Irregularities in genetic variation and mutation rates with environmental stresses is a research paper published in Environmental Microbiology (2019). On theSindex it has a DataRank of 1.1. It has been cited 64 times, with 62 citing works in its 1-hop citation network.
Summary The appearance of new mutations is determined by the equilibrium between DNA error formation and repair. In bacteria like Escherichia coli , stresses are thought shift this balance towards increased mutagenesis. Recent findings, however, suggest a very uneven relationship between stress and mutations. Only a subset of stressful environments increase the net rate of mutation and different forms of nutritional stress (such as oxygen, carbon or phosphorus limitations) result in markedly different mutation rates after similar reductions in growth rate. Moreover, different stresses result in altered mutational spectra, with some increasing transposition and others increasing indel formation. Single‐base substitution rates are lower with some stresses than in unstressed bacteria. Indeed, changes to the mix of mutations with stress are more widespread than a marked increase in net mutation rate. Much remains to be learned on how environments have unique mutational signatures and why some stresses are more mutagenic than others. Even beyond stress‐induced genetic variation, the fundamental unresolved question in the stress–mutation relationship is the adaptive value of different types of mutations and mutation rates; is transposition, for example, more advantageous under anaerobic conditions? It remains to be investigated whether stress‐specific genetic variation impacts on evolvability differentially in distinct environments.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.626
From this paper's citation signal
Citation Network Contribution
0.472
From 35 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 57% comes from its base citations and 43% from the citation network (35 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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