Method for detecting lycium barbarum glycopeptide metabolite in rat plasma and tissue based on non-targeted metabonomics

Abstract

The invention belongs to the technical field of drug detection, and particularly relates to a method for detecting lycium barbarum glycopeptide metabolites in rat plasma and tissues based on non-targeted metabonomics. According to the method, after rat lycium barbarum glycopeptides are administered through intragastric administration, blood, liver, intestine, lung and brain tissue samples after administration are collected. A UPLC-Q-TOF-MS (Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry) analysis method is combined with a statistical analysis method comprising principal component analysis (PCA) and orthogonal-partial least squares discriminant analysis (OPLS-DA) to carry out in-vivo metabonomics analysis on the wolfberry glycopeptide. Then, based on a human metabolome database (HMDB), lycium barbarum glycopeptide differential metabolite is searched, so that the pharmacological action mechanism of the lycium barbarum glycopeptide is clarified from the perspective of metabolism, and a foundation is laid for deep research and development of the medicinal value of the lycium barbarum glycopeptide in future.

Description

Technical Field

[0001] This invention belongs to the field of drug detection technology, specifically relating to a method for detecting Lycium barbarum glycopeptide metabolites in rat plasma and tissues based on non-targeted metabolomics. Background Technology

[0002] Ningxia wolfberry ( Lycium barbarum Lycium barbarum (L.) is an important plant resource in Ningxia Hui Autonomous Region. Favorable climate and geographical conditions have made Ningxia Hui Autonomous Region a major production area for high-quality medicinal Lycium barbarum, making Ningxia Lycium barbarum the most distinctive food and medicinal agricultural product of Ningxia Hui Autonomous Region. In addition, Lycium barbarum has unique medicinal and health benefits, reducing the incidence of various diseases and enhancing the body's immune function.

[0003] Lycium barbarum glycopeptides (LbGp), also known as Lycium barbarum glycoproteins and Lycium barbarum glycoconjugates, are water-soluble high-molecular-weight glycoproteins derived from Lycium barbarum. They possess well-defined and highly quantifiable biological activities. Their main components are polysaccharides, proteins, and almost all natural amino acids, with serine being the most abundant, accounting for one-quarter of the total amino acid content. Research reveals that Lycium barbarum glycopeptides can be divided into five homogeneous glycoconjugates (LbGp1). (LbGp5). Recent pharmacological studies have revealed that Lycium barbarum glycopeptides possess hepatoprotective, immunomodulatory, anti-aging, anti-tumor, antioxidant, cardiomyocyte repair, and nerve stress relief pharmacological effects. Studying the absorption and distribution process of Lycium barbarum glycopeptides in vivo is crucial for further clarifying their efficacy pathways. To date, there are few reports on the absorption, distribution, and metabolic degradation patterns of Lycium barbarum glycopeptides.

[0004] This study investigated the effects of oral administration of Lycium barbarum glycopeptides on metabolites in rat plasma, liver, brain, lungs, and intestines using UPLC-Q-TOF-MS metabolomics technology. The aim was to elucidate the pharmacological mechanism of Lycium barbarum glycopeptides from a metabolic perspective, laying the foundation for future in-depth research and development of the medicinal value of Lycium barbarum glycopeptides. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method for detecting Lycium barbarum glycopeptide metabolites in rat plasma and tissues based on non-targeted metabolomics. This method uses metabolites to reflect the organism's phenotype and identifies biomarkers in the metabolic process, aiming to elucidate the pharmacological mechanism of Lycium barbarum glycopeptides from a metabolic perspective and lay the foundation for future in-depth research and development of the medicinal value of Lycium barbarum glycopeptides.

