Detection system and method based on secondary metabolites in sweating process of salvia miltiorrhiza

Abstract

The invention discloses a detection system and method based on secondary metabolites in the sweating process of salvia miltiorrhiza, and relates to the technical field of detection of the sweating secondary metabolites of salvia miltiorrhiza. Comprising an ultra-high performance liquid phase system; a mass spectrometry detector; an electrospray ion source; the Proteowizard software package is used for converting an original mass spectrum offline file into an mz XML file format; the R XCMS software package is used for peak detection, peak filtration and peak alignment processing to obtain a substance quantitative list; and the R language ropls package is used for performing principal component analysis, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis and dimension reduction analysis on the sample data. According to the invention, an ultra-high performance liquid phase system and a mass spectrum detector ensure high efficiency and high accuracy of analysis; through optimized chromatography and mass spectrometry conditions, a wide variety of metabolites including amino acids, nucleotides, quinones, phenolic acids, flavonoids, organic acids and the like can be detected.

Description

Technical Field

[0001] The present invention relates to the technical field of detection of secondary metabolites of salvia miltiorrhiza sweating, and in particular to a detection system and method based on secondary metabolites in the process of salvia miltiorrhiza sweating. Background Art

[0002] In traditional Chinese medicine, Danshen is one of the commonly used medicinal materials, which has the effects of promoting blood circulation and removing blood stasis, clearing away heat and detoxifying. The quality evaluation and efficacy research of Danshen have always been the focus of traditional Chinese medicine research.

[0003] There are two types of salvia miltiorrhiza, one is "sweating" salvia miltiorrhiza and the other is "non-sweating" salvia miltiorrhiza. They treat similar diseases, but there are also some differences. For example, the cross section of "sweating" salvia miltiorrhiza is black and a little shiny, while non-sweating salvia miltiorrhiza does not have such characteristics; "sweating" salvia miltiorrhiza has a higher content of active ingredients, which can resist oxidation and protect the myocardium, and the treatment effect will be better.

[0004] In recent years, with the development of modern analytical technology, accurate and efficient qualitative and quantitative analysis of metabolites in complex biological samples has become possible. However, how to mine biomarkers related to the quality and efficacy of medicinal materials from a large amount of metabolite data and study the secondary metabolites in the sweating process of Danshen is a problem that needs to be considered.

[0005] After searching, the application scheme of Chinese patent application number CN201810678326.8 discloses a method for detecting danshensu and its metabolites in biological samples, comprising the following steps: (1) treating the biological sample: activating the C18 solid phase extraction column with 1 to 3 column volumes of methanol, and then balancing it with 1 to 3 column volumes of deionized water, then taking 0.2 to 2 column volumes of the biological sample and adding it to the solid phase extraction column, eluting it with 0.5 to 4 column volumes of deionized water and 0.5 to 4 column volumes of methanol in turn, collecting the methanol eluate, blowing it dry, and re-dissolving the residue with 0.02 to 0.2 column volumes of acetonitrile aqueous solution with a volume concentration of 2 vol% to 7 vol%, vortexing, centrifuging, and taking the supernatant as the test solution; wherein the biological sample includes a drug-containing plasma sample and / or a drug-containing urine sample containing danshensu and its metabolites after humans or animals absorb danshensu. The detection method in the above patent is still insufficient in detection sensitivity and accuracy, and the precision and efficiency of data processing need to be improved. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a detection system and method based on secondary metabolites in the sweating process of Salvia miltiorrhiza.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A detection system based on secondary metabolites in the sweating process of Danshen, comprising: Thermo Vanquish ultra-high performance liquid chromatography system using ACQUITY UPLC® HSST3 columns; Thermo Orbitrap Exploris 120 mass spectrometer; Electrospray ion source; Proteowizard software package, used to convert raw mass spectrometry files into mz XML file format; R XCMS software package, used for peak detection, peak filtering, peak alignment processing, and obtaining a quantitative list of substances; The R language ropls package is used to perform principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis on sample data for dimensionality reduction analysis; MetaboAnalyst software package is used to perform functional pathway enrichment and topological analysis on the screened differentially metabolized molecules.

[0008] Preferably, the detection method of the system comprises the following steps: S1: Preparation of test sample; S2: Definition of chromatographic conditions; S3: Definition of mass spectrometry conditions; S4: Data processing and statistical analysis; S5: Pathway analysis; S6: Basic analysis of results; S7: Multivariate statistical analysis of results.

[0009] Preferably: in S1, the preparation of the test sample comprises: S11: Send each prepared sample to the centrifuge for 5 min, 12000 rpm, 20°C to ensure that it falls from the lid to the bottom; S12: Then 100 μL of methanol was added to each microcentrifuge tube for ultrasonic extraction for 60 min; S13: then centrifuge again for 10 min, 12000 rpm, 20°C; S14: Transfer 90 μL of the supernatant into a plastic liner contained in an HPLC grade vial for LC-MS detection.

