Method for detecting changes of metabolites and florae of rats with chronic inflammation by using Hua Tuo beans

Abstract

The invention discloses a method for detecting changes of metabolites and florae of chronic inflammation rats by Hua Tuo beans, and relates to the technical field of pharmacodynamics, and the method comprises the following steps: S1, constructing a chronic inflammation rat model: 40 SPF-level male SD rats are adaptively fed for 7 days and are randomly divided into five groups, namely a blank group, a model group, a Hua Tuo bean high-dose group, a Hua Tuo bean low-dose group and a positive group, and the blank group, the model group, the Hua Tuo bean high-dose group, the model group, the Hua Tuo bean low-dose group and the positive group are matched with the model group; the model group, the positive group and the Hua Tuo bean administration group are subjected to tail vein injection of 200 mu g / kg of LPS once on the first day of a week. The excrement metabolites are analyzed by adopting a UPLC-Q-Exactive-MS technology, the intestinal flora is detected in combination with 16S rDNA high-throughput sequencing, the action path of Hua Tuo beans for regulating and controlling chronic inflammation through a flora-metabolic axis is disclosed for the first time, 13 differential metabolites are screened out through a multivariate statistical method, correlation analysis is carried out on the 13 differential metabolites and the differential flora, and the purpose of treating chronic inflammation is achieved. The regulation and control effects of Hua Tuo beans on metabolic pathways and flora structures are clarified, key metabolic pathways are disclosed, and molecular evidence is provided for a multi-target anti-inflammatory mechanism of natural products.

Description

Technical Field

[0001] This invention relates to the field of pharmacodynamics, specifically to a method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation caused by *Hua Tuo Dou* (a type of bean). Background Technology

[0002] Chronic inflammation is a core pathological basis for many major diseases, such as metabolic diseases, autoimmune diseases, malignant tumors, and cardiovascular diseases. Lipopolysaccharide (LPS), as a Gram-negative bacterial endotoxin, induces the abnormal release of pro-inflammatory factors (such as TNF-α, IL-6, and IL-1β) by activating the Toll-like receptor 4 (TLR4) signaling pathway, leading to multiple organ dysfunction. Existing anti-inflammatory drugs often have side effects and insufficient targeting, necessitating the development of multi-target intervention strategies based on natural products. Hua Tuo bean, a traditional Yao medicine, has purgative, detoxifying, and blood-stasis-dispersing effects. Previous studies have confirmed its ability to inhibit acute inflammation and LPS-induced cellular inflammatory responses. However, whether Hua Tuo bean can regulate metabolic disorders and gut microbiota dysbiosis in chronic inflammatory states remains unstudied. Chronic inflammation is closely related to metabolic pathways (such as lipid metabolism and steroid synthesis) and gut microbiota, with microbiota-host metabolic interactions being a key link in inflammation regulation. Currently, there is a lack of integrated metabolomics and microbiota analysis techniques to elucidate the mechanism of action of Hua Tuo bean. The existing technology has the following problems: The quality of Hua Tuo soybean is only indicated by Hua Tuo soybean alkaloid B, which cannot reflect its multi-component synergistic effect; moreover, there are no reports of Hua Tuo soybean regulating chronic inflammation through the metabolism-microbe axis; at the same time, traditional inflammation models do not combine multi-omics technologies to explore differential metabolites and key microbes. Summary of the Invention

[0003] This invention provides a method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo beans, in order to solve the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean includes the following steps: S1: Establishment of a chronic inflammation rat model: Forty SPF-grade male SD rats were randomly divided into 5 groups after 7 days of adaptive feeding: blank group, model group, high-dose Huatuo bean group, low-dose Huatuo bean group, and positive group, with 8 rats in each group. The model group, positive group, and Huatuo bean administration group were given LPS 200 μg / kg via tail vein injection once a week on the first day. The blank group was given an equal volume of sterile saline injection. Drug intervention was given the day after the first LPS injection. The blank group and model group were given the same volume of pure water by gavage. The positive group was given prednisolone acetate tablets 5 mg / kg by gavage.

[0005] S2: Hua Tuo Bean Intervention: Take 3.0 kg of Hua Tuo Bean crude powder, add 7 times the amount of 80% ethanol (calculated as L / kg) and reflux extract three times, 2 hours each time. Filter, combine the filtrates, evaporate and concentrate until there is no alcohol odor, and obtain ethanol extract paste. Store at 4℃ for later use. The high-dose group of Hua Tuo Bean was given 35 g / kg of raw Hua Tuo Bean by gavage, and the low-dose group was given 17.8 g / kg of raw Hua Tuo Bean by gavage for a total of 4 weeks.

[0006] S3: Sample collection: After the rats died, the heart, liver, spleen, lungs and kidneys of each group of rats were removed, washed with physiological saline, dried, weighed and fixed in 4% neutral paraformaldehyde solution for histopathological sectioning.

[0007] S4: Metabolite detection: UPLC-Q-Exactive-MS technology was used to study the metabolites in the feces of rats with chronic inflammation. Multivariate statistical analysis methods such as principal component analysis, partial least discriminant analysis, and orthogonal partial least squares discriminant analysis were used to analyze the metabolic profile of the feces and to look for potential biomarkers with differences.

[0008] S5: Microbial community change detection: 16S rDNA high-throughput sequencing technology was used to study the cecal contents of feces from rats with chronic inflammation. Differential microbiota related to chronic inflammation were obtained through α-diversity and β-diversity analysis and LEfSe analysis, and Spearman correlation analysis was performed with 13 differential metabolites.

[0009] S6: Statistical analysis.

[0010] A further improvement of the technical solution of the present invention is as follows: In S3, during the administration of the drug via gavage, the weight of each group of rats is recorded daily. After 4 weeks of drug intervention, each group of rats is anesthetized by intraperitoneal injection of 50 mg / kg of 0.5% sodium pentobarbital solution. Blood from the abdominal aorta of the rats is collected and placed in a vacuum blood collection tube. The tube is left to stand at room temperature for 30 min, then centrifuged at 4°C and 3500 rpm / min for 10 min. The serum is then aliquoted and stored at -80°C for detection by the ELISA kit. The levels of hs-CRP, IL-6, IL-1β, and TNF-α in the serum are measured according to the instructions of the ELISA kit.

[0011] A further improvement to the technical solution of this invention lies in the following: When washing with physiological saline and absorbing moisture with filter paper during step S3, the organ index is calculated using the following formula: Organ index (mg / g) = organ mass (mg) / rat body mass (g); The collected heart, liver, spleen, lung, and kidney specimens were dehydrated, embedded in paraffin, and sectioned to a thickness of 3 μm according to standard procedures. Appropriate paraffin sections were selected and routine hematoxylin-eosin staining was performed. The pathological morphological changes of the tissues were observed under a microscope.

