Synbiotics mixture capable of improving body metabolism homeostasis and intestinal microecology as well as preparation method and application of synbiotics mixture

Abstract

The invention relates to the technical field of probiotics and prebiotics, in particular to a synbiotics mixture capable of improving the metabolism homeostasis and intestinal microecology of an organism and a preparation method and application of the synbiotics mixture. Akkermansia muciniphila (A.muciniphila) is a novel probiotic, and the camellia oleosa seed oil disclosed by the invention can be used for exerting the prebiotic effect on the A.muciniphila (A.muciniphila). Compared with camellia oleosa seed oil or A.muciniphila which is independently used, the synbiotics mixture prepared by mixing the camellia oleosa seed oil and the A.muciniphila can obviously enhance the conditioning effect on the metabolic homeostasis of a host and the intestinal microecology.

Description

Technical Field

[0001] The present invention relates to the technical field of probiotics and prebiotics, and in particular to a synbiotic mixture capable of improving metabolic homeostasis and intestinal microecology, and a preparation method and application thereof. Background Art

[0002] Probiotics interact with the host or other intestinal flora through their cell structure molecules or metabolites, competing for nutrients and colonization sites, thus benefiting the host's health; while prebiotics are substrates that can be selectively utilized by host microorganisms and are beneficial to health. Synbiotics are a mixture of probiotics and prebiotics, in which prebiotics and probiotics can produce synergistic effects, thereby regulating intestinal metabolism, protecting the intestinal barrier, promoting beneficial microbial flora, and inhibiting potential pathogens; at the same time, synbiotics can reduce the level of harmful metabolites and significantly increase the levels of SCFAs, ketones, carbon disulfide, methyl acetate and other substances, which have a beneficial effect on the host and improve abnormal conditions such as obesity, type 2 diabetes, irritable bowel syndrome, and infection.

[0003] Akkermansia muciniphila (A.muciniphila) is a new type of probiotic. Akkermansia muciniphila (Akk bacteria) is a normal bacterium in the human intestine and a mucin-degrading bacterium. The strain MucT was isolated by Derrien in 2004. It is an oval, Gram-negative anaerobic bacterium and a representative of the Verrucomicrobia. A.muciniphila is negatively correlated with obesity, diabetes, cardiovascular disease and low-grade inflammation. Oral administration of Akkermansia muciniphila can improve the symptoms related to individual metabolic diseases. Akkermansia muciniphila is expected to become a candidate drug for the treatment of type 2 diabetes and obesity.

[0004] Currently, there is no public technical solution for synbiotics based on camellia oil or A. muciniphila that improves metabolic homeostasis and intestinal microecology.

[0005] Therefore, the current main research direction is to find a synbiotic mixture of Akkermansia muciniphila that can significantly enhance the regulatory effects on host metabolic homeostasis and intestinal microecology. Summary of the invention

[0006] In order to solve the above technical problems existing in the prior art, the present invention provides a synbiotic mixture that can improve the metabolic homeostasis of the body and the intestinal microecology, and a preparation method and application thereof. Specifically, this is achieved through the following technical solutions:

[0007] A synbiotic mixture capable of improving metabolic homeostasis and intestinal microecology comprises the following raw materials: Akkermansia muciniphila and camellia oleifera oil.

[0008] Furthermore, the Akkermansia muciniphila is a model strain of Akkermansia muciniphila.

[0009] Furthermore, the Akkermansia muciniphila is deposited in the China Industrial Microbiological Culture Collection Center with a deposit number of CICC: 24917.

[0010] Furthermore, the mixing ratio of Akkermansia muciniphila and camellia oil is: 1×10 8 CFU Akkermansia muciniphila corresponds to 0.7-0.8 mL of camellia oil.

[0011] The synbiotic mixture that can improve the body's metabolic homeostasis and intestinal microecology is used in the preparation of drugs for regulating intestinal metabolism, protecting the intestinal barrier, promoting beneficial microbial flora, controlling weight gain, and regulating blood lipids and blood sugar, as well as in the preparation of drugs for obesity, type 2 diabetes, irritable bowel syndrome, and infection.

[0012] Application of camellia oil in enhancing the regulatory effect of Akkermansia muciniphila on host metabolic homeostasis and intestinal microecology.

[0013] Furthermore, the invention specifically relates to the application of camellia seed oil in the preparation of related reagents that enhance the regulating effect of Akkermansia muciniphila on host metabolic homeostasis and intestinal microecology.

[0014] Compared with the prior art, the technical effects created by the present invention are embodied in:

[0015] (1) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can control the weight gain of individuals on a high-fat diet.

[0016] (2) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can reduce an individual's food intake, thereby reducing their energy intake.

[0017] (3) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can alleviate abnormal conditions such as kidney hyperplasia and hypertrophy.

[0018] (4) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can significantly improve individual blood lipid levels and significantly alleviate individual insulin resistance levels.

[0019] (5) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can significantly alleviate the level of liver cell damage in an individual, significantly alleviate the individual's circulatory inflammation, significantly increase the individual's serum intestinal hormone level, and significantly reverse the individual's serum adipokor level.

[0020] (6) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology produces serum-specific metabolites such as epicatechin, myristic acid, S-glutathione-L-cysteine, vitamin E, etc., compared with the intervention of a single component (camellia seed oil, A. muciniphila).

[0021] (7) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can significantly reduce the level of fatty degeneration of individual hepatocytes, significantly alleviate the level of oxidative stress of individual hepatocytes, reduce the relative expression levels of Tnf and Ccl2 genes in individual adipose tissue, and has no significant effect on the relative expression levels of genes related to fatty acid synthesis in the liver of an individual.

[0022] (8) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can reduce intestinal permeability, prevent intestinal FITC-dextran from leaking into the blood circulation through the intestinal gap, increase the number of goblet cells in colon tissue, and significantly affect the expression of tight junction protein ZO-1 in colon tissue.

[0023] (9) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology significantly increases the relative abundance of beneficial bacteria such as Blautia and Dubosiella compared with the intervention of a single component (camellia seed oil, A. muciniphila).

[0024] (10) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology produces unique metabolites of cecal contents, such as α-muricolic acid, compared with the intervention of a single component (camellia seed oil, A. muciniphila).

[0025] (11) The synbiotic mixture of the present application that can improve the body's metabolic homeostasis and intestinal microecology can significantly increase the level of SCFAs in the individual's cecal contents. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the weight of mice. A: weight change; B: final weight; C: weight gain, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0027] Figure 2 is the food and energy intake of mice. A: food intake; B: energy intake, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0028] Figure 3 is the blood lipid level of mice. A: TG; B: TC; C: LDL-C; D: HDL-C, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0029] Figure 4 The fasting blood glucose and insulin levels of mice. A: fasting blood glucose; B: fasting insulin, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0030] Figure 5 is the serum aminotransferase level of mice. A: AST; B: ALT, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0031] Figure 6 It is the level of serum inflammatory markers in mice. A: LPS; B: CRP; C: TNF-α; D: IL-6, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0032] Figure 7 It is the level of serum intestinal hormones in mice. A: GLP-1; B: PYY, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0033] Figure 8 is the level of serum adipokine in mice. A: ADPN; B: LEP; C: ADPN / LEP, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0034] Fig. 9 This is the PCA analysis of differential metabolites in mouse serum. A: anion mode; B: cation mode.

[0035] Fig.10 This is the OPLS-DA analysis of differential metabolites in mouse serum. A: HF vs. LF; B: POST vs. HF; C: PRO vs. HF; D: PRE vs. HF; E: SYN vs. HF.

[0036] Fig.11 These are the permutation test results of each OPLS-DA model. A: HF vs. LF; B: POST vs. HF; C: PRO vs. HF; D: PRE vs. HF; E: SYN vs. HF.

[0037] Fig.12 This is a volcano plot of differential metabolites in mouse serum. A: HF vs. LF; B: POST vs. HF; C: PRO vs. HF; D: PRE vs. HF; E: SYN vs. HF.

[0038] Fig.13 is the Venn diagram of differential metabolites among the groups.

[0039] Fig.14This is the KEGG enrichment analysis diagram of differential metabolites in mouse serum. A: HF vs. LF; B: POST vs. HF; C: PRO vs. HF; D: PRE vs. HF; E: SYN vs. HF.

[0040] Fig.15 Oil red O staining of mouse liver (20). A: LF; B: HF; C: POST; D: PRO; E: PRE; F: SYN; G: Hepatocyte fatty degeneration rate (%), different superscript letters indicate statistically significant differences (p<0.05), n=3, scale bar: 50 μm.

