Investigating the Mechanism of Astragalus in the Treatment of Periodontitis through Bioinformatics Analysis

doi:10.21203/rs.3.rs-4002520/v1

Background: Periodontitis, a common oral inflammatory disease which may cause premature tooth loss, was proved can be treated by Astragalus, but the detailed mechanisms are still not clear. We validated and discussed the molecular mechanism by using bioinformatics methods and cell experimental, and in order to clarify the mechanism of Astragalus during the treatment of periodontitis.

Methods:The active ingredients of Astragalus and their corresponding targets were obtained using the TCMSP database, and the periodontitis-related targets were obtained from DrugBank database, GeneCards database etc., then GO and KEGG analyses were performed based on Metascape database. Astragalus active ingredients and related targets network, Astragalus-active components-targets of periodontitis network, and Astragalus-active components- periodontitis targets-signaling pathways network were constructed by using Cytoscape3.9.0 software. Thereafter, Molecular docking and molecular dynamic simulation were analyzed in Discovery Studio 2019 software and Gromacs 2021.2 software package respectively, in order to evaluate the stability of combination between active components and core targets.

Results:17 compounds of Astragalus and 464 corresponding targets were obtained and 5 major active ingredients were screened from the drug active ingredients- periodontitis gene network. PPI network analysis revealed the top 10 core potential targets, 7 of them have suitable crystal structure and can be used for molecular docking, including interleukin-6 (IL6), tumor necrosis factor (TNF), RAC-α serine/threonine protein kinase (AKT1), interleukin-1β(IL1β), prostaglandin G/H synthase-2 (PTGS2), matrix metalloproteinase-9 (MMP9), and Caspase3 (CASP3). In addition, 58 GO terms and 146 KEGG pathways were identified. 5 major active ingredients and 7 core targets which mentioned above were docked molecularly in Discovery Studio 2019 software. Molecular dynamics simulations confirmed that there has a stable combination between Caspase3 and Kaempferol ligand system.

Conclusions: Based on the results of network pharmacology, molecular docking and molecular dynamics, it can be concluded that Astragalus has multiple active ingredients, and targets different signaling pathways to regulate the inflammatory response, immune response and oxidative stress in order to play a beneficial role in the treatment of periodontitis, especially Kaempferol can combine with Caspase3 stably to inhibit the cell apoptosis, our data provide solid evidences and enlightenment for the clinical application of Astragalus in future.

Astragalus

Periodontitis

Network pharmacology

Molecular docking

Molecular dynamics simulation

Kaempferol

Caspase3

Periodontitis is an inflammatory disease of the oral cavity that is characterized by the formation of periodontal pockets, resorption of alveolar bone, and loosening of teeth[1]. Local factors such as tartar accumulation, tooth crowding, and orthodontic treatment contribute to the development of periodontitis, which is a leading cause of tooth loss globally. Recent studies have identified an association between periodontitis and various systemic diseases, including rheumatoid arthritis, type 2 diabetes, atherosclerotic cardiovascular disease, cerebrovascular disease, respiratory disease, and osteoporosis[2, 3].

Currently, the clinical treatment of periodontitis mainly focuses on its basic, pharmacological, and surgical approaches[4]. In the aforementioned treatments, pharmacological interventions are primarily used as adjuncts to basic and surgical approaches. These interventions are particularly useful in cases of acute infections in periodontal tissues, infections in difficult-to-reach areas, patients with systemic diseases, or patients who cannot perform oral hygiene measures[4].

Traditional Chinese herbal medicine has attracted significant attention from researchers due to its long history of use, high safety profile, and affordability. It has emerged as a prominent area of research in the field of oral diseases, particularly in relation to the treatment of periodontitis. According to the Traditional Chinese Medicine (TCM) system, chronic periodontitis is classified as belonging to the categories of “tooth declaration” and “wind chancre” [5]. Astragalus, a traditional Chinese medicine, is known for its sweet taste and warm nature. It is believed to tonify qi and raise yang, consolidate the surface and stop sweating, detoxify and drain pus, promote water retention and reduce swelling, as well as strengthen muscles[5]. Astragalus is believed to have therapeutic potential for various common diseases, including diabetes, tumors, and autoimmune diseases. Astragalus polysaccharide, its active ingredient, has been found to mitigate bone destruction in ovariectomy rats, which serve as a model for osteoporosis[6]. These findings suggest a protective effect on the skeletal system of the host.

