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.

A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans

Nature Genetics volume 40, pages 181–188 (2008)Cite this article

Abstract

The fundamental aim of genetics is to understand how an organism's phenotype is determined by its genotype, and implicit in this is predicting how changes in DNA sequence alter phenotypes. A single network covering all the genes of an organism might guide such predictions down to the level of individual cells and tissues. To validate this approach, we computationally generated a network covering most C. elegans genes and tested its predictive capacity. Connectivity within this network predicts essentiality, identifying this relationship as an evolutionarily conserved biological principle. Critically, the network makes tissue-specific predictions—we accurately identify genes for most systematically assayed loss-of-function phenotypes, which span diverse cellular and developmental processes. Using the network, we identify 16 genes whose inactivation suppresses defects in the retinoblastoma tumor suppressor pathway, and we successfully predict that the dystrophin complex modulates EGF signaling. We conclude that an analogous network for human genes might be similarly predictive and thus facilitate identification of disease genes and rational therapeutic targets.

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

Get just this article for as long as you need it

$39.95

Learn more

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

Figure 1: Construction, accuracy and extent of the C. elegans gene network.
Figure 2: Network topology-based prediction of gene essentiality.
Figure 3: Network-based prediction of loss-of-function phenotypes.
Figure 4: Identification of genes that antagonize the retinoblastoma-synMuv pathway.
Figure 5: Verifying a functional interaction between the dystrophin associated protein complex (DAPC) and EGF-Ras-MAPK signaling.

References

  1. Bowers, P.M. et al. Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol. 5, R35 (2004).

    Article  Google Scholar 

  2. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 306, 1555–1558 (2004).

    Article  CAS  Google Scholar 

  3. Troyanskaya, O.G., Dolinski, K., Owen, A.B., Altman, R.B. & Botstein, D. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl. Acad. Sci. USA 100, 8348–8353 (2003).

    Article  CAS  Google Scholar 

  4. von Mering, C. et al. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 31, 258–261 (2003).

    Article  CAS  Google Scholar 

  5. Rhodes, D.R. et al. Probabilistic model of the human protein-protein interaction network. Nat. Biotechnol. 23, 951–959 (2005).

    Article  CAS  Google Scholar 

  6. Chen, Y. & Xu, D. Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae. Nucleic Acids Res. 32, 6414–6424 (2004).

    Article  CAS  Google Scholar 

  7. Jeong, H., Mason, S.P., Barabasi, A.L. & Oltvai, Z.N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    Article  CAS  Google Scholar 

  8. Famili, I., Forster, J., Nielsen, J. & Palsson, B.O. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA 100, 13134–13139 (2003).

    Article  CAS  Google Scholar 

  9. Gunsalus, K.C. et al. Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis. Nature 436, 861–865 (2005).

    Article  CAS  Google Scholar 

  10. Li, S. et al. A map of the interactome network of the metazoan C. elegans. Science 303, 540–543 (2004).

    Article  CAS  Google Scholar 

  11. Stuart, J.M., Segal, E., Koller, D. & Kim, S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

    Article  CAS  Google Scholar 

  12. Zhong, W. & Sternberg, P.W. Genome-wide prediction of C. elegans genetic interactions. Science 311, 1481–1484 (2006).

    Article  CAS  Google Scholar 

  13. Chen, N. et al. WormBase: a comprehensive data resource for Caenorhabditis biology and genomics. Nucleic Acids Res. 33, D383–D389 (2005).

    Article  CAS  Google Scholar 

  14. Giot, L. et al. A protein interaction map of Drosophila melanogaster. Science 302, 1727–1736 (2003).

    Article  CAS  Google Scholar 

  15. Lee, I., Li, Z. & Marcotte, E.M. An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae. PLoS ONE 2, e988 (2007).

    Article  Google Scholar 

  16. Remm, M., Storm, C.E. & Sonnhammer, E.L. Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J. Mol. Biol. 314, 1041–1052 (2001).

