Dataset Information

Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance.

ABSTRACT: BACKGROUND: Successful management of chronic human immunodeficiency virus type 1 (HIV-1) infection with a cocktail of antiretroviral medications can be negatively affected by the presence of drug resistant mutations in the viral targets. These targets include the HIV-1 protease (PR) and reverse transcriptase (RT) proteins, for which a number of inhibitors are available on the market and routinely prescribed. Protein mutational patterns are associated with varying degrees of resistance to their respective inhibitors, with extremes that can range from continued susceptibility to cross-resistance across all drugs. RESULTS: Here we implement statistical learning algorithms to develop structure- and sequence-based models for systematically predicting the effects of mutations in the PR and RT proteins on resistance to each of eight and eleven inhibitors, respectively. Employing a four-body statistical potential, mutant proteins are represented as feature vectors whose components quantify relative environmental perturbations at amino acid residue positions in the respective target structures upon mutation. Two approaches are implemented in developing sequence-based models, based on use of either relative frequencies or counts of n-grams, to generate vectors for representing mutant proteins. To the best of our knowledge, this is the first reported study on structure- and sequence-based predictive models of HIV-1 PR and RT drug resistance developed by implementing a four-body statistical potential and n-grams, respectively, to generate mutant attribute vectors. Performance of the learning methods is evaluated on the basis of tenfold cross-validation, using previously assayed and publicly available in vitro data relating mutational patterns in the targets to quantified inhibitor susceptibility changes. CONCLUSION: Overall performance results are competitive with those of a previously published study utilizing a sequence-based strategy, while our structure- and sequence-based models provide orthogonal and complementary prediction methodologies, respectively. In a novel application, we describe a technique for identifying every possible pair of RT inhibitors as either potentially effective together as part of a cocktail, or a combination that is to be avoided.

SUBMITTER: Masso M

PROVIDER: S-EPMC3849442 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance.

Masso Majid M Vaisman Iosif I II

BMC genomics 20131001

<h4>Background</h4>Successful management of chronic human immunodeficiency virus type 1 (HIV-1) infection with a cocktail of antiretroviral medications can be negatively affected by the presence of drug resistant mutations in the viral targets. These targets include the HIV-1 protease (PR) and reverse transcriptase (RT) proteins, for which a number of inhibitors are available on the market and routinely prescribed. Protein mutational patterns are associated with varying degrees of resistance to ...[more]

PMID: 24268064

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Project description:BACKGROUND:The presence of drug resistance mutations (DRMs) against antiretroviral agents is one of the main concerns in the clinical management of individuals with human immunodeficiency virus-1 (HIV-1) infection, especially in regions of the world where treatment options are limited. The current study aimed at assessing the prevalence of HIV-1 DRMs among naïve and treatment-experienced HIV-1-infected patients in Iran. METHODS:From April 2013 to September 2018, the HIV-1 protease and reverse transcriptase genes were amplified and sequenced in plasma specimens of 60 newly diagnosed antiretroviral-naive individuals and 46 participants receiving antiretroviral therapies (ARTs) for at least six months with an HIV viral load of more than 1000 IU/mL to determine the HIV-1 DRMs and subtypes. RESULTS:Among the 60 treatment-naïve HIV-1-infected participants, 8.3% were infected with HIV-1 variants with surveillance DRMs (SDRMs). The SDRMs, D67N and D67E, belonged to the NRTIs class in two patients and K103N and V106A belonged to the NNRTIs class in three patients. The phylogenetic analysis showed that 91.7% of the subjects were infected with subtype CRF35_AD, followed by subtype B (5.0%) and CRF01_AE (3.3%). Among the 46 ART-experienced participants, 33 (71.7%) carried HIV-1 variants with SDRMs (9.1% against PIs, 78.8% against NRTIs, and 100% against NNRTIs). M46I and I47V were the most common mutations for PIs, M184V was the most common mutation for the NRTIs, and K103N/S was the most common mutation for NNRTIs. Phylogenetic analysis of the polymerase region showed that all of the 46 HIV-1-infected patients who failed on ART carried CRF35_AD. CONCLUSIONS:The moderate prevalence of SDRMs (8.3%) in treatment-naïve and ART-failed (77.1%) Iranian patients with HIV-1-infection emphasizes the need for systematic viral load monitoring, expanding drug resistance testing, carefully surveilling individuals on ART regimens, and facilitating access to new antiretrovirals by health authorities.

Project description:BackgroundAs use of dolutegravir (DTG) becomes more common in resource limited settings (RLS), the demand for integrase resistance testing is increasing. Affordable methods for genotyping all relevant HIV-1 pol genes (i.e., protease (PR), reverse transcriptase (RT) and integrase (IN)) are required to guide choice of future antiretroviral therapy (ART). We designed an in-house HIV-1 drug resistance (HIVDR) genotyping method that is affordable and suitable for use in RLS.MethodsWe obtained remnant plasma samples from CAPRISA 103 study and amplified HIV-1 PR, RT and IN genes, using an innovative PCR assay. We validated the assay using remnant plasma samples from an external quality assessment (EQA) programme. We genotyped samples by Sanger sequencing and assessed HIVDR mutations using the Stanford HIV drug resistance database. We compared drug resistance mutations with previous genotypes and calculated method cost-estimates.ResultsFrom 96 samples processed, we obtained sequence data for 78 (81%), of which 75 (96%) had a least one HIVDR mutation, with no major-IN mutations observed. Only one sample had an E157Q INSTI-accessory mutation. When compared to previous genotypes, 18/78 (23%) had at least one discordant mutation, but only 2/78 (3%) resulted in different phenotypic predictions that could affect choice of subsequent regimen. All CAPRISA 103 study sequences were HIV-1C as confirmed by phylogenetic analysis. Of the 7 EQA samples, 4 were HIV-1C, 2 were HIV-1D, and 1 was HIV-1A. Genotypic resistance data generated using the IDR method were 100% concordant with EQA panel results. Overall genotyping cost per sample was estimated at ~ US$43-$US49, with a processing time of ~ 2 working days.ConclusionsWe successfully designed an in-house HIVDR method that is suitable for genotyping HIV-1 PR, RT and IN genes, at an affordable cost and shorter turnaround time. This HIVDR genotyping method accommodates changes in ART regimens and will help to guide HIV-1 treatment decisions in RLS.

