Rewiring of Genetic Networks in Response to DNA Damage

Abstract

Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. Using an approach called differential epistasis mapping, we have discovered widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage. Differential interactions uncover many gene functions that go undetected in static conditions. They are very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. The data also reveal that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. Differential networks chart a new type of genetic landscape that is invaluable for mapping cellular responses to stimuli.One of the most basic approaches to understanding gene function relies on the identification of genetic interactions, which occur when the phenotypic effects of one gene depend on the presence of a second. Recently, a number of technologies have been developed to systematically map genetic interaction networks over large sets of genes in budding yeast (1-3) and other model organisms (4,5 To gain insight into how genetic networks are altered by stress, we assembled a large genetic interactome with and without perturbation by the DNA-damaging agent methyl methane-sulfonate (MMS). Using the technique of epistatic miniarray profiles (E-MAP) (8), genetic interactions were interrogated among a set of 418 yeast genes selected to provide broad coverage of the cellular signaling and transcriptional machinery, including nearly all yeast kinases, phosphatases, and transcription factors, as well as known DNA repair factors ( fig. S1 and table S1). About 80,000 double-mutant strains were generated from all pairwise mutant combinations of the 418 genes, in which mutations were complete gene deletions (nonessential genes) or hypomorphic alleles (essential genes) as appropriate. Double-mutant combinations were grown with or without 0.02% MMS, and their colony sizes were analyzed statistically to compute a genetic interaction score (S score) in each condition (9), which indicates whether the strain was healthier or sicker than expected (positive or negative S, respectively) (10).From established score thresholds for positive and negative interactions (S ≥ +2.0, S ≤ −2.5) (9) we identified two genetic networks: a set of 1905 interactions for the untreated condition, and a set of 2297 interactions under MMS. Analysis of these "static" genetic maps showed strong associations with physical interaction networks of various kinds. For example, gene pairs with either positive or negative genetic interactions were highly enriched for proteins known to physically interact. In addition, both maps were enriched for known kinase-and phosphatase-substrate pairs, as well as transcription factor-target pairs ( fig. S2). The correspondence to physical...

Erk5 contributes to maintaining the balance of cellular nucleotide levels and erythropoiesis

“…47 An initial dNTP imbalance during the generation of shErk5 cells could later be compensated for by additional modifications to allow cell growth. Curiously, yeast Slt2, the functional homolog of human Erk5, is required for appropriate regulation of RNR genes in response to DNA damage, 48 and is necessary for resistance to dNTP deficiency induced by HU. 49,50 These data indicate different but related roles of Erk5 in mammals and yeast.…”

Bioinformatic identification of genes suppressing genome instability

“…One of the stronger counterexamples that can be cited is the observation that deletion of 12 of 31 tested genes in a high-scoring DNA damage cluster (cluster 4) did not cause increased GCR rates as single mutations. Sufficient genetic data exist for genes in cluster 4 to suggest that nonperturbed growth-based genetic interactions are only a crude surrogate for measuring similarity in suppressing GCRs, which is consistent with the substantial changes in synthetic lethal interactions between deletion mutations caused DNA damaging agents (31). Additionally, because only pair-wise interactions are typically identified, other kinds of important genetic results cannot be identified, such as suppression of the lethality of srs2Δ sgs1Δ double mutants by mutations causing homologous recombination defects (50), and because more complex genetic redundancies, which are particularly important in higher eukaryotes, cannot be handled.…”

“…However, the importance of these genes in the presence of DNA damaging agents is emphasized by the number of screens in which these genes were identified (Datasets S2 and S6) and is consistent with the known roles of these gene products. We note that the inability of unperturbed growth to capture the roles of these types of genes can be anticipated from decades of classic genetic studies as well as a recent report of changes in genetic interactions measured in a high-throughput manner due to the presence of MMS (31).…”

Gene coexpression measures in large heterogeneous samples using count statistics

“…MSN2 and YAP1 are both activators required for oxidative stress tolerance, and there is a partial overlap between their H 2 O 2 -inducible regulons (38). Studies using epistatic miniarray profiles (39,40) have shown that double mutations in MSN2 and YAP1 lead to severe fitness defect. Two more such examples can be found in SI Appendix, Fig.…”