Guominkang formula alleviates airway inflammation in HDM-induced asthma mice by regulating Wnt/β-Catenin pathway
Yuhan Zong, Jingwei Kong, Fan Yang, Manting Wang, Ji Wang, Qi Wang
TL;DR
The Guominkang formula reduces airway inflammation in asthma mice by regulating the Wnt/β-catenin pathway, offering a potential new treatment for allergic asthma.
Contribution
This study identifies the Wnt/β-catenin pathway as a novel therapeutic target for allergic asthma through the Guominkang formula.
Findings
GMK reduces Th2 and Th17 cell populations and restores immune balance in asthma mice.
GMK modulates the Wnt/β-catenin pathway, decreasing airway inflammation and remodeling.
GMK alters gut microbiome composition and cytokine levels in asthma mice.
Abstract
The Guominkang formula (GMK), formulated according to the principle of "treatment based on constitution differentiation," comprises Prunus mume (Siebold) Siebold & Zucc. (Wumei), Saposhnikovia divaricata (Turcz. ex Ledeb.) Schischk. (Fangfeng), Ganoderma lucidum (Curtis) P. Karst. (Lingzhi), and Periostracum Cicadae (Chantui). Clinically, GMK has been shown to modulate allergic constitution, effectively treating allergic asthma (AA) and various other allergic conditions, with a favorable safety profile and substantial therapeutic benefits. However, the precise mechanisms underlying its immune-modulatory effects, particularly in the context of AA, remain inadequately defined. This study aimed to investigate the therapeutic effects and underlying mechanisms of GMK in a mouse model of AA. The components of GMK were analyzed via LC–MS/MS. AA was induced in female mice through nasal…
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Figure 10- —the General program of National Natural Science Foundation of China
- —the General project of Beijing Natural Science Foundation
- —the High level Key Discipline of National Administration of Traditional Chinese Medicine - Traditional Chinese constitutional medicine
- —the Fundamental Research Funds for the Central Universities
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Taxonomy
TopicsAsthma and respiratory diseases · Phytochemistry and Biological Activities · Mast cells and histamine
Introduction
Asthma is a chronic inflammatory airway disease mediated by various immune cells and inflammatory factors [1]. Clinical manifestations include recurrent wheezing, shortness of breath, chest tightness, coughing, airway hyperresponsiveness (AHR), and reversible expiratory airflow limitation [2, 3]. Allergic asthma (AA) is the most prevalent phenotype, marked by eosinophilic airway inflammation, hyperresponsiveness, and elevated IgE levels [4, 5]. Allergies are implicated in up to 80% of childhood asthma cases and 40–50% of adult asthma cases [6]. Currently, nearly 300 million people worldwide are affected by asthma, with its global incidence increasing, thus posing a significant public health challenge [7]. Existing treatments, such as corticosteroids, beta2-agonists, anti-IgE monoclonal antibodies, and allergen immunotherapy, provide symptom relief but do not address the underlying disease mechanism [8]. Given AA’s chronic nature, recurrent flare-ups profoundly impact patients' daily lives and overall quality of life. Therefore, further exploration of AA's pathogenesis and the optimization of treatment strategies are essential for improved management.
Traditional Chinese Medicine (TCM) constitution theory, developed by Professor Wang Qi in the 1970s, classifies the human constitution into nine fundamental types based on an integrated assessment of physical structure, physiological function, and psychological state [9]. This approach to health assessment, which uses a non-invasive questionnaire, is considered a unique aspect of TCM [10]. According to this theory, the allergic constitution, categorized as a special constitution, is genetically predisposed and is characterized by reduced physiological function, compromised self-regulatory capacity, heightened sensitivity to allergens, and an increased susceptibility to allergic diseases [11]. The Guominkang formula (GMK), a patented TCM formula (China Patent No. ZL 201710584999.2), was developed to regulate the allergic constitution. GMK has shown clinical efficacy in treating a range of allergic conditions, including AA, allergic rhinitis, and atopic dermatitis, yielding significant therapeutic results. However, experimental evidence supporting GMK’s effectiveness specifically in the treatment of AA remains limited.
Th1 cells are CD4^+^ T cells primarily involved in the release of cytokines such as IL-2 and IFN-γ, playing a pivotal role in regulating cell-mediated immunity and facilitating cytotoxic T cell differentiation. In contrast, Th2 cells, driven by IL-4, secrete cytokines including IL-4, IL-5, and IL-13, which are key regulators of B lymphocyte proliferation, antibody production, and immediate hypersensitivity responses. Th2 cytokines not only promote the proliferation of Th2 cells but also inhibit the expansion of Th1 cells [12, 13]. It is well established that AA is predominantly driven by a Th2-dominant immune response, marked by an imbalance between Th1 and Th2 cell activities. Correcting this Th1/Th2 dysfunction is considered crucial for the effective management of AA. Recent research into asthma-related signaling pathways has garnered significant attention, particularly the Wnt/β-catenin pathway, which has been implicated in regulating the pathogenesis of AA [14]. Upon activation, the Wnt pathway prevents the phosphorylation of β-catenin in the cytoplasm, allowing it to accumulate and stabilize. Once a critical threshold is reached, β-catenin translocates to the nucleus, where it binds to TCF/LEF transcription factors to modulate the expression of downstream target genes [15, 16]. Dysregulation of the Wnt/β-catenin pathway contributes to epithelial-mesenchymal transition (EMT), promotes smooth muscle cell proliferation and hypertrophy, induces subepithelial fibrosis, and fosters extracellular matrix deposition, all of which drive airway remodeling in asthma [17]. Pharmacological interventions targeting the Wnt/β-catenin signaling pathway have been shown to alleviate airway inflammation, reduce AHR, and slow the progression of airway remodeling in AA [18, 19]. Therefore, exploring whether GMK can exert its anti-asthmatic effects through modulation of the Wnt/β-catenin pathway and T helper (Th) cells is crucial. In this study, an AA mouse model was established through intranasal administration of HDM extract. GMK was used as an intervention, and its therapeutic efficacy in treating AA was assessed. Additionally, the anti-inflammatory effects of GMK in asthma were investigated. The goal of this study is to offer a novel therapeutic strategy for the treatment of AA.
Materials and methods
Chemicals and reagents
HPLC-grade acetonitrile, methanol, and formic acid were sourced from Tanmo Quality Inspection Technology Co., Ltd. (Beijing, China). GMK quality control was performed using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) with a Vanquish UHPLC system (Thermo Fisher Scientific, USA). HDM (XPB91D3A25) was obtained from Beijing Biolead Biology Science & Technology Co., Ltd. Isoflurane (RS10-22-8) was acquired from Shenzhen Ruiwode Life Technology Co., Ltd. Acetyl-beta-methylcholine chloride (Phr1943), dexamethasone (D1756), collagenase IV (V900892), and EDTA solution (V900893) were purchased from Sigma-Aldrich Corporation. Anhydrous ethanol (G00004), hematoxylin staining solution (M1428), and eosin staining solution (E196384) were supplied by Beijing MREDA Technology Co., Ltd. HEPES (15630080) was provided by Thermo Fisher Scientific, and DNase (10104159001) by F. Hoffmann-La Roche Ltd. Perm/Wash Buffer (51-2091KZ) was obtained from Becton, Dickinson and Company. Percoll (17089101) was purchased from Cytiva Biotechnology (Hangzhou) Co., Ltd. D-Hanks (H1045) and Red Blood Cell Lysis Buffer (R1010) were procured from Beijing Solarbio Science & Technology Co., Ltd. Antibodies from BioLegend, Inc. included APC/Cyanine7 anti-CD3 (100222), APC/Cyanine7 anti-CD4 (100414), APC anti-CD4 (100516), PerCP anti-CD8a (100732), Pacific Blue anti-CD11b (101224), PerCP/Cyanine5.5 anti-CD11b (101228), anti-CD16/32 (101302), Brilliant Violet 510 anti-CD45 (103138), PE anti-CD86 (105008), PE anti-CD25 (113704), PerCP anti-CD11c (117326), Alexa Fluor 488 anti-F4/80 (123120), Brilliant Violet 421 anti-FOXP3 (126419), PE anti-Ly-6G (127608), APC/Cyanine7 anti-Ly-6G (128026), APC anti-CD206 (141708), FITC anti-CD45 (157214), PE anti-IL-4 (504104), FITC anti-IFN-γ (505806), and Brilliant Violet 421 anti-IL-17A (506926). RIPA lysis buffer (P0013B), proteinase inhibitors (C0101), phosphatase inhibitor (C0104), BCA Protein Assay Kit (B5001), HRP-conjugated Goat Anti-Rabbit IgG (S0101-1), HRP-conjugated Goat Anti-Mouse IgG (S0100-1), anti-GAPDH (G0100-1), Protein Marker (P1018), ECL Plus reagent (E1070), and SDS-PAGE Protein Sample Loading Buffer (5×) (G2527) were acquired from Beijing Huamei Shengke BioTechnology Co., Ltd. Polyvinylidene fluoride membrane was purchased from Merck Millipore. Wnt3a Rabbit pAb (ab172612) and anti-β-catenin antibody (AB32572) were obtained from Abcam Limited. Phospho-β-catenin (Ser29/33/37/Thr41), Rabbit pAb (AP1076) and GSK-3β Rabbit mAb (A11731) were sourced from Wuhan ABclonal Biotechnology Co., Ltd. Phospho-GSK-3β (Ser9) Rabbit mAb (5558S) was obtained from Cell Signaling Technology. Wnt/β-catenin activator and GSK-3α/β inhibitor (CHIR-99021) were procured from TargetMol.
