title: Quantification of Hepatitis C Virus Cell-to-Cell Spread Using a Stochastic Modeling Approach.
abstract: It has been proposed that viral cell-to-cell transmission plays a role in establishing and maintaining chronic infections. Thus, understanding the mechanisms and kinetics of cell-to-cell spread is fundamental to elucidating the dynamics of infection and may provide insight into factors that determine chronicity. Because hepatitis C virus (HCV) spreads from cell to cell and has a chronicity rate of up to 80% in exposed individuals, we examined the dynamics of HCV cell-to-cell spread in vitro and quantified the effect of inhibiting individual host factors. Using a multidisciplinary approach, we performed HCV spread assays and assessed the appropriateness of different stochastic models for describing HCV focus expansion. To evaluate the effect of blocking specific host cell factors on HCV cell-to-cell transmission, assays were performed in the presence of blocking antibodies and/or small-molecule inhibitors targeting different cellular HCV entry factors. In all experiments, HCV-positive cells were identified by immunohistochemical staining and the number of HCV-positive cells per focus was assessed to determine focus size. We found that HCV focus expansion can best be explained by mathematical models assuming focus size-dependent growth. Consistent with previous reports suggesting that some factors impact HCV cell-to-cell spread to different extents, modeling results estimate a hierarchy of efficacies for blocking HCV cell-to-cell spread when targeting different host factors (e.g., CLDN1 > NPC1L1 > TfR1). This approach can be adapted to describe focus expansion dynamics under a variety of experimental conditions as a means to quantify cell-to-cell transmission and assess the impact of cellular factors, viral factors, and antivirals.
  
  
  OBJECTIVE
  The ability of viruses to efficiently spread by direct cell-to-cell transmission is thought to play an important role in the establishment and maintenance of viral persistence. As such, elucidating the dynamics of cell-to-cell spread and quantifying the effect of blocking the factors involved has important implications for the design of potent antiviral strategies and controlling viral escape. Mathematical modeling has been widely used to understand HCV infection dynamics and treatment response; however, these models typically assume only cell-free virus infection mechanisms. Here, we used stochastic models describing focus expansion as a means to understand and quantify the dynamics of HCV cell-to-cell spread in vitro and determined the degree to which cell-to-cell spread is reduced when individual HCV entry factors are blocked. The results demonstrate the ability of this approach to recapitulate and quantify cell-to-cell transmission, as well as the impact of specific factors and potential antivirals.
authors.affiliation: Center for Modeling and Simulation in the Biosciences, BioQuant Center, Heidelberg University, Heidelberg, Germany frederik.graw@bioquant.uni-heidelberg.de suprichard@lumc.edu.
authors.forename: Frederik
authors.initials: F
authors.lastname: Graw
authors.affiliation: Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Loyola University Medical Center, Maywood, Illinois, USA.
authors.forename: Danyelle N
authors.initials: DN
authors.lastname: Martin
authors.affiliation: Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
authors.forename: Alan S
authors.initials: AS
authors.lastname: Perelson
authors.affiliation: Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Loyola University Medical Center, Maywood, Illinois, USA Department of Microbiology and Immunology, Loyola University Medical Center, Maywood, Illinois, USA frederik.graw@bioquant.uni-heidelberg.de suprichard@lumc.edu.
authors.forename: Susan L
authors.initials: SL
authors.lastname: Uprichard
authors.affiliation: Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Loyola University Medical Center, Maywood, Illinois, USA Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
authors.forename: Harel
authors.initials: H
authors.lastname: Dahari
mesh_terms: D002460:Cell Line; D016174:Hepacivirus / Q000502:physiology*; D022781:Hepatocytes / Q000821:virology*; D006801:Humans; D015233:Models, Statistical; D053586:Virus Internalization; D057074:Virus Release
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