2.3.1 Screening of xanthine oxidase inhibitors by AUF-MS

The potential anti-gout active ingredients in G. lucidum were screened using AUF-MS, and the affinity ultrafiltration results were analyzed by calculating the binding intensity (Chen et al., 2018; Han et al., 2016). 90 μL of XOD (0.2, 0.5, 1.0 U/mL), 30 μL of G. lucidum (130 mg/mL) sample solution, and 100 μL of 0.01 M PBS buffer were added to the incubation tube, and the mixture was incubated in a 37 °C water bath for 30 min after shaking. 220 μL of the mixture was pipetted into an ultrafiltration tube, which was then centrifuged at 12,000 rpm for 10 min for the first time, and filtered. The ultrafiltration tube was then washed three times with PBS buffer solution, centrifuged again under the same conditions as above, and the unbound small molecule ligands and bound complexes were separated through a 100 KD filter membrane. After that, 100 μL of 50 % methanol in water was added, the enzyme-bound triterpenoids were dissociated, centrifuged at 12,000 rpm for 10 min, and the experiment was repeated three times to obtain the active ligands. The blank test group replaced XOD with the same volume of PBS buffer, and other conditions were the same as in the experimental group, and finally the sample solution was entered into the detection system for analysis (Li et al., 2022). The binding strength (%) of the sample to XOD is calculated as follows: $$Bondstrength\left(\%\right)=\left(A_y-A_k\right)/A_k\times 100\%$$

In the formula, A_k was the initial peak area of the compound in the original extract, A_y was the peak area of the complex bound to XOD, and the binding intensity reflects the binding ability of triterpenoids to XOD (Coşkun et al., 2022).

The UF were under vacuum, and the released ligands were analyzed by LC-MS, with the average of three parallel measurements.

2.3.2 Molecular docking and molecular dynamics simulation verification of active compounds

First, we conducted molecular docking simulations utilizing Autodock Vina 1.2.0 software to predict the XOD-binding sites of the four specified compounds and their potential post-binding conformations. We employed Autodock software for ligand processing, followed by protein hydrogenation to compute the Gasteiger charge. Subsequently, the Lamarckian genetic algorithm was applied for docking analysis of the purified compounds within Autodock Vina. The binding energy of all receptor-ligand conformations was determined, with the conformation exhibiting the lowest binding energy being designated. The docking results of the active compounds and target protein were analyzed using Python 3.4. Subsequently, the protein structures were processed using Discovery Studio 2019, and the results were visualized and analyzed to generate mock-ups (Cui et al., 2022).

Coordinates of maps = (86.515, 89.565, 137.129).

Molecular dynamics simulation was performed using Gromacs 5.1.1. The ligands were preprocessing with AmberTools22 to introduce the GAFF force field, while Gaussian 16 W was used to hydrogenate the ligands and calculate the RESP potential. The potential data is included in the topology file of the molecular dynamics system. The simulations were carried out at a constant temperature of 300 K and atmospheric pressure (1.0 Bar). Amber99sb-ildn field was employed, with water molecules (Tip3p water model) serving as the solvent. The overall charge of the simulation system was neutralized by adding an appropriate number of Na⁺ ions. The free molecular dynamics simulation was performed, consisting of 5000 000 steps, each with a 2.0 fs step size, totaling a duration of 100 ns. Following the simulation, the trajectory was analyzed using software tools. The root mean square variance (RMSD), root mean square fluctuation (RMSF), and protein rotation radius of each amino acid trajectory were calculated. Moreover, MM/PBSA was calculated and a free energy morphology was plotted.

2.4.1 Distribution coefficient study

The active constituents in G. lucidum underwent screening and isolation using eleven solvent systems. Initial experiments were performed on each solvent system to optimize K-values for the target compounds. Once equilibrium was achieved for each solvent system, 1.0 mL of both the upper and lower phases was combined with 1.0 mg of G. lucidum extract in a tube and shaken until fully dissolved. The resulting two phases were filtered through a 0.45 μm filter membrane and dissolved in 1.0 mL of methanol for subsequent analysis via HPLC (Liu et al., 2023b).

