Method for analyzing and evaluating drug regulation metabolic pathway based on blood metabonomics

Abstract

The invention belongs to the technical field of traditional Chinese medicine research, and particularly relates to a method for analyzing and evaluating a drug regulation metabolic pathway based on blood metabonomics, which comprises the following steps: constructing a model animal, and taking part of the model animal for administration treatment; collecting blood of the normal animal, the model animal and the administration animal, and detecting and analyzing metabolites in the blood; detecting and analyzing the extracted metabolites, comparing the metabolites in the blood of the normal animal and the model animal, and screening out the blood biomarker of the model animal; comparing the blood biomarker of the model animal with metabolites in the blood of the administration animal, and screening out the blood biomarker of the administration animal; and performing pathway analysis on the blood biomarker of the drug-administered animal to obtain drug-regulated metabolic pathway data. According to the method, blood markers and metabolic pathways are used as parameters, a pharmacodynamic mechanism research normal form based on diabetes blood metabolism is constructed, and the problems that an existing method is long in pharmacodynamic evaluation period and low in efficiency are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of traditional Chinese medicine research, and particularly relates to a method for evaluating the regulation of metabolic pathways by drugs based on blood metabolomics analysis. Background Art

[0002] Diabetes Mellitus (DM) is a systemic metabolic disorder disease characterized by hyperglycemia and accompanied by a variety of acute and chronic complications caused by relative or absolute deficiency of insulin secretion. With the development of the economy and the improvement of people's living standards, the incidence of diabetes has been increasing year by year. In addition to insulin, oral antidiabetic drugs (OAHAs) can also achieve good blood glucose control in clinical practice. OAHAs mainly include five categories: sulfonylurea secretagogues, biguanides, α-glucosidase inhibitors, thiazolidinedione derivatives, and non-sulfonylurea secretagogues. Existing chemical hypoglycemic drugs all have varying degrees of toxic and side effects, and are expensive, which is not conducive to long-term use. Therefore, the development of traditional Chinese medicine with small toxic and side effects and affordable prices brings benefits to the majority of diabetic patients.

[0003] Reduced pancreatic function and relative deficiency of insulin secretion are the pathological roots of diabetes and its complications. β-cells continuously adjust their secretion activity according to the availability of substrates to keep blood glucose levels within the physiological range, and this process is called metabolic-secretory coupling. Glucose is considered the main regulator of insulin secretion. Once glucose is transported into pancreatic β-cells, it will be phosphorylated by hexokinase IV with low affinity (Km is 6-11 mmol / L), and then further metabolized to produce a series of metabolic-secretory coupling factors such as ATP, resulting in an increase in the ATP / ADP ratio, inhibiting the ATP-sensitive potassium channels (K ATP ) on the cell membrane and opening the L-type voltage-dependent calcium channels (L-VDCC) to allow Ca 2+ to enter, stimulating the fusion of insulin-containing secretory granules with the plasma membrane, and thus releasing insulin. The secretion of insulin by pancreatic β-cells is controlled by complex metabolic and energy changes caused by metabolic fuels. Some studies have shown that insufficient insulin secretion in diabetes is related to impaired metabolic-secretory coupling. In β-cells, interference with glucose metabolism and ATP production will lead to reduced insulin secretion. Therefore, regulating pancreatic islet function is the most effective way to treat diabetes. Traditional blood metabolism research only uses limited pathological and biochemical indicators, only focuses on the analysis of diabetes blood metabolism markers, and fails to effectively associate them with the overall efficacy evaluation, and cannot reflect the overall action characteristics of traditional Chinese medicine. Moreover, most of the early traditional drug evaluation studies only focused on biochemical indicators after administration to normal animals, and a few studies were conducted on disease animal models. However, the actual effect of the drug is related to the overall therapeutic effect of the drug, and the research results are disconnected from the overall efficacy. Summary of the Invention

[0004] In view of the above technical problems, the present invention provides a method for evaluating the regulation of metabolic pathways by drugs based on blood metabolomics analysis. Using blood metabolite markers and metabolic pathways as parameters, a research paradigm for the pharmacodynamic mechanism of targeting the regulation of diabetes blood metabolism is constructed, breaking through the technical bottleneck of confirming drug efficacy in traditional drug evaluation methods and solving the problems of long evaluation cycle and low efficiency of existing methods for evaluating drug efficacy.

[0005] The specific technical solution provided by the present invention is as follows: The present invention provides a method for evaluating the regulation of metabolic pathways by drugs based on blood metabolomics analysis, comprising the following steps: Construct model animals and administer drugs to some of the model animals. Collect blood samples from normal animals, model animals, and drug-administered animals, and detect and analyze the metabolites in the blood. Detect and analyze the extracted metabolites, compare the metabolites in the blood of normal animals and model animals, and screen out the blood biomarkers of model animals; compare the blood biomarkers of model animals with the metabolites in the blood of drug-administered animals, and screen out the blood biomarkers of drug-administered animals. Conduct pathway analysis on the above blood biomarkers of drug-administered animals to obtain the metabolic pathway data of drug-regulated blood metabolism.

[0006] Preferably, the drug is acanthopanax senticosus extract, which is obtained by extracting acanthopanax senticosus leaves with ethanol as a solvent.

