HIV-1 protease and reverse transcriptase mutations for drug resistance surveillance

Shafer, Robert W.; Rhee, Soo-Yon; Pillay, Deenan; Miller, Veronica; Sandstrom, Paul; Schapiro, Jonathan; Kuritzkes, Daniel R.; Bennett, Diane

doi:10.1097/qad.0b013e328011e691

Cited by 297 publications

(303 citation statements)

References 35 publications

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“…In recent years, the transmitted resistance occurrence has been increasing due to scaled-up antiretroviral treatments. In Europe, North America, and Brazil, it has been reported that the prevalence of drug resistance ranges from 5-15% in newly diagnosed individuals (14). Because our untreated sequence data were collected from 1982 to 2005, it is possible that there are several transmitted drug resistant sequences in the untreated group that may affect both the sensitivity of our BVP algorithm and the power of our Bayesian model structure inference method.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, HIV-1 drug resistance can be not only acquired (developing in a person receiving antiretroviral treatment) but also transmitted (occurring because a virus with drug-resistance mutations was transmitted to a drug-naive person) (14). In recent years, the transmitted resistance occurrence has been increasing due to scaled-up antiretroviral treatments.…”

Section: Discussionmentioning

confidence: 99%

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Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance

Zhang

Hou

Wang

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

108

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We propose a systematic approach for a better understanding of how HIV viruses employ various combinations of mutations to resist drug treatments, which is critical to developing new drugs and optimizing the use of existing drugs. By probabilistically modeling mutations in the HIV-1 protease or reverse transcriptase (RT) isolated from drug-treated patients, we present a statistical procedure that first detects mutation combinations associated with drug resistance and then infers detailed interaction structures of these mutations. The molecular basis of our statistical predictions is further studied by using molecular dynamics simulations and free energy calculations. We have demonstrated the usefulness of this systematic procedure on three HIV drugs, (Indinavir, Zidovudine, and Nevirapine), discovered unique interaction features between viral mutations induced by these drugs, and revealed the structural basis of such interactions.Bayesian model selection | free energy calculation | Markov chain Monte Carlo | molecular dynamics | mutation interactions H IV drug-resistance, which is caused by mutations of viral proteins that disrupt the drugs' binding but do not affect the viral survival, is a major hurdle that hinders a successful treatment of AIDS (1, 2). Due to the high rate and low fidelity of HIV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. By sequencing viral strains in the treated-patient isolates, genotypic data have been accumulated for the drugs targeting two viral enzymes, protease and reverse transcriptase, that are essential to the virus's replication. Because each mutation of the viral protein is not equally important for drug resistance, the observed, complicated mutation patterns are difficult to interpret (3, 4) and are limited in helping physicians design the best therapeutic regimen for a patient (5) (Fig. 1A).In past decades, many statistical learning methods (3, 4, 67-8) have been employed to help predict phenotypes from genotypes. There are also rule-based systems that infer drug-resistance levels from sequence information such as the Stanford University HIV Drug Resistance Database (Stanford HIVdb). However, these methods provide little insight on the genetic and molecular basis of drug resistance and often give inconsistent results when analyzing the same input mutation data (4, 6).In the present study, we investigated the problem of mutation interactions of the HIV induced by a certain drug treatment. Using a unique probabilistic model, we first detect resistant mutation combinations (9) and infer the interaction dependence structure of these combinations. Then, we use molecular dynamics (MD) simulations to reveal the molecular basis of how these mutations interact with each other to interfere with the drugs' binding. We have shown that our procedure is applicable to different antiretroviral drugs treating different types of HIV infection by analyzing the sequence mutations induced by three different drug treatments: a...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance

Zhang

Hou

Wang

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

108

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“…Due to rapid and error-prone viral replication, drug-resistant HIV-1 variants are inevitably selected during therapy with all currently available antiretroviral agents (3). Accumulation of mutations in the protease-encoding gene results in the emergence of multidrug-resistant (MDR) HIV-1 variants carrying a protease with an altered three-dimensional structure (4).…”

Section: H Uman Immunodeficiency Virus Type 1 (Hiv-1) Protease (Pr)mentioning

confidence: 99%

A Conserved Hydrogen-Bonding Network of P2 bis -Tetrahydrofuran-Containing HIV-1 Protease Inhibitors (PIs) with a Protease Active-Site Amino Acid Backbone Aids in Their Activity against PI-Resistant HIV

Yedidi

Garimella

Aoki

et al. 2014

Antimicrob Agents Chemother

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“…In Brazil, triple We have studied a group of HIV-1 perinatally infected children receiving prolonged antiretroviral treatment and we report in this paper the results concerning the drug resistance mutations profile observed in this group. These mutations were classified according to the current list of resistance mutations proposed by Shafer et al (2007).…”

Section: Introductionmentioning

confidence: 99%

Hiv-1 Drug Resistance Assessment in Pediatric Patients

Simonetti¹,

Lima²,

Schatzmayr³

et al. 2009

VR&R

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Twenty-one vertically HIV-1 infected children were assessed for drug resistance genotyping. Eighteen children presented at least one mutation conferring resistance to the nucleoside reverse transcriptase inhibitors, and seven children presented resistance to the non-nucleoside inhibitors. Among them, two children in whom the therapy had been discontinued two to three years before testing presented K101E, K103N, and G190A mutations conferring resistance to all the non-nucleoside drugs available for treatment. Protease gene mutations conferring resistance to at least two to up to twelve drugs were reported in 12 children. Studies in HIV-infected children have a particular meaning, and with antiretroviral therapy becoming more widely available, surveillance of viral resistance regional levels is relevant for treatment guidelines, supporting the rational use of antiretroviral drugs by treatment programs. Additional larger studies are still required to give more information on HIV-1 drug resistance in pediatric patients.

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HIV-1 protease and reverse transcriptase mutations for drug resistance surveillance

Abstract: This list, which should be updated regularly using the same or similar criteria, can be used for genotypic surveillance of transmitted HIV-1 drug resistance.

Cited by 297 publications

References 35 publications

Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance

Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance

A Conserved Hydrogen-Bonding Network of P2 bis -Tetrahydrofuran-Containing HIV-1 Protease Inhibitors (PIs) with a Protease Active-Site Amino Acid Backbone Aids in Their Activity against PI-Resistant HIV

Hiv-1 Drug Resistance Assessment in Pediatric Patients

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