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Pang X, Ma J, He Q, Tang K, Huang J, Fang N, Xie H, Lan G, Liang S. HIV molecular transmission networks among students in Guangxi: unraveling the dynamics of student-driven HIV epidemic. Emerg Microbes Infect 2025; 14:2459142. [PMID: 39869005 PMCID: PMC11809174 DOI: 10.1080/22221751.2025.2459142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Revised: 12/25/2024] [Accepted: 01/23/2025] [Indexed: 01/28/2025]
Abstract
In Guangxi, the number of newly diagnosed HIV-1 infections among students is continuously increasing, highlighting the need for a detailed understanding of local transmission dynamics, particularly focusing on key drivers of transmission. We recruited individuals newly diagnosed with HIV-1 in Nanning, Guangxi, and amplified and sequenced the HIV-1 pol gene to construct a molecular network. Bayesian phylogenetic analysis was utilized to identify migration events, and multivariable logistic regression was employed to analyze factors influencing clustering and high linkage. The predominant subtype among students was CRF07_BC (58.5%), followed by CRF01_AE (17.4%) and CRF55_01B (13.5%). Transmission network analysis identified a significant clustering rate of 64.3% among students, primarily within large clusters. The strongest transmission relationships were observed between students and MSM aged 25-39, as well as nonstudent youths. These migration events primarily occurred from MSM aged 25-39 to students and nonstudent youths for CRF01_AE, CRF07_BC, and CRF55_01B. Qingxiu was the main emigration region for for CRF01_AE, CRF07_BC, while Xixiangtang for CRF55_01B. Link with nonstudent youths (AOR = 5.11) and MSM aged 25-39 (AOR = 8.82) were significant factors contributing to the high linkage among students. Long-term infection was a key factor in super spreaders. These findings emphasize the critical role of MSM aged 25-39 in HIV-1 transmission among local youths, particularly regarding long-term infected individuals. The study advocates for targeted HIV-1 screening and intervention strategies for youths to strengthen early detection and treatment, thereby mitigating further transmission within this high-risk group.
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Affiliation(s)
- Xianwu Pang
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Jie Ma
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Qin He
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Kailing Tang
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Jinghua Huang
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Ningye Fang
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Haomin Xie
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Guanghua Lan
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
| | - Shujia Liang
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
- Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Center for Disease Control and Prevention, Nanning, Guangxi, China
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Li J, Mei J, Yu J, Chen X, Zhu J, Ye J, Zhang D, Cheng D, Chen X. Characteristics of molecular epidemiology and transmitted drug resistance among newly diagnosed HIV-1 infections in Lishui, China from 2020 to 2023. Virol J 2025; 22:111. [PMID: 40253369 PMCID: PMC12008846 DOI: 10.1186/s12985-025-02734-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2025] [Accepted: 04/10/2025] [Indexed: 04/21/2025] Open
Abstract
BACKGROUND Transmitted drug resistance (TDR) is becoming an obstacle to the success of antiretroviral therapy (ART) as the HIV epidemic continues to spread. This study aimed to investigate the characteristics of TDR and the molecular epidemiology of ART-naive HIV-1 infections in Lishui. METHODS A total of 481 plasma samples were collected from ART-naive HIV-1 infections in Lishui between 2020 and 2023. The sequences acquired from infections were used to analyze the characteristics of genotype, TDR, and molecular transmission network. RESULTS This study discovered that the three most prevalent subtypes among the 455 sequences successfully obtained from infections in Lishui were CRF08_BC (35.8%), CRF07_BC (26.4%), and CRF01_AE (25.9%). The overall prevalence of TDR was 12.1%, and the K103N (2.4%) was the most frequent mutation. Multivariate analysis showed that CRF08_BC (OR = 5.401, P < 0.001) and CD4+ cell concentration of 200-499 cells/µL (OR = 1.684, P = 0.030) were associated with a higher risk of entering the molecular transmission network and clustering, whereas the current address in other cities (OR = 0.328, P = 0.004), junior middle school (OR = 0.472, P = 0.006), and junior college or above (OR = 0.387, P = 0.045) were associated with a lower risk of clustering. CONCLUSIONS This study revealed that the prevalence of TDR was at an intermediate level of drug resistance, and high levels of resistance were predominantly concentrated in efavirenz (EFV) and nevirapine (NVP) among the NNRTIs. Middle-aged and older infections represented a significant proportion of the molecular transmission network. This suggests that HIV surveillance and targeted prevention and treatment interventions are essential to reduce the risk of HIV transmission.
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Affiliation(s)
- Jinkai Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, China
- Lishui Center for Disease Control and Prevention, Lishui, 323000, China
| | - Jianhua Mei
- Lishui Center for Disease Control and Prevention, Lishui, 323000, China
| | - Jie Yu
- Lishui Center for Disease Control and Prevention, Lishui, 323000, China
| | - Xiaolei Chen
- Lishui Center for Disease Control and Prevention, Lishui, 323000, China
| | - Jianliang Zhu
- Lishui Center for Disease Control and Prevention, Lishui, 323000, China
| | - Jiaji Ye
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Deyong Zhang
- Lishui Center for Disease Control and Prevention, Lishui, 323000, China.
| | - Dongqing Cheng
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
| | - Xiuying Chen
- Lishui Center for Disease Control and Prevention, Lishui, 323000, China.
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Liu Y, Hua L, Wu W, Ge Y, Li W, Wei P. Molecular network analysis for detecting HIV transmission clusters: insights and implications. Front Public Health 2025; 13:1429464. [PMID: 39944061 PMCID: PMC11814211 DOI: 10.3389/fpubh.2025.1429464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 01/13/2025] [Indexed: 05/09/2025] Open
Abstract
Objective In order to improve knowledge of HIV transmission dynamics and guide preventive and control strategies, this work uses molecular cluster analysis to objectively detect clusters of HIV genetic sequence similarity. Methods 89 HIV-positive couples provided blood samples, and plasma was separated for pol region gene sequence amplification. Furthermore, analyzed HIV-1 pol fragment sequences from Nanjing patients between 2015 and 2019. HYPHY and Cytoscape were used to generate and illustrate molecular networks. Results In this investigation of 89 double-positive pairs, it was discovered that the pairwise gene distance approach properly detected 82.02% of positive couples at an ideal gene distance of 0.014 substitution/loci. With an accuracy of 86.25%, the optimal parameter for the phylogenetic tree and gene distance approach was 90 + 0.045 substitution/loci. A molecular network was built for the Nanjing samples (2015-2019) using the optimum threshold of the previous technique. This network had 487 sequences with one misconnected cluster. There were 565 sequences in the network created by the latter approach that were not incorrectly connected. Conclusion For HIV research, molecular cluster analysis provides novel insights. It helps with preventive and control methods by objectively identifying clusters with comparable genetic sequences, which enhances our knowledge of HIV transmission. Further developments will increase its importance for HIV/AIDS research and worldwide prevention.
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Affiliation(s)
- Yangyang Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Lichun Hua
- Department of Ultrasound Diagnostic, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Wenqian Wu
- Department of Emergency, Pediatric Intensive Care Unit, Children’ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - You Ge
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Wei Li
- Department of Clinical Research Center, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Pingmin Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China
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Ye J, Lan Y, Wang J, Feng Y, Lin Y, Zhou Y, Liu J, Yuan D, Lu X, Guo W, Zheng M, Song X, Zhou Q, Yang H, Zheng C, Guo Q, Yang X, Yang K, Zhang L, Ge Z, Liu L, Yu F, Han Y, Huang H, Hao M, Chen Q, Ling X, Ruan Y, Dong Y, Zhou C, Liu X, Bai J, Tong X, Gao Y, Yang Z, Wang A, Wei W, Mei F, Qiao R, Luo X, Huang X, Chen J, Hu F, Shen X, Tan W, Tu A, Zhang X, He S, Ning Z, Fan J, Liu C, Xu C, Ren X, Sun Y, Li Y, Liu G, Li X, Li J, Duan J, Huang T, Liu S, Yu G, Wu D, Shao Y, Pan Q, Zhang L, Su B, Wu J, Jiang T, Zhao H, Zhang T, Chen F, Cai K, Hu B, Wang H, Zhao J, Gao B, Sun W, Ning T, Li J, Liang S, Huo Y, Fu G, Chen X, Li F, Xing H, Lu H. Improvement in the 95-95-95 Targets Is Accompanied by a Reduction in Both the Human Immunodeficiency Virus Transmission Rate and Incidence in China. J Infect Dis 2024; 230:1202-1214. [PMID: 39186695 DOI: 10.1093/infdis/jiae302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 06/04/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND In 2016, China has implemented the World Health Organization's "treat all" policy. We aimed to assess the impact of significant improvements in the 95-95-95 targets on population-level human immunodeficiency virus (HIV) transmission dynamics and incidence. METHODS We focused on 3 steps of the HIV care continuum: diagnosed, on antiretroviral therapy, and achieving viral suppression. The molecular transmission clusters were inferred using HIV-TRACE. New HIV infections were estimated using the incidence method in the European Centre for Disease Prevention and Control HIV Modelling Tool. RESULTS Between 2004 and 2023, the national HIV epidemiology database recorded 2.99 billion person-times of HIV tests and identified 1 976 878 new diagnoses. We noted a roughly "inverted-V" curve in the clustering frequency, with the peak recorded in 2014 (67.1% [95% confidence interval, 63.7%-70.5%]), concurrent with a significant improvement in the 95-95-95 targets from 10-13-<71 in 2005 to 84-93-97 in 2022. Furthermore, we observed a parabolic curve for a new infection with the vertex occurring in 2010. CONCLUSIONS In general, it was suggested that the improvements in the 95-95-95 targets were accompanied by a reduction in both the population-level HIV transmission rate and incidence. Thus, China should allocate more effort to the first "95" target to achieve a balanced 95-95-95 target.