[0041] Fig.16 is the antioxidant activity level of mouse liver. A: T-AOC; B: SOD; C: CAT; D: GSH-Px; E: MDA, different superscript letters indicate statistically significant differences (p<0.05), n≥8.

[0042] Fig.17 is the relative expression level of fatty acid synthesis-related genes in mouse liver. Different superscript letters indicate significant statistical differences (p<0.05), n=4.

[0043] Fig.18 is the relative expression level of inflammation-related genes in mouse adipose tissue. Different superscript letters indicate statistically significant differences (p<0.05). n=4.

[0044] Fig.19 is the fluorescence intensity of FITC-dextran in mouse serum, different superscript letters indicate statistically significant differences (p<0.05), n=3.

[0045] Fig. 20 This is AB-PAS staining of mouse colon (20). A: LF; B: HF; C: POST; D: PRO; E: PRE; F: SYN; G: number of goblet cells, different superscript letters indicate statistically significant differences (p<0.05), n=3, scale bar: 50μm.

[0046] Fig.21 The expression of ZO-1 in mouse colon (20). A: LF; B: HF; C: POST; D: PRO; E: PRE; F: SYN; G: Average optical density statistical graph, different superscript letters indicate statistically significant differences (p<0.05), n=3, scale bar: 50μm.

[0047] Fig. 22 is based on the dilution curve of ASV.

[0048] Fig.23 It is the hierarchical clustering curve based on ASV.

[0049] Fig.24 is the alpha diversity of mouse intestinal flora. A: Chao1 index; B: Shannon index, different superscript letters indicate statistically significant differences (p<0.05), n=4.

[0050] Fig.25 This is the PCoA analysis of mouse intestinal flora.

[0051] Fig.26 It is a cluster analysis of species abundance at the classification level of intestinal flora.

[0052] Fig. 27 is the F / B ratio of intestinal flora, different superscript letters indicate statistically significant differences (p<0.05), n=4.

[0053] Fig.28 It is a cluster analysis of species abundance at the genus classification level of intestinal flora.

[0054] Fig.29 This is the PCA analysis of differential metabolites in mouse cecal contents. A: anion mode; B: cation mode.

[0055] Fig.30 This is the OPLS-DA analysis of differential metabolites in mouse cecal contents. A: HF vs. LF; B: POST vs. HF; C: PRO vs. HF; D: PRE vs. HF; E: SYN vs. HF.

[0056] Fig.31 This is a volcano plot of differential metabolites in mouse cecal contents. A: HF vs. LF; B: POST vs. HF; C: PRO vs. HF; D: PRE vs. HF; E: SYN vs. HF.

[0057] Fig.32 is the Venn diagram of differential metabolites among groups of mouse cecal contents.

[0058] Fig.33 This is the KEGG enrichment analysis of differential metabolites in mouse cecal contents. A: HF vs. LF; B: POST vs. HF; C: PRO vs. HF; D: PRE vs. HF; E: SYN vs. HF.

[0059] Fig.34 is the content of short-chain fatty acids in the cecal contents of mice. Different superscript letters indicate significant statistical differences (p<0.05), n≥4. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further defined below in conjunction with specific implementation methods, but the scope of protection required is not limited to the description.

[0061] Synbiotic mixture based on camellia oil and A. muciniphila improves metabolic homeostasis and intestinal microecology in mice fed a high-fat diet

[0062] 1 Experimental Materials

[0063] Experimental mice (C57BL / 6J mice, 6-8 weeks old, weighing 20.0±2.0g) were housed in a constant temperature and humidity animal room (12-hour light / dark cycle, temperature 23℃±2℃, humidity 55%±5%), with free access to food (maintenance feed) and drinking water (sterile water). After 7 days of adaptation, they were randomly divided into cages, with 5 mice in each cage, for a total of 12 cages. All mice were divided into 6 groups, with 10 mice in each group. The specific grouping and feeding strategy are shown in Table 1. Among them, the Western diet high-fat diet (H10141) provided 41% energy from fat. Centralized gavage was performed from 9 to 11 am every day, feed and drinking water were replaced, and food intake was recorded; mouse cages and drinking water bottles were thoroughly disinfected every 3 days, and bedding was replaced; body weight was recorded every week. On the last day of the 8th week, mice were fasted but not watered 12 hours in advance. After 12 hours, mice were anesthetized with ether inhalation, and then killed by cervical dislocation, weighed, and samples were collected.

[0064] The A. muciniphila live bacteria (No.: CICC 24917) used in the experiment were obtained by anaerobic culture in BHI liquid medium for 24 hours, freshly prepared daily, and the number of passages did not exceed 5 generations. Before gavage, the medium was washed away with sterile, pre-cooled saline and the concentration was adjusted to 1×10 9 CFU / mL, which was used for oral gavage of mice in the PRO and SYN groups. At the same time, the live bacterial liquid of A. muciniphila treated in the same way was placed in a 70°C water bath for 30 min and cooled at 4°C overnight to obtain the pasteurized A. muciniphila bacterial liquid, which was used for oral gavage of mice in the POST group.

[0065] Table 1 Experimental mouse groups

[0066]

[0067] 2. Sample Collection

[0068] 2.1 Serum collection

[0069] The eyes of mice were enucleated and blood was collected. The whole blood samples were allowed to stand at room temperature for 30 min and then centrifuged at 4°C (3500 rpm, 15 min). The serum was divided into several 200 μL centrifuge tubes and frozen and temporarily stored in a -80°C refrigerator.

[0070] 2.2 Tissue Collection

[0071] 1.5 hours before killing the mice, 3 mice were randomly selected from each group, weighed and gavaged with 0.1 mg / μL FITC-dextran aqueous solution (dose 0.5 mg / g·bw). After gavage, the mice were returned to the cage, and anesthetized, killed, and tissue (whole blood) were collected synchronously with the other mice 1.5 hours after the action.

[0072] After the mice were disinfected with 75% ethanol, the heart, liver, spleen, kidney, fat, intestine and other tissues were separated on ice, weighed and rinsed several times with sterile pre-cooled PBS, wiped dry and divided into several 2mL cryotubes, frozen in liquid nitrogen and temporarily stored in a -80℃ refrigerator. The samples used for tissue staining were fixed in tissue fixative.

[0073] After separating the mouse cecum in a clean bench, the outer wall of the intestine was squeezed with sterile forceps to allow the cecum contents to flow into a 2 mL sterile cryopreservation tube, which was quickly frozen in liquid nitrogen and then temporarily stored in a -80°C refrigerator.

[0074] 3 Sample analysis

[0075] 3.1 Serum biochemical analysis

[0076] Various biochemical kits were used to detect mouse serum TC, TG, LDL-C, HDL-C, ALT, AST, blood glucose and other indicators on a Synergy H4 multi-function microplate reader according to the steps described in the manufacturer's instructions.

[0077] 3.2 Serum ELISA analysis

[0078] Various ELISA kits were used to detect mouse serum insulin, CRP, LPS, GLP-1, PYY, LEP, ADPN, IL-6, TNF-α and other indicators on a Synergy H4 multi-function microplate reader according to the steps described in the manufacturer's instructions.

[0079] 3.3 Serum metabolomics analysis

[0080] The serum samples were taken out from -80℃, thawed completely on ice, vortexed and mixed, and 50μL of the sample was pipetted into another 1.5mL centrifuge tube. 300μL of acetonitrile / methanol solution (1:4, v / v) was added and vortexed for 3min. After centrifugation at 4℃ (12000rpm, 10min), 200μL of the supernatant was pipetted into another 1.5mL centrifuge tube. After standing at -20℃ for 30min, the sample was centrifuged again at 4℃ (12000rpm, 3min). The supernatant was aspirated for UHPLC-MS analysis.

