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

Frontiers in Cellular and Infection Microbiology

Jianmin Liu¹  Xin Li¹  Lu Wang¹  Wei Song¹  Xue Dong¹  Haibo Ding²  Minghui An²  Wen Tian²  Zhen Wang²  Hong Shang²  Bin Zhao²  Xiaoxu Han² 

[1] Department of Food Safety and Nutrition, Shenyang Center for Health Service and Administrative Law Enforcement (Shenyang Center for Disease Control and Prevention), Shenyang, China;NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China;Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China;Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China;Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China;

关键词: HIV-1;    molecular networks;    genetic distance threshold;    molecular clusters;    sociodemographic characters;   

DOI  :  10.3389/fcimb.2021.642903

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY