Medicine, Faculty of
Medicine, Department of
DSpace
UBCV
Wang, Xuan
Master of Science - MSc
University of British Columbia
Acute myeloid leukemia (AML) is an aggressive blood disease associated with complex mutational patterns. Our previous AML sequencing study has revealed frequent somatic mutations in ubiquitin proteasome system (UPS) genes, with recurrent loss-of-function mutations in FBXO11, which codes substrate-recognition component for E3 ubiquitin ligase complex. Our mouse transplant model shows a biological cooperation between Fbxo11 knockdown and enforced AML1-ETO expression in driving mouse leukemogenesis. However, to-date the role of FBXO11 depletion in contributing to human leukemogenesis remains unclear. Gene set enrichment analysis (GSEA) on microarray data of FBXO11-knockdown human hematopoietic stem and progenitor cells (HSPCs) revealed a significant enrichment in stem cell signature, and dysregulated pathways centered on hypoxia and delay in cell cycle progression. Our work further shows that FBXO11 loss confers quiescence while enhancing more primitive populations ex vivo. Co-occurrence and exclusivity of UPS gene mutations with other frequent variants in AML were previously computed and reveal a significantly strong trend of RAS mutations co-occurring with UPS mutations in leukemogenesis. Given that FBXO11 knockdown confers quiescence on human HSPCs ex vivo and AML emerges as a heterogeneous disease with complex co-mutation patterns, we investigated in parallel the effect of KRASG12D (K) and AML1-ETO (A) alone and in combination with FBXO11 knockdown, to examine whether FBXO11 depletion can cooperate with leukemia-associated oncogenes to initiate AML in a xenograft model. Our results show that FBXO11 depletion restrains the growth of KA cells ex vivo, possibly through delaying Gâ exit, while enhancing their in vivo repopulating capacity, potentially leading to leukemogenesis.
THE ROLE OF FBXO11 IN HUMAN HEMATOPOIESIS by Xuan Wang B.Sc., University of Victoria, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2020 ©Xuan Wang, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: The role of FBXO11 in human hematopoiesis submitted by Xuan Wang in partial fulfillment of the requirements for the degree of Master of Science In Experimental medicine Examining Committee: Dr. Aly Karsan, Professor, Department of Pathology, UBC Supervisor Dr. Andrew Weng, Professor, Department of Pathology, UBC Supervisory Committee Member Dr. Florian Kuchenbauer, Associate Professor, Department of Medicine, UBC Supervisory Committee Member Dr. Edward Conway, Professor, Department of Medicine, UBC Additional Examiner iii Abstract Acute myeloid leukemia (AML) is an aggressive blood disease associated with complex mutational patterns. Our previous AML sequencing study has revealed frequent somatic mutations in ubiquitin proteasome system (UPS) genes, with recurrent loss-of-function mutations in FBXO11, which codes substrate-recognition component for E3 ubiquitin ligase complex. Our mouse transplant model shows a biological cooperation between Fbxo11 knockdown and enforced AML1-ETO expression in driving mouse leukemogenesis. However, to-date the role of FBXO11 depletion in contributing to human leukemogenesis remains unclear. Gene set enrichment analysis (GSEA) on microarray data of FBXO11-knockdown human hematopoietic stem and progenitor cells (HSPCs) revealed a significant enrichment in stem cell signature, and dysregulated pathways centered on hypoxia and delay in cell cycle progression. Our work further shows that FBXO11 loss confers quiescence while enhancing more primitive populations ex vivo. Co-occurrence and exclusivity of UPS gene mutations with other frequent variants in AML were previously computed and reveal a significantly strong trend of RAS mutations co-occurring with UPS mutations in leukemogenesis. Given that FBXO11 knockdown confers quiescence on human HSPCs ex vivo and AML emerges as a heterogeneous disease with complex co-mutation patterns, we investigated in parallel the effect of KRASG12D (K) and AML1-ETO (A) alone and in combination with FBXO11 knockdown, to examine whether FBXO11 depletion can cooperate with leukemia-associated oncogenes to initiate AML in a xenograft model. Our results show that iv FBXO11 depletion restrains the growth of KA cells ex vivo, possibly through delaying G0 exit, while enhancing their in vivo repopulating capacity, potentially leading to leukemogenesis. v Lay Summary Leukemia is a blood cancer caused by uncontrolled proliferation of immature white blood cells that spill out into the blood from bone marrow over a short period of time. Previous work from our lab has found that the FBXO11 gene is frequently mutated in AML patient samples, however, to date the role of FBXO11 depletion in contributing to AML development remains unknown. By genetic modulation of FBXO11 in normal human cord blood (CB) cells, we found that FBXO11 depletion restrains the growth of CB cells in culture dishes while improving human cells engraftment upon transplantation into the mice. vi Preface All work was completed at BC Cancer Research Center, located in Vancouver, Canada. The work presented in this thesis was mostly completed and written by the author, Xuan Wang. Other than the exceptions listed below, I performed the planning, set-up of all the experiments, data acquisition and interpretation for the work in Chapter 3 and 4. ï· Microarray data acquisition in chapter 3 was performed by Dr. Linda Chang. Microarray data normalization and GSEA pathway analysis were performed by Derek Tam. All animal work was performed under approval by the University of British Columbia Animal Care Committee (Animal Care Certificate A018-0186). Human materials and biohazardous material were approved by the UBC BCCA Research Ethics Board, and the Biosafety Committee (Human Ethics Certificate H19-01600; Biosafety B17-0025) vii Table of Contents Abstract ....................................................................................................................................................... iii Lay Summary .............................................................................................................................................. v Preface ......................................................................................................................................................... vi Table of Contents ...................................................................................................................................... vii List of Tables .............................................................................................................................................. ix List of Figures .............................................................................................................................................. x List of Abbreviations ................................................................................................................................. xi Acknowledgements .................................................................................................................................. xiv Dedication .................................................................................................................................................. xv Chapter 1: Introduction ............................................................................................................................. 1 1.1 Normal hematopoiesis................................................................................................................... 1 1.1.1 Myeloid differentiation ................................................................................................................ 5 1.2 Clonal hematopoiesis .......................................................................................................................... 6 1.2.1 Acute myeloid leukemia (AML) .................................................................................................. 7 1.2.2 Genomic lesions ........................................................................................................................... 8 1.2.3 Leukemic stem cells ..................................................................................................................... 9 1.2.5 Mouse modeling for AML ......................................................................................................... 11 1.3 The ubiquitin proteasome system (UPS) .......................................................................................... 15 1.3.1 The role of ubiquitin ligase in normal and disordered hematopoiesis ....................................... 17 1.3.2 FBXO11 ..................................................................................................................................... 20 1.3.3 Preliminary data for studying the role of FBXO11 in AML ...................................................... 21 1.4 Aim of the study ................................................................................................................................ 23 Chapter 2: Material and Methods ........................................................................................................... 24 2.1 Isolation of human HSPCs ................................................................................................................ 24 2.2 Lentiviral constructs and transduction .............................................................................................. 24 2.3 Methylcellulose colony-forming cell (CFC) assay ........................................................................... 26 2.4 Long-term ex vivo cell culture with mix 3 feeders ............................................................................ 27 2.4.1 Frozen mix 3 feeder cells preparation ........................................................................................ 27 2.4.2 Long-term culture (LTC) with mix 3 feeders ............................................................................ 28 2.4.3 Cellular output characterization of hematopoietic cells harvested from long term culture ....... 29 2.5 Xenotransplantation .......................................................................................................................... 29 2.6 Histology ........................................................................................................................................... 31 2.7 Ex vivo cord blood liquid culture ...................................................................................................... 31 2.7.1 Proliferation and cell cycle assays ............................................................................................. 32 2.8 Western blotting ................................................................................................................................ 33 2.9 Gene set enrichment analysis ............................................................................................................ 34 2.10 Statistical analysis ........................................................................................................................... 35 viii Chapter 3: Loss of FBXO11 promotes stem cell phenotype maintenance ex vivo ............................... 36 3.1 Aim ................................................................................................................................................ 36 3.2 FBXO11-knockdown CB cells show enrichment in HSC signature in GSEA pathway analysis ..... 36 3.2 FBXO11 deficiency enhances quiescence in CD34+ hematopoietic cells in short-term culture ....... 39 Chapter 4: Investigating the role of FBXO11 disruption in human leukemogenesis ......................... 43 4.1 Aim ................................................................................................................................................... 43 4.2 FBXO11 depletion impairs mature erythroid output in CFC assays ................................................. 43 4.3 FBXO11 loss confers inhibitory effect on growth of hematopoietic cells in long term culture ........ 46 4.4 FBXO11 deficiency improves in vivo repopulating capacity ............................................................ 49 Chapter 5. Discussion ............................................................................................................................... 58 Bibliography .............................................................................................................................................. 62 Appendix .................................................................................................................................................... 78 Top 150 upregulated pathways ............................................................................................................... 78 Top 150 downregulated pathways .......................................................................................................... 82 ix List of Tables Table 1. In vivo models of human de novo AML generated by genetic manipulation of primary human cells ............................................................................................................................................................. 12 Table 2. Total number of HSPCs produced from 3000 sorted EV, KA, and FKA cells in 40-day cultures .................................................................................................................................................................... 50 Table 3. Total number of cells injected and CD34+ cell number within the injected cells at transplant .... 52 Table 4. Total number of human cells harvested from BM per mouse at endpoint .................................... 56 x List of Figures Figure 1.1 The classical model of the hematopoietic hierarchy .................................................................... 4 Figure 1.2 Process and consequences of ubiquitin modification on a protein substrate ............................. 16 Figure 1.3 Ubiquitin proteasome system components are recurrently mutated in AML (unpublished) ..... 18 Figure 1.4 Co-occurrence or exclusivity between UPS gene mutations and common mutational events in AML using TCGA and AML-PMP combined datasets (unpublished) ....................................................... 19 Figure 1.5 Graphical representation of the E3 SKP1-CUL1-F-box (SCF) ubiquitin ligase complex ......... 20 Figure 1.6 FBXO11 transcript levels are significantly reduced in various types of AMLs compared to normal HSCs ............................................................................................................................................... 21 Figure 2.1 Construct with KRASG12D used in the study .............................................................................. 25 Figure 3.1 FBXO11 knockdown enhances stem cell signature in human HSPCs ...................................... 38 Figure 3.2 FBXO11 loss increases proportion of CD34+ cells but restricts expansion of HSPCs during ex vivo culture .................................................................................................................................................. 41 Figure 4.1 Knockdown of FBXO11 alters lineage balance by impairing erythroid output......................... 45 Figure 4.2 Comparison of the effects of FBXO11 knockdown on cellular outputs following 40-day cultures ........................................................................................................................................................ 48 Figure 4.3 Experimental design for xenotransplantation ............................................................................ 50 Figure 4.6 FBXO11 knockdown reduces survival of mice ......................................................................... 54 Figure 4.7 FBXO11 knockdown enhances human chimerism in vivo ........................................................ 57 xi List of Abbreviations A AML1-ETO AML Acute myeloid leukemia APL Acute promyelocytic leukemia ATRA All-trans retinoic acid BFU-E Burst-forming unit- erythrocyte BM Bone marrow BMA Bone marrow aspiration CB Cord blood CBF Core-binding factor CFC Colony-forming cell CFU-BFU-E Colony-forming unit-burst-forming unit- erythrocyte CFU-GEMM Colony-forming unit-granulocyte, erythrocyte, macrophage, megakaryocyte CFU-GM Colony-forming unit-granulocyte, macrophage CLP Common lymphoid progenitor CMP Common myeloid progenitor DLBCL Diffuse large B-cell lymphoma DNMT3A DNA methyltransferase 3 alpha ELN European LeukemiaNet EV Empty vector F shFBXO11 FAB French-American-British FBS Fetal bovine serum FBXO11 F-Box Protein 11 FDR False discovery rate FLT3 fms-like tyrosine kinase 3 xii FLT3L fms-like tyrosine kinase 3 ligand GSEA Gene set enrichment analysis GMP Granulocyte-macrophage progenitor GM-CSF Granulocyte-macrophage colony-stimulating factor HGB Hemoglobin HSC Hematopoietic stem cell HSPC Hematopoietic stem and progenitor cell IL3 Interleukin 3 IT Intermediate-term K KRASG12D LSC Leukemia stem cell LT Long-term LTC-IC Long-term culture-initiating cell M-CSF Macrophage colony-stimulating factor MDS Myelodysplastic syndrome MEP Megakaryocyte-erythroid progenitor MLL Mixed-lineage leukemia MLP Multi-lymphoid progenitor MPP Multipotent progenitor NES Normalized enrichment score NK cells Natural killer cells NPM1 Nucleophosmin1 NOD Nonobese diabetic NRG NOD-Rag1null-IL2rgnull NSG NOD-scid-IL2rgnull PB Peripheral blood PLT Platelet Rag1 Recombination-activating gene 1 RBC Red blood cell xiii SCF- E3 ligase SKP1-CUL1-F-box-E3 ligase SCF Stem cell factor SCID Severe combined immunodeficiency Sirpa Signal-regulatory protein α ST Short-term TCGA The Cancer Genome Atlas UPS Ubiquitin proteasome system WBC White blood cell WHO World Health Organization xiv Acknowledgements First and foremost I want to start by thanking my research supervisor, Dr. Aly Karsan, for taking me as a graduate student, providing mentorship and guidance throughout the duration of my Master of Science degree. I would also like to thank my supervisory committee members Drs. Andrew Weng and Florian Kuchenbauer for their useful comments and suggestions. I would like to thank all the past and present members of the Karsan lab. Jihong, Deborah, Patrick Coulombe, Angela, Grace, Patrick Lac, Aparna, Jen, Rod, Derek, Jeremy, and Nadia, thank you for making the lab such a great place to spend so much time. Particularly, I would like to thank Dr. Jennifer Grants, for showing me a lot of experimental designs and techniques when I first started in the lab. Jihong Jiang and Deborah Deng, thank you for your technical and emotional support. Angela Mo, thank you for putting up with my constant questions when I first started to work on FBXO11 project. Patrick Coulombe, thank you for teaching me a lot of useful lab techniques. Derek Tam, thank you for helping me with the bioinformatics analysis. Thank you to Colin Hammond, Glen Edin, and particularly Dr. Naoto Nakamichi from Eaves lab. I want to thank Colin for introducing me good papers to read at the beginning of my study, and patiently answering my questions regarding cord blood work. Thank you Glenn for sharing protocols with me for feeder cell culture. Special thanks to Dr. Naoto Nakamichi, who taught me a lot on flow analysis and xenograft work. Many thanks to go out to Wenbo Xu and Guillermo Simkin for their assistance with cell sorting. Last but certainly not least, I would like to thank my parents for their constant love and unconditional support. xv Dedication I would like to dedicate this thesis to my parents. 1 Chapter 1: Introduction 1.1 Normal hematopoiesis Hematopoiesis is a regulated process of blood production, through which approximately 1012 blood cells are generated daily in human adult bone marrow1. During steady-state hematopoiesis in a 70 kg adult human, approximately 3 Ã 105 red blood cells, and 3 Ã 104 white blood cells are produced per second2. Such life-long tremendous demand of blood production is achieved through hierarchical organization derived from hematopoietic stem cells (HSCs), which is a rare population historically identified by its unique ability to durably self-renew and repopulate the blood system upon transplantation into myeloablated recipients3,4. HSCs give rise to all the different mature blood cells through differentiation into highly proliferative lineage-committed progenitors, which then ultimately generate more than 10 different types of mature blood lineages2. The developmental transition from HSC to mature blood cells is characterized by progressively decreasing graft durability upon transplantation, and acquired lineage-restricted transcriptional programs during lineage specification 1,5,6. Quiescence, also referred to as G0 in the cell cycle, is characterized by absence of cycling7. Long-term HSCs (LT-HSC) reside in quiescence during homeostasis, and exhibit the most robust and longest repopulating potential upon transplantation8,9. Quiescence is widely considered to be a protective mechanism to prevent against HSC exhaustion and damage accumulation. p53 was reported to be a critical regulator for HSC quiescence, and HSC has higher p53 expression level compared to myeloid progenitors10. During hematopoietic stress, LT-HSC can be activated, and re-enter the cell cycle through extrinsic cues to replenish blood cells to meet hematopoietic demands11. Other features also associated with LT-HSCs during homeostasis are activation of 2 transcriptional programs associated with hypoxia12, low mitochondrial oxidative phosphorylation13, low MYC activity14,15, and tightly regulated protein synthesis16. Interestingly, dormant HSCs respond to stress or damage by leading to culling of damaged HSCs rather than triggering adaptive responses or activating repair pathways to resolve stress or damage. Although mechanisms leading to clearance of damaged HSCs still remain elusive, some of the possibilities are (1) p53-dependent apoptosis17, (2) unfolded protein response (UPR)-induced apoptosis18. Xenotransplantation assays are widely used to study human HSC biology, as this is considered the best assay to test self-renewal19. LT-HSCs are characterized by their multilineage repopulation capacity in sublethally irradiated primary (>16 weeks) and secondary mice, whereas intermediate term-HSC (IT-HSC), short term-HSC (ST-HSC), and multipotent progenitors (MPPs) are identified by transient multilineage engraftment in primary mice19. Using this xenotransplantation strategy, human cord blood (CB) cells with CD34+38-45RA+90+49f+ phenotype (hereafter referred to as â49f+ cellsâ) were revealed as highly enriched for HSCs (10% purity) by their long-term multilineage chimerism (30 weeks) upon transplantation in primary mice20. Later, single-cell analysis of CD49f+ population identified a CD33+ subset (with high CD90 expression) to demarcate HSCs, as shown by their serial multilineage repopulation activity in both of primary and secondary mice21. Downstream of HSCs lie multipotent progenitors (MPPs), which initiate blood differentiation by giving rise to increasingly lineage-restricted progenitorsâprogressing from oligopotent, to bipotent, and finally unipotent progenitor cells, which ultimately produce mature blood cells19,22. In the classical model of hematopoiesis (Figure 1.1), the myelo-lymphoid split occurs downstream of MPP, resulting in restricted myelopoiesis and lymphopoiesis via common 3 myeloid progenitor (CMP) and common lymphoid progenitor (CLP), respectively23â25. In myeloid differentiation, CMP give rise to megakaryocyte-erythroid progenitors (MEP) which make platelets and red blood cells (RBCs), and granulocyte-macrophage progenitors (GMP) which generate granulocytes and macrophages24. CLP only give rise to lymphoid precursors with no myeloid potential, which go on to make B cells, T cells, and natural killer (NK) cells25. However, through the development of sorting strategies (with more surface markers included) and improved functional assays to assess mixed differentiation potentials (both in vitro and in vivo), increasing evidence has been reported to challenge the classical model in both mouse26,27 and human hematopoiesis22,28,29. For example, CD34+38-90neg-lo45RA+10+ cells, previously referred to multi-lymphoid progenitors, were shown to retain myeloid potential28.GMP, reported as CD34+38+45RA+135+, were considered to be myeloid-restricted progenitors with granulocyte and macrophage output. However, recent work has reported that GMP in CB has residual lymphoid potential29. Moreover, recent evidence has pointed to earlier divergence of erythroid-megakaryocyte fates from a more primitive cell populationâHSC or MPP in mice26,27 and humans22, which contradicts the CMP-to-MEP route proposed by the classical model23. Nevertheless, the classical model provides a simple framework for understanding differentiation trajectory in blood development. Although the details remain to be worked out, classical approaches such as the use of cell surface markers, allow identification of hematopoietic stem and progenitor subpopulations that are tractable for further study. 4 Figure 1.1 The classical model of the hematopoietic hierarchy 5 1.1.1 Myeloid differentiation Progenitor cell differentiation down to a specific mature lineage is driven by activation of lineage-defining transcriptional programs through extrinsic cues1,19. PU.1 is a transcription factor specifically upregulated during myelopoiesis and confers a strong GM bias downstream of GMP30. Differentiation down the GMP to either macrophages or granulocytes is regulated by various transcription factors31,32. For example, C/EBPα and GFI1 activation increases granulocytic potential, whereas IRF8 induction results in macrophage specification31,32. Granulocytic differentiation ultimately gives rise to mature neutrophils, basophils, and eosinophils. Together, these three different types of granulocytes make up the first line of defense against pathogen invasion. 