[0081] Chromatographic separation was performed on a Shimadzu LC-30A ultra-high performance liquid chromatograph (containing a Waters 2.1 mm × 100 mm ACQUITY Premier HSS T3 1.8 μm column) with the following parameters:

[0082] Injection volume: 4 μL;

[0083] Column temperature: 40°C;

[0084] Flow rate: 0.4mL / min

[0085] Mobile phase A: 0.1% formic acid / water;

[0086] Mobile phase B: 0.1% formic acid / acetonitrile;

[0087] The gradient elution program is shown in Table 2:

[0088] Table 2 Gradient elution program for mouse serum UHPLC-MS analysis

[0089]

[0090] After chromatographic separation, the samples were analyzed by primary and secondary mass spectrometry using an AB Triple TOF 6600+ mass spectrometer, and electrospray ionization was used for detection in positive and negative ion modes. The parameters were as follows:

[0091] Collection time: 10min;

[0092] Spray gas: 50psi;

[0093] Auxiliary heating gas: 60psi;

[0094] Air curtain gas: 35psi;

[0095] Ion source temperature: 550°C (positive ion mode), 450°C (negative ion mode);

[0096] Spray voltage: 5000V (positive ion mode), -4000V (negative ion mode);

[0097] Declustering voltage: ±60V (positive and negative modes);

[0098] The first-stage mass spectrometer settings are as follows:

[0099] Scan range: 50-1000Da;

[0100] Scanning cumulative time: 0.20s / spectra;

[0101] Collision energy: ±10V;

[0102] The secondary mass spectrometer settings are as follows:

[0103] Scan range: 25-1000Da;

[0104] Scanning cumulative time: 0.04s / spectra;

[0105] Collision energy: ±30V.

[0106] The raw mass spectrometry data were converted into mzXML format using ProteoWizard software, and then XCMS software was used for chromatographic peak alignment, peak area extraction, and retention time correction. Subsequently, the obtained data were used for metabolite identification and analysis.

[0107] 3.4 Analysis of liver antioxidant levels

[0108] Weigh 0.1 g of tissue, add 0.9 mL of normal saline, homogenize the tissue in an ice bath, centrifuge at 4°C (12000 rpm, 10 min), and aspirate the supernatant. Use a protein quantification kit, as well as T-AOC, SOD, CAT, GSH-Px, and MDA kits to detect liver antioxidant levels on a Synergy H4 multifunctional microplate reader according to the steps described in the manufacturer's instructions.

[0109] 3.5 Liver Oil Red O staining analysis

[0110] The liver tissue samples placed in the tissue fixative were taken out, dehydrated with ethanol gradient, embedded in paraffin, cut into 5 μm thick slices, and stained with Oil Red O according to the instructions of the staining solution and analyzed under a microscope. 3.6 RT-qPCR analysis of liver and adipose tissue

[0111] 3.6.1 Total RNA extraction from tissues

[0112] Use the Animal Tissue Total RNA Extraction Kit (with gDNA Cleanup Column) (RE-03011) to extract total RNA from tissues. The steps are as follows:

[0113] 1. Weigh 0.02 g tissue, add 500 μL Buffer RL1, homogenize thoroughly in an ice bath, and transfer to the DNA-Cleaning column;

[0114] 2. After centrifugation at room temperature (12000 rpm, 2 min), remove the DNA-Cleaning purification column, add 800 μL Buffer RL2 to the supernatant in the lower tube, and mix gently;

[0115] 3. Pipette 700 μL of the solution in the tube into the RNA-Only purification column, centrifuge at room temperature (12000 rpm, 1 min), and discard the waste liquid in the collection tube;

[0116] 4. Add 500 μL Buffer RW1 to the RNA-Only purification column, centrifuge at room temperature (12000 rpm, 1 min), and discard the waste liquid in the collection tube;

[0117] 5. Add 700 μL Buffer RW2 to the RNA-Only purification column, centrifuge at room temperature (12000 rpm, 1 min), and discard the waste liquid in the collection tube;

[0118] 6. Repeat the previous step and centrifuge once more at room temperature (12000 rpm, 2 min) to remove residual Buffer RW2;

[0119] 7. Place the RNA-Only purification column into a new RNase-free centrifuge tube and drop 200 μL RNase-Free ddH 2 O, placed at room temperature for 2 min, and centrifuged (12000 rpm, 2 min) to obtain the total RNA solution of the tissue sample.

[0120] 8. Use Nanodrop micro-spectrophotometer to detect the concentration and purity of the extracted RNA solution.

[0121] 3.6.2 RNA Reverse Transcription

[0122] cDNA synthesis was performed using the PrimeScriptRT reagent Kit with gDNAEraser reverse transcription kit (RR047A). The specific steps are as follows:

[0123] 1. Add the following ingredients to an RNase-free centrifuge tube to prepare a reaction solution for genomic DNA removal: 2 μL of 5× gDNA Eraser Buffer, 1 μL of gDNA Eraser, and 1 μg of total RNA. 2 Add 10 μL of O and mix gently by pipetting, and react in a 42°C water bath for 2 min.

[0124] 2. Add 1 μL of PrimeScript RT Enzyme Mix I, 1 μL of RT Primer Mix, and 4 μL of 5× PrimeScript Buffer 2. 2 The volume of 4% cDNA was filled to 20 μL by gentle pipetting and mixing. After centrifugation, the first-strand cDNA synthesis reaction was performed on a PCR instrument (37°C, 15 min; 85°C, 5 s) to obtain the template cDNA required for the subsequent qPCR reaction.

[0125] 3.6.3 RT-qPCR detection

[0126] iTaq Universal SYBR Green Supermix PCR premix was used to perform PCR amplification of the target gene on an ABI 7500 real-time fluorescence quantitative PCR instrument. The primer sequences are shown in Table 3. The PCR amplification conditions are as follows:

[0127] PCR reaction system: 10 μL of premix, 1 μL of cDNA template, 1 μL of each bidirectional primer (final concentration 500 nM), and ultrapure water to 20 μL.

[0128] PCR reaction program: 95℃ pre-denaturation for 30s, 40 amplification cycles (95℃ denaturation for 15s, 60℃ annealing / extension for 60s). After the reaction was completed, Gapdh gene was used as the reference gene and 2 -ΔΔCt The relative expression of target genes was calculated by this method.

[0129] Table 3 RT-qPCR primers for mouse tissues

[0130]

[0131] 3.7 Intestinal permeability test

[0132] Whole blood of mice gavaged with FITC-dextran was kept at room temperature in the dark for 0.5 h and then centrifuged at 4°C (2500 g, 20 min). The serum was diluted one-fold with sterile, pre-cooled PBS and added to a 96-well plate. The serum fluorescence intensity was detected using a Varioskan Flash full-wavelength reader (excitation wavelength: 485 nm; emission wavelength: 528 nm).

[0133] 3.8 Staining of colon tissue sections

[0134] 3.8.1 Chemical staining of colon

[0135] The colon tissue samples fixed in tissue fixative were taken out, dehydrated with ethanol gradient, embedded in paraffin, cut into slices with a thickness of 5 μm, and stained with alcian blue-periodic acid schiff (AB-PAS) and analyzed under a microscope according to the instructions of the kit.

[0136] 3.8.2 Colon immunohistochemical staining

[0137] 1. Take out the colon tissue sample placed in tissue fixative, dehydrate it with ethanol gradient, embed it in paraffin, and cut it into slices with a thickness of 5 μm;

[0138] 2. Dewax and hydrate with gradient xylene and ethanol, rinse with running water, immerse in sufficient sodium citrate repair solution, heat in a microwave oven at high speed for 8 minutes, stop the heat and continue to work for 8 minutes, then heat in a microwave oven at low speed for 7 minutes, and then cool and slice naturally;

[0139] 3. Immerse the slices in PBS and decolorize them on a shaker for 3 times, 5 minutes each time;

[0140] 4. Gently shake off the water stains on the slices, draw a circle around the slice tissue with a tissue pen, add 3% BSA to cover all the tissues, and block at room temperature for 3 minutes;

[0141] 5. Gently spin-dry the blocking solution, add the primary antibody (anti-ZO1 purchased from Wuhan Saiweier Biotechnology Co., Ltd.) to the tissue, and place the slices flat in a humidified box at 4°C overnight;

[0142] 6. Immerse the slices in PBS and decolorize them on a shaker for 3 times, 5 minutes each time;

[0143] 7. Gently shake off the water stains, add secondary antibody to the tissue, and incubate at room temperature for 50 minutes;

[0144] 8. Immerse the slices in PBS and decolorize them on a shaker for 3 times, 5 minutes each time;

[0145] 9. Gently shake off the water stains, add the prepared DAB color developing solution to the tissue, observe under a microscope until the positive brown-yellow signal intensity is in the appropriate range, and rinse the slices with running water to terminate the reaction;

[0146] 10. Gently shake off the water stains, add hematoxylin staining solution to the tissue, incubate at room temperature for 3 minutes, then rinse with running water, add hematoxylin differentiation solution for 10 seconds, then rinse with running water again, then add hematoxylin blueing solution for 10 seconds, then rinse with running water;

[0147] 11. Dehydrate the prepared sections, seal the sections, and examine them under a microscope. Use Image-Pro Plus 6.0 software to analyze the positive signals.

