Exploring the mechanism of Poria cocos in treating allergic rhinitis based on network pharmacology and molecular docking

Abstract

Poria cocos (PC) is a traditional Chinese herbal medicine that plays an important role in the treatment of allergic rhinitis (AR); however, its specific mechanism of action has rarely been reported. This study was based on network pharmacology and molecular docking to explore the molecular mechanisms of PC in the treatment of AR. TCMSP, GeneCards, DrugBank, TTD, and OMIM databases were used to screen potential targets of Poria cocos for AR treatment. The STRING platform and Cytoscape 3.7.0 software (Cytoscape Consortium, San Diego) were used to construct a protein–protein interaction (PPI) network and screen core targets. Gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on potential target genes. Finally, molecular docking was conducted between the key active components and the core targets to validate their relevance. Fifteen active components of PC were identified and 94 common targets related to both PC and AR were screened. The top ten hub genes identified were TNF, IL1B, AKT1, prostaglandin G/H synthase 2 (PTGS2), epidermal growth factor receptor (EGFR), MMP9, PPARG, BCL2, NR3C1, and PTGS1. GO and KEGG enrichment analyses indicated that these core targets were involved in various biological processes, including the regulation of inflammatory responses, responses to exogenous stimuli, and modulation of defense responses. These targets influence AR through pathways such as the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway. Molecular docking results indicated that pachymic acid-PTGS2, polyporenic acid C-EGFR, and PTGS2 exhibited strong binding activity, while pachymic acid, polyporenic acid C-TNF and cerevisterol-AKT1 demonstrated good binding activity. Our study found that the key active components of PC for treating AR are pachymic acid, polyporenic acid C, and cerevisterol. PTGS2, EGFR, TNF, and AKT1 are the key targets, while the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway are the key pathways. These results indicate that PC may intervene in the intrinsic molecular mechanism of AR through multiple targets and pathways. Although further experimental verification is required, our study provides a theoretical basis for the clinical application of PC in AR treatment and subsequent related research.

1. Introduction

Allergic rhinitis (AR) is a noninfectious inflammatory disease of the nasal mucosa that is mediated by IgE following exposure to allergens in atopic individuals. This condition is multifactorial and results from both genetic and environmental factors. The global incidence rate ranges from 10 to 30% and has shown an increasing trend every year.^[1] In China, the incidence of allergic rhinitis in adults and children is approximately 19% and 22%, respectively.^[2] Typical symptoms include paroxysmal sneezing, clear watery nasal discharge, nasal congestion, and nasal itching, all of which significantly affect the patients’ quality of life. Consequently, the prevention and treatment of AR have become a global public health issue. Currently, Western medicine primarily employs antihistamines, nasal or oral corticosteroids, and oral leukotriene receptor antagonists. However, long-term use of these drugs has demonstrated that their efficacy is often unsatisfactory and can result in side effects. As a result, an increasing number of patients are opting for traditional Chinese medicine (TCM) or a combination of Chinese and Western medicines. TCM focuses on a holistic approach to improving a patient’s allergic constitution. Its unique overall regulatory function and notable long-term efficacy are hallmarks of TCM treatment.^[3]

In TCM, allergic rhinitis falls under the category of “Bi Qiu,” which is attributed to the combined action of internal and external factors. Internal factors are often linked to deficiencies in the lungs, spleen, and kidneys, leading to insufficient vital energy and weakened qi defense. External factors typically involve invasion by cold, wind, or other pathogenic entities, causing lung qi to fail in dispersing, resulting in sudden stagnation of body fluids, which culminates in Bi Qiu.^[4] As a traditional treatment modality, Chinese herbal medicine has garnered increasing attention owing to its multiple targets of action, minimal side effects, and low resistance. Poria cocos (PC), a traditional Chinese herb, is sweet and bland in taste, and acts on the heart, spleen, lung, and kidney meridians. It strengthens the spleen, tonifies the middle, promotes diuresis, drains dampness, calms the heart, and tranquilizes the mind. PC is also included in classic prescriptions for treating AR, such as Shenling Baizhu San, Zhenwu Tang, and Sijunzi Tang. Therefore, PC plays a significant role in the treatment of AR. However its mechanism of action requires further in-depth research.