6 1.2 Clonal hematopoiesis In a model of evolutionary trajectory starting from clonal hematopoiesis of indeterminate potential (CHIP) evolving to acute myeloid leukemia (AML)33,34, early mutations arise and confer proliferative advantage on HSCs. The resulting clonal expansion leads to the development of CHIP, which is a pre-malignant state characterized by presence of a hematologic malignancy-associated mutation at a minimum variant allele fraction of >2% in peripheral blood33. Subsequent acquisition of additional mutations in expanded clones can promote progression to AML directly, or through myelodysplastic syndrome (MDS), and eventually to AML33,34. The transition to MDS is characterized by ineffective hematopoiesis resulting in the presence of peripheral blood cytopenia (anemia, neutropenia, and thrombocytopenia),dysplastic myeloid differentiation, and <20% blasts in peripheral blood (PB) or bone marrow (BM) 34,35. Morphological dysplasia of neutrophils with nuclear hyposegmentation (pseudo-Pelger-Huët cells), and hypogranulation are frequently seen in blood smears from MDS patients35. MDS is associated with an increased risk of transformation to AML (approximately 1/3 progress to AML)34. 7 1.2.1 Acute myeloid leukemia (AML) AML is an aggressive blood disease that is one of the most common types of adult leukemias worldwide36. It is characterized by infiltrative proliferation of transformed myeloid progenitors with differentiation arrest (leukemic blasts) in BM, blood, and other tissues37. The 5-year survival for AML is only 21% in Canada38, and the incidence of AML has been rising in the last 40 years with an incidence rate reaching 3.46 cases per 100,000 persons between 2011 and 201536,39. Although multiple new drugs for AMLâwith the majority targeting specific gene mutations and/or cell survival pathwaysâhave been recently approved by the Food and Drug Administration 40,41, intensive induction chemotherapy followed by additional courses of chemotherapy or allogenic hematopoietic stem cell transplantation (HSCT) has been standard therapy for most AML patients for the last 30 years37,42. Sixty-85% of patients younger than 60 years of age enter complete remission with intensive induction chemotherapy37, however, long-term survival still remains poor due to a high relapse rate43. The outcomes of standard therapy in elderly patients are much worse due to poorer risk cytogenetics, higher comorbidities, and treatment-related mortalities that are associated with increased age37,44. Elderly patients are also not good candidates for allogeneic HSCT due to increased risk to develop graft-versus-host disease (GVHD) compared to younger patients44. As the median age at diagnosis is increasing (mean age increased from 49.1 to 62.5 between 1965 and 2003)39, these statistics and issues associated with increased age highlight the urgent need for improved ways to treat elderly AML patients. 8 1.2.2 Genomic lesions AML is a heterogenous disease with complex mutational patterns contributing to leukemogenesis in individual patients. At least 5234 driver mutations across 76 genes or genomic regions have been identified by previous high-throughput sequencing efforts, with 2 or more driver mutations detected in 86% of AML patients, and an average of 13 somatic mutations detected per patient45,46. Common driver events are categorized into 9 functional groups, which include signaling pathways , transcription-factor fusions, myeloid transcription factors, nucleophosmin (NPM1), spliceosome complex, cohesin complex, DNA methylation, chromatin modification, and tumor suppression37,46. The three most commonly mutated genes are fms-like tyrosine kinase 3 (FLT3), NPM1, and DNA methyltransferase 3 alpha (DNMT3A)45â47. Mutations associated with epigenetic regulators and cohesin complex often arise as initiating events in HSC45,48â50, and confer multilineage repopulation advantage over non-mutated cells, without causing overt leukemia50. Only through acquisition of additional mutations do these clonally expanded pre-leukemic cells transform to AML45,50. Further analysis of co-mutation patterns in AML cases identified 11 non-overlapping classes with distinct diagnostic and clinical features, and revealed a possible correlation between number of driver mutations and overall survival45. For example, co-occurrence of 3-gene mutation in FLT3, DNMT3A, and NPM1 has worse prognosis compared to FLT3 mutations alone, or with only one of the other two mutations (DNMT3A or NPM1)45. 9 1.2.3 Leukemic stem cells Leukemic stem cells (LSCs) are defined as populations capable of initiating disease and differentiating into bulk leukemic blasts in serial transplantation. Although mounting evidence has pointed to a myeloid progenitor origin of LSCs, they share many biological properties with normal HSCs, including capacity of self-renewal and quiescence51,52. Dormant LSCs have also been linked to chemotherapy failure and contribute to disease relapse53,54. Coexistence of two LSC populations, which are immunophenotypically characterized as CD34+38-45RA+ (LMPP-like) and CD34+38+45RA+ 123+ (GMP-like), were identified by Goardon et al. in most CD34+ AML patient samples55. These two populations exhibited serial repopulation capacity with myeloid-restricted grafts, and were hierarchically orderedâas LMPP-like population gave rise to GMP-like population upon transplantation, whereas the GMP-like population did not give rise to a LMPP-like population55. Global mRNA expression profile of these two populations revealed their similarity with normal progenitor populations, but the âself-renewalâ gene set was significantly upregulated55. Although AML is generally considered to be hierarchically organized and initiated by transformed myeloid cells arrested at a progenitor-like stage which retain CD34 expression56, evidence has pointed to the existence of CD34- LSCs, especially in CD34- AML cases (> 98% are CD34- cells)57â59. Within these CD34- AML samples, CD34+ and CD34- populations have similar LSC frequencies, and surprisingly, these two populations are not hierarchically arrangedâjust as CD34+ LSCs gave rise to both of CD34+ and CD34- progeny in primary and secondary mice, CD34- LSCs were also able to generate CD34+ and CD34- progeny in primary and secondary mice57. 10 Other evidence that LSCs do not follow the normal irreversible differentiation hierarchy was reported by McKenzie et al60. Interestingly, non-leukemogenic mature AML cells were able to reacquire LSC feature through de-differentiation upon withdrawal of established differentiation therapies, or suppression of lineage-determining transcription factors60. The plasticity of AML maturation suggests that differentiation of AML cells is reversible and LSCs may exist across a range of differentiation states, which cannot be classified into an immunophenotypically and morphologically distinct subset. 11 1.2.5 Mouse modeling for AML Mouse models have been extensively used to study normal and disordered hematopoiesis, due to their similarities in organization of the blood-forming system with humans, the ease of precise genetic modification in specific DNA sequences, and accessibility of mouse primitive hematopoietic cells, at any stage of development61. However, accumulating evidence has pointed to interspecies differences between human and mouse, suggesting the limitations of mice as a model for the human system61. For example, much evidence has shown that mouse cells are more easily transformed with generation of mouse leukemias by introduction of only one or two oncogenic variants, whereas the same oncogene combinations introduced in human hematopoietic cells only cause perturbations in hematopoiesis, without producing leukemia upon transplantation into immunodeficient mice62â65. Modeling leukemogenesis from human primary samples also more closely resembles human diseases compared to modeling with mouse cells. For example, MLL-ENL overexpression in human primary cells causes either a myeloid or lymphoid type leukemia, which resemble human disease66. In contrast, Mll-Enl murine model only exhibit an AML phenotype67. On the other hand, given that AML is a heterogeneous disease with complex mutational patterns (with 2 or more driver mutations detected in 86% of the AML patients and an average of 13 mutations detected per person)45â47, the few humanized leukemia models do not recapitulate all aspects of human disease considering that they are heavily biased towards the very few oncogenic events introduced68. Furthermore, technical challenges remain to model serial acquisition of oncogenic events in leukemogenesis in a humanized model. 12 Improved techniques in genetic manipulation and propagation of human primary cells, together with the development of immunodeficient mice, have enabled rapid progress towards xenograft modeling of normal and disordered human hematopoiesis. In 2007, Barabé et al. demonstrated the first de novo xenograft leukemia model by transplantation of MLL-AF9 or MLL-ENL-transduced human normal primitive hematopoietic cells into immunodeficient mice66. Although this approach of generating de novo leukemia model was reported in 2007, only very few humanized AML models have been successfully created thus far (Table 1)61. Table 1. In vivo models of human de novo AML generated by genetic manipulation of primary human cells Gene(s) Disease Phenotype Reference MLL-AF9/ENL B-ALL or AML Barabé et al., 200766 BCR-ABL1 + IK6 AML or myeloid sarcoma Theocharides et al., 201569 MLL-AF10 + KRAS G12V AML Moriya et al., 201270 NUP98-HOXD13 + MN1 AML Imren et al., 201471 NUP98-KDM5A AML, B- and T-cell acute lymphoblastic leukemia Cardin et al., 201972 13 1.2.5.1 Mouse strains for AML xenograft Advances in the ability to develop increasingly more permissive immunodeficient mice have enabled higher engraftment of AML patient samples for in vivo study. Key milestones involved in the development of immunodeficient mouse models are: (1) discovery of Sirpa (signal-regulatory protein α) in inhibiting phagocytosis of transplanted hematopoietic cells in nonobese diabetic (NOD) mice1. (2) deletion of IL-2R common γ chain to eliminate NK cells, mature lymphocytes and to prevent endogenous leukemias73. NOD-scid-IL2rgnull (NSG) and NOD-Rag1null-IL2rgnull (NRG) are the two most commonly used strains. Mice with the Rag mutation have higher tolerance to irradiation compared to the scid mutation, which confers defective DNA repair and results in radiosensitivity61,74. The current best immunodeficient host mice for human myelopoiesis are NRG and NSG constitutively expressing human IL-3, stem cell factor (SCF) and GM-CSF (NRG-3GS and NSG-3GS, respectively)75. Evidence has also shown that cytokines improve AML xenograft efficiency in 3GS-producing mice compared to the parental NRG or NSG mice76â78. However, even with the use of the most permissive immunodeficient mice available now, about 40% of human good-risk AML patient samples fail to engraft79. 14 1.2.5.1 Previous attempts to develop humanized AML models with AML1-ETO AML with AML1-ETO (AE) gene fusion, which is generated by chromosomal t(8,21) translocation targeting the transcription factor RUNX1 (AML1), accounts for approximately 4-12% of adult and 12-30% of pediatric AML patients80,81. As one of the most recurrent chromosomal abnormalities in AML82, several attempts have been made to develop a humanized AML model with AE. However, de novo generation of AE driven leukemia in human primary cells has not been achieved so far65,83,84. Xenotransplantation of AE-transduced human CD34+ CB cells into immunodeficient mice was first reported by Mulloy et al83. Although AE-transduced human CD34+ CB cells were able to proliferate ex vivo for more than 7 months while retaining 10% CD34+ expression (varied from as few as 1% and up to 90% between replicates; median CD34 percent was 10%), low level of human CD45 chimerism was detected upon transplantation (with or without sorting CD34+ cells), with a median engraftment of 0.11%83. Further incorporation of frequent cooperating events in AE-transduced CD34+ CB cells has led to increased engraftment in immunodeficient mice upon transplantation, without displaying overt leukemia development65,84. For example, as a recurrently mutated event in AE AML81, a mutated RAS gene was retrovirally transduced into AE-expressing human hematopoietic cells to examine their cooperativity in leukemogenesis65. Although addition of a mutated RAS cDNA was able to enhance in vivo engraftment compared to parental AE-expressing cells (~10-fold increase; Engraftment <1% with addition of mutated RAS), no mice developed overt leukemiaâsuggesting that additional genetic mutations may be required to initiate leukemogenesis65. 15 1.3 The ubiquitin proteasome system (UPS) The ubiquitin proteasome system (UPS) acts as a quality-control system in post-translational regulation for various cellular functions, including cell cycle progression, quiescence maintenance, signaling pathways, and migration to name a few85â88. Ubiquitin modification of proteins is performed by an enzymatic cascade (Figure 1.2), which consists of a ubiquitin-activating enzyme (E1), the ubiquitin-conjugating enzyme (E2), and the ubiquitin-protein ligase (E3)89. Briefly, E1 activates and transfers ubiquitin to E2 in an ATP-dependent manner. E2 then cooperates with E3âas E3 properly positions protein substrate in proximity, E2 transfers and tags protein substrates with either polyubiquitin or monoubiquitin88. The two most common linkage types for polyubiquitin are lysine48-linked (K48) and lysine 63-linked (K63) types. K48 linkage type are responsible for proteasomal degradation, whereas K63 linkage type tags the protein for protein trafficking and cell signalling90â92. Ubiquitin-mediated proteolysis is highly selective and precisely regulated, as protein turnover of individual proteins are different and vary ranging from several minutes to many days93. 