High screening method for anti-inflammatory components of three-fruit soup based on intestinal flora metabolism model

Abstract

The invention discloses a three-fruit soup anti-inflammatory component high screening method based on an intestinal flora metabolism model, and belongs to the technical field of traditional Chinese medicine active component screening. Comprising the following steps: constructing a special intestinal flora metabolism model (containing an in-vitro SHIME dynamic simulation system and an in-vivo flora humanized mouse model) of the three-fruit soup, firstly preparing a total extract of the three-fruit soup, different polar parts and a single prototype component, and co-culturing the in-vitro flora to obtain a metabolite; then locking high-activity prototype components and metabolites through a three-level anti-inflammatory activity screening system, and carrying out structure identification, anti-inflammatory mechanism verification, content and metabolic efficiency detection and in-vivo effectiveness confirmation, and carrying out repeated optimization to form a standardized method. The method solves the problems that traditional screening does not consider intestinal metabolism and results are incomplete, direct and indirect anti-inflammatory activity is considered, the screening result is precise and clinically fit, operation is controllable, repeatability is good, and the method can be used for quality control of the three-fruit soup, dosage form improvement and development of anti-inflammatory innovative drugs.

Description

Technical Field

[0001] This invention relates to the field of..., and more particularly to a high-resolution screening method for anti-inflammatory components of a three-fruit soup based on a gut microbiota metabolic model. Background Technology

[0002] Sanguo Tang is a classic Tibetan medicine formula for clearing heat, detoxifying, and reducing inflammation. It consists of three core medicinal herbs: Terminalia chebula, Terminalia chebula var. chinensis, and Phyllanthus emblica. Clinically, it is widely used to treat various inflammatory conditions such as lung heat, sore throat, and epidemic diseases. Its anti-inflammatory efficacy has been verified through long-term clinical practice. Modern pharmaceutical research indicates that the anti-inflammatory activity of Sanguo Tang mainly comes from its natural chemical components, such as polyphenols and tannins, including gallic acid, corilagin, ellagic acid, and chebulic acid. However, these components are only a part of the complex chemical composition of Sanguo Tang. The core challenge in the modern research of Sanguo Tang is how to accurately screen out highly active ingredients with strong anti-inflammatory activity, clear in vivo efficacy, and development value from among its many components.

[0003] Currently, existing methods for screening the anti-inflammatory components of Sanguotang (a traditional Chinese medicine formula) mainly employ a combination of traditional in vitro cell models (such as the RAW264.7 macrophage model) and in vivo animal models. The core idea is to isolate the original components in Sanguotang and screen for highly active original components by detecting their inhibitory effects on inflammatory factors (TNF-α, IL-6, IL-1β), NO, and other indicators. However, this screening method has significant limitations and cannot accurately reflect the actual pharmacokinetic process in humans: the core anti-inflammatory components in Sanguotang are polyphenols and tannins, which have large molecular weights and limited water / lipid solubility. After oral administration, only a small amount is directly absorbed by the small intestine, while more than 70% enters the colon and is converted into small molecule metabolites through hydrolysis, decarboxylation, and reduction by the intestinal flora. These metabolites are often the main forms in which the anti-inflammatory effect is exerted in vivo.

[0004] Existing screening methods fail to consider the metabolic transformation process of gut microbiota, resulting in two major drawbacks: First, they easily overlook hidden highly active components where the original component is inactive but the metabolites are highly active, leading to incomplete screening results. Second, they mistakenly target original components that are highly active in vitro but degraded and inactivated by gut microbiota in vivo as core development targets, wasting research resources. Furthermore, the in vivo efficacy of the screened components is disconnected from their in vitro activity, making it difficult to meet clinical application needs. In addition, existing screening methods only focus on the direct anti-inflammatory activity of components, failing to consider the indirect anti-inflammatory effects of the Sanguotang compound through regulating gut microbiota balance and repairing the intestinal barrier. This fails to capture the synergistic activity of the components' "direct anti-inflammatory + indirect anti-inflammatory" effects, which is inconsistent with the anti-inflammatory characteristics of Sanguotang, which involves "multiple components, multiple targets, and multiple pathways."

