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Identification of bioactive metabolites using activity metabolomics

Nature Reviews Molecular Cell Biology volume 20, pages 353–367 (2019)Cite this article

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Abstract

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.

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Fig. 1: Metabolites as active modulators of gene and protein activity.
Fig. 2: Examples of macromolecule modification by the active metabolome.
Fig. 3: Mechanisms for non-covalent modification of macromolecules by the active metabolome.
Fig. 4: Workflow to elucidate metabolite bioactivity.
Fig. 5: Metabolite activity for phenotype modulation.

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References

  1. Kahn, F. Man in Structure & Function (A. A. Knopf, 1943).

  2. Crick, F. Central dogma of molecular biology. Nature 227, 561–563 (1970).

    CAS  PubMed  Google Scholar 

  3. Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961).

    CAS  PubMed  Google Scholar 

  5. Yang, Q., Vijayakumar, A. & Kahn, B. B. Metabolites as regulators of insulin sensitivity and metabolism. Nat. Rev. Mol. Cell Biol. 19, 654–672 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Zoncu, R., Efeyan, A. & Sabatini, D. M. mTOR: from growth signal integration to cancer, diabetes and ageing. Nat. Rev. Mol. Cell Biol. 12, 21–35 (2011).

    CAS  PubMed  Google Scholar 

  7. Katsyuba, E. & Auwerx, J. Modulating NAD+ metabolism, from bench to bedside. EMBO J. 36, 2670–2683 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Magistretti, P. J. & Allaman, I. Lactate in the brain: from metabolic end-product to signalling molecule. Nat. Rev. Neurosci. 19, 235–249 (2018).

    CAS  PubMed  Google Scholar 

  9. Liu, P.-S. et al. α-Ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat. Immunol. 18, 985–994 (2017).

    CAS  PubMed  Google Scholar 

  10. Knobloch, M. et al. Metabolic control of adult neural stem cell activity by Fasn-dependent lipogenesis. Nature 493, 226–230 (2013).

    CAS  PubMed  Google Scholar 

  11. Branco Dos Santos, F. et al. Probing the genome-scale metabolic landscape of Bordetella pertussis, the causative agent of whooping cough. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01528-17 (2017). This study identifies nitrogen sinks, using metabolomics based on a computational prediction, with the aim of enhancing vaccine production.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Giera, M., Branco Dos Santos, F. & Siuzdak, G. Metabolite-induced protein expression guided by metabolomics and systems biology. Cell Metab. 27, 270–272 (2018).

    CAS  PubMed  Google Scholar 

  13. Yang, M., Soga, T. & Pollard, P. J. Oncometabolites: linking altered metabolism with cancer. J. Clin. Invest. 123, 3652–3658 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Guijas, C., Montenegro-Burke, J. R., Warth, B., Spilker, M. E. & Siuzdak, G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat. Biotechnol. 36, 316–320 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Metallo, C. M. & Vander Heiden, M. G. Understanding metabolic regulation and its influence on cell physiology. Mol. Cell 49, 388–398 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Rabinowitz, J. D. & Silhavy, T. J. Systems biology: metabolite turns master regulator. Nature 500, 283–284 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Choudhary, C., Weinert, B. T., Nishida, Y., Verdin, E. & Mann, M. The growing landscape of lysine acetylation links metabolism and cell signalling. Nat. Rev. Mol. Cell Biol. 15, 536–550 (2014).

    CAS  PubMed  Google Scholar 

  18. Weinert, B. T. et al. Time-resolved analysis reveals rapid dynamics and broad scope of the CBP/p300 acetylome. Cell 174, 231–244 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Rana, M. S. et al. Fatty acyl recognition and transfer by an integral membrane S-acyltransferase. Science 359, eaao6326 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. James, A. M. et al. The causes and consequences of nonenzymatic protein acylation. Trends Biochem. Sci. 43, 921–932 (2018).

