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Loss of miR-143 and miR-145 inhibits hematopoietic stem cell self-renewal via dysregulated TGF-beta signaling Lam, Jeffrey Churk-Hang 2015

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LOSS OF MIR-143 AND MIR-145 INHIBITS HEMATOPOIETIC STEM CELL SELF-RENEWAL VIA DYSREGULATED TGFβ SIGNALING    by   JEFFREY CHURK-HANG LAM   B.Sc., Simon Fraser University, 2009     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)    December 2015     © Jeffrey Churk-Hang Lam, 2015  ii ABSTRACT Myelodysplastic syndromes (MDS) are a collection of hematopoietic malignancies in which genomic abnormalities within the hematopoietic stem cell (HSC) compartment lead to pancytopenia and eventual bone marrow failure or progression to leukemia. The most common karyotypic abnormality in MDS is the interstitial deletion of chromosome 5q where a commonly deleted region (CDR) has been identified. Two microRNAs (miRNA) located within the CDR have been shown to be expressed at lower levels in del(5q) MDS compared to diploid MDS. Here I describe investigations of the functional consequence of loss of these two miRNAs, miR-143 and miR-145, on hematopoietic stem and progenitor cells. Loss of miR-143/145 in mice resulted in a decrease in hematopoietic stem cells and myeloid progenitors. I also identified a direct link between the loss of miR-143/145 and subsequent activation of both the canonical and non-canonical transforming growth factor beta (TGFβ) signaling pathways, mediated in part via the adaptor protein, Disabled-2 (DAB2). Analysis of del(5q) MDS patient bone marrow revealed an enriched TGFβ-signature compared to healthy controls and we show that TGFβ signaling is activated upon loss of miR-145 or enforced expression of its target, DAB2. Subsequent studies focused on the function of DAB2 within the hematopoietic system. Overexpression of DAB2 in mouse bone marrow resulted in a significant decrease in hematopoietic stem cell frequency, self-renewal, and colony forming activity.  In competitive transplants, vector-transduced bone marrow cells were able to outcompete DAB2-overexpressing marrow in both primary transplants as well as in secondary limiting dilution assays. However, a subset of mice with enforced DAB2 expression alone developed a transplantable acute myeloid leukemia. In conclusion, our data suggest a role for miR-145 haploinsufficiency in the inappropriate activation of TGFβ signaling through derepression of DAB2 which leads to abnormal HSC function in del(5q) MDS.   iii PREFACE  All written work and figures in this thesis is the original work of Jeffrey Lam (JL). The experiments presented in this thesis are currently unpublished and were performed by JL with the following exceptions:  Dr. Jeremy Parker provided the miRNA co-expression data in Figure 3.1a as well as the IPA analysis in Figure 3.7. Dr. Joanna Wegrzyn-Woltosz (JWW) performed the qPCR experiments in Figure 3.1b and performed the immunostaining experiment in Figure 3.2. Complete blood counts shown in Figure 4.12 were collected by JWW. The limiting dilution assay experiment in Figure 3.4 was a collaborative effort between JL and JWW. The gene expression dataset used in Figures 3.5 and 3.6 was published by Pellagatti, A., M. Cazzola, A. Giagounidis, J. Perry, L. Malcovati, M. G. Della Porta, M. Jadersten, S. Killick, A. Verma, C. J. Norbury, E. Hellstrom-Lindberg, J. S. Wainscoat and J. Boultwood (2010). "Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells." Leukemia 24(4): 756-764.  All experiments involving mouse models were approved by the UBC Animal Care and Ethics Committee on Animal Care (Certificate #A10-0126)  iv TABLE OF CONTENTS ABSTRACT .............................................................................................................................................................. ii PREFACE ............................................................................................................................................................... iii TABLE OF CONTENTS ............................................................................................................................................. iv LIST OF TABLES .................................................................................................................................................... vii LIST OF FIGURES .................................................................................................................................................. viii LIST OF ABBREVIATIONS ........................................................................................................................................ x ACKNOWLEDGEMENTS ....................................................................................................................................... xiii DEDICATION ....................................................................................................................................................... xiv Chapter 1 - INTRODUCTION ................................................................................................................................... 1 1.1 HEMATOPOIESIS AND HEMATOPOIETIC STEM CELLS ........................................................................................................ 1 1.1.1 Hematopoietic Stem Cells ............................................................................................................................ 1 1.1.2 Differentiated Progenitors ........................................................................................................................... 3 1.2 MYELODYSPLASTIC SYNDROMES .................................................................................................................................. 6 1.2.1 Classifications ............................................................................................................................................... 8 1.2.2 IPSS Classification in Relation to Prognosis ................................................................................................ 10 1.2.3 WHO Classification in Relation to Prognosis .............................................................................................. 10 1.2.4 Current Treatment Approaches.................................................................................................................. 11 1.3 MECHANISMS OF MDS PATHOGENESIS ...................................................................................................................... 12 1.3.1 Cytogenetic Abnormalities ......................................................................................................................... 13 1.3.2 Somatic Mutations ..................................................................................................................................... 19 1.3.3 Dysregulated Signaling Pathways .............................................................................................................. 25 1.4 MICRORNAS ......................................................................................................................................................... 26 1.4.1 MiRNA Biogenesis ...................................................................................................................................... 27 1.4.2 MiRNAs in Hematopoiesis .......................................................................................................................... 29 1.4.3 MiRNAs in MDS/AML ................................................................................................................................. 30 1.5 TGFΒ SIGNALING PATHWAY ..................................................................................................................................... 35 1.5.1 The Endocytic Pathway and Smad-independent TGFβ Signaling ............................................................... 39 1.5.2 TGFβ Signaling in MDS ............................................................................................................................... 41 1.5.3 TGFβ Signaling in Hematopoiesis ............................................................................................................... 43 1.5.4 Disabled-2 and MDS ................................................................................................................................... 46 1.6 AIMS OF THE STUDY ................................................................................................................................................ 48 Chapter 2 – MATERIALS AND METHODS .............................................................................................................. 50 2.1 GENERAL REAGENTS ............................................................................................................................................... 50 v 2.2 CELL CULTURE ....................................................................................................................................................... 50 2.2.1 Cell Lines ..................................................................................................................................................... 50 2.2.2 Gene Transfer ............................................................................................................................................. 51 2.2.3 RNA Interference ........................................................................................................................................ 52 2.3 IMMUNOBLOTTING ................................................................................................................................................. 52 2.4 CO-IMMUNOPRECIPITATION ..................................................................................................................................... 53 2.5 RNA COLLECTION AND QRT-PCR ............................................................................................................................. 54 2.5.1 mRNA Targets ............................................................................................................................................ 54 2.5.2 MicroRNA Targets ...................................................................................................................................... 55 2.6 FLOW CYTOMETRY .................................................................................................................................................. 55 2.7 MOUSE STRAINS .................................................................................................................................................... 56 2.7.1 Bone Marrow Transplants.......................................................................................................................... 57 2.8 DATA ANALYSIS ...................................................................................................................................................... 58 2.8.1 Statistics ..................................................................................................................................................... 58 2.8.2 ELDA ........................................................................................................................................................... 58 2.8.3 Ingenuity Pathway Analysis ....................................................................................................................... 58 2.8.4 Gene Set Enrichment Analysis .................................................................................................................... 59 Chapter 3 - LOSS OF MIR-143/145 INHIBITS HSC SELF-RENEWAL ......................................................................... 60 3.1 INTRODUCTION ...................................................................................................................................................... 60 3.2 RESULTS ............................................................................................................................................................... 61 3.2.1 Loss of miR-143/145 Results in a Decrease in Myeloid Progenitor and HSC Frequency ............................ 61 3.2.2 The TGFβ Pathway is Activated in miR-143/145-/- Bone Marrow .............................................................. 69 Chapter 4 – DAB2 IS CRITICAL FOR PROPER PROGENITOR AND HSC FUNCTION ................................................... 80 4.1 INTRODUCTION ...................................................................................................................................................... 80 4.2 RESULTS ............................................................................................................................................................... 80 4.2.1 DAB2 is a Specific Target of MiR-145 ......................................................................................................... 80 4.2.2 Defect in Colony Formation in MiR-143/145-/- Bone Marrow is Mimicked by DAB2 Overexpression ........ 84 4.2.3 DAB2-OE HSPC Show Reduced Function in Competitive Assays with a Defect in Self-Renewal ................. 86 4.2.4 Overexpression of DAB2 in Mouse Bone Marrow can Result in Myeloproliferation or Leukemic Transformation .......................................................................................................................................... 91 4.2.5 Aged MiR-143/145-/- Mice Show Mild Pancytopenia with Expansion of Leukocytes ................................. 96 Chapter 5 – SUMMARY AND SIGNIFICANCE OF THE STUDY .................................................................................. 98 5.1 MIR-143/145 AS REGULATORS OF HSC FREQUENCY ................................................................................................... 98 5.1.1 MiR-143/145 Regulates Multiple Signaling Pathways ............................................................................... 98 5.2 DAB2 OVEREXPRESSION IMPAIRS HSC SELF-RENEWAL WHILE PREDISPOSING CELLS TO MYELOPROLIFERATION OR FRANK AML TRANSFORMATION ..................................................................................................................................................... 101 vi REFERENCES ....................................................................................................................................................... 106 APPENDIX - PRIMERS AND VIRAL SEQUENCES USED .......................................................................................... 130    vii LIST OF TABLES Table 1.1 - World Health Organization 2008 Classification of MDS ............................................ 9 Table 1.2 - IPSS-R Classification of MDS .................................................................................10 Table 1.3 - Commonly Reoccurring Cytogenetic Abnormalities in MDS ....................................13 Table 1.4 - Mutational Landscape of MDS ................................................................................20 Table 1.5 - MicroRNAs Underexpressed in MDS ......................................................................34 Table 1.6 - MicroRNAs Overexpressed in MDS ........................................................................35 Table 1.7 - The TGFβ Superfamily of Receptors and Their Ligands ..........................................38 Table 2.1 – GSEA TGFβ gene set ............................................................................................59    viii LIST OF FIGURES Figure 1.1 - Model of Hematopoietic Hierarchy .......................................................................... 4 Figure 1.2 - Modified Model of Hematopoietic Hierarchy ............................................................ 6 Figure 1.3 - Commonly Deleted Region of 5q- Syndrome .........................................................17 Figure 1.4 - Schematic of DNMT3a and TET2 enzymatic activity ..............................................23 Figure 1.5 - MicroRNA Biogenesis ............................................................................................28 Figure 1.6 - Transcription Factor ChIP-Seq from ENCODE .......................................................34 Figure 1.7 - Simplified TGFβ Signaling Pathway .......................................................................38 Figure 1.8 - Smad-Independent Signaling and the Endocytic Pathway .....................................41 Figure 2.1 - Generation of MiR-143/145 Knockout Mice ............................................................57 Figure 3.1 - MiR-143 and MiR-145 are Enriched in the Stem Cell Population ...........................63 Figure 3.2 - Loss of MiR-143/145 Results in Reduced Hematopoietic Stem/Progenitor Cell Frequency ..........................................................................................................................65 Figure 3.3 - Loss of MiR-143/145 Results in Reduced Progenitor Activity .................................66 Figure 3.4 - Loss of MiR-143/145 Results in Reduced HSC Frequency ....................................68 Figure 3.5 - MiR-143 and MiR-145 are Predicted to Target the TGF-Beta Signaling Pathway ...70 Figure 3.6 - Expression Analysis of AML/MDS Patient Datasets ...............................................72 Figure 3.7 - Expression Analysis of AML/MDS Patient Datasets ...............................................73 Figure 3.8 - Loss of MiR-143 and MiR-145 Activates the TGFβ Pathway in Mouse HSPC ........75 Figure 3.9 - Knockdown of MiR-145 is Sufficient to Activate the TGFβ Pathway in vitro ............76 Figure 3.10 - Rescue of MiR-143/145-/- Progenitor Activity by a SMAD3 Inhibitor ......................77 Figure 3.11 - Loss of MiR-143 and MiR-145 Activates the Non-Canonical TGFβ Signaling Pathway .............................................................................................................................79 Figure 4.1 - DAB2 and MiR-145 Expression in Different HSPC Fractions .................................81 Figure 4.2 - MiR-145 Directly Binds the 3’UTR of DAB2 ............................................................82 ix Figure 4.3 - DAB2 Overexpression Activates TGFβ Signaling ...................................................83 Figure 4.4 - DAB2 Inhibits Colony Forming Activity ...................................................................85 Figure 4.5 - Knockdown of Dab2 Rescues MiR-143/145-/- Progenitor Activity ...........................85 Figure 4.6 - DAB2 Overexpression Impairs Peripheral Blood Repopulation ..............................87 Figure 4.7 - DAB2 Overexpression Impairs HSPC Repopulation ..............................................88 Figure 4.8 - DAB2 Overexpression Impairs HSC Self-Renewal .................................................90 Figure 4.9 - The HSC Self-Renewal Defect from DAB2 Overexpression is More Pronounced with Age .............................................................................................................................91 Figure 4.10 - Analysis of Mice Transplanted with Marrow Overexpressing DAB2 ......................92 Figure 4.11 - Mice Overexpressing DAB2 are Susceptible to Leukemic Transformation ...........93 Figure 4.12 - Immunophenotyping of Bone Marrow From Moribund DAB2-AML Mice ...............95 Figure 4.13 - Aged MiR-143/145-/- Mice Show Altered Hematopoiesis ......................................97 x LIST OF ABBREVIATIONS 5-FU  5-fluorouracil 5hmC  5-hydroxymethylcytosine 5mC  5-methylcytosine ALK  Activin receptor-like kinase AML  Acute Myeloid Leukemia BM  Bone marrow BMF  Bone marrow failure BMT  Bone marrow transplant BMP  Bone morphogenetic protein CDR  Commonly Deleted Region CLP  Common lymphoid progenitor CML  Chronic myeloid leukemia CMML  Chronic myelomonocytic leukemia CMP  Common myeloid progenitor CFU/CFC Colony Forming Unit/Cell CFU-E  Colony-forming unit erythroid CFU-GM Colony-forming unit-granulocyte erythrocyte macrophage megakaryoctye CFU-GEMM Colony-forming unit-granulocyte macrophage CRU  Competitive repopulating unit DAB2  Disabled-2 DNA  deoxyribonucleic acid DNMT  DNA methyltransferase DMSO  Dimethyl sulfoxide EMT  Epithelial-mesenchymal transition GO  Gene Ontology GM  Granulocyte/monocyte GSEA  Gene Set Enrichment Analysis xi GMP  Granulocyte-macrophage progenitor GFP  Green Fluorescent Protein HI-FBS Heat inactivated fetal bovine serum HSC  Hematopoietic stem cell HSPC  Hematopoietic stem and progenitor cell Hgb  Hemoglobin IPA  Ingenuity Pathway Analysis IL  Interleukin KO  Knockout Lin  Lineage LDA  Limiting Dilution Assay LT-HSC Long-term HSC LMPP  Lymphoid-primed multipotent progenitor MDS  Myelodysplastic Syndromes MPN  Myeloproliferative Neoplasm MEP  Megakaryocyte-erythroid progenitor MiRNA  Micro-RNA MIG  MSCV-IRES-GFP Vector MIY  MSCV-IRES-YFP Vector MPD  Myeloproliferative Disorder MPP  Multipotent progenitor NK  Natural killer PB  Peripheral Blood PBS  Phosphate Buffered Saline Pep3b  C57Bl/6J:Pep3b-Ly5.1 PI  Propidium iodide Plt  Platelet RNA  Ribonucleic acid RT-qPCR reverse transcription – quantitative polymerase chain reaction xii s-AML  Secondary Acute Myeloid Leukemia SD  Standard deviation SEM  Standard error of the mean ST-HSC Short-term HSC t-AML  Therapy-related Acute Myeloid Leukemia TGFβ  Transforming Growth Factor Beta TET2  Tet Methylcytosine Dioxygenase 2 WT  Wildtype WBC  White blood cell YFP  Yellow Fluorescent Protein 3’UTR  3’ Untranslated Region  xiii ACKNOWLEDGEMENTS I would like to express my sincerest gratitude to all those who have supported me during my PhD. Without their support, this thesis would not have been possible. I thank my parents, Winnie and Paul Lam, and my brother, Alfred Lam for their love and support. You have encouraged me in everything that I do and have helped me more than you know.  To my supervisory committee, Keith Humphries, Pamela Hoodless, and Sharon Gorski, I thank you all for your advice and guidance throughout this project.  In particular, I thank Sharon for her tremendous support and mentorship throughout my scientific career from when I first started as a Co-op student to this day. To my supervisor, Dr. Aly Karsan, I thank you for giving me the opportunity to work under your supervision and mentorship. You hold your students to the highest standard and I thank you for always demanding the best of me. I would also like to thank all the members of the Karsan Lab with whom I have had the pleasure of working. In particular, I thank Amanda Fentiman, Ada Kim, Kate Slowski, and Ashley Clayton for their support and friendship, as well as helping me remember that a PhD is more than just about lab work. I am also grateful to Dr. Joanna Wegrzyn-Woltosz, Dr. Linda Chang, Dr. Greg Paliouras, and Dr. Rawa Ibrahim who have been tremendous friends and colleagues throughout my PhD.  Financial support during my graduate studies was provided by a Master’s scholarship from the Canadian Institutes of Health Research, a UBC Doctoral Fellowship, and a Doctoral award from the Centre for Blood Research.   xiv  DEDICATION        Lily, your unwavering love and support is what keeps me going.  I could not have done this without you.1 Chapter 1 - INTRODUCTION 1.1 Hematopoiesis and Hematopoietic Stem Cells The mammalian blood system is comprised of multiple cell types which cooperate to perform key physiological processes such as nutrient and oxygen transport, assistance in wound healing, and immunity. These functions are performed by mature blood cells which can be broadly categorized into two populations, myeloid and lymphoid lineages. Mature myeloid cells consist of erythrocytes, platelets, megakaryocytes, mast cells, granulocytes (basophil, neutrophils, eosinophils), and macrophages. Lymphocytes are comprised of T-cells, B-cells, and natural killer (NK) cells. All the mature blood cells are formed through a process known as hematopoiesis where the origin of each mature cell can be traced back to a cell known as a hematopoietic stem cell (HSC). With the exception of certain lymphoid cells (CD4+Tcells, 222 days; CD8+Tcells, 357 days), the mature blood cells of the human hematopoietic system are relatively short lived and undergo rapid turnover (RBC, 100-120 days; platelets, 9 days; neutrophils, 1-3 days) (Westera et al. 2013, Westera et al. 2013). Therefore, numerous control systems are in place to maintain hematopoiesis under both homeostatic and stress conditions. 