Biomarker for interfering obesity-related metabolic dysfunction with proanthocyanidins and screening method of biomarker

Abstract

The invention provides a biomarker for intervening obesity-related metabolic dysfunction with procyanidine and a screening method of the biomarker, and belongs to the technical field of biological medicine. The biomarker provided by the invention comprises swainsonine, 6-hydroxypentadecanedioic acid, PE-NMe (20: 4 / 22: 6), PS (22: 6 / 22: 4), lactobacillus, Lactobacillus sp., Lactobacillus sp., Lactobacillus sp., CandidatusSaccharimonas sp., and a desulfurization vibrio. The invention also provides a preparation method of the swainsonine, the 6-hydroxypentadecanedioic acid and the application of the swainsonine, the 6-hydroxypentadecanedioic acid and the application of the swainsonine, the 6-hydroxypentadecanedioic acid, the PE-NMe, the PS and the lactobacillus sp. The biomarker provided by the invention has important significance in the aspects of early diagnosis of obesity-related metabolic dysfunction diseases, action targets of effective prevention and treatment of medicines or foods, formulation of personalized nutrition plans and the like.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly to biomarkers for proanthocyanidin intervention in obesity-related metabolic dysfunction and a screening method therefor. Background Art

[0002] Problems of overweight, obesity and diet-related chronic diseases are becoming increasingly serious, and obesity has gradually become a major public health problem endangering public health. As is well known, obesity is a risk factor for various chronic senile diseases, including cognitive impairment. Some studies have shown that metabolic function decreases with the increase of obesity level. Metabolic dysfunction generally refers to various degrees of metabolic function damage caused by various reasons, ranging from mild metabolic function damage to dementia. Obesity can promote pathophysiological processes such as neuroinflammation, oxidative stress, apoptosis, and mitochondrial dysfunction in the brain tissues of humans and animals, thereby disturbing neuron function, threatening their survival, and ultimately reducing cognitive level and causing cognitive impairment. Therefore, how to relieve or reduce obesity-induced metabolic dysfunction has become the focus of attention.

[0003] Intervening in obesity and metabolic dysfunction through nutrients is an effective means. For example, proanthocyanidins, as a common class of polyphenolic compounds in foods, have physiological functions such as antioxidant, antibacterial and anti-inflammatory, lipid-lowering, prevention and control of atherosclerosis, cardiovascular diseases, and immune regulation. They play an important role in reducing the incidence of metabolic diseases such as obesity and diabetes. They can also regulate the intestinal flora, promote the growth of beneficial bacteria and inhibit the growth of harmful bacteria, and have a positive impact on cognition. However, for the identification of obesity metabolic dysfunction, especially for the relief or prevention by means of nutrients, there is a lack of corresponding biomarkers. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide biomarkers for proanthocyanidin intervention in obesity-related metabolic dysfunction and a screening method therefor. The biomarkers provided by the present invention are of great significance for aspects such as early diagnosis of obesity-related metabolic dysfunction diseases, target points for effective prevention and treatment of drugs or foods, and formulation of personalized nutrition programs.

[0005] In order to achieve the above purpose, the present invention provides the following technical solutions:

[0006] Biomarkers for proanthocyanidin intervention in obesity-related metabolic dysfunction, the biomarkers including swainsonine, 6-hydroxypentadecanoic acid, PE-NMe(20:4 / 22:6), PS(22:6 / 22:4), Lactobacillus, Lachnospiraceae_NK4A136_group, Candidatus_Saccharimonas, Desulfovibrio.

[0007] The present invention also provides a method for screening the biomarker described in the above technical solution, comprising the following steps:

[0008] (1) Randomly divide mice into three groups: control group CON, high-fat group HFD, and proanthocyanidin group PAs. The HFD group and the PAs group are fed a high-fat purified diet with a fat energy ratio of 60%, and the CON group is fed a high-fat control diet with a fat energy ratio of 10% for 8 weeks;

[0009] (2) The PAs group is intragastrically administered with Pas at 100 mg / kg / d, and the CON group and the HFD group are respectively intragastrically administered with an equal dose of normal saline every day. Each group is intragastrically administered for 8 weeks. After the intragastric administration, sample collection is carried out;

[0010] (3) Extract the total microbial genome in the colon content sample, perform high-throughput sequencing, and analyze the microbial flora diversity, abundance, and its differential flora to obtain differential flora;

[0011] (4) Extract the metabolites in the colon content sample and perform non-targeted metabolomics analysis to obtain differential metabolites;

[0012] (5) Based on the differential flora and differential metabolites, perform correlation analysis between the intestinal flora and obesity metabolic function, and between the intestinal metabolites and obesity metabolic function, and then screen out the biomarkers for proanthocyanidin intervention in obesity-related metabolic dysfunction.

