Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Revealing disease-associated pathways by network integration of untargeted metabolomics

Nature Methods volume 13, pages 770–776 (2016)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: PIUMet identifies disease-associated pathways and hidden components from untargeted metabolomic data.
Figure 2: PIUMet.
Figure 3: The PIUMet subnetwork showing altered sphingolipid metabolism in the STHdh cell line model of HD.
Figure 4: Altered fatty acid and steroid metabolic processes identified by PIUMet.
Figure 5
Figure 6: Regions of the resulting network obtained from integrative analysis of lipidomics and phosphoproteomics, displaying the dysregulation of high-scoring, hidden components.

References

  1. DeBerardinis, R.J. & Thompson, C.B. Cellular metabolism and disease: what do metabolic outliers teach us? Cell 148, 1132–1144 (2012).

    Article  CAS  Google Scholar 

  2. Patti, G.J., Yanes, O. & Siuzdak, G. Innovation: Metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13, 263–269 (2012).

    Article  CAS  Google Scholar 

  3. Baker, M. Metabolomics: from small molecules to big ideas. Nat. Methods 8, 117–121 (2011).

    Article  CAS  Google Scholar 

  4. Dunn, W.B. et al. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics 9, 44–66 (2013).

    Article  CAS  Google Scholar 

  5. Johnson, C.H., Ivanisevic, J., Benton, H.P. & Siuzdak, G. Bioinformatics: the next frontier of metabolomics. Anal. Chem. 87, 147–156 (2015).

    Article  CAS  Google Scholar 

  6. Cho, K., Mahieu, N.G., Johnson, S.L. & Patti, G.J. After the feature presentation: technologies bridging untargeted metabolomics and biology. Curr. Opin. Biotechnol. 28, 143–148 (2014).

    Article  CAS  Google Scholar 

  7. Grapov, D., Wanichthanarak, K. & Fiehn, O. MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics 31, 2757–2760 (2015).

    Article  CAS  Google Scholar 

  8. Kuo, T.-C., Tian, T.-F. & Tseng, Y.J. 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).

    Article  Google Scholar 

  9. Karnovsky, A. et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28, 373–380 (2012).

    Article  CAS  Google Scholar 

  10. Krumsiek, J. et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8, e1003005 (2012).

    Article  CAS  Google Scholar 

  11. Li, S. et al. Predicting network activity from high throughput metabolomics. PLoS Comput. Biol. 9, e1003123 (2013).

    Article  CAS  Google Scholar 

  12. Yeger-Lotem, E. et al. Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat. Genet. 41, 316–323 (2009).

    Article  CAS  Google Scholar 

  13. Tuncbag, N. et al. Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. J. Comput. Biol. 20, 124–136 (2013).

    Article  CAS  Google Scholar 

  14. Huang, S.-S.C. & Fraenkel, E. Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci. Signal. 2, ra40 (2009).

    PubMed  PubMed Central  Google Scholar 

  15. Trettel, F. et al. Dominant phenotypes produced by the HD mutation in STHdh(Q111) striatal cells. Hum. Mol. Genet. 9, 2799–2809 (2000).

    Article  CAS  Google Scholar 

  16. Maceyka, M., Harikumar, K.B., Milstien, S. & Spiegel, S. Sphingosine-1-phosphate signaling and its role in disease. Trends Cell Biol. 22, 50–60 (2012).

    Article  CAS  Google Scholar 

  17. Di Pardo, A. et al. FTY720 (fingolimod) is a neuroprotective and disease-modifying agent in cellular and mouse models of Huntington disease. Hum. Mol. Genet. 23, 2251–2265 (2014).

    Article  CAS  Google Scholar 

  18. Di Menna, L. et al. Fingolimod protects cultured cortical neurons against excitotoxic death. Pharmacol. Res. 67, 1–9 (2013).

    Article  CAS  Google Scholar 

  19. Deogracias, R. et al. 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).

    Article  CAS  Google Scholar 

  20. Valenza, M. & Cattaneo, E. Emerging roles for cholesterol in Huntington's disease. Trends Neurosci. 34, 474–486 (2011).

    Article  CAS  Google Scholar 

  21. Kreilaus, F., Spiro, A.S., Hannan, A.J., Garner, B. & Jenner, A.M. 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).

    Article  CAS  Google Scholar 

  22. Yehuda, S., Rabinovitz, S. & Mostofsky, D.I. Essential fatty acids and the brain: from infancy to aging. Neurobiol. Aging 26 (Suppl. 1), 98–102 (2005).

    Article  Google Scholar 

  23. Block, R.C., Dorsey, E.R., Beck, C.A., Brenna, J.T. & Shoulson, I. Altered cholesterol and fatty acid metabolism in Huntington disease. J. Clin. Lipidol. 4, 17–23 (2010).