In this study, we retrieved active ingredients in Astragalus and genes related to periodontitis from various databases. We investigated the molecular mechanisms underlying the interaction between active ingredients in Astragalus and targets associated with periodontitis using network pharmacology. Additionally, we validated the identified active ingredients and disease targets using molecular docking and molecular dynamics techniques. These findings are anticipated to enhance the understanding of the underlying molecular mechanisms involved in periodontitis development and offer potential clinical applications for Astragalus in preventing periodontitis.

2.1 Screening and identification of the active compounds in Astragalus and corresponding targets

We retrieved 87 components of Astragalus from the TCMSP database. We identified and validated 17 active ingredients with potential preventive and curative effects on periodontitis. The screening criteria included OB ≥ 30%, DL ≥ 0.18, and the presence of appropriate target proteins in Homo sapiens, as presented in Table 1. We compiled a list of 184 potential targets (without duplication) that were targeted by the 17 active ingredients of the herbal extract, as illustrated in Fig. 1. Therefore, it is hypothesized that Astragalus exhibits complex pharmacological effects in the treatment of diseases in Homo sapiens, which may be attributed to the aforementioned 17 active ingredients and their multiple corresponding targets[7].

Table 1

Active compounds in astragalus
Number	MOL ID	Molecule Name	OB(%)	DL
HQ1	MOL000211	Mairin	55.38	0.78
HQ2	MOL000239	Jaranol	50.83	0.29
HQ3	MOL000296	hederagenin	36.91	0.75
HQ4	MOL000033	(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol	36.23	0.78
HQ5	MOL000354	Isorhamnetin	49.60	0.31
HQ6	MOL000371	3,9-di-O-methylnissolin	53.74	0.48
HQ7	MOL000378	7-O-methylisomucronulatol	74.69	0.30
HQ8	MOL000392	Formononetin	69.67	0.21
HQ9	MOL000422	Kaempferol	41.88	0.24
HQ10	MOL000433	FA	68.96	0.71
HQ11	MOL000439	isomucronulatol-7,2'-di-O-glucosiole	49.28	0.62
HQ12	MOL000442	1,7-Dihydroxy-3,9-dimethoxy pterocarpene	39.05	0.48
HQ13	MOL000098	Quercetin	46.43	0.28
HQ14	MOL000379	9,10-dimethoxypterocarpan-3-O-β-D-glucoside	36.74	0.92
HQ15	MOL000380	(6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol	64.26	0.42
HQ16	MOL000387	Bifendate	31.10	0.67
HQ17	MOL000417	Calycosin	47.75	0.24

2.2 Identification of targets associated with periodontitis

We isolated 280 differentially expressed genes related to periodontitis from the analysis of Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) using two datasets. The GSE10334 dataset included 89 up-regulated genes and 35 down-regulated genes, while the GSE16134 dataset included 123 up-regulated genes and 33 down-regulated genes. Figure 2 shows the volcano and heat map plots based on these screened differential genes. Up-regulated genes are indicated in red, while down-regulated genes are indicated in green[8]. To ensure that no differential genes were missed, we obtained 1880 differential genes related to periodontitis from GeneCards, OMIM, and DrugBank using the keywords “periodontitis”. We confirmed 1996 genes related to periodontitis by combining the above three databases with GEO analysis and removing duplicate genes, as shown in Fig. 3A. The targets corresponding to the active ingredients in Astragalus and the targets related to periodontitis were separately imported into a Venn diagram, resulting in a total of 94 intersection targets, as shown in Fig. 3B.

2.3 Construction and analysis of the network “Astragalus-active ingredients-targets-periodontitis”

The Astragalus-active ingredient-target-periodontitis network was mapped and analyzed using Cytoscape 3.9.0 software. As shown in Fig. 4, most of the active ingredients in Astragalus were found to have close interactions with targets related to periodontitis.

2.4 Construction and analysis of the PPI network

We constructed and analyzed a PPI network of intersection targets using Cytoscape 3.9.0 software to elucidate the potential mechanism of Astragalus in treating periodontitis. We calculated the node degree and arranged the nodes in a circular layout based on their degree values. A larger node area indicates a higher degree value[9]. After excluding 2 isolated nodes, the network consisted of 92 nodes and 3304 edges. Figure 5A shows the target nodes with degree values greater than 100 in pink color, placed in the inner layer. Figure 5B demonstrates the top ten key targets obtained using the MCC algorithm with the CytoHubba plug-in[10]. These targets include VEGFA, TNF, IL6, AKT1, MMP9, PTGS2, IL1B, CXCL8, CASP3, and IL10, which are recognized as core targets for the therapeutic effects of Astragalus in periodontitis treatment

2.5 Analyses of gene function and pathway enrichment

GO enrichment analysis was conducted on the 94 overlapping targets using the Metascape platform, with a threshold set at p.adjust < 0.01. The analysis revealed 19 BP-related entries, including responses to inorganic substances, positive regulation of cell migration, and responses to lipopolysaccharides. Additionally, 20 MF-related entries were identified, involving DNA-bound transcription factor binding, cytokine receptor binding, and kinase binding. Furthermore, 19 CC-related entries were found, which involved membrane rafts, transcriptional regulator complexes, and extracellular matrix. The analysis of BP, MF, and CC functions was visualized using a bar graph in Fig. 6A. Additionally, a total of 146 signaling pathways were obtained from KEGG analysis, with a threshold set at p.adjust < 0.01. The top 20 major signaling pathways are presented in a bubble diagram[11], which mainly include pathways related to cancer, lipids and atherosclerosis, AGE-RAGE signaling pathways in diabetic complications, and proteoglycans in cancer (as shown in Fig. 6B).

We constructed a network of signaling pathways targeted by the active components of Astragalus in periodontitis using Cytoscape 3.9.0 software, as depicted in Fig. 7, to comprehensively elucidate the mechanism of Astragalus in treating periodontitis. This network reveals that all 17 active ingredients in Astragalus can target and modulate multiple pathways, leading to therapeutic effects on periodontitis. Specifically, it highlights the modulation of lipids and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and several cancer pathways.

2.6 Molecular docking results and analysis

Total of 7 key targets were included in the molecular docking simulations, namely AKT1, TNF, IL6, MMP9, PTGS2, CAPS3, and IL 1β which shown in Table 2, with a threshold at RSMD value ≤ 2.0 Å. Another three core targets, namely VEGFA, CXCL8, and IL-10, were not analyzed using molecular docking due to the absence of suitable protein crystal structures in the PDB database[11].

Table 2

**Molecular information of key target proteins**
Target	PDBID	Ligand	Resolution/Å	RSMD/Å
IL 6	1ALU	TLA	1.90	1.6702
TNF	7KP9	ATG	2.15	0.9369
IL 1β	5R88	LWA	1.38	1.4982
AKT1	4EJN	OR4	2.19	0.4065
MMP9	2OW1	TMR	2.20	0.2574
PTGS2	5IKV	FLF	2.51	0.7210
CAPS3	1RHM	NA4	2.50	1.8978

The top five active components, namely quercetin, kaempferol, formalin, calycosin, and 7-O-methylisonicotinol, were selected for molecular docking due to their higher number of nodes and edges in the network of “Astragalus-Active Components-Targets of Periodontitis” (also shown in Fig. 4). The screened active ingredients were prepared using the “Prepare Ligands” module to obtain a validated 3D conformation. Subsequently, the “Prepare Protein” module was utilized to eliminate multiple conformations of the target protein and supplement any incomplete amino acid residues[12]. Subsequently, molecular docking was performed using the “CDOCKER” module, which utilizes -CIE to evaluate the affinity between the protein and active ingredients. A higher value indicates better docking activity, as shown in Table 3.

Table 3

Molecular docking of key target proteins and active compounds
Name	Molecular docking score for key targets (-CIE)
	IL1β	TNF	AKT1	IL6	PTGS2	MMP9	CAPS3
Quercetin	50.51	42.73	55.74	41.66	46.45	53.66	52.52
Kaempferol	47.88	58.51	60.21	41.97	59.20	61.39	72.18
Formononetin	41.04	47.03	45.73	24.78	44.23	50.55	50.75
Calycosin	43.58	48.78	47.92	27.56	51.22	54.67	52.29
7-O-methylisomucronulatol	33.62	43.10	44.95	26.70	27.64	47.23	28.28

In Fig. 8, we represented the molecular docking results of Astragalus active ingredients with key targets. Quercetin (MOL000098) and 7-O-methylisomucronulatol (MOL000378) showed a good binding affinity with AKT1 and MMP9, while Kaempferol (MOL000422), Formononetin (MOL000392) and Calycosin (MOL000417) showed a good binding affinity with CASP3 and MMP9. Despite showing good binding affinity with AKT1, CAPS3, and MMP9, these active ingredients also exhibit high -CIE values with IL 6, TNF, PTGS2, and IL I β, indicating their stable binding with the seven hub targets. Furthermore, our findings demonstrate that these active compounds interact with the amino acid residues of core targets by forming hydrogen bonds, Pi-Pi bonds, and van der Waals forces[13]. Moreover, the CASP3-Kaempferol complex was subjected to further investigation through molecular dynamics simulation due to its highest docking score.

2.7 Molecular dynamics stimulation

MD simulations were conducted on the CASP3-Kaempferol complex to assess its stability. The RMSD from the initial position serves as a crucial indicator of the stability of a ligand-target protein complex. A lower average RMSD indicates tighter binding between the ligand and the target protein, signifying greater stability of the complex. Initially, the complex system stabilizes around 2 ns, experiences fluctuations thereafter, and ultimately achieves a final equilibrium state (< 0.1 nm) by 24.5 ns, following a significant disturbance at 23 ns. These findings suggest that the binding between Kaempferol and CASP3 gradually stabilizes after 24.5 ns, as illustrated in Fig. 9A.

The root mean square fluctuation (RMSF) can track the fluctuation of amino acid residues in proteins during simulation, indicating local structural flexibility. A smaller RMSF value suggests greater stability in the complex system. While the overall structure of Kaempferol-CASP3 remains relatively stable, residues at positions 294 to 321 exhibit significant elasticity. Modifying these 27 residues could potentially enhance the stability of the Kaempferol-CASP3 complex, as depicted in Fig. 9B. The radius of rotation (Rg) is a crucial indicator for evaluating the compactness of the structure. Although there are some fluctuations initially, the Rg of the Kaempferol-CASP3 complex exhibits a peak at 24.5ns and subsequently maintains a relatively stable state, as illustrated in Fig. 9C. The solvent accessible surface area (SASA) is related to the solvation free energy of molecules, and it can be approximately proportional to their SASA when a completely hydrophobic surface is present. As depicted in Fig. 9D, SASA remains relatively stable for 50 ns. This finding is consistent with the RMSD and RMSF results, suggesting a stable binding between Kaempferol and CASP3. The stability of the system can be indirectly assessed by examining the number of hydrogen bonds. For instance, the number of hydrogen bonds exhibits a significant increase after 24 ns, suggesting a heightened level of stability in the system, as depicted in Fig. 9E. Figure 9F illustrates the three-dimensional structure of the CASP3-cannabinoid complex.

The binding affinity between the receptor and ligand was determined using MM-PBSA[14], with the results depicted in Fig. 10A. GGAS represents the overall gas-phase free energy, which is derived from the sum of van der Waals and electrostatic energies. The negative values (VDWAALS=-14.76, EEL=-9.22) indicate the involvement of both hydrophobic and electrostatic interactions in stabilizing the binding. GSOLV consists of contributions from EGB (polar solvation energy, where a value of 19.47 indicates a detrimental effect on complex formation) and ESURF (nonpolar solvation energy, with a value of -2.38 indicating favorable interactions between the ligand and receptor protein).

The total energy of the CASP3-Kaempferol complex was calculated to be -6.89 by summing the GGAS (-23.97) and GSOLV (17.09) energies. The negative value of the total energy indicates that the binding is stable. Figure 10B shows that the energy values of three residues (TYR:338=-0.51, TRP:340=-1.41, and ARG:341=-1.37) are all negative, indicating that Kaempferol mainly binds to these three residues to form a stable system.