[0012] A further improvement of the technical solution of the present invention is that: in S5, 16S rDNA high-throughput sequencing is used to analyze the species composition and abundance changes of gut microbiota, and diversity analysis is performed. Based on LEfSef analysis, differential microbiota are analyzed, and Spearman correlation analysis is performed on the relationship between differential microbiota and fecal metabolites.

[0013] A further improvement of the technical solution of this invention lies in the following: the diversity analysis includes changes at the phylum, class, family, and genus levels. Specifically: at the phylum level, the abundance of Firmicutes and Bacteroidetes in the Hua Tuo Dou section decreased (P<0.001), while the abundance of Verrucous Microbes significantly increased (P<0.01); at the class level, the relative abundance of Bacillus increased, while the abundance of Bacteroidetes decreased (P<0.001), the relative abundance of Verrucous Microbes increased (P<0.01), and the relative abundance of Clostridium decreased (P<0.001); at the family level, the relative abundance of Lactobacillusaceae (P<0.05), Akkermansiaceae and Bacteroidetesaceae increased (P<0.01), while the relative abundance of Trichophytonceae and Mulliebaceae decreased (P<0.001); at the genus level, the abundance of Lactobacillus (P<0.05), Akkermansia (P<0.01), and Bacteroidetes (P<0.01) significantly increased.

[0014] A further improvement of the technical solution of the present invention is that: the differential bacterial groups analyzed based on LESef are bacterial groups that play an important role in the Hua Tuo Dou section, including Verrucous phylum, Verrucous class, Verrucous orders, Akkermansia genus, Akkermansiaceae family, Akkermansia myxophilus, Lactobacillus genus, and Lactobacillus family.

[0015] A further improvement of the technical solution of the present invention is that: S6 uses Graphpad Pism 9.5 statistical software for statistical analysis. All measurement data are expressed as mean ± standard deviation. The statistical software is used to compare the means among multiple samples. When the variances are homogeneous, the one-way ANOVA LSD test is used. When the variances are unequal, Tamhane's T2 test is used for multiple comparisons. P < 0.05 is considered to be a difference.

[0016] A further improvement to the technical solution of the present invention is that step S4 includes the following steps: A1: Sample collection. 24 hours before the animals were sacrificed, the rats in each group were placed in metabolic cages and the feces produced by the rats in each group during the 12 hours of drug metabolism were collected. During the collection period, all animals were fasted but allowed to drink water. The fecal samples were stored in a -80℃ freezer for testing. A2: Sample pretreatment. The feces of rats in all groups will be analyzed by LC / MS. After thawing, 100 mg of fecal sample will be transferred to an EP tube, and 400 μL of 80% methanol aqueous solution will be added. After vortexing and mixing, the sample will be treated in an ice bath at 4℃ for 5 min, centrifuged at 15000g for 20 min, and an appropriate amount of supernatant will be taken to dilute the concentration with mass spectrometry grade water. After centrifugation at 15000g for 20 min at 4℃, the supernatant will be collected and analyzed by LC / MS. Equal volumes of fecal injection solutions from each group will be mixed to form quality control samples. The experimental samples will be replaced with methanol aqueous solution, and blank samples will be constructed according to the same pretreatment procedure as the experimental samples. A3: LC / MS data processing involves importing the mass spectrometry data into Compound Discoverer 3.3 database search software. A multidimensional mass spectrometry analysis strategy is employed. First, the retention time and mass-to-charge ratio of metabolites are preliminarily screened, and peak area correction is used to optimize identification accuracy. During data extraction, parameters such as 5 ppm mass deviation, 30% signal intensity deviation, minimum signal intensity threshold, and adduct ions are set as peak extraction standards. Quantitative peak area analysis is then conducted. Based on target ion integration analysis technology, the mass spectrometry information of molecular ion peaks and characteristic fragment ions is analyzed. Spectral matching verification is performed using the three major mass spectrometry databases: mzCloud, mzVault, and Masslist. In the experimental procedure, blank samples are used to eliminate background ion interference. A quantitative correction algorithm is applied: [original sample quantitative value / (total sample metabolite quantitative values ​​ / total QC1 sample metabolite quantitative values)] to normalize the original mass spectrometry data, obtaining the relative peak area values ​​of each metabolite. Compounds with a relative peak area CV value lower than 30% in the QC samples are then screened. A4: Multivariate statistical analysis was performed, importing the obtained data into the Maiwei platform for PCA, PLS-DA, and OPLS-DA analyses to obtain the VIP value for each metabolite. In the univariate statistical analysis, the independent samples t-test was used to analyze the significance of metabolite differences between the two groups. At the same time, the fold change index was used to quantify the degree of difference in metabolite expression levels between groups. VIP>1, P-value<0.05, and FC>1 were used as screening criteria to screen potential biomarkers of chronic inflammation. By integrating the KEGG, HMDB, and LIPIDMaps databases, systematic functional annotation and classification analysis were performed on the identified metabolites. Based on the Metaboanalyst 5.0 online analysis platform, metabolic pathway topology analysis was performed on differential metabolites in the chronic inflammation model to screen out key metabolic pathways in fecal metabolites.

