Effects of Danggui-Shaoyao-San on depression- and anxiety-like behaviors of rats induced by experimental tooth movement

Danggui-Shaoyao-San protects against non-alcoholic steatohepatitis via modulation of hepatic APP protein, Lysosomal CTSB release, and NF-κB activation

Background

Non-alcoholic steatohepatitis (NASH), an escalating global health concern, is a primary factor behind cirrhosis, liver transplantation, and hepatocellular carcinoma. Effective treatments remain elusive. Danggui-Shaoyao-San (DGSY), a classic famous prescription employed in treating NASH, could hold promise, although its molecular underpinnings are still under investigation. This study undertakes an exploration of the impacts of DGSY on NASH and seeks to illuminate the mechanisms at play.

Methods

UHPLC-Q-Orbitrap HRMS was employed to identify compounds within DGSY. Mice underwent a 25-week regimen of HFHC diet and high-sugar water, with 4 weeks of DGSY treatment for efficacy and pathogenic mechanism exploration in vivo. L02 cells were cultured with 0.2 mM FFA for 24 h, exposed to DGSY at 1 mg/ml and 2 mg/ml for efficacy and pathogenic mechanism exploration in vitro. Using online databases, we sought potential targets for NASH treatment, and through PPI networks, identified key targets. Expression levels of genes and proteins were examined by western blotting, RT-PCR, and immunofluorescence staining.

Results

Thirty-four compounds were identified within DGSY. DGSY brought about marked reductions in biochemical indicators and yielded significant improvements in NASH mice histological features. Additionally, it mitigated hepatic steatosis and inflammation both in vivo and in vitro. The top 10 targets from two network pharmacology analyses, one focusing on structural prediction and the other on literature mining, identified APOE and APP as potential therapeutic targets for DGSY in NASH treatment. PCR validation confirmed that DGSY reduced APP expression after treatment, and further investigation revealed that DGSY significantly suppressed hepatic APP and Aβ expression, indicating its effectiveness in treating NASH. Furthermore, it inhibited Aβ-induced Cathepsin B lysosomal release, reducing hepatic inflammation.

Conclusion

Danggui-Shaoyao-San has anti-steatohepatitis effects in ameliorating hepatic APP protein expression, reducing hepatic lysosomal CTSB release, and suppressing hepatic NF-κB activation. The study provided a more theoretical basis for the future clinical application of DGSY.

Mathematical algorithm–based identification of the functional components and mechanisms in depression treatment: An example of Danggui-Shaoyao-San

Depression, a complex epidemiological mental disorder, affects around 350 million people worldwide. Despite the availability of antidepressants based on monoamine hypothesis of depression, most patients suffer side effects from these drugs, including psychomotor impairment and dependence liability. Traditional Chinese medicine (TCM) is receiving more and more attention due to the advantages of high therapeutic performance and few side effects in depression treatment. However, complex multicomponents and multi-targets in TCM hinder our ability to identify the functional components and molecular mechanisms of its efficacy. In this study, we designed a novel strategy to capture the functional components and mechanisms of TCM based on a mathematical algorithm. To establish proof of principle, the TCM formula Danggui-Shaoyao-San (DSS), which possesses remarkable antidepressant effect but its functional components and mechanisms are unclear, is used as an example. According to the network motif detection algorithm, key core function motifs (CIM) of DSS in treating depression were captured, followed by a functional analysis and verification. The results demonstrated that 198 pathways were enriched by the target genes of the CIM, and 179 coincided with the enriched pathways of pathogenic genes, accounting for 90.40% of the gene enrichment pathway of the C-T network. Then the functional components group (FCG) comprising 40 components was traced from CIM based on the target coverage accumulation algorithm, after which the pathways enriched by the target genes of FCG were selected to elucidate the potential mechanisms of DSS in treating depression. Finally, the pivotal components in FCG of DSS and the related pathways were selected for experimental validation in vitro and in vivo. Our results indicated good accuracy of the proposed mathematical algorithm in sifting the FCG from the TCM formula, which provided a methodological reference for discovering functional components and interpreting molecular mechanisms of the TCM formula in treating complex diseases.

Using network pharmacology to explore the mechanism of Danggui-Shaoyao-San in the treatment of diabetic kidney disease

Danggui-Shaoyao-San (DSS) is one of traditional Chinese medicine, which recently was found to play a protective role in diabetic kidney disease (DKD). However, the pharmacological mechanisms of DSS remain obscure. This study would explore the molecular mechanisms and bioactive ingredients of DSS in the treatment of DKD through network pharmacology. The potential target genes of DKD were obtained through OMIM database, the DigSee database and the DisGeNET database. DSS-related targets were acquired from the BATMAN-TCM database and the STITCH database. The common targets of DSS and DKD were selected for analysis in the STRING database, and the results were imported into Cytoscape to construct a protein-protein interaction network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and Gene Ontology (GO) enrichment analysis were carried out to further explore the mechanisms of DSS in treating DKD. Molecular docking was conducted to identify the potential interactions between the compounds and the hub genes. Finally, 162 therapeutic targets of DKD and 550 target genes of DSS were obtained from our screening process. Among this, 28 common targets were considered potential therapeutic targets of DSS for treating DKD. Hub signaling pathways including HIF-1 signaling pathway, TNF signaling pathway, AMPK signaling pathway, mTOR signaling pathway, and PI3K-Akt signaling pathway may be involved in the treatment of DKD using DSS. Furthermore, TNF and PPARG, and poricoic acid C and stigmasterol were identified as hub genes and main active components in this network, respectively. In this study, DSS appears to treat DKD by multi-targets and multi-pathways such as inflammatory, oxidative stress, autophagy and fibrosis, which provided a novel perspective for further research of DSS for the treatment of DKD.