Amino Acid Differences from Consensus B Sequence at Drug-Resistance Positions in Protease and RT according to Subtype

<p>(A) shows data for protease, and (B) shows data for RT. In both, the first line lists the drug-resistance positions. The second line shows single-letter amino acid codes for the consensus B sequence. For each subtype (left column), the frequency of specific mutations in untreated persons is shown above the dashed line, whereas the frequency of specific mutations in treated persons is shown below the dashed line. Positions with significant differences in mutation frequency between B and non-B subtypes (<i>p</i> < 0.01, according to χ² test with Yate's correction) are circled. A pound sign indicates an insertion.</p

Subtype-Specific Polymorphisms

<p>Positions in protease (left) and RT (right) at which mutation frequency varied significantly between subtype B and at least one non-B subtype in untreated persons. Positions are shown along the x-axes, and the frequency of mutation for each subtype is shown along the y-axes. Positions related to drug resistance in subtype B are boxed. Bar colors denote statistical significance: black is statistically significant (Z_θ1 ≥ 3); gray is borderline significant (1 ≤ Z_θ1 < 3); white is not statistically significant (Z_θ1 < 1).</p

Binomial Response Model Used to Evaluate Subtype and Treatment Effects on Genotypic Evolution for Each Protease and RT Position

<p>A separate model was created for each non-B subtype. The frequencies of mutations at each position in four patient groups (untreated subtype B, treated subtype B, untreated non-B, and treated non-B) were converted to <i>Y</i> scores using a cube root transformation (similar to a logistic transform). Subtype effect was evaluated by calculating θ_1, the score differences between non-B and B subtypes in untreated persons_. The treatment effect was evaluated by calculating θ_2, the score differences between treated and untreated persons within the same subtype. The subtype–treatment interaction was evaluated by calculating θ₃, the difference of differences in the 2 × 2 table, or the difference in treatment effects between non-B and B subtypes.</p

Subtype-Specific Treatment-Related Mutations

<p>Positions in protease (left) and RT (right) at which mutations were significantly more prevalent in HIV-1 isolates from treated than from untreated persons infected with the same subtype. Positions are shown along the x-axes, and the proportion of mutant sequences in treated persons for each subtype is shown along the y-axes. For protease (left), treated persons are those receiving one or more PIs. For RT (right), treated persons are those receiving one or more NRTIs. Positions related to drug resistance in subtype B are boxed. Bar colors denote statistical significance: black is statistically significant (Z_θ2 ≥ 3); gray is borderline significant (1 ≤ Z_θ2 < 3); white is not statistically significant (Z_θ2 < 1).</p

Proportions of Persons Receiving Treatment with Specific NRTIs, NNRTIs, and PIs

<p>The number of persons with non-B virus receiving the NRTI tenofovir (ten), the NNRTI delavirdine (three), and the PIs amprenavir (13) and lopinavir (28) are not shown. 3TC, lamivudine; ABC, abacavir; AZT, zidovudine; D4T, stavudine; DDC, zalcitabine; DDI, didanosine; EFV, efavirenz; IDV, indinavir; NFV, nelfinavir; NVP, nevirapine; RTV, ritonavir; SQV, saquinavir.</p

Number of Treated and Untreated Persons Infected with B and Non-B HIV-1 Subtypes from Whom Protease and/or RT Sequences Were Available for Analysis

<p>Number of Treated and Untreated Persons Infected with B and Non-B HIV-1 Subtypes from Whom Protease and/or RT Sequences Were Available for Analysis</p

Impact of HIV-1 Subtype and Antiretroviral Therapy on Protease and Reverse Transcriptase Genotype: Results of a Global Collaboration

<div><h3>Background</h3><p>The genetic differences among HIV-1 subtypes may be critical to clinical management and drug resistance surveillance as antiretroviral treatment is expanded to regions of the world where diverse non-subtype-B viruses predominate.</p> <h3>Methods and Findings</h3><p>To assess the impact of HIV-1 subtype and antiretroviral treatment on the distribution of mutations in protease and reverse transcriptase, a binomial response model using subtype and treatment as explanatory variables was used to analyze a large compiled dataset of non-subtype-B HIV-1 sequences. Non-subtype-B sequences from 3,686 persons with well characterized antiretroviral treatment histories were analyzed in comparison to subtype B sequences from 4,769 persons. The non-subtype-B sequences included 461 with subtype A, 1,185 with C, 331 with D, 245 with F, 293 with G, 513 with CRF01_AE, and 618 with CRF02_AG. Each of the 55 known subtype B drug-resistance mutations occurred in at least one non-B isolate, and 44 (80%) of these mutations were significantly associated with antiretroviral treatment in at least one non-B subtype. Conversely, of 67 mutations found to be associated with antiretroviral therapy in at least one non-B subtype, 61 were also associated with antiretroviral therapy in subtype B isolates.</p> <h3>Conclusion</h3><p>Global surveillance and genotypic assessment of drug resistance should focus primarily on the known subtype B drug-resistance mutations.</p> </div