3.1 Identification of the bioactive constituents and their respective targets of Astragalus

The TCMSP (https://tcmsp-e.com/) is a database and analysis platform for traditional Chinese medicine systems pharmacology, which was developed based on the framework of systems pharmacology for herbal medicines[9]. Oral bioavailability (OB) is a crucial pharmacokinetic parameter that measures the ratio of the oral dose to the unchanged drug that enters systemic circulation[15]; On the other hand, drug-like properties (DL) is a qualitative concept employed to assess the molecule's potential to become a drug[16]. Based on the previously published data, the bioactive components and targets of Astragalus were identified using the criteria of OB ≥ 30% and DL ≥ 0.18[17].

3.2 Predicting the targets of Astragalus for periodontitis treatment

Target genes associated with periodontitis were searched using the GeneCards platform (https://www.genecards.org/)[18],, OMIM platform (https://www.omim.org/)[19], and DrugBank platform (https://www.drugbank.ca/)[20]. Two datasets, GSE10334 and GSE16134[8, 21, 22], were obtained by searching the GEO database (https://www.ncbi.nlm.nih.gov/geo/) for periodontitis-related datasets using the keyword “periodontitis”. The samples in both datasets were obtained from gingival tissue, with the experimental group consisting of samples from individuals with periodontitis and the control group consisting of samples from individuals with healthy gingiva. These two datasets were merged and analyzed using the GPL570 sequencing platform. Batch correction was applied prior to performing differential expression analysis to identify genes that were differentially expressed. Data correction was carried out using Perl. Differential gene analysis was performed using the Limma package in R software (version 4.2.2), and volcano and heat maps were generated using the ggplot2 package. A significance level of P < 0.05 and a fold change (FC) > 2 were used to determine significant differences between the two groups[23]. GSE10334 consisted of 183 samples, while GSE16134 consisted of 241 samples. All targets related to periodontitis were obtained from the aforementioned databases and confirmed. A crossover network was constructed using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/)[24] to identify the intersection of targets between the active ingredients in Astragalus and targets related to periodontitis. These intersection targets were then used for subsequent analysis.

3.3 Construction networks

We utilized Cytoscape 3.9.0 software to construct the network of active components of Astragalus and their corresponding targets, as well as the network of shared targets between Astragalus components and periodontitis[24]. Topological analysis was performed on the degree values of each node in this network to identify the key active ingredients of Astragalus.

3.4 Construction of protein-protein interaction (PPI) networks with overlapping targets

We utilized the STRING database (https://cn.string-db.org/) for the analysis of protein interactions[25]. The overlapping targets of active ingredients in Astragalus and periodontitis disease were inputted into the STRING database to acquire the TSV file, which was then used for constructing the PPI network. Subsequently, Cytoscape 3.9.0 software was employed for further topological analysis, allowing for adjustments in the size, shape, color, and layout of the nodes in order to construct a comprehensive PPI network. Key regulatory proteins were identified, and the top 10 nodes were filtered using the MCC algorithm with the CytoHubba plugin.

3.5 Enrichment of GO terms and KEGG pathways

We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to explore the effects of Astragalus on biological processes (BP), molecular functions (MF), cellular components (CC), and signaling pathways relevant to periodontitis treatment, utilizing the Metascape platform (http://metascape.prg/gp/index.html)[26]. The results were visualized using a bioinformatics platform (http://www.bioinformatics.com.cn/)[27]. Statistical significance was determined at p < 0.05. A smaller p-value indicates a higher level of enrichment, while a larger count represents a greater number of enriched genes[28]. Lastly, we constructed the network of “Astragalus-active ingredients-intersection targets” and “Astragalus-active components-periodontitis-signaling pathway” using Cytoscape 3.9.0 software.

