protein NM_006145 DnaJ (Hsp40) homolog, subfmaily B, member 1 AA526904 cDNA FLJ36515 fis, clone TRACH2001810 AJ005866 nucleotide-sugar transporter similar to C. elegans sqv-7 NM_022157 Rag C protein AI923675 hypothetical protein MGC19764 AI927931 LOC115209 AL037805 ESTs AF063020 PC4 and SFRS1 interacting protein 2 AL355685 EST from clone 491476, full insert AA694067 FLJ00180 protein AI472320 cDNA FLJ34482 fis, clone HLUNG2004067 BF339821 cDNA FLJ31439 fis, clone NT2NE2000707 AA176798 cytoplasmic linker associated protein 1 AA130247 protein kinase, cAMP-dependent, catalytic, beta BF679849 clone IMAGE:4692106 AI436803 N-acylsphingosine amidohydrolase-like NM_024633 hypothetical protein FLJ21276 R62453 cDNA FLJ32507 fis, clone SMINT1000048 AI611648 hypothetical protein DKFZp667I133 NM_002719 protein phosphatase 2 regulatory subunit B gamma isoform AA858297 hypothetical protein MGC27027 U70451 myeloid differentiation primary response gene (88) AL021707 unc-84 homolog A (C. elegans) D25304 Rac/Cdc42 guanine nucleotide exchange factor 6 BE504180 ESTs NM_024070 hypothetical protein MGC2463 AL520900 hypothetical protein BC017488 AF007155 PlSC domain containing hypothetical protein T58044 Enah/Vasp-like AI991252 butyrophilin, subfamily 3, member A2 NM_012417 retinal degeneration B beta AW271609 cDNA FLJ35633 fis, clone SPLEN2011737 BF344265 hematopoietic PBX-interacting protein NM_022476 fused toes homolog (mouse) NM_020139 oxidoreductase UCPA NM_016293 bridging integrator 2 AA975530 ESTs AB032951 protein kinase C binding protein 1 NM_005611 retinoblastoma-like 2 (p130) BF740152 myosin IF NM_001752 catalase BF057084 hypothetical zinc finger protein MGC2647 NM_006994 butyrophilin, subfamily 3, member A3 AV700930 ESTs NM_014315 host cell factor homolog AF198052 FYN binding protein AI821726 ESTs AI570531 chromobox homolog 4 AI743654 metallo phosphoesterase NM_007032 Tara-like protein NM_022074 hypothetical protein FLJ22794 Downregulated activation after NM_022898 B-cell CLL/lymphoma 11B AB015718 serine/threonine kinase 10 AA219354 Hermansky-Pudlak syndrome 3 NM_003789 TNFRSF1A-associated via death domain BE646618 mitogen-activated protein kinase kinase kinase kinase 1 NM_014744 TBC1 domain family, member 5 AV728606 programmed cell death 4 AA074597 myofibrillogenesis regulator 1 U87558 bridging integrator 1 AB014576 KIAA0676 protein AW296451 ESTs
NM_006399 basic leucine zipper transcription factor, ATF-like AI860150 hypothetical protein FLJ23306 NM_001665 ras homolog gene family, member G NM_024640 hypothetical protein FLJ23476 NM_006732 FBJ murine osteosarcoma viral oncogene homolog B NM_004049 BCL2-related protein A1 BC002538 serine proteinase inhibitor, clade B, member 9 AI870951 ESTs NM_002999 syndecan 4 AI356405 ESTs AA805622 ESTs BE778706 CDNA clone IMAGE:5092935 NM_004148 ninjurin 1 AA873350 ESTs NM_000584 interleukin 8 AF251062 RNA binding protein D86984 T-cell activation leucine repeat-rich protein NM_002188 interleukin 13 BF063657 ESTs NM_002460 interferon regulatory factor 4 NM_002981 CCL1 AB002344 KIAA0346 protein NM_003370 vasodilator-stimulated phosphoprotein NM_000588 interleukin 3 BF575514 pre-B-cell colony-enhancing factor NM_001394 dual specificity phosphatase 4 BE300521 insulin induced gene 1 AB051513 KIAA1726 protein AA669114 T-cell activation kelch repeat protein NM_024508 hypothetical protein MGC10796 NM_003897 immediate early response 3 AI924426 ELL-related RNA polymerase II, elongation factor AI743123 ESTs NM_004515 interleukin enhancer binding factor 2, 45kDa BC001423 proteasome activator subunit 3 AI916948 ESTs AI921844 clone IMAGE:3858719 NM_004728 DEAD/H box polypeptide 21 AI681558 ESTs NM_001674 activating transcription factor 3 NM_002135 nuclear receptor subfamily 4, group A, member 1
Upregulated after activation Upregulated M37435 colony stimulating factor 1 NM_018256 WD repeat domain 12 NM_020437 similar to aspartate beta hydroxylase NM_004973 jumonji homolog AB046775 KIAA1555 protein F09493 hypothetical protein BC008207
Figure 5.5 Genes that were most significantly regulated during T cell activation. Genes regulated during T cell activation were identified using 1-way ANOVA. Approximately 100 genes that were the most significantly different after T cell activation are shown.
113 Chapter 5 T cell gene expression signature
TNFSF15 IL-6
CCL1 CCR8 IL-11Ra TNFSF10 IL-3 ACVR2 ACVR1 VEGFb IL-5 ACVR2 VEGF
TNFSF11 ACVR1 TNFRSF25 IL-8 LIF IL-2Ra IL-2Rb CSF1 IL-2 ACVR1 CCR2 IL-2Rb
FLT3LG TNF ACVR1 CCL3 IL-4R IL-4 LTA CCL4
TNFSF14
IL-9 IL-9R ACVR2
TNFSF6 IL-15Ra ACVR2 IL-2Rb IFN-g TNFSF5 IFNGR2
IL-21R TNFSF8 TNFRSF8 IL-10Ra IL-10 IL-10Rb TNFSF9 TNFRSF9 IL-17R
IL-4R TNFRSF18 IL-4 XCL1
IL-4R IL-24 IL-13 TNFSF13b IL-10Rb
IL-1RAP
IL-12Rb2 IL-1RAP IL-10Rb IL-23
Downregulated Upregulated
Figure 5.6 Cytokine, chemokines and receptors regulated during T cell activation. GenMapp2 map of Cytokine/cytokine receptor interaction was used in GeneSpring software to show genes that were regulated during T cell activation. Upregulated genes are shown in red while those that were downregulated after activation are shown in yellow. Genes that did change are displayed in pale green.
A number of processes involving proliferation, cell cycle and apoptosis were regulated during T cell activation. Interestingly, resting T cells expressed higher levels of transcripts for genes involved in cytoskeleton organization including dynein, myosin VA and IXB, capping protein, tubulin-specific chaperones c, e and d and others. Cytoskeleton rearrangements are an integral part of the T cell activation process (Cannon and Burkhardt, 2002). These rearrangements are early events in the formation of the immunological synapse (minutes after activation) (Barda-Saad et al., 2004), and downregulation of these transcripts in T cells stimulated for 4 hours may serve as a feedback mechanism.
5.2.1.6 The effect of cell culture on T cell gene expression
Unsupervised hierarchical clustering (Figure 5.1) and principal component analysis (Figure 5.3) identified culturing as one of the biggest influences on gene expression patterns in T cells. To define gene expression changes induced in cultured T cells we compared them to isolated T cells. Despite the stringent p-value cut-off
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(p<0.0001) we still identified more than 2,000 transcripts that were affected by the culturing process (Figure 5.7a), with the majority of differentially expressed genes upregulated after culturing (1721 vs. 576). As expected, since T cell culture involves TCR stimulation, we found that a large proportion of genes upregulated in cultured cells were also the genes that were upregulated after T cell activation (Figure 5.7b). Few genes were shared between cultured activated and directly ex vivo isolated memory T cells. This likely reflects the differences between in vitro stimulated T cells which have a recently activated effector phenotype and ex vivo isolated memory T cells which are in a more resting state in the absence of an active immune response. To gain a more thorough understanding of the processes regulated between cultured and activated T cells we performed a gene ontology biological processes analysis. The majority of processes identified as significantly preferentially expressed by cultured cells were biosynthetic and metabolic pathways. The activation and division of cultured cells is reflected in their expression of genes involved in RNA and protein processing and also in cell cycle regulation. Isolated cells, on the other hand, expressed a large number of genes involved in regulation of gene transcription and immune responses. Although we found that many genes upregulated during cell culture were also upregulated during T cell activation, a large proportion of genes were uniquely upregulated during T cell culture. These genes are largely involved in protein synthesis and metabolic processes and are likely required for maintenance of T cells in culture.
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( A
regulation of transcription, DNA- 0.000283 immune response 0.0298 isolated amino acid metabolism 0.00361 anion transport 0.00772 apoptosis 1.45E-06 576 ATP synthesis coupled proton transport 0.000177 biological_process unknown 0.0328 carbohydrate metabolism 0.0425 carbohydrate transport 0.00432 cell proliferation 0.00127 defense response 0.0363 electron transport 5.36E-07 P<0.0001 energy pathways 0.0352 glucose transport 0.000841 glycolysis 2.06E-08 intracellular protein transport 0.000303 me mb r a n e fu si o n 0.00915 metal ion transport 4.11E-06 mRNA-nucleus export 0.00392 negative regulation of cell proliferation 0.04 NLS-bearing substrate-nucleus import 0.00127 nuclear mRNA splicing, via spliceosome 6.72E-08 nucleobase, nucleoside, nucleotide and 6.41E-06 one-carbon compound metabolism 0.00103 positive regulation of cell proliferation 0.01 protein biosynthesis 1. 9 0 E - 2 2 protein folding 8.85E-15 protein transport 0.00442 protein-mitochondrial targeting 4.98E-09 cultured protein-nucleus import 0.00915 protein-nucleus import, docking 0.000618 pyrimidine nucleotide biosynthesis 0.000235 regulation of cell cycle 0.000108 regulation of cell growth 0.000652 1721 regulation of translation 0.03 regulation of translational initiation 0.0484 response to oxidative stress 0.0367 response to stress 0.00284 ribosome biogenesis 0.00038 RNA splicing 1. 7 4 E - 0 8 rRNA processing 4 . 14 E - 11 small GTPase mediated signal transduction 0.0283 spliceosome assembly 0.00038 transcription 0.00323 transcription from Pol II promoter 0.0432 transcription from Pol III promoter 0.00347 translational elongation 0.00772 translational initiation 3.11E-07 tricarboxylic acid cycle 0.00915 tRNA processing 4.24E-05 ubiquitin-dependent protein catabolism 4.90E-05 vesicle-mediated transport 0.0311 0204060
Isolated Cultured B
Activated 2238 634 5 Cultured Isolated 989 98 436 135 Resting 4201
Figure 5.7 Gene expression profiles of cultured versus isolated T cells. (A) Differentially expressed genes were identified using 1-way ANOVA (p<0.0001). GO biological process was created using GeneSpring software and processes that showed a significant overlap with genes that increased or decreased after culturing are listed. (B) Genes that are differentially expressed by cultured and isolated T cells and are also expressed by resting or activated T cells are shown by Venn diagram.
5.2.1.7 Gene expression signatures of T cell subsets, including γδ T cells
We next determined gene expression signatures, that allowed the most reliable distinction between the different T cell subsets. A 1-way ANOVA with Student- Newman-Keuls post-hoc testing was used to identify the genes that distinguish particular subsets of T cells from all others. NK cells were also included in this study
116 Chapter 5 T cell gene expression signature
since we noted earlier that T cells share many genes with NK cells. In particular, γδ and NK cells seem to have largely overlapping gene expression profiles and cluster together (Figure 5.1) suggesting a large degree of similarity or relatedness. Unsupervised clustering of these genes (Figure 5.8) revealed easily identifiable and distinct gene expression signatures that distinguished each subset. Of note, gene expression signatures were not only characterized by highly preferentially expressed genes, but also by the genes whose expression was particularly low or absent in a particular subset. We have listed select genes from clusters including genes up and down- regulated in TFH cells (Figure 5.8i) and vi)), downregulated in γδ T cells (Figure 5.8ii)), down-regulated in TCM (Figure 5.8v)) and TEM (Figure 5.8vii)) and others. Several lines of evidence point to the importance of γδ T cells in immune responses (Carding and Egan, 2002; Hayday and Tigelaar, 2003). Despite this the precise role of these cells has remained elusive. In an effort to identify genes specific to γδ T cells that might underlie a function unique to this enigmatic subset, and enable distinction from αβ T cells, we compared γδ T cell genes against all other subsets. Surprisingly, we found that few genes were expressed specifically by γδ T cells. A number of genes highly expressed by γδ T cells were also expressed by NK cells, in particular a number of KIR genes (Figure 5.8iv)). The expression of these genes, however, was not confined to NK and γδ T cells since αβ effector T cells also expressed transcripts for some of these molecules. The gene expression signature of γδ T cells could be distinguished more easily by low or absent expression of a number of αβ T cell transcripts, rather than the expression of any γδ specific genes (Figure 5.8ii)). Even the γ chain of the TCR is expressed in some αβ T cells. Among the genes downregulated in γδ T cells were several genes involved in pre-mRNA splicing processes, including splicing factors, arginine/serine rich 10 and 16, and pre-mRNA splicing factor 16 (DEA/H box polypeptide 38). The downregulation of these splicing factors compared to αβ T cells suggests that γδ T cells may use alternative splicing mechanisms for the generation of TCR diversity than those used by αβ T cells.
