Biology subjects

Marrec, L.

Publications and source records attributed to Marrec, L..

12 recordsLinked to original sources

bioRxiv2025

Cluster dispersal shapes microbial diversity during community assembly

Identifying the drivers of diversity remains a central challenge in microbial ecology. In microbiota, within-community diversity is often linked to host health, which makes it all the more important to understand. Since many communities assemble de novo, microbial dispersal plays a critical role in shaping community structure. While theoretical models typically assume microbes disperse individually, this overlooks cases where microbes disperse in clusters, such as, for example, during host feeding. Here, we investigate how cluster dispersal impacts microbial community assembly, species richness, between-community dissimilarity, and species abundance. We developed a model in which microbes disperse from a pool into communities as clusters and then replicate locally. Using both analytical and numerical approaches, we show that cluster dispersal promotes community homogenization by increasing within-community richness and reducing dissimilarity across communities, even at low dispersal rates. Moreover, it modulates the influence of local selection on microbial community assembly and, consequently, on species abundance. Our results demonstrate that cluster dispersal has distinct effects from simply increasing the dispersal rate. We discuss how these predictions can inform the interpretation of experimental data and demonstrate their utility by reanalyzing a gut microbiota dataset from Caenorhabditis elegans. This analysis reveals new evidence for the role of cluster dispersal in microbial community assembly.

ecology↗

bioRxiv2024

Drivers of diversity within and between microbialcommunities during stochastic assembly

No two microbial communities share the same species richness and abundance. Experiments have shown that the assembly of new microbial communities from the same environmental pool is sufficient to generate diversity within and between communities. To identify the mechanisms behind these experimental results, we build a stochastic model that considers both the dispersal of microbes from a pool to communities and microbial division. By analyzing timescales, we identify distinct assembly regimes. Specifically, if dispersal is slower than division, microbial communities show low diversity within communities but high diversity between communities. Conversely, if dispersal is faster than division, microbial communities exhibit high diversity within communities but low diversity between them. We validate these predictions both numerically, using Gillespie simulations, and analytically, by deriving equations for species richness and abundance distributions. Our derivations pinpoint two key metrics, the bimodality coefficient and mean relative abundance, which identify the assembly regime and quantify trait differences. We apply these metrics to reanalyze an experimental data set, demonstrating their practical application. Overall, our study provides general predictions on how stochasticity, timescales, and traits impact diversity within and between communities during the assembly of new microbial communities, allowing for a better understanding of variation in microbiome formation.

ecology↗

bioRxiv2024

Fixation probability in a diploid sexually reproducing population

Classical population genetics models with haploid individuals and cloning reproduction, such as the Moran process, have limited power in predicting the evolutionary fate of mutants in diploid, sexually reproducing populations. In this work, we built a new stochastic population genetics model to fill this outstanding knowledge gap. We showed that sexual reproduction tends to suppress the fixation probability of beneficial mutants than clonal reproduction, except under overdominance. This effect is particularly large in small populations with biased sex ratios. We also found that small populations are prone to the invasion of mildly detrimental alleles when the heterozygotes have a fitness advantage. Our study highlights the pressing need to extend the research of stochastic population dynamics to consider the biology of sexual reproduction, such as allelic interactions and biased sex ratio. Our model extends the frontier of population genetics research into diploid, sexually reproducing populations and sets the foundation to explore the great variety of biological factors and processes, such as sexual selection and sexual conflict, on stochastic evolutionary dynamics. Future research in this direction will help make better predictions of the evolutionary fate of mutants in relevant contexts, including the conservation of endangered species and evolutionary rescue.

evolutionary biology↗

bioRxiv2023

Quantifying the impact of genotype-dependent gene flow on mutation fixation in subdivided populations

In the wild, any population is likely to be spatially structured. Whereas we deeply understand evolutionary dynamics in well-mixed populations, our understanding of evolutionary dynamics in subdivided populations needs to be improved. In this work, I quantify the impact of genotype-dependent gene flow on the evolutionary dynamics of a subdivided population. Specifically, I build a model of a population structured as the island or the stepping stone model in which genotype-dependent gene flow is represented by individuals migrating between its sub-populations at a rate depending on their genotype. I analytically calculate the fixation probability and time of a mutation arising in the subdivided population under the low migration limit, which I validate with numerical simulations. I find that the island and the stepping stone models lead to the same fixation probability. Moreover, comparing the fixation probability in these models to the one in a well-mixed population of the same total census size allows me to identify an effective selection coefficient and population size. In the island and the stepping stone models, the effective selection coefficient differs from the selection coefficient if the wild-type and the mutant migration rates are different, whereas the effective population size equals the total census size. Finally, I show that genotype-dependent gene flow increases the fixation time, which allows for distinguishing the island and the stepping stone models, as opposed to the fixation probability.

evolutionary biology↗

bioRxiv2022

Solving the stochastic dynamics of population growth

Population growth is a fundamental process in ecology, evolution, and epidemiology. The population size dynamics during growth are often described by deterministic equations derived from kinetic models. Here, we simulate several population growth models and compare the size averaged over many stochastic realizations with the deterministic predictions. We show that these deterministic equations are generically bad predictors of the average stochastic population dynamics. Specifically, deterministic predictions overestimate the simulated population sizes, especially those of populations starting with a small number of individuals. Describing population growth as a stochastic birth process, we prove that the discrepancy between deterministic predictions and simulated data is due to unclosed-moment dynamics. In other words, the deterministic approach does not take into account the variability of birth times, which is particularly important at small population sizes. We evaluate different moment-closure approximations and show that they do not satisfactorily reduce the error between analytical predictions and simulated data. We present two novel solutions to the stochastic growth dynamics, one of which applies to any population growth model. We show that our solution exactly quantifies the dynamics of a community composed of different strains and correctly predicts the fixation probability of a strain in a serial dilution experiment. Our work sets the foundations for a more faithful modeling of community dynamics. It provides tools for a more accurate analysis of experimental results, including the inference of important growth parameters.

ecology↗

bioRxiv2022

Evolutionary rescue in a fluctuating environment

No environment is constant over time, and environmental fluctuations impact the outcome of evolutionary dynamics. Survival of a population not adapted to some environmental conditions is threatened unless a mutation rescues it, an eco-evolutionary process termed evolutionary rescue. We here investigate evolutionary rescue in an environment that fluctuates between a favorable state, in which the population grows, and a harsh state, in which the population declines. We develop a stochastic model that includes both population dynamics and genetics. We derive analytical predictions for the mean extinction time of a non-adapted population given that it is not rescued, the probability of rescue by a generalist mutation, and the mean appearance time of a rescue mutant, which we validate using numerical simulations. We find that evolutionary rescue is less (respectively more) likely if the environmental fluctuations are stochastic rather than deterministic and if the mean time between each environmental change is less (respectively more) than the mean survival time of the population in the harsh environment. We demonstrate that high equilibrium population sizes and per capita growth rates maximize the chances of evolutionary rescue. We show that an imperfectly harsh environment, which does not fully prevent births but makes the death rate to birth rate ratio much greater than unity, has almost the same rescue probability as a perfectly harsh environment, which fully prevents births. Finally, we put our results in the context of antimicrobial resistance and conservation biology.

evolutionary biology↗

bioRxiv2022

Challenges and pitfalls of inferring microbial growth rates from lab cultures

After more than 100 years of generating monoculture batch culture growth curves, microbial ecologists and evolutionary biologists still lack a reference method for inferring growth rates. Our work highlights the challenges of estimating the growth rate from growth curve data and shows that inaccurate estimates of growth rates significantly impact the estimated relative fitness, a principal quantity in evolution and ecology. First, we conducted a literature review and found which different types of methods are currently used to estimate growth rates. These methods differ in the meaning of the estimated growth rate parameter. Kinetic models estimate the intrinsic growth rate {micro} whereas statistical methods - both model-based and model-free - estimate the maximum per capita growth rate {micro}max. Using math and simulations, we show the conditions in which {micro}max is not a good estimator of {micro}. Then, we demonstrate that inaccurate absolute estimates of {micro} is not overcome by calculating relative values. Importantly, we find that poor approximations for {micro} sometimes lead to wrongly classifying a beneficial mutant as deleterious. Finally, we re-analyzed four published data-sets using most of the methods found by our literature review. We detected no single best-fitting model across all experiments within a data-set and found that the Gompertz models, which were among the most commonly used, were often among the worst fitting. Our study provides suggestions for how experimenters can improve their growth rate and associated relative fitness estimates and highlights a neglected but fundamental problem for nearly everyone who studies microbial populations in the lab.

microbiology↗

bioRxiv2020

Toward a universal model for spatially structured populations

A key question in evolution is how likely a mutant is to take over. This depends on natural selection and on stochastic fluctuations. Population spatial structure can impact mutant fixation probabilities. We introduce a model for structured populations on graphs that generalizes previous ones by making migrations independent of birth and death. We demonstrate that by tuning migration asymmetry, the star graph transitions from amplifying to suppressing natural selection. Our results are universal in the sense that they do not hinge on a modeling choice of microscopic dynamics or update rules. Instead, they depend on migration asymmetry, which can be experimentally tuned and measured.