GMK preparation
GMK herbal formula was prepared as granules for experimental use. Each package contains 7.5 g of GMK, with a recommended dose of two bags (equivalent to 2.87 g of raw herbs per gram of granules). All granules were supplied by Guangdong Yifang Pharmaceutical Co., Ltd.
Quality control of GMK
Seventy-five milligrams of GMK were weighed, followed by the addition of 1 mL of water and vortexing for 60 s. Low-temperature ultrasound was applied for 30 min, after which the sample was centrifuged at 12,000 rpm for 10 min at 4 °C. The supernatant was collected and subjected to a second centrifugation at 14,000 × g for 15 min at 4 °C. The resulting supernatant was used for subsequent analysis. Liquid chromatography was performed using a Waters HSS T3 column (100 × 2.1 mm, 1.8 μm), with the mobile phases consisting of 0.1% formic acid in water (Phase A) and 0.1% formic acid in acetonitrile (Phase B). The flow rate was set to 0.3 mL/min, the column temperature to 40 °C, and the injection volume to 2 μL. A Q Exactive HFX high-resolution mass spectrometer was employed to acquire both primary and secondary spectra. Raw data were processed with Progenesis QI software (Waters Corporation, Milford, USA) for baseline filtering, peak detection, integration, retention time correction, and peak alignment.
Establishment of the asthmatic mouse model
The animal protocol was approved by the Institutional Animal Care and Use Committee of Beijing University of Chinese Medicine (approval No. BUCM-4-2022050503-2036; Beijing, China). Seventy-two SPF female C57BL/6J mice (8 weeks old, weighing 17–18 g) were purchased from SPF Biotechnology Co., Ltd. (License: SYXK (Jing) 2020-0033; Beijing, China) and housed in the Animal Laboratory of Beijing University of Chinese Medicine. The animal model was established as follows: After a 3-day adaptation period, the mice were randomly assigned to the following groups: normal control (Control), HDM-induced (Model), dexamethasone treatment (Dex, 10 mg/kg), GMK low-dose (GMK-L, 0.975 g/kg), GMK medium-dose (GMK-M, 1.95 g/kg), and GMK high-dose (GMK-H, 3.9 g/kg), with 12 mice in each group. Additionally, a Wnt/β-catenin activation group (CHIR-99021 + GMK, n = 4) was included, where mice received co-administration of the β-catenin activator CHIR-99021 (5 mg/kg, i.p.) and medium-dose GMK (1.95 g/kg) to validate pathway-specific mechanisms. All mice, except those in the control group, were sensitized with 20 μg/40 μL of HDM nasal drops on days 1, 2, 3, 8, 9, and 10, and challenged with 20 μg/40 μL of HDM nasal drops on days 18–22. Control mice received an equivalent volume of PBS at the same time points. Nasal drops were administered using an anesthesia machine after anesthetizing the mice with isoflurane to ensure effective HDM delivery. Starting from day 8 of the modeling period, mice in each group were treated with their respective doses via gavage 1 h prior to nasal drops for 15 consecutive days. The Dex group received 10 mg/kg dexamethasone, while the control and model groups were given an equivalent volume of saline. The experimental design is illustrated in Fig. 2A.
Measurement of AHR
Twenty-four hours after the final intranasal challenge, mice from each group were anesthetized via intraperitoneal injection of sodium pentobarbital (1%, 50 mg/kg). Once the absence of a pain response was confirmed, the mice were placed on their backs and secured. The skin over the neck was carefully incised with scissors, and the tissue surrounding the trachea was meticulously dissected using ophthalmic forceps. Once the trachea was fully exposed, a V-shaped incision was made, and a tracheal cannula was inserted. This cannula was connected to a pulmonary function apparatus to facilitate assisted ventilation. After stabilization of the baseline respiratory system resistance (Rrs), varying concentrations of methacholine (20 µL of each) were aerosolized in ascending order, specifically at concentrations of 0, 6.25, 12.5, 25, and 50 mg/mL. Each concentration was aerosolized for 30 s, and the response was monitored for 5 min. Changes in Rrs were measured to assess AHR in the mice.
Measurement of the serum levels of IgE and inflammatory cytokines
Twenty-four hours after the final intranasal challenge, blood samples were collected from the mice. The blood was allowed to stand at room temperature for 2 h before being centrifuged at 3,000 rpm for 15 min at 4 °C. The serum was then collected for further analysis. Enzyme-linked immunosorbent assay (ELISA) was used to quantify the levels of total IgE and HDM-sIgE in the serum. Additionally, levels of IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, IL-17, and IFN-γ were assessed using a flow cytometry-based fluorescence assay.
Pathological observation of lung tissue
Following euthanasia, lung tissue was harvested and fixed in 4% paraformaldehyde overnight. After embedding, tissue sections were prepared using a microtome. The sections were stained with Hematoxylin and Eosin (H&E), Periodic Acid-Schiff (PAS), and Masson’s trichrome (Masson) staining protocols. Following H&E staining, lung tissue was examined under a microscope to assess inflammatory cell infiltration and histopathological changes. Inflammation scores were assigned based on the following criteria: 0 points for no inflammatory cells around the airways; 1 point for a few inflammatory cells around the airways; 2 points for one layer of inflammatory cells surrounding the airways; 3 points for two to four layers of inflammatory cells surrounding the airways; 4 points for four or more layers of inflammatory cells surrounding the airways [20]. After PAS staining, the extent of goblet cell metaplasia and mucus secretion in the airway epithelium was evaluated microscopically, and photographs were taken. Scoring criteria were: 0 points for < 5% PAS-positive cells; 1 point for 5% to < 25% PAS-positive cells; 2 points for 25% to < 50% PAS-positive cells; 3 points for 50% to < 75% PAS-positive cells; and 4 points for ≥ 75% PAS-positive cells. PAS-positive cells, characterized by their magenta-stained cytoplasm, were used to assess the degree of goblet cell metaplasia in a single bronchial cross-section [21]. Following Masson staining, the basal membranes of airways with an internal diameter of 100 to 200 μm were examined under a light microscope. Six random fields from each group were selected for scoring and measurement.
Lung CD4+ T cell separation and flow cytometric testing
The excised right lung tissue from the mice was cut into small pieces and placed in separate Eppendorf (EP) tubes. A digestive solution, prepared using 1640 medium, collagenase IV, HEPES, and DNase, was added to the samples, which were then incubated in the dark on a shaker at 37 °C for 1 h. After digestion, the supernatant was centrifuged and discarded, and the pellet was resuspended in 1640 medium containing PBS and Percoll. The suspension was filtered through a 70 μm mesh sieve. Following a second centrifugation, the pellet was washed with 0.5% BSA, and RBC lysis buffer was added. The samples were then washed again before blocking the Fc receptor to facilitate detection. For granulocyte detection, staining was performed using anti-CD11b-PB, anti-CD11c-PerCP, anti-CD45-FITC, anti-Ly6C-APC-Cy7, and anti-Ly6G-PE, and flow cytometry was used for analysis. For macrophage detection, anti-F4/80-AF488, anti-CD11b-PerCP-Cy5.5, and anti-CD86-PE were used for staining. After staining, the cells were permeabilized using a permeabilization wash buffer, and anti-CD206-APC was added for intracellular staining. For Treg cell analysis, surface staining was performed using anti-CD3-APC Cy7, anti-CD4-APC, anti-CD8-PerCP, anti-CD25-PE, and anti-CD45-FITC, followed by intracellular staining with anti-Foxp3-BV421. For Th cell detection, cells were first stimulated with stimulants for 6 h, followed by surface staining with anti-CD3-APC Cy7, anti-CD4-APC, anti-CD8-PerCP, and anti-CD45-BV510, along with intracellular staining using anti-IFN-γ-FITC, anti-IL-4-PE, and anti-IL-17-BV421. The CytoFLEX flow cytometer was employed to assess the percentages of eosinophils, basophils, neutrophils, Th1 cells, Th2 cells, Th17 cells, and Treg cells in the lung tissues. Data acquisition and analysis were conducted using FlowJo V10 software.
Transcriptome sequencing of mouse lung tissues
The samples consisted of five from the Control group (labeled A1–5), ten from the Model group (labeled B1–10), and ten from the GMK-M group (labeled C1–10). Total RNA was extracted from the lung tissues of the mice, followed by quality assessment and library construction. The enriched mRNA fragments were reverse-transcribed into double-stranded cDNA. The cDNA was end-repaired, A-tailed, and ligated to Y-shaped sequencing adapters. PCR amplification was performed to enrich the library, and sequencing was carried out on the Illumina platform. Gene expression levels were quantified using Transcripts Per Million (TPM), representing the number of reads mapped to a transcript per million reads in the sample. The RSEM software was used to quantify overall gene expression levels in each sample. Correlation analysis and principal component analysis (PCA) were conducted to compare the samples. Differential gene expression was analyzed using DESeq2 software, with the Benjamini-Hochberg (BH) method applied for multiple testing correction. Differentially expressed genes (DEGs) were identified between the model and control groups, as well as between the GMK-M and model groups, based on the criteria of an adjusted P-value (P adj) < 0.05 and |log_2_FC|≥ 0.585. DEGs were then subjected to Venn diagram analysis, clustering, Gene Ontology (GO) functional annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.
Microbiome analysis
Fecal samples were collected from the control, model, and GMK-M groups for microbiome analysis. The groups comprised four samples from the Control group (Group A), labeled A1–4; six samples from the Model group (Group B), labeled B1–6; and seven samples from the GMK-M group (Group C), labeled C1–7. Total DNA was first extracted from the gut microbiota of the mice, and the quality of the genomic DNA was evaluated using 1% agarose gel electrophoresis. The V3–V4 hypervariable regions of the 16S rRNA gene were PCR-amplified using primers 338F (ACTCCTACGGGAGGCAGCAG) and 806R (GGACTACHVGGGTWTCTAAT) for high-throughput sequencing. The PCR products were then purified, quantified using fluorescence, and normalized. Products meeting sequencing standards were directly sequenced on the Illumina platform, generating paired-end (PE) reads, which were assembled based on overlapping regions. Quality control and filtering were applied to remove unusable sequences, optimizing the dataset. The sequencing data were matched against the SILVA database for taxonomic classification. Operational taxonomic units (OTUs) were defined using the UPARSE software platform, with the criterion that each species should have at least five sequences in a minimum of three samples and a total of at least 20 sequences across all samples. The sequences were rarefied to the minimum sequence number across the samples to generate the final dataset for analysis. Sequences were clustered into OTUs at 97% similarity, and the RDP Classifier was used for taxonomic classification. The alpha diversity indices (Sobs, Shannon, Simpson, ACE, Chao, and Coverage) were analyzed for the Control, Model, and GMK-M groups. Additionally, species composition at various taxonomic levels (phylum, class, order, family, genus, species) was evaluated statistically. PCA and Principal Coordinates Analysis (PCoA) were performed to assess differences in gut microbiota structure among the groups. Linear Discriminant Analysis Effect Size (LEfSe) was applied to identify taxonomic features with significant differences between the groups.
Correlation analysis between mouse gut microbiome and clinical factors
Distance-based redundancy analysis (db-RDA) and Spearman correlation analysis was used to correlate the gut microbiome data with the efficacy outcomes of GMK intervention in HDM-induced asthma. This analysis explored associations between gut microbial communities and immune cell populations, as well as factors related to AA, such as total IgE, HDM-specific IgE (HDM-sIgE), and inflammatory cytokines.
Integrated analysis of gut microbiome and DEGs in lung tissue
Additionally, Spearman correlation analysis was performed to integrate the gut microbiome data with lung tissue transcriptomic data, focusing on DEGs significantly enriched in GO and KEGG pathways, with a TPM threshold of ≥ 600 across all samples. This analysis aimed to identify correlations between the gut microbiome and gene expression profiles related to asthma pathology.
Western blot analysis
Lung tissue samples were lysed using RIPA lysis buffer supplemented with protease and phosphatase inhibitors. The protein concentration was determined using a BCA Protein Assay Kit. Equal amounts of protein were then subjected to Western blot (WB) analysis. Proteins were separated on a 10% SDS-PAGE gel and transferred onto a PVDF membrane. The membranes were blocked with 5% BSA in TBST and incubated overnight at 4 °C with primary antibodies against target proteins, using a primary antibody dilution buffer. Afterward, membranes were incubated with secondary antibodies for one hour at room temperature. WB signals were visualized using the Clarity Western ECL Substrate Kit, and protein density was quantified using ImageJ. The antibodies used included anti-GAPDH (1:5000), anti-Wnt3a (1:5000), anti-β-catenin (1:5000), and anti-Phospho-GSK-3β (1:1000).
For the assessment of cytosolic and nuclear β-catenin levels in mouse lung tissue, fresh tissue was homogenized by cutting into small pieces, followed by mixing with a tissue homogenate solution at a ratio of 200 μL of solution per 60 mg of tissue. Homogenization was performed using a glass homogenizer, and the mixture was incubated in an ice bath for 15 min. After centrifugation at 4 °C, 1500 × g for 5 min, the supernatant was collected as the cytoplasmic fraction. The pellet was resuspended with 50 μL of nuclear protein extraction reagent, vortexed, and centrifuged again at 4 °C, 16,000 × g for 10 min. The supernatant was collected as the nuclear protein fraction. Both cytoplasmic and nuclear proteins were then analyzed separately by WB.
Molecular docking
To investigate the 3D structure of Wnt3a, its protein sequence was retrieved from the UniProtKB website and imported into the SWISS-MODEL platform for structure prediction, and the resulting model was saved in PDB format. The ligand structure of GMK was obtained from a small molecule database and downloaded in SDF format. The protein file was loaded into PyMOL software, where excess ligands and water molecules were removed, hydrogen atoms added, charges assigned, and the protein was designated as the receptor. The small molecule was processed similarly. Molecular docking was performed using AutoDock Vina 1.5.6, and visualization was conducted with PyMOL 3.1 and Discovery Studio 2019.
Statistical analysis
Data organization, analysis, and figure creation were performed using Excel 2019 and GraphPad Prism 9.3.0. Statistical results are presented as the mean ± standard deviation (SD). For comparisons between independent samples across multiple groups, One-Way ANOVA was used if the data followed a normal distribution with homogeneity of variance. When data exhibited a normal distribution but lacked homogeneity of variance, Welch’s ANOVA was applied. Non-normally distributed data were analyzed using the Kruskal–Wallis test. A P-value of < 0.05 was considered statistically significant.
Results
Identification of GMK components
Typical total ion chromatograms (TICs) are shown in Fig. 1. Fourteen chemical compounds were identified by matching retention times, mass spectrometry data, and peak intensities with those of the reference standards, in accordance with the quality control standards specified in the Pharmacopoeia of the People's Republic of China (2020 edition). In positive ion mode, five compounds were detected: aspterric acid, 5-O-methylvisammioside, cimifugin, neochlorogenic acid, and D-alanine. Under negative ion conditions, nine compounds were identified: ganoderic acid A, ganoderic acid F, ganoderic acid C6, ferulic acid, vanillic acid, sec-O-glucosylhamaudol, gentisic acid, quinic acid, and L-pyroglutamic acid (Table 1).Fig. 1TICs of GMK showing the representative active ingredients. A TIC in positive ion mode. B TIC in negative ion modeTable 1The chemical components identified in the GMK water extractNoComponentFormulam/zAdductsRetention time(min)Mode1Ganoderic acid AC_30_ H_44_ O_7_497.29M-H2O-H8.72neg2Ganoderic acid FC_32_ H_42_ O_9_569.28M-H, M + Cl7.57neg3Ganoderic acid C6C_30_ H_42_ O_8_529.28M-H, M + Cl7.44neg4Aspterric acidC_15_ H_22_ O_4_533.312 M + H6.64pos5Ferulic acidC_10_ H_10_ O_4_193.05M-H6.27neg65-O-MethylvisammiosideC_22_ H_28_ O_10_453.17M + H, M + Na, M + K5.28pos7CimifuginC_16_ H_18_ O_6_307.12M + H4.79pos8Vanillic acidC_8_ H_8_ O_4_167.04M-H4.70neg9Sec-O-GlucosylhamaudolC_21_ H_26_ O_10_483.15M + FA-H4.58neg10Neochlorogenic acidC_16_ H_18_ O_9_355.10M + H4.31pos11Gentisic acidC_7_ H_6_ O_4_153.02M-H3.90neg12Quinic acidC_7_ H_12_ O_6_173.05M-H2O-H1.93neg13L-Pyroglutamic acidC_5_ H_7_ NO_3_110.02M-H2O-H1.03neg14D-alanineC_3_ H_7_ NO_2_90.06M + H, M + ACN + H0.78pos
The therapeutic effect of GMK in asthmatic mice
The therapeutic effect of GMK on AA was evaluated in an HDM-induced mouse model. Lung function was assessed through methacholine challenges, and AHR was quantified by measuring Rrs. As methacholine concentrations increased, corresponding changes in Rrs values were observed. Compared to the control group, the model group showed significantly higher Rrs values at methacholine concentrations of 6.25, 12.5, 25, and 50 mg/mL, indicating increased AHR (P < 0.05). Rrs values in the high, medium, and low dose GMK groups, as well as in the positive control dexamethasone group, were higher than in the control group, though less pronounced than those in the model group. These findings suggest that GMK and dexamethasone can partially alleviate AHR in HDM-induced asthmatic mice. The GMK-M group demonstrated the most significant improvement, followed by the GMK-H and GMK-L groups (Fig. 2B). Notably, the CHIR-99021 + GMK-M group exhibited significantly elevated Rrs values compared to the control group, approaching those of the model group (P < 0.001). This reversal effect suggests that activation of Wnt/β-catenin signaling antagonizes GMK's therapeutic effects, indicating that GMK likely exerts its action through the suppression of this pathway (Fig. S2A).Fig. 2. The therapeutic effect of GMK in asthmatic mice. A Experimental design for the HDM-induced asthma model. B Respiratory resistance (Rrs) overlaid plots of mice in each group, n = 3. C Histopathology of the lung tissue of mice in each group stained with H&E, PAS and Masson (Scale bar = 50 μm). D Inflammation scores in the lung tissue of mice in each group. E PAS-positive cells per bronchi in the lung tissue of mice in each group. F Masson-positive area in the lung tissue of mice in each group (n = 6 biologically independent experiments). Compared with the control group, ^^P < 0.05, ^^P < 0.01, ^^P < 0.001, ^****^P < 0.0001; Compared with the model group, ^#^P < 0.05, ^##^P < 0.01
H&E staining of lung tissue revealed that the alveoli, alveolar ducts, and alveolar sacs in the control group of mice were structurally intact, without signs of inflammatory cell infiltration around the airways. In contrast, the model group showed marked inflammatory cell infiltration around the airways, thickened alveolar walls, and fluid extravasation. Treatment with high and medium doses of GMK resulted in a reduction of inflammatory cell infiltration and less alveolar wall damage in asthmatic mice (Fig. 2C). Pathological inflammation scores indicated significantly higher inflammation in the model group compared to the control group. Inflammation scores were notably lower in the Dex group, as well as in the high, medium, and low dose GMK groups, relative to the model group (Fig. 2D).
Goblet cell metaplasia in the bronchial mucosa is a key indicator of asthma severity. PAS staining of lung tissue showed no significant goblet cell metaplasia in the airways of control group mice. In contrast, the model group exhibited a marked increase in goblet cell metaplasia. Treatment with dexamethasone and varying doses of GMK resulted in varying degrees of improvement in goblet cell metaplasia (Fig. 2C). Goblet cell metaplasia scores in the airway epithelium of the model group were predominantly between 3 and 4, while scores in the dexamethasone and GMK-treated groups exhibited varying reductions (Fig. 2E). Masson staining, which stains muscle fibers red and collagen fibers blue, was used to assess collagen deposition in the airways. The results showed that HDM-induced asthmatic mice had significantly higher Masson-positive staining areas around the trachea, indicating increased collagen deposition compared to the control group. In contrast, mice treated with different doses of GMK demonstrated substantial reductions in collagen deposition in the airways (Fig. 2C, F). Pathological findings revealed that the GMK-M group exhibited the most favorable outcomes, followed by the GMK-H group, while the GMK-L group showed the least effectiveness. These findings were consistent with pulmonary function results, which indicated superior therapeutic effects in both the medium and high-dose GMK groups. Importantly, the CHIR99021 + GMK-M group showed significantly exacerbated pathological features compared to both the control and GMK-M groups, including increased inflammatory cell infiltration, goblet cell hyperplasia, and a larger area of collagen deposition. This pathological deterioration indicates that pharmacological activation of Wnt/β-catenin signaling with CHIR-99021 counteracts the anti-inflammatory and anti-remodeling effects of GMK, suggesting that GMK exerts its therapeutic effects by suppressing this pathway (Fig. S2B-E).
Effects of GMK on the levels of asthmatic markers and inflammatory cytokines in the serum of mice
A flow-based fluorescent immunoassay was utilized to assess serum levels of IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, IL-17, and IFN-γ in each group of mice. Results showed that, compared to the control group, serum levels of IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, and IL-17 were elevated to varying degrees, with the most significant increase observed in the model group. Conversely, IFN-γ levels were significantly lower in the model group compared to the control group. Treatment with a medium dose of GMK in the model group led to a downregulation of cytokine levels, including IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, and IL-17, and an upregulation of IFN-γ (Fig. 3A–H).Fig. 3. Effects of GMK on the levels of asthmatic markers and inflammatory cells in serum of mice. A The levels of IL-1β in the serum of mice from each group. B The levels of IL-4 in the serum of mice from each group. C The levels of IL-5 in the serum of mice from each group. D The levels of IL-6 in the serum of mice from each group. E The levels of IL-10 in the serum of mice from each group. F The levels of IL-13 in the serum of mice from each group. G The levels of IL-17 in the serum of mice from each group. H The levels of IFN-γ in the serum of mice from each group. I The levels of total IgE in the serum of mice from each group. J The levels of HDM-sIgE in the serum of mice from each group. For IgE and HDM-sIgE, n = 6 biologically independent mice; for IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, IL-17 and IFN-γ, n = 4 biologically independent mice. Compared with the control group, ^^P < 0.05, ^^P < 0.01, ^^P < 0.001, ^****^P < 0.0001; compared with the model group, ^#^P < 0.05, ^##^P < 0.01, ^###^P < 0.001
ELISA results showed significantly elevated serum levels of total IgE and HDM-sIgE in HDM-induced asthmatic mice compared to controls. Treatment with dexamethasone or medium-dose GMK reduced both total IgE and HDM-sIgE levels compared to the model group (Fig. 3I–J).
Effects of GMK on the populations of immune cells in the lung tissue of mice
Lung function tests for AHR, along with H&E, PAS, and Masson staining, revealed that medium and high doses of GMK reduced airway inflammation, slowed airway remodeling, and decreased AHR in HDM-induced asthmatic mice. These effects were more pronounced in the GMK-M group than in the GMK-H group, making the GMK-M group the optimal choice for subsequent mechanistic investigations.
Flow cytometry analysis of lung tissues showed a significantly higher percentage of eosinophils and a significantly lower percentage of neutrophils in HDM-induced asthmatic mice compared to controls (P < 0.0001). Eosinophil infiltration was notably pronounced in the model group (Fig. 4A, F–G). Furthermore, the percentage of M2 macrophages was significantly elevated in the model group (P < 0.001) (Fig. 4B, H). Th2 cells were increased, and the Th1/Th2 ratio significantly decreased (P < 0.0001), indicating a Th2 bias (Fig. 4C, I–K). Additionally, Th17 and Treg cell percentages were significantly elevated (P < 0.01 and P < 0.05, respectively), with the Th17/Treg ratio also increased, though not significantly (P > 0.05), suggesting a Th17 bias in the model group (Fig. 4D, E, L, N).Fig. 4. Effects of GMK on the populations of immune cells in the lung tissue of mice. A Proportion of neutrophils and eosinophils. B Proportion of M2 macrophages. C Proportion of Th1 and Th2 cells. D Proportion of Th17 cells. E Proportion of Tregs. F Levels of neutrophils in lung tissue of mice in each group. G Levels of eosinophils in lung tissue of mice in each group. H Levels of M2 macrophages in lung tissue of mice in each group. I Levels of Th1 cells in lung tissue of mice in each group. J Levels of Th2 cells in lung tissue of mice in each group. K Ratios of Th1/Th2 in lung tissue of mice in each group. L Levels of Th17 cells in lung tissue of mice in each group. M Levels of Tregs in lung tissue of mice in each group. N Ratios of Th17/Treg in lung tissue of mice in each group. N = 6 biologically independent mice. Compared with the control group, ^^P < 0.05, ^^P < 0.01, ^^P < 0.001, ^****^P < 0.0001; compared with the model group, ^#^P < 0.05, ^##^P < 0.01, ^###^P < 0.001, ^####^P < 0.0001
Compared to the model group, eosinophil percentages were significantly lower and neutrophil percentages significantly higher in the GMK-M group (P < 0.001) (Fig. 4A, F, G), indicating that GMK effectively reduced eosinophil infiltration. Moreover, the percentage of M2 macrophages decreased in the GMK-M group (P < 0.05) (Fig. 4B, H). Medium-dose GMK also significantly reduced the percentages of Th2 and Th17 cells in the lung tissues of HDM-induced asthmatic mice (P < 0.001), thereby partially restoring the Th1/Th2 and Th17/Treg immune imbalances (Fig. 4C–E, I–N).
Effects of GMK on the identification of DEGs in lung tissue
In this sequencing study, approximately 53.14 million, 52.61 million, and 51.84 million raw reads were obtained from the control, model, and GMK-M groups, respectively. After quality control, the control, model, and treatment groups retained 51.47 million, 50.81 million, and 49.92 million high-quality clean reads, respectively (Supplement Table S1). HISAT2 software was used to align the clean reads to the reference genome, generating mapped reads for subsequent analysis. The alignment quality from this transcriptome sequencing was also evaluated. Genes with moderate to high expression levels were defined as those with a quantification value exceeding 3.5. Sequencing saturation analysis showed that near-saturation was achieved when 40% of the reads were aligned, suggesting that the sequencing depth was sufficient to capture the majority of expressed genes (Supplement Table S2).
