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.

  • Protocol
  • Published:

MetaNetwork: a computational protocol for the genetic study of metabolic networks

Nature Protocols volume 2, pages 685–694 (2007)Cite this article

Abstract

We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a two-part model to account for the often observed spike in the distribution of metabolite abundance data. MetaNetwork predicts and visualizes potential associations between metabolites using correlations of mQTL profiles, rather than of abundance profiles. Simulation and permutation procedures are used to assess statistical significance. Analysis of about 20 metabolite mass peaks from a mass spectrometer takes a few minutes on a desktop computer. Analysis of 2,000 mass peaks will take up to 4 days. In addition, MetaNetwork is able to integrate high-throughput data from subsequent metabolomics, transcriptomics and proteomics experiments in conjunction with traditional phenotypic data. This way MetaNetwork will contribute to a better integration of such data into systems biology.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: MetaNetwork flowchart.
Figure 2: The view of the R console for the MetaNetwork application.
Figure 3: The visualization of metabolic QTL profiles and networks.

Similar content being viewed by others

References

  1. Wink, M. Plant breeding: importance of plant secondary metabolites for protection against pathogens and herbivores. Theor. Appl. Genet. 75, 225–233 (1988).

    Article  CAS  Google Scholar 

  2. Baxter, J.D. & Webb, P. Metabolism: bile acids heat things up. Nature 439, 402–403 (2006).

    Article  CAS  Google Scholar 

  3. Keurentjes, J.J.B. et al. The genetics of plant metabolism. Nat. Genet. 38, 842–849 (2006).

    Article  CAS  Google Scholar 

  4. Jansen, R.C. & Nap, J.P. Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001).

    Article  CAS  Google Scholar 

  5. Brem, R.B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).

    Article  CAS  Google Scholar 

  6. Bystrykh, L. et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using “genetical genomics”. Nat. Genet. 37, 225–232 (2005).

    Article  CAS  Google Scholar 

  7. Chesler, E.J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat. Genet. 37, 233–242 (2005).

    Article  CAS  Google Scholar 

  8. Cheung, V.G. et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437, 1365–1369 (2005).

    Article  CAS  Google Scholar 

  9. DeCook, R., Lall, S., Nettleton, D. & Howell, S.H. Genetic regulation of gene expression during shoot development in Arabidopsis . Genetics 172, 1155–1164 (2006).

    Article  CAS  Google Scholar 

  10. Hubner, N. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat. Genet. 37, 243–253 (2005).

    Article  CAS  Google Scholar 

  11. Keurentjes, J.J.B. et al. Regulatory network construction in Arabidopsis using genome-wide expression QTLs. Proc. Natl. Acad. Sci. USA 104, 1708–1713 (2007).

    Article  CAS  Google Scholar 

  12. Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004).

    Article  CAS  Google Scholar 

  13. Schadt, E.E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003).

    Article  CAS  Google Scholar 

  14. Yvert, G. et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat. Genet. 35, 57–64 (2003).

    Article  CAS  Google Scholar 

  15. Bing, N. & Hoeschele, I. Genetical genomics analysis of a yeast segregant population for transcription network inference. Genetics 170, 533–542 (2005).

    Article  CAS  Google Scholar 

  16. Lan, H. et al. Combined expression trait correlations and expression quantitative trait locus mapping. PLoS. Genet. 2, e6 (2006).

    Article  Google Scholar 

  17. Zhu, J. et al. An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet. Genome Res. 105, 363–374 (2004).

    Article  CAS  Google Scholar 

  18. Broman, K.W. Mapping quantitative trait loci in the case of a spike in the phenotype distribution. Genetics 163, 1169–1175 (2003).

    PubMed  PubMed Central  Google Scholar 

  19. Sabatti, C., Service, S. & Freimer, N. False discovery rate in linkage and association genome screens for complex disorders. Genetics 164, 829–833 (2003).

    PubMed  PubMed Central  Google Scholar 

  20. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  Google Scholar 

  21. Broman, K.W. Mapping expression in randomized rodent genomes. Nat. Genet. 37, 209–210 (2005).

    Article  CAS  Google Scholar 

  22. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  Google Scholar 

  23. Zhang, P. et al. MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol. 138, 27–37 (2005).

    Article  CAS  Google Scholar 

  24. Mueller, L.A., Zhang, P. & Rhee, S.Y. AraCyc: a biochemical pathway database for Arabidopsis . Plant Physiol. 132, 453–460 (2003).

    Article  CAS  Google Scholar 

  25. Dijkstra, M., Vonk, R.J. & Jansen, R.C. SELDI-TOF mass spectra: a view on sources of variation. J. Chromatogr. B 847, 12–23 (2007).

    Article  CAS  Google Scholar 

  26. Tikunov, Y. et al. A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles. Plant Physiol. 139, 1125–1137 (2005).

    Article  CAS  Google Scholar 

  27. de Vos, C.H. et al. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Protoc. DOI 10.1038/nprot.2007.95 (2007).

  28. Lisec, J., Schauer, N., Kopka, J., Willmitzer, L. & Fernie, A.R. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat. Protoc. 1, 387–396 (2006).

    Article  CAS  Google Scholar 

  29. Kliebenstein, D.J., Gershenzon, J. & Mitchell-Olds, T. Comparative quantitative trait loci mapping of aliphatic, indolic and benzylic glucosinolate production in Arabidopsis thaliana leaves and seeds. Genetics 159, 359–370 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Hoheisel, J.D. Microarray technology: beyond transcript profiling and genotype analysis. Nat. Rev. Genet. 7, 200–210 (2006).

    Article  CAS  Google Scholar 

  31. Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).

    Google Scholar 

  32. Butte, A.J. & Kohane, I.S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 5, 418–429 (2000).

    Google Scholar 

  33. Jansen, R.C. Interval mapping of multiple quantitative trait loci. Genetics 135, 205–211 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Jansen, R.C. Quantitative trait loci in inbred lines. in Handbook of Statistical Genetics (eds. Balding, D.J., Bishop, M. & Cannings, C.) 445–476 (John Wiley & Sons, New York, 2003).

    Google Scholar 

  35. Swertz, M.A. & Jansen, R.C. Beyond standardization: dynamics software infrastructures for systems biology. Nat. Rev. Genet. 8, 235–243 (2007).

    Article  CAS  Google Scholar 

  36. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  37. Kliebenstein, D.J., Lambrix, V.M., Reichelt, M., Gershenzon, J. & Mitchell-Olds, T. Gene duplication in the diversification of secondary metabolism: tandem 2-oxoglutarate-dependent dioxygenases control glucosinolate biosynthesis in Arabidopsis . Plant Cell 13, 681–693 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Kliebenstein, D.J. et al. Genetic control of natural variation in Arabidopsis glucosinolate accumulation. Plant Physiol. 126, 811–825 (2001).

    Article  CAS  Google Scholar 

  39. Kroymann, J. et al. A gene controlling variation in Arabidopsis glucosinolate composition is part of the methionine chain elongation pathway. Plant Physiol. 127, 1077–1088 (2001).

    Article  CAS  Google Scholar 

  40. de la Fuente, A., Bing, N., Hoeschele, I. & Mendes, P. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20, 3565–3574 (2004).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Dr. Jan-Peter Nap for constructive comments on an earlier version of this paper, Bruno Tesson, Gonzalo Vera and Richard Scheltema for helping to develop the R-package, and Martijn Dijkstra and Rainer Breitling for helping to predict multiple peaks belonging to the same metabolite. This work was supported by grants from the Netherlands Organization for Scientific Research Program Genomics (050-10-029).

Author information

Authors and Affiliations

  1. Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Kerklaan 30, Haren, NL-9751 NN, The Netherlands

    Jingyuan Fu, Morris A Swertz & Ritsert C Jansen

  2. Laboratory of Genetics, Wageningen University, Arboretumlaan 4, Wageningen, NL-6703 BD, The Netherlands

    Joost JB Keurentjes

  3. Laboratory of Plant Physiology, Wageningen University, Arboretumlaan 4, Wageningen, NL-6703 BD, The Netherlands

    Joost JB Keurentjes

Authors
  1. Jingyuan Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Morris A Swertz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joost JB Keurentjes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ritsert C Jansen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ritsert C Jansen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Manual

Package 'MetaNetwork' (PDF 143 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fu, J., Swertz, M., Keurentjes, J. et al. MetaNetwork: a computational protocol for the genetic study of metabolic networks. Nat Protoc 2, 685–694 (2007). https://doi.org/10.1038/nprot.2007.96

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2007.96

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing