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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.


MI of estimated bivariate marginals versus MI of known bivariate marginals.Shown is the MI for 24 PR-PR pairs computed using the estimated bounds on the bivariate marginals (lower bound—red, upper bound—blue) versus using the known bivariate marginals.
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pcbi.1004249.g004: MI of estimated bivariate marginals versus MI of known bivariate marginals.Shown is the MI for 24 PR-PR pairs computed using the estimated bounds on the bivariate marginals (lower bound—red, upper bound—blue) versus using the known bivariate marginals.

Mentions: We have calculated bivariate counts from the deep sequencing pileup for 24 pairs of PR residues which have previously been shown to be correlated using conventional sequencing [3,4] where we had sufficient pileup (>10,000 overlapping reads). The estimated bounds (shown in S5 Fig) are typically very narrow, and the lower bound is often a very conservative estimate of the double mutant probability in each sample for all pairs examined. The mutual information (MI) computed for each pair using the bounding procedure is in good agreement with the MI computed using the known bivariate marginal probabilities, as shown in Fig 4. In practice, extracting the pileup can be computationally expensive for large regions of interest where the coverage is very high, and for systems with short reads, this method only allows for covariation analysis for residues that are relatively close together. Using our bounding procedure provides a computationally fast method to estimate bivariate marginals from single-site frequency counts for all pairs of interest, not just those within 200–300bp.


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)

MI of estimated bivariate marginals versus MI of known bivariate marginals.Shown is the MI for 24 PR-PR pairs computed using the estimated bounds on the bivariate marginals (lower bound—red, upper bound—blue) versus using the known bivariate marginals.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004249.g004: MI of estimated bivariate marginals versus MI of known bivariate marginals.Shown is the MI for 24 PR-PR pairs computed using the estimated bounds on the bivariate marginals (lower bound—red, upper bound—blue) versus using the known bivariate marginals.
Mentions: We have calculated bivariate counts from the deep sequencing pileup for 24 pairs of PR residues which have previously been shown to be correlated using conventional sequencing [3,4] where we had sufficient pileup (>10,000 overlapping reads). The estimated bounds (shown in S5 Fig) are typically very narrow, and the lower bound is often a very conservative estimate of the double mutant probability in each sample for all pairs examined. The mutual information (MI) computed for each pair using the bounding procedure is in good agreement with the MI computed using the known bivariate marginal probabilities, as shown in Fig 4. In practice, extracting the pileup can be computationally expensive for large regions of interest where the coverage is very high, and for systems with short reads, this method only allows for covariation analysis for residues that are relatively close together. Using our bounding procedure provides a computationally fast method to estimate bivariate marginals from single-site frequency counts for all pairs of interest, not just those within 200–300bp.

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.