[0005] Meanwhile, although some existing studies have focused on the relationship between gut microbiota and the anti-inflammatory effects of traditional Chinese medicine (such as studies on the regulation of gut microbiota by some traditional Chinese medicine compositions), none of them have formed a dedicated screening technology system for Sanguotang. They have not achieved precise matching between gut microbiota metabolic models and anti-inflammatory activity screening, nor have they established a closed-loop screening process of "metabolic transformation - stratified activity screening - mechanism verification - in vivo confirmation". They cannot solve the core pain point of "disconnect between in vitro activity and in vivo efficacy and incomplete screening results" in the screening of anti-inflammatory components of Sanguotang.

[0006] Therefore, a high-resolution screening method for the anti-inflammatory components of Sanguotang (a traditional Chinese medicine formula) is needed to solve the aforementioned technical problems. This method can simulate the human intestinal metabolic process, take into account both direct and indirect anti-inflammatory activities, and ensure that the screening results are accurate and consistent with clinical practice. Summary of the Invention

[0007] Purpose of the invention: This invention provides a high-resolution screening method for anti-inflammatory components of Sanguotang (a traditional Chinese medicine soup) based on a gut microbiota metabolic model. By constructing a gut microbiota metabolic model specific to Sanguotang, the metabolic process of the microbiota is embedded into the entire screening process, achieving dual screening of "prototype components + metabolites". This method takes into account both direct and indirect anti-inflammatory activities, significantly improving the accuracy and practicality of the screening results.

[0008] Technical Solution: To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically, a high-screening method for anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model, comprising the following steps: S1: Construct a gut microbiota metabolism model specific to Sanguotang, including an in vitro gut microbiota metabolism model and an in vivo gut microbiota metabolism model; S2: Prepare the total extract of Sanguotang, effective fractions of different polarities and single prototype components, co-culture them with an in vitro intestinal flora metabolism model, and separate the metabolite group and the unmetabolized prototype component. S3: Employs a three-tiered anti-inflammatory activity screening system, sequentially screening the overall anti-inflammatory activity of the post-metabolism system, screening the individual activity of the original components and metabolites, and screening the gut microbiota regulation activity to identify highly active ingredients. S4: Structural identification of highly active ingredients to verify their anti-inflammatory mechanism; S5: Detect the content and metabolic efficiency of highly active ingredients, verify its in vivo effectiveness through an in vivo gut microbiota metabolic model, and finally identify the core anti-inflammatory and highly active ingredients of the Three Fruit Soup. S6: Repeatedly screen and verify, optimize screening parameters, and form a standardized screening method.

[0009] Furthermore, in step S1, the in vitro gut microbiota metabolic model uses the SHIME dynamic gut simulation system, inoculating a standardized mixed gut microbiota. This standardized mixed gut microbiota is derived from fresh feces of 3-5 healthy volunteers, obtained through anaerobic gradient dilution and centrifugation purification, with a microbiota abundance ≥10. 9 CFU / mL, Shannon index ≥3.5, similarity to human colonic flora ≥85%, and the ability to metabolize ellagic acid into urolithin A.

[0010] Furthermore, in step S1, the in vivo gut microbiota metabolism model is a humanized mouse model of gut microbiota, which is obtained by gavage inoculation of SPF-grade germ-free mice with standardized mixed gut microbiota. The similarity between the mouse gut microbiota and the human colon microbiota is ≥85%.

[0011] Furthermore, in step S2, the preparation method of the three-fruit soup sample is as follows: Terminalia chebula, Terminalia chebula and Phyllanthus emblica are mixed in a mass ratio of 1:1:1, pulverized and extracted by reflux with 70% ethanol, and concentrated under reduced pressure to obtain the total extract; different polarity effective parts are obtained by gradient extraction with organic solvents; and single prototype components are separated by column chromatography, with a purity of ≥98%.

[0012] 5. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, in step S3, the specific method for screening the three-level anti-inflammatory activity is as follows: S31: Level 1: The co-culture supernatant was used to treat the LPS-induced RAW264.7 macrophage model to screen for highly active culture systems with an inflammatory factor inhibition rate of ≥50% and a NO inhibition rate of ≥45%. S32: Level 2: Separate and purify the metabolite group from the unmetabolized original component, and calculate the IC50 of each component. 50 Value, IC 50 A value ≤50μmol / L indicates a highly active ingredient, locking in Type A (inactive original, highly active metabolites) and Type B (active original, enhanced activity after metabolism); S33: Level 3: Detect the effect of the sample on the gut microbiota structure, and screen for components that can significantly increase the abundance of anti-inflammatory bacteria by ≥30%, reduce the abundance of pro-inflammatory bacteria by ≥25%, and increase the expression level of tight junction proteins of the intestinal barrier by ≥20%.

[0013] Furthermore, in step S4, the structure of the highly active ingredient is identified using UPLC-MS / MS and NMR techniques. The highly active ingredient includes the prototype component and its metabolites. The prototype component is gallic acid, corilagin, ellagic acid, and chebulic acid. The metabolites are gallolactone, urolithin A, urolithin B, and gallic acid-glucuronic acid conjugate.

[0014] Furthermore, in step S5, the content of the highly active prototype component in the Sanguotang compound is ≥0.1%, the amount of metabolites generated is ≥20% of the initial amount of the prototype component, the intestinal flora metabolic conversion rate of the prototype component is ≥30%, and the in vivo inflammation inhibition rate is ≥60%.

[0015] Furthermore, in step S2, the co-culture conditions are anaerobic culture at 37℃ for 24~72h, the sample concentration is 100~500μg / mL, and after co-culture, the sample is treated with 0.1% ascorbic acid for antioxidant treatment and stored at low temperature 4℃.

[0016] Furthermore, in step S4, Western blotting and dual-luciferase reporter gene assays were used to verify the inhibitory effects of the highly active ingredient on the NF-κB and MAPK inflammatory pathways, as well as its inhibitory effect on intestinal endotoxin LPS.

[0017] Beneficial effects: (1) This invention constructs a unique intestinal flora metabolism model for Sanguotang, and combines an in vitro dynamic intestinal simulation system with an in vivo humanized mouse model of intestinal flora to accurately simulate the metabolic environment of Sanguotang in the human intestine after oral administration, thus solving the problem that existing screening methods do not consider intestinal flora metabolism and are disconnected from the actual pharmacokinetic process in humans; A three-tiered activity screening system was established to achieve progressive screening of "overall post-metabolism activity - individual activity of prototype components and metabolites - gut microbiota regulation activity". This system not only captures highly active metabolites that are easily missed by traditional screening, but also takes into account the direct and indirect anti-inflammatory activities of the components, resulting in more comprehensive screening results. (2) The present invention adopts a closed-loop screening process of “metabolic transformation-activity screening-component identification-mechanism verification-in vivo confirmation”, and incorporates metabolic efficiency, component content and in vivo efficacy into the screening indicators to ensure that the screened high-activity components not only have strong in vitro activity, but also have clear in vivo efficacy, stable metabolism, qualified content and industrial development characteristics. To achieve precise matching between intestinal flora metabolism and the compound characteristics of Sanguotang, and to fully combine the compound advantages of Sanguotang's three medicinal materials containing prebiotics and capable of regulating intestinal flora, a combination of highly active ingredients that can form a synergistic effect with intestinal flora was screened out. This approach aligns with the anti-inflammatory properties of Sanguotang, which are characterized by "multiple components and multiple targets," and differs from existing general Chinese medicine screening methods. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Reference Figure 1 A high-level screening method for anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model includes the following steps: S1: Construct a gut microbiota metabolism model specific to Sanguotang (Three-Fruit Soup), including an in vitro gut microbiota metabolism model and an in vivo gut microbiota metabolism model, as detailed below: S11: Construction of an in vitro gut microbiota metabolic model: Fresh feces from healthy individuals were collected, and after anaerobic gradient dilution and centrifugation purification, standardized mixed intestinal flora were obtained. The SHIME dynamic gut microbiota simulation system was used to simulate the continuous digestive and metabolic environment of the human mouth, stomach, small intestine, and colon. Standardized mixed gut microbiota were inoculated into the colonic reaction zone of the SHIME system and cultured in GAM anaerobic medium at 37°C for 48 hours. The abundance and diversity of the microbiota were verified to be consistent with the human colon microbiota by 16S rRNA gene sequencing. It was also able to achieve the typical metabolic transformation of polyphenolic components (ellagic acid → urolithin A), thus obtaining an in vitro gut microbiota metabolic model. S12: Construction of an in vivo gut microbiota metabolic model: SPF-grade germ-free mice were selected and inoculated with standardized mixed intestinal flora in step S11 by gavage. After feeding for 7-10 days, 16S rRNA gene sequencing was used to verify that the similarity between the mouse intestinal flora structure and the human colonic flora was ≥85%, thus obtaining a humanized mouse model of intestinal flora as an in vivo intestinal flora metabolism model. S2: Preparation of Three-Fruit Soup Samples and Treatment of Microbial Metabolic Transformation: S21: Preparation of Three-Fruit Soup Sample: Take three medicinal materials: Terminalia chebula, Terminalia chebula and Phyllanthus emblica, mix them in a mass ratio of 1:1:1, pulverize them and pass them through an 80-mesh sieve. Extract them twice with 70% ethanol under reflux for 2 hours each time. Combine the extracts and concentrate them under reduced pressure until there is no alcohol taste to obtain the total extract of the three-fruit soup. An organic solvent gradient extraction method was used to extract the total extract sequentially with petroleum ether, ethyl acetate, and n-butanol to obtain effective fractions of different polarities (petroleum ether fraction, ethyl acetate fraction, n-butanol fraction, and water fraction). Meanwhile, column chromatography was used to separate the total extract and obtain single prototype components such as gallic acid, corilagin, ellagic acid, and chebulic acid. S22: Microbial community metabolic transformation treatment: The total extract of the three-fruit soup, effective fractions of different polarities, and single prototype components prepared in step S21 were divided into two groups. One group was co-cultured with the in vitro intestinal flora metabolism model in step S11 and anaerobically cultured at 37℃ for 24-72h. Samples were taken at regular intervals (0h, 6h, 12h, 24h, 48h, 72h). A blank flora control group (flora culture system without samples) and a positive drug control group (aspirin) were set up. Another group was used for in vivo metabolic verification in step S5; after co-culture, the samples were centrifuged to obtain the supernatant, which was then pretreated and separated into the "metabolite group" and the "unmetabolized original component". S3: Three-tiered high-screening for anti-inflammatory activity, targeting highly active