Epistasis detectably alters correlations between genomic sites in a narrow parameter window

2021

PLoS Comput Biol

Self Cite

An intriguing fact long defying explanation is the observation of a universal exponential distribution of beneficial mutations in fitness effect for different microorganisms. To explain this effect, we use a population model including mutation, directional selection, linkage, and genetic drift. The multiple-mutation regime of adaptation at large population sizes (traveling wave regime) is considered. We demonstrate analytically and by simulation that, regardless of the inherent distribution of mutation fitness effect across genomic sites, an exponential distribution of fitness effects emerges in the long term. This result follows from the exponential statistics of the frequency of the less-fit alleles, f, that we predict to evolve, in the long term, for both polymorphic and monomorphic sites. We map the logarithmic slope of the distribution onto the previously derived fixation probability and demonstrate that it increases linearly in time. Our results demonstrate a striking difference between the distribution of fitness effects observed experimentally for naturally occurring mutations, and the "inherent" distribution obtained in a directed-mutagenesis experiment, which can have any shape depending on the organism. Based on these results, we develop a new method to measure the fitness effect of mutations for each variable residue using DNA sequences sampled from adapting populations. This new method is not sensitive to linkage effects and does not require the one-site model assumptions.

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The evolutionary origin of the universal distribution of mutation fitness effect

Barlukova

2021

PLoS Comput Biol

Self Cite

“…The reason for the non-trivial validity of Fermi statistics far from equilibrium is the adiabatic (quasi-equilibrium) regime which exist in a broad parameter region in the traveling wave regime (Pedruzzi, et al 2018;Pedruzzi and Rouzine 2019a;Pedruzzi and Rouzine 2019c) This regime applies even for slowly changing selective pressure, such as exists in the case of influenza due to accumulation immune memory cells. This case can be mapped onto traveling wave for constant selection condition (Rouzine and Rozhnova 2018;Yan, et al 2019).…”

Section: Modelmentioning

confidence: 99%

“…A major complication in predicting evolutionary trajectory and estimating of mutational effects is that the fates of individual alleles are not independent due to clonal interference and genetic background effects (Fisher 1930;Gerrish and Lenski 1998). Another factor is epistasis (Pedruzzi, et al 2018;Pedruzzi and Rouzine 2019b). Recent advancements in theoretical population genetics provide accurate and general expressions for the speed of adaptation of an asexual population, its genetic diversity, mutation fixation probability, and phylogenetic properties within the framework of the traveling wave theory (Tsimring, et al 1996;Rouzine, et al 2003;Rouzine and Coffin 2005;Desai and Fisher 2007;Rouzine and Coffin 2007;Neher, et al 2010;Rouzine and Coffin 2010;Hallatschek 2011;Good, et al 2012;Walczak, et al 2012;Neher and Hallatschek 2013).…”

Section: Introductionmentioning

confidence: 99%

The evolutionary origin of the universal distribution of mutation fitness effect

Barlukova

Pedruzzi

2019

Preprint

Self Cite

10An intriguing fact long defying explanation is the observation of a universal exponential distribution of 11 beneficial mutations in fitness effect for different microorganisms. Here we use a general and 12 straightforward analytic model to demonstrate that, regardless of the inherent distribution of mutation 13 fitness effect across genomic sites, an observed exponential distribution of fitness effects emerges 14 naturally, as a consequence of the evolutionary process. Using this result, we develop a technique to 15 measure the mutation fitness effects for specific genomic sites from a single-time sequence set and apply 16 it to influenza A H1N1 hemagglutinin protein. Our results demonstrate the difference between the 17 distribution of fitness effects experimentally observed for naturally occurring mutations and the inherent 18 distribution obtained in directed-mutagenesis experiments. The technique will enable researchers to 19 measure fitness effects of mutations across the genome from a single DNA sample, which is important 20 for predicting the evolution of a population. 21 22 Recently, selection coefficients across the sites of the hemagglutinin gene of human influenza 48 A/H3N2 were estimated by fitting the deterministic one-locus model and its approximate 49 extension for two-loci (Illingworth and Mustonen 2012). The authors fit the model to time-series 50 data on allele frequencies of hemagglutinin (HA) gene of human influenza A H3N2. (Keightley and 51 Eyre-Walker 2007) proposed a method of DFE estimation in mutation-selection-drift equilibrium 52based on the assumption that DFE has the shape of the gamma distribution. They estimate 53 parameters of gamma distribution from maximization of the likelihood under the assumption that 54 the derived sites are binomially distributed. Thus, the two modeling papers used strong 55 assumptions about the dynamics of the system. 56

Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies

Rozhnova

2023

Commun Med

Self Cite

Once the first SARS-CoV-2 vaccine became available, mass vaccination was the main pillar of the public health response to the COVID-19 pandemic. It was very effective in reducing hospitalizations and deaths. Here, we discuss the possibility that mass vaccination might accelerate SARS-CoV-2 evolution in antibody-binding regions compared to natural infection at the population level. Using the evidence of strong genetic variation in antibody-binding regions and taking advantage of the similarity between the envelope proteins of SARS-CoV-2 and influenza, we assume that immune selection pressure acting on these regions of the two viruses is similar. We discuss the consequences of this assumption for SARS-CoV-2 evolution in light of mathematical models developed previously for influenza. We further outline the implications of this phenomenon, if our assumptions are confirmed, for the future design of SARS-CoV-2 vaccination strategies.