[0006] Specifically, the present invention is achieved through the following technical solutions: This invention provides a method for detecting Lycium barbarum glycopeptide metabolites in rat plasma and tissues based on non-targeted metabolomics, comprising the following steps: Step 1: Blood samples were collected from rats by orbital blood sampling 1 hour after gavage administration of wolfberry glycopeptide and saline. 24 hours later, the rats were immediately sacrificed and dissected to collect liver, intestine, lung and brain tissues. The group administered saline was the control group (N group), and the group administered wolfberry glycopeptide was the experimental group (G group). Step 2: Sample processing. 100 μL of plasma samples from groups N and G were transferred to 1.5 mL brown centrifuge tubes, moistened with a small amount of heparin, allowed to stand for 2 h, and then centrifuged at 15000 r / min for 10 min to obtain the supernatant for rat plasma. This supernatant was stored at -80 ℃ for later use. For liver, intestine, lung, and brain tissues collected from groups N and G, after accurate weighing, 2 times the volume of acetonitrile was added to prepare tissue homogenates. These homogenates were then centrifuged at 15000 r / min for 10 min and stored at -80 ℃ for later use. Step 3: Use UPLC-Q-TOF / MS to perform non-targeted detection on the test samples, and take samples sequentially to obtain raw data of metabolites in plasma and tissues; Step 4: Perform multivariate analysis on the original data and project the importance values ​​of the variables using the OPLS-DA model; Step 5: Perform KEGG pathway analysis on metabolic markers to determine the metabolic process of wolfberry glycopeptides.

[0007] The working principle of the above technical solution is as follows: The inventors of this application used UPLC-QTOF / MS to test and identify the structures of metabolites by searching databases. Finally, through statistical analysis based on OPLS-DA results, differentially expressed metabolites between different groups were initially screened based on VIP values. Metabolites with a VIP ≥ 1 are generally considered significantly different. Further screening of Lycium barbarum glycopeptide metabolic markers was conducted using univariate analysis and t-tests to determine the p-value. Metabolites with an expression level ≤ 0.5 were selected, and a p-value < 0.05 was considered significant, indicating that the selected differentially expressed metabolites were more reliable. This study aims to elucidate the pharmacological mechanism of Lycium barbarum glycopeptides from a metabolic perspective, laying the foundation for future in-depth research and development of their medicinal value.

[0008] As an optional implementation, in step 2 of the method, the rotation speed is set to 15000 r / min and the time is 10 min, which can completely separate small molecule compounds in a short time. The supernatant is immediately transferred to a refrigerator after collection. Storing at 80℃ can prevent protein aggregation and extend shelf life. Preserving plasma samples at 80℃ inhibits cell metabolism and stabilizes components with low temperature selectivity, thus avoiding freeze-thaw cycles and extending storage time. Since heat generated during centrifugation is detrimental to analyte stability, this application controls the collection temperature to 4℃. Because the blood is cryopreserved, recentrifugation before testing prevents interference from substances.

[0009] As an optional implementation, in the method, in step 3, the UPLC The acquisition conditions for QTOF / MS combined technology are: Chromatographic conditions: Column: CSH C18 column; Mobile phase: Phase A: 0.1% formic acid-water solution, Phase B: 0.1% formic acid-acetonitrile; Flow rate: 0.2 mL / min; Injection volume: 2 μL; Column temperature: 35℃; Gradient elution conditions for mobile phase: 15%-43% B phase 0-2.0 min, 43%-48% B phase 2.0-10.0 min, 48%-52% B phase 10.0-17.0 min, 52%-72% B phase 17.0-27.0 min, 72-88% B phase 27.0-37.0 min, 88%-86% B phase 37.0-45.0 min, 86%-15% B phase 45.0-46.0 min.

[0010] Mass spectrometry conditions: Acquisition mode: positive ion mode; TOFMS mass range: 100-1000 Da; IDA mass range: 50-1000 Da; TOFMS acquisition frequency: 0.18 secs; IDA secondary acquisition frequency: 0.03 secs; Ion Source Gas 1: 50 psi; Ion Source Gas 2: 50 psi; Curtain gas: 30 psi; Ion source temperature: 500℃; IonSpray voltage: 5500V; Declustering voltage: 80V; TOFMS collision energy: 10V; IDA secondary collision energy: 35V.

[0011] UPLC stands for Ultra-High Performance Liquid Chromatography, and MS stands for Quadrupole Time-of-Flight Mass Spectrometry.

[0012] Through the inventors' creative efforts, it was discovered that setting UPLC-QTOF / MS to the above acquisition conditions can overcome background interference, improve the signal-to-noise ratio, achieve high sensitivity for complex samples, and provide good quantitative results with more accurate results.

[0013] As an optional implementation, in the method, in step 4, the specific method for using the variable projection importance value of the OPLS-DA model is as follows: combining the VIP value with the p-value of the t-test and the FC value of the difference fold analysis, and using the condition VIP≥1, P<0.05 to find differential metabolites.

[0014] As an optional implementation, in the method, in step 5, the pathway includes a mineral absorption pathway, an aminoacyl-tRNA biosynthesis pathway, a lysine degradation pathway, a protein digestion and absorption pathway, and a central carbon metabolism pathway in cancer.

[0015] As an optional implementation, in the method, before performing multivariate analysis on the original data in step 4, the original data is first converted into mz ML format, and then the spectrum of the original data is subjected to peak alignment, retention time correction and peak area extraction.

[0016] Because mass spectrometry files have a fixed format, the output format is not compatible with analysis software that supports other data formats. Proteo Wizard offers a wide range of format options and supports mz ML format, which can be directly used for XCMS software processing. The processing workflow is smooth and highly versatile.

[0017] As an optional implementation, in the method, in step 4, the multivariate analysis of the original data specifically involves: after peak alignment, retention time correction and peak area extraction of the spectrum of the original data, the data is processed by MateboAnanlyst software, and statistical analyses such as principal component analysis and orthogonal partial least squares discriminant analysis are performed on the data.

[0018] As an optional implementation, in the method, before performing KEGG pathway analysis on the metabolic markers in step 4, a bioinformatics analysis is also performed to evaluate the rationality of differentially expressed metabolites. This bioinformatics analysis includes cluster analysis, correlation analysis, etc. Principal Component Analysis (PCA) is essentially an unsupervised multivariate statistical analysis method that reflects the overall differences between groups and the magnitude of variability within groups. The more similar the metabolite expression patterns of samples, the closer they are reflected in the PCA plot. The basic principle is to use mathematical methods to recombine the original variables into several new, mutually independent composite variables (i.e., principal components), ranking all factors by importance, typically ignoring minor factors at the lower end, thus simplifying the data through dimensionality reduction. The differences between multiple groups of data are reflected on a two-dimensional coordinate graph, with the axes using the two feature values ​​that best reflect the differences between samples. OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) is a derivative algorithm of PLS-DA. Compared to PLS-DA, OPLS-DA combines Orthogonal Signal Correction (OSC) and PLS-DA methods, decomposing the X matrix information into two categories: correlated and uncorrelated information with Y. By removing uncorrelated differences, the correlated information is concentrated in the first predictive component. OPLS-DA divides the differences in data table X into two parts based on the differences in data table Y: the first part represents differences related to Y, and the second part represents differences unrelated to Y (orthogonal and perpendicular). OPLS-DA can distinguish between these two parts of differences. In this way, OPLS-DA can better distinguish between group differences, improving the effectiveness and analytical power of the model.

[0019] KEGG (Kyoto Encyclopedia of Genes and Genomes, http: / / www.genome.jp / kegg / ) is a database for systematically analyzing gene function and genomic information. It integrates information from genomics, biochemistry, and systems functional omics, helping researchers study genes and their expression as a holistic network. A significant feature of KEGG is its powerful graphical capabilities, using diagrams rather than cumbersome text to illustrate numerous metabolic pathways and the relationships between them. This allows researchers to gain a direct and comprehensive understanding of the metabolic pathways they are studying. In organisms, gene products do not exist in isolation; different gene products coordinate with each other in an orderly manner to perform their specific biological functions. KEGG provides integrated metabolic pathways with comprehensive annotations of the enzymes catalyzing each step of the reaction and the gene products and metabolites involved. Therefore, the KEGG database is a powerful tool for conducting metabolic analysis and metabolic network analysis in organisms. By comparing with the KEGG compound database, the corresponding KEGG compound ID number for a metabolite can be obtained. Based on the KEGG compound ID number, the specific biological pathway involved by a particular metabolite and the biological function classification information of the metabolite can be obtained.

[0020] The Human Metabolomics Database (HMDB, www.hmdb.ca) contains comprehensive information on human metabolites and their biological functions, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra, and is currently updated to version 4.0. Significant advancements have also been made to HMDB's chemical taxonomy, chemical ontology, spectroscopic viewing, and spectroscopic / text search tools. HMDB 4.0 also adds a wealth of entirely new data. This includes a large amount of predicted MS / MS and GC-MS reference spectroscopic data, as well as predicted (physiologically feasible) metabolite structures to facilitate the identification of new metabolites. Additional information on metabolite-SNP interactions and the effects of drugs on metabolite levels has also been added (pharmacomics). The content, interface, and performance of the HMDB website have undergone many other significant improvements, which will greatly enhance its usability and potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics.

