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Dynamic correlation between intrahost HIV-1 quasispecies evolution and disease progression.

Lee HY, Perelson AS, Park SC, Leitner T - PLoS Comput. Biol. (2008)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, NY, USA. hayoun@bst.rochester.edu

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.

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Dynamics of divergence and diversity with emergence of X4viruses.Dynamics of divergence and diversity when imposing a greater level offitness for certain types of viruses which emerge and persist, forexample, by acquiring X4 tropism. The X4 viruses appear atd = 50 withgreater level of fitnessFhigh = 1.5in comparison to R5 viruses with fitnessF = 1. Thefraction of X4 viruses out of the total virus population withd≥50 is given byα. The rapid transient increasesboth in divergence and diversity upon the emergence of X4 virusesare observed. The scale factor for the divergence is 500, that forthe diversity is 100, andM(d) = 0.5for all d.
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pcbi-1000240-g006: Dynamics of divergence and diversity with emergence of X4viruses.Dynamics of divergence and diversity when imposing a greater level offitness for certain types of viruses which emerge and persist, forexample, by acquiring X4 tropism. The X4 viruses appear atd = 50 withgreater level of fitnessFhigh = 1.5in comparison to R5 viruses with fitnessF = 1. Thefraction of X4 viruses out of the total virus population withd≥50 is given byα. The rapid transient increasesboth in divergence and diversity upon the emergence of X4 virusesare observed. The scale factor for the divergence is 500, that forthe diversity is 100, andM(d) = 0.5for all d.

Mentions: Figure 6 plots thedynamics of divergence and diversity by changing the fraction(α) of X4 viruses that have a50% increase of fitness at distancedc = 50mutations. As we increase the value of α, weobserve an increase in divergence, then a transient rapid increase followedagain by the inital slope of linear increase. The emergence and persistencyof X4 viruses in the population leads to a rapid increase of diversityfollowed by a decline of diversity. Then at the final stage, diversitystarts to increase again. This trend is robust to both the amount of fitnessincrease and the value of dc. For example, whenwe chosedc = 30, thetransient rapid increases in the divergence and diversity still occur, butwere shifted to 4.2 years. An initial rapid increase both in diversity anddivergence due to the emergence of more fit virus is not compatible with thein vivo measurements from HIV-1 infected patients (Figure 3).


Dynamic correlation between intrahost HIV-1 quasispecies evolution and disease progression.

Lee HY, Perelson AS, Park SC, Leitner T - PLoS Comput. Biol. (2008)

Dynamics of divergence and diversity with emergence of X4viruses.Dynamics of divergence and diversity when imposing a greater level offitness for certain types of viruses which emerge and persist, forexample, by acquiring X4 tropism. The X4 viruses appear atd = 50 withgreater level of fitnessFhigh = 1.5in comparison to R5 viruses with fitnessF = 1. Thefraction of X4 viruses out of the total virus population withd≥50 is given byα. The rapid transient increasesboth in divergence and diversity upon the emergence of X4 virusesare observed. The scale factor for the divergence is 500, that forthe diversity is 100, andM(d) = 0.5for all d.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2602878&req=5

pcbi-1000240-g006: Dynamics of divergence and diversity with emergence of X4viruses.Dynamics of divergence and diversity when imposing a greater level offitness for certain types of viruses which emerge and persist, forexample, by acquiring X4 tropism. The X4 viruses appear atd = 50 withgreater level of fitnessFhigh = 1.5in comparison to R5 viruses with fitnessF = 1. Thefraction of X4 viruses out of the total virus population withd≥50 is given byα. The rapid transient increasesboth in divergence and diversity upon the emergence of X4 virusesare observed. The scale factor for the divergence is 500, that forthe diversity is 100, andM(d) = 0.5for all d.
Mentions: Figure 6 plots thedynamics of divergence and diversity by changing the fraction(α) of X4 viruses that have a50% increase of fitness at distancedc = 50mutations. As we increase the value of α, weobserve an increase in divergence, then a transient rapid increase followedagain by the inital slope of linear increase. The emergence and persistencyof X4 viruses in the population leads to a rapid increase of diversityfollowed by a decline of diversity. Then at the final stage, diversitystarts to increase again. This trend is robust to both the amount of fitnessincrease and the value of dc. For example, whenwe chosedc = 30, thetransient rapid increases in the divergence and diversity still occur, butwere shifted to 4.2 years. An initial rapid increase both in diversity anddivergence due to the emergence of more fit virus is not compatible with thein vivo measurements from HIV-1 infected patients (Figure 3).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, NY, USA. hayoun@bst.rochester.edu

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.

Show MeSH
Related in: MedlinePlus