[0007] Preferably, the analysis of metabolites in the blood is performed by liquid chromatography-mass spectrometry, and the analysis conditions are as follows: High-performance liquid chromatography conditions: ACQUITY UPLC BEH C 18 , 100 mm×2.1 mm, 1.7μm; a flow rate of 0.4 mL / min; Positive ion mode, mobile phase, A1: aqueous solution of 0.1% formic acid, B1: acetonitrile solution of 0.1% formic acid; gradient elution program: 0 - 1 min, 2% B1; 1 - 7 min, 2% - 20% B1; 7 - 7.5 min, 20% B1; 7.5 - 12 min, 20% - 40% B1; Negative ion mode, mobile phase, B2: acetonitrile, A2: aqueous solution of 5 mM ammonium formate; gradient elution program: 0 - 1 min, 2% B2; 1 - 7 min, 2% - 20% B2; 7 - 7.5 min, 20% B2; 7.5 - 12 min, 20% - 40% B2; Among them, % is the volume fraction; Mass spectrometry conditions: electrospray ionization source, collecting in positive and negative ion modes; positive ion spray voltage is 5.50 kV, negative ion spray voltage is -4.50 kV; capillary temperature is 550 °C.

[0008] Preferably, the model animal is a diabetic model animal.

[0009] Preferably, the blood biomarkers of the model animal are one or any combination of 5-aminovaleric acid, L-leucine, sphingomyelin (d18:1 / 24:1), 15-ketoprostaglandin F2α, arachidonic acid A3, octadecatetraenoic acid, (2'E,4'Z,7'Z,8E)-phthalic acid, 11β-hydroxyprogesterone, lysophosphatidylethanolamine(0:0 / 20:4(5Z,8Z,11Z,14Z)), lysophosphatidylethanolamine(0:0 / 20:4(8Z,11Z,14Z,17Z)), lysophosphatidylcholine(18:2 / 0:0), α-linolenic acid, 15-deoxy-Δ12,14-prostaglandin J2, 12-ketoeicosatetraenoic acid, 13-hydroxy-octadecadienoic acid, leukotriene A4, lysophosphatidylcholine(22:5(7Z,10Z,13Z,16Z,19Z) / 0:0), lysophosphatidylcholine(18:0 / 0:0), monoglyceride(0:0 / 20:5(5Z,8Z,11Z,14Z,17Z) / 0:0), monoglyceride(0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0), desogestrel, docosahexaenoic acid, palmitoleic acid, lysophosphatidylcholine(20:0 / 0:0), aldosterone, phosphatidic acid(P-16:0 / 18:2(9Z,12Z)), cholesterol sulfate, 3α,7α,12α-trihydroxy-5β-cholestan-26-al.

[0010] Preferably, the blood biomarkers of the dosed animal are one or any combination of L-leucine, α-linolenic acid, docosahexaenoic acid, palmitoleic acid, 12-ketoeicosatetraenoic acid, lysophosphatidylcholine(18:2 / 0:0), lysophosphatidylcholine(20:0 / 0:0), lysophosphatidylcholine(18:0 / 0:0), lysophosphatidylethanolamine(0:0 / 20:4(5Z,8Z,11Z,14Z)), lysophosphatidylcholine(22:5(7Z,10Z,13Z,16Z,19Z) / 0:0).

[0011] Preferably, the drug-regulated blood metabolism-related pathways are arachidonic acid metabolism, α-linolenic acid metabolism, biosynthesis of unsaturated fatty acids, steroid hormone biosynthesis, biosynthesis of valine, leucine and isoleucine, ether lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, degradation of valine, leucine and isoleucine, primary bile acid biosynthesis.

[0012] Preferably, the drug improves the blood metabolic disorder of the model by regulating the biosynthesis of unsaturated fatty acids, valine, leucine and isoleucine biosynthesis, α-linolenic acid metabolism, glycerophospholipid metabolism, valine, leucine and isoleucine degradation, and arachidonic acid metabolism.

[0013] Preferably, both the comparison of metabolites in the blood of normal animals and model animals and the comparison of blood biomarkers of model animals and metabolites in the blood of dosed animals are performed using principal component analysis, partial least squares discriminant analysis, and orthogonal-partial least squares discriminant analysis. The analysis results are illustrated by the VIP value to show the contribution degree of variables to the model. When the VIP of a certain variable > 1, it indicates that the variable is important and can be used as a potential biomarker.

[0014] Preferably, the screening of blood biomarkers of model animals is carried out by using the volcano plot analysis method.

[0015] The present invention also provides the use of a reagent for detecting the blood biomarkers of the model animals obtained by the above method in the preparation of products for diabetes prognosis or diagnosis.

