Analysis method for metabolic regulation mechanism of liver bean spirit in treatment of WD liver fibrosis

Abstract

The application discloses a metabolism regulation mechanism analysis method for treating WD liver fibrosis by using Gandouling, and belongs to the technical field of medicines, and comprises the following steps: blood component and network pharmacology research; obtaining metabolism markers significantly adjusted by Gandouling based on serum metabolomics, and performing enrichment analysis on the metabolism markers significantly adjusted by Gandouling to obtain significant metabolism pathways; constructing a complex metabolism network of "small molecule metabolite-reaction-enzyme-target"; screening a pharmacodynamic material basis of Gandouling by using molecular docking technology; and visualizing a multi-dimensional network of "pharmacodynamic material basis of Gandouling-core action target-core metabolism marker-core metabolism pathway-WD liver fibrosis". The application obtains 25 pharmacodynamic material bases, 11 core action targets, 12 core metabolism markers and 9 core metabolism pathways.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of medicine, in particular to a method for analyzing the metabolic regulation mechanism of liver bean spirit in treating WD liver fibrosis. BACKGROUND

[0002] Wilson disease (WD) is also known as hepatolenticular degeneration (HLD), which is a common autosomal recessive copper metabolism disorder caused by ATP7B gene mutation. The main clinical features are liver damage, neuropsychiatric symptoms, and corneal K-F ring.

[0003] For the treatment of WD, Western medicine often recommends the use of penicillamine, dimercaptosuccinic acid, etc. However, the occurrence of many adverse reactions during the treatment limits the use of such drugs. Previous studies have shown that traditional Chinese medicine plays a good role in the treatment of WD. For example, the traditional Chinese medicine preparation liver bean spirit (Gandouling, GDL).

[0004] Liver bean spirit is composed of six traditional Chinese medicines, including Danshen, turmeric, zedoary, cassia twig, Coptis chinensis, and raw rhubarb. In the prescription, turmeric and Coptis chinensis are the monarch, which can activate blood and promote qi, and eliminate phlegm and dampness; Danshen, zedoary, and cassia twig are the ministers, which can activate blood and resolve stasis, soften and resolve, and dredge channels and collaterals to expel copper turbidity and toxic evil; raw rhubarb is used as an adjuvant to dredge the bowels, remove dampness, and remove copper and detoxify. The combination of various drugs can activate blood and promote qi, eliminate phlegm and remove stasis, and remove copper and detoxify. Previous studies have shown that liver bean spirit can significantly improve the liver function and liver fibrosis degree of WD patients. Another study has confirmed that liver bean spirit can promote the secretion of bile by the liver, so that copper can enter the intestine with bile and be excreted outside the body, reducing the damage of copper to the liver and other systems; it can significantly improve the liver function indicators of patients and inhibit the further progress of cirrhosis. However, the mechanism of liver bean spirit in treating WD liver fibrosis has not been fully clarified.

[0005] Due to the characteristics of multi-component, multi-pathway, and multi-target of traditional Chinese medicine in exerting efficacy, and the prerequisite for traditional Chinese medicine to exert efficacy is that its components are absorbed into the blood. Therefore, the research on the material basis and mechanism of action of traditional Chinese medicine is a bottleneck problem restricting the modernization of traditional Chinese medicine. In recent years, the technology of metabolomics based on small molecule metabolites in vivo has developed rapidly. Its research strategy based on the detection of dynamic changes of overall metabolites is consistent with the overall concept of traditional Chinese medicine, which brings new opportunities to solve the bottleneck problem of the development of traditional Chinese medicine. At present, it has been widely used in the research of revealing the material basis and mechanism of action of traditional Chinese medicine. Therefore, by means of serum drug chemistry, metabolomics, and bioinformatics, the metabolic regulation mechanism of liver bean spirit in treating WD liver fibrosis is revealed, which provides experimental basis for the clinical application of GDL. SUMMARY

[0006] In view of the above shortcomings of the prior art, the present application provides a metabolic regulation mechanism analysis method for treating WD liver fibrosis by using Gandouling.

[0007] To achieve the above object, the present application is implemented by the following technical solutions:

[0008] The metabolic regulation mechanism analysis method for treating WD liver fibrosis by using Gandouling comprises the following steps:

[0009] Step one: based on the blood components of Gandouling and network pharmacology research, obtain the action target points of the blood components of Gandouling in the intervention of WD liver fibrosis;

[0010] Step two: obtain the metabolic markers significantly adjusted by Gandouling based on serum metabolomics, and perform enrichment analysis on the metabolic markers significantly adjusted by Gandouling to obtain significant metabolic pathways;

[0011] Step three: construct a “small molecule metabolite-reaction-enzyme-target point” complex metabolic network with the action target points of the blood components of Gandouling in the intervention of WD liver fibrosis and the metabolic markers significantly adjusted, and screen core action target points, core metabolic markers and core metabolic pathways;

[0012] Step four: screen Gandouling efficacy material basis by molecular docking technology, and visualize the “Gandouling efficacy material basis-core action target point-core metabolic marker-core metabolic pathway-WD liver fibrosis” multidimensional network; obtain 25 efficacy material bases, 11 core action target points, 12 core metabolic markers and 9 core metabolic pathways;