[0148] 3.9 16S rRNA gene sequencing analysis of cecal contents

[0149] 3.9.1 DNA extraction from cecal contents

[0150] The genomic DNA of mouse intestinal flora was extracted using the magnetic bead soil and feces genomic DNA extraction kit (DP712) as follows:

[0151] 1. Under sterile conditions, transfer 0.5 g of cecal content sample to another sterile centrifuge tube, add 500 μL buffer SA, 100 μL buffer SC, 10 μL RNase A and 0.25 g grinding beads, vortex mix for 3 minutes, heat and lyse in a 70°C water bath for 15 minutes, centrifuge at 12000 rpm for 1 minute, and take the supernatant to another sterile centrifuge tube;

[0152] 2. Add 200 μL buffer SH, vortex to mix, place at 4°C for 10 min, centrifuge at 12,000 rpm for 3 min, take the supernatant to another sterile centrifuge tube, add 500 μL buffer GFA, and mix by inversion;

[0153] 3. Add 10 μL of magnetic bead suspension G and shake to mix for 5 minutes;

[0154] 4. Place the centrifuge tube on the magnetic rack and let it stand for 30 seconds. After the magnetic beads are completely adsorbed, carefully discard the liquid;

[0155] 5. Remove the centrifuge tube from the magnetic rack, add 700 μL of deproteinized solution RD, and shake to mix for 5 minutes;

[0156] 6. Place the centrifuge tube on the magnetic rack again and let it stand for 30 seconds. After the magnetic beads are completely adsorbed, carefully discard the liquid;

[0157] 7. Remove the centrifuge tube from the magnetic rack, add 700 μL of rinse solution PWD, and shake to mix for 3 minutes;

[0158] 8. Place the centrifuge tube on the magnetic rack and let it stand for 30 seconds. After the magnetic beads are completely adsorbed, carefully discard the liquid;

[0159] 9. Add 700 μL of rinse solution PWD again, repeat the magnetic bead adsorption operation once, and carefully discard the liquid;

[0160] 10. Place the centrifuge tube on a magnetic rack and dry it at room temperature for 5-10 minutes to ensure that there is no ethanol residue;

[0161] 11. Remove the centrifuge tube from the magnetic rack, add 50-100 μL of elution buffer TB, shake and mix, and incubate at 56°C for 5 min. Shake and mix for 3 rounds, 3-5 times each round.

[0162] 12. Place the centrifuge tube on the magnetic rack and let it stand for 2 minutes. After the magnetic beads are completely adsorbed, carefully pipette the DNA solution into another sterile centrifuge tube for later use.

[0163] 3.9.2 Amplification of 16S rRNA gene of intestinal flora

[0164] 1. Use 1% agarose gel electrophoresis and Qubit 3.0 fluorescence detector to detect the concentration and purity of DNA extract, and dilute it to 1 ng / μL with sterile water;

[0165] 2. Use Phusion ultra-fidelity PCR premix (containing GC buffer) and Phusion ultra-fidelity DNA polymerase to perform PCR amplification of the V3-V4 variable region in the 16SrRNA gene of intestinal flora on a T100 gradient PCR instrument. The primer sequences and PCR amplification conditions are as follows:

[0166] Forward primer 341F: 5′-CCTAYGGGRBGCASCAG-3′, reverse primer 806R: 5′-GGACTACNNGGGTATCTAAT-3′.

[0167] PCR reaction system: 15 μL premix, 10 μL DNA extraction solution, 1 μL each of bidirectional primers, and ultrapure water to 30 μL.

[0168] The PCR reaction program was as follows: pre-denaturation at 94°C for 3 min, 35 amplification cycles (denaturation at 94°C for 45 s, annealing at 50°C for 60 s, and extension at 72°C for 90 s), and finally extension at 72°C for another 10 min.

[0169] 3. Use Qubit to detect the concentration of PCR products. Mix equal amounts of samples according to the concentration of PCR products in each group. Purify the PCR products by 1% agarose gel electrophoresis. Recover the target bands using QIAquick gel recovery kit (28704).

[0170] 3.9.3 Library construction and sequencing

[0171] The TruSeq DNA PCR-Free Library Preparation Kit was used for library construction, the library was quantified using the Qubit 3.0 fluorescence detector, and the qualified DNA library was loaded into the Illumina NovaSeq 6000 nucleic acid sequencer for sequencing.

[0172] 3.9.4 Analysis of Sequencing Data Information

[0173] 1. Split each sample data from the offline data, remove the barcode and primer sequence, and obtain the original sequence (rawtags);

[0174] 2. Use fastp v0.22.0 software to filter the raw tags and obtain high-quality sequences (cleantags);

[0175] 3. Use FLASH v2.2.11 software to splice double-end clean reads to obtain high-quality data (cleantags);

[0176] 4. Use vsearch software to compare clean tags with the database, detect and remove chimeras, and obtain effective tags;

[0177] 5. Use the Deblur module in the QIIME2 software to reduce the noise of effective tags to obtain ASV, and then use the Mothur algorithm to compare ASV with the Silva database to obtain the species information of each ASV;

[0178] 6. The composition of the microbial community of each sample was counted at each taxonomic level, and multiple sequence alignment was performed using MAFFT v7.520 software to obtain the phylogenetic relationship of ASVs;

[0179] 7. After homogenizing the data of each sample, use R software to perform alpha diversity and beta diversity analysis.

[0180] 3.10 UHPLC-MS analysis of cecal content metabolome

[0181] The cecal content sample was taken out from -80℃, thawed completely on ice, vortexed and mixed, and then 20 mg of the sample was weighed into another 1.5 mL centrifuge tube; 400 μL of methanol / water solution (7:3, v / v) was added and vortexed for 3 min, ultrasonicated in an ice bath for 10 min, and vortexed again for 1 min. After standing at -20℃ for 30 min, centrifuged at 4℃ (12000 rpm, 10 min), 300 μL of the supernatant was aspirated into another 1.5 mL centrifuge tube, and 200 μL of the supernatant was aspirated for UHPLC-MS analysis after centrifugation at 4℃ (12000 rpm, 3 min).

[0182] Chromatographic separation was performed on a Shimadzu LC-30A ultra-high performance liquid chromatograph (containing a Waters 2.1 mm × 100 mm ACQUITY Premier HSS T3 1.8 μm column) with the following parameters:

[0183] Injection volume: 4 μL;

[0184] Column temperature: 40°C;

[0185] Flow rate: 0.4mL / min

[0186] Mobile phase A: 0.1% formic acid / water;

[0187] Mobile phase B: 0.1% formic acid / acetonitrile;

[0188] The gradient elution program is shown in Table 4:

[0189] Table 4 Gradient elution program for UHPLC-MS analysis of mouse cecal contents

[0190]

[0191]

[0192] After chromatographic separation, the samples were analyzed by primary and secondary mass spectrometry using an AB Triple TOF 6600+ mass spectrometer, and electrospray ionization was used for detection in positive and negative ion modes. The parameters were as follows:

[0193] Collection time: 10min;

[0194] Spray gas: 50psi;

[0195] Auxiliary heating gas: 60psi;

[0196] Air curtain gas: 35psi;

[0197] Ion source temperature: 550°C (positive ion mode), 450°C (negative ion mode);

[0198] Spray voltage: 5000V (positive ion mode), -4000V (negative ion mode);

[0199] Declustering voltage: ±60V (positive and negative modes);

[0200] The first-stage mass spectrometer settings are as follows:

[0201] Scan range: 50-1000Da;

[0202] Scanning cumulative time: 0.20s / spectra;

[0203] Collision energy: ±10V;

[0204] The secondary mass spectrometer settings are as follows:

[0205] Scan range: 25-1000Da;

[0206] Scanning cumulative time: 0.04s / spectra;

[0207] Collision energy: ±30V.

[0208] The raw mass spectrometry data were converted into mzXML format using ProteoWizard software, and then XCMS software was used for chromatographic peak alignment, peak area extraction, and retention time correction. Subsequently, the obtained data were used for metabolite identification and analysis.