[0021] By searching the database, one can identify the types of differential metabolites. The databases mentioned above are rich and comprehensive in terms of the number of substances that can be detected, and both simple and complex searches can be performed to effectively identify differential metabolites.

[0022] Compared with the prior art, the present invention has the following advantages: (1) This invention uses non-targeted metabolomics to detect Lycium barbarum glycopeptide metabolites in rat plasma and tissue samples, uses metabolites to reflect the phenotype of organisms, identifies biomarkers in the metabolic process, and aims to elucidate the pharmacological mechanism of Lycium barbarum glycopeptide from a metabolic perspective, laying the foundation for further research and development of the medicinal value of Lycium barbarum glycopeptide. (2) The present invention uses UPLC-QTOF / MS, which, compared with traditional LC-MS, GC-MS and single detection, can overcome background interference, improve the signal-to-noise ratio, and still achieve high sensitivity for complex samples, with good quantitative effect and more accurate results; (3) The data processing module of this invention adopts a method of combining univariate analysis with multivariate analysis, which results in more accurate and comprehensive statistical results, is easy to operate, has strong universality, and has significant social progress significance. (4) This invention evaluates the rationality of differential metabolites from different dimensions through different methods such as cluster analysis and correlation analysis, and displays the relationship between samples more intuitively and comprehensively, thereby assisting us in accurately screening marker metabolites and ensuring the accuracy of the screened metabolic markers. Finally, KEGG pathway analysis is performed to obtain a standard, complete and accurate metabolic pathway map. Attached Figure Description

[0023] Figure 1 This is the total ion flow chromatogram in positive ion mode as described in the embodiments of the present invention; Figure 2 This is a pie chart showing the classification of compounds as described in the embodiments of the present invention; Figure 3 This is the PCA score graph described in the embodiments of the present invention; Figure 4 This is the Venn diagram described in the embodiments of the present invention; Figure 5 This is an OPLS-DA score graph of the metabolite described in the embodiments of the present invention; Figure 6 This is the model verification diagram described in the embodiment of the present invention; Figure 7 This is the differential expression volcano plot described in the embodiments of the present invention; Figure 8 This is a clustering heatmap of the differential metabolites described in the embodiments of the present invention; Figure 9 This is a KEGG metabolic pathway diagram as described in the embodiments of the present invention; Figure 10 This is the KEGG metabolic enrichment map described in the embodiments of the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to specific embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0025] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0026] Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods. Unless otherwise specified, the experimental materials used in the following embodiments are commercially available products.

[0027] Example: 1. Laboratory animals and grouping Twelve SPF-grade male SD rats, weighing 200-220 g, were provided by the Animal Experiment Center of Ningxia Medical University, license number: SCXK(Ning)2022-0001. They were divided into a control group (N group, n=6) and an experimental group (G group, n=6). After three days of acclimatization feeding, the experimental group was administered 200 mg / kg of Lycium barbarum glycopeptide via gavage, while the control group received an equal dose of physiological saline. Both groups were fasted for 12 hours prior to gavage, but water was permitted.

[0028] The "goji berry glycopeptide" powder used was provided by Ningxia Tianren Goji Berry Biotechnology Co., Ltd. The preparation method of the goji berry glycopeptide includes extracting and separating a polysaccharide and peptide chain covalently linked from a water extract of goji berries through pure physical centrifugation, membrane filtration purification, and vacuum freeze-drying. It contains LbGp1, LbGp2, LbGp3, LbGp4, and LbGp5. Specifically, LbGp1 has a protein content of 28.87%, LbGp2 has a protein content of 9.25%, LbGp3 has a protein content of 5.19%, LbGp4 has a protein content of 10.75%, and LbGp5 has a protein content of 59.87%. In the wolfberry glycopeptide, the mass ratio of LbGp1, LbGp2, LbGp3, LbGp4 and LbGp5 is 0.018:0.0077:0.018:0.029:0.014.

