Article

Revealing disease-associated pathways by network integration of untargeted metabolomics

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Abstract

Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.

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References

  1. 1.

    & Cellular metabolism and disease: what do metabolic outliers teach us? Cell 148, 1132–1144 (2012).

  2. 2.

    , & Innovation: Metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13, 263–269 (2012).

  3. 3.

    Metabolomics: from small molecules to big ideas. Nat. Methods 8, 117–121 (2011).

  4. 4.

    et al. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics 9, 44–66 (2013).

  5. 5.

    , , & Bioinformatics: the next frontier of metabolomics. Anal. Chem. 87, 147–156 (2015).

  6. 6.

    , , & After the feature presentation: technologies bridging untargeted metabolomics and biology. Curr. Opin. Biotechnol. 28, 143–148 (2014).

  7. 7.

    , & MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics 31, 2757–2760 (2015).

  8. 8.

    , & 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).

  9. 9.

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

  10. 10.

    et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8, e1003005 (2012).

  11. 11.

    et al. Predicting network activity from high throughput metabolomics. PLoS Comput. Biol. 9, e1003123 (2013).

  12. 12.

    et al. Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat. Genet. 41, 316–323 (2009).

  13. 13.

    et al. Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. J. Comput. Biol. 20, 124–136 (2013).

  14. 14.

    & Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci. Signal. 2, ra40 (2009).

  15. 15.

    et al. Dominant phenotypes produced by the HD mutation in STHdh(Q111) striatal cells. Hum. Mol. Genet. 9, 2799–2809 (2000).

  16. 16.

    , , & Sphingosine-1-phosphate signaling and its role in disease. Trends Cell Biol. 22, 50–60 (2012).

  17. 17.

    et al. FTY720 (fingolimod) is a neuroprotective and disease-modifying agent in cellular and mouse models of Huntington disease. Hum. Mol. Genet. 23, 2251–2265 (2014).

  18. 18.

    et al. Fingolimod protects cultured cortical neurons against excitotoxic death. Pharmacol. Res. 67, 1–9 (2013).

  19. 19.

    et al. Fingolimod, a sphingosine-1 phosphate receptor modulator, increases BDNF levels and improves symptoms of a mouse model of Rett syndrome. Proc. Natl. Acad. Sci. USA 109, 14230–14235 (2012).

  20. 20.

    & Emerging roles for cholesterol in Huntington's disease. Trends Neurosci. 34, 474–486 (2011).

  21. 21.

    , , , & Brain cholesterol synthesis and metabolism is progressively disturbed in the R6/1 mouse model of Huntington's disease: a targeted GC-MS/MS sterol analysis. J. Huntingtons Dis. 4, 305–318 (2015).

  22. 22.

    , & Essential fatty acids and the brain: from infancy to aging. Neurobiol. Aging 26 (Suppl. 1), 98–102 (2005).

  23. 23.

    , , , & Altered cholesterol and fatty acid metabolism in Huntington disease. J. Clin. Lipidol. 4, 17–23 (2010).

  24. 24.

    et al. Ethyl-EPA in Huntington disease: a double-blind, randomized, placebo-controlled trial. Neurology 65, 286–292 (2005).

  25. 25.

    et al. Reduction in cerebral atrophy associated with ethyl-eicosapentaenoic acid treatment in patients with Huntington's disease. J. Int. Med. Res. 36, 896–905 (2008).

  26. 26.

    & Brain lipogenesis and regulation of energy metabolism. Curr. Opin. Clin. Nutr. Metab. Care 11, 483–490 (2008).

  27. 27.

    & Huntingtin-protein interactions and the pathogenesis of Huntington's disease. Trends Genet. 20, 146–154 (2004).

  28. 28.

    , , & The role of Cockayne Syndrome group B (CSB) protein in base excision repair and aging. Mech. Ageing Dev. 129, 441–448 (2008).

  29. 29.

    Mechanisms of disease: DNA repair defects and neurological disease. Nat. Clin. Pract. Neurol. 3, 162–172 (2007).

  30. 30.

    , & iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9, 405 (2008).

  31. 31.

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

  32. 32.

    et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425 (2013).

  33. 33.

    et al. PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods 8, 528–529 (2011).

  34. 34.

    et al. SMPDB: The Small Molecule Pathway Database. Nucleic Acids Res. 38, D480–D487 (2010).

  35. 35.

    et al. SBML Forum. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).

  36. 36.

    , , & LibSBML: an API library for SBML. Bioinformatics 24, 880–881 (2008).

  37. 37.

    et al. Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling. PLoS Comput. Biol. 9, e1002887 (2013).

  38. 38.

    , , , & Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach. BMC Bioinformatics 11, 355 (2010).

  39. 39.

    , & Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

  40. 40.

    et al. Assignment of endogenous substrates to enzymes by global metabolite profiling. Biochemistry 43, 14332–14339 (2004).

  41. 41.

    , , & XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).

  42. 42.

    NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 41, D8–D20 (2013).

  43. 43.

    , & PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res. 39, D253–D260 (2011).

  44. 44.

    , , & Rapid detection of octamer binding proteins with 'mini-extracts', prepared from a small number of cells. Nucleic Acids Res. 17, 6419 (1989).

  45. 45.

    et al. Extensive changes in DNA methylation are associated with expression of mutant huntingtin. Proc. Natl. Acad. Sci. USA 110, 2354–2359 (2013).

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Acknowledgements

We thank A. Soltis and S. Dalin. This work was supported by grants from US National Institute of Health R01-GM089903, U54-NS091046 and U01-CA184898 (E.F.), and National Cancer Institute U54 CA112967 (E.F. and F.M.W.) and P30 CA014051 (F.M.W.) as well as Searle Scholars Program (A.S.).

Author information

Affiliations

  1. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Leila Pirhaji
    • , Pamela Milani
    • , Timothy Curran
    • , Forest M White
    •  & Ernest Fraenkel
  2. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.

    • Mathias Leidl
    •  & Alan Saghatelian
  3. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Timothy Curran
    •  & Forest M White
  4. Broad Institute, Cambridge, Massachusetts, USA.

    • Julian Avila-Pacheco
    • , Clary B Clish
    •  & Ernest Fraenkel
  5. Salk Institute for Biological Studies, La Jolla, California, USA.

    • Alan Saghatelian

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Contributions

L.P. and E.F. designed the approach. L.P. implemented the algorithm and performed the computational analyses. P.M. prepared cell cultures and performed western blot and cell viability experiments. M.L. and A.S. performed the untargeted lipidomic experiments. T.C. and F.M.W. measured global levels of phosphoproteins. J.A.-P. and C.B.C. performed targeted lipidomic experiments. L.P. and E.F. wrote the manuscript, and all the authors approved the final version.

Competing interests

L.P. and E.F. are co-founders of ReviveMed, Inc., and have filed a provisional patent based on the work described here (application number US 62/203,292).

Corresponding author

Correspondence to Ernest Fraenkel.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–11 and Supplementary Table 1

Excel files

  1. 1.

    Supplementary Table 2

    The summary of untargeted lipidomic data fromSTHdh cell lines.

  2. 2.

    Supplementary Table 3

    The summary of global phospho-proteomic data from STHdh cell lines.