Abstract
Objective: Ganoderma lucidum, a fungus of the genus Ganoderma in the family Polyporaceae, is often used in traditional medicine for the treatment of insomnia and amnesia, etc. Ganoderic acid B, a characteristic lanostane-type triterpenoid isolated from Ganoderma lucidum, has been preliminarily verified to possess mild sedative activity in existing studies. This study aims to explore the potential regulatory mechanism of Ganoderic acid B against PCPA-induced insomnia-like damage via multiple preclinical research strategies, and provide preliminary molecular and experimental clues for subsequent basic mechanistic research of this compound. Methods: Taking Ganoderic acid B as the research object, we established a PCPA-induced mouse insomnia model and evaluated its protective effects on insomnia-associated pathological lesions through behavioral assays, biochemical detection, and cerebral histopathological observation. Subsequently, UHPLC-MS/MS serum metabolomics analysis was performed to screen characteristic biomarkers and pivotal metabolic pathways linked to insomnia pathology. Next, compound and disease target databases were combined to obtain overlapping targets correlated with the alleviation of insomnia lesions by Ganoderic acid B; network pharmacology analyses, including GO and KEGG enrichment, were implemented to screen core hub targets, followed by molecular docking and molecular dynamics simulation to preliminarily verify the binding interaction between Ganoderic acid B and these targets. Finally, an in vitro CORT-triggered HT22 neuronal injury model was adopted for experimental validation, and Western blot assays were conducted on mouse brain tissue to detect the expression of core regulatory proteins. Results: In vivo experiments showed Ganoderic acid B alleviated weight loss and abnormal behaviors, raised serum 5-HT and GABA, and lowered DA and NE in PCPA insomnia model mice. Metabolomics screened six key insomnia-related serum markers, with phenylalanine, tyrosine, and tryptophan biosynthesis as the core pathway. CCK-8 assays showed that 4 and 8 μM Ganoderic acid B had no cytotoxicity and relieved CORT-induced neuronal injury, while 16 μM inhibited HT22 cell viability. Network pharmacology identified six core regulatory proteins (EP300, HDAC1, ESR1, IL-6, TNF, NCoR1) mediating Ganoderic acid B’s protective effects against insomnia lesions, with Pathways in cancer enriched. Molecular docking verified stable binding between Ganoderic acid B and these targets. Western blot results indicated Ganoderic acid B downregulated NCoR1, EP300, HDAC1, IL-6, and TNF and upregulated ESR1 expression Conclusion: Ganoderic acid B alleviates PCPA-triggered behavioral abnormalities, neurotransmitter disturbance, and hippocampal neuronal injury in mice via multitarget, multipathway synergistic regulation of NCoR1, EP300, ESR1, HDAC1, IL-6, and TNF, and restores the disrupted phenylalanine, tyrosine, and tryptophan biosynthetic network. This work offers preliminary preclinical and molecular evidence to clarify how Ganoderic acid B relieves insomnia-related pathological injuries
1. Introduction
Insomnia is a common sleep disorder characterized by difficulty falling asleep, sleep maintenance disorder, or low sleep quality, which in turn leads to impaired daytime function. Long-term insomnia can adversely affect the body’s immune, cardiovascular, neurocognitive, and psychological systems, seriously reduce the quality of life, and has become an important public health problem.
Epidemiological investigation shows that the prevalence of insomnia disorder in adults in China is 10–15%, mostly of a chronic course, and nearly half of patients with severe insomnia have had it for more than 10 years. The overall sleep quality of Chinese residents is poor, and insomnia has become an important public health problem. Insomnia is not only a risk factor for diseases such as cardiovascular disease, diabetes, obesity, and neurobehavioral dysfunction but also significantly increases the risk of death.
At present, commonly used drugs for insomnia include benzodiazepines, nonbenzodiazepines, melatonin receptor agonists, and orexin receptor antagonists, but long-term application is easy to produce tolerance, dependence, addiction, and other adverse reactions, which limits their long-term use. Therefore, safe and effective natural medicines have attracted much attention, and Chinese medicine has been paid more and more attention as an ideal alternative therapy. Data show that approximately 18.6% of adults (about 38 million people) in the United States have used Chinese medicine to intervene in psychiatric-related disorders.
Traditional Chinese herbal medicines have become an ideal treatment for insomnia. Ganoderma lucidum, a fungus belonging to the Ganoderma genus of the Polyporaceae family, is distributed throughout most of Southern China. In Chinese folk medicine, it is used as a health food and a traditional remedy for insomnia, forgetfulness, fatigue, and coughing with phlegm. It has been included in the Chinese Pharmacopoeia (2025 edition) for its calming and tranquilizing effects and is commonly used to alleviate and treat insomnia.
Ganoderma lucidum promotes sleep in mice by enhancing serotonin production and regulating specific transcripts in the hypothalamus. Ganoderic acids are a large family of highly oxygenated lanostane-type triterpenoids that are considered among the most important bioactive constituents of Ganoderma lucidum. To date, more than 50 ganoderic acids have been isolated and identified, including ganoderic acid A, ganoderic acid B, and so on, which mainly differ in the substitution patterns of hydroxyl, carbonyl, and carboxyl groups at C-3, C-7, C-15, and other positions. This structural diversity underpins their broad spectrum of pharmacological activities, including sedative-hypnotic, antitumor, anti-inflammatory, antioxidant, neuroprotective, cardioprotective, hepatoprotective, immunomodulatory, antibacterial, and antiviral effects. The triterpenoid compounds ganoderic acid B and ganoderic acid A isolated from Ganoderma lucidum exhibit significant sedative and hypnotic effects. Ganoderic acid B, a lanostane-type triterpenoid isolated from Ganoderma lucidum, has been previously reported to exhibit multiple biochemical and pharmacological activities. It demonstrates sedative-hypnotic, antitumor, and cardioprotective effects.
Metabolomics, network pharmacology, and molecular docking are widely integrated to clarify the multitarget regulatory mechanisms of natural small-molecule compounds against complex disorders like insomnia. Metabolomics captures systemic metabolic perturbations in animal models, while network pharmacology predicts the compound-disease target interaction network; molecular docking further verifies stable binding between active ingredients and core targets. Combined with in vivo and in vitro functional verification, this integrated technical system can systematically interpret the holistic regulatory characteristics of the TCM monomers.
This study utilized ganoderic acid B as the core research vehicle to establish a mouse insomnia model induced by p-chlorophenylalanine (PCPA). On the basis of the efficacy of ganoderic acid B in improving insomnia, the molecular regulatory network of ganoderic acid B was constructed by network pharmacology technology, and the target gene-mediated protein interaction (PPI) network was established. Combined with metabolomics, it screens important targets and identifies signaling pathways rich in target genes. Then, the binding mode and affinity of this small-molecule compound to core protein targets were explored by molecular docking technology, which provided experimental basis and theoretical support to elucidate the molecular mechanism of Ganoderic acid B in improving insomnia (Figure ).
1.
Experimental flowchart.
2. Materials and Methods
2.1. Materials
Ganoderic acid B (IUPAC Name:(2R,6R)-6-[(3S,5R,7S,10S,13R,14R,17R)-3,7-dihydroxy-4,4,10,13,14-pentamethyl-11,15-dioxo-2,3,5,6,7,12,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-2-methyl-4-oxoheptanoic acid, purity ≥ 98%, CAS: 81907-61-1, Batch No.: PQ070824-202511, Guangzhou Puji Biotechnology Co., Ltd.); PCPA (para-chlorophenylalanine) purchased from Shanghai Aladdin Biochemical Technology Co., Ltd., Batch No. E2416078; Estazolam (EST) purchased from Shandong Xinyi Pharmaceutical Co., Ltd., Batch No. 230612; Enzyme-Linked Immunosorbent Assay (ELISA) kits for dopamine (DA), 5-hydroxytryptamine (5-HT), γ-aminobutyric acid (GABA), and norepinephrine (NE) were purchased from Jiangsu Jingmei Biotechnology Co., Ltd., with batch numbers: JM-02906M1, JM-02726M1, JM-02725M1, JM-02907M1. Formaldehyde solution was purchased from Harbin Xinda Chemical Factory, batch number: 131020; HE staining reagents (Wuhan Savier Biotechnology Co., Ltd.); RIPA lysis buffer (Solarbio, Cat. No.: R0010); BCA protein concentration assay kit (Biyun Tian, Cat. No.: P0012); one-step PAGE gel rapid preparation kit (Yazhi, Cat. No.: PG222); PVDF membrane (MERCK, 0.45 μm, Cat No.: IPVH00010); 10 × TBST buffer (Biosharp, Cat No.: BL315B); NCoR1 (Cat No.: AF0270, Antibody Name: NCoR1 Antibody, Manufacturer: Affinity); EP300 (Cat No. 20695-1-AP, Antibody Name: p300 Polyclonal antibody, Manufacturer: proteintech); ESR1 (Cat No. 21244-1-AP, Antibody Name: ER Polyclonal antibody, Manufacturer: proteintech); HDAC1 (Cat No. 10197-1-AP, Antibody Name: HDAC1 Polyclonal Antibody, Manufacturer: proteintech); IL-6 (Cat No. DF6087, Antibody Name: IL6 Antibody, Manufacturer: Affinity); TNF (Cat No. AF7014, Antibody Name: TNF α Antibody, Manufacturer: Affinity); GAPDH (Cat No. 66009-1-Ig, Antibody Name: GAPDH Monoclonal antibody, Manufacturer: proteintech); Secondary Antibody (Goat Anti-Rabbit IgG(H+L)HRP -#S0001, Manufacturer: Affinity); Ultra-Sensitive ECL Chemiluminescent Substrate (Biosharp, Cat No.: BL523A); Fetal Bovine Serum (FBS, Gibco, Cat No. A5669701); 1% Penicillin-Streptomycin (Gibco, Cat No. 15140122); DMEM Medium (Gibco, Cat No. 11965118); Corticosterone (CORT, Solarbio, Cat No. IC05509); CCK-8 (Solarbio, Catalog No.: CA1210); Dimethyl sulfoxide (DMSO) (Biosharp, Catalog No.: BL165B); Annexin V-FITC/PI Apoptosis Detection Kit (Seven, Catalog No.: SC123).
2.2. Animal Experiments
Male ICR mice were purchased from Mudanjiang Medical University (animal ethics approval number: IACUC-20250301-101). Forty-eight age-matched healthy male ICR mice were ear-tagged and numbered 1-48. Group randomization was performed using Excel-generated random numbers to equally allocate animals into six groups. Mice were acclimatized for 7 days in a ventilated room at 25 °C ± 2 °C, 55% ± 5% relative humidity with a 12 h light-dark cycle, and had free access to food and water. All animal handling was performed in accordance with the institutional guidelines for laboratory animal care and use.
2.2.1. Animal Grouping and Model Establishment
The experimental design is illustrated in Figure A. Forty-eight mice were randomly assigned into six groups (n = 8 per group): control, model, low-, medium-, and high-dose Ganoderic acid B, and Estazolam positive control groups. The chemical structure of Ganoderic acid B is shown in Figure B. Following a previously reported protocol, mice in all groups except the control group received intraperitoneal injection of PCPA (300 mg/kg, dissolved in 1 mL/100 g saline) once daily for 3 days. General behavioral and phenotypic observations of mice were recorded throughout the experiment. Disrupted circadian rhythm, disheveled fur, decreased food intake, and marked stress responses after PCPA administration indicated successful model establishment. The control and model groups received equal volumes of normal saline. The low-, medium-, and high-dose ganoderic acid B groups were administered 7.5, 15, and 30 mg/kg/day ganoderic acid B, respectively. The Ganoderic acid B dose range was selected according to Chen et al. These doses exert sedative-hypnotic effects without obvious toxicity. The positive control group received 0.25 mg/kg estazolam by intragastric administration for 7 consecutive days. Both estazolam and ganoderic acid B were suspended in a vehicle containing 0.5% sodium carboxymethyl cellulose (CMC-Na) and 2% Tween 80. Estazolam and ganoderic acid B have low aqueous solubility. The intragastric administration volume was set at 20 mL/kg for all groups. The control and PCPA model groups received equal volumes of blank vehicle by gavage once daily for 7 consecutive days.
2.
Pharmacodynamic and behavioral evaluation of Ganoderic acid B in insomnia model mice. (A) Experimental procedure. (B) Chemical structure of Ganoderic acid B (IUPAC Name:(2R,6R)-6-[(3S,5R,7S,10S,13R,14R,17R)-3,7-dihydroxy-4,4,10,13,14-pentamethyl-11,15-dioxo-2,3,5,6,7,12,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-2-methyl-4-oxoheptanoic acid). (C) Effect of administration on mouse body weight. (D) Effect of Ganoderic acid B on unit time food intake in NSFT of insomnia mice. (E) Effects of Ganoderic acid B on open-field behavioral indices and activity trajectories in insomnia mice (n = 8, BB: Ganoderic acid B; EST: Estazolam; Compared with the blank 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).
2.2.2. Daily Activity and Weight Changes
Daily activity and body weight of mice in each group were monitored throughout the experiment, with behavioral and fur status recorded.
2.2.3. Open-Field Test (OFT)
The open-field test was performed 30 min after the last drug administration. The apparatus was a 40 cm × 40 cm × 30 cm chamber with black inner walls, divided into peripheral and central zones. Mice were placed at the chamber center and allowed to explore for 5 min. Animal movement was recorded, and trajectories were analyzed using the JL Behv Animal Behavior Video Analysis System (Shanghai Jiliang Software Technology Co., Ltd.) to obtain total travel distance, average speed, and time spent in the central zone. The chamber was cleaned with 75% ethanol between animals to remove odor cues. Video analysis was conducted independently by two single-blinded researchers to reduce observational bias.
2.2.4. Novelty Suppressed-Feeding Test (NSFT)
Mice were fasted for 24 h prior to NSFT. Animals were placed in a clean arena containing standard chow pellets and allowed to explore for 10 min. The end point was defined as the onset of chewing behavior (sniffing or touching food was excluded). The latency to feed and food consumption were recorded.
Food intake per unit time was calculated as food intake per unit time (g) = weight of food consumed within 10 min (g)/Mouse body weight (g).
2.2.5. Sample Collection and Preparation
After behavioral tests, all mice were fasted for 12 h with free access to water to avoid feeding-related interference with serum biomarkers. Blood was collected by eyeball puncture. Samples were kept at room temperature for 30 min, followed by centrifugation at 4000 rpm for 10 min at 4 °C. The serum supernatant was harvested, aliquoted into enzyme-free EP tubes, labeled, and stored at −80 °C. For UPLC-MS/MS detection, serum samples were thawed at room temperature; 100 μL serum was mixed with 300 μL methanol, vortexed for 30 s, and centrifuged at 13,000 rpm for 20 min at 4 °C. The supernatant was filtered and transferred to an injection vial for analysis.
After blood collection, the mice were euthanized by cervical dislocation. Whole-brain tissues were rapidly dissected on ice and reserved for pathological and subsequent analyses.
2.2.6. Hematoxylin-Eosin Staining
Brain tissue sections were subjected to hematoxylin-eosin (HE) staining to evaluate the pathological morphological alterations. Sections were observed under an optical microscope for neuronal damage assessment. All slices were de-identified with numerical codes. Two researchers independently performed blinded histological evaluation, and inconsistent findings were cross-checked to reach a consensus.
2.2.7. ELISA
Serum concentrations of 5-hydroxytryptamine (5-HT), γ-aminobutyric acid (GABA), dopamine (DA), and norepinephrine (NE) were quantified by ELISA following the manufacturer’s instructions.
2.2.8. Western Blotting
Brain tissues were lysed on ice using RIPA buffer supplemented with protease and phosphatase inhibitors, and the total protein was harvested by centrifugation. The protein concentration was quantified by the BCA assay. Protein samples were denatured and subjected to SDS-PAGE. After transfer to a PVDF membrane, the membrane was blocked and incubated with primary antibodies, followed by an HRP-labeled secondary antibody. Protein bands were visualized using an ECL chemiluminescent solution and captured with a chemiluminescence imager (Tanon 5200). Image analysis was performed using ImageJ with target protein expression normalized to GAPDH. The detailed method is described in the Supporting Information.
2.3. Metabolomics Analysis
2.3.1. Serum Metabolomics Analysis
Serum metabolomics was performed using a Triple TOF 5600+ tandem mass spectrometer (AB SCIEX, California, USA) equipped with an ESI source operating in both positive and negative ionization modes. Chromatographic separation was carried out on a Waters ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) at 40 °C with a binary linear gradient elution system. UHPLC-MS in both positive and negative ionization modes was adopted to capture metabolic fingerprint profiles. Raw MS data sets were processed using Waters Progenesis QI 2.2 for peak picking, alignment, and normalization, and postprocessed data sets were imported into EZinfo 3.0 for PCA and OPLS-DA. A complete QC system including pooled QC samples, random injection sequence, and feature filtering criteria was established. PCA was used to visualize intergroup clustering and separation patterns, and OPLS-DA with VIP was applied to filter candidate differential metabolites. Metabolite annotation was performed against the HMDB database and further identified using MS/MS spectra. Identified differential metabolites were submitted to the MetPA web platform for pathway enrichment and topological analyses. The detailed method was described in the Supporting Information.
2.3.2. Core Metabolic Biomarker Screening
Combined random forest (RF) classification and receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) metrics were utilized to screen core metabolic candidate differential metabolites for evaluating the therapeutic efficacy of ganoderic acid B. For biomarker screening, the mean decrease accuracy (MDA) cutoff was set at 0.006 for RF feature filtration, and an AUC value greater than 0.8 was defined as a valid criterion for ROC verification. RF modeling was performed using MetaboAnalyst 6.0, while ROC-AUC analysis was conducted in GraphPad Prism 8.0 (San Diego, CA, USA). Correlations between the screened metabolic candidate differential metabolites and four neurotransmitter indicators were visualized via the online ChiPlot platform. The detailed method is described in the Supporting Information.
2.4. Network Pharmacology Analysis
2.4.1. Identification of Potential Targets for Ganoderic Acid B
Log in to the Pharmmapper database (https://www.lilab-ecust.cn/pharmmapper/) (accessed 2 December 2025), the SwissTargetPrediction database (https://swisstargetprediction.ch/) (accessed 27 November 2025), the ChEMBL database (https://www.ebi.ac.uk/chembl/) (accessed 27 November 2025), and the SimilarityEnsembleApproach (SEA) database (https://sea.bkslab.org/) (accessed 28 November 2025). Input “Ganoderic acid B” and Submit the SMILES format of Ganoderic acid B to the four databases above and predict its potential targets. In the Swiss Target Prediction database, the probability must be greater than zero. Merge the targets predicted by the four databases, remove duplicate targets, and obtain the potential target set for Ganoderic acid B.
2.4.2. Acquisition of Insomnia-Related Targets
Log in to the GeneCards database (https://www.genecards.org/) (accessed 3 December 2025), OMIM database (https://www.omim.org/) (accessed 5 December 2025), the Therapeutic Target Database (TTD) (https://ttd.idrblab.cn/) (accessed 8 December 2025), the PharmGKB database (https://www.clinpgx.org/) (accessed 7 December 2025), and the DrugBank database (https://go.drugbank.com/) (accessed 9 December 2025). Using “Insomnia” as the keyword in the above databases, we searched the above databases to obtain the genes related to the above diseases; the inclusion criteria for insomnia targets using GeneCards databases were identified with gene score >1. The target sets identified from the five databases were merged, duplicate targets were removed, and the final insomnia-related target set was obtained.
2.4.3. Screening and Visualization of Intersecting Targets
Using the Venny 2.1.0 online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html) (accessed 14 December 2025), we performed an intersection analysis between the potential target set of ganoderic acid B and the insomnia-related target set. This identified the overlapping targets, representing the potential mechanisms by which ganoderic acid B alleviates insomnia, and generated a Venn diagram.
2.4.4. Construction of PPI Network and Core Target Screening
The intersecting targets were imported into the STRING database (https://cn.string-db.org/) (accessed 16 December 2025). The species was set to “Homo sapiens” with a confidence score >0.9. Nodes without interactions were hidden to obtain the PPI network data. Export the PPI network data in TSV format and import it into Cytosccape 3.9.1 to visualize the PPI network. Using the MCC, MNC, and Degree algorithms in CytoHubba plugin of Cytoscape, identify candidate therapeutic proteins for treating insomnia with Ganoderic acid B.
2.4.5. GO Functional Enrichment Analysis and KEGG Pathway Enrichment Analysis
Import the intersecting target points into the Metascape database (https://metascape.org/) (accessed 20 December 2025) and the DAVID database (https://davidbioinformatics.nih.gov/) (accessed 20 December 2025). Set the species to “Homo sapiens” and use default parameters to perform GO (Gene Ontology) functional enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. The GO functional enrichment analysis encompassed three dimensions: biological process (BP), cellular component (CC), and molecular function (MF). The KEGG pathway enrichment analysis screened for signaling pathways associated with insomnia. Finally, the GO results were analyzed using the CNSknowall web portal (https://www.cnsknowall.com/) (accessed 24 December 2025), and the KEGG results were analyzed using the MicroBioinformatics web portal (http://www.bioinformatics.com.cn/) (accessed 25 December 2025). The analysis results were visualized.
2.5. Molecular Docking
The 2D structure of ganoderic acid B was obtained from the PubChem database and energy-optimized in Chem3D 23.1.1 to obtain the lowest-energy stable conformation. The high-resolution crystal structures of the six core targets were obtained from the RCSB PDB database, and functionally related chains were retained during crystal pretreatment. PyMOL 2.6 was used to remove redundant groups, such as water molecules, phosphates, and irrelevant ions. Eutectic ligand redocking verification was first carried out to verify the reliability of the molecular docking program, grid, and parameter system. AutoDockTools 1.5.6 was used for protonation and ligand flexibility processing, and docking was performed with AutoDock Vina 1.2.5. Protein–ligand interaction visualization was completed by Discovery Studio 2019 and PyMOL 2.6. The detailed method is described in the Supporting Information.
2.6. Molecular Dynamics Simulation
GROMACS 2022.2 software was used to simulate the molecular dynamics of the protein–ligand complexes. The receptor protein topology file was generated by pdb2gmx with the Amber99SB-ILDN force field, and the topological parameters of the GAFF2 force field were constructed on the basis of the ligand structure using sobtop_1.0 (dev 3.1). The RESP method was used to distribute charges to the ligand atoms. The system was solvated with the TIP3P water model in a cube simulation box, and Na+ and Cl– were added to prepare a NaCl ion environment at 0.15 M. Long-range electrostatic interaction was solved by the particle mesh Ewald (PME) method, and the LINCS algorithm was selected for chemical bond constraints. Energy minimization, NVT, and NPT equilibrium simulations were carried out successively, followed by the molecular dynamics simulation without position constraints. After the simulation, RMSD, RMSF, R g, SASA, and FEL were analyzed using GROMACS 2022.2 companion analysis tools. The detailed method is described in the Supporting Information.
2.7. Cell Experiments
2.7.1. Cell Culture and Passaging
Mouse hippocampal neuronal HT22 cells were purchased from Procell (Cat. No. CL-0697) and authenticated by tandem repeat sequence analysis; mycoplasma testing confirmed no contamination. Cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin at 37 °C in a humidified 5% CO2 atmosphere. At 80–90% confluence, cells were passaged at a 1:3 ratio using 0.25% trypsin-EDTA, with medium changed every 2–3 days. Logarithmic-phase cells were used for all experiments.
2.7.2. Establishment of CORT-Induced HT22 Stress Neurotoxicity Model and Cell Grouping
A corticosterone (CORT)-induced HT22 neuronal injury model was used to mimic chronic stress-related neuronal damage. Logarithmic-phase HT22 cells were trypsinized, resuspended, and adjusted to 5 × 103 cells/mL. Two milliliters of cell suspension was seeded per well in 6-well plates and incubated at 37 °C with 5% CO2 for 24 h for cell attachment.
After attachment, the medium was replaced. Cells were assigned to control and model groups. The control group was cultured in 10% FBS-containing complete DMEM, whereas the model group received complete DMEM supplemented with 200 μM CORT. CORT was prepared as a 10 mM DMSO stock solution, freshly prepared before use. The final DMSO concentration was maintained ≤ 0.1% (v/v). Both groups were incubated for another 24 h at 37 °C, 5% CO2 to establish the model.
2.7.3. CCK-8 Assay for Cell Viability
HT22 cells were seeded in 96-well plates at 5 × 103 cells/well and cultured for 24 h to allow attachment. Cells were then treated with a series of Ganoderic acid B concentrations (0, 1, 2, 4, 8, 16, 32, 64 μM). Complete medium containing 0.1% DMSO served as the solvent control, and cell-free medium was used as the blank control. After 24 h of treatment, 10 μL CCK-8 reagent was added to each well, followed by incubation at 37 °C for 2 h. The absorbance at 450 nm was detected with a microplate reader. Cell viability was calculated as Viability (%) = [(ODtreated – ODblank)/(ODsolvent control – ODblank)] × 100. Six replicate wells were set for each group, and the experiment was repeated three times independently.
2.7.4. Flow Cytometry Analysis of Apoptosis Rate
HT22 cells were harvested by trypsin digestion (without EDTA) and washed with precooled PBS. Cell apoptosis was detected using the Annexin V-FITC/PI kit following the manufacturer’s instructions. Briefly, cells were resuspended in Annexin V binding buffer, stained with Annexin V-FITC and PI, incubated at room temperature in the dark for 15 min, and analyzed by flow cytometry. Data were processed with FlowJo V10 software.
2.7.5. Western Blotting
Total protein extraction, SDS-PAGE, membrane transfer, and antibody incubation for HT22 cells followed the protocol described in Section , except that cell scraping lysis was used instead of tissue grinding. Antibody dilutions for the target proteins remained unchanged.
2.8. Data Processing
Statistical analyses were conducted using GraphPad Prism 8.3.0 (San Diego, CA, USA) and SPSS 20.0 (IBM Corporation, Armonk, USA). Data are presented as mean ± standard deviation (SD). Two-tailed unpaired t tests were applied for two-group comparisons, while one-way ANOVA followed by Tukey’s post hoc test was used for multiple group comparisons.
3. Results
3.1. In Vivo Study of Ganoderic Acid B in an Insomnia Mouse Model
3.1.1. Effect of Ganoderic Acid B on Body Weight Changes in Mice
Body weight changes in each group are shown in Figure C. The experiment was divided into a 0–3-day modeling period and a 3–10-day oral administration period. The repeatedly measured body weight data were analyzed by two-way repeated-measures ANOVA followed by Tukey’s post hoc test, and significant intergroup differences were confirmed after multiple testing correction. Mice in the control group exhibited good mental status, normal activity, smooth coat, and normal food and water intake. After 3 days of modeling, compared with the control group, mice in the model group exhibited significantly reduced body weight, disrupted circadian rhythms, matted and dull fur, increased aggression, heightened daytime activity, and insomnia. Following intervention with Ganoderic acid B, body weight gradually recovered across all groups, general condition markedly improved, daytime activity decreased, sleep duration increased, and fur condition, food intake, and water consumption all showed significant improvement compared with the model group.
3.1.2. Effects of Ganoderic Acid B on Open-Field Test in Insomnia Mice
Figure E shows that compared to the control group, PCPA-induced insomnia model mice exhibited significantly reduced total distance traveled, average locomotor speed, and time spent in the central area. Their movement trajectories were fragmented and predominantly distributed in the periphery of the open field, confirming the successful model establishment. Compared with the model group, low-, medium-, and high-dose ganoderic acid B groups exhibited dose-dependent increases in total distance traveled and average locomotor speed, significantly prolonged central zone dwell time, and more uniform distribution of locomotor trajectories. The high-dose group demonstrated the most pronounced improvement, comparable to that of the positive control group. These findings indicate that ganoderic acid B significantly improves spontaneous activity and anxiety-like behavior in an insomnia model, demonstrating a marked intervention effect on insomnia-related behavioral disturbances.
