Speaker
Description
The fitness effects of all possible mutations available to an organism largely shapes the dynamics of evolutionary adaptation. Tremendous progress has been made in quantifying the strength and abundance of selected mutations available to single microbial species in simple environments, lacking strong ecological interactions. However, the adaptive potential of strains that are part of multi-strain communities remains largely unclear. We sought to fill this gap for a stable community of two closely related ecotypes (“L” and “S”) shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). To this end, we engineered genome-wide barcoded transposon libraries and developed a computational inference pipeline to measure the fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different conditions. We found that the fitness effect of most gene knockouts sensitively depends on the genetic background and the ecological conditions, as set by environmental perturbations and the relative frequency of both ecotypes. Despite the idiosyncratic behavior of individual knockouts, we still see consistent statistical patterns of fitness effect variation across both genetic background and community composition. The background dependence of mutational effects appears to reflect widespread changes in which gene functions are important for determining fitness, for all but the most strongly interacting genes. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to drive adaptive potential in recently diverged, coexisting ecotypes.