[0017] A further improvement to the technical solution of the present invention is that step S5 includes the following steps: B1: Sample collection. On the last day of drug administration, 0.5% sodium pentobarbital solution (50 mg / kg) was administered intraperitoneally to collect complete blood. After that, the contents of the cecum were collected into a sterile EP tube and temporarily stored on dry ice. After all samples were collected, they were stored at -80°C for analysis. B2: Extraction and PCR amplification of total DNA from cecal microorganisms. An appropriate amount of cecal contents was taken, and DNA was extracted using a DNA extraction kit. The purity and concentration of the extracted DNA were detected by 1% agarose gel electrophoresis. Then, an appropriate amount of sample DNA was taken into a centrifuge tube and diluted to 1 ng / μL with sterile water. The diluted genomic DNA sample was used as a template, and the 16SV4 region was selected as the PCR amplification region. The primer sequences were: 515F: 5'-GTGCCAGCMGCCGCGGTAA-3'; 806R: 5'-GGACTACHVGGGTWTCTAAT-3'. All PCR mixtures were added with 15 µL buffer, 0.2 µM primers, and 10 ng of genomic DNA template. The PCR reaction program was as follows: first denaturation at 98 °C for 1 minute, followed by 30 cycles at 98 °C (10 s), 50 °C (30 s), and 72 °C (30 s), and finally held at 72 °C for 5 minutes. B3: Mixing and purification of PCR products. The PCR products were initially detected by electrophoresis using a 2% agarose gel. The PCR products that passed the detection were purified by magnetic beads. The purified PCR products were mixed in equal volumes by enzyme-linked immunosorbent assay (ELISA). After thorough mixing, a second electrophoresis was performed. For the target band, a universal DNA purification and recovery kit was used to recover the product. B4: Library construction and sequencing. Library construction was performed using a library construction kit. After the library passed the test, it was sequenced using NovaSeq6000 with PE250 sequencing. B5: Data quality control. Bioinformatics techniques were used to systematically analyze high-throughput sequencing data. Barcode sequences and PCR amplification primer sequences were used as sample identification criteria to complete sample sorting of the sequencing data. After sequence trimming, sequences were assembled using FLASH software to obtain raw tag data. Strict quality control of RawTags was then performed using FASTP software to obtain high-quality Clean Tags data. To eliminate chimeric interference, Tag sequences were compared and detected with the Silva (16S / 18S) and Unie databases to obtain valid data. Based on this, the DADA2 functional module and QIIME2 software were used to perform sequence denoising, generating ASVs and their characteristic tables, and completing species annotation analysis. B6: Bioinformatics analysis. Based on the obtained feature sequence results, Venn diagrams are generated in R using the VennDiagram function to visually display the common and unique information among different samples. For the species composition of various samples at the biological taxonomic level, a histogram of abundance proportions is constructed using the SVG function. The α diversity index is calculated using the QIIME2 software. Species richness indices such as Richness, Chao1, and ACE, diversity indices such as Invsimpson, Shannon, and Simpson, as well as the Pielou evenness index and goodness index are used. The s_coverage sequencing depth metric comprehensively assesses the species composition, biodiversity level, and sequencing data quality of the gut microbiota. Dilution curves and rank clustering curves were plotted using R software. To assess community complexity and compare differences between samples, β-diversity was evaluated based on Bray-Curtis distance in QIME2. Principal coordinate analysis was used for data processing, and LEfSe analysis was employed to screen for differentially expressed microbiota between groups. The results were presented as LEfSe phylogenetic clade plots and LDA value distribution histograms. Finally, Spearman rank correlation analysis was used to specifically assess the bidirectional association between significantly differentially expressed microbiota screened by 16S rDNA sequencing and potential biomarkers identified by metabolomics.

[0018] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: 1. This invention provides a method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation caused by *Hypericum pertussis*. UPLC-Q-Exactive-MS technology is used to analyze fecal metabolites, combined with 16S rDNA high-throughput sequencing to detect gut microbiota. This method reveals for the first time the pathway by which *Hypericum pertussis* regulates chronic inflammation through the microbiota-metabolic axis. Furthermore, 13 differentially expressed metabolites were screened using multivariate statistical methods, and correlation analysis was performed with differentially expressed microbiota to clarify the regulatory role of *Hypericum pertussis* in metabolic pathways and microbiota structure, revealing key metabolic pathways and providing molecular evidence for the multi-target anti-inflammatory mechanism of natural products.

[0019] 2. This invention provides a method for detecting changes in metabolites and flora in rats with chronic inflammation caused by Hua Tuo soybeans. It significantly increases the abundance of probiotics. Based on LEfSe analysis, it is found that Hua Tuo soybeans specifically enrich anti-inflammatory bacteria such as Verrucae, Akkermansia, and Lactobacillus, confirming that it alleviates inflammation by repairing the intestinal barrier.

[0020] 3. This invention provides a method for detecting changes in metabolites and flora in rats with chronic inflammation using Hua Tuo Dou (a type of bean). Hua Tuo Dou, especially the low-dose group, significantly reduces serum pro-inflammatory factors, with effects comparable to the positive control drug prednisolone acetate. It can inhibit systemic inflammatory response, reduce pathological damage to multiple organs, significantly reduce the elevated spleen and kidney indices in the model group, and reverse organ inflammatory swelling. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the effect of the Hua Tuo bean on the body weight of rats with chronic inflammation according to the present invention; Figure 2 This is a schematic diagram of the organ indices of rats in each group according to the present invention; Figure 3 This is a schematic diagram illustrating the pathological changes in the cardiac tissue of rats with chronic inflammation caused by Hua Tuo Dou (a type of bean). Figure 4 This is a schematic diagram illustrating the pathological changes in liver tissue of rats with chronic inflammation caused by Hua Tuo Dou (a type of bean). Figure 5 This is a schematic diagram illustrating the pathological changes in the spleen tissue of rats with chronic inflammation caused by Hua Tuo Dou (a type of bean). Figure 6 This is a schematic diagram illustrating the pathological changes in the lung tissue of rats with chronic inflammation caused by Hua Tuo Dou (a type of bean). Figure 7 This is a schematic diagram illustrating the pathological changes in the kidney tissue of rats with chronic inflammation caused by Hua Tuo Dou (a type of bean). Figure 8 This is a schematic diagram illustrating the effect of the Hua Tuo bean of the present invention on the serum inflammatory factor content in rats with chronic inflammation. Figure 9 This is a total positive ion chromatogram of rat feces from various rats according to the present invention; Figure 10 This is a total ion flow diagram of negative ions in the feces of various rats according to the present invention; Figure 11 This is a schematic diagram of the PCA results of fecal samples from chronic inflammatory rats in each group according to the present invention; Figure 12 PLS-DA results of fecal samples from chronic inflammatory rats in each group in the positive ion mode of this invention; Figure 13 PLS-DA results of fecal samples from rats with chronic inflammation in each group using the negative ion mode of this invention; Figure 14 The OPLS-DA results and displacement diagrams of fecal samples from chronic inflammatory rats in each group under the positive ion mode of this invention are shown. Figure 15 The OPLS-DA results and displacement diagrams of fecal samples from chronic inflammatory rats in each group under the positive ion mode of this invention are shown. Figure 16 This is a KEGG classification diagram of differential metabolites in this invention; Figure 17 This is a bubble diagram of KEGG enrichment of differential metabolites of the present invention; Figure 18 This invention relates to the Venn diagram based on OTU. Figure 19 This is a sparse curve of the sample from the present invention; Figure 20 This is a clustering curve diagram of each group in this invention; Figure 21 This is a schematic diagram of the α-diversity analysis of the present invention; Figure 22 This is a schematic diagram of the PCoA analysis based on Bray-Curtis distance in this invention; Figure 23 This is a diagram showing the NMDS analysis results based on Bray-Curtis distance in this invention; Figure 24 This is a diagram showing the analysis results based on Bray-Curtis distance in this invention; Figure 25 This is a diagram showing the structural analysis of rat species at the phylum level in each group of the present invention; Figure 26 This is a diagram showing the structural analysis of rat-level species in each group of this invention; Figure 27 This is a structural analysis diagram of rat species at the family level for each group in this invention; Figure 28 This is a structural analysis diagram of rat species at the genus level for each group in this invention; Figure 29 This is a schematic diagram illustrating the correlation analysis between rat intestinal differentials and metabolites in this invention. Detailed Implementation

