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Alisa Pavel
Phd Student , Bioinformatics, Tampere University

Integrated network analysis reveals new genes  suggesting COVID-19 chronic effects and treatment 

The currently ongoing spread of SARS-CoV-2 has led to a global health emergency. Given the unavailability of a vaccine and distinct therapeutic measurements to counteract  the infection and possible long-term effects, understanding the interplay between SARS CoV-2 and the induced host response in greater detail is of paramount importance. Prior  to this health emergency, large quantities of biomedical data have been made available,  which is a necessary foundation in investigating molecular events of the COVID-19 pathogenesis, but data heterogeneity poses a significant limitation. Here, we applied a novel data integration method in order to construct a multilayer network, the Unified Knowledge Space (UKS). The UKS is then exploited to identify a new set of genes associated to the SARS-CoV-2 induced host response. Possible long-term systemic effects of SARS-CoV-2 infection, such as fibrosis and vascular remodeling, are revealed through functional analysis of these newly identified genes. By investigating drug – target  relationships of genes associated with the SARS-CoV-2 host response, we suggest possible highly interesting therapeutic classes with the ability to target multiple steps of the SARS-CoV-2 infection.