A Network Pharmacology Approach to Explore Mechanism of Action of Longzuan Tongbi Formula on Rheumatoid Arthritis

Schisandrin A Attenuates Diabetic Nephropathy via EGFR/AKT/GSK3β Signaling Pathway Based on Network Pharmacology and Experimental Validation

Diabetic nephropathy (DN) is one of the common complications of diabetes and the main cause of end-stage renal disease (ESRD) in clinical practice. Schisandrin A (Sch A) has multiple pharmacological activities, including inhibiting fibrosis, reducing apoptosis and oxidative stress, and regulating immunity, but its pharmacological mechanism for the treatment of DN is still unclear. In vivo, streptozotocin (STZ) and a high-fat diet were used to induce type 2 diabetic rats, and Sch A was administered for 4 weeks. At the same time, protein-protein interaction (PPI) networks were established to analyze the overlapping genes of DN and Sch A. Subsequently, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed to determine the hub pathway. In addition, molecular docking was used to preliminarily verify the affinity of hub proteins and Sch A. Further, H&E staining, Sirius red staining, immunohistochemistry, immunofluorescence, and western blot analysis were used to detect the location and expression of related proteins in DN. This study revealed the multi-target and multi-pathway characteristics of Sch A in the treatment of DN. First, Sch A could effectively improve glucose tolerance, reduce urine microprotein and urine creatinine levels, and alleviate renal pathological damage in DN rats. Second, EGFR was the hub gene screened in overlapping genes (43) of Sch A (100) and DN (2524). Finally, it was revealed that Sch A could inhibit the protein expression levels of EGFR and PTRF and reduced the expression of apoptosis-related proteins, and this effect was related to the modulation of the AKT/GSK-3β signaling pathway. In summary, Sch A has a protective effect in DN rats, EGFR may be a potential therapeutic target, throughout modulating AKT/GSK-3β pathway.

Toddalia asiatica extract attenuates adjuvant-induced arthritis by modulating colon Th17/Treg balance and colony homeostasis

ETHNOPHARMACOLOGICAL RELEVANCE

Given the adverse effects of the current principal treatments, there is still a great need for effective cures for rheumatoid arthritis (RA), an immune-mediated disease. Toddalia asiatica (L.) Lam is a traditional medicinal herb that can be used for RA treatment because of its anti-inflammatory and analgesic properties.

AIM OF THE STUDY

To investigate the possible effects of Toddalia asiatica extract (TAE) on intestinal immunity and the intestinal bacterial flora in a rat model of RA.

MATERIALS AND METHODS

The anti-arthritis effect of TAE was evaluated in arthritis rats induced by complete Freund's adjuvant-induced arthritis (AIA). Arthritis index (AI) scores, systemic inflammation scores, histopathologic changes in the colon and ankle were detected by hematoxylin and eosin staining. Western blot analysis was performed to assess the protein expression of IL-17A, RORC, IL-1β, IL-6, FOXP3, IL-10 in the colon. RT-PCR was performed to assess the expression of the colon's mRNA. Finally, changes to the gut microbiome by sequencing 16S rDNA. Microbial function prediction was performed using PICRUSt with the KEGG databases and correlation analysis was carried out by computing Spearman's rank correlations.

RESULTS

demonstrated that TAE administration at a dose of 3 g/kg dramatically decreased AI scores, systemic inflammation scores, and histopathologic lesions of the ankle and colon in AIA rats. TAE was found to significantly reduce the expression levels of Th17-related proteins and mRNAs (IL-17A, RORC, IL-1β and IL-6) in the colon, while increasing the expression levels of Treg-related proteins and mRNA (IL-10 and FOXP3), which helped restore the balance of Th17/Treg immune cells in the colon. Meanwhile, TAE was also found to be capable of remodeling the gut microbiota in AIA rats. Depleting RA-associated genera and thereby increasing α-diversity enriched the gut microbiota's diversity and shifted the community composition dramatically, leading to the increase of Firmicutes_unclassified, Ruminococcaceae_unclassified, Muribaculum, Subdoligranulum, Lachnospira, Marvinbryantia, and the reduction of RA-related bacteria Ligilactobacillus, Streptococcus and Eubacterium-eligens-group. Furthermore, PICRUSt analysis revealed that metabolic pathways were associated with TAE treatment, with metabolic pathways dominating. Among them, metabolic pathways were predominant. Correlation studies showed that a total of 9 microorganisms, including Ligilactobacillus, Eubacterium-eligens-group and Subdoligranulum, were significantly associated with Th17/Treg expression.