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Affiliation(s)
- Jingrong Ye
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Yun Lan
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou
| | - Juan Wang
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Yi Feng
- Division of Virology and Immunology, State Key Laboratory for Infectious Disease Prevention and Control and National Center for AIDS/STD Prevention and Control, China CDC, Beijing
| | - Yi Lin
- Division of Tuberculosis and HIV/AIDS Prevention, Shanghai CDC
- Shanghai Institutes of Preventive Medicine
- Shanghai Center for AIDS Research, Shanghai
| | - Ying Zhou
- Institute of AIDS/STD Control and Prevention, Jiangsu CDC, Nanjing
| | - Jinjin Liu
- Center for Translational Medicine, Affiliated Infectious Diseases Hospital of Zhengzhou University (Henan Infectious Diseases Hospital, The Sixth People's Hospital of Zhengzhou), Zhengzhou
| | - Dan Yuan
- Center for AIDS/STD Control and Prevention, Sichuan CDC, Chengdu
| | - Xinli Lu
- Department of AIDS Research, Hebei Key Laboratory of Pathogen and Epidemiology of Infectious Disease, Hebei CDC, Shijiazhuang
| | - Weigui Guo
- Institute of HIV/AIDS Prevention and Control, Beihai CDC, Beihai
| | - Minna Zheng
- Department of STDs/AIDS Control and Prevention, Tianjin CDC, Tianjin
| | - Xiao Song
- Institute for HIV/AIDS and STD Prevention and Control, Heilongjiang CDC, Harbin
| | - Quanhua Zhou
- Institute of Microbiology, Chongqing CDC, Chongqing
| | - Hong Yang
- STD/AIDS Prevention and Control Institute, Inner Mongolia CDC (Inner Mongolia Academy of Preventive Medicine), Hohhot
| | - Chenli Zheng
- Department of HIV/AIDS Control and Prevention, Shenzhen CDC, Shenzhen
| | - Qi Guo
- Virology Laboratory, Jilin CDC, Changchun
| | - Xiaohui Yang
- Institute for HIV/AIDS and STD Prevention and Control, Fuyang CDC, Fuyang
| | | | - Lincai Zhang
- Institute for HIV/AIDS and STD Prevention and Control, Gansu CDC, Lanzhou
| | - Zhangwen Ge
- Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang
| | - Lifeng Liu
- Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing
| | - Fengting Yu
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing
| | - Yang Han
- Department of Infectious Disease, Peking Union Medical College Hospital, Beijing
| | - Huihuang Huang
- Treatment and Research Center for Infectious Diseases, The Fifth Medical Center of People's Liberation Army General Hospital, Beijing
| | - Mingqiang Hao
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Qiang Chen
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Xuemei Ling
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou
| | - Yuhua Ruan
- Division of Virology and Immunology, State Key Laboratory for Infectious Disease Prevention and Control and National Center for AIDS/STD Prevention and Control, China CDC, Beijing
| | - Yuan Dong
- Division of Tuberculosis and HIV/AIDS Prevention, Shanghai CDC
- Shanghai Institutes of Preventive Medicine
- Shanghai Center for AIDS Research, Shanghai
| | - Chang Zhou
- Center for AIDS/STD Control and Prevention, Sichuan CDC, Chengdu
| | - Xuangu Liu
- Institute of HIV/AIDS Prevention and Control, Beihai CDC, Beihai
| | - Jianyun Bai
- Department of STDs/AIDS Control and Prevention, Tianjin CDC, Tianjin
| | - Xue Tong
- Institute for HIV/AIDS and STD Prevention and Control, Heilongjiang CDC, Harbin
| | - Ya Gao
- STD/AIDS Prevention and Control Institute, Inner Mongolia CDC (Inner Mongolia Academy of Preventive Medicine), Hohhot
| | - Zhengrong Yang
- Department of HIV/AIDS Control and Prevention, Shenzhen CDC, Shenzhen
| | - Ao Wang
- Virology Laboratory, Jilin CDC, Changchun
| | - Wei Wei
- Institute for HIV/AIDS and STD Prevention and Control, Fuyang CDC, Fuyang
| | | | - Ruijuan Qiao
- Institute for HIV/AIDS and STD Prevention and Control, Gansu CDC, Lanzhou
| | - Xinhua Luo
- Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang
| | - Xiaojie Huang
- Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing
| | - Jing Chen
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Fengyu Hu
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou
| | - Xin Shen
- Division of Tuberculosis and HIV/AIDS Prevention, Shanghai CDC
- Shanghai Institutes of Preventive Medicine
- Shanghai Center for AIDS Research, Shanghai
| | - Wei Tan
- Department of HIV/AIDS Control and Prevention, Shenzhen CDC, Shenzhen
| | - Aixia Tu
- Institute for HIV/AIDS and STD Prevention and Control, Gansu CDC, Lanzhou
| | - Xinhui Zhang
- Institute for Infectious Disease Prevention and Control, Guizhou CDC, Guiyang
| | - Shufang He
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Zhen Ning
- Division of Tuberculosis and HIV/AIDS Prevention, Shanghai CDC
- Shanghai Institutes of Preventive Medicine
- Shanghai Center for AIDS Research, Shanghai
| | | | | | - Conghui Xu
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Xianlong Ren
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Yanming Sun
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Yang Li
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Guowu Liu
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Xiyao Li
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Jie Li
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
| | - Junyi Duan
- Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing
| | - Tao Huang
- Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing
| | - Shuiqing Liu
- Department of Infectious Diseases, Guiyang Public Health Clinical Center, Guiyang
| | - Guolong Yu
- Institute of Pathogenic Microbiology, Guangdong CDC, Guangzhou
| | - Donglin Wu
- Virology Laboratory, Jilin CDC, Changchun
| | - Yiming Shao
- Division of Virology and Immunology, State Key Laboratory for Infectious Disease Prevention and Control and National Center for AIDS/STD Prevention and Control, China CDC, Beijing
| | - Qichao Pan
- Division of Tuberculosis and HIV/AIDS Prevention, Shanghai CDC
- Shanghai Institutes of Preventive Medicine
- Shanghai Center for AIDS Research, Shanghai
| | - Linglin Zhang
- Center for AIDS/STD Control and Prevention, Sichuan CDC, Chengdu
| | - Bin Su
- Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing
| | - Jianjun Wu
- Institute for HIV/AIDS and STD Prevention and Control, Anhui CDC, Hefei
| | - Tianjun Jiang
- Treatment and Research Center for Infectious Diseases, The Fifth Medical Center of People's Liberation Army General Hospital, Beijing
| | - Hongxin Zhao
- Clinical and Research Center of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing
| | - Tong Zhang
- Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing
| | - Faqing Chen
- Institute for HIV/AIDS and STD Prevention and Control, Gansu CDC, Lanzhou
| | | | - Bing Hu
- Institute for HIV/AIDS and STD Prevention and Control, Fuyang CDC, Fuyang
| | - Hui Wang
- Virology Laboratory, Jilin CDC, Changchun
| | - Jin Zhao
- Department of HIV/AIDS Control and Prevention, Shenzhen CDC, Shenzhen
| | - Baicheng Gao
- STD/AIDS Prevention and Control Institute, Inner Mongolia CDC (Inner Mongolia Academy of Preventive Medicine), Hohhot
| | - Wei Sun
- Institute for HIV/AIDS and STD Prevention and Control, Heilongjiang CDC, Harbin
| | - Tielin Ning
- Department of STDs/AIDS Control and Prevention, Tianjin CDC, Tianjin
| | - Jianjun Li
- Institute of HIV/AIDS Prevention and Control, Guangxi CDC, Nanning
| | - Shu Liang
- Center for AIDS/STD Control and Prevention, Sichuan CDC, Chengdu
| | - Yuqi Huo
- Center for Translational Medicine, Affiliated Infectious Diseases Hospital of Zhengzhou University (Henan Infectious Diseases Hospital, The Sixth People's Hospital of Zhengzhou), Zhengzhou
| | - Gengfeng Fu
- Institute of AIDS/STD Control and Prevention, Jiangsu CDC, Nanjing
| | - Xin Chen
- Division of Tuberculosis and HIV/AIDS Prevention, Shanghai CDC
- Shanghai Institutes of Preventive Medicine
- Shanghai Center for AIDS Research, Shanghai
| | - Feng Li
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou
| | - Hui Xing
- Division of Virology and Immunology, State Key Laboratory for Infectious Disease Prevention and Control and National Center for AIDS/STD Prevention and Control, China CDC, Beijing
| | - Hongyan Lu
- Institute for HIV/AIDS and STD Prevention and Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Academy of Preventive Medicine, Beijing
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Williams N. Complexity and Variation in Infectious Disease Birth Cohorts: Findings from HIV+ Medicare and Medicaid Beneficiaries, 1999-2020. ENTROPY (BASEL, SWITZERLAND) 2024; 26:970. [PMID: 39593914 PMCID: PMC11592912 DOI: 10.3390/e26110970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 10/24/2024] [Accepted: 11/04/2024] [Indexed: 11/28/2024]
Abstract
The impact of uncertainty in information systems is difficult to assess, especially when drawing conclusions from human observation records. In this study, we investigate survival variation in a population experiencing infectious disease as a proxy to investigate uncertainty problems. Using Centers for Medicare and Medicaid Services claims, we discovered 1,543,041 HIV+ persons, 363,425 of whom were observed dying from all-cause mortality. Once aggregated by HIV status, year of birth and year of death, Age-Period-Cohort disambiguation and regression models were constructed to produce explanations of variance in survival. We used Age-Period-Cohort as an alternative method to work around under-observed features of uncertainty like infection transmission, receiver host dynamics or comorbidity noise impacting survival variation. We detected ages that have a consistent, disproportionate share of deaths independent of study year or year of birth. Variation in seasonality of mortality appeared stable in regression models; in turn, HIV cases in the United States do not have a survival gain when uncertainty is uncontrolled for. Given the information complexity issues under observed exposure and transmission, studies of infectious diseases should either include robust decedent cases, observe transmission physics or avoid drawing conclusions about survival from human observation records.
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Affiliation(s)
- Nick Williams
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA
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Yan H, Luo Y, Wu H, Chen M, Li S, Tian Z, Zou G, Tang S, Bible PW, Hao Y, Gu J, Han Z, Liu Y. Evolving molecular HIV clusters revealed genotype-specific dynamics in Guangzhou, China (2008-2020). Int J Infect Dis 2024; 148:107218. [PMID: 39181438 DOI: 10.1016/j.ijid.2024.107218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVES This study investigated the genotype-specific dynamics of molecular HIV clusters (MHCs) in Guangzhou, China, aiming to enhance HIV control. METHODS HIV pol sequences from people with HIV (PWH) in Guangzhou (2008-2020) were obtained for genotyping and molecular network creation. MHCs were identified and categorized into three types: emerging, growing, or stable. Clustering rates, proportions of cluster types, and members within each type were calculated and their trends were assessed using joinpoint regression. RESULTS Among 8395 PWH, the most prevalent HIV-1 genotypes were CRF07_BC (39.7%) and CRF01_AE (32.6%). The genotype composition has been stable since 2012 (Ps > 0.05). The overall clustering rate was 43.3%, with significant variations across genotypes (P < 0.001), indicating genotype-specific transmission fitness. Significant declines in overall and genotype-specific clustering rates toward the end of 2020 (Ps < 0.05), potentially offer support for HIV control efforts in reducing local infections. The continuously increasing proportions of stable clusters and the gradually decreasing proportions of emerging and growing clusters (either Ps < 0.05 or Ps > 0.05) suggest a trend toward stable molecular network structure. However, growing clusters exhibited CRF55_01B, CRF07_BC, and CRF59_01B dominance that indicate their priority for interventions. CONCLUSION The evolving MHCs highlight the genotype-specific cluster dynamics, providing fresh insights for enhanced prevention and control strategies.
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Affiliation(s)
- Huanchang Yan
- School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, China; Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yefei Luo
- Department of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Hao Wu
- Department of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Mingyu Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Shunming Li
- Department of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Zhenming Tian
- School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Guanyang Zou
- School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shixing Tang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Paul W Bible
- Department of Computer Science, DePauw University, Greencastle, Indiana, USA
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jing Gu
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Zhigang Han
- Department of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China; Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Yu Liu
- School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, China.
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7
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Omar S, Woodman ZL. The evolution of envelope function during coinfection with phylogenetically distinct human immunodeficiency virus. BMC Infect Dis 2024; 24:934. [PMID: 39251948 PMCID: PMC11385138 DOI: 10.1186/s12879-024-09805-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 08/23/2024] [Indexed: 09/11/2024] Open
Abstract
BACKGROUND Coinfection with two phylogenetically distinct Human Immunodeficiency Virus-1 (HIV-1) variants might provide an opportunity for rapid viral expansion and the emergence of fit variants that drive disease progression. However, autologous neutralising immune responses are known to drive Envelope (Env) diversity which can either enhance replicative capacity, have no effect, or reduce viral fitness. This study investigated whether in vivo outgrowth of coinfecting variants was linked to pseudovirus and infectious molecular clones' infectivity to determine whether diversification resulted in more fit virus with the potential to increase disease progression. RESULTS For most participants, emergent recombinants displaced the co-transmitted variants and comprised the major population at 52 weeks postinfection with significantly higher entry efficiency than other co-circulating viruses. Our findings suggest that recombination within gp41 might have enhanced Env fusogenicity which contributed to the increase in pseudovirus entry efficiency. Finally, there was a significant correlation between pseudovirus entry efficiency and CD4 + T cell count, suggesting that the enhanced replicative capacity of recombinant variants could result in more virulent viruses. CONCLUSION Coinfection provides variants with the opportunity to undergo rapid recombination that results in more infectious virus. This highlights the importance of monitoring the replicative fitness of emergent viruses.
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Affiliation(s)
- Shatha Omar
- Department of Integrative Biomedical Sciences (IBMS), Division of Medical Biochemistry and Structural Biology, University of Cape Town, Cape Town, South Africa
- Department of Biomedical Sciences, Division of Molecular Biology and Human Genetics, TB Genomics Group, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Zenda L Woodman
- Department of Integrative Biomedical Sciences (IBMS), Division of Medical Biochemistry and Structural Biology, University of Cape Town, Cape Town, South Africa.
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8
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Bonetti Franceschi V, Volz E. Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2. Wellcome Open Res 2024; 9:85. [PMID: 39132669 PMCID: PMC11316176 DOI: 10.12688/wellcomeopenres.20704.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission. Methods We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics. Results We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein. Conclusions This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.