    CAS  PubMed  Google Scholar 

  21. Weinert, B. T., Moustafa, T., Iesmantavicius, V., Zechner, R. & Choudhary, C. Analysis of acetylation stoichiometry suggests that SIRT3 repairs nonenzymatic acetylation lesions. EMBO J. 34, 2620–2632 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Dennis, J. W. & Brewer, C. F. Density-dependent lectin-glycan interactions as a paradigm for conditional regulation by posttranslational modifications. Mol. Cell. Proteomics 12, 913–920 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hart, G. W., Slawson, C., Ramirez-Correa, G. & Lagerlof, O. Cross talk between O-GlcNAcylation and phosphorylation: roles in signaling, transcription, and chronic disease. Annu. Rev. Biochem. 80, 825–858 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Mills, E. L. et al. Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1. Nature 556, 113–117 (2018). This paper describes a new active metabolite, itaconate, that mediates inflammatory responses by protein modification.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Bambouskova, M. et al. Electrophilic properties of itaconate and derivatives regulate the IκBζ-ATF3 inflammatory axis. Nature 556, 556–504 (2018).

    Google Scholar 

  26. Cho-Park, P. F. & Steller, H. Proteasome regulation by ADP-ribosylation. Cell 153, 614–627 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Tan, M. et al. Lysine glutarylation is a protein posttranslational modification regulated by SIRT5. Cell Metab. 19, 605–617 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Masri, S. & Sassone-Corsi, P. The circadian clock: a framework linking metabolism, epigenetics and neuronal function. Nat. Rev. Neurosci. 14, 69–75 (2013).

    CAS  PubMed  Google Scholar 

  29. Warth, B. et al. Metabolomics reveals that dietary xenoestrogens alter cellular metabolism induced by palbociclib/letrozole combination cancer therapy. Cell Chem. Biol. 25, 291–300 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    PubMed Central  Google Scholar 

  31. Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Schaefer, M. et al. RNA methylation by Dnmt2 protects transfer RNAs against stress-induced cleavage. Genes Dev. 24, 1590–1595 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Helm, M. & Alfonzo, J. D. Posttranscriptional RNA modifications: playing metabolic games in a cell’s chemical Legoland. Chem. Biol. 21, 174–185 (2014).

    CAS  PubMed  Google Scholar 

  34. Hu, X.-L., Wang, Y. & Shen, Q. Epigenetic control on cell fate choice in neural stem cells. Protein Cell 3, 278–290 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Watanabe, A., Yamada, Y. & Yamanaka, S. Epigenetic regulation in pluripotent stem cells: a key to breaking the epigenetic barrier. Phil. Trans. R. Soc B 368, 20120292 (2013).

    PubMed  PubMed Central  Google Scholar 

  36. Serganov, A. & Patel, D. J. Molecular recognition and function of riboswitches. Curr. Opin. Struct. Biol. 22, 279–286 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Husted, A. S., Trauelsen, M., Rudenko, O., Hjorth, S. A. & Schwartz, T. W. GPCR-mediated signaling of metabolites. Cell Metab. 25, 777–796 (2017).

    CAS  PubMed  Google Scholar 

  38. Toma, I. et al. Succinate receptor GPR91 provides a direct link between high glucose levels and renin release in murine and rabbit kidney. J. Clin. Invest. 118, 2526–2534 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Syed, I. et al. Palmitic acid hydroxystearic acids activate GPR40, which is involved in their beneficial effects on glucose homeostasis. Cell Metab. 27, 419–427 (2018). This study shows how active metabolites, such as a novel identified class of endogenous lipids (PAHSAs), can signal to cells via GPCRs.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Yore, M. M. et al. Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell 159, 318–332 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Jones, C. P. & Ferré-D’Amaré, A. R. Long-range interactions in riboswitch control of gene expression. Annu. Rev. Biophys. 46, 455–481 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Rajniak, J. et al. Biosynthesis of redox-active metabolites in response to iron deficiency in plants. Nat. Chem. Biol. 14, 442–450 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Steinbusch, L., Labouèbe, G. & Thorens, B. Brain glucose sensing in homeostatic and hedonic regulation. Trends Endocrinol. Metab. 26, 455–466 (2015).

    CAS  PubMed  Google Scholar 

  44. Beyer, B. A. et al. Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation. Nat. Chem. Biol. 14, 22–28 (2018). This study demonstrates the metabolomics-based identification of taurine as an enhancer of oligodendrocyte differentiation from stem cells.

    CAS  PubMed  Google Scholar 

  45. Yang, M., Su, H., Soga, T., Kranc, K. R. & Pollard, P. J. Prolyl hydroxylase domain enzymes: important regulators of cancer metabolism. Hypoxia (Auckl.) 2, 127–142 (2014).

    Google Scholar 

  46. Xiao, M. et al. Inhibition of α-KG-dependent histone and DNA demethylases by fumarate and succinate that are accumulated in mutations of FH and SDH tumor suppressors. Genes Dev. 26, 1326–1338 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Adam, J. et al. Renal cyst formation in Fh1-deficient mice is independent of the Hif/Phd pathway: roles for fumarate in KEAP1 succination and Nrf2 signaling. Cancer Cell 20, 524–537 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Yang, M. et al. The succinated proteome of FH-mutant tumours. Metabolites 4, 640–654 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. Zheng, L. et al. Fumarate induces redox-dependent senescence by modifying glutathione metabolism. Nat. Commun. 6, 6001 (2015).

    CAS  PubMed  Google Scholar 

  50. McBrayer, S. K. et al. Transaminase inhibition by 2-hydroxyglutarate impairs glutamate biosynthesis and redox homeostasis in glioma. Cell 175, 101–116 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wishart, D. S. Is cancer a genetic disease or a metabolic disease? EBioMedicine 2, 478–479 (2015).

    PubMed  PubMed Central  Google Scholar 

  52. Boroughs, L. K. & DeBerardinis, R. J. Metabolic pathways promoting cancer cell survival and growth. Nat. Cell Biol. 17, 351–359 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Lee, C. K., Klopp, R. G., Weindruch, R. & Prolla, T. A. Gene expression profile of aging and its retardation by caloric restriction. Science 285, 1390–1393 (1999).

    CAS  PubMed  Google Scholar 

  54. Schvartzman, J. M., Thompson, C. B. & Finley, L. W. S. Metabolic regulation of chromatin modifications and gene expression. J. Cell Biol. 217, 2247–2259 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).

    CAS  PubMed  Google Scholar 

  56. Piazza, I. et al. A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172, 358–372 (2018). This paper presents a comprehensive study using structural proteomics and metabolomics to investigate the effects of small molecules on protein structure and complex assembly.

    CAS  PubMed  Google Scholar 

  57. Li, X., Gianoulis, T. A., Yip, K. Y., Gerstein, M. & Snyder, M. Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses. Cell 143, 639–650 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Alam, M. T. et al. The metabolic background is a global player in Saccharomyces gene expression epistasis. Nat. Microbiol. 1, 15030 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Buescher, J. M. et al. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell 173, 822–837 (2018). This review describes the state of the art of isotope tracing of metabolites.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Domingo-Almenara, X., Montenegro-Burke, J. R., Benton, H. P. & Siuzdak, G. Annotation: a computational solution for streamlining metabolomics analysis. Anal. Chem. 90, 480–489 (2018).

    CAS  PubMed  Google Scholar 

  62. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    CAS  PubMed  Google Scholar 

  63. Pluskal, T., Castillo, S., Villar-Briones, A. & Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010).

    PubMed  PubMed Central  Google Scholar 

  64. Pfeuffer, J. et al. OpenMS - a platform for reproducible analysis of mass spectrometry data. J. Biotechnol. 261, 142–148 (2017).

