Title:Identification of Novel Hub Genes Associated with Inflammation and Autophagy in Astragaloside Membranaceus ameliorates Lupus Nephritis by Bioinformatics Analysis and Molecular Dynamics Simulation
Volume: 28
Issue: 2
Author(s): Kaili Kong, Xiaomei Qiao, Ting Liu, Xiaoxia Wang, Rui Li, Jingai Fang and Xiaodong Zhang*
Affiliation:
- Department of Nephrology, The First Hospital of Shanxi Medical
University, Taiyuan, 030001, China
Keywords:
Lupus nephropathy, inflammation, autophagy, astragalus membranaceus, hub genes, bioinformatics, molecular dynamics simulation.
Abstract:
Background: Lupus nephritis is an autoimmune disease, and its pathogenesis involves
inflammation and autophagy disorders. Studies have demonstrated that Astragalus membranaceus
can effectively suppress the progression of LN, but the underlying therapeutic target is still unclear.
Objection: This study aimed to investigate the therapeutic target whereby AM ameliorates LN.
Method: We downloaded AM and LN-related chips from the TCMSP and GEO databases, respectively.
We selected the two compound targets for the subsequent analysis via WGCNA, and constructed
protein interaction networks of compound targets and determined the core targets. GO,
KEGG analyses were conducted on compound targets to identify enriched functional and genomic
pathways. The core genes were further validated in clinical and external datasets. Molecular docking
of AS with the core targets was performed using the AutoDock software, and molecular dynamics
simulation was conducted for the optimal core protein ligand obtained by molecular docking
by Gromacs 2020.6 software.
Result: We obtained 10 core targets, namely IL-1β, EGF, CCND1, CASP3, STAT1, PTGS2,
PPARγ, AR, CXCL10, and KDR, from the 24 compound targets identified. The results of the GO
enrichment analysis mainly included cell growth regulation. The results of the KEGG enrichment
analysis showed that 7 out of 23 valid targets were significantly enriched in the mitogen-activated
protein kinase pathway (p < 0.01). Combined with the clinical datasets, we found that IL-1β, EGF,
CCND1, CASP3, STAT1, PTGS2, and PPARγ have high diagnostic values for LN. In the validation
dataset, all the core targets were significantly differentially expressed, except for EGF deletion.
The molecular docking and molecular dynamics simulation results showed that AM and IL-
1β, CASP3, STAT1, and PPARγ all had binding energies < -5 kJ·mol-1 and good binding properties.
Conclusion: IL-1β, CASP3, STAT1, and PPARγ could be potential biomarkers and therapeutic
targets in AM ameliorates LN.
