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The role of Toll/interleukin-1 receptor adaptor protein in the pathogenesis of myelodysplastic syndromes Ibrahim, Rawa 2015

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  The Role of Toll/Interleukin-1 Receptor Adaptor Protein in the Pathogenesis of Myelodysplastic Syndromes   by   Rawa Ibrahim   BSc., The University of British Columbia, 2008   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF    DOCTOR OF PHILOSOPHY   in   The Faculty of Graduate and Postdoctoral Studies  (Interdisciplinary Oncology)   THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)    June, 2015   © Rawa Ibrahim, 2015 ii  ABSTRACT  Hematopoiesis is a process essential for the maintenance of cells that mediate many vital functions such as the production of red blood cells necessary for transportation of oxygen and the removal of carbon dioxide from the body.  It is also required for the production of platelets which are necessary for clotting, and the various types of white blood cells that make up the innate and adaptive immune systems that protect against viral, microbial, and parasitic infections.  The cell responsible for the generation of all the downstream effector cells, the hematopoietic stem cell (HSC), is generated during embryogenesis.  Through a series of symmetric and asymmetric cell divisions, the HSC is capable of maintaining all the cells of the hematopoietic hierarchy throughout the lifespan of an organism.  A variety of genetic and epigenetic cues are necessary to maintain homeostasis, and perturbations in these cues lead to the development of hematopoietic malignancies and myelodysplastic syndromes.  In recent years the innate immune pathway has emerged as an important player in hematopoietic homeostasis.   This dissertation examines the role of dysregulation of innate immune signaling in the development of the myelodysplastic syndromes, one of the most common hematological malignancies.  Using a murine bone marrow transplantation assay, I show that overexpression of the innate immune signaling adaptor, TIRAP, results in perturbations in normal hematopoiesis.  Overexpression of TIRAP in the hematopoietic compartment results in an inability to produce mature hematopoietic cells, leading to pancytopenia and bone marrow failure (BMF).  TIRAP-induced BMF is a result of both autonomous and non-autonomous effects mediated by the cytokine IFNγ.  Interestingly, in an environment depleted of IFNγ, TIRAP-transplanted mice develop a myeloproliferative neoplasm suggesting that TIRAP activates both myelosuppressive pathways iii  (through IFNγ) as well as myeloproliferative pathways.  IFNγ acts in a paracrine manner inhibiting osteoclast proliferation and maturation in the bone marrow microenvironment, thus disrupting the HSC niche.  In summary, this thesis shows the importance of immune regulation in hematopoietic homeostasis.  Furthermore, it shows how defects in the hematopoietic stem/progenitor compartment can translate into a defect in the stem cell niche, contributing further to marrow failure and disease progression.                   iv  PREFACE  This dissertation is an original intellectual product of the author, R. Ibrahim. All experiments presented in Chapter 3 of this thesis are original unpublished work performed by myself. Mouse bones were embedded by Michelle Ly and sectioning and H&E staining was performed by Vennie Chou at PMI Laboratories. The work presented in Chapter 4 was a collaborative effort. The MyD88-DN constructs were generated by Dr. Linda Chang. The MDS promoter methylation experiment presented in Chapter 4 was performed by Dr. Maria E. Figueroa and a version of that data has been published in [Figueroa, M.E., L. Skrabanek, Y. Li, A. Jiemjit, T.E. Fandy, E. Paietta, H. Fernandez, M.S. Tallman, J.M. Greally, H. Carraway, J.D. Licht, S.D. Gore, and A. Melnick. 2009. MDS and secondary AML display unique patterns and abundance of aberrant DNA methylation. Blood 114:3448-3458]. Patricia Umlandt performed busulfan injections for the marrow conditioning experiments. Patient bone marrow sections were provided by Dr. Hudoba at Vancouver General Hospital.  Immunohistochemical staining was performed by Megan Fuller, and the slides were imaged by Dr. Alistair Kyle, Patrick Coulombe and myself.   The use of mouse models, human material, and biohazardous material was approved by the UBC Animal Care and Ethics Committee, the UBC BCCA Research Ethics Board, and the Biosafety Committee (Animal Care Certificate A10-0126 and A14-0091; Human Ethics Certificate H13-02648 and H13-02687; Biohazard Approval Certificate B05-0182 and B13-0029).    v  TABLE OF CONTENTS ABSTRACT ....................................................................................................................................................... ii PREFACE ......................................................................................................................................................... iv TABLE OF CONTENTS ....................................................................................................................................... v LIST OF TABLES ............................................................................................................................................. viii LIST OF FIGURES ............................................................................................................................................. ix LIST OF ABBREVIATIONS ................................................................................................................................ xi ACKNOWLEDGEMENTS ............................................................................................................................... xvii DEDICATION ................................................................................................................................................. xix 1. INTRODUCTION ...................................................................................................................................... 1 1.1 HEMATOPOIESIS AND HOMEOSTASIS ................................................................................................................... 3 1.1.1 Hematopoietic stem cells ................................................................................................................. 4 1.1.2 Progenitor cells and differentiation ................................................................................................ 8 Myeloid differentiation ........................................................................................................................ 11 Osteoclast differentiation ................................................................................................................. 11 1.1.3 HSC niche ........................................................................................................................................ 15 Endosteal niche ..................................................................................................................................... 18 Osteoblasts ....................................................................................................................................... 18 Osteoclasts ....................................................................................................................................... 22 Vascular niche ....................................................................................................................................... 25 1.2 THE MYELODYSPLASTIC SYNDROMES ................................................................................................................. 28 1.2.1 Classification .................................................................................................................................. 29 1.2.2 Molecular mechanisms of MDS ..................................................................................................... 31 Del(5q) .................................................................................................................................................. 31 Mechanisms of clonal dominance in del(5q) MDS ............................................................................ 39 Other karyotypic abnormalities ........................................................................................................... 41 Genetic mutations ................................................................................................................................ 44 Mutations associated with regulation of the epigenome ................................................................. 46 DNMT3a .................................................................................................................................... 46 TET2 and IDH1/2 ....................................................................................................................... 48 EZH2 and AZXL1 ........................................................................................................................ 50 Mutations of RNA splicing machinery .............................................................................................. 52 1.2.3 MDS as a stem cell disorder ........................................................................................................... 54 1.2.4 Cellular mechanisms of MDS ......................................................................................................... 56 Progenitor cells in MDS ........................................................................................................................ 56 The bone marrow microenvironment in MDS ...................................................................................... 59 Contributions of other cells to the pathophysiology of MDS .............................................................. 61 1.2.5 Diagnosis of MDS in murine models .............................................................................................. 62 1.3 THE INNATE IMMUNE SYSTEM .......................................................................................................................... 64 1.3.1 Toll-like receptor signaling overview ............................................................................................ 66 vi  1.3.2 TLR signaling in hematopoietic progenitor cells ........................................................................... 70 1.4 AIMS OF THE STUDY ........................................................................................................................................ 72 2. MATERIALS AND METHODS .................................................................................................................. 74 2.1 CELL CULTURE ................................................................................................................................................ 75 2.1.1 Gene transfer.................................................................................................................................. 75 2.1.2 Osteoclast differentiation .............................................................................................................. 76 2.1.3 Long term culture-initiating cell (LTC-IC) assay ............................................................................. 77 2.2 OSTEOCLAST STAINING AND QUANTIFICATION .................................................................................................... 78 2.2.1 Histochemical staining of osteoclasts ........................................................................................... 78 2.2.2 Immunohistochemical staining of human FFPE marrow sections ................................................ 79 2.3 IMMUNOBLOTTING ......................................................................................................................................... 81 2.4 MOUSE STRAINS ............................................................................................................................................ 81 2.5 BONE MARROW TRANSPLANTATION ................................................................................................................. 81 2.5.1 Homing assay and homing efficiency ............................................................................................ 82 2.5.2 Niche conditioning transplant ....................................................................................................... 82 2.6 FLOW CYTOMETRY ......................................................................................................................................... 83 2.6.1 Progenitor staining ........................................................................................................................ 83 2.6.2 Ki67 staining ................................................................................................................................... 84 2.6.3 Activated caspase-3 staining ......................................................................................................... 84 2.6.4 Annexin staining ............................................................................................................................ 85 2.7 REAL-TIME PCR ............................................................................................................................................. 87 2.8 GENE SET ENRICHMENT ANALYSIS ..................................................................................................................... 88 2.9 PROMOTER METHYLATION ANALYSIS ................................................................................................................ 89 2.10 STATISTICAL ANALYSIS .............................................................................................................................. 93 3. TIRAP IS AN IMPORTANT MEDIATOR OF BONE MARROW FAILURE ...................................................... 94 3.1 TIRAP LEVELS ARE UPREGULATED IN DEL(5Q) MDS ........................................................................................... 95 3.2 OVEREXPRESSION OF TIRAP IN MURINE MARROW LEADS TO A MARROW FAILURE SYNDROME ................................. 97 4. TIRAP OVEREXPRESSION ACTIVATES NON-CANONICAL SIGNALING PATHWAYS ................................. 109 4.1 IFNΓ AND IL-10 EXPRESSION IS INDUCED FOLLOWING TIRAP OVEREXPRESSION ....................................................110 4.2 CANONICAL TLR SIGNALING GENES ARE HYPERMETHYLATED IN MDS ...................................................................113 4.3 IFNΓ IS RESPONSIBLE FOR TIRAP-MEDIATED BONE MARROW FAILURE .................................................................118 4.4 TIRAP OVEREXPRESSION ENHANCES SURVIVAL OF HEMATOPOIETIC CELLS EX VIVO ................................................123 4.5 IFNΓ CAUSES SECONDAY EFFECTS IN HSC NICHE ...............................................................................................125 4.6 TIRAP OVEREXPRESSION RESULTS IN A BLOCK IN OSTEOCLASTOGENESIS ...............................................................132 5. SUMMARY, DISCUSSION, AND FUTURE DIRECTIONS .......................................................................... 136 5.1 TIRAP OVEREXPRESSION AS A MODEL OF MARROW FAILURE IN MDS .................................................................137 5.1.1 Stem/progenitor cell involvement in TIRAP mediated marrow failure ......................................137 5.2 THE ROLE OF IFNΓ IN THE ONSET OF BONE MARROW FAILURE AND MPD .............................................................142 5.3 MDS ASSOCIATED MARROW FAILURE AS A DISORDER OF THE STEM CELL NICHE ....................................................144 5.4 THE ROLE OF ABERRANT TLR SIGNALING IN MDS .............................................................................................150 BIBLIOGRAPHY ............................................................................................................................................ 153 vii  APPENDICES ................................................................................................................................................ 182 APPENDIX A: FAB CLASSIFICATION OF MDS ............................................................................................................182 APPENDIX B: WHO CLASSIFICATION OF MDS ..........................................................................................................183 APPENDIX C: IPSS-R PROGNOSTIC SCORE VALUES, CYTOGENETIC RISK GROUPS, AND PROGNOSTIC RISK CATEGORIES ...........184 APPENDIX D: WHO CLASSIFICATION-BASED PROGNOSTIC SCORING SYSTEM FOR MDS ...................................................185 viii  LIST OF TABLES  TABLE 1.1 GENES ON CHROMOSOME ARM 5Q OUTSIDE OF CDR ASSOCIATED WITH MDS ................................ 38 TABLE 1.2 MINIMUM CRITERIA FOR DEFINING MYELOID DYSPLASIA IN MICE .................................................... 63 TABLE 1.3 MINIMUM CRITERIA FOR DEFINING MYELOID PROLIFERATION IN MICE ............................................ 63 TABLE 1.4 MAMMALIAN TOLL-LIKE RECEPTORS AND THEIR LIGANDS ................................................................. 67 TABLE 1.5 TOLL-LIKE RECEPTOR EXPRESSION IN HSPCS ....................................................................................... 71 TABLE 2.1 PRIMERS FOR QUANTITATIVE RT-PCR ................................................................................................. 87 TABLE 2.2 IFN STIMULATED GENES ...................................................................................................................... 88 TABLE 2.3 IL-10 SIGNALING PATHWAY GENES ..................................................................................................... 88 TABLE 2.4 NFΚB PATHWAY GENES ....................................................................................................................... 89 TABLE 2.5 TLR PATHWAY GENES .......................................................................................................................... 90 TABLE 2.6 TLR INTERACTING GENES ..................................................................................................................... 91 TABLE 2.7 INNATE DB PATHWAYS ........................................................................................................................ 92 TABLE 4.1 105 GENES ANALYZED IN PROMOTER METHYLATION ARRAY ........................................................... 114           ix  LIST OF FIGURES FIGURE 1.1 TYPES OF HSC DIVISIONS ..................................................................................................................... 5 FIGURE 1.2 HEMATOPOIETIC HIERARCHY .............................................................................................................. 6 FIGURE 1.3 SURFACE MARKERS FOR ISOLATION OF MOUSE HEMATOPOIETIC PROGENITOR CELLS. ................. 10 FIGURE 1.4 STAGES OF OSTEOCLAST DEVELOPMENT .......................................................................................... 12 FIGURE 1.5 RANKL/RANK/OPG REGULATORY AXIS .............................................................................................. 14 FIGURE 1.6 THE HEMATOPOIETIC STEM CELL NICHE ........................................................................................... 17 FIGURE 1.7 GENES LOCATED ON CHROMOSOME 5Q ........................................................................................... 32 FIGURE 1.8 MICRORNA BIOGENESIS..................................................................................................................... 35 FIGURE 1.9 IMMUNE TARGETS OF MIR-145 AND MIR-146 .................................................................................. 36 FIGURE 1.10 CHANGES IN HEMATOPOIETIC PROGENITOR CELL POPULATIONS IN MDS ..................................... 57 FIGURE 1.11 COMPARISON BETWEEN DROSOPHILA TOLL AND MAMMALIAN TLR SIGNALING .......................... 65 FIGURE 1.12 A BRIEF OVERVIEW OF THE TOLL-LIKE RECEPTOR SIGNALING PATHWAY. ...................................... 68 FIGURE 2.1 QUANTIFICATION OF OSTEOCLAST DIFFERENTIATION ...................................................................... 79 FIGURE 2.2 GATING STRATEGY FOR DISTINGUISHING WILD-TYPE AND TRANSDUCED CELLS IN CO-CULTURE SYSTEM ........................................................................................................................................................ 86 FIGURE 2.3 FILTERING CRITERIA FOR TLR-RELATED GENES .................................................................................. 90 FIGURE 3.1 TIRAP IS A TARGET OF MIR-145 AND LOSS OF MIR-145 RESULTS IN UPREGULATION OF ENDOGENOUS TIRAP LEVELS ....................................................................................................................... 96 FIGURE 3.2 TIRAP OVEREXPRESSION AND TRANSPLANT SCHEME ....................................................................... 97 FIGURE 3.3 TIRAP OVEREXPRESSION IN MURINE MARROW CAUSES INCREASED MORTALITY ......................... 100 FIGURE 3.4 TIRAP MARROWS DISPLAY INCREASED APOPTOSIS AND REDUCED CELLULARITY .......................... 100 FIGURE 3.5 TIRAP EXPRESSION DOES NOT ALTER HEMATOPOIETIC PROGENITOR HOMING TO MARROW...... 102 FIGURE 3.6 IMMUNOPHENOTYPING OF BONE MARROW AT TERMINATION OF EXPERIMENT ......................... 104 FIGURE 3.7 TIRAP OVEREXPRESSION DOES NOT ALTER STEM CELL FREQUENCY .............................................. 106 FIGURE 3.8 TIRAP OVEREXPRESSION ALTERS HEMATOPOIETIC PROGENITOR CELL POPULATIONS .................. 108 FIGURE 4.1 RT-QPCR SCREEN OF CYTOKINES AND CHEMOKINES INVOLVED IN MDS PATHOGENESIS.............. 111 FIGURE 4.2 GENE SET ENRICHMENT ANALYSIS .................................................................................................. 112 FIGURE 4.3 PROMOTER METHYLATION OF TLR-RELATED GENES IS INCREASED IN MDS PATIENTS .................. 115 FIGURE 4.4 IL-10 AND IFNΓ PRODUCTION OCCURS VIA NON-CANONICAL SIGNALING PATHWAY .................... 117 FIGURE 4.5 EXPRESSION LEVELS OF IL-10 AND IFNΓ IN WT AND KNOCKOUT BM CELLS .................................... 119 FIGURE 4.6 PERIPHERAL BLOOD COUNTS OF IL-10 -/- AND IFNΓ -/- TRANSPLANTED MICE ............................... 120 FIGURE 4.7 LOSS OF IFNΓ AND IL-10 PROMOTE DEVELOPMENT OF MYELOPROLIFERATIVE DISORDERS .......... 123 FIGURE 4.8 TIRAP OVEREXPRESSION ENHANCES SURVIVAL OF BM CELLS IN VITRO IN AN AUTONOMOUS AND NON-AUTONOMOUS MANNER ................................................................................................................. 124 FIGURE 4.9 CONSECUTIVE BONE MARROW TRANSPLANT EXPERIMENTAL DESIGN .......................................... 127 FIGURE 4.10 50 MG/KG BUSULFAN IS SUFFICIENT TO ERADICATE THE MAJORITY OF CELLS FROM A HEMATOPOIETIC TRANSPLANT 3 WEEKS POST TRANSPLANTATION. ........................................................ 128 FIGURE 4.11 TIRAP CONDITIONED NICHE AFFECTS HSC ENGRAFTMENT ........................................................... 130 FIGURE 4.12 LOW ENGRAFTMENT OF TIRAP EXPRESSING CELLS  DOES NOT RESULT IN MARROW FAILURE ... 132 FIGURE 4.13 IFNΓ INHIBITS OSTEOCLASTOGENESIS IN THE MONOCYTIC CELL LINE RAW264.7 ........................ 133 x  FIGURE 4.14 TIRAP EXPRESSING BONE MARROW CELLS BLOCK OSTEOCLASTOGENESIS IN THE RAW264.7 CELL LINE ............................................................................................................................................................ 134 FIGURE 4.15 OSTEOCLASTS ARE REDUCED IN DEL(5Q) MDS PATIENTS ............................................................. 135 FIGURE 5.1 PROGENITOR CELL DIFFERENTIATION DEFECTS IN TIRAP OVEREXPRESSION MODEL OF BMF ....... 141 FIGURE 5.2 PROPOSED MECHANISM FOR TIRAP MEDIATED MARROW FAILURE .............................................. 148 xi  LIST OF ABBREVIATIONS  5-FU  5-fluorouracil 5hmC  5-hydroxymethylcytosine 5mC  5-methylcytosine AGM  Aorta-gonad-mesonephros ALCAM  Activated leukocyte cell-adhesion molecule AML  Acute myeloid leukemia ASXL1  Additional sex comb like 1 BM  Bone marrow BMF  Bone marrow failure BMP  Bone morphogenetic protein Bmpr1a BMP receptor type 1A C/EBPα  CCAAT/enhancer-binding protein alpha Cbfa1  Core binding factor alpha 1 CDR  Commonly deleted region CD40  Cluster of differentiation 40  CFU  Colony forming unit CFU-S  Colony forming unit-spleen CLP  Common lymphoid progenitor CMML  Chronic myelomonocytic leukemia CMP  Common myeloid progenitor CRT  Calreticulin CSF1 (M-CSF) Colony stimulating factor 1 (Macrophage-colony stimulating factor) CSF2 (GM-CSF) Colony stimulating factor 2 (Granulocyte/macrophage-colony stimulating factor) xii  CSNK1A1 Casein kinase 1, alpha 1 DNMT  DNA methyltransferase  ES-cell  Embryonic stem cell EVI-1  Ectotropic virus integration site 1 protein homolog EZH2  Enhancer of Zeste Homolog 2 FAB  French-American-British FasL  Fas ligand FFPE  Formalin fixed paraffin embedded  Fli-1  Friend leukemia virus integration 1 FLT-3  Fms-like tyrosine kinase 3 GAPDH  Glyceraldehyde 3-phosphate dehydrogenase GSEA  Gene set enrichment analysis GMP  Granulocyte/macrophage progenitor H3K27  Histone H3 Lysine 27 H3K17me3 Trimethylated histone H3 Lysine 27 HPRD  Human Protein Reference Database HSC  Hematopoietic stem cell HSPC  Hematopoietic stem/progenitor cell IDH1/2  Isocitrate dehydrogenase 1 and 2 IFNβ  Interferon beta IFNγ  Interferon gamma IKK  IκB  Kinase IL-10  Interleukin 10 IL-12p40 Interleukin 12 p40 IL-1β  Interleukin 1 beta xiii  IL2-Rα  Interleukin 2 receptor alpha IL-6  Interleukin 6 IP-10  Interferon gamma-induced protein 10 IPSS  International Prognostic Scoring System IPSS-R  Revised International Prognostic Scoring System IRAK-1  Interleukin-1 receptor-associated kinase 1 IRAK-4   Interleukin-1 receptor-associated kinase 4 IRF-3  Interferon regulatory factor 3 JNK  Janus kinase LOH  Loss of heterozygosity LPS  Lipopolysaccharide LSK  Lineage- Sca-1+ c-kit+ LTC-IC  Long-term culture initiating cell LT-HSC  Long-term HSC MSC  Mesenchymal stromal cell MDS  Myelodysplastic syndrome MDS-U  MDS, unclassifiable MEP  Megakaryocyte/erythroid progenitor miR-145 MicroRNA-145 miR-146 MicroRNA-146 miRNA  MicroRNA MLL  Mixed-lineage leukemia Mo-MuLV  Moloney murine leukemia virus MPD  Myeloproliferative disorder MPN  Myeloproliferative neoplasm xiv  MPP  Multipotent progenitor MyD88  Myeloid differentiation primary response 88 NF-κB  Nuclear Factor-KappaB   Nup98  Nucleoporin 98kDa OPN  Osteopontin P53  Tumor suppressor p53 PAMPs  Pathogen associated molecular patterns PI3K  Phosphatidylinositol-4,5-bisphosphate 3-Kinase PPR  PTH/PTHrP receptor PRC1  Polycomb repressive complex 1 PRC2  Polycomb repressive complex 2 PRR  Pathogen recognition receptor PTH  Parathyroid hormone PTHrP  Parathyroid hormone related protein RA  Refractory anemia  RAEB-1  Refractory anemia with excess blasts-1 RAEB-2  Refractory anemia with excess blasts-2 RANK  Receptor activator of NF-κB RANKL  Receptor activator of NF-κB ligand RARS  Refractory anemia with ring sideroblasts RCMD  Refractory anemia with multilineage dysplasia RCUD  Refractory anemia with unilineage dysplasia RISC  RNA induced silencing complex RN  Refractory neutropenia RPS14  Ribosomal protein S14 xv  RT  Refractory thrombocytopenia RUNX1  Runt-related transcription factor 1 SCF  Stem cell factor SDF1  Stromal derived factor 1 Sl/Sld  Steel dickie mutant snRNP  Small nuclear ribonucleoprotein SPARC  Secreted protein acidic and rich in cysteine ST-HSC  Short-term HSC TBK-1  TANK-binding kinase 1 TCR  T-cell receptor TEL(ETV6) Ets variant 6 TET2  Ten-eleven translocation 2  TGF-β  Transforming growth factor-β TIR domain Toll/IL-1 receptor domain TIRAP  Toll/interleukin-1 receptor adaptor protein TLR  Toll-like receptor TNFα  Tumor necrosis factor alpha TRAF6  TNF receptor-associated factor 6  TRAM  TRIF related adaptor molecule TRAP  Tartrate-resistant acid phosphatase TRIF  TIR-domain containing adaptor-inducing interferon-β UBE2N  Ubiquitin-conjugating enzyme E2N UBE2V1 Ubiquitin-conjugating enzyme E2 variant 1 VGH  Vancouver General Hospital WHO  World Health Organization xvi  WPSS  WHO classification-based prognostic scoring system W/Wv  Dominant spotting mutant                        xvii  ACKNOWLEDGEMENTS       I offer my enduring gratitude to the faculty, staff and my fellow students at the University of British Columbia, who have inspired me to continue my work in this field.  I owe particular thanks to Dr. Aly Karsan for taking me on as a graduate student and providing guidance, mentorship, and support over the years.  I would also like to thank my supervisory committee members Drs. Gerry Krystal, Donna Hogge, and Sharon Gorski for their useful comments and suggestions.   I would like to thank the present and past lab members for many wonderful years of friendship and support.  I would like to thank Dr. Daniel Starczynowski for showing me the ropes when I first started in the lab and passing the torch to me for the MDS project.  I would also like to thank Dr. Linda Chang and Dr. Joanna Wegrzyn-Woltosz for all the thought provoking discussions, both scientific and otherwise.  This group of colleagues and friends are what made my years here truly enjoyable.     Many thanks to go out to Denise MacDougal for assistance with flow cytometry.  I would also like to thank members of the TFL Flow Core David Ko, Wenbo Xu and Gayle Thornbury for their assistance and expertise with multicolour flow cytometry, and the Animal Resource Centre for taking care of my mice. I would like to express my gratitude to the Canadian Institutes of Health Research for providing me with a Graduate Studentship and travel award, as well as the Canadian Cancer Society and the GSC’s John Bosdet Memorial Fund for providing funding that allowed me to attend conferences worldwide. xviii  Last but certainly not least, special thanks are owed to my parents who have supported me throughout my years of education both morally and financially.  Without your constant motivation and unwavering belief in my abilities I would not have been able to get where I am.  I would also like to thank my siblings Zahra, Fatima, Ahmad, and Muna for providing companionship and keeping me in touch with the real world.                      xix   Dedication        To my parents        1             1. INTRODUCTION         2  Hematopoiesis is the process by which the cellular components of blood are formed.  It is a process that occurs in different locations at different times through life, beginning in the blood islands of the yolk sac, the dorsal aorta, and the fetal liver during embyrogenesis, and ending in the bone marrow and primary and secondary lymphoid organs in the adult.  This process must be tightly regulated to ensure an adequate supply of blood cells throughout the life of an organism necessary for performing vital tasks such as tissue oxygenation, clotting, and immunological defense.  It is estimated that during steady-state, a 70-Kg adult human produces approximately 3 x 105 erythrocytes and 3 x 104 leukocytes every second (Takizawa et al., 2012).  These numbers increase several-fold following specific demands such as loss of blood or pathogenic infection.  Regulation of hematopoiesis is controlled by signals generated from within the cell as well as external cues from the neighboring cells in the microenvironment.  Wnt signaling, calcium-sensing receptors, and extracellular matrix components are among the factors necessary for maintaining the proper HSC niche, as well as regulating self-renewal and differentiation of the HSC (Nilsson et al., 2005).  One signaling pathway that has gained recognition in recent years for its role in hematopoietic homeostasis is the Toll-like receptor (TLR) signaling pathway.  Initially recognized for their ability to allow mature myeloid and lymphoid cells to respond to pathogen associated molecular patterns (PAMPs), TLRs have now been shown to be expressed on hematopoietic stem and progenitor cells (HSPCs) (Nagai et al., 2006).  TLR signaling in HSPCs has been shown to drive differentiation down the myeloid arm in the absence of cytokines and other differentiation cues  allowing for rapid generation of effector cells following infection and bypassing some of the differentiation stages  (Nagai et al., 2006).   Perturbations in the tight control of hematopoiesis can result in either excessive growth and proliferation, such as in leukemia or reactive conditions, or hematopoietic failure.  This 3  dissertation examines the role of innate immune signaling in the pathogenesis of the myelodysplastic syndromes, a disorder that progresses to either acute myeloid leukemia (AML), or bone marrow failure.  It will go on to show how immune signaling defects arising from genetic perturbations in the HSC compartment can result in stem cell niche dysfunction leading to marrow failure.           1.1 Hematopoiesis and Homeostasis The bone marrow (BM), the primary site of adult hematopoiesis, is comprised of blood cells, stromal cells (adipocytes, fibroblasts, osteoblasts, and osteoclasts), connective tissue, blood vessels, and trabecular bone (Taichman, 2005).  Within the bone marrow, two types of stem cells reside: Mesenchymal stem cells and hematopoietic stem cells (Taichman, 2005).   Mesenchymal stem cells give rise to the stromal cells of the marrow (Taichman, 2005).  Although they are not directly involved in blood cell formation, the stromal cells of the bone marrow play a supportive role by providing important growth factors, extracellular matrix proteins, and cell-cell interaction with HSPCs, and defects in the bone marrow stroma can lead to defects in hematopoiesis (Raaijmakers et al., 2010; Santamaria et al., 2012).   Hematopoietic stem cells give rise to all the mature cells of the blood cells as well as osteoclasts through a process called hematopoiesis.  Since these cells are short lived, blood cell production is an ongoing process.  Such a high turnover rate requires homeostatic control mechanisms at many levels, primarily at the level of the HSC, but also at the level of the highly proliferative committed multipotent and lineage restricted progenitor cells.        4  1.1.1 Hematopoietic stem cells The HSC is a rare cell representing approximately 0.01% of the adult mouse bone marrow (Goodell et al., 1996).   HSCs have the capacity for self-renewal and the potential to give rise to differentiated progeny.  The HSC is defined functionally by its ability to engraft a lethally irradiated recipient and establish long-term multilineage reconstitution (>16 weeks in mice) (Dykstra et al., 2006).  Residing at the top of the hematopoietic hierarchy, it is able to generate all the downstream progeny cells through a series of division (Figure 1.1) and differentiation steps (Figure 1.2). Hematopoietic stem cells can be purified from bulk bone marrow cells using a variety of surface markers.  In mice, the lineage negative Sca-1+ c-kit+ (LSK) fraction is a heterogeneous compartment containing all HSCs as well as  multipotent progenitors (MPPs) (Okada et al., 1992).  HSCs can be further purified from MPPs using the signaling lymphocyte activation molecules (SLAM) CD150, CD244, and CD48, where HSCs are represented in the CD150+ CD244- CD48- population, MPPs are represented in the CD150- CD244+ CD48- population, and restricted progenitors are represented in the CD150- CD244+ CD48+ population (Kiel et al., 2005).  More recently, the identification of EPCR as a marker for HSCs has enabled the purification of populations of hematopoietic cells in which 1 in 2 to 1 in 3 cells represents a true HSC capable of sustaining multilineage reconstitution (Kent et al., 2009).    5   Figure 1.1 Types of HSC divisions HSCs are responsible for replenishing the pool of downstream differentiated cells and maintaining an adequate supply of blood cells following a stress such as blood loss or infection.  They are also capable of replenishing the HSC pool to prevent stem cell exhaustion.  The HSC is capable of maintaining this homeostasis by undergoing different types of divisions.  Self-renewal divisions are those that result in the generation of at least one HSC and another cell.  Symmetrical self-renewal divisions result in the creation of two daughter cells that are both HSCs, where as asymmetrical divisions result in one HSC and one more differentiated daughter cell.  Differentiation divisions can also be symmetrical, whereby the two differentiated daughter cells produced are the same, or asymmetrical, where two different more differentiated daughter cells are produced.  Mechanisms for asymmetric divisions have been attributed to divisional asymmetry in the cell fate determinants in the cytoplasm of the stem cell and environmental asymmetry in the extrinsic environment of the stem cell.       Figure 1.2 Hematopoietic hierarchyA simplified view of the hematopoietic hierarchy.  The longhematopoietic hierarchy and is capable of self renewal to generate more LTdifferentiation to ST-HSCs and a bifurcation of the hierarchy into the myeloid arm and the lymphoid arm.  The myeloid arm is primarily responsible for the generation of megakaryocytes, erythrocytes, osteoclasts, and WBCs of the innate immune system, while the lymphoid arm is for generating WBCs involved in the adaptive immune system.           -term HSC lies at the apex of the multipotent progenitors (MPP).  Following the MPP 6  -HSCs or , there is responsible 7  The HSC compartment can be broken down into two functional groups, the long-term HSC (LT-HSC) and the short-term HSC (ST-HSC).  These two distinct subsets of HSCs can be discerned from one another and from the MPP based on the expression of CD34 and Flt-3 where LT-HSCs are LSK+ CD34- Flt-3- (Adolfsson et al., 2001; Osawa et al., 1996), ST-HSCs are LSK+ CD34+ Flt-3-, and MPPs are LSK+ CD34+ Flt-3+ (Adolfsson et al., 2001; Yang et al., 2005a).  This is in contrast to humans where CD34 expression marks the HSC (Baum et al., 1992; McCune et al., 1988; Seita and Weissman, 2010).  Although there are few phenotypic differences between these two classes of HSC, several functional distinctions exist.  LT-HSCs are quiescent, spending most of their time in G0 and have a high capacity for self-renewal divisions (Becker et al., 1963; Rossi et al., 2007; Siminovitch et al., 1963; Till and Mc, 1961; Wu et al., 1968).  In contrast, ST-HSCs are more proliferative, but have a reduced capacity for self-renewal, despite maintaining full lineage potential (Morrison and Weissman, 1994).  Since the LT-HSC is required to sustain the hematopoietic system for the entire lifespan of an organism, reduced cycling is thought to be important for preventing the accumulation of genetic mutations, and the increased capacity for self-renewal prevents the eventual loss of stem cells through stem cell exhaustion.    Even within the LT-HSC pool there exists diverse heterogeneity with respect to the HSC's capacity for myeloid and lymphoid output. A single HSC may fall into one of four differentiation patterns: α, β, γ, or δ. α HSCs are myeloid skewed in their output, β HSCs have a balanced ratio of myeloid to lymphoid cell output, and both γ and δ HSCs are lymphoid skewed (Dykstra et al., 2007). This heterogeneity can be attributed to a variety of factors such as chance differences in the external environment, stochastic events affecting different intrinsic pathways, or predetermined intrinsic diversity (Metcalf, 1998; Muller-Sieburg and Sieburg, 2006; Roeder et al., 2005).    8  1.1.2 Progenitor cells and differentiation The establishment of the various hematopoietic lineages occurs through a process known as differentiation which is mediated by the actions of a series of transcription factors that act in both a parallel and sequential manner, and extracellular factors such as environmental cues, cell-cell contact, and the cytokine milieu (Luc et al., 2008). Early models of hematopoietic differentiation suggested that the process of differentiation can occur in a stochastic manner, where a single multipotent progenitor is capable of differentiation along more than two pathways (Laiosa et al., 2006). This was mainly due to the finding that in colony forming assays single myeloid progenitors can generate a heterogeneous mix of colony types (Laiosa et al., 2006; Ogawa, 1993). However, most current models imply that differentiation proceeds along an ordered pathway consisting of binary decision steps (Laiosa et al., 2006). Moving down the hematopoietic hierarchy, the capacity for self-renewal decreases and proliferation increases (Reya et al., 2001).  It is these rapidly proliferating transit amplifying cells that are responsible for replenishing the mature blood cell pool following an increased demand for blood cells.  The first bifurcation after the MPP leads to the lineage restricted common myeloid progenitor (CMP) and the common lymphoid progenitor (CLP) (Figure 1.2 ) (Akashi et al., 2000; Kondo et al., 1997; Orkin and Zon, 2008; Reya et al., 2001). The CMP gives rise to more restricted progenitors such as the granulocyte/monocyte progenitor (GMP), the megakaryocyte/erythroid progenitor (MEP) and dendritic cell precursors, while the CLP is responsible for generating cells of the B and T lineages, NK lineages, and dendritic cell precursors (Akashi et al., 2000; Kondo et al., 1997). Progenitor cells can be isolated based on the presence of surface markers (Figure 1.3).  The CLP has been phenotypically defined as Lin-IL-7Rα+c-kitloSca-1lo, whereas the CMP is defined as Lin-c-kit+Sca-1-CD34+CD16/32lo, GMPs are defined as Lin-c-kit+Sca-9  1-CD34+CD16/32hi and MEPs are Lin-c-kit+Sca-1-CD34-CD16/32lo (Seita and Weissman, 2010). The finding that CMPs and CLPs give rise to mutually exclusive progeny suggests that this diversification point represents the earliest branching point. That being said, the identification of the CMP and CLP does not exclude the possibility of the existence of other commitment pathways. Studies of fetal as well as adult hematopoiesis have suggested the presence of lympho-myeloid restricted progenitors with combined B/T lymphocyte as well as GM lineage potential (Cumano et al., 1992; Luc et al., 2008; Montecino-Rodriguez et al., 2001).       Figure 1.3 Surface markers for isolation of mouse hematopoietic progenitor cells.Hematopoietic stem and progenitor cells can be prospectively isolated based on the expression of surface makers.negative for markers of differentiation and positive for the markers ScaAlthough the LSK population contains all HSCs, less than 10% of this population is HSC. LSKs can give rise to lineage restricted progenitors such as the common lymphoid progenitor and the common myeloid progenitor. the granulocyte/monocyte progenitor and the megakaryocyte/erythrocyte progeni      HSCs can be located with a population of cells that are  The CMP can differentiate further into 10   -1 and c-Kit. a true tor.  11 Myeloid differentiation Differentiation down the myeloid arm is orchestrated by the action of various transcription factors at different times during hematopoiesis.  The hematopoietic stem/progenitor cell is primed for differentiation down either the myeloid or lymphoid arm due to the expression of a combination of both myeloid and lymphoid transcription factors. These include transcription factors such as GATA-1, PU.1, C/EBPα, Notch-1, and GATA-3 (Laiosa et al., 2006). In order for commitment to the myeloid lineage to occur, lymphoid transcription factors such as Notch-1 and GATA-3 must first be down regulated (Laiosa et al., 2006). Further restriction to the granulocyte/monocyte lineage requires the subsequent downregulation of the transcription factor GATA-1 (Laiosa et al., 2006). The transcription factors PU.1 and C/EBPα co-operate to regulate the expression of various myeloid growth factor receptors such as the receptors for M-CSF, G-CSF, and GM-CSF (Friedman, 2002; Tenen, 2003). Conversely, differentiation down the megakaryocyte/erythroid lineage requires down regulation of PU.1 and C/EBPα and upregulation of Friend of GATA-1 (FOG-1) (Laiosa et al., 2006). Osteoclast differentiation Osteoclasts are a type of tissue specific-polykaryon created by the fusion and differentiation of monocyte/macrophage precursor cells (Boyle et al., 2003; Udagawa et al., 1990). M-CSF and RANKL are two key hematopoietic factors necessary for the process of osteoclastogenesis (Figure 1.4) and are produced by stromal cells in close contact with osteoclast precursors (Lacey et al., 1998; Takahashi et al., 1988; Yasuda et al., 1998).  The transcription factor PU.1 and the growth factor M-CSF act to promote formation and survival of osteoclast precursor cells (Tondravi et al., 1997).  The binding of RANKL to its receptor RANK initiates many different signaling pathways that 12  all begin with the recruitment of TRAF molecules to the cytoplasmic domain on RANK (Darnay et al., 1998; Galibert et al., 1998; Hsu et al., 1999). TRAF6 is specifically important for the process of osteoclastogenesis, as mutations in this gene result in osteopetrosis due to decreased osteoclast activity (Kobayashi et al., 2001; Lomaga et al., 1999). TRAF6 binding to RANK leads to downstream activation of NF-κB and AP-1 transcription factors which mediate the ability of the osteoclast precursor cells to undergo differentiation (Franzoso et al., 1997; Grigoriadis et al., 1994; Xing et al., 2002). Furthermore, RANK signaling activates p38 which leads to induction of osteoclast lineage specific genes such as tartrate-resistant acid phosphatase (TRAP) and cathepsin K which provide the lytic functions of mature osteoclasts (Li et al., 2002b; Mansky et al., 2002; Matsumoto et al., 2000).     Figure 1.4 Stages of osteoclast development Osteoclasts develop from hematopoietic precursors of the myeloid lineage. Exposure to M-CSF and RANKL allow peripheral blood-borne mononuclear cells to fuse into a multinucleated polykaryon and leads to upregulation of osteoclast lineage markers.  OPG acts as a decoy receptor for RANKL and can block osteoclastogenesis at various stages.      13  The process of bone remodeling and osteoclastogenesis is tightly regulated by osteoblasts.  Osteoblast expression of RANKL stimulates bone resorption by local osteoclasts (Udagawa et al., 2000).  This in turn stimulates bone formation by neighboring osteoblasts (Udagawa et al., 2000).  Osteoprotegerin (OPG) is also produced by osteoblasts, and acts to negatively regulate osteoclastogenesis and activation by sequestering RANKL from its receptor RANK on the osteoclast (Figure 1.5) (Lacey et al., 1998; Simonet et al., 1997; Yasuda et al., 1998).            14    Figure 1.5 RANKL/RANK/OPG regulatory axis RANKL is a type II transmembrane protein that is proteolytically released in a soluble form.   RANKL expressed on osteoblasts and other stromal cells binds to RANK on osteoclast  precursor cells and stimulates osteoclastogenesis.  OPG acts as a decoy receptor for RANKL and blocks RANKL binding to its cellular receptor.      15  1.1.3 HSC niche The anatomical site of hematopoiesis changes throughout the life of an organism, moving from the blood islands of the yolk sac to the dorsal aorta and the fetal liver during development and ending in the bone marrow during adult life (Dzierzak and Speck, 2008).  At each of these locations, a collection of cells exist in close proximity to the stem cell which are capable of exerting extrinsic influence on the behavior of the stem cell.  Collectively, these cells are known as the stem cell niche.  The concept of HSCs residing within a specific anatomical location which determines its behavior was first proposed by Schofield (Schofield, 1978).  It was proposed that this niche is capable of preventing maturation of the stem cell, and thus maintain the “stemness” of the cell as proliferation proceeds (Schofield, 1978).  The importance of the niche in hematopoiesis was first identified in studies of two mutant strains of mice (W/Wv strain and Sl/Sld strain) that display similar phenotypes (Russell, 1949; Sarvella and Russell, 1956).  Both strains of mice display anemia, lack of mast cells, lack of melanocytes, and lack of germ cells (Russell, 1949; Sarvella and Russell, 1956).  Transplantation of WT bone marrow into W mutant was found to restore normal hematopoiesis, however, transplantation of WT bone marrow into the Sl/Sld strain does not restore normal hematopoiesis (McCulloch et al., 1965).  Furthermore, transplantation of Sl/Sld mutant marrow into W/Wv mutant mice, but not the reciprocal transplant, restores hematopoiesis (McCulloch et al., 1965).  These were the first studies to highlight the crucial role of the microenvironment in maintaining HSC function and laid the foundation for the proposition of a model where some microenvironmental elements outside of the hematopoietic system are required for proper self-renewal and differentiation.  Later, the mutant genes in the W/Wv and Sl/Sld mutant strains were identified to be the receptor tyrosine kinase c-Kit and the membrane bound form of its ligand Stem Cell Factor (SCF) respectively (Nocka et al., 1989; Reith et al., 1990; Zsebo et al., 1990).     16  The spatial localization of the HSC niche at the surface of bone was first suggested by experiments in which the hematopoietic colony forming capacity of marginal (close to bone) and axial (center of bone marrow cavity) cells in the mouse femur was determined (Lord and Hendry, 1972).  The number of colony forming units-spleen (CFU-S) was found to increase as cells were assayed from positions closer to the bone (Lord and Hendry, 1972).   Furthermore, progenitor cells, measured by hematopoietic colony forming assays in agar culture were found to be low in concentration near the center of the bone marrow cavity, peak in concentration closer to the bone, and subsequently decrease in concentration in the region most proximal to the surface of the bone (Lord et al., 1975).  Taken together, these findings suggest that stem and progenitor cells occupy distinct locations within the bone marrow and are not randomly distributed throughout the marrow.   Experiments have shown a dependency of HSCs on cell-cell contact with bone marrow derived stromal cells in order for them to be maintained in vitro; and the absence of this direct contact significantly reduces  spleen colony formation (Bentley, 1981; Dexter et al., 1980; Sutherland et al., 1990).  Hematopoiesis supporting stromal cells are comprised of a variety of cell types including fibroblasts, macrophages, endothelial cells, and adipocytes, as well as cells of the endosteum,  such as bone forming osteoblasts and  bone resorbing osteoclasts, suggesting that these cells may make up the HSC niche in vivo (Figure 1.6) (Taichman, 2005; Wilson and Trumpp, 2006).  However, not all bone marrow stromal cells are capable of supporting hematopoiesis in vitro, suggesting that different stromal cell components have distinct hematopoietic supporting functions (Taichman and Emerson, 1998). 17   Figure 1.6 The hematopoietic stem cell niche Within the bone marrow cavity, HSCs are in contact with a variety of stromal cells including osteoblasts, fibroblasts, adipocytes, and endothelial cells that make up the HSC niche.  The combination of secreted growth factors and direct cell contact between HSCs and the stroma help to regulate HSC survival, growth and differentiation. Perturbations in intrinsic factors as well as extrinsic components of the HSC niche can lead to dysregulation of the process of hematopoiesis and lead to one of many hematological disorders ranging from marrow failure syndromes characterized by excessive apoptosis to hematological malignancies characterised by excessive growth and proliferation.    18  Two functionally distinct HSC niches exist: the vascular niche and the endosteal niche. The vascular niche has been shown to be the site of differentiation, and mobilization from the marrow into peripheral blood circulation (Kopp et al., 2005).  In contrast to the vascular HSC niche, the endosteal niche is responsible for regulating HSC number and maintaining them in a quiescent state (Kiel and Morrison, 2008). Endosteal niche The endosteum is formed by the inner walls of the bone marrow cavity, and is thought to be the major niche for HSCs.  The role of the endosteal niche is to regulate HSC function, and this is achieved through expression of a variety of signaling molecules and receptors that promote HSC quiescence or division and self renewal (Haylock and Nilsson, 2005). Although different cell types are present along the endosteal surface of the bone marrow cavity, the two major cell types comprising the endosteal niche are the osteoblasts and the osteoclasts. Osteoblasts Evidence for the involvement of osteoblasts in the HSC niche was first suggested from the finding that both human and mouse osteoblastic cell lines and mouse primary osteoblasts are capable of producing hematopoiesis supporting cytokines such as M-CSF (Ohtsuki et al., 1992).  Other studies have reported the production of G-CSF, GM-CSF, IL-1, and IL-6 from primary murine osteoblasts (Elford et al., 1987; Felix et al., 1988; Feyen et al., 1989; Hanazawa et al., 1987; Horowitz et al., 1989; Ishimi et al., 1990).  Studies showing direct contact between osteoblasts and HSCs provide further evidence for the involvement of osteoblasts in the HSC niche (Wilson et al., 19  2004; Zhang et al., 2003).  Furthermore, mouse models affecting bone development provide evidence linking the processes of hematopoiesis and osteogenesis.  Conditional deletion of the BMP receptor type 1A (Bmpr1a) gene was shown to increase the number of spindle-shaped N-Cadherin+ CD45- osteoblastic (SNO) cells, and this correlated with an increase in HSC numbers, suggesting that SNO cells are capable of regulating the size of the HSC niche via BMP signaling through BMPR1A (Zhang et al., 2003).  Parathyroid hormone (PTH) and parathyroid hormone related protein (PTHrP) have been shown to be important regulators of calcium homeostasis and activate osteoblasts (Chambers, 1980).  Intravenous administration of PTH was found to increase the number of HSCs in wildtype mice and improve survival after bone marrow transplantation (Calvi et al., 2003).  Furthermore, osteoblast-specific expression of constitutively active PTH/PTHrP receptor (PPR) leads to an increase in osteoblast numbers and LSK numbers without any alterations in the overall hematopoietic cell content of the marrow (Calvi et al., 2003).  These changes were found to be caused by an increase in osteoblast expression of the Notch ligand Jagged 1 (Calvi et al., 2003).  Interestingly, Notch signaling has been shown to alter the balance between self-renewal divisions and differentiation divisions (Stier et al., 2002), thus increasing stem cell numbers without increasing the number of mature progeny, similar to what is seen with constitutive activation of PPRs (Calvi et al., 2003).   Other factors expressed on osteoblast cells that have been found to positively regulate the maintenance and numbers of HSCs include angiopoietin-1 which binds to the receptor tyrosine kinase Tie2 expressed on HSCs, enhancing their ability to remain in a quiescent state (Arai et al., 2004).  Conversely, loss of Core binding factor alpha 1 (CBFA1), a transcriptional activator of osteoblast differentiation (Ducy et al., 1997)  results in a complete blockade of both intramembranous and endochondral ossification, and an absence of the bone marrow cavity (Deguchi et al., 1999; Komori et al., 1997).  Mice deficient in CBFA1 display normal embryonic extramedullary hematopoiesis up to embryonic day E17.5, however, at E18.5 these mice display 20  increased extramedullary hematopoiesis in the liver and the spleen (Deguchi et al., 1999). Histological examination of the skeletons of CBFA1 deficient embryos showed that the femurs consisted of non-calcified cartilage, whereas the tibiae consisted of calcified cartilage without the formation of the bone marrow cavity (Komori et al., 1997) suggesting an important role for osteoblasts in supporting adult hematopoiesis.   As discussed earlier, deletion of membrane bound SCF results in impaired hematopoiesis due to defective HSC-niche interactions.  It was later shown that the loss of membrane bound SCF results in decreased osteoblast development, reduced bone mass, and increased osteoclasts (Lotinun et al., 2005), providing further evidence for the importance of osteoblasts in creating a microenvironment capable of supporting and maintaining HSCs.   There is still some debate about the identity of the niche cell responsible for supporting HSC activity.  Cultured bone marrow stromal cells can form bony ossicles that become hematopoietic upon subcutaneous implantation in mice, however, implantation of osteoblasts results in the creation of bone that does not support hematopoiesis (Kaigler et al., 2005). Furthermore, deletion of the gene Dicer1 from osteolineage-committed mesenchymal cells that lack terminally differentiated osteoblasts, leads to increased LSK proliferation and impaired megakaryocytic differentiation, whereas deletion of Dicer1 in terminally differentiated osteoblasts did not result in any hematopoietic abnormalities (Raaijmakers et al., 2010). Conversely, other studies have been performed to identify components of the endosteal niche that regulate HSC function.  This led to the identification of three non-hematopoietic (CD45- Ter119-), non-endothelial (CD31-) populations based on the expression of activated leukocyte cell-adhesion molecule (ALCAM) and Sca-1 (Nakamura et al., 2010).  ALCAM- Sca-1+ immature mesenchymal cells, ALCAM+ Sca-1- osteoblast-enriched cells, and ALCAM- Sca1- cells were all capable of maintaining long-term reconstitution in 21  vitro (Nakamura et al., 2010).  Of these, the osteoblast-enriched ALCAM+Sca1- population showed the most robust HSC-supporting activity and were characterized by increased expression of cell adhesion molecules such as Cadherin 2 and Cadherin 11 (Nakamura et al., 2010).  In contrast, the ALCAM-Sca-1+ immature mesenchymal cells were characterized by increased expression of growth factors and cytokine-related genes such as Flt3 ligand, CXCL12, BMP4, and SCF (Nakamura et al., 2010).  These genes have been known to be involved in both quiescence and proliferation of HSCs (Nakamura et al., 2010).  In addition to their role in supporting HSCs and hematopoiesis, osteoblasts are also capable of negatively regulating the HSC pool size through expression of the glycoprotein osteopontin (OPN) (Stier et al., 2005).  Loss of OPN in the bone marrow microenvironment results in an increase in HSC numbers, but no increase in the number of mature differentiated cells and progenitor cells (Stier et al., 2005) similar to what is seen with constitutive activation of PPRs (Calvi et al., 2003).  The increase in HSC numbers observed in the OPN null environment is associated with increased Jagged1 and angiopoietin-1 expression on osteoblasts (Stier et al., 2005).  OPN also plays an important role in the regulation of the physical location of HSCs (Nilsson et al., 2005).  OPN expression is restricted to the endosteal bone surfaces and serves to anchor HSCs to the endosteum (Nilsson et al., 2005).  This has been demonstrated in mice lacking OPN, which display markedly aberrant HSC distribution following HSC transplantation (Nilsson et al., 2005).   Furthermore, OPN plays a critical role in maintaining HSC quiescence, as OPN -/- mice have significantly enhanced HSC cycling, and addition of exogenous OPN inhibits HSPC proliferation in vitro (Nilsson et al., 2005).      22 Osteoclasts Osteoclasts are bone resorbing cells of hematopoietic origin.  The role osteoclasts play in maintaining the endosteal HSC niche is controversial and not as well defined as the role of osteoblasts discussed above.  It is clear, however, that there is an interplay between bone forming osteoblasts and bone resorbing osteoclasts in maintaining bone homeostasis.  For example, osteoblasts secrete and express membrane-bound forms of receptor activator of NF-κB ligand (RANKL), an essential factor for osteoclastogenesis (Lacey et al., 1998; Yasuda et al., 1998).  Furthermore, as demonstrated above, it is clear that bone homeostasis is very closely linked to hematopoietic homeostasis.  The first line of evidence suggesting a role for osteoclasts in hematopoietic homeostasis comes from studies of two pathological conditions: osteoporosis and osteopetrosis.  Researchers have suggested a link between the increased demand for hematopoietic development due to blood loss in women and the onset of osteoporosis post-menopause (Gurevitch and Slavin, 2006).  Increased hematopoiesis (due to loss of blood) results in an increase in hematopoietic cells, including osteoclasts, leading to increased bone resorption and the creation of larger hematopoietic territory (Gurevitch and Slavin, 2006).  This has been demonstrated in mouse models of ectopic transplantation of bone marrow plugs into the renal capsule followed by blood-letting (Gurevitch and Slavin, 2006).  Mice that underwent blood withdrawal for 1/3 of their lifespan showed more hematopoiesis and reduced bone volume in the transplanted marrow plugs (Gurevitch and Slavin, 2006).  These findings however contradict what is seen in ovariectomized rats, where a gradual reduction in hematopoiesis over time is associated with increased osteoclast numbers (Lei et al., 2009).   23  In the case of osteopetrosis, a condition characterized by high bone mass, hematological failure (anemia, pancytopenia, and osteomyelitis) and dysfunctional osteoclasts, medullary hematopoiesis is impaired regardless of whether osteoclasts are increased or decreased in number (Del Fattore et al., 2008).  In the oc/oc mouse model of osteopetrosis, where osteoclasts are increased in number but ineffective at bone resorption, bone marrow cellularity is dramatically decreased compared to wildtype controls (Blin-Wakkach et al., 2004).  Furthermore, these mice display increased myelomonocytic differentiation, a block in B lymphopoiesis at the pro-B stage, and impaired T cell activation (Blin-Wakkach et al., 2004).  The op/op mouse model of osteopetrosis is characterized by inactivating mutations in the CSF-1 gene (Wiktor-Jedrzejczak et al., 1990; Yoshida et al., 1990) resulting in a complete absence of CSF-1.  In this model of osteopetrosis, bone density is increased and marrow cellularity is reduced to 10% that of control mice (Wiktor-Jedrzejczak et al., 1982).  These defects in the op/op mouse, however, progressively resolve with age, suggesting that other compensatory mechanisms are involved in the hematopoietic development of op/op mice (Begg et al., 1993).   The functional role of osteoclasts in maintaining the HSC niche and regulating HSPC mobilization and egress from the marrow is quite controversial.  Activated osteoclasts are known to secrete a variety of factors important for stem and progenitor cell mobilization from the bone marrow niche, including the mobilizing cytokine IL-8, the proteolytic enzyme matrix metalloproteinase-9 (MMP-9), and the bone resorbing enzyme cathepsin K (Heissig et al., 2002; Kollet et al., 2003; Pruijt et al., 1999; Rothe et al., 1998).  Osteoclasts also express the glycoprotein osteopontin which is involved in regulating stem cell migration, homing and anchorage, as well as proliferation and quiescence (Merry et al., 1993; Nilsson et al., 2005; Stier et al., 2005). Osteoclast degradation of the bone matrix results in the release of numerous factors that play a role in HSC and progenitor cell survival and proliferation such as transforming growth factor-β (TGF-β) and 24  bone morphogenetic proteins (BMPs) (An et al., 1996; Batard et al., 2000; Bhatia et al., 1999). Under conditions of stress such as blood loss or LPS stimulation (to mimic bacterial infection) endosteal TRAP+ osteoclasts increase in number as do the number of circulating progenitors (Kollet et al., 2006).  Concomitantly, levels of the chemoattractant molecule stromal derived factor 1 (SDF-1) in the bone marrow are reduced due to cleavage and inactivation by osteoclast secreted cathepsin K (Kollet et al., 2006).  Taken together, these findings suggest that osteoclasts play an active role in modifying adhesion factors necessary for retaining the HSC in its niche, and thus play a positive regulatory role in HSC mobilization and egress from the marrow. In contrast to this, other researchers have shown that circulating HSPCs are present at higher levels at steady-state in three different osteoclast deficient models of osteopetrosis (op/op mice, c-Fos-deficient mice, and RANKL-deficient mice), and that G-CSF induced mobilization of HSCs is enhanced in these mice compared to controls (Miyamoto et al., 2011). Also, inhibition of osteoclast function using bisphosphonates has been shown to increase HSPC mobilization to the peripheral blood as determined by colony-forming assays (Miyamoto et al., 2011; Takamatsu et al., 1998). Furthermore, OPG-deficient mice, which display osteoporosis due to accelerated osteoclastogenesis, have impaired HSC mobilization compared to controls (Miyamoto et al., 2011). Conversely, other researchers have shown that hydroxyapatite-bound calcium released following degradation of the bone matrix by osteoclasts acts as an important regulator of HSC retention at endosteal surfaces (Adams et al., 2006).    Osteoclasts are involved not only in HSC homing to the marrow and retention, they are also important in regulating the number and function of HSCs within the niche. Depletion of osteoclasts by administration of bisphosphonates results in a decrease in both the proportion and the number of primitive HSCs in the marrow, with a simultaneous increase in the proportion of progenitor cells due to increased HSC cycling and differentiation (Lymperi et al., 2011).   Furthermore, engraftment 25  of untreated BM cells in osteoclast depleted mice is impaired (Lymperi et al., 2011), further supporting a role for osteoclasts in the maintenance of the hematopoietic stem cell niche.  The importance of osteoclasts in the establishment and maintenance of the hematopoietic niche is further evidenced in the oc/oc mouse model of osteopetrosis where osteoclast function is lost. In this model, the absence of osteoclast activity results in reduced osteoblastic differentiation of mesenchymal progenitor cells in the marrow, as well as a decrease in key regulators of the HSC niche such as Ang-1, Jag-1, SDF-1 and Kit-L (Mansour et al., 2012). Futhermore, inhibition of osteoclasts by adminsitration of the bisphophonate drug aledronate abolishes the beneficial effects of PTH administation on the HSC niche (Lymperi et al., 2011). The effects of alendronate are not due to its direct action on osteoblasts, as osteoblasts are still increased following PTH and alendronate treatment, suggesting that osteoblasts alone are not sufficient for maintaining the function of the HSC niche (Lymperi et al., 2011). Vascular niche Evidence for the importance of the vascular niche comes from studies of fetal hematopoiesis. In mammalian fetal development, HSCs are able to self-renew and differentiate throughout most of the gestation period prior to the development of bone marrow cavities suggesting that microenvironments other than the bone and the endosteal surface are capable of supporting hematopoiesis (Deguchi et al., 1999; Kiel and Morrison, 2008). Furthermore, in organisms such as zebrafish, hematopoietic development never takes place in association with bone (Murayama et al., 2006). During embryogenesis, hematopoietic cells arise in various location such as the blood islands of the yolk sac, the aorta-gonad-mesonephros (AGM) region, the vitelline artery, and the placenta in association with endothelial cells (de Bruijn et al., 2000; Gekas et al., 2005; Ottersbach and 26  Dzierzak, 2005; Shalaby et al., 1995; Tavian et al., 1996). These findings suggest that a vascular or perivascular niche exists where HSCs reside during embryonic and fetal development. Throughout adult life, HSCs can be found within extramedullary tissue such as the spleen and the liver (Taniguchi et al., 1996) and can undergo extramedullary hematopoiesis within these tissues for prolonged periods of time, suggesting that cells other than bone marrow stromal cells can create an environment that supports adult hematopoiesis (Johnson et al., 1992; Yang et al., 1995). Furthermore, HSCs mobilized to the spleen are most often located adjacent to sinusoids, suggesting that HSCs reside within perivascular niches in extramedullary tissue (Kiel et al., 2005), however there is still no direct evidence that extramedullary vascular niches can maintain HSCs. The bone marrow vascular niche is composed of thin walled and fenestrated sinusoidal vessels.  Unlike other blood vessels throughout the body, the walls of sinusoidal vessels consist of a single layer of endothelial cells and are devoid of other supporting cells and connective tissue (Tavassoli, 1981). The major support for these vessel walls comes from the various hematopoietic cells that surround them, as evidenced by the collapse of sinusoids following depletion of hematopoietic cells due to cytotoxic therapy or radiation (Narayan et al., 1994). The relationship between hematopoietic cells and the bone marrow vasculature is a reciprocal relationship, as hematopoietic regeneration and revascularization following exposure to radiation are temporally related and hematopoietic regeneration does not occur in the absence of vascular reconstitution of the marrow (Fliedner et al., 2002; Shirota and Tavassoli, 1991). The lack of regular vessel walls renders sinusoidal vessels highly permeable, and ultimately facilitates the homing and mobilization of hematopoietic cells into and out of the marrow (Oghiso and Matsuoka, 1979). Localization studies have shown that approximately 60% of bone marrow HSCs can be found adjacent to sinusoids (Kiel et al., 2005) where they are poised to enter circulation.  27  The question still remains whether the perivascular niche helps to maintain HSCs or whether HSCs transiently pass through this niche on their way into and out of circulation. The isolation and characterization of bone marrow endothelial cells (BMECs) led to the finding that BMECs are capable of supporting the proliferation and differentiation of hematopoietic progenitors in vitro by the production of various cytokines and through physical contact (Rafii et al., 1997; Rafii et al., 1995; Rafii et al., 1994). Furthermore, normal endothelial function is required for hematopoiesis in vivo, as conditional deletion of the cytokine receptor subunit GP130 from endothelial cells leads to hypocellularity of the marrow and the development of anemia in mice (Yao et al., 2005). Interestingly, this hypocellularity was attributed to a marked decline of cell number at the sinusoids while the cellularity at the endosteum was unaffected (Yao et al., 2005).   In addition to endothelial cells, numerous other types of cells localized perivascularly are thought to be responsible for HSC maintenance (Kiel and Morrison, 2008). CXCL12, a factor required for the maintenance of HSCs is expressed at very high levels on perivascular reticular cells in the bone marrow (Sugiyama et al., 2006). HSCs localized to the endosteum are also in contact with CXCL12-expressing reticular cells, suggesting that these cells may be important in HSC maintenance at both the endosteal and the perivascular niches (Sugiyama et al., 2006). Furthermore, endothelial cells may be an important source of CXCL12 for HSCs in the bone marrow as they have been shown to internalized and re-secrete circulating CXCL12 (Dar et al., 2005). Mesenchymal progenitor cells localized perivascularly may also contribute to maintenance of HSCs at the perivascular niche (Sacchetti et al., 2007; Shi and Gronthos, 2003).  Human CD146-expressing mesenchymal progenitors residing in the bone marrow perivascular niche also express factors important for the maintenance of HSCs such as CXCL12 and angiopoietin and are capable of forming hematopoietic bone upon transplantation into mice (Sacchetti et al., 2007). Other cells located at the perivascular niche and adjacent to HSCs are megakaryocytes (Avecilla et al., 2004; Kiel et al., 2005). Mice with defects in megakaryocyte 28  development have been shown to have abnormalities in bone marrow hematopoiesis, however, it remains unclear whether the defect in hematopoiesis is due to defective HSC maintenance or function (Shivdasani and Orkin, 1995; Vannucchi et al., 2002). It still remains to be genetically tested whether vascular or perivascular cells are indeed capable of HSC maintenance. The bone marrow vascular niche is also important for the differentiation and maturation of megakaryocytes. Researchers have shown that the translocation of megakaryocyte progenitors to the bone marrow vascular sinusoids is enough to induce maturation even in the absence of thrombopoietin signaling (Avecilla et al., 2004). This was shown to be dependent on the chemokines CXCL12 and FGF-4, which are known to induce expression of adhesion molecules on both megakaryocytes and BMECs (Avraham et al., 1994; Avraham et al., 1993).  Disruption of BMEC adhesion molecules such as VE-cadherin severely impairs the ability of the vascular niche to support megakaryocytic differentiation (Avraham et al., 1994).   1.2 The Myelodysplastic Syndromes The myelodysplastic syndromes (MDS) are a heterogeneous group of hematological disorders characterized by ineffective hematopoiesis and manifested in patients as peripheral blood cytopenias (anemia, neutropenia, and thrombocytopenia), dysplastic morphology and cellular dysfunction.  This often results in transfusion dependence, increased risk of infection, hemorrhage, and the potential to transform to AML.  The age adjusted incidence rates of MDS in the United States ranges from 1.5 to 5.6 cases per 100,000 (Rollison et al., 2008).  These rates increase to 7.1 cases per 100,000 in those aged 60-69, and 35.5 cases per 100,000 in persons over 80 years of age (Rollison et al., 2008).  More recent studies have shown that MDS cases have been underreported to cancer registries and that actual annual incidence of MDS may be as high as  75 cases per 29  100,000 in persons 65 years or older (Cogle et al., 2011).  Improvements in diagnosis, geriatric medical care, and the aging of the population will all contribute to the increased incidence of MDS in the years to come.       1.2.1 Classification Prior to the development of the French-American-British (FAB) classification of MDS, the myelodysplastic syndromes were simply known as preleukemia (Block et al., 1953; Hamilton-Paterson, 1949).  The FAB group was the first to indicate that not all “preleukemia” patients develop AML, but rather, that some succumb to the complications of ineffective hematopoiesis and marrow failure.  The FAB group recognizes five subtypes of MDS based on the lineages affected by dysplasia and the percentage of blasts in the bone marrow and peripheral blood (Appendix A).  In 1997 the World Health Organization (WHO) revised the classification of hematopoietic neoplasms, and developed a new classification system for MDS.  The WHO classification was based on the framework set by the FAB group, but also incorporates clinical, morphologic, cytochemical, immunophenotypic, and genetic information (Appendix B).  Efforts to identify prognostic factors beyond the FAB classification led to the development of the International Prognostic Scoring System (IPSS) for MDS (Greenberg et al., 1997) which found that the percentage of marrow blasts, specific type of cytogenetic abnormality, and the number of cytopenias are independent prognostic variables.  Furthermore, multivariate analysis showed that patients can be stratified into low, intermediate-1, intermediate-2, or high risk for transformation to AML and median survival (Greenberg et al., 1997).  Despite all the effort put into developing a comprehensive cytogenetic scoring system, 14% of patients show cytogenetic abnormalities with unknown prognostic significance (Schanz et al., 2012). Furthermore, while our understanding of 30  isolated cytogenetic aberrations has increased, little is known about the prognostic relevance of pair-wise combinations of abnormalities (Schanz et al., 2012). A retrospective study of 2,902 patients found that double karyotypic abnormalities are characterized by extreme variability, as only one combination (del(5q) and trisomy 8) was found to occur in more than 5 patients (Schanz et al., 2012). Furthermore, 9% of patients were found to have complex karyotype (3-20 abnormalities per patient) with a median of 5 abnormalities (Schanz et al., 2012). Multivariate analysis considering overall survival and risk of AML transformation for all abnormalities was used to generate five prognostic categories (very good, good, intermediate, poor, and very poor) based on cytogenetics (Schanz et al., 2012). A revised International Prognostic Scoring System (IPSS-R) (Appendix C) was released that incorporates these five prognostic categories, and identifies features such as age, performance status, serum ferritin, and lactate dehydrogenase as being significant for survival, but not transformation to AML (Greenberg et al., 2012).   The WHO classification-based prognostic scoring system (WPSS) (Appendix D) was developed in 2007 based on a study of a large cohort of patients from Italy and Germany (Malcovati et al., 2007).  The aim of this study was to identify the most significant prognostic variables in MDS and develop a model for predicting survival and leukemic evolution that can be applied at any time during the course of the disease (Malcovati et al., 2007).  In addition to WHO subgroups and karyotype, transfusion dependence was identified as an important prognostic variable (Malcovati et al., 2007).  Five risk groups were identified (very low, low, intermediate, high, and very high) with significantly different overall survival rates and risk for transformation to AML (Malcovati et al., 2007).        31  1.2.2 Molecular mechanisms of MDS MDS is an extremely heterogeneous disease in terms of clinical presentation, disease course, and prognosis and survival.  These differences are reflected in the numerous genetic and epigenetic changes observed in MDS patients.  Transformation of a normal HSPC to a preleukemic state, and ultimately to a leukemic state is a multistep process that involves the accumulation of several genetic lesions (Nolte and Hofmann, 2010). Although the initiating mutation in every case of MDS is unknown, there are indications that epigenetic modifiers such as TET2, ASXL1, and DNMT3a may be involved (Cazzola et al., 2013; Delhommeau et al., 2009). Large scale genomic aberrations such as deletions and translocations are frequently associated with MDS.  It is estimated that 40-70% of patients with primary MDS and 95% of patients with therapy-related MDS have recurring chromosomal abnormalities (Catenacci and Schiller, 2005). Del(5q) The most common cytogenetic abnormality observed in MDS is an interstitial deletion of chromosome 5q, which accounts for approximately 10% of all cases of MDS (Haase et al., 2007).  The 5q- syndrome, a subtype of MDS characterized by loss of chromosome 5q as the sole genetic abnormality, presents with a distinct set of clinical and morphological features including macrocytic anemia, neutropenia, normal or elevated platelets, hypolobated megakaryocytes, and low risk for transformation to AML (Boultwood et al., 1994; Sokal et al., 1975).  Although the extent of the 5q deletion varies from patient to patient, researchers have mapped two commonly deleted regions (CDR) on chromosome 5q.  The CDR at 5q31 is associated with AML and high-risk MDS (Horrigan et al., 2000), and the CDR located at 5q32-33 is associated with the 5q- syndrome (Boultwood et al., 2002). The 1.5 Mb region at 5q32-33 harbours over 40 protein coding genes (Boultwood et al.,  2002; Jadersten and Karsan, 20111.7).    Figure 1.7 Genes located on chromosome 5q Genes located within the CDR as well as genes outside the CDR, but commonly deleted in del(5q) MDS.  Genes in red have been associated with MDS pathogenesis or sensitivity to lenalidomide. ) and non-coding microRNAs (Starczynowski et al., 2010  32 ) (Figure   33  No patients with the 5q- syndrome have been reported to have biallelic loss of genes within the CDR and for many years, no recurring mutations were reported on the remaining alleles (Gondek et al., 2008; Graubert et al., 2009; Heinrichs et al., 2009; Jerez et al., 2012a; Mallo et al., 2013). This led to the hypothesis that haploinsufficiency of one or several genes within the CDR is responsible for the characteristic phenotype of del(5q) MDS.  A systematic shRNA knockdown of every protein coding gene within the CDR in human CD34+ cells showed that haploinsufficiency of Ribosomal Protein S14 (RPS14) leads to impaired erythropoiesis with preserved megakaryopoiesis in vitro (Ebert et al., 2008).  Furthermore, overexpression of RPS14 in CD34+ cells from del(5q) MDS patients restores erythroid differentiation (Ebert et al., 2008).  In a mouse model of the 5q-syndrome in which the syntenic regions equivalent to the 5q- CDR have been deleted, loss of the interval containing the genes between Nid67 and Cd74  was found to induce a 5q- syndrome like phenotype including macrocytic anemia, impaired progenitor cell production, and increased apoptosis (Barlow et al., 2010).  Candidate genes lying within this region include RPS14, haploinsufficiency of which has been previously shown to impair normal erythropoiesis (Ebert et al., 2008).  In this model of del(5q) MDS, dysregulation of the ribosomal biogenesis pathways due to loss of RPS14 leads to activation of the p53 pathway and induces cell death in erythroid progenitors accounting for the macrocytic anemia seen in Cd74-Nid67 deleted mice and human 5q- syndrome (Barlow et al., 2010).  This phenotype can be rescued when the syntenic region is deleted in a p53 null background confirming that the erythroid defects seen in the Cd74-Nid67 deleted mice are due to elevated p53 in hematopoietic progenitors leading to increased cell death in the erythroid lineage (Barlow et al., 2010).       In addition to RPS14, the CDR contains several other candidate genes possibly responsible for the 5q- phenotype.  The gene for Secreted protein acidic and rich in cysteine (SPARC) lies within this region and its expression is induced in cultures of erythroid progenitors from del(5q) patients upon 34  treatment with the immunomodulatory drug Lenalidomide (Pellagatti et al., 2007).  Interestingly, heterozygous deletion in mice of regions within the 5q- CDR containing SPARC does not result in appreciable changes to the hematopoietic system even at 12-14 months of age (Barlow et al., 2010).  Furthermore, no evidence of SPARC involvement was found in the development of red blood cells or platelets in SPARC -/- mice (Barlow et al., 2010), suggesting that haploinsufficiency of SPARC may not play a major role in the pathogenesis of del(5q) MDS.   Although these studies have shown the importance of RPS14 haploinsufficiency in del(5q) MDS, other features of the disorder, namely thrombocytosis, neutropenia, and clonal dominance, are not explained by this model.  In addition to protein coding genes, the del(5q) CDR contains several non-coding genes and microRNAs (miRNA).  miRNAs are short single stranded RNA molecules approximately 19-22 nucleotides long generated from endogenous hairpin-shaped transcripts (Bartel, 2004; Lee et al., 1993).  miRNAs function in post-transcriptional gene silencing by pairing with target mRNA species in the RNA-induced silencing complex (RISC) and mediating mRNA degradation or translational repression (Olsen and Ambros, 1999; Wightman et al., 1993; Yekta et al., 2004).  The miRNA biogenesis pathway is summarized in Figure 1.8. Loss of miRNAs on chromosome 5q contribute to the pathogenesis of the 5q- syndrome by loss of inhibition of their target genes.  Of the 13 miRNAs present in the most frequently deleted regions between chromosomal bands 5q31 and 5q35, miR-143, miR-145, and miR-146a were found to be expressed at significantly lower levels in del(5q) MDS samples compared to normal karyotype MDS and healthy controls (Starczynowski et al., 2010).  miR-145 and miR-146a were shown to be enriched in the stem/prognitor cell compartment of both mouse and human hematopoietic cells (Lin- and CD34+ respectively) (Starczynowski et al., 2010).  Together, these two microRNAs target the innate immune signaling pathway (Figure 1.9) (Starczynowski et al., 2010).   Figure 1.8 MicroRNA biogenesis The primary microRNA (pri stem-loop structure called the precursor microRNA (pre Drosha.  The pre-miRNA is then exported out of the nucleus via the transport protein  Exportin-5, where it is further processed by the RNase Dicer into the miRNA:miRNA*  duplex.  The mature miRNA strand is then loaded onto the RISC complex where the  miRNA binds to  the seed site on the target mRNA leading to mRNA degradation or  translational repression.      -miRNA) is transcribed in the nucleus and is processed into a -miRNA), by the nuclear RNase  35    Figure 1.9 Immune targets of miROverview of innate immune signaling pathways targeted by miRPredicted and validated targets of miR  In a mouse bone marrow transplantation assay, knockdown of miRoverexpression of the miR-146a target TNF receptorneutropenia, thrombocytosis, and dysplasia along the megakaryocytic lineasyndrome (Starczynowski et al., 2010action of IL-6 on hematopoietic cells with TRAF6 overexpressing marrow cells progress to BMF or AML IRAK-1, another innate immune signaling gene has also been shown to be a validated target of miR-145 and miR-146 -145 and miR-145 and miR-146 are highlighted in red-145 and mirR-associated factor 6 (TRAF6), recapitulates the ge observed in the 5q).  These effects are mediated in part through the paracrine (Starczynowski et al., 2010).  Furthermore, mice transplanted (Starczynowski et al., 201036  -146. . -146a, or - ).  -37  146a (Taganov et al., 2006) and has been shown to be overexpressed and activated in MDS (Hofmann et al., 2002; Pellagatti et al., 2010; Rhyasen et al., 2013).  Friend leukemia virus integration 1 (Fli-1) is a transcription factor that regulates erythropoiesis and megakaryopoiesis, and is targeted by miR-145 (Kumar et al., 2011).  Fli-1 mRNA is detected at a higher level in both peripheral blood and bone marrow mononuclear cells from 5q- syndrome patients compared to healthy controls (Neuwirtova et al., 2013).  Using a construct containing synthetic miR-145 binding sites to sequester the miRNA from its endogenous mRNA targets results in an increase in the ratio of megakaryoctes to erythroid cells, and overexpressing the miR-145 target Fli-1 in mouse HSCs increases the number of hypolobated micromegakaryocytes, replicating one of the features characteristic of the 5q- syndrome (Kumar et al., 2011).   Other genes on chromosome 5q residing outside of the CDR have also been implicated in the pathogenesis of MDS.  These genes are involved in a variety of different pathways including Wnt signaling, cell cycle progression, and ribosomal biogenesis and tumor suppressors (Table 1.1).         38  Table 1.1 Genes on chromosome arm 5q outside of CDR associated with MDS Gene Location Function Evidence of involvement in MDS  Ref APC 5q21-q22 Negative regulator of canonical Wnt signaling. Tumor suppressor APC -/- : increased apoptosis, enhanced cell cycle entry, HSC exhaustion, BMF APCmin homozygote: embryonic lethal APCmin heterozygote: normal steady-state hematopoiesis. Lethal macrocytic anemia with age.  Expansion of HSC with reduced capacity to reconstitute hematopoiesis. (Lane et al., 2010; Qian et al., 2008) CTNNA1 5q31.2 Tumor suppressor Silenced by promoter hypermethylation Expressed at lower levels in leukemia initiating cells from AML/MDS with 5q deletion (Liu et al., 2007; Ye et al., 2009) PP2Acα 5q31.1 Dual specificity phosphatase Regulator of G2-M checkpoint Inhibited by Lenalidomide Treatment with Lenalidomide induces G2 arrest and apoptosis in del(5q) cells with no effect on non-del(5q) cells  (Wei et al., 2009) CDC25C 5q31 Dual specificity phosphatase  Regulator of G2-M checkpoint Inhibited by Lenalidomide Treatment with Lenalidomide induces G2 arrest and apoptosis in del(5q) cells with no effect on non-del(5q) cells (Wei et al., 2009) EGR1 5q31.1 Tumor suppressor Zinc finger transcription factor EGR1+/- and -/- mice treated with alkylating agents develop MPD at a higher rate than WT mice Secondary mutations cooperate with EGR1 haploinsufficiency for leukemic transformation  Loss of EGR1 causes enhanced proliferation of HSCs and HSC exhaustion (Joslin et al., 2007; Min et al., 2008) NPM1 5q35.1 Nucleolar protein NPM1-/- mouse is embryonic lethal. NPM1+/- mice have variable thrombocytopenia, variable leukopenia, dyplastic morphology, and hypercellular marrow.   (Grisendi et al., 2005)   39 Mechanisms of clonal dominance in del(5q) MDS Several 5q genes have been reported to alter normal HSC function and contribute to the pathogenesis of del(5q) MDS, however, until recently, it remained unknown how del(5q) MDS cells gain clonal dominance over their normal counterparts in the bone marrow. Ex vivo experiments knocking down miR-145 or miR-146a, two miRNAs commonly deleted in del(5q) MDS, have been shown to protect bone marrow cells from apoptosis (Starczynowski et al., 2010). In addition, transplantation of marrow cells overexpressing the target of miR-146a, TRAF6, results in massive expansion of the TRAF6-overexpressing clone in the marrows of moribund mice compared to healthy mice transplanted with TRAF6-overexpressing cells at 18-22 weeks post-transplant (Starczynowski et al., 2010). This suggests that expansion of the TRAF6-expressing clone is required for the development of AML or bone marrow failure. Also, TRAF6-transduced Lin- progenitor cells are protected from apoptosis compared to untransduced Lin- progenitors, further contributing to the clonal expansion of the malignant cells (Starczynowski et al., 2010).      Recently, Casein Kinase 1α1 (CSNK1A1), a gene located within the distal CDR in del(5q) MDS has been shown to play a role in the clonal dominance of del(5q) MDS cells.  CSNK1A1 has been reported to be a tumor suppressor in both melanoma and colon cancer, and controls proliferation by negatively regulating β-catenin activity (Elyada et al., 2011; Sinnberg et al., 2010). Haploinsufficiency of Csnk1a1 was shown to provide a clonal advantage to hematopoietic cells (Schneider et al., 2014). The cell intrinsic effects of Csnk1a1 deletion were determined by transplantation of Csnk1a1-/-Mx1Cre+ or Csnk1a1-/+Mx1Cre+ hematopoietic cells into wild type recipients (Schneider et al., 2014). Wild type mice transplanted with Csnk1a1-/-Mx1Cre+ whole marrow cells become moribund and die of marrow failure within two weeks of Csnk1a1 excision, however, in stark contrast, mice transplanted with Csnk1a1-/+Mx1Cre+ marrow show no difference 40  in survival compared to Mx1Cre+ control mice (Schneider et al., 2014). Transplanted Csnk1a1 haploinsufficient hematopoietic cells are capable of fully reconstituting the bone marrow resulting in normal to hypercellular marrows, normal hemoglobin levels, and elevated leukocyte numbers (Schneider et al., 2014). These mice also develop thrombocytosis and dysplastic megakaryocytes similar to that observed in del(5q) MDS (Schneider et al., 2014). Β-catenin activation due to haploinsufficieny of Csnk1a1 in the context of a normal bone marrow microenvironment leads to a cell intrinsic expansion of LSKs and LT-HSCs (Schneider et al., 2014). This expansion is due to exit of HSCs from quiescence and enhanced proliferation of HSPCs as evidenced by an increase in β-catenin and cyclin D1 in the LSK fraction (Schneider et al., 2014). In competitive transplantation experiments Csnk1a1 haploinsufficient cells show enhanced long-term repopulation potential compared to WT, while complete ablation of Csnk1a1 renders cells unable to compete leading to their rapid depletion over time due to activation of p53 (Schneider et al., 2014). In contrast, control Mx1Cre+ transplanted mice show stable reconstitution over time (Schneider et al., 2014).  Mutations in CSNK1A1 have been reported in MDS with normal karyotype (Graubert et al., 2012). Recently, recurrent mutations in CSNK1A1 (CSNK1A1 E98V and CSNK1A1 D140Y) were identified in del(5q) MDS  (Schneider et al., 2014; Woll et al., 2014). These are the first reports of mutation of the remaining allele of a gene located in the CDR in del5q(MDS). Expression of CSNK1A1 E98V in a homozygous Csnk1a1-deleted hematopoietic cells causes an increase in β-catenin and does not induce apoptosis nor activate p53 (Schneider et al., 2014). Furthermore, expression of CSNK1A1 E98V increases the frequency of cells in G1, suggesting that this mutation is not a loss of function mutation, but further contributes to the clonal dominance of MDS cells (Schneider et al., 2014). In addition to CSNK1A1, other regulators of the Wnt pathway have been implicated in the pathogenesis of del(5q) MDS. Haploinsufficiency of APC, another negative regulator of β-catenin 41  activity, is reported to occur in 95% of patients with -5/del(5q) (Wang et al., 2010). Mice heterozygous for the Apcmin mutation or with heterozygous deletion of Apc also have increased repopulation potential and loss of HSC quiescence (Lane et al., 2010; Wang et al., 2010). Combined heterozygous inactivation of Csnk1a1 and Apc significant increases in LT-HSCs, as well as accumulation of β-catenin, and increased cell cycle entry (Schneider et al., 2014). Other karyotypic abnormalities Other common deletions observed in MDS include monosomy 7/7q- and 20q- MDS. Monosomy 7 and 7q- MDS are associated with poor prognosis and a higher likelihood of transformation to AML (Hirai, 2003). Unlike MDS with an isolated 5q deletion, there is no clear genotype-phenotype relationship in cases of -7/7q-.  CDRs have been identified at 7q22, 7q32-33, and 7q35-36 (Fischer et al., 1997; Jerez et al., 2012b; Johnson et al., 1996; Le Beau et al., 1996).  Microarray expression data has shown significant reductions in several genes mapping to the CDRs of chromosome 7 including CUX1 and EZH2 (Jerez et al., 2012b). CUX1 is a transcription factor that is highly expressed in multipotent hematopoietic progenitor cells and has been mapped to 7q22 (Fischer et al., 1997; Lewis et al., 1996).  It regulates the expression of a variety of genes involved in DNA replication, chromosome segregation, cell cycle progression, cell motility, and invasion (Hulea and Nepveu, 2012).  RNA-sequencing data from patients with -7/del(7q) has shown a CUX1-associated cell cycle transcriptional gene signature (McNerney et al., 2013).  In xenograft models, shRNA mediated  knockdown of CUX1 results in enhanced expansion of  the transduced population compared to the non-transduced population, suggesting that CUX1 acts as a haploinsufficient tumor suppressor gene (McNerney et al., 2013).   42  EZH2, another gene down regulated in -7/del(7q) may be an important player in the pathogenesis of MDS.  Not only is this gene downregulated in del(7q) MDS, it has also been reported to be downregulated in MDS patients without chromosome 7 deletion (Jerez et al., 2012b).  In diploid MDS patients with uniparental disomy of chromosome 7q, recurrent inactivating mutations were detected involving EZH2 (Jerez et al., 2012b), further suggesting that loss of function of EZH2 may be a driver in the pathogenesis of MDS.   Del(20q) accounts for approximately 5% of primary MDS cases, and confers a favorable prognosis with a low risk for transformation to AML and prolonged survival (Braun et al., 2011; Hirai, 2003). Deletion of the long arm of chromosome 20 is not specific to MDS and is often seen in myeloproliferative neoplasms and AML (Braun et al., 2011).  Patients with del(20q) have significantly lower platelet counts and marrow blast percentage compared to non-del(20q) patients (Braun et al., 2011). The small size of chromosome 20 and the limited number of G-bands has made it difficult to identify breakpoints based on cytogenetics.  The use of genome-wide high resolution single nucleotide polymorphism (SNP) arrays has led to a more accurate definition of the commonly deleted region on 20q and identification of candidate genes involved in del(20q) pathogenesis (Huh et al., 2010).   Two CDRs have been mapped to 20q11.23-12 and 20q13.12 (Huh et al., 2010).  The two CDRs encompass a total of 89 genes known to be expressed in hematopoietic tissue (Huh et al., 2010). Less frequent deletions have also been reported to occur in MDS.  These include del(11q), del(12p), and del(13q) (Hirai, 2003).  The gene RB, located at 13q14 is often deleted in patients with del(13q).  Deletions in 12p are usually interstitial and involve the loss of the genetic region where TEL(ETV6) and CDKN1B are located (Hirai, 2003).  Both genes have been implicated in MDS pathogenesis. 43  Although more prevalent in AML, chromosomal translocations have been found to occur in MDS.  These translocation events result in the creation of fusion proteins.  The mixed lineage leukemia (MLL) gene located at chromosome 11q23 is often detected as a translocation partner in AML and MDS, with over 10 translocation partners.  The translocation t(11;16)(q23;p13) results in a gene product where the MLL gene is fused to the CREB Binding Protein (CBP) gene (Taki et al., 1997). CBP encodes a transcriptional adaptor/coactivator protein that has intrinsic histone acetyltransferase activity (Bannister and Kouzarides, 1996). The retained DNA binding abilities of MLL and the chromatin modifying abilities of CBP can result in changes in gene expression and altered cell cycle regulation (Rowley et al., 1997; Taki et al., 1997). One subtype of MDS, chronic myelomonocytic leukemia (CMML) has been associated with t(5;12)(q33;p13) (Berkowicz et al., 1991; Keene et al., 1987; Lerza et al., 1992; Srivastava et al., 1988; Wessels et al., 1993)which results in a fusion between platelet-derived growth factor receptor-β (PFGFRβ) and TEL(ETV6) (Golub et al., 1994). This leads to constitutive ligand independent activation of the PDGFRβ tyrosine kinase domain, leading to cellular transformation (Golub et al., 1994).  