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EVOLUTION OF INTRAHOST HIV ‐ 1 GENETIC DIVERSITY DURING CHRONIC INFECTION
Author(s) -
Shriner Daniel,
Liu Yi,
Nickle David C.,
Mullins James I.
Publication year - 2006
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.0014-3820.2006.tb01195.x
Subject(s) - biology , metapopulation , mutation rate , approximate bayesian computation , population , genetic variation , genetic diversity , evolutionary biology , genetics , effective population size , population genetics , gene , demography , biological dispersal , sociology
Abstract —HIV‐1 is one of the fastest evolving entities known. Given that census population sizes of HIV‐1 within individuals are much greater than the inverse mutation rate, every possible singel point mutation in the viral genome occurs each generation. This enormous capability to generate genetic variation allows for escape from immune surveillance and antiviral therapy. However, compared to generate genetic variation allows for escape from immune surveillance and ativiral therapy. However, compared to this potential, populations of HIV‐1 within individuals exhibit little genetic variation. This discrepancy between the known mutatiton rate of HIV‐1 and the average level of genetic variation in the env gene observed in vivo is reflected in comparisons of the actual numbers of productively infected cells, estimated as 10 7 , and the effectrive population size, estimated as 10 3 . Using approximate Bayesian computation, we evaluated several hypotheses based on a variety of selective and demorgaphic processes to explain the low effective populations size of HIV‐1. Of the models we examined, the metapopulation model, in which HIV‐1 evolves within an individual as a large collection of small subpopulations subject to frequent migration, extinction, and recolonization, was most consistent with the observed levels of genetic variation and the average frequencies of those variants. The metapopulation model links previous studies of viral dynamics and population genetics.