Gout damp-heat syndrome subtype modeling method, application of biomarker group and detection method

Abstract

The invention belongs to the technical field of animal model analysis, and particularly relates to a modeling method of a gout damp-heat syndrome subtype model, application of a biomarker group and a detection method. According to the invention, two subtype models of pain, rheumatism and severer heat and severer heat are constructed, a UPLC-MS (Ultra Performance Liquid Chromatography-Mass Spectrometry) detection method of serum biomarkers is established, and a biomarker group comprising lauroyl carnitine, taurocholic acid, palmitoyl-L-carnitine, L-leucine, leukotriene A4, 13-Oxo-9 and 11-tridecioic acid is obtained. Researches show that the biomarker group can simply and effectively distinguish models of pain and rheumatism heavier than heat and models of heat heavier than wet and judge whether modeling of two subtypes succeeds or not, so that the biomarker group can replace conventional characterization and biochemical pathology evaluation methods, and the objectivity and operability of judgment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of animal model analysis, and specifically relates to a method for modeling a gout-rheumatic-heat syndrome subtype model, and the use and detection method of a biomarker group. Background Art

[0002] Damp-heat syndrome is the key syndrome type of clinical gouty arthritis, and Lingnan region is known for damp-heat, and patients often have damp-heat in the spleen and stomach. According to the bias of the syndrome type, gout with damp-heat syndrome can be divided into two subtypes: dampness is heavier than heat and heat is heavier than dampness. According to the treatment principles of traditional Chinese medicine and ethnic medicine, in clinical treatment, gout with damp-heat syndrome needs to be treated according to whether dampness is heavier than heat or heat is heavier than dampness. In clinical treatment, professional physicians can be relied on for dialectical treatment or prescription.

[0003] In preclinical animal efficacy evaluation, it is often necessary to evaluate the efficacy of drugs on different damp-heat gout models (two subtypes: dampness is heavier than heat and heat is heavier than dampness). However, animals cannot be dialectical by looking, smelling, asking, and cutting. A large amount of semi-quantitative subjective evaluation is required. Semi-quantitative evaluations based on body shape, stool, rectal temperature, fur color, activity status, etc. often lack accuracy and the evaluation system is complex. This leads to the difficulty of distinguishing the two subtypes in animal model evaluation, and it is also difficult to judge whether the modeling is successful. For example, Zhang Junying used a variety of factors such as environment and diet to create rat models of dampness being heavier than heat and heat being heavier than dampness (Chinese medicine gavage, high-fat diet, high temperature and high humidity environment, Escherichia coli), and found that the body temperature of the heat-biased model group rose faster, the food intake was less, the water intake was more, the stool was drier, the urine was yellow and turbid, and the patient was more irritable. This requires a large number of behavioral, dietary, defecation and other observations as well as histopathological observations that require the killing of animals (Preliminary Study on the Model of Dampness More Than Heat and Heat More Than Dampness in Febrile Diseases with Dampness-Heat Syndrome, graduation thesis of Hubei University of Traditional Chinese Medicine, 2009).

[0004] As two subtypes, dampness is heavier than heat and heat is heavier than dampness, their metabolomics changes are extremely complex and difficult to distinguish or judge with a single blood marker. Therefore, developing a detection method that can simultaneously detect multiple markers of damp-heat syndrome gout animal model and screening a marker group that can simply and effectively judge or distinguish the two subtypes of dampness is heavier than heat and heat is heavier than dampness in damp-heat syndrome gout animal model has important practical significance for both traditional Chinese medicine evaluation and clinical diagnosis. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide a method for modeling a gout-rheumatic-heat syndrome subtype model, the use of a biomarker group and a detection method, so as to provide good convenience for clinical diagnosis and animal model evaluation and judgment.

[0006] The basic idea of ​​the present invention is as follows: first, a recognized and mature modeling method is used to induce the establishment of a gout rheumatism heavier than heat subtype group and a gout heat heavier than dampness subtype group; the traditional evaluation method based on Chinese medicine animal model and behavioral, pathological and other means are used to evaluate the success of modeling; then a new UPLC-MS analysis method is established to analyze animal blood samples, and on this basis, a metabolomics evaluation is performed to find potential biomarkers that can distinguish the two models or judge whether the modeling of the two models is successful; overlapping indicators in the two groups of differential metabolic biomarkers are selected, and receiver operating characteristic (ROC) analysis is performed to obtain cutoff values ​​and recognition ability indicators, thereby establishing an objective, simple and effective method for distinguishing gout rheumatism heavier than heat and heat heavier than dampness animal models, and can be used to judge whether the modeling of gout rheumatism heavier than heat or gout heat heavier than dampness is successful. The technical scheme of the present invention is as follows:

[0007] A method for modeling a subtype of gout and rheumatism syndrome, comprising inducing gout in animals using hypoxanthine, potassium oxonate, and sodium urate, inducing damp-heat syndrome in animals using high-fat feeding, ethanol feeding, dried ginger, and an artificial climate chamber, and collecting blood samples from animals for UPLC-MS analysis and screening; the animal model is a rodent model, and the method for modeling a subtype of gout and rheumatism syndrome further comprises the following steps:

[0008] S1: After the modeling is completed, animal blood samples are collected to separate serum, and the biomarker group in the serum of normal animals and model animals is analyzed by UPLC-MS. The biomarker group includes: lauroyl carnitine, taurocholic acid, palmitoyl L-carnitine, L-leucine, leukotriene A4, and 13-Oxo-9,11-tridecadienoic acid;

[0009] S2: The original index content of the biomarker group in S1 was divided by the average value of the corresponding index of the normal group to obtain the normalized value of each biomarker, and the model animals were screened according to the cutoff value.