evolutionary biology↗

bioRxiv2020

Adapt or perish: Evolutionary rescue in a gradually deteriorating environment

We investigate the evolutionary rescue of a microbial population in a gradually deteriorating environment, through a combination of analytical calculations and stochastic simulations. We consider a population destined for extinction in the absence of mutants, which can only survive if mutants sufficiently adapted to the new environment arise and fix. We show that mutants that appear later during the environment deterioration have a higher probability to fix. The rescue probability of the population increases with a sigmoidal shape when the product of the carrying capacity and of the mutation probability increases. Furthermore, we find that rescue becomes more likely for smaller population sizes and/or mutation probabilities if the environment degradation is slower, which illustrates the key impact of the rapidity of environment degradation on the fate of a population. We also show that our main conclusions are robust across various types of adaptive mutants, including specialist and generalist ones, as well as mutants modeling antimicrobial resistance evolution. We further express the average time of appearance of the mutants that do rescue the population and the average extinction time of those that do not. Our methods can be applied to other situations with continuously variable fitnesses and population sizes, and our analytical predictions are valid in the weak-to-moderate mutation regime.

evolutionary biology↗

bioRxiv2019

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

The evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action. Author summaryAntimicrobials select for resistance, which threatens to make antimicrobials useless. Understanding the evolution of antimicrobial resistance is therefore of crucial importance. Under what circumstances are microbial populations eradicated by antimicrobials? Conversely, when are they rescued by resistance? We address these questions employing a stochastic model that incorporates variations of both population composition and size. We consider periodic alternations of absence and presence of antimicrobial, which may model a treatment. We find a threshold period above which the first phase with antimicrobial fully determines the fate of the population. Faster alternations strongly select for resistance, and are inefficient to eradicate the microbial population, unless the death rate induced by the treatment is large enough. For longer alternation periods, we calculate the probability that the microbial population gets eradicated. We further demonstrate the different merits of biostatic antimicrobials, which prevent sensitive microbes from dividing, and of biocidal ones, which kill sensitive microbes.

evolutionary biology↗

bioRxiv2019

Antibody-mediated cross-linking of gut bacteria hinders the spread of antibiotic resistance

The body is home to a diverse microbiota, mainly in the gut. Resistant bacteria are selected for by antibiotic treatments, and once resistance becomes widespread in a population of hosts, antibiotics become useless. Here, we develop a multiscale model of the interaction between antibiotic use and resistance spread in a host population, focusing on an important aspect of within-host immunity. Antibodies secreted in the gut enchain bacteria upon division, yielding clonal clusters of bacteria. We demonstrate that immunity-driven bacteria clustering can hinder the spread of a novel resistant bacterial strain in a host population. We quantify this effect both in the case where resistance pre-exists and in the case where acquiring a new resistance mutation is necessary for the bacteria to spread. We further show that the reduction of spread by clustering can be countered when immune hosts are silent carriers, and are less likely to get treated, and/or have more contacts. We demonstrate the robustness of our findings to including stochastic within-host bacterial growth, a fitness cost of resistance, and its compensation. Our results highlight the importance of interactions between immunity and the spread of antibiotic resistance, and argue in the favor of vaccine-based strategies to combat antibiotic resistance.

evolutionary biology↗

bioRxiv2018

Quantifying the impact of a periodic presence of antimicrobial on resistance evolution in a homogeneous microbial population of fixed size

The evolution of antimicrobial resistance often occurs in a variable environment, as antimicrobial is given periodically to a patient or added and removed from a medium. This environmental variability has a huge impact on the microorganisms fitness landscape, and thus on the evolution of resistance. Indeed, mutations conferring resistance often carry a fitness cost in the absence of antimicrobial, which may be compensated by subsequent mutations. As antimicrobial is added or removed, the relevant fitness landscape thus switches from a fitness valley to an ascending landscape or vice-versa.\n\nHere, we investigate the effect of these time-varying patterns of selection within a stochastic model. We focus on a homogeneous microbial population of fixed size subjected to a periodic alternation of phases of absence and presence of an antimicrobial that stops growth. Combining analytical approaches and stochastic simulations, we quantify how the time necessary for fit resistant bacteria to take over the microbial population depends on the period of the alternations. We demonstrate that fast alternations strongly accelerate the evolution of resistance, and that a plateau is reached once the period gets sufficiently small. Besides, the acceleration of resistance evolution is stronger for larger populations. For asymmetric alternations, featuring a different duration of the phases with and without antimicrobial, we shed light on the existence of a broad minimum of the time taken by the population to fully evolve resistance. At this minimum, if the alternations are sufficiently fast, the very first resistant mutant that appears ultimately leads to full resistance evolution within the population. This dramatic acceleration of the evolution of antimicrobial resistance likely occurs in realistic situations, and can have an important impact both in clinical and experimental situations.

evolutionary biology↗