A computational method for predicting the most likely evolutionary trajectories in the step-wise accumulation of resistance mutations

  1. Ruth Charlotte Eccleston  Is a corresponding author
  2. Emilia Manko
  3. Susana Campino
  4. Taane G Clark
  5. Nicholas Furnham
  1. 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

The following data sets were generated

Article and author information

Author details

  1. Ruth Charlotte Eccleston

    Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    For correspondence
    charlotte.eccleston@lshtm.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1905-7942
  2. Emilia Manko

    Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Susana Campino

    Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Taane G Clark

    Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8985-9265
  5. Nicholas Furnham

    Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7532-1269

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.

Reviewing Editor

  1. Christian R Landry, Université Laval, Canada

Version history

  1. Preprint posted: January 27, 2022 (view preprint)
  2. Received: November 7, 2022
  3. Accepted: December 21, 2023
  4. Accepted Manuscript published: December 22, 2023 (version 1)
  5. Version of Record published: January 24, 2024 (version 2)

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.

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  1. Ruth Charlotte Eccleston
  2. Emilia Manko
  3. Susana Campino
  4. Taane G Clark
  5. Nicholas Furnham
(2023)
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

Share this article

https://doi.org/10.7554/eLife.84756

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