Evaluation of a HIV-1 drug resistance genotyping method based on high-throughput sequencing (HTS)

High-throughput sequencing (HTS) enables the detection of low-frequency HIV-1 drug resistance mutations (DRMs) that are often missed by conventional Sanger sequencing. However, the clinical implementation of HTS remains limited, and standardized evaluations of emerging platforms are still lacking. This study aims to evaluate the performance of the MGI HTS platform in detecting HIV-1 DRMs compared to Sanger sequencing. Plasma samples were collected from treatment-naïve and treatment-experienced individuals living with HIV. HIV-1 pol gene fragments, encompassing the protease (PR), reverse transcriptase (RT), and integrase (IN) regions, were amplified using a one-step reverse-transcription polymerase chain reaction (RT-PCR) protocol. Paired sequencing was performed on both the MGI DNBSEQ platform and Sanger platform. Sequences were analyzed for drug resistance mutations using the Stanford HIVdb algorithm. Detection rates and mutation frequencies were compared between methods. MGI HTS demonstrated higher overall amplification success, especially for the integrase region in samples with viral loads between 1,000 and 10,000 copies/mL. It also identified a greater number of DRMs, including low-frequency variants below the Sanger detection threshold (typically < 20.0% variant frequency). Among cases with discordant results, MGI detected additional resistance-associated mutations in both NNRTI and NRTI classes, potentially impacting treatment decisions. This study highlights the superior performance of the MGI HTS platform over traditional Sanger sequencing for HIV-1 drug resistance genotyping. The platform’s enhanced sensitivity, particularly for low-frequency variants, and its simplified one-step amplification protocol make it well-suited for routine resistance testing, especially in low-viral-load samples. These findings support the broader clinical adoption of MGI HTS as a reliable and scalable tool for comprehensive resistance profiling and timely optimization of antiretroviral therapy.