Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae.
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ABSTRACT:
SUBMITTER: Busby BP
PROVIDER: S-EPMC6776536 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference.ResultsWe describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org) and SGD (http://www.yeastgenome.org/) databases.ConclusionComprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.
| S-EPMC1561585 | biostudies-literature
Project description:The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and Ideker proposed a method to analyze together genetic and physical networks, which explains many of the known genetic interactions as linking different pathways in the physical network. Here, we extend this method and devise novel analytic tools for interpreting genetic interactions in a physical context. Applying these tools on a large-scale Saccharomyces cerevisiae data set, our analysis reveals 140 between-pathway models that explain 3765 genetic interactions, roughly doubling those that were previously explained. Model genes tend to have short mRNA half-lives and many phosphorylation sites, suggesting that their stringent regulation is linked to pathway redundancy. We also identify 'pivot' proteins that have many physical interactions with both pathways in our models, and show that pivots tend to be essential and highly conserved. Our analysis of models and pivots sheds light on the organization of the cellular machinery as well as on the roles of individual proteins.
| S-EPMC1865586 | biostudies-literature
Project description:BackgroundProtein interaction networks aim to summarize the complex interplay of proteins in an organism. Early studies suggested that the position of a protein in the network determines its evolutionary rate but there has been considerable disagreement as to what extent other factors, such as protein abundance, modify this reported dependence.ResultsWe compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans with those of closely related species to elucidate the recent evolutionary history of their respective protein interaction networks. Interaction and expression data are studied in the light of a detailed phylogenetic analysis. The underlying network structure is incorporated explicitly into the statistical analysis. The increased phylogenetic resolution, paired with high-quality interaction data, allows us to resolve the way in which protein interaction network structure and abundance of proteins affect the evolutionary rate. We find that expression levels are better predictors of the evolutionary rate than a protein's connectivity. Detailed analysis of the two organisms also shows that the evolutionary rates of interacting proteins are not sufficiently similar to be mutually predictive.ConclusionIt appears that meaningful inferences about the evolution of protein interaction networks require comparative analysis of reasonably closely related species. The signature of protein evolution is shaped by a protein's abundance in the organism and its function and the biological process it is involved in. Its position in the interaction networks and its connectivity may modulate this but they appear to have only minor influence on a protein's evolutionary rate.
| S-EPMC1079807 | biostudies-literature
Project description:BACKGROUND: Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. RESULTS: We created a novel glc7 catalytic mutant (glc7-E101Q). Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. CONCLUSION: We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.
| S-EPMC2481269 | biostudies-literature
Project description:Sodium metabisulfite (Na2S2O5) is widely used as a preservative in the food and wine industry. However, it causes varying degrees of cellular damage to organisms. In order to improve our knowledge regarding its cyto-toxicity, a genome-wide screen using the yeast single deletion collection was performed. Additionally, a total of 162 Na2S2O5-sensitive strains and 16 Na2S2O5-tolerant strains were identified. Among the 162 Na2S2O5 tolerance-related genes, the retromer complex was the top enriched cellular component. Further analysis demonstrated that retromer complex deletion leads to increased sensitivity to Na2S2O5, and that Na2S2O5 can induce mislocalization of retromer complex proteins. Notably, phosphatidylinositol 3-monophosphate kinase (PI3K) complex II, which is important for retromer recruitment to the endosome, might be a potential regulator mediating retromer localization and the yeast Na2S2O5 tolerance response. Na2S2O5 can decrease the protein expressions of Vps34, which is the component of PI3K complex. Therefore, Na2S2O5-mediated retromer redistribution might be caused by the effects of decreased Vps34 expression levels. Moreover, both pharmaceutical inhibition of Vps34 functions and deletions of PI3K complex II-related genes affect cell tolerance to Na2S2O5. The results of our study provide a global picture of cellular components required for Na2S2O5 tolerance and advance our understanding concerning Na2S2O5-induced cytotoxicity effects.