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Accession Gene name iv) NK and γδ i) Upregulated in TFH AI961231 thymus high mobility group box protein TOX NM_001557 IL-8R beta NM_002221 inositol 1,4,5-trisphosphate 3-kinase B AA524536 leucine-rich repeat-containing GPR6 AL122066 FK506 binding protein 5 AI814092 EDG8/S1P5 BF514552 Fc receptor-like protein 3 U79240 PAS domain containing serine/threonine kinase NM_016543 sialic acid binding Ig-like lectin 7 NM_004867 integral membrane protein 2A NM_007036 endothelial cell-specific molecule 1 AA142929 myocyte enhancer factor 2A NM_005211 colony stimulating factor 1 receptor U80918 NFATC1 AF022048 NK-receptor AF162428 sarcosine dehydrogenase M61900 prostaglandin D2 synthase 21kDa (brain) L32867 sialyltransferase 8A BF476502 metallo phosphoesterase NM_004999 myosin VI NM_005874 LILRB2 NM_014513 KIR2DS5 BF701166 trichorhinophalangeal syndrome I AF217487 KIR2DL5 NM_004968 islet cell autoantigen 1, 69kDa NM_014512 KIR2DS1 AF045451 NGFI-A binding protein 1 AF002256 KIR2DL4 U54826 MAD, mothers against decapentaplegic homolog 1 NM_002255 KIR2DL4 NM_030912 tripartite motif-containing 8 AF263617 KIR3DL2 NM_016010 CGI-62 protein NM_012445 spondin 2, extracellular matrix protein NM_006419 CXCL13 L76668 KIR2DS2 U24078 KIR2DL1 NM_022969 fibroblast growth factor receptor 2 L76669 KIR2DL2 i) N66633 lipoma HMGIC fusion partner-like 2 AC006293 KIR3DL7 NM_004364 CCAAT/enhancer binding protein (C/EBP), alpha NM_001553 insulin-like growth factor binding protein 7 X68829 CXCR5 AF135564 KIR2DS4 R61374 hairy/enhancer-of-split related with YRPW motif 1 U73396 KIR3DL1 U58515 chitinase 3-like 2 BE614255 BCL2-related ovarian killer U39226 myosin VIIA i) ii) AL040198 hairy/enhancer-of-split related with YRPW motif-like ii) AL567411 cyclin-dependent kinase 5, regulatory subunit 1 AK024964 nuclear factor I/A NM_001353 aldo-keto reductase family 1, member C1 v) Downregulated in TCM iii) AL390172 branched chain aminotransferase 1, cytosolic AL390144 nerve injury gene 283 NM_001335 cathepsin W (lymphopain) NM_005346 heat shock 70kDa protein 1B NM_003099 sorting nexin 1 U49396 purinergic receptor P2X, ligand-gated ion channel, 5 NM_005263 growth factor independent 1 AA687627 3-oxoacid CoA transferase 2 R13594 LIM domain only 4 NM_018945 phosphodiesterase 7B AV721789 discs, large (Drosophila) homolog 5 AK024280 synaptotagmin XII BF195718 cold shock domain protein A AF009961 myosin IA AK023913 CAMP-specific phosphodiesterase 8B1 ii) Downregulated in γδ T cells R38990 GALNT13 NM_002128 high-mobility group box 1 D13889 ID1, dominant negative helix-loop-helix protein NM_002475 myosin light chain 1 slow a NM_003323 tubby like protein 2 BF125756 GABA(A) receptor-associated protein like 1 NM_018660 papillomavirus regulatory factor PRF-1 iv) NM_000302 procollagen-lysine 2-oxoglutarate 5-dioxygenas iii) BC001169 esterase D/formylglutathione hydrolase AF279900 MCM7 minichromosome maintenance deficient 7 NM_017840 mitochondrial ribosomal protein L16 v) S72422 dihydrolipoamide S-succinyltransferase AK001081 dynein, cytoplasmic, light intermediate polypeptide 1 AJ295618 YME1-like 1 (S. cerevisiae) NM_018144 likely ortholog of mouse SEC61, alpha subunit 2 vi) Downregulated in TFH NM_016040 CGI-100 protein BC001650 putative methyltransferase NM_003853 interleukin 18 receptor accessory protein NM_004707 APG12 autophagy 12-like (S. cerevisiae) NM_014000 vinculin NM_022717 U1-snRNP binding protein homolog (70kD) NM_003608 G protein-coupled receptor 65 U92268 mitogen-activated protein kinase 11 AW014593 guanylate binding protein 1, interferon-inducible AK025020 adaptor-related protein complex 1, gamma 1 subunit AL353950 protein phosphatase 3 cat. subunit alpha isoform NM_022730 COP9 constitutive photomorphogenic homolog subunit 7B NM_005475 lymphocyte adaptor protein AK025696 trinucleotide repeat containing 6 NM_003151 STAT4 NM_004350 runt-related transcription factor 3 U87836 splicing factor, arginine/serine-rich 10 NM_001780 CD63 BE966876 GART AI693688 myosin IG X16354 carcinoembryonic antigen-related cell adhesion molecule 1 NM_002305 lectin, galactoside-binding, soluble, 1 AJ271091 butyrate-induced transcript 1 NM_001425 epithelial membrane protein 3 BG403660 heat shock 105kD BC000125 transforming growth factor, beta 1 vi) BF034906 PL6 protein NM_001456 filamin A, alpha AK000005 FLJ00005 protein BG537190 ferritin, light polypeptide NM_007056 suppressor of white apricot homolog 2 U07139 Ca channel, voltage-dependent, beta 3 subunit NM_014502 nuclear matrix protein 200 related to splicing factor PRP19 AW104453 monocyte to macrophage differentiation-assoc. NM_003254 tissue inhibitor of metalloproteinase 1 AI742553 protein kinase, lysine deficient 1 NM_003364 uridine phosphorylase AF254822 SMARCA4 NM_013272 solute carrier family 21 , member 11 AK025179 casein kinase 1, gamma 1 NM_001109 a disintegrin and metalloproteinase domain 8 AL136847 GRIP1 associated protein 1 NM_000889 integrin, beta 7 NM_024555 F-box and leucine-rich repeat protein 6 NM_003835 regulator of G-protein signalling 9 AF038391 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 38 NM_014624 S100 calcium binding protein A6 NM_001976 enolase 3, (beta, muscle) NM_001784 CD97 antigen AK001836 kelch-like 5 (Drosophila) NM_015675 growth arrest and DNA-damage-inducible, beta AA812224 Vam6/Vps39-like NM_013308 platelet activating receptor homolog/GPR171 AI674404 synaptotagmin-like 3 AK023141 FAST kinase BC001288 decay accelerating factor for complement /CD55 vii) NM_002480 protein phosphatase 1, regulatory (inhibitor) subunit 12A NM_002189 interleukin 15 receptor, alpha NM_030937 likely ortholog of mouse Paneth cell enhanced expression NM_006869 centaurin, alpha 1 T T CD8 NK γδ T Th1 Th2 Th1 Th2 Th1 Th2 AL565767 cold inducible RNA binding protein NM_002961 S100 calcium binding protein A4 CM EM FH AI768378 Williams-Beuren Syndrome critical region protein 20 B NM_021103 thymosin, beta 10 act act na na AI186666 mitochondrial Rho 2 NM_002966 S100 calcium binding protein A10 NM_018044 Williams Beuren syndrome chromosome region 20A NM_004039 annexin A2 AA583817 ribosomal protein S16 AI922599 vimentin AW511198 hypothetical protein FLJ13089 M62898 annexin A2 pseudogene 2 NM_000956 prostaglandin E receptor 2 BC004546 disrupter of silencing 10 AW295338 Ca/calmodulin-dependent protein kinase II gamm AA521311 T-cell activation protein phosphatase 2C NM_004753 short-chain dehydrogenase/reductase 1 iii) Common to αβ BE897074 CGI-09 protein U03858 fms-related tyrosine kinase 3 ligand vii) Downregulated in TEM NM_023077 hypothetical protein FLJ12439 NM_000700 annexin A1 NM_000074 CD40L NM_014481 APEX nuclease 2 NM_001929 deoxyguanosine kinase NM_004844 SH3-domain binding protein 5 NM_003037 SLAM NM_016279 cadherin 9, type 2 (T1-cadherin) AK021413 leucyl-tRNA synthetase NM_016594 FK506 binding protein 11, 19 kDa AJ300182 PBX4 NM_002677 peripheral myelin protein 2 NM_005804 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 39 M23254 calpain 2, (m/II) large subunit AL529104 CAMK4 AA521288 carbonic anhydrase VB, mitochondrial NM_001394 dual specificity phosphatase 4 AI949392 semaphorin 4C NM_018961 UBASH3A NM_000073 CD3 gamma M60725 P70 ribosomal S6 kinase alpha-II NM_000732 CD3 delta BF978262 CD109 NM_014450 SIT NM_002883 Ran GTPase activating protein 1 NM_014207 CD5 NM_012099 CD3-epsilon-associated protein; antisense to ERCC-1 M12959 TCR alpha NM_006521 transcription factor binding to IGHM enhancer 3 AB023135 ICOS AA541630 runt-related transcription factor 3 AB051487 DUSP16 NM_003151 STAT4 NM_006139 CD28 AJ240085 TRIM
Figure 5.8 Distinct gene expression signatures characterize effector/memory T cell subsets. Gene expression profiles of different T cell subsets were analysed using 1-way ANOVA with Benjamini multiple testing correction and Student-Newman-Keuls post-hoc tests to identify genes with significant changes in expression between the subsets. The union of all genes that distinguish the various cell types was created by combining genes that differentiate a single subset from all others. Unsupervised clustering using Pearson similarity measure was used to arrange the genes (y-axis) and microarrays (x- axis).
118 Chapter 5 T cell gene expression signature
The analysis presented above is useful for identifying specific signatures for particular T cell subsets. However it is not ideal for examining the differences between particular subsets especially if a proportion of the genes is shared by other T cell types.
For instance, we could identify few genes that would be specific to TEM or TCM cells since both of these memory T cell populations are heterogeneous and contain non- polarized and also Th1- and Th2-like cells, so many genes are shared between these memory and effector populations (Messi et al., 2003; Rivino et al., 2004). In order to characterize the differences between TCM and TEM cells, we made a direct comparison of gene expression in these two subsets (Figure 5.9). Predictably, TCM cells expressed higher levels of CCR7 and L-selectin which are key to their secondary lymphoid organ homing. TEM cells expressed a number of effector molecules including chemokines CCL4, CCL5, XCL1 and molecules associated with CTL activity such as granulysin and granzymes A and B. The expression of these molecules is consistent with potential effector function (Zaunders et al., 2004). In general, few genes were reproducibly differentially expressed between the two memory T cell populations, suggesting that
TEM and TCM cells are largely similar in their gene expression patterns.
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NM_001838 CCR7 AK021457 cDNA FLJ11395 fis, clone HEMBA1000594 AI675780 transcription elongation factor A (SII), 3 AI082078 actinin, alpha 1 NM_003328 TXK tyrosine kinase AF001540 clone alpha1 AF052160 clone 24629 Expression NM_014380 nerve growth factor receptor associated protein 1 D50925 PAS domain containing serine/threonine kinase NM_002371 mal, T-cell differentiation protein AL136944 solute carrier family 11 member 3 0 300 600 AL049265 cDNA DKFZp564F053 AA361361 mitogen-activated protein kinase kinase kinase 1 AK023042 cDNA FLJ12980 fis, clone NT2RP2006441 NM_000565 interleukin 6 receptor NM_020379 mannosidase, alpha, class 1C, member 1 Signal log ratio AI214464 ESTs AW593213 KIAA1078 protein NM_014832 TBC1 domain family, member 4 NM_020215 hypothetical protein DKFZp761F2014 -3 0 3 NM_000655 L-selectin NM_006159 NEL-like 2 (chicken) NM_006226 phospholipase C-like 1 NM_001242 CD27 BF589179 cDNA FLJ36527 fis, clone TRACH2003941 NM_006493 ceroid-lipofuscinosis, neuronal 5 NM_002012 fragile histidine triad gene BE676335 ESTs NM_005814 glycoprotein A33 AL512737 hypothetical protein FLJ22174 X06989 amyloid beta (A4) precursor protein AF146696 forkhead box P1 NM_006235 POU domain, class 2, associating factor 1 NM_017415 kelch-like 3 (Drosophila) AW006438 golgin-67 AI821791 phosphodiesterase 4D interacting protein AF008936 aminopeptidase-like 1 AL527430 glutathione S-transferase M3 (brain) AL545035 LDL receptor adaptor protein AA653300 zinc finger protein 36 (KOX 18) AF294627 lymphoid enhancer-binding factor 1 AW166711 phosphoinositide-binding protein PIP3-E AA743462 ESTs NM_024076 hypothetical protein MGC2628 NM_003595 tyrosylprotein sulfotransferase 2 NM_001814 cathepsin C BF475488 cDNA FLJ25967 fis, clone CBR01929 AI188445 similar to RIKEN cDNA 1200014N16 gene NM_003670 basic helix-loop-helix domain containing class B 2 AK000095 cDNA FLJ20088 fis, clone COL03869 AF070569 hypothetical protein MGC14376 AA911231 calcineurin A alpha AI801777 cDNA FLJ31353 fis, clone MESAN2000264 AL046017 hypothetical protein FLJ20202 NM_007182 Ras association domain family 1 AY029179 cell division cycle associated 7 BE500942 cDNA DKFZp761M0111 AF020314 leukocyte membrane antigen NM_016523 killer cell lectin-like receptor subfamily F member 1 NM_021822 phorbolin-like protein MDS019 BC000182 annexin A4 AW204712 cDNA DKFZp667P2012 AI250910 cDNA FLJ31655 fis, clone NT2RI2004284 AK026764 cDNA: FLJ23111 fis, clone LNG07835 BF446578 CG4853 gene product M27487 HLA-DPA1 NM_002121 HLA-DPB1 NM_002166 inhibitor of DNA binding 2 AI963476 RAP2A, member of RAS oncogene family AL096776 ras homolog gene family, member U BF969397 cDNA FLJ32437 fis, clone SKMUS2001398 BE620734 sterile alpha motif and leucine zipper containing kinase NM_004776 B4GALT5 BF508948 induced upon T-cell activation U65585 HLA-DRB1 AI129628 hypothetical protein MGC35163 AJ297586 HLA-DRB3 AU145277 cDNA FLJ11643 fis, clone HEMBA1004366 AA495984 GRB2-associated binding protein 3 AW193698 transforming growth factor, beta receptor III NM_000579 CCR5 AF081675 killer cell lectin-like receptor subfamily G member 1 NM_002985 CCL5 AB046817 synaptotagmin-like 2 AI768563 cDNA FLJ35169 fis, clone PLACE6012908 NM_013351 T-box 21/T-bet AI094580 ATPase, Na+/K+ transporting, beta 1 polypeptide BF968134 ESTs NM_002305 lectin, galactoside-binding, soluble, 1 M21121 CCL5 AF031824 cystatin F M30894 T cell receptor gamma locus AL121985 clone RP11-404F10 AB018580 aldo-keto reductase family 1, member C3 NM_006144 granzyme A BF342391 bobby sox homolog (Drosophila) NM_003853 interleukin 18 receptor accessory protein AI445650 Perforin BG397856 HLA-DQA1 U23772 XCL1 AB059408 homeodomain only protein AW157571 hypothetical protein FLJ31564 NM_000632 integrin, alpha M NM_002984 CCL4 NM_005248 FGR AI814092 EDG8 M85276 granulysin AL554008 G protein-coupled receptor 56 U20350 CX3CR1 NM_005601 natural killer cell group 7 sequence AB021123 Ksp37 protein M36118 granzyme B T SLR TCM EM
Figure 5.9 Genes differentially expressed by TCM and TEM cells. Genes that differed more than two- fold in 2 replicate experiments are shown. The colour scale indicates the level of expression. Signal log ratio (SLR) is the log of fold change.
120 Chapter 5 T cell gene expression signature
5.2.1.8 Identifying T cell subtype predictor genes
Classification and diagnosis of complex diseases is an important application of microarray gene expression profiling. We sought to identify the smallest subset of ‘predictor’ genes that could be used to distinguish between all of the different T cell subsets in this study. We selected one to two genes with the lowest p-value from each subset which most reliably distinguished that particular T cell subset from all other T cells, creating a list of 13 genes (Figure 5.10). We found that using this small subset of genes it was possible to correctly identify the T cell type used in each microarray experiment using a k-nearest neighbors method. As few as 13 genes were sufficient to distinguish between 11 different T cell subsets and NK cells. Our results demonstrate the utility of gene expression profiling for T cell type classification. An important qualification, however, is that a much larger number of replicates would be required for accurate classification of a completely unknown subset of T cells. Nevertheless, it demonstrates the applicability of this approach to T cell classification.
p ed cto ( atu Accession Gene title
U79240 PAS domain containing serine/threonine kinase U96180 phosphatase and tensin homolog NM_002121 HLA-DPB1 NM_002999 syndecan 4 U44836 cAMP responsive element modulator BC006146 hypothetical protein MGC13096 AF228422 normal mucosa of esophagus specific 1 AI766311 cDNA FLJ14059 fis, clone HEMBB1000573 X17013 Bacillus subtilis diaminopimelate decarboxylase NM_005572 lamin A/C BC001743 hypothetical protein FLJ10803 NM_001394 DUSP4 BE966247 F-box only protein 22
TCM TFH TCM TEM CD8 NK γδ Th1 Th2Th1act Th2act Th1na Th2na
Figure 5.10 A selection of differentially expressed genes that distinguish T cell subsets. 1-2 genes that most reliably (lowest p-value) differentiate between one particular T cell subset and all the others were selected and combined into the ‘predictor’ list. The classification value of this list was validated when the correct T cell subset was predicted using k-nearest neighbours method in GeneSpring software. The p-value for prediction was less than 0.05 for most T cell subsets.
5.3 Discussion
Gene expression profiling of all the major leukocyte subsets in human peripheral blood enabled the identification of a unique gene expression signature for each subset. We used our extensive database of gene expression profiles of leukocyte and T cell subsets to identify a T cell specific transcription signature. This signature
121 Chapter 5 T cell gene expression signature
contains both well characterized markers of T cell function and differentiation and also a number of novel genes not previously associated with T cells. For instance, GPR171 and PBX4 were highly specific to T cells according to their transcript expression in leukocytes, as well as other tissues. We have uncovered numerous genes selectively expressed by T cells in our study (~400 of these are listed in Appendix 2). An in-depth examination of many of these genes will undoubtedly provide many novel insights into T cell biology. Within the T cell subsets, T cell activation and culture induced the largest changes in gene expression. As has been noted in earlier studies, activation induced extensive changes to the transcriptional program of T cells (Diehn et al., 2002; Teague et al., 1999). We found that the changes were largely similar for both Th1 and Th2 cells, with the vast majority of genes regulated in the same direction in both T cell subsets. Predictably, there was a large overlap between the genes upregulated during T cell culture and after T cell activation. There were, however, important differences between the two signatures with the majority of genes upregulated during cell culture being unique to that process. These genes are involved in protein synthesis and in metabolic processes that are required for cell maintenance and proliferation during culture. An in depth examination of gene expression profiles of T cell subsets revealed specific signatures that distinguish these subsets. Interestingly, in this type of analysis some subsets were more easily distinguished by the genes that were downregulated or absent. This was especially evident for γδ T cells, which expressed lower levels of transcripts for many αβ T cell expressed genes. We demonstrated that T cell subsets can be easily distinguished from each other at the level of gene expression by selecting a small number of ‘predictor’ genes. Earlier studies have demonstrated the application of gene expression profiling to distinguish disease subtypes (Alizadeh et al., 2000; Alizadeh et al., 2001; Golub et al., 1999; van 't Veer et al., 2002). γδ T cells remain a largely enigmatic subset, and a unique role for these T cells is yet to be defined. Several recent studies attempted to identify a specific transcriptional profile for γδ T cells (Fahrer et al., 2001; Hedges et al., 2003; Meissner et al., 2003; Shires et al., 2001). Although these studies discovered many interesting features of γδ T cells such as their ‘activated yet resting’ phenotype (Shires et al., 2001), a unique γδ molecular and cellular profile remains to be identified. To the best
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of our knowledge, ours is the first study to examine the transcriptional profile of circulating human γδ T cells. We found that, at least in peripheral blood, γδ T cells shared some features of αβ T cells and NK cells, yet expressed few molecules that could be considered γδ-specific. This is surprising, as a γδ-specific marker termed T19 has been identified in sheep and cattle (Hein and Mackay, 1991). This result is, however, in accordance with a recent study which showed that γδ T cells share many features with an unconventional subset of αβ T cells (Pennington et al., 2003). Although we could not identify any γδ-specific markers, their gene expression signature, which included many genes that were expressed at a lower level compared to αβ T cells, did provide some interesting insights into the γδ T cell biology. Although γδ T cells have the potential for extensive receptor diversity, it appears that this potential is not fully realized (Carding and Egan, 2002). We found that compared to conventional αβ T cells, γδ T cells showed reduced expression of several proteins involved in pre-mRNA splicing which may provide important clues to the generation of TCR diversity in these cells. T cell memory can be divided into two distinct subsets based on the expression of homing molecules and effector function. TEM cells express tissue homing receptors and provide immediate effector function after stimulation, while TCM cells home to secondary lymphoid organs and provide long-term systemic protection (Sallusto et al.,
1999). We found surprisingly few differences between TCM and TEM cells isolated from human peripheral blood, at least at the level of gene expression. TEM cells expressed higher levels of several molecules associated with T cell effector function, while TCM cells expressed higher levels of CCR7 and CD62L (L selectin) which enable them to home to secondary lymphoid organs. Overall, TCM and TEM cells revealed similar gene expression profiles suggesting that at least in the periphery these two memory subsets may serve largely similar functions. Recent evidence indicates that the functional distinction between TEM and TCM subsets is less clearly defined than was previously thought. At least in mice, TCM cell were able to provide immediate protection (Wherry et al., 2003). In our study we only examined gene expression profiles of circulating memory T cells, it is likely however, that TEM cells found in tissues would have a more distinctive phenotype, possibly resembling more an effector cell. In addition, both TEM and TCM cells are heterogeneous (Sallusto et al., 2004) and include some Th1- and Th2- like cells, but also possibly some NK T and γδ T cells and skin and gut homing T cells.
123 Chapter 5 T cell gene expression signature
With recent advances in cRNA production techniques and multi-colour cell sorting, isolation of sub-subsets of TEM and TCM cells becomes feasible and will undoubtedly provide further insights into memory T cell biology. In conclusion, we have used an extensive dataset of leukocyte gene expression profiles to identify genes selectively expressed by T cells and to gain an understanding of the molecular changes that accompany T cell activation and differentiation. The identity of numerous novel genes selectively expressed by T cells will provide important insight into T cell function, in general, as well as the specialized roles of subsets of T cells.
124
6 General Discussion
Global analysis of gene expression has provided many important insights into the workings of the immune system, including the identification of genes specific to particular cell types and gene expression patterns or ‘signatures’ associated with specific immunological paradigms (Macian et al., 2002; Staudt, 2001; van der Pouw Kraan et al., 2004). This thesis describes some new insights into T cell development and function gained using gene expression profiling. Gene expression profiles of type 1 and type 2 mouse and human CD4+ and CD8+ T cells identified not only several novel genes with potentially important roles in T cell differentiation, but also teased out some of the major influences on gene expression patterns of in vitro polarized T cells. A significant caveat of microarray gene expression analysis to study Th1 and Th2 differentiation is the sensitivity of gene expression profiles to external factors. The use of different polarization protocols leads to large perturbations in the resulting gene expression profiles. Nevertheless, a comprehensive analysis of gene expression profiles of type 1 and type 2 T cells across a range of different conditions allowed identification of genes whose differential expression was conserved in several studies, suggesting a potentially important role in T cell development and/or function. We next used Th1 and Th2 gene expression profiles together with a CXCR5+ T cell gene expression profile to pinpoint the molecular signature of the third major effector T cell subset, TFH cells, and identified novel markers that may underlie their function. This TFH-specific signature provided possible mechanisms for the ability of these cells to provide help to B cells either via cell-cell contact (potentially mediated by CD84 and SAP) or via a soluble effector molecule IL-21, which is known to modify B cell development and function. In addition, BCL-6 was identified as a transcription factor which may direct T cell development towards the TFH fate. Many other genes that were specific to TFH cells will no doubt provide further insights into the biology of this subset of CD4+ effector T cells. The T cell subset gene expression profiles were integrated with other microarray datasets generated within our department which include all the major leukocyte subsets. This enabled us to conduct a broad study which identified numerous leukocyte subset-specific genes. In particular, we identified a large number of genes
125 Chapter 6 General Discussion
that were selectively expressed by T cells. An in-depth investigation of T cell subsets revealed T cell activation state and culture as the most significant influences on gene expression. Although gene expression profiles of ex vivo isolated T cell subsets were more similar to each other, we identified specific gene expression signatures for each subset. We found that genes selected from these signatures could be used for classification of multiple cell types, including closely related T cell subsets. Classification based on gene expression profiles may provide interesting insights into the relationship between different T cell and leukocyte subsets and the identity of newly discovered subsets. This and many other studies highlight the application of global gene expression analysis to identify genes involved in fundamental immunological processes. Microarrays have been used successfully to identify genes involved in effector T cell responses (Rogge et al., 2000), T cell tolerance (McHugh et al., 2002), anergy (Macian et al., 2002) and other key aspects of immune responses. The global scope of such studies avoids bias that might be introduced by model-driven analyses. Thus global gene expression studies often yield unexpected observations, and the identification of genes that may be better characterized in a completely different system (Staudt and Brown, 2000). Despite the obvious power of a high-throughput approach to gene expression analysis, there are several challenges that need to be addressed. One such challenge arises from the large volume of data generated in microarray experiments. Initial analysis of these datasets usually results in a large number of potential targets for future investigations. It is often very challenging to pick a handful of the most promising targets and commit the resources to downstream investigations, which in the case of novel genes often requires the generation of new reagents (such as mAbs) and animal models/knockouts to identify function. Gene annotation has been vastly improved since the early days of microarray technology. However, even at present out of ~30, 000 protein-encoding genes in the human genome only ~50% have been appropriately annotated (Su et al., 2004). This issue is especially salient when trying to select genes that are completely novel and uncharacterized. Sequence searches which allow the identification of protein functional domains are especially valuable for poorly annotated genes. Online databases such as Symatlas (http://symatlas.gnf.org/SymAtlas/) (Su et al., 2004) and Gene Expression Omnibus
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(http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=geo) proved very useful to assess the expression of possible target genes in other tissues and studies. It is likely that the coming years will see a vast improvement in gene annotations, and the availability of on-line resources for understanding gene/molecule function and biology. When large datasets are available (such as the one used in our study), clustering methods are useful for reducing data complexity and for providing some functional information about novel genes. Hierarchical clustering (based on Pearson correlation) is commonly used to identify co-regulated genes in a biological system. Clustering groups genes with similar expression patterns, and therefore allows one to identify genes that are coordinately regulated and may therefore be part of the same metabolic or functional pathway. This approach can be used to select novel/poorly annotated genes or hypothetical proteins or ESTs which are co-expressed with well characterized markers for a particular process. This was highlighted in our study of T cell specific genes, where genes that were strongly associated with T cells included well- characterized T cell markers, but also several ESTs and hypothetical proteins that clustered together with the well characterized genes. Identification of patterns or ‘signatures’ specific to particular cell types or processes is another useful application of clustering. For instance, using this approach we were able to identify leukocyte-specific and T cell subset-specific gene expression signatures, which contained both well-characterized and novel genes, as well as patterns of expression associated with T cell culturing and activation. The utility of microarray gene expression profiling for molecular diagnostics of complex diseases, especially cancers, has been well documented (Davis and Staudt, 2002; Rosenwald et al., 2002; Staudt, 2002). Classification systems based on gene expression patterns identified using microarrays have already been used successfully to identify the molecular nature of diseases and optimize therapy (Dave et al., 2004; Sturzebecher et al., 2003; Wiestner and Staudt, 2003; Wright et al., 2003a). Similarly, leukocyte- specific signatures can be used for classification of unknown cells based on their gene expression patterns. Where in earlier times leukocytes were characterized by the expression of cell surface markers, microarrays provide a new and comprehensive way to distinguish leukocytes by their characteristic gene expression signatures.
127 Chapter 6 General Discussion
The work presented in this thesis led to the identification of numerous new targets specific to particular cell types or immunological processes. It is expected that a number of different genes will be selected for generation of knockout mice, or production of mAbs. Since T cells are key players in normal immune responses and also important mediators of many pathological responses, targeting specific T cell genes may also prove of therapeutic importance. For instance, the identity of novel Th1 and Th2 genes may find therapeutic utility in treatment of T cell mediated immunopathologies such as asthma, arthritis, diabetes and many others. There is little doubt that comprehensive gene expression profiling using microarrays made a huge impact in the field of immunology. However, gene expression is just one of the many facets which when integrated provide a complete view of the system as a whole. A truly comprehensive approach requires the integration of several different components which together deal with information storage and processing, and execution of various cellular programs: genome, transcriptome, proteome and metabolome (Oltvai and Barabasi, 2002). The interaction of genetic and molecular components to create phenotypes at cellular level is the focus of an emerging interdisciplinary field termed systems biology (Ideker et al., 2001; Kitano, 2002a; Kitano, 2002b). In recent years microarray technology progressed rapidly to permit the analysis of an entire mammalian genome on a single chip. Currently microarray technology is being extended into proteomics, with microarrays being developed that enable examination of protein–protein interactions, protein-drug interactions and post- translational modification at the level of proteome (Klapa and Quackenbush, 2003). A combination of different high-throughput approaches, such as gene expression and protein microarrays, and metabolic activity data will undoubtedly lead to a more complete understanding biological networks and particularly, the immune system. In summary, the work presented in this thesis described the use of a comprehensive T cell transcriptome to gain significant insights into several aspects of T cell biology, and particularly T cell effector function. Microarray technology and associated bioinformatics are still in the development stages, and challenges involving systematic manipulation and analysis of data are still to be overcome. However, despite some challenges, the data presented in this thesis and in numerous other studies proves the value of large scale gene expression analysis for unraveling the mechanisms behind many immunological phenomena. This comprehensive analysis of the immune system
128 Chapter 6 General Discussion
provides a glimpse of an integrated set of gene expression programs that allow immune cells to respond to internal developmental signals and external antigenic challenges.
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155
Appendix 1
~50 selected genes for each leukocyte subset are listed. Leukocyte signature genes were identified using a 1-way ANOVA and genes highly expressed in particular leukocyte subsets were selected manually. P-value is shown.
