Limits...
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.

Show MeSH

Related in: MedlinePlus

Dynamics of synonymous and nonsynonymous evolutionary rates.Synonymous (blue lines) and nonsynonymous (black lines) evolutionaryrates as a function of the distance from the root of the tree for 9patients from Ref. [13]. Synonymous and nonsynonymousrates were calculated using maximum likelihood trees based on onlysynonymous and non-synonymous substitutions, respectively, whichwere inferred using HyPhy with optimized MG94xREV models [30].
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2602878&req=5

pcbi-1000240-g009: Dynamics of synonymous and nonsynonymous evolutionary rates.Synonymous (blue lines) and nonsynonymous (black lines) evolutionaryrates as a function of the distance from the root of the tree for 9patients from Ref. [13]. Synonymous and nonsynonymousrates were calculated using maximum likelihood trees based on onlysynonymous and non-synonymous substitutions, respectively, whichwere inferred using HyPhy with optimized MG94xREV models [30].

Mentions: We estimated overall synonymous and nonsynonymous evolutionary rates acrossmaximum likelihood trees based on synonymous and nonsynonymous changes onlyusing HyPhy [30]. Similar to the overall totalsubstitution rate, we found that neither synonymous nor nonsynonymousoverall evolutionary rates correlated with the disease progression rate. Forprogressors with progression time less than seven years (S-P1, S-P5, S-P6,S-P7, and S-P8), the average synonymous and nonsynonymous evolutionary rateswere estimated at6.6±3.5×10−4 and12±5×10−4 substitutionsper site per month, respectively. For slow disease progressors withprogression time greater than seven years (S-P2, S-P3, S-P7, S-P9 andSP-11), the average synonymous and nonsynonymous evolutionary rates wereestimated at 6.8±2.3×10−4 and13±4.5×10−4 substitutionsper site per month, respectively. Lemey et al. reportedlower overall synonymous evolutionary rates for these same slow diseaseprogressors [19]. These contradictory observationsmay be explained by the use of different methods in the estimation of theoverall evolutionary rates. While Lemey et al. used codonsubstitution models with a Bayesian relaxed clock model, we estimated theoverall synonymous and nonsynonymous evolutionary rates in separate maximumlikelihood trees based on synonymous and nonsynonymous changes [30] to allow for detecting rate changesacross the trees. A common finding with Lemey et al. isthat they also reported higher nonsynonymous rates(8.2±3.0×10−4) thansynonymous rates(3.8±1.9×10−4). Importantly,although the overall synonymous evolutionary rate did not correlate with thedisease progression rate in our calculations, we found that both synonymousand nonsynonymous evolutionary rates decline as disease progresses in 7 and8 out of 9 patients in Ref. [13],respectively (Figure 9).


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 synonymous and nonsynonymous evolutionary rates.Synonymous (blue lines) and nonsynonymous (black lines) evolutionaryrates as a function of the distance from the root of the tree for 9patients from Ref. [13]. Synonymous and nonsynonymousrates were calculated using maximum likelihood trees based on onlysynonymous and non-synonymous substitutions, respectively, whichwere inferred using HyPhy with optimized MG94xREV models [30].
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000240-g009: Dynamics of synonymous and nonsynonymous evolutionary rates.Synonymous (blue lines) and nonsynonymous (black lines) evolutionaryrates as a function of the distance from the root of the tree for 9patients from Ref. [13]. Synonymous and nonsynonymousrates were calculated using maximum likelihood trees based on onlysynonymous and non-synonymous substitutions, respectively, whichwere inferred using HyPhy with optimized MG94xREV models [30].
Mentions: We estimated overall synonymous and nonsynonymous evolutionary rates acrossmaximum likelihood trees based on synonymous and nonsynonymous changes onlyusing HyPhy [30]. Similar to the overall totalsubstitution rate, we found that neither synonymous nor nonsynonymousoverall evolutionary rates correlated with the disease progression rate. Forprogressors with progression time less than seven years (S-P1, S-P5, S-P6,S-P7, and S-P8), the average synonymous and nonsynonymous evolutionary rateswere estimated at6.6±3.5×10−4 and12±5×10−4 substitutionsper site per month, respectively. For slow disease progressors withprogression time greater than seven years (S-P2, S-P3, S-P7, S-P9 andSP-11), the average synonymous and nonsynonymous evolutionary rates wereestimated at 6.8±2.3×10−4 and13±4.5×10−4 substitutionsper site per month, respectively. Lemey et al. reportedlower overall synonymous evolutionary rates for these same slow diseaseprogressors [19]. These contradictory observationsmay be explained by the use of different methods in the estimation of theoverall evolutionary rates. While Lemey et al. used codonsubstitution models with a Bayesian relaxed clock model, we estimated theoverall synonymous and nonsynonymous evolutionary rates in separate maximumlikelihood trees based on synonymous and nonsynonymous changes [30] to allow for detecting rate changesacross the trees. A common finding with Lemey et al. isthat they also reported higher nonsynonymous rates(8.2±3.0×10−4) thansynonymous rates(3.8±1.9×10−4). Importantly,although the overall synonymous evolutionary rate did not correlate with thedisease progression rate in our calculations, we found that both synonymousand nonsynonymous evolutionary rates decline as disease progresses in 7 and8 out of 9 patients in Ref. [13],respectively (Figure 9).

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