Systematic review and meta-analysis identifies potential host therapeutic targets in COVID-19.

Parkinson, N.; Rodgers, N.; Head Fourman, M.; Wang, B.; Zechner, M.; Swets, M.; Millar, J. E.; Law, A.; Russell, C.; Baillie, J. K.; Clohisey, S.

An increasing body of literature describes the role of host factors in COVID-19 pathogenesis. There is a need to combine diverse, multi-omic data in order to evaluate and substantiate the most robust evidence and inform development of future therapies. We conducted a systematic review of experiments identifying host factors involved in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). Gene lists from these diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. 5,418 genes implicated in human betacoronavirus infection were identified from 32 datasets. The top ranked gene was *PPIA*, encoding cyclophilin A. Pharmacological inhibition with cyclosporine in vitro exerts antiviral activity against several coronaviruses including SARS-CoV. Other highly-ranked genes included proposed prognostic factors (*CXCL10*, *CD4*, *CD3E*) and investigational therapeutic targets (*IL1A*) for COVID-19, but also previously overlooked genes with potential as therapeutic targets. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating *FYCO1* over other nearby genes in a disease-associated locus on chromosome 3. Pathways enriched in gene rankings included T-cell receptor signalling, protein processing, and viral infections. We identified limited overlap of our gene list with host genes implicated in ARDS (innate immune and inflammation genes) and Influenza A virus infection (RNA-binding and ribosome-associated genes). We will continue to update this dynamic ranked list of host genes as the field develops, as a resource to inform and prioritise future studies. Updated results are available at https://baillielab.net/maic/covid19.