ingredients (original components + metabolites): S31: Level 1: Screening for overall anti-inflammatory activity of the post-metabolized system: The co-culture supernatant obtained in step S22 (containing metabolites + unmetabolized originals + microbial metabolites) was used to treat the LPS-induced RAW264.7 macrophage model. After culturing for 24 h, the inhibition rates of inflammatory factors TNF-α, IL-6, and IL-1β were detected by ELISA, and the inhibition rate of NO was detected by Griess reagent method. High-activity culture systems with inflammatory factor inhibition rate ≥50% and NO inhibition rate ≥45% were screened out, and inactive / low-activity systems were eliminated. S32: Level 2: Individual activity screening of prototype components and metabolites: Preparative HPLC was used to further separate and purify the "metabolite group" and "unmetabolized original component" separated in step S22, obtaining purified metabolites and unmetabolized original components; these were then applied to a RAW264.7 macrophage model, and the half-maximal inhibitory concentration (IC50) of each component was calculated. 50 IC 50 A value ≤50μmol / L is considered a highly active ingredient. At the same time, the direct activity of the original ingredient before microbial metabolism and its activity after metabolism are compared, and three categories are identified to lock in highly active ingredients: Type A (original ingredient is inactive, metabolites are highly active), Type B (original ingredient is active, activity is enhanced after metabolism), and Type C (original ingredient is highly active, activity is inactive / decreased after metabolism), among which Type A and Type B are the core screening targets. S33: Level 3: Screening for gut microbiota regulatory activity (indirect anti-inflammatory): The samples corresponding to the high-activity culture system in step S22 were used again in the in vitro gut microbiota metabolism model. After 48 hours of culture, 16S rRNA gene sequencing was used to detect changes in gut microbiota structure. Components that could significantly increase the abundance of anti-inflammatory bacteria (Bifidobacterium, Lactobacillus, Akkermansia) by ≥30%, decrease the abundance of pro-inflammatory bacteria (Escherichia coli, Fusobacterium) by ≥25%, and increase the expression level of intestinal barrier tight junction proteins (ZO-1, Occludin) by ≥20% were included in the high-activity component range. S4: Structural identification and mechanism of action verification of highly active ingredients: S41: Structural Assessment The highly active prototype components and metabolites screened in step S3 were chemically identified and their molecular structures were clarified using UPLC-MS / MS, NMR, and infrared spectroscopy (IR). Among them, the metabolites mainly include gallic acid metabolite gallolactone, ellagic acid metabolite urolithin A / B, and corilagin metabolite gallic acid-glucuronic acid conjugate. S42: Mechanism Verification: Western blotting and dual-luciferase reporter gene assays were used to verify the inhibitory effect of the highly active ingredient on the core inflammatory pathways (NF-κB and MAPK pathways); at the same time, the inhibitory effect of the highly active ingredient on intestinal endotoxin (LPS) was detected to clarify its dual mechanism of action of "direct anti-inflammatory + indirect anti-inflammatory". S5: Confirmation of content, metabolic efficiency, and in vivo effectiveness: S51: Content Detection: Uplift-lowering chromatography (UPLC) was used to determine the content of the highly active precursor components identified in step S4 in the Sanguotang compound and single medicinal materials, and to determine the amount of highly active metabolites generated in the in vitro microbial co-culture system, ensuring that the content of the precursor components was ≥0.1% and the amount of metabolites generated was ≥20% of the initial amount of the precursor components. S52: Metabolic efficiency detection: Calculate the intestinal microbiota metabolic transformation rate (amount of metabolites generated / initial amount of prototype component) of highly active prototype components, and screen for components with a metabolic transformation rate ≥30% and stable metabolites (degradation rate ≤15% within 48h); S53: In vivo efficacy verification: The highly active ingredients (prototype ingredients + metabolites) identified in step S4 were mixed in a certain proportion and administered to humanized mice of the gut microbiota from step S12 by gavage. At the same time, a DSS-induced mouse colitis inflammation model was established, and a model control group and a positive drug control group were set up. After 7 days of feeding, the degree of colonic inflammation, serum inflammatory factor levels, intestinal flora structure and intestinal barrier function of mice were tested to verify the in vivo anti-inflammatory effect of the highly active ingredients, ensuring that the in vivo inflammation inhibition rate was ≥60%, and that it could significantly regulate the balance of intestinal flora and repair the intestinal barrier, ultimately identifying the highly active anti-inflammatory core ingredient of Sanguotang. S6: Validation and optimization of screening results: The highly active core ingredients identified in step S5 were screened again 2-3 times using in vitro gut microbiota metabolism models and in vivo humanized mouse models of gut microbiota to ensure the repeatability of the screening results (coefficient of variation ≤10%). Meanwhile, the screening parameters (co-culture time, sample concentration, activity detection index) were optimized to form a standardized high screening method for the anti-inflammatory components of the three-fruit soup.

[0021] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A high-level screening method for anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model, characterized in that, Includes the following steps: S1: Construct a gut microbiota metabolism model specific to Sanguotang, including an in vitro gut microbiota metabolism model and an in vivo gut microbiota metabolism model; S2: Prepare the total extract of Sanguotang, effective fractions of different polarities and single prototype components, co-culture them with an in vitro intestinal flora metabolism model, and separate the metabolite group and the unmetabolized prototype component. S3: Employs a three-tiered anti-inflammatory activity screening system, sequentially screening the overall anti-inflammatory activity of the post-metabolism system, screening the individual activity of the original components and metabolites, and screening the gut microbiota regulation activity to identify highly active ingredients. S4: Structural identification of highly active ingredients to verify their anti-inflammatory mechanism; S5: Detect the content and metabolic efficiency of highly active ingredients, verify its in vivo effectiveness through an in vivo gut microbiota metabolic model, and finally identify the core anti-inflammatory and highly active ingredients of the Three Fruit Soup. S6: Repeatedly screen and verify, optimize screening parameters, and form a standardized screening method.

2. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S1, the in vitro gut microbiota metabolism model uses the SHIME dynamic gut simulation system, inoculating a standardized mixed gut microbiota. This standardized mixed gut microbiota is derived from fresh feces of 3-5 healthy volunteers, obtained through anaerobic gradient dilution and centrifugation purification, with a microbiota abundance ≥10. 9 CFU / mL, Shannon index ≥3.5, similarity to human colonic flora ≥85%, and the ability to metabolize ellagic acid into urolithin A.

3. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S1, the in vivo gut microbiota metabolism model is a humanized mouse model of gut microbiota, which is obtained by gavage inoculation of SPF-grade germ-free mice with standardized mixed gut microbiota. The similarity between the mouse gut microbiota and the human colon microbiota is ≥85%.

4. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S2, the preparation method of the three-fruit soup sample is as follows: Terminalia chebula, Terminalia chebula and Phyllanthus emblica are mixed in a mass ratio of 1:1:1, pulverized and extracted by reflux with 70% ethanol, and concentrated under reduced pressure to obtain the total extract; different polarity effective parts are obtained by gradient extraction with organic solvents; single prototype components are separated by column chromatography, and the purity of the single prototype component is ≥98%.

5. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S3, the specific method for screening the three-level anti-inflammatory activity is as follows: S31: Level 1: The co-culture supernatant was used to treat the LPS-induced RAW264.7 macrophage model to screen for highly active culture systems with an inflammatory factor inhibition rate of ≥50% and a NO inhibition rate of ≥45%. S32: Level 2: Separate and purify the metabolite group from the unmetabolized original component, and calculate the IC50 of each component. 50 Value, IC 50 A value ≤50μmol / L indicates a highly active ingredient, locking in Type A (inactive original, highly active metabolites) and Type B (active original, enhanced activity after metabolism); S33: Level 3: Detect the effect of the sample on the gut microbiota structure, and screen for components that can significantly increase the abundance of anti-inflammatory bacteria by ≥30%, reduce the abundance of pro-inflammatory bacteria by ≥25%, and increase the expression level of tight junction proteins of the intestinal barrier by ≥20%.

6. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S4, the structure of the highly active ingredient is identified using UPLC-MS / MS and NMR techniques. The highly active ingredient includes the prototype component and metabolites. The prototype component is gallic acid, corilagin, ellagic acid, and chebulic acid. The metabolites are gallolactone, urolithin A, urolithin B, and gallic acid-glucuronic acid conjugate.

7. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S5, the content of the highly active prototype component in the Sanguotang compound is ≥0.1%, the amount of metabolites generated is ≥20% of the initial amount of the prototype component, the intestinal flora metabolic transformation rate of the prototype component is ≥30%, and the in vivo inflammation inhibition rate is ≥60%.

8. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S2, the co-culture conditions are anaerobic culture at 37℃ for 24~72h, sample concentration is 100~500μg / mL, after co-culture, 0.1% ascorbic acid is added to the sample for antioxidant treatment, and it is stored at low temperature 4℃.

9. The method for high screening of anti-inflammatory components of three-fruit soup based on a gut microbiota metabolic model according to claim 1, characterized in that, In step S4, Western blotting and dual-luciferase reporter gene assays were used to verify the inhibitory effects of the highly active ingredients on the NF-κB and MAPK inflammatory pathways, as well as on the inhibitory effects on intestinal endotoxin LPS.

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