PHD3 Loss in Cancer Enables Metabolic Reliance on Fatty Acid Oxidation via Deactivation of ACC2.

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Mol Cell. Author manuscript; available in PMC 2017 September 15. Published in final edited form as: Mol Cell. 2016 September 15; 63(6): 1006–1020. doi:10.1016/j.molcel.2016.08.014.

PHD3 Loss in Cancer Enables Metabolic Reliance on Fatty Acid Oxidation via Deactivation of ACC2 Natalie J. German1, Haejin Yoon1, Rushdia Z. Yusuf2, J. Patrick Murphy1, Lydia W.S. Finley1,†, Gaëlle Laurent1, Wilhelm Haas1, F. Kyle Satterstrom1, Jlenia Guarnerio3, Elma Zaganjor1, Daniel Santos1, Pier Paolo Pandolfi3, Andrew H. Beck4, Steven P. Gygi1, David T. Scadden2, William G. Kaelin Jr.5,6, and Marcia C. Haigis1,*

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1Department

of Cell Biology, Ludwig Center at Harvard, Harvard Medical School, Boston, MA

02115 2Stem

Cell and Regenerative Biology Department, Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138; Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02114 USA 3Cancer

Research Institute, Beth Israel Deaconess Cancer Center, Department of Medicine and Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115 USA

4Department

of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02115 USA

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5Department 6Howard

of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215 USA

Hughes Medical Institute, Chevy Chase, MD 20815 USA

SUMMARY While much research has examined the use of glucose and glutamine by tumor cells, many cancers instead prefer to metabolize fats. Despite the pervasiveness of this phenotype, knowledge of pathways that drive fatty acid oxidation (FAO) in cancer is limited. Prolyl hydroxylase domain proteins hydroxylate substrate proline residues and have been linked to fuel switching. Here we reveal that PHD3 rapidly triggers repression of FAO in response to nutrient abundance via hydroxylation of acetyl-coA carboxylase 2 (ACC2). We find that PHD3 expression is strongly decreased in subsets of cancer including acute myeloid leukemia (AML) and is linked to a reliance

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*

Correspondence: [email protected] (MCH). †Current address: Memorial Sloan-Kettering Cancer Center, New York, NY 10065 USA SUPPLEMENTAL INFORMATION Supplemental Information includes Extended Experimental Procedures, one table and six figures.

AUTHOR CONTRIBUTIONS N.J.G. designed and performed experiments and wrote the paper. H.Y, R.Z., L.F., G.L., J.G., E.Z. and D.S. assisted with experiments. J.P.M, W.H. and S.P.G. designed and performed mass spec studies. A.B. and F.K.S. performed bioinformatics analyses. P.P.P, D.T.S. and W.G.K. assisted with experimental design. M.C.H. supervised and assisted with designing experiments and writing the paper. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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on fat catabolism regardless of external nutrient cues. Overexpressing PHD3 limits FAO via regulation of ACC2 and consequently impedes leukemia cell proliferation. Thus, loss of PHD3 enables greater utilization of fatty acids but may also serve as a metabolic and therapeutic liability by indicating cancer cell susceptibility to FAO inhibition.

Graphical Abstract

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In the past decade a resurgence of studies has provided mechanistic insight into why tumors upregulate glucose uptake and metabolism (Lunt and Vander Heiden, 2011). However, our understanding of tumor metabolism is incomplete because numerous tumors are FDG-PET negative (Long and Smith, 2011; Ono et al., 2007), suggesting many cancers utilize alternate carbon sources. Multiple cancer types have been suggested to rely on FAO for survival (Carracedo et al., 2013), highlighting a need to identify specific lipid metabolic programs that may go awry in cancer.

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Post-translational modifying enzymes are key components of metabolic reprogramming (German and Haigis, 2015; Hitosugi and Chen, 2013). PHDs (also called EGLN1-3) are one class of enzymes poised to coordinate metabolism in response to changing cellular conditions. PHDs are a conserved family of oxygen- and α-ketoglutarate dependent enzymes that are well known to regulate glycolytic metabolism through hydroxylation of hypoxia inducible factor (HIF) (Gorres and Raines, 2010). Hypoxia and a number of mutations in cancer repress activity of some PHDs, stabilizing HIFα and triggering a transcriptional program to increase glycolysis and anabolism while limiting mitochondrial bioenergetics (Masson and Ratcliffe, 2014). Recent reports suggest that PHDs are also responsive to cellular nutrient status (Kaelin and Ratcliffe, 2008). This may be linked to the use of α-ketoglutarate during prolyl Mol Cell. Author manuscript; available in PMC 2017 September 15.

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hydroxylation (Durán et al., 2012). PHD3 is notable for its particular sensitivity to αketoglutarate, or perhaps more generally to the high nutrient state that may be achieved by addition of α-ketoglutarate. Along these lines, treating mouse xenografts with cellpermeable α-ketoglutarate inhibited growth by a PHD3-dependent mechanism (Tennant and Gottlieb, 2010). This raises the question of whether PHD3 is responsive to fluctuations in the nutrient state. We hypothesized that PHD3 might link nutrient status with implementation of metabolic adaptations. Therefore, we aimed to identify metabolic pathways regulated by PHD3.

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In this study, we identify acetyl-CoA carboxylase 2 (ACC2), the gatekeeper of FAO, as a PHD3 substrate. By activating ACC2, PHD3 represses oxidation of long chain fatty acids. Fatty acid catabolism is a dynamic cellular process that responds to metabolic imbalances and restores homeostasis (Gerhart-Hines et al., 2007). We show that PHD3 represses FAO during nutrient abundance, and that cells with low PHD3 have persistent FAO regardless of external nutrient cues. In AML, PHD3 expression is dramatically decreased, contributing to a boost in fatty acid consumption that drives AML cell proliferation and disease severity.

RESULTS PHD3 binds and modifies ACC by prolyl hydroxylation

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To probe for PHD3 substrates, we performed immunoprecipitation of PHD3 followed by liquid chromatography tandem mass spectrometry (LC-MS2) and detected an interaction with acetyl-CoA carboxylase (ACC). 21 ACC peptides were identified in the PHD3 immunoprecipitation, while no ACC peptides were identified in PHD2 or negative control samples (Table S1). IP-Western blots confirmed that ACC interacted with PHD3 but not PHD1, PHD2 or anti-HA affinity resin alone (Figure 1A). ACC converts acetyl-CoA to malonyl-CoA, which serves as a precursor for fat synthesis and an inhibitor of FAO (AbuElheiga et al., 2003). Hence, ACC is a key regulator of fatty acid homeostasis that determines whether cells catabolize or synthesize fatty acids (Brownsey et al., 2006).

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As PHD3 belongs to a family of prolyl hydroxylases, we first examined ACC hydroxylation by PHD3. Using immunoprecipitation of ACC followed by Western blot with a panhydroxyproline antibody, we observed ACC hydroxylation. PHD3 overexpression boosted ACC hydroxylation (Figure 1B), while two catalytically inactive PHD3 mutants, H196A and R206K (Bruick, 2001; Rantanen et al., 2008), did not increase ACC hydroxylation (Figure 1C). ACC is present in two spatially and functionally distinct isoforms. Cytosolic ACC1 provides malonyl-CoA for fatty acid synthesis, while ACC2 localized to the outer mitochondrial membrane generates malonyl-CoA to inhibit the fatty acid transport protein CPT1, suppressing mitochondrial FAO (Brownsey et al., 2006). Several PHD3-interacting peptides found by mass spectrometry are shared between ACC1 and ACC2 (Table S1); we therefore interrogated whether PHD3 hydroxylates ACC1 or ACC2. Immunoprecipitation of endogenous ACC1 or ACC2 by isoform-specific antibodies demonstrated hydroxylation was specific to ACC2 and amplified by PHD3 (Figure 1D), indicating that PHD3 modulates ACC2 hydroxylation level.