Inversions and translocations involving chromosome 3q21 and 3q26 have been reported to occur in 2% of MDS and AML patients, and are associated with abnormalities in megakaryopoiesis and a poor prognosis (Jotterand Bellomo et al., 1992).  Inv(3)(q21q26) and t(3;3)(q21;q26) results in the aberrant expression of the ecotropic virus integration 1 (EVI-1) gene by bringing the EVI-1 gene under the control of the Ribophorin I gene enhancer (Hirai, 2003).  Another translocation involving EVI-1, t(3;21)(q26;q22), generates an AML1/EVI-1 chimeric gene which results in aberrant expression of the EVI-1 gene (Mitani et al., 1994). NUP98, a nucleoporin involved in the nuclear import and export of proteins and RNA, has been found to be a recurrent target of translocations with members of the HOX family of transcription factors in therapy related AML and MDS (Borrow et al., 1996; Nakamura et al., 1996; Raza-Egilmez et al., 1998; Shimada et al., 2000).  These 44  translocation events result in fusion proteins in which the DNA binding homeodomain of the HOX gene is separated from its regulatory elements, and brings the N-terminal GLFG repeats of NUP98 to the fusion partner.  The GLFG repeats have been shown to activate transcription by recruiting CBP and mediate oncogenetic activity of NUP98-HOX fusion proteins (Kasper et al., 1999; Nakamura et al., 1999).   The effect of multiple karyotypic abnormalities on overall survival and transformation to AML depends on the particular combination of aberrations present (Schanz et al., 2012). Patients with  del(5q) plus one additional karyotypic abnormality show a median survival of 44.4 months and median transformation to AML at 91.2 months, whereas those with -7/del(7q) plus an additional aberration have significantly worse prognosis (median overall survival = 13.4 months and the median time to AML transformation = 19.3 months) (Schanz et al., 2012). All other double cytogenetic abnormalities show an intermediate prognosis (Schanz et al., 2012). As for patients with complex karyotype, there is a significant difference in prognosis for those with exactly 3 abnormalities (median overall survival = 15.6 months) compared to those with more than 3 abnormalities (median overall survival = 5.7 months) (Schanz et al., 2012). Genetic mutations Analysis of 944 patients with MDS revealed that 89.5% of patients harbor at least one mutation, with the median being 3 mutations per patient (Haferlach et al., 2014). The high incidence of mutations is not restricted to high-risk MDS cases, as approximately 73% of low-risk MDS patients with normal karyotype (such as RA and RCMD) are reported to harbor point mutations (Haferlach et al., 2014). Exome sequencing of 9 patients with low-grade MDS revealed 64 mutations, ranging from 0-20 per patient (Papaemmanuil et al., 2011). In 5 of the 9 patients, great 45  variability in the proportion of reads for each mutation were detected, suggesting genetic heterogeneity within  the MDS clone and the restriction of some mutations to subclones within the malignant cell population (Papaemmanuil et al., 2011). The most commonly mutated genes include TET2, SF3B1, ASXL1, SRSF2, DNMT3a, and RUNX1, which are mutated in more than 10% of all patients (Haferlach et al., 2014).   Genetic mutations have been detected in pathways known to be important for proliferation and differentiation.  Activating mutations in RAS genes have been found in 10% of MDS cases and are associated with poor prognosis and an increased risk of leukemic transformation (Paquette et al., 1993).  FLT3 internal tandem duplication (FLT3-ITD) has also been reported in 5% of MDS cases (Yokota et al., 1997).  This mutation results in duplication of the juxtamembrane region leading to ligand independent activation of FLT3 (Kiyoi et al., 1998).  This mutation is also associated with a poor prognosis and thought to be an important genetic event in the progression from MDS to AML (Horiike et al., 1997).  The tumor suppressor gene p53 is often inactivated in 5-10% of MDS cases (Sugimoto et al., 1993).  This is most often seen in cases of complex karyotype MDS and may play a role in leukemic transformation (Sugimoto et al., 1993).  The heterodimeric transcription factor AML1/RUNX1, which has been implicated in translocations in MDS, is also a target of point mutations in MDS.  V105ter and the R139G mutations have been reported in MDS patients, and these mutations affect the Runt domain of AML1, causing loss of DNA binding ability of the mutant protein (Imai et al., 2000).  The V105ter mutant loses its ability to bind to core binding factor β (CBFβ), while the R139G mutant acts as a dominant negative by competing with WT RUNX1 for CBFβ binding, suggesting that altered transcriptional regulation by RUNX1 may important for development of MDS and leukemic transformation (Imai et al., 2000).  Various functional pathways are affected by mutations in MDS. These include RNA splicing, DNA methylation, chromatin modification, transcription, receptors and kinases, cohesin, RAS, and 46  DNA repair pathways (Haferlach et al., 2014). Intratumoral heterogeneity suggests a clonogenic hierarchy is present among these commonly mutated genes (Haferlach et al., 2014). Mutations in genes on DNA methylation pathways and RNA splicing pathways represent the highest mutational burden suggesting that these changes occur earlier during clonal evolution and disease progression (Haferlach et al., 2014). The sections below will further discuss mutations associated within the RNA splicing pathway, DNA methylation pathways, as well as other epigenetic modifiers. Mutations associated with regulation of the epigenome MDS is characterized by frequent epigenetic alterations, such as DNA methylation and histone modifications that drive stable, clonally propagated changes in gene expression and alter pathways that control proliferation and differentiation.  Patients with higher levels of DNA methylation have shorter overall survival and reduced time to progression to AML (Shen et al., 2010).  The important role epigenetics plays in the pathogenesis of MDS is evident from the success seen in inducing remission and prolonging life expectancy upon treatment with epigenetic modifying drugs such as  DNA methylation inhibitors and histone deacetylation inhibitors (Fenaux et al., 2009; Kantarjian et al., 2007; Lubbert et al., 2011; Shen et al., 2010).  Frequent mutations have also been detected in genes that code for epigenetic modifying proteins such as the methyltransferase DNMT3a, the DNA demethylase TET2, and histone modifiers such as EZH2 and ASLX1. DNMT3a The DNA methyltransferase (DNMT) family consists of the enzymes DNMT1, DNMT3a and DNMT3b. While DNMT1 is responsible for maintaining pre-existing methylation marks, DNMT3a 47  and DNMT3b are de novo methyltransferases, capable of creating new methylation on unmethylated DNA (Lei et al., 1996; Okano et al., 1999). All three DNA methyltransferases have been shown to be essential for embryonic development as inactivation of any one of these genes results in death.  Although the details of the mechanisms that control cell fate decisions such as self-renewal versus differentiation are not fully understood, a role for epigenetic modifications via promoter methylation and gene silencing has been demonstrated in embryonic stem (ES) cells and in the hematopoietic system.  The importance of epigenetic modifiers such as DNMTs in stem cell function and differentiation has been demonstrated in ES cells lacking both DNMT3a andDNMT3b.  In these cells, self-renewal abilities are maintained, while the ability to differentiate is lost following successive passages.  In the hematopoietic system, DNMT3a is highly enriched in the most primitive long-term HSC, compared to progenitors and differentiated cells (Challen et al., 2012). Similar to what is observed in ES cells, loss  of DNMT3a in HSCs results in an expansion of the stem cell pool and impaired differentiation abilities (Challen et al., 2012). In a mouse conditional knockout system, loss of DNMT3a in the hematopoietic system resulted in no overall change in global 5-methylcytosine, however, specific loci of the genome were affected by either hyper- or hypomethylation (Challen et al., 2012).  CpG rich and CpG poor regions were found to be affected differently by loss of DNMT3a.  For example, differentially methylated CpG island associated promoters were found to become hypermethylated, whereas differentially methylated promoters poor in CpG islands were found to be equally likely to be hyper or hypomethylated (Challen et al., 2012).  In both MDS and AML, mutations in DNMT3a have been reported (Ley et al., 2010; Walter et al., 2011).  The most common mutation occurring in DNMT3a (in both MDS and AML) affects amino acid R882 (Ley et al., 2010; Walter et al., 2011).  Amino acid R882 is required for the 48  homodimerization and activation of DNMT3a, and mutation of this residue reduces the catalytic activity of DNMT3a (Yamashita et al., 2010). TET2 and IDH1/2 Ten-eleven translocation 2 (TET2) is a member of the TET family of enzymes that catalyze the conversion of 5-methyl-cytosine (5mC) to 5-hydroxy-methylcytosine (5hmC). The role of hydroxylation of 5mC is not completely understood, but it is reported to be enriched in actively transcribed regions (Wu et al., 2011) suggesting that it may ultimately play a role in demethylation. Unlike the other members of the TET family, TET2 lacks the critical CXXC domain necessary for binding to CpG islands (Deaton and Bird, 2011; Song et al., 2011).  This suggests that TET2 plays in important role in DNA methylation outside of CpG islands (Issa, 2013). TET2 is located on chromosome 4q24 in a region that is often the target of microdeletions and loss of heterozygosity (LOH) in patients with myeloid malignancies (Viguie et al., 2005).  These mutations are frequent events in myeloid malignancies, occurring in about 15% of patients (Delhommeau et al., 2009), and in nearly half of all CMML cases (Langemeijer et al., 2009).  Somatic mutations in TET2 compromise the catalytic activity of the enzyme resulting in global low levels of 5hmC (Ko et al., 2010) and an increase in 5mC (Yamazaki et al., 2012).  The gene specific effect of TET2 mutations remain controversial, as studies in MDS show mostly CpG island hypomethylation when comparing MDS patients with TET2 mutations to those without TET2 mutations (Ko et al., 2010), while studies comparing AML patients with TET2 mutations to CD34+ bone marrow cells from healthy individuals show hypermethylation of CpG islands (Figueroa et al., 2010), and a comparison of CMML cases with and without mutations showed no difference (Yamazaki et al., 2012).   49  TET2 is an α-ketoglutarate-Fe(II) dioxygenase, and its function has been shown to be inhibited by 2-hydroxyglutarate produced by mutant forms of the isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) genes (Figueroa et al., 2010).  Interestingly, the effects of IDH1/2 mutation on global DNA methylation is the same as TET2 mutation, leading to an increase in 5mC (Figueroa et al., 2010).  Furthermore, both loss of TET2 by shRNA knockdown and expression of mutant forms of IDH1/2 lead to increased expression of the stem cell marker c-kit and reduced expression of mature myeloid markers (Figueroa et al., 2010).  Interestingly, TET2 mutations and IDH1/2 mutations are mutually exclusive suggesting that both mutations act on the same molecular pathway leading to global hypermethylation (Figueroa et al., 2010).  In a mouse model with conditional loss of TET2 in the hematopoietic compartment an increase in HSC self-renewal was measured by serial re-plating of colony-forming cell assay in vitro as well as in competitive transplantation assays in vivo, where 23 weeks after TET2 deletion more than 95% of peripheral blood myeloid and lymphoid cells were TET2-/- (Moran-Crusio et al., 2011).  Furthermore, TET2-deficient mice develop a CMML-like disease characterized by leukocytosis, myeloid dysplasia, and splenomegaly (Moran-Crusio et al., 2011). The role of TET2 mutation in myeloid malignancies is controversial.  TET2 mutation is a favorable prognostic indicator in MDS, as patients with mutations in TET2 have better overall survival and prolonged leukemia free survival compared to patients with unmutated TET2 (Kosmider et al., 2009), however, in AML TET2 mutation is associated with shorter overall survival in patients with intermediate risk cytogenetics (Chou et al., 2011).  Interestingly, recurrent somatic mutations in TET2 have been detected in normal elderly patients with age-associated X-inactivation skewing patterns and clonal hematopoiesis despite the absence of an overt hematological phenotype (Busque et al., 2012).  This is consistent with mouse studies showing that Tet2 deletion increases HSC self-renewal, provides a competitive growth advantage, and causes a bias toward the myeloid lineage (Ko et al., 2011; Li et al., 2011; Moran-Crusio et al., 2011; Quivoron et al., 2011).  50  Long term follow-up of seven healthy elderly patients with TET2 mutation revealed that one subsequently presented with a JAK2 V617F positive essential thrombocytosis (Busque et al., 2012).  These findings suggest that inactivating TET2 mutations may represent an initiating mutation that creates a more permissive landscape for the accumulation of other mutations leading to myeloid malignancies (Busque et al., 2012). EZH2 and AZXL1 Enhancer of Zeste Homolog 2 (EZH2) is a histone methyltransferase that comprises the catalytic subunit of the polycomb repressive complex 2 (PRC2) that initiates dimethylation and trimethylation of lysine 27 on histone H3 (H3K27), a histone modification associated with gene silencing (Simon and Lange, 2008).  The genomic locus of EZH2 is on chromosome 7q, a chromosome often deleted in MDS.  Patients with -7 or del(7q) display significantly worse survival compared with patients without EZH2 abnormalities (Ernst et al., 2010; Nikoloski et al., 2010).  Recurrent somatic mutations in EZH2 have been reported in MDS, MPN, and MDS/MPN (Ernst et al., 2010; Nikoloski et al., 2010).  One study has reported deletions or point mutations in EZH2 to be present in 23% of patients (Nikoloski et al., 2010).  Mutations detected in EZH2 include missense mutations, premature chain termination due to frameshift or stop mutations, in-frame deletions, and altered splice acceptor site mutations (Ernst et al., 2010; Nikoloski et al., 2010). These mutations are inactivating mutations affecting highly conserved amino acids in the SET domain, a domain essential for the catalytic activity of EZH2, and domain II, a region necessary for binding to other PRC2 components (Ernst et al., 2010; Nikoloski et al., 2010; Simon and Lange, 2008). EZH2 loss of function has been demonstrated in cells lines with mutant EZH2, where H3K27me3 is completely abolished compared to control cell lines without EZH2 mutation (Ernst et al., 2010).  51  This data suggests a tumor suppressor role for EZH2, however the mechanism by which loss of EZH2 promotes transformation is fully understood.  Interestingly, this contrasts the role of EZH2 in other malignancies, where activating mutations (lymphoma) (Morin et al., 2010) or overexpression of EZH2 (prostate cancer) (Varambally et al., 2002)  promotes cancer progression, suggesting a role for EZH2 as an oncogene. Alternative mechanisms exist that lead to inhibition of PRC2 and reduced H3K27me3 in MDS.  Additional sex combs like 1 (ASXL1) is another gene in the polycomb pathway that is frequently mutated in MDS (15-25%) and myeloid malignancies (43% in CMML, 10-15% AML and MPN) (Abdel-Wahab et al., 2011; Bejar et al., 2011; Gelsi-Boyer et al., 2009).  Mutation of ASXL1 is associated with adverse outcomes and poor survival (Bejar et al., 2011). The most common mutations occurring in ASXL1 are somatic nonsense mutations and insertion/deletions occurring in exon 12 (Abdel-Wahab et al., 2011) leading to loss of expression (Abdel-Wahab et al., 2012).  Knockdown of ASXL1 results in upregulation of HOX genes and global loss of H3K27me3, and promotes leukemic transformation (Abdel-Wahab et al., 2012).  This is seen despite preserved expression of PRC2 components, such as EZH2 responsible for trimethylation of H3K27 (Abdel-Wahab et al., 2012).  ASXL1 physically interacts with EZH2 and other members of the PRC2 complex, and is required for recruitment of PRC2 to target loci (Abdel-Wahab et al., 2012).  Furthermore, loss of ASXL1 has been shown to lead to loss of PRC2 occupancy at target loci. Overexpression of truncated mutants of ASXL1 has been shown to inhibit differentiation of myeloid cells (Inoue et al., 2013).  In a bone marrow transplantation assay, overexpression of mutant ASXL1 in hematopoietic cells results in the development of severe anemia, leukopenia, and thrombocytopenia similar to that seen in MDS patients (Inoue et al., 2013).  Hematopoietic cells from mice that have developed MDS showed an expression profile that inversely correlated with PRC target genes suggesting that mutant ASXL1 52  inhibits the polycomb repressive complex (Inoue et al., 2013). In addition, HOX gene expression was increased H3K27me3 was reduced in the promoters of these genes (Inoue et al., 2013). Mutations of RNA splicing machinery The RNA splicing pathway is the single most commonly mutated pathway in MDS, with mutations detected in 64% of patients (Haferlach et al., 2014). RNA splicing is catalyzed by the spliceosome, a complex of small nuclear ribonucleoprotein (snRNPs). Whole-exome sequencing has revealed recurrent mutations in components of the RNA splicing pathway in myeloid neoplasms showing features of myelodysplasia (Haferlach et al., 2014; Madan et al., 2015; Makishima et al., 2012; Papaemmanuil et al., 2011; Yoshida et al., 2011).  Mutations of the RNA splicing pathway are found in approximately 45-85% of myelodysplasia patients depending of the disease subtype (Yoshida et al., 2011). Six components of the splicing machinery (U2AF1, ZRSR2, SRSF2, SF3A1, SF3B1, and PRPF40B) have been found to be mutated in approximately 55% of patients with myelodysplasia in a mutually exclusive manner (Yoshida et al., 2011). Of these, mutations in U2AF1, ZRSR2, and SRSF2 were found to be recurrent, while the others were mutated in single cases (Yoshida et al., 2011). The mutually exclusive nature of these mutations suggests that these mutations have a common impact on RNA splicing and disease pathogenesis (Yoshida et al., 2011). The frequency of spliceosomal mutations vary across different disease subtypes, and are associated with different clinical outcomes. Mutations in SF3B1 are more prevalent in low-risk MDS with ringed sideroblasts and this mutation is associated with a favorable prognosis (Makishima et al., 2012; Papaemmanuil et al., 2011; Yoshida et al., 2011). In contrast, U2AF1 and SRSF2 are associated with CMML and advanced forms of MDS and predict shorter survival (Makishima et al., 2012).  Mutations in ZRSR2 are more prevalent in MDS subtypes without ringed sideroblasts and CMML, 53  and are associated with higher BM blast counts and higher rates of progression to AML (Madan et al., 2015). Mutations of U2AF1 may represent an early event in the progression of MDS to AML as serial analysis of secondary AML cases showed that U2AF1 mutations were present in the initial MDS samples (Makishima et al., 2012). The low frequency of SF3B1 mutations in advanced forms of AML suggests that it does not contribute to the evolution from MDS to AML (Makishima et al., 2012).   The functional consequences of mutations in RNA splicing machinery are largely unknown. Most mutations in splicing factors are heterozygous, suggesting that homozygous mutations may be lethal or that the mutated allele may have a dominant negative effect (Makishima et al., 2012). Overexpression of mutant forms of U2AF1 results in an increase in expression of genes in the nonsense-mediated mRNA decay pathway, and co-expression of wild type U2AF1 significantly suppressed activation of the nonsense-mediated decay pathway (Yoshida et al., 2011). Other studies have shown that while there is no genome wide increase in intron retention following mutation of spliceosomal machinery, the splicing pattern of several genes was affected (Makishima et al., 2012). For example, U2AF1 mutation results in defective splicing of intron 5 of TET2 and an increase in the prevalence of unspliced reads of RUNX1 (Makishima et al., 2012). This suggests that the main consequence of spliceosomal mutations is the accumulation of unspliced transcripts, and not abnormal alternative splicing (Makishima et al., 2012). Furthermore, phenotypic features of spliceosome mutations may be related to the transcriptional profile of cells in the mutation occurs (Makishima et al., 2012). SF3B1 mutations, which are common in MDS with ringed sideroblasts, may affect splicing of transcripts coding for protein associated with iron handling in erythroid precursors (Makishima et al., 2012).          54  1.2.3 MDS as a stem cell disorder MDS is generally referred to as a clonal stem cell disorder, however, there has been some debate about whether MDS arises in a myeloid restricted progenitor cell or a true stem cell with both myeloid and lymphoid potential (Nimer, 2008).  The dispute regarding the MDS initiating cell arose due to the apparent absence of clonal lymphoid involvement in earlier studies of MDS (Kibbelaar et al., 1992; Kroef et al., 1997; Soenen et al., 1995). Genetic and cytogenetic abnormalities were rarely found to occur in the lymphoid compartment, and the ineffective hematopoiesis and dysplasia seen in MDS were not found within cells of the lymphoid lineage (Vankamp et al., 1992). These observations challenged the idea that the MDS-initiating cell is a true stem cell capable of both myeloid and lymphoid differentiation, however they do not exclude the possibility that the MDS-initiating cell is a multipotent cell with impaired capacity for lymphoid differentiation (Nimer, 2008). This, along with the fact that cells of the lymphoid lineage are generally long lived (compared to the cells of the more rapidly turning over myeloid lineage), suggests that lymphoid cells detected in MDS patients may have arisen from a lymphoid-restricted progenitor prior to acquisition of the genetic lesion in the multipotent HPSC. More recently, fluorescent in situ hybridization (FISH) studies have shown that in the case of del(5q) MDS, virtually all CD34+CD38- cells arise from the 5q-deleted clone (Nilsson et al., 2000; Woll et al., 2014), and studies of monosomy 7 have demonstrated the presence of the deletion in the pluripotent HSC (CD34+Thy-1+) as well as in B (CD34+CD19+) and T/natural killer (CD34+CD7+) progenitors (Miura et al., 2000). These mutations arising in CD34+ hematopoietic stem/ progenitor cells (Miura et al., 2000; Nilsson et al., 2000; Nilsson et al., 2007; Pellagatti et al., 2006) have been shown to impair differentiation (Choi et al., 2008; Pang et al., 2013; Will et al., 2012). Moreover, clonal T cell populations have been identified in 50% of MDS patients regardless of prognostic subgroup (Epling-55  Burnette et al., 2007), and copy number variants in CD34+ cells from MDS patients have been detected in CD3+ T cells (Vercauteren et al., 2010). Furthermore, recent studies have shown that rare CD34+CD38- HSCs are capable of initiating disease in xenotransplant experiments (Choi et al., 2008; Pang et al., 2013; Will et al., 2012). The question still remained whether CD34+CD38- HSCs are the only cells capable of propagating MDS, or whether mutations occurring in myeloid progenitor populations could confer self-renewal abilities upon the short-lived progenitors, granting then the ability to maintain and propagate the disease. Stem cells (Lin-CD34+CD38-CD90+CD45RA-) and progenitor cells (CMPs, GMPs and MEPs) isolated from MDS patients were shown to be molecularly and functionally similar to normal hematopoietic stem and progenitor cells (Schneider et al., 2014; Woll et al., 2014). Of these stem and progenitor cell populations, only the MDS stem cell population was capable of sustaining long-term myeloid progenitors in vitro (Schneider et al., 2014; Woll et al., 2014). Furthermore, Lin-CD34+CD38-CD90+CD45RA- stem cells from del(5q) MDS patients, but not CMPs, MEPs, GMPs, or CD34- cells were the only cells capable of hematopoietic reconstitution of transplanted mice (Schneider et al., 2014; Woll et al., 2014). In xenografts, these MDS stem cells were capable of generating more clonally derived MDS stem cells as well as CMPs, MEPs, and GMPs that harbored the same molecular signatures as their respective populations isolated directly from the patient (Schneider et al., 2014; Woll et al., 2014). In order to validate that the MDS stem cell identified using in vitro and xenotransplantation assays is indeed the MDS-propagating cell in patients, Woll et al. tracked the origin of recurrent somatic driver mutations in bulk bone marrow cells from low- to intermediate-risk MDS patients.  They reasoned that due to the short lifespan of myeloid progenitors, any stable mutation contributing to the MDS clone must be acquired by a cell with self-renewal potential. Thus if Lin-CD34+CD38-CD90+CD45RA- stem cells are the only MDS stem cell, then all stable mutations detected in bulk bone marrow must have originated in that cell. 56  Alternatively, if the MDS-propagating cell is a myeloid progenitor cell that has gained self-renewal capabilities upon acquisition of driver mutations, then mutations should map to the progenitor cell population but not to the upstream stem cell compartment. All genetic lesions and driver mutations identified in the dominant MDS clones in 15 MDS patients were traced back to the Lin-CD34+CD38-CD90+CD45RA- stem cell compartment (Schneider et al., 2014; Woll et al., 2014).   1.2.4 Cellular mechanisms of MDS Progenitor cells in MDS MDS progenitor cells can be identified based on immunophenotypic markers (Choi et al., 2008; Manz et al., 2002; Pang et al., 2013; Will et al., 2012). CMPs are defined as Lin-CD34+CD38+CD123+CD45RA-, GMPs are Lin-CD34+CD38+CD123+CD45RA+, and MEPs are Lin-CD34+CD38+CD123-CD45RA- (Figure 1.10) (Choi et al., 2008; Manz et al., 2002; Pang et al., 2013; Will et al., 2012). MDS progenitors isolated using these surface markers are molecularly and functionally similar to normal hematopoietic progenitor cells as determined by their transcriptional expression profiles and their abilities to generate myeloid- and erythroid-restricted colonies in vitro (Schneider et al., 2014; Woll et al., 2014). The absolute number of CMPs is increased, while the number of GMPs is reduced in cases of isolated del(5q), other low- to intermediate-risk MDS with del(5q), and non-del(5q) MDS (Schneider et al., 2014; Woll et al., 2014). Similarly in another study, the number of GMPs was found to be reduced despite no difference in CMPs in low-risk MDS compared to healthy age-matched controls (Choi et al., 2008; Pang et al., 2013; Will et al., 2012). In contrast to low-risk MDS, in high-risk MDS, there is an expansion of the GMP (Will et al., 2012). MEPs are also suppressed in non-del(5q) cases, but not in del(5q) MDS (Schneider et al., 2014; Woll et al., 2014).    Figure 1.10 Changes in hematopoietic progenitor cell populations in MDSPatients with MDS present with normal or increased cellularity of the BM with paradoxical cytopenias in one or more lineage in the peripheral blood leading to anemia, neutropenia, and thrombocytopenia. However, in cases of isolated del5q patients display normal or elevated platelets. attributed to a block in differentiation of myeloid progenitorsto be expanded in MDS patientsdownstream progeny the expansion of the GMP population has been observed.    Peripheral blood cytopenias can be . CMPs have been shown , leading to reductions in the number of its MEPs and GMP low risk MDS. In cases of high    57   -risk MDS 58  Using FISH, researchers have been able to show that the del(5q) lesion is present at high frequencies in CMPs, GMPs, and MEPs, however the frequency of the lesion, as determined by sequencing, is slightly low within the GMP and MEP population (Schneider et al., 2014; Woll et al., 2014). This suggests GMPs and MEPs might be at a slight disadvantage with respect to their abilities to generate and maintain these populations (Schneider et al., 2014; Woll et al., 2014).MDS progenitor cells are organized in a hierarchal manner similar to normal hematopoietic progenitor cells (Schneider et al., 2014; Woll et al., 2014). Driver mutations identified in the Lin-CD34+CD38-CD90+CD45RA- MDS stem cells are also detectable within progenitor populations, as would be expected by their hierarchal relationship to the Lin-CD34+CD38-CD90+CD45RA- stem cell (Schneider et al., 2014; Woll et al., 2014). Increased apoptosis within hematopoietic cells is a hallmark of MDS. A recent study has shown that in low-risk MDS, the HSC does not undergo apoptosis more than its normal counterpart (Choi et al., 2008; Pang et al., 2013; Will et al., 2012), however, progenitor cells including CMPs, GMPs, and MEPs all display increased apoptosis, contributing to the reduction in frequency of progenitor cells (Choi et al., 2008; Pang et al., 2013; Will et al., 2012). In addition to increased apoptosis, increased phagocytosis of progenitor cells is another mechanism contributing to the reduced frequency of progenitor cells in low-risk MDS (Choi et al., 2008; Pang et al., 2013; Will et al., 2012). While the expression levels of the prophagocytic marker calreticulin (CRT) is not different between MDS HSCs and normal HSCs, this marker is significantly upregulated in CD34+CD38+ oligolineage progenitors, further contributing to the reduced frequency of GMPs in particular (Choi et al., 2008; Pang et al., 2013; Will et al., 2012). In contrast, myeloid progenitors from high-risk MDS express the anti-phagocytic marker CD47, thus enabling them to escape phagocytosis and leading 59  to the expansion of GMPs that is observed in high-risk MDS (Choi et al., 2008; Pang et al., 2013; Will et al., 2012). The bone marrow microenvironment in MDS Ineffective communication between HSCs and the microenvironment has been shown to contribute to the inability of MDS marrows to sustain normal hematopoiesis.  The first line of experimental evidence to suggest an important role for specific bone marrow stromal cells in the pathogenesis of MDS comes from studies of the  microRNA processing enzyme Dicer1 (Raaijmakers et al., 2010). Selective deletion of Dicer1 in mesenchymal osteoprogenitor cells results in reduced osteogenic colony formation in vitro, and reduced expression of the terminal differentiation marker osteocalcin, as well as a slight but significant reduction in osteoblast numbers in vivo (Raaijmakers et al., 2010). These osteoprogenitor differentiation defects result in markedly disordered hematopoiesis characterized by leukopenia in all white blood cell lineages, anemia, and thrombocytopenia despite normal stem and progenitor cell numbers and function (Raaijmakers et al., 2010). The presence of peripheral cytopenias and dysplastic maturation of neutrophils and megakaryocytes fulfill the criteria for the diagnosis of MDS in mice (Raaijmakers et al., 2010) . The hematopoietic defects observed in Dicer1-/- mice are environmentally induced, as transplantation of hematopoietic cells from severely cytopenic and dysplastic Dicer1-/- mice into wild type recipients rescues the cytopenias and dysplasia (Raaijmakers et al., 2010). Conversely, transplantation of wild type hematopoietic cells into Dicer1-/- recipients results in leukopenia, anemia, thrombocytopenia, and dysplasia (Raaijmakers et al., 2010). Similar to human MDS, Dicer1-/- mice display a predisposition for the development of AML (Raaijmakers et al., 2010). The implication of defective niche elements in the pathogenesis of MDS has been further corroborated 60  by the finding that DICER and DROSHA expression levels are reduced in mesenchymal cells from MDS patients compared to normal (Raaijmakers et al., 2010; Santamaria et al., 2012).  Mesenchymal stromal cells from MDS patients have been shown to be molecularly and functionally incapable of supporting normal hematopoiesis (Geyh et al., 2013). MSCs from MDS patients have reduced clonogenic potential compared to healthy controls and have impaired growth kinetics and increased cellular senescence (Geyh et al., 2013). The HSC-supporting capacity of MDS-derived MSCs is reduced 19-fold in long-term culture assays compared to healthy control MSCs (Geyh et al., 2013). Interestingly, culture of MDS CD34+ cells on healthy MSCs partially restores the LTC-IC frequency of MDS HSPCs (Geyh et al., 2013). Furthermore, MSCs from MDS patients have reduced osteogenic differentiation potential consistent with the finding that deletion of Dicer1 impairs osteogenic differentiation and leads to the development of MDS in mice (Geyh et al., 2013; Raaijmakers et al., 2010).       