Biology subjects

Berrios-Caro, E.

Publications and source records attributed to Berrios-Caro, E..

3 recordsLinked to original sources

bioRxiv2025

Resistance variation and bacterial interactions shape the adaptation of a genetically diverse bacterial population to antimicrobial treatment

Bacterial infections are often polymicrobial and subject to the evolution of antimicrobial resistance (AMR). Existing knowledge on AMR in such polymicrobial infections usually relies on observational patient data, for which cause-effect relationships are difficult to infer, or on studying interactions between different bacterial species, ignoring the commonly encountered variation within species. Here, we therefore asked how mixed populations with strains from the same species evolve under antibiotic treatment. We used a genetically diverse population of the high-risk human pathogen Pseudomonas aeruginosa, and first identified strain variation in both AMR and pairwise bacteria-bacteria interactions, the latter ranging from beneficial, neutral, to competitive. Using experimental evolution, we subsequently demonstrate that the response to selection by different antibiotic treatments is significantly influenced by AMR strain variation, bacterial interactions, and also spatial population structure. Moreover, de novo AMR evolution was additionally impacted by variation in resistance rates towards the two considered antibiotics. A second evolution experiment emphasized the central role of strain variation and bacterial interactions in determining the evolutionary outcome. We conclude that ecological dynamics in genetically diverse pathogen populations are key for our general understanding of infection characteristics and AMR evolution, and, therefore, deserve particular attention during treatment of polymicrobial infections.

evolutionary biology↗

bioRxiv2020

Switching environments, synchronous sex, and the evolution of mating types

While facultative sex is common in sexually reproducing species, for reasons of tractability most mathematical models assume that such sex is asynchronous in the population. In this paper, we develop a model of switching environments to instead capture the effect of an entire population transitioning synchronously between sexual and asexual modes of reproduction. We use this model to investigate the evolution of the number of self-incompatible mating types in finite populations, which empirically can range from two to thousands. When environmental switching is fast, we recover the results of earlier studies that implicitly assumed populations were engaged in asynchronous sexual reproduction. However when the environment switches slowly, we see deviations from previous asynchronous theory, including a lower number of mating types at equilibrium and bimodality in the stationary distribution of mating types. We provide analytic approximations for both the fast and slow switching regimes, as well as a numerical scheme based on the Kolmogorov equations for the system to quickly evaluate the model dynamics at intermediate parameters. Our approach exploits properties of integer partitions in number theory. We also demonstrate how additional biological processes such as selective sweeps can be accounted for in this switching environment framework, showing that beneficial mutations can further erode mating type diversity in synchronous facultatively sexual populations.

evolutionary biology↗

bioRxiv2020

Competition delays multi-drug resistance evolution during combination therapy

Combination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

evolutionary biology↗