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Nat Methods. 2016 Sep;13(9):770-6. doi: 10.1038/nmeth.3940. Epub 2016 Aug 1.

Revealing disease-associated pathways by network integration of untargeted metabolomics.

Author information

1
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
2
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.
3
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
4
Broad Institute, Cambridge, Massachusetts, USA.
5
Salk Institute for Biological Studies, La Jolla, California, USA.

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.

PMID:
27479327
PMCID:
PMC5209295
DOI:
10.1038/nmeth.3940
[Indexed for MEDLINE]
Free PMC Article

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