T cell genes Probe set name P-value Symbol Genbank Gene name 210448_s_at 1.23E-23 P2RX5 U49396 purinergic receptor P2X, ligand-gated ion channel, 5 223514_at 1.48E-30 CARD11 AF322641 caspase recruitment domain family, member 11 228677_s_at 2.11E-16 FLJ21438 AI028474 hypothetical protein FLJ21438 235721_at 1.10E-10 N62126 Unnamed protein product [Homo sapiens], mRNA sequence 236782_at 2.24E-08 MGC35163 AI129628 hypothetical protein MGC35163 240413_at 4.21E-08 AI827431 ESTs 213193_x_at 1.31E-30 TRB@ AL559122 T cell receptor beta locus 211796_s_at 2.47E-28 TRB@ AF043179 T cell receptor beta locus 213958_at 1.31E-20 CD6 AW134823 CD6 antigen 203828_s_at 4.24E-20 NK4 NM_004221 natural killer cell transcript 4 206804_at 2.60E-26 CD3G NM_000073 CD3G antigen, gamma polypeptide (TiT3 complex) 213539_at 1.38E-29 CD3D NM_000732 CD3D antigen, delta polypeptide (TiT3 complex) 211902_x_at 7.43E-20 TRA@ L34703 T cell receptor alpha locus 210972_x_at 1.21E-20 TRA@ M15565 T cell receptor alpha locus 209671_x_at 1.57E-21 TRA@ M12423 T cell receptor alpha locus 209670_at 2.50E-27 TRA@ M12959 T cell receptor alpha locus 220418_at 8.12E-20 UBASH3A NM_018961 ubiquitin associated and SH3 domain containing, A 209604_s_at 1.23E-21 GATA3 BC003070 GATA binding protein 3 210031_at 4.82E-25 CD3Z J04132 CD3Z antigen, zeta polypeptide (TiT3 complex) 211841_s_at 6.64E-14 TNFRSF25 U94510 tumor necrosis factor receptor superfamily, member 25 230536_at 1.70E-14 PBX4 AJ300182 pre-B-cell leukemia transcription factor 4 207892_at 1.46E-14 TNFSF5 NM_000074 tumor necrosis factor (ligand) superfamily, member 5 210439_at 2.04E-16 ICOS AB023135 inducible T-cell co-stimulator 217147_s_at 3.33E-23 TRIM AJ240085 T-cell receptor interacting molecule 230489_at 1.81E-16 CD5 AI797836 CD5 antigen (p56-62) 206485_at 4.45E-14 CD5 NM_014207 CD5 antigen (p56-62) 240102_at 8.00E-16 AW024095 Human mRNA for T cell receptor, clone IGRB40 239744_at 2.24E-14 AW151660 ESTs 205291_at 5.39E-20 IL2RB NM_000878 interleukin 2 receptor, beta 241871_at 5.36E-13 CAMK4 AL529104 calcium/calmodulin-dependent protein kinase IV 220485_s_at 1.96E-13 SIRPB2 NM_018556 signal-regulatory protein beta 2 206545_at 1.80E-14 CD28 NM_006139 CD28 antigen (Tp44) 229029_at 2.98E-12 AI745230 ESTs 218086_at 1.31E-10 NPDC1 NM_015392 neural proliferation, differentiation and control, 1 205376_at 1.45E-12 INPP4B NM_003866 inositol polyphosphate-4-phosphatase, type II, 105kDa 204777_s_at 3.90E-09 MAL NM_002371 mal, T-cell differentiation protein 209890_at 4.77E-09 TM4SF9 AF065389 tetraspan 5 220132_s_at 2.02E-12 LLT1 NM_013269 lectin-like NK cell receptor 219566_at 1.15E-13 PLEKHF1 NM_024310 apoptosis-inducing protein D 224784_at 1.83E-08 MLLT6 BG024886 myeloid/lymphoid or mixed-lineage leukemia; translocated to 6 210321_at 4.08E-07 CTLA1 M36118 similar to granzyme B 231776_at 4.35E-10 EOMES NM_005442 eomesodermin homolog (Xenopus laevis) 214617_at 3.03E-14 PRF1 AI445650 Perforin, mRNA sequence 244764_at 1.49E-10 BG250907 ESTs 211685_s_at 2.32E-09 NCALD AF251061 neurocalcin delta 211478_s_at 1.38E-07 DPP4 M74777 dipeptidylpeptidase IV (CD26) 212758_s_at 3.41E-09 TCF8 U19969 transcription factor 8 (represses interleukin 2 expression) 235154_at 1.75E-07 TAF3 BG250498 TAF3 RNA polymerase II TATA box binding protein-associated factor 211861_x_at 1.01E-06 CD28 AF222343 CD28 antigen (Tp44) 241692_at 1.39E-07 AA868729 ESTs 236428_at 3.42E-05 D59900 ESTs
156 Appendix
NK cell genes Probe set name P-value Symbol Genbank Gene name 207314_x_at 9.80E-07 KIR3DL2 NM_006737 killer cell Ig-like receptor 3 domains long cytoplasmic tail 2 235291_s_at 9.17E-07 BE875232 Homo sapiens cDNA FLJ32255 fis, clone PROST1000226 205839_s_at 4.14E-07 BZRAP1 NM_004758 peripheral benzodiazepine receptor-associated protein 1 210321_at 4.08E-07 CTLA1 M36118 similar to granzyme B 234165_at 3.33E-07 AK026202 Homo sapiens cDNA: FLJ22549 fis clone HSI00650 mRNA sequence 226625_at 2.27E-07 TGFBR3 AW193698 transforming growth factor, beta receptor III (betaglycan, 300kDa) 226549_at 1.52E-07 LOC112868 AI935915 Homo sapiens mRNA; cDNA DKFZp547J047 52940_at 9.76E-08 SIGIRR AA085764 single Ig IL-1R-related molecule 218921_at 9.34E-08 SIGIRR NM_021805 single Ig IL-1R-related molecule 208426_x_at 8.49E-08 KIR2DL4 NM_002255 killer cell Ig-like receptor 2 domains, long cytoplasmic tail, 4 35671_at 6.44E-08 GTF3C1 U02619 general transcription factor IIIC, polypeptide 1, alpha 220kDa 240413_at 4.21E-08 AI827431 ESTss] 214450_at 3.73E-08 CTSW NM_001335 cathepsin W (lymphopain) 226314_at 3.30E-08 D4ST-1 AA039350 dermatan-4-sulfotransferase-1 206828_at 2.79E-08 TXK NM_003328 TXK tyrosine kinase 205171_at 2.68E-08 PTPN4 NM_002830 protein tyrosine phosphatase, non-receptor type 4 (megakaryocyte) 205277_at 2.31E-08 PRDM2 NM_012231 PR domain containing 2, with ZNF domain 236782_at 2.24E-08 MGC35163 AI129628 hypothetical protein MGC35163 224784_at 1.83E-08 MLLT6 BG024886 myeloid/lymphoid or mixed-lineage leukemia translocated to, 6 216676_x_at 3.54E-09 KIR3DL7 AC006293 killer cell Ig-like receptor KIR3DL7 207313_x_at 3.09E-09 KIR3DL2 L76666 killer cell Ig-like receptor, three domains, long cytoplasmic tail, 2 211685_s_at 2.32E-09 NCALD AF251061 neurocalcin delta 218638_s_at 5.60E-10 SPON2 NM_012445 spondin 2, extracellular matrix protein 210890_x_at 5.02E-10 KIR2DL1 U24078 killer cell Ig-like receptor, two domains, long cytoplasmic tail, 1 231776_at 4.35E-10 EOMES NM_005442 eomesodermin homolog (Xenopus laevis) 211397_x_at 3.11E-10 KIR2DL2 L76669 killer cell Ig-like receptor, two domains, long cytoplasmic tail, 2 208203_x_at 2.77E-10 KIR2DS5 NM_014513 killer cell Ig-like receptor, two domains, short cytoplasmic tail, 5 209815_at 1.84E-10 PTCH U43148 patched homolog (Drosophila) 244764_at 1.49E-10 BG250907 ESTs 235721_at 1.10E-10 N62126 Unnamed protein product [Homo sapiens], mRNA sequence 212863_x_at 1.02E-10 CTBP1 BF337195 C-terminal binding protein 1 210763_x_at 2.34E-11 NCR3 AF031137 natural cytotoxicity triggering receptor 3 203823_at 5.47E-13 RGS3 NM_021106 regulator of G-protein signalling 3 227394_at 1.34E-13 NCAM1 W94001 neural cell adhesion molecule 1 219566_at 1.15E-13 PLEKHF1 NM_024310 apoptosis-inducing protein D 214617_at 3.03E-14 PRF1 AI445650 Perforin, mRNA sequence 239744_at 2.24E-14 AW151660 ESTs 227228_s_at 6.72E-16 KIAA1509 AB040942 KIAA1509 protein 228677_s_at 2.11E-16 FLJ21438 AI028474 hypothetical protein FLJ21438 205291_at 5.39E-20 IL2RB NM_000878 interleukin 2 receptor, beta 203828_s_at 4.24E-20 NK4 NM_004221 natural killer cell transcript 4 213958_at 1.31E-20 CD6 AW134823 CD6 antigen 209604_s_at 1.23E-21 GATA3 BC003070 GATA binding protein 3 226991_at 7.04E-22 AA489681 Homo sapiens cDNA: FLJ22105 fis, clone HEP17660 228442_at 4.59E-24 AI770171 ESTs 210031_at 4.82E-25 CD3Z J04132 CD3Z antigen, zeta polypeptide (TiT3 complex) 206804_at 2.60E-26 CD3G NM_000073 CD3G antigen, gamma polypeptide (TiT3 complex) 213193_x_at 1.31E-30 TRB@ AL559122 T cell receptor beta locus
157 Appendix
B cell genes Probe set name P-value Symbol Genbank Gene name 201254_x_at 4.14E-17 RPS6 NM_001010 ribosomal protein S6 224406_s_at 4.10E-17 IRTA2 AF343664 Ig superfamily receptor translocation associated 2 209134_s_at 4.05E-17 RPS6 BC000524 ribosomal protein S6 215121_x_at 3.69E-17 IGLJ3 AA680302 Ig lambda joining 3 216576_x_at 3.51E-17 AF103529 Ig kappa light chain variable region [Homo sapiens], mRNA sequence 214271_x_at 3.27E-17 RPL12 AA281332 ribosomal protein L12 200029_at 2.83E-17 RPL19 NM_000981 ribosomal protein L19 211927_x_at 2.70E-17 BE963164 ESTs 200010_at 2.45E-17 RPL11 NM_000975 ribosomal protein L11 230983_at 1.76E-17 BE646461 FLJ00140 protein [Homo sapiens], mRNA sequence 200082_s_at 1.53E-17 RPS7 AI805587 ribosomal protein S7 209138_x_at 1.38E-17 IGLJ3 M87790 Ig lambda joining 3 200858_s_at 1.16E-17 RPS8 NM_001012 ribosomal protein S8 214669_x_at 1.12E-17 IGKC BG485135 Ig kappa constant 200038_s_at 1.09E-17 RPL17 NM_000985 ribosomal protein L17 212933_x_at 9.00E-18 RPL13 AA961748 ribosomal protein L13 206478_at 8.11E-18 KIAA0125 NM_014792 KIAA0125 gene product 213890_x_at 7.76E-18 RPS16 AI200589 ribosomal protein S16 207655_s_at 7.66E-18 BLNK NM_013314 B-cell linker 215946_x_at 7.49E-18 LOC91316 AL022324 similar to bK246H3.1 (Ig lambda-like polypeptide 1, pre-B-cell specific) 216401_x_at 6.35E-18 IGKV AJ408433 0 233132_at 5.58E-18 AK026408 Unnamed protein product [Homo sapiens], mRNA sequence 211345_x_at 3.01E-18 EEF1G AF119850 eukaryotic translation elongation factor 1 gamma 211666_x_at 2.45E-18 RPL3 L22453 ribosomal protein L3 200081_s_at 1.79E-18 RPS6 BE741754 ribosomal protein S6 200705_s_at 1.26E-18 EEF1B2 NM_001959 eukaryotic translation elongation factor 1 beta 2 200651_at 7.85E-19 GNB2L1 NM_006098 guanine nucleotide binding protein beta polypeptide 2-like 1 200024_at 6.38E-19 RPS5 NM_001009 ribosomal protein S5 213414_s_at 4.99E-19 RPS19 BE259729 EST, Weakly similar to I52692 ribosomal protein S19, cytosolic 200834_s_at 4.99E-19 RPS21 NM_001024 ribosomal protein S21 217378_x_at 4.39E-19 IGKV1OR2-1 X51887 0 200909_s_at 4.37E-19 RPLP2 NM_001004 ribosomal protein, large P2 228518_at 3.29E-19 IGHG3 AW575313 Ig heavy constant gamma 3 (G3m marker) 200933_x_at 3.10E-19 RPS4X NM_001007 ribosomal protein S4, X-linked 208929_x_at 2.00E-19 RPL13 BC004954 ribosomal protein L13 200689_x_at 1.35E-19 EEF1G NM_001404 eukaryotic translation elongation factor 1 gamma 202649_x_at 4.81E-20 RPS19 NM_001022 ribosomal protein S19 211710_x_at 2.17E-20 RPL4 BC005817 ribosomal protein L4 213687_s_at 1.89E-20 RPL35A BE968801 ribosomal protein L35a 200018_at 1.67E-20 RPS13 NM_001017 ribosomal protein S13 212191_x_at 7.31E-21 RPL13 AW574664 ribosomal protein L13 200088_x_at 4.27E-21 RPL12 AK026491 ribosomal protein L12 212734_x_at 3.23E-21 RPL13 AI186735 ribosomal protein L13 211644_x_at 1.56E-21 IGKC L14458 Ig kappa constant 213347_x_at 1.22E-21 RPS4X AW132023 ribosomal protein S4, X-linked 201154_x_at 1.02E-21 RPL4 NM_000968 ribosomal protein L4 213377_x_at 2.35E-22 C1S AI799007 ribosomal protein S12 200937_s_at 1.50E-22 RPL5 NM_000969 ribosomal protein L5 214351_x_at 1.16E-22 RPL13 AA789278 ribosomal protein L13 200034_s_at 8.56E-23 RPL6 NM_000970 ribosomal protein L6 200089_s_at 2.54E-23 RPL4 AI953886 ribosomal protein L4 214167_s_at 1.49E-24 RPLP0 AA555113 ribosomal protein, large, P0
158 Appendix
Macrophage genes Probe set name P-value Symbol Genbank Gene name 214770_at 8.90E-13 MSR1 AI299239 macrophage scavenger receptor 1 201647_s_at 5.12E-13 SCARB2 NM_005506 scavenger receptor class B, member 2 205888_s_at 5.03E-13 KIAA0555 AI962693 KIAA0555 gene product 204517_at 4.98E-13 PPIC BE962749 peptidylprolyl isomerase C (cyclophilin C) 228873_at 4.68E-13 COL22A1 BE349115 collagen type XXII, alpha 1 215134_at 4.18E-13 PI4KII H84390 phosphatidylinositol 4-kinase type II 214040_s_at 4.01E-13 GSN BE675337 gelsolin (amyloidosis, Finnish type) 207071_s_at 2.59E-13 ACO1 NM_002197 aconitase 1, soluble 202767_at 1.24E-13 ACP2 NM_001610 acid phosphatase 2, lysosomal 232662_x_at 1.13E-13 AK022598 Homo sapiens cDNA FLJ12536 fis, clone NT2RM4000265 204983_s_at 9.23E-14 GPC4 AF064826 glypican 4 201125_s_at 5.66E-14 ITGB5 NM_002213 integrin, beta 5 222736_s_at 5.02E-14 FLJ10493 BC000049 hypothetical protein FLJ10493 219890_at 4.13E-14 CLECSF5 NM_013252 C-type lectin, superfamily member 5 208168_s_at 4.12E-14 CHIT1 NM_003465 chitinase 1 (chitotriosidase) 203920_at 3.64E-14 NR1H3 NM_005693 nuclear receptor subfamily 1, group H, member 3 205404_at 2.02E-14 HSD11B1 NM_005525 hydroxysteroid (11-beta) dehydrogenase 1 223204_at 9.50E-15 DKFZp434L1 AF260333 hypothetical protein DKFZp434L142 217975_at 7.35E-15 LOC51186 NM_016303 pp21 homolog 232277_at 5.52E-15 AA643687 Homo sapiens cDNA FLJ11980 fis, clone HEMBB1001304 216243_s_at 4.77E-15 IL1RN BE563442 interleukin 1 receptor antagonist 222939_s_at 1.84E-15 SLC16A10 N30257 solute carrier family 16 member 10 218126_at 5.27E-16 FLJ10579 NM_018145 hypothetical protein FLJ10579 209727_at 4.66E-16 GM2A M76477 GM2 ganglioside activator protein 210663_s_at 3.94E-16 KYNU BC000879 kynureninase (L-kynurenine hydrolase) 33646_g_at 3.29E-16 GM2A X61094 GM2 ganglioside activator protein 214875_x_at 3.02E-16 APLP2 AW001847 amyloid beta (A4) precursor-like protein 2 205306_x_at 2.91E-16 KMO AI074145 kynurenine 3-monooxygenase (kynurenine 3-hydroxylase) 202575_at 1.98E-16 CRABP2 NM_001878 cellular retinoic acid binding protein 2 224435_at 1.69E-16 MGC4248 BC005871 hypothetical protein MGC4248 228220_at 8.88E-17 LOC115548 AI627666 hypothetical protein BC014311 225847_at 1.92E-17 KIAA1363 AB037784 KIAA1363 protein 240873_x_at 1.83E-17 DAB2 R62907 disabled homolog 2, mitogen-responsive phosphoprotein (Drosophila) 204259_at 1.62E-17 MMP7 NM_002423 matrix metalloproteinase 7 (matrilysin, uterine) 228155_at 6.18E-18 MGC4248 BF512388 hypothetical protein MGC4248 221530_s_at 4.54E-18 BHLHB3 AB044088 basic helix-loop-helix domain containing, class B, 3 209396_s_at 1.79E-18 CHI3L1 M80927 chitinase 3-like 1 (cartilage glycoprotein-39) 227995_at 5.03E-19 AI051950 ESTs 206214_at 3.92E-19 PLA2G7 NM_005084 phospholipase A2, group VII 211404_s_at 3.46E-19 APLP2 BC004371 amyloid beta (A4) precursor-like protein 2 213553_x_at 1.39E-19 APOC1 W79394 apolipoprotein C-I 223344_s_at 6.20E-20 MS4A7 AB026043 membrane-spanning 4-domains, subfamily A, member 7 224357_s_at 4.36E-20 MS4A4A AF237912 membrane-spanning 4-domains, subfamily A, member 4 203381_s_at 1.58E-20 APOE N33009 apolipoprotein E 204416_x_at 8.61E-21 APOC1 NM_001645 apolipoprotein C-I 230422_at 3.14E-21 AW026543 ESTs, Moderately similar to cytochrome P450 isoform 4F12 204561_x_at 1.02E-21 APOC2 NM_000483 apolipoprotein C-II 205819_at 2.73E-22 MARCO NM_006770 macrophage receptor with collagenous structure 207861_at 1.10E-23 CCL22 NM_002990 chemokine (C-C motif) ligand 22 212884_x_at 6.22E-26 APOE AI358867 apolipoprotein E 209696_at 1.63E-26 FBP1 D26054 fructose-1,6-bisphosphatase 1 237731_at 6.79E-33 LOC154092 AW665570 Homo sapiens cDNA FLJ30604 fis, clone CD34C2000152
