The function of mitochondrial pyruvate in stem cells and diffuse large B cell lymphoma

The Mitochondrial Pyruvate Carrier (MPC) is an important carrier that transports pyruvate from cytosol into the mitochondria in a cell. Since its discovery, various studies have investigated the expression and impact of MPC in stem and cancer cells. Stem cell metabolism are often characterized by increased anaerobic glycolysis and a lower fraction of mitochondrial carbohydrate oxidation. Initially, the low mitochondrial carbohydrate oxidation feature had been thought to be induced by the environment, rather than an intrinsic feature determined by the cell itself. However, recent studies have revealed that metabolism is rather influential on stem cell fate. For instance, expression of the MPC, which often correlates with carbohydrate oxidation, is low in intestinal stem cells, but comparatively high in their differentiated progeny. Furthermore, deletion of MPC and reduced pyruvate oxidation enhances stem cell function and reinforces the stem cell markers expression. Despite this common understanding of MPC in stem cells, there is still no unified understanding of the relationship between a given type of cancer and its dependence on MPC. For example: re-expression of the MPC in colon cancer cells represses their tumor growth; while in prostate cancer and hepatocellular carcinoma, high MPC activity is required for their rapid proliferation. Diffuse large B-cell lymphoma (DLBCL) is the most common type of non- Hodgkin lymphoma. It is a heterogenous cancer type with different subgroups, such as iv Oxphos-DLBCLs and BCR-DLBCLs. However, the differences of carbohydrate metabolism across the two subgroups were unclear. Accordingly, we investigated the expression of the MPC in two DLBCL subgroups: Oxphos-DLBCLs and BCR-DLBCLs. Although MPC expression was higher in Oxphos-DLBCLs than BCR-DLCLs, we found both subgroups oxidized minimal pyruvate. Unexpectedly, mitochondrial pyruvate was mainly consumed to support α-ketoglutarate production, which, surprisingly, is the major carbon source feeding the TCA cycle in these cells. Furthermore, we discovered that MPC inhibition decreases DLBCL proliferation in a solid 3D environment, but not in a suspension environment. This is likely because MPC inhibition could not be compensated for by glutamate dehydrogenase in 3D environment. This metabolic program unveils a non-canonical connection between the consumption and assimilation of carbohydrates and glutamine.

THE FUNCTION OF MITOCHONDRIAL PYRUVATE IN STEM CELLS AND DIFFUSE LARGE B CELL LYMPHOMA by Peng Wei A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Biochemistry The University of Utah December 2021 Copyright © Peng Wei 2021 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL The dissertation of Peng Wei has been approved by the following supervisory committee members: Jared P. Rutter , Chair 09/02/2021 Date Approved Adam Lucas Hughes , Member 09/03/2021 Date Approved Donald E. Ayer , Member 09/01/2021 Date Approved Carl Sennrich Thummel , Member 09/01/2021 Date Approved Janet M. Shaw , Member 09/01/2021 Date Approved and by Wesley I. Sundquist the Department/College/School of and by David B. Kieda, Dean of The Graduate School. , Chair/Dean of Biochemistry ABSTRACT The Mitochondrial Pyruvate Carrier (MPC) is an important carrier that transports pyruvate from cytosol into the mitochondria in a cell. Since its discovery, various studies have investigated the expression and impact of MPC in stem and cancer cells. Stem cell metabolism are often characterized by increased anaerobic glycolysis and a lower fraction of mitochondrial carbohydrate oxidation. Initially, the low mitochondrial carbohydrate oxidation feature had been thought to be induced by the environment, rather than an intrinsic feature determined by the cell itself. However, recent studies have revealed that metabolism is rather influential on stem cell fate. For instance, expression of the MPC, which often correlates with carbohydrate oxidation, is low in intestinal stem cells, but comparatively high in their differentiated progeny. Furthermore, deletion of MPC and reduced pyruvate oxidation enhances stem cell function and reinforces the stem cell markers expression. Despite this common understanding of MPC in stem cells, there is still no unified understanding of the relationship between a given type of cancer and its dependence on MPC. For example: re-expression of the MPC in colon cancer cells represses their tumor growth; while in prostate cancer and hepatocellular carcinoma, high MPC activity is required for their rapid proliferation. Diffuse large B-cell lymphoma (DLBCL) is the most common type of nonHodgkin lymphoma. It is a heterogenous cancer type with different subgroups, such as Oxphos-DLBCLs and BCR-DLBCLs. However, the differences of carbohydrate metabolism across the two subgroups were unclear. Accordingly, we investigated the expression of the MPC in two DLBCL subgroups: Oxphos-DLBCLs and BCR-DLBCLs. Although MPC expression was higher in Oxphos-DLBCLs than BCR-DLCLs, we found both subgroups oxidized minimal pyruvate. Unexpectedly, mitochondrial pyruvate was mainly consumed to support α-ketoglutarate production, which, surprisingly, is the major carbon source feeding the TCA cycle in these cells. Furthermore, we discovered that MPC inhibition decreases DLBCL proliferation in a solid 3D environment, but not in a suspension environment. This is likely because MPC inhibition could not be compensated for by glutamate dehydrogenase in 3D environment. This metabolic program unveils a non-canonical connection between the consumption and assimilation of carbohydrates and glutamine. iv This dissertation is dedicated to my family And to Qing TABLE OF CONTENTS ABSTRACT....................................................................................................................... iii LIST OF FIGURES .......................................................................................................... vii ACKNOWLEDGMENTS ............................................................................................... viii Chapters 1. INTRODUCTION .......................................................................................................... 1 Metabolism in Stem Cells ....................................................................................... 2 Metabolism in Diffuse Large B Cell Lymphoma ................................................... 4 References ............................................................................................................... 7 2. THE FORCE IS STRONG WITH THIS ONE: METABOLISM (OVER)POWERS STEM CELL FATE .......................................................................................................... 10 Stem Cells and Their Metabolism......................................................................... 12 Metabolic Signatures of Tissue Stem Cells .......................................................... 13 Pyruvate Oxidation Limits Stem Cell Maintenance and Proliferation ................. 13 MPC: An Important Effector of the APC/Wnt/β-catenin Pathway? .................... 16 Fatty Acids Promote ISC Proliferation and Maintenance .................................... 16 Concluding Remarks............................................................................................. 18 References ............................................................................................................. 20 3. A NON-CANONICAL CONVERGENCE OF CARBOHYDRATE AND GLUTAMINE METABOLISM IS REQUIRED AFTER METABOLIC REWIRING IN 3D ENVIRONMENT ....................................................................................................... 21 Abstract ................................................................................................................. 22 Introduction ........................................................................................................... 22 Results ................................................................................................................... 24 Discussion ............................................................................................................. 36 Materials and Methods.......................................................................................... 39 References ............................................................................................................. 46 4. CONCLUDING REMARKS ........................................................................................ 73 References ............................................................................................................. 78 LIST OF FIGURES Figures 2.1. Overview of glucose and fatty acid metabolism pathways ...................................... 15 2.2. Potential mechanisms by which glucose and fatty acid metabolism might affect stem cell homeostasis ................................................................................................................ 17 3.1. MPC expression and pyruvate metabolism in Oxphos- and BCR-DLBCLs............ 52 3.2. Additional data of MPC expression and pyruvate metabolism in Oxphos- and BCRDLBCLs ............................................................................................................................ 54 3.3. MPC inhibition affects glutamine to TCA cycle flux in DLBCL cells .................... 56 3.4. α-KG production is essential for DLBCLs, and MPC inhibition affects glutamine to TCA cycle flux in DLBCL ............................................................................................... 58 3.5. MPC inhibition reduces DLBCL proliferation in Matrigel ...................................... 60 3.6. DLBCL proliferation in suspension and its viability in Matrigel with MPC inhibition, and metabolism impact of MPC inhibition on TCA cycle in cells from Matrigel ........... 62 3.7. Environmental change reshapes the metabolic landscape of DLBCLs ..................... 64 3.8. MPC inhibition enhances the sensitivity of DLBCLs to ammonia in Matrigel ........ 66 3.9. α-KG production is important for DLBCL proliferation in Matrigel ....................... 68 3.10. BCAA degradation pathway is affected by MPC inhibition, and α-KG is important for DLBCLs proliferation in Matrigel environment ......................................................... 70 3.11. α-KG production paths that add net carbons to TCA cycle from glutamine .......... 71 3.12. SLC25A1 dependency in cancer cell lines ............................................................. 72 ACKNOWLEDGMENTS I would like to thank my mentor, Dr. Jared Rutter, for his support, patience, and scientific insights in guiding me through my Ph.D. training. It has been a long and winding road, but Jared always believed in me, even when sometimes I don’t. I will always be gracious for his generosity, support, and encouragement. I would also like to thank my graduate committee: Dr. Adam Hughes, Dr. Carl Thummel, Dr. Don Ayer, and Dr. Janet Shaw. I am very grateful for their support and guidance through my training. Furthermore, I need to thank Dr. Ralph DeBerardinis and his lab for help with metabolomic experiments. I am also grateful for the Department of Biochemistry, its collaborative environment greatly facilitated my research. I also want to show my appreciation to all past and present members of the Rutter lab. In particular, I need to thank Alex Bott for his inspiration, scientific input, and general help during my bumpy science journey; Sarah Fogarty for managing the lab and make things run smoothly; John Schell, Jon Van Vranken, and Yu-Chan Chen for their help and training when I joined the lab. Ahmad Cluntun, Corey Cunningham, and Yeyun Ouyang for their great help with experiments; Jeff Morgan, Jake Winter, Katja Dove, Sara Nowinski, Olga Zurita-Rendon, Chintan Kikani, Jordan Berg, Ashish Toshniwal, Kevin Hicks, Bill McKean for their scientific input and stimulus conversations. Finally, I want to thank my family for all their support throughout my education. They always encouraged me to pursue my interest, and without their support, I could not be where I am now. Also, I want to thank my partner, Qing, for all the love, support, and joy she gives me. ix CHAPTER 1 INTRODUCTION 2 Metabolism in Stem Cells Glucose and fatty acids are two major fuels that drive cellular ATP production. Fatty acids are activated in the cytosol and then transported into mitochondria via the carnitine shuttle system. Once in the matrix, β-oxidation of fatty acids yields acetyl-CoA, which can then be further oxidized in the tricarboxylic acid (TCA) cycle to fuel the electron transport chain and oxidative phosphorylation (OXPHOS), to eventually power the production of ATP. Mitochondrial acetyl-CoA can also be derived from glucose. First, glucose is converted to pyruvate in the cytosol via glycolysis, which generates limited quantities of ATP and NADH. From there pyruvate has two major fates: Pyruvate imported into mitochondria can be decarboxylated to acetyl-CoA and enter the TCA cycle to ultimately produce NADH, which can support ATP synthesis through OXPHOS. Alternatively, cytosolic pyruvate can either be reduced to lactate or it (and/or its precursors) can be used in biosynthetic processes through different metabolic pathways. Therefore, a major determinant of the fate of pyruvate is whether it enters the mitochondrial compartment. The protein complex responsible for transporting pyruvate into mitochondria is known as the Mitochondria Pyruvate Carrier (MPC) (Bricker et al., 2012; Herzig et al., 2012). The fly and mammalian MPC is composed of two types of subunits, MPC1 and MPC2, and deletion of either one is sufficient to disrupt the formation of a functional MPC complex (Bricker et al., 2012; Herzig et al., 