Purple sweet potato leaf uric acid reducing active component and potential target and screening method thereof

Abstract

The invention belongs to the technical field of active component and target screening, and relates to a purple sweet potato leaf uric acid reducing active component and a potential target and a screening method thereof, the active component comprises rhododendron yellow, 3, 4, 2 ', 4', 6 '-pentahydroxychalcone, apigenin, quercetin glycoside, 6-hydroxycoumarin, 6, 6-dihydroxycoumarin, 3, 4, 2', 4 ', 6'-pentahydroxychalcone, 3, 4, 2 ', 4', 6 '-pentahydroxychalcone, 3, 4, 2', 4 '-pentahydroxychalcone, the component A is prepared from 2, 7-dihydroxy coumarin, daphnetin, trigonelline, serotonin, salidroside, sesamin and nicotinamide; the potential target spots comprise INS, PPAR-gamma, IL1-beta, PPAR-alpha, XDH and ABCG2; the method comprises the following steps: preparing purple sweet potato leaves and a sweet potato leaf water extract, determining the uric acid generation inhibition activity of the purple sweet potato leaves through an in-vitro experiment, and carrying out differential metabonomics combined network pharmacology analysis to excavate functional substances for reducing uric acid in the purple sweet potato leaves, thereby providing a material basis for high-valued utilization of the purple sweet potato leaves and development of functional foods for reducing uric acid.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of active ingredient screening, and particularly relates to a purple sweet potato leaf uric acid reducing active ingredient, a potential target and a screening method. BACKGROUND

[0002] Hyperuricemia (HUA) is a common basic metabolic disease characterized by excessive accumulation of uric acid. Xanthine oxidase (XOD) is a key enzyme for the synthesis of uric acid, which catalyzes the formation of uric acid from purine. The homeostasis of serum uric acid level depends on the balance of uric acid metabolism and excretion. Clinically, drugs for reducing uric acid include XOD inhibitors and drugs for promoting uric acid excretion. However, such drugs usually have irreversible liver and kidney toxicity, and the drug treatment for asymptomatic HUA patients is also controversial. Drug treatment is often limited in practical application. Studies have found that natural ingredients from food have potential efficacy for treating hyperuricemia. Ethanol extract of camellia leaf can effectively inhibit XOD activity and reduce uric acid production. Baicalein can inhibit XOD activity in serum and liver and promote uric acid excretion in the kidney. Other food-derived natural compounds such as phenolic acids, stilbenes, saponins and terpenes have also been reported to reduce blood uric acid levels. Therefore, it is of great significance to find safe and effective functional ingredients from food sources to replace chemically synthesized drugs.

[0003] Purple sweet potato (Ipomoea batatas) is an annual herbaceous plant of the Convolvulaceae Ipomoea genus, with purple to dark purple skin and flesh. Purple sweet potato leaves are rich in nutrients such as dietary fiber, vitamins, and bioactive compounds such as polyphenols, flavonoids and anthocyanins. Sweet potato leaves are a byproduct of agricultural product processing, and the utilization rate is low. However, studies have found that a variety of secondary metabolites produced in sweet potato leaves have pharmacological activities such as antibacterial, antidiabetic, antioxidant, anti-inflammatory, etc. Studies have found that the antioxidant activity of sweet potato leaf water extract has good reducing power and superoxide radical scavenging effect, showing strong antioxidant capacity. In addition, a specific bioactive component in sweet potato leaves has anti-inflammatory effect. Oxidative stress, inflammation and hyperuricemia have a strong correlation, which also indirectly reflects the potential efficacy of uric acid-lowering ingredients in sweet potato leaves. At the same time, purple sweet potato leaf polyphenols can reduce the activity of XOD and directly hinder the production of uric acid. However, different varieties usually have significant differences, and the analysis of the differences between sweet potato and purple sweet potato leaves can determine their function of reducing uric acid disease and the potential material basis. At present, there is a lack of systematic and efficient research methods for targeting the main components of sweet potato leaves with uric acid-lowering activity.

[0004] Employing metabolomics alone has the drawbacks of broad scope and lack of scientific foresight. Combining metabolomics with network pharmacology, however, allows for precise targeting, providing empirical targets for screening bioactive substances. A method (CN117517536A) based on metabolomics combined with network pharmacology to analyze the differences in anticancer activity between the carapace and plastron of turtles, by comparing the omics differences in anticancer activity between the carapace and plastron and using network pharmacology, clarifies that the carapace exhibits superior anticancer activity. Therefore, by leveraging the descending differences within the same genus and adopting a source-tracing approach, more precise targeting of substances can be achieved for component screening. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an active ingredient in purple sweet potato leaves that lowers uric acid, its potential targets, and a screening method.

