Variations and predictability of epistasis on an intragenic fitness landscape.

How epistasis hinders or facilitates movement on fitness landscapes has been a longstanding question in evolutionary biology. High-throughput experiments have revealed that, despite their idiosyncratic nature, epistatic interactions can exhibit reproducible global statistical patterns. Recently, Papkou et al. constructed a fitness landscape for a 9-base pair region of the folA gene in Escherichia coli, which encodes dihydrofolate reductase (DHFR), and showed that this landscape is both rugged and highly navigable. Here, we analyze this landscape to address two questions: How does the nature of epistasis between two mutations change with genetic background? and How predictable is epistasis within a gene? We find that epistasis is "fluid": higher-order interactions cause the relationship between two mutations to shift strongly across genetic backgrounds. Mutations fall into two distinct categories: a small subset exhibit strong global epistasis, while the majority do not. Nonetheless, we find that the distribution of fitness effects (DFE) of a genotype is highly predictable from its fitness. These findings provide a gene-level perspective on how epistasis operates, revealing both its unpredictability and its statistical regularities, and offer a framework for predicting mutational effects from high-dimensional fitness landscapes. Significance Statement.The effect of a mutation on fitness depends on the genome in which it occurs, a phenomenon known as epistasis. Epistasis makes evolution difficult to predict, but recent work has uncovered statistical regularities in how it manifests. Using a fitness landscape of [~]260,000 variants of an E. coli gene, we show that higher-order interactions make pair-wise epistasis "fluid": the relationship between two mutations changes with genetic background. We also find that epistasis is "binary" - a small subset of mutations exhibits strong global statistical patterns, while most do not. These findings reveal new principles of how epistasis shapes protein evolution and, ultimately, organismal adaptation.