Epistatic Interaction Maps Relative to Multiple Metabolic Phenotypes

Abstract

An epistatic interaction between two genes occurs when the phenotypic impact of one gene depends on another gene, often exposing a functional association between them. Due to experimental scalability and to evolutionary significance, abundant work has been focused on studying how epistasis affects cellular growth rate, most notably in yeast. However, epistasis likely influences many different phenotypes, affecting our capacity to understand cellular functions, biochemical networks adaptation, and genetic diseases. Despite its broad significance, the extent and nature of epistasis relative to different phenotypes remain fundamentally unexplored. Here we use genome-scale metabolic network modeling to investigate the extent and properties of epistatic interactions relative to multiple phenotypes. Specifically, using an experimentally refined stoichiometric model for Saccharomyces cerevisiae, we computed a three-dimensional matrix of epistatic interactions between any two enzyme gene deletions, with respect to all metabolic flux phenotypes. We found that the total number of epistatic interactions between enzymes increases rapidly as phenotypes are added, plateauing at approximately 80 phenotypes, to an overall connectivity that is roughly 8-fold larger than the one observed relative to growth alone. Looking at interactions across all phenotypes, we found that gene pairs interact incoherently relative to different phenotypes, i.e. antagonistically relative to some phenotypes and synergistically relative to others. Specific deletion-deletion-phenotype triplets can be explained metabolically, suggesting a highly informative role of multi-phenotype epistasis in mapping cellular functions. Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact. Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes. The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.

Author Summary

An epistatic interaction between two genes occurs when the phenotypic impact of one gene is dependent on the other. While different phenotypes have been used to uncover epistasis in different contexts, little is known about how cell-scale genetic interaction networks vary across multiple phenotypes. Here we use a genome-scale mathematical model of yeast metabolism to compute a three-dimensional matrix of interactions between any two gene deletions with respect to all metabolic flux phenotypes. We find that this multi-phenotype epistasis map contains many more interactions than found relative to any single phenotype. The unique contribution of examining multiple phenotypes is further demonstrated by the fact that individual interactions may be synergistic relative to some phenotypes and antagonistic relative to others. This observation indicates that different phenotypes are indeed capturing different aspects of the functional relationships between genes. Furthermore, the observation that genes involved in many epistatic interactions across all metabolic flux phenotypes are found to be highly expressed and under strong selective pressure seems to indicate that these interactions are important to the cell and are not just the unavoidable consequence of the connectivity of biological networks. Multi-phenotype epistasis maps may help elucidate the functional organization of biological systems and the role of epistasis in the manifestation of complex genetic diseases.