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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 the                                rate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a function                                of time relative to seroconversion. Based on the estimation of the                                evolutionary rate as a function of distance to the root (Figure 1A), the                                evolutionary rate is plotted as a function of time (average sampled                                time point of all the sequences within the window). Error bars                                indicate ±1 standard deviation. The dynamics of the                                evolutionary rate is linked to that of the CD4+ T-cell                                count: While the CD4+ T-cell level is stable, the                                evolutionary rate is stable or increasing; the evolutionary rate                                starts to decrease when the CD4+ T-cell population is                                depleted. In patients S-P1 to S-P11, the dashed line indicates the                                stage 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 antiretroviral                                therapy for each patient. (B) Correlation between the slope of                                CD4+ T cell count and the slope of the evolutionary rate                                (r = 0.68,                                P = 0.0014). For patients S-P1                                to S-P11, the slopes are calculated separately before and after the                                dashed line. For W-P1 to W-P6, the slopes are measured over the                                whole range of the data. Note that the slope of the evolutionary                                rate for W-P6 is very large due to tight sampling, and the slope of                                the CD4+ T cell count is also high in the corresponding                                time interval, leading to W-P6 becoming an outlier. The inset shows                                the average evolutionary rate for different rates of disease                                progression. Each subject's average evolutionary rate is measured as                                the ratio between the root distance difference and the sampling time                                difference, averaged over all the sequence pairs in each tree. The                                error bars indicate ±1 standard deviation. Because we                                rooted our trees using a sequence from the initial time point, and                                not the clade B consensus as done by Wolinsky et al.                                [22], our calculated                                evolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7,                                S-P9, S-P11, W-P5, and W-P6 were classified as slow disease                                progressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 as                                intermediate 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 the rate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a function of time relative to seroconversion. Based on the estimation of the evolutionary rate as a function of distance to the root (Figure 1A), the evolutionary rate is plotted as a function of time (average sampled time point of all the sequences within the window). Error bars indicate ±1 standard deviation. The dynamics of the evolutionary rate is linked to that of the CD4+ T-cell count: While the CD4+ T-cell level is stable, the evolutionary rate is stable or increasing; the evolutionary rate starts to decrease when the CD4+ T-cell population is depleted. In patients S-P1 to S-P11, the dashed line indicates the stage 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 antiretroviral therapy for each patient. (B) Correlation between the slope of CD4+ T cell count and the slope of the evolutionary rate (r = 0.68, P = 0.0014). For patients S-P1 to S-P11, the slopes are calculated separately before and after the dashed line. For W-P1 to W-P6, the slopes are measured over the whole range of the data. Note that the slope of the evolutionary rate for W-P6 is very large due to tight sampling, and the slope of the CD4+ T cell count is also high in the corresponding time interval, leading to W-P6 becoming an outlier. The inset shows the average evolutionary rate for different rates of disease progression. Each subject's average evolutionary rate is measured as the ratio between the root distance difference and the sampling time difference, averaged over all the sequence pairs in each tree. The error bars indicate ±1 standard deviation. Because we rooted our trees using a sequence from the initial time point, and not the clade B consensus as done by Wolinsky et al. [22], our calculated evolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7, S-P9, S-P11, W-P5, and W-P6 were classified as slow disease progressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 as intermediate 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 of change of CD4+ T-cell counts (Figure 8A), a significant correlation (r = 0.68, P = 0.0014) was observed (Figure 8B). In the initial interval where CD4+ T-cell counts were relatively stable (to the left of the dashed bar in Figure 8A), the evolutionary rate stayed relatively stable too. As CD4+ T-cell counts decreased and disease progressed in the patients the evolutionary rate slowed down. However, if one compares the overall (average) evolutionary rate from the whole study period (as defined by Eq. (20) in Materials and Methods), not its slope, with the disease progression rate, no clear correlation was seen (Figure 8B inset). The overall evolutionary rate of 15 patients was 10.4±3.14×10−4 substitutions per site per month. Note that increased or stable viral RNA counts rather than contraction in viral loads were observed in 7 patients under antiretroviral therapy in [13]. Thus, the decrease in the rate of evolution seems not to be associated with the 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 the                                rate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a function                                of time relative to seroconversion. Based on the estimation of the                                evolutionary rate as a function of distance to the root (Figure 1A), the                                evolutionary rate is plotted as a function of time (average sampled                                time point of all the sequences within the window). Error bars                                indicate ±1 standard deviation. The dynamics of the                                evolutionary rate is linked to that of the CD4+ T-cell                                count: While the CD4+ T-cell level is stable, the                                evolutionary rate is stable or increasing; the evolutionary rate                                starts to decrease when the CD4+ T-cell population is                                depleted. In patients S-P1 to S-P11, the dashed line indicates the                                stage 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 antiretroviral                                therapy for each patient. (B) Correlation between the slope of                                CD4+ T cell count and the slope of the evolutionary rate                                (r = 0.68,                                P = 0.0014). For patients S-P1                                to S-P11, the slopes are calculated separately before and after the                                dashed line. For W-P1 to W-P6, the slopes are measured over the                                whole range of the data. Note that the slope of the evolutionary                                rate for W-P6 is very large due to tight sampling, and the slope of                                the CD4+ T cell count is also high in the corresponding                                time interval, leading to W-P6 becoming an outlier. The inset shows                                the average evolutionary rate for different rates of disease                                progression. Each subject's average evolutionary rate is measured as                                the ratio between the root distance difference and the sampling time                                difference, averaged over all the sequence pairs in each tree. The                                error bars indicate ±1 standard deviation. Because we                                rooted our trees using a sequence from the initial time point, and                                not the clade B consensus as done by Wolinsky et al.                                [22], our calculated                                evolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7,                                S-P9, S-P11, W-P5, and W-P6 were classified as slow disease                                progressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 as                                intermediate progessors; and W-P1 and W-P2 as rapid progressors.
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Related In: Results  -  Collection

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pcbi-1000240-g008: Dynamic correlation between the rate of HIV-1 evolution and the rate of CD4+ T cell count decline.(A) Evolutionary rate and CD4+ T-cell level as a function of time relative to seroconversion. Based on the estimation of the evolutionary rate as a function of distance to the root (Figure 1A), the evolutionary rate is plotted as a function of time (average sampled time point of all the sequences within the window). Error bars indicate ±1 standard deviation. The dynamics of the evolutionary rate is linked to that of the CD4+ T-cell count: While the CD4+ T-cell level is stable, the evolutionary rate is stable or increasing; the evolutionary rate starts to decrease when the CD4+ T-cell population is depleted. In patients S-P1 to S-P11, the dashed line indicates the stage 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 antiretroviral therapy for each patient. (B) Correlation between the slope of CD4+ T cell count and the slope of the evolutionary rate (r = 0.68, P = 0.0014). For patients S-P1 to S-P11, the slopes are calculated separately before and after the dashed line. For W-P1 to W-P6, the slopes are measured over the whole range of the data. Note that the slope of the evolutionary rate for W-P6 is very large due to tight sampling, and the slope of the CD4+ T cell count is also high in the corresponding time interval, leading to W-P6 becoming an outlier. The inset shows the average evolutionary rate for different rates of disease progression. Each subject's average evolutionary rate is measured as the ratio between the root distance difference and the sampling time difference, averaged over all the sequence pairs in each tree. The error bars indicate ±1 standard deviation. Because we rooted our trees using a sequence from the initial time point, and not the clade B consensus as done by Wolinsky et al. [22], our calculated evolutionary rate differs from theirs. Subjects S-P2, S-P3, S-P7, S-P9, S-P11, W-P5, and W-P6 were classified as slow disease progressors; S-P1, S-P5, S-P6, S-P7, S-P8, W-P3, and W-P4 as intermediate 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 of change of CD4+ T-cell counts (Figure 8A), a significant correlation (r = 0.68, P = 0.0014) was observed (Figure 8B). In the initial interval where CD4+ T-cell counts were relatively stable (to the left of the dashed bar in Figure 8A), the evolutionary rate stayed relatively stable too. As CD4+ T-cell counts decreased and disease progressed in the patients the evolutionary rate slowed down. However, if one compares the overall (average) evolutionary rate from the whole study period (as defined by Eq. (20) in Materials and Methods), not its slope, with the disease progression rate, no clear correlation was seen (Figure 8B inset). The overall evolutionary rate of 15 patients was 10.4±3.14×10−4 substitutions per site per month. Note that increased or stable viral RNA counts rather than contraction in viral loads were observed in 7 patients under antiretroviral therapy in [13]. Thus, the decrease in the rate of evolution seems not to be associated with the 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