Machine learning identifies molecular regulators and therapeutics for targeting SARS-CoV2-induced cytokine release

Although 15-20% of COVID-19 patients experience hyper-inflammation induced by massive cytokine production, cellular triggers of this process and strategies to target them remain poorly understood. Here, we show that the N-terminal domain (NTD) of the SARS-CoV-2 spike protein substantially induces multiple inflammatory molecules in myeloid cells and human PBMCs. Using a combination of phenotypic screening with machine learning-based modeling, we identified and experimentally validated several protein kinases, including JAK1, EPHA7, IRAK1, MAPK12, and MAP3K8, as essential downstream mediators of NTD-induced cytokine production, implicating the role of multiple signaling pathways in cytokine release. Further, we found several FDA-approved drugs, including ponatinib, and cobimetinib as potent inhibitors of the NTD-mediated cytokine release. Treatment with ponatinib outperforms other drugs, including dexamethasone and baricitinib, inhibiting all cytokines in response to the NTD from SARS-CoV-2 and emerging variants. Finally, ponatinib treatment inhibits lipopolysaccharide-mediated cytokine release in myeloid cells in vitro and lung inflammation mouse model. Together, we propose that agents targeting multiple kinases required for SARS-CoV-2-mediated cytokine release, such as ponatinib, may represent an attractive therapeutic option for treating moderate to severe COVID-19.