The K-values is calculated as follows: $$K=A_{\mathit{upper}}/A_{\mathit{lower}}$$

Here, the peak area of the compound in the upper phase is denoted by A_upper, while the peak area of the compound in the lower phase is represented by A_lower.

2.4.2 HSCCC separation conditions

Following our experimental findings, the solvent system composed of ethyl acetate:n-butanol:water (3.0:1.0:4.0, v/v/v) was employed. The flow rate was set to 1.5 mL/min, and the rotation speed was maintained at 800 r/min. The stationary phase utilized was the upper phase of the solvent system, while the lower phase served as the mobile phase. Detection was conducted at a wavelength of 257 nm, and the sample concentration was maintained at 50 mg/mL.

2.4.3 SPLC separation conditions

In SPLC, water (A) and acetonitrile (B) were employed as mobile phases utilizing gradient elution. The mobile phase gradient program was structured as follows: 0–5 min, 10–20 % B; 5–30 min, 20–50 % B; and 30–60 min, 50–80 % B. The flow rate was sustained at 4.0 mL/min, with detection performed at a wavelength of 257 nm. An injection volume of 4.5 mL was utilized, and the crude extract of G. lucidum (40 mg/mL) was dissolved in a 50 % methanol aqueous solution (Xia et al., 2023).

2.4.4 Purity anaiysis and identification

In this investigation, LC-MS was employed to identify xanthine oxidase inhibitors in G. lucidum, while their purity was assessed via HPLC. Compounds within G. lucidum extracts were analyzed using a Waters 2695 extend C₁₈ column (250 × 4.6 mm, 5.0 μm). The mobile phases consisted of acetonitrile (A) and water (C), employing a gradient program as follows: 0–5 min, 2 % A; 5–22 min, 2–22 % A; 22–24 min, 22 % A; 24–60 min, 22–40 % A. The flow rate was set at 0.2 mL/min, with an injection volume of 8.0 μL. Detection was performed at a wavelength of 257 nm, and the column temperature was maintained at 30 °C.

UPLC-Q-Exactive-MS (Thermo Scientific) analysis was performed utilizing an analytical C₁₈ column (100 × 2.1 mm ID). With acetonitrile (A) and water (C), following the gradient program: 0–5 min, 2 % A; 5–20 min, 2–40 % A; 20–60 min, 40–80 % A. The flow rate was set at 0.3 mL/min, with a sample injection volume of 10 μL. Detection was conducted at a wavelength of 257 nm, and the column temperature was maintained at 20 °C. For ionization, an electrospray ion source (ESI) was utilized in negative ion mode. The scanning range for m/z was set from 100 to 1000. Ion trap conditions included a spray voltage of 4.5 kV, nitrogen as the sheath gas auxiliary with a flow rate of 20 L/min, an ion trap pressure of 3.1 × 107 Pa, a metal capillary temperature of 350 °C, and a metal capillary voltage of 3.5 V.

2.5.1 Plot uric acid standard curve

The uric acid solution was injected in volumes of 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, and 16.0 μL, respectively. Subsequently, the injection volume (x) and the corresponding area (y) were employed to establish the standard curve for obtaining the regression equation.

2.5.2 Study on the inhibitory types of xanthine oxidase from active ingredients

In a 200 μL reaction system, aliquots of G. lucidum sample solution at concentrations of 10, 25, 50, 100, and 200 μmol/L, respectively, were dispensed at 50 μL each. Subsequently, 50 μL of XOD solution was added, and the mixture was thoroughly homogenized before being incubated in a 37 °C water bath for 10 min. Following the initial incubation, 100 μL of 0.4 mmol/L xanthine solution was introduced, thoroughly mixed, and the mixture was further incubated in a 37 °C water bath for 30 min. Subsequently, the mixture was subjected to a 10 min incubation in a 100 °C constant temperature water bath for inactivation (Zhao et al., 2021). The absorbance at a wavelength of 257 nm was measured thrice using HPLC, and the average of the three parallel measurements was considered as the experimental data.

The inhibition rate of four xanthine oxidase inhibitors in G. lucidum can be calculated using the following formula: $$XODInhibitionrate\left(\%\right)=\left[A-\left(B-C\right)\right]/A\times 100\%$$

A represents the peak area of uric acid in the blank control sample, B represents the peak area of uric acid in the sample to be tested, and C represents the peak area of uric acid in the positive control sample.

