title: Revealing disease-associated pathways by network integration of untargeted metabolomics.
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.
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Leila
authors.initials: L
authors.lastname: Pirhaji
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Pamela
authors.initials: P
authors.lastname: Milani
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Mathias
authors.initials: M
authors.lastname: Leidl
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Curran
authors.affiliation: Broad Institute, Cambridge, Massachusetts, USA.
authors.forename: Julian
authors.initials: J
authors.lastname: Avila-Pacheco
authors.affiliation: Broad Institute, Cambridge, Massachusetts, USA.
authors.forename: Clary B
authors.identifier: 0000-0001-8259-9245
authors.initials: CB
authors.lastname: Clish
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Forest M
authors.initials: FM
authors.lastname: White
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.|Salk Institute for Biological Studies, La Jolla, California, USA.
authors.forename: Alan
authors.initials: A
authors.lastname: Saghatelian
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|Broad Institute, Cambridge, Massachusetts, USA.
authors.forename: Ernest
authors.identifier: 0000-0001-9249-8181
authors.initials: E
authors.lastname: Fraenkel
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D005227:Fatty Acids / Q000378:metabolism; D006801:Humans; D006816:Huntington Disease / Q000378:metabolism*; D000069550:Machine Learning; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000295:instrumentation / Q000379:methods*; D016571:Neural Networks, Computer; D013107:Sphingolipids / Q000378:metabolism; D013256:Steroids / Q000378:metabolism
chemical_list: D005227:Fatty Acids; D013107:Sphingolipids; D013256:Steroids