View Program - 2022 Population, Evolutionary, and Quantitative Genetics Conference

Biological complexity is built on different types of molecular interactions, such as those in protein-protein interaction (PPI) networks. The highly connected 'hubs' in a PPI network interact with many of the less-connected proteins and often exhibit different types of epistasis. For example, Heat shock protein 90 (Hsp90), a hub and conserved molecular chaperone, can sometimes buffer mutations in other proteins (i.e., dampen their phenotypic effects) but in other cases can potentiate (i.e., enhance) these effects. The mechanisms behind these differential epistatic interactions are not well understood. Understanding mechanisms of epistatic interactions will result in better predictions about when a mutation will be buffered vs. potentiated, and an improved understanding of how epistasis contributes to the genotype-phenotype map.

Previous high throughput studies of epistasis focus on gene knockouts and therefore may fail to describe and understand the mechanisms underlying epistasis involving single-nucleotide mutations. To study epistasis in a high throughput manner, we used ‘Cas9 retron precise parallel editing via homology’ (CRISPEY). to generate ~4982 unique single-nucleotide mutations. These mutations were a mixture of randomly chosen variants and standing genetic variation among natural populations of S.cerevisiae in the genomic region of 92 proteins with known or predicted interactions with Hsp90.

This project focuses on studying the impact of Hsp90 on mutations in its PPI network, including the Ras/PKA signaling pathway of S.cerevisiae. Genes in the Ras/PKA signaling pathway among other pathways in the Hsp90 network are hotspots for rapid adaptation in yeasts and diseases such as cancers. Understanding how mutations in these pathways interact could be particularly useful for evolutionary forecasting. Using a barcode-lineage tracking approach, the fitness of these mutations will be measured relative to unmutated control strains in two conditions, one in which Hsp90 is inhibited and one in which it is not. This study will reveal the prevalence of epistasis within the Hsp90 PPI network, and whether certain types of mutations demonstrate predictable patterns of epistasis, for example, whether mutations within the same gene tend to have the same type of epistasis with Hsp90. By looking for predictable patterns of epistasis, this study may suggest underlying mechanisms that cause epistasis in PPI networks. Ultimately, a better understanding of epistasis will help us make better clinical and evolutionary predictions about how mutations will interact.

Understanding epistasis in the Hsp90 network