2.5.3 Study on the inhibition mechanism of monomer compounds

During the enzymatic reaction, the concentration of xanthine remained constant at 0.4 mmol/L, whereas the concentrations of XOD were varied at 0.1, 0.2, 0.5, and 0.7 U/mL. This experimental setup aimed to evaluate the influence of four inhibitors on XOD activity in G. lucidum. A graphical representation was generated to elucidate the correlation between enzymatic reaction velocity and XOD concentration. The formation of a linear relationship passing through the origin in the plotted data signifies reversible inhibition by the inhibitor. Conversely, the appearance of a series of parallel lines intersecting the X_-axis suggests irreversible inhibition (Hou et al., 2024).

In the aforementioned reaction system, the concentration of XOD was 1.0 U/mL, while the concentration of xanthine substrate was varied at 0.05, 0.10, 0.20, 0.30, and 0.40 mmol/L, respectively. Subsequently, we investigated the impact of these diverse concentrations of monomeric samples on the catalytic activity of XOD in metabolizing the xanthine. The relationship between enzymatic reaction velocity and the concentration of substrate xanthine in the reaction system was assessed. Utilizing the L-Burk double reciprocal plotting method, we constructed a linear plot of 1/V against 1/[S] under various concentrations of inhibitors. By analyzing the intersections of the plotted lines with the axes, we determined the type of inhibitory effect exerted by monomers on XOD (Liu et al., 2024).

2.6.1 Active ingredient and disease related target prediction

G. lucidum was searched as the keyword to identify its potential active ingredients in various databases including TCMSP, TCMID, ETCM, and chemical professional databases, supplemented by published literature. The structural files for predicting active compounds were retrieved from the PubChem database and subsequently imported into the Swiss Target Prediction database for the initial prediction of component targets. Upon integration, the protein names were standardized using the Uniprot database. Finally, Cytoscape software was employed for visual analysis (Gao et al., 2024).

The keywords “gout” were inputted into the GeneCards and Drugbank databases to gather potential disease targets associated with gout. Following integration based on Score values, the targets underwent standardization through the Uniprot database.

2.6.2 Active ingredient-Gout common target construction

To explore the correlation between G. lucidum and targets related to gout, both drug targets and gout-related targets were uploaded to a Venn diagram website. The resultant intersection targets were subsequently imported into Cytoscape 3.6.1 software for visualization, generating a network diagram.

2.6.3 Protein-protein interaction networks construction

The obtained results were imported into the STRING database, where the PPI network was constructed. This network was saved in CSV format, facilitating the analysis of interactions between active components and gout disease targets through integrated analysis. Subsequently, the data were imported into Cytoscape 3.6.1 software for visualization, enabling the identification of potential core target genes.

2.6.4 GO and KEGG analysis

The collected targets were imported into the Metscape database with the default filter threshold set to P < 0.01. Subsequently, the condensed data folder was obtained, and integrated processing was conducted. The top 10 items were selected based on P-value as the next research data. Utilizing a bioinformatics platform, GO analysis and KEGG analysis were performed, and a GO and KEGG network diagram was constructed.

3.1.1 Screening of xanthine oxidase inhibitors by AUF-MS

As depicted in the Fig. 1, the liquid chromatographic peak areas of compounds 1, 2, 3, and 4 in the sample group with XOD were notably higher than those in the blank control sample, indicating a close association of these four compounds with xanthine oxidation enzymes. This suggests a biological affinity between these compounds and XOD, thereby speculating their potential inhibitory effect on XOD and potential anti-gout activity.

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Fig. 1

High performance liquid chromatogram of G. lucidum combined with xanthine oxidase. a: control group, b: 0.2 U/mL, c: 0.5 U/mL, d: 1.0 U/mL, e: comparison between 0.5 U/mL XOD and control group; 1: ganoderic acid C₂, 2: ganoderic acid B, 3: ganoderic acid A, 4: ganoderic acid D₂.

