Revealing disease-associated pathways by network integration of untargeted metabolomics

• Main Authors: Leidl, Mathias (Author), Avila-Pacheco, Julian (Author), Pirhaji, Leila (Contributor), Milani, Pamela (Contributor), Curran, Timothy G. (Contributor), Clish, Clary (Contributor), Saghatelian, Alan (Contributor), Fraenkel, Ernest (Contributor), White, Forest M. (Author)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Biology (Contributor), White, Forest M (Contributor)
• Format: Article
• Language: English
• Published: Springer Nature, 2018-09-05T17:26:25Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Institutes of Health (U.S.) (grant R01-GM089903)
National Institutes of Health (U.S.) (grant U54-NS091046)
National Institutes of Health (U.S.) (grant U01-CA184898)
National Cancer Institute (U.S.) (grant U54 CA112967)
National Cancer Institute (U.S.) (grant P30 CA014051)
Searle Scholars Program