[0209] 3.11 Gas chromatography analysis of short-chain fatty acids in cecal contents

[0210] 3.11.1 Preparation of analytical standards

[0211] Accurately pipette glacial acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid, hexanoic acid and other standard substances to prepare 10-500 μmol / L series standard substance mixed solution and 100 mmol / L 2-ethylbutyric acid internal standard solution. 3.12.2 Processing of cecal contents samples

[0212] Weigh the cecal content sample, transfer it to a 5 mL centrifuge tube, add 263 μL concentrated hydrochloric acid, 21 μL diethylbutyric acid internal standard solution and 2100 μL ultrapure water, shake and mix for 5 minutes, then add 2100 μL ether, shake and mix again for 5 minutes, extract at 4 ° C for 20 minutes, centrifuge at 4 ° C (12000 rpm, 20 minutes), take the supernatant and filter it through a 0.22 μm filter membrane before testing.

[0213] 3.13.3 Gas chromatography detection

[0214] Chromatographic detection was performed on an Agilent 7890A gas chromatograph (with a flame ionization detector and an Agilent DB-FFAP 30m×0.25mm×0.25μm chromatographic column) with the following parameters:

[0215] Injection volume: 1 μL;

[0216] Split ratio: 1:10;

[0217] Injector temperature: 230°C;

[0218] Detector temperature: 240°C;

[0219] Nitrogen (carrier gas) flow rate: 1 mL / min;

[0220] Air flow rate: 270mL / min;

[0221] Hydrogen flow rate: 30 mL / min;

[0222] The heating program is shown in Table 5:

[0223] Table 5 Temperature program for gas chromatography analysis of mouse cecal contents

[0224]

[0225] result

[0226] 1. Physiological index detection

[0227] 1.1 Body weight and food intake

[0228] The body weight of mice in each group is as follows Figure 1 As shown in the figure, as the gavage experiment progressed, the weight of the mice gradually increased from the initial value (20g±0.2g), and the high-fat diet further increased the weight gain of the mice. The final weight of the mice in the SYN group was lower than that in the HF group (p<0.05); while no significant difference was observed between the final weight and weight gain of the POST, PRO, PRE and HF groups (p>0.05). This shows that compared with a single gavage component, the synbiotic preparation can control the weight gain of mice on a high-fat diet.

[0229] The average daily food intake and energy intake of mice in each group were Figure 2 As shown. The energy density of the normal diet (3.42 kcal / g) is lower than that of the high-fat diet (4.7 kcal / g), so the food intake of mice in the LF group was significantly higher than that of mice in the other groups (p<0.05), and the energy intake was lower than that of mice in the other groups (p<0.05). Among mice fed a high-fat diet, the food intake and energy intake of mice in the PRE and SYN groups were significantly lower than those in the HF group (p<0.05). It can be seen that compared with a single oral gavage component, the synbiotic preparation can reduce the food intake of mice, thereby reducing their energy intake.

[0230] 1.2 Organ index

[0231] After weighing the organs of mice in each group, the organ index was calculated according to formula 1, and the results are shown in Table 6. There was no significant difference in the weight and organ index of the heart, liver, spleen, white fat and other organs of mice in each group compared with the HF group (p>0.05); in addition, only the kidney weight of the SYN group was significantly lower than that of the HF group (p<0.05). It can be seen that compared with a single oral gavage component, the synbiotic preparation can alleviate abnormal conditions such as hyperplasia, hypertrophy, and congestion of the kidney.

[0232] Organ index = (organ weight / mouse body weight) × 100 (Formula 1) Table 6 Mouse organ weight and organ index

[0233]

[0234] *Different superscript letters in each row of data indicate statistically significant differences (p<0.05), n≥8

[0235] 1.3 Serum testing and analysis

[0236] 1.3.1 Blood lipid levels

[0237] Serum lipid levels of mice in each group Figure 3As shown. Compared with the LF group, the high-fat diet significantly increased the levels of TG, TC, and LDL-C in mice (p<0.05), and significantly decreased the level of HDL-C (p<0.05), resulting in lipid metabolism disorders. In all intervention groups except the PRO group (p>0.05), a significant decrease in the levels of TG, TC, and LDL-C (p<0.05) and a significant increase in the level of HDL-C (p<0.05) were observed, and the lipid metabolism disorder of mice was reversed. The reversal of various indicators in the SYN group was greater than that in the PRE group, and the intervention effect of the POST group was not significantly different from that of the SYN group (p>0.05).

[0238] It can be seen that the synbiotic preparation can significantly improve the blood lipid level of mice. At the same time, the pasteurized A. muciniphila showed similar blood lipid regulation effect as the synbiotic preparation.

[0239] 1.3.2 Blood sugar level

[0240] The fasting blood glucose and fasting insulin levels of mice in each group were Figure 4 Compared with the LF group, the high-fat diet significantly increased the fasting blood glucose and fasting insulin levels of mice (p<0.05), which may lead to insulin resistance.

[33] Except for the fasting insulin level in the PRO group (p>0.05), all intervention groups observed a significant decrease in fasting blood glucose and fasting insulin levels (p<0.05), and the insulin resistance of mice was alleviated to varying degrees. Among them, all indicators in the PRE group and the SYN group decreased significantly (p<0.05), which was greater than that in the PRO group; the intervention effect of the POST group was not significantly different from that of the SYN group (p>0.05).

[0241] It can be seen that the synbiotic preparation can significantly alleviate the insulin resistance level of mice. At the same time, the pasteurized A. muciniphila showed similar blood sugar regulation effect as the synbiotic preparation.

[0242] 1.3.3 Transaminase levels

[0243] The serum AST and ALT levels of mice in each group were as follows Figure 5As shown. Compared with the LF group, the high-fat diet significantly increased the AST and ALT levels of mice (p<0.05), and obvious hepatocyte damage occurred. Except for the AST level in the PRO group (p>0.05), all intervention groups observed a significant decrease in serum transaminase levels (p<0.05), and the hepatocyte damage was alleviated. Among them, the PRO group had a relatively small relief, and the intervention effect on AST was not significant (p>0.05); the two transaminase indicators in the PRE group decreased significantly (p<0.05), which was greater than that in the PRO group; the two transaminase indicators in the SYN group decreased more than those in the PRO and PRE groups (p<0.05); the intervention effect of the POST group was not significantly different from that of the SYN group (p>0.05).

[0244] It can be seen that the synbiotic preparation can significantly alleviate the level of liver cell damage in mice. At the same time, pasteurized A. muciniphila showed similar transaminase regulation effect as the synbiotic preparation.

[0245] 1.3.4 Levels of inflammatory markers

[0246] The levels of serum inflammatory markers in each group of mice were as follows Figure 6 As shown. Compared with the LF group, the high-fat diet significantly increased the levels of LPS, CRP, TNF-α, and IL-6 in mice (p<0.05), showing obvious systemic inflammation. A significant decrease in the levels of inflammatory markers was observed in all intervention groups (p<0.05), except for the CRP level in the PRO group (p>0.05). Among them, from the PRO group, PRE group to the SYN group, the decrease in the levels of inflammatory markers showed an overall significant increasing trend (p<0.05); there was no significant difference in the levels of LPS, CRP, and IL-6 in the SYN group from the POST group (p>0.05), and the TNF-α level was lower than that in the SYN group (p<0.05).

[0247] It can be seen that the synbiotic preparation can significantly alleviate the circulatory inflammation in mice. At the same time, pasteurized A. muciniphila showed similar inflammatory regulation effects as the synbiotic preparation.

[0248] 1.3.5 Intestinal hormone levels

[0249] The levels of serum intestinal hormones GLP-1 and PYY in each group of mice were as follows Figure 7As shown. Compared with the LF group, the high-fat diet significantly reduced the serum GLP-1 and PYY levels of the HF group mice (p<0.05). These reduced satiety hormone levels can increase the appetite of the HF group mice, leading to energy surplus. Except for the PRO group (p>0.05), all intervention groups observed a significant increase in serum intestinal hormone levels (p<0.05). Among them, there was no significant difference in the intervention effect between the PRE group and the SYN group (p>0.05); the GLP-1 level in the POST group was significantly higher than that in the SYN group (p<0.05), and there was no significant difference in the PYY level (p>0.05).

[0250] It can be seen that the synbiotic preparation can significantly increase the serum intestinal hormone levels of mice. At the same time, pasteurized A. muciniphila showed similar serum intestinal hormone regulation effects as the synbiotic preparation.