    Article  Google Scholar 

  24. Puri, B.K. et al. Ethyl-EPA in Huntington disease: a double-blind, randomized, placebo-controlled trial. Neurology 65, 286–292 (2005).

    Article  CAS  Google Scholar 

  25. Puri, B.K. et al. Reduction in cerebral atrophy associated with ethyl-eicosapentaenoic acid treatment in patients with Huntington's disease. J. Int. Med. Res. 36, 896–905 (2008).

    Article  CAS  Google Scholar 

  26. López, M. & Vidal-Puig, A. Brain lipogenesis and regulation of energy metabolism. Curr. Opin. Clin. Nutr. Metab. Care 11, 483–490 (2008).

    Article  Google Scholar 

  27. Li, S.-H. & Li, X.-J. Huntingtin-protein interactions and the pathogenesis of Huntington's disease. Trends Genet. 20, 146–154 (2004).

    Article  Google Scholar 

  28. Stevnsner, T., Muftuoglu, M., Aamann, M.D. & Bohr, V.A. The role of Cockayne Syndrome group B (CSB) protein in base excision repair and aging. Mech. Ageing Dev. 129, 441–448 (2008).

    Article  CAS  Google Scholar 

  29. Subba Rao, K. Mechanisms of disease: DNA repair defects and neurological disease. Nat. Clin. Pract. Neurol. 3, 162–172 (2007).

    Article  Google Scholar 

  30. Razick, S., Magklaras, G. & Donaldson, I.M. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9, 405 (2008).

    Article  Google Scholar 

  31. Wishart, D.S. et al. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).

    Article  CAS  Google Scholar 

  32. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425 (2013).

    Article  CAS  Google Scholar 

  33. Aranda, B. et al. PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods 8, 528–529 (2011).

    Article  CAS  Google Scholar 

  34. Frolkis, A. et al. SMPDB: The Small Molecule Pathway Database. Nucleic Acids Res. 38, D480–D487 (2010).

    Article  CAS  Google Scholar 

  35. Hucka, M. et al. SBML Forum. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).

    Article  CAS  Google Scholar 

  36. Bornstein, B.J., Keating, S.M., Jouraku, A. & Hucka, M. LibSBML: an API library for SBML. Bioinformatics 24, 880–881 (2008).

    Article  CAS  Google Scholar 

  37. Huang, S.S. et al. Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling. PLoS Comput. Biol. 9, e1002887 (2013).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  39. Huang, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  Google Scholar 

  40. Saghatelian, A. et al. Assignment of endogenous substrates to enzymes by global metabolite profiling. Biochemistry 43, 14332–14339 (2004).

    Article  CAS  Google Scholar 

  41. Tautenhahn, R., Patti, G.J., Rinehart, D. & Siuzdak, G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).

    Article  CAS  Google Scholar 

  42. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 41, D8–D20 (2013).

  43. Gnad, F., Gunawardena, J. & Mann, M. PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res. 39, D253–D260 (2011).

    Article  CAS  Google Scholar 

  44. Schreiber, E., Matthias, P., Müller, M.M. & Schaffner, W. Rapid detection of octamer binding proteins with 'mini-extracts', prepared from a small number of cells. Nucleic Acids Res. 17, 6419 (1989).

    Article  CAS  Google Scholar 

  45. Ng, C.W. et al. Extensive changes in DNA methylation are associated with expression of mutant huntingtin. Proc. Natl. Acad. Sci. USA 110, 2354–2359 (2013).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank A. Soltis and S. Dalin. This work was supported by grants from US National Institute of Health R01-GM089903, U54-NS091046 and U01-CA184898 (E.F.), and National Cancer Institute U54 CA112967 (E.F. and F.M.W.) and P30 CA014051 (F.M.W.) as well as Searle Scholars Program (A.S.).

Author information

Authors and Affiliations

  1. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Leila Pirhaji, Pamela Milani, Timothy Curran, Forest M White & Ernest Fraenkel

  2. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA

    Mathias Leidl & Alan Saghatelian

  3. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Timothy Curran & Forest M White

  4. Broad Institute, Cambridge, Massachusetts, USA

    Julian Avila-Pacheco, Clary B Clish & Ernest Fraenkel

  5. Salk Institute for Biological Studies, La Jolla, California, USA

    Alan Saghatelian

Authors
  1. Leila Pirhaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pamela Milani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mathias Leidl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Timothy Curran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Julian Avila-Pacheco
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Clary B Clish
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Forest M White
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alan Saghatelian
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ernest Fraenkel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.P. and E.F. designed the approach. L.P. implemented the algorithm and performed the computational analyses. P.M. prepared cell cultures and performed western blot and cell viability experiments. M.L. and A.S. performed the untargeted lipidomic experiments. T.C. and F.M.W. measured global levels of phosphoproteins. J.A.-P. and C.B.C. performed targeted lipidomic experiments. L.P. and E.F. wrote the manuscript, and all the authors approved the final version.

