Medicine, Faculty of
Pathology and Laboratory Medicine, Department of
DSpace
UBCV
Mo, Ya-Chi Angela
Doctor of Philosophy - PhD
University of British Columbia
We examined the AML-PMP, TCGA, TARGET pediatric, and Beat AML public datasets to identify genetic drivers of AML. We identified frequent somatic mutations in ubiquitin proteasome system genes, with recurrent inactivating mutations in FBXO11. FBXO11 encodes the substrate-recognizing component of the SKP1-CUL1-F-BOX (SCF) ubiquitin E3-ligase complex. In a mouse marrow transplant model generated by previous members of the Karsan lab, Fbxo11 knockdown cooperated with AML1-ETO expression to initiate AML. These leukemias were serially transplantable, consistent with the presence of leukemic stem cells (LSC). We hypothesized that FBXO11 depletion in hematopoietic stem and progenitor cells (HSPC) promotes self-renewal and drives leukemogenesis through dysregulated expression of its ubiquitination targets. First, we developed a serially transplantable human xenotransplant myeloid leukemia model based on FBXO11 depletion with cooperating mutations, and observed increased engraftment and expansion of primitive CD34⁺ cells with FBXO11 knockdown. Next, to identify FBXO11 targets that are dysregulated with FBXO11 depletion, we performed mass spectrometry analysis of FBXO11 co-immunoprecipitating proteins in the myeloid K562 cell line. We identified LONP1 as a top target, but LONP1 protein expression did not change with FBXO11 over-expression or loss, suggesting that LONP1 is not targeted for degradation. Knockdown of LONP1 in human CD34⁺ HSPCs resulted in similar phenotypes as FBXO11 knockdown: enriched hematopoietic stem cell, LSC, and mitochondrial defect transcriptomic signatures, increased myeloid cells, and reduced erythroid and megakaryocytic populations. In parallel, FBXO11 knockdown reduced mitochondrial membrane potential. These data suggest that FBXO11 normally positively regulates LONP1. We found that K63-linked polyubiquitination—a modification known to regulate protein activation and trafficking— of LONP1 is increased with FBXO11 overexpression, and reduced with FBXO11 loss. LONP1 overexpression alone had negligible effect on the transcriptome, and LONP1 knockdown blocked or reversed the effects of FBXO11 overexpression. This supports a model of FBXO11 regulating LONP1 mitochondrial function, presumably through K63-linked ubiquitination. These findings suggest that FBXO11 positively regulates LONP. We propose FBXO11 depletion in AML patients results in reduced LONP1 mitochondrial function, which promotes a primitive cell state. We have identified novel candidate targets of FBXO11, and demonstrated a unique link between the ubiquitin-proteosome system and the mitochondria.
南無本師大自在王佛 ELUCIDATING THE MECHANISMS OF LEUKEMOGENESIS DRIVEN BY FBXO11 DEPLETION by Ya-Chi Angela Mo B.Sc., University of British Columbia, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Pathology and Laboratory Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2022 ©Ya-Chi Angela Mo, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Elucidating the Mechanisms of Leukemogenesis Driven by FBXO11 Depletion Submitted by _Ya-Chi Angela Mo in partial fulfillment of the requirements for the degree of _Doctor of Philosophy in _Pathology and Laboratory Medicine Examining Committee: Dr. Aly Karsan, Professor, Pathology and Laboratory Medicine, UBC ______ Supervisor Dr. Andrew Weng, Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member Dr. Fabio Rossi, Professor, Medical Genetics, UBC_____________________ University Examiner Dr. Kirk Schultz, Professor, Pathology and Laboratory Medicine, UBC______ University Examiner Additional Supervisory Committee Members: Dr. Peter Stirling, Associate Professor, Medical Genetics, UBC Supervisory Committee Member Dr. Thibault Mayor, Professor, Biochemistry and Molecular Biology, UBC Supervisory Committee Member iii Abstract We examined the AML-PMP, TCGA, TARGET pediatric, and Beat AML public datasets to identify genetic drivers of AML. We identified frequent somatic mutations in ubiquitin proteasome system genes, with recurrent inactivating mutations in FBXO11. FBXO11 encodes the substrate-recognizing component of the SKP1-CUL1-F-BOX (SCF) ubiquitin E3-ligase complex. In a mouse marrow transplant model generated by previous members of the Karsan lab, Fbxo11 knockdown cooperated with AML1-ETO expression to initiate AML. These leukemias were serially transplantable, consistent with the presence of leukemic stem cells (LSC). We hypothesized that FBXO11 depletion in hematopoietic stem and progenitor cells (HSPC) promotes self-renewal and drives leukemogenesis through dysregulated expression of its ubiquitination targets. First, we developed a serially transplantable human xenotransplant myeloid leukemia model based on FBXO11 depletion with cooperating mutations, and observed increased engraftment and expansion of primitive CD34+ cells with FBXO11 knockdown. Next, to identify FBXO11 targets that are dysregulated with FBXO11 depletion, we performed mass spectrometry analysis of FBXO11 co-immunoprecipitating proteins in the myeloid K562 cell line. We identified LONP1 as a top target, but LONP1 protein expression did not change with FBXO11 over-expression or loss, suggesting that LONP1 is not targeted for degradation. Knockdown of LONP1 in human CD34+ HSPCs resulted in similar phenotypes as FBXO11 knockdown: enriched hematopoietic stem cell, LSC, and mitochondrial defect transcriptomic signatures, increased myeloid cells, and reduced erythroid and megakaryocytic populations. In parallel, FBXO11 knockdown reduced mitochondrial membrane potential. These data suggest that FBXO11 normally positively regulates LONP1. We found that K63-linked polyubiquitination—a modification known to regulate protein activation and trafficking— of LONP1 is increased with FBXO11 overexpression, and reduced with FBXO11 loss. LONP1 overexpression alone had negligible effect on the transcriptome, and LONP1 knockdown blocked or reversed the effects of FBXO11 overexpression. This supports a model of FBXO11 regulating LONP1 mitochondrial function, presumably through K63-linked ubiquitination. iv These findings suggest that FBXO11 positively regulates LONP. We propose FBXO11 depletion in AML patients results in reduced LONP1 mitochondrial function, which promotes a primitive cell state. We have identified novel candidate targets of FBXO11, and demonstrated a unique link between the ubiquitin-proteosome system and the mitochondria. v Lay Summary Acute myeloid leukemia (AML) is a type of cancer where the bone marrow produces large amounts of abnormal immature cells that are incapable of maturing into functional blood cells. This can affect red blood cells, white blood cells, and platelets. We found that AML bone marrow cells have lower levels of products encoded by the gene, FBXO11, compared to normal bone marrow cells. Depletion of FBXO11 in human cord blood cells results in irregular blood cell production, and increases the maintenance of an immature cell population. Our findings suggest this happens because FBXO11 depletion reduces the activity of a protein, LONP1. We showed that depletion of LONP1 results in abnormal function of the mitochondria, a structure important for producing energy for the cell. This has been found to contribute to an immature cell state. vi Preface The preliminary data presented were the work of Dr. Linda Chang and Dr. Gerben Duns. AML patient data presented in Chapter 3 were based off of work done by Dr. Linda Chang, Dr. Gerben Duns, and Dr. Roderick Docking, which only included the PMP data set. The data was reanalyzed and expanded to include more data sets. Dr. Gerben Duns compiled the UPS genes for query, and patient mutation data was assessed, compiled and plotted by Harwood Kwan. The K562 CRISPR clones used for experiments presented in Chapters 3 and 4 were generated, validated, and characterized previously by Dr. Gerben Duns. The quantitative tandem mass spectrometry experiment was performed by Dr. Gerben Duns and Dr. Linda Chang in collaboration with the Canada’s Michael Smith Genome Sciences Centre proteomics core. Preprocessing of the data was performed by the proteomics core. The data was analyzed and plotted by Angela Mo. The xenotransplant data were based on and combined with preliminary data from Xuan Wang. Xuan Wang designed the experiment, cloned the constructs, and performed all long-term culture experiments. Endpoint processing and data analysis were done by both Xuan Wang and Angela Mo. Xuan performed endpoint processing for 2 of the 3 primary transplant experiments, and 1 of the 2 secondary transplant experiments. Bone marrow smears presented were prepared by Angela Mo, and stained and imaged by Hayle Kincross. The Seahorse assay was done with the assistance and equipment of Dr. Ramon Klein Geltink at BC Children’s Hospital Research Institute. Cells were transduced and prepared by Angela Mo. Data analysis was performed by Angela Mo. Differential gene expression and Gene Set Enrichment Analysis of CB RNAseq data was done with the assistance of Elijah Willie and Tammy Lau. Elijah Willie performed the read alignments and count generations. Tammy ran GSEA and exported results for replotting by Angela Mo. vii Cesium irradiation, intrafemoral injection, tail vein injection, and bone marrow aspirates were performed by Jessica Tran. Jessica Tran also assisted with weekly monitoring of the mice. 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).viii Table of Contents Abstract ............................................................................................................................... iii Lay Summary ......................................................................................................................... v Preface ................................................................................................................................. vi Table of Contents ................................................................................................................ viii List of Tables ........................................................................................................................xii List of Figures ...................................................................................................................... xiii List of Abbreviations ............................................................................................................. xv Acknowledgements ............................................................................................................. xix Chapter 1: Introduction ......................................................................................................... 1 1.1 Hematopoiesis and hematopoietic stem cells ...................................................................... 1 1.1.1 Defining and studying HSCs and progenitors ................................................................. 4 1.1.2 Myeloid differentiation .................................................................................................. 5 1.2 Acute myeloid leukemia (AML) ............................................................................................. 6 1.3 Leukemia Stem Cells (LSCs) ................................................................................................. 10 1.4 Comparing HSCs and LSCs ................................................................................................... 11 1.5 Mitochondrial function and stem cell maintenance ........................................................... 13 1.5.1 Reactive Oxygen Species .............................................................................................. 13 1.5.2 Mitochondrial membrane potential ............................................................................. 15 1.5.3 Mitochondrial regulation of the epigenome ................................................................ 16 1.5.4 Fatty acid oxidation ...................................................................................................... 16 1.5.5 Mitochondrial proteases .............................................................................................. 17 1.6 Functional classes of mutations in AML .............................................................................. 18 1.7 Ubiquitin Proteasome System (UPS) ................................................................................... 20 1.7.1 The UPS in AML............................................................................................................. 22 ix 1.7.2 SCF ubiquitin ligases ..................................................................................................... 23 1.7.3 FBXO11 ......................................................................................................................... 24 1.7.4 SCFFBXO11 ........................................................................................................................ 25 1.7.5 Fbxo11 dysregulation in leukemogenesis .................................................................... 26 1.8 Aim of the study .................................................................................................................. 27 Chapter 2: Materials and Methods....................................................................................... 30 2.1 Mutation calling for oncoprint inclusion ............................................................................. 30 2.2 Odds ratios calculations ...................................................................................................... 30 2.3 Isolation of human HSPCs ................................................................................................... 30 2.4 shRNA constructs and CRISPR clones .................................................................................. 31 2.5 Lentiviral constructs and transduction ............................................................................... 32 2.6 Ex vivo cord blood liquid culture for differentiation assay ................................................. 32 2.7 Cell differentiation analysis ................................................................................................. 32 2.8 Long-term initiating cell (LTC-IC) ......................................................................................... 33 2.9 Colony-forming cell (CFC) assay .......................................................................................... 33 2.10 Xenotransplantation ......................................................................................................... 34 2.11 Calculating CD34+ cell output for xenotransplants ........................................................... 35 2.12 RNA extraction and RNAseq .............................................................................................. 36 2.13 Ki-67 Staining ..................................................................................................................... 37 2.14 Annexin V staining ............................................................................................................. 37 2.15 Histology ............................................................................................................................ 37 2.16 Immunoprecipitation ........................................................................................................ 37 2.17 Quantitative tandem mass spectrometry ......................................................................... 38 2.18 Measuring mitochondrial membrane potential ............................................................... 39 x 2.19 Mitochondrial mass measurements ................................................................................. 39 2.20 UAE and NAE inhibitor treatment ..................................................................................... 39 2.21 Western blots .................................................................................................................... 40 2.22 Seahorse XF Cell MitoStress Test ...................................................................................... 41 2.23 RNAseq Analysis ................................................................................................................ 41 2.24 Statistical Analysis ............................................................................................................. 42 Chapter 3: Investigating the tumor suppressive role of FBXO11 in a humanized model ......... 43 3.1 Introduction ......................................................................................................................... 43 3.2 Results ................................................................................................................................. 44 3.2.1 UPS mutations are frequent in AML ............................................................................. 44 3.2.2 FBXO11 depletion promotes stem cell maintenance with myeloid bias ..................... 49 3.2.3 FBXO11 depletion promotes a quiescent cell state and protects against primitive cell death ...................................................................................................................................... 52 3.2.4 FBXO11 depletion in vivo reduces leukemia free survival in a humanized xenotransplant model ........................................................................................................... 54 3.3 Discussion ............................................................................................................................ 62 Chapter 4: FBXO11 depletion promotes a stem cell phenotype through loss of LONP1 mitochondrial function ........................................................................................................ 67 4.1 Introduction ......................................................................................................................... 67 4.2 Results ................................................................................................................................. 68 4.2.1 LONP1 is a top FBXO11 interacting protein ................................................................. 68 4.2.2 FBXO11 regulates LONP1 in a degradation-independent manner .............................. 74 4.2.3 FBXO11 positively regulates LONP1 ............................................................................. 76 4.2.4 FBXO11 depletion reduces mitochondrial membrane potential ................................. 77 4.2.5 FBXO11 loss reduces OXPHOS compared to FBXO11 over-expression ........................ 82 xi 4.6 FBXO11 and LONP1 depletion result in similar transcriptomic responses ..................... 85 4.7 Transcriptomic patterns indicate FBXO11 functions upstream of LONP1 and depends on the mitochondrial functions of LONP1 ................................................................................ 100 4.3 Discussion .......................................................................................................................... 107 Chapter 5: Conclusion ........................................................................................................ 111 5.1 Summary and contributions to the field ........................................................................... 111 5.2 Future directions ............................................................................................................... 116 5.2.1 FBXO11 depletion leukemia models .......................................................................... 116 5.2.2 Exploration of other FBXO11 targets ......................................................................... 118 5.2.3 FBXO11 regulation of LONP1 ...................................................................................... 119 5.3 Concluding remarks ........................................................................................................... 120 References ........................................................................................................................ 121 Appendix ........................................................................................................................... 152 xii List of Tables Table 4.1 Proteins co-immunoprecipitated with FLAG-FBXO11 without Benzonase® treatment........................................................................................................................................................ 71 Table 4.2 Proteins co-immunoprecipitated with FLAG-FBXO11 with Benzonase® treatment. ... 73 Table 4.3 Upregulated GO terms categorized by node clusters. .................................................. 91 Table 4.4 Downregulated GO terms categorized by node clusters. ............................................. 95 Table A.1 Variant allele frequencies (VAFs) for FBXO11 mutations in Figure 3.2 ...................... 152 Table A.2 SCF genes used for Figure 3.4A ................................................................................... 153 xiii List of Figures Figure 1.1 Classical model of hematopoietic differentiation ......................................................... 3 Figure 1.2 Revised model of hematopoietic hierarchy incorporating LMPP population ............... 4 Figure 1.3 The electron transport chain ....................................................................................... 15 Figure 1.4 The ubiquitination system ........................................................................................... 21 Figure 1.5 Graphical representation of the SKP1-CUL1-FBXO11 (SCFFBXO11) E3 ubiquitin-ligase complex ......................................................................................................................................... 21 Figure 3.1 Mutations in ubiquitin proteasome system genes occur frequently in AML-PMP AML patients. ........................................................................................................................................ 45 Figure 3.2 UPS mutations occur frequently and in a mutually exclusive trend across datasets. . 47 Figure 3.3 UPS mutations cooccur with t(8;21) translocations and RAS mutations.. .................. 48 Figure 3.4 FBXO11 transcript expression is lower in AML cells compared to normal HSPCs. ...... 49 Figure 3.5 FBXO11 depletion promotes myeloid bias and maintenance of primitive cells ......... 51 Figure 3.6 FBXO11 depletion promotes apoptosis in bulk cells but not in primitive CD34+ cells........................................................................................................................................................ 53 Figure 3.7 FKA mice had significantly increased engraftment compared to controls.................. 55 Figure 3.8 FKA mice had reduced survival and increased human cell engraftment. ................... 57 Figure 3.9 FKA mice have significantly increased human cell engraftment in spleen and liver... 58 Figure 3.10 FKA mice expand primitive CD34+ cell population. ................................................... 60 Figure 3.11 FKA mice exhibit signs of myeloid malignancy. ......................................................... 62 Figure 4.1 LONP1 is a top FBXO11-interacting target in K562 cells. ............................................. 70 Figure 4.2 K63-linked polyubiquitination and neddylation of LONP1 correlates with FBXO11 expression ..................................................................................................................................... 75 Figure 4.3 FBXO11 and LONP1 depletion both promote myeloid bias and inhibit erythroid and megakaryocytic differentiation..................................................................................................... 77 xiv Figure 4.4 FBXO11 and LONP1 depletion reduces MMP but not mitochondrial mass ............... 81 Figure 4.5 FBXO11 loss reduces OXPHOS activity but increases coupling efficiency .................. 84 Figure 4.6 GO-term analysis of genes dysregulated with FBXO11 and LONP1 depletion. ........... 90 Figure 4.7 LONP1 knockdown blocks effects of FBXO11 overexpression. ................................. 102 Figure 4.8 Transcript expression of ETC complex genes are reduced with FBXO11 and LONP1 knockdown. ................................................................................................................................. 103 Figure 4.9 FBXO11 and LONP1 depletion enriched for stem and myeloid cell states................ 106 Figure 5.1 Schematic of a model of FBXO11 depletion promoting a leukemic cell state through reduced LONP1 function ............................................................................................................. 115 Figure A.1 Fbxo11 depletion cooperates with AML1-ETO expression to drive leukemogenesis in a mouse bone marrow transplant leukemia model. .................................................................. 154 Figure A.2 Schematic representing regions of FBXO11 where shRNA and gRNA sequences target..................................................................................................................................................... 155 Figure A.3 Schematic representing regions of LONP1 where shRNA sequences target ............ 155 xv List of Abbreviations α-KG: α-ketoglutarate acetyl-CoA: acetyl coenzyme A AML: Acute Myeloid Leukemia AML-PMP: AML-Personalized Medicine Project CASH domain: carbohydrate binding and hydrolase domain CB: CD34+ cord blood CFC: colony forming cell assay CHIP: clonal hematopoiesis of intermediate potential CLP: common lymphoid progenitors CMP: common myeloid progenitors CvT: coefficient of variance (transformed) ΔF-box-FBXO11: wild-type FLAG-tagged FBXO11 with deleted ΔF-box domain DLBCL: diffuse large B-cell lymphoma ECAR: extracellular acidification rate ELN: European LeukemiaNet EMP: erythro-myeloid progenitors ETC: electron transport chain EV: empty vector FAO: fatty acid oxidation FLAG-FBXO11: FLAG-tagged wild-type FBXO11 FRET: fluorescence resonance energy transfer G-CSF: granulocyte-colony stimulating factor GMP: granulocyte and macrophage progenitors xvi GO: Gene Ontology GSEA: Gene Set Enrichment Analysis H&E: hematoxylin and eosin HAT: histone acetyltransferase enzymes HCT: hematopoietic cell transplant HDM: histone demethylase HSC: hematopoietic stem cells HSPC: hematopoietic stem and progenitor cells IF: injected femur IL-3: interleukin-3 KO: knockout LK: Lin-c-Kit+ cell population LMPP: lymphoid-primed multipotent progenitors LSC: leukemia stem cell LT-HSC: long-term HSC MAMs: mitochondrial associated membranes MDS: myelodysplastic syndrome MFI: mean fluorescence intensity MLP: multi-lymphoid progenitors MMP: mitochondrial membrane potential MPO: myeloperoxidase MPP: multipotent progenitors MTS: mitochondrial targeting signal NAE: neddylation activating enzyme xvii NES: normalized enrichment scores OCR: oxygen consumption rate OXPHOS: oxidative phosphorylation PES: protein enrichment score PLA: proximity ligation assay PRMT: protein arginine methyltransferases ProA: protein abundance score ROS: reactive oxygen species s-AML: secondary AML SCF: stem cell factor SCFFBXO11: Skp1-Cul1-FBXO11 E3 ubiquitin-ligase complex sgRNA: single guide RNAs shCTR: non-targeting control shRNA shFBXO11: shRNA targeting FBXO11 shLONP1: shRNA targeting LONP1 SNP: single nucleotide polymorphism ST-HSC: short-term HSC t-AML: therapy-related AML TARGET: Therapeutically Applicable Research to Generate Effective Treatments TCA cycle: tricarboxylic acid cycle TCGA: The Cancer Genome Atlas Program TMRE: tetramethylrhodamine methyl ester perchlorate UAE: ubiquitin activating enzyme UPS: ubiquitin proteasome system xviii WHO: World Health Organization WT: wild-type xix Acknowledgements I am thankful to my parents, Tracy Hsiung and Mok Mo for providing the resources and support necessary for me to reach where I am today. I am thankful for my teacher for teaching me patience and resilience, and for always showing me new perspectives. I am thankful to my childhood friend, Flora Su, for our friendship which quietly supported me through this journey. My supervisory committee contributed to the advancement of my project with their questions and suggestions, but also my development as a person with their feedback and encouragement. I chose my supervisor, Dr. Aly Karsan, because I wanted a challenge and to grow beyond my comfort zone, and he did not disappoint. I have also come to appreciate a humorous and warm side of Aly outside of the lab. I am thankful to Dr. Sergio Martinez-Høyer for showing me the ropes in the lab. It set a standard I tried to emulate and surpass when working with new trainees. Although my interaction with Dr. Ramon Klein Geltink was brief, his insightfulness and the effort he took to boost my confidence left an impression. Dr. Ping Xian is one of the most knoweldgable, patient, and supportive mentors I have had. I appreciate the technicians in our lab for their guidance, expertise, and support: Deborah Deng, Jihong Jiang, Patricia Umlandt, Jessica Tran, and Lemlem Degefie. The friends I made in the lab contributed to my growth and my sense of fulfillment. Deborah Deng has been a friend and a rock for me since my first day in the lab. I will cherish the many memories I shared with Aparna Gopal, Samantha Mar, Patrick Lac, and Patrick Coulombe, Xuan Wang, Melody Lu, Jessica Tran, Joshua Bridgers, Hayle Kincross, and Grace Cole. I have learned so much from each of you both inside and outside the lab. I benefited from your support through the years, and you have all shaped me into who I am now, at the end of my degree. Knowing I get to see your faces gave me the extra motivation to make it to the lab every day. Thank you, Marion Shadbolt, for being my number one cheerleader. I am lucky to have you back again. 