[0010] Preferably, in step S2, when the desired gout model is a wet weight to heat subtype, the normalized value of lauroyl carnitine>1.4330, the normalized value of taurocholic acid>1.4463, the normalized value of palmitoyl L-carnitine>1.5429, the normalized value of L-leucine<0.8317, the normalized value of leukotriene A4>2.8835, and the normalized value of 13-Oxo-9,11-tridecadienoic When the normalized value of acid is greater than 1.1770 and less than 1.3379, and at least three of the above conditions are met, the model is considered to be successful. When the required gout model is a heat-gravity-wet subtype, the normalized value of lauroylcarnitine is greater than 1.1361 and less than 1.4330, the normalized value of taurocholic acid is greater than 1.2556 and less than 1.4463, the normalized value of palmitoyl-L-carnitine is greater than 0.9739 and less than 1.5429, the normalized value of L-leucine is greater than 0.8317, the normalized value of leukotriene A4 is greater than 1.8198 and less than 2.8835, and the normalized value of 13-Oxo-9,11-tridecadienoic acid is greater than 1.3379, and at least three of the above conditions are met, the model is considered to be successful.

[0011] A UPLC-MS detection method for a biomarker group of a gout-rheumatic-heat syndrome subtype model, wherein the gout-rheumatic-heat syndrome subtype model is a rodent model, and the biomarker group is a biomarker group for distinguishing between two subtypes of gout-rheumatic syndrome that is more severe than heat and gout-heat that is more severe than dampness and / or a biomarker group for judging whether the modeling of two subtypes of gout-rheumatic syndrome that is more severe than heat and gout-heat that is more severe than dampness is successful or not, and the detection method comprises the following steps:

[0012] J1 Take animal blood sample to separate serum, add methanol, vortex for 0.5min to 1.5min, centrifuge, and filter the supernatant through 0.22μm filter membrane to obtain the sample treatment solution to be tested;

[0013] J2 takes the sample treatment solution to be tested in step S1 and performs UPLC-MS analysis:

[0014] UPLC chromatographic conditions: ACQUITY UPLC HSS T3 column; column specifications: 2.1 mm x 100 mm, particle size: 1.8 μm; mobile phase A: acetonitrile, mobile phase B: 0.1% formic acid water; column temperature: 30°C to 35°C; flow rate: 0.3 mL / min; gradient elution program is as follows:

[0015] Elution time (min) Mobile phase A (%) Mobile phase B (%) Initial 5.0 95.0 1.00 25.0 75.0 2.00 60.0 40.0 7.50 90.0 10.0 10.50 99.0 1.0 12.50 99.0 1.0 13.00 5.0 95.0 15.00 5.0 95.0

[0016] Mass spectrometry conditions are as follows: positive ion mode: heater temperature 350°C, capillary temperature 330°C, sheath gas flow rate 40arb, auxiliary gas flow rate 15arb, sweep gas flow rate 1arb, electrospray voltage 3.5KV, S-Lens RF Level is 50%; negative ion mode: heater temperature 350°C, capillary temperature 330°C, sheath gas flow rate 45arb, auxiliary gas flow rate 15arb, sweep gas flow rate 1arb, electrospray voltage 3.2KV, S-Lens RF Level is 50%; scanning mode: primary full scan: m / z is 80-1200, data-dependent secondary mass spectrometry scan: dd-MS2, Top N=3, resolution: Full MS is 70,000, dd-MS2 is 13,500, collision mode: high energy collision dissociation, collision voltage: 10V, 25V, 40V.

[0017] Preferably, the amount of methanol used is 200 μL of methanol per 50 μL of serum.

[0018] Preferably, the vortex time is 1 min.

[0019] Preferably, the column temperature is 35°C.

[0020] Preferably, the injection volume for UPLC detection is 3 μL.

[0021] Preferably, the rodent animal model is a mouse animal model.

[0022] The use of a reagent containing a biomarker group in preparing an animal model for distinguishing between two subtypes of gout with rheumatism being more serious than heat and heat being more serious than moisture and / or judging whether the modeling of two subtypes of gout with rheumatism being more serious than heat and heat being more serious than moisture is successful or not, the animal model is a rodent model, and the biomarker group includes the following 6 biomarkers: lauroyl carnitine, taurocholic acid, palmitoyl L-carnitine, L-leucine, leukotriene A4 and 13-Oxo-9,11-tridecadienoic acid.

[0023] Preferably, the reagent is selected from: rodent serum and / or an artificially prepared solution containing the biomarker.

[0024] That is, the reagent can be a reagent of an artificially synthesized compound, or a substance separated from animal serum.

[0025] The use of reagents for detecting a group of biomarkers in the preparation of a detection kit for distinguishing gout rheumatism that is more serious than heat, or heat that is more serious than moisture models and / or judging whether the modeling of gout rheumatism that is more serious than heat, or heat that is more serious than moisture is successful or not, characterized in that the animal model is a rodent model, and the biomarker group includes the following 6 biomarkers: lauroyl carnitine, taurocholic acid, palmitoyl L-carnitine, L-leucine, leukotriene A4 and 13-Oxo-9,11-tridecadienoic acid.

[0026] Beneficial effects of the present invention:

[0027] The present invention establishes a new UPLC-MS method that can be used to simultaneously detect 6 serum biomarkers for distinguishing animal models of gout and rheumatism being more serious than heat, or heat being more serious than dampness, and / or for judging whether the two animal models of gout and rheumatism being more serious than heat, or heat being more serious than dampness are successfully established, as well as more than 40 other differential serum biomarkers.