    CAS  PubMed  Google Scholar 

  65. Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Vinaixa, M. et al. Mass spectral databases for LC/MS- and GC/MS-based metabolomics: state of the field and future prospects. Trends Analyt. Chem. 78, 23–35 (2016).

    CAS  Google Scholar 

  67. Wishart, D. S. et al. HMDB 4.0: The Human Metabolome Database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    CAS  PubMed  Google Scholar 

  68. Wishart, D. S. et al. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).

    CAS  PubMed  Google Scholar 

  69. Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90, 3156–3164 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Ludwig, C. et al. Birmingham Metabolite Library: a publicly accessible database of 1D 1H and 2D 1H J-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics 8, 8–18 (2012).

    CAS  Google Scholar 

  71. King, Z. A. et al. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515–D522 (2016).

    CAS  PubMed  Google Scholar 

  72. Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703–714 (2010).

    CAS  PubMed  Google Scholar 

  73. Fahy, E. et al. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50 (Suppl.), S9–S14 (2009).

    PubMed  PubMed Central  Google Scholar 

  74. Lai, Z. et al. Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat. Methods 15, 53–56 (2018).

    CAS  PubMed  Google Scholar 

  75. Hummel, J., Selbig, J., Walther, D. & Kopka, J. in Metabolomics: A Powerful Tool in Systems Biology (eds Nielsen, J. & Jewett, M. C.) 75–95 (Springer Berlin Heidelberg, 2007).

  76. Vinaixa, M. et al. A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites 2, 775–795 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Huan, T. et al. Systems biology guided by XCMS Online metabolomics. Nat. Methods 14, 461–462 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Cottret, L. et al. MetExplore: collaborative edition and exploration of metabolic networks. Nucleic Acids Res. 46, W495–W502 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Boekel, J. et al. Multi-omic data analysis using Galaxy. Nat. Biotechnol. 33, 137–139 (2015).

    CAS  PubMed  Google Scholar 

  80. Kuo, T.-C., Tian, T.-F. & Tseng, Y. J. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst. Biol. 7, 64 (2013).

    PubMed  PubMed Central  Google Scholar 

  81. Karnovsky, A. et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28, 373–380 (2012).

    CAS  PubMed  Google Scholar 

  82. Zhu, H. et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).

    CAS  PubMed  Google Scholar 

  83. Roelofs, K. G., Wang, J., Sintim, H. O. & Lee, V. T. Differential radial capillary action of ligand assay for high-throughput detection of protein-metabolite interactions. Proc. Natl Acad. Sci. USA 108, 15528–15533 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Savitski, M. M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).

    PubMed  Google Scholar 

  85. Diether, M. & Sauer, U. Towards detecting regulatory protein-metabolite interactions. Curr. Opin. Microbiol. 39, 16–23 (2017).

    CAS  PubMed  Google Scholar 

  86. Tran, D. T., Adhikari, J. & Fitzgerald, M. C. StableIsotope labeling with amino acids in cell culture (SILAC)-based strategy for proteome-wide thermodynamic analysis of protein-ligand binding interactions. Mol. Cell. Proteomics 13, 1800–1813 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Feng, Y. et al. Global analysis of protein structural changes in complex proteomes. Nat. Biotechnol. 32, 1036–1044 (2014).

    CAS  PubMed  Google Scholar 

  88. Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).

    CAS  PubMed  Google Scholar 

  89. Kirchmair, J. et al. Predicting drug metabolism: experiment and/or computation? Nat. Rev. Drug Discov. 14, 387–404 (2015).

    CAS  PubMed  Google Scholar 

  90. Warth, B. et al. Exposome-scale investigations guided by global metabolomics, pathway analysis, and cognitive computing. Anal. Chem. 89, 11505–11513 (2017).

    CAS  PubMed  Google Scholar 

  91. Ge, H., Walhout, A. J. M. & Vidal, M. Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends Genet. 19, 551–560 (2003).