1 Chapter 1: Introduction 1.1 Hematopoiesis and hematopoietic stem cells The hematopoietic system is made up of stem cells, progenitor cells, and differentiated mature cells. Hematopoiesis is the highly regulated process of blood production that occurs in the adult bone marrow1. Production of all hematopoietic cells is dependent on the relatively rare multipotent hematopoietic stem cell (HSC) population which is capable of self-renewing to generate additional HSCs, or differentiate to form progenitor cells. The HSC population is unique in its ability to repopulate the entire blood system2. HSC function is reflected in cell cycle status. Quiescent HSCs are in the G0 state of the cell cycle, and contain long-term HSC (LT-HSC) which have the greatest repopulating potential3,4. Under acute stress, such as with blood loss and infection, HSCs exit dormancy to divide and differentiate to generate mature myeloid and erythroid cells to meet hematopoietic demands5,6. When subjected to chronic stress in situations like bone marrow transplantation, chemotherapy, chronic inflammation, and aging, HSC exhaustion is accelerated by continuous proliferation and differentiation of HSCs to reconstitute the hematopoietic system7. HSCs accumulate DNA damage during self-renewal and differentiation divisions, and as a result, display decreased self-renewal and reconstitution potential8–10. Accordingly, it has been proposed that quiescence is a protective mechanism for HSCs to avoid damage caused by physiological stress6,11. The earlier models of the hematopoietic differentiation tree had the first branching point at multipotent progenitors (MPP) (Figure 1.1). This separates the lymphoid lineages from the myeloid lineages. The former includes B cells, T cells, and natural killer cells, which are differentiated from common lymphoid progenitors (CLP), and the latter includes myeloid, erythroid, and megakaryocytic lineages that are derived from common myeloid progenitors (CMP)12,13. However, the identification of lymphoid-primed multipotent progenitors (LMPP) that exhibit some myeloid potential suggest that certain typically myeloid-associated populations like granulocytes can be generated from either the myeloid or lymphoid branches 2 based on the standard model (Figure 1.2). With the increased resolution provided by additional identifying cell surface markers, the hematopoietic tree continued to evolve over time. From 2005-2015, the human hematopoietic tree was revised to use cell surface expression of CD133 to identify functional progenitor cells12. In this model, the HSC population is discovered to be more heterogenous than previously described. MPPs, LMPPs, multi-lymphoid progenitors (MLP), and granulocyte and macrophage progenitors (GMP) are found within the CD133+CD34+ cell population. MPPs specifically are CD133+CD34+CD38lowCD45RA− and LMPPs characterized as CD133+CD34+CD38lowCD45RA+CD10− 14. In mice, the MPP population is further categorized: MPP1 also known as ST-HSCs (Lin− Sca1+ cKit+CD135−CD150−CD48−), MPP2 (Lin−Sca1+cKit+oCD135−CD150+CD48+), MPP3 (Lin−Sca1+cKit+oCD135−CD150+CD48+), and MPP4 (Lin− Sca1+ cKit+oCD135+CD150−CD48+)12. MPP2 and MPP3 are myeloid-biased populations and can enhance production of GMPs when they are in demand12,15. MPP4 cells have more transient myeloid cell output, and are uniquely able to produce B/myeloid mixed colonies in CFCs (colony forming cell assays)15. In general, MPPs give rise to erythro-myeloid progenitors (EMP) and LMPPs13. Eosinophilic and basophilic granulocytes, erythrocytes, and megakaryocytes are derived from EMPs. LMPPs give rise to MLPs and GMPs. MLPs can be further subdivided into 4 subtypes based on CD10 and CD7 expression, where each subtype varies in lymphoid and myeloid cell output16. Neutrophilic granulocytes are derived from CD133+CD34+ cells independently from the eosinophilic and basophilic granulocytes, which differentiate from CD133low/-CD34+ cells. Importantly, CMPs are absent from this updated model, resulting in a more balanced, symmetrical hierarchy dividing the lymphoid and myeloid branches. Findings from recent single-cell sequencing efforts suggest that rather than a restrictive, stage-wise process, the hematopoietic differentiation model is a continuous process, and lineage-restricted cells arise from a continuum of undifferentiated HSPCs instead of transitioning through multipotent and bipotent states12. However, this model is based on computational predictions derived from single-cell RNA-seq data. Contradictory to this model, cell surface marker combinations can, in fact, distinguish between HSPC subpopulations 3 functionally. Some explanations for this discrepancy are that steady-state mRNA levels can be decoupled from protein levels, or that single-cell RNA-seq methods are currently insufficient for differentiating between highly related cell types due to similarity in biological processes. Figure 1.1 Classical model of hematopoietic differentiation. Showing cell surface markers described in this dissertation for identifying corresponding HSPCs17–25. 4 Figure 1.2 Revised model of hematopoietic hierarchy incorporating LMPP population. Modified from figure by Laurenti et al12. EoBP: eosinophil–basophil progenitor. 1.1.1 Defining and studying HSCs and progenitors HSCs are essentially defined by their self-renewal capability and their multipotency. Progenitors, on the other hand, are defined by the lack of extended self-renewal, and differentiation is often limited to bi-lineage or uni-lineage capacity. The earliest in vivo method for assaying stem-cell function was by bone marrow transplantation to rescue lethal irradiation in mice, followed by counting hematopoietic colonies in the spleens of transplanted mice26. The HSC population itself is a highly heterogeneous, considering that even within HSCs and MPP 5 populations, cells display distinct differentiation activities15,27. The heterogeneity of HSCs is captured even by serial transplantation28. Xenotransplantations of human cells into immunocompromised mice are more widely used recently to assay self-renewal capability. LT-HSCs are defined by their multilineage repopulation ability in primary and serial transplants, while intermediate and short-term HSCs as well as MPPs display only transient multilineage engraftment in primary transplants12. Newer models allow transplantation without irradiation, and incorporate human bone marrow stromal cells with implanted ossicles or co-transplantation with mesenchymal cells29–31. While xenotransplant models provide the advantage of being able to study hematopoiesis and leukemia in human cells, transplantation in general can provoke cellular changes that would be absent in unperturbed HSPCs. Despite this limitation, transplantation experiments remain highly informative with a wide range of research applications. In human cord blood (CB) based xenotransplant models, cell surface marker combination CD34+38-45RA-90+49f+ enriches for HSCs based on the ability of this cell population to achieve long-term multilineage repopulation capability14. Additional work based on single-cell analysis of cell division activity, RNA-seq data, DNA-methylation patterns, and proteomics analysis revealed that CD33 expression further distinguishes cells with high regenerative potential within this population32. However, using only the CD34+CD90+CD45RA- cell surface marker expression combination is sufficient to enrich for the LT-HSC population within cultured CD34+ cells, confirmed by their ability to generate human CD45+ human grafts 16 weeks post-xenotransplantaion33. The CD34+CD90-CD45RA- cultured progenitor population, on the other hand, cannot. 1.1.2 Myeloid differentiation HPSC differentiation is driven by transcription factor activity. The transcription factor, C/EBPα, promote the expression of genes important for granulocyte differentiation and competes with PU.1 to inhibit monocyte differentiation. PU.1 promotes myeloid and B-lymphocyte differentiation, and plays an important regulatory role in determining lineage 6 commitment at the myeloid- erythroid cell bifurcation, and at the granulocyte-monocyte bifurcation34. Other important myeloid-associated transcription factors include ELF1, RUNX1, IRF8, and GFI135. Myeloid differentiation can also be guided or stimulated by cytokine signaling. Human CD34+ progenitors undergo expansion and differentiation to form early promyelocytes in the presence of stem cell factor (SCF) and interleukin-3 (IL-3) ex vivo, and granulocyte-colony stimulating factor (G-CSF) promotes promyelocyte differentiation to form mature neutrophils36. Mature myeloid populations can be identified by Wright-Giemsa staining to assess morphology, as well as by assessment of myeloid-specific cell-surface markers. In mouse models, neutrophil differentiation is accompanied by expression of Gr1 and Mac-1 (CD11b/CD18), and macrophage expression by F4/80 and Mac136. In human cells, CD33 is expressed on both normal and malignant myeloid cells37. CD11b is expressed on human myeloid-derived suppressor cells and peripheral blood neutrophils, CD14 is associated with monocytes and macrophages, and neutrophils generally also express high levels of CD1522,38. Another hallmark of myeloid cells is myeloperoxidase (MPO), a microbicidal enzyme that is found in the primary granules of myeloid precursors—specifically in late myeloblasts and promyelocytes— as well as myeloid leukemia cells39. 1.2 Acute myeloid leukemia (AML) AML is the most common adult acute leukemia, with an incidence rate of 3.46/100,000 in Canada in 201540,41. The average estimated 5-year survival is under 30%42–44. The suppression of normal blood production is typically the cause of death in AML45. Other hematological disorders such as myelodysplastic syndromes or myeloproliferative neoplasms have high likelihood of progression to AML, known as secondary AML (s-AML)46. Therapy-related AML (t-AML) results from exposure to prior therapy, which includes exposure to topoisomerase II, alkylating agents, or radiation47. Otherwise, occurrence of AML without any known exposure to treatment or other identifiable hematological syndromes is categorized as de novo AML. 7 Diagnosis of AML requires a bone marrow or blood myeloid blast count of at least 20% with the exception of AML with certain translocations45. While AML is characterized by myeloid blasts, acute lymphoblastic leukemia (ALL) is characterized by blast transformation of B and T cells, chronic lymphocytic leukemia (CLL) is characterized by proliferation of monoclonal lymphoid cells, and Chronic myelogenous leukemia (CML) is characterized by monoclonal, dysfunctional granulocytes48. Diagnosis of leukemia is based on morphology and immunophenotype according to the WHO system49,50. Additional tests may help distinguish between the types of leukemia, and cytogenetics and molecular testing can inform therapeutic decisions51. Incidence of AML is increasing in developed countries, along with the mean age of diagnosis45,52–54. Increasing age is well established to be correlated with adverse cytogenetics and treatment related mortalities. This results in older patients being less likely to be selected as candidates for intensive chemotherapy or allogeneic stem cell transplant due to toxicity and associated mortality risks43,44,55. Despite this, the standard treatment regimen for AML has advanced little in the last 40 years. AML treatment was advanced by the discovery of treatment with cytarabine (ara-C) and anthracyclines. To this day, the standard of care established in the 1970s for AML patients combines the two, and is known as induction therapy, or the “3 + 7 regimen”: 3 days of daunrubicin and 7 days of cytarabine56. This treatment regimen results in long-term cure of up to 40% of younger AML patients, but a 5-survival rate of less than 15% for patients 60 years or older 56. Importantly, AML is a heterogeneous disease — for example, different cytogenic and molecular classes of AMLs have different outcomes and benefit from different types of therapies57. Consolidation therapy may include combinations of intermediate or high-dose cytarabine, allogeneic hematopoietic cell transplant (HCT), or autologous HCT, depending on age and risk-category based on genetics45. Patients that are not suitable for intensive chemotherapy may be treated with azacitidine, decitabine, or low-dose cytarabine instead45. 8 AML is a clonal myeloid disease characterized by abnormal proliferation and impaired HSPC differentiation58. This prevents normal hematopoiesis in the bone marrow. AML is a hetergenous disease, and is typically driven by the cooperation of a small number of mutations59. Earlier studies in patient xenograft models suggested that AML arose from genetic alterations in the HSC population, while flow cytometry and next generation sequencing data indicate that a combination of both genetic and epigenetic changes in HSPCs contribute to a preleukemic state59. Data from more recent studies support a model of AML originating from cells ranging from early multipotent populations to differentiated progenitors, but in up 20% of AML patients, AML may be derived from the immature LT-HSC population59. Classification of AML is useful for determining prognosis and informing disease management. The French-American-British (FAB) classification system established in 1976 established 8 subtypes from M0 to M7, and compares the morphological and cytochemical characteristics of leukemic cells40. The World Health Organization (WHO) classification established in 2001, in collaboration with the European Association for Haematopathology and the Society for Hematopathology, was revised in 2008 and in 2016 to incorporate genetic information with morphologic, cytochemical, immunophenotypic, and clinical information for diagnosis49,50. The WHO classification system stratifies AML into 6 groups: (1) AML with recurrent genetic abrnomalities, (2) AML with myelodysplasia-related features, (3) therapy related AML, (4), AML not otherwise specified, (5), myeloid sarcoma, and (6) myeloid proliferation related to Down syndrome. Based on the WHO classification, within the AML with recurrent genetic abnormalities group exists 11 subtypes represented by chromosomal translocations. Cytogenetic profiles can be used to stratify AML, but there still exists variation in prognosis within these cytogenetic groups40. Additional identification of recurrent mutations in genes which regulate hematopoietic proliferation and differentiation, made possible with development of high-throughput sequencing techniques, can further refine prognostication. The European LeukemiaNet (ELN) published in 2010 and revised in 2017 a set of recommendations for diagnosis and management of adult AML. The ELN classification 9 incorporates both cytogenetic abnormalities and recurrent genetic mutations to stratify AML into favorable, intermediate, and adverse risk categories. According to this classification, RUNX1-RUNX1T1 fusion mutation, also known as AML1-ETO, NPM1 mutations without or with low (<0.5 allelic ratio) FLT3-ITD, and biallelic mutated CEBPA are common mutations associated with favorable outcomes. Common gene mutations include RUNX1, ASXL1, and TP53 are associated with adverse outcomes. Mutations in SRSF2, SF3B1, U2AF1, ZRSR2, ASXL1, EZH2, BCOR, or STAG2 genes are more frequently identified in secondary AML60. The presence of these mutations can be used to help support a diagnosis for sAML and possibly distinguish them from t-AML and elderly de novo AML categories. The presence of these mutations is also associated with poor outcomes and lower complete remission rates. Many genetic and environmental risks that contribute to predisposition to AML have been identified. However, the heterogeneity of AML is a barrier for identifying less common recurrent mutations. In some cases, de novo human cell AML models have been characterized by the presence of blast cells in the blood and bone marrow, with >10% of peripheral blood cells expressing human CD45 and >20% leukocytes being human myeloid blast cells61,62. Myeloid sarcomas in the legs and abdomen have also been observed61,62. In a multi-step human AML model, expression of MLL-AF10 in human CD34+ cord blood cells were determined to be insufficient to generate leukemia in humanized mice, since no weight loss or appearance of abnormal peripheral leukocytes were observed63. Humanized mice transplanted with CD34+ cells expressing both K-rasG12V and MLL-AF10, however, showed rough coats, slow movement, and weight loss63. Morphological leukemia cells with abnormal nuclei and cytoplasmic vacuoles—aligning with the M5 FAB classification— were also present in the peripheral blood. These mice also had reduced survival compared to control mice63. There is a lack of consistent characterization of CD34 expression in human AML models described. In a human AML model based on MN1 and NUP98HOXD13 10 coexpression in CD34+ cord blood cells that were long-term cultured before transplantation, engrafted cells were all CD34- and described as aberrant progenitors64. In other human AML models, CD34+ cells were transplanted without prior long-term culture, and engrafted cells were >90% CD34+ or not characterized for CD34 positivity following transplant at all61–63,65,66. Thus there are no consistent or formal criteria for defining a synthetic human cell model as producing AML. 1.3 Leukemia Stem Cells (LSCs) Cancer stem cells (CSC), also known as cancer-initiating cells, are a small population of cells capable of maintaining and propagating the corresponding cancer67. CSCs are capable of self-renewal and differentiation, and are typically difficult to eliminate, resulting in the reoccurrence of cancer67. Similar to normal HSPCs, hierarchical cellular organization has been observed in AML patient samples as well, where leukemia stem cells (LSCs), also known as leukemia-initiating cells, represent a minor population of AML cells and sit at the apex of the hierarchy, much like HSCs68–70. LSCs are defined by their self-renewal ability, demonstrated by their ability to initiate leukemia when serially-transplanted into immunodeficient mice, whereas non-LSC blasts cannot self-renew68,71,72. LSCs are characterized by their increased chemoresistance, which allows them to evade treatment and re-establish leukemia73. This can be attributed to their quiescent state, which protects them against cytotoxic effects that target proliferating cells, as well as increased expression of multidrug resistance genes that contribute to greater drug efflux69,70,74–77. While the majority of AML patients achieve remission, up to 70% of adult patients relapse and do not survive beyond 5 years after initial clinical response74. Given the evasive nature of LSCs, which can give rise to all leukemic cells, their elimination is crucial for AML treatment, and a better understanding of their biology can contribute to better targeting strategies. LSCs reside predominantly within the CD38-CD34+ population, like HSCs, but various additional cell surface markers have been shown to be upregulated in CD38-CD34+ LSCs 11 compared to normal CD38-CD34+ HSPCs68. These include CD25, CD32, CD96, CLEC12a, CD123, CD44, CD47, TIM3, CD99, CLL-1, IL1RAP, GPR56, and CD9368,73. However, the stability of these markers is not well understood, as many of them were not studied at relapse68. Analysis of primary AML samples has resulted in the identification of LSC gene expression signatures, which have been linked to poor survival in AML74. LSC gene expression profiles are distinct from bulk AML cells, but resemble progenitors more than HSCs, suggesting LSCs originate from progenitors rather than HSCs, and acquire self-renewal properties68. LSC properties and origins can be deduced from clonal hematopoiesis studies as well. In a preleukemic state known as clonal hematopoiesis of intermediate potential (CHIP), the acquisition of pre-leukemic mutations results in the expansion and dominance of a small number of HSC clones78. For example, mutations in TET2 and DMNT3A have been associated with high preleukemic burden in AML79. Further accumulation of mutations in these clones result in the uncontrolled proliferation of abnormal blast cells that are unable to undergo differentiation. Findings from some clonality studies indicate that leukemogenic mutations do not necessarily originate in HSCs, but can occur in multipotent hematopoietic cells that either already possess self-renewal capacity, or acquire self-renewal ability from the mutations79–82. This is consistent with the similar genetic profiles of LSCs and progenitors68. 1.4 Comparing HSCs and LSCs HSCs are characterized by self-renewal and pluripotency, and maintain quiescent states83,84. A critical function of HSCs is their ability to give rise to the hematopoietic system despite existing in low numbers in the bone marrow27,85. Despite their general dormancy, HSCs are capable of reversibly entering cell cycle to repopulate the hematopoietic system, and use repair mechanisms to reestablish functional HSC pools when challenged by hematopoietic stress, such as injury to the bone marrow4. Transition between dormancy and self-renewal states are tightly regulated, as HSCs have limited cell divisions before losing repopulating potential and the ability to exit cell cycle to return to a quiescent state. 12 Similar to the normal hematopoietic system, AML patient samples are often organized hierarchically. Leukemia stem cells (LSC) are similar to HSCs in their self-renewal capacity and ability to initiate and maintain long-term disease in immunodeficient mice in serial transplantations68. These similarities between HSCs and LSCs can make differentiating them, and selectively targeting LSCs challenging. Both HSCs and LSCs have lower metabolic rates relative to the bulk hematopoietic cell population, as expected based on their quiescent states86. Paradoxically, LSCs have been shown to be dependent on oxidative phosphorylation (OXPHOS), and, unlike HSCs, are unable to increase glycolysis rates in response to OXPHOS inhibition87–89. While LSCs are more dependent on OXPHOS, they do not necessarily have increased OXPHOS activity. Functional LSCs have been shown to have lower levels of reactive oxygen species (ROS) and oxygen consumption rates, which could represent reduced oxidative metabolism68. ROS-low LSCs demonstrate impaired reserve glycolytic capacity compared to normal CD34+ cells from the bone marrow, supporting an increased dependence on OXPHOS68. LSCs are not necessarily enriched in chemotherapy resistant cells, but AML cell lines with high OXPHOS activity are more chemoresistant compared to low OXPHOS lines88. This suggests LSCs may have adapted metabolic pathways for survival advantages, particularly under stressful conditions. SCF complexes been shown to regulate key transcription factors involved in controlling the glycolysis-OXPHOS balance and responding to oxidative stress; perturbations in these regulatory pathways result in various disease states90. The preferential use of OXPHOS—which is a more efficient source of energy metabolism than glycolysis—observed in LSCs might allow LSCs to meet greater energy demands to support activities like increased protein synthesis73,91. Reduction of OXPHOS in AML selectively eliminates quiescent LSCs86. Although LSCs are characterized as having an OXPHOS-biased profile, cancers are heterogeneous diseases, and this is reflected in the metabolic heterogeneity between different cells within the same cancer, as well as in the ability of cancer cells to adapt to environmental stresses92–95. The versatility of cancer cells can impart a selective advantage under unfavorable conditions. LSCs have unique properties distinct from bulk AML cells and are similar to HSCs in the ability to give 13 rise to both identical daughter cells and more differentiated cells carrying the same or additional genetic defects69,75. 1.5 Mitochondrial function and stem cell maintenance HSCs are found in the hypoxic bone marrow niche, where HSCs communicate with and are supported by the microenvironment through cell-cell interactions96. Importantly, the hypoxic environment is not just tolerated, but required for HSC function96,97. Given that homeostatic regulation of OXPHOS is essential for regulating HSC maintenance and self-renewal, and LSCs have increased dependence on OXPHOS, the study of mitochondria is highly relevant to HSC biology. Mitochondria are the site of OXPHOS, which contributes to the main ATP source in an oxygenated environment. Without oxygen as the terminal electron acceptor for OXPHOS, the proton gradient, generated by the electron transport chain (ETC) complexes located on the inner mitochondrial membrane, is lost, halting mitochondrial ATP production. Cytosolic glycolysis is the dominant method of ATP production in hypoxic or anoxic conditions, where pyruvate is converted to lactate in the process of glucose fermentation. Consistent with this, proteomic data show that glycolysis pathway proteins are more abundant in multipotent HSCs and progenitors compared to more differentiated precursors98. LT-HSCs, specifically, predominantly utilize anaerobic glycolysis for ATP production and consume lower levels of ATP even in the presence of oxygen; in fact, LT-HSCs and LSCs are distinguishable from the rest of the normal, more differentiated HSPC population based on mitochondrial metabolism87,98,99. Evidence suggests that self-renewing stem cells can metabolically switch from glycolysis to OXPHOS with activated mitochondria when differentiating11,98. 1.5.1 Reactive Oxygen Species OXPHOS can generate ATP more efficiently compared to glycolysis: OXPHOS generates a maximum yield of 36 ATPs with complete oxidation of a glucose molecule, while anaerobic 14 glycolysis alone only produces 2 ATPs100. However, the consequence of the high productivity of OXPHOS is high levels of free radical production by the mitochondria. Free radical production occurs in the mitochondria when electrons that are passing through the ETC during OXPHOS are leaked and reduce oxygen. This forms the superoxide anion radical (O2-), which is the precursor of other reactive oxygen species (ROS) and can trigger other oxidative chain reactions101. Enzymes like superoxide dismutase in the mitochondrial matrix and in the mitochondrial intermembrane space can convert the superoxide anion into hydrogen peroxide, which can be converted by catalase to form H2O. Normal regulation of cellular redox status is dependent on balancing ROS production with antioxidant mechanisms. Oxidative stress occurs when the cell’s antioxidant capacity is overwhelmed by ROS levels. ROS can oxidize and damage various cellular material, including lipids, proteins, and nucleic acids102–104. High levels of ROS can induce apoptosis and senescence in cells, and has been shown to disrupt mitochondria, activating the protease cascade through cytochrome release105–107. Compared to less primitive progenitors, HSCs have increased number of mitochondria. There are studies showing that HSCs in fact have higher mitochondrial mass compared to committed hematopoietic populations, but HSCs are typically reported to have reduced mitochondrial mass, which is in line with a quiescent and dormant phenotype108,109. The conflicting results could be attributed to differing methods in measurement of mitochondrial mass, as certain mitochondrial mass dyes are not mitochondrial potential-independent, and dyes can be eliminated from the mitochondria by xenobiotic efflux pumps110,111. Accordingly, both HSCs and LSCs are characterized by low levels of ROS87,112. The ROSlow cells have been reported to have greater self-renewal potential compared to the ROShigh population based on serial transplantation capacity, and inhibition of ROS production can restore HSC function in ROShigh cells112. It is proposed that the ROS-production limiting environment of the hypoxic bone marrow niche provides protective effects for HSCs from oxidative stress112. 15 1.5.2 Mitochondrial membrane potential Mitochondrial membrane potential (MMP) is generated by the ETC complexes I, II, and IV, by pumping protons from the mitochondrial matrix across the inner mitochondrial membrane into the intermembrane space during OXPHOS (Figure 1.3). MMP reflects mitochondria activity and, accordingly, there is a strong positive correlation between MMP and mitochondrial ROS production101,113,114. Aligning with the low mitochondrial mass and ROSlow phenotypes of HSCs, LT-HSCs and human CD34+ cord blood (CB) with greater reconstitution potential have reduced mitochondrial membrane potential115,116. In fact, the disruption of MMP promotes self-renewal in HSCs117. Of note, a negative correlation between MMP and ROS levels has been observed under pathological conditions118. Figure 1.3 The electron transport chain. Dotted lines represent the path of electrons passing through the components of the electron transport chain. Just as HSCs must maintain flexibility in metabolism and cell cycle state in order to respond to hematopoietic needs, mitochondria are also characterized by functional and 16 morphological plasticity, allowing them to support cell needs. Aside from changes in mitochondrial membrane potential and ROS production discussed above, mitochondria can also undergo ongoing fission, to form fragmented mitochondria, and fusion, to form elongated mitochondrial. LT-HSCs have been observed to exhibit fragmented mitochondria, and more differentiated short-term HSCs (ST-HSC) have longer mitochondria119,120. 