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File S1 - Mutations.xlsx (110.72 kB)
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File S2 - R source.R (19.75 kB)
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File S3 - Mutant counts.csv (1.67 kB)
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File S4 - Nt counts.csv (1.55 kB)
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Data from: Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli

dataset
posted on 2024-04-04, 13:51 authored by Danna GiffordDanna Gifford, Anish Bhattacharyya, Alexandra Geim, Eleanor Marshall, Rok Krašovec, Christopher KnightChristopher Knight

Abstract: Spontaneous mutations are the ultimate source of novel genetic variation on which evolution operates. Although mutation rate is often discussed as a single parameter in evolution, it comprises multiple distinct types of changes at the level of DNA. Moreover, the rates of these distinct changes can be independently influenced by genomic background and environmental conditions. Using fluctuation tests, we characterised the spectrum of spontaneous mutations in Escherichia coli grown in low and high glucose environments. These conditions are known to affect the rate of spontaneous mutation in wild-type MG1655, but not in a ΔluxS deletant strain—a gene with roles in both quorum sensing and the recycling of methylation products used in Escherichia coli’s DNA repair process. We find an increase in AT>GC transitions in the low glucose environment, suggesting that processes relating to the production or repair of this mutation could drive the response of overall mutation rate to glucose concentration. Interestingly, this increase in AT>GC transitions is maintained by the glucose non-responsive ΔluxS deletant. Instead, an elevated rate of GC>TA transversions, more common in a high glucose environment, leads to a net non-responsiveness of overall mutation rate for this strain. Our results show how relatively subtle changes, such as the concentration of a carbon substrate or loss of a regulatory gene, can substantially influence the amount and nature of genetic variation available to selection.

File S1 contains the mutations found in sequenced rifampicin-resistant strains originating from fluctuation test and used to assess changes in mutational spectrum.

File S2 contains the R analysis code used to perform all statistical analyses and generate figures.

File S3 contains mutant counts used to estimate mutation rates to rifampicin resistance for MG1655 and ΔluxS strains grown at low and high glucose in the fluctuation test.

File S4 contains population density data (Nt) for MG1655 and ΔluxS strains grown at low and high glucose in the fluctuation test.

Funding

Predicting evolutionary dynamics of multi-drug resistance

Medical Research Council

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Spatio-temporal dynamics of mutation avoidance and antimicrobial resistance

UK Research and Innovation

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Manchester-Bangor DTP2

Biotechnology and Biological Sciences Research Council

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Institutional Strategic Support Fund

Wellcome Trust

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