3.1.3. Effects of Ganoderic Acid B on Food Intake in Insomnia-Induced Mice
In the NSFT (Figure D), compared with the blank group, PCPA-induced insomnia model mice exhibited a highly significant reduction in food intake per unit time, demonstrating marked anxiety-like feeding suppression. Following intervention with low, medium, and high doses of ganoderic acid B, food intake increased in a dose-dependent manner. The high-dose group exhibited the most pronounced improvement, comparable to that of the positive control group. These findings indicate that ganoderic acid B effectively alleviates anxiety-like feeding suppression in insomnia model mice, suggesting potential therapeutic benefits for anxiety-related behaviors associated with insomnia.
3.1.4. Effects on Hippocampal Histopathological Changes in Insomnia-Induced Mice
HE staining results (Figure A) revealed that neurons in the hippocampal region of the control group exhibited regular morphology, dense arrangement, large round nuclei, and distinct nucleoli, with only a few shrunken neurons visible in the cortex. In contrast, the model group showed marked neuronal damage in the hippocampus, including shrunken neurons, minor hemorrhages, and round vacuoles. Following intervention with different doses of ganoderic acid B, neuronal damage in the hippocampal region improved across all dose groups. In the high-dose group, neuronal morphology and arrangement in the hippocampal region largely recovered to levels comparable to those of the blank group, with only occasional round vacuoles and a small number of cortical shrunken neurons observed. In the positive control group, neuronal morphology and arrangement in the hippocampal region were largely normal, although a small number of shrunken neurons remained visible in the cortex. These results indicate that ganoderic acid B ameliorates neuronal damage in the hippocampal region of insomnia-induced mice, with the highest dose demonstrating the most pronounced reparative effects.
3.
Effects of Ganoderic acid B on improving brain tissue damage in insomnia mice and influencing related neurotransmitter and protein expression. (A) HE staining evaluation of Ganoderic acid B’s impact on brain tissue. Serum 5-HT (B), GABA (C), DA (D), and NE (E). (F) Effects of Ganoderic acid B on related protein expression in hippocampal tissue of insomnia mice and its quantification (G) (n = 3, *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).
3.1.5. Regulation of Neurotransmitter Levels by Ganoderic Acid B
Compared with the control group, serum levels of 5-hydroxytryptamine (5-HT) and γ-aminobutyric acid (GABA) decreased, while dopamine (DA) and norepinephrine (NE) levels increased in the model group. Different doses of Ganoderic acid B exerted varying degrees of regulatory effects on these indicators, elevating 5-HT and GABA levels while reducing DA and NE levels, with higher doses demonstrating superior therapeutic efficacy (Figure B–E).
3.1.6. Effects of Ganoderic Acid B on Hippocampal Tissue-Related Protein Expression in Model Mice
Compared with the blank group, the model group mice exhibited upregulation of NCoR1, EP300, HDAC1, IL-6, and TNF protein expression in hippocampal tissue, while ESR1 protein expression was downregulated. Compared with the model group, the high-dose ganoderic acid B group showed downregulation of NCoR1, EP300, HDAC1, IL-6, and TNF protein expression, while ESR1 protein expression was upregulated (Figure F,G).
3.2. Metabolism Analysis
3.2.1. Analysis of the Insomnia Model Metabolism Profile and Regulatory Role of Ganoderic Acid B
All mice were fasted overnight prior to blood collection to eliminate dietary interference and ensure the credibility of the metabolomics data. Blood samples from the blank group, the model group, and the drug administration group were taken, and the blood samples were scanned in positive and negative ion patterns by the established UPLC/MS analysis method according to the sample processing method, and the metabolic profile base peak intensity chromatogram (BPI) diagram, which can reflect the information on each group of samples, was obtained (Figure ). The raw blood ESI-MS metabolic profile data were imported into Progenesis QI software to complete peak matching and identification. The processed data were then analyzed by multivariate statistical analysis using EZinfo 3.0 software to obtain a PCA score map, which was used to distinguish the model group, the blank group, and the Ganoderic acid B administration group (Figure A,B). It can be seen that the samples of the control group and the model group are separated, indicating that the model group was established. The samples of each administration group tend to be close to the control group, and the separation from the model group is obvious, showing a certain metabolic profile callback trend, indicating that ganoderic acid B exerts obvious regulatory effects on the metabolic disorder of insomnia model mice.
4.
Serum BPI of positive and negative chromatograms of control groups (A, B), model groups (C, D), low-dose Ganoderic acid B group (E, F), medium-dose Ganoderic acid B group (G, H), high-dose Ganoderic acid B group (I, J), and EST groups (K, L).
5.
Metabolomic analysis of the control group, Insomnia model group, Ganoderic acid B group, and EST control group. PCA score plots of blood positive ion patterns (A) and negative ion patterns (B) in the control group, model group, Ganoderic acid B-treated group, and positive control group insomnia model mice. PCA score plots of serum samples in positive modes (C) and negative modes (D). OPLS-DA analysis charts of positive mode (E) and negative mode (F) for the control group and insomnia model group. Splot of blood metabolic analysis in Insomnia model mice in positive (G) and negative modes (H). VIP of serum samples from the control and model groups of Insomnia model mice in both positive (I) and negative (J) modes.
3.2.2. Identification of the Potential Candidate Differential Metabolites and Pathway Analysis
The metabolic profile can clearly reflect the global metabolic disorder state of mice induced by the insomnia model. We used EZinfo 3.0 software to perform multivariate statistical analysis on the blood sample data of the blank group and the model group and constructed PCA score maps reflecting the dispersion between groups (Figure C,D) and OPLS-DA models for discriminant analysis between groups (Figure E,F), respectively. The results showed that all samples were distributed within 95% confidence intervals with no outliers; moreover, the two groups of samples showed significant separation along the t[1] axis, suggesting that there were obvious differences in metabolic patterns between the model group and the blank group, and the model was successfully replicated. Differential metabolites were further screened by S-plot (Figure G,H) and VIP plot (Figure I,J) of OPLS-DA, and blood metabolic candidate differential metabolites significantly related to the insomnia model were screened with VIP > 1 and P < 0.05 as thresholds.
The reliability of OPLS-DA models between control and model groups was further quantitatively evaluated using built-in leave-one-out (LOO) cross-validation in EZinfo 3.0. For ESI positive ion mode: R 2 X = 0.772, R 2 Y = 0.9909, and Q 2 = 0.9136. All samples fell within the 95% Hotelling T2 confidence ellipse without outliers, and control/model groups were clearly separated along the t[1] axis. R 2 X > 0.7 indicated that the model explained 77.2% of total metabolome variation, within the acceptable range for serum untargeted metabolomics. High R 2 Y and Q 2 values far exceeding the general threshold of 0.4 demonstrated strong explanatory ability for group differences and robust cross-validation prediction performance. No obvious overfitting was observed, as R 2 X, R 2 Y, and Q 2 were close to 1 without extreme contrast between high R 2 Y and low Q 2. For ESI negative ion mode: Model parameters were R 2 X = 0.6185, R 2 Y = 0.9909, Q 2 = 0.9228, all satisfying standard OPLS-DA evaluation criteria, with complete separation between control and insomnia model groups and no overfitting risk.
In this experiment, the accurate mass number of ion m/z value was determined by the UPLC-MS analysis method, and the possible elemental composition was obtained by data analysis by Progenesis QI software. Retrieved and validated through online databases, such as HMDB and KEGG. A total of 10 potential insomnia candidate differential metabolites were identified by comparison with the secondary mass spectrometry data and chromatographic behavior of the standard (Table ). Taking Cyclic AMP as an example, the process of identifying its metabolites is detailed, as shown in Figure . In the metabolite relative content map (Figure ), among the 10 differential metabolites, 4 of them had significant differences (P < 0.05) and 6 showed extremely significant differences (P < 0.01). Biomarker heat map analysis further illustrated the differences between the control group and the model group. The above results indicate that the model is effective. Ganoderic acid B has a callback effect on 6 of 10 blood candidate differential metabolites in insomnia, which suggests that ganoderic acid B exerts regulatory activity on the metabolic disturbance in insomnia model mice. The dynamic changes of 10 blood candidate differential metabolites were visually evaluated and analyzed, and the intensity of the color blocks reflected the relative signal intensity and cluster recognition. Statistically significant differences were observed between the control and model groups, indicating significant changes in metabolites in insomnia model mice (Figure A). In clinical trials, it is an important method to screen core disease candidate differential metabolites by ROC analysis. When the AUC of the curve reaches 0.7, it indicates that the diagnostic accuracy is at a low level. However, an AUC greater than 0.8 indicates good diagnostic accuracy. As shown in Figure , except for Eugenol, Cyclic AMP, Taurohyocholate, and Prostaglandin E1, which have AUC values less than 0.7, the AUC values of the above six candidate metabolites were all higher than 0.8. However, since feature screening and ROC evaluation were performed on the same animal samples without external validation in an independent cohort, these metabolites possess only potential distinguishing capacity and cannot be regarded as candidate differential metabolites. We further screened the selected insomnia candidate differential metabolites by using a random forest approach. As shown in Figure B, the MDA of six insomnia candidate differential metabolites was >0.05, and the MDA of tyramine, l-tyrosine, l-phenylalanine, and serotonin was >0.08. Figure D shows the correlation analysis of four key neurotransmitters for sleep regulation and 10 candidate blood markers. The color and depth of color blocks represent the positive and negative correlation coefficients and magnitudes of the two. The results showed that 5-HT, GABA, DA, and NE neurotransmitters were significantly related to the markers of urate, l-tyrosine, serotonin, l-phenylalanine, tyramine, and 6-hydroxymelatonin, which proved that the above markers were involved in the regulation of the pathological process of insomnia at the molecular interaction level.
1. Information of Candidate Differential Metabolites of Insomnia Model Mice .
| No | Metabolite name | Rt/min | Adducts | Determined (m/z) | Mass Error (ppm) | Formula | Fragment | HMDB | Change Trend | Statistical significance |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Eugenol | 0.68 | [M + H]+ | 165.0912 | 1.2690 | C10H12O2 | 133.0648 | HMDB0005809 | ↓ | * |
| 2 | Urate | 2.47 | [M – H]− | 169.0288 | 2.6164 | C5H4N4O3 | 124.0142 | HMDB0000289 | ↑ | ** |
| 3 | Cyclic AMP | 3.48 | [M + H]+ | 330.3030 | 2.4155 | C10H12N5O6P | 136.0618, 312.0493 | HMDB0000058 | ↓ | * |
| 4 | l-Tyrosine | 4.52 | [M + H]+ | 182.0866 | –1.0984 | C9H11NO3 | 91.2365, 123.0466 | HMDB0000158 | ↓ | ** |
| 5 | Serotonin | 4.80 | [M + H]+ | 177.1025 | 1.2540 | C10H12N2O | 132.0808, 160.0757 | HMDB0000259 | ↓ | ** |
| 6 | l-Phenylalanine | 6.36 | [M + H]+ | 166.0866 | –1.2042 | C9H11NO2 | 120.0853 | HMDB0000159 | ↑ | ** |
| 7 | Tyramine | 8.54 | [M + H]+ | 138.0914 | 0.7810 | C8H11NO | 103.0543, 121.0651 | HMDB0000306 | ↓ | ** |
| 8 | Taurohyocholate | 8.76 | [M + H]+ | 514.2847 | 0.6126 | C26H45NO7S | 124.0067 | HMDB0011637 | ↑ | * |
| 9 | Prostaglandin E1 | 9.45 | [M – H]− | 353.2327 | 1.1146 | C20H34O5 | 235.1286, 317.2316 | HMDB0001442 | ↑ | * |
| 10 | 6-Hydroxymelatonin | 10.90 | [M + H]+ | 249.1230 | –3.6127 | C13H16N2O3 | 51.0268 | HMDB0004081 | ↓ | ** |
“↑” stands for upgrading, “↓” stands for downgrading. * Stands for very significant difference (p < 0.01). ** Stands for significant difference (p < 0.05).
6.
Identification of cyclic AMP by ion extraction and fragmentation analysis of MSe data.
7.
Relative abundance of potential serum metabolites in the effect of Ganoderic acid B on insomnia. (*p < 0.05 and **p < 0.01 vs control group. # p < 0.05 and ## p < 0.01 vs model group).
8.
Identification of the potential lipid candidate differential metabolites and pathway analysis. (A) Heat map analysis of the relative content of Insomnia candidate differential metabolites in the control group and model group. (B) Mean decrease accuracy values for metabolomics metabolic candidate differential metabolites in random forest classification. (C) Analysis of metabolic pathways regulated by Ganoderic acid B therapy for Insomnia. 1. Phenylalanine, tyrosine, and tryptophan biosynthesis; 2. Phenylalanine metabolism; 3. Tryptophan metabolism; 4. Tyrosine metabolism; 5. Ubiquinone and other terpenoid-quinone biosynthesis; 6. Purine metabolism. (D) Correlation analysis of differential metabolites with key neurotransmitters.
9.
ROC curve analysis of 10 metabolites.
In summary, the differential metabolites were screened by OPLS-DA, further screened by VIP values and P values, and verified by ROC, correlation analysis, and RF values, and finally six insomnia candidate differential metabolites were identified.
We input the 10 insomnia candidate differential metabolites into the MetaboAnalyst website (http://www.metaboanalyst.ca/) and analyzed the metabolic pathways of the identified candidate differential metabolites using the MetaboAnalyst 6.0 database. The metabolic pathway topological features revealed six metabolic pathways closely associated with insomnia: phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; tyrosine metabolism; ubiquinone and other terpenoid-quinone biosynthesis; and purine metabolism. Among these, the phenylalanine, tyrosine, and tryptophan biosynthesis had the highest impact value (Figure C). A metabolic network diagram based on the KEGG database was constructed to visualize the regulatory mechanism of ganoderic acid B on insomnia. The phenylalanine, tyrosine, and tryptophan biosynthesis pathway, associated with the upstream and downstream metabolic enzymes of candidate differential metabolites, was identified as the key pathway (Figure ).
10.
Network analysis of potential serum metabolism candidate differential metabolites for the treatment of Insomnia by Ganoderic acid B.