[0006] To achieve the objectives of this invention, the following technical solutions are adopted.

[0007] A purple sweet potato leaf uric acid-lowering active ingredient and its potential targets, wherein: the active ingredient includes azadirachtin, 3,4,2',4',6'-pentahydroxychalcone, apigenin, quercetin glycosides, 6-hydroxycoumarin, 6,7-dihydroxycoumarin, daphnetin, trigonelline, serotonin, rhodioloside, sesamin, and nicotinamide; the potential targets of the active ingredient include INS, PPAR-γ, IL1-β, PPAR-α, XDH, and ABCG2.

[0008] Furthermore, the INS is strongly correlated with sesamin, rhodioloside, serotonin, trigonelline, and quercetin glycosides; the PPAR-α is strongly correlated with sesamin, quercetin glycosides, catechin, and serotonin; the PPAR-γ is strongly correlated with serotonin and quercetin glycosides; the ABCG2 is strongly correlated with azadirachtin, quercetin glycosides, apigenin, and 3,4,2',4',6'-pentahydroxychalcone; the XDH is strongly correlated with nicotinamide, quercetin glycosides, apigenin, fraxin, azadirachtin, daphnetin, and 6-hydroxycoumarin; and the IL1-β is strongly correlated with nicotinamide, quercetin glycosides, and sesamin.

[0009] A method for screening uric acid-lowering active components of purple sweet potato leaves and their potential targets includes the following steps: S1. Preparation of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract; S2. An in vitro uric acid-lowering activity experiment was conducted using aqueous extracts of sweet potato leaves and purple sweet potato leaves as raw materials. After the in vitro uric acid-lowering activity experiment, the uric acid production inhibition rates of the two aqueous extracts were compared, and it was determined that the uric acid-lowering activity of purple sweet potato leaves was higher than that of sweet potato leaves. The in vitro uric acid-lowering activity experiment involved adding aqueous extracts of sweet potato leaves and purple sweet potato leaves to a xanthine sodium-xanthine oxidase system for reaction. After the reaction, the uric acid content was determined by high performance liquid chromatography to evaluate the ability of the aqueous extracts of sweet potato leaves and purple sweet potato leaves to inhibit uric acid production, i.e., the uric acid production inhibition rate. The xanthine sodium-xanthine oxidase system was formed by incubating 0.05 U / mL xanthine oxidase XOD in a constant temperature incubator at 37℃ for 10 min, then adding 500 μL of 0.06 mmol / L xanthine to start the reaction, and incubating at 37℃ for 30 min. S3. Differential metabolomics analysis was conducted using sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract as raw materials. 395 differential metabolites from sweet potato leaves and 662 differential metabolites from purple sweet potato leaves were preliminarily screened. It was determined that there are more types of uric acid-lowering active ingredients in purple sweet potato leaves than in sweet potato leaves. S4. Using the 662 differential metabolites obtained from purple sweet potato leaves as targets, and with hyperuricemia and gout as targets, network pharmacology was used to analyze the uric acid-lowering active ingredients and their potential targets from purple sweet potato leaves. 65 uric acid-lowering active ingredients and 7 potential targets were screened. Among these, the 65 uric acid-lowering active ingredients were further screened using a three-dimensional network analysis of components, targets, and pathways, resulting in 12 uric acid-lowering active ingredients from purple sweet potato leaves. The 6 potential targets were INS, PPAR-γ, IL1-β, PPAR-α, XDH, and ABCG2. The 12 uric acid-lowering active ingredients were azadirachtin, 3,4,2',4',6'-pentahydroxychalcone, apigenin, quercetin glycosides, 6-hydroxycoumarin, 6,7-dihydroxycoumarin, daphnetin, trigonelline, serotonin, rhodioloside, sesamin, and nicotinamide.

[0010] As a preferred embodiment of the present invention, the preparation method of the sweet potato leaf aqueous extract and the purple sweet potato leaf aqueous extract includes the following steps: S31. Select fresh sweet potato leaves and purple sweet potato leaves; S32. Dry fresh purple sweet potato leaves and sweet potato leaves separately to constant weight, pulverize them with a pulverizer, and after passing through a 60-mesh sieve, obtain sweet potato leaf powder and purple sweet potato leaf powder respectively, and store them in a desiccator for later use. S33. Accurately weigh 0.5g each of sweet potato leaf powder and purple sweet potato leaf powder, and extract them separately by heating in a water bath. Finally, centrifuge the centrifuge tube at 6000rpm for 10min, remove the filter residue, collect the supernatant and filter it to obtain sweet potato leaf water extract and purple sweet potato leaf water extract respectively.