1.5.3 Mitochondrial regulation of the epigenome Another avenue in which mitochondrial activity could influence stem cell state could be through regulation of the epigenome. The mitochondrion is the site of the tricarboxylic acid cycle (TCA cycle) that oxidizes pyruvate and feeds electron donors to the electron transport chain for OXPHOS. TCA cycle metabolites can also function as enzyme cofactors for epigenetic processes. TCA cycle metabolites like α-ketoglutarate (α-KG) are a source of carbon units required for acetylation and methylation, and contributes to demethylation through α-KG-dependent dioxygenase activity121. α-KG is required for Jumonji C domain histone demethylase (HDM) function122. The pyruvate dehydrogenase complex also functions in the mitochondria, and converts pyruvate generated from glycolysis into acetyl coenzyme A (acetyl-CoA). Acetyl-CoA is also the substrate for histone acetyltransferase enzymes (HAT) for modifying histone tails122. These metabolites affect DNA and histone modifications, and in turn, influence nuclear transcription. Interestingly, aside from its damaging effects, ROS can also act as signaling molecules and second messengers to affect transcriptional reprogramming123,124. 1.5.4 Fatty acid oxidation Another process that occurs in the mitochondria is fatty acid oxidation (FAO), which is an aerobic process that breaks down fatty acid into acetyl-CoA units. Inhibition of mitochondrial FAO has been demonstrated to reduce asymmetric divisions and promote HSC exhaustion, while treatment with an agonist of the FAO pathway can improve HSC maintenance125. FAO can also reduce ROS levels by increasing levels of NADPH (reduced form of nicotinamide adenine dinucleotide phosphate). NADPH is an electron donor that is used by many ROS-detoxifying enzymes, including glutathione peroxidases, glutaredoxins, and peroxiredoxins, to neutralize 17 ROS126. Rapid proliferation occurring with HSC exit from quiescence could also be supported by mitochondrial FAO fueling the TCA cycle with acetyl-CoA. 1.5.5 Mitochondrial proteases One of the major regulators of mitochondrial plasticity is mitochondrial proteases. The mitochondrion contains more than 1,200 proteins that vary depending on cell state and response to environmental cues, but there are 45 characterized proteases that exist in different mitochondrial compartments to maintain homeostasis127. Mitochondrial proteases, or mitoproteases, can be categorized into 3 groups: Mitoproteases with functional activity that is mostly present in the mitochondria, pseudo-mitoproteases without catalytic protease activity but exhibiting other mitochondrial functions, and transient mitoproteases that translocate to the mitochondria and exhibit functions other than proteolysis128. Of the 45 proteases, 23 are localized exclusively in the mitochondria while the rest can also be found in the cytosol128. The central role of the mitochondrial proteolytic system is assumed by ATP-dependent proteases, which are active throughout the mitochondria129. These proteases included CLPXP and LONP1. LONP1 has been reported to target misfolded subunits of ETC complex II and complex V for degradation; consequently, LONP1 loss increases ROS production and reduces oxygen consumption rate130. Accordingly, LONP1 knockdown has been shown to reduce MMP131. LONP1 also exhibits mitochondrial DNA binding activity and chaperone activity132. The latter is mediated by the ATP-binding domain and is important for promoting proper folding of mitochondrial proteins such as mitochondrial membrane complex proteins. There is also evidence supporting LONP1 chaperone function being important for sequestering and stabilizing p53 in the mitochondrial matrix to inhibit apoptosis when the cell is under oxidative stress133. Given these diverse roles, LONP1 is considered a major regulator of mitochondrial function in cancer, and currently studied as a potential therapeutic target. However, LONP1 has predominantly been studied in colorectal cancer, and the role it plays in AML has not yet been investigated. 18 Another mitochondrial protease, ClpP, is overexpressed in AML stem cells134. ClpP interacts with mitochondrial respiratory chain complex proteins and mitochondrial metabolism enzymes and is suggested to maintain the integrity of mitochondrial metabolism134. While ClpP loss has no impact on normal hematopoietic, ClpP knockdown in leukemic cells results in impaired complex II function and inhibited OXPHOS134. It is proposed that ClpP is upregulated in response to increased mitochondrial stress in a subset of AML, making ClpP a promising therapeutic target134. Aside from ClpP however, there is little to no investigation of the role of mitochondrial proteases in AML, despite the role of mitochondrial proteases in regulating mitochondrial function, and the importance of mitochondrial homeostasis in normal stem cell maintenance. 1.6 Functional classes of mutations in AML Understanding the mutational landscape and trends in AML can reveal underlying mechanisms of leukemogenesis. AML differs from normal HSPCs in its abnormal proliferation, myeloid differentiation, and cell cycle or apoptotic activity, and AML-associated mutations can also be categorized in this manner135. AML was originally proposed to require mutations in both Class I genes, which promote proliferation and include FLT3, RAS, JAK2, and KIT, and Class II genes, which impairs hematopoietic differentiation and include transcription factors like PMR/RARA, RUNX1/CBFA2T1, MLL fusions, and CEBPα136. It was thought that mutations in both classes of genes were required for AML development, while mutations in only Class I genes result in myeloproliferative neoplasms, and mutations in only Class II genes result in myelodysplastic syndrome136. However, later research identified recurring mutations in AML in genes that fall outside of Class I and II genes, suggesting that AML development is more complex than can be described by a two-class system137. The WHO classifies AML into cytogenetic and molecular genetic subgroups. When 20% or more of peripheral blood or bone marrow blasts are present, cytogenetic abnormalities are sufficient to diagnose AML with myelodysplasia-related changes according to the WHO cytogenetic abnormality classes50. The WHO cytogenetics classification system is made up of 19 complex karyotype (3 or more abnormalities present), 7 classes of unbalanced abnormalities, and 9 classes of balanced abnormalities. The WHO classification of AML based on genetic abnormalities includes the following 11 subtypes: AML with RUNX1-RUNX1T1; AML with CBFB-MYH11; APL with PML-RARA; AML with KMT2A-MLLT3; AML with DEK-NUP214; AML with GATA2, MECOM; megakaryoblastic AML with RBM15-MKL1, AML with BCR-ABL1, AML with mutated NPM1, AML with biallelic mutation of CEBPA, and AML with mutated RUNX150. A study by Papaemmanuil et al. established classification rules to minimize overlap between genetic abnormality categories and generated 11 subtypes of AML based on driver mutations and cytogenetic abnormalities137. There is some overlap between these subtypes and the WHO classification system, where some classes include AML with mutated chromatin genes, RNA-splicing genes, or both; AML with TP53 mutations, aneuploidy, or both; and AML with IDH2R172 mutations and no other class-defining lesions137. In this study, the researchers were not able to classify 48% of the AML patients according to the WHO molecular groups classification system. Using their new classification system that defined 11 AML genetic abnormality classes, the authors could still not classify 11% of the patients137. These findings suggest that there are additional genetic, epigenetic, or clinical parameters that impact on outcomes in AML which have not yet been defined. The ELN recommendations stratify AML patients into favourable, intermediate, and adverse risk categories based on 18 genomic abnormality groups consisting of a combination of cytogenetic abnormalities and recurrent mutations138. Since the 2010 ELN recommendations, cytogenetics and mutational screening of NPM1, FLT3, and CEBPA have been incorporated into routine practice138. Updates to these recommendations were made in 2017 to account for new data—for example, RUNX1, ASXL1, and TP53 mutations are now associated with poor prognosis138. As new molecular information is validated in additional clinical studies, it is expected that these will be incorporated into the ELN classification. 20 Patterns of co-occurrence and mutual exclusivity between genetic mutations, within subgroups of mutations, and among mutation hotspots within genes have previously been characterized137. For example, NPM1 mutations occur frequently with NRASG12/13 mutations but not with NRASQ61 mutations137. KIT is frequently mutated with core-binding-factor alterations including t(8;21) and inv(16)/t(16;16), IDH1 and IDH2 mutations co-occur with NPM1 mutations, and DNMT3A mutations co-occur with NPM1, FLT3, and IDH1 mutations139. Co-occurrence of mutations can indicate potential biological interactions and certain combinations of mutations might identify AML subtypes with unique features140. IDH1 and IDH2 mutations occur mutually exclusively with TET2 and WT1 mutations139. Mutual exclusivity for mutations within biological classes of genes, including cohesins, splicesome proteins, signaling proteins, and histone-modifying proteins indicate that only one mutation in these pathways is sufficient for development of AML140. 1.7 Ubiquitin Proteasome System (UPS) The UPS is the major intracellular, non-lysosomal regulator of protein expression through its protein degradation function. The UPS regulates the degradation of old, damaged, misfolded and abnormal proteins, as well as normal short-lived and important regulatory proteins for regular homeostasis and quality control purposes. The UPS can regulate proteins involved in various processes, including cell cycle, tumor suppressors, cell signaling, cell division, cell growth, cellular stress response, inflammatory response, cellular localization, and DNA damage response141–145. The UPS functions by imparting covalently attached ubiquitin molecules to protein substrates. The UPS is composed of multi-step enzyme cascade process (Figure 1.3). An E1 ubiquitin-activating enzyme (UAE) activates ubiquitin in an ATP-dependent manner. The ubiquitin molecule is then transferred to an E2 ubiquitin-conjugating enzyme at the cysteine residue of the active site. Humans encode for roughly 40 E2 enzymes that can transfer ubiquitin or ubiquitin-like molecules such as NEDD8 (neddylation) or SUMO (sumoylation)146. Finally, E3 ubiquitin ligases guide the E2 enzyme conjugation of the ubiquitin molecule to a lysine residue 21 of the target substrate. The human genome encodes over 600 E3 ubiquitin-ligase enzymes, which determine substrate specificity of the ubiquitin molecule147. Linked ubiquitin chains are formed with the modification of the N-terminus, or one of the seven K6, K11, K27, K33, K48, K63 lysine residues of ubiquitin molecules already covalently attached to target proteins. Figure 1.4 The ubiquitination system. Ub: ubiquitin monomer; Dub: deubiquitinase. Different types of ubiquitin modifications have different effects on the modified protein. The most abundant, and most studied, forms of ubiquitin modifications are K4-linked and K63-linked polyubiquitination148,149. K48-linked polyubiquitination is the canonical ubiquitin modification that targets proteins for degradation150. Polymerized ubiquitin chains typically signal the shuttling of proteins for degradation by the proteasome, which is composed of proteolytic enzymes that form a tubular shape with two major components: one 20S catalytic core complex, and two 19S regulatory complexes that act as proteasome activators151,152. Multiple monoubiquitin modifications, K11-linked polyubiquitination, and K29-linked chains are also proteasomal in nature153,154. K63-linked polyubiquitination can also target proteins for proteasomal degradation, but has also been shown to regulate proteasome-independent processes such as cell signaling, DNA repair, endocytosis, and cellular localization145,149. Dysregulated K63-linked polyubiquitination has been implicated in cancers and myeloid malignancies. For example, TNF receptor-associated factor (TRAF) proteins have been reported to promote auto-polyubiquitination and ubiquitination of surrounding proteins with K63-linked polyubiquitin chains that result in the nuclear translocation and subsequent activation of NF-κB 22 characteristic of many cancers155,156. Inhibition of USP9X, which normally inhibits K63-linked polyubiquitination of FLT3-ITD, results in anti-leukemic effects by blocking signaling downstream of FLT3157. Homogenous chains are more frequently studied, but heterogenous ubiquitin chains can also be formed with modification of different ubiquitin residues within a chain. Ubiquitin chains can also be linear or branched158. Mixed modifications can also exist within a chain, and these ubiquitin chains can exhibit functions unique from the corresponding homozygous chains149,159. Proteins targeted for degradation are degraded by the 26S proteasome complex, which releases ubiquitin for reuse. Ubiquitin can be found in most tissues in eukaryotes, and is one of the key post-translational modifications in the cell as a part of a highly selective protein degradation system, regulating the degradation of over 80% of cellular proteins151. Disruption of the ubiquitin proteasome system (UPS) plays an important role in many diseases, including in cancer development, as suggested by recurrent genetic aberrations affecting E3 ligases and deubiquitinases in multiple cancers160–163. Emerging evidence implicates deregulated ubiquitination pathways in leukemogenesis163,164. 1.7.1 The UPS in AML Disruption of the UPS plays an important role in cancer development, based on recurrent genetic aberrations affecting E3 ligases and deubiquitinases in multiple cancers161,162,165–169. The UPS also plays a crucial role in the development and function of normal HSPCs and leukemic stem cells170–174. While abnormally high proteasome activity has been previously observed in AML, normalization of the data indicates that proteasome activity is, in fact, significantly lower in AML compared to normal164,175–178. This supports the loss-of-function nature of the UPS mutations observed in the combined patient samples including several truncating and frameshift mutations (present in 22%, or 19 of 88 patients). The UPS plays an important role in regulating various cellular functions such as apoptosis, differentiation, inflammatory responses, cell cycle progression and DNA repair141,143,179. Several components of the UPS have also been shown to be important 23 regulators of HSC fate determination and maintenance172,174. Proteasome enzymatic activity is generally lower in leukemic cells in AML patients compared to mature hematopoietic cells, and more recent studies have also implicated some UPS proteins in regulating leukemia stem cell activity. Several UPS genes are recurrently mutated across the combined patient sample sets. In our patient cohort, CBL is mutated in 3 patients, while FBXO11 and ASB1 are mutated in two samples each, with one truncating FBXO11 mutation (Figure 1.2). CBL mutations contribute to pathogenesis of myeloid malignancy, while FBXO11 gene aberrations have not been well studied in AML171. 1.7.2 SCF ubiquitin ligases The human genome encodes over 600 E3 ubiquitin ligases, and they fall within 3 families: the homolog of the E6-AP carboxyl terminus (HECT) family, the plant homeodomain/U-box (PHD/U-box) family, and the RING finger family180. Cullin-RING ubiquitin ligases (CRLs) are the most representative RING finger E3 ligases, and are characterized by Cullins that bridge E2 ubiquitin-conjugating enzymes to substrates181. CRLs can be formed with CUL1, CUL2, CUL3, CUL4, CUL5, CUL7, and CUL9181. The organization of CRLs is typically well conserved across the different type of cullins, but they can vary in the assembly and use of adaptors, resulting in differences in methods of substrate interaction and modification181. CRLs regulate various biological processes, including cell cycling, DNA repair, hypoxia response, STAT signaling, ROS regulation181. Consequentially, CRLs play an important role in regulating cancer pathogenesis181. Within the CRLs, the SCF E3 ubiquitin ligase family is the most researched182. SCF ligases also make up the biggest family of E3 ligases180. The SCF complex contains a catalytic core composed of CUL1 and RBX1, and a substrate recognition module composed of SKP1 and a variable F-box protein182. CUL1 provides the structural scaffold for the complex, binding SKP1 and the F-box protein at the N-terminus and RBX1 at the C-terminus183. Substrate ubiquitination is regulated by neddylation of CUL1, where CUL1 neddylation stimulates substrate ubiquitination, and substrate dissociation results in removal of NEDD8 from CUL1182. 24 CUL1 neddylation may also increase the affinity of the SCF complex for some E2 enzymes180. SKP1 is important for the identification of, and binding to, the F-box protein, and RBX1 contains the RING finger zinc-binding domain responsible for binding E2-ubiquitin and facilitating the transfer of ubiquitin to a lysine site on the target protein180. The variable F-box protein determines the substrate specificity of the SCF complex. 1.7.3 FBXO11 The FBXO11 gene encodes the 927 amino acid FBXO11 protein, a member of the F-box protein family. F-box proteins contain at least one F-box domain. F-box proteins fall within 3 classes based on characteristics of the F-box domain: the F-box domain of FBXW class proteins contain amino acid repeats of the WD40 (beta-transducin) structural motif that frequently terminates with a tryptophan-aspartic acid (WD) di-peptide, the F-box domain of the FBXL class contains leucine-rich amino acid repeats, and the F-box domain of the FBXO class contains only uncharacterized domains184. There are currently 22 FBXLs, 10 FBXWs, and 37 FBXO F-box human proteins characterized184. The FBXO11 gene is highly conserved, sharing 99.5% amino acid identity with mice and 60% with C. elegans185. The F-box domain is a 50 amino acid motif that is encoded within the N-terminal end of FBXO11. The F-box domain is bipartite in nature: the shared F-box motif mediates the interaction between FBXO11 and SKP1 in the SCF complex and important for substrate recruitment, while additional protein-protein interaction motifs contribute to binding of substrates186,187. FBXO11 also contains 3 carbohydrate binding and hydrolase (CASH) domains, and a zinc finger-UBR domain—the latter accounting for FBXO11 also being known as UBR6185,188. CASH domains are composed of right-handed β helices that form elongated structures, are typically present in carbohydrate binding proteins, and contribute to substrate recognition in F-box proteins167,189,190. Deletion of the CASH domains in FBXO11 reduces the interaction between FBXO11 and its target, BCL6167. The UBR-box is a 70 amino-acid zinc finger domain, forming a dimer structure that is stabilized by 7 zinc atoms188. The UBR domain is responsible for binding 25 the N-terminal residues of short-lived proteins which contain degradation signals of (N-degrons) to target for proteasomal degradation191,192. Despite the presence of the UBR domain, FBXO11 has not been shown to bind N-degrons, which is attributed to the absence of an amino acid binding pocket based on crystal structure analysis188,191. FBXO11 is also known as PRMT9 because it contains domains that are typically present in protein arginine methyltransferases (PRMTs) as well193. FBXO11 methylates arginine residues, but is unique in how its sequence shares little homology with other members of the PRMT family193. FBXO11 has been reported to localize to the nucleus and the cytosol167,193. 1.7.4 SCFFBXO11 Although the Skp1-Cul1-FBXO11 (SCFFBXO11) E3 ubiquitin-ligase complex is predominantly known for its ubiquitin-ligase activities, it has previously been shown to also exhibit NEDD8-ligase activity as well, with p53 being the only characterized neddylation target194. FBXO11 encodes the substrate recognizing component of SCFFBXO11 ,which is responsible for interacting with ubiquitination targets for targeting to proteasomal degradation; this can occur through a phosphorylation-dependent mechanism (Figure 1.2)167,195–197. SCFFBXO11 targets have been characterized in other disease models. FBXO11 depletion can be observed in melanocytes in vitiligo patients, and FBXO11 single nucleotide polymorphism (SNP) variants have been associated with otitis media198,199. FBXO11-inactivating mutations have also been implicated in diffuse large B-cell lymphoma (DLBCL), where wild-type (WT) FBXO11 targets BCL6 for ubiquitination, exhibiting tumor suppressor function167. De novo variants in FBXO11 that are predicted to destabilize the protein have been implicated in neurodevelopmental disorders185. FBXO11 has also been found to regulate cell cycle through targeting of CRL4 for proteasomal degradation; inhibition of CRL4 degradation delays cell-cycle exit196,200. FBXO11 has been proposed to regulate the TGFβ pathway, where its inactivation causes accumulation of phospho-Smad2 in the nucleus likely due to stabilization of FBXO11 26 neddylation target, p53, contributing to developmental defects in affected tissues201,202. Loss of ubiquitination of Snail family members with FBXO11 depletion has also been shown to dysregulate invasion, epithelial migration, and metastasis195,197. Reported mechanisms of FBXO11 regulation has predominantly been by microRNAs—including miRNA-582-5p, miRNA-21, miRNA-621, miRNA-26a—in various diseases, including cancers203–206. The role of FBXO11 dysregulation, particularly in the context of FBXO11, has been studied in a few disease models, but not yet in AML. Figure 1.5 Graphical representation of the SKP1-CUL1-FBXO11 (SCFFBXO11) E3 ubiquitin-ligase complex. 1.7.5 Fbxo11 dysregulation in leukemogenesis Dysregulation of FBXO11 has not been studied extensively in the context of AML or even hematopoiesis. FBXO11 has been shown to be an important regulator of erythroid maturation by targeting BADH1, an H3K27me3 reader that represses PRC2-dependent transcription207. In another, recent study, FBXO11 loss was implicated in the transformation of myelodysplastic syndrome to acute myeloid leukemia163. This study found that FBXO11 loss caused changes in proliferation, differentiation, and apoptosis pathways at the transcriptomic level. Aside from 27 these findings, there have not been any studies investigating the role of FBXO11 depletion in AML, in particular, in de novo AML. Previous work done in our lab found that Fbxo11 depletion cooperated with AML1-ETO expression to reduce leukemia free survival in a mouse bone marrow transplant model (Appendix Figure A.2). AML1-ETO (RUNX1-RUNX1T1) fusion mutation, which results from t(8;21) translocation, was selected as a cooperating mutation in this model. AML1-ETO has a high odds-ratio of co-occurrence with ubiquitin proteasome system mutations (Figure 3.3). Importantly, AML1-ETO occurs frequently in AML—reported in 22% of AML patients—and its expression, alone, is not sufficient to drive leukemogenesis208,209. Fbxo11 depletion also cooperated with AML1-ETO to expand the Lin-c-Kit+ (LK) population in the blood at the expense of T-cell CD3+, B-cell CD19+ and myeloid CD11b+/Gr-1+ cell populations. The LK population contains the myeloid progenitors and is reported to contain leukemia initiating cells in some mouse leukemia models210–212. Similarly, at endpoint, flow cytometry analysis of cell surface marker expression showed an increase in the phenotypically long-term HSC (LT-HSC) like population (Lineage- c-kithi Sca1+ CD34- Flt3-) in mice with Fbxo11 depletion and AML1-ETO expression compared to controls, exhibiting an HSC-maintenance and expansion phenotype. Of significance, Fbxo11 depletion cooperated with AML1-ETO expression to generate a transplantable leukemia, indicating the presence of leukemia stem cells. Altogether, these findings support a model of Fbxo11 depletion expanding a stem-cell like population with dysregulated differentiation capabilities, and driving leukemogenesis. 1.8 Aim of the study While there has been recent development in genetic classification of AML and therapeutic targeting, AML prognosis has not improved much, incidence is increasing, and standard therapeutic practices have generally remained the same. The sequencing effort that resulted in the identification of recurrent UPS mutations in our patient cohort was initiated with the intention of identifying genetic drivers of AML. The ultimate goal is to identify novel therapeutic targets to increase or improve treatment options for AML patients, as well as to 28 contribute to the collective investigation on the molecular and genetic basis behind leukemogenesis. Thus far, our findings have highlighted the importance of UPS class of mutations and identified the leukemogenic effect of Fbxo11 depletion. FBXO11 is mutated recurrently in AML, but more importantly, AML patients in general have lower FBXO11 transcript expression compared to normal HSPCs, implying that characterization of the effects of FBXO11 depletion can be widely applicable to the heterogeneous disease. Based on previous findings, and given the role of SCFFBXO11 as an E3 ubiquitin ligase most well known for targeting proteins for degradation by ubiquitination, we hypothesize that FBXO11 depletion in hematopoietic stem and progenitor cells (HSPC) promotes self-renewal and drives leukemogenesis through dysregulated expression of its ubiquitination targets. The aims of the study are to (i) characterize the effects of FBXO11 depletion in HSPCs and its contribution to leukemogenesis, and to (ii) investigate the mechanisms behind leukemogenesis driven by FBXO11 depletion. The first aim is explored in Chapter 3, where I solidify the role of FBXO11 depletion in leukemogenesis, and investigate the effect of FBXO11 depletion on self-renewal. Previous work was predominantly done in a mouse model. To confirm the validity of the findings in the context of humans, and to better understand the dysregulation in hematopoiesis that occurs with FBXO11 depletion in the process of leukemia development, I investigate the hematopoietic effects of FBXO11 depletion in human cells, both ex vivo in normal human CD34+ CB cells and leukemic leukemic cell lines, as well as in vivo in a humanized xenotransplant model. Importantly, in this chapter, we identify the effects of FBXO11 depletion on stem cell function. Additionally, we compare some of our findings with previously reported effects of FBXO11 depletion in HSPCs. In Chapter 4, I focus on the molecular mechanism proposed through which FBXO11 depletion promotes a stem cell phenotype that is important in leukemogenesis, particularly in the LSC population. SCFFBXO11 ubiquitination targets have been identified in other diseases, but 29 not in AML. Through unbiased mass spectrometry analysis of FBXO11 interacting proteins, we identify novel candidates for SCFFBXO11 substrates. I establish an association between FBXO11 and the mitochondrial protein, LONP1, which has not previously been reported to interact with FBXO11, or studied in the context of AML. To address the hypothesis, we investigated how FBXO11 regulates LONP1, and how LONP1 dysregulation resulting from FBXO11 depletion may contribute to leukemogenesis. We validate our mechanistic findings at the transcriptomic level in human CD34+ CB cells, where we also find additional support for FBXO11 depletion promoting stem cell characteristics. Overall, the work completed during my PhD further solidifies the tumor suppressor role of FBXO11 in leukemogenesis, and highlights a novel link between the ubiquitin proteasome system and the mitochondrial protease system. 30 Chapter 2: Materials and Methods 2.1 Mutation calling for oncoprint inclusion Patient mutations were determined pathogenic and included in the oncoprint if they had a REVEL score > 0.250 and CADD score > 10.5. Germline variants were excluded. Hugo Gene Nomenclature Committee (HGNC) gene data was used to generate a list of ubiquitination genes to query, and the list was manually filtered to retain genes functionally relevant to the UPS. 2.2 Odds ratios calculations Odds ratios were calculated as “odds of a mutation occurring in a sample with a UPS mutation” vs “odds of a mutation occurring in a sample without UPS mutations”. Odd ratio for mutations in gene X= 2.3 Isolation of human HSPCs Anonymized normal human CB samples were obtained with informed consent from normal full-term Cesarean 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+ cord blood (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 manufacturer’s protocols and/or FACS sorting. CB cells were thawed by dropwise addition into HBSS + 10% fetal bovine serum (FBS) supplemented with DNase (100 µg/mL final concentration). CB cells were then seeded into 96-well round bottom plates (cell density at 100k cells/100 µL) and prestimulated in StemSpanTM SFEM (Stem Cell Technologies #09650) supplemented with StemSpan™ CC100 (Stem Cell Technologies #02690), 750 nM SR1 (Stem Cell Technologies #72344) and 35 nM UM171(Stem Cell Technologies #72912) overnight. 31 2.4 shRNA constructs and CRISPR clones K562 control and FBXO11 knockout CRISPR clones were generated by Dr. Gerben Duns. Guide RNAs (gRNAs) were cloned into the pSpCas9n(BB)-2A-GFP (PX461) plasmid (Addgene #48140) and delivered to parental K562 cells by Amaxa nucleofection using the Amaxa Cell line nucleofector kit V (Lonza VCA-1003). GFP+ transfected cells were single-cell sorted into 96-well plates, expanded. FBXO11 targeting was confirmed by Sanger sequencing, and FBXO11 depletion was confirmed by western blot. shRNAs used are in the pLKO.1 vector, from the MISSION library, and received from the UBC Centre for High-Throughput Biology, and GFP or mCherry was inserted in place of the puromycin resistance marker. gRNA and shRNA information are listed below. More information for gRNA and shRNA targeting can be found in Appendix Figures A.3 and A.4. FBXO11 CRISPR gRNAs gRNA sequence: AACCACTGTAGGGTTAGCATAGG Target exon: Exon 12/23 (antisense, positive strand) shRNAs shCTR Target sequence: CCTAAGGTTAAGTCGCCCTCGC shFBXO11 #9 TRC Clone ID: TRCN0000004303 Target exon: Exon16/23 (isoform 4 NM_001190274.2) Target sequence: GAGTGCTAGAAGACAATGATA shFBXO11 #10 TRC Clone ID: TRCN0000004304 Target exon: Exon 5/23 (of isoform 4 NM_001190274.2) Target sequence: GAGAGTTTCCAGCAGTTGTAT shLONP1 #1 TRCN0000046793, Exon 11/18 (isoform 1 NM_004793.4) Target sequence: CCAGTGTTTGAAGAAGACCAA (negative strand) shLONP1 #3 TRCN0000046797 Exon 2/18 (isoform 1 NM_004793.4) 32 Target sequence: CCAGCCTTATGTCGGCGTCTT (negative strand) 2.5 Lentiviral constructs and transduction 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 4°C; Beckman SW32Ti rotor). CB cells were transduced in 96-well plates (Thermo ScientificTM #12-565-65) with concentrated lentivirus. After 6 hours of incubation, cells were washed once with PBS and transferred to culture medium (StemSpanTM SFEM with StemSpan™ CC100 to culture medium at a 1 in 100 dilution, 750 nM SR1 and 35nM UM171). Following 72-hour ex vivo culture, transduced cells were then isolated by sorting for reporter fluorophore expression on a Fusion sorter (Becton Dickenson) using purity mode. All cultures were incubated in a humidified atmosphere at 37°C in room air supplemented with CO2 to achieve a final concentration of 5%. 2.6 Ex vivo cord blood liquid culture for differentiation assay Transduced cells were sorted into 96-well plates at 10k cells/well into 100 µL in StemSpanTM SFEM supplemented with StemSpan™ CC100, 20 ng/mL recombinant human thrombopoietin (hTPO) (BioLegend #597402), DNase (100 ug/mL final concentration), and antibiotics. Cells were incubated in a humidified atmosphere at 37°C in room air supplemented with CO2 to achieve a final concentration of 5%. 2.7 Cell differentiation analysis CB cells were harvested from culture and washed with PBS with 2% FBS before staining with antibodies at the dilutions listed in Table 2.1 for 1 hour on ice. Cells were pelleted at 300 x g for 5 min, resuspended in PBS with 2% FBS and 1 µg/mL DAPI, and filtered before flow cytometry analysis. 33 Antibody target Fluorochrome Dilution Clone Vendor Catalogue # CD34 AF700 1:50 581 BD 561440 CD14 APCCy7 1:100 63D3 BioLegend 367108 CD11b BV510 1:100 ICRF44 BioLegend 301334 CD41 PE 1:100 HIP8 BioLegend 303706 CD61 PECy7 1:100 VI-PL2 BioLegend 336416 CD235a BV711 1:100 HI264 BD Biosciences 740785 CD71 PerCP/Cy5.5 1:100 CY1G4 BioLegend 334114 CD33 BV510 1:100 WM53 BD 563257 CD15 BV786 1:100 HI98 BD 563838 Table 2.1 CB differentiation marker antibodies. 2.8 Long-term initiating cell (LTC-IC) Two-three days before cell sorting for long-term co-culture with MS5 feeder cells, frozen and irradiated feeder cells were thawed via dropwise addition into 9 mL DMEM with 15% FBS (15% DMEM) (Invitrogen, 12100-046). The cell mixture was spun down at 300 × 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 was neutralized with DMEM medium before cell plating). The feeder cells were incubated in a 37 ֯C incubator overnight. The media was replaced with 2 mL fresh 15% DMEM medium the next day. On the day of CB cell sort, the media was removed and replaced sorted CB cells in 2.5 mL of LTC media (STEMCELL Technologies #05150) supplemented with 10 µM Hydrocortisone (STEMCELL Technologies #07904). Cells were incubated in a humidified atmosphere in a 37°C incubator at 5% CO2. Weekly half-medium changes were performed by removing one half of the medium containing cells and replacing with freshly prepared H5100 with 10 µM hydrocortisone over the 40-day culture period. 2.9 Colony-forming cell (CFC) assay For primary CFCs, 500 of sorted lentivirally transduced CB cells were seeded into 1 mL of MethoCultTM H4434 Classic media (STEMCELL Technologies #04434) supplemented with 34 StemSpan™ CC100 and antibiotics, and plated in 35mm CFC dishes (STEMCELL Technologies #27150). Cells were incubated in a humidified atmosphere in a 37 ֯C incubator at 5% CO2 for 10 days before colonies were counted. For secondary CFCs, the media containing the cells were collected from the CFC dishes and transferred into 15mL tubes. PBS with 2% FBS was used to rinse the plates and pooled with the collected media. Cells were spun down at 300 x g for 5min, washed with PBS with 2% FBS. After cell counting, the cells were seeded into fresh CFC dishes in fresh MethoCultTM H4434 Classic media supplemented with StemSpan™ CC100 and antibiotics at 50k cells/mL in each plate. 2.10 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 pathogen-free conditions. Female NRG-3GS mice 8-15 weeks of age were sublethally irradiated with 850 cGy (cesium irradiation, medium dose rate) on the day of intrafemoral injection. 100k CB cells harvested from LTC were injected per mouse. 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 µg/mL final concentration, Sigma-Aldrich D9542). 35 Bone marrow aspirates were performed on the non-injected femur (BM, weeks 6, 20, and 35 weeks post-transplant) or the injected femur (IF, weeks 12 and 27 weeks post-transplant) to determine human cell chimerism. Transplanted mice were sacrificed at predefined, humane clinical morbidity endpoints. At endpoint, human chimerism in bone marrow (cells flushed from femurs, pelvis and 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 µg/mL final concentration, Sigma-Aldrich D9542). For secondary transplants, viably frozen bone marrow harvested at primary transplant endpoints were thawed and added drop-wise into 8mL thawing medium (PBS with 10% FBS and 100 µg/mL DNAse final concentration). Cells were spun down at 400 x g for 5 min to remove the supernatant. Cells were counted, and the total amount of harvested bone marrow for each EV and KA mice were transplanted into each sublethally irradiated recipient NRG-3GS by tail vein injection. Half of the harvested bone marrow from each donor FKA mouse was transplanted into each FKA recipient mouse. Secondary transplanted mice were monitored, processed, and analyzed following the procedures for primary transplants. 2.11 Calculating CD34+ cell output for xenotransplants The number of human CD34+ cells generated per input CD34+ cell after the LTC-IC and xenotransplant process is calculated based on the assumption that the ratio of the (A) number of CD34+ cells harvested from the LTC to (B) the number of CD34+ cells that were injected into the mice (C) X = the number of CD34+ cells that would have been harvested at xenotransplant endpoint if the total amount of CD34+ cells harvested from the LTC were injected into the mice to (D) the number of CD34+ cells that were actually harvested from at xenotransplant endpoint. 36 Since equal numbers (100k) of total cells harvested from LTC was injected into recipient mice for xenotransplantation, the number of CD34+ transplanted varied between conditions. To calculate the number of CD34+ cell generated by the end of the experiment per CD34+ cell inputted into the LTC-IC experiment, we divide “X” by the number of LTC-IC input CD34+ cell, which is the same for each condition. 2.12 RNA extraction and RNAseq CB cells were cultured for 24 hours in StemSpanTM SFEM supplemented with StemSpan™ CC100, 20 ng/mL recombinant hTPO, 750 nM SR1, 35 nM UM171, DNase (100 ug/mL final concentration), and antibiotics after sorting for lentivirally transduced cells as described above before pelleting at 300 x g for 5 min. Cell pellets were resuspended in TRIzolTM reagent (ThermoFisher #15596026). Total RNA was extracted according to manufacturer guidelines. For RNA precipitation, 5 µg glycogen (Roche #10901393001) was added per sample. 40 ng of RNQA per sample was submitted based on quantification by NanoDropTM. Down stream processing, quality control, and RNAseq was performed at Michael Smith laboratories facilities by 1 x Illumina seq with 150bp PET Indexed Lanes. cDNA was labelled using the Biotin Allonamide Triphosphate protocol. Target hybridization was performed at 45°C, for 16-18hr using the FS450_0001 fluidics protocol for washing. Data was acquired using the Affymetrix GeneChip Scanner 3000. CEL files were imported into R and the probe expression values were normalized using the Robust Multichip Average (RMA) method from the 'oligo' (v.1.50.0) package. Probes that were lowly expressed, defined as not having normalized expression values greater than 4 in at least 3 samples, were filtered out. Probes that ambiguously mapped to multiple genes were filtered out. 37 2.13 Ki-67 Staining Lentivirally transduced K562 cells were cultured in Iscove’s media with 10% FBS and antibiotics for a week after transduction. Cells were fixed and permeabilized with the BD Fixation and Permeabilization kit (#554714) according to manufacturer protocol. Fixed and permeabilized cells were stained with PE anti-human Ki-67 antibody (BioLegend #350504) diluted 1:10 in the BD Perm/Wash buffer from the kit for 30 min on ice. Unbound antibody was washed with the Perm/Wash buffer. Pelleted cells were resuspended in BD Perm/Wash buffer with 2 µg/ml DAPI and incubated for 10 min in the dark, on ice. Cells were centrifuged at 1500 x g for 5 min and resuspended in PBS with 2% FBS and strained for flow cytometry analysis. 2.14 Annexin V staining Annexin V staining was done with APC Annexin V (BD Biosciences # 550475) in Annexin V binding buffer (BD Biosciences # 556454) according to manufacturer’s protocol. 2.15 Histology Mouse brain samples were fixed in 10% buffered formalin and stored at 4°C for a week before paraffin embedding. H&E and myeloperoxidase staining of brain samples were performed at BC Cancer Vancouver Center Pathology office. Bone marrow smears were stained with Wright-Giemsa staining solution. 2.16 Immunoprecipitation For co-immunoprecipitations for identifying protein-protein interactions, cells were washed in PBS then lysed with modified RIPA buffer (50 mM Tris-HCl pH 7.6, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, protease inhibitor cocktail 1 tablet/1 mL at 1:100 (Sigma-Aldrich 11697498001), phosphatase inhibitor cocktail 1:100 (Sigma-Aldrich P0044-5ML), 1 mM Na3VO4 and 10 mM NaF) for 1 hour at 4°C on a rotating platform, then passed 10x through a 22-G needle. Lysate was centrifuged at 16,000 x g for 10 min at 4°C. The supernatant was collected and protein quantification was performed using the BCA assay (BioRad DC Protein assay kit, Bio-Rad 5000112). 1 mg of protein per condition was used for co-immunoprecipitation, and the 38 volume was made up to 1 mL with the lysis buffer. Lysate was precleared with Protein G agarose beads (EMD Millipore 16-266) for FLAG immunoprecipitation or Protein A agarose beads (EMD Millipore 16-125) for LONP1 immunoprecipitation on a rotating platform at 4°C, overnight (16 hours) for FLAG immunoprecipitation or for 3 hours for LONP1 immunoprecipitation. Beads were spun down at 1500 x g for 1 min, and the lysate was transferred to a fresh tube with 50 µL equilibrated ANTI-FLAG® M2 Affinity Gel (Sigma-Aldrich A2220-1ML) for FLAG immunoprecipitation, or with Protein A agarose beads incubated with 2 µg anti-LONP1 antibody (ThermoFisher Scientific 15440-1-AP) overnight (16 hours) and incubated on a rotating platform at 4°C for 1 hour for FLAG immunoprecipitation, and for 3 hours for LONP1 immunoprecipitation. The beads were washed with wash buffer (50 mM Tris-HCl pH 7.6, 150 mM NaCl, 0.05% Triton X-100) 5 times with 1 min 1500 x g spins in between, and eluted in 1x Laemmli sample buffer at 95°C for 15 min. For LONP1 immunoprecipitation to identify post-translational modifications, cells are lysed with RIPA buffer (25mM Tris, 150mM NaCl, 1% NP40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate (SDS) instead of modified RIPA buffer. 2.17 Quantitative tandem mass spectrometry K562 cells lentivirally expressing FLAG-FBXO11 were lysed in modified RIPA buffer with 10 µM MG132 (ApexBio A2585-25) as described above. Whole cell lysate was incubated with Anti-FLAG® M2 Magnetic Beads (Sigma-Aldrich M8823-1ML) or IgG beads for 3 hours then washed with lysis buffer 3 times. Three replicates per condition were directly eluted with SP3 elution buffer (200 mM HEPES pH 8.0, 10% SDS, 200 mM DTT). Four replicates each were treated with Benzonase® (EMD Millipore 70664-3) before elution. TMT labelling was performed by the mass spectrometry core at the Genome Sciences Centre. 8-plex TMT labels were used. Quantification results were calculated based on published methods213,214. Stringent filtering was applied to the total list of proteins identified in each immunoprecipitation experiment, and then the shortlisted proteins were intersected to identify commonly immunoprecipitated proteins. For the set of immunoprecipitations performed 39 without Benzonase®, proteins had normalized protein enrichment score (PESn) greater than 0.6, and were enriched in 2 of the 3 replicates by at least 1.1 fold were shortlisted. For the immunoprecipitations performed with Benzonase® treatment, proteins that had PESn greater than 0.45 and were enriched in 3 of 4 replicates by at least 1.2 fold were shortlisted. 2.18 Measuring mitochondrial membrane potential Mitochondrial membrane potential (MMP) was measured by TMRE for cell lines (Cedarlane labs ENZ-52309), or with the JC-1 dye (eBioscience 65-0851-38) for CB. For TMRE, cells were cultured in their regular culture media and conditions with 400 nM TMRE for 30 min. Cells were spun down at 400 x g for 5 min to remove the media and TMRE, and the cell pellets were resuspended in PBS with 2% FBS and 1 µg/mL DAPI and kept on ice before analysis by flow cytometry. JC-1 dye was used according to manufacturer protocol. Stained cells were resuspended in PBS with 2% FBS and 1 µg/mL DAPI for flow cytometry analysis. 2.19 Mitochondrial mass measurements MitoTracker™ Deep Red FM (FisherScientific M22426) was used to measure mitochondrial mass in K562 cells. Cell staining was performed according to manufacturer protocol. Cells were stained for 30 min. Stained cells were washed with PBS with 2% FBS and resuspended in PBS with 2% FBS and 1 µg/mL DAPI for flow cytometry analysis. 2.20 UAE and NAE inhibitor treatment Ubiquitin activating enzyme (UAE) inhibitors PYR-41 (Sigma-Aldrich N2915-5MG) was used at 50 nM, and TAK-243 (also known as MLN7243, Selleck Chemicals S8341) at 0.1 μM. Neddylation activating enzyme (NAE) inhibitors MLN4924 (Cayman Chemical Company 15217) was used at 50 nM, and TAS4464 (Selleck Chemicals 15217) was used at 50nM. Concentrations were determined by published concentrations used on K562 cells or other myeloid cell lines, and inhibitory effect on global ubiquitination or neddylation levels were confirmed by western 40 blot. Cells were seeded at 300k cells/mL and treated with above inhibitors added directly to culture media for 16 hours. Inhibitor stock solutions were prepared in DMSO. 2.21 Western blots Cells were washed with PBS and lysed for 30 min on a rotating platform at 4°C in RIPA buffer (25mM Tris, 150mM NaCl, 1% NP40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate (SDS), protease inhibitor cocktail, phosphatase inhibitor cocktail, 1 mM Na3VO4 and 10 mM NaF). Lysed cells were centrifuged for 16,000 x g for 10 min at 4°C, and the supernatant was collected for protein quantification by BCA. Samples were mixed with Laemmli sample buffer incubated at 95°C on a heat block for 15 min. Proteins were separated by SDS-polyacrylamide gel electrophoresis at 120 V at room temperature and transferred to nitrocellulose membrane at 100 V at room temperature for 1 hour for proteins of interest under 55 kDa, and at 35 V at 4°C for 16 hours for proteins of interest over 55 kDa. After transfer, blots were rinsed briefly with tris-buffered saline with 0.1% Tween 20 (TBST), and then blocked for 1 hour at room temperature in 5% skim milk in TBST. Membranes were washed 3 times for 10 minutes each with TBST. Blots were probed with primary antibodies (FBXO11 antibody at 1:1000, Bethyl Laboratories A301-178A; LONP1 antibody at 1:1000, ThermoFisher Scientific 15440-1-AP; GAPDH antibody at 1:10,000, Sigma-Aldrich G8795-200UL, K63 linkage specific antibody at 1:1000, Cell Signaling Technology 5621S, NEDD8 antibody at 1:1000, Cell Signaling Technology 2745S; α-tubulin at 1:1000, Sigma-Aldrich T5168-.2ML; TOM20 at 1:1000, New England Biolabs 42406S) prepared in TBST with 1% bovine serum albumin (BSA), based on manufacturer recommended incubation times. Membranes were washed 3 times for 10 minutes each with TBST and probed with appropriate HRP secondary antibody (anti-Rabbit Immunoglobulin/HRP, Dako P044801 or aniti-mouse Immunoglobulin/HRP, Dako P0260) at 1:1000 in TBST with 1% BSA. Blots were developed with Western Lightning Plus, Chemiluminescent Substrate (PerkinElmer NEL105) and imaged with a BioRad ChemiDoc MP Imaging system. 41 2.22 Seahorse XF Cell MitoStress Test K562 wild-type control clone and FBXO11 KO clone cells were in the growth phase at the time of the assay. The cells were counted and seeded at 80k cells per well in the 96-well Agilent Seahorse XF Cell Mito Stress Test (Agilent 103010-100) assay plate. Assay was performed as outlined in the manufacturer protocol and run on the Seahorse XFe bioanalyzer. Analysis and interpretation were done according to protocol. 2.23 RNAseq Analysis RNAseq read alignment was performed using STAR aligner, and count generation was done with Salmon. Variance stabilizing transformation was performed to allow comparison between samples. Transcript per million (TPM) values were filtered to remove genes that have low counts (TPM < 0.5, equivalent to a count of 10 for the dataset) for at least 2 samples. Differential gene expression analysis was performed on the RNAseq data using DeSeq2 package with R. Gene Set Enrichment Analysis was performed using the Hallmark, C2, and C5 gene set collections from MSigDB. GSEA was performed using pre-ranked gene lists for each comparison presented, where the gene list was ranked based on the log2 fold change value. Metascape analysis was done using the online user interface (https://metascape.org/) and further interpreted and visualized with the Cytoscape software (https://cytoscape.org/), with differentially expressed genes defined as genes with log2 foldchange > 1.5 or < -1.5, and adjusted P value < 0.05 inputted for analysis. CellRadar plots from Figure 4.9 were generated using the public user interface provided by the Karlsson lab (https://karlssong.github.io/cellradar/). The same definition of differentially expressed genes was used, and HSPC data was taken from the BloodSpot HemaExplorer human normal hematopoeisis dataset. 42 2.24 Statistical Analysis All statistical tests were performed using GraphPad Prism version 8.0 (San Diego, California). All data are presented as mean ± standard error of the mean (SEM). Statistical significance was set at a threshold of P < 0.05. 43 Chapter 3: Investigating the tumor suppressive role of FBXO11 in a humanized model 3.1 Introduction Previous work in our lab implicated FBXO11 depletion in driving leukemogenesis in a mouse bone marrow transplant model. A recent study also showed that FBXO11 loss resulted in transcriptomic changes that could contribute to leukemic transformation of MDS163. Findings from mouse models often do not extrapolate to human cells, and similarly, murine leukemia models often do not always translate to humanized leukemia models215. Another advantage of humanized leukemia mouse models is the ability to study human cells in vivo in the presence of the bone marrow microenvironment that is difficult to replicate in vitro. Establishing an FBXO11 depletion-based humanized leukemia model would provide a more relevant tool to study AML and for drug and molecular screening in the disease. Previous findings from the FBXO11 depletion mouse model demonstrated that Fbxo11 depletion cooperated with AML1-ETO expression to expand the LT-HSC like population and reduce leukemia-free survival when compared to empty vector and control shRNA controls, as well as to AML1-ETO expression alone. Based on these findings, we hypothesize that FBXO11 depletion enriches for a primitive cell phenotype with increased stem-cell characteristics in human HSPCs, and also cooperates with AML-associated mutations to drive de novo AML in a humanized model. In this chapter, I demonstrate the prevalence of UPS mutations in AML, and characterize the effects of FBXO11 depletion on human hematopoiesis ex vivo. I also examine how FBXO11 depletion cooperates with common AML-associated genetic mutations to dysregulate hematopoiesis in a humanized mouse xenotransplant model. 44 3.2 Results 3.2.1 UPS mutations are frequent in AML Analysis of whole exome and RNA sequencing data from 140 AML patient samples from our published AML-Personalized Medicine Project (AML-PMP) data-set was performed to identify genetic drives of AML. Mutations in genes belonging to functional classes that have been reported to be recurrently mutated in myeloid neoplasms previously were identified (Figure 3.1)60,137. Mutations in genes that code for UPS components (UPS genes) were also identified in a substantial portion of the patient cohort (11.4%, 16 of 140 patients). This finding is also observed in The Cancer Genome Atlas Program (TCGA) AML (11.2%, 22 of 200 patients), The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) pediatric AML (4.38%, 30 in 684 patients), and Beat AML (11.4%, 95 in 832 patients) datasets as well, revealing UPS mutations as a previously un-identified class of AML mutations (Figure 3.1). 45 Figure 3.1 Mutations in ubiquitin proteasome system genes occur frequently in AML-PMP AML patients. Shown are somatic, functionally relevant mutations that satisfy pathogenicity prediction tool REVEL scores > 0.250 and CADD scores > 10.5 in the AML-PMP patient cohort. Rows represent genetic mutations or chromosomal aberrations, grouped into different functional categories. Columns represent individual AML samples. 11.4% (16/140) patients contain one or more mutation affecting genes belonging to the ubiquitin proteasome system, and these patients are grouped together to the left. AML: de novo AML, sAML: transformed AML derived from myelodysplastic syndrome (MDS), tAML: therapy-related AML. 152 of 163 AML patients with UPS mutations have only one UPS gene mutation, exhibiting a mutually exclusive trend (Figure 3.2). This signifies the functional importance of the UPS class of genes, and only one, or few mutations in UPS genes is required to drive leukemogenesis. Recurrently mutated UPS genes (identified in more than 1 patient) include 46 CBL, FBXO11, CUL1, TRIM24, ASB1, HECW1, USP48, USP34, HERC1, MYCBP2, UBR5, RBBP6, USP9X, BRCC3, TRIM67, PDZRN4, UBR1, TRAF7, BTBD2, CBLB, RNF168, and TRIM41. The most frequently mutated UPS gene was CBL, which was mutated in 41 patients. CBL mutations have already been previously reported to occur frequently in AML, and leukemogenic effects of these mutations have been demonstrated216–219.47 Figure 3.2 UPS mutations occur frequently and in a mutually exclusive trend across datasets. Distribution of UPS mutations among UPS-mutated patients from the AML-PMP cohort (16 mutated from 140 patients), the Beat AML dataset (95 of 832), the TCGA AML dataset (22 of 200), and the TARGET pediatric AML dataset (30 of 684) for a total of 163 UPS mutated patients. 152 of 163 patients have mutations in only 1 UPS gene. FBXO11 is mutated in 4 patients, and one of the patients also has an AML1-ETO fusion. 48 Figure 3.3 UPS mutations cooccur with t(8;21) translocations and RAS mutations. Showing odds ratios of various AML-associated mutations for AML-PMP AML samples occurring in UPS-mutated samples compared to UPS wild-type samples. Mutations with odds ratio > 1 are likely to co-occur with UPS mutations, and odds ratios < 1 are likely to occur mutually exclusively with UPS mutations. Asterisks (*) denote P < 0.05, representing Fisher’s exact test. UPS gene mutations were significantly enriched in AML samples that harbor mutations affecting RAS or CEBPA, and show a trend toward co-occurrence with t(8;21) translocations (odds ratios of 3.13, 2.68 and 2.14 respectively), suggesting these mutations have cooperative effects in driving leukemogenesis (Figure 3.3). UPS mutations are mutually exclusive with some mutations affecting hydroxymethylation (TET2, IDH1) (odds ratio = 0.38), suggesting the two systems regulate common functions impacted in leukemia (Figure 3.3). 49 3.2.2 FBXO11 depletion promotes stem cell maintenance with myeloid bias Of the SCF E3 ubiquitin ligase complex proteins, FBXO11 transcript expression is most significantly downregulated in AML patient cells compared to normal hematopoietic stem and progenitor cells (HSPC) (Figure 3.4A). This is consistent across many AML subtypes (Figure 3.4B). FBXO11 is also mutated in 4 of the patient samples across the 4 AML data sets (Figure 3.2 Appendix Table A.1). This pattern of expression suggests that depletion of FBXO11 may drive leukemogenesis. FBXO11 encodes the substrate recognizing component of the Skp1-Cul1-FBXO11 (SCFFBXO11) E3 ubiquitin-ligase complex, which is responsible for interacting with ubiquitination targets for targeting to proteasomal degradation; this can occur through a phosphorylation-dependent mechanism167,195–197. Figure 3.4 FBXO11 transcript expression is lower in AML cells compared to normal HSPCs. A-B. Transcriptomic data from Bloodspot database. Datasets used include GSE42519 and GSE13159. A. Volcano plot representing the ratio of transcript expression of SCF (Skp, Cullin, F-box containing complex) genes in AML patient cells compared to normal HSPCs. Horizontal dotted line represents P = 0.05. P values represent t-tests followed by Bonferroni correction for multiple testing. Refer to Appendix Table A.2 for gene list and input values. B. FBXO11 transcript expression across different AML subtypes and normal hematopoietic populations. Plotted based on data for the 219208_at probe. AML samples are composed of whole unsorted BM. AML complex = AML with aberrant karyotype. Early_PM = early promyelocyte. Late_PM = late promyelocyte. MY = myelocyte. MM = metamyelocytes. BC = band cell. PMN = polymorphonuclear cells. Mono = monocytes. P values represent two-tailed unpaired t-tests. Error bars represent mean ± SEM. 50 To determine how FBXO11 depletion dysregulates human hematopoiesis, we investigated the effects of FBXO11 knockdown on the differentiation of cultured CB CD34+ cells. CB cells were lentivirally transduced to stably express shRNA targeting FBXO11 (shFBXO11) or control shRNA (shCTR). Transduced cells were sorted based on expression of reporter fluorescence expression, and cultured ex vivo for 2 weeks. FBXO11 knockdown by shRNA decreased expression of erythroid markers (CD71, glycophorin A) and megakaryocytic markers (CD41/CD61), while the CD15+ myeloid population expanded after 14 days of culture (Figure 3.5A, B). The inhibition of erythroid differentiation aligns with reports of FBXO11 relieving PRC2-mediated repression during erythroid maturation207. To further investigate the effect of FBXO11 knockdown on the progenitor population in human HSPC, we performed colony-forming cell (CFC) assays with CB. FBXO11 knockdown reduced primary CFC counts, indicating a reduction in progenitors (Figure 3.5C). Secondary CFCs showed increased colony formation (Figure 3.5D). A possible explanation for these findings is that depletion of FBXO11 reduces the proportion of progenitors in the population while increasing more primitive populations with greater stem cell characteristics, which were able to differentiate and give rise to progenitor cells with more time in the culture conditions in the secondary CFCs. Alternatively, FBXO11 depletion could have an apoptotic effect on the bulk population, but cells that survive have an increased proliferation and differentiation activity captured in the secondary CFC. FBXO11 depletion has previously been shown to upregulate cell death-associated genes163. To better understand the effect of FBXO11 depletion on HSPCs, we investigated the effect of FBXO11 depletion on apoptosis, cell cycle, and stem cell maintenance in the following experiments. 51 Figure 3.5 FBXO11 depletion promotes myeloid bias and maintenance of primitive cells. A-D. shFBXO11 #9 and #10 are two different shRNA constructs targeting FBXO11. P values representWelch’s t-tests. Error bars represent mean ± SEM. A. Representative flow cytometry analysis plots of immunophenotyping analysis of CB cells cultured for 2 weeks. Representative plots for shFBXO11 are for shRNA #9. B. Megakaryocytic (CD41+CD61+), erythroid (CD71+ glycophorin A+), and myeloid (CD15+) populations as a percentage of live transduced cells. C. Primary CFC counts 10 days after plating. D. Secondary CFC counts 10 days after plating. 