1.1.1 Hematopoietic Stem Cells HSCs are specialized multipotential cells located in the bone marrow with two major functional characteristics: (1) the ability to self-renew (give rise to identical daughter cells) for the lifetime of an organism; and (2) the ability to differentiate into all the mature blood cell types. This hierarchical organization of the blood system places the long-term HSC (LT-HSC) at the apex of the hematopoietic hierarchy. Given their importance, a tremendous amount of effort has been put into identifying phenotypic markers that can identify true LT-HSCs.  In mice, the lineage negative-Sca-1+c-Kit+ (LSK) fraction makes up approximately 0.05% (1 in 2,000) of nucleated bone marrow and is comprised of a heterogeneous group of cells which contain HSCs as well as more differentiated multipotential progenitors (MPPs) (Okada et al. 1992, 2 Uchida and Weissman 1992, Morrison and Weissman 1994). MPPs or short-term HSCs (ST-HSCs) have a finite self-renewal potential (Osawa et al. 1996). Within this LSK fraction, it is estimated that only 3-7% of these cells are true LT-HSCs (0.002% or 1 in 50,000 BM cells), while no HSC activity is detectable outside of this LSK population (Okada et al. 1992). Many protocols have been developed to further purify this LSK population. These protocols include the use of signaling lymphocyte activation molecule (SLAM) markers to define HSCs as LSK+CD150+CD48-CD244-  (Kiel et al. 2005), defining HSCs as the Hoechst-effluxing side population (SPLSK) (Goodell et al. 1996, Challen et al. 2010) or low staining for Rhodamine123 dye (Rho) HSCs (Bertoncello et al. 1985). In addition, the endothelial protein C receptor (EPCR/CD201) was shown to be expressed at high levels within HSCs (Balazs et al. 2006). Combining EPCR with SLAM markers, so-called ESLAM staining (CD45+EPCR+CD48-CD150+) has allowed for the purification of HSCs in which 1 in 2 to 1 in 3 ESLAM+ cells represent a true LT-HSC, capable of reconstituting all hematopoietic lineages in a mouse (Kent et al. 2009). However, even within the LT-HSC pool, functional heterogeneity exists where an HSC’s capacity for myeloid or lymphoid differentiation is different. Broadly, these HSCs are categorized as α- β- γ- or δ-HSCs. α- HSCs are myeloid-biased, γ- and δ-HSCs are lymphoid-biased, while β-HSCs produce a balanced ratio of myeloid to lymphoid output. Interestingly, the ratios of lineage output from these HSCs are conserved through serial transplants, indicating that there exists a cell intrinsic property that dictates the stem cell program. In comparison to mice, our ability to purify human HSCs is still lacking. Human HSCs are loosely defined as CD34+CD38-Lineage- while more stringent methods define HSCs as RholowCD49f+CD34+CD38-CD90+CD45RA-Lin-. However, by xenotransplantation assays, these purified populations comprise <1% and 15% of HSCs respectively (Bhatia et al. 1997, Notta et al. 2011).   3 1.1.2 Differentiated Progenitors Differentiation is the process in which cells become more specialized in their function. When necessary, an HSC is able to differentiate into more mature and lineage restricted subtypes (Fig. 1.1). The decision to undergo differentiation is influenced by cell-intrinsic factors such as transcription factor and co-factor expression, as well as extrinsic factors including environmental cues from cell-cell contact, stimulation from cytokines and growth factors, or hypoxia. Along this differentiation process, ST-HSCs or MPPs are able to give rise to all the lineages of the blood but persist for a shorter period of time than LT-HSCs and are not maintained for the full lifetime of an organism (Benveniste et al. 2010). Following MPPs, differentiation is restricted as it diverges along either the myeloid lineage or the lymphoid lineage through the common myeloid progenitor (CMP; Lin-c-kit+Sca-1-CD34+CD16/32lo) or common lymphoid progenitor (CLP; Lin-IL-7Rα+c-kitloSca-1lo) respectively (Rieger and Schroeder 2012). CMPs give rise to yet further lineage restricted progenitors as either megakaryocyte/erythroid progenitors (MEP; Lin-c-kit+Sca-1-CD34-CD16/32lo) or granulocyte-macrophage progenitors (GMP; Lin-c-kit+Sca-1-CD34+CD16/32hi) (Rieger and Schroeder 2012). These progenitors produce all the myeloid cells of the body. Conversely, CLPs produce all cells of the lymphoid lineage. Together, these rapidly proliferating progenitors, or transit amplifying cells, are the cells responsible for replenishing the mature terminally differentiated cells in the hematopoietic system.  4  Figure 1.1 - Model of Hematopoietic Hierarchy  Simplified model of the hematopoietic hierarchy where the LT-HSC, capable of self-renewal and differentiation, is situated at the apex. LT-HSCs give rise to ST-HSCs and MPPs which then bifurcates into CMPs or CLPs. The CMPs gives rise to cells of myeloid lineage including megakaryocytes, erythrocytes, granulocytes and monocytes, while CLPs give rise cells of lymphoid lineage such as B-cells and T-cells. LT-HSC, long-term hematopoietic stem cells; ST-HSC, short-term HSC; MPP, multipotent progenitors; LSK, Lin-Sca1+ckit+; CMP, common myeloid progenitor; MEP, megakaryocyte/erythroid progenitor;  GMP, granulocyte- macrophage progenitor; CLP, common lymphoid progenitor.    Although the classical hierarchy depicted in Figure 1.1 conveys the core concept that all lymphoid and myeloid cells are derived from the same HSC, its depiction of myeloid and lymphoid differentiation has been challenged by other models. The classical model describes that the first branch point occurs when an HSC produces one of two progenitors, the myeloid-erythroid progenitor or the lymphoid progenitor. However, other models such as the myeloid-5 based model (Kawamoto 2001) have been described in which myeloid potential is retained in T- and B-cell branches. In this model, myeloid cells represent typical blood cells, whereas erythroid, T- and B- cells represent specially derived types. This is supported by observations that T-cell progenitors in adult thymus lose B-cell potential, yet retain macrophage potential (Kawamoto et al. 2010, Wada H 2008, Bell JJ 2008). Currently, the classical model has been modified to recognize the fact that while CLPs can be purified, an early progenitor described as LMPPs (lymphoid-primed MPP) can generate both myeloid and lymphoid progeny through GMPs and CLPs respectively (Adolfsson et al. 2005) (Fig. 1.2). In addition, by analyzing LMPPs which have been infected with lentivirus containing barcoding sequences, it was shown that most LMPPs have a restricted lineage output and their differentiation fate is imprinted as early as at the HSC level and then becomes more restricted throughout hematopoiesis, possibly influenced by the microenvironment (Naik et al. 2013). This suggests that hematopoiesis more closely resembles a “graded commitment” model, where lineage imprinting takes place early on, rather than the classical bifurcation model (Mansson et al. 2007, Naik et al. 2013).   6  Figure 1.2 - Modified Model of Hematopoietic Hierarchy Current model of adult hematopoiesis which highlights the fact that MPPs first lose their megakaryocytic and erythroid potenital to become LMPPs but still retain their myeloid potential. LT-HSC, long-term hematopoietic stem cells; ST-HSC, short-term HSC; MPP, multipotent progenitors; LMPP, lymphoid-primed MPP; LSK, Lin-Sca1+ckit+; CMP, common myeloid progenitor; MEP, megakaryocyte/erythroid progenitor;  GMP, granulocyte- macrophage progenitor; CLP, common lymphoid progenitor.   1.2 Myelodysplastic Syndromes Myelodysplastic syndromes (MDS) are a heterogeneous group of hematopoietic stem cell disorders characterized by ineffective hematopoiesis in the myeloid lineage. Patients with MDS typically present with chronic peripheral blood cytopenia as well as morphological dysplasia in one or more lineages (Greenberg et al. 1997). However, the bone marrow is generally normo- or hypercellular. Paradoxically, MDS patients can succumb to bone marrow failure if untreated but are also at a greater risk of transformation into acute myeloid leukemias (AML) where there is an expansion and accumulation of immature blasts in the bone marrow and peripheral blood (Fedeli et al. 2014).   In the United States, MDS occurs in 3-4 individuals per 100,000 (Rollison et al. 2008). However, the risk of MDS increases with age where MDS occurs at a rate of 35.49 per 100,000 7 in individuals 80 years and older, compared to a rate of 1.95 per 100,000 in individuals aged 50-59 (Rollison et al. 2008). Incidence of MDS is also higher among males (4.4 per 100,000) compared to females (2.5 per 100,000). However, these estimates are believed to be lower than reality as under-reporting of MDS in cancer registries is often a problem largely because of the difficulty of diagnosis as well as instances where patients with mild anemias may not be referred for bone marrow biopsy to confirm an MDS diagnosis. A report using a claims-based algorithim to assess the 2000-2008 SEER-medicare database suggests that the incidence rate is closer to 75 per 100,000 individuals 65 years of older, in contrast to 20 per 100,000 from the study by Rollison et al. (Rollison et al. 2008, Cogle et al. 2011). At the far end of the spectrum, a report has suggested a rate of incidence as high as 162 per 100,000 in patients ≥ 65years of age, but this study was based on US Medicare reporting that is not based on histopathological diagnosis (Goldberg et al. 2010).  Risk factors for the development of MDS include prior exposure to radiation or chemotherapy as well as smoking and other environmental toxins. The prognosis for MDS patients varies greatly depending on numerous factors including age and disease subtype. As a result, the median survival ranges from a few months to years. In a registry of 1,056 MDS patients in Italy who died between 2008 and 2011, MDS was coded as the underlying cause of death in 41% of cases while 16% died from progression to AML from MDS (Fedeli et al. 2014). Similarly, another study of more than 28,000 deaths listed MDS as the primary cause in 35% of patients (Polednak 2011). Mortality in MDS patients is a result of complications due to blood cytopenias and blood cell defects. Specifically, the causes of death include infections from neutropenia and granulocyte dysfunction (15-38%), hemorrhage from thrombocytopenia and impaired platelet activity (10-24%), and progression to AML (15-24%). One of the harder groups to estimate accurately is those who die of cardiovascular complications like ischemia or myocardial infarction as a result of anemia. In a study of 467 patients, heart failure or 8 arrhythmias represented 51% of non-MDS causes of death (Malcovati et al. 2005). Also, due to the high median age of diagnosis, unrelated geriatric conditions and comorbidities account for a quarter of deaths (Kantarjian et al. 2007, Wang et al. 2009, Dayyani et al. 2010, Naqvi et al. 2011, Greenberg et al. 2012, Sullivan et al. 2013). 1.2.1 Classifications Prior to the French-American-British classification of MDS, all individuals with anemia and abnormal blood production were grouped together under the term “refractory anemia” as they were refractory to treatment (Rhoads and Barker 1938). Similarly, it was observed that some patients with AML had a preceding refractory anemia and therefore, patients were described to have a “preleukemia” or “smoldering leukemia” (Block et al. 1953, Bjorkman 1956, Rheingold et al. 1963). However, this classification was problematic as it was recognized that not all preleukemia patients developed AML, but rather, some developed bone marrow failure. The term MDS reflects the presence of dysplasia in the bone marrow and peripheral blood. In 1976, the FAB classification was published to aid in the classification of MDS and then revised in 1982 (Bennett et al. 1976, Bennett et al. 1982).  This classification system was based primarily on bone marrow blast counts and the percentage of abnormal nucleated erythroblasts with mitochondrial iron known as ringed-sideroblasts. This allowed for the classification of five groups defined as refractory anemia (RA), RA with ringed sideroblasts (RARS), RA with excess blasts (RAEB), RAEB in transformation (RAEB-T) and chronic myelomonocytic leukemia (CMML). This system was widely used for many years until this classification system was modified by the World Health Organization (WHO) to include cytogenetic and genetic aberrations as well as the presence or absence of Auer rods in blood or bone marrow smears (Table 1.1). The current WHO classification of MDS (last revised in 2008) now subdivides RA into groups with or without multilineage dysplasia and subdivides RAEB into two groups based on the percentage of bone marrow blasts. RA patients with an isolated deletion of chromosome 9 5q are classified as del(5q) MDS. Lastly, a category called MDS-unclassifiable (MDS-U) was added, which includes patients with certain MDS features but do not fulfill all the criteria. Recently, Steensma et al. proposed the idea that the minimal diagnostic criteria for MDS should be reconsidered due to our growing knowledge of clonally restricted somatic mutations in MDS-associated genes (Steensma et al. 2015).  Steensma et al. put forward the term “clonal hematopoiesis of indeterminate potential” or CHIP, to describe individuals who do not meet the diagnostic criteria to be classified as MDS but do carry somatic mutations associated with hematopoietic malignancies. This is an important point because it has been shown that individuals with normal blood counts (and no signs of disease) who carry MDS/AML-associated mutations have an increased risk of developing hematological malignancies in the future (Busque et al. 2012, Xie et al. 2014, Genovese et al. 2015). Recurrent somatic mutations found in MDS will be discussed in section 1.3.2. Table 1.1 - World Health Organization 2008 Classification of MDS Disease PB Findings PB Blasts (%) BM Findings BM Blasts (%) BM Ringed Sideroblasts (%) RCUD (RA, RN, RT) Unicytopenia <1 unilineage dysplasia <5 <15 RARS Anemia <1 Erythroid dysplasia <5 >15 RCMD Cytopenias No Auer rods <1 Multlineage dysplasia <5 <15 RCMD-RS Cytopenias No Auer rods <1 Multlineage dysplasia <5 >15 RAEB-1 Cytopenias No Auer rods <5 Unilineage or multilineage dysplasia 5-9 <15 RAEB-2 Cytopenias +/- Auer rods 5-19 Unilineage or multilineage dysplasia 10-19 <15 MDS-U Cytopenias <1 Unilineage or multilineage dysplasia with recurrent cytogenetic abnormality <5 <15 MDS with isolated del(5) Anemia Normal or increased platelets <1 Normal to increased megakaryocyteswith hypolobulated nuclei <5 <15  RCUD, Refractory cytopenia with unilineage dysplasia; RA, refractory anemia; RN, refractory neutropenia; RT, refractory thrombocytopenia; RARS, Refractory anemia with ringed sideroblasts; RCMD, Refreactory cytopenia with multilineage dysplasia; RCMD-RS, RCMD with ringed sideroblasts; RAEB-1, Refractory anemia with excess blasts type-1; RAEB-1, Refractory anemia with excess blasts type-2; MDS-U, Myelodysplastic syndrome, unclassified 10 1.2.2 IPSS Classification in Relation to Prognosis The first widely adopted prognostic scoring system for MDS was developed in 1997 by Peter Greenberg and the International MDS Risk Analysis Workshop (IMRAW) (Greenberg et al. 1997).  This system, named the “International Prognostic Scoring System” (IPSS) is based on cytopenias, BM blasts, and cytogenetics and stratifies patients into four risk categories. This system was modified in 2012 to incorporate the risk of progression to AML and other factors such as age, serum ferritin and lactate dehydrogenase levels. This revised IPSS system (IPSS-R) now stratifies patients into five risk categories which predict distinct survival outcomes and risk of AML transformation (Table 1.2) (Greenberg et al. 2012).  Table 1.2 - IPSS-R Classification of MDS  Score 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 - - - - -  Risk Category  Very Low Low Intermediate High Very High Risk Score ≤ 1.5 > 1.5-3 > 3-4.5 > 4.5-6 >6 Survival, median years 8.8 5.3 3 1.6 0.8  1.2.3 WHO Classification in Relation to Prognosis The WHO classification-based Prognostic Scoring System (WPSS) was introduced in 2007 to provide a dynamic scoring system that can be used at any time during the course of the disease to predict survival and leukemic evolution (Malcovati et al. 2007). This is in contrast to the IPSS where the estimated patient survival or risk of transformation is based on data at the 11 time of diagnosis irrespective of disease evolution. The study in 2007 utilized a cohort of 426 primary MDS patients plus a 739 patient validation cohort and identified WHO classification, cytogenetics, and transfusion requirement as the most significant prognostic variables. Therefore, the WPSS uses these three variables to stratify patients into five prognostic groups (Malcovati et al. 2007).  1.2.4 Current Treatment Approaches Currently, the only curative therapy is an allogeneic hematopoietic stem cell transplant (HSCT). However, the number of patients who are eligible to receive this treatment is relatively low (<1000 patients per year in the United States) due to a variety of reasons including the age of the patient, difficulty in finding a compatible donor, or the inherent risk of the procedure itself (Cutler et al. 2004, Pasquini and Zhu 2015). All together, only about 6% of MDS patients will undergo an allogeneic transplant where only a third of patients will be cured. Half of those transplanted will relapse and as much as 50-60% of patients will develop chronic graft-versus-host disease (cGVHD) (Deeg and de Lima 2013, Koreth et al. 2013). Indeed, patients with a low-risk disease are generally not recommended to undergo a HSCT because the beneficial survival effect relative to the survival benefit by other therapies does not overcome the potential risk of a HSCT (Pasquini and Zhu 2015). Therefore, therapy is selected based on the patient’s overall risk group, transfusion dependence, blast count, and cytogenetic profile. Besides HSCT, current available therapies include treatment with growth factors, lenalidomide, hypomethylating agents, and chemotherapy. Low-risk patients are treated with supportive care to alleviate the effects of cytopenia. RBC blood transfusions or erythropoietin are used for the treatment of anemia and thrombopoietin agonists are used to treat thrombocytopenia. Antimicrobial agents are also used to treat infections which are a significant cause of MDS-related mortality (Pomeroy et al. 1991, Sullivan et al. 2013).  12 In low-risk MDS with del(5q), the immunomodulatory drug lenalidomide (a derivative of thalidomide) has been approved for first line treatment. Treatment in these patients is effective in reducing RBC transfusion-dependence and achieving complete cytogenetic response in approximately 50% of patients  with a median duration of 2 years (List et al. 2006, Fenaux et al. 2011). However, myelosuppression is a major side effect in 50-60% of patients, requiring a dose reduction or discontinuation of treatment. Even in patients who achieve complete remission, the primitive del(5q) clone is not eliminated (List et al. 2006, Tehranchi et al. 2010). The presence of TP53 mutations in the del(5q) clone is a predictor of resistance and reduced treatment response. While lenalidomide treatment is effective in some patients, approximately 20% of responders still progress to AML after 5 years (60% in non-responders) (List et al. 2006, Fenaux et al. 2011). 1.3 Mechanisms of MDS Pathogenesis MDS is a clinically heterogeneous disease which is reflective of the diverse mechanisms that drive disease pathogenesis. Through the advancement of sequencing technology, researchers have identified many genetic lesions which have provided insight into MDS biology. Chromosomal lesions, point mutations, and epigenetic changes play important roles in the pathogenesis of the vast majority of MDS patients. Approximately 50% of MDS patients present with chromosomal abnormalities by conventional metaphase cytogenetic analyses (Haase et al. 2007, Schanz et al. 2012), while more sensitive techniques such as single-nucleotide polymorphism (SNP)-arrays or array comparative genomic hybridization (aCGH) can detect abnormalities in approximately 90% of cases (Gondek et al. 2008, Starczynowski et al. 2008, Haferlach et al. 2014). Likewise, with the advent of next-generation sequencing, somatic point mutations can be identified in more than 90% of MDS cases when looking at 40 recurrently mutated MDS genes (Bejar et al. 2014). Recurrent mutations have expanded our understanding of important biological pathways including RNA splicing, epigenetic regulation, DNA repair, and 13 growth factor response. Indeed, the mutational status of these recurrently mutated genes can be tightly associated with distinct morphological features or clinical phenotypes.   1.3.1 Cytogenetic Abnormalities Approximately half of de novo MDS patients present with karyotypic abnormalities (Haase et al. 2007, Schanz et al. 2012). Evaluation of cytogenetic abnormalities in patient BM holds important diagnostic and prognostic value. The most commonly reoccurring abnormalities are shown in Table 1.3 (Schanz et al. 2012). The frequency of abnormalities is also associated with the severity of the disease and the risk of transformation, where high-risk patients (RAEB-1 and RAEB-2) generally carry more karyotypic abnormalities than low risk (RA and RARS) patients. Several of these cytogenetic aberrations are also observed in AML patients supporting the idea that there is overlap between these two diseases. However, certain karyotypes such as del(5q) and del(20q) are more frequently found in MDS. Conversely, fusion genes that are commonly observed in AML such as AML-ETO (t(8;21)), PML-RARA (t(15;17)), and MYH11-CBFB (inv(16)), are not frequently observed in MDS. Table 1.3 - Commonly Reoccurring Cytogenetic Abnormalities in MDS Very Good Good Intermediate Poor Very Poor del(11q) (0.7%) Normal (55%) -7/7q- (2%) del(3q) (0.4%) >3 abnormalities (7%) -Y (2.2%) der(1;7) (0.3%) +8 (4.7%)    del(5q) (6.4%) iso(17q) (0.4%) Abnormality Plus -7/del(7q)   del(12p) (0.6%) +19 (0.4%)    del(20q) (1.7%) +21 (0.3%)   Median OS (months) 60.8 48.6 26 15.8 5.9 Table shows frequency of single abnormalities in a cohort of 2801 patients (n=1543 normal and n=1258 abnormal karyotype patients). Abbreviations: OS, overall survival. (Schanz et al. 2012)  14 Del(5q) MDS Interstitial deletion of chromosome 5q (del(5q)) is the most common structural genomic abnormality in MDS. It accounts for 7-15% of all MDS cases and approximately 14-29% of patients with a karyotypic abnormality (Haase et al. 2007, Schanz et al. 2012). Patients with isolated deletion of chromosome 5q can also be classified as having 5q- syndrome, characterized by macrocytic anemia, variable neutropenia, normal or elevated platelets with dysplastic hypolobulated megakaryocytes. The extent of chromosome loss varies from patient to patient but two distinct commonly deleted regions (CDR) on chromosome 5q have been identified (Jaju et al. 1998, Boultwood et al. 2002) (Fig. 1.3). The more distal CDR at 5q32-33.1(GRCh38/hg38) spanning approximately 1.5-megabase is comprised of forty protein coding genes and five microRNAs. The second more centromeric CDR of chromosome 5q is located at band 5q31 and contains eighteen coding genes including EGR1 and CTNNA1. This centromeric CDR is associated with more advanced MDS with a higher risk of leukemic transformation (Horrigan et al. 2000). Similarly, patients with larger deletions, either distal or proximal to the CDR, have a greater risk of AML transformation (Jerez et al. 2012). This suggests a separate mechanism of disease progression distinct from the classical low-risk 5q- syndrome with the distal CDR described above.  