Gene expression was quantified using TPM, which measures the number of transcript reads per million reads. RSEM software was used to perform clustering and PCA based on TPM values from different samples, allowing for the assessment of gene correlation. Clustering analysis revealed a high degree of correlation within the control group, reflecting robust biological replicates. In contrast, the correlation between the control and model groups was lower, indicating significant differences in gene expression profiles in the lung tissue of HDM-induced asthmatic mice compared to controls (Fig. 5A). PCA showed substantial separation between the control and model groups along the first principal component (PC1), highlighting distinct differences between HDM-induced asthmatic mice and the control group. This observation was consistent with the sample correlation heatmap results (Fig. 5B).Fig. 5. Effects of GMK on the identification of DEGs in lung tissue. A Heatmap of Correlation between samples. The colors of the squares indicate the degree of correlation between pairs of samples, with higher values representing stronger correlations. B PCA analysis between samples. C Venn diagram showing intersection of DEGs among groups. D Volcano plot (the model group vs the control group). Red dots indicate significantly upregulated genes, blue dots indicate significantly downregulated genes, and gray dots represent genes that are not significantly differentially expressed. E Volcano plot (the GMK-M group vs the model group). F Cluster analysis heatmap of different groups. G GO enrichment analysis (the model group vs the control group). H GO enrichment analysis (the GMK-M group vs the model group). I KEGG enrichment analysis (the model group vs the control group). J KEGG enrichment analysis (the GMK-M group vs the model group)
RNA-seq comparison of the model and control groups revealed a total of 5,036 DEGs, including 2,872 upregulated and 2,164 downregulated genes in the model group. The top 10 DEGs, ranked by P-value, included Ccl8, C1qb, C1qa, C1qc, Fcgr2b, Lpxn, Pdcd1, Ly6i, Trem2, and Mmp12, all showing upregulation in the model group. In the GMK-M vs model comparison, a total of 6 DEGs were identified, of which 4 genes (Camk1g, Sfrp2, Wnt2, and Dynlt2a3) were upregulated in the treatment group, while 2 genes (Igkv1-110 and Ccl21d) were downregulated. Based on the P-values, the rankings were as follows: Ccl21d, Wnt2, Camk1g, Dynlt2a3, Igkv1-110, and Sfrp2. Notably, Igkv1-110 and Wnt2 were differentially expressed in both the model versus control and GMK-M versus model comparisons. In comparison to the control group, Igkv1-110 was upregulated, while Wnt2 was downregulated in the lung tissues of HDM-induced asthmatic mice. After GMK treatment, Igkv1-110 expression was downregulated, while Wnt2 expression was upregulated (Fig. 5C–E). In the lung tissues of HDM-induced asthmatic mice, upregulated DEGs outnumbered downregulated genes compared to the control group, suggesting that HDM induction altered the functions of certain genes. Conversely, following GMK treatment, the disparity between upregulated and downregulated genes diminished, indicating that GMK effectively mitigated the gene expression changes induced by HDM (Fig. 5F).
GO analysis revealed that in HDM-induced asthmatic mice, DEGs were predominantly localized in the cell membrane, organelles, and extracellular regions. These dysregulated genes were primarily involved in immune and metabolic processes, potentially triggering inflammatory responses through the regulation of molecular functions and the regulation of catalytic activities (Fig. 5G, Supplement Table S3). After GMK intervention, the cellular components, molecular functions, and biological processes of the DEGs partially recapitulated the functional profile derived from the model versus control comparison (Fig. 5H, Supplement Table S4). KEGG enrichment analysis identified 329 signaling pathways enriched in the DEGs from the lung tissues in the model group compared to the control group. These pathways were largely associated with immune system interactions, signaling molecules, and the differentiation of Th1, Th2, and Th17 cells, indicating that HDM induction primarily impacts immune system functions and signaling (Fig. 5I, Supplement Table S5). Following GMK treatment, DEGs in lung tissues were enriched in seven signaling pathways: Wnt, chemokine, mTOR, Hippo, calcium, oxytocin, and NF-κB. The P-adjust for the Wnt signaling pathway was < 0.05, suggesting that GMK may exert its effects by modulating the Wnt signaling pathway in asthmatic mice (Fig. 5J, Supplement Table S6).
Effects of GMK on the gut microbiota
To verify the intestinal microbiota in the model, 16S rDNA high-throughput sequencing was employed to analyze the microbial DNA from fecal samples of the mice. A total of 776,848 optimized sequences and 329,482,719 optimized sequence bases were obtained.
Analysis of the gut microbiota abundance and diversity across different groups of mice revealed that, at the OTU level, the model group had higher Sobs, Chao, ACE, and Shannon indices, along with a lower Simpson index compared to the control group. The difference in the Shannon index was statistically significant (P < 0.05), suggesting that HDM-induced asthmatic mice exhibited greater species richness and diversity, along with a more even microbial community distribution. In contrast, the GMK-M group showed increased Sobs, Chao, ACE, and Simpson indices but a decreased Shannon index compared to the model group, indicating that GMK treatment enhanced species richness while reducing species diversity. The Coverage index for each group approached 1, confirming robust community coverage and reliable microbial community representation (Fig. 6A–F).Fig. 6. Effects of GMK on the gut microbiota. A Sobs index of OTU level. B Chao index of OTU level. C ACE index of OTU level. D Simpson index of OTU level. E Shannon index of OTU level. F Coverage index of OTU level. G Percent of community abundance at the phylum level. H Ternary analysis at the phylum level. I Kruskal–Wallis H test at the phylum level. J Percent of community abundance at the genus level. K Ternary analysis at the genus level. L Kruskal–Wallis H test at the genus level. M Hierarchical clustering tree at OTU level. N PCA at OTU level. O PCoA at OTU level. P Cladogram of relative abundance differences among gut microbiota. Q Bar chart of relative gut microbiota differences between groups. Differences compared with the control group were statistically significant (^*^P < 0.05)
Taxonomic analysis of the gut microbiota composition across the different groups at the phylum level revealed that all groups contained Firmicutes, Bacteroidota, Actinobacteriota, and Verrucomicrobiota (Fig. 6G). The ternary plot showed that the dominant phylum in the control group was Firmicutes, in the model group it was Bacteroidota, and in the GMK-M group it was Actinobacteriota (Fig. 6H). Significance tests identified differences in the abundance of microbial taxa at the phylum level among the groups. Compared to the control group, the model group had significantly lower Firmicutes (P < 0.01) and significantly higher Bacteroidota. GMK treatment partially restored Firmicutes abundance while reducing Bacteroidota abundance (P < 0.05) (Fig. 6I). At the genus level, Lactobacillus, norank_f__Muribaculaceae, Dubosiella, and Bifidobacterium were present in relatively high proportions across all groups (Fig. 6J). The ternary plot demonstrated that Dubosiella was the dominant genus in the control group, norank_f__Muribaculaceae in the model group, and Bifidobacterium in the GMK-M group (Fig. 6K). Significance tests revealed 11 differentially abundant bacterial genera among the groups. The top five differentially abundant genera included norank_f__Muribaculaceae, Dubosiella, Campylobacter, Akkermansia, and Coriobacteriaceae_UCG-002 (Fig. 6L). Hierarchical clustering analysis of the gut microbiota composition revealed a significant distinction between the gut microbiota of HDM-induced asthmatic mice and that of the control group. After GMK treatment, the gut microbiota composition showed a trend toward normalization (Fig. 6M).
PCA revealed distinct clustering patterns in the gut microbiota structure between HDM-induced asthmatic mice and the control group. After treatment with GMK, the gut microbiota structure of the treated mice showed greater similarity to that of the control group. Samples within both the control and model groups were tightly clustered, indicating the stability and high reproducibility of the AA model established through HDM intranasal administration (Fig. 6N). PCoA showed a clear separation between the model group and the other two groups along PC1, highlighting the structural changes in the gut microbiota of HDM-induced asthmatic mice compared to the control group. Following GMK treatment, the sample points of the GMK-M group moved closer to those of the control group, suggesting that the gut microbiota composition in the GMK-M group increasingly resembled that of the control group (Fig. 6O).
LEfSe analysis was conducted to identify differentially abundant microbial taxa across various taxonomic levels, including phylum, class, order, family, genus, and species. The LEfSe multi-level cladogram illustrated the significantly influential microbial taxa from the kingdom to the species level within each group. Red, blue, and green nodes represented microbial taxa that were significantly enriched and had a considerable impact on inter-group differences in the control, model, and GMK-M groups, respectively (P < 0.05). Yellow nodes indicated microbial taxa that did not show significant differences among the groups (Fig. 6P). Linear Discriminant Analysis (LDA) scores were used to quantify the influence of each taxa on the observed differences; higher LDA scores reflect a greater impact of the species' abundance on the differences. According to the LDA discriminant bar plot, 10 taxa in the control group, 30 taxa in the model group, and 11 taxa in the GMK-M group were identified as having a significant impact on the observed differences (Fig. 6Q).
Correlation analysis of clinical factors
The gut microbiome findings from Sect. 3.6 were integrated with the pharmacodynamic effects of GMK on HDM-induced asthmatic mice, as outlined in Sects. 3.3 and 3.4. Db-RDA and Spearman correlation analysis were applied to explore the relationships between these datasets. The Db-RDA analysis demonstrated that gut microbial genera levels in HDM-induced asthmatic mice were positively correlated with immune cells (Th2, Th17, eosinophils, and M2 macrophages), the Th17/Treg ratio, total IgE, HDM-sIgE, and inflammatory cytokines such as IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, and IL-17 (Fig. 7A). After GMK treatment, gut microbial genera levels in asthmatic mice were positively correlated with immune cells (Th1, Treg, and neutrophils), the Th1/Th2 ratio and IFN-γ (Fig. 7B). Spearman correlation heatmap analysis further indicated that Treg was strongly correlated with Bifidobacterium and norank_f_norank_o__RF39, Th2 with Akkermansia, IL-1β with norank_f_norank_o__RF39, and IL-4 and IL-13 with Parasutterella (Fig. 7C).Fig. 7. Correlation analysis of clinical factors. A Db-RDA analysis of immune cells (Th1, Th2, Th17, Treg, eosinophils, neutrophils, M2 macrophages, Th1/Th2 ratio, and Th17/Treg ratio) against gut microbiota at the genus level. B Db-RDA analysis of cytokines (IL-1β, IL-4, IL-5, IL-6, IL-10, IL-13, IL-17, and IFN-γ) against gut microbiota at the genus level. C Spearman correlation heatmap analysis. In this figure A, B, the red, blue, and green dots represent samples from the control, model, and GMK groups, respectively. The length of red arrows signifies the degree of influence on the species. Acute, obtuse, and right angles between the arrows represent positive correlation, negative correlation, and no correlation, respectively. Projections from the sample points to the clinical factors are made along the arrows. The distance of the projection points from the origin indicates the relative impact of the clinical factors on the sample community distribution. The direction of the sample points relative to the arrows indicates positive or negative correlation. The R values are displayed in different colors in the figure, with ^^P < 0.05, ^^P < 0.01, and ^^P < 0.001 in this figure C
Results of the integrated analysis of gut microbiota and DEGs in lung tissue
To integrate the top 50 bacterial genera by total abundance with the significantly enriched DEGs (TPM ≥ 600) across all samples from the transcriptomic study, a comparison was made between the model and control groups, as well as the treatment and model groups, utilizing the GO and KEGG databases. This analysis identified significant genes and dominant bacterial taxa. A correlation analysis heatmap was created to visualize the relationship between gene expression and gut microbiota, revealing several genes that correlated with bacterial genera with higher relative abundance. Notably, genes H2-Aa, Cd74, Mgp, Rps11, Rps26, and Gm10443 exhibited significant positive correlations with Bifidobacterium (P < 0.001), while H2-D1 was positively correlated with Parasutterella (P ≤ 0.001). In contrast, Hba-a2 showed a significant negative correlation with Bifidobacterium (P < 0.001), Hba-a1 with norank_f__norank_o__RF39 (P < 0.001), and Hba-a2 with Turicibacter (P ≤ 0.001) (Fig. 8).Fig. 8. Results of the integrated analysis of gut microbiota and DEGs in lung tissue. The R values are displayed in different colors in the figure, with ^^P < 0.05, ^^P < 0.01, and ^^P < 0.001
Effect of GMK on AA via modulating the Wnt/β-Catenin pathway in lung tissue
WB was utilized to evaluate the protein expression levels of Wnt3a, β-catenin, and p-GSK-3β in lung tissues. Compared to the control group, the model group showed a significant increase in the relative protein expression of Wnt3a (P < 0.01), while the relative expressions of β-catenin and p-GSK-3β were notably reduced (P < 0.05). These results suggest that activation of the Wnt signaling pathway in the lung tissues of the model group led to a negative regulation of β-catenin accumulation in the cytoplasm through inhibition of GSK-3β phosphorylation. In contrast, the positive drug and GMK-M groups displayed reduced Wnt3a expression and increased β-catenin expression compared to the model group; however, the differences were not statistically significant (P > 0.05). Importantly, p-GSK-3β expression was elevated and the difference was statistically significant (P < 0.05), indicating that GMK intervention in HDM-induced asthma mice inhibits the Wnt signaling pathway, promoting GSK-3β phosphorylation and facilitating β-catenin accumulation in the cytoplasm by reducing Wnt3a expression (Fig. 9A–D).Fig. 9. Effect of GMK on AA via modulating the Wnt/β-Catenin pathway in lung tissue. A Protein expression of Wnt3a, β-catenin and p-GSK-3β in lung tissues of mice from each group. B Statistical plots of the differences in protein expression of Wnt3a in the lung tissues of mice in each group. C Statistical plots of the differences in protein expression of β-catenin in the lung tissues of mice in each group. D Statistical plots of the differences in protein expression of p-GSK-3β in the lung tissues of mice in each group. n = 3 biological replicates per group, compared with the control group, ^*^P < 0.05, ^**^P < 0.01; compared with the model group, ^#^P < 0.05
Cytosolic and nuclear protein levels of β-catenin in mouse lung tissue revealed that, compared to the control group, β-catenin expression in the cytoplasm was reduced, while its nuclear expression significantly increased in the model group, suggesting β-catenin pathway activation. Conversely, the GMK treatment group showed increased cytoplasmic β-catenin expression and significantly reduced nuclear β-catenin accumulation compared to the model group, indicating inhibition of the β-catenin pathway (Fig. S1A-D).
Interactions between the chemical components identified in the GMK and the Wnt3a protein
Docking results of key compounds in GMK with the Wnt3a protein revealed that multiple compounds were predicted to form hydrogen bonds with Wnt3a. The binding energies from these interactions, shown in Table S7, suggest a strong interaction between the compounds and Wnt3a. In the surface model, the compounds are positioned within a cavity on Wnt3a's surface, consistent with their molecular structures. The 2D diagram further illustrates that the screened compounds interact with amino acid residues of Wnt3a via van der Waals forces and can also form π-interactions with Wnt3a's residues. Overall, several compounds in GMK demonstrate relatively strong interactions with Wnt3a (Fig. S1E).
Discussion
In China, GMK is commonly used to regulate allergic constitution and treat various allergic conditions, including AA, allergic rhinitis, and atopic dermatitis. Our previous studies demonstrated that after three months of GMK intervention, levels of allergy-related markers in peripheral blood—such as IgE, sIgE, IL-4, IL-5, IL-13, IL-25, thymic stromal lymphopoietin (TSLP), and Eotaxin—were significantly reduced, leading to an improved allergic constitution [22]. In clinical studies on GMK’s effect on allergic rhinitis, it was found that after 4, 8, 12 weeks, and 1 year of intervention, the total nasal symptom score (TNSS), total non-nasal symptom score (TNNSS), and rhinoconjunctivitis quality of life questionnaire (RQLQ) all decreased significantly (P < 0.05) [23]. Additionally, animal studies showed that GMK treatment in rats with allergic rhinitis increased IFN-γ expression while decreasing IL-4, IL-6, IL-17, and TGF-β1 levels. This modulation inhibited Th2 and Th17 cells, improved Th cell balance, and reduced inflammatory responses [24]. These findings suggest that GMK can regulate allergic constitution and treat allergic rhinitis by modulating Th immune balance. However, the chemical composition of GMK remains unclear, and its specific targets and mechanisms for inhibiting inflammation in AA have yet to be fully elucidated. Therefore, we aimed to uncover the potential targets and mechanisms of GMK through pharmacodynamic studies, transcriptomics, and microbiome analyses.
Establishing a robust asthma model is critical for advancing our understanding of the disease. The mouse asthma model shares several key features with human asthma; for example, exposure to allergens induces the production of Th2 cytokines such as IL-4, IL-5, and IL-13, goblet cell hyperplasia, and elevated IgE levels. These characteristics make mice a valuable model for studying asthma [25]. However, since asthma does not develop spontaneously in mice, it is necessary to induce the condition to establish an AA model [26]. Traditional methods typically choose ovalbumin (OVA) combined with aluminum hydroxide adjuvant for intraperitoneal sensitization, followed by nebulized challenges to establish acute and chronic asthma models. However, this method does not fully replicate the disease onset observed in clinical asthma patients [27]. House dust mites (HDM) are prevalent in everyday environments and are major allergens responsible for allergic conditions like asthma and rhinitis. Therefore, in this study, HDM-induced nasal drip was used to establish the mouse model of AA, as this method more closely mimics clinical scenarios. It is crucial to observe the immune response in the lung tissues of HDM-induced asthma mice. Given that C57BL/6 J mice do not exhibit a strong predisposition toward a Th2 immune response and that female mice show a stronger humoral immune response than males [28, 29], alongside epidemiological data indicating a higher prevalence of asthma in adult females [30], female C57BL/6 J mice were selected for the experiments.
