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Standardized comparison of the relative impacts of HIV-1 reverse transcriptase (RT) mutations on nucleoside RT inhibitor susceptibility.

ABSTRACT: Determining the phenotypic impacts of reverse transcriptase (RT) mutations on individual nucleoside RT inhibitors (NRTIs) has remained a statistical challenge because clinical NRTI-resistant HIV-1 isolates usually contain multiple mutations, often in complex patterns, complicating the task of determining the relative contribution of each mutation to HIV drug resistance. Furthermore, the NRTIs have highly variable dynamic susceptibility ranges, making it difficult to determine the relative effect of an RT mutation on susceptibility to different NRTIs. In this study, we analyzed 1,273 genotyped HIV-1 isolates for which phenotypic results were obtained using the PhenoSense assay (Monogram, South San Francisco, CA). We used a parsimonious feature selection algorithm, LASSO, to assess the possible contributions of 177 mutations that occurred in 10 or more isolates in our data set. We then used least-squares regression to quantify the impact of each LASSO-selected mutation on each NRTI. Our study provides a comprehensive view of the most common NRTI resistance mutations. Because our results were standardized, the study provides the first analysis that quantifies the relative phenotypic effects of NRTI resistance mutations on each of the NRTIs. In addition, the study contains new findings on the relative impacts of thymidine analog mutations (TAMs) on susceptibility to abacavir and tenofovir; the impacts of several known but incompletely characterized mutations, including E40F, V75T, Y115F, and K219R; and a tentative role in reduced NRTI susceptibility for K64H, a novel NRTI resistance mutation.

SUBMITTER: Melikian GL

PROVIDER: S-EPMC3346663 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Standardized comparison of the relative impacts of HIV-1 reverse transcriptase (RT) mutations on nucleoside RT inhibitor susceptibility.

Melikian George L GL Rhee Soo-Yon SY Taylor Jonathan J Fessel W Jeffrey WJ Kaufman David D Towner William W Troia-Cancio Paolo V PV Zolopa Andrew A Robbins Gregory K GK Kagan Ron R Israelski Dennis D Shafer Robert W RW

Antimicrobial agents and chemotherapy 20120213 5

Determining the phenotypic impacts of reverse transcriptase (RT) mutations on individual nucleoside RT inhibitors (NRTIs) has remained a statistical challenge because clinical NRTI-resistant HIV-1 isolates usually contain multiple mutations, often in complex patterns, complicating the task of determining the relative contribution of each mutation to HIV drug resistance. Furthermore, the NRTIs have highly variable dynamic susceptibility ranges, making it difficult to determine the relative effect ...[more]

PMID: 22330916

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Project description:Equilibrative nucleoside transporters (ENTs) transport nucleosides across the blood-testis barrier (BTB). ENTs are of interest to study the disposition of nucleoside reverse-transcriptase inhibitors (NRTIs) in the human male genital tract because of their similarity in structure to nucleosides. HeLa S3 cells express ENT1 and ENT2 and were used to compare relative interactions of these transporters with selected NRTIs. Inhibition of [3H]uridine uptake by NBMPR was biphasic, with IC50 values of 11.3 nM for ENT1 and 9.6 μM for ENT2. Uptake measured with 100 nM NBMPR represented ENT2-mediated transport; subtracting that from total uptake represented ENT1-mediated transport. The kinetics of ENT1- and ENT2-mediated [3H]uridine uptake revealed no difference in Jmax (16.53 and 30.40 pmol cm-2 min-1) and an eightfold difference in Kt (13.6 and 108.9 μM). The resulting fivefold difference in intrinsic clearance (Jmax/Kt) for ENT1- and ENT2 transport accounted for observed inhibition of [3H]uridine uptake by 100 nM NBMPR. Millimolar concentrations of the NRTIs emtricitabine, didanosine, lamivudine, stavudine, tenofovir disoproxil, and zalcitabine had no effect on ENT transport activity, whereas abacavir, entecavir, and zidovudine inhibited both transporters with IC50 values of ∼200 µM, 2.5 mM, and 2 mM, respectively. Using liquid chromatography-tandem mass spectrometry and [3H] compounds, the data suggest that entecavir is an ENT substrate, abacavir is an ENT inhibitor, and zidovudine uptake is carrier-mediated, although not an ENT substrate. These data show that HeLa S3 cells can be used to explore complex transporter selectivity and are an adequate model for studying ENTs present at the BTB. SIGNIFICANCE STATEMENT: This study characterizes an in vitro model using S-[(4-nitrophenyl)methyl]-6-thioinosine to differentiate between equilibrative nucleoside transporter (ENT) 1- and ENT2-mediated uridine transport in HeLa cells. This provides a method to assess the influence of nucleoside reverse-transcriptase inhibitors on natively expressed transporter function. Determining substrate selectivity of the ENTs in HeLa cells can be effectively translated into the activity of these transporters in Sertoli cells that comprise the blood-testis barrier, thereby assisting targeted drug development of compounds capable of circumventing the blood-testis barrier.

Project description:BackgroundLinear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.ResultsCompared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.ConclusionsWe successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.

Dataset Information

Standardized comparison of the relative impacts of HIV-1 reverse transcriptase (RT) mutations on nucleoside RT inhibitor susceptibility.

Publications

Standardized comparison of the relative impacts of HIV-1 reverse transcriptase (RT) mutations on nucleoside RT inhibitor susceptibility.

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