[0016] Preferably, an increase in the expression level of any one or more of 5-aminovaleric acid, L-leucine, arachidonic acid A3, octadecatetraenoic acid, (2'E,4'Z,7'Z,8E)-phthalic acid, lysophosphatidylethanolamine(0:0 / 20:4(8Z,11Z,14Z,17Z)), lysophosphatidylcholine(18:2 / 0:0), α-linolenic acid, 15-deoxy-Δ12,14-prostaglandin J2, 12-ketoeicosatetraenoic acid, 13-hydroxy-octadecadienoic acid, leukotriene A4, lysophosphatidylcholine(22:5(7Z,10Z,13Z,16Z,19Z) / 0:0), lysophosphatidylcholine(18:0 / 0:0), aldosterone, monoglyceride(0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0) or 3α,7α,12α-trihydroxy-5β-cholestane-26-al in the blood biomarkers of the model animals indicates good diabetes prognosis; or A decrease in the expression level of any one or more of sphingomyelin(d18:1 / 24:1), 15-ketoprostaglandin F2α, 11β-hydroxyprogesterone, lysophosphatidylethanolamine(0:0 / 20:4(5Z,8Z,11Z,14Z)), docosahexaenoic acid, palmitoleic acid or lysophosphatidylcholine(20:0 / 0:0), desogestrel, phosphatidic acid(P-16:0 / 18:2(9Z,12Z)) or cholesterol sulfate in the blood biomarkers of the model animals indicates good diabetes prognosis.

[0017] Compared with the prior art, the beneficial effects of the present invention are: The present invention provides a method for evaluating drug-regulated metabolic pathways based on blood metabolomics analysis, which utilizes ultra-performance liquid chromatography tandem with high-definition mass spectrometry to evaluate the regulatory effects of traditional Chinese medicine with blood metabolic profiles and metabolic markers, macroscopically characterizes the overall characteristics of drug efficacy with blood metabolic profiles, and microscopically expresses the fine characteristics of drug efficacy with quantitative changes in blood metabolic markers, mines metabolic markers that significantly change in drug-regulated diabetic blood, analyzes blood metabolic pathways related to drug efficacy biomarkers, and provides technical support for traditional Chinese medicine efficacy evaluation, new drug screening and development.

[0018] The present invention uses the blood of diabetic mice as the research object, establishes a drug efficacy evaluation model through multivariate analysis, and combines pattern recognition and metabolic pathway analysis methods to clarify the diabetic blood metabolism biomarkers and their metabolic pathways, reveal the changing rules of diabetic blood metabolism under diabetic pathological conditions, and use blood metabolic markers and metabolic profiles as parameters to accurately evaluate the pharmacodynamic effect of Acanthopanax senticosus leaves intervening in diabetic blood metabolic pathways, and clarifies that the regulatory effect of Acanthopanax senticosus leaves on abnormal metabolic pathways is mainly related to the biosynthesis of unsaturated fatty acids, valine, leucine and isoleucine biosynthesis, α-linolenic acid metabolism, glycerophospholipid metabolism, valine, leucine and isoleucine degradation, and arachidonic acid metabolism, which provides an important scientific basis for the clinical treatment of diabetes with Acanthopanax senticosus leaves.

[0019] The present invention uses blood metabolic markers and metabolic pathways as parameters. The entire research process is directly related to the drug efficacy. It constructs a drug efficacy mechanism research model based on targeted regulation of diabetic blood metabolism, breaks through the technical bottleneck of traditional drug evaluation methods for confirming drug efficacy, and solves the problems of long drug efficacy evaluation cycle and low efficiency of existing methods, providing a new model for innovative Chinese medicine research.

[0020] The present invention focuses on the changes in blood metabolites in diabetes and uses targeted blood metabolomics to screen biomarkers, which has the advantages of rapidity, high throughput, and high coverage.

[0021] By constructing a diabetes model, 28 potential blood biomarkers related to the model group were screened, including 5-aminovaleric acid, L-leucine, sphingomyelin (d18:1 / 24:1), 15-ketoprostaglandin F2α, arachidonic acid A3, octadecatetraenoic acid, (2'E,4'Z,7'Z,8E)-phthalic acid, 11β-hydroxyprogesterone, lysophosphatidylethanolamine (0:0 / 20:4(5Z,8Z,11Z,14Z)), lysophosphatidylethanolamine (0:0 / 20:4(8Z,11Z,14Z,17Z)), lysophosphatidylcholine (18:2 / 0:0), α-linolenic acid, 15-deoxy-Δ12,14-prostaglandin J2, 12-keto-eicosatetraenoic acid, 13-hydroxy-octadecadienoic acid, leukotriene A4, lysophosphatidylcholine (22:5(7Z,10Z,13Z,16Z,19Z) / 0:0), lysophosphatidylcholine (18:0 / 0:0), monoglyceride (0:0 / 20:5(5Z,8Z,11Z,14Z,17Z) / 0:0), monoglyceride (0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0), desogestrel, docosahexaenoic acid, palmitoleic acid, lysophosphatidylcholine (20:0 / 0:0), aldosterone, phosphatidic acid (P-16:0 / 18:2(9Z,12Z)), cholesterol sulfate, 3α,7α,12α-trihydroxy-5β-cholestane-26-al. The up-regulation and down-regulation of the expression levels of these biomarkers can be used for the diagnosis and prognosis efficacy judgment of diabetes, and can screen target drugs capable of treating diabetes. Description of the Drawings