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Affiliation(s)
- Vinicius Bonetti Franceschi
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, England, W2 1PG, UK
| | - Erik Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, England, W2 1PG, UK
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9
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Carrasco-Hernández R, Valenzuela-Ponce H, Soto-Nava M, García-Morales C, Matías-Florentino M, Wertheim JO, Smith DM, Reyes-Terán G, Ávila-Ríos S. Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors. Epidemics 2024; 47:100770. [PMID: 38761432 PMCID: PMC11213286 DOI: 10.1016/j.epidem.2024.100770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/07/2024] [Accepted: 05/07/2024] [Indexed: 05/20/2024] Open
Abstract
In the context of infectious diseases, the dynamic interplay between ever-changing host populations and viral biology demands a more flexible modeling approach than common fixed correlations. Embracing random-effects regression models allows for a nuanced understanding of the intricate ecological and evolutionary dynamics underlying complex phenomena, offering valuable insights into disease progression and transmission patterns. In this article, we employed a random-effects regression to model an observed decreasing median plasma viral load (pVL) among individuals with HIV in Mexico City during 2019-2021. We identified how these functional slope changes (i.e. random slopes by year) improved predictions of the observed pVL median changes between 2019 and 2021, leading us to hypothesize underlying ecological and evolutionary factors. Our analysis involved a dataset of pVL values from 7325 ART-naïve individuals living with HIV, accompanied by their associated clinical and viral molecular predictors. A conventional fixed-effects linear model revealed significant correlations between pVL and predictors that evolved over time. However, this fixed-effects model could not fully explain the reduction in median pVL; thus, prompting us to adopt random-effects models. After applying a random effects regression model-with random slopes and intercepts by year-, we observed potential "functional changes" within the local HIV viral population, highlighting the importance of ecological and evolutionary considerations in HIV dynamics: A notably stronger negative correlation emerged between HIV pVL and the CpG content in the pol gene, suggesting a changing immune landscape influenced by CpG-induced innate immune responses that could impact viral load dynamics. Our study underscores the significance of random effects models in capturing dynamic correlations and the crucial role of molecular characteristics like CpG content. By enriching our understanding of changing host-virus interactions and HIV progression, our findings contribute to the broader relevance of such models in infectious disease research. They shed light on the changing interplay between host and pathogen, driving us closer to more effective strategies for managing infectious diseases. SIGNIFICANCE OF THE STUDY: This study highlights a decreasing trend in median plasma viral loads among ART-naïve individuals living with HIV in Mexico City between 2019 and 2021. It uncovers various predictors significantly correlated with pVL, shedding light on the complex interplay between host-virus interactions and disease progression. By employing a random-slopes model, the researchers move beyond traditional fixed-effects models to better capture dynamic correlations and evolutionary changes in HIV dynamics. The discovery of a stronger negative correlation between pVL and CpG content in HIV-pol sequences suggests potential changes in the immune landscape and innate immune responses, opening avenues for further research into adaptive changes and responses to environmental shifts in the context of HIV infection. The study's emphasis on molecular characteristics as predictors of pVL adds valuable insights to epidemiological and evolutionary studies of viruses, providing new avenues for understanding and managing HIV infection at the population level.
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Affiliation(s)
- Rocío Carrasco-Hernández
- Centro de Investigación en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico.
| | - Humberto Valenzuela-Ponce
- Centro de Investigación en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico
| | - Maribel Soto-Nava
- Centro de Investigación en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico
| | - Claudia García-Morales
- Centro de Investigación en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico
| | - Margarita Matías-Florentino
- Centro de Investigación en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico
| | - Joel O Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Davey M Smith
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gustavo Reyes-Terán
- Coordinación de Institutos Nacionales de Salud y Hospitales de Alta Especialidad, Secretaría de Salud, Mexico
| | - Santiago Ávila-Ríos
- Centro de Investigación en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico; Centro de Investigación en Enfermedades Infecciosas (CIENI), Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Calz. de Tlalpan 4502, Belisario Domínguez Secc 16, Tlalpan, Ciudad de México 14080, Mexico
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10
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He Y, Tang Y, Hua Q, Li X, Ge Y, Liu Y, Tang R, Tian Y, Li W. Exploring Dynamic Changes in HIV-1 Molecular Transmission Networks and Key Influencing Factors: Cross-Sectional Study. JMIR Public Health Surveill 2024; 10:e56593. [PMID: 38810253 PMCID: PMC11170051 DOI: 10.2196/56593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 02/19/2024] [Accepted: 05/05/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND The HIV-1 molecular network is an innovative tool, using gene sequences to understand transmission attributes and complementing social and sexual network studies. While previous research focused on static network characteristics, recent studies' emphasis on dynamic features enhances our understanding of real-time changes, offering insights for targeted interventions and efficient allocation of public health resources. OBJECTIVE This study aims to identify the dynamic changes occurring in HIV-1 molecular transmission networks and analyze the primary influencing factors driving the dynamics of HIV-1 molecular networks. METHODS We analyzed and compared the dynamic changes in the molecular network over a specific time period between the baseline and observed end point. The primary factors influencing the dynamic changes in the HIV-1 molecular network were identified through univariate analysis and multivariate analysis. RESULTS A total of 955 HIV-1 polymerase fragments were successfully amplified from 1013 specimens; CRF01_AE and CRF07_BC were the predominant subtypes, accounting for 40.8% (n=390) and 33.6% (n=321) of the specimens, respectively. Through the analysis and comparison of the basic and terminal molecular networks, it was discovered that 144 sequences constituted static molecular networks, and 487 sequences contributed to the formation of dynamic molecular networks. The findings of the multivariate analysis indicated that the factors occupation as a student, floating population, Han ethnicity, engagement in occasional or multiple sexual partnerships, participation in anal sex, and being single were independent risk factors for the dynamic changes observed in the HIV-1 molecular network, and the odds ratio (OR; 95% CIs) values were 2.63 (1.54-4.47), 1.83 (1.17-2.84), 2.91 (1.09-7.79), 1.75 (1.06-2.90), 4.12 (2.48-6.87), 5.58 (2.43-12.80), and 2.10 (1.25-3.54), respectively. Heterosexuality and homosexuality seem to exhibit protective effects when compared to bisexuality, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. Additionally, the National Eight-Item score and sex education experience were also identified as protective factors against dynamic changes in the HIV-1 molecular network, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. CONCLUSIONS The HIV-1 molecular network analysis showed 144 sequences in static networks and 487 in dynamic networks. Multivariate analysis revealed that occupation as a student, floating population, Han ethnicity, and risky sexual behavior were independent risk factors for dynamic changes, while heterosexuality and homosexuality were protective compared to bisexuality. A higher National Eight-Item score and sex education experience were also protective factors. The identification of HIV dynamic molecular networks has provided valuable insights into the characteristics of individuals undergoing dynamic alterations. These findings contribute to a better understanding of HIV-1 transmission dynamics and could inform targeted prevention strategies.
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Affiliation(s)
- Yan He
- Department of Infection Management, Nanjing Drum Tower Hospital, Nanjing, China
| | - Ying Tang
- Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Qun Hua
- Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Xin Li
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - You Ge
- School of Public Health, Southeast University, Nanjing, China
| | - Yangyang Liu
- School of Public Health, Southeast University, Nanjing, China
| | - Rong Tang
- Nanjing Qixia District Center for Disease Control and Prevention, Nanjing, China
| | - Ye Tian
- Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Li
- Children's Hospital of Nanjing Medical University, Nanjing, China
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11
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Switzer WM, Shankar A, Jia H, Knyazev S, Ambrosio F, Kelly R, Zheng H, Campbell EM, Cintron R, Pan Y, Saduvala N, Panneer N, Richman R, Singh MB, Thoroughman DA, Blau EF, Khalil GM, Lyss S, Heneine W. High HIV diversity, recombination, and superinfection revealed in a large outbreak among persons who inject drugs in Kentucky and Ohio, USA. Virus Evol 2024; 10:veae015. [PMID: 38510920 PMCID: PMC10953796 DOI: 10.1093/ve/veae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 03/22/2024] Open
Abstract
We investigated transmission dynamics of a large human immunodeficiency virus (HIV) outbreak among persons who inject drugs (PWID) in KY and OH during 2017-20 by using detailed phylogenetic, network, recombination, and cluster dating analyses. Using polymerase (pol) sequences from 193 people associated with the investigation, we document high HIV-1 diversity, including Subtype B (44.6 per cent); numerous circulating recombinant forms (CRFs) including CRF02_AG (2.5 per cent) and CRF02_AG-like (21.8 per cent); and many unique recombinant forms composed of CRFs with major subtypes and sub-subtypes [CRF02_AG/B (24.3 per cent), B/CRF02_AG/B (0.5 per cent), and A6/D/B (6.4 per cent)]. Cluster analysis of sequences using a 1.5 per cent genetic distance identified thirteen clusters, including a seventy-five-member cluster composed of CRF02_AG-like and CRF02_AG/B, an eighteen-member CRF02_AG/B cluster, Subtype B clusters of sizes ranging from two to twenty-three, and a nine-member A6/D and A6/D/B cluster. Recombination and phylogenetic analyses identified CRF02_AG/B variants with ten unique breakpoints likely originating from Subtype B and CRF02_AG-like viruses in the largest clusters. The addition of contact tracing results from OH to the genetic networks identified linkage between persons with Subtype B, CRF02_AG, and CRF02_AG/B sequences in the clusters supporting de novo recombinant generation. Superinfection prevalence was 13.3 per cent (8/60) in persons with multiple specimens and included infection with B and CRF02_AG; B and CRF02_AG/B; or B and A6/D/B. In addition to the presence of multiple, distinct molecular clusters associated with this outbreak, cluster dating inferred transmission associated with the largest molecular cluster occurred as early as 2006, with high transmission rates during 2017-8 in certain other molecular clusters. This outbreak among PWID in KY and OH was likely driven by rapid transmission of multiple HIV-1 variants including de novo viral recombinants from circulating viruses within the community. Our findings documenting the high HIV-1 transmission rate and clustering through partner services and molecular clusters emphasize the importance of leveraging multiple different data sources and analyses, including those from disease intervention specialist investigations, to better understand outbreak dynamics and interrupt HIV spread.
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Affiliation(s)
- William M Switzer
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Anupama Shankar
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Hongwei Jia
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Sergey Knyazev
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
- Oak Ridge Institute for Science and Education, 1299 Bethel Valley Rd, Oak Ridge, TN 37830, USA
| | - Frank Ambrosio
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Reagan Kelly
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
- General Dynamics Information Technology, 3150 Fairview Park Dr, Falls Church, VA 22042, USA
| | - HaoQiang Zheng
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | | | - Roxana Cintron
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Yi Pan
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | | | - Nivedha Panneer
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Rhiannon Richman
- HIV Surveillance Program, Bureau of HIV/STI/Viral Hepatitis, Ohio Department of Health, 246 North High Street, Colombus, OH 43215, USA
| | - Manny B Singh
- Division of Epidemiology and Health Planning, Kentucky Department for Public Health, Frankfort, KY 40621, USA
| | - Douglas A Thoroughman
- Division of Epidemiology and Health Planning, Kentucky Department for Public Health, Frankfort, KY 40621, USA
- ORR/Division of State and Local Readiness/Field Services Branch/CEFO Program, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Erin F Blau
- Division of Epidemiology and Health Planning, Kentucky Department for Public Health, Frankfort, KY 40621, USA
- Epidemic Intelligence Service, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - George M Khalil
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
| | - Sheryl Lyss
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
- HIV Surveillance Program, Bureau of HIV/STI/Viral Hepatitis, Ohio Department of Health, 246 North High Street, Colombus, OH 43215, USA
- Division of Epidemiology and Health Planning, Kentucky Department for Public Health, Frankfort, KY 40621, USA
- Hamilton County Public Health, 250 William Howard Taft Rd, Cincinnati, OH 45219, USA
- Northern Kentucky Health Department, 8001 Veterans Memorial Drive, Florence, KY 41042, USA
| | - Walid Heneine
- Division of HIV Prevention, CDC, 1600 Clifton Rd, Atlanta, GA 30329, USA
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12
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Cummins B, Johnson K, Schneider JA, Del Vecchio N, Moshiri N, Wertheim JO, Goyal R, Skaathun B. Leveraging social networks for identification of people with HIV who are virally unsuppressed. AIDS 2024; 38:245-254. [PMID: 37890471 PMCID: PMC10843229 DOI: 10.1097/qad.0000000000003767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/06/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023]
Abstract
OBJECTIVES This study investigates primary peer-referral engagement (PRE) strategies to assess which strategy results in engaging higher numbers of people with HIV (PWH) who are virally unsuppressed. DESIGN We develop a modeling study that simulates an HIV epidemic (transmission, disease progression, and viral evolution) over 6 years using an agent-based model followed by simulating PRE strategies. We investigate two PRE strategies where referrals are based on social network strategies (SNS) or sexual partner contact tracing (SPCT). METHODS We parameterize, calibrate, and validate our study using data from Chicago on Black sexual minority men to assess these strategies for a population with high incidence and prevalence of HIV. For each strategy, we calculate the number of PWH recruited who are undiagnosed or out-of-care (OoC) and the number of direct or indirect transmissions. RESULTS SNS and SPCT identified 256.5 [95% confidence interval (CI) 234-279] and 15 (95% CI 7-27) PWH, respectively. Of these, SNS identified 159 (95% CI 142-177) PWH OoC and 32 (95% CI 21-43) PWH undiagnosed compared with 9 (95% CI 3-18) and 2 (95% CI 0-5) for SPCT. SNS identified 15.5 (95% CI 6-25) and 7.5 (95% CI 2-11) indirect and direct transmission pairs, whereas SPCT identified 6 (95% CI 0-8) and 5 (95% CI 0-8), respectively. CONCLUSION With no testing constraints, SNS is the more effective strategy to identify undiagnosed and OoC PWH. Neither strategy is successful at identifying sufficient indirect or direct transmission pairs to investigate transmission networks.