[0010] Preferably: in said S2, the limitation of chromatographic conditions includes: Thermo Vanquish ultra-high performance liquid chromatography system was used, using an ACQUITY UPLC® HSST3 column, a flow rate of 0.25 mL / min, a column temperature of 40°C, and an injection volume of 2 μL; In positive ion mode, the mobile phase was 0.1% formic acid acetonitrile and 0.1% formic acid water, and the gradient elution program was: 0-1 min, 2% C; 1-9 min, 2%-50% C; 9-12 min, 50%-98% C; 12-13.5 min, 98% C; 13.5-14 min, 98%-2% C; 14-20 min, 2% C; In negative ion mode, the mobile phase was acetonitrile and 5 mM ammonium formate water, and the gradient elution program was: 0-1 min, 2% A; 1-9 min, 2%-50% A; 9-12 min, 50%-98% A; 12-13.5 min, 98% A; 13.5-14 min, 98%-2% A; 14-17 min, 2%.

[0011] Preferably: in S3, the definition of mass spectrometry conditions includes: Thermo Orbitrap Exploris 120 mass spectrometer was used with an electrospray ion source to collect data in positive and negative ion modes respectively; the positive ion spray voltage was 3.50 kV, the negative ion spray voltage was -2.50 kV, the sheath gas was 30 arb, and the auxiliary gas was 10 arb; the capillary temperature was 325°C, the first-level full scan was performed with a resolution of 60,000, the first-level ion scan range was m / z 100-1000, and HCD was used for secondary fragmentation with a collision voltage of 30%, a secondary resolution of 15,000, and the first 4 ions of the collected signal were fragmented. At the same time, dynamic exclusion was used to remove unnecessary MS / MS information.

[0012] Preferably: in S4, data processing and statistical analysis include: The original mass spectrum file was converted into mz XML file format by using the MSConvert tool in the Proteowizard software package; peak detection, peak filtering, and peak alignment were performed using the R XCMS software package to obtain a quantitative list of substances; Public databases such as HMDB, massbank, LipidMaps, mzclound, KEGG and self-built substance libraries were used to identify substances. The LOESS signal correction method based on QC samples was used to correct data and eliminate systematic errors. Substances with RSD>30% in QC samples were filtered out during data quality control. The R language ropls package was used to perform principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis dimensionality reduction analysis on the sample data; score graphs, loading graphs, and S-plot graphs were drawn to show the differences in metabolite composition among the samples; and the permutation test method was used to test the overfitting of the model.

[0013] Preferably: in S5, the pathway analysis includes: The MetaboAnalyst software package was used to perform functional pathway enrichment and topological analysis on the screened differential metabolic molecules; the enriched pathways were visualized using the KEGG Mapper tool to browse the differential metabolites and pathway maps.

[0014] Preferably, in S6, the basic analysis of the results includes: S61: Analysis of instrument system stability investigation results; S62: Total ion current analysis.

[0015] Preferably: in S7, the result multivariate statistical analysis includes: S71: principal component analysis; S72: Overall metabolite clustering heat map analysis; S73: Identification of chemical substances in various tissues; S74: Metabolite pathway analysis.

[0016] Preferably: in S71, the principal component analysis includes: Principal component analysis was used to compare the main contents in different tissues of all Salvia miltiorrhiza samples; supervised methods and partial least squares discriminant analysis were used to study the differences in metabolites in different parts of "sweating" and "non-sweating" Salvia miltiorrhiza.

[0017] The beneficial effects of the present invention are: The present invention uses an ultra-high performance liquid phase system and a mass spectrometer detector to ensure high efficiency and high accuracy of analysis; through optimized chromatographic and mass spectrometric conditions, a wide range of metabolite types can be detected, including amino acids, nucleotides, quinones, phenolic acids, flavonoids and organic acids.

[0018] The present invention utilizes the R XCMS software package for peak detection, filtering and alignment, and the R language ropls package for multivariate statistical analysis such as PCA, PLS-DA, and OPLS-DA, thereby improving the accuracy and reliability of data analysis.

[0019] The present invention introduces QC samples to conduct system stability inspection and data quality assurance to ensure the repeatability and reliability of the results; through the screening of differential metabolites and pathway analysis, potential biomarkers related to the sweating process of Danshen can be discovered, providing a scientific basis for the pharmacological research of Danshen. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a PCA score graph of QC samples in a detection method for secondary metabolites in the sweating process of Salvia miltiorrhiza proposed in the present invention; Figure 2 This is a PCA score diagram of the QA results in a detection method for secondary metabolites in the sweating process of Danshen proposed by the present invention; Figure 3 It is a principal component analysis score diagram of different parts of the sweating and non-sweating Salvia miltiorrhiza samples in the present invention; Figure 4 The hierarchical clustering tree of samples and the heat map of overall metabolite clustering in a detection method of secondary metabolites in the sweating process of Danshen proposed by the present invention; Figure 5 This is a metabolic pathway analysis diagram closely related to the sweating mechanism in a detection method for secondary metabolites in the sweating process of Salvia miltiorrhiza proposed by the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods. Example 1