[0028] The present invention found that the six serum biomarkers obtained can effectively distinguish the animal models of gout rheumatism being more serious than heat and heat being more serious than moisture, and can also effectively judge whether the modeling of gout rheumatism being more serious than heat and heat being more serious than moisture is successful or not. This provides a simple, effective and objective new method for the dialectical typing of animal models and the judgment of modeling results. It overcomes the defects of insufficient professional personnel, lack of objectivity in judgment and the need for animal death when traditional dialectical typing and modeling judgment based on animal fur color, behavior, feces and stool, and pathological means are used. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a comparison of the general conditions of mice in each group;

[0030] Figure 2 This is a comparison of ankle swelling in each group of mice;

[0031] Figure 3 This is a comparison chart of serum uric acid, creatinine, and blood lipid indicators of mice in each group;

[0032] Figure 4 Comparison of pathological changes in ankle joints and livers of mice in each group;

[0033] Figure 5 Comparison of pathological changes in kidney and small intestine of mice in each group;

[0034] Figure 6 This is a comparison of the immunofluorescence detection results of NLRP3, IL-6, and IL-1β in the ankle joint tissues of mice in each group;

[0035] Figure 7 This is the metabolomics chromatogram of serum of normal control group mice in positive ion mode;

[0036] Figure 8 This is the metabolomics chromatogram of serum of mice in the wet weight and heat groups under positive ion mode;

[0037] Fig. 9 This is the thermogravimetric metabolomics chromatogram of serum from mice in the wet group under positive ion mode;

[0038] Fig.10 This is the metabolomics chromatogram of serum of normal control group mice in negative ion mode;

[0039] Fig.11 This is the metabolomics chromatogram of serum of mice in the wet weight and heat groups under negative ion mode;

[0040] Fig.12 This is the metabolomics chromatogram of serum from mice in the negative ion mode under thermogravimetric analysis. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are not intended to limit the protection scope of the present invention.

[0042] The sources, models and other information of the main instruments and reagents used in this invention are as follows: Dionex TM Ultimate TM RSLC3000 liquid chromatograph (Thermo Scientific); Q-Exactive-Focus-Orbitrap mass spectrometer (Thermo Scientific); Progenesis QI software (Waters). Customized high-fat diet (Guangdong Medical Experimental Animal Center, composition: 80% ordinary diet, 12% lard, 8% honey); hypoxanthine (Aladdin); potassium oxonate, sodium urate, i.e., MSU (Sigma); high-density lipoprotein cholesterol (HDL-C) test kit, low-density lipoprotein cholesterol (LDL-C) test kit, total cholesterol (TC) test kit, triglyceride (TG) test kit, uric acid test kit, creatinine test kit, xanthine oxidase (XOD) test kit (Nanjing Jiancheng); NLRP3 antibody, IL-6 antibody, IL-1β antibody (Sevier).

[0043] Example 1 Modeling method and model evaluation, data processing and statistical analysis method

[0044] 1 Construction of animal model

[0045] 1.1 Solution and preparation

[0046] Preparation of 12.5% ​​ethanol solution: Take anhydrous ethanol and purified water and prepare an ethanol volume percentage of 12.5%.

[0047] Preparation of 0.013g / mL dried ginger aqueous solution: Take 50.0g of dried ginger raw material, add 500mL of distilled water and soak for 1h, boil over high heat, simmer over low heat for 1h, filter through gauze, and place the filtrate in a beaker; then add 10 times the volume of distilled water, repeat the above process twice, combine the filtrates three times, concentrate under reduced pressure to 500mL, and freeze in a -80℃ refrigerator. When used, melt 15.6mL of the concentrate, add 104.4mL of distilled water to dilute, and prepare a dried ginger aqueous solution with a concentration of 0.013g / mL.

[0048] Hypoxanthine intragastric solution: Dissolve hypoxanthine in 0.5% sodium carboxymethylcellulose solution to prepare a hypoxanthine intragastric solution with a concentration of 30 mg / mL.

[0049] Potassium oxonate injection: Dissolve potassium oxonate in normal saline to prepare an injection with a concentration of 15 mg / mL.

[0050] MSU crystal suspension: Dissolve MSU in 1 mol / L NaOH, add sterile 0.9% sodium chloride solution, boil and stir until dissolved, adjust pH to 7.2 with 1 mol / L HCl after cooling, transfer supernatant to a volumetric flask, and store overnight at room temperature. Centrifuge, dry and grind the precipitate at 70°C to obtain MSU crystals, and prepare a 60 mg / mL MSU crystal suspension with 0.9% sodium chloride solution.

[0051] 1.2 Experimental animals

[0052] SPF C57BL / 6 male mice, weighing 18-22 g, 7-8 weeks old, were provided by Guangdong Sijia Jingda Biotechnology Co., Ltd. They were adaptively fed for one week, with free access to water and food, and a 12-h day-night cycle.

[0053] 1.3 Grouping and modeling

[0054] Mice were randomly divided into 3 groups. Conventional TCM animal modeling methods were used to establish the model according to the factors in Table 1. Among them, high fat, ethanol, dried ginger, and climate chamber are the modeling factors of damp-heat syndrome, high fat and ethanol focus on dampness, and dried ginger and climate chamber focus on heat.

[0055] Table 1 Modeling factors of each group in the gout-rheumatic-heat syndrome subtype model

[0056] Grouping High fat Ethanol Dried ginger Climate Chamber Hypoxanthine Potassium Oxonate MSU normal - - - - - - - Humidity is heavier than heat √ √ - - √ √ √ Heat is more important than moisture √ √ √ √ √ √ √

[0057] Normal group (normal control group): 0.5% CMC-Na solution (0.1 mL / 10 g) was gavaged every morning, and normal saline (0.2 mL / 10 g) was injected intraperitoneally. On the 28th day, 25 μL of 0.9% sodium chloride solution was injected into the right ankle joint of the mice, and the samples were collected 48 hours later. During the experiment, the mice were given ordinary feed and free access to water.

[0058] Model group 1 (wet weight is higher than heat group): 30 mg / mL hypoxanthine (0.1 mL / 10 g) and 15 mg / mL potassium oxonate (0.2 mL / 10 g) were intraperitoneally injected every morning, and 12.5% ​​ethanol solution (0.1 mL / 10 g) was intraperitoneally injected in the afternoon. On the 28th day, 25 μL of 60 mg / mL MSU crystal suspension was injected into the right hind ankle joint of the mice, and the samples were collected 48 hours later. During the experiment, the mice in this group were given a high-fat diet and free access to water.

[0059] Model group 2 (heat weight and humidity group): 30 mg / mL hypoxanthine (0.1 mL / 10 g) was gavaged every morning and 15 mg / mL potassium oxonate (0.2 mL / 10 g) was intraperitoneally injected. The mice were placed in an artificial climate box with a temperature (32 ± 2 ° C) and humidity (95% ± 2%) for 6 hours a day. In the afternoon, the mice were taken out of the climate box and gavaged with 12.5% ​​ethanol aqueous solution (0.1 mL / 10 g) and 0.013 g / mL dried ginger aqueous solution (0.1 mL / 10 g). On the 28th day, 25 μL of 60 mg / mL MSU crystal suspension was injected into the right hind ankle joint of the mice, and the samples were collected 48 hours later. During the experiment, the mice in this group were given a high-fat diet and free access to water.

[0060] 2. Sample collection and processing

[0061] On the 31st day of the experiment, mice were anesthetized with sodium pentobarbital, blood samples were collected from the eyeballs, left to stand for 1 hour, centrifuged at 4000 rpm for 10 minutes at 4°C, and the supernatant (serum) was stored at -80°C. Liver, kidney, small intestine and ankle joint tissues of mice were obtained, and some were fixed with paraformaldehyde for HE staining. Part of the liver, kidney and small intestine tissue samples were embedded in OCT and stored at -80°C, and the rest were stored at -80°C.

[0062] 3. Evaluation of animal models

[0063] 3.1 Macro-indicator evaluation

[0064] On the 30th day of the experiment, the mice's overall demeanor, willingness to move, group behavior, hair condition, bowel movements, body weight, abdominal circumference, rectal temperature, 12-hour urine volume, and average daily diet and drinking water in the past two weeks were observed.

[0065] After the injection of MSU crystal suspension on the 28th day of the experiment, the diameter of the right hind ankle joint of the mice was measured with an electronic vernier caliper at 0, 12, 24, and 48 hours after injection, and the ankle joint swelling was calculated [(ankle joint diameter after modeling-ankle joint diameter before modeling) / ankle joint diameter before modeling]. The 0-48h swelling curve was drawn according to the ankle joint swelling, and the area under the curve was calculated.

[0066] 3.2 Evaluation of biochemical indicators

[0067] The obtained serum was taken and the kit was used to detect the serum TG, TC, HDL-C, LDL-C, creatinine and uric acid levels of mice in different groups. Serum samples were taken and the kit was used to detect the serum uric acid and creatinine levels of mice in each group, and the uric acid excretion fraction was calculated based on the serum uric acid and creatinine results [uric acid excretion fraction = urine uric acid × blood creatinine / (blood uric acid × urine creatinine)].

[0068] 3.3 Histopathological evaluation

[0069] Well-fixed liver, kidney, small intestine, and ankle joint tissues were routinely prepared for pathological sections and HE staining, and the tissue sections were observed in detail under a microscope at different magnifications. Basic pathological changes in the sections, such as congestion, congestion, hemorrhage, edema, degeneration, necrosis, hyperplasia, fibrosis, organization, granulation tissue, and inflammatory changes, were observed, and pathological changes in the selected tissues were scored. The scoring criteria are shown in Table 2, and typical lesions are marked with arrows.

[0070] Table 2 Histopathological scoring criteria

[0071]

[0072] 3.4 Evaluation of ankle joint tissue inflammation

[0073] Ankle joint tissue was taken to make paraffin sections, and the changes in NLRP3, IL-6, and IL-1β levels in the ankle joint tissue sections were measured using immunofluorescence technology. The results were expressed as positive cell density (number of positive cells / area of ​​tissue to be tested).

[0074] 3.5 Evaluation of damp-heat syndrome

[0075] With reference to the "Evaluation Form for Animal (Mice and Rats) Models of Dampness Syndrome in Traditional Chinese Medicine", the "Guidelines for the Combined Diagnosis and Treatment of Hyperuricemia and Gout (2021-01-20)" and related research on damp-heat syndrome and gout, the damp-heat syndrome evaluation is divided into two parts: dampness syndrome score and heat syndrome score.

[0076] The dampness syndrome score includes: overall demeanor, group behavior, weight gain, increased abdominal circumference, increased serum TG, increased serum TC, increased serum uric acid, increased serum creatinine, liver pathological damage, kidney pathological damage, and small intestinal pathological damage. Overall demeanor and group behavior are scored according to abnormal conditions (1-4 points from low to high). Other indicators are scored according to significant differences. Compared with the normal group, 1 point is given if the trend is in line with the trend and p<0.05, 2 points are given if the trend is in line with the trend and p<0.01, 3 points are given if the trend is in line with the trend and p<0.001, and 4 points are given if the trend is in line with the trend and p<0.0001.

[0077] Heat syndrome scores include abnormal gait, weight loss, increased joint swelling, increased rectal temperature, decreased urine output, decreased diet, increased water intake, ankle pathological damage, increased ankle NLRP3, increased ankle IL-6, and increased ankle IL-1β. Gait analysis is scored according to abnormal conditions (1-4 points from low to high). Other indicators are scored according to significant differences. Compared with the normal group, 1 point is given if the trend is in line with the p<0.05, 2 points are given if the trend is in line with the p<0.01, 3 points are given if the trend is in line with the p<0.001, and 4 points are given if the trend is in line with the p<0.0001.

[0078] The dampness syndrome score and the heat syndrome score were combined to evaluate whether the gout rheumatism-heat syndrome model was successfully constructed. The dampness syndrome and heat syndrome scores were used to compare whether the two model groups (the dampness-heavy than heat group and the heat-heavy than dampness group) were successfully constructed.

[0079] 3.6 Serum metabolomics analysis

[0080] 3.6.1 Preparation and optimization of serum samples

[0081] Take 50 μL of serum sample, add 200 μL of methanol, vortex for 1 min, let stand for 20 min, centrifuge at 4°C, 13000 rpm for 15 min, take the supernatant and filter through 0.22 μm filter membrane for UPLC-MS analysis.

