Exploring the mechanism of action of total glucosides of paeony against autoimmune thyroiditis based on network pharmacology and molecular docking

Abstract

The objective of this study is to explore the potential mechanism of action of Total glucosides of paeony (TGP) in the treatment of autoimmune thyroiditis (AIT). The study utilized literature mining to obtain the active ingredients of TGP. Databases such as Super-PRED, similarity ensemble approach, and Swiss Target Prediction were utilized to predict the targets of the active ingredients. DisGeNET, Dangbank, GeneCards, online mendelian inheritance in man, and Pharmgkb databases were used to obtain the targets related to AIT. The Venn Online tool was used to screen the intersecting genes between the active ingredients and AIT targets. The STRING database was employed to analyze protein protein interaction. Gene ontology bio-enrichment and Kyoto encyclopedia of genes and genomes enrichment of common targets were analyzed using R language. Finally, molecular docking was performed using AutoDockTools-1.5.6 software for validation. The study identified 5 active ingredients of TGP, 283 ingredient targets, 7120 disease targets, 220 intersecting targets, 30 entries for gene ontology analysis, and 30 pathways for Kyoto encyclopedia of genes and genomes analysis. The important targets of the protein protein interaction network were identified as interleukin-6, proto-oncogene tyrosine-protein kinase, epidermal growth factor receptor, among others. The molecular docking validation results showed that Paeoniflorin, albiflorin, and benzoylpaeoniflorin and oxypaeoniflor all bind well to interleukin-6, epidermal growth factor receptor, and proto-oncogene tyrosine-protein kinase. This study reveals the multi-component, multi-target and multi-pathway mechanism of action of TGP in regulating AIT and provides a reference for subsequent basic research.

1. Introduction

Autoimmune thyroiditis (AIT) is an immune disease caused by a combination of genetic and environmental factors. It is characterized by hypothyroidism and is common in females, with a prevalence of about 5% to 15%, and can occur in all age groups, with a higher incidence in adults aged 20 to 60 years.^[1–3] This disease may manifest as transient hypothyroidism, normal thyroid function, and eventually develops into lifelong hypothyroidism, which is also related to the occurrence of thyroid cancer,^[4] so early detection and timely intervention are of great clinical significance. The etiology of AIT has not been fully elucidated, and there is no treatment for the etiology. Clinical treatment is mostly based on symptomatic treatments, such as anti-inflammatory and alternative therapies, or surgical treatments.^[5] The search for new safe drugs is therefore an important goal of AIT treatment.

Chinese medicine focuses on the regulation of the whole body and has fewer toxic side effects, and its rational development and application are of great clinical significance for the treatment of this disease. Total glucosides of paeony (TGP) is a Chinese herbal extract of Radix paeoniae alba, which is a group of glucosides including paeoniflorin, paeonin, albiflorin, oxypaeoniflorin, benzoylpaeoniflorin.^[6]TGP has various pharmacological activities, including anti-inflammatory, antioxidant, immunomodulatory and antitumor.^[7]Studies have shown that TGP can effectively reduce the autoimmune response, attenuate the pathological damage of AIT, and have a certain protective effect on thyroid function.^[8] TGP can also inhibit the inflammatory response mediated by T-helper 17 cells (Th17 cells and promote the generation of Treg cells, thereby increasing the Treg/Th17 ratio and delaying the progression of AIT.^[9] However, the therapeutic mechanism of TGP in autoimmune thyroiditis still need to be further studied and explored.

Network pharmacology uses data mining to obtain drug action networks and biological networks and further analyze their interactions, which is an essential technical means for the discovery and development of multi-target, multi-pathway drugs, and is of great significance in analyzing the therapeutic mechanism of traditional Chinese medicine. Meanwhile molecular bond technique was applied to predict the binding mode of the simulated components with the corresponding receptors to elucidate the target of action. This study investigates the mechanism of action of TGP in the treatment of AIT based on the above 2 approaches, predicted the main potential targets of TGP, and provided a reference for the clinical intervention treatment of AIT.

2. Materials and methods

2.1. Active ingredient and target screening of TGP

Using the chemical composition were paeoniflorin, paeonin, albiflorin, oxypaeoniflorin, benzoylpaeoniflorin as keywords, the Canonical SMILES of this pharmaceutical ingredient gained through pubchem (https://pubchem.ncbi.nlm.nih.gov/), and then through Super-PRED (https://prediction.charite.de/subpages/target_prediction.php), Swiss TargetPrediction (http://www.swisstargetprediction.ch) and similarity ensemble approach (https://sea.bkslab.org/) databases to retrieve Canonical SMILES to predict the targets of action of chemical components in TGP. The objectives from the 3 comprehensive databases were finally merged and duplicate targets were removed to create a TGP component target dataset.