[0022] A detection system based on secondary metabolites in the sweating process of Danshen, comprising: Thermo Vanquish ultra-high performance liquid chromatography system using ACQUITY UPLC® HSST3 columns; Thermo Orbitrap Exploris 120 mass spectrometer; Electrospray ion source; Proteowizard software package, used to convert raw mass spectrometry files into mz XML file format; R XCMS software package, used for peak detection, peak filtering, peak alignment processing, and obtaining a quantitative list of substances; The R language ropls package is used to perform principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) dimensionality reduction analysis on sample data; MetaboAnalyst software package, used for functional pathway enrichment and topological analysis of screened differentially metabolized molecules; The detection method of the system includes the following steps: S1: Preparation of test sample; S2: Definition of chromatographic conditions; S3: Definition of mass spectrometry conditions; S4: Data processing and statistical analysis; S5: Pathway analysis; S6: Basic analysis of results; S7: Multivariate statistical analysis of results; Wherein, in said S1, the preparation of the test sample comprises: Each prepared sample was sent to a centrifuge for 5 min (12000 rpm, 20°C) to ensure that it fell from the lid to the bottom, and then 100 μL of methanol was added to each microcentrifuge tube for ultrasonic extraction for 60 min, followed by another centrifugation for 10 min (12000 rpm, 20°C). 90 μL of the supernatant was transferred to a plastic liner tube, which was contained by an HPLC-grade vial for LC-MS detection.

[0023] Wherein, in said S2, the limitation of chromatographic conditions includes: Thermo Vanquish (Thermo Fisher Scientific, USA) ultra-high performance liquid chromatography system was used, using an ACQUITY UPLC® HSST3 (2.1×150 mm, 1.8 µm) (Waters, Milford, MA, USA) column, with a flow rate of 0.25 mL / min, a column temperature of 40°C, and an injection volume of 2 μL. In positive ion mode, the mobile phases were 0.1% formic acid in acetonitrile (C) and 0.1% formic acid in water (D), and the gradient elution program was: 0-1 min, 2% C; 1-9 min, 2%-50% C; 9-12 min, 50%-98% C; 12-13.5 min, 98% C; 13.5-14 min, 98%-2% C; 14-20 min, 2% C. In negative ion mode, the mobile phase was acetonitrile (A) and 5 mM ammonium formate water (B), and the gradient elution program was: 0-1 min, 2% A; 1-9 min, 2%-50% A; 9-12 min, 50%-98% A; 12-13.5 min, 98% A; 13.5-14 min, 98%-2% A; 14-17 min, 2%.

[0024] Wherein, in said S3, the limitation of mass spectrometry conditions includes: Thermo Orbitrap Exploris 120 mass spectrometer (Thermo Fisher Scientific, USA) was used with an electrospray ion source (ESI) to collect data in positive and negative ion modes. The positive ion spray voltage was 3.50 kV, the negative ion spray voltage was -2.50 kV, the sheath gas was 30 arb, and the auxiliary gas was 10 arb. The capillary temperature was 325 °C, the primary full scan was performed at a resolution of 60,000, the primary ion scan range was m / z 100-1000, and HCD was used for secondary fragmentation, the collision voltage was 30%, the secondary resolution was 15,000, the first 4 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information.

[0025] Wherein, in said S4, data processing and statistical analysis include: The original mass spectrum file was converted into mz XML file format by using the MSConvert tool in the Proteowizard software package (v3.0.8789). The peak detection, peak filtering, and peak alignment were performed using the R XCMS software package to obtain a quantitative list of substances. The parameters were set as bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdiff=0.01, and method=”centWave”.

[0026] Public databases HMDB, massbank, LipidMaps, mzclound, KEGG and self-built substance libraries were used for substance identification, and the parameter was set to ppm < 30 ppm. The LOESS signal correction method based on QC samples was used to correct data and eliminate systematic errors. Substances with RSD> 30% in QC samples were filtered out during data quality control.

[0027] The R language ropls package was used to perform principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) on the sample data. Score plots, loading plots, and S-plots were drawn to show the differences in metabolite composition among samples. The permutation test method was used to test the overfitting of the model. R2X and R2Y represent the explanatory rate of the constructed model for the X and Y matrices, respectively, and Q2 indicates the predictive ability of the model. The closer their values ​​are to 1, the better the fit of the model, and the more accurately the samples in the training set can be divided into their original attributes. According to the statistical test, the P value was calculated, the variable projection importance (VIP) was calculated by the OPLS-DA dimensionality reduction method, and the fold change was calculated to calculate the component difference fold, measure the influence and explanatory power of each metabolite component content on the sample classification and discrimination, and assist in the screening of marker metabolites. When the P value is <0.05 and the VIP value is >1, the metabolite molecule is considered to be statistically significant.

[0028] Wherein, in said S5, the pathway analysis includes: MetaboAnalyst software package was used to perform functional pathway enrichment and topological analysis on the screened differential metabolites. The enriched pathways were visualized using KEGG Mapper to browse differential metabolites and pathway maps.

[0029] Wherein, in said S6, the basic analysis of the results includes: S61: Analysis of instrument system stability investigation results; S62: Total ion current analysis.

[0030] Among them, in said S61, the analysis of the instrument system stability investigation results includes: In metabolomics research based on mass spectrometry, quality control is required to obtain reliable and high-quality metabolomics data. This experiment uses QC samples for quality control during LC-MS detection. In theory, QC samples are the same, but there will be systematic errors in the sample extraction, detection and analysis process, resulting in differences between QC samples. The smaller the difference, the higher the stability of the method and the better the data quality, which is reflected in Figure 1 The PCA analysis chart shows that QC samples are densely distributed, indicating that the data is reliable.