[0029] 2. Test Characterization 2.1 Instruments and Reagents ACQUITY UPLC I-Class PLUS ultra-high performance liquid chromatograph (Waters Corporation, USA); ACQUITY Premier Peptide CSH C18 column (130 Å, 1.7 µm, 2.1 x 150 mm) (Waters Corporation, USA); Triple TOF 5600+ mass spectrometer (AB SCIEX Corporation, USA); vortex mixer (Shanghai Jingke Industrial Co., Ltd.); Flying Pigeon ultracentrifuge (Shanghai Anting Scientific Instrument Factory); wolfberry glycopeptide (Ningxia Tianren Wolfberry Biotechnology Co., Ltd.); methanol, acetonitrile, formic acid, and ultrapure water used in LC-MS (purchased from Fisher Chemical Company, USA).

[0030] 2.2 Sample Collection and Processing In this embodiment, blood was collected from the orbital cavity 1 hour after drug administration. The extracted blood was placed in a 1.5 mL brown centrifuge tube, moistened with a small amount of heparin, and allowed to stand for 2 hours. Then, it was centrifuged at 15,000 r / min for 10 min to obtain the supernatant as rat plasma, which was stored at -80°C for later use. 24 hours later, the rats were immediately sacrificed and dissected. Liver, intestine, lung, and brain tissues were collected. After accurate weighing, twice the volume of acetonitrile was added to prepare tissue homogenates, which were then centrifuged at 15,000 r / min for 10 min to obtain the rat tissue homogenates, which were stored at -80°C for later use.

[0031] 2.3 Non-targeted detection Using UPLC The QTOF / MS coupled technology is used to perform non-targeted detection on the test samples, and samples are taken sequentially to obtain raw data of metabolites in plasma and tissue samples.

[0032] Chromatographic conditions: Column: ACQUITY Premier Peptide CSH C18 column (130 Å, 1.7 µm, 2.1 x 150 mm); Mobile phase: Phase A: 0.1% formic acid-water solution, Phase B: 0.1% formic acid-acetonitrile; Flow rate: 0.2 mL / min; Injection volume: 2 μL; Column temperature: 35℃; Gradient elution conditions: 15%-43% B phase 0-2.0 min, 43%-48% B phase 2.0-10.0 min, 48%-52% B phase 10.0-17.0 min, 52%-72% B phase 17.0-27.0 min, 72-88% B phase 27.0-37.0 min, 88%-86% B phase 37.0-45.0 min, 86%-15% B phase 45.0-46.0 min.

[0033] Mass spectrometry conditions: Acquisition mode: positive ion mode; TOFMS mass range: 100-1000 Da; IDA mass range: 50-1000 Da; TOFMS acquisition frequency: 0.18 secs; IDA secondary acquisition frequency: 0.03 secs; Ion Source Gas1: 50 psi; Ion Source Gas2: 50 psi; Curtain gas: 30 psi; Ion source temperature: 500℃; IonSpray voltage: 5500 V; Declustering voltage: 80 V; TOFMS collision energy: 10 V; IDA secondary collision energy: 35 V.

[0034] 3. Multivariate analysis of data First, the raw data was converted to mz ML format using Proteo Wizard. Then, XCMS software was used to perform peak alignment, retention time correction, and peak area extraction on the spectra of the raw data. After XCMS processing, the data was processed using MateboAnanlyst software for statistical analysis, including principal component analysis and orthogonal partial least squares discriminant analysis. Finally, statistical analysis was performed based on OPLS. The DA results show that differentially expressed metabolites between different groups can be initially screened from the VIP value. Metabolites with VIP ≥ 1 are generally considered to be significantly different. At the same time, the p-value of the t-test in univariate analysis is used to further screen for the biomarkers of Lycium barbarum glycopeptides. The expression level of metabolites is set to be less than 0.5 times, that is, metabolites with ≤ 0.5 are selected. It is considered that the p-value of the t-test < 0.05 is meaningful, and the more reliable the differentially expressed metabolites are.