Interestingly, patients with 5q- syndrome have not been observed to carry biallelic loss of genes within the CDR. With the exception of CSNK1A, which is mutated in 5-7% of del(5q) MDS patients (Bello et al. 2015) (Schneider et al. 2014), point mutations are not observed in the remaining allele of these CDR genes. Therefore, it is believed that haploinsufficiency of genes within the CDR is sufficient for the pathogenesis of del(5q) MDS. Extensive research has been done to find the important disease-mediating genes within either CDR. Through a systematic knockdown of these coding genes in human CD34+ cells by shRNA, researchers demonstrated that the loss of the 40S ribosomal subunit, RPS14, resulted in erythroid cell apoptosis in a p53-15 dependent manner, ultimately leading to macrocytic anemia (Ebert et al. 2008). In a mouse model of 5q- syndrome, researchers deleted the syntenic regions equivalent to the human 5q- CDR. This deleted region, containing genes between Nid67 and Cd74, was sufficient to induce apoptosis in erythroid progenitors leading to macrocytic anemia in the mice (Barlow et al. 2010). The candidate genes within this syntenic region include RPS14, fitting with what has been previously shown. The observed increase in apoptosis in the erythroid progenitors was shown to be facilitated through a p53-dependent manner. Crossing Cd74-Nid67 mice with p53-null mice resulted in a rescue of the anemia phenotype. Interestingly, another ribosomal protein, RPS19, was shown to be frequently mutated in Diamond Blackfan Anemia; a congenital disorder characterized by macrocytic anemia (Torihara et al. 2011). These studies highlight the importance of proper ribosomal biogenesis in erythroid maturation and anemia. While these studies have shown that RPS14 happloinsufficiency is important in mediating the erythroid defect in del(5q) MDS, the Cd74-Nid67 model does not explain the other clinical features of MDS such as thrombocytosis or neutropenia, nor the mechanism of clonal dominance.  Another gene encoded in the distal CDR of del(5q) MDS is the serine/threonine kinase, Casein Kinase 1α1 (CSNK1A1). CSNK1A1 is an important component of the β-Catenin destruction complex in the Wnt signaling pathway (Sinnberg et al. 2010, Elyada et al. 2011). Schneider et al. investigated the role of CSNK1A1 loss in the hematopoietic system and showed that heterozygous inactivation of Csnk1a1 in mice resulted in β-catenin activation and cell-intrinsic expansion of HSCs with increased self-renewal ability (Schneider et al. 2014). CSNK1A1 mutations are found in approximately 5-7% of del(5q) MDS patients meaning that a small proportion of patients carry homozygous loss of CSNK1A1 (Bello et al. 2015) (Schneider et al. 2014). Unlike Csnk1a1-/+Mx1Cre+, homozygous deletion of Csnk1a1 in the hematopoietic system (Csnk1a1-/-Mx1Cre+) resulted in significant reduction in LT-HSCs, ST-HSCs and MPPs. Mice developed bone marrow failure and presented with peripheral pancytopenia leading to 16 death after just 5-17 days post gene excision. The loss in HSCs was attributed to an increase in apoptosis through the activation of p53 and p21 which is not seen in mice with heterozygous deletion (Schneider et al. 2014). Interestingly, it was shown that lenalidomide induces the ubiquitination and degradation of CSNK1A1, leading to growth inhibition (Kronke et al. 2015).  In addition to CSNK1A1, another important regulator of Wnt/β-catenin signaling has been identified on chromosome 5q22.2. Like CSNK1A1, the adenomatous polyposis coli (APC) gene is another member of the β-catenin destruction complex which acts to negatively regulate Wnt/β-catenin signaling. Deletion of both alleles of Apc by a Mx1-Cre-inducible Apcfl/fl model resulted in rapid lethality due to bone marrow failure, and loss of hematopoietic stem and progenitor cells (HSPCs) through apoptosis and exit from cell cycle (Qian et al. 2008). Conversely, heterozygous deletion of Apc or the Apcmin allele that results in a premature stop codon and loss of function showed no abnormality in steady state hematopoiesis. However, Apcmin marrow showed enhanced repopulating activity in primary transplants but decreased repopulation in secondary recipients due to loss of HSC quiescence. Aged mice also developed MDS/MPD (myeloproliferative disorder) attributed to a cell extrinsic mechanism (Qian et al. 2008, Lane et al. 2010, Wang et al. 2010). Interestingly, compound heterozygous deletion of Apc and Csnk1a1 (Csnk1a1-/+Apc-/+Mx1Cre+) resulted in significantly increased LT-HSCs and activation of HSC cell cycle in long term transplants (Schneider et al. 2014). In addition to the protein coding genes within the CDR, five microRNAs (miRNAs) have also been identified within this region. MicroRNAs are a group of evolutionarily conserved small non-coding RNAs (Lee et al. 1993, Wightman et al. 1993). MiRNAs negatively regulate gene expression at the post transcriptional level by binding to complementary sequences of target messenger RNAs (mRNAs) (Nahvi et al. 2009). Our lab and other groups have previously investigated the role of miR-145 and miR-146a in hematopoiesis and this will be discussed in section 1.4.3.  17  Figure 1.3 - Commonly Deleted Region of 5q- Syndrome Genes within two defined commonly deleted regions (CDR) in del(5q)MDS. MicroRNAs are highlighted in green while genes highlighted in red indicate genes which have been previously linked to MDS/AML progression or lenalidomide response. Del(20q) Patients with isolated del(20q) MDS make up approximately 2-5% of MDS patients and carries an overall favourable prognosis with a median survival of 4-6 years (Braun et al. 2011, Pan et al. 2014). Interestingly, ASXL1 is found on chromosome 20q11 and is frequently mutated in 10-20% of MDS (Bacher et al. 2014). However, ASXL1 is located just outside the defined CDR of del(20q). Within the CDR, genes which have been implicated in the pathogenesis of the 18 disease include the polycomb protein coding gene, L3MBTL1, which is frequently lost in MPN, AML and MDS (MacGrogan et al. 2004). Trisomy 8  In approximately 5% of all MDS cases, trisomy 8 is the sole cytogenetic abnormality and carries an intermediate prognostic score with a median overall survival of 23 months (Haase et al. 2007, Schanz et al. 2011). Analysis of CD34+ hematopoietic progenitors carrying trisomy 8 revealed upregulation of numerous anti-apoptotic genes such as BIRC5, MYC, and CD1A (Sloand et al. 2007). While the gain of chromosome 8 is an important prognostic feature in MDS, it appears to be a late event as the amplification is present only in myeloid restricted progenitors and is not found in the more primitive CD34+CD38-CD90+ stem cell compartment (Nilsson et al. 2002), suggesting that trisomy 8 may be a mechanism of clonal advantage rather than disease initiation. Monosomy 7 The loss of chromosome 7 (monosomy 7 or interstitial deletion of 7q) is associated with worse overall survival compared to normal karyotype and represents approximately 10% of MDS. Interestingly, chromosome 7 abnormalities are present in approximately 50% of MDS patients who relapse after treatment with alkylating agents (Christiansen et al. 2004, Haase et al. 2007, Schanz et al. 2012). Three CDRs have been described in del(7q) MDS patients which are localized in bands 7q22, 7q34, and another spanning 7q35-36.1 (Le Beau et al. 1996, Jerez et al. 2012). In a murine model with a deletion of the syntenic region to 7q22, no hematological phenotype was observed (Wong et al. 2010). However, deletion of the histone methyltransferase, MLL5, which is located within the 7q22 CDR, resulted in impaired erythropoiesis and reduced competitive repopulating capacity (Heuser et al. 2009). A frequently mutated chromatin modifier gene, EZH2, is also located on 7q (though not in the CDR) and will be discussed below. 19 1.3.2 Somatic Mutations While identification of cytogenetic anomalies is an important diagnostic and prognostic factor, more than half of de novo MDS patients carry no detectable karyotypic abnormalities. With the advent of next-generation sequencing platforms, an increasing number of molecular alterations and somatic mutations have been observed to be recurrent in MDS. Approximately 78-90% of MDS patients carry at least one mutation when screened against a selected panel of 100 gene targets (Papaemmanuil et al. 2013, Haferlach et al. 2014). Conversely, conventional cytogenetic studies only detected abnormalities in 33% of patients (Papaemmanuil et al. 2013). From large-scale sequencing projects, researchers have identified recurrent mutations in five major categories: DNA methylation (DNMT3A, TET2, IDH1/2); chromatin modification (EZH2, ASXL1) ;RNA splicing (SF3B1, U2AF1, SRSF2, ZRSR2); signal transduction/kinase signaling (JAK2, KRAS, NRAS, CBL); and transcriptional regulation (EVI1, RUNX1, GATA2) (Table 1.4). With the exception of SF3B1 and TET2, most of the above mutations are associated with a worse prognosis (Delhommeau et al. 2009, Bejar et al. 2011, Malcovati et al. 2011, Visconte et al. 2012).  At the same time, SF3B1 is highly associated with the presence of ringed sideroblasts (RARS or RCMD-RS) (Papaemmanuil et al. 2011, Yoshida et al. 2011), while TET2 mutations are enriched in MDS/MPN patients with myelomonocytic differentiation. It is likely that a combination of point mutations and/or cytogenetic abnormalities underlies the heterogeneity in MDS.    20 Table 1.4 - Mutational Landscape of MDS Gene Approximate Frequency Function Pathway TET2 21% Control of Cytosine hydroxymethylation Epigenetic Regulation DNMT3A 8-12% DNA methyltransferase IDH1/2 1-5% Metabolism/ Epigenetic regulation EZH2 5-6% Polycomb group protein ASXL1 14-15% Polycomb group protein SF3B1 18-28% Splicing factor RNA Splicing SRSF2 11-12% Splicing factor U2AF1 7-12% Splicing factor ZRSR2 5% Splicing factor SF3A1 ~1% Splicing factor PRPF40B ~1% Splicing factor JAK2 3% Tyrosine kinase Signal Transduction/ Proliferation KRAS 1% GTPase NRAS 4% GTPase CBL 2% E3 ubiquitin ligase PTEN <1% Phosphatase CDKN2A <1% Cell cycle control RUNX1 9% Transcription factor Transcriptional Regulation ETV6 3% Transcription factor TP53 8% Transcription factor Other SETBP1 7% Nuclear localizing protein NPM1 2% Nucleolar phosphoprotein STAG2 and other cohesins 5-10% Cohesin complex member Estimated mutation frequency in MDS was currated from the following sources: (Bejar et al. 2011, Malcovati et al. 2011, Papaemmanuil et al. 2011, Bejar et al. 2012, Thol et al. 2012, Papaemmanuil et al. 2013, Haferlach et al. 2014) Epigenetic Alterations Epigenetic alterations encompass all modifications to gene expression which are not a result of a change in nucleotide sequence. Mechanisms of epigenetic regulation include chromatin modification, DNA methylation, and RNA splicing. It has been established that genes involved in these processes are frequently mutated in MDS. Within the MDS stem cell pool, aberrant methylation is observed suggesting that some epigenetic alterations may be important drivers of MDS pathogenesis (Figueroa et al. 2009, Jiang et al. 2009, Will et al. 2012). Indeed, hypomethylating agents such as 5-azacytidine (azacitidine) and 5-azadeoxycytidine (decitabine) 21 have shown effective clinical activity in MDS where 40-60% of patients who respond to treatment demonstrate improved overall survival (Silverman LR et al. 2002, Fenaux P et al. 2009). DNA Methylation (DNMT3A, TET2, IDH1/2) Perhaps the most well studied form of epigenetic modification is the methylation of DNA which involves the transfer of a methyl group to a cytosine nucleotide primarily at CpG sites, converting cytosine to 5-methylcytosine (5mC). Generally, areas of the genome that are highly methylated tend to display lower transcriptional activity (Clark and Melki 2002, Mutskov and Felsenfeld 2004). A well characterized example is the aberrant promoter methylation of the tumor suppressor gene p15/INK4B, in approximately one-third of MDS patients (Quesnel et al. 1998, Paul et al. 2010). P15/INK4B is a cyclin-dependent kinase inhibitor which inhibits cell cycle G1 progression and may be an important driver of MDS to AML transformation as its inactivation in mice results in an MDS/MPN (myeloproliferative neoplasm) phenotype with frequent progression to AML (Bies et al. 2010).  DNA methylation is carried out by three DNA methyltransferase (DNMT) enzymes: de novo methylation by DNMT3A and DNMT3B, and maintenance of methylation patterns by DNMT1. DNMT3a mutations are frequently found in MDS where 2-8% of patients harbor loss of function or dominant-negative mutations (Walter et al. 2011). The most common of which is an arginine to histidine mutation at position 882 (R882H) which reduces its methyltransferase activity by approximately 80% compared to the wildtype enzyme. Patients with DNMT3A mutations have worse overall survival and higher risk of progression to AML. In a murine model of DNMT3A loss, HSCs lacking Dnmt3a showed enhanced repopulating potential following serial transplantation (Challen et al. 2012). Dnmt3a-/- HSCs also showed an increase in expression of genes related to stem cell multipotency in tandem with a decrease in differentiation genes. Therefore, it appears that Dnmt3a is important in regulating the balance 22 between self-renewal and differentiation where its loss results in a distinct bias towards self-renewal as opposed to differentiation.  The Tet methylcytosine dioxygenase 2 (TET2) gene is frequently mutated in MDS (15-30%) and other myeloid malignancies (Haferlach et al. 2014).  TET2 is a member of the TET family of enzymes which catalyzes the conversion of 5mC to 5-hydroxymethylcytosine (5hmC). This conversion is dependent on the presence of 2-oxoglutarate/alpha-ketoglutarate (α-KG) which is generated by the enzymes isocitrate dehydrogenases (IDH)-1 and IDH2. 5hmC is an intermediate species leading to DNA demethylation and is found predominantly at promoter CpG islands where it is associated with active gene expression. Indeed, genomic DNA of bone marrow cells from TET-mutated patients and cell line models of TET2 mutants, show reduced level of 5hmC (Ko et al. 2010, Ko et al. 2011, Quivoron et al. 2011). In addition to TET2 loss of function mutations, IDH1/2 neomorphic mutations are also recurrent in MDS (5% of MDS), leading to overall reduced 5hmC content (Langemeijer et al. 2009). The mutations result in IDH1/2 catalyzing the reduction of isocitrate to α-2-hydroxyglutarate which inhibits the function of α-KG dependent genes such as TET2 (Figueroa et al. 2010, Ko et al. 2010). Inactivation of Tet2 in mice results in a rapid expansion of HSPC as well as enhanced self-renewal and replating capability. Tet2-deficient mice also frequently developed a myeloproliferative phenotype with a bias toward myelomonocytic differentiation, comparable to that of human CMML (Moran-Crusio et al. 2011). TET2 mutations are also frequently observed in CMML (42-50%) and AML (20-30%) (Abdel-Wahab et al. 2009, Kohlmann et al. 2010) and this suggests that loss of TET2 activity is important in contributing to clonal dominance in hematopoietic malignancies.  23   Figure 1.4 - Schematic of DNMT3a and TET2 enzymatic activity DNA methyl transferases catalyze the conversion of cytosine to 5-methylcytosine (5mC), leading to DNA methylation. The TET family of demethylases converts 5mC to 5-hydroxymethylcytosine (5hmC) resulting in DNA demethylation. Red font indicates the effect on the enzymatic activity of genes frequently mutated in MDS. Chromatin Modification (EZH2, ASXL1)  The presence of 5hmC is known to influence the recruitment of polycomb repressive complex 2 (PRC2) and influence target gene expression. The protein enhancer of zeste homolog 2 (EZH2) is a member of the PRC2 complex and represses gene expression through trimethylation of histone H3 at lysine 27 (H3K27me3). Approximately 6% of MDS patients carry mutations in EZH2 with the majority of mutations clustering in the methyltransferase domain of EZH2, resulting in loss of function (Ernst et al. 2010, Makishima et al. 2010, Bejar et al. 2011). Conversely, EZH2 mutations found in B-cell lymphoma and follicular lymphoma occurs predominantly at Y641 which alters the enzymatic activity of EZH2 resulting in increased H3K27me3 (Morin et al. 2010, Yap et al. 2011). Therefore, the contrasting effects of EZH2 mutations suggest that this gene can function either as an oncogene or as a tumor suppressor depending on the context and specific mutation. The EZH2 gene is located on chromosome 7q and is a prognostic factor for worse overall survival. However, EZH2 mutations are rarely found in AML suggesting that the negative clinical outcome in patients with EZH2 mutations may not be due to leukemia progression. 24 Another key member of the polycomb pathway is Additional Sex Combs Like 1 (ASXL1). ASXL1 is a homolog of the Drosophila Asx gene which encodes a chromatin-binding protein required for normal determination of segment identity during embryogenesis. ASXL1 is frequently mutated in MDS (15-25%) and other myeloid malignancies including AML (10-15%), CMML (43%) and MPN (10-15%), conferring worse prognosis, including a shorter time to leukemic transformation, reduced rate of response to therapy, and worse overall survival  (Abdel-Wahab 2011, Bejar 2011, Gelsi-Boyer 2009). ASXL1 is also located on chromosome 20q11 but falls outside the CDR of del(20q) MDS. In an Asxl1 loss-of-function mouse model, myeloid and lymphoid differentiation is mildly affected but gene targeting of the Asxl1 locus did not cause myelodysplasia or leukemia (Fisher et al. 2010).  However, conditional deletion of Asxl1 in the hematopoietic system results in myelodysplasia, multilineage cytopenias, and a reduction in HSC self-renewal (Abdel-Wahab et al. 2013). RNA Splicing (SF3B1, SRSF2, U2F1, ZRSR2) It is estimated that 90-95% of multiexon genes undergo alternative splicing, allowing for the production of over 100,000 mRNA species from approximately 25,000 protein-coding genes (Pan et al. 2008, Wang et al. 2008). RNA splicing is the process in which introns are removed from precursor-mRNA transcripts and the flanking exons are joined together to form a mature mRNA. Alternative splicing is an evolutionarily conserved process which allows for multiple mRNA and protein isoforms to be encoded by a single gene.  This is accomplished primarily via exon skipping in which exons and introns are spliced from the transcript.  As a class, RNA splicing genes are targeted in approximately 45-85% of MDS cases (Papaemmanuil et al. 2011, Yoshida et al. 2011, Graubert et al. 2012) and are generally enriched in samples with increased levels of morphological dysplasia such as MDS, CMML, t-AML and s-AML. Conversely, diseases with low levels of dysplasia such as de novo AML and MPN, have a lower frequency of RNA spliceosome mutations.   25 The process of RNA splicing is carried out by the spliceosome which consists of 5 small nuclear RNAs (snRNA U1, U2, U4, U5 and U6) and a combination of accessory proteins to form small nuclear ribonucleoproteins (snRNPs). snRNPs are critical for the enzymatic function of the spliceosome while the non-snRNPs are important for structural assembly, splice-site selection, and coordination of alternative splicing events. Interestingly, RNA splicing mutations in MDS occurs in a mutually exclusive manner, targeting genes in the early steps of U2 snRNP assembly/function (SF3A1, SFS3B1, ZRSR2, SRSF2), 3’ splice-site recognition (SF1), PPT binding (U2AF2), or 3’ splice site binding (U2AF1) (Yoshida et al. 2011). Of note is the splicing factor 3b, subunit 1 (SF3B1) gene which is the most frequently mutated genes in MDS (28%) and correlates strongly with the presence of ring sideroblasts. The presence of an SF3B1 mutation carries a 98%-positive predictive value for the presence of ring sideroblasts. Overall, the presence of SF3B1 mutations appears to be a favorable prognostic marker where patients show prolonged overall survival and increased leukemia-free survival (Malcovati et al. 2011, Papaemmanuil et al. 2011). However, a more recent study concluded that SF3B1 mutation status did not provide additional prognostic information (Patnaik et al. 2012). 1.3.3 Dysregulated Signaling Pathways For normal hematopoiesis to occur, HSCs require the appropriate level of intracellular signaling following the binding of cytokines and growth factors to surface receptors. Mutations that result in constitutive activation or differential response to exogenous stimuli can ultimately affect cell fate in terms of self-renewal, differentiation, proliferation or apoptosis. The RAS family of proto-oncogenes are small guanosine triphosphatases (GTPases) that are important in the signal transduction of many pathways involved in the regulation of cell growth and survival. Activating mutations in NRAS, KRAS, and BRAF are found in approximately 10%, 1-2%, and <1% of MDS patients respectively (Bacher et al. 2007, Bejar et 26 al. 2011). The presence of NRAS mutation is associated with severe thrombocytopenia and increased marrow blasts (Bejar et al. 2011). CBL (c-Casitas B lymphoma), JAK2 V617F (Janus kinase 2), and FLT3 (FMS-like tyrosine kinase 3) activating mutations are relatively infrequent compared to other myeloid malignancies, detected in approximately 2%, 3-4%, and 2-3% of MDS, respectively. The E3 ubiquitin ligase, CBL, is an adaptor protein that negatively regulates receptor tyrosine kinase signaling by marking various receptors for lysosomal degradation. CBL mutations are frequently found in myeloid malignancies including CMML (15%), JMML (15%), and MDS (3%) (Loh et al. 2009, Bejar et al. 2011). Interestingly, CBL mutations are mutually exclusive from other recurrently mutated signaling pathway components such as FLT3, KIT, NRAS/KRAS/BRAF and JAK2 (Reindl et al. 2009, Bejar et al. 2011). Like CBL, JAK2 mutations are not frequently found in MDS, but are commonly found in other myeloid malignancies including MPN like polycythemia vera (74-97%), essential thrombocytopenia (32-57%) and myelofibrosis (35-50%) (Baxter et al. 2005, Levine et al. 2005, Steensma et al. 2005). Interestingly, approximately 50% of RARS-T patients carry JAK2 V617F mutations (Malcovati et al. 2009, Papaemmanuil et al. 2011).  1.4 MicroRNAs MicroRNAs (miRNAs) are a group of evolutionarily conserved small non-coding RNAs, 18-25 nucleotides in length (Lee et al. 1993, Wightman et al. 1993). MiRNAs negatively regulate gene expression at the post transcriptional level by binding to complementary sequences (known as miRNA response elements, MREs) of target messenger RNAs (mRNAs), generally at the 3’UTR region (Nahvi et al. 2009). A short 6-8bp sequence at the 5’ terminus, known as the miRNA seed site is responsible for the recognition of target mRNAs. Binding at this location leads to gene silencing via inhibition of translation initiation, inhibition of translational elongation, or mRNA deadenylation which results in mRNA degradation. There are approximately 1900 27 miRNAs in the human genome with each miRNA capable of binding multiple targets while a given target may be regulated by multiple miRNAs (Kozomara and Griffiths-Jones 2014). MiRNAs are also thought to be more stable than mRNA, suggesting that miRNA expression profiles may also be more stable than commonly used transcriptional signatures (Petriv et al. 2010). 1.4.1 MiRNA Biogenesis  MiRNAs are transcribed within the nucleus by RNA polymerase II or III where the transcript forms a hairpin loop termed a primary-miRNA (pri-miRNA) (Fig. 1.5). Subsequently, the pri-miRNA transcript is cleaved at the stem of the hairpin structure by a protein complex known as the microprocessor complex which consists of the nuclear RNase III-type protein, Drosha, and its cofactor DGCR8. The remaining stem-loop structure (approximately 65 nt in length), termed a pre-miRNA, is subsequently bound and exported to the cytoplasm by the nuclear export factor Exportin 5 (EXP5) and Ran-GTP complex. Once in the cytoplasm, the pre-miRNA undergoes an additional processing step where the RNase III enzyme Dicer cleaves near the terminal loop to create a 22 nt miRNA duplex where the strand at the 5’ end of the miRNA transcript is designated -5p while the complementary strand is designated the -3p strand. At this stage, one of the two mature miRNA strands is loaded onto an Argonaute protein (Ago1-4) to form an effector complex known as RISC (RNA-induced silencing complex). The RISC complex facilitates miRNA-mRNA binding and subsequent gene silencing. 