Targeting the Mitochondrial Peptidase Neurolysin in Acute Myeloid Leukemia

TARGETING THE MITOCHONDRIAL PEPTIDASE NEUROLYSIN IN ACUTE MYELOID LEUKEMIA

Sara Mirali

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of the Institute of Medical Science, University of Toronto

Sara Mirali

Doctor of Philosophy

Institute of Medical Science

University of Toronto

2020

Abstract

Acute myeloid leukemia (AML) cells and stem cells have unique mitochondrial characteristics with an increased reliance on oxidative phosphorylation (OXPHOS).

Through a genetic screen to find novel mitochondrial targets in AML, we identified the mitochondrial peptidase, neurolysin (NLN). NLN is a metallopeptidase whose mitochondrial function is not well understood and whose role in AML has not been reported.

We analyzed the expression of NLN in AML cells and normal hematopoietic cells. NLN gene expression was enhanced in 41% of AML samples and overexpression was confirmed by immunoblotting. Next, we assessed the effects of knocking down NLN in

AML cell lines. NLN knockdown reduced AML growth by up to 70%, clonogenic growth by up to 80%, and engraftment into mouse marrow by 90%.

We next identified NLN’s mitochondrial interactors by BioID-MS. NLN interacted extensively with the respiratory chain and NLN knockdown reduced OXPHOS. Moreover,

NLN knockdown impaired the formation of respiratory chain supercomplexes (RCS), which promote efficient oxidative metabolism.

RCS have not been previously studied in leukemia. We found that RCS assembly was increased in a subset of AML patients compared to normal hematopoietic cells and positively correlated with NLN protein expression (R2 = 0.80, p < 0.05), suggesting that

NLN mediates RCS assembly in AML.

Finally, we used a small molecule inhibitor of NLN (R2) in mice engrafted with primary

AML and normal hematopoietic cells. Treatment with R2 reduced leukemic burden without toxicity. R2 targeted AML stem cells as evidenced by reduced engraftment in secondary experiments. In contrast, R2 did not reduce the engraftment of normal hematopoietic cells. Thus, we show that inhibition of NLN preferentially targets AML cells and stem cells.

In summary, we defined a novel role of NLN in RCS formation and highlighted NLN inhibition as a potential therapeutic strategy for AML.

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Acknowledgements

Throughout my academic experiences, I have been privileged to meet many inspiring and driven individuals who encouraged me to pursue science. I have learned a great deal during my time at the Schimmer lab and I am indebted to everyone who has helped me on this journey.

I extend my sincerest gratitude to my supervisor, Dr. Aaron D. Schimmer. Aaron is truly dedicated to his students. His patience and support gave me the space to grow as a scientist and I am grateful for his advice and mentorship over the years. I could not have asked for a more kind and understanding supervisor.

I thank my colleagues in the Schimmer lab who made my time in the lab both enjoyable and memorable. In particular, I thank Rose Hurren, who approached every experiment with dedication and enthusiasm. I also thank Marcela Gronda for her technical and scientific support. Rose and Marcela helped me greatly throughout graduate school with their expertise. I am grateful for their friendship and I will miss our daily discussions and laughs.

I’d like to thank my committee members, Dr. Steven M. Chan and Dr. G. Angus McQuibban, for their excellent guidance and insight over the years. I especially thank Steven, who welcomed me into his lab meetings and journal club over the past four years. Steven has been a trusted source of advice and a cherished clinician-scientist mentor.

Finally, I sincerely thank my parents and my brother for their unconditional love and support throughout my academic endeavors. I especially thank my loving Mom, who I lost to cancer 4 months ago. She was, and still is, my hero, role model, and best friend. I could not have completed this project without the tireless efforts of my dad and brother, who helped me immensely through the most difficult times.

Statement of Contributions

The majority of the work was performed by Sara Mirali. Technical assistance was contributed as follows:

Aaron Botham performed and analyzed the BioID-MS experiments, under the supervision of Dr. Aaron D. Schimmer.

Veronique Voisin and ChangJiang Xu performed all bioinformatics analyses under the supervision of Dr. Gary D. Bader (Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada).

Rose Hurren provided technical assistance for qRT-PCR experiments, under the supervision of Dr. Aaron D. Schimmer.

Neil Maclean provided technical assistance with lentiviral production for experiments involving genetic knockdowns, under the supervision of Dr. Aaron D. Schimmer.

Rose Hurren and Xiaoming Wang performed and analyzed data for in vivo experiments, under the supervision of Dr. Aaron D. Schimmer.

Marcela Gronda provided technical assistance for colony formation assays of primary patient samples, under the supervision of Dr. Aaron D. Schimmer.

This work has been published in Science Translational Medicine (Mirali S et al. The mitochondrial peptidase, neurolysin, regulates respiratory chain supercomplex formation and is necessary for AML viability. Sci Transl Med 2020;12.)

Table of Contents List of Abbreviations ...... ix List of Figures ...... xi List of Tables ...... xii CHAPTER 1: INTRODUCTION ...... 1 1. 1. Acute Myeloid Leukemia ...... 1 1.1.1. Pathogenesis ...... 1 1.1.2. Classification ...... 4 1.1.3. Treatment ...... 5 1.2. Mitochondria ...... 7 1.2.1. Structure and Function ...... 7 1.2.2. Mitochondrial Respiratory Chain ...... 10 1.2.3. Mitochondrial Quality Control ...... 12 1.2.4. Mitochondrial Proteases ...... 15 1.3. Respiratory Chain Supercomplexes ...... 18 1.3.1. Structure and Assembly ...... 18 1.3.2. LETM1 ...... 20 1.3.3. Function ...... 21 1.3.4. Respiratory Chain Supercomplexes in Cancer ...... 23 1.4. Neurolysin ...... 24 1.4.1. Structure and Function ...... 24 1.4.2. Localization ...... 26 1.4.3. NLN knockout mouse ...... 27 1.4.4. NLN inhibitors ...... 28 1.5. Targeting the mitochondria in AML ...... 28 1.5.1. Mitochondrial properties of AML cells ...... 28 1.5.2. Targeted therapies ...... 29 CHAPTER 2: RATIONALE AND HYPOTHESIS ...... 34 OBJECTIVES ...... 35 2.1. AIM 1: To determine the effects of NLN knockdown on the growth of leukemic cells and progenitors ...... 35 2.2. AIM 2: To characterize NLN’s mitochondrial interactors and mitochondrial function ...... 35

2.3. AIM 3: To investigate the effects of pharmacologically inhibiting NLN in leukemia ...... 35 CHAPTER 3: MATERIALS AND METHODS ...... 36 3.1. Statistical Analysis ...... 36 3.2. Bioinformatic Analysis ...... 36 3.3. Cell Lines ...... 37 3.4. Primary AML and Normal Hematopoietic Cells ...... 40 3.5. Animals ...... 42 3.6. Viral Infections ...... 42 3.7. shRNA Knockdown of AML Cell Lines ...... 44 3.8. NLN Overexpression ...... 44 3.9. Mitochondrial Protein Lysates ...... 45 3.10. Immunoblotting ...... 45 3.11. Cell Growth and Viability Assays ...... 46 3.12. Colony Formation Assays ...... 47 3.13. Proximity-Dependent Biotinylation ...... 47 3.14. Liquid Chromatography-Mass Spectrometry ...... 48 3.15. Mass Spectrometry Data Analysis ...... 48 3.16. Seahorse ...... 49 3.17. Electron Microscopy ...... 49 3.18. Mitochondrial Membrane Potential ...... 50 3.19. Mitochondrial Mass ...... 50 3.20. Mitochondrial Reactive Oxygen Species ...... 51 3.21. Cellular Reactive Oxygen Species ...... 51 3.22. Blue Native Polyacrylamide Gel Electrophoresis ...... 51 3.23. Image Quantification ...... 52 3.24. Protein Purification and Crystallization ...... 52 3.25. ClpP Activity in Isolated Mitochondria ...... 53 3.26. Hypoxia ...... 54 3.27 RNA Isolation and Quantitative Reverse Transcriptase-Real Time Polymerase Chain Reaction ...... 54 3.28 Calcium Measurements ...... 54 3.29 8227 Flow Cytometry ...... 55

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3.30. Xenograft Models of Human AML ...... 55 CHAPTER 4: RESULTS ...... 57 4.1. NLN is necessary for the viability of leukemic bulk and progenitor cells ...... 57 4.1.1. NLN is overexpressed in a subset of AML patients ...... 57 4.1.2. Genetic knockdown of NLN impairs the growth of leukemic cells and progenitors .... 61 4.1.3. NLN knockdown impairs engraftment of leukemic cells in vivo ...... 65 4.2. NLN interacts with the mitochondrial respiratory chain and genetic knockdown of NLN impairs mitochondrial function ...... 67 4.2.1. NLN’s interactors identified by BioID-MS ...... 67 4.2.2. Genetic knockdown of NLN impairs oxidative metabolism and cristae structure ...... 73 4.2.3. Knockdown of NLN reduces RCS levels ...... 78 4.2.4. Genetic knockdown of NLN does not affect viability under hypoxic conditions ...... 83 4.2.5 Respiratory chain supercomplex assembly is enhanced in a subset of AML patients and correlates with NLN expression ...... 87 4.2.6. NLN knockdown impairs LETM1 complex formation ...... 89 4.2.7. LETM1 knockdown reduces viability and oxidative metabolism in AML cells ...... 94 4.3. A small molecule inhibitor of NLN (R2) targets AML cells and stem cells ...... 96 4.3.1. R2 is cytotoxic to AML cell lines and low passage primary AML cultures in vitro ...... 96 4.3.2. R2 targets AML progenitors in vitro ...... 98 4.3.3. R2 impairs RCS formation and oxidative metabolism ...... 100 4.3.4. R2 demonstrates anti-leukemic activity in vivo ...... 105 CHAPTER 5: CONCLUSIONS AND FUTURE DIRECTIONS ...... 109 5.1. Conclusions ...... 109 5.2. Future Directions ...... 112 REFERENCES ...... 115 COPYRIGHT PERMISSIONS ...... 134

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List of Abbreviations

AML Acute myeloid leukemia

CHIP Clonal hematopoiesis of indeterminate potential

DNMT3A DNA methyltransferase 3A

TET2 Tet methylcytosine dioxygenase 2

ASXL1 ASXL transcriptional regulator 1

LSC Leukemic stem cell

CD34 Cluster of differentiation 34

CD38 Cluster of differentiation 38

HSCT Hematopoietic stem cell transplantation

LDAC Low-dose cytarabine

MDS Myelodysplastic syndromes

OS Overall survival

EFS Event-free survival

OMM Outer mitochondrial membrane

IMM Inner mitochondrial membrane

IMS Intermembrane space mtDNA Mitochondrial DNA

ATP Adenosine triphosphate

CI Complex I

CII Complex II

CIII Complex III

CIV Complex IV

CIV Complex V

NADH Reduced nicotinamide adenine dinucleotide

BN-PAGE Blue native polyacrylamide gel electrophoresis

RCS Respiratory chain supercomplexes

ROS Reactive oxygen species

OXPHOS Oxidative phosphorylation

ClpP Caseinolytic protease P

NLN Neurolysin

THOP1 Thimet oligopeptidase 1

LETM1 Leucine zipper-EF-hand containing transmembrane protein 1

HSPC Hematopoietic stem and progenitor cell

OCR Oxygen consumption rate

List of Figures

Figure 1. AML is driven by LSCs 3 Figure 2. Proposed models for the organization of respiratory chain complexes 12 Figure 3. Summary of the preclinical and clinical drugs that target AML cells 33 and stem cells Figure 4. NLN mRNA is overexpressed in a subset of AML patients 58 Figure 5. NLN is overexpressed in a subset of AML patients 59 Figure 6. Target knockdown of NLN confirmed by immunoblot 62 Figure 7. Genetic knockdown of NLN reduces the growth of leukemic cells in 63 vitro Figure 8. Genetic knockdown of NLN targets leukemic progenitor cells 64 Figure 9. Genetic knockdown of NLN impairs engraftment of leukemic cells in 66 vivo Figure 10. NLN interacts with the mitochondrial respiratory chain 72 Figure 11. Knockdown of NLN disrupts oxidative metabolism 74 Figure 12. NLN knockdown disrupts cristae ultrastructure 75 Figure 13. NLN knockdown does not alter individual OXPHOS subunits 76 Figure 14. NLN knockdown does not affect mitochondrial membrane potential, 77 mitochondrial mass, or ROS levels Figure 15. NLN knockdown impairs RCS formation 79 Figure 16. Overexpression of NLN comfirmed by immunoblot 80 Figure 17. NLN knockdown does not affect OPA1 levels 81 Figure 18. ClpXP degrades respiratory chain complex subunits and impairs 82 supercomplex formation Figure 19. RCS is downregulated under hypoxia 84 Figure 20. NLN is downregulated under hypoxia 85 Figure 21. NLN is not necessary for the growth of leukemic cells under hypoxia 86 Figure 22. Respiratory chain supercomplex assembly is enhanced in a subset 88 of AML patients and correlates with NLN expression Figure 23. LETM1 complex assembly is impaired under hypoxia 91

Figure 24. NLN knockdown impairs LETM1 complex formation 92 Figure 25. NLN knockdown does not alter cellular or mitochondrial calcium 93 levels Figure 26. LETM1 knockdown disrupts leukemic growth and oxidative 95 metabolism Figure 27. R2 is cytotoxic to AML cell lines and low passage primary AML 97 cultures in vitro Figure 28. R2 targets AML progenitors in vitro 99 Figure 29. R2 impairs RCS formation in leukemic cells 101 Figure 30. Immunoblot equal loading confirmed by Amido Black staining 102 Figure 31. R2 impairs LETM1 complex formation 103 Figure 32. R2 disrupts oxidative metabolism 104 Figure 33. R2 reduces tumor growth in an OCI-AML2 xenograft model 106 Figure 34. A small molecule inhibitor of NLN shows no evidence of toxicity in 107 a xenograft model of AML Figure 35. A small molecule inhibitor of NLN reduces the growth of leukemic 108 cells

List of Tables

Table 1. Submitochondrial localization of intrinsic mitochondrial proteases. 16 Table 2. Cell lines 39 Table 3. Clinical data of primary AML samples 41 Table 4. Correlation of NLN with mutations 60 Table 5. Complete list of NLN’s interactors identified by BioID-MS 68 Table 6. Comparison of NLN’s mitochondrial interactors with ClpP’s 70 mitochondrial interactors Table 7. LETM1 is a top mitochondrial protein interactor of NLN 90

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CHAPTER 1: INTRODUCTION

1. 1. Acute Myeloid Leukemia

1.1.1. Pathogenesis

Acute myeloid leukemia (AML) is a clinically and genetically heterogeneous hematological malignancy that is characterized by the clonal proliferation of immature blast cells (Khwaja et al., 2016). The uncontrolled proliferation of these immature blasts, called myeloblasts, impairs normal hematopoiesis, resulting in infection, anemia, and haemorrhage (Short et al., 2018). Despite recent advances and new therapies for this disease, the prognosis for most patients with AML remains poor. Patients with relapsed and refractory disease have a particularly dismal outcome. Likewise, patients who are unfit for chemotherapy, have a median survival of only 5-10 months (Dohner et al., 2010).

AML develops from a single malignant hematopoietic progenitor cell that has acquired a series of genetic and epigenetic changes, leading to transformation. It is often preceded by a premalignant state called clonal hematopoiesis of indeterminate potential (CHIP) (Jan et al., 2012; Shlush et al., 2014). CHIP can be detected in individuals without evidence of hematological malignancy. The most common mutations observed in CHIP are mutations in DNMT3A (DNA methyltransferase 3A), TET2 (tet methylcytosine dioxygenase), or ASXL1 (ASXL transcriptional regulator 1) (Genovese et al., 2014). CHIP mutations are rare in persons under the age of 40, but increase in frequency with age. For instance, CHIP is detectable in 5.6% of persons between 60 to 69 years of age, but this figure increases substantially to 18.4% in persons over the age of 90 (Jaiswal et al., 2014). Although the presence of CHIP is associated with an increased risk of developing a hematological malignancy, the absolute risk is low (0.5% per year) (Jaiswal et al., 2014).

AML is maintained by a population of cells called leukemic stem cells (LSCs). LSC- enriched fractions can be identified by immunophenotyping: the majority of LSCs reside in the CD34+CD38- fraction (Bonnet and Dick, 1997). Unlike differentiated myeloblasts,

LSCs have the capacity to self-renew and differentiate (Bonnet and Dick, 1997). Similar to normal hematopoiesis, AML is organized as a hierarchy, with LSCs at the apex (Bonnet and Dick, 1997; Lapidot et al., 1994). While normal hematopoiesis gives rise to terminally differentiated hematopoietic cells, such as red bloods cells, white blood cells, and platelets, LSCs give rise to immature myeloblasts. The rapid proliferation of these immature myeloblasts compromises the production of normal hematopoietic cells and leads to the accumulation of myeloblasts in the bone marrow, peripheral blood, and other organs (Khwaja et al., 2016).

Figure 1. AML is driven by LSCs

In normal hematopoiesis, the HSC gives rise to restricted progenitors and terminally differentiated functional blood cells. In AML, normal HSCs or progenitors acquire a series of genetic and epigenetic changes and transform into LSCs. These LSCs produce leukemic blasts, which impair normal hematopoiesis, resulting in complications such as infection, anemia, and haemorrhage. Reproduced from (Tan et al., 2006) with permission.

1.1.2. Classification

AML was originally classified based on the French-American-British (FAB) classification, in which AML subtypes were defined based on their cellular morphology after routine staining. The FAB subtypes were termed M0 to M7 and were grouped based on the differentiation state of their cell of origin. Subtypes M0 to M5 originated in leukocyte progenitors with M0 being the most primitive subtype (undifferentiated acute myeloblastic leukemia), while M6 and M7 originated in erythroid and megakaryocytic precursors, respectively (acute erythroid leukemia and acute megakaryoblastic leukemia) (Bennett et al., 1976).

While the FAB classification is useful in limited circumstances, it does not provide prognostic value for the majority of patients. In response, the World Health Organization (WHO) created a new classification system for AML, formally called the World Health Organization (WHO) Classification of Haematopoietic and Lymphoid Tissues. The WHO classification incorporates clinical information, morphology, cytogenetics, molecular genetics, and immunophenotyping to classify AML into four major groups of clinical significance: AML with recurrent genetic abnormalities, AML with myelodysplasia-related changes, therapy-related AML (secondary to exposure to cytotoxic agents and/or radiation), and AML not otherwise specified (Arber et al., 2016).

AML with recurrent generic abnormalities can be stratified into four risk groups with prognostic significance: favorable, intermediate-I, intermediate-II, and adverse (Dohner et al., 2010). In a study of 1550 adults with primary AML, 31% were classified as favorable, 18% as intermediate-I, 24% as intermediate-II, and 26% as adverse. The distribution of patients was heavily skewed with age: twice as many young patients (defined as younger than 60 years of age) were within the favorable group, with more than half presenting with core binding factor AML. Conversely, a greater proportion of older patients were classified as adverse risk (Mrozek et al., 2012). Interestingly, the same genetic mutation can confer a different prognosis depending on the patient’s age. In younger patients, NPM1 mutations are considered a favorable genetic alteration in the

4 absence of a co-occurring FLT3-ITD mutation (Dohner et al., 2005). However, in older patients, NPM1 mutations were independently associated with a favorable prognosis (Becker et al., 2010).

1.1.3. Treatment

Until recently, therapeutic options for AML were limited to intensive induction chemotherapy, followed by consolidation therapy for patients with lower risk disease or allogeneic hematopoietic stem cell transplantation (HSCT) for patients with high risk disease. Standard induction chemotherapy (also called 7+3) is a combination of 7-days of continuous infusion of 100 or 200 mg/m2 cytarabine per day and 60 mg/m2 of an anthracycline (i.e. daunorubicin or idarubicin) per day for the first three days (Dombret and Gardin, 2016). For patients under the age of 60 with favourable ELN (European Leukemia Net) risk, post-remission consolidation chemotherapy generally consists of 2-4 cycles of intermediate dose cytarabine (i.e. 1-3 g/m2 every 12 hours for 3 days per cycle). For patients with intermediate risk, consolidation therapy as described above or allogeneic HSCT can be considered, depending on the patient’s risk of relapse, donor, comorbidities, and treatment goals. Finally, patients with adverse risk disease are offered HSCT (Short et al., 2018). Allogeneic HSCT is more effective compared to consolidation chemotherapy in these patients because of pre-transplantation cytoreductive conditioning and graft-versus-leukemia effect (Gupta et al., 2011).

For patients who are not eligible for intensive chemotherapy, treatment options include low-dose cytarabine (LDAC) and hypomethylating agents (Dohner et al., 2015). Although 18% of patients achieved complete remission (CR) with LDAC, the median survival was poor at 8 months (Burnett et al., 2007). The response to the hypomethylating agents decitabine and azacitidine is more promising. Patients treated with either of these agents had longer median survival and higher 1-year survival rates compared to LDAC, but long- term survival rates were comparable (Dombret et al., 2015; Kantarjian et al., 2012).

The landscape of AML treatment has changed dramatically in the last few years. In 2017 and 2018, the Food and Drug Administration (FDA) approved several new drugs for the treatment of AML. One of these drugs is a new formulation of the classic cytotoxic agents, cytarabine and daunorubicin. CPX-351 is a liposomal formulation of cytarabine and daunorubicin in a fixed 5:1 molar ratio within liposomes that are approximately 100 nm in diameter. A phase 3 study was recently completed for this drug, which recruited patients with a history of cytotoxic therapy, antecedent myelodysplastic syndromes (MDS), chronic myelomonocytic leukemia, or AML with MDS-related cytogenetic abnormalities. This study found that CPX-351 treatment increased overall survival in older patients with newly diagnosed secondary AML (defined as AML which developed from a previous hematological disorder or ionizing radiation) compared to 7+3 chemotherapy (Lancet et al., 2018).

