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      Mathematical model calibrated to in vitro data predicts mechanisms of antiviral action of the influenza defective interfering particle “OP7”

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          Summary

          Defective interfering particles (DIPs) are regarded as potent broad-spectrum antivirals. We developed a mathematical model that describes intracellular co-infection dynamics of influenza standard virus (STV) and “OP7”, a new type of influenza DIP discovered recently. Based on experimental data from in vitro studies to calibrate the model and confirm its predictions, we deduce OP7’s mechanisms of interference, which were yet unknown. Simulations suggest that the “superpromoter” on OP7 genomic viral RNA enhances its replication and results in a depletion of viral proteins. This reduces STV genomic RNA replication, which appears to constitute an antiviral effect. Further, a defective viral protein (M1-OP7) likely causes the deficiency of OP7’s replication. It appears unable to bind to genomic viral RNAs to facilitate their nuclear export, a critical step in the viral life cycle. An improved understanding of OP7’s antiviral mechanism is crucial toward application in humans as a prospective antiviral treatment strategy.

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          Highlights

          • Systems biology investigation of viral co-infection

          • Impact of mutations in OP7 genome on its replication

          • Antiviral effect likely mediated by OP7 superpromoter inducing depletion of proteins

          • Mechanistic understanding promotes development of OP7 as broad-spectrum antiviral

          Abstract

          Biological sciences; Biological sciences research methodologies; Natural sciences

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          IEEE Transactions on Automatic Control, 19(6), 716-723
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            Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

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              Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.

              Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                05 March 2024
                19 April 2024
                05 March 2024
                : 27
                : 4
                : 109421
                Affiliations
                [1 ]Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
                [2 ]Institute for Computational Systems Biology, University of Hamburg, 20148 Hamburg, Germany
                [3 ]Chair of Bioprocess Engineering, Otto-von-Guericke University, 39106 Magdeburg, Saxony-Anhalt, Germany
                Author notes
                []Corresponding author ruedigerd@ 123456mpi-magdeburg.mpg.de
                [∗∗ ]Corresponding author kupke@ 123456mpi-magdeburg.mpg.de
                [4]

                Lead contact

                Article
                S2589-0042(24)00642-4 109421
                10.1016/j.isci.2024.109421
                10959662
                38523782
                29933941-ba1d-4884-ba6d-5784c4fcf45f
                © 2024 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 23 August 2023
                : 8 February 2024
                : 29 February 2024
                Categories
                Article

                biological sciences,biological sciences research methodologies,natural sciences

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