[0022] Figure 1 It is the PCA score plot of the positive ion mode (A) and negative ion mode (B) of the blood of the normal group and the model group; the abscissa represents the score value of the first principal component, and the ordinate represents the score value of the second principal component. The dots represent the samples, the circles represent the 95% confidence interval, and the colors represent different groups. Control, blank group; Model, model group;

[0023] Figures 2 - 5 It is the positive ion mode of the blank group of blood metabolites UPLC / MS ( Figure 2 ), the negative ion mode of the blank group ( Figure 3 ), the positive ion mode of the model group ( Figure 4 ), and the negative ion mode of the model group ( Figure 5 ) base peak chromatograms; the abscissa is the retention time of 10 min, and the ordinate is the ion intensity; Figure 6It is the overall metabolite clustering heatmap of the blood of the normal group and the model group; the relative content in the figure is shown by different colors. The redder the color, the higher the expression level, and the bluer the color, the lower the expression level. Among them, the columns represent samples, the rows represent metabolite names, and the clustering tree on the left side of the figure is the metabolite clustering tree. Control, blank group; Model, model group;

[0024] Figure 7 It is the PLS-DA score plot of the positive ion mode (A) and negative ion mode (B) of the blood of the normal group and the model group; the abscissa represents the score value of the first principal component, and the ordinate represents the score value of the second principal component. The points represent samples, the circles represent the 95% confidence interval, and the colors represent different groups. Control, blank group; Model, model group;

[0025] Figure 8 It is the OPLS-DA S-plot of the positive ion mode (A) and negative ion mode (B) of the blood of the normal group and the model group; the abscissa represents the covariance correlation coefficient between the principal component and the metabolite, and the ordinate represents the correlation coefficient between the principal component and the metabolite; Figure 9 It is the volcano plot of differential metabolites of the positive ion mode (A) and negative ion mode (B) of the blood of the normal group and the model group; the abscissa represents the relative abundance change of a metabolite between two samples; the ordinate represents the -log10 value of the P-value. Each point in the figure represents a metabolite. The size of the point represents the VIP value. The red points represent differential metabolites (VIP>1), and the black points represent metabolites that do not meet the differential screening conditions;

[0026] Figures 10 - 11 It is the positive ion mode of the blood of mice in the acanthopanax senticosus leaf group ( Figure 10 ) and negative ion mode ( Figure 11 ) of the TIC chromatogram; Figure 12 It is the PLS-DA score plot of the positive ion mode (A) and negative ion mode (B) of the blood of the normal group, the model group and the acanthopanax senticosus leaf group; the abscissa PC1 represents the score value of the first principal component, and the ordinate PC2 represents the score value of the second principal component. The points represent samples, the circles represent the 95% confidence interval, and the colors represent different groups. ASL, acanthopanax senticosus leaf group; Control, blank group; Model, model group;

[0027] Figure 13 It is the clustering heatmap of the blank group, the model group and the acanthopanax senticosus leaf group based on blood metabolite markers; Figure 14 It is the regulation of the blood metabolic pathway of mice in the diabetic model group by acanthopanax senticosus leaves. Specific implementation mode

[0028] The features and advantages of the present invention can be further understood through the following detailed description in conjunction with the accompanying drawings. The provided embodiments are only illustrative of the method of the present invention and do not limit the remaining content disclosed by the present invention in any way.

[0029] Example 1 Screening of blood metabolic biomarkers 1. Experimental animals 7-week-old SPF-grade male C57BL / Ksj-db / db mice and C57BL / Ksj-db / db mice of the same age and sex were purchased from Mudanjiang Medical College, with the license number 20240607. All mice were housed separately in the animal laboratory of Mudanjiang Normal University. Breeding conditions: temperature (25±2)°C, relative humidity (90±5)%, 12h / 12h light / dark cycle, free to move, eat (standard feed) and drink water.

[0030] 2. Drugs Acanthopanax senticosus leaves (picked from the third team of Dahuanggou, Xinbei Village, Shanzhhen Town, Hailin City, Heilongjiang Province) were dried to a constant weight at 55°C, then the Acanthopanax senticosus leaves were crushed and passed through a 100-mesh sieve, and stirred with 75% ethanol at 75°C for 3 hours. The supernatant was separated from the residue by centrifugation (4000g). The extraction was repeated 3 times. After the supernatants were mixed and concentrated, they were freeze-dried under vacuum conditions to obtain the ethanol extract of Acanthopanax senticosus leaves.

[0031] 3. Experimental methods 3.1. Experimental grouping and drug administration After all mice were adaptively fed for 1 week, the db / db mice were randomly divided into 5 groups according to the random number table method: model group (MOD), Acanthopanax senticosus group (ASL), with 5 mice in each group; 5 db / m mice were used as the normal control group (CON). This animal experiment was approved by the Ethics Center of Mudanjiang Medical College.

[0032] According to the equivalent dose ratio calculated by body surface area, the equivalent clinical dose of Acanthopanax senticosus group mice in this experiment was 300 g / kg. The normal group and the model group were given the same volume of normal saline, and intragastric administration was carried out once a day at a fixed time, and the administration volume was 5 mL·kg -1 , and the administration was continued for 6 weeks. All drugs in this experiment were dissolved in water and stored at 4°C, and the drugs were prepared every two days. Before intragastric administration to mice, the drugs were taken out, and after being restored to room temperature, they were mixed well and used.