The results of this study suggest that GMK can alleviate eosinophilic airway inflammation in asthmatic mice. Specifically, the level of eosinophils serves as a key indicator of both the severity of inflammation and the manifestation of asthma symptoms. Eosinophilia is a hallmark of AA. The findings also demonstrate that HDM induction caused lung inflammation in mice, leading to significant migration and aggregation of immune cells at the site of inflammation. Compared to the control group, the proportion of eosinophils in the lung tissues of the model group was significantly elevated. Pathological H&E staining further revealed that the model group exhibited marked inflammatory cell infiltration around the airways, thickening of the alveolar walls, and fluid extravasation. These observations suggest that HDM-induced asthma is characterized by heightened airway inflammation. In contrast, GMK treatment resulted in reduced airway inflammatory cell infiltration and a decrease in the extent of alveolar wall damage in asthmatic mice. The elevation of total IgE and specific IgE levels also reflects the severity of AA in patients. As a key biomarker for asthma, IgE exerts its effects through the Fc region of the antibody, which binds to FcεRI receptors on mast cells and basophils, sensitizing the body to the antigen. Additionally, IgE can bind to FcεRII receptors on eosinophils and B lymphocytes, triggering the release of inflammatory mediators. Clinically, anti-IgE therapy in mild to moderate asthma reduces IgE levels in the airway mucosa and decreases the expression of several markers of airway inflammation, particularly eosinophils and high-affinity IgE receptors [31]. In the present study, serum levels of total IgE and HDM-sIgE in HDM-induced asthmatic mice were significantly elevated compared to the control group. Treatment with a medium dose of GMK led to a reduction in these levels, indicating that medium-dose GMK may be effective in treating asthma. The medium dose (1.95 g/kg) was prioritized due to its optimal balance between efficacy and safety, exhibiting superior histopathological improvement over higher doses, which aligns with bell-shaped dose–response curves often observed in natural products. This divergence from lung function metrics (which showed a linear dose-dependence) suggests that tissue repair pathways may be more vulnerable to target saturation, offering critical insights for the optimization of future asthma therapies.
Imbalances in the ratios of Th cell subsets play a pivotal role in the pathogenesis of asthma. GMK has the ability to reduce the proportion of Th2 and Th17 cells, thereby restoring the balance in the Th1/Th2 and Th17/Treg cytokine networks. The study results showed that in the lung tissues of HDM-induced asthmatic mice, the percentage of Th2 cells and the levels of Th2-type inflammatory cytokines (IL-4, IL-5 and IL-13) were elevated, while the Th1/Th2 ratio was decreased compared to the control group. This finding indicates an immune imbalance in the lung tissues of the model group, characterized by a disrupted Th1/Th2 cell and cytokine network. GMK treatment significantly reduced the percentage of Th2 cells and partially restored the Th1/Th2 immune balance. In addition to the classical Th1/Th2 imbalance, the Th17/Treg immune imbalance has also gained increasing attention. The mechanisms of Th17 and Treg cells complement the effector functions of Th1 and Th2 cells. Th17 cells, a distinct subset of CD4^+^ T cells, are named for their ability to secrete IL-17. This cytokine promotes eosinophil infiltration in the airways, and elevated levels of Th17 cells and IL-17 expression are closely associated with asthma [32]. Treg cells represent another subset of T cells with regulatory functions within the body. Normally, a balance is maintained between Th17 and Treg cells; however, during inflammation, an increased differentiation ratio of Th17 cells can disrupt this balance. Clinical studies indicate that the percentage of Th17 cells in the peripheral blood of AA patients is significantly higher than in the general population [33]. Similarly, the current study demonstrated an elevated percentage of Th17 cells and a higher Th17/Treg ratio in the model group. Treatment with a medium dose of GMK significantly reduced the percentage of Th17 cells and partially corrected the Th17/Treg immune imbalance. Moreover, in AA, alveolar macrophages contribute to airway remodeling by promoting airway smooth muscle contraction through mediators such as prostaglandin E1 (PGE1) [34]. Macrophages can be classified into two main types: classically activated (M1) and alternatively activated (M2) macrophages. The balance between M1 and M2 macrophages reflects Th1/Th2 immune homeostasis, and macrophage polarization is associated with the development of asthma. Under the stimulation of type 2 cytokines, such as IL-4 and IL-13, airway macrophages differentiate into the M2 phenotype. These M2 macrophages produce various cytokines and chemokines that further regulate the recruitment of eosinophils and Th2 cells to the lungs, exacerbating asthma. In the present study, the percentage of M2 macrophages was elevated in the lung tissues of mice in the model group [35, 36]. This elevation correlated with high expression levels of Th2 inflammatory factors, such as IL-4 and IL-13, which promote macrophage differentiation into the M2 phenotype. The presence of M2 macrophages likely contributed to the increased recruitment of eosinophils and Th2 cells, worsening asthma. GMK treatment reduced the number of M2 macrophages in the lung tissues of asthmatic mice, demonstrating a favorable therapeutic effect.
Transcriptomics, the study of RNA expression and regulation in cells, enables the analysis of gene expression at the RNA level. To further elucidate the mechanistic effects of GMK in treating allergic diseases, transcriptomic studies were conducted to investigate its modulatory mechanisms at the genetic level. The results revealed that 2,872 genes were upregulated and 2,164 genes were downregulated in the lung tissues of HDM-induced asthmatic mice compared to controls. These DEGs were primarily enriched in functions related to the immune system and signaling molecule interactions, including cytokine-cytokine receptor interactions, Th17 cell differentiation, and the differentiation of Th1 and Th2 cells. Previous studies investigating the pharmacodynamic effects of GMK in HDM-induced models have highlighted a Th2-type inflammatory response and imbalances in the Th1/Th2 and Th17/Treg cytokine networks, suggesting that GMK may regulate the differentiation of Th1, Th2, and Th17 cells to control AA effectively. Following GMK intervention, a total of six DEGs were identified when comparing the treatment group to the model group, with four genes upregulated and two genes downregulated in the treatment group. Notably, the small number of DEGs identified between the model and treatment groups emphasizes the importance of the Igkv1-110 and Wnt2 genes, which were differentially expressed in both the model vs. control and treatment vs. model comparisons. This suggests that these genes play a relatively significant role in the modeling and treatment processes. Previous studies have shown that polymorphisms in Wnt genes such as WISP1 and WIF1 are closely associated with lung function in asthma. Moreover, several Wnt genes, including Wnt3A, have been positively correlated with Th2-type inflammation in asthma patients [37–39]. This finding is consistent with the HDM-induced asthma model, which reflects a Th2 dominance and elevated levels of Th2 inflammatory factors, indicating that GMK intervention may mitigate the Th2-type immune response. Furthermore, the KEGG functional enrichment analysis of the DEGs between the treatment and model groups revealed that these genes were predominantly enriched in several signaling pathways, including the Wnt signaling pathway, chemokine signaling pathway, and mTOR signaling pathway. Notably, among these pathways, only the Wnt signaling pathway exhibited a P-adjusted value of less than 0.05, suggesting that GMK likely exerts its effects on asthmatic mice by modulating the Wnt signaling pathway.