3.6 Molecular docking simulation

Molecular docking simulations were performed using Discovery Studio 2019 software (hereafter referred to as DS)[7]. DS is commonly used in protein structure-function studies and drug discovery, providing a user-friendly tool for protein simulation, optimization, and drug design. The ligand molecules in the complex were separated and docked into the protein pocket. The root-mean-square deviation (RMSD) value was used to assess the computational accuracy of the docking method. Typically, an RMSD ≤ 2.0 Å indicates accurate docking and reliable results[29]. The ligand molecules in the complex were separated and docked into the protein pocket. The root-mean-square deviation (RMSD) value was used to assess the computational accuracy of the docking method. Typically, an RMSD ≤ 2.0 Å indicates accurate docking and reliable results. Molecular docking technology enables rapid identification of drug targets and evaluation of their pharmacological activities and molecular mechanisms, thereby providing valuable technical support for the modernization research of Chinese medicine[30].

The top 10 key targets derived from the protein-protein interaction (PPI) network using the MCC algorithm with the CytoHubba plugin, along with the five core compounds obtained from the “Astragalus active ingredients-intersection targets-periodontitis” network, were used as receptors and ligands for molecular docking. Two-dimensional structures of the active ingredient small molecules were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Subsequently, mechanistic structure optimization was performed using Chem3D software[31]. Three-dimensional structures of protein macromolecules were obtained from the PDB database (https://www.rcsb.org/)[32]. The ligand and receptor were then input into DS for molecular docking, and the docking score value, -CIE (CDOCKER INTERACTION ENERGY), was also calculated. A larger -CIE value indicates a stronger affinity between the small ligand and the protein’s 3D structure.

3.7 Molecular dynamics analysis

Molecular dynamics (MD) simulations were performed with the gromacs-2022 software package to assess the credibility and stability of the docking outcomes between small molecules and the protein’s 3D structure. Specifically, simulations focused on the protein-ligand complexes exhibiting the highest docking scores. The TIP3 water model was used to neutralize the system charge by adding Na+. The solvent system was first energy minimized and then optimized through 5000 steps. Then, the entire system was preheated in NVT ensemble by applying harmonic constraints to the protein backbone with a force constant of 2 kcal/mol/Å2. This process allowed the system to gradually increase its temperature from 0 to 310 K over a period of 100 ps. After the ramp was completed, the system was equilibrated in NVT ensemble for 100 ps using a time step of 2 fs. This process enabled the control of temperature and pressure, keeping them at 310 K and 1 atm, respectively. Finally, the MM/GBSA method was used to calculate the binding free energy of the protein-ligand complex[33].

The etiology and pathogenesis of periodontitis are extremely complex, with plaque being the initiating factor, while the interaction between bacteria and the host accelerates the development and progression of periodontitis[34]. However, although the administration of antibiotics can reduce the pathogenic bacteria that invade periodontal tissues, the drugs are difficult to reach the periodontal pockets, and long-term use of antibiotics can lead to drug resistance as well as other systemic side effects[35]. The TCM system mainly focuses on target therapy based on different clinical symptoms using various herbal medicines. These herbs have numerous advantages, especially in controlling plaque, inhibiting the growth of microorganisms, promoting tissue regeneration, and providing anti-inflammatory effects, all with minimal toxic effects. These factors prove their suitability for the treatment of periodontitis[2]. The TCM system mainly focuses on target therapy based on different clinical symptoms using various herbal medicines. These herbs have numerous advantages, especially in controlling plaque, inhibiting the growth of microorganisms, promoting tissue regeneration, and providing anti-inflammatory effects, all with minimal toxic effects. These factors prove their suitability for the treatment of periodontitis[36]. However, the detailed molecular mechanisms are not clear because Astragalus contains numerous components. Modern bioinformatics analysis, including network pharmacology, molecular docking, and molecular dynamics simulation, can partly reveal the mechanisms, although confirmation is still needed by using in/ex vivo experiments[37]. In this current manuscript, we utilize the aforementioned methods to further study the mechanisms of Astragalus in the treatment of periodontitis.

