Deciphering the Underlying Mechanisms of Sanleng-Ezhu for the Treatment
of Idiopathic Pulmonary Fibrosis Based on Network Pharmacology and
Single-cell RNA Sequencing Data
Authors: Zhou Xianqiang , Tan Fang , Zhang Suxian , Zhang Tiansong 
Published: August 2023

Aims:

To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic
pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data.

Background:

Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease.
Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the
management of IPF, its underlying mechanisms remain unknown.

Method:

Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology
Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ.
After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database,
we performed the differential expression analysis and the weighted gene co-expression network analysis
(WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing
Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related
IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of
Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression
MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted
for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell
infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated
the cell types which expressed different MCODE subgroup feature genes. Molecular docking and
animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary
fibrosis.

Result:

We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate
targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained
1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules.
Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated
modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate
targets to obtain direct targets of action. After constructing the protein interaction networks
between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the
highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug
targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the
single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality
reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2
cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups.
Molecular docking revealed the mode of interaction between targets and their corresponding
components. Animal experiments confirmed the efficacy of SL-EZ.

Conclusion:

We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in
the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle
cells mainly through the viral infection, apoptosis, and p53 signaling pathway.