Quantifying the Local Adaptive Landscape of a Nascent Bacterial Community

Abstract

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.

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

“…We sought to infer the fitness effects of individual lineages, so that we could then determine if putatively selected lineages are influencing the estimation of the time-varying effective population sizes. We first used a deterministic method to estimate lineage fitness effects, similar to the method described in [66].…”

Bayesian inference of relative fitness on high-throughput pooled competition assays

“…The advent of DNA barcoding-the ability to uniquely identify cell lineages with DNA sequences integrated at a specific locus-and high-throughput sequencing has opened new venues for understanding microbial evolutionary dynamics with an unprecedented level of temporal resolution [1][2][3]. These experimental efforts rely on our ability to reliably infer the relative fitness of an ensemble of diverse genotypes.…”

“…We present a Bayesian inference pipeline to quantify the uncertainty about the parametric information we can extract from high-throughput competitive fitness assays given a model of the data generation process and experimental data. In these assays, the fitness of an ensemble of genotypes is determined relative to a reference genotype [3,4]. Fig 1(A) shows a schematic of the experimental procedure in which an initial pool of barcoded strains are mixed with a reference strain and inoculated into fresh media.…”

Barcoding Populations of Pseudomonas fluorescens SBW25

“…When changes are measured over the course of hundreds of generations, detailed understanding of eco-evolutionary dynamics can emerge (Levy et al 2015 ; Jasinska et al 2020 ; Acar et al 2020 ; Boyer et al 2021 ; Aggeli et al 2021 ), including insight into the connection between genotype and phenotype of even complex traits (Venkataram et al 2016 ; Kinsler et al 2020 ; Jagdish and Nguyen Ba 2022 ; Nguyen Ba et al 2022 ; Aggeli et al 2022 ; Ascensao et al 2022 ). Recent developments allowing the regeneration of barcode diversity extend the opportunity to observe evolutionary change over the course of thousands of generations (Ba et al 2019 ).…”