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

Abstract

Abstract 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.

Keywords

• kinases
• machine learning
• N‐terminal domain
• Ponatinib
• SARS‐CoV‐2