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
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Abstract 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.
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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 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 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 Yuanyuan Xu, Hongjie Shi, Xin Li, Tingyi Jiang, Mengkai Qiao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6539216/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Virology Journal → Version 1 posted 9 You are reading this latest preprint version Abstract 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. HIV-1 CRF119_0107 molecular network transmission cluster spatial analysis Figures Figure 1 Figure 2 Figure 3 Introduction Due to high mutation rate, rapid replication dynamics, and frequent dual infection/superinfection events, HIV recombinant forms are being increasingly complex and unique recombinant forms (URFs) are continually emerging each year[ 1 ]. The Los Alamos HIV Sequence Database reported that at least 158 circulating recombinant forms (CRFs) have been confirmed globally, including 157 HIV-1 CRFs and one HIV-2 CRF. Notably, China has reported over 50 distinct CRFs and they predominantly circulated in sexual contact population, particularly men who have sex with men (MSM). This epidemiological pattern indicates intense HIV-1 recombination activity in China. In recent years, CRF01_AE and CRF07_BC have been the dominant recombinant strains nationally [ 2 – 4 ]. Through sustained viral evolution and transmission, dual infection of CRF01_AE and CRF07_BC among MSM have facilitated emergence of mutiple second-generation recombinants. Multiple such strains have been reported nationwide in recent years, such as CRF117_0107, CRF123_0107, CRF136_0107, CRF 163_0107 and so on [ 5 – 9 ]. CRF119_0107, as a second-generation recombinant of CRF01_AE and CRF07_BC, was first detected among MSM in Nanjing in 2017 [ 10 ]. CRF119_0107 has rapidly ascended to become local third most prevalent HIV-1 strain, representing 6.29% in recent study [ 11 ]. In recent years, HIV-1 molecular transmission network has emerged as a novel methodology for investigating transmission patterns among HIV-infected populations [ 12 ]. By leveraging the genetic sequence similarity among infected individuals, this network is constructed based on the principle that smaller genetic distances correspond to higher genetic similarities, thereby reflecting potential transmission relationships between HIV-1 individuals. Such networks provide critical insights into the transmission dynamics of HIV-1, enabling identification of key transmission clusters, drug resistance transmission clusters, and associated risk factors[ 13 , 14 ]. Current research on HIV/AIDS mainly focuses on etiology, epidemiology, clinical features, prevention and control, and medical treatment, often neglecting the spatial attributes of HIV/AIDS transmission, which results in incomplete epidemiological understanding. Spatial epidemiological studies consistently demonstrate that the emergence, transmission patterns, and distribution of infectious diseases are closely linked to geographical and spatial attributes [ 15 – 17 ]. Given that molecular transmission networks alone cannot capture spatial characteristics, the joint analysis of molecular and spatial epidemiology enables a deeper understanding of the active areas of the transmission network from a spatial dimension, providing an evidence for optimizing regional HIV prevention strategies and healthcare resource allocation[ 17 – 19 ]. As the provincial capital of Jiangsu and a pivotal economic hub within China's Yangtze River Delta region, Nanjing possesses distinct geographical features that profoundly influence its HIV transmission pattern. By the end of 2024, the HIV infection rate of population in Nanjing was over 0.07%, maintaining the province's highest case burden. Contrasting with the national predominance of heterosexual transmission[ 20 ], homosexual transmission has been the most frequent transmission route of HIV-1 in Nanjing, accounting for more than 68% of newly reported cases[ 21 ]. Additionally, MSM and even male students who have sex with men both have stable high HIV prevalence [ 22 , 23 ]. Nanjing confronts multifaceted challenges characterized by severe HIV epidemic in MSM, viral diversity and escalating transmitted drug resistance [ 11 , 24 ]. In this study, newly reported HIV cases identified as CRF119_0107 in Nanjing were selected to construct transmission network. We combined the spatial epidemiology with molecular network analysis to systematically elucidate transmission pattern and dynamic change, spatial characteristics of transmission clusters and links, along with drug resistance prevalence. These evidence-based insights will establish a foundation for implementing geographically targeted prevention strategies and optimizing antiretroviral therapy (ART) regimens to curb CRF119_0107 dissemination in the Nanjing. Material and methods Study subjects In our study, 138 newly reported HIV-1 infections between January 1, 2019, and December 31, 2024 were enrolled. The inclusion criteria were as follows: (1) Confirmed diagnosis of HIV-1 infection; (2) Residence in Nanjing at diagnosis; (3) Without history of ART; (4) Identified as CRF119_0107. Sample collection and data acquisition After informed consent was obtained, peripheral blood samples (5–10 mL) were collected by venipuncture into EDTA anticoagulant tubes. Samples were centrifuged at 1500 rotations per minute for 15 minutes to separate plasma, which was then stored at -80°C freezer until further analysis. Demographic data, including age, gender, and residential district, along with clinical parameters including routes of transmission, screening source, initial CD4 + T lymphocyte cells (CD4) count, and initial viral load (VL) before ART were extracted from the National AIDS Prevention and Control Basic Information System. HIV-1 RNA extraction, amplification and sequencing HIV-1 RNA was extracted from 200 µL plasma samples using the QIAamp Viral RNA Mini Kit (Qiagen, Hilden,Germany) following the manufacturer's instructions. A nested reverse transcription-polymerase chain reaction (RT-PCR) was performed to amplify the HIV-1 pol gene fragment (HXB2:2253–3313) as previously described [ 24 ]. The PCR products were dealt with electrophoresis with 1% agarose gel, and the amplified positive products were purified and sequenced by Sangon Biotechnology Co., Ltd. The resulting sequence database was curated, excluding duplicate sequence, as well as sequences not compliant with quality control [ 25 ].Specifically, sequences exhibiting inadequate length, presence of stop codons, bad insertions/deletions and hyper-mutations were excluded to ensure analytical validity. Subtype identification Sequences were edited, trimmed, and assembled using Sequencer 4.10.1 software (GeneCodes, Ann Arbor, MI). A comprehensive reference dataset encompassing major epidemic clades A-D, F-H, and J-K, along with prevalent Chinese CRFs, was downloaded from the Los Alamos HIV Database ( https://www.hiv.lanl.gov/content/index ). Sequences were aligned against reference strains using BioEdit 7.0.9 (Informer Technologies Inc.), followed by phylogenetic analysis. Maximum likelihood (ML) phylogenetic tree was constructed using FastTree 2.1 software under GTR model. The phylogenetic framework was validated using bootstrap resampling (1,000 iterations) and a bootstrap value > 80% was used to determine subtype classification. Drug resistance analysis All sequences were submitted to HIV Drug Resistance Database ( https://hivdb.stanford.edu/hivdb/by-sequences/ ) to identify Drug resistant mutation (DRMs) against nucleoside/nonnucleoside reverse transcriptase inhibitors (NRTIs/NNRTIs) and protease inhibitors (PIs). Drug resistant levels were categorized using the Stanford HIVDB scoring system as follows: Sensitive (S, 0–9); Potential resistance (P, 10–14); Low-level resistance (L, 15–29); Intermediate-level resistance (I, 30–59); High-level resistance (H, ≥ 60). Transmitted drug resistance (TDR) was defined as low-level resistance or higher in ART-naïve individuals. Molecular network construction and cluster analysis Pairwise genetic distances (GD) were calculated via the Tamura-Nei 93 model. The optimal genetic distance threshold of 0.005 substitutions/site was selected based on sensitivity analysis ranged from 0.005 to 0.015 substitutions/site, to construct molecular transmission networks via HIV-TRACE ( https://veg.github.io./hivtrace-viz/ ) [ 26 ]. According to Technical Guideline for HIV Transmission Network Monitoring and Intervention (revision in 2021) released by Chinese Center for Disease Control and Prevention, nodes represent HIV sequences or individuals, edges denote potential transmission relationship between nodes. Node degree quantifies the number of connections per node. In ou study, degree was calculated to quantify transmission complexity and large clusters were defined as those containing more than 10 nodes. We classified dynamic change of clusters as follows:(1) High-growth cluster: comprising ≥ 2 cases diagnosed in 2019–2021, and ≥ 3 cases diagnosed in 2022–2024; (2) Low-growth cluster: comprising ≥ 2 cases diagnosed in 2019–2021, and < 3 cases diagnosed in 2022–2024; (3) Stable cluster: all pre-2022 cases; (4) Emerging cluster: those formed during 2022–2024. Spatial autocorrelation analysis The Global Moran’s I, a widely used spatial autocorrelation metric, was employed to assess whether there was spatial aggregation. If I > 0, it indicted there was a positive spatial correlation, reflecting a clustered distribution; if I = 0, it suggested that there was no spatial autocorrelation, implying random spatial distribution. if I < 0, it indicted that there was a negative spatial autocorrelation, showing a discrete distribution[ 27 ]. Statistical significance of observed spatial autocorrelation was evaluated using one-sample z-tests (α = 0.05). All the spatial descriptions and analyses were performed in ArcGIS 10.3. To reflect HIV transmission types geographically, the proportion of intra-district transmission in each district was calculated by dividing the number of links between cases in the district by the total number of links with any cases in the district, while the remaining proportion was referred to as the proportion of inter-district transmission. Additionally, the transmission links of CRF119_0107 was visualized and colored differently in intensity matrices and sankey diagram. The color of the grid cell at the intersection of two districts in an intensity matrix represented the number of links between the cases in these two districts. Sankey diagrams visualized the intensity of inter-district and intra-district HIV transmission by scaling the flow width by the number of links, which