2012; Schell et al., 2014), thereby impairing the direct transport of pyruvate into mitochondria and reducing pyruvate oxidation. Accordingly, the MPC is a significant node in the metabolic network of carbohydrate metabolism. In most differentiated mammalian cells, the pyruvate is transported into 3 mitochondria is used in/for oxidation and anabolic reactions (Bricker et al., 2012). In stem cells and many cancers, pyruvate is primarily converted to lactate and excreted from the cell (Vander Heiden et al., 2009). Most adult stem cells appear to have distinct metabolic profiles from their fully differentiated progeny (Stringari et al., 2012; Varum et al., 2011; Zhang et al., 2012). One of these differences is a stem cell’s preference for aerobic glycolysis, with a lower ratio mitochondrial oxidation of carbohydrate fuels (Stringari et al., 2012; Varum et al., 2011; Zhang et al., 2012). Although this metabolic distinction is known, it is not commonly considered to be a major regulator of stem cell proliferation or tissue regeneration. For example, it has been hypothesized that the hematopoietic stem cell’s hypoxia niche might cause this non-oxidation metabolism program (Simsek et al., 2010; Varum et al., 2011). On the contrary, tissue-resident stem cells often have active mitochondria and oxidize fatty acids and amino acids (Kilberg et al., 2016; Knobloch et al., 2017; Rodríguez-Colman et al., 2017). Now growing evidence suggests that this non-oxidative metabolic feature is intrinsically determined and important for the maintenance of adult tissue stem cells (Shyh-Chang et al., 2013; Zhang et al., 2012). Many fully developed organs can renew and recover from injury due to the tissueresident adult stem cells. The fact that stem cells promote proliferation in response to injury is widely appreciated, but recent studies have shown that adult tissue stem cells can also take cues from environmental nutrients to then change their homeostatic status (Chen et al., 2003; Ertl et al., 2008). Therefore, some great examples of how metabolism impacts intestinal stem cells (ISCs) and hair follicle stem cells (HFSCs) homeostasis and maintenance will be discussed in detail (see Chapter 2). 4 Metabolism in Diffuse Large B Cell Lymphoma Cancer cells often reprogram their metabolism to increase glycolysis, even when provided with sufficient oxygen to support oxidative phosphorylation. This alteration, known as the Warburg effect or aerobic glycolysis, was first misinterpreted as a mere consequence of damaged mitochondrial respiration function (Koppenol et al., 2011; Warburg et al., 1927). However, it is now clear that this change in metabolism functions to divert pyruvate and its precursors to fuel other anabolic pathways (Vander Heiden et al., 2009). While multiple mechanisms contribute to this metabolic alteration, the ultimate fate of pyruvate is crucial in this change (Bayley and Devilee, 2012). To enter into the TCA cycle, pyruvate requires MPC-dependent import into the mitochondria (Bricker et al., 2012; Herzig et al., 2012). In a variety of tumor types, the Warburg effect can be caused by low MPC activity (Schell et al., 2014; Li et al., 2017; Tang et al., 2019; Zou et al., 2019). However, low MPC activity and the Warburg effect is not a universal feature of all cancers. For example, in prostate cancer and hepatocellular carcinoma, high MPC activity is required to support the Tricarboxylic Acid (TCA) cycle and oxidative phosphorylation (Bader et al., 2019; Tompkins et al., 2019). Therefore, for a given type of cancer, it is still hard to predict its MPC activity preference. Pyruvate metabolism plays an important role in cancer cell metabolic adaptation. First, cancer cells tend to express a less active isoform of pyruvate kinase, PKM2, which leads to decreased pyruvate production (Christofk et al., 2008). Second, lactate dehydrogenase A (LDHA), the enzyme that converts pyruvate to lactate, is commonly upregulated in cancer cells (Feron, 2009). Thirdly, pyruvate dehydrogenase (PDH), the complex that converts pyruvate to acetyl-CoA, is frequently inhibited in cancer cells by 5 phosphorylated PDH kinase, PDK1 (McFate et al., 2008). Regarding DLBCLs specifically, it is known that OxPhos-DLBCLs have higher PDH activity than BCRDLBCLs (Caro et al., 2012), which correlates with their respective MPC abundance. In the context of colon cancer, re-expressing MPC leads to decreased expression of PKM2, LDHA, PDK1, decreased PDH phosphorylation, and increased PDH activity (Schell et al., 2014). Based on this evidence, it is clear that pyruvate metabolism is tightly regulated in cancer. Colon cancer clearly favors low MPC expression. However this may not be true for all cancers. DLBCL is the most common non-Hodgkin lymphoma and comprises close to 40% of all lymphoid tumors (Monti, 2005). DLBCLs are believed to arise specifically from B cells that have been exposed to antigen, and then migrated to or through germinal centers (GCs) in secondary lymphoid organs (Küppers et al., 1999). DLBCLs are genetically heterogeneous and can be distinguished into three subsets by genome-wide microarray and multiple clustering algorithms: oxidative phosphorylation (OxPhos) DLBCLs, B-cell receptor (BCR) DLBCLs, and host response (HR) DLBCLs (Monti, 2005). DLBCLs that cluster as OxPhos-DLBCL are enriched in genes involved in mitochondrial oxidative phosphorylation, the BCR-DLBCL cluster displays an upregulation of genes involved in B-cell receptor signaling, and the HR-BCR cluster is characterized by a brisk host inflammatory infiltrate (Monti, 2005). Unlike BCRDLBCLs, OxPhos-DLBCLs can survive without the BCR signaling pathway (Monti, 2005). Furthermore, OxPhos-DLBCLs import more carbon into the tricarboxylic acid (TCA) cycle than BCR-DLBCLs and exhibit enhanced mitochondrial energy production (Caro et al., 2012). Because of these metabolism differences between the different 6 subgroups, DLBCL is a great model to study the effect of MPC on cancer cell metabolism and growth. Also, it is worth to mention that although in the laboratory DLBCLs are routinely passaged and studied in suspension media, they are primarily forming solid tumors (Bakhshi and Georgel, 2020; Chiche et al., 2019). In ex vivo culturing conditions, cell profiles are better recapitulated when culturing condition mimics their native environment, for example, stem cell organoids could only form in 3D environment. Therefore, I predict an extra cellular solid tumor microenvironment may be a key factor in recapitulating aspects of DLBCL biology ex vivo. Taken together, I hypothesize that altering MPC expression in different DLBCL subgroups will affect their metabolism and proliferation by changing nutrient flux into mitochondria. In this dissertation, I set out to examine the metabolic function of MPC in Oxphos-DLBCLs and BCR-DLBCLs utilizing different culturing conditions (suspension and Matrigel-based solid 3D environments.). 7 References Bader, D.A., Hartig, S.M., Putluri, V., Foley, C., Hamilton, M.P., Smith, E.A., Saha, P.K., Panigrahi, A., Walker, C., Zong, L., et al. (2019). Mitochondrial pyruvate import is a metabolic vulnerability in androgen receptor-driven prostate cancer. 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Copyright 2018 Trends in Cell Biology 11 12 Review The Force Is Strong with This One: Metabolism (Over)powers Stem Cell Fate Peng Wei,1 Katja K. Dove,1 Claire Bensard,1 John C. Schell,1 and Jared Rutter1,2,* Compared to their differentiated progeny, stem cells are often characterized by distinct metabolic landscapes that emphasize anaerobic glycolysis and a lower fraction of mitochondrial carbohydrate oxidation. Until recently, the metabolic program of stem cells had been thought to be a byproduct of the environment, rather than an intrinsic feature determined by the cell itself. However, new studies highlight the impact of metabolic behavior on the maintenance and function of intestinal stem cells and hair follicle stem cells. This Review summarizes and discusses the evidence that metabolism is not a mere consequence of, but rather influential on stem cell fate. Highlights Reduced pyruvate oxidation enhances stem cell function and reinforces the stem cell molecular signature. Expression of the mitochondrial pyruvate carrier (MPC) is low in intestinal stem cells (ISCs), but high in their differentiated progeny. Lactate dehydrogenase is highly expressed and active in hair follicle stem cells (HFSCs). Stem Cells and Their Metabolism Deletion of MPC leads to increased function of ISCs, while deletion of Ldha prevents activation of HFSCs and promotes their quiescence. Most fully developed organs, principally populated with terminally differentiated cells, can renew and recover from injury due to the function of tissue-resident adult stem cells. The demand for stem cell proliferation in response to injury is widely appreciated, but recent studies have demonstrated that adult tissue stem cells also take cues from environmental nutrients that impact their homeostatic fate [1,2]. For example, caloric restriction increases the healthspan and lifespan of various organisms and one mechanism is likely to be the promotion of tissue stem cell function [3–7]. Recent studies have highlighted the impact of metabolic behavior on intestinal stem cells (ISCs) and hair follicle stem cells (HFSCs) and this phenomenon is the primary focus of this Review [8–10]. Both quiescent and proliferative adult stem cells appear to have metabolic profiles that are distinct from their fully differentiated progeny [11–13]. One common metabolic feature is their preference to perform aerobic glycolysis, with a lower fractional mitochondrial oxidation of carbohydrate fuels [11–13]. Although this metabolic distinction is known, it is not widely considered to be a major driving factor in the management of stem cell homeostasis and tissue regeneration. Typically, the metabolic program of adult stem cells has been viewed to be a product of the environment, rather than being intrinsically determined by the cell itself [14]. This view was supported by the observation that hematopoietic stem cells (HSCs) homeostasis is augmented by hypoxia within their niche, perhaps through promotion of a glycolytic/nonoxidative metabolic program [14–17]. By contrast, tissue-resident stem cells frequently have active mitochondria and oxidize other fuels, such as fatty acids and amino acids [18–20]. This is suggestive of a highly specific mode of metabolic control operating in tissue stem cells. Notably, metabolic profiles of pluripotent stem cells (PSCs) vary with developmental stage and the metabolic regulation of PSC fate has been reviewed extensively elsewhere [21–23]. Evidence is accumulating that the metabolic program of stem cells is intrinsically determined and essential for the maintenance of those cells, perhaps through impacting the balance of selfrenewal and differentiation [11,21]. Here, we focus on how the consumption and metabolism of different substrates (Box 1) differentially influences adult stem cell homeostasis and function. Trends in Cell Biology, July 2018, Vol. 28, No. 7 Altered levels of pyruvate oxidation might be transduced through the Wnt/b-catenin pathway. High levels of fatty acids also promote ISC proliferation and maintenance. 1 Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA 2 Howard Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA *Correspondence: [e-mail] (J. Rutter). https://doi.org/10.1016/j.tcb.2018.02.007 © 2018 Elsevier Ltd. All rights reserved. 551 13 Metabolic Signatures of Tissue Stem Cells Nutrient availability could provide cues that impact stem cell fate decisions [1,2] and the intestinal epithelium is particularly interesting because ISC progeny have intimate access to dietary nutrients. Caloric restriction, while maintaining adequate nutrition, leads to increased numbers of ISCs in mice and enhances their regeneration capacity [3]. Other studies have shown that different epithelial stem cells in various species prefer to utilize glycolysis and lactate production over mitochondrial carbohydrate oxidative [24,25]. Now, multiple groups have set out to understand the regulation and impact of this stem cell metabolism signature across different systems and species. The expression of Mpc1, one of the subunits of the mitochondrial pyruvate carrier (MPC) complex, is low in ISCs and high in the differentiated progeny of those ISCs, the intestinal epithelium [10]. In both mouse and human proximal small intestine (jejunum), immunohistochemistry demonstrated that MPC is nearly absent from the base of the intestinal crypt, where ISCs are located, but is much more abundantly expressed as one moves up the crypts to the villi, which is populated with differentiated epithelial cells. Furthermore, using mice in which enhanced GFP is expressed from the stem cell-specific LGR5 promoter, it was observed that MPC1 abundance inversely correlated with LGR5 gene expression [10]. Together, these data indicate that ISCs exhibit low MPC1 expression and presumably low MPC activity. It is critical to note that the low MPC1 protein level in ISCs is not due to low abundance of mitochondria overall. Protein analysis and electron microscopy both show high mitochondrial content in mouse and human ISCs [10,18]. This implies that the mitochondria in ISCs might be directed toward other functions, including biosynthetic programs as well as the oxidation of fatty acids or other noncarbohydrate fuels, rather than carbohydrate oxidation. Unlike Lgr5-positive ISCs that appear to divide almost continuously, HFSCs undergo cycles of proliferation and quiescence corresponding to the beginning and end of the hair cycle, respectively [9]. Parallel to the discovery that pyruvate oxidation is low in ISCs, lactate dehydrogenase (LDH), the cytosolic enzyme that converts pyruvate to lactate, is highly expressed and active in HFSCs [9]. Ldha expression is enriched in HFSCs relative to the total epidermis [9] and LDH activity assays on skin tissue sections and lysates from sorted cells also showed high LDH activity in HFSCs across the hair cycle [9]. Furthermore, Ldha expression and activity is induced at the beginning of the hair cycle, which is when HFSCs start their rapid proliferation phase [9]. These data are consistent with proliferative HFSCs also being highly glycolytic. The observation of low MPC expression in ISCs and high LDH expression in HFSCs suggest that these tissue-resident stem cells might preferentially utilize glucose through glycolysis coupled with lactate production rather than with mitochondrial oxidation. Beyond these correlations, manipulation of pyruvate metabolism through MPC or LDH modulation also profoundly affects stem cell homeostasis and function, suggesting that metabolism is not a mere response to, but rather influential on stem cell fate. Pyruvate Oxidation Limits Stem Cell Maintenance and Proliferation Using multiple species and multiple stem cell populations, recent studies have demonstrated that stem cell number and proliferation is increased when mitochondrial pyruvate entry is blocked through MPC deletion or inhibition [10]. These discoveries support the notion that the metabolic program of stem cells is not a byproduct of their environments or a passive feature of their cell biology, but rather a driving force that influences their fate and function. 552 Trends in Cell Biology, July 2018, Vol. 28, No. 7 14 Loss of the Drosophila ortholog of MPC1, known as dMPC1 causes overgrowth of the intestinal epithelium [10]. ISCs from these mutant flies also exhibited increased proliferation, similar to what was observed with RNAi-mediated disruption of dMPC1 or dMPC2 in wild-type flies [10]. Drosophila ISC proliferation was also increased when pyruvate dehydrogenase (PDH), the mitochondrial enzyme that oxidizes MPC-imported pyruvate to acetyl-CoA, was disrupted by RNAi [10]. This collection of phenotypes indicates that MPC inhibition promotes stem cell proliferation, almost certainly through reducing pyruvate incorporation into the TCA cycle. Mice that lack MPC1 selectively in Lgr5-positive ISCs also exhibit increased stem cell number in the intestinal crypt and these stem cells are hyperproliferative [10]. Moreover, in the proximal small intestine where deletion of MPC1 is incomplete, MPC1-deficient cells appear to be positively selected to repopulate the intestinal epithelium over time [10]. In spite of these apparently stem-cell-intrinsic phenotypes, the intestine is overtly normal, with no morphological changes observed in intestinal crypt length, villus height, or total intestinal length [10]. This suggests that although the homeostasis of the stem cell compartment is perturbed by MPC deletion, MPC-deficient stem cell progeny are still competent to differentiate. Similar effects have also been observed in intestinal organoid cultures, which are an in vitro model that faithfully recapitulates many aspects of the architecture and function of the mammalian intestinal epithelium [10,26]. MPC1 deletion enhances organoid formation from both whole crypts and from sorted Lgr5-positive stem cells [10]. MPC inhibition by the wellestablished MPC inhibitor, UK-5099, has similar effects. UK-5099-treated or MPC1-deleted organoids display increased expression of stem cell markers (Lgr5, Ascl2, Cd44, and Myc) and decreased expression of differentiation markers (Krt20, Villin1, Chga, and Fabp2) [10]. Metabolomics experiments have also confirmed that MPC1-deleted organoids exhibit a steady-state increase in pyruvate and decrease in tricarboxylic acid (TCA) cycle intermediates, such as citrate, malate, and a-ketoglutarate. In addition, 13C-glucose flux tracing experiments in intestinal organoids have also shown impaired glucose carbon entry into the mitochondrial TCA cycle in response to MPC1 deletion [10]. Together, these observations support the hypothesis that impaired mitochondrial pyruvate import enhances stem cell function and reinforces the stem cell molecular signature. Like MPC abundance in ISCs, LDH abundance and activity also appear to affect stem cell function. Deletion of Ldha in HFSCs prevents activation and HFSCs fail to enter the start of the hair cycle and remain quiescent [9]. Similarly, enforcement of a glycolytic and nonoxidative metabolic program by deleting MPC1 accelerates HFSC activation [9], providing further evidence that such a program promotes stem cell maintenance and proliferation. Box 1. Metabolism Fundamentals Glucose and fatty acids are two major fuels that drive ATP production (Figure I). Fatty acids are activated in the cytosol and then transported into mitochondria via the carnitine shuttle system. Once in the matrix, b-oxidation of fatty acids yields acetyl-CoA, which can then be further oxidized in the TCA cycle to liberate reducing equivalents that fuel the electron transport chain and oxidative phosphorylation (OXPHOS) to eventually power the production of ATP. However, mitochondrial acetylCoA can also be derived from glucose. First, glucose is converted to pyruvate in the cytosol via glycolysis, which generates limited quantities of ATP and NADH. From there pyruvate has two major fates. Pyruvate imported into mitochondria can be decarboxylated to acetyl-CoA and enter the TCA cycle to ultimately produce NADH, which can fuel ATP synthesis through OXPHOS. Alternatively, pyruvate that remains in the cytosol can either be reduced to lactate or it (and/or its precursors) can be used in biosynthetic processes. Therefore, a major determinant of the fate of pyruvate is whether it enters the mitochondrial compartment or remains in the cytosol. The protein complex responsible for transporting pyruvate into mitochondria is known as the MPC [38,39]. The yeast, fly, and mammalian MPC is composed of two types of subunits, exemplified by MPC1 and MPC2 in mammals, and deletion of either one is sufficient to disrupt the formation of a functional MPC complex [27,38,39], thereby impairing the direct transport of pyruvate into mitochondria and reducing pyruvate oxidation. This places the MPC at an important position in determining the mode of carbohydrate metabolism and raises the possibility that its expression and activity could impact stem cell homeostasis. Trends in Cell Biology, July 2018, Vol. 28, No. 7 553 15 Fa!y Acids Glucose NAD+ ADP Biosynthesis NAD+ Lactate LDH Glycolysis ACS NADH ATP NADH Pyruvate Fa!y acyl-CoA CPT1 MPC1 MPC2 Acyl-carni"ne Pyruvate NAD+ PDH NADH Acetyl-CoA TCA cycle Citrate Cytosol Matrix NAD+ FAD NADH FADH2 CAT CPT2 n β-oxida!o Fa!y Acyl-CoA NADH FADH2 Oxaloacetate Acyl-carni"ne NAD+ FAD NADH FADH2 ADP NAD+ FAD I II III IV V ETC ATP Citrate ACLY Low MPC High LDH High fa!y acids level Stem cell maintenance & func"on Acetyl-CoA High MPC Low LDH Stem cell maintenance & func"on Figure I. Overview of Glucose and Fatty Acid Metabolism Pathways. As the end product of glycolysis, pyruvate has two major fates. In the cytosol, it can either be reduced to lactate by LDH or it (and/or its precursors) can be used for biosynthesis. Alternatively, MPC can import pyruvate into mitochondria where it can be converted to acetyl-CoA by pyruvate dehydrogenase. In the cytosol, fatty acyl-CoA synthases activate fatty acids by converting them to fatty acyl-CoAs. Transport of (Figure legend continued on the bottom of the next page.) 554 Trends in Cell Biology, July 2018, Vol. 28, No. 7 16 As might be expected from the observations described above, overexpression of MPC1 and MPC2 is sufficient to reduce stem cell proliferation, consistent with previous reports that expression of these two genes together establishes a functional MPC complex and increases mitochondrial pyruvate uptake [10,27]. Exogenous MPC expression in mouse ISCs decreases expression of the Lgr5 stemness marker [10]. Furthermore, intestines from flies overexpressing MPC1 and MPC2 display reduced size, consistent with impaired stem cell proliferation [10]. Therefore, it appears that MPC overexpression decreases ISC proliferation and probably eventually causes defects in tissue homeostasis. MPC: An Important Effector of the APC/Wnt/b-Catenin Pathway? The Wnt/b-catenin pathway is a highly conserved signaling pathway that regulates cell proliferation as well as the fate of many cell populations [28,29]. In particular, this pathway plays an important role in the development and maintenance of ISCs and HFSCs [30]. Due to its preferential expression and function in stem cells, it is not surprising that targets of the Wnt/ b-catenin pathway are upregulated in MPC-depleted mouse organoids [10]. Additionally, HFSC activation, which is associated with high LDH activity, also induces expression of Myc; a known target of the Wnt/b-catenin pathway [9]. This suggests that these conditions of low pyruvate oxidation in stem cells may stimulate activity of the Wnt/b-catenin pathway. Adenomatous polyposis coli (APC), which acts by suppressing Wnt/b-catenin signaling, is a well-known tumor suppressor in the context of colorectal cancer [31]. APC also controls the expression of MPC1 and MPC2 during intestinal development in zebrafish [32]. Indeed, MPC1 knockdown recapitulates the impaired intestinal differentiation phenotype caused by APC mutation. Moreover, the defective intestinal differentiation observed in APC mutant zebrafish is rescued by expression of human MPC1. This unexpected finding raises the possibility that maintaining low MPC1 expression could be one of the important effectors of the Wnt/b-catenin pathway during stem cell maintenance (see Outstanding Questions). These disparate observations lead to a hypothetical model wherein APC activity induces expression and activity of the MPC complex to limit stemness. By contrast, impaired APC activity leads to decreased expression and activity of the MPC complex and this impairs differentiation. There appears to also be a feedback mechanism whereby low MPC activity also stimulates activation of the Wnt/b-catenin pathway (Figure 1). Fatty Acids Promote ISC Proliferation and Maintenance As expected, MPC inhibition in mouse intestinal organoids decreases pyruvate flux into the mitochondrial TCA cycle and decreases carbohydrate oxidation [10]. In this MPC-deficient setting, maximal respiration becomes highly dependent on fatty acid oxidation [10]. This suggests that loss of MPC activity and pyruvate oxidation leads to a compensatory increased dependence on fatty acid oxidation. This is particularly intriguing because fatty acids have a fatty acyl-CoA across the mitochondrial membrane requires CPT1, CAT, and CPT2. In the mitochondrial matrix, fatty acyl-CoA is oxidized to acetyl-CoA through b-oxidation. Acetyl-CoA, whether derived from carbohydrates, fatty acids or other fuels, enters the TCA cycle to generate NADH and FADH2, which ultimately fuel the ETC to produce ATP. The TCA cycle intermediate citrate can be transported to the cytosol, where it can be converted to cytosolic acetyl-CoA by ACLY. While this process depletes TCA cycle intermediates, they can be replenished through the conversion of pyruvate to oxaloacetate by PC. Decreased pyruvate oxidation caused by low MPC function and high LDH activity or potentially by high fatty acid oxidation can promote stem cell maintenance and function. Similarly, enhanced pyruvate oxidation by high MPC activity or low LDH activity reduces stem cell maintenance and function. Transporters/enzymes are shown in boxes; major metabolic pathways are shown in italics; metabolites are shown in regular type. Abbreviations: ACLY, ATP citrate lyase; ACS, acyl-CoA synthase; CAT, carnitinetranslocase; CPT1, carnitine palmitoyltransferase 1; CPT2, carnitinepalmitoytransferase 2; ETC, electron transport chain; LDH, lactatedehydrogenase; MPC, mitochondrial pyruvate carrier; PC, pyruvate carboxylase; PDH, pyruvate dehydrogenase; TCA cycle, tricarboxylic acid cycle. Trends in Cell Biology, July 2018, Vol. 28, No. 7 555 17 Glucose LDH Lactate Fa y acid mTOR Pyruvate Redox balance (NADH/NAD+, etc.) APC EGFR SIRT MPC1 MPC2 Acetyl-coA TCA cycle Citrate I II III IV V ETC -Catenin Citrate ACLY Acetyl-coA H3K4 Ac H3K9 Ac H3K27 Ac Vegfa Ccnd1 NF- B etc. Histone acetyla on PPAR-δ PPAR- Prolifera on genes -Catenin TCF Stemness Genes Bmp4 Jag1 Jag2 Edn3 etc. Cytosol Nucleus Krt20 Villin1 Chga Fabp2 etc. Di eren a on genes Figure 1. Potential Mechanisms by Which Glucose and Fatty Acid Metabolism Might Affect Stem Cell Homeostasis. The Wnt/b-catenin pathway is important in the development, maintenance, and proliferation of epithelial stem cells. In this review, we propose a model wherein APC, most likely through the Wnt/ b-catenin pathway, modulates the expression of MPC1 and MPC2 to alter the cellular metabolic program. This metabolic program then profoundly impacts cell fate, perhaps through feedback regulation on the Wnt/b-catenin pathway itself. Targets of the Wnt/b-catenin pathway are also upregulated in response to fatty acids, possibly through peroxisome proliferator-activated receptor d-driven gene expression. Potential mechanisms underlying the effects of metabolic program on gene expression and stemness are depicted. Differential redox balance in the cytosol and mitochondria is influenced by the flux of glycolysis, lactate production, pyruvate oxidation, and fatty acid oxidation. Redox balance could act through various pathways to modulate gene expression related to stem cell homeostasis. Finally, MPC inhibition also impairs differentiation through epigenetic modifications as MPC deletion decreases H3K4, H3K9, and H3K27 histone acetylation marks. This observation could be explained by a depleted cytosolic acetyl-CoA pool through the reduction of its precursor, citrate, as a result of limited mitochondrial pyruvate. Transporters and enzymes are shown in rounded boxes; transcriptional regulation is depicted by blue arrows; possible relationships are shown in dashed lines; metabolites and histone modifications are shown in regular type. Abbreviations: ACLY, ATP citrate lyase; APC, adenomatous polyposis coli; EGFR, epidermal growth factor receptor; ETC, electron transport chain; LDH, lactatedehydrogenase; MPC, mitochondrial pyruvate carrier; mTOR, mammalian target ofrapamycin; PPAR-d, peroxisome proliferator activated receptor d; SIRT, sirtuin; TCA cycle, tricarboxylic acid cycle; TCF, T-cell factor. 