scholarly journals Prevalence of HIV Drug Resistance Mutations in HIV Type 1 Isolates in Antiretroviral Therapy Naïve Population from Northern India

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
S. Sinha ◽  
H. Ahmad ◽  
R. C. Shekhar ◽  
N. Kumar ◽  
L. Dar ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Antiretroviral Therapy ◽  
Resistance Mutations ◽  
Subtype C ◽  
Hiv Drug Resistance ◽  
Northern India ◽  
Subtype B ◽  
Hiv Type 1 ◽  
Hiv 1

Objective. The increased use of antiretroviral therapy (ART) has reduced the morbidity and mortality associated with HIV, adversely leading to the emergence of HIV drug resistance (HIVDR). In this study we aim to evaluate the prevalence of HIVDR mutations in ART-naive HIV-1 infected patients from northern India.Design. Analysis was performed using Viroseq genotyping system based on sequencing of entire protease and two-thirds of the Reverse Transcriptase (RT) region ofpolgene.Results. Seventy three chronic HIV-1 infected ART naïve patients eligible for first line ART were enrolled from April 2006 to August 2008. In 68 patients DNA was successfully amplified and sequencing was done. 97% of HIV-1 strains belonged to subtype C, and one each to subtype A1 and subtype B. The overall prevalence of primary DRMs was 2.9% [2/68, 95% confidence interval (CI), 0.3%–10.2%]. One patient had a major RT mutation M184V, known to confer resistance to lamivudine, and another had a major protease inhibitor (PI) mutation D30N that imparts resistance to nelfinavir.Conclusion. Our study shows that primary HIVDR mutations have a prevalence of 2.9% among ART-naive chronic HIV-1 infected individuals.

scholarly journals Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

2020 ◽  
Author(s):  
Susana Posada-Céspedes ◽  
Gert Van Zyl ◽  
Hesam Montazeri ◽  
Jack Kuipers ◽  
Soo-Yon Rhee ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Genotype Data ◽  
Resistance Mutations ◽  
Subtype C ◽  
Cross Sectional ◽  
Data Set ◽  
Hiv Drug Resistance ◽  
Subtype B ◽  
Hiv 1 ◽  
Temporal Ordering

AbstractAlthough combination antiretoviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Here, we present a methodology for the comparison of mutational pathways in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational pathways from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models on a large number of resistance mutations and develop a statistical test to assess differences in the inferred mutational pathways between two groups. We apply this method to the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional data set of South African individuals living with HIV-1 subtype C, as well as a genotype data set of subtype B infections derived from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. Our results also show that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Furthermore, the maximum likelihood mutational networks for subtypes B and C share only 7 edges (Jaccard distance 0.802) and imply many different evolutionary pathways. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational pathways between any two groups.Author summaryThere is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Western Europe and Australia, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we study lopinavir resistance pathways in HIV-1 subtypes B versus C, but the methodology can be generally applied to compare the temporal ordering of genetic events in different subgroups.

scholarly journals Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

2021 ◽  
Vol 17 (9) ◽  
pp. e1008363
Author(s):  
Susana Posada-Céspedes ◽  
Gert Van Zyl ◽  
Hesam Montazeri ◽  
Jack Kuipers ◽  
Soo-Yon Rhee ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Strong Support ◽  
Monte Carlo Sampling ◽  
Resistance Mutations ◽  
Subtype C ◽  
Cross Sectional ◽  
Data Set ◽  
Hiv Drug Resistance ◽  
Subtype B ◽  
Hiv 1

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.

scholarly journals Nationwide Study of Drug Resistance Mutations in HIV-1 Infected Individuals under Antiretroviral Therapy in Brazil

2021 ◽  
Vol 22 (10) ◽  
pp. 5304
Author(s):  
Ana Santos-Pereira ◽  
Vera Triunfante ◽  
Pedro M. M. Araújo ◽  
Joana Martins ◽  
Helena Soares ◽  
...  
Keyword(s):  
Drug Resistance ◽  
People Living With Hiv ◽  
Resistance Mutations ◽  
Subtype C ◽  
Viral Loads ◽  
Drug Resistance Mutations ◽  
Subtype B ◽  
Low Prevalence ◽  
Living With Hiv ◽  
Hiv 1

