Transcriptional Potential Determines the Adaptability of Escherichia coli Strains with Different Fitness Backgrounds

Abstract

Predicting the adaptive potential of bacterial populations can be difficult due to their complexity and dynamic environmental conditions. Also, epistatic interaction between mutations affects the adaptive trajectory. Adaptation through the fitness landscape may be influenced by the gene pool or expression network. However, genetic factors that determine the contribution of beneficial mutations during adaptive evolution are poorly understood. In this study, we experimentally evolved wild-type Escherichia coli K-12 MG1655 and its isogenic derivative that has two additional replication origins and shows higher background fitness. During the short time of experimental evolution, the fitness gains of the two E. coli strains with different fitness backgrounds converged. Populational genome sequencing revealed various mutations with different allele frequencies in evolved populations. Several mutations occurred in genes affecting transcriptional regulation (e.g., RNA polymerase subunit, RNase, ppGpp synthetase, and transcription termination/antitermination factor genes). When we introduced mutations into the ancestral E. coli strains, beneficial effects tended to be lower in the ancestor with higher initial fitness. Replication rate analysis showed that the various replication indices do not correlate with the growth rate. Transcriptome profiling showed that gene expression and gene ontology are markedly enriched in populations with lower background fitness after experimental evolution. Further, the degree of transcriptional change was proportional to the fitness gain. Thus, the evolutionary trajectories of bacteria with different fitness backgrounds can be complex and counterintuitive. Notably, transcriptional change is a major contributor to adaptability.IMPORTANCE Predicting the adaptive potential of bacterial populations can be difficult due to their complexity and dynamic environmental conditions. Also, epistatic interaction between mutations affects the adaptive trajectory. Nevertheless, next-generation sequencing sheds light on understanding evolutionary dynamics through high-throughput genome and transcriptome information. Experimental evolution of two E. coli strains with different background fitness showed that the trajectories of fitness gain, which slowed down during the later stages of evolution, became convergent. This suggests that the adaptability of bacteria can be counterintuitive and that predicting the evolutionary path of bacteria can be difficult even in a constant environment. In addition, transcriptional change is associated with fitness gain during the evolutionary process. Thus, the adaptability of cells depends on their intrinsic genetic capacity for a given evolutionary period. This should be considered when genetically engineered bacteria are optimized through adaptive evolution.