    Article  CAS  Google Scholar 

  17. Gandhi, T.K. et al. Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat. Genet. 38, 285–293 (2006).

    Article  CAS  Google Scholar 

  18. Deplancke, B. et al. A gene-centered C. elegans protein-DNA interaction network. Cell 125, 1193–1205 (2006).

    Article  CAS  Google Scholar 

  19. Kamath, R.S. et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421, 231–237 (2003).

    Article  CAS  Google Scholar 

  20. Marcotte, E.M., Pellegrini, M., Thompson, M.J., Yeates, T.O. & Eisenberg, D. A combined algorithm for genome-wide prediction of protein function. Nature 402, 83–86 (1999).

    Article  CAS  Google Scholar 

  21. Walker, M.G., Volkmuth, W. & Klingler, T.M. Pharmaceutical target discovery using Guilt-by-Association: schizophrenia and Parkinson's disease genes. Proc. Int. Conf. Intell. Syst. Mol. Biol. 1999, 282–286 (1999).

    Google Scholar 

  22. Hansen, M., Hsu, A.L., Dillin, A. & Kenyon, C. New genes tied to endocrine, metabolic, and dietary regulation of lifespan from a Caenorhabditis elegans genomic RNAi screen. PLoS Genet. 1, 119–128 (2005).

    Article  CAS  Google Scholar 

  23. Hamilton, B. et al. A systematic RNAi screen for longevity genes in C. elegans. Genes Dev. 19, 1544–1555 (2005).

    Article  CAS  Google Scholar 

  24. Curran, S.P. & Ruvkun, G. Lifespan regulation by evolutionarily conserved genes essential for viability. PLoS Genet. 3, e56 (2007).

    Article  Google Scholar 

  25. Lu, X. & Horvitz, H.R. lin-35 and lin-53, two genes that antagonize a C. elegans Ras pathway, encode proteins similar to Rb and its binding protein RbAp48. Cell 95, 981–991 (1998).

    Article  CAS  Google Scholar 

  26. Lehner, B. et al. Loss of LIN-35, the Caenorhabditis elegans ortholog of the tumor suppressor p105Rb, results in enhanced RNA interference. Genome Biol. 7, R4 (2006).

    Article  Google Scholar 

  27. Wang, D. et al. Somatic misexpression of germline P granules and enhanced RNA interference in retinoblastoma pathway mutants. Nature 436, 593–597 (2005).

    Article  CAS  Google Scholar 

  28. Cui, M., Kim, E.B. & Han, M. Diverse chromatin remodeling genes antagonize the Rb-involved SynMuv pathways in C. elegans. PLoS Genet. 2, e74 (2006).

    Article  Google Scholar 

  29. Segalat, L. Dystrophin and functionally related proteins in the nematode Caenorhabditis elegans. Neuromuscul. Disord. 12 Suppl 1, S105–S109 (2002).

    Article  Google Scholar 

  30. Sundaram, M.V. RTK/Ras/MAP kinase signaling. WormBook Vol. 1.80.1 <http://www.wormbook.org>(2005).

  31. Yochem, J., Sundaram, M. & Han, M. Ras is required for a limited number of cell fates and not for general proliferation in Caenorhabditis elegans. Mol. Cell. Biol. 17, 2716–2722 (1997).

    Article  CAS  Google Scholar 

  32. Chockalingam, P.S. et al. Dystrophin-glycoprotein complex and Ras and Rho GTPase signaling are altered in muscle atrophy. Am. J. Physiol. Cell Physiol. 283, C500–C511 (2002).

    Article  CAS  Google Scholar 

  33. Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  Google Scholar 

  34. Gollub, J. et al. The Stanford microarray database: data access and quality assessment tools. Nucleic Acids Res. 31, 94–96 (2003).

    Article  CAS  Google Scholar 

  35. Jiang, M. et al. Genome-wide analysis of developmental and sex-regulated gene expression profiles in Caenorhabditis elegans. Proc. Natl. Acad. Sci. USA 98, 218–223 (2001).