Corresponding author

Correspondence to Ernest Fraenkel.

Ethics declarations

Competing interests

L.P. and E.F. are co-founders of ReviveMed, Inc., and have filed a provisional patent based on the work described here (application number US 62/203,292).

Integrated supplementary information

Supplementary Figure 1 A flow chart describing different steps of the PIUMet algorithm.

Supplementary Figure 2 The comparison of the disease-specific score distributions from real and random results.

The figure shows that the scores for the real results are significantly higher than random results (P value = 4.238E-37).

Supplementary Figure 3 The frequency of resulting hidden components from real data in the networks inferred from randomly selected metabolite features.

Supplementary Figure 4 A Venn diagram comparing the potential metabolites matching disease features based on mass compared to the putative ones inferred by PIUMet.

The metabolites inferred by PIUMet are significantly enriched for the metabolites detected by a targeted metabolomic platform (hypergeometric test P value= 6.00×10− 4). All of the metabolites identified by the targeted platform were dysregulated in diseased cells.

Supplementary Figure 5 Altered sphingolipids in STHdh Q111 cells compared to STHdh Q7 cells. These sphingolipids were measured in nine biological replicates.

Two-sided student t-test analysis showed significant changes in C24:0 Ceramide (d18:1) with P value =1.2×10−11 (a), C16:1 Ceramide (d18:1) with P value =7.74×10−5 (b), C24:1 Ceramide (d18:1) with P value =0.02 (c), and C24:1 sphingomyelin (SM) with P value =2.07×10−8 (d). The height of the bar plot shows the average of each sphingolipid levels, while the error bar shows their standard deviation.

Supplementary Figure 6 PIUMet inferred Sphingosine-1-phosphate (S1P) as a disease-modifying hidden component of dysregulated sphingolipid pathway.

(a) S1P was significantly downregulated (P value = 1.99×10−4, two-sided student t-test) in STHdh Q111 cells compared to STHdh Q7 cells. The bar plot shows the average levels of S1P that were measured in nine biological replicates. The error bars show the standard deviation of S1P levels (b). The treatment of diseased cells with an analogue of S1P (FTY720-P) significantly decreased apoptosis (P value = 7.98×10−5, two-sided student t-test). The bar plot shows the average percentage of cell death, and the error bars show the standard deviation from two independent experiments with twenty replicates each. (c) Calcein (green) detects viable cells and monitors changes in cell shape and morphology, while propidium iodide (PI – red) stains late apoptotic cells with damaged membranes.

Supplementary Figure 7 Western blot results showed that the DHCR7-encoded protein was significantly downregulated (P value = 0.025, two-sided student t-test) in the STHdh Q111 cells compared to STHdh Q7 cells.

The boxplot shows the measured protein levels in six biological replicates (black dots).

Supplementary Figure 8 Altered fatty acids in STHdh Q111 cells compared to STHdh Q7 cells.

These fatty acids were measured in nine biological replicates. Two-sided student t-test analysis showed significant changes in eicosapentaenoic acid (EPA, P value=7.8×10−6, a) and dihomo-gamma-linolenic acid (DHGLA, P value=0.01, b). The height of the bar plot shows the average of each fatty acid levels, while the error bars show their standard deviation.

Supplementary Figure 9 Western blot results showed that fatty acid synthase enzyme, encoded by the FASN gene, was significantly upregulated (P value = 0.025, two-sided student t-test) in the STHdh Q111 cells compared to STHdh Q7 cells.

The boxplot shows the measured protein levels in six biological replicates (black dots).

Supplementary Figure 10 Western blot results of measuring RASA1 and ERCC6 protein levels.

(a) Western blot results showed a significant increase (P value = 0.008, two-sided student t-test) in the RASA1 protein levels. The boxplot shows the measured protein levels in three biological replicates (black dots). (b) The plot shows Western blot results indicating a significant increase (P value =0.028, two-sided student t-test) in the ERCC6 protein levels. The boxplot shows the measured protein levels in six biological replicates (black dots).

Supplementary Figure 11 The comparison of degree distributions of features from terminal and detectable metabolite feature (DMF) sets.

The degree of a feature shows the number of metabolites corresponding to the feature.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11 and Supplementary Table 1 (PDF 4541 kb)

Supplementary Table 2

The summary of untargeted lipidomic data fromSTHdh cell lines. (XLSX 26 kb)

Supplementary Table 3

The summary of global phospho-proteomic data from STHdh cell lines. (XLSX 11 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pirhaji, L., Milani, P., Leidl, M. et al. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 13, 770–776 (2016). https://doi.org/10.1038/nmeth.3940

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3940

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research