Spatial Structure Facilitates Evolutionary Rescue by Drug Resistance
Cecilia Fruet, Ella Linxia Müller, Claude Loverdo, Anne-Florence Bitbol.
Abstract
Bacterial populations often have complex spatial structures, which can impact their evolution. Here, we study how spatial structure affects the evolution of antibiotic resistance in a bacterial population. We consider a minimal model of spatially structured populations where all demes (i.e., subpopulations) are identical and connected to each other by identical migration rates. We show that spatial structure can facilitate the survival of a bacterial population to antibiotic treatment, starting from a sensitive inoculum. Specifically, the bacterial population can be rescued if antibiotic resistant mutants appear and are present when drug is added, and spatial structure can impact the fate of these mutants and the probability that they are present.
Introduction
Antibiotic resistance is a crucial challenge in public health. Resistant bacteria emerge and spread through Darwinian evolution, driven by random mutations, genetic drift and natural selection. Mutations allow the emergence of strains adapted to challenging environments, including various antibiotic types. These strains are selected when antibiotics are present in the environment. Critically, the evolution of antibiotic resistance can occur quickly, within days in specific conditions, while the development of a new antibiotic typically takes around ten years. In this context, it is crucial to understand what conditions favor or hinder the development and spread of antibiotic resistance.
Materials and Methods:
We aim to assess the impact of spatial structure on the establishment of antibiotic resistance, in a minimal model where spatial structure is as simple as possible. Thus, we consider a spatially structured bacterial population comprising D demes (i.e. subpopulations) on the nodes of a clique (i.e. a fully connected graph). This corresponds to the island population model. Each deme has the same carrying capacity K (Fig 1, center). For comparison, we also consider a well-mixed population with the same total carrying capacity DK (Fig 1, left), and a fully subdivided population composed of D demes with carrying capacity K without migrations between them (Fig 1, right). We model migrations from one deme to another through a per capita migration rate γ, which is the same between all pairs of demes.
Discussion
Here, we showed that spatial structure can facilitate the survival of a bacterial population to antibiotic treatment, starting from a sensitive inoculum. Indeed, the bacterial population can be rescued if antibiotic resistant mutants are present when drug is added. While the emergence of resistant bacteria by random mutations only depends on total population size and not on spatial structure, their fate can be affected by spatial structure. If the mutation that provides resistance is neutral or deleterious, which is usually the case, its probability of fixation is increased in smaller populations.
Citation: Fruet C, Müller EL, Loverdo C, Bitbol A-F (2025) Spatial structure facilitates evolutionary rescue by drug resistance. PLoS Comput Biol 21(4): e1012861. https://doi.org/10.1371/journal.pcbi.1012861
Editor: Samir Suweis, University of Padova: Universita degli Studi di Padova, ITALY
Received: September 2, 2024; Accepted: February 9, 2025; Published: April 3, 2025.
Copyright: © 2025 Fruet et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Python code for our stochastic simulations is freely available at https://github.com/Bitbol-Lab/ DrugRes_StructPop. All relevant data are within the manuscript and its Supporting information files.
Funding: This research was partly funded by the Swiss National Science Foundation (SNSF) (grant No. 315230_208196, to A.-F.B.) and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 851173, to A.-F.B.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
