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Chemical approaches to study metabolic networks

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Pflügers Archiv - European Journal of Physiology Aims and scope Submit manuscript

Abstract

One of the more provocative realizations that have come out of the genome sequencing projects is that organisms possess a large number of uncharacterized or poorly characterized enzymes. This finding belies the commonly held notion that our knowledge of cell metabolism is nearly complete, underscoring the vast landscape of unannotated metabolic and signaling networks that operate under normal physiological conditions, let alone in disease states where metabolic networks may be rewired, dysregulated, or altered to drive disease progression. Consequently, the functional annotation of enzymatic pathways represents a grand challenge for researchers in the post-genomic era. This review will highlight the chemical technologies that have been successfully used to characterize metabolism, and put forth some of the challenges we face as we expand our map of metabolic pathways.

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References

  1. Adibekian A, Martin BR, Wang C, Hsu KL, Bachovchin DA, Niessen S, Hoover H, Cravatt BF (2011) Click-generated triazole ureas as ultrapotent in vivo-active serine hydrolase inhibitors. Nat Chem Biol 7(7):469–478. doi:10.1038/nchembio.579

    Article  PubMed  CAS  Google Scholar 

  2. Ahn K, Johnson DS, Mileni M, Beidler D, Long JZ, McKinney MK, Weerapana E, Sadagopan N, Liimatta M, Smith SE, Lazerwith S, Stiff C, Kamtekar S, Bhattacharya K, Zhang Y, Swaney S, Van Becelaere K, Stevens RC, Cravatt BF (2009) Discovery and characterization of a highly selective FAAH inhibitor that reduces inflammatory pain. Chem Biol 16(4):411–420. doi:10.1016/j.chembiol.2009.02.013

    Article  PubMed  CAS  Google Scholar 

  3. Ahn K, Smith SE, Liimatta MB, Beidler D, Sadagopan N, Dudley DT, Young T, Wren P, Zhang Y, Swaney S, Van Becelaere K, Blankman JL, Nomura DK, Bhattachar SN, Stiff C, Nomanbhoy TK, Weerapana E, Johnson DS, Cravatt BF (2011) Mechanistic and pharmacological characterization of PF-04457845: a highly potent and selective fatty acid amide hydrolase inhibitor that reduces inflammatory and noninflammatory pain. J Pharmacol Exp Ther 338(1):114–124. doi:10.1124/jpet.111.180257

    Article  PubMed  CAS  Google Scholar 

  4. An WF, Tolliday N (2010) Cell-based assays for high-throughput screening. Mol Biotechnol 45(2):180–186. doi:10.1007/s12033-010-9251-z

    Article  PubMed  CAS  Google Scholar 

  5. Bachovchin DA, Brown SJ, Rosen H, Cravatt BF (2009) Identification of selective inhibitors of uncharacterized enzymes by high-throughput screening with fluorescent activity-based probes. Nat Biotechnol 27(4):387–394. doi:10.1038/nbt.1531

    Article  PubMed  CAS  Google Scholar 

  6. Bachovchin DA, Cravatt BF (2012) The pharmacological landscape and therapeutic potential of serine hydrolases. Nat Rev Drug Discov 11(1):52–68. doi:10.1038/nrd3620

    Article  PubMed  CAS  Google Scholar 

  7. Bachovchin DA, Ji T, Li W, Simon GM, Blankman JL, Adibekian A, Hoover H, Niessen S, Cravatt BF (2010) Superfamily-wide portrait of serine hydrolase inhibition achieved by library-versus-library screening. Proc Natl Acad Sci U S A 107(49):20941–20946. doi:10.1073/pnas.1011663107

    Article  PubMed  CAS  Google Scholar 

  8. Bachovchin DA, Mohr JT, Speers AE, Wang C, Berlin JM, Spicer TP, Fernandez-Vega V, Chase P, Hodder PS, Schurer SC, Nomura DK, Rosen H, Fu GC, Cravatt BF (2011) Academic cross-fertilization by public screening yields a remarkable class of protein phosphatase methylesterase-1 inhibitors. Proc Natl Acad Sci U S A 108(17):6811–6816. doi:10.1073/pnas.1015248108

    Article  PubMed  CAS  Google Scholar 

  9. Barglow KT, Cravatt BF (2004) Discovering disease-associated enzymes by proteome reactivity profiling. Chem Biol 11(11):1523–1531. doi:10.1016/j.chembiol.2004.08.023

    Article  PubMed  CAS  Google Scholar 

  10. Blum G, von Degenfeld G, Merchant MJ, Blau HM, Bogyo M (2007) Noninvasive optical imaging of cysteine protease activity using fluorescently quenched activity-based probes. Nat Chem Biol 3(10):668–677

    Article  PubMed  CAS  Google Scholar 

  11. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21(1 Suppl):33–37. doi:10.1038/4462

    Article  PubMed  CAS  Google Scholar 

  12. Chang JW, Moellering RE, Cravatt BF (2012) An activity-based imaging probe for the integral membrane hydrolase KIAA1363. Angew Chem Int Ed Engl 51(4):966–970. doi:10.1002/anie.201107236

    Article  PubMed  CAS  Google Scholar 

  13. Chang JW, Niphakis MJ, Lum KM, Cognetta AB 3rd, Wang C, Matthews ML, Niessen S, Buczynski MW, Parsons LH, Cravatt BF (2012) Highly selective inhibitors of monoacylglycerol lipase bearing a reactive group that is bioisosteric with endocannabinoid substrates. Chem Biol 19(5):579–588. doi:10.1016/j.chembiol.2012.03.009

    Article  PubMed  CAS  Google Scholar 

  14. Chang JW, Nomura DK, Cravatt BF (2011) A potent and selective inhibitor of KIAA1363/AADACL1 that impairs prostate cancer pathogenesis. Chem Biol 18(4):476–484. doi:10.1016/j.chembiol.2011.02.008

    Article  PubMed  CAS  Google Scholar 

  15. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, Palma MJ, Roberts LD, Dejam A, Souza AL, Deik AA, Magnusson M, Fox CS, O'Donnell CJ, Vasan RS, Melander O, Clish CB, Gerszten RE, Wang TJ (2012) Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125(18):2222–2231. doi:10.1161/CIRCULATIONAHA.111.067827

    Article  PubMed  CAS  Google Scholar 

  16. Chiang KP, Niessen S, Saghatelian A, Cravatt BF (2006) An enzyme that regulates ether lipid signaling pathways in cancer annotated by multidimensional profiling. Chem Biol 13(10):1041–1050. doi:10.1016/j.chembiol.2006.08.008

    Article  PubMed  CAS  Google Scholar 

  17. Chowdhury R, Yeoh KK, Tian YM, Hillringhaus L, Bagg EA, Rose NR, Leung IKH, Li XS, Woon ECY, Yang M, McDonough MA, King ON, Clifton IJ, Klose RJ, Claridge TDW, Ratcliffe PJ, Schofield CJ, Kawamura A (2011) The oncometabolite 2-hydroxyglutarate inhibits histone lysine demethylases. Embo Rep 12(5):463–469. doi:10.1038/embor.2011.43

    Article  PubMed  CAS  Google Scholar 

  18. Crown SB, Antoniewicz MR (2012) Selection of tracers for C-13-metabolic flux analysis using elementary metabolite units (EMU) basis vector methodology. Metab Eng 14(2):150–161. doi:DOI10.1016/j.ymben.2011.12.005

    Article  PubMed  CAS  Google Scholar 

  19. Dang L, White DW, Gross S, Bennett BD, Bittinger MA, Driggers EM, Fantin VR, Jang HG, Jin S, Keenan MC, Marks KM, Prins RM, Ward PS, Yen KE, Liau LM, Rabinowitz JD, Cantley LC, Thompson CB, Heiden MGV, Su SM (2009) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462(7274):739–U752. doi:10.1038/Nature08617

