Frontiers of high-throughput metabolomics

Current Opinion in Chemical Biology

Volumn 36, Issue , 2017, Pages 15-23

  Zampieri, Mattia ^a   Sekar, Karthik ^a   Zamboni, Nicola ^a   Sauer, Uwe ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; LARGE SCALE PRODUCTION; METABOLITE; METABOLOME; METABOLOMICS; NONHUMAN; REVIEW; SINGLE CELL ANALYSIS; ANIMAL; DRUG DEVELOPMENT; HIGH THROUGHPUT SCREENING; MASS SPECTROMETRY; PERSONALIZED MEDICINE; PLANT; PROCEDURES; SYSTEMS BIOLOGY;

ANIMALS; DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; MASS SPECTROMETRY; METABOLOME; METABOLOMICS; PLANTS; PRECISION MEDICINE; SYSTEMS BIOLOGY;

EID: 85008391609     PISSN: 13675931     EISSN: 18790402     Source Type: Journal    
DOI: 10.1016/j.cbpa.2016.12.006     Document Type: Review

References (89)

1. 1 Sévin, D.C., Sauer, U., Ubiquinone accumulation improves osmotic-stress tolerance in Escherichia coli. Nat Chem Biol 10 (2014), 266–272.
2. 2 Zenobi, R., Single-cell metabolomics: analytical and biological perspectives. Science, 342, 2013, 1243259.
3. 3 Hsu, C.-C., Dorrestein, P.C., Visualizing life with ambient mass spectrometry. Curr Opin Biotechnol 31 (2015), 24–34.
4. 4 Patti, G.J., Yanes, O., Siuzdak, G., Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13 (2012), 263–269.
5. 5 Creek, D.J., Dunn, W.B., Fiehn, O., Griffin, J.L., Hall, R.D., Lei, Z., Mistrik, R., Neumann, S., Schymanski, E.L., Sumner, L.W., et al. Metabolite identification: are you sure? And how do your peers gauge your confidence?. Metabolomics 10 (2014), 350–353.
6. 6 Dona, A.C., Kyriakides, M., Scott, F., Shephard, E.A., Varshavi, D., Veselkov, K., Everett, J.R., A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Comput Struct Biotechnol J 14 (2016), 135–153.
7. 7 Williams, E.G., Wu, Y., Jha, P., Dubuis, S., Blattmann, P., Argmann, C.A., Houten, S.M., Amariuta, T., Wolski, W., Zamboni, N., et al. Systems proteomics of liver mitochondria function. Science, 352, 2016, aad0189.
8. 8 Fuhrer, T., Heer, D., Begemann, B., Zamboni, N., High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. Anal Chem 83 (2011), 7074–7080.
9. 9•• Shin, S.-Y., Fauman, E.B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., Yang, T.-P., et al. An atlas of genetic influences on human blood metabolites. Nat Genet 46 (2014), 543–550 In this study the authors used high-throughput metabolic profiling to generate one of the most comprehensive genome wide association study, monitoring more than 400 metabolites in blood samples from nearly 8000 individuals.
10. 10 Hartiala, J.A., Wilson Tang, W.H., Wang, Z., Crow, A.L., Stewart, A.F.R., Roberts, R., McPherson, R., Erdmann, J., Willenborg, C., Hazen, S.L., et al. Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat Commun, 7, 2016, 10558.
11. 11 Ma, S., Yim, S.H., Lee, S.-G., Kim, E.B., Lee, S.-R., Chang, K.-T., Buffenstein, R., Lewis, K.N., Park, T.J., Miller, R.A., et al. Organization of the mammalian metabolome according to organ function, lineage specialization, and longevity. Cell Metab 22 (2015), 332–343.
12. 12 Rhee, E.P., Ho, J.E., Chen, M.-H., Shen, D., Cheng, S., Larson, M.G., Ghorbani, A., Shi, X., Helenius, I.T., O'Donnell, C.J., et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab 18 (2013), 130–143.
13. 13• Balog, J., Sasi-Szabó, L., Kinross, J., Lewis, M.R., Muirhead, L.J., Veselkov, K., Mirnezami, R., Dezső, B., Damjanovich, L., Darzi, A., et al. Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med, 5, 2013, 194ra93 A direct clinical application of real time metabolome characterization of human tissue in vivo.
