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Metabolomics activity screening for identifying metabolites that modulate phenotype

Nature Biotechnology volume 36, pages 316–320 (2018)Cite this article

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Abstract

Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype1,2,3. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

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Figure 1: MAS for the identification of endogenous metabolites that modulate phenotype.
Figure 2: MAS demonstrated in stem-cell differentiation, a mouse model of type 2 diabetes, T-cell function and activity, macrophage response to a fungal stimulus, and a remyelination model for multiple sclerosis.

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References

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Patti, G.J., Yanes, O. & Siuzdak, G. Metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13, 263–269 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fiehn, O. Metabolomics--the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    Article  CAS  PubMed  Google Scholar 

  4. Woolf, L.I., Griffiths, R. & Moncrieff, A. Treatment of phenylketonuria with a diet low in phenylalanine. BMJ 1, 57–64 (1955).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kamanna, V.S. & Kashyap, M.L. Mechanism of action of niacin. Am. J. Cardiol. 101 8A, 20B–26B (2008).

    Article  CAS  PubMed  Google Scholar 

  6. Banach, M. et al. Statin therapy and plasma coenzyme Q10 concentrations--A systematic review and meta-analysis of placebo-controlled trials. Pharmacol. Res. 99, 329–336 (2015).

    Article  CAS  PubMed  Google Scholar 

  7. Tautenhahn, R., Patti, G.J., Rinehart, D. & Siuzdak, G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Xia, J., Sinelnikov, I.V., Han, B. & Wishart, D.S. MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res. 43, W251–W257 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tautenhahn, R. et al. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat. Biotechnol. 30, 826–828 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wishart, D.S. et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521–D526 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fahy, E. et al. A comprehensive classification system for lipids. J. Lipid Res. 46, 839–861 (2005).

    Article  CAS  PubMed  Google Scholar 

  13. 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).

    Article  CAS  Google Scholar 

  14. Yanes, O. et al. Metabolic oxidation regulates embryonic stem cell differentiation. Nat. Chem. Biol. 6, 411–417 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sperber, H. et al. The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition. Nat. Cell Biol. 17, 1523–1535 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gil-de-Gómez, L. et al. A phosphatidylinositol species acutely generated by activated macrophages regulates innate immune responses. J. Immunol. 190, 5169–5177 (2013).

    Article  PubMed  CAS  Google Scholar 

  17. Guijas, C., Meana, C., Astudillo, A.M., Balboa, M.A. & Balsinde, J. Foamy monocytes are enriched in cis-7-hexadecenoic fatty acid (16:1n-9), a possible biomarker for early detection of cardiovascular disease. Cell Chem. Biol. 23, 689–699 (2016).

    Article  CAS  PubMed  Google Scholar 

  18. Hinz, C. et al. Human platelets utilize cycloxygenase-1 to generate dioxolane A3, a neutrophil-activating eicosanoid. J. Biol. Chem. 291, 13448–13464 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Geiger, R. et al. L-arginine modulates t cell metabolism and enhances survival and anti-tumor activity. Cell 167, 829–842.e13 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Beyer, B.A. et al. Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation. Nat. Chem. Biol. 14, 22–28 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Patti, G.J. et al. Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin. Nat. Chem. Biol. 8, 232–234 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Prentice, K.J. et al. The furan fatty acid metabolite CMPF is elevated in diabetes and induces β cell dysfunction. Cell Metab. 19, 653–666 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wohlgemuth, G. et al. SPLASH, a hashed identifier for mass spectra. Nat. Biotechnol. 34, 1099–1101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Koeth, R.A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang, Z. et al. Non-lethal inhibition of gut microbial trimethylamine production for the treatment of atherosclerosis. Cell 163, 1585–1595 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wikoff, W.R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl. Acad. Sci. USA 106, 3698–3703 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Panopoulos, A.D. et al. The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Res. 22, 168–177 (2012).

    Article  CAS  PubMed  Google Scholar 

  31. Ezashi, T., Das, P. & Roberts, R.M. Low O2 tensions and the prevention of differentiation of hES cells. Proc. Natl. Acad. Sci. USA 102, 4783–4788 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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, 7074–7080 (2011).

    Article  CAS  PubMed  Google Scholar 

  33. Franklin, R.J.M. & Ffrench-Constant, C. Remyelination in the CNS: from biology to therapy. Nat. Rev. Neurosci. 9, 839–855 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Deshmukh, V.A. et al. A regenerative approach to the treatment of multiple sclerosis. Nature 502, 327–332 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fernández-Pisonero, I. et al. Synergy between sphingosine 1-phosphate and lipopolysaccharide signaling promotes an inflammatory, angiogenic and osteogenic response in human aortic valve interstitial cells. PLoS One 9, e109081 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Cardoso, C., Afonso, C. & Bandarra, N.M. Dietary DHA and health: cognitive function ageing. Nutr. Res. Rev. 29, 281–294 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Ng, C.M., Blackman, M.R., Wang, C. & Swerdloff, R.S. The role of carnitine in the male reproductive system. Ann. NY Acad. Sci. 1033, 177–188 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Wise, L.E., Shelton, C.C., Cravatt, B.F., Martin, B.R. & Lichtman, A.H. Assessment of anandamide's pharmacological effects in mice deficient of both fatty acid amide hydrolase and cannabinoid CB1 receptors. Eur. J. Pharmacol. 557, 44–48 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Hardeland, R. et al. Melatonin—a pleiotropic, orchestrating regulator molecule. Prog. Neurobiol. 93, 350–384 (2011).

    Article  CAS  PubMed  Google Scholar 

  40. Cohen, L.J. et al. Commensal bacteria make GPCR ligands that mimic human signalling molecules. Nature 549, 48–53 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  42. Kosina, S.M. et al. Exometabolomics assisted design and validation of synthetic obligate mutualism. ACS Synth. Biol. 5, 569–576 (2016).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge financial support from the National Institutes of Health (Grants R01 GM114368-03, P30 MH062261-10, P01 DA026146-02), and support was also received from Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley Laboratory for the US Department of Energy, Office of Science, Office of Biological and Environmental Research under Contract DE-AC02-05CH11231.

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Author notes

  1. Carlos Guijas, J Rafael Montenegro-Burke and Benedikt Warth: These authors contributed equally to this work.

Authors and Affiliations

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

    Carlos Guijas, J Rafael Montenegro-Burke, Benedikt Warth, Mary E Spilker & Gary Siuzdak

  2. Department of Food Chemistry and Toxicology, University of Vienna, Vienna, Austria

    Benedikt Warth

  3. Department of Integrative and Computational Biology, The Scripps Research Institute, La Jolla, California, USA

    Gary Siuzdak

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  1. Carlos Guijas
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Correspondence to Gary Siuzdak.

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Guijas, C., Montenegro-Burke, J., Warth, B. et al. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol 36, 316–320 (2018). https://doi.org/10.1038/nbt.4101

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