were consistent with values in the intensity matrices[ 19 ]. Statistical Analysis Statistical analyses were performed in SPSS Statistics 18.0 (IBM Corporation, Armonk, NY). Continuous variables with normal distributions were reported as mean ± standard deviation (SD), nonparametric measures as median (interquartile range [IQR]), and categorical variables as counts (%). Group differences were analyzed using χ² tests for categorical data. To identify independent predictors of transmission clustering, a multivariate logistic regression model was employed, incorporating variables that showed significance (p < 0.05) in χ² tests. Statistical significance was determined using two-tailed tests, with p-values < 0.05 considered statistically significant. Results 1. Basic characteristics A total of 138 sequences were identified as CRF119_0107 during 2019–2024. The study population had a mean age of 26.5 ± 8.0 years (range: 16–61) and were predominantly male (99.3%), unmarried (86.2%), college-educated (75.4%), and infected through homosexual transmission (84.8%). Initial CD4 counts were primarily 200–499 cells/µL (65.2%), and initial VL before ART predominantly ranged 10,000–99,999 copies/mL (49.3%) (Table 1 ). Cases were distributed across all 12 districts of Nanjing, with the highest proportions in Gulou District (23, 16.7%) and Jiangbei New Area (23, 16.7%), followed by Jiangning (18, 13.0%), Lishui (16, 11.6%), Qixia (13, 9.4%), Yuhuatai (11, 8.0%), Xuanwu (10, 7.2%), Qinhuai (10, 7.2%), Jianye (5, 3.6%), Liuhe (4, 2.9%), Gaochun District (3, 2.2%) and Pukou District (2, 1.4%). Table 1 Basic information on the molecular transmission network of newly reported HIV CRF119_0107 cases in Nanjing Variables Total(%) Clustering(%) Chi-square Test Multivariate Analysis χ 2 P value aOR(95% CI) P value Gender Male 137(99.3) 77(56.2) 1.000 * Female 1(0.7) 1(100.0) Age group (yrs) 16 ~ 24 71(51.4) 44(62.0) 1.728 0.184 ≥ 25 67(48.6) 34(50.7) Marital status Single 119(86.2) 66(55.5) 0.395 0.530 Others 19(13.8) 12(63.2) Education degree Senior or below 34(24.6) 21(61.8) College or above 104(75.4) 57(54.8) Location Urban area 72(52.2) 39(54.2) 5.619 0.060 1.000 0.693 Suburban area 43(31.2) 21(48.8) 0.688(0.292 ~ 1.622) Outer suburban area 23(16.7) 18(78.3) 0.882(0.158 ~ 4.920) Occupation Student 35(25.4) 22(62.9) 0.766 0.381 Others 103(74.6) 56(54.4) Transmission route Homosexual 117(84.8) 67(57.3) 4.920 0.085 Commercial heterosexual 7(5.1) 6(85.7) Non-commercial heterosexual 14(10.1) 5(35.7) Screening source VCT 77(55.8) 42(54.5) 0.859 0.651 Medical institution 53(38.4) 32(60.4) Others 8(5.8) 4(50.0) Initial CD4 counts (cells/µL) <200 21(15.2) 3(14.3) 22.294 < 0.001 1.000 < 0.001 200 ~ 499 90(65.2) 53(58.9) 7.578(1.950 ~ 29.452) ≥ 500 27(19.6) 22(81.5) 26.501(5.200 ~ 135.052) Initial viral load before ART (copies/mL) < 10000 20(14.5) 12(60.0) 0.699 0.705 10000 ~ 99999 68(49.3) 36(52.9) ≥ 100000 50(36.2) 30(60.0) TDR No 116(84.1) 58(50.0) 12.594 < 0.001 1.000 0.025 Yes 22(15.9) 20(90.9) 9.661(1.323 ~ 70.559) 2. Drug resistance of CRF119_0107 The CRF119_0107 exhibited a TDR rate of 15.9% (22/138), with resistance to NNRTIs observed in 15.2% (21/138) of cases and NRTIs resistance in 0.7% (1/138). Six mutations were identified: NNRTI-associated mutations K103N (14.4%), V179D (11.6%), and K103KN (0.7%), along with NRTI-associated mutations M41ML (1.4%), S68G (0.7%), and S68SN (0.7%). No PI-associated mutations were detected. The V179D mutation conferred potential resistance to efavirenz (EFV), etravirine (ETR), nevirapine (NVP), and rilpivirine (RPV), while K103N and K103KN mutations were associated with high-level resistance to EFV and NVP. The M41ML mutation induced low-level resistance to zidovudine (AZT) and stavudine (D4T), along with potential resistance to didanosine (DDI). Additionally, S68G and S68SN mutations did not demonstrate resistance to NRTI antiretrovirals (Table 2 ). Table 2 Genetic drug resistance mutations of HIV-1 CRF119_0107 strains in Nanjing Mutation site n(detection rate/%) ART drugs(Degree of resistance) NNRTIs K103N 20(14.4) EFV、NVP(H) V179D 16(11.6) EFV、ETR、NVP、RPV(P) K103KN 1(0.7) EFV、NVP(H) NRTIs M41ML 1(0.7) AZT、D4T(L),DDI(P) S68G 1(0.7) - S68SN 1(0.7) - 3. Characteristics and dynamic change of CRF119_0107 clusters We performed a sensitivity analysis spanning a spectrum of GD thresholds ranging from 0.005 to 0.015 substitutions/site (Additional file 1). At a 0.005 substitutions/site GD threshold, the total number of clusters within the molecular network reached its peak, with 78 sequences (56.6%, 78/138) forming 11 distinct clusters. The majority of clustering cases were male (98.8%), aged < 25 years (56.5%), unmarried (84.7%), college-educated (73.1%), and infected through homosexual transmission (85.9%). Cluster sizes ranged from 2 to 21 nodes, with a median node degree of 3 (IQR: 1–6). Transmission route analysis identified 67 nodes infected through homosexual transmission, 5 through commercial heterosexual transmission, and 6 through non-commercial heterosexual transmission, with corresponding median node degrees of 4 (IQR: 1–6), 2 (IQR: 1–2), and 3 (IQR: 1.25–4), respectively. Regarding the drug resistance, the network contained 20 TDR cases, including 19 NNRTI-resistant cases in cluster 1 (K103N: 18; K103KN: 1) and one NRTI-resistant case in cluster 7 (M41ML). Of particular concern, cluster 1 gained annual additions of 2, 6, 5, and 5 resistant infections from 2021 to 2024 and K103N mutation persistently spread in this cluster (Fig. 1 ). Four large clusters (Clusters 1–4) with 21, 14, 11, and 10 nodes, respectively, were identified, accounting for 70.6% (56/78) of all the clustering cases. These clusters were exclusively male, dominated by homosexual transmission (85.8%, 48/56), with minor contributions from commercial (8.9%, 5/56) and non-commercial (5.4%, 3/56) heterosexual transmission. Geographically, cluster 1 spanned eight districts, with over half (57.2%, 12/21) of cases in Lishui District and 61.5% (8/13) of its 2022–2024 additions in Lishui District. Clusters 2, 3, and 4 spanned nine, seven, and six districts, respectively (Fig. 1 ). The analysis of dynamic change revealed 48 nodes entering the network during 2019–2021 and 30 during 2022–2024.From 2022 to 2024, newly clustering nodes across 11 clusters exhibited a median growth of 2 (IQR: 0.5–2). Clusters 1 and 3 were classified as high-growth cluster, gaining 13 and 7 new clustering nodes, respectively, and presenting over 2 nodes/year growth. Clusters 2, 4, and 5 showed low-growth cluster, gaining 2, 1, and 2 new clustering nodes, respectively. Cluster 6, 7, and 8 were classified as stable clusters without new nodes added during 2022–2024, whereas clusters 9, 10, and 11 were identified as emerging clusters (Fig. 1 ). 4. Factors influencing HIV-1 CRF119_0107 molecular transmission network The χ² test revealed statistically significant differences in clustering rates based on initial CD4 counts and TDR status ( P < 0.05). Multivariable analysis revealed that cases with initial CD4 counts of 200–499 cells/µL ( OR = 7.58, 95% CI : 1.95–29.45) and ≥ 500 cells/µL ( OR = 26.50, 95% CI : 5.20–135.05) exhibited significantly higher risk of clustering compared to those with CD4 counts < 200 cells/µL. Additionally, TDR cases (a OR = 9.66, 95% CI : 1.32–70.56) were more likely to enter the network than those without TDR (Table 1). 5. Spatial analysis of HIV-1 CRF119_0107 molecular network The clustering cases were distributed across all 12 districts of Nanjing, with higher clustering cases observed in Lishui (13), Gulou (12), Jiangbei New District (10), and Jiangning (10) and higher clustering rates observed in Lishui (81.3%), Luhe (75.0%) and Qinhuai (70.0%). The standardized clustering rate was calculated according to the age composition of participants for each district, and analyzed for spatial autocorrelation. Global Moran’s I value revealed no significant spatial autocorrelation ( I = -0.121, P = 0.774), indicating a random distribution pattern at the district level (Fig. 2). Spatial connection patterns among newly reported HIV CRF119_0107 cases were analyzed across 12 districts in Nanjing using Sankey diagram of transmission links and transmission intensity matrices. Impressively, inter-district links accounted for 83.9% (260/310) and intra-district links only constituted 16.1% (50/310) of all the 310 links. Moreover, inter-district transmission predominated across all 12 districts, whereas Yuhuatai and Lishui District demonstrated mixed spatial connection pattern due to exhibition of 38.5% and 34.0% intra-district transmission. (Fig. 3, Table 3). Notably, except for geographically adjacent districts (between Gulou and Qixia, Lishui and Jiangning), strong inter-district transmission linkage was also observed even between geographically non-adjacent districts (between Qixia and Lishui) (Fig. 3). Table 3 Analysis of inter-district and intra-district links of newly reported HIV CRF119_0107 cases between 12 districts of Nanjing District Inter-district links Intra-district links Inter-district proportion Rank Jiangning 34 0 100 1 Jianye 23 0 100 1 Luhe 7 0 100 1 Pukou 5 0 100 1 Qixia 39 4 90.7 5 Qinhuai 17 2 89.5 6 Jiangbei 26 4 86.7 7 Xuanwu 13 2 86.7 7 Gulou 34 10 77.3 9 Gaochun 11 0 76.3 10 Lishui 35 18 66 11 Yuhuatai 16 10 61.5 12 Total 260 50 83.9 – Inter-district links: the number of links between cases in different districts; Intra-district links: the number of links between cases within the same district; Inter-district proportion: the proportion of inter-district links over all links (inter-district and intra-district). Discussion Our study revealed that newly reported HIV-1 CRF119_0107 infections predominantly occurred among MSM, characterized by a demographic profile of males aged < 25 years with high education. Since initial detection among MSM in 2017, CRF119_0107 has expanded beyond this population, with over one-seventh of cases now emerging in commercial heterosexual contacts and non-commercial heterosexual contacts. The HIV-1 transmission network identified 11 CRF119_0107 clusters with an overall clustering rate approaching 60%, exceeding rates for CRF01_AE(46.4%) and CRF07_BC (53.1%) observed in our historical study (2019–2021)[ 11 ]. The high rate illustrated a relatively active dissemination of CRF119_0107 in Nanjing. This rapid transmission was further evidenced by integration of a female CRF119_0107 case infected through non-commercial heterosexual transmission into Cluster 7, confirming cross-population dissemination of this subtype from MSM to other risk groups. The network exhibited a high proportion of young, unmarried, highly educated MSM, underscoring this population’s vulnerability to rapid transmission. These observations highlighted the importance of targeted interventions that address the key populations, such as young MSM. Furthermore, cases with high initial CD4 counts demonstrated significantly higher clustering