28  Figure 1.5 - MicroRNA Biogenesis Primary microRNA (pri-miRNA) transcripts are transcribed in the nucleus by RNA pol II and III as a hairpin-loop structure. Pri-miRNA transcripts are processed by the nuclear RNase Drosha and exported out to the cytoplasm by the nuclear export protein, Exportin-5. The Pre-miRNA is then processed by the RNase Dicer to yield a miRNA-duplex. One strand of this duplex is loaded into the RISC complex where it facilitates the binding of the mature miRNA to the seed sites of its target mRNAs. Binding leads to mRNA degradation or translational repression.  29 1.4.2 MiRNAs in Hematopoiesis The mammalian hematopoietic system produces a large number of specialized cell types through the process of differentiation. It is now recognized that miRNAs play an important role in HSC maintenance and lineage commitment. By performing RT-qPCR at the single cell level using a high-throughput microfluidic system, researchers have analyzed the miRNA expression of 27 phenotypically distinct cell populations throughout the differentiation hierarchy from normal adult mouse hematopoietic cells (Petriv et al. 2010). Grouping of the cell populations based on miRNA expression profiling revealed a clear separation between myeloid, lymphoid, and hematopoietic stem/progenitor populations. Likewise, it was possible to distinguish between all 27 analyzed cell populations based on miRNA expression. While no single miRNA was found to be solely expressed in a specific cell type, it was found that several miRNAs are generally upregulated or expressed exclusively in HSPC compared to more differentiated cells (Gentner et al. 2010, O'Connell et al. 2010, Petriv et al. 2010). These HSPC-enriched miRNAs include miR-99a, miR-125a/b, miR-126, miR-196a, miR-196b, miR-130a, let-7d, miR-148b and miR-351. Conversely, several miRNAs, including miR-484, miR-200c, miR-331, miR-320, miR-210, miR-324-5p, miR-212 and miR-690, are widely expressed across differentiated cell types but have reduced expression in stem and progenitor cells.  Similarly, miRNA expression profiles can distinguish between myeloid and lymphoid populations. Three miRNAs that are highly upregulated in myeloid cells include miR-25, miR-221, and miR-223 while let-7, let-7d, miR-126, miR-148b, miR-351 and miR-10a expression is reduced in lymphoid lineages. Even at single-cell resolution, Petriv et al. and Gentner et al. were able to confirm the enrichment of these miRNAs in HSCs and observed remarkably low variability in biologically homogenous populations (Gentner et al. 2010, Petriv et al. 2010). This study highlights the fact that miRNA expression is tightly regulated and its dysregulation may affect the differentiation state and lineage potential of a cell.  30 1.4.3 MiRNAs in MDS/AML Numerous studies have shown that miRNAs are differentially expressed between normal and tumor samples in various cancers. Indeed, several of the miRNAs shown to be highly enriched in normal HSCs are often dysregulated in hematopoietic malignancies such as MDS or MPD (Ooi et al. 2010, Petriv et al. 2010, Rhyasen and Starczynowski 2012). Some of these dysregulated miRNAs are summarized in Table 1.5 and Table 1.6. One of the most extensively studied miRNAs in the hematopoietic system is miR-125 (Guo et al. 2010, O'Connell et al. 2010, O'Connell et al. 2010). This miRNA is highly conserved and highly expressed in HSCs. It has been shown to be a critical regulator of HSC function where overexpression of miR-125a increases the HSC pool size (Guo et al. 2010) while miR-125b overexpression results in improved engraftment and increases self-renewal (of lymphoid-biased HSCs) by serial transplantation assays (Ooi et al. 2010). Interestingly, a rare but recurrent chromosomal translocation in AML and MDS, t(2;11)(p21;q23), involving miR-125b leads to a 90-fold increase of miR-125b compared to normal bone marrow (Bousquet et al. 2008). Similarly, other groups have shown an upregulation of this miRNA in a variety of hematopoietic malignancies including del(5q) MDS, AML with AML1/ETO translocations, AML with FLT3 mutations, CML, multiple myeloma and acute lymphoblastic leukemia (ALL). Two separate murine transplantation models of miR-125b overexpression have been conducted. One study resulted in transformation to fatal AML while the other resulted in B- and T-ALL (Bousquet et al. 2008, O'Connell et al. 2010, Ooi et al. 2010). Taken together, miR-125b appears to be a leukemogenic oncomiR. Several members of the pro-apoptotic network, including BAK1, PUMA, BMF, KLF13, and TRP53INP1 have been shown to be targets of miR-125b (Bousquet et al. 2012). MiR-155 has been shown to be more highly expressed in MDS bone marrow compared to healthy controls and sustained expression in mouse bone marrow results in the development of an MPN (O'Connell et al. 2008, O'Connell et al. 2010). It has been shown that miR-155 can 31 target and downregulate CEBPα in AML (O'Connell et al. 2008). The transcription factor CEBPα activates PU.1 and both have been shown to be required for myeloid differentiation, specifically in granulopoiesis and macrophage development respectively (Friedman 2002, Karpurapu et al. 2011). As discussed in section, CSNK1A1 is located on the CDR of del(5q) MDS where haploinsufficiency confers a clonal advantage. MiR-155 has also been shown to target CSNK1A1 leading to enhanced β-catenin signaling in liposarcomas (Zhang et al. 2012).  The miR-29 family of miRNAs consists of three members (miR-29a, –b, and –c). MiR-29 has been extensively studied and was shown to regulate a number of cellular processes including epigenetic regulation and myeloid differentiation (Han et al. 2010, Wang et al. 2012, Wang et al. 2012). As described in section, epigenetic alterations are commonly found in myeloid malignancies such as MDS and AML. Ectopic expression of miR-29 in AML cell lines leads to global DNA hypomethylation and reduces 5hmC levels through its targeting of DNMT3a, DNMT3b, and SP1 (a transcriptional regulator of DNMT1). However, in contradiction to this earlier study, Cheng et al. (2013) found that overexpressing miR-29 family members in mouse BM cells reduced the level of TET1, TET2 and TET3, possibly contributing to global DNA hypermethylation. Downregulation of TETs by miR-29 induced aberrant GMP self-renewal and development of a MPN with progression to AML (Han et al. 2010). Furthermore, transplanting mouse bone marrow cells overexpressing miR-29 led to biased myeloid differentiation, splenomegaly, and an increased percentage of donor-derived monocytes in the bone marrow, inducing a chronic myelomonocytic leukemia (CMML)-like disease (Cheng et al., 2013). We and others have shown miR-146a to be an important regulator of hematopoiesis (Starczynowski et al. 2010, Boldin et al. 2011, Starczynowski et al. 2011, Yang et al. 2012). MiR-146a is differentially expressed during hematopoietic development where it is expressed at relatively high levels in LT-HSCs and lower levels in myeloid and lymphoid progenitors. MiR-146a is also highly expressed in certain T-cell compartments yet lowly expressed in 32 granulocytes (Mac1+) and erythrocytes (Ter119+) (Starczynowski et al. 2010, Starczynowski et al. 2011). MiR-146a is a negative regulator of megakaryopoiesis and granulocyte/macrophage differentiation and is downregulated during megakaryopoisis (Labbaye et al. 2008).  Although not located within the CDR of del(5q) MDS, the miR-146a locus is still frequently lost in many cases. Our lab has shown that the knockdown of miR-146a using a miRNA-decoy system in vivo results in increased megakaryopoiesis through the derepression of its target TRAF6 (Starczynowski et al. 2010). In the same study, we showed that transplant of HSPCs with knockdown of miR-145 and miR-146a or overexpression of TRAF6 results in many of the features observed in human del(5q) MDS including neutropenia, thrombocytosis and hypolobulated megakaryocytes. By 12 weeks post transplant, most recipient mice progressed to bone marrow failure while mice older than 5 months developed AML. Interestingly, miR-146a-null mice produce increased numbers of granulocyte/monocyte cells with age leading to a myeloproliferative phenotype. These mice are also hypersensitive to LPS challenge where chronic inflammation results in myeloproliferation even in young mice (Zhao et al. 2011, Zhao et al. 2013).  Located within the CDR of del(5q) MDS, miR-143 and miR-145 are co-transcribed as a single transcriptional unit (Xin et al. 2009). However, analysis of transcription factor binding sites from the Encyclopedia of DNA Elements (ENCODE) through chromatin immunoprecipitation followed by DNA sequencing (ChIP-Seq) as well as regions of DNaseI hypersensitivity suggests that miR-143 and miR-145 can also be regulated by distinct regulatory factors as there are transcription factors that bind upstream of miR-145 but not miR-143 and vice versa (Fig. 1.6). In cardiac and smooth muscle cells, it was shown that a ~900bp region upstream of miR-143 is sufficient for miR-143/145 expression (Cordes et al. 2009). This region contains binding sites for the serum response factor (SRF) and the cardiac NK-2 transcription factor (Nkx2-5). In addition, myocardin has been shown to be a potent coactivator of SRF where myocardin and SRF can 33 upregulate miR-143/145 expression in vascular smooth muscle cells (Chen et al. 2002, Xin et al. 2009). Recently, it was shown that inactivation of Srf or both myocardin-related transcription factors Mkl1 and Mkl2 in mouse bone marrow results in reduced engraftment and chemotactic response to SDF1 (Cxcl12) (Costello et al. 2015). MiR-145 was shown to be upregulated during granulopoiesis and down regulated during erythropoiesis (Raghavachari et al. 2014). Likewise, miR-145 was shown to play a role in megakaryocyte and erythroid differentiation (Kumar et al. 2011). Kumar et al. identified the megakaryocyte and erythroid regulatory transcription factor, FLI1, as a target of miR-145 where overexpression of FLI1 or knockdown of miR-145 in a human erythroleukemia cell line shifted the balance between megakaryocyte and erythroid differentiation towards megakaryocytes. This finding suggests that loss of miR-145 may be partially responsible for the anemia and thrombocytosis observed in del(5q) MDS. In agreement, in a study of a clonal myeloproliferative disorder, polycythemia vera, it was found that miR-143 and miR-145 are highly upregulated in mononuclear cells (Bruchova et al. 2008). MiR-145 has also been studied in the context of vascular smooth muscle cell function and it was shown that loss of miR-143 and miR-145 leads to smooth muscle cell dedifferentiation resulting in pathologies similar to artherosclerosis (Elia et al. 2009). MiR-143 and miR-145 have also been implicated in numerous solid cancers, including ovarian, bladder, and prostate, where their expression is significantly downregulated (Nam et al. 2008, Avgeris et al. 2015, Coarfa et al. 2015).  34 Figure 1.6 - Transcription Factor ChIP-Seq from ENCODE Schematic of selected transcription factor binding sites of the miR-143 and miR-145 locus from ENCODE. Green arrows represent the pre-miRNA region of miR-143 and miR-145. Transcription factor binding sites are shown as white boxes. Size of boxes are not to scale.    Table 1.5 - MicroRNAs Underexpressed in MDS miRNA Chromosome MDS subtype Reference miR-93 7q22.1 RA/RCMD(tri8) (Hussein et al. 2010) miR-143 5q33.1 Del(5q), RA/RCMD (tri8) (Hussein et al. 2010) (Starczynowski et al. 2010) miR-145 5q33.1 Del(5q), RA/RCMD (tri8) (Hussein et al. 2010) (Starczynowski et al. 2010) (Kumar et al. 2011) miR-146a 5q33.3 Del(5q), all MDS (Sokol et al. 2011) (Votavova et al. 2011) (Starczynowski et al. 2010) miR-150 19q13.33 Del(5q), all MDS (Sokol et al. 2011) (Erdogan et al. 2011) (Hussein et al. 2010)       35 Table 1.6 - MicroRNAs Overexpressed in MDS miRNA Chromosome MDS subtype Reference miR-34a 1p36.23 Del(5q) (Votavova et al. 2011) (Merkerova et al. 2015) miR-181 1q31.3 High-risk MDS, low-risk MDS (Sokol et al. 2011) (Pons et al. 2009) miR-206 6p12.2 Del(5q), all MDS (Hussein et al. 2010) (Sokol et al. 2011) miR-148a 7p15.2 Del(5q) (Hussein et al. 2010) (Votavova et al. 2011) miR-130a 11q12.1 High-risk MDS, del(5q) (Sokol et al. 2011) (Dostalova Merkerova et al. 2011) miR-125b 11q24.1, 21q21.1 Del(5q), t(2;11), high-risk (Hussein et al. 2010) (Sokol et al. 2011) (Dostalova Merkerova et al. 2011) (Metcalf et al. 2005) miR-342 14q32.2 Low-risk MDS, del(5q) (Hussein et al. 2010) (Erdogan et al. 2011) miR-10a 17q21.32 Del(5q), low-risk MDS, CMML (Sokol et al. 2011) (Dostalova Merkerova et al. 2011) (Votavova et al. 2011) (Hussein et al. 2011) miR-199a 19p13.2, 1q24.3 Del(5q) (Hussein et al. 2010) (Votavova et al. 2011) miR-125a 19q13.33 Del(5q) (Hussein et al. 2010) (Dostalova Merkerova et al. 2011) miR-155 21q21.3 High-risk MDS, low-risk MDS (Sokol et al. 2011) (Pons et al. 2009) miR-222 Xp11.3 Low-risk MDS (Sokol et al. 2011) (Pons et al. 2009)  1.5 TGFβ Signaling Pathway The effect of TGF-beta signaling on the hematopoietic system is extremely complex and varies greatly depending on the context and cell type. With respect to hematopoiesis, numerous studies have pointed to an important role of TGFβ signaling in HSC quiescence and self-renewal. Given its ability to elicit dramatic cellular responses even in the most primitive hematopoietic population, it is not surprising that aberrant TGFβ signaling has previously been implicated in MDS and other hematological malignancies. Soluble ligands of the Transforming growth factor β (TGFβ) family include numerous growth factors of structurally similar polypeptide ligands such as Activin (-βA, -βB, -βC, βE), Bone Morphogenetic Proteins (BMP2-7, 8A, 8B, 10, 15), Growth and Differentiation Factors (GDF1-3, 5-11, 15), Anti-Müllerian hormone (AMH), Nodal and TGFβ (-1, -2, -3). These proteins act on a range of processes that work to regulate cell proliferation, growth arrest, pluripotency, differentiation, cell survival and apoptosis. Not surprisingly, this pathway plays a pivotal role 36 during development as well as cancer progression. TGFβ is secreted in a latent form, complexed with two other polypeptides, latent TGF-beta binding protein (LTBP) and latency-associated peptide (LAP) (Gentry et al. 1988, Dallas et al. 1994, Taipale et al. 1994). This latent complex undergoes processing by serum proteases such as plasmin, matrix metalloproteinases (MMPs), thrombospondins, and calpains to release the active form of TGFβ (Schultz-Cherry and Murphy-Ullrich 1993, Abe et al. 1998, Yu and Stamenkovic 2000).  The TGFβ family of receptors are single pass serine/threonine kinase receptors which can be broadly grouped into three categories based on their structural and functional properties; type I, type II and type III families. The type I family of receptors is comprised of seven receptors termed the activin receptor-like kinase (ALK)-1 to ALK-7. Five receptors make up the Type II family which include the Activin type II Receptor (ActRII), ActRIIB, AMHR-II, BMPRII and TGFΒRII. Lastly, the type III receptors include Endoglin, and β-glycan. However, Endoglin and β-glycan do not participate in signal transduction directly as they do not possess serine/threonine kinase domains. Instead, they act as accessory receptors that facilitate TGFβ signaling by presenting ligand to the type I and II receptors. TGFβ signaling involves the binding of a ligand to a receptor complex consisting of a type II homodimer which then recruits and phosphorylates a type I homodimer at a conserved glycine-serine rich domain (GS domain) to form a heterotetrameric receptor (Wrana et al. 1992) (Fig. 1.6). This is followed by downstream signaling through SMAD (Sma and mothers against decapentaplegic) proteins. In humans, there are eight SMAD proteins which are divided into three groups based on their functional role; Receptor-regulated SMADs (R-SMADs), Co-SMADs and Inhibitory-SMADs (I-SMADs). R-SMADs include SMAD1, 2, 3, 5, and 8 which are phosphorylated and activated by TGFβ receptor complexes. The co-SMAD, SMAD4, acts by binding to activated R-SMADs to facilitate their translocation into the nucleus where they act as transcription factors that regulate gene expression. These five R-SMAD proteins are essentially part of two intracellular pathways 37 where SMAD2 and 3 are activated upon TGFβ /Activin signaling, while BMP and AMH typically signal through SMADs 1, 5 and 8. Two inhibitory SMADs, SMAD6 and SMAD7, act to regulate TGFβ signaling activity and are involved in negative feedback as they are also downstream targets of TGFβ (Table 1.7).    R-SMAD and Co-SMAD proteins share two conserved Mad-homology domains (MH1 and MH2).  The N-terminal MH1 domain is connected to the C-terminal MH2 domain by a less well-conserved proline-rich linker region.  This is in contrast to the I-SMADs which only have the MH2 domain. The MH1 domain contains a nuclear localization signal (NLS) and is responsible for DNA binding while the MH2 domain contains a nuclear export signal (NES) and is also responsible for protein-protein interactions such as SMAD oliogomerization, interaction with transcription factors and co-factors such as CBP and p300. Activation of SMADs by TGFβ ligand results in the phosphorylation at the C-terminal SSXS motif and is critical for the stabilization of SMAD-SMAD complexes (Abdollah et al. 1997, Souchelnytskyi et al. 1997, Chacko et al. 2004).  The interactions between receptor and R-SMADs are coordinated and regulated by numerous proteins including DAB2, SARA, HGS, DOK1, TGFΒRAP1, AXIN1 and MAGI2 (Xu et al. 2000, Hocevar et al. 2001, Yamakawa et al. 2002, Di Guglielmo et al. 2003, Hocevar et al. 2005). One of the most well characterized SMAD-binding proteins is the Smad Anchor for Receptor Activation (SARA) protein (Xu et al. 2000). This protein contains a Smad-interacting domain as well as a FYVE phospholipid-binding domain which targets the protein to the membrane. This allows for SARA to promote the interaction of SMAD proteins to TGFΒRI receptors.  Likewise, SARA and the adaptor protein, Disabled-2 (DAB2), have been shown to be important in receptor internalization in endosomes, a process that is required for efficient TGFβ signaling through SMADs (Di Guglielmo et al. 2003). The role of DAB2 in TGFβ signaling will be further discussed below. 38 Table 1.7 - The TGFβ Superfamily of Receptors and Their Ligands Ligands Type II Receptors Type I Receptors Smads TGFβ TGFΒR-II TGFΒRI (ALK5) ALK1 Smad2/3 BMP2/4 BMPR-II BMPR-IA (ALK3) BMPR-IB (ALK6) ActR-IA (ALK2) Smad1/5/8 BMP7 BMPR-II ActR-II BMPR-IB (ALK6) ActR-IA (ALK2) Smad1/5/8 GDF5 BMPR-II ActR-II BMPR-IB (ALK6)  Smad1/5/8 Nodal ActR-II ActR-IB (ALK4) Smad2/3 Activins ActR-II ActR-IIB ActR-IA (ALK2) ActR-IB (ALK4) Smad2/3   Figure 1.7 - Simplified TGFβ Signaling Pathway A simplified schematic of the canonical TGFΒ signaling pathway. Binding of active ligand results in the formation of a heterotetrameric complex of Type I and Type II receptors. Phosphorylation of R-SMADs by TGFΒ receptors and binding with co-SMAD leads to translocation into the nucleus and target gene expression. 39 1.5.1 The Endocytic Pathway and Smad-independent TGFβ Signaling Adding to the complexity of the TGFβ signaling pathway is that binding of ligand to receptor not only initiates the signal transduction events depicted in Fig. 1.6, but it also initiates the internalization of receptor and ligand through endocytosis which can regulate signal transduction both positively and negatively (Fig.1.7). Two major receptor mediated endocytic pathways have been described: clathrin-mediated endocytosis and non-clathrin-mediated endocytosis. The latter includes lipid-raft or caveolae-mediated endocytosis. The most well studied form is the clathrin-mediated pathway where receptor internalization involves the formation of a clathrin coated pit along the cytoplasmic face of the plasma membrane through the recruitment of several proteins including the adaptor complex AP2, and DAB2. Internalization in this manner appears to promote the phosphorylation and activation of SMADs through the formation of a complex between the TGFβ receptors, SARA and SMADs. Alternatively, others have shown that inhibition of clathrin mediated endocytosis only slightly affects SMAD2 phosphorylation but decreases the accumulation of SMAD2 in the nucleus. Conversely, disruption of lipid rafts results in enhanced SMAD signaling. Therefore, clathrin-mediated endocytosis promotes TGFβ/SMAD signaling whereas caveolae-mediated endocytosis inhibits it.  In addition to the canonical pathway, TGFβ can also activate other signaling molecules such as MAPKs, PI3K/AKT, and NF-κB signaling independent of SMADs (Conery et al. 2004, Remy et al. 2004, Sorrentino et al. 2008, Yamashita et al. 2008, Neil et al. 2009). Rather than signaling through SMADs, the activated TGFβ receptor complex transmits its signal through factors such as tumor necrosis factor (TNF) receptor-associated 4 (TRAF4), TRAF6, TGFβ-activated kinase 1 (TAK1), p38 mitogen-activated protein kinase (p38 MAPK), RHO, phosphoinositide-3-kinase (PI3K), AKT, extracellular signal-regulated kinase (ERK), JUN N-40 terminal kinase (JNK) or nuclear factor-kB (NF-kB). However, it is unclear whether TGFβ-mediated activation of some or all of these molecules is dependent on the endocytic pathway.   Perhaps the best characterized SMAD-independent pathway is the JNK/p38 signaling pathway. Experiments using dominant negative SMAD3 or cells deficient for SMAD3 or SMAD4, showed that SMADs were dispensable for TGFβ induced activation of JNK. Similarly, researchers using a mutant TGFΒRI which retains its kinase activity but is defective for SMAD binding and activation, is still capable of mediating TGFβ induced activation of p38 and JNK (Yu et al. 2002, Itoh et al. 2003). Both JNK and p38 are part of the MAPK cascade where they are activated by MKK4 and MKK3/6 respectively. Recently, it has been shown that DAB2 negatively regulates TGFβ-induced JNK activation in an ovarian cancer cell line (Shapira et al. 2014). Upstream of MKK4 and MKK3/6 is the MAP3K, TGFβ-activated kinase 1 (TAK1). TAK1 has been shown to physically interact with TGFΒRII and is required for TGFβ induced JNK and NF-kB activation (Shim et al. 2005, Watkins et al. 2006). The TRAF6 protein plays an important role in the activation of TAK1 in Toll-like receptor (TLR) signaling, but it is also crucial for TGFβ induced activation of the TAK1/JNK/p38 signaling pathway (Sorrentino et al. 2008, Yamashita et al. 2008). TRAF6 associates with TGFΒ type II and type I receptors through its C-terminal TRAF domain leading to activation of its E3 ligase activity and subsequent lysine-63 linked polyubiquitination (Wang et al. 2001, Haglund and Dikic 2005). This association leads to activation of TRAF6 and the recruitment and activation of TAK1. 41  Figure 1.8 - Smad-Independent Signaling and the Endocytic Pathway Schematic of the TGFβ signaling pathway and its regulation by the endocytic pathway. Clathrin-dependent internalization brings Smad2/3 and adaptor proteins like SARA and DAB2 together to promote TGFβ signaling. Conversely, the lipid raft-caveolar rich vesicles contain SMAD7 bound receptors to facilitate rapid receptor turnover. TGFβ signaling can also occur in a SMAD independent manner leading to the activation of MAPK pathways.    