16 Figure 1.2 Process and consequences of ubiquitin modification on a protein substrate 17 1.3.1 The role of ubiquitin ligase in normal and disordered hematopoiesis Hematopoiesis is tightly regulated through intrinsic and extrinsic elements1,19,94. Although much attention is given to the transcriptional mechanisms, epigenetics and signaling pathways 1,19,95,96, surprisingly little is known about the role of UPS in hematopoiesis considering its importance in post-translational regulation for various cellular functions. To date only very few components of UPS have been recognized as important regulators of HSC maintenance and lineage specification, and the majority of them are associated with substrate-binding constituents of E3 ubiquitin ligase12,15,86,87,97,98. For example, a Fbw7-c-Myc interaction was reported by Reavie et al. to regulate HSC maintenance15. Deletion of Fbw7 stabilizes c-Myc protein activity, resulting in substantial loss of LT-HSCs15. Huwe1, was also recognized as a crucial regulator for HSC maintenance, and essential for early lymphoid commitment86. Moreover, previous work has shown that deletion of Cbl, an E3 ubiquitin ligase, results in hyperproliferation of HSC in mice97,99. Mutations in CBL have also been reported in various myeloid malignancies99â102. Disruption of UPS components is mainly studied in lymphoid malignancies thus far103,104. Although treatment with proteasome inhibitors has been investigated in clinical studies for MDS and AML, no effect or highly variable results among patients were reported after treatment105,106. Our previous genomic analyses of AML patient sample data from The Cancer Genome Atlas (TCGA)46 and in-house datasets (AML-PMP dataset, unpublished) have revealed that genes involved in UPS are frequently disrupted (50 of 345 cases, 14.5%; Figure 1.1). These recurrent 18 UPS mutations are mainly loss-of-function mutations, suggesting that ubiquitin-mediated protein degradation is expected to be reduced in most AML cases bearing UPS mutations. This resultant reduced proteasome activity by UPS mutations likely explain no effects or highly variable results in AML patients treated with proteasome inhibitors. Finally, co-occurrence and exclusivity of UPS mutations with other common mutational events in AML were assessed (unpublished; Figure 1.2). According to this co-occurrence and exclusivity plot, RAS mutations have the highest odds ratio of co-occurrence (odds ratio = 3.13, p-value = 0.016), indicating a significantly strong trend of RAS mutations co-occurring with UPS mutations in leukemogenesis (Figure 1.2). Figure 1.3 Ubiquitin proteasome system components are recurrently mutated in AML (unpublished) Mutational spectrum of TCGA and our in-house AML-PMP samples (total patient sample size=345 cases) that harbor somatic, functionally relevant mutations affecting one or more genes encoding components of the ubiquitin proteasome system (UPS, patient size with UPS mutations=50). Columns represent individual AML samples, rows represent individual genes or genesets, with names of recurrently mutated UPS genes shown in red. AML cohorts and subtypes are depicted at the top of the chart. AML-MDS: transformed AML derived from myelodysplastic syndrome (MDS); tAML: therapy-related AML. 19 Figure 1.4 Co-occurrence or exclusivity between UPS gene mutations and common mutational events in AML using TCGA and AML-PMP combined datasets (unpublished) The odds ratio plot shows co-occurrence (odds ratio >1) or exclusivity (odds ratio <1) with rows representing individual genes or gene sets. p-value is shown at the right of each row. 20 1.3.2 FBXO11 F-Box Protein 11 (FBXO11) is a constituent of SKP1-CUL1-F-box (SCF) E3-ubiquitin ligase complex, and functions as the substrate-recognition component for E3 ligase by binding ubiquitination targets through its protein interaction domains107 (Figure 1.3). In lymphoid cells, one known FBXO11 target is the BCL6 protein103, which is frequently overexpressed in aggressive diffuse large B-cell lymphoma (DLBCL)108â110. Inactivating mutations of FBXO11 gene are reported to be disrupted in ~6% of DLBCL111, and Fbxo11 knockout in germinal center-derived B cells results in lymphoproliferative disorders in mice111. FBXO11 is also reported to be a p53-interacting protein and inhibits p53 activity by neddylation (analogous to ubiquitylation but mediated by a ubiquitin-like protein, NEDD8)112. Figure 1.5 Graphical representation of the E3 SKP1-CUL1-F-box (SCF) ubiquitin ligase complex FBXO11 is a component of SCF E3-ubiquitin ligase complex, and it determines the substrate specificity of E3 ligase by binding to the ubiquitination targets. S-phase kinase-associated protein 1 (SKP1), Cullin-1 (CUL1), and ring-box 1 (RBX1) are also components of the SCF E3 ubiquitin ligase complex. 21 1.3.3 Preliminary data for studying the role of FBXO11 in AML Recurrent loss-of-function mutations in FBXO11 gene were revealed by an AML sequencing study from our lab (unpublished). In addition, FBXO11 has significantly reduced transcript expression in various types of AML compared to normal hematopoietic stem and progenitor cells (HSPCs; Figure 1.4, data obtained from Bloodspot database). Figure 1.6 FBXO11 transcript levels are significantly reduced in various types of AMLs compared to normal HSCs Data were re-plotted from the online tool Bloodspot (http://servers.binf.ku.dk/bloodspot/; data from GSEA GSE42519 and GSE13159). 22 Previous work from our lab has shown retroviral shRNA-mediated knockdown of Fbxo11 cooperates with AML1-ETO (AE) overexpression to cause transplantable leukemia in a mouse BM transplant model (unpublished). At the endpoint, the shFbxo11+AE doubly transduced mice had increased blasts in BM and PB, and the complete blood count of sick shFbxo11+AE mice exhibited increased white blood cells, lower platelets and hemoglobin relative to controls (unpublished). Flow cytometry analysis on BM cells harvested from sick shFbxo11+AE mice also revealed significantly increased LT-HSC-like population compared to controls, and the peripheral blood showed a massive increase in myeloid progenitors (unpublished). 23 1.4 Aim of the study Our preliminary data have identified that the FBXO11 gene is recurrently mutated with loss-of-function mutations in AML, and FBXO11 transcript expression is significantly reduced in various types of AML compared to normal HSPCs. In addition, our mouse transplant work revealed biological cooperation between Fbxo11 knockdown and enforced AE expression to cause serially transplantable AMLs. However, to date the role of FBXO11 loss in contributing to human leukemogenesis has not been studied. The objective of this project is to investigate the role of FBXO11 in normal hematopoiesis and assess its potential role in human leukemogenesis. The specific aims are: Aim 1: To determine the effects of FBXO11 disruption on human HSPCs during ex vivo culture (Chapter 3). Aim 2: To determine whether FBXO11 depletion can cooperate with leukemia-associated oncogenes to model human AML (Chapter 4). 24 Chapter 2: Material and Methods 2.1 Isolation of human HSPCs Anonymized normal human CB samples were obtained with informed consent from normal full-term Cesarian section deliveries in accordance with procedures approved by the Research Ethics Board of the University of British Columbia and samples from a single day pooled for further processing. CD34+ CB cells were obtained at >95% purity from pooled collections using a two-step Rosette-Sep/EasySep human CD34-positive selection kit (STEMCELL Technologies) according to the manufacturerâs protocols and/or FACS sorting. CD34+ cells were thawed by dropwise addition into HBSS + 10% fetal bovine serum (FBS) supplemented with DNase (100 ug/mL final concentration). CD34+ cells were then seeded into 96-well round bottom plates (cell density at 80k cells/100 uL) and prestimulated in StemSpanTM SFEM (STEMCELL Technologies #09650) supplemented with StemSpanâ¢ CC100 (STEMCELL Technologies #02690), 750 nM SR1 (STEMCELL Technologies #72344) and 35 nM UM171(STEMCELL Technologies #72912)for overnight. 2.2 Lentiviral constructs and transduction PLKO.1-shFBXO11-GFP, and MND-AML1ETO-YFP were constructed in the lab by Dr. Linda Chang. Sequence of KRASG12D-2A-mCherry was ordered as gBlock gene fragments (IDT). The gBlock gene fragment was digested with BamHI-HF and KpnI-HF (NEB) and subcloned into the pLKO.1-shScramble, and pLKO.1-shFBXO11 lentiviral vector. The resulting constructs are shown in Figure 2.1, and were validated by Sanger sequencing (forward primer 5â- TCTCCCCAGGGGGATCC and reverse primer 5â-GCTGCCTTGTAAGTCATTGGTC). 25 FBXO11 and KRAS protein expression were validated in UT-7 cells (a human leukemic cell line derived from the bone marrow cells of a patient with acute megakaryoblastic leukemia. Sequencing113 has shown that UT-7 does not have FBXO11 or RAS mutations and FBXO11 protein expression was further confirmed by western blotting) expressing PLKO.1-shSCR-hPGK-KRASG12D-2A-mCherry and PLKO.1-shFBXO11-hPGK-KRASG12D-2A-mCherry, by western blotting. UT-7 cells were resuspended in DMEM supplemented with 20% FBS and 5 ng/mL GM-CSF (BioLegend #572902) at 5 Ã 105 cells/mL, and transduction was performed by direct addition of concentrated viral supernatants (500 uL total cell mixture with addition of 1 uL concentrated viral supernatants). Figure 2.1 Construct with KRASG12D used in the study (A)Schematic diagram for construct containing KRASG12D, construct validated by sequencing. (B) Western blotting for FBXO11 and KRAS expression level in UT-7 cells transduced with either PLKO.1-shSCR-hPGK-KRASG12D-2A-mCherry or PLKO.1-shFBXO11-hPGK-KRASG12D-2A-mCherry. 26 High-titre lentiviral supernatants were produced by transient transfection of 293T/17 cells with second-generation packaging/envelope vectors pRRE (Addgene #12251), pREV (Addgene #115989), and pMD2.g (Addgene #12259) using CalPhosTM mammalian transfection kit (Clontech #631312), followed by ultracentrifuge concentration (25,000 rpm for 90 min at 4oC; Beckman SW32Ti rotor).The titers obtained were typically in the range of 109 transducing units per mL as measured by transducing 150,000 Hela cells (transduction and culture media for Hela cells were DMEM supplemented with 10% FBS), followed by flow cytometry analysis of > 20% GFP/YFP/mCherry+ at 1:50000 dilution (Virus titre = dilution factor Ã GFP/YFP/mCherry+ Ã cell number at transduction Ã· 100). CB cells were transduced in 96-well plates (Thermo ScientificTM #12-565-65) by direct addition of concentrated viral supernatants (100 uL total cell mixture with addition of < 5uL concentrated viral supernatants). After 6 hours of virus exposure, cells were washed once with PBS and transferred to culture medium (StemSpanTM SFEM with StemSpanâ¢ CC100 to culture medium at a 1 in 100 dilution, 750 nM SR1 and 35nM UM171). Following 72-hour ex vivo culture, GFP+YFP+ or mCherry+YFP+ were then isolated by sorting on a Fusion sorter (Becton Dickenson) using purity mode. All cultures were incubated in a humidified atmosphere at 37oC in room air supplemented with CO2 to achieve a final concentration of 5%. 2.3 Methylcellulose colony-forming cell (CFC) assay At day 3 post-transduction, transduced CB cells (GFP+YFP+ or mCherry+YFP+) were sorted and transferred to a pre-aliquoted 2.5mL methylcellulose containing h-IL-3, h-erythropoietin, h-GM-27 CSF, and h-SCF (H4434; STEMCELL Technologies #04434) containing tube (cell density at 500 sorted cells/mL). CFC media was mixed before plating into 35mm dishes at 1 mL/dish. Dishes were maintained in a humidified 37oC incubator with 5% CO2 for 17 days. At day 17 post-plating, colonies were counted and classified into BFU-E (burst-forming unit- erythrocyte), CFU-GM (colony-forming unit-granulocyte, macrophage), and CFU-GEMM (colony-forming unit-granulocyte, erythrocyte, macrophage, megakaryocyte). 2.4 Long-term ex vivo cell culture with mix 3 feeders 2.4.1 Frozen mix 3 feeder cells preparation Mix 3 feeder cells consist of the M210B4 murine fibroblast cell line engineered to express human IL3 and G-CSF, Sl/Sl mouse fibroblasts engineered to express human stem cell factor (SCF) and interleukin-3 (IL3), and Sl/Sl mouse fibroblasts engineered to express human fms-like tyrosine kinase 3 ligand (FLT3L). Non-irradiated individual mix 3 feeder cells were gifts from C. Eaves114. M210B4 cells engineered to express human IL3 and G-CSF were maintained in 10% RPMI supplemented with 10% FBS, 0.4 mg/mL G418 (Thermo FisherTM 10131-035) and 0.06 mg/mL hygromycin (Sigma-Aldrich H3274). Sl/Sl mouse fibroblasts engineered to express human SCF and IL3 were maintained in DMEM supplemented with 15% FBS, 0.8 mg/mL G418 (Thermo FisherTM 10131-035) and 0.125 mg/mL hygromycin (sigma-Aldrich H3274). Sl/Sl mouse fibroblasts engineered to express human FLT3L were maintained in DMEM supplemented with 15% FBS, 0.4 mg/mL G418 (Thermo FisherTM 10131-035). The culture flasks were coated with collagen solution (STEMCELL Technologies #04902), air dried in the 28 biosafety cabinet hood for 2 hours before plating the feeder cell lines. 3 mL of individual cell line (density at 2-3 Ã 106 cells/mL) was placed into a 5 mL snap cap polystyrene tube (Falcon®) and irradiated at 80 Gy by X-ray irradiation. After irradiation, 1.5 Ã 106 cells/cell line was aliquoted to a new 5 mL snap cap polystyrene tube (4.5 Ã 106 cells in total), and cells were spun down at 300 Ã g for 5min. The cell pellet was then resuspended in 1 mL FBS supplemented with 10% DMSO, and frozen down in a 2 mL cryovial. Mix 3 feeder cells were stored at -185oC in a liquid nitrogen tank. 2.4.2 Long-term culture (LTC) with mix 3 feeders Three-four days before cell sorting for long-term co-culture with feeders, frozen irradiated mix 3 feeders were thawed via dropwise addition into 9 mL DMEM supplemented with 15% FBS (15% DMEM). The cell mixture was spun down at 300 Ã g for 5 min and the supernatant was decanted. The cell pellet was then resuspended in 6 mL 15% DMEM (no antibiotics) and plated at 1 mL/dish in collagen-coated 35 mm dishes (collagen coating was acidic and needed to be rinsed and neutralized with DMEM medium before cell plating). The mix 3 feeders were incubated in a 37oC incubator overnight and changed to 2 mL fresh 15% DMEM medium the next day. On the day of sorting for the LTC-IC assay, the 15% DMEM was carefully removed and replaced with 2.5 mL of sorted-cell containing LTC medium (MyeloCultTM H5100 supplemented with 10-6 M Hydrocortisone; STEMCELL Technologies #05150 and #07904). 3000 sorted cells were plated in each dish and co-cultured with mix 3 feeders in a humidified atmosphere at 37oC incubator supplemented with CO2 to achieve a final concentration of 5%. 29 Weekly half-medium changes were performed by removing one half of the medium and replacing with one half of the original volume of freshly prepared LTC medium over the 40-day cell culture. 2.4.3 Cellular output characterization of hematopoietic cells harvested from long term culture On day 40, both adherent and non-adherent cells were harvested from LTC (according to manufacturerâs instructions) and followed by assay for clonogenic progenitors in methylcellulose-based media (CFC assay) supplemented with h-SCF, h-IL-3, h-IL-6, h-erythropoietin, h-G-CSF (H4435; STEMCELL Technologies #04435). 10,000 cells were plated for each CFC assay. Harvested cells were also analyzed by flow cytometry analysis (using a BD LSR Fortessa analyzer or BD FACSymphony) with the following: AF700-anti-CD34 (1:50; BD biosciences 561440), and APC/Cy7 anti-CD45 (1:50; BioLegend 304014). 2.5 Xenotransplantation All experimental procedures were done in accordance with protocols approved by the University of British Columbia Animal Care Committee. NRG-3GS mice (NOD.Cg-Rag1tm1Mom Il2rgtm1Wjl Tg(CMV-IL3,CSF2,KITLG)1Eav/J) were bred and maintained in the Animal Resource Centre of the British Columbia Cancer Research Centre under specific pathogen-free conditions. 30 Female NRG-3GS mice 8-15 weeks of age were sublethally irradiated with 850 cGy (cesium irradiation, medium dose rate) on the same day before intrafemoral injection. For transplant analysis of ex vivo cultured cells, bone marrow aspirates were performed every 6 weeks post-transplant and human chimerism was tracked by flow cytometry analysis (using a BD LSR Fortessa analyzer or BD FACSymphony) and analyzed with the following: APC-anti-CD45 2D1(1:50; Invitrogen 17-9459-42), APC/Cy7 anti-CD45 HI30 (1:50; BioLegend 304014), AF700-anti-CD34 (1:50; BD biosciences 561440), PE/Cy7-anti-CD33 (1:100; Thermo fisher 25-0338-42), PE/Cy7-anti-CD15 (1:100; BD biosciences 560827), BV650-anti-CD19 (1:100; BD biosciences 560827), SB600-anti-CD3(1:50; Invitrogen 83-0037-42), DAPI (1 ug/mL final concentration, Sigma-Aldrich D9542). Peripheral blood was collected from the tail vein every 7 weeks post-transplant and human chimerism was tracked by flow cytometry analysis (using a BD LSR Fortessa analyzer or BD FACSymphony) and analyzed with the following: APC-anti-CD45 2D1(1:50; Invitrogen 17-9459-42), APC/Cy7 anti-CD45 HI30 (1:50; BioLegend 304014), AF700-anti-CD34 (1:50; BD biosciences 561440), PE/Cy7-anti-CD33 (1:100; Thermo fisher 25-0338-42), PE/Cy7-anti-CD15 (1:100; BD biosciences 560827), BV650-anti-CD19 (1:100; BD biosciences 560827), SB600-anti-CD3(1:50; Invitrogen 83-0037-42), DAPI (1 ug/mL final concentration, Sigma-Aldrich D9542). Counting beads (Invitrogen PCB100) were used according to manufacturerâs instructions. Transplanted mice were sacrificed at predefined, humane clinical morbidity endpoints. At endpoint, human chimerism in bone marrow (cells flushed from femurs, pelvis and 31 tibias), spleen, and liver were tracked by flow cytometry analysis (using a BD LSR Fortessa analyzer or BD FACSymphony) and analyzed with the following: APC-anti-CD45 2D1(1:50; Invitrogen 17-9459-42), APC/Cy7 anti-CD45 HI30 (1:50; BioLegend 304014), AF700-anti-CD34 (1:50; BD biosciences 561440), PE -anti-CD33 (1:50; eBioscience 8012-0337-120), PE/Cy7-anti-CD15 (1:100; BD biosciences 560827), BV480-anti-CD14 (1:50; BD biosciences 566190), DAPI (1 ug/mL final concentration, Sigma-Aldrich D9542). 2.6 Histology Mouse tissues (spleen and liver) were fixed in 10% neutral-buffered formalin at 4oC for 16 hours overnight, and then stored in 70% ethanol at 4oC before paraffin embedding. Bone marrow and peripheral blood smears were performed and stained with Wright-Giemsa staining solution. 2.7 Ex vivo cord blood liquid culture Lin-CD34+ enriched CB cells were thawed via dropwise addition into HBSS + 10% FBS supplemented with DNase (100 µg/mL final concentration). CD34+ cells were then seeded into 96-well round bottom plates at 80,000 cells/100 µL ((Thermo Fisher TM #12-565-65) and pre-stimulated in StemSpanTM SFEM (STEMCELL Technologies #09650) with StemSpanâ¢ CC100 (STEMCELL Technologies #02690) to culture medium at a 1 in 100 dilution, 750 nM SR1 (STEMCELL Technologies #72344) and 35 nM UM171(Stem Cell Technologies #72912) for 16h overnight. CB cells were transduced in 96-well plates by direct addition of concentrated 32 viral supernatants. After 6-hour viral exposure, cells were washed with PBS and transferred to culture medium (StemSpanTM SFEM supplemented with StemSpanâ¢ CC100, 750 nM SR1 and 35 nM UM171). Following 72-hour ex vivo culture, 20,000 cells GFP+ cells were then isolated into each well (containing 100 μL culture medium) in a 96-well round bottom plate by sorting on a Fusion sorter (Becton Dickenson) using purity mode. All cultures were incubated in a humidified atmosphere at 37oC in room air supplemented with CO2 to achieve a final concentration of 5%. Four days after sorting, cells from each well were split into two wells in a 96-well round bottom plate (50 µL/well) and then topped up with fresh culture medium to 100 µL final volume each well. One week after sorting, cells were harvested either for proliferation and cell cycle analysis (on fixed cells), or live-cell flow staining with AF647-anti-CD34 (1:50; BioLegend 343508), PE/Cy7 -anti-CD90 (1:200; BD biosciences 561558), AF700-anti-CD45RA (1:50; BioLegend 304120), DAPI (1 ug/mL final concentration, Sigma-Aldrich D9542) and counting beads (Invitrogen PCB100). 2.7.1 Proliferation and cell cycle assays Cultured CB cells from liquid culture experiment were harvested and transferred to a low-protein binding 1.5 ml eppendorf tube. The cells were washed with PBS, stained with fixable viability stain 780 (BD biosciences 565388) and incubated on ice for 15 min. Cells were then washed once in 10% PBS (spun down at 300 Ã g for 5 min), resuspended in 100 µL human blocking buffer consisting of PBS supplemented with 4% FBS and 1.5 µg/mL anti-human CD32 antibody (Clone IV.3; Stem Cell Technologies #60012) and stained for 1 hour with antibody cocktail, including AF647-anti-CD34 (1:50; BioLegend 343508), PE/Cy7 -anti-CD90 (1:200; BD 33 biosciences 561558), AF700-anti-CD45RA (1:50; BioLegend 304120). After one hour antibody staining, cells were washed with PBS supplemented with 2% FBS, followed by fixing with Cytofix/Cytoperm buffer (BD 554714) for 15 min on ice. After fixation, cells were washed twice in 500 uL 1x BD perm/wash buffer and spun at 2100 rpm for 3 min and supernatant was aspirated. The pellet was then resuspended in 100 µL PBS supplemented with 2% FBS and added 1 µL of RNase A/sample (Qiagen, stock 10mg/mL). PE Mouse Anti-Ki67 antibody and PE mouse IgG1 isotype control antibody (both 1:50; BD biosciences 556027) was added to the respective samples and stained overnight at 4oC. The next morning, all samples were washed with PBS supplemented with 2% FBS and spun at 2100 rpm for 3 min, aspirated the supernatant. The samples were then resuspended in 200 µL PBS supplemented with 2% FBS plus DAPI (final concentration 8 ug/mL), and transferred to a matrix tube (Thermo Fisher; #4140) in a 5 mL snap cap polystyrene tube. Samples were analyzed on a BD LSR Fortessa analyzer or BD FACSymphony. 2.8 Western blotting Cell lysates were prepared for each cell line by collecting 3 Ã 107 cells, washing once with PBS to remove media, and lysing the cells for 30min using 250 µL of RIPA buffer (25âmM Tris, 150âmM NaCl, 1% TX-100, 0.25% sodium deoxycholate, 0.1% sodium dodecyl sulfate (SDS)) with freshly added protease inhibitor and phosphatase inhibitor, followed by sonication. The solubilized cells were then centrifuged at 16,000 Ã g for 20 min at 4oC, and the supernatant lysate was collected for BCA assay (BioRad DC Protein assay kit, Bio-Rad 5000112) to 34 determine protein concentrations. Samples containing 20-25 µg protein were mixed with Laemmli sampling buffer and heated in 95oC water block for 5 min. The protein was then separated by SDS-polyacrylamide gel electrophoresis at 120 V for ~1 hour at room temperature and transferred onto nitrocellulose membrane overnight at 30 V and 4oC. Next morning, the membrane was blocked for 1h at room temperature using 5% skim milk in tris-buffered saline with 0.1% Tween 20 (TBST), followed by washing 3 times with TBST, 10min/wash. Blots were probed with primary antibody (FBXO11 antibody 0.2 mg/mL at 1:1000 dilution with 3% BSA in TBST, Bethyl Laboratories A310-395A; RAS antibody 0.048 mg/mL at 1:1000 dilution with 3% BSA in TBST, Cell Signaling Technology 3339T), and anti-rabbit-HRP secondary antibody (1:10000 dilution). Proteins were detected using enhanced chemiluminescence (ECL) kit in dark room with exposures of 10s, 30s, 90s, and 5 min onto X-ray film. 2.9 Gene set enrichment analysis Gene set enrichment analysis software (Broad Institute)115,116 was used to analyzed microarray data of FBXO11-knockdown CB cells versus shSCR control CB cells. Microarray data normalization and GSEA pathway analysis was performed by Derek Tam. MSigDB v7.0 Hallmark and C2 collection (total 5579 gene sets) were combined and used for pathway analysis. 35 2.10 Statistical analysis All statistical tests were performed using GraphPad Prism version 8.0 (San Diego, California). All data are presented as mean Â± standard error of the mean (SEM). In chapter 3, all statistical comparisons were performed using the Student T-test. In chapter 4, all statistical comparisons were performed by one-way ANOVA corrected by Sidak multiple comparison test, except for the survival curves of miceâwhich were compared using the Log-rank (Mantel-Cox) test. Statistical significance was set at a threshold of p < 0.05. P-values are labeled as follows p > 0.05 (actual p-value listed), p â¤ 0.05 (*), p â¤ 0.01 (**), p â¤ 0.001 (***), p â¤ 0.0001 (****). 36 Chapter 3: Loss of FBXO11 promotes stem cell phenotype maintenance ex vivo 3.1 Aim To determine the effects of FBXO11 disruption on human HSPCs during ex vivo culture 3.2 FBXO11-knockdown CB cells show enrichment in HSC signature in GSEA pathway analysis Previous mouse transplant work revealed a significantly increased LT-HSC-like population in BM of sick shFbxo11+AE mice compared to AE control mice (unpublished). To gain genome-wide insights into the impact of FBXO11 depletion on stem cell population, we performed gene set enrichment analysis (GSEA) on microarray data of shFBXO11 relative to shSCR human HSPCs with the MSigDB v7.0 Hallmark and C2 collection (Total 5579 gene sets; top 150 deregulated pathways shown in Appendix A.1 and A.2). Many of the dysregulated pathways identified following FBXO11 knockdown were centered on HSC-associated characteristics, including hypoxia and delay in cell cycle progression (Figure 3.1, Appendix A.1 and A.2). Upregulation in transcriptional programs associated with hypoxia ( Figure 3.1A and C, Appendix A.1), together with decreased oxidative phosphorylation (normalized enrichment score (NES) = -1.85, false discovery rate (FDR) = 0.01; Figure 3.1B) were identified in GSEA, both of which have been characterized as important aspects of HSC metabolism13. Downregulation of MYC targets (NES=-2.47 and -2.44, FDR=0.00; Figure 3.1B and C) and E2F targets (NES=-2.13, FDR=0.00; Figure 3.1B) were also noted in GSEA, which are associated with delay in cell cycle 37 progression15,117. Furthermore, GSEA revealed a significant enrichment in shared HSC and LSC signature (NES=2.12, FDR=0.00; Figure 3.1A and C), and a downregulation in progenitor signature (NES=-2.17, FDR=0.00; Figure 3.1B and C) following FBXO11 knockdown relative to control. Significant upregulation in the p53 pathway following FBXO11 knockdown was also revealed in GSEA analysis (NES=1.97, FDR=0.0; Figure 3.1A), consistent with the evidence that FBXO11 is a p53-interacting protein and inhibits p53 activity by neddylation112. p53 has been previously shown to be a critical regulator for HSC quiescence, and HSC has higher p53 expression level compared to myeloid progenitors10. On the basis of the above findings, we hypothesized that FBXO11 knockdown conferred a possible quiescence effect on human HSPCs. 