乙型肝炎动物模型的研究现状与挑战
Current research status and challenges of animal models for hepatitis B
乙型肝炎病毒(HBV)感染是全球面临的主要健康问题之一,是导致肝硬化和肝细胞癌的重要病因之一。由于HBV感染具有严格的种属特异性,目前尚未建立能够支持完整HBV感染周期并真实模拟宿主免疫和发病机制的动物模型。现有用于HBV研究的动物模型包括黑猩猩、树鼩和小鼠等多种宿主,以及利用相关嗜肝病毒建立的替代模型。尽管这些模型在研究HBV复制、免疫反应及抗病毒药物评估方面发挥了重要作用,但仍存在伦理、感染效率低、成本高或缺乏持续感染等局限。近年来,人源化肝脏与免疫系统的小鼠模型、转基因模型及病毒载体介导的感染模型等新策略的出现,显著推动了HBV生物学研究进展。未来,通过结合基因编辑、组织工程及多系统整合等新技术,构建更符合人类病理生理特征的HBV感染动物模型,将为深入理解病毒-宿主互作机制、探索HBV清除途径及开发根治性治疗策略提供坚实基础。
Hepatitis B virus (HBV) infection is one of the major global health problems, and it can lead to the development of liver cirrhosis and hepatocellular carcinoma. Due to the strict species specificity of HBV infection, no animal model has yet been established to fully support the complete life cycle of HBV infection and accurately reflect host immune responses and pathogenesis. Current animal models used for HBV research include various hosts such as chimpanzees, tree shrews, and mice, as well as surrogate models based on related hepatotropic viruses. Although these models have contributed significantly to the research on HBV replication, immune response, and antiviral drug evaluation, they still have certain limitations such as ethical concerns, low infection efficiency, high costs, and a lack of persistent infection. In recent years, the development of novel strategies, such as humanized mouse models with reconstituted human liver and immune systems, transgenic models, and viral vector-mediated infection systems, has greatly promoted the research on HBV biology. In the future, with the integration of emerging technologies including gene editing, tissue engineering, and multi-system reconstruction, it is possible to establish HBV infection models that can more closely mimic human pathophysiology, thereby laying a robust foundation for understanding virus-host interactions, exploring the pathways for viral clearance, and developing radical treatment strategies.
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新发突发与重大传染病防控国家科技重大专项(2025ZD01906700)
中央高校基本科研业务费(2042024kf0026)
国家自然科学基金(32570193)
国家自然科学基金(81971936)
国家自然科学基金(32300131)
中国博士后科学基金(2023M732695)
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