Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis.
BACKGROUND
High-throughput genomics has enabled the global mapping of genetic interactions based on the phenotypic impact of combinatorial genetic perturbations. An important next step is to understand how these networks are dynamically remodelled in response to environmental stimuli. Here, we report on the development and testing of a method to identify such interactions. The method was developed from first principles by treating the impact on cellular growth of environmental perturbations equivalently to that of gene deletions. This allowed us to establish a novel neutrality function marking the absence of epistasis in terms of sensitivity phenotypes rather than fitness. We tested the method by identifying fitness- and sensitivity-based interactions involved in the response to drug-induced DNA-damage of budding yeast Saccharomyces cerevisiae using two mutant libraries - one containing transcription factor deletions, and the other containing deletions of DNA repair genes.


RESULTS
Within the library of transcription factor deletion mutants, we observe significant differences in the sets of genetic interactions identified by the fitness- and sensitivity-based approaches. Notably, among the most likely interactions, only ~50% were identified by both methods. While interactions identified solely by the sensitivity-based approach are modulated in response to drug-induced DNA damage, those identified solely by the fitness-based method remained invariant to the treatment. Comparison of the identified interactions to transcriptional profiles and protein-DNA interaction data indicate that the sensitivity-based method improves the identification of interactions involved in the DNA damage response. Additionally, for the library containing DNA repair mutants, we observe that the sensitivity-based method improves the grouping of functionally related genes, as well as the identification of protein complexes, involved in DNA repair.


CONCLUSIONS
Our results show that the identification of response-modulated genetic interactions can be improved by incorporating the effect of a changing environment directly into the neutrality function marking the absence of epistasis. We expect that this extension of conventional epistatic analysis will facilitate the development of dynamic models of gene networks from quantitative measurements of genetic interactions. While the method was developed for growth phenotype, it should apply equally well for other phenotypes, including the expression of fluorescent reporters.
Batenchuk
Cory
C
Ottawa Institute of Systems Biology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5, Canada. mkaern@uottawa.ca
Tepliakova
Lioudmila
L
Kaern
Mads
M
D004777:Environment; D004843:Epistasis, Genetic / Q000187:drug effects; D015966:Gene Expression Regulation, Fungal / Q000187:drug effects; D053263:Gene Regulatory Networks / Q000235:genetics*; D056084:Genetic Fitness / Q000187:drug effects; D053858:Metabolic Networks and Pathways / Q000187:drug effects / Q000235:genetics; D008741:Methyl Methanesulfonate / Q000494:pharmacology; D009154:Mutation / Q000235:genetics; D010641:Phenotype; D012441:Saccharomyces cerevisiae / Q000187:drug effects / Q000235:genetics* / Q000254:growth & development; D014157:Transcription Factors / Q000235:genetics
D014157:Transcription Factors; D008741:Methyl Methanesulfonate