1.5.2 TGFβ Signaling in MDS There is accumulating evidence that the TGFβ pathway plays an important role in hematological malignancies such as myelodysplastic syndromes. Powers et al. showed that SNPs associated with increased expression of TGFβ1 are overrepresented in the MDS 42 population (Powers et al. 2007). Similarly, Akiyama et al. showed that in MDS with myelofibrosis, both the myelofibrosis and megakaryocytosis can be attributed to the overproduction of TGFβ1 from blast cells (Akiyama et al. 2005). Importantly, it was shown that SMAD2 is upregulated and overactivated in CD34+ BM progenitors from MDS patients while the inhibition of TGFβ receptor I kinase was able to promote hematopoiesis and alleviate the anemia in an MDS mouse model (Zhou et al. 2008). The same group also demonstrated that miR-21 is markedly higher expressed in MDS patients and that this miRNA is able to bind the mRNA of the inhibitory-SMAD, SMAD7, resulting in an activation of TGFβ signaling (Bhagat et al. 2013).   Indeed, therapies targeting the TGFβ signaling pathway are currently in development in clinical trials. The TGFβRI inhibitor, LY2157299, is a small molecule that inhibits the kinase activity of TGFΒRI and has been shown to stimulate hematopoiesis in a mouse model of bone marrow failure as well as in primary MDS bone marrow samples ex vivo (Zhou et al. 2011). The TGFβ ligand, activin, is known to be important in erythroid differentiation (Shiozaki et al. 1998, Akel et al. 2003, Maguer-Satta et al. 2003).  The compound ACE-536 is a modified type-II activin receptor fusion protein and acts as a TGFβ superfamily ligand trap. This drug has shown effectiveness in reducing anemia and stimulating erythropoiesis in an EPO independent mechanism in a mouse model of MDS/AML (Lin et al. 2005, Attie et al. 2014, Suragani et al. 2014, Suragani et al. 2014). Similarly, Sotatercept (ACE-011) is also a TGFβ pathway inhibitor which functions as a soluble fusion protein with an extracellular domain of the activin receptor type IIA linked to the Fc protein of human IgG1. It too has shown to improve ineffective erythropoiesis in a mouse model of β-thalassemia and appears to work by a similar mechanism as ACE-536 (Dussiot et al. 2014).  43 1.5.3 TGFβ Signaling in Hematopoiesis The effect of TGFβ signaling on the hematopoietic system is extremely complex and varies depending on the context and cell type. In vitro, TGFβ has been shown to be a potent inhibitor of HSC or progenitor growth while neutralization of TGFβ is able to release HSPCs from quiescence (Keller et al. 1988, Hatzfeld et al. 1991, Soma et al. 1996, Batard et al. 2000, Fortunel et al. 2000, Fortunel et al. 2000). In agreement, constitutive TGFβ1 secretion in a transgenic mouse results in anemia, dysplastic megakaryocytes and bone marrow failure (Zhou et al. 2008). Based on its anti-proliferative effect in hematopoietic cells, TGF-beta is regarded as a tumor suppressor. The mechanism by which TGFβ1 induces HSC quiescence has been shown to be in part through the upregulation of the cyclin-dependent kinase (CDK) p57Kip2 and p19INK4d (Scandura et al. 2004, Yamazaki et al. 2006, Hilpert et al. 2014). p57Kip2 expression is enriched in the more primitive CD34-LSK cells compared to the CD34+LSK fraction and correlates with the activation status of SMAD2/3 (Yamazaki et al. 2009). These studies suggest that TGFβ induces p57Kip2 specifically in the primitive HSC compartment and promotes a quiescent state in vivo. Despite what was learned from in vitro studies, mouse models which knockout key TGFβ-signaling molecules suggest a more complex role in adult hematopoiesis. TGFβ1 ligand and TGFβRII receptor knockout mice develop a lethal inflammatory disorder affecting multiple organs. Transplantation of TGFβRII knockout marrow into normal recipient mice also caused a lethal inflammation (Letterio et al. 1996, Yaswen et al. 1996, Leveen et al. 2002). Interestingly, analysis of BM cells from Tgfb1 knockout neonates (before inflammation occurs) revealed reduced reconstitution ability which the authors attributed to impaired homing ability (Capron et al. 2010). Similarly, Tgfb2+/- mice have a lower frequency of LSKs and reduced repopulating potential while Tgfb2-/- fetal liver cells only display a defect upon serial transplantations (Langer et al. 2004). These findings suggest that TGFβ1 and TGFβ2 are positive regulators of adult 44 HSCs. However, Tgfbr1 (ALK5) knockout HSCs showed normal self-renewal and homing capacity even under stress conditions of serial transplantation or repeated 5-FU treatments (Larsson et al. 2003, Larsson et al. 2005) To investigate the role of SMAD signaling in HSCs, researchers have over expressed the inhibitory Smad7 or deleted the co-SMAD, Smad4. Enforced expression of Smad7 resulted in increased self-renewal of HSCs with no effect on differentiation (Blank et al. 2006). This suggests that SMAD signaling negatively regulates self-renewal of HSCs independent of differentiation. Conversely, overexpression of SMAD7 in human Lin- cord blood using a NOD/SCID xenograft system resulted in a shift from lymphoid to myeloid differentiation (Chadwick et al. 2005). Using a Smad4 inducible knockout mouse model (Mx1-Cre), Smad4-deficient HSCs displayed reduced repopulating ability in primary and secondary transplants (Karlsson et al. 2007). Therefore, contrary to what was observed in Smad7-overexpressing studies, Smad4 knockout experiments suggest that SMAD signaling is critical for HSC self-renewal.  The majority of HSCs within the BM niche are in a hibernating or quiescent state (Foudi 2009, Sudo 2000). This quiescence is linked to the maintenance of the stem cell pool and loss of quiescence can result in HSC exhaustion. The decision to enter the cell-cycle and undergo self-renewal or differentiation is regulated by numerous factors including signals from the stem cell niche within the bone marrow. Lipid raft clustering was previously shown to be crucial for HSC entry into the cell cycle (Yamazaki et al. 2006). In a follow-up study, Yamazaki et al. performed a screen of candidate niche signals and identified TGFβ as one that efficiently inhibited cytokine-mediated lipid raft clustering and induced HSC hibernation ex vivo (Yamazaki 2009). Similarly, Tgfbr2del/-Rag2-/-CD34-LSK HSCs showed reduced pSmad2/3, increased cycling, and reduced long term repopulating activity compared to Tgfbr2+/+Rag2-/-CD34-LSKs. Therefore, TGFβ/Smad signaling plays an important role in maintaining HSC dormancy 45 (Yamazaki et al. 2011). The primary source of active TGFβ in the BM niche is from nonmyelinating Schwann cells, which ensheath sympathetic nerves in the BM (Yamazaki et al. 2011). Interestingly, it was shown that a large proportion of HSCs are in direct contact with Schwann cells in the BM (Yamazaki et al. 2011). Autonomic nerve denervation of BM reduced the number of TGFβ-secreting cells, resulting in significant loss of CD34-LSK HSCs. Another major source of TGFβ is megakaryocytes which physically associate with approximately 20% of HSCs in the BM (Bruns et al. 2014, Zhao et al. 2014). These HSCs show activation of SMAD2/3 while the ablation of megakaryocytes or the conditional deletion of TGFβ1 in megakaryocytes, results in reduced SMAD2/3 phosphorylation and a loss of HSC quiescence. Therefore, SMAD2/3 signaling appears to be important for the maintenance of HSC quiescence.  Goodell and colleagues recently described two distinct HSC subpopulations which respond differently to TGFβ signaling (Challen et al. 2010). Two HSC populations were purified based on Hoechst dye efflux and designated as lower or upper side population+ LSK HSCs (Lower-SPLSK and Upper-SPLSK). It was shown that Lower-SPLSK HSCs possessed a myeloid differentiation bias (My-HSCs) while the Upper-SPLSK HSCs were lymphoid-biased (Ly-HSCs). While both are able to repopulate the entire hematopoietic hierarchy long-term, My-HSCs are relatively quiescent and longer lived than Ly-HSCs. Interestingly, My- and Ly-HSCs respond differently to TGFβ1 stimulation both in vitro and in vivo. TGFβ1 and TGFβ2 stimulate My-HSC proliferation while inhibiting Ly-HSCs (Challen et al. 2010). The exact mechanisms behind this differential response is not fully understood but My-HSCs showed up-regulation of the myeloid transcription factors, PU.1, when treated with TGFβ1 while Ly-HSCs expressed the lymphoid lineage factor, Ikaros. It was previously shown that with age, HSCs preferentially differentiate towards the myeloid lineage, resulting in a decrease in lymphoid cells (de Haan and Van Zant 1999, Sudo et al. 2000, Rossi et al. 2005). As previously described, TGFβ can differentially regulate My- and 46 Ly-HSCs. Therefore, TGFβ is implicated in the aging of HSCs. Recently, an epigenomic profile (transcriptome, DNA methylome, and histone modifications) of young and old HSCs revealed significant changes in the TGFβ pathway at the transcriptome level, representing 19% of all differentially expressed genes (Sun et al. 2014). These changes result in an overall reduction in TGFβ signaling in aged HSCs. It was proposed that because low concentrations of TGFβ can stimulate proliferation in My-HSCs, the reduction in TGFβ signaling in aged mice may provide a more suitable environment for My-HSC expansion. In addition, the stimulatory effect of TGFβ2 was shown to have an age component as its effects on the LSK population increases with age. Another factor that has been shown to promote physiological aging of HSCs is the tripartite motif containing 33 protein (TRIM33/Tif1γ). Tif1γ has been shown to be crucial for hematopoiesis by competing with Smad4 to bind pSmad2/3. Smad2/3-Smad4 mediates anti-proliferative effects in HSPCs while Smad2/3-Tif1γ complex stimulates differentiation (He et al. 2006, Xi et al. 2011). Quéré et al showed that Tif1γ expression decreases with age. Aged wild type mice as well as young Tif1γ-/- mice, both display an aging HSC phenotype where there are a greater number of myeloid biased HSCs but they have a reduced capacity to differentiate and generate white cells in the peripheral blood (Quere et al. 2014). Tif1γ was also shown to control Tgfbr1 turnover, leading myeloid-biased Tif1γ-/- HSCs and old HSCs to have increased levels of Tgfbr1 making cells more sensitive to TGFβ signaling than young HSCs. 1.5.4 Disabled-2 and MDS Disabled-2 (DAB2) is an important mitogen-responsive phosphoprotein and endocytic adaptor protein. Originally named DOC2 for “Differentially expressed in Ovarian Cancer”, it is frequently downregulated in ovarian cancer and therefore considered a tumor suppressor. Likewise, numerous studies have shown reduced or loss of expression in a variety of solid tumors including cancers of the breast, colon, and lung (Mok et al. 1998, Kleeff et al. 2002, Bagadi et al. 2007, Tong et al. 2010, Xu et al. 2011, Xie et al. 2013). However, despite the 47 observation that DAB2 is lost or downregulated in these cancers, the investigators do not provide an explanation of the molecular mechanisms by which DAB2 acts on the initiation or pathogenesis of the disease. DAB2 is a multi-domain adaptor protein best characterized for its role in clathrin-mediated endocytosis of selected cargo proteins and mediator of signal transduction in numerous signaling pathways through its N-terminal phosphotyrosine-binding (PTB) domain as well as its C-terminal proline-rich SH3 domain (Xu et al. 1998, Mishra et al. 2002). In addition, DAB2 is able to mediate endocytosis by associating with clathrin (Mishra et al. 2002), the clathrin adaptor protein-2 (AP2) (Morris and Cooper 2001) and myosin VI (Morris et al. 2002). DAB2 is able to bind phosphatidylinositol 4,5-bisphosphate (PtdIns(4,5)P2) and cargos containing NPXY internalization motifs to recruit them to clathrin coated pits. This has been demonstrated for proteins such as the low density lipoprotein (LDL) receptor (Morris and Cooper 2001), integrin beta-1(Huang et al. 2006, Chao and Kunz 2009), cystic fibrosis transmembrane conductance regulator (CFTR) (Madden and Swiatecka-Urban 2012), and TGF-beta receptors (Hocevar et al. 2001).  Within the TGF-beta pathway, DAB2 was shown to be important in facilitating the phosphorylation of SMAD2 and SMAD3 by TGF-beta receptors (Hocevar et al. 2001). DAB2 was shown to be required for the recycling of TGF-beta receptors back to the cell surface. Loss of DAB2 results in enlarged early endosomes, reflecting the inability of type I and II TGFβ receptors to traffic from early endosomes to recycling endsomes and also leads to reduced SMAD2 phosphorylation (Penheiter et al. 2010). Interestingly, in a study of epithelial-mesenchymal-transdifferentiation (EMT) in mammary gland epithelial cells, it was shown that TGFβ treatment can stimulate DAB2 translation through a post-transciptional mechanism (Chaudhury et al. 2010, Hussey et al. 2011). This suggests that DAB2 is both a mediator of TGFβ signaling as well as a downstream target of TGFβ. DAB2 is also involved in SMAD-48 independent TGFΒ signaling pathways where it positively regulates TGF-beta-mediated JNK activation (Hocevar et al. 2005) in mouse fibroblasts and rat aortic smooth muscle cells.  However, Ehrlich et al. published a conflicting report where Dab2 inhibits the cholesterol-dependent activation of JNK by TGFΒ in ovarian cancer cell lines (Shapira et al. 2014).  Beyond TGFβ signaling, DAB2 has been shown to inhibit the canonical Wnt/beta-catenin pathway by stabilizing Axin and preventing its interaction and dephosphorylation by protein phosphatase-1 (PP1). This results in the stabilization of the beta-catenin destruction complex and attenuation of the signal (Jiang et al. 2009). Similarly, DAB2 sequesters the low density liproprotein-related protein 6 (LRP6) coreceptor of WNT, thereby inhibiting the pathway (Jiang et al. 2012). 1.6 Aims of the Study Research to date has allowed for a tremendous understanding of the function and processes involved in hematopoiesis. In spite of the knowledge we have obtained in studying this vital system, our ability to treat many of the hematological disorders is still lacking. Therefore, a better understanding of the factors affecting proper blood production is essential so that more effective treatments can be developed.   It is clear that miRNAs play an important role in development as well as in maintaining normal adult hematopoiesis, where their dysregulation can have drastic consequences. We and others have reported on the importance of miR-146a in HSC function where haploinsufficiency results in a decline of HSCs and bone marrow failure along with progression to acute myeloid leukemia (Starczynowski et al. 2010, Starczynowski et al. 2011, Zhao et al. 2013). However, miR-146a is not located in the CDR of del(5q) MDS (5q33.3). Using two available datasets of del(5q) MDS patients in which SNP-A or aCGH analysis was performed,  we estimate that only 30-42% of del(5q) MDS patients have a deletion spanning the miR-146a locus (Evers et al. 49 2007, Mallo et al. 2013). Interestingly, it has been shown that patients with larger 5q deletions telomeric to the CDR have a more aggressive disease and worse overall survival (Jerez et al. 2012). This suggests that del(5q) MDS patients with deletion of miR-146a will have a different disease prognosis than the majority of patients with miR-143/145 deletion alone. Therefore, it is important to study the effect of miR-143/145 loss in the hematopoietic system given that they are the only two miRNAs located in the CDR which are differentially expressed between diploid chromosome 5q and del(5q) MDS patients (Starczynowski et al. 2010). The overall aim of this thesis was to characterize a murine model of miR-143 and miR-145 loss in order to better understand the molecular and cellular processes affected by these two miRNAs within the hematopoietic system.  In Chapter 3, we investigated the effect of miR-143/145 loss specifically in the HSC and myeloid progenitor compartment. We worked to characterize miR-143/145-/- mice through immunophenotypic analysis of peripheral blood and bone marrow. Additionally, to further characterize the effect of these miRNAs on HSC function, we performed limiting dilution assays (LDA) coupled with secondary competitive repopulating unit (CRU) assays to assess the frequency of functional HSCs and their ability to self-renew in vivo. We went on to explore the molecular pathways that are affected by these miRNAs through the use of bioinformatic tools.  The results of these assays led us to believe that miR-143 and miR-145 act to target the TGFβ-signaling pathway and are critical regulators of HSPC function. The results generated in Chapter 3 suggested that miR-143/145 loss results in the derepression of DAB2. We hypothesize that differential expression of DAB2 might influence HSC frequency and self-renewal. In Chapter 4, we test this hypothesis by performing a series of experiments to test the effect of DAB2 overexpression on hematopoietic repopulation and HSC self-renewal capacity.50 Chapter 2 – MATERIALS AND METHODS 2.1 General Reagents SuperScript II reverse transcriptase, Trizol and Alexa Fluor secondary antibodies were acquired from Invitrogen (Carlsbad, CA). DAPI (4’,6-diamidino-2-phenylindole), goat anti-mouse horse radish peroxidase (HRP), rat anti-mouse HRP, were purchased from Sigma-Aldrich (St. Louis, MO). The Dual-Luciferase Assay was from Promega (Madison, WI). All primers were acquired from Integrated DNA Technologies (San Diego, CA). Bradford reagents were acquired from Bio-Rad (Mississauga, Ontario). Complete protease inhibitor cocktail tablets were acquired from Roche Applied Science (Mississauga, Ontario). TransIT®-LT1 Transfection Reagent was acquired from Mirus (Madison, WI). Western Lightning® Plus-ECL was acquired from Perkin Elmer (Waltham, MA). Bioflex – Scientific Imaging Films were acquired from InterScience (Markham, ON). Pre-stained protein ladders were acquired from Fermantas/Thermo Scientific (Burlington, ON). pDrive cloning kit, mini- and midi-prep extraction kits and gel extraction kits were acquired from Qiagen (Toronto, ON) or Promega (Madison, WI). Cell culture media and supplements were purchased from Thermo Fisher Scientific (Burlington, ON). 2.2 Cell Culture 2.2.1  Cell Lines The amphotropic retroviral packaging cell line, Phoenix, is derived from the HEK-293T/17 cell line containing viral gag-pol regions (Swift et al. 2001). The PhoenixTM-Ampho cell line and lentiviral producing HEK-293T cell line were obtained from Dr. Gary Nolan (Stanford University, Palo Alto, CA). The ecotropic retroviral packaging cell line, GP+E-86 (GPE86), was acquired from the American Type Culture Collection (ATCC). This line was originally generated through the electroporation of two plasmids into the NIH-3T3 cell line. One plasmid contained the gag and pol regions of the Moloney murine leukemia virus (Mo-MuLV), while the other contained the env region of Mo-MuLV. Ampho-Phoenix, HEK-293T, and GPE86 cells were 51 cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Thermo Fisher Scientific, Burlington, ON) supplemented with 10% heat inactivated fetal-bovine serum (FBS), 2 mM L-glutamine and 100U each of penicillin and streptomycin (Thermo Fisher Scientific, Burlington, ON). In vitro work was carried out using the acute megakaryoblastic cell line UT-7 acquired from ATCC. UT-7 cells were maintained in DMEM containing 10% HI-FBS and 1ng/ml GM-CSF. All cell lines were maintained cultured at 37°C with atmospheric air and 5% CO2. 2.2.2 Gene Transfer To generate retroviral producer cell lines, the PhoenixTM-Ampho cell line was transfected with our viral construct of interest and the viral supernatant was used to infect GPE86 cells. All retroviral vectors used in this thesis utilized a MSCV-IRES-GFP/YFP backbone (MIG or MIY). Human cell lines were infected with lentivirus produced from HEK293T transiently transfected with pLL3.7 miR-143/145decoy. Briefly, constructs were transiently transfected into either the PhoenixTM-Ampho or HEK293T using TransIT®-LT1 Transfection Reagent (Mirus Bio, Madison, WI) according to manufacturer’s instructions. Retroviral and lentiviral supernatants were collected and filtered through a 0.45 m filter, supplemented with 8 g/mL Polybrene (Sigma-Aldrich, St. Louis, MO) and fresh medium before applying to cells. The virus producing cells were replenished with fresh medium and this procedure was repeated 24 hours later. The MIG/MIY and pLentilox transduced cells were sorted by fluorescent activated cell sorting for yellow fluorescent protein (YFP) or green fluorescent protein (GFP) using a BD FACSAriaTM III or BD Influx II flow-sorter (BD, Franklin Lakes, NJ).   For transplantation and ex vivo assays, mouse bone marrow was isolated and transduced with retrovirus containing our constructs of interest. Briefly, retrovirus producing GPE86 cells were irradiated (40Gy) by X-ray and co-cultured with mouse bone marrow in DMEM containing 15% HI-FBS, 10ng/ml rhIL-6, 6ng/ml rhIL-3, 100ng/ml mSCF and 8µg/ml 52 Polybrene.  GPE86 and bone marrow were co-cultured for 72-96hrs and sorted for positively transduced GFP/YFP positive cells. Progenitor assays were performed using whole bone marrow from miR-143/145 mice or 5-FU enriched bone marrow. Cells were plated in duplicate onto MethoCultTM GF-M3434 methylcellulose as per the manufacturer’s instructions and counted 10-12 days post plating. For secondary replatings, cells were collected using cell scrapers. Cells were washed with PBS, counted by Trypan blue exclusion, and then plated again onto MethoCultTM GF-M3434 methylcellulose at equal ratios.  2.2.3 RNA Interference Stable knockdown of target genes were accomplished using the retroviral construct pSIREN to express short-hairpin RNA (pSIREN-RetroQ-DsRed, Clontech, Mountain View, CA). The construct contains a dsRED marker for selection. shRNA sequences were taken from the Broad Institutes RNAi Consortium shRNA Library and listed in Appendix A. All experiments used the empty shRNA construct (shNeg) or a sequence targeting Luciferase (shLuc) as a control for non-specific targeting. 2.3 Immunoblotting Cell lysates were collected in RIPA buffer (50 mM HEPES pH7.4, 150 mM NaCl, 10% glycerol, 1.5 mM MgCl2, 1 mM EGTA, 1 mM sodium vanadate, 10 mM sodium pyrophosphate, 10 mM NaF, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 1 mM PMSF, 1x phosphatase inhibitor cocktail (Sigma-Aldrich) and 1x protease inhibitor cocktail (Roche Applied Science) as previously described (Fu et al. 2009). Briefly, cells are washed once with phosphate-buffered solution (PBS) and lysed with RIPA buffer on ice. Cell lysates were collected into an eppendorf tube and were placed on a rotator for 2hrs at 4°C. Samples were then centrifugated at 13,000 rpm for 10 minutes and the supernatant cell lysates were 53 transferred into a new eppendorf tube and protein concentrations were determined using Bio-Rad DC Protein Assay System (Bio-Rad Laboratories, Hercules, CA). Samples (30-80 g) were denatured in 1X loading dye at 90°C for 10 minutes and loaded onto a 7.5% or 10% SDS-PAGE gel. Electrophoresis was accomplished by applying 100-120 V current through the SDS-PAGE gel. The protein samples were then transferred onto nitrocellulose membrane (Bio-Rad Laboratories, Hercules, CA), blocked in 5% skim milk/PBS/0.1% Tween for 1 hour at room temperature, incubated with primary antibody in 5% skim milk/PBS/0.1% Tween at 4°C overnight, washed three times with PBS/Tween, incubated with secondary antibody (HRP) in 5% skim milk/PBS/0.1% Tween at room temperature for 1 hour, washed three times with PBS/Tween, and developed by enhanced chemiluminescence (PerkinElmer Life Sciences, Boston, MA). Membranes were probed using the following antibodies: mouse anti-DAB2, rabbit anti-Smad2 (D43B4), rabbit phospho-Smad2 (ser465/467) (138D4), rabbit anti-Smad3 (C67H9), rabbit Phospho-Smad3 (Ser423/425),  rabbit anti-Smad4, rabbit anti-phospho-p38 MAPK (Thr180/Tyr182), rabbit anti-p38 MAPK, rabbit anti-SAPK/JNK (56G8), mouse anti-phopho-SAPK/JNK (Thr183/Tyr185) (G9), rabbit anti-Akt, rabbit anti-phospho-Akt (Ser473) (D9E), rabbit anti-TAK1 (D94D7), rabbit anti-phospho-TAK1 (Thr184/187), 1:50,000 mouse anti-GAPDH and appropriate secondary antibody (1:10000 HRP-conjugated IgG, Sigma-Aldrich, St. Louis, MO). Cells from lineage negative bone marrow were isolated by FACS using lineage markers described above or were isolated using the EasySepTM mouse hematopoietic progenitor enrichment kit (Stem Cell Technologies).  