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We next mapped ACC2 sites of hydroxyproline, as detected by a +15.9949 molecular weight shift (Figures 1E and 1F). Three candidate hydroxylated prolines had >5 redundant peptides per site: prolines 343, 450 and 2131 (Figure 1G). These sites are located in the biotin carboxylase, ATP-grasp and carboxyltransferase domains, respectively (Figure 1H). To validate hydroxylation of these residues, we generated proline to alanine point mutants at each site and found that P450A mutagenesis most dramatically decreased hydroxylation (Figure 1I). Using a reconstituted in vitro hydroxylation assay, recombinant PHD3 hydroxylated a synthetic ACC2 peptide containing P450, but not a peptide containing P2131 or a control ACC2 proline-containing peptide (P966) (Figure 1J). These data identify P450 as a major hydroxylation site and suggest that modification of this residue may coordinate ACC2 function. Hydroxylation at residue P450 promotes ACC2 activity and ATP binding

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At 16 daltons, hydroxylation is among the smallest posttranslational modifications. Nevertheless, the electronegativity it imparts alters proline residue pucker, favors transconfiguration of the peptide bond and can induce structural changes significant enough to alter protein-protein interactions or activity (Gorres and Raines, 2010; Loenarz and Schofield, 2011). Thus, we investigated whether site-specific hydroxylation might regulate ACC2 function. P450 is conserved from yeast to humans (Figure 2A) in the ATP-grasp domain, a 196-amino acid region within the biotin carboxylase domain that includes nucleotide-binding amino acids at residues 458-463 (Almarza-Ortega, 1997). We mapped site P450 in the human ACC2 biotin carboxylase domain crystal structure (PDB: 3JRW) (Cho et al., 2010) superposed with the e. coli ATP-bound ACC biotin carboxylase domain (PDB: 1DV2) (Thoden, 2000). Modeling revealed that P450 is in close proximity to the catalytic site ATP (Figure 2B). P450 caps the adenine ring of ATP, while the phosphate groups of ATP abut the previously described nucleotide-binding site within ACC2. The proximity of P450 to ATP led us to hypothesize that hydroxylation modulates the ability of ACC2 to bind ATP. We purified ATP-binding proteins from dialyzed cell lysates using ATP affinity chromatography and determined levels of ACC bound. ACC2 lacking the major hydroxylation site upon P450 mutation to alanine or glycine showed decreased ATP binding versus wild type (Figures 2C and S1A), highlighting the importance of this residue for ATP binding. Strikingly, knockdown of PHD3 also diminished ATP binding of ACC2 (Figure 2D), demonstrating that hydroxylation is critical for optimal ATP binding. Thus, PHD3 may activate ACC2 by enabling greater affinity for the co-substrate ATP.

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To evaluate this hypothesis, we performed in vitro ACC activity assays from 293T cells, based on production of [14C]malonyl-CoA from [14C]bicarbonate and acetyl-CoA. ACC2 overexpression resulted in higher activity, as did addition of the known allosteric modulator citrate (Ruderman and Prentki, 2004), validating the specificity of this assay (Figure 2E). The P450A mutation strongly decreased ACC activity (Figure 2E). Of note, PHD3 overexpression increased wild type ACC2 activity (Figure 2F) but had no effect on P450A ACC2. Conversely, PHD3 knockdown decreased ACC2 activity (Figure 2G). These findings support the model that PHD3 boosts ACC2 activity via hydroxylation of P450.


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PHD3 represses FAO

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ACC2 inhibits FAO by limiting long chain fatty acid import into the mitochondrial matrix (Ruderman and Prentki, 2004). Because ACC2 is activated by PHD3, we hypothesized that PHD3 represses FAO. PHD3 knockdown enhanced palmitate oxidation in 293T and HepG2 cells (Figures 3A, S2A–D). PHD1 and PHD2 expression were not consistently altered by PHD3 knockdown, indicating that the effect was not due to other PHDs (Figure S2B). To assess the role of ACC2 prolyl hydroxylation on FAO, we measured palmitate oxidation in cells overexpressing P450A ACC2. While overexpression of wild type ACC2 decreased FAO, ACC2 lacking the proline hydroxylation site did not repress FAO (Figure 3B). In contrast, P343A and P2131A variants behaved similarly to wild type ACC2 (Figure S2E).

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ACC2 knockdown increased FAO in 293T cells by 2.5-fold, while the effect of PHD3 knockdown was of similar magnitude (1.5–2 fold), demonstrating that PHD3 is a major contributor to FAO regulation (Figure S2F compared to Figures 3A and 3C; extent of ACC2 knockdown shown in Figure S2G). Immunoblots with control ACC2 knockdown confirmed that ACC2 is ~276 kDa and often appears as two bands that may correspond to different oligomerization states (Figures S2G and S2H), consistent with studies showing that ACC2 exists in oligomeric complexes and that phosphorylation pushes ACC to a monomeric state (Cho et al., 2010). The presence of ACC2 in both bands was confirmed by IP-mass spectrometry (data not shown). Collectively, our findings demonstrate that PHD3 modulates FAO at a physiologically significant magnitude similar to the effect of other known lipid metabolism regulators including ACC2 itself, CPT1, adiponectin and sirtuins (Fullerton et al., 2013; Gerhart-Hines et al., 2007; Laurent et al., 2013; Yoon, 2006).

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ACC2 gates long chain fatty acid import into the mitochondria, whereas short and medium chain fatty acids freely diffuse and bypass ACC2 regulation (Madden et al., 1995; O’Donnell et al., 2002). To validate the role of ACC2 in PHD3-mediated regulation of FAO, we probed whether PHD3 specifically modulates long chain FAO. Comparison of the long chain,16-C palmitate versus 6-C hexanoate oxidation revealed that PHD3 knockdown specifically boosted long chain FAO (Figure 3C). We next tested the effect of PHD3 overexpression on fatty acid synthesis, a process mediated by ACC1. Consistent with our studies showing that ACC2, but not ACC1, is hydroxylated by PHD3, we observed no effect of PHD3 on fatty acid synthesis (Figures S2I and S2J). In sum, PHD3 regulates FAO at the level of ACC2. PHD3 repression of FAO occurs independently of HIF and AMPK

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Because HIF is the most well characterized target of PHD family members, we assessed dependence on the HIF regulatory axis. PHD3 modulated FAO regardless of the presence or absence of HIF. PHD3 overexpression repressed FAO, while PHD3 knockdown amplified FAO, in mouse hepatoma 4 (B13NBii1) HIFβ-null cells which lack HIF1 transcriptional activity (Wood et al., 1996) (Figure 3D–F). The absence of HIF activity was validated by treatment with CoCl2, which stabilizes HIFα, but did not induce HIF target genes in these cells (Figure 3F). Additionally, HIF1/2α protein levels were not changed by PHD3 knockdown (Figure 3G). Furthermore, PHD3 knockdown boosted FAO in 786-O VHLdeficient renal carcinoma cells with constitutively stabilized HIF (Iliopoulos et al., 1996)


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(Figures 3H and S2K). We also found that PHD3 knockdown amplified FAO both under normoxia (21% O2) and hypoxia (1% O2) (Figure 3I), consistent with reports that PHD3 is less sensitive to oxygen levels than PHD1 or PHD2 (Luo et al., 2011). In total, these data demonstrate that PHD3 represses FAO independently of HIF.

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We next examined whether PHD3 activates ACC2 in concert with the major known regulator of this metabolic node, AMP-activated protein kinase (AMPK) (Ruderman and Prentki, 2004). Upon detecting a low cellular energy status, AMPK inhibits ACC2 by phosphorylating serine 222, disrupting the dimer-dimer interface to block formation of the more active ACC oligomer (Cho et al., 2010). In this way, AMPK activates FAO as part of a program to restore ATP levels. PHD3 knockdown amplified FAO in both wildtype and AMPKα-knockout mouse embryonic fibroblasts (MEFs) (Figure 3J, S2L and S2M). PHD3 overexpression repressed FAO in the absence of AMPKα (Figure S2N). Additionally, we found that AMPK phosphorylated ACC under low nutrient conditions in both control and PHD3-knockdown MEFs, and ACC phosphorylation decreased upon nutrient restoration regardless of PHD3 status (Figure S2O). Thus, our data suggest that PHD3 regulates FAO through a mechanism independent of AMPK. PHD3 hydroxylates ACC and represses FAO in response to nutrient abundance


