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. 2024 Aug 12;398(2):1597–1612. doi: 10.1007/s00210-024-03353-8

Network pharmacology analysis of the Huangqi-Gancao herb pair reveals quercetin as a therapeutics for allergic rhinitis via the RELA-regulated IFNG/IRF1 axis response

Yongjun Deng 1, Limin Shen 1, Huilan Zhu 1, Yanying Zhou 1, Xin Hu 2,✉
PMCID: PMC11825621  PMID: 39133272

Abstract

Despite the complexity of allergic rhinitis (AR) pathogenesis, no FDA-approved drug has been developed to achieve optimal therapeutic effects. The present study explored the efficacy and mechanism of Huangqi (Hedysarum Multijugum Maxim)-Gancao (Glycyrrhizae Radix et Rhizoma or licorice) herb pair in treating AR by network pharmacology and experimental approaches. The bioactive ingredients of Huangqi and Gancao were identified and used to predict the targets of these herbs in AR and generate the pharmacological network. Ovalbumin (OVA)-induced AR mouse model was established to assess the anti-AR effect of the Huangqi decoction (HQD) prepared based on both herbs. We identified 90 active ingredients of the Huangqi-Gancao pair, targeting 69 AR-related genes. Quercetin (QUE) was identified as the hub ingredient of this pair, with 57 targets in AR. The protein–protein interaction (PPI) network analysis and molecular docking revealed IL1B, TNF, STAT1, IL6, PTGS2, RELA, IL2, NFKBIA, IFNG, IL10, IL1A, IRF1, EGFR, and CXCL10 as important targets of QUE in AR treatment. Experimentally, QUE or HQD significantly alleviated the AR-induced histopathological changes, AR symptoms, and IgE level and counteracted AR-induced expression changes of IFNG, IRF1, RELA, and NFKBIA. These effects were promoted by the NF-kB inhibitor helenalin, indicating that HQD and QUE counteracted AR in mice by regulating the IFNG/IRF1 signaling via the NF-κB pathway in AR mice. These findings shed light on the efficacy of the constituents of Huangqi-Gancao pair, their potential targets, and the molecular mechanisms of HQD in treating AR, which could advance the development of tailored therapeutic interventions for this disorder.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00210-024-03353-8.

Keywords: Allergic rhinitis, Network pharmacology, Huangqi, Quercetin, NF-κB signaling, IFNG/IRF1 signaling

Introduction

Allergic rhinitis (AR) is an allergic disease of the nasal mucosa caused by various factors (Baroody et al. 2008; Peroni et al. 2012; Segboer et al. 2013; Steelant et al. 2018; Struß et al. 2020). In addition to a substantial economic burden, AR alters the quality of life of patients (Dalal et al. 2008; Hellgren et al. 2010; Wang et al. 2016; Tkacz et al. 2021). To date, no practical or efficient curative technique for AR has been formulated so far, urging the demand for safe and efficient natural candidate drugs. Consequently, in-depth investigations are critically needed to develop potent drugs from natural sources for AR.

For centuries, traditional Chinese medicine (TCM) has been used to treat a range of human diseases, including AR. For example, the possible therapeutic effect of acupuncture for AR treatment has been reported in numerous studies (Sun et al. 2016; Shou et al. 2020). The efficiency of herbal medicines from TCM in the treatment of AR has also been reported, suggesting the importance of developing new TCM drugs for AR (Yang et al. 2021; Qu et al. 2022). Hedysarum Multijugum Maxim. (Huangqi in Chinese) is a Chinese herbal medicine (CHM) comprising the root of the herbaceous plant Astragalus (Radix Astragali) (Chen et al. 2020). Huangqi is known for its antioxidant properties and holds promise as a potential treatment for many human diseases, including ulcerative colitis (Zhang et al. 2022), rhinitis (Geng et al. 2021), and seasonal AR (Matkovic et al. 2010). The effect of Huangqi in AR has been noted (Bing et al. 2019). Earlier reports demonstrated that Astragalus polysaccharide, a biologically active substance, relieves immune response in AR by enhancing the instability in Treg/Th17 mediated by NF-kB (He et al. 2022). Astragalus polysaccharides can also hinder AR in rats by suppressing NLRP3 inflammasome and NOD2-regulated NF-κB signaling (Xu et al. 2021). Alternative research demonstrated that the Huangqi constituent Astragaloside IV exerts protective effects against AR via controlling T-box in T cells/GATA-3 and forkhead box protein 3/retinoic acid-linked orphan nuclear receptor γt (Li et al. 2017). Huangqi has additionally been reported to be effective and safe in treating seasonal AR (Matkovic et al. 2010). Consequently, it would be critical to develop formulations relying on Huangqi, either solely or in combination with alternative organic substances, and to investigate the basic mechanism for improved and controlled AR treatment.

