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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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Dynamic correlation between the rate of HIV-1 evolution and therate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a functionof time relative to seroconversion. Based on the estimation of theevolutionary rate as a function of distance to the root (Figure 1A), theevolutionary rate is plotted as a function of time (average sampledtime point of all the sequences within the window). Error barsindicate ±1 standard deviation. The dynamics of theevolutionary rate is linked to that of the CD4+ T-cellcount: While the CD4+ T-cell level is stable, theevolutionary rate is stable or increasing; the evolutionary ratestarts to decrease when the CD4+ T-cell population isdepleted. In patients S-P1 to S-P11, the dashed line indicates thestage when stable CD4+ T-cell count starts to decline.CD4+ T-cell counts were provided by J. Mullins and J.Learn. Red horizontal line denotes the period of antiretroviraltherapy for each patient. (B) Correlation between the slope ofCD4+ T cell count and the slope of the evolutionary rate(r = 0.68,P = 0.0014). For patients S-P1to S-P11, the slopes are calculated separately before and after thedashed line. For W-P1 to W-P6, the slopes are measured over thewhole range of the data. Note that the slope of the evolutionaryrate for W-P6 is very large due to tight sampling, and the slope ofthe CD4+ T cell count is also high in the correspondingtime interval, leading to W-P6 becoming an outlier. The inset showsthe average evolutionary rate for different rates of diseaseprogression. Each subject's average evolutionary rate is measured asthe ratio between the root distance difference and the sampling timedifference, averaged over all the sequence pairs in each tree. Theerror bars indicate ±1 standard deviation. Because werooted our trees using a sequence from the initial time point, andnot the clade B consensus as done by Wolinsky et al.[22], our calculatedevolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7,S-P9, S-P11, W-P5, and W-P6 were classified as slow diseaseprogressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 asintermediate progessors; and W-P1 and W-P2 as rapid progressors.
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pcbi-1000240-g008: Dynamic correlation between the rate of HIV-1 evolution and therate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a functionof time relative to seroconversion. Based on the estimation of theevolutionary rate as a function of distance to the root (Figure 1A), theevolutionary rate is plotted as a function of time (average sampledtime point of all the sequences within the window). Error barsindicate ±1 standard deviation. The dynamics of theevolutionary rate is linked to that of the CD4+ T-cellcount: While the CD4+ T-cell level is stable, theevolutionary rate is stable or increasing; the evolutionary ratestarts to decrease when the CD4+ T-cell population isdepleted. In patients S-P1 to S-P11, the dashed line indicates thestage when stable CD4+ T-cell count starts to decline.CD4+ T-cell counts were provided by J. Mullins and J.Learn. Red horizontal line denotes the period of antiretroviraltherapy for each patient. (B) Correlation between the slope ofCD4+ T cell count and the slope of the evolutionary rate(r = 0.68,P = 0.0014). For patients S-P1to S-P11, the slopes are calculated separately before and after thedashed line. For W-P1 to W-P6, the slopes are measured over thewhole range of the data. Note that the slope of the evolutionaryrate for W-P6 is very large due to tight sampling, and the slope ofthe CD4+ T cell count is also high in the correspondingtime interval, leading to W-P6 becoming an outlier. The inset showsthe average evolutionary rate for different rates of diseaseprogression. Each subject's average evolutionary rate is measured asthe ratio between the root distance difference and the sampling timedifference, averaged over all the sequence pairs in each tree. Theerror bars indicate ±1 standard deviation. Because werooted our trees using a sequence from the initial time point, andnot the clade B consensus as done by Wolinsky et al.[22], our calculatedevolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7,S-P9, S-P11, W-P5, and W-P6 were classified as slow diseaseprogressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 asintermediate progessors; and W-P1 and W-P2 as rapid progressors.

Mentions: When the rate of change of the evolutionary rate was compared to the rate ofchange of CD4+ T-cell counts (Figure 8A), a significant correlation(r = 0.68,P = 0.0014) was observed (Figure 8B). In the initialinterval where CD4+ T-cell counts were relatively stable (to theleft of the dashed bar in Figure 8A), the evolutionary rate stayed relatively stable too.As CD4+ T-cell counts decreased and disease progressed in thepatients the evolutionary rate slowed down. However, if one compares theoverall (average) evolutionary rate from the whole study period (as definedby Eq. (20) in Materials and Methods),not its slope, with the disease progression rate, no clear correlation wasseen (Figure 8B inset).The overall evolutionary rate of 15 patients was10.4±3.14×10−4substitutions per site per month. Note that increased or stable viral RNAcounts rather than contraction in viral loads were observed in 7 patientsunder antiretroviral therapy in [13].Thus, the decrease in the rate of evolution seems not to be associated withthe onset of therapy.


