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Revealing disease-associated pathways by network integration of untargeted metabolomics

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Pirhaji, Leila, Milani, Pamela, Leidl, Mathias, Curran, Timothy, Avila-Pacheco, Julian, Clish, Clary B, White, Forest M, Saghatelian, Alan +1 more·2016·Nature Methods
DOI 10.1038/nmeth.3940PMC PMC5209295
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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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  • PIUMet discovers dysregulated molecular pathways and components associated with changes in untargeted metabolomics data by analyzing a network built from curated protein-protein and protein-metabolite interactions (PPMI).

    “PIUMet discovers dysregulated molecular pathways and components associated with changes in untargeted metabolomics data by analyzing a network built from curated protein-protein and protein-metabolite interactions (PPMI).”

    From APOE4 is Associated with Differential Regional Vulnerability to Bioenergetic Deficits in Aged APOE Mice (2020) · cites this paper as [34]

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  1. [1]DeBerardinis RJ Thompson CB Cellular metabolism and disease: what do metabolic outliers teach us? Cell 148 1132 1144 2012 22424225 PMC3337773
  2. [2]Patti GJ Yanes O Siuzdak G Innovation: Metabolomics: the apogee of the omics trilogy Nat Rev Mol Cell Biol 13 263 269 2012 22436749 PMC3682684
  3. [5]Johnson CH Ivanisevic J Benton HP Siuzdak G Bioinformatics: the next frontier of metabolomics Anal Chem 87 147 156 2015 25389922 PMC4287838
  4. [6]Cho K Mahieu NG Johnson SL Patti GJ After the feature presentation: technologies bridging untargeted metabolomics and biology Curr Opin Biotechnol 28 143 148 2014 24816495 PMC4111999
  5. [7]Grapov D Wanichthanarak K Fiehn O MetaMapR: pathway independent metabolomic network analysis incorporating unknowns Bioinformatics 31 2757 2760 2015 25847005 PMC4528626
  6. [8]Kuo TC Tian TF Tseng YJ 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data BMC Syst Biol 7 64 2013 23875761 PMC3723580
  7. [9]Karnovsky A Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data Bioinformatics 28 373 380 2012 22135418 PMC3268237
  8. [11]Li S Predicting network activity from high throughput metabolomics PLOS Comput Biol 9 e1003123 2013 23861661 PMC3701697
  9. [12]Yeger-Lotem E Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity Nat Genet 41 316 323 2009 19234470 PMC2733244
  10. [13]Tuncbag N Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem J Comput Biol 20 124 136 2013 23383998 PMC3576906
  11. [14]Huang SSC Fraenkel E Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks Sci Signal 2 ra40 2009 19638617 PMC2889494
  12. [15]Trettel F Dominant phenotypes produced by the HD mutation in STHdh(Q111) striatal cells Hum Mol Genet 9 2799 2809 2000 11092756
  13. [16]Maceyka M Harikumar KB Milstien S Spiegel S Sphingosine-1-phosphate signaling and its role in disease Trends Cell Biol 22 50 60 2012 22001186 PMC3253987
  14. [17]Di Pardo A FTY720 (fingolimod) is a neuroprotective and disease-modifying agent in cellular and mouse models of Huntington disease Hum Mol Genet 23 2251 2265 2014 24301680
  15. [18]Di Menna L Fingolimod protects cultured cortical neurons against excitotoxic death Pharmacol Res 67 1 9 2013 23073075
  16. [19]Deogracias R Fingolimod, a sphingosine-1 phosphate receptor modulator, increases BDNF levels and improves symptoms of a mouse model of Rett syndrome Proc Natl Acad Sci USA 109 14230 14235 2012 22891354 PMC3435172
  17. [20]Valenza M Cattaneo E Emerging roles for cholesterol in Huntington’s disease Trends Neurosci 34 474 486 2011 21774998
  18. [21]Kreilaus F Spiro AS Hannan AJ Garner B Jenner AM Brain cholesterol synthesis and metabolism is progressively disturbed in the R6/1 mouse model of Huntington’s disease: a targeted GC-MS/MS sterol analysis J Huntingtons Dis 4 305 318 2015 26639223
  19. [22]Yehuda S Rabinovitz S Mostofsky DI Essential fatty acids and the brain: from infancy to aging Neurobiol Aging 26 Suppl 1 98 102 2005 16226347
  20. [24]Puri BK Ethyl-EPA in Huntington disease: a double-blind, randomized, placebo-controlled trial Neurology 65 286 292 2005 16043801
  21. [25]Puri BK Reduction in cerebral atrophy associated with ethyl-eicosapentaenoic acid treatment in patients with Huntington’s disease J Int Med Res 36 896 905 2008 18831882
  22. [26]López M Vidal-Puig A Brain lipogenesis and regulation of energy metabolism Curr Opin Clin Nutr Metab Care 11 483 490 2008 18542011
  23. [27]Li SH Li XJ Huntingtin-protein interactions and the pathogenesis of Huntington’s disease Trends Genet 20 146 154 2004 15036808
  24. [28]Stevnsner T Muftuoglu M Aamann MD Bohr VA The role of Cockayne Syndrome group B (CSB) protein in base excision repair and aging Mech Ageing Dev 129 441 448 2008 18541289 PMC2538557
  25. [29]Subba Rao K Mechanisms of disease: DNA repair defects and neurological disease Nat Clin Pract Neurol 3 162 172 2007 17342192
  26. [30]Razick S Magklaras G Donaldson IM iRefIndex: a consolidated protein interaction database with provenance BMC Bioinformatics 9 405 2008 18823568 PMC2573892
  27. [31]Wishart DS HMDB 3.0—The Human Metabolome Database in 2013 Nucleic Acids Res 41 D801 D807 2013 23161693 PMC3531200
  28. [33]Aranda B PSICQUIC and PSISCORE: accessing and scoring molecular interactions Nat Methods 8 528 529 2011 21716279 PMC3246345
  29. [34]Frolkis A SMPDB: The Small Molecule Pathway Database Nucleic Acids Res 38 D480 D487 2010 19948758 PMC2808928
  30. [35]Hucka M SBML Forum The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models Bioinformatics 19 524 531 2003 12611808
  31. [37]Huang SS Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling PLOS Comput Biol 9 e1002887 2013 23408876 PMC3567149
  32. [38]Bailly-Bechet M Braunstein A Pagnani A Weigt M Zecchina R Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach BMC Bioinformatics 11 355 2010 20587029 PMC2909222
  33. [39]Huang W Sherman BT Lempicki RA Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources Nat Protoc 4 44 57 2009 19131956
  34. [40]Saghatelian A Assignment of endogenous substrates to enzymes by global metabolite profiling Biochemistry 43 14332 14339 2004 15533037
  35. [41]Tautenhahn R Patti GJ Rinehart D Siuzdak G XCMS Online: a web-based platform to process untargeted metabolomic data Anal Chem 84 5035 5039 2012 22533540 PMC3703953
  36. [42]NCBI Resource Coordinators Database resources of the National Center for Biotechnology Information Nucleic Acids Res 41 D8 D20 2013 23193264 PMC3531099
  37. [43]Gnad F Gunawardena J Mann M PHOSIDA 2011: the posttranslational modification database Nucleic Acids Res 39 D253 D260 2011 21081558 PMC3013726
  38. [44]Schreiber E Matthias P Müller MM Schaffner W Rapid detection of octamer binding proteins with ‘mini-extracts’, prepared from a small number of cells Nucleic Acids Res 17 6419 1989 2771659 PMC318318
  39. [45]Ng CW Extensive changes in DNA methylation are associated with expression of mutant huntingtin Proc Natl Acad Sci USA 110 2354 2359 2013 23341638 PMC3568325

