Metabonomics method and device for detecting serum and kidney tissue of hyperuricemia renal injury rat based on HPLC-MS (High Performance Liquid Chromatography-Mass Spectrometer)

Abstract

The invention discloses a metabonomics method and a metabonomics device for detecting serum and kidney tissue of a hyperuricemia renal injury rat based on HPLC-MS. The metabonomics method comprises the following steps: (1) collecting and storing a sample; (2) sample pretreatment; (3) carrying out UHPLC-MS detection; (4) data analysis; according to the method, by optimizing a sample pretreatment process and detection conditions, metabolite spectrums of serum and kidney tissues of the hyperuricemia renal injury rats can be reserved to the greatest extent for subsequent data analysis, the extraction efficiency and detection sensitivity of the metabolite are improved, and meanwhile, by combining an advanced data analysis means, the detection sensitivity of the metabolite is improved. Metabolic characteristics of the hyperuricemia renal injury are comprehensively disclosed, and the method is stable and reproducible, so that the detection result has higher directivity.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical detection, and particularly relates to a metabolomics method and device for detecting serum and kidney tissues of rats with hyperuricemia-induced kidney injury based on HPLC-MS. Background Art

[0002] Hyperuricemia (HUA) is a common metabolic disease and an important risk factor for gout, cardiovascular diseases, metabolic syndrome, kidney diseases, etc. It is manifested as an excessive concentration of serum urate (sUA), and the diagnostic basis is that the fasting sUA concentration exceeds 420 μmol / L (~7 mg / dL) on two non-consecutive days. The prevalence of HUA shows an upward trend globally and a trend of getting younger. HUA is mainly caused by excessive production of uric acid in the body, reduced excretion, or both, and has a close connection and mutual influence with kidney diseases.

[0003] Metabolomics is a new discipline that emerged after genomics, transcriptomics, and proteomics. Different from what other omics focus on "what might happen", metabolomics focuses on "what has happened". It is defined as a discipline that systematically identifies and quantifies all or most of the small molecule metabolites present in biological samples (such as plasma, tissue, or urine) at a specific time point. Therefore, metabolomics is widely used to search for characteristic biomarkers in disease diagnosis, pathogenesis, and the material basis of drug efficacy, to clarify disease mechanisms, as well as for drug efficacy evaluation and drug development, etc.

[0004] However, there is currently no non-targeted metabolomics detection method for the serum and kidney tissues of rats with hyperuricemia-induced kidney injury, especially in terms of sample pretreatment and detection sensitivity. Therefore, inventing a non-targeted metabolomics detection and analysis method for the serum and kidney tissues of rats with hyperuricemia-induced kidney injury is of great significance for in-depth study of its pathological metabolism and related pathogenesis and clarification of characteristic biomarkers in disease mechanisms. Summary of the Invention

[0005] The object of the present invention is to provide a metabolomics method for detecting serum and kidney tissues of rats with hyperuricemia-induced kidney injury based on HPLC-MS.

[0006] The object of the present invention is also to provide a metabolomics device for detecting serum and kidney tissues of rats with hyperuricemia-induced kidney injury based on HPLC-MS.

[0007] The present invention provides an efficient, sensitive and reliable non-targeted metabolomics detection method for analyzing metabolite changes in the serum and kidney tissues of rats with hyperuricemia-induced kidney injury. By optimizing the sample pretreatment process and detection conditions, this method improves the extraction efficiency and detection sensitivity of metabolites, and at the same time combines advanced data analysis means to comprehensively reveal the metabolic characteristics of hyperuricemia-induced kidney injury.

[0008] The above first object of the present invention can be achieved by the following technical solution: A metabolomics method for detecting the serum and kidney tissues of rats with hyperuricemia-induced kidney injury based on HPLC-MS, comprising the following steps:

[0009] (1) Sample collection and preservation:

[0010] Collect serum and kidney tissue samples from normal rats and rats with hyperuricemia-induced kidney injury respectively;

[0011] The serum samples are stored at low temperature, and the kidney tissue samples are frozen in liquid nitrogen and then stored at low temperature;

[0012] (2) Sample pretreatment:

[0013] (2.1) Serum samples

[0014] Thaw the serum samples on ice and take the serum;

[0015] Add a protein precipitant and vortex. The volume ratio of the serum to the protein precipitant is 1:3 - 5, and the protein precipitant is methanol;

[0016] Centrifuge and take the supernatant, which is the non-targeted metabolomics sample of the serum of normal rats and rats with hyperuricemia-induced kidney injury. Place it in a low-temperature refrigerator, let it stand, and then filter it into an HPLC-MS injection vial for injection and detection;

[0017] (2.2) Kidney tissue samples

[0018] Place the kidney tissue samples on ice and thaw until they can be cut, and perform subsequent operations on ice;

[0019] Weigh the kidney tissue, place it in a homogenization tube, add an internal standard extraction solution, and place magnetic beads. The dosage relationship between the kidney tissue and the internal standard extraction solution is 1 mg: 10 - 20 μL. The extraction solution is a methanol aqueous solution, and the volume ratio of methanol to water in the methanol aqueous solution is 4:1;

[0020] Homogenize it with a low-temperature homogenizer;

[0021] Place the homogenized sample in an ultrasonic instrument and ultrasonicate it on ice;

[0022] After high-speed centrifugation of the extracted sample, take the supernatant;

[0023] After standing the supernatant at low temperature and then centrifuging it at high speed again, the supernatant, which is the untargeted metabolomics sample of the kidney tissues of normal rats and rats with hyperuricemia-induced kidney injury, is placed in a container for future measurement;

[0024] (2.3) Quality control samples

[0025] Take equal volumes of the untargeted metabolomics samples of the sera of normal rats and rats with hyperuricemia-induced kidney injury prepared in step (2.1) and the untargeted metabolomics samples of the kidney tissues of normal rats and rats with hyperuricemia-induced kidney injury prepared in step (2.2), and mix them separately to prepare quality control samples, namely serum QC samples and kidney tissue QC samples. During the instrument analysis process, one QC sample is inserted into every six samples to examine the repeatability and stability of the entire analysis process;

[0026] (3) UHPLC-MS detection:

[0027] Use a UHPLC system and a high-resolution Q Exactive Focus mass spectrometer to detect the metabolomics samples of the sera and kidneys of normal rats, the metabolomics samples of the sera and kidneys of rats with hyperuricemia-induced kidney injury, as well as the serum QC samples and kidney tissue QC samples;

[0028] The UHPLC system uses a gradient program with an aqueous solution of 0.1% (v / v) formic acid as phase A and acetonitrile solution as phase B. The gradient program is as follows: 0 - 2.5 min, 2% B; 2.5 - 5 min, 2% - 40% B; 5 - 9 min, 40% - 60% B; 9 - 14 min, 60% - 100% B; 14 - 16 min, 100% B; 16 - 16.1 min, 100% - 2% B; 16.1 - 20 min, 2% B for gradient elution, and stop collecting after 18 min;

[0029] (4) Data analysis: Use Progenesis QI 2.0 and Compound Discoverer 3.3 software to process the mass spectrometry data of untargeted metabolomics, perform peak alignment and area normalization operations, then perform multivariate statistical analysis and differential metabolite identification, and then determine the significantly differential metabolites through the variable importance in projection VIP value and t-test, and calculate their fold change.

[0030] In the above metabolomics method for detecting the sera and kidney tissues of rats with hyperuricemia-induced kidney injury based on HPLC-MS:

[0031] Preferably, it is stored at -80 °C in step (1).

[0032] Preferably, the protein precipitant in step (2.1) is pre-cooled in a -20°C refrigerator for 30 min, shaken and mixed evenly for 2 min, and then centrifuged at 13,000 rpm for 10 min in a 4°C refrigerated centrifuge.

[0033] Preferably, in step (2.1), the supernatant, which is the non-targeted metabolomics sample of the sera of normal rats and rats with hyperuricemic kidney injury, is placed in a -30°C refrigerator and allowed to stand for 1 h, then filtered through a needle filter into an HPLC-MS injection vial for injection and detection.

[0034] Preferably, in step (2.2), the kidney tissue preferably contains as much medulla and cortex as possible.

[0035] Preferably, in step (2.2), the internal standard is L-2-chlorophenylalanine, and the concentration of the internal standard is 0.02 mg / mL.

[0036] Preferably, in step (2.2), the homogenization conditions of the cryogenic homogenizer are 5.65 m / s, 2 cycles, 45 s, pause for 30 s, and the cavity temperature is controlled below 8°C.

[0037] Preferably, in step (2.2), the homogenized sample is placed in an ultrasonic instrument and ultrasonically treated on ice for 30 min.

[0038] Preferably, in step (2.2), the extracted sample is centrifuged at 12,000 rpm / min at 4°C for 15 min, and then the supernatant is taken.

[0039] Preferably, in step (2.2), the supernatant is placed at -20°C and allowed to stand for 20 min, then centrifuged again at 12,000 rpm / min at 4°C for 3 min, and the supernatant, which is the non-targeted metabolomics sample of the kidney tissues of normal rats and rats with hyperuricemic kidney injury, is placed in a container for further measurement.

[0040] Preferably, in step (3), the chromatographic column used in the UHPLC system is ACQUITY UPLC HSS T3, 100×2.1 mm, 1.8 μm, the flow rate is 0.2 mL / min; the injection volume is 1 μL, and the column temperature is 40°C.

[0041] Preferably, the mass spectrometry conditions of the high-resolution Q Exactive Focus mass spectrometer in step (3) include: separate detection of positive and negative ions; the full MS scan range is m / z 70 - 1050, and the resolution is 70000; the MS parameters are set as follows: the sheath gas flow rate is set to 35 L / min, the auxiliary gas flow rate is set to 10 L / min, the positive ion spray voltage is 3.5 kV, the negative ion spray voltage is -3.2 kV, the capillary temperature is 320 °C, and the auxiliary gas heater temperature is 350 °C; the AGC target is 1e6, and the maximum injection time is 100 ms; the MS / MS scan mode is data-dependent ms2 scan, i.e., dd-ms2, the resolution is 17500, and the collision energy is set in a step mode of 20, 30, and 40 eV; subsequently, the AGC target is 5e4, the maximum IT is set to auto, and the positive and negative ion modes are the same.

[0042] Preferably, the multivariate statistical analysis in step (4) includes principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA analysis examines the anomalies in the data and the distribution trends of each group as a whole, and visualizes these data. On the basis of PCA analysis, OPLS-DA analysis of the metabolomics samples of normal rat serum and kidneys, the metabolomics samples of hyperuricemia-induced renal injury rat serum and kidneys, and the serum QC samples and kidney tissue QC samples is performed to evaluate the differences before and after modeling.

[0043] The method of the present invention can maximize the retention of the metabolite profiles of the serum and kidney tissues of hyperuricemia-induced renal injury rats for subsequent data analysis by optimizing the sample pretreatment process and detection conditions, improve the extraction efficiency and detection sensitivity of metabolites, and at the same time, combined with advanced data analysis means, comprehensively reveal the metabolic characteristics of hyperuricemia-induced renal injury. This method is stable and reproducible, making the test results more directive.

[0044] The above second object of the present invention can be achieved by the following technical solution: A metabolomics device for detecting the serum and kidney tissues of hyperuricemia-induced renal injury rats based on HPLC-MS, comprising:

[0045] A sample processing unit for pretreating serum and kidney tissue samples;

[0046] An HPLC-MS detection unit for analyzing the pretreated serum and kidney tissue samples;

[0047] A data processing unit for preprocessing and multivariate statistical analysis of the mass spectrometry data.

[0048] Preferably, the sample processing unit includes a serum processing module and a kidney tissue processing module, and the serum processing module and the kidney tissue processing module are respectively used to perform the pre-processing steps of serum samples and kidney tissue samples.

[0049] The present invention has the following advantages:

[0050] (1) The optimized sample pre-processing method provided by the present invention can efficiently extract metabolites from serum and kidney tissue, minimize the loss and degradation of metabolites to the greatest extent, and improve the extraction efficiency and detection sensitivity of metabolites;

[0051] (2) The present invention realizes the high-sensitivity and high-resolution detection of metabolites through ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS), improves the accuracy and reliability of detection, and can also comprehensively and unbiasedly detect the metabolite changes in serum and kidney tissue;

[0052] (3) The present invention combines multivariate statistical analysis methods to screen differential metabolites and construct a metabolic pathway network, which can reveal the metabolic disorder mechanism of hyperuricemia-induced kidney injury;

[0053] (4) This method is applicable to the study of hyperuricemia-induced kidney injury rat models, and can provide a theoretical basis for the diagnosis and treatment of related diseases;

[0054] (5) The present invention provides new ideas and methods for the early diagnosis, disease progression monitoring and treatment target discovery of hyperuricemia-induced kidney injury. Description of the Drawings

[0055] Figure 1 It is a diagram of the metabolomics method and device for detecting the serum and kidney tissue of rats with hyperuricemia-induced kidney injury in Examples 1-3;

[0056] Figure 2 It is the TIC diagram obtained by 5 detection methods in Example 1;

[0057] Figure 3 It is the TIC diagram of serum and kidney QC samples in positive and negative ion modes in Example 3. The UHPLC-MS system detects positive and negative ions separately. Serum POS is the TIC diagram of serum QC samples in positive ion mode, and Serum NEG is the TIC diagram of serum QC samples in negative ion mode; Kidney POS is the TIC diagram of kidney tissue QC samples in positive ion mode, and Kidney NEG is the TIC diagram of kidney tissue QC samples in negative ion mode;

[0058] Figure 4 It is the multivariate statistical analysis result of serum samples in Example 3;

[0059] Figure 5 It is the multivariate statistical analysis result of the kidney samples in Example 3;

[0060] Figure 6 It is the differential metabolic pathway of CON vs MOD in the serum metabolome of Example 3;

[0061] Figure 7 It is the differential metabolic pathway of CON vs MOD in the kidney metabolome of Example 3. Detailed implementation mode

[0062] The present invention will be further described below in conjunction with specific embodiments. The following embodiments are only for illustrative purposes and should not be construed as limiting the present invention. Unless otherwise specified, the raw materials used in the following embodiments are conventional commercially available or obtained through commercial channels, and unless otherwise specified, the methods and equipment used in the following embodiments are the methods and equipment commonly used in the art.