Network pharmacology is an emerging discipline integrating systems biology, bioinformatics, and pharmacology. It systematically and comprehensively analyzes existing molecular biology data to elucidate the mechanisms of interactions between organisms and diseases at the protein, molecular, and gene levels.^[5] Molecular docking is a computer simulation technique that predicts the binding modes and affinities of small-molecule ligands for target proteins, thereby facilitating the identification of potential drug targets.^[6]

In recent years, the integration of network pharmacology and molecular docking technology has provided new insights into the mechanisms underlying the multicomponent, multi-target, and multipathway nature of TCM.^[7] This study aimed to screen the active ingredients and core targets of PC for the treatment of AR through network pharmacology, verify the binding patterns of key components and targets via molecular docking, explore the potential targets and molecular mechanisms of PC in the treatment of AR, and offer new perspectives for both the research and therapeutic applications of PC. The workflow of network pharmacology and molecular docking is shown in Figure 1.

Figure 1.

Workflow for exploring the mechanism of PC in treating AR based on network pharmacology. AR = allergic rhinitis, PC = Poria cocos.

2. Research methods

2.1. Screening of active constituents from PC and prediction of target sites

The active ingredients of PC were retrieved from the TCM Systems Pharmacology Database and Analysis Platform (TCMSP, https://www.tcmsp-e.com/tcmsp.php) using oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 as screening criteria. The canonical SMILES of these active ingredients were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The canonical SMILES were then entered into the SwissTargetPrediction database (http://www.swisstargetprediction.ch/index.php) to identify potential target sites for the active ingredients of PC. Duplicate targets were removed and the obtained targets were normalized using the UniProt database (https://www.uniprot.org/) to derive standard gene names.

2.2. Get AR targets

Pathogenic target sites of AR were searched using the GeneCards database (https://www.genecards.org/), DrugBank database (https://go.drugbank.com/), TTD database (https://db.idrblab.net/ttd/), and the OMIM database (https://www.omim.org/). The disease target sites retrieved from these 4 databases were input into the Venny platform (https://jvenn.toulouse.inra.fr/app/index.html). Duplicate values were removed, and a Venn diagram was generated. The UniProt database was used to standardize the gene names of the acquired targets, ultimately yielding potential targets for AR.

2.3. Screening of potential targets of PC for treating AR and visual analysis

The drug component targets and pathogenic targets for AR were input into the Venny platform for screening, PC-AR intersection targets were obtained, and a Venny diagram was drawn. Subsequently, the intersection targets were imported into Cytoscape 3.7.0 software to construct a visual network.

2.4. Construction of protein–protein interaction (PPI) network

The common targets of the PC-AR intersection were imported into the STRING platform (https://cn.string-db.org/), and the species to Homo sapiens was set with a medium confidence level >0.4, to construct a protein–protein interaction (PPI) network, and the PPI network data were downloaded. Visual analysis using Cytoscape 3.7.0 software, the network topology parameters, the Centiscape plugin, and the Degree algorithm were used to screen for hub genes in the protein–protein interaction network.

2.5. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

The DAVID database (https://davidbioinformatics.nih.gov/) and Microbiome Bioinformatics Platform (https://www.bioinformatics.com.cn/) were used to conduct gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the potential therapeutic targets of PC for AR and visualization of the results.

2.6. Construction of the “component-target-pathway-disease” network diagram

The results of the KEGG pathway enrichment analysis, core targets, and active drug components were imported into Cytoscape 3.7.0 software to construct a “component-target-pathway-disease” network diagram.

2.7. Molecular docking

The top 5 core targets with the highest degree values in the PPI network were selected as protein receptors, and their corresponding active components were used as small-molecule ligands for molecular docking. The gene names of the core targets were input into UniProt to find the corresponding Entry IDs. The 3D structures of the target proteins were downloaded from the PDB database (https://www.rcsb.org/) and optimized using PyMOL, saving them in the pdb format. The 2D structures of the active components were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and converted into 3D structures after optimization using the Chem3D 23.01 64-bit software. The optimized structures were imported into AutoDock Tools 1.5.6 for processing and converted into pdbqt file format for molecular docking, providing both protein receptors and small-molecule ligands was performed using Autodock Vina, and the docking results were imported into PyMOL for image processing.

