Taking Multiple Infections of Cells and Recombination into Account Leads to Small Within-Host Effective-Population-Size Estimates of HIV-1

Balagam, Rajesh and Singh, Vasantika and Sagi, Aparna Raju and Dixit, Narendra M (2011) Taking Multiple Infections of Cells and Recombination into Account Leads to Small Within-Host Effective-Population-Size Estimates of HIV-1. In: PLos One, 6 (1).

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Abstract

Whether HIV-1 evolution in infected individuals is dominated by deterministic or stochastic effects remains unclear because current estimates of the effective population size of HIV-1 in vivo, N-e, are widely varying. Models assuming HIV-1 evolution to be neutral estimate N-e similar to 10(2)-10(4), smaller than the inverse mutation rate of HIV-1 (similar to 10(5)), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates N-e>10(5), suggesting that deterministic forces would hold sway. The consequent uncertainty in the nature of HIV-1 evolution compromises our ability to describe disease progression and outcomes of therapy. We perform detailed bit-string simulations of viral evolution that consider large genome lengths and incorporate the key evolutionary processes underlying the genomic diversification of HIV-1 in infected individuals, namely, mutation, multiple infections of cells, recombination, selection, and epistatic interactions between multiple loci. Our simulations describe quantitatively the evolution of HIV-1 diversity and divergence in patients. From comparisons of our simulations with patient data, we estimate N-e similar to 10(3)-10(4), implying predominantly stochastic evolution. Interestingly, we find that N-e and the viral generation time are correlated with the disease progression time, presenting a route to a priori prediction of disease progression in patients. Further, we show that the previous estimate of N-e>10(5) reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with N-e similar to 10(3)-10(4) may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence.

Item Type:	Journal Article
Publication:	PLos One
Publisher:	Public Library of Science
Additional Information:	Copyright of this article belongs to Public Library of Science.
Department/Centre:	Division of Information Sciences (Doesn't exist now) > BioInformatics Centre Division of Mechanical Sciences > Chemical Engineering
Date Deposited:	04 Mar 2011 06:46
Last Modified:	04 Mar 2011 06:46
URI:	http://eprints.iisc.ac.in/id/eprint/35788

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