Failure of cells to respond to DNA damage is a primary event associated with mutagenesis and environmental toxicity. To map the transcriptional network controlling the damage response, we measured genomewide binding locations for 30 damage-related transcription factors (TFs) after exposure of yeast to methyl-methanesulfonate (MMS). The resulting 5272 TF-target interactions revealed extensive changes in the pattern of promoter binding and identified damage-specific binding motifs. As systematic functional validation, we identified interactions for which the target changed expression in wild-type cells in response to MMS but was nonresponsive in cells lacking the TF. Validated interactions were assembled into causal pathway models that provide global hypotheses of how signaling, transcription, and phenotype are integrated after damage.Exposure of cells to chemical and physical damaging agents can result in DNA lesions that contribute to the onset of cancer, aging, immune deficiencies, and other degenerative diseases (1). DNA damage is sensed by a highly conserved mechanism involving the ATM/ATR protein kinases in humans (ataxia-telangiectasia mutated/ataxia-telangiectasia and Rad3-related; homologous to Tel1 and Mec1 in yeast). These aggregate at DNA lesions (2) and activate signaling cascades that include the Chk protein kinases (Chk1, Rad53, and Dun1 in yeast). Chk kinases, in turn, trigger both transcriptional and transcription-independent responses, including activation of DNA repair machinery and cell-cycle arrest (1).Beyond the known DNA repair genes, genomewide expression profiling in yeast has identified several hundred genes (3-5) whose expression is increased or decreased in response to alkylation damage by methyl-methanesulfonate (MMS). At the level of growth phenotype, systematic deletion studies have also identified several hundred genes that are required for normal recovery from alkylation damage (6)(7)(8). Surprisingly, the set of genes that, when deleted, affect damage recovery is not enriched for genes whose transcript levels change upon damage exposure (7,9). Thus, neither transcriptional profiling alone, nor genomic phenotyping alone, adequately defines the cellular response to DNA-damaging agents. However, these studies do suggest that the DNA damage response involves multiple levels of regulation, †To whom correspondence should be addressed. trey@bioeng.ucsd.edu. * These authors contributed equally to this work. affecting not only DNA repair genes but also genes that influence protein and lipid turnover, cytoskeleton remodeling, and general stress pathways.To construct a global model of yeast transcriptional networks activated by MMS, we applied a systems approach (10) that integrated data from genomewide chromatin immunoprecipitation (ChIP) assays, expression profiling, systematic phenotyping, and protein interaction databases (Fig. 1). First, we performed a systematic screen for transcription factors (TFs) involved in the MMS response. TFs were chosen from a set of 141 yeast DNA bind...

As genome-scale measurements lead to increasingly complex models of gene regulation, systematic approaches are needed to validate and refine these models. Towards this goal, we describe an automated procedure for prioritizing genetic perturbations in order to discriminate optimally between alternative models of a gene-regulatory network. Using this procedure, we evaluate 38 candidate regulatory networks in yeast and perform four high-priority gene knockout experiments. The refined networks support previously unknown regulatory mechanisms downstream of SOK2 and SWI4.

Transcription factors are most commonly thought of as proteins that regulate expression of specific genes, independently of the order of those genes along the chromosome. By screening genome-wide chromatin immunoprecipitation (ChIP) profiles in yeast, we find that more than 10% of DNA-binding transcription factors concentrate at the subtelomeric regions near to chromosome ends. None of the proteins identified were previously implicated in regulation at telomeres, yet genomic and proteomic studies reveal that a subset of factors show many interactions with established telomere binding complexes. For many factors, the subtelomeric binding pattern is dynamic and undergoes flux toward or away from the telomere as physiological conditions shift. We find that subtelomeric binding is dependent on environmental conditions and correlates with the induction of gene expression in response to stress. Taken together, these results underscore the importance of genome structure in understanding the regulatory dynamics of transcriptional networks.

CellCircuits () is an open-access database of molecular network models, designed to bridge the gap between databases of individual pairwise molecular interactions and databases of validated pathways. CellCircuits captures the output from an increasing number of approaches that screen molecular interaction networks to identify functional subnetworks, based on their correspondence with expression or phenotypic data, their internal structure or their conservation across species. This initial release catalogs 2019 computationally derived models drawn from 11 journal articles and spanning five organisms (yeast, worm, fly, Plasmodium falciparum and human). Models are available either as images or in machine-readable formats and can be queried by the names of proteins they contain or by their enriched biological functions. We envision CellCircuits as a clearinghouse in which theorists may distribute or revise models in need of validation and experimentalists may search for models or specific hypotheses relevant to their interests. We demonstrate how such a repository of network models is a novel systems biology resource by performing several meta-analyses not currently possible with existing databases.

Cell Systems invited 16 experts to share their views on the field of systems genetics. In questions repeated in the headings, we asked them to define systems genetics, highlight its relevance to researchers outside the field, discuss what makes a strong systems genetics paper, and paint a picture of where the field is heading in the coming years. Their responses, ordered by the journal but otherwise unedited, make it clear that deciphering genotype to phenotype relationships is a central challenge of systems genetics and will require understanding how networks and higher-order properties of biological systems underlie complex traits. In addition, our experts illuminate the applications and relevance of systems genetics to human disease, the gut microbiome, development of tools that connect the global research community, sustainability, drug discovery, patient-specific disease and network models, and personalized treatments. Finally, a table of suggested reading provides a sample of influential work in the field.