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Kurpas, M. K.

Publications and source records attributed to Kurpas, M. K..

3 recordsLinked to original sources

Mutation patterns in SARS-COV-2 Alpha and Beta variants indicate non-neutral evolution

Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Two early emergent strains, Alpha and Beta, characterized by a significant number of missense mutations, provide natural testing samples. In this study we are exploring the history of each of the segregating sites present in Alpha and Beta variants of concern, to address the question whether defining mutations were accumulating gradually leading to the formation of sequence characteristic of these variants. Our analysis exposes data features that suggest other than neutral evolution of SARS-CoV-2 genomes, leading to emergence of variants of concern. We observe only small number of possible combinations of mutations indicating rapid evolution of genomes. In addtion, mutation patterns observed in whole genome samples of Alpha and Beta variants also indicate presence of stronger selection than in remaining genome samples.

evolutionary biology↗

Evolutionary analysis of genomes of SARS-CoV-2-related bat viruses suggests old roots, constant effective population size, and possible increase of fitness

It is of vital practical interest to understand the co-evolution of bat {beta}-coronaviruses with their hosts, since a number of these most likely crossed the species boundaries and infected humans. Complete sequences of 47 consensus genomes are available for bat {beta}-coronaviruses related to the SARS-CoV-2 human virus. We carried out several types of evolutionary analyses using these data. First, using the publicly available BEAST 2 software, we generated phylogenetic trees and skyline plots. The roots of the trees, both for the entire sequences and subsequences coding for the E and S proteins as well as the 5 and 3 UTR regions, are estimated to be located from several decades to more than a thousand years ago, while the effective population sizes remained largely constant. Motivated by this, we developed a simple estimator of the effective population size in a Moran model with constant population, which, under the model is equal to the expected age of the MRCA measured in generations. Comparisons of these estimates to those produced by BEAST 2 shows qualitative agreement. We also compared the site frequency spectra (SFS) of the bat genomes to those provided by the Moran Tug-of-War model. Comparison does not exclude the possibility that overall fitness of the bat {beta}-coronaviruses was increasing over time as a result of directional selection. Stability of interactions of bats and their viruses was considered likely on the basis of specific manner in which bat immunity is tuned, and it seems consistent with our analysis.

evolutionary biology↗

Moran Process Version of the Tug-of-War Model: Complex Behavior Revealed by Mathematical Analysis and Simulation Studies

AO_SCPLOWBSTRACTC_SCPLOWIn a series of publications McFarland and co-authors introduced the tug-of-war model of evolution of cancer cell populations. The model is explaining the joint effect of rare advantageous and frequent slightly deleterious mutations, which may be identifiable with driver and passenger mutations in cancer. In this paper, we put the Tug-of-War model in the framework of a denumerable-type Moran process and use mathematics and simulations to understand its behavior. The model is associated with a time-continuous Markov Chain (MC), with a generator that can be split into a sum of the drift and selection process part and of the mutation process part. Operator semigroup theory is then employed to prove that the MC does not explode, as well as to characterize a strong-drift limit version of the MC which displays "instant fixation" effect, which was an assumption in the original McFarlands model. Mathematical results are fully confirmed by simulations of the complete and limit versions. They also visualize complex stochastic transients and genealogies of clones arising in the model.

evolutionary biology↗