[0013] The Gandouling efficacy material basis is specifically: gallic acid, magnolia base, camphor glycoside A, 2,6-edible astragalus ester glycoside, phlorizin, lucidin, fucoidin E, 1,3-dihydroxy-9,10-dioxanthrene-2-carboxylic acid, kaempferol, (+)-1,5-epoxy-norketone guaiac-11-ene, 8-isopentenyl daidzein, 2-hydroxy-5-[3,4,5-trihydroxy-6-(hydroxymethyl)oxa-2-yl]oxybenzoic acid, protocatechuic acid-3-glucoside, (2S,3R,4S,5S,6R)-2-(4-hydroxy-2,6-dimethoxyphenoxy)-6-(hydroxymethyl)oxane-3,4,5-triol, bambusin A, evodia glycoside, codonopsis lactone, celtuce dialdehyde, 5-O-methyl visamminol-4-O-β-D-furan serfyl-(1→6)-β-D-glucopyranoside, methyl syringin, kopsin B, jambu ketone, 2-[(9Z,12Z)-heptadeca-9,12-dienyl]-6-hydroxybenzoic acid, 7,2'-dihydroxy-3',4'-methylenedioxy isoflavone and pseudoerythrin.

[0014] Further, the core action targets are AGR1, GLUL, TRY, HPRT1, PDE11A, NOS3, MPO, ADHIC, CYP1A2, CYP3A4 and GSTM1.

[0015] Further, the core metabolic markers are N-formiminoglutamate, dihydrofolic acid, 5'-phosphoribosyl-N-formylglycinamide, 3',5'-cyclic phosphoradenosine, pyruvic acid, fumaric acid, 2-oxoglutaric acid, N-acetyl-L-aspartate, 3,4-dihydroxy mandelic acid, 4-trimethylammonium butyraldehyde, L-3-hydroxykynurenine and (S)-malate.

[0016] Further, the core metabolic pathways are purine metabolism, histidine metabolism, vitamin B9 metabolism, tyrosine metabolism, lysine metabolism, glycolysis and gluconeogenesis metabolism, TCA cycle, tryptophan metabolism, urea cycle and metabolism of arginine, proline, glutamic acid, aspartic acid and asparagine.

[0017] Compared with the prior art, the application has the beneficial effects that the application studies the metabolic regulation molecular mechanism of Gandouling, obtains the potential targets of Gandouling intervention in WD liver fibrosis based on the blood components of Gandouling and network pharmacology research, obtains the metabolic regulation mechanism of Gandouling treatment in WD liver fibrosis based on serum metabolomics, obtains the metabolic markers significantly regulated by Gandouling, and carries out enrichment analysis on the metabolic markers significantly regulated by Gandouling to obtain significant metabolic pathways, constructs a “small molecule metabolite-reaction-enzyme-target” complex metabolic network by using the potential targets of Gandouling intervention in WD liver fibrosis and the metabolic markers significantly regulated by Gandouling, and constructs a multi-dimensional network diagram of “Gandouling pharmacodynamic material basis-core action target-core metabolic marker-core metabolic pathway-WD liver fibrosis” to obtain 25 pharmacodynamic material bases, 11 core action targets, 12 core metabolic markers and 9 core metabolic pathways. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0019] Figure 1 FIG. 1 is a TIC graph of Gandouling in vitro chemical components in positive and negative ion modes; wherein, A: TIC graph of Gandouling in vitro chemical components in positive ion mode, B: TIC graph of Gandouling in vitro chemical components in negative ion mode.

[0020] Figure 2The in vitro chemical composition classification quantity distribution map of Gan Dou Ling.

[0021] Figure 3 The TIC map of Gan Dou Ling blood components in positive and negative ion modes, wherein A: the TIC map of Gan Dou Ling blood components in positive ion mode, B: the TIC map of Gan Dou Ling blood components in negative ion mode.

[0022] Figure 4 The PPI network map of the intersection of Gan Dou Ling blood component action targets and WD liver fibrosis targets.

[0023] Figure 5 The serum metabolite marker volcano plot of TX mice after Gan Dou Ling intervention. A: serum metabolite marker volcano plot analysis of TX mice in the model group and the control group; B: serum metabolite marker volcano plot analysis of TX mice in the Gan Dou Ling administration group and the model group.

[0024] Figure 6 The metabolic pathway of Gan Dou Ling intervention, wherein A: metabolic pathway bubble chart; B: metabolic pathway Sankey chart. The deeper the color of P value, the higher the degree of enrichment, and the smaller the corresponding P value.

[0025] Figure 7 The multi-dimensional network of “Gan Dou Ling pharmacodynamic material basis-core action target-core metabolite marker-core metabolic pathway-WD liver fibrosis”. The red oval in the figure represents WD liver fibrosis, the red polygon represents Gan Dou Ling, the blue represents the core action target, the purple represents the core metabolite, the orange represents the core metabolic pathway, and the green represents the pharmacodynamic material basis of Gan Dou Ling. DETAILED DESCRIPTION

[0026] To make the purposes, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0027] Embodiment one: study on in vitro chemical composition of Gan Dou Ling based on UPLC-Q Exactive / MS technology

[0028] 1. Experimental method

[0029] 1.1, Preparation of test solution: Take out the Gan Dou Ling tablets, grind them evenly with liquid nitrogen, and then weigh about 100 mg of the sample into a 1.5 mL centrifuge tube. Add 1 mL of water, vortex for 1 min, and add a steel ball. Pre-cool in a -40°C refrigerator for 2 min, then grind in a grinder (60 Hz, 2 min). After ice water bath ultrasonic extraction for 60 min, centrifuge for 10 min (12000 rpm, 4°C). Dilute with water 2 times, take 200 μL of supernatant and load into a LC-MS sample vial with an inner liner for analysis.