[0022] The present invention will be further described in detail below with reference to embodiments: Example 1 like Figures 1-8 As shown, this invention provides a method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo Dou (a type of bean). In this embodiment, rat body weight was recorded daily during the experiment. Before modeling, there was no difference in body weight among the groups. After 4 weeks of intervention, compared with the blank group, the body weight parameters of the model group rats showed no change and no statistical difference. Compared with the model group, the positive group and the high- and low-dose Hua Tuo Dou groups showed a decreasing trend, but there was no statistical difference. (See attached...) Figure 1 As shown in the attached figure; the effects of Hua Tuo bean on organ indices in rats with chronic inflammation. Figure 2 As shown, compared with the blank group, the heart and liver indices of rats in the chronic inflammation model group were decreased, but there was no statistical difference; the spleen, lung, and kidney indices were significantly increased (P<0.05 or P<0.01). Compared with the model group, the heart and liver indices of the positive group were increased, while the spleen and lung indices were decreased, but there was no statistical difference; the kidney index was significantly decreased (P<0.01). The heart index was increased and the lung index was decreased in the high- and low-dose Hua Tuo Dou groups, but there was no statistical difference; the liver index was increased in the high-dose Hua Tuo Dou group, but there was no statistical difference; the liver index was decreased in the low-dose Hua Tuo Dou group, but there was no statistical difference; the spleen and kidney indices were significantly decreased in the high- and low-dose Hua Tuo Dou groups (P<0.05 or P<0.01).

[0023] Example 2 like Figures 1-8 As shown, based on Example 1, the present invention provides a technical solution: for heart tissue, see Appendix Figure 3 In the blank group, the cardiomyocytes were oval, tightly and neatly arranged, and in good condition. In the model group, the cardiomyocytes showed focal degeneration, vacuolation of cytoplasm, capillary network of myocardial fibers, focal congestion and hemorrhage, loose edema between myocardial fibers, and inflammatory cell infiltration. The high- and low-dose groups of Hua Tuo Dou and the positive group improved the arrangement of cardiac tissue and the size of intercellular spaces to varying degrees. For liver tissue, see appendix. Figure 4 In the control group, the liver tissue of rats was normal and intact, with hepatocytes in normal condition, arranged radially around the central vein, and the hepatic cords were orderly arranged without breakage. No obvious inflammatory cell infiltration, degeneration, or necrosis was observed. In the model group, the liver tissue of rats showed obvious pathological changes, with disordered and polygonal hepatocytes, obvious swelling, severe edema and degeneration, no clear portal area, unclear hepatic plate structure, narrowed hepatic sinusoids, and obvious inflammatory cell infiltration. The disordered hepatocytes in the high- and low-dose Hua Tuo Dou groups and the positive group were improved to some extent compared with the model group, and some hepatocytes showed mild edema, which was most obvious in the low-dose Hua Tuo Dou group. For spleen tissue, see appendix. Figure 5 In the control group, microscopic examination revealed red and white pulp structures, with medullary cords and sinus structures within the red pulp. Cell count was normal, with no obvious infiltrating congestion or inflammatory cells. In the model group, microscopic examination revealed red and white pulp structures, with medullary cords and sinus structures within the red pulp, numerous lymphoid tissue structures, and a few eosinophil infiltrations. The red pulp was slightly congested, with a few hyalinated vascular structures visible. The white pulp consisted of irregular lymphoid follicles, with some white pulp structures slightly atrophied. Splenic marginal zone structures were visible around the follicles. Overall, the degree of splenic tissue lesions was mild. Compared with the model group, no significant reduction in cell count was observed in the high- and low-dose Hua Tuo Dou groups, and the degree of lesions in each group was mild. For lung tissue, see appendix. Figure 6 In the blank control group, the lung parenchyma structure of rats was intact, the alveolar cavities were evenly distributed, and no obvious apoptosis or inflammatory cell infiltration was observed. There was no congestion between the alveolar septa. In the model group, under microscopic examination, more alveolar cavities were compressed and deformed, some alveolar cavities were closed, some alveolar septa were broken, alveoli were fused, more inflammatory cell infiltration was observed in the alveolar cavities and alveolar septa, a few focal hemorrhages were observed, the alveolar septa were slightly thickened, the fibrous tissue was mildly proliferated, and the bronchiolar epithelium was damaged and sloughed off. The high- and low-dose Hua Tuo Dou groups and the positive control group could alleviate the above pathological conditions. A few inflammatory cell infiltrations were observed in the alveolar cavities and alveolar septa, and the pulmonary interstitial edema was significantly improved. For kidney tissue, see appendix. Figure 7In the control group, the kidney tissue of rats was intact, with abundant glomeruli, renal tubules, and collecting ducts, and no obvious pathological changes. In the model group, the renal parenchyma of rats showed significant pathological abnormalities, with severely damaged nephrons, mild cytoplasmic loosening and edema-like changes in some tubular cells, casts in some lumens, and inflammatory cell infiltration. The pathological damage to the kidney tissue and the inflammatory cell infiltration were reduced in the high- and low-dose Hua Tuo Dou and positive groups. Appendix Figures 3-7 (a): Blank group, (b): Model group, (c): High-dose group, (d): Low-dose group, (e): Positive group.

[0024] Example 3 like Figures 1-8 As shown in Example 1, this invention provides a technical solution: the levels of hs-CRP, IL-6, IL-1β, and TNF-α in each group of rats were detected, and the results are shown in the table below and appendix. Figure 8 As shown, compared with the blank group, the serum levels of hs-CRP, IL-6, IL-1β, and TNF-α in the model group rats were significantly increased (P<0.05 or P<0.01). Compared with the model group rats, the levels of hs-CRP, IL-6, IL-1β, and TNF-α in the low-dose Hua Tuo Dou group and the positive group were significantly decreased (P<0.05 or P<0.01). The IL-1β level in the high-dose Hua Tuo Dou group was decreased, but there was no statistical difference, while the levels of hs-CRP, IL-6, and TNF-α were significantly decreased (P<0.05 or P<0.01).

