Application of ginger source exosome-like nano-vesicle marker combination in quality control

Abstract

The invention provides application of a ginger source exosome-like nano-vesicle marker combination in quality control, and relates to the technical field of nano-vesicles. According to the application provided by the invention, the seven markers are combined, and the quality of the nano vesicles can be effectively judged by detecting the concentrations of the seven markers in the nano vesicles, so that the quality of the nano vesicles can be effectively improved by performing specific treatment on the seven markers in the extraction process of the ginger-derived nano vesicles.

Description

Technical Field

[0001] The present invention relates to the field of nanovesicle technology, and in particular to an application of a ginger-derived exosome-like nanovesicle marker combination in quality control. Background Art

[0002] Ginger-derived exosome-like nanovesicles (GELNs) are lipid bilayer nanoparticles with diameters ranging from 100nm to 300nm, naturally secreted from ginger rhizome cells. They are commonly used as delivery systems targeting the gastrointestinal tract, lungs, and liver. Functionally, GELNs can mediate intercellular communication and transport a variety of bioactive compounds inherent in ginger, including gingerols, gingerols, and various microRNAs. Existing studies have demonstrated that GELNs have anti-inflammatory effects, regulate the gut microbiome, and prevent insulin resistance. Furthermore, as plant-derived nanovesicles, GELNs have higher biocompatibility, improved immune tolerance, and greater safety compared to animal-derived exosomes, potentially increasing their potential for application in functional foods and nutritional supplements.

[0003] The commonly used method for extracting GELNs in the prior art is differential centrifugation, which involves filtering the ginger juice and then centrifuging it at a low speed to remove debris, then performing high-speed centrifugation to remove organelles, and finally performing ultra-high-speed centrifugation to allow the GELNs to complete sedimentation. The GELNs are then washed and resuspended with ultra-pure water / PBS and repeated ultra-high-speed centrifugation to obtain relatively pure GELNs. However, it is well known that ginger is a natural plant raw material, and different ginger varieties have significantly different genetic backgrounds and secondary metabolite contents, which directly affect the quality of cell vesicle components. In addition, changes in parameters in the centrifugation scheme used will also change parameters such as vesicle yield and particle size distribution. GELNs of different qualities will inevitably lead to fluctuations in the performance of the final product in actual application, making it difficult to achieve stable and uniform production. Therefore, there is an urgent need to provide a nanovesicle quality control method for guiding the extraction and application of ginger-derived nanovesicles. Summary of the Invention

[0004] The purpose of the present invention is to provide an application of a ginger-derived exosome-like nanovesicle marker combination in quality control. By detecting the concentration of seven markers in the nanovesicles, the quality of the nanovesicles can be effectively judged, so that specific treatment of these seven markers during the ginger-derived nanovesicle extraction process can effectively improve the quality of the nanovesicles.

[0005] In the first aspect, the present invention provides an application of a ginger-derived exosome-like nanovesicle marker combination in quality control, wherein the marker combination includes: penicillin I with CAS number 1016605-29-0, cypermethrin with CAS number 52315-07-8, Mammea coumarin D type (A / AD type) with CAS number 30563-62-3, 1-methylhistidine with CAS number 332-80-9, PubChem 11,12-dihydroxy-13-methoxy-4,5,8-trimethyl-3-(2-methylpropyl)-1H,2H,3H,4H,6aH,9H,10H,11H,12H,13H,14H,15H,15bH-cycloundeca[e]isoindole-1,15-dione with CID 24868391, cytosine with CAS number 71-30-7, and paroxetine with CAS number 110429-35-1.

[0006] Optionally, the quality control includes the antioxidant activity of ginger-derived exosome-like nanovesicles, the concentration of 1-methylhistidine is positively correlated with the antioxidant activity, and the concentration of penicillin I, cypermethrin, Mammea coumarin type D (A / AD type), cytosine, paroxetine, and 11,12-dihydroxy-13-methoxy-4,5,8-trimethyl-3-(2-methylpropyl)-1H,2H,3H,4H,6aH,9H,10H,11H,12H,13H,14H,15H,15bH-cycloundeca[e]isoindole-1,15-dione are negatively correlated with the antioxidant activity.

[0007] In the second aspect, a screening method for a combination of ginger-derived exosome-like nanovesicle markers comprises: isolating and purifying different ginger pieces to obtain nanovesicle samples; characterizing the physical and chemical properties of the nanovesicle samples; obtaining analysis results based on the physical and chemical properties characterization results through OPLS model and ANOVA difference analysis of VIP values; and obtaining a marker combination by pooling the analysis results to obtain markers whose VIP values are greater than a preset threshold and p < 0.05.

[0008] Optionally, when separating and purifying ginger pieces to obtain nanovesicle samples, the method includes: squeezing the ginger pieces into phosphate buffer solution, centrifuging them at 1000×g for 10 min, 4000×g for 20 min, 10000×g for 25 min, and 10000×g for 25 min at 4°C, filtering the supernatant through a 450nm aqueous polyethersulfone membrane to obtain a primary filtrate; ultracentrifuging the primary filtrate at 100,000×g for 2h, collecting the precipitate, resuspending the precipitate, and filtering it through 450nm and 200nm aqueous polyethersulfone membranes to obtain a nanovesicle sample.

[0009] Optionally, when characterizing the physicochemical properties of the nanovesicle samples, non-targeted metabolomics analysis is performed on the metabolites of the nanovesicle samples based on UPLC-MS / MS, the analysis results are normalized to obtain a data matrix, and the metabolome database and the standard compound database are queried based on the data matrix to obtain different metabolites from different ginger sources, which are combined into the physicochemical property characterization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a method for screening a combination of ginger-derived exosome-like nanovesicle markers provided by the present invention; Figure 2 A schematic diagram of a specific process for obtaining a nanovesicle sample in step S1 of a method for screening a combination of ginger-derived exosome-like nanovesicle markers provided by the present invention; Figure 3 The present invention provides characterization diagrams of the three gingers used in Preparation Example 1; wherein: Figure 3 A in the figure is the phenotypic representation diagram of three types of ginger; Figure 3 B in the figure is the transmission electron microscopy characterization of three types of GELNs; Figure 3 C in the figure is a schematic diagram of the sizes of three types of GELNs particles; Figure 3 D in the figure is a comparison diagram of the zeta potential of three types of GELNs particles; Figure 3 E in the figure is a comparison of protein concentrations in three types of GELNs; Figure 4 This is the result of multivariate analysis of three GELNs metabolites using OPLS-DA in the present invention; Figure 4 A in the figure is the sample volume normalized data graph; Figure 4 B in the figure is the protein concentration normalized data graph; Figure 5 This is a test result diagram of the data reliability of the OPLS-DA method using random permutation test in the present invention; wherein: Figure 5 A in the figure is the test result diagram for the standardized sample volume; Figure 5 B in the figure is the test result graph normalized to protein concentration; Figure 6 This is a classification result diagram after identifying differential metabolites based on metabolic data and classifying them according to the secondary classification database when screening potential markers in the present invention; wherein: Figure 6 A in the figure is the classification result diagram after identification and classification based on volume-normalized metabolite data; Figure 6 B in the figure is the classification result diagram after identification and classification based on the metabolite data normalized by protein concentration; Figure 7 This is a comparison chart of the Caco-2 cell experiment conducted on three types of GELNs obtained in Preparation Example 1 of the present invention; wherein: Figure 7 A in the figure represents the effects of three GELNs on DPPH, ABTS + Comparison of free radical scavenging activity; Figure 7 B is a comparison of the effects of three GELNs on Caco-2 cell activity; Figure 7 Figure C is a comparison of the effects of three GELNs on reactive oxygen species in Caco-2 cells that were not treated with H2O2; Figure 7 D in the figure is a comparison of the effects of three GELNs on reactive oxygen species in Caco-2 cells treated with H2O2; Figure 8 This is a laser confocal microscopy characterization image of the three GELNs after the cell uptake experiment of the present invention; Figure 9 Schematic diagram of the analysis of potential marker screening according to the present invention; wherein: Figure 9 A in the figure is the correlation diagram between seven markers and antioxidant activity; Figure 9 Figure B is the result of correlation analysis of VIP values of seven markers; Figure 9 Figure C is the cluster analysis result of VIP value DM of seven markers. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the present invention belongs.