[0030] 1.2, Chromatographic conditions: The experimental analysis instrument is an ACQUITY UPLC I-Class HF ultra-high performance liquid chromatograph coupled with a QExactive high-resolution mass spectrometer to form a liquid chromatograph-mass spectrometer system. Chromatographic column: ACQUITY UPLC HSS T3 (100 mm x 2.1 mm, 1.8 μm); column temperature: 45°C; mobile phase: mobile phase A: water containing 0.1% formic acid, B: acetonitrile; flow rate: 0.35 mL / min; injection volume: 5 μL. PDA scanning range: 210-400 nm. TM

[0031] Table 1 Gradient elution program table

[0032]

[0033] 1.3, Mass spectrometry conditions: Ion source: HESI; sample mass spectrum signal acquisition uses positive and negative ion scanning modes respectively. Data acquisition mode: DDA; scanning mode: Full MS / dd-MS2 (TOP 8).

[0034] Table 2 Mass spectrometry parameter setting table

[0035]

[0036] 2, Experimental results (in vitro chemical composition identification of Gan Dou Ling and prescription medicinal material attribution)

[0037] The in vitro chemical composition of Gan Dou Ling was characterized by UPLC-Q Exactive / MS technology. The total ion chromatogram (TIC) in positive and negative ion modes is shown in Figure 1 . A total of 1299 chemical compositions were identified in Gan Dou Ling, 596 in positive ion mode and 703 in negative ion mode. In order to more intuitively describe the composition type distribution, the composition classification number distribution graph was drawn based on the data matrix Figure 2 ), which contains 20 types of compositions. The top five compositions are phenylpropanoids, terpenes, sugars and glycosides, organic heterocyclic compounds and flavonoids. ​

[0038] Combined with the prescription of Gandouling, the source of the chemical components of Gandouling was attributed based on the HerbDB database. There were 217 chemical components attributed to Rheum palmatum, 169 chemical components attributed to Salvia miltiorrhiza, 179 chemical components attributed to Coptis chinensis, 8 chemical components attributed to Curcuma zedoaria, 16 chemical components attributed to Curcuma longa, and 14 chemical components attributed to Spatholobus suberectus, covering all the medicinal flavors of Gandouling. The 439 chemical components that could be attributed to the prescription of Gandouling were classified according to the compound structure, including 74 phenylpropanoids, 43 terpenoids, 39 flavonoids, 14 alkaloids, 7 anthraquinones, and 262 other compounds.

[0039] Example 2: Exploring the pharmacodynamic substances and action targets of Gandouling in the treatment of WD liver fibrosis based on serum pharmacochemistry and network pharmacology

[0040] Based on the identification results of the in vitro chemical components of Gandouling in Example 1, taking the classic WD animal model - TX (Toxic milk, TX) mice as the research object, the blood components of Gandouling were traced to provide a basis for the subsequent research on the pharmacodynamic substances of Gandouling.

[0041] 3. Experimental methods

[0042] 3.1. Animal experiments and sample collection

[0043] 3.1.1. Experimental animals and grouping

[0044] TX mice, male, 5 months old, SPF grade, weighing (30 ± 5) g, provided by the Animal Experiment Center of Sun Yat - sen University in Guangzhou, with the license number SCXK (Yue) 2021 - 0029. DL mice were from the Animal Experiment Center of the First Affiliated Hospital of Anhui University of Chinese Medicine. The mice were housed in the SPF - level animal room of the First Affiliated Hospital of Anhui University of Chinese Medicine (room temperature 24 - 26 °C, relative humidity 40 - 60%). The light - dark environment alternated for 12 h each, and they were randomly grouped and caged, with free access to food and water. The experiment started after 1 week of adaptive feeding. Twenty TX mice were randomly divided into a model group (Model) and a Gandouling administration group (GDL), with 10 mice in each group, and 10 DL mice were used as the control group (Control). The experimental protocol was approved by the Animal Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Animal Ethics Number: AZYFY - 2024 - 2003).

[0045] 3.1.2. Experimental drug administration protocol

[0046] Preparation of the gavage liquid: Put the Gandouling tablets into a mortar and grind them into powder. Weigh an appropriate dose and add distilled water to make a Gandouling suspension.

[0047] Gavage dose: the administration group was given gavage to TX mice at 0.2 mL / 10 g / d, and the control group and the model group were given the same volume of distilled water (0.2 mL / 10 g / d), and the administration was continued for 8 weeks.

[0048] 3.1.3, Sample acquisition

[0049] At the end of the 8th week of administration, the mice in each group were fasted for 12 h before sampling. The mice were given gavage 30 min before the eyeball was removed for blood collection. After standing for 60 min, the blood was centrifuged at 3000 rpm for 15 min, and the supernatant was stored at -80 ℃ for subsequent studies. After blood collection, the liver was immediately removed, the surface blood was washed with physiological saline, the water was absorbed with filter paper, and the liver was fixed with 10% neutral formalin.