    Article  PubMed  CAS  Google Scholar 

  20. Dennis EA, Deems RA, Harkewicz R, Quehenberger O, Brown HA, Milne SB, Myers DS, Glass CK, Hardiman G, Reichart D, Merrill AH Jr, Sullards MC, Wang E, Murphy RC, Raetz CR, Garrett TA, Guan Z, Ryan AC, Russell DW, McDonald JG, Thompson BM, Shaw WA, Sud M, Zhao Y, Gupta S, Maurya MR, Fahy E, Subramaniam S (2010) A mouse macrophage lipidome. J Biol Chem 285(51):39976–39985. doi:10.1074/jbc.M110.182915

    Article  PubMed  CAS  Google Scholar 

  21. Edgington LE, Berger AB, Blum G, Albrow VE, Paulick MG, Lineberry N, Bogyo M (2009) Noninvasive optical imaging of apoptosis by caspase-targeted activity-based probes. Nat Med 15(8):967–973. doi:10.1038/nm.1938

    Article  PubMed  CAS  Google Scholar 

  22. Evans MJ, Cravatt BF (2006) Mechanism-based profiling of enzyme families. Chem Rev 106(8):3279–3301. doi:10.1021/cr050288g

    Article  PubMed  CAS  Google Scholar 

  23. Figueroa ME, Abdel-Wahab O, Lu C, Ward PS, Patel J, Shih A, Li YS, Bhagwat N, Vasanthakumar A, Fernandez HF, Tallman MS, Sun ZX, Wolniak K, Peeters JK, Liu W, Choe SE, Fantin VR, Paietta E, Lowenberg B, Licht JD, Godley LA, Delwel R, Valk PJM, Thompson CB, Levine RL, Melnick A (2010) Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Canc Cell 18(6):553–567. doi:10.1016/j.ccr.2010.11.015

    Article  CAS  Google Scholar 

  24. Giang DK, Cravatt BF (1997) Molecular characterization of human and mouse fatty acid amide hydrolases. Proc Natl Acad Sci U S A 94(6):2238–2242

    Article  PubMed  CAS  Google Scholar 

  25. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537

    Article  PubMed  CAS  Google Scholar 

  26. Hankin JA, Farias SE, Barkley R, Heidenreich K, Frey LC, Hamazaki K, Kim HY, Murphy RC (2011) MALDI mass spectrometric imaging of lipids in rat brain injury models. J Am Soc Mass Spectrom 22(6):1014–1021. doi:10.1007/s13361-011-0122-z

    Article  PubMed  CAS  Google Scholar 

  27. Hellerstein MK, Murphy E (2004) Stable isotope-mass spectrometric measurements of molecular fluxes in vivo: emerging applications in drug development. Curr Opin Mol Ther 6(3):249–264

    PubMed  CAS  Google Scholar 

  28. Hiller K, Metallo C, Stephanopoulos G (2011) Elucidation of cellular metabolism via metabolomics and stable-isotope assisted metabolomics. Curr Pharm Biotechnol 12(7):1075–1086

    Article  PubMed  CAS  Google Scholar 

  29. Ho PA, Alonzo TA, Kopecky KJ, Miller KL, Kuhn J, Zeng R, Gerbing RB, Raimondi SC, Hirsch BA, Oehler V, Hurwitz CA, Franklin JL, Gamis AS, Petersdorf SH, Anderson JE, Reaman GH, Baker LH, Willman CL, Bernstein ID, Radich JP, Appelbaum FR, Stirewalt DL, Meshinchi S (2010) Molecular alterations of the IDH1 gene in AML: a Children’s Oncology Group and Southwest Oncology Group study. Leukemia 24(5):909–913. doi:10.1038/Leu.2010.56

    Article  PubMed  CAS  Google Scholar 

  30. Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T, Souza AL, Kafri R, Kirschner MW, Clish CB, Mootha VK (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336(6084):1040–1044. doi:10.1126/science.1218595

    Article  PubMed  CAS  Google Scholar 

  31. Jessani N, Liu Y, Humphrey M, Cravatt BF (2002) Enzyme activity profiles of the secreted and membrane proteome that depict cancer cell invasiveness. Proc Natl Acad Sci U S A 99(16):10335–10340. doi:10.1073/pnas.162187599