[0012] See also Figure 1The present invention provides a method for screening a combination of ginger-derived exosome nanovesicle markers, comprising the following steps: S1, separating and purifying different ginger pieces to obtain nanovesicle samples; S2, characterizing the physicochemical properties of the nanovesicle samples; S3, obtaining analysis results based on the physicochemical property characterization results by performing OPLS model on VIP values and ANOVA difference analysis; S4, obtaining a marker combination for markers whose VIP values in the collective analysis results are greater than a preset threshold and p < 0.05.

[0013] See also Figure 2 In performing step S1, the method of separating and purifying the ginger pieces to obtain a nanovesicle sample comprises: squeezing the ginger pieces into a phosphate buffer solution, centrifuging the ginger pieces at 4°C for 10 minutes at 1000×g, 20 minutes at 4000×g, 25 minutes at 10,000×g, and 25 minutes at 10,000×g, respectively; filtering the supernatant through a 450nm aqueous polyethersulfone membrane to obtain a primary filtrate; ultracentrifuging the primary filtrate at 100,000×g for 2 hours, collecting the precipitate, resuspending the precipitate, and filtering it through 450nm and 200nm aqueous polyethersulfone membranes to obtain a nanovesicle sample. In fact, filtering the filtrate through an aqueous polyethersulfone membrane before ultracentrifugation in step S1 can effectively improve the purity and yield of the obtained nanovesicle sample.

[0014] In fact, when executing step S2 to characterize the physicochemical properties of the nanovesicle sample, a non-targeted metabolomics analysis of the metabolites of the nanovesicle sample is performed based on UPLC-MS / MS, the analysis results are normalized to obtain a data matrix, and the metabolome database and the standard compound database are queried based on the data matrix to obtain different metabolites from different ginger sources, which are combined into the physicochemical property characterization results.

[0015] Specifically, when executing step S4, the preset threshold can be set according to an empirical value, or it can be set according to the expected number of markers obtained by screening. For example, the markers are pre-sorted according to the size of the VIP value, and 7 markers are taken from large to small as the markers obtained by screening and a marker combination is constructed.

[0016] Preparation Example 1

[0017] This preparation example 1 provides a method for isolating and purifying ginger-derived exosome-like nanovesicles, comprising: S11. Take 80.00g of washed and peeled Jiushan (JS) ginger, Chiling (CL) ginger, and Boai (BA) ginger (all harvested in November 2024) and cut them into pieces with a volume of about 1cm. 3 of ginger pieces (JS, CL, BA); S12. Soak ginger pieces (JS, CL, BA) in ice-cold phosphate buffered saline (PBS, 10 mmol / L, pH ≈ 7.4) and squeeze the juice using a slow juicer to separate the ginger juice. Centrifuge the ginger juice at 1,000 × g for 10 min, 4,000 × g for 20 min, 10,000 × g for 25 min, and 10,000 × g for 25 min at 4°C, and then filter through a 450 nm filter membrane to obtain the primary filtrate (JS, CL, BA). S13. Take 36 mL of the primary filtrate (JS, CL, BA) and ultracentrifuge at 100,000 × g for 2 h to separate the precipitate. Resuspend it in 5 mL of ice-cold phosphate buffer (PBS, 10 mmol / L, pH = 7.4), and filter it through a 450 nm filter membrane and a 200 nm filter membrane to isolate ginger (JS, CL, BA) exosome-like nanovesicles (GELNs).

[0018] Preparation Example 1 was repeated three times and the yields of the obtained GELNs (JS, CL, BA) were 5.328 mg / mL±0.426 mg / mL, 3.767 mg / mL±0.559 mg / mL, and 4.851 mg / mL±0.637 mg / mL, respectively. The phenotypes of the three gingers used in Preparation Example 1 were characterized as follows: Figure 3 As shown in A in FIG, the GELNs prepared in Preparation Example 1 were observed using a transmission electron microscope. Figure 3 As shown in B, the particle size of the GELNs prepared in Preparation Example 1 was detected as follows Figure 3 As shown in C, the zeta potential of the GELNs particles prepared in Preparation Example 1 was detected as follows Figure 3 As shown in D in the figure, the protein concentration of the GELNs particles prepared in Preparation Example 1 was detected as follows Figure 3 As shown in E.