[0013] In the present invention, it is preferably further included to measure the biochemical indexes related to serum lipid metabolism; the biochemical indexes related to serum lipid metabolism preferably include alanine aminotransferase, aspartate aminotransferase, blood glucose, triglyceride, total cholesterol, low-density lipoprotein, and high-density lipoprotein contents.

[0014] In the present invention, it is also preferably included to measure the expression levels of inflammatory factors and their related genes in the hippocampal tissue; among them, the inflammatory factors preferably include interleukin-1β, lipopolysaccharide, and tumor necrosis factor; the related genes include ULK1, Sirt1, Becline-1, P62, Atg3, Lc3, FGF21.

[0015] In the present invention, the samples in step (2) preferably include blood, liver, abdominal fat, hippocampus, colon, and its colon content; the method for sample collection is preferably that after the experiment, all mice are fasted for 12 h, then anesthetized with tribromoethanol by intraperitoneal injection, and blood is collected from the tail. Serum is prepared by centrifuging at 3000 rpm for 10 min at 4°C. The collected liver, abdominal fat, and colon tissues are fixed with 4% paraformaldehyde, and at the same time, part of the liver, abdominal fat, hippocampus, colon, and its colon content are quickly frozen in liquid nitrogen and transferred to -80°C for storage.

[0016] In the present invention, the analysis of the microbial flora diversity, abundance and differential flora in step (3) preferably includes using Alpha diversity and between-group difference significance test.

[0017] In the present invention, the specific method for extracting metabolites from the colon content sample in step (4) is as follows: Weigh 50 mg of the colon content sample, add 400 μL of the extraction solution for metabolite extraction, grind it with a cryogenic freezing tissue grinder for 6 min and then perform low-temperature ultrasonic extraction for 30 min; Let the sample stand at -20 °C for 30 min, centrifuge at 4 °C at 13,000×g for 15 min, and take the supernatant for LC-MS analysis.

[0018] In the present invention, the extraction solution is preferably methanol:water with a volume ratio of 4:1 and containing 0.02 mg / mL of the internal standard L-2-chlorophenylalanine.

[0019] In the present invention, the non-targeted metabolomics analysis preferably includes LC-MS differential metabolite analysis, HMDB compound classification, and KEGG pathway enrichment analysis.

[0020] In the present invention, the determination criteria for differential metabolites in the LC-MS differential metabolite analysis are VIP > 1 and P < 0.05.

[0021] The present invention also includes the application in the development of a product for preventing or alleviating obesity-related metabolic dysfunction using the biomarker described in the above technical solution.

[0022] Beneficial technical effects: The present invention provides biomarkers for the intervention of procyanidins in obesity-related metabolic dysfunction and a screening method therefor. The biomarkers include swainsonine, 6-hydroxypentadecanoic acid, PE-NMe(20:4 / 22:6), PS(22:6 / 22:4), Lactobacillus, Lachnospiraceae_NK4A136_group, Candidatus_Saccharimonas, and Desulfovibrio. The biomarkers provided by the present invention are of great significance for the early diagnosis of obesity-related metabolic dysfunction diseases, the target of effective prevention and treatment of drugs or foods, and the formulation of personalized nutrition programs. Description of the Drawings

[0023] Figure 1 Shows the effects of different treatment groups on the body weight change of mice;

[0024] Figure 2 Shows the effects of PAs on the abdominal fat and colon morphology of mice evaluated by H&E staining, original magnification ×100, ×200;

[0025] Figure 3To evaluate the effect of PAs on the mouse liver by H&E staining, original magnifications ×100, ×200;

[0026] Figure 4 For the effects of different treatment groups on mouse serum biochemistry; among them, Figure 4 A is triglyceride (TG) Figure 4 B is total cholesterol (TC); Figure 4 C is low density lipoprotein (LDL); Figure 4 D is high density lipoprotein (HDL); Figure 4 E is alanine aminotransferase (ALT); Figure 4 F is aspartate aminotransferase (AST); Figure 4 G is blood glucose (BG);