    Article  PubMed  CAS  Google Scholar 

  32. Jessani N, Niessen S, Wei BQ, Nicolau M, Humphrey M, Ji Y, Han W, Noh DY, Yates JR 3rd, Jeffrey SS, Cravatt BF (2005) A streamlined platform for high-content functional proteomics of primary human specimens. Nat Meth 2(9):691–697. doi:10.1038/nmeth778

    Article  CAS  Google Scholar 

  33. Kinsey SG, Long JZ, O'Neal ST, Abdullah RA, Poklis JL, Boger DL, Cravatt BF, Lichtman AH (2009) Blockade of endocannabinoid-degrading enzymes attenuates neuropathic pain. J Pharmacol Exp Ther 330(3):902–910. doi:10.1124/jpet.109.155465

    Article  PubMed  CAS  Google Scholar 

  34. Kinsey SG, O'Neal ST, Long JZ, Cravatt BF, Lichtman AH (2011) Inhibition of endocannabinoid catabolic enzymes elicits anxiolytic-like effects in the marble burying assay. Pharmacol Biochem Behav 98(1):21–27. doi:10.1016/j.pbb.2010.12.002

    Article  PubMed  CAS  Google Scholar 

  35. Knuckley B, Jones JE, Bachovchin DA, Slack J, Causey CP, Brown SJ, Rosen H, Cravatt BF, Thompson PR (2010) A fluopol-ABPP HTS assay to identify PAD inhibitors. Chem Comm (Camb) 46(38):7175–7177. doi:10.1039/c0cc02634d

    Article  CAS  Google Scholar 

  36. Kobe B, Kemp BE (1999) Active site-directed protein regulation. Nature 402(6760):373–376. doi:10.1038/46478

    Article  PubMed  CAS  Google Scholar 

  37. Kodadek T (2001) Protein microarrays: prospects and problems. Chem Biol 8(2):105–115

    Article  PubMed  CAS  Google Scholar 

  38. Lea WA, Simeonov A (2011) Fluorescence polarization assays in small molecule screening. Expert Opin Drug Discov 6(1):17–32. doi:10.1517/17460441.2011.537322

    Article  PubMed  CAS  Google Scholar 

  39. Li W, Blankman JL, Cravatt BF (2007) A functional proteomic strategy to discover inhibitors for uncharacterized hydrolases. J Am Chem Soc 129(31):9594–9595. doi:10.1021/ja073650c

    Article  PubMed  CAS  Google Scholar 

  40. Locasale JW, Grassian AR, Melman T, Lyssiotis CA, Mattaini KR, Bass AJ, Heffron G, Metallo CM, Muranen T, Sharfi H, Sasaki AT, Anastasiou D, Mullarky E, Vokes NI, Sasaki M, Beroukhim R, Stephanopoulos G, Ligon AH, Meyerson M, Richardson AL, Chin L, Wagner G, Asara JM, Brugge JS, Cantley LC, Vander Heiden MG (2011) Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nat Genet 43(9):869–874. doi:10.1038/ng.890

    Article  PubMed  CAS  Google Scholar 

  41. Long JZ, Cravatt BF (2011) The metabolic serine hydrolases and their functions in mammalian physiology and disease. Chem Rev 111(10):6022–6063. doi:10.1021/cr200075y

    Article  PubMed  CAS  Google Scholar 

  42. Long JZ, Li W, Booker L, Burston JJ, Kinsey SG, Schlosburg JE, Pavon FJ, Serrano AM, Selley DE, Parsons LH, Lichtman AH, Cravatt BF (2009) Selective blockade of 2-arachidonoylglycerol hydrolysis produces cannabinoid behavioral effects. Nat Chem Biol 5(1):37–44. doi:10.1038/nchembio.129

    Article  PubMed  CAS  Google Scholar 

  43. Long JZ, Nomura DK, Cravatt BF (2009) Characterization of monoacylglycerol lipase inhibition reveals differences in central and peripheral endocannabinoid metabolism. Chem Biol 16(7):744–753. doi:10.1016/j.chembiol.2009.05.009