[0019] from Figure 3 It can be seen from the A in JS, CL and BA ginger that there are obvious morphological differences. Figure 3 As can be seen from B in the figure, the three varieties of GELNs all have characteristic membrane structures and saucer-like morphologies. Figure 3 From C in FIG, it can be seen that the particle diameters are all around 200 nm and there are differences in particle size, which is CL>JS≈BA. The polydispersity index (PDI) values of the three GELNs are all between 0.13 and 0.14, which can explain that the particle size distribution of the GELNs obtained by the method provided by the present invention is narrow and uniform.

[0020] from Figure 3 It can be seen from the D in the figure that the zeta potential of the three types of GELNs are different, in the order of JS>BA>CL. Figure 3The protein concentrations of GELNs obtained by the same separation and purification methods were significantly different, in the order of JS>BA>CL. Figure 3 Overall, it can be seen that the separation and purification method provided by the present invention can effectively obtain GELNs, and it is proved that the properties and compositions of GELNs derived from different varieties of ginger are different.

[0021] 100 μL of GELNs (JS, CL, BA) were taken respectively and mixed with pre-cooled solution (4°C, methanol, acetonitrile and water in a volume ratio of 2:2:1), then sonicated at -20°C for 30 min and centrifuged for 20 min. The supernatant was separated, dried, and redissolved in 100 μL of acetonitrile aqueous solution (acetonitrile and water in a volume ratio of 1:1). After vortex mixing, the supernatant was separated and centrifuged at 14,000 g for 15 min to obtain metabolic samples (JS, CL, BA).

[0022] The metabolic samples (JS, CL, BA) were analyzed using a Vanquish ultra-high performance liquid chromatography system and a QE mass spectrometer, respectively, as follows: The metabolic samples (JS, CL, BA) were analyzed by an ACQUITY UPLC system with a 1.7 μm, 2.1 mm × 100 mm ® The BEH column was packed and separated at a flow rate of 0.3 mL / min at 25°C. The mobile phase consisted of 25 mmol ammonium acetate / ammonia water and acetonitrile. The specific gradient elution was as follows: 0 min-1.5 min, 98% acetonitrile in the mobile phase; 1.5 min-12 min, 2% acetonitrile in the mobile phase; 12 min-14 min, 2% acetonitrile in the mobile phase; 14 min-14.1 min, 98% acetonitrile in the mobile phase. Metabolic samples were stored in an autosampler at 4°C for random sequence analysis. Metabolic samples (JS, CL, BA) were ionized by electrospray ionization (ESI) with a nebulizer and auxiliary gas pressure of 60 psi, a curtain pressure of 30 psi, a source temperature of 600°C, and a spray voltage of ±5.5 kV. The main mass spectrometer detection range was 80 Da-1200 Da, the resolution was 60,000, the scan time was 100 ms, and the dynamic exclusion time was 4 s.

[0023] The metabolomics data obtained in ESI(+) and ESI(-) modes were peak identified, filtered, and aligned to keep the mass error within 25 ppm. The peak intensity was normalized relative to the total spectral intensity to obtain a metabolic data matrix including retention time, m / z value, and peak intensity. The metabolic data matrix was used to compare the chemical composition of the metabolites against the Human Metabolome Database (HMDB), MassBank, Metlin, MoNA, and the Standard Compound Database (Shenzhen Weike Technology Co., Ltd.). Approximately 1,272 different metabolites were obtained in the metabolic samples (JS, CL, BA). The metabolites were classified into 15 superclasses and 91 categories using chemical classification.

[0024] Among the metabolites of GELNs, organic acids and their derivatives are the most abundant metabolite superclass, accounting for about 25.15% of the total metabolites, which also reflects the core role of this type of substance in plant cell metabolism. The content of lipids and lipid-like molecules is second only to organic acids and their derivatives, accounting for about 24.36% of the total metabolites. Among the top 20 metabolite categories, fatty acyl groups, pro-alcohol lipids, steroids and their derivatives, and glycerophospholipids belong to the lipid and lipid-like molecule superclass, which shows the importance of this substance in the formation of stable vesicle membranes and further verifies the role of this substance in vesicle structure and function.

[0025] OPLS-DA was used to perform multivariate analysis on the metabolite data normalized for sample volume and protein concentration, where the data normalized for sample volume were as follows: Figure 4 The data normalized to protein concentration are shown in A. Figure 4 As shown in B. Figure 4 In A, the first two principal components (component 1 and component 2) explained 49.2% of the variance of JS, CL, and BA metabolites. Figure 4 In B, the first two principal components (component 1 and component 2) explain 49.3% of the variation between different varieties. Figure 4 As can be seen from the figure, multiple metabolites were highly correlated with the principal components with higher VIP values in both normalization methods, and the clear separation between the groups indicated the differences in metabolite composition.

[0026] The reliability of the OPLS-DA method for sample volume standardization and protein concentration standardization was confirmed by 200 random permutation tests. The test results for sample volume standardization are as follows: Figure 5 As shown in A, the test results for protein concentration standardization are as follows Figure 5 As shown in B. Figure 5It can be seen that the values of R2Y and Q2 are higher than 0.99 and 0.6, respectively, and there are significant differences in Q2 and R2Y before and after replacement (p < 0.05), which shows that the OPLS-DA model has a good fit and predictive ability. The credibility and VIP value of the model are indicated and verified for the screening of differential metabolites, which further shows that GELNs from different ginger sources have unique metabolic profiles.

[0027] Differential metabolites were identified as potential markers and screened according to the VIP>1 and ANOVA (p<0.05) criteria. A total of 116 differential metabolites were identified between the three ginger-derived GELNs using volume-normalized metabolite data, of which 105 metabolites were classified into nine categories according to the secondary database classification, including lipids and lipid-like molecules (36.19%), benzyl ring compounds (18.10%), organic acids and derivatives (15.24%), heterocyclic compounds (13.33%), phenylpropanoids and polyketides (5.71%), organic oxygen compounds (4.76%), alkaloids and their derivatives (2.86%), lignans (neolignans and related compounds) (1.90%), and organic nitrogen-containing compounds (1.90%). Figure 6 As shown in Figure A; 190 differential metabolites were identified between the three ginger-derived GELNs using protein concentration-normalized metabolite data, of which 117 metabolites were classified into 12 categories based on the secondary database, including lipids and lipid-like molecules (28.81%), organic acids and derivatives (19.77%), heterocyclic compounds (13.56%), benzene ring compounds (12.99%), organic oxygen compounds (11.86%), organic nitrogen-containing compounds (5.08%), phenylpropanoids and polyketides (3.95%), alkaloids and their derivatives (1.13%), homogeneous non-metallic compounds (1.13%), organic 1,3-polar molecules (0.56%), nucleosides and nucleotides and analogs (0.56%), and lignans (neolignans and related compounds) (0.56%). Figure 6 As shown in B.

[0028] A 100.48 μmol / L DPPH solution was prepared in methanol and mixed with 20 μL of a 1 mg / mL GELNs (JS, CL, BA) solution. The absorbance was measured at 517 nm using a microplate reader at 30-min intervals. A 7 mmol / L ABTS stock solution was mixed with a 2.45 mmol / L potassium persulfate solution in a 96-well plate and incubated in the dark for 15 h to obtain ABTS. +The free radical cation solution was prepared by adjusting the pH of the solution to 7.4 and the absorbance to 0.70±0.02 using PBS. 20 μL of 1 mg / mL GELNs (JS, CL, BA) solution was mixed with 275 μL of ABTS cation solution. The absorbance was measured at 734 nm using a microplate reader in the dark for 6 minutes. The free radical scavenging activity of GELNs (JS, CL, BA) was calculated according to the following formula. The results are shown in the figure: Figure 7 As shown in A: ; Where, is the absorbance value after GELNs treatment, is the absorbance value of the untreated free radical solution, is the free radical scavenging activity of GELNs.