2.4 Co-Immunoprecipitation HEK293T cells were co-transfected with 2-5 μg of each expression plasmid using the TransIT®-LT1 Transfection Reagent (Mirus Bio Corporation, Madison, WI) according to the manufacturer’s protocol. Briefly, cell lysates were collected for immunoprecipitation 48 hours post-transfection using a modified RIPA buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM 54 EDTA, 1% NP-40, 0.25% sodium deoxycholate, plus 1x protease inhibitor cocktail (Roche Applied Science). Cell lysates were assayed for protein concentration using the Bio-Rad DC Protein Assay System (Bio-Rad Laboratories, Hercules, CA) where 2-3 mg of lysates were pre-cleared by incubation with Protein A agarose beads (EMD Millipore Corporation, Billerica, MA) for 1 h at 4 °C. Precleared lysates were subsequently used for immunoprecipitation with anti-Dab2 (1 μg; Cell Signaling Technology), anti-HA (1 μg; Covance, Emeryville, CA) or control isotype IgG (Sigma- Aldrich, St. Louis, MO) overnight at 4°C, followed by an incubation with TrueBlot® Anti-Rabbit Ig IP Beads (eBioscience, San Diego, CA) for an additional 3 h at 4°C. Following immunoprecipitation, the beads were washed four times with RIPA buffer and boiled in Laemmli sample buffer for subsequent immunoblot analysis. For the immunoprecipitation of Flag-tagged proteins, cell lysates were collected 48hr post-transfection using a Flag-modified RIPA buffer (50 mM Tris-HCl, pH 7.6, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, plus protease inhibitors). Cell lysates were assayed for protein concentration and 1-2 mg of lysates were precleared by incubation with Protein G agarose beads (EMD Millipore Corporation, Billerica, MA) for 1 h at 4 °C. Pre-cleared lysates were added to 50 μL anti-FlagM2-agarose beads or control isotype IgG-agarose (Sigma-Aldrich, St. Louis, MO) and incubated overnight at 4°C. Following immunoprecipitation, beads were washed four times with a Flag-modified wash buffer (50 mM Tris-HCl, pH 7.6, 150 mM NaCl, 0.05% Triton X-100, plus protease inhibitors) and incubated with 0.2 mg/mL FlagM2 peptide (Sigma-Aldrich, St. Louis, MO) for an additional hour at 4°C to elute Flag-tagged proteins. Following elution, samples were boiled in Laemmli sample buffer for subsequent immunoblot analysis. 2.5 RNA Collection and qRT-PCR 2.5.1 mRNA Targets Cultured cells or mouse bone marrow cells were isolated to study gene expression. Total RNA was isolated by Trizol (Invitrogen, USA) isolation according to manufacturer’s instructions. 55 2-5 g RNA samples were pretreated with DNase I before synthesis of cDNA using SuperScript II reverse transcriptase reagent and random primers (Invitrogen, Carlsbad, CA). RT-qPCR was carried out using FastStart Universal SYBR Green Master kit (Roche Applied Science, Germany) on an Applied Biosystems 7900HT with primers listed in Appendix B. Results were normalized to Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) or Beta-Actin (ACTB) expression. All primers targeted transcripts spanning exon-exon junctions and were designed using the Roche Probe Library website (Roche Applied Science, Indianapolis, IN). 2.5.2 MicroRNA Targets Total RNA was isolated by Trizol according to manufacturer’s instructions (Invitrogen, Carlsbad, CA). cDNA synthesis was performed using the TaqMan microRNA Assay kit. Briefly, 100-200ng of total RNA was reverse transcribed using Multiscribe RT and miRNA-specific RT primers. RT-qPCR was performed on a ViiA 7 Real-time qPCR instrument (LifeTechnologies) using the TaqMan Universal PCR Master Mix and miRNA-specific primers. U47 and Sno202 were used as loading controls in human and mouse samples respectively.  2.6 Flow Cytometry To perform bone marrow immunophenotyping, cells were harvested from mice and resuspended in PBS containing 10% rat serum, and anti-CD16/32. Cells were incubated with primary antibody for one hour at 4°C and then analyzed on a BD FACSCalibur or BD Fortessa flow cytometer. Primary antibodies used were APC-conjugated anti-mouse CD45.1 (clone A20; eBioscience), PE- or APC-conjugated anti-mouse Mac1/CD11b (clone M1/70; BD), PE-conjugated anti-mouse Ly6G/Gr1 (clone RB6-8C5; BD) or PE-conjugated anti-mouse Ly6G/Gr1 (clone 1A8; BD),  PE- or APC-conjugated anti-mouse CD19 (clone 1D3; BD), PE-conjugated anti-mouse CD45R/B220 (clone RA3-6B2; BD), PE-conjugated anti-mouse CD3 (clone 17A2; BD) or PE-conjugated anti-mouse CD3e (clone 145-2C11; BD), PE-conjugated anti-mouse CD4 (clone GK1.5, BD), APC-conjugated anti-mouse CD8a (clone 53-6.7; BD), PE-conjugated anti-56 mouse CD71 (clone C2; BD), APC-conjugated anti-mouse Ter119 (clone Ter119; Biolegend), and PE-conjugated anti-mouse CD41 (clone MWReg30; BD).  Hematopoietic stem and progenitor staining were performed on a BD Fortessa flow cytometer. Primary antibodies used to mark mature (Lin-) cells were PerCP-Cy5.5-conjugated anti-mouse Gr1 (clone RB6-85C; BD), anti-mouse Ter119 (clone Ter119; eBioscience), anti-mouse B220 (clone RA3-6B2; eBioscience), anti-mouse CD3 (clone 172A; BD), anti-mouse CD4 (clone RM4-5; BD), anti-mouse CD8a (clone 53-6.7; BD), and anti-mouse IL-7R (clone A7R34; eBioscience). Primary antibodies used to differentiate myeloid progenitors were PE-Cy7-conjugated antimouse Sca1 (clone 7; BD), APC-conjugated anti-mouse c-Kit (clone 2B8; eBioscience), APC-Cy7-conjugated anti-mouse CD16/32 (clone 93; eBioscience), biotinylated anti-mouse CD34 (clone RAM34; eBioscience) with Streptavidin-PE-TxRed (BD). Propidium iodide (PI) or DAPI were used as viability markers. All flow cytometry results were analyzed using FlowJo v7.6. 2.7 Mouse Strains MiR-143/145 mice were obtained from the Condorelli lab and maintained on a C57Bl/6J:Pep3b-Ly5.1 (Pep3b) background (Elia et al. 2009). MiR-143/145 knockout mice were generated by inserting a LacZ/neo cassette into the miR-143/145 locus through homologous recombination (Fig. 2.1). The genotype of miR-143/145 mice was determined using primers in Appendix C. C57Bl/6J-TyrC2J (C2J) mice were used as recipients in bone marrow transplants (Ly5.2). All strains were bred and maintained in-house at the BC Cancer Research Centre Animal Resource Centre.  All animal experiments were carried out by following animal protocols approved by the Animal Care Committee of the University of British Columbia (Vancouver, British Columbia) (Animal Care Protocol# A10-0126).  57  Figure 2.1 - Generation of MiR-143/145 Knockout Mice MiR-143/145 knockout mice were generated through the insertion of a LacZ/neo cassette into the miR-143/145 locus via homologous recombination. DTA, diptheria toxin A.  2.7.1 Bone Marrow Transplants Bone marrow transplants were performed using Pep3b (Ly45.1) mice as donors and C2J (Ly45.2) mice as recipients. To enrich for HSPCs, Pep3b mice were injected with 5-fluorouracil (5-FU) at a dose of 150mg/kg. After four days, mice were euthanized by CO2 inhalation and bone marrow cells were harvested from femurs, tibiae and pelvic bones by flushing with 5ml PBS containing 2% FBS using a 5ml syringe and 28-guage needle. Red blood cells were lysed using 10mls of ammonium chloride lysis solution (0.8% NH4Cl with 0.1mM EDTA, StemCell Technologies) and incubated for 10mins on ice. Cells were then washed with PBS+2%FBS and forced through a 40µm nylon cell strainer (Becton Dickinson, Franklin Lakes, NJ, USA). BM cells were prestimulated in DMEM media containing 15% fetal bovine serum, 6ng/ml rhIL-3, 10ng/ml rhIL-6 and 100ng/ml mSCF overnight. BM cells were retrovirally transduced by co-culturing with an ecotropic viral packaging cell line (GPE86) for 72 hours and then sorted for GFP. Recipient mice were lethally irradiated with 810 rads by X-ray prior to being intravenously injected with 3x105 test cells and 1x105 unmanipulated helper cells unless otherwise stated. Irradiated mice were housed with drinking water containing HCl and ciproflaxacin for 2months. 58 In competitive transplants with MIY and MIG-DAB2, 2.5x105 cells from each group were transplanted into the same recipient mouse along with 5x104 helper cells. For limiting dilution assays using miR-143/145+/+ or miR-143/145-/- marrow, whole bone marrow was isolated from the femurs and tibiae of 12 week old mice. Selected doses were injected into lethally irradiated C2J mice (810 rads by X-ray) along with 1x105 unmanipulated helper cells. In secondary LDA transplants, bone marrow from the femurs and tibiae of primary mice were harvested, pooled, and transplanted into secondary recipients. The doses used represent fractions of the total bone marrow isolated from primary mice. 2.8 Data Analysis 2.8.1 Statistics All data are shown as the mean ± SEM of multiple experiments unless otherwise stated. All statistical analyses were performed using a two-tailed Student t-test with Welch’s correction, a One-Way Anova using the Dunnett multiple testing correction, 2-Way Anova using Turkey’s multiple testing correction, or Multiple t-tests using the Holm-Sidak multiple testing correction. Results were considered to be statistically significant at P < 0.05. 2.8.2 ELDA The results of limiting dilution assays experiments were analyzed using the Extreme Limiting Dilution Analysis online tool (Hu and Smyth 2009). P-values and confidence intervals were calculated using this software. 2.8.3 Ingenuity Pathway Analysis Pathway analysis was performed using the Ingenuity® Pathway Analysis tool (IPA®, Qiagen Redwood City, www.qiagen.com/ingenuity). A non-redundant list of predicted miR-143 or miR-145 targets was collected from TargetScan v6.2 (Friedman et al. 2009) (www.targetscan.org) and analyzed for canonical pathways that were significantly overrepresented in the gene list. 59 The list of miR-143/145 targets used in the IPA analysis represent 1081 transcripts with broadly conserved (across most vertebrates) miRNA binding sites.  2.8.4 Gene Set Enrichment Analysis The Gene Set Enrichment Analysis software from the Broad institute (www.broadinstitute.org/gsea/index.jsp) was used to analyze data from a previously published microarray dataset (Mootha et al. 2003, Subramanian et al. 2005, Pellagatti et al. 2010). The genes in the TGFβ gene set are listed in Table 2.1.  Table 2.1 – GSEA TGFβ gene set ACVR1 FMOD IL17F SMAD2 ACVRL1 FNTA JUN SMAD3 BMPR1A FOXH1 KLF10 SMAD4 CCND1 GDF10 LEFTY1 SNX6 CDKN1C GDF15 LEFTY2 STUB1 CDKN2B GDF5 LTBP2 TGFB1 CIDEA GDF9 MAP3K7 TGFBR1 CITED2 HIPK2 PEG10 TGFBR3 DAB2 HOXA7 RBPMS TGFBRAP1 ENG HPGD SERPINE1 ZFYVE9 Gene-set of TGFβ-signaling associated genes used in Gene Set Enrichment Analysis  60 Chapter 3 - LOSS OF MIR-143/145 INHIBITS HSC SELF-RENEWAL 3.1 Introduction Myelodysplastic syndromes (MDS) are a collection of hematopoietic malignancies characterized by ineffective hematopoiesis and dysplasia of the marrow cells. Therefore, the primary cause of mortality in these patients is bone marrow (BM) failure presenting as peripheral pancytopenia. MDS patients also have a significantly increased risk of transformation to acute myeloid leukemia (Greenberg et al. 2012, Fedeli et al. 2014).  Although MDS is a heterogeneous disease, numerous recurrent genetic alterations have been identified and have been used for diagnosis and patient stratification. The most common karyotypic abnormality found in MDS is the interstitial deletion of chromosome 5q (del(5q) MDS) (Boultwood et al. 2002, Haase et al. 2007). Previous groups have identified a commonly deleted region spanning 1.5-megabases on band 5q32, encoding for 40 protein coding genes, and five microRNAs (miRNAs) (mir-584, miR-143, miR-145, miR-378 and miR-6499) (Boultwood et al. 2002). MicroRNAs are short non-coding RNAs that regulate gene expression of target mRNAs by targeting them for degradation or translational repression. Our lab has previously shown that miR-143, miR-145 and miR-146a are expressed at significantly lower levels in del(5q) MDS CD34+ cells compared to MDS cells diploid at chromosome 5q, and that knockdown of miR-145 and miR-146a together is able to recapitulate some of the features of del(5q) MDS (Starczynowski et al. 2010). However, since miR-146a is located outside the CDR and is only deleted in approximately 30-42% of del(5q) MDS cases, we were interested in directly testing the role of miR-143 and miR-145 in del(5q) MDS. The effect of TGF-beta signaling on the hematopoietic system is extremely complex and varies greatly depending on the context and cell type. Mouse models which knock out key TGFβ-signaling molecules have pointed to a vital role of this pathway in both developmental and 61 adult hematopoiesis. Given its ability to elicit dramatic cellular responses, it is not surprising that aberrant TGFβ signaling was previously implicated in MDS. It was observed that MDS patient plasma shows higher levels of TGFβ (Allampallam et al. 1999, Zorat et al. 2001, Allampallam et al. 2002). Likewise, SMAD2, a key protein in the signal transduction of TGFβ was also shown to be upregulated in MDS bone marrow progenitors (Zhou et al. 2008). Conversely, the negative regulator, SMAD7 is downregulated in MDS patients (Zhou et al. 2011, Bhagat et al. 2013). Together, this suggests that the inappropriate activation of this key pathway in MDS progenitors may be important in the pathogenesis of the disease. However, it is unclear if the haploinsufficency of miR-143 or miR-145 contributes to dysregulated TGFβ signaling and how it affects the pathogenesis or progression of this disease. To address this question, we used miRNA-target prediction algorithms and observed that genes in the canonical TGFβ signaling pathway are overrepresented when we examined the predicted targets of miR-145. We hypothesize that the TGFβ signaling pathway is dysregulated in del(5q) MDS due to the haploinsufficiency of miR-145 and we investigate the functional role of the TGFβ-related adaptor protein DAB2 in hematopoiesis and MDS. In the first part of this thesis, I examined the functional loss of miR-143 and miR-145 in a mouse model with deletion of these two microRNAs. Specifically, I investigated the effect of miR-143 and miR-145 loss on progenitor activity, HSC frequency, and fitness in a competitive transplant. In addition, I investigated the possible molecular mechanisms by which observed changes occur. 3.2 Results 3.2.1 Loss of miR-143/145 Results in a Decrease in Myeloid Progenitor and HSC Frequency MicroRNA-143 and miR-145 are co-transcribed as a single transcript (Xin et al. 2009). As such, we observed that in primary MDS/AML patient samples, miR-143 and miR-145 62 expression are positively correlated (R2 = 0.69, P = 8.8x10-17, Fig. 3.1A). However, no correlation was observed with miR-146a (R2 = 0.069, P = 0.069, Fig. 3.1B). Given that miR-143 and miR-145 are located within the commonly deleted region (CDR) of del(5q) MDS patients, we hypothesized that these two miRNAs play a vital role in the HSC compartment. To examine the endogenous expression of both miR-143 and miR-145, we isolated primitive human CD34+ cells and observed significantly higher expression of both miRNAs compared to CD34- cells by RT-qPCR (Fig. 3.1C). Likewise, we examined the level of expression of these two miRNAs in mouse LT-HSCs (ESLAM, Lin-CD45+EPCR+CD48−CD150+) as well as multipotent common myeloid progenitors (CMP), granulocyte-monocyte progenitors (GMP), and megakaryocyte-erythrocyte progenitors (MEP). Both miR-143 and miR-145 expression are significantly higher in LT-HSCs compared to differentiated progenitors (Fig. 3.1D). The enrichment of these two miRNAs within the stem cell pool suggests that they play key roles in their function or maintenance.    63   Figure 3.1 - MiR-143 and MiR-145 are Enriched in the Stem Cell Population (A,B) Correlation between expression of miR-143, miR-145, and miR-146a in primary AML CD34+ cells.(C) Expression of miR-143 and miR-145 in human CD34- and CD34+ cord blood cells by RT-qPCR. (D) Expression of endogenous miR-143 and miR-145 in mouse Long-Term HSC (LT-HSC, CD45+EPCR+CD48−CD150+), short-term HSC (LSK, Lin−Sca1+cKit−) and myeloid progenitors (CMP, Lin-Sca1-cKit-CD34+CD16/32-; GMP, Lin-Sca1-cKit-CD34+CD16/32+; MEP, Lin-Sca1-cKit-CD34-CD16/32-) by RT-qPCR.  To investigate the role of miR-143 and miR-145, we utilized a mouse model of miR-143 and miR-145 loss. MiR-143/145 knockout mice (miR-143/145-/-) were first generated by the Condorelli lab by inserting a LacZ/neo cassette into the miR-143/145 locus through homologous recombination (Elia et al. 2009). We hypothesized that loss of miR-143 and miR-145 in these 64 mice would affect the frequency of hematopoietic stem and progenitor cells (HSPC). Therefore, we isolated bone marrow from miR-143/145+/+, miR-143/145+/- and miR-143/145-/- mice to analyze the frequency of LT-HSCs, short-term repopulating cells (Lin−Sca1+cKit− (LSK)), as well as CMPs, GMPs, and MEPs. At 8-12 weeks of age, miR-143/145-/- mice showed a 1.44 fold decrease in LT-HSCs as defined by the ESLAM staining phenotype compared to miR-143/145+/+ mice (P = 0.039) (Fig. 3.2A) while LSKs (P = 0.092) and committed progenitors showed a trend towards decreased frequencies but this did not reach statistical significance (CMP, P = 0.244; GMP, P = 0.177; MEP, P = 0.388). Given that MDS is predominantly a disease of the elderly, we hypothesized that the decrease in LT-HSCs would be reflected in downstream progenitors with time. Therefore, we evaluated the frequency of ST-HSCs and committed progenitors in older mice at 80 weeks of age (Fig. 3.2B). At this point, the defect in miR-143/145-/- progenitors became evident as they showed significantly fewer ST-HSCs (1.67 fold, P = 0.037), CMP (2.36 fold, P = 0.016), GMPs (2.33 fold, P = 0.0145) and MEPs (1.89 fold, P = 0.068). Similarly, heterozygous deletion of miR-143/145 also resulted in fewer progenitors but this did not reach statistical significance with the exception of the GMP population (2.44–fold decrease, P = 0.008). There was no difference in total cellularity of the bone marrow suggesting that in terms of frequency and absolute numbers, miR-143/145-/- mice have a defect in HSCs and myeloid progenitors.  65  Figure 3.2 - Loss of MiR-143/145 Results in Reduced Hematopoietic Stem/Progenitor Cell Frequency (A,B) HSC and progenitor frequency in young (2-3 months) and old (18 months) miR-143/145+/+, miR-143/145+/- and miR-143/145-/- mice by flow cytometry. Bone marrow cellularity from 18 month old mice was assessed by trypan-blue exclusion counts (2 femur and 2 tibias).   To examine the effects of miR-143/145 loss on progenitor activity, we performed colony forming cell (CFC) assays. Bone marrow cells from 143/145+/+, 143/145+/- or 143/145-/- mice were plated onto methylcellulose and colonies were counted 10-14 days later. As shown in Fig. 3.3A, primary CFCs showed a decrease in total colonies between 143/145+/+ and 143/145-/- (54.5 vs 43.25 colonies per 104 cells plated, P = 0.014). Colonies were also visually inspected and tallied as BFU-E, CFU-G, CFU-M, CFU-GM, or CFU-GEMM colonies. There was no significant difference in the type of colonies formed between groups. Interestingly, when cells from primary CFCs were collected and replated, a defect in miR-143/145-/- CFCs became more apparent. MiR-143/145-/- cells formed 2.22-fold fewer colonies compared to wildtype cells (26.91 ± 3.95 vs 12.11 ± 2.24, P =0.0067) (Fig. 3.3B,C).  66  Figure 3.3 - Loss of MiR-143/145 Results in Reduced Progenitor Activity (A) Colony forming unit (CFU) assay in primary and secondary platings (BFU-E, Blast forming unit-eryhtroid; GEMM, Granulocyte-erythrocyte-monocyte-megakaryocyte; GM, Granulocyte-monocyte; G, Granulocyte; M, monocyte). (B) Quantification of total colonies from primary and secondary CFU assays. Secondary CFU assays were performed by replating primary CFUs at equal proportions. (C) Representative images of secondary colonies from miR-143/145+/+ and miR-143/145-/- bone marrow are shown. CFC counts were from 4 independent experiments.    67 The decrease in CFC activity following secondary replating led us to hypothesize that the loss of miR-143/145 leads to an HSC defect. To test this, we performed a limiting dilution assay to quantify the number of functional HSCs in miR-143/145-/- bone marrow cells. Whole bone marrow was harvested from 12-week old miR-143/145+/+ or miR-143/145-/- mice and transplanted into lethally-irradiated recipients over a range of cell doses along with a life-sparing dose of unmanipulated Ly5.2 helper cells. Mice were followed over time and positively engrafted mice were defined as those with >1% reconstitution by donor cells (Ly5.1+) in the GM (Gr1/Mac1) myeloid population. Using the LDA statistical analysis tool, ELDA, we quantified the number of HSCs (Hu and Smyth 2009). As shown in Fig. 3.4A,B, miR-143/145-/- mice possess 2.4-fold fewer HSCs compared to miR-143/145+/+ mice (1 in 21665 vs. 1 in 52794, P = 0.0257). Analysis of mice transplanted with 105 cells showed that all mice were positively engrafted after 20 weeks (>1% reconstitution by donor cells). However, even though these mice are positively engrafted, the total engraftment was significantly lower in miR-143/145-/- compared to wildtype mice (Fig. 3.4C). Overall, these experiments demonstrate that miR-143 and miR-145 are important regulators of HSPC function where their loss results in significantly reduced HSC and progenitors.  68  Figure 3.4 - Loss of MiR-143/145 Results in Reduced HSC Frequency (A,B) Estimate of HSC frequency in wildtype and miR-143/145-/- mice by limiting dilution assay (CI, confidence interval). (C) Assessment of total engraftment in the peripheral blood of positively engrafted recipient mice transplanted with miR-143/145+/+, miR-143/145+/- or miR-143/145-/- donor marrow at 16weeks.    69 3.2.2 The TGFβ Pathway is Activated in miR-143/145-/- Bone Marrow We observed that loss of miR-143/145 results in an HSPC defect in vivo. Therefore, we sought to investigate the mechanism by which these miRNAs affect HSPC function. Using the miRNA-target prediction algorithm, TargetScan (v6.2) (Friedman et al. 2009) (www.targetscan.org), a non-redundant list of 1081 predicted miR-143 and miR-145 targets was generated and then organized into known canonical pathways using Ingenuity Pathway Analysis (IPA®, Qiagen Redwood City, www.qiagen.com/ingenuity) (Fig. 3.5A). As shown in Fig. 3.5B, the analysis revealed several pathways known to have key roles in HSC maintenance and self-renewal. Among these, the TGFβ-signaling pathway was one of the highest ranked canonical pathways with an enrichment score of 7.13, second only to axonal guidance signaling with an enrichment score of 7.5. However, TGFβ-signaling had a greater ratio of miRNA-targets to total pathway members (0.218 of TGFβ Signaling vs 0.118 of Axonal Guidance Signaling). Therefore, our IPA analysis suggests that miR-143/145 heavily target TGFβ pathway members. These predicted targets include ligands of the TGFβ superfamily, numerous receptors, canonical SMAD proteins and key adaptor proteins including DAB2 (Fig. 3.5B). In addition, the predicted targets also include members of the non-canonical TGFβ signaling pathway such as those in the TAK1/p38/JNK signaling axis.   70  Figure 3.5 - MiR-143 and MiR-145 are Predicted to Target the TGF-Beta Signaling Pathway (A) Analysis of miR-143 and miR-145 predicted targets using Ingenuity Pathway Analysis. Targets were grouped into canonical signaling pathways and enrichment scores were calculated. The top fifteen canonical pathways enriched for miR-143 and miR-145 targets are shown. (B) A selection of pathways previously described to be important in HSC function are shown. Dashed line represents P-value = 0.05. (C) Simplified TGFβ signaling pathway with predicted targets of miR-143, miR-145 and miR-143/145, are highlighted in red, green, and yellow, respectively.  