Grant: Project Grant

Chemicals that target specific bioprocesses and proteins provide valuable insight into gene function. They also represent tool compounds to probe cell function and potential therapeutic leads. A chemical-genetic screen involves testing genome-scale collections of mutant cells for sensitivity to a specific compound and thus, has the potential to monitor all cellular pathways in an unbiased manner. A specific chemical-genetic interaction describes a mutant cell that shows a phenotype when grown in the presence of a specific compound. Quantifying chemical-genetic interactions across a large collection of mutants in response to compound treatment generates a chemical-genetic interaction profile indicative of a compound's general mode-of-action (MOA), thereby facilitating both gene function and drug target discovery. We previously developed a yeast cell-based platform to screen thousands of compounds and curated the general cellular roles for a set of ~1,500 compounds. We also developed a CRISPR-based platform to map chemical-genetic interactions in human cells. Here, we propose to: [1] apply novel yeast essential gene mutant libraries to precisely characterize the cellular targets of our curated set of ~1,500 bioactive compounds; [2] generate a genome-scale, chemical-genetic network for a model human cell line and, utilizing a deep-learning approach we developed previously, to integrate this network with other datasets and build a more functionally representative map of human cell. The resultant integrated network will allow us to functionally annotate roles of previously uncharacterized human genes and comparative analysis will identify bioactive compounds with highly conserved MOAs, from yeast to human; [3] validate the predicted targets of previously uncharacterized compounds with conserved MOAs, which will facilitate tool compound and drug discovery.