38 Figure 3.1 FBXO11 knockdown enhances stem cell signature in human HSPCs NES scores for the MSigDB v7.0 Hallmark and HSC/LSC-related gene sets (from the C2 collection) that are significantly deregulated following FBXO11 knockdown compared to control. (A) Upregulated pathways with NES > 1.75 are shown. (B) Downregulated pathways with NES < -1.80 are shown. (C) GSEA plots of deregulated pathways (only NES > 2.1 and NES < - 2.1 are shown). 39 3.2 FBXO11 deficiency enhances quiescence in CD34+ hematopoietic cells in short-term culture To determine how FBXO11 perturbation impacts the phenotype of CB HSPCs, we transduced and sorted CB CD34+ cells with either shSCR or shFBXO11 vectors co-expressing green fluorescent proteins (GFP), followed by liquid culture for a week before flow analysis (Figure 3.2A). At the endpoint, FBXO11 knockdown slightly decreased cell viability (3% decrease in shFBXO11 cells compared to shSCR control, p = 0.00007; Figure 3.2B), and significantly enhanced the percentage of CD34+ cells (5-9% increase, p=0.0002, Figure 3.2C), but the absolute number of CD34+ cells was reduced (p=0.000004, Figure 3.2C). Next, we further analyzed subpopulations within the CD34+ compartment to see how FBXO11 knockdown alters CD34+ subset distribution. CD34+ subpopulations can be divided into four subpopulations in vitro based on the expression of CD45RA and CD90: CD45RA-CD90-, CD45RA-CD90+, CD45RA+CD90-, CD45RA+CD90+. Although FBXO11 depletion significantly limited the number of all CD34+ subpopulations examined, the percentage of CD45RA-CD90- population was significantly enriched within the CD34+ fraction (20-22% increase relative to control, p < 0.000001, Figure 3.2D and E). Cultured CD45RA-CD90-, CD45RA-CD90+ and CD45RA+CD90- subpopulations within CD34+ fraction were previously referred to as cultured ST-HSC, cultured LT-HSC and cultured progenitors, respectively, in normal CB cells118. Substantial enrichment in CD45RA-CD90- (equivalent to cultured ST-HSC populations118) and decrease in percentage of CD45RA+CD90 â population (equivalent to cultured progenitor population118) reflect our findings from GSEA pathway analysis (Figure 3.1). Of note, cell distribution within the CD34+CD45RA+ gate following FBXO11 knockdown seemed different 40 from that of controlâa relatively distinct CD45RA+CD90+ population could be identified in shFBXO11 cells whereas the majority of the CD45RA+ population exhibited CD90neg-low expression in controls (Figure 3.2D). This suggests FBXO11 knockdown may confer different effects on different progenitor populations. Despite fewer CD34+ cells following FBXO11 knockdown, increase in percentage of CD34+ population implicates the possibility of quiescence conferred by FBXO11 depletion relative to control. GSEA pathway analysis also suggested this dormancy following FBXO11 knockdown (Figure 3.1). To directly test the impact of FBXO11 knockdown on quiescence, cell cycle analysis was performed at the same endpoint (workflow shown in Figure 3.3A). Data showed CD34+ population in shFBXO11 condition had significantly 3-4 fold increase in percentage of G0 quiescent state (ki67- 2n DNA content, p=0.00004), significantly decrease in G1 state (ki67+ 2n DNA content, p=0.000008), and similar in S-G2-M (p=0.133) relative to control (Figure 3.3B and C). Next, we investigated the proportions of each cell cycle phase within CD34+ subpopulations. The cell cycle analysis showed a similar pattern (significantly increased G0 quiescent state, decreased G1 state and similar in S-G2-M state following FBXO11 knockdown) in all CD90/CD45RA subsets (Figure 3.3D). Collectively, these data indicate that FBXO11 loss restrains proliferation and confers quiescence on CD34+ CB cells, including all CD90/CD45RA subpopulations. 41 Figure 3.2 FBXO11 loss increases proportion of CD34+ cells but restricts expansion of HSPCs during ex vivo culture (A) Experimental scheme for ex vivo culture of shFBXO11 (n=4) or shSCR (n=4) transduced lin- CB CD34+ cells followed by flow analysis. Flow cytometry for (B) total cell viability, (C) percentage and count (number) of CD34+ cells at day 11 transduced with shFBXO11 (green dots) or shSCR(black dots). (D) Representative flow cytometry plots for gating of CD90/CD45RA subpopulations within the CD34+ fraction of lin- CB progeny at day 11 of each treatment group. (E) Flow cytometry for the percentage (left panel) and cell count (right panel) of CD34+ CD90/CD45RA subpopulations at day 11. (All data are represented by mean ± SEM; *p â¤ 0.05; **p â¤ 0.01; *** p â¤ 0.001; **** p â¤ 0.0001 as determined by t tests relative to control). n = 4 technical replicates. 42 Figure 3.3 FBXO11 deficiency confers quiescence on HSPCs in vitro (A) Schematic showing the experimental design used. (B) Cell cycle analysis for Ki67 and DNA content by flow cytometry of CD34+ fraction transduced with shFBXO11 (green dots) or shSCR (black dots). (C) Representative flow cytometry plots of cell cycle analysis for Ki67 and DNA content. (D) Cell cycle distribution within each CB CD34+ subpopulations transduced with shFBXO11 or shSCR. Cell cycle distribution for CD45RA-CD90+ (top left panel), CD45RA+CD90+ (top right panel), CD45RA-CD90- (bottom left panel), CD45RA+CD90- (bottom right). (All data are represented by mean ± SEM; *p â¤ 0.05; **p â¤0.01; *** p â¤ 0.001; **** p â¤ 0.0001 as determined by t tests relative to control). 43 Chapter 4: Investigating the role of FBXO11 disruption in human leukemogenesis 4.1 Aim To determine whether FBXO11 depletion can cooperate with leukemia-associated oncogenes to model human AML 4.2 FBXO11 depletion impairs mature erythroid output in CFC assays To examine whether FBXO11 depletion alters functional outputs of different progenitor cells, we utilized a colony-forming cell (CFC) assays. Considering that FBXO11 loss limited the proliferation of CB CD34+ cells and resulted in more quiescent cells compared to control, we investigated in parallel the effects of two frequently co-occurring mutationsâKRASG12D (hereafter referred to as K) and AML1-ETO (hereafter referred to as A), alone and in combination with shFBXO11 (F). Figure 4.1A shows the workflow we designed to perform CFC assays for shFBXO11 CB cells alone, or together with K or A, or both. First, CB CD34+ cells were transduced with respective lentiviral vector combinations for control (empty vector, hereafter referred as EV), F, K, A, F+A (FA), F+K (FK), K+A (KA), or all 3 oncogenes together (F+K+A, referred as FKA); then transduced cells were sorted from each condition based on fluorescent markers for CFC assays. The results showed conditions with FBXO11-knockdown had significantly fewer colonies compared to respective controls (EV vs. F, p < 0.0001; A vs. FA, p = 0.017; K vs. FK, p < 0.0001, KA vs. FKA, p = 0.62; Figure 4.1B), suggesting that FBXO11 loss inhibited clonogenic activity of progenitor cells. Morphologic CFC assessment revealed that FBXO11 knockdown led to a reduced BFU-E output compared to corresponding controls (EV vs. 44 F, p < 0.0001; A vs. FA, p = 0.24; K vs. FK, p < 0.0001, KA vs. FKA, p = 0.84; Figure 4.1C), suggesting that FBXO11 depletion inhibited erythroid lineage output. GM output remained similar in all 8 conditions tested (Figure 4.1D), indicating that FBXO11 knockdown did not affect myeloid lineage differentiation. FBXO11 knockdown significantly decreased GEMM output in F relative to EV (>50% decrease, p=0.001; Figure 4.1E), and in FA relative to A (p=0.035; Figure 4.1E). Overall, the data suggest that FBXO11 loss decreases mature erythroid output and restrains GEMM output, without affecting myeloid lineage differentiation. 45 Figure 4.1 Knockdown of FBXO11 alters lineage balance by impairing erythroid output (A) Experimental design. Lin-CD34+ CB cells were transduced with empty control (EV), or the indicated combination of F, K, or A. Transduced cells were then sorted based on fluorescent markers and plated for CFC assay, showing (B) Total colonies/500 cells input. Within the total colonies counted, colony distribution was characterized based on morphologic assessment of colony types (C) BFU-E, (D) GM, (E) GEMM. Green dots indicate conditions with the presence of shFBXO11. (All data are represented by mean ± SEM; *p â¤ 0.05; **p â¤ 0.01; *** p â¤ 0.001; **** p â¤ 0.0001; as determined by one-way ANOVA corrected by Sidak multiple comparison test relative to EV control, or to the corresponding controls without FBXO11 KD). Data were from two independent replicates. 46 4.3 FBXO11 loss confers inhibitory effect on growth of hematopoietic cells in long term culture Next, we utilized a long-term culture-initiating cell (LTC-IC) assay with long-term cultures that contained a feeder layer of irradiated mouse fibroblasts expressing human IL-3, G-CSF, SCF, and FLT3L114,119, to detect and characterize more primitive progenitor-like populations within sorted CB cells expressing shFBXO11 alone or in combination with either K or A, or both (workflow shown in Figure 4.2A). To determine the cellular outputs of long-term culture (LTC), we harvested cells from each LTC plate on day 40 for CFC assay and flow analysis of surface markers. Conditions with shFBXO11 had reduced cell numbers relative to corresponding controls at harvest (EV vs. F, p = 0.12; A vs. FA, p = 0.056; K vs. FK, p = 0.097; KA vs. FKA, p = 0.0007; Figure 4.2B), but a trend towards more consistent increase in proportion of CD34+ cells when combined with KA (Figure 4.2C). K and A alone did not increase percentage and numbers of CD34+ cells over EV, and KA produced CD34+ population in highly variable numbersâboth in percentage and cell numbers (Figure 4.2C). FKA resulted in significant enrichment of CD34+ population over EV control (23.2-fold increase in percentage, p = 0.015, Figure 4.2C). Next, CFC assay was performed to assess the clonogenic activity of harvested cells from LTC. Conditions with shFBXO11 trended to decrease colony number relative to corresponding controls (Figure 4.2D). No colonies or only 1 colony could be detected in F, FA, or FK conditions (Figure 4.2D). Despite higher CD34+ cells input at plating for CFC, FA and FKA had reduced colony number than A, and KA, respectively (Figure 4.2D). Colony number was similar between FKA and EV control where far higher CD34+ cell input was plating for FKA (Figure 47 4.2D). Overall, these data indicate that FBXO11 deficiency has an inhibitory effect on the growth of hematopoietic cells during LTC, and impedes clonogenic activity while trending to enrich the CD34+ population within harvested hematopoietic cells when combined with KA. These findings suggest that FKA may induce a more immature phenotype of stem/progenitor cells that are known not to be read out on CFC assays, as is also seen when primary AML cells are subjected to a CFC assay120. 48 Figure 4.2 Comparison of the effects of FBXO11 knockdown on cellular outputs following 40-day cultures (A) Experimental design. Lin-CD34+ CB cells were transduced with EV, or the indicated combination of F, K, or A (total 8 different combinations). Doubly transduced cells were then sorted based on fluorescent markers, and plated for LTC at 3000 cells/plate. Half media change was performed weekly. On day 40, all cells/LTC plate were harvested for flow analysis or CFC assay. (B) Count (number) of live cells harvested at day 40. (C) percentage of CD34+ cells (left panel) and CD34+ cell absolute number (right panel) at harvest. (D) The number of CD34+ cells within 10,000 harvested cells input for CFC assay (left panel). The number of colonies at the endpoint of CFC assay for all 8 combinations (right panel). Green 49 dots indicate conditions with the presence of shFBXO11. All data are represented by mean ± SEM; *p â¤ 0.05; **p â¤ 0.01; *** p â¤ 0.001 ; as determined by one-way ANOVA corrected by Sidak multiple comparison test relative to EV control, or to the corresponding controls without FBXO11 KD). Data were from n = 3-4 technical replicates. 4.4 FBXO11 deficiency improves in vivo repopulating capacity Our findings from long-term culture experiments revealed that sorted cultured FKA cells consistently maintain higher proportions of CD34+ cells compared to sorted cultured KA and EV cells (Table 2). To assess whether FBXO11 knockdown caused a potential disconnect between surface phenotype and functional activity, or whether addition of shFBXO11 leads to leukemogenesis in mice when combined with KA, we utilized the xenograft assay and transplanted cultured EV, KA, and FKA cells (harvested from LTC) into the immunodeficient mice (hereafter referred to as EV, KA, and FKA mice; workflow shown in Figure 4.3). 50 Table 2. Total number of HSPCs produced from 3000 sorted EV, KA, and FKA cells in 40-day cultures Figure 4.3 Experimental design for xenotransplantation Lin-CB cells were transduced with EV, KA and FKA. Doubly transduced cells were then sorted based on fluorescent markers, and plated for LTC at 3000 cells/plate. Half media change was performed weekly. On day 40, 100,000 cells/mouse were transplanted via intrafemoral (IF) injection for each condition (4 mice injected/condition). 