[0031] In order to discover biomarkers, the relative standard deviation of potential characteristic peaks in QC samples, that is, the coefficient of variation, cannot exceed 30%, and characteristic peaks that do not meet the requirements should be deleted. Therefore, on the basis of quality control, quality assurance is usually performed to delete characteristic peaks with poor weight in QC samples in order to obtain a higher quality data set, which is more conducive to the detection of biomarkers. Figure 2 In the QA result PCA, the proportion of characteristic peaks with RSD < 30% can reach about 65%, indicating that the data is good.

[0032] Wherein, in said S62, the total ion current analysis includes: UPLC-ESI-MS total ion current chromatograms (TIC) of various tissues of "sweating" and "non-sweating" Salvia miltiorrhiza were constructed. The differential metabolites of various tissues of "sweating" and "non-sweating" Salvia miltiorrhiza were concentrated in the retention time stages of 1.0-4.0 min, 5.5-7.5 min, and 11.5-14 min.

[0033] Wherein, in said S7, the result multivariate statistical analysis includes: S71: principal component analysis; S72: Overall metabolite clustering heat map analysis; S73: Identification of chemicals in various tissues; S74: Metabolite pathway analysis.

[0034] Wherein, in said S71, the principal component analysis includes: like Figure 3As shown, principal component analysis (PCA) was used to compare the main contents in different tissues of all Salvia miltiorrhiza samples to further verify the experimental results. According to the PCA scores of different parts of "sweating" and "non-sweating" Salvia miltiorrhiza, the first two components explained 52% (PC1=28%, PC2=24%) of the changes respectively. The data were clearly divided into 10 groups (non-sweating Salvia miltiorrhiza cork layer, non-sweating Salvia miltiorrhiza cortex, non-sweating Salvia miltiorrhiza xylem, mild "sweating" Salvia miltiorrhiza cortex, mild "sweating" Salvia miltiorrhiza xylem, severe "sweating" Salvia miltiorrhiza cortex, severe "sweating" Salvia miltiorrhiza xylem and QC group), indicating that the metabolome of different parts of "sweating" and "non-sweating" Salvia miltiorrhiza was different; The differences in metabolites between different parts of "sweating" and "non-sweating" Salvia miltiorrhiza were investigated by partial least squares discriminant analysis (PLS-DA) using a supervised approach. The R2 calculated values ​​of PCA and the Q values ​​of PLS-DA indicated that there were statistically significant differences between different parts of "sweating" and "non-sweating" Salvia miltiorrhiza. The characteristics of the OPLS-DA model were R2X 0.69, R2Y 0.989, and Q2 0.923, respectively, indicating that the model had good predictive ability and most of the data were verified.

[0035] Wherein, in said S72, the overall metabolite clustering heat map analysis includes: In order to further understand the significant changes in metabolites between the "sweating" and non-"sweating" parts of Danshen, hierarchical clustering analysis was performed using the features of VIP>1 and P<0.05. The results of differential metabolite screening were visualized by heat maps, and cluster analysis was used to determine the metabolic patterns of metabolites under different experimental conditions. The relative values ​​of metabolites under different experimental conditions were used as metabolic levels, and hierarchical clustering analysis was performed. The results are shown in Figure 2. Figure 4 As shown. According to the results of the sample hierarchical clustering tree, the three replicates of each sample were first clustered together, and then the cork layers of non-sweating, mildly sweating, and heavily sweating were clustered together into one category, while the other category of non-sweating xylem and cortex were clustered together and then clustered together with the xylem cortex of Salvia miltiorrhiza after "sweating" treatment. The hierarchical clustering tree accurately separates the cortical xylem from the cork layer, and the hierarchical clustering of "sweating" Salvia miltiorrhiza and non-sweating Salvia miltiorrhiza can be clearly distinguished. The overall metabolite clustering heat map is basically consistent with the results of the sample hierarchical clustering tree, which mainly reflects that the three parts of Salvia miltiorrhiza are divided into two categories, namely, the cortex and xylem are clustered into one category, and the cork layer is a separate category.

[0036] Wherein, in said S73, identifying chemical substances in various tissues comprises: Different degrees of sweating in the same part with different degrees of "sweating" (1-1 vs 2-1 vs 3-1; 1-2 vs 2-2 vs3-2; 1-3 vs 2-3 vs 3-3); different tissues under the same conditions (1-1 vs 1-2 vs 1-3; 2-1 vs 2-2 vs2-3; 3-1 vs 3-2 vs 3-3) 1-1 vs 1-2 screened 29798 differential metabolites, including 5796 up-regulated differential metabolites and 3696 down-regulated differential metabolites. 1-1 vs 1-3 screened 29798 differential metabolites, including 6865 up-regulated differential metabolites and 4743 down-regulated differential metabolites. 1-2 vs 1-3 screened 29798 differential metabolites, including 5652 up-regulated differential metabolites and 5024 down-regulated differential metabolites. 1-2 vs 1-3 screened out 29798 differential metabolites, including 5652 up-regulated differential metabolites and 5024 down-regulated differential metabolites. 1-1 vs 2-1 screened out 29798 differential metabolites, including 5652 up-regulated differential metabolites and 5024 down-regulated differential metabolites.