3. Results

3.1. Screening of active ingredients and target prediction of PC

In the TCMSP database, 34 active ingredients of PC were identified (Table 1). Using oral bioavailability (OB) and drug-likeness (DL) as screening criteria for target genes, the top 15 active ingredients that met these criteria were selected as the effective components of PC. Subsequently, the corresponding targets were obtained from the SwissTargetPrediction database, duplicate values were removed, and gene names were standardized using the UniProt database, ultimately resulting in 268 target genes.

Table 1.

Active components of Poria cocos.

Component ID	Mol ID	Molecule name	MW	OB (%)	DL
Fuling1	MOL000273	(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-Dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6-methylhept-5-enoic acid	470.76	30.93	0.81
Fuling2	MOL000275	Trametenolic acid	456.78	38.71	0.8
Fuling3	MOL000276	7,9(11)-Dehydropachymic acid	526.83	35.11	0.81
Fuling4	MOL000279	Cerevisterol	430.74	37.96	0.77
Fuling5	MOL000280	(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-Dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid	484.79	31.07	0.82
Fuling6	MOL000282	Ergosta-7,22E-dien-3beta-ol	398.74	43.51	0.72
Fuling7	MOL000283	Ergosterol peroxide	430.74	40.36	0.81
Fuling8	MOL000285	(2R)-2-[(5R,10S,13R,14R,16R,17R)-16-Hydroxy-3-keto-4,4,10,13,14-pentamethyl-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid	482.77	38.26	0.82
Fuling9	MOL000287	3β-Hydroxy-24-methylene-8-lanostene-21-oic acid	470.81	38.7	0.81
Fuling10	MOL000289	Pachymic acid	528.85	33.63	0.81
Fuling11	MOL000290	Poricoic acid A	498.77	30.61	0.76
Fuling12	MOL000291	Poricoic acid B	484.74	30.52	0.75
Fuling13	MOL000292	Poricoic acid C	482.77	38.15	0.75
Fuling14	MOL000296	Hederagenin	414.79	36.91	0.75
Fuling15	MOL000300	Dehydroeburicoic acid	453.75	44.17	0.83
Fuling16	MOL000274	3β-Hydroxylanosta-7,9(11),24-trien-21-oic acid	454.76	24.92	0.8
Fuling17	MOL000277	Tumulosic acid	486.81	15.95	0.81
Fuling18	MOL000278	Beta-Glucan	516.56	0.73	0.7
Fuling19	MOL000281	Dimethyl L-malate	162.16	8.59	0.03
Fuling20	MOL000284	L-Uridine	244.23	23.4	0.11
Fuling21	MOL000286	β-Amyrin acetate	468.84	9.11	0.74
Fuling22	MOL000288	Pachyman	500.56	0.45	0.68
Fuling23	MOL000293	Poricoic acid D	514.77	22.38	0.78
Fuling24	MOL000294	Poricoic acid DM	528.8	29.32	0.78
Fuling25	MOL000295	Alexandrin	576.95	20.63	0.63
Fuling26	MOL000297	Tumulosic acid	486.81	29.88	0.81
Fuling27	MOL000298	Ergosterol	396.72	14.29	0.72
Fuling28	MOL000299	Trimethyl citrate	234.23	67.61	0.07
Fuling29	MOL000301	2-Lauroleic acid	198.34	31.42	0.04
Fuling30	MOL000302	Undekansaeure	186.33	30.14	0.03
Fuling31	MOL000303	Caprylic acid	144.24	16.4	0.02
Fuling32	MOL000304	Ethyl glucoside	208.24	15.21	0.06
Fuling33	MOL000305	Lauric acid	200.36	23.59	0.04
Fuling34	MOL000069	Palmitic acid	256.48	19.3	0.1

3.2. Acquisition of AR target sites

Pathogenic targets for AR were retrieved from GeneCards, DrugBank, TTD, and OMIM databases. The disease targets obtained from these 4 databases were entered into the Venny platform, where they were merged, and duplicates were removed, resulting in the generation of a Venn diagram, as illustrated in Figure 2A. The targets were then standardized for gene names using the UniProt database, ultimately yielding 1739 potential targets for AR.

