Revealing disease-associated pathways by network integration of untargeted metabolomics
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Author(s)
Leidl, Mathias
Avila-Pacheco, Julian
Pirhaji, Leila
Milani, Pamela
Curran, Timothy G.
Clish, Clary
Saghatelian, Alan
Fraenkel, Ernest
White, Forest M.
Date Issued
August 2016
Journal
Nature Methods
Publisher
Springer Nature
Citation
Pirhaji, Leila, Pamela Milani, Mathias Leidl, Timothy Curran, Julian Avila-Pacheco, Clary B Clish, Forest M White, Alan Saghatelian, and Ernest Fraenkel. “Revealing Disease-Associated Pathways by Network Integration of Untargeted Metabolomics.” Nature Methods 13, no. 9 (August 1, 2016): 770–776.
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Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
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DOI of Published Version
https://doi.org/10.1038/NMETH.3940
