flan: An R Package for Inference on Mutation Models.

Mazoyer, Adrien; Drouilhet, Rémy; Despréaux, Stéphane; Ycart, Bernard

doi:10.32614/rj-2017-029

Cited by 38 publications

(44 citation statements)

References 25 publications

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“…To see whether our dilution model could explain this, we simulated the 40-replicate, 20 generation fluctuation test experiment of Lee et al [46], using the mutation probability as estimated by whole-genome sequencing (µ = 3.98 × 10 −9 , total for all mutations producing sufficient resistance to nalidixic acid), for differing values of the number n of target "units" (gyrase molecules). For each n we simulated 1000 realisations of the 40-replicate experiment, and for each realisation we estimated the mutation probability under the no-delay model using the maximum likelihood method [45] (the same as used by Lee et al) implemented in the package flan [75]. This procedure correctly reproduced the mutation probability for data from simulations without delay (n = 0; SI Fig.…”

Section: Phenotypic Delay Due To Dilution Changes the Luria-delbrück mentioning

confidence: 98%

Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications

Carballo-Pacheco

Nicholson

Lilja

et al. 2019

Preprint

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Phenotypic delay -the time delay between genetic mutation and expression of the corresponding phenotype -is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data. Author SummaryUnderstanding precisely how some bacteria survive exposure to antibiotics is a major research focus. Specific mutations in the bacterial genome are known to provide protection. However, it remains unclear how much time passes between a bacterium acquiring the genetic change and being able to tolerate antibiotics -termed the phenotypic delay -and what controls this delay. Here, using computer simulations and mathematical arguments we discuss three biologically plausible mechanisms of phenotypic delay. We investigate how each mechanism would affect the outcome of laboratory experiments often used to study the evolution of antibiotic resistance, and we highlight ¶These authors contributed equally to this work. *Corresponding authors: rosalind.allen@ed.ac.uk, bwaclaw@staffmail.ed.ac.uk 1 how the delay might be detected in such experiments. We also show that the existence of the delay could explain an observed discrepancy in the measurement of mutation rates, and demonstrate that one of our models provides a superior fit to experimental data. Our work exposes how molecular details at the intracellular level can have a direct effect on evolution at the population level.

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Section: Phenotypic Delay Due To Dilution Changes the Luria-delbrück mentioning

confidence: 98%

Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications

Carballo-Pacheco

Nicholson

Lilja

et al. 2019

Preprint

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“…The death parameters γ and δ are assumed to be zero. These simulation studies have been implemented in R [26], using the R package flan [24], which is available on CRAN (https://cran.r-project.org/package=flan).…”

Section: Simulation Studymentioning

confidence: 99%

Fluctuation analysis on mutation models with birth-date dependence

Mazoyer

2018

Mathematical Biosciences

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The classic Luria-Delbrück model can be interpreted as a Poisson compound (number of mutations) of exponential mixtures (developing time of mutant clones) of geometric distributions (size of a clone in a given time). This "three-ingredients" approach is generalized in this paper to the case where the split instant distributions of cells are not i.i.d. : the lifetime of each cell is assumed to depend on its birth date. This model takes also into account cell deaths and non-exponentially distributed lifetimes. Previous results on the convergence of the distribution of mutant counts are recovered. The particular case where the instantaneous division rates of normal and mutant cells are proportional is studied. The classic Luria-Delbrück and Haldane models are recovered. Probability computations and simulation algorithms are provided. Robust estimation methods developed for the classic mutation models are adapted to the new model: their properties of consistency and asymptotic normality remain true; their asymptotic variances are computed. Finally, the estimation biases induced by considering classic mutation models instead of an inhomogeneous model are studied with simulation experiments.

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“…All simulations and inference were implemented in R. We wrote our own code to account for polyploidy, but in the future our methods could potentially be integrated into recently published R packages for fluctuation analysis (47,48). We simulated culture growth in non-selective media with stochastic appearance of spontaneous de novo mutations (for details see Supplementary Text, Section 3.1).…”

Section: Simulated Fluctuation Testsmentioning

confidence: 99%

Effective polyploidy causes phenotypic delay and influences bacterial evolvability

Sun

Alexander

Bogos

et al. 2017

Preprint

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Whether mutations in bacteria exhibit a noticeable delay before expressing their corresponding mutant phenotype was discussed intensively in the 1940s-50s, but the discussion eventually waned for lack of supportive evidence and perceived incompatibility with observed mutant distributions in fluctuation tests. Phenotypic delay in bacteria is widely assumed to be negligible, despite lack of direct evidence. Here we revisited the question using recombineering to introduce antibiotic resistance mutations into E. coli at defined time points and then tracking expression of the corresponding mutant phenotype over time. Contrary to previous assumptions, we found a substantial median phenotypic delay of 3-4 generations. We provided evidence that the primary source of this delay is multifork replication causing cells to be effectively polyploid, whereby wild-type gene copies transiently mask the phenotype of recessive mutant gene copies in the same cell. Using modeling and simulation methods, we explored the consequences of effective polyploidy for mutation rate estimation by fluctuation tests and sequencing-based methods. For recessive mutations, despite the substantial phenotypic delay, the per-copy or per-genome mutation rate is accurately estimated. However, the per-cell rate cannot be estimated by existing methods. Finally, with a mathematical model, we showed that effective polyploidy increases the frequency of costly recessive mutations in the standing genetic variation, and thus their potential contribution to evolutionary adaptation, while drastically reducing the chance that de novo recessive mutations can rescue populations facing a harsh environmental change such as antibiotic treatment. Overall, we have identified phenotypic delay and effective polyploidy as previously overlooked but essential components in bacterial evolvability, including antibiotic resistance evolution. Author summaryWhat is the time delay between the occurrence of a genetic mutation in a bacterial cell and manifestation of its phenotypic effect? We show that antibiotic resistance mutations in E.coli show a remarkably long phenotypic delay of 3-4 bacterial generations. The primary underlying mechanism of this delay is effective polyploidy. In a polyploid cell with multiple chromosomes, once a mutation arises on one of the chromosomes, the presence of non-mutated, wild-type gene copies on other chromosomes may mask the . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/226654 doi: bioRxiv preprint first posted online Nov. 29, 2017; 2 phenotype of the mutation. One implication of this finding is that conventional methods to determine the mutation rates of bacteria do not detect polyploidy and thus underestimate their potential for adaptation. More generally, the effect that a new mutation may become useful only in the "grand-children of the grand-children" suggests that pre-existing mutations are more importan...

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flan: An R Package for Inference on Mutation Models.

Cited by 38 publications

References 25 publications

Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications

Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications

Fluctuation analysis on mutation models with birth-date dependence

Effective polyploidy causes phenotypic delay and influences bacterial evolvability

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