BackgroundThe 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.Methods and FindingsTo 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.ConclusionGlobal surveillance and genotypic assessment of drug resistance should focus primarily on the known subtype B drug-resistance mutations.

No abstract

The objective of this study was to assess if a simple evaluation, adherence to antiretroviral therapy, would correlate to clinical and laboratory outcomes. We followed an open cohort of patients from a public teaching hospital AIDS outpatient clinic. Patients were categorized according to adherence as: regular (Reg), optimal, all doses all days, tolerating only irregular timing (+/- 2 hours) of intake; quasi-regular (qReg), those missing up to four doses or 1 full day during a month; irregular (Irreg), all other irregular regimens, and ignored (Ign), those without information. The results from a simple questionnaire were compared to CD4+ cell counts and human immunodeficiency virus type 1 (HIV-1) RNA plasma viremia. One hundred eighty-two HIV-1-infected patients (126 males, 69%; 56 females, 31%) were analyzed. Information on adherence was available for 168 (90%). Reg adherence was reported by 75 (41%) patients, qReg adherence by 35 (19%), and Irreg by 53 (29%) of patients. The main reasons for nonadherence were forgetfulness, intolerance, use of alcohol, and misunderstanding of prescription. A significant increase of CD4+ T-cell counts and absolute gain were only observed among Reg and qReg users (p < 0.001). The median viral RNA load log10 decreases were -1.68, -1.45, -0.9 log, respectively, for Reg, qReg, and Irreg patients (p = 0.043, Kruskal-Wallis). Development of and death from AIDS occurred almost exclusively among those with Ign or Irreg adherence. Previous use of antiretroviral therapy may have had an impact in treatment response. Individuals who were treatment-naive were more likely to be Reg users (41%). Although more refined methods to assess adherence should be implemented when available, the inability to do so should not prevent simple, albeit subjective measurements that also correlate with favorable outcome. Mechanisms to improve adherence should be considered an integral part of antiretroviral therapy.

HIV-1 B is predominant in Brazil, but HIV-1 C has increasingly been reported in the south of the country. However, many samples clustering with clade C are actually a recombinant, with a small B segment at RT (CRF31). Samples (209) from the three cities with the highest aids prevalence rate are analyzed. Partial polymerase sequences from HIV RNA made it possible to determine HIV clades and recombination patterns and to identify primary drug resistance mutations (DRMs). The incidence was estimated with a BED assay. HIV-1 C and CRF31 patterns were twice as frequent as clade B at all sites, but the proportion of C and CRF31 patterns was significantly different among sites. The incidence estimate for SC was 2.6 persons-years. Infection in recent or younger cases showed no association with clade C. Surveillance DRM was observed in 8.3% (95% CI 5-13), mostly to NNRTIs. Clade F pol genomes had significantly more primary DRM.

The major limitation of drug resistance genotyping for human immunodeficiency virus remains the interpretation of the results. We evaluated the concordance in predicting therapy response between four different interpretation algorithms (Rega 6.3, HIVDB-08/04, ANRS [07/04], and VGI 8.0). Sequences were gathered through a worldwide effort to establish a database of non-B subtype sequences, and demographic and clinical information about the patients was gathered. The most concordant results were found for nonnucleoside reverse transcriptase (RT) inhibitors (93%), followed by protease inhibitors (84%) and nucleoside RT inhibitor (NRTIs) (76%). For therapy-naive patients, for nelfinavir, especially for subtypes C and G, the discordances were driven mainly by the protease (PRO) mutational pattern 82I/V ؉ 63P ؉ 36I/V for subtype C and 82I ؉ 63P ؉ 36I ؉ 20I for subtype G. Subtype F displayed more discordances for ritonavir in untreated patients due to the combined presence of PRO 20R and 10I/V. In therapy-experienced patients, subtype G displayed a lot of discordances for saquinavir and indinavir due to mutational patterns involving PRO 90 M and 82I. Subtype F had more discordance for nelfinavir attributable to the presence of PRO 88S and 82A ؉ 54V. For the NRTIs lamivudine and emtricitabine, CRF01_AE had more discordances than subtype B due to the presence of RT mutational patterns 65R ؉ 115 M and 118I ؉ 215Y, respectively. Overall, the different algorithms agreed well on the level of resistance scored, but some of the discordances could be attributed to specific (subtypedependent) combinations of mutations. It is not yet known whether therapy response is subtype dependent, but the advice given to clinicians based on a genotypic interpretation algorithm differs according to the subtype.Genotyping for the assessment of anti-human immunodeficiency virus (HIV) drug resistance is often used in the management of individual patient therapy. Currently, it is recommended in European as well as American guidelines (17,38). In several retrospective and prospective studies, genotyping proved beneficial in optimizing treatment for individual patients (5,10,16,23,25,31,37).Although genotyping is commonly used, there are still many uncertainties with respect to the value of genotype in the assignment of a new regimen. The current genotypic assays are not always able to report all drug resistance mutations among non-B subtypes (11,18,19,24). Regardless of subtype, genotyping is not sensitive to mutations that are present as a minor variant in the population (22,40). Genotyping results also differ depending on the laboratory where they are performed. Quality control studies indicate that mutations, even present as a pure variant, are often underestimated (32).However, separate from the quality and sensitivity issues, the interpretation of genotypic results is still not standardized.

No abstract

Human immunodeficiency virus type 1 subtype C (HIV-1C) represents 30-65 % of HIV infections in southern Brazil, and isolated cases of HIV-1C infection have also been reported in Argentina, Uruguay, Paraguay and Venezuela. Phylogenetic studies have suggested that the Brazilian subtype C epidemic was initiated by the introduction of closely related strains. Nevertheless, because of sampling limitations, the point of entry and the timing of subtype C introduction into Brazil, as well as the origin of the founder lineage, remain controversial. The present study investigated the origin, spread and phylogeography of HIV-1C in South America. Phylogenetic analysis showed a wellsupported monophyletic clade including all available strains from Brazil, Uruguay and Argentina. Only one lineage from Venezuela was unrelated to the epidemic involving the other three countries. Molecular clock and likelihood mapping analysis showed that HIV-1C introduction in Brazil dated back to the period 1960-1970, much earlier than previously thought, and was followed by a nearly simultaneous star-like outburst of viral lineages, indicating a subsequent rapid spread. Phylogeographic patterns suggested Paraná or Rio Grande do Sul as the possible entrance points of subtype C and an asymmetrical gene flow from Paraná to Sao Paulo, Santa Catarina and Rio Grande do Sul, as well as from Rio Grande do Sul to Sao Paulo fostered by the strong interconnectivity between population centres in southern Brazil. The study illustrates how coupling phylogeography inference with geographical information system data is critical to understand the origin and dissemination of viral pathogens and potentially predict their future spread.

No abstract