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Affiliation(s)
- Breschine Cummins
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - Kara Johnson
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - John A. Schneider
- Department of Medicine, University of Chicago
- Department of Public Health Sciences, University of Chicago, Chicago, IL
| | | | | | - Joel O. Wertheim
- Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Ravi Goyal
- Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Britt Skaathun
- Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
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13
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Zhou Y, Cui M, Hong Z, Huang S, Zhou S, Lyu H, Li J, Lin Y, Huang H, Tang W, Sun C, Huang W. High Genetic Diversity of HIV-1 and Active Transmission Clusters among Male-to-Male Sexual Contacts (MMSCs) in Zhuhai, China. Viruses 2023; 15:1947. [PMID: 37766353 PMCID: PMC10535991 DOI: 10.3390/v15091947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/15/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Monitoring genetic diversity and recent HIV infections (RHIs) is critical for understanding HIV epidemiology. Here, we report HIV-1 genetic diversity and RHIs in blood samples from 190 HIV-positive MMSCs in Zhuhai, China. MMSCs with newly reported HIV were enrolled from January 2020 to June 2022. A nested PCR was performed to amplify the HIV polymerase gene fragments at HXB2 positions 2604-3606. We constructed genetic transmission network at both 0.5% and 1.5% distance thresholds using the Tamura-Nei93 model. RHIs were identified using a recent infection testing algorithm (RITA) combining limiting antigen avidity enzyme immunoassay (LAg-EIA) assay with clinical data. The results revealed that 19.5% (37/190) were RHIs and 48.4% (92/190) were CRF07_BC. Two clusters were identified at a 0.5% distance threshold. Among them, one was infected with CRF07_BC for the long term, and the other was infected with CRF55_01B recently. We identified a total of 15 clusters at a 1.5% distance threshold. Among them, nine were infected with CRF07_BC subtype, and RHIs were found in 38.8% (19/49) distributed in eight genetic clusters. We identified a large active transmission cluster (n = 10) infected with a genetic variant, CRF79_0107. The multivariable logistic regression model showed that clusters were more likely to be RHIs (adjusted OR: 3.64, 95% CI: 1.51~9.01). The RHI algorithm can help to identify recent or ongoing transmission clusters where the prevention tools are mostly needed. Prompt public health measures are needed to contain the further spread of active transmission clusters.
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Affiliation(s)
- Yi Zhou
- Faculty of Medicine, Macau University of Science and Technology, Macau SAR, China;
- Department of HIV Prevention, Zhuhai Center for Disease Control and Prevention, Zhuhai 519060, China; (H.L.); (H.H.)
| | - Mingting Cui
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China;
| | - Zhongsi Hong
- Department of Infectious Diseases, Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519001, China
| | - Shaoli Huang
- School of Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
| | - Shuntai Zhou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hang Lyu
- Department of HIV Prevention, Zhuhai Center for Disease Control and Prevention, Zhuhai 519060, China; (H.L.); (H.H.)
| | - Jiarun Li
- Department of HIV Prevention, Zhuhai Center for Disease Control and Prevention, Zhuhai 519060, China; (H.L.); (H.H.)
| | - Yixiong Lin
- Department of HIV Prevention, Zhuhai Center for Disease Control and Prevention, Zhuhai 519060, China; (H.L.); (H.H.)
| | - Huitao Huang
- Department of HIV Prevention, Zhuhai Center for Disease Control and Prevention, Zhuhai 519060, China; (H.L.); (H.H.)
| | - Weiming Tang
- Dermatology Hospital of Southern Medical University, Guangzhou 510315, China
- Southern Medical University Institute for Global Health and Sexually Transmitted Diseases, Guangzhou 510315, China
- University of North Carolina Project-China, Guangzhou 510315, China
| | - Caijun Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China;
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou 510080, China
| | - Wenyan Huang
- Department of HIV Prevention, Zhuhai Center for Disease Control and Prevention, Zhuhai 519060, China; (H.L.); (H.H.)
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14
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Kun Á, Hubai AG, Král A, Mokos J, Mikulecz BÁ, Radványi Á. Do pathogens always evolve to be less virulent? The virulence–transmission trade-off in light of the COVID-19 pandemic. Biol Futur 2023:10.1007/s42977-023-00159-2. [PMID: 37002448 PMCID: PMC10066022 DOI: 10.1007/s42977-023-00159-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 03/09/2023] [Indexed: 04/03/2023]
Abstract
AbstractThe direction the evolution of virulence takes in connection with any pathogen is a long-standing question. Formerly, it was theorized that pathogens should always evolve to be less virulent. As observations were not in line with this theoretical outcome, new theories emerged, chief among them the transmission–virulence trade-off hypotheses, which predicts an intermediate level of virulence as the endpoint of evolution. At the moment, we are very much interested in the future evolution of COVID-19’s virulence. Here, we show that the disease does not fulfill all the assumptions of the hypothesis. In the case of COVID-19, a higher viral load does not mean a higher risk of death; immunity is not long-lasting; other hosts can act as reservoirs for the virus; and death as a consequence of viral infection does not shorten the infectious period. Consequently, we cannot predict the short- or long-term evolution of the virulence of COVID-19.
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15
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Skoupý S, Stanojković A, Pavlíková M, Poulíčková A, Dvořák P. New cyanobacterial genus Argonema is hidding in soil crusts around the world. Sci Rep 2022; 12:7203. [PMID: 35504986 PMCID: PMC9065122 DOI: 10.1038/s41598-022-11288-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Cyanobacteria are crucial primary producers in soil and soil crusts. However, their biodiversity in these habitats remains poorly understood, especially in the tropical and polar regions. We employed whole genome sequencing, morphology, and ecology to describe a novel cyanobacterial genus Argonema isolated from Antarctica. Extreme environments are renowned for their relatively high number of endemic species, but whether cyanobacteria are endemic or not is open to much current debate. To determine if a cyanobacterial lineage is endemic is a time consuming, elaborate, and expensive global sampling effort. Thus, we propose an approach that will help to overcome the limits of the sampling effort and better understand the global distribution of cyanobacterial clades. We employed a Sequencing Read Archive, which provides a rich source of data from thousands of environmental samples. We developed a framework for a characterization of the global distribution of any microbial species using Sequencing Read Archive. Using this approach, we found that Argonema is actually cosmopolitan in arid regions. It provides further evidence that endemic microbial taxa are likely to be much rarer than expected.
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Affiliation(s)
- Svatopluk Skoupý
- Department of Botany, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71, Olomouc, Czech Republic
| | - Aleksandar Stanojković
- Department of Botany, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71, Olomouc, Czech Republic
| | - Markéta Pavlíková
- Department of Botany, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71, Olomouc, Czech Republic
| | - Aloisie Poulíčková
- Department of Botany, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71, Olomouc, Czech Republic
| | - Petr Dvořák
- Department of Botany, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71, Olomouc, Czech Republic.
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Zhao L, Wymant C, Blanquart F, Golubchik T, Gall A, Bakker M, Bezemer D, Hall M, Ong SH, Albert J, Bannert N, Fellay J, Grabowski MK, Gunsenheimer-Bartmeyer B, Günthard HF, Kivelä P, Kouyos RD, Laeyendecker O, Meyer L, Porter K, van Sighem A, van der Valk M, Berkhout B, Kellam P, Cornelissen M, Reiss P, Fraser C, Ferretti L. Phylogenetic estimation of the viral fitness landscape of HIV-1 set-point viral load. Virus Evol 2022; 8:veac022. [PMID: 35402002 PMCID: PMC8986633 DOI: 10.1093/ve/veac022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/25/2022] [Accepted: 03/15/2022] [Indexed: 11/16/2022] Open
Abstract
Set-point viral load (SPVL), a common measure of human immunodeficiency virus (HIV)-1 virulence, is partially determined by viral genotype. Epidemiological evidence suggests that this viral property has been under stabilising selection, with a typical optimum for the virus between 104 and 105 copies of viral RNA per ml. Here we aimed to detect transmission fitness differences between viruses from individuals with different SPVLs directly from phylogenetic trees inferred from whole-genome sequences. We used the local branching index (LBI) as a proxy for transmission fitness. We found that LBI is more sensitive to differences in infectiousness than to differences in the duration of the infectious state. By analysing subtype-B samples from the Bridging the Evolution and Epidemiology of HIV in Europe project, we inferred a significant positive relationship between SPVL and LBI up to approximately 105 copies/ml, with some evidence for a peak around this value of SPVL. This is evidence of selection against low values of SPVL in HIV-1 subtype-B strains, likely related to lower infectiousness, and perhaps a peak in the transmission fitness in the expected range of SPVL. The less prominent signatures of selection against higher SPVL could be explained by an inherent limit of the method or the deployment of antiretroviral therapy.
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Affiliation(s)
- Lele Zhao
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - François Blanquart
- Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, Cedex 05, Paris 75231, France
| | - Tanya Golubchik
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Astrid Gall
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Margreet Bakker
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, MB 1007, Netherlands
| | - Daniela Bezemer
- Stichting HIV Monitoring, Amsterdam, Amsterdam, AZ 1105, Netherlands
| | - Matthew Hall
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Swee Hoe Ong
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Jan Albert
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Stockholm 171 77, Sweden
- Department of Clinical Microbiology, Karolinska University Hospital, Solna, Stockholm S-171 76, Sweden
| | - Norbert Bannert
- Division for HIV and Other Retroviruses, Department of Infectious Diseases, Robert Koch Institute, Berlin 13353, Germany
| | - Jacques Fellay
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne CH-1015, Switzerland
| | - M Kate Grabowski
- Department of Pathology, John Hopkins University, Baltimore, MD 21287, USA
| | | | - Huldrych F Günthard
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich CH-8091, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland
| | - Pia Kivelä
- Department of Infectious Diseases, Helsinki University Hospital, Helsinki FI-00029, Finland
| | - Roger D Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich CH-8091, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland
| | | | - Laurence Meyer
- INSERM CESP U1018, Université Paris Saclay, APHP, Service de Santé Publique, Hôpital de Bicêtre, Le Kremlin-Bicêtre 94270, France
| | - Kholoud Porter
- Institute for Global Health, University College London, London WC1N 1EH, UK
| | - Ard van Sighem
- Stichting HIV Monitoring, Amsterdam, Amsterdam, AZ 1105, Netherlands
| | - Marc van der Valk
- Stichting HIV Monitoring, Amsterdam, Amsterdam, AZ 1105, Netherlands
| | - Ben Berkhout
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, MB 1007, Netherlands
| | - Paul Kellam
- Kymab Ltd, Babraham Research Campus, Cambridge CB22 3AT, UK
- Department of Infectious Diseases, Faculty of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Marion Cornelissen
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, MB 1007, Netherlands
- Molecular Diagnostic Unit, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, MB 1007, Netherlands
| | - Peter Reiss
- Stichting HIV Monitoring, Amsterdam, Amsterdam, AZ 1105, Netherlands
- Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam and Amsterdam Institute for Global Health and Development, Amsterdam, DE 1100, Netherlands
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK
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17
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CRF07_BC is associated with slow HIV disease progression in Chinese patients. Sci Rep 2022; 12:3773. [PMID: 35260599 PMCID: PMC8904811 DOI: 10.1038/s41598-022-07518-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/21/2022] [Indexed: 11/09/2022] Open
Abstract
HIV subtypes convey important epidemiological information and possibly influence the rate of disease progression. In this study, HIV disease progression in patients infected with CRF01_AE, CRF07_BC, and subtype B was compared in the largest HIV molecular epidemiology study ever done in China. A national data set of HIV pol sequences was assembled by pooling sequences from public databases and the Beijing HIV laboratory network. Logistic regression was used to assess factors associated with the risk of AIDS at diagnosis ([AIDSAD], defined as a CD4 count < 200 cells/µL) in patients with HIV subtype B, CRF01_AE, and CRF07_BC. Of the 20,663 sequences, 9,156 (44.3%) were CRF01_AE. CRF07_BC was responsible for 28.3% of infections, followed by B (13.9%). In multivariable analysis, the risk of AIDSAD differed significantly according to HIV subtype (OR for CRF07_BC vs. B: 0.46, 95% CI 0.39─0.53), age (OR for ≥ 65 years vs. < 18 years: 4.3 95% CI 1.81─11.8), and transmission risk groups (OR for men who have sex with men vs. heterosexuals: 0.67 95% CI 0.6─0.75). These findings suggest that HIV diversity in China is constantly evolving and gaining in complexity. CRF07_BC is less pathogenic than subtype B, while CRF01_AE is as pathogenic as B.
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18
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Abstract
[Figure: see text].
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Affiliation(s)
- Joel O Wertheim
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
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19
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Wymant C, Bezemer D, Blanquart F, Ferretti L, Gall A, Hall M, Golubchik T, Bakker M, Ong SH, Zhao L, Bonsall D, de Cesare M, MacIntyre-Cockett G, Abeler-Dörner L, Albert J, Bannert N, Fellay J, Grabowski MK, Gunsenheimer-Bartmeyer B, Günthard HF, Kivelä P, Kouyos RD, Laeyendecker O, Meyer L, Porter K, Ristola M, van Sighem A, Berkhout B, Kellam P, Cornelissen M, Reiss P, Fraser C, Aubert V, Battegay M, Bernasconi E, Böni J, Braun DL, Bucher HC, Burton-Jeangros C, Calmy A, Cavassini M, Dollenmaier G, Egger M, Elzi L, Fehr J, Fellay J, Furrer H, Fux CA, Gorgievski M, Günthard H, Haerry D, Hasse B, Hirsch HH, Hoffmann M, Hösli I, Kahlert C, Kaiser L, Keiser O, Klimkait T, Kouyos R, Kovari H, Ledergerber B, Martinetti G, de Tejada BM, Marzolini C, Metzner K, Müller N, Nadal D, Nicca D, Pantaleo G, Rauch A, Regenass S, Rudin C, Schöni-Affolter F, Schmid P, Speck R, Stöckle M, Tarr P, Trkola A, Vernazza P, Weber R, Yerly S, van der Valk M, Geerlings SE, Goorhuis A, Hovius JW, Lempkes B, Nellen FJB, van der Poll T, Prins JM, Reiss P, van Vugt M, Wiersinga WJ, Wit FWMN, van Duinen M, van Eden J, Hazenberg A, van Hes AMH, et alWymant C, Bezemer D, Blanquart F, Ferretti L, Gall A, Hall M, Golubchik T, Bakker M, Ong SH, Zhao L, Bonsall D, de Cesare M, MacIntyre-Cockett G, Abeler-Dörner L, Albert J, Bannert N, Fellay J, Grabowski MK, Gunsenheimer-Bartmeyer B, Günthard HF, Kivelä P, Kouyos RD, Laeyendecker O, Meyer L, Porter K, Ristola M, van Sighem A, Berkhout B, Kellam P, Cornelissen M, Reiss P, Fraser C, Aubert V, Battegay M, Bernasconi E, Böni J, Braun DL, Bucher HC, Burton-Jeangros C, Calmy A, Cavassini M, Dollenmaier G, Egger M, Elzi L, Fehr J, Fellay J, Furrer H, Fux CA, Gorgievski M, Günthard H, Haerry D, Hasse B, Hirsch HH, Hoffmann M, Hösli I, Kahlert C, Kaiser L, Keiser O, Klimkait T, Kouyos R, Kovari H, Ledergerber B, Martinetti G, de Tejada BM, Marzolini C, Metzner K, Müller N, Nadal D, Nicca D, Pantaleo G, Rauch A, Regenass S, Rudin C, Schöni-Affolter F, Schmid P, Speck R, Stöckle M, Tarr P, Trkola A, Vernazza P, Weber R, Yerly S, van der Valk M, Geerlings SE, Goorhuis A, Hovius JW, Lempkes B, Nellen FJB, van der Poll T, Prins JM, Reiss P, van Vugt M, Wiersinga WJ, Wit FWMN, van Duinen M, van Eden J, Hazenberg A, van Hes AMH, Rajamanoharan S, Robinson T, Taylor B, Brewer C, Mayr C, Schmidt W, Speidel A, Strohbach F, Arastéh K, Cordes C, Pijnappel FJJ, Stündel M, Claus J, Baumgarten A, Carganico A, Ingiliz P, Dupke S, Freiwald M, Rausch M, Moll A, Schleehauf D, Smalhout SY, Hintsche B, Klausen G, Jessen H, Jessen A, Köppe S, Kreckel P, Schranz D, Fischer K, Schulbin H, Speer M, Weijsenfeld AM, Glaunsinger T, Wicke T, Bieniek B, Hillenbrand H, Schlote F, Lauenroth-Mai E, Schuler C, Schürmann D, Wesselmann H, Brockmeyer N, Jurriaans S, Gehring P, Schmalöer D, Hower M, Spornraft-Ragaller P, Häussinger D, Reuter S, Esser S, Markus R, Kreft B, Berzow D, Back NKT, Christl A, Meyer A, Plettenberg A, Stoehr A, Graefe K, Lorenzen T, Adam A, Schewe K, Weitner L, Fenske S, Zaaijer HL, Hansen S, Stellbrink HJ, Wiemer D, Hertling S, Schmidt R, Arbter P, Claus B, Galle P, Jäger H, Jä Gel-Guedes E, Berkhout B, Postel N, Fröschl M, Spinner C, Bogner J, Salzberger B, Schölmerich J, Audebert F, Marquardt T, Schaffert A, Schnaitmann E, Cornelissen MTE, Trein A, Frietsch B, Müller M, Ulmer A, Detering-Hübner B, Kern P, Schubert F, Dehn G, Schreiber M, Güler C, Schinkel CJ, Gunsenheimer-Bartmeyer B, Schmidt D, Meixenberger K, Bannert N, Wolthers KC, Peters EJG, van Agtmael MA, Autar RS, Bomers M, Sigaloff KCE, Heitmuller M, Laan LM, Ang CW, van Houdt R, Jonges M, Kuijpers TW, Pajkrt D, Scherpbier HJ, de Boer C, van der Plas A, van den Berge M, Stegeman A, Baas S, Hage de Looff L, Buiting A, Reuwer A, Veenemans J, Wintermans B, Pronk MJH, Ammerlaan HSM, van den Bersselaar DNJ, de Munnik ES, Deiman B, Jansz AR, Scharnhorst V, Tjhie J, Wegdam MCA, van Eeden A, Nellen J, Brokking W, Elsenburg LJM, Nobel H, van Kasteren MEE, Berrevoets MAH, Brouwer AE, Adams A, van Erve R, de Kruijf-van de Wiel BAFM, Keelan-Phaf S, van de Ven B, van der Ven B, Buiting AGM, Murck JL, de Vries-Sluijs TEMS, Bax HI, van Gorp ECM, de Jong-Peltenburg NC, de Mendonç A Melo M, van Nood E, Nouwen JL, Rijnders BJA, Rokx C, Schurink CAM, Slobbe L, Verbon A, Bassant N, van Beek JEA, Vriesde M, van Zonneveld LM, de Groot J, Boucher CAB, Koopmans MPG, van Kampen JJA, Fraaij PLA, van Rossum AMC, Vermont CL, van der Knaap LC, Visser E, Branger J, Douma RA, Cents-Bosma AS, Duijf-van de Ven CJHM, Schippers EF, van Nieuwkoop C, van Ijperen JM, Geilings J, van der Hut G, van Burgel ND, Leyten EMS, Gelinck LBS, Mollema F, Davids-Veldhuis S, Tearno C, Wildenbeest GS, Heikens E, Groeneveld PHP, Bouwhuis JW, Lammers AJJ, Kraan S, van Hulzen AGW, Kruiper MSM, van der Bliek GL, Bor PCJ, Debast SB, Wagenvoort GHJ, Kroon FP, de Boer MGJ, Jolink H, Lambregts MMC, Roukens AHE, Scheper H, Dorama W, van Holten N, Claas ECJ, Wessels E, den Hollander JG, El Moussaoui R, Pogany K, Brouwer CJ, Smit JV, Struik-Kalkman D, van Niekerk T, Pontesilli O, Lowe SH, Oude Lashof AML, Posthouwer D, van Wolfswinkel ME, Ackens RP, Burgers K, Schippers J, Weijenberg-Maes B, van Loo IHM, Havenith TRA, van Vonderen MGA, Kampschreur LM, Faber S, Steeman-Bouma R, Al Moujahid A, Kootstra GJ, Delsing CE, van der Burg-van de Plas M, Scheiberlich L, Kortmann W, van Twillert G, Renckens R, Ruiter-Pronk D, van Truijen-Oud FA, Cohen Stuart JWT, Jansen ER, Hoogewerf M, Rozemeijer W, van der Reijden WA, Sinnige JC, Brinkman K, van den Berk GEL, Blok WL, Lettinga KD, de Regt M, Schouten WEM, Stalenhoef JE, Veenstra J, Vrouenraets SME, Blaauw H, Geerders GF, Kleene MJ, Kok M, Knapen M, van der Meché IB, Mulder-Seeleman E, Toonen AJM, Wijnands S, Wttewaal E, Kwa D, van Crevel R, van Aerde K, Dofferhoff ASM, Henriet SSV, Ter Hofstede HJM, Hoogerwerf J, Keuter M, Richel O, Albers M, Grintjes-Huisman KJT, de Haan M, Marneef M, Strik-Albers R, Rahamat-Langendoen J, Stelma FF, Burger D, Gisolf EH, Hassing RJ, Claassen M, Ter Beest G, van Bentum PHM, Langebeek N, Tiemessen R, Swanink CMA, van Lelyveld SFL, Soetekouw R, van der Prijt LMM, van der Swaluw J, Bermon N, van der Reijden WA, Jansen R, Herpers BL, Veenendaal D, Verhagen DWM, Lauw FN, van Broekhuizen MC, van Wijk M, Bierman WFW, Bakker M, Kleinnijenhuis J, Kloeze E, Middel A, Postma DF, Schölvinck EH, Stienstra Y, Verhage AR, Wouthuyzen-Bakker M, Boonstra A, de Groot-de Jonge H, van der Meulen PA, de Weerd DA, Niesters HGM, van Leer-Buter CC, Knoester M, Hoepelman AIM, Arends JE, Barth RE, Bruns AHW, Ellerbroek PM, Mudrikova T, Oosterheert JJ, Schadd EM, van Welzen BJ, Aarsman K, Griffioen-van Santen BMG, de Kroon I, van Berkel M, van Rooijen CSAM, Schuurman R, Verduyn-Lunel F, Wensing AMJ, Bont LJ, Geelen SPM, Loeffen YGT, Wolfs TFW, Nauta N, Rooijakkers EOW, Holtsema H, Voigt R, van de Wetering D, Alberto A, van der Meer I, Rosingh A, Halaby T, Zaheri S, Boyd AC, Bezemer DO, van Sighem AI, Smit C, Hillebregt M, de Jong A, Woudstra T, Bergsma D, Meijering R, van de Sande L, Rutkens T, van der Vliet S, de Groot L, van den Akker M, Bakker Y, El Berkaoui A, Bezemer M, Brétin N, Djoechro E, Groters M, Kruijne E, Lelivelt KJ, Lodewijk C, Lucas E, Munjishvili L, Paling F, Peeck B, Ree C, Regtop R, Ruijs Y, Schoorl M, Schnörr P, Scheigrond A, Tuijn E, Veenenberg L, Visser KM, Witte EC, Ruijs Y, Van Frankenhuijsen M, Allegre T, Makhloufi D, Livrozet JM, Chiarello P, Godinot M, Brunel-Dalmas F, Gibert S, Trepo C, Peyramond D, Miailhes P, Koffi J, Thoirain V, Brochier C, Baudry T, Pailhes S, Lafeuillade A, Philip G, Hittinger G, Assi A, Lambry V, Rosenthal E, Naqvi A, Dunais B, Cua E, Pradier C, Durant J, Joulie A, Quinsat D, Tempesta S, Ravaux I, Martin IP, Faucher O, Cloarec N, Champagne H, Pichancourt G, Morlat P, Pistone T, Bonnet F, Mercie P, Faure I, Hessamfar M, Malvy D, Lacoste D, Pertusa MC, Vandenhende MA, Bernard N, Paccalin F, Martell C, Roger-Schmelz J, Receveur MC, Duffau P, Dondia D, Ribeiro E, Caltado S, Neau D, Dupont M, Dutronc H, Dauchy F, Cazanave C, Vareil MO, Wirth G, Le Puil S, Pellegrin JL, Raymond I, Viallard JF, Chaigne de Lalande S, Garipuy D, Delobel P, Obadia M, Cuzin L, Alvarez M, Biezunski N, Porte L, Massip P, Debard A, Balsarin F, Lagarrigue M, Prevoteau du Clary F, Aquilina C, Reynes J, Baillat V, Merle C, Lemoing V, Atoui N, Makinson A, Jacquet JM, Psomas C, Tramoni C, Aumaitre H, Saada M, Medus M, Malet M, Eden A, Neuville S, Ferreyra M, Sotto A, Barbuat C, Rouanet I, Leureillard D, Mauboussin JM, Lechiche C, Donsesco R, Cabie A, Abel S, Pierre-Francois S, Batala AS, Cerland C, Rangom C, Theresine N, Hoen B, Lamaury I, Fabre I, Schepers K, Curlier E, Ouissa R, Gaud C, Ricaud C, Rodet R, Wartel G, Sautron C, Beck-Wirth G, Michel C, Beck C, Halna JM, Kowalczyk J, Benomar M, Drobacheff-Thiebaut C, Chirouze C, Faucher JF, Parcelier F, Foltzer A, Haffner-Mauvais C, Hustache Mathieu M, Proust A, Piroth L, Chavanet P, Duong M, Buisson M, Waldner A, Mahy S, Gohier S, Croisier D, May T, Delestan M, Andre M, Zadeh MM, Martinot M, Rosolen B, Pachart A, Martha B, Jeunet N, Rey D, Cheneau C, Partisani M, Priester M, Bernard-Henry C, Batard