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Convergent evolution as an indicator for selection during acute HIV-1 infectionuse asterix (*) to get italics
Frederic Bertels, Karin J Metzner, Roland R RegoesPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2018
<p>Convergent evolution describes the process of different populations acquiring similar phenotypes or genotypes. Complex organisms with large genomes only rarely and only under very strong selection converge to the same genotype. In contrast, independent virus populations with very small genomes often acquire identical mutations. Here we test the hypothesis of whether convergence in early HIV-1 infection is common enough to serve as an indicator for selection. To this end, we measure the number of convergent mutations in a well-studied dataset of full-length HIV-1 env genes sampled from HIV-1 infected individuals during early infection. We compare this data to a neutral model and find an excess of convergent mutations. Convergent mutations are not evenly distributed across the env gene, but more likely to occur in gp41, which suggests that convergent mutations provide a selective advantage and hence are positively selected. In contrast, mutations that are only found in an HIV-1 population of a single individual are significantly affected by purifying selection. Our analysis suggests that comparisons between convergent and private mutations with neutral models allow us to identify positive and negative selection in small viral genomes. Our results also show that selection significantly shapes HIV-1 populations even before the onset of the adaptive immune system.</p>
https://www.biorxiv.org/content/10.1101/168260v4.supplementary-materialYou should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
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HIV-1, parallel evolution, convergent evolution, selection
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Bioinformatics & Computational Biology, Evolutionary Applications, Genome Evolution, Molecular Evolution
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2017-07-26 08:39:17
Guillaume Achaz