[0033] 3.2. Sample collection After 6 weeks of drug intervention, the blood of each group of mice was taken from the eyeballs and placed in a centrifuge tube for storage and standby.

[0034] 3.3. Metabolite extraction (1) Blood was taken from the eyeballs of mice and collected with a 1.5 mL centrifuge tube; (2) Let the blood sample stand for 15 - 20 min; (3) Place it in a centrifuge with a rotation speed of 4000 r·min -1 , and centrifuge for 20 min (4 °C); (4) Take all the supernatant into a centrifuge tube and store it at -20 °C; (5) Take 200 μL of the serum after thawing at room temperature, accurately add 200 μL of 50% acetonitrile solution to prepare a 2-chloro-L-phenylalanine solution (4 ppm) to re-dissolve the sample, and add the filtrate to the detection bottle for LC-MS detection.

[0035] 3.4 Analytical conditions Chromatographic conditions: A chromatographic column of model ACQUITY UPLC BEH C 18 (100 mm × 2.1 mm, 1.7 μm); column temperature 40 °C; injection volume 5 μL; flow rate 0.4 mL / min; detection time 10 min. In positive ion mode, the mobile phase is an acetonitrile solution (B1) with 0.1% formic acid by volume and an aqueous solution (A1) with 0.1% formic acid by volume. The gradient elution program is: 0 - 1 min, 2%B1; 1 - 7 min, 2% - 20%B1; 7 - 7.5 min, 20%B1; 7.5 - 12 min, 20% - 40%B1. In negative ion mode, the mobile phase is acetonitrile (B2) and a 5 mM ammonium formate aqueous solution (A2), and the gradient elution program is: 0 - 1 min, 2%B1; 1 - 7 min, 2% - 20%B1; 7 - 7.5 min, 20%B1; 7.5 - 12 min, 20% - 40%B1.

[0036] Mass spectrometry conditions: Thermo Orbitrap Exploris 120 mass spectrometer detector (Thermo Fisher Scientific, USA), electrospray ionization source (ESI), and data are collected in positive and negative ion modes respectively. In positive ion detection mode, the scanning ranges of the first-stage mass spectrometry and the second-stage mass spectrometry are 100 - 2000 m / z and 50 - 2000 m / z respectively, and nitrogen is used for each gas path. The positive ion spray voltage is 5.50 kV, the negative ion spray voltage is -4.50 kV, the sheath gas is 35 arb, and the auxiliary gas is 65 arb. The capillary temperature is 550 °C. TOF is used for the first-stage full scan, the first-stage ion scanning range is 100 - 2000 m / z, and collision-induced dissociation is used for the second-stage fragmentation. The first-stage collision energy is 10 V, the second-stage collision energy is 40 V, the ions are fragmented before signal collection, and unnecessary MS / MS information is removed by dynamic exclusion at the same time.

[0037] 3.5 Data analysis method 3.5.1 Data preprocessing: The R XCMS (v3.12.0) software package was used for peak detection, peak filtering, and peak alignment to obtain a metabolite quantification list. Then, through support vector regression calibration based on QC samples, systematic errors were eliminated, and substances with RSD > 30% in the QC samples were filtered out during the quality control process for subsequent data analysis. Spectral databases such as the public databases HMDB, massbank, LipidMaps, mzcloud, KEGG, and the self-built standard substance library were used for substance identification (database searching). The parameter was set to ppm < 30 ppm to obtain metabolite qualitative results. The specific principle is to determine the molecular weight of metabolites according to the mass-to-charge ratio (m / z) of precursor ions in the mass spectrum, predict the molecular formula through information such as mass number deviation (ppm) and adduct ions, and then match with the database to achieve metabolite identification.

[0038] 3.5.2, Data Analysis The R software was used to perform dimensionality reduction analysis on the sample data, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). According to statistical tests, the P-value was calculated, and the variable importance in the projection (VIP), fold change between groups were calculated by the OPLS-DA dimensionality reduction method to measure the influence intensity and explanatory ability of the content of each metabolite on sample classification discrimination, and assist in the screening of metabolic markers. When the P-value < 0.05 and the VIP value > 1, it was considered that the metabolite molecules had statistically significant differences.

[0039] 3.5.3, Pathway Analysis The MetaboAnalyst software was used to perform functional pathway enrichment and topological analysis on the screened differential metabolites using the hypergeometric test algorithm. The enriched pathways were browsed with a visualization tool for differential metabolites and pathway maps. The threshold for highly correlated metabolic pathways affected by path topology was set to P-value < 0.05.

[0040] 4. Statistical Analysis Statistical analysis and graphing were performed using the origin software. Measurement data were expressed as mean ± standard deviation (x ± SD). The Student's t test was used to compare the statistical significance of multiple parameters between two groups. P < 0.05 was considered to have statistical significance, and P < 0.01 was considered to have significant statistical significance.

[0041] 5. Experimental Results 5.1, Principal Component Analysis Principal Component Analysis (PCA) is an efficient unsupervised data analysis method. From the PCA score plot, the aggregation and dispersion degree of the samples can be observed (Figure 1 )。The closer the sample distribution points are, the closer the composition and concentration of these samples are; conversely, the farther the sample points are from each other, the greater the difference.

[0042] 5.2, Base peak chromatogram When analyzing blood metabolites using UPLC / MS, the blood metabolites eluted through chromatographic separation continuously enter the mass spectrometer, and the mass spectrometer continuously scans for data acquisition. Each scan obtains a mass spectrum. The blood metabolite ions with the highest intensity in each mass spectrum are continuously depicted. With the ion intensity as the vertical coordinate and time as the horizontal coordinate, the obtained blood metabolite spectrum ( Figures 2 - 5 ), which is the base peak chromatogram (TIC) of blood metabolites in the model group.

[0043] 5.3, Overall metabolite clustering heatmap The hierarchical clustering method is used to cluster the characteristics of samples (vertically) and metabolites (horizontally) respectively, and a heatmap is used to display the metabolite levels of each sample in the blood. Blood metabolites with similar metabolic patterns have similar functions, or jointly participate in the same metabolic process or metabolic pathway. In this study, two-way clustering of blood samples and metabolites in the model group was performed to draw a clustering heatmap ( Figure 6 ), and metabolites with the same or similar metabolic patterns of blood metabolites are clustered together.

[0044] 5.4, Pattern recognition analysis and multivariate statistical analysis In this analysis, multivariate statistical analysis using R software is performed. The methods include: principal component analysis (PCA); partial least squares discriminant analysis (PLS-DA); orthogonal partial least squares discriminant analysis (OPLS-DA).

[0045] 5.4.1, Principal component analysis Principal component analysis (PCA) is an efficient unsupervised data analysis method.

[0046] 5.4.2, Partial least squares discriminant analysis (PLS-DA) It can be seen from the score plot of partial least squares discriminant analysis that there is an obvious separation trend between the blank group and the model group ( Figure 7 ), indicating that there are multiple differential metabolites between the blank group and the model group in the blood.

[0047] 5.4.3, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) Further, orthogonal partial least squares discriminant analysis was performed on the blood metabolites of the blank group and the model group. The OPLS-DA plot can illustrate the contribution degree of variables to the model through the VIP value (Variable Importance for the Projection) Figure 8 ). Generally speaking, when the VIP of a certain variable > 1, it indicates that this variable is important and can be used as a potential biomarker.

[0048] 5.5, Screening of differential blood metabolites UPLC / MS was used to analyze blood metabolites, and it was found that [number of ions] were detected in the positive ion mode and [number of ions] were detected in the negative ion mode. We used the volcano plot analysis method to display the distribution and change trend of differential metabolites in the two groups of samples. The metabolite volcano plot was drawn using the preset screening conditions for differential blood metabolites in the model group such as FC value, P-value, and VIP Figure 9 ), and a total of 28 potential biomarkers significantly related to the blood model group of the model group were screened out.

[0049] 5.6, Identification of blood metabolic biomarkers The structural characterization of potential biomarkers mainly includes the following steps: accurate molecular weight determination, derivation of the structure from multi-stage mass spectrometry fragments, database and retention time alignment, and correlation analysis. According to their mass spectrometry data, potential biomarkers were retrieved and confirmed in public databases, and a total of 28 potential blood biomarkers in the model group were identified (Table 1), including 5-aminovaleric acid, L-leucine, sphingomyelin (d18:1 / 24:1), 15-ketoprostaglandin F2α, arachidonic acid A3, octadecatetraenoic acid, (2'E,4'Z,7'Z,8E)-phthalic acid, 11β-hydroxyprogesterone, lysophosphatidylethanolamine (0:0 / 20:4(5Z,8Z,11Z,14Z)), lysophosphatidylethanolamine (0:0 / 20:4(8Z,11Z,14Z,17Z)), lysophosphatidylcholine (18:2 / 0:0), α-linolenic acid, 15-deoxy-Δ12,14-prostaglandin J2, 12-keto-eicosatetraenoic acid, 13-hydroxy-octadecadienoic acid, leukotriene A4, lysophosphatidylcholine (22:5(7Z,10Z,13Z,16Z,19Z) / 0:0), lysophosphatidylcholine (18:0 / 0:0), monoglyceride (0:0 / 20:5(5Z,8Z,11Z,14Z,17Z) / 0:0), monoglyceride (0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0), desogestrel, docosahexaenoic acid, palmitoleic acid, lysophosphatidylcholine (20:0 / 0:0), aldosterone, phosphatidic acid (P-16:0 / 18:2(9Z,12Z)), cholesterol sulfate, 3α,7α,12α-trihydroxy-5β-cholestane-26-al.

[0050] Table 1 Information on blood metabolic biomarkers in blood samples of mice in the diabetes model group 5.7. Metabolic pathway analysis Using the collection of differential metabolites in the blood of the model group as the background, topological methods were used for pathway enrichment calculation. The results showed that the main differential metabolites in the blood of the model group were mainly enriched in the following metabolic pathways after enrichment: biosynthesis of valine, leucine, and isoleucine, degradation of valine, leucine, and isoleucine. The serum metabolic disorder in diabetic mice was most closely related to the biosynthesis of valine, leucine, and isoleucine and the degradation of valine, leucine, and isoleucine.

