Biology subjects

Waclaw, B.

Publications and source records attributed to Waclaw, B..

13 recordsLinked to original sources

bioRxiv2025

Simple theoretical models predict some - but not all - aspects of the experimental evolution of antibiotic resistance

Mathematical modelling of antibiotic resistance plays an important role in understanding the mechanisms of resistance emergence and spreading, testing the feasibility of new treatment protocols, and antimicrobial stewardship. However, many assumptions underlying some of the most commonly used mathematical models have not been rigorously tested experimentally. We verify whether one of these models - a birth-death-mutation process - is able to quantitatively predict the outcome of laboratory experiments. We grow bacteria in a bioreactor in conditions that closely resemble the assumptions of the model, and compare the model predictions with experimental observables such as the probability and time to resistance evolution, mutant number distribution, and the genetic composition of the evolved populations. We show that the model fails to reproduce some aspects of the experiments (failing differently for different antibiotics) but that simple modifications of the model significantly improve its predictive power. These modifications give insight into the population dynamics of resistant mutants for each antibiotic tested, and highlight the importance of quantitative modelling for accurate prediction of antibiotic resistance evolution.

evolutionary biology↗

bioRxiv2024

A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.

cancer biology↗

bioRxiv2023

Infection dynamics in the urinary tract - The importance of non-planktonic phases during early infection and resistance evolution

Treatment of urinary tract infections (UTIs) and the prevention of their recurrence is a pressing global health problem. During the infection establishment, pathogenic bacteria may attach to and invade into the bladder tissue, and thus access non-planktonic phases besides the bladder lumen. Planktonic, attached, and intracellular bacteria face different selection pressures from physiological responses (frequent micturition and immune responses), or antibiotic treatment. Acknowledging this heterogeneity of selection pressures is essential to increase treatment efficacy, reduce resistance evolution and develop new treatments. Here, we present a mathematical model capturing the initial infection phase to unravel the effects of this heterogeneity on the ecological and evolutionary dynamics of UTIs. We explicitly represent planktonic bacteria in the bladder lumen, bacteria attached to epithelial cells of the bladder wall, and bacteria that have invaded these epithelial cells. We find that access to non-planktonic compartments increases the risk of infection establishment substantially. With antibiotic treatment, the trajectories of resistance evolution are shaped by the accessibility of the intracellular compartment and antibiotic permeation into the epithelial cells. Further, we evaluate conditions where probiotic pretreatment can reduce the pathogen load during an infection and increase the efficacy of accompanying antibiotic therapies. We find that faster-growing probiotic strains achieve a stronger increase in antibiotic treatment efficacy, although too strong antibiotic treatment can diminish this effect. Our study shows that accounting for the selection heterogeneity is crucial to understand the eco-evolutionary dynamics of UTIs, and that mathematical modeling can help to steer this complex system into a healthy state. Competing InterestsThe authors declare no competing interests.

evolutionary biology↗

bioRxiv2023

Substrate geometry affects population dynamics in a bacterial biofilm

Biofilms inhabit a range of environments, such as dental plaques or soil micropores, often characterized by intricate, non-even surfaces. However, the impact of surface irregularities on the population dynamics of biofilms remains elusive as most biofilm experiments are conducted on flat surfaces. Here, we show that the shape of the surface on which a biofilm grows influences genetic drift and selection within the biofilm. We culture E. coli biofilms in micro-wells with an undulating bottom surface and observe the emergence of clonal sectors whose size corresponds to that of the undulations, despite no physical barrier separating different areas of the biofilm. The sectors are remarkably stable over time and do not invade each other; we attribute this stability to the characteristics of the velocity field within the growing biofilm, which hinders mixing and clonal expansion. A microscopically-detailed computer model fully reproduces these findings and highlights the role of mechanical (physical) interactions such as adhesion and friction in microbial evolution. The model also predicts clonal expansion to be severely limited even for clones with a significant growth advantage - a finding which we subsequently confirm experimentally using a mixture of antibiotic-sensitive and antibiotic-resistant mutants in the presence of sub-lethal concentrations of the antibiotic rifampicin. The strong suppression of selection contrasts sharply with the behavior seen in bacterial colonies on agar commonly used to study range expansion and evolution in biofilms. Our results show that biofilm population dynamics can be controlled by patterning the surface, and demonstrate how a better understanding of the physics of bacterial growth can pave the way for new strategies in steering microbial evolution.

biophysics↗

bioRxiv2022

Rapid Evolution of Colistin Resistance in a Bioreactor Model of Infection of Klebsiella pneumoniae

SynopsisO_ST_ABSObjectivesC_ST_ABSWe seek to understand the dynamics of de novo resistant mutations arising during colistin treatment of an antibiotic-naive population of K. pneumoniae. MethodsWe used a bioreactor model of infection and colistin treatment against the colistin susceptible K. pneumoniae Ecl8. Whole-genome sequencing and MIC profiling was used to characterize genetic and phenotypic state of the bacterial culture at three time points: before treatment, immediately after regrowth following challenge, and at the end point of the experiment. A mathematical model based on the birth-death process was used to gain further insights on the population dynamics of emerging resistant variants. ResultsWe find that, after an initial decline, the population recovers within 24h due to the evolution of highly resistant clones which exhibit MICs >100-fold higher than the parental strain. Recovery is caused by a small number of "founder cells" which have single point mutations mainly in the regulatory genes encoding crrB and pmrB. The mutants arise through spontaneous mutations prior to colistin treatment. ConclusionsDevelopment of colistin resistance during treatment of K. pneumoniae infections is readily achieved if colistin is used as a monotherapy.