[0050] 3.2, Analysis of blood components of Hepa-Lin

[0051] 3.2.1, Preparation of serum samples

[0052] The sample stored at -80 ℃ was taken out and slowly thawed on ice. 120 μL of the sample was transferred to a 1.5 mL EP tube. 480 μL of protein precipitant methanol-acetonitrile (V:V=2:1) was added, and vortexed for 1 min. Ultrasonic extraction was performed in an ice water bath for 10 min, and the sample was placed at -40 ℃ for 30 min. Centrifugation was performed for 10 min (12000 rpm, 4 ℃), and 450 μL of supernatant was taken and placed in an LC-MS sample vial to dry. 200 μL of water-methanol-acetonitrile (V:V:V=1:2:1) was used for reconstitution, vortexed for 1 min, and ultrasonicated for 3 min. The sample was placed at -40 ℃ overnight. Centrifugation was performed for 10 min (12000 rpm, 4 ℃), and 120 μL of supernatant was taken and placed in an LC-MS sample vial with a foot inner liner for analysis.

[0053] 3.2.2, Chromatographic conditions and mass spectrometric conditions: same as Example Step 1.2 and Step 1.3.

[0054] 3.2.3, Data processing and blood component identification

[0055] The raw data were processed by the metabolomics processing software XCMS v4.5.1 for baseline filtering, peak identification, integration, retention time correction, peak alignment and normalization. The identification of exogenous compounds in serum was based on accurate mass number, secondary fragments and isotope distribution, combined with the in vitro chemical composition identification results of liver bean spirit in Example One for comparison analysis. An automatic judgment program combined with manual review was used to optimize the accuracy of the identification results. Automatic judgment criteria: the liver bean spirit in vitro chemical composition (TCM group) has a peak, the liver bean spirit administration group has a peak, and the peak area ratio of the liver bean spirit administration group / model group is ≥FC (Fold Change, fold change). Manual review criteria: ① The liver bean spirit administration group and the TCM group must be detected at the same time and the retention time (RT) must be consistent; ② The peak area ratio of the liver bean spirit administration group to the model group is ≥FC, or the model group is not detected. For the substances identified in the library, the FC value of the substance in the liver bean spirit administration group and the model group is ≥10, which is considered as a blood-borne component, and the substances in the positive and negative ion modes are combined and de-duplicated.

[0056] 3.3, Network pharmacology research

[0057] 3.3.1, Obtain the target points of liver bean spirit blood-borne components

[0058] Based on the blood-borne components of liver bean spirit, the Swiss Target Prediction website, BATMAN-TCM database, TCMSP database, and Superpred database were used to construct the target point library of liver bean spirit blood-borne components. The obtained target protein names were input into the UniprotKB database in the Uniprot database, the species was set to Homo sapiens, the target protein was standardized named, and the corresponding gene name was arranged, combined, and de-duplicated to obtain the target points of liver bean spirit blood-borne components.

[0059] 3.3.2, Obtain disease target points

[0060] The related gene targets of WD liver fibrosis were predicted by GeneCards, OMIM, and pharmGKB databases, the obtained disease target points were de-duplicated and redundant, and the related disease target points of WD liver fibrosis were obtained.

[0061] 3.3.3, Obtain intersection target points and construct PPI network

[0062] The target points of the screened liver beanling blood components and the WD liver fibrosis disease target points screened were introduced into the micro-signal online website to draw a wein diagram, and the intersection target points of liver beanling blood components and WD liver fibrosis disease were obtained. The obtained intersection target points were introduced into the Multiple proteins analysis box of STRING database, the species was set as Homo sapiens, the confidence was set as ≥0.4, and the protein-protein interaction network (PPI) was constructed.

[0063] 4、Results

[0064] 4.1 Characterization, identification and medicinal material attribution of liver beanling blood components

[0065] The blood components of liver beanling were characterized by UPLC-Q Exactive / MS technology. The total ion chromatogram (TIC) in positive and negative ion modes is shown in Figure 3 In positive and negative ion modes, 41 blood components were detected in liver beanling (Table 3), of which 13 blood components were detected in positive ion mode and 28 blood components were detected in negative ion mode.

[0066] According to the HerbDB database, the prescription medicinal material attribution analysis of chemical components was carried out. Six blood components could be attributed to Coptis chinensis, six blood components could be attributed to Salvia miltiorrhiza, ten blood components could be attributed to Rheum officinale, one blood component could be attributed to Curcuma zedoary, one blood component could be attributed to Curcuma zedoary, and one blood component could be attributed to Spatholobus suberectus, covering the whole prescription of liver beanling.

[0067] Table 3 Identification results of liver beanling blood components

[0068]

[0069]

[0070] 4.2 Classification identification of liver beanling blood components

[0071] 4.2.1 Identification of phenylpropanoid components

[0072] Two phenylpropanoid components were identified in liver beanling in vivo. Compound 7 showed a quasi-molecular ion of m / z 235.0602 [M+H]+ in low-energy mass spectrum, and m / z 189.0527, m / z 217.0495, m / z235.0600, according to the cleavage rule of phenylpropanoids, it is judged that the compound is lucidenin. Compound 10, in the low-energy mass spectrum, shows that the quasi-molecular ion is m / z 197.0448 [M-H]-, detected in high-energy mass spectrum m / z 179.03438, m / z 135.04468, m / z 123.04475, m / z 72.99288, it is judged that the compound is 2,3,4-trihydroxyphenylpropanoic acid.