[0037] Differential metabolites were found from the sample primary substance list, and the P value and VIP threshold preset in the statistical test were used for screening. Compared with the control group, the chemical composition of the "sweating" samples of each group of Danshen changed to a certain extent. It mainly contains amino acids and their derivatives, nucleotides and their derivatives, quinones and phenolic acids. In addition, there are flavonoids and organic acids. A total of 84 differential compounds (up-regulated and down-regulated) before and after Danshen "sweating" were screened out in different "sweating" groups of Danshen. See the table below: The main different compounds before and after Danshen "sweating" serial number Compound Molecular formula Substance Category Mild / no sweating Severe / no sweating 1 L-(-)-Tyrosine <h2 style=";text-align:left;direction:ltr"><![CDATA[C9H <h2 style=";text-align:left;direction:ltr"> 11 <h2 style=";text-align:left;direction:ltr"> NO3]]><h2 style=";text-align:left;direction:ltr"> Amino acids and their derivatives up up 2 L-Valine <![CDATA[C5H 11 NO2]]> Amino acids and their derivatives up up 3 L-Isoleucine <![CDATA[C6H 13 NO2]]> Amino acids and their derivatives up up 4 L-Leucine <![CDATA[C6H 13 NO2]]> Amino acids and their derivatives up up 5 L-Serine <![CDATA[C3H7NO3]]> Amino acids and their derivatives up - 6 L-Asparagine <![CDATA[C4H8N2O3]]> Amino acids and their derivatives down - 7 L-(-)-Threonine <![CDATA[C4H9NO3]]> Amino acids and their derivatives up - 8 L-Glutamine <h2 style=";text-align:left;direction:ltr"><![CDATA[C5H <h2 style=";text-align:left;direction:ltr"> 10 <h2 style=";text-align:left;direction:ltr"> N2O3<h2 style=";text-align:left;direction:ltr"> Amino acids and their derivatives up - 9 L-Methionine <![CDATA[C5H 11 NO2S]]> Amino acids and their derivatives down - 10 L-Proline <![CDATA[C5H7NO3]]> Amino acids and their derivatives up - 11 L-Glutamic Acid <![CDATA[C5H9N3]]> Amino acids and their derivatives up - 12 L-Piperidinic acid <![CDATA[C5H9NO2]]> Amino acids and their derivatives up - 13 L-(-)-Cystine <![CDATA[C5H9NO4]]> Amino acids and their derivatives up - 14 L-(+)-Lysine <![CDATA[C6H 11 NO2]]> Amino acids and their derivatives up - 15 L-(+)-Arginine <![CDATA[C6H 12 N2O4S2]]> Amino acids and their derivatives up - 16 L-Histidine <h2 style=";text-align:left;direction:ltr"><![CDATA[C6H <h2 style=";text-align:left;direction:ltr"> 14 <h2 style=";text-align:left;direction:ltr"> N2O2<h2 style=";text-align:left;direction:ltr"> Amino acids and their derivatives up - 17 Tyramine <![CDATA[C6H 14 N4O2]]> Amino acids and their derivatives up - 18 5-Oxyproline <![CDATA[C6H9N3O2]]> Amino acids and their derivatives up - 19 histamine <![CDATA[C8H 11 NO]]> Amino acids and their derivatives down - 20 N-Acetyl-L-Tyrosine <![CDATA[C 11 H 13 NO4]]> Amino acids and their derivatives up up 21 2'-deoxyguanosine <![CDATA[C 10 H 13 N5O4]]> Nucleotides and their derivatives down down 22 Adenosine <![CDATA[C 10 H 13 N5O4]]> Nucleotides and their derivatives down up 23 Adenosine 3'-monophosphate <h2 style=";text-align:left;direction:ltr"><![CDATA[C <h2 style=";text-align:left;direction:ltr"> 10 <h2 style=";text-align:left;direction:ltr"> H<h2 style=";text-align:left;direction:ltr"> 14 <h2 style=";text-align:left;direction:ltr"> N5O7P]]><h2 style=";text-align:left;direction:ltr"> Nucleotides and their derivatives down up 24 Adenosine 5'-monophosphate <h2 style=";text-align:left;direction:ltr"><![CDATA[C <h2 style=";text-align:left;direction:ltr"> 10 <h2 style=";text-align:left;direction:ltr"> H<h2 style=";text-align:left;direction:ltr"> 14 <h2 style=";text-align:left;direction:ltr"> N5O7P]]><h2 style=";text-align:left;direction:ltr"> Nucleotides and their derivatives up up 25 Adenine <![CDATA[C5H5N5]]> Nucleotides and their derivatives up up 26 Guanine <![CDATA[C5H5N5O]]> Nucleotides and their derivatives down down 27 Guanosine <![CDATA[C 10 H 13 N5O5]]> Nucleotides and their derivatives up up 28 Thymidine <![CDATA[C 10 H 14 N2O5]]> Nucleotides and their derivatives down down 29 Cytosine <![CDATA[C4H5N3O]]> Nucleotides and their derivatives down down 30 Deoxycytidine <h2 style=";text-align:left;direction:ltr"><![CDATA[C9H <h2 style=";text-align:left;direction:ltr"> 13 <h2 style=";text-align:left;direction:ltr"> N3O4<h2 style=";text-align:left;direction:ltr"> Nucleotides and their derivatives down down 31 Cytidine <h2 style=";text-align:left;direction:ltr"><![CDATA[C9H <h2 style=";text-align:left;direction:ltr"> 13 <h2 style=";text-align:left;direction:ltr"> N3O5]]><h2 style=";text-align:left;direction:ltr"> Nucleotides and their derivatives up down 32 Cytidine-5'-monophosphate <h2 style=";text-align:left;direction:ltr"><![CDATA[C9H <h2 style=";text-align:left;direction:ltr"> 14 <h2 style=";text-align:left;direction:ltr"> N3O8P]]><h2 style=";text-align:left;direction:ltr"> Nucleotides and their derivatives up down 33 Carnosine <![CDATA[C 18 H 16 O6]]> Flavonoids down down 34 Tanshinone IIA <h2 style=";text-align:left;direction:ltr"><![CDATA[C <h2 style=";text-align:left;direction:ltr"> 19 <h2 style=";text-align:left;direction:ltr"> H<h2 style=";text-align:left;direction:ltr"> 18 <h2 style=";text-align:left;direction:ltr"> O3]]><h2 