Analyses of HIV-1 integrase sequences prior to South African national HIV-treatment program and available of integrase inhibitors in Cape Town, South Africa

HIV-Integrase (IN) has proven to be a viable target for highly specific HIV-1 therapy. We aimed to characterize the HIV-1 IN gene in a South African context and identify resistance-associated mutations (RAMs) against available first and second generation Integrase strand-transfer inhibitors (InSTIs). We performed genetic analyses on 91 treatment-naïve HIV-1 infected patients, as well as 314 treatmentnaive South African HIV-1 IN-sequences, downloaded from Los Alamos HIV Sequence Database. Genotypic analyses revealed the absence of major RAMs in the cohort collected before the broad availability of combination antiretroviral therapy (cART) and INSTI in South Africa, however, occurred at a rate of 2.85% (9/314) in database derived sequences. RAMs were present at IN-positions 66, 92, 143, 147 and 148, all of which may confer resistance to Raltegravir (RAL) and Elvitegravir (EVG), but are unlikely to affect second-generation Dolutegravir (DTG), except mutations in the Q148 pathway. Furthermore, protein modeling showed, naturally occurring polymorphisms impact the stability of the intasome-complex and therefore may contribute to an overall potency against InSTIs. Our data suggest the prevalence of InSTI RAMs, against InSTIs, is low in South Africa, but natural polymorphisms and subtype-specific differences may influence the effect of individual treatment regimens

Epidemiological study of phylogenetic transmission clusters in a local HIV-1 epidemic reveals distinct differences between subtype B and non-B infections

<p>Abstract</p> <p>Background</p> <p>The number of HIV-1 infected individuals in the Western world continues to rise. More in-depth understanding of regional HIV-1 epidemics is necessary for the optimal design and adequate use of future prevention strategies. The use of a combination of phylogenetic analysis of HIV sequences, with data on patients' demographics, infection route, clinical information and laboratory results, will allow a better characterization of individuals responsible for local transmission.</p> <p>Methods</p> <p>Baseline HIV-1 <it>pol </it>sequences, obtained through routine drug-resistance testing, from 506 patients, newly diagnosed between 2001 and 2009, were used to construct phylogenetic trees and identify transmission-clusters. Patients' demographics, laboratory and clinical data, were retrieved anonymously. Statistical analysis was performed to identify subtype-specific and transmission-cluster-specific characteristics.</p> <p>Results</p> <p>Multivariate analysis showed significant differences between the 59.7% of individuals with subtype B infection and the 40.3% non-B infected individuals, with regard to route of transmission, origin, infection with <it>Chlamydia </it>(p = 0.01) and infection with Hepatitis C virus (p = 0.017). More and larger transmission-clusters were identified among the subtype B infections (p < 0.001). Overall, in multivariate analysis, clustering was significantly associated with Caucasian origin, infection through homosexual contact and younger age (all p < 0.001). Bivariate analysis additionally showed a correlation between clustering and syphilis (p < 0.001), higher CD4 counts (p = 0.002), <it>Chlamydia </it>infection (p = 0.013) and primary HIV (p = 0.017).</p> <p>Conclusions</p> <p>Combination of phylogenetics with demographic information, laboratory and clinical data, revealed that HIV-1 subtype B infected Caucasian men-who-have-sex-with-men with high prevalence of sexually transmitted diseases, account for the majority of local HIV-transmissions. This finding elucidates observed epidemiological trends through molecular analysis, and justifies sustained focus in prevention on this high risk group.</p

Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations

<p>Abstract</p> <p>Background</p> <p>Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.</p> <p>Results</p> <p>Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.</p> <p>Conclusions</p> <p>We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.</p

Monitoring Virologic Responses to Antiretroviral Therapy in HIV-Infected Adults in Kenya: Evaluation of a Low-Cost Viral Load Assay

A key advantage of monitoring HIV viral load (VL) in persons receiving antiretroviral therapy (ART) is the ability to detect virologic failure before clinical deterioration or resistance occurs. Detection of virologic failure will help clarify the need for enhanced adherence counseling or a change to second- line therapy. Low-cost, locally performable alternates to expensive VL assays are needed where resources are limited.We monitored the response to 48-week ART in 100 treatment-naïve Kenyan adults using a low-cost VL measurement, the Cavidi reverse transcriptase (RT) assay and gold-standard assays, Roche RNA PCR and Bayer Versant HIV-1 RNA (bDNA) assays. In Altman-Bland plots, the mean difference in viral loads between the three assays was small (<0.5 log(10) copies/mL). However, the limits of agreement between the methods exceeded the biologically relevant change of 0.5 log copies/ml. Therefore, the RT assay cannot be used interchangeably with the other assays to monitor individual patients. The RT assay was 100% sensitive in detecting viral loads of > or =400 copies/ml compared to gold-standard assays. After 24 weeks of treatment, viral load measured by the RT assay was undetectable in 95% of 65 patients with undetectable RNA PCR VL (<400 copies/ml), 90% of 67 patients with undetectable bDNA VL, and 96% of 57 patients with undetectable VL in both RNA PCR and bDNA assays. The negative predictive value of the RT assay was 100% compared to either assay; the positive predictive value was 86% compared to RNA PCR and 70% compared to bDNA.The RT assay compared well with gold standard assays. Our study highlights the importance of not interchanging viral load assays when monitoring an individual patient. Furthermore, the RT assay may be limited by low positive predictive values when used in populations with low prevalence of virologic failure

Mutational Correlates of Virological Failure in Individuals Receiving a WHO-Recommended Tenofovir-Containing First-Line Regimen: An International Collaboration.

