Integration of Network Pharmacology, Transcriptomics, and Metabolomics Strategies to Uncover the Mechanism of Chaihuang Qingfu Pill in Treating Sepsis-Induced Liver Injury

Abstract

Background: Sepsis is a critical condition triggered by infection and characterized by systemic inflammation and subsequent multiorgan failure. Chaihuang Qingfu Pill (CHQF), an in-hospital formulation developed by Hunan Provincial People's Hospital, is derived from the traditional Chinese medicine compound Qingyi Decoction through optimized herbal compatibility. It possesses pharmacological activities including heat-clearing and purgation, choleretic and anti-jaundice effects, as well as Qi-regulation and mass-resolving properties. Clinically, CHQF is primarily used in the treatment of cholecystitis, pancreatitis, and hepatitis, and has shown potential therapeutic effects in alleviating sepsis-associated liver injury. However, the precise molecular mechanisms and omics-based investigations of CHQF in the context of sepsis remain poorly understood. The NF-κB signaling pathway serves as a central regulatory hub of the inflammatory response. Its activation leads to the excessive expression of pro-inflammatory mediators and cytokines, thereby exacerbating tissue damage and promoting the progression of inflammatory diseases. Consequently, targeting the NF-κB pathway may represent an effective therapeutic strategy for the treatment of sepsis. This study aims to systematically investigate the molecular basis of CHQF in the mitigation of sepsis-associated liver damage.

Purpose: To explore the mechanism of CHQF for the treatment of sepsis-induced liver injury.

Methods: A sepsis mouse model was established via cecal ligation and puncture (CLP). The pharmacological mechanisms of CHQF were explored using network pharmacology, transcriptomics, and metabolomics, which enabled the identification of potential therapeutic targets and pathways, as further validated by in vivo and in vitro experiments.

Results: CHQF administration significantly improved the survival rates, reduced systemic inflammation, and restored liver function in CLP-induced sepsis mice, while also mitigating liver tissue injury. Network pharmacological analysis revealed paeoniflorin, quercetin, hyperforin, and wogonin as the core bioactive compounds of CHQF. Transcriptomic profiling identified key targets, including CD14, CXCL2, CCL2, BIRC5, and CXCL8, and demonstrated a significant downregulation of inflammatory cytokines such as TNF-α, IL-6, IL-1β, IL-17, CCL2, CCL3, CCL4, CXCL2, CXCL3, and CXCL5, alongside NF-κB signaling pathway inhibition. Metabolomic analysis indicated that CHQF treatment reduced the levels of sepsis-related metabolites, including lysophosphatidylcholine (22:6), lysophosphatidylcholine (18:1), 1-LGPC, and C17-sphinganine.

Conclusion: Collectively, these findings suggest that CHQF alleviates sepsis-induced liver injury by modulating the inflammatory response via NF-κB signaling pathway inhibition. This study provides novel insights into the complex molecular mechanisms underlying the therapeutic effects of CHQF in sepsis and enhances the understanding of the pharmacological actions of traditional Chinese medicine in managing sepsis.

Keywords: NF-κB signaling pathway; liver injury; multi-omics; network pharmacology; sepsis; traditional Chinese medicine.

Graphical abstract

Figure 1

Total ion chromatogram of the CHQF. The peak values represent the top ten ranked compounds (positive ions).

Figure 2

Total ion chromatogram of the CHQF. The peak values represent the top ten ranked compounds (negative ions).

Figure 3

CHQF reduces the mortality rate, hepatic injury, and production of pro-inflammatory cytokines in serum in a mouse model of sepsis. (A) Survival curve analysis in a murine sepsis model. Serum levels of (B) ALP, (C) ALT, (D) AST. The levels of (E) IL-2, (F) IL-4, (G) IL-6, (H) IL-10, (I) IFN-γ, (J) TNF-α, (K) IL-17A (n=5). (L) H&E staining and (M) Liver injury score to visualize the histomorphological features and quantitation analysis (n=5). Data are expressed as mean ± SEM. **P < 0.01, ***P < 0.001, ****P < 0.0001 vs CLP group; ns, not significant.

Figure 4

CHQF inflammatory cell infiltration. IHC staining for (A) CCL2, (B) CXCL2, (C) IL-6, (D) TNF-α (100×). (E–H) Statistical analysis of CCL2, CXCL2, IL-6, TNF-α values. Data presented as mean ± SD(n = 5 /group); *P < 0.05, **P < 0.01, ***P < 0.001 vs CLP group; ns, not significant.

Figure 5

Network Pharmacology uncovers the mechanism of CHQF for sepsis. (A) Venn diagram of targets shared by CHQF and sepsis. (B) The PPI network of intersection targets. (C) Degree of top 30 common targets (number of node-connected edges) of common targets (top 30) in the PPI network. (D) Active ingredient−target−disease network diagram. (E) GO-enrichment analysis of common targets. (F) KEGG pathway analyses of the hub genes.

Figure 6

CHQF regulates the expression of genes related to sepsis. (A) PCA mapping of Sham, CLP, and CHQF treated samples. (B) Volcano plots of upregulated and downregulated DEGs between CLP and Sham groups. (C) Volcano plots of upregulated and downregulated DEGs between CHQF and CLP groups. (D) Heatmap for hierarchical cluster analysis of DEGs between the samples. (E) GO functional enrichment analysis. (F) KEGG functional annotation analysis. (G) KEGG functional enrichment analysis.(n=6 /group, one data point in the CH group was identified as an outlier and was excluded from the analysis).

Figure 7

Effects of CHQF on key gene expression and NF-κB pathway proteins in CLP-induced sepsis. (A) CHQF inhibits NF-κB signaling in a CLP-induced sepsis mouse model. Protein lysates were subjected to SDS-PAGE followed by immunoblotting for P65, P-P65, TLR4 and MYD88. (B–F) Statistical analysis for P65, P-P65, P-P65/P65, TLR4 and MYD88 levels. Data are presented as mean ± SD (n = 5 /group). *P < 0.05, **P < 0.01 vs CLP group; ns, not significant. (G–Q) Detection of IL-7, TNF-α, IL-6, TNFAIP3, BIRC3, BIRC2, GADD45A, CD14, CXCL2, CXCL12, and CCL2 mRNA expression levels in sepsis mice. Data are presented as mean ± SD (n = 5 /group). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 vs CLP group; ns, not significant.

Figure 8

Metabolomics analysis. (A) PCA of Sham, CLP, and CLP+CHQF groups. (B and C) PLS-DA score plot of CLP vs Sham and CLP vs CLP+CHQF. (D–F) Metabolic pathways of the significant metabolites in Sham vs CLP and CLP vs CLP+CHQF.(n=8 /group, one data point in the Sham group was identified as an outlier and was excluded from the analysis).

Figure 9

Result of molecular docking. (A) CD14-paeoniflorin (−6.9 kcal/mol), (B) CXCL8-hyperforin (−7.8 kcal/mol), (C) BIRC5-quercetin (−7.6 kcal/mol), (D) CCL2-quercetin (−7.6 kcal/mol), (E) CXCL2-quercetin (−7.8 kcal/mol), (F) CCL2-wogonin (−7.0 kcal/mol), (G) CXCL8-wogonin (−6.0 kcal/mol).