[0025] The batch numbers of the reagents used in this experiment are shown in the table below: The models of the instruments used in this experiment are shown in the table below: Example 4 like Figures 1-8 As shown in Example 1, this invention provides a technical solution: using liquid chromatography-mass spectrometry (LC-MS) to expand the number of detectable metabolites, quantitatively determining endogenous metabolites in rat feces using LC-MS, and analyzing the metabolic profile of feces using multivariate statistical analysis methods such as PCA, PLS-DA, and OPLS-DA, aiming to screen potential differential biomarkers and explore related metabolic pathways in depth, thereby elucidating the therapeutic mechanism of Hua Tuo Dou in LPS-induced inflammatory diseases; In this embodiment, the experimental results are as follows: fecal metabolic profile analysis: total ion chromatograms of positive and negative ions in the feces of each rat are shown in the figure. Figure 9 , Figure 10In the figure, (A) total ion chromatogram of blank solvent, (B) total ion chromatogram of rat feces in blank group, (C) total ion chromatogram of rat feces in model group, (D) total ion chromatogram of rat feces in Hua Tuo Dou group, and (E) total ion chromatogram of quality control sample. As shown in the figure, the total ion chromatograms of rat feces samples from each group show similar spectral characteristics, but there are certain differences in peak shape and peak area parameters. This phenomenon suggests that there are certain differences in metabolite content among samples from different experimental groups. Principal component analysis of chronic inflammatory rats and their metabolome after drug administration: Principal component analysis was performed on the fecal samples of chronic inflammatory rats from each group, and the results are shown in the figure. Figure 11 In positive ion mode, principal components 1 and 2 together accounted for 43.27% and 21.94% of the total variance, respectively, with a cumulative contribution rate of 65.21%. In negative ion mode, principal components 1 and 2 together accounted for 37.96% and 17.84% of the variance, respectively, with a cumulative contribution rate of 55.80%. Both were within the 95% confidence interval. The samples from each group showed aggregation, indicating significant differences in fecal metabolic levels among the groups. The control group and the model group exhibited ideal clustering characteristics within a certain region, and there was a certain separation trend between the two groups. The metabolic profiles of the Hua Tuo Dou group and the control group overlapped. The chronic inflammatory rats showed a trend towards normalization, suggesting that Hua Tuo Dou intervention may have altered the metabolic regulation of chronic inflammatory rats. Partial least squares discriminant analysis (PLS-DA) of the metabolic groups of chronic inflammatory rats and those after drug administration: PLS-DA results are shown in […]. Figure 12 , Figure 13 The figures (A) show the control group vs. the model group, (B) the Hua Tuo bean group vs. the model group, and (C) the Hua Tuo bean group vs. the control group. An interaction phenomenon was observed between the two groups, and the degree of separation of metabolites between them was not significant. Therefore, further OPLS-DA analysis was conducted to seek a more useful analytical model to explain the differences in fecal metabolites among rats with chronic inflammation. Orthogonal partial least squares discriminant analysis was performed on the chronic inflammatory rats and their metabolites after drug administration. OPLS-DA analysis is a multivariate statistical analysis method with supervised pattern recognition characteristics, which can effectively eliminate interfering factors irrelevant to the study, thereby achieving the screening of differential metabolites. The OPLS-DA method was used to draw score plots for the control group, model group, and Hua Tuo bean group. The results are shown in [Figure 1]. Figures 2-6 , Figures 2-7 ,result( Figure 14 , Figure 15 (A), (B), and (C) show that under positive and negative ion modes, the blank group and the model group were significantly separated, indicating successful modeling. This suggests differences in metabolic regulation and metabolites between the blank and model groups. The trend between the Hua Tuo bean group and the blank group is similar, revealing that the metabolic regulation of chronic inflammation after Hua Tuo bean intervention is close to normal levels. Figure 14 , Figure 15(D), (E), and (F) represent the explanatory power of the model, and (Q) represents the predictive power. For both positive and negative ion models, R²Y is close to 1 and Q²Y is not less than 0.5, indicating that the statistical model has good explanatory and predictive capabilities. The Q² and R² regression models constructed after 200 random permutation tests show that the R² index is higher than the Q² index, and the intercept of the Q² regression line on the Y-axis is negative. This statistical characteristic excludes the possibility of overfitting, ensuring the reliability of subsequent analysis results. Figure 14 and Figure 15 In the figure, (A) OPLS-DA plot of blank group vs. model group; (B) OPLS-DA plot of Hua Tuo bean group vs. model group; (C) OPLS-DA plot of Hua Tuo bean group vs. blank group; (D) Permutation plot of blank group vs. model group; (E) Permutation plot of Hua Tuo bean group vs. model group; (F) Permutation plot of Hua Tuo bean group vs. blank group. Based on the analysis of inter-group differences and the reliability of the detection model in combination with OPLS-DA, the importance projection value (VIP), fold change (FC), and significance level (P value) are comprehensively considered in the screening of differential metabolites. Based on the indicators set by the projection importance variable (VIP) score greater than or equal to 1, P less than 0.05, and FC value greater than 1, differentially expressed metabolites were screened. Abnormal fluctuations in fecal metabolite expression were observed in the model group rats. A total of 13 metabolites showed increases or decreases after intervention with Hua Tuo Dou (a type of herb), as shown in Table 1. The results showed that compared with the control group, the model group exhibited significantly increased levels of 3 metabolites and decreased levels of 10 metabolites. Compared with the model group, after Hua Tuo Dou intervention, the increased levels of the 3 metabolites in the model group were reduced, and the decreased levels of the 10 metabolites were increased. Table 1: Differential metabolite results before and after drug administration in rats with chronic inflammation The results are as follows Figure 16 and Figure 17 As shown, the 13 differentially metabolized metabolic pathways involved are mainly: steroid hormone biosynthesis, linoleic acid metabolism, purine metabolism, steroid biosynthesis, unsaturated fatty acid synthesis, arachidonic acid metabolism, glycerol ester metabolism, lysine degradation, glycerophospholipid metabolism, amino sugar and nucleotide metabolism, with lipid metabolism-related pathways being the most significantly enriched metabolic pathways.

[0026] Example 5 like Figures 1-8As shown in Example 1, this invention provides experimental results for gut microbiota detection: In biodiversity research, operable taxa (OTUs) serve as key indicators, and their application value is mainly reflected in the characterization and analysis of species information. Venn diagrams, a visualization tool, are typically used to quantitatively analyze the distribution characteristics of OTUs among samples, thereby revealing the similarity in species composition and common characteristics of different sample groups. The number of OTUs in each sample group is obtained through species annotation, as shown in the figure. Figure 18 The study found 473 OTUs in the control group, 219 in the model group, and 439 in the Hua Tuo Dou group, with a total of 219 OTUs across the three groups. These results indicate that Hua Tuo Dou treatment can affect the OTU composition of the rat gut microbiota. Differential analysis of microbial community α-diversity index, sparse curve: The curve analysis method used in this study has a dual function: on the one hand, it can assess the adequacy of sequencing depth; on the other hand, it can serve as an important parameter for evaluating microbial community diversity. Specifically, the trend of the curve's slope angle clearly demonstrates the effect level of sequencing intensity on the characterization of sample heterogeneity, such as... Figure 19 As shown, the results indicate that the curves for all three groups of samples exhibit a stable trend, suggesting that the sequencing depth has reached saturation and can fully reflect the diversity characteristics of existing species in the samples; hierarchical clustering curves: as shown Figure 20 As shown, the sample size used for sequencing is sufficient, and the species coverage in the samples is broad. α-diversity index: Gut microbiota α-diversity analysis is mainly used to assess the richness and evenness of the gut microbiota community distribution, reflecting the equilibrium state of the gut microbiota. In the α-diversity assessment system, Richness, Chao1, and ACE are used to characterize the species richness of the gut microbiota community; Invsimpson, Shannon, and Simpson indices are used to measure the species heterogeneity of the community; the Pielou index is used to determine the evenness of microbial distribution; and the goods_coverage index is used to assess sequencing depth and community coverage. The results are as follows: Figure 21As shown in the figure, (A) Richness index, (B) Shannon index, (C) Simpson index, (D) Pielou index, (E) Invsimpson index, (F) Chao1 index, (G) ACE index, and (H) goods_coverage index are all lower in the chronic inflammation model group compared to the control group. *P<0.05, **P<0.01, ***P<0.001. The total number of intestinal flora in the chronic inflammation model group rats was significantly lower in all four groups: Richness index (P<0.01), Chao1 index (P<0.01), ACE index (P<0.01), Invsimpson index (P<0.05), Shannon index (P<0.01), Simpson index (P<0.05), and Pielou index (P<0.05). Compared with other model groups, the Hua Tuo Dou group showed higher Richness index (P<0.01), Chao1 index (P<0.01), ACE index (P<0.01), Invsimpson index (P<0.001), Shannon index (P<0.01), Simpson index (P<0.01), and Pielou index (P<0.001). This indicates that the diversity and richness of the gut microbiota in rats with chronic inflammation are decreased, and the abundance distribution of species is uneven. However, intervention with Hua Tuo Dou increased the diversity and richness of the rat gut microbiota, and the species abundance distribution became more even. Although there was no significant difference in the goods_coverage index among the groups, all groups had a goods_coverage index greater than 0.9, indicating sufficient sequencing depth. Microbial community β-diversity analysis, PCoA analysis: The results of PCoA analysis among the three groups showed that ( Figure 22 The control group and the model group were significantly separated by a large distance, indicating a significant difference in the microbial community composition between the two groups. However, there was a significant overlap between the samples from the Hua Tuo bean group and the control group, suggesting that the microbial community composition of rats with chronic inflammation after Hua Tuo bean intervention was similar to that of normal rats. NMDS analysis: Bray-Curtis distance was used to perform NMDS analysis on the samples from the three groups, and the results are as follows... Figure 23 As shown in the figure, the model group and the control group are distributed in two regions and are far apart, indicating that there are differences in the gut microbiota composition between the model group and the control group. The control group and the Hua Tuo Dou group show spatial clustering in the two-dimensional scatter plot, and the distribution areas of the two groups overlap significantly, indicating that they have high similarity in gut microbiota structure. The stress value is less than 0.2, indicating that the NMDS results have good explanatory power. Anosim and Adonis analyses: Anosim and Adonis analyses were performed on different groups based on Bray-Curtis distance, and the results are as follows. Figure 24As shown, the R-values ​​for the blank group and the model group, and the model group and the *Haloxylon ammodendron* group, were all equal to 1, indicating a significant difference in community structure between the two groups (P < 0.05); Species composition analysis: as shown... Figure 25 As shown in the figure, (A) the relative abundance of species at the phylum level, (B) the bar chart at the phylum level, and (C) the circular diagram at the phylum level, indicate that the abundance of bacterial communities differed significantly among different treatment groups at the phylum level. Firmicutes accounted for 66%, 61%, 44%, and 10% of the total abundance in the control group, model group, and *Eriocaulon buergerianum* group, respectively; Bacteroidetes accounted for 31%, 36%, and 24% of the total abundance in the control group, model group, and *Eriocaulon buergerianum* group, respectively; and Verrucomicrobiota accounted for 0.29%, 0.2%, and 27% of the total abundance in the control group, model group, and *Eriocaulon buergerianum* group, respectively. These results suggest that Firmicutes dominated the control group. The relative abundance of Firmicutes and Bacteroidota in the model group rat intestine was significantly higher than that of other bacterial groups, with Bacteroides being the second most abundant, and the ratio of the two reaching 2.12. Compared with the control group, Firmicutes and Bacteroides remained dominant in the intestine of the model group rats, but the relative abundance of Firmicutes showed a decreasing trend, while the relative abundance of Bacteroides increased, and the ratio decreased to 1.69. After intervention with Hua Tuo Dou, the abundance of Firmicutes and Bacteroides (P<0.001) decreased, and the ratio rebounded to 1.83. At the same time, Verrucomicrobiota showed a significant increasing trend (P<0.01), which suggests that Verrucomicrobiota has a high biological sensitivity to Hua Tuo Dou. like Figure 26 The figure shows (A) relative abundance of species at the class level and (B) bar chart at the class level. At the class level, compared with the control group, the relative abundance of Bacilli in the intestine of rats in the model group decreased and the relative abundance of Bacteroidia increased. Compared with the model group, the relative abundance of Bacilli in the intestine of rats in the Hua Tuo Dou group increased and the relative abundance of Bacteroidia decreased (P<0.001). In addition, Hua Tuo Dou also increased the relative abundance of Verrucomicrobiae (P<0.01) and decreased the relative abundance of Clostridia (P<0.001). like Figure 27As shown in the figure, (A) is the relative abundance of species at the family level and (B) is the bar chart at the family level. At the family level, compared with the control group, the relative abundance of Lactobacillaceae and Prevotellaceae in the model group decreased (P<0.01), while the relative abundance of Lachnospiraceae and Muribaculaceae increased (P<0.01). Compared with the model group, the relative abundance of Lactobacillaceae increased (P<0.05), while the relative abundance of Lachnospiraceae and Muribaculaceae decreased (P<0.001). However, the relative abundance of Prevotellaceae and Lachnospiraceae was not reversed, while the relative abundance of Akkermansiaceae and Bacteroidetes significantly increased (P<0.01). like Figure 28 As shown in the figure, (A) is the relative abundance of species at the genus level, (B) is the bar chart at the genus level, and (C) is the bar chart of sample distribution at the genus level. At the genus level, the relative abundance of Lactobacillus in the control group, model group, and Hua Tuo Dou group were 4.2%, 2.9%, and 21%, respectively; the relative abundance of Akkermansia in the control group, model group, and Hua Tuo Dou group were 0.29%, 0.22%, and 27%, respectively. Compared with the control group, the abundance of Lactobacillus, Alloprevotella (P<0.001), and Lachnospiraceae_NK4A136_group (P<0.001) in the model group was reduced. Compared with the model group, the abundance of Lactobacillus (P<0.05), Akkermansia (P<0.01), and Bacteroides (P<0.01) in the Huatuo bean group was significantly increased, accounting for 21%, 27%, and 10%, respectively. It can be inferred that Huatuo bean may regulate chronic inflammation through the abundance of these three genera. Correlation analysis between differentially expressed gut microbiota and fecal metabolism: In this study, Spearman hierarchical cluster analysis was performed on the screened fecal gut microbiota and differentially expressed fecal metabolism. Red represents positive correlation, and green represents negative correlation; the results are shown in […]. Figure 29Glycerol 1-hexadecanoate showed a significant positive correlation with the relative abundance of Muribacterium (f__Muribaculaceae) (P<0.05); and a significant negative correlation with the abundance of Lactobacillus (g__Lactobacillus), Prevotella (g__Prevotella_9g__Prevotella_9), and Akkermansia (s__Akkermansia_muciniphila) (P<0.01). Propionylcarnitine showed a significant positive correlation with the abundance of *Muribaculaceae* (P<0.01), while trimethyllysine showed a significant negative correlation with the abundance of *Muribaculaceae* (P<0.05) and a significant positive correlation with the abundance of *Lactobacillus*, *Prevotella*, and *Akkermansia muciniphila* (P<0.01 or P<0.05).

[0027] The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation caused by Hua Tuo bean, characterized in that: Includes the following steps: S1: Establishment of a chronic inflammation rat model: 40 SPF-grade male SD rats were randomly divided into 5 groups after 7 days of adaptive feeding: blank group, model group, high-dose Huatuo bean group, low-dose Huatuo bean group, and positive group, with 8 rats in each group. The model group, positive group, and Huatuo bean administration group were given LPS 200 μg / kg via tail vein injection once a week on the first day. The blank group was given an equal volume of sterile saline injection. Drug intervention was given the day after the first LPS injection. The blank group and model group were given the same volume of pure water by gavage. The positive group was given prednisolone acetate tablets 5 mg / kg by gavage. S2: Hua Tuo Bean Intervention: Take 3.0 kg of Hua Tuo Bean crude powder, add 7 times the amount of 80% ethanol (calculated as L / kg) and reflux extract three times, 2 hours each time. Filter, combine the filtrates, evaporate and concentrate until there is no alcohol odor, and obtain ethanol extract paste. Store at 4℃ for later use. The high-dose group of Hua Tuo Bean was given 35 g / kg of raw Hua Tuo Bean by gavage, and the low-dose group was given 17.8 g / kg of raw Hua Tuo Bean by gavage for a total of 4 weeks. S3: Sample collection: After the rats died, the heart, liver, spleen, lungs and kidneys of each group of rats were removed, washed with physiological saline, dried, weighed and fixed in 4% neutral paraformaldehyde solution for histopathological sectioning. S4: Metabolite detection: UPLC-Q-Exactive-MS technology was used to study the metabolites in the feces of rats with chronic inflammation. Principal component analysis, partial least discriminant analysis, orthogonal partial least squares discriminant analysis and other multivariate statistical analysis methods were used to analyze the metabolic profile of the feces and to look for potential biomarkers of difference. S5: Microbial community change detection: 16S rDNA high-throughput sequencing technology was used to study the cecal contents of feces from rats with chronic inflammation. Differential microbiota related to chronic inflammation were obtained through α-diversity and β-diversity analysis and LEfSe analysis, and Spearman correlation analysis was performed with 13 differential metabolites. S6: Statistical analysis.

2. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 1, characterized in that: During the administration of the drug via gavage in S3, the body weight of each group of rats was recorded daily. After 4 weeks of drug intervention, each group of rats was anesthetized by intraperitoneal injection of 50 mg / kg of 0.5% sodium pentobarbital solution. Blood was collected from the abdominal aorta of the rats and placed in a vacuum blood collection tube. The tube was left to stand at room temperature for 30 min, then centrifuged at 3500 rpm / min for 10 min at 4℃. The serum was aliquoted and stored at -80℃ for detection by the ELISA kit. The levels of hs-CRP, IL-6, IL-1β, and TNF-α in the serum were measured according to the instructions of the ELISA kit.

3. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 1, characterized in that: When the organ index is calculated in step S3 after washing with physiological saline and blotting dry with filter paper, the organ index is as follows: Organ index (mg / g) = organ mass (mg) / rat body mass (g); The collected heart, liver, spleen, lung, and kidney specimens were dehydrated, embedded in paraffin, and sectioned to a thickness of 3 μm according to standard procedures. Appropriate paraffin sections were selected and routine hematoxylin-eosin staining was performed. The pathological morphological changes of the tissues were observed under a microscope.

4. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 1, characterized in that: In S5, 16S rDNA high-throughput sequencing was used to analyze the species composition and abundance changes of gut microbiota, and diversity analysis was performed. Based on LEfSef analysis, differential microbiota were analyzed, and Spearman correlation analysis was performed on the relationship between differential microbiota and fecal metabolites.

5. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 4, characterized in that: The diversity analysis included changes at the phylum, class, family, and genus levels. Specifically: at the phylum level, the abundance of Firmicutes and Bacteroidetes in the Hua Tuo Dou section decreased (P<0.001), while the abundance of Verrucous Microbes significantly increased (P<0.01); at the class level, the relative abundance of Bacillus increased, while the abundance of Bacteroidetes decreased (P<0.001), the relative abundance of Verrucous Microbes increased (P<0.01), and the relative abundance of Clostridium decreased (P<0.001); at the family level, the relative abundance of Lactobacillusaceae (P<0.05), Akkermansiaceae and Bacteroidetesaceae increased (P<0.01), while the relative abundance of Trichophytonceae and Mulliebaceae decreased (P<0.001); at the genus level, the abundance of Lactobacillus (P<0.05), Akkermansia (P<0.01), and Bacteroidetes (P<0.01) significantly increased.

6. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 4, characterized in that: The differentially expressed microbiota based on LESef analysis are important microbiota in the Hua Tuo Dou section, including Verrucous phylum, Verrucous class, Verrucous orders, Akkermansia genus, Akkermansiaceae family, Akkermansia myxophilus, Lactobacillus genus, and Lactobacillus family.

7. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 1, characterized in that: S6 uses Graphpad Pism 9.5 statistical software for statistical analysis. All measurement data are expressed as mean ± standard deviation. The statistical software is used to compare the means among multiple samples. When the variances are homogeneous, the one-way ANOVA LSD test is used. When the variances are unequal, Tamhane's T2 test is used for multiple comparisons. P < 0.05 is considered to be a difference.

8. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 1, characterized in that: S4 includes the following steps: A1: Sample collection. 24 hours before the animals were sacrificed, the rats in each group were placed in metabolic cages and the feces produced by the rats in each group during the 12 hours of drug metabolism were collected. During the collection period, all animals were fasted but allowed to drink water. The fecal samples were stored in a -80℃ freezer for testing. A2: Sample pretreatment. The feces of rats in all groups will be analyzed by LC / MS. After thawing, 100 mg of fecal sample will be transferred to an EP tube, and 400 μL of 80% methanol aqueous solution will be added. After vortexing and mixing, the sample will be treated in an ice bath at 4℃ for 5 min, centrifuged at 15000g for 20 min, and an appropriate amount of supernatant will be taken to dilute the concentration with mass spectrometry grade water. After centrifugation at 15000g for 20 min at 4℃, the supernatant will be collected and analyzed by LC / MS. Equal volumes of fecal injection solutions from each group will be mixed to form quality control samples. The experimental samples will be replaced with methanol aqueous solution, and blank samples will be constructed according to the same pretreatment procedure as the experimental samples. A3: LC / MS data processing involves importing the mass spectrometry data into Compound Discoverer 3.3 database search software. A multidimensional mass spectrometry analysis strategy is employed. First, the retention time and mass-to-charge ratio of metabolites are preliminarily screened, and peak area correction is used to optimize identification accuracy. During data extraction, parameters such as 5 ppm mass deviation, 30% signal intensity deviation, minimum signal intensity threshold, and adduct ions are set as peak extraction standards. Quantitative peak area analysis is then conducted. Based on target ion integration analysis technology, the mass spectrometry information of molecular ion peaks and characteristic fragment ions is analyzed. Spectral matching verification is performed using the three major mass spectrometry databases: mzCloud, mzVault, and Masslist. In the experimental procedure, blank samples are used to eliminate background ion interference. A quantitative correction algorithm is applied: [original sample quantitative value / (total sample metabolite quantitative values ​​ / total QC1 sample metabolite quantitative values)] to normalize the original mass spectrometry data, obtaining the relative peak area values ​​of each metabolite. Compounds with a relative peak area CV value lower than 30% in the QC samples are then screened. A4: Multivariate statistical analysis was performed, importing the obtained data into the Maiwei platform for PCA, PLS-DA, and OPLS-DA analyses to obtain the VIP value for each metabolite. In the univariate statistical analysis, the independent samples t-test was used to analyze the significance of metabolite differences between the two groups. At the same time, the fold change index was used to quantify the degree of difference in metabolite expression levels between groups. VIP>1, P-value<0.05, and FC>1 were used as screening criteria to screen potential biomarkers of chronic inflammation. By integrating the KEGG, HMDB, and LIPIDMaps databases, systematic functional annotation and classification analysis were performed on the identified metabolites. Based on the Metaboanalyst 5.0 online analysis platform, metabolic pathway topology analysis was performed on differential metabolites in the chronic inflammation model to screen out key metabolic pathways in fecal metabolites.

9. The method for detecting changes in metabolites and gut microbiota in rats with chronic inflammation using Hua Tuo bean according to claim 1, characterized in that: S5 includes the following steps: B1: Sample collection. On the last day of drug administration, 0.5% sodium pentobarbital solution (50 mg / kg) was administered intraperitoneally to collect complete blood. After that, the contents of the cecum were collected into a sterile EP tube and temporarily stored on dry ice. After all samples were collected, they were stored at -80°C for analysis. B2: Extraction and PCR amplification of total DNA from cecal microorganisms. An appropriate amount of cecal contents was taken, and DNA was extracted using a DNA extraction kit. The purity and concentration of the extracted DNA were detected by 1% agarose gel electrophoresis. Then, an appropriate amount of sample DNA was taken into a centrifuge tube and diluted to 1 ng / μL with sterile water. The diluted genomic DNA sample was used as a template, and the 16SV4 region was selected as the PCR amplification region. The primer sequences were: 515F: 5'-GTGCCAGCMGCCGCGGTAA-3'; 806R: 5'-GGACTACHVGGGTWTCTAAT-3'. All PCR mixtures were added with 15 µL buffer, 0.2 µM primers, and 10 ng of genomic DNA template. The PCR reaction program was as follows: first denaturation at 98 °C for 1 minute, followed by 30 cycles at 98 °C (10 s), 50 °C (30 s), and 72 °C (30 s), and finally held at 72 °C for 5 minutes. B3: Mixing and purification of PCR products. The PCR products were initially detected by electrophoresis using a 2% agarose gel. The PCR products that passed the detection were purified by magnetic beads. The purified PCR products were mixed in equal volumes by enzyme-linked immunosorbent assay (ELISA). After thorough mixing, a second electrophoresis was performed. For the target band, a universal DNA purification and recovery kit was used to recover the product. B4: Library construction and sequencing. Library construction was performed using a library construction kit. After the library passed the test, it was sequenced using NovaSeq6000 with PE250 sequencing. B5: Data quality control. Bioinformatics techniques were used to systematically analyze high-throughput sequencing data. Barcode sequences and PCR amplification primer sequences were used as sample identification criteria to complete sample sorting of the sequencing data. After sequence trimming, sequences were assembled using FLASH software to obtain raw tag data. Strict quality control of the raw tags was then performed using FASTP software to obtain high-quality Clean Tags data. To eliminate chimeric interference, the tag sequences were compared and detected with the Silva (16S / 18S) and Unie databases to obtain valid data. Based on this, the DADA2 functional module and QIIME2 software were used to perform sequence denoising, generating ASVs and their characteristic tables, and completing species annotation analysis. B6: Bioinformatics analysis. Based on the obtained feature sequence results, Venn diagrams are generated in R using the VennDiagram function to visually display the common and unique information among different samples. For the species composition of various samples at the biological taxonomic level, a histogram of abundance proportions is constructed using the SVG function. The α diversity index is calculated using the QIIME2 software. Species richness indices such as Richness, Chao1, and ACE, diversity indices such as Invsimpson, Shannon, and Simpson, as well as the Pielou evenness index and goodness index are used. The s_coverage sequencing depth metric comprehensively assesses the species composition, biodiversity level, and sequencing data quality of the gut microbiota. Dilution curves and rank clustering curves were plotted using R software. To assess community complexity and compare differences between samples, β-diversity was evaluated based on Bray-Curtis distance in QIME2. Principal coordinate analysis was used for data processing, and LEfSe analysis was employed to screen for differentially expressed microbiota between groups. The results were presented as LEfSe phylogenetic clade plots and LDA value distribution histograms. Finally, Spearman rank correlation analysis was used to specifically assess the bidirectional association between significantly differentially expressed microbiota screened by 16S rDNA sequencing and potential biomarkers identified by metabolomics.

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