Fms-related tyrosine kinase 3 (FLT3) is a commonly mutated gene in AML. FLT3 internal tandem duplications (FLT3-ITD) are found in approximately 30% of patients and are associated with a poor prognosis (Frohling et al., 2002). Mutations in the tyrosine kinase domain (FLT3-TKD) are rarer, with a frequency of less than 10%, and their prognostic significance is ill-defined (Bacher et al., 2008). First-generation FLT3 inhibitors, such as sorafenib, have not shown promising results in AML (Serve et al., 2013). However, two new FLT3 inhibitors have recently been FDA approved for the treatment of AML.

Midostaurin is a multikinase inhibitor of FLT3, c-KIT, PDGFRB (platelet derived growth factor receptor beta), VEGFR-2 (vascular endothelial growth factor receptor 2), and protein kinase C (Stein and Tallman, 2016). FDA approval was based on the pivotal RATIFY study; a randomized, double-blinded trial in 717 patients between the ages of 18-59 with FLT3 mutated AML. The trial combined midostuarin or placebo with standard induction and consolidation chemotherapy. Patients who achieved remission after consolidation chemotherapy received midostaurin or placebo maintenance therapy for one year. The trial showed that the addition of midostaurin to conventional chemotherapy significantly increased OS (overall survival) and EFS (event-free survival) in patients with FLT3 mutated AML (Stone et al., 2017). Although the study did not include patients 60

6 years of age or older, the FDA did not place an age restriction on the use of midostaurin (FDA, 2017b). However, a more recent trial showed a significant increase in cardiac adverse events in patients over the age of 60 (Schlenk et al., 2019). In addition to midostaurin, another multikinase inhibitor called gilteritinib was approved for the treatment of patients with relapsed or refractory FLT3-mutated AML. Gilteritinib inhibits FLT3, c-Kit, and AXL (Mori et al., 2017). In a phase 3 randomized trial of 347 patients over the age of 18 with relapsed or refractory FLT3 mutated AML, treatment with gilteritinib improved OS and remission rates compared to salvage chemotherapy (Perl et al., 2019).

Gemtuzumab ozogamicin (GO) is an antibody-drug conjugate that was recently FDA approved as frontline therapy in combination with daunorubicin and cytarabine or as a monotherapy for the treatment of adult patients with CD33 positive AML. GO is a monoclonal anti-CD33 antibody joined to the antitumor antibiotic, calicheamicin (Hamann et al., 2002). A randomized, open label, phase 3 study showed that the addition of GO to standard 7+3 chemotherapy in patients with de novo AML improved OS by 15 months and EFS by 8 months (Castaigne et al., 2012). Subsequent analysis showed that improved EFS was only evident in patients with over 70% CD33+ expression in AML blasts (Olombel et al., 2016). GO’s approval as a monotherapy was based on a randomized trial of 237 patients over the age of 60 with newly diagnosed AML who were unfit for intensive chemotherapy. Patients who received GO had significantly greater OS compared to patients receiving best supportive care (Amadori et al., 2016).

Several recently approved therapies, such as IDH inhibitors and venetoclax, target the unique mitochondrial properties of AML cells. These drugs are discussed in section 1.5.2.

1.2. Mitochondria

1.2.1. Structure and Function

According to the endosymbiotic theory, mitochondria originated two billion years ago from the engulfment of an α-proteobacterium by a nucleus-containing eukaryotic cell (Gray et

7 al., 1999). Much like their prokaryotic ancestor, mitochondria have their own double membrane. The outer mitochondrial membrane (OMM) is a phospholipid bilayer that is permeable to small molecules, such as ions, nutrients, ATP (adenosine triphosphate), and ADP (adenosine diphosphate). Molecules larger than 5000 Da pass through the OMM via translocases (Kuhlbrandt, 2015; Mannella, 1992). The protein-rich inner mitochondrial membrane (IMM) protrudes into the matrix, forming a series of invaginations known as cristae. Compared to the OMM, the IMM has much stricter diffusion requirements and is only permeable to small, uncharged molecules. The majority of molecules and proteins cross the IMM through specific membrane transporters (Kuhlbrandt, 2015). Thus, the IMM is extremely protein rich, with 60-70% of its mass consisting of proteins (Becker et al., 2009).

Unlike other organelles, mitochondria contain their own circular genome and translation machinery, another remnant of their prokaryotic origin (Gray et al., 1999). Mitochondrial DNA (mtDNA) is found within the matrix and each mitochondrion contains several copies (Shuster et al., 1988). mtDNA consists of two strands which differ in their G+T base composition and buoyant densities in a cesium chloride gradient, resulting in a heavy and light chain (Kasamatsu and Vinograd, 1974). mtDNA is approximately 16,600 base pairs in length and does not have introns. The displacement loop, or D-loop, is a short non- coding stretch of the genome containing regulatory elements for mtDNA transcription and replication. Transcription begins from a single promotor on the light chain, whereas the heavy chain initiates transcription from two separate sites that are differentially regulated. The heavy chain encodes 2 rRNAs (ribosomal ribonucleic acid), 14 tRNAs (transfer ribonucleic acid), and 12 polypeptides. The light chain produces 8 tRNAs and 1 polypeptide (Anderson et al., 1981; Chomyn et al., 1986). Each protein and rRNA gene is flanked by a tRNA sequence. These flanking tRNA regions are excised from the polycystronic precursor RNA to produce mRNA and rRNA transcripts, a process referred to as the tRNA punctuation model (Ojala et al., 1981). Maturation of the tRNA transcripts is completed by the addition of a CCA sequence to their 3’ end (Rossmanith et al., 1995), while mitochondrial mRNAs are polyadenylated and rRNAs undergo a 3’ addition of short adenyl sequences (Dubin et al., 1982; Rose et al., 1975).

The 13 polypeptides encoded by mtDNA are constituents of the mitochondrial respiratory chain complexes I, III, IV, and V. Their corresponding mRNAs are unique in that they have few 5’ untranslated nucleotides, are uncapped, and their polyadenylated tail is closely associated with a stop codon (Rossmanith et al., 1995). These mRNAs are recognized and bound by mitochondrial ribosomes, with no assistance from initiation factors or initiation tRNAs (Smits et al., 2010). Translation initiation is mediated by IF2 (translation initiation factor 2) and IF3 (translation initiation factor 3): IF2 selects the initiator tRNA and IF3 aids in ribosomal recycling by preventing the re-association of ribosomal subunits post-translation (Kuzmenko et al., 2014).

Although the mitochondria retain a unique genome and translational machinery, they are not self-supporting organelles. There are over 1000 proteins within the mitochondria and, with the exception of the few produced by mtDNA, they are all encoded by nuclear DNA (nDNA) and transported to the mitochondria. These proteins are transcribed in the nucleus, translated in the cytoplasm, and associate with cytosolic chaperones to maintain an unfolded state. They are escorted to the mitochondria and imported through translocation channels in the OMM that recognize their mitochondrial targeting sequence (Schmidt et al., 2010). Some proteins, mostly of prokaryotic ancestry, are made on ribosomes that are directly bound to the OMM, coupling translation and transport (Verner, 1993). Most nDNA encoded proteins enter the mitochondria via the translocase of the OMM (TOM). Depending on their final localization within the mitochondria, several routes are possible. If the protein is destined for the OMM, the sorting and assembly machinery (SAM) mediates their insertion. IMS proteins are drawn in by the translocase of the IMM 23 (TIM23) complex. IMM proteins can be integrated directly into the IMM by TIM22 or TIM23, or they can initially be imported into the matrix by TIM23 and then exported to the IMM. Finally, matrix proteins reach their destination by passing through the TIM23 channel. Following their proper localization, the targeting sequence is cleaved and the proteins are free to fold into their tertiary structures (Smits et al., 2010).

1.2.2. Mitochondrial Respiratory Chain

The mitochondrial respiratory chain is organized as a series of protein complexes embedded in the inner mitochondrial membrane (IMM). These complexes transfer electrons from NADH (reduced nicotinamide adenine dinucleotide) or FADH2 (reduced flavin adenine dinucleotide) to oxygen while simultaneously generating an electrochemical gradient across the IMM. Complex I (CI) transfers electrons from NADH to complex III (CIII) via ubiquinone, an electron carrier. CIII then transfers electrons to complex IV (CIV) via cytochrome c, after which they are transferred to the terminal electron acceptor, molecular oxygen, and reduced to water. Electrons can also enter the chain through complex II (CII), through the oxidation of FADH2. CI, CIII, and CIV directly contribute to the electrochemical proton gradient by pumping protons across the membrane, whereas CII contributes indirectly by reducing the ubiquinone pool. This gradient is then used by complex V (CV) to produce ATP (Newmeyer and Ferguson- Miller, 2003).

The structural organization of the mitochondrial respiratory chain has been debated for decades. The initial “solid state” model was proposed by Chance and Williams in 1955, in which all the complexes exist as a single unit with a dedicated pool of electron carriers (Chance and Williams, 1955). This was later replaced by the “fluid state” or “random collision” model, which was outlined by Hackenbrock and colleagues in 1986 (Hackenbrock et al., 1986b). In this model, respiratory chain complexes and redox components exist independently and freely diffuse within the IMM, and electron transfer occurs by the diffusion based collisions of the respiratory chain complexes and electron carriers (Chazotte and Hackenbrock, 1988; Gupte and Hackenbrock, 1988; Hochli and Hackenbrock, 1976). The fluid state model was supported by kinetic analyses showing that ubiquinone and cytochrome c obeyed pool behavior and that individual respiratory chain complexes retain their enzymatic activity in isolation (Hatefi et al., 1962; Kroger and Klingenberg, 1973b). The fluid state model was called into question after several studies isolated complexes containing both CIII and CIV from bacteria (Berry and Trumpower, 1985; Iwasaki et al., 1995; Sone et al., 1987). With the advent of blue native

10 polyacrylamide gel electrophoresis (BN-PAGE) in 2000, Schagger and colleagues demonstrated that yeast and mammalian respiratory chain complexes exist in isolation and in association as supercomplexes (RCS) (Cruciat et al., 2000; Schagger and Pfeiffer, 2000). RCS were initially met with skepticism as some considered them to be artifacts of mild detergent solubilization. However, their existence was later confirmed by other methods, such as solubilization by additional non-ionic detergents, separation by ultracentrifugation on sucrose density gradients, and single-particle cryo-electron microscopy (Acin-Perez et al., 2008; Dudkina et al., 2005; Gu et al., 2016; Letts et al., 2016b; Wu et al., 2016). Most recently, a third model has been proposed in which the complexes coexist in both individual and higher-order structures. In this “plasticity” model, the relative abundance and composition of the individual subunits and RCS may vary depending on tissue type or energetic demands (Acin-Perez and Enriquez, 2014).

RCS from mammalian mitochondria vary in composition (Fig. 2) (Greggio et al., 2017;

Schagger and Pfeiffer, 2001). The majority of CI assembles with CIII dimers (SC I+III2) and CIV (SC I+III2+IV). The SC I+III2+IV supercomplex is also referred to as a respirasome as it can transfer electrons from NADH to oxygen (Schagger and Pfeiffer,

2001). CIII exists as a dimer (CIII2) and in complex with CI (SC I+III2) and CIV (SC III2+IV) (Schagger and Pfeiffer, 2000). CIV and CV exist independently, but can also form dimers

(CIV2 and CV2) (Davies et al., 2011; Tsukihara et al., 1996). While CV2 is essential for membrane bending and cristae formation (Hahn et al., 2016), the function of CIV2 is unknown. Finally, there is conflicting evidence for the incorporation of Complex II (CII) into supercomplexes. While small amounts of supercomplex-associated CII have been described in mouse mitochondria (Acin-Perez et al., 2008), BN-PAGE analysis and kinetic studies suggest that CII is a fully independent entity (Genova and Lenaz, 2014; Schagger and Pfeiffer, 2000).

1.2.3. Mitochondrial Quality Control

Figure 2. Proposed models for the organization of respiratory chain complexes.

The traditional model postulates that each complex is independent of one another and electron transfer depends on the random collision between complexes and electron carriers (A). Recent studies have found that respiratory complexes organize into large macromolecular structures called RCS (B).

Mitochondria produce high levels of reactive oxygen species (ROS) as a consequence of oxidative phosphorylation (OXPHOS). Elevated ROS levels can disrupt proteins, induce lipid peroxidation, and cause DNA damage. In this harsh environment, mitochondria depend on several quality control mechanisms. The first mechanism relies on the selective elimination of damaged or misfolded mitochondrial proteins. The mitochondrial proteome is tightly monitored by the collaboration of the ubiquitin-proteome system (UPS) in the cytosol and a mitochondrial-specific unfolded protein response.

The cytosolic UPS is highly selective and tags proteins for degradation by the covalent linkage of ubiquitin. The cytosolic UPS is tightly linked with mitochondrial quality control: reduced UPS activity impairs mitochondrial function (Segref et al., 2014). The cytosolic UPS influences mitochondrial quality control through two main mechanisms. The first mechanism is within the cytosol before nuclear-encoded proteins are targeted to the mitochondria. Preproteins that are destined for the mitochondria can be ubiquitinated and degraded by the cytosolic UPS. These include OMM, IMS, and matrix mitochondrial proteins (Jeon et al., 2007). For instance, 2-oxoglutarate dehydrogenase complex, a rate limiting enzyme within the mitochondrial Krebs cycle, is ubiquitinated and degraded shortly after translation and before its translocation to the mitochondria (Habelhah et al., 2004). Secondly, the cytosolic UPS directly targets proteins imbedded within the OMM. Key mitochondrial proteins that control apoptosis and mitochondrial dynamics are susceptible to UPS mediated degradation, such as BAX (BCL2 associated X protein), DRP1 (dynamin-related protein 1), and MFN1/2 (mitofusin 1/2) (Benard et al., 2010; Burchell et al., 2013; Karbowski et al., 2007; Nakamura et al., 2006). The cytosolic UPS can also degrade proteins embedded within the IMM. For example, upon mitochondrial stress, a subunit of respiratory chain complex V is translocated from the IMM to the OMM and tagged for degradation by the cytosolic UPS (Margineantu et al., 2007). This is analogous to the UPS mediated degradation of proteins within the ER lumen, which is partitioned away from the cytosol. Upon induction of ER stress, proteins are translocated from the ER lumen into the cytosol, where they are ubiquitinated and degraded through the ER-associated degradation pathway (Hiller et al., 1996; Ye et al., 2004).

Within the mitochondria, chaperones and proteases work in tandem to repair or swiftly remove defective proteins. The Hsp60 and Hsp70 chaperones prevent protein aggregation and facilitate proper folding, while ATP-dependent proteases destroy defective proteins, which are then degraded by oligopeptidases (Koppen and Langer, 2007; Voos and Rottgers, 2002). These multimeric ATP-dependent proteases can be divided into three major classes: Lon, Clp-like, and other AAA proteases. Lon and Clp- like proteases are found in the matrix and are also capable of acting as chaperones. The AAA proteases are embedded in the IMM and target dysfunctional IMM proteins. Their activity is counteracted by the prohibin complex, which protects OXPHOS subunits from degradation by AAA proteases (Bohovych et al., 2015). The AAA proteases can be further divided into two subclasses: those with their active site exposed to the intermembrane space (i-AAA) and those that act in the matrix (m-AAA). In addition to the ATP-dependent proteases described above, two ATP-independent proteases (ATP23 and HTRA2) and two oligopeptidases (P1TRM1 and neurolysin) also monitor mitochondrial protein control (Quiros et al., 2015).

The mitochondrial network is constantly being shaped by two processes called fission and fusion. Dysfunctional mitochondria can fuse with intact mitochondria and exchange their contents, DNA, and metabolites to retain the integrity of the mitochondrial network. Conversely, fission secludes irreparable mitochondria from the network to be degraded by mitophagy (Westermann, 2010). Mitochondrial fusion is mediated by three membrane bound GTPases: Mfn1/2 within the OMM and OPA1 (optic atrophy 1) within the IMM (Cipolat et al., 2004; Santel and Fuller, 2001). Mitochondrial fission is mediated by the cytosolic protein, Drp1 (Smirnova et al., 2001).

The balance between fission and fusion shapes the mitochondrial network (Westermann, 2010). For instance, the post-translational processing of the mitochondrial fusion protein, OPA1, is regulated by two proteases within the IMM: YME1L and OMA1. Cleavage by these enzymes creates the pro-fission short isoform of OPA1. A balance of short and long OPA1 isoforms is maintained by YME1L. However, under conditions of mitochondrial stress, OMA1 is induced and cleaves OPA1, which blocks the fusion of damaged

14 mitochondria with intact mitochondria (Anand et al., 2014). Moreover, OPA1 is also a key regulator of cristae morphology and its cleavage disrupts cristae structure and initiates apoptosis through the release of cytochrome c (Cipolat et al., 2006; Frezza et al., 2006).

1.2.4. Mitochondrial Proteases

Mitochondrial proteases are a highly diverse group of enzymes. Traditionally, the main function of mitochondrial proteases was thought to be the cleavage of target sequences following mitochondrial import and non-specific degradation of defective mitochondrial proteins (Quiros et al., 2015). However, recent work has found that mitochondrial proteases also undergo highly specific proteolytic reactions. By modulating the activity of an expansive range of mitochondrial proteins, mitochondrial proteases are involved in various biochemical pathways such as fission and fusion dynamics, mitophagy, cellular aging, and apoptosis (Anand et al., 2013; Lopez-Otin et al., 2013).

Mitochondrial proteases can be classified based on several properties, including location, function, structure, and catalysis. Catalytically, they are categorized into 3 groups: serine proteases, cysteine proteases, and metalloproteases. Location-wise, three subgroups exist: 20 intrinsic, 5 pseudo-mitochondrial proteases, and over 20 transient mitochondrial proteases. Intrinsic mitochondrial proteases are mainly active in the mitochondria and are present in all compartments. Pseudo-mitochondrial proteases are structurally similar to other proteases but cannot catalyze proteins. Instead, they regulate proteolytic activity (i.e. α-MPP) or have their own unique function (i.e. UQCRC1). Transient mitochondrial proteases are located elsewhere in the cell but localize to the mitochondria when needed, such as during apoptosis or autophagy (Quiros et al., 2015). These classes can be further categorized based on the specific mitochondrial compartment they act in. Table 1 summarizes the location of the intrinsic mitochondrial proteases.

Protease Subcompartment Reference AFG3L2 IMM (Maltecca et al., 2008) ATP23 IMS (Osman et al., 2007) ClpP Matrix (Gispert et al., 2013) HtrA2 IMS (Clausen et al., 2011) IMMP1L IMM (Nunnari et al., 1993) IMMP2L IMM (Nunnari et al., 1993) LactB IMS (Polianskyte et al., 2009) LonP Matrix (Quiros et al., 2014) MetAP1D Matrix (Kremmidiotis et al., 2001) MIP Matrix (Vogtle et al., 2011) NLN Matrix (Teixeira et al., 2018) OMA1 IMM (Quiros et al., 2012) OSGEPL1 Matrix (Oberto et al., 2009) PARL IMM (Cipolat et al., 2006) PITRM1 Matrix (Alikhani et al., 2011) PMPCB Matrix (Gakh et al., 2002) SPG7 IMM (Ferreirinha et al., 2004) USP30 OMM (Nakamura and Hirose, 2008) XPNPEP3 Matrix (O'Toole et al., 2010) YME1L IMM (Stiburek et al., 2012)

Table 1. Submitochondrial localization of intrinsic mitochondrial proteases.

AFG3L, AFG3-like protein; ATP23, Mitochondrial inner membrane protease ATP23; ClpP, caseinolytic protease P; HtrA2, Serine protease HtrA2; IMMP, mitochondrial inner membrane protease subunit; LactB, Ser protease β-lactamase-like protein; LonP, Lon protease homologue; MetAP1D, Met aminopeptidase 1D; MIP, mitochondrial intermediate peptidase; NLN, neurolysin; OMA1, Metalloendopeptidase OMA1; OSGEPL1, O-sialoglycoprotein endopeptidase-like protein 1; PARL, presenilins- associated rhomboid-like protein; PITRM1, presequence protease; PMPC, mitochondrial-processing peptidase subunit; SPG7, paraplegin; USP, ubiquitin carboxyl- terminal hydrolase; XPNPEP3, X-Pro aminopeptidase 3; YME1L1, ATP-dependent zinc metalloprotease YME1L1; IMM, inner mitochondrial membrane; OMM, outer mitochondrial membrane; IMS, intermembrane space.

Our knowledge on the function of these enzymes has been accelerated by studying pathologies caused by deficient or defective mitochondrial proteases. Mutations in mitochondrial proteases have been implicated in neurological and neurodegenerative disorders, such as Parkinson’s (HTRA2), Alzheimer’s (PITRM1), and Tourette syndrome (IMMP2L) (Bertelsen et al., 2014; Falkevall et al., 2006; Strauss et al., 2005). Mutations in PARL have been linked to type 2 diabetes and knockout mouse models of OMA1 and LACTB suffer from obesity (Civitarese et al., 2010; Polianskyte et al., 2009; Quiros et al., 2012). LONP overexpression has been shown to help tumors adapt to hypoxic conditions while upregulation of HTRA2 pushes hepatocellular carcinoma cells to apoptosis (Fukuda et al., 2007; Xu et al., 2015). These examples highlight the importance of mitochondrial protein homeostasis in disease. However, many of these proteases are poorly characterized and little is known about their physiology and function.

The consequences of inhibiting mitochondrial proteases in AML cells remain largely unknown. The matrix mitoprotease, ClpP, was recently identified as a promising target in AML. Cole et al. found that the majority of ClpP’s 48 interacting proteins were components of the respiratory chain or metabolic enzymes. (Cole et al., 2015). Accordingly, ClpP deficient AML cells demonstrated reduced basal oxygen consumption rates and impaired respiratory chain complex II activity. Consistent with previous findings that showed AML cells relied on OXPHOS, chemical and genetic inhibition of ClpP was cytotoxic to AML cells and stem cells (Cole et al., 2015; Lagadinou et al., 2013; Skrtic et al., 2011). Within the same shRNA screen that initially identified ClpP as a promising target in leukemic cells, three other mitochondrial proteases, NLN, PARL, and PITRM1, were among the top hits.

Interestingly, another study later demonstrated that a small molecule hyperactivator of ClpP, ONC201, also demonstrated anti-leukemic activity (Ishizawa et al., 2019). ONC201 binds to ClpP and increases the size of its axial entrance pore from 12 Å to 17 Å, maintaining ClpP’s open state. ONC201 treatment increased ClpP mediated proteolysis and impaired leukemic growth in vitro and in vivo. Similar to ClpP inhibition, ClpP activated

17 by ONC201 disrupted oxidative metabolism and increased mitochondrial ROS. Collectively, these findings suggest that mitochondrial proteases are tightly regulated in leukemia, as both inhibition and hyperactivation of ClpP is cytotoxic (Cole et al., 2015; Ishizawa et al., 2019).

1.3. Respiratory Chain Supercomplexes

1.3.1. Structure and Assembly

Supercomplexes from several mammalian species have been characterized by single- particle cryo-microscopy (Althoff et al., 2011; Dudkina et al., 2011; Gu et al., 2016; Guo et al., 2017; Letts et al., 2016b; Wu et al., 2016). In the mammalian I+III2+IV supercomplex, the membrane arm of CI loops around the CIII dimer and interacts with CIV at its distal tip. CIV interacts with both CI and III, and its location within the supercomplex varies. Letts et al. identified two conformations of the I+III2+IV supercomplex in ovine mitochondria: loose and tight. These two states were defined by the position of complex IV. In the loose respirasome, complex IV was positioned away from complex III while in the tight respirasome, complex IV remained closely associated

(Letts et al., 2016b). In addition to the loose and tight I+III2 +IV structures, Letts et al. also detected a supercomplex lacking CIV, supercomplex I+III2 (Letts et al., 2016b). However, it is not clear whether these conformations represent distinct structures or different stages of assembly.

Two main models for the assembly of RCS have recently been proposed. In the first model, Moreno-Lastres et al. treated cells with doxycycline to deplete RCS (Moreno- Lastres et al., 2012). They then collected samples over a period of 96 hours after the removal of doxycycline and analyzed supercomplex re-assembly by 2D BN-PAGE. In this model, CIII and CIV are assembled independently. Eventually, their assembly plateaus and there is an accumulation of free CIII and CIV subunits. These free subunits assemble onto a partially assembled CI, which forms the first of five subcomplexes. Subsequent subcomplexes are formed by the addition of more CI, CIII, and CIV subunits. In the final

18 stage of RCS assembly, the respirasome is made functional by the incorporation of the N catalytic module of complex I (Moreno-Lastres et al., 2012). Based on this model, CI is only active when incorporated within a supercomplex and does not exist in the absence of CIII and/or CIV. While this model explains why certain mutations that disrupt CIII and CIV assembly result in secondary deficiencies in CI (D'Aurelio et al., 2006; Moran et al., 2010), it contradicts previous studies which show the presence of enzymatically active CI in the absence of CIV assembly (Balsa et al., 2012). Moreover, it does not align with previous reports that show CI is enzymatically active independent of its incorporation into supercomplexes (Acin-Perez et al., 2008; Hatefi et al., 1962; Hou et al., 2019).

A more recent model of RCS assembly was established by dynamic complexome profiling of CI assembly. Guerrero-Castillo et al. treated cells with chloramphenicol and monitored the re-assembly of CI by tracking 60 CI subunits and assembly factors over a period of 24 hours after chloramphenicol withdrawal (Guerrero-Castillo et al., 2017). In this model, CI is fully assembled before integrating into supercomplexes. This is in line with previous reports that CI and supercomplex assembly occur sequentially (Acin-Perez et al., 2008).

Supercomplex assembly is closely related to mitochondrial cristae structure and cellular conditions (Cogliati et al., 2013). Cristae shape is modulated according to the energetic requirements of the cell. When exposed to acute stressors, such as nutrient deprivation or short-term increases in ROS, mitochondria fuse and elongate to increase energy production and efficiency (Gomes et al., 2011; Jendrach et al., 2008). However, under conditions of nutrient excess, mitochondria undergo fission, mitochondrial uncoupling, and ATP reduction (Molina et al., 2009). Collectively, these findings demonstrate the link between cristae structure and mitochondrial bioenergetics.

Within the cristae, the respiratory chain is strictly compartmentalized. Supercomplexes are located along the planar surface, while ATP synthase dimers reside on the cristae’s highly curved tips (Davies et al., 2011). This creates a proton gradient within the cristae, facilitating the flow of electrons from the planar edges to the tips where ATP is being consumed (Rieger et al., 2014). Disruption of the highly organized cristae structure

19 impairs RCS assembly and mitochondrial respiration: cells with disorganized cristae do not grow well in galactose depleted media, where the majority of ATP is derived from OXPHOS (Cogliati et al., 2013).

1.3.2. LETM1

RCS assembly depends on the presence of specific proteins that play important roles in their formation and stabilization. These include cardiolipin, COX7A2L (cytochrome c oxidase subunit 7A2 like), and Rcf1-3 (respiratory supercomplex factor 1-3) (Chen et al., 2012; Lapuente-Brun et al., 2013; Pfeiffer et al., 2003; Strogolova et al., 2012; Vukotic et al., 2012). One assembly factor which will be explored later in this thesis is LETM1 (leucine zipper-EF-hand containing transmembrane protein 1) (Tamai et al., 2008). LETM1 is an IMM protein containing one transmembrane domain. Its N-terminus, which resides in the IMS, contains a mitochondrial targeting sequence and a protein kinase C phosphorylation site, while its C-terminus, which extends into the matrix, contains two EF- hand domains (Endele et al., 1999; Shao et al., 2016). LETM1 forms a hexamer with a central cavity, which functions as a Ca2+/H+ antiporter (Jiang et al., 2009b; Shao et al., 2016). It plays an important role in calcium homeostasis under low cytosolic calcium concentrations and its activity is limited by the proton gradient across the IMM (Jiang et al., 2009b; Jiang et al., 2013). The LETM1 gene is frequently deleted in Wolf-Hirschhorn syndrome, a genetic disorder characterized by intellectual disability, muscle weakness, and seizures (Endele et al., 1999). In particular, LETM1 is a candidate gene for seizures in this disorder, as LETM1 is deleted in Wolf-Hirschhorn patients presenting with seizures but is not deleted in patients without seizures (Zollino et al., 2003). LETM1 has also been studied in malignancy: increased expression of LETM1 has been observed in multiple types of cancers and overexpression is associated with poor outcomes in breast cancer and lung cancer (Li et al., 2015; Piao et al., 2009; Piao et al., 2019).

LETM1 also modulates mitochondrial morphology and respiration. Knockdown of LETM1 results in decreased oxygen consumption, mitochondrial swelling, disrupted cristae formation, and perturbed mitochondrial tubular networks (Dimmer et al., 2008; Doonan et

20 al., 2014; Tamai et al., 2008). Moreover, although knockdown of LETM1 did not reduce the amount of respiratory chain complex proteins, it did impair their assembly into RCS. LETM1 forms two major complexes: the minor and the major complex. The AAA-ATPase, BCS1L, binds to LETM1 and stimulates the formation of the LETM1 major complex. Similar to LETM1, knockdown of BCS1L disrupted supercomplex formation without impacting any of the individual respiratory chain subunit levels (Tamai et al., 2008). Tamai et al. proposed a model in which BCS1L promotes the formation of the LETM1 major complex, which maintains mitochondrial morphology and the assembly of respiratory chains. Collectively, these results suggest that LETM1 is important in mitochondrial structure and function, as well as supercomplex assembly.

1.3.3. Function

Given that supercomplexes are evolutionary conserved, several studies have attempted to identify the structural and/or functional advantages of supercomplex assembly. Functionally, increased mitochondrial supercomplex formation has been associated with increased oxidative metabolism (Greggio et al., 2017; Hou et al., 2019; Lopez-Fabuel et al., 2016) but the mechanism underlying this functional advantage is debated. Initially, supercomplexes were thought to sequester coenzyme Q and cytochrome c pools, preventing their exchange with the rest of the respiratory chain (Lapuente-Brun et al., 2013). However, this data contradicted several kinetic studies suggesting that coenzyme Q and cytochrome c are freely exchanged within the IMM (Blaza et al., 2014; Gupte et al., 1984; Kroger and Klingenberg, 1973a; Kroger and Klingenberg, 1973b). Moreover, in another study, it was shown that the same model which proposed the existence of dedicated electron carrier pools could exhibit both additive and non-additive kinetics, based on the amount of cytochrome c present (Blaza et al., 2014).

A similar theory postulated that supercomplex assembly confers a kinetic advantage by channeling substrates between respiratory complexes (Bianchi et al., 2004; Trouillard et al., 2011). This was later challenged by single particle cryo-microscopy of mammalian supercomplexes, which demonstrated that there were no channels connecting the binding

21 sites for ubiquinone or cytochrome c between CI and CIII or CIII and CIV, respectively (Gu et al., 2016; Wu et al., 2016). However, these findings do not exclude the possibility that supercomplexes promote the diffusion of electron carriers within the membrane. In grana thylakoid membranes, which contain high protein/lipid ratios, supercomplexes increase the rate of quinone diffusion (Tremmel et al., 2003). Structural data suggest that supercomplexes have a similar role in mammalian mitochondria. In mammalian mitochondria, only one of the CIII dimers is active. The quinol oxidation side of the active CIII monomer faces the complex I quinol site (Sousa et al., 2016). In this case, ubiquinol reduced by CI is trapped quickly by complex III, increasing the efficiency of electron transfer. Likewise, the cytochrome c binding site of the active complex III monomer is in close proximity to CIV (Sousa et al., 2016). In summary, supercomplexes confer a functional advantage, likely by promoting the diffusion of electron carriers by optimizing the orientation of the respiratory chain complexes.

It has been suggested that respiratory chain supercomplexes reduce ROS production. Lopez-Fabuel et al. correlated the amount of ROS production in neurons and astrocytes with the amount of CI incorporated into supercomplexes. Astrocytes have more free CI, higher ROS production, and impaired oxygen consumption compared to neurons (Lopez- Fabuel et al., 2016). Another study demonstrated that CI mediated ROS production is reduced when CI joins CIII to form the I+III2 supercomplex (Maranzana et al., 2013). However, CI and CIII were separated by high concentrations of dodecyl maltoside detergent, which would inhibit complex I activity and disrupt the membrane (Letts et al., 2016a). Results from both of these studies were later questioned as their conclusions were inconsistent with structural data (reviewed in (Milenkovic et al., 2017)). Thus, more work is required to understand the role of supercomplexes in ROS production.

Respiratory chain complexes may be more stable when integrated into supercomplexes. Certain mutations in CIII and CIV result in secondary defects in CI and supercomplex assembly (Acin-Perez et al., 2004; Diaz et al., 2006). Conversely, mutations in CI do not impact CIII or CIV to the same extent (Schagger et al., 2004). Collectively, these results suggest that CI is stabilized by CIII and CIV. However, there are notable exceptions.

Assembled CI can be detected in the absence of CIV (Balsa et al., 2012) and there are multiple instances in which a CIV deficiency does not alter other complexes (Antonicka et al., 2003; Huigsloot et al., 2011; Weraarpachai et al., 2009). Moreover, impaired CI stability in the context of CIII or CIV mutations may be due to increased ROS levels, rather than CI’s stability in supercomplexes. When electron transfer is blocked at CIII or CIV, accumulation of electron carriers triggers reverse electron transport and increased superoxide production at CI, leading to CI instability (Diaz et al., 2012; Guaras et al., 2016). Therefore, while the combined deficiencies observed in respiratory chain supercomplexes are interesting, they do not confirm that supercomplexes enhance the stability of any one complex.

Another theory suggests that mitochondrial supercomplexes prevent protein aggregation within the protein-rich inner mitochondrial membrane, which is composed of 30% phospholipids (Fleischer et al., 1961). A comparable adaptation is observed in the eye lens, which is densely packed by proteins called crystallins. The weak association between these proteins maintains the lens’s gel state and prevents aggregation (Slingsby et al., 2013). Likewise, by promoting weak interactions between the complexes, supercomplexes may prevent clumping that would be detrimental to their function.

1.3.4. Respiratory Chain Supercomplexes in Cancer

The role of supercomplex assembly in malignancy has not been fully characterized but recent work has demonstrated the importance of supercomplex assembly in cancer metabolism. Supercomplex assembly was recently studied in breast cancer because the supercomplex assembly factor COX7RP (cytochrome c oxidase subunit VII-related protein, also known as COX7A2L) is an estrogen-responsive gene (Ikeda et al., 2019; Ikeda et al., 2013; Lapuente-Brun et al., 2013; Watanabe et al., 1998). COX7RP expression was increased in breast cancer compared to normal breast tissue and increased COX7RP expression was associated with inferior OS and disease free survival in breast cancer patients. Mechanistically, COX7RP overexpression promoted RCS assembly under hypoxic conditions, which reduced mitochondrial ROS, increased the

23 expression of TCA (tricarboxylic acid) cycle enzymes, and enhanced oxidative metabolism. Moreover, genetic inhibition of COX7RP reduced tumor growth in mice (Ikeda et al., 2019). Collectively, these data suggest that increased assembly of supercomplexes promotes hypoxia resistance and tumor growth in breast cancer. It also provides a rationale for targeting supercomplex assembly as a therapeutic strategy in malignancy.

In addition to breast cancer, supercomplex assembly has also been investigated in metastatic lung cancer (Elkholi et al., 2019). MDM2 (mouse double minute 2 homolog) is an E3 ubiquitin ligase and a transcriptional repressor of p53 (Momand et al., 1992). MDM2 levels are dramatically increased in cancer and high expression is correlated with poor prognosis in multiple cancer types (Bueso-Ramos et al., 1995; Bueso-Ramos et al., 1993; Matsumura et al., 1996; Reifenberger et al., 1993). In addition to its role as a transcriptional repressor of p53, MDM2 is also involved in several p53 independent pathways and demonstrates both tumor suppressor and oncogenic properties (Jones et al., 1998; Manfredi, 2010).

Interestingly, in metastatic lung cancer cell, MDM2 overexpression induced cell death independently of its known ubiquitin ligase activity or its transcriptional regulation of p53. Instead, Elkholi et al. found that MDM2 overexpression binds NDUFS1 and holds it in the cytoplasm, preventing its translocation into the mitochondria. In the absence of NDUFS1, supercomplexes fail to form, leading to increased ROS production, impaired oxidative metabolism, and increased cell death (Elkholi et al., 2019). These findings further highlight the importance of supercomplex assembly in cancer metabolism.

1.4. Neurolysin

1.4.1. Structure and Function

Neurolysin (NLN) (also known as oligopeptidase M, EP 24.16, and EC 3.4.24.16) is a 78 kDa zinc metalloprotease with a conserved His-Glu-X-X-His (HEXXH) sequence motif

(Brown et al., 2001). The two histidine residues bind a zinc ion cofactor and the glutamate polarizes a water molecule, which acts as the attacking nucleophile. Its ellipsoid structure is divided by a deep cleft, forming two domains that are loosely connected at the base (Brown et al., 2001). The two domains move together as a hinge to close around the ligand, with domain II rotating 15º along its hinge axis and domain I rotating less than 5º (Teixeira et al., 2018). NLN’s active site is covered by large structural elements, which limits substrate access, and is buried within an increasingly narrowing channel at the bottom of the second domain (Brown et al., 2001). Consequently, its substrates are small peptides without bulky secondary or tertiary structures and include neurotensin, bradykinin, angiotensin I and II, substance P, hemopressin, dynorphin A, metorphamide, and somatostatin (Checler et al., 1995; Dahms and Mentlein, 1992; Rioli et al., 2003; Rioli et al., 1998; Vincent et al., 1996b).

In addition to maintaining an unfolded structure, NLN’s substrates must meet additional specifications. NLN’s cleft cannot accommodate peptides that contain more than 10 residues from the N-terminus of the cleavage site. A longer N-terminus will result in improper alignment of the catalytic substrate and the active site. There is more flexibility at the C-terminus, because the substrate is not restrained by the enzyme and can extend out of the channel (Brown et al., 2001). NLN’s active site must recognize specific cleavage sequences of several substrates, who themselves are cleaved by many other proteases. NLN uses two strategies to accommodate for sequence variation, without sacrificing binding affinity and substrate positioning. First, the active site is surrounded by flexible loops and open coils that freely alter their conformation to accommodate different substrates. Secondly, one of the loops within the active site is highly flexible. Depending on the substrate, the loop rearranges to expose the appropriate residue required for interaction of the substrate with the active site (Brown et al., 2001).

NLN is very similar to thimet oligopeptidase (THOP1) (also known as EP 24.15 and EC 3.4.24.15) from both a structural and functional perspective. THOP1 and NLN are both M3 metallopeptidases and because of their unique sensitivity to thiol reagents, they both fall under the thimet oligopeptidase subfamily. THOP1 and NLN have considerable

25 sequence homology: 60% of their sequence is identical (Brown et al., 2001). The main protein sequence of NLN is encoded by exons 5-16, which is exactly the same as THOP1’s exons 2-13 and contain matching splice sites (Kato et al., 1997). Both THOP1 and NLN range in size from 680-700 amino acids, are around 78-80 kDa, and act on many of the same substrates (Rioli et al., 1998). However, they have a number of important differences. In contrast to THOP1, NLN is inhibited by proline-isoleucine and is not activated by thiols (Serizawa et al., 1995). NLN can be found within the mitochondria and cytosol, while THOP1, lacking a targeting sequence, remains in the cytosol (Kato et al., 1997). Although they act on many of the same substrates, they can reliably be differentiated enzymatically based on where they hydrolyze neurotensin. THOP1 cleaves neurotensin at its Arg-Arg bond, whereas NLN cleaves at the Pro-Tyr bond (Checler et al., 1986; Serizawa et al., 1995). THOP1 and NLN also differ at the genetic level. THOP1’s sequence spans 45 kilobases, nearly half the size of NLN. It has a simpler 5’ structure, fewer exons, and shorter introns (Kato et al., 1997).

1.4.2. Localization

NLN is found in a wide range of tissues and its subcellular distribution varies with cell type. In neurons and glia, NLN is localized solely within the cytosol and plasma membrane (Vincent et al., 1996a). However, in rat hepatocytes and HeLa cells, NLN is predominately found within the mitochondria, with lower levels detected in the cytosol and plasma membrane (Krause et al., 1997; Serizawa et al., 1995). NLN is secreted from astrocytes as well as murine melanoma cells, where it has been shown to modulate angiogenesis and tumor growth (Paschoalin et al., 2007; Vincent et al., 1996a). While its subcellular localization has been described in several cell lines, its presumed submitochondrial localization to the IMS is based off of a single study. Serizawa et al. treated isolated mitochondria from rat hepatocytes with digitonin to form mitoplasts. They concluded that NLN is within the IMS through proteolytic assays (Serizawa et al., 1995). However, two independent studies mapping the mitochondrial proteome suggest that NLN is localized to the mitochondrial matrix (Hung et al., 2014; Rhee et al., 2013). More recently, a study

26 showed that in mouse hepatocytes, NLN is detected in the matrix by immunoblotting, with only trace amounts being present in intact mitoplasts (Teixeira et al., 2018).

1.4.3. NLN knockout mouse

Neurolysin acts on a diverse range of substrates that have been implicated in several biological functions, such as pain, blood pressure regulation, sepsis, and stroke (Jeske et al., 2006; Piliponsky et al., 2008; Rashid et al., 2014) 54–59. Despite the wide variety of proteins NLN acts upon in vitro, its most established role in vivo is its cleavage of neurotensin at its Pro-Tyr bond, rendering the 13-residue peptide inactive (Barelli et al., 1994; Chabry et al., 1990a). However, its physiological role and activity within the mitochondria is not well understood. Recently, the generation of a knockout mouse has shed more light on NLN’s mechanism and clinical significance. NLN knockout mice demonstrated heightened insulin sensitivity, increased glucose tolerance, and liver gluconeogenesis (Cavalcanti et al., 2014). Histological analysis of the gastrocnemius muscle revealed a significant reduction of oxidative fibers that stained for sorbitol dehydrogenase and cytochrome c oxidase in the knockout mouse, indicative of reduced mitochondrial oxidative activity (Cavalcanti et al., 2014). The authors also studied the effect of NLN knockout on exercise capacity. Slow-twitch muscle fibers have higher oxidative enzyme activity than their fast-twitch counterparts and increase in response to endurance training. Mice were monitored during a low intensity running exercise until exhaustion. The knockout mice exhausted earlier than wild-type mice, further linking NLN with impairment in mitochondrial oxidative activity. Surprisingly, despite its reported cleavage of bradykinin, neurotensin, angiotensin I and II, and hemopressin, there was no observed effect in blood pressure regulation (Cavalcanti et al., 2014). One potential explanation could be that other peptidases are compensating for some of NLN’s proteolytic effects, but not others. Another explanation could be that the primary enzymatic activity of NLN is within the mitochondria, as demonstrated by the mouse’s specific metabolic phenotype. Taken together, these in vivo findings suggest that loss of NLN impairs metabolism.