[0027] Figure 5 For the effects of different treatment groups on inflammatory factors in mouse hippocampal tissue; among them, Figure 5 A is interleukin 1β (IL-1β); Figure 5 B is tumor necrosis factor (TNF-α); Figure 5 C is lipopolysaccharide (LPS);

[0028] Figure 6 For the effects of different treatment groups on gene expression in mouse hippocampal tissue; among them, Figure 6 A is the expression level of Atg3; Figure 6 B is the expression level of p62; Figure 6 C is the expression level of Becline-1; Figure 6 D is the expression level of Lc3; Figure 6 E is the expression level of Sirt1; Figure 6 F is the expression level of FGF21; Figure 6 G is the expression level of ULK1;

[0029] Figure 7 For the effect of PAs on the mouse colonic microbial composition; among them, Figure 7 A is the species composition at the phylum level; Figure 7 B is the species composition at the genus level;

[0030] Figure 8 For the effect of PAs on the mouse colonic microbial composition; among them, Figure 8 A is the differential species composition at the phylum level; Figure 8 B is the differential species composition at the genus level;

[0031] Figure 9 For the volcano plot of differential metabolites between the CON group and the HFD group;

[0032] Figure 10 For the volcano plot of differential metabolites between the HFD group and the PAs group;

[0033] Figure 11 It is the KEGG pathway enrichment bubble chart for the CON group and the HFD group;

[0034] Figure 12 It is the KEGG pathway enrichment bubble chart for the HFD group and the PAs group;

[0035] Figure 13 It is the clustering heat map and VIP bar chart of differential metabolites for the CON group and the HFD group;

[0036] Figure 14 It is the clustering heat map and VIP bar chart of differential metabolites for the HFD group and the PAs group;

[0037] Figure 15 It is the effect of PAs on mouse colon metabolites; Figure 15 A is the PCA score for the CON group and the HFD group; Figure 15 B is the PCA score for the HFD group and the PAs group; Figure 15 C is the PLS-DA score for the CON group and the HFD group; Figure 15 D is the PLS-DA score for the HFD group and the PAs group; Figure 15 E is the OPLS-DA score for the CON group and the HFD group; Figure 15 F is the OPLS-DA score for the HFD group and the PAs group;

[0038] Figure 16 It is the effect of PAs on mouse colon metabolites; Figure 16 A, Figure 16 C are the HMDB compounds of the differential metabolites identified for the CON group and the HFD group; Figure 16 B, Figure 16 D are the HMDB compounds of the differential metabolites identified for the HFD group and the PAs group;

[0039] Figure 17 It is the correlation analysis between colonic microbiota, metabolic function, and obesity phenotype; where, * indicates P < 0.05, with significant difference; ** indicates P < 0.01, with extremely significant difference;

[0040] Figure 18 It is the correlation analysis between colonic metabolites, metabolic function, and obesity phenotype; where, * indicates P < 0.05, with significant difference; ** indicates P < 0.01, with extremely significant difference. Specific implementation manner

[0041] In order to better understand the present invention, the content of the present invention is further explained in conjunction with the following examples, but the content of the present invention is not limited to the following examples. The materials, reagents, etc. used in the examples and test examples of the present invention, unless otherwise specified, can be obtained from commercial channels; the methods used in the examples and test examples of the present invention, unless otherwise specified, are conventional methods.

[0042] Example 1

[0043] 1. Establish a high-fat mouse model and give nutritional intervention of proanthocyanidins (PAs) to clarify its effects on obesity and its metabolic function

