BackgroundGlobal HIV-1 genetic diversity and evolution form a major challenge to treatment and prevention efforts. An increasing number of distinct HIV-1 recombinants have been identifiedworldwide, but their contribution to the global epidemic is unknown. We aimed to estimate the global and regional distribution of HIV-1 recombinant forms during 1990-2015. MethodsWe assembled a global HIV-1 molecular epidemiology database through a systematic literature review and a global survey. We searched PubMed, EMBASE (Ovid), CINAHL (Ebscohost), and Global Health (Ovid) for HIV-1 subtyping studies published from Jan 1, 1990, to Dec 31, 2015. Unpublished original HIV-1 subtyping data was collected through a survey among experts in the field who were members of the WHO-UNAIDS Network for HIV Isolation and Characterisation. We included prevalence studies with HIV-1 subtyping data collected during 1990-2015. Countries were grouped into 14 regions and analyses conducted for four time periods (1990-99, 2000-04, 2005-09 and 2010-15). The distribution of circulating recombinant forms (CRFs), and unique recombinant forms (URFs) in individual countries was weighted according to the UNAIDS estimates of the number of people living with HIV in each country to generate regional and global estimates of numbers and proportions of HIV-1 recombinants in each time period. The systematic review is registered with PROSPERO, number CRD42017067164.

In countries with a low prevalence of human T-lymphotropic virus (HTLV) infection, indeterminate HTLV serologies are a major problem in blood bank screening because of the uncertainties about infection in these cases. The recent discovery of two new types of simian T-lymphotropic viruses (STLV), which give an HTLV-indeterminate serology, raises the question whether indeterminate serologies in humans may be linked to new types of HTLV. Starting from a Tax sequence alignment of all available primate T-cell lymphotropic virus strains (PTLV), including the two new types STLV-PH969 and STLV-PP1664, we developed generic and type-specific nested polymerase chain reactions (PCRs). The generic PCR proved to be highly sensitive and cross-reactive for all four types of PTLV, while the discriminatory PCRs had a high sensitivity and a specificity of 100%. There was no cross-reactivity with human immunodeficiency virus (HIV), ensuring correct interpretation of results from coinfected patients. Among the 77 serologically indeterminate samples tested, 6 were found to be HTLV-1 PCR positive and 1 was HTLV-II PCR positive. Sequencing of one of the HTLV-I PCR positives excluded PCR contamination, and revealed a divergent type of HTLV-I. The majority of the seroindeterminate samples (91%) were however HTLV-PCR negative, and no new types of HTLV were found. This new assay can identify otherwise undetected HTLV-I or HTLV-II infections and is a useful tool of screening for new types of HTLV among seroindeterminate samples.

Objectives The development of a genotypic drug resistance interpretation algorithm, and the evaluation of its power to predict therapy outcome. Design A rule-based algorithm was established by an individual expert and was based on published and inhouse results, independently from the data of the patients used in this evaluation. The predictive value of the algorithm for virological outcomes was retrospectively evaluated using the baseline genotype observed in patients on highly active antiretroviral therapy, failing virologically and subsequently starting a salvage regimen. Methods The independent association between the susceptibility score (calculated according to the algorithm) and the virological response at 3 months, was analysed using multivariable logistic regression and multiple linear regression models. Results In two clinical centres 240 patients were studied. At 3 months 35% had a viral load of <500 RNA copies/ml. Using multivariable logistic regression, the odds ratio of achieving a viral load <500 RNA copies/ml at month 3 per unit increase of susceptibility score was 2.0 (95% CI 1.3–3.1; P=0.002) after adjusting for baseline viral load, genotype-driven salvage therapy, number of new drugs in the regimen, use of a new drug class in the regimen, nelfinavir-containing salvage therapy and history of prior viral load <500 RNA copies/ml. Using multiple linear regression, the susceptibility score showed a significant linear correlation with the log viral load change (slope=–0.27 log10 RNA copies/ml; 95% CI –0.11 to –0.43; P=0.001) after adjusting for history of prior viral load <500 RNA copies/ml, number of new drugs in the salvage therapy, use of a new drug class in the salvage therapy and baseline viral load. Conclusions This algorithm proved to be a significant independent predictor of therapy response at 3 months in this cohort of HIV-1-infected patients on salvage therapy. However, it should be subject to regular updates as is needed in this fast developing field.