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Spatial Models of Intrahepatic Hepatitis C Virus Propagation in Humans

丙型肝炎病毒在人体中传播的空间模型

基本信息

批准号：
9882937
负责人：
ASHWIN BALAGOPAL
金额：
$ 74.65万
依托单位：
TRIAD NATIONAL SECURITY, LLC
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-02-01 至 2022-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9882937
关键词：
Address Animal Model Antiviral Therapy Blood Cause of Death Cells Characteristics Chronic Chronic Hepatitis C Cirrhosis Complement Data Dendritic Cells Disease model Equilibrium Focal Infection Future Hepatic Hepatitis B Hepatitis C Hepatitis C virus Hepatocyte Human Immune Immune response Immune signaling Immunity In Situ Infection Innate Immune Response Interferon-alpha Investigation Kupffer Cells Laboratories Liver Liver Fibrosis Maps Mathematics Measures Minority Modeling Organ Pathology Patients Persons Phylogenetic Analysis Primary carcinoma of the liver cells Property Research Personnel Sampling Solid Spatial Distribution Statistical Models System Systemic infection Testing Tissues Universities Validation Variant Viral Virus Virus Diseases Virus Replication cell type chronic infection in vivo innovation insight intrahepatic laser capture microdissection liver biopsy liver transplantation macrophage mathematical methods model building multidisciplinary novel strategies public health relevance repository response simulation single cell analysis skills statistics tissue culture tool viral RNA virology

项目摘要

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.