Bioinformatics prediction of HIV coreceptor usage

Lengauer, Thomas; Sander, Oliver; Sierra, Saleta; Thielen, Alexander; Kaiser, Rolf

doi:10.1038/nbt1371

Cited by 305 publications

(300 citation statements)

References 27 publications

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“…The tropism prediction was performed using the geno2pheno (coreceptor) 2.5 algorithm. 17 The significance level was defined by a false-positive rate threshold of 10% as recommended by the European guidelines for tropism testing. 18 …”

Section: Tropism Testingmentioning

confidence: 99%

Recent HIV-1 Outbreak Among Intravenous Drug Users in Romania: Evidence for Cocirculation of CRF14_BG and Subtype F1 Strains

Niculescu

Paraschiv

Paraskevis

et al. 2015

AIDS Research and Human Retroviruses

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Since 2011, Romania has faced an HIV outbreak among injecting drug users (IDUs). Our aim was to identify and describe clinical and epidemiological patterns of this outbreak. A cross-sectional study enrolled 138 IDUs diagnosed with HIV infection between 2011 and 2013 with 58 sexually infected individuals included as the control group. The IDUs had a long history of heroin abuse (10 years) and a recent history of new psychostimulant injection (3-4 years). Classical epidemiological data and molecular techniques were used to describe the transmission dynamics. A high prevalence of hepatitis C virus (HCV) coinfection was noted (98.6%) compared to the control group (10.3%) ( p < 0.001). IDUs had initially been infected with HCV. HIV infection was more recent, linked to starting injecting stimulants. HIV subtype analysis showed a predominance of the local F1 strain in both IDUs and sexually infected patients; in IDUs it also identified 28 CRF14_BG recombinants and six unique recombinant forms (URFs) between F1 and CRF14_BG. A few patients from both risk groups were infected with subtype B. Among IDUs, CRF14_BG was associated with a lower CD4 cell count and more advanced stages of disease, which correlated with CXCR4 tropism. Phylogenetic analysis revealed the spread of HIV through three major IDU clusters of recent date. Among IDUs with CRF14_BG, some reported travel abroad (Spain, Greece). By identifying clusters of IDUs with related viruses, molecular epidemiologic methods provide valuable information on patterns of HIV transmission that can be useful in planning appropriate harm reduction interventions.

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Section: Tropism Testingmentioning

confidence: 99%

Recent HIV-1 Outbreak Among Intravenous Drug Users in Romania: Evidence for Cocirculation of CRF14_BG and Subtype F1 Strains

Niculescu

Paraschiv

Paraskevis

et al. 2015

AIDS Research and Human Retroviruses

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“…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.…”

mentioning

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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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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“…A false-positive rate (FPR) <5% are mainly X4 and ≥ 20% are mainly R5 variants. Therefore, we classified V3 loop sequences with FPR ≥ 20% as R5 viruses, with FPR ≥ 5% and <20% as dual-tropic viruses (R5X4) and with FPR <5% as X4 viruses as described previously [20,28,29].…”

Section: Viral Tropism and Co-receptor Usementioning

confidence: 99%