Gancao (Glycyrrhizae Radix et Rhizoma or licorice), another TCM herb, has been studied and well-documented. Gancao has been utilized in the control of many diseases, encompassing respiratory and urinary tract infections, dyspepsia, gastritis, peptic ulcers, kidney stones, Alzheimer’s disease, and acne vulgaris (Chen et al. 2019; Wang et al. 2023; Xie et al. 2024; Yang et al. 2024). Through systems pharmacology, Gancao active ingredients with therapeutic effects via regulation of various signaling pathways, encompassing the PI3K-Akt pathway, have been pinpointed (Pei et al. 2023). It is additionally recognized that the combination of Gancao with alternative TCM formulations is effective for curing various diseases and regulating various biological processes and inflammatory reaction pathways (Hong et al. 2023). In a previous study, the treatment of AR through the utilization of a preparation composed of black cumin, Gancao, anise, and black tea was conveyed, and its effect was achieved via balancing the activity of helper T cells in the lung (Liao et al. 2021). Earlier research relying on in vitro, in vivo, and clinical trials applying Gancao extract for nasal lavage has proven that it is a natural and effective treatment method for AR (Chang et al. 2021). Thus, combining Huangqi and Gancao could potentially increase the efficiency of each of these drugs in a synergistic manner. Nonetheless, additional exploration is needed to thoroughly understand the curative potential of the Huangqi-Gancao pair functioning as a safe and secure substitute for managing AR. Recently, network pharmacology and molecular docking have developed into clear potent algorithmic procedures in drug discovery and are employed to assist in the design of innovative therapies for numerous diseases (Liang et al. 2019; Yao et al. 2020; Cheng et al. 2022; Cong et al. 2023; Liu et al. 2023; Zhang et al. 2023). By combining different data sources, both methodologies can offer more comprehensive insights into the potential therapeutic targets and active compounds of natural products (Liang et al. 2019; Yao et al. 2020; Cheng et al. 2022; Cong et al. 2023; Liu et al. 2023; Zhang et al. 2023) such as Huangqi and Gancao.

Our current study utilized system pharmacology in the finding of target genes of the bioactive compounds of Huangqi and Gancao for AR. This included the utilization of integrated data sources to build and examine herb drug-target and protein–protein interactions, functional enrichment analysis, and experimental confirmation in vivo based on the mouse model of AR. The findings discovered that quercetin (QUE), a key biologically efficient constituent discovered in Huangqi and Gancao, as well as the decoction made by combining both plants, was efficient as AR remedy, which is valuable for establishing innovative therapeutic strategies for AR. Moreover, our study explored the molecular mechanisms of Huangqi and Gancao combination and QUE in AR treatment and found that Huangqi-Gancao pair decoction and QUE can decrease inflammation, regulate essential cytokines, and enhance the expression of crucial immune response regulators and suggest that HQD, especially its ingredient QUE, holds promise for treating AR.

Materials and methods

Retrieval of main bioactive ingredients

The TCM system pharmacology database and analysis platform (TCMSP, https://tcmsp-e.com) was searched to obtain the active ingredients of Huangqi (Hedysarum Multijugum Maxim) and Gancao (Glycyrrhizae Radix et Rhizoma). The screening of the bioactive ingredients was according to the optimal toxicologic ADME criteria, which requires oral bioavailability (OB) and drug-like features (DL) of at least 30% and 0.18, respectively. OB stands for the amount of the active substances that get into the whole blood after the biological absorption by the body. DL properties comprise physicochemical and pharmacokinetic features such as permeability, metabolic stability, and solubility profile similar to that identified for known effective drugs. By ranking the compounds following the OB and DL features, it is possible to find the candidates that hold the possible therapeutic effects during drug discovery.

Identification of potential targets of Huangqi and Gancao and AR-related genes

To obtain the full names of the targets of the bioactive ingredients of Huangqi and Gancao, we used the TCMSP database. The UniProt database contains information on the protein names and their gene symbols, which allows retrieving gene symbols by search using the full target protein names as keywords. Thus, the protein names of target proteins were used to search in the UniProt database (www.uniprot.org) and retrieve the gene symbols for subsequent analysis.

To screen genes associated with AR, we conducted a keyword search for “allergic rhinitis” in various databases, namely GeneCards (http://www.genecards.org/), DrugBank (https://www.drugbank.ca/), OMIM (https://omim.org/), DisGeNET, and PharmGkb. The use of these databases, besides TCMSP, provided a more comprehensive list of genes related to AR because integrating data from these supplementary databases helped uncover additional AR-related genes that are not reported in TCMSP. The lists of genes obtained from different databases were combined, and duplicated ones were removed to obtain a unique list of AR genes.

Compound-gene-protein–protein interaction (PPI) network construction

To construct the drug-gene target network, we curated and integrated data from different databases by mapping drugs to their respective gene targets to obtain the drug-gene association network. To generate the protein–protein interaction (PPI) networks, we uploaded the sets of proteins to analyze into the online STRING database (https://string-db.org/) to build the PPI network. The network construction parameters were set as follows: input type, multiple proteins; species, “Homo sapiens”; and a confidence score of 0.4, which is defined as a moderate confidence level in the predicted interaction, allowing for the inclusion of potential interactions while filtering out weaker, less reliable associations. The PPI network was then downloaded in TSV format and merged with the herb-bioactive ingredient-target network to obtain the herb-bioactive ingredient-PPI network. The herb-bioactive ingredient-PPI and the PPI networks were visualized and analyzed using Cytoscape 3.8.0 (https://cytoscape.org/), which was used for visualization using the “grid layout” or the “apply preferred layout” algorithms. The “style” menu was used to change the colors, fonts, and size of the labels and nodes. The “analyze network” tool in Cytoscape was used without applying the force-directed layout algorithm. The network analysis output contained, among other measures, the degree (number of direct connections a node has in the network), betweenness centrality (the extent to which a node lies on the shortest paths between other nodes), and closeness centrality (which measures how close a node is to all other nodes in the network). The nodes with high centrality are central and vital in the network, while those with high betweenness are “hubs” that connect various parts of the network. Also, nodes involved in many inter-node relationships, especially those with high closeness centrality, can easily interact with other nodes in the network. The MCODE plugin was used to detect hub clusters.

Functional analysis of target genes

Enrichment analysis was conducted using the ClusterProfiler package in R to obtain the enrichment terms of target genes. The enrichment analysis was done based on the “org.Hs.eg.db” database. Terms with a p-value below 0. 05 were considered significant and were screened as enriched terms.