2.2. AIT target predictions

The database was accessed through DisGeNET (www.disgenet.org/), GeneCards (https://www.genecards.org),Dangbank (https://go.drugbank.com/), online mendelian inheritance in man (OMIM) (https://omim.org), Pharmgkb database (https://www.pharmgkb.org/), entered the keyword test “Autoimmune Thyroiditis,” and the species setting was “Homo sapiens.” We searched for human genes related to AIT and analyzed them, and then summarized the results of each database to remove duplicate genes, and finally obtained the AIT-related targets.

2.3. Key target protein protein interaction network construction

In order to clarify the interactions between TGP and AIT targets, the screened targets were transferred to String (https://string-db.org/), and the species was set to “Homo sapiens” to construct a target interaction network (PPI), with a confidence of 0.4, enwomb the discrete points in the network, preserve the outcome as a tsv file, and then transfer them into Cytoscape 3.7.2 (https://cytoscape.org/download_old_versions.html). The core target of TGP anti-AIT is determined by using its “Network Analyzer” option.

2.4. Gene ontology (GO) enrichment analysis of bioprocesses and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathways

Based on the predicted targets of TGP treatment of AIT, GO and KEGG analysis of the predicted intersection targets were performed using R language, and the results were visualized. The GO analysis is structured into 3 sections: molecular function, biological processes and cellular components.

2.5. Docking of drug components with target molecules

The structure files of the Active Chinese medicine ingredients were obtained as ligands from the Pubchem database, and the protein structures of the intersection targets of AIT were obtained from the PDB database. The corresponding ligands and the receptors were introduced into AutoDockTools-1.5.6 for multi-ligand molecular docking, and the binding energies were calculated.

3. Results

3.1. Major component targets of TGP

According to the component analysis results of TGP system (Fig. 1), 5 effective components were obtained, namely paeoniflorin, paeonin, albiflorin, benzoylpaeoniflorin, and oxypaeoniflorin. The 5 chemical components were entered into Super-PRED, similarity ensemble approach, swiss target prediction, and a total of 283 potential targets were obtained after deleting the resulting target duplicates.

Figure 1.

Effective components of total glucosides of paeony.

3.2. Screening of disease targets and prediction of common targets with drugs

The DisGeNET, Dangbank, GeneCards, OMIM, and Pharmgkb databases were searched with the disease name “Autoimmune Thyroiditis” respectively. Among them, DisGeNET database acquired 161 disease targets for the treatment of AIT. Dangbank database screening yielded 22 disease targets. GeneCards database screened for 6856 disease targets. OMIM database got 28 disease targets. Pharmgkb database returned 744 disease targets. And a total of 7120 disease targets were obtained from 5 databases. By mapping and matching TGP drug targets with AIT disease targets, 220 targets for TGP treatment of AIT were obtained, and a Venn diagram was drawn (Fig. 2).

Figure 2.

Intersection targets of total glucosides of paeony and autoimmune thyroiditis.

3.3. Topological parameters analysis of the direct target of TGP anti-AIT

The String database was used to construct the interaction map between key targets. To obtain a protein interaction network diagram, the TGP common target of AIT treatment was imported into String (Fig. 3), Among them, LDHB and AMD1 did not find the corresponding protein names in the string database. The graph consists of 218 nodes and 1975 edges, with an average node degree of 18.1. The average local clustering coefficient is 0.482. The expected number of edges in the graph is calculated to be 669. The results were exported in TSV format, and the topological parameters in PPI network were obtained by Cytoscape3.7.2. Cytoscape3.7.2 plug-in Network Analyzer was used to analyze the Degree, Betweenness centrality and Closeness centrality of common targets. Results interleukin-6 (IL-6) (Degree = 109), epidermal growth factor receptor (EGFR) (Degree = 98), serine rich coiled-coi (SRC) (Degree = 96), and other comprehensive ranking, as shown in Figure 4, indicate that these targets play an important role in the treatment of AIT by TGP.

Figure 3.

Protein interaction network.

Figure 4.

The top 30 core target proteins of total glucosides of paeony in the treatment of autoimmune thyroiditis.

