AI Article Synopsis
- The study aims to analyze and compare how different HIV-1 subtypes (C, F, G, and CRF02_AG) respond to protease inhibitors to better understand variations in susceptibility and related genetic factors.
- Using samples from drug-naive patients, researchers employed both phenotyping and genotyping techniques to identify specific polymorphisms linked to drug susceptibility, revealing unique susceptibility patterns across the subtypes.
- Results indicated that certain polymorphisms, like 70R and 37D/S/T, significantly influenced drug response, suggesting that understanding these genetic variations can enhance the accuracy of genotyping algorithms across different HIV subtypes.
Article Abstract
Objective: To compare baseline susceptibility to protease inhibitors among HIV-1 isolates of subtypes C, F, G and CRF02_AG, and to identify polymorphisms that determine the differences in susceptibility.
Methods: A total of 42 samples of drug-naive patients infected with subtypes G (n=19), CRF02_AG (n = 10), F (n = 6) and C (n = 7) were phenotyped and genotyped with the Antivirogram and the ViroSeq 2.0 genotyping system, respectively. A Bayesian network approach was used for a preliminary analysis of the collected data and the dependencies indicated by the network were statistically confirmed.
Results: CRF02_AG samples were found to be more susceptible to nelfinavir and ritonavir than other subtypes. Hypersusceptibility to these drugs was associated with the 70R polymorphism. 37D/S/T was associated with reduced susceptibility to indinavir and 89M with reduced susceptibility to lopinavir. Susceptibility to tipranavir was the lowest among the subtype F samples and the highest for subtype G samples, with samples carrying 57R being more susceptible than samples carrying 57K.
Conclusions: Our study suggests that there are baseline susceptibility differences between subtypes and these differences are due to naturally occurring polymorphisms in these subtypes. The predictive value for phenotype of these polymorphisms was even valid in subtypes where these polymorphisms are less prevalent. Taking into account such polymorphisms should improve current algorithms for interpretation of genotyping results in a subtype-independent way.
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