Molecular docking

The data, including the source files for molecular structure and images of ingredients, are stored in the TCMSP database. The MOL2 files of the identified key ingredients were downloaded from the TCMSP database by searching with keywords corresponding to these ingredients. The PDB files and images of their structures for the hub targets were acquired from the PDB website. Before docking, the original ligands and water molecules were removed, and polar hydrogens were added to the target proteins using Discovery Studio (version 2016), which allowed the preparation of protein structures via removing undesired molecules such as ligand proteins or ligand small molecules, and water molecules, and adding key hydrogens to facilitate an accurate and reliable molecular docking. The molecular docking simulation with the default parameters was performed using iGEMDOCK (version 2.1).

Preparation of Huangqi decoction

Astragali Radix and Glycyrrhizae Radix et Rhizoma were authenticated according to the Chinese Pharmacopoeia (2015 version) (Chinese Pharmacopoeia Commission 2015). The extract powder of the Huangqi decoction (HQD), expertly prepared by Jiangyin Tianjiang Pharmaceutical Co., Ltd. in Jiangsu, China, contains 6 g of Radix Astragali and 1 g of Radix Glycyrrhizae (Wu et al. 2017). The 6:1 ratio for Astragali Radix and Glycyrrhizae Radix et Rhizoma used in the study was optimized based on the TCM theory for the formulation of mixed herbs, which explains the reason for using the ratio of 6:1 for the herbs because the ratio plays a significant role in the improvement of the action of the mixed herb pair. Thus, herein, these herbs were blended in a 6:1 ratio and then methodically extracted using boiling water. The aqueous extract was vacuum-dried (60 °C) to obtain the powdered extract.

Ovalbumin-induced AR mouse model and drug treatments

The OVA-induced AR mouse model is the most traditionally used in research because of its characteristics, which closely replicate the pathological processes, including induction of AHR, TH2 responses, and inflammatory airway infiltration (Moldaver et al. 2014), of AR in humans, enabling scientists to examine the physiological and pathophysiological pathways of AR and assess the effectiveness of various treatments in a laboratory environment. Therefore, an OVA-induced AR mouse model was successfully developed in the present study. We obtained male BALB/C mice aged 6–8 weeks from Shanghai SLAC Laboratory Animal Co. Ltd. The mice were bred in an environment that was free of any specific pathogen. They were fed standardized sterile food and water, which had been sterilized by high-pressure steam and cooled to room temperature. All animal experiments were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Ethics Committee of Shanghai Pudong New Area Guangming Hospital of Traditional Chinese Medicine.

The mice were divided into control, AR, AR + HQD-Low (20 mg/kg), AR + HQD-High (50 mg/kg), AR + QUE, AR + helenalin, AR + helenalin + HQD-High (50 mg/kg), and AR + helenalin + QUE.

The inclusion of helenalin and QUE in the study groups was because QUE was identified in earlier steps as the main ingredient of Huangqi and Gancao, which had the highest number of targets in AR, and its hub targets belonged to the NF-kB signaling pathway. On the other hand, helenalin is known as an inhibitor of the NF-kB signaling pathway; thus, it was used to investigate the effect of the NF-kB signaling on AR and its involvement in the mechanism of action of QUE and HQD. The establishment of AR in mice was conducted in two stages, namely, the basic sensitization stage and the stimulation stage. In the basic sensitization stage, we injected intraperitoneally a mixture comprising 40 µg OVA (Grade V, Sigma, St. Louis, MO, USA) and 25 µL Imject™ Alum Adjuvant (Thermo Scientific, Rockford, MD, USA) three times on days 0, 7, and 14. Next, mice received treatment once daily with low and high doses of Huangqi decoction extract (20 or 50 mg/kg) and QUE (100 mg/kg body weight) by intragastric administration from day 15 to day 27. The dose of QUE (100 mg/kg body weight) was selected based on a previous study (Gao et al. 2020). QUE with a purity of at least 95% was purchased from Sigma in St. Louis, USA. Helenalin (Cayman Chemicals) was mixed with distilled water and 0.05% polysorbate 80 and given to mice by intraperitoneal injection at a dose of 25 mg of helenalin per kg of body weight every 2 days. Mice in the AR group received OVA dissolved in saline. Briefly, after being sensitized, mice were given daily treatment from day 21 to day 27. Following this, they were challenged with OVA by administering it intranasally at a concentration of 10 mg/mL (20 µL in each nasal cavity) 1 h after the treatment. The mice were then put to sleep using ether and sacrificed 24 h after the final OVA challenge.

Nasal symptoms

To assess any early allergic reactions, the number of nasal rubs, sneezes, and secretions was recorded for 15 min after the most recent OVA challenge.

HE staining

The nasal mucosa samples from AR mice were stained using the standard HE protocols. A 10% formalin solution-based fixation preceded paraffin embedding and dewaxing through a series of alcohols. For 5 min, hematoxylin (Sigma-Aldrich, H&E, HHS16) was applied, followed by a water rinse. For 3 min, eosin (Sigma-Aldrich, E4009-100ML) was applied, followed by another rinse with distilled water. Dehydration and mounting preceded microscopic observation, with a coverslip as the final touch.