ML, Fischer P, Berger JL, Kmiec I, Robineau O, Huleux T, Ajana F, Alcaraz I, Allienne C, Baclet V, Meybeck A, Valette M, Viget N, Aissi E, Biekre R, Cornavin P, Merrien D, Seghezzi JC, Machado M, Diab G, Raffi F, Bonnet B, Allavena C, Grossi O, Reliquet V, Billaud E, Brunet C, Bouchez S, Morineau-Le Houssine P, Sauser F, Boutoille D, Besnier M, Hue H, Hall N, Brosseau D, Souala F, Michelet C, Tattevin P, Arvieux C, Revest M, Leroy H, Chapplain JM, Dupont M, Fily F, Patra-Delo S, Lefeuvre C, Bernard L, Bastides F, Nau P, Verdon R, de la Blanchardiere A, Martin A, Feret P, Geffray L, Daniel C, Rohan J, Fialaire P, Chennebault JM, Rabier V, Abgueguen P, Rehaiem S, Luycx O, Niault M, Moreau P, Poinsignon Y, Goussef M, Mouton-Rioux V, Houlbert D, Alvarez-Huve S, Barbe F, Haret S, Perre P, Leantez-Nainville S, Esnault JL, Guimard T, Suaud I, Girard JJ, Simonet V, Debab Y, Schmit JL, Jacomet C, Weinberck P, Genet C, Pinet P, Ducroix S, Durox H, Denes É, Abraham B, Gourdon F, Antoniotti O, Molina JM, Ferret S, Lascoux-Combe C, Lafaurie M, Colin de Verdiere N, Ponscarme D, De Castro N, Aslan A, Rozenbaum W, Pintado C, Clavel F, Taulera O, Gatey C, Munier AL, Gazaigne S, Penot P, Conort G, Lerolle N, Leplatois A, Balausine S, Delgado J, Timsit J, Tabet M, Gerard L, Girard PM, Picard O, Tredup J, Bollens D, Valin N, Campa P, Bottero J, Lefebvre B, Tourneur M, Fonquernie L, Wemmert C, Lagneau JL, Yazdanpanah Y, Phung B, Pinto A, Vallois D, Cabras O, Louni F, Pialoux G, Lyavanc T, Berrebi V, Chas J, Lenagat S, Rami A, Diemer M, Parrinello M, Depond A, Salmon D, Guillevin L, Tahi T, Belarbi L, Loulergue P, Zak Dit Zbar O, Launay O, Silbermann B, Leport C, Alagna L, Pietri MP, Simon A, Bonmarchand M, Amirat N, Pichon F, Kirstetter M, Katlama C, Valantin MA, Tubiana R, Caby F, Schneider L, Ktorza N, Calin R, Merlet A, Ben Abdallah S, Weiss L, Buisson M, Batisse D, Karmochine M, Pavie J, Minozzi C, Jayle D, Castel P, Derouineau J, Kousignan P, Eliazevitch M, Pierre I, Collias L, Viard JP, Gilquin J, Sobel A, Slama L, Ghosn J, Hadacek B, Thu-Huyn N, Nait-Ighil L, Cros A, Maignan A, Duvivier C, Consigny PH, Lanternier F, Shoai-Tehrani M, Touam F, Jerbi S, Bodard L, Jung C, Goujard C, Quertainmont Y, Duracinsky M, Segeral O, Blanc A, Peretti D, Cheret A, Chantalat C, Dulucq MJ, Levy Y, Lelievre JD, Lascaux AS, Dumont C, Boue F, Chambrin V, Abgrall S, Kansau I, Raho-Moussa M, De Truchis P, Dinh A, Davido B, Marigot D, Berthe H, Devidas A, Chevojon P, Chabrol A, Agher N, Lemercier Y, Chaix F, Turpault I, Bouchaud O, Honore P, Rouveix E, Reimann E, Belan AG, Godin Collet C, Souak S, Mortier E, Bloch M, Simonpoli AM, Manceron V, Cahitte I, Hiraux E, Lafon E, Cordonnier F, Zeng AF, Zucman D, Majerholc C, Bornarel D, Uludag A, Gellen-Dautremer J, Lefort A, Bazin C, Daneluzzi V, Gerbe J, Jeantils V, Coupard M, Patey O, Bantsimba J, Delllion S, Paz PC, Cazenave B, Richier L, Garrait V, Delacroix I, Elharrar B, Vittecoq D, Bolliot C, Lepretre A, Genet P, Masse V, Perrone V, Boussard JL, Chardon P, Froguel E, Simon P, Tassi S, Avettand Fenoel V, Barin F, Bourgeois C, Cardon F, Chaix ML, Delfraissy JF, Essat A, Fischer H, Lecuroux C, Meyer L, Petrov-Sanchez V, Rouzioux C, Saez-Cirion A, Seng R, Kuldanek K, Mullaney S, Young C, Zucchetti A, Bevan MA, McKernan S, Wandolo E, Richardson C, Youssef E, Green P, Faulkner S, Faville R, Herman S, Care C, Blackman H, Bellenger K, Fairbrother K, Phillips A, Babiker A, Delpech V, Fidler S, Clarke M, Fox J, Gilson R, Goldberg D, Hawkins D, Johnson A, Johnson M, McLean K, Nastouli E, Post F, Kennedy N, Pritchard J, Andrady U, Rajda N, Donnelly C, McKernan S, Drake S, Gilleran G, White D, Ross J, Harding J, Faville R, Sweeney J, Flegg P, Toomer S, Wilding H, Woodward R, Dean G, Richardson C, Perry N, Gompels M, Jennings L, Bansaal D, Browing M, Connolly L, Stanley B, Estreich S, Magdy A, O'Mahony C, Fraser P, Jebakumar SPR, David L, Mette R, Summerfield H, Evans M, White C, Robertson R, Lean C, Morris S, Winter A, Faulkner S, Goorney B, Howard L, Fairley I, Stemp C, Short L, Gomez M, Young F, Roberts M, Green S, Sivakumar K, Minton J, Siminoni A, Calderwood J, Greenhough D, DeSouza C, Muthern L, Orkin C, Murphy S, Truvedi M, McLean K, Hawkins D, Higgs C, Moyes A, Antonucci S, McCormack S, Lynn W, Bevan M, Fox J, Teague A, Anderson J, Mguni S, Post F, Campbell L, Mazhude C, Russell H, Gilson R, Carrick G, Ainsworth J, Waters A, Byrne P, Johnson M, Fidler S, Kuldanek K, Mullaney S, Lawlor V, Melville R, Sukthankar A, Thorpe S, Murphy C, Wilkins E, Ahmad S, Green P, Tayal S, Ong E, Meaden J, Riddell L, Loay D, Peacock K, Blackman H, Harindra V, Saeed AM, Allen S, Natarajan U, Williams O, Lacey H, Care C, Bowman C, Herman S, Devendra SV, Wither J, Bridgwood A, Singh G, Bushby S, Kellock D, Young S, Rooney G, Snart B, Currie J, Fitzgerald M, Arumainayyagam J, Chandramani S. A highly virulent variant of HIV-1 circulating in the Netherlands. Science 2022; 375:540-545. [PMID: 35113714 DOI: 10.1126/science.abk1688] [Show More Authors] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log10 increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV-CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences-is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence.
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Affiliation(s)
- Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - François Blanquart
- Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, Paris, France.,IAME, UMR 1137, INSERM, Université de Paris, Paris, France
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Astrid Gall
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Matthew Hall
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Margreet Bakker
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Swee Hoe Ong
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Lele Zhao
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David Bonsall
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mariateresa de Cesare
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jan Albert
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
| | - Norbert Bannert
- Division for HIV and Other Retroviruses, Department of Infectious Diseases, Robert Koch Institute, Berlin, Germany
| | - Jacques Fellay
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - M Kate Grabowski
- Department of Pathology, John Hopkins University, Baltimore, MD, USA
| | | | - Huldrych F Günthard
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Pia Kivelä
- Department of Infectious Diseases, Helsinki University Hospital, Helsinki, Finland
| | - Roger D Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | | | - Laurence Meyer
- INSERM CESP U1018, Université Paris Saclay, APHP, Service de Santé Publique, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Kholoud Porter
- Institute for Global Health, University College London, London, UK
| | - Matti Ristola
- Department of Infectious Diseases, Helsinki University Hospital, Helsinki, Finland
| | | | - Ben Berkhout
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Paul Kellam
- Kymab Ltd., Cambridge, UK.,Department of Infectious Diseases, Faculty of Medicine, Imperial College London, London, UK
| | - Marion Cornelissen
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Molecular Diagnostic Unit, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Peter Reiss
- Stichting HIV Monitoring, Amsterdam, Netherlands.,Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam and Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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20
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Oster AM, Lyss SB, McClung RP, Watson M, Panneer N, Hernandez AL, Buchacz K, Robilotto SE, Curran KG, Hassan R, Ocfemia MCB, Linley L, Perez SM, Phillip SA, France AM. HIV Cluster and Outbreak Detection and Response: The Science and Experience. Am J Prev Med 2021; 61:S130-S142. [PMID: 34686282 DOI: 10.1016/j.amepre.2021.05.029] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/28/2021] [Accepted: 05/06/2021] [Indexed: 11/30/2022]
Abstract
The Respond pillar of the Ending the HIV Epidemic in the U.S. initiative, which consists of activities also known as cluster and outbreak detection and response, offers a framework to guide tailored implementation of proven HIV prevention strategies where transmission is occurring most rapidly. Cluster and outbreak response involves understanding the networks in which rapid transmission is occurring; linking people in the network to essential services; and identifying and addressing gaps in programs and services such as testing, HIV and other medical care, pre-exposure prophylaxis, and syringe services programs. This article reviews the experience gained through 30 HIV cluster and outbreak responses in North America during 2000-2020 to describe approaches for implementing these core response strategies. Numerous jurisdictions that have implemented these response strategies have demonstrated success in improving outcomes related to HIV care and viral suppression, testing, use of prevention services, and reductions in transmission or new diagnoses. Efforts to address important gaps in service delivery revealed by cluster and outbreak detection and response can strengthen prevention efforts broadly through multidisciplinary, multisector collaboration. In this way, the Respond pillar embodies the collaborative, data-guided approach that is critical to the overall success of the Ending the HIV Epidemic in the U.S. initiative.
