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

Abstract

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.

Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial

“…Besides, it would be interesting to explicitly model horizontal gene transfer of resistance mutations, to include realistic pharmacodynamics and pharmacokinetics [10], and also to compare the impact of periodic alternations to that of random switches of the environment [5][6][7][8][9][68][69][70]. Other effects such as single-cell physiological properties [3], phenotypic delay [71] or density dependence of drug efficacy [72] can further enrich the response of microbial populations to variable concentrations of antimicrobials.…”

Molecular Mechanism and Kinetics of Amyloid-β42 Aggregate Formation: A Simulation Study

“…S2), the dimerization time will be 1018 ns which is much slower than the value (140 ns) predicted using the diffusion equation. 14 Our hypothesis is that at low monomer concentrations, the peptides are far from each other and Coulomb interactions are dominant over the van der Waals (vdW) interactions since the former decay much slower than the latter as a function of distance. Additionally, the Aβ42 peptide has a net charge of −3.…”

“…[10][11][12][13] The Aβ aggregated structures can be formed within minutes when the monomer concentration is in the micromolar (μM) range or in days when the concentration is the hundred nanomolar (nM) range. 11,12,14 The dependence of amyloid aggregation rate on the monomer concentrations has been investigated in many experimental studies with a wide concentration range from nM to millimolar (mM). However, the experiments were performed in different conditions and current technologies cannot accurately determine the exact time oligomers formed.…”

Antibiotic resistance: Insights from evolution experiments and mathematical modeling

“…As a result, the initial dynamics of mutant establishment are far more random than the process of mutant fixation at higher cell numbers [18,19]. This effect can be amplified by phenotypic delay, in which the protective effect of a resistance mutation takes several generations to manifest, e.g., because the new mutant variant of a protein must first be produced and replace the antibiotic-sensitive ancestral variant [20,21]. The effects of phenotypic delay on mutation rate estimates via Luria-Delbrück fluctuation tests were recently shown using a combination of numerical simulations and experiments [20,21].…”