    CAS  PubMed  Google Scholar 

  92. Hakimi, A. A. et al. An integrated metabolic atlas of clear cell renal cell carcinoma. Cancer Cell 29, 104–116 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Davidson, R. L., Weber, R. J. M., Liu, H., Sharma-Oates, A. & Viant, M. R. Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. GigaScience 5, 10 (2016).

    PubMed  PubMed Central  Google Scholar 

  94. Villiers, F. et al. Investigating the plant response to cadmium exposure by proteomic and metabolomic approaches. Proteomics 11, 1650–1663 (2011).

    CAS  PubMed  Google Scholar 

  95. Zhang, W., Li, F. & Nie, L. Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology 156, 287–301 (2010).

    CAS  PubMed  Google Scholar 

  96. Haas, R. et al. Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology. Curr. Opin. Syst. Biol. 6, 37–45 (2017).

    Google Scholar 

  97. Yugi, K., Kubota, H., Hatano, A. & Kuroda, S. Trans-omics: how to reconstruct biochemical networks across multiple ‘omic’ layers. Trends Biotechnol. 34, 276–290 (2016).

    CAS  PubMed  Google Scholar 

  98. Jewison, T. et al. SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res. 42, D478–D484 (2014).

    CAS  PubMed  Google Scholar 

  99. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 42, D459–D471 (2014).

    CAS  PubMed  Google Scholar 

  100. Xia, J. & Wishart, D. S. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 38, W71–W77 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Fabregat, A. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    CAS  PubMed  Google Scholar 

  102. Swainston, N. et al. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12, 109 (2016).

    PubMed  PubMed Central  Google Scholar 

  103. Barupal, D. K. & Fiehn, O. Chemical similarity enrichment analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci. Rep. 7, 14567 (2017).

    PubMed  PubMed Central  Google Scholar 

  104. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).

    CAS  PubMed  Google Scholar 

  105. Haug, K. et al. MetaboLights — an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).

    CAS  PubMed  Google Scholar 

  106. Sud, M. et al. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 44, D463–D470 (2016).

    CAS  PubMed  Google Scholar 

  107. Knepper, M. A. Proteomic pearl diving versus systems biology in cell physiology. Focus on “Proteomic mapping of proteins released during necrosis and apoptosis from cultured neonatal cardiac myocytes”. Am. J. Physiol. Cell Physiol. 306, C634–C635 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).

    CAS  PubMed  Google Scholar 

  109. Chassagnole, C., Noisommit-Rizzi, N., Schmid, J. W., Mauch, K. & Reuss, M. Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. 79, 53–73 (2002).

    CAS  PubMed  Google Scholar 

  110. Covert, M. W., Xiao, N., Chen, T. J. & Karr, J. R. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24, 2044–2050 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Zelezniak, A. et al. Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts. Cell Syst. 7, 269–283 (2018). This study shows how the yeast metabolome can be predicted from omics data sets, showing the wide applicability of machine learning approaches in multi-omics integration.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Kim, M., Rai, N., Zorraquino, V. & Tagkopoulos, I. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat. Commun. 7, 13090 (2016). This study shows how cellular behaviour can be computationally predicted by mathematical modelling combined with omics integration.

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Frainay, C. et al. Mind the gap: mapping mass spectral databases in genome-scale metabolic networks reveals poorly covered areas. Metabolites 8, 51 (2018).

    PubMed Central  Google Scholar 

  114. Chae, Y. K., Kim, S. H. & Markley, J. L. Relationship between recombinant protein expression and host metabolome as determined by two-dimensional NMR spectroscopy. PLOS ONE 12, e0177233 (2017).

    PubMed  PubMed Central  Google Scholar 

  115. Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).

    Google Scholar 

  116. Corrêa-Oliveira, R., Fachi, J. L., Vieira, A., Sato, F. T. & Vinolo, M. A. R. Regulation of immune cell function by short-chain fatty acids. Clin. Transl Immunol. 5, e73 (2016).