We conducted a database search to screen 17 active ingredients of Astragalus and their corresponding 184 targets. Subsequently, we constructed a network representing the relationship between Astragalus active ingredients and their targets, which was then analyzed. We found Quercetin (MOL000098), Kaempferol (MOL000422), Formononetin (MOL000392), Calycosin (MOL000417), 7-O-methylisomucronulatol (MOL000378) are the most important components in Astragalus in treating periodontitis, and Quercetin with the highest degree value, which is the most critical active component in Astragalus. Quercetin, a natural flavonoid, already been proved have anti-inflammatory, antibacterial and antioxidant effects, which can kill P. gingivalis and inhibit a variety of virulence factors[38]. Quercetin can attenuate the impairing of the pathogenicity of P. gingivalis, hinder the progression of periodontitis and contribute to the treatment of periodontal disease[39], administration of quercetin also can reduce the alveolar bone loss by increasing osteoblast activity, decreasing osteoclast activity, inhibiting inflammation reactions and cell apoptosis in periodontitis rats [40]. Kaempferol inhibited the release of TNF-α, increased the expression and secretion of IL-10, and exerted significant anti-inflammatory effects[41]. In experimental periodontitis rats models, kaempferol reduce the alveolar bone resorption, inhibit the attachment loss and MMP-1 production[42]. In periodontal ligament stem cells (PDLSCs) cultured in vitro, it also can enhance the proliferation and osteogenesis of PDLSC by activating Wnt/beta-catenin signaling pathway, which suggesting that kaempferol have a potential of clinical application for the periodontal tissue regeneration[43]. Formononetin, an active ingredient in Astragalus, also preserve therapeutic effects to prevent periodontal disease[44].

TNF, IL1B (also known as IL1β), IL6, PTGS2 (also known as COX-2), CXCL8 (also known as IL8), MMP9, IL10, AKT1, VEGFA, and CASP3 were identified as the top 10 core targets among the 92 targets in the PPI network using the MCC algorithm of the CytoHubba plugin. These core target proteins are involved in immune regulation, inflammatory response, angiogenesis, oxidative stress, cell proliferation, and apoptosis. TNF-α is mainly generated during inflammation reactions, which can cause continuous production of osteoclasts and increase their resorption activity, thus leading to the loss of alveolar bone[45]. The Interleukin (IL) family plays a crucial role in the immune response and inflammatory reactions, including periodontitis and other oral diseases. IL-1β secretion was increased in experimental periodontitis in rats[46], and IL8 was proven to be associated with the susceptibility of periodontitis[47]. IL-6 is the most potent proinflammatory cytokine and is involved in the occurrence of periodontitis in human gingival fibroblasts (HGFs) cultured in vitro[48], IL-10 is widely expressed in inflamed periodontal tissues and can inhibit the severity of periodontitis, making it beneficial for the control of periodontal disease[49]. PTGS2, also known as COX-2, was upregulated in experimental periodontitis induced by bilateral ovariectomy (OVX) rats and resulted in alveolar bone resorption in animal models[50]. MMP-9 has been shown to be a sensitive marker for periodontal inflammation during orthodontic treatment[51]. VEGF-A, which belongs to the vascular endothelial growth factors (VEGFs), plays an important role in the pathological process of periodontitis[52]. Caspase-3, which mainly regulates cell apoptosis, is also involved in the pathogenesis of periodontal disease[53]. All the above-mentioned genes have been confirmed by other researchers to participate in the development of periodontitis, which further validates the accuracy and reliability of our results.

GO analysis helps us identify the biological processes, cellular components, and molecular functions involved in Astragalus therapy for periodontitis, while KEGG analysis helps us identify the pathways involved. Astragalus active ingredients mainly involve several signaling pathways during the treatment of periodontitis, including cancer pathways, lipid and atherosclerosis pathways, the AGE-RAGE signaling pathway in diabetic complications, and proteoglycan pathways in cancer. Oxidative stress and lipid peroxidation are implicated in various pathological conditions, including inflammation, atherosclerosis, neurodegenerative diseases, cancer, and periodontitis[54]. In a rat model of ligation-induced periodontitis, increased lipid peroxidation was observed in the serum, aorta, and periodontal tissue[55]. Periodontitis is a risk factor for the development of atherosclerosis due to its induction of aortic lipid peroxidation and close association with the early stages of atherosclerosis[55]. The AGE-RAGE signaling pathway is associated with the regulation of oxidative stress and inflammation[56], RAGE is strongly expressed in the gingiva of periodontitis patients, regardless of whether they have diabetes or not[57]. Advanced glycosylation end products (AGEs) increase the expression of IL-6 and ICAM-1 through the RAGE, MAPK, and NF-κB pathways in HGF, which may exacerbate the progression of periodontal disease pathogenesis[58]. In vitro cultured HGFs, the accumulation of AGEs may upregulate the expression of MMP-1. The RAGE/NF-κB pathway is

involved in the metabolism of MMP-1, and both are important in the development of diabetes-associated periodontitis[59]. Based on the above results of GO and KEGG analysis, we hypothesize that Astragalus treats periodontitis by regulating the accumulation of advanced glycosylation end products, oxidative stress response, inflammatory response, and atherosclerosis, ultimately reducing the inflammatory response of periodontitis.