3.3. Network Pharmacology Analysis
3.3.1. Predicted Targets of Ganoderic Acid B and Insomnia-Related Targets
In the above database, a total of 1130 targets were predicted for the component targets of ganoderic acid B, and after summarization and deduplication, a total of 1010 targets were obtained. The results showed that a total of 2292 targets related to insomnia were obtained after searching the disease database and summarizing the deduplication. The drug target and the disease target are intersected to obtain the human core targets, and human-mouse orthologous gene mapping is completed. Only the homologous targets that can be successfully matched are retained, and the genes without homologous correspondence are eliminated so that the network pharmacological analysis results can be adapted to the in vivo experimental system of mice. After eliminating nonhomologous targets, ganoderic acid B yielded 1002 targets and 2292 insomnia-related targets. Drug targets and disease targets were imported and intersected through the online Venny 2.1.0 tool, and a total of 228 intersection targets were obtained. These targets were considered to be potential targets for Ganoderic acid B in the treatment of insomnia (Figure A).
11.
Mechanism analysis of Ganoderic acid B treating insomnia based on network pharmacology. (A) Venn diagram of common targets between Ganoderic acid B and insomnia. (B) Interaction network diagram of Ganoderic acid B-active components and insomnia targets. (C) Protein–protein interaction (PPI) network of common targets between Ganoderic acid B and insomnia. (D) Three algorithms for CytoHubba plugins. (E) Gene Ontology (GO) functional enrichment analysis results. (F) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results.
3.3.2. Compound-Target (C-T) Interaction Network for Ganoderic Acid B in Insomnia Treatment
By intersecting the predicted targets of ganoderic acid B with insomnia-related targets, we identified 236 common targets and constructed a compound-target (C-T) interaction network diagram (Figure B). Network topology analysis revealed that the diagram comprises 1 compound node (purple, representing ganoderic acid B), 1 disease node (red, representing insomnia), and 236 common target nodes (green). Gray lines between nodes represent interactions between compounds and targets. The connectivity indicates that ganoderic acid B directly acts on numerous targets closely associated with the pathological processes of insomnia, suggesting it may exert multitarget protective activity through synergistic regulation. This result provides a clear target set for elucidating its specific molecular mechanisms in the subsequent studies.
3.3.3. Establishing the PPI Network for Ganoderic Acid B Targets in Insomnia Treatment
To investigate interactions among intersecting targets, we performed online analysis via the STRING database, generating a protein–protein interaction (PPI) network diagram. Interaction data were imported into Cytoscape 3.9.1 for visualization and topological optimization. Within the PPI network, nodes were filtered based on degree values, with unconnected nodes removed. Node size and color intensity were positively correlated with degree values (Figure C). Three algorithms according to CytoHubba were further applied to core target screening, and finally, the first six intersecting targets of the three algorithms were selected: EP300, HDAC1, ESR1, IL-6, TNF, and NCoR1 (Figure D), indicating that they play a key role in the mechanism of ganoderic acid B treatment of insomnia.
3.3.4. GO and KEGG Enrichment Analysis Results
To elucidate the potential biological mechanisms underlying ganoderic acid B’s treatment of insomnia, we performed gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the Metoscape tool, setting p < 0.01 as the statistical significance threshold. Results showed that GO enrichment analysis categorized targets into three major categories: biological process (BP), molecular function (MF), and cellular component (CC) (Figure E). In the figure, differently colored sectors represent distinct functional categories, with the sector size reflecting the number of genes within that category. The intensity of the color bars on the right indicates the significance of enrichment results (-log10(P value)), where darker colors denote smaller P-values and more significant enrichment.
For KEGG pathway enrichment analysis, we retained the top 10 most significantly enriched pathways using P < 0.01 as the threshold and visualized them via a bubble plot. In the bubble chart, bubble size represents the number of enriched target genes in each pathway, while bubble color indicates enrichment significance level (-log10(P value)), with redder colors corresponding to smaller P-values and stronger enrichment. Figure F shows that further analysis of TGF-β signaling pathway is the most significant enrichment, and other enriched pathways include the thyroid hormone signaling pathway, pathways in cancer, Hepatitis B, and other signaling pathways. These results systematically illustrate the multipathway synergistic mechanism of ganoderic acid B in the treatment of insomnia at the pathway level.
3.4. Analysis of Molecular Docking Results
In this study, high-resolution protein crystals of EP300 (PDB ID: 5LPM), ESR1 (PDB ID: 3UUD), HDAC1 (PDB ID: 5ICN), IL6 (PDB ID: 1ALU), TNF (PDB ID: 9OJO), and NCOR1 (PDB ID: 4WVD) were selected for molecular simulation. In order to ensure the reliability of docking parameters and grid settings, this study first carried out cocrystallized ligand redocking verification, as shown in Figure and Table .
12.
(A–F) Visualization results of molecular docking of cocrystallized ligand with core target proteins.
2. Molecular Docking Experiments and RMSD Validation of Cocrystallized Ligand, Ganoderic Acid B, and Estazolam.
The residues of GLN1082 and ARG1055 on the EP300 receptor formed hydrogen bond interactions with the cocrystallized ligand, the residues of PHE1090, PRO1051, and PRO1081 on the EP300 receptor formed hydrophobic interactions with the cocrystallized ligand, the residues of THR1108, GLY1109, PRO1087, GLU1052, and ASP1107 on the EP300 receptor formed van der Waals interactions with the cocrystallized ligand, the residues of PRO1081 on the EP300 receptor formed carbon–hydrogen bond interactions with the cocrystallized ligand, the residues of ASP1080 on the EP300 receptor formed pi-anion interactions with the cocrystallized ligand, and the residues of VAL1079 on the EP300 receptor formed halogen (C1, Br, I) interactions with the cocrystallized ligand with a binding energy of −8.3 kcal/mol.
PHE404 residues on the ESR1 receptor formed a Pi-Pi T-shaped interaction force with the cocrystallized ligand, LEU391, MET388, ILE424, LEU384, LEU525, MET421, LEU387, ALA350 residues on the ESR1 receptor formed a hydrophobic interaction force with the cocrystallized ligand, and LEU428, GLY521, HIS524, MET528, LEU346, GLU353 residues on the ESR1 receptor formed a van der Waals interaction force with the cocrystallized ligand with a binding energy of −11 kcal/mol.
The residues of TYR15, GLY17, and ARG36 on the HDAC1 receptor formed hydrogen bond interactions with the cocrystallized ligand, the residues of VAL198, ASP256, ASN21, ASP16, ILE249, MET329, and ASN40 on the HDAC1 receptor formed van der Waals interactions with the cocrystallized ligand, and the residues of PHE252, ARG36, HIS39, and ARG55 on the HDAC1 receptor formed Pi-anion interactions with the cocrystallized ligand with a binding energy of −5.9 kcal/mol.
ASP160, THR43, ARG104, and THR163 residues on the IL6 receptor formed hydrogen bond interaction force with cocrystallized ligand, and PHE105, LYS46, GLN156, and SER47 residues on IL6 receptor formed van der Waals interaction force with cocrystallized ligand, with a binding energy of −4.4 kcal/mol.
TYR59 residues on the TNF receptor formed a Pi-Pi stacked interaction force with the cocrystallized ligand, TYR119 and LEU57 residues on the TNF receptor formed a hydrophobic interaction force with the cocrystallized ligand, and VAL123, GLN61, TYR119, LEU120, GLYB121, GLYC121, TYRC59, GLYC122, LEUB57, LEUA157, ILEA155, ILEB155, LYSA11, ALAA156, LEUC157 residues on the TNF receptor formed a van der Waals interaction force with the cocrystallized ligand, and LEU57 residues on the TNF receptor formed a Pi-Sigma interaction force with the cocrystallized ligand, with a binding energy of −12.9 kcal/mol.
SER358 residues on the NCOR1 receptor formed hydrogen bond interactions with the cocrystallized ligand, HIS294, ARG331, TYR361, MET328, PHE329, HIS447, HIS446, ILE362, MET365, LEU287, PHE336, LEU348, PHE366 residues on the NCOR1 receptor formed hydrophobic interactions with the cocrystallized ligand, ALA291, ILE335, SER332, MET290, LEU347, HIS344, ASN283, and ASP359 residues on the NCOR1 receptor formed van der Waals interactions with the cocrystallized ligand, and ILE352 residues on the NCOR1 receptor formed carbon hydrogen bond interactions with the cocrystallized ligand with a binding energy of −10 kcal/mol.
In order to verify the reliability and rationality of the docking parameters and scheme, redocking is carried out. The original ligand molecule is extracted from the protein crystal structure, and the ligand is redocked to a predetermined active site of the target protein. The root-mean-square deviation (RMSD) was calculated between the ligand conformation and the original ligand conformation (crystal structure) after redocking; RMSD < 2.0 Å. This is considered acceptable, indicating that the established docking scheme and parameters have good reliability and can accurately replicate the binding mode of ligand to protein. During the redocking validation of cocrystallized ligands, the RMSD values of ligands for the systems 5LPM, 3UUD, 5ICN, 1ALU, 9OJO, and 4WVD were 0.156, 0.000, 1.81, 0.716, 0.001, and 0.447 Å, respectively. All RMSD values were less than 2.0 Å, within the acceptable threshold, which verified the reliability of the molecular docking protocol and guaranteed the credibility of subsequent docking results (Figure , Table ).
13.
(A–F) Verification of cocrystallized ligand docking.
Molecular docking was separately performed using EP300 (PDB ID: 5LPM), ESR1 (PDB ID: 3UUD), HDAC1 (PDB ID: 5ICN), IL6 (PDB ID: 1ALU), TNF (PDB ID: 9OJO), and NCOR1 (PDB ID: 4WVD) as receptors, with Ganoderic acid B and estazolam serving as ligands, as shown in Figure and Table . First, for docking with Ganoderic acid B, the grid box center coordinates and dimensions were set as follows: EP300 (5LPM): 34.608, 21.314, 8.12 with a box size of 47.25 Å; ESR1 (3UUD): 17.384, 3.303, −11.847 with a box size of 47.25 Å; HDAC1 (5ICN): −57.451, 17.097, −4.164 with a box size of 47.25 Å; IL6 (1ALU): 2.668, −19.933, 8.838 with a box size of 30.0 Å; TNF (9OJO): −7.458, −0.757, 17.449 with a box size of 47.25 Å; NCOR1 (4WVD): −6.921, −11.719, −28.85 with a box size of 66.15 Å. Second, for docking with estazolam, the grid box parameters were listed as below: EP300 (5LPM): 34.64, 21.266, 8.16 with a box size of 47.25 Å; ESR1 (3UUD): 17.833, 3.179, −11.742 with a box size of 47.25 Å; HDAC1 (5ICN): −57.451, 17.097, −4.164 with a box size of 62.65 Å; IL6 (1ALU): 2.668, −19.933, 8.838 with a box size of 47.25 Å; TNF (9OJO): −7.458, −0.757, 17.449 with a box size of 52.15 Å; NCOR1 (4WVD): 4.152, −24.374, −39.86 with a box size of 47.25 Å.
14.
Molecular docking diagram of chemical composition to target. (A) Molecular docking diagram of Ganoderic acid B with its target. (B) Molecular docking diagram of Estazolam with its target.
First, the docking results of Ganoderic acid B and its target Figure A are as follows: GLN1110, GLN1116, and SER1095 residues on EP300 receptor form hydrogen bond interaction force with Ganoderic acid B, MET1097, LEU1123, and PRO1078 residues on EP300 receptor form hydrophobic interaction force with Ganoderic acid B, and LYS1105, ARG1104, THR1101, PRO1096, PHE1090, GLN1112, TYR1111, VAL1079, and ASP1119 residues on EP300 receptor form van der Waals interaction force with Ganoderic acid B, and ASP1120 residues on EP300 receptor form Carbon Hydrogen Bond interaction force with Ganoderic acid B, with a binding energy of −8 kcal/mol.
The residues of His516, Arg515, Thr460, Glu423, and Gly420 on the ESR1 receptor formed hydrogen bond interactions with ganoderic acid B, the residues of His377, Arg548, Ile424, and Lys520 on the ESR1 receptor formed hydrophobic interactions with ganoderic acid B, the residues of Ala546, Glu380, Ala382, Ser456, His547, His524, Met421, and Met427 on the ESR1 receptor formed van der Waals interactions with ganoderic acid B, and the residues of Gly457 on the ESR1 receptor formed carbon hydrogen bond interactions with ganoderic acid B with a binding energy of −8.7 kcal/mol.
The residues of TYR48, HIS39, TYR15, GLY17, ARG36, and GLU284 on the HDAC1 receptor formed hydrogen bond interaction force with ganoderic acid B, the residues of ILE249, ILE53, ARG55, VAL198, TYR15, and HIS39 on the HDAC1 receptor formed hydrophobic interaction force with ganoderic acid B, the residues of HIS253, ASP256, TYR54, ASP16, PRO56, GLU325, ASP283, ARG282, and GLU287 on the HDAC1 receptor formed van der Waals interaction force with ganoderic acid B, and the residues of PHE252 on the HDAC1 receptor formed Pi-Sigma interaction force with ganoderic acid B with binding energy of −9.2 kcal/mol.
TYR97 and ASN63 residues on the IL6 receptor formed hydrogen bond interactions with ganoderic acid B, PRO65 residues on the IL6 receptor formed hydrophobic interactions with ganoderic acid B, THR143, LEU147, LEU62, ASN61, LYS86, LYS66, LEU64, and GLU93 residues on the IL6 receptor formed van der Waals interactions with ganoderic acid B, and PRO65 residues on the IL6 receptor formed carbon–hydrogen bond interactions with ganoderic acid B with a binding energy of −6.6 kcal/mol.
The residues of PRO100, LYS98, ILE97, and SER99 on TNF receptor formed hydrogen bond interaction with Ganoderic acid B, the residues of CYS69 on TNF receptor formed hydrophobic interaction with Ganoderic acid B, and the residues of HIS73, LYS112, TRP114, PRO100, SER99, TYR115, GLU116, PROB100, GLUB116, TYRB115, TRPB114, PROB113, LYSB112, CYSA101, and CYSC101 on TNF receptor formed hydrophobic interaction with Ganoderic acid B, the van der Waals interaction force was formed with a binding energy of −7.7 kcal/mol.