rates compared to those with low counts, consisting with previous studies[ 28 ]. This finding suggested group with high CD4 counts may had persistent sexual activity and elevated secondary transmission risks, thereby facilitating network expansion. While late-presenting cases remain critical for mortality reduction, our data advocate for parallel surveillance intensification targeting behaviorally active populations with higher CD4 counts to disrupt the rapid dissemination of CRF119_0107. The molecular network analysis uncovered the presence of distinct transmission clusters and the emergence of super clusters or rapidly growing clusters, which offer a nuanced view of the transmission dynamics and guide the accurate prevention and control of HIV [ 29 , 30 ]. Our molecular network analysis determined four large CRF119_0107 clusters exhibiting hybrid transmission dynamics, predominantly MSM-driven with secondary contributions from commercial heterosexual contacts and non-commercial heterosexual contacts, reflecting intricate connectivity across risk groups in Nanjing's sexual networks. Dynamic change analysis identified two priority clusters (Clusters 1 and 3) demonstrating sustained expansion (> 2 nodes/year growth during 2022–2024), indicative of heightened transmission efficiency [ 31 , 32 ]. To optimize cost-efficiency in epidemic containment and attain ambitious incidence and mortality reduction targets by 2030, these high-growth clusters should prioritize implementation of three-tiered prevention measures as follows: (1) behavioral interventions promoting consistent condom utilization; (2) biomedical prophylaxis through timely post-exposure prophylaxis (PEP) or pre-exposure prophylaxis (PrEP) administration and rapid ART initiation; (3) contact management via partner notification for HIV/STI testing. Of particular concern, cluster 1 had several transmission linkages between homosexual contact and commercial heterosexual contact, and even a node infected through commercial heterosexual contact showed the highest value of degree, which implied that bridging population facilitated cross-risk-group spread as amplifier. These results were useful to find out key persons which should be monitored and intervened at an individual level, such as the apparent bridge nodes or highly linked nodes. Therefore, we will enhance tailored intervention targeting these key nodes, so as to effectively cut off transmission chain. Alarmingly, TDR prevalence of HIV-1 CRF119_0107 reached 15.9%, far higher than TDR prevalence of all newly reported HIV cases in Nanjing and the nationwide prevalence of TDR [ 4 , 24 , 33 ]. Meanwhile, it even exceeded the WHO-recommended high-alert threshold (> 15%) [ 34 ]. The worrying prevalence constitutes a major public health challenge, as TDR undermines therapeutic efficacy and accelerates resistance propagation within ART-naïve populations. Consistent with prior studies[ 4 ], NNRTI resistance remained the primary driver of TDR, accounting for 95% of resistant cases in our study. This study identified four key drug resistance mutations (DRMs) in CRF119_0107 strains: K103N, V179D, K103KN, and M41ML. K103N is the most common drug resistance mutation and severely reduce binding affinity of EFV and NVP to reverse transcriptase, conferring high-level resistance to these first-line NNRTI drugs and NNRTI-based ART failure[ 35 – 37 ]. V179D is a polymorphic accessory NNRTI-selected mutation. It contributes low-level reductions in susceptibility to each of the NNRTIs. The combination of V179D and K103R acts synergistically to reduce NVP and EFV susceptibility [ 38 ]. In this study, a single V179D mutation could result in potentially low-level HIV-1 resistance to the NNRTI, as previously found in Shanghai[ 39 ]. Our analysis determined a rapidly expanding TDR cluster, which comprised 19 resistant cases and contained nearly 95% of all the K103 mutations. This resistant cluster formed in 2021 and continued to expand during 2022–2024. That also explained 9.6 times higher risk of TDR cases entering the network compared to drug-sensitive cases. The persistent circulation of K103N-harboring strains threatens the efficacy of China’s current free ART regimen especially NVP/EFV-based regimen [ 36 , 37 ], highlighting the urgency of surveillance for drug-resistant transmission clusters and DRMs. In June 2023, China’s updated National Free ART Guidelines conditionally incorporated dolutegravir (DTG), a second-generation integrase strand transfer inhibitor (ISTI) with a high genetic barrier to resistance, into first-line regimens. Therefore, following the WHO recommendation[ 34 ], we urgently need to take a three-pronged action for CRF119_0107 cases: (1) implementation of real-time resistance surveillance; (2) urgent transition to WHO-recommended DTG-based ART; (3) enhanced VL monitoring through semiannual free VL testing and close follow-up of individuals with viral non-suppression, enabling prompt regimen adjustment for virological failure. These measures are critical to sustaining the long-term success of ART and further mitigating TDR proliferation. We spatialized HIV-1 genetic transmission network and analyzed spatial distribution of CRF119_0107 across districts. Geospatial mapping of HIV-1 transmission networks revealed that the network was distributed in all 12 districts of Nanjing and 4 large clusters spanned multiple districts. Spatial autocorrelation analysis demonstrated random distribution of clustering rates at the district level, indicative of widespread across 12 districts, non-focal transmission. However, Lishui District with relatively more clustering cases showed higher clustering rates, and even nearly six in ten in cluster1 were also distributed in Lishui District, thus Lishui was a priority intervention zone. In addition, we observed frequent inter-district transmission of CRF119_0107. The districts with strong transmission linkages (e.g., between Qixia and Gulou, between Jiangning and Lishui), especially those strong linkage between geographically distants districts (e.g.,between Qixia and Lishui) should be concerned. Consist with previous study, our study have demonstrated that spatial analysis of transmission network can be used to reveal spatial connection patterns [ 17 – 19 , 40 ]. These findings underscores that local health sectors in districts with strong linkages should strengthen inter-district coordination, information exchange and joint interventions for effective containment of CRF119_0107 transmission, aligning with WHO/UNAIDS recommendations for geo-targeted HIV control strategies. Hence, our cross-disciplinary method undoubtedly provides a novel framework for optimizing resource allocation: prioritizing districts of high clustering rates and enhancing joint intervention in districts of strong linkages. This study has several limitations. First, the sample size was constrained by the relatively recent emergence of CRF119_0107, which was initially identified in 2017 and has circulated in Nanjing for only six years. Nevertheless, we systematically collected all available CRF119_0107 sequences spanning this six-year period to comprehensively characterize its its transmission pattern and temporal growth patterns. Second, while its current transmission remains localized to Nanjing, the potential for inter-region spread through population mobility necessitates expanded surveillance across broader geographical areas to monitor potential cross-provincial dissemination. Future research priorities include implementing prospective genomic surveillance to expand the CRF119_0107 sequence dataset, and establishing real-time alerts for emerging drug resistance mutations throughout the Yangtze River Delta region. Conclusion We used cross-disciplinary approaches to disentangle the transmission pattern and dynamic change of CRF119_0107 at the local level. CRF119_0107 primarily circulated among highly educated young MSM and spread from MSM to other population. Sustained monitoring of CRF119_0107 strain is critical to track its evolving transmission patterns. At the individual level, real-time surveillance and rapid response mechanisms should prioritize high-growth or drug-resistant clusters. At the regional level, frequent inter-district transmission demand inter-district coordination, information-sharing and joint interventions especially in districts with strong linkages. Our cross-disciplinary approach provides an evidence-based framework for containing CRF119_0107 dissemination in Nanjing’s complex transmission context. Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the Ethics Committee of the Nanjing Center for Disease Control and Prevention (Approval No: PJ2020-A001-03). Participants provided written informed consent to participate in this study. Consent for publication Not applicable Availability of data and materials All sequences in this study have been deposited in the GenBase in National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation. The accession numbers can be obtained in additional file 2. All sequences can be found online at https://ngdc.cncb.ac.cn/genbase. Competing interests The authors declare that they have no competing interests. Funding This study was funded by Nanjing Medical Science and Technology Development Project (ZKX23059, YKK23192), Nanjing Preventive Medicine Research Project (NJYFKT202402), Innovation Program of Nanjing Institute of Public Health, Nanjing Medical University (NCX2403), Opening Foundation of Key Laboratory (JSHD202329) and Jiangsu Province Capability Improvement Project through Science, Technology and Education (ZDXYS202210). Authors' contributions ZZ and YX conceived and designed the study. YX, HS and XL analyzed and wrote the manuscript. MQ and DX provided laboratory supports. TJ , XY, RW, JW and XZ performed investigation, data collection and data cleansing. All authors read and approved the final manuscript. Acknowledgments We are highly grateful for Centers for Disease Control and Prevention in 12 districts of Nanjing for all the support to conduct this study. We thank all the participants for their participation in this study and our colleagues for their support. References Nasir A, Dimitrijevic M, Romero-Severson E, Leitner T: Large Evolutionary Rate Heterogeneity among and within HIV-1 Subtypes and CRFs . 2021, 13 (9). Yin Y, Liu Y, Zhu J, Hong X, Yuan R, Fu G, Zhou Y, Wang B: The prevalence, temporal trends, and geographical distribution of HIV-1 subtypes among men who have sex with men in China: A systematic review and meta-analysis . Epidemiology and infection 2019, 147 :e83. Li X, Li W, Zhong P, Fang K, Zhu K, Musa TH, Song Y, Du G, Gao R, Guo Y et al : Nationwide Trends in Molecular Epidemiology of HIV-1 in China . AIDS research and human retroviruses 2016, 32 (9):851-859. 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Dávila-Conn V, García-Morales C, Matías-Florentino M, López-Ortiz E, Paz-Juárez HE, Beristain-Barreda Á, Cárdenas-Sandoval M, Tapia-Trejo D, López-Sánchez DM, Becerril-Rodríguez M et al : Characteristics and growth of the genetic HIV transmission network of Mexico City during 2020 . 2021, 24 (11):e25836. Zuo L, Liu K, Liu H, Hu Y, Zhang Z, Qin J, Xu Q, Peng K, Jin X, Wang JH et al : Trend of HIV-1 drug resistance in China: A systematic review and meta-analysis of data accumulated over 17 years (2001-2017) . EClinicalMedicine 2020, 18 :100238. HIV drug resistance report 2021 . Ouyang F, Yuan D, Zhai W, Liu S, Zhou Y: HIV-1 Drug Resistance Detected by Next-Generation Sequencing among ART-Naïve Individuals: A Systematic Review and Meta-Analysis . 2024, 16 (2). Li JZ, Paredes R, Ribaudo HJ, Svarovskaia ES, Metzner KJ, Kozal MJ, Hullsiek KH, Balduin M, Jakobsen MR, Geretti AM et al : Low-frequency HIV-1 drug resistance mutations and risk of NNRTI-based antiretroviral treatment failure: a systematic review and pooled analysis . Jama 2011, 305 (13):1327-1335. Mbunkah HA, Bertagnolio S, Hamers RL, Hunt G, Inzaule S, Rinke De Wit TF, Paredes R, Parkin NT, Jordan MR, Metzner KJ: Low-Abundance Drug-Resistant HIV-1 Variants in Antiretroviral Drug-Naive Individuals: A Systematic Review of Detection Methods, Prevalence, and Clinical Impact . The Journal of infectious diseases 2020, 221 (10):1584-1597. Liu Y, Zhang Y, Li H, Wang X, Jia L, Han J, Li T, Li J, Li L: Natural presence of the V179D and K103R/V179D mutations associated with resistance to nonnucleoside reverse transcriptase inhibitors in HIV-1 CRF65_cpx strains . BMC infectious diseases 2020, 20 (1):313. Wang Z, Zhang M, Wang J, Liu L, Chen J, Zhang R, Tang Y, Shen Y, Qi T, Song W et al : Efficacy of Efavirenz-Based Regimen in Antiretroviral-Naïve Patients with HIV-1 V179D/E Mutations in Shanghai, China . Infectious diseases and therapy 2023, 12 (1):245-255. Xu Y, Jiang T, Jiang L, Shi H, Li X, Qiao M, Wu S, Wu R, Yuan X, Wang J et al : Combining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China . Virology journal 2024, 21 (1):218. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file 1. Sensitivity analysis graph of genetic distance thresholds. Additionalfile2.docx Additional file 2. The Genbase accession numbers of all the 138 CRF119_0107 sequences. Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Virology Journal → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 28 Jul, 2025 Reviews received at journal 28 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 28 Apr, 2025 Submission checks completed at journal 28 Apr, 2025 First submitted to journal 27 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6539216","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483394337,"identity":"871c84d4-aff9-40fd-80ca-1e7e7afbf6bb","order_by":0,"name":"Yuanyuan Xu","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Xu","suffix":""},{"id":483394338,"identity":"472fcfad-93c0-4550-b7e6-99d0e936c847","order_by":1,"name":"Hongjie Shi","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Hongjie","middleName":"","lastName":"Shi","suffix":""},{"id":483394339,"identity":"fe16a2fe-e3fd-466c-a06c-f2d770ca0fdc","order_by":2,"name":"Xin Li","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":483394340,"identity":"53cbf48a-bbd9-4c69-8c0a-85297fd562d5","order_by":3,"name":"Tingyi Jiang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingyi","middleName":"","lastName":"Jiang","suffix":""},{"id":483394341,"identity":"05a29d95-7346-4468-9fe6-4d35d4c79398","order_by":4,"name":"Mengkai Qiao","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Mengkai","middleName":"","lastName":"Qiao","suffix":""},{"id":483394342,"identity":"c7ffad64-26f1-460e-b44f-197ecf1f221a","order_by":5,"name":"Dandan Xu","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Xu","suffix":""},{"id":483394343,"identity":"88b3838d-f834-4c42-a4d9-3a2290ec9786","order_by":6,"name":"Rong Wu","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wu","suffix":""},{"id":483394344,"identity":"088f9590-9c11-4c26-8059-a92e1e03e6f3","order_by":7,"name":"Xin Yuan","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yuan","suffix":""},{"id":483394345,"identity":"b22a4ac0-1640-4dfd-b500-2746b45769f4","order_by":8,"name":"Jingwen Wang","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Wang","suffix":""},{"id":483394346,"identity":"4ddf5f72-7f29-45de-b6e7-7f77688947d8","order_by":9,"name":"Xiajie Zhou","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xiajie","middleName":"","lastName":"Zhou","suffix":""},{"id":483394347,"identity":"4b7f0229-cf62-43d9-bc39-6d4002ea182d","order_by":10,"name":"Zhengping Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYNACHgbGBvbGxocfSNPCc7jZWIIUexgbJNLbBHiIUSrff/YAc4GMnWz/zIdtDBIMdnK6DQRNP5fAPIMn2XjG7cS2BwUMycZmBwhoYWbsMWDm4TmQuEE6sd1AguFA4jZCWtiYeaBaJA+2SfAQo4WHDaZFgpFILRI8fAlALUC/nEkEBrIBEX4BhxhvDzDE2o8/fPihwk6OoBag09h/MPbAOAYElYO1APEPolSOglEwCkbBSAUAO1Q6grJcgRoAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Zhengping","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-04-27 08:53:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6539216/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6539216/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12985-025-02932-2","type":"published","date":"2025-09-26T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86664641,"identity":"bdbc1e44-17f8-458d-bee4-4fa35da52092","added_by":"auto","created_at":"2025-07-14 10:55:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1882406,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular network characteristics of newly reported HIV CRF119_0107 cases in Nanjing. (A): Demographic characteristics; (B): Drug resistance; (C): Dynamic change.\u003c/p\u003e\n\u003cp\u003eNRTI, nucleoside reverse transcriptase inhibitors; NNRTI, non-nucleoside reverse transcriptase inhibitors; Sensitive, sensitive to antiretroviral drugs.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6539216/v1/ea36176aca4fb2318a7ddeb1.png"},{"id":86664646,"identity":"e0e9df29-56d7-4112-a7a3-d30363db9568","added_by":"auto","created_at":"2025-07-14 10:55:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2480971,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution graph of molecular transmission network of newly reported HIV CRF119_0107 cases in Nanjing.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6539216/v1/da240ebe62a85b71ecf844e4.png"},{"id":86665386,"identity":"9e616fa4-e259-469c-b989-5c88cabdb874","added_by":"auto","created_at":"2025-07-14 11:03:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186249,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram (A) and intensity matrices (B) of transmission links observed in HIV CRF119_0107 transmission network between 12 districts of Nanjing.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6539216/v1/af1ff1084294fa74594a9eba.png"},{"id":92430409,"identity":"ca629271-e8ca-4f9c-8f4c-a41d3f7ad7b3","added_by":"auto","created_at":"2025-09-29 16:00:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7552509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6539216/v1/3724829b-87c2-4a8e-b8ca-8b6fa898be5a.pdf"},{"id":86664644,"identity":"78fc2a1f-666f-4d4b-b8e7-4c79c6307fb5","added_by":"auto","created_at":"2025-07-14 10:55:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16323,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1. Sensitivity analysis graph of genetic distance thresholds.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6539216/v1/feb05ed7218d625a8413760b.docx"},{"id":86664643,"identity":"ab8ba1ba-4ec9-4d84-90bb-7e08c8381c8d","added_by":"auto","created_at":"2025-07-14 10:55:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19827,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2. The Genbase accession numbers of all the 138 CRF119_0107 sequences.\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6539216/v1/9e4178495c734c74f29304e8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"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","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDue to high mutation rate, rapid replication dynamics, and frequent dual infection/superinfection events, HIV recombinant forms are being increasingly complex and unique recombinant forms (URFs) are continually emerging each year[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Los Alamos HIV Sequence Database reported that at least 158 circulating recombinant forms (CRFs) have been confirmed globally, including 157 HIV-1 CRFs and one HIV-2 CRF. Notably, China has reported over 50 distinct CRFs and they predominantly circulated in sexual contact population, particularly men who have sex with men (MSM). This epidemiological pattern indicates intense HIV-1 recombination activity in China. In recent years, CRF01_AE and CRF07_BC have been the dominant recombinant strains nationally [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Through sustained viral evolution and transmission, dual infection of CRF01_AE and CRF07_BC among MSM have facilitated emergence of mutiple second-generation recombinants. Multiple such strains have been reported nationwide in recent years, such as CRF117_0107, CRF123_0107, CRF136_0107, CRF 163_0107 and so on [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. CRF119_0107, as a second-generation recombinant of CRF01_AE and CRF07_BC, was first detected among MSM in Nanjing in 2017 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. CRF119_0107 has rapidly ascended to become local third most prevalent HIV-1 strain, representing 6.29% in recent study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, HIV-1 molecular transmission network has emerged as a novel methodology for investigating transmission patterns among HIV-infected populations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. By leveraging the genetic sequence similarity among infected individuals, this network is constructed based on the principle that smaller genetic distances correspond to higher genetic similarities, thereby reflecting potential transmission relationships between HIV-1 individuals. Such networks provide critical insights into the transmission dynamics of HIV-1, enabling identification of key transmission clusters, drug resistance transmission clusters, and associated risk factors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Current research on HIV/AIDS mainly focuses on etiology, epidemiology, clinical features, prevention and control, and medical treatment, often neglecting the spatial attributes of HIV/AIDS transmission, which results in incomplete epidemiological understanding. Spatial epidemiological studies consistently demonstrate that the emergence, transmission patterns, and distribution of infectious diseases are closely linked to geographical and spatial attributes [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given that molecular transmission networks alone cannot capture spatial characteristics, the joint analysis of molecular and spatial