[0063] Example 1

[0064] As Figure 1 shown, the metabolomics method and device for detecting the serum and kidney tissues of hyperuricemia renal injury rats based on HPLC-MS provided in this embodiment are as follows:

[0065] 1. Sample collection and preservation:

[0066] A hyperuricemia renal injury rat model was constructed by continuously intragastrically administering 750 mg / kg potassium oxonate combined with 300 mg / kg uric acid to normal rats for 49 days (Yan Meixia, Huo Shuai, Tian Ruimin, et al. Study on the establishment of a rat model of uric acid nephropathy [J]. Chinese Journal of Comparative Medicine, 2022, 32(2): 1-9), and it was divided into a normal group (CON, normal rats) and a model group (MOD, hyperuricemia renal injury rats). Serum and kidney tissue samples of normal rats and hyperuricemia renal injury rats were collected. The serum samples were stored at -80°C, and the kidney tissue samples were frozen in liquid nitrogen and then stored at -80°C.

[0067] 2. Sample pretreatment:

[0068] (2.1) Pretreatment of serum samples: The serum samples of normal rats and hyperuricemia renal injury rats were thawed on ice. 40 μL of each blood sample was precisely pipetted, and 160 μL of protein precipitant methanol pre-cooled in a -20°C refrigerator for 30 min was added. After shaking and mixing for 2 min, it was centrifuged at 13,000 rpm for 10 min in a 4°C freezing centrifuge. 170 μL of the supernatant, which was the non-targeted metabolomics sample of the serum of normal rats and hyperuricemia renal injury rats, was taken and placed in a -30°C refrigerator and left to stand for 1 h, then passed through a needle filter into an injection vial for injection detection.

[0069] (2.2) Pre-treatment of kidney tissue samples:

[0070] ① Thaw the kidney tissue samples of normal rats and rats with hyperuricemia renal injury on ice until they can be cut, and perform subsequent operations on ice;

[0071] ② Weigh 20 mg of kidney tissue, including the medulla and cortex as much as possible, place it in a homogenization tube, add 400 μL of extraction solution, and place two magnetic beads. The extraction solution, i.e., protein precipitant, is a methanol-water solution containing an internal standard. The volume ratio of methanol to water is 4:1. The internal standard is L-2-chlorophenylalanine, and the concentration of the internal standard is 0.02 mg / mL.

[0072] ③ Place in a low-temperature homogenizer for homogenization treatment, the conditions are 5.65m / s, 2 cycles, 45 seconds, pause 30s, and the chamber temperature is controlled below 8°;

[0073] ④ Place the homogenized sample in an ultrasonic instrument and sonicate on ice for 30 minutes;

[0074] ⑤ Place the extracted sample at 4°C, 12000rpm / min for 15min and take 300μL of the supernatant;

[0075] ⑥ Place the supernatant at -20°C for 20 min, and then centrifuge it again at 4°C and 12,000 rpm / min for 3 min. Take 200 μL of the supernatant, which is the non-targeted metabolomics sample of kidney tissue of normal rats and rats with hyperuricemia renal injury, and place it in an inner liner tube for testing.

[0076] (2.3) Quality control samples

[0077] Take equal volumes of the non-targeted metabolomics samples of serum of normal rats and rats with hyperuricemia renal injury prepared in step (2.1) and the non-targeted metabolomics samples of kidney tissue of normal rats and rats with hyperuricemia renal injury prepared in step (2.2), and mix them to prepare quality control samples, namely serum QC samples and kidney tissue QC samples, respectively. During the instrument analysis process, one QC sample is inserted into every 6 samples to examine the repeatability and stability of the entire analysis process.

[0078] 3.HPLC-MS detection:

[0079] The analysis was performed using a UHPLC system and a high-resolution Q Exactive Focus mass spectrometer, the mass spectrometer was equipped with a heated electrospray ionization source, the chromatographic column was ACQUITY UPLC HSS T3, 100×2.1 mm, 1.8 μm, the flow rate was 0.2 mL / min, the injection volume was 1 μL, the column temperature was 40°C, and a gradient program of 0.1% (volume ratio) formic acid aqueous solution as phase A and acetonitrile solution as phase B was used. The gradient application was as follows:

[0080] Usage Method 1: The gradient is applied as follows: 0 - 1 min, 2% B; 1 - 2 min, 2% - 15% B; 2 - 8 min, 15% - 45% B; 8 - 9 min, 45% - 65% B; 9 - 14 min, 65% - 100% B; 14 - 16 min, 100% B; 16 - 16.1 min, 100% - 2% B; 16.1 - 20 min, 2% B; Acquisition is stopped after 18 min.

[0081] Usage Method 2: The gradient is applied as follows: 0 - 1 min, 2% B; 1 - 9 min, 2% - 50% B; 9 - 14 min, 50% - 100% B; 14 - 16 min, 100% B; 16 - 16.1 min, 100% - 2% B; 16.1 - 20 min, 2% B. Acquisition is stopped after 17 min.

[0082] Usage Method 3: The gradient is applied as follows: 0 - 1 min, 2% B; 1 - 5 min, 2% - 10% B; 5 - 9 min, 10% - 65% B; 9 - 14 min, 65% - 100% B; 14 - 16 min, 100% B; 16 - 16.1 min, 100% - 2% B; 16.1 - 20 min, 2% B; Acquisition is stopped after 17 min.

[0083] Usage Method 4: The gradient is applied as follows: 0 - 2.5 min, 2% B; 2.5 - 5 min, 2% - 40% B; 5 - 9 min, 40% - 60% B; 9 - 14 min, 60% - 100% B; 14 - 16 min, 100% B; 16 - 16.1 min, 100% - 2% B; 16.1 - 20 min, 2% B; Acquisition is stopped after 17 min.

[0084] Usage Method 5: The gradient is the same as that in Method 4; Acquisition is stopped after 18 min.

[0085] The mass spectrometry conditions include: separate detection of positive and negative ions; the full MS scanning range is m / z 70 - 1050, with a resolution of 70000; the MS parameters are set as follows: the sheath gas flow rate is set to 35 L / min, the auxiliary gas flow rate is set to 10 L / min, the positive ion spray voltage is 3.5 kV, the negative ion spray voltage is -3.2 kV, the capillary temperature is 320 °C, and the auxiliary gas heater temperature is 350 °C; the AGC target is 1e6, and the maximum injection time is 100 ms; the MS / MS scanning mode is data-dependent ms2 scanning, i.e., dd-ms2, with a resolution of 17500, and the collision energy is set in a step mode of 20, 30, and 40 eV; subsequently, the AGC target is 5e4, the maximum IT is set to auto, and the positive and negative ion modes are the same.

[0086] (4) Data analysis: The data of untargeted metabolomics were processed using Progenesis QI 2.0 and Compound Discoverer 3.3 software. Peak alignment and area normalization operations were performed, followed by multivariate statistical analysis and differential metabolite identification. The main methods included principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA analysis was used to examine the anomalies in the data and the distribution trends of each group as a whole, and to visualize these data. On the basis of PCA analysis, OPLS-DA analysis was performed between the CON and MOD groups to evaluate the differences before and after modeling.

[0087] The results are as Figure 2 shown. In the chromatogram obtained by Method 5, each peak can be separated independently and is smoother. Therefore, the elution gradient of Method 5 was selected for subsequent experiments.

[0088] Example 2

[0089] As Figure 1 shown, the metabolomics method and device for detecting the serum and kidney tissues of hyperuricemia-induced kidney injury rats based on HPLC-MS provided in this example are as follows:

[0090] 1. Sample collection and preservation:

[0091] Same as Example 1.

[0092] 2. Sample pretreatment:

[0093] Taking the serum QC sample as an example, the precipitant was optimized. Five kinds of precipitants, namely ACN (acetonitrile), MeOH (methanol), 1MeOH:1ACN (volume ratio), 1MeOH:4ACN, and 4MeOH:1ACN, were used for the protein precipitation method. The specific operation was as follows: The serum sample was thawed on ice, 40 μL of blood sample was precisely pipetted, and after adding 160 μL of the precipitant that had been pre-cooled in a -20°C refrigerator for 30 min, it was shaken and mixed evenly for 2 min, centrifuged at 12000 rpm / min at 4°C for 10 min. After taking 170 μL of the supernatant, it was placed in a -20°C refrigerator and allowed to stand for 1 h, then passed through a needle filter membrane into the injection vial for injection detection.

[0094] 3. HPLC-MS detection:

[0095] The optimized detection method in Example 1.

[0096] 4. Data analysis:

[0097] The original data chromatogram was imported into Compound Discoverer 3.3 software to roughly identify the total number of its compounds. The specific method was the same as that in Example 1.

[0098] 5. Results

[0099] According to the results shown in Table 1, when pure MeOH is used as the precipitant, more compounds can be extracted, reaching 8972. Since the ratio of serum to protein precipitant is 1:4 and the serum contains a large amount of water, the kidney precipitant is selected as methanol:water = 4:1 (v:v).

[0100] Table 1 Number of compounds extracted by different precipitants

[0101] Precipitant Number of compounds 1MeOH:1ACN 8513 ACN 8723 1MeOH:4ACN 8853 MeOH 8972 4MeOH:1ACN 8786

[0102] Example 3

[0103] As Figure 1 shown, the metabolomics method and device for detecting serum and kidney tissues of hyperuricemic nephropathy rats based on HPLC-MS provided in this example are as follows:

[0104] 1. Sample collection and preservation:

[0105] Using the aforementioned method to establish models to obtain the normal group (CON) and the model group (MOD), and using low, medium, and high doses of Clerodendranthus spicatus (purchased from Guangzhou Zhixin Pharmaceutical Co., Ltd., product number 200901) as the therapeutic drug, and allopurinol as the positive control drug. That is, the low dose of Clerodendranthus spicatus (OL) is gavaged with the aqueous decoction of Clerodendranthus spicatus at 3.125 g / kg, the medium dose of Clerodendranthus spicatus (OM) is gavaged with the aqueous decoction of Clerodendranthus spicatus at 6.25 g / kg, the high dose of Clerodendranthus spicatus (OH) is gavaged with the aqueous decoction of Clerodendranthus spicatus at 12.5 g / kg, and the positive control group (AP) is gavaged with allopurinol at 30 mg / kg. Collect serum and kidney tissue samples from the normal group (CON), the model group (MOD), the low-dose Clerodendranthus spicatus group (OL), the medium-dose Clerodendranthus spicatus group (OM), the high-dose Clerodendranthus spicatus group (OH), and the positive control group (AP), and store them at -80 °C respectively.

[0106] 2. Sample pretreatment:

[0107] Pretreat the serum and kidney tissue samples according to the method in Example 2.

[0108] 3. HPLC-MS detection:

[0109] Detect according to the optimized detection method in Example 1.

[0110] 4. Data analysis:

[0111] Pretreat and perform multivariate statistical analysis on the mass spectrometry data according to the method in Example 1.

[0112] 5. Results:

[0113] The total ion current chromatograms of the serum and kidney tissues are as Figure 3As shown, the spectral peaks in the TIC diagrams of each group under the same positive or negative ion mode are generally consistent. At this time, the differences between groups cannot be directly observed from the spectral diagrams. Therefore, quality control (QC) spectra are presented to obtain the most comprehensive spectral peaks.

[0114] Multivariate statistical analysis of serum and kidney tissues. Import the TIC spectra into Progenesis QI 2.0 software. Use the QC samples as the control samples for peak alignment. According to the automatic evaluation of the software, select the optimal control samples in the QC for automatic peak alignment. After checking the peak alignment results, manually align the areas with poor alignment results to ensure normal subsequent analysis. Divide them into CON, MOD, AP, OL, OM, OH groups and the QC group. Use the HMDB2022 database for preliminary peak identification. Screen the identified compounds with VIP > 1, fold change > 0.5, p ≤ 0.05 and exclude the compounds with minimum CV ≥ 30 for multivariate statistical analysis. The serum results are as Figure 4 , from Figure 4 it can be seen that the QC samples gather at the origin in both positive and negative ion modes, indicating good data reproducibility. And each group is distributed in a clustered manner and there are obvious distribution differences from other groups. The OL, OM, and OH groups are significantly different from the CON, MOD, and AP groups in distribution, and there are also subtle differences among these three groups, indicating that the treatment with Clerodendranthus spicatus has a certain impact on the metabolite distribution in rats. According to Hotelling’s T2, it can be seen that there are no outliers, indicating that the data is stable and reproducible.

[0115] And perform multivariate statistical analysis on the CON, MOD, and OH groups again. The results show that there are obvious metabolic differences among the three groups. The kidney samples are as Figure 5 It can be seen that the QC gathers at the origin in both positive and negative ion modes, especially obvious in the negative ion mode, indicating that the data is repeatable. Also, in the Hotelling’s T2 diagram, C9 and OL11 are outliers in the positive ion mode, and OM10 is an outlier in the negative ion mode. Therefore, this data is excluded from subsequent analysis to ensure the reliability of the data. And perform multivariate statistical analysis on the CON, MOD, and OH groups again. The results show that there are obvious metabolic differences among the three groups.

[0116] Analysis of differential metabolites. The TIC chromatogram was imported into Compound Discoverer 3.3 software for compound identification by alignment, and differential metabolites were screened with the screening conditions of an error value between -5 and 5 ppm, log2 fold change greater than or equal to 0.3 or less than or equal to -0.3, and P less than 0.05. Differential metabolite screening of CON vs MOD was performed on serum and kidneys respectively. The screened compounds were retrieved in the pubchem, KEGG, and HMDB databases, and compounds without HMDB serial numbers were excluded. Then the obtained HMDB serial numbers were imported into the MetaboAnalyst 5.0 online analysis software for matching, and compounds with failed matching were deleted to obtain the final differential metabolites. And the metabolic differential metabolites were imported into the MetaboAnalyst 5.0 online analysis software for pathway enrichment. Table 2 shows the differential metabolites of CON vs MOD in serum samples (a total of 20). Figure 6 are the metabolic pathways obtained for this differential metabolite. It was found that the pathways involved in hyperuricemic nephropathy rats in the serum samples of rats were mainly pyrimidine metabolism, non-sugar glucuronic acid metabolism, alanine aspartate and glutamate metabolism, etc. Similarly, analyzing the kidney samples gave Table 3 and Figure 7 , and 32 differential metabolites were found in the kidney tissue samples. The metabolic pathways involved in this differential metabolite included the biosynthesis of phenylalanine, tyrosine and tryptophan, vitamin B6 metabolism, biosynthesis of pantothenic acid and coenzyme A, etc. It shows that there are many abnormal metabolic disorders in hyperuricemic nephropathy rats that do not exist in normal rats, and corrective therapies can be targeted at them in the future, thus providing a feasible scientific hypothesis for the treatment of hyperuricemic nephropathy.