    Article  PubMed  CAS  Google Scholar 

  44. Long JZ, Nomura DK, Vann RE, Walentiny DM, Booker L, Jin X, Burston JJ, Sim-Selley LJ, Lichtman AH, Wiley JL, Cravatt BF (2009) Dual blockade of FAAH and MAGL identifies behavioral processes regulated by endocannabinoid crosstalk in vivo. Proc Natl Acad Sci U S A 106(48):20270–20275. doi:10.1073/pnas.0909411106

    Article  PubMed  CAS  Google Scholar 

  45. Lu C, Ward PS, Kapoor GS, Rohle D, Turcan S, Abdel-Wahab O, Edwards CR, Khanin R, Figueroa ME, Melnick A, Wellen KE, O'Rourke DM, Berger SL, Chan TA, Levine RL, Mellinghoff IK, Thompson CB (2012) IDH mutation impairs histone demethylation and results in a block to cell differentiation. Nature 483(7390):474–U130. doi:10.1038/Nature10860

    Article  PubMed  CAS  Google Scholar 

  46. Luo WB, Hu HX, Chang R, Zhong J, Knabel M, O'Meally R, Cole RN, Pandey A, Semenza GL (2011) Pyruvate kinase M2 is a PHD3-stimulated coactivator for hypoxia-inducible factor 1. Cell 145(5):732–744. doi:10.1016/j.cell.2011.03.054

    Article  PubMed  CAS  Google Scholar 

  47. Metallo CM, Gameiro PA, Bell EL, Mattaini KR, Yang J, Hiller K, Jewell CM, Johnson ZR, Irvine DJ, Guarente L, Kelleher JK, Vander Heiden MG, Iliopoulos O, Stephanopoulos G (2012) Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature 481(7381):380–384. doi:10.1038/nature10602

    CAS  Google Scholar 

  48. Moellering RE, Cravatt BF (2012) How chemoproteomics can enable drug discovery and development. Chem Biol 19(1):11–22. doi:10.1016/j.chembiol.2012.01.001

    Article  PubMed  CAS  Google Scholar 

  49. Mullen AR, Wheaton WW, Jin ES, Chen PH, Sullivan LB, Cheng T, Yang Y, Linehan WM, Chandel NS, DeBerardinis RJ (2012) Reductive carboxylation supports growth in tumour cells with defective mitochondria. Nature 481(7381):385–388. doi:10.1038/nature10642

    CAS  Google Scholar 

  50. Murphy RC, Hankin JA, Barkley RM (2009) Imaging of lipid species by MALDI mass spectrometry. J Lipid Res 50:S317–S322. doi:10.1194/jlr.R800051-JLR200

    Article  PubMed  Google Scholar 

  51. Nobusawa S, Watanabe T, Kleihues P, Ohgaki H (2009) IDH1 mutations as molecular signature and predictive factor of secondary glioblastomas. Clin Cancer Res 15(19):6002–6007. doi:10.1158/1078-0432.Ccr-09-0715

    Article  PubMed  CAS  Google Scholar 

  52. Nomura DK, Dix MM, Cravatt BF (2010) Activity-based protein profiling for biochemical pathway discovery in cancer. Nat Rev Cancer 10(9):630–638. doi:10.1038/nrc2901

    Article  PubMed  CAS  Google Scholar 

  53. Nomura DK, Lombardi DP, Chang JW, Niessen S, Ward AM, Long JZ, Hoover HH, Cravatt BF (2011) Monoacylglycerol lipase exerts dual control over endocannabinoid and fatty acid pathways to support prostate cancer. Chem Biol 18(7):846–856. doi:10.1016/j.chembiol.2011.05.009

    Article  PubMed  CAS  Google Scholar 

  54. Nomura DK, Long JZ, Niessen S, Hoover HS, Ng SW, Cravatt BF (2010) Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell 140(1):49–61. doi:10.1016/j.cell.2009.11.027

    Article  PubMed  CAS  Google Scholar 

  55. Nomura DK, Morrison BE, Blankman JL, Long JZ, Kinsey SG, Marcondes MCG, Ward AM, Hahn YK, Lichtman AH, Conti B, Cravatt BF (2011) Endocannabinoid hydrolysis generates brain prostaglandins that promote neuroinflammation. Science 334(6057):809–813. doi:10.1126/science.1209200