[0029] The CCK-8 method was used to evaluate the effect of GELNs (JS, CL, BA) on cell viability: Caco-2 cells were cultured at a density of 5 × 10 3 The cells were seeded into 96-well plates at a density of 100 μL / well and incubated at 37°C for 24 h. 100 μL of GELNs (JS, CL, BA) solutions with concentration gradients of 0, 0.060, 0.120, and 0.240 mg / mL were added and incubated for 1 h. The absorbance was measured at 450 nm using a microplate reader, and the cell viability of Caco-2 cells was calculated compared with that of the blank control according to the following formula: Figure 7 As shown in B: ; Where, is the absorbance value after incubation with GELNs, is the absorbance value of the blank group without treatment, is the absorbance value of the blank group without GELNs and Caco-2 cells.

[0030] Evaluation of the effect of GELNs (JS, CL, BA) on intracellular reactive oxygen species: Caco-2 cells were cultured at a rate of 5×10 3After inoculation into a 96-well plate at a density of 100 cells / well and incubation at 37°C for 24 hours, a 1.75 mmol / L H2O2 solution was added to each well for 3 hours, and then the culture medium in the wells was replaced with a concentration gradient of 0, 60, 120, and 240 μg / mL GELNs (JS, CL, BA) solution. After incubation for 24 hours, DCFH-DA was added to each well and the final concentration was adjusted to 10 μmol / L. After incubation at 37°C for 30 minutes, the fluorescence intensity was measured using a microplate reader at an excitation wavelength of 485 nm and an emission wavelength of 528 nm, and the reactive oxygen species level in the Caco-2 cells was calculated according to the following formula. The results are shown in the figure. Figure 7 As shown in C and D: ; Where, is the absorbance value after treatment with H2O2 solution and GELNs, The absorbance value after treatment with H2O2 solution without GELNs treatment is shown in Table 1. is the absorbance value of the blank group without treatment.

[0031] Evaluation of the cell uptake capacity of GELNs (JS, CL, BA): 500 μL of 1 mg / mL GELNa (JS, CL, BA) was labeled with PLH26 and adjusted to a final concentration of 5 μmol / L. The cells were washed with PBS and centrifuged at 4°C for 70 min to separate the precipitate. The precipitate was resuspended in 200 μL PBS. Caco-2 cells were cultured at 1×10 5 The cells were seeded into 96-well plates at a density of 100 μL / well and incubated at 37°C for 12 h. Labeled GELNs (JS, CL, BA; 0.24 mg / mL) were added and incubated at 37°C for 24 h. After fixation with 4% paraformaldehyde for 15 min, 100 μL of 5 mg / mL phalloidin-FITC was added and incubated at room temperature for 60 min. The cells were then rinsed three times with phosphate-buffered saline (PBST) containing 0.1% Tween-20. The cell nuclei were stained with 4',6-diamidino-2-phenylindole (DAPI) for 5 min. The excess DAPI was removed by rinsing with PBST, and the cells were observed using a laser confocal microscope. Figure 8 shown.

[0032] from Figure 7 As can be seen from Figure A, the order of antioxidant performance is JS>CL>BA, among which the DPPH radical scavenging rate of JS is higher than that of BA and CL, while the scavenging rate of CL is higher than that of BA. A similar size relationship is observed in the ABTS radical scavenging rate, but there are statistically significant differences between JS and CL, and between BA and CL.

[0033] from Figure 7 As can be seen from Figure B, the cell viability of GELNs changed in a dose-dependent manner. At 0.24 mg / mL, the cell viability of JS, BA, and CL reached the maximum, which were 125.16% ± 2.87%, 105.72 ± 7.18%, and 124.10% ± 5.75%, respectively. Compared with CL and BA, JS showed a stronger ability to promote intestinal epithelial cell proliferation in the concentration range of 0.06 mg / mL-0.24 mg / mL.

[0034] from Figure 7 As shown in Figures C and D, all GELN samples reduced reactive oxygen species (ROS) levels in Caco-2 cells without H2O2 treatment within the concentration range of 0.06–0.24 mg / ml. JS exhibited the strongest ROS-reducing ability. BA exhibited a stronger ROS-reducing ability than CL at concentrations of 0.12 and 0.24 mg / ml, while CL exhibited a stronger ROS-scavenging ability at 0.06 mg / ml, an effect that correlated with cell viability. While there was no statistically significant difference in cell viability between BA and CL at 0.06 mg / ml, CL exhibited a stronger ROS-scavenging ability than BA at higher concentrations. Under H2O2-induced oxidative stress in Caco-2 cells, the three GELNs showed significant differences in their ROS-scavenging ability, with the order JS > CL > BA. This ability to mitigate H2O2-induced oxidative stress was dose-dependent, consistent with the effects of GELN on cell viability and ROS scavenging in the absence of H2O2 treatment. The differences in antioxidant capacity among the three varieties were confirmed. Figure 8 It can be seen that under the same protein content conditions, the absorption rate of JS by Caco-2 cells was the highest, followed by CL, while the absorption rate of BA was the lowest.

[0035] The markers with VIP values TOP7 and p < 0.05 were screened to determine the potential markers as shown in Table 1 below, and then the correlation between the potential markers and antioxidant activity was evaluated.

[0036] Table 1 Potential markers

[0037] The seven markers in Table 1 were correlated with antioxidant activity. Figure 9 As shown in A, the results of the correlation analysis of VIP values of seven markers are as follows Figure 9 As shown in B, the cluster analysis results of the VIP values DM of the seven markers are as follows Figure 9 As shown in C. Figure 9As can be seen from the table, the correlation coefficients of the six markers were negatively correlated with antioxidant activity, while only one marker was positively correlated with antioxidant activity. Pearson correlation analysis showed that all seven markers in Table 1 showed strong correlation.

[0038] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. Application of a ginger-derived exosome-like nanovesicle marker combination in quality control, characterized in that: The marker combination includes: penicillin I with CAS number 1016605-29-0, cypermethrin with CAS number 52315-07-8, Mammea coumarin type D (A / AD type) with CAS number 30563-62-3, 1-methylhistidine with CAS number 332-80-9, 11,12-dihydroxy-13-methoxy-4,5,8-trimethyl-3-(2-methylpropyl)-1H,2H,3H,4H,6aH,9H,10H,11H,12H,13H,14H,15H,15bH-cycloundeca[e]isoindole-1,15-dione with PubChem CID 24868391, cytosine with CAS number 71-30-7, and paroxetine with CAS number 110429-35-1.

2. The use according to claim 1, characterized in that The quality control included the antioxidant activity of ginger-derived exosome-like nanovesicles. The concentration of 1-methylhistidine was positively correlated with the antioxidant activity, while the concentrations of penicillin I, cypermethrin, Mammea coumarin type D (A / AD type), cytosine, paroxetine, and 11,12-dihydroxy-13-methoxy-4,5,8-trimethyl-3-(2-methylpropyl)-1H,2H,3H,4H,6aH,9H,10H,11H,12H,13H,14H,15H,15bH-cycloundeca[e]isoindole-1,15-dione were negatively correlated with the antioxidant activity.

3. A method for screening a combination of ginger-derived exosome-like nanovesicle markers, characterized in that: include: Nanovesicle samples were obtained by separating and purifying different ginger pieces; Characterize the physical and chemical properties of nanovesicle samples; Based on the results of physicochemical property characterization, the analysis results were obtained by OPLS model and ANOVA difference analysis of VIP values; in the pooled analysis results, the marker combination was obtained for markers with VIP values greater than the preset threshold and p < 0.

05.

4. The screening method according to claim 3, wherein When separating and purifying ginger pieces to obtain nanovesicle samples, the method includes: squeezing the ginger pieces into a phosphate buffer solution, centrifuging the ginger pieces at 1000×g for 10 minutes, 4000×g for 20 minutes, 10000×g for 25 minutes, and 10000×g for 25 minutes at 4°C, filtering the supernatant through a 450nm aqueous polyethersulfone membrane to obtain a primary filtrate; ultracentrifuging the primary filtrate at 100,000×g for 2 hours, collecting the precipitate, resuspending the precipitate, and filtering it through 450nm and 200nm aqueous polyethersulfone membranes to obtain the nanovesicle sample.

5. The screening method according to claim 3, wherein When characterizing the physicochemical properties of the nanovesicle samples, non-targeted metabolomics analysis of the metabolites of the nanovesicle samples was performed based on UPLC-MS / MS. The analysis results were normalized to obtain a data matrix. Based on the data matrix, the metabolome database and the standard compound database were queried to obtain different metabolites from different ginger sources, and the metabolites were combined into the physicochemical property characterization results.

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