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Verfasst von:Graw, Frederik [VerfasserIn]   i
Titel:Inferring viral dynamics in chronically HCV infected patients from the spatial distribution of infected hepatocytes
Verf.angabe:Frederik Graw, Ashwin Balagopal, Abraham J. Kandathil, Stuart C. Ray, David L. Thomas, Ruy M. Ribeiro, Alan S. Perelson
E-Jahr:2014
Jahr:November 13, 2014
Fussnoten:Gesehen am 01.09.2020
Titel Quelle:Enthalten in: Public Library of SciencePLoS Computational Biology
Ort Quelle:San Francisco, Calif. : Public Library of Science, 2005
Jahr Quelle:2014
Band/Heft Quelle:10(2014,11) Artikel-Nummer e1003934, 15 Seiten
ISSN Quelle:1553-7358
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 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. Our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.
DOI:doi:10.1371/journal.pcbi.1003934
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1371/journal.pcbi.1003934
 Volltext: https://dx.plos.org/10.1371/journal.pcbi.1003934
 DOI: https://doi.org/10.1371/journal.pcbi.1003934
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1728491886
Verknüpfungen:→ Zeitschrift

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