52 3.2.3 FBXO11 depletion promotes a quiescent cell state and protects against primitive cell death While the ex vivo CB data indicates an increased proportion of a primitive cell population relative to the progenitor population, the changes in these populations could be due to either increased expansion of HSCs or increased progenitor cell death. In fact, FBXO11 has a repressive effect on apoptosis, and FBXO11 depletion has previously been reported to upregulate apoptotic transcriptomic pathways in myelodysplastic syndrome (MDS) cells163,198. In order to determine if the hematopoietic dysregulation is attributed to differential apoptosis or dysregulated cell cycling, we first measured the apoptotic effect of FBXO11 depletion on CB by Annexin V staining. FBXO11 depletion significantly increased the proportion of apoptotic cells consistent with previous reports (Figure 3.6A, F), although the effect on the absolute number of apoptotic cells is not consistent (Figure 3.6B). Total live cell counts are lower for cells with FBXO11 depletion, and a lack of a consistent effect on apoptosis suggests that FBXO11 depletion may impact proliferation as well (Figure 3.6C). Importantly, while FBXO11 depletion increased apoptosis in the bulk cell population, the proportion of Annexin V+ cells is significantly lower in the more primitive CD34+ population with FBXO11 depletion, indicating that FBXO11 depletion has a protective effect on the primitive cell population (Figure 3.6D). These results confirm FBXO11 depletion has apoptotic effects on human hematopoietic cells, but not in the primitive cell population. Given that we interpret the above results as FBXO11 depletion enriching for and maintaining the primitive cell population, we expect cells with FBXO11 knockdown to assume a quiescent cell state consistent with HSCs. We measured cell cycle in K562 CRISPR control and two FBXO11 knockout (KO) clones generated from two different single guide RNAs (sgRNA), and found that FBXO11 loss significantly increased the quiescent Go population (Figure 3.6E, G). Similar trends were identified in CB cells with FBXO11 knockdown by lab member Xuan Wang Overall, our ex vivo findings suggest that FBXO11 depletion promotes the maintenance of a quiescent primitive cell population with myeloid bias. 53 Figure 3.6 FBXO11 depletion promotes apoptosis in bulk cells but not in primitive CD34+ cells. A. Percentage of Annexin V+ cells within the live, transduced CB population. B. Absolute cell numbers of Annexin V+ live, transduced CB cells. C. Total viable cell count of cells based on trypan blue staining. D. Percentage of Annexin V+ cells within CD34+ live, transduced CB cells. E. Percentages of Ki-67 negative (Go) cells within the viable K562 cell population. Showing 2 FBXO11 KO CRISPR clones generated from different sgRNAs. F. Representative flow plots for percentage of Annexin V+ cells of all live, transduced CB cells. G. Representative Ki-67 flow 54 cytometry plots for K562 CRISPR control clone and 2 FBXO11 KO clones. For A-D, P values represent Welch’s t-test. Error bars represent the mean ± SEM. 3.2.4 FBXO11 depletion in vivo reduces leukemia free survival in a humanized xenotransplant model The ex vivo CB data indicates FBXO11 depletion increases the maintenance of a primitive cell population, but stem cell function can only be reliably assessed by in vivo transplantation assays. We developed a human xenotransplant FBXO11 depletion model to determine the effects of FBXO11 depletion on HSPCs in vivo, and importantly, to confirm whether FBXO11 depletion can also generate leukemia in a humanized model. Considering genetic aberration combinations that drive leukemogenesis in mouse models do not always translate in humanized xenotransplant models, that human AMLs generally result from an accumulation of several mutations that impart clonal advantages135,220, and AML1-ETO expression alone or combined with some other cooperating mutations in CD34+ CB have previously failed to generate leukemia when xenotransplanted into mice215,221, we opted to include an additional cooperating mutation, KRASG12D, with the AML1-ETO and FBXO11 knockdown combination used previously in the mouse bone marrow transplant model for the human xenotransplant model. Like AML1-ETO, RAS mutations also have high odds of co-occurring with UPS mutations based on analysis of our patient cohort (Figure 1.3). RAS mutations are common in AML137, and importantly, AML1-ETO expression with mutation RAS is not sufficient to produce overt leukemia when expressed in human HSPCs xenotransplanted into mice215. CB cells were lentivirally transduced to express combinations of constructs of interest, then transduced cells were sorted for fluorescent marker expression and cultured with MS5 stromal cells for 5 weeks before xenotransplantation into sublethally irradiated NRG-3GS mice (Figure 3.7A). In this long—term culture system prior to transplantation, committed progenitors that were present in the initial cell population should mature and disappear from the population within 3-5 weeks due to limitations in proliferative potential222,223. Thus, the cells transplanted into recipient mice represent more primitive populations. EV mice were 55 transplanted with cells expressing empty vector and control shRNA. KA mice were transplanted with cells expressing the cooperating mutations only, AML1-ETO and KRASG12D. FKA mice were transplanted with cells containing the cooperating mutations, AML1-ETO and KRASG12D, in addition to shFBXO11. Engraftment was determined by bone marrow aspirates from the intrafemorally injected femur (IF) or non-injected femur (BM) over the course of the experiment (Figure 3.7B). Human cells were identified by expression of human CD45 as detected by two different antibody clones, and FKA mice consistently had the highest level of engraftment, at each time point. Overall, FKA had significantly increased engraftment compared to both the EV (P = 0.003) and KA (P = 0.018) control mice. While KA mice had greater engraftment than EV mice, the effect was not consistent or significant (P = 0.96). Figure 3.7 FKA mice had significantly increased engraftment compared to controls. 56 A. Schematic of human xenotransplant experimental design. B. Percentage of cells positive for two different human CD45+ antibody clones from bone marrow aspirate samples. Aspirates were taken from intrafemorally transplanted femur (IF) or the other femur (BM). P values represent one-way ANOVAs. Table shows sample sizes for conditions at each engraftment monitoring time point. Results are comprised of 3 independent experiments. Error bars represent mean ± SEM. The FKA mice had significantly reduced survival compared to both EV (P = 0.003) and KA (P = 0.0002) control mice, while survival of EV and KA did not differ significantly (P > 0.99) (Figure 3.8A). Of interest, several of the FKA mice displayed neurological symptoms, with persistent head tilts to one side and inability to maintain balance at humane endpoint. These symptoms were absent in all EV and KA mice. Taken together, these results suggest an ability of FBXO11 knockdown not only to improve long-term in vivo human reconstitution, but also to cooperate with AML1-ETO and KRASG12D expression to reduce survival of mice. To better understand the nature of the illness generated in the FKA mice, we analyzed the bone marrow, spleen and liver of the mice, which are all common sites of engraftment. The cellularity of the bone marrow in FKA mice varied greatly, but did not differ significantly from the EV or KA mice (Figure 3.8B). Human hematopoietic cell chimerism was assessed based on a minimum threshold of 0.01% engraftment as is commonly used224,225. Significantly greater proportion of FKA mice showed human cell chimerism at endpoint than EV mice (P = 0.008) or KA mice (P = 0.008) (Figure 3.8C). Absolute cell numbers of FKA humans cells also expanded by endpoint, producing significantly greater human cells compared to EV (P = 0.0014) or KA (P = 0.0015) cells (Figure 3.8D). 57 Figure 3.8 FKA mice had reduced survival and increased human cell engraftment. A. Kaplan-Meier survival curve for EV, KA, and FKA xenotransplanted NRG-3GS mice. Log-rank test comparing EV to FKA is P = 0.003, KA to FKA is P = 0.0002, EV to KA is P > 0.99). B-D P values representWelch’s t-test. Error bars represent mean ± SEM. B. Absolute total (human and mouse) live cell counts of bone marrow harvested at humane or experimental endpoints. C. Human cell engraftment in bone marrow at humane or experimental endpoints based on expression of human CD45 as detected by two different antibody clones. Horizontal dashed line indicates the 0.01% threshold for engraftment. ND = not detected. P values for Fisher’s exact test comparing EV to FKA is P = 0.0005, and KA to FKA is P = 0.063) D. Absolute cell numbers of human cells harvested from the bone marrow at humane or experimental endpoints, calculated from absolute total cell counts and percentage of engrafted human cells. Although spleen weights for all 3 experimental conditions were not significantly different from each other (Figure 3.9A), FKA mice had had approximately 1000 times greater infiltration of human cells compared to EV (P = 0.03) and approximately 10 times more than KA mice (P = 0.04) (Figure 3.9B). FKA mice liver weights were also not greater than EV and KA mice 58 (Figure 3.9C), but infiltration of human cells was significantly greater than both EV (P = 0.035) and KA mice (P = 0.032) (Figure 3.9D). Despite not reaching the extent of causing splenomegaly or hepatomegaly, these findings indicate that the FKA cells were not only able to reconstitute the mouse bone marrow at the sites of transplantation, but also give rise to mobile cells capable of infiltrating distant hematopoietic organs. Figure 3.9 FKA mice have significantly increased human cell engraftment in spleen and liver. A-C. P values representWelch’s t-tests. Error bars represent mean ± SEM. A. Weights of spleens harvested at endpoint. B. Human cell engraftment in spleens at humane or experimental endpoints based on expression of human CD45 as detected by two different antibody clones. C. Weights of livers harvested at endpoint. D. Human cell engraftment in spleens at humane or experimental endpoints based on expression of human CD45 as detected by two different antibody clones. 59 Given the stem cell enrichment observed with FBXO11 knockdown in the ex vivo experiments, we assessed the human CD34+ cell population in the xenotransplanted mice, which generally characterizes the more primitive cell population226. We used total viable bone marrow cell and human CD34+ percentages to determine the absolute count of CD34+ human cells and found that FKA mice had a significantly greater number of CD34+ cells at endpoint compared to EV (P = 0.039) and KA mice (P = 0.039) (Figure 3.10A). In fact, CD34+ cells were below the detectable threshold by flow cytometry analysis for all of the EV mice and all except one of the KA mice. This indicates that the FBXO11 depletion promotes maintenance of the primitive cell population. Calculating the output of each CD34+ cell initially placed in culture after vector transduction following serial bone marrow transplant (see methods for calculation), we see that there is an absence of CD34+ cell maintenance in EV and KA conditions (Figure 3.10B) but a substantialexpansion of the CD34+ population in FKA-transduced cells, with each input CD34+ cell generating an average of approximately 6,000 CD34+ cells following primary transplant. We performed Giemsa staining of bone marrow smears prepared at humane or experimental endpoint to determine if the histology of the bone marrow cells reflects the primitive cell expansion observed based on CD34+ marker expression (Figure 3.10C). We identified the presence of bone marrow cells with blast cell characteristics — larger cell size and enlarged nuclei with open chromatin — in the FKA mice that were absent in the EV or FKA mice (Figure 3.10B). To functionally confirm that the primitive cell population we characterized in the FKA mice possess stem cell properties, we performed secondary transplants with bone marrow cells harvested from these mice at endpoint into sublethally-irradiated NRG-3GS recipients to compare the reconstitution potential of these cells. A significantly greater proportion of FKA mice reached the engraftment threshold of 0.01% compared to KA mice (P < 0.009) mice (Figure 3.10D). Based on these findings, we conclude that FBXO11 depletion promotes the maintenance and expansion of a primitive cell population with stem-cell characteristics. 60 Figure 3.10 FKA mice expand primitive CD34+ cell population. A. Number of CD34+ human cells harvested from the bone marrow, calculated from total bone marrow cell counts and percentage of human CD34+ cells. P values representWelch’s t-tests. ND = not detected. B. Table listing number of CD34+ cells at different stages of the experiment. LTC = long term culture. “BM CD34+ output per initial LTC CD34+ cell input” calculates the mean number of CD34+ cells generated at the end of the transplant for each CD34+ cell that went 61 into the assay at the beginning, starting at the LTC step. Standard deviation for values used for mean calculations in this column listed under the “standard deviation” column. C. Giemsa-stained bone marrow smears prepared from mice at humane or experimental endpoint. Cells with blast cell characteristics are indicated by white arrows. D. Human cell engraftment in secondary transplants assessed by bone marrow aspirates (weeks 8 and 22) and at experimental endpoint (week 24). P values represent Fisher’s exact test for number of engrafted mice (engraftment ≥ 0.01% threshold) compared to number of mice with engraftment below detection threshold (<0.01%). Several of the FKA mice displayed persistent and severe head tilts and inability to stay upright or walk straight at around 10 weeks post-primary transplant. None of the EV or KA mice that were transplanted displayed these symptoms. Fecal pellet cultures were negative for fungi and bacteria. Although the brain leptomeningeal tissue exhibited dense infiltration of mononuclear cells with left-shifted myeloid appearance, in addition to a small number of neutrophils based on hematoxylin and eosin (H&E) staining, there was an absence of bacteria in the brain tissue (Figure 3.11A). Considering the myeloid appearance of the infiltrate, we followed up with a myeloperoxidase stain and confirmed that the cell infiltrate in the leptomeninges were myeloid cells (Figure 3.11B). Abnormal leptomeningeal infiltration of myeloid HSPCs has been reported in around 5% of AML patients, most commonly in AML patients classified in the FAB sytem as M4 or M5, which represent cases with monocytic differentiation of AML blasts227,228. The myeloid infiltration of the leptomeningeal tissue is consistent with the dominance of CD33+ myeloid cells in the bone marrow of FKA mice at endpoint, making up over 90% of the engrafted human cell population (Figure 3.11C). This myeloid expansion occurs at the expense of lymphoid cells (CD3+ T-cell and CD19+ B-cell populations). In contrast, CD33+ cells were below the threshold of detection for EV mice. Compared to the EV mice, there is some expansion of myeloid cells in the KA mouse bone marrow (36% in KA compared to 0% in EV), but not to the extent of FKA mice (P = 0.08). In summary, the FKA cells exhibit stem-cell characteristics with myeloid bias, and are able to produce abnormal HSPCs capable of infiltrating the leptomeninges. We conclude that FBXO11 cooperates with AML1-ETO and KRASG12D expression to generate a myeloid leukemia in this human xenotransplant model. 62 Figure 3.11 FKA mice exhibit signs of myeloid malignancy. A. H&E stain of brain sections of an FKA mouse with persistent head tilt at endpoint. Showing same leptomeningeal region at 2 magnifications. B. Myeloperoxidase stain of brain sections of a wild-type NRG-3GS mouse and an FKA mouse, showing the same leptomeningeal regions for the respective mice at 2 magnifications. C. Proportions of myeloid (CD33+) or lymphoid (CD3+ or CD19+) cells within the hCD45+ population. “Other” category accounts for cells that are negative for all 3 cell surface markers. P values represent Welch’s t-test for percentage of CD33+ cells within the hCD45+ cell population. 3.3 Discussion While FBXO11 depletion has been implicated in other diseases and recently published ex vivo data suggests FBXO11 depletion could drive transformation of MDS to AML163, FBXO11 depletion has not been previously shown to drive leukemogenesis in vivo before, and little is 63 known about the role of FBXO11 dysregulation in the context of AML. In addition to the preliminary mouse bone marrow transplant data, the xenotransplant data further supports the role of FBXO11 as a tumor suppressor, and implicates FBXO11 depletion in leukemogenesis. In our ex vivo work, we observed that FBXO11 depletion resulted in dysregulated hematopoietic differentiation in cultured CB based on the reduced expression of megakaryocytic and erythroid cell surface markers, and increased expression of myeloid markers. Dysregulation in erythropoiesis is supported by findings from another study by Xu et al. supporting an activating role of FBXO11 on erythropoiesis207. However, the same study also found FBXO11 depletion does not alter myeloid or megakaryocytic differentiation of peripheral blood CD34+ HSPCs. These discrepancies could be due to the different sources of CD34+ HSPCs used. In our work, we used umbilical CB HSPCs for all our primary cell experiments. While the study also used CB for their results they based their erythropoiesis dysregulation conclusions on, they used peripheral blood mobilized human CD34+ CB for their myeloid and megakaryocyte data. The conflicting conclusions could be due to the fact that FBXO11 depletion has variable effects depending on the source of HPSC cells, or more specifically, depending on cell age. The latter is relevant because aged HSPCs are typically genetically and epigenetically altered over time, and these factors are closely intertwined with the driver events that contribute to leukemogenesis103,229. A more likely explanation for the contradicting conclusions is that different myeloid and megakaryocytic markers were analyzed in our respective studies. Xu et al. defined myeloid cells as CD33+CD11b+ and did not detect a significant difference between control and FBXO11 depleted cells. We also do not observe notable differences in the proportion of CD33+ cells with FBXO11 knockdown ex vivo. However, we consistently observed depletion in the CD14+ and CD15+ myeloid markers in independent experimental replicates. CD11b is expressed on monocytes, macrophages, and granulocytes; CD14 is expressed on monocytes, macrophages, and dendritic cells; and CD15 is expressed on granulocyte-series cells 230–232. There is some overlap between cell populations expressing each of the 3 markers. FBXO11 depletion could be specifically affecting either dendritic populations and CD11blow granulocytes, a niche and 64 uncharacterized myeloid population that is CD14+ or CD15+ population that does not express CD11b, or it only downregulates the expression of CD14 and CD15 without changing the cell population composition, hence the absence of an effect on overall CD11b expression. Similarly, Xu et al. identified megakaryocytic cells by CD41a and CD42b expression while we observed a reduction in CD41+CD61+ cells. CD61 expression occurs earlier on relative to CD42b expression during megakaryocyte maturation, indicating FBXO11 depletion could be impacting more primitive megakaryocytic cells233. Additionally, FBXO11 mutations have also been described to result in abnormal platelet function in a mouse Otitis media model234. Of interest, while expression of the myeloid CD33 marker is not affected in either of our ex vivo studies where majority of the cells are also CD33+, there is substantial expansion of CD33+ cells in the in vivo human xenotransplant model with FBXO11 depletion in cooperation with AML1-ETO and KRASG12D expression that is entirely absent in the EV control, and present at less than half the extent in terms of proportion of human cells, and even lower in terms of cell number, in KA mice. This observation is supportive of the conclusion that the FKA mice have developed myeloid malignancies given that 85-90% of AML patient blast cells express CD33 as well235. CD33 is expressed on more primitive myeloid cells236, and the majority of the ex vivo CD34+ CB cultured for short term in the presented data and in the experiments conducted by Xu et al. were predominantly CD33+. Therefore, the difference in the effect between the ex vivo and in vivo data could be attributed to the EV and KA cells losing CD33+ expression through the duration of the LTC and transplantation due to differentiation with insufficient self-renewal of the CD33+ population. In the FKA mice, FBXO11 depletion contributes to the maintenance and expansion of the primitive cell population with myeloid features. In addition to the differentiation defects, FBXO11 depletion also increased apoptosis as measured by Annexin V staining. FBXO11 depletion has previously been suggested to promote cell death in MDS, but the conclusions were drawn from transcriptomic data163. Increased apoptotic activity appears to contradict a survival advantage effect expected with the depletion of a tumor suppressor. However, while FBXO11 depletion increased the proportion of apoptotic cells in the bulk population, it also significantly reduced apoptosis within the primitive CD34+ 65 population, which is in line with the stem cell maintenance and expansion effect that also resulted from FBXO11 depletion. What signaling or metabolic changes that FBXO11 depletion imparts may be selectively advantageous to more primitive cells, while having measurable apoptotic effects on the bulk cell population. We have shown both ex vivo and in vivo that FBXO11 depletion promotes the maintenance and expansion of primitive cell populations with myeloid bias. We saw a depletion of progenitors with FBXO11 knockdown based on CFC colony counts in CB, followed by an enrichment in colonies in the secondary platings, which we interpreted as FBXO11 depletion enriching for more primitive cells in the population that were able to differentiate while in culture to generate progenitors captured in the secondary plating. Aligning with that interpretation, we also observed that CB and K562 cells with depleted FBXO11 assumed a quiescent Go cell cycle state, reflecting the dormant cell state typical of HSCs3. The in vivo human xenotransplant experiment further supports this, with increased human cell reconstitution and serial transplantation capability of cells with FBXO11 depletion. The fact that the cells that engrafted in the transplanted mice went through long-term culture first—a process that reduces the committed progenitor population—provides additional support for their primitive cell state. These findings align with the mouse bone marrow transplant model, where Fbxo11 depletion cooperated with AML1-ETO expression to expand a cell population immunophenotypically and functionally reminiscent of the LT-HSCs. While the stem cell maintenance and expansion effect of FBXO11 depletion occurs in cooperation with AML1-ETO and KRASG12D expression, these effects are not produced in the conditions with the cooperating mutations only, indicating that FBXO11 depletion is responsible for the differential outcomes. Whether FBXO11 depletion alone is sufficient to drive the same effects in vivo is not known. Cooperating mutations were included in the human transplant experiment to provide additional cell expansion and survival advantages to compensate for the general apoptotic effect of FBXO11 depletion in order to generate enough cells for the experiment. For example, while AML1-ETO expression alone is not sufficient to drive leukemogenesis, it supports the expansion of HSCs208,209,237. This also reflects human leukemias, 66 which are typically characterized by the presence of 2 or more mutations rather than single isolated mutations238. Aside from the increased maintenance of primitive myeloid populations and infiltration of human cells in the spleen and liver, a key result from the xenotransplant experiment is the infiltration of human myeloid cells in the leptomeninges. This abnormal mobilization of immature myeloid cells from the femoral bone marrow, the site of transplantation, to the leptomeningnes is consistent with myeloid malignancy, particularly acute leukemia239. In conclusion, in this chapter, we established a humanized xenotransplant myeloid leukemia model based on FBXO11 depletion in cooperation with AML1-ETO and KRASG12D, and characterized the myeloid bias and stem cell maintenance effect of FBXO11 depletion in human cells. 67 Chapter 4: FBXO11 depletion promotes a stem cell phenotype through loss of LONP1 mitochondrial function 4.1 Introduction In Chapter 3, we characterized the leukemogenic effects of FBXO11 depletion. However, the mechanism of how this occurs is unclear. FBXO11 encodes the substrate-recognizing component of the SCFFBXO11 E3 ubiquitin-ligase complex known to target proteins for ubiquitination and subsequent proteasomal degradation. Based on this previously characterized function of FBXO11, we hypothesized that FBXO11 depletion results in an accumulation of proteins normally targeted by SCFFBXO11 for degradation, causing dysregulation in the respective pathways the proteins normally function in. Considering FBXO11 depletion causes hematopoietic dysregulation and stem cell maintenance, we predicted that FBXO11 ubiquitination targets responsible for mediating the effects of FBXO11 loss would be involved in processes that regulate stem cell function and differentiation. Previously identified FBXO11 ubiquitination targets include CRL4, Snail factors, and BCL6167,195,200. However, the interactions of FBXO11 with these targets were studied in the context of other diseases, and FBXO11 function in the context of HSPCs has not studied previously. Additionally, while SCFFBXO11 is known as an E3 ubiquitin-ligase complex, it has previously been reported to exhibit neddylation activity as well201. Given FBXO11 has not yet been extensively studied, we are interested in what other post translational modifications are imparted by FBXO11 on protein targets relevant to the FBXO11 depletion phenotypes we observed. In this chapter, we identify and validate the mitochondrial protease and chaperone protein, LONP1, as a top FBXO11 target. We characterize the commonalities of LONP1 and FBXO11 dysregulation, and investigate the mechanisms of LONP1 regulation by FBXO11. 68 4.2 Results 4.2.1 LONP1 is a top FBXO11 interacting protein In order to identify protein targets regulated by FBXO11 in AML cells, we performed quantitative tandem mass spectrometric analysis of FLAG-tagged FBXO11 (FLAG-FBXO11) co-immunoprecipitating proteins in the K562 myeloid cell line. This experiment was performed in the absence (n = 3) or presence (n = 4) of Benzonase® endonuclease treatment to reduce interactions facilitated by binding of nucleic acids. Immunoprecipitated proteins were ranked by protein enrichment score (PES), which prioritizes greater abundance of the protein immunoprecipitated with FLAG-FBXO11 (ProA), and lower coefficient of variance between replicates (CvT) (Figure 4.1A and B, Table 4.1, Table 4.2). Intersection of the shortlisted proteins from each experiment after stringent filtering based on PES score, magnitude of protein enrichment, and reproducibility, produced a list of 5 proteins (Figure 4.1A). Three of the 5 proteins are FBXO11, CUL1, and SKP1, which up the SCFFBXO11 complex. This indicates that the top targets identified by immunoprecipitation and this method of ranking and filtering selected for high confidence protein targets. The other 2 proteins are THAP5 and LONP1. THAP5 is a nuclear protein that has previously been described as a substrate of mitochondrial protease, Omi/HterA2, an inhibitor of cell cycle, as well as a transcriptional repressor with DNA binding activity240. However, we were not able to validate the co-immunoprecipitation of THAP5 and FBXO11. Since SKP1 and CUL1 are already known components of the SCFFBXO11 complex and their functions are well understood, we focussed our investigations on LONP1. LONP1 is a nuclear-encoded ATP-dependent mitochondrial serine protease that has also been shown to have chaperone function132,241. FBXO11 interaction with LONP1 was validated by reciprocal co-immunoprecipitation in K562 cells (Figure 4.1C and D). We conclude that FBXO11 interacts with LONP1. 69 Dysregulation of LONP1 has been associated with colon cancer and melanoma, but has not been studied in the context of AML131,242,243. LONP1 is a mitochondrial protease and chaperone protein, and has been reported to target ETC complex proteins for degradation and to be important for proper folding of inner mitochondrial proteins130,244. Accordingly, LONP1 knockdown has been shown to reduce MMP and oxygen consumption rate130,131. 70 Figure 4.1 LONP1 is a top FBXO11-interacting target in K562 cells. A. List of FLAG-FBXO11 co-immunoprecipitating proteins that make the applied enrichment thresholds for the respective mass spectrometry experiments. Numbers represent the total number of proteins that satisfy the filtering criteria in the respective experiments. B. Mean protein abundance (log2(ProA)) between replicates and transformed coefficient of variation of the FLAG-FBXO11 co-immunoprecipitated proteins in the 2 versions of the mass spectrometry experiment, plotting all detected proteins. C-D. LONP1 and FBXO11 blots of proteins co-immunoprecipitated with C. FLAG-FBXO11 or D. LONP1 in FBXO11 CRISPR KO K562 cells expressing empty vector (-) or FLAG-FBXO11 (+). Sup = supernatant. 