Deep Sequencing of Protease Inhibitor Resistant HIV Patient Isolates Reveals Patterns of Correlated Mutations in Gag and Protease

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.

尽管针对HIV蛋白酶（HIV protease）中的耐药突变（drug resistance mutations）的作用已得到全面研究，但其底物Gag多聚蛋白（Gag polyprotein）中的突变尚未得到广泛编目。本研究借助深度测序（deep sequencing）技术，对来自93名接受含蛋白酶抑制剂（PIs）治疗的患者的独特纵向病毒样本队列进行了分析。由于每份样本的测序覆盖度极高，我们可高精度计算单个位点上的突变频率。我们利用该信息对Gag多聚蛋白的变异特征及其对蛋白酶抑制剂治疗结局的影响进行了表征。为借助深度测序数据探究两个不同位点间的突变共变异情况，我们开发了一种方法，用于估算每份病毒样本中两位点双变量概率的严格边界，并基于所有边界计算位点间的互信息（mutual information）。借助这一新方法，我们发现基质蛋白（matrix）与p6蛋白（p6）中的突变会导致治疗持续失败，并在Gag多聚蛋白内部以及Gag与蛋白酶之间的强相关突变网络中发挥核心作用。尽管共变异并非结构倾向的直接证据，但我们发现同一Gag蛋白的衣壳蛋白（capsid）与基质蛋白残基间的最强相关性，往往源于结构上的空间邻近性。这表明部分最强的涉及Gag的蛋白间相关性源于结构邻近性。此外，N端的基质蛋白与衣壳蛋白残基，以及C端的p1与p6蛋白残基间的强共变异，与病毒生命周期（viral life cycle）中这些蛋白间存在残基接触的现象相一致。