GLU276 residues on NCOR1 receptor formed hydrogen bond interaction with Ganoderic acid B, MET290, ARG331, ILE335, PHE336, LEU348, HIS344, LEU347 residues on NCOR1 receptor formed hydrophobic interaction with Ganoderic acid B, and PHE278, LYS272, ILE286, LEU287, THR270, MET265, ARG264, SER332, MET328, and ASN283 residues on NCOR1 receptor formed van der Waals interaction with Ganoderic acid B with binding energy of −8.5 kcal/mol.
Next, the docking results of estazolam and its target are Figure B, as follows: the GLN1110 residue on the EP300 receptor forms a hydrogen bond interaction force with estazolam, the PRO1096 residue on the EP300 receptor forms a hydrophobic interaction force with estazolam, the PRO1078, GLN1116, GLN1112, TYR111, LYS1105, and ASP1120 residues on the EP300 receptor form a van der Waals interaction force with estazolam, the ASP1119 residue on the EP300 receptor forms a Pi-anion interaction force with estazolam, and the PHE1090 residue on the EP300 receptor forms a Pi-Pi stacked interaction force with estazolam, with a binding energy of −7.7 kcal/mol.
Residues of LEU346 on the ESR1 receptor formed hydrogen bond interactions with estazolam, residues of PHE404, ALA350, LEU391, LEU387, PHE425, LEU428, MET421, and ILE424 on the ESR1 receptor formed hydrophobic interactions with estazolam, residues of ARG394, LEU349, GLU353, THR347, LEU540, TRP383, LEU525, LEU384, and MET388 on the ESR1 receptor formed van der Waals interactions with estazolam, and residues of PHE404 on the ESR1 receptor formed Pi-Pi T-shaped interactions with estazolam, with a binding energy of −8.9 kcal/mol.
ASN40 residues on the HDAC1 receptor formed hydrogen bond interactions with estazolam, ILE249, and VAL198 residues on the HDAC1 receptor formed hydrophobic interactions with estazolam, TYR48, GLY17, TYR15, TYR333, ASP332, and ASP248 residues on the HDAC1 receptor formed van der Waals interactions with estazolam, HIS39 and PHE252 residues on the HDAC1 receptor formed pi-cation interactions with estazolam, and PHE252 residues on the HDAC1 receptor formed Pi-Pi T-shaped interactions with estazolam, with a binding energy of −8.6 kcal/mol.
ASN63 residues on the IL6 receptor formed a hydrogen bond interaction force with estazolam, LEU147 residues on the IL6 receptor formed a pi-sigma interaction force with estazolam, ASP140, THR143, VAL96, LEU64, and PRO65 residues on the IL6 receptor formed a van der Waals interaction force with estazolam, HIS39, and PHE252 residues on the IL6 receptor formed a pi-cation interaction force with estazolam, and GLU93 residues on the IL6 receptor formed a carbon hydrogen bond force with estazolam, with a binding energy of −6.6 kcal/mol.
ILE58 residues on TNF receptor formed hydrogen bond interaction with estazolam, tyrra119, and tyrb119, and tyr151 residues on TNF receptor formed hydrophobic interaction with estazolam, tyrc119, glyc121, gly122, tyr59, leuc57, ile155, leub57, leua157, val123, and gln61 residues on TNF receptor formed van der Waals interaction with estazolam, leua57 residues on TNF receptor formed pi-sigma interaction with estazolam, and tyrb59 residues on TNF receptor formed Pi-Pi stacked interaction with estazolam, with binding energy of −11.4 kcal/mol.
The residues of LEU348 on the NCOR1 receptor formed hydrophobic interactions with estazolam, the residues of SER355, PHE284, ASN354, ILE352, ILE362, ASN283, and MET328 on the NCOR1 receptor formed van der Waals interactions with estazolam, the residues of LEU287 and ILE357 on the NCOR1 receptor formed pi-sigma interactions with estazolam, and the residues of ARG353 and ILE357 on the NCOR1 receptor formed carbon hydrogen bond interactions with estazolam with a binding energy of −8.2 kcal/mol.
In summary, estazolam, a classic clinical sedative-hypnotic agent, was selected as the positive control for insomnia in this study, and parallel molecular docking was carried out with identical protein pretreatment, grid setting, and docking parameters. As presented in Table , the binding free energies of estazolam against all six core target proteins were lower than −5 kcal/mol, indicating its stable binding capacity to each target. Similarly, ganoderic acid B exhibited binding free energies below −5 kcal/mol for all targets. Both compounds could form multiple noncovalent interactions, including hydrogen bonds and hydrophobic interactions, within the active pockets, with comparable binding affinities. These results verify that ganoderic acid B possesses target-binding potential equivalent to the standard clinical hypnotic drug for insomnia.
3.5. Analysis of Molecular Dynamics Simulation Results
The root-mean-square deviation (RMSD) is a good measure of the conformational stability of proteins and ligands, as well as the degree to which the atomic position deviates from the starting position. The smaller the deviation, the better is the conformational stability. Therefore, the balance of the simulation system is evaluated using the RMSD. As shown in Figure A, both EP300 protein and EP300-Ganoderic acid B complex systems reached equilibrium after 80 ns, and finally fluctuated at 0.36 and 0.32 nm, respectively. Both ESR1 protein and ESR1-Ganoderic acid B complex system reached equilibrium after 60 ns, and finally fluctuated at 0.28 and 0.21 nm, respectively. Both HDAC1 protein and HDAC1-Ganoderic acid B complex system reached equilibrium after 25 ns, and finally fluctuated at 0.3 and 0.24 nm, respectively. Both IL6 protein and IL6-Ganoderic acid B complex system reached equilibrium after 60 ns, and finally fluctuated at 0.38 and 0.32 nm, respectively. Both NCOR1 protein and NCOR1-Ganoderic acid B complex system reached equilibrium after 20 ns and finally fluctuated at 0.46 and 0.38 nm, respectively. Both TNF protein and TNF-ganoderic acid B complex systems reached equilibrium after 50 ns and finally fluctuated at 0.28 and 0.22 nm, respectively. Therefore, the ganoderic acid B small molecule exhibits high stability when binding to EP300, ESR1, HDAC1, IL6, NCOR1, and TNF target proteins, respectively.
15.
Molecular dynamics simulation of protein–ligand complexes. (A) RMSD values of protein–ligand complexes over time; (B) R g values of protein–ligand complexes over time; (C) SASA values of protein–ligand complexes over time; (D) RMSF value of the protein–ligand complex; (E) free energy landscape diagram.
The radius of gyration (R g) can be used to describe the change of the overall structure and can be used to characterize the compactness of the protein structure. The EP300-Ganoderic acid B, IL6-Ganoderic acid B, and NCOR1-Ganoderic acid B complex systems exhibited slight fluctuations during exercise. This indicates that the small-molecule-target protein complex undergoes conformational changes during movement. ESR1-Ganoderic acid B, HDAC1-Ganoderic acid B, and TNF-Ganoderic acid B complexes fluctuated steadily during exercise. This indicated that the small-molecule-target protein complex did not expand and contract significantly during exercise (Figure B).
The solvent-accessible surface area (SASA) is an index to evaluate the surface area of proteins. This simulation calculated the solvent-accessible surface area between the target protein and small molecules (Figure C). The results showed that EP300-Ganoderic acid B, IL6-Ganoderic acid B, and NCOR1-Ganoderic acid B The complex system exhibits slight fluctuations. It was demonstrated that binding small molecules affects the binding microenvironment and leads to changes in the SASA to some extent. After binding of ESR1-Ganoderic acid B, HDAC1-Ganoderic acid B, and TNF-Ganoderic acid B receptors to ligands, the SASA of the complexes did not change significantly, further indicating that small protein molecules can achieve relatively stable binding.
The root-mean-square fluctuation (RMSF) can represent the flexibility of amino acid residues in proteins. As shown in Figure D, the RMSF values of the EP300-Ganoderic acid B complex are relatively low (mostly below 0.5 nm), the RMSF values of the ESR1-Ganoderic acid B complex are relatively low (mostly below 0.4 nm), the RMSF values of the HDAC1-Ganoderic acid B complex are relatively low (mostly below 0.5 nm), the RMSF values of the IL6-Ganoderic acid B complex are relatively low (mostly below 0.3 nm), the RMSF values of the NCOR1-Ganoderic acid B complex are relatively low (mostly below 0.4 nm), and the RMSF values of the TNF-Ganoderic acid B complex are relatively low (mostly below 0.35 nm), thus, their flexibility is lower and stability is higher.
The Free Energy Landscape (FEL) is displayed in Figure E. Color mapping was adopted in this figure to distinguish different free energy values, where red regions correspond to high free energy and blue regions represent low free energy. As observed from the FEL plots, the EP300-Ganoderic acid B complex exhibited low free energy at PC1 (representing RMSD) = 0.3 and PC2 (representing R g) = 2.07; the ESR1-Ganoderic acid B complex showed minimal free energy at PC1 = 0.22 and PC2 = 2.25; the HDAC1-Ganoderic acid B complex had the lowest free energy at PC1 = 0.23 and PC2 = 2.385; the IL6-Ganoderic acid B complex displayed low free energy at PC1 = 0.27 and PC2 = 1.58; the NCOR1-Ganoderic acid B complex reached the energy minimum at PC1 = 0.39 and PC2 = 1.8; the TNF-Ganoderic acid B complex possessed low free energy at PC1 = 0.2 and PC2 = 2.04. These findings demonstrated that the conformations corresponding to these coordinate positions were the most stable conformations throughout the simulation.
In summary, the EP300-Ganoderic acid B, ESR1-Ganoderic acid B, HDAC1-Ganoderic acid B, IL6-Ganoderic acid B, NCOR1-Ganoderic acid B, and TNF-Ganoderic acid B complex systems exhibited relatively stable fluctuations during the simulation. Accordingly, the small molecule displayed favorable binding interactions with the target proteins.
3.6. In Vitro Verification of the Protective Effect of Ganoderic Acid B against CORT-Induced HT22 Stress Neurotoxicity
3.6.1. CCK-8 Detects the Effect of Ganoderic Acid B on Cell Viability
In order to screen for safe and effective dosing concentrations, CCK-8 method was used to detect the effect of different concentrations of Ganoderic acid B on cell survival after 24 h and 48 h treatment in CORT-induced HT22 stress neurotoxicity model. The results showed that at 24 h (Figure A), Ganoderic acid B had no significant effect on cell viability in the concentration range of 0–8 μM. When the concentration increased to 16 μM, cell viability began to decrease significantly and showed a decreasing trend with the increase in concentration. At 48 h (Figure B), the downward trend of cell viability is more obvious, with significant inhibition at 8 μM, and cell viability has dropped to about 50% at 64 μM. Based on the above results, in order to explore the drug effect on the premise of avoiding obvious cytotoxicity, 4 μM (low dose), 8 μM (medium dose), and 16 μM (high dose) were selected as intervention concentrations in subsequent experiments of this study.
16.
Regulation of Ganoderic acid B on the survival, apoptosis, and key protein expression of HT22 cells. Changes in cell survival rate after Ganoderic acid B treatment for 24 h (A) and 48 h (B). (C, D) Flow cytometry detected the apoptosis rate of HT22 cells in each group. (E, F) Effect of Ganoderic acid B on the expression of key proteins in HT22 cells. (n = 6, *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).
3.6.2. Effect of Ganoderic Acid B on Apoptosis Rate of HT22 Cells Detected by Flow Cytometry
In order to explore the regulatory effect of Ganoderic acid B on HT22 cell apoptosis under CORT-induced stress neurotoxicity, the results showed that compared with the blank group, the apoptosis rate of the model group was significantly increased, indicating that the model successfully induced HT22 cell apoptosis. After different doses of Ganoderic acid B intervention, the apoptosis rate of each dose group was significantly lower than that of the model group in a dose-dependent manner, and the apoptosis rate of the high-dose group (16 μM) was closest to the level of the control group (Figure C,D). The results indicate that Ganoderic acid B can effectively inhibit the apoptosis of HT22 cells under CORT-induced stress neurotoxicity, and this protective effect is concentration-dependent.
3.6.3. Effect of Ganoderic Acid B on the Expression of Key Proteins in HT22 Cells
In order to further explore the molecular mechanism of Ganoderic acid B’s neuroprotective effect against CORT-induced HT22 stress neurotoxicity, the results showed that compared with the blank group, the expression of NCoR1, EP300, HDAC1, IL-6, and TNF proteins in the CORT-induced stress neurotoxicity model group was upregulated, while the expression of ESR1 protein was downregulated. After different doses of Ganoderic acid B intervention, the expression of NCoR1, EP300, HDAC1, IL-6, and TNF proteins was downregulated, while the expression of ESR1 protein was upregulated, and the protein expression level of the 8 μM group (noncytotoxic high concentration) was closest to that of the blank group (Figure E,F).
4. Discussion
As a high-incidence sleep-wake disorder worldwide, insomnia is characterized by difficulty in falling asleep, sleep maintenance disorder, and decreased sleep quality. The long-term course of the disease can lead to a series of complications such as immune dysfunction, increased risk of cardiovascular disease, and cognitive decline, which has become an important problem that seriously threatens public health. At present, sedative and hypnotic drugs such as benzodiazepines and nonbenzodiazepines commonly used in clinics can quickly improve sleep symptoms, but long-term use can easily lead to adverse reactions such as tolerance, dependence, and withdrawal. Therefore, it is of great clinical value and social significance to develop safe and effective natural active ingredient replacement therapy. Originally recorded in Shen Nong’s Materia Medica 2000 years ago, Ganoderma lucidum is a traditional herbal remedy used to treat arthritis, hearing loss, and sleep problems. In current clinical practice, Ganoderma lucidum is used to treat cancer, liver disease, hypertension, and insomnia. In this study, lanostane triterpenoid ganoderic acid B, the main active ingredient in Ganoderma lucidum, was selected as the research object, which has been proven to have various pharmacological activities such as sedation and hypnosis, anti-inflammatory effects, neuroprotection, and lung injury repair, , which provides a solid theoretical basis for further exploration of its anti-insomnia effect and molecular mechanism. Unlike previous studies relying on Ganoderma crude extracts or single-pathway triterpenoid detection, this study systematically verified the multitarget synergistic effect of Ganoderic acid B via metabolomics and dual in vivo/in vitro models, proving it to be a promising candidate for novel anti-insomnia agents.
Insomnia is closely related to neurotransmitter imbalances, key chemicals in the brain that regulate sleep, which can lead to brain hyperexcitation and affect sleep-wake rhythms. Studies have shown that insomnia is closely related to neurotransmitters such as 5-HT, GABA, DA, and NE. 5-HT and GABA are inhibitory neurotransmitters, and their content increases can effectively promote sleep, while DA and NE are excitatory neurotransmitters, and their content decreases can effectively promote sleep. In this study, the contents of 5-HT and GABA in mice in the administration group increased, while the contents of DA and NE decreased, , among which the high-dose treatment of ganoderic acid B had a better effect. It is revealed that regulating neurotransmitter release is one of the pathways of ganoderic acid B in the treatment of insomnia. In addition, the results of HE staining showed that neurons in the hippocampus of model mice showed shrinkage, bleeding, and vacuolar-like changes, while ganoderic acid B could significantly improve the pathological damage of neurons, and the morphology and arrangement of neurons in the high-dose group basically returned to normal, indicating that ganoderic acid B had a significant protective effect on brain tissue damage caused by insomnia, which provided pathological evidence for its anti-insomnia effect.
Serum metabolomics complements the regulatory mechanisms of ganoderic acid B at the global metabolic level. In this study, a total of 10 serum candidate metabolites with insomnia characteristics were identified, and 6 core markers (urate, l-tyrosine, serotonin, l-phenylalanine, tyramine, and 6-hydroxymelatonin) were finally screened according to the methods of AUC curve, random forest and correlation analysis with biochemical indexes, which initially possessed biomarkers for diagnosing insomnia. Among them, l-phenylalanine, l-tyrosine, and tyramine belong to the core products of the homologous phenylalanine-tyrosine monoamine metabolism pathway in the human body. l-Phenylalanine is an essential upstream amino acid, which is catalyzed by liver hydroxylase to generate l-tyrosine, and l-tyrosine is further decarboxylated to generate tyramine. The three participate in the synthesis of central excitatory neurotransmitters and the regulation of nerve homeostasis. Metabolomics studies have shown that the metabolic pathway in insomnia patients is disordered as a whole, and the anabolism of upstream l-phenylalanine and l-tyrosine is insufficient, which directly causes the imbalance of central neurotransmitter synthesis, while the metabolic accumulation of downstream tyramine is abnormally increased, which will overactivate the central sympathetic nerve, increase nerve excitability, inhibit the activity of sedation and tranquilization-related pathways, and eventually induce insomnia symptoms such as difficulty falling asleep and fragmented sleep. Regulating the homeostasis of this metabolic axis can effectively correct the nerve excitation-inhibition imbalance and improve sleep quality. The abnormal linkage of three substances in the pathway can be used as a specific biomarker to assess monoamine metabolism disorder and central nervous system excitability imbalance in insomnia patients. ,
6-Hydroxymelatonin is the most active metabolite of melatonin in the body. It is produced by the hydroxylation of melatonin catalyzed by the liver CYP1A2 enzyme, accounting for 80–90% of melatonin metabolites. Like melatonin, it can activate MT1/MT2 receptors, and its antioxidant and neuroprotective effects are stronger than melatonin. It can penetrate the blood-brain barrier and finally be excreted in urine in a combined form. After melatonin is supplemented, the production of 6-Hydroxymelatonin in the body increases, which is the key link for melatonin to improve insomnia. It can be used as a biomarker to evaluate melatonin metabolism and sleep rhythm.
Urate is the core end product of purine metabolism in the human body, which is mainly excreted by the kidneys. It is an important endogenous antioxidant in the body and can participate in the regulation of oxidative stress in the central nervous system. Metabolomics studies have confirmed that the purine metabolism pathway in insomnia patients is disordered; serum and urine urate levels are abnormally elevated, and excessive uric acid accumulation will aggravate oxidative damage and neuroinflammation in the central nervous system, increase the body’s arousal level, and destroy the normal sleep structure. As a core characterization of oxidative stress and purine metabolism disorder in the body, urate can be used as a key peripheral biomarker to assess the onset of insomnia and the imbalance of sleep metabolic homeostasis.
Serotonin is a key central active product of the tryptophan metabolism pathway, which is mainly synthesized by tryptophan hydroxylase. It is a core inhibitory neurotransmitter that regulates the sleep-wake cycle and provides a direct precursor for melatonin synthesis. Metabolomics analysis showed that there was significant tryptophan metabolism arrest in insomnia people, and the synthesis and secretion levels of central and peripheral serotonin were significantly reduced, which directly led to difficulty in sleep initiation and sleep maintenance and, at the same time, inhibited the production of endogenous melatonin and aggravated circadian rhythm disorder. Increasing the level of serotonin in the body can effectively improve sleep disorders, and its abnormal content can be used as a core biomarker to evaluate the balance of central neurotransmitters and sleep rhythm disorders. ,
Metabolomics data analysis shows that phenylalanine, tyrosine, and tryptophan biosynthetic pathways directly regulate the supply of 5-HT and melatonin synthetic precursors, which are the key links between peripheral metabolism and central sleep regulation. Ganoderma B can regulate the levels of 6 key differential metabolites, reshape the disordered amino acid metabolic network, and synergistically exert a sedative effect by increasing the supply of 5-HT synthetic precursors and reducing the accumulation of pro-inflammatory metabolites; the correlation analysis also confirmed that the above characteristic metabolites were significantly associated with four key neurotransmitters, 5-HT, GABA, DA, and NE, establishing a “metabolic neurotransmitter sleep” regulatory axis.
The above-mentioned metabolic markers related to insomnia have been reported in a number of insomnia metabolomic studies. The results of this study further support the findings of the existing literature and realize the reverification and consolidation of the conclusions of previous studies. In contrast, the effect of ganoderic acid B on insomnia and its metabolic regulation mechanism was first proposed in this study. At the same time, the potential targets obtained by nontargeted metabolomics screening in this study partially intersect with the target set obtained by network pharmacology prediction, which corroborate each other from two different dimensions of metabolic phenotype and molecular network, further supporting the multitarget action characteristics of ganoderic acid B in the treatment of insomnia and providing multiomics cross-evidence for explaining its potential pharmacological mechanism.
Targets related to insomnia were discovered for the first time in the network pharmacology studies. In order to verify the molecular mechanism of ganoderic acid B anti-insomnia through the above targets, we found through Western blot that NCoR1, EP300, HDAC1, IL-6, and TNF protein expressions were upregulated, and ESR1 expression was downregulated in the hippocampus and HT22 cells of the model group. Ganoderic acid B can reverse this change in a dose-dependent manner, with in vitro and in vivo results mutually confirmed. In the present study, consistent in vivo and in vitro protein expression results suggest that mannanate B simultaneously regulates six core targets: NCoR1, EP300, HDAC1, ESR1, IL-6, and TNF. Combined with molecular docking validation, suggesting a stable binding between ganoderic acid B and these proteins, we speculate that this compound alleviates insomnia through synergistic multitarget regulation. Upregulation of ESR1 and downregulation of epigenetic regulators (NCoR1, EP300, HDAC1) and pro-inflammatory cytokines (IL-6, TNF) together alleviated hippocampal neuronal damage and central nervous system inflammation observed in PCPA mice and CORT-induced HT22 cells. This protein regulatory network also coordinates with metabolomically identified alterations in the metabolic pathways of phenylalanine, tyrosine, and tryptophan to rebalance the central inhibitory/excitatory neurotransmitters 5-HT, GABA, DA, and NE to improve sleep-related behavioral disorders. Multilayer correlations between animal phenotypes, serum metabolic profiles, and core target proteins construct the complete pharmacological mechanism of ganoderic acid B against insomnia.
In vitro CORT-induced HT22 cell injury model was adopted to mimic stress-triggered neuronal neurotoxic damage. CCK-8 results confirmed that 0–8 μmol/L Ganoderic acid B had no obvious cytotoxicity. Flow apoptosis detection showed that Ganoderic acid B could significantly reduce the apoptosis rate of model cells, and the high-dose neuroprotective effect was the best. Cellular protein detection was completely consistent with the results of brain tissue WB, which again proved that Ganoderic acid B inhibited neuronal apoptosis and alleviated cell inflammatory damage by downregulating pro-inflammatory and apparent inhibition-related proteins and upregulating ESR1 hormone receptor protein, explaining the underlying mechanism of hippocampal protection and insomnia improvement at the cellular level.
This study combines multidimensional data in vivo, in vitro, and bioinformatics to comprehensively reveal the “multitarget, multipathway” synergistic anti-insomnia mechanism of Ganoderic acid B: Ganoderic acid B stably binds to the core targets of NCoR1, EP300, HDAC1, ESR1, IL-6, and TNF. On the one hand, it downregulates the pro-inflammatory factors IL-6 and TNF to inhibit CNS inflammation and downregulates the epigenetic regulatory proteins EP300, HDAC1, and NCoR1 to alleviate neuronal damage; on the other hand, upregulation of ESR1 activates sleep protective signals and repairs the structure of hippocampal neurons; at the same time, it remodels the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan, enhances the synthesis of inhibitory neurotransmitters such as 5-HT and GABA, downregulates excitatory DA and NE, and ultimately improves insomnia-related disorders.
5. Conclusion
In this study, we employed serum metabolomics and network pharmacology to elucidate the potential mechanisms of action of Ganoderic acid B in treating insomnia. We identified its key targets and further validated these findings through theoretical analysesincluding molecular docking and molecular dynamics simulationsas well as in vitro experiments and Western blot assays, thereby preliminarily confirming the interactions between the active compound and its key targets. Our findings suggested that Ganoderic acid B exerts its therapeutic effect on insomnia by targeting NCoR1, EP300, HDAC1, ESR1, IL-6, and TNF, among others. These results provide a theoretical basis for the clinical application of ganoderic acid B and offer new insights for the development of novel hypnotic drugs.
Supplementary Material
Glossary
Abbreviations
- BB
Ganoderic acid B
- PCPA
para-chlorophenylalanine
- EST
Estazolam
- OFT
open-field test
- NSFT
novelty suppressed-feeding test
- ELISA
enzyme-linked immunosorbent assay
- 5-HT
5-hydroxytryptamine
- GABA
γ-aminobutyric acid
- DA
dopamine
- NE
norepinephrine
- NCoR1
nuclear receptor corepressor 1
- EP300
E1A-binding protein p300
- ESR1
estrogen receptor 1
- HDAC1
histone deacetylase 1
- IL-6
nterleukin 6
- TNF
tumor necrosis factor
- GAPDH
glyceraldehyde-3-phosphate dehydrogenase
- HRP
horseradish peroxidase
- OPLS-DA
orthogonal partial least-squares discriminant
- PCA
rincipal component analysis
- PPI
protein–protein interaction
- GO
Gene Ontology
- BP
biological process
- CC
cellular component
- MF
molecular function
- KEGG
Kyoto Encyclopedia of Genes and Genomes
- FBS
fetal bovine serum
- CORT
corticosterone
- DMSO
dimethyl sulfoxide
- PBS
phosphate-buffered saline
All data supporting the findings of this study are fully available within the article and its Supporting Information files. No additional public data repository deposition is required.
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.6c07355.
§.
C.W. and H.B. contributed equally to this work and share the last authorship.
This work was supported by Education Department of Heilongjiang (Grant No. LH2024H044); Heilongjiang Provincial Traditional Chinese Medicine Scientific Research Project (Grant No. ZHY2025-272); Natural Science Foundation of Heilongjiang Province (Grant No. QC2025H016); and National Natural Science Foundation of China Youth Fund (82405036).
The authors declare no competing financial interest.
