Genotype Distribution and Migration Patterns of Hepatitis C Virus in Shandong Province, China: Molecular Epidemiology and Phylogenetic Study.

IF 3.8 3区 医学 Q2 MEDICAL INFORMATICS
Lin Lin, Guoyong Wang, Lianzheng Hao, Tingbin Yan
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引用次数: 0

Abstract

Background: Hepatitis C virus (HCV) remains a significant public health concern in China, particularly in Shandong Province, where detailed molecular epidemiological data are limited. HCV exhibits substantial genetic diversity, and understanding its genotype distribution and transmission dynamics is critical for developing effective control strategies.

Objective: This study aimed to investigate the genetic diversity, geographic dissemination, and evolutionary history of HCV genotypes in Shandong Province, China, using molecular techniques and phylogenetic methods.

Methods: A total of 320 HCV-positive serum samples were collected from multiple hospitals across Shandong Province between 2013 and 2021. HCV RNA was extracted and amplified targeting the 5' untranslated region (UTR), Core, and NS5B regions. Sequencing was conducted, and genotypes were determined using the National Center for Biotechnology Information's Basic Local Alignment Search Tool (NCBI BLAST). Phylogenetic trees were constructed using maximum likelihood methods with the general time reversible with Gamma-distributed rate variation among sites [(GTR)+Gamma model]. The temporal and geographic evolution of the major subtypes (1b and 2a) was analyzed using Bayesian Markov chain Monte Carlo (MCMC) methods implemented in Bayesian Evolutionary Analysis Sampling Trees (BEAST). The Bayesian skyline plot (BSP) was used to infer population dynamics and estimate the time to the most recent common ancestor (tMRCA).

Results: Genotypes 1b (n=165) and 2a (n=131) were identified as the predominant subtypes, with a small number of genotypes 3b, 6a, 6k, and potential recombinant strains also detected. Phylogenetic analysis revealed distinct evolutionary clustering of 1b and 2a strains, suggesting multiple diffusion events within the province. The tMRCA of subtypes 1b and 2a were estimated to be 1957 and 1979, respectively. Bayesian skyline analysis showed that both subtypes experienced long-term population stability, followed by a rapid expansion period between 2014 and 2019 (1b) and 2014 to 2016 (2a), respectively. The analysis also identified key transmission hubs such as Jinan, Liaocheng, Tai'an, and Dezhou, indicating city-level variations in HCV spread.

Conclusions: This study provides data-supported insights into the genotypic landscape and evolutionary patterns of HCV in Shandong Province. The identification of dominant subtypes, potential recombinant strains, and regional transmission pathways enhances our understanding of local HCV epidemiology. These findings have implications for public health policy, resource allocation, and targeted treatment strategies. The integration of molecular epidemiology and phylogenetics offers a valuable model for infectious disease surveillance and control in similar settings.

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山东省丙型肝炎病毒基因型分布和迁移模式:分子流行病学和系统发育研究
背景:丙型肝炎病毒(HCV)在中国仍然是一个重要的公共卫生问题,特别是在山东省,那里详细的分子流行病学数据有限。HCV具有丰富的遗传多样性,了解其基因型分布和传播动态对于制定有效的控制策略至关重要。目的:利用分子技术和系统发育方法,研究山东省丙型肝炎病毒基因型的遗传多样性、地理分布和进化历史。方法:2013 - 2021年在山东省多家医院采集320份hcv阳性血清样本。提取并扩增HCV RNA,靶向5'非翻译区(UTR)、Core和NS5B区。测序完成,使用国家生物技术信息中心的基本局部比对搜索工具(NCBI BLAST)确定基因型。采用最大似然方法构建系统发育树,一般时间可逆,位点间的Gamma分布速率变化[(GTR)+Gamma模型]。利用贝叶斯进化分析采样树(BEAST)中的贝叶斯马尔可夫链蒙特卡罗(MCMC)方法分析了主要亚型(1b和2a)的时间和地理演变。利用贝叶斯天际线图(BSP)推测种群动态,并估计到最近共同祖先(tMRCA)的时间。结果:以1b基因型(n=165)和2a基因型(n=131)为主,同时检出少量3b、6a、6k基因型及潜在重组菌株。系统发育分析显示,菌株1b和2a具有明显的进化聚类性,表明该省存在多次扩散事件。1b和2a亚型的tMRCA分别为1957年和1979年。贝叶斯天际线分析表明,这两种亚型分别在2014 - 2019年(1b)和2014 - 2016年(2a)经历了长期的种群稳定,随后分别经历了快速扩张期。分析还确定了济南、聊城、泰安和德州等主要传播中心,表明HCV传播在城市层面存在差异。结论:本研究为山东省丙型肝炎病毒的基因型格局和进化模式提供了数据支持。对优势亚型、潜在重组菌株和区域传播途径的鉴定增强了我们对当地HCV流行病学的了解。这些发现对公共卫生政策、资源分配和有针对性的治疗策略具有启示意义。分子流行病学和系统遗传学的结合为类似环境下的传染病监测和控制提供了一个有价值的模型。
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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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