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Updated: Jun 13, 2025

Bacterial Artificial Chromosomes: A Functional Genomics Tool for the Study of Positive-strand RNA Viruses
Published on: December 29, 2015
Distinct negative-sense RNA viruses induce a common set of transcripts encoding proteins forming an extensive network
Nina Hofmann1, Marek Bartkuhn2,3, Stephan Becker4
1Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen, Germany.
None:
The large group of negative-strand RNA viruses (NSVs) comprises many important pathogens. To identify conserved patterns in host responses, we systematically compared changes in the cellular RNA levels after infection of human hepatoma cells with nine different NSVs of different virulence degrees. RNA sequencing experiments indicated that the amount of viral RNA in host cells correlates with the number of differentially expressed host cell transcripts. Time-resolved differential gene expression analysis revealed a common set of 178 RNAs that are regulated by all NSVs analyzed. A newly developed open access web application allows downloads and visualizations of all gene expression comparisons for individual viruses over time or between several viruses. Most of the genes included in the core set of commonly differentially expressed genes (DEGs) encode proteins that serve as membrane receptors, signaling proteins and regulators of transcription. They mainly function in signal transduction and control immunity, metabolism, and cell survival. One hundred sixty-five of the DEGs encode host proteins from which 47 have already been linked to the regulation of viral infections in previous studies and 89 proteins form a complex interaction network that may function as a core hub to control NSV infections.IMPORTANCEThe infection of cells with negative-strand RNA viruses leads to the differential expression of many host cell RNAs. The differential spectrum of virus-regulated RNAs reflects a large variety of events including anti-viral responses, cell remodeling, and cell damage. Here, these virus-specific differences and similarities in the regulated RNAs were measured in a highly standardized model. A newly developed app allows interested scientists a wide range of comparisons and visualizations.
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