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Mutation Induced Extinction in Finite Populations: Lethal Mutagenesis and Lethal Isolation

Overview of attention for article published in PLoS Computational Biology, August 2012
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Title
Mutation Induced Extinction in Finite Populations: Lethal Mutagenesis and Lethal Isolation
Published in
PLoS Computational Biology, August 2012
DOI 10.1371/journal.pcbi.1002609
Pubmed ID
Authors

C. Scott Wylie, Eugene I. Shakhnovich

Abstract

Reproduction is inherently risky, in part because genomic replication can introduce new mutations that are usually deleterious toward fitness. This risk is especially severe for organisms whose genomes replicate "semi-conservatively," e.g. viruses and bacteria, where no master copy of the genome is preserved. Lethal mutagenesis refers to extinction of populations due to an unbearably high mutation rate (U), and is important both theoretically and clinically, where drugs can extinguish pathogens by increasing their mutation rate. Previous theoretical models of lethal mutagenesis assume infinite population size (N). However, in addition to high U, small N can accelerate extinction by strengthening genetic drift and relaxing selection. Here, we examine how the time until extinction depends jointly on N and U. We first analytically compute the mean time until extinction (τ) in a simplistic model where all mutations are either lethal or neutral. The solution motivates the definition of two distinct regimes: a survival phase and an extinction phase, which differ dramatically in both how τ scales with N and in the coefficient of variation in time until extinction. Next, we perform stochastic population-genetics simulations on a realistic fitness landscape that both (i) features an epistatic distribution of fitness effects that agrees with experimental data on viruses and (ii) is based on the biophysics of protein folding. More specifically, we assume that mutations inflict fitness penalties proportional to the extent that they unfold proteins. We find that decreasing N can cause phase transition-like behavior from survival to extinction, which motivates the concept of "lethal isolation." Furthermore, we find that lethal mutagenesis and lethal isolation interact synergistically, which may have clinical implications for treating infections. Broadly, we conclude that stably folded proteins are only possible in ecological settings that support sufficiently large populations.

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The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Venezuela, Bolivarian Republic of 1 2%
Canada 1 2%
Unknown 49 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 28%
Student > Ph. D. Student 14 26%
Student > Bachelor 6 11%
Student > Master 5 9%
Professor > Associate Professor 3 6%
Other 5 9%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 38%
Biochemistry, Genetics and Molecular Biology 12 23%
Physics and Astronomy 6 11%
Medicine and Dentistry 3 6%
Computer Science 2 4%
Other 5 9%
Unknown 5 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 March 2024.
All research outputs
#14,614,930
of 25,399,318 outputs
Outputs from PLoS Computational Biology
#6,140
of 8,972 outputs
Outputs of similar age
#100,448
of 179,290 outputs
Outputs of similar age from PLoS Computational Biology
#69
of 108 outputs
Altmetric has tracked 25,399,318 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,972 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 179,290 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.