The importance of stromal cell support in the pathogenesis of MDS is further demonstrated by the fact that for many years xenograft models of low-risk MDS into immunodeficient mice have not been able to sustain robust engraftment for prolonged periods of time (Martin et al., 2010; Medyouf et al., 2014; Muguruma et al., 2011; Thanopoulou et al., 2004). However, co-transplantation of MDS CD34+ cells with in vitro expanded MDS-derived MSCs allowed long-term engraftment of MDS hematopoietic cells in NSG mice (Medyouf et al., 2014). Furthermore, MDS derived MSCs provide MDS CD34+ cells with significantly enhanced engraftment capacities compared to healthy MSCs (Medyouf et al., 2014). Analysis of the transcriptomes of MDS-derived MSCs revealed an enrichment in osteoprogenitor signatures, as well as cellular adhesion, extracellular matrix remodeling, and cytokine-cytokine receptor interactions (Medyouf et al., 2014). Interestingly, MDS hematopoietic cells were found to be able to directly induce changes in MSCs, 61  indicating that MDS cells can reprogram the cells of the bone marrow niche during the course of the disease, converting it into an MDS-supporting niche (Medyouf et al., 2014). Contributions of other cells to the pathophysiology of MDS While many of the molecular mechanisms leading to the development of MDS have been thoroughly investigated, little has been revealed about the cellular players (beyond the role of defective stem and progenitor cells) responsible for the manifestations of MDS. Specifically, the role of non-clonally derived hematopoietic cells in the pathogenesis of MDS has not been fully investigated. Recently, it was shown in low-risk MDS that Lin-HLA-DR-CD33+ myeloid derived suppressor cells (MDSCs) from MDS patients may play a role in the induction of MDS (Chen et al., 2013). MDSCs are immature myeloid cells that are site-specific inflammatory and immunosuppressive to T cells (Gabrilovich and Nagaraj, 2009). They are known to accumulate in cancer patients and tumor bearing mice and contribute to cancer progression (Gabrilovich and Nagaraj, 2009). MDSCs are present in MDS patients at higher frequencies than in healthy controls and non-MDS cancer patients (Chen et al., 2013). Interestingly, FISH analysis of MDSCs from del(5q) or del(7q) patients showed that these MDSCs were not derived from the MDS clone as indicated by the absence of the cytogenetic abnormalities within this population (Chen et al., 2013). Also, in MDS patients with no chromosomal abnormalities, qPCR analysis of the most common gene mutations (such as EZH2, IDH1/2, RUNX1) in MDS revealed that these mutations are restricted to the MDSC-depleted fraction, indicating that MDSCs are not part of the malignant clone (Chen et al., 2013). MDS-MDSCs demonstrate suppressive activity toward T cells, inhibiting proliferation and IFNγ production , as well as overproducing immunosuppressive cytokines such as IL-10 and TGF-β, and nitric oxide and arginase compared to MDSCs from healthy controls (Chen et al., 2013). These 62  effects combine to create a proinflammatory environment leading to increased immune tolerance within the bone marrows of MDS patients (Chen et al., 2013). These cells further contribute to the increased apoptosis of hematopoietic progenitor cells by releasing caspase-activating effector proteases such as granzyme B at the site of cell contact with erythroid precursors  (Chen et al., 2013). In addition, the finding that depletion of MDSCs from MDS bone marrow increases the clonogenic potential of HSPCs in colony forming assays, indicates that MDSCs have a direct suppressive effect on myeloid and erythroid progenitor cells  (Chen et al., 2013).      The increased accumulation and activation of MDSCs in MDS patients is attributed to their elevated expression of the surface transmembrane glycoprotein, CD33, which binds to the inflammatory signaling molecule S100A9 (Chen et al., 2013). CD33 overexpression in healthy bone marrow cells and treatment with S100A9 was found to induce expression and secretion of the suppressive cytokines IL-10 and TGFβ, and reduction in S100A9 expression attenuated IL-10 and TGFβ production (Chen et al., 2013). Furthermore, it rescued myeloid and erythroid colony formation, showing that the inflammatory molecule S100A9 plays an important part in the suppression of normal hematopoiesis in MDS patients (Chen et al., 2013).     1.2.5 Diagnosis of MDS in murine models In order to better understand the molecular mechanisms and the pathogenesis of MDS, researchers have employed three different strategies to generate mouse models of MDS.  These include the manipulation of mouse hematopoietic cells to virally overexpress or knockdown gene expression, the generation of transgenic/knockout mice, and xenograft techniques to introduce human MDS cells into immunocompromised mice (Wegrzyn et al., 2011).  Guidelines have been proposed for the diagnosis of MDS in mice by the hematopathology subcommittee of the Mouse 63  Models of Human Cancer Consortium (Kogan et al., 2002).  The criteria for diagnosing myelodysplastic syndromes and myeloproliferative disease in mice are presented in Table 1.2 and Table 1.3 respectively. Table 1.2 Minimum criteria for defining myeloid dysplasia in mice Defining Criteria  1. In peripheral blood mice exhibit one or more of the following: (a) Neutropenia (± anemia, ± thrombocytopenia) (b) Thrombocytosis (without leukocytosis or erythrocytosis) (c) Anemia (without leukocytosis or thrombocytosis) 2. Mice exhibit defects in maturation of nonlymphoid hematopoietic cells as evidenced by one of the following: (a) Dysgranulopoiesis, dyserythropoiesis and/or dysplastic    megakaryocytes ± increased nonlymphoid immature forms/blasts (b) At least 20% nonlymphoid immature forms/blasts in BM and/or spleen 3. Disorder is not a nonlymphoid leukemia  MDS =  disorder fulfills criterion 2a Cytopenia with increased blasts = disorder fulfills criterion 2b  Table 1.3 Minimum criteria for defining myeloid proliferation in mice Defining Criteria  1. Lesions are nonreactive, persistant, genetically determined, and meet criteria 2-4 below   2. Mice exhibit increased nonlymphoid hematopoietic cells as evidenced by any combination of the following: (a) Erythrocytoisis, leukocytosis of myeloid cells, and/or thrombocytosis/circulating micromegakaryocytes (b) Increased nonlymphoid hematopoietic cells in spleen and/or bone marrow 3. Disorder is not a nonlymphoid leukemia  4. Disorder is not a myeloid dysplasia  Myeloproliferation (genetic) = Disorder fulfills criterion 2b but not 2a Myeloproliferative disease= Disorder fulfills criterion 2a and 2b    64  1.3 The Innate Immune System The mammalian immune system can be divided into two arms: innate and adaptive.  Following a breach of physical barriers against infection, such as skin and the mucosa, the innate immune system is the host’s first line of defense (Delves and Roitt, 2000; Gallo and Nizet, 2008; Parkin and Cohen, 2001). Unlike the adaptive immune response which has developed within jawed vertebrates, the innate immune system is found in all forms of life. This evolutionarily conserved response to infection is rapid, does not generate immunological memory, and lacks the specificity of the adaptive immune system (Delves and Roitt, 2000; Gallo and Nizet, 2008; Parkin and Cohen, 2001). In contrast to the antigen specific receptors present on T and B lymphocytes of the adaptive immune system, cells of the innate immune system utilize pattern recognition receptors (PRRs) that recognize conserved pathogen associated molecular patterns (PAMPs) on the invading microbes(Medzhitov, 2001). Although the innate immune system was described over a century ago, its importance in the detection of pathogens was not fully recognized until the discovery that the Drosophila protein Toll is required for mounting an effective immune response against the fungus Aspergillus fumigates (Lemaitre et al., 1996). Researchers were very surprised at the discovery of Toll that they exclaimed in German, "Das ist ja toll!" which translates to "That's great!" (Hansson and Edfeldt, 2005). Toll-like receptors (TLRs) are an evolutionarily conserved family of PRRs (Figure 1.11) named after Drosophila Toll, and are primarily responsible for the host’s ability to recognize and response to pathogens. While the role of TLRs on mature cells of the immune system has been very well characterized, it was only within the last decade that an emerging role has been identified for Toll-like receptor on hematopoietic stem and progenitor cells (Nagai et al., 2006).      Figure 1.11 Comparison between Drosophila Toll and  The homology between Drosophila Toll and the mammalian TLR signaling pathway is demonstrated.  In both species, ligation of the cell surface receptor to its ligand results in the recruitment of intracellular adaptor proteins and thkinases leading to the eventual translocation of transcription factors into the nucleus leading to changes in gene expression.     Mammalian TLR signalinge activation of downstream  65   66  1.3.1 Toll-like receptor signaling overview Toll-like receptors are non-catalytic transmembrane proteins consisting of extracellular leucine rich repeats that form horseshoe shaped solenoids responsible for ligand binding, a transmembrane domain, and a cytoplasmic Toll/IL-1 receptor (TIR) domain (Park et al., 2009). In mammals, 13 TLRs have been identified that recognize a variety of microbial components such as bacterial peptidoglycan, lipopolysaccharides (LPS) and viral components such as dsRNA (Table 1.4) (Lauw et al., 2005; Yamamoto and Takeda, 2010). In addition to microbial components, several TLRs have been shown to recognize host-derived components exposed during cellular injury, such as High-mobility group protein B1 (HMGB1), heat shock proteins, and endogenous RNAs (Table 1.4) (Akira et al., 2006; Yu et al., 2010). Following ligand binding, Toll-like receptors dimerize and undergo conformational changes that recruit downstream signaling molecules to the receptor complex and activate signaling through one of two pathways: Myeloid differentiation primary response 88 (MyD88)-dependent or MyD88-independent (Figure 1.12) (Akira and Takeda, 2004; Vogel et al., 2003). All TLRs, with the exception of TLR3, signal through the MyD88-dependent pathway (Akira and Takeda, 2004).  This pathway is characterized by the recruitment and association of the adaptor protein MyD88 with TLRs through TIR-TIR interactions (Akira and Takeda, 2004). In the case of TLR2 and TLR4, an additional adaptor protein, Toll/interleukin-1 receptor adaptor protein (TIRAP), is recruited to the TLR complex prior to MyD88 binding (Bovijn et al., 2013; Horng et al., 2002; O'Neill et al., 2003; Yamamoto et al., 2002). Through its N-terminal death domain, MyD88 recruits the serine/threonine kinases IL-1 receptor associated kinase (IRAK)-4 and IRAK-1 (Burns et al., 2003; Li et al., 2002a). IRAK-4 mediated phosphorylation of IRAK-1 leads to its activation and autophosphorylation of its N-terminus, enabling binding of the E3 ubiquitin ligase Tumor necrosis  67  Table 1.4 Mammalian Toll-like receptors and their ligands Toll-like Receptor Species Ligand Reference TLR1 Human Mouse Triacylated lipoprotein *Pam3Cys (Akira and Takeda, 2004; Takeuchi et al., 2002) TLR2 Human Mouse Peptidoglycan TLR1/2: Triacylated lipoprotein TLR2/6: Diacylated lipoprotein †HSP60 † HSP70 †HMGB1 (Akira and Takeda, 2004; Asea et al., 2002; Curtin et al., 2009; Vabulas et al., 2001; Vabulas et al., 2002) TLR3 Human Mouse dsRNA *PolyI:C †Endogenous RNA (Akira and Takeda, 2004; Alexopoulou et al., 2001; Cavassani et al., 2008; Kariko et al., 2004) TLR4 Human Mouse LPS † HSP60 † HSP70 †HMGB1 (Apetoh et al., 2007; Asea et al., 2002; Poltorak et al., 1998; Tang et al., 2007; Vabulas et al., 2001; Vabulas et al., 2002) TLR5 Human Mouse Flagellin (Akira and Takeda, 2004; Hayashi et al., 2001) TLR6 Human Mouse Diacylated lipoprotein MALP-2 (macrophage activating lipopeptide 2) (Akira and Takeda, 2004; Takeuchi et al., 2001) TLR7 Human Mouse ssRNA40 *resiquimod *loxoribine (Akira and Takeda, 2004; Heil et al., 2004) TLR8 Human Mouse ssRNA *resiquimod (Heil et al., 2004) TLR9 Human Mouse Unmethylated CpG DNA *CpG ODN †DNA-IC (DNA-immunoglobulin complex) (Hemmi et al., 2000; Leadbetter et al., 2002; Viglianti et al., 2003) TLR10 Human Mouse (non-functional) Unknown  TLR11 Mouse Profilin (Yarovinsky et al., 2005) TLR12 Human (pseudogene; non-functional) Mouse Unknown  TLR13 Human (non-functional) Mouse Unknown  * Synthetic TLR agonist, † Endogenous ligand    Figure 1.12 A brief overview of the TollTLR signaling is initiated by ligation of the receptor to its ligand at the surface of the cellwithin endosomes, leading to homomolecules such as MyD88, TIRAP, phophorylation events leads to the activation and translocation of transcriptisuch as NFκB, JNK, IRF-3inflammatory cytokines, apoptosis and survival signals.   factor receptor associated factoret al., 2002a). The IRAK-1/TRAF6 complex interacts with membrane bound TAK1/TAB1/2/3 complex and dissociate from the TLR complex membrane and the TRAF6/TAK1/TAB1/2/3 complex is released into the cytoplasm 2002; Takaesu et al., 2001; Yamin and Miller, 1997-like receptor signaling pathway.   - or heterodimerization of the receptor.  Adaptor TRAM and TRIF are recruited to the receptors.  A series of , and IRF-7 into the nucleus where they activate transcription of  -6 (TRAF6) (Burns et al., 2003; Cao et al., 1996; Deng et al., 2000(Jiang et al., 2002). IRAK-1 is then degraded at the plasma ). In the cytoplasm, TRAF6 interacts with the 68   or on factors ; Li (Jiang et al., 69  E1/E2 ubiquitin-conjugating complex UBC13/Uev1A leading to the autoubiquitination and activation of TRAF6, which further leads to the phosphorylation and activation of TAK1 (Deng et al., 2000; Wang et al., 2001). Activation of TAK1 can lead to downstream activation of both p38/JNK MAP kinase signaling pathways as well as the transcription factor NFκB (Wang et al., 2001). Activated TAK1 phosphorylates and activates the inhibitor of NFκB (IκB) kinase (IKK) complex, which then phosphorylates IκB leading to its lysine-48 linked ubiquitination, which targets IκB for degradation by the proteasome (Adhikari et al., 2007; Wang et al., 2001). This degradation releases the inhibition on NFκB and allows for its subsequent translocation into the nucleus where it can cause changes in gene expression patterns such as the upregulation of proinflammatory cytokines such as IL-6, TNFα, and IL-1β (Akira and Takeda, 2004; Barton and Medzhitov, 2003).  MyD88-independent signaling is restricted to TLR3 and TLR4.  This pathway depends on the recruitment of the adaptor protein TRIF (and in the case of TLR4, TRAM) to the TLR signaling complex following receptor ligation (Kenny and O'Neill, 2008; Oshiumi et al., 2003; Yamamoto et al., 2003).  Association of TRIF with TRAF3 and TBK1 leads to the subsequent phosphorylation and translocation of Interferon regulatory factor (IRF3) into the nucleus were it mediates the production of type one interferons and interferon inducible chemokines such as IP-10, CCL5, and CCL2 (Hacker et al., 2006; Kawai et al., 2001; Oganesyan et al., 2006). In parallel, TRIF can recruit TRAF6 and RIP1 leading to late phase activation of NFκB (Sato et al., 2003). Several signaling molecules exist that act to negatively regulate TLR signaling.  Among them are IRAK-M, which lacks kinase activity, a splice isoform of MyD88 (MyD88s), which lacks the intermediate domain rendering it unable to bind to IRAK4 (Burns et al., 2003), and ST2, which associates with and sequesters TIRAP and MyD88 from TLRs. In endothelial cells, the Fas-associated 70  death domain protein (FADD) also serves  as a negative regulator of TLR4 signaling by impairing the interaction of MyD88 with IRAK1 (Zhande et al., 2007).    1.3.2 TLR signaling in hematopoietic progenitor cells For quite some time, it was unknown at which stage in hematopoietic development cells acquire expression of Toll-like receptors and whether TLR expression plays a role in hematopoiesis. Flow cytometric analysis has revealed that TLR2 and TLR4, as well as their associated co-receptors CD14 and MD-2 are expressed on HSPCs and early progenitors enriched in the Lin-c-kit+ subset of hematopoietic cells (Nagai et al., 2006). In humans, TLR4, TLR7 and TLR8 expression is detectable on freshly isolated CD34+ progenitor cells (Sioud et al., 2006). TLR expression within the different hematopoietic compartments is summarized in Table 1.5. Furthermore, signaling through these receptors was found to drive proliferation and differentiation of stem/progenitor cells into mature monocyte/macrophage cells at the expense of lymphoid differentiation in the absence of exogenous cytokines (Nagai et al., 2006; Sioud et al., 2006). Genetic experiments with the TLR signaling protein TAK1 further point to an important role of this pathway in normal hematopoiesis (Tang et al., 2008). TAK1 is expressed and activated in LSK cells under normal physiological conditions, and knockout of this gene in mice results in ineffective hematopoiesis leading to pancytopenia and depletion of primitive hematopoietic cells in the marrow due to apoptosis (Tang et al., 2008). These findings suggest that expression of TLRs within HSPCs serves an important role in pro-survival signaling and rapid replenishment of the immune system during infection (Nagai et al., 2006; Sioud et al., 2006; Takizawa et al., 2012; Tang et al., 2008).  71  Table 1.5 Toll-like receptor expression in HSPCs Hematopoietic compartment Toll-like receptor Expression level Reference HSC 1,3,5,6,8,11 Not determined  2,4 High (Nagai et al., 2006; Schmid et al., 2011) 7,9 Low/inducible (Pelayo et al., 2008; Schmid et al., 2011) MPP 1,3,5,6,8,11 Not determined  2 High (Nagai et al., 2006; Schmid et al., 2011)  4,7,9 Low (Nagai et al., 2006; Pelayo et al., 2008; Schmid et al., 2011) CMP 1,3,5,6,8,11 Not determined  2,4,9 Low (Nagai et al., 2006; Pelayo et al., 2008; Schmid et al., 2011) 7 No expression (Schmid et al., 2011) MEP 1,3,5,6,8,11 Not determined  9 Low (Pelayo et al., 2008; Schmid et al., 2011) 2,4,7 No expression (Nagai et al., 2006; Schmid et al., 2011) GMP 1,3,5,6,8,11 Not determined  2,4 High (Nagai et al., 2006; Schmid et al., 2011) 7,9 Low (Pelayo et al., 2008; Schmid et al., 2011) CLP 1,3,5,6,8,11 Not determined  7,9 High (Pelayo et al., 2008; Schmid et al., 2011) 2,4 Low (Nagai et al., 2006; Schmid et al., 2011)   72  While it is critical that hematopoietic stem/progenitor cells are able to respond rapidly to infectious agents, dysregulation and chronic activation of innate immune signaling pathways in stem/progenitor cells is speculated to have detrimental outcomes such as exhaustion of the HSC pool or the accumulation of genetic alterations (Takizawa et al., 2012).    1.4 Aims of the Study The hematopoietic hierarchy is one of the better characterized stem cell systems in the mouse and human.  Despite the breadth of knowledge we have about the organization and function of hematopoietic cells, we are still left with the problem of our inability to effectively treat all disorders that arise within this system, warranting further study in the field.  Despite being one of the most common hematological disorders, there are no curative therapeutic options for MDS other than bone marrow transplantation. With the difficulties in finding histocompatible donors and the vast majority of MDS patients being elderly and too frail to undergo the intense conditioning regimen necessary prior to transplantation, this is usually not a viable option for most patients. Furthermore, immunomodulatory drugs and other chemotherapies are beneficial only to a small subset of MDS patients, leaving blood transfusion as the only option for managing MDS for many patients. These gaps in our understanding of MDS pathogenesis warrant further investigation into the mechanisms underlying disease onset and progression. Furthermore, the propensity of MDS to progress to the two extremes of hematopoietic dysfunction (AML and BM failure) makes this the ideal disorder to study in order to increase our basic knowledge of the process of hematopoiesis. Also, findings from such studies may have broader implications applicable not only to MDS, but also for other hematological malignancies and marrow failure syndromes.    73  In recent years the role immune dysregulation plays in the pathogenesis of MDS has taken a front and center position.  Immune signaling, in particular TLR signaling, plays an important role in maintaining hematopoietic homeostasis under steady state conditions as well as under pathological conditions such as microbial infections, stress, and cancer.  The overarching aim of this dissertation is to develop and characterize a murine model that recapitulates the complexity of the myelodysplastic syndromes in order to help further our understanding of the molecular and cellular basis of this disease.  To this end, we have shown that upregulation of the innate immune signaling molecule TIRAP in hematopoietic stem/progenitor cells results in aberrant hematopoiesis leading to marrow failure due to both intrinsic and extrinsic factors. Overexpression of TIRAP results in an altered cytokine milieu within the bone marrow, and this altered environment allows malignant cells to co-opt the assistance of non-malignant T cells in further potentiating an environment that is non-supportive of hematopoiesis by inhibiting stromal cells required for maintaining the hematopoietic stem cell niche.  Furthermore, we have identified key immune signaling pathways that are important for allowing progression of MDS from an indolent hematopoietic state to a myeloproliferative state setting the stage for the development of myeloid leukemias.  This study is the first to our knowledge to show a direct link between HSPC, immune cell and stromal defects in the development of the myelodysplastic syndromes. It sheds light on some of the deregulated processes occurring in MDS and will lead to better comprehension of the delicate balancing act that prevents the hematopoietic hierarchy from tipping one way or the other.  Furthermore, it may be possible to generalize some of the findings presented here in order to better understand the molecular and cellular mechanisms underlying other hematological disorders such as leukemias, myeloproliferative neoplasms and marrow failure syndromes.  74              2. MATERIALS AND METHODS             75  2.1 Cell Culture The GP+E-86 cell line is an ecotropic retroviral producer cell line derived from the NIH-3T3 cells (Markowitz et al., 1988).  It was generated by electroporation of two plasmids, one containing the gag and pol regions and the other containing the env region of Moloney murine leukemia virus (Mo-MuLV)(Markowitz et al., 1988).  GP+E-86 cells were provided by the American Type Culture Collection (ATCC).  GP+E-86 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Sigma-Aldrich, St. Louis, MO) supplemented with 10%  heat inactivated calf serum (CS) (Hyclone, Logan, Utah), 2 mM glutamine and 100 U each of penicillin and streptomycin (Gibco, Invitrogen, Carlsbad, CA).  0.015mg/ml hypoxanthine, 0.25mg/ml xanthine, and 0.025mg/ml mycophenolic acid were supplemented for selection of GP+E-86 cells. The retroviral producer cell line AmphoPhoenix was obtained from Dr. Gary Nolan (Stanford University, Pal Alto, CA) and was cultured in DMEM supplemented with 10% heat inactivated CS, 2mM glutamine and 100 U each of penicillin and streptomycin.   Mouse bone marrow (BM) cells were cultured in DMEM supplemented with 15% heat inactivated fetal bovine serum (FBS),  10 ng/ml rhIL-6 (eBioscience, San Diego, CA), 6 ng/ml rhIL-3 (eBioscience, San Diego, CA), and 100 ng/ml mSCF (eBioscience, San Diego, CA).   All cells were maintained in 5% CO2 at 37°C.          2.1.1 Gene transfer GP+E-86 cells were transduced using the retroviral vectors MSCV-IRES-GFP (MIG; gift from R.K. Humphries), MIG-TIRAP (gift from D.T. Starczynowski (Starczynowski et al., 2010)), MIG-miR145 (gift from D.T. Starczynowski (Starczynowski et al., 2010)), MIG-MyD88C (gift from L. Chang), MIG-MyD88N (gift from L. Chang), MSCV-IRES-YFP (MIY; gift from R.K. Humphries), MIY-TRAF6DN (gift from D.T. Starczynowski (Starczynowski et al., 2010)), MIY-TIRAP (subcloned from MIG-TIRAP), 76  MSCV-PGK-GFP (PGK; gift from R.K. Humphries), or PGK-miR145decoy (subcloned from PGK-miRdecoy (Starczynowski et al., 2010)).  Breifly, constructs were transiently transfected into the retroviral packaging cell line AmphoPhoenix using TransIT®-LT1 Transfection Reagent (Mirus Bio, Madision, WI).  Retroviral supernatants supplemented with 8μg/ml Polybrene (Sigma-Aldrich, St. Louis, MO) were collected, filtered through a 0.45μm filter, and applied to cultures of GP+E-86 cells along with fresh medium.  This procedure was repeated once over the next 24 hours.  Stably transduced cells were sorted by fluorescent activated cell sorting (FACS) for green fluorescent protein (GFP) or yellow fluorescent protein (YFP) using a FACS-440 flow-sorter (BD, Franklin Lakes, NJ). For retroviral transduction of mouse bone marrow, BM cells were co-cultured with irradiated (40 Gy) GP+E-86 cells transduced with the aforementioned constructs.  After two days of infection, BM cells were removed from co-culture, and expanded for one day prior to sorting stably transduced cells for GFP or YFP.  2.1.2 Osteoclast differentiation The RAW264.7 monocyte cell line was passaged in RPMI or DMEM supplemented with 10%  heat inactivated fetal bovine serum (FBS) (Hyclone, Logan, Utah), 2 mM glutamine and 100 U each of penicillin and streptomycin (Gibco, Invitrogen, Carlsbad, CA).  To induce differentiation into osteoclasts, RAW264.7 cells were moved into αMEM supplemented with 10% FBS, 2 mM glutamine and 100 U each of penicillin and streptomycin, and RANKL (50ng/mL).  Media was changed every 2-3 days. Osteoclast differentiation was inhibited with the addition of mouse recombinant IFNγ (1ng/mL) (StemCell Technoligies, Vancouver, BC).  77  To test the effects of TIRAP expression in BM cells on osteoclast differentiation, TIRAP or MIG transduced BM cells were co-cultured in transwell inserts with RAW264.7 cells in a 24-well tissue culture plate in 400 uL of osteoclast differentiation media.  Half-media changes were performed every 2-3 days. Osteoclast differentiation was confirmed by TRAP staining.        2.1.3 Long term culture-initiating cell (LTC-IC) assay LTC-IC assays were performed according to manufacturer's protocol (StemCell Technologies, Vancouver, BC).  Briefly, bone marrow cells were collected from 8-12 week old Pep3b mice, red blood cells were lysed using ammonium chloride solution (StemCell Technologies, Vancouver, BC) and resuspended in MyeloCult M5300 (StemCell Technologies, Vancouver, BC) supplemented with 1 μM hydrocortisone (StemCell Technologies, Vancouver, BC).  3 x 105 cells/per well were plated in collagen coated 96 well plates and cultured for 2-3 weeks with weekly half medium changes until cells reached more than 70-80% confluence.  Stromal layers were then irradiated at 15 Gy.  Test cells were added to 12 wells at doses of 300, 150, 75, and 37.5 cells per well and were cultured for four weeks with weekly half medium changes.  Non-adherent cells were then collected in 12 x 75mm sterile tubes, and the wells were rinsed with 0.1 mL Ca++ Mg++-free Hanks (StemCell Technologies, Vancouver, BC) and added to the appropriate tube.  Adherent cells were removed using 0.25% Trypsin-EDTA and added to the appropriate tube.  Cells were rinsed in Iscove's MDM containing 2% FBS, resuspended 0.1 mL Iscove's MDM and added to 1 mL methylcellulose medium (Methocult M3434, StemCell Technologies, Vancouver, BC), and CFCs were plated.  Colonies were counted on day 11 and wells were scored as positive (≥ 1 CFC) or negative (no CFC).  LTC-IC frequencies were calculated by the method of maximum likelihood from the proportion of wells 78  that are negative and limiting dilution analysis using ELDA (Extreme Limiting Dilution Analysis) (Hu and Smyth, 2009) software available online (    2.2 Osteoclast Staining and Quantification 2.2.1 Histochemical staining of osteoclasts TRAP staining of differentiated RAW264.7 cells was performed using a TRAP and ALP Double Stain kit (Clontech, Mountain View, CA) according to the manufacturer’s instructions. In brief, cells were washed once with PBS and fixed using the fixation solution provided for 5 minutes at room temperature. Next, the fixative was diluted with 2 ml sterile water and aspirated.  Cells were then washed one more time with sterile water. Single staining for TRAP was then performed by incubating cells with acid phosphatase substrate solution plus sodium tartrate at 37 °C for 20 minutes.  The substrate solution was then discarded and cells were washed three times with sterile water. Cells were then imaged with an inverted microscope and the amount of TRAP staining was quantified with the NIH ImageJ software (Research Service Branch, National Institute of Health, Bethesda, MD). Briefly, the measurement scale was first set using an image of micrometer taken at the same magnification at the images of the cells. Next, TRAP+ cells were outlined (Figure 2.1b) and the total area within the outlines was calculated using the Analyze → Measure funcon. To determine the total area of all cells within the field of view, automated thresholding (Process → Binary → Make Binary) was used to create a binary image (Figure 2.1c) which was then used to create a perimeter around all cells and calculate the total area (Analyze → Analyze Parcles). The area measurements obtained from Figure 2.1b and Figure 2.1d were then used to calculate percent TRAP+ area.           79   Figure 2.1 Quantification of osteoclast differentiation (A) Bright field image of differentiated RAW264.7 cells stained for TRAP. (B) The same image with the perimeter of TRAP+ cells outlined for computation of TRAP+ staining area. (C) The same image after processing to threshold all cells in the field of view. (D) The perimeter of the thresholded cells used to compute total area of all cells.   2.2.2 Immunohistochemical staining of human FFPE marrow sections Formalin fixed paraffin embedded (FFPE) sections from del(5q) MDS patients and normal controls were obtained from the Anatomic Pathology Lab at Vancouver General Hospital (VGH). Slides were dried overnight at 37°C in a mini incubator oven (VWR, Radnor, PA). Sections were then deparaffinized by incubating sections in xylene twice for 3 minutes each.  Slides were then moved to a 1:1 (v/v) mix of xylene and ethanol for 3 minutes, followed by two 3 minute washes in 100% ethanol. Sections were then moved into successively lower concentrations of ethanol (95%, 70%, 50%) for 3 minutes each.  Slides were then placed in H2O until antigen retrieval.  Antigen retrieval 80  was performed by incubating slides in Buffer CC2 (Ventana, Tuscon, AZ) in a pre-heated vegetable steamer for 30 minutes.  Sections were then washed 3 x 10 minutes in PBS. Endogenous peroxidase activity was then blocked by incubating in 0.3% H2O2 (Sigma-Aldrich, St. Louis, MO) for one hour at room temperature. Slides were then washed briefly in PBS. Next, slides were blocked in blocking buffer (10% goat serum in PBS + 0.1% Triton X-100) for 2 hours at room temperature. Sections were then stained with mouse anti-human TRAcP (clone 9C5, R&D Systems, Minneapolis, MN) diluted 1:100 in blocking buffer for 32 minutes at 37°C. Slides were then transfered to 4°C overnight. The following day, slides were washed in PBS 3 times for 10 minutes each and a secondary biotinylated goat anti-mouse Ig (Vector Laboratories, Burlingame, CA) diluted 1:100 in blocking buffer was applied for 90 minutes at room temperature.  VECTASTAIN ABC kit (Vector Laboratories, Burlingame, CA) was then used to stain the sections.  In brief, AB complex was prepared (9ul/ml A and 9ul/ml B in blocking buffer) at room temperature 30 minutes prior to use.  Cells were then incubated in AB complex for 90 minutes at room temperature, followed by 3x 10 minute washes in PBS.  DAB staining was then performed for 3-5 minutes at room temperature followed by one 10 minute wash in PBS. Slides were then counterstained with full strength hematoxylin solution for 10 seconds at RT (one slide at a time).  Slides were then rinsed in tap water until water runs clear.  Next, slides were dipped 10 times in Scott's tap water (1.0 g sodium bicarbonate, 10.0 g magnesium sulphate, 100ml dH2O) and then rinsed in tap water.  Slides were then dehydrated by performing successive 2 minute washes in ethanol (35%, 50%, 70%, 80%, 90%, 100%).  Next, slides were incubated twice in xylene for two minutes at a time.  