51 Engraftment potential was assessed by bone marrow aspiration (BMA) and peripheral blood (PB) analysis at week 6 and 7, respectively. The gating strategies for human cells, maturation stage, lineage distribution and fluorescent markers are shown in Figure 4.4. Although slightly fewer CD34+ cells (within 100,000 cells input/mouse) at transplant for KA cells comparable to FKA cells (Table 3), it was interesting to note that KA mice had <0.01% engraftment in the non-injected bone at week 6 while FKA mice had 15-41% engraftment (Figure 4.5A). Almost no human cells (<0.01%) could be detected in EV mice at week 6 BMA (Figure 4.5A). The next step was to assess percentage of CD34+ cells, lineage distribution of myeloid cells (identified by hCD33 and hCD15 antibodies, to maximize myeloid lineage detection), B cells (hCD19+ population), and T cells (hCD3+ population) within the engrafted human cells in FKA mice. Our results showed over half of engrafted human cells were myeloid cells (>50%), only 1-4% were CD34+ and 3-6% were B lymphoid cells (Figure 4.5B). No T cells could be detected at this timepoint (Figure 4.5B). Week 7 PB analysis was also consistent with the week 6 BMA findings. Human cells chimerism could only be detected in FKA mice whereas no human cells were observed for EV and KA mice (Figure 4.5A). 52 Figure 4.4 Representative flow cytometry profiles from BMA analysis (A) Examples of gating for human cells chimerism. Positive staining determined by 2 independent anti-human CD45 antibody clones (clone 2D1 and clone HI30). (B) Example of the maturation stage (characterized by CD34) and lineage gating (characterized by CD33/15, CD19 and CD3) within human chimerism levels measured in (A). Table 3. Total number of cells injected and CD34+ cell number within the injected cells at transplant 53 Figure 4.5 FBXO11 knockdown improves repopulation potentials in vivo at week 6 BMA and week 7 PB analyses Human CD45+ cell chimerism levels in the non-injected bone (BM) assessed at (A) week 6 BMA (left panel) and peripheral blood at week 7 (right panel). Human blood cell count calculated by using counting beads. (B) CD34+ percentage and lineage distribution of myeloid cells, CD19+ B cells, and CD3+ T cells within human grafts in FKA mice at week 6 BMA. (All data are represented by mean ± SEM; *p â¤ 0.05; **p â¤ 0.01; *** p â¤ 0.001 as determined by one-way ANOVA corrected by Sidak multiple comparison test relative to EV control, or to the KA). n = 4 mice / group. By 18 weeks, three FKA mice (3/4 FKA mice) had reached humane endpoint due to weight loss (>10% weight loss), lower activity and pallor (Figure 4.6A). At necropsy, sick FKA mice exhibited splenomegaly (Figure 4.6B). Analysis of BM, spleen, and liver revealed human cell infiltration with a predominant population of CD33+34-14-15-19-3- phenotype (Figure 4.6C-E). 54 Giemsa staining showed the presence of blast-like cells, dysplastic myeloid cells, and mature myeloid cells in BM of sick FKA mice (Figure 4.6F). Figure 4.6 FBXO11 knockdown reduces survival of mice A) Kaplan-Meier survival curves for mice transplanted with cultured EV, KA, and FKA cells. Mice sacrificed upon signs of illness. (B) Splenomegaly was observed in FKA mice compared with KA and EV-recipient mice. (C) Human CD45+ chimerism levels in BM, spleen, and liver of sick FKA mice. CD34+ percentage and lineage differentiation within engrafted human cells of sick FKA mice were characterized in (D) BM, and (E) spleen. (F) Giemsa-stained BM smear from sick FKA mice showing blast-like cells (white arrow), dysplastic myeloid cells (black arrow), and mature myeloid cells (asterisk). In panel A, *p â¤ 0.05 was determined by log-rank (Mantel-Cox) test relative to EV control, or to the KA mice. All data in (B-E) are represented by mean ± SEM. In panel B, **p â¤ 0.01; *** p â¤ 0.001 were 55 determined by one-way ANOVA corrected by Sidak multiple comparison test relative to EV control, or to the KA). n = 4 mice / group. In contrast, recipients of cultured EV and KA cells showed no signs of overt pathology at 24 weeks. At experimental endpoint (week 24 after transplantation), we compared human hematopoietic reconstitution in EV and KA mice to FKA mice (Table 4). FKA mice had significantly reduced total BM cells at harvest relative to EV and KA control mice (Figure 4.7A), suggesting that shFBXO11 may have an effect in suppressing normal mouse hematopoiesis. FKA mice had a near significant increase in human cell chimerismâboth in percentage and absolute numbers, relative to KA control mice (17.5-fold increase in percentage, p = 0.067; 3.6-fold increase in absolute numbers, p = 0.061; Figure 4.7B and C). No or very few human CD45+ cells (<0.1%) could be detected in the BM of EV mice (Figure 4.7B and C). Spleen showed significantly increased human engraftment in FKA mice compared to KA mice (12.8-fold increase in percentage, p = 0.016). Further analysis of the maturation stage of engrafted human cells in BM revealed that FKA mice had significantly increased absolute numbers of CD34+ cells compared to KA mice (p = 0.027, Figure 4.7D). Consistent with previous evidence that the majority of engrafted RAS+AE cells in immunodeficient mice exhibited myeloid phenotype in vivo65, our results also showed majority of human CD45+ grafts were myeloid cells in BM of KA mice (Figure 4.7E). Taken together, these results suggest an ability of FBXO11 knockdown not only to improve in vivo human reconstitution, but also to cooperate with K and A to reduce survival of mice, with potential progression towards leukemia if longer period of observation is continued or secondary transplantation is performed. 56 Table 4. Total number of human cells harvested from BM per mouse at endpoint 57 Figure 4.7 FBXO11 knockdown enhances human chimerism in vivo Experimental endpoint for EV, KA and one remaining FKA mouse at week 24 after transplantation. In vivo human engraftment of control EV and KA mice (n = 4 /group) was compared with FKA mice (3 sick mice + one remaining mouse sacrificed at week 24). (A) Count (number) of live cells harvested from bone marrow at endpoint. Human CD45+ cell (B) percentage and (C) count (number) in BM at endpoint. (D) Human CD45+ cell percentage in spleen at endpoint. (E) Human CD34+ cell percentage and count (number) in BM at endpoint. (F) Lineage distribution of human grafts in BM at endpoint. (All data are represented by mean ± SEM. In panel A-C, *p â¤ 0.05; **p â¤ 0.01; *** p â¤ 0.001 were determined by one-way ANOVA corrected by Sidak multiple comparison test relative to EV control, or to the KA control; in panel D-E, *p â¤ 0.05 was determined by Student T-test relative to KA control). n = 4 mice / group. 58 Chapter 5. Discussion Two findings emerge from our work: (1) FBXO11 knockdown confers quiescence on human HSPCs; (2) FBXO11 perturbation improves in vivo engraftment and cooperates with KRASG12D and AML1-ETO to induce a leukemic state and reduce survival of mice. Since recurrent inactivating FBXO11 mutations were identified in our previous AML sequencing study, one would therefore speculate that FBXO11 knockdown confers proliferative advantage of human HSPCs over shSCR control. However, our GSEA pathway analysis revealed a possible dormancy following FBXO11 knockdown, and liquid culture experiments provide direct evidence to confirm that FBXO11 depletion induces a stem cell phenotype, including quiescence in human HSPCs. GSEA pathway analysis also identified a significant enrichment in a shared HSC and LSC signature, and downregulation of a progenitor signature following FBXO11 knockdown in normal human HSPCs. Consistent with these findings, FBXO11 depletion significantly limited the percentage of CD34+CD45RA+CD90â population (equivalent to a normal cultured progenitor population118) while enriching CD34+CD45RA-CD90- population (equivalent to a normal cultured ST-HSC population118) relative to control in our liquid culture experiments. Although the CD38 marker was not included in our flow cytometry analysis for cultured cell immunophenotyping (CD38 expression is not a reliable marker for cultured cells considering its rapid loss during ex vivo culture121), significant enrichment in CD34+CD45RA-CD90- population following FBXO11 loss may be of particular interest given that CD34+ AML cases with a dominant Lin-CD34+CD38-CD90-CD45RA- population have previously been reported51,55. 59 Further a unique population that remained in the CD34+CD45RA+ gate of shFBXO11 knockdown cells showed higher CD90 expression, which is a well-established HSC marker20,122. High CD90 expression is associated with the most primitive human CB cell population identified so far21. However, these immunophenotypic CD34+ subpopulations were only characterized by flow cytometric analysis. It will be of substantial interest to perform functional assays, such as xenotransplant assay (currently the best assay to test self-renewal capacity19,52), to further study their biological properties. Evidence that dormant LSCs survive antiproliferative chemotherapy and contribute to disease relapse have been previously reported52,54, and these chemotherapy-resistant LSCs share many biological properties with normal HSC, including capacity of self-renewal and quiescence51. A stemness gene-expression signature in AML samples have also been reported to be highly prognostic for clinical outcome123. Our study shows that FBXO11 depletion restrains the growth of KA cells in LTC, possibly through delaying G0 exit, while consistently maintaining more primitive population and enhancing their in vivo repopulating capacity, with potential progression towards leukemia if longer period of observation is continued or secondary transplantation is performed. Given that FBXO11 knockdown enriches a CD34+CD45RA-CD90- population and results in a CD34+CD45RA+ CD90+ unique population in normal human HSPCs, it will be of particular interest to investigate whether these two populations are enriched in cultured FKA primitive population, and which population(s) exhibit long-term in vivo repopulating ability and potentially lead to leukemogenesis. Interestingly, majority of engrafted FKA cells exhibited CD34-CD33+ phenotype in vivo. Evidence of phenotype change in major populations before and after transplantation has 60 previously been reported in >60% AML samples (M0-M5 AML subtypes), and CD34 expression loss accompanied by an increase in CD33 expression was most frequently seen124. However, secondary transplantation will be required to test if these engrafted FKA cells have self-renewal capacity. AML is a genetically heterogeneous disease that requires cooperation of at least two driver events in initiating leukemogenesis45. A â2-hitâ model of leukemogenesis has been proposed to explain the cooperativity of genetic events, where class I mutations â which confer proliferative advantage â must collaborate with a class II mutation that promotes differentiation block, in driving AML development125. Here, we show that FBXO11 loss can be classified as a type II mutation given its ability to induce differentiation block by impairing erythroid lineage output. Although in our work, the decision of introducing RAS to model human AML was based on co-occurrence plot (Figure 1.4), retrospectively, this â2-hitâ model likely explains the cooperation between RAS (class I mutation126,127), AE (class II mutation80), and FBXO11 deficiency in enhancing human reconstitution in immunodeficient mice, potentially leading to leukemogenesis. Increased levels of UPS and proteasome activity in AML patient samples were previously suggested128. However, further normalization revealed that ubiquitin-mediated proteasome activity is actually lower in AML patient samples relative to that of normal controls128. This likely explains why no effect or highly variable results were seen in patients after treatment with proteasome inhibitors106. Our previous work showed that UPS genes are recurrently mutated in AML with loss-of-function mutations, and revealed a biological cooperation of FBXO11 loss with AE in contributing to leukemogenesis in a murine model. FBXO11 is the second most 61 frequently mutated UPS gene in our analysis; the most frequent UPS mutated gene is CBL and had been extensively studied84,100,129; Figure 1.3. Furthermore, my work provides key insights into the role of FBXO11 in normal human hematopoiesis, and the biological cooperation of FBXO11 perturbations with AML1-ETO and KRASG12D in enhancing human reconstitution in immunodeficient mice, potentially leading to leukemogenesis. 