epidemiology enables a deeper understanding of the active areas of the transmission network from a spatial dimension, providing an evidence for optimizing regional HIV prevention strategies and healthcare resource allocation[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs the provincial capital of Jiangsu and a pivotal economic hub within China's Yangtze River Delta region, Nanjing possesses distinct geographical features that profoundly influence its HIV transmission pattern. By the end of 2024, the HIV infection rate of population in Nanjing was over 0.07%, maintaining the province's highest case burden. Contrasting with the national predominance of heterosexual transmission[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], homosexual transmission has been the most frequent transmission route of HIV-1 in Nanjing, accounting for more than 68% of newly reported cases[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, MSM and even male students who have sex with men both have stable high HIV prevalence [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nanjing confronts multifaceted challenges characterized by severe HIV epidemic in MSM, viral diversity and escalating transmitted drug resistance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, newly reported HIV cases identified as CRF119_0107 in Nanjing were selected to construct transmission network. We combined the spatial epidemiology with molecular network analysis to systematically elucidate transmission pattern and dynamic change, spatial characteristics of transmission clusters and links, along with drug resistance prevalence. These evidence-based insights will establish a foundation for implementing geographically targeted prevention strategies and optimizing antiretroviral therapy (ART) regimens to curb CRF119_0107 dissemination in the Nanjing.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy subjects\u003c/h2\u003e\u003cp\u003eIn our study, 138 newly reported HIV-1 infections between January 1, 2019, and December 31, 2024 were enrolled. The inclusion criteria were as follows: (1) Confirmed diagnosis of HIV-1 infection; (2) Residence in Nanjing at diagnosis; (3) Without history of ART; (4) Identified as CRF119_0107.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample collection and data acquisition\u003c/h3\u003e\n\u003cp\u003eAfter informed consent was obtained, peripheral blood samples (5\u0026ndash;10 mL) were collected by venipuncture into EDTA anticoagulant tubes. Samples were centrifuged at 1500 rotations per minute for 15 minutes to separate plasma, which was then stored at -80\u0026deg;C freezer until further analysis. Demographic data, including age, gender, and residential district, along with clinical parameters including routes of transmission, screening source, initial CD4\u0026thinsp;+\u0026thinsp;T lymphocyte cells (CD4) count, and initial viral load (VL) before ART were extracted from the National AIDS Prevention and Control Basic Information System.\u003c/p\u003e\n\u003ch3\u003eHIV-1 RNA extraction, amplification and sequencing\u003c/h3\u003e\n\u003cp\u003eHIV-1 RNA was extracted from 200 \u0026micro;L plasma samples using the QIAamp Viral RNA Mini Kit (Qiagen, Hilden,Germany) following the manufacturer's instructions. A nested reverse transcription-polymerase chain reaction (RT-PCR) was performed to amplify the HIV-1 pol gene fragment (HXB2:2253\u0026ndash;3313) as previously described [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The PCR products were dealt with electrophoresis with 1% agarose gel, and the amplified positive products were purified and sequenced by Sangon Biotechnology Co., Ltd. The resulting sequence database was curated, excluding duplicate sequence, as well as sequences not compliant with quality control [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].Specifically, sequences exhibiting inadequate length, presence of stop codons, bad insertions/deletions and hyper-mutations were excluded to ensure analytical validity.\u003c/p\u003e\n\u003ch3\u003eSubtype identification\u003c/h3\u003e\n\u003cp\u003eSequences were edited, trimmed, and assembled using Sequencer 4.10.1 software (GeneCodes, Ann Arbor, MI). A comprehensive reference dataset encompassing major epidemic clades A-D, F-H, and J-K, along with prevalent Chinese CRFs, was downloaded from the Los Alamos HIV Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hiv.lanl.gov/content/index\u003c/span\u003e\u003cspan address=\"https://www.hiv.lanl.gov/content/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Sequences were aligned against reference strains using BioEdit 7.0.9 (Informer Technologies Inc.), followed by phylogenetic analysis. Maximum likelihood (ML) phylogenetic tree was constructed using FastTree 2.1 software under GTR model. The phylogenetic framework was validated using bootstrap resampling (1,000 iterations) and a bootstrap value\u0026thinsp;\u0026gt;\u0026thinsp;80% was used to determine subtype classification.\u003c/p\u003e\n\u003ch3\u003eDrug resistance analysis\u003c/h3\u003e\n\u003cp\u003eAll sequences were submitted to HIV Drug Resistance Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hivdb.stanford.edu/hivdb/by-sequences/\u003c/span\u003e\u003cspan address=\"https://hivdb.stanford.edu/hivdb/by-sequences/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify Drug resistant mutation (DRMs) against nucleoside/nonnucleoside reverse transcriptase inhibitors (NRTIs/NNRTIs) and protease inhibitors (PIs). Drug resistant levels were categorized using the Stanford HIVDB scoring system as follows: Sensitive (S, 0\u0026ndash;9); Potential resistance (P, 10\u0026ndash;14); Low-level resistance (L, 15\u0026ndash;29); Intermediate-level resistance (I, 30\u0026ndash;59); High-level resistance (H, \u0026ge;\u0026thinsp;60). Transmitted drug resistance (TDR) was defined as low-level resistance or higher in ART-na\u0026iuml;ve individuals.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMolecular network construction and cluster analysis\u003c/h2\u003e\u003cp\u003ePairwise genetic distances (GD) were calculated via the Tamura-Nei 93 model. The optimal genetic distance threshold of 0.005 substitutions/site was selected based on sensitivity analysis ranged from 0.005 to 0.015 substitutions/site, to construct molecular transmission networks via HIV-TRACE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://veg.github.io./hivtrace-viz/\u003c/span\u003e\u003cspan address=\"https://veg.github.io./hivtrace-viz/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. According to Technical Guideline for HIV Transmission Network Monitoring and Intervention (revision in 2021) released by Chinese Center for Disease Control and Prevention, nodes represent HIV sequences or individuals, edges denote potential transmission relationship between nodes. Node degree quantifies the number of connections per node. In ou study, degree was calculated to quantify transmission complexity and large clusters were defined as those containing more than 10 nodes. We classified dynamic change of clusters as follows:(1) High-growth cluster: comprising\u0026thinsp;\u0026ge;\u0026thinsp;2 cases diagnosed in 2019\u0026ndash;2021, and \u0026ge;\u0026thinsp;3 cases diagnosed in 2022\u0026ndash;2024; (2) Low-growth cluster: comprising\u0026thinsp;\u0026ge;\u0026thinsp;2 cases diagnosed in 2019\u0026ndash;2021, and \u0026lt;\u0026thinsp;3 cases diagnosed in 2022\u0026ndash;2024; (3) Stable cluster: all pre-2022 cases; (4) Emerging cluster: those formed during 2022\u0026ndash;2024.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpatial autocorrelation analysis\u003c/h3\u003e\n\u003cp\u003eThe Global Moran\u0026rsquo;s I, a widely used spatial autocorrelation metric, was employed to assess whether there was spatial aggregation. If I\u0026thinsp;\u0026gt;\u0026thinsp;0, it indicted there was a positive spatial correlation, reflecting a clustered distribution; if I\u0026thinsp;=\u0026thinsp;0, it suggested that there was no spatial autocorrelation, implying random spatial distribution. if I\u0026thinsp;\u0026lt;\u0026thinsp;0, it indicted that there was a negative spatial autocorrelation, showing a discrete distribution[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Statistical significance of observed spatial autocorrelation was evaluated using one-sample z-tests (α\u0026thinsp;=\u0026thinsp;0.05). All the spatial descriptions and analyses were performed in ArcGIS 10.3. To reflect HIV transmission types geographically, the proportion of intra-district transmission in each district was calculated by dividing the number of links between cases in the district by the total number of links with any cases in the district, while the remaining proportion was referred to as the proportion of inter-district transmission. Additionally, the transmission links of CRF119_0107 was visualized and colored differently in intensity matrices and sankey diagram. The color of the grid cell at the intersection of two districts in an intensity matrix represented the number of links between the cases in these two districts. Sankey diagrams visualized the intensity of inter-district and intra-district HIV transmission by scaling the flow width by the number of links, which were consistent with values in the intensity matrices[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed in SPSS Statistics 18.0 (IBM Corporation, Armonk, NY). Continuous variables with normal distributions were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), nonparametric measures as median (interquartile range [IQR]), and categorical variables as counts (%). Group differences were analyzed using χ\u0026sup2; tests for categorical data. To identify independent predictors of transmission clustering, a multivariate logistic regression model was employed, incorporating variables that showed significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in χ\u0026sup2; tests. Statistical significance was determined using two-tailed tests, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e1. Basic characteristics\u003c/h2\u003e\u003cp\u003eA total of 138 sequences were identified as CRF119_0107 during 2019\u0026ndash;2024. The study population had a mean age of 26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0 years (range: 16\u0026ndash;61) and were predominantly male (99.3%), unmarried (86.2%), college-educated (75.4%), and infected through homosexual transmission (84.8%). Initial CD4 counts were primarily 200\u0026ndash;499 cells/\u0026micro;L (65.2%), and initial VL before ART predominantly ranged 10,000\u0026ndash;99,999 copies/mL (49.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cases were distributed across all 12 districts of Nanjing, with the highest proportions in Gulou District (23, 16.7%) and Jiangbei New Area (23, 16.7%), followed by Jiangning (18, 13.0%), Lishui (16, 11.6%), Qixia (13, 9.4%), Yuhuatai (11, 8.0%), Xuanwu (10, 7.2%), Qinhuai (10, 7.2%), Jianye (5, 3.6%), Liuhe (4, 2.9%), Gaochun District (3, 2.2%) and Pukou District (2, 1.4%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic information on the molecular transmission network of newly reported HIV CRF119_0107 cases in Nanjing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eClustering(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eChi-square Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMultivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eaOR(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137(99.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77(56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group (yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u0026thinsp;~\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71(51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44(62.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67(48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34(50.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119(86.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66(55.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19(13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(61.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104(75.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57(54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72(52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39(54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuburban area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43(31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.688(0.292\u0026thinsp;~\u0026thinsp;1.622)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOuter suburban area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23(16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(78.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.882(0.158\u0026thinsp;~\u0026thinsp;4.920)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103(74.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransmission route\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomosexual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117(84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67(57.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial heterosexual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(85.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-commercial heterosexual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14(10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScreening source\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77(55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42(54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical institution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53(38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32(60.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eInitial CD4 counts (cells/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21(15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e200\u0026thinsp;~\u0026thinsp;499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90(65.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53(58.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e7.578(1.950\u0026thinsp;~\u0026thinsp;29.452)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27(19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e26.501(5.200\u0026thinsp;~\u0026thinsp;135.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eInitial viral load before ART (copies/mL)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10000\u0026thinsp;~\u0026thinsp;99999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68(49.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36(52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;100000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50(36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116(84.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22(15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(90.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e9.661(1.323\u0026thinsp;~\u0026thinsp;70.559)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2. Drug resistance of CRF119_0107\u003c/h2\u003e\u003cp\u003eThe CRF119_0107 exhibited a TDR rate of 15.9% (22/138), with resistance to NNRTIs observed in 15.2% (21/138) of cases and NRTIs resistance in 0.7% (1/138). Six mutations were identified: NNRTI-associated mutations K103N (14.4%), V179D (11.6%), and K103KN (0.7%), along with NRTI-associated mutations M41ML (1.4%), S68G (0.7%), and S68SN (0.7%). No PI-associated mutations were detected. The V179D mutation conferred potential resistance to efavirenz (EFV), etravirine (ETR), nevirapine (NVP), and rilpivirine (RPV), while K103N and K103KN mutations were associated with high-level resistance to EFV and NVP. The M41ML mutation induced low-level resistance to zidovudine (AZT) and stavudine (D4T), along with potential resistance to didanosine (DDI). Additionally, S68G and S68SN mutations did not demonstrate resistance to NRTI antiretrovirals (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic drug resistance mutations of HIV-1 CRF119_0107 strains in Nanjing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMutation site\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en(detection rate/%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eART drugs(Degree of resistance)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNNRTIs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK103N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEFV、NVP(H)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV179D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEFV、ETR、NVP、RPV(P)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK103KN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEFV、NVP(H)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRTIs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM41ML\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAZT、D4T(L),DDI(P)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS68G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS68SN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3. Characteristics and dynamic change of CRF119_0107 clusters\u003c/h2\u003e\u003cp\u003eWe performed a sensitivity analysis spanning a spectrum of GD thresholds ranging from 0.005 to 0.015 substitutions/site (Additional file 1). At a 0.005 substitutions/site GD threshold, the total number of clusters within the molecular network reached its peak, with 78 sequences (56.6%, 78/138) forming 11 distinct clusters. The majority of clustering cases were male (98.8%), aged\u0026thinsp;\u0026lt;\u0026thinsp;25 years (56.5%), unmarried (84.7%), college-educated (73.1%), and infected through homosexual transmission (85.9%). Cluster sizes ranged from 2 to 21 nodes, with a median node degree of 3 (IQR: 1\u0026ndash;6). Transmission route analysis identified 67 nodes infected through homosexual transmission, 5 through commercial heterosexual transmission, and 6 through non-commercial heterosexual transmission, with corresponding median node degrees of 4 (IQR: 1\u0026ndash;6), 2 (IQR: 1\u0026ndash;2), and 3 (IQR: 1.25\u0026ndash;4), respectively. Regarding the drug resistance, the network contained 20 TDR cases, including 19 NNRTI-resistant cases in cluster 1 (K103N: 18; K103KN: 1) and one NRTI-resistant case in cluster 7 (M41ML). Of particular concern, cluster 1 gained annual additions of 2, 6, 5, and 5 resistant infections from 2021 to 2024 and K103N mutation persistently spread in this cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFour large clusters (Clusters 1\u0026ndash;4) with 21, 14, 11, and 10 nodes, respectively, were identified, accounting for 70.6% (56/78) of all the clustering cases. These clusters were exclusively male, dominated by homosexual transmission (85.8%, 48/56), with minor contributions from commercial (8.9%, 5/56) and non-commercial (5.4%, 3/56) heterosexual transmission. Geographically, cluster 1 spanned eight districts, with over half (57.2%, 12/21) of cases in Lishui District and 61.5% (8/13) of its 2022\u0026ndash;2024 additions in Lishui District. Clusters 2, 3, and 4 spanned nine, seven, and six districts, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe analysis of dynamic change revealed 48 nodes entering the network during 2019\u0026ndash;2021 and 30 during 2022\u0026ndash;2024.From 2022 to 2024, newly clustering nodes across 11 clusters exhibited a median growth of 2 (IQR: 0.5\u0026ndash;2). Clusters 1 and 3 were classified as high-growth cluster, gaining 13 and 7 new clustering nodes, respectively, and presenting over 2 nodes/year growth. Clusters 2, 4, and 5 showed low-growth cluster, gaining 2, 1, and 2 new clustering nodes, respectively. Cluster 6, 7, and 8 were classified as stable clusters without new nodes added during 2022\u0026ndash;2024, whereas clusters 9, 10, and 11 were identified as emerging clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003e4. Factors influencing HIV-1 CRF119_0107 molecular transmission network\u003c/h2\u003e\n\u003cp\u003eThe χ² test revealed statistically significant differences in clustering rates based on initial CD4 counts and TDR status (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Multivariable analysis revealed that cases with initial CD4 counts of 200–499 cells/µL (\u003cem\u003eOR\u003c/em\u003e = 7.58, 95% \u003cem\u003eCI\u003c/em\u003e: 1.95–29.45) and ≥ 500 cells/µL (\u003cem\u003eOR\u003c/em\u003e = 26.50, 95% \u003cem\u003eCI\u003c/em\u003e: 5.20–135.05) exhibited significantly higher risk of clustering compared to those with CD4 counts \u0026lt; 200 cells/µL. Additionally, TDR cases (a\u003cem\u003eOR\u003c/em\u003e = 9.66, 95% \u003cem\u003eCI\u003c/em\u003e: 1.32–70.56) were more likely to enter the network than those without TDR (Table 1).\u003c/p\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e5. Spatial analysis of HIV-1 CRF119_0107 molecular network\u003c/h2\u003e\n \u003cp\u003eThe clustering cases were distributed across all 12 districts of Nanjing, with higher clustering cases observed in Lishui (13), Gulou (12), Jiangbei New District (10), and Jiangning (10) and higher clustering rates observed in Lishui (81.3%), Luhe (75.0%) and Qinhuai (70.0%). The standardized clustering rate was calculated according to the age composition of participants for each district, and analyzed for spatial autocorrelation. Global Moran’s I value revealed no significant spatial autocorrelation (\u003cem\u003eI\u003c/em\u003e = -0.121, \u003cem\u003eP\u003c/em\u003e = 0.774), indicating a random distribution pattern at the district level (Fig. 2).