[0011] As a preferred embodiment of the present invention, the uric acid content determination process is as follows: 250 μL of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract of different concentrations are added to the xanthine sodium-xanthine oxidase system for reaction. After the reaction is completed, the uric acid content is determined by ultraviolet detector and C18 column high-performance liquid chromatography. The liquid chromatography conditions are: flow rate 0.8 mL / min, mobile phase 0.1% phosphate aqueous solution-methanol, the ratio of phosphate aqueous solution to methanol by mass is 85:15, detection wavelength is 292 nm, injection volume is 20 μL, column temperature is 30℃, and phosphate buffer is used as a blank control. Based on the determined uric acid content, the uric acid production inhibition rates of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract are calculated to be 49.92% and 74.73%, respectively.

[0012] As a preferred embodiment of the present invention, the specific process of the differential metabolomics analysis includes the following steps: S51. Take the sweet potato leaf aqueous extract and the purple sweet potato leaf aqueous extract out of the -80℃ freezer and thaw until there is no ice in the sample. Vortex for 10s to mix well. Take 200μL of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract respectively and add them to the corresponding numbered 1.5mL centrifuge tubes. Add 200μL of 70% methanol and internal standard extraction solution to each tube. Vortex for 15min at 4℃ and 12000r / min, centrifuge for 3min, take the supernatant, filter it through a microporous membrane with a pore size of 0.22μm to obtain the two sample extracts. Store them in injection bottles for LC-MS / MS detection. S52. LC-MS / MS Detection: The extracts of the two samples were analyzed using UPLC-esi-MS / MS and tandem mass spectrometry systems. The UPLC-esi-MS / MS system analytical conditions were as follows: Column: Agilent SB-C18 1.8µm, 2.1 mm * 100 mm; Mobile phase: Phase A was ultrapure water, Phase B was acetonitrile; Elution gradient: 0.00 min, Phase B proportion was 5%, within 9.00 min, the proportion of Phase B increased linearly to 95% and maintained at 95% for 1 min, from 10.00 to 11.10 min, the proportion of Phase B decreased to 5%, and equilibrated to 5% for 14 min; Flow rate: 0.35 mL / min; Column temperature: 40℃; Injection volume: 2 μL. The mass spectrometry conditions of the tandem mass spectrometry system included: an electrospray ionization source temperature of 500℃; an ion spray voltage of 5500V / -4500V; ion source gas I, ion gas II, and curtain gas were set to 50, 60, and 25 psi, respectively; and the collision-induced ionization parameter was set to high. Triple quadrupole scanning used MRM mode with the collision gas set to medium. The declustering voltage and collision energy were then optimized to determine the declustering voltage and collision energy for each MRM ion pair. Based on the metabolites eluted in each period, a specific set of MRM ion pairs was monitored in each period. S53. Mass spectrometry data were processed using Analyst 1.6.3 software. Based on a self-built metabolic database, Metlin, and HMDB database, qualitative and quantitative mass spectrometry analysis was performed on the metabolites of the samples. Unsupervised principal component analysis was performed using the statistical function prcomp in R software to distinguish the overall differences between samples. After log2 transformation of the raw data, centering was performed to establish an OPLS-DA model. Based on the variable importance projection (VIP) obtained from the OPLS-DA model, and combined with the P / FDR or FC values ​​from univariate analysis, differential metabolites were further screened. Using VIP > 1, FC ≥ 2, FC ≤ 0.5, and p < 0.05 as screening criteria, a total of 1057 differential metabolites were screened, of which 395 differential metabolites were screened from sweet potato leaf aqueous extract and 662 differential metabolites were screened from purple sweet potato leaf aqueous extract.

[0013] As a preferred embodiment of the present invention, the specific process of the network pharmacology analysis includes the following steps: S61. The 662 differential metabolites of purple sweet potato leaves were screened using three databases: TCMSP, SwissDrugDesign, and SymMap. The differential metabolites with uric acid-lowering activity and their target predictions were then performed. The information of the screened differential metabolites with uric acid-lowering activity and the predicted target was extracted and summarized. S62. Collect disease / symptom-related target information for hyperuricemia and gout using DisGeNET, SymMap and TTD databases respectively, and extract and summarize the predicted disease / symptom target information. S63. The metabolite target information predicted by TCMSP, SymMap, and SwissDrugDesign and the disease / symptom target information predicted by DisGeNET, SymMap, and TTD were subjected to intersection analysis using Venn diagrams. At the same time, the targets were screened by assigning parameter scores based on the target predictions in the database. The screening criteria were: component targets: the Probability* parameter in SwissTargetPrediction was set to greater than 0.12; disease targets: the score parameter in DisGeNET was set to greater than 0.5. A total of 23 common targets were obtained. S64. Import the 23 common targets into the string database, select Homo sapiens as the species, perform protein interaction analysis, export the network relationship CRV file, directly import it into Cytoscape 3.1.10 software for visualization analysis, use the CytoNCA plugin to perform betweenness centrality analysis (BC), and screen core targets based on the BC value. The core target proteins include INS, PPAR-α, PPAR-γ, IL1-β, XDH, and ABCG2.