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Affiliation(s)
- Alexandra M Oster
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia; U.S. Public Health Service, Atlanta, Georgia.
| | - Sheryl B Lyss
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia; U.S. Public Health Service, Atlanta, Georgia
| | - R Paul McClung
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia; U.S. Public Health Service, Atlanta, Georgia
| | - Meg Watson
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Nivedha Panneer
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Angela L Hernandez
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Kate Buchacz
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Susan E Robilotto
- Division of State HIV/AIDS Programs, HIV/AIDS Bureau, Health Resources and Services Administration, Rockville, Maryland
| | - Kathryn G Curran
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Rashida Hassan
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - M Cheryl Bañez Ocfemia
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Laurie Linley
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Stephen M Perez
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia; U.S. Public Health Service, Atlanta, Georgia
| | - Stanley A Phillip
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Anne Marie France
- Division of HIV Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Atlanta, Georgia; U.S. Public Health Service, Atlanta, Georgia
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21
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Novitsky V, Steingrimsson J, Howison M, Dunn C, Gillani FS, Manne A, Li Y, Spence M, Parillo Z, Fulton J, Marak T, Chan P, Bertrand T, Bandy U, Alexander-Scott N, Hogan J, Kantor R. Longitudinal typing of molecular HIV clusters in a statewide epidemic. AIDS 2021; 35:1711-1722. [PMID: 34033589 PMCID: PMC8373695 DOI: 10.1097/qad.0000000000002953] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND HIV molecular epidemiology is increasingly integrated into public health prevention. We conducted cluster typing to enhance characterization of a densely sampled statewide epidemic towards informing public health. METHODS We identified HIV clusters, categorized them into types, and evaluated their dynamics between 2004 and 2019 in Rhode Island. We grouped sequences by diagnosis year, assessed cluster changes between paired phylogenies, t0 and t1, representing adjacent years and categorized clusters as stable (cluster in t0 phylogeny = cluster in t1 phylogeny) or unstable (cluster in t0 ≠ cluster in t1). Unstable clusters were further categorized as emerging (t1 phylogeny only) or growing (larger in t1 phylogeny). We determined proportions of each cluster type, of individuals in each cluster type, and of newly diagnosed individuals in each cluster type, and assessed trends over time. RESULTS A total of 1727 individuals with available HIV-1 subtype B pol sequences were diagnosed in Rhode Island by 2019. Over time, stable clusters and individuals in them dominated the epidemic, increasing over time, with reciprocally decreasing unstable clusters and individuals in them. Conversely, proportions of newly diagnosed individuals in unstable clusters significantly increased. Within unstable clusters, proportions of emerging clusters and of individuals in them declined; whereas proportions of newly diagnosed individuals in growing clusters significantly increased over time. CONCLUSION Distinct molecular cluster types were identified in the Rhode Island epidemic. Cluster dynamics demonstrated increasing stable and decreasing unstable clusters driven by growing, rather than emerging clusters, suggesting consistent in-state transmission networks. Cluster typing could inform public health beyond conventional approaches and direct interventions.
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Affiliation(s)
| | | | - Mark Howison
- Research Improving People’s Life, Providence, RI, USA
| | | | | | | | | | | | | | | | | | - Philip Chan
- Brown University, Providence, RI, USA
- Rhode Island Department of Health, Providence, RI, USA
| | | | - Utpala Bandy
- Rhode Island Department of Health, Providence, RI, USA
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22
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Brenner BG, Ibanescu RI, Osman N, Cuadra-Foy E, Oliveira M, Chaillon A, Stephens D, Hardy I, Routy JP, Thomas R, Baril JG, Leblanc R, Tremblay C, Roger M, The Montreal Primary HIV Infection (PHI) Cohort Study Group. The Role of Phylogenetics in Unravelling Patterns of HIV Transmission towards Epidemic Control: The Quebec Experience (2002-2020). Viruses 2021; 13:1643. [PMID: 34452506 PMCID: PMC8402830 DOI: 10.3390/v13081643] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 01/23/2023] Open
Abstract
Phylogenetics has been advanced as a structural framework to infer evolving trends in the regional spread of HIV-1 and guide public health interventions. In Quebec, molecular network analyses tracked HIV transmission dynamics from 2002-2020 using MEGA10-Neighbour-joining, HIV-TRACE, and MicrobeTrace methodologies. Phylogenetics revealed three patterns of viral spread among Men having Sex with Men (MSM, n = 5024) and heterosexuals (HET, n = 1345) harbouring subtype B epidemics as well as B and non-B subtype epidemics (n = 1848) introduced through migration. Notably, half of new subtype B infections amongst MSM and HET segregating as solitary transmissions or small cluster networks (2-5 members) declined by 70% from 2006-2020, concomitant to advances in treatment-as-prevention. Nonetheless, subtype B epidemic control amongst MSM was thwarted by the ongoing genesis and expansion of super-spreader large cluster variants leading to micro-epidemics, averaging 49 members/cluster at the end of 2020. The growth of large clusters was related to forward transmission cascades of untreated early-stage infections, younger at-risk populations, more transmissible/replicative-competent strains, and changing demographics. Subtype B and non-B subtype infections introduced through recent migration now surpass the domestic epidemic amongst MSM. Phylodynamics can assist in predicting and responding to active, recurrent, and newly emergent large cluster networks, as well as the cryptic spread of HIV introduced through migration.
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Affiliation(s)
- Bluma G. Brenner
- McGill Centre for Viral Diseases, Lady Davis Institute for Medical Research, Montréal, QC H3T 1E2, Canada; (R.-I.I.); (N.O.); (E.C.-F.); (M.O.)
- Department of Microbiology and Immunology, McGill University, Montréal, QC H4A 3J1, Canada
- Department of Medicine (Surgery, Infectious Disease), McGill University, Montréal, QC H3A 2M7, Canada
| | - Ruxandra-Ilinca Ibanescu
- McGill Centre for Viral Diseases, Lady Davis Institute for Medical Research, Montréal, QC H3T 1E2, Canada; (R.-I.I.); (N.O.); (E.C.-F.); (M.O.)
| | - Nathan Osman
- McGill Centre for Viral Diseases, Lady Davis Institute for Medical Research, Montréal, QC H3T 1E2, Canada; (R.-I.I.); (N.O.); (E.C.-F.); (M.O.)
- Department of Microbiology and Immunology, McGill University, Montréal, QC H4A 3J1, Canada
| | - Ernesto Cuadra-Foy
- McGill Centre for Viral Diseases, Lady Davis Institute for Medical Research, Montréal, QC H3T 1E2, Canada; (R.-I.I.); (N.O.); (E.C.-F.); (M.O.)
- Department of Microbiology and Immunology, McGill University, Montréal, QC H4A 3J1, Canada
| | - Maureen Oliveira
- McGill Centre for Viral Diseases, Lady Davis Institute for Medical Research, Montréal, QC H3T 1E2, Canada; (R.-I.I.); (N.O.); (E.C.-F.); (M.O.)
| | - Antoine Chaillon
- Department of Medicine, University of California, San Diego, CA 93903, USA;
| | - David Stephens
- Department of Mathematics and Statistics, McGill University, Montréal, QC H3A 0B9, Canada;
| | - Isabelle Hardy
- Département de Microbiologie et d’Immunologie et Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC H2X 0C1, Canada; (I.H.); (C.T.); (M.R.)
| | - Jean-Pierre Routy
- Chronic Viral Illness Service, McGill University Health Centre, Montréal, QC H3A 3J1, Canada;
| | - Réjean Thomas
- Clinique Médicale l’Actuel, Montréal, QC H2L 4P9, Canada;
| | - Jean-Guy Baril
- Clinique Médicale Urbaine du Quartier Latin, Montréal, QC H2L 4E9, Canada;
| | - Roger Leblanc
- Clinique Médicale OPUS, Montréal, QC H3A 1T1, Canada;
| | - Cecile Tremblay
- Département de Microbiologie et d’Immunologie et Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC H2X 0C1, Canada; (I.H.); (C.T.); (M.R.)
| | - Michel Roger
- Département de Microbiologie et d’Immunologie et Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC H2X 0C1, Canada; (I.H.); (C.T.); (M.R.)
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23
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Li M, Zhao J, Tang Q, Zhang Q, Wang Y, Zhang J, Hao Y, Bai X, Lu Z. Lamivudine improves cognitive decline in SAMP8 mice: Integrating in vivo pharmacological evaluation and network pharmacology. J Cell Mol Med 2021; 25:8490-8503. [PMID: 34374199 PMCID: PMC8419189 DOI: 10.1111/jcmm.16811] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/03/2021] [Accepted: 07/08/2021] [Indexed: 12/14/2022] Open
Abstract
The reverse transcriptase inhibitors such as lamivudine (3TC) play important roles in anti‐ageing, but their effects on neurodegenerative diseases caused by ageing are not clear, especially on the functions of the nervous system such as cognition. In this study, we administered 3TC to senescence‐accelerated mouse prone 8 (SAMP8) mice by gastric perfusion (100 mg/kg) for 4 weeks. Our results showed that 3TC significantly improved the ageing status of SAMP8 mice, especially the decline of cognitive ability evaluated by the Morris water maze test. To further investigate the molecular mechanisms of improving the ageing status of SAMP8 mice by 3TC, the qPCR and tissue staining methods were used to study the brain tissues (i.e., hippocampus and cortex) of mice, while the network pharmacology analysis was applied to investigate the potential targets of 3TC. The results showed that the mRNA levels of genes related to long interspersed element‐1, type 1 interferon response, the senescence‐associated secretion phenotype and the Alzheimer's disease in the hippocampus and cortex of SAMP8 mice were increased due to senescence, but this trend was reversed partially by 3TC. Results of histological studies showed that 3TC reduced the death of hippocampal neurons, while the results of network pharmacology analysis indicated that 3TC may exert its influence through multiple pathways, including the oestrogen signalling and the PI3K/Akt and neuroactive ligand‐receptor interaction signalling pathways, which we have verified through in vitro experiments. These findings provide evidence for the therapeutic potential of 3TC in the treatment of neurodegenerative diseases.
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Affiliation(s)
- Ming Li
- Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Jie Zhao
- Department of Radiology, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qi Tang
- Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qingchen Zhang
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yong Wang
- Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Jian Zhang
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yingying Hao
- Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Xiaohui Bai
- Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhiming Lu
- Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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24
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Bousali M, Dimadi A, Kostaki EG, Tsiodras S, Nikolopoulos GK, Sgouras DN, Magiorkinis G, Papatheodoridis G, Pogka V, Lourida G, Argyraki A, Angelakis E, Sourvinos G, Beloukas A, Paraskevis D, Karamitros T. SARS-CoV-2 Molecular Transmission Clusters and Containment Measures in Ten European Regions during the First Pandemic Wave. Life (Basel) 2021; 11:life11030219. [PMID: 33803490 PMCID: PMC8001481 DOI: 10.3390/life11030219] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/12/2021] [Accepted: 03/03/2021] [Indexed: 12/23/2022] Open
Abstract
Background: The spatiotemporal profiling of molecular transmission clusters (MTCs) using viral genomic data can effectively identify transmission networks in order to inform public health actions targeting SARS-CoV-2 spread. Methods: We used whole genome SARS-CoV-2 sequences derived from ten European regions belonging to eight countries to perform phylogenetic and phylodynamic analysis. We developed dedicated bioinformatics pipelines to identify regional MTCs and to assess demographic factors potentially associated with their formation. Results: The total number and the scale of MTCs varied from small household clusters identified in all regions, to a super-spreading event found in Uusimaa-FI. Specific age groups were more likely to belong to MTCs in different regions. The clustered sequences referring to the age groups 50–100 years old (y.o.) were increased in all regions two weeks after the establishment of the lockdown, while those referring to the age group 0–19 y.o. decreased only in those regions where schools’ closure was combined with a lockdown. Conclusions: The spatiotemporal profiling of the SARS-CoV-2 MTCs can be a useful tool to monitor the effectiveness of the interventions and to reveal cryptic transmissions that have not been identified through contact tracing.
