Literature DB >> 21875619

Prediction of drug-resistance in HIV-1 subtype C based on protease sequences from ART naive and first-line treatment failures in North India using genotypic and docking analysis.

Jaideep S Toor1, Aman Sharma, Rajender Kumar, Pawan Gupta, Prabha Garg, Sunil K Arora.   

Abstract

Genotyping reveal emergence of drug resistance (DR)-related mutations in HIV-1 protease (PR) gene in the first-line treatment failure patients as per Stanford DR database. In order to have a subtype C specific prediction model, a three dimensional structure of local wild type C variant is created and the identified mutations were introduced to assess the mutational effects on protease inhibitors (PI) in a homology model. We estimated viral load, CD4 count and conducted DR genotyping in HIV isolates from 129 therapy naive and 20 first-line treatment failure individuals. Several genotypic variations, as compared to subtype B sequence in the Stanford gene database were detected in HIV-1 subtype C isolates from treatment naive individuals. Among these, nine mutations (12S, 15V, 19I, 36I, 41K, 63P, 69K, 89M, 93L) occurred in more than 60% of the isolates and were considered as local wild type for molecular modelling studies. No major mutations were seen in the PR sequences in isolates from treatment-naive individuals, although isolates from two patients had T74S mutation, known to be associated with reduced susceptibility to nelfinavir (NFV) and a combination of M36I, H69K and L89M mutations found in isolates from 77 patients (59.7%), considered to be conferring resistance to tipranavir (TPV) according to ANRS algorithm. Among the first-line treatment failures, an isolate from one patient showed L33F, I47T, M46G, and G48E mutations conferring intermediate resistance to saquinavir (SQV) and lopinavir (LPV). Though the docking energy scores are in agreement with this interpretation for SQV, it, however, indicated these mutations to be causing intermediate to high level resistance to atazanavir (ATV) and tipranavir (TPV) but making it susceptible to LPV. The patient finally responded to a second-line regimen containing 3TC, AZT and LPV with significant viral suppression. All the DR genotyping studies analyse the results using available databases which are all based on subtype B specific sequences. The proposed homology model in this study is unique, as it may predict subtype C specific susceptibility criteria for the available PIs.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21875619     DOI: 10.1016/j.antiviral.2011.08.005

Source DB:  PubMed          Journal:  Antiviral Res        ISSN: 0166-3542            Impact factor:   5.970


  9 in total

1.  Emergence of drug resistance-associated mutations in HIV-1 subtype C protease gene in north India.

Authors:  Mohd Azam; Abida Malik; Meher Rizvi; Supriya Singh; Poonam Gupta; Arvind Rai
Journal:  Virus Genes       Date:  2013-07-26       Impact factor: 2.332

2.  Predicting, Diagnosing, and Treating Acute and Early HIV Infection in a Public Sector Facility in Eswatini.

Authors:  Bernhard Kerschberger; Aung Aung; Qhubekani Mpala; Nombuso Ntshalintshali; Charlie Mamba; Michael Schomaker; Marie Luce Tombo; Gugu Maphalala; Dumile Sibandze; Lenhle Dube; Rufaro Kashangura; Simangele Mthethwa-Hleza; Alex Telnov; Roberto de la Tour; Alan Gonzalez; Alexandra Calmy; Iza Ciglenecki
Journal:  J Acquir Immune Defic Syndr       Date:  2021-12-15       Impact factor: 3.771

3.  Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance.

Authors:  Yashik Singh
Journal:  Healthc Inform Res       Date:  2017-10-31

4.  Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics.

Authors:  Olivier Sheik Amamuddy; Nigel T Bishop; Özlem Tastan Bishop
Journal:  Sci Rep       Date:  2018-12-18       Impact factor: 4.379

5.  Primary HIV Drug Resistance among Recently Infected Cases of HIV in North-West India.

Authors:  C K Chauhan; P V M Lakshmi; V Sagar; A Sharma; S K Arora; R Kumar
Journal:  AIDS Res Treat       Date:  2019-02-27

6.  Prediction and molecular field view of drug resistance in HIV-1 protease mutants.

Authors:  Baifan Wang; Yinwu He; Xin Wen; Zhen Xi
Journal:  Sci Rep       Date:  2022-02-21       Impact factor: 4.379

7.  Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2.

Authors:  Dharmeshkumar Patel; Suzane K Ono; Leda Bassit; Kiran Verma; Franck Amblard; Raymond F Schinazi
Journal:  Molecules       Date:  2022-08-24       Impact factor: 4.927

8.  RHIVDB: A Freely Accessible Database of HIV Amino Acid Sequences and Clinical Data of Infected Patients.

Authors:  Olga Tarasova; Anastasia Rudik; Dmitry Kireev; Vladimir Poroikov
Journal:  Front Genet       Date:  2021-06-10       Impact factor: 4.599

9.  An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.

Authors:  Nuno G Alves; Ana I Mata; João P Luís; Rui M M Brito; Carlos J V Simões
Journal:  Front Chem       Date:  2020-04-29       Impact factor: 5.221

  9 in total

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