Figure 2.

Venn diagram. (A) AR-related targets in GeneCards, TTD, Drugbank and OMIM databases. (B) Common targets of PC and AR. AR = allergic rhinitis, PC = Poria cocos.

3.3. Screening and visual analysis of potential targets of PC for treating AR

Using the Venny platform, the identified AR action targets and PC active ingredient targets were imported, yielding 94 potential targets of PC for the treatment of AR. A Venn diagram was created (Fig. 2B). The 94 potential action targets were then imported into Cytoscape 3.7.0 software for visual analysis, as shown in Figure 3.

Figure 3.

The potential targets network diagram of PC in the treatment of AR. AR = allergic rhinitis, PC = Poria cocos.

3.4. Construction of protein–protein interaction (PPI) network

The intersecting PC-AR genes were imported into the STRING platform to construct a PPI network diagram. Each node represents a target protein, whereas the connecting lines between nodes indicate interactions between pairs of proteins. The network comprises 94 nodes and 721 edges, yielding an average node degree of 15.3, as illustrated in (Fig. 4A). The resulting data were imported into Cytoscape 3.7.0 software for visual analysis. Utilizing network topology parameters, the Centiscape plugin, and the degree algorithm, 16 hub genes were identified from the protein interaction network. Select the top 10 hub genes based on the “Degree” value and plot a network diagram, where a higher “Degree” value corresponds to a darker color, as shown in (Fig. 4B).

Figure 4.

Network construction. (A) Protein–protein interaction network. (B) Hub genes of PC for AR treatment. AR = allergic rhinitis, PC = Poria cocos.

3.5. GO and KEGG enrichment analysis

GO functional enrichment analysis yielded 2010 GO terms, including 1814 biological process (BP) terms. These terms primarily pertain to the regulation of the inflammatory response, adenylate cyclase-modulating G protein-coupled receptor signaling pathway, and positive regulation of both the response to external stimuli and defense response. There were 58 entries related to cellular components, including membrane rafts, presynaptic membranes, postsynaptic membranes, and the nuclear envelope. Additionally, 138 entries pertained to molecular functions, including icosanoid receptor activity, nuclear receptor activity, ligand-activated transcription factor activity, and oxidoreductase activity acting on single donors with the incorporation of molecular oxygen. The top 10 enriched results for BP, cellular components (CC), and molecular functions were identified based on P-values, and bar charts and circle plots were generated (Fig. 5A and B).

Figure 5.

Go function enrichment analysis. (A) Bar chart of GO analysis. (B) Circle plot of GO analysis. GO = gene ontology.

The KEGG pathway enrichment analysis revealed that a total of 131 pathways were identified when the threshold was set at P < .05. The top 20 pathways are displayed based on the P-value. The pathways with the most significant P-values included Neuroactive ligand-receptor interaction, PD-L1 expression and PD-1 checkpoint pathway in cancer, human cytomegalovirus infection, Leishmaniasis, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, and the PI3K-Akt signaling pathway (Fig. 6A and B). PI3K Akt signaling pathway plays an important role in the occurrence and development of allergic rhinitis. Therefore, we selected the PI3K Akt signaling pathway diagram for display, in (Fig. 7), where the red bars represent common genes within the pathway.

Figure 6.

Enrichment analysis of KEGG pathway. (A) Bubble chart of KEGG analysis. (B) Cnet plot of KEGG analysis. KEGG=Kyoto Encyclopedia of Genes and Genomes.

Figure 7.

PI3K-AKT signaling pathway.

3.6. Construct a “component-target-pathway-disease” network diagram

Based on the KEGG pathway enrichment results, the corresponding data were imported into Cytoscape 3.7.0 software to construct a “component-target-pathway-disease” network diagram, as illustrated in Figure 8. This network illustrates that PC can treat AR through multiple targets and pathways, with TNF, IL1B, AKT1, prostaglandin G/H synthase 2 (PTGS2), and EGFR being identified as key targets.

Figure 8.

PC components-targets-pathways-AR network diagram. Green rectangles represent pathways, purple ellipses denote core targets, and blue hexagons signify the components of PC. AR = allergic rhinitis, PC = Poria cocos.

3.7. Molecular docking

Using AutoDock Tools, PyMOL, and AutoDock Vina software, the top 5 hub genes with the highest degree values in the PPI network were subjected to molecular docking analysis with their corresponding components. The molecular structures of the drugs and their components are shown in Figure 9A–F. A binding energy <0 indicates that the target protein and the active molecule can spontaneously bind, and the more stable the binding conformation, the lower the required binding energy, with a binding energy ≤ −5 kcal/mol indicating good binding activity and a binding energy ≤ −7 kcal/mol indicating strong binding activity. The results showed that TNF-Tumulosic acid (−6.4 kcal/mol), TNF-Polyporenic acid C (−6.4 kcal/mol), TNF-Eburicoic acid (−6.8 kcal/mol), TNF-Pachymic acid (−6.7 kcal/mol), IL-1β-Pachymic acid (−6.4 kcal/mol), AKT1-Cerevisterol (−6.9 kcal/mol), PTGS2-Polyporenic acid C (−8.0 kcal/mol), and PTGS2-Eburicoic acid (−6.9 kcal/mol), PTGS2-Pachymic acid (−8.0 kcal/mol), EGFR-Polyporenic acid C (−8.3 kcal/mol), as shown in (Fig. 9G). The partial molecular docking visualization results are shown in Figure 10.

Figure 9.

Molecular docking analysis. (A) Pictures of PC. (B–F) The 2D molecular structure of corresponding drug components. (G) Minimum binding energies of core targets and corresponding components. PC = Poria cocos.

Figure 10.

Molecular docking results. (A) Polyporenic acid C-EGFR. (B) Pachymic acid-PTGS2. (C) Pachymic acid-TNF. (D) Cerevisterol-AKT1. EGFR=epidermal growth factor receptor, PTGS2=prostaglandin G/H synthase 2, TNF = tumor necrosis factor.

4. Discussion

Poria cocos (Schw.) Wolf, the dried sclerotium of the fungus from the family Polyporaceae, is a traditional Chinese medicinal material with a compatibility rate of 70% in commonly used Chinese herbal prescriptions.^[8] Its chemical constituents include polysaccharides, triterpenoids (such as pachymic acid, Poria cocos alcohol, and poriatin), sterols, and others, which exhibit pharmacological activities such as anti-inflammatory, antioxidant, immunomodulatory, and antitumor effects.^[9–12] From the perspective of TCM, allergic rhinitis is often caused by a deficiency of internal organs, insufficient righteous qi, and the invasion of external pathogenic factors. The “fundamentals of TCM” (ISBN: 9787513269056, edited by Zheng Hongxin, China TCM Publishing House) states that the lungs govern qi, control respiration, and are open to the nose. The spleen is considered the mother of the lungs, and strengthening the spleen can benefit the lung qi. When lung qi is abundant, it can resist the invasion of external pathogens, preventing the nasal passages from being affected and developing allergic rhinitis. In “Huangdi Neijing” (ISBN: 9787117067225, edited by Xing Ruwen, People’s Health Publishing House) and “Practical Internal Medicine of TCM” (ISBN: 9787513212700, edited by Zhou Zhongying, China TCM Publishing House), various TCM treatment methods for diseases are discussed, emphasizing that for lung-related diseases, methods such as strengthening the spleen are often highlighted to support the righteous qi in resisting external pathogens, providing both theoretical and practical basis for PC treatment of allergic rhinitis. PC had a strong effect on strengthening the spleen and promoting dampness. By strengthening the spleen, the transportation function of the spleen and stomach can be enhanced, allowing for the normal metabolism of water and dampness, thereby reducing the production of phlegm-dampness improving the physiological environment of the nasal passages, and alleviating symptoms such as nasal mucosal edema and increased secretion. Additionally, PC has a calming effect on the mind, which can stabilize the patient’s emotions and bring tranquility to the spirit, helping to regulate the overall functional state of the body, ensuring smooth circulation of qi and blood, and coordinating the functions of the internal organs, thus enhancing the body’s resistance and alleviating the symptoms of AR, while reducing the frequency of attacks.