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Affiliation(s)
- Maria Bousali
- Bioinformatics and Applied Genomics Unit, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (M.B.); (A.D.); (V.P.)
| | - Aristea Dimadi
- Bioinformatics and Applied Genomics Unit, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (M.B.); (A.D.); (V.P.)
| | - Evangelia-Georgia Kostaki
- Department of Hygiene Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, 15772 Athens, Greece; (E.-G.K.); (G.M.)
| | - Sotirios Tsiodras
- 4th Department of Internal Medicine & Infectious Diseases, School of Medicine, National and Kapodistrian University of Athens, 15772 Athens, Greece;
| | | | - Dionyssios N. Sgouras
- Laboratory of Medical Microbiology, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (D.N.S.); (E.A.)
| | - Gkikas Magiorkinis
- Department of Hygiene Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, 15772 Athens, Greece; (E.-G.K.); (G.M.)
| | - George Papatheodoridis
- Department of Gastroenterology, Medical School of National and Kapodistrian University of Athens, “Laiko” General Hospital of Athens, 11527 Athens, Greece;
| | - Vasiliki Pogka
- Bioinformatics and Applied Genomics Unit, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (M.B.); (A.D.); (V.P.)
- Laboratory of Medical Microbiology, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (D.N.S.); (E.A.)
| | - Giota Lourida
- Infectious Diseases Clinic A, Sotiria Chest Diseases Hospital, 11527 Athens, Greece; (G.L.); (A.A.)
| | - Aikaterini Argyraki
- Infectious Diseases Clinic A, Sotiria Chest Diseases Hospital, 11527 Athens, Greece; (G.L.); (A.A.)
| | - Emmanouil Angelakis
- Laboratory of Medical Microbiology, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (D.N.S.); (E.A.)
- IRD, APHM, VITROME, IHU-Mediterranean Infections, Aix Marseille University, 13005 Marseille, France
| | - George Sourvinos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71500 Heraklion, Greece;
| | - Apostolos Beloukas
- Department of Biomedical Sciences, University of West Attica, 12243 Athens, Greece
- Institute of Infection and Global Health, University of Liverpool, Liverpool L69 7BE, UK
- Correspondence: (A.B.); (D.P.); (T.K.); Tel.: +30-210-5385697 (A.B.); +30-210-7462114 (D.P.); +30-210-6478871 (T.K.)
| | - Dimitrios Paraskevis
- Department of Hygiene Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, 15772 Athens, Greece; (E.-G.K.); (G.M.)
- Correspondence: (A.B.); (D.P.); (T.K.); Tel.: +30-210-5385697 (A.B.); +30-210-7462114 (D.P.); +30-210-6478871 (T.K.)
| | - Timokratis Karamitros
- Bioinformatics and Applied Genomics Unit, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (M.B.); (A.D.); (V.P.)
- Laboratory of Medical Microbiology, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece; (D.N.S.); (E.A.)
- Correspondence: (A.B.); (D.P.); (T.K.); Tel.: +30-210-5385697 (A.B.); +30-210-7462114 (D.P.); +30-210-6478871 (T.K.)
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25
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Stansfield SE, Herbeck JT, Gottlieb GS, Abernethy NF, Murphy JT, Mittler JE, Goodreau SM. Test-and-treat coverage and HIV virulence evolution among men who have sex with men. Virus Evol 2021; 7:veab011. [PMID: 33633867 PMCID: PMC7893213 DOI: 10.1093/ve/veab011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
HIV set point viral load (SPVL), the viral load established shortly after initial infection, is a proxy for HIV virulence: higher SPVLs lead to higher risk of transmission and faster disease progression. Three models of test-and-treat scenarios, mainly in heterosexual populations, found that increasing treatment coverage selected for more virulent viruses. We modeled virulence evolution in a population of men who have sex with men (MSM) with increasing test-and-treat coverage. We extended a stochastic, dynamic network model (EvoNetHIV). We varied relationship patterns (MSM vs. heterosexual), HIV transmission models (increasing vs. plateauing probability of transmission at very high viral loads), and treatment roll-out (with explicit testing or fixed intervals between infection and treatment). In scenarios most similar to previous models (longer relational durations and the plateauing transmission function), we replicated trends previously found: increasing treatment coverage led to increased virulence (0.12 log10 increase in mean population SPVL between 20% and 100% treatment coverage). In scenarios reflecting MSM behavioral data using the increasing transmission function, increasing treatment coverage selected for viruses with lower virulence (0.16 log10 decrease in mean population SPVL between 20% and 100% treatment coverage). These findings emphasize the impact of sexual network conditions and transmission function details on predicted epidemiological and evolutionary outcomes. Varying these features creates very different evolutionary environments, which in turn lead to opposite effects in mean population SPVL evolution. Our results suggest that, under some realistic conditions, effective test-and-treat strategies may not face the previously reported tradeoff in which increasing coverage leads to evolution of greater virulence. This suggests instead that a virtuous cycle of increasing treatment coverage and diminishing virulence is possible.
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Affiliation(s)
- Sarah E Stansfield
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - Joshua T Herbeck
- Department of Global Health, University of Washington, Seattle, WA 98195, USA
| | - Geoffrey S Gottlieb
- Departments of Medicine & Global Health, University of Washington, Seattle, WA 98195, USA
| | - Neil F Abernethy
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - James T Murphy
- Department of Microbiology, University of Washington, Seattle, WA 98195, USA
| | - John E Mittler
- Department of Microbiology, University of Washington, Seattle, WA 98195, USA
| | - Steven M Goodreau
- Department of Anthropology, University of Washington, Seattle, WA 98195, USA
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26
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Hassan A, De Gruttola V, Hu YW, Sheng Z, Poortinga K, Wertheim JO. The Relationship Between the Human Immunodeficiency Virus-1 Transmission Network and the HIV Care Continuum in Los Angeles County. Clin Infect Dis 2020; 71:e384-e391. [PMID: 32020172 PMCID: PMC7904072 DOI: 10.1093/cid/ciaa114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/03/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Public health action combating human immunodeficiency virus (HIV) includes facilitating navigation through the HIV continuum of care: timely diagnosis followed by linkage to care and initiation of antiretroviral therapy to suppress viral replication. Molecular epidemiology can identify rapidly growing HIV genetic transmission clusters. How progression through the care continuum relates to transmission clusters has not been previously characterized. METHODS We performed a retrospective study on HIV surveillance data from 5226 adult cases in Los Angeles County diagnosed from 2010 through 2014. Genetic transmission clusters were constructed using HIV-TRACE. Cox proportional hazard models were used to estimate the impact of transmission cluster growth on the time intervals between care continuum events. Gamma frailty models incorporated the effect of heterogeneity associated with genetic transmission clusters. RESULTS In contrast to our expectations, there were no differences in time to the care continuum events among individuals in clusters with different growth dynamics. However, upon achieving viral suppression, individuals in high growth clusters were slower to experience viral rebound (hazard ratio 0.83, P = .011) compared with individuals in low growth clusters. Heterogeneity associated with cluster membership in the timing to each event in the care continuum was highly significant (P < .001), with and without adjustment for transmission risk and demographics. CONCLUSIONS Individuals within the same transmission cluster have more similar trajectories through the HIV care continuum than those across transmission clusters. These findings suggest molecular epidemiology can assist public health officials in identifying clusters of individuals who may benefit from assistance in navigating the HIV care continuum.
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Affiliation(s)
- Adiba Hassan
- Department of Medicine, University of California, San Diego, California, USA
| | - Victor De Gruttola
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Family Medicine, University of California, San Diego, California, USA
| | - Yunyin W Hu
- Division of HIV and STD Programs, Los Angeles County Department of Public Health, Los Angeles, California, USA
| | - Zhijuan Sheng
- Division of HIV and STD Programs, Los Angeles County Department of Public Health, Los Angeles, California, USA
| | - Kathleen Poortinga
- Division of HIV and STD Programs, Los Angeles County Department of Public Health, Los Angeles, California, USA
| | - Joel O Wertheim
- Department of Medicine, University of California, San Diego, California, USA
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27
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Hecht LB, Thompson PC, Rosenthal BM. Assessing the evolutionary persistence of ecological relationships: A review and preview. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2020; 84:104441. [PMID: 32622083 PMCID: PMC7327472 DOI: 10.1016/j.meegid.2020.104441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 12/13/2022]
Abstract
Species interactions, such as pollination, parasitism and predation, form the basis of functioning ecosystems. The origins and resilience of such interactions therefore merit attention. However, fossils only occasionally document ancient interactions, and phylogenetic methods are blind to recent interactions. Is there some other way to track shared species experiences? "Comparative demography" examines when pairs of species jointly thrived or declined. By forging links between ecology, epidemiology, and evolutionary biology, this method sheds light on biological adaptation, species resilience, and ecosystem health. Here, we describe how this method works, discuss examples, and suggest future directions in hopes of inspiring interest, imitators, and critics.
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Affiliation(s)
| | - Peter C. Thompson
- USDA-Agricultural Research Service, Animal Parasitic Diseases Lab, Beltsville, MD 20705 USA
| | - Benjamin M. Rosenthal
- USDA-Agricultural Research Service, Animal Parasitic Diseases Lab, Beltsville, MD 20705 USA,Corresponding author
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28
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High frequency of new recombinant forms in HIV-1 transmission networks demonstrated by full genome sequencing. INFECTION GENETICS AND EVOLUTION 2020; 84:104365. [PMID: 32417307 DOI: 10.1016/j.meegid.2020.104365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/08/2020] [Accepted: 05/12/2020] [Indexed: 11/22/2022]
Abstract
The HIV-1 epidemic in Belgium is primarily driven by MSM. In this patient population subtype B predominates but an increasing presence of non-B subtypes has been reported. We aimed to define to what extent the increasing subtype heterogeneity in a high at risk population induces the formation and spread of new recombinant forms. The study focused on transmission networks that reflect the local transmission to an important extent. One hundred and five HIV-1 transmission clusters were identified after phylogenetic analysis of 2849 HIV-1 pol sequences generated for the purpose of baseline drug resistance testing between 2013 and 2017. Of these 105 clusters, 62 extended in size during the last two years and were therefore considered as representing ongoing transmission. These 62 clusters included 774 patients in total. From each cluster between 1 and 3 representative patients were selected for near full-length viral genome sequencing. In total, the full genome sequence of 101 patients was generated. Indications for the presence of a new recombinant form were found for 10 clusters. These 10 clusters represented 105 patients or 13.6% of the patients covered by the study. The findings clearly show that new recombinant strains highly contribute to local transmission, even in an epidemic that is largely MSM and subtype B driven. This is an evolution that needs to be monitored as reshuffling of genome fragments through recombination may influence the transmissibility of the virus and the pathology of the infection. In addition, important changes in the sequence of the viral genome may challenge the performance of tests used for diagnosis, patient monitoring and drug resistance analysis.
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29
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Molecular network-based intervention brings us closer to ending the HIV pandemic. Front Med 2020; 14:136-148. [PMID: 32206964 DOI: 10.1007/s11684-020-0756-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 02/13/2020] [Indexed: 01/08/2023]
Abstract
Precise identification of HIV transmission among populations is a key step in public health responses. However, the HIV transmission network is usually difficult to determine. HIV molecular networks can be determined by phylogenetic approach, genetic distance-based approach, and a combination of both approaches. These approaches are increasingly used to identify transmission networks among populations, reconstruct the history of HIV spread, monitor the dynamics of HIV transmission, guide targeted intervention on key subpopulations, and assess the effects of interventions. Simulation and retrospective studies have demonstrated that these molecular network-based interventions are more cost-effective than random or traditional interventions. However, we still need to address several challenges to improve the practice of molecular network-guided targeting interventions to finally end the HIV epidemic. The data remain limited or difficult to obtain, and more automatic real-time tools are required. In addition, molecular and social networks must be combined, and technical parameters and ethnic issues warrant further studies.
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