[0082] 3.6.2 Analysis conditions

[0083] (1) Chromatographic conditions: ACQUITY UPLC TM HSS T3 chromatographic column (2.1 mm x 100 mm, 1.8 μm) (Waters, USA); mobile phase A: acetonitrile, mobile phase B: 0.1% formic acid water; column temperature: 35°C; flow rate: 0.3 mL / min; injection volume: 3 μL, gradient elution program is as follows:

[0084] Table 3 Gradient elution program

[0085]

[0086] (2) Mass spectrometry conditions: Positive ion mode: heater temperature 350°C; capillary temperature: 330°C; sheath gas flow rate: 40arb; auxiliary gas flow rate: 15arb; purge gas flow rate: 1arb; electrospray voltage: 3.5KV; S-Lens RF Level: 50%. Negative ion mode: heater temperature 350°C; capillary temperature: 330°C; sheath gas flow rate: 45arb; auxiliary gas flow rate: 15arb; purge gas flow rate: 1arb; electrospray voltage: 3.2KV; S-Lens RF Level: 50%. Scan mode: full scan (Full Scan, m / z 80~1200) and data-dependent secondary mass spectrometry scan (dd-MS2, Top N=3); resolution: 70,000 (Full MS) & 13,500 (dd-MS2). Collision mode: high energy collision dissociation (HCD), collision voltage (NCE): 10V, 25V, 40V.

[0087] 3.6.3 Data processing

[0088] The obtained metabolic profile data were imported into the Progenesis QI software processing system for analysis, and data preprocessing such as peak alignment, peak extraction, data normalization, and deconvolution analysis was performed. EZinfo 3.0 software was used for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Ions with VIP>1 in OPLS-DA analysis and p<0.05 in t-test were considered as potential biomarkers for further analysis. Element composition analysis was used to determine the possible molecular formula. Based on accurate mass, possible molecular formula and secondary mass spectrometry data (MS / MS), databases and literature such as Human Metabolome Database (HMDB, http: / / www.hmdb.ca) and Lipidomics Gatewa (http: / / www.lipidmaps.org / ) were searched. Biomarkers were characterized according to the possibility of chemical structure fragmentation and the biological mass spectrometry fragmentation rules, and pathway analysis was performed using the MetaboAnalyst 3.0 web tool.

[0089] 3.7 Statistical analysis

[0090] The experimental data were analyzed using IBM SPSS Statistics 25 software. One-way ANOVA was used for measurement data that met the normal distribution. LSD test was used for pairwise comparison between groups when the variance was equal. Dunnett's T3 test was used for data that did not meet the variance. Measurement data were expressed as mean ± standard deviation. Kruskal-Wallis test was used for nonparametric test of measurement data and rank data that did not meet the normal distribution. Mann-Whitney U was used for pairwise comparison between groups. Data were expressed as median and interquartile range. Results were considered to be significantly different when p < 0.05.

[0091] Example 2 Screening of metabolomics markers for differentiation of subtypes of gout-rheumatic-heat syndrome model

[0092] In this study, an animal model was established according to the method of Example 1. After the model was successfully constructed using conventional evaluation methods (8-10 animals per group), a marker group was screened to distinguish between the two subtypes of dampness being heavier than heat and heat being heavier than dampness.

[0093] 1 Macro indicators

[0094] 1.1 General Characterization Changes

[0095] On the 30th day of the experiment, compared with the normal group, the mice in the model group were in a state of depression and curling up, and their group behavior was in a state of huddling. The overall demeanor of the wet weight than heat group was worse than that of the heat weight than wet group, and the huddling behavior was more obvious. The weight of mice in the normal control group was stable, and there was no significant difference in the weight of mice in the wet weight than heat group and the heat weight than wet group compared with the normal group (see Figure 1 A). On the 30th day of the experiment, the abdominal circumference of mice in the wet weight-than-heat group and the hot weight-than-wet weight group was significantly greater than that in the normal group (p<0.001, p<0.01) (see Figure 1 B); The rectal temperature of mice in the wet weight than heat group and the heat weight than wet weight group was significantly higher than that in the normal group (p<0.05, p<0.01) (see Figure 1 C); the urine volume of mice in the wet weight-than-heat group and the hot weight-than-wet weight group was significantly lower than that in the normal group (p<0.05) (see Figure 1 D); the average daily food intake of mice in the hot weight-to-wet group was significantly lower than that in the normal group (p<0.001) (see Figure 1 E); the average daily water intake of mice in the heat-weight-to-humidity group was significantly higher than that in the normal group (p<0.0001) (see Figure 1 F).

[0096] 1.2 Ankle swelling

[0097] On the 28th day of the experiment, after the injection of MSU suspension, the ankle joints of mice in the wet weight-than-heat group and the hot weight-than-wet weight group showed different degrees of swelling (see Figure 2A). Vernier calipers were used to measure the swelling of the ankle joint at 0, 12, 24, and 48 hours after injection of MSU suspension, and the swelling index was calculated. A line graph was drawn and the area under the curve was calculated. The results showed that compared with the normal group, the ankle joints in the wet weight group and the hot weight group were significantly swollen (p<0.0001).

[0098] 2 Biochemical indicators

[0099] The results of serum biochemical indexes showed that (see Figure 3 ), the serum uric acid content of mice in wet weight than heat group and heat weight than wet group was significantly higher than that in normal group (p<0.0001, p<0.01); the serum TG content of mice in heat weight than wet group was significantly higher than that in normal group (p<0.05); the serum TC content of mice in wet weight than heat group and heat weight than wet group was significantly higher than that in normal group (p<0.0001, p<0.001); the serum HDL-C content of mice in wet weight than heat group was significantly higher than that in normal group (p<0.05); the serum LDL-C content of mice in wet weight than heat group and heat weight than wet group was significantly higher than that in normal group (p<0.0001).

