Basic component of Chinese herbaceous peony bark fermentation liquor capable of exerting anti-inflammatory efficacy and analysis method

Abstract

The invention discloses a component analysis method of a Chinese herbaceous peony cork fermentation liquor material basis for exerting anti-inflammatory efficacy, and belongs to the technical field of biotechnology and environmental protection. The method comprises the following steps: identifying and analyzing metabolites before and after fermentation of the Chinese herbaceous peony suppository skin; a non-targeted metabonomics method based on a UHPLC-ESI-QE-Orbitrap-MS / MS technology is adopted to analyze products before and after PLC fermentation, then Compressed Discovery 3.3 software is utilized to extract characteristic peaks of sample metabolites, and the characteristic peaks of the sample metabolites are analyzed according to the mass-to-charge ratio (error lt; the method comprises the following steps: comparing (10 ppm), retention time, splitting decomposition mode and collision energy, identifying metabolite structures, carrying out classified counting on metabolites in a PLC sample so as to clarify the classification of fermentation products, and quantifying the difference of metabolite composition before and after fermentation under an unsupervised model through principal component analysis (PCA) so as to clarify the sample clustering effect and separation trend before and after fermentation of the Chinese herbaceous peony suppository skin. The invention is applied to a component analysis method of a material basis for exerting an anti-inflammatory drug effect.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology and environmental protection technology, and specifically relates to a component analysis method for the material basis of anti-inflammatory efficacy of peony cork fermentation liquid. Background Art

[0002] Paeonia lactiflora Pall. is a plant of the genus Paeonia in the family Paeoniaceae. Its dried root without cork is called white peony, which has the effects of anti-arthritis, treatment of systemic lupus erythematosus, liver protection, muscle growth, etc. In the process of processing peony into white peony, a large amount of by-product, namely peony cork, is produced, which also contains rich monoterpenoid glycoside compounds, but this part is often directly discarded and not effectively utilized, resulting in a large amount of resource waste. Therefore, how to increase the utilization rate of peony resources and effectively utilize the by-product peony cork is of great significance to reducing the waste of peony resources and promoting the sustainable development of traditional Chinese medicine.

[0003] Traditional Chinese medicine fermentation is one of the methods to improve the activity of traditional Chinese medicine and increase the utilization of traditional Chinese medicine. Microorganisms secrete various extracellular enzymes that can effectively destroy the cell walls of plants, decompose dense structural components such as cellulose, hemicellulose and pectin in the cell walls, provide channels for the transport and diffusion of substances inside and outside the cells, and improve the absorption, utilization and extraction rate of effective ingredients. In addition, the fermentation strain is inexpensive, the fermentation process is simple, and no organic solvents are introduced into the system, so the overall cost is low. It also greatly increases the added value of waste cork and effectively reduces environmental pollution, which is in line with the concept of green development.

[0004] Due to the complex composition of Chinese medicine, and the synergistic effect of the active ingredients, the research on the effective material basis of Chinese medicine faces certain obstacles. The traditional method of studying Chinese medicine is to use various chromatography and other techniques to study the chemical components in Chinese medicine and its compound, and detect its corresponding activity in vitro, so as to clarify the active substance of Chinese medicine, but this method is relatively cumbersome, and ignores the metabolic absorption process of the drug in the body, and can not fully illustrate the effective material basis of Chinese medicine. And Chinese medicine serum drug chemistry can more directly reflect the relationship between material basis and efficacy and effect, making the experiment more real and reliable, so the present invention will analyze the blood migration components of peony cortex fermented liquid by Chinese medicine serum drug chemistry, explore the material basis of the anti-inflammatory effect of peony cortex fermented liquid, and provide certain reference for the quality evaluation standard of peony cortex fermented liquid. Summary of the invention

[0005] The present invention provides a fermentation process for peony cork which is a byproduct, and determines the material basis for the anti-inflammatory effect of peony cork, and provides a method for screening the material basis of the efficacy of peony cork fermentation liquid, which is fast, simple, accurate, low-cost, low-cost and has good versatility, and provides a reference for the anti-inflammatory effect and quality evaluation standard of peony cork fermentation liquid, and has excellent market application prospects and economic potential.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] The object of the present invention is to provide a component analysis method for the material basis of the anti-inflammatory efficacy of the fermented broth of peony root cork, comprising the following steps:

[0008] S1: Fermentation of Peony Cork: Peony Cork was crushed and sieved, mixed with distilled water, and sterilized by high pressure to obtain a fermentation substrate; the strain was inoculated, fermented, and centrifuged through a 0.22 μm filter membrane to obtain a PLC fermentation liquid;

[0009] S2: HPLC method was used to determine the content of total peony glucosides (TGP);

[0010] S3: Determination of the external antioxidant capacity of the fermented liquid of Peony Cork;

[0011] S4: Anti-inflammatory effect of peony root cork fermentation liquid;

[0012] S5: Identification and analysis of metabolites of peony cork before and after fermentation;

[0013] The products before and after PLC fermentation were analyzed using a non-targeted metabolomics method based on UHPLC-ESI-QE-Orbitrap-MS / MS technology;

[0014] Compound Discoverer 3.3 software was then used to extract the characteristic peaks of sample metabolites, and the metabolite structures were identified by comparing the mass-to-charge ratio (error <10 ppm), retention time, fragmentation pattern, and collision energy with those of standard compounds and databases;

[0015] Metabolites in PLC samples were classified and counted to clarify the classification of fermentation products;

[0016] The principal component analysis (PCA) was used to quantify the differences in metabolite composition before and after fermentation under an unsupervised model to clarify the clustering effect and separation trend of samples of Peony Root Cork before and after fermentation.

[0017] Orthogonal partial least squares discriminant analysis (OPLS-DA) analysis was performed on the samples of Peony Cork before and after fermentation to screen the differential metabolites during the fermentation process;

[0018] The significantly different metabolites among different groups were screened and compared based on the VIP value and P value of the OPLS-DA model to obtain the differential metabolites during the fermentation of peony cork.

[0019] Analysis of differential metabolites before and after fermentation of peony cork. Pathway enrichment analysis of differential metabolites in the fermentation process of peony cork was performed using the KEGG database.

[0020] EC of metabolites and antioxidant capacity of Peony Root Cortex 50 Value, RP 0.5AU The correlation analysis was performed between the values ​​and the levels of inflammatory factors in mouse serum to clarify the relationship between metabolites and anti-inflammatory ability during the fermentation of peony root cork.

[0021] S6: Collect base peak chromatograms of drug-containing serum and blank serum samples under positive and negative ion modes, and then analyze the serum samples of Paeonia lactiflora cortex fermented liquid according to the analytical method under the data processing method, combined with the database and references, to identify the migratory components in the bleeding, that is, to obtain the components that form the material basis for the anti-inflammatory effect of Paeonia lactiflora cortex fermented liquid.

[0022] It is further defined that in step S1, the strain used is Saccharomyces cerevisiae.

[0023] It is further defined that in step S1, the fermentation time is 52 hours, the inoculation amount is 4%, and the material-liquid ratio is 1:21.

[0024] Further defined, the TGP content of the sample is calculated according to the following formula:

[0025]

[0026] Further, in step S3, the external antioxidant capacity includes the total reducing power, ·OH - Clearance rate, DPPH clearance rate, ABTS + Clearance rate.

[0027] Further defined, in step S4, the modeling results and determination of acute inflammation in mice induced by lipopolysaccharide; the effect of peony cortex fermented liquid on the organ coefficient of inflammatory mice; histopathological observation; the effect of peony cortex fermented liquid on the cytokine level of inflammatory mice is further defined, in step S5, a total of 106 compounds were identified before and after PLC fermentation, of which positive ions (ESI + ) peaks, 61 negative ions (ESI - ) 45 peaks.

[0028] Further defined, in step S6, gradient elution: the chromatographic column is a Thermo Fisher Science HypersilGold AQ C18 column (10 0×2.1 mm, 1.9 μm); the column temperature is room temperature; the injection volume is 5 μL; the mobile phase A is 0.1% (v / v) formic acid in water, the mobile phase B is 0.1% (v / v) formic acid in acetonitrile, and the flow rate is 0.30 mL·min -1， The elution procedure is as follows:

[0029]

[0030] It is further defined that in step S6, the components of the anti-inflammatory drug substance of the fermented broth of peony root cork are paeonol, benzoic acid, paeoniflorin, paeoniflorin, protocatechuic aldehyde, kaempferol, quercetin, peony metabolite II, C 10 H 14 O 3 Sulfate, peony metabolin I glucuronide, 4-O-methylgallic acid.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The method of the invention has the advantages of being rapid, simple, accurate, low in consumption, low in cost and good in versatility.

[0033] In order to further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the attached drawings are only provided for reference and illustration and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a mouse liver pathological section (10×10), Note: The black arrow in the figure points to the mouse central vein, A blank group, B model group, C positive group, D PLC-U group, E PLC-F low-dose group, F PLC-F medium-dose group, G PLC-F high-dose group;

[0035] Figure 2 are mouse lung pathological sections (10×10), Note: The black arrows in the figure point to the infiltrating inflammatory cells, and the red arrows point to the broken alveolar septa, A blank group, B model group, C positive group, D PLC-U group, E PLC-F low-dose group, FPLC-F medium-dose group, G PLC-F high-dose group;

[0036] Figure 3 This is a bar graph of the cytokine levels of mice in each drug group. Note: Same as Table 3-7. ANO level, B IL-1β level, C IL-6 level, D TNF-α level;

[0037] Figure 4 It is the total ion current chromatogram of the quality control sample in positive and negative ion modes, A-negative ion mode, B-positive ion mode;

[0038] Figure 5 This is the PCA score graph of samples from different parts of Peony Cork before and after fermentation;

[0039] Figure 6 This is the OPLS-DA score diagram of the pairwise comparison of the peony cork before and after fermentation;

[0040] Figure 7 It is the differential metabolite of Peony Bark before and after fermentation;

[0041] Figure 8 It is the volcano diagram and difference bar diagram of metabolites before and after fermentation of peony cork;

[0042] Fig. 9 This is the heat map of the differences between the bark of peony root before and after fermentation;

[0043] Fig.10 It is a bubble diagram of the topological analysis of metabolic pathways in different fermentation parts of peony root;

[0044] Fig.11 is the Pierce correlation between metabolites and anti-inflammatory ability during the fermentation of Paeonia lactiflora cortex;

[0045] Fig.12 These are the base peak chromatograms of drug-containing serum and blank serum samples in positive and negative ion modes, A in negative ion mode, B in positive ion mode; DETAILED DESCRIPTION

[0046] Experimental methods

[0047] 1Peony root fermentation method

[0048] Paeonia lactiflora Cork (PLC) was obtained by boiling the root of Paeonia lactiflora in boiling water for 30 seconds, peeling the cork, and drying it at 50°C. The sample was crushed and sieved through a 60-mesh sieve, then mixed with distilled water at a ratio of 1:21 (w / v), and sterilized at 121°C for 20 minutes to obtain the fermentation substrate. The fermentation substrate of Paeonia lactiflora Cork was inoculated with the yeast liquid of Capsella fusca at a 4% inoculation rate, and fermented under the condition of 52 hours. The fermentation liquid was centrifuged through a 0.22μm filter membrane to obtain the PLC fermentation liquid (PLC-F). In order to maintain consistency, the bacterial liquid was taken from the same culture bottle, and the unfermented PLC sample liquid (PLC-U) on day 0 was taken as a blank for comparison.

[0049] 2HPLC determination of total glycosides of paeonia lactiflora

[0050] 2.1 Sample processing

[0051] Accurately pipette the fermentation liquid of "2.2.1.3" (equivalent to about 0.4g crude drug), evaporate it in an evaporating dish, add 10mL of 1% NaOH solution to dissolve it, take 0.5mL of the sample solution and place it in a 10mL volumetric flask, add 8mL of mobile phase, shake well, and then add 1mol·L -1 Adjust the pH of the hydrochloric acid solution to 3-6, make up to the mark with the mobile phase, shake well, and the test solution is obtained.

[0052] 2.2 Chromatographic conditions and system suitability test

[0053] Ilite C18 (250 mm × 4.6 mm, 5 μm) chromatographic column; mobile phase A was 0.05 mol·L -1 Potassium dihydrogen phosphate-acetic acid (350:8), phase B is methanol-isopropanol (100:6), A:B=70:30; detection wavelength 230nm; flow rate 1mL·min -1 ; Column temperature 35℃.

[0054] 2.3 Drawing of standard curve

[0055] Accurately weigh 1.00 mg of benzoic acid standard to make 10 μg mL -1 Accurately pipette 1, 3, 5, 10, and 20 μL of the reference solution into the HPLC to determine the peak area. Draw a standard curve with the peak area integral as the ordinate and the reference sample injection volume (μg) as the abscissa.

[0056] 2.4 Determination of TGP content in the fermented broth of peony bark

[0057] Since the common structural feature of peony total glycosides is that they contain benzoyl groups and have the characteristics of generating benzoic acid after alkaline hydrolysis, this experiment selected alkaline hydrolysis-HPLC method, and adopted external standard method to use benzoic acid as standard to quantitatively measure the content of peony total glycosides in the sample. Take 1mL of the fermentation sample solution of each strain in "2.2.1.3", pass it through a 0.22μm microporous membrane, and perform HPLC analysis according to the chromatographic conditions of "2.2.2". Measure the peak area, substitute it into the above linear equation, calculate the benzoic acid content in the sample, and calculate the TGP content of the sample according to the following formula, where 480.27 is the molecular weight of paeoniflorin and 122.12 is the molecular weight of benzoic acid.

[0058]

[0059] 2.5 Methodological Review

[0060] ①Precision investigation: Accurately pipette one portion of fermentation liquid, inject it 6 times continuously under the chromatographic conditions of "2.2.2.2", record the peak area and retention time, and calculate its RSD value.

[0061] ② Repeatability study: Prepare 6 portions of fermentation broth according to the preparation method in "2.2.1.3", repeat the injection under the above chromatographic conditions, determine the content of monoterpene glycosides in the samples and record the retention time, and determine its RSD value.

[0062] ③ Stability study: Take 1 mL of fermentation liquid, filter it through a 0.22 μm microporous membrane, and measure it at 0, 2, 4, 6, 8, and 24 h according to the above chromatographic conditions. Calculate the content and its RSD value.

[0063] ④ Sample recovery rate: Take 3 portions of fermentation broth with known content, add a certain amount of standard solution at low, medium and high levels. Perform the measurement under the above chromatographic conditions, measure the peak area of ​​each sample, substitute it into the linear equation, calculate the TGP content in each sample and calculate the sample recovery rate.

[0064] 3 Determination of the in vitro antioxidant capacity of the fermented liquid of peony bark

[0065] 3.1 Determination of total reducing power

[0066] Take different test tubes and add 2.0mL of different concentrations of PLC sample solutions before and after fermentation, and then add 2.0mL of phosphate buffer (pH=7.2) and 1% potassium ferricyanide solution in sequence. Mix well, water bath at 50℃ for 20min, then add 2.0mL of 10% trichloroacetic acid, let stand for 10min; rotate at 3000r·min -1 Centrifuge for 10 min under the same conditions, take 2.0 mL of supernatant, add 2.0 mL of pure water and 0.4 mL of ferric chloride in sequence, react for 10 min, and measure the absorbance at a wavelength of 700 nm.

[0067] 3.2 OH- scavenging capacity determination

[0068] Add 2mL of 6mmol·L -1 FeSO 4 solution, 2 mL of different concentrations of PLR and PLC sample solutions before and after fermentation, and 2 mL of 6 mmol·L -1 H 2 O 2 The solution was reacted in a water bath at 37°C for 10 min, and then 2 mL of 6 mmol·L -1 Place the salicylic acid-ethanol solution in a 37°C water bath for 30 minutes and measure its absorbance at a wavelength of 510 nm. For blank control, add 2 mL of distilled water to the test tube instead of the sample solution.

[0069] OH - The clearance rate was calculated as follows:

[0070]

[0071] Where: P%-OH - Clearance rate; A 0 -Absorbance value of blank control group; A s -The absorbance value of the sample solution before and after PLR and PLC fermentation; A x -Absorbance without addition of salicylic acid solution.

[0072] 3.3DPPH free radical scavenging rate determination

[0073] After the test solution is operated according to the data in Table 2-2, it is shaken evenly and placed in the dark at 37°C for 30 minutes. With anhydrous ethanol as a blank control, the absorbance value is measured at a wavelength of 517nm. According to the absorbance, the DPPH scavenging rate is calculated using the following formula. The calculation formula for DPPH free radical scavenging rate is as follows:

[0074]

[0075] Where: P%-DPPH·free radical scavenging rate; A 0 -Blank absorbance value; A 1 -Sample absorbance value; A 2 - Sample blank absorbance.

[0076] Table 1 Determination method for scavenging DPPH free radicals

[0077]

[0078] 3.4 ABTS+ clearance determination method

[0079] 0.8 mL of ABTS + The working solution was mixed with 0.2 mL of anhydrous ethanol and 0.2 mL of different concentrations of PLC sample solutions before and after fermentation, respectively, and oscillated for 10 seconds. After standing for 6 minutes, the absorbance was measured at 734 nm.

[85] .

[0080] ABTS + The clearance rate was calculated as follows:

[0081]

[0082] Where: P%-ABTS + Clearance rate; A 0 -Absorbance value after adding anhydrous ethanol; A x -Absorbance value of added sample solution.

[0083] 3.5 Statistical analysis

[0084] SPSS29.0 software was used to process the antioxidant clearance rate and total reducing power data. The quantitative data were expressed in the form of mean ± standard deviation (X ± S, n = 3), and the data were subjected to one-way analysis of variance. P < 0.05 was considered to have a significant difference, and when P < 0.01, it indicated that the data had an extremely significant difference. In order to further compare the changes in the antioxidant capacity of PLR and PLC before and after fermentation, Graphpad prism 8.0 was used to calculate the EC of the samples. 50 (the effective mass concentration of the sample at which the antioxidant index clearance rate reaches 50%) and RP 0.5AU (The mass concentration of the sample when the absorbance of the reaction solution reaches 0.5 in the total reducing power determination).

[0085] 4 Study on the anti-inflammatory effect of peony root and its cork fermentation liquid on lipopolysaccharide-induced acute inflammation model in mice

[0086] 4.1 Animal grouping

[0087] The mice were randomly divided into 11 groups, with 10 mice in each group and named according to the sample numbers, including blank control group (Blank), model group (Model), positive control group (Control), unfermented peony cortex group (PLC-U), low-dose peony cortex fermented liquid group (PLC-FS), medium-dose peony cortex fermented liquid group (PLC-FM), and high-dose peony cortex fermented liquid group (PLC-FL).

[0088] 4.2 Animal Dosage

[0089] The Blank and Model groups were gavaged with 0.9% saline solution; the Control group was gavaged with dexamethasone dissolved in saline at a dose of 5 mg kg -1 ·d -1 ; The low, medium and high dose groups of PLC-F were 0.91, 1.82 and 3.64 g·kg -1 ·d -1 .

[0090] 4.3 Modeling and drug administration

[0091] The mice in each group were gavaged continuously for 7 days. All mice were fasted but not watered for 12 hours before the last administration. One hour after the last administration, mice in all groups except the Blank group were intraperitoneally injected with LPS (5 mg kg -1 ), and the Blank group was intraperitoneally injected with an equal volume of saline. Mice were killed 6 hours after intraperitoneal injection of LPS, and samples were collected for further analysis.

[0092] 4.4 Organ removal and organ index determination

[0093] Acute inflammation induced by intraperitoneal injection of LPS may be a systemic inflammation that attacks the respiratory and immune systems, leading to lung damage and local hepatocyte necrosis.

[86] Therefore, the mice were killed by cervical dislocation and then dissected to remove their lungs and livers, which were washed in 0.9% saline and the surface moisture was absorbed with filter paper. The weight was recorded with an analytical balance and the organ index was determined using the following formula.

[0094]

[0095] Where: W-organ index; m 0 - Mouse weight; m 1 - Mouse organ weight

[0096] 4.5 Histopathological observation

[0097] The liver and lung of mice were fixed in 10% formalin solution, embedded in paraffin, and stained with HE. The tissue changes were observed under a microscope.

[0098] 4.6 Determination of mouse serum biochemical indicators

[0099] After the mice were killed by dislocation, blood was collected from the eyeballs and placed in EP tubes. The supernatant was collected and the changes in serum NO, IL-1β, IL-6 and TNF-α levels were detected using an ELISA instrument according to the instructions of the kit.

[0100] 4.7 Statistical analysis

[0101] SPSS 29.0 software was used to process the data involved in the above experiments, and the measurement data were expressed as mean ± standard deviation. The data were expressed in the form of (n=3) and subjected to T test or one-way analysis of variance. P<0.05 was considered to have significant difference, and P<0.01 indicated that the data had extremely significant difference.

[0102] 5 Metabolomics study on the fermentation liquid of Peony Root Cork

[0103] 5.1 Preparation of test samples

[0104] Take 5 mL of each of PLR-U, PLR-F, PLC-U, and PLC-F prepared under "2.2.1.3", evaporate to dryness at 50°C, add to 5 mL of pre-cooled acetonitrile / water solution (1:1, V / V) and vortex mix. Ultrasonicate at 4°C for 30 min (40 KHz), centrifuge the sample (12000 × g, 4°C) for 20 min, take the supernatant for detection; in addition, take 100 μL of the supernatant of each sample and mix as a quality control (QC) sample.