556 Trends in Cell Biology, July 2018, Vol. 28, No. 7 18 profound effect on ISC stemness that is opposite that of glucose oxidation described above [8]. Indeed, a high-fat diet (HFD), wherein 60% of the calories come from fat, affects ISCs in mice. Mice on a long-term HFD exhibit shorter intestinal villi with a decrease in crypt depth, but experience no change in crypt-villus density [8]. This finding is consistent with a block in ISC differentiation. Staining for ISCs and Paneth cells further reveals that HFD increases ISC abundance, but decreases Paneth cell abundance [8]. Paneth cells intercalate with ISCs and support ISC function in the stem cell niche by secreting specific growth factors [33]. Moreover, an HFD not only decreases Paneth cell abundance, but also decreases the dependence of ISCs on Paneth cells for organoid formation [8]. In addition, HFD feeding increases ISC proliferation and regeneration in vivo as assessed by bromodeoxyuridine incorporation [8]. Recapitulating these in vivo findings, crypts isolated from HFD-fed mice show enhanced primary and secondary organoid formation capacity in vitro [8]; probably related to the increased frequency of Lgr5-positive ISCs compared to control organoids. Single ISCs from HFD-fed mice also exhibit increased capacity to form new organoids. When mice are switched from an HFD to a standard normal chow diet, the enhanced organoid formation from crypts or single ISCs disappears within 4 weeks [8]. These findings demonstrate that HFD feeding causes profound, reversible and acute effects on ISC homeostasis, maintenance, and proliferation (see Outstanding Questions). Outstanding Questions By which mechanisms does decreased pyruvate oxidation promote ISC and HFSC function? What is the role of MPC in the APC/ Wnt/b-catenin pathway? Do reduced cytosolic citrate levels (and with that acetyl-CoA) as a result of decreased mitochondrial pyruvate oxidation limit histone acetylation? Lastly, while recent studies suggest that fatty acids regulate ISC function, it remains to be seen if this phenomenon is due to increase fatty acid oxidation or other mechanisms such as fatty-acid-responsive transcription or signal transduction. It appears that this effect of a HFD is likely related to direct effects of fatty acids. Treatment with the fatty acid palmitate in vitro is sufficient to increase ISC abundance and decrease the stem cell niche dependence of organoids isolated from normal-chow-fed mice [8]. Similar results are obtained when mouse and human intestinal crypts are treated with other fatty acids, such as oleic acid or a mixture of fatty acids. It is important to emphasize that this phenotype is diet mediated and is not related to obesity. Leptin-receptor-deficient (db/db) mice, which develop profound obesity, show no increase in ISC proliferation when fed a normal chow diet [8]. These data suggest that ISC maintenance and function is affected by the diet directly and this is specifically related to fatty acid content. While other mechanisms might also be at play, it has been demonstrated that fatty acids might stimulate peroxisome proliferator-activated receptor d-driven expression of components of the Wnt/b-catenin pathway to increase stemness [8]. Concluding Remarks While both carbohydrate and fatty acid oxidation generate mitochondrial acetyl-CoA and ultimately ATP in mitochondria, the simplest interpretation of these data is that pyruvate and fatty acid oxidation have opposing effects on stem cell homeostasis. Enhanced pyruvate oxidation decreases stem cell maintenance and proliferation, whereas increased fatty acid abundance promotes stem cell function. As noted above, these two metabolic programs are highly interlinked as decreased pyruvate oxidation leads to increased fatty acid oxidation, likely to compensate for the loss of pyruvate-derived mitochondrial acetyl-CoA. Therefore, it becomes important to determine whether elevated fatty acid availability and oxidation similarly reduces pyruvate oxidation as part of its effect on stem cell function. Probably the most important question, however, and one of the most difficult to answer is how do these two metabolic pathways, with ostensibly similar outputs, have such different effects on cell fate. Is the critical distinction derived from the activity level of the different oxidative pathways per se and the generation of products like acetyl-CoA, or is it due to differential side effects, for example, lactate production and redox changes? One thing seems clear; eventually this effect has to arrive at the nucleus to impact gene expression, and given the molecules involved, this seems likely to be related to epigenetic modification of histones and/or DNA. It is, therefore, noteworthy that histone acetylation is decreased in MPC-deficient organoids [10], Trends in Cell Biology, July 2018, Vol. 28, No. 7 557 19 which exhibit enhanced ISC proliferation and maintenance. It is tempting to speculate that decreased pyruvate-derived acetyl-CoA, due to MPC loss, might increase stem cell function through a secondary decrease in cytosolic acetyl-CoA. Cytosolic acetyl-CoA is thought to be primarily derived from citrate or acetate. Mitochondrial citrate is a TCA cycle intermediate that can be exported to the cytosol and converted to acetyl-CoA by ATP citrate lyase [34]. However, this process depletes TCA cycle intermediates, known as cataplerosis, which need to be replenished through anaplerosis. Although pyruvate-derived mitochondrial acetyl-CoA drives the TCA cycle, it is not an anaplerotic substrate to restore TCA intermediates. However, besides being decarboxylated to acetyl-CoA, mitochondrial pyruvate can also be converted to the TCA cycle intermediate oxaloacetate via pyruvate carboxylase, which does replenish the TCA cycle [35]. Therefore, in the absence of MPC activity, pyruvate import into mitochondria is lost, which reduces production of mitochondrial acetyl-CoA as well as the production of TCA cycle anaplerotic substrates. As a result, citrate availability for cytosolic acetyl-CoA production would be decreased and this might be predicted to decrease the acetyl-CoA pool that is available for histone acetylation (Figure 1; see Outstanding Questions). While fatty acid oxidation can generate mitochondria acetyl-CoA to compensate for loss of pyruvate-derived acetyl-CoA, it does not generate anaplerotic substrates to replenish TCA cycle intermediates. Therefore, the impact of these two oxidative pathways on the generation of cytosolic acetyl-CoA from mitochondrion-derived citrate could be different. In the setting of elevated fatty acid oxidation, it is possible that mitochondrial pyruvate uptake and conversion to acetyl-CoA and oxaloacetate might be limited, which might further decrease the availability of cytosolic acetyl-CoA for histone acetylation. It is also possible that altered redox state might be a key intermediate in the effects of metabolic program on stem cell function (Figure 1). Complete glucose oxidation produces NADH from NAD+ in the cytosol (via glycolysis) as well as in the mitochondrial matrix. In mitochondria, the electron transport chain recycles NADH to NAD+ by passing those electrons to oxygen, while LDH recycles NAD+ in the cytosol by converting pyruvate to lactate. Compared to differentiated cells, stem cells use a reduced fraction of their pyruvate for mitochondrial oxidation, which leads to predictable and differential effects on the cytosolic and mitochondrial NADH/NAD+ ratio. Different redox states might affect nuclear epigenetics and gene expression through altering the activity of NAD+ dependent deacetylases (sirtuins), or through any of a number of other redoxdependent mechanisms, including the mammalian target of rapamycin or epidermal growth factor receptor pathways [36]. This type of differential compartmentalization of reducing equivalents between glycolysis, glucose oxidation, and fatty acid oxidation could constitute part of the mechanistic foundation underlying the differential effects on stemness [37]. At this point, it is critical to note that although high fatty acid abundance has been shown to promote ISC function, it remains to be determined whether this phenomenon depends primarily on elevated fatty acid oxidation or on other mechanisms like fatty-acid-responsive transcription or signal transduction. 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Herzig, S. et al. (2012) Identification and functional expression of the mitochondrial pyruvate carrier. Science 337, 93–96 Trends in Cell Biology, July 2018, Vol. 28, No. 7 559 CHAPTER 3 A NON-CANONICAL CONVERGENCE OF CARBOHYDRATE AND GLUTAMINE METABOLISM IS REQUIRED AFTER METABOLIC REWIRING IN 3D ENVIRONMENT Peng Wei and Jared Rutter 22 Abstract The fate of pyruvate, which is modulated mitochondrial pyruvate carrier (MPC) activity, is a defining metabolic feature in many cancers. Diffuse large B-cell lymphomas (DLBCLs) are a genetically and metabolically heterogenous cancer. Although MPC expression and activity differed between DLBCL subgroups, mitochondrial pyruvate oxidation was uniformly minimal. Mitochondrial pyruvate was instead robustly consumed by glutamate pyruvate transaminase 2 to support αketoglutarate production as part of glutamine catabolism. This led us to discover that glutamine exceeds pyruvate as a carbon source for the TCA cycle, but, MPC function is required to enable GPT2-mediated glutamine catabolism. Furthermore, we found that MPC inhibition only decreased DLBCL proliferation in a solid culture environment, but not in a suspension environment. Thus, the non-canonical connection between the consumption and assimilation of carbohydrates and glutamine in DLBCLs enables their proliferation in a solid 3D environment. Introduction The central pathway of carbohydrate metabolism is the conversion of glucose to pyruvate via glycolysis in the cytosol. The fate of this pyruvate is a critical metabolic node in mammalian cells. In most differentiated mammalian cells, the mitochondrial pyruvate carrier (MPC) transports pyruvate into mitochondria where it is used to fuel oxidation and anabolic reactions (Bricker et al., 2012). In contrast, in stem cells and many cancers, pyruvate is primarily converted to lactate and excreted from the cell (Vander Heiden et al., 2009), a process known as the Warburg effect. Several groups 23 have shown in a variety of tumor types that the Warburg effect can be caused by low activity of the MPC (Schell et al., 2014; Li et al., 2017; Tang et al., 2019; Zou et al., 2019). For instance, re-expression of the MPC in colon cancer cells, which have very low native expression, increased mitochondrial pyruvate oxidation and repressed tumor growth (Schell et al., 2014). However, repression of the MPC is not a universal feature of all cancers. Indeed, in prostate cancer, high MPC activity is required for lipogenesis and oxidative phosphorylation, and, in hepatocellular carcinoma, high MPC activity is required to supply mitochondrial pyruvate for the tricarboxylic acid (TCA) cycle (Bader et al., 2019; Tompkins et al., 2019). Therefore, we lack a unified understanding of the relationship between a given cancer type and its dependence on the MPC or mitochondrial pyruvate. Diffuse large B-cell lymphomas (DLBCLs) are the most common type of nonHodgkin lymphoma and are genetically and phenotypically heterogeneous (Abramson and Shipp, 2005; Lenz and Staudt, 2010). This genetic heterogeneity has been captured by independent classification schemes (Alizadeh et al., 2000; Monti, 2005). In particular, consensus cluster classification has identified three subgroups of DLBCL based on gene expression and metabolic signatures: BCR-DLBCL, which are characterized by the expression of genes encoding B-cell receptor (BCR) signaling pathways; OxPhos-DLBCL, which have high expression of genes involved in mitochondrial oxidative phosphorylation; and the HR-DLBCL, which have increased expression of genes involved in host inflammatory infiltration (Monti, 2005; Caro et al., 2012; Norberg et al., 2017). In terms of metabolism, OxPhos-DLBCLs display greater fatty acid oxidation 24 than BCR-DLBCLs, whereas BCR-DLBCLs have a higher rate of glycolysis (Caro et al., 2012). Moreover, initial glucose tracing studies in the two subgroup suggested there might be differences in the fate of pyruvate for citrate and lactate synthesis (Caro et al., 2012). However, the qualitative and quantitative differences of carbohydrate metabolism and the broader spectrum of metabolic substrates feeding the TCA cycle, across DLBCL subgroups, including glutaminolysis and the interplay between different fuels, is not fully understood. Understanding these basic metabolism features in DLBCLs might inform their therapeutic vulnerabilities. Although mature B cells can transition from the solid lymph node environment to the liquid intravascular environment, DLBCLs primarily form solid tumors (Bakhshi and Georgel, 2020; Chiche et al., 2019). Therefore, although DLBCLs are routinely passaged and studied in suspension media in the laboratory, mimicry of the native architecture of a solid 3D tumor microenvironment could be a key experimental factor in recapitulating DLBCL biology ex vivo. Therefore, we set out to examine the metabolic architectures