1.4.4. NLN inhibitors

Several small-molecule NLN inhibitors have previously been characterized. The dipeptide proline-isoleucine was the first identified selective inhibitor of NLN. Although proline- isoleucine is specific for NLN and has been used in both in vitro and in vivo studies, it has a low affinity for the endopeptidase (Krause et al., 1997). Consequently, it must be administered at high concentrations, which is impractical for in vivo applications. Shortly after the discovery of proline-isoleucine, Jiracek et al. identified P33 (Pro-Phe-

Ψ(PO2CH2)-Leu-Pro-NH2) as a more potent and specific inhibitor of NLN (Jiracek et al., 1996). They tested P33 in primary mouse neuron cultures and in vivo by intracerebroventricular administration in mice (Vincent et al., 1997).

Since the identification of P33, neurotensin, a substrate of NLN, has been implicated in various pathologies such as addiction, schizophrenia, stroke, neurodegenerative disorders, and cancer (Boules et al., 2013; Ouyang et al., 2015; Zhu et al., 2019). In response to the growing demand for NLN inhibitors, Hines et al. recently constructed a small allosteric inhibitor of NLN (Hines et al., 2014). This allosteric inhibitor (R2) binds largely though nonpolar contacts near NLN’s hinge axis and induces mild conformational changes which lock NLN in an open, inactive conformation. Furthermore, it is predicted to be more bioavailable than P33 because it lacks phosphinic and carboxylic acid moieties (Hines et al., 2014).

1.5. Targeting the mitochondria in AML

1.5.1. Mitochondrial properties of AML cells

AML cells and stem cells have unique mitochondrial properties compared to normal hematopoietic stem and progenitor cells (HSPCs). While normal HSPCs rely on anaerobic glycolysis, LSCs are enriched for hallmarks of oxidative metabolism and rely on OXPHOS for their survival (Ho et al., 2017; Lagadinou et al., 2013; Raffel et al., 2017; Simsek et al., 2010; Vannini et al., 2016). Moreover, compared to normal blood cells, AML

28 cells have higher mitochondrial mass, increased mitochondrial DNA, higher basal oxygen consumption, and decreased spare reserve capacity (Skrtic et al., 2011; Sriskanthadevan et al., 2015). These unique properties make targeting mitochondrial metabolism a promising therapeutic strategy in AML: AML cells are sensitive to inhibitors of mitochondrial translation, mitochondrial DNA replication, and respiratory chain complexes (Baccelli et al., 2019; Liyange et al., 2017; Molina et al., 2018; Skrtic et al., 2011).

In addition to differences between AML and normal hematopoietic cells, AML LSCs have a distinct metabolic profile compared to bulk AML cells. Compared to bulk AML cells, quiescent LSC-enriched populations have lower rates of oxygen consumption. Moreover, under hypoxic conditions, LSC-enriched populations cannot upregulate glycolysis to meet their metabolic demands (Lagadinou et al., 2013). AML LSC-enriched populations are also uniquely dependant on specific metabolites: LSCs rely on amino acids for survival, whereas normal HSPCs and bulk AML cells do not. Moreover, while bulk AML cells can compensate for amino acid depletion by upregulating fatty acid metabolism, the LSC- enriched fractions are metabolically inflexible. Consequently, amino acid depletion impairs OXPHOS and viability in LSC-enriched fractions (Jones et al., 2018). These data suggest that AML LSCs are more reliant on OXPHOS for energy production and viability compared to bulk AML cells, and that they are metabolically rigid.

1.5.2. Targeted therapies

The FDA has recently approved several drugs that target the unique mitochondrial properties of AML cells. Isocitrate dehydrogenase (IDH) 1 mutations are present in 6-16% of patients and IDH2 mutations are present in 8-19% of patients (Im et al., 2014). IDH1/2 are metabolic enzymes involved in citrate metabolism within the Krebs cycle. Wild-type IDH1/2 catalyze the oxidative decarboxylation of isocitrate, producing α-ketoglutarate (Dang et al., 2009; Gross et al., 2010). In contrast, mutant IDH1/2 produce the oncometabolite 2-hydroxyglutarate (2-HG), which disrupts epigenetic alterations and myeloid differentiation (Figueroa et al., 2010).

Ivosidenib and enasidenib are small molecule inhibitors of IDH1 and IDH2, respectively. Ivosidenib was approved for the treatment of adults with relapse or refractory IDH1 mutated AML, as well as for newly-diagnosed IDH1 mutated AML in patients that are 75 years or older who are not eligible for intensive chemotherapy (Norsworthy et al., 2019). Enasidenib was approved for the treatment of adults with relapsed or refractory IDH2 mutated AML (FDA, 2017a).

The use of ivosidenib as a front-line therapy was approved based on the results of an open label, single arm clinical trial in 28 patients aged 75 years or older with newly- diagnosed IDH1 mutated AML. The primary endpoint was CR + CR with partial hematological recovery (CRh), defined as CR but with platelets >50 Gi/L and absolute neutrophil count > 0.5 Gi/L. 42.9% of patients treated with ivosidenib achieved CR + CRh (FDA, 2019). The use of ivosidenib in relapsed or refractory AML was approved based on an open label, single arm clinical trial of 174 patients with relapsed or refractory IDH1 mutated AML. The primary endpoints were CR + CRh and the conversion rate from transfusion dependence to independence. The CR + CRh rate was 32.8% (CR and CRh rates of 24.7% and 8.0%, respectively) and 37.3% of patients who were transfusion dependent became independent (Norsworthy et al., 2019).

Enasidenib was approved for the treatment of relapsed or refractory AML based on the results of an open-label, single-arm clinical trial of 199 adults with relapsed or refractory IDH2 mutated AML. After a median follow-up time of approximately 7 months, 23% of patients had CR or CRh and 34% of patients who were transfusion dependent became transfusion independent. Of those patients who were transfusion independent at the start of the trial, 76% remained transfusion independent (FDA, 2017a).

B-cell lymphoma 2 (BCL-2) is an antiapoptotic protein that is important for the survival of AML cells and is overexpressed in LSCs (Lagadinou et al., 2013; Pan et al., 2014). BCL- 2 prevents the mitochondrial pathway of apoptosis by binding to pro-apoptotic proteins and inhibiting their function (Hockenbery et al., 1990). Venetoclax is a potent and selective inhibitor of BCL-2 that has been shown to have anti-AML and anti-LSC activity

(Chan et al., 2015; Lagadinou et al., 2013; Pan et al., 2014). In preclinical studies, inhibition of BCL-2 disrupted oxidative metabolism in both AML cells and stem cells. However, while bulk cells were able to compensate for this reduction in OXPHOS by upregulating glycolysis, LSC-enriched cells could not induce glycolysis. Importantly, venetoclax treatment did not deplete ATP production in normal CD34+ bone marrow, although it did induce a metabolic shift towards glycolysis (Lagadinou et al., 2013). These preclinical studies suggest that BCL-2 inhibition selectively targets LSCs. However, clinically, only 19% of relapsed/refractory AML patients responded (CR or complete remission with incomplete hematologic recovery) to venetoclax monotherapy (Konopleva et al., 2016).

Venetoclax has shown more promising results in combination with hypomethylating agents (HMAs). In a non-randomised, open-label, phase 1b trial of elderly treatment- naïve patients, venetoclax combined with azacitidine or decitibine showed a response rate of 78% and an increase in OS by 15 months (DiNardo et al., 2018). Mechanistically, venetoclax and azacitidine impair amino acid uptake in AML LSCs (Jones et al., 2018). In particular, the amino acid cysteine is important for the maintenance of LSCs (Jones et al., 2019). Venetoclax and azacitidine treatment depleted intracellular cysteine levels, which in turn reduced intracellular glutathione levels and glutathionylation of SDHA (Jones et al., 2019). Impaired glutathionylation of SDHA decreased CII activity and reduced OXPHOS in AML LSCs (Pollyea et al., 2018). These studies further highlight the importance of targeting LSCs by impairing mitochondrial metabolism.

Resistance to venetoclax is associated with changes in mitochondrial structure. Venetoclax treatment disrupts cristae structure, leading to caspase activation and apoptosis. Conversely, venetoclax-resistant leukemic cells have mitochondria with tight cristae and increased expression of OPA1. Targeting proteins important for mitochondrial structure, such as CLPB, re-sensitizes venetoclax-resistant cells. Knockdown of CLPB in venetoclax-resistant cells disrupts cristae ultrastructure, activates the mitochondrial stress response, and primes AML cells for apoptosis. Loss of CLPB also re-sensitizes cells resistant to both venetoclax and azacitidine. Collectively, these findings suggest that

31 targeting mitochondrial structure can overcome resistance in venetoclax based therapies (Chen et al., 2019).

Figure 3. Summary of the preclinical and clinical drugs that target AML cells and stem cells

CHAPTER 2: RATIONALE AND HYPOTHESIS

AML cells and stem cells have unique mitochondrial properties with increased reliance on OXPHOS (Cole et al., 2015; Pollyea et al., 2018; Skrtic et al., 2011; Sriskanthadevan et al., 2015). Mitochondrial functions, including OXPHOS, are tightly regulated by protein quality control systems, including mitochondrial proteases. We previously found that select mitochondrial proteases are important for AML viability (Cole et al., 2015). Mitochondrial proteases maintain the integrity of mitochondrial pathways such as mitochondrial dynamics, metabolism, and apoptosis (Anand et al., 2013). For example, the mitochondrial matrix protease, caseinolytic ClpP, degrades damaged and misfolded respiratory chain proteins to maintain the integrity of the respiratory chain (Cole et al., 2015).

NLN is a zinc metallopeptidase that is localized to the mitochondria and is also secreted into the circulation. In the circulation, NLN cleaves vasoactive peptides such as neurotensin and bradykinin to regulate physiological processes such as blood pressure (Chabry et al., 1990b; Checler et al., 1995; Checler et al., 1986; Rioli et al., 2003; Rioli et al., 1998). Despite this reported function, NLN knockout mice are viable with normal blood pressure, suggesting that NLN’s effects on vasoactive peptides are redundant in vivo. However, NLN knockout mice demonstrate mild metabolic defects with reduced mitochondrial oxidative activity in their muscle fibers (Cavalcanti et al., 2014). Although the role of NLN in the circulation has been well characterized, its mitochondrial function is largely unknown. Initially, NLN was thought to localize to the mitochondrial IMS, but more recent work localizes NLN to the mitochondrial matrix (Hung et al., 2014; Rhee et al., 2013; Teixeira et al., 2018). However, the function of NLN in the matrix is unclear.

OBJECTIVES

2.1. AIM 1: To determine the effects of NLN knockdown on the growth of leukemic cells and progenitors

We previously identified the mitochondrial preotease NLN as a top hit in an shRNA screen to identify new biological vulnerabilities in the mitochondrial proteome of AML cells (Cole et al., 2015). Thus, we hypothesize that genetic inhibition of NLN will be cytotoxic to leukemic cells and progenitors.

2.2. AIM 2: To characterize NLN’s mitochondrial interactors and mitochondrial function

We hypothesize that NLN’s mitochondrial interactors will be localized to the mitochondrial matrix, as recent studies have described NLN as a matrix protease (Hung et al., 2014; Rhee et al., 2013; Teixeira et al., 2018). We postulate that genetic inhibition of NLN will disrupt mitochondrial function and that this is the basis by which NLN knockdown impairs leukemic cell growth. We will use BioID-MS to identify NLN’s mitochondrial interactors and we will characterize the mitochondrial consequences of knocking down NLN.

2.3. AIM 3: To investigate the effects of pharmacologically inhibiting NLN in leukemia

We hypothesize that chemical inhibition of NLN will impair leukemic cell viability and mitochondrial function. We will assess the effects of a small molecule inhibitor of NLN on AML cell viability, RCS formation, oxidative phosphorylation, and in vivo efficacy in xenograft models of AML.

CHAPTER 3: MATERIALS AND METHODS

3.1. Statistical Analysis Prism Graph Pad 6.0 was used to perform statistical analysis and data plotting. A one- way or two-way ANOVA followed by Dunnett’s (one-way) or Bonferroni’s (two-way) post hoc testing was used to compare mean values between multiple groups. An unpaired Student’s t-test was used to compare the mean between two groups. Statistical significance values were indicated as follows: ∗ p ≤ 0.05, ∗∗ p ≤ 0.01, ∗∗∗ p ≤ 0.001, ∗∗∗∗ p ≤ 0.0001.

3.2. Bioinformatic Analysis Affymetrix gene expression data of AML (536 samples) and healthy bone marrow samples (73 samples) from the Haferlach data set (Haferlach et al., 2010) were downloaded from the Leukemia Gene Atlas (LGA) portal in March 2016. The platform used is Affymetrix Human Genome U133 Plus 2.0 Array and the data values correspond to robust multichip average expression measures. Official gene symbols from the HUGO Gene Nomenclature Committee were retrieved from the Affymetrix probe identifiers using the R package biomaRt (biomaRt_2.26.1 and R version 3.2.3) and searched against Ensembl Genes version 84. Data were reduced at the gene level by selecting the probe with the highest median absolute deviation across samples per gene.

In order to study the gene expression pattern within the AML samples, and between AML and normal patients, data were centered, scaled (z-score) and clustered using the heatmap.2 function available from the gplots R package (gplots_2.17.0). For each pair of samples, the euclidean distance based on NLN expression values was calculated to create the distance matrix. Hierarchical clustering using the complete linkage method was applied to identify the main groups of samples that showed a minimum distance between them and the result was visualized using a dendrogram. The samples and associated gene expression values for the 4 main AML clusters were retrieved from the hierarchical

36 cluster results using the cutree function in R. A boxplot was constructed using the z score values for the 4 AML groups and the group of normal samples. Overexpression was defined as 1 standard deviation above the mean NLN expression of normal bone marrow.

One-way analysis of variance (ANOVA) and pairwise t-tests were applied to the data to test the significance of the differences in the mean values between the groups. Using the default parameters, each row (gene) in the result has mean 0 and sample standard deviation 1. The z score is a normalized value, indicating how many standard deviations away the gene expression is compared with the mean expression of all samples for the same gene: z = (x − μ)/σ, where x is gene expression, μ is mean gene expression across samples, and σ is standard deviation of the population.

For correlation of NLN with mutations, Beat AML data was downloaded from Supplementary Tables in the paper, Functional genomic landscape of acute myeloid leukemia, Nature 2018 (Tyner et al., 2018). The counts per million table, Supplementary table S9, consists of 22843 genes and 451 patients. The clinical table, Supplementary table S5, includes 672 tumour specimens collected from 562 patients. We tested correlations of NLN with mutations using three methods: (1 and 2) t-test and Wilcoxon rank sum test between mutated and non-mutated patients: The t-test and Wilcoxon rank sum test are to compare NLN gene expressions between two groups of the mutated and non-mutated patients. (3) Fisher exact test for the up and down NLN in mutated and non- mutated patients: The Fisher's exact test is to test the null of independence of the counts of the up (greater than the median) and down (less than the median) NLN in mutated and non-mutated patients.

3.3. Cell Lines Flp-In T-REx HEK293 cells were grown in Dulbecco’s Modified Eagles Medium (DMEM) supplemented with 10% fetal bovine serum, 100 units/mL penicillin, and 100 µg/mL

37 streptomycin. OCI-AML2 and MV4-11 cells were grown in Iscove’s Modified Dulbecco’s Medium (IMDM), supplemented with 10% fetal bovine serum (FBS), 100 units/mL penicillin, and 100 µg/mL of streptomycin. NB4 cells were grown in RPMI media supplemented with 10% fetal bovine serum, 100 units/mL penicillin, and 100 µg/mL streptomycin. TEX leukemia cells obtained from Dr. John Dick’s lab (Warner et al., 2005) were maintained in IMDM with 20% FBS, 2 mM L-glutamine, 20 ng/mL human recombinant stem cell factor (SCF), and 2 ng/mL human recombinant interleukin-3 (IL-3). 8227 cells were obtained from Dr. John Dick’s lab (Lechman et al., 2016) and were cultured in X-VIVO-10 with 20% bovine serum albumin-insulin-transferrin (BIT), human Fms-related tyrosine kinase 3 ligand (Flt3-L, 50 ng/ml), interleukin-6 (IL-6, 10 ng/ml), SCF (50 ng/ml), thrombopoietin (TPO, 25 ng/ml), IL-3 (10 ng/ml), and granulocyte colony- stimulating factor (G-CSF, 10 ng/ml). Lentiviral packing cells (293T) were cultured in DMEM with 10% FBS for seeding, and DMEM with 10% FBS, 100 units/mL penicillin, 100 μg/mL of streptomycin, and 1% BSA for harvesting of virus. All cell lines were maintained at 37°C, supplemented with 5% CO2 in humidified atomosphere. The sex and age of the patients from whom the cell lines were generated are indicated in table 2.

Cell Line Species Tissue Sex Age

OCI-AML2 Homo sapiens Peripheral Blood Male 65

MV4-11 Homo sapiens Peripheral Blood Male 10

NB4 Homo sapiens Bone Marrow Female 20

TEX Homo sapiens Cord Blood n/a n/a

HEK293 TREx Homo sapiens Fetal Kidney n/a n/a

8227 Homo sapiens n/a n/a n/a

130578 Homo sapiens n/a n/a n/a

TableTable 2.S3. Cell Cell lines lines

3.4. Primary AML and Normal Hematopoietic Cells Primary human AML samples were obtained from peripheral blood or the bone marrow of consenting male or female AML patients, with a malignant cell frequency of 80% among mononuclear cells. Differential density centrifugation was used to isolate AML cells. Peripheral blood stem cells (PBSCs) were obtained from healthy consenting male or female volunteers, donating PBSCs for allogenic stem cell transplantation. PBSCs were isolated by G-CSF stimulation and leukapheresis. Primary AML cells and PBSCs were frozen in alpha MEM + 5% FBS or 90% FBS +15U/mL of heparin + 10% DMSO. The University Health Network institutional review board approved the collection and use of human tissue for this study (Research Ethics Board protocol #13-7163). All specimens were de-identified and each experiment was performed using a single aliquot from a donor. Information about the patients who were the source of the cells is indicated in table 3.

Sample Collection Date Gender Age at Dx Diagnosis FLT3-ITD FLT3-TKD NPM1 Cytogenetics ID AML with mutated 120287 20-Apr-12 Male 77 Positive Undetectable Positive 46,XY[20] NPM1

AML with mutated 120541 24-Jul-12 Female 51 Positive Undetectable Positive 46,XX[20] NPM1

AML with mutated 120887 7-Nov-12 Female 68 Undetectable Undetectable Positive 46,XX[20] NPM1

AML with mutated 120968 4-Dec-12 Female 59 Undetectable Undetectable Positive 46,XX[20] NPM1

140176 4-Mar-14 Male 77 AML NOS Positive Undetectable Undetectable 46,XY[20]

AML with mutated 141065 27-Nov-14 Male 57 Positive Undetectable Positive 46,XY[20] NPM1

AML with mutated 150375 30-Apr-15 Male 66 Positive Undetectable Positive 46,XY[10] NPM1

AML with mutated 151257 19-Nov-15 Male 58 Undetectable Undetectable Positive 46,XY[20] NPM1

AML with mutated 160053 12-Jan-16 Female 38 Undetectable Undetectable Positive unsuccessful NPM1

160114 20-Jan-16 Male 71 AML NOS Positive Undetectable Undetectable 46,XY[20]

AML with mutated 160406 25-Feb-16 Female 64 Positive not done Positive unsuccessful NPM1

46,XY,t(3;5)(q 160556 16-Mar-16 Male 55 AML not done not done not done 21;q35)[10]

161476 27-Jul-16 Female 56 AML Positive not done Positive not done

AML with mutated 276968 20-Apr-18 Female 58 Undetectable Undetectable Positive 46,XX[20] NPM1

290985 4-Jul-18 Female 88 AML Undetectable Undetectable Undetectable not done

Table S4. Clinical data of primary AML samples

Table 3. Clinical data of primary AML samples

3.5. Animals Six-twelve week male or female immunodeficient NOD.Cg-Prkdcscid IL2rgtm1Wjl Tg (CMV- IL3,CSF2,KITLG)1Eav/MloySzJ (NOD-SCID-GF) mice used to transplant TEX cells were obtained from Dr. Connie J. Eaves and bred in our facility (Nicolini et al., 2004). Eight- twelve week female immunodeficient NOD.CB17-Prkdcscid/J (NOD-SCID) and 6-8 week immunodeficient male Prkdcscid (SCID) mice, used for the transplantation of primary AML and OCI-AML2 cells respectively were obtained from the University Health Network. Mice were randomly assigned to each experimental group.

During all experiments, the weights of the mice were approximately 18-30 g with no animals losing greater than 10% body weight. All animals were housed in microisolator cages with temperature-controlled conditions under a 12-hour light/dark cycle with access to drinking water and food. Only one experimental procedure was performed on each mouse and all mice used were drug naive prior to the experiment. Furthermore, all animal studies were performed in accordance with the University Health Network Animal Use Protocol (AUP): #1251.34 (NOD-SCID-GF, NOD-SCID, and SCID).

3.6. Viral Infections The hairpin-pLKO.1 vectors (carrying the puromycin antibiotic resistance gene) containing the shRNA sequences used are as described previously (Cole et al., 2015). The hairpin-pLKO.1 vector was then isolated using the E.N.Z.A® Plasmid Midi Kit system (Omega bio-tek), and then quantified with a NanoDrop™ (ThermoFisher Scientific) spectrophotometer. Lentiviruses were made in a 25 cm2 flask format, by transfecting HEK293T cells with a three-plasmid system (hairpin-pLKO.1 vector, packaging plasmid with: gag, pol, and rev genes, and envelope plasmid). All plasmids were validated by Sanger sequencing before use.

