Biology subjects

Igler, C.

Publications and source records attributed to Igler, C..

7 recordsLinked to original sources

bioRxiv2024

Bacterial cell size modulation along the growth curve across nutrient conditions

Under stable growth conditions, bacteria maintain cell size homeostasis through coordinated elongation and division. However, fluctuations in nutrient availability result in dynamic regulation of the target cell size. Using microscopy imaging and mathematical modelling, we examine how bacterial cell volume changes over the growth curve in response to nutrient conditions. We find that two rod-shaped bacteria, Escherichia coli and Salmonella enterica, exhibit similar cell volume distributions in stationary phase cultures irrespective of growth media. Cell resuspension in rich media results in a transient peak with a five-fold increase in cell volume {approx} 2h after resuspension. This maximum cell volume, which depends on nutrient composition, subsequently decreases to the stationary phase cell size. Continuous nutrient supply sustains the maximum volume. In poor nutrient conditions, cell volume shows minimal changes over the growth curve, but a markedly decreased cell width compared to other conditions. The observed cell volume dynamics translate into non-monotonic dynamics in the ratio between biomass (optical density) and cell number (colony-forming units), highlighting their non-linear relationship. Our findings support a heuristic model comparing modulation of cell division relative to growth across nutrient conditions and providing novel insight into the mechanisms of cell size control under dynamic environmental conditions.

microbiology↗

bioRxiv2023

Prophage maintenance is determined by environment-dependent selective sweeps rather than mutational availability

Prophages, viral sequences integrated into bacterial genomes, can be beneficial and costly. Despite the risk of prophage activation and subsequent bacterial death, active prophages are present in most bacterial genomes. However, our understanding of the selective forces that maintain prophages in bacterial populations is limited. Combining experimental evolution with stochastic modelling, we show that prophage maintenance and loss are primarily determined by environmental conditions that alter the net fitness effect of a prophage. When prophages are too costly, they are rapidly lost through environment-specific sequences of selective sweeps. Conflicting selection pressures that select against the prophage but for a prophage-encoded accessory gene can maintain prophages. The dynamics of prophage maintenance additionally depends on the sociality of this accessory gene. Non-cooperative genes maintain prophages at higher frequencies than cooperative genes, which can protect phage-free cheaters that may emerge if prophage costs outweigh their benefits. Our simulations suggest that environmental variation plays a larger role than mutation rates in determining prophage maintenance. These findings highlight the complexity of selection pressures that act on mobile genetic elements and challenge our understanding of the role of environmental factors relative to random chance events in shaping the evolutionary trajectory of bacterial populations.

evolutionary biology↗

bioRxiv2022

Assessing the importance of resistance, persistence and hyper-mutation for antibiotic treatment success with stochastic modelling

Antimicrobial resistance poses a rising threat to global health, making it crucial to understand the routes of bacterial survival during antimicrobial treatments. Treatment failure can result from genetic or phenotypic mechanisms, which diminish the effect of antibiotics. By assembling empirical data, we find that, for example, Pseudomonas aeruginosa infections in cystic fibrosis patients frequently contain persisters, transiently non-growing and antibiotic-refractory subpopulations, and hyper-mutators, mutants with elevated mutation rates and thus higher probability of genetic resistance emergence. Resistance, persistence and hyper-mutation dynamics are difficult to disentangle experimentally. Hence, we use stochastic population modelling and deterministic fitness calculations of bacterial evolution under antibiotic treatment to investigate how genetic resistance and phenotypic mechanisms affect treatment success. We find that treatment failure is caused by resistant mutants at lower antibiotic concentrations (with high final bacterial numbers), but by persistence phenotypes at higher antibiotic concentrations (with low final bacterial numbers). Facilitation of resistance occurs through hyper-mutators during treatment, but through persistence only after treatment is discontinued, which allows for persisters to resume growth and evolve resistance in the absence of antibiotics. Our findings highlight the time- and concentration-dependence of different bacterial mechanisms to escape antibiotic killing, which should be considered when designing resistance-proof antimicrobial treatments.

evolutionary biology↗

bioRxiv2021

Limited specificity of molecular interactions incurs an environment-dependent fitness cost in bacteria

Reliable operation of cellular programs depends crucially on the specificity of biomolecular interactions. In gene regulatory networks, the appropriate expression of genes is determined through the specific binding of transcription factors (TFs) to their cognate DNA sequences. However, the large genomic background likely contains many DNA sequences showing similarity to TF target motifs, potentially allowing for substantial non-cognate TF binding with low specificity. Whether and how non-cognate TF binding impacts cellular function and fitness remains unclear. We show that increased expression of different transcriptional regulators in Escherichia coli and Salmonella enterica can significantly inhibit population growth across multiple environments. This effect depends upon (i) TF binding to a large number of DNA sequences with low specificity, (ii) TF cooperativity, and (iii) the ratio of TF to DNA. DNA binding due to the limited specificity of promiscuous or non-native TFs can thus severely impact fitness, giving rise to a fundamental biophysical constraint on gene regulatory design and evolution.

molecular biology↗

bioRxiv2021

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid-host interactions, but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same host cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection-from systems to exclude infection by other plasmids to interactions in the regulation of plasmid copy number per cell-are well characterised at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics, and in turn, the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts eco-evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection.

microbiology↗

bioRxiv2021

Conjugative plasmid transfer is limited by prophages but can be overcome by high conjugation rates

Antibiotic resistance spread via plasmids is a serious threat to successfully fight infections and makes understanding plasmid transfer in nature crucial to prevent the rise of antibiotic resistance. Studies addressing the dynamics of plasmid conjugation have yet neglected one omnipresent factor: prophages (viruses integrated into bacterial genomes), whose activation can kill host and surrounding bacterial cells. To investigate the impact of prophages on conjugation, we combined experiments and mathematical modelling. Using E. coli, prophage{lambda} and the multidrug-resistant plasmid RP4 we find that prophages can substantially limit the spread of conjugative plasmids. This inhibitory effect was strongly dependent on environmental conditions and bacterial genetic background. Our empirically parameterized model reproduced experimental dynamics of cells acquiring either the prophage or the plasmid well but could only reproduce the number of cells acquiring both elements by assuming complex interactions between conjugative plasmids and prophages in sequential infections. Varying phage and plasmid infection parameters over empirically realistic ranges revealed that plasmids can overcome the negative impact of prophages through high conjugation rates. Overall, the presence of prophages introduces an additional death rate for plasmid carriers, the magnitude of which is determined in non-trivial ways by the environment, the phage and the plasmid.

evolutionary biology↗

bioRxiv2020

Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics and treatment regimens

The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: i) a single mutation, which provides a large MIC increase, or ii) multiple mutations, each conferring a small increase, which combine to yield high-level resistance. Using stochastic modeling we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the most efficacious drug type depends on the pharmacokinetic profile. Further, we demonstrate that, for resistance evolution in multiple steps, adaptive treatment, which only suppresses the bacterial population, is favored over aggressive treatment, which aims at eradication.

evolutionary biology↗