Dataset Information

Adaptive laboratory evolution of antimicrobial resistance in bacteria for genetic and phenotypic analyses.

ABSTRACT: Adaptive laboratory evolution (ALE) of bacteria has the potential to provide many insights like revealing novel mechanisms of resistance and elucidating the impact of drug combinations and concentrations on AMR evolution. Here, we describe a step-by-step ALE protocol for the model bacterium Escherichia coli that can be easily adapted to answer questions related to evolution and genetics of AMR in diverse bacteria. Key issues to consider when designing ALE experiments as well as some downstream mutation mapping analyses are described. For complete details on the use and execution of this protocol, please refer to Patel and Matange (2021)¹ and Matange et al. (2019).².

SUBMITTER: Vinchhi R

PROVIDER: S-EPMC9843481 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Adaptive laboratory evolution of antimicrobial resistance in bacteria for genetic and phenotypic analyses.

Vinchhi Rhea R Jena Chinmaya C Matange Nishad N

STAR protocols 20230109 1

Adaptive laboratory evolution (ALE) of bacteria has the potential to provide many insights like revealing novel mechanisms of resistance and elucidating the impact of drug combinations and concentrations on AMR evolution. Here, we describe a step-by-step ALE protocol for the model bacterium Escherichia coli that can be easily adapted to answer questions related to evolution and genetics of AMR in diverse bacteria. Key issues to consider when designing ALE experiments as well as some downstream m ...[more]

PMID: 36625217

Similar Datasets

Project description:Adaptive laboratory evolution (ALE) experiments are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the observed distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications.IMPORTANCE Evolution and natural selection inexorably lead to an organism's improved fitness in a given environment, whether in a laboratory or natural setting. However, despite the frequent natural occurrence of complex and dynamic growth environments, laboratory evolution experiments typically maintain simple, static culturing environments so as to reduce selection pressure complexity. In this study, we investigated the adaptive strategies underlying evolution to fluctuating environments by evolving Escherichia coli to conditions of frequently switching growth substrate. Characterization of evolved strains via a number of different data types revealed the various genetic and phenotypic changes implemented in pursuit of growth optimality and how these differed across the different growth substrates and switching protocols. This work not only helps to establish general principles of adaptation to complex environments but also suggests strategies for experimental design to achieve desired evolutionary outcomes.

Dataset Information

Adaptive laboratory evolution of antimicrobial resistance in bacteria for genetic and phenotypic analyses.

Publications

Adaptive laboratory evolution of antimicrobial resistance in bacteria for genetic and phenotypic analyses.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets