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...
We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual-guide RNAs in three cell lines, altogether comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified and these patterns replicated with combinatorial drugs at 75% precision. Based on these results we anticipate cellular context will be critical to synthetic-lethal therapies.
SUMMARY HIV-infected individuals are living longer on antiretroviral therapy, but many patients display signs that in some ways resemble premature aging. To investigate and quantify the impact of chronic HIV infection on aging, we report a global analysis of the whole blood DNA methylomes of 137 HIV+ individuals under sustained therapy along with 44 matched HIV− individuals. First, we develop and validate epigenetic models of aging that are independent of blood cell composition. Using these models, we find that both chronic and recent HIV infection lead to an average aging advancement of 4.9 years, increasing expected mortality risk by 19%. In addition, sustained infection results in global deregulation of the methylome across >80,000 CpGs and specific hypomethylation of the region encoding the human leukocyte antigen locus (HLA). We find that decreased HLA methylation is predictive of lower CD4/CD8 T cell ratio, linking molecular aging, epigenetic regulation and disease progression.
Summary An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here, we use a multi-species approach to develop a resource of synthetic-lethal interactions among genes mutated in cancer, including tumor suppressor genes (TSG) and druggable genes. First, we screen in yeast ~169,000 potential interactions amongst TSG orthologs and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic-lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules we prioritize >105 human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.
Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These “somatic eQTLs” (expression Quantitative Trait Loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines, and that increasing DAAM1 expression leads to invasive cell migration. Collectively the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer.
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