[0100] 3. Evaluation results of tissue pathological changes

[0101] 3.1 Pathological changes and scores of ankle joints and liver

[0102] The results of ankle joint HE staining showed that (see Figure 4 A), the ankle joint structure of the normal group was clear and stained evenly, the surface of the tibial and talar cartilage was smooth, the chondrocyte morphology and structure were normal, no obvious bone erosion was observed, no obvious hyperplasia of the synovium on both sides, and no obvious inflammatory cell infiltration was observed. In the wet weight than heat group, the ankle joint synovial cells were slightly hyperplastic, a large number of synovial cells were necrotic and detached, the nuclei were condensed, dark stained and fragmented, and few cell fragments were found in the joint cavity; there were few basophilic masses in the synovial connective tissue, and more granulocytes and macrophages were seen infiltrating. In the heat weight than wet group, a small number of synovial cells in the ankle joint were necrotic and detached, the nuclei were condensed, dark stained and fragmented, and few cell fragments were found in the joint cavity; basophilic masses were seen locally in the tibial periosteum, and a small number of granulocytes and macrophages were seen infiltrating around them. The results of ankle joint pathological scoring showed that the scores of the wet weight than heat group and the heat weight than wet group were significantly higher than those of the normal group (p<0.05, p<0.01).

[0103] The results of liver HE staining showed that (see Figure 4B), the capsule structure of the liver tissue in the normal group was clear, without obvious hyperplasia; the boundaries of the hepatic lobule were not obvious, the center of the lobule was the central vein, surrounded by hepatocytes and hepatic sinusoids arranged roughly radially, and the hepatocytes were round and plump; there was no obvious expansion or compression of the hepatic sinusoids; there was no obvious abnormality in the portal area; no obvious necrosis and inflammatory cell infiltration were observed. In the wetness heavier than heat group, a small number of hepatocytes in the liver tissue had watery degeneration, and the cytoplasm was loose and lightly stained. In the heat heavier than wetness group, a small number of hepatocytes in the liver tissue had watery degeneration, and the cytoplasm was loose and lightly stained. The results of liver pathology scoring showed that the scores of the wetness heavier than heat group and the heat heavier than wetness group were significantly higher than those of the normal group (p<0.01, p<0.05).

[0104] 3.2 Pathological changes and scoring of kidney and small intestine tissues

[0105] The results of HE staining of kidney tissue showed that (see Figure 5 A), the surface capsule of the kidney tissue in the normal control group was composed of dense connective tissue of uniform thickness, the glomeruli were evenly distributed in the cortex, the number of cells and matrix in the glomeruli were uniform, the renal tubular epithelial cells were round and plump, the brush borders were arranged neatly and regularly, and no obvious abnormalities were found in the medulla; the connective tissue between the urinary tubules was the renal interstitium, and there was no obvious hyperplasia of the interstitium; no obvious abnormalities such as necrosis and inflammatory cell infiltration were found. In the renal tissue of the wet weight than heat group, a small number of renal tubular epithelial cells showed watery degeneration, cell swelling, and loose and light stained cytoplasm. In the renal tissue of the heat weight than wet group, a small number of renal tubular epithelial cells showed watery degeneration, cell swelling, and loose and light stained cytoplasm, and occasionally dilated renal tubules were seen at the local edges, the epithelium was flattened, the tubular lumen volume was enlarged, and the shape was irregular. Eosinophils were seen in the tubular lumen, and a small amount of surrounding connective tissue was slightly hyperplastic, accompanied by a small amount of lymphocyte infiltration; the results of renal pathological scoring showed that the renal pathological damage of mice in the wet weight than heat group was significantly higher than that in the normal group (p<0.01).

[0106] The results of HE staining of small intestine tissue were (see Figure 5 B), intestinal villi were distributed on the surface of the intestinal tissue of the normal group, and the surface was a single layer of columnar epithelium with normal morphology and structure. Goblet cells were distributed between the epithelial cells. The submucosal layer was composed of connective tissue. The remaining part of the intestinal wall included the muscular layer and serosal layer composed of smooth muscle cells, and no obvious abnormalities were found. In the wet weight than heat group, intestinal villi were distributed on the surface of the intestinal tissue. The intestinal villi were short and the surface was a single layer of columnar epithelium. It was seen that many mucosal epithelial cells fell off and the lamina propria was exposed. In the heat weight than wet group, a large number of mucosal epithelial cells fell off and the lamina propria was exposed; the local mucosal layer structure was unclear, and a small amount of eosinophilic flocs were seen. The results of small intestinal tissue pathological scoring showed that there was no significant difference in small intestinal pathological damage between the wet weight than heat and heat weight than wet groups and the normal group.

[0107] 4Ankle joint tissue inflammation evaluation results

[0108] Paraffin section immunofluorescence was used to detect the levels of NLRP3, IL-6, and IL-1β, the inflammatory markers in ankle joint tissue. The results showed that (see Figure 6 ), the contents of NLRP3, IL-6 and IL-1β in the normal group were low, and increased in each model group. The results of the positive cell density of each index showed that compared with the normal group, the density of NLRP3 positive cells in the wet weight than heat and heat weight than wet groups increased significantly (p<0.05); the density of IL-6 positive cells in the wet weight than heat and heat weight than wet groups increased significantly (p<0.01, p<0.05); the density of IL-1β positive cells in the wet weight than heat and heat weight than wet groups increased significantly (p<0.001, p<0.0001).

[0109] 5. Evaluation of damp-heat syndrome (evaluation of dampness syndrome, evaluation of heat syndrome)

[0110] The results of the dampness syndrome score combined with serum and pathological analysis showed that the scores of the dampness-heavy group and the heat-heavy group were higher than those of the normal group (see Tables 4 to 6), and the dampness syndrome element score of the dampness-heavy group was significantly higher than that of the other two groups (p<0.05); the heat syndrome element score of the heat-heavy group was significantly higher than that of the other two groups (p<0.05). The models were successfully constructed.