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The results showed that the triterpenoid active ingredients 1, 2, 3 and 4 in G. lucidum combined with xanthine oxidase had significant activity (Table 1). Four potential xanthine oxidase inhibitors(namely, ganoderic acids C₂, B, A and D₂) were screened by affinity ultrafiltration experiment, and the four triterpenoid active ingredients had good binding ability with xanthine oxidase, and their binding strengths were 60.21 %, 72.63 %, 50.82 % and 55.28 %, respectively. The order of strength and weakness of the XOD inhibition effect of each active ingredient 1, 2, 3 and 4: (2) ganoderic acid B> (1) ganoderic acid C₂> (4) ganoderic acid D₂> (3) ganoderic acid A. In order to realize the activity verification of active ingredients 1, 2, 3 and 4, and the data analysis of the inhibition of xanthine oxidase by these four components, HPLC was further used for detection and analysis. The above method was used to detect the inhibition rate of XOD activity of triterpenoids of G. lucidum, and it was proved that the triterpenoids active ingredients of G. lucidum had inhibitory effect on XOD.

Although numerous studies have separately reported various bioactivities of ganoderic acid compounds, there have been no specific reports on the simultaneous screening of ganoderic acids C₂, B, A, and D₂ for their inhibitory effects on xanthine oxidase. Most of the literature focuses on individual ganoderic acids or employs different detection methods. Affinity ultrafiltration mass spectrometry, as an efficient screening method, provides data support for a comprehensive study of these compounds together in future research (Zhang et al., 2023).

3.1.2 Simulation results of molecular docking and molecular dynamics simulation

The affinity of the four active ingredients was further validated using molecular docking techniques. The results of docking of triterpenoid active components of G. lucidum with XOD molecules showed that the main binding forces between the four active components and target protein were hydrogen bond or van der Waals force, accompanied by electrostatic and hydrophobic interaction. Ganoderic acid C₂ demonstrated strong interactions with PRO 132, GLN 131, and ARG 129 of XOD; ganoderic acid B exhibited significant interactions with LYS 340, VAL 313, and LYS 310; ganoderic acid A displayed notable interactions with LEU 1294, LYS 1292, THR 934, and VAL 731. Lastly, ganoderic acid D₂ showed strong interactions with ALA 1284, ALA 1281, ARG 1280, and LYS 996 of XOD amino acid residues (Fig. 2).

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Fig. 2

Three-dimensional and two-dimensional models of four active triterpenes in G. lucidum by xanthine oxidase. A(a): ganoderic acid C₂; B(b): ganoderic acid B; C(c): ganoderic acid A; D(d): ganoderic acid D₂.

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The docking binding energies of the triterpenoid active components ganoderic acid C₂, B, A, and D₂ were found to be −6.29, −6.65, −4.29, and −5.96 kcal/mol, respectively, for 2ckj. These docking results are consistent with the findings from ultrafiltration. Additionally, molecular docking technology confirmed their strong binding affinity with XOD (Table 2).

Table 2 Molecular docking analysis of xanthine oxidase by triterpenoid active ingredients in G. lucidum.

NO.	Ligand name	Ligand structure	Intermol energy (kcal/mol)	Interactions amino acid
A	ganoderic acid C2		−6.29	PRO132 GLN131 ARG129
B	ganoderic acid B		−6.65	LYS340 VAL313 LYS310
C	ganoderic acid A		−4.29	LEU1294 LYS1292 THR934 VAL731
D	ganoderic acid D2		−5.96	ALA1284 ALA1281 ARG1280 LYS996

We calculate the RMSD for the protein, the ligand, and the protein–ligand complex RMSD by kinetic simulations (Fig. 3A1-4). For the initial 100 ns of the simulation, the protein–ligand RMSD values are quite high, and then gradually become steady, indicating that protein–ligand binding is stable. It can be seen that ganoderic acid D₂ has sustained small fluctuations, while the state of other small molecules RMSD is flat or remains flat after some twists and turns. On the whole, RMSD values are maintained in a small and reasonable range.

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Fig. 3

RMSD and gibbs energy landscape by kinetic simulations of active compouds from G. lucidum were butted with xanthine oxidase. A: RMSD diagram, B: Gibbs energy landscape diagram.