[0051] Table 2 Analysis of blood metabolic pathways in mice in the diabetes model group Note: Total, the total number of metabolites in the target metabolic pathway; Hits, the total number of differential metabolites in the target metabolic pathway; P-value, the P-value of the hypergeometric distribution test. The smaller the P-value, the more significant the impact of the detected differential metabolites on this pathway; Impact, the metabolic pathway impact value, the larger the value, the greater the impact of the detected differential metabolites on the target pathway.

[0052] Example 2 Analyze the differential metabolic markers in different experimental groups according to the method in Example 1.

[0053] Experimental results 1. Metabolic chromatogram of the blood protection effect of Eleutherococcus senticosus leaves on the model group Processed according to the method in Example 1, and UPLC-MS was used to separate and collect data of the blood of db / db mice. Figures 10 - 11 They are the total ion current (TIC) chromatograms of the Eleutherococcus senticosus intervention group in positive and negative ion modes. The TIC chromatogram provides basic information on endogenous metabolites in the blood of the model group.

[0054] 2. Pattern recognition analysis Perform multi-dimensional statistical analysis on the metabolite information collected in positive and negative ion modes. The metabolic profile reflects overall disorder. Perform multivariate statistical analysis on the LC-MS metabolic profile data of the normal group, model group, and Eleutherococcus senticosus intervention group. It can be seen from the PLS-DA score plot that there is an obvious separation trend among the normal group, model group, and Eleutherococcus senticosus group ( Figure 12 ).

[0055] 5. Cluster evaluation of blood metabolic markers to evaluate the effect of Eleutherococcus senticosus on diabetic blood The relative contents of blood metabolic markers in the blood of each group were visualized in the form of a heat map for the relative contents of 28 blood metabolic markers in each sample ( Figure 13 ). The abscissa is the group of samples, and the ordinate reflects the results of the cluster analysis of metabolites. Different colors reflect the relative contents of blood metabolites in the blood, with red representing up-regulation and blue representing down-regulation. The results show that compared with the model group, the content levels of most metabolites have been adjusted back in the Eleutherococcus senticosus group.

[0056] 6. Study on the regulation of blood metabolic pathways by Eleutherococcus senticosus Based on the differential analysis of blood metabolite biomarkers (Table 3), Acanthopanax senticosus mainly improves the abnormal levels of blood metabolites by regulating L-leucine, α-linolenic acid, docosahexaenoic acid, palmitoleic acid, 12-keto-eicosatetraenoic acid, lysophosphatidylcholine (18:2 / 0:0), lysophosphatidylcholine (20:0 / 0:0), lysophosphatidylcholine (18:0 / 0:0), lysophosphatidylethanolamine (0:0 / 20:4(5Z,8Z,11Z,14Z)), lysophosphatidylcholine (22:5(7Z,10Z,13Z,16Z,19Z) / 0:0), and there is an obvious recovery trend.

[0057] Pathway analysis of blood metabolite differences found that the pharmacological action pathways of Acanthopanax senticosus regulating blood metabolism are mainly related to the biosynthesis of unsaturated fatty acids, the biosynthesis of valine, leucine and isoleucine, α-linolenic acid metabolism, glycerophospholipid metabolism, the degradation of valine, leucine and isoleucine, and arachidonic acid metabolism ( Figure 14 , Table 4). Through the biological significance analysis of the blood metabolic pathways, it was found that Acanthopanax senticosus can improve the blood metabolic disorder pathways in the model group by regulating metabolic pathways such as the biosynthesis of unsaturated fatty acids, the biosynthesis of valine, leucine and isoleucine, α-linolenic acid metabolism, glycerophospholipid metabolism, the degradation of valine, leucine and isoleucine, and arachidonic acid metabolism.

[0058] Table 3 Regulatory trends of Acanthopanax senticosus on blood metabolite biomarkers in the model group Table 4 Acanthopanax senticosus regulates the blood metabolic pathways in diabetic model group mice Note: Total is the total number of compounds in the pathway; Hits is the number of exact matches in the uploaded biomarker data; Rawp is the original P value obtained through pathway analysis; Impact is the pathway impact value obtained through topological analysis.

