Cell Type Mediated Resistance of Vesicular Stomatitis Virus and Sendai Virus to Ribavirin

Ribavirin (RBV) is a synthetic nucleoside analog with broad spectrum antiviral activity. Although RBV is approved for the treatment of hepatitis C virus, respiratory syncytial virus, and Lassa fever virus infections, its mechanism of action and therapeutic efficacy remains highly controversial. Recent reports show that the development of cell-based resistance after continuous RBV treatment via decreased RBV uptake can greatly limit its efficacy. Here, we examined whether certain cell types are naturally resistant to RBV even without prior drug exposure. Seven different cell lines from various host species were compared for RBV antiviral activity against two nonsegmented negative-strand RNA viruses, vesicular stomatitis virus (VSV, a rhabdovirus) and Sendai virus (SeV, a paramyxovirus). Our results show striking differences between cell types in their response to RBV, ranging from virtually no antiviral effect to very effective inhibition of viral replication. Despite differences in viral replication kinetics for VSV and SeV in the seven cell lines, the observed pattern of RBV resistance was very similar for both viruses, suggesting that cellular rather than viral determinants play a major role in this resistance. While none of the tested cell lines was defective in RBV uptake, dramatic variations were observed in the long-term accumulation of RBV in different cell types, and it correlated with the antiviral efficacy of RBV. While addition of guanosine neutralized RBV only in cells already highly resistant to RBV, actinomycin D almost completely reversed the RBV effect (but not uptake) in all cell lines. Together, our data suggest that RBV may inhibit the same virus via different mechanisms in different cell types depending on the intracellular RBV metabolism. Our results strongly point out the importance of using multiple cell lines of different origin when antiviral efficacy and potency are examined for new as well as established drugs in vitro

2013 WSES guidelines for management of intra-abdominal infections

Peer reviewe

Biological Activities of Influenza Virus Subpopulations: Characterization of Infectious, Interferon-Inducing and Interferon Induction-Suppressing Particles

This dissertation explores the complex nature of the influenza virus quasispecies. Influenza populations exist as a mixture of mostly replication-deficient biologically-active particles. These particles are measured and quantified based upon the phenotype they manifest upon entry into the host. Three subpopulations are characterized: infectious, interferon-inducing, and interferon induction-suppressing particles. It has been observed that only 5 out of every 100 particles of influenza are infectious. A novel biological assay was developed and, for the first time, directly demonstrated that infectious particles are the progenitors of all other virus particles. From the disparity observed to exist between the numbers of physical and infectious particles, a mathematical approach is presented which quantifies the frequency of expression of individual gene segments. Interferon-inducing particles are demonstrated to be resistant to physical and chemical inactivation when measured in avian cells –the best evidence to date that the interferon-inducing moiety is preformed within the virion. In contrast, interferon-inducing particles are inactivated at a rate consistent with inactivation of the NS gene in mammalian cells, suggesting that primary transcription is necessary for interferon-induction in these cells. Interferon induction-suppressing particles inhibit interferon production in cells otherwise programed to induce. This activity is nonspecific, blocking interferon-induction by viruses belonging to multiple families, and is dependent upon expression of the NS1 protein. A mathematical model, based on a random (Poisson) distribution of virus particles amongst the cell monolayer, is presented that predicts the fraction of cells that will induce interferon, dependent upon the ratio of interferon-inducing and interferon induction-suppressing particles present in the virus stock. A novel method for quantifying interferon-inducing particles in preparations with high interferon induction-suppressing particle content is described. A highly potent interferon induction-suppression activity was also found to be associated with exposure to lipopolysaccharide. The functional heterogeneity demonstrated by a single strain of influenza was quantified, utilizing interferon-induction and interferon induction-suppression as phenotypic markers of the quasispecies. The activities of 117 plaque-derived isolates were measured, and variability in these phenotypes was observed over a 1000-fold range. The genetic basis of this phenotypic variance was investigated through sequencing the NS genes of several isolates demonstrating extreme phenotypes

Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry.MethodsSeveral machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index.ResultsOf 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers.ConclusionsThis model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics