Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance
Abstract
Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction, DI) and how resistance to one drug impacts resistance to the other (collateral effects, CE). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug sensitive (ancestral) and drug resistant (mutant) populations. We show that evolved resistance to the component drugs-and in turn, the adaptation of growth rate-is governed by a Price equation whose covariance terms encode geometric features of both the two-drug response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multi-drug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally-explicit selective outcomes.
Data availability
Data used in this paper was taken from a public repository:Dean, Ziah; Maltas, Jeff; Wood, Kevin (2020), Antibiotic interactions shape short-term evolution of resistance in Enterococcus faecalis, Dryad, Dataset, https://doi.org/10.5061/dryad.j3tx95x92There are no restrictions on any new results.
-
Data from: Antibiotic interactions shape short-term evolution of resistance in Enterococcus faecalisDryad Digital Repository, doi:10.5061/dryad.j3tx95x92.
Article and author information
Author details
Funding
National Institutes of Health (1R35GM124875)
- Kevin B Wood
National Science Foundation (1553028)
- Kevin B Wood
Fundação Luso-Americana para o Desenvolvimento (274/2016)
- Erida Gjini
Instituto Gulbenkian de Ciencia
- Erida Gjini
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Gjini & Wood
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,207
- views
-
- 267
- downloads
-
- 29
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Epidemiology and Global Health
- Evolutionary Biology
Several coronaviruses infect humans, with three, including the SARS-CoV2, causing diseases. While coronaviruses are especially prone to induce pandemics, we know little about their evolutionary history, host-to-host transmissions, and biogeography. One of the difficulties lies in dating the origination of the family, a particularly challenging task for RNA viruses in general. Previous cophylogenetic tests of virus-host associations, including in the Coronaviridae family, have suggested a virus-host codiversification history stretching many millions of years. Here, we establish a framework for robustly testing scenarios of ancient origination and codiversification versus recent origination and diversification by host switches. Applied to coronaviruses and their mammalian hosts, our results support a scenario of recent origination of coronaviruses in bats and diversification by host switches, with preferential host switches within mammalian orders. Hotspots of coronavirus diversity, concentrated in East Asia and Europe, are consistent with this scenario of relatively recent origination and localized host switches. Spillovers from bats to other species are rare, but have the highest probability to be towards humans than to any other mammal species, implicating humans as the evolutionary intermediate host. The high host-switching rates within orders, as well as between humans, domesticated mammals, and non-flying wild mammals, indicates the potential for rapid additional spreading of coronaviruses across the world. Our results suggest that the evolutionary history of extant mammalian coronaviruses is recent, and that cases of long-term virus–host codiversification have been largely over-estimated.
-
- Evolutionary Biology
Haldane’s rule occupies a special place in biology as one of the few ‘rules’ of speciation, with empirical support from hundreds of species. And yet, its classic purview is restricted taxonomically to the subset of organisms with heteromorphic sex chromosomes. I propose explicit acknowledgement of generalized hypotheses about Haldane’s rule that frame sex bias in hybrid dysfunction broadly and irrespective of the sexual system. The consensus view of classic Haldane’s rule holds that sex-biased hybrid dysfunction across taxa is a composite phenomenon that requires explanations from multiple causes. Testing of the multiple alternative hypotheses for Haldane’s rule is, in many cases, applicable to taxa with homomorphic sex chromosomes, environmental sex determination, haplodiploidy, and hermaphroditism. Integration of a variety of biological phenomena about hybrids across diverse sexual systems, beyond classic Haldane’s rule, will help to derive a more general understanding of the contributing forces and mechanisms that lead to predictable sex biases in evolutionary divergence and speciation.