Systematic identification of changes in the yeast protein interaction network in response to environmental, chemical, and genetic perturbation
Ontology highlight
ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae Schizosaccharomyces pombe
PROVIDER: GSE72425 | GEO | 2017/04/30
SECONDARY ACCESSION(S): PRJNA294009
REPOSITORIES: GEO
Similar Datasets
Project description:To understand the principles underlying protein-protein interaction (PPI) complex changes in response to external perturbations, we created a highly multiplexed version of the murine dihydrofolate reductase protein complementation assay (mDHFR PCA) in Saccharomyces cerevisiae, allowing quantitative PPI complex profiling in vivo. We investigated the effects of 14 different conditions (including small molecules, abiotic stress factors, and nutrient composition) on a total of 1383 PPIs. More than half of PPIs (758) were found to be variable, and their Gene Ontology (GO) annotations were found to be informative of both the nature of the perturbation within each condition, as well as the overall variability of the interactions across conditions. Many perturbations triggered network changes characterized by large connected modules centered around highly connected proteins ('hubs'), suggesting that cellular control of a few proteins (e.g., by mRNA levels) can induce widespread PPI remodeling. Under a diauxic shift from glucose to ethanol as the main carbon source, we found a striking relationship between PPI changes measured by our assay and those predicted by mRNA expression under a simple law of mass action based model. We chose to investigate the relationship between interactome and transcriptome changes in the shift from glucose to ethanol as the sole carbon source. To obtain relative mRNA expression data in our pool, cells were grown in selective media supplemented with dextrose as a pre-culture and then shifted to selective media containing ethanol as the sole carbon source, with samples taken at 0, 0.5, 1, 4, and 12 hours after the transfer.
2017-04-30 | E-GEOD-72423 | biostudies-arrayexpress
Project description:To understand the principles underlying protein-protein interaction (PPI) complex changes in response to external perturbations, we created a highly multiplexed version of the murine dihydrofolate reductase protein complementation assay (mDHFR PCA) in Saccharomyces cerevisiae, allowing quantitative PPI complex profiling in vivo. We investigated the effects of 14 different conditions (including small molecules, abiotic stress factors, and nutrient composition) on a total of 1383 PPIs. More than half of PPIs (758) were found to be variable, and their Gene Ontology (GO) annotations were found to be informative of both the nature of the perturbation within each condition, as well as the overall variability of the interactions across conditions. Many perturbations triggered network changes characterized by large connected modules centered around highly connected proteins ('hubs'), suggesting that cellular control of a few proteins (e.g., by mRNA levels) can induce widespread PPI remodeling. Under a diauxic shift from glucose to ethanol as the main carbon source, we found a striking relationship between PPI changes measured by our assay and those predicted by mRNA expression under a simple law of mass action based model.
2017-04-30 | GSE72336 | GEO
Project description:To understand the principles underlying protein-protein interaction (PPI) complex changes in response to external perturbations, we created a highly multiplexed version of the murine dihydrofolate reductase protein complementation assay (mDHFR PCA) in Saccharomyces cerevisiae, allowing quantitative PPI complex profiling in vivo. We investigated the effects of 14 different conditions (including small molecules, abiotic stress factors, and nutrient composition) on a total of 1383 PPIs. More than half of PPIs (758) were found to be variable, and their Gene Ontology (GO) annotations were found to be informative of both the nature of the perturbation within each condition, as well as the overall variability of the interactions across conditions. Many perturbations triggered network changes characterized by large connected modules centered around highly connected proteins ('hubs'), suggesting that cellular control of a few proteins (e.g., by mRNA levels) can induce widespread PPI remodeling. Under a diauxic shift from glucose to ethanol as the main carbon source, we found a striking relationship between PPI changes measured by our assay and those predicted by mRNA expression under a simple law of mass action based model.