[0105] 5.2 UPLC-MS / MS detection conditions

[0106] 5.2.1 Chromatographic conditions

[0107] The Dionex Ultimate 3000 HPLC from Thermo Fisher Scientific, USA, consists of a vacuum exhaust system, a pump system, a sample tray with a temperature control system, an automatic sampler, a chromatographic column and a column temperature control system, and a DAD detector. The pre-column is Thermo Scientific Hypersil GOLD aQ (2.1 mm × 10 mm, 1.9 μm); the chromatographic column is ThermoFisher Science Hypersil Gold AQ C18 column (10 0 × 2.1 mm, 1.9 μm); the column temperature is room temperature; the injection volume is 5 μL; the mobile phase A is water containing 0.1% formic acid (v / v), and the mobile phase B is acetonitrile containing 0.1% formic acid (v / v). The gradient elution program is shown in Table 2.

[0108] Table 2 Gradient elution program

[0109]

[0110] 5.2.2 Mass spectrometry conditions

[0111] The high-resolution mass spectrum parameters were set as follows: sheath gas flow rate, 30 psi; auxiliary gas pressure, 10 psi; sweep gas flow rate, 0 psi; capillary voltage, 3.5 kV; capillary temperature, 320 °C; Aux gas heater temperature, 350 °C; collision gas, nitrogen; normalized collision energy, 20, 40, 60 eV; s-lens 60.0. Combined with the selection of a full mass spectrometry scan to automatically trigger a secondary mass spectrometry scan mode (FullMS-DDMS 2 ). The resolution conditions were: primary and secondary resolutions were 70000 FWHM / 17500 FWHM, respectively; ion scanning range, m / z 50-1500; cycle count 3 times; fourth-level isolation window, 1.5 m / z; dynamic exclusion time was 5 s.

[0112] 5.3 Metabolite identification

[0113] UHPLC-MS Raw data were processed by Compound Discoverer 3.3 software (Thermo Fisher Scientific, Waltham, MA, USA), and the non-targeted metabolomics workflow was used for peak alignment, peak detection, feature filtering, and metabolite annotation. Peak alignment parameters mainly included retention time (RT) and mass tolerance, which were set to 0.2 min and 5 ppm, respectively. A signal-to-noise ratio (S / N) of 1.5 and a peak intensity threshold of 10 000 were used for peak detection. Metabolites were inferred and annotated using ChemSpider atabase, mzCloud, and in-house databases by comparing theoretical mass spectrometry data with detected mass spectrometry data.

[0114] 5.4 Data Analysis

[0115] 5.4.1 Analysis of differential metabolites

[0116] The obtained quantitative data were imported into MetaboAnalyst (https: / / www.metaboanalyst.ca / MetaboAnalyst) for centralization and automatic scaling to reduce the discrimination between high-abundance and low-abundance chromatographic peaks. Then, multivariate statistical analysis was performed. Principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and heat maps were generated using MetaboAnalyst and R language (version 4.2.2). 2 Y≥0.5 and Q 2 When ≥0.5, the model evaluation obtained by OPLS analysis was statistically significant. The values ​​of important predictors (Variables Important for Prediction, VIP) were calculated to identify characteristic metabolites that changed with the variables. Metabolites with VIP>1.0 and P<0.05 were used as differential metabolites that could distinguish between taxa.

[0117] 5.4.2 Metabolic pathway analysis

[0118] The metabolic pathways of differential metabolites were annotated using the KEGG database (https: / / www.kegg.jp / ) to obtain the metabolic pathways that were mainly affected by fermentation.

[0119] 5.4.3 Pierce correlation analysis between differential metabolites and anti-inflammatory activity

[0120] The R language was used to analyze the peak areas of the differential metabolites before and after fermentation of peony cork and the inflammation-related index, namely the EC of the free radical scavenging rate in the antioxidant activity. 50 value, total reduction power RP 0.5AUThe Pierce correlation analysis was performed on the values ​​and levels of inflammatory factors (NO, IL-1β, IL-6 and TNF-α) to interpret the relationship between the differential metabolites and anti-inflammatory activity.

[0121] 6Study on the anti-inflammatory efficacy of the fermented broth of Peony Root

[0122] 6.1 Preparation of drugs

[0123] Take the fermented broth of peony root cork, evaporate it at 50℃, and add appropriate amount of physiological saline to make it equivalent to 3.64g·kg before administration. -1 of intragastric solution.

[0124] 6.2 Preparation of serum samples

[0125] ICR mice were adaptively fed for one week and randomly divided into a blank group and a PLC-F group, with 6 mice in each group. The drugs were continuously administered for 7 days. The blank group was gavaged with distilled water as a control. The mice were fasted for 12 hours before the last gavage and had free access to water. Blood was collected from the orbits 1 hour after the last administration to collect serum samples, which were allowed to stand for 30 minutes and centrifuged for 15 minutes (3500×g, 4°C) to collect the supernatant. 120μL of blank serum and drug-containing serum were taken, and 480μL of methanol was added to each to precipitate protein. The samples were vortexed for 1 minute, centrifuged at 4°C, 13000×g for 15 minutes, and the supernatant was taken and dried with nitrogen. The residue was reconstituted with 120μL of methanol, centrifuged at 4°C, 13000×g for 15 minutes, and the supernatant was taken for subsequent sampling and analysis.

[0126] 6.3 UPLC-MS / MS detection conditions

[0127] 6.3.1 Chromatographic conditions

[0128] The Dionex Ultimate 3000 HPLC from Thermo Fisher Scientific, USA, consists of a vacuum exhaust system, a pump system, a sample tray with a temperature control system, an automatic sampler, a chromatographic column and a column temperature control system, and a DAD detector. The pre-column is Thermo Scientific Hypersil GOLD aQ (2.1 mm × 10 mm, 1.9 μm); the chromatographic column is ThermoFisher Science Hypersil Gold AQ C18 column (10 0 × 2.1 mm, 1.9 μm); the column temperature is room temperature; the injection volume is 5 μL; the mobile phase A is water containing 0.1% formic acid (v / v), and the mobile phase B is acetonitrile containing 0.1% formic acid (v / v). The gradient elution program is shown in Table 3.

[0129] Table 3 Gradient elution program

[0130]

[0131] 6.3.2 Mass spectrometry conditions

[0132] The high-resolution mass spectrum parameters were set as follows: sheath gas flow rate, 30 psi; auxiliary gas pressure, 10 psi; sweep gas flow rate, 0 psi; capillary voltage, 3.5 kV; capillary temperature, 320 °C; Aux gas heater temperature, 350 °C; collision gas, nitrogen; normalized collision energy, 20, 40, 60 eV; s-lens 60.0. Combined with the selection of a full mass spectrometry scan to automatically trigger a secondary mass spectrometry scan mode (FullMS-DDMS 2 ). The resolution conditions were: primary and secondary resolutions were 70000 FWHM / 17500 FWHM, respectively; ion scanning range, m / z 50-1500; cycle count 3 times; fourth-level isolation window, 1.5 m / z; dynamic exclusion time was 5 s.

[0133] 6.4 Data Collection and Identification of Transient Components in Blood

[0134] The UPLC-MS / MS spectra and data of drug-containing serum and blank serum were imported into Compound Discoverer 3.3 software (Thermo Fisher Scientific, Waltham, MA, USA) for pre-processing, including peak extraction and peak matching. Blank serum was used as a control to remove the common compounds between the two. The MS molecular ion data were fitted using the Thermo Scientific Xcalibur workstation to obtain the molecular formula of the compound. The allowable calculation error was set at 5 ppm. The components of peony root fermentation broth that entered the blood were identified by combining relevant literature and mzCloud-Advance Mass and ChemSpider databases.

