Inferring Viral Dynamics in Chronically HCV Infected Patients from the Spatial Distribution of Infected Hepatocytes

Journal Article · Thu Nov 13 00:00:00 EST 2014 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1003934· OSTI ID:1259480

Graw, Frederik ^[1]; Balagopal, Ashwin ^[2]; Kandathil, Abraham J. ^[2]; Ray, Stuart C. ^[2]; Thomas, David L. ^[2]; Ribeiro, Ruy M. ^[3]; Perelson, Alan S. ^[3]; Yates, Andrew J. ^[4]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Heidelberg Univ. (Germany)
2. Johns Hopkins Univ., Baltimore, MD (United States)
3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
4. Univ. of Glasgow, Scotland (United Kingdom)

Chronic liver infection by hepatitis C virus (HCV) is a major public health concern. Despite partly successful treatment options, several aspects of intrahepatic HCV infection dynamics are still poorly understood, including the preferred mode of viral propagation, as well as the proportion of infected hepatocytes. Answers to these questions have important implications for the development of therapeutic interventions. In this study, we present methods to analyze the spatial distribution of infected hepatocytes obtained by single cell laser capture microdissection from liver biopsy samples of patients chronically infected with HCV. By characterizing the internal structure of clusters of infected cells, we are able to evaluate hypotheses about intrahepatic infection dynamics. We found that individual clusters on biopsy samples range in size from 4-50 infected cells. In addition, the HCV RNA content in a cluster declines from the cell that presumably founded the cluster to cells at the maximal cluster extension. These observations support the idea that HCV infection in the liver is seeded randomly (e.g. from the blood) and then spreads locally. Assuming that the amount of intracellular HCV RNA is a proxy for how long a cell has been infected, we estimate based on models of intracellular HCV RNA replication and accumulation that cells in clusters have been infected on average for less than a week. Further, we do not find a relationship between the cluster size and the estimated cluster expansion time. Lastly, our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-06NA25396; GM103452; DA016078l; EY001765
• OSTI ID: 1259480
• Journal Information: PLoS Computational Biology (Online), Vol. 10, Issue 11; ISSN 1553-7358
• Publisher: Public Library of Science
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES