Biology subjects

de Visser, A.

Publications and source records attributed to de Visser, A..

7 recordsLinked to original sources

bioRxiv2026

Antibiotic tolerance due to filamentation shapes β-lactam pharmacodynamics in Escherichia coli

Pharmacodynamic curves describe how changes in drug concentration affect pathogen growth. They are essential for designing treatments that promote pathogen eradication and minimize the evolution of antibiotic resistance. The classical function for modelling pharmacodynamics is a phenomenological, S-shaped curve with stable growth and death rates separated by a single drop. In this study, we characterized the pharmacodynamic curve of the {beta}-lactam antibiotic cefotaxime (CTX) acting against Escherichia coli. We found that the relationship between CTX concentration and net growth rate diverged from classical model predictions, instead yielding a two-step curve defined by distinct phases of growth, population maintenance, and killing. We hypothesized that the intermediate phase arose from antibiotic tolerance conferred by bacterial filaments. Microscopic assessment of treated cells indeed showed a difference in degree of filamentation with concentration. We further sought to explain this with a semi-mechanistic pharmacodynamic function, modelling the binding of CTX to its cellular targets, penicillin binding proteins (PBP) 1 and 3. By incorporating the preferential concentration-dependent binding of CTX to PBP3 and then PBP1, yielding filaments or lysed cells respectively, we replicated the two-step curve in silico. We also assessed the pharmacodynamics of CTX against mutants conferring resistance; these displayed further altered curves, in line with their fitness costs. Altogether, our results show that CTX has a two-step pharmacodynamic curve against E. coli arising from multiple targets separated in their affinity for the antibiotic. We present a model offering a mechanistically grounded framework for capturing such dynamics. These pharmacodynamic curves deserve careful consideration when defining optimal dosing.

microbiology↗

bioRxiv2026

Anaerobic conditions increase plasmid transfer rates across Escherichia coli strains

Bacterial conjugation is the primary mechanism by which antibiotic resistance genes spread in microbial populations, yet our understanding of this process has been largely based on experiments conducted under aerobic conditions. This creates a fundamental disconnect: environments that are considered hotspots for gene exchange (e.g., the gut, abscesses, chronic wounds, and wastewater systems) are predominantly anaerobic. In this study, we investigate whether oxygen availability influences the transfer rate of a set of common ESBL-IncI1- and qnrS1-IncF plasmids in commensal Escherichia coli strains from chickens. We found that oxygen availability significantly shapes conjugation dynamics in a recipient strain-specific manner, with anaerobic conditions promoting higher ESBL-IncI1- plasmid transfer rates to commensal E. coli recipients. Conjugation rates of the ESBL-IncI1- plasmids to a laboratory strain of E. coli were several orders of magnitude higher and independent of oxygen level, while two qnrS1-IncF plasmids showed higher anaerobic rates. Our study reveals critical "oxygen blind spots" in conventional conjugation assays and suggests that conventional aerobic conjugation assays underestimate plasmid transfer rates in natural environments such as the chicken caeca. These findings highlight the importance of aligning experimental conditions with the physiological and ecological environments in which gene exchange naturally occurs. Tailoring these variables is essential for generating results that accurately reflect, predict, and potentially intervene in the horizontal spread of antimicrobial resistance.

microbiology↗

bioRxiv2023

Conditions for coexistence of β-lactamase mutants

{beta}-lactamases play an important role in antibiotic resistant bacterial infections. Understanding the spread of these enzymes may inform the development of better drug therapies. However, this is complicated by the fact that {beta}-lactamases reduce the antibiotic concentration in their environment, thereby altering their own selective advantage via eco-evolutionary feedback. We investigated the effect of such feedback on the spread of bacterial strains expressing {beta}-lactamase enzymes conferring different levels of resistance to the cephalosporin cefotaxime. Specifically, we conducted head-to-head competitions between two clinically observed {beta}-lactamase mutants, TEM-19 and TEM-52, with low and high activity against cefotaxime, respectively. By experimentally varying nutrient levels, we altered cell density and hence the strength of ecological feedback and examined its impact on competitive fitness and strain coexistence across a range of cefotaxime concentrations. A population dynamic model, parameterized solely with independently measured traits, revealed cell density as the key mediator of this feedback. Our results show that cell density dictates whether the resistant strain (TEM-52) outcompetes or stably coexists with the susceptible strain (TEM-19). By validating our model with experimental data, we showed that it can reasonably predict the equilibrium frequencies based on dose-dependent growth rates and antibiotic degradation rates of both strains. Our study emphasizes the importance of considering ecological feedback for understanding the fate of antibiotic-degrading mutants, including in clinical environments. ImportanceSince the discovery of penicillin, {beta}-lactam antibiotics have become the most widely used antibiotics to treat bacterial infections. Their applicability is decreasing because bacteria evolve resistance via expression of antibiotic-degrading {beta}-lactamase enzymes. Because the spread of resistance is a large health problem, th prediction of resistance evolution is a big target. Several mutations increasing resistance have been studied, but as enzymes with increased activity spread, cells deactivate the {beta}-lactam antibiotics and thereby open a niche for susceptible variants. This eco-evolutionary feedback complicates the prediction of resistance evolution and makes it context dependent. Here, we show that both the cell density and the antibiotic concentration affect the success of a new {beta}-lactamase variant and whether it can invade, replace, or coexist with an ancestor variant. With a mathematical model we can predict the success of new variants and therefore predict evolutionary paths depending on the environmental variables.

evolutionary biology↗

bioRxiv2023

Interaction between mutation type and gene pleiotropy drives parallel evolution in the laboratory