microbiology↗

bioRxiv2020

Predictable Properties of Fitness Landscapes Induced by Adaptational Tradeoffs

Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations. Our model generates a succession of landscapes with predictable properties as antibiotic concentration is varied. The landscape is nearly smooth at low and high concentrations, but the tradeoff induces a high ruggedness at intermediate antibiotic concentrations. Despite this high ruggedness, however, all the fitness maxima in the landscapes are evolutionarily accessible from the wild type. This implies that selection for antibiotic resistance in multiple mutational steps is relatively facile despite the complexity of the underlying landscape.

evolutionary biology↗

bioRxiv2019

Phenotypic delay: mechanistic models and their implications for the evolution of resistance to antibiotics

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-Delbruck 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-Delbruck 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 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.

evolutionary biology↗

bioRxiv2019

A roadblock-and-kill model explains the action of the DNA-targeting antibiotic ciprofloxacin

Fluoroquinolones - antibiotics that cause DNA damage by inhibiting DNA topoisomerases - are clinically important, but their mechanism of action is not yet fully understood. In particular, the dynamical response of bacterial cells to fluoroquinolone exposure has hardly been investigated, although the SOS response, triggered by DNA damage, is often thought to play a key role. Here we investigate growth inhibition of the bacterium Escherichia coli by the fluoroquinolone ciprofloxacin at low doses (up to 5x the minimum inhibitory concentration). We measure the long-term and short-term (dynamic) response of the growth rate and DNA production rate to ciprofloxacin, at both population- and single-cell level. We show that despite the molecular complexity of DNA metabolism, a simple `roadblock-and-kill model focusing on replication fork blockage and DNA damage by ciprofloxacin-poisoned DNA topoisomerase II (gyrase) quantitatively reproduces long-term growth rates. The model also predicts dynamical changes in DNA production rate in wild type E. coli and in an SOS-deficient mutant, following a step-up of ciprofloxacin. Our work reveals new insights into the dynamics of fluoroquinolone action, with important implications for predicting the rate of resistance evolution. Most importantly, our model explains why the response is delayed: it takes many doubling times to fragment the DNA sufficiently to inhibit gene expression. Our model also challenges the view that the SOS response plays a central role: the dynamical response is controlled by the timescale of DNA replication and gyrase binding/unbinding to the DNA rather than by the SOS response. More generally, our work highlights the importance of including biophysical processes in biochemical-systems models to fully understand bacterial response to antibiotics.

systems biology↗

bioRxiv2019

On measuring selection in cancer from subclonal mutation frequencies

Recently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides precise quantification of the validity of the 1/f statistic across the entire range of all relevant parameter values. Our results are also applicable to the question of distinguishing driver and passenger mutations in a general exponentially expanding population.

cancer biology↗

bioRxiv2018

Mathematical modelling reveals how MeCP2 restrains transcriptional elongation in human neurons

Patterns of gene expression are primarily determined by proteins that locally enhance or repress transcription. While many transcription factors target a restricted number of genes, others appear to modulate transcription levels globally. An example is MeCP2, an abundant methylated-DNA binding protein that is mutated in the neurological disorder Rett Syndrome. Despite much research, the molecular mechanism by which MeCP2 regulates gene expression is not fully resolved. Here we integrate quantitative, multi-dimensional experimental analysis and mathematical modelling to show that MeCP2 is a novel type of global transcriptional regulator whose binding to DNA creates "slow sites" in gene bodies. Waves of slowed-down RNA polymerase II formed behind these sites travel backward and indirectly affect initiation, reminiscent of defect-induced shock waves in non-equilibrium physics transport models. This mechanism differs from conventional gene regulation mechanisms, which often involve direct modulation of transcription initiation. Our findings uncover a genome-wide function of DNA methylation that may account for the reversibility of Rett syndrome in mice. Moreover, our combined theoretical and experimental approach provides a general method for understanding how global gene expression patterns are choreographed.

biophysics↗

bioRxiv2017

A physics-explicit model of bacterial conjugation shows the stabilizing role of the conjugative junction

Conjugation is a process in which bacteria exchange DNA through a physical connection (conjugative junction) between mating cells. Despite its significance for processes such as the spread of antibiotic resistance, the role of physical forces in conjugation is poorly understood. Here we use computer models to show that the conjugative junction not only serves as a link to transfer the DNA but it also mechanically stabilises the mating pair which significantly increases the conjugation rate. We discuss the importance of our findings for biological evolution and suggest experiments to validate them.

biophysics↗

bioRxiv2017

Mechanical interactions in bacterial colonies and the surfing probability of beneficial mutations

Bacterial conglomerates such as biofilms and microcolonies are ubiquitous in nature and play an important role in industry and medicine. In contrast to well-mixed, diluted cultures routinely used in microbial research, bacteria in a microcolonv interact mechanically with one another and with the substrate to which they are attached. Despite their ubiquity, little is known about the role of such mechanical interactions on growth and biological evolution of microbial populations. Here we use a computer model of a microbial colony of rod-shaped cells to investigate how physical interactions between cells determine their motion in the colony, this affects biological evolution. We show that the probability that a faster-growing mutant \"surfs\" at the colonys frontier and creates a macroscopic sector depends on physical properties of cells (shape, elasticity, friction). Although all these factors contribute to the surfing probability in seemingly different ways, they all ultimately exhibit their effects by altering the roughness of the expanding frontier of the colony and the orientation of cells. Our predictions are confirmed by experiments in which we measure the surfing probability for colonies of different front roughness. Our results show that physical interactions between bacterial cells play an important role in biological evolution of new traits, and suggest that these interaction may be relevant to processes such as de novo evolution of antibiotic resistance.

evolutionary biology↗

bioRxiv2016

An exactly solvable, spatial model of mutation accumulation in cancer

One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.

cancer biology↗