3.4. Enrichment analysis of GO function and KEGG signaling pathway

To further explore the multiple mechanisms of TGP in the therapy, of AIT GO analysis and KEGG analysis of 220 common targets were performed using R language. GO functional enrichment analysis showed that in biological processes (Fig. 5), the therapeutic target genes of TGP were mainly involved in positive regulation of response to external stimulus, positive regulation of MAPK cascade, and ERK1 and ERK2 cascade. In molecular function, the target genes of TGP treatment were mainly involved in protein tyrosine kinase activity, protein serine/threonine kinase activity, and G protein-coupled peptide receptor activity. In closeness centrality, TGP treatment target genes mainly acted on membrane raft, membrane microdomain, and vesicle lumen. KEGG pathway enrichment results showed that Neuroactive ligand-receptor interaction, MicroRNAs in cancer, PI3K − Akt signaling pathway and so on. It is suggested that TGP treatment of AIT may act synergistically through multiple signaling pathways as described above. Figure 6 shows the enrichment data for the 30 most important signaling pathways. The size of the circle represents the amount of genes, and the larger the number of target genes related in the signaling pathway, the larger the diameter of the circle; the circle color from blue to red indicates the smaller P value. The smaller the P value, the higher the enrichment of the target genes involved in the signaling pathway.

Figure 5.

Histogram of GO enrichment results. GO = gene ontology.

Figure 6.

Bubble diagram of KEGG enrichment results. KEGG = Kyoto encyclopedia of genes and genomes.

4. Molecular docking

Multi-ligand molecular docking of paeoniflorin, albiflorin, benzoylpaeoniflorin, and oxypaeoniflorin in TGP with the key targets (IL-6, SRC, EGFR). The docking results were then visualized, and the results are shown in Figure 7. Amino acid residues of key targets of the bound component molecules are clearly presented, and hydrogen bonds are indicated by dashed lines. In Figure 7; albiflorin, paeoniflorin, benzoylpaeoniflorin and oxypaeoniflorin dock with IL-6; Binding of albiflorin, paeoniflorin, benzoylpaeoniflorin, and oxypaeoniflorin to epidermal growth factor receptor; albiflorin, paeoniflorin, benzoylpaeoniflorin, and oxypaeoniflorin dock with SRC. Molecular docking results are shown in Table 1.

Figure 7.

molecular docking.

Table 1.

Results of molecular docking.

Compound	Molecular formula	Molecular weight (g/mol)	Target protein	Bingding energy (KJ/mol)
Paeoniflorin	C23H28O11	480.5	IL-6	−11.96
Albiflorin	C23H28O11	480.5
Benzoylpaeoniflorin	C30H32O12	584.6
Oxypaeoniflorin	C23H28O12	496.5
Paeoniflorin	C23H28O11	480.5	EGFR	−14.68
Albiflorin	C23H28O11	480.5
Benzoylpaeoniflorin	C30H32O12	584.6
Oxypaeoniflorin	C23H28O12	496.5
Paeoniflorin	C23H28O11	480.5	SRC	−12.41
Albiflorin	C23H28O11	480.5
Benzoylpaeoniflorin	C30H32O12	584.6
Oxypaeoniflorin	C23H28O12	496.5

EGFR = epidermal growth factor receptor, IL-6 = interleukin-6, SRC = serine rich coiled-coi.

5. Discussion

AIT is a group of T-cell-mediated autoimmune diseases with thyroid damage as the main manifestation, its main features are infiltration of lymphocytes in the thyroid gland, destruction of follicular cells and increased levels of thyroid autoantibodies in serum.^[10]At present, the treatment of AIT mainly relies on antithyroid drugs, immunosuppressants, and anti-inflammatory agents. However, there are great shortcomings in the clinical application of these drugs, such as poor efficacy and serious side effects.^[11] TGP is one of the extracts of the Chinese herb Paeonia lactiflora. Modern pharmacological research have shown that TGP has anti-inflammatory, immunomodulatory, hepatoprotective and analgesic, anti-oxidative stress and a variety of other effects.^[12] In addition, TGP can also reduce the level of AIT thyroid autoantibodies, regulate the regulatory T-cells, reduce the inflammatory reaction of the thyroid tissue, but the mechanism of action needs to be further study.^[13] Therefore, by constructing protein PPI network, GO enrichment, KEGG, etc, this study deeply and systematically analyzed the possible mechanism of TGP regulating AIT.

