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.
Bibliographical noteFunding Information:
EK is an NSF Graduate Research Fellow. EK, JB, RD and CLM are partially supported by a grant from the Minnesota Partnership for Biotechnology and Medical Genomics program, a grant from the National Institutes of Health (1R01HG005084-01A1), and the National Science Foundation (DBI0953881). EK, MC, JB, RD, CLM, BJA, and CB are also supported by a grant from the National Institutes of Health (1R01HG005853-01). MC, BJA, and CB are supported by the Canadian Institutes of Health Research (MOP-57830) and the Ontario Research Fund (GL2-01-22). GC was supported by funds from a CIHR-Operating grant. KC-R was supported by a Queen Elizabeth II Graduate Scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank Jeff Piotrowski for providing S. pombe query strains, Karen Dowell and Matt Hibbs for providing co-expression network software, Valerie Wood for providing orthology maps, and Tahin Syed for help with image processing.