References
- Kim S. Y., Lee K. H., Jeon J. E., Lee H. Y., You J. H., Shin J., Seo M. C., Seo W. W., Lee Y. J.. Reduced prefrontal activation during cognitive control under emotional interference in chronic insomnia disorder. J. Sleep Res. 2025;34(3):e14383. doi: 10.1111/jsr.14383. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Calcaterra, V. ; Rossi, V. ; Tagi, V. M. ; Baldassarre, P. ; Grazi, R. ; Taranto, S. ; Zuccotti, G. . Food Intake and Sleep Disorders in Children and Adolescents with Obesity. Nutrients. 2023, 15(22) 4736 10.3390/nu15224736. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Rowe R. K., Griesbach G. S.. Immune-endocrine interactions in the pathophysiology of sleep-wake disturbances following traumatic brain injury: A narrative review. Brain Res. Bull. 2022;185:117–128. doi: 10.1016/j.brainresbull.2022.04.017. [DOI] [PubMed] [Google Scholar]
- Benjafield A. V., Sert Kuniyoshi F. H., Malhotra A., Martin J. L., Morin C. M., Maurer L. F., Cistulli P. A., Pépin J. L., Wickwire E. M.. Estimation of the global prevalence and burden of insomnia: a systematic literature review-based analysis. Sleep Med. Rev. 2025;82:102121. doi: 10.1016/j.smrv.2025.102121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li L. J., Yang Y., Guan B. Y., Chen Q., Wang A. X., Wang Y. J., Zhang N., Wang C. X.. Insomnia is associated with increased mortality in patients with first-ever stroke: a 6-year follow-up in a Chinese cohort study. Stroke Vasc. Neurol. 2018;3(4):197–202. doi: 10.1136/svn-2017-000136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Riemann D., Nissen C., Palagini L., Otte A., Perlis M. L., Spiegelhalder K.. The neurobiology, investigation, and treatment of chronic insomnia. Lancet Neurol. 2015;14(5):547–558. doi: 10.1016/S1474-4422(15)00021-6. [DOI] [PubMed] [Google Scholar]
- Robertson I., Cheung A., Fan X.. Insomnia in patients with schizophrenia: current understanding and treatment options. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2019;92:235–242. doi: 10.1016/j.pnpbp.2019.01.016. [DOI] [PubMed] [Google Scholar]
- Sarris J., Ravindran A., Yatham L. N., Marx W., Rucklidge J. J., McIntyre R. S., Akhondzadeh S., Benedetti F., Caneo C., Cramer H.. et al. Clinician guidelines for the treatment of psychiatric disorders with nutraceuticals and phytoceuticals: The World Federation of Societies of Biological Psychiatry (WFSBP) and Canadian Network for Mood and Anxiety Treatments (CANMAT) Taskforce. World J. Biol. Psychiatry. 2022;23(6):424–455. doi: 10.1080/15622975.2021.2013041. [DOI] [PubMed] [Google Scholar]
- a Qiu Y., Mao Z. J., Ruan Y. P., Zhang X.. Exploration of the anti-insomnia mechanism of Ganoderma by central-peripheral multi-level interaction network analysis. BMC Microbiol. 2021;21(1):296. doi: 10.1186/s12866-021-02361-5. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Segain J. P., Raingeard de la Blétière D., Sauzeau V., Bourreille A., Hilaret G., Cario-Toumaniantz C., Pacaud P., Galmiche J. P., Loirand G.. Rho kinase blockade prevents inflammation via nuclear factor κB inhibition: evidence in Crohn’s disease and experimental colitis. Gastroenterology. 2003;124(5):1180–1187. doi: 10.1016/S0016-5085(03)00283-X. [DOI] [PubMed] [Google Scholar]; c Gibson C. L., Srivastava K., Sprigg N., Bath P. M., Bayraktutan U.. Inhibition of Rho-kinase protects cerebral barrier from ischaemia-evoked injury through modulations of endothelial cell oxidative stress and tight junctions. J. Neurochem. 2014;129(5):816–826. doi: 10.1111/jnc.12681. [DOI] [PubMed] [Google Scholar]; d Ishikura K., Yamada N., Ito M., Ota S., Nakamura M., Isaka N., Nakano T.. Beneficial acute effects of rho-kinase inhibitor in patients with pulmonary arterial hypertension. Circ J. 2006;70(2):174–178. doi: 10.1253/circj.70.174. [DOI] [PubMed] [Google Scholar]
- Yao C., Wang Z., Jiang H., Yan R., Huang Q., Wang Y., Xie H., Zou Y., Yu Y., Lv L.. Ganoderma lucidum promotes sleep through a gut microbiota-dependent and serotonin-involved pathway in mice. Sci. Rep. 2021;11(1):13660. doi: 10.1038/s41598-021-92913-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Wang, S. ; Wang, L. ; Shangguan, J. ; Jiang, A. ; Ren, A. . Research Progress on the Biological Activity of Ganoderic Acids in Ganoderma lucidum over the Last Five Years. Life. 2024, 14 (10)1339 10.3390/life14101339. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Ahmad R., Riaz M., Khan A., Aljamea A., Algheryafi M., Sewaket D., Alqathama A.. Ganoderma lucidum (Reishi) an edible mushroom; a comprehensive and critical review of its nutritional, cosmeceutical, mycochemical, pharmacological, clinical, and toxicological properties. Phytother. Res. 2021;35(11):6030–6062. doi: 10.1002/ptr.7215. [DOI] [PubMed] [Google Scholar]; c Shangguan J., Wu T., Tian L., Liu Y., Zhu L., Liu R., Zhu J., Shi L., Zhao M., Ren A.. Hydrogen sulfide maintains mitochondrial homeostasis and regulates ganoderic acids biosynthesis by SQR under heat stress in Ganoderma lucidum. Redox Biol. 2024;74:103227. doi: 10.1016/j.redox.2024.103227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Chen W., Deng Y. Y., Yu J. W., Leung Y. T., Bai J. X., Chen Y. J., Wu Y., Wang L., Fan X. Y., Wang X. Q.. et al. A tri-herb formulation protects against ethanol-induced mouse liver injury and downregulates mitogen-activated protein kinase phosphatase 1. Phytomedicine. 2023;114:154802. doi: 10.1016/j.phymed.2023.154802. [DOI] [PubMed] [Google Scholar]; b Bashir M. A., Abdalla M., Shao C. S., Wang H., Bondzie-Quaye P., Almahi W. A., Swallah M. S., Huang Q.. Dual inhibitory potential of ganoderic acid A on GLUT1/3: computational and in vitro insights into targeting glucose metabolism in human lung cancer. RSC Adv. 2024;14(39):28569–28584. doi: 10.1039/D4RA04454A. [DOI] [PMC free article] [PubMed] [Google Scholar]; c Chen L., Wu B., Mo L., Chen H., Yin X., Zhao Y., Cui Z., Cui F., Chen L., Deng Q.. et al. High-content screening identifies ganoderic acid A as a senotherapeutic to prevent cellular senescence and extend healthspan in preclinical models. Nat. Commun. 2025;16(1):2878. doi: 10.1038/s41467-025-58188-5. [DOI] [PMC free article] [PubMed] [Google Scholar]; d Cui J., Meng Y. H., Wang Z. W., Wang J., Shi D. F., Liu D.. Ganoderic Acids A and B Reduce Okadaic Acid-Induced Neurotoxicity in PC12 Cells by Inhibiting Tau Hyperphosphorylation. Biomed. Environ. Sci. 2023;36(1):103–108. doi: 10.3967/bes2023.011. [DOI] [PubMed] [Google Scholar]; e Xiao C., Chen J., Zhou X., Liu H.. Ganoderic acid A alleviate psoriasis by inhibiting GSDMD-mediated pyroptosis. Tissue Cell. 2025;97:103078. doi: 10.1016/j.tice.2025.103078. [DOI] [PubMed] [Google Scholar]; f Sui Q., Zhu C., Shi S., Xu J., Zhang J., Wang A., Chen P., Liang G., Zhang Y.. Ganoderic acid A: an in-depth review of pharmacological effects and molecular docking analysis. J. Ethnopharmacol. 2025;349:119868. doi: 10.1016/j.jep.2025.119868. [DOI] [PubMed] [Google Scholar]
- Shi J., Wang H., Liu J., Zhang Y., Luo J., Li Y., Yang C., Jiang J.. Ganoderic acid B attenuates LPS-induced lung injury. Int. Immunopharmacol. 2020;88:106990. doi: 10.1016/j.intimp.2020.106990. [DOI] [PubMed] [Google Scholar]
- a Guedes I. A., de Magalhães C. S., Dardenne L. E.. Receptor-ligand molecular docking. Biophys. Rev. 2014;6(1):75–87. doi: 10.1007/s12551-013-0130-2. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Pagadala N. S., Syed K., Tuszynski J.. Software for molecular docking: a review. Biophys. Rev. 2017;9(2):91–102. doi: 10.1007/s12551-016-0247-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sharma, B. ; Yadav, D. K. . Metabolomics and Network Pharmacology in the Exploration of the Multi-Targeted Therapeutic Approach of Traditional Medicinal Plants. Plants 2022, 11 (23). 3243 10.3390/plants11233243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Dong Y. J., Jiang N. H., Zhan L. H., Teng X., Fang X., Lin M. Q., Xie Z. Y., Luo R., Li L. Z., Li B.. et al. Soporific effect of modified Suanzaoren Decoction on mice models of insomnia by regulating Orexin-A and HPA axis homeostasis. Biomed. Pharmacother. 2021;143:112141. doi: 10.1016/j.biopha.2021.112141. [DOI] [PubMed] [Google Scholar]; b Kukuia K. K. E., Appiah F., Dugbartey G. J., Takyi Y. F., Amoateng P., Amponsah S. K., Adi-Dako O., Koomson A. E., Ayertey F., Adutwum-Ofosu K. K.. Extract of Mallotus oppositifolius (Geiseler) Müll. Arg. increased prefrontal cortex dendritic spine density and serotonin and attenuated para-chlorophenylalanine-aggravated aggressive and depressive behaviors in mice. Front. Pharmacol. 2022;13:962549. doi: 10.3389/fphar.2022.962549. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chen W., Yu J. W., Deng Y. Y., Wong L. Y., Wang C., Liang Y. L., Leung Y. T., Tian J. Y., Wu Y., Leung K. S.. et al. Identification of sedative-hypnotic compounds shared by five medicinal Polyporales mushrooms using UPLC-Q-TOF-MS/MS-based untargeted metabolomics. Phytomedicine. 2024;128:155355. doi: 10.1016/j.phymed.2024.155355. [DOI] [PubMed] [Google Scholar]
- Liu W. L., Wu B. F., Shang J. H., Wang X. F., Zhao Y. L., Huang A. X.. Moringa oleifera seed ethanol extract and its active component kaempferol potentiate pentobarbital-induced sleeping behaviours in mice via a GABAergic mechanism. Pharm. Biol. 2022;60(1):810–824. doi: 10.1080/13880209.2022.2056207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhong Y., Zheng Q., Hu P., Huang X., Yang M., Ren G., Du Q., Luo J., Zhang K., Li J.. et al. Sedative and hypnotic effects of compound Anshen essential oil inhalation for insomnia. BMC Complementary Altern. Med. 2019;19(1):306. doi: 10.1186/s12906-019-2732-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cheng J., Wu Q., Sun R., Li W., Wang Z., Zhou M., Yang T., Wang J., Lyu Y., Yue C.. Protective effects of a probiotic-fermented germinated grain complex on neurotransmitters and sleep quality in sleep-deprived mice. Front. Microbiol. 2024;15:1438928. doi: 10.3389/fmicb.2024.1438928. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ren L., Fan Y., Luo H., Hu J., Hu J.. PACAP/VIP in the prefrontal cortex mediates the rapid antidepressant effects of zhizichi decoction. J. Ethnopharmacol. 2024;335:118638. doi: 10.1016/j.jep.2024.118638. [DOI] [PubMed] [Google Scholar]
- Juarez-Gomez C., Aguilar-Vazquez A., Gonzalez-Gauna E., Garcia-Ordoñez G. P., Martin-Marquez B. T., Gomez-Rios C. A., Becerra-Jimenez J., Gaspar-Ruiz A., Vazquez-Del Mercado M.. Integrating Machine Learning into Myositis Research: a Systematic Review. Clin. Rev. Allergy Immunol. 2025;61(1):62. doi: 10.1007/s12016-025-09076-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Chen X., Zou G., Yang Z., Qi X., Song F., Peng L., Wang D., Zhou J., Ma J., He H.. et al. Serum metabolomic profiling uncovered metabolic shifts in individuals upon moderate-altitude exposure and identified the potentiality of beta-alanine to ameliorate hyperuricemia. Redox Biol. 2025;81:103546. doi: 10.1016/j.redox.2025.103546. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Zhai J., Jiang C., Yan H., Jin M., Xie B., Yu F., Ding Y., Ma C., Cong B., Wen D.. Integrative metabolomics and machine learning reveal diagnostic biomarkers for gelsenicine intoxication. J. Ethnopharmacol. 2026;358:121022. doi: 10.1016/j.jep.2025.121022. [DOI] [PubMed] [Google Scholar]; c Anh N. K., Phat N. K., Thu N. Q., Tien N. T. N., Eunsu C., Kim H. S., Nguyen D. N., Kim D. H., Long N. P., Oh J. Y.. Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning. Sci. Rep. 2024;14(1):15312. doi: 10.1038/s41598-024-66113-x. [DOI] [PMC free article] [PubMed] [Google Scholar]; d Wang M. G., Wu S. Q., Zhang M. M., He J. Q.. Plasma metabolomic and lipidomic alterations associated with anti-tuberculosis drug-induced liver injury. Front. Pharmacol. 2022;13:1044808. doi: 10.3389/fphar.2022.1044808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang X., Shen Y., Wang S., Li S., Zhang W., Liu X., Lai L., Pei J., Li H.. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017;45(W1):W356–W360. doi: 10.1093/nar/gkx374. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li, X. ; Nong, X. ; Yang, J. ; Li, M. ; Wang, Q. ; Sun, M. ; Ma, Q. ; Xu, L. ; Wang, Y. . Exploring the Frontier of Cyclic Dipeptides: A Bioinformatics Approach to Potential Therapeutic Applications in Schizophrenia. Int. J. Mol. Sci. 2024, 25 (21) 11421 10.3390/ijms252111421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zdrazil B.. Fifteen years of ChEMBL and its role in cheminformatics and drug discovery. J. Cheminf. 2025;17(1):32. doi: 10.1186/s13321-025-00963-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gu S., Lai L. H.. Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach. Acta Pharmacol. Sin. 2020;41(3):432–438. doi: 10.1038/s41401-019-0306-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rebhan M., Chalifa-Caspi V., Prilusky J., Lancet D.. GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics. 1998;14(8):656–664. doi: 10.1093/bioinformatics/14.8.656. [DOI] [PubMed] [Google Scholar]