2017-04-30 | GSE72423 | GEO
Project description:Cytoscape is a bioinformatic data analysis and visualization platform that is well-suited to the analysis of gene expression data. To facilitate the analysis of yeast microarray data using Cytoscape, we constructed an interaction network (interactome) using the curated interaction data available from the Saccharomyces Genome Database (www.yeastgenome.org) and the database of yeast transcription factors at YEASTRACT (www.yeastract.com). These data were formatted and imported into Cytoscape using semi-automated methods, including Linux-based scripts, that simplified the process while minimizing the introduction of processing errors. The methods described for the construction of this yeast interactome are generally applicable to the construction of any interactome. Using Cytoscape, we illustrate the use of this interactome through the analysis of expression data from a recent yeast diauxic shift experiment. We also report and briefly describe the complex associations among transcription factors that result in the regulation of thousands of genes through coordinated changes in expression of dozens of transcription factors. These cells are thus able to sensitively regulate cellular metabolism in response to changes in genetic or environmental conditions through relatively small changes in the expression of large numbers of genes, affecting the entire yeast metabolome.
| S-EPMC2673449 | biostudies-literature
Project description:To study the evolution of the yeast protein interaction network, we first classified yeast proteins by their evolutionary histories into isotemporal categories, then analyzed the interaction tendencies within and between the categories, and finally reconstructed the main growth path. We found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life. These observations suggest synergistic selection during network evolution and provide insights into the hierarchical modularity of cellular networks.
| S-EPMC240702 | biostudies-literature
Project description:A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein-protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70-80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).
| S-EPMC3337474 | biostudies-literature
Project description:Cryptic genetic variation could arise from, for example, Gene-by-Gene (G-by-G) or Gene-by-Environment (G-by-E) interactions. The underlying molecular mechanisms and how they influence allelic effects and the genetic variance of complex traits is largely unclear. Here, we empirically explored the role of environmentally influenced epistasis on the suppression and release of cryptic variation by reanalysing a dataset of 4,390 haploid yeast segregants phenotyped on 20 different media. The focus was on 130 epistatic loci, each contributing to segregant growth in at least one environment and that together explained most (69-100%) of the narrow sense heritability of growth in the individual environments. We revealed that the epistatic growth network reorganised upon environmental changes to alter the estimated marginal (additive) effects of the individual loci, how multi-locus interactions contributed to individual segregant growth and the level of expressed genetic variance in growth. The estimated additive effects varied most across environments for loci that were highly interactive network hubs in some environments but had few or no interactors in other environments, resulting in changes in total genetic variance across environments. This environmentally dependent epistasis was thus an important mechanism for the suppression and release of cryptic variation in this population. Our findings increase the understanding of the complex genetic mechanisms leading to cryptic variation in populations, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits in breeding and evolution.
| S-EPMC7241848 | biostudies-literature
Project description:The widely observed dispensability of duplicate genes is typically interpreted to suggest that a proportion of the duplicate pairs are at least partially redundant in their functions, thus allowing for compensatory affects. However, because redundancy is expected to be evolutionarily short lived, there is currently debate on both the proportion of redundant duplicates and their functional importance. Here, we examined these compensatory interactions by relying on a genome wide data analysis, followed by experiments and literature mining in yeast. Our data, thus, strongly suggest that compensated duplicates are not randomly distributed within the protein interaction network but are rather strategically allocated to the most highly connected proteins. This design is appealing because it suggests that many of the potentially vulnerable nodes that would otherwise be highly sensitive to mutations are often protected by redundancy. Furthermore, divergence analyses show that this association between redundancy and protein connectivity becomes even more significant among the ancient duplicates, suggesting that these functional overlaps have undergone purifying selection. Our results suggest an intriguing conclusion-although redundancy is typically transient on evolutionary time scales, it tends to be preserved among some of the central proteins in the cellular interaction network.
| S-EPMC2234123 | biostudies-literature
Project description:The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.
| S-EPMC5127411 | biostudies-literature
Project description:Many cellular functions are mediated by protein-protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment-dependent protein complex dynamics, we used a DNA-barcode-based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that "concerted" protein-centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass-action-based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.
| S-EPMC5527849 | biostudies-literature