The coding sequence of shRNAs targeting NLN (accession no. NM_ 020726) are as follows: shRNA1

5’- CCGGGCAGATGTAGAAGTAAAGTATCTCGAGATACTTTACTTCTACATCTGCTTTTT G-3’; shRNA2

5’- CCGGGCATGGACATGCTCCACAATTCTCGAGAATTGTGGAGCATGTCCATGCTTTT TG-3’;

3’ UTR shRNA

5’- CCGGTGGAGCTCTGTGTCAACTTTGCTCGAGCAAAGTTGACACAGAGCTCCATTTT TG-3’

The coding sequence of shRNA targeting LETM1 (accession no. NM_ 012318) are as follows: shRNA

5’- CCGGGCTATGGATCGACACCAAGATCTCGAGATCTTGGTGTCGATCCATAGCTTTT TG-3’;

The coding sequence of shRNA targeting BCS1L (accession no. NM_004328) are as follows: shRNA

5’- CCGGGCTGAGAACTTTGCAGAACATCTCGAGATGTTCTGCAAAGTTCTCAGCTTTT T-3’;

3.7. shRNA Knockdown of AML Cell Lines To perform lentiviral transductions 5 x 106 OCI-AML2, NB4, and TEX cells or 2 x 105 Flp- In T-REx HEK293 cells were resuspended in 5 mL of medium containing 5 µg/mL of protamine sulfate. 2 mL of virus was added to OCI-AML2 and TEX cells, 1mL to NB4 cells, or 300uL of virus was added to Flp-In T-REx HEK293 cells, followed by an overnight incubation (37°C, 5% CO2). The following day, fresh medium with puromycin (1.5 µg/mL for OCI-AML2, T-REx HEK293, and NB4 or 2 µg/mL for TEX) was added to cells. Three days later, the medium was replaced with non-puromycin containing media.

To perform lentiviral transductions on MV4-11 cells, 12 well non-TC treated plates were coated with retronectin, blocked with 2% BSA, and stored at 4°C overnight. The next day, the BSA was aspirated and 2mL of virus was added to each well and the plates were spun at 3000 RPM for 2.5 hours. Viral supernatant was aspirated and 1 x 106 MV4-11 cells in 1mL of medium was added to each well. Two days later, fresh medium with puromycin (0.5 µg/mL) was added to cells. Three days later, the medium was replaced with non-puromycin containing media.

3.8. NLN Overexpression For experiments overexpressing NLN, the human NLN ORF clone (OriGene, #RC212447) was subcloned into the pLentiEF1α vector (carrying the blasticidin antibiotic resistance gene) and a STOP codon was added at the end of the ORF so that the vector expresses NLN without a tag (pLentiEF1α-hNLN). The empty vector, pLentiEF1α, served as a control. OCI-AML2 cells were seeded in T25 flasks at 5 x 105 cells/mL (3 mL per flask). The culture was supplemented with 1 µg/mL of protamine sulfate. To each flask, 1

44 mL of either pLentiEF1α or pLentiEF1α -NLN viral stock was added, followed by overnight incubation (37°C, 5% CO2). The following day, 1 mL of virus containing shRNA targeting a control sequence (GFP) or the 3’ UTR of NLN was added and incubated overnight. The next day, cells were resuspended in 20 mL of media containing both blasticidin (10 µg/mL) and puromycin (1.5 µg/mL) and incubated for three days and then subcultured at a concentration of 1:20 in 20 mL of fresh media containing blasticidin (10 µg/mL) and puromycin (1.5 µg/mL) for three days. Cells were then subcultured at a concentration of 2 x 104 cells/mL in fresh media with 10 µg/mL blasticidin for five days. Cells were collected for immunoblot lysate and seeded at a concentration of 1 x 105 cells/mL in fresh media and counted over four days.

3.9. Mitochondrial Protein Lysates To isolate mitochondria from cell lines and primary patient samples, 10-40 x 106 cells were washed in phosphate buffered saline pH 7.4, resuspended in 500μL mitochondrial isolation buffer (200mM sucrose, 10mM Tris/MOPS, pH 7.2, and 1mM EGTA/Tris) with protease inhibitors (ThermoFisher 87786), and transferred to a glass dounce homogenizer. Cells were homogenized until 5-10% of cells were viable as assessed by trypan blue exclusion staining. The lysate was then spun at 600g for 10 minutes at 4°C. The supernatant was removed and spun at 7000g for 10 minutes at 4°C. The supernatant was discarded and the pellet was resuspended in cold mitochondrial isolation buffer with protease inhibitors. Protein concentration was quantified using the Pierce™ BCA Protein Assay Kit (ThermoFisher 23225).

3.10. Immunoblotting Mitochondrial isolations or total cell lysates from cell lines or primary patient samples were lysed using RIPA buffer and mitochondrial protein concentration was measured by the Bradford assay (Bio Rad, Hercules, CA). Equal amounts of protein were run on 10-12% SDS- polyacrylamide gels and transferred to a PVDF membrane. Membranes were blocked with 5% milk in TBST for one hour, then incubated with primary antibody

45 dissolved in 5% milk in TBST overnight at 4°C. Primary antibodies included anti-GAPDH (Cell Signaling Technology CST-2118S), anti-tubulin (Cell Signaling Technology CST 2144), anti-actin (Santa Cruz sc-69879), anti-NLN (Abcam ab119802), anti-THOP1 (ThermoFisher PA5-48031), anti-LETM1 (Abcam 55434), anti-NDUFA9 (Abcam ab14713), anti-NDUFB8 (Abcam ab110242), anti-SDHA (Abcam ab14715), anti- UQCRC2 (Abcam ab14745), anti-MTCO1 (Abcam ab14705), anti-COXIV (ThermoFisher A-21347), anti-ATP5B (Abcam ab14730), anti-MnSOD (Enzo ADI-SOD-110), and anti- BCS1L (Abnova H00000617-M01). The membrane was washed three times before incubation for one hour at room temperature with secondary HRP-conjugated donkey anti-rabbit antibody (GE Healthcare, Buckinghamshire, UK), sheep anti-mouse antibody (GE Healthcare, Buckinghamshire, UK), or donkey anti-sheep antibody (ThermoFisher).

3.11. Cell Growth and Viability Assays Three days after shRNA knockdown of target genes, equal numbers of cells were plated for assaying growth. Cells were counted by trypan blue exclusion staining for a period of 8 days post-transduction. To confirm target knockdown, 5 - 20 × 106 cells were collected at day 7 post-transduction for immunoblot analysis.

R2 was obtained from Dalriada and J&C consulting with purity over 90%, For R2 dose response curves, cells were plated in 96 well plates at 10000 cells in 100 µL media per well. Following 3 days of treatment, 100 µL of CellTiter-Fluor (Promega) was added to each well. Plates were incubated at 37 oC for 2-3 hours prior to fluorescence measurements by using SpectraMax M3 plate reader at excitation of 380-400 nm and emission of 505 nm. Background fluorescence value of medium only wells was subtracted from each reading. Viability was determined following normalization with vehicle control treatment.

3.12. Colony Formation Assays Seven days after transduction of OCI-AML2 and NB4 cells with control shRNA or one of two different shRNA sequences targeting NLN, 750 cells were plated in duplicate 35mm dishes (Nunclon, Rochester, USA) to a final volume of 1 mL/dish in MethoCult H4100 media (StemCell Technologies, BC, Canada) supplemented with 30% FBS. After incubating the dishes for 10 days at 37°C, 5% CO2 with 95% humidity, the number of colonies containing 10 or more cells were counted on an inverted microscope. The mean of the duplicate plates for each condition is presented.

To assess clonogenic growth in primary samples, 4 × 105 fresh AML mononuclear cells were incubated with R2 or vehicle control for 72 hours in MyelocultH5100 (StemCell Technologies), supplemented with 100 ng/mL rhSCF, 10 ng/mL rhFlt3-L, 20 ng/mL rhIL- 7, 10 ng/mL rhIL-3, 20 ng/mL rhIL-6, 20 ng/mL rhG-CSF, 20 ng/mL rhGM-CSF. Treated AML patient samples were plated in MethoCult H4434 medium (StemCell Technologies,

BC, Canada). After incubating the dishes for 7 days at 37°C with 5% CO2 and 95% humidity, AML colonies containing 10 or more cells were counted. The mean of duplicate plates for each condition are presented.

3.13. Proximity-Dependent Biotinylation BioID and mass spectrometry were conducted as described previously (Coyaud et al., 2015). NLN or ClpP cDNA was fused in-frame with a mutant E. coli biotin conjugating enzyme, BirA R118G (or BirA*) into a tetracycline-inducible pcDNA5 FRT/TO expression vector, which was then transfected into Flp-In T-REx HEK293 Flp-In cells. Cells were lysed, sonicated twice for 10 sec at 35% amplitude (Sonic Dismembrator 500; Fisher Scientific) and centrifuged at 35,000 × g for 30 min at 4°C. Supernatants were passed through a Micro Bio-Spin Chromatography column (Bio-Rad 732-6204, Hercules, California) and were incubated with 30 µl of high performance streptavidin packed beads (GE Healthcare, Chicago, Illinois) for 3 hrs at 4°C on an end-over-end rotator. Beads were collected (2,000 RPM, 2 min) and washed 6 times with 50 mM ammonium bicarbonate

(pH 8.3). Beads were then treated with L-1-Tosylamide-2-phenylethyl chloromethyl ketone (TPCK)-trypsin (Promega, Madison, Wisconsin) for 16 h at 37°C on an end-over- end rotator. After 16 hrs, another 1 µl of TPCK-trypsin was added for 2 hrs and incubated in a water bath at 37°C. Supernatants were lyophilized and stored at 4°C for downstream mass spectrometry analysis. Two biological and two technical replicates were completed for each protease. NLN’s interactors were compared to ClpP’s interactors, which were normalized to each protease’s respective BirA* spectral counts.

3.14. Liquid Chromatography-Mass Spectrometry Liquid chromatography was conducted using a C18 pre-column (Acclaim PepMap 100, 2 cm x 75 µm ID, Thermo Scientific, Waltham, Massachusetts) and a C18 analytical column (Acclaim PepMap RSLC, 50 cm x 75 µm ID, Thermo Scientific), running a 120 min reversed-phase gradient (0-40% ACN in 0.1% formic acid) at 225 nl/min on an EASY- nLC1200 pump (Proxeon, Odense, Denmark) in-line with a Q-Exactive HF mass spectrometer (Thermo Scientific). An MS scan was performed with a resolution of 60,000 (FWHM) followed by up to twenty MS/MS scans (minimum ion count of 1,000 for activation) using higher energy collision induced dissociation (HCD) fragmentation. Dynamic exclusion was set for 5 seconds (10 ppm; exclusion list size = 500).

3.15. Mass Spectrometry Data Analysis For peptide and protein identification, Thermo .RAW files were converted to the .mzML format using ProteoWizard (v3.0.10800) (Kessner et al., 2008), then searched using X!Tandem (X!TANDEM Jackhammer TPP (v2013.06.15.1) (Craig and Beavis, 2004) and Comet (v2014.02 rev.2) (Eng et al., 2013) against the human Human RefSeq v45 database (containing 36113 entries). Search parameters specified a parent ion mass tolerance of 10 ppm, and an MS/MS fragment ion tolerance of 0.4 Da, with up to 2 missed cleavages allowed for trypsin (excluding K/RP). Variable modifications included deamidation on N and Q, oxidation on M, GG on K, and acetylation on protein N-terminal

48 in the search. Data were filtered through the TPP (v4.7 POLAR VORTEX rev 1) with general parameters set as –p0.05 -x20 –PPM.

Proteins identified with an iProphet cut-off of 0.9 and at least two unique peptides were analyzed with SAINT Express (v.3.6) (Choi et al., 2011; Teo et al., 2014). Control runs (21 runs from cells expressing the FlagBirA* epitope tag only) were collapsed to the 2 highest spectral counts for each prey, and high confidence interactors were defined as those with BFDR ≤ 0.01. Prohits-viz was used for bait-bait Pearson Correlations and heat map generation. All raw mass spectrometry files have been deposited at the MassIVE archive (massive.ucsd.edu), ID MSV000084182.

3.16. Seahorse Oxygen consumption rate was measured in AML cells after shRNA knockdown or drug treatment using the Seahorse XF Cell Energy Phenotype Test Kit (Agilent, 103325-100) following manufacturer’s protocol. Data were collected using the Seahorse XF-96 analyzer (Seahorse Bioscience, MA, USA). Seven days after transduction or 3 days after treatment with R2, cells were resuspended in unbuffered XF assay medium (Agilent, 102365-100) supplemented with 2.5 mM glucose and 1 mM sodium pyruvate and seeded at 1.2 × 105 cells/well in Cell-Tak coated (0.15 μg/well) XF96 plates. Cells were equilibrated in the unbuffered XF assay medium for 1 hour at 37°C in a CO2-free incubator before being transferred to the XF96 analyzer.

3.17. Electron Microscopy OCI-AML2 cells were transduced with shRNA targeting NLN or control sequences in lentiviral vectors. Seven days post-transduction, cells were harvested and imaged by transmission electron microscopy. Briefly, cells were harvested, and fixed in a Graham- Karnovsky’s style fixative (4% paraformaldehyde and 2% glutaraldehyde in 0.1 M phosphate buffer pH 7.2) for 1 hour at room temperature. Cells were postfixed with 1%

49 osmium tetroxide, dehydrated with ethanol, washed with propylene oxide, treated with epoxy resin polymerized at 60°C for 48 hours, sectioned on a Reichert Ultracut E microtome to 90 nm thickness, collected on 300 mesh copper grids, and counterstained with uranyl acetate and lead citrate. A Hitachi H7000 (Hitachi, Tokyo, Japan) transmission electron microscope was used to evaluate the sections at an accelerating voltage of 75 kV.

3.18. Mitochondrial Membrane Potential To measure mitochondrial membrane potential, OCI-AML2 cells were plated in 96 well suspension u-bottom plates and incubated for 15 minutes with 2.5 μM of 5,5',6,6'- tetrachloro-1,1',3,3'-tetraethyl benzimidazolylcarbocyanine iodide (JC-1, Cayman) at o 37 C, 5% CO2 in the dark. After incubation, cells were centrifuged at 1000 RPM for 3 minutes, supernatant was removed, and cells were resuspended in 0.2 mL of Annexin V- APC and run on Fortessa HTS flow cytometer using 561 nm laser (Perelman et al., 2012). Analysis was conducted using FlowJo version 7.7.1 (TreeStar, Ashland, OR). To obtain the mitochondrial membrane potential (FL2/FL1) from Annexin V negative cells, emission from the red channel was divided by emission from the green channel.

3.19. Mitochondrial Mass To measure mitochondrial mass, OCI-AML2 cells were centrifuged, and stained with 100 nM MitoTracker Deep Red FM (Molecular Probes, Eugene, OR) in Phenol Red-free Hanks Buffer for 30 minutes at 37oC in the dark seven days after transduction. After incubation cells were centrifuged and stained with Annexin V-FITC and flow cytometry performed in a Fortessa HTS cytometer (BD Biosciences). Data were analyzed with FlowJo version 7.7.1 (TreeStar, Ashland, OR).

3.20. Mitochondrial Reactive Oxygen Species To measure mitochondrial reactive oxygen species (ROS), OCI-AML2 cells were suspended in 0.2 mL of 5 μM MitoSox (Molecular Probes/Life Technologies, Eugene, OR,

o USA) and incubated in the dark for 30 minutes at 37 C and 5% CO2 in humidified atmosphere seven days after transduction. Cells were then centrifuged to remove the dye, resuspended in 0.2 mL binding buffer containing Annexin V-FITC (BioVision, Milpitas, CA, USA) and analyzed by flow cytometry on a Fortessa HTS cytometer (BD Biosciences). 50 μM of antimycin A (Sigma, Saint Louis, MO, USA) treatment was used as positive control for increased ROS production. The percentage of Annexin V negative and MitoSox positive cells was determined and the fold increase of ROS production was calculated.

3.21. Cellular Reactive Oxygen Species

To measure cellular ROS, OCI-AML2 cells were suspended in 10 μM carboxyl-H2DCFDA (Molecular Probes/Life Technologies, Eugene, OR, USA) and incubated in the dark for o 30 minutes at 37 C and 5% CO2 in humidified atmosphere seven days after transduction. Cells were then centrifuged to remove the dye, resuspended in 0.2 mL binding buffer containing Annexin V-APC (eBioscience, San Diego, CA, USA) and analyzed by flow cytometry on a Fortessa HTS cytometer (BD Biosciences). 0.03% (v/v) of hydrogen peroxide (Sigma, Saint Louis, MO, USA) treatment was used as positive control for increased ROS production. The percentage of Annexin V negative and carboxyl-

H2DCFDA positive cells was determined and the fold increase of ROS production was calculated.

3.22. Blue Native Polyacrylamide Gel Electrophoresis For cell lines, mitochondria were harvested seven days after transduction or 3 days after treatment with R2. For primary patient samples and normal PBSCs, mitochondria were harvested immediately after thawing. Protein lysis and extraction were performed with 8 g/g digitonin and NativePAGE™ (Invitrogen) buffer for 20 min on ice. The lysates were

51 cleared by centrifugation at 20,000 × g for 10 min at 4 ºC. Lysates were quantified using the Pierce™ BCA Protein Assay Kit (ThermoFisher 23225) and 2 g/g of G-250 dye was added immediately before the samples were loaded on a 3–12% Invitrogen NativePAGE gel (BN1003BOX; Invitrogen) and transferred to a PVDF membrane.

3.23. Image Quantification Images were scanned and converted to 16-bit with ImageJ. Quantification was performed with the Plot Lanes function. The peaks were selected and quantified from the plots.

3.24. Protein Purification and Crystallization Human ClpP was expressed and purified as described previously (Kang et al., 2004; Kimber et al., 2010; Wong et al., 2018). Wild type human ClpP (without mitochondrial targeting sequences) was cloned into pETSUMO2 expression vectors and expressed in E. coli SG1146 (Kimber et al., 2010). To induce protein expression, bacteria were treated with 1 mM isopropyl-1-thio-B-D-galactopyranoside (IPTG) for 4 h at 37°C after reaching

OD600∼0.6, harvested by centrifugation, and disrupted in lysis buffer (25 mM Tris-HCl, pH 7.5, 500 mM NaCl, 10% Glycerol), by sonication. Following cell lysis, the insoluble material was removed by centrifugation (27,000 x g (Sorvall rotor SS-34) for 30 min twice) and the supernatant was passed through a 10 mL Ni-sepharose high-performance column (GE) pre-equilibrated with lysis buffer. The protein was washed with four wash buffers (25 mM Tris-HCl, pH 7.5, 500 mM NaCl, 10% Glycerol) containing 50 mM, 100 mM, 150 mM and 200 mM imidazole, respectively and eluted with 25 mM Tris-HCl, pH 7.5, 500 mM NaCl, 10% Glycerol, and 400 mM imidazole. The protein was diluted with 25 mL of dialysis buffer (50 mM Tris-HCl (pH 7.5), 0.3M NaCl, 10% glycerol), mixed with SUMO protease (1:100) (Lee et al., 2008), and dialyzed overnight at 4°C with light stirring into 4 L of dialysis buffer using Slide-A-Lyzer™ Dialysis Cassettes 3K (ThermoScientific, Waltham, MA). The dialyzed material was then passed through a second 10 mL Ni- column (ThermoScientific, Waltham, MA) and the flow-through solution containing untagged ClpP was collected. All fractions were analyzed by SDS-PAGE.

Human ClpX was expressed and purified as described previously (Cole et al., 2015). His– tagged wild type human ClpX was transformed into B21 Gold DE3. Transformed bacteria were plated on LB-Agar plates containing 100 μg/mL Ampicilin and incubated overnight at 37 °C. The following day individual colonies were grown overnight at 37 °C in LB medium containing 100 μg/mL Ampicillin. To induce protein expression, bacteria were treated with 1 mM isopropyl-1-thio-B-D-galactopyranoside for 4 h at 37°C in Terrific medium containing 100 μg/mL Ampicillin after reaching OD600∼0.6, harvested by centrifugation, and disrupted in lysis buffer (30 mM Hepes, pH 8.0, 300 mM NaCl, 10% Glycerol, 2 mM mercaptoethanol and 10 mM imidazole), by sonication. Following cell lysis, the insoluble material was removed by centrifugation (27,000 x g (Sorvall rotor SS- 34) for 30 min twice) and the supernatant was passed through a 1 mL Co-Agarose high- performance column (GE) pre-equilibrated with lysis buffer. The protein was washed with wash buffer (30 mM Hepes, pH 8.0, 500 mM NaCl, 10% Glycerol, 2 mM mercaptoethanol, 0.02% Triton X-100 and 20 mM imidazole) and eluted with 30 mM Hepes, pH 8.0, 300 mM NaCl, 10% Glycerol, 2 mM mercaptoethanol, and 200 mM imidazole. The protein was dialyzed overnight at 4 °C in 4 L dialysis buffer (50 mM Tris-HCl, pH 7.5, 200 mM KCl, 25mM MgCl2, 10% g1ycerol, 1 mM DTT and 0.1 mM EDTA) using Slide-A-Lyzer™ Dialysis Cassettes 3K (ThermoScientific, Waltham, MA). The recombinant proteins were stored at −70 °C. All fractions were analyzed by SDS-PAGE.

3.25. ClpP Activity in Isolated Mitochondria Intact mitochondria from OCI-AML2 cell lines were isolated as described above. The 1.5 µM ClpP and 4.5 µM ClpX were added to mitochondrial lysates in ClpXP protease assay buffer (25 mM Tris-Cl pH 8.0 supplemented with 3 mM ATP, 10 mM MgCl2, 20 mM KCl, 0.03% Tween 20, 2 mM β-mercaptoethanol, 5% glycerol with an ATP regeneration system of 16 mM creatine phosphate and 13 U/ml creatine kinase). This mixture was incubated for 1.5 or 3 hours at 37°C and was subjected to SDS-PAGE and BN-PAGE.