[0117] Table 2 Differential metabolites of CON vs MOD in serum samples (a total of 20)

[0118]

[0119] Note: A positive Log2 Fold Change indicates upregulation, and a negative one indicates downregulation.

[0120] Table 3 Differential metabolites of CON vs MOD in kidney tissue homogenate (a total of 32)

[0121]

[0122]

[0123] Note: The name is the name of the differentially expressed metabolite it discovered, the molecular formula is its corresponding molecular formula, the error value is the error between the detected molecular weight and the actual molecular weight, the exact molecular weight is its actual molecular weight, m / z is the molecular weight obtained after losing or adding ions in mass spectrometry detection, the retention time is the peak time of the compound in the TIC chromatogram, Log2Fold Change represents the fold difference in the expression level of the compound between the CON and MOD groups on a logarithmic scale, P-value is the criterion for measuring the significance of the difference of the metabolite between CON and MOD, and HMDB is the number of the metabolite in HMDB. Among them, Gly-DL-Phe exists in both positive and negative ion modes and shows a downward trend in both cases.

[0124] The above embodiments are only used to illustrate the present invention, and the protection scope of the present invention is not limited to the above embodiments. Those of ordinary skill in the art can achieve the purpose of the present invention based on the content disclosed above. Any improvements and modifications made based on the concept of the present invention fall within the protection scope of the present invention. The specific protection scope shall be subject to the claims.

Claims

1. A metabolomics method for detecting serum and kidney tissues of hyperuricemia-induced kidney injury rats based on HPLC-MS, characterized in that, It includes the following steps: (1) Sample collection and preservation: Collect serum and kidney tissue samples from normal rats and rats with hyperuricemic kidney injury respectively; The serum samples are stored at low temperature, and the kidney tissue samples are frozen in liquid nitrogen and then stored at low temperature; (2) Sample pretreatment: (2.1) Serum samples Thaw the serum samples on ice and take the serum; Add a protein precipitant, vortex, the volume ratio of the serum to the protein precipitant is 1:3 - 5, and the protein precipitant is methanol; Centrifuge and take the supernatant, which is the untargeted metabolomics sample of the serum of normal rats and rats with hyperuricemic kidney injury. Place it in a low-temperature refrigerator, let it stand still, and then filter it into an HPLC-MS injection vial for injection and detection; (2.2) Kidney tissue samples Place the kidney tissue samples on ice until they can be cut, and perform subsequent operations on ice; Weigh the kidney tissue, place it in a homogenization tube, add an internal standard extraction solution, and place magnetic beads. The dosage relationship between the kidney tissue and the internal standard extraction solution is 1mg:10 - 30μL. The extraction solution is a methanol aqueous solution, and the volume ratio of methanol to water in the methanol aqueous solution is 4:1; Homogenize it in a low-temperature homogenizer; Place the homogenized sample in an ultrasonic instrument and ultrasonicate it on ice; After high-speed centrifugation of the extracted sample, take the supernatant; After the supernatant is left standing at low temperature, centrifuge it at high speed again, and take the supernatant, which is the untargeted metabolomics sample of the kidney tissue of normal rats and rats with hyperuricemic kidney injury, and place it in a container for testing; (2.3) Quality control samples Take equal volumes of the untargeted metabolomics samples of the serum of normal rats and rats with hyperuricemic kidney injury prepared in step (2.1) and the untargeted metabolomics samples of the kidney tissue of normal rats and rats with hyperuricemic kidney injury prepared in step (2.2), and mix them respectively to prepare quality control samples, namely serum QC samples and kidney tissue QC samples. During the instrument analysis process, insert one QC sample every 6 samples to examine the repeatability and stability of the entire analysis process; (3) UHPLC-MS detection: Use a UHPLC system and a high-resolution Q Exactive Focus mass spectrometer to detect the metabolomics samples of the serum and kidneys of normal rats, the metabolomics samples of the serum and kidneys of rats with hyperuricemic kidney injury, as well as the serum QC samples and kidney tissue QC samples; The UHPLC system uses a gradient program with a 0.1% (v / v) formic acid aqueous solution as phase A and an acetonitrile solution as phase B. The gradient program is as follows: 0 - 2.5 min, 2% B; 2.5 - 5 min, 2% - 40% B; 5 - 9 min, 40% - 60% B; 9 - 14 min, 60% - 100% B; 14 - 16 min, 100% B; 16 - 16.1 min, 100% - 2% B; 16.1 - 20 min, 2% B for gradient elution, and stop collection after 18 min; (4) Data analysis: The mass spectrometry data of untargeted metabolomics were processed using Progenesis QI 2.0 and Compound Discoverer 3.3 software for peak alignment and area normalization operations. Then, multivariate statistical analysis and differential metabolite identification were performed. Subsequently, significant differential metabolites were determined through the variable importance in projection (VIP) value and t-test, and their fold change was calculated.