Project description:BackgroundViral resistance to antiretroviral therapy threatens our best methods to control and prevent HIV infection. Current drug resistance genotyping methods are costly, optimized for subtype B virus, and primarily detect resistance mutations to protease and reverse transcriptase inhibitors. With the increasing use of integrase inhibitors in first-line therapies, monitoring for integrase inhibitor drug resistance mutations is a priority. We designed a universal primer pair to PCR amplify all major group M HIV-1 viruses for genotyping using Illumina MiSeq to simultaneously detect drug resistance mutations associated with protease, nucleoside reverse transcriptase, non-nucleoside reverse transcriptase, and integrase inhibitors.ResultsA universal primer pair targeting the HIV pol gene was used to successfully PCR amplify HIV isolates representing subtypes A, B, C, D, CRF01_AE and CRF02_AG. The universal primers were then tested on 62 samples from a US cohort of injection drug users failing treatment after release from prison. 94% of the samples were successfully genotyped for known drug resistance mutations in the protease, reverse transcriptase and integrase gene products. Control experiments demonstrate that mutations present at ≥ 2% frequency are reliably detected and above the threshold of error for this method. New drug resistance mutations not found in the baseline sample were identified in 54% of the patient samples after treatment failure. 86% of patients with major drug resistance mutations had 1 or more mutations associated with drug resistance to the treatment regimen at the time point of treatment failure. 59% of the emerging mutations were found at frequencies between 2% and 20% of the total sequences generated, below the estimated limit of detection of current FDA-approved genotyping techniques. Primary plasma samples with viral loads as low as 799 copies/ml were successfully genotyped using this method.ConclusionsHere we present an Illumina MiSeq-based HIV drug resistance genotyping assay. Our data suggests that this universal assay works across all major group M HIV-1 subtypes and identifies all drug resistance mutations in the pol gene known to confer resistance to protease, reverse transcriptase and integrase inhibitors. This high-throughput and sensitive assay could significantly improve access to drug resistance genotyping worldwide.

Project description:UnlabelledHIV-1 protease (PR), reverse transcriptase (RT), and integrase (IN) variability presents a challenge to laboratories performing genotypic resistance testing. This challenge will grow with increased sequencing of samples enriched for proviral DNA such as dried blood spots and increased use of next-generation sequencing (NGS) to detect low-abundance HIV-1 variants. We analyzed PR and RT sequences from >100,000 individuals and IN sequences from >10,000 individuals to characterize variation at each amino acid position, identify mutations indicating APOBEC-mediated G-to-A editing, and identify mutations resulting from selective drug pressure. Forty-seven percent of PR, 37% of RT, and 34% of IN positions had one or more amino acid variants with a prevalence of ≥1%. Seventy percent of PR, 60% of RT, and 60% of IN positions had one or more variants with a prevalence of ≥0.1%. Overall 201 PR, 636 RT, and 346 IN variants had a prevalence of ≥0.1%. The median intersubtype prevalence ratios were 2.9-, 2.1-, and 1.9-fold for these PR, RT, and IN variants, respectively. Only 5.0% of PR, 3.7% of RT, and 2.0% of IN variants had a median intersubtype prevalence ratio of ≥10-fold. Variants at lower prevalences were more likely to differ biochemically and to be part of an electrophoretic mixture compared to high-prevalence variants. There were 209 mutations indicative of APOBEC-mediated G-to-A editing and 326 mutations nonpolymorphic treatment selected. Identification of viruses with a high number of APOBEC-associated mutations will facilitate the quality control of dried blood spot sequencing. Identifying sequences with a high proportion of rare mutations will facilitate the quality control of NGS.ImportanceMost antiretroviral drugs target three HIV-1 proteins: PR, RT, and IN. These proteins are highly variable: many different amino acids can be present at the same position in viruses from different individuals. Some of the amino acid variants cause drug resistance and occur mainly in individuals receiving antiretroviral drugs. Some variants result from a human cellular defense mechanism called APOBEC-mediated hypermutation. Many variants result from naturally occurring mutation. Some variants may represent technical artifacts. We studied PR and RT sequences from >100,000 individuals and IN sequences from >10,000 individuals to quantify variation at each amino acid position in these three HIV-1 proteins. We performed analyses to determine which amino acid variants resulted from antiretroviral drug selection pressure, APOBEC-mediated editing, and naturally occurring variation. Our results provide information essential to clinical, research, and public health laboratories performing genotypic resistance testing by sequencing HIV-1 PR, RT, and IN.

Dataset Information

Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance.

Publications

Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance.

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