[0014] S65. Using the DAVID database, GO functional enrichment analysis was performed on 23 common targets, including biological process (BP), cellular component (CC), and molecular function (MF) analysis. A p-value of <0.05 was set, and the top-ranked GO entries were selected based on the p-value. A bar chart was generated using the MicroBioinformatics platform. Pathway enrichment analysis was then performed on the common targets using the KEGG database, with a p-value of <0.05. The top 20 enriched pathways were selected for visualization, resulting in a substance-component-disease-target-pathway network map. A total of 12 differentially metabolites were identified: azadirachtin, 3,4,2',4',6'-pentahydroxychalcone, apigenin, quercetin glycosides; 6-hydroxycoumarin, 6,7-dihydroxycoumarin, daphnetin; trigonelline, serotonin; rhodioloside; sesamin; and nicotinamide.

[0015] Beneficial effects: This invention reveals for the first time the differentially metabolized components in sweet potato leaves and purple sweet potato leaves and their effects on uric acid-lowering activity. Among them, the differentially active components in purple sweet potato leaves have a significant effect on lowering uric acid, providing a material basis and theoretical support for finding functional components in food and developing uric acid-lowering functional foods. Attached Figure Description

[0016] Figure 1Principal component analysis plot of differential metabolomics between sweet potato leaves and purple sweet potato leaves; where: PC1 (64%) represents the core inter-sample differences that retain 64.3% of the original data information, and PC1 represents the largest dispersion (largest variance), which can distinguish the sample differences in the original data to the greatest extent; 64.3% is the variance explained rate of PC1, indicating that it can retain 64.3% of the inter-sample differences in the original data; PC2 (8.66%) represents the ability to capture 8.66% of the minor differences in the original data that PC1 did not cover, and PC2 represents the ability to explain the remaining variance to the greatest extent without repeating the information explained by PC1; 8.66% is the variance explained rate of PC2, indicating that PC2 can explain an additional 8.66% of the total variance of the original data; Figure 2 Volcano plots of differential metabolites in the PSPL and SPL groups; Figure 3 For intersection Venn diagrams; Figure 4 For protein-protein interaction network analysis (PPI); Figure 5 For GO enrichment analysis and KEGG pathway enrichment analysis; Figure 6 This is a network map of substances, components, diseases, targets, and pathways. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0018] As an embodiment of the present invention, such as Figures 1 to 6 As shown, a method for screening uric acid-lowering active components of purple sweet potato leaves and their potential targets includes the following steps: Step 1: Preparation of aqueous extracts of sweet potato leaves and purple sweet potato leaves Fresh sweet potato leaves and purple sweet potato leaves were selected. Sample pretreatment was performed. Fresh purple sweet potato leaves and sweet potato leaves were dried to constant weight, pulverized using a pulverizer, and sieved through a 60-mesh sieve to obtain sweet potato leaf powder, which was then stored in a desiccator for later use. 0.5 g of the dried purple sweet potato leaf powder was accurately weighed and extracted in a water bath. The mixture was then centrifuged at 6000 rpm for 10 min, the filter residue was removed, and the supernatant was collected and filtered to obtain purple sweet potato leaf aqueous extract (PLPL) and sweet potato leaf aqueous extract (SPL).

[0019] Step 2: Detection of the uric acid-lowering activity of aqueous extracts of sweet potato leaves and purple sweet potato leaves 0.05 U / mL xanthine oxidase (XOD) was incubated at 37°C for 10 min. Then, 500 μL of 0.06 mmol / L xanthine was added to initiate the reaction, and the mixture was incubated at 37°C for 30 min to obtain an in vitro xanthine oxidase inhibition model. Different concentrations (0, 20, 40, 60, 80, 100 mg / mL) of PSPL and SPL were added to the above xanthine oxidase inhibition model for reaction. After the reaction, the UA content was determined by high-performance liquid chromatography (HPLC) using a C18 column (4.6 mm * 250 mm * 5 μm) with a UV detector. HPLC conditions: flow rate 0.8 mL / min, mobile phase 0.1% phosphate aqueous solution-methanol (85:15), detection wavelength 292 nm, injection volume 20 μL, column temperature 30°C, and phosphate buffer as a blank control. The uric acid production inhibition rates of PSPL and SPL reached 74.73% and 49.92%, respectively.