CONCLUSIONS

This study demonstrates that TAE is a low-toxicity poly alkaline drug that can rapidly and effectively improve joint symptoms in RA rats and increases beneficial intestinal bacteria and decreases harmful ones, which is associated with modulating Th17/Treg interactions in intestinal T cells and reversing microbial disorders.

Network pharmacology study of Yishen capsules in the treatment of diabetic nephropathy

OBJECTIVE

In this study, we used network pharmacology to explore the possible therapeutic mechanism underlying the treatment of diabetic nephropathy with Yishen capsules.

METHODS

The active chemical constituents of Yishen capsules were acquired using the Traditional Chinese Medicine Systems Pharmacology platform and the Encyclopedia of Traditional Chinese Medicine. Component target proteins were then searched and screened in the BATMAN database. Target proteins were cross-validated using the Comparative Toxicogenomics Database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the target proteins were performed. Then, protein-protein interaction (PPI) analysis was performed using the STRING database. Finally, a pharmacological network was constructed to show the component-target-pathway relationships. Molecular docking was used to analyse the interaction between drug components and target proteins.

RESULTS

In total, 285 active chemical components were found, including 85 intersection targets against DN. In the pharmacological network, 5 key herbs (A. membranaceus, A. sinensis, E. ferox, A. orientale, and R. rosea) and their corresponding 12 key components (beta-sitosterol, beta-carotene, stigmasterol, alisol B, mairin, quercetin, caffeic acid, 1-monolinolein, kaempferol, jaranol, formononetin, and calycosin) were screened. Furthermore, the 12 key components were related to 24 target protein nodes (e.g., AGT, AKT1, AKT2, BCL2, NFKB1, and SIRT1) and enriched in 24 pathway nodes (such as the NF-kappa B, AGE-RAGE, toll-like receptor, and relaxin signaling pathways). Molecular docking revealed that hydrogen bond was formed between drug components and target proteins.

CONCLUSION

In conclusion, the active constituents of Yishen capsules modulate targets or signaling pathways in DN pathogenesis.

Explore the Potential Ingredients for Detoxification of Honey-Fired Licorice (ZGC) Based on the Metabolic Profile by UPLC-Q-TOF-MS

Licorice is well known for its ability to reduce the toxicity of the whole prescription in traditional Chinese medicine theory. However, honey-fired licorice (ZGC for short), which is made of licorice after being stir-fried with honey water, is more commonly used for clinical practice. The metabolism in vivo and detoxification-related compounds of ZGC have not been fully elucidated. In this work, the chemical constituents in ZGC and its metabolic profile in rats were both identified by high ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). The network pharmacology was applied to predict the potential detoxifying ingredients of ZGC. As a result, a total of 115 chemical compounds were identified or tentatively characterized in ZGC aqueous extract, and 232 xenobiotics (70 prototypes and 162 metabolites) were identified in serum, heart, liver, kidneys, feces, and urine. Furthermore, 41 compounds absorbed in serum, heart, liver, and kidneys were employed for exploring the detoxification of ZGC by network pharmacology. Ultimately, 13 compounds (five prototypes including P5, P24, P30, P41 and P44, and 8 phase Ⅰ metabolites including M23, M47, M53, M93, M100, M106, M118, and M134) and nine targets were anticipated to be potential mediums regulating detoxification actions. The network pharmacology analysis had shown that the ZGC could detoxify mainly through regulating the related targets of cytochrome P450 and glutathione. In summary, this study would help reveal potential active ingredients in vivo for detoxification of ZGC and provided practical evidence for explaining the theory of traditional Chinese medicine with modern technology.

Virtual Screening Technique Used to Estimate the Mechanism of Adhatoda vasica Nees for the Treatment of Rheumatoid Arthritis Based on Network Pharmacology and Molecular Docking

Adhatoda vasica Nees (AVN) is commonly used to treat joint diseases such as rheumatoid arthritis (RA) in ethnic minority areas of China, especially in Tibetan and Dai areas, and its molecular mechanisms on RA still remain unclear. Network pharmacology, a novel strategy, utilizes bioinformatics to predict and evaluate drug targets and interactions in disease. Here, network pharmacology was used to investigate the mechanism by which AVN acts in RA. The chemical compositions and functional targets of AVN were retrieved using the systematic pharmacological analysis platform PharmMapper. The targets of RA were queried through the DrugBank database. The protein-protein interaction network (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets were constructed in the STRING database, and the network visualization analysis was performed in Cytoscape. Maestro 11.1, a type of professional software, was used for verifying prediction and analysis based on network pharmacology. By comparing the predicted target information with the targets of RA-related drugs, 25 potential targets may be related to the treatment of RA, among which MAPK1, TNF, DHODH, IL2, PTGS2, and JAK2 may be the main potential targets for the treatment of RA. Finally, the chemical components and potential target proteins were scored by molecular docking, and compared with the ligands of the protein, the prediction results of network pharmacology were preliminarily verified. The active ingredients and mechanism of AVN against RA were firstly investigated using network pharmacology. Additionally, this research provided a solid foundation for further experimental studies.