5.1. TGP is an important chemical component in the treatment of AIT

Paeoniflorin is the main component in TGP, with antitumor, antioxidant, and immunomodulatory effects, and less toxic side effects, and its medicinal value is receiving more and more attention.^[14] Paeoniflorin inhibited the proliferation of RL95-2 cells and upregulated the protein expression of p38 mitogen-activated protein kinase (p38) MAPK and nuclear factor-κB (NF-κB).^[15] These results suggest that paeoniflorin may exert its antiproliferative activity by activating p38 MAPK and NF-κB signaling pathways. Zhong et al^[16] found in the oxygen-glucose deprivation experiment of cerebral cortex neurons that paeoniflorin activation of Akt and extracellular signal-regulated kinase 1/2 depends on the transfer of adenosine A1 receptor to activate EGFR, thereby exerting its protective effect on nerve cells with antioxidant sugar deficiency. In addition, paeoniflorin can inhibit apoptosis, down-regulate the expression of Hypoxia-inducible factor 1α (HIF-1α), promote the regeneration and repair of epithelial cells, and reduce inflammation and oxidative stress by affecting the PI3K/Akt pathway.^[17–20] Other components of TGP can also affect the signaling pathways such as MAPK and PI3K/Akt. For example, albiflorin can reduce inflammation and fibrosis by inhibiting the PI3K/AKT/NF-κB signaling pathway,^[21] and it can also inhibit the activation of the MAPK/NF-κB signaling pathway, improving lung inflammation in mouse models of asthma.^[22] Oxypaeoniflorin inhibits inflammation and oxidative stress by regulating the PTEN/AKT pathway in a SIRT1-dependent manner.^[23] Benzoylpaeoniflorin the release of inflammatory cytokines by blocking extracellular signal-regulated kinase 1/2, c-Jun N-terminal Kinase and p38 in the MAPK signaling pathway.^[24]

5.2. Important targets of TGP in the treatment of AIT

A PPI network analysis of common targets of TGP and AIT revealed that core targets with high degree values included IL-6, EGFR, and SRC. Studies have shown that IL-6 concentrations are found to be significantly elevated in the serum of female AIT patients.^[25] IL-6 regulates thyroid cell growth and differentiation, induces follicular cells to produce monocyte chemokine protein 1, and its expression in thyroid cells positively correlates with the degree of lymphocyte infiltration.^[26]EGFR is a tyrosine kinase involved in cell reproduction, division and mitosis that induces cancer,^[27] EGFR binds to its ligand and activates and transduces a range of intracellular signals, and aberrant EGFR expression can cause thyroid cell deterioration.^[28] SRC is a non-receptor tyrosine kinase that acts as a suprachiasmatic activator that positively regulates the EGFR signaling pathway and enhances phosphorylation of EGFR and its downstream signaling pathways.^[29]

5.3. Important biological pathways of TGP in treating AIT

The results of GO enrichment analysis indicated that TGP could affect AIT through the role of related biological processes, molecular functions and cellular composition. KEGG results indicated that TGP treatment of AIT mainly involved the MAPK signaling pathway, the PI3K-Akt signaling pathway, the HIF-1 signaling pathway and so on, which were closely associated with the pathogenesis of AIT. MAPK signaling pathway is widely involved in the process of apoptosis, proliferation, and differentiation, and is an important participant in AIT tissue injury, and inhibition of the MAPK signaling pathway can reduce thyroid tissue injury and protect thyroid function.^[30] Autophagy is a lysosome-mediated catabolic process in eukaryotic cells and is a cellular self-protection mechanism. Defective autophagy can bias T-cells toward pro-inflammatory cell subsets (Th17), which in turn promotes the development of AIT.^[31] Mammalian target of rapamycin (mTOR) is a major inhibitor of intracellular autophagy, and the PI3K/Akt pathway is its upstream pathway. Studies have shown that inhibition of the PI3K/AKT/mTOR signaling pathway promotes cellular autophagy to maintain T-cell homeostasis, which in turn reduces the inflammatory response in thyroid tissues and lowers circulating thyroid autoantibody titers, and delays the development of AIT.^[31,32] HIF-1α in the HIF-1 signaling pathway can act as an inducer of malignant tumorigenesis and is overexpressed in a wide range of malignant tumors, and HIF-1α is also overexpressed in AIT tissues.^[32] Moreover, activation of the PI3K/AKt/mTOR signaling pathway enhances HIF-1α activity and initiates HIF-1α protein translation, which in turn leads to inhibition of apoptosis.^[33,34]

6. Conclusion

In summary, the treatment of AIT by TGP is recognized for its multi-component, multi-target, and multi-pathway effects. in which important targets such as IL-6 and EGFR have been proven to be closely related to AIT. TGP may mainly play a role in the treatment of AIT through the MAPK signaling pathway, PI3K-Akt signaling pathway, and is related to the IL-6 and EGFR, which have been shown to be related to AIT. This study used a network pharmacology approach to investigate the possible mechanism of action of TGP in the treatment of AIT, which may offer a theoretical reference for further research.

Acknowledgements

Because this analysis collects data downloaded from public database searches, it does not require ethical approval.

Author contributions

Data curation: Jin Su, Xinran Yu, Limin Zhang.

Funding acquisition: Wen Li.

Supervision: Wen Li.

Visualization: Jin Su, Xinran Yu.

Writing – original draft: Jin Su, Youqing Dong.

Writing – review & editing: Jin Su, Wen Li.