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Furthermore, we analyzed Protein allosterism as shown in Fig. 3B1-4. The RMSD and cyclotron radius indicate that the protein has several lowest energy states, with blue indicating lower energy states and red signifying higher energy states. For example, if an allosteric protein has two allosteric states, it will have two lowest energy points. It is shown that during the simulation, the conjugated proteins of ganoderic acids C₂, A, and D₂ have one allosteric state. Ganoderic acid B binds to the protein have two allosteric state allosteric state. Among these compounds, including ganoderic acid B and amino acid residues within XOD, form hydrogen bonds primarily with benzene ring hydroxyl groups. These interactions occur at key sites responsible for catalytic activity, preventing the substrate from accessing or occupying the binding site. In addition, the primary amino acid residues in ganoderic acids C₂, A, and D₂ act as proton donors in catalytic reactions and contribute to enzyme reactions through hydrogen bonding. These interactions occur relatively far from the XOD active site. Enzymes are predominantly proteins, linear macromolecules composed of peptide bonds connecting various amino acid molecules. These amino acid molecules contain functional groups such as –NH₂ and –COOH, as well as various side chains. Interactions between these amino acids involve forces such as hydrogen bonds, ionic bonds, van der Waals forces, and other attractive forces. The Table 3 provided show the energy calculations for proteins and small molecules, resulting in the total free energy of each small molecule and protein. These results can be effectively combined, with the following ranking of total free energies: ganoderic acid B (−45.74 kcal/mol) < ganoderic acid D₂ (−28.02 kcal/mol) < ganoderic acid C₂ (−26.86 kcal/mol) < ganoderic acid A (−16.44 kcal/mol). These findings indicate that monomeric components bind reversibly and non-covalently when inhibiting enzymes. Various interactions between enzymes and monomeric components induce changes in the microenvironment of enzyme secondary structures and specific amino acid residues, driven primarily by van der Waals potentials, electrostatic interactions, and hydrogen bonding.

Table 3 MM/GBSA computational combination free.

NO.	Ligand name	Ligand structure	Binding free energy（kcal/mol）
			ΔEvdw	ΔEeel	ΔEPB	ΔENPOLAR	ΔGgas	ΔGsolv	ΔTOTAL
1	ganoderic acid C2		−31.56	−25.05	33.78	−4.03	−56.61	29.75	−26.86
2	ganoderic acid B		−52.64	−0	35.99	−6.75	−74.98	29.24	−45.74
3	ganoderic acid A		–23.59	–23.58	33.80	−3.07	−47.17	30.73	−16.44
4	ganoderic acid D2		−41.43	0	38.25	−5.16	−61.11	33.09	−28.02

All evidence suggests that the protein model’s acceptability andgood quality. These components could be the material basis for the anti-gout effect of G. lucidum. Molecular docking and molecular dynamics simulation techniques provides an intuitive and vivid understanding of the interaction mechanism between ligand compounds and target proteins.

3.2.1 HSCCC separation conditions

In solvent systems 1, 5, 10, and 11, the K-values of the target compound were notably less than 0.5, suggesting that the separation of triterpenoid components in G. lucidum was deemed unsuitable. It was speculated that the high polarity of the target compound might have contributed to this observation (Table 4). Therefore, n-butanol was incorporated into the solvent system to adjust its polarity, aiming to achieve enhanced separation efficiency for the target compound. To ensure optimal separation, a certain distance between the K-values of the target components was deemed necessary. Consequently, solvent system 7 (ethyl acetate: n-butanol: water, 3.0: 1.0: 4.0) was chosen for separation, with K-values of compounds 1, 2, 3, and 4 being determined as 0.53, 0.84, 0.60, and 0.91, respectively, falling within the appropriate range (Table 4). Ultimately, ethyl acetate: n-butanol: water (3.0: 1.0: 4.0, v/v/v) was deemed the optimal solvent system.

3.2.2 Separation by HSCCC

HSCCC separation was conducted for G. lucidum using a flow rate of 1.5 mL/min, a solvent system composed of ethyl acetate: n-butanol: water in a ratio of 3.0: 1.0: 4.0 (v/v/v), a rotational speed of 800 rpm, and a detection wavelength of 257 nm (Fig. 4(A)). Three compounds were isolated from a 50 mg/mL G. lucidum extract under single extraction conditions, identified as ganoderic acid C₂, ganoderic acid B, and ganoderic acid A, with purities of 99.10, 93.58, and 95.76 %, respectively. The stationary phase retention value was approximately 47.85 %, indicating the rapid and effective nature of HSCCC separation for G. lucidum.

