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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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Evolutionary rate as a function of the distance from the root ofthe maximum likelihood tree of each patient.(A) Maximum likelihood tree for the viral sequences sampled frompatient S-P6 over 6 years [13]. (B) Evolutionary rate as afunction of the distance from the root of the tree for 9 patientsfrom Ref. [13] and 6 patients from Ref.[22] (black lines). Theevolutionary rate between sequence i andj is estimated by the distance difference,dj−di,divided by the sampling time difference,tj−ti.The evolutionary rate at a certain distance from the rootd was averaged over all possible sequence pairs(i, j) within a slidingwindow. The distance from the root for a particular windowd̅ is the average distance for all thesequences within that window. The size of the window (Δ)was 0.09 substitutions per site for S-P1 to S-P11 and 0.02 for W-P1to W-P6. Error bars indicate ±1 standard deviation. Thefitted rate of evolution with the full model to the divergence anddiversity dynamics of each patient is depicted as blue line.
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pcbi-1000240-g007: Evolutionary rate as a function of the distance from the root ofthe maximum likelihood tree of each patient.(A) Maximum likelihood tree for the viral sequences sampled frompatient S-P6 over 6 years [13]. (B) Evolutionary rate as afunction of the distance from the root of the tree for 9 patientsfrom Ref. [13] and 6 patients from Ref.[22] (black lines). Theevolutionary rate between sequence i andj is estimated by the distance difference,dj−di,divided by the sampling time difference,tj−ti.The evolutionary rate at a certain distance from the rootd was averaged over all possible sequence pairs(i, j) within a slidingwindow. The distance from the root for a particular windowd̅ is the average distance for all thesequences within that window. The size of the window (Δ)was 0.09 substitutions per site for S-P1 to S-P11 and 0.02 for W-P1to W-P6. Error bars indicate ±1 standard deviation. Thefitted rate of evolution with the full model to the divergence anddiversity dynamics of each patient is depicted as blue line.

Mentions: To test the prediction made by the model, i.e., a slowdown of theevolutionary rate as virus population evolves further from the founderstrain, we calculated the rate of HIV-1 sequence evolution in consecutivewindows over a maximum likelihood (ML) tree from each patient, starting fromthe root (see Materials and Methods).We used longitudinal sequence samples for 15 patients from two independentstudies [13],[22]. As anexample, Figure 7A showsthe tree describing the HIV-1 evolution in patient S-P6. Figure 7B shows theresulting evolutionary rate as a function of the distance from the root forall patients. Interestingly, the rate is not constant but rather displays adynamic behavior as HIV-1 evolves. In agreement with our model predictions,13 out of the 15 patients showed a decline of the evolutionary rate as thesequence population evolved further from its founder strain. The samedynamic behavior was observed using other window sizes(Δ = 0.06 for theShankarappa data andΔ = 0.03 for the Wolinskydata). Thus, the observed decline of the evolutionary rate was robust to thesize of the window. In Figure7B, we also plotted the evolutionary profile obtained by a fit tothe divergence and diversity dynamics with the full model. The dynamics ofthe evolutionary rate calculated from the maximum likelihood tree wasreasonably consistent with that obtained by a model fit to the divergenceand diversity dynamics for each patient.


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

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

Evolutionary rate as a function of the distance from the root ofthe maximum likelihood tree of each patient.(A) Maximum likelihood tree for the viral sequences sampled frompatient S-P6 over 6 years [13]. (B) Evolutionary rate as afunction of the distance from the root of the tree for 9 patientsfrom Ref. [13] and 6 patients from Ref.[22] (black lines). Theevolutionary rate between sequence i andj is estimated by the distance difference,dj−di,divided by the sampling time difference,tj−ti.The evolutionary rate at a certain distance from the rootd was averaged over all possible sequence pairs(i, j) within a slidingwindow. The distance from the root for a particular windowd̅ is the average distance for all thesequences within that window. The size of the window (Δ)was 0.09 substitutions per site for S-P1 to S-P11 and 0.02 for W-P1to W-P6. Error bars indicate ±1 standard deviation. Thefitted rate of evolution with the full model to the divergence anddiversity dynamics of each patient is depicted as blue line.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000240-g007: Evolutionary rate as a function of the distance from the root ofthe maximum likelihood tree of each patient.(A) Maximum likelihood tree for the viral sequences sampled frompatient S-P6 over 6 years [13]. (B) Evolutionary rate as afunction of the distance from the root of the tree for 9 patientsfrom Ref. [13] and 6 patients from Ref.[22] (black lines). Theevolutionary rate between sequence i andj is estimated by the distance difference,dj−di,divided by the sampling time difference,tj−ti.The evolutionary rate at a certain distance from the rootd was averaged over all possible sequence pairs(i, j) within a slidingwindow. The distance from the root for a particular windowd̅ is the average distance for all thesequences within that window. The size of the window (Δ)was 0.09 substitutions per site for S-P1 to S-P11 and 0.02 for W-P1to W-P6. Error bars indicate ±1 standard deviation. Thefitted rate of evolution with the full model to the divergence anddiversity dynamics of each patient is depicted as blue line.
Mentions: To test the prediction made by the model, i.e., a slowdown of theevolutionary rate as virus population evolves further from the founderstrain, we calculated the rate of HIV-1 sequence evolution in consecutivewindows over a maximum likelihood (ML) tree from each patient, starting fromthe root (see Materials and Methods).We used longitudinal sequence samples for 15 patients from two independentstudies [13],[22]. As anexample, Figure 7A showsthe tree describing the HIV-1 evolution in patient S-P6. Figure 7B shows theresulting evolutionary rate as a function of the distance from the root forall patients. Interestingly, the rate is not constant but rather displays adynamic behavior as HIV-1 evolves. In agreement with our model predictions,13 out of the 15 patients showed a decline of the evolutionary rate as thesequence population evolved further from its founder strain. The samedynamic behavior was observed using other window sizes(Δ = 0.06 for theShankarappa data andΔ = 0.03 for the Wolinskydata). Thus, the observed decline of the evolutionary rate was robust to thesize of the window. In Figure7B, we also plotted the evolutionary profile obtained by a fit tothe divergence and diversity dynamics with the full model. The dynamics ofthe evolutionary rate calculated from the maximum likelihood tree wasreasonably consistent with that obtained by a model fit to the divergenceand diversity dynamics for each patient.

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