ArticleNature methods2016
Revealing disease-associated pathways by network integration of untargeted metabolomics.
Article in Nature methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed.
- Network propagation in bipartite metabolite-reaction graphs for metabolomic data exploration.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Huntington's disease vasculopathy is barrier-type-selective and arteriolar-dominant with spatially-coupled astrogliosis and reversible by S1pr1 stimulation.Nature communications · 2026Article
- PCIPG: A comprehensive framework for protein complex identification based on a probabilistic graphical model.PLoS computational biology · 2026Article
- Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.Analytical chemistry · 2026Article
- Circulating metabolites associated with protection against Mycobacterium tuberculosis infection and inhibition of mycobacterial growth.Communications biology · 2026Article
- iModMix: integrative module analysis for multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- TidyMass2: advancing LC-MS untargeted metabolomics through metabolite origin inference and metabolic feature-based functional module analysis.Nature communications · 2026Article
- Filtration-enriched metabolites and their association with salivary microbiota: a combined two-cohort analysis.Journal of oral microbiology · 2026Article
- Untargeted Metabolomics for Diagnosis, Monitoring, and Understanding the Pathophysiology of Inherited Metabolic Disorders.Journal of inherited metabolic disease · 2026Review
- mzLearn as a data-driven LC/MS signal detection algorithm that enables pre-trained generative models for untargeted metabolomics.Communications chemistry · 2025Article
- A machine learning framework for classifying lipids in untargeted metabolomics using mass-to-charge ratios and retention times.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- An integrative systems-biology approach defines mechanisms of Alzheimer's disease neurodegeneration.Nature communications · 2025Article
- CellPHIE: Integrating Pathway Discovery With Pooled Profiling of Perturbations Uncovers Pathways of Huntington's Disease, Including Genetic Modifiers of Neuronal Development and Morphology.bioRxiv : the preprint server for biology · 2025Article
- Lipidomics of Huntington's Disease: A Comprehensive Review of Current Status and Future Directions.Metabolites · 2025Review
- Differential metabolic responses of mouse Leydig and spermatogonia cells to radiofrequency electromagnetic field exposure.Frontiers in public health · 2025Article
- Inflammatory and metabolic markers mediate the association between urinary metals and non-alcoholic fatty liver disease in U.S. adults: a cross-sectional study.Frontiers in public health · 2025Article
- Longitudinal urine metabolic profiling and gestational age prediction in human pregnancy.Briefings in bioinformatics · 2024Article
- An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.Nature communications · 2024Article
- A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study.International journal of cancer · 2024Article
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29 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
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.
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