Cargando…
Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area
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 divers...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8039375/ https://www.ncbi.nlm.nih.gov/pubmed/33854982 http://dx.doi.org/10.3389/fcimb.2021.642903 |
_version_ | 1783677576489730048 |
---|---|
author | Zhao, Bin Song, Wei An, Minghui Dong, Xue Li, Xin Wang, Lu Liu, Jianmin Tian, Wen Wang, Zhen Ding, Haibo Han, Xiaoxu Shang, Hong |
author_facet | Zhao, Bin Song, Wei An, Minghui Dong, Xue Li, Xin Wang, Lu Liu, Jianmin Tian, Wen Wang, Zhen Ding, Haibo Han, Xiaoxu Shang, Hong |
author_sort | Zhao, Bin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8039375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80393752021-04-13 Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area Zhao, Bin Song, Wei An, Minghui Dong, Xue Li, Xin Wang, Lu Liu, Jianmin Tian, Wen Wang, Zhen Ding, Haibo Han, Xiaoxu Shang, Hong Front Cell Infect Microbiol Cellular and Infection Microbiology 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. Frontiers Media S.A. 2021-03-29 /pmc/articles/PMC8039375/ /pubmed/33854982 http://dx.doi.org/10.3389/fcimb.2021.642903 Text en Copyright © 2021 Zhao, Song, An, Dong, Li, Wang, Liu, Tian, Wang, Ding, Han and Shang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cellular and Infection Microbiology Zhao, Bin Song, Wei An, Minghui Dong, Xue Li, Xin Wang, Lu Liu, Jianmin Tian, Wen Wang, Zhen Ding, Haibo Han, Xiaoxu Shang, Hong Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area |
title | Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area |
title_full | Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area |
title_fullStr | Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area |
title_full_unstemmed | Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area |
title_short | Priority Intervention Targets Identified Using an In-Depth Sampling HIV Molecular Network in a Non-Subtype B Epidemics Area |
title_sort | priority intervention targets identified using an in-depth sampling hiv molecular network in a non-subtype b epidemics area |
topic | Cellular and Infection Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8039375/ https://www.ncbi.nlm.nih.gov/pubmed/33854982 http://dx.doi.org/10.3389/fcimb.2021.642903 |
work_keys_str_mv | AT zhaobin priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT songwei priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT anminghui priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT dongxue priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT lixin priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT wanglu priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT liujianmin priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT tianwen priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT wangzhen priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT dinghaibo priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT hanxiaoxu priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea AT shanghong priorityinterventiontargetsidentifiedusinganindepthsamplinghivmolecularnetworkinanonsubtypebepidemicsarea |