title: Dynamic epistasis under varying environmental perturbations.
abstract: Epistasis describes the phenomenon that mutations at different loci do not have independent effects with regard to certain phenotypes. Understanding the global epistatic landscape is vital for many genetic and evolutionary theories. Current knowledge for epistatic dynamics under multiple conditions is limited by the technological difficulties in experimentally screening epistatic relations among genes. We explored this issue by applying flux balance analysis to simulate epistatic landscapes under various environmental perturbations. Specifically, we looked at gene-gene epistatic interactions, where the mutations were assumed to occur in different genes. We predicted that epistasis tends to become more positive from glucose-abundant to nutrient-limiting conditions, indicating that selection might be less effective in removing deleterious mutations in the latter. We also observed a stable core of epistatic interactions in all tested conditions, as well as many epistatic interactions unique to each condition. Interestingly, genes in the stable epistatic interaction network are directly linked to most other genes whereas genes with condition-specific epistasis form a scale-free network. Furthermore, genes with stable epistasis tend to have similar evolutionary rates, whereas this co-evolving relationship does not hold for genes with condition-specific epistasis. Our findings provide a novel genome-wide picture about epistatic dynamics under environmental perturbations.
authors.affiliation: Center for Advanced Computing, Cornell University, Ithaca, New York, United States of America.
authors.forename: Brandon
authors.initials: B
authors.lastname: Barker
authors.affiliation: Division of Hematology/Oncology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
authors.forename: Lin
authors.initials: L
authors.lastname: Xu
authors.affiliation: Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America; Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, United States of America.
authors.forename: Zhenglong
authors.initials: Z
authors.lastname: Gu
mesh_terms: D003198:Computer Simulation; D004843:Epistasis, Genetic; D019143:Evolution, Molecular; D053263:Gene Regulatory Networks; D059647:Gene-Environment Interaction; D005800:Genes, Fungal; D064688:Metabolic Flux Analysis; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000235:genetics