| S-EPMC8699849 | biostudies-literature
Project description:We investigated the dynamics of a gene regulatory network controlling the cold shock response in budding yeast, Saccharomyces cerevisiae. The medium-scale network, derived from published genome-wide location data, consists of 21 transcription factors that regulate one another through 31 directed edges. The expression levels of the individual transcription factors were modeled using mass balance ordinary differential equations with a sigmoidal production function. Each equation includes a production rate, a degradation rate, weights that denote the magnitude and type of influence of the connected transcription factors (activation or repression), and a threshold of expression. The inverse problem of determining model parameters from observed data is our primary interest. We fit the differential equation model to published microarray data using a penalized nonlinear least squares approach. Model predictions fit the experimental data well, within the 95% confidence interval. Tests of the model using randomized initial guesses and model-generated data also lend confidence to the fit. The results have revealed activation and repression relationships between the transcription factors. Sensitivity analysis indicates that the model is most sensitive to changes in the production rate parameters, weights, and thresholds of Yap1, Rox1, and Yap6, which form a densely connected core in the network. The modeling results newly suggest that Rap1, Fhl1, Msn4, Rph1, and Hsf1 play an important role in regulating the early response to cold shock in yeast. Our results demonstrate that estimation for a large number of parameters can be successfully performed for nonlinear dynamic gene regulatory networks using sparse, noisy microarray data.
| S-EPMC4636536 | biostudies-literature
Project description:The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ?6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome. Analyzing ?90,000 negative genetic interactions observed to date, we found that more than 10% of them are incorrectly annotated due to NGEs. We developed a novel algorithm, GINGER, to identify and correct erroneous interaction annotations. We validated the algorithm using a comparative analysis of interactions from Schizosaccharomyces pombe. We further showed that our predictions are significantly more concordant with diverse biological data compared to their mis-annotated counterparts. Our work uncovered about 9500 new genetic interactions in yeast.
| S-EPMC4797274 | biostudies-literature
Project description:The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call 'synergistic outcome determination' (SOD), a concept similar to 'Synthetic Lethality'. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.
| S-EPMC2978107 | biostudies-literature
Project description:A functionally diverse subset of yeast temperatures sensitive deletion mutants were transformed with a pool of barcoded yeast ORFs to identify dosage suppressors. Objective: To assess the relative strain proportion after growing DNA barcoded yeast deletion strains in a mixed pools. The barcodes are amplified from the mixed population, and the intensity of each labelled barcodes on the Tag4 array indicate the relative contribution of that strain to the mixed pool. Note: File MoBY_strains.txt available in E-TABM-1147.additional.zip
2012-04-21 | E-TABM-1147 | biostudies-arrayexpress
Project description:The concept of "protein-based inheritance" defines prions as epigenetic determinants that cause several heritable traits in eukaryotic microorganisms, such as Saccharomyces cerevisiae and Podospora anserina. Previously, we discovered a non-chromosomal factor, [NSI+], which possesses the main features of yeast prions, including cytoplasmic infectivity, reversible curability, dominance, and non-Mendelian inheritance in meiosis. This factor causes omnipotent suppression of nonsense mutations in strains of S. cerevisiae bearing a deleted or modified Sup35 N-terminal domain. In this work, we identified protein determinants of [NSI+] using an original method of proteomic screening for prions. The suppression of nonsense mutations in [NSI+] strains is determined by the interaction between [SWI+] and [PIN+] prions. Using genetic and biochemical methods, we showed that [SWI+] is the key determinant of this nonsense suppression, whereas [PIN+] does not cause nonsense suppression by itself but strongly enhances the effect of [SWI+]. We demonstrated that interaction of [SWI+] and [PIN+] causes inactivation of SUP45 gene that leads to nonsense suppression. Our data show that prion interactions may cause heritable traits in Saccharomyces cerevisiae.
| S-EPMC5189945 | biostudies-literature