Slides were mounted with Permount Mounting Media (Fisher Scientific, Waltham, MA) and allowed to dry overnight.        81  2.3 Immunoblotting Cells were lysed in RIPA buffer (PBS containing 1% NP-40 (Sigma-Aldrich, St. Louis, MO), 0.5% sodium deoxycholate, and 0.1% SDS (Sigma-Aldrich, St. Louis, MO)) with addition of fresh protease inhibitor cocktail (Roche Applied Science, Indianapolis, IN).  50 μg of total protein, as measured using Bio-Rad DC Protein Assay System (Bio-Rad Laboratories, Hercules, CA), were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis, transferred to nitrocellulose membranes (Bio-Rad Laboratories, Hercules, CA), and developed by enhanced chemiluminescence (PerkinElmer Life Sciences, Boston, MA).  Membranes were probed with the following antibodies: 1:500 rabbit anti-TIRAP (Santa Cruz), 1:10000 mouse anti-GAPDH (Sigma-Aldrich, St. Louis, MO).     2.4 Mouse Strains Pep3b (CD45.1),  C57Bl/6J-TyrC2J (CD45.2), IFNγ KO (B6.129S7-Ifngtm1Ts/J) (stock # 002287) (CD45.2), and  IL-10 KO (B6.129P2-Il10tm1Cgn/J) (stock# 002251)(CD45.2) mice were originally obtained from The Jackson Laboratory and were bred and maintained at the Animal Resource Centre of the British Columbia Cancer Research Centre.    2.5 Bone Marrow Transplantation Six- to 12-week old donor mice were injected i.v. with 5-fluorouracil (5-FU) (150 mg/kg) and bone marrow was harvested after four days.  Bone marrow cells were retrovirally transduced with MSCV-IRES-GFP (MIG) or MIG-TIRAP, and GFP+ cells were sorted.  Lethally irradiated (810 rads) recipient mice were transplanted i.v. with 300,000 GFP positive cells and 100,000 non-transduced 82  helper cells.  Irradiated mice were given ciprofloxacin/HCl in their drinking water for one month following transplantation.    2.5.1 Homing assay and homing efficiency Mice were euthanized 18-22 hours following transplantation bone marrow was collected from two tibiae and 2 femurs.  Flow cytometry was performed and the total number of cells that homed to the marrow was determined by multiplying the number of cells flushed from the marrow by the percentage of GFP+ cells detected by flow cytometry.  Homing efficiency of progenitor cells was determined by comparing the proportion of GFP+ colonies formed in colony forming assay before transplantation and 22 hrs after transplantation.  GFP+ colonies were counted using an Axiovert S100 fluorescent microscope (Zeiss, Oberkochen, Germany).  Homing efficiency was calculated using the following formula:   	 =%	()		%	()		       2.5.2 Niche conditioning transplant Bone marrow transplants were performed as described above.  After three weeks of marrow conditioning, 50 mg/kg of Busulfan was administered i.p. over two days and mice were allowed to recover from weight loss for two days following the last injection.  200,000 GFP or YFP labeled marrow cells along with 100,000 helper cells were then injected i.v. into mice with TIRAP or MIG pre-conditioned marrows.  GFP and YFP expressing cells were allowed to engraft for 2 weeks.  BM cells were then collected from the TIRAP and MIG conditioned mice, mixed together, and one mouse-equivalent of marrow was then transplanted into lethally irradiated recipients.  GFP and YFP chimerism in the peripheral blood was then monitored.    83  2.6 Flow Cytometry For immunophenotypic analysis, bone marrow cells were washed and resuspended in PBS containing 2% calf serum, followed by primary monoclonal antibody (phycoerythrin (PE)- or allophycocyanin (APC)-labled) staining overnight, followed by analysis on a BD FACSCalibur flow cytometer.  Antibodies used were PE-conjugated anti-mouse Gr1 (clone RB6-8C5; BD, Franklin Lakes, NJ), APC-conjugated anti-mouse Mac1 (clone M1/70;  BD, Franklin Lakes, NJ), PE-conjugated anti-mouse CD3 (clone 17A2;  BD, Franklin Lakes, NJ), APC-conjugated anti-mouse CD19 (clone 1D3;  BD, Franklin Lakes, NJ), PE-conjugated anti-mouse CD45R/B220 (clone RA3-6B2;  BD, Franklin Lakes, NJ), PE-conjugated anti-mouse CD4 (clone GK1.5,  BD, Franklin Lakes, NJ), APC-conjugated anti-mouse CD8a (clone 53-6.7;  BD, Franklin Lakes, NJ), PE-conjugated anti-mouse CD41 (clone MWReg30;  BD, Franklin Lakes, NJ), PE-conjugated anti-mouse CD71 (clone C2;  BD, Franklin Lakes, NJ), and APC-conjugated anti-mouse Ter119 (clone Ter119; Biolegend, San Diego, CA).  2.6.1 Progenitor staining Antibodies used for progenitor staining were FITC-conjugated antimouse CD45.1 (clone A20; eBioscience, San Diego, CA), PerCP-Cy5.5-conjugated antimouse Gr1 (clone RB6-85C;  BD, Franklin Lakes, NJ) PerCP-Cy5.5-conjugated antimouse Ter119 (clone Ter119; eBioscience, San Diego, CA), PerCP-Cy5.5-conjugated antimouse B220 (clone RA3-6B2; eBioscience, San Diego, CA), PerCP-Cy5.5-congugated antimouse CD3 (clone 172A;  BD, Franklin Lakes, NJ), PerCP-Cy5.5-conjugated antimouse CD4 (clone RM4-5;  BD, Franklin Lakes, NJ), PerCP-Cy5.5-conjugated antimouse CD8a (clone 53-6.7;  BD, Franklin Lakes, NJ), PerCP-Cy5.5-conjugated antimouse IL-7R (clone A7R34; eBioscience, San Diego, CA), APC-conjugated antimouse c-Kit (clone 2B8; eBioscience, San Diego, CA), APC-Cy7-conjugated antimouse CD16/32 (clone 2.4G2; BD, Franklin Lakes, NJ), PE-Cy7-84  conjugated antimouse Sca1 (clone 7;  BD, Franklin Lakes, NJ), biotinylated antimouse CD34 (cloneRAM34; eBioscience, San Diego, CA), and Streptavidin-PE-TxRed (BD, Franklin Lakes, NJ).  Cells were analyzed on BD Fortessa.  2.6.2 Ki67 staining Cells were washed in buffer PBA (PBS + 0.5% BSA + 2mM EDTA, pH 7.4) and resuspended in 250 μL Fix/Perm solution (FOXP3 staining Kit, eBioscience, San Diego, CA) while vortexing.  Cells were incubated in the dark at 4°C overnight.  Cells were then washed in 1 mL Perm buffer (FOXP3 staining kit, eBioscience, San Diego, CA) +0.5% BSA and resuspended in 100 μL Perm buffer (FOXP3 staining kit, eBioscience, San Diego, CA) + 2ul Ki67/isotype (BD, Franklin Lakes, NJ) + 5 μL normal mouse serum (Sigma-Aldrich, St. Louis, MO) + 0.5 μL anti-CD16/32 (eBioscience, San Diego, CA).  Following 30 minutes incubation at room temperature, cells were washed with buffer PBA and resuspended in 300 μL PBS and analyzed by flow cytometry.  2.6.3 Activated caspase-3 staining Active Caspase-3 Apoptosis Kit (BD, Franklin Lakes, NJ) was used according to manufacturer's instructions.  In brief, cells were washed twice in cold PBS and resuspended in 0.5 ml BD Cytofix/Cytoperm solution and were incubated for 20 min on ice.  Cells were then pelleted and washed twice in 1X BD Perm/Wash buffer.  Cells were then incubated with anti-activated caspase-3 antibody diluted in 1X BD Perm/Wash buffer (0.10 ml 1X Perm/Wash : 20 μL antibody) for 30 min at room temperature.  Cells were then washed in 1X BD Perm/Wash buffer, resuspended PBS + 2% FBS and analyzed by flow cytometry on BD Fortessa.    85  2.6.4 Annexin staining Cells were washed in cold PBS and resuspended in 100μl of Annexin binding buffer (10mM HEPES (Sigma-Aldrich, St. Louis, MO), pH 7.4; 140 mM NaCl (Sigma-Aldrich, St. Louis, MO); 2.5mM CaCl2 (Sigma-Aldrich, St. Louis, MO)) at a concentration of ~1x106 cells/mL.  Cells were stained with 3μL of APC-conjugated Annexin-V and PI and incubated for 15 min.  300 μL of ice cold annexin binding buffer was then added to cells and samples were acquired on flow cytometer.  For co-culture experiments, non-transduced WT bone marrow cells were cocultured with TIRAP or MIG-transduced cells overnight in serum-free medium supplemented with 1% BSA. Annexin-V and PI staining was performed as described above. The differences in forward scatter and side scatter profiles of the non-transduced WT and transduced bone marrow cells were used to determine the amount of apoptosis within each population in the co-culture system (Figure 2.2).   86   Figure 2.2 Gating strategy for distinguishing wild-type and transduced cells in co-culture system    87  2.7 Real-time PCR Total RNA was isolated from cells using the RNeasy kit (QIAGEN, Duesseldorf, Germany) according to manufacturer’s instructions.   2.5μg of total RNA was treated with DNase I (Invitrogen, Carlsbad, CA) and was reverse transcribed using Superscript II and random primers (Invitrogen, Carlsbad, CA).  cDNA was diluted 2.5X and 2.5μl of the diluted cDNA was used for each PCR reaction.  Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as a loading control for comparison across samples.  Real time quantitative RT-PCR was performed on the Applied Biosystems 7900HT Fast Real-Time PCR System using the Power SYBR Green PCR kit (Applied Biosystems, Foster City, CA) or the FastStart Universal SYBR Green Master kit (Roche Applied Science, Penzberg, Germany).  Primers are described in Table 2.1.  Table 2.1 Primers for quantitative RT-PCR  Forward primer sequence 5’→3’ Reverse primer sequence 5’→3’ Mouse IL-1β AAGGCTGCTTCCAAACCTTTGACC ATACTGCCTGCCTGAAGCTCTTGT  Mouse IL-6 TACCACTTCACAAGTCGGAGGCTT CAATCAGAATTGCCATTGCACAAC Mouse IL-10 GCAGGACTTTAAGGGTTACTTGGG CCTTGATTTCTGGGCCATGCTTCT Mouse IL-12p40 ATGTGGGAGCTGGAGAAAGACGTT ATCTTCTTCAGGCGTGTCACAGGT  Mouse TNFα TCTCAGCCTCTTCTCATTCCTGCT GCCATTTGGGAACTTCTCATCCCT Mouse IFNβ TGAACTCCACCAGCAGACAGTGTT TCAAGTGGAGAGCAGTTGAGGACA Mouse IFNγ TGCCAAGTTTGAGGTCAACAACCC TTTCCGCTTCCTGAGGCTGGATT Mouse IP-10 GCTGCAACTGCATCCATATC GTGGCaATGATCTCAACACG Mouse CSF2 AGGGTCTACGGGGCAATTT ACAGTCCGTTTcCGGAGTT Mouse IL2-Rα GCAAGAGAGGTTTCCGAAGA CGATTTGTCATGGgAGTTGC Mouse Fas-L TTTAACAGgGAACCCCCACT GATCACAAGGCCACCTTTCT Mouse TIRAP CCCAATGCCTGCTCTTTCATGGTT AGATCGGCATCTTCTTGGGCTTCT Human TIRAP AGATGGCTgACTGGTTCAGG TCCTGTGAGGTAGGCTGTGA Mouse GAPDH TGCAGTGGCAAAGTGGAGAT TTTGCCGTGAGGAGTCATA   88  2.8 Gene Set Enrichment Analysis Gene set enrichment analysis software (Broad Institute) (Mootha et al., 2003; Subramanian et al., 2005) was used to analyze data from previously published microarray data sets.  The tables below list the genes included in each gene set.   Table 2.2 IFN stimulated genes ABCD3 CD38 FGFR1 IFIT3 ISG15 SECTM1 TP63 ATF3 CEACAM1 GBP1 IFIT5 ISG20 STAT1 TYMP BAX CFLAR GBP2 IFITM1 ITGB7 STX11 UBE4B BRCA2 CXCL10 IFI16 IFITM3 KNTC1 TAP1 VCAM1 CASP1 CXCL11 IFI27 IFNG LAMP3 TMOD1 WARS CASP10 CXCL9 IFI30 IGF1R MX1 TNF  CASP8 DNAJA2 IFI35 IGFBP4 NAMPT TNFAIP2  CCL5 EIF2AK2 IFI44 IL10RA OPTN TNFAIP6  CCL8 FAS IFI6 IL12RB1 PML TNFSF10  CCNA1 FASLG IFIT2 IRF1 RAB7L1 TOP1   Table 2.3 IL-10 signaling pathway genes BLVRA IL10 IL1A STAT1 STAT4 STAT6 BLVRB IL10RA IL6 STAT2 STAT5A TNF HMOX1 IL10RB JAK1 STAT3 STAT5B         89  Table 2.4 NFκB pathway genes ABI3 CCL5 DOCK4 HIVEP1 MAP3K8 POU2F3 SIX5 TRAF4 ACAN CD40 DSC2 HNRNPR MAPK6 PPP1R13B SLAMF8 TRIB2 ACTN3 CD69 E2F3 HSD3B7 MIA PRDM12 SLC12A2 TRIM47 ADCK4 CD70 EBF1 HTR3B MIR17HG PRRT2 SLC16A6 TRPC4 AKT1S1 CD86 EHF ICAM1 MITF PTGES SLC44A1 TSEN54 ALG6 CDC42SE1 EIF4A2 IER3 MLLT11 PTHLH SLC6A12 TSLP AMOTL1 CDK6 EIF4G1 IER5 MLLT6 PURG SMOC1 TSNAXIP1 APPL1 CHD4 EIF5A IFNB1 MMP9 RANBP10 SMPD3 TUT1 ARHGEF2 CHD6 ENO3 IL13 MOB3C RAP2C SOX10 UACA ASCL3 CLCN1 ERN1 IL17C MSC RASGRP4 SOX3 UBD ASH1L CLCN2 FAM117A IL1RAPL1 MSX1 RASSF2 SOX5 UBE2D3 ATP1B1 CLDN5 FAM43B IL27 NDUFB9 RBMS1 SP6 UBE2H BAZ2B CLOCK FGF1 IL6ST NFAT5 REL STAT6 UBE2I BCL3 COL11A2 FGF12 ILK NFKB2 RELB STX19 UPF2 BCL6B COL16A1 FGF17 ITPKC NFKBIA RFX5 STX4 VEZF1 BDNF CREB1 FLJ39739 JAK3 NFKBIB RIN2 TATDN1 WNT10A BIRC3 CSF1R FLOT1 KAT7 NFKBID RND1 TBC1D17 WNT10B BLCAP CSF2RB FOXS1 KCNN2 NLK RNF43 TCEA2 WNT4 BMF CTDSP1 FTHL17 KCNT2 NR2F2 RPS19 TFE3 WRAP53 BMP2K CTDSPL2 FUT7 KLK9 NXPH4 RPS6KA4 TIAL1 WRN C14orf43 CUEDC1 G3BP1 KRT23 ORAI1 RRAS TJAP1 YWHAQ C16orf47 CXCL10 GADD45B KRT36 PAN2 RRP8 TLX1 YWHAZ C1orf9 CXCL11 GATA4 KY PARP8 RSF1 TLX3 ZDHHC24 C1QL1 CXCL16 GDPD5 LAMA1 PCBP4 S1PR2 TNFRSF1B ZDHHC8 C20orf160 CXCR5 GNG4 LIX1L PCDH10 SDC4 TNFRSF9 ZEB1 C2orf67 CYLD GNGT2 LRCH1 PCDH12 SEC63 TNFSF15 ZIC4 CACNG3 CYP2D6 GPBP1 LTB PCSK2 SH2B3 TNFSF18 ZMYND15 CALCOCO1 DAP3 GPM6A MADCAM1 PFN1 SIN3A TNIP1  CASKIN2 DCLK1 GREM1 MAML2 PLXNB1 SIRT2 TP53  CCDC107 DDR1 GRK5 MAP3K11 PNKD SIX4 TP63    2.9 Promoter Methylation Analysis An analysis of the promoter methylation status in MDS patient bone marrow cells and normal CD34+ cord blood cells was performed using a publicly available data set. The analysis was restricted to innate immune signaling genes. 67 TLR pathway genes (Table 2.5) as well as 233 TLR4 90  pathway interacting genes identified using the Human Protein Reference Database (HPRD) (Prasad et al., 2009) (Table 2.6) were selected for the analysis. Genes within this list were further filtered based on their presence within innate immune/TLR related pathways identified using InnateDB (Table 2.7) and their representation on the HG17 custom design oligonucleotide array, leading to a final list of 105 genes.      Figure 2.3 Filtering criteria for TLR-related genes  Table 2.5 TLR Pathway genes ARRB1 FADD IFNB1 IRF1 PPARA TAB2 TLR4 TRAF6 ARRB2 HMGB1 IFNG IRF3 PPP4C TBK1 TLR5 UBE2N BTK HRAS IFNK LY86 PPP4R1 TICAM1 TLR6 UBE2V1 CASP8 HSPA1A IL10 LY96 PPP4R2 TICAM2 TLR7 IRAK4 CD14 HSPD1 IL18 MAP3K7 PRKRA TIRAP TLR8  CD180 IFNA1 IL1RL1 MAPK8IP3 RIPK2 TLR1 TLR9  CXCL10 IFNA4 IRAK1 MYD88 SARM1 TLR10 TNFAIP3  ECSIT IFNA5 IRAK2 NR2C2 SIGIRR TLR2 TOLLIP  EIF2AK2 IFNA8 IRAK3 PELI1 TAB1 TLR3 TRAF3  91  Table 2.6 TLR interacting genes SEPT1 CD40 HDAC3 MALT1 NTRK3 PSMD6 SRC TRAF2 ABL1 CD55 HGS MAP2K1 NTSR1 PSMD7 STAP2 TRAF3IP1 ADRB1 CDC37 HIPK2 MAP2K4 NUMBL PTAFR STAT3 TRAF3IP2 ADRB2 CLTC HIPK3 MAP2K7 NUP62 PTH1R STC2 TRAF5 ADRBK1 CNPY4 HLTF MAP3K1 OPN1LW PTHLH STRADB TRAF7 AKAP12 CREBBP HSP90AA1 MAP3K14 OTUD5 PTPN6 SUMO1 TRAM1 AKT1 CS HSP90AB1 MAP3K3 OTUD7B RAD18 SYK TRH ALPL CSNK2A1 HSP90B1 MAP3K5 OXTR RALGDS TAX1BP1 TRIM37 AP2B1 CXCR4 HSPA4 MAP3K8 PCNA RAMP1 TBKBP1 TXLNA AP2M1 CYLD HTR2C MAP4K1 PEBP1 RBPMS TBPL1 UBB ARF6 CYTH2 IGBP1 MAPK1 PELI2 REL TGFBR1 UBE2B ATP6V1E1 DAZAP2 IKBKAP MAPK10 PELI3 RELA TGFBR3 VRK2 ATXN1 DDX20 IKBKB MAPK11 PIAS4 RIPK3 TIFA XIAP AURKA DHX38 IKBKE MAPK14 PIK3CA RRAS2 TMED1 XRN2 AVPR2 DNAJC3 IKBKG MAPK3 PPIB SERPINB2 TMEM189;TMEM189-UBE2V1 YWHAB AZI2 DVL2 IL17RB MAST2 PPM1B SETDB1 TMSB4X YWHAG BCL10 EDA2R IL17RD MBP PPP2R1A SIKE1 TNFRSF12A YWHAH BIRC2 EDAR IL1R1 MDM2 PPP2R5D SLC2A4 TNFRSF13C YWHAZ BIRC3 EP300 IL1RAP MED8 PPP4R4 SLC9A5 TNFRSF14 ZBP1 BMPR1A FGR IRF4 NCK1 PRDX1 SMAD3 TNFRSF17 ZBTB16 BMPR1B FHL2 IRF5 NDUFS7 PRKCI SMAD6 TNFRSF18 ZHX1 CALCRL FKBP5 IRF7 NFKB1 PRKCZ SMAD7 TNFRSF1B ZMYND11 CARD11 FLII IRF8 NFKBIA PSMB5 SMARCC2 TNFRSF4 ZRANB1 CASP3 FLNA IRF8 NFKBIB PSMC1 SMEK2 TNFRSF8  CAV1 FOS ITGB2 NGFR PSMC2 SMURF1 TNFRSF9  CBL FZD4 JUN NLK PSMC3 SMURF2 TNIP1  CCAR1 GEMIN4 KCNQ1 NLRP12 PSMD1 SOCS1 TNIP2  CCDC50 GRIN1 KITLG NOX4 PSMD12 SPHK1 TOM1  CCDC6 GRIN2D LDB1 NTRK1 PSMD13 SPOP TRADD  CD247 HCK LGALS3BP NTRK2 PSMD3 SQSTM1 TRAF1        92  Table 2.7 Innate DB Pathways activated TAK1 mediates p38 MAPK activation  IRAK2 mediated activation of TAK1 complex upon TLR7/8 or 9 stimulation Toll-like receptor pathway  TRAF6 mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation Activation of IRF3/IRF7 mediated by TBK1/IKK epsilon MyD88 cascade initiated on plasma membrane Toll-like receptor signaling pathway  TRAF6 Mediated Induction of proinflammatory cytokines Atypical NF-kappaB pathway  MyD88:Mal cascade initiated on plasma membrane Toll-like receptor signaling pathway (p38 cascade) ( Toll-like receptor signaling pathway (p38 cascade) ) TRAF6 mediated induction of TAK1 complex Canonical NF-kappaB pathway  NF-kB signaling ( Toll-like receptor signaling pathway (trough NF-kappaB) ) Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, JNK cascade) ( Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, JNK cascade) ) TRAF6 mediated IRF7 activation  Endogenous TLR signaling  P38 cascade ( Toll-like receptor signaling pathway (p38 cascade) ) Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, p38 cascade) ( Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, p38 cascade) ) TRAF6 mediated IRF7 activation in TLR7/8 or 9 signaling IRAK1 recruits IKK complex  P38 cascade ( Toll-like receptor signaling pathway (through ECSIT, MEKK1, MKKs, p38 cascade) ) Toll-like receptor signaling pathway (through JNK cascade)(Canonical) ( Toll-like receptor signaling pathway (through JNK cascade) ) TRAF6 mediated NF-kB activation  IRAK1 recruits IKK complex upon TLR7/8 or 9 stimulation  Regulation of target gene expression by NF-kB ( Toll-like receptor signaling pathway (trough NF-kappaB) ) Toll-like receptor signaling pathway (through LPS, TLR4, MyD88, IRAK, TAK1 and IKK-NF-kappaB cascade)(Canonical) ( Toll-like receptor signaling pathway (trough NF-kappaB) ) Viral dsRNA:TLR3:TRIF Complex Activates RIP1 IRAK2 mediated activation of TAK1 complex TAK1 activates NFkB by phosphorylation and activation of IKKs complex TRAF3-dependent IRF activation pathway  Viral dsRNA:TLR3:TRIF Complex Activates TBK1/IKK epsilon    93  2.10 Statistical Analysis Results were expressed as means ± standard error of mean (SEM).  Data were analyzed using a two-tailed Student’s t-test, the Chi square test (for proportions analysis) or the Log-rank test (for Kaplan-Meier plots) using the GraphPad Prism statistical program.                      94              3. TIRAP IS AN IMPORTANT MEDIATOR OF BONE MARROW FAILURE             95  3.1 TIRAP Levels are Upregulated in Del(5q) MDS Using the TargetScan algorithm (release 4.2a) (Lewis et al., 2003), TIRAP was found to be a predicted target of miR-145.  Using a luciferase reporter assay in which the firefly luciferase is under the control of the WT or mutant TIRAP 3’ UTR, miR-145 TIRAP was shown to be a valid target of miR-145 (Starczynowski et al., 2010).  Overexpression of miR-145 results in a reduction of TIRAP protein expression (Starczynowski et al., 2010).  Conversely, overexpression of a miR-145/146 decoy construct containing eight repeats of the miR-145 and miR-146 binding sites results in an increase in TIRAP and TRAF6 protein expression (Starczynowski et al., 2010).  We further confirmed TIRAP is a target of miR-145 by overexpression of a miR-145 decoy construct consisting of eight repeats of the miR-145 binding site only and found that it does indeed result in an upregulation of endogenous TIRAP levels (Figure 3.1b).  We hypothesized that the haploinsufficiency of the negative regulator of TIRAP, miR-145, in del(5q) MDS would result in aberrant expression of this protein.  We analyzed the results of a published microarray study performed on CD34+ cells from MDS patients and controls (Pellagatti et al., 2006) and found that TIRAP mRNA levels are increased 1.5-fold in del(5q) patients compared to patients diploid for chromosome 5 (Figure 3.1c).  However, it is well established that mRNA expression levels do not always correlate with protein levels.  Our lab has previously quantified the protein expression levels of TIRAP in MDS patients with del(5q) and diploid controls and found that TIRAP protein is indeed upregulated in del(5q) patients (Starczynowski et al., 2010).  This finding, along with other published reports of the involvement of TLR signaling components in the pathogenesis of MDS (Maratheftis et al., 2007; Rhyasen et al., 2013; Starczynowski et al., 2010) led us to further investigate the role of TIRAP upregulation in the pathogenesis of MDS.         96    Figure 3.1 TIRAP is a target of miR-145 and loss of miR-145 results in upregulation of endogenous TIRAP levels (A)Human TIRAP 3’ UTR showing location of predicted miRNA binding sites. (B) Schematic of miR-145 decoy construct, and Western blot of NIH-3T3 cells retrovirally transduced with miR-145 decoy or vector control. Cells were sorted for GFP expression and analyzed for protein expression. (C) TIRAP mRNA expression obtained from a gene expression study on CD34+ cells isolated from del(5q) and N(5q) marrows. N(5q): diploid at chormosome 5q     97  3.2 Overexpression of TIRAP in Murine Marrow Leads to a Marrow Failure Syndrome   To determine the role of elevated TIRAP levels in the hematopoietic system, we overexpressed TIRAP or an MIG vector control in 5-fluorouracil treated mouse hematopoietic cells by retroviral transduction, and reintroduced these marrow cells into lethally irradiated congenic recipient mice (Figure 3.2a).  Overexpression of TIRAP was confirmed by Western blot analysis (Figure 3.2b). In addition to the GFP+ transduced cells, mice were co-transplanted with 1 x 105 non-transduced helper cells.  These helper cells provide the transplant recipient with a sufficient amount of mature cells to allow survival following the initial irradiation period, and also act to create a chimeric transplant model.  We monitored the mice for the development of hematological abnormalities indicative of progression to AML or marrow failure by performing peripheral blood counts once a month following transplantation.      Figure 3.2 TIRAP overexpression and transplant scheme (A)Workflow for retroviral transduction of 5FU treated bone marrow cells and transplantation scheme. Donor mice were treated with 5-FU four days prior to marrow harvest to enrich for stem and progenitor cells.  BM cells were then co-cultured with GP+E86 retroviral producer cells to infect them with TIRAP or MIG-vector control. GFP+ sorted cells were then transplanted along with untransduced helper cells into lethally irradiated recipients. (B) Western blot analysis confirming TIRAP overexpression in transduced marrow. Cells were lysed 3 days post transduction.    98  Mice transplanted with TIRAP-transduced marrow exhibited significantly reduced survival compared to MIG controls (P < 0.0001) and become moribund as early as four weeks post-transplant (Figure 3.3a).  The median survival of TIRAP transplanted mice was 9.7 weeks, while median survival had not been achieved for MIG mice as far as 45 weeks post transplant.  Blood smears revealed an absence of leukocytes and increased reticulocytes in the peripheral blood of TIRAP transplanted mice compared to MIG controls (Figure 3.3b,c). No blast cells where observed in the marrows of TIRAP transplanted mice (Figure 3.3d,e) ruling out the possibility that mice were succumbing to leukemia, however, moribund mice did have enlarged spleens (0.34g ± 0.04g) compared to healthy controls (0.10g ± 0.01g) (Figure 3.3f). The bone marrow engraftment of TIRAP transplanted mice was not significantly different from MIG control transplanted mice (Figure 3.3g), however, the contribution of TIRAP expressing cells to peripheral blood output was significantly reduced (64.8% ± 4.2%) compared to healthy controls (85.0% ±7.4%) (Figure 3.3h). This was reflected in the complete blood counts as moribund mice displayed pancytopenia with significantly reduced cell numbers across the leukocyte lineages, platelets, and erythrocytes (Figure 3.3i-k). Complete blood counts at the time of death suggest that the cause of mortality is marrow failure due to an inability to sustain normal hematopoiesis.   Histological examination of bone sections and quantification of the cellularity of the marrows revealed a five-fold reduction in total cell number in TIRAP transplanted mice compared to vector control (Figure 3.4a-c). A concomitant reduction in total cell viability was observed in TIRAP marrows compared to MIG (Figure 3.4d). A breakdown of the contribution of GFP+ and GFP- components of the marrow shows that  while there is more apoptosis in the GFP- cells in both MIG and TIRAP transplanted mice, the reduction in overall survival of TIRAP transduced cells is attributed to the increase in apoptosis observed in the GFP+ fraction of the marrow (Figure 3.4e-f) 99      100  Figure 3.3 TIRAP overexpression in murine marrow causes increased mortality (A) Kaplan-Meier survival curves for primary transplanted mcie reconstituted with marrow transduced with MIG or TIRAP. (B,C) Giemsa staining of peripheral blood smears and (D,E) bone marrow cytospins from MIG and TIRAP transplanted mice at endpoint. (F) Spleen weights from MIG and TIRAP transplanted mice. Representative flow cytometry plots and quantification of GFP engraftment in (G) bone marrow and (H) peripheral blood  of transplanted mice at endpoint. (I-K) Hemoglobin (Hgb), granulocyte, and platelet (Plt) counts in moribund TIRAP-transduced mice and control MIG-transduced mice.  Error bars represent SEM.        Figure 3.4 TIRAP marrows display increased apoptosis and reduced cellularity (A) Representative hematoxylin and eosin staining of marrow sections from MIG (top) and TIRAP (bottom) transplanted mice. (C) Cellularity (live + dead) of marrows from mice transplanted with TIRAP-transduced (n = 15) or MIG-transduced (n = 6) marrow cells was determined by trypan blue staining. (D) Viability of BM cells from TIRAP (n = 4) or MIG (n = 4) transplanted mice 3-4 weeks post transplant. (E) Annexin-V staining on GFP- and (F) GFP+ fractions of whole bone marrow from mice transplanted with TIRAP or MIG-transduced cells 3-4 weeks post transplant. Data represents mean ± SEM.      101  The presence of splenomegaly in moribund TIRAP transplanted mice (Figure 3.3f), led us to ask whether ectopic expression of TIRAP alters the inherent ability of hematopoietic cells to migrate to the bone marrow following transplantation, leading to extramedullary hematopoiesis and contributing to the marrow failure observed. To exclude the possibility that TIRAP overexpression alters the ability of hematopoietic cells to home to the marrow, thus contributing to the hypocellularity of the marrow and the subsequent pancytopenia observed, we performed a homing assay to measure the number of transduced cells migrating to the marrow one day post-transplant.  In brief, mice were transplanted with TIRAP or MIG transduced BM cells and were sacrificed 22 hours post-transplantation. Bone marrow was collected and analyzed by flow cytometry for GFP chimerism.  We found no difference in the total number of cells migrating into the marrow at this time point (Figure 3.5a).  This assay, however, does not differentiate between the different types of cells (stem, progenitor, mature differentiated) homing to the marrow.  As only HSPCs, and not mature differentiated cells, contribute to the reconstitution of the hematopoietic system, we assessed the homing abilities of progenitor cells from TIRAP and MIG control transplanted mice by performing colony forming cell (CFC) assays on a mixture of transduced and helper cells prior to transplantation, and the cells recovered from the marrow one day post-transplant.  Colonies were counted under a fluorescent microscope and the ratio of GFP+ colonies to GFP- colonies was then compared to determine the homing efficiency of progenitor cells (Figure 3.5b).  No significant difference was observed in the homing efficiency of TIRAP transduced progenitor cells compared to MIG vector control (Figure 3.5) allowing us to rule out the possibility that a homing defect exists in hematopoietic cells overexpressing TIRAP.  Furthermore, these findings led us to conclude that the peripheral pancytopenia and early lethality observed in TIRAP transplanted mice is due to an inherent defect in the ability of TIRAP expressing cells to 102  reconstitute the hematopoietic system following irradiation after homing to the marrow has occurred.            Figure 3.5 TIRAP expression does not alter hematopoietic progenitor homing to marrow (A)Flow cytometric analysis of BM collected from mice transplanted with MIG (n = 4) or TIRAP (n = 4) transduced marrow 22 hours post transplant and quantification of the total number of transduced cells homed to the marrow/mouse. (B) Schematic illustrating progenitor homing assay and quantification of progenitor homing efficiency.  Homing efficiency was calculated using the following formula: 	 =%	()		%	()		           103  Immunophenotyping for markers of lineage-restricted hematopoietic cells from moribund mice shows significant changes in the composition of the bone marrow in all three hematopoietic compartments.  The TIRAP-transduced compartment was characterized by a shift in the ratio of granulocytes (Gr-1+) to macrophages (Mac-1+), with TIRAP-transplanted mice showing a significant reduction in Gr-1+ cells and a concomitant significant increase in Mac-1+ cells (Figure 3.6).  Aberrant hematopoiesis is also evident in the lymphoid compartment where there is a significant reduction in CD19+ B-cells in TIRAP-transduced bone marrow transplants compared to MIG controls (Figure 3.6).  Interestingly, a similar finding has been previously reported in low-risk MDS patients (Sternberg et al., 2005).  The changes in the erythroid lineage are most evident in the CD71+Ter119- late proerythroblast and the CD71+ Ter119+ erythroblast.  Both these populations are significantly reduced in TIRAP-transduced cells compared to MIG-transduced (Figure 3.6).  Interestingly, the effects of ectopic TIRAP expression on normal hematopoiesis are both cell autonomous and cell non-autonomous.  Examination of the non-transduced fraction of the marrow shows that the aberrant hematopoiesis seen in TIRAP-transduced cells is also reflected in the wild-type helper cell population.  There is a nearly 50% reduction in the Mac1/Gr double-positive population as well as a reduction in the Gr1+ population (Figure 3.6).  A similar reduction in CD19+ B cells is observed in the non-transduced population, with a concomitant increase in CD3+ T cells (Figure 3.6).  Similar to the GFP+ population, non-transduced marrow cells from TIRAP transplanted mice show reductions in the late proerythroblasts (CD71+Ter119-) and erythroblasts (CD71+Ter119+).  This is accompanied by a concomitant increase in CD71-Ter119+ erythrocytes and might be a compensatory mechanism to correct the erythroid defect in the GFP+ marrow cells.  Taken together, overexpression of TIRAP results in both a cell intrinsic defect in hematopoiesis, as well as a suppression of hematopoiesis in normal cell populations as indicated by a reduction in peripheral blood counts despite co-transplantation of sufficient wild type cells to reconstitute hematopoiesis.    104    Figure 3.6 Immunophenotyping of bone marrow at termination of experiment Immunophenotyping of (A,E,I) myeloid, (B,F,J) lymphoid, (C,G,K) erythroid and (D,H,L) megakaryocytic lineages in marrows of mice transplanted with MIG (n = 3) or TIRAP-transduced (n = 6) cells at 3-4 weeks post transplant. Percentage BM engraftment was determined based on the percent of cells staining positive for each phenotypic marker listed.         105  The inability of mice to restore peripheral blood counts following transplantation led us to question whether the defect in mice transplanted with TIRAP transplanted marrow is due solely to the reduced survival of mature differentiated cells or whether there is a block in differentiation at the level of the more primitive progenitor cells.  Because stem cell frequency can only be measured after long term engraftment (which is estimated to be 16 weeks in mice) and the median survival of TIRAP transplanted mice is less than 10 weeks, we utilized the LTC-IC assay as a surrogate for estimating stem cell frequency TIRAP- and MIG-transduced marrow (Figure 3.7a).  We found no significant difference in HSC frequency between TIRAP-and MIG-transduced cells (1/11.7 and 1/16.8 respectively; P = 0.162) (Figure 3.7b), ruling out the possibility that ectopic TIRAP expression reduces the number of stem cells present.  106   Figure 3.7 TIRAP overexpression does not alter stem cell frequency (A) Schematic representation of LTC-IC procedure. (B) Estimation of stem cell frequency in TIRAP- and MIG-transduced marrow.   Next, we quantified the absolute number of progenitor cells (LSK: Lin-, c-Kit+, Sca-1+; GMP: Lin-, c-Kit+, Sca-1-, CD34+, CD16/32+; CMP: Lin-, c-Kit+, Sca-1-, CD34+, CD16/32-; MEP: Lin-, c-Kit+, Sca-1-, CD34-, CD16/32-) in the transplanted mice and determined the amount of apoptosis and proliferation within each compartment.  We found significant reductions in the total number of viable progenitors at the CMP and MEP level in the TIRAP-transduced marrow of transplanted mice (Figure 3.8b).  The reduction in cell number in the CMPs and MEPs is not caused by an increase in apoptosis as measured by activated Caspase 3 staining (Figure 3.8c) suggesting that there is increased differentiation toward the GMP progenitors at the expense of MEPs, accounting for the 107  peripheral anemia and thrombocytopenia observed.  Interestingly, although differentiation is driven toward the granulocyte/macrophage lineage, these cells are more apoptotic (Figure 3.8c), accounting for the leukopenia seen in the peripheral blood.  There were no differences observed in proliferation of the different progenitor populations between TIRAP and MIG transplanted mice (Figure 3.8d).  As mice began showing the signs of marrow failure, reductions in the total viable cell counts across all progenitor populations and a concomitant increase in apoptosis were noted (Figure 3.8).   