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      Hierarchical effects of pro-inflammatory cytokines on the post-influenza susceptibility to pneumococcal coinfection

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          Abstract

          In the course of influenza A virus (IAV) infections, a secondary bacterial infection frequently leads to serious respiratory conditions provoking high hospitalization and death tolls. Although abundant pro-inflammatory responses have been reported as key contributing factors for these severe dual infections, the relative contributions of cytokines remain largely unclear. In the current study, mathematical modelling based on murine experimental data dissects IFN- γ as a cytokine candidate responsible for impaired bacterial clearance, thereby promoting bacterial growth and systemic dissemination during acute IAV infection. We also found a time-dependent detrimental role of IL-6 in curtailing bacterial outgrowth which was not as distinct as for IFN- γ. Our numerical simulations suggested a detrimental effect of IFN- γ alone and in synergism with IL-6 but no conclusive pathogenic effect of IL-6 and TNF- α alone. This work provides a rationale to understand the potential impact of how to manipulate temporal immune components, facilitating the formulation of hypotheses about potential therapeutic strategies to treat coinfections.

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          Most cited references38

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          Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

          Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html. Supplementary data are available at Bioinformatics online.
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            The co-pathogenesis of influenza viruses with bacteria in the lung.

            Concern that a highly pathogenic virus might cause the next influenza pandemic has spurred recent research into influenza and its complications. Bacterial superinfection in the lungs of people suffering from influenza is a key element that promotes severe disease and mortality. This co-pathogenesis is characterized by complex interactions between co-infecting pathogens and the host, leading to the disruption of physical barriers, dysregulation of immune responses and delays in a return to homeostasis. The net effect of this cascade can be the outgrowth of the pathogens, immune-mediated pathology and increased morbidity. In this Review, advances in our understanding of the underlying mechanisms are discussed, and the key questions that will drive the field forwards are articulated.
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              Insights into the interaction between influenza virus and pneumococcus.

              Bacterial infections following influenza are an important cause of morbidity and mortality worldwide. Based on the historical importance of pneumonia as a cause of death during pandemic influenza, the increasingly likely possibility that highly pathogenic avian influenza viruses will trigger the next worldwide pandemic underscores the need to understand the multiple mechanisms underlying the interaction between influenza virus and bacterial pathogens such as Streptococcus pneumoniae. There is ample evidence to support the historical view that influenza virus alters the lungs in a way that predisposes to adherence, invasion, and induction of disease by pneumococcus. Access to receptors is a key factor and may be facilitated by the virus through epithelial damage, by exposure or up-regulation of receptors, or by provoking the epithelial regeneration response to cytotoxic damage. More recent data indicate that alteration of the immune response by diminishing the ability of the host to clear pneumococcus or by amplification of the inflammatory cascade is another key factor. Identification and exploration of the underlying mechanisms responsible for this synergism will provide targets for prevention and treatment using drugs and vaccines.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                22 November 2016
                2016
                : 6
                : 37045
                Affiliations
                [1 ]Infection Immunology Group, Institute of Medical Microbiology, Disease Prevention and Control, Otto-von-Guericke University Magdeburg, Germany
                [2 ]Systems Medicine of Infectious Disease Group, Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research , Braunschweig, Germany
                [3 ]Immune Regulation Group, Helmholtz Centre for Infection Research , Braunschweig, Germany
                [4 ]Chair for Automation/Modeling, Institute for Automation Engineering, Otto-von-Guericke University Magdeburg, Germany
                Author notes
                [*]

                These authors contributed equally to this work.

                [†]

                These authors jointly supervised this work.

                Article
                srep37045
                10.1038/srep37045
                5181841
                27872472
                45e4c131-b120-4ba4-a2df-f68c01be0f14
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 26 August 2016
                : 20 October 2016
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