159 Appendix
DC genes Probe set name P-value Symbol Genbank Gene name 213324_at 2.35E-10 SRC AK024281 v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) 226556_at 2.29E-10 BF431260 Homo sapiens, clone IMAGE:4815204, mRNA, mRNA sequence 207277_at 7.94E-11 CD209 AF290886 CD209 antigen 210402_at 5.37E-11 KCNJ1 U03884 potassium inwardly-rectifying channel, subfamily J, member 1 225915_at 2.01E-11 FLJ12577 AW242839 0 211887_x_at 1.85E-11 MSR1 AF037351 macrophage scavenger receptor 1 214953_s_at 1.14E-11 APP X06989 amyloid beta (A4) precursor protein 228285_at 8.69E-12 C14orf75 AI989706 chromosome 14 open reading frame 75 206407_s_at 7.79E-12 CCL13 NM_005408 chemokine (C-C motif) ligand 13 218613_at 3.94E-12 DKFZp761K1 NM_018422 hypothetical protein DKFZp761K1423 32625_at 2.73E-12 NPR1 X15357 natriuretic peptide receptor A/guanylate cyclase A 211470_s_at 2.15E-12 SULT1C1 AF186255 sulfotransferase family, cytosolic, 1C, member 1 213706_at 1.43E-12 GPD1 AI368018 glycerol-3-phosphate dehydrogenase 1 (soluble) 206749_at 1.13E-12 CD1B NM_001764 CD1B antigen, b polypeptide 214770_at 8.90E-13 MSR1 AI299239 macrophage scavenger receptor 1 229566_at 7.22E-13 AA149250 Homo sapiens full length insert cDNA clone ZC45B12 201647_s_at 5.12E-13 SCARB2 NM_005506 scavenger receptor class B, member 2 204517_at 4.98E-13 PPIC BE962749 peptidylprolyl isomerase C (cyclophilin C) 228873_at 4.68E-13 COL22A1 BE349115 collagen type XXII, alpha 1 211748_x_at 1.37E-13 PTGDS BC005939 prostaglandin D2 synthase 21kDa (brain) 204983_s_at 9.23E-14 GPC4 AF064826 glypican 4 208168_s_at 4.12E-14 CHIT1 NM_003465 chitinase 1 (chitotriosidase) 203920_at 3.64E-14 NR1H3 NM_005693 nuclear receptor subfamily 1, group H, member 3 205404_at 2.02E-14 HSD11B1 NM_005525 hydroxysteroid (11-beta) dehydrogenase 1 223204_at 9.50E-15 DKFZp434L1 AF260333 hypothetical protein DKFZp434L142 232277_at 5.52E-15 AA643687 Homo sapiens cDNA FLJ11980 fis, clone HEMBB1001304 222939_s_at 1.84E-15 SLC16A10 N30257 solute carrier family 16 member 10 209727_at 4.66E-16 GM2A M76477 GM2 ganglioside activator protein 210663_s_at 3.94E-16 KYNU BC000879 kynureninase (L-kynurenine hydrolase) 33646_g_at 3.29E-16 GM2A X61094 GM2 ganglioside activator protein 205306_x_at 2.91E-16 KMO AI074145 kynurenine 3-monooxygenase (kynurenine 3-hydroxylase) 207900_at 2.73E-16 CCL17 NM_002987 chemokine (C-C motif) ligand 17 202575_at 1.98E-16 CRABP2 NM_001878 cellular retinoic acid binding protein 2 224435_at 1.69E-16 MGC4248 BC005871 hypothetical protein MGC4248 229074_at 4.37E-17 EHD4 AI692267 EH-domain containing 4 225847_at 1.92E-17 KIAA1363 AB037784 KIAA1363 protein 240873_x_at 1.83E-17 DAB2 R62907 disabled homolog 2, mitogen-responsive phosphoprotein (Drosophila) 204259_at 1.62E-17 MMP7 NM_002423 matrix metalloproteinase 7 (matrilysin, uterine) 209396_s_at 1.79E-18 CHI3L1 M80927 chitinase 3-like 1 (cartilage glycoprotein-39) 213553_x_at 1.39E-19 APOC1 W79394 apolipoprotein C-I 224357_s_at 4.36E-20 MS4A4A AF237912 membrane-spanning 4-domains, subfamily A, member 4 225283_at 3.82E-20 AV701177 Homo sapiens cDNA: FLJ22783 fis, clone KAIA1993 203381_s_at 1.58E-20 APOE N33009 apolipoprotein E 204416_x_at 8.61E-21 APOC1 NM_001645 apolipoprotein C-I 230422_at 3.14E-21 AW026543 ESTs, Moderately similar to cytochrome P450 isoform 4F12 204561_x_at 1.02E-21 APOC2 NM_000483 apolipoprotein C-II 205819_at 2.73E-22 MARCO NM_006770 macrophage receptor with collagenous structure 207861_at 1.10E-23 CCL22 NM_002990 chemokine (C-C motif) ligand 22 212884_x_at 6.22E-26 APOE AI358867 apolipoprotein E 209696_at 1.63E-26 FBP1 D26054 fructose-1,6-bisphosphatase 1 237731_at 6.79E-33 LOC154092 AW665570 Homo sapiens cDNA FLJ30604 fis, clone CD34C2000152
160 Appendix
Neutrophil genes Probe set name P-value Symbol Genbank Gene name 226047_at 7.85E-10 N66571 Homo sapiens cDNA FLJ11177 fis, clone PLACE1007402 227341_at 6.07E-10 AW195407 Unknown (protein for MGC:35247) 209287_s_at 5.90E-10 CDC42EP3 AF104857 CDC42 effector protein (Rho GTPase binding) 3 228950_s_at 5.53E-10 FLJ23091 AL534095 hypothetical protein FLJ23091 206515_at 5.38E-10 CYP4F3 NM_000896 cytochrome P450, subfamily IVF, polypeptide 3 55705_at 4.48E-10 LOC91300 W07773 hypothetical protein BC012775 239555_at 4.09E-10 W87626 ESTs 206209_s_at 3.93E-10 CA4 NM_000717 carbonic anhydrase IV 203691_at 2.86E-10 PI3 NM_002638 protease inhibitor 3, skin-derived (SKALP) 236423_at 2.54E-10 AI022821 ESTs 202878_s_at 2.52E-10 C1QR1 NM_012072 complement component 1, q subcomponent, receptor 1 235483_at 2.45E-10 AA858058 Homo sapiens cDNA FLJ30906 fis, clone FEBRA2006055 207384_at 2.19E-10 PGLYRP NM_005091 peptidoglycan recognition protein 218136_s_at 1.63E-10 MSCP NM_018579 mitochondrial solute carrier protein 218624_s_at 1.33E-10 MGC2752 NM_023939 hypothetical protein MGC2752 241631_at 1.30E-10 AI640434 EST 203585_at 1.09E-10 ZNF185 NM_007150 zinc finger protein 185 (LIM domain) 225043_at 1.03E-10 PTR4 AW304786 peptide-histidine transporter 4 227425_at 4.64E-11 AI984607 Homo sapiens cDNA FLJ40165 fis, clone TESTI2015962 207842_s_at 3.30E-11 MLN51 NM_007359 MLN51 protein 226179_at 2.13E-11 N63920 ESTs, Weakly similar to hypothetical protein FLJ20378 231972_at 1.97E-11 AK024681 Homo sapiens cDNA: FLJ21028 fis, clone CAE07155 231274_s_at 1.49E-11 MSCP R92925 mitochondrial solute carrier protein 229770_at 1.35E-11 FLJ31978 AI041543 hypothetical protein FLJ31978 228527_s_at 1.27E-11 MSCP BE221818 mitochondrial solute carrier protein 238893_at 1.22E-11 AI377324 Homo sapiens cDNA FLJ37198 fis, clone BRALZ2005992 221541_at 9.71E-12 DKFZP434B0AL136861 hypothetical protein DKFZp434B044 226064_s_at 6.60E-12 DGAT2 BF979495 diacylglycerol O-acyltransferase homolog 2 (mouse) 210119_at 5.21E-12 KCNJ15 U73191 potassium inwardly-rectifying channel, subfamily J, member 15 229054_at 4.47E-12 AI935766 Unnamed protein product [Homo sapiens], mRNA sequence 204308_s_at 2.83E-12 KIAA0329 NM_014844 KIAA0329 gene product 212860_at 2.73E-12 DKFZp667O2BG168720 hypothetical protein DKFZp667O2416 214784_x_at 2.53E-12 XPO6 BE966299 RAN binding protein 20 243465_at 2.37E-12 AI033097 ESTs 243395_at 1.91E-12 AI679555 ESTs, Weakly similar to hypothetical protein FLJ20489 234044_at 1.49E-12 AK026261 Homo sapiens cDNA: FLJ22608 fis, clone HSI04854 225899_x_at 1.19E-12 AL040396 Homo sapiens cDNA FLJ39761 fis, clone SPLEN1000083 218023_s_at 1.01E-12 C5orf6 NM_016605 chromosome 5 open reading frame 6 226397_s_at 5.82E-13 BG502771 Homo sapiens cDNA: FLJ21028 fis, clone CAE07155 205068_s_at 3.70E-13 GRAF BE671084 GTPase regulator associated with focal adhesion kinase pp125(FAK) 233217_at 2.90E-13 AV741679 HSPC102 [Homo sapiens], mRNA sequence 201244_s_at 2.47E-13 RAF1 NM_002880 v-raf-1 murine leukemia viral oncogene homolog 1 226928_x_at 2.02E-13 MSCP BE677761 mitochondrial solute carrier protein 225987_at 1.63E-13 FLJ23153 AA650281 likely ortholog of mouse TN-alpha-induced adipose-related protein 220746_s_at 1.47E-13 RAP80 NM_016290 receptor associated protein 80 211806_s_at 8.34E-14 KCNJ15 D87291 potassium inwardly-rectifying channel, subfamily J, member 15 207094_at 1.96E-14 IL8RA NM_000634 interleukin 8 receptor, alpha 211982_x_at 7.41E-15 XPO6 AL546600 RAN binding protein 20 227129_x_at 4.07E-15 AW006934 Homo sapiens cDNA FLJ39457 fis, clone PROST2011105 229967_at 3.03E-15 CKLFSF2 AA778552 chemokine-like factor super family 2 232045_at 2.28E-15 KIAA1733 AW468218 0 224327_s_at 4.66E-16 DGAT2 AB048286 diacylglycerol O-acyltransferase homolog 2 (mouse) 221345_at 2.28E-16 GPR43 NM_005306 G protein-coupled receptor 43 231908_at 1.05E-16 AL034380 0 241627_x_at 5.32E-17 AI640434 EST 223552_at 1.01E-17 NAG14 AF196976 NAG14 protein 203435_s_at 5.15E-19 MME NM_007287 membrane metallo-endopeptidase
161 Appendix
Eosinophil genes Probe set name P-value Symbol Genbank Gene name 228536_at 6.50E-05 LOC90826 AA574240 hypothetical protein BC004337 223660_at 5.91E-05 AD026 AF226731 AD026 protein 206698_at 4.03E-05 XK NM_021083 Kell blood group precursor (McLeod phenotype) 235086_at 3.88E-05 AW956580 Homo sapiens, clone IMAGE:3934391, mRNA, mRNA sequence 206171_at 3.86E-05 ADORA3 NM_000677 adenosine A3 receptor 201751_at 1.99E-05 KIAA0063 NM_014876 KIAA0063 gene product 243586_at 1.84E-05 AA707317 ESTs 222830_at 1.66E-05 LBP-32 BE566136 LBP protein 32 223198_x_at 1.64E-05 HT002 BC002672 HT002 protein; hypertension-related calcium-regulated gene 207594_s_at 1.36E-05 SYNJ1 NM_003895 synaptojanin 1 243092_at 1.08E-05 AI140189 ESTs 202668_at 9.33E-06 EFNB2 BF001670 ephrin-B2 228915_at 8.41E-06 DACH AI650353 dachshund homolog (Drosophila) 230604_at 6.44E-06 BE670600 Homo sapiens cDNA FLJ36005 fis, clone TESTI2015350 202696_at 4.75E-06 OSR1 NM_005109 oxidative-stress responsive 1 228723_at 4.62E-06 AL360198 Homo sapiens mRNA full length insert cDNA clone EUROIMAGE 34988 242538_at 3.96E-06 TFDP1 AW007021 transcription factor Dp-1 223930_at 3.76E-06 MGC3413 BC004969 hypothetical protein MGC3413 225287_s_at 2.71E-06 C14orf9 AI992151 chromosome 14 open reading frame 9 201108_s_at 2.35E-06 THBS1 AI812030 thrombospondin 1 203269_at 2.20E-06 NSMAF NM_003580 neutral sphingomyelinase (N-SMase) activation associated factor 214108_at 1.83E-06 MAX AI346181 MAX protein 224303_x_at 1.47E-06 NIN AF223938 ninein (GSK3B interacting protein) 224470_at 9.75E-07 SEC22C BC006178 vesicle trafficking protein 223819_x_at 8.97E-07 HT002 BC003055 HT002 protein; hypertension-related calcium-regulated gene 208304_at 7.28E-07 CCR3 NM_001837 chemokine (C-C motif) receptor 3 238029_s_at 4.32E-07 FLJ30794 R15072 hypothetical protein FLJ30794 240131_at 3.06E-07 H78083 ESTs 210744_s_at 2.37E-07 IL5RA M75914 interleukin 5 receptor, alpha 229230_at 1.95E-07 MGC39807 AA702685 hypothetical protein MGC39807 211810_s_at 6.27E-08 GALC D25284 galactosylceramidase (Krabbe disease) 201109_s_at 5.77E-08 THBS1 AI812030 thrombospondin 1 236402_at 2.84E-08 AW184034 ESTs 203394_s_at 2.69E-08 HES1 BE973687 hairy homolog (Drosophila) 203395_s_at 2.48E-08 HES1 NM_005524 hairy homolog (Drosophila) 202980_s_at 1.98E-08 SIAH1 AI953523 seven in absentia homolog 1 (Drosophila) 210036_s_at 1.34E-08 KCNH2 AB044806 potassium voltage-gated channel, subfamily H , member 2 237904_at 8.46E-09 BF056965 ESTs 201563_at 5.98E-09 SORD L29008 sorbitol dehydrogenase 239056_at 3.24E-09 AA096421 ESTs 232149_s_at 2.20E-09 BF056507 Homo sapiens cDNA FLJ11963 fis, clone HEMBB1001051 223981_at 1.32E-09 NIN AF223937 ninein (GSK3B interacting protein) 206111_at 9.40E-11 RNASE2 NM_002934 ribonuclease, RNase A family, 2 (liver, eosinophil-derived neurotoxin) 237403_at 8.23E-11 GFI1B AI097490 Homo sapiens, clone IMAGE:5205687, mRNA, mRNA sequence 209122_at 3.15E-11 ADFP BC005127 adipose differentiation-related protein 223828_s_at 9.05E-12 LGALS12 AF222694 lectin, galactoside-binding, soluble, 12 (galectin 12) 205033_s_at 2.38E-12 DEFA1 NM_004084 defensin, alpha 1, myeloid-related sequence 207111_at 8.63E-13 EMR1 NM_001974 egf-like module containing mucin-like hormone receptor-like sequence 1 218829_s_at 2.25E-15 KIAA1416 NM_017780 KIAA1416 protein 206207_at 6.97E-17 CLC NM_001828 Charot-Leyden crystal protein
162 Appendix
Appendix 2
Genes selectively expressed in T cells as identified using a 1-way ANOVA to compare T cells to all other leukocytes. P-value is shown.
