Revealing disease-associated pathways by network integration of untargeted metabolomics

• Published in: Nature methods Vol. 13; no. 9; pp. 770 - 776
• Main Authors: Pirhaji, Leila, Milani, Pamela, Leidl, Mathias, Curran, Timothy, Avila-Pacheco, Julian, Clish, Clary B, White, Forest M, Saghatelian, Alan, Fraenkel, Ernest
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.09.2016; Nature Publishing Group
• Subjects: 631/114/2397; 631/114/2401; 631/1647/320; 631/45/320; 631/553/794; Algorithms; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; Chromatography; Databases, Protein; Disease susceptibility; Fatty acids; Fatty Acids - metabolism; Genetic aspects; Humans; Huntington Disease - metabolism; Life Sciences; Machine Learning; Mass spectrometry; Metabolic Networks and Pathways; Metabolites; Metabolomics; Metabolomics - instrumentation; Metabolomics - methods; Molecular biology; Neural Networks (Computer); Proteins; Proteomics; Sphingolipids - metabolism; Steroids; Steroids - metabolism; United States
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.3940

A network-based method and computational tool, PIUMet, reveals disease-associated molecular pathways from untargeted metabolomics data without requiring mass-spectral feature identification. 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.