14. 14 Guo, L., Milburn, M.V., Ryals, J.A., Lonergan, S.C., Mitchell, M.W., Wulff, J.E., Alexander, D.C., Evans, A.M., Bridgewater, B., Miller, L., et al. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci U S A 112 (2015), E4901–E4910.
15. 15 Chen, R., Mias, G.I., Li-Pook-Than, J., Jiang, L., Lam, H.Y.K., Chen, R., Miriami, E., Karczewski, K.J., Hariharan, M., Dewey, F.E., et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148 (2012), 1293–1307.
16. 16 Fearnley, L.G., Inouye, M., Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks. Int J Epidemiol, 2016, 10.1093/ije/dyw046.
17. 17 Ritchie, M.D., Holzinger, E.R., Li, R., Pendergrass, S.A., Kim, D., Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet 16 (2015), 85–97.
18. 18 Alam, M.T., Zelezniak, A., Mülleder, M., Shliaha, P., Schwarz, R., Capuano, F., Vowinckel, J., Radmaneshfar, E., Krüger, A., Calvani, E., et al. The metabolic background is a global player in Saccharomyces gene expression epistasis. Nat Microbiol, 1, 2016, 15030.
19. 19 Dumas, M.-E., Wilder, S.P., Bihoreau, M.-T., Barton, R.H., Fearnside, J.F., Argoud, K., D'Amato, L., Wallis, R.H., Blancher, C., Keun, H.C., et al. Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models. Nat Genet 39 (2007), 666–672.
20. 20• Breunig, J.S., Hackett, S.R., Rabinowitz, J.D., Kruglyak, L., Genetic basis of metabolome variation in yeast. PLOS Genet, 10, 2014, e1004142 By profiling the metabolome variance in ∼100 segregants from Saccharomyces cerevisiae, the authors infer metabolic regulatory links.
21. 21•• Lewis, I.A., Wacker, M., Olszewski, K.L., Cobbold, S.A., Baska, K.S., Tan, A., Ferdig, M.T., Llinás, M., Metabolic QTL analysis links chloroquine resistance in Plasmodium falciparum to impaired hemoglobin catabolism. PLOS Genet, 10, 2014, e1004085 The authors employ metabolic quantitative trait locus mapping to analyze the metabolic differences between chloroquine-susceptible and resistant Plasmodium falciparum, and unraveled a functional link between hemoglobin digestion and fitness of resistant parasites.
22. 22 Zimmermann, M., Kuehne, A., Boshoff, H.I., Barry, C.E., Zamboni, N., Sauer, U., Dynamic exometabolome analysis reveals active metabolic pathways in non-replicating mycobacteria. Environ Microbiol 17 (2015), 4802–4815.
23. 23 Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A.L., Kafri, R., Kirschner, M.W., Clish, C.B., Mootha, V.K., Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336 (2012), 1040–1044.
24. 24•• Baran, R., Bowen, B.P., Price, M.N., Arkin, A.P., Deutschbauer, A.M., Northen, T.R., Metabolic footprinting of mutant libraries to map metabolite utilization to genotype. ACS Chem Biol 8 (2013), 189–199 This study exploit the high throughput of untargeted mass spectrometry to generate a systematic profiling of spent media from each individual knockout mutant in two model organisms: Escherichia coli and Shewanella oneidensis MR-1.
25. 25 Fukushima, A., Kusano, M., Mejia, R.F., Iwasa, M., Kobayashi, M., Hayashi, N., Watanabe-Takahashi, A., Narisawa, T., Tohge, T., Hur, M., et al. Metabolomic characterization of knockout mutants in Arabidopsis: development of a metabolite profiling database for knockout mutants in Arabidopsis. Plant Physiol 165 (2014), 948–961.
26. 26• Schulz, J.C., Zampieri, M., Wanka, S., von Mering, C., Sauer, U., Large-scale functional analysis of the roles of phosphorylation in yeast metabolic pathways. Sci Signal, 7, 2014, rs6 Examined the metabolomic state of kinase and phosphate knockouts in yeast. The authors, by integrating phosphoproteomics and metabolome data using a network-based approach, were able to predict functional phosphorylation events in 47 enzymes.
27. 27 Hoerr, V., Duggan, G.E., Zbytnuik, L., Poon, K.K.H., Große, C., Neugebauer, U., Methling, K., Löffler, B., Vogel, H.J., Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics. BMC Microbiol, 16, 2016, 82.
28. 28 Leveridge, M., Collier, L., Edge, C., Hardwicke, P., Leavens, B., Ratcliffe, S., Rees, M., Stasi, L.P., Nadin, A., Reith, A.D., A high-throughput screen to identify LRRK2 kinase inhibitors for the treatment of Parkinson's disease using RapidFire mass spectrometry. J Biomol Screen 21 (2016), 145–155.
29. 29• Tiziani, S., Kang, Y., Choi, J.S., Roberts, W., Paternostro, G., Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library. Nat Commun, 2, 2011, 545 The authors developed an experimental workflow based on whole cell broth extraction to systematically and rapidly monitor the metabolic changes induced by a library of kinase inhibitors in mammalian cells.
30. 30• Kurita, K.L., Glassey, E., Linington, R.G., Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries. Proc Natl Acad Sci U S A 112 (2015), 11999–12004 In this study, integration of image-based phenotypic screening and untargeted high-throughput metabolomics analysis is used to categorize the mode of action of bioactive constituents.
31. 31 Sampson, J.N., Boca, S.M., Shu, X.O., Stolzenberg-Solomon, R.Z., Matthews, C.E., Hsing, A.W., Tan, Y.T., Ji, B.-T., Chow, W.-H., Cai, Q., et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Prev Biomark 22 (2013), 631–640.
32. 32• Sinclair, I., Stearns, R., Pringle, S., Wingfield, J., Datwani, S., Hall, E., Ghislain, L., Majlof, L., Bachman, M., Novel acoustic loading of a mass spectrometer toward next-generation high-throughput MS screening. J Lab Autom 21 (2016), 19–26 This technical paper describes a novel method that sets a new standard in high-throughput, non-targeted metabolomics with 3 samples/s while preserving sensitivity.
33. 33 Sévin, D.C., Kuehne, A., Zamboni, N., Sauer, U., Biological insights through nontargeted metabolomics. Curr Opin Biotechnol 34 (2015), 1–8.
34. 34 Pirhaji, L., Milani, P., Leidl, M., Curran, T., Avila-Pacheco, J., Clish, C.B., White, F.M., Saghatelian, A., Fraenkel, E., Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods, 2016, 10.1038/nmeth.3940.
35. 35 Jha, A.K., Huang, S.C.-C., Sergushichev, A., Lampropoulou, V., Ivanova, Y., Loginicheva, E., Chmielewski, K., Stewart, K.M., Ashall, J., Everts, B., et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42 (2015), 419–430.
36. 36 Inouye, M., Kettunen, J., Soininen, P., Silander, K., Ripatti, S., Kumpula, L.S., Hämäläinen, E., Jousilahti, P., Kangas, A.J., Männistö, S., et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol Syst Biol, 6, 2010, 441.
37. 37•• Bordbar, A., McCloskey, D., Zielinski, D.C., Sonnenschein, N., Jamshidi, N., Palsson, B.O., Personalized whole-cell kinetic models of metabolism for discovery in genomics and pharmacodynamics. Cell Syst 1 (2015), 283–292 In this study the authors developed a network based kinetic model approach to interpret the variability in the metabolome of erythrocytes from 24 individuals, and to predict drug side effect susceptibility.
38. 38 Zelezniak, A., Sheridan, S., Patil, K.R., Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes. PLoS Comput Biol, 10, 2014, e1003572.
39. 39 Gerosa, L., Haverkorn van Rijsewijk, B.R.B., Christodoulou, D., Kochanowski, K., Schmidt, T.S.B., Noor, E., Sauer, U., Pseudo-transition analysis identifies the key regulators of dynamic metabolic adaptations from steady-state data. Cell Syst 1 (2015), 270–282.
40. 40 Hackett, S.R., Zanotelli, V.R.T., Xu, W., Goya, J., Park, J.O., Perlman, D.H., Gibney, P.A., Botstein, D., Storey, J.D., Rabinowitz, J.D., Systems-level analysis of mechanisms regulating yeast metabolic flux. Science, 2016, 354.
41. 41 Plata, G., Fuhrer, T., Hsiao, T.-L., Sauer, U., Vitkup, D., Global probabilistic annotation of metabolic networks enables enzyme discovery. Nat Chem Biol 8 (2012), 848–854.
42. 42 Krumsiek, J., Suhre, K., Illig, T., Adamski, J., Theis, F.J., Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol, 5, 2011, 21.