[0020] Step 3: Differential metabolomics analysis between sweet potato leaves and purple sweet potato leaves (1) Sample pretreatment. Remove the sample from the -80℃ freezer and thaw until no ice remains. Vortex for 10 seconds to mix thoroughly. Take 200 μL of sample and add it to a corresponding numbered 1.5 mL centrifuge tube. Add 200 μL of 70% methanol and internal standard extraction solution (for samples smaller than 200 μL, add the extraction solution at a ratio of 1:1 (V / V)). Vortex for 15 min at 4℃ and 12000 r / min, centrifuge for 3 min, collect the supernatant, filter it through a 0.22 μm microporous membrane, and store it in a syringe for LC-MS / MS detection.

[0021] (2) LC-MS detection. The sample extract was analyzed using an UPLC-esi-MS / MS system (UPLC, ExionLC™AD) and a tandem mass spectrometry system. Analytical conditions: Column: Agilent SB-C18 1.8 µm, 2.1 mm * 100 mm; Mobile phase: Phase A was ultrapure water (with 0.1% formic acid), and Phase B was acetonitrile (with 0.1% formic acid); Elution gradient: Phase B ratio was 5% at 0.00 min, linearly increased to 95% within 9.00 min and maintained at 95% for 1 min, and decreased to 5% from 10.00 to 11.10 min, and equilibrated to 5% at 14 min; Flow rate: 0.35 mL / min; Column temperature: 40 °C; Injection volume: 2 μL.

[0022] The mass spectrometry conditions mainly included: an electrospray ionization (ESI) source temperature of 500 °C; an ion spray voltage (IS) of 5500 V (positive ion mode) / -4500 V (negative ion mode); and source gas I (GSI), gas II (GSII), and curtain gas (CUR) set to 50, 60, and 25 psi, respectively, with collision-induced ionization parameters set to high. Triple quadrupole scanning used MRM mode, with the collision gas (nitrogen) set to medium. The DP and CE of each MRM ion pair were further optimized through further optimization of the declustering potential (DP) and collision energy (CE). A specific set of MRM ion pairs was monitored at each epoch based on the metabolites eluted within each epoch.

[0023] (3) Data Processing. Mass spectrometry data were processed using Analyst 1.6.3 software. Based on a self-built metabolic database, Metlin, and the HMDB database, qualitative and quantitative mass spectrometry analyses of the metabolites in the samples were performed. Unsupervised principal component analysis (PCA) was primarily performed using the statistical function prcomp in R software (V4.1.2) to distinguish population differences between samples, such as... Figure 1 As shown, PSPL and SPL are far apart along the PC1 direction, indicating a significant core difference between them. PSPL and SPL separate along the PC2 direction, suggesting the difference originates from minor differences. After log2 transformation of the original data, centering was performed to establish an OPLS-DA model. The variable importance projection (VIP) obtained from the OPLS-DA model (biological replicates ≥ 3) was combined with the P / FDR (biological replicates ≥ 2) or FC value from univariate analysis to further screen differentially expressed metabolites. Using VIP > 1, FC ≥ 2, FC ≤ 0.5, and p < 0.05 as screening criteria, a total of 1057 differentially expressed metabolites were identified. The volcano plots of the two groups of differentially expressed metabolites are shown below. Figure 2 As shown.

[0024] Step 4: Network pharmacological analysis of sweet potato leaves and purple sweet potato leaves (1) Screening of active substances and prediction of targets in PSPL and SPL extracts. Active substances and targets were screened and predicted for all detected substances using three databases: TCMSP, SwissDrugDesign, and SymMap. The union of the screened substances and targets was then summarized.

[0025] (2) Disease target prediction. For the diseases to be treated with traditional Chinese medicine, disease / symptom-related target information was collected using the DisGeNET, SymMap, and TTD databases. The union of the target information predicted using these databases was then summarized. (3) Intersection analysis of substance targets and disease targets. An intersection analysis was performed between substance target information obtained using TCMSP, SymMap, and SwissDrugDesign and disease target information predicted using DisGeNET, SymMap, and TTD. To further reduce interference from too many substance and disease targets, the target prediction scores in the database were used to filter targets. The filtering criteria were: for substance targets, the Probability* parameter in SwissTargetPrediction was set to greater than 0.12; for disease targets, the score parameter in DisGeNET was set to greater than 0.5. Through the intersection Venn diagram, 23 common targets were obtained, such as... Figure 3 As shown.