Pharmacological Effects of Agastache rugosa against Gastritis Using a Network Pharmacology Approach

Agastache rugosa is used as a Korean traditional medicine to treat gastric diseases. However, the active ingredients and pharmacological targets of A. rugosa are unknown. In this study, we aimed to reveal the pharmacological effects of A. rugosa on gastritis by combining a mice model and a network pharmacology method. The macrophage and gastritis-induced models were used to evaluate the pharmacological effects of A. rugosa. The results show that A. rugosa relieved mucosal damage induced by HCl/EtOH in vivo. Network analysis identified 99 components in A. rugosa; six components were selected through systematic screening, and five components were linked to 45 gastritis-related genes. The main components were acacetin and luteolin, and the identified core genes were AKT serine/threonine kinase 1 (AKT1), nuclear factor kappa B inhibitor alpha (NFKBIA), and mitogen-activated protein kinase-3 (MAPK3) etc. in this network. The network of components, target genes, protein–protein interactions, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was closely connected with chemokines and with phosphoinositide 3-kinase-Akt (PI3K/AKT), tumor-necrosis-factor alpha (TNFα), mitogen-activated protein kinase, nuclear factor kappa B, and Toll-like receptor (TLR) pathways. In conclusion, A. rugosa exerts gastro-protective effects through a multi-compound and multi-pathway regulatory network and holds potential for treating inflammatory gastric diseases.

Prediction of triptolide targets in rheumatoid arthritis using network pharmacology and molecular docking

Network pharmacology is a novel approach that uses bioinformatics to predict and identify multiple drug targets and interactions in disease. Here, we used network pharmacology to investigate the mechanism by which triptolide acts in rheumatoid arthritis (RA). We first searched public databases for genes and proteins known to be associated with RA, as well as those predicted to be targets of triptolide, and then used Ingenuity Pathway Analysis (IPA) to identify enriched gene pathways and networks. Networks and pathways that overlapped between RA-associated proteins and triptolide target proteins were then used to predict candidate protein targets of triptolide in RA. The following proteins were found to occur in both RA-associated networks and triptolide target networks: CD274, RELA, MCL1, MAPK8, CXCL8, STAT1, STAT3, c-JUN, JNK, c-Fos, NF-κB, and TNF-α. Docking studies suggested that triptolide can fit in the binding pocket of the six top candidate triptolide target proteins (CD274, RELA, MCL1, MAPK8, CXCL8 and STAT1). The overlapping pathways were activation of Th1 and Th2 cells, macrophages, fibroblasts and endothelial cells in RA, while the overlapping networks were involved in cellular movement, hematological system development and function, immune cell trafficking, cell-to-cell signaling and interaction, inflammatory response, cellular function and maintenance, and cell death and survival. These results show that network pharmacology can be used to generate hypotheses about how triptolide exerts therapeutic effects in RA. Network pharmacology may be a useful method for characterizing multi-target drugs in complex diseases.

Mechanism of action of celastrol against rheumatoid arthritis: A network pharmacology analysis

Network pharmacology uses bioinformatics to broaden our understanding of drug actions and thereby to advance drug discovery. Here we apply network pharmacology to generate testable hypotheses about the multi-target mechanism of celastrol against rheumatoid arthritis. We reconstructed drug-target pathways and networks to predict the likely protein targets of celastrol and the main interactions between those targets and the drug. Then we validated our predictions of four candidate targets (IKK-β, JNK, COX-2, MEK1) by performing docking studies with celastrol. The results suggest that celastrol acts against rheumatoid arthritis by regulating the function of several signaling proteins, including MMP-9, COX-2, c-Myc, TGF-β, c-JUN, JAK-1, JAK-3, IKK-β, SYK, MMP-3, JNK and MEK1, which regulate the functions of Th1 and Th2 cells, macrophages, fibroblasts and endothelial cells in rheumatoid arthritis. Celastrol is predicted to affect networks involved mainly in cancer, connective tissue disorders, organismal injury and abnormalities, tissue development, cell death and survival. This network pharmacology strategy may be useful for discovery of multi-target drugs against complex diseases.