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 algorithm13 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.

Metabolomics: A Primer

“…A further complication is that chromatographic retention times (RT) are highly dependent on the LC or GC setup, are difficult to reproduce from external databases, and also vary over time even within a given lab. Lots of efforts have been made to advance untargeted analysis and feature identification, including new MS/MS workflow or network integration (Table 2) [62, 63]. A more detailed discussion of unknown metabolite identification is contained in a recent article [4].…”

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

“…For example, using a general linear model, Padayachee et al designed a statistical model to integrate metabolomic and transcriptomic data to understand the biological pathways leading to complex disease [114]. Using a network-based approach, PIUMet (prize-collecting Steiner forest algorithms for integrative analysis of untargeted metabolomics) combines untargeted metabolomic data and proteomic data to analyze molecular changes in disease [115]. Multivariate statistical analyses have given us the power to put multiple omics data into a statistical framework to understand the ageing process at a systems level.…”

Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies

“…Gene knock‐outs can by simulated in tissue‐specific models to direct further molecular validation of regulatory mechanisms 55. Methods to infer missing or unidentified metabolites in untargeted metabolomic studies, incorporating network techniques, will facilitate a tissue‐specific understanding 56. These studies, in due course, will provide the input to the development of coupled, multi‐tissue whole joint metabolic models.…”