[0135] Results and Discussion

[0136] 1. Determination of total glycosides of paeonia lactiflora by HPLC

[0137] 1.1 Standard linearity and linear range

[0138] The peak area results of the benzoic acid standard solution are shown in Table 4. A benzoic acid standard curve was drawn based on the determination results, and its linear fitting equation was y=4811.3x+12.37, R 2=0.9983, indicating that the linear relationship is good when the injection amount of benzoic acid standard is between 0.05 and 0.2 μg. The theoretical plate number calculated based on the benzoic acid peak is not less than 4000.

[0139] Table 4 Benzoic acid standard curve data

[0140]

[0141] 1.2 Methodological test

[0142] ①Precision investigation: The experiment was carried out according to the method under "2.2.2.5". Table 5 shows the peak area determination results of benzoic acid reference solution. The RSD is 0.61%, indicating that the instrument has good precision.

[0143] Table 5 Precision investigation results (n=6)

[0144]

[0145] ② Repeatability study: Repeatability test was carried out according to the method under “2.2.2.6”. The results are shown in Table 6. The RSD is 0.96%, indicating that the method has good repeatability.

[0146] Table 6 Repeatability test results (n=6)

[0147]

[0148] ③ Stability study: The experiment was carried out according to the method under "2.2.2.7" to obtain Table 7, which is the peak area measurement result of the sample solution within 24 hours. The RSD is 0.28%, indicating that the sample solution has good stability.

[0149] Table 7 Stability test results (n = 6)

[0150]

[0151] ④ Investigation of sample recovery rate: The experiment was carried out according to the method under "2.2.2.8" to obtain Table 8. The sample recovery test showed that the average recovery rate of benzoic acid was 98.53% and the RSD was 0.69% (n=3), indicating that the method was accurate and reliable.

[0152] Table 8 Results of sample recovery investigation (n=3)

[0153]

[0154] 1.3 Determination of total peony glycosides in peony cork fermentation liquid

[0155] The results of TGP content in fermentation broth of different strains are shown in Table 9. As can be seen from the table, fermentation can effectively increase the TGP content.

[0156] Table 9 TGP content of fermentation broth of different strains

[0157]

[0158] 2 Determination of the in vitro antioxidant capacity of the fermented liquid of peony bark

[0159] 2.1 Total reducing power determination results

[0160] The total reducing power can be reflected by measuring the absorbance value of the sample at a wavelength of 700nm. The higher the absorbance value, the stronger the antioxidant capacity. The results of the sample solution before and after fermentation of each concentration of PLC are shown in Table 10. Within the concentration measurement range (0.0156~4.000mg·mL -1 ), the absorbance values ​​of each sample solution increased with the increase of concentration, showing a certain dose dependence. As shown in Table 10, the absorbance value of PLC-F is higher than that of the unfermented sample solution, and RP 0.5AU The values ​​showed that there were significant differences before and after PLC fermentation (P<0.05), proving that fermentation can effectively enhance the total reducing capacity of peony roots.

[0161] Table 10 Total reducing power RP 0.5AU Compare( n=3)

[0162]

[0163] Note: Duncan's multiple range test was used for analysis. When two groups of data were compared, data without the same letters indicated significant differences (P<0.05), while data with the same letters indicated no significant differences (P>0.05).

[0164] 2.2 OH- clearance rate measurement results

[0165] PLC fermentation liquid sample OH - The clearance results are shown in Table 11. Within the concentration range (0.0156~4.000mg·mL -1 ), the clearance rate of each sample solution increased with the increase of concentration, showing a certain dose dependence. As can be seen from the table, the ·OH - The clearance rates were higher than those before fermentation, and the EC of PLC-F 50 The values ​​were significantly lower than those before fermentation (P<0.05), indicating that the fermentation broth can achieve half clearance rate at a lower concentration. Similar results were obtained by Li Bing et al.

[47] It was also reported in the study that fermentation can effectively improve the antioxidant capacity of peony root.

[0166] Table 11 OH - Free radical scavenging rate EC 50Compare( n=3)

[0167]

[0168] Note: Same as Table 10.

[0169] 2.3 DPPH clearance test results

[0170] The results of DPPH clearance of the samples before and after PLC fermentation are shown in Table 12. As can be seen from the figure, the clearance rate of each sample increased with the increase of concentration, showing a dose-dependency. The DPPH clearance rate of PLC-F at different concentrations was higher than that of PLC-U, and the EC 50 The value was significantly lower than that before fermentation (P<0.05), indicating that fermentation significantly improved the antioxidant capacity of PLC.

[0171] Table 12 DPPH free radical scavenging rate EC 50 Compare( n=3)

[0172]

[0173] Note: Same as Table 10.

[0174] 2.4ABTS + Clearance determination results

[0175] ABTS of PLR and PLC samples before and after fermentation + The clearance results are shown in Table 13. The clearance of each sample solution increases with the increase of concentration, showing a dose-dependency. It can be seen from the table that the clearance rates of PLR-F and PLC-F are higher than before fermentation, and the EC 50 The value was also significantly lower than that before fermentation (P<0.05), proving that fermentation can effectively improve the antioxidant capacity of peony root. 50 The values ​​were all lower than those of PLR, indicating that the antioxidant capacity of PLC was stronger than that of PLR, and as the fermentation continued to increase, it further demonstrated that the byproduct PLC had excellent potential to replace PLR.

[0176] Table 13 ABTS + Free radical scavenging rate EC 50 Compare( n=3)

[0177]

[0178] Note: Duncan's multiple range test was used for analysis. When two groups of data were compared, data without the same letters indicated significant differences (P<0.05), while data with the same letters indicated no significant differences (P>0.05).

[0179] 3 Experiment on the anti-inflammatory effect of peony root fermentation liquid in vivo

[0180] 3.1 Results and judgment of lipopolysaccharide-induced acute inflammation modeling in mice

[0181] NO is an important regulatory factor in the immune system. When inflammation occurs in the body, immune cells are activated and release NO to exert a certain anti-inflammatory effect, which increases the NO content in the body. Therefore, we use the NO content as an indicator to verify whether the in vivo inflammation model is successfully established. The results show that compared with the blank group, the NO content in the model group is significantly increased, which preliminarily indicates that the modeling is successful and subsequent experiments can be carried out.

[0182] 3.2 Effect of Peony Root Cortex Fermentation Liquid on Organ Coefficients of Inflammatory Mice

[0183] As shown in Table 14, compared with the blank group, the liver coefficient of the model group was significantly increased (P<0.01), and the lung coefficient was significantly increased (P<0.05); compared with the model group, the organ coefficients of the positive control group and the drug group were reduced, and according to the picture, the organ coefficients of the drug group gradually decreased with the increase of the dose, indicating that the organ coefficients of the drug group were concentration-dependent. Among them, the organ coefficients of PLC-U were not significantly different from those of the model group (P>0.05); at the same dosage, the liver coefficients of PLC-FM were significantly reduced compared with the model group (P<0.01), and the lung coefficients of PLC-FM were significantly reduced compared with the model group (P<0.05), indicating that fermentation can improve the ability of peony root to repair organ swelling.

[0184] Table 14 Results of organ coefficients of mice in each drug administration group ( n=8)

[0185]

[0186]

[0187] Note: Compared with the blank group, * indicates P < 0.05, ** Indicates P < 0.01; compared with the model group, # indicates P < 0.05, ## Indicates P<0.01.

