Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

Abstract

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous patterns seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption leads to persistent coexistence, which is robust over a wide range of treatment coverage, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

Spectral analysis of transient amplifiers for death–birth updating constructed from regular graphs

“…The results of this paper may also be of interest beyond computational biology as amplifiers and suppressors of natural selection have also significant implications on our knowledge about the interplay between the population structure and evolutionary dynamics of real biological processes. How spatial population structure influences evolutionary dynamics has, for instance, been shown for cancer initiation and progression (Hindersin et al 2016b;Komarova et al 2003;Komarova 2006;Nowak et al 2003;Vermeulen et al 2013), ageing of tissues (Cannataro et al 2016(Cannataro et al , 2017, the spread of infections (Ottino-Löffler et al 2017a, b) and the microbial evolution of antibiotic resistance (Krieger et al 2020). Furthermore, a direct biotechnological application of the construction of amplifiers has been suggested with in vitro evolution, where amplifiers could help to discover optimized protein or nucleotide sequences with selected medical or biotechnical functions (Pavlogiannis et al 2018;Möller et al 2019).…”

“…By these mechanisms the spatial population structure becomes influential on the balance between fitness-dependent selection and random drift. The influence of the spatial population structure on evolutionary dynamics has been shown, for instance, for cancer initiation and progression (Hindersin et al 2016b;Komarova et al 2003;Komarova 2006;Nowak et al 2003;Vermeulen et al 2013), ageing of tissues (Cannataro et al 2016(Cannataro et al , 2017, the spread of infections (Ottino-Löffler et al 2017a, b) and the microbial evolution of antibiotic resistance (Krieger et al 2020).…”

How unequal vaccine distribution promotes the evolution of vaccine escape

“…A further lesson from drug resistance studies derives from the observation that variability in drug distribution can have more of an impact on the evolution of resistance than overall drug concentration [5, 18]. Extrapolating to vaccine escape, this would indicate that geographic variability in vaccine distribution can pose a bigger threat of vaccine escape than factors such as public distrust and fake news that reduce vaccine participation throughout the population.…”

How unequal vaccine distribution promotes the evolution of vaccine escape

“…The lessons learned from drug resistance point to two key factors that could facilitate the evolution of vaccine escape, namely, population size and population structure [21,22]. Population size is important because the overwhelming majority of mutations occur during replication.…”

“…A further lesson from drug resistance studies derives from the observation that variability in drug distribution can have more of an impact on the evolution of resistance than overall drug concentration [21,22].…”