[0059] The above embodiments are the preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A method for evaluating the regulation of metabolic pathways by drugs based on blood metabolomics analysis, characterized in that, Comprising the following steps: Construct model animals, and administer drugs to some of the model animals; Collect blood samples from normal animals, model animals and drug-administered animals, and detect and analyze metabolites in the blood; Compare the metabolites in the blood of normal animals and model animals, and screen out the blood biomarkers of model animals; compare the blood biomarkers of model animals with the metabolites in the blood of drug-administered animals, and screen out the blood biomarkers of drug-administered animals; Conduct pathway analysis on the blood biomarkers of the drug-administered animals to obtain metabolic pathway data of drug-regulated blood metabolism; Among them, the detection and analysis of metabolites are carried out by liquid chromatography-mass spectrometry, and the analysis conditions are: High performance liquid chromatography conditions: Chromatographic column: ACQUITY UPLC BEH C 18 , 100 mm×2.1 mm, 1.7 μm; flow rate of 0.4 mL / min; Positive ion mode, mobile phase, A1: aqueous solution of 0.1% formic acid by volume, B1: acetonitrile solution of 0.1% formic acid by volume; gradient elution program: 0-1 min, 2% B1; 1-7 min, 2%-20% B1; 7-7.5 min, 20% B1; 7.5-12 min, 20%-40% B1; Negative ion mode, mobile phase, B2: acetonitrile, A2: aqueous solution of 5 mM ammonium formate; gradient elution program: 0-1 min, 2% B2; 1-7 min, 2%-20% B2; 7-7.5 min, 20% B2; 7.5-12 min, 20%-40% B2; Mass spectrometry conditions: electrospray ionization source, positive and negative ion mode acquisition; positive ion spray voltage is 5.50 kV, negative ion spray voltage is -4.50 kV; capillary temperature is 550 °C.

2. The method according to claim 1, characterized in that, The drug is extracted from acanthopanax senticosus leaves as the raw material and ethanol as the solvent.

3. The method according to claim 2, wherein The model animals are diabetic model animals.

4. The method according to claim 3, wherein The blood biomarkers of the model animal are one or any combination of 5-aminovaleric acid, L-leucine, sphingomyelin (d18:1 / 24:1), 15-ketoprostaglandin F2α, arachidonic acid A3, octadecatetraenoic acid, (2'E,4'Z,7'Z,8E)-phthalic acid, 11β-hydroxyprogesterone, lysophosphatidylethanolamine (0:0 / 20:4(5Z,8Z,11Z,14Z)), lysophosphatidylethanolamine (0:0 / 20:4(8Z,11Z,14Z,17Z)), lysophosphatidylcholine (18:2 / 0:0), α-linolenic acid, 15-deoxy-Δ12,14-prostaglandin J2, 12-ketoeicosatetraenoic acid, 13-hydroxy-octadecadienoic acid, leukotriene A4, lysophosphatidylcholine (22:5(7Z,10Z,13Z,16Z,19Z) / 0:0), lysophosphatidylcholine (18:0 / 0:0), monoglyceride (0:0 / 20:5(5Z,8Z,11Z,14Z,17Z) / 0:0), monoglyceride (0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0), desogestrel, docosahexaenoic acid, palmitoleic acid, lysophosphatidylcholine (20:0 / 0:0), aldosterone, phosphatidic acid (P-16:0 / 18:2(9Z,12Z)), cholesterol sulfate, 3α,7α,12α-trihydroxy-5β-cholestane-26-al.

5. The method according to claim 4, wherein The blood biomarkers of the administered animal are one or any combination of L-leucine, α-linolenic acid, docosahexaenoic acid, palmitoleic acid, 12-ketoeicosatetraenoic acid, lysophosphatidylcholine (18:2 / 0:0), lysophosphatidylcholine (20:0 / 0:0), lysophosphatidylcholine (18:0 / 0:0), lysophosphatidylethanolamine (0:0 / 20:4(5Z,8Z,11Z,14Z)), lysophosphatidylcholine (22:5(7Z,10Z,13Z,16Z,19Z) / 0:0).

6. The method according to claim 5, wherein The drug-regulated blood metabolism-related pathways are arachidonic acid metabolism, α-linolenic acid metabolism, biosynthesis of unsaturated fatty acids, steroid hormone biosynthesis, biosynthesis of valine, leucine and isoleucine, ether lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, degradation of valine, leucine and isoleucine, primary bile acid biosynthesis.

7. Use of a reagent for detecting the blood biomarkers of the model animal obtained by the method described in claim 4 in the preparation of a product for diabetes prognosis or diagnosis.

8. The use according to claim 7, wherein An increase in the expression level of any one or more of 5-aminovaleric acid, L-leucine, eicosatetraenoic acid A3, octadecatetraenoic acid, (2'E,4'Z,7'Z,8E)-phthalic acid, lysophosphatidylethanolamine(0:0 / 20:4(8Z,11Z,14Z,17Z)), lysophosphatidylcholine(18:2 / 0:0), α-linolenic acid, 15-deoxy-Δ12,14-prostaglandin J2, 12-ketoeicosatetraenoic acid, 13-hydroxyoctadecadienoic acid, leukotriene A4, lysophosphatidylcholine(22:5(7Z,10Z,13Z,16Z,19Z) / 0:0), lysophosphatidylcholine(18:0 / 0:0), aldosterone, monoglyceride(0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0) or 3α,7α,12α-trihydroxy-5β-cholestan-26-al in the blood biomarker of the model animal indicates a good prognosis for diabetes; or A decrease in the expression level of any one or more of sphingomyelin(d18:1 / 24:1), 15-ketoprostaglandin F2α, 11β-hydroxyprogesterone, lysophosphatidylethanolamine(0:0 / 20:4(5Z,8Z,11Z,14Z)), docosahexaenoic acid, palmitoleic acid or lysophosphatidylcholine(20:0 / 0:0), desogestrel, phosphatidic acid(P-16:0 / 18:2(9Z,12Z)) or cholesterol sulfate indicates a good prognosis for diabetes.

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