Immunohistochemistry detection of RELA

The mice nasal mucosa tissue sections were processed to determine the RELA expression levels by using immunohistochemistry. Briefly, the nasal mucosa tissue was afterward paraffin-embedded followed by slicing it into 5-μm-thick sections. After deparaffinizing them, the sections were rehydrated, and antigen retrieval was performed with a citrate buffer. After the use of hydrogen peroxide to block endogenous peroxidase activity. Next, the sections were treated with RELA antibody and incubated at a temperature of 4 °C for 12 h. Subsequently, the sections were incubated with the secondary antibody tagged to peroxidase of horseradish peroxidase (HRP). Diaminobenzidine was used as a substrate to achieve precipitation of brown crystals at the areas expressing RELA. After the counterstaining with the hematoxylin was performed, the slices were processed and stained. Subsequently, they underwent examination under a microscope, and images were captured to analyze RELA expression.

Enzyme-linked immunosorbent assay (ELISA)

Mouse ELISA kits for IL-4, IgE, IL-5, TNF-α, IL-13, and IFNG were all purchased from Invitrogen (Shanghai, China) and used to detect the levels of these proteins in the nasal lavage fluid (NALF). Briefly, following the sacrifice, the trachea was opened, and a sterile saline solution of 1 mL was gently pumped into the nasal cavities using an 18-gauge catheter. The NALF was collected from the front nostril and centrifuged at 10,000 rpm for 10 min at 4 °C. The resulting supernatant was transferred to another tube and stored at a temperature of − 80 °C for subsequent analysis of cytokine levels. ELISA kits were used to detect the levels of the above proteins following the manufacturer’s instructions. The absorbance was detected using a microplate reader at the wavelength of 560 nm. Standard curves were used to quantify the levels of detected proteins in the NALF samples.

RT-qPCR

Total RNA was isolated from the nasal mucosa specimens using TRIzol RNA extraction reagent as per the manufacturer’s guidelines. The purity of the extracted RNA was checked with the NanoDrop Spectrophotometer (Thermo Fisher Scientific). Next, cDNA synthesis was achieved using the SuperScript IV Reverse Transcriptase Kit (Thermo Fisher Scientific). The cDNA samples were subsequently amplified by quantitative PCR using appropriate specific primers (Table 1). The PCR reactions were carried out in Applied Biosystems QuantStudio 7 Flex Real-Time PCR System. The mRNA expression levels were normalized to GAPDH as an internal reference gene. The comparative Ct method was used to compute the relative mRNA levels of the genes of interest.

Table 1.

Primers used in this study

Gene name Forward primer sequence (5′- > 3′) Reverse primer sequence (5′- > 3′)
Il4 5′-CACTTGCAAGCTTTTGCCCT-3′ 5′-AGCCAACAGCCTCCTGTATTG-3′
Il5 5′-CGTGGGGGTACTGTGGAAAT-3′ 5′-AGGGTCCCTGGGGAACTTAC-3′
Il13 5′-CTTGAGCCCAGGCACTTGTA-3′ 5′-TATGCTACCCGAGGGATGCT-3′
Tnf-α 5′-ACTGATGAGAGGGAGGCCAT-3′ 5′-CCGTGGGTTGGACAGATGAA-3′
Ifng 5′-ATCAAGCTGCCTCCCGTATG-3′ 5′-CTGTCTGCAGTGGGGAAACA-3′
Irf1 5′-AACAGGGGACCATCCTCCTT-3′ 5′-GATCGACGCATGTCAATGCT-3′
Rela 5′-AGTTCTGAAAGGGGAGGGAGA-3′ 5′-CACCCCTTAGTTTCACCGCA-3′
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Statistical analysis

The data obtained from the experiment were analyzed with the help of GraphPad Prism 9.0 software (v9.0, La Jolla, CA, USA). For statistical analysis, one-way ANOVA followed by Turkey’s test was employed to conduct multiple comparisons between the study groups. The values for all measurements were expressed as means ± standard deviation (SD). A P value of less than 0.05 was considered to indicate statistically significant results.

Results

Network pharmacology of Huangqi-Gancao pair identifies QUE as the key bioactive ingredient in AR treatment

A total of 20 and 92 bioactive ingredients were respectively screened from the TCMSP database as bioactive ingredients of Huangqi and Gancao according to the ADME criteria (OB > 30%, DL > 0.18) (Additional File S1). The prediction of target genes of each bioactive ingredient from the TCMSP database allowed the identification of 201 targets for Huangqi ingredients and 223 targets for Gancao ingredients (Additional Files S1 and S2). After appending and removing the sets of genes from OMIM, GeneCards, DrugBank, PharmGkb, and DisGeNET platforms, a total of 1232 AR-related genes were retrieved (Additional File S2). The overlap of the AR-related genes and the target genes of Huangqi and Gancao allowed the identification of 63 intersection genes between Huangqi, Gancao, and AR-related genes and 69 intersection genes between Gancao and AR-related genes (Fig. 1A). In total, 69 genes were identified as the targets of the Huangqi-Gancao pair in AR treatment (Fig. 1A). Based on these targets, we identified 13 and 83 ingredients of Huangqi and Gancao, respectively, as those targeting AR (Fig. 1B). After merging, 90 unique ingredients were identified as the bioactive ingredients of the Huangqi-Gancao pair that could have therapeutic effects against AR (AR-targeting ingredients) (Fig. 1B). In addition, among them, six compounds (MOL000354 (isorhamnetin), MOL000417 (calycosin), MOL000239 (jaranol), MOL000422 (kaempferol), MOL000098 (quercetin), and MOL000392 (formononetin)) were identified as common AR-targeting ingredients of both Huangqi and Gancao (Fig. 1B). The network of herb-bioactive ingredients-protein–protein interactions of the Huangqi-Gancao pair in AR treatment was constructed and visualized using the Cytoscape software (Fig. 1C). The network parameters for all of the nodes were depicted in Additional File S3, while those of the top ten key ingredients are depicted in Table 2. This pharmacological network indicated QUE as the hub ingredient of the Huangqi-Gancao pair in AR treatment (MOL000098, degree: 59, betweenness: 0.03636526, closeness: 0.610687023), followed by kaempferol (MOL000422, degree: 23, betweenness: 0.005581507, closeness: 0.533333333) that was also a potential bioactive ingredient in treating AR (Fig. 1C, Additional File S3). The most targeted genes were PTGS2 (degree: 142, betweenness: 0.261112237, closeness: 0.898876404), PPARG (degree: 116, betweenness: 0.119400755, closeness: 0.784313725), NOS2 (degree: 103, betweenness: 0.092360775, closeness: 0.730593607), and MAPK14 (degree: 85, betweenness: 0.054162449, closeness: 0.675105485) (Fig. 1C, Additional File S3). Functional enrichment analysis indicated that the 69 genes in the network were enriched in the biological processes (BP), mainly including positive regulation of cytokine production, response to molecules of bacterial origin, response to oxidative stress, and regulation of inflammatory response (Fig. 2, Additional File S4). The molecular function (MF) terms of DNA-binding transcription factor binding, cytokine activity, RNA polymerase II-specific DNA-binding, and cytokine receptor binding, while the most enriched cellular ingredient (CC) terms were membrane raft, membrane microdomain, and external side of plasma membrane (Fig. 2, Additional File S4). These genes were also mainly involved in numerous pathways, including TNF signaling, Th17 cell differentiation, HIF-1 signaling, inflammatory bowel disease, and IL-17 signaling pathways (Fig. 2, Additional File S4). These results indicated that the combination of Huangqi and Gancao is a potential therapeutic approach for AR treatment, and its effect may be driven by the regulation of inflammation and immune-related pathways.

Fig. 1.

Fig. 1

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Construction of Huangqi-Gancao pair ingredients-targets interaction network for AR treatment. A Ven diagram showing the intersection between the list of Huangqi target genes (predicted based on the ingredients screened based on the ADME criteria of OB greater than 30 and DL greater than 0.18), the list of Gancao target genes (predicted based on the ingredients screened based on the ADME criteria of OB greater than 30 and DL greater than 0.18), and AR-related genes. B Venn diagram showing the intersection of AR-targeting ingredients (having targets as AR-related genes), and Huangqi- and Gancao-related AR-targeting ingredients. C Herbs-ingredients-gene interaction indicating the interaction of Huangqi and Gancao targets in AR and the active ingredients of AR

Table 2.

Network parameters of the top 10 key ingredients of the Huangqi-Gancao pair

Ingredients Betweenness centrality Closeness centrality Degree Neighborhood connectivity
MOL000098 0.036365 0.610687 59 39.86441
MOL000422 0.005582 0.533333 23 52.17391
MOL000392 0.001345 0.507937 13 69.07692
MOL000497 0.000997 0.511182 13 68.69231
MOL004328 0.009187 0.511182 13 53.61538
MOL000354 0.001129 0.507937 12 67.16667
MOL000378 0.00308 0.509554 12 64.16667
MOL004959 0.00066 0.509554 12 71.5
MOL003896 0.001873 0.501567 11 69.81818
MOL005003 0.002196 0.507937 11 71
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Fig. 2.

Fig. 2

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Functional roles of Huangqi-Gancao target genes in AR. BP, biological process; MF, molecular function; CC, cellular component; KEGG, Kyoto Encyclopedia of Genes and Genomes

Protein–protein interaction network of QUE targets in AR and their function

Because QUE was identified as the common ingredient of Huangqi and Gancao with the greatest number of targets in AR treatment, its 57 targets in AR were used for generating the PPI network (Fig. 3A). The PPI network was composed of 784 edges and 56 nodes. The network diameter was 3, with a radius of 2 and an average number of neighbors of 28.00 (Fig. 3A). Clustering with MCODE identified 36 proteins as hub targets of QUE (yellow color) in the treatment of AR (Fig. 3A). Successive clustering analysis from the string database indicated that the important targets of QUE were IL1B, TNF, STAT1, IL6, PTGS2, RELA, IL2, NFKBIA, IFNG, IL10, IL1A, IRF1, EGFR, and CXCL10 (Fig. 3B). The data in Fig. 3C and Additional File S5 illustrates that the QUE targets were involved in various BP terms such as response to oxidative stress, response to molecules of bacterial origin, wound healing, response to lipopolysaccharide, regulation of apoptotic signaling pathway, regulation of inflammatory response, and positive regulation of cytokine production. Regarding CC, the predominant terms were membrane raft, membrane microdomain, and external side of plasma membrane (Fig. 3C, Additional File S5). The predominantly enriched MF terms were DNA-binding transcription factor binding, cytokine activity, cytokine receptor binding, and ubiquitin-like protein ligase binding (Fig. 3C, Additional File S5). Furthermore, the most enriched target KEGG pathways of QUE targets, as depicted in Fig. 3C and Additional File S5, included the lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and numerous inflammation-related pathways such as TNF signaling, NF-KB signaling pathway, Th17 cell differentiation, inflammatory bowel disease, and IL-17 signaling pathway.

Fig. 3.

Fig. 3

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QUE targets in AR treatment and their functional roles. A PPI shows the interaction between AR-related genes and QUE target genes. B Successive clustering for the identification of key hub targets of QUE. C Functional enrichment of QUE target genes in AR therapy

Molecular docking analysis

To confirm the interaction between QUE and AR-related genes, its 14 hub targets, IL1B, TNF, STAT1, IL6, PTGS2, JUN, PPARG, RELA, IL2, NFKBIA, IFNG, IL10, IL1A, IRF1, and MAPK1, were selected for molecular docking. VDWs can be calculated in terms of the added attractive and repulsive forces between the atoms of the protein and the ligand. A lower contribution of VDW energy implies that there is a better attraction between the atoms and, hence, a more stable complex. The energy values derived from the docking simulations show the stability of the protein–ligand complexes. Reduced energy values indicate that the protein–ligand interaction in the model is more likely to be favorable and less likely to change during the interaction. The hydrogen bonding (Hbond) contribution outlines the extent of hydrogen bond interactions between the protein and the ligand. It also means that there are more hydrogen bonds, and the interaction is much stronger in the molecule or system under comparison. Our docking results showed that the energy values ranged from − 121 for RELA to − 83.8233 for IL6, indicating that QUE had an excellent binding affinity for RELA and a highly favorable interaction with these proteins (Fig. 4A and B). The VDW values varied between − 81.64 for EGFR and 4764 for NFKBIA-MOL000098, respectively. IFNG-QUE, RELA-QUE, IRF1-QUE, and NFKBIA-QUE showed the highest values of VDW, indicating strong interactions between the studied proteins and QUE. RELA-QUE showed the lowest Hbond value (− 93), which indicated a significant implication of hydrogen bonding in the stability of the interaction (Fig. 4A and B). These results suggested that the RELA-QUE interaction exhibited the most favorable docking properties based on energy, hydrogen bonding, and VDW interactions. This confirmed it as a potential therapeutic active ingredient of the Huangqi-Gancao pair for AR treatment and showed that targeting RELA could be valuable for further studies or drug development.

Fig. 4.

Fig. 4

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Docking of QUE with key hub targets of QUE. A Interaction complexes of QUE and key hub targets of QUE. B Energy, VWD, and Hbong of complexes of QUE and key hub targets of QUE

HQD and its ingredient QUE alleviate AR via regulating RELA-regulated response of IFNG/IRF1 axis

To investigate the effect of HQD and the active ingredient QUE in the treatment of AR and the potential involvement of RELA, IFNG, and IRF1 in the underlying mechanism, a mouse model of AR was established and subjected to the treatment with different concentrations of HQD, QUE, and RELA inhibitor (Helenalin). HE staining was performed to analyze the histopathological changes of the nasal mucosa tissue (Fig. 5A). Compared to the control group, increased infiltration of inflammatory cells in the AR model group was observed (Fig. 5A). In addition, the treatment of AR mice with QUE or HQD significantly alleviated the AR-induced histopathological changes, and the effect of HQD was found to be dose dependent (Fig. 5A). Moreover, treating RA mice with helenalin inhibited the extent of infiltrating inflammatory cells (Fig. 5A). Interestingly, the combined treatment of helenalin with HQD or QUE further promoted the effect of Huangqi or QUE (Fig. 5A). In addition, we found that the number of sneezing episodes (Fig. 5B), the number of rubs on the eyes (Fig. 5C), nasal secretions (Fig. 5D), and the level of IgE (Fig. 5E) were all increased in the AR group compared to the control group. Moreover, the treatments with QUE, HQD, or helenalin significantly decreased the number of sneezing episodes, the number of rubs, the amount of nasal secretion, and the level of IgE compared with the model group, which implied relief of nasal irritability and hypersensitivity (Fig. 5B–E). However, the effect of the HQD or QUE on the number of sneezing episodes, the number of rubs, the amount of nasal secretion, and the level of IgE was significantly promoted by helenalin (Fig. 5B–E). These findings showed that the HQD and its active ingredient QUE have potential as treatments for AR symptoms.

Fig. 5.

Fig. 5

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HQD, QUE, and the RELA inhibitor alleviate AR-induced pathological changes. A H&E staining of nasal mucosa tissue of mice under different treatments. B The number of sneezing of mice under different treatments. C Number of rub of mice under different treatments. D Nasal secretion of mice under different treatments. E IgE detection in NAFL of mice under different treatments by ELISA. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001 among the compared groups

The qRT-PCR assay indicated that RELA was upregulated in the nasal mucosa of mice in the AR group (Fig. 6A). At the same time, the IFNG and its receptor IRF1 were downregulated compared to mice in the control group (Fig. 6A). Furthermore, we observed that HQD dose dependently decreased the mRNA expression of RELA but increased the mRNA expression levels of IFNG and IRF1 compared to the AR model group (Fig. 6A). Moreover, the treatment of AR mice with QUE also decreased the expression of RELA but increased the levels of IFNG and IRF1 (Fig. 6A). Similarly, the treatment of AR mice with helenalin decreased the expression of RELA but increased the expression of IFNG and IRF1 (Fig. 6A). Further immunohistochemical analysis of RELA confirmed the effect of HQD, QUE, and helenalin on the protein expression of RELA (Fig. 6B).

Fig. 6.

Fig. 6

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HQD and QUE regulate the RELA/IFNG/IRF1 axis in AR mice. A qRT-PCR detection of RELA, IFNG, and IRF1 in different treatment groups. B Immunohistochemistry detection of RELA in different treatment groups. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001 among the compared groups

By utilizing qRT-PCR, we also measured the levels of inflammatory cytokines in the nasal mucosa (Fig. 7A). The findings revealed an increase in mRNA expression of IL-13, IL-4, TNF-α, and IL-5 in the nasal mucosa (Fig. 7A) of mice with AR compared to the control group. Moreover, treatment with the HQD, QUE, or helenalin resulted in a reduction in the levels of these mediators when compared to the AR model group (Fig. 7A). In addition, the combination with helenalin further promoted the effect of HQD and QUE (Fig. 7A). Furthermore, ELISA was used to detect the protein levels of IL-13, IL-4, TNF-α, IL-5, and IFNG in the NALF of mice. The protein levels of IL-13, IL-4, TNF-α, and IL-5 in different groups were similar to those observed in qRT-PCR (Fig. 7B). In addition, the protein level of IFNG in the NALF was decreased in AR but promoted by the treatment with HQD, QUE, or helenalin (Fig. 7B). This indicated that HQD or QUE may have an effect on alleviating inflammation associated with AR via regulating the RELA/IFNG/IRF1 axis.

Fig. 7.

Fig. 7

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HQD and QUE regulate the inflammation and immunity in AR mice. A qRT-PCR detection of IL-4, IL-5, TNF- and immunity in AR mice.groups. B ELISA detection of IL-4, IL-5, TNF- ofof IL-4, IL-5, TNF- and immunity in AR mice.groups. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001 among the compared groups

Discussion

AR is a serious disease encountered in the human population, and its prevention and treatment options are limited. It is, therefore, important to find and validate candidate drugs for this disease. Past research indicated the potential of Huangqi and Gancao in treating AR. However, the therapeutic mode of action of the Huangqi-Gancao herb pair in AR has to be clarified. Herein, we intend to pinpoint the bioactive ingredients of HQD in AR therapy by network pharmacology and examine their possible molecular mechanism. We screened 90 bioactive ingredients in the Huangqi-Gancao pair, targeting 69 genes related to the pathogenesis of AR. Among these bioactive ingredients, QUE was found to have the highest number of targets (57 targets) in AR treatment, showing that QUE may be used as a key drug for treating AR. Based on the 61 QUE targets, the PPI network indicated IL1B, TNF, STAT1, IL6, PTGS2, JUN, PPARG, RELA, IL2, NFKBIA, IFNG, IL10, IL1A, IRF1, and MAPK1 as the key hub genes. The targets of QUE were genes primarily participating in numerous biological processes, including response to oxidative stress, wound healing, and regulation of apoptotic signaling pathway; these genes were also involved in inflammation-related processes and pathways such as TNF signaling, NF-kB signaling, inflammatory bowel disease, and IL-17 signaling pathways. Experimentally, we demonstrated that both the decoction of the Huangqi-Gancao herb pair (HQD) and QUE alleviate AR-associated symptoms and processes by regulating the RELA/IFNG/IRF1 axis. Our study is the first pharmacological network analysis demonstrating the molecular mechanism of the effectiveness of the Huangqi-Gancao pair against AR, shedding light on the mode of action of Huangqi and QUE in the treatment of AR.

Studies on the capacity of TCM as a remedy for AR have yielded optimistic results. Data indicates that incorporating TCM practices, such as herbal remedies and acupuncture, can substantially improve AR symptoms, which encompass congestion and runny nose (Sun et al. 2016; Shou et al. 2020; Ding et al. 2021). The effect of TCM treatments can offer persistent relief, with symptoms subsiding for up to a year following treatment (Yang et al. 2021; Zhang et al. 2023). Interestingly, TCM remedials show low side effects and are well received by patients (Zhang et al. 2020). Thus, the application of TCM may be a safe and robust complementary approach for alleviating AR (Zhang et al. 2020). Increasing research implies that Huangqi may hold significant promise in managing AR symptoms. Indeed, a previous study indicated that Huangqi contains anti-allergic ingredients (Lv et al. 2017). Another study discovered that Huangqi extract significantly alleviated nasal symptoms, including sneezing and congestion, in patients with AR (Matkovic et al. 2010). These studies indicate that Huangqi might be a valuable supplement to conventional AR treatments. In addition, emerging evidence indicates that Gancao and some of its ingredients exert anti-inflammatory and anti-allergic effects (Chen et al. 2017; Li et al. 2018; Chang et al. 2021; Liao et al. 2021), with ill-defined mechanism of action in AR treatment. However, network analysis of the Huangqi-Gancao pair in human diseases such as AR has yet to be conducted. In this study, we conducted network pharmacology to elucidate, for the first time, the mechanism of the Huangqi-Gancao pair in AR. We found that the therapeutic mechanism of this herbal combination is possibly driven by active ingredients that target genes mainly regulating the inflammation-related processes and pathways, oxidative stress, and apoptosis. During AR, pathogenesis, inflammation, and oxidative stress are associated with changes in signal transduction pathways (Qin et al. 2024). The early stage of AR is characterized mainly by inflammation. In contrast, in the late stage, eosinophils emit a series of pro-inflammation mediators that are involved in the pathogenesis of AR (Qin et al. 2024). In the present study, we showed that, by regulating oxidative stress and inflammation-related pathways, the Huangqi-Gancao pair and the constituent bioactive ingredients of these herbs may alleviate AR symptoms (Qin et al. 2024), which is in agreement with previous studies exposing that anti-inflammation and antioxidant therapies can lower the risk of AR (Qin et al. 2024). In addition, apoptosis is among the factors that result in enhanced oxidative stress which is involved in the regulation of various signaling pathways that, in the end, cause AR (Qin et al. 2024). Moreover, it has been broadly believed that stimulation of eosinophil apoptosis has the potential as a substantial pharmacological method to treat AR (Li et al. 2017; Yu et al. 2017). In the present study, we found that the main ingredients of the Huangqi-Gancao pair in AR treatment could target the apoptotic signaling pathway, potentiating the Huangqi-Gancao pair and QUE as candidate therapeutics for AR and indicating the potential of these treatments in apoptotic death of eosinophils. Further studies aimed at modulating this pathway with the Huangqi-Gancao pair and QUE is highly encouraged.