43. 43 Sévin, D.C., Fuhrer, T., Zamboni, N., Sauer, U., Nontargeted in vitro metabolomics for high-throughput identification of novel enzymes in Escherichia coli. Nat Methods, 2016.
44. 44 Mukherjee, K., Twyman, R.M., Vilcinskas, A., Insects as models to study the epigenetic basis of disease. Prog Biophys Mol Biol 118 (2015), 69–78.
45. 45 Baryshnikova, A., Costanzo, M., Myers, C.L., Andrews, B., Boone, C., Genetic interaction networks: toward an understanding of heritability. Annu Rev Genomics Hum Genet 14 (2013), 111–133.
46. 46• Kuehne, A., Emmert, H., Soehle, J., Winnefeld, M., Fischer, F., Wenck, H., Gallinat, S., Terstegen, L., Lucius, R., Hildebrand, J., et al. Acute activation of oxidative pentose phosphate pathway as first-line response to oxidative stress in human skin cells. Mol Cell 59 (2015), 359–371 On the basis of time-resolved metabolomics, the authors found that glucose flux is routed to the pentose phosphate pathway in response to oxidative stress due to a release of allosteric inhibition of G6P dehydrogenase during glycolysis.
47. 47 Ralser, M., Wamelink, M.M., Kowald, A., Gerisch, B., Heeren, G., Struys, E.A., Klipp, E., Jakobs, C., Breitenbach, M., Lehrach, H., et al. Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress. J Biol, 6, 2007, 10.
48. 48 Link, H., Kochanowski, K., Sauer, U., Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo. Nat Biotechnol 31 (2013), 357–361.
49. 49 Kwon, Y.K., Lu, W., Melamud, E., Khanam, N., Bognar, A., Rabinowitz, J.D., A domino effect in antifolate drug action in Escherichia coli. Nat Chem Biol 4 (2008), 602–608.
50. 50 Reaves, M.L., Young, B.D., Hosios, A.M., Xu, Y.-F., Rabinowitz, J.D., Pyrimidine homeostasis is accomplished by directed overflow metabolism. Nature 500 (2013), 237–241.
51. 51 Altman, B.J., Hsieh, A.L., Sengupta, A., Krishnanaiah, S.Y., Stine, Z.E., Walton, Z.E., Gouw, A.M., Venkataraman, A., Li, B., Goraksha-Hicks, P., et al. MYC disrupts the circadian clock and metabolism in cancer cells. Cell Metab 22 (2015), 1009–1019.
52. 52 Hao, D., Sarfaraz, M.O., Farshidfar, F., Bebb, D.G., Lee, C.Y., Card, C.M., David, M., Weljie, A.M., Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment [Internet]. Metabolomics, 12, 2016.
53. 53 Shaham, O., Wei, R., Wang, T.J., Ricciardi, C., Lewis, G.D., Vasan, R.S., Carr, S.A., Thadhani, R., Gerszten, R.E., Mootha, V.K., Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol Syst Biol, 4, 2008, 214.
54. 54• Wen, H., An, Y.J., Xu, W.J., Kang, K.W., Park, S., Real-time monitoring of cancer cell metabolism and effects of an anticancer agent using 2D in-cell NMR spectroscopy. Angew Chem Int Ed Engl 54 (2015), 5374–5377 An NMR-based real-time tool that can monitor cells in real-time. The authors use the tool to study the mechanistic details of galloflavin treament on cancer cells.
55. 55 Ewald, J.C., Kuehne, A., Zamboni, N., Skotheim, J.M., The yeast cyclin-dependent kinase routes carbon fluxes to fuel cell cycle progression. Mol Cell 62 (2016), 532–545.
56. 56 Doucette, C.D., Schwab, D.J., Wingreen, N.S., Rabinowitz, J.D., α-Ketoglutarate coordinates carbon and nitrogen utilization via enzyme I inhibition. Nat Chem Biol 7 (2011), 894–901.
57. 57 Zaitsu, K., Hayashi, Y., Murata, T., Ohara, T., Nakagiri, K., Kusano, M., Nakajima, H., Nakajima, T., Ishikawa, T., Tsuchihashi, H., et al. Intact endogenous metabolite analysis of mice liver by probe electrospray ionization/triple quadrupole tandem mass spectrometry and its preliminary application to in vivo real-time analysis. Anal Chem 88 (2016), 3556–3561.