Looking at the co-transplanted WT progenitor populations there are reduced progenitor numbers in the GMP compartment (Figure 3.8e), as well as increases in apoptosis in the GMP and MEP population (Figure 3.8f), suggesting that TIRAP overexpression leads to a block in normal differentiation via both intrinsic and extrinsic mechanisms.  Although there is no difference in the proliferation of the different progenitor populations prior to the onset of the signs of marrow failure, a 40% reduction in LSK proliferation is seen in moribund mice (Figure 3.8g).  108    Figure 3.8 TIRAP overexpression alters hematopoietic progenitor cell populations (A) Gating strategy for hematopoietic progenitors.  (B,E) Enumeration of progenitor cells (C,F) apoptosis and (D,G) and proliferation in marrows of MIG (n = 4) or TIRAP-transplanted (n = 5) mice 4 weeks post-transplant.    109              4. TIRAP OVEREXPRESSION ACTIVATES NON-CANONICAL SIGNALING PATHWAYS            110  4.1 IFNγ and IL-10 Expression is Induced Following TIRAP Overexpression It is well known that cytokines play a critical role in the pathogenesis of MDS.  Inflammatory cytokines such as IL-1β and TNFα have been shown to be upregulated in MDS (Allampallam et al., 1999; Gersuk et al., 1998; Kornblau et al., 2010; Mundle et al., 1996; Tsimberidou et al., 2008).  IL-10 levels have been shown to be elevated in high-risk MDS patients (Kordasti et al., 2009; Tsimberidou et al., 2008), and IFNγ has been shown to be overexpressed in the marrows of MDS patients compared to AML patients and normal controls (Kitagawa et al., 1997; Kordasti et al., 2009). Furthermore, one study has shown the interferon signaling pathway to be the most significantly upregulated pathway in MDS (Pellagatti et al., 2010).  Previous work from our lab suggests that IL-6 plays an important role in hematopoietic cytopenias as well as thrombocytosis (Starczynowski et al., 2010).  In order to identify soluble factors which may be playing a role in the non-autonomous suppression of normal hematopoiesis in the TIRAP-overexpressing transplant model, we performed cytokine expression profiling of TIRAP-expressing marrow and MIG controls to determine the involvement of cytokines in TIRAP-mediated marrow failure.  IL-1β, IL-10, TNFα, IFNγ, and IP-10 transcript levels were all increased in TIRAP-expressing marrow (P < 0.05), with IL-10 and IFNγ showing the greatest induction (17.1 and 17.8 fold respectively) (Figure 4.1). Surprisingly, IL-6, a known downstream target of TLR-4 signaling and NFκB activation, was not induced following TIRAP overexpression (Figure 4.1).  This is contrary to what we have previously shown following overexpression of the downstream signaling molecule TRAF6 (Starczynowski and Karsan, 2010). 111    Figure 4.1 RT-qPCR screen of cytokines and chemokines involved in MDS pathogenesis  Significant increase in IL-1β, IL-10, TNFα, IFNγ, and IP-10 following TIRAP expression in murine marrow cells.  * P < 0.05.  P-value was determined by Student's t-test. Data represents the mean ± SEM of 3-6 biological replicates.    Since IL-10 and IFNγ showed the greatest increase following TIRAP overexpression we wanted to assess the clinical significance of upregulation of these two genes.  We performed Gene Set Enrichment Analysis on two published microarray datasets looking at gene expression in CD34+ cells from MDS patients and healthy controls (Pellagatti et al., 2010; Pellagatti et al., 2006).  Using a list of IFN-stimulated genes and IL-10 signaling pathway genes (Table 2.2 and Table 2.3) we found a significant enrichment of an IFNγ signature in low-risk MDS patients compared to healthy controls in both datasets, and a significant enrichment of the IL-10 signaling pathway in one of the two datasets (Figure 4.2).  In line with our findings regarding the absence of IL-6 induction following TIRAP overexpression, the NFκB signaling pathway was not found to be enriched in low-risk MDS compared to healthy controls (Figure 4.2). In this data set there was a positive correlation between TIRAP expression levels and IFNγ expression levels, but no correlation with IL-10 (Figure 4.2).   112   Figure 4.2 Gene set enrichment analysis (A) GSEA profiles from low-risk MDS marrow compared to normal marrow generated from  list of IFNγ stimulated genes, (B) IL-10 pathway genes and (C) NF-κB pathway genes. (D) Correlation between IFNγ gene expression and TIRAP gene expression in low-risk MDS patients. (E) Correlation between IL-10 gene expression and TIRAP gene expression in low-risk MDS patients.   113  4.2 Canonical TLR Signaling Genes are Hypermethylated in MDS The enrichment of IFNγ and IL-10 signaling pathways and the lack of enrichment of the canonical NFκB pathway in low-risk MDS marrow compared to normal led us to question whether this is a common finding in MDS. To test whether the NFκB signaling pathway is activated through TLR signaling in MDS, we analyzed the results of a published promoter methylation array comparing bone marrow cells from MDS patients to normal CD34+ cells. We generated a list of 105 TLR pathway genes (canonical TLR signaling genes and negative regulators of the TLR pathway) as well as interacting genes (Figure 2.3) and determined the methylation status of their promoters. Analysis of the methylation status of these 105 promoters revealed 38 differentially methylated promoters in MDS patients compared to CD34+ normal controls (Table 4.1 and Figure 4.3).  Of the 38 differentially methylated promoters, 37 were found to be hypermethylated in MDS (Figure 4.3). Among the hyper methylated promoters were key genes in the TLR-4 signaling pathway such as CD14, MyD88, IRAK4, IKKβ, and NFκB (Table 4.1 and Figure 4.3).  Furthermore, negative regulators of the TLR signaling pathway such as SIGIRR, TOLLIP and IRAK3 were not found to be hypermethylated, suggesting an alternate mechanism for the inhibition of MyD88-depedent TLR signaling. Interestingly, the promoter for TIRAP was not hypermethylated in MDS. This, combined with the hypermethylation of key potentiators of the TLR signaling pathway in the absence of hypermethylation of negative regulators of the pathway, suggests that gene expression changes following the upregulation of TIRAP in MDS may result from the activation of a non-canonical signaling pathway.     114  Table 4.1 105 genes analyzed in promoter methylation array 105 genes analyzed in promoter methylation array ARRB1 HSPA1B HSPBAP1 IRAK3 MAP3K1 PIK3CA TIRAP ARRB2 HSPA1L HSPBP1 IRAK4 MAP3K14 PPARA TLR1 BCL10 HSPA2 HSPC157 IREB2 MAP3K7IP2 PPP4C TLR2 BIRC2 HSPA4 HSPC159 IRF2 MAP3K8 PPP4R1 TLR3 BTK HSPA4L HSPD1 IRF2BP1 MAPK1 PRKRA TLR7 CD14 HSPA5 IFNA13 IRF2BP2 MAPK10 REL TLR8 CD180 HSPA6 IFNAR1 IRF3 MAPK14 RELA TLR9 CD40 HSPA8 IFNAR2 IRF5 MAPK3 RIPK2 TMED7-TICAM2 CREBBP HSPA9 IFNGR2 IRF7 MYD88 RIPK3 TNFAIP3 CYLD HSPB11 IKBKB LBP NFKB1 SARM1 TOLLIP EIF2AK2 HSPB2 IKBKE LY86 NFKBIA SIGIRR TRAF2 FADD HSPB6 IKBKG MALT1 NFKBIB SIKE1 TRAF3 HMGB1 HSPB7 IL10 MAP2K1 NR2C2 SRC TRAF6 HRAS HSPB8 IL18 MAP2K4 PELI2 SYK UBB HSPA1A HSPB9 IRAK1 MAP2K7 PELI3 TBK1 ZBP1 Bold = Differentially methylated 115  1 Figure 4.3 Promoter methylation of TLR-related genes is increased in MDS patients                                                            1 Kanehisa, M.; “Post-genome Informatics”, Oxford University Press (2000) 116    To address the question of whether TIRAP overexpression induces changes in gene expression through an alternative signaling pathway, we assessed the ability of TIRAP to induce IL-10 and IFNγ following blockade of the TLR pathway downstream of TIRAP at the level of MyD88 and TRAF6.  We co-expressed TIRAP or vector control with either a TRAF6-dominant negative (TRAF6-DN) or one of two MyD88-dominant negative constructs.  The TRAF6-DN construct is a C-terminus construct consisting of the TRAF domain and the coiled-coil only.   This construct is able to bind and dimerize, but in the absence of the RING domain and the Zn finger domain it is unable to ubiquinate and activate signaling downstream of TRAF6 (Hull et al., 2002; Raschi et al., 2003).  The MyD88-DN constructs used were either an N-terminus deletion consisting of the TIR domain only, or a C-terminus deletion consisting of the death domain only.  Both these constructs have been shown to function as dominant negatives (Medzhitov et al., 1998; Raschi et al., 2003).  We found that blockade of the TLR signaling pathway at the level of MyD88 or TRAF6 did not inhibit induction of IL-10 or IFNγ, but rather augments the IL-10 and IFNγ signals (Figure 4.4) suggesting that TIRAP signals through a non-canonical pathway in order to activate IL-10 and IFNγ production.    117   Figure 4.4 IL-10 and IFNγ production occurs via non-canonical signaling pathway (A) Structure of WT and dominant negative TRAF6. (B,C) qRT-PCR for IL-10 and IFNγ  following TIRAP overexpression and knockdown of TRAF6 (D) qRT-PCR for IL-10 and IFNγ  following TIRAP overexpression and knockdown of MyD88. Data represents mean ± SEM of 2-4  biological replicates. * P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001 118  4.3 IFNγ is Responsible for TIRAP-mediated Bone Marrow Failure Since IL-10 and IFNγ showed the greatest induction following TIRAP overexpression, and because these two signaling pathways are enriched in low-risk MDS patients compared to normal, we chose to focus the rest of the investigation on the role of these two cytokines in the study of TIRAP-induced marrow failure.  We obtained IL-10-/- and IFNγ-/- mice from the Jackson Laboratories and confirmed knock out of these two genes by qPCR. IL-10 transcript levels were not detectable in IL-10 -/- BM cells, and IFNγ transcript levels were reduced by 50% (Figure 4.5a). Furthermore, we confirmed that knockout of either one of these genes alone does not affect expression of the other nor does it affect expression of endogenous mouse TIRAP at steady state (Figure 4.5a). We also assessed the induction of IL-10 and IFNγ in the two knockout mouse strains following transduction with TIRAP or MIG control.  Loss of either one of these cytokines did not attenuate the induction of the other cytokine in the respective mouse strains (Figure 4.5b).   119   Figure 4.5 Expression levels of IL-10 and IFNγ in WT and knockout BM cells (A)RT-qPCR for IL-10, IFNγ and mouse TIRAP in whole bone marrow from wild type, IL-10-/- and IFNγ-/-. (B) RT-qPCR for IL-10 and IFNγ in IL-10-/- and IFNγ-/- bone marrow cells transduced with TIRAP or MIG control. Data represents mean ± SEM of three biological replicates (ND = not detected)    To determine the role IL-10 and IFNγ play in the marrow failure associated with increased TIRAP expression, we performed transplants as described earlier. In brief, bone marrow from IL-10-/- and IFNγ-/- mice was transduced to overexpress TIRAP or MIG vector control and was transplanted into lethally-irradiated syngenic Pep3b recipients.  Mice transplanted with IL-10 -/- cells transduced with TIRAP displayed significant reductions in white blood cell counts and hemoglobin levels 4 weeks post-transplant (Figure 4.6).  This reduced peripheral blood output is 120  maintained even after long term engraftment has occurred at 28 weeks post-transplant.  In contrast, transplantation of IFNγ -/- marrow transduced with TIRAP restored white blood cell and hemoglobin levels at 4 weeks post-transplant, and this is maintained until after long-term engraftment has occurred (Figure 4.6), suggesting a role for IFNγ but not IL-10 in TIRAP-induced anemia and neutropenia.  Platelet counts in mice transplanted with TIRAP transduced  IL-10 -/- or IFNγ -/- marrow were significantly reduced 4 weeks post transplant, however the reduction is greater in the IL-10 -/- transplanted mice (Figure 4.6).  After longterm engraftment has occurred, the platelet defect is normalized in both transplant backgrounds, however more so in the IFNγ -/- background, suggesting that IFNγ and to a lesser extent IL-10 play roles in the thrombocytopenia observed (Figure 4.6).    Figure 4.6 Peripheral blood counts of IL-10 -/- and IFNγ -/- transplanted mice White blood cell, hemoglobin, and platelet counts of mice transplanted with (A) IL-10 KO or  (B) IFNγ KO BM  transduced with TIRAP or MIG vector control at four weeks post transplant  and after long term engraftment at 28 weeks post transplant.    121  Loss of IL-10 does not improve the overall survival of mice transplanted with TIRAP-transduced marrow (Figure 4.7a).  Similar to mice transplanted with wild type TIRAP-transduced marrow, mice transplanted with TIRAP-transduced IL-10 -/- marrow succumb to the symptoms of bone marrow failure 4 weeks after transplant.  A second wave of mortality is observed starting after 24 weeks due to a myeloproliferative disorder (Figure 4.7a,c-e). Transplantation of IFNγ -/- marrow overexpressing TIRAP delayed the lethality observed in WT and IL-10-/- transplants.  Interestingly, the cause of death in this case was not marrow failure, but rather a myeloproliferative disorder (Figure 4.7b,f-h).  Analysis of the cause of death revealed no significant difference between mice transplanted with WT and IL-10 -/- TIRAP-transduced marrow cells, but a significant improvement in IFNγ -/- vs. IL-10 -/- and IFNγ -/- vs. WT (P < 0.05 and P < 0.0001 respectively) (Figure 4.7i). 122   123  Figure 4.7 Loss of IFNγ and IL-10 promote development of myeloproliferative disorders (A,B)Kaplan-Meier survival curves for primary transplanted mice reconstituted with BM from IL-10 KO or IFNg KO mice transduced with MIG or TIRAP. Median survival for TIRAP transplanted mice reconstituted with WT, IL-10 KO or IFNγ KO BM is 9.7 weeks, 27.2 weeks, and undefined, respectively. WT vs. IL-10 KO, P = 0.9197; WT vs. IFNγ KO, P = 0.0218. (C,F) Hemoglobin levels, (D,G) granulocyte counts, and (E,H) platelet counts in moribund mice transplanted with IL-10 KO or IFNγKO BM trandsuced with MIG or TIRAP. (I) Chi-square analysis of the difference in cause of mortality between mice transplanted with WT, IL-10-/- and IFNγ-/- TIRAP-transduced marrow. (J) GSEA showing an enrichment in IFNγ signature in MDS patients compared to AML  4.4 TIRAP Overexpression Enhances Survival of Hematopoietic Cells Ex Vivo IFNγ production by TIRAP-expressing cells and in our transplant model can have paracrine effects on both hematopoietic cells as well as non-hematopoietically derived cell types present in the bone marrow microenvironment.  To elucidate whether the elevated apoptosis leading to marrow failure observed following transplantation with TIRAP-expressing cells is due to the direct effects of IFNγ on hematopoietic cells, we devised an ex vivo culture system to evaluate survival of transduced bone marrow cells in the absence of the bone marrow microenvironment. 5-fluorouracil treated murine marrow was retrovirally transduced with TIRAP or MIG vector control. GFP positive cells were flow sorted and cultured overnight in serum free media supplemented with 1% BSA.  Annexin-V/PI staining was performed to assess survival under these conditions. It was found that cells transduced with TIRAP have enhanced survival compared to vector control (56.47% and 39.64% respectively) (Figure 4.8). This suggest that in the absence of extrinsic factors, TIRAP expression may provide cells with a survival advantage and can contribute to clonal dominance of the MDS clone in patients.   124   Figure 4.8 TIRAP overexpression enhances survival of BM cells in vitro in an autonomous and non-autonomous manner (A) Representative flow plots displaying AnnexinV/PI staining in transduced and WT populations in ex vivo coculture experiments. (B) Quantification of cell viability (Annexin V/PI double negative) following overnight serum starvation. Data shown is the mean ± SEM of thee biological replicates.  125  Next, to test whether TIRAP-expressing cells are capable of inducing apoptosis and suppressing normal hematopoietic cells, we cocultured non-transduced WT bone marrow cells with TIRAP or MIG-transduced cells overnight in serum-free medium supplemented with 1% BSA and assessed survival by Annexin-V/PI staining.  The differences in the forward scatter vs. side scatter profiles of transduced and non-transduced WT cells were used to differentiate between these two cell populations in our analysis. Using the gating strategy described earlier in Figure 2.2, we were able to determine the percent survival (Annexin-V- PI-) of both transduced and non-transduced cells under these co-culture conditions.  Interestingly, we found that co-culture of WT cells with either TIRAP transduced or MIG transduced cells enhances survival of the WT cells to the same level as the transduced cells they are co-cultured with (Figure 4.8).  These findings are in contrast to the in vivo findings of increased overall cell death in the TIRAP transplant model and point to a possible role for bone marrow stromal cells in apoptosis leading to marrow failure.     4.5 IFNγ Causes Seconday Effects in HSC Niche The findings presented in Chapter 3 suggest that upregulation of TIRAP has both cell autonomous and cell non-autonomous effects on hematopoiesis leading to marrow suppression.  Furthermore, data presented in earlier sections of this chapter suggest that this marrow suppression is mediated by upregulation of several cytokines, namely IL-10 and IFNγ, which can act in both an autocrine and paracrine manner on hematopoietic elements in the marrow. However, the data presented in section 4.4 suggests that the actions of IL-10 and IFNγ leading to marrow failure are not due to their effects directly on hematopoietic cells, but rather, due to an effect on an intermediary cell type present in the bone marrow microenvironment and absent in ex vivo culturing systems. Thus, modification of the functional hematopoietic stem cell niche may be 126  responsible for the impaired hematopoietic reconstitution following transplantation with TIRAP expressing marrow cells leading to bone marrow failure.    To address the question of whether the bone marrow microenvironment is the target of TIRAP induced cytokine production, we devised a transplantation assay to measure the ability of hematopoietic cells to engraft in a microenvironment conditioned by TIRAP or MIG (Figure 4.9).  In brief, primary transplants were performed as previously described, with the following modifications: 300,000 TIRAP- or MIG-transduced CD45.2+ cells were transplanted into lethally irradiated syngeneic recipients, along with 100,000 CD45.2+ helper cells.  Cells were allowed to engraft and condition the marrows of the transplant recipients for 3 weeks.  This time point was chosen as it provides enough time for the conditioning to occur without the recipients succumbing to marrow failure.  Following the three-week conditioning period, the marrows of the primary transplant recipients was myeloablated using the chemotherapeutic drug busulfan.  A dose of 125 mg/kg administered over five days is equivalent to 10 Gy lethal irradiation (Lewis et al., 2013),     however, as mice in our model were subjected to lethal irradiation just three weeks prior, only 50 mg/kg busulfan was used to limit mortality. Three days after the final busulfan dose, mice were transplanted with 200,000 MIG (MIG primary recipient) or MIY (TIRAP primary recipient) transduced CD45.1+ bone marrow cells (Figure 4.9). At the time of the competitive transplant, the amount of residual CD45.2+ GFP+ cells remaining from the primary transplant were measured to determine the effectiveness of busulfan treatment. Less than 10% of the total marrow consisted of CD45.2+ GFP+ cells (8.8% in MIG-conditioned mice and 4.8% in TIRAP conditioned mice) (Figure 4.10).               127      Figure 4.9 Consecutive bone marrow transplant experimental design 5-FU treated CD45.2+ BM was retrovirally transduced to express TIRAP or MIG control.  300,000 GFP+ cells were transplanted along with 100,000 helper cells into syngeneic  recipients.  Transplanted cells were allowed to condition the marrow for three weeks prior  to myeloablation with Busulfan.  Consecutive transplants with MIG or MIY transduced  CD45.1+ marrow were performed and cells were allowed to engraft for two weeks.   Following this engraftment period bone marrow cells from TIRAP and MIG conditioned  marrows were collected and combined in order to perform competitive transplants.   Competitive transplant recipients were transplanted with one mouse equivalent of bone  marrow.      128   Figure 4.10 50 mg/kg busulfan is sufficient to eradicate the majority of cells from a hematopoietic transplant 3 weeks post transplantation.  Bone marrow was collected from consecutive transplant recipients two weeks after  transplantation.  Cells were stained for the markers CD45.1 and CD45.2 and the residual GFP content of CD45.2+ cells was determined by flow cytometry.  Hematopoietic cells from the consecutive bone marrow transplant were allowed to engraft for a two weeks period. Following this two week engraftment period, whole bone marrow was collected from the MIG-conditioned and TIRAP-conditioned mice and combined together at a 1 to 1 ratio. One mouse equivalent of the mixed marrow was then transplanted into lethally irradiated CD45.2+ recipients.  This system allows for a direct comparison between engraftment capabilities of GFP+ cells from MIG-conditioned marrows (GFP+ CD45.1+) and MIY cells from TIRAP-conditioned marrows (YFP+ CD45.1+). The contribution of GFP+ CD45.1+ and YFP+ CD45.1+ cells to the hematopoietic reconstitution in peripheral blood and bone marrow was determined (Figure 4.11). At five weeks post-transplant, the myeloid contribution of hematopoietic cells derived from TIRAP-conditioned marrows (YFP+ CD45.1+) was significantly lower than that of cells derived from MIG-  129   130  Figure 4.11 TIRAP conditioned niche affects HSC engraftment (A)Gating strategy for analysis of GFP and YFP chimerism in competitive transplants. (B-D) Representative flow plots showing the contribution of cells from TIRAP and MIG-conditioned marrow to myeloid, T- and B-lymphocyte lineage reconstitution in competitive transplants. (E,F) Peripheral blood and BM chimerism at 5 weeks and 11 weeks respectively post competitive transplant (G,H) Residual GFP+ CD45.2+ cells from primary transplant in peripheral blood and BM at 5 weeks and 11 weeks respectively post competitive transplant.       conditioned marrows (GFP+ CD45.1+) (14.0% compared to 86.0%; P < 0.0001). This is in line with the findings presented in Chapter 3 that show reduced myeloid output in mice transplanted with cells overexpressing TIRAP. Furthermore, both the T and B lymphoid peripheral blood output of cells from TIRAP-conditioned marrows is significantly more than that of MIG-conditioned marrow at 5 weeks post transplant (Figure 4.11). These differences are not due to outgrowth of residual TIRAP expressing cells from the primary transplant, as the total CD45.2+ GFP+ cells in the peripheral blood at this time point is less than 6% (Figure 4.11). At later times post transplant when we looked at the bone marrow chimerism between GFP+ CD45.1+ cells from MIG-conditioned marrows and YFP+ CD45.1+ cells from TIRAP-conditioned marrows, we saw a similar trend towards a significant reduction in myeloid output of cells derived from TIRAP-conditioned mice (4.1% compared to 95.9%; P < 0.0001) (Figure 4.11). Again, residual CD45.2+ GFP+ bone marrow cells from the primary transplant were present at approximately 5% of total BM (Figure 4.11). The bone marrow chimerism in the lymphoid compartment, however, was the opposite of what was observed in peripheral blood at early time points after transplantation. The contribution of hematopoietic cells derived from TIRAP-conditioned marrows to the T and B lymphocyte compartments in the bone marrow was significantly reduced compared to MIG-conditioned marrow cells (15.2% vs 84.8%, P < 0.0001; and 28.7% vs. 71.3%, P < 0.0001 respectively) (Figure 4.11). These findings suggest that TIRAP overexpression in hematopoietic cells has an effect on the bone marrow microenvironment rendering it unable to functionally support hematopoiesis.       131    We next wanted to rule out the possibility that the low-grade residual hematopoietic cell derived from the primary transplant may be influencing the bone marrow niche in the consecutive and competitive transplants.  To do this we modified the ratio of TIRAP or MIG transduced cells to helper cells, and also extended the time in culture prior to transplantation in order to achieve lower engraftment (Figure 4.12a).  We monitored the mice for the signs of bone marrow failure but found no significant difference in the hemoglobin, leukocyte, or platelet levels over the course of 16 weeks (Figure 4.12b-d), demonstrating that low levels of engraftment of TIRAP transduced cells is not sufficient to induce marrow failure.  This allowed us to conclude that the observed engraftment deficiencies in CD45.1+ YFP+ cells from TIRAP-conditioned marrows are due to the effect of a dysfunctional niche and not due to the direct effect of TIRAP expressing cells.    132   Figure 4.12 Low engraftment of TIRAP expressing cells  does not result in marrow failure (A) Peripheral blood engraftment, (B) hemoglobin, (C) leukocyte, and (D) platelet levels in mice transplanted with TIRAP (n = 14) or MIG (n = 11) transduced bone marrow. To achieve lower peripheral blood engraftment transduced bone marrow cells were cultured for 3 days longer than in the original bone marrow transplantation scheme prior to transplantation.  Mice were transplanted with 100,000 transduced cells and 100,000 helper cells.    4.6 TIRAP Overexpression Results in a Block in Osteoclastogenesis The hematopoietic stem cell niche is comprised of a large variety of cells types that may be the target of IFNγ or IL-10 production by TIRAP-expressing cells and other cells recruited to the bone marrow. Published reports have shown that IFNγ produced by T-cells inhibits bone resorbing osteoclasts (Takayanagi et al., 2000). As discussed earlier in the introductory chapter of this 133  dissertation, osteoclasts have been shown to be important in the maintenance of the HSC niche (Lymperi et al., 2011; Mansour et al., 2012), thus we postulated that the marrow failure observed following TIRAP overexpression may be secondary to the effects of IFNγ on components of the hematopoietic stem cell niche, such as osteoclasts.  To address this question, we utilized the monocyte/macrophage cell line, RAW264.7, which has the potential to undergo osteoclastic differentiation upon stimulation with RANKL.  As proof-of-principle we tested the ability of RANKL to induce differentiation towards the osteoclast lineage and the ability of IFNγ to inhibit this differentiation in the presence of RANKL. Stimulation of monocytic cells with RANKL results in upregulation of the osteoclast marker Tartrate Resistant Acid Phosphatase (TRAP) and fusion of osteoclast precursor cells into large multinucleated polykaryons. Osteoclastogenesis was measured by the presence of TRAP+ large multinucleated giant cells. We found that a concentration as low as 1ng/ml IFNγ was capable of inhibiting osteoclastogenesis in RAW264.7 cells in the presence of 50 ng/ml RANKL (Figure 4.13).    Figure 4.13 IFNγ inhibits osteoclastogenesis in the monocytic cell line RAW264.7 RAW264.7 cells cultured for 7-10 days in α-MEM or α-MEM supplemented with 50 ng/ml RANKL with or without IFNγ (1ng/ml). Osteoclasts are identified as large, red, TRAP positive cells (arrows).    134  Next, to test the ability of TIRAP transduced hematopoietic cells to inhibit osteoclastogenesis we set up a coculture system with RAW264.7 cells and TIRAP or MIG-transduced bone marrow cells. After 7-10 days in culture, cells were fixed, stained for TRAP, and the percent of total area staining TRAP positive was quantified. We found that co-culture of RAW264.7 cells with MIG transduced cells resulted in 27.8% TRAP+ area (Figure 4.14). Co-culture of RAW264.7 cells with TIRAP expressing bone marrow cells in the presence of RANKL resulted in a significant inhibition in differentiation as demonstrated by the complete absence of TRAP staining (Figure 4.14).    Figure 4.14 TIRAP expressing bone marrow cells block osteoclastogenesis in the RAW264.7 cell line (A)TRAP stained RAW264.7 cells after 7-10 days in co-culture with MIG or TIRAP-tranduced bone marrow cells. Cells outlined in yellow represent cells that have undergone differentiation into osteoclasts. (B) Quantification of the percent area staining TRAP+ (ND, not detected)      Next, we wanted to investigate whether the observed inhibition of is also reflected in MDS patients. We obtained normal and del(5q) marrow sections under a protocol approved by the Research Ethics Board of the University of British Columbia and performed immunohistochemical staining for TRAP toidentified as DAB+ cells at the boundaries between the endosteum and the bone marrow. TRAP+ osteoclasts were detected at higher frequencies in biopsy specimens from healthy controls compared to del(5q) MDS patientsmay play a role in the pathogenesis of MDS in vivo. Figure 4.15 Osteoclasts are reduced in del(5q) MDS patients(A)Immunohistochemical staining of bone marrow biopsy sections from normal and del(5q)  MDS patient for the osteoclast marker TRAP. Arrows point to TRAP+ osteoclasts at the  endosteal surface of trabecular bone. (B) Quantification of osteoclast in normal and del(5q)  biopsy sections normalized to the length of bone surface.    osteoclast differentiation  stain the osteoclasts.  Osteoclasts were  (Figure 4.15), suggesting that regulation of osteoclastogenesis    135  136             5. SUMMARY, DISCUSSION, AND FUTURE DIRECTIONS            137  5.1 TIRAP Overexpression as a Model of Marrow Failure in MDS Much effort has been invested into generating animal models of the myelodysplastic syndromes in order to further our understanding of the processes underlying the pathogenesis of MDS. Despite all of these efforts, no single model has been able to fully recapitulate all the characteristic features of dysplastic hematopoiesis observed in MDS and much remains to be understood about the molecular and cellular mechanisms regulating progression to marrow failure or acute myeloid leukemia and leading to clonal dominance of MDS stem cells over their normal hematopoietic counterparts. The work presented in this dissertation aimed to elucidate the importance of innate immune regulation in the development of MDS-related marrow failure. Here we have presented a new model of MDS based on the retroviral overexpression of the innate immune signaling adaptor protein TIRAP. We have shown that overexpression of this protein results in the development of a very rapid and severe marrow failure syndrome characterized by pancytopenia and increased bone marrow apoptosis in both mature differentiated cells as well as progenitor cell populations. Furthermore, we have shown that the effects are cell extrinsic affecting both the TIRAP transduced cells as well as the WT cells co-transplanted at the same time. This led us to identify IFNγ and, to a lesser extent IL-10 as  soluble mediators of marrow failure through their action in the bone marrow stem cell niche. Furthermore, we have been able to show a clinical correlation between IFNγ signaling, low-risk MDS, and a block in progression to AML.   5.1.1 Stem/progenitor cell involvement in TIRAP mediated marrow failure While the model we have developed fulfills the criteria for the diagnosis of MDS in mice (Kogan et al., 2002), some features of the disease have not been reproduced. For one, the rapid 138  onset of marrow failure following transplantation is not observed in human manifestations of MDS in which patients survive for many years with an indolent form of disease before progression to AML or bone marrow failure. Such rapid disease progression leads one to postulate that the results seen following TIRAP overexpression are not due to an intrinsic defect in the hematopoietic stem cell, limiting its maintenance and self-renewal abilities, but rather due to a defect at the level of the transit amplifying progenitor cells that are responsible for repopulating the marrow immediately following transplantation. This idea is supported by our findings in Chapter 3 that overexpression of TIRAP in murine bone marrow cells does not alter the frequency of HSPCs compared to MIG vector control. We also found no significant difference in the size of the LSK population which contains both long-term and short-term HSCs. Researchers have reported similar findings that the HSC frequencies in low-risk MDS patients are not significantly different from age-matched non-anemic normal controls (Pang et al., 2013). This is in contrast to high-risk MDS, where the pool of primitive LT-HSCs is significantly increased (Will et al., 2012). Since neither the stem cell frequency nor the size of the primitive HSC compartment are affected in TIRAP transplanted mice, the observed cytopenias and marrow failure cannot be directly attributed to a loss of stem cell function. Furthermore, the progeny of transplanted HSCs can only be measured after long term engraftment has occurred (at least 16 weeks post-transplant in mice). Given the short time to marrow failure in this model, it is unlikely that the observed effects are due to a stem cell defect, rather, overexpression of TIRAP in the progenitor populations may be responsible for the ineffective hematopoietic reconstitution following transplantation.  