[0073] 4.2.2, identification of terpenoid components

[0074] Six terpenoid components are identified in the in vivo original components of Ganoderma lucidum. Taking compound 36 as an example, in the low-energy mass spectrum, the quasi-molecular ion peak is detected m / z 413.2167 [M+FA-H]-, detected in high-energy mass spectrum m / z 305.1853, m / z 251.1648, m / z 101.0243, m / z 85.0293, according to the cleavage rule of terpenoids, it is judged that the compound is gallic alcohol B. Compounds 11, 16, 26, 30, and 25 are identified as fucoidin E, cerebroside, codonopsis lactone, methyl syringin, and cornus glycoside, respectively, according to the cleavage rule of terpenoids and combined with database comparison.

[0075] 4.2.3, identification of flavonoid components

[0076] Six flavonoid components are identified in the in vivo original components of Ganoderma lucidum. Taking compound 38 as an example, in the low-energy mass spectrum, the quasi-molecular ion is shown m / z 515.2405 [M+Na]+, in the high-energy mass spectrum, three ion fragments are detected, which are m / z 310.1414, m / z 287.1247, m / z 269.1148. According to the cleavage rule of flavonoids, it is judged that the compound is amurensin B. Compounds 6, 14, 39, 41, and 20 are identified as phloridzin, kaempferol, 7,2'-dihydroxy-3',4'-methylenedioxy isoflavone, kaempferol-3-O-glucoside-7-O-rhamnoside, and 8-isopentenyl daidzein, respectively, based on the cleavage rule of flavonoids and combined with database comparison.

[0077] 4.2.4, identification of alkaloid components

[0078] One alkaloid component was detected in the liver bean original forming part. Compound 3 showed a quasi-molecular ion at m / z 342.1699 [M+H]+ in low-energy mass spectrum, and four fragment ions were detected in high-energy mass spectrum, respectively m / z 342.1699 [M+H]+, four fragment ions were detected in high-energy mass spectrum, respectively m / z 297.1120, m / z 282.0861, m / z 265.0846. According to the cleavage rules of alkaloid compounds and combined with database comparison, it was judged that the compound was magnoflorine.

[0079] 4.3, WD liver fibrosis disease target collection

[0080] Based on 41 WD liver fibrosis disease targets, Swiss Target Prediction, BATMAN-TCM, TCMSP, Superpred, Uniprot, Pubchem databases were used to predict the WD liver fibrosis disease targets, and 778 WD liver fibrosis disease targets were obtained after removing the repeated values.

[0081] 4.4, WD liver fibrosis disease target collection

[0082] With "Wilson's disease and Liver Fibrosis, Wilson disease and Liver Fibrosis" as the keyword, relevant targets were downloaded from GeneCards, OMIM, pharmGKB online databases, and 778 WD liver fibrosis disease targets were obtained after removing the repeated values.

[0083] 4.5, WD liver fibrosis disease target collection

[0084] After intersection analysis of WD liver fibrosis disease targets and WD liver fibrosis disease targets, 128 intersection targets were obtained; the intersection targets were imported into STRING website to construct PPI network, and the results showed that there were 128 points and 2338 edges (PPI network) Figure 4 ).

[0085] Example Three: Metabolic regulation mechanism of WD liver fibrosis treated by liver bean

[0086] Metabolic regulation mechanism of WD liver fibrosis treated by liver bean was studied by metabolomics. TX mice were used as models, UPLC-Q Exactive / MS technology was used to characterize serum metabolic profiles of control group, model group and liver bean administration group, and metabolic markers of WD liver fibrosis and the regulation effect of liver bean on metabolic markers were screened, in order to reveal the metabolic regulation mechanism of WD liver fibrosis treated by liver bean.

[0087] 5. Experimental methods

[0088] 5.1. Animal experiments and sample collection: same as step 3.1 in Example 2.

[0089] 5.2. Pretreatment of biological samples

[0090] The sample stored at -80 ℃ was taken out, thawed in an ice-water mixture, and 50 μL of the sample was transferred to a 1.5 mL EP tube; 200 μL of protein precipitant methanol-acetonitrile (V:V=2:1, containing mixed internal standards 1, 2, 3, 4, 5, each 4 μg / mL) was added, vortexed for 1 min; ultrasonic extraction in ice water bath for 10 min, overnight standing at -40 ℃; centrifuged for 20 min (12000 rpm, 4 ℃), 150 μL of supernatant was loaded into a LC-MS sample vial with a foot inner liner for analysis; the quality control sample (QC) was prepared by mixing all sample extracts in equal volumes. Note: all extraction reagents were pre-cooled at -20 ℃ before use.

[0091] 5.3. Liquid chromatography-mass spectrometry analysis conditions

[0092] 5.3.1. Chromatographic conditions

[0093] Chromatographic column: ACQUITY UPLC HSS T3 (100 mm x 2.1 mm, 1.8 μm); column temperature: 45 ℃; mobile phase: mobile phase: A-water (containing 0.1% formic acid), B-acetonitrile; flow rate: 0.35 mL / min; injection volume: 4 μL.

[0094] 5.3.2. Mass spectrometry conditions

[0095] Table 4. Mass spectrometry parameter setting table

[0096]

[0097] 5.4. Data quality control

[0098] Precisely take 50 μL of each sample extract, mix uniformly, and use as the QC sample, according to the same method in 5.2. Insert a QC sample every 10 samples during the analysis process to verify the stability of the LC-MS system during the entire analysis process.

[0099] 5.5. Metabolite identification

[0100] In the process of identifying metabolites, the original data were first processed by the metabolomics processing software XCMS v4.5.1 to filter the baseline, identify the peaks, integrate, and correct the retention time. Then, the retention time, accurate mass number, secondary fragments, and isotope distribution of the compounds were comprehensively compared and analyzed by using The Human Metabolome Database, Lipidmaps (v2.3), METLIN database, and PubChem local database to identify the metabolites, and the data matrices of the quality control group, the Zhusuoling administration group, the model group, and the control group were obtained, respectively.

[0101] 5.6. Multivariate statistical analysis

[0102] Before screening the differential metabolites, the data matrices were imported into the SIMCA 14.1 software for unsupervised principal component analysis (PCA), followed by supervised partial least squares analysis (PLS-DA), and finally supervised orthogonal partial least squares-discriminant analysis (OPLS-DA). The PLS-DA and OPLS-DA models were verified by 200 times of permutation test.

[0103] 5.7. Metabolic pathway enrichment analysis

[0104] Based on the results of multivariate statistical analysis and T test, P < 0.05 and |FC| ≥ 2 were used as the screening criteria for differential metabolites. The screened differential metabolites were imported into the online software MetaboAnalyst 6.0, and the enrichment analysis (Enrichment analysis) and pathway analysis (Pathway analysis) modules were used for metabolic pathway enrichment analysis.

[0105] 6. Results

[0106] 6.1. Quality control of metabolomics data

[0107] According to the strongest ion intensity of the quality control sample (QC) in the chromatogram at each time point, the chromatogram was continuously drawn. In the positive and negative ion acquisition modes, it was found that the response intensity and retention time repeatability of the QC sample chromatographic peak were good, indicating that the variability caused by instrument error was very small during the entire experiment.

[0108] To further evaluate the stability of the instrument, principal component analysis (PCA) was performed on the overall data of the collected QC samples. The PCA model figure was obtained by 7-fold cross-validation; the QC samples were closely clustered together, with good repeatability, indicating that the liquid chromatography-mass spectrometry system was stable throughout the experiment. To more intuitively show the correlation between the QC samples, correlation analysis and plotting were performed on the QC samples (the first and last 10 of each group were displayed when the number of QCs was greater than 20). The results showed that the QC sample metabolite contents tested in the positive and negative ion modes were basically linear, and the correlation coefficients were all > 0.99, indicating that the entire test system had good repeatability.

[0109] 6.2, Metabolite identification and overall analysis

[0110] A total of 4583 metabolites were detected from the samples of the three groups. The substance statistical classification results of the metabolites showed that the top five were: 1649 lipid and lipid-like molecular compounds, 849 organic acids and their derivatives, 812 organic heterocyclic compounds, 456 benzene compounds, and 358 organic oxidation compounds.

[0111] 6.3, Verification of the consistency of the serum samples within the mouse serum group and the difference in the metabolic level between the groups based on the serum metabolic profile

[0112] 6.3.1, Principal component analysis (PCA)

[0113] To observe the overall distribution between the samples and the stability of the entire analysis process, PCA analysis was performed on the serum samples of the three groups of mice. The results showed that there was a clear separation trend between the control group, the model group, and the Gan Dou Ling administration group, indicating that the serum metabolite level of the TX mice changed significantly, and there were differences in the metabolic profiles between the three groups.

[0114] 6.3.2, Partial least squares discriminant analysis (PLS-DA)

[0115] To distinguish the overall differences in the metabolic profiles between the groups, PLS-DA analysis was performed. The 200 times permutation test results of the PLS-DA model showed that the R 2 X, R 2 Y and Q 2 values were 0.633, 0.994, and 0.967, respectively; and the Q 2 < R (Table 5), indicating that the model did not appear to be over-fitted, and had reliable explanation and prediction ability. In the PLS-DA multi-group analysis model, the control group, the model group, and the Gan Dou Ling administration group had good discrimination between the groups, and the differences within the groups were relatively small.

[0116] 6.3.3, Orthogonal partial least squares discriminant analysis (OPLS-DA)

[0117] In order to filter out the noise irrelevant to the classification information, and maximize the differences between different groups within the model, OPLS-DA analysis was performed. The results showed that the explanation rates of the OPLS-DA model of the model group and the control group to the X and Y matrices were R 2 X=0.501, R 2 Y=0.999, and the Q 2 value of the model prediction was 0.962; the R 2 X, R 2 Y and Q 2 values of the liver doulouling administration group and the model group were 0.553, 0.994, 0.951, respectively (Table 5). It can be seen that the OPLS-DA model constructed in this study has good explanation and prediction ability, and the model is effective.

[0118] Table 5 Model evaluation parameters

[0119]

[0120] 6.4, Screening of serum metabolic markers of TX mice and pathway analysis

[0121] 6.4.1, Screening of serum metabolic markers of TX mice

[0122] Between the model group and the control group, the screening criteria were P<0.05 and |FC|≥2, and a total of 1142 differential metabolites were screened, of which 991 markers were significantly increased and 151 markers were significantly decreased (P<0.05, |FC|≥2). Figure 5 It is shown that there is a significant difference in serum metabolite levels between the model group and the control group, and the serum metabolic state of TX mice is abnormal.

[0123] 6.4.2, Metabolic pathway enrichment analysis of serum metabolic markers of TX mice

[0124] The 1142 TX mouse metabolic markers were introduced into the metaboanalyst 6.0 software for enrichment analysis, and the results showed that 32 metabolic pathways changed, mainly involving steroid hormone biosynthesis, citric acid cycle (TCA cycle), arginine biosynthesis, alanine, aspartate and glutamate metabolic pathway, pyruvate metabolic pathway, tryptophan metabolic pathway, and tyrosine metabolic pathway.

[0125] 6.5, Screening of serum metabolic markers of TX mice significantly adjusted by liver doulouling

[0126] In order to explore the intervention effect of Gandouling on serum metabolic abnormalities of TX mice, based on the serum metabolic markers of TX mice screened above, taking Log2(FC_MvsC) x Log2(FC_GvsM) < 0 and P < 0.05 as the standard, after Gandouling treatment, 266 serum metabolic markers were significantly adjusted, among which 253 metabolites were significantly increased, and 13 metabolites were significantly decreased, indicating that Gandouling had a significant improvement effect on serum metabolic disorder of TX mice.

[0127] 6.6, Metabolic pathway analysis of Gandouling intervention

[0128] The 266 Gandouling significantly adjusted metabolic markers were introduced into the metaboanalyst 6.0 software for enrichment analysis. 21 metabolic pathways were obtained, mainly enriched in the tricarboxylic acid cycle (TCA cycle) pathway, alanine, aspartate and glutamate metabolism pathway, pyruvate metabolism pathway, glyoxylate and dicarboxylate metabolism pathway, cysteine and methionine metabolism pathway, tyrosine metabolism pathway, arginine biosynthesis pathway, lipoic acid metabolism pathway, glycine, serine, threonine metabolism pathway Figure 6 ).

[0129] Example Four: Joint analysis to explore the pharmacodynamic material basis and metabolic regulation mechanism of Gandouling in the treatment of WD liver fibrosis

[0130] 7, Experimental method

[0131] 7.1, Joint analysis of network pharmacology and metabonomics to construct a "small molecule metabolite-reaction-enzyme-target" complex metabolic network

[0132] The action targets of Gandouling blood components in the intervention of WD liver fibrosis, and the Gandouling significantly adjusted metabolic markers were introduced into the MetScape plug-in of Cytoscape v3.9.1 software to construct a "small molecule metabolite-reaction-enzyme-target" complex metabolic network, and the core action targets, core metabolic markers and core metabolic pathways were screened.

[0133] 7.2, Screening of the pharmacodynamic material basis of Gandouling

[0134] The obtained core action targets were introduced into the GeneCard database, and the protein structure of the corresponding target was downloaded by clicking Protein; Gandouling blood components were introduced into the Pubchem database, and the three-dimensional structure of the corresponding components was downloaded and saved in SDF format; finally, the 3D structure of Gandouling blood components and the protein structure of the core action targets were introduced into CB-Dock2 website for molecular docking, and Gandouling blood components that can bind to the core action targets were taken as the pharmacodynamic material basis.

[0135] 7.3, Construction of the multi-dimensional network of “Hegu Ling active substance basis-core action target-core metabolic marker-core metabolic pathway-WD liver fibrosis”

[0136] After the core action targets, core metabolic markers and core metabolic pathways obtained from the “small molecule metabolite-reaction-enzyme-target” complex metabolic network, and the data of the active substance basis of Hegu Ling obtained from molecular docking were sorted into “Network” and “Type” tables, they were imported into Cytoscape v3.9.1 software for visualization of the multi-dimensional network of “Hegu Ling active substance basis-core action target-core metabolic marker-core metabolic pathway-WD liver fibrosis”.

[0137] 8, Results

[0138] 8.1, Core action targets, core metabolic markers and core metabolic pathways of Hegu Ling in the treatment of WD liver fibrosis

[0139] In the constructed “small molecule metabolite-reaction-enzyme-target” complex metabolic network, 11 core action targets (AGR1, GLUL, TRY, HPRT1, PDE11A, NOS3, MPO, ADHIC, CYP1A2, CYP3A4 and GSTM1) of Hegu Ling in the treatment of WD liver fibrosis were screened out, 12 core metabolic markers (N-iminomethyl glutamate, dihydrofolic acid, 5'-phosphoribosyl-N-formylglycinamide, 3', 5'-cyclic phosphoadenosine, pyruvic acid, fumaric acid, 2-oxoglutaric acid, N-acetyl-L-aspartate, 3, 4-dihydroxy mandelic acid, 4-trimethylammonium butyraldehyde, L-3-hydroxykynurenine and (S)-malic acid) of Hegu Ling were significantly adjusted, and 9 core metabolic pathways (purine metabolism; histidine metabolism; vitamin B9 metabolism; tyrosine metabolism; lysine metabolism; glycolysis and gluconeogenesis metabolism; TCA cycle; tryptophan metabolism; urea cycle and metabolism of arginine, proline, glutamic acid, aspartic acid and asparagine) of Hegu Ling were significantly intervened.

