Biology subjects

Alexander, H. K.

Publications and source records attributed to Alexander, H. K..

3 recordsLinked to original sources

bioRxiv2023

Estimating mutation rates under heterogeneous stress responses

Exposure to environmental stressors, including certain antibiotics, induces stress responses in bacteria. Some of these responses increase mutagenesis and thus potentially accelerate resistance evolution. Many studies report increased mutation rates under stress, often using the standard experimental approach of fluctuation assays. However, single-cell studies have revealed that many stress responses are heterogeneously expressed in bacterial populations, which existing estimation methods have not yet addressed. We develop a population dynamic model that considers heterogeneous stress responses (subpopulations of cells with the response off or on) that impact both mutation rate and cell division rate, inspired by the DNA-damage response in Escherichia coli (SOS response). We derive the mutant count distribution arising in fluctuation assays under this model and then implement maximum likelihood estimation of the mutation-rate increase specifically associated with the expression of the stress response. Using simulated mutant count data, we show that our inference method allows for accurate and precise estimation of the mutation-rate increase, provided that the induction of the response also reduces the division rate. Moreover, we find that in many cases, our model of heterogeneous stress responses and the standard model with mutant fitness cost reproduce fluctuation assay data equally well, suggesting that separate experiments would be required to identify the true underlying process. In cases where stress responses are heterogeneous, current methods still correctly infer the effective increase in population mean mutation rate, but we provide a novel method to infer heterogeneity in stress-induced mutation rates. Author summaryHow does environmental stress, especially from antibiotics, affect mutation rates in bacteria? This question has often been examined by estimating mutation rates using fluctuation assays, a standard experiment dating back to Luria and Delbruck in the 1940s. We address a gap in currently available estimation methods: we consider cell-to-cell variation in stress responses within bacterial populations, as revealed by recent single-cell studies. Our approach involves a population dynamic model inspired by the DNA-damage response in E. coli (SOS response). It accounts for a subpopulation with high expression of the stress response, which increases the mutation rate and decreases the division rate of a cell. Notably, we find that existing models and our heterogeneous-response model reproduce simulated fluctuation assay data equally well in certain scenarios. This emphasises the need for separate experiments to uncover the true underlying processes. Nevertheless, when stress responses are known to be heterogeneous, our study offers a novel method for accurately estimating mutation rates specifically associated with the high expression of the stress response. Uncovering the heterogeneity in stress-induced mutation rates could be important for predicting the evolution of antibiotic resistance.

evolutionary biology↗

bioRxiv2018

Stochastic bacterial population dynamics prevent the emergence of antibiotic resistance within the mutant selection window

A better understanding of how antibiotic exposure impacts the evolution of resistance is crucial for designing more sustainable treatment strategies. The conventional approach to relating antibiotic dose to resistance evolution within a bacterial population is to measure the range of concentrations over which resistant strain(s) are selectively favoured over a sensitive strain - the "mutant selection window". Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using two Pseudomonas aeruginosa strains carrying distinct resistance plasmids, we show that single resistant cells have <5% probability of outgrowth at antibiotic concentrations as low as 1/8th of the resistant strains minimum inhibitory concentration. This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, within the traditional mutant selection window, may be more effective at preventing de novo emergence of resistance than predicted by deterministic approaches. Significance statementThe emergence of antibiotic resistance poses a critical threat to the efficacy of antibiotic treatments. A resistant bacterial population must originally arise from a single cell that mutates or acquires a resistance gene. This single cell may, by chance, fail to successfully reproduce before it dies, leading to loss of the nascent resistant lineage. Here we show that antibiotic concentrations that selectively favour resistance are nonetheless sufficient to reduce the chance of outgrowth from a single cell to a very low probability. Our findings suggest that lower antibiotic concentrations than previously thought may be sufficient to prevent, with high probability, emergence of resistance from single cells.

microbiology↗

bioRxiv2017

Effective polyploidy causes phenotypic delay and influences bacterial evolvability

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-copyor 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.\n\nAuthor 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 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 important for surviving sudden environmental catastrophe.

evolutionary biology↗