71 Using a published RNA expression dataset of 29 del(5q) MDS patients and 17 healthy controls (Pellagatti et al. 2010), we performed Gene Set Enrichment Analysis (GSEA) and observed a significant enrichment of TGFβ associated genes (Gene Ontology) in the del(5q) MDS group compared to healthy controls (NES=1.785, P = 0.0018) (Fig. 3.6A), suggesting an activation of TGFβ signaling in del(5q) MDS patients. This dataset showed DAB2 and TGFβ1 as two of the most differentially expressed TGFβ pathway genes in del(5q) MDS patients compared to healthy samples (P = 0.009 and P = 0.0003 respectively) (Fig. 3.6B). Interestingly, DAB2 is positively correlated with TGFβ1 and SMAD2 expression within del(5q) MDS patients (R2 = 0.2161, P = 0.0111 and R2 = 0.5526, P < 0.0001 respectively) (Fig. 3.6C). In a separate cohort of 83 primary AML/MDS cases, we examined differentially expressed genes (1.5-fold cutoff) between patients with high expression of miR-143/145 (top quartile) vs low expression of miR-143/145 (bottom quartile). With this list of differentially expressed genes, Ingenuity Pathway Analysis identified TGFβ1 (P = 1.2x10-14) as a putative upstream regulator of a significant number of these differentially expressed genes. A selected list of upstream regulators is shown in Fig. 3.7A (full list in Appendix D). TGFβ1 regulated genes that are differentially expressed between high versus low expressers of miR-143/145 are shown in Fig. 3.7B. This supports the idea that loss of miR-143/145 in del(5q) MDS is sufficient to activate the TGFβ-signaling pathway through combined derepression of TGFβ pathway members.  In addition to TGFβ1, we also identified TP53, NFκB, p38 MAPK and IFNG as putative upstream regulators. This is in agreement with previously published reports that TP53 can regulate miR-145 expression (Sachdeva et al. 2009) while miR-143 has been shown to regulate NFκB p65 expression in colorectal cancer and hepatocarcinoma (Borralho et al. 2009, Zhang et al. 2009). Analysis of differentially expressed genes between high miR-146a and low miR-146a expressing patients revealed a different set of upstream regulators. IFNG and NFκB regulated genes showed a high level of overlap with the differentially expressed genes in our dataset. This also fits with previous reports that loss of miR-146a activates NFκB signaling through the 72 activation of IRAK1 and TRAF6 (Starczynowski et al. 2011, Rhyasen et al. 2013, Zhao et al. 2013).    Figure 3.6 - Expression Analysis of AML/MDS Patient Datasets (A) Gene set enrichment analysis of 29 del(5q) MDS and 17 healthy samples using a TGFβ Gene Ontology Terms dataset. Expression dataset was from Pellagatti et al. 2010. (B) Expression values of selected TGFβ genes in healthy controls, del(5q) MDS and normal karyotype MDS. (C) Correlation between DAB2 with TGFβ1 and SMAD2 expression.   73  Figure 3.7 - Expression Analysis of AML/MDS Patient Datasets  (A) Eighty-three primary AML/MDS cases were binned into quartiles based on expression of the indicated miRNAs. Normalized mRNA coverage values (RPKMs) were averaged and low-expressing genes were filtered out. Fold change values were calculated (low-miRNA vs high-miRNA quartile) with a 1.5 fold cutoff and analysed by IPA. P-values shown here are for the significance of overlap between differentially expressed genes and the targets of the indicated upstream regulator. (B) Differentially expressed genes regulated by TGFβ1 are shown.  74 Our analysis of two MDS patient datasets suggests that miR-143/145 loss is sufficient to activate the TGFβ signaling pathway. To verify this, we isolated lineage negative BM from miR-143/145+/+, miR-143/145+/-, and miR-143/145-/- mice and found that DAB2 expression as well as phosphorylated Smad3 and total Smad2 were higher in miR-143/145-/- marrow (Fig. 3.8A). Furthermore, when we compared the expression of these proteins between mature lineage-positive cells and more primitive lineage-negative cells, we observed that Smad3 is significantly enriched in the lineage-negative fraction compared to differentiated lineage-positive cells. This suggests that the inappropriate activation of TGFβ signaling is particularly pronounced in the HSPC compartment (Fig. 3.8B). This is in agreement with previous reports that Smad2 and Smad3 are highly phosphorylated in long-term HSCs (Yamazaki et al. 2009).   To confirm the activation of TGFβ-signaling in human cells, we used the 5q diploid human myeloid cell line UT-7 and knocked down the expression of miR-145 using a lentiviral decoy construct containing tandem repeats of miR-145 binding sites (Fig. 3.9A). Knockdown of miR-145 resulted in greater expression of its predicted targets DAB2, SMAD2, and SMAD4 (Fig. 3.9B). Furthermore, we observed that knockdown of miR-145 in UT-7 cells resulted in an increase in expression of known TGFβ downstream targets including p57Kip2 (CDKN1C) and SERPINE1 (Scandura et al. 2004, Brenet et al. 2013) as well as miR-145 targets themselves such as INHBB, SMAD3 and TGFβ1 (Fig. 3.9C).   75  Figure 3.8 - Loss of MiR-143 and MiR-145 Activates the TGFβ Pathway in Mouse HSPC (A) Lineage negative bone marrow isolated from miR-143/145+/+, miR-143/145+/- and miR-143/145-/- mice were blotted for phospho-SMAD proteins or predicted targets of miR-143 or miR-145. (B) Representative blot showing the relative levels of Smad3 between lineage negative and lineage positive bone marrow cells. Images are representative of 2 independent experiments with 2-4 mice in each group.  76   Figure 3.9 - Knockdown of MiR-145 is Sufficient to Activate the TGFβ Pathway in vitro (A) Schematic of a lentiviral miRNA-decoy construct and quantification of miR-145 knockdown in UT-7 cells. (B) UT-7 cells transduced with miR-145 decoy immunoblotted against TGFβ targets. (C) RT-qPCR of TGFβ genes in UT-7cells transduced with a miR-145 decoy construct.   We observed that miR-143/145-/- BM cells are defective for progenitor activity, especially upon secondary replating. Given that loss of miR-143/145 activates TGFβ-signaling, we hypothesized that inhibition of SMAD-dependent signaling by the chemical inhibitor SIS3, would rescue the loss of progenitor activity. As shown in Fig. 3.10, SIS3 treatment did not affect colony formation in miR-143/145+/+ BM cells. However, there was a significant decrease in the 77 number of colonies formed in SIS3 treated miR-143/145-/- cells (P = 0.0026). As expected, when we performed secondary CFC replatings, miR-143/145-/- cells formed significantly fewer colonies than miR-143/145+/+ when treated with DMSO (P = 0.033). However, treatment of miR-143/145-/- cells with SIS3 completely rescued this defect, resulting in significantly more colonies in SIS3-treated miR-143/145-/- cells than miR-143/145-/- cells treated with DMSO (P = 0.003). Therefore, we conclude that loss of miR-143/145 results in the derepression of key TGFβ signaling proteins resulting in an HSPC defect mediated through SMAD3.  Figure 3.10 - Rescue of MiR-143/145-/- Progenitor Activity by a SMAD3 Inhibitor Whole bone marrow isolated from miR-143/145+/+ or miR-143/145-/- mice were plated in methylcellulose media containing 10µM SIS3 or DMSO. At Day10, colonies were counted and cells from primary cultures were harvested and replated in secondary cultures in equal proportions. Secondary cultures were counted 10 days later. CFC counts were from 2 independent experiments done in triplicate.  TGFβ signaling can also activate SMAD-independent pathways. These include signaling through the TRAF6-TAK1-p38/JNK or Ras-ERK axis. P38 MAPK (MAPK14) has been implicated in both MDS and myeloproliferative disorders (Shahjahan et al. 2008). P38 78 overactivation has been observed in CD34+ cells of MDS patients where its inhibition is able to effectively restore erythropoiesis and myelopoiesis in patients (Katsoulidis et al. 2005, Navas et al. 2006). Interestingly, p38 staining is significantly weaker in MPD cases compared to MDS (Shahjahan et al. 2008). This suggests that decreased p38 activation may result in reduced inhibition of hematopoiesis and potentiate myeloproliferation. Conversely, activation of ERK signaling can promote cellular proliferation and survival. ERK1/2 was also shown to be important in regulating granulopoiesis and monopoiesis (Ross et al. 2004, Geest et al. 2009). Therefore, we investigated the status of non-canonical TGFβ signaling concurrently in our model of miR-143/145 loss. To assess non-canonical TGFβ signaling, we immunoblotted against phospho-Tak1, phospho-p38, phospho-Jnk, phospho-Akt, and phospho-Erk1/2 in lineage-negative bone marrow (Fig. 3.11A). MiR-143/145-/- marrow showed higher expression of many SMAD-independent signaling members including phospho and total TAK1, pAKT, pERK1/2 and p-p38. Similar to what we observed in Fig. 3.8B, p-p38 and pErk1/2 protein levels were noticeably higher in the lineage-negative fraction compared to lineage positive cells (Fig. 3.11B).  It was previously shown that DAB2 mediates non-canonical TGFβ signaling by binding to TAK1. Similarly, others have shown that TRAF6 mediates SMAD-independent activation of JNK and p38 through TAK1. Given that TRAF6 has previously been shown to be important in the pathogenesis of MDS, we decided to explore this connection and performed a DAB2 co-IP to test for interaction with TRAF6. We confirmed the interaction between DAB2 and TAK1 by co-IP and also show that DAB2 is able to bind and pull down TRAF6 (Fig. 3.11C). Collectively, this data show that miR-143 and miR-145 are highly enriched in the HSPC pool and the loss of these two miRNAs in mice results in the loss of HSCs and hematopoietic progenitors. These two miRNAs regulate members of the SMAD-dependent and SMAD-independent signaling, and DAB2 appears to play a role in both these pathways. 79  Figure 3.11 - Loss of MiR-143 and MiR-145 Activates the Non-Canonical TGFβ Signaling Pathway (A) Lineage negative bone marrow isolated from miR-143/145+/+ miR-143/145+/- and miR-143/145-/- mice were blotted for proteins involved in Smad-independent TGFβ signaling. (B) Representative blot showing the relative levels of total and phospho-p38 and pErk1/2 between lineage negative and lineage positive bone marrow cells (C) HEK293T cells were transfected with TAK1-HA and TRAF6-HA constructs and immunoprecipitated with anti-DAB2 or IgG isotype control.   80 Chapter 4 – DAB2 IS CRITICAL FOR PROPER PROGENITOR AND HSC FUNCTION 4.1 Introduction  In chapter 3, we showed that DAB2 is upregulated and TGFβ signaling is activated in miR-143/145-/- marrow. We also showed that DAB2 binds to TRAF6 and TAK1 suggesting that it has an important role in mediating SMAD-independent signaling in miR-143/145-/- cells. In this part of the thesis, I examined the functional consequence of DAB2 overexpression within the hematopoietic system. Specifically, I investigate the effect of DAB2 overexpression on HSC frequency, progenitor activity, and their level of fitness in a competitive transplant. In addition, I investigate the possible molecular mechanisms by which these changes occur. 4.2 Results 4.2.1 DAB2 is a Specific Target of MiR-145 DAB2 is a key positive regulator of TGFβ signaling through the facilitation of SMAD2/3 phosphorylation. Our GSEA analysis (Fig. 3.6A) revealed DAB2 to be the most differentially expressed TGFβ gene that is also predicted to be a direct target of miR-145. As previously shown in Fig. 3.1A, miR-145 is differentially expressed in different progenitor subsets where miR-145 is lower expressed in GMP and higher expressed in MEPs relative to LSKs. When we examined the level of Dab2 expression in these fractions, we observed an inverse relationship with higher expression in the GMP fraction and decreased expression in the MEP fraction relative to LSK (Fig. 4.1A). Therefore, we decided to validate DAB2 as a direct target of miR-145. As shown in Fig. 4.2A, the 3’UTR of DAB2 contains three broadly conserved putative miR-145 seed sites and one miR-143 binding site that is conserved among mammals. However, the miR-143 site was only recently revealed in the latest version of TargetScan (v7.0). The ability of miR-143 to bind this site was not assessed. To test the ability of miR-145 to bind these seed sites and inhibit translation, we cloned the 1757bp DAB2 3’UTR downstream of a luciferase reporter (Fig. 4.2B). Co-transfection with a miR-145-overexpression construct resulted in a 81 significant reduction in luciferase signal, suggesting that miR-145 is capable of inhibiting DAB2 translation (P < 0.0001). Mutation of any of the predicted miR-145 binding sites alone was not sufficient to restore luciferase activity. However, luciferase activity was restored upon site-directed mutagenesis of sites 1 and 3 together or all three miR-145 binding sites (Fig. 4.2B). Therefore, miR-145 inhibits DAB2 by binding its 3’UTR miR-145 seed sites.   Figure 4.1 - DAB2 and MiR-145 Expression in Different HSPC Fractions (A) Progenitor fractions were isolated from wildtype mice and assessed for miR-145 and Dab2 expression by qRT-PCR. Values represent fold-change relative to the LSK fraction. (B) Expression of human DAB2 and mouse Dab2 in HSPC fractions from the publically available web server HemaExplorer (Bagger et al. 2013). 82  Figure 4.2 - MiR-145 Directly Binds the 3’UTR of DAB2 (A) Schematic of the 3’UTR of DAB2 with its predicted miRNA binding sites (www.targetscan.org) (Friedman et al. 2009). (B) Wildtype or mutated full length DAB2 3’UTR was cloned downstream of a luciferase reporter and transfected into 293T cells. A combination of the three putative miR-145 seed sites were mutated by site-direct mutagenesis. Cells were co-transfected with either vector alone or miR-145-overexpression. Luciferase signal was assessed 48hrs post transfection and normalized to renilla signal.   Previous reports have shown that DAB2 is an essential component of the TGFβ signaling pathway and functions by facilitating the phosphorylation of SMAD proteins (Hocevar et al. 2001, Hocevar et al. 2005, Penheiter et al. 2010). To confirm that overexpression of DAB2 (DAB2-OE) is able to activate TGFβ signaling, we transduced the UT-7 cell line with an MSCV-83 IRES-GFP vector (Vector) or a DAB2-OE vector and transfected these cells with 3TP-LUX, a luciferase reporter containing TGFβ responsive elements. As expected, overexpression of DAB2 alone was sufficient to increase 3TP-LUX luciferase signal (P = 0.03) and sensitized the cells to TGFβ1 stimulation (P = 0.035) (Fig. 4.3A). In addition, analysis of UT7-DAB2 cells by qRT-PCR showed higher expression of TGFβ downstream target genes including p57, MMP2 and EGR2 (Fig. 4.3B). Therefore, our data show DAB2 to be a bona fide miR-145 target, and its overexpression is sufficient to activate the TGFβ signaling pathway.    Figure 4.3 - DAB2 Overexpression Activates TGFβ Signaling (A) The TGFβ activation reporter construct, 3TP-LUX, was transfected into UT-7 cells transduced with a control vector or a DAB2-overexpressing construct (DAB2-OE). Cells were treated with DMSO or TGFβ1(5ng/ml). Luciferase signal was assessed 24hrs later and normalized to renilla. (B) UT-7 cells overexpressing DAB2 were assessed for expression of TGFβ target genes by qRT-PCR. Values are displayed as fold change relative to vector.      84 4.2.2 Defect in Colony Formation in MiR-143/145-/- Bone Marrow is Mimicked by DAB2 Overexpression To investigate the role of DAB2 in HSPC activity, we overexpressed DAB2 in 5-fluorouracil (5-FU)-treated BM using a retroviral construct containing a GFP tag (Fig. 4.4A,B). Similar to what was observed in miR-143/145-/- marrow, DAB2-OE reduced colony formation in the primary plating (P = 0.0333) and the defect was more pronounced upon serial replating (P = 0.0325) (Fig. 4.4B). Given this result, we decided to investigate the effect of Dab2 knockdown on the ability of miR-143/145+/+ or miR-143/145-/- marrow to form colonies. 5-FU treated bone marrow from miR-143/145+/+ or miR-143/145-/- mice were retrovirally transduced with a shDab2 or shNegative construct and sorted for the presence of dsRED fluorescence and plated onto methylcellulose (Fig. 4.5A,B). In agreement with our previous data, the knockdown of Dab2 resulted in an increase in colony formation. Interestingly, the increase was especially pronounced in miR-143/145-/- marrow compared to miR-143/145+/+ (Fig. 4.5C). Therefore the progenitor defect in miR-143/145-/- marrow can be attributed to the activation of TGFβ signaling, mediated in part by Dab2.   85  Figure 4.4 - DAB2 Inhibits Colony Forming Activity (A) Schematic of CFC assay using mouse bone marrow enriched for HSPCs and retrovirally transduced with either control or DAB2-OE vector. (B) Immunoblot of DAB2 from GFP(+) sorted bone marrow. (C) Primary colony forming cell assays were counted 10-12days after plating. Total cells were harvested from primary CFCs and replated in equal proportions. Secondary CFCs were counted 10-12days later and total colonies were normalized to the number of input cells. CFCs counts were from 8 independent experiments done in duplicate.  Figure 4.5 - Knockdown of Dab2 Rescues MiR-143/145-/- Progenitor Activity (A) Schematic of CFC assay using miR-143/145+/+ or miR-143/145-/- bone marrow enriched for HSPCs and retrovirally transduced with either control or shDab2 vector. (B) Immunoblot of DAB2 from dsRed(+) sorted bone marrow. (C) Colony forming cell assays were counted 10-12days after plating. CFC counts were from 2 indepdendent experiements done in duplicate. 86 4.2.3 DAB2-OE HSPC Show Reduced Function in Competitive Assays with a Defect in Self-Renewal Given that we observed reduced colony forming activity in marrow cells overexpressing DAB2, we hypothesized that DAB2-OE marrow would elicit a hematopoietic defect in vivo. To test this, we performed a competitive transplant where equal numbers of DAB2-OE-GFP and Vector-YFP bone marrow were transplanted into the same lethally irradiated mouse and followed over time (Fig. 4.6A). Peripheral blood analysis showed that the overall level of engraftment by DAB2-GFP marrow was significantly lower than Vector-YFP and gradually decreased over a span of 20 weeks (P < 0.0001) (Fig. 4.6B). As shown in Fig. 4.6C, DAB2-GFP cells contributed far less to both myeloid (Mac1+/Gr1+, P = 0.0097) and lymphoid lineages (CD19+, P = 0.0004 and CD3+, P < 0.0001) compared to Vector cells in the peripheral blood. Following long term engraftment at twenty weeks, mice were euthanized and BM analysis showed a significantly higher proportion of Vector-YFP cells compared to DAB2-GFP cells in GM (Mac1+/Gr1+, P = 0.026), B-cell (CD19+, P = 0.0001) and T-cell (CD3+, P < 0.0001) lineages (Fig. 4.7A,B). Analysis of LSK (P = 0.091), CMP (P = 0.053), GMP (P = 0.042) and MEP (P = 0.032) progenitor fractions also revealed significantly lower repopulation by DAB2-GFP cells (Fig. 4.7C). Interestingly, when YFP or GFP cells were examined for their contribution to the different progenitor populations, there was a greater percent of DAB2-GFP cells in the GMP subpopulation (P = 0.052) (Fig. 4.7D). This fits with our observation that wildtype GMPs have the highest level of endogenous Dab2 relative to other progenitors (Fig. 4.1A,B). This suggests the DAB2 overexpression may cause a skewing towards GMP. 87  Figure 4.6 - DAB2 Overexpression Impairs Peripheral Blood Repopulation (A) Schematic of competitive transplant where lethally irradiated mice were transplanted with equal numbers of Vector-YFP transduced BM and DAB2-GFP BM. (B,C) Mice were monitored for engraftment and reconstitution of myeloid, B- and T-cell populations by peripheral blood analysis.  88  Figure 4.7 - DAB2 Overexpression Impairs HSPC Repopulation (A) Vector-YFP and DAB2-GFP BM isolated from long-term engrafted mice were transplanted into secondary recipients at limiting dilution. (B,C) Sixteen weeks following secondary transplant, mice were euthanized and analyzed for the level of reconstitution by Vector-YFP or DAB2-GFP bone marrow cells in hematopoetic progenitors and mature myeloid and lymphoid lineages. (D) To evaluate whether DAB2-OE cells preferentially differentiated towards a particular progenitor type, contribution to LSK, CMP, GMP, and MEP progenitors was evaluated within YFP or GFP gated cells. 89 We previously observed that DAB2-OE CFCs showed an exaggerated defect upon serial replating (Fig.4.4A). Therefore, we suspected that there may be a defect in DAB2-OE cells to self-renew or maintain their stem/progenitor activity. To investigate the effect of DAB2-OE in HSC self-renewal, we isolated bulk BM from positively engrafted primary mice and performed a secondary transplant at limiting dilutions (1:100, 1:500, 1:2500, and 1:10,000). As early as four weeks post-transplant, contribution to the myeloid population by DAB2-OE cells was significantly lower than Vector cells (Fig. 4.8A). To estimate the number of HSCs capable of self-renewal, we calculated the number of positively-engrafted mice at each dilution after long-term engraftment (16 weeks). Positively-engrafted mice were defined as having >1% contribution to the myeloid (Mac1/Gr1) population. DAB2-GFP BM had significantly fewer positively-engrafted secondary mice than Vector-YFP. From secondary limiting dilution assays, we estimate that DAB2-GFP BM have 3.9 fold fewer HSCs than Vector BM cells (P = 0.0335) (Fig.4.8B).  Given that the observed defects in miR-143/145-/- cells became more pronounced with age, we performed a secondary limiting dilution assay (LDA) using bone marrow isolated from 52 week old mice which were transplanted with either Vector-GFP or DAB2-OE-GFP (Fig. 4.9). The defect was indeed more pronounced with age where mice transplanted with DAB2-OE BM had an estimated 8.33 fold decrease in functional HSCs compared to Vector-GFP cells (40.28 vs. 335.54 HSC/million BM, P  = 0.0170).  90  Figure 4.8 - DAB2 Overexpression Impairs HSC Self-Renewal (A) Vector-YFP and DAB2-GFP BM isolated from long-term engrafted mice (20 weeks) were transplanted into secondary recipients at limiting dilution. Four weeks following secondary transplant, peripheral blood was analyzed for myeloid reconstitution. (B) Sixteen weeks post transplant, the number of positively engrafted mice was determined and the frequency of self-renewing HSCs was calculated.  91  Figure 4.9 - The HSC Self-Renewal Defect from DAB2 Overexpression is More Pronounced with Age Lethally-irradiated recipient mice were transplanted with bone marrow retrovirally transduced with Vector-GFP or DAB2-GFP. Fifty-two weeks post transplant, bone marrow from transplanted mice were harvested and transplanted into secondary recipients at limiting dilution. The number of positively engrafted mice was determined and the frequency of self-renewing HSCs was calculated 16 weeks later.  4.2.4 Overexpression of DAB2 in Mouse Bone Marrow can Result in Myeloproliferation or Leukemic Transformation To determine whether over expression of DAB2 in mouse HSPCs is sufficient to recapitulate the defects observed in miR-143/145-/- mice, we retrovirally transduced HSPCs with GFP-tagged control vector (MIG) or vector overexpressing DAB2 (DAB2) and injected 3x105 cells (Ly5.1) along with 1x105 wildtype cells (Ly5.2) into lethally irradiated C2J recipient mice (Ly5.2). At 24 weeks post transplant, the majority of mice transplanted with DAB2-OE BM showed no significant differences in overall WBC, RBC, HGB, or PLT counts compared to MIG control (Fig 4.10A). When we examined the level of engraftment in either myeloid, B-cell or T-cell lineages, we saw no significant difference except for a minor skewing towards the T-cell lineage in DAB2-OE mice (P = 0.0465) (Fig 4.10B). However, when examining either the myeloid or lymphoid lineages, DAB2-OE cells were less in each lineage compared to the MIG marrow (Fig. 4.10C). Therefore, at 24 weeks post transplant, while there was no skewing of 92 DAB2-OE marrow to either myeloid or lymphoid lineages, the overall contribution of DAB2 overexpressing cells to marrow engraftment was less than MIG cells.   Figure 4.10 - Analysis of Mice Transplanted with Marrow Overexpressing DAB2 (A) Complete blood counts of mice transplanted with MIG or DAB2 BM up to 24 weeks post transplant. (B) Peripheral blood analysis of transplanted mice by flow cytometry using markers against myeloid (Mac1/Gr1), B-cell (CD19) and T-cell (CD3) population (n=8-12 mice). (C) Percent contribution of MIG or DAB2-OE cells within myeloid (Mac1/Gr1), B-cell (CD19) and T-cell (CD3) lineages (n=8-12 mice).  