3.26. Hypoxia Wild-type OCI-AML2 cells or OCI-AML2 cells seeded at equal cell concentrations four days after transduction with shRNA targeting NLN or control sequences were incubated for 48 hours under 0.2% oxygen (Hypoxygen HypOxystation H45) or 1% oxygen (Hypoxygen HypOxystation H35). For proliferation assays, cells were counted daily by trypan blue exclusion staining from days 3 to 7 post-transduction.

3.27 RNA Isolation and Quantitative Reverse Transcriptase-Real Time Polymerase Chain Reaction Total RNA was isolated from AML cells using the RNeasy Plus Mini Kit (QIAGEN), and cDNA was prepared using SuperScript IV Reverse Transcriptase (ThermoFisher, MA, USA). Equal amounts of cDNA for each sample were added to a prepared master mix (Power SYBR Green PCR Master mix; Applied Biosystems, CA, USA). Quantitative Reverse Transcriptase-Polymerase Chain Reaction (qRT-PCR) was performed on an ABI Prism 7900 sequence detection system (Applied Biosystems, CA, USA). The relative abundance of a transcript was represented by the threshold cycle of amplification (CT), which is inversely correlated to the amount of target RNA/first-strand cDNA being amplified. To normalize for equal amounts of cDNA we assayed the transcript amounts of 18s rRNA gene. The comparative CT method was calculated as per manufacturer’s instructions. Primer sequences for NLN-F: 5’-GGCTGAACTTGGTGCTCTTC-3’ and NLN-R: 5’-TAGTTTGGCCACCTTGGTTC-3’. Primer sequences for 18s rRNA-F: 5’- AGGAATTGACGGAAGGGCAC-3’ and 18s rRNA-R: 5’-GGACATCTAAGGGCATCACA- 3’.

3.28 Calcium Measurements Seven days after transduction with shRNA targeting NLN or a control sequence, OCI- AML2 cells were stained for either the mitochondrial calcium indicator Rhod-2 AM (ThermoFisher Scientific R1244) or the cellular calcium indicator Fluo-8 AM (Abcam ab142773). Cells were acquired in a Fortessa cytometer (BD Biosciences) and data were analyzed with the FlowJo software (TreeStar, OR, USA).

3.29 8227 Flow Cytometry 8227 cells were co-immunostained with Annexin V-FITC (BD Biosciences, BD 556419), and anti-human antibodies recognizing CD34 (BD Biosciences, BD 340411) and CD38 (ThermoFisher Scientific 12-0388-42). Flow cytometry data were acquired using a BD Accuri flow cytometer (BD Biosciences) and frequency of viable 34+, 38- cells were analyzed with the FlowJo software (TreeStar, OR, USA).

3.30. Xenograft Models of Human AML OCI-AML2 human leukemia cells (1 × 106) were injected subcutaneously into the flanks of SCID mice. After the appearance of a palpable tumor (5 days), the mice were treated with 100mg/kg R2 or vehicle (10% (v/v) DMSO + 10% (v/v) cremophor + 0.9% (w/v) NaCl) by i.p. once daily (n = 10 per group) for a total of 10 days. Tumor volumes were measured 3 times per week based on caliper measurements of tumor length, width, and height (volume = tumor length × width × height x 0.52). At the end of treatment, mice were sacrificed, and tumor volumes and mass were measured from excised tumors. Peripheral blood was collected (n = 4 per group) and biochemical markers of liver (AST; aspartate transaminase, ALP; alkaline phosphatase, bilirubin), muscle (creatine kinase), and renal (creatinine) toxicity were measured by Idexx Laboratories (Ontario, Canada).

Equal numbers of TEX (2 x 105) transduced with an shRNA sequence in lentiviral vectors targeting NLN or control sequences were injected into the right femur of sublethally irradiated NOD-SCID-GF mice with human IL-3, GM-CSF, and Steel factor (SF) (Nicolini et al., 2004). Five weeks after injection, mice were sacrificed, and the percentage of human CD45+ (BD Biosciences) was enumerated in TEX cells by flow cytometry.

To assess NLN knockdown in a primary AML patient sample, cells from AML patient 140005 were transduced with NLN shRNA or control shRNA sequences in lentiviral vectors. NOD-SCID mice were conditioned with 208 rad of irradiation from a 137 rad source, and 200 μg of anti-mouse CD122 24 hours before transplantation. 48 hours after

55 transduction, 4.3 x 105 cells were injected into the right femurs of conditioned NOD-SCID mice. 8 weeks later, mice were sacrificed, cells were flushed from the femora, and stained with CD45 and CD33. The percentage of human GFP+, CD45+, and CD33+ cells in the bone marrow was determined by flow cytometry. The engraftment of transduced human AML cells into the bone marrow was assessed by enumerating relative human cell engraftment as described in (Pei et al., 2018).

To assess R2 in mouse models of primary AML engraftment, a frozen aliquot of AML cells was thawed, counted and re-suspended in PBS and 2.5 × 106 viable trypan blue-negative cells were injected into the right femur of 10 week-old female NOD-SCID mice that had been irradiated 24 hours previously with 2.08 Gy from a 137Cs source, and injected with 200 µg anti-mouse CD122. Similarly, engraftment of normal hematopoietic cells was assessed by the injection of 1.5 x 106 viable trypan blue-negative human cord blood cells into equivalent mice. Ten days after injection of AML or human cord blood cells, mice were treated with R2 (100 mg/kg by i.p. injection) or vehicle control (n = 9-10 per group) 3 of 7 days for four weeks. Mice were then sacrificed, and the cells were flushed from the femurs. Engraftment of human AML cells into the marrow of the non-injected left femur was assessed by enumerating the percentage of human CD45+CD33+CD19− (BD Biosciences) cells by flow cytometry using the BD FACS Calibur. Data were analyzed with FlowJo version 7.7.1 (TreeStar).

To assess secondary engraftment, primary human AML cells were isolated from the bone marrow of control and R2-treated mice. Cells were pooled and equal numbers of viable cells were transplanted into the right femur of secondary untreated mice. After 6 weeks, mice were sacrificed and human CD45+CD33+CD19− (BD Biosciences) cells were assessed by flow cytometry. All in vivo studies were carried out according to the regulations of the Canadian Council on Animal Care and with the approval of the University Health Network Ethics Review Board.

CHAPTER 4: RESULTS

4.1. NLN is necessary for the viability of leukemic bulk and progenitor cells

4.1.1. NLN is overexpressed in a subset of AML patients

We previously identified the mitochondrial peptidase NLN as a top hit in an shRNA screen to identify new biological vulnerabilities in the mitochondrial proteome of AML cells (Cole et al., 2015). NLN’s mitochondrial function is not well understood and its role in AML has not been previously reported. We initiated our study into the role of NLN in leukemia by analyzing NLN gene expression in AML cells and stem cells. In a database of 536 AML and 73 normal bone marrow samples, NLN was overexpressed in 41% of AML samples (Fig. 4A). NLN was equally expressed in the CD34+ and CD34- AML cell populations, as well as in the functionally defined leukemic stem cell (LSC)+ and LSC- fractions (Fig. 4B, C). NLN expression correlated with RUNX1, FLT3, NPM1, and ASXL1 mutations (Table 4). We confirmed the over-expression of NLN in primary AML cells compared to normal hematopoietic cells by immunoblotting (Fig. 5A, B).

B. C.

Figure 4. NLN mRNA is overexpressed in a subset of AML patients (A) Expression pattern and hierarchical clustering of microarray data from 536 primary human AML and 73 normal bone marrow samples for NLN expression. The four main AML NLN expression clusters were designated as 1, 2, 3, and 4. A whisker boxplot displays the NLN z-score normalized gene expression values for each sample in each cluster through their quartiles. The midline represents the median value for each group. One way ANOVA was applied to test the significance of the differences in the mean expression values between the groups. Pairwise t-tests were applied between each AML cluster and the normal data. Each t- test was significant at p < 2 x 10-16. (B) Expression of NLN in the CD34- and CD34+ populations of primary human AML samples. p > 0.05 by t- test. 58 (C) Expression of NLN in functionally defined LSCs of primary human AML samples. p > 0.05 by t-test.

Normal AML A.

180706 150375 180117 170106 180704 180720 120887 120968 160053 160114 160556 276968 290985 151252 160406

NLN

SDHA

B. AML Normal

0556 151257 160406 151104 161476 141065 140176 16 141202 151120

NLN

GAPDH

Figure 5. NLN is overexpressed in a subset of AML patients (A) NLN expression was assessed in isolated mitochondria from primary AML samples and normal hematopoietic cells. NLN and SDHA were analyzed by immunoblotting. (B) NLN expression was assessed in whole cell lysates from primary AML samples and normal hematopoietic cells. The membrane was immunoblotted with antibodies against NLN and GAPDH.

Mutation Mutation t.value t.pvalue Wilcoxon.pvalue Fisher.pvalue 0/1

RUNX1 -4.205 6.91e-05 1.72e-04 5.92e-04 46/34

FLT3 3.62 3.70e-04 2.45e-04 1.20e-03 171/39

NPM1 3.257 1.21e-03 2.77e-04 5.61e-03 340/108

ASXL1 -2.361 2.39e-02 2.53e-02 4.63e-02 4/33

JAK2 -2.497 1.39e-02 4.22e-02 0.095 113/10

MPL -2.914 4.83e-03 3.92e-02 0.493 68/2

WT1 -2.896 5.07e-03 0.081 0.805 44/26

CEBPA -0.14 0.889 0.421 0.151 164/28

DNMT3A 0.197 0.844 0.456 0.253 100/60

KRAS 1.019 0.31 0.142 0.399 128/14

IDH2 -1.352 0.178 0.18 0.456 158/35

BRAF 1.48 0.147 0.205 0.487 38/1

KIT 1.245 0.215 0.276 0.495 155/9

NRAS 0.771 0.442 0.335 0.601 114/46

TP53 -0.407 0.684 0.821 0.67 119/27

KMT2A 1.094 0.283 0.418 0.728 14/19

IDH1 -1.055 0.293 0.497 0.843 168/30

Table 4. Correlation of NLN with mutations

4.1.2. Genetic knockdown of NLN impairs the growth of leukemic cells and progenitors

We assessed whether NLN is necessary for the growth and viability of AML cells. OCI- AML2, NB4, TEX, and MV4-11 leukemia cells were transduced with shRNA targeting NLN or control sequences. Target knockdown was confirmed by immunoblotting (Fig. 6A-D). Of note, NLN knockdown did not affect the expression of the closely related cytoplasmic peptidase, THOP1 (Fig. 6E). NLN knockdown reduced the growth and viability of OCI- AML2, NB4, TEX, and MV4-11 cells (Fig. 7A-D). Knockdown of NLN also reduced the clonogenic growth of AML cells (Fig. 8A, B).

Figure 6. Target knockdown of NLN confirmed by immunoblot (A-D) Target knockdown of NLN in OCI-AML2 (A), NB4 (B), TEX (C), and MV4-11 (D) cells was assessed by immunoblotting eight days after transducing cells with shRNA targeting NLN or control sequences. Membranes were immunoblotted with antibodies against NLN and actin or GAPDH. (E) Knockdown of THOP1 in OCI-AML2 cells was assessed by immunoblotting four and eight days after transducing cells with shRNA targeting NLN or control sequences. The membrane was immunoblotted with antibodies against THOP1 and GAPDH.

OCI-AML2

A. e ) 3 0 C o n tro l v 1 0 0 r s ) u n s h R N A 1 l o C o i

r l l s h R N A 2 t h i t 2 0 n m o w (

c o

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i r a ( V e

0 r 0 1 2 3 4 5 A C o n tr o l s h R N A 1 s h R N A 2 D a y NB4 Figure 7. Genetic knockdown B.

e of NLN reduces the growth of v ) 3 0 C o n tro l 1 0 0 r s ) u l n s h R N A 1 leukemic cells in vitro C o o

i r l t h l s h R N A 2 t i n (A-D) Viable cell counts and area o 2 0 w m ( c

o ****

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G 5 0 l under the growth curve of OCI-

e r e e C v

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t **** e AML2 (A), NB4 (B), TEX (C) and l n a l b U e

a r i a ( MV4-11 (D) cells seeded four V e

0 r 0 1 2 3 4 5 A C o n tr o l s h R N A 1 s h R N A 2 days after transducing cells with D a y TEX shRNA targeting NLN or control C. e

) 1 5 C o n tro l v 1 0 0 sequences. Viable cell count data r s ) u n s h R N A 1 l o C o i

r l points represent mean viable cell l s h R N A 2 t h i t 1 0 n m o w (

c counts ± SD of a representative o

**** r **** s o l t

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a r i a ( V 0 e r 0 curve data represent the mean 1 2 3 4 5 A C o n tr o l s h R N A 1 s h R N A 2 D a y percent area under the curve ± MV4-11 D. SD compared to control shRNA e v ) 1 5 C o n tro l 1 0 0 r s of a representative experiment ) u n s h R N A 1 l C o o

i r l t l

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i from N = 3 biological t 1 0 n o m w ( c

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r replicates. p ≤ 0.0001 by one- o s l t

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d t e n a l l b U e

hoc test. r a i a ( e V 0 r 0 1 2 3 4 5 A T o ta l A r e a s h R N A 1 s h R N A 2 D a y

OCI-AML2 A. 1 0 0 ) l o r s t e n i o n c o

l o

t 5 0

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e r **** **** ( 0 C o n tr o l s h R N A 1 s h R N A 2

Figure 8. Genetic knockdown of NLN targets leukemic progenitor cells (A, B) Clonogenic growth of OCI-AML2 (A) and NB4 (B) cells transduced with shRNA targeting NLN or control sequences. Mean ± SD colony counts are shown of a representative experiment from N = 2 biological replicates. ∗∗∗p ≤ 0.001, ∗∗∗∗p ≤ 0.0001 by one-way ANOVA and Dunnett’s post hoc test.

4.1.3. NLN knockdown impairs engraftment of leukemic cells in vivo

To further test the effects of NLN on leukemia initiating cells in vivo, we evaluated NLN knockdown in TEX cells that have properties of leukemic stem cells, including hierarchal organization and self-renewal (Nicolini et al., 2004). Knockdown of NLN in TEX cells reduced engraftment into the marrow of immune deficient mice (Fig. 9A). NLN knockdown also reduced engraftment of a primary AML sample into mice by 90% (Fig. 9B).

A. TEX B. Patient 140005

** ** 5 0 2 0 0 +

4 0 3 3 1 5 0 D C +

3 0 + 5 5 4

4 1 0 0 D

2 0 D C C +

P 5 0 1 0 F G 0 0 C o n tr o l N L N s h R N A C o n tr o l N L N s h R N A

Figure 9. Genetic knockdown of NLN impairs engraftment of leukemic cells in vivo (A) Equal number of viable TEX cells (2 × 105) transduced with shRNA targeting NLN or control sequences were injected into the right femur of sublethally irradiated immune deficient mice. Six weeks after injection, mice were sacrificed and the percentage of human CD45+ cells in the non-injected femur was determined by flow cytometry (n = 10 mice per group). Bar represents mean engraftment. ∗∗p ≤ 0.01 by Student’s t-test. (B) Engraftment of primary AML cells from patients 140005 in NOD-SCID mice after NLN knockdown (n = 7 mice/group). Bar represents mean engraftment potential. ∗∗p ≤ 0.01 by Student’s t test.

4.2. NLN interacts with the mitochondrial respiratory chain and genetic knockdown of NLN impairs mitochondrial function

4.2.1. NLN’s interactors identified by BioID-MS

To understand the mitochondrial function of NLN, we used proximity-dependent biotin labeling (BioID) coupled with mass spectrometry (MS) to identify the proteins that interact with NLN. We induced expression of Flag-BirA*-NLN in Flp-In T-REx HEK293 cells and identified interacting proteins by MS. Interacting proteins were compared to Flag-BirA* and the known mitochondrial matrix protease, ClpP. From our BioID screen, we identified 86 proteins that preferentially interacted with NLN over the Flag-BirA* control, of which 72 localized to the mitochondria. Of these 72 mitochondrial proteins, 32 preferentially interacted with NLN over ClpP (Table 5, 6). NLN’s interactors were enriched for functions including respiratory electron transport, mitochondrion organization, and respiratory chain complex assembly (Fig. 10A, B).

Complete list of NLN's interactors identified by BioID-MS NLN Pool A Pool B Gene ID Gene Name Full Name Top 2 Controls Run #1 Run #2 Run #1 Run #2 Total SAINT BFDR 948469 birA Biotin ligase (E. coli) 2815 2807 607 582 584 541 2314 0.79 57486 NLN Neuroysin 0 0 290 265 285 263 1103 8192 CLPP Caseinolytic mitochondrial matrix peptidase proteolytic subunit 7 7 4 5 5 5 19 0.64 0 55699 IARS2 isoleucyl-tRNA synthetase 2, mitochondrial 4 4 168 158 169 158 653 1 0 641371 ACOT1 acyl-CoA thioesterase 1 43 42 127 126 122 127 502 1 0 10128 LRPPRC leucine rich pentatricopeptide repeat containing 18 14 96 95 102 88 381 1 0 3954 LETM1 leucine zipper and EF-hand containing transmembrane protein 1 0 0 92 84 76 74 326 1 0 2746 GLUD1 glutamate dehydrogenase 1 21 18 74 72 59 57 262 1 0 4720 NDUFS2 NADH:ubiquinone oxidoreductase core subunit S2 3 3 61 62 50 44 217 1 0 2108 ETFA electron transfer flavoprotein subunit alpha 14 7 59 57 52 46 214 1 0 4722 NDUFS3 NADH:ubiquinone oxidoreductase core subunit S3 10 7 58 50 50 52 210 1 0 4719 NDUFS1 NADH:ubiquinone oxidoreductase core subunit S1 6 5 45 52 46 44 187 1 0 3301 DNAJA1 DnaJ heat shock protein family (Hsp40) member A1 15 14 45 47 50 43 185 1 0 10939 AFG3L2 AFG3 like matrix AAA peptidase subunit 2 8 8 41 40 41 39 161 1 0 10845 CLPX caseinolytic mitochondrial matrix peptidase chaperone subunit 6 4 29 36 28 29 122 1 0 4698 NDUFA5 NADH:ubiquinone oxidoreductase subunit A5 7 5 29 28 29 20 106 1 0 10469 TIMM44 translocase of inner mitochondrial membrane 44 8 6 25 26 27 27 105 1 0 2109 ETFB electron transfer flavoprotein subunit beta 5 4 26 24 26 24 100 1 0 515 ATP5F1 ATP synthase peripheral stalk-membrane subunit b 4 2 25 21 23 21 90 1 0 873 CBR1 carbonyl reductase 1 6 4 21 18 23 20 82 1 0 3336 HSPE1 heat shock protein family E (Hsp10) member 1 0 0 20 19 15 20 74 1 0 57176 VARS2 valyl-tRNA synthetase 2, mitochondrial 0 0 18 17 20 16 71 1 0 4191 MDH2 malate dehydrogenase 2 0 0 20 19 13 17 69 1 0 28976 ACAD9 acyl-CoA dehydrogenase family member 9 0 0 18 20 15 16 69 1 0 2744 GLS glutaminase 0 0 19 16 16 14 65 1 0 10105 PPIF peptidylprolyl isomerase F 0 0 17 15 17 14 63 1 0 10463 SLC30A9 solute carrier family 30 member 9 0 0 15 16 13 18 62 1 0 79736 TEFM transcription elongation factor, mitochondrial 0 0 22 10 15 14 61 1 0 26073 POLDIP2 DNA polymerase delta interacting protein 2 2 0 18 13 13 16 60 1 0 4728 NDUFS8 NADH:ubiquinone oxidoreductase core subunit S8 0 0 16 13 13 18 60 1 0 29078 NDUFAF4 NADH:ubiquinone oxidoreductase complex assembly factor 4 0 0 13 15 15 14 57 1 0 51204 TACO1 translational activator of cytochrome c oxidase I 0 0 14 14 16 12 56 1 0 5831 PYCR1 pyrroline-5-carboxylate reductase 1 0 0 11 14 15 13 53 1 0 64756 ATPAF1 ATP synthase mitochondrial F1 complex assembly factor 1 0 0 14 18 8 12 52 1 0 87178 PNPT1 polyribonucleotide nucleotidyltransferase 1 0 0 14 14 13 10 51 1 0 29920 PYCR2 pyrroline-5-carboxylate reductase 2 0 0 12 14 9 12 47 1 0 617 BCS1L BCS1 homolog, ubiquinol-cytochrome c reductase complex chaperone 0 0 13 16 12 6 47 1 0 1892 ECHS1 enoyl-CoA hydratase, short chain 1 0 0 14 12 10 8 44 1 0 4731 NDUFV3 NADH:ubiquinone oxidoreductase subunit V3 0 0 13 11 10 10 44 1 0 85476 GFM1 G elongation factor mitochondrial 1 0 0 9 13 8 8 38 1 0 55967 NDUFA12 NADH:ubiquinone oxidoreductase subunit A12 0 0 9 8 10 10 37 1 0 4528 MTIF2 mitochondrial translational initiation factor 2 0 0 10 9 9 9 37 1 0 9692 KIAA0391 KIAA0391 0 0 11 7 9 8 35 1 0 27068 PPA2 pyrophosphatase (inorganic) 2 0 0 14 6 9 4 33 1 0 55066 PDPR pyruvate dehydrogenase phosphatase regulatory subunit 0 0 8 12 7 6 33 1 0 64949 MRPS26 mitochondrial ribosomal protein S26 0 0 9 8 6 9 32 1 0 6389 SDHA succinate dehydrogenase complex flavoprotein subunit A 0 0 5 8 10 8 31 1 0 91942 NDUFAF2 NADH:ubiquinone oxidoreductase complex assembly factor 2 0 0 11 6 6 7 30 1 0 390916 NUDT19 nudix hydrolase 19 0 0 8 5 8 8 29 1 0 4282 MIF macrophage migration inhibitory factor 0 0 9 7 5 7 28 1 0 36 ACADSB acyl-CoA dehydrogenase short/branched chain 0 0 8 7 6 5 26 1 0 84274 COQ5 coenzyme Q5, methyltransferase 0 0 5 6 7 7 25 1 0 117145 THEM4 thioesterase superfamily member 4 0 0 6 5 6 6 23 1 0 79133 C20orf7 NADH:ubiquinone oxidoreductase complex assembly factor 5 0 0 6 5 7 4 22 1 0 4695 NDUFA2 NADH:ubiquinone oxidoreductase subunit A2 0 0 6 5 5 6 22 1 0 25915 NDUFAF3 NADH:ubiquinone oxidoreductase complex assembly factor 3 0 0 5 7 5 5 22 1 0 326625 MMAB metabolism of cobalamin associated B 0 0 5 4 7 5 21 1 0 5442 POLRMT RNA polymerase mitochondrial 0 0 8 3 6 4 21 1 0 51295 ECSIT ECSIT signalling integrator 0 0 4 7 4 5 20 1 0 51181 DCXR dicarbonyl and L-xylulose reductase 0 0 5 6 5 4 20 1 0 26284 ERAL1 Era like 12S mitochondrial rRNA chaperone 1 0 0 6 4 5 4 19 1 0 64951 MRPS24 mitochondrial ribosomal protein S24 0 0 5 4 5 5 19 1 0 4729 NDUFV2 NADH:ubiquinone oxidoreductase core subunit V2 3 3 11 13 14 14 52 0.99 0 23078 KIAA0564 von Willebrand factor A domain containing 8 2 0 12 12 10 12 46 0.99 0 374291 NDUFS7 NADH:ubiquinone oxidoreductase core subunit S7 2 2 9 8 12 12 41 0.99 0 10797 MTHFD2 methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2, methenyltetrahydrofolate0 cyclohydrolase0 6 4 3 3 16 0.99 0 9581 PREPL prolyl endopeptidase like 0 0 4 4 3 4 15 0.99 0 81892 SLIRP SRA stem-loop interacting RNA binding protein 0 0 4 4 4 2 14 0.99 0 55037 PTCD3 pentatricopeptide repeat domain 3 9 9 27 27 29 28 111 0.98 0 5824 PEX19 peroxisomal biogenesis factor 19 5 3 22 16 16 14 68 0.98 0 55028 C17orf80 chromosome 17 open reading frame 80 0 0 8 6 4 2 20 0.98 0 200205 IBA57 IBA57, iron-sulfur cluster assembly 0 0 4 4 6 2 16 0.98 0 9054 NFS1 NFS1, cysteine desulfurase 0 0 4 3 4 2 13 0.97 0 5879 RAC1 Rac family small GTPase 1 0 0 3 2 4 4 13 0.97 0 219927 MRPL21 mitochondrial ribosomal protein L21 0 0 4 3 2 3 12 0.97 0 4594 MUT methylmalonyl-CoA mutase 0 0 4 3 3 2 12 0.97 0 6472 SHMT2 serine hydroxymethyltransferase 2 29 16 83 87 79 81 330 0.96 0 4200 ME2 malic enzyme 2 2 0 9 9 8 12 38 0.96 0 204 AK2 adenylate kinase 2 2 0 7 10 10 12 39 0.95 0 8801 SUCLG2 succinate-CoA ligase GDP-forming beta subunit 0 0 3 3 2 2 10 0.95 0 23395 LARS2 leucyl-tRNA synthetase 2, mitochondrial 2 0 7 9 10 8 34 0.93 0 4723 NDUFV1 NADH:ubiquinone oxidoreductase core subunit V1 4 3 12 10 14 13 49 0.91 0.01 23646 PLD3 phospholipase D family member 3 0 0 2 2 2 2 8 0.91 0.01 29979 UBQLN1 ubiquilin 1 2 0 8 7 6 9 30 0.89 0.01 8803 SUCLA2 succinate-CoA ligase ADP-forming beta subunit 2 0 9 8 6 5 28 0.84 0.01 23386 NUDCD3 NudC domain containing 3 13 11 32 38 32 38 140 0.77 0.01 25902 MTHFD1L methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1 like 5 5 15 16 14 16 61 0.76 0.01 100529144 CORO7-PAM16 CORO7-PAM16 readthrough 0 0 0 9 9 12 30 0.75 0.01 84816 RTN4IP1 reticulon 4 interacting protein 1 68 0 0 9 8 5 0 22 0.75 0.01

Table 5. Complete list of NLN’s interactors identified by BioID-MS List of all proteins that interacted with NLN, as determined by BioID-MS. Interacting proteins were compared to Flag-BirA* and the known mitochondrial matrix protease, ClpP.

Comparison of NLN's mitochondrial interactors with ClpP's mitochondrial interactors

NLN ClpP NLN/ClpP BirA 2314 14740 IARS2 0.282195333 0.029986431 9.410767432 LRPPRC 0.164649957 0.036635007 4.494334006 LETM1 0.14088159 0.027137042 5.191486603 GLUD1 0.113223855 0.023880597 4.74124892 NDUFS2 0.09377701 0.027679783 3.387924314 ETFA 0.092480553 0.015061058 6.140375466 NDUFS3 0.090751945 0.020488467 4.429416108 NDUFS1 0.080812446 0.045183175 1.788551732 DNAJA1 0.079948142 0.004206242 19.00702596 AFG3L2 0.069576491 0.013975577 4.978434351 CLPX 0.052722558 0.023134328 2.278975102 NDUFA5 0.045808124 0.0078019 5.871406561 TIMM44 0.045375972 0.013432836 3.377989052 ETFB 0.043215212 0.007598372 5.687430547 ATP5F1 0.038893691 0.002306649 16.8615588 HSPE1 0.031979257 0.007734057 4.134861787 VARS2 0.0306828 0.00312076 9.831836459 ACAD9 0.029818496 0.004816825 6.190487784 MDH2 0.029818496 0.026729986 1.115544753 GLS 0.028089888 0.019470828 1.442665309 PPIF 0.027225583 0.010447761 2.605877269 TEFM 0.026361279 0.00027137 97.14131374 NDUFS8 0.025929127 0.007191316 3.605616347 NDUFAF4 0.024632671 0.004545455 5.419187554 TACO1 0.024200519 0.015332429 1.578387805 PYCR1 0.022904062 0.008819539 2.596968287 ATPAF1 0.02247191 0.011329715 1.983448833 PNPT1 0.022039758 0.013839891 1.592480553 BCS1L 0.02031115 0.013432836 1.512052242 PYCR2 0.02031115 0.015196744 1.336546179 ECHS1 0.019014693 0.023473541 0.810047911 NDUFV3 0.019014693 0.019674355 0.966470956 GFM1 0.01642178 0.008616011 1.905960977 MTIF2 0.015989628 0.003188602 5.014619614 NDUFA12 0.015989628 0.01275441 1.253654904 KIAA0391 0.015125324 0.00027137 55.73681936 PDPR 0.01426102 0.002103121 6.780884936 PPA2 0.01426102 0.009023066 1.580507015 MRPS26 0.013828868 0.006919946 1.998406969 SDHA 0.013396716 0.006784261 1.974675886 NDUFAF2 0.012964564 0.016689281 0.776819782 ACADSB 0.011235955 0.004816825 2.332647571 COQ5 0.010803803 0.000135685 79.62402766 THEM4 0.009939499 0.010108548 0.983276583 C20orf7 0.009507347 0.006580733 1.444724626 NDUFA2 0.009507347 0.005427408 1.751728608 NDUFAF3 0.009507347 0.00305291770 3.114184193

Table 6. Comparison of NLN’s mitochondrial interactors with ClpP’s mitochondrial interactors List of mitochondrial proteins that interacted with NLN, as determined by BioID-MS. NLN’s mitochondrial interactors were compared to the matrix protease, ClpP. Bolded proteins indicate preferential interaction with NLN defined as >3-fold spectral counts for NLN versus ClpP. "-" indicates proteins that only interacted with NLN and not ClpP.

C itric a c id c y c le a n d re sp ira to ry e le c tro n tra n sp o rt Re sp ira to ry e le c tro n tra n sp o rt M ito c h o n d ria l re sp ira to ry c h a in c o m p le x I a sse m b ly M ito c h o n d rio n o rg a n iza tio n M ito c h o n d ria l re sp ira to ry c h a in c o m p le x a sse m b ly D ru g m e ta b o lic p ro c e ss C e llu la r re sp ira tio n Nu c le o tid e m e ta b o lic p ro c e ss Nu c le o sid e p h o sp h a te m e ta b o lic p ro c e ss G e n e ra tio n o f p re c u rso r m e ta b o lite s a n d e n e rg y

0 1 0 2 0 3 0 4 0 -lo g (P v a lu e ) Figure 10. NLN interacts with the mitochondrial respiratory chain1 0 (A ) List of proteins that interacted with NLN, as determined by proximity-dependent biotin labeling (BioID) coupled with mass spectrometry. 72

(B) Top Gene Ontology terms in the NLN interaction network. 4.2.2. Genetic knockdown of NLN impairs oxidative metabolism and cristae structure

As NLN interacted with respiratory chain complex proteins, we investigated the effects of NLN knockdown on OXPHOS. Knockdown of NLN decreased oxygen consumption rates (OCR) (Fig. 11A-C) and disrupted mitochondrial cristae formation (Fig. 12) in AML cells. However, knockdown of NLN did not alter the amounts of complex I, II, III, IV, or V subunits (Fig. 13A-C). In addition, no changes in mitochondrial membrane potential, mitochondrial mass, or reactive oxygen species (ROS) were observed (Fig. 14A-D).

A. NB4

O lig o m y c in F C C P

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) s h R N A 1 n i 4 0 0 s h R N A 2 m / s

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m 2 0 0 p (

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i l i l

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o o e e v v m M i i t t p p a a ( ( l l e e R R 0 0 C o n tr o l s h R N A 1 s h R N A 2 C o n tr o l s h R N A 1 s h R N A 2

Figure 11. Knockdown of NLN disrupts oxidative metabolism (A) Effects of NLN knockdown on oxidative metabolism in NB4 cells. Data points represent mean ± SD, n = 5 technical replicates per group. (B, C) Effects of NLN knockdown on relative basal oxygen consumption in NB4 (B) and OCI- AML2 (C) cells. Values are shown as relative OCR compared to control shRNA. Mean ± SD OCR values are shown, n = 5-10 technical replicates. ∗∗∗∗p ≤ 0.0001 by one-way ANOVA and Dunnett’s post hoc test.

Figure 12. NLN knockdown disrupts cristae ultrastructure OCI-AML2 cells transduced with shRNA targeting NLN or control sequences were assessed by electron microscopy. Images were captured at 20,000X and 60,000X from different micrographs. Scale bars indicate 500 nm (top) and 100 nm (bottom).

Figure 13. NLN knockdown does not alter individual OXPHOS subunits (A-C) Lysates were collected from OCI-AML2 (A), NB4 (B), and T-REx HEK293 (C) cells seven days after transduction with shRNA targeting NLN or a control sequence. NDUFA9 (complex I), SDHA (complex II), UQCRC2 (complex III), MTCO1 (OCI-AML2 and NB4) or COXIV (T-REx H293) (complex IV), ATP5B (complex V), tubulin, GAPDH, and actin were measured by immunoblotting.

A. Membrane Potential B. Mitochondrial Mass

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C. Mitochondrial ROS D. Cellular ROS

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Figure 14. NLN knockdown does not affect mitochondrial membrane potential, mitochondrial mass, or ROS levels (A-D) OCI-AML2 cells were transduced with shRNA targeting NLN or control sequences. Mitochondrial membrane potential (A), mass (B), mitochondrial ROS (C), and cellular ROS (D) were measured seven days after transduction as described in the materials and methods. Data represent mean ± SD, n = 3 technical replicates. ∗∗p ≤ 0.01 by one-way ANOVA and Dunnett’s post hoc test.

4.2.3. Knockdown of NLN reduces RCS levels

To understand how NLN affects OXPHOS, we examined the formation of respiratory chain complexes and supercomplexes. Respiratory chain complexes I, III, and IV assemble into large higher order quaternary structures called respiratory chain supercomplexes (RCS), which promote efficient oxidative metabolism. NLN knockdown impaired RCS formation in T-REx HEK293, NB4, and OCI-AML2 cells (Fig. 15A-C). In contrast, there was little to no effect on the assembly of respiratory chain complex II, which does not participate in supercomplex formation. Of note, overexpressing wild-type shRNA-resistant NLN reversed the effects of NLN knockdown on supercomplex formation (Fig. 15D, Fig. 16).The effects of NLN knockdown on RCS were independent of changes in amounts of the master cristae regulator, OPA1 (Fig. 17).

The effects of NLN on RCS formation stand in contrast to the effects of another mitochondrial matrix protease, ClpP. ClpP is activated by the mitochondrial chaperone ClpX (caseinolytic mitochondrial matrix peptidase chaperone subunit X) to form ClpXP. NLN knockdown inhibited the formation of RCS, but did not affect individual complex subunits. In contrast, ClpXP impaired RCS assembly (Fig. 18A), but also degraded individual subunits of respiratory complexes I, III, and IV (Fig. 18B).

Figure 15. NLN knockdown impairs RCS formation (A-C) Mitochondrial fractions were collected seven days after Flp-In T-REx HEK293 (A), NB4 (B), and OCI-AML2 (C) cells were transduced with shRNA targeting NLN or control sequences. Isolated mitochondria were solubilized with digitonin and analyzed by BN-PAGE with antibodies against NDUFA9 (complex I), SDHA (complex II), UQCRC2 (complex III), and MTCO1 (complex IV). (D) OCI-AML2 cells overexpressing an empty vector (EV) or NLN were transduced with shRNA targeting the 3’ untranslated region (UTR) of NLN or a control sequence. Isolated mitochondria were solubilized with digitonin and analyzed by BN-PAGE with antibodies against NDUFA9 (complex I), SDHA (complex II), UQCRC2 (complex III), and MTCO1 (complex IV).

Figure 16. Overexpression of NLN confirmed by immunoblot OCI-AML2 cells overexpressing an empty vector (EV) or NLN were transduced with shRNA targeting the 3’ untranslated region (UTR) of NLN or a control sequence. Wild-type non-transduced (NT) OCI-AML2 cells served as an additional control. NLN and actin were assessed by immunoblotting.

Figure 17. NLN knockdown does not affect OPA1 levels Lysates were collected from OCI-AML2 cells seven days after transduction with shRNA targeting NLN or a control sequence. OPA1 and GAPDH were measured by immunoblotting.

Figure 18. ClpXP degrades respiratory chain complex subunits and impairs supercomplex formation (A) Mitochondrial fractions were collected after OCI-AML2 cells were treated with 1.5 μM recombinant ClpXP for 1.5 and 3 h. Complex and RCS assembly was measured by BN-PAGE with antibodies against NDUFB8 (complex I), SDHA (complex II), UQCRC2 (complex III), MTCO1 (complex IV), and citrate synthase (CS). (B) Mitochondrial fractions were collected after OCI-AML2 cells were treated with 1.5 μM recombinant ClpXP for 1.5 and 3 h. NDUFB8 (complex I), SDHA (complex II), UQCRC2 (complex III), MTCO1 (complex IV), and citrate synthase (CS) were assessed by immunoblotting.

4.2.4. Genetic knockdown of NLN does not affect viability under hypoxic conditions

To further investigate the requirement for NLN and respiratory supercomplexes in oxidative metabolism in AML, we analyzed NLN and RCS under hypoxia and near anoxia (1% and 0.2% oxygen). Under these hypoxic conditions, RCS assembly was reduced but the amounts of individual complex subunits were unchanged (Fig. 19, Fig. 20A, B). In addition, the amount of NLN protein and mRNA were decreased (Fig. 20A-C). Moreover, under near anoxic conditions, NLN was not necessary for the growth of AML cells (Fig. 21A-C).

BN-PAGE

48h

2 2 2 2 2 2 2 2

2 2 2 2 20% O 20% O 20% O 1% O 20% O 1% 1% O 1% O 0.2% O 0.2% O 20% O 1% O 0.2% O 0.2%

-I + III + IV

2 n RCS

-III2 + IV

-III2, IV2 -IV

-II

NDUFA9 SDHA UQCRC2 MTCO1

Figure 19. RCS is downregulated under hypoxia Mitochondrial fractions were collected after OCI-AML2 cells were incubated at 20%, 1%, or 0.2% oxygen for 48 h. Isolated mitochondria were solubilized with digitonin. Complex and RCS assembly was measured by BN-PAGE with antibodies against NDUFA9 (complex I), SDHA (complex II), UQCRC2 (complex III), and MTCO1 (complex IV).

2 2 2 2 2 2

2 2 2 A. B. 1% O 20% O 1% O 20% O 0.2% O 20% O 1% O 0.2% O 0.2% O 48h

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NDUFA9 α-NDUFA9 α-UQCRC2 α-NLN α-SDHA α-COXIV SDHA C.

n 1 0 0 o ) i UQCRC2 l s o r s t e n r **** o p c x

E o COXIV **** t 5 0

A e N v i R t a m l

e N r ( L

N 0 N o r m o x ia 1 % O 2 0 .2 % O 2

Figure 20. NLN is downregulated under hypoxia (A) Mitochondrial fractions were collected after OCI-AML2 cells were incubated at 20%, 1%, or 0.2%

O2 for 48 h. NLN, NDUFA9 (complex I), SDHA (complex II), UQCRC2 (complex III), and COXIV (complex IV) were measured by immunoblotting. (B) Mitochondrial fractions were collected after OCI-AML2 cells were incubated at 20%, 1%, or 0.2%

O2 for 48 h. Equal protein loading was confirmed by staining with Amido Black. (C) Relative mRNA expression of NLN was assessed by qRT-PCR after OCI-AML2 cells were grown under conditions of normoxia, 1% O2, or 0.2% O2 for 48 h. Data represent mean ± SD, n = 3 technical replicates. ∗∗∗∗p ≤ 0.0001 by one-way ANOVA and Dunnett’s post hoc test.

e N o rm o x ia

v 1 5 0

)

n o

w 1 0 0

o **

e v

i 5 0

( e

r 0 A C o n tro l s h R N A 1 s h R N A 2 B.

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)

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w 1 0 0 c

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r *

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)

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w 1 0 0

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i 5 0

( e

r 0 Figure 21. NLN is notA necessaryC o n t rforo l thes hgrowthR N A 1 ofs lheukemicR N A 2 cells under hypoxia (A-C) OCI-AML2 cells were transduced with shRNA targeting NLN or control sequences.

Cells were incubated at 20% (A), 1% (B), or 0.2% (C) O2 and counted daily for five days by trypan blue exclusion staining. Data are shown as the mean area under the growth curve relative to control shRNA ± SD, Data points represent mean viable cell counts ± SD of a representative experiment from N = 2 biological replicates. ∗∗∗∗p ≤ 0.0001 by one-way ANOVA and Dunnett’s post hoc test. 86

4.2.5 Respiratory chain supercomplex assembly is enhanced in a subset of AML patients and correlates with NLN expression

To further investigate the importance of RCS in AML, we analyzed RCS in primary AML patient samples and normal hematopoietic cells. RCS assembly was increased in a subset of AML patient samples compared to normal hematopoietic cells (Fig. 22A). Increased amounts of RCS positively correlated with increased NLN expression (Fig. 22B), but did not correlate with the amount of individual respiratory chain complex subunits (Fig. 22C, D). Collectively, these data suggest that RCS assembly is increased in a subset of AML patient samples and is correlated with NLN expression.

Figure 22. Respiratory chain supercomplex assembly is enhanced in a subset of AML patients and correlates with NLN expression (A) Lysates from mitochondria isolated from primary AML and normal hematopoietic cells were analyzed by BN-PAGE with a mixture of antibodies against NDUFA9 (complex I), SDHA (complex II), UQCRC2 (complex III), and MTCO1 (complex IV). (B) RCS and NLN in primary AML samples from (A) were semi-quantified by densitometry. R2 = 0.8032, p < 0.05. (C) RCS and NDUFA9 in primary AML samples from (A) were semi-quantified by densitometry. R2 = 0.4743, p > 0.05. (D) RCS and UQCRC2 in primary AML samples from (A) were semi-quantified by densitometry. R2 = 0.6394, p > 0.05.

4.2.6. NLN knockdown impairs LETM1 complex formation

To investigate how NLN may be regulating RCS assembly, we analyzed our BioID results. Among the top mitochondrial interactors with NLN was LETM1 (leucine zipper EF-hand containing transmembrane protein 1) (Table 7). LETM1 is a known regulator of RCS formation (Tamai et al., 2008). LETM1 forms two multi protein complexes, termed the minor and major complex. To investigate the role of LETM1 in oxidative metabolism in AML, we assessed LETM1 under hypoxia and near anoxia. Assembly of the minor and major complexes of LETM1 were impaired under hypoxia, but the amount of total LETM1 protein was only slightly reduced (Fig. 23A-D). Knockdown of NLN in AML cells reduced the formation of the minor and major LETM1 complexes as assessed by non-denaturing gels, but only slightly changed total amounts of LETM1 protein (Fig. 24A, B). LETM1 is also reported to regulate mitochondrial calcium (Jiang et al., 2009a), but NLN knockdown did not alter cellular or mitochondrial calcium (Fig. 25A, B).