Probe set P-value Symbol Genbank Description 209671_x_at 3.17E-15 TRA@ M12423 T cell receptor alpha locus 210972_x_at 3.17E-15 TRA@ M15565 T cell receptor alpha locus 220418_at 3.17E-15 UBASH3A NM_018961 ubiquitin associated and SH3 domain containing, A 209670_at 3.17E-15 TRA@ M12959 T cell receptor alpha locus 230489_at 3.17E-15 CD5 AI797836 CD5 antigen (p56-62) 206485_at 3.17E-15 CD5 NM_014207 CD5 antigen (p56-62) 206804_at 3.17E-15 CD3G NM_000073 CD3G antigen, gamma polypeptide (TiT3 complex) 213539_at 3.17E-15 CD3D NM_000732 CD3D antigen, delta polypeptide (TiT3 complex) 206545_at 3.17E-15 CD28 NM_006139 CD28 antigen (Tp44) 211902_x_at 3.61E-15 TRA@ L34703 T cell receptor alpha locus 241871_at 3.61E-15 CAMK4 AL529104 calcium/calmodulin-dependent protein kinase IV 213958_at 3.61E-15 CD6 AW134823 CD6 antigen 240102_at 5.74E-15 AW024095 Human mRNA for T cell receptor, clone IGRB40, mRNA sequence 205831_at 5.74E-15 CD2 NM_001767 CD2 antigen (p50), sheep red blood cell receptor 220485_s_at 5.74E-15 SIRPB2 NM_018556 signal-regulatory protein beta 2 209604_s_at 7.64E-15 GATA3 BC003070 GATA binding protein 3 211211_x_at 9.49E-15 SH2D1A AF100542 SH2 domain protein 1A, Duncan's disease (lymphoproliferative syndrome) 213193_x_at 9.49E-15 TRB@ AL559122 T cell receptor beta locus 210439_at 9.49E-15 ICOS AB023135 inducible T-cell co-stimulator 207892_at 1.23E-14 TNFSF5 NM_000074 tumor necrosis factor (ligand) superfamily, member 5 (hyper-IgM syndrome) 211796_s_at 1.26E-14 TRB@ AF043179 T cell receptor beta locus 210915_x_at 1.56E-14 TRB@ M15564 T cell receptor beta locus 219423_x_at 1.56E-14 TNFRSF25 NM_003790 tumor necrosis factor receptor superfamily, member 25 203828_s_at 1.56E-14 NK4 NM_004221 natural killer cell transcript 4 211841_s_at 1.94E-14 TNFRSF25 U94510 tumor necrosis factor receptor superfamily, member 25 228109_at 1.94E-14 AI912976 Homo sapiens cDNA FLJ34852 fis, clone NT2NE2012113, mRNA sequence 230536_at 1.94E-14 PBX4 AJ300182 pre-B-cell leukemia transcription factor 4 213598_at 2.55E-14 HSA9761 W87688 putative dimethyladenosine transferase 232001_at 2.55E-14 AW193600 Homo sapiens clone CDABP0095 mRNA sequence 211209_x_at 4.21E-14 SH2D1A AF100540 SH2 domain protein 1A, Duncan's disease (lymphoproliferative syndrome) 206761_at 4.21E-14 TACTILE NM_005816 T cell activation, increased late expression 209602_s_at 4.21E-14 GATA3 AI796169 GATA binding protein 3 228320_x_at 5.43E-14 LOC92558 R61322 Human clone 295, 5cM region surrounding hepatocyte 1a/MODY3 mRNA 211210_x_at 5.43E-14 SH2D1A AF100539 SH2 domain protein 1A, Duncan's disease (lymphoproliferative syndrome) 222895_s_at 5.43E-14 BCL11B AA918317 B-cell CLL/lymphoma 11B (zinc finger protein) 229029_at 9.79E-14 AI745230 ESTs, Highly similar to T17345 hypothetical protein DKFZp586M1824.1 211282_x_at 9.79E-14 TNFRSF25 U94506 tumor necrosis factor receptor superfamily, member 25 236346_at 1.26E-13 BF115793 ESTs, Weakly similar to hypothetical protein FLJ20378 210847_x_at 1.26E-13 TNFRSF25 AF026071 tumor necrosis factor receptor superfamily, member 25 202747_s_at 1.59E-13 ITM2A NM_004867 integral membrane protein 2A 205456_at 1.59E-13 CD3E NM_000733 CD3E antigen, epsilon polypeptide (TiT3 complex) 204777_s_at 2.01E-13 MAL NM_002371 mal, T-cell differentiation protein 209890_at 2.01E-13 TM4SF9 AF065389 tetraspan 5 205376_at 2.01E-13 INPP4B NM_003866 inositol polyphosphate-4-phosphatase, type II, 105kDa 209603_at 2.65E-13 GATA3 AI796169 GATA binding protein 3 210116_at 3.02E-13 SH2D1A AF072930 SH2 domain protein 1A, Duncan's disease (lymphoproliferative syndrome) 218086_at 4.38E-13 NPDC1 NM_015392 neural proliferation, differentiation and control, 1 217147_s_at 8.87E-13 TRIM AJ240085 T-cell receptor interacting molecule 237194_at 8.87E-13 T58048 ESTs 204890_s_at 1.86E-12 LCK U07236 lymphocyte-specific protein tyrosine kinase 211339_s_at 1.86E-12 ITK D13720 IL2-inducible T-cell kinase 211856_x_at 2.35E-12 CD28 AF222341 CD28 antigen (Tp44) 214032_at 2.35E-12 ZAP70 AI817942 zeta-chain (TCR) associated protein kinase 70kDa 221558_s_at 2.96E-12 LEF1 AF288571 lymphoid enhancer-binding factor 1 202523_s_at 3.73E-12 SPOCK2 AI952009 KIAA0275 gene product 213623_at 4.64E-12 KIF3A NM_007054 kinesin family member 3A 225387_at 4.64E-12 TM4SF9 BG252118 tetraspan 5 210948_s_at 5.96E-12 LEF1 AF294627 lymphoid enhancer-binding factor 1 210038_at 7.57E-12 AL137145 0 211207_s_at 9.52E-12 FACL6 AF129166 fatty-acid-Coenzyme A ligase, long-chain 6 64064_at 9.52E-12 IAN4L1 AI435089 immune associated nucleotide 4 like 1 (mouse) 229725_at 9.52E-12 AV705292 ESTs, Moderately similar to hypothetical protein FLJ20378 240015_at 1.45E-11 AI299467 ESTs 204891_s_at 1.45E-11 LCK NM_005356 lymphocyte-specific protein tyrosine kinase 202746_at 1.45E-11 ITM2A; E25A AL021786 integral membrane protein 2A 218805_at 1.45E-11 IAN4L1 NM_018384 immune associated nucleotide 4 like 1 (mouse) 207735_at 1.45E-11 FLJ20456 NM_017831 hypothetical protein FLJ20456 219528_s_at 1.45E-11 BCL11B NM_022898 B-cell CLL/lymphoma 11B (zinc finger protein) 210424_s_at 1.81E-11 GOLGIN-67 AF163441 golgin-67 210349_at 1.81E-11 CAMK4 L24959 calcium/calmodulin-dependent protein kinase IV 214049_x_at 2.29E-11 CD7 AI829961 CD7 antigen (p41) 235046_at 2.83E-11 AA456099 ESTs 233302_at 3.40E-11 AU146285 Homo sapiens cDNA FLJ10224 fis, clone HEMBB1000025 203408_s_at 3.40E-11 SATB1 NM_002971 special AT-rich sequence binding protein 1 223949_at 3.40E-11 TMPRSS3 AB038160 transmembrane protease, serine 3 210039_s_at 3.40E-11 PRKCQ L01087 protein kinase C, theta 207651_at 3.40E-11 H963 NM_013308 platelet activating receptor homolog 206099_at 4.09E-11 PRKCH NM_006255 protein kinase C, eta 214551_s_at 4.09E-11 CD7 NM_006137 CD7 antigen (p41)
163 Appendix
Probe set P-value Symbol Genbank Description 225533_at 5.00E-11 DKFZP727G0AL117477 DKFZP727G051 protein 227868_at 5.00E-11 AI928764 0 210837_s_at 6.04E-11 PDE4D AF012074 phosphodiesterase 4D, cAMP-specific 225388_at 7.44E-11 TM4SF9 BG252118 tetraspan 5 236610_at 9.01E-11 AI082004 ESTs 219315_s_at 9.01E-11 FLJ20898 NM_024600 hypothetical protein FLJ20898 235154_at 1.10E-10 TAF3 BG250498 TAF3 RNA polymerase II 36545_s_at 1.34E-10 KIAA0542 AB011114 KIAA0542 gene product 213431_x_at 1.62E-10 KIAA0542 AB011114 KIAA0542 gene product 221744_at 1.62E-10 HAN11 AK026008 WD-repeat protein 211478_s_at 1.94E-10 DPP4 M74777 dipeptidylpeptidase IV (CD26, adenosine deaminase complexing protein 2) 205590_at 1.94E-10 RASGRP1 NM_005739 RAS guanyl releasing protein 1 (calcium and DAG-regulated) 208602_x_at 1.94E-10 CD6 NM_006725 CD6 antigen 222266_at 1.94E-10 C19orf2 BF796940 ESTs, Highly similar to RPB5-mediating protein [Homo sapiens] 207460_at 2.33E-10 GZMM NM_005317 granzyme M (lymphocyte met-ase 1) 211893_x_at 2.33E-10 CD6 U66145 CD6 antigen 211596_s_at 2.33E-10 LRIG1 AB050468 leucine-rich repeats and immunoglobulin-like domains 1 220177_s_at 2.83E-10 TMPRSS3 NM_024022 transmembrane protease, serine 3 241692_at 2.83E-10 AA868729 ESTs 232234_at 3.44E-10 SLA2 AA305476 chromosome 20 open reading frame 24 218764_at 4.20E-10 PRKCH NM_024064 protein kinase C, eta 224428_s_at 5.02E-10 CDCA7 AY029179 cell division cycle associated 7 202524_s_at 5.02E-10 SPOCK2 NM_014767 KIAA0275 gene product 220132_s_at 5.02E-10 LLT1 NM_013269 lectin-like NK cell receptor 213683_at 5.02E-10 FACL6 AV727634 ESTs, Highly similar to T00636 hypothetical protein F21856_2 - human 236862_at 5.99E-10 AA279958 Homo sapiens mRNA full length insert cDNA clone EUROIMAGE 704987 218650_at 5.99E-10 FLJ22127 NM_022775 hypothetical protein FLJ22127 228071_at 5.99E-10 hIAN7 AA858297 hypothetical protein MGC27027 211208_s_at 5.99E-10 CASK AB039327 calcium/calmodulin-dependent serine protein kinase (MAGUK family) 244272_s_at 7.08E-10 C14orf47 AW275132 chromosome 14 open reading frame 47 205291_at 7.08E-10 IL2RB NM_000878 interleukin 2 receptor, beta 241435_at 7.08E-10 AA702930 ESTs 212906_at 7.08E-10 KIAA1201 BE044440 Homo sapiens, clone IMAGE:3880654, mRNA, mRNA sequence 210031_at 7.08E-10 CD3Z J04132 CD3Z antigen, zeta polypeptide (TiT3 complex) 234970_at 8.40E-10 C14orf47 AI741469 chromosome 14 open reading frame 47 228658_at 8.40E-10 R54042 Homo sapiens cDNA FLJ25887 fis, clone CBR02996, mRNA sequence 204491_at 9.99E-10 PDE4D R40917 phosphodiesterase 4D, cAMP-specific 224851_at 9.99E-10 AW274756 Homo sapiens cDNA FLJ31360 fis, clone MESAN2000572 202971_s_at 9.99E-10 DYRK2 NM_006482 dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 2 205790_at 9.99E-10 SCAP1 NM_003726 src family associated phosphoprotein 1 230229_at 9.99E-10 AI692879 ESTs, Weakly similar to hypothetical protein FLJ20378 [Homo sapiens] 203086_at 1.19E-09 KIF2 BE872563 kinesin heavy chain member 2 203717_at 1.19E-09 DPP4 NM_001935 dipeptidylpeptidase IV (CD26, adenosine deaminase complexing protein 2) 244428_at 1.19E-09 DNMT3A AW572279 ESTs, Highly similar to DNA (cytosine-5-)-methyltransferase 3 alpha 231776_at 1.41E-09 EOMES NM_005442 eomesodermin homolog (Xenopus laevis) 236796_at 1.41E-09 AI052447 ESTs 212758_s_at 1.41E-09 TCF8 U19969 transcription factor 8 (represses interleukin 2 expression) 227312_at 1.66E-09 AI694536 ESTs, Weakly similar to hypothetical protein FLJ20489 [Homo sapiens] 216033_s_at 1.66E-09 FYN S74774 FYN oncogene related to SRC, FGR, YES 215338_s_at 1.66E-09 NKTR AI688640 Homo sapiens, Similar to natural killer tumor recognition sequence 244766_at 1.66E-09 BG180003 0 205488_at 1.98E-09 GZMA NM_006144 granzyme A (cytotoxic T-lymphocyte-associated serine esterase 3) 212638_s_at 1.98E-09 WWP1 BF131791 WW domain-containing protein 1 232466_at 1.98E-09 AU155661 Homo sapiens cDNA FLJ13368 fis, clone PLACE1000599, mRNA sequence 242438_at 2.34E-09 AI819150 ESTs 211900_x_at 2.34E-09 CD6 U66146 CD6 antigen 238656_at 2.76E-09 RAD50 AA877043 RAD50 homolog (S. cerevisiae) 209881_s_at 2.76E-09 LAT AF036905 linker for activation of T cells 224832_at 2.76E-09 DUSP16 AB051487 dual specificity phosphatase 16 240265_at 3.26E-09 AI214464 ESTs 226991_at 3.26E-09 AA489681 Homo sapiens cDNA: FLJ22105 fis, clone HEP17660, mRNA sequence 204755_x_at 3.82E-09 HLF M95585 hepatic leukemia factor 227801_at 3.82E-09 N90779 ESTs, Moderately similar to hypothetical protein FLJ20378 [Homo sapiens] 207681_at 3.82E-09 CXCR3 NM_001504 chemokine (C-X-C motif) receptor 3 233411_at 3.82E-09 AU147253 Homo sapiens cDNA FLJ12123 fis, clone MAMMA1000133 243808_at 3.82E-09 AW193531 ESTs, Weakly similar to 2109260A B cell growth factor [Homo sapiens] 209841_s_at 3.82E-09 LRRN3 AL442092 leucine-rich repeat protein, neuronal 3 232055_at 4.43E-09 AA960991 ESTs, Weakly similar to hypothetical protein FLJ20489 [Homo sapiens] 203579_s_at 4.43E-09 SLC7A6 NM_003983 solute carrier family 7 (cationic amino acid transporter, y+ system), member 6 214925_s_at 4.43E-09 SPTAN1 AK026484 spectrin, alpha, non-erythrocytic 1 (alpha-fodrin) 232469_x_at 5.23E-09 AK023883 Homo sapiens cDNA FLJ13821 fis, clone THYRO1000484, mRNA sequence 217326_x_at 5.23E-09 TRB@ AF009787 T cell receptor beta locus 242261_at 5.23E-09 AW470799 ESTs, Weakly similar to neuronal thread protein [Homo sapiens] 219566_at 6.06E-09 PLEKHF1 NM_024310 apoptosis-inducing protein D 239726_at 6.06E-09 AI743588 ESTs 242439_s_at 6.06E-09 AI819150 ESTs 214617_at 6.06E-09 PRF1 AI445650 Perforin, mRNA sequence 206974_at 6.06E-09 CXCR6 NM_006564 chemokine (C-X-C motif) receptor 6 238994_at 7.10E-09 AW022496 ESTs
164 Appendix
Probe set P-value Symbol Genbank Description 211861_x_at 7.10E-09 CD28 AF222343 CD28 antigen (Tp44) 228694_at 7.10E-09 BE670036 Homo sapiens, clone IMAGE:3352913, mRNA, mRNA sequence 207351_s_at 7.10E-09 SH2D2A NM_003975 SH2 domain protein 2A 220940_at 7.10E-09 KIAA1641 NM_025190 KIAA1641 protein 210836_x_at 8.10E-09 PDE4D AF012073 phosphodiesterase 4D, cAMP-specific 211685_s_at 8.10E-09 NCALD AF251061 neurocalcin delta 244764_at 8.10E-09 BG250907 ESTs 208798_x_at 8.10E-09 GOLGIN-67 AF204231 golgin-67 212926_at 8.10E-09 SMC5 AB011166 SMC5 protein 239744_at 8.10E-09 AW151660 ESTs 224726_at 8.10E-09 KIAA1323 W80418 KIAA1323 protein 205315_s_at 8.10E-09 SNTB2 NM_006750 syntrophin, beta 2 221090_s_at 8.10E-09 FLJ10826 NM_018233 hypothetical protein FLJ10826 224580_at 9.51E-09 AK024263 Homo sapiens cDNA FLJ14201 fis, clone NT2RP3002955, mRNA sequence 219629_at 9.51E-09 FLJ20635 NM_017911 hypothetical protein FLJ20635 233500_x_at 9.51E-09 LLT1 AF285089 lectin-like NK cell receptor 235522_at 1.10E-08 AA262688 Homo sapiens mRNA; cDNA DKFZp667F1814 227156_at 1.10E-08 AK025872 Homo sapiens cDNA: FLJ22219 fis, clone HRC01637, mRNA sequence 237333_at 1.10E-08 SYNCOILIN T90771 intermediate filament protein syncoilin 215699_x_at 1.10E-08 PISD; PSSC; AL096768 KIAA0542 gene product 201414_s_at 1.10E-08 NAP1L4 NM_005969 nucleosome assembly protein 1-like 4 218494_s_at 1.29E-08 SLC2A4RG NM_020062 SLC2A4 regulator 230961_at 1.29E-08 BE856980 ESTs 244872_at 1.50E-08 BE514107 Homo sapiens cDNA FLJ31988 fis, clone NT2RP7008863 212229_s_at 1.50E-08 FBXO21 AK001699 F-box only protein 21 218392_x_at 1.50E-08 SFXN1 NM_022754 sideroflexin 1 228962_at 1.74E-08 BF507941 ESTs 81811_at 1.74E-08 AI744451 ESTs 204976_s_at 1.74E-08 AMMECR1 AK023637 Alport syndrome, mental retardation, gene 1 215524_x_at 2.02E-08 TRA@ AW966434 T cell receptor alpha locus 222920_s_at 2.02E-08 KIAA0748 BG231515 KIAA0748 gene product 208807_s_at 2.31E-08 CHD3 U91543 chromodomain helicase DNA binding protein 3 226003_at 2.31E-08 KIAA1708 AB051495 KIAA1708 protein 228570_at 2.31E-08 FLJ33957 BF510581 hypothetical protein FLJ33957 239479_x_at 2.31E-08 AI718964 ESTs 234994_at 2.31E-08 KIAA1913 AA088177 KIAA1913 protein 229488_at 2.31E-08 AW450442 Homo sapiens, clone IMAGE:3909104, mRNA, mRNA sequence 219717_at 2.62E-08 FLJ20280 NM_017741 hypothetical protein FLJ20280 221191_at 2.62E-08 DKFZP434A0NM_018991 DKFZp434A0131 protein 212596_s_at 2.62E-08 HMG2L1 AL079310 high-mobility group protein 2-like 1 241365_at 2.62E-08 AA002140 ESTs 227168_at 2.62E-08 BF475488 Homo sapiens cDNA FLJ25967 fis, clone CBR01929, mRNA sequence 209678_s_at 2.62E-08 PRKCI L18964 protein kinase C, iota 213317_at 2.62E-08 AL049313 Homo sapiens mRNA; cDNA DKFZp564B076 204863_s_at 2.62E-08 IL6ST BE856546 interleukin 6 signal transducer (gp130, oncostatin M receptor) 233614_at 2.99E-08 AU145361 Homo sapiens cDNA FLJ11661 fis, clone HEMBA1004617, mRNA sequence 209501_at 2.99E-08 CDR2 AL582414 cerebellar degeneration-related protein 2, 62kDa 206118_at 2.99E-08 STAT4 NM_003151 signal transducer and activator of transcription 4 217655_at 2.99E-08 BE552409 Unnamed protein product [Homo sapiens], mRNA sequence 220035_at 2.99E-08 NUP210 NM_024923 nucleoporin 210 221499_s_at 2.99E-08 NPEPL1 AF008936 aminopeptidase-like 1 237839_at 2.99E-08 BF433975 ESTs, Moderately similar to hypothetical protein FLJ20234 226433_at 2.99E-08 KIAA1917 BF056204 KIAA1917 protein 228613_at 3.37E-08 BF183535 Homo sapiens cDNA FLJ37498 fis, clone BRAWH2016028 209840_s_at 3.37E-08 LRRN3 AI221950 leucine-rich repeat protein, neuronal 3 237347_at 3.37E-08 AL039379 Homo sapiens cDNA FLJ37166 fis, clone BRACE2027408 228442_at 3.37E-08 AI770171 ESTs 237388_at 3.37E-08 BF224204 ESTs 231976_at 3.37E-08 FLJ10583 AL512693 hypothetical protein FLJ10583 221905_at 3.37E-08 CYLD AJ250014 cylindromatosis (turban tumor syndrome) 212538_at 3.92E-08 zizimin1 AL576253 zizimin1 210607_at 4.49E-08 FLT3LG U03858 fms-related tyrosine kinase 3 ligand 234362_s_at 4.49E-08 CTLA4 U90273 cytotoxic T-lymphocyte-associated protein 4 208806_at 4.49E-08 BE379542 Homo sapiens cDNA FLJ39236 fis, clone OCBBF2007892 203653_s_at 4.49E-08 COIL BG391060 coilin 207556_s_at 4.49E-08 DGKZ NM_003646 diacylglycerol kinase, zeta 104kDa 208376_at 4.49E-08 CCR4 NM_005508 chemokine (C-C motif) receptor 4 201194_at 4.49E-08 SEPW1 NM_003009 selenoprotein W, 1 223093_at 5.15E-08 ANKH AF274753 ankylosis, progressive homolog (mouse) 225584_at 5.15E-08 BE880820 Homo sapiens cDNA FLJ31598 fis, clone NT2RI2002549, mRNA sequence 230069_at 5.15E-08 SFXN1 BF593817 sideroflexin 1 203497_at 5.15E-08 PPARBP NM_004774 PPAR binding protein 237346_at 5.15E-08 AA976208 Homo sapiens clone IMAGE:1567660, mRNA sequence 221046_s_at 5.15E-08 HSPC135 NM_014170 HSPC135 protein 231241_at 5.91E-08 MGC13114 AW469714 hypothetical protein MGC13114 244287_at 5.91E-08 SFRS12 R94399 splicing factor, arginine/serine-rich 12 208930_s_at 5.91E-08 ILF3 BG032366 interleukin enhancer binding factor 3, 90kDa 213650_at 6.72E-08 GOLGIN-67 AW006438 golgin-67 203578_s_at 6.72E-08 SLC7A6 NM_003983 solute carrier family 7 (cationic amino acid transporter, y+ system), member 6
165 Appendix
Probe set P-value Symbol Genbank Description 215336_at 6.72E-08 AKAP11 AK002166 A kinase (PRKA) anchor protein 11 210425_x_at 6.72E-08 GOLGIN-67 AF164622 golgin-67 201321_s_at 6.72E-08 SMARCC2 NM_003075 SWI/SNF related, matrix associated,subfamily c, member 2 210185_at 6.72E-08 CACNB1 AB054985 calcium channel, voltage-dependent, beta 1 subunit 202773_s_at 6.72E-08 SFRS8 AI023864 splicing factor, arginine/serine-rich 8 239543_s_at 6.72E-08 AW275049 ESTs 228298_at 7.68E-08 MGC16044 BF056901 hypothetical protein BC008360 205039_s_at 7.68E-08 ZNFN1A1 NM_006060 zinc finger protein, subfamily 1A, 1 (Ikaros) 235167_at 7.68E-08 BE972419 ESTs, Weakly similar to hypothetical protein FLJ20489 203345_s_at 7.68E-08 M96 AI566096 likely ortholog of mouse metal response transcription factor 2 221745_at 7.68E-08 HAN11 AK026008 WD-repeat protein 212487_at 7.68E-08 KIAA0553 AB011125 KIAA0553 protein 232489_at 7.68E-08 FLJ10287 AK001149 hypothetical protein FLJ10287 217653_x_at 7.68E-08 AW150065 ESTs, Weakly similar to hypothetical protein FLJ20489 219359_at 8.77E-08 FLJ22635 NM_025092 hypothetical protein FLJ22635 227213_at 8.77E-08 AA706895 Similar to RIKEN cDNA 4933426M09 gene [Homo sapiens] 202412_s_at 8.77E-08 USP1 AW499935 ubiquitin specific protease 1 212808_at 8.77E-08 FLJ14639 AA152202 hypothetical protein FLJ14639 230256_at 8.77E-08 FLJ35976 AW009436 Unnamed protein product [Homo sapiens], mRNA sequence 235423_at 1.00E-07 AI274840 ESTs 215757_at 1.00E-07 AK022387 Homo sapiens cDNA FLJ12325 fis, clone MAMMA1002125 210354_at 1.00E-07 IFNG M29383 interferon, gamma 238600_at 1.00E-07 FLJ31564 AW157571 hypothetical protein FLJ31564 205484_at 1.00E-07 SIT NM_014450 SHP2 interacting transmembrane adaptor AFFX-r2-Bs-p 1.00E-07 0 232483_at 1.00E-07 CRSP6 AK022156 cofactor required for Sp1 transcriptional activation, subunit 6, 77kDa 241947_at 1.14E-07 MGC16044 AI824855 hypothetical protein BC008360 220582_at 1.30E-07 FLJ12190 NM_025071 hypothetical protein FLJ12190 210105_s_at 1.30E-07 FYN M14333 FYN oncogene related to SRC, FGR, YES 214402_s_at 1.30E-07 AA521233 Unnamed protein product [Homo sapiens], mRNA sequence 225383_at 1.30E-07 NM_020636 Homo sapiens mRNA; cDNA DKFZp667K1122 228426_at 1.30E-07 LLT1 AW268886 lectin-like NK cell receptor 208797_s_at 1.30E-07 GOLGIN-67 AI829170 golgin-67 215114_at 1.48E-07 SENP3 AK000923 sentrin/SUMO-specific protease 3 236428_at 1.48E-07 D59900 ESTs 218237_s_at 1.48E-07 SLC38A1 NM_030674 solute carrier family 38, member 1 232279_at 1.48E-07 KIAA0239 AK025001 KIAA0239 protein 214298_x_at 1.48E-07 6-Sep AL568374 septin 6 241779_at 1.48E-07 BE898126 ESTs 232834_at 1.48E-07 AU146764 Homo sapiens cDNA FLJ11994 fis, clone HEMBB1001436, mRNA sequence 232752_at 1.48E-07 AK001164 Homo sapiens cDNA FLJ10302 fis, clone NT2RM2000042, mRNA sequence 210865_at 1.48E-07 TNFSF6 D38122 tumor necrosis factor (ligand) superfamily, member 6 237158_s_at 1.68E-07 MPHOSPH9 AW449069 M-phase phosphoprotein 9 235476_at 1.68E-07 TSBF1 AW182459 ESTs 228655_at 1.68E-07 BE466077 Homo sapiens cDNA FLJ12786 fis, clone NT2RP2001936 243414_at 1.68E-07 H62221 ESTs 212994_at 1.68E-07 THOC2 BE543527 THO complex 2 206652_at 1.93E-07 ZNF237 NM_016384 zinc finger protein 237 238880_at 1.93E-07 AI241331 ESTs 229380_at 1.93E-07 BF509573 ESTs 206506_s_at 2.18E-07 SUPT3H NM_003599 suppressor of Ty 3 homolog (S. cerevisiae) 220702_at 2.18E-07 PRO2037 NM_018616 hypothetical protein PRO2037 224848_at 2.18E-07 AW274756 Homo sapiens cDNA FLJ20653 fis, clone KAT01739, mRNA sequence 211005_at 2.18E-07 LAT AF036906 linker for activation of T cells 229558_at 2.18E-07 MGC16824 AI927643 esophageal cancer associated protein 230884_s_at 2.18E-07 SPG7 BE670386 spastic paraplegia 7, paraplegin (pure and complicated autosomal recessive) 202786_at 2.18E-07 STK39 NM_013233 serine threonine kinase 39 (STE20/SPS1 homolog, yeast) 230270_at 2.18E-07 N32872 ESTs, Highly similar to hypothetical protein FLJ10330 201229_s_at 2.18E-07 ARIH2 BC000422 ariadne homolog 2 (Drosophila) 236294_at 2.18E-07 R37670 ESTs, Moderately similar to upstream regulatory element binding protein 1 216748_at 2.45E-07 AK024890 Homo sapiens cDNA: FLJ21237 fis, clone COL01114, mRNA sequence 226480_at 2.45E-07 HSA9761 N21475 putative dimethyladenosine transferase 244443_at 2.45E-07 BE247450 ESTs 238651_at 2.45E-07 BF512491 ESTs 229528_at 2.45E-07 LOC283378 AI670935 Homo sapiens cDNA FLJ35199 fis, clone PLACE6018031, mRNA sequence 229035_s_at 2.45E-07 DKFZp434G0AI797462 hypothetical protein DKFZp434G0522 201143_s_at 2.45E-07 EIF2S1 BC002513 eukaryotic translation initiation factor 2, subunit 1 alpha, 35kDa 205926_at 2.45E-07 WSX1 NM_004843 class I cytokine receptor 227286_at 2.45E-07 FLJ90652 AA743390 FLJ00210 protein [Homo sapiens], mRNA sequence 212000_at 2.45E-07 KIAA0365 AB002363 KIAA0365 gene product 212107_s_at 2.45E-07 DDX9 BE910323 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 9 226044_at 2.45E-07 TDP1 AK023514 tyrosyl-DNA phosphodiesterase 1 202384_s_at 2.78E-07 TCOF1 NM_000356 Treacher Collins-Franceschetti syndrome 1 239892_at 2.78E-07 AW593666 ESTs, Weakly similar to hypothetical protein FLJ20378 215390_at 2.78E-07 AU147194 Homo sapiens cDNA FLJ12102 fis, clone HEMBB1002684 212520_s_at 2.78E-07 SMARCA4 AI684141 SWI/SNF related, matrix associated, subfamily a, member 4 232614_at 3.14E-07 AU146963 Homo sapiens cDNA FLJ12049 fis, clone HEMBB1001996 227232_at 3.14E-07 EVL T58044 RNB6 212825_at 3.14E-07 PAXIP1L AI357401 PAX transcription activation domain interacting protein 1 like
166 Appendix
Probe set P-value Symbol Genbank Description 213594_x_at 3.14E-07 FUSIP1 AU130523 FUS interacting protein (serine-arginine rich) 1 238341_at 3.14E-07 BF677084 ESTs, Weakly similar to hypothetical protein FLJ20378 218309_at 3.52E-07 PRO1489 NM_018584 hypothetical protein PRO1489 215623_x_at 3.52E-07 SMC4L1 AK002200 SMC4 structural maintenance of chromosomes 4-like 1 (yeast) 205917_at 3.52E-07 ZNF264 NM_003417 zinc finger protein 264 226037_s_at 3.52E-07 TAF9L; DN7; AL049589 TAF9-like RNA polymerase II 204459_at 3.52E-07 CSTF2 NM_001325 cleavage stimulation factor, 3' pre-RNA, subunit 2, 64kDa 237107_at 3.52E-07 AA279462 ESTs 216022_at 3.52E-07 AL049278 Homo sapiens mRNA; cDNA DKFZp564I153 236924_at 3.52E-07 AA814383 ESTs 232210_at 3.52E-07 AU146384 Homo sapiens cDNA FLJ14056 fis, clone HEMBB1000335 242146_at 3.52E-07 AA872471 ESTs 242024_at 3.52E-07 T90999 ESTs, Weakly similar to hypothetical protein FLJ20489 206975_at 3.96E-07 LTA NM_000595 lymphotoxin alpha (TNF superfamily, member 1) 240513_at 3.96E-07 AI417992 ESTs 212384_at 3.96E-07 BAT1 BG341380 HLA-B associated transcript 1 215201_at 3.96E-07 AW166925 Homo sapiens cDNA FLJ14135 fis, clone MAMMA1002728 240482_at 3.96E-07 AI955094 ESTs 226625_at 3.96E-07 TGFBR3 AW193698 transforming growth factor, beta receptor III (betaglycan, 300kDa) 243996_at 3.96E-07 BE670974 ESTs 209433_s_at 4.50E-07 PPAT AI457120 phosphoribosyl pyrophosphate amidotransferase 236782_at 4.50E-07 MGC35163 AI129628 hypothetical protein MGC35163 227884_at 4.50E-07 AW296067 Homo sapiens cDNA FLJ10080 fis, clone HEMBA1001987 239427_at 4.50E-07 AA131524 ESTs 238438_at 4.50E-07 R67226 ESTs 238783_at 4.50E-07 MGC33214 AA213701 hypothetical protein MGC33214 232672_x_at 5.00E-07 MEF-2 AK023133 myelin gene expression factor 2 230297_x_at 5.00E-07 SYNGAP1 AW002361 synaptic Ras GTPase activating protein 1 homolog (rat) 242874_at 5.00E-07 AI741506 0 229367_s_at 5.00E-07 hIAN2 AW130536 hypothetical protein FLJ22690 226475_at 5.00E-07 AI650582 Homo sapiens cDNA: FLJ22586 fis, clone HSI02774, mRNA sequence 222214_at 5.00E-07 AK024988 Homo sapiens cDNA: FLJ21335 fis, clone COL02546, mRNA sequence 214948_s_at 5.00E-07 AL050136 Homo sapiens mRNA; cDNA DKFZp586L141 (from clone DKFZp586L141) 236620_at 5.00E-07 FLJ10599 AU150841 hypothetical protein FLJ10599 207237_at 5.00E-07 KCNA3 NM_002232 potassium voltage-gated channel, shaker-related subfamily, member 3 228028_at 5.00E-07 AW139151 HRIHFB2063 [Homo sapiens], mRNA sequence 228677_s_at 5.00E-07 FLJ21438 AI028474 hypothetical protein FLJ21438 208524_at 5.67E-07 GPR15 NM_005290 G protein-coupled receptor 15 233827_s_at 5.67E-07 SUPT16H AK024072 suppressor of Ty 16 homolog (S. cerevisiae) 233832_at 5.67E-07 AK021824 Homo sapiens cDNA FLJ11762 fis, clone HEMBA1005670 206589_at 6.40E-07 GFI1 NM_005263 growth factor independent 1 220704_at 6.40E-07 ZNFN1A1 NM_018563 hypothetical protein PRO0758 213242_x_at 6.40E-07 KIAA0284 AB006622 KIAA0284 protein 224336_s_at 6.40E-07 DUSP16 AB052156 dual specificity phosphatase 16 238430_x_at 6.40E-07 MGC19764 AI923675 hypothetical protein MGC19764 210556_at 7.18E-07 NFATC3 U85430 nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 3 34726_at 7.18E-07 CACNB3 U07139 calcium channel, voltage-dependent, beta 3 subunit 241402_at 7.18E-07 AA504269 Unnamed protein product [Homo sapiens], mRNA sequence 244185_at 7.18E-07 AA921841 ESTs 217119_s_at 7.18E-07 CXCR3; GPRZ79783 chemokine (C-X-C motif) receptor 3 229193_at 7.18E-07 AA005430 Homo sapiens cDNA FLJ34170 fis, clone FCBBF3015396, mRNA sequence 221869_at 7.18E-07 KIAA1196 AL118506 KIAA1196 protein 235287_at 7.18E-07 AW192700 ESTs 202789_at 8.00E-07 AL022394 0 216682_s_at 8.00E-07 AK021457 Homo sapiens cDNA FLJ11395 fis, clone HEMBA1000594, mRNA sequence 211713_x_at 8.00E-07 KIAA0101 BC005832 KIAA0101 gene product 215921_at 8.00E-07 NPIP AC002544 nuclear pore complex interacting protein 207966_s_at 8.00E-07 GLG1 NM_012201 golgi apparatus protein 1 202046_s_at 8.00E-07 GRLF1 NM_004491 glucocorticoid receptor DNA binding factor 1 231165_at 8.00E-07 BE857355 ESTs 243729_at 8.00E-07 AI457984 Homo sapiens cDNA FLJ37931 fis, clone CTONG2004397 241798_at 8.00E-07 AI339930 ESTs, Weakly similar to hypothetical protein FLJ20294 239333_x_at 8.00E-07 BF525633 Homo sapiens cDNA FLJ30541 fis, clone BRAWH2001355 228314_at 8.90E-07 BE877357 Homo sapiens cDNA FLJ37485 fis, clone BRAWH2014379 237768_x_at 8.90E-07 AA825925 ESTs, Moderately similar to hypothetical protein FLJ20378 232171_x_at 8.90E-07 DKFZp434G0AK001742 hypothetical protein DKFZp434G0522 200702_s_at 8.90E-07 DDX24 BG421209 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 24 218798_at 8.90E-07 FLJ12949 NM_023008 hypothetical protein FLJ12949 214670_at 8.90E-07 ZNF36 AA653300 zinc finger protein 36 (KOX 18) 39729_at 8.90E-07 NKEFB L19185 peroxiredoxin 2 235038_at 8.90E-07 HRB2 BF665176 HIV-1 rev binding protein 2 243995_at 8.90E-07 N36417 Homo sapiens mRNA; cDNA DKFZp313P0917 235662_at 8.90E-07 AI125867 ESTs 232369_at 8.90E-07 AF339768 Homo sapiens clone IMAGE:119716, mRNA sequence 230885_at 8.90E-07 SPG7 BE670386 spastic paraplegia 7, paraplegin 202361_at 9.99E-07 SEC24C NM_004922 SEC24 related gene family, member C (S. cerevisiae) 236250_at 9.99E-07 AFG3L1 AI859065 AFG3 ATPase family gene 3-like 1 (yeast) 222790_s_at 9.99E-07 FLJ11220 BE888593 hypothetical protein FLJ11220 213940_s_at 9.99E-07 FNBP1 AU145053 formin binding protein 1 243869_at 9.99E-07 AW205685 ESTs, Weakly similar to hypothetical protein FLJ20234 226987_at 9.99E-07 HUMAGCGB W68720 chromosome 3p21.1 gene sequence 209315_at 9.99E-07 AK024258 ESTs 213283_s_at 9.99E-07 SALL2 BG285616 sal-like 2 (Drosophila)
