A computational method for predicting the most likely evolutionary trajectories in the step-wise accumulation of resistance mutations
- London School of Hygiene & Tropical Medicine, United Kingdom
Abstract
Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf ) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterized using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.
Data availability
Code, Rosetta Flex ddG predictions, structural models and isolate mutation frequency data deposited in Zenodo at 10.5281/zenodo.7082168
Article and author information
Author details
Ruth Charlotte Eccleston
Funding
Medical Research Council (MR/T000171/1)
- Ruth Charlotte Eccleston
Medical Research Council (MR/R025576/1)
- Susana Campino
Medical Research Council (MR/R020973/1)
- Susana Campino
British Council (261868591)
- Emilia Manko
Medical Research Council (MR/T000171/1)
- Nicholas Furnham
Medical Research Council (MR/M01360X/1)
- Taane G Clark
Medical Research Council (MR/N010469/1)
- Taane G Clark
Medical Research Council (MR/R025576/1)
- Taane G Clark
Medical Research Council (MR/R020973/1)
- Taane G Clark
Medical Research Council (MR/T000171/1)
- Taane G Clark
Biotechnology and Biological Sciences Research Council (BB/R013063/1)
- Taane G Clark
Medical Research Council (MR/M01360X/1)
- Susana Campino
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Eccleston et al.
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
-
- 772
- views
-
- 1
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
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)
A computational method for predicting the most likely evolutionary trajectories in the step-wise accumulation of resistance mutations
eLife 12:e84756.
https://doi.org/10.7554/eLife.84756