[0044] (1) Thirty healthy male C57BL / 6J mice (3 weeks old, weighing 12 g to 13 g, purchased from Henan Sikebes Biotechnology Co., Ltd.) with similar body weight were selected and randomly divided into a control group (CON), a high-fat diet group (HFD) and a proanthocyanidins group (PAs), with 10 mice in each group. For 8 weeks before the experiment, the HFD group and the PAs group were fed with a high-fat purified diet (fat 60%, protein 20%, carbohydrate 20%) with a fat energy supply ratio of 60%, and the CON group was fed with a control diet (fat 10.2%, protein 18%, carbohydrate 71.8%) with a fat energy supply ratio of 10% (high-fat purified diet and control diet were purchased from Beijing Huafukang Biotechnology Co., Ltd., product codes H10060 and H10010, respectively). After the end of HFD feeding, the PAs group was gavaged with PAs (100 mg / kg / d), and the CON group and the HFD group were gavaged with the same dose of normal saline every day, and the body weight of the mice was recorded weekly. Samples were collected at the 16th week of the experiment. The results showed that at the 8th week of the experiment, the weight of mice in the HFD group and the PAs group was higher than that in the CON group and met the requirements of the high-fat mouse model, but there was no significant difference in the weight of mice between the groups (P>0.05). At the 15th week of the experiment, compared with the HFD group, the weight of mice in the PAs group decreased significantly (P<0.05). Figure 1 ).

[0045] Sample collection: The weight of mice was recorded weekly, and blood, liver, abdominal fat, hippocampus, colon and colon contents were collected. After the experiment, all mice were fasted for 12 hours, then intraperitoneally injected with tribromoethanol anesthetic, and blood was collected from the tail, and serum was prepared by centrifugation at 3000rpm for 10 minutes at 4°C. The collected liver, abdominal fat and colon tissues were fixed with 4% paraformaldehyde, and part of the liver, abdominal fat, hippocampus, colon and colon contents were quickly frozen with liquid nitrogen and transferred to -80°C for storage.

[0046] (2) The abdominal fat, colon, and liver fixed in 4% paraformaldehyde were serially dehydrated with ethanol, then cleared with xylene and embedded in paraffin. Subsequently, the embedded wax blocks were sectioned and stained with hematoxylin and eosin (H&E). The morphological changes of the liver, colon, and abdominal fat tissues were observed using a microscope (Olymbus BX53, Tokyo, Japan). The results showed that there was almost no vacuole fusion in the abdominal fat of the CON group mice, while a large number of broken and fused vacuoles and inflammatory cell infiltration were observed in the HFD group. In addition, compared with the HFD group, only partial fusion of vacuoles and a small number of inflammatory cells were observed in the PAs group. Analysis of the morphological changes of the mouse colon showed that there were more goblet cells and fewer inflammatory cells in the colon of the CON group and PAs group mice, while the vacuoles and inflammatory cells in the intestinal mucosa of the HFD group increased, and the goblet cells decreased ( Figure 2 ). In addition, there was basically no inflammation in the liver of the CON group mice, while fatty cell hyperplasia, fatty degeneration, and hepatocyte edema were observed in the liver of the HFD group. Compared with the HFD group, PAs reduced the number of hepatic adipocytes and alleviated the hepatocyte edema ( Figure 3 ).

[0047] 2. To clarify the effect of proanthocyanidins (PAs) on blood lipids in high-fat diet mice through serum lipid metabolism-related biochemical indexes

[0048] (1) The determination of serum biochemical indexes included the contents of alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood glucose (BG), triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL). The results showed that compared with the HFD group, the contents of total cholesterol (TC) and low-density lipoprotein (LDL) in the PAs group mice were significantly decreased (P < 0.01), the content of high-density lipoprotein (HDL) increased, and the contents of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) decreased, but the differences were not significant (P > 0.05). In addition, the contents of triglyceride (TG) and blood glucose (BG) in the HFD group and PAs group were higher than those in the CON group, but the differences were not significant (P > 0.05) ( Figure 4 ).

[0049] 3. To clarify the effect of proanthocyanidins (PAs) on metabolic dysfunction in mice through the expression of hippocampal inflammatory factors and their related genes

[0050] (1) The levels of interleukin-1β (IL-1β), lipopolysaccharide (LPS), and tumor necrosis factor (TNF-α) in the hippocampal tissue were measured by enzyme-linked immunosorbent assay. The results showed that the contents of interleukin 1β (IL-1β), tumor necrosis factor (TNF-α), and lipopolysaccharide (LPS) in the mice of the PAs group were extremely significantly decreased compared with those in the HFD group (P<0.01). However, compared with the CON group, the tumor necrosis factor (TNF-α) in the PAs group was significantly increased (P<0.05), and the interleukin 1β (IL-1β) and lipopolysaccharide (LPS) showed an increasing trend, but the differences were not significant (P>0.05)( Figure 5 ).