167 Appendix
Appendix 3
Genes selectively expressed in T cells (identified using 1-way ANOVA, p-value is shown) and involved in the TCR complex, costimulation and signaling. Relevant probe sets were compiled by Diego Silva (ANU Medical School, Canberra, Australia). The direction of change after activation is indicated; nc – no change. Probe set Genbank accession Gene name p-value change with activation 206545_at NM_006139 CD28 3.17E-15 nc 209670_at M12959 TCR alpha 3.17E-15 nc 209671_x_at M12423 TCR alpha 3.17E-15 nc 210972_x_at M15565 TCR alpha 3.17E-15 nc 213539_at NM_000732 CD3 delta 3.17E-15 nc 220418_at NM_018961 UBASH3A 3.17E-15 nc 206804_at NM_000073 CD3 gamma 3.17E-15 downregulated 211902_x_at L34703 TCR alpha 3.61E-15 nc 240102_at AW024095 T-cell receptor rearranged beta-chain V-region 5.74E-15 nc 211211_x_at AF100542 SH2D1A 9.49E-15 nc 213193_x_at AL559122 T cell receptor beta chain BV20S1 BJ1-5 BC1 9.49E-15 nc 210439_at AB023135 ICOS 9.49E-15 upregulated 207892_at NM_000074 CD154 1.23E-14 upregulated 211796_s_at AF043179 TCR beta 1.26E-14 nc 210915_x_at M15564 beta chain BV20S1 BJ1-5 BC1 1.56E-14 nc 228109_at AI912976 RASGRF2 1.94E-14 nc 211209_x_at AF100540 SH2D1A 4.21E-14 nc 211210_x_at AF100539 SH2D1A 5.43E-14 nc 205456_at NM_000733 CD3 epsilon 1.59E-13 nc 210116_at AF072930 SH2D1A 3.02E-13 nc 211339_s_at D13720 itk 1.86E-12 upregulated 204890_s_at U07236 Lck 1.86E-12 downregulated 211856_x_at AF222341 CD28 2.35E-12 nc 214032_at AI817942 ZAP70 2.35E-12 downregulated 204891_s_at NM_005356 Lck 1.45E-11 downregulated 205590_at NM_005739 ras-GRP 1.94E-10 nc 232234_at AA305476 SLA2 3.44E-10 downregulated 211208_s_at AB039327 CASK 5.99E-10 downregulated 210031_at J04132 CD3 zeta 7.08E-10 nc 205790_at NM_003726 SCAP1 9.99E-10 downregulated 230229_at AI692879 --- 9.99E-10 downregulated 227312_at AI694536 --- 1.66E-09 nc 216033_s_at S74774 Fyn 1.66E-09 upregulated 209881_s_at AF036905 LAT 2.76E-09 nc 214925_s_at AK026484 SPTAN1 4.43E-09 nc 217326_x_at AF009787 T cell receptor beta chain (TCRB) 5.23E-09 nc 219566_at NM_024310 PLEKHF1 6.06E-09 downregulated 211861_x_at AF222343 CD28 7.1E-09 nc 207351_s_at NM_003975 SH2D2A 7.1E-09 upregulated 205315_s_at NM_006750 SNTB2 8.1E-09 nc 215524_x_at AW966434 TCR alpha 2.02E-08 nc 206118_at NM_003151 STAT4 2.99E-08 upregulated 212538_at AL576253 DOCK9 3.92E-08 nc 234362_s_at U90273 CTLA-4 4.49E-08 nc 210185_at AB054985 CACNB1 6.72E-08 upregulated 210105_s_at M14333 FYN 0.00000013 upregulated 211005_at AF036906 LAT 2.18E-07 nc 227232_at T58044 evl 3.14E-07 downregulated 210556_at U85430 NFAT 7.18E-07 nc 202789_at AL022394 PLC-gamma 1 0.0000008 downregulated 213940_s_at AU145053 FNBP1 9.99E-07 nc 39835_at U93181 SBF1 0.00000125 nc 217170_at AE000659 T-cell receptor active alpha-chain 0.00000154 nc 234886_at M11950 cell receptor gene (polymorphism associated with 0.00000154 nc 234852_at AE000660 T-cell receptor alpha delta locus 0.00000171 nc 232874_at AU146550 DOCK9 0.00000434 nc 204852_s_at NM_002832 PTPN7 0.00000434 upregulated 216540_at X61072 TCR V alpha 14.1/J alpha 32/C alpha 0.00000481 nc 212550_at AL080218 STAT5B 0.00000481 upregulated 212486_s_at N20923 FYN 0.00000531 upregulated 221331_x_at NM_005214 CTLA-4 0.00000579 nc 232461_at AI962891 AHI1 0.00000579 upregulated 234819_at AE000660 T-cell receptor alpha delta locus 0.00000638 nc 207732_s_at NM_021120 DLG3 0.0000122 nc 234013_at AE000659 T cell receptor alpha chain 0.0000133 nc 232344_at AK021812 RASA1/GAP 0.0000133 downregulated 205026_at NM_012448 STAT5B 0.0000146 upregulated 208406_s_at NM_004810 GRAP2/gads 0.0000174 nc 207620_s_at NM_003688 CASK 0.0000268 nc 214054_at AI828929 DOK2 0.0000268 upregulated 208325_s_at NM_006738 AKAP13 0.0000343 nc 244699_at AV658469 --- 0.0000403 upregulated 227677_at BF512748 JAK3 0.000044 nc 231794_at BG536887 CTLA-4 0.0000517 nc 224533_s_at M77498 TCR beta chain Vbeta 13S3-LLQGYTF-Jbeta1.2 0.0000557 nc 236664_at AA448167 --- 0.0000557 upregulated 213688_at N25325 Calmodulin 1 0.0000557 downregulated 217208_s_at AL121981 DLG1 0.0000652 nc 205285_s_at AI633888 ADAP/FYB 0.0000652 downregulated 210555_s_at U85430 NFAT 0.0000756 nc 212240_s_at M61906 PIK3-alpha 0.0000817 downregulated 215923_s_at AK023421 PSD4 0.0000886 nc 217328_at AF009787 T cell receptor beta chain 0.0000886 nc 207187_at NM_000215 JAK3 0.0000953 nc 217412_at AE000659 T-cell receptor alpha delta locus 0.0000953 nc 217381_s_at X69383 T cell receptor gamma V region 5 0.000103 nc 216551_x_at AL110247 PLC-gamma 1 0.000111 nc 207634_at NM_005018 PD-1 0.000111 upregulated 216920_s_at M27331 T cell receptor gamma locus 0.000119 nc 230086_at AA937109 FNBP1 0.000119 nc
168 Appendix
Probe set Genbank accession Gene name p-value change with activation 234399_at AE000660 T-cell receptor alpha delta locus 0.000119 nc 203055_s_at NM_004706 --- 0.000137 nc 211144_x_at M30894 T cell receptor gamma locus 0.000137 nc 227817_at R51324 PKC beta 0.000147 downregulated 215796_at BF976764 T-cell receptor alpha chain VDJC region 0.000158 nc 215806_x_at M13231 T cell receptor gamma locus 0.000158 nc 220841_s_at NM_017651 AHI1 0.000158 nc 212727_at AB033058 DLG3 0.000182 nc 212983_at NM_005343 RAS 0.000182 nc 225098_at BF245400 ABI2 0.000182 nc 222024_s_at AK022014 AKAP13 0.000195 nc 236707_at AA521016 DAPP1 0.00021 nc 236341_at AI733018 CTLA-4 0.000243 upregulated 209813_x_at M16768 T cell receptor gamma variable 9 0.000259 nc 218501_at NM_019555 ARHGEF3 0.000259 upregulated 212249_at M61906 PIK3-alpha 0.000259 downregulated 209999_x_at AI056051 SOCS1 0.000277 nc 200623_s_at NM_005184 Calmodulin 3 0.000296 nc 222023_at AK022014 AKAP13 0.000317 nc 202547_s_at AA778936 ARHGEF7 0.000362 nc 225234_at AV710415 cbl 0.000362 downregulated 230469_at AW665138 PLEKHK1 0.000387 nc 206828_at NM_003328 txk/rlk 0.000412 nc 219994_at NM_019043 APBB1IP 0.000412 nc 225979_at AK024429 CLG 0.000412 nc 212729_at AB033058 DLG3 0.000439 nc 234440_at X13954 T-cell receptor alpha chain 0.000466 nc 205839_s_at NM_004758 BZRAP1 0.000498 nc 225137_at AL515755 NFAT 0.000498 nc 230337_at AW241962 SOS1 0.000563 downregulated 212393_at U93181 SBF1 0.000642 nc 238699_s_at AI659225 CASK 0.000681 nc 209504_s_at AF081583 PLEKHB1 0.000724 nc 209856_x_at U31089 --- 0.000724 nc 225112_at BF245400 ABI2 0.000724 nc 209534_x_at BF222823 AKAP13 0.000922 nc 234388_at AE000660 TCR alpha 0.000922 nc 241619_at BF526558 Calmodulin 1 0.000922 nc 205868_s_at L07527 PTPN11 0.000976 nc 219513_s_at NM_005490 SH2D3A 0.000976 nc 223766_at AF130105 PARG 0.000976 upregulated 226685_at AI695684 --- 0.00103 nc 221569_at AL136797 AHI1 0.00103 upregulated 234849_at AE000659 TCR alpha 0.0011 nc 242549_at AW008270 PRKCN 0.0011 upregulated 201488_x_at BC000717 Sam68 0.00116 nc 215639_at AK000861 SH2D3C 0.00116 nc 207268_x_at NM_005759 ABI2 0.00123 nc 229854_at AW614056 OBSCN 0.00123 nc 202315_s_at NM_004327 BCR 0.0013 nc 217056_at X61070 T cell recepto 0.0013 nc 209626_s_at AI202969 OSBPL3 0.0013 upregulated 228985_at N22896 --- 0.00146 nc 203617_x_at NM_005229 ELK1 0.00146 upregulated 221223_x_at NM_013324 CISH 0.00154 downregulated 239476_at AW152166 PIK3-alpha 0.00173 nc 221293_s_at NM_022047 DEF6 0.00173 downregulated 218697_at NM_016453 NCKIPSD 0.00183 nc 214228_x_at AJ277151 CD134 0.00183 upregulated 225564_at AW269397 SPATA13 0.00183 upregulated 203809_s_at NM_001626 AKT2 0.00228 nc 212239_at M61906 PIK3-alpha 0.00228 downregulated 238563_at AV762916 --- 0.00228 downregulated 235692_at AW024527 SH3KBP1 0.0024 nc 228986_at AW978375 OSBPL8 0.0024 downregulated 244578_at AA992040 SLP-76/LCP2 0.0024 downregulated 217397_at AE000659 T-cell receptor alpha delta locus from bases 250 0.00313 nc 201469_s_at AI809967 SHC1 0.0033 nc 202516_s_at NM_004087 DLG1 0.0033 nc 213093_at AI471375 PKC 0.00347 nc 214844_s_at AL050069 DOK5 0.00347 nc 33197_at U39226 --- 0.00347 nc 234427_at AE000659 Human T-cell receptor rearranged beta chain ge 0.00347 nc 212801_at AI861788 CIT 0.00366 nc 217394_at AE000659 TCR alpha 0.00366 nc 223961_s_at D83532 CISH 0.00366 downregulated 234402_at AE000659 T-cell receptor alpha delta locus from bases 250 0.00385 nc 230538_at AI027957 RaLP 0.00385 upregulated 211252_x_at U36759 pre-alpha 0.00426 nc 218180_s_at NM_022772 EPS8L2 0.00426 nc 234895_at U90273 CTLA4 0.00426 nc 217838_s_at NM_016337 evl 0.00426 downregulated 210376_x_at M25269 ELK1 0.00448 nc 212777_at L13857 SOS1 0.00448 downregulated 234377_at M11952 T-cell receptor beta chain 0.00472 nc 202932_at NM_005433 YES1 0.00521 nc 234848_at AE000659 T-cell receptor alpha delta locus from bases 250 0.00546 nc 203760_s_at U44403 slap 0.00546 upregulated 205867_at NM_002834 PTPN11 0.00574 nc 215195_at AF035594 PKC 0.00574 nc
169 Appendix
Probe set Genbank accession Gene name p-value change with activation 209158_s_at BC004361 PSCD2 0.00602 downregulated 215797_at AE000659 T-cell receptor alpha delta locus 0.00632 nc 230389_at BE046511 FNBP1 0.00632 nc 223377_x_at AF035947 CISH 0.00632 downregulated 230669_at W38444 RASA2 0.00632 downregulated 211795_s_at AF198052 ADAP/FYB 0.00663 downregulated 216069_at AL050065 HRMT1L1 0.00696 nc 220842_at NM_017651 AHI1 0.00762 upregulated 203317_at NM_012455 PSD4 0.00762 downregulated 208611_s_at U83867 SPTAN1 0.00797 nc 213618_at AB011152 CENTD1 0.00835 upregulated 216133_at AA284903 T-cell receptor alpha chain 0.00873 nc 212197_x_at AB020671 M-RIP 0.00873 downregulated 228725_x_at BF003112 HRMT1L1 0.00873 downregulated 221971_x_at BE672818 FLJ00312 0.00955 nc 225141_at AL515755 NFAT 0.00998 nc 227151_at BE464841 MGC32065 0.00998 nc 234883_x_at M97943 TCR beta gene for T cell receptor beta chain var 0.00998 nc 212549_at AL080218 STAT5B 0.00998 upregulated 209878_s_at M62399 NF-kappa-B p65delta3 0.0104 nc 222169_x_at N71739 SH2D3A 0.0104 nc 240613_at AW070459 JAK1 0.0104 downregulated 206508_at NM_001252 CD70 0.0109 nc 212288_at AB011126 FNBP1 0.0109 nc 235238_at BF676462 RaLP 0.0109 nc 210384_at U79286 HRMT1L1 0.0114 nc 218849_s_at NM_006663 RAI 0.0124 nc 214339_s_at AA744529 hpk 0.0129 downregulated 206220_s_at NM_007368 RASA3 0.0135 nc 228596_at AW009638 ARHGEF5 0.0135 nc 220439_at NM_024892 RIN3 0.0141 nc 222128_at U80764 CACNB2 0.0147 nc 214771_x_at AK025604 M-RIP 0.0147 downregulated 209627_s_at AY008372 OSBPL3 0.0154 nc 243213_at BF508977 STAT3 0.0154 upregulated 206296_x_at NM_007181 hpk 0.0154 downregulated 217048_at Y09846 --- 0.016 nc 202933_s_at NM_005433 YES1 0.0167 nc 203372_s_at AB004903 SOCS2 0.0167 nc 214735_at AW166711 PIP3-E 0.0167 nc 210001_s_at AB005043 SOCS1 0.0167 upregulated 205212_s_at NM_014716 CENTB1 0.0173 downregulated 213830_at AW007751 T cell receptor delta locus 0.018 nc 215238_s_at AW450751 DOCK9 0.018 nc 216114_at AL049430 NCKIPSD 0.018 nc 221610_s_at BC000795 STAP2 0.018 nc 221850_x_at AI826075 CTGLF1 0.018 nc 232017_at AK025185 TJP2 0.0187 nc 236835_at AI654093 FUT8 0.0195 upregulated 227946_at AI955239 OSBPL7 0.0195 downregulated 205728_at AL022718 SH2D1A 0.0203 nc 238356_at AW968823 DOCK11 0.0203 downregulated 217585_at BE502910 NEBL 0.0212 nc 217999_s_at AA576961 PHLDA1 0.0212 upregulated 226659_at Z97832 DEF6 0.022 downregulated 205213_at NM_014716 CENTB1 0.0259 downregulated 234865_at AJ389983 TCR beta 0.0265 nc 206152_at NM_014770 CENTG1 0.0269 nc 221718_s_at M90360 AKAP13 0.0269 nc 217576_x_at BF692958 SOS2 0.029 nc 214219_x_at BE646618 hpk 0.029 downregulated 219393_s_at NM_005465 AKT3 0.0301 nc 226829_at AW138743 KIAA1914 0.0301 nc AFFX-HUMISGF3A/MM97935 STAT1 0.0312 upregulated 216218_s_at AK023546 PLCL2 0.0312 downregulated 203516_at NM_003098 SNTA1 0.0324 nc 217998_at AA576961 PHLDA1 0.0324 nc 204740_at NM_006314 CNKSR1 0.0349 nc 225542_at AI492175 CENTB5 0.0349 nc 229642_at AW139757 ARHGEF7 0.0349 nc 206150_at NM_001242 CD27 0.0375 nc 207776_s_at NM_000724 CACNB2 0.0375 nc 218813_s_at NM_020145 SH3GLB2 0.0375 downregulated 222880_at AF135794 AKT3 0.0404 nc 214185_at AW592227 Sam68 0.0418 nc 227750_at AL137629 TRAD 0.0418 nc 226602_s_at T30183 BCR 0.0418 downregulated 229116_at AI670947 CNKSR2 0.0433 nc 203563_at NM_021638 AFAP 0.0448 nc 225139_at AL515755 NFAT 0.0448 nc 216755_at AK024945 OSBPL10 0.0479 nc 216191_s_at X72501 T cell receptor delta locus 0.0496 nc 217143_s_at X06557 T cell receptor delta locus 0.0496 nc 224148_at AF116653 --- 0.0496 downregulated
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