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Fig. 4

HSCCC and SPLC chromatographic separation plots and separation monomer chromatographic of G. lucidum. A: HSCCC separation diagram, B: SPLC separation diagram, C: Liquid chromatogram of monomer composition purity; 1: ganoderic acid C₂, 2: ganoderic acid B, 3: ganoderic acid A, 4: ganoderic acid D_2.

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3.2.3 Separation of active compounds by SPLC

Prior to the experiment, the experimental conditions, including the mobile phase, flow rate, and injection volume of the separation system, were optimized. Upon using water and methanol in the mobile phase, the chromatogram lacked an independent monomer peak, rendering compound separation challenging. Therefore, in this experiment, acetonitrile was selected as a substitute for methanol in the mobile phase. Subsequently, flow rates of 2.0, 4.0, 6.0, and 8.0 mL/min were compared. Eventually, a flow rate of 4.0 mL/min was chosen, the mobile phase gradient program was structured as follows: 0–5 min, 10–20 % B; 5–30 min, 20–50 % B; and 30–60 min, 50–80 % B, and the injection volume was fixed at 4.5 mL.

One triterpenoid active ingredient was isolated from G. lucidum by SPLC, which was ganoderic acid D₂ (purity is 90.01 %) (Fig. 4(B)).

The results demonstrated that HSCCC and SPLC, as contemporary liquid–liquid partitioning techniques, significantly improved the efficiency, purity, and selectivity of purifying active compounds from G. lucidum. These methods provided robust tools for effective purification, complementing the enduring value of traditional techniques in small-scale and preliminary research. The potential applications of these advanced methods in pharmaceuticals and biotechnology were substantial, particularly for extracting and purifying high-value compounds. HSCCC and SPLC, efficient separation technologies, showed excellent scalability, meeting diverse needs from laboratory to industrial production scales. Validation in small-scale experiments suggested potential for scaling up production through increased equipment capacity and optimized processes, setting a foundation for further exploration in drug development and natural product manufacturing.

3.2.4 Screening of xanthine oxidase inhibitors by AUF-MS purity analysis and identification

The triterpenoids in the G. lucidum extract were well separated. The four monomer compounds are ganoderic acid C₂, B, A, D₂, and the purity is 99.10, 93.58, 95.76, and 90.01 % (Fig. 4(C)).

The mass spectrometry data corresponding to the chromatographic peaks of the main compounds in the HPLC were analyzed and determined using the LC-MS method and comparison with standard products. Compound 1 exhibited a retention time of 21.49 min, with a precursor ion [M−H]^-m/z of 517.32 in the first-order mass spectrometry. Multi-stage spectrum analysis revealed the desorption of one molecule of H₂O from the quasi-molecular ion, followed by the removal of one molecule of CO₂, generating ion fragments with m/z [M−H−H₂O−CO₂]^-=455.32. Additionally, fragmentation information obtained through electron transfer and molecular rearrangement after ganoderic acid C₂ ionization showed peaks at m/z [M−H−C₁₁H₂₀O₄]^-= 301.19, m/z [M−H−C₁₂H₂₂O₄]^-= 287.17, and m/z [M−H−C₁₅H₂₀O₃]^-= 269.18. This fragmentation pattern aligns with reported mass spectrometry fragmentation information of ganoderic acid C₂ in the literature. Moreover, the retention time of compound 1 matched that of the ganoderic acid C₂ standard, indicating that this compound is likely ganoderic acid C₂ (Guo et al., 2012). Employing the same method, experimentally obtained [M + H]^-(m/z) values for compounds 2 − 4 were determined to be 515, 513, and 529, respectively. Comparison of the MS data and LC retention times led to the inference that compound 2, 3, and 4 correspond to ganoderic acid B, ganoderic acid A, and ganoderic acid D₂, respectively (Zhou et al., 2013; Guo et al., 2013; Yang et al., 2007; Xu et al., 2022) (Table 5).