[0140] 8.2, Screening of the active substance basis of Hegu Ling in the treatment of WD liver fibrosis by molecular docking

[0141] In order to screen the active substance basis of Hegu Ling in the treatment of WD liver fibrosis, molecular docking was performed by CB-Dock2 platform. The molecular docking results showed that 25 blood-entering components of Hegu Ling could combine with the core action targets, and the binding energy was less than-6 kcal / mol (Table 6). According to the negative correlation between binding energy and binding strength, and the judgment that the binding energy is less than-5 kcal / mol indicates strong receptor affinity, it is indicated that they all have strong binding capacity and good binding activity, which can be used as the active substance basis of Hegu Ling in the treatment of WD liver fibrosis.

[0142] Table 6. Molecular docking information on the pharmacodynamic material basis and core target of liver-derived sildenafil.

[0143]

[0144]

[0145] 8.3, A multidimensional network of "Pharmacodynamic material basis of hepatoretin - core target of action - core metabolic marker - core metabolic pathway - WD liver fibrosis"

[0146] By constructing a multidimensional network of "hepatoretin's pharmacodynamic material basis - core target of action - core metabolic marker - core metabolic pathway - WD liver fibrosis" ( Figure 7 It is known that 25 pharmacodynamic substances of hepatoxin, such as gallic acid, magnoflorine, and camphoridine A, can act on 11 core targets, including AGR1, GLUL, and TRY, regulate 12 core metabolites, such as N-iminomethylglutamate, dihydrofolate, and 5'-phospribose-N-formylglycine, and affect 9 core metabolic pathways, including the TCA cycle, purine metabolism, and histidine metabolism, thereby exerting a therapeutic effect on WD liver fibrosis.

[0147] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for analyzing the metabolic regulation mechanism of liver bean spirit in treating WD liver fibrosis, characterized in that, Comprise the following steps: Step one, based on the blood components and network pharmacology research of liver bean spirit, obtain the target points of liver bean spirit blood components intervention WD liver fibrosis; Step two, based on serum metabolomics to obtain liver bean spirit significant callback metabolic markers, and liver bean spirit significant callback metabolic markers are analyzed by enrichment analysis, and the significant metabolic pathways are obtained; Step three, with liver bean spirit blood components intervention WD liver fibrosis target points, significant callback metabolic markers to construct "small molecule metabolites-reaction-enzyme-target" complex metabolic network, screen core target points, core metabolic markers and core metabolic pathways; Step four, with molecular docking technology to screen liver bean spirit pharmacodynamic material basis, and can visualize "liver bean spirit pharmacodynamic material basis-core target points-core metabolic markers-core metabolic pathways-WD liver fibrosis" multidimensional network; Obtain 25 pharmacodynamic material basis, 11 core target points, 12 core metabolic markers and 9 core metabolic pathways; The liver bean spirit pharmacodynamic material basis is as follows: gallic acid, magnolia base, camphor glycoside A, 2, 6- edible astragalus ester glycoside, phlorizin, bright fungus A, fucoidin E, 1, 3-dihydroxy-9, 10-dioxanthrene-2-carboxylic acid, kaempferol, (+) 1, 5-epoxy- demethyl ketone guaiac-11-alkene, 8-isopentenyl daidzein, 2-hydroxy-5-[3, 4, 5-trihydroxy-6- (hydroxymethyl) oxy-2-yl] oxybenzoic acid, protocatechuic acid-3-glucoside, (2S, 3R, 4S, 5S, 6R) -2- (4-hydroxy-2, 6-dimethoxy phenoxy) -6- (hydroxymethyl) oxy ane-3, 4, 5-triol, bamboo fungus A, evodia glycoside, codonopsis lactone, cress dihydric alcohol, 5-O-methyl visamir alcohol-4-O-β-D-furan sercosyl- (1→6) -β-D-glucopyranoside, methyl syringin, bauhinia purpurea glycoside B, junco ketone, 2-[(9Z, 12Z)-heptadeca-9, 12-dienyl]-6-hydroxybenzoic acid, 7, 2'-dihydroxy-3', 4'-methylenedioxy isoflavone and pseudo red pigment; The core target points are AGR1, GLUL, TRY, HPRT1, PDE11A, NOS3, MPO, ADHIC, CYP1A2, CYP3A4 and GSTM1; The core metabolic markers are N-iminomethyl glutamic acid, dihydrofolic acid, 5'-phosphoribosyl-N-formylglcyamine, 3', 5'-cyclic phosphoadenosine, pyruvic acid, fumaric acid, 2-oxoglutaric acid, N-acetyl-L-aspartic acid, 3, 4-dihydroxy mandelic acid, 4-trimethylammonium butyl aldehyde, L-3-hydroxykynurenine and (S) -malic acid; The core metabolic pathways are purine metabolism, histidine metabolism, vitamin B9 metabolism, tyrosine metabolism, lysine metabolism, glycolysis and gluconeogenesis metabolism, TCA cycle, tryptophan metabolism, urea cycle and metabolism of arginine, proline, glutamic acid, aspartic acid and asparagine.

Images