Dynamic change and spatial distribution of HIV-1 CRF119_0107 transmission clusters 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 change, spatial characteristics, and transmitted drug resistance (TDR) prevalence, we conducted a joint analysis of genomic and spatial epidemiology. Methods From 2019 to 2024, a total of 138 antiretroviral therapy (ART)-naïve individuals newly diagnosed with HIV-1 CRF119_0107 infection were enrolled. HIV-1 pol gene sequence was obtained by viral RNA extraction and nested PCR. Molecular transmission network was constructed using HIV-TRACE while spatial distribution analyses were performed in ArcGIS. Multivariate logistic regression was used to analyze the factors associated with clustering. The transmission links of the network was visualized and colored differently in intensity matrices and sankey diagram. Results The 138 CRF119_0107-infected individuals predominantly consisted of unmarried, college-educated MSM. A notably high TDR prevalence of 15.9% was observed, with 15.2% (21/138) of cases showing 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%. Network analysis identified two drug-resistant clusters including 19 NNRTI-resistant cases predominantly driven by the K103N mutation and one nucleoside reverse transcriptase inhibitor (NRTI)-resistant, respectively. Four large male-exclusive clusters dominated by MSM were identified, with two high-growth clusters expanding at over 2 nodes/year during 2022–2024. Multivariate logistic regression analysis revealed that cases with high initial CD4 counts and TDR cases had significantly higher clustering rate compared to those with CD4 counts < 200 cells/µL and without TDR. Spatial analysis demonstrated no significant autocorrelation in clustering rate at district-level (Moran's I=-0.121, P  = 0.774). The sankey diagram and intensity matrices demonstrated extensive inter-district transmission across all 12 districts and inter-district transmission accounted for 83.8%. Notably, strong inter-district transmission linkage was observed even between geographically non-adjacent districts except for geographically 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 for districts with intensive transmission linkages. Our cross-disciplinary approach could provide an evidence-based framework for curbing CRF119_0107 dissemination.