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Authors
Pirhaji, L., Milani, P., Leidl, M., Curran, T., Avila-Pacheco, J., Clish, C. B., White, F. M., Saghatelian, A., & Fraenkel, E.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Harvard University (US)
  • Koch Institute for Integrative Cancer Research At MIT (US)
  • Broad Institute (US)
  • Salk Institute for Biological Studies (US)
Corresponding author
Fraenkel, Ernest
ORCID
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2016-08-01
Volume
13
Issue
9
Pages
770-776
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/nmeth.3940
PMC
PMC5209295
OpenAlex
W2483024785
Cited by
167
References
4
Citations by year
2026: 15 · 2025: 13 · 2024: 8 · 2023: 11 · 2022: 17 · 2021: 14 · 2020: 21 · 2019: 19 · 2018: 24 · 2017: 22 · 2016: 3
FWCI
9.68
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Methods (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
Funders
  • National Cancer Institute (U54CA112967, U01CA184898, P30CA014195, P30CA014051)
  • National Institute of General Medical Sciences (R01GM089903)
  • National Institute of Neurological Disorders and Stroke (U54NS091046, R01NS089076)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords
untargeted metabolomics, network integration, metabolite identification, sphingolipid metabolism, fatty acid metabolism, steroid metabolism, Huntington's disease, dysregulated proteins
MeSH terms
Machine Learning, Algorithms, Fatty Acids, Humans, Huntington Disease, Sphingolipids, Steroids, Neural Networks, Computer, Databases, Protein, Metabolic Networks and Pathways, Metabolomics

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