[0026] (4) Protein-protein interaction network. The intersection points of the obtained substance targets and disease targets were imported into the string database. The species selected was *Homo sapiens*, and protein-protein interaction analysis was performed. The network relationship CRV file was exported and directly imported into Cytoscape 3.1.10 software for visualization analysis. Betweenness centrality (BC) analysis was performed using the CytoNCA plugin. The BC value measures the mediating role of nodes in the shortest path of the network. Nodes with high betweenness centrality play an important role in communication between different targets. Core targets were selected based on the BC value. PPI results are shown below. Figure 4 As shown. The key target proteins are mainly INS, PPAR-α, PPAR-γ, IL1-β, XDH, and ABCG2.

[0027] (5) Intersection analysis of substance targets and disease targets. Using the DAVID database, GO functional enrichment analysis was performed on the common targets, including biological process (BP), cellular component (CC), and molecular function (MF) analyses. A p-value of <0.05 was set, and the top-ranked GO entries were selected based on the p-value, and bar charts were generated using the MicroBio platform. Using the KEGG database, pathway enrichment analysis was performed on the intersecting targets. A p-value of <0.05 was set, and the top 20 enriched pathways were selected for display. The GO enrichment analysis and KEGG results are shown below. Figure 5 As shown. A comprehensive analysis of the results from the above steps yields a substance-component-disease-target-network map, as shown below. Figure 6As shown. A total of 12 core compounds were obtained, including four flavonoids, three coumarins, two alkaloids, one phenolic acid, one lignan, and one vitamin, namely azadirachtin, 3,4,2',4',6'-pentahydroxychalcone, apigenin, quercetin glycosides; 6-hydroxycoumarin, fraxin (6,7-dihydroxycoumarin), daphnetin; trigonelline, serotonin; rhodioloside; sesamin; and nicotinamide. The six core targets were identified as INS, PPAR-γ, IL1-β, PPAR-α, XDH, and ABCG2.

[0028] The relationships between the active ingredients corresponding to the potential targets are as follows: INS is strongly correlated with sesamin, rhodioloside, serotonin, trigonelline, and quercetin glycosides; PPAR-α is strongly correlated with sesamin, quercetin glycosides, and serotonin; PPAR-γ is strongly correlated with serotonin and quercetin glycosides; ABCG2 is strongly correlated with azadirachtin, quercetin glycosides, apigenin, and 3,4,2',4',6'-pentahydroxychalcone; XDH is strongly correlated with nicotinamide, quercetin glycosides, apigenin, fraxin, azadirachtin, daphnetin, and 6-hydroxycoumarin; IL1-β is strongly correlated with nicotinamide, quercetin glycosides, and sesamin.

[0029] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A uric acid-lowering active ingredient from purple sweet potato leaves and its potential targets, characterized in that: The active ingredients include azadirachtin, 3,4,2',4',6'-pentahydroxychalcone, apigenin, quercetin glycosides, 6-hydroxycoumarin, 6,7-dihydroxycoumarin, daphnetin, trigonelline, serotonin, rhodioloside, sesamin, and nicotinamide; the potential targets include INS, PPAR-γ, IL1-β, PPAR-α, XDH, and ABCG2.

2. The uric acid-lowering active ingredient of purple sweet potato leaves and its potential target as described in claim 1, characterized in that: The INS is strongly correlated with sesamin, rhodioloside, serotonin, trigonelline, and quercetin glycosides; the PPAR-α is strongly correlated with sesamin, quercetin glycosides, catechin, and serotonin; the PPAR-γ is strongly correlated with serotonin and quercetin glycosides; the ABCG2 is strongly correlated with azadirachtin, quercetin glycosides, apigenin, and 3,4,2',4',6'-pentahydroxychalcone; the XDH is strongly correlated with nicotinamide, quercetin glycosides, apigenin, fraxin, azadirachtin, daphnetin, and 6-hydroxycoumarin; and the IL1-β is strongly correlated with nicotinamide, quercetin glycosides, and sesamin.

