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Open Access Research

Conserved rules govern genetic interaction degree across species

Elizabeth N Koch1, Michael Costanzo23, Jeremy Bellay14, Raamesh Deshpande1, Kate Chatfield-Reed5, Gordon Chua5, Gennaro D'Urso6, Brenda J Andrews23, Charles Boone23 and Chad L Myers1*

Author Affiliations

1 Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA

2 Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada

3 Department of Molecular Genetics, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada

4 Institute for Advanced Computer Studies, University of Maryland College Park, 3115 Biolmolecular Sciences Bldg #296, College Park, MD 20742, USA

5 Institute of Biocomplexity and Informatics, Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4 Canada

6 Department of Molecular and Cellular Pharmacology, University of Miami School of Medicine, PO Box 016189, Miami, FL 33101, USA

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Genome Biology 2012, 13:R57  doi:10.1186/gb-2012-13-7-r57

Published: 2 July 2012

Abstract

Background

Synthetic genetic interactions have recently been mapped on a genome scale in the budding yeast Saccharomyces cerevisiae, providing a functional view of the central processes of eukaryotic life. Currently, comprehensive genetic interaction networks have not been determined for other species, and we therefore sought to model conserved aspects of genetic interaction networks in order to enable the transfer of knowledge between species.

Results

Using a combination of physiological and evolutionary properties of genes, we built models that successfully predicted the genetic interaction degree of S. cerevisiae genes. Importantly, a model trained on S. cerevisiae gene features and degree also accurately predicted interaction degree in the fission yeast Schizosaccharomyces pombe, suggesting that many of the predictive relationships discovered in S. cerevisiae also hold in this evolutionarily distant yeast. In both species, high single mutant fitness defect, protein disorder, pleiotropy, protein-protein interaction network degree, and low expression variation were significantly predictive of genetic interaction degree. A comparison of the predicted genetic interaction degrees of S. pombe genes to the degrees of S. cerevisiae orthologs revealed functional rewiring of specific biological processes that distinguish these two species. Finally, predicted differences in genetic interaction degree were independently supported by differences in co-expression relationships of the two species.

Conclusions

Our findings show that there are common relationships between gene properties and genetic interaction network topology in two evolutionarily distant species. This conservation allows use of the extensively mapped S. cerevisiae genetic interaction network as an orthology-independent reference to guide the study of more complex species.