- Hamosh A., Scott A. F., Amberger J. S., Bocchini C. A., McKusick V. A.. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2004;33(Database issue):D514–D517. doi: 10.1093/nar/gki033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chen X., Ji Z. L., Chen Y. Z.. TTD: Therapeutic Target Database. Nucleic Acids Res. 2002;30(1):412–415. doi: 10.1093/nar/30.1.412. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Leabman M. K., Huang C. C., Stryke D., Johns S. J., Kawamoto M., Ferrin T. E., DeYoung J., Taylor T. R., De La Cruz M., Herskowitz I., Giacomini K. M.. PharmGKB update: I. Genetic variants of the organic cation transporter 2 (OCT2, SLC22A2) Pharmacol Rev. 2003;55(3):399. doi: 10.1124/pr.55.3.6. [DOI] [PubMed] [Google Scholar]
- Tasneem S., Liu B., Li B., Choudhary M. I., Wang W.. Molecular pharmacology of inflammation: Medicinal plants as anti-inflammatory agents. Pharmacol. Res. 2019;139:126–140. doi: 10.1016/j.phrs.2018.11.001. [DOI] [PubMed] [Google Scholar]
- Kang J., Jiang J., Xiang X., Zhang Y., Tang J., Li L.. Identification of a new gene signature for prognostic evaluation in cervical cancer: based on cuproptosis-associated angiogenesis and multi-omics analysis. Cancer Cell Int. 2024;24(1):23. doi: 10.1186/s12935-023-03189-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhao A., Sun Q., Zhang J., Hu T., Zhou X., Wang C., Liu J., Wang B.. Substance basis and pharmacological mechanism of heat-clearing herbs in the treatment of ischaemic encephalopathy: a systematic review and network pharmacology. Ann. Med. 2024;56(1):2308077. doi: 10.1080/07853890.2024.2308077. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Hong Y., Qiao L. C., Wang Y., Song S., Meng Y., Zhou J. X., Li S.. Multi-dimensional insights into the association between PFOA exposure and NAFLD: An integrative analysis of epidemiology, transcriptomics, and molecular docking. Ecotoxicol. Environ. Saf. 2025;307:119408. doi: 10.1016/j.ecoenv.2025.119408. [DOI] [PubMed] [Google Scholar]; b Tan H., Gong H., Li H., Huang M., Zhang J., Liu Y., Li Y., Song Y., Feng Q.. A NETs-centric multi-omics framework prioritizes PGLYRP1 and MMP9 as subtype-associated thromboinflammatory biomarker candidates and putative therapeutic hypotheses in ischemic stroke. Neurobiol. Dis. 2026;225:107422. doi: 10.1016/j.nbd.2026.107422. [DOI] [PubMed] [Google Scholar]; c Zhao S., Feng Y., Zhang J., Zhang Q., Wang J., Cui S.. Comparative analysis of gene expression between mice and humans in acetaminophen-induced liver injury by integrating bioinformatics analysis. BMC Med. Genomics. 2024;17(1):80. doi: 10.1186/s12920-024-01848-0. [DOI] [PMC free article] [PubMed] [Google Scholar]; d Kong M., Jia J., Liu C., Liu H., Zhang S.. Liver fibrosis in biliary atresia: identification of the key gene EDIL3 via integrated bioinformatics. Front. Med. 2026;12:1726141. doi: 10.3389/fmed.2025.1726141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li J., Wang X., Zhang Y., Wei M., Qi J., Liu D., Wu R., Chen Q., Huang J.. Ginsenoside Rg1 alleviates PCPA-induced insomnia by inhibiting NLRP3 inflammasome activation and pyroptosis through the Nrf2/HO-1 pathway in mice. Psychopharmacology. 2025;242(12):2739–2755. doi: 10.1007/s00213-025-06828-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Yang Y., Liu H., Ren J., Liu L., Sun H., Wang L., Yan G., Han Y., Wang X.. Danggui Buxue decoction ameliorates blood deficiency syndrome by suppressing IL-6/JAK2/STAT3 signaling pathway: An integrated Chinmedomics and bioinformatics study. J. Ethnopharmacol. 2026;364:121541. doi: 10.1016/j.jep.2026.121541. [DOI] [PubMed] [Google Scholar]; b Liu H., Ren J., Hu Y., Yang Y., Sun H., Fang H., Yan G., Han Y., Wang X.. Multi-omics analysis reveals that Danggui Buxue decoction ameliorates benzene-induced blood deficiency syndrome via the restoration of hematopoietic lineage and remodeling of the immune microenvironment. Chin. Med. 2026;21(1):143. doi: 10.1186/s13020-026-01365-5. [DOI] [PMC free article] [PubMed] [Google Scholar]; c Wan C., Sun S., Han Y., Du Y., Li X., Zhang L., Yang Y., Hao J., Wu Y.. Integrating lipid metabolomics, serum medicinal chemistry, network pharmacology and experimental validation to explore the mechanism of Sanmiao wan in the treatment of rheumatoid arthritis. J. Ethnopharmacol. 2025;340:119295. doi: 10.1016/j.jep.2024.119295. [DOI] [PubMed] [Google Scholar]; d Wan C., Sun S., Du Y., Han Y., Li X., Yang Y., Cao J., Miu J., Chen P., Zhang Y.. et al. A study on the mechanism of action of Syringin in the treatment of type 2 diabetes by integrating metabolomics, network pharmacology, molecular docking technology and experimental validation. Int. Immunopharmacol. 2025;167:115716. doi: 10.1016/j.intimp.2025.115716. [DOI] [PubMed] [Google Scholar]
- a Way J. A. H., Mathieu E., Chau J., Cistulli P. A., Bin Y. S.. Assessment tools for population sleep health surveillance in adults: A systematic review of validity and reliability. Sleep Med. Rev. 2025;84:102196. doi: 10.1016/j.smrv.2025.102196. [DOI] [PubMed] [Google Scholar]; b Chino Y., Sugiyama H., Chaki S., Sato Y.. Current and novel dual orexin receptor antagonists for the treatment of insomnia: the emergence of vornorexant. Int. J. Neuropsychopharmacol. 2025;28(12):pyaf077. doi: 10.1093/ijnp/pyaf077. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Zee, P. C. ; Bertisch, S. M. ; Morin, C. M. ; Pelayo, R. ; Watson, N. F. ; Winkelman, J. W. ; Krystal, A. D. . Long-Term Use of Insomnia Medications: An Appraisal of the Current Clinical and Scientific Evidence. J. Clin. Med.. 2023, 12 (4) 1629 10.3390/jcm12041629. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Pan B., Ge L., Lai H., Hou L., Tian C., Wang Q., Yang K., Lu Y., Zhu H., Li M.. et al. The Comparative Effectiveness and Safety of Insomnia Drugs: A Systematic Review and Network Meta-Analysis of 153 Randomized Trials. Drugs. 2023;83(7):587–619. doi: 10.1007/s40265-023-01859-8. [DOI] [PubMed] [Google Scholar]; c Noble S., Langtry H. D., Lamb H. M.. Zopiclone. An update of its pharmacology, clinical efficacy and tolerability in the treatment of insomnia. Drugs. 1998;55(2):277–302. doi: 10.2165/00003495-199855020-00015. [DOI] [PubMed] [Google Scholar]
- a Huie C. W., Di X.. Chromatographic and electrophoretic methods for Lingzhi pharmacologically active components. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2004;812(1–2):241–257. doi: 10.1016/S1570-0232(04)00678-6. [DOI] [PubMed] [Google Scholar]; b Wang M., Wang F., Xu F., Ding L. Q., Zhang Q., Li H. X., Zhao F., Wang L. Q., Zhu L. H., Chen L. X., Qiu F.. Two pairs of farnesyl phenolic enantiomers as natural nitric oxide inhibitors from Ganoderma sinense. Bioorg. Med. Chem. Lett. 2016;26(14):3342–3345. doi: 10.1016/j.bmcl.2016.05.037. [DOI] [PubMed] [Google Scholar]
- a Qin Y., Zhao Y., Hu X., Chen X., Jiang Y. P., Jin X. J., Li G., Li Z. H., Yang J. H., Zhang G. L.. et al. Ganoderma lucidum spore extract improves sleep disturbances in a rat model of sporadic Alzheimer’s disease. Front. Pharmacol. 2024;15:1390294. doi: 10.3389/fphar.2024.1390294. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Chen W., Deng Y. Y., Yu J. W., Wang Z. H., Bai J. X., Wu Y., Fan X. Y., Dou X., Fu X. Q., Yu Z. L.. Sedative-hypnotic effects of an herbal formulation comprising Ganoderma, Gastrodiae Rhizoma, and Chrysanthemi Flos in mice. Phytomedicine. 2025;148:157374. doi: 10.1016/j.phymed.2025.157374. [DOI] [PubMed] [Google Scholar]; c Sheng, F. ; Zhang, L. ; Wang, S. ; Yang, L. ; Li, P. . Deacetyl Ganoderic Acid F Inhibits LPS-Induced Neural Inflammation via NF-κB Pathway Both In Vitro and In Vivo. Nutrients 2020, 12 (1) 85 10.3390/nu12010085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ye X., Pang S., Ren X., Wang H., Chen M.. Neurotransmitter modulation of sleep-wake States: From molecular mechanisms to therapeutic potential. Sleep Med. 2025;132:106547. doi: 10.1016/j.sleep.2025.106547. [DOI] [PubMed] [Google Scholar]
- Wu J., Zhao Z.. Acupuncture in circadian rhythm sleep-wake disorders and its potential neurochemical mechanisms. Front. Neurosci. 2024;18:1346635. doi: 10.3389/fnins.2024.1346635. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Ren Q., Gu M., Fan X.. Circadian clock genes and insomnia: molecular mechanisms and therapeutic implications. Ann. Med. 2025;57(1):2576643. doi: 10.1080/07853890.2025.2576643. [DOI] [PMC free article] [PubMed] [Google Scholar]; b Chagraoui A., Thibaut F., De Deurwaerdère p.. 5-HT6 receptors: Contemporary views on their neurobiological and pharmacological relevance in neuropsychiatric disorders. Dialogues Clin. Neurosci. 2025;27(1):112–128. doi: 10.1080/19585969.2025.2502028. [DOI] [PMC free article] [PubMed] [Google Scholar]; c Wang S., Liu L., Liang S., Yang J., Zhang Y., Liu X.. Effects of BXSMD on ESR1 and ESR2 expression in CSD female mice. J. Ethnopharmacol. 2024;318(Pt B):116973. doi: 10.1016/j.jep.2023.116973. [DOI] [PubMed] [Google Scholar]
- a Wang M., Wang G., Zhao M., Hou L., Ma D., Yang H., Luo Z., Mi B., Lv S.. Jujuboside A in ameliorating insomnia in mice via GABAergic modulation of the PVT. J. Ethnopharmacol. 2025;349:119939. doi: 10.1016/j.jep.2025.119939. [DOI] [PubMed] [Google Scholar]; b Liu J., Aikebaier G., Lu X., Zhang X.. Abdominal massage modulates gut microbiota and brain-gut peptides in insomnia model rats. Front. Microbiol. 2025;16:1720248. doi: 10.3389/fmicb.2025.1720248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Chu Y., Zhang Y., Liu J., Du C., Yan Y.. An integrated liver, hippocampus and serum metabolomics based on UPLC-Q-TOF-MS revealed the therapeutical mechanism of Ziziphi Spinosae Semen in p-chlorophenylalanine-induced insomnia rats. Biomed. Chromatogr. 2024;38(3):e5796. doi: 10.1002/bmc.5796. [DOI] [PubMed] [Google Scholar]; b Yang Y., Chen Z., Yan G., Kong L., Yang L., Sun H., Han Y., Zhang J., Wang X.. Mass spectrum oriented metabolomics for evaluating the efficacy and discovering the metabolic mechanism of Naoling Pian for insomnia. J. Pharm. Biomed. Anal. 2023;236:115756. doi: 10.1016/j.jpba.2023.115756. [DOI] [PubMed] [Google Scholar]; c Li, J. ; Li, B. ; Shi, X. ; Yang, Y. ; Song, Z. . Extraction Methods and Sedative-Hypnotic Effects of Total Flavonoids from Ziziphus jujuba Mesocarp. Pharmaceuticals. 2025, 18 (9) 1272 10.3390/ph18091272. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu H., Yang L., Wan C., Li Z., Yan G., Han Y., Sun H., Wang X.. Evaluation of the pharmacological effects and exploration of the mechanism of traditional Chinese medicine preparation Ciwujia tablets in treating insomnia based on ethology, energy metabolism, and urine metabolomic approaches. Front. Pharmacol. 2022;13:1009668. doi: 10.3389/fphar.2022.1009668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Ren H., Kong X., Zhang Y., Deng F., Li J., Zhao F., Li P., Pei K., Tan J., Cheng Y.. et al. The therapeutic potential of Ziziphi Spinosae Semen and Polygalae Radix in insomnia management: Insights from gut microbiota and serum metabolomics techniques. J. Ethnopharmacol. 2024;330:118255. doi: 10.1016/j.jep.2024.118255. [DOI] [PubMed] [Google Scholar]; b Zhou H. P., Su J., Wei K. J., Wu S. X., Yu J. J., Yu Y. K., Niu Z. W., Jin X. H., Yan M. Q., Chen S. H., Lyu G. y.. Beneficial Effects of Dendrobium officinale Extract on Insomnia Rats Induced by Strong Light and Noise via Regulating GABA and GABA(A) Receptors. Chin. J. Integr. Med. 2025;31(6):490–498. doi: 10.1007/s11655-025-3925-7. [DOI] [PubMed] [Google Scholar]; c Huang, N. ; Zhuang, Z. ; Liu, Z. ; Huang, T. . Observational and Genetic Associations of Modifiable Risk Factors with Aortic Valve Stenosis: A Prospective Cohort Study of 0.5 Million Participants. Nutrients. 2022, 14(11) 2273 10.3390/nu14112273. [DOI] [PMC free article] [PubMed] [Google Scholar]
- a Nguyen P.-Q., Dang-Vu T. T., Forest G., Desjardins S., Forget M. F., Vu T. T., Nguyen Q. D., Kouassi E., Desmarais P.. Mirtazapine for chronic insomnia in older adults: a randomised double-blind placebo-controlled trial-the MIRAGE study. Age Ageing. 2025;53(3):afaf050. doi: 10.1093/ageing/afaf050. [DOI] [PubMed] [Google Scholar]; b Sochal, M. ; Witkowska, A. ; Binienda, A. ; Gabryelska, A. ; Białasiewicz, P. ; Fichna, J. ; Talar-Wojnarowska, R. ; Małecka-Wojciesko, E. . The Effect of Serotonin Transmission on Depressive and Insomnia Symptoms in Inflammatory Bowel Diseases. J. Clin. Med.. 2023, 12 (19) 6353 10.3390/jcm12196353. [DOI] [PMC free article] [PubMed] [Google Scholar]; c Zhang S., Guo D., Zhao C., Shi Y., Zou J., Shi H., Cheng J., Sun J., Zhang X.. Integrating serum pharmacochemistry with network pharmacology and pharmacological validation to elucidate the mechanism of KaiXinSan in treating insomnia. BMC Complementary Med. Ther. 2026;26(1):5. doi: 10.1186/s12906-025-05196-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Data Availability Statement
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