Table_1_Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area.xlsx

Molecular network analysis based on the genetic similarity of HIV-1 is increasingly used to guide targeted interventions. Nevertheless, there is a lack of experience regarding molecular network inferences and targeted interventions in combination with epidemiological information in areas with diverse epidemic strains of HIV-1.We collected 2,173 pol sequences covering 84% of the total newly diagnosed HIV-1 infections in Shenyang city, Northeast China, between 2016 and 2018. Molecular networks were constructed using the optimized genetic distance threshold for main subtypes obtained using sensitivity analysis of plausible threshold ranges. The transmission rates (TR) of each large cluster were assessed using Bayesian analyses. Molecular clusters with the characteristics of ≥5 newly diagnosed cases in 2018, high TR, injection drug users (IDUs), and transmitted drug resistance (TDR) were defined as priority clusters. Several HIV-1 subtypes were identified, with a predominance of CRF01_AE (71.0%, 1,542/2,173), followed by CRF07_BC (18.1%, 393/2,173), subtype B (4.5%, 97/2,173), other subtypes (2.6%, 56/2,173), and unique recombinant forms (3.9%, 85/2,173). The overall optimal genetic distance thresholds for CRF01_AE and CRF07_BC were both 0.007 subs/site. For subtype B, it was 0.013 subs/site. 861 (42.4%) sequences of the top three subtypes formed 239 clusters (size: 2-77 sequences), including eight large clusters (size ≥10 sequences). All the eight large clusters had higher TR (median TR = 52.4/100 person-years) than that of the general HIV infections in Shenyang (10.9/100 person-years). A total of ten clusters including 231 individuals were determined as priority clusters for targeted intervention, including eight large clusters (five clusters with≥5 newly diagnosed cases in 2018, one cluster with IDUs, and two clusters with TDR (K103N, Q58E/V179D), one cluster with≥5 newly diagnosed cases in 2018, and one IDUs cluster. In conclusion, a comprehensive analysis combining in-depth sampling HIV-1 molecular networks construction using subtype-specific optimal genetic distance thresholds, and baseline epidemiological information can help to identify the targets of priority intervention in an area epidemic for non-subtype B.

基于人类免疫缺陷病毒1型（HIV-1）遗传相似性的分子网络分析，正日益被用于指导靶向干预措施。然而，在HIV-1流行毒株多样的地区，结合流行病学信息开展分子网络推断与靶向干预的相关经验仍较为匮乏。我们收集了2016年至2018年间中国东北地区沈阳市新确诊HIV-1感染病例中84%对应的2173条pol基因序列。通过对合理阈值范围进行敏感性分析，获得针对主要流行亚型的最优遗传距离阈值，并据此构建分子网络。采用贝叶斯分析评估各大型传播簇的传播率（TR）。将2018年新确诊病例数≥5、高传播率、涉及注射毒品使用者（IDUs）或存在传播性耐药（TDR）的分子传播簇定义为优先干预簇。本研究共鉴定出多种HIV-1亚型，其中以CRF01_AE为主（占比71.0%，1542/2173），其次为CRF07_BC（18.1%，393/2173）、B亚型（4.5%，97/2173）、其他亚型（2.6%，56/2173）以及独特重组型（3.9%，85/2173）。CRF01_AE与CRF07_BC的整体最优遗传距离阈值均为0.007替换/位点（subs/site），B亚型的最优阈值为0.013替换/位点（subs/site）。占前三大亚型的861条序列（占比42.4%）共形成239个传播簇（簇大小为2~77条序列），其中包含8个大型传播簇（簇大小≥10条序列）。这8个大型传播簇的传播率均高于沈阳市普通HIV感染者的传播率（中位传播率为52.4/100人年，普通人群为10.9/100人年）。共计确定10个传播簇（涉及231名感染者）作为靶向干预的优先簇，其中包括8个大型传播簇：5个2018年新确诊病例数≥5的簇、1个涉及注射毒品使用者的簇、2个存在传播性耐药的簇（分别携带K103N、Q58E/V179D耐药突变），另有1个满足2018年新确诊病例数≥5的簇与1个注射毒品使用者簇。综上，结合通过深度采样、基于亚型特异性最优遗传距离阈值构建的HIV-1分子网络，与基线流行病学信息开展综合分析，可帮助在非B亚型流行地区识别优先干预目标。