2. The method according to claim 1, characterized in that, In step (1), it is stored at -80 °C.

3. The method according to claim 1, wherein In step (2.1), the protein precipitant is pre-cooled in a -20 °C refrigerator for 30 min. After shaking and mixing evenly for 2 min, it is centrifuged at 13,000 rpm for 10 min in a 4 °C refrigerated centrifuge. In step (2.1), the supernatant, which is the untargeted metabolomics sample of the sera of normal rats and hyperuricemic kidney injury rats, is placed in a -30 °C refrigerator and left to stand for 1 h, and then passed through a syringe filter into an HPLC-MS injection vial for injection and detection.

4. The method according to claim 1, wherein In step (2.2), the kidney tissue contains medulla and cortex; the internal standard is L-2-chlorophenylalanine, and the concentration of the internal standard is 0.02 mg / mL.

5. The method according to claim 1, characterized in that In step (2.2), the homogenization conditions of the cryogenic homogenizer are 5.65 m / s, 2 cycles, 45 seconds, with a 30 s pause, and the chamber temperature is controlled below 8 °C. In step (2.2), the homogenized sample is placed in an ultrasonic bath and sonicated on ice for 30 min. In step (2.2), the extracted sample is centrifuged at 12,000 rpm / min at 4 °C for 15 min, and the supernatant is taken. In step (2.2), the supernatant is placed at -20 °C and left to stand for 20 min, and then centrifuged again at 12,000 rpm / min at 4 °C for 3 min. The supernatant, which is the untargeted metabolomics sample of the kidney tissues of normal rats and hyperuricemic kidney injury rats, is placed in a container for further testing.

6. The method according to claim 1, characterized in that In step (3), the chromatographic column used in the UHPLC system is ACQUITY UPLC HSS T3, 100×2.1 mm, 1.8 μm, and the flow rate is 0.2 mL / min; the injection volume is 1 μL, and the column temperature is 40 °C.

7. The method according to claim 1, characterized in that, The mass spectrometry conditions of the high-resolution QExactive Focus mass spectrometer in step (3) include: separate detection of positive and negative ions; the full MS scan range is m / z 70 - 1050, and the resolution is 70,000; the MS parameters are set as follows: the sheath gas flow rate is set to 35 L / min, the auxiliary gas flow rate is set to 10 L / min, the positive ion spray voltage is 3.5 kV, the negative ion spray voltage is -3.2 kV, the capillary temperature is 320 °C, and the auxiliary gas heater temperature is 350 °C; the AGC target is 1e6, and the maximum injection time is 100 ms; the MS / MS scan mode is data-dependent ms2 scan, i.e., dd-ms2, with a resolution of 17,500, and the collision energy is set in a stepped mode of 20, 30, and 40 eV; subsequently, the AGC target is 5e4, the maximum IT is set to auto, and the positive and negative ion modes are the same.

8. The method according to claim 1, wherein The multivariate statistical analysis described in step (4) includes principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA analyzes the anomalies in the data and the distribution trends of each group as a whole, and visualizes these data. On the basis of the PCA analysis, OPLS-DA analysis is performed on the metabolomics samples of normal rat serum and kidneys, the metabolomics samples of hyperuricemia-induced kidney injury rat serum and kidneys, as well as the serum QC samples and kidney tissue QC samples to evaluate the differences before and after modeling.

9. A metabolomics device for detecting serum and kidney tissues of hyperuricemia kidney injury rats based on HPLC-MS, characterized in that, It includes: A sample processing unit for preprocessing the serum and kidney tissue samples described in claim 1; An HPLC-MS detection unit for analyzing the preprocessed serum and kidney tissue samples of claim 1; A data processing unit for preprocessing and performing multivariate statistical analysis on the mass spectrometry data described in claim 1.

10. The device according to claim 9, characterized in that, The sample processing unit includes a serum processing module and a kidney tissue processing module, and the serum processing module and the kidney tissue processing module are respectively used to execute the preprocessing steps of serum samples and kidney tissue samples.

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