Network-based systems pharmacology combined UHPLC-Q-extractive-Orbitrap-MS reveals the mechanism of Jinhua Qinggan Granule Reducing cellular inflammation in COVID-19
This repository contains the scripts to reproduce the result of the manuscript Network-based systems pharmacology combined UHPLC-Q-extractive-Orbitrap-MS reveals the mechanism of *Jinhua Qinggan* Granule Reducing cellular inflammation in COVID-19
Compound-Target network: https://starlitnightly.github.io/Analysis_JHQG_COVID/
Introduction: The outbreak of SARS-CoV-2, leading to COVID-19, poses a major global health threat. While specific treatments and vaccines are under development, Traditional Chinese Medicine (TCM) has historically been effective against pandemics, including viral pneumonias. Our study explores the efficacy and mechanisms of Jinhua Qinggan Granules (JHQG) in treating COVID-19.
Methods: We analyzed JHQG’s components using UHPLC-Q-Exactive-Orbitrap-MS, identifying 73 compounds. Network pharmacology and single-cell RNA sequencing (scRNA-seq) were used to assess JHQG’s effects on immune cells from peripheral blood mononuclear cells (PBMCs). Literature review supported the antiviral and anti-inflammatory effects of JHQG.
Results: JHQG targets were found to interact with immune cells, including neutrophils, monocytes, plasmablasts, and effector T cells, reducing their overactivation in severe COVID-19. JHQG’s modulation of these cells’ activity likely contributes to reduced inflammation and improved clinical outcomes.
Discussion: Our findings provide insights into JHQG's mechanism of action, highlighting its potential in controlling the inflammatory response in COVID-19 patients. The study supports the use of JHQG as a safe and effective treatment for COVID-19 and similar viral infections, leveraging its ability to modulate immune cell activity and reduce inflammation.
/result/: the precessed data and result of ipynb1.herb_analysis.ipynb: the raw code to reproduce the result12.sc.ipynb: the raw code to reproduce the result23.dock.ipynb: the raw code to reproduce the molecular docking
- Zehua Zeng (starlitnightly@163.com)