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

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

Dynamic correlation between the rate of HIV-1 evolution and therate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a functionof time relative to seroconversion. Based on the estimation of theevolutionary rate as a function of distance to the root (Figure 1A), theevolutionary rate is plotted as a function of time (average sampledtime point of all the sequences within the window). Error barsindicate ±1 standard deviation. The dynamics of theevolutionary rate is linked to that of the CD4+ T-cellcount: While the CD4+ T-cell level is stable, theevolutionary rate is stable or increasing; the evolutionary ratestarts to decrease when the CD4+ T-cell population isdepleted. In patients S-P1 to S-P11, the dashed line indicates thestage when stable CD4+ T-cell count starts to decline.CD4+ T-cell counts were provided by J. Mullins and J.Learn. Red horizontal line denotes the period of antiretroviraltherapy for each patient. (B) Correlation between the slope ofCD4+ T cell count and the slope of the evolutionary rate(r = 0.68,P = 0.0014). For patients S-P1to S-P11, the slopes are calculated separately before and after thedashed line. For W-P1 to W-P6, the slopes are measured over thewhole range of the data. Note that the slope of the evolutionaryrate for W-P6 is very large due to tight sampling, and the slope ofthe CD4+ T cell count is also high in the correspondingtime interval, leading to W-P6 becoming an outlier. The inset showsthe average evolutionary rate for different rates of diseaseprogression. Each subject's average evolutionary rate is measured asthe ratio between the root distance difference and the sampling timedifference, averaged over all the sequence pairs in each tree. Theerror bars indicate ±1 standard deviation. Because werooted our trees using a sequence from the initial time point, andnot the clade B consensus as done by Wolinsky et al.[22], our calculatedevolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7,S-P9, S-P11, W-P5, and W-P6 were classified as slow diseaseprogressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 asintermediate progessors; and W-P1 and W-P2 as rapid progressors.
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Show All Figures
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pcbi-1000240-g008: Dynamic correlation between the rate of HIV-1 evolution and therate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a functionof time relative to seroconversion. Based on the estimation of theevolutionary rate as a function of distance to the root (Figure 1A), theevolutionary rate is plotted as a function of time (average sampledtime point of all the sequences within the window). Error barsindicate ±1 standard deviation. The dynamics of theevolutionary rate is linked to that of the CD4+ T-cellcount: While the CD4+ T-cell level is stable, theevolutionary rate is stable or increasing; the evolutionary ratestarts to decrease when the CD4+ T-cell population isdepleted. In patients S-P1 to S-P11, the dashed line indicates thestage when stable CD4+ T-cell count starts to decline.CD4+ T-cell counts were provided by J. Mullins and J.Learn. Red horizontal line denotes the period of antiretroviraltherapy for each patient. (B) Correlation between the slope ofCD4+ T cell count and the slope of the evolutionary rate(r = 0.68,P = 0.0014). For patients S-P1to S-P11, the slopes are calculated separately before and after thedashed line. For W-P1 to W-P6, the slopes are measured over thewhole range of the data. Note that the slope of the evolutionaryrate for W-P6 is very large due to tight sampling, and the slope ofthe CD4+ T cell count is also high in the correspondingtime interval, leading to W-P6 becoming an outlier. The inset showsthe average evolutionary rate for different rates of diseaseprogression. Each subject's average evolutionary rate is measured asthe ratio between the root distance difference and the sampling timedifference, averaged over all the sequence pairs in each tree. Theerror bars indicate ±1 standard deviation. Because werooted our trees using a sequence from the initial time point, andnot the clade B consensus as done by Wolinsky et al.[22], our calculatedevolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7,S-P9, S-P11, W-P5, and W-P6 were classified as slow diseaseprogressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 asintermediate progessors; and W-P1 and W-P2 as rapid progressors.
Mentions: When the rate of change of the evolutionary rate was compared to the rate ofchange of CD4+ T-cell counts (Figure 8A), a significant correlation(r = 0.68,P = 0.0014) was observed (Figure 8B). In the initialinterval where CD4+ T-cell counts were relatively stable (to theleft of the dashed bar in Figure 8A), the evolutionary rate stayed relatively stable too.As CD4+ T-cell counts decreased and disease progressed in thepatients the evolutionary rate slowed down. However, if one compares theoverall (average) evolutionary rate from the whole study period (as definedby Eq. (20) in Materials and Methods),not its slope, with the disease progression rate, no clear correlation wasseen (Figure 8B inset).The overall evolutionary rate of 15 patients was10.4±3.14×10−4substitutions per site per month. Note that increased or stable viral RNAcounts rather than contraction in viral loads were observed in 7 patientsunder antiretroviral therapy in [13].Thus, the decrease in the rate of evolution seems not to be associated withthe onset of therapy.

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