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Genomic signatures of UV resistance evolution in Escherichia coli depend on the growth phase during exposure

Citation

Selveshwari, S; Lele, Kasturi; Dey, Sutirth (2021), Genomic signatures of UV resistance evolution in Escherichia coli depend on the growth phase during exposure, Dryad, Dataset, https://doi.org/10.5061/dryad.ngf1vhht1

Abstract

Physiological states can determine the ability of organisms to handle stress. Does this mean that the same selection pressure will lead to different evolutionary outcomes, depending on the organisms’ physiological state? If yes, what will be the genomic signatures of such adaptation(s)? We used experimental evolution in Escherichia coli followed by whole-genome whole-population sequencing to investigate these questions. The sensitivity of Escherichia coli to ultraviolet (UV) radiation depends on the growth phase during which it experiences the radiation. We evolved replicate E. coli populations under two different conditions of UV exposures, namely exposure during the lag and the exponential growth phases. Initially, the UV sensitivity of the ancestor was greater during the exponential phase than the lag phase. However, at the end of 100 cycles of exposure, UV resistance evolved to similar extents in both treatments. Genome analysis showed that mutations in genes involved in DNA repair, cell membrane structure and RNA polymerase were common in both treatments. However, different functional groups were found mutated in populations experiencing lag and exponential UV treatment. In the former, genes involved in transcriptional and translational regulations and cellular transport were mutated, whereas the latter treatment showed mutations in genes involved in signal transduction and cell adhesion. Interestingly, the treatments showed no phenotypic differences in a number of novel environments. Taken together, these results suggest that selection pressures at different physiological stages can lead to differences in the genomic signatures of adaptation, which need not necessarily translate into observable phenotypic differences.

Methods

These datasets represent Minimum Inhibitory Concentrations (MICs) and growth rates of bacterial populations that have been evolved in presence of ultraviolet radiation. The detailed methodologies are presented in the paper.

Funding

Department of Biotechnology, Ministry of Science and Technology, India, Award: BT/PR22328/BRB/10/1569/2016