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

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 mu...

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Bibliographic Details
Main Authors: Chan, M. (Author), Gujral, T.S (Author), Holland, E.C (Author), McElrath, M.J (Author), McNevin, J. (Author), Vijay, S. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2021
Subjects:
animal
Animals
Antiviral Agents
antivirus agent
azetidine derivative
Azetidines
baricitinib
C57BL mouse
coronavirus spike glycoprotein
cytokine
Cytokines
drug effect
host pathogen interaction
Host-Pathogen Interactions
human
Humans
imidazole derivative
Imidazoles
interleukin 1 receptor associated kinase
Interleukin-1 Receptor-Associated Kinases
IRAK1 protein, human
JAK1 protein, human
Janus kinase 1
Janus Kinase 1
kinases
lipopolysaccharide
Lipopolysaccharides
machine learning
Machine Learning
male
Male
metabolism
Mice
Mice, Inbred C57BL
mouse
neutrophil
Neutrophils
N-terminal domain
pathogenicity
physiology
ponatinib
Ponatinib
protein kinase inhibitor
Protein Kinase Inhibitors
purine derivative
Purines
pyrazole derivative
Pyrazoles
pyridazine derivative
Pyridazines
RAW 264.7 cell line
RAW 264.7 Cells
SARS-CoV-2
Spike Glycoprotein, Coronavirus
spike protein, SARS-CoV-2
sulfonamide
Sulfonamides
virology
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Internet

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