[0188] 3.3 Histopathological observation

[0189] The livers of mice in each group were stained with HE, observed under a microscope and photographed. Figure 1 It can be seen that no abnormalities in the structure and morphology of hepatocytes were observed in the blank group mice. Hepatocyte damage, loose arrangement of hepatocytes, infiltration of inflammatory cells, narrow hepatic sinusoids, and disappearance of hepatic cord structure were observed in the model group mice. The symptoms of liver inflammation and focal necrosis in the positive control group and mice in each drug-treated group were improved. Among them, the infiltration of inflammatory cells in the PLC-U and PLC-FS groups was repaired, but the recovery of the hepatic cord structure was not obvious; the hepatocyte morphology of mice in the positive control group, PLC-F medium-dose and high-dose groups was close to the normal cell morphology, with close arrangement, clear and normal hepatic sinusoids, and radially arranged hepatic cords, which were neatly arranged. This shows that PLC has a stronger liver repair function after fermentation, and it is dose-dependent.

[0190] In addition, if Figure 2 As shown in the figure, inflammatory cells in the lungs of mice induced by lipopolysaccharide obviously infiltrated into the alveolar cavity, and the peribronchial wall was thickened and vascular congested. After drug treatment, the positive control group and each drug group significantly reduced these histological changes. Among them, the inflammatory infiltration phenomenon in the PLC-U and PLC-FS groups was reduced, but there were still alveolar septal ruptures and peribronchial wall thickening; a small amount of inflammatory cell infiltration was found in the positive control group and the PLC-F medium and high dose groups of mice, and the alveolar wall capillaries were occasionally dilated and congested, and the peribronchial wall in the high dose group returned to normal thickness, and the lesion range was small, indicating that the fermentation liquid has significant anti-inflammatory activity and is dose-dependent.

[0191] 3.4 Effect of Peony Root Cortex Fermented Liquid on Cytokine Levels in Inflammatory Mice

[0192] Effects of peony root fermentation liquid on four inflammatory factors, NO, IL-1β, IL-6, and TNF-α, in the LPS-induced acute inflammation model in mice Figure 3 .

[0193] The NO levels in the serum of mice in each group were shown in Figure 3 A. Compared with the blank group, the NO level in the model group increased significantly, indicating that the model was successfully constructed. After drug treatment, the NO level in each group decreased to a certain extent, and with the increase of the dosage; the NO level of PLC-F showed a certain dose dependence, among which the PLC-F low-dose group and the PLC-U group were significantly different from the model group (P<0.05), and the other drug-treated groups were extremely significantly different (P<0.01); the NO level in the PLC-F medium- and high-dose groups was not significantly different from the blank group (P>0.05), indicating that the PLC fermentation broth has excellent anti-inflammatory activity, and with the increase of concentration, the activity is further enhanced. In order to compare the effect of fermentation on anti-inflammatory activity, this experiment investigated the effect of PLC-U on the NO level in mouse serum. The results also showed that at the same dose (1.82g·kg-1 ), the NO level of PLC-F was significantly lower than that of PLC-U, indicating that fermentation can effectively enhance the anti-inflammatory ability of peony root, thereby inhibiting the increase of NO.

[0194] The levels of IL-1β in the serum of mice in each group were shown in Figure 3 B. Compared with the blank group, the IL-1β level in the model group increased significantly, indicating that the model was successfully constructed. After drug treatment, the IL-1β level in each group decreased significantly or extremely significantly compared with the model group (P<0.05 or P<0.01), and showed a certain dose dependence. At the same dose (1.82 g·kg -1 ), the fermentation broth showed a stronger ability to downregulate IL-1β than before fermentation, indicating that peony root has excellent ability to inhibit IL-1β, and the activity is further enhanced with fermentation.

[0195] The levels of IL-6 in the serum of mice in each group were shown in Figure 3 C. Compared with the blank group, the IL-6 level in the model group increased significantly, indicating that the model was well constructed. Compared with the model group, the IL-6 levels in each group decreased significantly (P<0.01), and the degree of inhibition showed a dose-dependent trend. At the same dose (1.82 g·kg -1 ), PLR-F and PLC-F showed lower IL-6 levels than before fermentation, indicating that fermentation improved the anti-inflammatory ability of peony root to a certain extent.

[0196] The levels of TNF-α in the serum of mice in each group were shown in Figure 3 D. Compared with the blank group, the TNF-α level in the model group increased significantly, indicating that the model was well constructed. Compared with the model group, the TNF-α levels in each group decreased significantly (P<0.01), and the activity gradually increased with the increase of the dose; at the same dose (1.82 g·kg -1 )PLC-F showed lower TNF-α levels than PLC-U. Shrijana et al. also reported that white peony root fermented with Lactobacillus plantarum had an excellent inhibitory effect on abnormally elevated levels of inflammatory factors such as NO, IL-1, and TNF-α in the serum of acute inflammatory mice, which was similar to the trend observed in this study, further demonstrating that fermentation is one of the excellent means to improve the anti-inflammatory ability of crude drugs.

[0197] 4 Metabolomics study of peony cork fermentation liquid

[0198] 4.1 Identification and analysis of metabolites of peony bark before and after fermentation

[0199] 4.1.1 Identification of metabolites of peony bark before and after fermentation

[0200] In order to clarify the changes in metabolites during the fermentation of peony roots, a non-targeted metabolomics method based on UHPLC-ESI-Q-Orbitrap-MS / MS technology was used to analyze the products before and after PLC fermentation. The total ion chromatogram (TIC) of the quality control sample in positive and negative ion modes is shown in Figure 2. Figure 4 As shown in the figure, under this detection condition, the peak shape of the quality control sample is good, the distribution is relatively uniform, and the chromatogram overlap of the quality control sample is high, which proves that the instrument has good stability.

[0201] The characteristic peaks of sample metabolites were extracted using Compound Discoverer 3.3 software, and the metabolite structures were identified by comparing the mass-to-charge ratio (error < 10 ppm), retention time, fragmentation pattern, and collision energy with those of standard compounds and the local database. A total of 106 compounds were identified before and after PLC fermentation, of which the positive ion (ESI + ) peaks, 61 negative ions (ESI - ) peaks 45. The identification results of the compounds are shown in Table 15.

[0202] Table 15 Metabolite identification results before and after fermentation of Peony Cork

[0203]

[0204]

[0205]

[0206]

[0207] 4.1.2 Analysis of metabolites of peony bark before and after fermentation

[0208] In order to further clarify the classification of fermentation products, this experiment classified and counted the metabolites in PLC samples. The compounds in PLC include 14 terpenes, 13 amino acids and their derivatives, 12 polyphenols, 24 flavonoids, 10 sugars, 15 organic acids, 9 fatty acids, 6 coumarins and 2 other types. According to the type and proportion of metabolites, terpenes, flavonoids, phenolic acids, organic acids, amino acids and their derivatives are the five dominant metabolites in PLC.

[0209] In order to study the clustering effect and separation trend of samples of peony root before and after fermentation, principal component analysis (PCA) was used to quantify the differences in metabolite composition before and after fermentation under an unsupervised model. Figure 5It can be seen that there is a highly significant distinction before and after fermentation, indicating that the metabolites of peony root before and after fermentation have significant changes, and the distribution of sample data points shows a high degree of cohesion and good separation within the group before and after fermentation. In addition, the PCA score diagram shows that the PLC group has good separation, indicating that there is a large difference in the metabolite content between the two.

[0210] In order to screen the differential metabolites during the fermentation process, the samples of PLC-U and PLC-F were subjected to pairwise comparison orthogonal partial least squares discriminant analysis (OPLS-DA). The results are shown in Figure 6 As shown in the figure, the separation effect of each group is good, R 2 Y=0.985,Q 2 =0.95, indicating that the model is well constructed.

[0211] 4.1.3 Screening of differential metabolites in peony bark before and after fermentation

[0212] In order to obtain the differential metabolites during the fermentation of peony cork, the VIP value and P value of the OPLS-DA model were used to screen and compare the significantly different metabolites between different groups. The screening thresholds were VIP>1 and P<0.05. The specific results are shown in Figure 7 A total of 56 differential metabolites were identified between PLC-U and PLC-F, of which 50 were upregulated and 6 were downregulated in PLC-F. The results are shown in the table. They can be divided into 7 different categories, mainly including 13 flavonoids, 10 terpenes, 4 fatty acids, 8 amino acids and their derivatives, 10 organic acids, 7 polyphenols, 3 sugars and 1 coumarin. In order to further clarify the differences in metabolites, the volcano plot and difference bar graph of metabolites are shown in Figure 8 ,It can be seen from the figure that the compounds showed significant changes after fermentation, and most metabolites showed an upward trend, indicating that fermentation is an effective strategy to enhance the active ingredients in peony roots.

[0213] 4.1.3 Analysis of differential metabolites in peony bark before and after fermentation

[0214] Fig. 9The heat map of differential metabolites between PLC-U and PLC-F. In PLC-F, except for Mudanpioside J, which was not detected in PLC-F, the contents of other terpenoid compounds increased. Among them, the peak areas of Paeoniflorin and Albiflorin increased by 3.13 and 17 times, respectively. In addition, the contents of Benzoylpaeoniflorin, Oxypaeoniflorin, Galloylpaeoniflorin, and Benzoyloxy-paeoniflorin increased by 179.97, 1.25, 1.85, and 1.65 times, respectively, after fermentation, indicating that fermentation is an excellent means to increase the content of various monoterpene glycosides in PLC. In addition, except for 3,4,2',3',4',6',alpha-Heptahydroxychalcone 2'-glucoside, which disappeared in PLC-F, the contents of other polyphenols and flavonoids increased significantly. In addition, the contents of all amino acids except L-(+)-Aspartic acid in PLC-F were upregulated, which may be caused by bacterial metabolism or degradation of proteins in PLC. Studies have shown that supplementation with Tryptophan and Threonine can inhibit the expression of proinflammatory cytokines, thereby preventing or treating inflammation, and their contents in PLC-F increased by 1.74 and 11.27 times. Secondly, we found that the contents of organic acids and fatty acids in PLC-F also showed an upward trend, and the content of Benzoic Acid, an important anti-inflammatory active substance in peony, increased by 1.7 times. In summary, fermentation can effectively increase the content of active ingredients in PLC and is an excellent means of treating peony by-products.

[0215] 4.1.4 Analysis of metabolic pathways in the fermentation process of peony bark

[0216] (S6) The KEGG database was used to conduct pathway enrichment analysis of differential metabolites during the fermentation process of peony cork ( Fig.10 The most important metabolic pathways of PLC include the biosynthesis of flavonoids and flavonols, aminoacyl-tRNA biosynthesis, glycine, serine and threonine metabolism, glyoxylate and dicarboxylic acid metabolism, and the citric acid cycle (TCA cycle).

[0217] 4.1.5 Correlation between metabolites and anti-inflammatory activity during fermentation of peony cork

[0218] To clarify the relationship between metabolites and anti-inflammatory activity during the fermentation of peony cork, the EC 50 Value, RP 0.5AU The correlation analysis was performed between the values ​​and the levels of inflammatory factors (NO, IL-1, IL-6, TNF-α) in mouse serum. Fig.11 As shown in the figure, the closer the color is to blue, the negative correlation between the two is present. As the anti-inflammatory ability of the sample is stronger, its EC 50 Value, RP 0.5AU The lower the value and the level of inflammatory factors, the more negatively correlated the anti-inflammatory indicators are, indicating that the compound has a greater contribution to anti-oxidation. As can be seen from the figure, TGP is significantly or extremely significantly negatively correlated with the indicators of oxidative stress and inflammation (P<0.05 or 0.01, r<-0.6), indicating that TGP has a good role in regulating oxidative stress and inflammatory processes, and the anti-inflammatory effect is enhanced as the content increases, which is consistent with the above-mentioned content detection and Elisa test results. Among them, Paeoniflorin and Oxypaeoniflorin, which are components of TGP, are significantly negatively correlated with NO levels (P<0.05, r<-0.6), and Benzoylpaeoniflorin is significantly or extremely significantly negatively correlated with oxidative stress levels and NO, IL-6, and TNF-α (P<0.05 or 0.01, r<-0.6), and is negatively correlated with IL-1 levels (r<-0.6), indicating that Benzoylpaeoniflorin may be one of the important active ingredients in peony that play an anti-inflammatory role. In addition, metabolites such as Pyrogallol, Kaempferol, Benzoic Acid, L-Serine, 4-Anisic acid, L-Tryptophan, and Geranylacetone also showed significant or extremely significant negative correlations with oxidative stress and inflammation levels (P<0.05 or 0.01, r<-0.6). Combined with the metabolomics results, it was shown that fermentation can significantly increase the content of various compounds, and most compounds showed a negative correlation with the inflammation level indicators, indicating that the enhancement of anti-inflammatory activity of Peony Cork after fermentation with Saccharomycopsis fibuligera Y2 is closely related to the upregulation of the content of active ingredients during the fermentation process.

[0219] 5 Metabolomics study on the fermentation liquid of Peony Root Cork

[0220] 5.3.1 Sample chromatogram collection

[0221] Test the prepared drug-containing serum and blank serum samples according to the method in "5.2.3" and collect the base peak chromatograms (BPC) of the drug-containing serum and blank serum samples in positive and negative ion modes. Fig.12 As shown in the figure, it can be seen that the chromatographic peak after administration is somewhat different from that of blank serum.

[0222] 5.3.2 Identification of Transition Components in Blood

[0223] According to the analysis method under the data processing method, combined with the database and references, the serum samples of peony root fermentation liquid were analyzed, and a total of 12 blood-transmitted components were identified, of which 8 were original components and 4 were metabolites of original components. The identification results are shown in Table 12.

[0224] Compound 1, in positive ion mode, showed a quasi-molecular ion peak of 167.0123 [M+H] + , loses 1 molecule of water and breaks into fragments m / z 149.0229 [MH 2 O+H] + Then, one molecule of CO is removed to form a fragment m / z 121.0285 [MH 2 O-CO+H] + Combined with the literature, compound 1 was identified as Paeonol.

[0225] Compounds 4 and 5 both produced an adduct ion peak m / z 525.0946 [M+HCOO] in negative ion mode. - , we know that its molecular formula is C 23 H 28 O 11 The retention times were 10.03 and 10.92 min, respectively. By analyzing the fragment ion peaks of the secondary mass spectrometry, there was a fragment ion m / z 449.1457 [M-CH 2 OH] - and the m / z 431.1356 [M-CH 2 OH 2 OH] - , there are benzoyl characteristic ion peaks m / z 121.0279 and pinane skeleton m / z 165.0539; in addition, compound 4 contains m / z 357.1194 [MC 7 H 6 O 2 -H] - , which is a characteristic fragment of paeoniflorin. Compound 5 contains m / z 327.1078 [M-CH 2 OC 7 H 6 O 2 -H] - , which are characteristic fragments of paeoniflorin, so compounds 4 and 5 were identified as Albiflorin and Paeoniflorin, respectively.