3.3.1 Evaluation of XOD inhibitory

Taking the concentration of uric acid as the abscissa, and the peak areas of uric acid solutions of different concentrations as the ordinate, draw the standard curve of uric acid, the regression equation: y = 672.74x-1287.1, R² = 0.9995, when the concentration is 2 ∼ 16 μg/mL. The linearity of the standard curve was good in the range of 16 μg/mL (method validation in the supplementary file 1).

As illustrated in the Table 6, the influence of ganoderic acid C₂, B, A, D₂, and positive controls on XOD inhibition was investigated. It can be seen from the IC₅₀ value that the four triterpenoid active ingredients in G. lucidum and positive controls have inhibitory effect on XOD, and when the sample concentration is 10 ∼ 200 μg/mL, the inhibition order of XOD by these four triterpenoids active ingredients is: ganoderic acid B (27.94 μg/mL) > ganoderic acid C₂ (34.05 μg/mL) > ganoderic acid D₂ (40.09 μg/mL) > ganoderic acid A (46.83 μg/mL).

It had been reported that plant extracts exhibiting > 50 % xanthine oxidase inhibition at 50 μg/mL warranted further investigation (Re et al., 1999). In this study, the lowest IC₅₀ value was 46.83 μg/mL, exhibited by G. lucidum extract which indicating that G. lucidum extract could inhibit 50 % of xanthine oxidase activity at specified IC₅₀ value and showed better inhibition of xanthine oxidase activity.

3.3.2 Inhibition types and mechanisms

Identify the inhibition induced by XOD inhibitors, distinguishing between reversible and irreversible inhibition. Reversible inhibition could be categorized into C., NC., UC., and MT., (Competitive, Non-competitive, Anticompetitive, Mixed inhibition, respectively) based on the relationship among the inhibitor, substrate, and XOD. The conditions of compounds, all the plotted lines originated from the origin, with a gradual decrease as the inhibitor concentration increased, signifying a reversible inhibition (Fig. 5A1-4).

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Fig. 5

Enzymatic reaction kinetics screening of active ingredients of G. lucidum. A: The determination curve of inhibition type that separated compounds for XOD, B: double-reciprocal plot for the determination of the inhibitory mechanism of separated compounds for XOD; 1: ganoderic acid C₂, 2: ganoderic acid B, 3: ganoderic acid A, 4: ganoderic acid D₂.

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Concurrently, while maintaining a fixed of XOD at 1.0 U/mL, the xanthine was varied, and the rate of the compound was assessed at xanthine concentrations of 0.05, 0.10, 0.20, 0.30, and 0.40 mmol/L. As shown in Fig. 5B1-4, a L-Burk double reciprocal plot was constructed, with 1/V plotted on the vertical axis and 1/[S] on the horizontal axis. ganoderic acid C₂, ganoderic acid A, and ganoderic acid D₂ intersect on the Y_-axis, this indicates that for the point corresponding to C, the intersection of the line and the Y_-axis remains unchanged with the increase in the concentration of active ingredients, and the intercept remains nearly constant, signifying a typical competitive inhibition effect. While all straight lines of ganoderic acid B it intersects on the X_-axis, and the slope of the straight line gradually increases with the increase of active ingredients, but its intersection is always on the X_-axis, so ganoderic acid B belongs to non-competitive inhibition. The affinity between the enzyme and the substrate could be diminished due to the competition between the compound and the substrate for the XOD catalytic site, thereby further impeding enzyme activity.

Multiple screening results consistently indicate that ganoderic acid B exhibits the strongest effect, followed by ganoderic acids C₂, D₂, and A. This efficient coupling of various screening methods provides valuable data for subsequent experiments. Ganoderic acids C₂, B, A, and D₂, as natural compounds, typically carry lower risks of adverse effects compared to synthetic drugs, making them safer options. Their inhibition of xanthine oxidase and comparison with other compounds showcase their potential in the treatment of gout. By effectively inhibiting xanthine oxidase activity, these compounds reduce the production of uric acid. This inhibitory effect helps lower uric acid levels, thereby reducing the risk of gout attacks.