[0251] 1.3.6 Adipokine levels

[0252] The levels of serum adipokines ADPN and LEP in each group of mice Figure 8 As shown. Compared with the LF group, the high-fat diet significantly reduced the ADPN level of mice (p<0.05), significantly increased the LEP level (p<0.05), significantly reduced the ADPN / LEP ratio (p<0.05), and abnormal adipocytes. In addition to the ADPN level and ADPN / LEP ratio of the PRO and PRE groups (p>0.05), all intervention groups observed a significant reversal of serum adipokine levels (p<0.05); the reversal of all three indicators in the SYN group was significantly greater than that in the PRO group (p<0.05), and there was no significant difference in the intervention effect between the POST group and the SYN group (p>0.05).

[0253] It can be seen that the synbiotic preparation can significantly reverse the serum adipokine levels in mice. At the same time, pasteurized A. muciniphila showed similar serum adipokine regulation effects as the synbiotic preparation.

[0254] 1.4 UHPLC-MS analysis of serum metabolome

[0255] 1.4.1 PCA analysis of metabolite differences among groups

[0256] PCA analysis performs orthogonal transformation on possibly correlated variables and converts them into a set of linearly uncorrelated variables, thereby reducing the dimension of metabolite variables and classifying them, thereby reflecting the overall degree of variation between groups (or samples). Fig. 9This is the PCA analysis of serum metabolites of each group of mice. The horizontal and vertical axes represent the first and second principal component scores, respectively. It can be seen from the figure that there is an obvious separation trend between the serum metabolites of the high-fat diet group and the normal diet group; at the same time, there is a certain degree of separation trend between each intervention group and the HF group, indicating that there are differences in serum metabolites between each intervention group and the HF group.

[0257] 1.4.2 OPLS-DA analysis of metabolite differences among groups

[0258] Orthogonal partial least squares discriminant analysis (OPLS-DA) modeling can be used to further analyze differential metabolites with less correlation. Fig.10 The OPLS-DA analysis diagram of serum metabolites of mice in each group compared with those in the HF group. The horizontal and vertical axes represent the predicted component score and orthogonal component score, respectively, and the percentage of each axis is the explanation rate of the component to the data set. As can be seen from the figure, each group can be completely separated from the HF group, indicating that there are large differences between the serum metabolites of each group and the serum metabolites of the HF group, and different intervention methods can have a greater impact on serum metabolites.

[0259] The permutation test can further evaluate the OPLS-DA model. Fig.11 The validation results of each OPLS-DA model, the horizontal axis is the model R 2 Y, Q 2 The vertical axis is the frequency of the OPLS-DA model classification effect during the 200 random permutation and combination experiments. When p<0.05, the OPLS-DA model is well modeled, which shows that all OPLS-DA models in this experiment are stable and reliable, and the metabolites between different groups are significantly different.

[0260] 1.4.3 Screening and analysis of differential metabolites

[0261] Based on PCA and OPLS-DA analysis, the VIP value of each metabolite can be obtained. With VIP>1 and p<0.05 as the standard, the selected DEMs are as follows: Fig.12As shown. The horizontal axis represents the fold change (log2FoldChange) of metabolites between groups, and the vertical axis represents the significance of metabolite differences. Significantly up-regulated metabolites are marked in red, and significantly down-regulated metabolites are marked in green. The size of the dots represents the VIP value of the metabolites. Compared with the LF group, 300 significantly up-regulated metabolites and 199 significantly down-regulated metabolites were identified in the HF group. Compared with the HF group, 225 significantly up-regulated metabolites and 262 significantly down-regulated metabolites were identified in the POST group; 76 significantly up-regulated metabolites and 265 significantly down-regulated metabolites were identified in the PRO group; 163 significantly up-regulated metabolites and 400 significantly down-regulated metabolites were identified in the PRE group; 166 significantly up-regulated metabolites and 466 significantly down-regulated metabolites were identified in the SYN group.

[0262] Compared with the LF group, the high-fat diet decreased the levels of folic acid, serotonin and other substances in the serum of mice, and increased the levels of chenodeoxycholic acid and other substances. Compared with the HF group, the levels of L-tyrosine, prostaglandin E3, citric acid and other substances in the serum of mice in the POST group increased; the levels of coenzyme Q10, 11-dehydrocorticosterone and other substances in the serum of mice in the PRO group increased; the levels of coenzyme Q10, prostaglandin E3 and other substances in the serum of mice in the PRE group increased; the levels of coenzyme Q10, prostaglandin E3, epicatechin, vitamin E and other substances in the serum of mice in the SYN group increased.

[0263] The Venn diagram can intuitively display the relationship between different groups of metabolites, such as Fig.13 As shown in the figure, each circle is the number of differential metabolites between different intervention groups and the HF group. The overlapping parts of the circles are the common differential metabolites between the comparison groups, and the non-overlapping parts are the unique differential metabolites of the corresponding groups. It can be seen that the SYN group has the most types of unique differential metabolites (155 species), and the PRO group has the least types of unique differential metabolites (50 species). Among them, there are 202 differential metabolites that are not shared by the SYN group and the PRO and PRE groups, that is, the unique metabolites produced by the combination of camellia oil and A. muciniphila compared with the intervention of the single component, such as epicatechin, myristic acid, S-glutathione-L-cysteine, vitamin E and other substances.

[0264] 1.4.4 KEGG enrichment analysis of differential metabolites

[0265] KEGG enrichment can intuitively display the relevant functions of DEMs and help to deeply analyze the differences between groups. Fig.14 This is the KEGG enrichment analysis diagram of serum DEMs of mice in each group. The vertical axis is the KEGG pathway name, and the horizontal axis is the enrichment factor. The larger the scatter point, the more enriched metabolites there are.

[0266] Compared with the LF group, high-fat diet can change the serum metabolites of mice, and these differential metabolites are mainly enriched in KEGG pathways such as choline metabolism in cancer, Cushing's syndrome, and chemical carcinogen-receptor activation. Compared with the HF group, serum DEMs of mice in the POST group were mainly enriched in KEGG pathways such as amino acid biosynthesis, mineral absorption, and nucleotide metabolism; serum DEMs of mice in the PRO group were mainly enriched in KEGG pathways such as retrograde endocannabinoid signaling, arachidonic acid metabolism, and autophagy; serum DEMs of mice in the PRE group were mainly enriched in KEGG pathways such as linolenic acid metabolism, arachidonic acid metabolism, and renin secretion; serum DEMs of mice in the SYN group were mainly enriched in KEGG pathways such as glycerophospholipid metabolism, retrograde endocannabinoid signaling, and autophagy.

[0267] 1.5 Liver tissue analysis

[0268] 1.5.1 Liver tissue morphology

[0269] Oil red O staining of liver tissue sections Fig.15 The high-fat diet caused the liver cells of mice to be arranged in disorder and to show severe fatty degeneration, which was significantly different from the LF group (p<0.05); the fatty degeneration of liver cells in each intervention group was significantly alleviated (p<0.05), among which the fatty degeneration rate of liver cells in the PRE group was relatively high, and there was no significant difference in the fatty degeneration rate of liver cells among the POST, PRO, and SYN groups (p>0.05).

[0270] It can be seen that the synbiotic preparation can significantly reduce the level of fatty degeneration in mouse liver cells. At the same time, pasteurized A. muciniphila showed a similar effect of reversing liver fatty degeneration as the synbiotic preparation.

[0271] 1.5.2 Liver antioxidant index

[0272] The antioxidant levels of liver in each group of mice Fig.16 As shown. Compared with the LF group, the high-fat diet significantly reduced the total antioxidant activity T-AOC and antioxidant enzyme levels of mice (p<0.05), and significantly increased the MDA level (p<0.05), showing obvious hepatocyte oxidative stress. Except for some indicators of the PRO and PRE groups, all intervention groups observed a significant reversal of liver oxidative stress (p<0.05). Among them, the reversal amplitude of the PRO group was relatively small, and only the MDA level was significantly reduced (p<0.05); only the T-AOC and MDA indicators in the PRE group were significantly reversed (p<0.05); all indicators of the SYN and POST groups were reversed (p<0.05), among which the T-AOC level in the POST group was significantly higher than that in the SYN group (p<0.05), and the other indicators were not significantly different from those in the SYN group (p>0.05).

[0273] It can be seen that synbiotic preparations can significantly alleviate the oxidative stress level of mouse hepatocytes. At the same time, pasteurized A. muciniphila showed similar oxidative stress regulation effects as synbiotic preparations.