\u003c/p\u003e\n \u003cp\u003eSpatial connection patterns among newly reported HIV CRF119_0107 cases were analyzed across 12 districts in Nanjing using Sankey diagram of transmission links and transmission intensity matrices. Impressively, inter-district links accounted for 83.9% (260/310) and intra-district links only constituted 16.1% (50/310) of all the 310 links. Moreover, inter-district transmission predominated across all 12 districts, whereas Yuhuatai and Lishui District demonstrated mixed spatial connection pattern due to exhibition of 38.5% and 34.0% intra-district transmission. (Fig. 3, Table 3). Notably, except for geographically adjacent districts (between Gulou and Qixia, Lishui and Jiangning), strong inter-district transmission linkage was also observed even between geographically non-adjacent districts (between Qixia and Lishui) (Fig. 3).\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of inter-district and intra-district links of newly reported HIV CRF119_0107 cases between 12 districts of Nanjing\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInter-district links\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntra-district links\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInter-district proportion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJiangning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJianye\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuhe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePukou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQixia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQinhuai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJiangbei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXuanwu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGulou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGaochun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLishui\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYuhuatai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e–\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003eInter-district links: the number of links between cases in different districts; Intra-district links: the number of links between cases within the same district; Inter-district proportion: the proportion of inter-district links over all links (inter-district and intra-district).\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study revealed that newly reported HIV-1 CRF119_0107 infections predominantly occurred among MSM, characterized by a demographic profile of males aged\u0026thinsp;\u0026lt;\u0026thinsp;25 years with high education. Since initial detection among MSM in 2017, CRF119_0107 has expanded beyond this population, with over one-seventh of cases now emerging in commercial heterosexual contacts and non-commercial heterosexual contacts. The HIV-1 transmission network identified 11 CRF119_0107 clusters with an overall clustering rate approaching 60%, exceeding rates for CRF01_AE(46.4%) and CRF07_BC (53.1%) observed in our historical study (2019\u0026ndash;2021)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The high rate illustrated a relatively active dissemination of CRF119_0107 in Nanjing. This rapid transmission was further evidenced by integration of a female CRF119_0107 case infected through non-commercial heterosexual transmission into Cluster 7, confirming cross-population dissemination of this subtype from MSM to other risk groups. The network exhibited a high proportion of young, unmarried, highly educated MSM, underscoring this population\u0026rsquo;s vulnerability to rapid transmission. These observations highlighted the importance of targeted interventions that address the key populations, such as young MSM. Furthermore, cases with high initial CD4 counts demonstrated significantly higher clustering rates compared to those with low counts, consisting with previous studies[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This finding suggested group with high CD4 counts may had persistent sexual activity and elevated secondary transmission risks, thereby facilitating network expansion. While late-presenting cases remain critical for mortality reduction, our data advocate for parallel surveillance intensification targeting behaviorally active populations with higher CD4 counts to disrupt the rapid dissemination of CRF119_0107.\u003c/p\u003e\u003cp\u003eThe molecular network analysis uncovered the presence of distinct transmission clusters and the emergence of super clusters or rapidly growing clusters, which offer a nuanced view of the transmission dynamics and guide the accurate prevention and control of HIV [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our molecular network analysis determined four large CRF119_0107 clusters exhibiting hybrid transmission dynamics, predominantly MSM-driven with secondary contributions from commercial heterosexual contacts and non-commercial heterosexual contacts, reflecting intricate connectivity across risk groups in Nanjing's sexual networks. Dynamic change analysis identified two priority clusters (Clusters 1 and 3) demonstrating sustained expansion (\u0026gt;\u0026thinsp;2 nodes/year growth during 2022\u0026ndash;2024), indicative of heightened transmission efficiency [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To optimize cost-efficiency in epidemic containment and attain ambitious incidence and mortality reduction targets by 2030, these high-growth clusters should prioritize implementation of three-tiered prevention measures as follows: (1) behavioral interventions promoting consistent condom utilization; (2) biomedical prophylaxis through timely post-exposure prophylaxis (PEP) or pre-exposure prophylaxis (PrEP) administration and rapid ART initiation; (3) contact management via partner notification for HIV/STI testing. Of particular concern, cluster 1 had several transmission linkages between homosexual contact and commercial heterosexual contact, and even a node infected through commercial heterosexual contact showed the highest value of degree, which implied that bridging population facilitated cross-risk-group spread as amplifier. These results were useful to find out key persons which should be monitored and intervened at an individual level, such as the apparent bridge nodes or highly linked nodes. Therefore, we will enhance tailored intervention targeting these key nodes, so as to effectively cut off transmission chain.\u003c/p\u003e\u003cp\u003eAlarmingly, TDR prevalence of HIV-1 CRF119_0107 reached 15.9%, far higher than TDR prevalence of all newly reported HIV cases in Nanjing and the nationwide prevalence of TDR [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Meanwhile, it even exceeded the WHO-recommended high-alert threshold (\u0026gt;\u0026thinsp;15%) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The worrying prevalence constitutes a major public health challenge, as TDR undermines therapeutic efficacy and accelerates resistance propagation within ART-na\u0026iuml;ve populations. Consistent with prior studies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], NNRTI resistance remained the primary driver of TDR, accounting for 95% of resistant cases in our study. This study identified four key drug resistance mutations (DRMs) in CRF119_0107 strains: K103N, V179D, K103KN, and M41ML. K103N is the most common drug resistance mutation and severely reduce binding affinity of EFV and NVP to reverse transcriptase, conferring high-level resistance to these first-line NNRTI drugs and NNRTI-based ART failure[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. V179D is a polymorphic accessory NNRTI-selected mutation. It contributes low-level reductions in susceptibility to each of the NNRTIs. The combination of V179D and K103R acts synergistically to reduce NVP and EFV susceptibility [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study, a single V179D mutation could result in potentially low-level HIV-1 resistance to the NNRTI, as previously found in Shanghai[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur analysis determined a rapidly expanding TDR cluster, which comprised 19 resistant cases and contained nearly 95% of all the K103 mutations. This resistant cluster formed in 2021 and continued to expand during 2022\u0026ndash;2024. That also explained 9.6 times higher risk of TDR cases entering the network compared to drug-sensitive cases. The persistent circulation of K103N-harboring strains threatens the efficacy of China\u0026rsquo;s current free ART regimen especially NVP/EFV-based regimen [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], highlighting the urgency of surveillance for drug-resistant transmission clusters and DRMs. In June 2023, China\u0026rsquo;s updated National Free ART Guidelines conditionally incorporated dolutegravir (DTG), a second-generation integrase strand transfer inhibitor (ISTI) with a high genetic barrier to resistance, into first-line regimens. Therefore, following the WHO recommendation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], we urgently need to take a three-pronged action for CRF119_0107 cases: (1) implementation of real-time resistance surveillance; (2) urgent transition to WHO-recommended DTG-based ART; (3) enhanced VL monitoring through semiannual free VL testing and close follow-up of individuals with viral non-suppression, enabling prompt regimen adjustment for virological failure. These measures are critical to sustaining the long-term success of ART and further mitigating TDR proliferation.\u003c/p\u003e\u003cp\u003eWe spatialized HIV-1 genetic transmission network and analyzed spatial distribution of CRF119_0107 across districts. Geospatial mapping of HIV-1 transmission networks revealed that the network was distributed in all 12 districts of Nanjing and 4 large clusters spanned multiple districts. Spatial autocorrelation analysis demonstrated random distribution of clustering rates at the district level, indicative of widespread across 12 districts, non-focal transmission. However, Lishui District with relatively more clustering cases showed higher clustering rates, and even nearly six in ten in cluster1 were also distributed in Lishui District, thus Lishui was a priority intervention zone. In addition, we observed frequent inter-district transmission of CRF119_0107. The districts with strong transmission linkages (e.g., between Qixia and Gulou, between Jiangning and Lishui), especially those strong linkage between geographically distants districts (e.g.,between Qixia and Lishui) should be concerned. Consist with previous study, our study have demonstrated that spatial analysis of transmission network can be used to reveal spatial connection patterns [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These findings underscores that local health sectors in districts with strong linkages should strengthen inter-district coordination, information exchange and joint interventions for effective containment of CRF119_0107 transmission, aligning with WHO/UNAIDS recommendations for geo-targeted HIV control strategies. Hence, our cross-disciplinary method undoubtedly provides a novel framework for optimizing resource allocation: prioritizing districts of high clustering rates and enhancing joint intervention in districts of strong linkages.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the sample size was constrained by the relatively recent emergence of CRF119_0107, which was initially identified in 2017 and has circulated in Nanjing for only six years. Nevertheless, we systematically collected all available CRF119_0107 sequences spanning this six-year period to comprehensively characterize its its transmission pattern and temporal growth patterns. Second, while its current transmission remains localized to Nanjing, the potential for inter-region spread through population mobility necessitates expanded surveillance across broader geographical areas to monitor potential cross-provincial dissemination. Future research priorities include implementing prospective genomic surveillance to expand the CRF119_0107 sequence dataset, and establishing real-time alerts for emerging drug resistance mutations throughout the Yangtze River Delta region.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe used cross-disciplinary approaches to disentangle the transmission pattern and dynamic change of CRF119_0107 at the local level. CRF119_0107 primarily circulated among highly educated young MSM and spread from MSM to other population. Sustained monitoring of CRF119_0107 strain is critical to track its evolving transmission patterns. At the individual level, real-time surveillance and rapid response mechanisms should prioritize high-growth or drug-resistant clusters. At the regional level, frequent inter-district transmission demand inter-district coordination, information-sharing and joint interventions especially in districts with strong linkages. Our cross-disciplinary approach provides an evidence-based framework for containing CRF119_0107 dissemination in Nanjing\u0026rsquo;s complex transmission context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by\u0026nbsp;the Ethics Committee of the Nanjing Center for Disease Control and Prevention (Approval No: PJ2020-A001-03).\u0026nbsp;Participants provided written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequences in this study have been deposited in the GenBase in National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation. The accession numbers can be obtained in additional file 2. All sequences can be found online at https://ngdc.cncb.ac.cn/genbase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Nanjing Medical Science and Technology Development Project (ZKX23059, YKK23192), Nanjing Preventive Medicine Research Project (NJYFKT202402), Innovation Program of Nanjing Institute of Public Health, Nanjing Medical University (NCX2403), Opening Foundation of Key Laboratory (JSHD202329) and Jiangsu Province Capability Improvement Project through Science, Technology and Education (ZDXYS202210).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZZ and YX conceived and designed the study. YX, HS and XL analyzed and wrote the manuscript. MQ and DX provided laboratory supports. TJ , XY, RW, JW and XZ performed investigation, data collection and data cleansing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are highly grateful for Centers for Disease Control and Prevention in 12 districts of Nanjing for all the support to conduct this study. We thank all the participants for their participation in this study and our colleagues for their support.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNasir A, Dimitrijevic M, Romero-Severson E, Leitner T: \u003cstrong\u003eLarge Evolutionary Rate Heterogeneity among and within HIV-1 Subtypes and CRFs\u003c/strong\u003e. 2021, \u003cstrong\u003e13\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003eYin Y, Liu Y, Zhu J, Hong X, Yuan R, Fu G, Zhou Y, Wang B: \u003cstrong\u003eThe prevalence, temporal trends, and geographical distribution of HIV-1 subtypes among men who have sex with men in China: A systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eEpidemiology and infection \u003c/em\u003e2019, \u003cstrong\u003e147\u003c/strong\u003e:e83.\u003c/li\u003e\n\u003cli\u003eLi X, Li W, Zhong P, Fang K, Zhu K, Musa TH, Song Y, Du G, Gao R, Guo Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNationwide Trends in Molecular Epidemiology of HIV-1 in China\u003c/strong\u003e. \u003cem\u003eAIDS research and human retroviruses \u003c/em\u003e2016, \u003cstrong\u003e32\u003c/strong\u003e(9):851-859.\u003c/li\u003e\n\u003cli\u003eLiu X, Wang D, Hu J, Song C, Liao L, Feng Y, Li D, Xing H, Ruan Y: \u003cstrong\u003eChanges in HIV-1 Subtypes/Sub-Subtypes, and Transmitted Drug Resistance Among ART-Na\u0026iuml;ve HIV-Infected Individuals - China, 2004-2022\u003c/strong\u003e. \u003cem\u003eChina CDC weekly \u003c/em\u003e2023, \u003cstrong\u003e5\u003c/strong\u003e(30):664-671.\u003c/li\u003e\n\u003cli\u003eZhang L, Feng Y: \u003cstrong\u003eNear-Full-Length Genomic Characterization of Two Novel HIV-1 Unique Recombinants (CRF01_AE/CRF07_BC) and (CRF01_AE/CRF68_01B) in Shijiazhuang, Hebei Province, China\u003c/strong\u003e. 2025.\u003c/li\u003e\n\u003cli\u003eWang X, Zhu B: \u003cstrong\u003eIdentification of a Novel HIV-1 Second-Generation Circulating Recombinant Form (CRF117_0107) in China\u003c/strong\u003e. 2025.\u003c/li\u003e\n\u003cli\u003eXing W, An M, Zhao B, Wang L, Zhang H, Hu Q, Ding H, Shang H, Han X: \u003cstrong\u003eIdentification of a novel CRF01_AE/CRF07_BC (CRF163_0107) circulating recombinant form in Shenyang city, the economic center of Northeast China\u003c/strong\u003e. \u003cem\u003eThe Journal of infection \u003c/em\u003e2024, \u003cstrong\u003e89\u003c/strong\u003e(6):106320.\u003c/li\u003e\n\u003cli\u003eXing Y, Wang L, Li Y, Wang Y, Han L, Huang G, Han J, Zhang W, Jia L, Liu Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIdentification of a new HIV-1 intersubtype circulating recombinant form (CRF123_0107) in Hebei province, China\u003c/strong\u003e. \u003cem\u003eThe Journal of infection \u003c/em\u003e2022, \u003cstrong\u003e84\u003c/strong\u003e(3):e36-e39.\u003c/li\u003e\n\u003cli\u003eZhang YQ, Li QH, Li EL, Wang YR, Tang ZY, Gao X, Lu RR, Liu SY, Chen XH, Wang FX\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIdentification of a novel HIV-1 second-generation circulating recombinant form (CRF136_0107) among MSM in China\u003c/strong\u003e. \u003cem\u003eAIDS (London, England) \u003c/em\u003e2023, \u003cstrong\u003e37\u003c/strong\u003e(8):F19-f23.\u003c/li\u003e\n\u003cli\u003eYin Y, Zhou Y, Lu J, Guo H, Chen J, Xuan Y, Yuan D, Hu H, Xu X, Fu G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFirst Detection of a Cluster Novel HIV-1 Second-Generation Recombinant (CRF01_AE/CRF07_BC) among Men Who Have Sex with Men in Nanjing, Eastern China\u003c/strong\u003e. \u003cem\u003eIntervirology \u003c/em\u003e2021, \u003cstrong\u003e64\u003c/strong\u003e(2):81-87.\u003c/li\u003e\n\u003cli\u003eShi H, Li X, Wang S, Dong X, Qiao M, Wu S, Wu R, Yuan X, Wang J, Xu Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMolecular transmission network analysis of newly diagnosed HIV-1 infections in Nanjing from 2019 to 2021\u003c/strong\u003e. \u003cem\u003eBMC infectious diseases \u003c/em\u003e2024, \u003cstrong\u003e24\u003c/strong\u003e(1):583.\u003c/li\u003e\n\u003cli\u003eLong JE, Tordoff DM, Reisner SL, Dasgupta S, Mayer KH, Mullins JI, Lama JR, Herbeck JT, Duerr A: \u003cstrong\u003eHIV transmission patterns among transgender women, their cisgender male partners, and cisgender MSM in Lima, Peru: A molecular epidemiologic and phylodynamic analysis\u003c/strong\u003e. \u003cem\u003eThe Lancet Regional Health \u0026ndash; 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and therapy \u003c/em\u003e2023, \u003cstrong\u003e12\u003c/strong\u003e(1):245-255.\u003c/li\u003e\n\u003cli\u003eXu Y, Jiang T, Jiang L, Shi H, Li X, Qiao M, Wu S, Wu R, Yuan X, Wang J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCombining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China\u003c/strong\u003e. \u003cem\u003eVirology journal \u003c/em\u003e2024, \u003cstrong\u003e21\u003c/strong\u003e(1):218.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV-1, CRF119_0107, molecular network, transmission cluster, spatial analysis","lastPublishedDoi":"10.21203/rs.3.rs-6539216/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6539216/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSince 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFrom 2019 to 2024, a total of 138 antiretroviral therapy (ART)-na\u0026iuml;ve individuals newly diagnosed with HIV-1 CRF119_0107 infection were enrolled. HIV-1 \u003cem\u003epol\u003c/em\u003e 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe 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\u0026ndash;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\u0026thinsp;\u0026lt;\u0026thinsp;200 cells/\u0026micro;L and without TDR. Spatial analysis demonstrated no significant autocorrelation in clustering rate at district-level (Moran's I=-0.121, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;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.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eReal-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.\u003c/p\u003e","manuscriptTitle":"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","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:55:12","doi":"10.21203/rs.3.rs-6539216/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T03:25:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T18:13:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T08:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10649990658515407798557935573744644238","date":"2025-07-14T13:45:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100609846887499015033289499601713599962","date":"2025-07-10T02:31:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T13:23:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T10:00:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-28T09:29:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Virology Journal","date":"2025-04-27T08:38:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9c1ef92-5515-4714-9316-7cad4f59a59b","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T15:58:48+00:00","versionOfRecord":{"articleIdentity":"rs-6539216","link":"https://doi.org/10.1186/s12985-025-02932-2","journal":{"identity":"virology-journal","isVorOnly":false,"title":"Virology Journal"},"publishedOn":"2025-09-26 15:56:59","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2025-07-14 10:55:12","video":"","vorDoi":"10.1186/s12985-025-02932-2","vorDoiUrl":"https://doi.org/10.1186/s12985-025-02932-2","workflowStages":[]},"version":"v1","identity":"rs-6539216","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6539216","identity":"rs-6539216","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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