58. 58 Heinemann, J., Noon, B., Mohigmi, M.J., Mazurie, A., Dickensheets, D.L., Bothner, B., Real-time digitization of metabolomics patterns from a living system using mass spectrometry. J Am Soc Mass Spectrom 25 (2014), 1755–1762.
59. 59 Marasco, C.C., Enders, J.R., Seale, K.T., McLean, J.A., Wikswo, J.P., Real-time cellular exometabolome analysis with a microfluidic-mass spectrometry platform. PLOS ONE, 10, 2015, e0117685.
60. 60•• Link, H., Fuhrer, T., Gerosa, L., Zamboni, N., Sauer, U., Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat Methods 12 (2015), 1091–1097 A automated method for high-resolution metabolomics down to a frequency of 10 s. The determined rapid abundances changes were used to infer allosteric regulation of biosynthesis pathways by kinetic modeling.
61. 61 Storey, J.D., Xiao, W.Z., Leek, J.T., Tompkins, R.G., Davis, R.W., Significance analysis of time course microarray experiments. Proc Natl Acad Sci U S A 102 (2005), 12837–12842.
62. 62 Berk, M., Ebbels, T., Montana, G., A statistical framework for biomarker discovery in metabolomic time course data. Bioinform Oxf Engl 27 (2011), 1979–1985.
63. 63 Ren, S., Hinzman, A.A., Kang, E.L., Szczesniak, R.D., Lu, L.J., Computational and statistical analysis of metabolomics data. Metabolomics 11 (2015), 1492–1513.
64. 64 Chechik, G., Oh, E., Rando, O., Weissman, J., Regev, A., Koller, D., Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nat Biotechnol 26 (2008), 1251–1259.
65. 65 Jovanovic, M., Rooney, M.S., Mertins, P., Przybylski, D., Chevrier, N., Satija, R., Rodriguez, E.H., Fields, A.P., Schwartz, S., Raychowdhury, R., et al. Dynamic profiling of the protein life cycle in response to pathogens. Science, 347, 2015, 1259038.
66. 66 Oliveira, A.P., Dimopoulos, S., Busetto, A.G., Christen, S., Dechant, R., Falter, L., Haghir Chehreghani, M., Jozefczuk, S., Ludwig, C., Rudroff, F., et al. Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome. Mol Syst Biol, 11, 2015, 802.
67. 67 Stoeger, T., Battich, N., Pelkmans, L., Passive noise filtering by cellular compartmentalization. Cell 164 (2016), 1151–1161.
68. 68 Lin, Y., Sohn, C.H., Dalal, C.K., Cai, L., Elowitz, M.B., Combinatorial gene regulation by modulation of relative pulse timing. Nature 527 (2015), 54–58.
69. 69 Liu, Y., Link, H., Liu, L., Du, G., Chen, J., Sauer, U., A dynamic pathway analysis approach reveals a limiting futile cycle in N-acetylglucosamine overproducing Bacillus subtilis. Nat Commun, 7, 2016, 11933.
70. 70 King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.G.K., Bryant, C.H., Muggleton, S.H., Kell, D.B., Oliver, S.G., Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427 (2004), 247–252.
71. 71 Besnard, J., Ruda, G.F., Setola, V., Abecassis, K., Rodriguiz, R.M., Huang, X.-P., Norval, S., Sassano, M.F., Shin, A.I., Webster, L.A., et al. Automated design of ligands to polypharmacological profiles. Nature, 492, 2012, 215.
72. 72 Fujii, T., Matsuda, S., Tejedor, M.L., Esaki, T., Sakane, I., Mizuno, H., Tsuyama, N., Masujima, T., Direct metabolomics for plant cells by live single-cell mass spectrometry. Nat Protoc 10 (2015), 1445–1456.
73. 73 Zhang, L., Vertes, A., Energy charge, redox state, and metabolite turnover in single human hepatocytes revealed by capillary microsampling mass spectrometry. Anal Chem 87 (2015), 10397–10405.
74. 74 Zhang, X.-C., Wei, Z.-W., Gong, X.-Y., Si, X.-Y., Zhao, Y.-Y., Yang, C.-D., Zhang, S.-C., Zhang, X.-R., Integrated droplet-based microextraction with ESI–MS for removal of matrix interference in single-cell analysis. Sci Rep, 6, 2016, 24730.
