Speaker
Description
Quantitative estimates of fitness, particularly of individual genetic mutations, are crucial to predict the evolutionary dynamics of biological systems. Estimating fitness is challenging due to linkage disequilibrium of mutations on the same genome as well as clonal interference between competing genotypes. We estimate the fitness effects of individual mutations that are observed in a long-term HIV-1 evolution experiment, in which we evolved four evolutionary lines of HIV-1 for around 1500 generations, or almost 5 years, and sequenced the population using a high-throughput method every 10 transfers. We used MPL to infer mutational fitness effects, which takes into account genetic linkage and clonal interference. We compared the fitness effects to the results of competition assays against the ancestor virus population with which we determined the viral population fitness every 100 transfers. Combining the mutational fitness estimates to predict population fitness over time, we find good agreement with experimental results. In two of the lines, the agreement diminishes when we do not account for linkage. We analysed the mutational fitness effects distributions and found that mutations in non-coding regions have higher fitness effects, as do mutations that evolve independently in multiple evolution lines. Overall, we show that mutational fitness effects can be inferred from high-throughput sequencing data, allowing for the prediction of population fitness over many generations.
Bibliography
@article{stroud_long-term_2025,
title = {Long-term studies provide unique insights into evolution},
volume = {639},
issn = {0028-0836, 1476-4687},
url = {https://www.nature.com/articles/s41586-025-08597-9},
doi = {10.1038/s41586-025-08597-9},
language = {en},
number = {8055},
urldate = {2026-03-13},
journal = {Nature},
author = {Stroud, James T. and Ratcliff, William C.},
month = mar,
year = {2025},
pages = {589--601},
}
@article{sohail_mpl_2021,
title = {{MPL} resolves genetic linkage in fitness inference from complex evolutionary histories},
volume = {39},
issn = {1087-0156, 1546-1696},
url = {https://www.nature.com/articles/s41587-020-0737-3},
doi = {10.1038/s41587-020-0737-3},
language = {en},
number = {4},
urldate = {2026-03-13},
journal = {Nature Biotechnology},
author = {Sohail, Muhammad Saqib and Louie, Raymond H. Y. and McKay, Matthew R. and Barton, John P.},
month = apr,
year = {2021},
pages = {472--479},
}
@article{li_estimating_2023,
title = {Estimating linkage disequilibrium and selection from allele frequency trajectories},
volume = {223},
copyright = {https://academic.oup.com/pages/standard-publication-reuse-rights},
issn = {1943-2631},
url = {https://academic.oup.com/genetics/article/doi/10.1093/genetics/iyac189/6969379},
doi = {10.1093/genetics/iyac189},
abstract = {Abstract
Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.},
language = {en},
number = {3},
urldate = {2026-03-13},
journal = {GENETICS},
author = {Li, Yunxiao and Barton, John P},
editor = {Coop, G},
month = mar,
year = {2023},
pages = {iyac189},
}
@article{bons_long-term_2020,
title = {Long-term experimental evolution of {HIV}-1 reveals effects of environment and mutational history},
volume = {18},
issn = {1545-7885},
url = {https://dx.plos.org/10.1371/journal.pbio.3001010},
doi = {10.1371/journal.pbio.3001010},
abstract = {An often-returning question for not only HIV-1, but also other organisms, is how predictable evolutionary paths are. The environment, mutational history, and random processes can all impact the exact evolutionary paths, but to which extent these factors contribute to the evolutionary dynamics of a particular system is an open question. Especially in a virus like HIV-1, with a large mutation rate and large population sizes, evolution is expected to be highly predictable if the impact of environment and history is low, and evolution is not neutral. We investigated the effect of environment and mutational history by analyzing sequences from a long-term evolution experiment, in which HIV-1 was passaged on 2 different cell types in 8 independent evolutionary lines and 8 derived lines, 4 of which involved a switch of the environment. The experiments lasted for 240–300 passages, corresponding to approximately 400–600 generations or almost 3 years. The sequences show signs of extensive parallel evolution—the majority of mutations that are shared between independent lines appear in both cell types, but we also find that both environment and mutational history significantly impact the evolutionary paths. We conclude that HIV-1 evolution is robust to small changes in the environment, similar to a transmission event in the absence of an immune response or drug pressure. We also find that the fitness landscape of HIV-1 is largely smooth, although we find some evidence for both positive and negative epistatic interactions between mutations.},
language = {en},
number = {12},
urldate = {2026-03-13},
journal = {PLOS Biology},
author = {Bons, Eva and Leemann, Christine and Metzner, Karin J. and Regoes, Roland R.},
editor = {Sanjuán, Rafael},
month = dec,
year = {2020},
pages = {e3001010},
}
@article{bertels_parallel_2019,
title = {Parallel {Evolution} of {HIV}-1 in a {Long}-{Term} {Experiment}},
volume = {36},
copyright = {http://creativecommons.org/licenses/by/4.0/},
issn = {0737-4038, 1537-1719},
url = {https://academic.oup.com/mbe/article/36/11/2400/5524887},
doi = {10.1093/molbev/msz155},
abstract = {Abstract
One of the most intriguing puzzles in biology is the degree to which evolution is repeatable. The repeatability of evolution, or parallel evolution, has been studied in a variety of model systems, but has rarely been investigated with clinically relevant viruses. To investigate parallel evolution of HIV-1, we passaged two replicate HIV-1 populations for almost 1 year in each of two human T-cell lines. For each of the four evolution lines, we determined the genetic composition of the viral population at nine time points by deep sequencing the entire genome. Mutations that were carried by the majority of the viral population accumulated continuously over 1 year in each evolution line. Many majority mutations appeared in more than one evolution line, that is, our experiments showed an extreme degree of parallel evolution. In one of the evolution lines, 62\% of the majority mutations also occur in another line. The parallelism impairs our ability to reconstruct the evolutionary history by phylogenetic methods. We show that one can infer the correct phylogenetic topology by including minority mutations in our analysis. We also find that mutation diversity at the beginning of the experiment is predictive of the frequency of majority mutations at the end of the experiment.},
language = {en},
number = {11},
urldate = {2026-03-13},
journal = {Molecular Biology and Evolution},
author = {Bertels, Frederic and Leemann, Christine and Metzner, Karin J and Regoes, Roland R},
month = nov,
year = {2019},
pages = {2400--2414},
}