93  Figure 4.11 - Mice Overexpressing DAB2 are Susceptible to Leukemic Transformation (A) Kaplan-Meier survival curves for mice transplanted with MIG-vector (n=18) or DAB2 transduced marrow (n=18). Secondary (yellow) mice were transplanted with marrow from DAB2-AML/MPD mice from two independent experiments (n=4 in each experiment). (B,C,D) Complete blood counts from transplanted mice. Charts show blood counts of MIG and 1°DAB2 mice at 35-40 weeks post transplant, and blood counts from moribund 1°DAB2-AML/MPD and 2°DAB2 mice. (E) Spleen weight measurements from MIG, 1°DAB2-AML/MPD and 2°DAB2 94 mice at endpoint. Representative images of spleens from MIG mice or a 2°DAB2 mouse are shown. (F) BM cells from 1°DAB2-AML/MPD moribund were harvested, sorted for GFP, and plated onto methylcellulose. Total colonies were counted 10 days later. (G-L) Wright-Giemsa stain of representative peripheral blood smears and BM cytospins from a MIG mouse (G,H), a 1°DAB2-AML mouse (I,J), and a1°DAB2-MPD mouse (K,L) . Hgb, hemoglobin; WBC, white blood cells; Plt, platelet.  When DAB2-OE mice were followed over a longer period of time (52 weeks), DAB2-OE mice showed a significantly worse overall survival (P = 0.031) (Fig. 4.11A). Beyond 24 weeks post transplant, a portion of DAB2 mice (n= 6/18) developed a myeloproliferative disease or an acute myeloid leukemia -like disease. Blood counts from moribund DAB2 mice (DAB2-AML/MPD) showed a dramatic increase in WBCs compared to MIG mice at 30-40 weeks (75.90 x103/mm3 vs. 8.867x103/mm3, P < 0.0001) (Fig. 4.11B). Likewise, DAB2-AML/MPD mice presented with macrocytic anemia (MCV, P = 0.0002; HGB, P < 0.0001), and thrombocytopenia (Plt, P < 0.0001) (Fig. 4.11C,D). DAB2-AML/MPD mice also had enlarged spleens indicative of extramedullary hematopoiesis (Fig. 4.11E). To confirm that the malignant clone was coming from the DAB2-overexpressing cells, bone marrow isolated from moribund mice were sorted into GFP(+) and GFP(-) fractions and plated onto semisolid medium. The GFP(+) fraction formed significantly more colonies than GFP(-) marrow (P = 0.0064) (Fig. 4.11F).  Histopathological examinations of moribund DAB2-OE mice were performed using peripheral blood smears and bone marrow cytospins (Figure 4.11G-L). Approximately 30% of moribund DAB2-OE mice (n=3/10) developed an acute myeloid leukemia (DAB2-AML) where the peripheral blood contained a large proportion of blast cells with the bone marrow containing mostly immature myeloblasts (Fig.4.11I,J). Conversely, 70% (n=7/10) of moribund DAB2-OE mice developed a disease resembling a myeloproliferative disorder (DAB2-MPD) where the bone marrow contained myeloid precursors in various stages of maturation (Figure 4.11K,L)  In addition, unsorted bone marrow from DAB2-AML and DAB2-MPD mice were transplanted into lethally irradiated secondary recipients (2°DAB2) to see if the disease persists. 95 Within 4 weeks post transplant, 2°DAB2 mice died of a disease similar to the primary DAB2-AML/MPD mice from which it originated, presenting with myeloproliferation, anemia, thrombocytopenia, and splenomegaly (Fig. 4.11A-E).   Immunophenotyping of DAB2-AML/MPD mice was performed using various myeloid, lymphoid, megakaryocyte, and erythroid markers (Fig 4.12). Within the GFP(+) population, DAB2-AML mice showed a significant increase in Mac1/Gr-1 double-positive immature myeloid cells (P = 0.0012) but no difference in Mac1(+)Gr1(-) macrophages or Gr1(+)/Mac1(-) granulocytes. DAB2-AML/MPD mice also had a significant expansion of Ter119(-)/CD71(+) immature erythroid progenitors (P < 0.0001). Although it did not reach significance, a concomitant reduction in mature Ter119(+)/CD71(-) erythroid cells was observed (P = 0.081). Within the lymphoid lineage, DAB2-AML/MPD mice showed a significant reduction of the CD19(+) B-cell population compared to MIG mice (P = 0.009). No significant difference was observed in CD41(+) megakaryocytes. Therefore, DAB2 overexpression results in a greater risk of leukemic transformation with an expansion of Mac1+/Gr1+ and CD71+ blasts.   Figure 4.12 - Immunophenotyping of Bone Marrow From Moribund DAB2-AML Mice (A) Flow cytometry analysis of myeloid (Mac1/Gr1), erythroid (Ter119/CD71), lymphoid (CD19/CD3), and megakaryocytic (CD41) lineages from bone marrow of DAB2-AML/MPD (n=3) mice at endpoint or MIG mice 30-40weeks post transplant (n=11).    96 4.2.5 Aged MiR-143/145-/- Mice Show Mild Pancytopenia with Expansion of Leukocytes  Despite the observed HSC defect, miR-143/145-/- mice did not succumb to a bone marrow failure phenotype in the period observed. In young mice (2-3 months), analysis of blood counts showed comparable blood counts between miR-143/145+/+, miR-143/145+/- and miR-143/145-/- mice. However, similar to what was observed in DAB2-OE mice, miR-143/145-/- mice began to show signs of abnormal hematopoiesis with age. MiR-143/145-/- mice displayed mild anemia (52 weeks, P = 0.032) and thrombocytopenia (80 weeks, P = 0.0018) (Fig. 4.13A,B). Interestingly, at 80 weeks, miR-143/145-/- mice displayed elevated WBCs compared to miR-143/145+/+ mice (P = 0.005) and also had enlarged spleens (P = 0.0272) (Fig. 4.13C,D). Altogether, both miR-143/145 loss and DAB2 overexpression results in a clear HSC and progenitor defect. Additionally, we show that mice with loss of miR-143/145 or overexpression of DAB2 resulted in myeloproliferation. 97  Figure 4.13 - Aged MiR-143/145-/- Mice Show Altered Hematopoiesis Complete blood counts from miR-143/145+/+, miR-143/145+/-, and miR-143/145-/- mice over time. Spleen weights were measured from 80 week old mice.      98 Chapter 5 – SUMMARY AND SIGNIFICANCE OF THE STUDY MiR-143/145 as Regulators of HSC Frequency The overall goal of this dissertation was to investigate the effect of miR-143 and miR-145 loss in the hematopoietic system and to better understand the molecular and cellular processes affected by these two miRNAs. Results from a series of experiments using a murine model of miR-143/145 loss indeed point to miR-143 and miR-145 as important regulators of HSC function.  In Chapter 3, we describe experiments in which we examine the function and frequency of HSPCs in miR-143/145-/- mice. In relatively young mice, we observed a reduction in the most primitive HSC population (CD45+EPCR+CD48-CD150+) but not in more committed myeloid progenitors. However, a defect in HSPC frequency in both miR-143/145 knockout and heterozygous mice became apparent with age. Eighteen month old miR-143/145+/- and miR-143/145-/- mice showed significant reductions in progenitor fractions such as LSKs or CMPs. The affect of age was a common theme in this study where differences became more evident with time or age. This is in line with the manifestation of MDS which is generally an indolent disease, where symptoms can be managed for many years before progressing to bone marrow failure or transformation to AML. Likewise, CFC progenitor assays showed greater differences upon serial replating. This suggests that time and stress conditions act to exaggerate the defects in miR-143/145-/- HSPC. An HSC defect was confirmed when we tested for the number of functional HSCs by limiting dilution assay and observed significantly fewer HSCs in miR-143/145-/- mice. Therefore, we show that miR-143 and miR-145 are important regulators of HSC activity in vivo. 5.1.1  MiR-143/145 Regulates Multiple Signaling Pathways With an evident defect in miR-143/145-/- HSCs, we utilized various bioinformatic tools to identify which molecular pathways would be most greatly affected by the loss of these two 99 miRNAs. We identified several pathways which would be predicted to be dysregulated by miR-143/145 loss, including TGFβ-signaling. In addition to TGFβ-signaling, Ingenuity Pathway Analysis also identified HGF, IGF-1, Wnt/β-catenin, ERK/MAPK, SAPK/JNK, and Clathrin-mediated endocytosis signaling pathways enriched for miR-143 or miR-145 targets (Fig 3.5A). As previously discussed, TGFβ signaling can also activate numerous SMAD-independent pathways such as the Ras-ERK or the TRAF6-TAK1-p38/JNK axis. CD34+ cells of MDS patients show an over activation of p38 where its inhibition is able to effectively restore erythropoiesis and myelopoiesis in patients (Katsoulidis E et al. 2005). In studying SMAD-independent TGFβ signaling, we showed that TAK1, ERK and p38 signaling are also activated in miR-143/145-/- lineage- marrow. This is in agreement with our IPA analysis from Fig. 3.5 as well as our analysis of upstream regulators when comparing AML/MDS patients with low versus high expression of miR-143 and miR-145 in Fig. 3.7A. While we did not investigate the role of clathrin mediated endocytosis in these signaling events, others have previously shown that endocytosis is an important regulator of SMAD-dependent and -independent TGFβ signaling. To our knowledge this work is the first to demonstrate a link between the loss of miR-143 and miR-145 in the activation of both SMAD-dependent and SMAD-independent TGFβ signaling pathways in the hematopoietic system.  In this work, we focused our study on TGFβ signaling. However, we cannot rule out the possibility that the other pathways identified in our IPA analysis such as EMT, HGF, IGF-1 and Wnt/β-catenin signaling, are also dysregulated in miR-143/145-/- mice. Epithelial-mesenchymal transition (EMT) signaling is vital process involved in embryogenesis, wound healing, and cancer metastasis. EMT involves the loss of epithelial cell features such as cell polarity and cell-cell adhesion with the acquisition of migratory and invasive features of mesenchymal cells. A critical EMT transcription factor, SNAI2 (also known as SLUG), has been shown to be highly expressed in hematopoietic stem cells (Wu et al. 2005). Slug-deficient mice show enhanced 100 HSC repopulating potential as well as increased HSC proliferation following myelosuppression (Sun et al. 2010). Therefore, it appears that Slug is important in controlling the transition from HSC quiescence to proliferation under stress conditions.  It has been well established that TGFβ family members are potent extracellular factors that can induce and maintain EMT signaling (Lamouille et al. 2014). TGFβ can induce the transcription of key EMT genes such as Snail, Slug, ZEB1, ZEB2, Twist and Mdm2 in a Smad2/3 dependent manner (Bracken et al. 2008, Vincent et al. 2009, Araki et al. 2010). Interestingly, it was shown that TGFβ can also regulate EMT in a post-transcriptional manner. Heterogeneous nuclear ribonucleoprotein-E1 (hnRNP-E1, also known as PCBP1) is able to bind a TGFβ-activated translation (BAT) element located in the 3’UTR of Dab2 and interleukin-like EMT inducer (ILEI) and repress their translation. Upon TGFβ signaling, hnRNP-E1 is phosphorylated and released from BAT elements to allow for the translation of Dab2 and ILEI, leading to EMT and metatstatic progression. Therefore, crosstalk between EMT and TGFβ signaling in del(5q) MDS may warrant further study.  Our IPA analysis also suggested Wnt/β-catenin signaling to be upregulated upon miR-143/145 loss. Wnt//β-catenin is known to have a vital role in HSC self-renewal. However, much like TGFβ1, it appears the level of Wnt/B-catenin signaling is important. Current evidence suggests that activation of Wnt signaling is capable of conferring stem cell like properties on leukemic stem cells/leukemia initiating cells (LSCs/LICs).  As previously discussed, heterozygous deletion of CSNK1A or APCmin leads to an increase in β-catenin signaling which results in greater HSC expansion and self-renewal. However, homozygous deletion of CSNK1A1 or Apc results in HSPC apoptosis and bone marrow failure. Interestingly, miR-145 has also been shown to target members of the Wnt/β-catenin signaling pathway in other tissue types such as colon and stomach (Yamada et al. 2013, Xing et al. 2015). Furthermore, in a study of acute myocardial infarction, it was shown that TGFβ1 can actually stimulate miR-145 expression leading to the down-regulation of Dab2 and concomitant increase in β-catenin 101 activity (Mayorga and Penn 2012). Studies in ovarian cancer have also demonstrated that DAB2 acts to attenuate Wnt signaling (Jiang et al. 2009, Jiang et al. 2012). Therefore, it is possible that DAB2 overexpression in the hematopoietic system leads to reduced β-catenin signaling, resulting in the HSPC defects we have observed.  5.2  DAB2 Overexpression Impairs HSC Self-Renewal While Predisposing Cells to Myeloproliferation or Frank AML Transformation In our analysis of a gene expression dataset of del(5q) MDS patients, we identified DAB2 as the most differentially expressed gene within the TGFβ signaling pathway that was predicted to be a miR-145 target. In addition, DAB2 was shown to function in several pathways identified from our IPA analysis such as ERK signaling, Wnt/β-catenin signaling and clathrin-mediated endocytosis. Therefore in Chapter 4, we confirm DAB2 as a bona fide target of miR-145 and focused our efforts in describing its function within the HSPC compartment. We demonstrate that DAB2 overexpression recapitulates many of the observed defects in miR-143/145-/- mice. Specifically, DAB2 overexpression in the HSPC compartment leads to defective hematopoietic repopulation in progenitors as well as in mature myeloid and lymphoid cells. At the stem cell level, DAB2 overexpression reduces the self-renewal capability of HSCs and this defect becomes more pronounced with age. One of the most interesting observations from our study of DAB2 is that despite its inhibitory effect on HSC self-renewal and overall hematopoietic repopulation, DAB2 overexpression can result in myeloproliferation or leukemic transformation. DAB2-AML/MPD mice showed rapid expansion of Mac1/Gr1 myeloid cells as well as CD71 cells which marks proliferative hematopoietic precursors. Conversely, there was a reduction in CD19 B-cells. A reduction in B-cells is often observed in MDS patients (Ogata et al. 2006, Ribeiro et al. 2006). CD71 marks the iron transporter transferrin and is regulated by iron depletion. This marker has been shown to be highly expressed in RA and RARS MDS patients (Maynadie et al. 2002). In 102 addition, CD71 is highly expressed in poorly differentiated AMLs (M0, M1, M2, and M4 FAB subtypes) and has been shown to be an effective marker for erythroid precursors and erythroblasts in MDS and AML bone marrow biopsies (Marsee et al. 2010, Liu et al. 2014). Therefore, DAB2 overexpression may have a role in leukemic transformation, specifically in poorly differentiated AML subtypes. It is unclear what role DAB2 has in two seemingly contradictory outcomes between inhibiting HSPC function and leukemic transformation. Challen et al. have shown that myeloid-biased My-HSCs and lymphoid biased Ly-HSCs respond differently to TGFβ1. My-HSCs are more quiescent but are stimulated by TGFβ1. Convsersely, Ly-HSCs are more proliferative, and are inhibited by TGFβ1 (Challen et al. 2010). It is possible that DAB-OE is augmenting the sensitivity of HSCs to TGFβ1, shifting them towards this lymphoid-biased or myeloid-deficient phenotype. This would result in a more proliferative phenotype and earlier stem cell exhaustion. This more proliferative phenotype would increase the likelihood of secondary mutations. At the same time, DAB2-OE within My-HSCs could also lead to an expansion of myeloid progenitors due to the stimulatory effects of TGFβ1. Numerous studies have described a connection between TGFβ signaling, HSC function, and the phenomenon of increased myeloid differentiation in ageing HSCs (Quere et al. 2014, Sun et al. 2014).  Analysis of young and old HSCs revealed significant changes in the TGFβ pathway at the transcriptome level (Sun et al. 2014). These changes result in an overall reduction in TGFβ signaling in aged HSCs. It was proposed that because low concentrations of TGFβ can stimulate proliferation in My-HSCs, the reduction in TGFβ signaling in aged mice may provide a more suitable environment for My-HSC expansion. Conversely, Quere et al. showed that old HSCs and myeloid-biased HSCs are more sensitive to TGFβ1 signaling due to higher levels of Tgfbr1 while myeloid-lymphoid-balanced HSCs have low expression of Tgfbr1 (Quere et al. 2014). Our data suggest that miR-143/145 loss can lead the derepression of TGFβ 103 signaling and defects in HSPC function. It is unclear whether the level of TGFβ signaling is different between old and young miR-143/145 or DAB2-OE mice and would be worth further investigation. It is possible that the myeloproliferation or leukemic transformation is attributable to the sensitivity of the HSPCs to TGFβ signaling.  One possible explanation for the dual role of DAB2 in eliciting an HSC defect while predisposing mice to myeloproliferation is that its role is dependent upon the specific cell population. Specifically, DAB2 may have an inhibitory role in the LT-HSC fraction but a proliferative function in a particular progenitor fraction. Krivtsov et al. have previously shown in mice that leukemic stem cells generated by expression of the MLL-AF9 fusion protein in GMPs, can maintain their GMP identity from which they arose but are able to activate a stem cell or self-renewal signature (Krivtsov et al. 2006). It is possible that DAB2-OE in a progenitor population acts in a similar manner where it results in leukemic transformation and acquisition of self-renewal potential. Rosenbauer et al. previously showed that the Ets-transcription factor PU.1 binds to the Dab2 promoter to activate transcription (Rosenbauer et al. 2002). Given that PU.1 is a potent inducer of myeloid lineage commitment, this may point to a unique role for DAB2 in myeloid progenitors distinct from HSCs. However, Challen et al. showed that in My-HSCs, PU.1 is upregulated. It is possible to speculate that DAB2 may also have a role in the intrinsic myeloid-bias observed in My-HSCs, which have also been shown to be more sensitive to TGFβ1 induced proliferation (Challen et al. 2010, Quere et al. 2014). To address the question of where the leukemic clone originates, it would be necessary to isolate different HSC and progenitor fractions from moribund DAB2-AML/MPD mice and perform secondary transplants with each subpopulation.  Our IPA analysis of differentially expressed genes between patients with high versus low expression of miR-143/145 revealed that, in addition to TGFβ1, IFNγ is also a putative upstream regulator of a significant number of differentially expressed genes in MDS/AML patients. The 104 interferon consensus sequence binding protein, also known as interferon regulatory factor 8 (ICSBP/IRF8) is a transcription factor of the interferon regulatory factor (IRF) family. It was shown that Dab2 is significantly upregulated in ICSBP-/- marrow cells and that Dab2 is negatively regulated by ICSBP in myeloid progenitors (Rosenbauer et al. 2002). Interestingly, approximately 33% of ICSBP-/- mice become moribund by 50 weeks of age after transitioning from a chronic phase to a blast crisis with clonal expansion of the granulocytic lineage; a characteristic feature of human CML (Holtschke et al. 1996, Scheller et al. 1999). Therefore, it is possible that the CML-like phenotype is attributable to the derepression of Dab2 in ICSBP-/- mice. Dysregulated IFNγ signaling has previously been implicated in the MDS clone or the BM microenvironment of MDS patients (Kitagawa et al. 1997, Pellagatti et al. 2010, Kim et al. 2015). Given that Dab2 is regulated by IRF8, the link between DAB2 and IFNγ signaling warrants further investigation. Analyzing the gene expression dataset by Pellagatti et al. (2010), we observed significantly reduced expression of IRF8 in del(5q) MDS. In addition, IRF8 expression is significantly anti-correlative to DAB2 expression in del(5q) MDS patients (data not shown). Also, the IRF8 promoter is frequently hypermethylated in MDS and AML patients (Otto et al. 2011). Therefore, loss or inactivation of IRF8 may be an alternate mechanism by which DAB2 is overexpressed in MDS.  Although subtle, aged miR-143/145+/- and miR-143/145-/- mice show signs of leukocyte expansion, thrombocytopenia, and splenomegaly. The more exaggerated phenotype seen in DAB2 mice likely speaks to the overexpression system we used. While loss of miR-143/145 results in higher expression of Dab2, it is not to the same degree as our DAB2-OE mice. It is possible that in patients with del(5q) MDS, the physiological increase of Dab2 as a result of miR-143/145 loss simply requires a greater length of time before the effects described in this thesis are observed. Nevertheless, these findings provide evidence that the loss in HSC self-renewal potential is a consequence of miR-143/145 loss, which in turn activates the TGFβ 105 signaling pathway by permitting the higher expression of DAB2. While overexpression of DAB2 plays an inhibitory role in HSCs, our data also suggests a role in leukemic transformation.  106 REFERENCES Abdel-Wahab, O., J. Gao, M. Adli, A. Dey, T. Trimarchi, Y. R. Chung, C. Kuscu, T. Hricik, D. Ndiaye-Lobry, L. M. Lafave, R. Koche, A. H. Shih, O. A. Guryanova, E. Kim, S. Li, S. Pandey, J. Y. Shin, L. Telis, J. Liu, P. K. Bhatt, S. Monette, X. Zhao, C. E. Mason, C. Y. Park, B. E. Bernstein, I. Aifantis and R. L. Levine (2013). "Deletion of Asxl1 results in myelodysplasia and severe developmental defects in vivo." 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"Inhibition of the TGF-beta receptor I kinase promotes hematopoiesis in MDS." Blood 112(8): 3434-3443. Zorat, F., V. Shetty, D. Dutt, L. Lisak, F. Nascimben, K. Allampallam, S. Dar, A. York, S. Gezer, P. Venugopal and A. Raza (2001). "The clinical and biological effects of thalidomide in patients with myelodysplastic syndromes." Br J Haematol 115(4): 881-894.     130 APPENDIX - PRIMERS AND VIRAL SEQUENCES USED Appendix A. shRNA sequences for knockdown of mouse Dab2 Gene Target Sense Sequence shDab2-1 TCGAACTTTCTGGATCTCTTCAA shDab2-2 CCAAGTCTGCTGACAATTCACTC    131 Appendix B. Human qPCR primer sequences  Gene Target Sense Sequence p57(CDKN1C)_Fwd_human GAGCGAGCTAGCCAGCAG p57(CDKN1C)_Rev_human GCGACAAGACGCTCCATC SMAD3_Fwd_human CACCACGCAGAACGTCAA SMAD3_Rev_human GATGGGACACCTGCAACC TGFB1_Fwd_human ACTACTACGCCAAGGAGGTCAC TGFB1_Rev_human TGCTTGAACTTGTCATAGATTTCG SERPINE1_Fwd_human AAGGCACCTCTGAGAACTTCA SERPINE1_Rev_human CCCAGGACTAGGCAGGTG KLF10_Fwd_human AGCCAACCATGCTCAACTTC KLF10_Rev_human CTCTTTTGGCCTTTCAGAAATC DAB2_Fwd_human TGCCTGTTGTCTGGGGCCCT DAB2_Rev_human AGGGCCAGCTCTGGGAGGTG INHBA_Fwd_human CTCGGAGATCATCACGTTTG INHBA_Rev_human CCTTGGAAATCTCGAAGTGC INHBB_Fwd_human TTTCAGGTAAAGCCACAGGC INHBB_Rev_human ATCATCAGCTTCGCCGAG ACVR1B_Fwd_human ACTGGTGGCAGAGTTATGAGG ACVR1B_Rev_human GCATACCAACACTCTCGCATC ZFYVE9(SARA)_Fwd_human GTGTAATCTGCCATTCAGTGCT ZFYVE9(SARA)_Rev_human TCCCATCAGCAAACCAAACT MMP2_Fwd_human ACTGCTGGCTGCCTTAGAACCTTT MMP2_Rev_human ACTATGTGGGCTGAGATGCACTGT EGR2_Fwd_Human TGGGATATGGGAGATCCAAC EGR2_Rev_Human GGTGACCATCTTTCCCAATG     BMPR2_Fwd_Human CTTTGCCCTCCTGATTCTTG     BMPR2_Rev_Human AAACGCACATAGCCGTTCTT     GAPDH_Fwd_human TGATTCCACCCATGGCAAATTCC GAPDH_Rev_human GCTCCTGGAAGATGGTGATGGATT ACTB_Fwd_human TTCCTTCCTGGGCATGGAGTCCT ACTB_Rev_human ACTGTGTTGGCGTACAGGTCTTTG    132 Appendix C. Genotyping primer sequences for miR-143/145-/- mice  Primer Name Primer Sequence GP1 AACAGGGGAGCCACAGGTTGAGCAG GP2 GCTACAGTGCTTCATCTCAGACTCCCAAC GP3 GGAGCTTGGGCTGCAGGTCGAGG    133 Appendix D. IPA Upstream Regulator Analysis  Upstream Regulator (miR-143/145) P-value of overlap (miR-143/145) Upstream Regulator (miR-146a) P-value of overlap (miR-146a) E2F4 5.88E-21 TGM2 1.07E-21 TP53 4.60E-17 TNF 1.47E-21 TGFB1 1.27E-14 CEBPA 1.23E-16 IL13 3.31E-14 IL13 1.14E-14 ERBB2 4.39E-14 IFNG 2.76E-14 CEBPA 7.22E-14 TREM1 1.10E-12 FOXO1 1.27E-13 TCR 9.59E-12 IFNG 2.25E-13 MAPK1 1.72E-10 CCND1 5.79E-13 PGR 2.66E-10 TREM1 1.10E-12 CSF2 2.75E-10 CDK4 2.20E-12 NR3C1 2.78E-10 FOXM1 3.39E-12 CCL5 1.32E-09 E2F1 3.52E-12 NFkB (complex) 2.12E-09 NFkB (complex) 3.86E-12 IL10 2.52E-09 GATA1 1.13E-11 Immunoglobulin 3.64E-09 TNF 2.81E-11 IgG 9.88E-09 TGM2 1.06E-10 ESR1 1.28E-08 NUPR1 1.34E-10 JUN 1.59E-08 CDKN1A 4.41E-10 SELPLG 1.69E-08 ESR1 6.27E-10 SATB1 2.87E-08 P38 MAPK 2.20E-09 SPI1 3.62E-08 BRCA1 3.29E-09 Interferon alpha 5.99E-08 PGR 5.72E-09 estrogen receptor 9.04E-08 IL1B 1.18E-08 SP1 1.78E-07 TCR 1.82E-08 IL1A 2.82E-07  


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