71 Protein ratio1 ratio2 ratio3 PES_n log2(proA) log2(cvT) SKP1 9.37 12.55 5.34 1.00 3.18 1.88 FBXO11 6.10 6.32 3.86 0.83 2.44 1.22 VARS 2.69 2.68 2.26 0.72 1.35 -0.17 PLCD1 2.33 2.06 2.29 0.72 1.16 -0.74 CUL1 4.06 4.55 2.65 0.70 1.91 1.29 RPL27 1.43 1.47 1.50 0.69 0.55 -2.09 RPL10 1.48 1.43 1.38 0.68 0.52 -1.66 COPS2 3.35 3.33 2.27 0.68 1.58 0.93 RBM14 2.67 3.05 2.16 0.68 1.39 0.67 NUP205 1.21 1.26 1.26 0.67 0.31 -2.07 RPL34 1.32 1.25 1.31 0.67 0.37 -1.74 RPL7 2.01 2.01 1.68 0.67 0.93 -0.11 FBL 1.39 1.27 1.32 0.67 0.41 -1.26 PRAME 1.35 1.46 1.33 0.67 0.46 -1.06 HIST1H1B 1.20 1.13 1.21 0.66 0.24 -1.52 RPS24 1.20 1.17 1.29 0.66 0.29 -1.14 NACA 1.40 1.29 1.25 0.66 0.39 -0.84 RPL7A 1.25 1.35 1.19 0.65 0.34 -0.77 NPM1 1.38 1.21 1.25 0.65 0.36 -0.70 RPL13A 1.25 1.36 1.19 0.65 0.34 -0.72 YBX1 1.26 1.32 1.15 0.64 0.32 -0.65 RPLP0 1.24 1.47 1.35 0.64 0.44 -0.34 DDX21 1.29 1.24 1.12 0.64 0.29 -0.60 LONP1 1.98 1.79 1.51 0.64 0.81 0.31 RPL11 1.26 1.27 1.11 0.64 0.28 -0.52 THAP5 3.25 3.14 2.00 0.64 1.48 1.20 NCL 1.66 1.56 1.33 0.64 0.60 0.04 ILF3 1.42 1.25 1.20 0.63 0.37 -0.29 RBM28 1.27 1.17 1.43 0.62 0.37 -0.08 RPL10A 1.68 1.83 1.38 0.62 0.70 0.38 RPL21 1.45 1.48 1.19 0.62 0.46 0.11 HNRNPA0 1.28 1.34 1.61 0.62 0.50 0.23 ILF2 1.53 1.23 1.26 0.61 0.42 0.23 RPS26 1.11 1.32 1.40 0.61 0.35 0.15 C8orf59 1.10 1.24 1.39 0.61 0.31 0.15 RPL18A 1.99 2.22 3.20 0.60 1.30 1.27 … 72 Protein ratio1 ratio2 ratio3 PES_n log2(proA) log2(cvT) RPS15A 1.30 1.46 1.08 0.59 0.36 0.46 C1orf131 1.30 1.06 1.44 0.59 0.34 0.50 TRMT10C 1.04 1.43 1.47 0.57 0.39 0.75 UTP14A 1.74 1.24 1.29 0.57 0.51 0.86 RPL3 2.96 2.87 1.56 0.56 1.30 1.56 HSD17B10 1.13 1.56 1.79 0.56 0.58 1.05 KPNA2 1.54 1.63 1.06 0.56 0.50 1.00 MYBBP1A 1.34 0.96 1.47 0.55 0.33 0.97 BYSL 1.22 0.99 1.53 0.54 0.32 1.01 ELOF1 0.97 1.60 1.28 0.53 0.36 1.18 RPL30 1.23 2.00 1.33 0.52 0.60 1.35 GPRASP1 1.24 0.85 1.42 0.51 0.22 1.21 RPL32 0.95 1.26 1.67 0.50 0.37 1.37 MYH1 2.12 2.83 1.25 0.49 1.05 1.82 RBMX 1.35 0.81 1.59 0.47 0.32 1.57 MYH4 1.74 2.24 1.00 0.46 0.73 1.80 TPM1 2.35 3.72 1.32 0.45 1.30 2.18 STAU1 1.64 1.27 0.72 0.42 0.27 1.84 ACTN2 1.76 4.04 1.17 0.32 1.22 2.59 ACTA2 2.16 5.53 1.18 0.29 1.57 2.84 MYH3 1.15 5.46 1.51 0.19 1.44 3.03 Table 4.1 Proteins co-immunoprecipitated with FLAG-FBXO11 without Benzonase® treatment. Proteins that make the normalized protein enrichment score (PESn) > 0.6, and must be enriched in 2/3 of replicates by at least 1.1 fold. Ratio1- ratio3 columns represent the ratio of protein abundance measured in the FLAG immunoprecipitated samples to the abundance measured in the IgG control immunoprecipitated samples for each of the 3 replicates performed. 73 Protein ratio1 ratio2 ratio3 ratio4 PESn log2(proA) log2(cvT) FBXO11 4.49 4.52 4.48 4.91 1.00 2.20 -2.93 ABCF1 3.21 3.29 3.55 3.78 0.87 1.79 -5.97 CUL1 2.84 2.91 2.82 2.71 0.79 1.50 -3.91 SKP1 2.20 2.47 2.31 1.44 0.70 1.07 -4.64 GLO1 1.89 2.08 1.99 1.09 0.65 0.82 -2.79 ZYX 1.43 1.54 1.57 1.33 0.63 0.55 -6.60 CLNS1A 1.56 1.52 1.62 1.54 0.63 0.64 -3.16 THAP5 1.88 1.80 1.31 0.97 0.62 0.58 -4.38 FECH 1.16 1.35 1.44 1.87 0.62 0.54 -5.26 EEF1B2 1.56 1.79 1.56 1.15 0.62 0.60 -2.99 CHD7 1.64 1.77 1.23 0.89 0.62 0.47 -7.53 YTHDC1 1.50 1.45 1.35 1.09 0.61 0.43 -6.57 BPNT1 1.59 1.45 1.66 0.69 0.61 0.43 -6.36 C1QBP 1.36 1.10 1.67 1.30 0.61 0.44 -4.92 DEK 1.47 1.29 1.46 1.07 0.61 0.40 -5.57 TAGLN2 1.38 1.58 1.63 0.72 0.61 0.41 -4.84 TXNDC17 1.61 1.65 1.41 0.94 0.61 0.49 -3.15 IDI1 1.27 1.57 1.24 1.17 0.60 0.39 -4.76 P4HA1 1.39 1.34 1.19 1.33 0.60 0.39 -4.44 IDH3A 1.37 1.45 1.34 1.12 0.60 0.40 -3.99 LONP1 1.42 1.36 1.30 1.31 0.60 0.43 -3.14 PSMB2 1.26 1.25 1.44 1.13 0.60 0.35 -4.68 SREK1IP1 1.52 1.41 1.31 0.99 0.60 0.39 -3.68 PSMD14 1.29 1.29 1.46 1.05 0.60 0.35 -4.54 AIMP2 1.39 1.24 1.34 1.14 0.60 0.35 -4.05 LRRC37A2 1.14 1.24 1.23 1.28 0.59 0.29 -5.46 CLIC2 1.25 1.36 1.33 1.13 0.59 0.34 -3.56 PPIH 1.36 1.53 1.34 0.98 0.59 0.38 -3.06 TMED6 1.38 1.45 1.31 1.18 0.59 0.41 -2.70 NOA1 1.22 1.30 1.19 1.23 0.59 0.30 -3.55 BLVRB 1.16 1.38 1.36 1.23 0.58 0.36 -2.40 LASP1 1.29 1.38 1.32 1.09 0.58 0.34 -2.46 STAMBP 1.21 1.22 1.22 1.11 0.58 0.25 -3.16 RPS12 1.43 1.49 1.37 0.90 0.58 0.38 -2.05 ACAT1 1.38 1.43 1.43 1.10 0.57 0.42 -1.50 MAT2A 1.20 1.21 1.21 1.29 0.56 0.30 -1.79 UBE2M 1.22 1.24 1.31 1.11 0.56 0.29 -1.62 SRP9 1.31 1.29 1.28 1.09 0.56 0.31 -1.48 74 … Protein ratio1 ratio2 ratio3 ratio4 PES_n log2(proA) log2(cvT) METTL2B 1.30 1.38 1.14 1.20 0.55 0.33 -1.18 ARF4 1.23 1.19 1.21 1.24 0.54 0.29 -1.23 CLIC1 1.38 1.38 1.38 1.03 0.51 0.37 -0.40 PFN1 1.61 1.59 1.60 1.01 0.46 0.54 0.41 Table 4.2 Proteins co-immunoprecipitated with FLAG-FBXO11 with Benzonase® treatment. Proteins that make the normalized protein enrichment score (PESn) > 0.45, and must be enriched in 3/4 of replicates by at least 1.2 fold. Ratio1- ratio4 columns represent the ratio of protein abundance measured in the FLAG immunoprecipitated samples to the abundance measured in the IgG control immunoprecipitated samples for each of the 4 replicates performed. 4.2.2 FBXO11 regulates LONP1 in a degradation-independent manner If LONP1 is ubiquitinated by SCFFBXO11 and subsequently targeted for proteasomal degradation, we predicted that LONP1 protein expression would inversely correlate with FBXO11 expression. Contrary to our expectations, LONP1 protein neither accumulated with FBXO11 knockout in 2 different K562 CRISPR KO clones, nor decreased with FBXO11 over-expression (Figure 4.2A). This suggests that if LONP1 is regulated by FBXO11, it is in a degradation-independent mechanism. Since SCFFBXO11 is an E3 ubiquitin-ligase and NEDD8-ligase, we investigated whether post-translational modifications of LONP1 are affected by changes in FBXO11 expression. Due to the degradation-independent nature of the interaction between FBXO11 and LONP1, we assessed post-translational modifications that can regulate protein fates in ways other than degradation. This includes K63-linked polyubiquitination, known to regulate processes like endocytosis and cellular localization, and neddylation, which can also regulate cellular localization145,149,245. We found that levels of both K63-linked polyubiquitination and neddylation of LONP1 increased with FLAG-FBXO11 expression in K562 cells (Figure 4.2 B and C), consistent with the potential for FBXO11 having an activating effect on LONP1, mediated through either one or both of these modifications. 75 Figure 4.2 K63-linked polyubiquitination and neddylation of LONP1 correlates with FBXO11 expression. A. Western blot of lysates from K562 parental cells (CTR) and 2 different FBXO11 CRISPR KO clones expressing empty vector (-) or FLAG-FBXO11 (+). Numbers below LONP1 bands represent LONP1 signal intensities normalized to GAPDH signal from the same sample, then normalized to the K562 parental condition value. B. K63-linked polyubiquitin of immunoprecipitated LONP1 in K562 parental cells (CTR) and FBXO11 CRISPR KO clones expressing empty vector (-) or FLAG-FBXO11 (+). C. NEDD8 modification of immunoprecipitated LONP1 in K562 CRISPR KO clones expressing empty vector (-) or FLAG-FBXO11 (+). D. MMP measured by TMRE mean fluorescence intensity in parental K562 cells expressing FLAG-FBXO11 measured 24 hours after treatment with indicated drug. P values represent Welch’s t-tests. 76 4.2.3 FBXO11 positively regulates LONP1 Although we concluded that FBXO11 likely regulates LONP1 in a degradation independent manner through post-translational modifications, we did not know whether this had a positive or negative effect on LONP1 function. Since we have established the effects of FBXO11 depletion on human CB differentiation, we knocked down FBXO11 and LONP1 using 2 different shRNAs targeting each gene (shFBXO11 and shLONP1) in CB in parallel, and measured cell surface marker expression by flow cytometry analysis after 1 week of culture to compare the effects (Figure 4.3). As expected, FBXO11 knockdown reduced the proportion of CD41+CD61+ megakaryocytic cells (P < 0.0001 for both shRNAs), reduced the proportion of CD71+CD235a+ erythroid cells (P = 0.0017 for #9 and P = 0.0004 for #10), and increased the myeloid CD15+ population (P < 0.0001 for both shRNAs) (Figure 4.3 A, B, D). In this experiment, we assessed an alternative myeloid marker, CD14, instead of CD33, because CD33 expression was not significantly affected by FBXO11 knockdown in our previous experiment. We found that the proportion of CD14+ cells also increased with FBXO11 knockdown, confirming a myeloid bias effect (Figure 4.3C). Importantly, CB with LONP1 knockdown mirrored the differentiation trends of CB with FBXO11 knockdown. We observed a consistent significant reduction in CD41+CD61+ megakaryocytic cells (P < 0.0001 for #1, P = 0.0002 for #3), and significant increase in myeloid CD14+ (P = 0.0003 for #1, P < 0.0001 for #3) and CD15+ (P < 0.0001 for both shRNAs) (Figure 4.3 A, C, D). While the reduction in CD71+CD235a+ erythroid cells was only significant for cells expressing one of the shRNAs targeting LONP1 (P = 0.0004 for #1, P = 0.083 for #3), the trend is a reduction for both shRNAs, as with FBXO11 knockdown. Based on the similarity of these differentiation trends, we concluded that if FBXO11 is an upstream regulator of LONP1, FBXO11 has an activating effect on LONP1 function. 77 Figure 4.3 FBXO11 and LONP1 depletion both promote myeloid bias and inhibit erythroid and megakaryocytic differentiation. A-D. Flow cytometry analysis of cell surface markers after 1 week of culture. P values representWelch’s t-tests. A. Knockdown of both genes reduced the megakaryocytic CD41+CD61+ CB population. B. Knockdown of both genes reduced the erythroid CD71+CD235a+ population. C. Knockdown of both genes increased the proportion of myeloid CD14+ cells. D. Knockdown of both genes increased the proportion of myeloid CD15+ cells. 4.2.4 FBXO11 depletion reduces mitochondrial membrane potential While we have determined that LONP1 knockdown phenocopies FBXO11 depletion, we have yet to connect FBXO11 to any mitochondrial functions. If FBXO11 is an upstream regulator of LONP1 and has an activating effect on LONP1 function, then FBXO11 depletion should have similar effects on mitochondrial function as LONP1 depletion. 78 LONP1 knockdown has previously been shown to reduce mitochondrial membrane potential in melanoma cells131. LT-HSCs and CD34+ CB that have greater reconstitution potential, and are both characterized by low MMP115–117. This is consistent with the stem cell phenotype we observed with FBXO11 depletion, assuming that FBXO11 positively regulates LONP1 function. Disruption of MMP also promotes self-renewal in HSCs, indicating low MMP is essential for maintaining HSC state117. To determine if LONP1 knockdown also has the same effect on MMP in HSPCs, and if FBXO11 depletion affects mitochondrial function, we knocked down FBXO11 and LONP1 in parallel in the K562 and OCI-AML3 myeloid cell lines, and in CB cells cultured for 2 weeks, then measured MMP by MMP-dependent dyes, TMRE (cell lines) and JC-1 (CB). As expected, LONP1 knockdown significantly reduced MMP compared to control (P = 0.016 in K562, P = 0.0001 in OCI-AML3, P = 0.0003 in CB) (Figure 4.4A). Similarly, FBXO11 knockdown also significantly reduced MMP in all 3 cell types (P = 0.017 in K562, P = 0.0002 in OCI-AM3, P = 0.01 in CB). This finding further supports FBXO11 as a regulator of LONP1 and confirms that FBXO11 depletion affects mitochondrial function. FBXO11 and LONP1 knockdowns were confirmed at the protein level by western blot (Figure 4.4B). Given the effect of FBXO11 depletion on MMP, we predict that over-expression of FBXO11 would have the opposite effect, increasing MMP. We confirmed this by over-expressing WT FLAG-FBXO11 (WT-FBXO11) in K562 cells, and found significant increase in MMP (P = 0.0003) (Figure 4.4D). Although the magnitude of the effect is much smaller compared to FBXO11 depletion, we observe the same effect in independent experimental replicates. FBXO11 over-expression was confirmed by western blot (Figure 4.4C). We propose that the effects of FBXO11 dysregulation on mitochondrial function is mediated through LONP1 mitochondrial function. Thus, we predict that the expression of an FBXO11 mutant with a deleted F-box domain (ΔF-box-FBXO11) would have no, or reduced, effect on MMP compared to over-expression of WT-FBXO11, given the importance of the F-box domain on substrate modification. The assembly of the SCF complex and subsequent 79 recruitment of substrates is dependent on the F-box domain in FBXO11. We determined that over-expression of ΔF-box-FBXO11 has no effect on MMP in K562 cells (P = 0.57) (Figure 4.4D). Our findings so far suggest that FBXO11 positively regulates LONP1, suggesting that FBXO11 functions upstream of LONP1. Thus, we predict that LONP1 depletion would block or reverse the effects of FBXO11 over-expression. To test this, we doubly transduced K562 cells with lentivirus to express control shRNA (shCTR) or shLONP1 with over-expression of an empty vector control (EV) or WT-FBXO11, then measured the effect on MMP. We found that expression of WT-FBXO11 with shCTR increased MMP as we had seen with WT-FBXO11 expression alone (P = 0.028), while WT-FBXO11 expression with shLONP1 reduced MMP (P = 0.028) (Figure 4.4E). This suggests that LONP1 function is required for FBXO11 mitochondrial functional effects, supporting a model of LONP1 as a downstream mediator of mitochondrial regulation by FBXO11. With an established phenotype linking FBXO11 and LONP1 function, we revisited the investigation of FBXO11 modification of LONP1. If FBXO11 is activating LONP1 function through ubiquitination or neddylation, then inhibition of the modification should mimic the effects of FBXO11 and LONP1 depletion by reducing MMP. To test this, we lentivirally over-expressed FLAG-FBXO11 in K562 cells to increase basal FBXO11-mediated ubiquitination and neddylation activity for better detection, then treated the cells with neddylation activating enzyme (NAE) inhibitors, MLN4924 and TAS4464, or with ubiquitin activating enzyme (UAE) inhibitors, PYR-41 and TAK-243. We found the UAE inhibitors significantly reduced MMP (P = 0.002 for PYR-41, P < 0.0001 for TAK-243) while the NAE inhibitors did not (Figure 4.2D). According to these findings, we conclude that FBXO11 ubiquitination of LONP1 likely plays a more relevant role on regulating LONP1 mitochondrial function. MMP in these experiments were measured by detection of accumulation of positively charged dyes (TMRE or JC-1) in a negatively charged mitochondrial environment. The negatively charged mitochondrial matrix is generated by efflux of protons into the intermembrane space by the electron transport chain (ETC). This means that differences in MMP detected could be 80 attributed to either differences in ETC function or to mitochondrial mass. We measured mitochondrial mass in CB cells with FBXO11 or LONP1 depletion using mitochondrial mass dye MitoTracker Red, and found that mitochondrial mass is not reduced, but in fact, increased with LONP1 knockdown (P < 0.0001 for #1, P = 0.0002 for #3) and with expression of one of the FBXO11 shRNAs (P = 0.096 for #9, P = 0.0003 for #3) (Figure 4.4F). Mitotracker dye is retained in the mitochondria by binding to thiol groups, but MitoTracker Red uptake is dependent on MMP. However, if the MitoTracker readings were confounded by differences in MMP, we would expect a reduction in mitochondrial mass with FBXO11 and LONP1 knockdown. We compared the expression of outer mitochondria translocase protein, TOM20, as an alternative for determining mitochondrial mass, and found that TOM20 levels were stable with FBXO11 KO and over-expression (Figure 4.4G). Based on these findings, we determined that FBXO11 and LONP1 knockdown reduces MMP independent of changing mitochondrial content. MMP is generated by ETC Complexes, I, III, and IV during oxidative phosphorylation (OXPHOS), and is a form of energy storage used to drive the synthesis of ATP by Complex V (Figure 1.3). To understand the impact of FBXO11 depletion on ETC function, we investigated the effects of FBXO11 loss on OXPHOS. 81 Figure 4.4 FBXO11 and LONP1 depletion reduces MMP but not mitochondrial mass A. Mitochondrial membrane potential (MMP) represented as mean fluorescence intensities (MFI) of reporter dyes (TMRE for K562 and OCI-AML3 cell lines, JC-1 for CB). = B. Representative 82 of 3 western blots confirming FBXO11 and LONP1 knockdown in K562 myeloid cells. C. Representative western blot confirming WT FBXO11 (WT-FBXO11) and F-box domain-deleted FBXO11 mutant (ΔF-box-FBXO11) expression. D. MMP represented as TMRE MFI of K562 cells lentivirally expressing WT-FBXO11 or ΔF-box-FBXO11. E. MMP represented as TMRE MFI of K562 cells lentivirally expressing WT-FBXO11 with control shRNA or shLONP1. F. Mitochondrial mass, represented as Mitotracker Red dye MFI in CB cultured for 2 weeks, lentivirally expressing indicated constructs. G.Western blot of mitochondrial marker TOM20 in K562 FBXO11 CRISPR KO clones lentivirally expressing indicated constructs. Numbers below TOM20 bands represent TOM20 signal intensities normalized to α-tubulin signal from the same sample, then normalized to the K562 parental condition value. All P values represent Welch’s t-tests. 4.2.5 FBXO11 loss reduces OXPHOS compared to FBXO11 over-expression We measured OXPHOS activity in two K562 FBXO11 KO CRISPR clones expressing empty vector (EV) or FLAG-tagged FBXO11 (FLAG-FBXO11) with the Seahorse XF Cell Mito Stress Test Kit from Agilent. The cells were treated with various inhibitors of the ETC, and perturbations in oxygen consumption rate (OCR) were measured (Figure 4.5A and B). FBXO11 KO cells consistently had lower OCR throughout the duration of the experiment (Figure 4.5C). We did not observe differences (P = 0.13 and 0.42) between FBXO11 loss and high FBXO11 expression on the ratio of OCR to extracellular acidification rate (ECAR) (Figure 4.5D). OCR reflects aerobic glycolysis and OXPHOS activity, while extracellular acidification occurs as a consequence of lactate released from anaerobic glycolysis—and to a smaller extent, the release of CO2. These findings indicate that FBXO11 loss does not affect relative levels of anaerobic to aerobic respiration. However, FBXO11 loss results in significantly lower basal respiration (P = 0.0003 and P = 0.0018) compared to high FBXO11 expression (4.5E). This suggests that FBXO11 loss results in a reduction of OXPHOS, and also a reduction in overall metabolic activity given the OCR:ECAR ratio does not differ with FBXO11 loss. Reduced OXPHOS resulting from FBXO11 loss is also consistent with the reduced MMP observed with FBXO11 depletion, since MMP is generated by the ETC during OXPHOS. While OXPHOS is lower with FBXO11 loss, coupling efficiency—referring to the efficiency in which basal respiration is converted to ATP production—is higher with FBXO11 loss, although 83 the effect is only significant in one of the CRISPR clones (P = 0.30 and P < 0.0001) (Figure 4.5F). Coupling efficiency is inversely correlated with proton leak, because stored energy from basal respiration that is not converted to ATP production is lost to protons leaking through the inner mitochondrial membrane, down the gradient, into the matrix. As expected, proton leak is significantly lower with FBXO11 loss in both clones (P = 0.0005 and P < 0.0001) (Figure 4.5G). OCR associated with ATP-production, which is calculated by taking the difference in OCR at basal respiration and OCR when ATP synthase (Complex V) is inhibited by oligomycin treatment, is lower with FBXO11 KO, but only significantly in one clone (P = 0.0008 and 0.12) (Figure 4.5H). The assay also allows for measurement of maximal respiration, which is the OCR achieved when the cells are treated with the uncoupler, FCCP, to disrupt the proton gradient. This forces the cell to work at maximal capacity by rapid oxidation of cellular metabolic substrates, such as sugars, amino acids, and fats. The spare respiratory capacity is the difference between maximal and basal respiration, and can be interpreted to reflect the cell’s flexibility in its ability to respond to energetic demands. We found that the maximal respiration achieved is significantly greater in FBXO11-expressing cells (P = 0.015 and 0.039), but the spare respiratory capacity does not differ significantly between FBXO11 KO or high cells in either clone (P = 0.39 and 0.67) (Figure 4.5I and J). Based on the Mito Stress test results, we confirmed that OXPHOS is reduced with FBXO11 loss, which supports the reduced MMP observed with FBXO11 and LONP1 depletion. Considering that OCR:ECAR ratios are not affected by changes in FBXO11 expression, this suggests that overall metabolic activity, whether aerobic or anerobic, is lower with FBXO11 loss. This is also consistent with a quiescent phenotype characteristic of primitive HSPCs. Although FBXO11 KO cells are metabolically less active than high FBXO11 expressing cells, FBXO11 KO cells are metabolically more efficient and have comparable stress response to high FBXO11 cells based on spare respiratory capacity. These findings confirm a reduced mitochondrial metabolism phenotype in FBXO11 low cells, and also suggest that while more dormant, FBXO11 depleted cells possess increased metabolic efficiency and flexibility compared to FBXO11 expressing cells in the face of metabolic stress. 84 Figure 4.5 FBXO11 loss reduces OXPHOS activity but increases coupling efficiency. A. Schematic of a typical Seahorse Mito Stress test result with expected effects of indicated inhibitors on oxygen consumption rate (OCR), and how OXPHOS-related activity values are 85 calculated. Based on information and schematic from Agilent Mito Stress test user manual. B. Table to ETC inhibitors and corresponding targets. C. OCR of K562 FBXO11 KO CRISPR clones expressing empty vector (EV) or WT FLAG-FBXO11 (FLAG-FBXO11) during MitoStress test. D-J. P values represent Welch’s t-tests. D. OCR to extracellular acidification rate (ECAR) ratio. E. Basal respiration rate in the absence of ETC component inhibitors. F. Coupling efficiency of respiration to ATP production, calculated by taking ATP-linked respiration as a percentage of basal respiration. G. OCR associated with proton leak from intermembrane space back into the mitochondrial matrix. H. OCR associated with ATP production. I. Maximal OCR achieved when MMP is perturbed. J. Spare respiratory capacity measured by taking the difference between maximal and basal respiration. 4.6 FBXO11 and LONP1 depletion result in similar transcriptomic responses According to our investigations so far, we propose that FBXO11 depletion promotes a quiescent primitive cell state, characterized by reduced mitochondrial metabolic activity caused by reduced LONP1 function. To confirm these findings at the transcriptomic level, investigate the mechanistic relationship between FBXO11 and LONP1, and further characterize the effects of FBXO11 and LONP1 dysregulation, we performed RNA-Seq in CB cells lentivirally transduced to express combinations of FBXO11 and LONP1 shRNA or over-expression constructs. First, we compared the general effects of FBXO11 and LONP1 depletion with Metascape analysis. FBXO11 knockdown has a greater transcriptomic effect in terms of the number of differentially expressed genes compared to changes in LONP1 expression. We found 1470 genes to be upregulated (log2fold > 1.2, P-adjusted < 0.05) and 1275 genes downregulated (log2fold < -1.2, P-adjusted < 0.05) with FBXO11 knockdown, while only 488 upregulated genes and 483 downregulated genes were identified with LONP1 knockdown (Figure 4.6A). The difference in the number of genes dysregulated with FBXO11 and LONP1 knockdown could be explained by their functions and the extent in which they interact and regulate other proteins and cellular processes. While both genes are encoded in the nuclear genome, LONP1 predominantly localizes and functions in the mitochondria as a protease and chaperone protein, and thus, transcriptomic effects of LONP1 dysregulation are likely more restricted to mitochondrial-associated processes. FBXO11, on the other hand, encodes the substrate-recognizing component of SCFFBXO11 E3 ubiquitin-ligase complex. Although only a handful of FBXO11 targets have been characterized so far, over 500 proteins were pulled down in the 86 FLAG-immunoprecipitation mass spectrometry experiments we performed to identify FBXO11-interactors. Identified proteins are involved in a wide variety of cellular functions, including regulating cell cycle, epigenetics, and cell detoxification pathways. Of the differentially expressed genes, 309 of the upregulated genes and 253 of the downregulated genes were shared between FBXO11 and LONP1 knockdown. That is equivalent to over 60% of upregulated genes and over 50% of downregulated genes with LONP1 knockdown. Within both shared and unique genes that were differentially regulated by FBXO11 and LONP1 knockdown, genes also shared the same Gene Ontology (GO) terms, as indicated by the blue lines in the Circos plots (Figure 4.6A). These differentially regulated genes and pathways shared between FBXO11 and LONP1 depletion could represent shared pathways regulated by both of the corresponding proteins. We investigated the GO terms commonly enriched with FBXO11 and LONP1 knockdown, assessing the upregulated and downregulated genes separately to elucidate what pathways were affected at the transcriptomic level (Figure 4.6B and C). GO terms containing genes upregulated with FBXO11 and LONP1 knockdown include a large cluster of inflammatory response related terms (Figure 4.6B). However, the cells were not cultured in an inflammatory environment, indicating that this is an intrinsic response to FBXO11 or LONP1 depletion. The inflammatory response-related GO terms include inflammatory response, cytokine production, chemotaxis, leukocyte activation, and other similar terms. Typically, inflammatory signals impair self-renewal and prompt HSCs to exit quiescence and to proliferate and differentiate to compensate for disruptions in homeostasis or to support an immune response to the inflammatory trigger246,247. However, multiple inflammatory have been implicated in promoting HSC emergence246. Interestingly, a recent study on genetic drivers of clonal hematopoiesis also identified an inflammatory response signature in cells carrying mutations that promote clonal dominance; the authors interpret this as an anti-inflammatory response to better equip cells to resist inflammatory environments, and propose that the response imparts enhanced clonal fitness to mutant cells248. Their findings also imply that abrogating the inflammatory response reduces clonal fitness248. Additionally, transcriptomic analysis of AML samples reveal pro-87 inflammatory signatures associated with AML progression249. These reports are all consistent with LSC characteristics. Apart from the inflammation related terms, the GO terms enriched in genes upregulated with FBXO11 and LONP1 knockdown are all interrelated according to the edges connecting the different GO term node clusters, and the other clusters including MAPK signaling, GPCRs, cAMP signaling, and cell adhesion interactions with HSCs are all involved in the inflammatory response process, although MAPK inhibition, not activation, has previously been shown to promote HSC self-renewal247,250–252. Of importance, GPCRs and pathways involving GPCR signaling are important for HSC engraftment, mobilization, self-renewal, and proliferation253. A subset of adhesion GPCRs are also upregulated in AML and associated with poorer clinical outcomes254. Additionally, adhesion GPCRs are known to regulate inflammation, and could be contributing to upregulation of many inflammation-related GO terms255. GPCRs have been shown to localize to various subcellular localizations, including the mitochondria, where dysregulation contributes to leukemogenesis in our model256. Interestingly, GO terms related to heart contraction and synaptic signaling were also enriched in the upregulated genes. Certain FBXO11 variants have been associated with cardiomyopathy and neurodevelopmental disorders, but the relevance of these signatures in the context of HSPCs is unclear185,257. Focusing on GO terms enriched in downregulated genes, we found that MAPK, adhesion, and cytokine signaling were shared with the upregulated GO terms, suggesting a general dysregulation of these pathways (Figure 4.6C). While MAPK inhibition promotes HSC self renewal, MAPK activation in HSCs generates myeloproliferative neoplasms258. The MAPK related GO term clusters in the genes upregulated and downregulated with FBXO11 and LONP1 depletion differ in that the upregulated cluster contains more terms involving upregulation of MAPK activity and other kinase activity terms, and the downregulated cluster contains ERK1 and ERK2 cascade terms (Tables 4.3 and 4.4). The GO Term “positive regulation of MAP kinase activity” is defined as “any process that activates or increases the frequency, rate or extent of 88 MAP kinase activity”. In the GO terms hierarchy, MAPK cascade is a parent term to the ERK1 and ERK2 cascade term, which contains fewer genes. Overall, the 2 clusters of terms are still very similar, but the downregulated terms are more specific to the MAPK cascade itself, while the upregulated terms are more generic to kinase activity related terms, and processes involved in upregulating MAPK activity. An interpretation of these findings is that MAPK activity is indeed lower with FBXO11 and LONP1 depletion, and positive regulatory pathways are upregulated in response to maintain homeostasis. However, there is not enough information to form concrete conclusions, especially based on transcriptomic data, and additional experiments to determine MAPK/ERK signaling activity could shed more light on this. Cell cycle and chromatin assembly terms were enriched in downregulated genes, which is consistent with an increased quiescence characteristic of stem cells. The hemostasis cluster of nodes include platelet activation and megakaryocyte development GO terms, which supports the reduced megakaryocytic differentiation effect observed with FBXO11 and LONP1 depletion in CB. The reduced detoxification related GO terms include several ROS-related detoxification pathways, and is also consistent with the reduced OXPHOS phenotype observed with low FBXO11 expressing cells. Angiogenesis and ion transport related GO-terms were also recurrently enriched in downregulated genes. The enrichment of angiogenesis-related GO-terms in downregulated genes was unexpected, given FBXO11 has been reported to destabilize HIF-1α, suggesting that FBXO11 depletion should promote angiogenesis instead259. Additionally, cells from AML patients have previously been reported to exhibit endothelial cell-like qualities, including expression of VEGF and VEGFRs260,261. However, the reduction in angiogenesis could contribute to a hypoxic environment similar to the bone marrow niche that home chemoresistant LSCs capable of maintaining minimal residual disease262. The metabolic differences between bulk AML cells and LSCs could explain this contradicting finding, but more investigations are necessary to better understand these results. The ion transport cluster of GO terms includes several generic transmembrane transport terms that could be involved in a wide variety of processes. Calcium ion transport and cation transport terms are also included in this cluster. Calcium can act as a second messenger involve 89 in stem cell proliferation, differentiation, and maintenance; low levels of intracellular calcium has been shown to promote HSC maintenance by stabilizing TET enzymes, but in general, the role ion transport in HSCs is not well understood263. Reduced transmembrane transport activity could also imply reduced uptake of chemotherapy drugs. In summary, analysis of the effects of FBXO11 and LONP1 depletion at the transcriptomic level reveals substantial overlap in differentially regulated genes and processes. A majority of the dysregulated processes support our previously characterized stem cell and leukemogenic characteristics observed in cells with FBXO11 or LONP1 knockdown. These results point to potential unexplored mechanisms through which FBXO11 depletion contributes to stem cell maintenance and leukemogenesis through reduced LONP1 function. 