of OxPhos-DLBCLs and BCR-DLBCLs under both suspension and Matrigel-based solid 3D growth conditions. Results OxPhos-DLBCLs exhibit elevated expression of numerous genes encoding the mitochondrial electron transport chain (ETC) subunits complexes compared to BCRDLBCLs (Monti, 2005). Since MPC expression typically correlates with an oxidative metabolic phenotype, we hypothesized that OxPhos-DLBCLs would have higher MPC levels to compared to BCR-DLBCLs. From patient tumor microarray data (GSE10846), we found the mRNA levels of MPC1 and MPC2, the genes that encode the obligatory 25 MPC1 and MPC2 subunits of the MPC, were higher in OxPhos-DLBCL than in BCRDLBCLs (Fig. 3.1A). Across ten DLBCL cell lines, we found that OxPhos-DLBCLs generally had higher MPC1 and MPC2 protein levels than BCR-DLBCLs (Fig. 3.1B). By analyzing proteomics from isolated mitochondria (Norberg et al., 2017), MPC2 was 7-fold more abundant in OxPhos-DLBCLs than BCR-DLBCLs (MPC1-derived peptides were not detected) (Fig. 3.2A, 3.2B). MPC1 and MPC2 form an obligate heterodimer and their protein abundances are typically tightly linked, which suggests that the MPC complex is upregulated in OxPhos-DLBCLs (Schell et al., 2014). We hypothesized that increased MPC expression in OxPhos-DLBCLs compared to BCR-DLBCLs would result in greater incorporation of carbons from glucose into the TCA cycle through increased mitochondrial transport and oxidation of pyruvate. To test this, we used D-[U-13C]-glucose tracing. Surprisingly, Pfeiffer OxPhos-DLBCL cells reached only 25% incorporation of D-[U-13C]-glucose into citrate within 2 hours (Fig. 3.1D). MPC inhibition with UK-5099, a well-established MPC inhibitor (Halestrap, 1975), decreased glucose incorporation into citrate to 10% (Fig. 3.1D). On the other hand, U2932 BCR-DLBCL cells reached a maximal incorporation of D-[U-13C]glucose into citrate of only 15% (Fig. 3.1D), which was further decreased by MPC inhibition, although this change was not statistically significant (Fig. 3.1D). These results indicate that the expression difference in the MPC between DLBCL subgroups is reflected in their glucose to citrate labeling, with greater glucose contribution to citrate in the OxPhos subgroup–consistent with a previous study (Caro et al 2012), but is minimal in both subgroups. Despite clearly detectable, albeit low, glucose-to-citrate labeling in DLBCL 26 cells, we found minimal labeling of other TCA cycle intermediates, such as αketoglutarate (α-KG) and succinate, from D-[U-13C]-glucose (Fig. 3.1E,3.1F,3.1G; Fig. 3.2D). This was especially evident in the BCR-DLBCL cell line, where very little α-KG and succinate labeling occurred even after four hours (Fig. 3.1F,3.1G). Overall, this suggests that glucose does not make a substantial contribution to the TCA cycle in DLBCLs, regardless of subtype classification. Interestingly, MPC inhibition did not increase labeling of pyruvate and lactate in either OxPhos-DLBCL or BCR-DLBCL cell lines (Fig. 3.2C, 3.2D). This is in contrast to other cell types, where inhibiting mitochondrial pyruvate import leads to an increased labeling of intracellular lactate, likely to compensate for the loss of ATP production from mitochondrial pyruvate oxidation (Cluntun et al., 2021). Altogether, these data suggest that the direct contribution of pyruvate to mitochondrial TCA cycle metabolism and ATP production is likely minimal. Given the very limited degree to which glucose carbons were incorporated into the TCA cycle, even in OxPhos-DLBCLs that exhibit high MPC expression, we sought to uncover the destination of glucose-derived carbon once it entered the mitochondria as pyruvate. Surprisingly, we found a striking incorporation of D-[U-13C]-glucose carbons into alanine. This alanine labeling was dependent on MPC activity, as MPC inhibition with UK-5099 substantially decreased labeling in both OxPhos-DLBCL and BCRDLBCL cells (Fig. 3.1H; Fig. 3.2C, 3.2D). Alanine can be generated by the amination of pyruvate in either the mitochondria or cytosol. Since MPC inhibition significantly decreased the ratio of labeled alanine in DLBCLs, these data support a model wherein alanine synthesis is predominantly mediated by the mitochondrial glutamate pyruvate 27 transaminase 2 (GPT2) enzyme, which catalyzes the reversible transamination of pyruvate and glutamate to generate alanine and α-KG (Fig. 3.1C). Thus, despite differences in MPC abundance and pyruvate oxidation in the TCA cycle, alanine is a major fate of glucose carbon in both OxPhos-DLBCLs and BCR-DLBCLs. The robust glucose-to-alanine labeling implies that substantial amounts of glutamine would need to be converted to mitochondrial glutamate for use by GPT2. The GPT2 reaction and accompanying glutamate conversion to α-KG is dependent on mitochondrial pyruvate and therefore is likely dependent upon MPC activity. Therefore, we next tested how MPC inhibition affects glutamine consumption by DLBCLs. First, we grew DLBCLs under UK-5099 treatment for five days at a series of glutamine concentrations. Neither OxPhos-DLBCL nor BCR-DLBCL cells exhibited decreased proliferation when MPC is inhibited in lower glutamine concentration media (Fig. 3.3A), which suggests that MPC inhibition does not induce increased glutamine consumption or dependence in DLBCLs. This is in contrast with the metabolic responses of glioma cells, cortical neurons, and prostate cancer cells, where MPC inhibition induced increased glutamine consumption (Bader et al., 2019; Divakaruni et al., 2017; Yang et al., 2014). To directly test if glutaminolysis is important for DLBCL growth, we inhibited the conversion of glutamine to glutamate using the well-established glutaminase (GLS1) inhibitor CB-839 (Gross et al., 2014). Treatment with CB-839 decreased proliferation in both OxPhos and BCR-BLBCL cells (Fig. 3.3B; Fig. 3.4A). Furthermore, adding a cell-permeable form of α-KG, dimethyl-α-ketoglutarate (dmKG), to DLBCLs rescued the effects of CB-839 (Fig. 3.3B; Fig. 3.4A). This result shows that 28 DLBCLs require α-KG generation through glutaminolysis, regardless of their subgroup classification. To further understand how MPC inhibition affects TCA cycle metabolism, we performed a L-[U-13C]-glutamine isotope tracing experiment in BCR-DLBCL cells. We found that 16% of citrate exists as the M+5 isotopologue (Fig. 3.3D), which was completely eliminated upon MPC inhibition (Fig. 3.3D). M+5 citrate is indicative of reductive carboxylation, wherein α-KG is converted to citrate through a backwards turn of a portion of the TCA cycle (Fig. 3.3C-green) and this is thought to enable the production of citrate to fuel acetyl-CoA synthesis in the cytosol (Metallo et al., 2012; Mullen et al., 2012). Since the α-KG to citrate conversion is reversible, this result suggests the enzymes that could mediate citrate oxidation, namely isocitrate dehydrogenase 2 (IDH2) and aconitase 2 (ACO2), are active in DLBCLs. In contrast to the minimal labeling of TCA cycle intermediates from D-[U-13C]glucose, we observed substantial labeling of M+4 succinate, M+4 fumarate, M+4 malate, and M+4 citrate from L-[U-13C]-glutamine (Fig. 3.3D; Fig. 3.4B). All of these intermediates are derived from the first turn of M+5 α-KG through the TCA cycle in the oxidative direction (Fig. 3.3C-orange). Surprisingly, the labeling of these TCA cycle metabolites from glutamine was significantly decreased by inhibiting the MPC (Fig. 3.3D; Fig. 3.4B). Again, this is in contrast to the increased glutamine anaplerosis observed in other cells upon MPC inhibition (Bader et al., 2019; Divakaruni et al., 2017; Yang et al., 2014). As expected, labeling from L-[U-13C]-glutamine of glutamate and various isotopomers of TCA cycle intermediates is increased by 2 hours, but the same patterns of labeling remain evident (Fig. 3.3E). MPC inhibition decreases this L- 29 [U-13C]-glutamine labeling and the dominant isotopomers are from reductive or the first oxidative turn of the TCA cycle (Fig. 3.3E; Fig. 3.4C). These results suggest that DLBCLs have active glutaminolysis and α-KG oxidation and that MPC activity is required for these metabolic processes by enabling α-KG production. The extensive production of alanine from glucose also suggests that alanine could play an important role in DLBCL biosynthesis processes. To address the fate of that alanine, we cultured Pfeiffer OxPhos-DLBCL cells with D-[U-13C]-glucose for 4 hours and collected the media for isotope tracing analysis. We found that M+3 alanine is robustly excreted from the cell and that this is dependent on MPC activity (Fig. 3.3F; Fig. 3.4D). This result supports the idea that α-KG is likely the important product of GPT2 and alanine is primarily a byproduct. We also observed M+3 lactate in the media, but–unlike M+3 alanine–MPC inhibition did not affect medium M+3 lactate abundance (Fig. 3.3F; Fig. 3.4D), similar to our previous findings for intracellular lactate labeling (Fig. 3.2D). To summarize the above findings using L-[U-13C]-glutamine isotope tracing: DLBCLs have an intact and active TCA cycle, but it is primarily fed by glutamine rather than glucose. Although glucose-to-citrate labeling occurred, the glucose-derived carbon in citrate mostly did not progress through the remainder of the TCA cycle. This is likely because of citrate export to the cytosol to support biosynthesis of fatty acid and cholesterol, as well as acetylation events via acetyl-CoA synthesis (Carrer et al., 2019; Sivanand et al., 2018). Given that glutamine is required as a TCA cycle fuel and thus for DLBCL proliferation, and mitochondrial pyruvate is required to sustain said glutamine 30 oxidation, we hypothesized that loss of MPC function should impair proliferation of DLBCLs. However, inhibiting the MPC in cells grown in suspension culture had no effect on their proliferation (Fig. 3.5A; Fig. 3.6A). Because DLBCLs form solid tumors (Chiche et al., 2019) and we had previously observed that MPC-dependent effects on proliferation were particularly evident in a 3D environment (Schell et al., 2014), we decided to investigate if MPC inhibition impairs DLBCL proliferation in Matrigel, an extracellular matrix (ECM) used to mimic the in vivo 3D environment (Benton et al., 2014). Indeed, multiple DLBCL lines treated with UK-5099 exhibited 30–70% fewer cells than their corresponding control group after cultured in growth factor reduced Matrigel for 10 days (Fig. 3.5B) without a change in viability (Fig. 3.5C). These results indicate that MPC inhibition decreases proliferation rate of DLBCLs grown in an ECM 3D environment. When grown in ECM, DLBCLs form compact colonies within 4–5 days of seeding. To address whether this colony formation was necessary for the MPCdependent decrease in proliferation, we assayed DLBCL cell concentration 24 and 48 hours after plating in ECM, and found that MPC inhibition significantly decreased the cell concentration of all four DLBCL cell lines by 24–48 hours after plating (Fig. 3.5D). As before, MPC inhibition does not decrease DLBCLs viability in ECM at these time points (Fig. 3.6B). These results show that growth in an ECM 3D environment is sufficient to reveal an MPC-dependent growth phenotype in DLBCLs. Given the environment-dependent effects on proliferation of MPC inhibition, we asked whether the metabolic effects we had previously observed in UK-5099-treated suspension cells were also evident in ECM-grown cells. We performed D-[U-13C]- 31 glucose tracing experiments with cells in ECM. As in suspension culture, alanine labeling was very robust and largely MPC-dependent (Fig. 3.5E). We observed minimal 13C-glucose labeling of most TCA cycle intermediates, including α-KG and succinate, which was MPC-dependent (Fig. 3.5E; Fig. 3.6C). As before, MPC inhibition did not increase intracellular pyruvate and lactate labeling (Fig. 3.6C). These results confirm that MPC inhibition has similar effects on glucose metabolism in DLBCLs in both suspension and ECM environments. To understand the full metabolic impact of transitioning from suspension to a solid ECM environment, we collected U2932 BCR-DLBCL cells for steady-state metabolomic analysis after growth in either suspension or ECM environment, with or without MPC inhibition, for 4 hours, 8 hours, 12 hours, or 24 hours. Through unbiased clustering of both samples and metabolites, we found that 4 hours was sufficient to induce robust changes in the metabolic landscape of ECM-grown cells (Fig. 3.7A). This change at 4 hours occurs well before we observed a significant impact of MPC inhibition, which is most apparent at the 24-hour time point (Fig. 3.7A). These results indicate that the growth environment has a broad and rapid impact on DLBCL metabolism. Next, we focused on how this environmental shift affects the glutamine and TCA cycle metabolic phenotypes. We observed more glutamine and less glutamate in ECM-grown cells than in suspension-grown cells (Fig. 3.7B). As for TCA-cycle metabolites, α-KG was higher in ECM relative to suspension (Fig. 3.7C), but MPC inhibition did not affect α-KG abundance in either growth environment (Fig. 3.7C). We observed decreased abundance of the remaining TCA cycle intermediates in ECM, especially fumarate and malate (Fig. 3.7C). These changes result in an increased α- 32 KG/citrate ratio in the ECM environment, which could further increase the reductive αKG to citrate conversion (Fendt et al., 2013). It also has been previously reported that changing from monolayer culture to spheroid growth enhanced the reductive α-KG to citrate reaction (Jiang et al., 2016). These results together demonstrate that the ECM environment significantly impacts DLBCL TCA cycle metabolism. Furthermore, MPC inhibition consistently decreased citrate and isocitrate abundance in ECM and suspension environments (Fig. 3.7C). This is likely due to a combinatorial effect of decreased mitochondrial pyruvate to both limit