style=";text-align:left;direction:ltr"> Quinones down down 36 1,2-Dihydrotanshinone I <h2 style=";text-align:left;direction:ltr"><![CDATA[C <h2 style=";text-align:left;direction:ltr"> 18 <h2 style=";text-align:left;direction:ltr"> H<h2 style=";text-align:left;direction:ltr"> 14 <h2 style=";text-align:left;direction:ltr"> O3]]><h2 style=";text-align:left;direction:ltr"> Quinones down down 37 1-Oxoisocryptotanshinone <![CDATA[C 18 H 14 O5]]> Quinones down down 38 Purple tanshinone A <![CDATA[C 18 H 14 O5]]> Quinones down down 39 Danshendiol C <![CDATA[C 18 H 16 O5]]> Quinones down down 40 Danshendiol B <![CDATA[C 18 H 16 O5]]> Quinones down down 41 1,2,15,16-Tetrahydrotanshinone <![CDATA[C 19 H 20 O2]]> Quinones down down 42 Dehydrodanshinone <![CDATA[C 19 H 20 O2]]> Quinones down down 43 Tanshinone I <h2 style=";text-align:left;direction:ltr"><![CDATA[C <h2 style=";text-align:left;direction:ltr"> 18 <h2 style=";text-align:left;direction:ltr"> H<h2 style=";text-align:left;direction:ltr"> 12 <h2 style=";text-align:left;direction:ltr"> O3]]><h2 style=";text-align:left;direction:ltr"> Quinones down down 44 Danshen aldehyde <![CDATA[C 18 H 12 O5]]> Quinones down down 45 Red Ginseng Quinone A <![CDATA[C 18 H 14 O4]]> Quinones down down 46 Tanshinone IIB <![CDATA[C 19 H 18 O4]]> Quinones down down 47 Neocryptoshinone <![CDATA[C 19 H 22 O4]]> Quinones down down 48 Methyltanshinone <![CDATA[C 20 H 18 O5]]> Quinones down down 49 Red ginseng quinone <![CDATA[C 20 H 20 O5]]> Quinones down down 50 Ganserione A <![CDATA[C 36 H 28 O6]]> Quinones down down 51 Hydroxysesaquinone <![CDATA[C 15 H 14 O5]]> Quinones down down 52 Cryptotanshinone <h2 style=";text-align:left;direction:ltr"><![CDATA[C <h2 style=";text-align:left;direction:ltr"> 19 <h2 style=";text-align:left;direction:ltr"> H<h2 style=";text-align:left;direction:ltr"> 20 <h2 style=";text-align:left;direction:ltr"> O3]]><h2 style=";text-align:left;direction:ltr"> Quinones down down 53 Danshenxin <![CDATA[C 19 H 22 O2]]> Quinones down down 54 A <![CDATA[C 20 H 28 O6]]> Terpenes up up 55 Caffeic acid ethyl ester <![CDATA[C 11 H 12 O4]]> Organic Acid down down 56 Lithospermic acid <![CDATA[C 27 H 22 O 12 ]]> Organic Acid up up 57 Isoferulic acid <![CDATA[C 10 H 10 O4]]> Phenolic acids down down 58 Ferulic acid <![CDATA[C 10 H 10 O4]]> Phenolic acids down down 59 Neochlorogenic Acid <![CDATA[C 16 H 18 O9]]> Phenolic acids down down 60 Chlorogenic acid <![CDATA[C 16 H 18 O9]]> Phenolic acids up up 61 Feruloyl Glucose <![CDATA[C 16 H 20 O9]]> Phenolic acids down up 62 Salvianolic acid F <![CDATA[C 17 H 14 O6]]> Phenolic acids down down 63 Salvianolic acid G <![CDATA[C 20 H 18 O 10 ]]> Phenolic acids up up 64 Salvianolic acid N <![CDATA[C 26 H 22 O 10 ]]> Phenolic acids up up 65 Salvianolic acid A <![CDATA[C 26 H 22 O 10 ]]> Phenolic acids up up 66 Salvianolic acid I <![CDATA[C 27 H 22 O 12 ]]> Phenolic acids up down 67 Salvianic acid methyl ester H / I (iso) <![CDATA[C 28 H 24 O12]]> Phenolic acids up up 68 Salvianic acid methyl ester H / I <![CDATA[C 28 H 24 O 12 ]]> Phenolic acids up up 69 Protocatechuic acid <![CDATA[C7H6O4]]> Phenolic acids down down 70 Danshensu <![CDATA[C9H 10 O5]]> Phenolic acids up up 71 Rosmarinic acid <![CDATA[C 18 H 16 O8]]> Phenolic acids up up 72 Salvianolic acid C <![CDATA[C 26 H 20 O 10 ]]> Phenolic acids up up 73 Salvianolic acid K <![CDATA[C 27 H 24 O 13 ]]> Phenolic acids up down 74 Caffeic acid tetramer <![CDATA[C 36 H 32 O 16 ]]> Phenolic acids up up 75 4-Methoxy-salvianolic acid B <![CDATA[C 37 H 32 O 16 ]]> Phenolic acids up up 35 Protocatechuic aldehyde <![CDATA[C7H6O3]]> Phenolic acids down down 76 Caffeic acid <![CDATA[C9H8O4]]> Phenolic acids down up 77 Dihydrotanshinone <h2 style=";text-align:left;direction:ltr"><![CDATA[C <h2 style=";text-align:left;direction:ltr"> 18 <h2 style=";text-align:left;direction:ltr"> H<h2 style=";text-align:left;direction:ltr"> 14 <h2 style=";text-align:left;direction:ltr"> O3]]><h2 style=";text-align:left;direction:ltr"> Other categories down down 78 Carnosol <![CDATA[C 20 H 26 O4]]> Other categories up up 79 Caffeoyltartaric acid <![CDATA[C 13 H 12 O9]]> Phenolic acids down down 80 Feruloyl Malate <![CDATA[C 14 H 14 O8]]> Phenolic acids up up 81 Feruloyl tartaric acid <![CDATA[C 14 H 14 O9]]> Phenolic acids up up 82 Epicatechin <![CDATA[C 21 H 24 O 11 ]]> Flavanols down down 83 Epicatechin Gallate <![CDATA[C 22 H 18 O 10 ]]> Flavanols up down 84 Epigallocatechin gallate <![CDATA[C 22 H 18 O 11 ]]> Flavanols down down Compared with the non-sweating group, 36 differential metabolites (L-tyrosine, adenine, lithospermic acid, chlorogenic acid, salvianolic acid A, danshensu, rosmarinic acid, salvianolic acid C, etc.) increased, and 39 differential metabolites (L-asparagine, 2'-deoxyguanosine, tanshinone IIA, isoferulic acid, protocatechuic aldehyde, etc.) decreased in the "sweating" group; in addition, 4 metabolites (adenosine, adenosine 3'-monophosphate, feruloyl glucose, and caffeic acid) decreased when danshen sweated mildly, and increased when sweating severely; and 5 metabolites (cytidine, cytidine-5'-monophosphate, salvianolic acid I, salvianolic acid K, and epicatechin gallate) increased when danshen sweated mildly, and decreased when sweating severely.