Table 7. LETM1 is a top mitochondrial protein interactor of NLN The top 10 putative mitochondrial protein interactors with NLN identified by BioID-MS compared to their interaction with ClpP. Spectral counts were normalized to each bait’s respective BirA* peptide counts. Bolded proteins indicate preferential interaction with NLN defined as > 3-fold spectral counts for NLN versus ClpP.

Figure 23. LETM1 complex assembly is impaired under hypoxia (A) Mitochondrial fractions were collected after OCI-AML2 cells were incubated at 20%, 1%, or 0.2% O2 for 48 h. LETM1 and MnSOD were measured by immunoblotting, and equal protein loading was confirmed by staining with Amido Black. (B) Mitochondrial fractions were collected after OCI-AML2 cells were incubated at 20%, 1%, or 0.2% O2 for 48 h. Isolated mitochondria were solubilized with digitonin. LETM1 complexes were measured by BN-PAGE with antibodies against LETM1. (C) Mitochondrial fractions were collected after OCI-AML2 cells were incubated at 20%, 1%, or 0.2% O2 for 48 h. LETM1 and MnSOD protein was semi-quantified by densitometry of SDS-PAGE results (A). (D) Mitochondrial fractions were collected after OCI-AML2 cells were incubated at 20%, 1%, or 0.2% O2 for 48 h. LETM1 complex assembly was semi-quantified by densitometry of BN- PAGE results from (B). 91

Figure 24. NLN knockdown impairs LETM1 complex formation (A, B) Lysates were collected seven days after OCI-AML2 (A) and NB4 (B) cells were transduced with shRNA targeting NLN or control sequences. LETM1 expression was measured by denaturing SDS-PAGE and non-denaturing BN-PAGE gels. LETM1 and SDHA were run in parallel in BN- PAGE.

1 5 0

) l o r m t

u i n

c 1 0 0 o l c a

C o

t r

a e l v u i l

t 5 0 l a e l C e r (

0 C o n tro l s h R N A 1 s h R N A 2 B.

1 5 0 m )

l u i o c r l t a n 1 0 0 C o

l c

a i o r t

e n v o i

t 5 0 h a c l o e t r i ( M 0 C o n tro l s h R N A 1 s h R N A 2

Figure 25. NLN knockdown does not alter cellular or mitochondrial calcium levels (A, B) Cellular (A) and mitochondrial (B) calcium were assessed by flow cytometry in OCI-AML2 cells seven days after cells were transduced with shRNA targeting NLN or control sequences. Data represent mean values ± SD compared to control shRNA, N = 2 biological replicates. p > 0.05 by one-way ANOVA and Dunnett’s post hoc test.

4.2.7. LETM1 knockdown reduces viability and oxidative metabolism in AML cells

To further test whether RCS formation is necessary for oxidative phosphorylation and growth in AML, we knocked down LETM1. We also knocked down BCS1L, a known component of the LETM1 major complex (Tamai et al., 2008) and an NLN interactor in our BioID screen. Knockdown of LETM1 and BCS1L decreased basal and maximal OCR and reduced the growth of AML cells (Fig. 26A-H).

Figure 26. LETM1 knockdown disrupts leukemic growth and oxidative metabolism (A) LETM1 expression in OCI-AML2 cells was measured by immunoblotting eight days after transducing cells with shRNA targeting LETM1 or a control sequence. (B) Viable cell counts of OCI-AML2 cells seeded four days after transducing cells with shRNA targeting LETM1 or a control sequence. Data points represent mean viable cell counts ± SD of a representative experiment from N = 2 biological replicates. (C, D) Basal (C) and maximal (D) OCR after LETM1 knockdown in OCI-AML2 cells. Data represent mean ± SD percent OCR, n = 10 technical replicates. ∗∗∗∗p ≤ 0.0001 by Student’s t-test. (E) BCS1L expression in OCI-AML2 cells were measured by immunoblotting eight days after transducing cells with shRNA targeting BCS1L or a control sequence. (F) Viable cell counts of OCI-AML2 cells seeded four days after transducing cells with shRNA targeting BCS1L or a control sequence. Data points represent mean viable cell counts ± SD of a representative experiment from N = 2 biological replicates. (G, H) Basal (G) and maximal (H) OCR after BCS1L knockdown in OCI-AML2 cells. Data represent mean ± SD percent OCR, n = 10 technical replicates. ∗∗∗∗p ≤ 0.0001 by Student’s t-test. 95

4.3. A small molecule inhibitor of NLN (R2) targets AML cells and stem cells

4.3.1. R2 is cytotoxic to AML cell lines and low passage primary AML cultures in vitro

3-[(2S)-1-[(3R)-3-(2-Chlorophenyl)-2-(2-fluorophenyl)pyrazolidin-1-yl]-1-oxopropan-2-yl]- 1-(adamantan-2-yl)urea (R2) is a reported inhibitor of NLN (Hines et al., 2014), but its anti-cancer effects have not been previously reported. Inhibiting NLN with R2 reduced the growth and viability of AML cell lines (OCI-AML2, NB4, MV4-11, and TEX) (Fig. 27A) as well as the primary AML culture models 8227 and 130578 (Fig. 27B, C).

Figure. 27. R2 is cytotoxic to AML cell lines and low passage primary AML cultures in vitro (A) OCI-AML2, NB4, MV4-11, and TEX cells were treated with R2 for 72 h. The mean percent ± SD of viable cells was measured by CellTiter-FluorTM, n = 3 technical replicates. (B, C) 8227 (B) and 130578 (C) cells were treated with R2 (35 μM) for 72 h. The mean percent ± SD of viable cells was measured by CellTiter-FluorTM, n = 3 technical replicates. ∗∗∗∗p ≤ 0.0001 by Student’s t-test.

4.3.2. R2 targets AML progenitors in vitro

We next assessed the effects of R2 on AML progenitors in vitro. Treatment with R2 reduced the viability of functionally defined stem cells that reside in the CD34+CD38- fraction of 8227 cells (Fig. 28A). R2 also decreased the clonogenic growth of primary AML cells (Fig. 28B).

Figure. 28. R2 targets AML progenitors in vitro (A) 8227 cells were treated with R2 (25 μM) for 72 h. The numbers of viable CD34+ CD38- stem cells were measured by flow cytometry. Data represents mean ± SD, n = 3 technical replicates. ∗∗p ≤ 0.01 by Student’s t-test. (B) Clonogenic growth of primary AML cells from patient 160053 after treatment with R2 (25 μM) or vehicle control. Data represent mean number of colonies ± SD, n = 2 technical replicates. ∗∗∗∗p ≤ 0.0001 by Student’s t-test.

4.3.3. R2 impairs RCS formation and oxidative metabolism

Similar to genetic knockdown of NLN, chemical inhibition of NLN did not affect individual complex subunits but did impair complex and RCS formation in OCI-AML2 and NB4 cells; as well as in the 8227 primary AML culture model (Lechman et al., 2016) and primary AML cells (Fig. 29A-D, Fig. 30A, B). Moreover, inhibition of NLN with R2 also reduced the formation of LETM1 minor and major complexes with only slight changes in the total amount of LETM1 protein (Fig. 31). R2 also reduced the basal and maximal oxygen consumption rate in OCI-AML2 and 8227 cells (Fig. 32A, B).

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Figure 29. R2 impairs RCS formation in leukemic cells (A-C) OCI-AML2 (A), NB4 (B), and 8227 (C) cells were treated with R2 (11 μM, 8.6 μM, and 35 μM, respectively) for 72 h. Mitochondria were isolated and respiratory chain complexes were measured by immunoblotting denaturing SDS-PAGE and non-denaturing BN-PAGE gels. (D) Primary AML patient sample 160053 was treated with 15 μM R2 for 96 h. Isolated mitochondria were solubilized with digitonin and RCS formation was measured by BN-PAGE using a mixture of antibodies against NDUFA9 (complex I), SDHA (complex II), UQCRC2 (complex III), and MTCO1 (complex IV). (E) Primary AML patient sample 160053 was treated with 15 μM R2 for 96 h. RCS and SDHA were semi- quantified by densitometry of BN-PAGE results from Fig. 6D.

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Figure 30. Immunoblot equal loading confirmed by Amido Black staining (A, B) Mitochondrial fractions were collected after OCI-AML2, NB4 (A) and 8227 (B) cells were treated with R2 (11 μM, 8.6 μM, and 35 μM, respectively) for 72 h and immunoblotted (Fig. 29A- C). Equal protein loading was confirmed by staining with Amido Black.

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Figure 31. R2 impairs LETM1 complex formation OCI-AML2, NB4, and 8227 cells were treated with R2 (11 μM, 8.6 μM, and 25 μM, respectively) for 72 h. After treatment, LETM1 expression was measured by immunoblotting denaturing SDS-PAGE and non- denaturing BN-PAGE gels. LETM1 and SDHA were run in parallel in BN-PAGE.

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Figure 32. R2 disrupts oxidative metabolism (A, B) Basal and maximal OCR were measured 72 h after treatment of OCI-AML2 (A) and 8227 (B) cells with 5 μM and 25 μM of R2, respectively. Data represent mean percent ± SD OCR, n = 10-15 technical replicates. ∗∗∗∗p ≤ 0.0001 by Student’s t-test.

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4.3.4. R2 demonstrates anti-leukemic activity in vivo

To determine whether inhibiting NLN has anti-leukemia activity in vivo, we assessed the efficacy and toxicity of R2 in an OCI-AML2 xenograft mouse model. Daily treatment with R2 reduced the growth of OCI-AML2 cells in immune deficient mice (Fig. 33A, B) without affecting body weight or causing toxicity (Fig. 34A-G).

Finally, we assessed the effects of inhibiting NLN on primary AML and normal hematopoietic cells in vivo. Primary AML and normal hematopoietic cells were injected into the femurs of immune deficient mice. Two weeks later, mice were treated with R2 or vehicle control. Treatment of mice with R2 reduced the leukemic burden in these mice without toxicity (Fig. 35A-C). Moreover, inhibiting NLN targeted the AML stem cells as evidenced by decreased engraftment in secondary experiments (Fig. 35D). In contrast, inhibiting NLN did not reduce the engraftment of normal hematopoietic cells (Fig. 35E). Collectively, these results demonstrate that pharmacological inhibition of NLN impairs leukemic cell growth in vitro and in vivo.

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Figure 33. R2 reduces tumor growth in an OCI-AML2 xenograft model (A) Immune deficient mice with OCI-AML2 xenografts were treated with 100 mg/kg of R2 or vehicle control daily for 5 of 7 days for 10 days. Tumor volume was measured over time (n = 10 mice/group). Data represent mean ± SD. ∗p ≤ 0.05, ∗∗∗∗p ≤ 0.0001 by two-way ANOVA and Bonferroni’s post hoc test. (B) Immune deficient mice with OCI-AML2 xenografts were treated with 100 mg/kg of R2 or vehicle control daily for 5 of 7 days for 10 days. Tumor weight was measured at the end of the experiment (n = 10 mice per group). Data represent mean ± SD, ∗∗∗∗p ≤ 0.0001 by Student’s t-test.

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Figure 34. A small molecule inhibitor of NLN shows no evidence of toxicity in a xenograft model of AML (A) Body weight of mice with OCI-AML2 xenografts treated with 100 mg/kg of R2 or vehicle control daily for 5 of 7 days for 10 days. Data points represent mean body weight ± SD (n = 10 mice per group). (B-F) At the end of the experiment in (A), peripheral blood was collected (n = 4 mice/group) and the amount of creatinine (B), creatine kinase (C), aspartate transaminase (AST) (D), alkaline phosphatase (ALP) (E), and bilirubin (F) were measured. p > 0.05 by Student’s t-test. (G) Hematoxylin and eosin stained sections of mouse heart, liver, muscle, kidney, and lung from a 107 representative control and R2-treated mouse from (A). Scale bars indicate 200 µm.

Figure 35. A small molecule inhibitor of NLN reduces the growth of leukemic cells (A, B) Primary AML cells from patient 120541 (A) and 120287 (B) were injected into the femurs of sublethally irradiated female immune deficient mice. Ten days after injection, mice were treated with R2 (100 mg/kg by i.p. injection) or vehicle control on alternate days 5 of 7 days. Four weeks after treatment, mice were sacrificed and the number of human CD45+CD33+CD19- cells in the non-injected femur was measured by flow cytometry. Horizontal bar represents mean engraftment of human cells (n = 8-10 mice in control group, n = 9-10 mice in R2 group). ∗∗∗∗p ≤ 0.0001 by Student’s t-test. (C) Body weight of mice with primary AML patient 120541 xenograft treated with 100 mg/kg of R2 or vehicle control alternate days 5 of 7 days for four weeks. Data points represent mean body weight ± SD (n = 9 mice in control group, n = 10 mice in R2 group). (D) Secondary engraftment of AML patient 120541 was assessed by injecting equal numbers of viable leukemia cells from the bone marrow of R2-treated and vehicle mice into the right femur of untreated irradiated female immune deficient mice. Six weeks after injection, mice were sacrificed and the number of human CD45+CD33+CD19- cells in the non-injected femur was measured by flow cytometry. Horizontal bar represents mean engraftment of human cells (n = 6 mice in control group, n = 5 mice in R2 group). ∗p ≤ 0.05 by Student’s t-test. (E) Normal human cord blood was injected into the femurs of sublethally irradiated female immune deficient mice. Ten days after injection, mice were treated with R2 (100 mg/kg by i.p. injection) or vehicle control on alternate days 5 of 7 days. Four weeks after treatment, mice were sacrificed and the number of human CD45+CD33+CD19- cells in the non-injected femur was measured by flow cytometry. Horizontal bar represents mean engraftment of human cells (n = 4 mice in control group, n = 5 mice in R2 group). p > 0.05 by Student’s t-test. 108

CHAPTER 5: CONCLUSIONS AND FUTURE DIRECTIONS

5.1. Conclusions

The function of the mitochondrial peptidase NLN is largely unknown. We showed that

NLN maintains efficient oxidative phosphorylation by promoting the formation of RCS.

Inhibiting NLN impaired oxidative phosphorylation and targeted AML cells and stem cells in vitro and in vivo. Thus, this work describes the importance of NLN and RCS in AML and highlights a biological vulnerability in this disease.

NLN is a zinc metallopeptidase localized to the mitochondria and secreted into the circulation. NLN cleaves peptides such as neurotensin and bradykinin in the circulation to regulate blood pressure (Chabry et al., 1990b; Checler et al., 1995; Checler et al., 1986;

Rioli et al., 2003; Rioli et al., 1998), but its mitochondrial function is unclear. To better understand NLN’s role in the mitochondria, we profiled NLN’s mitochondrial interactors using BioID-MS. An earlier study placed NLN in the mitochondrial intermembrane space

(Serizawa et al., 1995), but more recent reports localize NLN to the mitochondrial matrix

(Hung et al., 2014; Rhee et al., 2013; Teixeira et al., 2018). Our data are consistent with these more recent reports, as almost all of NLN’s mitochondrial interactors are located in the matrix.

NLN interacted with the mitochondrial respiratory chain and we showed that NLN is necessary for the formation of respiratory chain complexes and supercomplexes. The mitochondrial respiratory chain comprises a series of protein complexes embedded in the

109 inner mitochondrial membrane. The structural organization of the respiratory chain has traditionally been explained by the “fluid state” or “random collision” model. In this model, each complex is an independent entity and electron transfer depends on the random and transient encounters between the complexes and electron carriers (Hackenbrock et al.,

1986a). However, recent studies using blue native polyacrylamide gel electrophoresis

(BN-PAGE) (Schagger and Pfeiffer, 2000) and cryo-electron microscopy (Letts et al.,

2016b) support the “solid state” model, in which respiratory complexes are assembled into large quaternary structures called RCS. RCS comprise complexes I, III, and IV assembled into distinct stoichiometries, such as I+III2+IVn and III2+IV. The I+III2+IVn supercomplex is also referred to as the “respirasome” because of its ability to directly transfer electrons from NADH to oxygen. Recently, a third model called the “plasticity” model has been proposed in which the complexes coexist in both individual and higher order structures (Acin-Perez and Enriquez, 2014).

While the existence of supercomplexes is now widely accepted, there is debate regarding the functional advantage of RCS (Letts and Sazanov, 2017). Prior studies suggest that

RCS may decrease ROS formation or maintain the stability of individual respiratory chain complexes (Acin-Perez et al., 2004; Lopez-Fabuel et al., 2016; Maranzana et al., 2013;

Schagger et al., 2004). We observed no change in the amounts of ROS after decreasing

RCS formation. Rather, in AML cells, RCS formation appears to be necessary for optimal oxidative phosphorylation as disrupting RCS assembly decreased basal and maximal oxygen consumption. Moreover, RCS formation was decreased under near anoxic conditions, where the cell would have less reliance on oxidative phosphorylation. Thus,

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RCS are necessary for optimal oxidative phosphorylation in AML. However, RCS may serve different functions in different cell types, which is supported by the fact that the ratio of RCS varies between different tissues and physiological states (Cogliati et al., 2016;

Cogliati et al., 2013; Lapuente-Brun et al., 2013; Williams et al., 2016).

We discovered that inhibiting NLN preferentially targets AML cells and stem cells over normal hematopoietic cells, suggesting a potential therapeutic window for NLN inhibitors in AML. A potentially favorable toxicity profile for NLN inhibitors is supported by studies of NLN knockout mice. NLN knockout mice are viable and are born following normal

Mendelian distribution. They are similar to wild-type mice in external appearance and are fertile. Although NLN degrades several vasoactive peptides, such as neurotensin and bradykinin (Chabry et al., 1990b; Checler et al., 1995; Checler et al., 1986; Rioli et al.,

2003; Rioli et al., 1998), NLN knockout mice have normal blood pressure, indicating that

NLN’s function in the circulation is redundant (Cavalcanti et al., 2014).

Consistent with our findings that NLN is necessary for RCS formation, NLN knockout mice demonstrate mild metabolic defects. Knockout mice have greater insulin sensitivity, increased glucose tolerance, and increased liver gluconeogenesis. Moreover, muscle from knockout mice show less oxidative fibers and knockout mice perform worse on measures of exercise endurance (Cavalcanti et al., 2014).

In conclusion, we report that NLN is necessary for RCS formation and RCS are necessary for oxidative metabolism in AML. Thus, RCS formation represents a biological

111 vulnerability in AML cells. Moreover, we highlight inhibition of NLN as a therapeutic strategy for AML.

5.2. Future Directions

Our study has a number of limitations. As NLN is present in the central nervous system, pharmacological inhibition of NLN may affect nociception and dopaminergic pathways.

These side effects may be mitigated by ensuring the drug does not cross the blood-brain barrier. Moreover, while R2 was initially designed to inhibit NLN, it may also inhibit the closely related cytosolic peptidase, THOP1. Thus, we cannot exclude the fact that some of R2’s effects may be due to THOP1 inhibition and future work will study the relative contribution of THOP1 and NLN inhibition to impairment of leukemic growth. Although R2 may inhibit both peptidases, we found that NLN knockdown does not affect the expression of THOP1.

NLN may be mediating RCS formation dependent or independent of its peptidase activity.

To address this question, we have generated homozygous mutant NLN peptidase-dead

HL-60 cells using CRISPR/Cas9. NLN has previously been shown to degrade mitochondrial presequence peptides (Teixeira et al., 2018). Future work will investigate if

NLN is processing LETM1 or respiratory chain complex subunits upon their import into the mitochondria, and whether this function is dependent or independent of NLN’s peptidase activity. Based on these results, we will screen for more potent small molecule inhibitors.

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LETM1 is a 70 kDa mitochondrial protein that forms minor and major complexes. LETM1 is necessary for RCS assembly and proper mitochondrial cristae formation (Dimmer et al., 2008; Tamai et al., 2008). The subunits of the LETM1 complexes have not been fully mapped, but the AAA-ATPase chaperone, BCS1L, is a known component of the LETM1 major complex (Tamai et al., 2008). We discovered that NLN interacts with LETM1 and

BCS1L and inhibition of NLN disrupts the formation of LETM1 complexes. However, the components of the LETM1 complexes are poorly characterized. To address this, we identified LETM1’s mitochondrial interactors using BioID-MS. In addition, we performed in-gel digestion coupled with mass spectrometry analysis of the LETM1 major and minor complexes after BN-PAGE. We found that the major complex consists of proteins involved in respiratory electron transport and that the minor complex consists of proteins involved in complex I assembly. Moreover, consistent with previous reports of LETM1’s function, the major and the minor complex interact with proteins involved with calcium regulation and mitochondrial translation, respectively. Further studies will be necessary to characterize the components of LETM1’s minor and major complexes and how NLN regulates LETM1 complex formation

The preferential effects of NLN on AML cells over normal hematopoietic cells are in-line with previous reports that AML cells and stem cells have increased flux of substrates into the tricarboxylic acid cycle and decreased spare reserve capacity in their respiratory chain

(Sriskanthadevan et al., 2015). As such, hampering oxidative phosphorylation in AML by disrupting RCS formation with NLN inhibitors could selectively target AML cells. Further

113 studies will be necessary to determine if NLN or RCS expression can serve as a biomarker to predict sensitivity to R2 in AML.

These experiments will build upon our current understanding of NLN’s mitochondrial function and its significance in AML. Moreover, these experiments will characterize the potential of targeting RCS in malignancy.

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Copyright permission was granted for the use of Figure 1 from Tan et al. 2006 (Springer Nature) on April 14, 2020 (license number 4807731492513).

Copyright permission was granted from Mirali et al. 2020 (Springer Nature) on April 14, 2020 (license number 4807731168311).

Excerpts have been accepted for publication in Stem Cells Translational Medicine (Mirali S and Schimmer AD. The role of mitochondrial proteases in leukemic cells and leukemic stem cells. Stem Cells Transl Med 2020).

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