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Cancer Cell. 2010;18(4):341-352. doi:10.1016/j.ccr.2010.09.008 78 Appendix Top 150 upregulated pathways Upregulated pathway NES FDR q-val ELVIDGE_HYPOXIA_UP 2.56 0.00 ELVIDGE_HIF1A_TARGETS_DN 2.51 0.00 ELVIDGE_HYPOXIA_BY_DMOG_UP 2.47 0.00 ELVIDGE_HIF1A_AND_HIF2A_TARGETS_DN 2.46 0.00 MENSE_HYPOXIA_UP 2.36 0.00 GRAHAM_CML_DIVIDING_VS_NORMAL_QUIESCENT_DN 2.35 0.00 CASORELLI_ACUTE_PROMYELOCYTIC_LEUKEMIA_UP 2.31 0.00 GRAHAM_CML_QUIESCENT_VS_NORMAL_QUIESCENT_DN 2.30 0.00 KEGG_INTESTINAL_IMMUNE_NETWORK_FOR_IGA_PRODUCTION 2.29 0.00 LEONARD_HYPOXIA 2.25 0.00 FARDIN_HYPOXIA_11 2.24 0.00 TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_HSC_UP 2.23 0.00 MANALO_HYPOXIA_UP 2.22 0.00 ODONNELL_TARGETS_OF_MYC_AND_TFRC_UP 2.21 0.00 REACTOME_INTERFERON_GAMMA_SIGNALING 2.21 0.00 PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_DN 2.21 0.00 ZHENG_FOXP3_TARGETS_IN_THYMUS_UP 2.19 0.00 HALLMARK_HYPOXIA 2.16 0.00 WINTER_HYPOXIA_METAGENE 2.16 0.00 VALK_AML_WITH_EVI1 2.15 0.00 WANG_IMMORTALIZED_BY_HOXA9_AND_MEIS1_UP 2.15 0.00 TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_3D_UP 2.13 0.00 EPPERT_CE_HSC_LSC 2.12 0.00 WANG_RESPONSE_TO_GSK3_INHIBITOR_SB216763_UP 2.11 0.00 SANA_RESPONSE_TO_IFNG_UP 2.11 0.00 MACAEVA_PBMC_RESPONSE_TO_IR 2.11 0.00 KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS 2.11 0.00 CHUNG_BLISTER_CYTOTOXICITY_DN 2.11 0.00 HUANG_GATA2_TARGETS_UP 2.09 0.00 DUNNE_TARGETS_OF_AML1_MTG8_FUSION_UP 2.08 0.00 RHEIN_ALL_GLUCOCORTICOID_THERAPY_UP 2.07 0.00 GRAHAM_NORMAL_QUIESCENT_VS_NORMAL_DIVIDING_UP 2.06 0.00 BASAKI_YBX1_TARGETS_DN 2.06 0.00 UZONYI_RESPONSE_TO_LEUKOTRIENE_AND_THROMBIN 2.05 0.00 79 MORI_IMMATURE_B_LYMPHOCYTE_UP 2.03 0.00 PID_HIF1_TFPATHWAY 2.02 0.00 TERAO_AOX4_TARGETS_HG_UP 2.02 0.00 MULLIGHAN_MLL_SIGNATURE_2_UP 2.02 0.00 MULLIGHAN_MLL_SIGNATURE_1_UP 2.01 0.00 HERNANDEZ_ABERRANT_MITOSIS_BY_DOCETACEL_2NM_UP 2.00 0.00 DIAZ_CHRONIC_MEYLOGENOUS_LEUKEMIA_DN 1.99 0.00 SEMENZA_HIF1_TARGETS 1.99 0.00 DUTERTRE_ESTRADIOL_RESPONSE_24HR_DN 1.98 0.00 KEGG_AUTOIMMUNE_THYROID_DISEASE 1.98 0.00 WARTERS_IR_RESPONSE_5GY 1.98 0.00 HALLMARK_P53_PATHWAY 1.97 0.00 TOMLINS_METASTASIS_DN 1.97 0.00 RUIZ_TNC_TARGETS_UP 1.97 0.00 REACTOME_REGULATED_NECROSIS 1.96 0.00 MITSIADES_RESPONSE_TO_APLIDIN_UP 1.96 0.00 EPPERT_HSC_R 1.96 0.00 WIELAND_UP_BY_HBV_INFECTION 1.96 0.00 YU_MYC_TARGETS_DN 1.96 0.00 PARK_APL_PATHOGENESIS_DN 1.96 0.00 KEGG_ALLOGRAFT_REJECTION 1.94 0.00 PEDERSEN_TARGETS_OF_611CTF_ISOFORM_OF_ERBB2 1.94 0.00 MIZUSHIMA_AUTOPHAGOSOME_FORMATION 1.93 0.01 DANG_REGULATED_BY_MYC_DN 1.93 0.01 GROSS_HYPOXIA_VIA_ELK3_AND_HIF1A_UP 1.93 0.01 PID_TRAIL_PATHWAY 1.93 0.01 AMUNDSON_DNA_DAMAGE_RESPONSE_TP53 1.92 0.01 BIOCARTA_PEPI_PATHWAY 1.92 0.01 REACTOME_NEUTROPHIL_DEGRANULATION 1.91 0.01 BILBAN_B_CLL_LPL_DN 1.91 0.01 IKEDA_MIR133_TARGETS_UP 1.91 0.01 QUINTENS_EMBRYONIC_BRAIN_RESPONSE_TO_IR 1.90 0.01 FISCHER_DIRECT_P53_TARGETS_META_ANALYSIS 1.90 0.01 ZHENG_BOUND_BY_FOXP3 1.90 0.01 WARTERS_RESPONSE_TO_IR_SKIN 1.90 0.01 IVANOVSKA_MIR106B_TARGETS 1.90 0.01 BIOCARTA_LYM_PATHWAY 1.89 0.01 THEILGAARD_NEUTROPHIL_AT_SKIN_WOUND_DN 1.89 0.01 CHIBA_RESPONSE_TO_TSA 1.89 0.01 GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_TURQUOISE_UP 1.89 0.01 80 BROWN_MYELOID_CELL_DEVELOPMENT_UP 1.89 0.01 VERHAAK_AML_WITH_NPM1_MUTATED_DN 1.88 0.01 HIRSCH_CELLULAR_TRANSFORMATION_SIGNATURE_UP 1.88 0.01 AZARE_NEOPLASTIC_TRANSFORMATION_BY_STAT3_UP 1.88 0.01 RUTELLA_RESPONSE_TO_HGF_DN 1.88 0.01 GUTIERREZ_WALDENSTROEMS_MACROGLOBULINEMIA_1_DN 1.88 0.01 REACTOME_TRAIL_SIGNALING 1.87 0.01 FERRARI_RESPONSE_TO_FENRETINIDE_UP 1.87 0.01 GABRIELY_MIR21_TARGETS 1.86 0.01 DUTERTRE_ESTRADIOL_RESPONSE_6HR_DN 1.86 0.01 REACTOME_REGULATION_BY_C_FLIP 1.86 0.01 REN_ALVEOLAR_RHABDOMYOSARCOMA_DN 1.86 0.01 JOHNSTONE_PARVB_TARGETS_3_UP 1.86 0.01 WINTER_HYPOXIA_DN 1.86 0.01 MARTORIATI_MDM4_TARGETS_FETAL_LIVER_UP 1.86 0.01 REACTOME_REGULATION_OF_ACTIN_DYNAMICS_FOR_PHAGOCYTIC_CUP_FORMATION 1.86 0.01 REACTOME_PD_1_SIGNALING 1.86 0.01 WALLACE_PROSTATE_CANCER_RACE_UP 1.85 0.01 LEE_DIFFERENTIATING_T_LYMPHOCYTE 1.85 0.01 MORI_MATURE_B_LYMPHOCYTE_UP 1.85 0.02 CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_5 1.84 0.02 TARTE_PLASMA_CELL_VS_B_LYMPHOCYTE_DN 1.84 0.02 SMIRNOV_RESPONSE_TO_IR_6HR_UP 1.84 0.02 KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION 1.84 0.02 CASORELLI_APL_SECONDARY_VS_DE_NOVO_UP 1.84 0.02 KEGG_GRAFT_VERSUS_HOST_DISEASE 1.83 0.02 KIM_MYCN_AMPLIFICATION_TARGETS_DN 1.83 0.02 TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_ERYTHROCYTE_UP 1.83 0.02 GROSS_ELK3_TARGETS_DN 1.83 0.02 MORI_PRE_BI_LYMPHOCYTE_DN 1.83 0.02 LUI_THYROID_CANCER_CLUSTER_4 1.83 0.02 GARY_CD5_TARGETS_UP 1.83 0.02 ROSS_LEUKEMIA_WITH_MLL_FUSIONS 1.83 0.02 BAKKER_FOXO3_TARGETS_UP 1.83 0.02 GAZDA_DIAMOND_BLACKFAN_ANEMIA_MYELOID_UP 1.82 0.02 FULCHER_INFLAMMATORY_RESPONSE_LECTIN_VS_LPS_DN 1.82 0.02 HALLMARK_INTERFERON_ALPHA_RESPONSE 1.82 0.02 KEGG_TYPE_I_DIABETES_MELLITUS 1.82 0.02 QI_HYPOXIA 1.82 0.02 LIU_SOX4_TARGETS_UP 1.82 0.02 81 SCHUETZ_BREAST_CANCER_DUCTAL_INVASIVE_UP 1.82 0.02 REACTOME_TRANSLOCATION_OF_ZAP_70_TO_IMMUNOLOGICAL_SYNAPSE 1.82 0.02 INGA_TP53_TARGETS 1.82 0.02 GROSS_HYPOXIA_VIA_HIF1A_DN 1.81 0.02 JAATINEN_HEMATOPOIETIC_STEM_CELL_UP 1.81 0.02 ZWANG_CLASS_1_TRANSIENTLY_INDUCED_BY_EGF 1.81 0.02 ZHENG_FOXP3_TARGETS_IN_T_LYMPHOCYTE_DN 1.81 0.02 KEGG_CELL_ADHESION_MOLECULES_CAMS 1.81 0.02 GUTIERREZ_WALDENSTROEMS_MACROGLOBULINEMIA_2 1.81 0.02 VISALA_RESPONSE_TO_HEAT_SHOCK_AND_AGING_UP 1.80 0.02 PRAMOONJAGO_SOX4_TARGETS_UP 1.80 0.02 KEGG_ASTHMA 1.80 0.02 DUNNE_TARGETS_OF_AML1_MTG8_FUSION_DN 1.80 0.02 SASAI_RESISTANCE_TO_NEOPLASTIC_TRANSFROMATION 1.80 0.02 KIM_LIVER_CANCER_POOR_SURVIVAL_UP 1.80 0.03 WAKABAYASHI_ADIPOGENESIS_PPARG_BOUND_36HR 1.79 0.03 REACTOME_RETROGRADE_TRANSPORT_AT_THE_TRANS_GOLGI_NETWORK 1.79 0.03 WINNEPENNINCKX_MELANOMA_METASTASIS_DN 1.79 0.03 HALLMARK_INTERFERON_GAMMA_RESPONSE 1.79 0.03 REACTOME_ACETYLCHOLINE_REGULATES_INSULIN_SECRETION 1.78 0.03 ZHAN_MULTIPLE_MYELOMA_CD2_UP 1.78 0.03 PID_SYNDECAN_4_PATHWAY 1.78 0.03 BRACHAT_RESPONSE_TO_METHOTREXATE_UP 1.78 0.03 CROMER_METASTASIS_UP 1.78 0.03 REACTOME_PHOSPHORYLATION_OF_CD3_AND_TCR_ZETA_CHAINS 1.78 0.03 FAELT_B_CLL_WITH_VH3_21_DN 1.77 0.03 JIANG_HYPOXIA_NORMAL 1.77 0.03 MIKKELSEN_PLURIPOTENT_STATE_DN 1.77 0.03 BIOCARTA_TH1TH2_PATHWAY 1.77 0.03 LU_IL4_SIGNALING 1.77 0.03 LINDGREN_BLADDER_CANCER_CLUSTER_2B 1.77 0.03 BIOCARTA_TOB1_PATHWAY 1.77 0.03 REACTOME_FATTY_ACIDS_BOUND_TO_GPR40_FFAR1_REGULATE_INSULIN_SECRETION 1.77 0.03 BOWIE_RESPONSE_TO_EXTRACELLULAR_MATRIX 1.77 0.03 KEGG_VIBRIO_CHOLERAE_INFECTION 1.76 0.03 MORI_LARGE_PRE_BII_LYMPHOCYTE_DN 1.76 0.04 82 Top 150 downregulated pathways NAME NES FDR q-val MANALO_HYPOXIA_DN -2.57 0.00 GRAHAM_CML_QUIESCENT_VS_NORMAL_QUIESCENT_UP -2.54 0.00 REACTOME_RRNA_PROCESSING_IN_THE_NUCLEUS_AND_CYTOSOL -2.53 0.00 REACTOME_RRNA_PROCESSING -2.53 0.00 HALLMARK_MYC_TARGETS_V2 -2.47 0.00 HALLMARK_MYC_TARGETS_V1 -2.44 0.00 WONG_EMBRYONIC_STEM_CELL_CORE -2.39 0.00 REACTOME_RRNA_MODIFICATION_IN_THE_NUCLEUS_AND_CYTOSOL -2.38 0.00 REACTOME_TRANSLATION -2.35 0.00 ROSS_AML_OF_FAB_M7_TYPE -2.32 0.00 SCHUHMACHER_MYC_TARGETS_UP -2.30 0.00 WELCH_GATA1_TARGETS -2.28 0.00 TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_10D_DN -2.28 0.00 REACTOME_BINDING_AND_UPTAKE_OF_LIGANDS_BY_SCAVENGER_RECEPTORS -2.26 0.00 REACTOME_EUKARYOTIC_TRANSLATION_INITIATION -2.26 0.00 REACTOME_MITOCHONDRIAL_TRANSLATION -2.23 0.00 HALLMARK_HEME_METABOLISM -2.23 0.00 VALK_AML_CLUSTER_8 -2.19 0.00 VALK_AML_CLUSTER_7 -2.18 0.00 REACTOME_SELENOAMINO_ACID_METABOLISM -2.18 0.00 REACTOME_NONSENSE_MEDIATED_DECAY_NMD_INDEPENDENT_OF_THE_ EXON_JUNCTION_COMPLEX_EJC -2.18 0.00 EPPERT_PROGENITOR -2.17 0.00 REACTOME_SCAVENGING_OF_HEME_FROM_PLASMA -2.17 0.00 GRAHAM_CML_DIVIDING_VS_NORMAL_QUIESCENT_UP -2.17 0.00 GRAHAM_NORMAL_QUIESCENT_VS_NORMAL_DIVIDING_DN -2.17 0.00 BIOCARTA_AHSP_PATHWAY -2.17 0.00 NAKAJIMA_MAST_CELL -2.15 0.00 KOBAYASHI_EGFR_SIGNALING_24HR_DN -2.14 0.00 KEGG_RIBOSOME -2.14 0.00 HALLMARK_E2F_TARGETS -2.13 0.00 KIM_MYC_AMPLIFICATION_TARGETS_UP -2.12 0.00 MUELLER_PLURINET -2.10 0.00 REACTOME_MITOCHONDRIAL_PROTEIN_IMPORT -2.09 0.00 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_11 -2.09 0.00 REACTOME_TRNA_PROCESSING -2.05 0.00 REACTOME_ACTIVATION_OF_THE_PRE_REPLICATIVE_COMPLEX -2.04 0.00 83 IRITANI_MAD1_TARGETS_DN -2.04 0.00 DANG_MYC_TARGETS_UP -2.04 0.00 CUI_TCF21_TARGETS_2_UP -2.03 0.00 MOOTHA_VOXPHOS -2.02 0.00 REACTOME_DNA_REPLICATION_PRE_INITIATION -2.02 0.00 REACTOME_TRNA_MODIFICATION_IN_THE_NUCLEUS_AND_CYTOSOL -2.01 0.00 KEGG_SPLICEOSOME -2.01 0.00 RHODES_CANCER_META_SIGNATURE -2.00 0.00 REACTOME_PLATELET_ADHESION_TO_EXPOSED_COLLAGEN -1.99 0.00 NIKOLSKY_BREAST_CANCER_16P13_AMPLICON -1.99 0.00 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_3 -1.98 0.00 REACTOME_REGULATION_OF_EXPRESSION_OF_SLITS_AND_ROBOS -1.98 0.00 REACTOME_METABOLISM_OF_NON_CODING_RNA -1.98 0.00 WELCSH_BRCA1_TARGETS_DN -1.98 0.00 SCHLOSSER_MYC_TARGETS_AND_SERUM_RESPONSE_DN -1.97 0.00 REACTOME_ACTIVATION_OF_THE_MRNA_UPON_BINDING_OF_THE_ CAP_BINDING_COMPLEX_AND_EIFS_AND_SUBSEQUENT_BINDING_TO_43S -1.97 0.00 REACTOME_NONSENSE_MEDIATED_DECAY_NMD -1.97 0.00 MOOTHA_MITOCHONDRIA -1.96 0.00 WORSCHECH_TUMOR_EVASION_AND_TOLEROGENICITY_UP -1.96 0.00 BENPORATH_PROLIFERATION -1.95 0.00 JAATINEN_HEMATOPOIETIC_STEM_CELL_DN -1.95 0.00 REACTOME_MRNA_SPLICING_MINOR_PATHWAY -1.95 0.00 BILD_MYC_ONCOGENIC_SIGNATURE -1.95 0.00 KEGG_PORPHYRIN_AND_CHLOROPHYLL_METABOLISM -1.95 0.00 BROWN_MYELOID_CELL_DEVELOPMENT_DN -1.95 0.00 REACTOME_INFLUENZA_INFECTION -1.94 0.00 SCHLOSSER_MYC_TARGETS_REPRESSED_BY_SERUM -1.94 0.01 MULLIGAN_NTF3_SIGNALING_VIA_INSR_AND_IGF1R_UP -1.94 0.01 SONG_TARGETS_OF_IE86_CMV_PROTEIN -1.94 0.01 GROSS_HYPOXIA_VIA_HIF1A_UP -1.94 0.01 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP -1.94 0.01 REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS -1.93 0.01 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT_ATP_SYNTHESIS_BY_ CHEMIOSMOTIC_COUPLING_AND_HEAT_PRODUCTION _BY_UNCOUPLING_PROTEINS -1.93 0.01 DAIRKEE_CANCER_PRONE_RESPONSE_BPA -1.93 0.01 WONG_MITOCHONDRIA_GENE_MODULE -1.92 0.01 HESS_TARGETS_OF_HOXA9_AND_MEIS1_UP -1.92 0.01 STEINER_ERYTHROCYTE_MEMBRANE_GENES -1.92 0.01 KARLSSON_TGFB1_TARGETS_UP -1.92 0.01 REACTOME_HEME_BIOSYNTHESIS -1.92 0.01 REACTOME_COMPLEX_I_BIOGENESIS -1.92 0.01 84 MORI_EMU_MYC_LYMPHOMA_BY_ONSET_TIME_UP -1.92 0.01 REACTOME_NUCLEOBASE_BIOSYNTHESIS -1.91 0.01 GARCIA_TARGETS_OF_FLI1_AND_DAX1_DN -1.91 0.01 HALLMARK_UNFOLDED_PROTEIN_RESPONSE -1.91 0.01 NAKAMURA_CANCER_MICROENVIRONMENT_DN -1.91 0.01 FERRANDO_T_ALL_WITH_MLL_ENL_FUSION_DN -1.91 0.01 REACTOME_METABOLISM_OF_PORPHYRINS -1.91 0.01 SANSOM_APC_TARGETS_REQUIRE_MYC -1.91 0.01 KUROZUMI_RESPONSE_TO_ONCOCYTIC_VIRUS -1.90 0.01 SCHLOSSER_MYC_AND_SERUM_RESPONSE_SYNERGY -1.90 0.01 PAL_PRMT5_TARGETS_UP -1.90 0.01 ABRAMSON_INTERACT_WITH_AIRE -1.90 0.01 REACTOME_DNA_REPLICATION -1.89 0.01 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_TERMINATION -1.89 0.01 MOOTHA_HUMAN_MITODB_6_2002 -1.89 0.01 KEGG_ONE_CARBON_POOL_BY_FOLATE -1.89 0.01 VECCHI_GASTRIC_CANCER_ADVANCED_VS_EARLY_DN -1.89 0.01 GRADE_COLON_AND_RECTAL_CANCER_UP -1.89 0.01 REACTOME_COOPERATION_OF_PREFOLDIN_AND_TRIC_CCT_IN_ ACTIN_AND_TUBULIN_FOLDING -1.89 0.01 KEGG_DNA_REPLICATION -1.89 0.01 SCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_DN -1.88 0.01 COLLER_MYC_TARGETS_UP -1.88 0.01 REACTOME_ORC1_REMOVAL_FROM_CHROMATIN -1.88 0.01 VECCHI_GASTRIC_CANCER_EARLY_UP -1.88 0.01 FOURNIER_ACINAR_DEVELOPMENT_LATE_2 -1.87 0.01 REACTOME_O2_CO2_EXCHANGE_IN_ERYTHROCYTES -1.87 0.01 REACTOME_PROCESSING_OF_CAPPED_INTRON_CONTAINING_PRE_MRNA -1.87 0.01 REACTOME_ASSEMBLY_OF_THE_PRE_REPLICATIVE_COMPLEX -1.87 0.01 REACTOME_RESPONSE_TO_ELEVATED_PLATELET_CYTOSOLIC_CA2PLUS -1.86 0.01 RHODES_UNDIFFERENTIATED_CANCER -1.86 0.01 CHNG_MULTIPLE_MYELOMA_HYPERPLOID_UP -1.86 0.01 REACTOME_ATF4_ACTIVATES_GENES_IN_RESPONSE_ TO_ENDOPLASMIC_RETICULUM_STRESS -1.86 0.01 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT -1.86 0.01 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_INITIATION _FROM_TYPE_1_PROMOTER -1.85 0.01 KONG_E2F3_TARGETS -1.85 0.01 REACTOME_MRNA_SPLICING -1.85 0.01 HALLMARK_OXIDATIVE_PHOSPHORYLATION -1.85 0.01 GARY_CD5_TARGETS_DN -1.85 0.01 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION -1.85 0.01 REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_ PROTEIN_TARGETING_TO_MEMBRANE -1.85 0.01 85 PUJANA_BREAST_CANCER_WITH_BRCA1_MUTATED_UP -1.85 0.01 REACTOME_ERYTHROCYTES_TAKE_UP_OXYGEN_AND_RELEASE_CARBON_DIOXIDE -1.85 0.01 BIOCARTA_MCM_PATHWAY -1.85 0.01 ZHOU_CELL_CYCLE_GENES_IN_IR_RESPONSE_24HR -1.85 0.01 CHYLA_CBFA2T3_TARGETS_DN -1.85 0.01 REACTOME_PREFOLDIN_MEDIATED_TRANSFER_OF_SUBSTRATE_TO_CCT_TRIC -1.84 0.01 CROONQUIST_IL6_DEPRIVATION_DN -1.84 0.01 BIOCARTA_SM_PATHWAY -1.84 0.01 BILANGES_SERUM_AND_RAPAMYCIN_SENSITIVE_GENES -1.83 0.02 POMEROY_MEDULLOBLASTOMA_PROGNOSIS_DN -1.83 0.02 REACTOME_CRISTAE_FORMATION -1.83 0.02 MORI_PRE_BI_LYMPHOCYTE_UP -1.83 0.02 REACTOME_FORMATION_OF_TUBULIN_FOLDING_INTERMEDIATES_ BY_CCT_TRIC -1.83 0.02 REACTOME_TRNA_AMINOACYLATION -1.83 0.02 ZHOU_TNF_SIGNALING_4HR -1.83 0.02 REACTOME_ACTIVATION_OF_APC_C_AND_APC_C:CDC20_MEDIATED_ DEGRADATION_OF_MITOTIC_PROTEINS -1.83 0.02 REACTOME_SWITCHING_OF_ORIGINS_TO_A_POST_REPLICATIVE_STATE -1.83 0.02 RAGHAVACHARI_PLATELET_SPECIFIC_GENES -1.82 0.02 GNATENKO_PLATELET_SIGNATURE -1.82 0.02 ANDERSEN_LIVER_CANCER_KRT19_UP -1.82 0.02 REACTOME_CITRIC_ACID_CYCLE_TCA_CYCLE -1.82 0.02 GAJATE_RESPONSE_TO_TRABECTEDIN_DN -1.82 0.02 KAMMINGA_EZH2_TARGETS -1.81 0.02 CHAUHAN_RESPONSE_TO_METHOXYESTRADIOL_UP -1.81 0.02 REACTOME_PERK_REGULATES_GENE_EXPRESSION -1.81 0.02 CROONQUIST_NRAS_SIGNALING_DN -1.81 0.02 KAUFFMANN_DNA_REPLICATION_GENES -1.81 0.02 DUTERTRE_ESTRADIOL_RESPONSE_6HR_UP -1.81 0.02 FU_INTERACT_WITH_ALKBH8 -1.80 0.02 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_14 -1.80 0.02 MADAN_DPPA4_TARGETS -1.80 0.02 KEGG_PARKINSONS_DISEASE -1.80 0.02 GOTZMANN_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_UP -1.80 0.02 VIETOR_IFRD1_TARGETS -1.80 0.02
Thesis/Dissertation
eng
Experimental Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
The role of FBXO11 in human hematopoiesis
Text