Search

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes. [ABSTRACT FROM AUTHOR]

Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemicalgenetic (CG) interactions in yeast that can be scaled to screen large compound collections and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-ofaction as well as computational biologists interested in analysing CG interaction networks. [ABSTRACT FROM AUTHOR]

Momilactone B is a natural product with dual biological activities, including antimicrobial and allelopathic properties, and plays a major role in plant chemical defense against competitive plants and pathogens. The pharmacological effects of momilactone B on mammalian cells have also been reported. However, little is known about the molecular and cellular mechanisms underlying its broad bioactivity. In this study, the genetic determinants of momilactone B sensitivity in yeast were explored to gain insight into its mode of action. We screened fission yeast mutants resistant to momilactone B from a pooled culture containing genome-wide gene-overexpressing strains in a drug-hypersensitive genetic background. Overexpression of pmd1, bfr1, pap1, arp9, or SPAC9E9.06c conferred resistance to momilactone B. In addition, a drug-hypersensitive, barcoded deletion library was newly constructed and the genes that imparted altered sensitivity to momilactone B upon deletion were identified. Gene Ontology and fission yeast phenotype ontology enrichment analyses predicted the biological pathways related to the mode of action of momilactone B. The validation of predictions revealed that momilactone B induced abnormal phenotypes such as multiseptated cells and disrupted organization of the microtubule structure. This is the first investigation of the mechanism underlying the antifungal activity of momilactone B against yeast. The results and datasets obtained in this study narrow the possible targets of momilactone B and facilitate further studies regarding its mode of action. [ABSTRACT FROM AUTHOR]