In many tissues, fatty acids are not a predominant fuel choice under nutrient replete conditions but rather are reserved for times of nutrient deprivation to restore metabolic homeostasis (Gerhart-Hines et al., 2007). During conditions of stress or low energy, cells ramp up ATP production by activating FAO via AMPK signaling. While AMPK boosts FAO by inhibiting ACC2, our data show PHD3 has the opposite effect of repressing FAO by activating ACC2. Because recent reports underscore a possible link between PHDs and cellular nutrient status (Kaelin and Ratcliffe, 2008; MacKenzie et al., 2007), we wondered whether PHD3 might be a candidate for dynamically repressing FAO in response to nutrient abundance. In control vector-treated cells, ACC was strongly hydroxylated in the presence of high glucose medium containing serum (high) versus cells treated 12 h with serum-free, low glucose medium (low) (Figure 3K, vector lanes). However, PHD3 overexpression restored ACC hydroxylation to nearly that observed in the high nutrient state (Figure 3K). This suggests that endogenous PHD3 hydroxylates and activates ACC particularly when nutrients are abundant. In support of this model, PHD3 knockdown abrogated ACC hydroxylation in high nutrient medium (Figure 3L, left). In comparison, the effect of PHD3 knockdown was less evident in low nutrient conditions (Figure 3L, right). We next performed a time course analysis of ACC2 hydroxylation under high versus low nutrient conditions. ACC2 hydroxylation decreased following 6 h in low glucose, serum-free medium, and hydroxylation increased in a PHD3-dependent manner after only 10 minutes of restoring high nutrient medium (Figures 3M and S2P). PHD3 silencing most potently represses ACC2 activity in the time frame immediately after restoring high nutrients (Figures S2Q and S2R). Thus, our data suggest that PHD3 is an acute metabolic toggle responsive to cellular nutrient abundance. We reasoned that cells with low PHD3 would lack this metabolic switch, uncoupling FAO repression in nutrient abundance. In multiple cell lines, palmitate oxidation was enhanced in


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serum-free, low glucose medium but blunted in the presence of high glucose and serum (Figures 3N and S2S). However, upon PHD3 knockdown, cells lost sensitivity to external nutrient cues and displayed elevated FAO even in the presence of high nutrients. Similarly, supplementing low nutrient medium with cell-permeable versions of the TCA cycle intermediate α-ketoglutarate for 6 h prior to FAO analysis repressed palmitate oxidation, unless PHD3 was downregulated by shRNA (Figure 3O). This indicates that PHD3 limits FAO in nutrient-replete conditions, and that nutrient deprivation lifts PHD3-mediated repression of FAO.

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Our findings suggest PHD3 activates ACC2 to inhibit CPT1. In support, metabolomics analysis revealed that PHD3 knockdown by shRNA increased long chain acylcarnitines – FAO intermediates generated by CPT1 – but short and medium chain acylcarnitines, which bypass the ACC2/CPT1 regulatory node, were unchanged (Figure 3P). This profile is reminiscent of the way in which glycolytic intermediates increase upon upregulation of glycolysis (Finley et al., 2011), provided the initial substrate is not limiting; here, too, we observe that long chain FAO intermediates are increased when FAO is upregulated by PHD3 silencing. Our data suggests that, together, AMPK and PHD3 toggle FAO in a manner that is sensitive to both high and low nutrient status (Figure 3Q). Low PHD3 expression drives altered metabolism in AML

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In some cancers, FAO fuels energy production, antioxidant defense via NADPH production, nucleotide synthesis and maintenance of the mitochondrial membrane potential to prevent apoptosis induction (Jeon et al., 2012; Samudio et al., 2010; Schafer et al., 2009; Schoors et al., 2015; Zaugg et al., 2011). PHD3 downregulation is a common feature of multiple cancer types (Rawluszko et al., 2013), and we hypothesized that low PHD3 levels might predict a dependency on FAO in cancer.

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We used bioinformatics to assess the metabolic impact of low PHD3 expression in cancer. Analysis of the Ramaswamy, Valk and Andersson leukemia datasets from the Oncomine cancer gene expression database (http://www.oncomine.org) showed that PHD3 expression was lower in AML compared to a panel of other cancerous tissues (Figure 4A–C) (Andersson et al., 2007; Valk et al., 2004). AML has been previously linked to increased fatty acid catabolism (Carracedo et al., 2013; Liu et al., 2013); thus, we assessed the impact of PHD3 on this phenotype. Using gene expression data from patient samples in The Cancer Genome Atlas (TCGA), we clustered AML patients into two groups (PHD3-low and PHD3high) using a Gaussian mixture model based on the level of PHD3 expression. Nearly 80% of patients fell into the low PHD3 group (Figures 4D and 4E). Gene Set Enrichment Analysis revealed that the top curated gene sets inversely correlated with high PHD3 expression in AML are largely markers of oxidative metabolism (Figures 4F and S3A–S3D). Of note, we found no significant link between PHD3 and expression of ACC2, AMPK or LKB1 (Figures S3E–S3G) in TCGA data. These data support a model in which high PHD3 expression may indicate AML cells are not fueled by oxidative metabolism, while low PHD3 expression can enable a switch toward FAO. In line with patient data, PHD3 gene expression was nearly undetectable in a panel of AML cell lines (MOLM14, KG1, THP1, NB4 and U937) compared to the K562 chronic myeloid Mol Cell. Author manuscript; available in PMC 2017 September 15.

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leukemia (CML) cell line (Figure 4G). Low-PHD3 AML cells show reduced ACC hydroxylation and ATP binding (Figures 4H and 4I) and markedly increased palmitate oxidation (Figure 4J). PHD1 and PHD2 are not repressed to the same extent as PHD3 in AML cells (Figures S3H and S3I), indicating that PHD3 expression is specifically linked to the observed traits.

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Recent studies revealed that AML frequently displays enhanced dependence on FAO (Samudio et al., 2010). Inhibition of FAO increases sensitivity to apoptosis in cell culture and in a murine model of human AML (Estañ et al., 2014; Lee et al., 2015). Furthermore, FAO was shown to be critical for maintenance of hematopoietic stem cells, and was suggested to be involved in the maintenance of leukemic initiation cells (Ito et al., 2012). We hypothesized that low-PHD3 leukemia cells possess a metabolic liability rooted in their dependency on FAO, so we examined leukemia cell sensitivity to etomoxir or ranolazine, FAO inhibitors that have shown success in treating angina and heart disease (Holubarsch et al., 2007; Nash and Nash, 2008). Etomoxir inhibits CPT1, and ranolazine inhibits 3ketoacylthiolase, the enzyme catalyzing the final step of β-oxidation (Carracedo et al., 2013). Within 96 h, FAO inhibition led to substantial cell death in a panel of low-PHD3 leukemia cells, while viability was maintained for high-PHD3 K562 cells (Figures 4K–4N). Another high-PHD3 CML cell line, MEG01, was less sensitive to a high dose of ranolazine compared to low-PHD3 AML cells (Figures S3J and S3K). Sensitivity to FAO inhibition was more strongly linked to PHD3 status than to classification as AML or CML; a CML cell line with low PHD3 expression (KU812) was found to be sensitive to treatment with etomoxir and more closely resembled another low-PHD3 AML cell line (NB4) than a highPHD3 CML line (K562) (Figures S3K and S3L). Thus, blocking fatty acid catabolism has a strong cytotoxic effect specific to low-PHD3 leukemia cells.

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In high-PHD3 K562 cells, PHD3 knockdown enabled higher FAO (Figures 4O and S3M), however it did not create a fixed dependency on FAO or cause susceptibility to FAO inhibitors (Figure 4P). K562 cells have a preference for glycolytic metabolism (Barger et al., 2012), and although PHD3 knockdown allowed higher FAO, it did not force these cells to rely on fats. In contrast, cancer cells with low levels of PHD3 displayed limited metabolic plasticity, required sustained FAO and were particularly susceptible to pharmacological inhibitors of FAO. These data indicate that low PHD3 expression may be a promising candidate as a biomarker for leukemia cells that may be successfully targeted with FAO inhibitors. Restoring PHD3 expression in AML limits FAO and cell growth through activation of ACC2

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Our data suggest that low PHD3 expression is advantageous in AML by boosting FAO. We hypothesized that restoring PHD3 would limit the proliferation and potency of leukemia cells. Indeed, stable PHD3 overexpression repressed FAO by over 50% (Figures 5A and S4A), a level similar to that achieved by etomoxir (Figure S4B). This suggests that PHD3 affects FAO at a magnitude similar to direct CPT1 inhibition. PHD3 overexpression also diminished cell proliferation and viability (Figures 5B–C and S5A–B). To assess whether PHD3 overexpression was generally toxic, we overexpressed PHD3 in K562 cells. Endogenous PHD3 levels in MOLM14 and THP1 cells are 1% of that in K562 Mol Cell. Author manuscript; available in PMC 2017 September 15.