[0038] The results showed that after Salvia miltiorrhiza "sweated", the contents of components such as tanshinone acid, small molecule phenolic acid, and organic acid increased, while the contents of tanshinone components such as tanshinone IIA decreased. The reason may be that the large molecular compounds were decomposed into small molecular compounds during the "sweating" process.

[0039] Wherein, in said S74, metabolite pathway analysis includes: KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database that systematically analyzes gene function and genomic information by treating genes and their expression information as an overall network. MetaboAnalyst5.0 website function was used to analyze metabolic pathways. The relative response values ​​of metabolic pathways were obtained based on the relative response values ​​of metabolites identified in metabolic pathways. The correlation coefficients between metabolic pathways were calculated and a metabolic pathway association network diagram was drawn to obtain more information on potential biomarkers. In this study, metabolic pathways with response values ​​greater than 0.05 and higher significance levels (-log p) were considered to be important metabolic pathways. Figure 5 , (Note: The dots represent the number of corresponding metabolic molecules on the pathway. The color is related to the P value. The redder the color, the smaller the P value, and the bluer the color, the larger the P value). The results showed that five major metabolic pathways may be closely related to the "sweating" mechanism. The first category is the amino acid anabolic pathway, which mainly involves the biosynthesis of tyrosine, lysine, phenylalanine, arginine and tryptophan; the second category is the amino acid metabolic pathway, which mainly involves the metabolism of tryptophan, tryptophan, alanine, histidine, arginine, proline, aspartic acid, glutamate and glutathione; the third category is the carboxylic acid metabolic pathway, which mainly includes linoleic acid metabolism, citric acid cycle (TCA cycle), taurine and hypotaurine metabolism, glyoxylic acid and dicarboxylic acid metabolism, C5-branched dicarboxylic acid metabolism, ascorbic acid and aldonic acid metabolism; the fourth category is the sugar metabolic pathway, such as galactose metabolism; the fifth category is the plant hormone signal transduction pathway. The results of metabolic pathway analysis showed that amino acid metabolism is the main metabolic pathway of compounds in the "sweating" process of Salvia miltiorrhiza, and plant hormone signal transduction, sugar metabolism and carboxylic acid metabolism are necessary conditions for plants to survive.