75. 75 Ibáñez, A.J., Fagerer, S.R., Schmidt, A.M., Urban, P.L., Jefimovs, K., Geiger, P., Dechant, R., Heinemann, M., Zenobi, R., Mass spectrometry-based metabolomics of single yeast cells. Proc Natl Acad Sci U S A 110 (2013), 8790–8794.
76. 76• Krismer, J., Sobek, J., Steinhoff, R.F., Fagerer, S.R., Pabst, M., Zenobi, R., Screening of Chlamydomonas reinhardtii populations with single-cell resolution by using a high-throughput microscale sample preparation for matrix-assisted laser desorption ionization mass spectrometry. Appl Environ Microbiol 81 (2015), 5546–5551 A milestone study that combined single cell metabolomics with high throughputs using a generally applicable technology.
77. 77 Park, J.O., Rubin, S.A., Xu, Y.-F., Amador-Noguez, D., Fan, J., Shlomi, T., Rabinowitz, J.D., Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nat Chem Biol 12 (2016), 482–489.
78. 78 Raamsdonk, L.M., Teusink, B., Broadhurst, D., Zhang, N., Hayes, A., Walsh, M.C., Berden, J.A., Brindle, K.M., Kell, D.B., Rowland, J.J., et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 19 (2001), 45–50.
79. 79 Clasquin, M.F., Melamud, E., Singer, A., Gooding, J.R., Xu, X., Dong, A., Cui, H., Campagna, S.R., Savchenko, A., Yakunin, A.F., et al. Riboneogenesis in yeast. Cell 145 (2011), 969–980.
80. 80 Allen, J., Davey, H.M., Broadhurst, D., Heald, J.K., Rowland, J.J., Oliver, S.G., Kell, D.B., High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol 21 (2003), 692–696.
81. 81 Behrends, V., Ryall, B., Zlosnik, J.E.A., Speert, D.P., Bundy, J.G., Williams, H.D., Metabolic adaptations of Pseudomonas aeruginosa during cystic fibrosis chronic lung infections. Environ Microbiol 15 (2013), 398–408.
82. 82 Keurentjes, J.J.B., Fu, J., de Vos, C.H.R., Lommen, A., Hall, R.D., Bino, R.J., van der Plas, L.H.W., Jansen, R.C., Vreugdenhil, D., Koornneef, M., The genetics of plant metabolism. Nat Genet 38 (2006), 842–849.
83. 83 Kettunen, J., Demirkan, A., Würtz, P., Draisma, H.H.M., Haller, T., Rawal, R., Vaarhorst, A., Kangas, A.J., Lyytikäinen, L.-P., Pirinen, M., et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun, 7, 2016, 11122.
84. 84 Illig, T., Gieger, C., Zhai, G., Römisch-Margl, W., Wang-Sattler, R., Prehn, C., Altmaier, E., Kastenmüller, G., Kato, B.S., Mewes, H.-W., et al. A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42 (2010), 137–141.
85. 85 Hou, Y., Yin, M., Sun, F., Zhang, T., Zhou, X., Li, H., Zheng, J., Chen, X., Li, C., Ning, X., et al. A metabolomics approach for predicting the response to neoadjuvant chemotherapy in cervical cancer patients. Mol Biosyst 10 (2014), 2126–2133.
86. 86 Laye, M.J., Tran, V., Jones, D.P., Kapahi, P., Promislow, D.E.L., The effects of age and dietary restriction on the tissue-specific metabolome of Drosophila. Aging Cell 14 (2015), 797–808.
87. 87 Park, Y.H., Shi, Y.P., Liang, B., Medriano, C.A.D., Jeon, Y.H., Torres, E., Uppal, K., Slutsker, L., Jones, D.P., High-resolution metabolomics to discover potential parasite-specific biomarkers in a Plasmodium falciparum erythrocytic stage culture system. Malar J, 14, 2015, 122.
88. 88 Oliveira, A.P., Ludwig, C., Zampieri, M., Weisser, H., Aebersold, R., Sauer, U., Dynamic phosphoproteomics reveals TORC1-dependent regulation of yeast nucleotide and amino acid biosynthesis. Sci Signal, 8, 2015, rs4.
89. 89 Berney, M., Berney-Meyer, L., Wong, K.-W., Chen, B., Chen, M., Kim, J., Wang, J., Harris, D., Parkhill, J., Chan, J., et al. Essential roles of methionine and S-adenosylmethionine in the autarkic lifestyle of Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 112 (2015), 10008–10013.