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cells (Figure 4G), and PHD3 overexpression in these cells achieved an amount roughly 10 to 60-fold greater than that found in K562 cells (Figure S4A). PHD3 overexpression in K562 cells had little effect on proliferation (Figures 5D–5F), suggesting PHD3 overexpression is not generally toxic. Moreover, knocking down PHD3 did not alter proliferation in K562 cells (Figure 5G and S5C), supporting the idea that metabolic alterations due to modulating PHD3 are not detrimental to all cancer cells. Instead, low-PHD3 cancer cells uniquely experience severe effects when PHD3 is restored.

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Our model suggests that PHD3 overexpression impairs AML cells by activating ACC2 and repressing fatty acid catabolism. To test directly the requirement for ACC, we assessed the effect of PHD3 overexpression when individual ACC isozymes were silenced. We generated MOLM14 cells that overexpressed PHD3 and also expressed shRNA against ACC2, ACC1 or a non-silencing control. Importantly, shRNA were specific for each isozyme, and knockdown of one did not lead to compensatory upregulation of the other (Figures S4C and S4D). As anticipated, ACC2 knockdown amplified FAO more than ACC1 (Figure 5H), and knockdown of either isozyme enhanced cell growth (Figure 5I), fitting with previously established roles of the enzymes (Jeon et al., 2012). Strikingly, in the absence of ACC2, PHD3 overexpression was not able to blunt FAO or impair cellular proliferation (Figures 5H and 5I; PHD3 expression levels in Figure S4E). ACC1 was not required for these PHD3mediated phenotypes. Consistent results were obtained in THP1 cells (Figures 5J and S4F – S4I). These data clearly demonstrate that ACC2 is essential for the inhibitory action of PHD3 on FAO, AML survival and growth. Restoring PHD3 expression impedes AML potency in cell culture and in vivo

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After establishing that PHD3 alters AML cell metabolism and proliferation, we probed the impact of PHD3 overexpression on leukemic potency using colony formation assays to measure viable and functional progenitor cells. Overexpressing PHD3 dramatically decreased the number of clonogenic MOLM14 and THP1 cells in methylcellulose assays (Figures 6A and 6B). However in the absence of ACC2, AML colonies grew robustly regardless of PHD3 overexpression (Figures 6C and S6A). In contrast, PHD3 overexpression impaired clonogenic capacity independently of ACC1 (Figures 6C and S6A). We observed that high-PHD3 K562 cell colony formation is inherently less sensitive to PHD3 modulation (Figures 6D and 6E). These results indicate that the growth advantage of low-PHD3 AML cells is indeed due at least in part to PHD3 levels, and that re-introducing PHD3 is detrimental to this subset of leukemia. Moreover, ACC2 but not ACC1 is required for the inhibitory effects of PHD3.

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We next asked whether PHD3 overexpression inhibits proliferation in primary AML samples. Leukemic cells from patient samples obtained from the University of Pennsylvania showed decreased PHD3 expression compared to healthy CD34+ control bone marrow cells (Figure 6F). Overexpressing PHD3 decreased proliferation in 2 of 3 samples, while the remaining sample trended toward a decrease (Figure 6G). PHD3 overexpression led to similar results in leukemic cells derived from the MLL/AF9 mouse model of AML. MLL/AF9 chromosomal translocation is a causative factor in a substantial subset of human AML and is associated with a 5-year survival rate of only 40% (Noordermeer et al., 2012).


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Compared to healthy CD11b control cells, PHD3 was strongly decreased in leukemic cells obtained from the MLL/AF9 mouse model of AML and decreased to a lesser extent in the Hoxa9 Meis1 mouse model of AML (Figure 6H). In MLL-AF9 lineage-negative bone marrow cells, PHD3 overexpression decreased AML clonogenic capacity (Figures 6I and 6J). Thus, in low-PHD3 systems, PHD3 overexpression limits leukemic potency.

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Finally, we evaluated the in vivo impact of PHD3 overexpression in low-PHD3 AML cells using an aggressive mouse xenotransplanation model. NOD-scid IL2Rgammanull (NSG) mice were chosen for this analysis due to their superiority in allowing engraftment of human AML cells (Shultz et al., 2005). Cohorts of NSG mice were injected via tail vein with MOLM14 cells overexpressing PHD3 or vector. The length of survival post-injection was used as a readout of AML severity. We observed that PHD3 overexpression in AML enhanced survival (Figure 6K). Taken together, these data strengthen our model that lowPHD3 leukemia cells possess a metabolic liability rooted in ACC2 activation and a dependency on FAO, and that restoring PHD3 levels limits the proliferation and potency of AML cells.

DISCUSSION

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Here we reveal a PHD3-mediated, nutrient-dependent signaling pathway. In response to nutrient abundance, PHD3 hydroxylates and activates ACC2 in order to repress mitochondrial FAO. The PHD3/ACC2 axis couples cellular nutrient status with dynamic regulation of FAO via a mechanism that is parallel to, but distinct from, AMPK. When cellular energy supplies are low, AMPK inhibits ACC2 to activate FAO and restore bioenergetic homeostasis (Cho et al., 2010). Here we reveal that PHD3 contributes to regulation of this metabolic node by activating ACC2 and limiting FAO when nutrients are abundant, thus enabling fuel conservation and metabolic efficiency. Together, AMPK and PHD3 can achieve dual modulation of FAO in response to changing nutrient levels. From our studies, a strong correlation between ACC2 hydroxylation and oligomerization could not be seen, but it will be interesting for future studies to explore in detail any possible feedback between PHD3 and AMPK. The link between PHD3 and nutrient status aligns with previous reports suggesting that PHD3 might be sensitive to α-ketoglutarate abundance, or more generally to a high nutrient state achieved by addition of α-ketoglutarate (Koivunen et al., 2007). We hypothesize that PHD3 may be responsive to a pool of α-ketoglutarate near the outer mitochondrial membrane, in close proximity to ACC2. Although there is not currently a robust and reliable technique for measuring different intracellular pools of α-ketoglutarate, future advancements in metabolomics may make investigating this idea possible.

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Because PHD3 acts on ACC2 and not ACC1, PHD3 loss may provide a way for the cell to elevate FAO while maintaining lipid synthesis. One traditional view of lipid metabolism considers both ACC isozymes to be similarly regulated, such that FAO is suppressed when lipogenesis is elevated, and vice versa. However, our results support a mechanism by which PHD3 can specifically activate ACC2 in order to repress FAO, without impinging upon lipid synthesis. Studies of calorie restriction establish a precedent for this idea – collectively, three studies in c. elegans indicate that FAO and lipogenesis can be concurrently upregulated (Amrit, 2016; Ratnappan et al., 2014; Steinbaugh et al., 2015). Further studies are needed to


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identify how PHD3 specifically targets ACC2, perhaps via localization to the outer mitochondrial membrane.

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Our studies offer insight into mechanisms that drive fatty acid addiction in tumors. We find that reduced PHD3 expression is a common feature of AML that enables amplification of FAO. The regulatory node revealed here may have implications for a broad range of cancers. In addition to AML, PHD3 is epigenetically silenced due to promoter hypermethylation in patient samples of glioblastoma, B-cell lymphoma subtypes, invasive breast cancer and multiple myeloma, as well as in some prostate and colon cancer cell lines (Rawluszko et al., 2013). Several of these cancers are linked to FAO dependency, in particular prostate, colorectal and breast cancers, as well as B-cell lymphoma subtypes (Carracedo et al., 2013; Liu et al., 2013). Thus, it is possible that PHD3 loss may play a role in driving FAO in these settings as well. PHD3 suppression likely is not the only determinant of FAO dependency in cancer, in the same way that multiple enzymes contribute to increased glycolysis in other tumor types. For example, PML and PPARδ have been linked to the maintenance and function of HSCs (Ito et al., 2012), and future studies may reveal the role of other enzymes in cancer cell reliance on fats.

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Our data suggest a unique opportunity in the treatment of hematological malignancies. FAO inhibitors are promising candidates for consideration in cancer treatment (Carracedo et al., 2013; Heřmanová et al., 2015; Rodríguez-Enríquez et al., 2015). The utility of FAO inhibitors would be greatly aided by biomarkers that identify individuals who might most likely benefit from such a treatment. Based on the knowledge of the regulatory axis identified here, low PHD3 expression could be considered as a marker in cancer to identify patients who may be successfully treated with FAO inhibitors, thus moving the field toward metabolically-based treatment options in the future.