the minimal pyruvate oxidation and to decrease α-KG generation via GPT2. Growth in a solid ECM environment increased α-KG abundance (Fig. 3.7C), so we next asked if any of the following major α-KG-producing mitochondrial enzymes are responsible for this increase. One candidate is glutamate dehydrogenase (GDH), which converts glutamate to α-KG and produces free ammonia in the process (Fig. 3.8A). Therefore, excessive GDH activity could be toxic if free ammonia cannot be efficiently cleared (Eng et al., 2010; Kappler et al., 2017; Spanaki and Plaitakis, 2012). Furthermore, it has been reported that GDH could synthesize glutamate from α-KG and environmental ammonia, to both detoxify and recycle ammonia nitrogen for use in biosynthesis processes (Spinelli et al., 2017). A second enzyme is the mitochondrial aspartate aminotransferase (GOT2), which converts glutamate to α-KG through consumption of another TCA cycle intermediate, oxaloacetate, and so does not add net carbons into the TCA cycle. (Fig. 3.8A). The third enzyme, GPT2, consumes glutamate and pyruvate and yields α-KG and alanine, and thus its activity is dependent upon mitochondrial pyruvate and likely MPC activity (Fig 3.8A). Accordingly, the relative 33 contribution of each of these enzymes—GDH, GOT2, and GPT2—to α-KG production can be differentiated based on their consumption and production of specific metabolites. To determine if GOT2 activity is increased in response to MPC inhibition, we cultured DLBCL cells in L-[alpha-15N]-glutamine-containing media for 4 hours and analyzed incorporation of 15N into aspartate. We found that, in both suspension and ECM environments, MPC inhibition increased labeling of M+1 aspartate (Fig. 3.8B), suggesting that MPC inhibition increases GOT2 activity in both environments. Interestingly, we also found that only 42% of glutamate was labeled from L-[alpha15N]-glutamine at 4 hours (Fig. 3.8B), but 75% of glutamate was labeled from L-[U13C]-glutamine in a similar timeframe (Fig. 3.4C). This is likely because of robust 14Nglutamate synthesis by GDH from α-KG and environmental 14N-ammonia, which has been reported to occur in human breast cancer cells (Spinelli et al., 2017). Therefore, we hypothesized that this GOT2 activity change is due to increased cellular demand for αKG, due to impaired GPT2-mediated α-KG production. In addition, this impaired α-KG production could impair GDH-mediated incorporation of free ammonia into glutamate. Since glutamine-to-aspartate nitrogen labeling is increased upon MPC inhibition, we next questioned if the steady-state aspartate abundance is affected by MPC inhibition. We found that in a suspension environment, MPC inhibition increased aspartate abundance by 4.5-fold; while in an ECM environment, aspartate abundance was only increased by 2-fold (Fig. 3.8C). Because aspartate synthesis is directly tied to GOT2-mediated α-KG production, this result suggests that GOT2-mediated α-KG production might be lower in ECM, perhaps due to decreased availability of oxaloacetate. Therefore, we next asked if glutamate synthesis via GDH is also affected 34 in ECM. We found that the ECM environment caused glutamate abundance to decrease by 70% (Fig. 3.8C). MPC inhibition decreased glutamate abundance by about 30% in the suspension environment, but glutamate abundance was not affected by MPC inhibition in ECM (Fig. 3.8C). This decreased glutamate abundance from suspension to ECM raises the possibility that either the GDH-mediated α-KG and ammonia production could have increased, or GDH-mediated ammonia recycling ability could have decreased. We next asked if DLBCLs have increased ammonia sensitivity when cultured in an ECM versus in a suspension environment. In suspension conditions, we observed dose-dependent toxicity of NH4Cl with MPC inhibition having no additional effect (Fig. 3.8D). In contrast, cells cultured in ECM were hypersensitive to NH4Cl, and MPC inhibition caused an even further sensitization (Fig. 3.8E). These data support two interesting conclusions: first, DLBCLs in ECM are more sensitive to ammonia than in suspension; and second, MPC inhibition increases ammonia sensitivity in ECMcultured DLBCLs. Because MPC inhibition decreased DLBCL proliferation in ECM, we next asked which metabolic pathways are affected by MPC inhibition in ECM. Through a metabolite set enrichment analysis on our steady-state metabolomics data, we found that after 12 or 24 hours of MPC inhibition in ECM, the most affected metabolite sets were those related to branched-chain amino acid (BCAA) degradation pathways (Fig. 3.9A; Fig. 3.10A). BCAA degradation includes the transamination of BCAAs such as valine, leucine, and isoleucine into their branched-chain keto acids (BCKA) i.e. alphaketoisovalerate (KIV), ketoisocaproate (KIC), and alpha-keto-beta-methylvalerate (KMV). In these transamination reactions, α-KG is aminated to glutamate. Therefore, 35 our results suggest that BCAA degradation to BCKAs could be regulated by the MPC through its role in α-KG production. Although the growth environment and MPC inhibition had inconsistent effects on individual BCAAs, BCKAs were all more abundant in ECM than in suspension, and decreased by MPC inhibition (Fig. 3.9B). These results suggest that MPC inhibition decreases BCKA production presumably by limiting α-KG production. To test the hypothesis that the ECM-dependent growth defects caused by MPC inhibition were due to loss of GPT2-mediated α-KG production, we generated a GPT2 knockdown cell line with scrambled shRNA control (Fig. 3.9C). Knockdown of GPT2 decreased DLBCLs proliferation in ECM to a similar extent as MPC inhibition (Fig. 3.9C). Importantly, we found no additive effect when the MPC was inhibited in GPT2knockdown cells (Fig. 3.9C). These results strongly support our model that MPC inhibition decreases DLBCL proliferation in ECM mainly by restricting mitochondrial pyruvate for the GPT2 reaction. To confirm that our GPT2 findings were specific with MPC, we generated GDH knockdown cells (Fig. 3.9D). GDH also produces α-KG, but does not require pyruvate for its reaction. Therefore, we expected GDH knockdown would have MPCindependent effects on DLBCL proliferation. As with GPT2, GDH knockdown decreased DLBCL proliferation in ECM to a similar extent as MPC inhibition (Fig. 3.9D). However, unlike GPT2 knockdown, MPC inhibition further suppressed proliferation of GDH knockdown cells (Fig. 3.9D). This additive effect of MPC inhibition suggests that GDH is important for the proliferation of DLBCLs in ECM, but is likely acting through a distinct pathway from the MPC and mitochondrial pyruvate. 36 Finally, we added the cell-permeable form of α-KG, dmKG, to cells grown in ECM to determine if directly increasing α-KG is sufficient to rescue the effects of MPC inhibition. We found that adding dmKG alone had no effect DLBCL proliferation. Importantly, however, dmKG completely rescued the MPC inhibition-dependent proliferation defect in all of the cell lines that we tested (Fig. 3.9E). This further supported the hypothesis that impaired α-KG production is the metabolic defect that underlies the MPC inhibition-induced loss of proliferation in ECM. Discussion Based on the difference in MPC expression between OxPhos- and BCR-DLBCL subgroups, we initially set out to study potential differences in the utilization of mitochondrial pyruvate in these cell types. Indeed, OxPhos-DLBCLs display greater incorporation of pyruvate into citrate, and are more sensitive to MPC inhibition for this incorporation metric. However, although there are some differences between the two subgroups, the maximum pyruvate-to-citrate labeling ratio in both OxPhos- and BCRDLBCL subgroups is low, and labeling of TCA-cycle metabolites downstream of citrate is almost non-existent. These findings indicate that although there are differences between OxPhos- and BCR-DLBCL pyruvate metabolism, these differences are dwarfed by the effects of a common, yet unexpected source of TCA carbon, which we discovered to be pyruvate enabled glutamine. Nevertheless, in the light of a greater understanding of the role of MPC in DLBCLs, it will be interesting to understand the differential expression of the MPC in DLBCL subtypes and whether this leads to additional phenotypes that we have yet to uncover. Although glucose does not substantially contribute to the TCA cycle in 37 DLBCLs, glucose-derived pyruvate does facilitate the utilization of glutamine as a TCA cycle fuel by supporting GPT2 mediated α-KG production (Fig. 3.11). Glutamine tracing experiments demonstrated that glutamine-derived α-KG can be converted to citrate through either the reductive or oxidative modes of the TCA cycle (Fig. 3.11). This suggests that DLBCLs have an intact and active TCA cycle, but we consistently observed limited oxidation of citrate to other TCA cycle intermediates. We speculate this is because citrate is being exported to the cytosol, where it could be used to produce acetyl-CoA for lipogenesis and acetylation, both of which are critical for cancer cells (Fig. 3.11) (Carrer et al., 2019; Sivanand et al., 2018). In support of this hypothesis, the mitochondrial citrate exporter SLC25A1 appears to be essential in DLBCLs (Fig. 3. 12A, 12B). Therefore, DLBCLs appear to have a noncanonical TCA cycle pattern that includes export of most glucose-derived citrate to the cytosol. This is likely why glutamine, and not glucose, is so prominently incorporated into the TCA cycle in DLBCL cells: pyruvate-derived citrate is not used to fuel the TCA cycle, but glutamatederived α-KG is. We also speculate this previously underappreciated MPC—GPT2—α-KG axis could also play an important metabolic role outside of DLBCLs or other cancers. For example, this surprising metabolic feature of DLBCLs might be established during Bcell activation. A previous study showed that B cells increase their glucose consumption during activation, but that this increased consumption—reminiscent of our study—does not lead to labeling of TCA cycles metabolites via pyruvate (Waters et al., 2018). Besides GPT2, GDH is another enzyme that produces α-KG from glutamate and thereby can add net carbons into the TCA cycle (Fig. 3.11). Because GDH necessarily 38 produces free ammonia while making α-KG, high GDH activity could be toxic to the cell if free ammonia does not diffuse away and cannot be efficiently recycled. Here, we show that transitioning DLBCLs from a suspension environment to a solid Matrigelbased ECM environment makes them more sensitive to ammonia. In addition, MPC inhibition further sensitizes cells to ammonia in this solid environment. We speculate that GDH-produced ammonia does not sufficiently diffuse away from cells in a solid environment, which could feed back on the GDH reaction to prevent α-KG production. As a consequence of this decreased α-KG production, less ammonia can be recycled to glutamate by GDH, resulting in a yet additional defect in the ability of the cell to detoxify excess ammonia (Fig. 3.11). A limitation of cell culture has always been an inability to fully recapitulate aspects of an individual cell’s organismal context. For example, the function of pyruvate metabolism for breast cancer metastasis could only be revealed in a 3D growth environment (Elia et al., 2019). The importance of this limitation varies depending on cell type. It is now clear that immune cells, including B cells, function in tissues more than in the bloodstream, as previously thought (Farber, 2021). Recent studies have reported that the tissue microenvironment could influence DLBCL gene expression (Sangaletti et al., 2020), and physical properties of the extracellular matrix environment could also change cancer cells’ mitochondrial structure and function (Tharp et al., 2021). Here, we have shown that transitioning DLBCLs from a suspension environment to a 3D Matrigel-based ECM rapidly reshapes their metabolome. Indeed, we found that a growth condition that better recapitulates the in vivo environment is sufficient to unveil entire facets of the DLBCL metabolic landscape not apparent in standard 39 suspension cell culture. We anticipate that other aspects of DLBCL biology are also better reflected in ECM environment, and that the metabolic requirements of many types of solid-tumor cancers may be similarly revealed by 3D culture systems. DLBCLs are typically more vascularized compared to follicular lymphoma (Passalidou et al., 2003; Solimando et al., 2020). Aggressive and chemotherapyresistant DLBCLs often have high vascular endothelial growth factor expression and high microvessel density (Cardesa-Salzmann et al., 2011; Ruan and Leonard, 2009; Solimando et al., 2020). Besides importing nutrients, tumor blood vessels could also function to export metabolic byproducts, such as ammonia and lactate from the tumors. In addition, cancer cells can change their metabolism to adapt to their microenvironment, such that byproducts such as ammonia and lactate become a nutrient source or anabolic substrate (Faubert et al., 2017; Spinelli et al., 2017). Since MPC inhibition affects DLBCL metabolism and induces ammonia sensitivity, it is possible that a combination therapy of an MPC inhibitor and a drug that blocks tumor blood vessel growth could sensitize cancer cells to their byproduct ammonia and also limit essential glutaminolysis. Materials and Methods DLBCL cell lines DLBCL cell lines used in this study (Pfeiffer, Toledo, OCI-Ly4, Karpas 422, U2932, OCI-Ly1, OCI-Ly7, SU-DHL-4, SU-DHL-6, and HBL-1) have been acquired from Dr. Nika Danial’s laboratory. All DLBCL cell lines were grown in RPMI 1640 medium with 2 g/L glucose, 0.3 g/L glutamine (Thermo Fisher 11875) supplemented 40 with 10% FBS (Sigma, F0926) and 1% penicillin/streptomycin (HyClone) at 37 °C in a humidified atmosphere containing 5% CO2. Stable GDH and GPT2 knockdown cell lines HEK293T cells were transiently transfected with pLKO.1 shGDH-1 (Sigma Aldrich TRCN0000028600), shGDH-2 (Sigma Aldrich TRCN0000028611), shGPT2 (Sigma Aldrich TRCN0000035028) or scramble shRNA control (Addgene 8453) along with the lentiviral packaging plasmids pRSV-Rev, pMDLg/pRRE and pMD2.G using Lipofectamine 2000 transfection reagent. Forty-eight hours after transfection, viral supernatant was collected, filtered through a 0.45 µm polyethersulfone membrane, and stored at 4 °C. Ten µg/mL Polybrene (EMD Millipore, TR-1003-G) was added and a 1:1 mixture of viral supernatant and fresh growth medium (RPMI 1640 + 10% FBS) was applied directly to Pfeiffer cells, which were then incubated for 16 hours at 37 °C in a humidified incubator with 5% CO2. Viral media was discarded and replaced with fresh growth media and cells were allowed to recover and expand for 48 hours. After the recovery period, stably infected cells were selected with 1 µg/mL puromycin for 1 week. Knockdown of GDH and GPT2 were confirmed via immunoblotting as described below. SDS-PAGE and immunoblotting Whole cell lysates (WCLs) were prepared by scraping cells directly into RIPA buffer (50mM Tris-HCl, 1% NP-40, 0.5% Sodium Deoxycholate, 0.1% SDS, 150 mM NaCl, 2 mM EDTA) supplemented with protease and phosphatase inhibitors (Sigma 41 Aldrich P8340, Roche Molecular 04906845001), incubated on ice for 45 minutes with vortexing every 5 minutes, and then spun at 16,000 × g for 10 minutes at 4 °C to remove insoluble material. WCL was normalized for total protein content via BCA Assay (Thermo Scientific 23225). Samples were resolved on SDS-PAGE gels and transferred to nitrocellulose membranes. Immunoblotting was performed using the indicated primary antibodies, which are listed in Table 3.1 according to the manufacturers’ recommendations, and analyzed on a LICOR Odyssey CLx. Cell growth and proliferation assays Cells from suspension culture were removed from flasks, mixed at a 1:1 ratio with 0.4% trypan blue solution (Sigma Aldrich T8154), and counted using an automated cell counter (Bio-rad 1450102). Cells from matrigel culture were scraped off plates with matrigel and media, spun at 600 × g for 10 minutes at 4 °C to remove media and matrigel. Cell pellets were resuspended in PBS, mixed at a 1:1 ratio with 0.4% trypan blue solution (Sigma Aldrich T8154), and counted using an automated cell counter (Bio-rad 1450102) Matrigel cell culture Cells were resuspended in ice-cold fresh RPMI 1640 growth medium at 2× concentration, then mixed at a 1:1 ratio with growth factor reduced matrigel (Corning 356231). 200 μL of mix was spotted into seven small drops in one well of a 6-well plate. After incubating for 30 minutes at 37 °C and 5% CO2 to allow the mix to polymerize, 3 mL of warm RPMI 1640 growth medium was added. Media was changed 42 every 48 hours. C13-Glucose tracing experiments For suspension-culture tracing, 2 million cells were resuspended in 3 mL tracing medium. For matrigel-culture tracing, 1.5 million cells were plated with matrigel then incubated with 3 mL growth medium for 48 hours before the tracing experiment. To start experiment, media was changed to tracing media: glucose free RPMI 1640 (Thermo Fisher 11879020) supplemented with 11.11mM [U-13C]glucose, and 10% dialyzed FBS. Cells were harvested 30 minutes, 1 hour, 2 hours, and 4 hours later by centrifuge (scraped then centrifuge for cells from matrigel) and then quenched with 800 μL 80:20 methanol:water. Methanol lysates were then subjected to three rapid freezethaw cycles and then spun at 16,000 × g for 10 minutes at 4 °C. The supernatants were evaporated using a SpeedVac concentrator. C13-Glutamine tracing experiments For suspension-culture tracing, 2 million cells were resuspended in 3 mL tracing medium. For matrigel-culture tracing, 1.5 million cells were plated with matrigel then incubated with 3 mL growth medium 48 hours before tracing experiment. To start experiment, media was changed to tracing media: glutamine free RPMI 1640 (Thermo Fisher 21870076) supplemented with 2.05 mM [U-13C]glutamine, and 10% dialyzed FBS. Cells were harvested 30 minutes, and 2 hours later by centrifuge (scrape then centrifuge for cells from matrigel) and then quenched with 800 μL 80:20 methanol:water. Methanol lysates were then subjected to three rapid freeze-thaw cycles 43 and then spun at 16,000 × g for 10 minutes at 4 °C. The supernatants were evaporated using a SpeedVac concentrator. N15-Glutamine tracing experiments For suspension-culture tracing, 2 million cells were resuspended in 3 mL tracing medium. For matrigel-culture tracing, 1.5 million cells were plated with matrigel then incubated with 3 mL growth medium 48 hours before tracing experiment. To start experiment, media was changed to tracing media: glutamine free RPMI 1640 (Thermo Fisher 21870076) supplemented with 2.05 mM [Alpha-15N]glutamine, and 10% dialyzed FBS. Cells were harvested 4 hours later by centrifuge (scrape then centrifuge for cells from matrigel) and then quenched with 800 μL 80:20 methanol:water. Methanol lysates were then subjected to three rapid freeze-thaw cycles and then spun at 16,000 × g for 10 minutes at 4 °C. The supernatants were evaporated using a SpeedVac concentrator. Gas Chromatography Mass Spectrometry (GCMS) derivatization The supernatants from tracing experiments were evaporated using a SpeedVac. Dried metabolites were re-suspended in 30 μL anhydrous pyridine with 10 mg/mL methoxyamine hydrochloride and incubated at room temperature overnight. The following morning, the samples were heated at 70 °C for 10–15 minutes and then centrifuged at 16,000 × g for 10 minutes. The supernatant was transferred to a preprepared GC/MS autoinjector vial containing 70 μL N-(tert-butyldimethylsilyl)-Nmethyltrifluoroacetamide (MTBSTFA) derivitization reagent. The samples were 44 incubated at 70 °C for 1 hour following which aliquots of 1 μL were injected for analysis. Samples were analyzed using either an Agilent 6890 or 7890 gas chromotograph coupled to an Agilent 5973N or 5975C Mass Selective Detector, respectively. The observed distributions of mass isotopologues were corrected for natural abundance. Steady state metabolomics experiments For matrigel samples, 2 million U2932 cells were plated with matrigel then incubated with 3 mL fresh growth medium in each plate. In parallel, 2 million same passaged cells were resuspended in 3 mL fresh growth medium in each flask. Cells were harvested 4 hours, 8 hour, 12 hours, and 24 hours later by centrifuge (scrape then centrifuge for cells from matrigel) and then quenched with 800 μL 80:20 methanol:water. Methanol lysates were then subjected to three rapid freeze-thaw cycle and then spun at 16,000 × g for 10 minutes at 4 °C. The supernatants were evaporated using a SpeedVac concentrator. Liquid Chromatography Mass Spectrometry (LCMS) The metabolite supernatants were evaporated using a SpeedVac. Dried metabolites were reconstituted in 100 μL of 0.03% formic acid in analytical-grade water, vortexed and centrifuged to remove insoluble material. The supernatant was collected and subjected to screening metabolomics analysis as described on an AB SCIEX QTRAP 5500 liquid chromatography/triple quadrupole mass spectrometer (Applied Biosystems SCIEX) (Kim et al., 2017). The injection volume was 20 μL. 45 Chromatogram review and peak area integration were performed using MultiQuant (version 2.1, Applied Biosystems SCIEX). The peak area for each detected metabolite was normalized against the total ion count of that sample. 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Cell Death Dis 10, 148. 50 Table 3.1 -- Key Resources Table REAGENT or RESOURCE Antibodies MPC1, Rabbit monoclonal SOURCE IDENTIFIER Cell Signaling MPC2, Rabbit monoclonal Cell Signaling VDAC, Rabbit monoclonal Cell Signaling GDH, Rabbit monoclonal Cell Signaling REAGENT or RESOURCE GPT2, Rabbit polyclonal SOURCE Sigma Aldrich α-Tubulin, Mouse monoclonal Cell Signaling Cat# 14462; RRID:AB_2773729 Cat# 46141; RRID:AB_2799295 Cat# 4866; RRID:AB_2272627 Cat# 12793; RRID:AB_2750880 IDENTIFIER Cat# HPA051514; RRID:AB_2681516 Cat# 3873; RRID:AB_1904178 Bacterial and Virus Strains pLKO.1 Addgene Cat# 8453 Biological Samples Chemicals, Peptides, and Recombinant Proteins D-[U-13C]glucose Cambridge Isotopes L-[U-13C]glutamine Cambridge Isotopes L-[alpha-15N]glutamine Cambridge Isotopes Lipofectamine 2000 transfection Invitrogen reagent Polybrene EMD Millipore UK-5099 Sigma Aldrich CB-839 Sigma Aldrich Dimethyl-α-ketoglutarate Sigma Aldrich Ammonium chloride (NH4Cl) Sigma Aldrich Matrigel (growth factor reduced) Corning Critical Commercial Assays Pierce BCA Assay 0.4% trypan blue solution Deposited Data Thermo Sigma Aldrich Cat# CLM-1396 Cat# CLM-1822 Cat# NLM-1016 Cat# 11668019 Cat# TR-1003-G Cat# PZ0160 Cat# 5337170001 Cat# 349631 Cat# A9434 Cat# 356231 Cat# 23225 Cat# T8154 51 Table 3.1 continued REAGENT or RESOURCE SOURCE Experimental Models: Cell Lines Human: HEK293T cell line ATCC Human: Pfeiffer cell line Human: Toledo cell line Human: OCI-Ly4 cell line Human: Karpas 422 cell line Human: U2932 cell line Human: OCI-Ly1 cell line Human: OCI-Ly7 cell line Human: SU-DHL-4 cell line Human: SU-DHL-6 cell line Human: HBL-1 cell line Recombinant DNA pLKO.1 scramble (scramble shRNA) Human shGDH-1 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Caro et al., 2012 Cat# CRL-11268; RRID:CVCL_1926 RRID:CVCL_3326 RRID:CVCL_3611 RRID:CVCL_8801 RRID:CVCL_1325 RRID:CVCL_1896 RRID:CVCL_1879 RRID:CVCL_1881 RRID:CVCL_0539 RRID:CVCL_2206 RRID:CVCL_4213 Addgene Cat# 8453 Sigma Aldrich Cat# NM_005271; TRCN0000028600 Cat# NM_005271; TRCN0000028611 Cat# NM_133443; TRCN0000035028 Human shGDH-2 Sigma Aldrich Human shGPT2 Sigma Aldrich Software and Algorithms Prism 9 R Project for Statistical Computing pheatmap ggplot2 limma IDENTIFIER R Core Team, 2020 RRID:SCR_001905 Kolde, 2019 Wickham, 2016 Ritchie et al., 2015 RRID:SCR_016418 RRID:SCR_014601 RRID:SCR_010943 52 Figure 3.1. MPC expression and pyruvate metabolism in Oxphos- and BCRDLBCLs. (A) Aggregated MPC1 and MPC2 mRNA expression level data from 71 Oxphos- and 83 BCR-DLBCLs microarray studies. (B) Western blot analysis of MPC1, MPC2, VDAC, and α-tubulin from a panel of Oxphos- and BCR-DLBCL cell lines. (C) Schematic of D-[U-13C]-glucose tracing. (D) Quantification of the isotopologue abundance of M+2 citrate in Oxphos- and BCRDLBCL cells cultured with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 30 minutes, 1 hour, and 2 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (E, F, G) Quantification of the isotopologue abundances of M+0 and M+2 citrate, M+0 and M+2 α-KG, and M+0 and M+2 succinate in Oxphos- and BCR-DLBCL cells cultured with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (H) Quantification of the isotopologue abundances of M+0 and M+3 alanine in Oxphosand BCR-DLBCL cells cultured with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. 53 54 Figure 3.2. Additional data of MPC expression and pyruvate metabolism in Oxphos- and BCR-DLBCLs. (A and B) MS/MS spectra corresponding to MPC2-derived peptide 40TVFFWAPIMK49 (A) and 28LRPLYNHPAGPR39 (B) acquired during multidimensional LC-MS/MS analysis of purified mitochondria from three independent OxPhos- (Karpas 422, Pfeiffer, and Toledo) and non-OxPhos/BCR- (Ly1, DHL4, and DHL6) DLBCL cell lines using DEEP SEQ mass spectrometry. Ions of b- and y-type are shown in green and orange, respectively. Relative ratios in BCR- and OxPhosDLBCL cell lines are derived from iTRAQ reporter ion intensities shown in inset mass spectrum. (C) Quantification of the isotopologue abundance of M+2 pyruvate, M+3 lactate, and M+3 alanine in Oxphos- and BCR-DLBCL cells cultured with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 30 minutes, 1 hour, and 2 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (D) Quantification of the isotopologue abundances of alanine, citrate, α-KG, succinate, pyruvate, and lactate in Oxphos- and BCR-DLBCL cells cultured with D-[U-13C]glucose ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. 55 56 Figure 3.3. MPC inhibition affects glutamine to TCA cycle flux in DLBCL cells. (A) Growth assay of Oxphos- and BCR-DLBCL cells cultured in suspension in media supplemented with 2 mM, 1 mM, 0.75 mM, 0.5 mM, 0.25 mM, or 0 mM glutamine ± the MPC inhibitor UK-5099. Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. (B) Growth assay of Oxphos- and BCR-DLBCL cells cultured in suspension and treated with either vehicle, GLS inhibitor CB-839, or CB-839 with dimethyl-α-ketoglutarate (dmKG). Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. (C) Schematic of L-[U-13C]-glutamine tracing. (D) Quantification of the isotopologue abundances of M+5 citrate, M+4 succinate, M+4 malate, M+4 citrate, M+2 succinate, and M+2 malate in DLBCL cells cultured with L[U-13C]-glutamine ± the MPC inhibitor UK-5099 for 30 minutes. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (E) Quantification of the isotopologue abundances of M+5 citrate, M+4 succinate, M+4 malate, M+4 citrate, M+2 succinate, and M+2 malate in DLBCL cells cultured with L[U-13C]-glutamine ± the MPC inhibitor UK-5099 for 2 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (F) Quantification of the isotopologue abundances of M+0 and M+3 alanine and M+0 and M+3 lactate in the medium collected from DLBCLs grown with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation). ns p >0.05; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. Data were analyzed by one-way Anova followed by Dunnett’s multiple comparison test. 57 58 Figure 3.4. α-KG production is essential for DLBCLs, and MPC inhibition affects glutamine to TCA cycle flux in DLBCL. (A) Growth assay of Oxphos- and BCR-DLBCL cells cultured in suspension and treated with either vehicle, GLS inhibitor CB-839, or CB-839 with dimethyl-α-ketoglutarate (dmKG). Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. (B) Quantification of the isotopologue abundances of glutamate, citrate, succinate, fumarate, and malate in DLBCL cells cultured with L-[U-13C]-glutamine ± the MPC inhibitor UK-5099 for 30 minutes. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (C) Quantification of the isotopologue abundances of glutamate, citrate, succinate, fumarate, and malate in DLBCL cells cultured with L-[U-13C]-glutamine ± the MPC inhibitor UK-5099 for 2 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (D) Quantification of the isotopologue abundances of alanine and lactate in the medium collected from DLBCLs grown with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation). 59 60 Figure 3.5. MPC inhibition reduces DLBCL proliferation in Matrigel. (A) Growth assay of DLBCL cell lines cultured in suspension ± the MPC inhibitor UK5099 for five days. Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. (B) Growth assay of DLBCL cell lines cultured in Matrigel ± the MPC inhibitor UK5099 for ten days. Cell number (relative to vehicle treatment) for each cell line is the mean of n = 3 independent biological experiments, ± standard deviation. (C) Cell viability measured by Trypan blue staining of DLBCL cell lines cultured in Matrigel ± the MPC inhibitor UK-5099 for 10 days. Cell viability is the mean of n = 3 independent biological experiments, ± standard deviation). (D) Growth assay of DLBCL cell lines cultured in Matrigel ± the MPC inhibitor UK5099 for 24 and 48 hours. Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation). (E) Quantification of the isotopologue abundances of M+0 and M+3 alanine, and M+0 and M+2 α-KG from DLBCL cells cultured in Matrigel with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation). ns p >0.05; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. Data were analyzed by one-way Anova followed by Dunnett’s multiple comparison test. 61 62 Figure 3.6. DLBCL proliferation in suspension and its viability in Matrigel with MPC inhibition, and metabolism impact of MPC inhibition on TCA cycle in cells from Matrigel. (A) Growth assay of DLBCL cell lines cultured in suspension ± the MPC inhibitor UK5099 for five days. Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. (B) Cell viability measured by trypan blue staining of DLBCL cell lines cultured in Matrigel ± the MPC inhibitor UK-5099 for 48 hours. Cell viability is the mean of n = 3 independent biological experiments, ± standard deviation). (C) Quantification of the isotopologue abundances of alanine, citrate, α-KG, succinate, fumarate, malate, lactate, and pyruvate from Oxphos- and BCR-DLBCL cells cultured in Matrigel with D-[U-13C]-glucose ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation). ns p >0.05; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. Data were analyzed by one-way Anova followed by Dunnett’s multiple comparison test. 63 64 Figure 3.7. Environmental change reshapes the metabolic landscape of DLBCLs. (A) Heatmaps showing the steady-state abundances of metabolites from U2932 BCRDLBCL cells grown either in suspension or in Matrigel ± the MPC inhibitor UK-5099 for 4 hours, 8 hours, 12 hours, and 24 hours. (B) Relative steady-state abundances of glutamine and glutamate from cells grown either in suspension or in Matrigel ± the MPC inhibitor UK-5099 for 24 hours. Metabolite abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (C) Relative steady-state abundances of citrate, isocitrate, α-KG, succinate, fumarate, and malate from cells grown either in suspension or in Matrigel ± the MPC inhibitor UK-5099 for 24 hours. Metabolite abundance is the mean of n = 3 independent biological experiments, ± standard deviation. ns p >0.05; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. Data were analyzed by one-way Anova followed by Dunnett’s multiple comparison test. 65 66 Figure 3.8. MPC inhibition enhances the sensitivity of DLBCLs to ammonia in Matrigel. (A) Schematic of the metabolic interactions of α-KG production by GPT2, GOT2, and GDH with the Urea cycle. (B) Top, schematic of L-[alpha-15N]glutamine tracing. Bottom, quantification of the isotopologue abundances of M+1 aspartate, M+1 glutamate, and M+1 glutamine in DLBCL cells cultured either in suspension or in Matrigel with L-[alpha-15N]glutamine ± the MPC inhibitor UK-5099 for 4 hours. Isotopologue abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (C) Relative steady-state abundance of unlabeled (12C) aspartate from cells cultured either in suspension or in Matrigel ± the MPC inhibitor UK-5099 for 24 hours. Metabolite abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (D) Growth assay of cells cultured in suspension with 0 mM, 0.3 mM, 0.75 mM, 1 mM, 1.5 mM, 2 mM, and 5mM of NH4Cl ± the MPC inhibitor UK-5099 for 48 hours. Cell number (relative to 0 mM NH4Cl without UK-5099 treatment) is the mean of n = 3 independent biological experiments, ± standard deviation. (E) Growth assay of cells cultured in Matrigel with 0 mM, 0.3 mM, 0.75 mM, 1 mM, 1.5 mM, 2 mM, and 5mM of NH4Cl ± the MPC inhibitor UK-5099 for 48 hours. Cell number (relative to 0 mM NH4Cl without UK-5099 treatment) is the mean of n = 3 independent biological experiments, ± standard deviation. ns p >0.05; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. Data were analyzed by one-way Anova followed by Dunnett’s multiple comparison test. 67 68 Figure 3.9. α-KG production is important for DLBCL proliferation in Matrigel. (A) Metabolite set enrichment analysis based on MPC inhibition (vehicle vs. UK-5099) of cells grown in Matrigel for 24 hours. Metabolite abundances are n = 3 independent biological experiments. (B) Top, schematic of BCAA degradation pathway. Bottom, relative steady-state abundances of metabolites of the BCAA degradation pathway from cells grown either in suspension or in Matrigel ± the MPC inhibitor UK-5099 for 24 hours. Metabolite abundance is the mean of n = 3 independent biological experiments, ± standard deviation. (C) Top, schematic of GPT2 mediated α-KG production path. Bottom left, western blot analysis of GPT2 and α-tubulin in control (Scramble) and GPT2 knock-down (shGPT2) cell lines. Bottom right, growth assay of these cell lines cultured in Matrigel ± the MPC inhibitor UK-5099 for 48 hours. Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. (D) Top, schematic of GDH mediated α-KG production path. Bottom left, western blot analysis of GDH and α-tubulin in control (Scramble) and GPT2 knock-down (shGDH1, shGDH-2) cell lines. Bottom right, growth assay of these cell lines cultured in Matrigel ± the MPC inhibitor UK-5099 for 48 hours. Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. (E) Growth assay of cells cultured in Matrigel and treated with either vehicle, UK-5099, dimethyl-α-ketoglutarate (dmKG), or UK-5099 with dmKG for 48 hours. Cell concentration is the mean of n = 3 independent biological experiments, ± standard deviation. ns p >0.05; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. Data were analyzed by one-way Anova followed by Dunnett’s multiple comparison test. 69 70 Figure 3.10. BCAA degradation pathway is affected by MPC inhibition, and α-KG is important for DLBCLs proliferation in Matrigel environment. (A) Metabolite set enrichment analysis based on MPC inhibition (vehicle vs. UK-5099) of cells grown in Matrigel for 4 hours, 8 hours, and 12 hours. Metabolite abundances are n = 3 independent biological experiments. Arrowheads are pointing to BCAA degradation passways. ns p >0.05; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. Data were analyzed by one-way Anova followed by Dunnett’s multiple comparison test. 71 Figure 3.11. α-KG production paths that add net carbons to TCA cycle from glutamine. 72 Figure 3.12. SLC25A1 dependency in cancer cell lines. (A) Distribution of dependency for SLC25A1 comparing cancer cell lines of the lymphocyte lineage versus cancer cell lines from all other lineages. (B) Distribution of dependency for SLC25A1 restricted to the lymphocyte lineage, comparing DLBCL versus other subtypes. Tick marks indicated individual cell lines. CHAPTER 4 CONCLUDING REMARKS 74 In adult tissue stem cells, it is very interesting that although carbohydrate and fatty acid oxidation both generate mitochondrial acetyl-CoA and ultimately ATP, they have contrasting effects on stem cell homeostasis. Enhanced pyruvate oxidation decreases stem cell maintenance and proliferation, while increased fatty acid abundance promotes stem cell function. The difficult to answer question, is how do these two metabolic pathways, with ostensibly similar outputs, have opposite effects on stem cell fate? Is the critical distinction derived from the activity level of the different oxidative pathways? Is it due to differential side effects, for example, lactate production and redox changes? One thing is clear, this seems likely to be related to epigenetic modification of histones and/or DNA. Nevertheless, several studies now have demonstrated that metabolism is not a mere signature of, but instead is highly influential on, tissue-resident stem cell fate. In DLBCLs, despite Oxphos-DLBCLs display greater incorporation of pyruvate into citrate, and being more sensitive to MPC inhibition for this incorporation metric, we were unable to identify further differences in the utilization of mitochondrial pyruvate between the Oxphos- and BCR-DLBCL subgroups. Instead, we found that both DLBCL subgroups non-canonically use pyruvate to support glutaminolysis in an unusual crosstalk pathway. I speculate this interesting metabolism feature of DLBCLs might be established during B-cell activation, as a previous study has shown that B cells increase their glucose consumption during activation, and this study also did not detect further glucose labeling in the TCA cycle besides citrate (Waters et al., 2018). First, this suggests that there could be some common metabolic targets in a heterogenous DLBCL; second, this suggests that there could be an interesting and not well charactered metabolism prolife shift during B 75 cell activation. Nevertheless, now in the light of a greater understanding of the metabolic landscape of DLBCLs, it will also be interesting to further investigate how MPC expression is differentially regulated in these two subgroups of DLBCLs and if there are additional phenotypes associated with MPC abundance. From our data, it suggests most mitochondrial citrate is not further oxidized through the TCA cycle, but transported to cytosol. In the cytosol, citrate is used to produce acetyl-CoA for lipogenesis and acetylation. It is tempting to speculate if citrate mediated lipogenesis is important for DLBCLs proliferation in ECM; and if so, it would be also interesting to understand what particular lipid species are essential for DLBCL proliferation in ECM. Also, we found that the ECM environment that better recapitulates the endogenous environment of DLBCLs in organisms is sufficient to reshape these cells’ metabolic landscape. This is supported by other trends in the cancer metabolism field, such as the use of physiological human plasma-like media (HPLM) to study features of metabolism that are otherwise intractable in non-physiologic—but experimentally commonplace—growth environments (Rossiter et al., 2021). In this study of specific aspects of DLBCL metabolism, we did not find a benefit to using HPLM, but we expect future studies in this system could benefit from both a more physiologic 3D culture system as well as a more physiologic milieu of extracellular metabolites. We anticipate that other aspects of DLBCL biology are better reflected in matrigel-based environment, and that the metabolic requirements of many types of solid-tumor cancers may be similarly revealed by 3D culture systems. MPC activity might also affect DLBCLs stemness markers. Based on DLBCL 76 gene expression signatures, it is possible to determine the developmental stage at which individual DLBCLs are derived. Derivation can occur early during the Germinal Center (GC) B-cells stage or late during the activated B-cell stage. More differentiated DLBCLs will typically express lower levels of GC B-cell signature genes (CD10, JAW1, BCL-6 and CD77 synthase), and higher levels of activated B-cell signature genes (FOXP1, CD44, Cyclin D2 and IRF4) (Shaffer et al., 2002). Interestingly, MPC re-expression in colon cancers decreases their stemness markers indicating that high MPC activity might decrease cell stemness (Schell et al., 2014). Therefore, modulating MPC expression in DLBCLs may not only affect cancer cell metabolism but may also alter expression of stemness markers. Besides rewired metabolism and growth, cancer cells can also reprogram their signaling pathways to be resistant to cell death and to sustain proliferation. Many B-cell tumors carry chromosomal translocations of genes that regulate cell growth, such as MYC and BCL-2 (Hanahan and Weinberg, 2011). In DLBCLs, high expression of MYC, BCL-2 and BCL-6, is correlated with worse outcomes after treatment when compared to patients without abnormalities in these genes (Caponetti et al., 2015). In addition, elevated expression of BRAF and MAPK activation can lead to an aggressive malignancy resembling human DLBCL (Karreth et al., 2015). Interestingly, targets of the Wnt/βcatenin pathway are upregulated in MPC-depleted mouse organoids(Schell et al., 2017), potentially suggesting that low MPC activity condition in stem cells may result metabolism profile that stimulates activity of the Wnt/β-catenin pathway. Therefore, it is reasonable to speculate if MPC activity could have further impact in additional signaling pathways that could provide a selective growth advantage to DLBCLs. 77 Last but not least, after the discovery of MPC and studies of its impact in stem cells and cancer cells, an outstanding question still remains is that how is the expression of MPC regulated. 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