What causes evolution to be repeatable is a fundamental question in evolutionary biology. Pleiotropy, i.e. the effect of an allele on multiple traits, is thought to enhance repeatability by constraining the number of available beneficial mutations. Additionally, pleiotropy may promote repeatability by allowing large fitness benefits of single mutations via adaptive combinations of phenotypic effects. Yet, this latter evolutionary potential may be reaped solely by specific types of mutations able to realize optimal combinations of phenotypic effects while avoiding the costs of pleiotropy. Here, we address the interaction of gene pleiotropy and mutation type on evolutionary repeatability in a meta-analysis of experimental evolution studies with Escherichia coli. We hypothesize that single-nucleotide polymorphisms are principally able to yield large fitness benefits by targeting highly pleiotropic genes, whereas indels and structural variants provide smaller benefits and are restricted to genes with lower pleiotropy. By using gene connectivity as proxy for pleiotropy, we show that nondisruptive single-nucleotide polymorphisms (SNPs) in highly pleiotropic genes yield the largest fitness benefits, since they contribute more to parallel evolution, especially in large populations, than inactivating SNPs, indels and structural variants. Our findings underscore the importance of considering genetic architecture together with mutation type for understanding evolutionary repeatability.

evolutionary biology↗

bioRxiv2022

Race to survival during antibiotic breakdown determines the minimal surviving population size

A common strategy used by bacteria to resist antibiotics is enzymatic degradation or modification. Such a collective mechanism also enhances the survival of nearby cells, an effect that increases with the number of bacteria that are present. Collective resistance is of clinical significance, yet a quantitative understanding at the population level is lacking. Here we develop a general theoretical framework of collective resistance under antibiotic degradation. Our modeling reveals that population survival crucially depends on the ratio of timescales of two processes: the rates of population death and antibiotic removal. However, it is insensitive to molecular, biological and kinetic details of the underlying processes that give rise to these timescales. Another important aspect for this race to survival is the degree of cooperativity, which is related to the permeability of the cell wall for antibiotics and enzymes. These observations motivate a coarse-grained, phenomenological model and simple experimental assay to measure the dose-dependent minimal surviving population size. From this model, two dimensionless parameters can be estimated, representing the populations race to survival and single-cell resistance. Our simple model may serve as reference for more complex situations, such as heterogeneous bacterial communities.

microbiology↗

bioRxiv2022

The fitness landscape of TEM-1 β-lactamase is stratified and inverted by sublethal concentrations of cefotaxime

Adaptive evolutionary processes are constrained by the availability of mutations which cause a fitness benefit - a concept that may be illustrated by fitness landscapes which map the relationship of genotype space with fitness. Experimentally derived landscapes have demonstrated a predictability to evolution by identifying limited mutational routes that evolution by natural selection may take between low and high-fitness genotypes. However, such studies often utilise indirect measures to determine fitness. We estimated the competitive fitness of each mutant relative to all of its single-mutation neighbours to describe the fitness landscape of three mutations in a {beta}-lactamase enzyme at sub-lethal concentrations of the antibiotic cefotaxime in a structured and unstructured environment. We found that in the unstructured environment the antibiotic selected for higher-resistance types - but with an equivalent fitness for subsets of mutants, despite substantial variation in resistance - resulting in a stratified fitness landscape. In contrast, in a structured environment with low antibiotic concentration, antibiotic-susceptible genotypes had a relative fitness advantage, which was associated with antibiotic-induced filamentation. These results cast doubt that highly resistant genotypes have a unique selective advantage in environments with sub-inhibitory concentrations of antibiotics, and demonstrate that direct fitness measures are required for meaningful predictions of the accessibility of evolutionary routes. ImportanceThe evolution of antibiotic resistant bacterial populations underpins the ongoing antibiotic-resistance crisis. We aim to understand how antibiotic-degrading enzymes can evolve to cause increased resistance, how this process is constrained and whether it can be predictable. To this end we performed competition experiments with a combinatorially-complete set of mutants of a {beta}-lactamase gene subject to sub-inhibitory concentrations of the antibiotic cefotaxime. While some mutants confer their hosts with high resistance to cefotaxime, in competition these mutants do not always confer a selective advantage. Similarly, we identified conditions involving spatial structure where mutations causing high resistance result in a selective disadvantage. Together, this work suggests that the relationship between resistance level and fitness at sub-inhibitory concentrations is complex; predicting the evolution of antibiotic resistance requires knowledge of the conditions that select for resistant genotypes and the selective advantage evolved types have over their predecessors.

evolutionary biology↗

bioRxiv2021

Stochastic effects on the establishment of mutants resistant to β-lactam antibiotics

For antibiotic resistance to arise, new resistant mutants must establish in a bacterial population before they can spread via natural selection. Comprehending the stochastic factors that influence mutant establishment is crucial for a quantitative understanding of antibiotic resistance emergence. Here, we quantify the single-cell establishment probability of four Escherichia coli strains expressing {beta}-lactamase alleles with different activity against the antibiotic cefotaxime, as a function of antibiotic concentration in both unstructured (liquid) and structured (agar) environments. We show that concentrations well below the minimum inhibitory concentration (MIC) can substantially hamper establishment, particularly for highly resistant mutants. While the pattern of establishment suppression is comparable in both tested environments, we find greater variability in establishment probability on agar. Using a simple branching model, we investigate possible sources of this stochasticity, including environment-dependent lineage variability. Lastly, we use the single-cell establishment probability to predict each strains MIC in the absence of social interactions. We observe substantially higher measured than predicted MIC values, particularly for highly resistant strains, which indicates cooperative effects among resistant cells at large cell numbers, such as in standard MIC assays.

evolutionary biology↗