[0035] 4. Results 4.1 Identification of Lycium barbarum glycopeptide metabolites like Figure 1 As shown, the sample data were analyzed and processed using LC-MS data analysis software, and a database search was performed in HMDB 4.0. In positive ion mode, a total of 591 metabolites were qualitatively identified. These metabolites included 364 lipids and lipid-like compounds, 82 organic acids and their derivatives, 40 organic heterocyclic compounds, 30 oxygen-containing organic compounds, 23 benzene ring derivatives, 21 phenylpropanoids and polyketides, 14 nitrogen-containing organic compounds, 9 nucleosides / nucleotides and analogs, 5 alkaloids and their derivatives, 1 lignan / neoligan and related compound, 1 sulfur-containing organic compound, and 1 other type of compound. The proportions of each type of compound were 61.59%, 13.87%, 6.77%, 5.08%, 3.89%, 3.55%, 2.37%, 1.52%, 0.85%, 0.17%, 0.17%, and 0.17%, respectively. Figure 2 As shown.

[0036] 4.2 Multivariate statistical analysis of sample metabolic profiles 4.2.1 Principal Component Analysis (PCA) like Figure 3 As shown in the figure, PCA was used to perform metabolic profile analysis on the two groups of samples. The contribution rates of PC1 and PC2 were 27.8% and 17.90%, respectively. All samples were within the 95% confidence interval, indicating that the differences within the sample groups were small. There was a certain angle between the metabolic profiles of the control group and the drug-treated group, indicating that the drug-containing group was significantly separated from the normal group. This indicates that there are differences in the chemical composition of drug-containing plasma and tissues after oral administration of Lycium barbarum glycopeptide compared with normal plasma and tissues. Compared with the normal group, the chemical composition of the drug-treated group changed.

[0037] 4.2.2 Venn Analysis (Venn) Venn analysis can be used to display metabolites specific to each group or shared across groups. Different colors represent different groups; overlapping numbers represent the number of metabolites shared across multiple groups, while non-overlapping numbers represent the number of metabolites specific to a given group. Figure 4 It is known that after oral administration of wolfberry glycopeptide, there are a total of 70 unique metabolites, including 34 in the intestine, 13 in the brain, 10 in the liver, 8 in the lungs, and 5 in the plasma.

[0038] 4.3 Screening and Analysis of Differential Metabolites like Figure 5 , Figure 6 As shown, the OPLS-DA model of plasma and tissue metabolites established in the experiment can clearly distinguish between the control group and the drug-treated group. Under positive ion mode, the R values ​​of the control group and the drug-treated group were significantly different. 2 Y, Q 2 The values ​​are 0.956 and 0.452 respectively, R 2 Y is close to 1, Q 2 ≥0.4 indicates that the established OPLS-DA model fits well and there is no overfitting.

[0039] In the OPLS-DA model, variables with VIP≥1 are considered to be of significant importance for classification. Therefore, metabolites with VIP≥1 are selected for univariate t-tests, and differential expression volcano plots are drawn. Figure 7In the figure, each point represents a specific metabolite. The horizontal axis represents the FC value of each substance in the comparison group, the vertical axis represents the P value of the t-test, and the size of the point represents the Vip value. Points on the left represent metabolites with differentially downregulated expression, and points on the right represent metabolites with differentially upregulated expression. The further to the left, right, and top of the points, the more significant the expression difference. Ultimately, 16 differentially expressed metabolites (VIP≥1, P<0.05) were screened. Compared with the control group, 8 were upregulated and 8 were downregulated. The names of the differentially expressed metabolites, average peak values, trends, VIP, and P values ​​are shown in Table 1. The cluster analysis heatmap shows that the 16 screened metabolites can clearly distinguish between the two groups (…). Figure 8 This indicates that the screened differential metabolites have high reliability. Table 1 shows the results of the differential metabolites and related parameters.

[0040] Table 1: Differential metabolites and related parameters in rats of the treatment group and control group Note: ↑ indicates upward adjustment; ↓ indicates downward adjustment.

[0041] 4.4 Pathway enrichment analysis of differentially metabolites Differential metabolites of Lycium barbarum glycopeptides were annotated to primary pathways in metabolism, biological systems, human diseases, environmental information processing, and genetic information processing. Sixteen screened differential metabolites were enriched into metabolic pathways, and pathway analysis was used to identify related metabolic pathways of Lycium barbarum glycopeptides in vivo. The top 30 metabolic pathways were enriched, and those with an impact >0.05 and P < 0.05 were selected as key metabolic pathways, mainly including mineral absorption pathways, aminoacyl-tRNA biosynthesis pathways, lysine degradation pathways, protein digestion and absorption pathways, and central carbon metabolism pathways in cancer.