In the in vivo experiments, we found that HQD counteracted the action of AR on the levels of cytokines such as IL-13, IL-4, TNF-α, IL-5, and IFNG, confirming its anti-inflammatory and immune-regulating properties. This finding is supported by a previous investigation implying that Huangqi-Guizhi-Wuwu decoction is efficient in regulating the differentiation of CD4( +) T cells and preventing the progression of experimental autoimmune encephalomyelitis in mice (Xu et al. 2024). However, our study is the first to demonstrate the therapeutic effect of HQD in AR.

QUE, a flavonoid compound, is commonly encountered in various fruits and vegetables. Through consistent research findings, QUE has been shown to offer therapy and protection properties against various human conditions. In the present study, we found that QUE is a common active ingredient of Huangqi and Gancao with the highest number of targets as AR-related genes. Experimentally, the effect of QUE was similar to that of HQD on AR-associated inflammation and pathological changes. These anti-AR properties of QUE have been reported in previous studies. In fact, it was previously reported that AR can be alleviated by QUE, which contributes to restoring the balance of the Treg/Th17 cells and Th1/Th2 cells (Ke et al. 2023). This previous finding was also corroborated by our functional analysis of the targets of QUE, which showed that these target genes were involved in Th17 cell differentiation. A clinical study indicated that repeated oral intake of a QUE-containing supplement can effectively reduce allergic reactions (Yamada et al. 2022). The PPI network established from the 56 QUE targets has revealed important hub genes, including RELA, JUN, NFKBIA, IRF1, and IFNG. RELA and JUN coordinate gene expression, whereas NFKBIA serves as a restrictive agent in the NF-κB pathway. IRF1 regulates immune responses, while IFNG participates in innate and adaptive immune responses. The targeting of these critical genes implies that QUE may influence their activity to produce therapeutic effects through associated pathways.

The NF-κB pathway is crucial for immune homeostasis. The RELA subunit of NF-κB is responsible for the transcriptional activation of pro-inflammatory genes, and its activity is tightly controlled by various mechanisms to prevent excessive inflammation and ensure proper immune responses. RELA has been reported to be significantly involved in AR. For instance, it was reported that miR-302e hinders allergic inflammation by inhibiting NF-kB activation by targeting RELA (Xiao et al. 2018). Moreover, fucoxanthin was reported to alleviate AR via RELA and STAT3 signaling (Li et al. 2019). In the present study, the targets of QUE were enriched in the NF-kB signaling pathway, while docking results indicated a strong interaction between QUE and RELA. Our study revealed that treatment with HQD, QUE, and RELA inhibitor helenalin resulted in a considerable decrease in AR symptoms in mice. These treatments also inhibited the expression of proinflammatory cytokines in the nasal mucosa and activated the IFNG/IRF1 axis. The results point towards HQD, QUE, and RELA inhibition as potentially effective anti-inflammatory interventions for AR, which needs further clinical trials for validation.

The investigation presents some limitations. Indeed, we acknowledge that the present investigation chiefly focused on HQD and QUE and did not take into account further possible TCM ingredients with potential efficacy in healing AR. In addition, we did not analyze the possible side effects or interactions with other drugs used for healing AR patients. Moreover, the investigation was based on experimental results from systems pharmacology and preclinical in vivo studies. It did not include medical trials evaluating the safety and efficacy of HQD and QUE in healing AR patients, which requires subsequent medical trials for evaluating the safety and efficacy of these ingredients in humans. Moreover, we did not analyze the content of QUE and other active ingredients in HQD, which needs to be done in future studies. Additionally, further research is encouraged to identify further potential TCM ingredients with efficient medicinal impacts for AR.

Conclusion

The present study proved the in silico and in vivo efficacy of the Huangqi-Gancao pair and its active ingredient, QUE, in curing AR. We identified 90 active ingredients from the Huangqi-Gancao pair and found that QUE was the main bioactive ingredient since it targeted the highest number (57) of targets as AR-related genes. This study also showed that HQD and QUE could modulate the IFNG/IRF1 signaling via the NF-κB pathway in mice with AR. These results suggest that the Huangqi-Gancao pair, particularly its active ingredient QUE, holds promise as a curative option for curing AR. Further investigation is necessary to validate the medical efficacy of the Huangqi-Gancao pair and QUE in the treatment of AR and to elucidate the mechanisms of action beneath their curative impacts.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 70 KB) (70.4KB, xlsx)
Supplementary file2 (XLSX 45 KB) (45.3KB, xlsx)
Supplementary file3 (XLSX 30 KB) (30.2KB, xlsx)
Supplementary file4 (XLSX 209 KB) (209.3KB, xlsx)
Supplementary file5 (XLSX 179 KB) (178.8KB, xlsx)

Acknowledgements

We are grateful to Pudong New Area Science and Technology Development Fund Special Project for Civil Livelihood Research for the financial support of this study.

Author contribution

YD contributed to the study conception and design. LS performed data collection. HZ and YZ performed data analysis. YD wrote the frst draft of the paper. XH commented on the frst draft and read and approved the fnal manuscript. The authors declare that no paper mill was used and that all data were generated in-house. The authors declare that all data were generated in-house and that no paper mill was used.

Funding

This study was supported by the Pudong New Area Science and Technology Development Fund Special Project for Civil Livelihood Research (PKJ2022-Y29).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Consent for publication

Not applicable because publicly available.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (XLSX 70 KB) (70.4KB, xlsx)
Supplementary file2 (XLSX 45 KB) (45.3KB, xlsx)
Supplementary file3 (XLSX 30 KB) (30.2KB, xlsx)
Supplementary file4 (XLSX 209 KB) (209.3KB, xlsx)
Supplementary file5 (XLSX 179 KB) (178.8KB, xlsx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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