We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log 10 increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV—CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences—is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence.

Compared with our previous studies, the proportion of NRTI- and first-generation NNRTI-related TDR has continued to decline in French seroconverters. However, subtype B-infected MSM could drive the spread of resistant HIV strains. Finally, we suggest preferring PI- or II- to NNRTI-based combinations to treat PHI patients.

Introduction HIV controllers (HIC) maintain viraemia at low levels without antiretroviral treatment and have small HIV reservoirs. Nevertheless, they are heterogeneous regarding their risk of infection progression. The study of reservoirs can help elucidate this control. This study aimed to explore the factors implicated in the pathogenesis of HIV infection that are potentially associated with HIV reservoirs and their dynamics in HIC.MethodsIndividuals living with HIV included in the ANRS‐CODEX cohort with at least two HIV‐DNA measurements between 2009 and 2016 were selected. The total HIV‐DNA levels had been quantified prospectively from blood samples. Mixed‐effect linear models estimated the HIV‐DNA dynamics over time.ResultsThe median (interquartile range (IQR)) HIV‐DNA level was 1.5 (1.3 to 1.9) log copies/million peripheral blood mononuclear cells at inclusion (n = 202 individuals). These low levels showed heterogeneity among HIC. Lower levels were then associated with the protective HLA‐B*27/B*57 alleles and/or lower HIV‐RNA level at inclusion, negative hepatitis C virus serology, lower HIV‐suppressive capacity of specific CD8 T cells and lower levels of immune activation and inflammation. Interestingly, mathematical modelling of the dynamics of HIV‐DNA over time (840 measurements) showed that the number of infected cells decreased in 46% of HIC (follow‐up: 47.6 months) and increased in 54% of HIC. A multivariate analysis indicated that HLA‐B*27/B*57 alleles, a low level of HIV‐RNA and a low level of HIV‐DNA at inclusion were markers independently associated with this decrease.ConclusionsThese results offer new insights into the mechanisms of long‐term control in HIC. In half of HIC, the decrease in HIV‐DNA level could be linked to tighter viral control and progressive loss of infected cells. These findings allow the identification of HIC with a low risk of progression who may not need treatment.

Concordant and discordant genotypic predictions of HIV-1 co-receptor tropism were analyzed. V3 region was sequenced from plasma samples of patients screened for R5 tropism by the Trofile® assay, before CCR5 antagonist prescription. Ten tools including geno2pheno, PSSM, an "11/25" and "net charge" rule, and other published algorithms were used. Patients were grouped according to concordance or discordance between tools and Trofile® result. Trofile® tropism reports from 50 patient samples were R5 in 38 and Dual/Mixed (DM) in 12. Prediction with the genotypic tools were concordant for 23 R5 samples, and discordant for the 15 other ones. From Trofile® DM strains were concordant in 6 and discordant in 6. V3 sequences were not clearly distinct between R5 and DM strains, except a greater diversity in the later. Discordances were found with any tool or combination of them, so that no one can be proposed as better than the others. Predictive values of each algorithm were similar and rather good (efficacy ranged from 74% to 84%), but the rate of non-confirmed prediction is greater when compelling the results of all tools with each individual sample. The mean of quantitative values obtained with one tool when another tool give the opposite prediction were different from those obtained when all tools agree with that prediction. The two discordant groups were often not distinguishable from each other. These results suggest that viruses giving discordant prediction with bioinformatic tools could be functionally distinct and/or in a different evolutionary state compared to those with concordant prediction.