Between viral targets and differentially expressed genes in COVID-19: the sweet spot for therapeutic intervention
Professor Natasa Przulj is an ICREA Research Professor and a Group Leader at Barcelona Supercomputing Center. She is a leader in network science and AI algorithms for biomedical data fusion applied to precision medicine. Her research has been cited around 10,000 times, h-index=43, i10-index=70 (Google Scholar) and supported by over €15 million in competitive funding. Notably, she received three prestigious, single PI, European Research Council (ERC) grants: Consolidator (2018-2023), Proof of Concept (2020-2022) and Starting (2012-2017). She has been elected into: The Serbian Royal Academy of Scientists and Artists in 2019; Academia Europaea, The Academy of Europe, in 2017; Fellow of the British Computer Society (BCS) Academy of Computing, in 2013. In 2014, she received a BCS Roger Needham Award, sponsored by Microsoft Research, in recognition of the potential her research has to revolutionize health and pharmaceutics. She obtained a PhD in Computer Science from the University of Toronto.
The COVID-19 pandemic is raging. It revealed the importance of rapid scientific advancement towards understanding and treating new diseases. To address this challenge, we build onto our previous methods for extracting new biomedical knowledge from the wiring patterns of systems-level, heterogeneous biomedical networks. These methods are needed due to the flood of molecular and clinical data, measuring interactions between various bio-molecules in and around a cell that form large, complex systems. These systems-level network data provide heterogeneous, but complementary information about cells, tissues and diseases. The challenge is how to mine them collectively to answer fundamental biological and medical questions. This is nontrivial, because of computational intractability of many underlying problems on networks (also called graphs), necessitating the development of approximate algorithms (heuristic methods) for finding approximate solutions.
We adapt an explainable artificial intelligence algorithm for data fusion and utilize it on new omics data on viral-host interactions, human protein interactions, and drugs to better understand SARS-CoV-2 infection mechanisms and predict new drug-target interactions for COVID-19. We discover that in the human interactome, the human proteins targeted by SARS-CoV-2 proteins and the genes that are differentially expressed after the infection have common neighbors central in the interactome that may be key to the disease mechanisms. We uncover 185 new drug-target interactions targeting 49 of these key genes and suggest re-purposing of 149 FDA-approved drugs, including drugs targeting VEGF and nitric oxide signaling, whose pathways coincide with the observed COVID-19 symptoms. Our integrative methodology is universal and can enable insight into this and other serious diseases.