[0051] (2) The total RNA of the hippocampal tissue was extracted using the TransZol Up Plus RNA Kit, and the purity and integrity of the RNA were evaluated by a Thermofisher nanodrop2000 visible spectrophotometer. The RNA was reverse transcribed into cDNA using the HiFiScript cDNA Synthesis Kit. Primers were designed by Primer 5 (Table 1), with β-actin as the internal reference gene, and qRT-PCR was performed, and relative quantitative analysis was carried out using the 2 -ΔΔCt method. PCR amplification conditions: pre-denaturation at 94°C for 30 s, denaturation at 94°C for 45 cycles of 5 s, annealing at 60°C for 15 s, and extension at 72°C for 10 s. The results showed that compared with the CON group, the expression levels of Atg3 and Becline in the HFD group and the PAs group were extremely significantly decreased (P<0.01), and the expression level of Lc3 in the HFD group was extremely significantly increased (P<0.01), the expression level of Sirt1 was extremely significantly decreased (P<0.01), and the expression level of FGF21 was significantly decreased (P<0.05). In addition, compared with the HFD group, the expression level of Lc3 in the PAs group was extremely significantly decreased (P<0.01), and the expression levels of Sirt1 and FGF21 were extremely significantly increased (P<0.01). At the same time, the expression levels of Atg3 and p62 showed an upward trend, and the expression levels of Becline and ULK1 showed a downward trend, but the differences were not significant (P>0.05)( Figure 6 )。

[0052] Table 1 Primer sequences

[0053]

[0054]

[0055] 4. Screening for biomarkers of high-fat diet-induced obesity-related metabolic dysfunction by combining 16s rRNA high-throughput sequencing of colonic contents and untargeted metabolomics

[0056] (1) 16S rRNA high-throughput sequencing. Using soil DNA kit (Omega Bio-tek, Norcross, GA, U.S.) to extract total microbial genomic DNA from colonic contents, and using 1% agarose gel electrophoresis to detect the quality of the extracted genomic DNA. Then PCR amplification was carried out, and the PCR recovery products were detected and quantified using a TM Quantus Fluorometer (Promega, USA). The purified PCR products were used to construct a library using the Figure 7 Rapid DNA-Seq Kit, sequenced using the NovaSeq PE250 platform of Illumina, and analyzed for microbial flora diversity, abundance, and differential flora, etc. The results showed that at the phylum level, the dominant phyla were Firmicutes, Bacteroidota, Patescibacteria, Actinobacteriota, and Desulfobacterota. Among them, compared with the CON group, the Desulfobacterota in the HFD group increased extremely significantly (P < 0.01), and the Firmicutes and Desulfobacterota in the PAs group increased significantly (P < 0.05). And the Patescibacteria, Campilobacterota, and Proteobacteria in the HFD group decreased extremely significantly (P < 0.01). The Bacteroidota, Campilobacterota, and Proteobacteria in the PAs group decreased extremely significantly (P < 0.01). In addition, compared with the HFD group, the Patescibacteria in the PAs group increased extremely significantly (P < 0.01), while the Bacteroidota, Desulfobacterota, and Proteobacteria decreased extremely significantly (P < 0.01)( Figure 7 A, Figure 8A). At the genus level, compared with the HFD group, the abundances of Candidatus_Saccharimonas, Lactobacillus, Akkermansia, and Lachnospiraceae_UCG - 006 microbial flora in the PAs group increased extremely significantly (P < 0.01), the abundance of Lachnospiraceae_NK4A136_group increased (P > 0.05), while the abundances of unclassified_f__Lachnospiraceae, Desulfovibrio, Alistipes, Bacteroides, norank_f__Muribaculaceae, and norank_f__Lachnospiraceae decreased extremely significantly (P < 0.01)( Figure 7 B, Figure 8 B).

