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在中国成人低水平病毒血症中使用 Sanger 和二代测序检测 HIV-1 基因型耐药性
Authors Li Q, Yu F, Song C, Zhao H, Xiao Q, Lao X, Yang S, Tang Y, Zhang F
Received 23 August 2022
Accepted for publication 27 October 2022
Published 21 November 2022 Volume 2022:15 Pages 6711—6722
DOI https://doi.org/10.2147/IDR.S387215
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Héctor M Mora-Montes
Objective: In this study, we aimed to determine drug-resistance mutations (DRMs) in HIV-1 patients with low-level viremia (LLV) and explored the performance of next-generation sequencing (NGS) in detecting HIV DRMs by using LLV samples.
Methods: Overall, 80 samples with LLV were amplified and sequenced using a commercial Sanger sequencing (SS) genotyping kit. Furthermore, 51 samples successfully sequenced using SS were simultaneously subjected to NGS. Genotyping success rates of various viremia categories by two sequencing methods were calculated. Stanford HIV-1 drug-resistance database (HIVdb version 8.9) was used to analyze the DRMs. In the NGS assay, a threshold of 5% was considered for reporting low-frequency variants, and the DRMs detected using SS and NGS were compared.
Results: The overall success rate of PR/RT regions was 88.1% (67/80) using SS and 86.3% (44/51) using NGS. Furthermore, a significant linear trend was noted between viral load and the genotyping success rate. A total of 38.8% (26/67) participants harbored at least one mutation, as revealed through SS. Moreover, the prevalence of DRMs in persistent LLV was significantly higher than that in intermittent LLV (62.1% vs. 21.1%; P < 0.05). A total of 69 DRMs were detected using the two sequencing methods at the threshold of 5%. Moreover, 10 DRMs missed by SS were detected using NGS, whereas 8 DRMs missed by NGS were detected by SS.
Conclusion: Our data suggested that the genotyping resistance testing is necessary to guide antiretroviral therapy optimization in LLV patients.
Keywords: HIV-1, low-level viremia, drug-resistance mutation, next-generation sequencing