This study systematically elucidates the potential mechanisms of PC in treating allergic rhinitis (AR) through network pharmacology and molecular docking techniques, providing new insights for modern pharmacological research on TCM. The results indicate that the active components of PC, including triterpenoids (pachymic acid, polyporenic acid C) and cerevisterol, can interact with multiple key targets (PTGS2, EGFR, TNF, and AKT1) to participate in biological cellular processes, such as inflammatory response, immune imbalance, and epithelial barrier repair, thereby exerting therapeutic effects on AR. This is consistent with the core pathological features of AR, which include inflammation-driven, immune disorders, and barrier damage. Pachymic acid acts on pro-inflammatory factors such as TNF and IL-1β, which is consistent with previous studies showing that pachymic acid reduces the release of inflammatory factors by inhibiting the NF-κB pathway,^[13,14] suggesting that it may alleviate mucosal inflammation in AR by downregulating Th2 type immune response. Tumor necrosis factor (TNF) is a multifunctional cytokine that plays a crucial role in the occurrence and development of AR. Upon allergen stimulation, sensitized epithelial barriers and immune cells produce TNF, which promotes the Th2 immune response, leading to increased secretion of IL-4, IL-5, and IL-13. This further activates mast cells, eosinophils, and basophils, resulting in a sustained inflammatory response within the body.^[15–17] PTGS2 (cyclooxygenase-2) is involved in the pathological process of AR. Normally, nasal mucosa is lowly expressed. After exposure to allergens, it stimulates mast cells to release IL-1β, TNF-α, activates the NF-κB pathway, induces rapid upregulation of PTGS2, and promotes the synthesis of prostaglandin E2 (PGE2), exacerbating mucosal inflammation.^[18] As a transmembrane tyrosine kinase receptor, EGFR plays a dual regulatory role of “repair-inflammation” in maintaining the homeostasis of the nasal mucosa. Under normal circumstances, EGFR promotes the proliferation of nasal mucosal epithelial cells and the expression of tight junction proteins by binding to epidermal growth factor (EGF), thereby participating in the repair process following barrier damage.^[19] However, in the chronic inflammatory state of AR, persistent allergen stimulation leads to abnormal activation of EGFR, which induces nasal mucosal epithelial cells to secrete inflammatory factors such as IL-6 and IL-8^[20] and upregulates the expression of eosinophil chemotactic factor (eotaxin), exacerbating chronic inflammatory infiltration.^[21,22] This study found that the binding affinity of cerevisterol for AKT1 was relatively strong (molecular docking binding energy -6.9 kcal/mol). As a core molecule in the PI3K-Akt pathway, AKT1 plays a crucial role in cell survival, proliferation, and the regulation of inflammation.^[23,24] The aberrant activation of the PI3K-Akt pathway has been confirmed to be closely related to mast cell degranulation and eosinophil infiltration in AR.^[25,26] Research by Xiaohan et al suggested that hyperglycemia (HG) may exert its effects in AR treatment by activating AKT1 to inhibit the activation of the c-Jun and NF-κB signaling pathways.^[27] This finding revealed that cerevisterol may alleviate symptoms such as sneezing and rhinorrhea in AR by inhibiting the activity of the PI3K-Akt pathway and blocking the inflammatory signaling cascade.