Transcriptomic studies have revealed that most of the DEGs in the lung tissues of asthmatic mice, compared to normal mice, are significantly enriched in signaling pathways associated with the immune system and signal transduction. Notably, pathways related to immunoglobulin A (IgA) were found to be enriched, suggesting the involvement of the intestinal immune network. This indicates that changes in the intestinal immune system of the mice may warrant further investigation. The intestinal tract houses the body’s largest immune system, containing a vast array of intestinal flora that can be classified into beneficial bacteria, conditionally pathogenic bacteria, and pathogenic bacteria based on their functions. The gut microbial genome contains over 3 million genes, approximately 100 times larger than the human genome, which has led to the characterization of the intestinal flora as the “second genome” of the human body [40]. The interaction between the intestinal flora and the host forms the intestinal microecology, playing a critical role in metabolism and the regulation of immune responses. Imbalances in this microecology can influence the onset and progression of numerous diseases. Given that the mucosal epithelia of both the gut and the respiratory tract originate from the endoderm, the structural similarities between the intestinal and respiratory mucosa, derived from their shared embryonic origin, underpin their functional parallels [41]. Although it was once believed that healthy lungs were sterile, recent studies have revealed the presence of specific microbiota residing in the lungs. These findings indicate bidirectional communication between the lungs and the gut, mediated by the microbiota. The existence of a “gut-lung axis” suggests that gut flora can influence the development and progression of lung diseases [42]. Wang et al. demonstrated that adult asthmatics exhibit a higher abundance of Clostridium species and a lower abundance of E. faecalis in their intestines [43]. Furthermore, early-life imbalances in gut flora have been linked to an increased risk of developing asthma later in life. A human birth cohort study found that Ecuadorian children with higher relative abundances of Streptococcus and Anaplasma species, and lower relative abundances of Bifidobacterium and Ruminococcus species in their fecal samples at three months of age, were at greater risk of experiencing atopy and wheezing by the age of five [44]. In addition to specific gut bacteria, the diversity of the microbiome plays a significant role in the pathogenesis of asthma. Low alpha diversity and reduced levels of certain gut commensal genera during the first year of life have been associated with an increased risk of respiratory diseases, particularly asthma, later in life [45]. Therefore, examining the abundance and diversity of gut microorganisms in asthma patients and analyzing how these factors differ based on age, disease stage, pharmacological interventions, and other influencing variables could provide valuable insights. Targeting characteristic gut microbiota for therapeutic intervention may offer promising avenues for the clinical diagnosis and management of AA. The sequencing results revealed significant differences in the composition of the intestinal microbiota among the control, model, and treatment groups. At the OTU level, the species richness and diversity of the intestinal flora in HDM-induced asthmatic mice were higher than those in the control group. In comparison to the model group, the intestinal microbiota of mice treated with GMK showed an increase in species richness but a decrease in species diversity. This suggests that GMK treatment for AA may modulate the characteristic intestinal flora, contributing to its therapeutic effects. The species composition of intestinal microorganisms in each group was analyzed, and at the phylum level, the dominant taxa in the intestines of mice in the control, model, and treatment groups were Firmicutes, Bacteroidota, and Actinobacteria, respectively. In the model group, the abundance of Firmicutes decreased, while the abundance of Bacteroidota increased compared to the control group. An imbalance in the ratio of Firmicutes to Bacteroidota (F/B ratio) has been associated with various pathological responses. It was demonstrated that the lung function index, specifically the one-second forced expiratory volume (FEV1), was significantly associated with the phyla Firmicutes and Bacteroidota at the phylum taxonomy level. Moreover, the relative abundance of Bacteroidota and Firmicutes phyla in the gut of adult asthmatics was found to be much lower than that of healthy individuals [46]. In our study, the trend of a lower F/B ratio in asthma was reversed following GMK treatment. Although we do not have data on the detection of gut microbiota metabolites, such as short-chain fatty acids (SCFAs), to explain the specific mechanism of the "gut-lung axis", it is plausible that microbial metabolites like SCFAs (acetate, propionate, and butyrate), which are influenced by the F/B ratio and possess immunomodulatory properties, could serve as key mediators in this "gut-lung axis" communication [47]. SCFAs have been reported to enter systemic circulation and influence distant organs, including the lungs, potentially modulating inflammatory responses and signaling pathways, such as Wnt [48]. For instance, butyrate has been shown to regulate histone deacetylase (HDAC) activity and influence Wnt signaling in various contexts [49, 50]. Furthermore, sample-level cluster analysis, PCA, and PCoA of the gut microbial composition in each group of mice revealed significant differences between the HDM-induced asthmatic mice and the control group, with clear segregation observed. After GMK treatment, the intestinal microbial structure of the mice exhibited greater similarity to that of the control group, indicating a trend toward normalization. Additionally, we identified the family Erysipelotrichaceae as a bacterial taxon present in all three groups of mice. Erysipelotrichaceae is associated with immune activation, and research has demonstrated a positive correlation between its abundance and TNF-α levels. This suggests that Erysipelotrichaceae may be a characteristic component of the intestinal flora in cases of AA. However, further studies are necessary to confirm this association. As a multi-target therapy, GMK may possess unique advantages over single-target Western drugs in regulating complex immune networks. This potential provides a basis for developing precision treatment strategies for asthma based on microbiome or immune status.
β-catenin is a pivotal signaling molecule in the evolutionarily conserved Wnt/β-catenin signaling pathway, which plays a critical role in regulating lung development during the embryonic period. In animal models, abnormal expression of β-catenin disrupts normal lung morphology, impairs peripheral lung formation and differentiation, and promotes the development of conducting airways [51]. Glycogen synthase kinase-3β (GSK-3β), a subtype of the serine/threonine protein kinase GSK-3, forms a multiprotein complex with casein kinase 1 (CK1), adenomatous polyposis coli protein (APC), and axin to phosphorylate β-catenin. This phosphorylation negatively regulates β-catenin accumulation in the cytoplasm, maintaining its levels at low concentrations. Upon activation of the Wnt pathway, the phosphorylation of β-catenin in the cytoplasm is inhibited, resulting in β-catenin accumulation and stabilization. It then translocates to the nucleus, where it binds to TCF/LEF transcription factors and regulates the expression of downstream target genes. Dysregulation of the Wnt/β-catenin pathway can induce EMT, promote smooth muscle cell hyperplasia and hypertrophy, subepithelial airway fibrosis, and extracellular matrix deposition—factors contributing to airway remodeling in asthma. At the genetic level, a genome-wide microarray analysis by Choy et al. revealed that several Wnt genes, including Wnt3A, are positively associated with Th2-type inflammation in asthma patients [52]. This observation aligns with the Th1/Th2 imbalance noted in the HDM-induced asthma model, characterized by elevated expression of Th2 inflammatory factors. GMK intervention has been shown to mitigate this Th2-type immune response, reducing the levels of these inflammatory factors. Although abnormalities in the Wnt/β-catenin signaling pathway are observed in asthma, it cannot be universally concluded that activation of the Wnt pathway results in increased β-catenin levels. The expression of the Wnt/β-catenin pathway varies across different asthma models. In the OVA-induced acute asthma mouse model (up to 4 weeks), a reduction in β-catenin expression is observed, which may be associated with diminished β-catenin activity during the allergen challenge period, protecting the host from excessive β-catenin-induced physiological responses. Moreover, inhibiting β-catenin degradation with lithium chloride (LiCl) has been shown to alleviate inflammation and hyperreactivity in allergic airways [53–55]. Conversely, in OVA-induced chronic asthma mouse models (10 weeks or longer), elevated levels of β-catenin have been reported. This increase may result from heightened β-catenin activation and expression, correlating with the progressive severity of airway remodeling observed with prolonged allergen exposure. Suppressing β-catenin expression with siRNA reduces epithelial fibrosis and smooth muscle proliferation, thereby mitigating airway remodeling [56]. Additionally, Koopmans et al. found that β-catenin expression in the OVA-induced acute asthma model did not differ significantly from the normal group. However, despite the overall β-catenin expression remaining relatively unchanged, the researchers noted that protein binding mediated by β-catenin may still be affected [57]. The results of this study indicate that, compared to the control group, the expression level of Wnt3a protein in the lung tissue of HDM-induced asthmatic mice was elevated, whereas the relative protein expression levels of β-catenin and p-GSK-3β were significantly reduced. This suggests that in this acute asthma model, the increased expression of Wnt3a likely inhibits the phosphorylation of GSK-3β, leading to a transient decrease in the accumulation of β-catenin in the cytoplasm. This downregulation of β-catenin may help protect the organism from inflammatory damage. Following treatment with GMK, the relative protein expression level of Wnt3a decreased, while the expression levels of β-catenin and p-GSK-3β increased. These findings imply that GMK treatment can reduce Wnt3a expression and promote GSK-3β phosphorylation, facilitating β-catenin accumulation. This study demonstrates that GMK mitigated HDM-induced airway inflammation, slowed airway remodeling, and reduced AHR by modulating the Wnt/β-catenin signaling pathway in HDM-induced AA mice.
The primary innovation of this study is the exploration of the mechanisms by which GMK treats AA, advancing our understanding of the underlying processes involved in managing allergic conditions in clinical practice. This approach aligns with the TCM principle of "treating different diseases with the same therapy." Furthermore, the mechanisms of GMK intervention in AA were elucidated through transcriptomic and microbiomic studies at multiple levels. The pathways enriched from the DEGs in the transcriptomic analysis provided additional insights into the molecular mechanisms underlying GMK’s effects, making the study more focused and directed. Notably, the Wnt signaling pathway, identified as a key pathway in the transcriptomic analysis, was examined to investigate the role of the Wnt/β-catenin signaling pathway in mediating the Th1/Th2 immune response within the AA model. This investigation aimed to clarify how GMK intervenes in AA. However, the study does have some limitations. First, our findings specifically focus on asthma mechanisms in females, a population with higher clinical prevalence and severity [30]. The absence of male controls was necessary to prevent hormonal confounding (e.g., testosterone-associated elevation of FEV1/FVC masking asthma severity in men [30], though this limits the generalizability of sex-based differences. Future studies will prioritize male models in pharmacokinetic investigations. Second, while multiple key signaling pathways involved in GMK’s intervention in AA were identified through transcriptomic analysis, only one of these pathways was experimentally validated. Third, our study lacks data on the detection of gut microbiota metabolites, such as SCFAs, to elucidate the specific mechanisms of the "gut-lung axis." Future investigations will focus on verifying causality through fecal microbiota transplantation and analysis of microbial metabolites (e.g., SCFAs), which will uncover the underlying molecular mechanisms. Fourth, the study did not validate the downstream targets within the Wnt/β-catenin signaling pathway. Future research could focus on further elucidating the Wnt/β-catenin signaling pathway or experimentally validating other identified signaling pathways.
Conclusion
This study suggests that GMK modulates the Wnt/β-catenin pathway, likely serving as a key mechanism for alleviating airway inflammation and the type 2 immune response in HDM-induced asthmatic mice. This conclusion is supported by pharmacological studies, lung tissue mRNA sequencing, gut microbiome analysis, and in vivo experimental validation. These findings reveal potential mechanisms by which GMK exerts therapeutic effects and provide scientific evidence for its potential clinical applications.
Supplementary Information
Additional file1 (DOCX 29394 kb)
The reference list from the paper itself. Each links out to its DOI / PubMed record.
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