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

Graw F, Balagopal A, Kandathil AJ, Ray SC, Thomas DL, Ribeiro RM, Perelson AS (2014)

Publication Type: Journal article

Publication year: 2014

Journal

PLoS Computational Biology Public Library of Science

Book Volume: 10

Journal Issue: 11

DOI: 10.1371/journal.pcbi.1003934

Abstract

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 (Formula presented.) 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. Our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.

Authors with CRIS profile

Frederik Graw

Involved external institutions

Los Alamos National Laboratory US United States (USA) (US) Johns Hopkins University (JHU) US United States (USA) (US)

How to cite

APA:

Graw, F., Balagopal, A., Kandathil, A.J., Ray, S.C., Thomas, D.L., Ribeiro, R.M., & Perelson, A.S. (2014). Inferring Viral Dynamics in Chronically HCV Infected Patients from the Spatial Distribution of Infected Hepatocytes. PLoS Computational Biology, 10(11). https://doi.org/10.1371/journal.pcbi.1003934

MLA:

Graw, Frederik, et al. "Inferring Viral Dynamics in Chronically HCV Infected Patients from the Spatial Distribution of Infected Hepatocytes." PLoS Computational Biology 10.11 (2014).

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