Revealing disease-associated pathways by network integration of untargeted metabolomics.
Nat Methods
; 13(9): 770-6, 2016 09.
Article
in En
| MEDLINE
| ID: mdl-27479327
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Neural Networks, Computer
/
Huntington Disease
/
Metabolic Networks and Pathways
/
Metabolomics
Type of study:
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Nat Methods
Journal subject:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Year:
2016
Type:
Article
Affiliation country:
United States