The success of antiretroviral treatment (ART) is threatened by the emergence of drug resistance mutations (DRM). Since Brazil presents the largest number of people living with HIV (PLWH) in South America we aimed at understanding the dynamics of DRM in this country. We analyzed a total of 20,226 HIV-1 sequences collected from PLWH undergoing ART between 2008–2017. Results show a mild decline of DRM over the years but an increase of the K65R reverse transcriptase mutation from 2.23% to 12.11%. This increase gradually occurred following alterations in the ART regimens replacing zidovudine (AZT) with tenofovir (TDF). PLWH harboring the K65R had significantly higher viral loads than those without this mutation (p < 0.001). Among the two most prevalent HIV-1 subtypes (B and C) there was a significant (p < 0.001) association of K65R with subtype C (11.26%) when compared with subtype B (9.27%). Nonetheless, evidence for K65R transmission in Brazil was found both for C and B subtypes. Additionally, artificial neural network-based immunoinformatic predictions suggest that K65R could enhance viral recognition by HLA-B27 that has relatively low prevalence in the Brazilian population. Overall, the results suggest that tenofovir-based regimens need to be carefully monitored particularly in settings with subtype C and specific HLA profiles.

scholarly journals Drug Resistance Mutations in HIV Type 1 Isolates from Patients Failing Antiretroviral Therapy in Morocco

2012 ◽  
Vol 28 (8) ◽  
pp. 944-948 ◽  
Author(s):  
Hicham El Annaz ◽  
Patricia Recordon-Pinson ◽  
Rida Tagajdid ◽  
Toufik Doblali ◽  
Bouchra Belefquih ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Antiretroviral Therapy ◽  
Resistance Mutations ◽  
Drug Resistance Mutations ◽  
Hiv Type 1

HIV-1 Subtype C Drug-Resistance Background among ARV-Naive Adults in Botswana

2005 ◽  
Vol 16 (2) ◽  
pp. 103-115 ◽  
Author(s):  
Hermann Bussmann ◽  
Vladimir Novitsky ◽  
William Wester ◽  
Trevor Peter ◽  
Kereng Masupu ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Population Level ◽  
Resistance Mutations ◽  
Subtype C ◽  
Coding Region ◽  
Drug Pressure ◽  
Phenotypic Properties ◽  
Subtype B ◽  
Protease Enzyme ◽  
Hiv 1

Current HIV-1 antiretroviral (ARV) drug resistance knowledge is limited to HIV-1 subtype B (HIV-1B). We addressed whether unique genetic and phenotypic properties of HIV-1 subtype C (HIV-1C), southern Africa's most prevalent subtype, may foment earlier and/or distinct resistance mutations. Population-level HIV-1C genotypes were evaluated with respect to drug resistance prevalence before Botswana's public ARV treatment programme began. Viruses were genotyped from 11 representative districts of northern and southern Botswana, and consensus sequences from these 71 individuals and 51 previously reported sequences from HIV-positive blood donors were constructed. Phylogenetic analysis classified all 71 sequences but one, which exhibited pol gene mosaicism, as HIV-1C. The protease and reverse transcriptase coding region had no detectable known primary mutations associated with HIV-1B protease inhibitor (PI) drug resistance. Secondary mutations associated with PI drug resistance were found in all sequences. Several HIV-1C—specific polymorphic sites were found across the pol gene. Northern and southern Botswana viral sequences showed no significant differences from each other. Population genotyping shows that, without countrywide ARV treatment, HIV-1C—infected Batswana harbour virtually no primary mutations known to confer resistance to the three major HIV-1B ARV drug classes. Some secondary PI mutations and polymorphic sites in the protease enzyme necessitate continuous population monitoring, particularly after introduction of countrywide ARV treatment in Botswana. Although its PI resistance development rate and kinetics are not known, our data may suggest increased susceptibility and readiness of HIV-1C to develop resistance under drug pressure when the PI class of drugs is used.

scholarly journals HIV Type 1 Subtype C Drug Resistance among Pediatric and Adult South African Patients Failing Antiretroviral Therapy

2008 ◽  
Vol 24 (11) ◽  
pp. 1449-1454 ◽  
Author(s):  
Visva Pillay ◽  
Candice Pillay ◽  
Rami Kantor ◽  
Francois Venter ◽  
Leon Levin ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Antiretroviral Therapy ◽  
South African ◽  
Subtype C ◽  
Hiv Type 1 ◽  
African Patients

scholarly journals Drug Resistance Mutations in Drug-Naive HIV Type 1 Subtype C-Infected Individuals from Rural Malawi

2011 ◽  
Vol 27 (4) ◽  
pp. 439-444 ◽  
Author(s):  
Vijay Bansode ◽  
Zuzanna J. Drebert ◽  
Simon A.A. Travers ◽  
Emmanuel Banda ◽  
Anna Molesworth ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Resistance Mutations ◽  
Subtype C ◽  
Drug Resistance Mutations ◽  
Drug Naïve ◽  
Hiv Type 1
Sign in / Sign up

Export Citation Format

Share Document