Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae

Abstract

Eukaryotic genetic interaction networks (GINs) are extensively described in the Saccharomyces cerevisiae S288C model using deletion libraries, yet being limited to this one genetic background, not informative to individual drug response. Here we created deletion libraries in three additional genetic backgrounds. Statin response was probed with five queries against four genetic backgrounds. The 20 resultant GINs representing drug–gene and gene–gene interactions were not conserved by functional enrichment, hierarchical clustering, and topology-based community partitioning. An unfolded protein response (UPR) community exhibited genetic background variation including different betweenness genes that were network bottlenecks, and we experimentally validated this UPR community via measurements of the UPR that were differentially activated and regulated in statin-resistant strains relative to the statin-sensitive S288C background. These network analyses by topology and function provide insight into the complexity of drug response influenced by genetic background.

Genetic interaction networks underlie statin efficacy

Genes are wired together in functional genetic interaction networks (GINs) specific for different traits. We asked a simple question: do GINs for particular traits vary with individuals? The trait we investigated was response to statins, cholesterol-lowering drugs prescribed to 30 million people worldwide. Building comprehensive GINs requires a library of cells with a different gene deleted covering the entire genome that is available only in Saccharomyces cerevisiae (Baker’s yeast), a well-defined model for human genetics. However, the yeast libraries being limited to one genetic background are not informative of individuals. Therefore, we constructed GINs in additional genetic backgrounds and showed statin-specific GINs were not conserved, albeit there was a common fundamental process mediating statin-resistance. Our results identify a mechanism to further investigate in the millions of people that do not respond to statins and the methodology will enhance the discovery and development of drugs to treat other major diseases.