    Article  PubMed  CAS  Google Scholar 

  56. Patti GJ, Yanes O, Shriver LP, Courade JP, Tautenhahn R, Manchester M, Siuzdak G (2012) Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin. Nat Chem Biol 8(3):232–234. doi:10.1038/nchembio.767

    Article  PubMed  CAS  Google Scholar 

  57. Piro Justin R, Benjamin Daniel I, Duerr James M, Pi Y, Gonzales C, Wood Kathleen M, Schwartz Joel W, Nomura Daniel K, Samad Tarek A (2012) A dysregulated endocannabinoid-eicosanoid network supports pathogenesis in a mouse model of Alzheimer’s disease. Cell Rep 1(6):617–623

    Article  PubMed  CAS  Google Scholar 

  58. Reaves ML, Rabinowitz JD (2011) Metabolomics in systems microbiology. Curr Opin Biotechnol 22(1):17–25. doi:10.1016/j.copbio.2010.10.001

    Article  PubMed  CAS  Google Scholar 

  59. Saghatelian A, Cravatt BF (2005) Discovery metabolite profiling—forging functional connections between the proteome and metabolome. Life Sci 77(14):1759–1766. doi:10.1016/j.lfs.2005.05.019

    Article  PubMed  CAS  Google Scholar 

  60. Saghatelian A, Jessani N, Joseph A, Humphrey M, Cravatt BF (2004) Activity-based probes for the proteomic profiling of metalloproteases. Proc Natl Acad Sci U S A 101(27):10000–10005. doi:10.1073/pnas.0402784101

    Article  PubMed  CAS  Google Scholar 

  61. Saghatelian A, McKinney MK, Bandell M, Patapoutian A, Cravatt BF (2006) A FAAH-regulated class of N-acyl taurines that activates TRP ion channels. Biochemistry 45(30):9007–9015. doi:10.1021/bi0608008

    Article  PubMed  CAS  Google Scholar 

  62. Saghatelian A, Trauger SA, Want EJ, Hawkins EG, Siuzdak G, Cravatt BF (2004) Assignment of endogenous substrates to enzymes by global metabolite profiling. Biochemistry 43(45):14332–14339. doi:10.1021/bi0480335

    Article  PubMed  CAS  Google Scholar 

  63. Salisbury CM, Cravatt BF (2007) Activity-based probes for proteomic profiling of histone deacetylase complexes. Proc Natl Acad Sci U S A 104(4):1171–1176. doi:10.1073/pnas.0608659104

    Article  PubMed  CAS  Google Scholar 

  64. Santoni V, Molloy M, Rabilloud T (2000) Membrane proteins and proteomics: un amour impossible? Electrophoresis 21(6):1054–1070. doi:10.1002/(SICI)1522-2683(20000401)21:6<1054::AID-ELPS1054>3.0.CO;2-8

    Article  PubMed  CAS  Google Scholar 

  65. Sauer U (2006) Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2:62. doi:10.1038/msb4100109

    Article  PubMed  Google Scholar 

  66. Schlosburg JE, Blankman JL, Long JZ, Nomura DK, Pan B, Kinsey SG, Nguyen PT, Ramesh D, Booker L, Burston JJ, Thomas EA, Selley DE, Sim-Selley LJ, Liu QS, Lichtman AH, Cravatt BF (2010) Chronic monoacylglycerol lipase blockade causes functional antagonism of the endocannabinoid system. Nat Neurosci 13(9):1113–U1111. doi:10.1038/Nn.2616

    Article  PubMed  CAS  Google Scholar 

  67. Scott DA, Richardson AD, Filipp FV, Knutzen CA, Chiang GG, Ronai ZA, Osterman AL, Smith JW (2011) Comparative metabolic flux profiling of melanoma cell lines: beyond the Warburg effect. J Biol Chem 286(49):42626–42634. doi:10.1074/jbc.M111.282046

