Limits...
Taking multiple infections of cells and recombination into account leads to small within-host effective-population-size estimates of HIV-1.

Balagam R, Singh V, Sagi AR, Dixit NM - PLoS ONE (2011)

Bottom Line: 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.From comparisons of our simulations with patient data, we estimate N(e)~10³-10⁴, implying predominantly stochastic evolution.Further, we show that the previous estimate of N(e)>10⁵ reduces as the frequencies of multiple infections of cells and recombination assumed increase.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.

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)~10²-10⁴, smaller than the inverse mutation rate of HIV-1 (~10⁵), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates N(e)>10⁵, 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)~10³-10⁴, 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)>10⁵ reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with N(e)~10³-10⁴ may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence.

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Fits of our simulations to data from patients.Best-fit predictions of our simulations (solid lines) presented with experimental data (symbols) of the evolution of viral diversity, dG, (cyan) and divergence, dS, (purple) for each patient. Each cell is assumed to be infected with M virions drawn from a distribution based on a viral dynamics model (see text). The values of Ne (cells) and τ (days) employed for the predictions are indicated.
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pone-0014531-g007: Fits of our simulations to data from patients.Best-fit predictions of our simulations (solid lines) presented with experimental data (symbols) of the evolution of viral diversity, dG, (cyan) and divergence, dS, (purple) for each patient. Each cell is assumed to be infected with M virions drawn from a distribution based on a viral dynamics model (see text). The values of Ne (cells) and τ (days) employed for the predictions are indicated.

Mentions: Best-fit predictions of our simulations (solid lines) presented with experimental data (symbols) of the evolution of viral diversity, dG, (cyan) and divergence, dS, (purple) for each patient. Each cell is assumed to be infected with M = 3 virions in our simulations. The values of Ne (cells) and τ (days) employed for the predictions are indicated.


Taking multiple infections of cells and recombination into account leads to small within-host effective-population-size estimates of HIV-1.

Balagam R, Singh V, Sagi AR, Dixit NM - PLoS ONE (2011)

Fits of our simulations to data from patients.Best-fit predictions of our simulations (solid lines) presented with experimental data (symbols) of the evolution of viral diversity, dG, (cyan) and divergence, dS, (purple) for each patient. Each cell is assumed to be infected with M virions drawn from a distribution based on a viral dynamics model (see text). The values of Ne (cells) and τ (days) employed for the predictions are indicated.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3020941&req=5

pone-0014531-g007: Fits of our simulations to data from patients.Best-fit predictions of our simulations (solid lines) presented with experimental data (symbols) of the evolution of viral diversity, dG, (cyan) and divergence, dS, (purple) for each patient. Each cell is assumed to be infected with M virions drawn from a distribution based on a viral dynamics model (see text). The values of Ne (cells) and τ (days) employed for the predictions are indicated.
Mentions: Best-fit predictions of our simulations (solid lines) presented with experimental data (symbols) of the evolution of viral diversity, dG, (cyan) and divergence, dS, (purple) for each patient. Each cell is assumed to be infected with M = 3 virions in our simulations. The values of Ne (cells) and τ (days) employed for the predictions are indicated.

Bottom Line: 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.From comparisons of our simulations with patient data, we estimate N(e)~10³-10⁴, implying predominantly stochastic evolution.Further, we show that the previous estimate of N(e)>10⁵ reduces as the frequencies of multiple infections of cells and recombination assumed increase.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.

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)~10²-10⁴, smaller than the inverse mutation rate of HIV-1 (~10⁵), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates N(e)>10⁵, 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)~10³-10⁴, 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)>10⁵ reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with N(e)~10³-10⁴ may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence.

Show MeSH
Related in: MedlinePlus