The molecular docking results were consistent with the network pharmacology results. The main active components in Astragalus, such as Quercetin (MOL000098), Kaempferol (MOL000422), Formononetin (MOL000392), Calycosin (MOL000417), 7-O methylisomucronulatol (MOL000378) have good binding stability to periodontitis target proteins such as AKT1, TNF, IL 6, IL1β, PTGS2, MMP9, CASP3, etc, which provides strong evidence to support our previous results from network pharmacological analysis. It worth noting that the complex of Kaempferol-Caspase3 got the highest binding score (-CIE = 72.181), it should have a stable binding structure, and these results were confirmed by molecular dynamics simulations which got consistent results with the molecular docking, it was proved that interactions such as van der Waals forces and hydrogen bonds play an important role in the stability of the complex. Therefore, kaempferol which target Caspase3 to inhibit the cell apoptosis can partly explain the pharmacological effect of Astragalus in the treatment of periodontitis.

In summary, the potential biological mechanisms of Astragalus for periodontitis treatment involve multiple components, targets, and pathways. Certain signaling pathways play a dual role, contributing not only to cancer but also to the development of periodontitis. Additionally, oxidative stress and the AGE-RAGE signaling pathway play crucial roles in the onset of periodontitis. These pathways and associated key genes present promising targets for periodontitis treatment and the development of novel medicines. Although there have multi-components in Astragalus, Quercetin, Kaempferol, Formononetin, Calycosin, 7-O methylisomucronulatol are the important ingredients based on our network pharmacology analysis, these natural products were proved related to the occurrence and treatment of periodontitis more or less, so we also need focus on these components during our research in periodontitis in future. Kaempferol has multiple pharmacological effects, such as inhibiting cell apoptosis, stimulating bone formation, and inhibiting the activity of osteoclasts. Interestingly, we found that Kaempferol can bind closely and stably with Caspase3. These findings suggest that kaempferol may be one of the key natural products for the treatment of periodontitis, and its molecular mechanisms may be related to Caspase3 as the target. Our current study explains the molecular mechanism of Astragalus in the treatment of periodontitis, providing a new perspective and approach for researching Astragalus and other Chinese medicines, as well as the treatment of periodontitis and other oral diseases. However, further cellular and animal experiments, as well as clinical studies, are still needed to verify the above results in the future.

Ethics Approval and Consent to Participate

Not applicable.

Funding Sources

This work was supported by grants from the Foundation of Science & Technology Department of Henan Province, China (no. 212102310103), Natural Science Foundation of Education Department of Henan Province, China (no. 21A320004), the Foundation of Science & Technology Department of Luoyang City, Henan Province, China (no. 2101065A), the Foundation of Science & Technology Department of Kaifeng City, Henan Province, China (no. 2203015).

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflict of Interest Statement

The authors assert that there are no conflicts of interest regarding the research, authorship, and publication of this manuscript.

Author Contributions

NingLi Li performed data analysis, constructed figures, and drafted an overview of the original manuscript. Jixian Feng constructed tables and thoroughly revised the manuscript. Mingzhen Yang and Mingyuan Jiang edited and reviewed the manuscript. Yingying Li thoroughly edited and revised the manuscript. Yuankun Zhai conceived the idea, reviewed and revised the manuscript, and approved the final version. All authors have agreed to be accountable for all aspects of this study.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Supplementary Material

N/A

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No competing interests reported.

Investigating the Mechanism of Astragalus in the Treatment of Periodontitis through Bioinformatics Analysis

Status:

Version 1

Abstract

Figures

1 Introduction

2 Results