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Deep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and protease.

Flynn WF, Chang MW, Tan Z, Oliveira G, Yuan J, Okulicz JF, Torbett BE, Levy RM - PLoS Comput. Biol. (2015)

Bottom Line: Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease.Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity.This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, United States of America; Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, United States of America.

ABSTRACT
While the role of drug resistance mutations in HIV protease has been studied comprehensively, mutations in its substrate, Gag, have not been extensively cataloged. Using deep sequencing, we analyzed a unique collection of longitudinal viral samples from 93 patients who have been treated with therapies containing protease inhibitors (PIs). Due to the high sequence coverage within each sample, the frequencies of mutations at individual positions were calculated with high precision. We used this information to characterize the variability in the Gag polyprotein and its effects on PI-therapy outcomes. To examine covariation of mutations between two different sites using deep sequencing data, we developed an approach to estimate the tight bounds on the two-site bivariate probabilities in each viral sample, and the mutual information between pairs of positions based on all the bounds. Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease. Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity. This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity. Moreover, the strong covariation between residues in matrix and capsid at the N-terminus with p1 and p6 at the C-terminus is consistent with residue-residue contacts between these proteins at some point in the viral life cycle.

No MeSH data available.


Single nucleotide polymorphism (SNP) frequencies between independent replicates are strongly correlated.Three patient samples with viral loads of 2,000, 8,500, and 67,000 copies/mL (low, moderate, and high, respectively), were extracted, reverse transcribed, amplified, and sequenced in duplicate. A comparison of SNP frequencies between these replicates shows R>0.99 in all cases. Even when ignoring SNPs that occur with <10% or >90% frequency in paired samples, R>0.95 for each pair.
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pcbi.1004249.g001: Single nucleotide polymorphism (SNP) frequencies between independent replicates are strongly correlated.Three patient samples with viral loads of 2,000, 8,500, and 67,000 copies/mL (low, moderate, and high, respectively), were extracted, reverse transcribed, amplified, and sequenced in duplicate. A comparison of SNP frequencies between these replicates shows R>0.99 in all cases. Even when ignoring SNPs that occur with <10% or >90% frequency in paired samples, R>0.95 for each pair.

Mentions: To evaluate possible biases resulting from our RT-PCR procedure, we compared SNP frequencies in technical replicates, finding a high level of concordance. Specifically, for three clinical samples, we obtained multiple aliquots that were processed independently throughout the entire process of preparation and sequencing. These samples spanned a range of clinically relevant viral loads, from 2,000 copies/mL to 30,000 copies/mL. In each case, the paired replicates showed SNP frequencies that were well correlated even when ignoring SNPs that occur with <10% or >90% frequency in paired samples, R>0.95 for each pair (Fig 1). The difference between replicates appeared smallest for the sample with greatest viral load, indicating that a higher number of template molecules can reduce stochastic effects, as might be expected. Aside from the RT-PCR process, the high level of sequencing coverage afforded by the use of the Illumina HiSeq 1000 could also be a factor in the strong correlation between replicates.


Deep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and protease.

Flynn WF, Chang MW, Tan Z, Oliveira G, Yuan J, Okulicz JF, Torbett BE, Levy RM - PLoS Comput. Biol. (2015)

Single nucleotide polymorphism (SNP) frequencies between independent replicates are strongly correlated.Three patient samples with viral loads of 2,000, 8,500, and 67,000 copies/mL (low, moderate, and high, respectively), were extracted, reverse transcribed, amplified, and sequenced in duplicate. A comparison of SNP frequencies between these replicates shows R>0.99 in all cases. Even when ignoring SNPs that occur with <10% or >90% frequency in paired samples, R>0.95 for each pair.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4404092&req=5

pcbi.1004249.g001: Single nucleotide polymorphism (SNP) frequencies between independent replicates are strongly correlated.Three patient samples with viral loads of 2,000, 8,500, and 67,000 copies/mL (low, moderate, and high, respectively), were extracted, reverse transcribed, amplified, and sequenced in duplicate. A comparison of SNP frequencies between these replicates shows R>0.99 in all cases. Even when ignoring SNPs that occur with <10% or >90% frequency in paired samples, R>0.95 for each pair.
Mentions: To evaluate possible biases resulting from our RT-PCR procedure, we compared SNP frequencies in technical replicates, finding a high level of concordance. Specifically, for three clinical samples, we obtained multiple aliquots that were processed independently throughout the entire process of preparation and sequencing. These samples spanned a range of clinically relevant viral loads, from 2,000 copies/mL to 30,000 copies/mL. In each case, the paired replicates showed SNP frequencies that were well correlated even when ignoring SNPs that occur with <10% or >90% frequency in paired samples, R>0.95 for each pair (Fig 1). The difference between replicates appeared smallest for the sample with greatest viral load, indicating that a higher number of template molecules can reduce stochastic effects, as might be expected. Aside from the RT-PCR process, the high level of sequencing coverage afforded by the use of the Illumina HiSeq 1000 could also be a factor in the strong correlation between replicates.

Bottom Line: Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease.Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity.This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, United States of America; Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, United States of America.

ABSTRACT
While the role of drug resistance mutations in HIV protease has been studied comprehensively, mutations in its substrate, Gag, have not been extensively cataloged. Using deep sequencing, we analyzed a unique collection of longitudinal viral samples from 93 patients who have been treated with therapies containing protease inhibitors (PIs). Due to the high sequence coverage within each sample, the frequencies of mutations at individual positions were calculated with high precision. We used this information to characterize the variability in the Gag polyprotein and its effects on PI-therapy outcomes. To examine covariation of mutations between two different sites using deep sequencing data, we developed an approach to estimate the tight bounds on the two-site bivariate probabilities in each viral sample, and the mutual information between pairs of positions based on all the bounds. Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease. Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity. This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity. Moreover, the strong covariation between residues in matrix and capsid at the N-terminus with p1 and p6 at the C-terminus is consistent with residue-residue contacts between these proteins at some point in the viral life cycle.

No MeSH data available.