[0111] Table 4 Scoring table of dampness syndrome in each group

[0112]

[0113] Table 5 Scoring table of heat syndrome in each group

[0114]

[0115] Table 6 Dampness-heat syndrome scores of each group (dampness syndrome score + heat syndrome score)

[0116] Grouping Wetness score Heat syndrome score Total (damp-heat syndrome score) normal 0 0 0 Humidity is heavier than heat 26 13 39 Heat is more important than moisture 12 27 39

[0117] 6. Metabolomics Experiment Results

[0118] Serum metabolomics chromatograms of the control and model groups in positive and negative ion modes Figure 7-12 shown. Figure 7-9 Serum metabolomics chromatograms of the control group, wet weight-to-heat group, and heat weight-to-wet group in positive ion mode. Figure 10-12 Serum metabolomics chromatograms of the control group, wet weight-to-heat group, and heat weight-to-wet group under negative ion mode.

[0119] The serum metabolic profile data were imported into Progenesis QI for data preprocessing, and multivariate statistical analysis (PCA and OPLA-DA) was performed on each model group and the control group. The results showed that there was a significant separation between the two model groups and the control group, and the metabolic profile data of the model group showed significant changes.

[0120] PCA and OPLS-DA analysis of the wet weight and heat group and the control group showed that the two groups were clustered significantly and there was significant separation between the groups. Comparison of the content of indicators used to evaluate wet weight and heat showed that 39 metabolites such as uric acid, prostaglandin E2, and leukotriene A4 were significantly different from those in the normal group (see Table 7).

[0121] Table 7 Identification results of serum differential metabolites in wet weight and heat groups

[0122]

[0123]

[0124]

[0125] PCA and OPLS-DA analysis of the thermogravimetric and wet-control groups showed that the two groups were clustered significantly and there was significant separation between the groups. Comparison of the content of indicators used to evaluate thermogravimetric and wet-control showed that 30 metabolites such as uric acid, prostaglandin E2, and linoleic acid were significantly different from those in the normal group (see Table 8).

[0126] Table 8 Identification results of differential metabolites in serum of thermogravimetric and wet groups (30)

[0127]

[0128]

[0129] There were 16 overlapping indicators of the two groups of differential metabolites (Tables 7 and 8), indicating that these 16 differential metabolic biomarkers may be used to distinguish the wet weight than heat group, the heat weight than wet group and the normal group. On this basis, ROC was used to analyze whether the 16 indicators can distinguish the wet weight than heat group and the heat weight than wet group.

[0130] Thirty SPF-grade C57BL / 6 male mice were selected according to the method of Example 1 and randomly divided into three groups: a normal group, a dampness-over-heat gout model group, and a heat-over-dampness gout model group. After determining that the model was successfully constructed using the conventional evaluation method of Example 1, blood was taken for testing. Numerical normalization was performed by dividing the original index content of each index by the average value of the corresponding normal group. According to the ROC curve, the discrimination ability of the above 16 indexes for the model was judged. Among them, 6 differential metabolic biomarkers, namely Dodecanoylcarnitine, Taurocholic acid, Palmitoylcarnitine, L-Leucine, Leukotriene A4, and 13-Oxo-9,11-tridecadienoic acid, had good discrimination ability for the model. An AUC higher than 0.85 had a good discrimination and judgment effect, and the sensitivity and 1-specificity met the requirements. For example, AUC = 0.5 was equivalent to judging by randomly flipping a coin; 0.5 < AUC < 0.7 had a poor effect, and the sensitivity and 1-specificity did not meet the requirements; 0.7 < AUC < 0.85 had a discrimination and judgment effect less accurate than traditional Chinese medicine syndromes. Therefore, the discrimination ability of the 6 indexes combined as a biomarker group was investigated. At this time, the AUC was 1 for all, and the sensitivity and 1-specificity were greatly improved, reaching and exceeding the usage requirements of ordinary discrimination ability. The results are shown in Table 9.

[0131] Table 9 AUC of individual and combined diagnosis of six key indexes

[0132]

[0133] The Cut-off value corresponding to each index was obtained by using the ROC curve. The results are shown in Table 10. The numerical range for using the differential metabolic biomarker to distinguish different subtypes can be obtained through the Cut-off value (see Table 11).

[0134] Table 10 Cut-off values corresponding to six key indexes

[0135]

[0136] Table 11 Diagnostic ranges corresponding to six key indexes

[0137] name Wet weight to heat range Thermal gravity to wet range Dodecanoylcarnitine >1.4330 1.4330>X>1.1361 Taurocholic acid >1.4463 1.4463>X>1.2556 Palmitoylcarnitine >1.5429 1.5429>X>0.9739 L-Leucine <0.8317 >0.8317 13-Oxo-9,11-tridecadienoic acid 1.3379>X>1.1770 >1.3379 Leukotriene A4 >2.8835 2.8835>X>1.8198

[0138] Example 3 Using the biomarker group to judge whether the dampness-over-heat and heat-over-dampness gout models are successfully constructed

[0139] In addition, two damp-heat syndrome gout models, namely, dampness heavier than heat and heat heavier than dampness, were induced according to the method of Example 1, and the judgment method of Example 2 "5. Damp-heat syndrome evaluation (dampness syndrome evaluation, heat syndrome evaluation)" and the detection and data processing method of "6. Metabonomics experimental results" were used to judge the modeling situation based on the normalized values ​​of the six markers. The results are shown in Table 12. The modeling rate of the wet weight over heat group is 100%, and the modeling rate of the heat weight over wetness group is 100%. The judgment results of the two methods are consistent. The judgment method based on the six marker groups can evaluate the success of modeling of a single model mouse, which can provide a reference for pharmacological research and new drug development.

[0140] Table 12 The success rate of mice in the two groups

[0141] Group Number of molds Number of unformed molds Molding rate Wet weight compared with hot group (n=8) 8 0 100％ Heat and humidity group (n=8) 8 0 100％

Claims

1. A method for modeling a subtype of gout and rheumatism syndrome, comprising inducing gout in animals using hypoxanthine, potassium oxonate, and sodium urate, inducing rheumatism syndrome in animals using high-fat feeding, ethanol feeding, dried ginger, and an artificial climate chamber, and collecting blood samples from animals for UPLC-MS analysis and screening; the animal model is a rodent model, characterized in that: The method for making the gout-dampness-heat syndrome subtype animal model further comprises the following steps: S1: After the modeling is completed, animal blood samples are collected to separate serum, and the biomarker group in the serum of normal animals and model animals is analyzed by UPLC-MS. The biomarker group includes: lauroyl carnitine, taurocholic acid, palmitoyl L-carnitine, L-leucine, leukotriene A4, and 13-Oxo-9,11-tridecadienoic acid; S2: The original index content of the biomarker group in S1 was divided by the average value of the corresponding index of the normal group to obtain the normalized value of each biomarker, and the model animals were screened according to the cutoff value.

2. The method for modeling a gout-rheumatic-heat syndrome subtype model according to claim 1, characterized in that: In step S2, when the desired gout model is a wet weight over heat subtype, the normalized value of lauroyl carnitine>1.4330, the normalized value of taurocholic acid>1.4463, the normalized value of palmitoyl L-carnitine>1.5429, the normalized value of L-leucine<0.8317, the normalized value of leukotriene A4>2.8835, and the normalized value of 13-oxo-9,11-tridecadienoic When the normalized value of acid is greater than 1.1770 and less than 1.3379, and at least three of the above conditions are met, the model is considered to be successful. When the required gout model is a heat-gravity-wet subtype, the normalized value of lauroylcarnitine is greater than 1.1361 and less than 1.4330, the normalized value of taurocholic acid is greater than 1.2556 and less than 1.4463, the normalized value of palmitoyl-L-carnitine is greater than 0.9739 and less than 1.5429, the normalized value of L-leucine is greater than 0.8317, the normalized value of leukotriene A4 is greater than 1.8198 and less than 2.8835, and the normalized value of 13-Oxo-9,11-tridecadienoic acid is greater than 1.3379, and at least three of the above conditions are met, the model is considered to be successful.

3. A UPLC-MS detection method for a biomarker panel of a gout-rheumatic-heat syndrome subtype model, characterized in that: The gout-rheumatism-heat syndrome subtype model is a mouse animal model, the biomarker group is a biomarker group for distinguishing the gout-rheumatism-heavy-heat and heat-heavy-heat models and / or a biomarker group for judging whether the gout-rheumatism-heavy-heat and heat-heavy-heat models are successfully established, and the detection method comprises the following steps: J1 Take animal blood sample to separate serum, add methanol, vortex for 0.5min to 1.5min, centrifuge, and filter the supernatant through 0.22μm filter membrane to obtain the sample treatment solution to be tested; J2 takes the sample treatment solution to be tested in step S1 and performs UPLC-MS analysis: UPLC chromatographic conditions: ACQUITY UPLC HSS T3 column; column specifications: 2.1 mm x 100 mm, particle size: 1.8 μm; mobile phase A: acetonitrile, mobile phase B: 0.1% formic acid water; column temperature: 30°C to 35°C; flow rate: 0.3 mL / min; gradient elution program is as follows: Mass spectrometry conditions are as follows: positive ion mode: heater temperature 350°C, capillary temperature 330°C, sheath gas flow rate 40arb, auxiliary gas flow rate 15arb, sweep gas flow rate 1arb, electrospray voltage 3.5KV, S-Lens RF Level is 50%; negative ion mode: heater temperature 350°C, capillary temperature 330°C, sheath gas flow rate 45arb, auxiliary gas flow rate 15arb, sweep gas flow rate 1arb, electrospray voltage 3.2KV, S-Lens RF Level is 50%; scanning mode: primary full scan: m / z is 80-1200, data-dependent secondary mass spectrometry scan: dd-MS2, Top N=3, resolution: Full MS is 70,000, dd-MS2 is 13,500, collision mode: high energy collision dissociation, collision voltage: 10V, 25V, 40V.

4. The method for detecting a subtype marker group of a gout-rheumatic-heat syndrome model according to claim 3, characterized in that: The amount of methanol used is 200 μL of methanol per 50 μL of serum.

5. The method for detecting a gout-rheumatic-heat syndrome model subtype marker group according to claim 3, characterized in that: The vortexing time was 1 min.

6. The method for detecting a subtype marker group of a gout-rheumatic-heat syndrome model according to claim 3, characterized in that: The column temperature was 35°C.

7. The method for detecting a gout-rheumatic-heat syndrome model subtype marker group according to claim 3, characterized in that: The injection volume for the UPLC detection was 3 μL.

8. The method for detecting a subtype marker group of a gout-rheumatic-heat syndrome model according to claim 3, characterized in that: The rodent model is a mouse animal model.

9. Use of a reagent containing a biomarker group in the preparation of a detection kit for distinguishing gout rheumatism heavier than heat, heat heavier than moisture models and / or judging whether the gout rheumatism heavier than heat, heat heavier than moisture models are successfully established, characterized in that: The animal model is a rodent model, and the biomarker group includes the following 6 biomarkers: lauroyl carnitine, taurocholic acid, palmitoyl L-carnitine, L-leucine, leukotriene A4 and 13-Oxo-9,11-tridecadienoic acid.

10. Use of a reagent for detecting a group of biomarkers in preparing a detection kit for distinguishing a gout-rheumatism-heavy-heat-heavy-heat-heavy-dampness model and / or determining whether the gout-rheumatism-heavy-heat-heavy-dampness model is successfully established, characterized in that: The animal model is a rodent model, and the biomarker group includes the following 6 biomarkers: lauroyl carnitine, taurocholic acid, palmitoyl L-carnitine, L-leucine, leukotriene A4 and 13-Oxo-9,11-tridecadienoic acid.

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