[0226] Compound 7, in negative ion mode, precursor ion m / z 285.0401 [MH] -In the secondary mass spectrum, ion fragment peaks of 267.0307, ​​211.0380, 133.0278, and 93.0328 were found, and m / z 285.0401 lost H 2 O obtained m / z 267.0307 [MH 2 OH] - , m / z 285.0401 loses CO to give m / z 257.0453 [M-CO-H] - , further losing one molecule of CO to obtain m / z 229.0483 [M-2CO-H] - It was speculated that compound 7 was kaempferol.

[0227] Compound 8, in negative ion mode, precursor ion m / z 301.0352 [MH] - , lose 1 molecule C 11 H 6 O 5 The fragment ion with m / z of 83.0121 was obtained, and one molecule of H 2 O formed a fragment m / z 65.0015; the precursor ion produced RDA fragmentation at the 1,3 position to a fragment ion of m / z 151.0023, and then one molecule of CO was removed 2 The secondary fragment ion m / z 107.0120 is formed; the precursor ion undergoes RDA fragmentation to obtain a fragment ion with m / z 149.02304, and then one molecule of CO is removed. 2 The secondary fragment ion m / z 121.02796 was formed, and the secondary fragment further removed one molecule of CO to form a secondary fragment ion m / z 93.03327; the precursor ion produced RDA cleavage at positions 1 and 4 to a fragment ion of m / z 178.9973. The mass spectrum information and retention time of the compound were compared with relevant literature and databases, confirming that compound 8 was quercetin.

[0228] Metabolite 10, retention time 6.38min, in negative ion mode, produced ion peaks m / z 183.0287, m / z 168.0050 and m / z 124.0150, among which the quasi-molecular ion peak m / z 183.0293 [MH] - Compared with the fragment m / z 168.0059 [M-CH 3 -H] - , prompting that 1 CH is lost 3, it is speculated that the compound is the methylation product of a certain original component; the secondary fragment was found to have a characteristic ion peak m / z124.0188, which is the characteristic product of gallic acid decarboxylation, indicating that the compound may be the methylation product of gallic acid, and the compound was identified as 4-O-Methylgallicacid.

[0229] Metabolite M19, retention time is 10.41min, and the quasi-molecular ion peak is 373.0087[MH] in negative ion mode. - , losing 1 molecule of glucuronic acid and breaking into fragments m / z 197.9971 [MC 6 H 8 O 6 -H] - , which is consistent with the fragment ion of paeoniflorin metabolin I, so metabolite M19 was identified as the glucuronidation product of paeoniflorin metabolin I (PaeonimetabolinIglucuronide).

[0230] Table 16 Identification results of drug-containing serum transition components in blood

[0231]

[0232] The above describes the specific embodiments of the present invention. It should be noted that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the essential content of the present invention.

Claims

1. A method for analyzing the components of the material basis for the anti-inflammatory efficacy of the fermented broth of peony root cortex, characterized in that: The following steps are involved: S1: Fermentation of Peony Cork: Peony Cork is crushed and sieved, mixed with distilled water, and sterilized by high pressure to obtain a fermentation substrate; Inoculate the strain, ferment, and centrifuge through a 0.22 μm filter membrane to obtain PLC fermentation broth; S2: HPLC method was used to determine the content of total peony glucosides (TGP); S3: Determination of the external antioxidant capacity of the fermented liquid of Peony Cork; S4: Anti-inflammatory effect of peony root cork fermentation liquid; S5: Identification and analysis of metabolites of peony cork before and after fermentation; The products before and after PLC fermentation were analyzed using a non-targeted metabolomics method based on UHPLC-ESI-QE-Orbitrap-MS / MS technology. Compound Discoverer 3.3 software was then used to extract the characteristic peaks of sample metabolites, and the metabolite structures were identified by comparing the mass-to-charge ratio (error < 10 ppm), retention time, fragmentation pattern, and collision energy with those of standard compounds and databases. The metabolites in the PLC samples were classified and counted to clarify the classification of the fermentation products. Principal component analysis (PCA) was used to quantify the differences in metabolite composition before and after fermentation under an unsupervised model to clarify the clustering effect and separation trend of samples of peony cork before and after fermentation. Orthogonal partial least squares discriminant analysis (OPLS-DA) analysis was performed on the samples of peony cork before and after fermentation to screen the differential metabolites in the fermentation process. The significantly different metabolites among different groups were screened and compared based on the VIP value and P value of the OPLS-DA model to obtain the differential metabolites during the fermentation of peony cork. Analysis of differential metabolites of Peony Bark before and after fermentation, The KEGG database was used to analyze the pathway enrichment of differential metabolites in the fermentation process of peony cork; EC of metabolites and antioxidant capacity of Peony Root Cortex 50 Value, RP 0.5AU The correlation analysis was performed between the values ​​and the levels of inflammatory factors in mouse serum to clarify the relationship between metabolites and anti-inflammatory ability during the fermentation of peony root cork. S6: Collect base peak chromatograms of drug-containing serum and blank serum samples under positive and negative ion modes, and then analyze the serum samples of Paeonia lactiflora cortex fermented liquid according to the analytical method under the data processing method, combined with the database and references, to identify the migratory components in the bleeding, that is, to obtain the components that form the material basis for the anti-inflammatory effect of Paeonia lactiflora cortex fermented liquid.

2. The method according to claim 1, characterized in that: In step S1, the strain used is Saccharomyces cerevisiae.

3. The method according to claim 2, characterized in that: In step S1, the fermentation time is 52 h, the inoculation amount is 4%, and the material-liquid ratio is 1:

21.

4. The method according to claim 1, characterized in that: Calculate the TGP content of the sample according to the following formula:

5. The method according to claim 1, characterized in that: In step S3, the external antioxidant capacity includes total reducing power, ·OH - Clearance rate, DPPH clearance rate, ABTS + Clearance rate.

6. The method according to claim 1, characterized in that: The results and judgment of the modeling of acute inflammation in mice induced by lipopolysaccharide in step S4; the effect of the fermented broth of peony cortex on the organ coefficients of mice with inflammation; histopathological observation; the effect of the fermented broth of peony cortex on the cytokine levels of mice with inflammation.

7. The method according to claim 1, characterized in that: In step S5, a total of 106 compounds were identified before and after PLC fermentation, of which positive ions (ESI + ) peaks, 61 negative ions (ESI - ) 45 peaks.

8. The method according to claim 2, characterized in that: In step S6, gradient elution: the chromatographic column is a ThermoFisher Science Hypersil GoldAQ C18 column (100×2.1 mm, 1.9 μm); the column temperature is room temperature; the injection volume is 5 μL; the mobile phase A is 0.1% (v / v) formic acid in water, the mobile phase B is 0.1% (v / v) formic acid in acetonitrile, and the flow rate is 0.30 mL·min -1， The elution procedure is as follows:

9. The method according to claim 2, characterized in that: In step S6, the components of the fermented broth of peony root cork that exert anti-inflammatory effects are paeonol, benzoic acid, new peony glycosides, paeoniflorin, protocatechuic aldehyde, kaempferol, quercetin, peony metabolite II, C 10 H 14 O3 sulfate, peony metabolin I glucuronide, 4-O-methyl gallic acid.

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