    Google Scholar 

  117. Li, Z. et al. Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit. Gut 67, 1269–1279 (2017).

    PubMed  Google Scholar 

  118. Levy, B. D., Clish, C. B., Schmidt, B., Gronert, K. & Serhan, C. N. Lipid mediator class switching during acute inflammation: signals in resolution. Nat. Immunol. 2, 612–619 (2001).

    CAS  PubMed  Google Scholar 

  119. Funk, C. D. Prostaglandins and leukotrienes: advances in eicosanoid biology. Science 294, 1871–1875 (2001).

    CAS  PubMed  Google Scholar 

  120. Kalinski, P. Regulation of immune responses by prostaglandin E2. J. Immunol. 188, 21–28 (2012).

    CAS  PubMed  Google Scholar 

  121. Kaisar, M. M. M. et al. Dectin-1/2-induced autocrine PGE2 signaling licenses dendritic cells to prime Th2 responses. PLOS Biol. 16, e2005504 (2018). This study shows, starting from lipidomic data, the identification of prostaglandin E2 as a key modulator of T helper 2 immune cell responses.

    PubMed  PubMed Central  Google Scholar 

  122. Lipworth, B. J. Leukotriene-receptor antagonists. Lancet 353, 57–62 (1999).

    CAS  PubMed  Google Scholar 

  123. Veselinovic, M. et al. Clinical benefits of n-3 PUFA and γ-linolenic acid in patients with rheumatoid arthritis. Nutrients 9, 325 (2017).

    PubMed Central  Google Scholar 

  124. Bhatt, D. L. et al. Cardiovascular risk reduction with icosapent ethyl for hypertriglyceridemia. N. Engl. J. Med. 380, 11–22 (2019).

    CAS  PubMed  Google Scholar 

  125. Siscovick, D. S. et al. Omega-3 polyunsaturated fatty acid (fish oil) supplementation and the prevention of clinical cardiovascular disease: a science advisory from the American Heart Association. Circulation 135, e867–e884 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Kris-Etherton, P. M., Harris, W. S. & Appel, L. J., American Heart Association Nutrition Committee. Fish consumption, fish oil, omega-3 fatty acids, and cardiovascular disease. Circulation 106, 2747–2757 (2002).

    PubMed  Google Scholar 

  127. Hur, J. et al. Cerebrovascular β-amyloid deposition and associated microhemorrhages in a Tg2576 Alzheimer mouse model are reduced with a DHA-enriched diet. FASEB J. 32, 4972–4983 (2018).

    CAS  PubMed  Google Scholar 

  128. Grandison, R. C., Piper, M. D. W. & Partridge, L. Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila. Nature 462, 1061–1064 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Denzel, M. S. et al. Hexosamine pathway metabolites enhance protein quality control and prolong life. Cell 156, 1167–1178 (2014).

    CAS  PubMed  Google Scholar 

  130. Serhan, C. N. Treating inflammation and infection in the 21st century: new hints from decoding resolution mediators and mechanisms. FASEB J. 31, 1273–1288 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Niihara, Y. et al. A phase 3 trial of l-glutamine in sickle cell disease. N. Engl. J. Med. 379, 226–235 (2018). This study shows that metabolic interventions with active metabolites can potentially have a large impact on human disease.

    CAS  PubMed  Google Scholar 

  132. Morris, C. R. et al. Erythrocyte glutamine depletion, altered redox environment, and pulmonary hypertension in sickle cell disease. Blood 111, 402–410 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. 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, 894–901 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Chin, R. M. et al. The metabolite α-ketoglutarate extends lifespan by inhibiting ATP synthase and TOR. Nature 510, 397–401 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Carey, B. W., Finley, L. W. S., Cross, J. R., Allis, C. D. & Thompson, C. B. Intracellular α-ketoglutarate maintains the pluripotency of embryonic stem cells. Nature 518, 413–416 (2015).