[0040] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A detection system for secondary metabolites in the sweating process of Danshen, characterized in that: include: Ultra-high performance liquid phase system; Mass spectrometer detector; Electrospray ion source; Proteowizard software package, used to convert raw mass spectrometry files into mz XML file format; R XCMS software package, used for peak detection, peak filtering, peak alignment processing, and obtaining a quantitative list of substances; The R language ropls package is used to perform principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis on sample data for dimensionality reduction analysis; MetaboAnalyst software package is used to perform functional pathway enrichment and topological analysis on the screened differentially metabolized molecules.

2. A detection system for secondary metabolites in the sweating process of Danshen according to claim 1, characterized in that: The detection method of the system comprises the following steps: S1: Preparation of test sample; S2: Definition of chromatographic conditions; S3: Definition of mass spectrometry conditions; S4: Data processing and statistical analysis; S5: Pathway analysis; S6: Basic analysis of results; S7: Multivariate statistical analysis of results.

3. A detection system for secondary metabolites in the sweating process of Danshen according to claim 2, characterized in that: In S1, the preparation of the test sample includes: S11: Send each prepared sample to the centrifuge for 5 min, 12000 rpm, 20°C to ensure that it falls from the lid to the bottom; S12: Then 100 μL of methanol was added to each microcentrifuge tube for ultrasonic extraction for 60 min; S13: then centrifuge again for 10 min, 12000 rpm, 20°C; S14: Transfer 90 μL of the supernatant into a plastic liner contained in an HPLC grade vial for LC-MS detection.

4. A detection system for secondary metabolites in the sweating process of Danshen according to claim 2, characterized in that: In said S2, the limitation of chromatographic conditions includes: Thermo Vanquish ultra-high performance liquid chromatography system was used, using an ACQUITY UPLC® HSST3 column, a flow rate of 0.25 mL / min, a column temperature of 40°C, and an injection volume of 2 μL; In positive ion mode, the mobile phase was 0.1% formic acid acetonitrile and 0.1% formic acid water, and the gradient elution program was: 0-1 min, 2% C; 1-9 min, 2%-50% C; 9-12 min, 50%-98% C; 12-13.5 min, 98% C; 13.5-14 min, 98%-2% C; 14-20 min, 2% C; In negative ion mode, the mobile phase was acetonitrile and 5 mM ammonium formate water, and the gradient elution program was: 0-1 min, 2% A; 1-9 min, 2%-50% A; 9-12 min, 50%-98% A; 12-13.5 min, 98% A; 13.5-14 min, 98%-2% A; 14-17 min, 2%.

5. A detection system for secondary metabolites in the sweating process of Danshen according to claim 2, characterized in that: In S3, the mass spectrometry conditions are limited to: Thermo Orbitrap Exploris 120 mass spectrometer was used with an electrospray ion source to collect data in positive and negative ion modes respectively; the positive ion spray voltage was 3.50 kV, the negative ion spray voltage was -2.50 kV, the sheath gas was 30 arb, and the auxiliary gas was 10 arb; the capillary temperature was 325°C, the first-level full scan was performed with a resolution of 60,000, the first-level ion scan range was m / z 100-1000, and HCD was used for secondary fragmentation with a collision voltage of 30%, a secondary resolution of 15,000, and the first 4 ions of the collected signal were fragmented. At the same time, dynamic exclusion was used to remove unnecessary MS / MS information.

6. A detection system for secondary metabolites in the sweating process of Danshen according to claim 2, characterized in that: In S4, data processing and statistical analysis include: The original mass spectrum file was converted into mz XML file format by using the MSConvert tool in the Proteowizard software package; peak detection, peak filtering, and peak alignment were performed using the R XCMS software package to obtain a quantitative list of substances; Public databases such as HMDB, massbank, LipidMaps, mzclound, KEGG and self-built substance libraries were used to identify substances. The LOESS signal correction method based on QC samples was used to correct data and eliminate systematic errors. Substances with RSD>30% in QC samples were filtered out during data quality control. The R language ropls package was used to perform principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis dimensionality reduction analysis on the sample data; score graphs, loading graphs, and S-plot graphs were drawn to show the differences in metabolite composition among the samples; and the permutation test method was used to test the overfitting of the model.

7. A detection system for secondary metabolites in the sweating process of Danshen according to claim 2, characterized in that: In S5, the pathway analysis includes: The MetaboAnalyst software package was used to perform functional pathway enrichment and topological analysis on the screened differential metabolic molecules; the enriched pathways were visualized using the KEGG Mapper tool to browse the differential metabolites and pathway maps.

8. A detection system for secondary metabolites in the sweating process of Danshen according to claim 2, characterized in that: In S6, the basic analysis of the results includes: S61: Analysis of instrument system stability investigation results; S62: Total ion current analysis.

9. A detection system for secondary metabolites in the sweating process of Danshen according to claim 2, characterized in that: In S7, the multivariate statistical analysis of the results includes: S71: principal component analysis; S72: Overall metabolite clustering heat map analysis; S73: Identification of chemical substances in various tissues; S74: Metabolite pathway analysis.

10. A detection system for secondary metabolites in the sweating process of Danshen according to claim 9, characterized in that: In S71, the principal component analysis includes: Principal component analysis was used to compare the main contents in different tissues of all Salvia miltiorrhiza samples; supervised method and partial least squares discriminant analysis were used to study the differences in metabolites in different parts of "sweating" and "non-sweating" Salvia miltiorrhiza.

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