[0274] 1.5.3 RT-qPCR analysis of liver and adipose tissue

[0275] 1.5.3.1 Genes related to liver fatty acid synthesis

[0276] The relative mRNA expression levels of fatty acid synthesis-related genes in the liver of each group of mice are as follows Fig.17 As shown. Among them, Srebf1 gene encodes sterol regulatory element binding protein, Fasn gene encodes fatty acid synthase, and Acaca gene encodes acetyl-CoA carboxylase. Except that the relative expression levels of Srebf1 and Fasn genes in the liver of mice in the POST group were significantly higher than those in the HF group (p<0.05), no other significant differences were observed (p>0.05).

[0277] It can be seen that the synbiotic preparation had no significant effect on the relative expression level of genes related to fatty acid synthesis in the liver of mice. At the same time, pasteurized A. muciniphila can significantly increase the expression level of genes related to fatty acid synthesis in the liver.

[0278] 1.5.3.2 Genes associated with adipose tissue inflammation

[0279] The relative expression levels of mRNA of inflammation-related genes in adipose tissue of mice in each group were as follows Fig.18 As shown. Among them, Nfkb1 gene encodes nuclear factor κ-B, Tnf gene encodes tumor necrosis factor α, Il6 gene encodes interleukin 6, and Ccl2 gene encodes monocyte chemoattractant factor 1. High-fat diet significantly increased the relative expression levels of Tnf gene and Ccl2 gene in adipose tissue of HF group mice (p<0.05), the relative expression level of Tnf gene in adipose tissue of mice in each intervention group was significantly lower than that in HF group (p<0.05), and the relative expression level of Ccl2 gene in adipose tissue of mice in POST group and SYN group was significantly lower than that in HF group (p<0.05). Apart from this, no other significant differences were observed.

[0280] It can be seen that the synbiotic preparation can reduce the relative expression levels of Tnf and Ccl2 genes in adipose tissue of mice. At the same time, pasteurized A. muciniphila showed similar regulatory effects on inflammatory genes in adipose tissue.

[0281] 1.6 Intestinal permeability test

[0282] After mice were gavaged with FITC-dextran for 1.5 hours, blood was collected to measure serum fluorescence intensity. Fig.19The fluorescence intensity of FITC-dextran in the POST, PRO, and SYN groups was significantly lower than that in the HF group (p<0.05), indicating that live A. muciniphila and its inactivated bacteria can reduce intestinal permeability and prevent intestinal FITC-dextran from leaking into the blood circulation through the intestinal gap.

[0283] 1.7 Colon staining analysis

[0284] 1.7.1 AB-PAS staining

[0285] The results of AB-PAS staining of colon tissues of mice in each group are shown in Fig. 20 As shown. High-fat diet decreased the number of colonic goblet cells in HF group mice, but there was no significant difference compared with LF group (p>0.05). Among the intervention groups, only PRO group and SYN group had significantly increased goblet cell numbers (p<0.05), but there was no significant difference in goblet cell numbers between the two groups (p>0.05), suggesting that live A. muciniphila can increase the number of goblet cells in colon tissue.

[0286] 1.7.2ZO-1

[0287] Immunohistochemical staining was used to analyze the expression of tight junction protein ZO-1 in the colon tissue of each group of mice. Fig.21 As shown, the ZO-1 protein in the colon tissue was stained brown. Compared with the high-fat diet control HF group, the expression of ZO-1 protein in the PRE and SYN groups was significantly increased (p<0.05). There was no significant difference in the expression of ZO-1 protein in the PRO and POST groups compared with the HF group (p>0.05), indicating that camellia seed oil can significantly affect the expression of tight junction protein ZO-1 in colon tissue.

[0288] 1.8 16S rRNA gene sequencing analysis of cecal contents

[0289] 1.8.1 Quality control and analysis of mouse intestinal flora sequencing data

[0290] High-quality sequencing data can lay the foundation for the reliability and authenticity of subsequent analysis. Table 7 is a statistical table of 16SrRNA gene sequencing data of mouse cecal contents, which lists the raw tags, high-quality clean tags and effective tags; Q20 and Q30 are the percentages of bases with base quality values ​​greater than 20 (error rate less than 1%) and greater than 30 (error rate less than 0.1%) in effective tags; GC represents the percentage of GC base content in effective tags. It can be seen that the effective sequence Q20 percentage of each sample is greater than 99%, indicating that the sequencing data quality is high and can reflect the true situation of the mouse intestinal flora, laying the foundation for subsequent analysis.

[0291] Table 7 Sequencing data statistics

[0292]

[0293] 1.8.2 Analysis of alpha diversity of mouse intestinal flora

[0294] The rarefaction curve is a curve that randomly extracts a certain amount of sequencing data from a sample, observes the number of species under different sampling amounts, and how the number of species observed changes with the increase in sampling amount. It is usually used to evaluate the biodiversity and integrity of samples, and can also be used as an evaluation criterion for whether the sequencing amount of each sample is sufficient.

[0295] The horizontal axis of the rarefaction curve represents the amount of sequencing data, and the vertical axis represents the corresponding alpha diversity index. A sharply rising curve indicates that more sample volume or sequencing volume is needed to fully describe the biodiversity in the sample. Conversely, it indicates that the amount of sequencing data is gradually reasonable and the sequencing depth is sufficient to meet the subsequent data analysis. Fig. 22 As shown in the figure, the amount of mouse intestinal flora sequencing data is 3×10 4 More sequencing will not have a significant effect on the alpha diversity of the intestinal flora, indicating that the amount of sequencing data is reasonable and sufficient to meet the needs of subsequent analysis.

[0296] The hierarchical clustering curve is drawn by arranging the ASVs in the sample in descending order of relative abundance to obtain the corresponding ranking number, and then connecting these points with a broken line with the number as the horizontal axis and the relative abundance in the ASV as the vertical axis. This curve can intuitively present the richness and uniformity of species in the sample. The richness of the species is proportional to the span of the curve on the horizontal axis, and the uniformity of the species is proportional to the smoothness of the curve on the vertical axis. Fig.23 As shown, the species distribution uniformity of samples in each group was similar, among which the species richness of the HF group was lower, the species richness of the POST group was higher, and the PRE group was second.

[0297] Alpha diversity is often used to describe the biodiversity in a sample, of which the Chao1 index can be used to describe species richness, and the Shannon index can be used to describe species diversity. However, in this experiment, the Chao1 and Shannon indices of the intestinal flora of mice in each group were not significantly different (p>0.05). Fig.24 shown.

[0298] 1.8.3 Analysis of beta diversity of mouse intestinal flora

[0299] Beta diversity is often used to compare the composition of microbial communities in different samples, such as PCoA analysis. This experiment uses Bray-Curtis distance for beta diversity analysis, which can be used for general comparative studies of microbial community structure and takes into account the abundance information of species in the microbial community.

[0300] In the PCoA analysis based on Bray-Curtis distance, multidimensional data is reduced to two-dimensional data, and the principal coordinate combination with the largest contribution rate is selected for plotting. The horizontal axis represents the first principal component, and the percentage is the contribution of the first principal component to the sample difference; the vertical axis represents the second principal component, and the percentage is the contribution of the second principal component to the sample difference. The distance between samples is positively correlated with the similarity of community structure. The greater the difference in beta diversity between two samples, the more dispersed they appear in the PCoA graph, and vice versa. Fig.25 PCoA analysis of the intestinal flora of mice showed that the high-fat diet samples of each group were significantly separated from the LF group; in addition, there was no significant separation between the intervention groups and the HF group.

[0301] 1.8.4 ASV analysis of mouse intestinal flora

[0302] The Deblur module in the QIIME2 software was used to perform noise reduction on the effective tags to obtain ASV information. Based on the ASV annotation results, the species abundance information of each classification level of the sample can be obtained. Subsequently, according to the quantitative information of the intestinal flora species of each group of mice, the top-ranked species were clustered and analyzed to obtain the species abundance heat map at the phylum classification level and genus classification level. The horizontal axis is the mouse group, the vertical axis is the species classification information, and the depth of the color block changes according to the Z-Score value (standardized relative quantitative data).

[0303] At the phylum level ( Fig.26 ), the intestinal flora of mice in each group was mainly composed of Firmicutes, Bacteroidota, Actinobacteriota, Deferribacteres, Proteobacteria, Verrucomicrobiota and other species. According to the quantitative data of Firmicutes and Bacteroidetes in each group, the F / B ratio was calculated, as shown in Fig. 27 As shown, the F / B values ​​of mice in each high-fat diet group were significantly higher than those of mice in the normal diet LF group (p<0.05), but there was no significant difference between the intervention groups and the HF group (p>0.05).