    CAS  PubMed  Google Scholar 

  136. Klysz, D. et al. Glutamine-dependent α-ketoglutarate production regulates the balance between T helper 1 cell and regulatory T cell generation. Sci. Signal. 8, ra97 (2015).

    PubMed  Google Scholar 

  137. Grimm, P. R. et al. Integrated compensatory network is activated in the absence of NCC phosphorylation. J. Clin. Invest. 125, 2136–2150 (2015).

    PubMed  PubMed Central  Google Scholar 

  138. Grimm, P. R. & Welling, P. A. α-Ketoglutarate drives electroneutral NaCl reabsorption in intercalated cells by activating a G-protein coupled receptor, Oxgr1. Curr. Opin. Nephrol. Hypertens. 26, 426–433 (2017).

    CAS  PubMed  Google Scholar 

  139. Coudray-Lucas, C., Le Bever, H., Cynober, L., De Bandt, J. P. & Carsin, H. Ornithine alpha-ketoglutarate improves wound healing in severe burn patients: a prospective randomized double-blind trial versus isonitrogenous controls. Crit. Care Med. 28, 1772–1776 (2000).

    CAS  PubMed  Google Scholar 

  140. Patti, G. J. et al. Meta-analysis of global metabolomic data identifies metabolites associated with life-span extension. Metabolomics 10, 737–743 (2014).

    CAS  PubMed  Google Scholar 

  141. Kamburov, A., Cavill, R., Ebbels, T. M. D., Herwig, R. & Keun, H. C. Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 27, 2917–2918 (2011).

    CAS  PubMed  Google Scholar 

  142. Peters, K. et al. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Preprint at bioRxiv https://www.biorxiv.org/content/early/2018/09/13/409151 (2018).

  143. Cravatt, B. F. et al. Chemical characterization of a family of brain lipids that induce sleep. Science 268, 1506–1509 (1995).

    CAS  PubMed  Google Scholar 

  144. Boneschansker, L., Yan, J., Wong, E., Briscoe, D. M. & Irimia, D. Microfluidic platform for the quantitative analysis of leukocyte migration signatures. Nat. Commun. 5, 4787 (2014).

    CAS  PubMed  Google Scholar 

  145. Laan, L. C. et al. The whipworm (Trichuris suis) secretes prostaglandin E2 to suppress proinflammatory properties in human dendritic cells. FASEB J. 31, 719–731 (2016).

    PubMed  PubMed Central  Google Scholar 

  146. Shi, S.-Y. et al. Coupling HPLC to on-line, post-column (bio)chemical assays for high-resolution screening of bioactive compounds from complex mixtures. Trends Analyt. Chem. 28, 865–877 (2009).

    CAS  Google Scholar 

  147. Tammela, P., Wennberg, T., Vuorela, H. & Vuorela, P. HPLC micro-fractionation coupled to a cell-based assay for automated on-line primary screening of calcium antagonistic components in plant extracts. Anal. Bioanal. Chem. 380, 614–618 (2004).

    CAS  PubMed  Google Scholar 

  148. Veyel, D. et al. PROMIS, global analysis of PROtein–metabolite interactions using size separation in Arabidopsis thaliana. J. Biol. Chem. 293, 12440–12453 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Annis, D. A., Nickbarg, E., Yang, X., Ziebell, M. R. & Whitehurst, C. E. Affinity selection-mass spectrometry screening techniques for small molecule drug discovery. Curr. Opin. Chem. Biol. 11, 518–526 (2007).

    CAS  PubMed  Google Scholar 

  150. Huber, K. V. M. et al. Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat. Methods 12, 1055–1057 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Sergushichev, A. A. et al. GAM: a web-service for integrated transcriptional and metabolic network analysis. Nucleic Acids Res. 44, W194–W200 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104–1115 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Li, S. et al. Predicting network activity from high throughput metabolomics. PLOS Comput. Biol. 9, e1003123 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Pirhaji, L. et al. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat. Methods 13, 770–776 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Olivon, F. et al. Bioactive natural products prioritization using massive multi-informational molecular networks. ACS Chem. Biol. 12, 2644–2651 (2017).

    CAS  PubMed  Google Scholar 

  157. Péresse, T. et al. Cytotoxic prenylated stilbenes isolated from Macaranga tanarius. J. Nat. Prod. 80, 2684–2691 (2017).

    PubMed  Google Scholar 

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Acknowledgements

M.M.R. was supported by a fellowship from the Deutsche Forschungsgemeinschaft (DFG-Ri2811/1-1 and Ri2811/1-2). This effort was partially funded by Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA), a Scientific Focus Area Program at the Lawrence Berkeley National Laboratory for the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract DE-AC02-05CH11231 (G.S.) and US National Institutes of Health grants R01 GM114368-03, P30 MH062261-17 P01 DA026146-02 and the NIH Cloud Credits Model Pilot, a component of the NIH Big Data to Knowledge (BD2K) program (G.S.).

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Nature Reviews Molecular Cell Biology thanks C. Burant, C. Frezza and M. Ralser for their contribution to the peer review of this work.

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Authors and Affiliations

  1. The Scripps Research Institute, Center for Metabolomics and Mass Spectrometry, La Jolla, CA, USA

    Markus M. Rinschen & Gary Siuzdak

  2. Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

    Julijana Ivanisevic

  3. Leiden University Medical Center, Center for Proteomics & Metabolomics, Leiden, Netherlands

    Martin Giera

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  1. Markus M. Rinschen
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  2. Julijana Ivanisevic
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  3. Martin Giera
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The authors contributed equally to all aspects of the article.

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Glossary

Sirtuin

An enzyme that is involved in the deacetylation of proteins.

Cognitive computing

A term that describes computational platforms that are based on artificial intelligence.

Cloud-based computing

The use of computational resources that are not physically present but are deposited at a server at a remote location and are accessed via Internet connection.

Itaconate

An organic dicarbonic acid that has recently emerged as a modifier of cysteine residues and a modulator of inflammatory phenotypes.

Riboswitches

Parts of an mRNA molecule that change structure upon binding of small molecules.

Protein arrays

A high-throughput method used to determine the interactions of proteins (for example, with candidate metabolites).

Flux analysis

A mass-spectrometry-based technique that is used to examine production and consumption rates of metabolites by tracking isotopes.

Metabolite features

Peaks or a set of peaks across samples with a unique mass-to-charge ratio (m/z value) and retention time that define the metabolite and enable its unique identification.

Triple quadrupole mass spectrometer

A mass spectrometer consisting of three quadrupole mass spectrometers in a row designed for targeted metabolomics quantification.

High-resolution mass spectrometry

An Orbitrap or a quadrupole-time-of-flight mass spectrometer with high mass resolution and accuracy. It is a commonly used instrument for untargeted metabolomics acquisition.

Neural networks

A machine learning technique. Neural networks consist of artificial neurons that translate an input into an output.

Exploratory metabolomics

The use of (untargeted) metabolomics to identify global regulation states of molecules in a biological system.

Chemical space

The actual physico-chemical space (degree of freedom) defined by the chemical structure in which binding and/or activity might occur.

Law of mass action

A chemical law defining that a reversible chemical reaction in equilibrium is directly proportional to the product of the concentrations of the reactants.

Metabolite set enrichment

A method of identifying patterns of regulated metabolites using predefined metabolite lists. It is an analogue of gene set enrichment.

Untargeted metabolomics

A global method that attempts to measure all or as many molecules as possible in a sample. By contrast, targeted metabolomics is a method in which a specified, predefined entity of molecules is measured. Both methods can be based on mass spectrometry or NMR.

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Rinschen, M.M., Ivanisevic, J., Giera, M. et al. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol 20, 353–367 (2019). https://doi.org/10.1038/s41580-019-0108-4

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