Prevalence, dynamic change and spatial distribution of HIV-1 CRF119_0107 from 2019 to 2024 in Nanjing, China: a genomic and spatial epidemiological analysis.

BACKGROUND

Since its initial detection among men who have sex with men (MSM) in Nanjing, CRF119_0107 has rapidly emerged as the third most prevalent HIV-1 subtype. To elucidate its transmission dynamic, spatial characteristics, and the prevalence of transmitted drug resistance (TDR), we conducted a joint genomic and spatial epidemiological analysis.

METHODS

HIV-infected individuals diagnosed from 2109 to 2024 who were identified as CRF119_0107 and didn't undergo antiretroviral therapy (ART) were included in the study. The HIV-1 pol gene sequence was obtained by viral RNA extraction and nested PCR. A molecular transmission network was constructed using HIV-TRACE, and spatial analysis was performed in ArcGIS. Multivariate logistic regression was employed to analyze factors associated with clustering. Transmission links of the network were visualized in intensity matrices and spatial transmission graph.

RESULTS

The 138 CRF119_0107 individuals predominantly consisted of unmarried, college-educated MSM. A notably high TDR prevalence of 15.9% was observed, with 15.2% (21/138) resistance to non-nucleoside reverse transcriptase inhibitor (NNRTI). At the genetic distance threshold of 0.005 substitutions/site, 78 sequences formed 11 transmission clusters, with a clustering rate of 56.6%. The transmission network analysis identified a drug-resistant cluster containing 19 drug-resistant individuals, all harboring K103N/KN mutation. Four large MSM-dominated clusters were identified, including two high-growth clusters expanding at over 2 nodes per year between 2022 and 2024. Multivariate logistic regression analysis revealed that individuals with high initial CD4 counts and TDR individuals had significantly higher clustering rate compared to those with CD4 counts < 200 cells/µL and without TDR. Spatial analysis revealed no significant autocorrelation in clustering rate at the district-level (Moran's I=-0.121, P = 0.774). Intensity matrices revealed extensive inter-district transmission across all 12 districts, with this transmission accounting for 83.8% of total transmissions. The spatial transmission graph showed that the strength of the connections between districts varied, and strong inter-district transmission linkages were also observed between geographically non-adjacent districts.

CONCLUSIONS

Real-time surveillance and rapid response mechanisms should prioritize high-growth or drug-resistant transmission clusters. Cross-district coordination and joint interventions should be strengthened in districts with intensive transmission linkages. Our interdisciplinary approach offers an evidence-based framework for curbing dissemination of CRF119_0107.