Analysis of the Luria–Delbrück distribution using discrete convolution powers

T., W.; Sandri, G.; Sarkar, Sahotra

doi:10.2307/3214564

Cited by 130 publications

(46 citation statements)

References 15 publications

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“…Estimates were obtained using fluctuation tests (Luria and Delbruck 1943), in which many replicate cultures were grown from a few founder cells and the numbers of cells expressing the mutant phenotype in each culture were counted. Mutation rates were then calculated from the distribution of mutants across the replicate cultures using a maximum-likelihood method (Ma et al 1992;Stewart 1994). Mutation rates were estimated for single clones that had been chosen at random from each population after 10,000 generations.…”

Section: Estimation Of Mutation Ratesmentioning

confidence: 99%

Long-Term Experimental Evolution in Escherichia coli. VII. Mechanisms Maintaining Genetic Variability Within Populations

Elena¹,

Lenski²

1997

Evolution

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Six replicate populations of the bacterium Escherichia coli were propagated for more than 10,000 generations in a defined environment. We sought to quantify the variation among clones within these populations with respect to their relative fitness, and to evaluate the roles of three distinct population genetic processes in maintaining this variation. On average, a pair of clones from the same population differed from one another in their relative fitness by approximately 4%. This within-population variation was small compared with the average fitness gain relative to the common ancestor, but it was statistically significant. According to one hypothesis, the variation in fitness is transient and reflects the ongoing substitution of beneficial alleles. We used Fisher's fundamental theorem to compare the observed rate of each population's change in mean fitness with the extent of variation for fitness within that population, but we failed to discern any correspondence between these quantities. A second hypothesis supposes that the variation in fitness is maintained by recurrent deleterious mutations that give rise to a mutation-selection balance. To test this hypothesis, we made use of the fact that two of the six replicate populations had evolved mutator phenotypes, which gave them a genomic mutation rate approximately 100-fold higher than that of the other populations. There was a marginally significant correlation between a population's mutation rate and the extent of its within-population variance for fitness, but this correlation was driven by only one population (whereas two of the populations had elevated mutation rates). Under a third hypothesis, this variation is maintained by frequency-dependent selection, whereby genotypes have an advantage when they are rare relative to when they are common. In all six populations, clones were more fit, on average, when they were rare than when they were common, although the magnitude of the advantage when rare was usually small (~1% in five populations and ~5% in the other). These three hypotheses are not mutually exclusive, but frequency-dependent selection appears to be the primary force maintaining the fitness variation within these experimental populations.

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Section: Estimation Of Mutation Ratesmentioning

confidence: 99%

Long-Term Experimental Evolution in Escherichia coli. VII. Mechanisms Maintaining Genetic Variability Within Populations

Elena¹,

Lenski²

1997

Evolution

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“…This permitted credible intervals of the mutation rate ratios to be accurately calculated. Posterior distributions were calculated using the MSS (Ma-Sandri-Sarkar) likelihood function (Ma, Sandri and Sarkar, 1992) for the Lea-Coulson model (Lea and Coulson 1949) with a non-informative (constant) prior over log µ. The results were insensitive to the choice of prior: Changing to a constant prior over µ or to a 1/µ 2 prior changed the estimated mutation rate ratios by <10% of the distance to the credible interval boundaries (RMS deviation = 4%).…”

Section: Methodsmentioning

confidence: 99%

Chromatin modifiers alter recombination between divergent DNA sequences

Chakraborty

Mackenroth

Alani

2019

Preprint

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Recombination between divergent DNA sequences is actively prevented by heteroduplex rejection mechanisms. In baker's yeast such anti-recombination mechanisms can be initiated by the recognition of DNA mismatches in heteroduplex DNA by MSH proteins, followed by recruitment of the Sgs1-Top3-Rmi1 helicase-topoisomerase complex to unwind the recombination intermediate. We previously showed that the repair/rejection decision during single-strand annealing recombination is temporally regulated by MSH protein levels and by factors that excise non-homologous single-stranded tails. These observations, coupled with recent studies indicating that mismatch repair factors interact with components of the histone chaperone machinery, encouraged us to explore roles for epigenetic factors and chromatin conformation in regulating the decision to reject vs. repair recombination between divergent DNA substrates. This work involved the use of an inverted repeat recombination assay thought to measure sister chromatid repair during DNA replication. Our observations are consistent with the histone chaperones CAF-1 and Rtt106 and the histone deacetylase Sir2 acting to suppress heteroduplex rejection and the Rpd3, Hst3 and Hst4 deacetylases acting to promote heteroduplex rejection. These observations and double mutant analysis have led to a model in which nucleosomes located at DNA lesions stabilize recombination intermediates and compete with mismatch repair factors that mediate heteroduplex rejection.

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“…All fluctuation tests were repeated in two independent blocks. The total number of colonies and the number of resistant colonies were then used to estimate mutation rate (and 95% confidence intervals) with FALCOR (using the Ma-Sandri-Sarkar maximum likelihood estimator; http://www.keshavsingh.org/protocols/FALCOR.html ) [87][88][89][90][91]. Significant differences in mutation rates between clones were identified from non-overlapping 95% confidence intervals.…”

Section: Mutator Screen and Fluctuation Tests To Estimate Mutation Ratementioning

confidence: 99%

Low Mutational Load Allows for High Mutation Rate Variation in Gut Commensal Bacteria

Ramiro

Durão

Bank

et al. 2019

Preprint

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Bacteria generally live in species-rich communities, such as the gut microbiota. Yet, little is known about bacterial evolution in natural ecosystems. Here, we followed the long-term evolution of commensal Escherichia coli in the mouse gut. We observe the emergence of polymorphism for mutation rate, ranging from wild-type levels to 1000-fold higher. By combining experiments, whole-genome sequencing and in silico simulations, we identify the molecular causes and evolutionary conditions that allow these hypermutators to emerge and coexist within a complex microbiota. The hypermutator phenotype is caused by mutations in DNA polymerase III, which increase mutation rate by~1000-fold (a mutation in the proofreading subunit) and stabilize hypermutator fitness (mutations in the catalytic subunit). The strong mutation rate variation persists for >1000 generations, with coexistence between lineages carrying 4 to >600 mutations. This in vivo molecular evolution pattern is consistent with deleterious mutations of~0.01-0.001% fitness effects, 100 to 1000-fold lower than current in vitro estimates. Despite large numbers of deleterious mutations, we identify multiple beneficial mutations that do not reach fixation over long periods of time. This indicates that the dynamics of beneficial mutations are not shaped by constant positive Darwinian selection but by processes leading to negative frequency-dependent or temporally fluctuating selection. Thus, microbial evolution in the gut is likely characterized by partial sweeps of beneficial mutations combined with hitchhiking of very slightly deleterious mutations, which take a long time to be purged but impose a very weak mutational load. These results are consistent with the pattern of genetic polymorphism that is emerging from metagenomics studies of the human gut microbiota, suggesting that we identified key evolutionary processes shaping the genetic composition of this community.

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Analysis of the Luria–Delbrück distribution using discrete convolution powers

Cited by 130 publications

References 15 publications

Long-Term Experimental Evolution in Escherichia coli. VII. Mechanisms Maintaining Genetic Variability Within Populations

Long-Term Experimental Evolution in Escherichia coli. VII. Mechanisms Maintaining Genetic Variability Within Populations

Chromatin modifiers alter recombination between divergent DNA sequences

Low Mutational Load Allows for High Mutation Rate Variation in Gut Commensal Bacteria

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