广州市2008-2017年注射吸毒人群HIV-1分子网络特征分析

曾文婷; 韩志刚; 吴昊; 黎庆梅; 梁彩云; 徐理倩; 赵新华

Abstract

曾文婷,韩志刚,吴昊,黎庆梅,梁彩云,徐理倩,赵新华.广州市2008-2017年注射吸毒人群HIV-1分子网络特征分析[J].Chinese journal of Epidemiology,2021,42(7):1260-1265

广州市2008-2017年注射吸毒人群HIV-1分子网络特征分析

Analysis on characteristics of HIV-1 molecular network in injected drug users in Guangzhou, 2008-2017

Received:December 09, 2020

DOI：10.3760/cma.j.cn112338-20201209-01393

KeyWord: 艾滋病病毒注射吸毒人群分子网络成簇分析中心性分析

English Key Word: HIV Injected drug user Molecular network Cluster analysis Centrality analysis

FundProject:广州市科技计划（201707010184，201704020219，201607010008）；广州市卫生健康科技（20201A011068）；广东省科技计划（2017A020224029）

Author Name	Affiliation	E-mail
Zeng Wenting	School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510310, China
Han Zhigang	Department of HIV/AIDS Control and Prevention, Guangzhou Municipal Center for Disease Control and Prevention, Guangzhou, 510440, China
Wu Hao	Department of HIV/AIDS Control and Prevention, Guangzhou Municipal Center for Disease Control and Prevention, Guangzhou, 510440, China
Li Qingmei	Department of HIV/AIDS Control and Prevention, Guangzhou Municipal Center for Disease Control and Prevention, Guangzhou, 510440, China
Liang Caiyun	Department of HIV/AIDS Control and Prevention, Guangzhou Municipal Center for Disease Control and Prevention, Guangzhou, 510440, China
Xu Liqian	School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510310, China
Zhao Xinhua	School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510310, China	zhaoxinhua007@163.com

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Abstract:

目的分析2008-2017年广州市注射吸毒人群HIV-1分子网络的分布特征，为该人群艾滋病防控提供参考依据。方法选取2008-2017年广州市新确证的注射吸毒HIV-1感染者血清样本，进行pol区基因扩增及测序后，利用Cluster Picker 1.2.3软件识别系统进化树中的分子簇，使用HyPhy 2.2.4中TN93模型计算成簇序列间的基因距离，通过可视化软件Cytoscape 3.8.2构建分子网络，采用χ²检验或确切概率法进行成簇分析和中心性分析。结果成功扩增获得pol区基因片段586条（73.9%，586/793），共产生80个分子簇，成簇率为46.6%（273/586）。成簇样本与未成簇样本比较，汉族（48.4%，260/537）、广东籍（52.7%，146/277）和CRF55_01B（93.3%，14/15）的成簇比例更高。分子网络度值范围为1~7，度值≥ 3的节点占12.8%（24/187），其与网络中另外81个节点相关联（43.3%，81/187）。中心性分析结果显示，HIV-1感染者职业为家务/待业者在高程度中心性（19.0%，19/100）、高中介中心性（22.0%，22/100）和高亲近中心性（32.0%，32/100）的比例更高。结论广州市注射吸毒人群HIV-1分子成簇风险较高，应将具有本地及广西籍、家务/待业特征的注射吸毒人群作为重点干预目标，并实施精准干预，降低该人群HIV-1感染率。

English Abstract:

Objective To understand the characteristics of the HIV-1 molecular networks in injected drug users (IDUs) in Guangzhou from 2008 to 2017, and provide reference for the prevention and control of AIDS in this population. Methods The serum samples of newly diagnosed HIV-1 positive IDUs in Guangzhou from 2008 to 2017 were collected, HIV-1 RNA was extracted for pol gene amplification and sequencing. The molecular cluster in the phylogenetic tree was identified by Cluster Picker 1.2.3 for cluster analysis. TN93 model in HyPhy2.2.4 was used to calculate the gene distance between the cluster sequences. Software Cytoscape3.8.2 was used to visualize the molecular network, and χ² test or exact probability method was used for cluster analysis and centrality analysis. Results A total of 586 sequences were successfully amplified (73.9%, 586/793), and 80 molecular clusters were produced, with a clustering rate of 46.6% (273/586). In molecular clusters, the proportions of the strains from IDUs in Han ethnic group (48.4%, 260/537), IDUs who were local residents in Guangdong (52.7%, 146/277) and IDUs whose strain sequence subtype was CRF55_01B (93.3%, 14/15) were higher. In the molecular network, the degree range was 1-7, and nodes with degree ≥ 3 accounted for 12.8% (24/187), which was associated with another 81 nodes in the molecular network (43.3%, 81/187). The centrality analysis showed that the proportions of housework/unemployed with high degree centrality (19.0%, 19/100), high intermediary (22.0%, 22/100), and high proximity centrality (32.0%, 32/100) were higher in IDUs infected with HIV-1. Conclusion The risk of HIV-1 clustering in IDUs in Guangzhou was high, suggesting that IDUs who were from both Guangdong and Guangxi and were house workers or unemployed should be viewed as the key targets, and precise intervention should be implemented to reduce the HIV-1 infection rate in this population.

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