90 Figure 4.6 GO-term analysis of genes dysregulated with FBXO11 and LONP1 depletion. A. Circos plots comparing genes upregulated (left) or downregulated (right) with FBXO11 (red portion of outer ring) or LONP1 (blue portion of outer ring) knockdown in CB. Orange portions of the inner ring represents shared genes between FBXO11 and LONP1 knockdown and are connected by purple lines, yellow portions represent unique genes. Blue lines connect genes 91 that fall under the same GO terms. Up/downregulated genes are defined by a threshold of 1.2 log2fold change in expression, and an adjusted P value of 0.05. GO term enrichment restricted to terms containing a maximum of 100 genes to exclude generic terms, and all terms have logP < -2.27 (0.005). B-C. GO networks containing genes commonly upregulated (B) or downregulated (C) with FBXO11 knockdown (contributing genes in red) or LONP1 knockdown (contributing genes in blue). Each node represents a GO term. Edges represent direct connections between 2 nodes and could represent either an “is a…” or “part of…” relationship. Cluster labels were manually generated based on common themes in cluster nodes (Tables 4.3 and 4.4). Response to cAMP Description LogP Enrichment Log_q_value Z_score response to cAMP -8.07 5.20 -5.48 7.98 response to purine-containing compound -7.92 3.89 -5.38 7.35 response to organophosphorus -7.59 4.02 -5.11 7.24 cellular response to organic cyclic compound -5.51 2.09 -3.48 5.21 cellular response to cAMP -3.84 4.53 -2.16 5.07 Cytokine Production Description LogP Enrichment Log_q_value Z_score response to bacterium -12.65 2.48 -8.98 8.55 regulation of cytokine production -12.05 2.39 -8.47 8.26 response to molecule of bacterial origin -11.38 3.30 -8.10 8.62 positive regulation of cytokine production -11.30 2.97 -8.07 8.38 response to lipopolysaccharide -11.08 3.18 -7.87 8.41 cellular response to biotic stimulus -8.64 3.44 -5.89 7.49 cellular response to lipid -8.54 2.46 -5.81 6.90 cellular response to molecule of bacterial origin -7.92 3.46 -5.34 7.16 cellular response to lipopolysaccharide -7.24 3.40 -4.79 6.78 tumor necrosis factor superfamily cytokine production -6.40 3.36 -4.14 6.31 regulation of tumor necrosis factor superfamily cytokine production -6.40 3.36 -4.14 6.31 tumor necrosis factor production -6.06 3.32 -3.89 6.11 regulation of tumor necrosis factor production -6.06 3.32 -3.89 6.11 regulation of interleukin-8 production -5.34 3.79 -3.29 5.86 interleukin-8 production -5.29 3.75 -3.24 5.81 positive regulation of interleukin-6 production -5.20 3.89 -3.18 5.81 positive regulation of tumor necrosis factor superfamily cytokine production -5.12 3.64 -3.13 5.67 lipopolysaccharide-mediated signaling pathway -3.49 4.08 -1.90 4.66 92 Leukocyte activation Description LogP Enrichment Log_q_value Z_score leukocyte differentiation -11.27 2.67 -8.02 8.17 regulation of cell activation -10.65 2.45 -7.58 7.78 negative regulation of immune system process -10.64 2.84 -7.58 8.03 regulation of leukocyte activation -10.18 2.47 -7.21 7.60 regulation of cell adhesion -10.00 2.28 -7.08 7.40 lymphocyte activation -9.89 2.25 -7.02 7.34 leukocyte cell-cell adhesion -9.89 2.93 -7.02 7.77 T cell activation -9.85 2.64 -7.00 7.58 mononuclear cell differentiation -9.39 2.72 -6.65 7.43 immune effector process -8.25 2.32 -5.61 6.69 response to cAMP -8.07 5.20 -5.48 7.98 response to purine-containing compound -7.92 3.89 -5.38 7.35 response to organophosphorus -7.59 4.02 -5.11 7.24 cellular response to organic cyclic compound -5.51 2.09 -3.48 5.21 cellular response to cAMP -3.84 4.53 -2.16 5.07 Antigen Processing Description LogP Enrichment Log_q_value Z_score antigen processing and presentation of exogenous antigen -11.29 9.25 -8.07 11.07 antigen processing and presentation -9.59 5.39 -6.72 8.84 Hematopoietic cell lineage -8.60 4.98 -5.92 8.15 Hematopoietic cell lineage -8.60 4.98 -5.92 8.15 Rheumatoid arthritis -8.50 5.22 -5.80 8.21 antigen processing and presentation of peptide or polysaccharide antigen via MHC class II -8.50 9.06 -5.80 9.46 Rheumatoid arthritis -8.39 5.44 -5.71 8.24 antigen processing and presentation of exogenous peptide antigen via MHC class II -8.34 9.97 -5.68 9.60 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell -8.29 4.20 -5.65 7.67 Cell adhesion molecules (CAMs) -8.22 4.02 -5.60 7.56 Cell adhesion molecules (CAMs) -8.22 4.02 -5.60 7.56 Adaptive Immune System -5.75 1.90 -3.60 5.23 Heart Contraction/Blood Circulation Description LogP Enrichment Log_q_value Z_score regulation of system process -12.65 2.85 -9.06 8.82 blood circulation -10.30 2.76 -7.37 7.84 93 Description (Heat contraction/blood circulation continued) LogP Enrichment Log_q_value Z_score circulatory system process -9.53 2.42 -6.76 7.30 regulation of blood circulation -7.56 3.17 -5.05 6.84 regulation of heart contraction -6.13 3.15 -3.92 6.07 muscle system process -6.05 2.35 -3.88 5.64 muscle contraction -5.68 2.51 -3.60 5.52 heart contraction -5.43 2.81 -3.41 5.52 heart process -5.12 2.70 -3.17 5.29 regulation of muscle system process -5.12 2.70 -3.17 5.29 GPCR Signaling/Ion Homeostasis Description LogP Enrichment Log_q_value Z_score GPCR ligand binding -11.43 2.84 -8.07 8.35 Signaling by GPCR -11.28 2.43 -8.02 8.01 Class A/1 (Rhodopsin-like receptors) -10.82 3.17 -7.69 8.31 second-messenger-mediated signaling -10.53 3.40 -7.58 8.32 GPCR downstream signalling -9.34 2.46 -6.50 7.25 ion homeostasis -8.99 2.16 -6.28 6.90 divalent inorganic cation homeostasis -8.20 2.42 -5.59 6.73 cation homeostasis -8.16 2.23 -5.55 6.59 metal ion homeostasis -7.55 2.16 -5.07 6.27 calcium-mediated signaling -7.50 3.50 -5.01 6.97 positive regulation of cytosolic calcium ion concentration -7.49 2.90 -5.00 6.66 inorganic ion homeostasis -7.48 2.15 -5.00 6.23 Inflammatory Response Description LogP Enrichment Log_q_value Z_score NABA MATRISOME ASSOCIATED -17.34 2.93 -12.98 10.49 leukocyte migration -14.98 3.50 -10.92 10.10 chemotaxis -13.93 2.78 -10.07 9.23 taxis -13.83 2.77 -10.07 9.18 myeloid leukocyte migration -11.67 4.14 -8.34 9.21 leukocyte chemotaxis -11.17 3.98 -7.99 8.92 cytokine-mediated signaling pathway -10.61 2.88 -7.59 8.05 Cytokine-cytokine receptor interaction -10.57 3.62 -7.59 8.47 Cytokine-cytokine receptor interaction -10.57 3.62 -7.59 8.47 cell chemotaxis -10.53 3.40 -7.58 8.32 NABA SECRETED FACTORS -9.40 2.95 -6.65 7.57 mononuclear cell migration -9.06 3.88 -6.25 7.92 94 Description (Inflammatory response continued) LogP Enrichment Log_q_value Z_score positive regulation of response to external stimulus -9.03 2.80 -6.24 7.33 Cytokine Signaling in Immune system -8.52 2.28 -5.80 6.78 regulation of inflammatory response -7.30 2.54 -4.86 6.37 positive regulation of inflammatory response -7.01 3.74 -4.64 6.80 positive regulation of defense response -6.32 2.85 -4.08 6.03 regulation of defense response -5.76 2.01 -3.61 5.30 Signaling by Interleukins -4.65 2.12 -2.79 4.74 Interleukin-4 and Interleukin-13 signaling -3.27 2.90 -1.66 4.12 Synaptic Signaling Description LogP Enrichment Log_q_value Z_score behavior -7.90 2.28 -5.36 6.50 learning or memory -4.62 2.46 -2.71 4.87 cognition -3.93 2.20 -2.18 4.32 positive regulation of synaptic transmission -2.95 2.49 -1.51 3.73 chemical synaptic transmission -3.05 1.61 -1.50 3.44 anterograde trans-synaptic signaling -3.05 1.61 -1.50 3.44 trans-synaptic signaling -2.95 1.59 -1.42 3.36 synaptic signaling -2.89 1.57 -1.38 3.31 long-term synaptic potentiation -2.45 2.75 -1.16 3.40 memory -2.44 2.45 -1.03 3.29 Cell Junction Assembly Description LogP Enrichment Log_q_value Z_score synapse assembly -11.58 4.31 -8.17 9.25 cell junction organization -10.67 2.40 -7.58 7.75 synapse organization -9.89 2.78 -7.02 7.68 cell junction assembly -9.08 2.69 -6.35 7.28 regulation of synapse assembly -8.28 4.78 -5.65 7.91 regulation of cell junction assembly -6.97 3.19 -4.61 6.54 regulation of synapse organization -6.67 3.09 -4.36 6.33 regulation of synapse structure or activity -6.38 2.99 -4.11 6.13 presynapse assembly -6.16 5.79 -3.93 7.05 positive regulation of cell junction assembly -5.89 4.18 -3.76 6.35 anterograde trans-synaptic signaling -3.05 1.61 -1.50 3.44 MAPK activity Description LogP Enrichment Log_q_value Z_score regulation of MAPK cascade -12.23 2.66 -8.77 8.53 95 Description (MAPK activity continued) LogP Enrichment Log_q_value Z_score MAPK cascade -12.18 2.53 -8.77 8.41 positive regulation of MAPK cascade -10.82 2.88 -7.74 8.14 cytokine-mediated signaling pathway -10.61 2.88 -7.59 8.05 positive regulation of MAP kinase activity -6.72 4.29 -4.37 6.88 positive regulation of protein phosphorylation -6.63 2.08 -4.31 5.78 regulation of kinase activity -6.04 1.99 -3.88 5.43 regulation of MAP kinase activity -6.02 3.30 -3.86 6.08 positive regulation of transferase activity -5.22 2.08 -3.24 5.04 positive regulation of kinase activity -5.03 2.15 -3.10 4.97 regulation of protein kinase activity -4.77 1.94 -2.90 4.71 Table 4.3 Upregulated GO terms categorized by node clusters. GO terms commonly enriched in genes upregulated with FBXO11 knockdown and LONP1 knockdown in CB. Genes with log2foldchange > 1.2 and adjusted P-value < 0.05 were inputted as upregulated genes. Chromatin Assembly Description LogP Enrichment Log_q_value Z_score DNA conformation change -8.43 3.31 -5.92 7.33 protein-DNA complex subunit organization -7.35 3.36 -5.04 6.83 DNA packaging -6.60 3.46 -4.38 6.48 protein-DNA complex assembly -6.53 3.43 -4.31 6.42 nucleosome organization -4.89 3.23 -2.99 5.37 nucleosome assembly -3.97 3.25 -2.30 4.76 chromatin remodeling -3.84 2.47 -2.20 4.37 chromatin assembly or disassembly -3.51 2.71 -1.96 4.24 chromatin assembly -3.26 2.78 -1.77 4.07 DNA replication-independent nucleosome assembly -2.35 4.54 -1.17 3.78 Cell Cycle Description LogP Enrichment Log_q_value Z_score Cell Cycle -24.65 3.69 -20.29 13.27 sister chromatid segregation -22.47 6.43 -18.41 14.50 Cell Cycle, Mitotic -22.04 3.85 -18.16 12.66 chromosome segregation -21.85 4.81 -18.09 13.30 mitotic sister chromatid segregation -20.99 6.84 -17.33 14.22 nuclear chromosome segregation -20.58 5.17 -16.99 13.12 nuclear division -20.25 4.13 -16.73 12.32 cell division -18.54 3.42 -15.08 11.26 96 Description (Cell cycle continued) LogP Enrichment Log_q_value Z_score organelle fission -17.94 3.72 -14.54 11.28 mitotic cell cycle process -17.89 3.14 -14.53 10.83 Retinoblastoma gene in cancer -11.06 6.81 -8.15 10.14 regulation of sister chromatid segregation -10.77 7.49 -7.87 10.25 regulation of mitotic sister chromatid separation -10.60 7.23 -7.74 10.04 mitotic sister chromatid separation -10.36 7.02 -7.53 9.84 regulation of mitotic metaphase/anaphase transition -10.23 7.40 -7.41 9.90 metaphase/anaphase transition of mitotic cell cycle -9.98 7.16 -7.20 9.69 regulation of metaphase/anaphase transition of cell cycle -9.85 7.05 -7.13 9.59 regulation of chromosome separation -9.79 6.53 -7.08 9.37 regulation of chromosome segregation -9.86 6.26 -7.06 9.33 metaphase/anaphase transition of cell cycle -9.62 6.83 -6.92 9.39 DNA replication -9.54 3.66 -6.81 8.04 PID FOXM1 PATHWAY -9.51 9.74 -6.80 10.28 chromosome separation -9.43 5.93 -6.73 8.99 positive regulation of cell cycle -8.73 3.26 -6.17 7.45 meiotic nuclear division -8.59 3.81 -6.00 7.65 meiotic cell cycle process -7.75 3.49 -5.28 7.09 meiotic cell cycle -7.20 3.22 -4.90 6.68 positive regulation of cell cycle process -6.96 3.30 -4.70 6.60 Phosphorylation of Emi1 -6.62 24.98 -4.39 10.91 Mitotic G1 phase and G1/S transition -6.27 3.85 -4.10 6.45 p53 signaling pathway -5.78 4.63 -3.66 6.44 Cell cycle -5.70 3.86 -3.63 6.12 p53 signaling pathway -5.73 4.92 -3.61 6.51 Cell cycle -5.41 3.87 -3.41 5.94 PID E2F PATHWAY -5.44 4.65 -3.37 6.23 meiosis I -5.18 3.72 -3.23 5.75 G1/S Transition -5.14 3.69 -3.19 5.71 meiosis I cell cycle process -4.93 3.55 -3.02 5.52 DNA-dependent DNA replication -4.86 3.35 -2.98 5.40 Cell cycle -4.85 3.69 -2.97 5.52 Condensation of Prometaphase Chromosomes -4.80 13.62 -2.93 7.78 Cell cycle kinase complex CDC2 -4.76 19.98 -2.90 8.64 PID PLK1 PATHWAY -4.75 5.86 -2.90 6.13 G1/S-Specific Transcription -4.54 7.49 -2.73 6.39 97 Description (Cell cycle continued) LogP Enrichment Log_q_value Z_score positive regulation of mitotic cell cycle -4.27 3.47 -2.53 5.05 meiotic chromosome segregation -4.21 3.87 -2.48 5.15 S Phase -3.99 2.98 -2.31 4.68 Activation of ATR in response to replication stress -3.79 5.83 -2.17 5.38 positive regulation of mitotic cell cycle phase transition -3.65 3.37 -1.99 4.57 cell cycle DNA replication -3.36 5.00 -1.85 4.81 DNA strand elongation -3.31 5.80 -1.81 4.97 positive regulation of cell cycle phase transition -3.33 2.95 -1.75 4.19 Synthesis of DNA -3.19 3.02 -1.73 4.11 DNA replication -2.59 4.28 -1.33 3.95 Activation of the pre-replicative complex -2.41 4.68 -1.21 3.87 homologous chromosome segregation -2.31 3.33 -1.14 3.44 Unwinding of DNA -2.30 8.17 -1.14 4.42 Ion Transport Description LogP Enrichment Log_q_value Z_score regulation of ion transport -9.97 2.40 -7.20 7.48 circulatory system process -9.50 2.49 -6.81 7.34 regulation of system process -9.08 2.47 -6.42 7.15 regulation of ion transmembrane transport -7.56 2.45 -5.14 6.45 muscle system process -6.86 2.43 -4.57 6.10 regulation of transmembrane transport -6.79 2.21 -4.51 5.94 regulation of muscle system process -5.79 2.80 -3.66 5.72 muscle contraction -5.32 2.41 -3.27 5.27 inorganic cation transmembrane transport -5.24 1.86 -3.22 4.93 regulation of muscle contraction -5.09 3.09 -3.10 5.44 regulation of cation channel activity -4.32 2.93 -2.57 4.88 calcium ion transport -4.37 2.10 -2.52 4.56 regulation of metal ion transport -4.34 2.12 -2.49 4.54 regulation of transporter activity -4.16 2.27 -2.36 4.50 regulation of transmembrane transporter activity -4.12 2.35 -2.33 4.51 regulation of ion transmembrane transporter activity -3.98 2.35 -2.23 4.41 Hemostasis Description LogP Enrichment Log_q_value Z_score Hemostasis -13.06 2.78 -9.95 8.92 Kinesins -8.38 6.64 -5.81 8.65 98 Description (Hemostasis continued) LogP Enrichment Log_q_value Z_score Factors involved in megakaryocyte development and platelet production -7.98 4.10 -5.55 7.49 COPI-dependent Golgi-to-ER retrograde traffic -5.28 4.24 -3.30 6.00 Golgi-to-ER retrograde transport -4.86 3.34 -2.91 5.39 MHC class II antigen presentation -4.13 3.18 -2.34 4.84 Intra-Golgi and retrograde Golgi-to-ER traffic -3.52 2.46 -1.90 4.14 Vesicle-mediated transport -2.90 1.69 -1.55 3.37 microtubule-based movement -2.56 1.87 -1.32 3.19 MAPK cascade Description LogP Enrichment Log_q_value Z_score regulation of ERK1 and ERK2 cascade -9.37 5.77 -6.67 8.97 ERK1 and ERK2 cascade -8.86 5.39 -6.25 8.55 positive regulation of ERK1 and ERK2 cascade -8.42 6.57 -5.92 8.78 positive regulation of MAPK cascade -5.72 2.22 -3.61 5.39 regulation of MAPK cascade -5.34 1.96 -3.29 5.05 MAPK cascade -4.63 1.81 -2.72 4.56 Kinase Activity Description LogP Enrichment Log_q_value Z_score regulation of kinase activity -9.24 2.27 -6.57 7.09 regulation of protein kinase activity -7.68 2.25 -5.24 6.39 positive regulation of transferase activity -7.27 2.32 -4.91 6.24 positive regulation of protein phosphorylation -6.85 2.08 -4.55 5.89 positive regulation of kinase activity -6.43 2.34 -4.19 5.83 positive regulation of protein kinase activity -5.13 2.29 -3.13 5.10 activation of protein kinase activity -3.70 2.92 -2.03 4.45 Angiogenesis Description LogP Enrichment Log_q_value Z_score blood vessel development -13.41 2.59 -10.28 8.91 blood vessel morphogenesis -12.92 2.66 -9.86 8.79 angiogenesis -11.99 2.73 -9.01 8.49 regulation of angiogenesis -7.73 2.81 -5.26 6.73 regulation of vasculature development -7.54 2.76 -5.11 6.61 positive regulation of vasculature development -7.52 3.61 -5.11 7.02 positive regulation of angiogenesis -7.52 3.61 -5.11 7.02 epithelial cell proliferation -3.87 1.98 -2.15 4.18 endothelial cell proliferation -3.58 2.48 -1.94 4.19 regulation of endothelial cell proliferation -3.55 2.54 -1.91 4.20 99 Focal Adhesion Description LogP Enrichment Log_q_value Z_score ECM-receptor interaction -10.60 6.37 -7.74 9.72 ECM-receptor interaction -10.19 6.07 -7.38 9.40 Extracellular matrix organization -9.90 3.30 -7.16 7.99 Focal adhesion -8.52 3.67 -5.94 7.55 Focal adhesion -7.79 3.62 -5.38 7.18 Focal adhesion -6.58 3.23 -4.32 6.35 PI3K-Akt signaling pathway -6.18 2.47 -3.99 5.77 Integrin cell surface interactions -6.13 4.61 -3.96 6.65 ECM proteoglycans -6.00 4.81 -3.83 6.64 PI3K-Akt signaling pathway -5.81 2.50 -3.67 5.58 Detoxification Description LogP Enrichment Log_q_value Z_score response to toxic substance -9.54 3.66 -6.81 8.04 cellular oxidant detoxification -6.63 4.75 -4.39 7.01 cellular detoxification -6.50 4.39 -4.29 6.80 cellular response to toxic substance -6.08 4.11 -3.92 6.45 detoxification -4.86 3.35 -2.98 5.40 hydrogen peroxide metabolic process -4.20 4.50 -2.39 5.33 reactive oxygen species metabolic process -3.84 2.40 -2.14 4.35 hydrogen peroxide catabolic process -3.76 5.71 -2.08 5.32 oxygen transport -2.90 7.99 -1.55 5.03 reactive oxygen species biosynthetic process -2.27 3.28 -1.12 3.39 Cytokine Signaling Description LogP Enrichment Log_q_value Z_score Cytokine-cytokine receptor interaction -9.92 3.18 -7.17 7.94 Cytokine-cytokine receptor interaction -8.90 3.29 -6.25 7.54 response to tumor necrosis factor -4.80 2.58 -2.86 5.04 cellular response to tumor necrosis factor -4.58 2.62 -2.68 4.92 cytokine-mediated signaling pathway -4.10 1.99 -2.33 4.33 tumor necrosis factor-mediated signaling pathway -2.84 2.90 -1.38 3.78 Table 4.4 Downregulated GO terms categorized by node clusters. GO terms commonly enriched in genes downgregulated with FBXO11 knockdown and LONP1 knockdown in CB. Genes with log2foldchange < -1.2 and adjusted P-value < 0.05 were inputted as downregulated genes. 100 4.7 Transcriptomic patterns indicate FBXO11 functions upstream of LONP1 and depends on the mitochondrial functions of LONP1 Having explored the effects of FBXO11 and LONP1 depletion and established their similarities, we assessed all the experimental conditions to better grasp the mechanistic relationship between FBXO11 and LONP1 function. Based on Euclidian distances between samples calculated from the expression of all detectable genes, expression of LONP1 (LONP1) clusters closely with the shRNA control (shCTR) and empty vector control (Empty), indicating that overexpression of LONP1 alone on top of endogenous LONP1 expression has little to no effect on the transcriptome. This is expected if LONP1 protein function is dependent on activation by FBXO11-mediated posttranslational modification, and thus, is limited by FBXO11-dependent activation. This places LONP1 function downstream of FBXO11. Similarly, FBXO11 knockdown samples (shFBXO11) also cluster closely with FBXO11 knockdown combined with LONP1 expression (shFBXO11_LONP1), likely for the same reason. Next, we performed Gene Set Enrichment Analysis (GSEA) to determine which pathways and functions are affected by the manipulations at the transcriptomic level. FBXO11 and LONP1 knockdown positively enriched for genes in the Eppert HSC/LSC (EPPERT_CE_HSC_LSC) and HSC (EPPERT_HSC_R) genesets, which confirms our previous findings of FBXO11 depletion promoting a stem cell phenotype, and our prediction that this occurs through reduced LONP1 function (Figure 4.7B and C). Importantly, FBXO11 over-expression downregulates genes in the same gene set (FDR = 0.18), while LONP1 knockdown follows the trend of blocking, or reversing the effect of FBXO11 overexpression, though not significant (FDR = 0.77). This further supports that FBXO11 regulation of stem cell characteristics are dependent on LONP1 function. Most importantly, we identified a positive enrichment for a mitochondrial respiratory chain defect gene set with FBXO11 and LONP1 depletion that was downregulated with FBXO11 overexpression, and FBXO11 overexpression when combined with LONP1 knockdown reverses the effect of FBXO11 overexpression alone (Figure 4.7B and D). This result confirms at the transcriptomic level that FBXO11 plays a role in regulating mitochondrial function. Although LONP1 is known to possess mitochondrial protease and chaperone functions, we also 101 confirmed this in HSPCs transcriptionally for the first time. Importantly, we pinpointed the defect to the respiratory chain, or the ETC, as observed in our MMP and OXPHOS experiments. To determine how the ETC is defective or differentially modulated, we compared expression of genes that encode components of the ETC between control and FBXO11 and LONP1 knockdown conditions, and found that expression of genes encoding components of ETC Complex I (NDUFB8, P = 0.005 for shFBXO11, P = 0.02 for shLONP1), II (SDHB, P = 0.002 for shFBXO11, P = 0.03 for shLONP1), III (UQCR2, P = 0.005 for shFBXO11, P = 0.01 for shLONP1), and V (ATP5A1, P = 0.02 for shFBXO11, P = 0.04 for shLONP1), are all downregulated by either FBXO11 or LONP1 depletion (Figure 4.8A). Expression of MT-CO2, which encodes COX II of Complex IV, shows a trend for upregulation with FBXO11 knockdown (P = 0.07), and is significantly upregulated with LONP1 knockdown (P = 0.005). Overall, there is a trend for reduced expression of ETC complex proteins, consistent with the reduced MMP phenotype observed with FBXO11 and LONP1 knockdown. 102 Figure 4.7 LONP1 knockdown blocks effects of FBXO11 overexpression. A. Heatmap representing Euclidian distances between samples based on expression of all detected genes. B-D. FDR < 0.25 is considered significant. B. Summary of relevant GSEA pathways. Plotting normalized enrichment scores (NES) for each condition (rows). FDR represented by color. C. GSEA enrichment plots for EPPERT_CE_HSC_LSC gene set. D. GSEA enrichment plots for HP_MITOCHONDRIAL_RESPIRATORY_CHAIN_DEFECTS gene set. 103 Figure 4.8 Transcript expression of ETC complex genes are reduced with FBXO11 and LONP1 knockdown. Transcript expression of ETC complex genes in lentivirally transduced CB cells based on RNAseq. NDUFB8 encodes a component of Complex I, SDHB8 encodes a component of Complex II, UQCRC2 encodes a component of Complex III, MT-CO2 encodes a component of Complex IV, and ATP5A encodes a component of Complex V. P values represent Welch’s t-tests. Our analyses so far have confirmed that FBXO11 and LONP1 depletion have similar effects on pathways and processes related to stem cell maintenance and mitochondrial function. The gene set enrichment trends and sample clustering also indicate that LONP1 functions depend on FBXO11 regulation, likely through ubiquitination based on our previous findings. Aside from maintenance of stem cell phenotypes, we have also previously observed FBXO11 and LONP1 depletion to promote myeloid bias. We looked at the transcriptional cell states of the RNA-seq samples next to determine if transcriptional trends reflect similar effects. To do this, we assessed significantly (adjusted P value < 0.05) upregulated (log2fold > 1.2) or downregulated (log2fold < -1.2) genes for representation of genes associated with various hematopoietic populations according to the Bloodspot HemaExplorer human HSPC 104 transcriptomic data set. We first compared genes that are upregulated with FBXO11 depletion and LONP1 depletion. Based on our in vitro CB differentiation experiments and the xenotransplant model, we expect upregulated genes to be enriched in genes associated with primitive and myeloid cell populations. We confirmed this in the RNA-seq data, where we observe an enrichment in HSC and myeloid lineage populations (CMP, GMP, promyelocytes, myelocytes, monocytes, and mDC) (Figure 4.9A). Interestingly, there is an enrichment of CD4+ T cell associated genes within the upregulated genes as well. While CD4 expression was not assessed in the CB differentiation and xenotransplant experiments, the CD3 T cell marker was included in the xenotransplant experiment, and FBXO11 depletion combined with the cooperating mutation had reduced the proportion of CD3+ population instead. FBXO11 is frequently inactivated in lymphomas, and NK T cell populations are higher in mice heterozygous for an FBXO11 mutation affecting the regulation of p53 stabilization167,234. Our findings confirm that FBXO11 depletion may affect both myeloid and lymphoid populations, though the proliferative effect was restricted to myeloid cells in the xenotransplant model in Chapter 3. Next, we investigated genes that were downregulated with overexpression of WT FBXO11 (Figure 4.9B). We expected these genes to overlap considerably with genes upregulated with FBXO11 depletion, given the MMP and Mito Stress test results indicate that low and high FBXO11 expressing cells exhibit opposite cell states. As predicted, genes downregulated with FBXO11 overexpression also enriched for primitive (HSC) and myeloid populations (CMP, GMP, promyelocytes, myelocytes). The MMP experiments as well as the GSEA analysis trends also showed that LONP1 depletion blocked the effects of FBXO11 overexpression, so we compared the FBXO11 overexpression cell states with FBXO11 overexpression combined with LONP1 depletion. We found that with the addition of LONP1 depletion, the magnitude of HSC/HPC enrichment was reduced compared to cells with only FBXO11 overexpression. This is also true for the myeloid populations, with the exception of myelocytes. This suggests that genes downregulated with LONP1 depletion are associated with more mature myeloid cells, which is in line with the role of FBXO11 and LONP1 depletion in the maintenance of primitive hematopoietic populations. FBXO11 overexpression with LONP1 105 depletion also downregulated genes associated with the megakaryocyte-erythroid progenitor (MEP) population. This is consistent with the loss of CD41+CD61+ megakaryocytic and CD71+CD235a+ erythroid populations with FBXO11 and LONP1 depletion observed in the CB in vitro culture experiments. Another observation we made in our previous RNA-seq analysis is that LONP1 overexpression has little to no transcriptomic effect, likely due to LONP1 function being restricted by post-translational modification by FBXO11. To determine if this is true for transcriptional state, we compared genes that are downregulated with FBXO11 depletion alone, to FBXO11 depletion combined with LONP1 overexpression (Figure 4.9C). As we have seen previously, LONP1 overexpression has little to no effect, and the transcriptional states of the two conditions share very similar profiles. This provides further support that LONP1 acts downstream of FBXO11 and is dependent on FBXO11 expression for functional effects. Overall, the CB RNA-seq results confirmed a mitochondrial association between FBXO11 and LONP1 function, provided support for a model of LONP1 functioning downstream of FBXO11, and mirrored previous findings on the effects of FBXO11 and LONP1 depletion on maintenance of a stem cell phenotype with myeloid bias. 106 Figure 4.9 FBXO11 and LONP1 depletion enriched for stem and myeloid cell states. Cell radar plots based on human normal hematopoiesis (HemaExplorer) data from the Bloodspot database comparing enrichment of genes associated with different hematopoietic populations. The closer the outline is to the edge of the plot at a labelled point, the greater the proportion of differentially expressed genes are associated with the corresponding cell population. Thresholds for differential gene expression set at Log2fold > |1.2| (fold change 2.3) and adjusted P value < 0.05. A. Genes upregulated with FBXO11 or LONP1 knockdown compared to control shRNA. B. Genes downregulated with WT FBXO11 expression, or with FBXO11 expression combined with LONP1 knockdown, relative to empty vector control. C. 107 Genes down regulated with FBXO11 knockdown, or with FBXO11 knockdown combined with LONP1 expression, relative to control shRNA. P values represent t-tests. 4.3 Discussion In this chapter, we identified a previously unknown target of FBXO11, and characterized a mitochondrial regulatory function of FBXO11 for the first time. Based on our findings, we propose that WT FBXO11 normally positively regulates LONP1 mitochondrial function through ubiquitination of LONP1. However, in AML, where FBXO11 expression is typically lower than normal HSPCs, there is a loss of LONP1 ubiquitination, resulting in reduced LONP1 function in the mitochondria. This results in reduced MMP, which likely contributes to a stem cell state given the association of reduced MMP with primitive HSCs, and that disruption of MMP has been shown to promote HSC self-renewal115–117. The reduced MMP effect imparted by FBXO11 and LONP1 depletion can be attributed to previously characterized roles of LONP1 as a mitochondrial protease and chaperone protein: LONP1 has been reported to target misfolded ETC Complex II and V proteins for degradation, and also promotes the proper folding of inner mitochondrial membrane complex proteins130,132. If LONP1 function is dependent on modification by FBXO11, then reduced FBXO11 expression and the resulting loss in LONP1 function would lead to an accumulation of misfolded ETC complexes. A dysfunctional ETC would not be able to effectively pump protons across the inner mitochondrial membrane, which would result in the low MMP we observed. LONP1 loss has also been shown to reduce OCR130. Our findings were in line with this as well, where FBXO11 loss resulted in reduced baseline OCR compared to high FBXO11 expressing cells. The MMP and OXPHOS activities we observed have been consistent with the proposed model and previous reports on LONP1 function. GSEA analysis revealed enrichment in an ETC defect signature with FBXO11 and LONP1 depletion, which corresponds with the reduced MMP and OCR activity, as well as reduced transcript expression of ETC components. 108 Another aspect of the FBXO11 depletion model that requires further investigation is the effect of FBXO11-mediated post-translational modification of LONP1 on LONP1 function. We have shown that increased FBXO11 expression results in increased K63-linked polyubiquitination and neddylation of LONP1, and found support in our mitochondrial and RNA-seq experiments for FBXO11 having an activating effect on LONP1. However, the exact mechanisms of how this activating effect occurs is also unclear. LONP1 is an ATP-dependent protease (AAA+, or ATPases associated with diverse cellular activities), and its protease activity is dependent on the protease active site, as well as the ATP-binding domain, which contains a spiral staircase structure that also interacts with substrate241. LONP1 chaperone function is also dependent on the ATP-binding domain132. Ubiquitination or neddylation of LONP1 could be required for forming the proper conformation of either one or both of these 2 domains. Additionally, LONP1 functions in a hexamer configuration, where the N-terminus has been shown to be important for hexamer stability as well as both protease and chaperone functions of LONP1264. In this case, the formation of the hexamer configuration of LONP1 could also be dependent on post-translation modifications. Lastly, LONP1 is a nuclear encoded protein, but has only been shown to localize in the mitochondria and mitochondrial associated membranes (MAMs) after synthesis in the cytosol as a precursor protein, prior to importation to the mitochondria244. LONP1 is targeted to the mitochondria by its N-terminus mitochondrial targeting signal (MTS). FBXO11, on the other hand, is known to localize in the cytoplasm and the nucleus265. Post-translational modification of LONP1 by FBXO11 in the cytoplasm could assist LONP1 shuttling to the mitochondria. Alternatively, LONP1 modification in the cytoplasm could have a stabilizing effect to prevent LONP1 degradation before it reaches the mitochondria. However, since whole cell levels of LONP1 is not reduced with depletion of FBXO11, this is likely not the case. Preliminary cell fractionation and immunofluorescence labeling of LONP1 data indicate that mitochondrial levels of LONP1 is not altered with FBXO11 over-expression or depletion, indicating that the modification of LONP1 by FBXO11 could be directly regulating LONP1 active sites or structural configuration. To test this hypothesis, we could perform mass spectrometry analysis of immunoprecipitated LONP1 in cells with FBXO11 depletion or over-expression to identify differentially modified sites on LONP1. This would be 109 followed by site directed mutagenesis for mutating target lysine sites to inhibit LONP1 modification by FBXO11 followed by analysis of MMP and Mito Stress test again. This would provide more direct evidence of FBXO11 regulation of LONP1. Analysis of the crystal structures of the resulting LONP1 mutants could shed light on the conformational effects of LONP1 modification by FBXO11. This experiment would also offer a more specific and targeted alternative to the generic UAE and NAE inhibition experiment we performed to measure effects on MMP. Based on our findings, we propose that the reduction in MMP due to reduced LONP1 function—which occurs at the protein level—primes the cells for a more primitive cell state. The enriched stem cell function was observed functionally in the xenotransplant model, and it could be driven at the transcriptomic level, as seen in the CB RNA-seq data. This model is supported by studies demonstrating how metabolic changes and products of metabolic pathways can impact nuclear transcription as well as chromatin and epigenetic remodeling, particularly in cancers266,267. In fact, MMP specifically has previously been shown to regulate nuclear gene expression in macrophages268. We could further investigate this proposed mechanism by performing RNA-seq on cells treated with FCCP, which would collapse MMP by allowing transport of protons across the inner mitochondrial membrane. We would expect enrichment in the HSC/LSC gene sets compared to control conditions. The effect of FBXO11 and LONP1 depletion on MMP could potentially also explain the differential apoptotic effect of FBXO11 depletion on bulk CB cells compared to more primitive CD34+ cells. Reduction in MMP has been shown to cause mitochondrial matrix remodeling that precedes the release of cytochrome c, and this could be contributing to the increased apoptotic activity in the overall cell population 269. Since more primitive cells typically have lower MMP, there may be protective mechanisms in place that circumvent the apoptotic side effects of any further reduction in MMP caused by FBXO11 depletion. Aside from the common, characterized effects of FBXO11 and LONP1 depletion discussed, the RNA-seq data also reveals other pathways that could be dysregulated by the 110 depletion of these 2 genes. Defects in these pathways and processes could also contribute to leukemogenesis and the promotion of stem cell characteristics, either on conjunction with our proposed mechanisms, or through completely independent avenues, as we’ve discussed. These include inflammatory response, GPCR signaling, and cellular detoxification. Overall, the model of LONP1 as a downstream target of FBXO11 responsible for regulating mitochondrial function has been supported by our own findings as well as previously characterized LONP1 functions. Of interest, the reduction in erythroid differentiation we found with LONP1 depletion, which mimicked the effects of FBXO11 depletion, is also supported by data highlighting the role of LONP1 in heme biosynthesis244. We also confirm a defect in this specific pathway ourselves in our transcriptomic data, where both FBXO11 and LONP1 depletion downregulate genes associated with a heme biosynthesis gene set in GSEA. Contrary to intuition, LONP1 transcript expression is significantly upregulated, not downregulated, in many human cancers compared to normal tissues, and overexpression of LONP1 has been shown to contribute to tumorigenesis, although reduced levels correlate with aging270,271. Increased transcript expression of LONP1 in other cancers could be attributed to a transcriptional response to reduced LONP1 protein function, which is what we propose to be the case in AML. Alternatively, regulation and function of LONP1 could differ between tissues and cancers. Importantly, LONP1 transcript expression is not significantly different in majority of AML subtypes compared to normal HSCs according to the Bloodspot database. Our findings also suggest that it is not the expression, but the reduced post-translational modification and protein function of LONP1 that contributes to the leukemogenic effects. To summarize, in this chapter we have established correlative as well as mechanistic support for a model of FBXO11 depletion causing loss of mitochondrial LONP1 function in leukemia. Current results encourage further investigation of the functional effects of FBXO11 post-translational modification of LONP1, as well as other unexplored effects of FBXO11 depletion. 111 Chapter 5: Conclusion 5.1 Summary and contributions to the field In this body of work, we demonstrated the role of FBXO11 as a tumor suppressor in AML, characterized LONP1 as a novel modification target of FBXO11, and identified several links between FBXO11 expression and mitochondrial function. FBXO11 loss has been studied in various other diseases, but not well in leukemias. While tumor suppressive functions of FBXO11 have been investigated previously, specifically in the transformation of MDS to AML, there are no in vivo AML models based on FBXO11 depletion, and the role of FBXO11 depletion in de novo leukemogenesis has not been studied163. In the work discussed in Chapter 3, we demonstrated for the first time the stem cell phenotype resulting from FBXO11 depletion. This was implied by the quiescent cell state of FBXO11 depleted cells, and the expansion in progenitors identified in secondary CFCs plating. This was also demonstrated functionally in the human xenotransplant leukemia model based on FBXO11 depletion. The stem cell phenotype is particularly relevant considering LSCs frequently display chemoresistant properties and contribute to relapse in AML due to their unique metabolic characteristics compared to the bulk AML cell population272,273. Better characterization of the effects of FBXO11 depletion could potentially contribute to the identification of effective therapeutic targets for eliminating LSCs. While the block in erythroid differentiation caused by FBXO11 has been previously characterized, our work is also the first to demonstrate a defect in megakaryopoiesis and a bias for myeloid differentiation, which actually contradicts previous reports207. The megakaryocytic differentiation defect phenotype is further supported by the CB transcriptomic data, where the hemostasis cluster of GO terms, which included several megakaryocyte development and platelet activation terms, were enriched in genes downregulated with FBXO11 and LONP1 depletion. The hematopoietic defect and myeloid bias is consistent with traits of myeloid malignancies, further supporting the role of FBXO11 depletion in leukemogenesis. Importantly, the myeloid bias effect was observed both ex vivo and in vivo. The apoptotic effect of FBXO11 112 depletion has also been demonstrated in melanocytes, and suggested in MDS based on transcriptomic data. Although we confirmed this effect in bulk HSPCs directly by Annexin V staining, we also discovered that the CD34+ primitive cell population actually displayed reduced apoptotic activity with FBXO11 depletion. This supports the stem cell maintenance phenotype observed with FBXO11 and LONP1 depletion later on in our work, although the mechanism behind the differential apoptotic effect on primitive cells compared to bulk cells is not clear. The human xenotransplant leukemia model we developed is the first to establish FBXO11 depletion as a driver of de novo AML. The serial transplantation capacity of the FKA cells indicates the presence of LSCs, providing a valuable model to study the biology and formation of LSCs in a human model. The xenotransplant model could also serve as a tool for drug screening experiments where the findings are likely more applicable to human patients compared to mouse models. In Chapter 4, we identified and validated LONP1 as a novel FBXO11 target. This also revealed a novel role of LONP1 depletion in leukemogenesis, which has also not been studied in AML. Interestingly, protein interactions with SCFFBXO11 typically results in protein degradation or inhibition in function. Our work is also the first to demonstrate a positively regulatory function of FBXO11. This explains why LONP1 escaped detection by an shRNA screen for identifying mitochondrial contributors to leukemogenesis, because the readout was for reduced leukemia cell viability with target gene knockdown134. In our model, it is reduced LONP1 function due to FBXO11 depletion, not LONP1 activation, that is responsible for leukemogenesis. Also, while SCFFBXO11 has previously been reported to exhibit neddylation and ubiquitination activity, our findings are also the first to observe K63-linked polyubiquitination by SCFFBXO11. However, since these conclusions are based on associations between levels of LONP1 post-translational modifications and FBXO11 protein expression, further work is necessary to solidify this claim. In this chapter, we demonstrated the hematopoietic defects caused by LONP1 depletion. The alignment in the trends in erythroid, megakaryocytic, and myeloid differentiation, as well as the common enrichment in HSC/LSC signatures observed in 113 FBXO11 and LONP1 depletion provide further support that LONP1 is a true target of FBXO11 regulation, and is a relevant target in mediating hematopoietic regulatory functions of FBXO11. While the MMP and OXPHOS effects caused by LONP1 depletion have been published previously, we confirm these effects in HSPCs for the first time. Additionally, while FBXO11 depletion has previously been shown to impart apoptotic effects, our work is also the first to demonstrate specifically the effects on MMP and on ETC function. We identified a novel link between the cytosolic UPS system and the mitochondrial protease system. Importantly, again, the effects of FBXO11 and LONP1 depletion on mitochondrial function align again, whether it is reduced MMP, the enrichment in the mitochondrial defect signature at the transcriptomic level, or the reduction in transcript expression of ETC proteins in the absence of changes to expression of the corresponding proteins. While we have not confirmed this in our own experiments, LONP1 loss has previously been shown to reduce OCR, which we also observe with FBXO11 loss130. The consistency of the phenotypes of FBXO11 and LONP1 depletion at the level of hematopoietic differentiation, mitochondrial function, and the transcriptome, provide confidence that the 2 proteins function in the same pathway. Our results not only support a model of the 2 proteins functioning in the same pathway, but also of FBXO11 functioning upstream of LONP1, and LONP1 function being dependent on FBXO11 activity. The former is reflected in how LONP1 depletion blocks the effects of WT FBXO11 overexpression on MMP, on enrichment of transcriptomic pathways, and in transcriptomic cell states. The latter is supported by the fact the LONP1 over-expression has little to no effect, whether it is on transcriptomic signatures of interest, or on the transcriptomic landscape in general based on our Euclidian-distances-based clustering. LONP1 over-expression also does not reverse the transcriptomic cell state caused by FBXO11 depletion. These findings provide important clues regarding the mechanistic relationship between FBXO11 and LONP1. However, as discussed previously, further elaboration on the nature and effect of FBXO11 post-translational modification of LONP1 would further strengthen this connection. 114 While the CB RNA-seq experiment served as validation for our previous findings, Gene Ontology analysis results reveal previously unexplored, and common, aspects of FBXO11 and LONP1 function. While we discussed interpretations of how the enriched terms could potentially support or contribute to the phenotypes we have characterized, follow-up experiments to determine how the enriched pathways are dysregulated and how they affect stem cell maintenance and leukemogenesis could reveal additional mechanisms of how FBXO11 depletion drives AML. Based on our findings, we propose that FBXO11 normally promotes LONP1 mitochondrial activity by ubiquitinating LONP1 (Figure 5.1). In AML, where patients have lower FBXO11 expression compared to normal HSPCs, FBXO11 depletion and resulting loss of LONP1 ubiquitination causes a loss of mitochondrial LONP1 activity. This reduces ETC function. The resulting reduction in MMP and metabolic stress alters the transcriptomic – and possibly epigenetic—landscape of cells, priming the cells for a stem cell state with myeloid bias, as well as for defective hematopoiesis in the erythroid and megakaryocytic lineages. 115 Figure 5.1 Schematic of a model of FBXO11 depletion promoting a leukemic cell state through reduced LONP1 function. Mitochondria images from BioRender. 116 5.2 Future directions 5.2.1 FBXO11 depletion leukemia models We have discussed potential causes of the discrepant conclusions regarding the effect of FBXO11 depletion on megakaryocytic and myeloid differentiation. However, it would be of interest to characterize the megakaryocytic and myeloid cell surface markers that have been reported to be unaffected by FBXO11 depletion. Assuming we can replicate previously reported results, the differential effect of FBXO11 depletion on different erythroid and myeloid markers could provide some insight on how the differentiation and function of the corresponding cell populations are being impacted by FBXO11 depletion. The Fbxo11 depletion mouse leukemia model previously established in our lab used AML1-ETO as the cooperating mutation. The FBXO11 depletion xenotransplant leukemia model used both AML1-ETO and KRASG12D as cooperating mutations. The conditions with FBXO11 depletion had significant effects on survival and hematopoiesis compared to the control construct conditions or the conditions with only cooperating mutations, indicating that it is FBXO11 depletion that is driving the effects. This is most apparent in the xenotransplant model, where some of the effects, such as expansion of the primitive CD34+ cells and the metastasis of the myeloid cells to the leptomeningeal tissue, were entirely absent in the conditions without FBXO11 depletion. While the cooperating mutations provided cell survival and proliferative advantages, these findings indicate that the leukemogenic effect is primarily driven by FBXO11 depletion208,209,237. The cooperating mutations were selected because they are not capable of driving leukemogenesis on their own, which we confirmed in our experiments as well. AML1-ETO and KRASG12D mutations are also prevalent in AML, meaning our conclusions have some generalizability to AMLs broadly. These mutations also have high rates of co-occurrence with UPS mutations based on analysis of our patient cohort, which led us to predict that they would cooperate with FBXO11 depletion to drive leukemogenesis. Considering the proliferative and survival advantage effects of the cooperating mutations discussed previously, the cooperating 117 mutations likely compensate for or protect against the apoptotic effects of FBXO11 depletion, allowing the self-renewing leukemic population to survive and proliferate. However, it is unknown whether FBXO11 directly interacts with either of the cooperating mutations. If there is direct interaction by protein-protein contact, or by convergence of functional pathways, then these interactions or processes could be targetable in AMLs bearing these mutations. While we have established in our models that the leukemogenic effect is driven by FBXO11 depletion, and that the cooperating mutations are prevalent in AML, it would be informative to test the effect of FBXO11 depletion with other mutations that occur commonly in AML. This would test whether the phenomena observed in our models are specific to the cooperating mutations we selected. Based on our patient cohort, candidate cooperating mutations include CEBPα mutations, which also have high odds of co-occurrence with UPS mutations, and occur frequently in AML at around 10% of patients (Figure 1.3)274. Although not specific, TP53 mutations also have a trend of co-occurring with UPS mutations, and also occur in roughly 10% of de novo AML275. AML models based on mutations of these genes also require the assistance of cooperating mutations276–279. Mutations in the 2 most commonly mutated genes, NPM1 and FLT3—neither of which display trends of co-occurrence with UPS mutations—would be useful to test as cooperating mutations to determine the generalizability of the effects of FBXO11 depletion in our experiments. All of our human primary cell experiments were done in CB cells. However, AML is typically a disease associated with age, with an average age of diagnosis of 68 years229. Thus, the observations we made in the CB experiments might not translate to adult AML. Our plan is to repeat some of our CB experiments, including the differentiation, MMP, and xenotransplants, using adult BM-derived CD34+ cells to determine if our conclusions based on CB experiments hold. Our lab has also been acquiring human patient AML BM samples with FBXO11 mutations or low FBXO11 expression. These cells can be used to generate human AML xenotransplant models based on FBXO11 depletion for validation of our CB xenotransplant leukemia model, and for further characterization of the effects of FBXO11 depletion in an in vivo model. 118 5.2.2 Exploration of other FBXO11 targets LONP1 was one of the highest ranking, high confidence targets that was immunoprecipitated with FLAG-FBXO11 in K562 cells, and the only target of the ones we investigated that we were able to validate by reciprocal co-immunoprecipitation with FLAG-FBXO11. Aside from LONP1, there were several other high confidence proteins that were immunoprecipitated with FLAG-FBXO11 that we have not yet validated, and have also not been previously identified as FBXO11 targets. For example, several of the top targets were proteins involved in protein synthesis, including ribosomal proteins and translation elongation factors. Deregulation of translation is typical not only in AML, but many cancers280. SCFFBXO11 could be a regulator of this process by controlling protein expression or function of targets involved in protein synthesis. Other targets of interest include splicing factors and epigenetic regulators. While we have found support for LONP1 as a target for mediating FBXO11 regulation of hematopoiesis and stem cell maintenance through regulating mitochondrial function, it is unlikely that the leukemogenic effects of FBXO11 depletion are entirely generated through loss of LONP1 function. Thus, following up on other top targets could reveal alternative or cooperative targets involved in regulation of hematopoiesis. However, manual screening of candidate FBXO11 targets based on previously characterized protein functions relevant to leukemogenesis would limit findings to those that conform to already established models and mechanisms. A more unbiased approach we could pursue next would be a CRISPR screen of candidate FBXO11 targets. However, the selective pressure applied to identify FBXO11 targets would also be based on already characterized effects of FBXO11 depletion, including increased apoptosis, reduced OXPHOS, dysregulated hematopoiesis. Additionally, based on our investigations of FBXO11 regulation of LONP1, FBXO11 does not necessarily only target proteins for degradation as previously assumed. These complexities would make it difficult to design a screen that captures all FBXO11-regulated targets, but would still prove to be more efficient and unbiased than manual validation. Mass spectrometry analysis of FBXO11 targets was performed on FLAG-FBXO11 co-immunoprecipitating proteins. However, transient or weak interactions could be sufficient for 119 SCFFBXO11 to modify target proteins, and these interactions may evade detection by analysis of co-immunoprecipitating proteins. Some alternative methods that can be used to detect these proteins include fluorescence resonance energy transfer (FRET) and proximity ligation assay (PLA). 5.2.3 FBXO11 regulation of LONP1 As discussed in Chapter 4, the model of FBXO11 regulation of LONP1 by post-translation modification can be solidified by determining the exact lysine residues that are modified by FBXO11. Demonstrating that inhibition of LONP1 modification by site directed mutagenesis of the target lysine residues would provide support for this model, and may shed light on which LONP1 domains or functions are affected. In our work, we have demonstrated that MMP and OCR are affected by FBXO11 and LONP1 depletion. Changes in MMP and OCR both reflect altered mitochondrial metabolism. Important metabolic processes like the TCA cycle and oxidative phosphorylation occur in the mitochondria. Cancer cell metabolism is often the focus of study for therapeutic targeting, since dysregulated metabolism in cancer cells allows for specific targeting by chemotherapy drugs281. The trends we observed in mitochondrial defect is consistent with stem cell characteristics and supported by previous reports on LONP1 function, but how FBXO11 depletion affects cells metabolically in HSPCs is still unclear. Differentially expressed proteins based on preliminary whole cell proteome profiling data of cell lines with low or high FBXO11 expression revealed enrichment in transsulfuration pathway GO terms. H2S is eliminated through the mitochondrial sulfide oxidation pathway, which works with the ETC, particularly Complex III282. CBS is a degradation target of LONP1, and is a heme protein that regulates homocysteine and cysteine metabolism, and endogenous H2S production283. This suggests that the transsulfuration pathway is likely dysregulated in HSPCs with FBXO11 depletion. Considering that H2S has been shown to behave as a signaling molecule and protect against oxidative stress, further investigation of how this pathway is regulated by FBXO11 might provide insight on how the LSC 120 population interacts with bulk AML cells, and how the stem cell population is protected from the apoptotic effects of FBXO11 depletion. Cysteine is also an essential substrate for glutathione synthesis284. Glutathione plays an important role in the detoxification of free radicals, and is elevated in CD34+ AML cells285. Several FLAG-FBXO11 co-immunoprecipitating proteins we identified also play roles in glutathione synthesis and metabolism. Additionally, RNA-seq analysis of CB cells had shown that both FBXO11 and LONP1 depletion resulted in differential expression of detoxification-related genes. All of this point to FBXO11 depletion dysregulating detoxification pathways, and investigating relevant metabolomic processes such as glutathione synthesis and activity may clarify this and potentially reveal targetable options as well. 5.3 Concluding remarks Through the work described, we highlighted the importance of the UPS class of mutations in leukemogenesis. We showed for the first time how depletion of FBXO11 drives leukemogenesis, and identified LONP1 as a novel ubiquitination target. Unlike previously identified FBXO11 targets, our data suggest that LONP1 function is promoted by FBXO11-mediated ubiquitination. With the identification of LONP1 as an FBXO11 target, we also characterized a rare direct interaction between the cytosolic UPS and the mitochondrial protease system. Our human xenotransplant model will be useful for further characterization of FBXO11 depletion driven leukemogenesis, and may eventually be used to test potential mitochondrial related targets for therapy. Further investigation on the metabolic effects of FBXO11 depletion could provide more details on its leukemogenic effects, and elucidating the LONP1 modification sites regulated by FBXO11 would further solidify the mechanistic link and shed light on how the interaction impacts LONP1 function. 121 References 1. Doulatov, S., Notta, F., Laurenti, E. & Dick, J. E. Hematopoiesis: a human perspective. Cell Stem Cell 10, 120–136 (2012). 2. Morrison, S. J. & Weissman, I. L. 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Kaplan-Meier survival curve plotting leukemia-free survival of bone marrow transplanted C56BL/6J-c2J mice. Log-rank test P = 0.06 for AML-ETO + shFBXO11 compared to AML1-ETO + shLuc. B. Immunophenotyping of peripheral blood cells taken at endpoint. C. Immunophenotyping of bone marrow cells harvested at endpoint. D. Leukemia-free survival of secondary transplanted mice. Log-rank test P = 0.07. 155 Figure A.2 Schematic representing regions of FBXO11 targeted by shRNA and gRNA sequences in this thesis. Figure modified from isoform graphics view on National Center for Biotechnology Information (NCBI) gene database (https://www.ncbi.nlm.nih.gov/gene/80204). Canonical isoform (NM_001190274.2) is indicated by a blue frame. Showing nucleotide number on the corresponding transcript aligning with the beginning of the shRNA or gRNA sequence provided. Figure A.3 Schematic representing regions of LONP1 targeted by shRNA sequences in this thesis. Figure modified from isoform graphics view on NCBI gene database. (https://www.ncbi.nlm.nih.gov/gene/9361). Canonical isoform (NM_004793.4) is indicated by a 156 blue frame. Showing nucleotide numbers on the corresponding transcript aligning with the beginning of the shRNA sequence provided.
Thesis/Dissertation
eng
Pathology and Laboratory Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Karsan, Aly
Elucidating the mechanisms of leukemogenesis driven by FBXO11 depletion
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