EXPERIMENTAL PROCEDURES Mass Spectrometry To identify hydroxyproline sites, ACC2 was transiently overexpressed in 293T cells and immunoprecipitated with ACC2 antibody (Cell Signaling). Bound material was washed and separated by SDS-PAGE. The Coomassie stained band was analyzed by LC-MS2 and searched against the Uniprot Human database using Sequest with proline hydroxylation set as a variable modification (+15.9949 MW shift). ACC Activity

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Reactions were performed with 50 μg 293T cell protein lysates as previously described (Pulinilkunnil et al., 2011), with the exception of using 16.7 mM MgCl2. Further details provided in supplemental procedures. Growth Curves Live cells were sorted by FACS on day 0, then counted and plated in the wells of a 24 well or 96 well plate. At indicated times, cells were counted on the Beckman Z1 Coulter Counter.


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Colony-Forming Cell Assays

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After sorting for live leukemic cells, cells were resuspended in methylcellulose-based medium (MethoCult H4434 or M3434, StemCell Technologies) in the presence of puromycin and also hygromycin, where indicated. For K562, 375 cells were plated per well of a 6-well plate. For MOLM14, THP1 and MLL-AF9 cells, 5,000 cells were plated. CFU were counted 8–21 d later. Xenotransplantation Studies

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Male NOD-scid IL2Rgammanull (NSG) mice were obtained from Jackson Labs and housed in specific pathogen-free environments. Mice were 7 wk at the time of xenotransplantation. On day 0, mice were sublethally irradiated (2.5 Gy) and injected via tail vein with 7 x 105 Molm14 cells overexpressing vector or HA-PHD3 in 250 μl PBS. Statistical evaluation of survival was based on the log-rank test as well as the Gehan-Breslow-Wilcoxon test for comparison of the Kaplan-Meier curves. Statistical Analysis Unpaired two-tailed Student’s t tests were used for comparison of FAO, gene expression, activity assays and growth and cell viability experiments. All experiments were performed at least 2–3 times.

Supplementary Material Refer to Web version on PubMed Central for supplementary material.

Acknowledgments Author Manuscript

We thank David Sykes, Giovanni Roti, Ninib Baryawno and Qing Zhang for assay advice. We thank Peppi Karppinen at the University of Oulu for recombinant PHD3. We thank the Stable Isotope and Metabolomics Core Facility at the Albert Einstein College of Medicine (NIH/NCI grant P60DK020541) for acylcarnitine measurements. N.J.G. is supported by NSF Graduate Research Fellowship and NIH Training Grants. P.P.P, A.B., S.P.G., D.T.S. and W.G.K are supported by NIH grants. W.G.K. is a Howard Hughes Medical Institute investigator. M.C.H. is supported by NIH grants, the Ludwig Center at Harvard, Glenn Foundation for Medical Research and American Cancer Society New Scholar Award. This work is funded in part by the Alexander and Margaret Stewart Trust Grant.

References

Author Manuscript

Abu-Elheiga L, Oh W, Kordari P, Wakil SJ. Acetyl-CoA carboxylase 2 mutant mice are protected against obesity and diabetes induced by high-fat/high-carbohydrate diets. Proc Natl Acad Sci USa. 2003; 100:10207–10212. [PubMed: 12920182] Almarza-Ortega DB. Human Acetyl-CoA Carboxylase 2. Molecular Cloning, Characterization, Chromosomal Mapping, and Evidence for Two Isoforms. Journal of Biological Chemistry. 1997; 272:10669–10677. [PubMed: 9099716] Amrit FRG, Steenkiste EM, Ratnappan R, Chen S-W, McClendon TB, Kostka D, Yanowitz J, Olsen CP, Ghazi A. DAF-16 and TCER-1 Facilitate Adaptation to Germline Loss by Restoring Lipid Homeostasis and Repressing Reproductive Physiology in C. elegans. PLoS Genetics. 2016; 12(2):1– 35. Andersson A, Ritz C, Lindgren D, Edén P, Lassen C, Heldrup J, Olofsson T, Råde J, Fontes M, PorwitMacDonald A, et al. Microarray-based classification of a consecutive series of 121 childhood acute leukemias: prediction of leukemic and genetic subtype as well as of minimal residual disease status. Leukemia. 2007; 21:1198–1203. [PubMed: 17410184] Mol Cell. Author manuscript; available in PMC 2017 September 15.

German et al.

Page 13

Author Manuscript Author Manuscript Author Manuscript Author Manuscript

Barger JF, Gallo CA, Tandon P, Liu H, Sullivan A, Grimes HL, Plas DR. S6K1 determines the metabolic requirements for BCR-ABL survival. Oncogene. 2012; 32:453–461. [PubMed: 22391570] Brownsey RW, Boone AN, Elliott JE, Kulpa JE, Lee WM. Regulation of acetyl-CoA carboxylase. Biochem Soc Trans. 2006; 34:223–227. [PubMed: 16545081] Bruick RK. A Conserved Family of Prolyl-4-Hydroxylases That Modify HIF. Science. 2001; 294:1337–1340. [PubMed: 11598268] Carracedo A, Cantley LC, Pandolfi PP. Cancer metabolism: fatty acid oxidation in the limelight. Nature Reviews Cancer. 2013; 13:227–232. Cho YS, Lee IlJ, Shin D, Kim HT, Jung HY, Lee TG, Kang L-W, Ahn Y-J, Cho H-S, Heo YS. Molecular mechanism for the regulation of human ACC2 through phosphorylation by AMPK. Biochemical and Biophysical Research Communications. 2010; 391:187–192. [PubMed: 19900410] Durán RV, MacKenzie ED, Boulahbel H, Frezza C, Heiserich L, Tardito S, Bussolati O, Rocha S, Hall MN, Gottlieb E. HIF-independent role of prolyl hydroxylases in the cellular response to amino acids. Oncogene. 2012; 32:4549–4556. [PubMed: 23085753] Estañ MC, Calviño E, Calvo S, Guillén-Guío B, Boyano-Adánez MDC, de Blas E, Rial E, Aller P. Apoptotic Efficacy of Etomoxir in Human Acute Myeloid Leukemia Cells. Cooperation with Arsenic Trioxide and Glycolytic Inhibitors, and Regulation by Oxidative Stress and Protein Kinase Activities. PLoS ONE. 2014; 9:e115250. [PubMed: 25506699] Finley LWS, Carracedo A, Lee J, Souza A, Egia A, Zhang J, Teruya-Feldstein J, Moreira PI, Cardoso SM, Clish CB, et al. SIRT3 Opposes Reprogramming of Cancer Cell Metabolism through HIF1α Destabilization. Cancer Cell. 2011; 19:416–428. [PubMed: 21397863] Fullerton MD, Galic S, Marcinko K, Sikkema S, Pulinilkunnil T, Chen ZP, O’Neill HM, Ford RJ, Palanivel R, O’Brien M, et al. Single phosphorylation sites in Acc1 and Acc2 regulate lipid homeostasis and the insulin-sensitizing effects of metformin. Nat Med. 2013; 19:1649–1654. [PubMed: 24185692] Gerhart-Hines Z, Rodgers JT, Bare O, Lerin C, Kim SH, Mostoslavsky R, Alt FW, Wu Z, Puigserver P. Metabolic control of muscle mitochondrial function and fatty acid oxidation through SIRT1/ PGC-1alpha. The EMBO Journal. 2007; 26:1913–1923. [PubMed: 17347648] German NJ, Haigis MC. Sirtuins and the Metabolic Hurdles in Cancer. Current Biology. 2015; 25:R569–R583. [PubMed: 26126285] Gorres KL, Raines RT. Prolyl 4-hydroxylase. Critical Reviews in Biochemistry and Molecular Biology. 2010; 45:106–124. [PubMed: 20199358] Heřmanová I, Arruabarrena-Aristorena A, Vališ K, Nůsková H, Alberich-Jorda M, Fišer K, FernándezRuiz S, Kavan D, Pecinová A, Niso-Santano M, et al. Pharmacological inhibition of fatty-acid oxidation synergistically enhances the effect of l-asparaginase in childhood ALL cells. Leukemia. 2015 Hitosugi T, Chen J. Post-translational modifications and the Warburg effect. 2013; 33:4279–4285. Holubarsch CJF, Rohrbach M, Karrasch M, Boehm E, Polonski L, Ponikowski P, Rhein S. A doubleblind randomized multicentre clinical trial to evaluate the efficacy and safety of two doses of etomoxir in comparison with placebo in patients with moderate congestive heart failure: the ERGO (etomoxir for the recovery of glucose oxidation) study. Clin Sci. 2007; 113:205–212. [PubMed: 17319797] Iliopoulos O, Levy AP, Jiang C, Kaelin WG, Goldberg MA. Negative regulation of hypoxia-inducible genes by the von Hippel-Lindau protein. Proc Natl Acad Sci USa. 1996; 93:10595–10599. [PubMed: 8855223] Ito K, Carracedo A, Weiss D, Arai F, Ala U, Avigan DE, Schafer ZT, Evans RM, Suda T, Lee CH, et al. A PML–PPAR-δ pathway for fatty acid oxidation regulates hematopoietic stem cell maintenance. Nat Med. 2012; 18:1350–1358. [PubMed: 22902876] Jeon SM, Chandel NS, Hay N. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature. 2012; 485:661–665. [PubMed: 22660331] Kaelin WG Jr, Ratcliffe PJ. Oxygen Sensing by Metazoans: The Central Role of the HIF Hydroxylase Pathway. Molecular Cell. 2008; 30:393–402. [PubMed: 18498744]


German et al.

Page 14


Koivunen P, Hirsilä M, Remes AM, Hassinen IE, Kivirikko KI, Myllyharju J. Inhibition of hypoxiainducible factor (HIF) hydroxylases by citric acid cycle intermediates: possible links between cell metabolism and stabilization of HIF. J Biol Chem. 2007; 282:4524–4532. [PubMed: 17182618] Laurent G, German NJ, Saha AK, de Boer VCJ, Davies M, Koves TR, Dephoure N, Fischer F, Boanca G, Vaitheesvaran B, et al. SIRT4 Coordinates the Balance between Lipid Synthesis and Catabolism by Repressing Malonyl CoA Decarboxylase. Molecular Cell. 2013; 50:686–698. [PubMed: 23746352] Lee EA, Angka L, Rota SG, Hanlon T, Mitchell A, Hurren R, Wang XM, Gronda M, Boyaci E, Bojko B, et al. Targeting Mitochondria with Avocatin B Induces Selective Leukemia Cell Death. Cancer Research. 2015; 75:2478–2488. [PubMed: 26077472] Liu Y, Zuckier LS, Ghesani NV. Dominant uptake of fatty acid over glucose by prostate cells: a potential new diagnostic and therapeutic approach. Anticancer Research. 2013:1–6. [PubMed: 23267122] Loenarz C, Schofield CJ. Physiological and biochemical aspects of hydroxylations and demethylations catalyzed by human 2-oxoglutarate oxygenases. Trends in Biochemical Sciences. 2011; 36:7–18. [PubMed: 20728359] Long NM, Smith CS. Causes and imaging features of false positives and false negatives on 18FPET/CT in oncologic imaging. Insights Imaging. 2011; 2:679–698. [PubMed: 22347986] Lunt SY, Vander Heiden MG. Aerobic Glycolysis: Meeting the Metabolic Requirements of Cell Proliferation. Annu Rev Cell Dev Biol. 2011; 27:441–464. [PubMed: 21985671] Luo W, Hu H, Chang R, Zhong J, Knabel M, O’Meally R, Cole RN, Pandey A, Semenza GL. Pyruvate kinase M2 is a PHD3-stimulated coactivator for hypoxia-inducible factor 1. Cell. 2011; 145:732– 744. [PubMed: 21620138] MacKenzie ED, Selak MA, Tennant DA, Payne LJ, Crosby S, Frederiksen CM, Watson DG, Gottlieb E. Cell-Permeating -Ketoglutarate Derivatives Alleviate Pseudohypoxia in Succinate Dehydrogenase-Deficient Cells. Molecular and Cellular Biology. 2007; 27:3282–3289. [PubMed: 17325041] Madden MC, Wołkowicz PE, Pohost GM, McMillin JB, Pike MM. Acylcarnitine accumulation does not correlate with reperfusion recovery in palmitate-perfused rat hearts. Am J Physiol. 1995; 268:H2505–H2512. [PubMed: 7611501] Masson N, Ratcliffe PJ. Hypoxia signaling pathways in cancer metabolism: the importance of coselecting interconnected physiological pathways. Cancer Metab. 2014; 2:3. [PubMed: 24491179] Nash DT, Nash SD. Ranolazine for chronic stable angina. Lancet. 2008; 372:1335–1341. [PubMed: 18929905] Noordermeer SM, Monteferrario D, Sanders MA, Bullinger L, Jansen JH, van der Reijden BA. Improved classification of MLL-AF9-positive acute myeloid leukemia patients based on BRE and EVI1 expression. Blood. 2012; 119:4335–4337. [PubMed: 22555662] O’Donnell JM, Alpert NM, White LT, Lewandowski ED. Coupling of Mitochondrial Fatty Acid Uptake to Oxidative Flux in the Intact Heart. Biophysical Journal. 2002; 82:11–18. [PubMed: 11751291] Ono K, Ochiai R, Yoshida T, Kitagawa M, Omagari J, Kobayashi H, Yamashita Y. The detection rates and tumor clinical/pathological stages of whole-body FDG-PET cancer screening. Ann Nucl Med. 2007; 21:65–72. [PubMed: 17373338] Pulinilkunnil T, He H, Kong D, Asakura K, Peroni OD, Lee A, Kahn BB. Adrenergic Regulation of AMP-activated Protein Kinase in Brown Adipose Tissue in Vivo. Journal of Biological Chemistry. 2011; 286:8798–8809. [PubMed: 21209093] Rantanen K, Pursiheimo J, Hogel H, Himanen V, Metzen E, Jaakkola PM. Prolyl Hydroxylase PHD3 Activates Oxygen-dependent Protein Aggregation. Molecular Biology of the Cell. 2008; 19:2231– 2240. [PubMed: 18337469] Ratnappan R, Amrit FRG, Chen SW, Gill H, Holden K, Ward J, Yamamoto KR, Olsen CP, Ghazi A. Germline Signals Deploy NHR-49 to Modulate Fatty-Acid β-Oxidation and Desaturation in Somatic Tissues of C. elegans. PLoS Genet. 2014; 10:e1004829. [PubMed: 25474470]


German et al.

Page 15

Author Manuscript Author Manuscript Author Manuscript

Rawluszko AA, Bujnicka KE, Horbacka K, Krokowicz P, Jagodziński PP. Expression and DNA methylation levels of prolyl hydroxylases PHD1, PHD2, PHD3 and asparaginyl hydroxylase FIH in colorectal cancer. BMC Cancer. 2013; 13:526. [PubMed: 24195777] Rodríguez-Enríquez S, Hernández-Esquivel L, Marín-Hernández A, El Hafidi M, Gallardo-Pérez JC, Hernández-Reséndiz I, Rodríguez-Zavala JS, Pacheco-Velázquez SC, Moreno-Sánchez R. Mitochondrial free fatty acid β-oxidation supports oxidative phosphorylation and proliferation in cancer cells. International Journal of Biochemistry and Cell Biology. 2015; 65:209–221. [PubMed: 26073129] Ruderman N, Prentki M. AMP kinase and malonyl-CoA: targets for therapy of the metabolic syndrome. Nat Rev Drug Discov. 2004; 3:340–351. [PubMed: 15060529] Samudio I, Harmancey R, Fiegl M, Kantarjian H, Konopleva M, Korchin B, Kaluarachchi K, Bornmann W, Duvvuri S, Taegtmeyer H, et al. Pharmacologic inhibition of fatty acid oxidation sensitizes human leukemia cells to apoptosis induction. J Clin Invest. 2010; 120:142–156. [PubMed: 20038799] Schafer ZT, Grassian AR, Song L, Jiang Z, Gerhart-Hines Z, Irie HY, Gao S, Puigserver P, Brugge JS. Antioxidant and oncogene rescue of metabolic defects caused by loss of matrix attachment. Nature. 2009; 461:109–113. [PubMed: 19693011] Schoors S, Bruning U, Missiaen R, Queiroz KCS, Borgers G, Elia I, Zecchin A, Cantelmo AR, Christen S, Goveia J, et al. Fatty acid carbon is essential for dNTP synthesis in endothelial cells. Nature. 2015; 520:192–197. [PubMed: 25830893] Shultz LD, Lyons BL, Burzenski LM, Gott B, Chen X, Chaleff S, Kotb M, Gillies SD, King M, Mangada J, et al. Human Lymphoid and Myeloid Cell Development in NOD/LtSz-scid IL2R null Mice Engrafted with Mobilized Human Hemopoietic Stem Cells. The Journal of Immunology. 2005; 174:6477–6489. [PubMed: 15879151] Steinbaugh MJ, Narasimhan SD, Robida-Stubbs S, Moronetti Mazzeo LE, Dreyfuss JM, Hourihan JM, Raghavan P, Operaña TN, Esmaillie R, Blackwell TK. Lipid-mediated regulation of SKN-1/Nrf in response to germ cell absence. Elife. 2015:4. Tennant DA, Gottlieb E. HIF prolyl hydroxylase-3 mediates alpha-ketoglutarate-induced apoptosis and tumor suppression. J Mol Med. 2010; 88:839–849. [PubMed: 20383689] Thoden JB. Movement of the Biotin Carboxylase B-domain as a Result of ATP Binding. Journal of Biological Chemistry. 2000; 275:16183–16190. [PubMed: 10821865] Valk PJM, Verhaak RGW, Beijen MA, Erpelinck CAJ, Barjesteh van Waalwijk van Doorn-Khosrovani S, Boer JM, Beverloo HB, Moorhouse MJ, van der Spek PJ, Löwenberg B, et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med. 2004; 350:1617–1628. [PubMed: 15084694] Wood SM, Gleadle JM, Pugh CW, Hankinson O, Ratcliffe PJ. The role of the aryl hydrocarbon receptor nuclear translocator (ARNT) in hypoxic induction of gene expression. Studies in ARNTdeficient cells. J Biol Chem. 1996; 271:15117–15123. [PubMed: 8662957] Yoon MJ. Adiponectin Increases Fatty Acid Oxidation in Skeletal Muscle Cells by Sequential Activation of AMP-Activated Protein Kinase, p38 Mitogen-Activated Protein Kinase, and Peroxisome Proliferator-Activated Receptor. Diabetes. 2006; 55:2562–2570. [PubMed: 16936205] Zaugg K, Yao Y, Reilly PT, Kannan K, Kiarash R, Mason J, Huang P, Sawyer SK, Fuerth B, Faubert B, et al. Carnitine palmitoyltransferase 1C promotes cell survival and tumor growth under conditions of metabolic stress. Genes & Development. 2011; 25:1041–1051. [PubMed: 21576264]

