NIH: Spatial Models of Intrahepatic Hepatitis Virus Propagation in Humans

摘要 意义：全球约有2.6亿人感染乙肝病毒，是导致肝脏疾病的主要原因 世界上最严重的疾病和肝细胞癌。尽管有有效的预防感染的疫苗， 慢性乙型肝炎病毒感染(CHB)一旦确诊就很难治愈。病毒遗传物质进入 感染细胞的细胞核产生稳定的共价闭合环状DNA(CccDNA)分子或线性 整合到宿主细胞染色体中的形式(IDNA)。现有的治疗方法对cccdna的影响很小。 因此成功率很低。治疗中断往往会导致HBVDNA反弹和 复发性肝炎。此外，即使接受治疗，慢性乙肝患者的肝病也往往进展为失代偿。 需要肝移植的肝硬变。这一过程因几种病毒联合感染而加剧，例如与 HIV或丙型肝炎病毒(HCV)。因此，需要改进治疗慢性乙肝的疗法。 创新：我们的建议在三个方面是创新的。一)获取珍贵和独特的肝脏样本 组织；二)使用最先进的实验技术进行单细胞分析和同期血液 生物标志物；iii)根据我们丰富的数据集开发新的多尺度乙肝病毒感染模型， 都是为临床使用而设计的。 方法：乙肝病毒有一个复杂的生命周期，包括细胞内和细胞外两个阶段。我们队有 开发了在单个肝细胞水平上询问乙肝病毒胞内阶段的工具。我们已经做出了 对单个细胞中乙肝病毒的初步观察，但直到最近才开始将细胞内和 乙肝病毒的胞外期。因此，我们准备揭开乙肝病毒生物学中血液和肝脏之间的关键联系。 使用一种综合的方法，我们建议开发乙肝病毒的模型，以表征 生物体统一了我们对病毒胞内和胞外阶段的理解。我们的具体目标是： I)开发细胞内和细胞外乙肝病毒复制的综合模型，测量和 在血液和肝脏的单细胞水平上对一组全面的乙肝相关生物标志物进行建模， 未治疗和治疗的慢性乙肝；以及ii)建立乙肝病毒在肝脏中的空间传播和持续的模型， 利用单细胞病毒测序技术在中国人群中建立遗传相关的乙肝病毒变异株的空间联系 肝脏。这些数据和模型将允许推断本地和非本地的乙肝病毒传播。具体来说， 我们将测试感染了乙肝病毒的肝细胞的克隆性增殖是否有助于持续 感染细胞，这不仅对治疗研究具有重要意义，而且对肝细胞癌的发生也有重要意义。 影响：随着多种治疗乙肝病毒的新化合物正在开发中，在 这一建议将填补我们在了解乙肝病毒复制和发病机制方面的空白。我们和之前的努力 其他人在模拟丙型肝炎病毒和艾滋病毒方面对开发成功的治疗方法也很重要 感染。我们预计，这里产生的新数据和模型将在推动乙肝病毒方面产生同样的影响 心理治疗。

Summary Significance: Hepatitis B virus (HBV) infects ~260 million people worldwide and is the leading cause of liver disease and hepatocellular carcinoma (HCC) in the world. Despite an effective vaccine for infection prevention, chronic hepatitis B virus infection (CHB) is difficult to cure once established. The viral genetic material enters the infected cell's nucleus generating a stable covalently closed-circular DNA (cccDNA) molecule or a linear form that integrates into the host cell's chromosome (iDNA). Existing treatments have little effect on cccDNA and as a result have poor success rates. Treatment interruption often leads to rebound of HBV DNA and recurrent hepatitis. In addition, even with treatment, liver disease in CHB often progresses to decompensated cirrhosis requiring liver transplantation. This process is exacerbated by several viral co-infections, such as with HIV or hepatitis C virus (HCV). Thus, improved therapeutics for CHB cure are required. Innovation: Our proposal is innovative in three aspects. i) access to precious and unique samples of liver tissue; ii) using state-of-the-art experimental techniques for single cell analyses and contemporaneous blood biomarkers; iii) development of new multi-scale models of HBV infection informed by our rich data sets, which are designed for clinical usefulness. Approach: HBV has a complex lifecycle comprising intracellular and extracellular phases. Our team has developed tools to interrogate the intracellular phase of HBV at the single-hepatocyte level. We have made preliminary observations of HBV in single cells but have only recently begun to link the intracellular and extracellular phases of HBV. Thus, we are poised to uncover key links in HBV biology between blood and liver. Using an integrated approach, we propose developing models of HBV that characterize replication in the organism uniting our understanding of intracellular and extracellular phases of the virus. Our specific aims are: i) To develop integrated models of intracellular and extracellular HBV replication, measuring and modeling a comprehensive set of HBV-related biomarkers in the blood and at the single-cell level in the liver, in untreated and treated CHB; and ii) To develop models of HBV spatial spread and persistence in the liver, using single-cell viral sequencing of HBV to make spatial linkages between genetically related HBV variants in the liver. These data and models will allow inferences about local versus non-local HBV spread. Specifically, we will test if clonal proliferation of HBV infected hepatocytes may contribute to continued persistence of infected cells, which has important implications not only for cure research but also for HCC oncogenesis. Impact: As multiple novel compounds for HBV treatment are in development, the timely insights generated in this proposal will fill gaps in our understanding of HBV replication and pathogenesis. Previous efforts by us and others in modeling HCV and HIV were important in the development of successful therapies for those infections. We expect the new data and models generated here to have the same impact in advancing HBV therapy.