Evaluation of capsid inhibitor resistance profiles and HIV-1 subtype distribution via near-full-length genome NGS in ART-naive HIV-1 patients in Guangxi, China.
Abstract
Objectives
Lenacapavir, a first-in-class, long-acting capsid inhibitor (CAI), has been approved for HIV-1 treatment and prevention. However, naturally occurring resistance to the HIV-1 CAI at the quasi-species level is rarely reported.
Methods
Using next-generation sequencing (NGS), we obtained 495 HIV-1 near-full-length genome (NFLG) sequences derived from plasma specimens of ART-naive individuals in Guangxi, China. Drug resistance mutations (DRMs) conferring resistance to CAIs, as well as to NNRTIs, NRTIs, PIs and INSTIs, were detected at a 5% frequency threshold for variant calling using the HIVdb program. To analyse naturally occurring polymorphisms, the gag sequences of CRF08_Bc subtype were retrieved from two public sequence databases, followed by CAI-related DRM detection. Subtype-specific capsid polymorphism was analysed with a 0.5% prevalence cut-off.
Results
Of the 495 individuals, 16.77% (83/495) were infected with unique recombinant forms (URFs). Overall, 4.44% (22/495) of the sequences were carrying at least DRMs to CAIs, including one major DRM (Q67H, 0.80%) and two accessory DRMs (T107A, 2.22%; T107S, 1.81%). No cross-resistance to other classes of drugs, including PIs, NRTIs, NNRTIs or INSTIs, was observed in individuals with CAI-related DRMs. According to the definition of subtype-specific polymorphism (>0.5% prevalence), Q67H (1.60%, 3/188) and T107S (1.60%, 3/188) were CRF01_AE-specific polymorphisms, whereas T107A (5.05%, 11/218) and T107S (3.03%, 5/218) were CRF08_Bc-specific polymorphisms.
Conclusions
HIV URFs are commonly misclassified by conventional partial pol gene subtyping among treatment-naive patients in Guangxi, indicating a need for enhanced full-genome genotyping surveillance locally. Subtype-specific capsid polymorphisms that have not been identified in previous studies further highlight the value of geographically diverse cohorts in future CAI research to improve real-world applicability.