Viral Disease Mapping Initiative

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Viral infectious diseases are one of the leading causes of disease burden in human population. Recent pandemics, such as 2003 SARS, 2009 influenza A, 2016 Zika and the 2020 SARS-CoV-2, highlighted the requirement for rapid development of antiviral treatments orthogonal to vaccines. As observed in the SARS-CoV-2 pandemic, even developed nations can suffer massively on health, political and socioeconomic levels until the development of a suitable vaccine. Small molecule treatment options would in part prevent this and facilitate the control of a pandemic on the global scale. In order to identify compounds with antiviral efficacy against emerging threats, it is vital to gain biological and molecular understanding of viruses and viral families before they begin to spread in the human population. Multi-level analysis of biological networks perturbed by these viruses, including context-dependent gene expression regulation and subsequent identification of factors promoting or inhibiting pathogeneses, can facilitate the identification of viable treatment targets belonging to either the virus or the host. Providing sufficient molecular knowledge becomes available, repurposing of pharmacologically tested drugs that are in clinical use for non-infectious diseases (e.g. oncology, metabolic diseases etc…) can be achieved. Here we propose to comprehensively analyse the regulation and rewiring of the host’s gene expression networks after infection with highly pathogenic epidemic and pandemic viruses. Our preliminary data offers evidence that modulation of the RNA quality (alternative splicing and alternative polyadenylation) is a common theme seen for many clinically relevant viruses and that modifications of RNA quality may be a dramatically underestimated determinant of viral pathogenicity. We teamed up with multiple laboratories who will provide primary cells that are infected with highly pathogenic viruses, which we will use for in depth sequence analysis. Computational analyses based on state of the art statistical and machine learning algorithms will be applied to understand how specific viruses or viral families are influencing the cellular transcriptome on a quantitative and qualitative basis. The transcriptomics data will be complemented by proteomics and phosphoproteomics data (funded from ERC-consolidator grant PRODAP) which will allow to identify functional modules and links between individual viral infections. We expect to gain mechanistic insights in alterations of virus-affected mRNA quality control. Moreover, through integration of drug interaction networks we expect to aid rational identification of host-directed drugs with antiviral properties. This initiative will promote the use of advanced systematic molecular research on emerging viruses to prime responses against future pandemics with an explicit purpose of using the obtained knowledge in a clinical setting with high public health relevance.