    Article  PubMed  CAS  Google Scholar 

  68. Shields DJ, Niessen S, Murphy EA, Mielgo A, Desgrosellier JS, Lau SK, Barnes LA, Lesperance J, Bouvet M, Tarin D, Cravatt BF, Cheresh DA (2010) RBBP9: a tumor-associated serine hydrolase activity required for pancreatic neoplasia. Proc Natl Acad Sci U S A 107(5):2189–2194. doi:10.1073/pnas.0911646107

    Article  PubMed  CAS  Google Scholar 

  69. Simon GM, Cravatt BF (2010) Activity-based proteomics of enzyme superfamilies: serine hydrolases as a case study. J Biol Chem 285(15):11051–11055. doi:10.1074/jbc.R109.097600

    Article  PubMed  CAS  Google Scholar 

  70. Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–787. doi:10.1021/ac051437y

    Article  PubMed  CAS  Google Scholar 

  71. Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84(11):5035–5039. doi:10.1021/Ac300698c

    Article  PubMed  CAS  Google Scholar 

  72. Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, Campos C, Fabius AWM, Lu C, Ward PS, Thompson CB, Kaufman A, Guryanova O, Levine R, Heguy A, Viale A, Morris LGT, Huse JT, Mellinghoff IK, Chan TA (2012) IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483(7390):479–U137. doi:10.1038/Nature10866

    Article  PubMed  CAS  Google Scholar 

  73. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K, Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M, Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di Francesco V, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W, Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV, Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I, Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S, Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T, McMullen I, Moy M, Moy L, Murphy B, Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S, Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K, Abril JF, Guigo R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B, Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M, Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H, Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan J, Kasha J, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen N, Nodell M, Pan S, Peck J, Peterson M, Rowe W, Sanders R, Scott J, Simpson M, Smith T, Sprague A, Stockwell T, Turner R, Venter E, Wang M, Wen M, Wu D, Wu M, Xia A, Zandieh A, Zhu X (2001) The sequence of the human genome. Science 291(5507):1304–1351. doi:10.1126/science.1058040

    Article  PubMed  CAS  Google Scholar 

  74. Weerapana E, Simon GM, Cravatt BF (2008) Disparate proteome reactivity profiles of carbon electrophiles. Nat Chem Biol 4(7):405–407. doi:10.1038/nchembio.91

    Article  PubMed  CAS  Google Scholar 

  75. Weerapana E, Wang C, Simon GM, Richter F, Khare S, Dillon MB, Bachovchin DA, Mowen K, Baker D, Cravatt BF (2010) Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 468(7325):790–795. doi:10.1038/nature09472

    Article  PubMed  CAS  Google Scholar 

  76. Wise DR, Ward PS, Shay JE, Cross JR, Gruber JJ, Sachdeva UM, Platt JM, Dematteo RG, Simon MC, Thompson CB (2011) Hypoxia promotes isocitrate dehydrogenase-dependent carboxylation of alpha-ketoglutarate to citrate to support cell growth and viability. Proc Natl Acad Sci U S A 108(49):19611–19616. doi:10.1073/pnas.1117773108

    Article  PubMed  CAS  Google Scholar 

  77. Yan H, Parsons DW, Jin GL, McLendon R, Rasheed BA, Yuan WS, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD (2009) IDH1 and IDH2 mutations in gliomas. New Engl J Med 360(8):765–773

    Article  PubMed  CAS  Google Scholar 

  78. Yates JR 3rd (2004) Mass spectral analysis in proteomics. Annu Rev Biophys Biomol Struct 33:297–316. doi:10.1146/annurev.biophys.33.111502.082538

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

We thank the members of the Nomura Research Group for helpful discussion and critical reading of the manuscript. This work was supported by the University of California, Berkeley startup funds, Searle Scholars Program, and the National Institutes of Health (NIDA/NIH) (R00DA030908).

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Correspondence to Daniel K. Nomura.

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This article is published as part of the special issue on “Molecular Sensors”.

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Medina-Cleghorn, D., Nomura, D.K. Chemical approaches to study metabolic networks. Pflugers Arch - Eur J Physiol 465, 427–440 (2013). https://doi.org/10.1007/s00424-012-1201-0

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