Researchers have shown the importance of the MDS stem cell in the initiation of disease despite the absence of differences in stem cell frequencies between low risk MDS patients and age matched normal controls (Pang et al., 2013). This was accomplished by transplanting phenotypic hematopoietic stem cells from del(7q) MDS patients into sublethally irradiated NSG recipient pups 139  (Pang et al., 2013). In this xenotransplant model, human CD45+ cells were recovered in the bone marrow of the recipients showing that MDS stem cells are capable of successful engraftment (Pang et al., 2013). Furthermore, the proportion of human-derived cells harbouring the 7q deletion was the same as what was present in the patient, proving that the MDS stem cell is capable of initiating clonal disease (Pang et al., 2013). Although the TIRAP-overexpressing stem cell was not shown to be responsible for the marrow failure observed, it is still inconclusive whether the TIRAP-overexpressing stem cell is important for initiation of the disease. Future experiments to address this question could be to transplant purified stem cell populations expressing TIRAP and measuring the clonal expansion of these cells as well as monitoring for the development of bone marrow failure. Such an experiment may more accurately represent the human manifestations of MDS, in which the disease arises in the stem cell population and gradually expands to take over the marrow.  This may also allow for a longer latency before marrow failure sets in, as it would take more time for the TIRAP expressing stem cells to generate enough abnormal progenitor cell progeny to disrupt normal hematopoiesis.  The longer latency period may allow for the accumulation of mutations that can act as the second hit necessary to promote leukemogenesis. Researchers have shown that the hematopoietic defects observed in MDS are due to defects in expansion at specific stages of the hematopoietic hierarchy and a block of differentiation, however, the exact nature of the differentiation defects are still debatable (Pang et al., 2013; Will et al., 2012). Some researchers have shown that patients with high risk MDS have an expansion in both LT-HSCs and GMPs at the expense of MEPs, while low risk MDS patients show skewed expansion of the CMP population (Will et al., 2012). In contrast, others have shown significant reductions in the frequency of GMPs that is not accounted for by an increase in the frequency of CMPs and MEPs, suggesting that the defect in low-risk MDS lies in a significant absolute reduction in the GMP population and not due to expansion of the CMP or MEP populations (Pang et al., 140  2013). Furthermore, the reduction in GMPs was not always found to be associated with neutropenia, and while the most common cytopenia in MDS patients is anemia, MEPs were not found to be significantly reduced (Pang et al., 2013). Furthermore, in the ARE-del mouse model of aplastic anemia, IFNγ has been shown to be responsible for dramatic decreases in the number of hematopoietic progenitors (CMPs, GMPs, and MEPs), but not ST-HSCs, MPPs, and CLPs (Lin et al., 2014). Analysis of the various progenitor cell populations in our TIRAP overexpression transplant model also revealed skewed differentiation at various stages of the hematopoietic hierarchy. Unlike in the published studies using MDS patient bone marrow, in our model we can trace the contribution of normal marrow cells and TIRAP-overexpressing cells to hematopoietic reconstitution due to the presence of a GFP tag in transduced cells. In our model we found an absolute reduction in the numbers of CMP and MEP populations derived from TIRAP-overexpressing cells, but no difference in the number of GMP (Figure 3.8). While there was a trend towards elevated apoptosis within the CMP and MEP populations this was not significant due to high variability between samples. Interestingly, while there was no difference in the absolute number of GMPs within the transduced population, there was in fact a significant increase in apoptosis in this population. Increased apoptosis within the GMPs has been previously reported (Pang et al., 2013). Taken together, this suggests that overexpression of TIRAP in the progenitor population may lead to the observed cytopenias due to skewed differentiation of CMPs towards GMPs at the expense of MEPs. Furthermore, elevated apoptosis within the GMP population contributes to the development of neutropenias. Examination of progenitors derived from the non-transduced population further reveals a trend similar to that observed by Pang et al. where there is a reduction in the absolute number of GMPs due to increased apoptosis and no effect on the absolute numbers of CMPs and MEPs. Taken together, our model suggests that TIRAP-expressing progenitor cells, as well their normal counterparts are defective in terms of their ability to survive  and differentiate into mature functional progeny, leading to the pancytopenias and marrow failure observed (Figure 5.1).  Figure 5.1 Progenitor cell differentiation defects In mice transplanted with MIGbalanced in both GFP+ and GFPtransplantation. In mice transplanted with TIRAPand skewed differentiation prevent hematopoietic reconstitution. Within the GFP+ fraction of TIRAP-transduced marrow, there is increased differentiation toward GMPs at the expense of MEPs. This leads to exhaustion of CMPs and a reduction in the absolute nof MEPs, contributing to the development of anemia and thrombocytopenia in the peripheral blood. Increased apoptosis in the GMP lineage, despite the increase in differentiation toward GMPs accounts for peripheral blood leucopenia. WT hematopoietic cells cotransplanted with TIRAPhematopoieisis due to the nonincreased cell death in the GMPs and MEPs.    in TIRAP overexpression model -transduced marrow, differentiation and apoptosis are - cells, allowing hematopoietic reconstitution following -transduced marrow, increased apoptosis-transduced marrow are also unable to restore -autonomous effects of TIRAP  overexpression leading to  141  of BMF  umber 142  5.2 The Role of IFNγ in the Onset of Bone Marrow Failure and MPD IFNγ is a pleiotropic cytokine with potent immunomodulatory effects. Traditionally recognized as a cytokine important in the anti-viral response of effector cells of the immune system, the effects of IFNγ on hematopoietic stem/progenitor cells and the role it plays in the pathogenesis of bone marrow failure syndromes are beginning to be understood. The work presented in this dissertation highlights the importance of IFNγ in the onset of marrow failure in the context of TIRAP-overexpressing marrow. We have shown that TIRAP overexpression in mouse hematopoietic stem/progenitor cells leads to the upregulation of several cytokines including IFNγ. Several published studies have also reported elevated levels of IFNγ in low-risk MDS and in CD68+ macrophages from MDS patients (Kitagawa et al., 1997; Kordasti et al., 2009). Furthermore, CD34+ cells from MDS patients have been shown to have a gene expression profile similar to the IFNγ response in normal CD34+ cells (Pellagatti et al., 2006). IFNγ has been shown to be important in the development of anemia in patients with chronic immune activation, and has been implicated in bone marrow failure disorders such as aplastic anemia (Lin et al., 2014; Weiss and Goodnough, 2005). IFNγ can influence the process of erythropoiesis by regulating intracellular ferritin levels (Taetle and Honeysett, 1988; Wang et al., 1995). Furthermore, IFNγ suppresses proliferation and induces apoptosis in maturing erythroid cells (Dai and Krantz, 1999; Mamus et al., 1985; Selleri et al., 1996; Zoumbos et al., 1985). More recently, the effects of IFNγ on hematopoietic stem/progenitor cells have been described. Studies of chronic Mycobacterium avium infection in mice show that strong upregulation of IFNγ in the bone marrow following infection leads to a reduction in the frequency of HSCs in G0 and an increase in proliferation of the LT-HSC compared to uninfected mice (Baldridge et al., 2010). Furthermore, these cycling LT-HSCs have impaired engraftment abilities (Baldridge et al., 2010). Studies of human CD34+CD38- cord blood have shown 143  that treatment with IFNγ inhibits the long-term reconstitution of cultures in vitro and multilineage engraftment in vivo (Yang et al., 2005b). Furthermore, although CD34+CD38- cord blood cells treated with IFNγ are capable of proliferation, they undergo more differentiation divisions and fewer self-renewal divisions (Yang et al., 2005b). IFNγ has also been shown to decrease the number and function of myeloid progenitor cells in colony forming assays (Lin et al., 2014). Taken together, the inhibitory effects of IFNγ on erythropoiesis, the impaired engraftment abilities of HSCs following exposure to IFNγ, and the quantitative and qualitative impairment of myeloid progenitors all contribute to the development of bone marrow failure in our TIRAP-overexpressing bone marrow transplant model.   Progression from MDS to AML requires the expansion of clonal blasts within the marrow, while normal hematopoietic cells become less effective at supporting hematopoiesis. The exact mechanisms involved have not been fully elucidated. The results presented in Chapter 4 suggest that IFNγ not only plays an important role in the development of bone marrow failure, but also plays an important role in controlling progression from MDS to AML. We showed that in the context of bone marrow transplants with TIRAP-overexpressing marrow, depletion of IFNγ is sufficient to reverse the marrow failure phenotype observed, and also allow progression to a myeloproliferative disorder (Figure 4.7). Furthermore, we showed a significant enrichment in IFNγ signatures in MDS patients compared to AML patients (Figure 4.7), suggesting that IFNγ signaling may inhibit progression to AML. We also showed that in low-risk MDS patients, IFNγ signatures are enriched, whereas NFκB signatures are not (Figure 4.2). In contrast, NFκB has been shown to be constitutively active in high-risk MDS and AML (Carvalho et al., 2007; Guzman et al., 2001). NFκB activation has been shown to be important for the upregulation of the immunoinhibitory molecule B7-H1 on MDS blasts (Kondo et al., 2010). This molecule is expressed at higher levels on blasts from high-risk MDS compared to low-risk MDS, and provides MDS blasts with a proliferative advantage 144  while suppressing T cell proliferation (Kondo et al., 2010). Taken together, this suggests that progression from MDS to AML may require inactivation of the IFNγ signaling pathway as well as activation of NFκB. Genomic instability and the acquisition of additional mutations may be necessary for full blown AML to manifest.      5.3 MDS Associated Marrow Failure as a Disorder of the Stem Cell Niche As hematopoietic malignancies are clonal disorders, the development of these malignancies has been mainly attributed to genetic or epigenetic aberrations occurring within the HSPC pool that provide the malignant clone with an intrinsic growth and survival advantage over normal hematopoietic elements in the bone marrow.  Deregulation of the hematopoietic microenvironment due to genetic aberrations in stromal cells has also been reported to lead to the development of hematological malignancies. This has been demonstrated in cases of deletion of the retinoic acid receptor-γ or the retinoblastoma genes in bone marrow stromal cells that lead to the development of myeloproliferative neoplasms (MPN) (Walkley et al., 2007a; Walkley et al., 2007b). Also, inactivation of the microRNA processing enzyme Dicer in osteoprogenitor cells causes the development of myelodysplastic syndromes in mice (Raaijmakers et al., 2010). Others have shown that constitutive activation of β-catenin in osteoblasts stimulates expression of the Notch ligand Jagged-1 and subsequently leads to aberrant Notch signaling in hematopoietic stem/progenitor cells and the development of myeloid leukemias with recurrent chromosomal alterations (Kode et al., 2014). Transplantation of LT-HSC from these mice leads to leukemic progression in wild-type recipients, however transplantation of hematopoietic cells from fetal livers does not result in leukemogenesis, as fetal HSCs have not been in contact with the β-catenin expressing osteoblasts (Kode et al., 2014).           145  The discrepancies between the in vivo and ex vivo survival studies on TIRAP-overexpressing cells described in Chapter 3 and Chapter 4 were the first clues to suggest the involvement of bone marrow-derived stromal cells in the development of marrow failure following TIRAP overexpression. Since one of the main difference between the in vivo and ex vivo experiments was the absence of stromal support ex vivo, this suggested that the interaction of TIRAP cells with other cells in the bone marrow microenvironment may be playing an important role in the increased apoptosis and aberrant hematopoiesis, leading to the onset of marrow failure in the TIRAP-transplanted mice. To address this question, we developed a consecutive transplant model in which we transplanted TIRAP or MIG control cells into lethally irradiated recipients and allowed these cells to condition the bone marrow microenvironment. Following a three week conditioning period, the marrows were myeloablated and GFP or YFP-tagged cells were then transplanted into the preconditioned recipient mice. By myeloablating the marrows prior to the subsequent transplant, we were able to eliminate any confounding effects due to the continued presence of TIRAP-expressing cells.  Furthermore, it allowed us to determine whether the non-autonomous marrow suppression of the wild type helper cells observed in the TIRAP transplanted mice is due to the direct effect of TIRAP-overexpressing cells on the co-transplanted helper cells or whether it is secondary to the effects on the marrow stroma. If the interaction of TIRAP cells with bone marrow stromal cells creates a hostile environment that does not support hematopoietic reconstitution and leads to marrow failure, then this should be reflected in the quantity and quality of hematopoietic cells that we recover after a two week engraftment period in the preconditioned bone marrow microenvironment. By performing competitive transplants of marrow cells derived from TIRAP and MIG preconditioned marrows and measuring the contribution of each to the myeloid, T, and B cell lineages we found that hematopoietic cells transplanted into a TIRAP conditioned microenvironment are severely impaired in their abilities to compete with cells from the MIG 146  vector control conditioned mice. These findings clearly show that transplantation of TIRAP expressing cells alters the bone marrow microenvironment in such a way that it can no longer support hematopoiesis and contributes to the non-autonomous suppression of hematopoiesis in the normal co-transplanted helper cell population.  While we have been able to show that TIRAP-conditioning of the bone marrow microenvironment reduces the functional output of normal hematopoietic stem cells, we have not determined whether the reduced output is due to functional impairment and reprogramming of the hematopoietic stem cell exposed to such an environment or due to a reduction in the total number of hematopoietic stem cells residing in the marrow secondary to homing or retention defects. To determine whether there is a reduction in the total number of hematopoietic stem cells in TIRAP conditioned marrows, a limiting dilution assay could be performed in the subsequent transplant to determine the stem cell frequency. In addition, RNA-seq could be performed to compare the transcriptomes of HSCs from TIRAP-conditioned and MIG control-conditioned microenvironments. This could be used to identify changes in transcriptional networks that are responsible for the functional impairment observed.                   Recently, other researchers have shown similar findings suggesting that malignant cells are capable of modifying their microenvironment to create a more suitable and supportive environment for the malignant clone that is inhibitory to normal cells (Medyouf et al., 2014; Schepers et al., 2013). For example, in the inducible double transgenic Scl-tTA::TRE-BCR/ABL mouse model of CML, osteoblastic lineage cells are expanded in the bone marrow stroma despite restriction of expression of the transgenes to hematopoietic bone marrow cells and some endothelial cells (Schepers et al., 2013). Furthermore, transplantation of this BCR/ABL bone marrow into wild type recipients leads to expansion of endosteal osteoblastic cells and bone marrow fibrosis as MPN progressively sets in (Schepers et al., 2013). It was found that the leukemic myeloid 147  cells are necessary and sufficient to induce changes to the bone marrow stroma and architecture, and these changes require direct cell contact between stromal cells and leukemic myeloid cells (Schepers et al., 2013). Most importantly, MPN-expanded osteoblastic stromal cells are impaired in their hematopoietic supportive capabilities of normal HSCs, while BRC/ABL HSCs are resistant to the impaired supportive capabilities of MPN-expanded stromal cells (Schepers et al., 2013). Also, in MDS, a recent report has shown that MDS-MSCs significantly enhance the engraftment capacities of CD34+ MDS cells (Medyouf et al., 2014). These MDS-MSCs exhibit specific molecular features such as an enrichment in mesenchymal and osteoprogenitor cell fate signatures, response to an inflammatory environment, and a depletion of adipogenesis signatures (Medyouf et al., 2014). Furthermore, this report has shown that exposure of healthy mesenchymal stromal cells to MDS hematopoietic cells results in changes to the healthy MSCs such that they adopt MDS-like molecular features such as upregulation of the gene Leukemia inhibitory factor (LIF)(Medyouf et al., 2014).      The study we have presented here provides a novel explanation of the cellular mechanism behind the pathogenesis of MDS. Innate immune dysregulation within the hematopoietic compartment contributes to the creation of an environment inhibitory to normal hematopoiesis through upregulation of the cytokines IFNγ, and to a lesser extent, IL-10. In the absence of a stromal support system, cytokine induction following TIRAP overexpression provides a survival advantage to hematopoietic cells through autocrine and paracrine mechanisms (Figure 4.8). In vivo, however, these cytokines play a role in promoting marrow failure and blocking progression to AML. IFNγ has been shown to be inhibitory to osteoclasts (Kamolmatyakul et al., 2001; Takayanagi et al., 2000). Since osteoclasts serve a key regulatory role in the maintenance of the hematopoietic stem cell niche, this led us to postulate that osteoclasts are the components of the bone marrow stoma that are the targets of these inflammatory cytokines. This model is summarized in Figure 5.2 below.  Using the RAW264.7 monocytic cell line, we were able to show TIRAP overexpressing cells are indeed able to inhibit osteoclastogenesis. These findings were corroborated with clinical datashowing reduced osteoclast staining in del(5q) MDS patients compared to healthy normal control (Figure 4.15) (Mellibovsky et al., 1996Figure 5.2 Proposed Mechanism for TIRAP mediated marrIn the absence of stromal support, TIRAP expressing cells display an intrinsic survival advantage, and blocking IFNγ and ILmyeloproliferative disorders. TIRAP overexpression leads to the develthrough both cell autonomous and nonthrough the effects of IFNγ and ILosteoclastogenesis. Hematopoietic stem cells residing in a TIRAPmicroenvironment have a reduced ability to regenerate both myeloid and lymphoid impaired, and this may be due to increased mobilization and/or reduced homing or retention of HSCs in the endosteal HSC niche due to defective osteoclasts. Alternatively, the suppression of osteoclastogenesis may lead to changes in intracellular signaling within the HSC and reprogramming of the hematopoietic stem cell.      ).  ow failure -10 signaling in vivo can lead to the development of opment of BMF -autonomous mechanisms. This is possibly mediated -10 on the HSC niche through inhibition of  148   -conditioned 149  What still remains to be shown is that direct inhibition of osteoclasts leads to marrow failure or anemia. In osteoporosis, a disease characterized by reduced bone mineral density due to increased bone resorption by osteoclasts, bisphosphonates are often utilized as a treatment option (Inderjeeth et al., 2015). These drugs work by inducing apoptosis in osteoclasts, thereby slowing down bone loss (Weinstein et al., 2009). Interestingly, other studies have shown that treatment with bisphosphonates induces anemia, however, this anemia has been attributed to the depletion of macrophages, and not osteoclasts (Giuliani et al., 2005; Lisa Giuliani et al., 2007; Ramos et al., 2013; Sadahira et al., 2000). Since osteoclasts and macrophages share a common progenitor, both are susceptible to the inhibitory effects of bisphosphonate treatment, and one cannot rule out the involvement of osteoclasts in the anemia observed following treatment with bisphosphonates. To test the role of osteoclast inhibition in the development of anemia, one must selectively inhibit osteoclasts without affecting macrophages. Osteoclasts can be inhibited in vivo by targeting key molecules important in the differentiation and maturation of osteoclasts. The anti-RANKL antibody Denosumab has been used in the clinical setting to treat giant cell tumor of bone, which is characterized by the presence of numerous osteoclasts that destroy bone (Xu et al., 2013). Unfortunately, this antibody does not bind to mouse RANKL (Kostenuik et al., 2009). There is a human RANKL knock-in mouse model that is responsive to Denosumab, however the effects of Denosumab on hematological parameters have not been reported (Kostenuik et al., 2009). Alternatively, the RANKL knockout mouse could be utilized to study the effects of osteoclasts on the development of anemia, however, these mice often display severe osteopetrosis, due to the absence of osteoclasts neonatally, which may further confound any hematopoietic defects observed (Kong et al., 1999). These problems could be avoided by using an inducible RANKL knockout mouse model, or by administering the selective osteoclast inhibitor AMGN-0007, which consists of the OPG molecule fused to the Ig Fc domain at its C-terminus. This fusion molecule has 150  been shown to have greater in vivo potency than native OPG and disrupts osteoclast differentiation by blocking RANK-RANKL interactions, sparing macrophages.   Both IFNγ and IL-10 have been shown to inhibit osteoclastogenesis through different mechanisms (Hong et al., 2000; Kamolmatyakul et al., 2001; Owens et al., 1996). We have shown using both IL-10 and IFNγ knockout bone marrow cells that loss of either one of these genes does not inhibit upregulation of the other following overexpression of TIRAP, suggesting that osteoclast inhibition is still possible in IFNγ or IL-10 knockout marrow following TIRAP overexpression. It is interesting to note that only loss of IFNγ completely blocked progression to marrow failure, although loss of IL-10 did partially restore the platelet counts, increased median survival, and slightly reduced the incidence of marrow failure. These results suggest that the effects of IFNγ may not be restricted to osteoclasts, but other cell types affected by IFNγ may also be contributing to the development of marrow failure.   5.4 The Role of Aberrant TLR Signaling in MDS Over the past decade several studies have highlighted the importance of innate immune signaling in the maintenance of hematopoietic homeostasis (Boiko and Borghesi, 2012; Nagai et al., 2006; Takizawa et al., 2012). Furthermore, dysregulation of TLR signaling pathways appears to be a common feature of myeloid malignancies, especially the myelodysplastic syndromes (Dimicoli et al., 2013; Maratheftis et al., 2007; Rhyasen et al., 2013; Starczynowski et al., 2010; Wei et al., 2013). The work presented here highlights the role of TIRAP in the development of bone marrow failure in a mouse model of the myelodysplastic syndromes. One interesting finding was the difference in the outcome of overexpression of TIRAP compared to what was previously published for TRAF6 151  (Starczynowski et al., 2010). Although TIRAP and TRAF6 are components of the same signaling pathway, overexpression of these genes results in different outcomes in terms of disease latency, disease progression, and the cytokine mediators involved, suggesting that TIRAP may be mediating signal transduction via an alternative pathway independent of TRAF6. In support of this hypothesis, we found that blockade of the TLR pathway downstream of TIRAP at the level of both MyD88 and TRAF6 did not abrogate the signal leading to upregulation of IL-10 and IFNγ. Furthermore, promoter methylation analysis of MDS patient marrow compared to normal CD34+ cells revealed hypermethylation of key components of the TLR4 pathway including CD14, MyD88, IRAK4, IKKβ, and NFκB, while the promoters of TIRAP and negative regulators of TLR signaling (such as SIGIRR, TOLLIP, and IRAK3) were not affected, suggesting there is an overall suppression of the canonical TLR4 pathway in MDS patients. Furthermore, SOCS1, a negative regulator of immune signaling has been shown to interact with TIRAP, and this interaction leads to the ubiquitination and degradation TIRAP (Scott et al., 2009). Researchers have shown that the SOCS1 locus is hypermethylated in 30% of MDS patients, resulting in reduced mRNA expression (Brakensiek et al., 2005). Reduced expression of SOCS1 may ultimately lead to increased TIRAP activation.                       In addition to hypermethylation of promoters of TLR signaling genes, other mechanisms may exist that ultimately lead to suppression of the canonical TLR4 pathway and activation of TIRAP dependent signaling pathways. Mutations of RNA splicing factors has been shown to be very prevalent in MDS (Makishima et al., 2012; Papaemmanuil et al., 2011; Yoshida et al., 2011). Furthermore, the splicing factors SF3B1 and SF3A1 have been shown to regulate innate immunity (De Arras and Alper, 2013). Inhibition of these splicing factors enhances production of the short isoform of MyD88 (MyD88s), which is an inhibitor of MyD88 dependant signaling, and diminishes IL-6 production in response to LPS (De Arras and Alper, 2013).  This is similar to what is seen in our model, as the induction of IL-6 seen upon TRAF6 overexpression is diminished upon overexpression 152  of TIRAP. Thus, the defect in RNA splicing machinery seen in MDS patients may contribute to an increase in TIRAP-dependent signaling in MDS patients.  The exact signaling pathway that is activated upon TIRAP overexpression remains to be elucidated. It is unlikely that TIRAP participates in TRIF-dependent signaling, as the genes induced downstream of TRIF are the type-I interferons (IFNα and IFNβ), not IFNγ. Furthermore, promoter methylation analysis we performed showed that components of the TRIF-dependent pathway (TBK1 and IRF3) are also hypermethylated. A recently published paper has reported that TIRAP is required for endosomal TLR9 signaling (Bonham et al., 2014), which is activated in response to viral infection by recognizing unmethylated CpG DNA. CpG DNA has been show to be a potent inducer of the proinflammatory cytokine IL-12 in bone marrow-derived macrophages (Sweet et al., 2002), which can then stimulate production of IFNγ by other cells. 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Cell 63:213-224.             182  APPENDICES Appendix A: FAB Classification of MDS2 Category Dysplasia % BM blasts % PB blasts Refractory anemia (RA) Erythroid <5 <1 Refractory anemia with ringed sideroblasts (RARS) Erythroid <5 <1 Refractory anemia with excess blasts (RAEB) 2 or more lineages 5-20 0-4 Refractory anemia with excess blasts in transformation (RAEB-T) Usually 2 or more lineages 21-30 ≥5 Chronic myelomonocytic leukemia (CMML) Variable ≥ 1 x 109/L monocytes <20                                                                       2 Bennett, J.M., D. Catovsky, M.T. Daniel, G. Flandrin, D.A. Galton, H.R. Gralnick, and C. Sultan. 1976. Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. Br J Haematol 33:451-458. 183  Appendix B: WHO Classification of MDS3 Disease entity Blood findings Bone marrow findings Refractory cytopenias with unilineage dysplasia (RCUD): Refractory anemia (RA), Refractory neutropenia (RN), Refractory thrombocytopenia (RT) Unicytopenia or bicytopenia No or rare blasts (<1%) Unilineage dysplasia: ≥10% of the cells in one myeloid lineage <5% blasts 15% of erythroid precursors are ringed sideroblasts Refractory anemia with ring sideroblasts (RARS) Anemia  No blasts ≥15% of the erythroid precursors are ring sideroblasts Dyserythropoiesis only <5% blasts Refractory anemia with multilineage dysplasia (RCMD) Cytopenia(s), no or rare blasts No Auer rods <1x109/L monocytes Dysplasia in >10% of cells in 2 or more lineages <5% blasts in marrow No Auer rods <1x109/L monocytes Refractory anemia with excess blasts-1 (RAEB-1) Cytopenia(s) <5% blasts No Auer rods <1x109/L monocytes Unilineage or multilineage dysplasia 5-9% blasts No Auer rods Refractory anemia with excess blasts-2 (RAEB-2) Cytopenia(s) 5-19% blasts  Auer rods ± <1x109/L monocytes Unilineage or multilineage dysplasia 0-19% blasts Auer rods± MDS, unclassifiable (MDS-U) Cytopenias <1% blasts Unequivocal dysplasia in less than 10% of cells in one or more myeloid lines when accompanied by a cytogenetic abnormality considered as presumptive evidence for a diagnosis of MDS <5% blasts MDS associated with isolated del(5q) Anemia Usually normal to elevated platelets No or rare blasts Normal to increased megakayrocytes with hypolobated nuclei <5% blasts del(5q) is the sole cytogenetic abnormality No Auer rods                                                           3 Swerdlow, S.H., International Agency for Research on Cancer., and World Health Organization. 2008. WHO classification of tumours of haematopoietic and lymphoid tissues. International Agency for Research on Cancer, Lyon, France. 439 p. pp. 184  Appendix C: IPSS-R Prognostic Score Values, Cytogenetic Risk Groups, and Prognostic Risk Categories4  Prognostic Variable 0 0.5 1 1.5 2 3 4 Cytogenetics Very good  Good  Intermediate Poor Very poor BM Blast % ≤2%  >2-<5%  5-10% >10%  Hemoglobin ≥10  8-<10 <8    Platelets ≥100 50-<100 <50     Absolute Neutrophil Count ≥0.8 <0.8      Cytogenetic Prognostic Subgroups Cytogenetic Abnormalities Very good -Y, del(11q) Good Normal, del(5q), del(20q), double including del(5q) Intermediate Del(7q), +8, +19, i(17q), any other single or double independent clones Poor -7, inv(3)/t(3q)/del(3q), double including -7/del(7q), Complex: 3 abnormalities Very Poor Complex: >3 abnormalities  Very low Low Intermediate High Very high Risk Score ≤1.5 >1.5-3 >3-4.5 >4.5-6 >6                                                            4 Greenberg, P.L., H. Tuechler, J. Schanz, G. Sanz, G. Garcia-Manero, F. Sole, J.M. Bennett, D. Bowen, P. Fenaux, F. Dreyfus, H. Kantarjian, A. Kuendgen, A. Levis, L. Malcovati, M. Cazzola, J. Cermak, C. Fonatsch, M.M. Le Beau, M.L. Slovak, O. Krieger, M. Luebbert, J. Maciejewski, S.M. Magalhaes, Y. Miyazaki, M. Pfeilstocker, M. Sekeres, W.R. Sperr, R. Stauder, S. Tauro, P. Valent, T. Vallespi, A.A. van de Loosdrecht, U. Germing, and D. Haase. 2012. Revised international prognostic scoring system for myelodysplastic syndromes. Blood 120:2454-2465. 185  Appendix D: WHO Classification-Based Prognostic Scoring System for MDS5  Variable 0 1 2 3 WHO category RA, RARS, 5q- RCMD, RCMD-RS RAEB-1 RAEB-2 Karyotype Good Intermediate Poor - Transfusion requirement No Regular - - Risk groups are as follows: Very low (score = 0), low (score = 1), intermediate (score = 2), high (score = 3-4), and very high (score = 5-6)                                                                    5 Malcovati, L., U. Germing, A. Kuendgen, M.G. Della Porta, C. Pascutto, R. Invernizzi, A. Giagounidis, B. Hildebrandt, P. Bernasconi, S. Knipp, C. Strupp, M. Lazzarino, C. Aul, and M. Cazzola. 2007. Time-dependent prognostic scoring system for predicting survival and leukemic evolution in myelodysplastic syndromes. J Clin Oncol 25:3503-3510. 


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