Spatial Models of Intrahepatic Hepatitis C Virus Propagation in Humans

 描述（由申请人提供）：丙型肝炎病毒（HCV）感染全球超过1.7亿人。虽然慢性HCV可以治疗，但它是美国慢性病毒感染中的主要死亡原因，导致肝纤维化，肝硬化和肝细胞癌（HCC），这些都不能通过治疗完全逆转。HCV研究具有挑战性：受感染的器官（肝脏）不容易接近，血液是感染动力学的一个小窗口;没有代表性的组织培养模型;动物模型缺乏代表性的免疫力。尽管存在挑战，但慢性HCV感染是研究病毒通过实体组织传播的理想模型，因为i）HCV感染主要局限于一种细胞类型（肝细胞），ii）HCV复制在慢性感染中主要处于稳态，以及iii）外部HCV多样性是巨大的，形成准种，其允许对局部病毒传播进行系统发育推断。我们组建了一个来自约翰霍普金斯大学（JHU）、洛斯阿拉莫斯国家实验室（LANL）和英国兰开斯特大学的多学科团队，该团队结合了翻译病毒学、动态病毒建模和空间统计学方面的互补技能。JHU团队已经开发出独特的能力，通过单细胞激光捕获显微切割（scLCM）测量单个肝细胞中的HCV感染和序列变异，并获得了HCV感染者肝脏活检的珍贵资料库。LANL的研究人员已经开发了多尺度的HCV感染模型（例如，细胞内到全身），而兰开斯特大学的研究人员是空间统计学的世界领导者。我们的假设是，丙型肝炎病毒传播，并限制本地之间的平衡，从受感染的肝细胞和先天免疫控制的传播。我们将使用scLCM从HCV感染者的数千个细胞空间映射HCV复制，多样化和局部免疫反应;我们将使用这些数据来开发HCV通过组织传播的机制模型。我们将利用这些工具来实现以下具体目标。目标1.详细描述感染细胞簇、肝内HCV RNA景观及其与血清HCV RNA水平的关系。目标二。确定肝内HCV传播是否是由于局部感染而不是由于全身传播。目标3。确定局部先天性免疫反应是否抑制HCV肝内传播。我们的目标是综合的、创新的，但又是可行的。我们的策略不仅对理解HCV传播很重要，而且可以广泛应用于其他慢性感染：事实上，我们的系统是此类研究的理想模型。此外，我们的方法开辟了新的途径，并为未来的调查提供了新的工具。

 DESCRIPTION (provided by applicant): Hepatitis C virus (HCV) infects >170 million people worldwide. Although chronic HCV can be treated, it is the leading cause of death in the US among chronic viral infections, resulting in liver fibrosis, cirrhosis, and hepatocellular carcinom (HCC), that are not fully reversed by therapy. HCV is challenging to study: the infected organ (the liver) is not easily accessible, and blood is a small window on infection dynamics; there is no representative tissue culture model; and animal models lack representative immunity. Despite the challenges, chronic HCV infection is an ideal model to study the spread of viruses through solid tissues because i) HCV infection is predominantly constrained to one cell type (hepatocyte), ii) HCV replication is largely at steady state in chronic infection, and iii) within-ost HCV diversity is immense, forming a quasispecies that permits phylogenetic inference of local viral spread. We assembled a multidisciplinary team from Johns Hopkins University (JHU), Los Alamos National Laboratory (LANL) and Lancaster University, UK, that combines complementary skills in translational virology, dynamic viral modeling and spatial statistics. The JHU team has developed unique capabilities to measure HCV infection and sequence variation in single hepatocytes by single-cell laser capture microdissection (scLCM) and has access to a precious repository of liver biopsies from HCV-infected persons. The researchers at LANL have developed models of HCV infection at multiple scales (e.g., intracellular to whole body), and the investigators at Lancaster University are world leaders in spatial statistics. Our hypothesis is that HCV spreads and is constrained locally by a balance between propagation from infected hepatocytes and innate immune control. We will employ scLCM for thousands of cells from HCV-infected people to spatially map HCV replication, diversification, and local immune responses; we will use this data to develop mechanistic models of HCV spread through tissues. We will employ these tools to address the following specific aims. Aim 1. To characterize in detail infected cell clusters, the intrahepatic HCV RNA landscape, and their relation to serum HCV RNA levels. Aim 2. To determine if intrahepatic HCV spread occurs due to localized infection rather than due to systemic spread. Aim 3. To determine if local innate immune responses constrain intrahepatic HCV spread. Our aims are integrated and innovative, yet feasible. Not only is our strategy important in understanding HCV spread, but can be broadly applied to other chronic infections: indeed our system is an ideal model for such studies. In addition, our approaches open new avenues and provide new tools for future investigations.