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A combined modeling framework to break the lethal alliance between influenza and bacterial coinfections

Subject Area Medical Microbiology and Mycology, Hygiene, Molecular Infection Biology
Immunology
Term from 2018 to 2022
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 408736049
 
Final Report Year 2023

Final Report Abstract

Respiratory virus infections have traditionally been thought to be caused by single pathogens, however, an increasing number of studies highlighted that viral coinfections are more common than what we think, especially among pediatric patients. Coinfections have the potential to alter disease severity compared to infection by individual pathogens. For our study case, influenza-pneumococcus infection, an overt proinflammatory immune response is a key factor contributing to lethal pneumococcal infection in an influenza pre-infected host and represents a potential target for therapeutic intervention. However, there is a paucity of knowledge about the level of contribution of individual cytokines, which was the main contribution we did in this project. A key result of this project was based on the predictions of mathematical models that highlighted the potential benefit of gamma interferon (IFN-γ) and/or and interleukin 6 (IL-6)-specific antibody-mediated cytokine neutralization, which we explored in C57BL/6 mice infected with the influenza A/PR/8/34 strain that were subsequently infected with the Streptococcus pneumoniae strain TIGR4 on day 7 post influenza. While single IL-6 neutralization had no effect on respiratory bacterial clearance, single IFN-γ neutralization enhanced local bacterial clearance in the lungs. Concomitant neutralization of IFN-γ and IL-6 significantly reduced the degree of pneumonia as well as bacteremia compared to the control group, indicating a positive effect for the Host during secondary bacterial infection. The results of our model-driven experimental study reveal that the predicted therapeutic value of IFN-γ and IL-6 neutralization in secondary pneumococcal infection following influenza infection is tightly dependent on the experimental protocol while at the same time paving the way toward the development of effective immune therapies. Another major finding in this proposal relates to monitoring respiratory infections and guiding treatment decisions requires minimally invasive pathogen burden and host response tracking. Utilizing a standardized murine model of respiratory influenza A virus infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independent in vivo infection experiments to acquire extensive data for training and testing of the models. We show here that lung viral load, neutrophil counts, cytokines (such as IFN- γ and IL-6), and other lung infection markers can be predicted from hematological data. The proposed in silico tools developed in this project has a large potential to pave the path toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. The scientific impact of our research has been demonstrated by a broad interest in this project at national and international conferences. In addition, the clinical relevance of bacterial superinfection after previous viral infection - probably due to the broad public discussion about respiratory infections in connection with the SARS-CoV-2 pandemic - is obviously perceived more strongly. This is expressed, for example, by recent invitations to contribute and article on influenza-pneumococcal coinfections to the recognized German Medical Journal Deutsches Ärzteblatt and the invitation to give a lecture to the interested public on our current research in this field at the “Medical Sunday” in Magdeburg in October. The data from this project also formed the basis for new successfully acquired projects in third-party funded scientific consortia.

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