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Figure 1. ACC interacts with PHD3 and is modified by hydroxylation at Pro450

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(A) HA-tagged PHD1-3 or empty vector was transfected into 293T cells and immunoprecipitated with HA affinity resin. ACC co-immunoprecipitated with PHD3, as detected by immunoblot. (B–C) Immunoblot to detect ACC hydroxylation. ACC was immunoprecipitated from 293T cells overexpressing HA-PHD3, vector, or catalytically inactive PHD3 mutants (R206K and H196A). Cells had been treated in serum-free, low glucose medium for 12 h prior to immunoprecipitation (IP). WT PHD3 increased hydroxylation, as detected by immunoblot with hydroxyproline (OH-Pro) antibody. (D) Immunoblot to measure hydroxylation of ACC1 versus ACC2 in 293T cells overexpressing vector or PHD3. ACC1 and ACC2 were immunoprecipitated using isoformspecific antibodies. Cells were treated 12 h with serum-free, low glucose medium prior to IP. (E–F) Representative mass spectra identifying the hydroxylated and non-hydroxylated versions of residue P450 in ACC2 peptides. ‘b’ fragments (blue) contain the N-terminal


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amino acid and are labeled from the N to C terminus. ‘y’ fragments (green) contain the Cterminal amino acid and are labeled from the C to N terminus. (G–H) Hydroxyproline residues and locations in ACC2 domains. ACC2 was overexpressed in 293T cells and immunoprecipitated with ACC antibody. Hydroxylation sites were identified using LC-MS2. BT = biotin transferase domain. BCCP = biotin carboxyl carrier protein. Xcorr = cross correlation score. (I) Hydroxylation of transiently overexpressed WT ACC2 versus proline to alanine point mutants. Relative hydroxyproline were quantified using ImageJ software. (J) In vitro hydroxylation assay. ACC2 peptides (12.5 nmol) with the indicated proline residue were incubated in reactions containing 0.02 μmol [1-14C]α-ketoglutarate and 1.2 μg recombinant PHD3. [14C]CO2 formed upon hydroxylation was captured on Whatman paper inside capped vials and analyzed by scintillation counting (n = 2). ***p < 0.001. Data represent mean ± SEM. See also Table S1.

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Author Manuscript Author Manuscript Figure 2. Hydroxylation at site P450 promotes ACC2 activity and ATP binding


(A) Residue P450 (purple) in ACC2 is conserved in the ATP grasp domain throughout species. Alignment shows the ACC2 isoform in human, rat and mouse, and ACC in C. elegans, drosophila and S. cerevisiae, organisms lacking distinct ACC isoforms. Other conserved residues are in green. (B) P450 (purple) is located in the ACC2 ATP-grasp domain (green) adjacent to the catalytic site ATP (magenta). ATP is capped at the phosphate end by the known nucleotide binding residues (orange). This model was generated by superposition of human ACC2 biotin carboxylase domain (PDB: 3JRW) with e. coli ATP-bound ACC biotin carboxylase domain (PDB: 1DV2). (C) ATP-affinity of overexpressed WT versus P450A ACC2. ATP-binding was assessed by IP with ATP-affinity resin (Jena Bioscience) and immunoblot with ACC antibody. Relative levels were quantified by ImageJ. (D) ATP-affinity assay of transiently overexpressed ACC2 from 293T cells stably expressing shRNA against PHD3 or non-targeting control. Bound ACC2 was analyzed by immunoblot and quantified by ImageJ. (E) ACC activity in cells overexpressing vector, WT ACC2 or P450A mutant (n = 3). 10 μg ACC2 plasmid was overexpressed. Reactions (50 μg protein lysate) were performed ± 2 mM citrate. Immunoblots show loading controls. (F) ACC activity in cells co-overexpressing ACC2 or P450A and HA-PHD3 or empty vector (n = 4). 10 μg HA-PHD3 or vector was overexpressed plus 2 μg ACC2 plasmid. Less ACC2 plasmid was required here compared to (E) so the additive effect of PHD3 could be better observed. Reactions were done with citrate.


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(G) ACC activity in cells overexpressing 2 μg ACC2 plasmid with PHD3 knockdown or control (n = 3). Reactions were done with citrate. Knockdown was performed with shPHD3 #2 and confirmed in Figure S2A. For panels (E–G), *p < 0.05, **p < 0.01, ***p < 0.001. Data represent mean ± SEM. See also Figure S1.

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Figure 3. PHD3 represses FAO in response to nutrient abundance and independently of HIF and AMPK

(A) Palmitate oxidation by 293T cells following stable PHD3 knockdown by shRNA (shPHD3.1 and shPHD3.2) or non-targeting control (shControl) (n = 4). (B) Palmitate oxidation in complete medium in 293T cells overexpressing WT or mutant ACC2 (n = 3). (C) Oxidation of long chain palmitic acid versus medium chain hexanoic acid in 293T cells expressing shPHD3 or non-targeting control shRNA (n = 3).


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(D–E) Palmitate oxidation in HIF-deficient mouse hepatoma 4 (B13NBii1) cells. FAO was assessed 48 h after transfection with human HA-PHD3 or vector or with siPHD3 or siControl (n = 3). (F) Validation of HIF deficiency in HIFβ-null hepatoma cells. PHD3 expression and HIF target gene expression in HIFβ-deficient cells transfected with siRNA against PHD3 or control, and treated with or without the HIFα-stabilizing compound CoCl2 (250 μM, 6 h) (n = 4). (G) Immunoblot of HIF1α and 2α levels in 293T cells with PHD3 knockdown or control. HIFα was made more identifiable by 6 h treatment with 250 μM CoCl2 in separate control samples. (H) Effect of PHD3 knockdown on palmitate oxidation in 786-O VHL−/− cells (n = 3). (I) Palmitate oxidation in 293T cells following 12 h pre-incubation in normoxia or hypoxia (1% O2). Cells were maintained under normoxia or hypoxia during FAO analysis (n = 4). (J) Palmitate oxidation in WT versus AMPKα KO MEFs expressing shRNA against PHD3 or non-silencing control (n = 3). (K) ACC hydroxylation in 293T cells following 12 h incubation in high versus low nutrient medium. High nutrient DMEM contains 4.5 g/L glucose and serum. Low nutrient DMEM contains 1 g/L glucose without serum. ACC was immunoprecipitated and hydroxylation was detected by immunoblot. (L) 293T cells expressing shRNA against PHD3 or control were incubated 12 h in high or low nutrient media prior to analyzing ACC hydroxylation. (M) ACC hydroxylation dynamically responds to cellular nutrient cues. WT immortalized MEFs were incubated in high or low nutrient medium for 6 h, or in low nutrient medium for 6 h followed by adding back high nutrient medium for 5 or 10 min. ACC-IP was performed in lysis buffer containing the PHD inhibitor DMOG (1 mM) to minimize hydroxylation in the lysis buffer. Hydroxylation was detected by immunoblot. (N) Impact of PHD3 knockdown on the ability of MEFs to modulate FAO in response to low or high nutrient medium (n = 3). (O) Impact of PHD3 knockdown on the ability of 293T cells to suppress FAO in response to supplementing low glucose, serum-free medium with dimethyl ketoglutarate (+kg, 5 mM) for 6 h prior to and during 2 hr FAO analysis (n = 3). (P) Acylcarnitine levels measured by metabolomics analysis of 293T cells grown in high nutrient medium following stable knockdown of PHD3 or control. Levels were normalized to cell count in parallel plates (n = 6 for control, n = 3 for shPHD3). (Q) Two-part model of ACC2 regulation. Under low nutrient conditions, AMPK responds to the AMP/ATP ratio to phosphorylate and inhibit ACC2, thus promoting long chain FAO. Under high nutrient conditions, PHD3 hydroxylates and activates ACC2 to limit long chain FAO. *p < 0.05, **p < 0.01, ***p < 0.001. Data represent mean ± SEM. See also Figure S2.


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Figure 4. PHD3 expression is repressed in AML, contributing to altered ACC and a dependency on FAO

(A) Gene expression of PHD3 in patient samples across cancer types. Data obtained from Ramaswamy multi-type cancer analysis Oncomine dataset. (B–C) Relative PHD3 gene expression in normal marrow versus AML patient samples. Data obtained from Valk and Andersson Leukemia Oncomine datasets. (D) PHD3 gene expression across AML patient samples from datasets in TCGA. Patients were classified as low vs. high PHD3 based on performing univariate clustering on PHD3 levels using a Gaussian mixture model with two clusters.


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(E) Box plot showing stratification of low and high PHD3expression in TCGA AML patient samples, as calculated in (D). Nearly 80% of patients fell into the low PHD3 group. (F) Table of top curated gene sets that are inversely correlated with high-PHD3 AML patient samples, as determined by GSEA. Pathways were ranked by false discovery rate (FDR) q value and normalized enrichment score (NES). (G) PHD3 expression using PPIA as a reference gene. K562 = CML cell line. MOLM14, KG1, THP1, NB4 and U937 = AML cell lines. (H) ACC2 hydroxylation in leukemia cell lines. Because the ACC2 antibody does not work well for detecting endogenous input levels of ACC2 by immunoblot, an ACC antibody was used to show input. (I) ATP-affinity of ACC in leukemia cell lines. (J) Palmitate oxidation by leukemia cell lines in complete medium (n = 3). (K–L) Viability of leukemia cells assessed by PI staining and FACS after 96 h treatment with etomoxir (n = 3). All data points with drug treatment are significant by p < 0.001 for all cell lines compared to K562. Bar graph highlights sensitivity to 150 μM etomoxir. (M–N) Viability of leukemia cells after 96 h treatment with ranolazine (n = 3). All data points with drug treatment are significant by at least p < 0.05 or p < 0.01 compared to K562. Bar graph highlights sensitivity to 500 μM ranolazine. (O) PHD3 knockdown boosts FAO in K562 cells (n = 3). (P) In K562 cells that normally express high PHD3, PHD3 knockdown does not create a dependency on FAO. K562 cells expressing shPHD3 or shControl were treated 96 h +/− ranolazine, and viability was assessed by PI staining (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001. Bar graphs and cell viability curves represent mean ± SEM. See also Figure S3.

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Author Manuscript Author Manuscript Figure 5. PHD3 overexpression in low-PHD3 AML cells blunts FAO and cell proliferation in an ACC2-dependent manner


(A) Palmitate oxidation in MOLM14 and THP1 cells following stable overexpression of empty vector or PHD3 (n = 3). Immunoblots show stable overexpression of HA-PHD3. (B) Growth curves of MOLM14 and THP1 cells overexpressing vector or PHD3 (n = 3). (C) ATP CellTiter-Glo analysis in MOLM14 and THP1 cells overexpressing vector or PHD3 (n = 4). (D–E) PHD3 gene expression (n = 3) and immunoblot in K562 cells following overexpression of PHD3 or vector. (F–G) Growth curves of K562 cells overexpressing HA-PHD3 or empty vector (n = 3), or shRNA against PHD3 or control. (H) ACC2, but not ACC1, is required for PHD3 to repress palmitate oxidation in MOLM14 cells (n = 3). Cells were first infected with lentivirus expressing shRNA against ACC isozymes or control and conferring puromycin resistance. Following puromycin selection, PI-negative cells were sorted by FACS. These cells were infected with PHD3 or vector conferring hygromycin resistence. Following selection with hygromycin D, PI-negative cells were collected and used for subsequent assays. (I–J) Overexpressed PHD3 acts through ACC2, but not ACC1, to blunt AML cell proliferation. Growth curves were assessed by cell counting in MOLM14 and THP1 cells following knockdown of ACC1 or ACC2, as well as overexpression of empty vector or PHD3 (n = 3). Relative cell number at 72 h, compared to count at time 0, is highlighted on the right. MOLM14 cells were generated as above. THP1 cells were generated by viral


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overexpression of PHD3 or vector, followed by FACS selection of PI-negative cells. Subsequently, ACC1, ACC2 or control knockdown was achieved by siRNA. *p < 0.05, **p < 0.01, ***p < 0.001. Data represent mean ± SEM. See also Figures S4 and S5.


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Author Manuscript Author Manuscript Figure 6. Restoring high PHD3 expression impairs AML colony formation and potency


(A–B) CFU and representative images of MOLM14 and THP1 cells overexpressing vector or PHD3. CFU were imaged 8 d after plating MOLM14 and 20 d after plating THP1 using a Nikon Eclipse Ti-U microscope at 200× magnification and SPOT camera software 5.0. (C) CFU and representative images of THP1 cells overexpressing vector or PHD3 in the presence of siRNA against ACC1, ACC2 or control. CFU were imaged 18 d after plating (n = 3). (D–E) CFU and representative images from K562 cells expressing PHD3 or vector, or shRNA against PHD3 or control. CFU were imaged 10 d after plating (n = 2–3). (F) PHD3 gene expression in primary human CD34+ cells from bone marrow filtrate of a healthy control or AML patient samples (690a, 2093 and 2266, blue bars). PPIA was the reference gene. (G) ATP CellTiter-Glo analysis in AML patient samples following overexpression of vector or PHD3 (n = 4). (H) PHD3 gene expression in primary mouse CD11b control cells or AML cells obtained from Hoxa9 Meis1 and MLL-AF9 mouse models. (I) PHD3 gene expression in primary mouse MLL-AF9 AML cells following stable overexpression of empty vector or PHD3 (n = 2). (J) CFU from MLL-AF9 cells overexpressing vector or PHD3, counted 10 d after plating (n = 3).


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(K) Kaplan-Meier survival curves of NSG mice xenotransplanted with MOLM14 cells overexpressing vector or PHD3 (n = 5). *p < 0.05, **p < 0.01, ***p < 0.001. Data represent mean ± SEM. See also Figure S6.


Fatty acid oxidation disorders: a new class of metabolic diseases.

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Defects of fatty-acid oxidation in muscle.

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Combined Effects of Phytochemicals and Exercise on Fatty Acid Oxidation.

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Fatty acid oxidation by human liver peroxisomes.

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The involvement of fatty acid binding protein in peroxisomal fatty acid oxidation.

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Astragaloside IV alleviates heart failure via activating PPARα to switch glycolysis to fatty acid β-oxidation.

The role of intermediates in mitochondrial fatty acid oxidation.

Birth Prevalence of Fatty Acid β-Oxidation Disorders in Iberia.

Inborn errors of fatty acid oxidation in man.

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Metabolic autopsy with next generation sequencing in sudden unexpected death in infancy: Postmortem diagnosis of fatty acid oxidation disorders.

Fibrosis: dysfunctional fatty acid oxidation in renal fibrosis.