3. A method for screening uric acid-lowering active components of purple sweet potato leaves and their potential targets, characterized in that: Includes the following steps: S1. Preparation of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract; S2. An in vitro uric acid-lowering activity experiment was conducted using sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract as raw materials. After the in vitro uric acid-lowering activity experiment, the uric acid production inhibition rate of the two aqueous extracts was compared, and it was determined that the uric acid-lowering activity of purple sweet potato leaf was higher than that of sweet potato leaf. The in vitro uric acid-lowering activity experiment involved adding sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract to a xanthine sodium-xanthine oxidase system for reaction. After the reaction, the uric acid content was determined by high performance liquid chromatography to evaluate the ability of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract to inhibit uric acid production, i.e., the uric acid production inhibition rate. The xanthine sodium-xanthine oxidase system was formed by incubating 0.05 U / mL xanthine oxidase XOD in a constant temperature incubator at 37℃ for 10 min, then adding 500 μL of 0.06 mmol / L xanthine to start the reaction, and incubating at 37℃ for 30 min. S3. Differential metabolomics analysis was conducted using sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract as raw materials. 395 differential metabolites from sweet potato leaves and 662 differential metabolites from purple sweet potato leaves were preliminarily screened. It was determined that there are more types of uric acid-lowering active ingredients in purple sweet potato leaves than in sweet potato leaves. S4. Using the 662 differential metabolites obtained from purple sweet potato leaves as targets, and with hyperuricemia and gout as targets, network pharmacology was used to analyze the uric acid-lowering active ingredients and their potential targets from purple sweet potato leaves. 65 uric acid-lowering active ingredients and 7 potential targets were screened. Among these, the 65 uric acid-lowering active ingredients were further screened using a three-dimensional network analysis of components, targets, and pathways, resulting in 12 uric acid-lowering active ingredients from purple sweet potato leaves. The 6 potential targets were INS, PPAR-γ, IL1-β, PPAR-α, XDH, and ABCG2. The 12 uric acid-lowering active ingredients were azadirachtin, 3,4,2',4',6'-pentahydroxychalcone, apigenin, quercetin glycosides, 6-hydroxycoumarin, 6,7-dihydroxycoumarin, daphnetin, trigonelline, serotonin, rhodioloside, sesamin, and nicotinamide.

4. The method for screening the uric acid-lowering active components of purple sweet potato leaves and their potential targets according to claim 3, characterized in that: The preparation methods of the sweet potato leaf aqueous extract and the purple sweet potato leaf aqueous extract include the following steps: S31. Select fresh sweet potato leaves and purple sweet potato leaves; S32. Dry fresh purple sweet potato leaves and sweet potato leaves separately to constant weight, pulverize them with a pulverizer, and after passing through a 60-mesh sieve, obtain sweet potato leaf powder and purple sweet potato leaf powder respectively, and store them in a desiccator for later use. S33. Accurately weigh 0.5g each of sweet potato leaf powder and purple sweet potato leaf powder, and extract them separately by heating in a water bath. Finally, centrifuge the centrifuge tube at 6000rpm for 10min, remove the filter residue, collect the supernatant and filter it to obtain sweet potato leaf water extract and purple sweet potato leaf water extract respectively.

5. The method for screening the uric acid-lowering active ingredients and potential targets of purple sweet potato leaves according to claim 4, characterized in that: The procedure for determining uric acid content was as follows: 250 μL of aqueous extracts of sweet potato leaves and purple sweet potato leaves at different concentrations were added to a xanthine sodium-xanthine oxidase system for reaction. After the reaction, the uric acid content was determined by ultraviolet detector and C18 column high-performance liquid chromatography (HPLC). The HPLC conditions were: flow rate 0.8 mL / min, mobile phase 0.1% phosphate aqueous solution-methanol (85:15), detection wavelength 292 nm, injection volume 20 μL, column temperature 30℃, and phosphate buffer as a blank control. Based on the determined uric acid content, the uric acid production inhibition rates of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract were calculated to be 49.92% and 74.73%, respectively.