    Article  CAS  Google Scholar 

  36. Lund, J. et al. Transcriptional profile of aging in C. elegans. Curr. Biol. 12, 1566–1573 (2002).

    Article  CAS  Google Scholar 

  37. Reinke, V. et al. A global profile of germline gene expression in C. elegans. Mol. Cell 6, 605–616 (2000).

    Article  CAS  Google Scholar 

  38. Romagnolo, B. et al. Downstream targets of let-60 Ras in Caenorhabditis elegans. Dev. Biol. 247, 127–136 (2002).

    Article  CAS  Google Scholar 

  39. Wang, J. & Kim, S.K. Global analysis of dauer gene expression in Caenorhabditis elegans. Development 130, 1621–1634 (2003).

    Article  CAS  Google Scholar 

  40. Alfarano, C. et al. The biomolecular interaction network database and related tools 2005 update. Nucleic Acids Res. 33, D418–D424 (2005).

    Article  CAS  Google Scholar 

  41. Joshi-Tope, G. et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 33, D428–D432 (2005).

    Article  CAS  Google Scholar 

  42. Peri, S. et al. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 13, 2363–2371 (2003).

    Article  CAS  Google Scholar 

  43. Ramani, A.K., Bunescu, R.C., Mooney, R.J. & Marcotte, E.M. Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biol. 6, R40 (2005).

    Article  Google Scholar 

  44. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).

    Article  CAS  Google Scholar 

  45. Rual, J.F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).

    Article  CAS  Google Scholar 

  46. Date, S.V. & Marcotte, E.M. Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. Nat. Biotechnol. 21, 1055–1062 (2003).

    Article  CAS  Google Scholar 

  47. Kanehisa, M., Goto, S., Kawashima, S. & Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42–46 (2002).

    Article  CAS  Google Scholar 

  48. Efron, B. & Tibshirani, R. An introduction to the bootstrap, 439 (Chapman & Hall, New York, 1993).

    Book  Google Scholar 

  49. Eppig, J.T. et al. The Mouse Genome Database (MGD): from genes to mice—a community resource for mouse biology. Nucleic Acids Res. 33, D471–D475 (2005).

    Article  CAS  Google Scholar 

  50. Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A.G. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat. Genet. 38, 896–903 (2006).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Worm strains used in this work were provided by the Caenorhabditis Genetics Center, which is funded by the US National Institutes of Health National Center for Research Resources (NCRR). SAGE data were obtained from the Genome BC C. elegans Gene Expression Consortium and were produced at the Michael Smith Genome Sciences Centre with funding from Genome Canada. This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01), Welch (F1515), and a Packard Fellowship (E.M.M.). B.L. was supported by a Sanger Institute Postdoctoral Fellowship and the EMBL-CRG Systems Biology Program, and the Institució Catalana de Recerca i Estudis Avançats (ICREA). A.G.F., W.W. and C.C. are supported by the Wellcome Trust. We thank A. Enright for help with constructing the web interface for analyzing this data.

Author information

Author notes

  1. Insuk Lee and Ben Lehner: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas, 2500 Speedway, MBB 3.210, Austin, 78712, Texas, USA

    Insuk Lee & Edward M Marcotte

  2. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK

    Ben Lehner, Catriona Crombie, Wendy Wong & Andrew G Fraser

  3. European Molecular Biology Laboratory-Centre for Genomic Regulation (EMBL-CRG) Systems Biology Unit and Institució Catalana de Recerca i Estudis Avançats (ICREA), Center for Genomic Regulation, Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, 08003, Spain

    Ben Lehner

Authors
  1. Insuk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ben Lehner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Catriona Crombie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wendy Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew G Fraser
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Edward M Marcotte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Andrew G Fraser or Edward M Marcotte.

Supplementary information

Supplementary Text and Figures

Supplementary Methods, Supplementary Figure 1, Supplementary Tables 1–3 (PDF 1859 kb)

Supplementary Data File 1

Reference gene pairs (TXT 13816 kb)

Supplementary Data File 2

Network linkages and evidence (TXT 29223 kb)

Supplementary Data File 3

Identified gene clusters. (TXT 459 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lee, I., Lehner, B., Crombie, C. et al. A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat Genet 40, 181–188 (2008). https://doi.org/10.1038/ng.2007.70

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.2007.70

This article is cited by

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