[0042] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for detecting Lycium barbarum glycopeptide metabolites in rat plasma and tissues based on non-targeted metabolomics, characterized in that: Includes the following steps: Step 1: Blood samples were collected from rats by orbital blood sampling 1 hour after gavage administration of wolfberry glycopeptide and saline. 24 hours later, the rats were immediately sacrificed and dissected to collect liver, intestine, lung and brain tissues. The group administered saline was the control group (N group), and the group administered wolfberry glycopeptide was the experimental group (G group). Step 2: Sample processing. 100 μL of plasma samples from groups N and G were transferred to 1.5 mL brown centrifuge tubes, moistened with a small amount of heparin, allowed to stand for 2 h, and then centrifuged at 15000 r / min for 10 min to obtain the supernatant for rat plasma. This supernatant was stored at -80 ℃ for later use. For liver, intestine, lung, and brain tissues collected from groups N and G, after accurate weighing, 2 times the volume of acetonitrile was added to prepare tissue homogenates. These homogenates were then centrifuged at 15000 r / min for 10 min and stored at -80 ℃ for later use. Step 3: Use UPLC-Q-TOF / MS to perform non-targeted detection on the test samples, and take samples sequentially to obtain raw data of metabolites in plasma and tissues; Step 4: Perform multivariate analysis on the original data and project the importance values ​​of the variables using the OPLS-DA model; Step 5: Perform KEGG pathway analysis on metabolic markers to determine the metabolic process of wolfberry glycopeptides.

2. The method according to claim 1, characterized in that: The chromatographic conditions used in step 3 were as follows: Column: CSHC18 column; Mobile phase: Phase A: 0.1% formic acid-water solution, Phase B: 0.1% formic acid-acetonitrile; Flow rate: 0.2 mL / min; Injection volume: 2 μL; Column temperature: 35℃; The gradient elution conditions of the mobile phase were as follows: 15%-43% B phase 0-2.0 min, 43%-48% B phase 2.0-10.0 min, 48%-52% B phase 10.0-17.0 min, 52%-72% B phase 17.0-27.0 min, 72-88% B phase 27.0-37.0 min, 88%-86% B phase 37.0-45.0 min, 86%-15% B phase 45.0-46.0 min.

3. The method according to claim 1, characterized in that: The mass spectrometry conditions used in step 3 were: acquisition mode: positive ion mode; TOFMS mass range: 100-1000 Da; IDA mass range: 50-1000 Da. TOFMS acquisition frequency: 0.18 secs; IDA secondary acquisition frequency: 0.03 secs; Ion Source Gas1 spray: 50 psi; Ion Source Gas2 auxiliary heating gas: 50 psi; curtain gas: 30 psi; ion source temperature: 500℃; IonSpray voltage: 5500V; declustering voltage: 80V; TOFMS collision energy: 10V; IDA secondary collision energy: 35V.

4. The method according to claim 1, characterized in that: In step 4, the specific method for determining the variable projection importance value of the OPLS-DA model is as follows: the VIP value is combined with the p-value of the t-test and the FC value of the fold change analysis, and differential metabolites are identified with the condition VIP≥1 and P<0.

05.

5. The method according to claim 1, characterized in that: In step 5, the pathways include mineral absorption pathways, aminoacyl-tRNA biosynthesis pathways, lysine degradation pathways, protein digestion and absorption pathways, and central carbon metabolism pathways in cancer.

6. The method according to claim 1, characterized in that: In step 4, before performing multivariate analysis on the original data, the original data is first converted into mz ML format, and then the spectrum of the original data is subjected to peak alignment, retention time correction and peak area extraction.

7. The method according to claim 1, characterized in that: In step 4, the multivariate analysis of the original data specifically involves: after peak alignment, retention time correction and peak area extraction of the original data spectrum, the data is processed by MateboAnanlyst software, and statistical analyses such as principal component analysis and orthogonal partial least squares discriminant analysis are performed.

8. The method according to claim 1, characterized in that: In step 4, before performing KEGG pathway analysis on metabolic markers, bioinformatics analysis is also performed to evaluate the rationality of differential metabolites. The bioinformatics analysis includes cluster analysis, correlation analysis, etc.

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