Bioinformatics based exploration of the anti-NAFLD mechanism of Wang's empirical formula via TLR4/NF-κB/COX2 pathway

Abstract

Background: Nonalcoholic fatty liver disease (NAFLD) has developed as a leading public wellness challenge as a result of changes in dietary patterns. Unfortunately, there is still a lack of effective pharmacotherapy methods for NAFLD. Wang's empirical formula (WSF) has demonstrated considerable clinical efficacy in treating metabolic disorders for years. Nevertheless, the protective effect of WSF against NAFLD and its underlying mechanism remains poorly understood.

Methods: The NAFLD model was established using a 17-week high-sucrose and high-fat (HSHF) diet with 32 ICR mice. In assessing the therapeutic efficacy of WSF on NAFLD, we detected changes in body weight, viscera weight, biomarkers of glycolipid metabolism in serum and liver, transaminase levels and histopathology of liver with H&E and Oil Red O staining after oral administration. The chemical components in WSF were extensively identified and gathered utilizing the HPLC-Q-TOF/MS system, database mining from HMDB, MassBank, and TCMSP databases, alongside literature searches from CNKI, Wanfang and VIP databases. The forecast of network pharmacology approach was then utilized to investigate the probable mechanisms by which WSF improves NAFLD based on the performance of prospective target identification and pathway enrichment analysis. Besides, molecular docking was also conducted for the verification of combination activities between active components of WSF and core proteins related to NAFLD. In final, validation experiments of obtained pathways were conducted through ELISA, immunohistochemistry (IHC), and western blot (WB) analysis.

Results: Pharmacodynamic outcomes indicated that WSF intervention effectively mitigated obesity, fat accumulation in organs, lipid metabolism disorders, abnormal transaminase levels and liver pathology injury in NAFLD mice (P < 0.05, 0.01). A total of 72 existent ingredients of WSF were acquired by HPLC-Q-TOF/MS and database, and 254 common targets (11.6% in total targets) of NAFLD and WSF were identified. Network pharmacology revealed that WSF presses NAFLD via modulating TNF, IL6, AKT1, IL1B, PTGS2 (COX2), and other targets, and the probable pathways were primarily inflammatory signaling pathways, as confirmed by molecular docking. Molecular biology experiments further conformed that WSF could decrease levels of inflammatory factors like IL-1β, IL-6 and TNF-α (P < 0.01) and expression of TLR4, NF-κB and COX-2 (P < 0.05, 0.01) in the liver.

Conclusion: WSF treatment effectively protects against lipid metabolism disorders and liver inflammation injury in HSHF diet-induced NAFLD mice, and its molecular mechanism might be via suppressing the TLR4/NF-κB/COX-2 inflammatory pathway to reduce the release of inflammatory cytokines in the liver.

Keywords: Bioinformatics; HPLC-Q-TOF/MS; Liver inflammation; Network pharmacology; Nonalcoholic fatty liver disease; Wang’s empirical formula.

Fig. 1

Effects of WSF on body weight and visceral fat accumulation. A Animal experiment procedures. B Body weight change during the whole experiment. C Weight gain. D Liver mass. E Liver index. F Epididymis adipose mass. G Epididymis adipose index. All values were presented as mean ± SD with significance markers of ^*P < 0.05 and ^**P < 0.01

Fig. 2

Effects of WSF on liver function and hepatic pathology. A Representative graphs of morphology and pathological changes in liver (H&E 400 × ; Oil Red O 400 ×). B Serum AST. C Serum ALT. D NAFLD activity scores. E Oil Red O staining area ratio. All values were presented as mean ± SD with significance markers of ^*P < 0.05 and ^**P < 0.01

Fig. 3

Effects of WSF on glycolipid metabolism. A–D Levels of TC, TG, HDL-c, and LDL-c in serum. E, F Levels of TC and TG in liver. G Levels of GLU in serum. All values were presented as mean ± SD with significance markers of ^*P < 0.05 and ^**P < 0.01

Fig. 4

The total ion chromatogram (TIC) profiles in positive (A) and negative ion mode (B). Peaks 1–39 correlate with the compounds enumerated in Table 2

Fig. 5

Analysis of core components and proteins for WSF on NAFLD treatment. A Venn diagram of targets. B Herbs-active components-targets network. C PPI network of core targets possessing the highest 50-degree values

Fig. 6

Enrichment analysis for WSF on NAFLD treatment. A BP results of GO analysis (top 20). B KEGG pathway analysis results (top 20). C Analytical results of selected pathways. D Active components-target-pathway network of WSF on treating NAFLD

Fig. 7

Molecular docking results of ingredients in WSF with pivotal proteins. A Heat map in accordance with minimum binding energies. B–D The docking conformation of JGL5 and JGL8 with TLR4, NF-κB and COX-2

Fig. 8

Effects of WSF on liver inflammation via IHC and ELISA analysis. A IHC figures at the magnification of 400 × . B–E Expression levels of TLR4, NF-κB, COX-2 and IL-6 in liver quantified by IHC assays. F–G Expression levels of IL-1β and TNF-α in liver quantified by ELISA assays. Black arrows refer to the positive expression sites of specific proteins. All values were presented as mean ± SD with significance markers of ^*P < 0.05 and ^**P < 0.01

Fig. 9

Effects of WSF on hepatic inflammation via WB analysis. A Representative figures of WB. B Relative expression levels of TLR4, NF-κB and COX-2 to β-actin in the liver quantified by WB assays. All values were presented as mean ± SD with significance markers of ^*P < 0.05 and ^**P < 0.01.