Shannon R. Sweeney

Inhibiting glutaminase in acute myeloid leukemia: metabolic dependency of selected AML subtypes

Metabolic reprogramming has been described as a hallmark of transformed cancer cells. In this study, we examined the role of the glutamine (Gln) utilization pathway in acute myeloid leukemia (AML) cell lines and primary AML samples. Our results indicate that a subset of AML cell lines is sensitive to Gln deprivation. Glutaminase (GLS) is a mitochondrial enzyme that catalyzes the conversion of Gln to glutamate. One of the two GLS isoenzymes, GLS1 is highly expressed in cancer and encodes two different isoforms: kidney (KGA) and glutaminase C (GAC). We analyzed mRNA expression of GLS1 splicing variants, GAC and KGA, in several large AML datasets and identified increased levels of expression in AML patients with complex cytogenetics and within specific molecular subsets. Inhibition of glutaminase by allosteric GLS inhibitor bis-2-(5-phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide or by novel, potent, orally bioavailable GLS inhibitor CB-839 reduced intracellular glutamate levels and inhibited growth of AML cells. In cell lines and patient samples harboring IDH1/IDH2 (Isocitrate dehydrogenase 1 and 2) mutations, CB-839 reduced production of oncometabolite 2-hydroxyglutarate, inducing differentiation. These findings indicate potential utility of glutaminase inhibitors in AML therapy, which can inhibit cell growth, induce apoptosis and/or differentiation in specific leukemia subtypes.

Abstract 3: Amino acid profiles indicate dependence on different metabolic pathways between leukemia subtypes

Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA Despite the growing body of evidence that the tumor microenvironment protects leukemia cells from chemotherapeutic stresses (1-3), the effect of many extracellular metabolites remains largely unknown. To explore the influence of extracellular metabolites on different leukemia subtypes, cells were treated for 24 hours in vitro with either a supplemental amino acid or amino acid derivative. From this initial screening, a subset of metabolites were chosen for metabolomics analysis. Mass spectrometry (UPLC-MS/MS) was performed on intracellular fractions to identify metabolic differences that resulted from supplementation. Metabolite profiles were also compared between leukemia cell types, namely AML, pre-B cell ALL, and T cell ALL. Of the metabolites tested, lysine and 4-hydroxyphenylpyruvate, an intermediate of tyrosine and phenylalanine metabolism, had the greatest impact on global amino acid profiles. In AML and T cell ALL cell lines, intracellular glutamate, glutamine, proline, and aspartate were increased relative to their respective controls. These amino acids can enter the tricarboxylic acid (TCA) cycle as either α-ketoglutarate or oxaloacetate, suggesting a central role of the TCA cycle in both AML and T cell ALL metabolism. Interestingly, these metabolites were not significantly increased in pre-B cell ALL, signifying the inverse it true for pre-B cells. This observation provides metabolomics evidence that is consistent with a previous study that reported downregulated expression of TCA cycle related genes in pre-B cell ALL (4). Our findings indicate that uptake and metabolism of amino acids and their derivatives is distinct for different leukemia types. Moreover, supplementation with a single metabolite can result in global changes in intracellular metabolite profiles, suggesting an influence not only as an energy substrate, but on overall metabolic pathway activity. Specifically, we conclude that the TCA cycle is more active in AML and T cell ALL and can be modulated by changing the extracellular environment, while pre-B cells are less sensitive to amino acid modulation. (1) Meads MB, et al. Clin Cancer Res 2008;14(9):2519-2526. (2) Ayala F, et al. Leukemia 2009;23:2233-2241. (3) Konopleva M, et al. Drug Resist Updat 2009;12:103-113. (4) Boag JM, et al. Leukemia 2006;20:1731-1737. Citation Format: Shannon R. Sweeney, Enrique Sentandreu, Stefano Tiziani. Amino acid profiles indicate dependence on different metabolic pathways between leukemia subtypes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3.

Deletion of the neural tube defect-associated gene Mthfd1l disrupts one-carbon and central energy metabolism in mouse embryos

One-carbon (1C) metabolism is a universal folate-dependent pathway essential for de novo purine and thymidylate synthesis, amino acid interconversion, universal methyl-donor production, and regeneration of redox cofactors. Homozygous deletion of the 1C pathway gene Mthfd1l encoding methylenetetrahydrofolate dehydrogenase (NADP+-dependent) 1-like, which catalyzes mitochondrial formate production from 10-formyltetrahydrofolate, results in 100% penetrant embryonic neural tube defects (NTDs), underscoring the central role of mitochondrially derived formate in embryonic development and providing a mechanistic link between folate and NTDs. However, the specific metabolic processes that are perturbed by Mthfd1l deletion are not known. Here, we performed untargeted metabolomics on whole Mthfd1l-null and wildtype mouse embryos in combination with isotope tracer analysis in mouse embryonic fibroblast (MEF) cell lines to identify Mthfd1l deletion–induced disruptions in 1C metabolism, glycolysis, and the TCA cycle. We found that maternal formate supplementation largely corrects these disruptions in Mthfd1l-null embryos. Serine tracer experiments revealed that Mthfd1l-null MEFs have altered methionine synthesis, indicating that Mthfd1l deletion impairs the methyl cycle. Supplementation of Mthfd1l-null MEFs with formate, hypoxanthine, or combined hypoxanthine and thymidine restored their growth to wildtype levels. Thymidine addition alone was ineffective, suggesting a purine synthesis defect in Mthfd1l-null MEFs. Tracer experiments also revealed lower proportions of labeled hypoxanthine and inosine monophosphate in Mthfd1l-null than in wildtype MEFs, suggesting that Mthfd1l deletion results in increased reliance on the purine salvage pathway. These results indicate that disruptions of mitochondrial 1C metabolism have wide-ranging consequences for many metabolic processes, including those that may not directly interact with 1C metabolism.

A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an R MetaboList Package to Study Small-Molecule Metabolites

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z < 250) polar metabolites by LC-ESI-HRMSn analysis powered by a hybrid Quadrupole-Orbitrap. MS data were simultaneously acquired in fast polarity switching mode operating in MS1 and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive metabolite assignments were achieved by AIF analysis considering at least two characteristic transitions. Targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking of their respective deuterated counterparts. Manual data processing of MS1 and AIF scans were compared for the accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList R package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were, respectively, identified outside those studied by labeling. The main limitation of this method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. The combination of AIF analysis with MetaboList R package represents an opportunity to move towards automated, faster, and more global metabolomics approaches supported by an entirely flexible open source data processing platform freely available from Comprehensive R Archive Network (CRAN, https://CRAN.R-project.org/package=MetaboList).