Predicting Parallelism and Quantifying Divergence in Microbial Evolution Experiments

Evolution recovers the fitness of Acinetobacter baylyi strains with large deletions through mutations in deletion-specific targets and global post-transcriptional regulators

Organelles and endosymbionts have naturally evolved dramatically reduced genome sizes compared to their free-living ancestors. Synthetic biologists have purposefully engineered streamlined microbial genomes to create more efficient cellular chassis and define the minimal components of cellular life. During natural or engineered genome streamlining, deletion of many non-essential genes in combination often reduces bacterial fitness for idiosyncratic or unknown reasons. We investigated how and to what extent laboratory evolution could overcome these defects in six variants of the transposon-free Acinetobacter baylyi strain ADP1-ISx that each had a deletion of a different 22- to 42-kilobase region and two strains with larger deletions of 70 and 293 kilobases. We evolved replicate populations of ADP1-ISx and each deletion strain for ~300 generations in a chemically defined minimal medium or a complex medium and sequenced the genomes of endpoint clonal isolates. Fitness increased in all cases that were examined except for two ancestors that each failed to improve in one of the two environments. Mutations affecting nine protein-coding genes and two small RNAs were significantly associated with one of the two environments or with certain deletion ancestors. The global post-transcriptional regulators rnd (ribonuclease D), csrA (RNA-binding carbon storage regulator), and hfq (RNA-binding protein and chaperone) were frequently mutated across all strains, though the incidence and effects of these mutations on gene function and bacterial fitness varied with the ancestral deletion and evolution environment. Mutations in this regulatory network likely compensate for how an earlier deletion of a transposon in the ADP1-ISx ancestor of all the deletion strains restored csrA function. More generally, our results demonstrate that fitness lost during genome streamlining can usually be regained rapidly through laboratory evolution and that recovery tends to occur through a combination of deletion-specific compensation and global regulatory adjustments.

Identifying Targets of Selection in Laboratory Evolution Experiments

Adaptive evolution navigates a balance between chance and determinism. The stochastic processes of mutation and drift generate phenotypic variation; however, once mutations reach an appreciable frequency in the population, their fate is governed by the deterministic action of selection, enriching for favorable genotypes and purging the less-favorable ones. The net result is that replicate populations will traverse similar-but not identical-pathways to higher fitness. This parallelism in evolutionary outcomes can be leveraged to identify the genes and pathways under selection. However, distinguishing between beneficial and neutral mutations is challenging because many beneficial mutations will be lost due to drift and clonal interference, and many neutral (and even deleterious) mutations will fix by hitchhiking. Here, we review the best practices that our laboratory uses to identify genetic targets of selection from next-generation sequencing data of evolved yeast populations. The general principles for identifying the mutations driving adaptation will apply more broadly.