3.4.1 Prediction analysis of intersection target of gout and active ingredients

Based on various in vitro screening experiments, we identified four triterpenoid active components from G. lucidum as the material basis for disease intervention and conducted further network pharmacology studies. TCMSP, Pubchem database and Swiss Target Prediction database were used to predict the action targets of the triterpenoid active components ganoderic acid A, B, C₂ and D₂. “gout” was searched in the GeneGards database to collect disease-related targets. As shown in Fig. 6(A), 184 targets were obtained after deduplication by predicting compound-related targets through the database, 1780 gout disease-related targets were retrieved through the GeneCards database, and a total of 71 common targets were obtained after the analysis of the two potentially related target genes by Wayne.

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Fig. 6

Network pharmacological analysis pathway mechanism map. A: Venn diagram of Gout-target, B: PPI interaction network diagram, C: GO enrichment analysis diagram and D: KEGG action pathway analysis diagram.

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3.4.2 PPI network construction and network topology analysis

Through the String platform, import the intersection gene, set the object to (Homo Sapiens), take the highest confidence level of 0.9, hide the free gene node, and obtain the protein interaction relationship. Import the results into the Cytoscape 3.9.2 software, select “Network Analyzer”, and obtain the network topology parameters. Then import the downloaded TSV file into Cytoscape software to make a PPI map, and filter the top 10 core targets according to the Degree value.

After obtaining the interaction information of intersecting target proteins and removing the non-interacting targets, the network has a total of 355 edges and 39 nodes. The larger the node diameter and darker the color, the higher the node Degree value and the more target proteins interacting with it. The thicker the edges and darker the color, the stronger the interaction between the two target proteins. The Cyto NCA plug-in was used to perform topological analysis of the network, as shown in Fig. 6(B) TNF, AKT1, STAT3, EGFR, SRC, PTGS2, MMP9, MAPK3, PPARG, MAPK1, MTOR, and PPARA major targets may be closely related to gout.

G. lucidum exerted therapeutic effects in gout treatment by inhibiting TNF (tumor necrosis factor) and PTGS2 (cyclooxygenase-2), thereby reducing inflammation associated with gout. It also reduced cellular damage from oxidative stress, indirectly affecting the activity of AKT1, STAT3, and EGFR signaling pathways, contributing to alleviating symptoms and conditions of gout patients. Furthermore, it could modulate immune system function, potentially influencing the activity of SRC, MAPK (such as MAPK1, MAPK3) signaling pathways, and thereby impacting inflammation and pathological processes related to gout treatment. Additionally, it affected the activity of PPARG, MMP9, MTOR, and PPARA targets, which may have played roles in gout pathogenesis, particularly in inflammation and metabolic regulation. Its anti-inflammatory, antioxidant, and immune-modulating properties positioned it as a promising natural candidate for adjunctive treatment in the comprehensive management of gout.

3.4.3 GO and KEGG analysis

Based on the parameters Min overlap = 3, P-value cutoff = 0.01 and Min Enrichment = 1.5 set by the metascape database, GO enrichment analysis was performed for the intersection of triterpenoid active ingredients and gout targets in G. lucidum.

The 71 common targets were enriched by Go enrichment to obtain Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) parts. Among them, there are 995 biological process entries (response to lipopolysaccharide, response to molecule of bacterial origin, etc.), 200 cell component entries (membrane raft, membrane microdomain, etc.), and 356 molecular function items (nuclear receptor activity, etc.) (Fig. 6(C)).

There were 227 KEGG biological pathways mainly enriched by triterpenoids potential active ingredients in the treatment of gout, and the top 10 pathways were sorted by P value to obtain a bubble map of KEGG pathway analysis (Fig. 6(D)). It can be seen that target cells are mainly enriched to Lipid and atherosclerosis, Endocrine resistance, Relaxin signaling pathway, Prostate cancer and other key pathways.

These findings underscored the multifaceted pharmacological potential of G. lucidum's active ingredients in addressing gout, implicating broad impacts on biological processes, cellular components, and molecular functions, as well as involvement in key pathways relevant to disease pathogenesis and treatment. Meanwhile, among the four potential active ingredients screened by network pharmacology, all of them were the four active ingredients screened by AUF-MS. And the four active components have higher degree values than other active ingredients and have more related targets, indicating that G. lucidum is used in the treatment of gout. These four active components play an important role in the process of the disease. The network pharmacology results further verified the accuracy of the experiment and made the whole experimental design more complete.