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Dynamic Correlation between Intrahost HIV-1 Quasispecies Evolution and Disease Progression

Overview of attention for article published in PLoS Computational Biology, December 2008
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Title
Dynamic Correlation between Intrahost HIV-1 Quasispecies Evolution and Disease Progression
Published in
PLoS Computational Biology, December 2008
DOI 10.1371/journal.pcbi.1000240
Pubmed ID
Authors

Ha Youn Lee, Alan S. Perelson, Su-Chan Park, Thomas Leitner

Abstract

Quantifying the dynamics of intrahost HIV-1 sequence evolution is one means of uncovering information about the interaction between HIV-1 and the host immune system. In the chronic phase of infection, common dynamics of sequence divergence and diversity have been reported. We developed an HIV-1 sequence evolution model that simulated the effects of mutation and fitness of sequence variants. The amount of evolution was described by the distance from the founder strain, and fitness was described by the number of offspring a parent sequence produces. Analysis of the model suggested that the previously observed saturation of divergence and decrease of diversity in later stages of infection can be explained by a decrease in the proportion of offspring that are mutants as the distance from the founder strain increases rather than due to an increase of viral fitness. The prediction of the model was examined by performing phylogenetic analysis to estimate the change in the rate of evolution during infection. In agreement with our modeling, in 13 out of 15 patients (followed for 3-12 years) we found that the rate of intrahost HIV-1 evolution was not constant but rather slowed down at a rate correlated with the rate of CD4+ T-cell decline. The correlation between the dynamics of the evolutionary rate and the rate of CD4+ T-cell decline, coupled with our HIV-1 sequence evolution model, explains previously conflicting observations of the relationships between the rate of HIV-1 quasispecies evolution and disease progression.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
French Polynesia 1 <1%
Switzerland 1 <1%
Italy 1 <1%
France 1 <1%
India 1 <1%
South Africa 1 <1%
Unknown 102 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 24%
Student > Ph. D. Student 22 19%
Student > Master 17 15%
Professor 12 11%
Professor > Associate Professor 8 7%
Other 16 14%
Unknown 11 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 34%
Biochemistry, Genetics and Molecular Biology 18 16%
Medicine and Dentistry 11 10%
Computer Science 8 7%
Immunology and Microbiology 5 4%
Other 19 17%
Unknown 14 12%