In terms of pathway enrichment, this study found that the treatment of allergic rhinitis with Poria cocos primarily involves pathways such as the neuroactive ligand-receptor interaction pathway and the PI3K-Akt signaling pathway. Neuroimmune communication participates in the pathogenesis of AR, and is classified as a neuroimmune disease. Immune cells colocalize with nerves in specific anatomical locations, forming neuroimmune cell units that can modulate the severity of type 2 inflammation in AR.^[28,29] During the progression of AR, when the nasal mucosal epithelium is damaged, the exposed nerve fibers promote the release of granular contents, leading to a Th2 immune response, resulting in the accumulation of eosinophils, basophils, and T cells to sustain the inflammatory response in AR.^[30] The PI3K-Akt pathway is a key signaling pathway that regulates mucosal homeostasis and inflammatory balance in AR. Under normal physiological conditions, this pathway is activated by receptors such as EGFR, promoting the proliferation of nasal epithelial cells and synthesis of tight junction proteins such as occludin and ZO-1, thereby enhancing barrier defense functions and participating in the repair process following mucosal injury.^[31,32] In the pathological state of AR, this pathway exhibits abnormal activation, allergen stimulation leads to excessive phosphorylation of EGFR, resulting in sustained activation of the PI3K-Akt pathway, which causes a massive release of pro-inflammatory factors, exacerbating eosinophil infiltration and nasal mucosal edema.^[33,34] Poria cocos, through its diuretic and dampness-draining properties, can facilitate the expulsion of damp pathogens from the nasal passages, thereby promoting nasal patency and alleviating the common symptoms of AR, such as nasal congestion and rhinorrhea. Previous studies have reported that PC can stimulate aquaporin-3 via the PI3K/Akt/mTOR signaling pathway, exerting immunomodulatory and barrier functions.^[35]

However, this study had certain limitations. First, network pharmacology analysis relies on the completeness of existing databases, and some in vivo metabolic products of the active components of Poria cocos and unrecorded targets may be overlooked, resulting in an incomplete elucidation of the mechanism of action. Second, molecular docking is merely an in vitro virtual prediction, and lacks experimental validation both in vivo and in vitro. The actual effective concentrations, duration of action, and dynamic processes of target binding of the active components require further investigation.

In summary, this study revealed the “multi-component–multi-target–multi-pathway” characteristics of PC in the treatment of AR, which aligns with the therapeutic philosophy of TCM emphasizing “holistic regulation and addressing both root causes and symptoms.” Compared to single-target drugs, PC exerts its therapeutic advantages through the synergistic action of multiple components on various aspects, such as inflammation, immunity, and barrier repair, demonstrating the benefits of TCM in treating AR. The results of molecular docking further validated the binding capacity of the core components with key targets, providing candidate combinations for subsequent experimental validation. For instance, the interactions of pachymic acid-PTGS2, polyporenic acid C-EGFR and PTGS2, and cerevisterol-AKT1 can serve as subjects for in vitro cellular experiments.

5. Conclusions

In this study, we used network pharmacology and molecular docking to analyze the potential mechanisms of PC therapy for AR. The results showed that pachymic acid, poronic acid C, and cerevisterol in PC were the key active ingredients that exert therapeutic effects, and PTGS2, EGFR, TNF, and AKT1 were the key targets. Further analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were key pathways that may play important signaling and regulatory roles in the treatment process. These findings provide a theoretical basis for a deeper understanding of the molecular mechanism of PC in treating AR, and also provide direction for the clinical application and subsequent research of PC.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant No: 8246040193), Key Research and Development Program of Ningxia Hui Autonomous Region (Grant No: 2023BEG02019), Yinchuan Science and Technology Plan Project (Grant No: 2024SFZD003), and Ningxia Hui Autonomous Region Science and Technology Special Project for the Benefit of the People (Grant No: 2024CMG03002).

Author contributions

Conceptualization: Ruixia Ma.

Data curation: Zhijuan Zhang.

Formal analysis: Zhijuan Zhang, Yuqiao Zhang, Shiqi Yan.

Funding acquisition: Ruixia Ma.

Software: Xinran Niu, Shurong Li, Yuqiao Zhang, Shiqi Yan.

Supervision: Ruixia Ma.

Visualization: Zhijuan Zhang, Xinran Niu, Shurong Li.

Writing – original draft: Zhijuan Zhang.

Writing – review & editing: Ruixia Ma.