Tenofovir disoproxil fumarate (TDF) genotypic resistance defined by K65R/N and/or K70E/Q/G occurs in 20% to 60% of individuals with virological failure (VF) on a WHO-recommended TDF-containing first-line regimen. However, the full spectrum of reverse transcriptase (RT) mutations selected in individuals with VF on such a regimen is not known. To identify TDF regimen-associated mutations (TRAMs), we compared the proportion of each RT mutation in 2873 individuals with VF on a WHO-recommended first-line TDF-containing regimen to its proportion in a cohort of 50,803 antiretroviral-naïve individuals. To identify TRAMs specifically associated with TDF-selection pressure, we compared the proportion of each TRAM to its proportion in a cohort of 5805 individuals with VF on a first-line thymidine analog-containing regimen. We identified 83 TRAMs including 33 NRTI-associated, 40 NNRTI-associated, and 10 uncommon mutations of uncertain provenance. Of the 33 NRTI-associated TRAMs, 12 - A62V, K65R/N, S68G/N/D, K70E/Q/T, L74I, V75L, and Y115F - were more common among individuals receiving a first-line TDF-containing compared to a first-line thymidine analog-containing regimen. These 12 TDF-selected TRAMs will be important for monitoring TDF-associated transmitted drug-resistance and for determining the extent of reduced TDF susceptibility in individuals with VF on a TDF-containing regimen

A pre-registered, multi-lab non-replication of the Action-sentence Compatibility Effect (ACE)

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward) matches the direction of the action in the to-be-judged sentence (e.g., Art gave you the pen describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.Fil: Morey, Richard. Cardiff University; Reino UnidoFil: Kaschak, Michael. Florida State University; Estados UnidosFil: Díez Álamo, Antonio. Universidad de Salamanca; España. Arizona State University; Estados UnidosFil: Glenberg, Arthur. Arizona State University; Estados Unidos. Universidad de Salamanca; EspañaFil: Zwaan, Rolf A.. Erasmus University Rotterdam; Países BajosFil: Lakens, Daniël. Eindhoven University of Technology; Países BajosFil: Ibáñez, Santiago Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of San Francisco; Estados Unidos. Universidad Adolfo Ibañez; Chile. Trinity College Dublin; IrlandaFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of San Francisco; Estados Unidos. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina. Universidad de Santiago de Chile; ChileFil: Gianelli, Claudia. Universitat Potsdam; Alemania. Scuola Universitaria Superiore; ItaliaFil: Jones, John L.. Florida State University; Estados UnidosFil: Madden, Julie. University of Tennessee; Estados UnidosFil: Alifano Ferrero, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bergen, Benjamin. University of California at San Diego; Estados UnidosFil: Bloxsom, Nicholas G.. Ashland University; Estados UnidosFil: Bub, Daniel N.. University of Victoria; CanadáFil: Cai, Zhenguang G.. The Chinese University; Hong KongFil: Chartier, Christopher R.. Ashland University; Estados UnidosFil: Chatterjee, Anjan. University of Pennsylvania; Estados UnidosFil: Conwell, Erin. North Dakota State University; Estados UnidosFil: Wagner Cook, Susan. University of Iowa; Estados UnidosFil: Davis, Joshua D.. University of California at San Diego; Estados UnidosFil: Evers, Ellen R. K.. University of California at Berkeley; Estados UnidosFil: Girard, Sandrine. University of Carnegie Mellon; Estados UnidosFil: Harter, Derek. Texas A&m University Commerce; Estados UnidosFil: Hartung, Franziska. University of Pennsylvania; Estados UnidosFil: Herrera, Eduar. Universidad ICESI; ColombiaFil: Huettig, Falk. Max Planck Institute for Psycholinguistics; Países BajosFil: Humphries, Stacey. University of Pennsylvania; Estados UnidosFil: Juanchich, Marie. University of Essex; Reino UnidoFil: Kühne, Katharina. Universitat Potsdam; AlemaniaFil: Lu, Shulan. Texas A&m University Commerce; Estados UnidosFil: Lynes, Tom. University of East Anglia; Reino UnidoFil: Masson, Michael E. J.. University of Victoria; CanadáFil: Ostarek, Markus. Max Planck Institute for Psycholinguistics; Países BajosFil: Pessers, Sebastiaan. Katholikie Universiteit Leuven; BélgicaFil: Reglin, Rebecca. Universitat Potsdam; AlemaniaFil: Steegen, Sara. Katholikie Universiteit Leuven; BélgicaFil: Thiessen, Erik D.. University of Carnegie Mellon; Estados UnidosFil: Thomas, Laura E.. North Dakota State University; Estados UnidosFil: Trott, Sean. University of California at San Diego; Estados UnidosFil: Vandekerckhove, Joachim. University of California at Irvine; Estados UnidosFil: Vanpaeme, Wolf. Katholikie Universiteit Leuven; BélgicaFil: Vlachou, Maria. Katholikie Universiteit Leuven; BélgicaFil: Williams, Kristina. Texas A&m University Commerce; Estados UnidosFil: Ziv Crispel, Noam. BehavioralSight; Estados Unido