Higher Level of Replication Efficiency of 2009 (H1N1) Pandemic Influenza Virus than Those of Seasonal and Avian Strains: Kinetics from Epithelial Cell Culture and Computational Modeling

Abstract

The pathogenicity and transmission of influenza A viruses are likely determined in part by replication efficiency in human cells, which is the net effect of complex virus-host interactions. H5N1 avian, H1N1 seasonal, and H1N1 2009 pandemic influenza virus strains were compared by infecting human differentiated bronchial epithelial cells in air-liquid interface cultures at relatively low virus particle/cell ratios. Differential equation and computational models were used to characterize the in vitro kinetic behaviors of the three strains. The models were calibrated by fitting experimental data in order to estimate difficult-to-measure parameters. Both models found marked differences in the relative values of p, the virion production rate per cell, and R 0 , an index of the spread of infection through the monolayer, with the values for the strains in the following rank order (from greatest to least): pandemic strain, followed by seasonal strain, followed by avian strain, as expected. In the differential equation model, which treats virus and cell populations as well mixed, R 0 and p varied proportionately for all 3 strains, consistent with a primary role for productivity. In the spatially explicit computational model, R 0 and p also varied proportionately except that R 0 derived for the pandemic strain was reduced, consistent with constrained viral spread imposed by multiple host defenses, including mucus and paracrine antiviral effects. This synergistic experimental-computational strategy provides relevant parameters for identifying and phenotyping potential pandemic strains.Influenza viruses cause annual epidemics and occasional pandemics, and the recent pandemic due to the swine-origin 2009 (H1N1) virus provides a fresh opportunity to explore in vitro and in vivo measures of pathogenicity and transmission among different strains (12,23,31). Comparative analysis of virulence and transmission in animal models has been informative (21,24,30), although the sensitivity of this approach in identifying a pandemic strain is not yet clear. The ultimate goal of linking detailed phenotype with detailed genotype is impeded by a limited repertoire of tools to characterize the strain phenotype.Pathogenicity and transmission are in part functions of virus replication, but the complex kinetics of replication are not necessarily reflected in simple counts of virus particles harvested from infected mucosa in vivo or from cell monolayers in vitro. Strains differ dramatically in their entry and replication efficiencies within specific cell types, and they display various potencies in suppressing cell defenses (43). Strains also differ in their efficiencies of person-to-person transmission as measured in closed populations and expressed as the basic reproductive number R 0 (12). The kinetics of viral replication in respiratory mucosal epithelial cells may reflect critical determinants in person-to-person transmission.Using a seasonal strain as a reference point, we compared its replication kinetics to that of an avian strain (as a ...

Non-animal models of epithelial barriers (skin, intestine and lung) in research, industrial applications and regulatory toxicology

“…These cells express basal cell markers including cytokeratin 5 and the transcription factor p63 (Hackett et al, 2011a) and have been used as models of basal cells. al., 2010), mucociliary transport (Seagrave et al, 2012), airway toxicology (Mathis et al, 2013;Watson et al, 2010), electrophysiology (Hirsh et al, 2008), and bacterial and viral infection studies (Deng et al, 2014;Mitchell et al, 2011;Palmer et al, 2012;Ren and Daines, 2011;Ren et al, 2012). The MucilAir cultures have been characterized electrophysiologically and are fully functional: the activity of the main epithelial ionic channels, such as CFTR, EnaC, Na/K ATPase, etc.…”

Effects of Aging on Influenza Virus Infection Dynamics

“…Thus, mathematical modeling has been used to capture the dynamics of influenza virus infection and to understand the interaction of the virus with the immune system (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). Much of the work has been focused on the basic relationship between the host and the virus (25,26,32,34,35), whereas other work has strived to quantify the interplay between viral replication and adaptive immunity (27)(28)(29)(30)36). These models have been important to estimate the kinetic parameters describing influenza virus infection (25, 26, 28-30, 35, 36).…”

Quantification system for the viral dynamics of a highly pathogenic simian/human immunodeficiency virus based on an in vitro experiment and a mathematical model

“…Our improved method for quantifying viral kinetics in vitrowhich depends crucially on detailed time-course information about the infection of cells in addition to that of virus (both total particle count and infectious titer) -could be applied to other viral infections. The method could likely improve the understanding of the differences in replication across different strains [25,55] or between complete and protein-deficient viruses [53,54]; the differences in viral pathogenesis [6]; and the effects of anti-viral therapies [9,13]. Quantifying the in vitro viral kinetics for viruses such as HCV [56,57], for which a convenient animal experimental model has not been established, is of particular interest.…”

“…Despite these successes, the available virological data, even for in vitro experiments, have often been limited in that many modeling analyses have been based only on total viral load data (e.g., RNA or DNA copies, hemagglutination assay (HA)) [9][10][11][12][13][15][16][17]20,22,23,26,27] or infectious viral load data (e.g., 50% tissue culture infection dose (TCID 50 ) or plaque forming units (PFU)) [18,19,25]. Thus, while the applied mathematical models typically depend on the interaction of many components of the infection -including the populations of susceptible and infected cells -they are often only confronted by a single biological quantity: the time-course of the viral load.…”

“…Mathematical analysis of clinical data is an increasingly popular tool for the evaluation of drugs, the elaboration of diagnostic criteria, and the generation of recommendations for effective therapies [9][10][11][12][13][14][15][16][17]. Analyses of animal and cell culture studies have revealed fundamental aspects of viral infections including the specification of the half-life of infected cells and virus, the virus burst size, and the relative contribution of the immune response [18][19][20][21][22][23][24][25][26][27][28][29]. Important results have also been obtained in the analysis of purely in vitro experiments.…”