[0304] At the genus level ( Fig.28), the intestinal flora of mice in each group was mainly composed of Faecalibaculum, Alistipes, Bilophila, Blautia, Olsenella and other genera. High-fat diet increased the abundance of Lachnoclostridium and other genera in the intestinal flora of mice in the HF group, and decreased the abundance of Alistipes and other genera. The abundance of Negativibacillus and other genera in the POST group increased, and the abundance of Parvibacter and other genera decreased; the abundance of Butyricicoccus, Acetatifactor, Desulfovibrio and other genera in the PRO group increased; the abundance of unidentified_Oscillospiraceae and other genera in the PRE group increased; the abundance of Blautia, Dubosiella and other genera in the SYN group increased, and the abundance of Monoglobus, Tyzzerella and other genera decreased.

[0305] 1.9 UHPLC-MS analysis of cecal content metabolome

[0306] 1.9.1 PCA analysis of differences in metabolites among groups

[0307] PCA analysis performs orthogonal transformation on possibly correlated variables and converts them into a set of linearly uncorrelated variables, thereby reducing the dimension of metabolite variables and classifying them, thereby reflecting the overall degree of variation between groups (or samples). Fig.29 This is the PCA analysis of the intestinal flora metabolites of each group of mice. The horizontal axis and vertical axis represent the scores of the first and second principal components, respectively. As can be seen from the figure, the separation trend between the groups is not significant, and the degree of metabolite variation is small.

[0308] 1.9.2 OPLS-DA analysis of differences in metabolites among groups

[0309] Through OPLS-DA modeling, differential metabolites with less correlation can be further analyzed. Fig.30 The OPLS-DA analysis diagram of the intestinal flora metabolites of each group of mice compared with those of the HF group. The horizontal axis and vertical axis represent the predicted component score and the orthogonal component score, respectively, and the percentage is the component's explanation rate for the data set. As can be seen from the figure, each group can be completely separated from the HF group, indicating that there are large differences between the intestinal flora metabolites of each group and those of the HF group, and different intervention methods can have a greater impact on the intestinal flora metabolites.

[0310] 1.9.3 Screening and analysis of differential metabolites

[0311] Based on PCA and OPLS-DA analysis, the VIP value of each metabolite can be obtained. With VIP>1 and p<0.05 as the standard, the selected DEMs are as follows: Fig.31 As shown. The horizontal axis represents the fold change (log2FoldChange) of metabolites between groups, and the vertical axis represents the significance of metabolite differences. Significantly up-regulated metabolites are marked in red, and significantly down-regulated metabolites are marked in green. The size of the dots represents the VIP value of the metabolites. Compared with the LF group, 1228 significantly up-regulated metabolites and 310 significantly down-regulated metabolites were identified in the HF group. Compared with the HF group, 31 significantly up-regulated metabolites and 146 significantly down-regulated metabolites were identified in the POST group; 69 significantly up-regulated metabolites and 67 significantly down-regulated metabolites were identified in the PRO group; 152 significantly up-regulated metabolites and 97 significantly down-regulated metabolites were identified in the PRE group; 199 significantly up-regulated metabolites and 82 significantly down-regulated metabolites were identified in the SYN group.

[0312] Among them, compared with the LF group, the high-fat diet increased the levels of leukotriene C4 and other substances in the cecal contents of the HF group mice. Compared with the HF group, the levels of γ-linolenic acid and other substances in the cecal contents of the POST group mice increased; the levels of resolvin E1 and other substances in the cecal contents of the PRO group mice increased; the levels of lipoxin A4 and other substances in the cecal contents of the PRE group mice increased; the levels of chenodeoxycholic acid and other substances in the cecal contents of the SYN group mice decreased, and the levels of citric acid, L-tryptophan-L-glutamine and other substances increased.

[0313] The Venn diagram can intuitively display the relationship between different groups of metabolites, such as Fig.32 As shown in the figure, each circle represents the number of differential metabolites between different intervention groups and the HF group. The overlapping parts of the circles represent the common differential metabolites between the comparison groups, and the non-overlapping parts represent the unique differential metabolites of the corresponding groups. It can be seen that the SYN group has the most types of unique differential metabolites (189 species), and the PRO group has the least types of unique differential metabolites (76 species). Among them, there are 204 differential metabolites that are not shared by the SYN group and the PRO and PRE groups, such as α-muricholic acid. 1.9.4 KEGG enrichment analysis of differential metabolites

[0314] KEGG enrichment can intuitively display the relevant functions of DEMs and help to deeply analyze the differences between groups. Fig.33 The KEGG enrichment analysis diagram of differential metabolites in each group is shown. The vertical axis is the KEGG pathway name, and the horizontal axis is the enrichment factor. The larger the scatter point, the more enriched DEMs there are.

[0315] Compared with the LF group, high-fat diet can change the metabolites of intestinal flora in the HF group, and these differential metabolites are mainly enriched in KEGG pathways such as ovarian steroidogenesis, Cushing's syndrome, and prostate cancer. Compared with the HF group, the differential metabolites of intestinal flora in the POST group are mainly enriched in KEGG pathways such as carbohydrate digestion and absorption, biosynthesis of unsaturated fatty acids, and fat digestion and absorption; the differential metabolites of intestinal flora in the PRO group are mainly enriched in KEGG pathways such as fat digestion and absorption and autophagy; the differential metabolites of intestinal flora in the PRE group are mainly enriched in KEGG pathways such as arachidonic acid metabolism and tryptophan metabolism; the differential metabolites of intestinal flora in the SYN group are mainly enriched in KEGG pathways such as primary bile acid metabolism and riboflavin metabolism.

[0316] 1.10 Analysis of SCFAs in cecal contents

[0317] SCFAs produced by intestinal flora fermentation of dietary fiber and polysaccharides can be effectively absorbed by intestinal epithelial cells, providing energy for intestinal epithelial cells and having a beneficial effect on the prevention and treatment of metabolic syndrome. The cecal contents of each group of mice were extracted, chromatographically separated and detected, and the data were calculated using the standard curve (Table 8). The results are shown in Table 8. Fig.34 Among the intervention groups, compared with the HF group, the levels of various SCFAs in the POST, PRO and PRE groups did not change significantly (p>0.05); the levels of acetate, isobutyrate and valeric acid in the SYN group were significantly increased (p<0.05).

[0318] It can be seen that synbiotic preparations can significantly increase the level of SCFAs in the cecal contents of mice.

[0319] Table 8 Standard curve of short-chain fatty acids

[0320]

[0321] Finally, it should be pointed out that the above embodiments are only representative examples of the present invention. Obviously, the technical solution of the present invention is not limited to the above embodiments, and there are many variations. All variations that can be directly derived or associated with the content disclosed by ordinary technicians in this field should be considered as the protection scope of the present invention.

Claims

1. A synbiotic mixture that can improve metabolic homeostasis and intestinal microecology, characterized in that: Contains the following raw materials: Akkermansia muciniphila, camellia oil.

2. The synbiotic mixture capable of improving metabolic homeostasis and intestinal microecology according to claim 1, characterized in that: The Akkermansia muciniphila is a model strain of Akkermansia muciniphila.

3. The synbiotic mixture capable of improving metabolic homeostasis and intestinal microecology according to claim 2, characterized in that: The Akkermansia muciniphila is deposited in the China Industrial Microbiological Culture Collection Center with a deposit number of CICC: 24917.

4. The synbiotic mixture capable of improving metabolic homeostasis and intestinal microecology according to claim 1, characterized in that: The mixing ratio of Akkermansia muciniphila and camellia oil is: 1×10 8 CFU Akkermansia muciniphila matched 0.07-0.08mL camellia oil.

5. Use of the synbiotic mixture capable of improving metabolic homeostasis and intestinal microecology as claimed in claim 1 in the preparation of drugs for regulating intestinal metabolism, protecting intestinal barrier, promoting beneficial microbial flora, controlling weight gain, and regulating blood lipids and blood sugar.

6. Use of the synbiotic mixture capable of improving metabolic homeostasis and intestinal microecology as claimed in claim 1 in the preparation of drugs for obesity, type 2 diabetes, irritable bowel syndrome and infection.

7. Application of camellia oil in enhancing the regulatory effect of Akkermansia muciniphila on host metabolic homeostasis and intestinal microecology.

8. The use according to claim 7, characterized in that: Specifically, camellia oil is used to prepare related reagents that enhance the regulatory effects of Akkermansia muciniphila on host metabolic homeostasis and intestinal microecology.

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