6. The method for screening the uric acid-lowering active ingredients and potential targets of purple sweet potato leaves according to claim 5, characterized in that: The specific process of the differential metabolomics analysis includes the following steps: S61. Take the sweet potato leaf aqueous extract and the purple sweet potato leaf aqueous extract out of the -80℃ freezer and thaw until there is no ice in the sample. Vortex for 10s to mix evenly. Take 200μL of sweet potato leaf aqueous extract and purple sweet potato leaf aqueous extract respectively and add them to the corresponding numbered 1.5 mL centrifuge tubes. Add 200μL of 70% methanol and internal standard extraction solution to each tube. Vortex for 15min at 4℃ and 12000r / min, centrifuge for 3min, take the supernatant, filter it through a microporous membrane with a pore size of 0.22μm to obtain the two sample extracts. Store them in injection bottles for LC-MS / MS detection. S62. LC-MS / MS Detection: The extracts of the two samples were analyzed using UPLC-esi-MS / MS and tandem mass spectrometry systems. The UPLC-esi-MS / MS system analysis conditions were as follows: Column: Agilent SB-C18 1.8µm, 2.1mm*100mm; Mobile phase: Phase A was ultrapure water, Phase B was acetonitrile; Elution gradient: 0.00 min, Phase B proportion was 5%, increasing linearly to 95% within 9.00 min and maintaining at 95% for 1 min; from 10.00 to 11.10 min, Phase B proportion decreased to 5% and equilibrated to 5% for 14 min; Flow rate: 0.35 mL / min; Column temperature: 40℃; Injection volume: 2 μL. The mass spectrometry conditions of the tandem mass spectrometry system included: an electrospray ionization source temperature of 500 °C; an ion spray voltage of 5500 V / -4500 V; ion source gas I, ion gas II, and curtain gas were set to 50, 60, and 25 psi, respectively; and the collision-induced ionization parameter was set to high. Triple quadrupole scanning used MRM mode with the collision gas set to medium. The declustering voltage and collision energy were then optimized to determine the declustering voltage and collision energy for each MRM ion pair. Based on the metabolites eluted in each period, a specific set of MRM ion pairs was monitored in each period. S63. Mass spectrometry data were processed using Analyst 1.6.3 software. Based on a self-built metabolic database, Metlin, and HMDB database, qualitative and quantitative mass spectrometry analysis was performed on the metabolites of the samples. Unsupervised principal component analysis was performed using the statistical function prcomp in R software to distinguish the overall differences between samples. After log2 transformation of the raw data, centering was performed to establish an OPLS-DA model. Based on the variable importance projection (VIP) obtained from the OPLS-DA model, and combined with the P / FDR or FC values ​​from univariate analysis, differential metabolites were further screened. Using VIP > 1, FC ≥ 2, FC ≤ 0.5, and p < 0.05 as screening criteria, a total of 1057 differential metabolites were screened, of which 395 differential metabolites were screened from sweet potato leaf aqueous extract and 662 differential metabolites were screened from purple sweet potato leaf aqueous extract.

7. The method for screening the uric acid-lowering active ingredients and potential targets of purple sweet potato leaves according to claim 6, characterized in that: The specific process of the network pharmacology analysis includes the following steps: S71. The 662 differential metabolites of purple sweet potato leaves were screened using three databases: TCMSP, SwissDrugDesign, and SymMap. The differential metabolites with uric acid-lowering activity and their target predictions were then performed. The information of the screened differential metabolites with uric acid-lowering activity and the predicted target was extracted and summarized. S72. Collect disease / symptom-related target information for hyperuricemia and gout using DisGeNET, SymMap and TTD databases respectively, and extract and summarize the predicted disease / symptom target information. S73. The metabolite target information predicted by TCMSP, SymMap, and SwissDrugDesign and the disease / symptom target information predicted by DisGeNET, SymMap, and TTD were subjected to intersection analysis using Venn diagrams. At the same time, the targets were screened by assigning parameter scores to the target predictions in the database. The screening criteria were: component targets: the Probability* parameter in SwissTargetPrediction was set to greater than 0.12; disease targets: the score parameter in DisGeNET was set to greater than 0.

5. 23 common targets were obtained. S74. Import 23 common targets into the string database, select Homo sapiens as the species, perform protein interaction analysis, export the network relationship CRV file, directly import it into Cytoscape 3.1.10 software for visualization analysis, use the CytoNCA plugin to perform betweenness centrality analysis (BC), and screen core targets based on the BC value. The core target proteins include INS, PPAR-α, PPAR-γ, IL1-β, XDH and ABCG2. S75. Using the DAVID database, GO functional enrichment analysis was performed on 23 common targets, including biological process (BP), cellular component (CC), and molecular function (MF) analysis. With a p-value of <0.05, the top-ranked GO entries were selected based on the p-value, and bar charts were generated using the MicroBioinformatics platform. Pathway enrichment analysis was then performed on the common targets using the KEGG database, with a p-value of <0.

05. The top 20 enriched pathways were selected for visualization, resulting in a substance-component-disease-target-pathway network map. A total of 12 differentially metabolites were identified: azadirachtin, 3,4,2',4',6'-pentahydroxychalcone, apigenin, quercetin glycosides; 6-hydroxycoumarin, fraxin (6,7-dihydroxycoumarin), daphnetin; trigonelline, serotonin; rhodioloside; sesamin; and nicotinamide.

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