A drug-gene network for understanding drug mechanism of action

Official URL: http://risc01.sabanciuniv.edu/record=b1499079? (Table of Contents)

Chemogenomics experiments, where genetic and chemical perturbations are combined, provide data for discovering the relationships between genotype and phenotype. Here, we computationally analyzed the largest chemogenomics dataset, which combines more than 300 chemicals with virtually all gene deletion strains in the yeast S. cerevisiae. Traditionally, analysis of chemogenomic datasets has been done considering the sensitivity of the deletion strains to chemicals, and this has shed light into drug mechanism of action and finding drug targets. We also considered the deletion strains which are resistant to chemicals. We found a small set of genes whose deletion makes the yeast cell resistant to many chemicals. Curiously, these genes were enriched for functions related to RNA metabolism. Our approach allowed us to generate a network of drugs and genes that are connected with resistance or sensitivity relationships. As a quality assessment, we showed that the higher order motifs found in this network make biological sense. Moreover, by using this network, we constructed a biologically relevant network projection pertaining to drug similarities, and subsequently analyzed this network projection in detail. We propose the drug similarity network as a useful tool for understanding drug mechanism of action.