[0057] (2) Untargeted metabolomics analysis. Weigh 50 mg of colon content samples, add 400 μL of extraction solution (methanol: water = 4:1, containing 0.02 mg / mL internal standard L - 2 - chlorophenylalanine) for metabolite extraction. Use a cryogenic (-10 °C, 50 Hz) tissue grinder to grind for 6 min and then perform cryogenic ultrasonic extraction for 30 min (5 °C, 40 kHz). Then let the samples stand at -20 °C for 30 min, and finally centrifuge the samples at 13000×g for 15 min at 4 °C. Take the supernatant and transfer it to an injection vial with an inner cannula for on - machine analysis. LC - MS was performed at Shanghai Majorbio Bio - PharmTechnology Co., Ltd. The results showed that PCA, PLS - DA, and OPLS - DA all showed an obvious separation trend of colonic metabolites in CON group, HFD group, and PAs group mice( Figure 15 ), indicating that high - fat diet and PAs supplementation had a certain impact on colonic metabolites in mice. Metabolites with VIP > 1 and P < 0.05 were classified as differential metabolites. A total of 8892 differential metabolites were detected in the CON group, HFD group, and PAs group. Figure 9 、 Figure 10 shows the up - regulated and down - regulated metabolites in the expression differences between the CON group and the HFD group, as well as between the HFD group and the PAs group. Figure 13 、 Figure 14showed significant differences in the expression of metabolites and the trend of changes in metabolite expression levels. HMDB compound classification showed that the most abundant compounds in the CON group and the HFD group, as well as in the HFD group and the PAs group, were Lipids and lipid-like molecules( Figure 16 A, B). Among the CON group and the HFD group, the differential metabolites were mainly concentrated in Fatty Acyls and Glycerophospholipids compounds( Figure 16 C). Among them, compared with the CON group, the HFD significantly down-regulated the metabolites of Osmaronin and 6-Hydroxypentadecanedioic acid in the Fatty Acyls compounds, and significantly up-regulated the metabolites of PE-NMe(20:4 / 22:6)) and PS(22:6 / 22:4) in the Glycerophospholipids compounds. Among the HFD group and the PAs group, the differential metabolites were mainly concentrated in Fatty Acyls and Steroids and steroid derivatives compounds( Figure 16 D)). Among them, in the Steroids and steroid derivatives compounds, compared with the PAs group, the HFD significantly up-regulated the metabolites of Tetrahydrocortisol and Doxercalciferol, and significantly down-regulated the metabolite of Chembl4569322. KEGG pathway enrichment analysis was performed to explore the changes in metabolic pathways. The results showed that the Glycerophospholipid metabolism pathway was more enriched in the differential metabolites between the CON group and the HFD group( Figure 11 ), while the Linoleic acid metabolism, Steroid hormone biosynthesis, and Tryptophan metabolism pathways were more enriched in the differential metabolites between the HFD group and the PAs group( Figure 12 ). Among them, PAs significantly up-regulated the level of the metabolite 2-Methoxyestradiol in the Steroid hormone biosynthesis metabolic pathway.

[0058] (3) Biomarker screening. Based on the above differential flora and metabolites, correlation analysis of the mouse intestinal flora, metabolites and obesity metabolic functions was carried out, and then biomarkers related to high-fat-induced obesity-related metabolic dysfunction were screened. Biomarker screening was comprehensively determined based on metabolites (VIP>1; P<0.05), the differential significance of flora (P<0.05), the correlation between differential metabolites and flora and obesity metabolic indicators, and their biological roles in obesity metabolism. From the correlation analysis of mouse intestinal flora, metabolites and obesity metabolic functions ( Figure 17 、 Figure 18)It can be seen that PAs are positively correlated with Lactobacillus, Lachnospiraceae_NK4A136_group, Candidatus_Saccharimonas in the intestinal flora of mice and intestinal metabolites Osmaronin and 6-Hydroxypentadecanedioic acid, and negatively correlated with PE-NMe(20:4 / 22:6), PS(22:6 / 22:4), neuroinflammatory factors IL-1β, TNF-α, LPS in the hippocampal tissue and some obesity phenotype indicators. At the same time, neuroinflammatory factors IL-1β, TNF-α, LPS in the hippocampal tissue and some obesity phenotype indicators are negatively correlated with intestinal metabolites Osmaronin and 6-Hydroxypentadecanedioic acid, and positively correlated with PE-NMe(20:4 / 22:6) and PS(22:6 / 22:4). Desulfovibrio is negatively correlated with metabolites Osmaronin and 6-Hydroxypentadecanedioic acid, and positively correlated with PE-NMe(20:4 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PS(22:6 / 22:4), neuroinflammatory factors IL-1β, TNF-α, LPS in the hippocampal tissue and some obesity phenotype indicators. At the same time, neuroinflammatory factors IL-1β, TNF-α, LPS in the hippocampal tissue and some obesity phenotype indicators are negatively correlated with Osmaronin and 6-Hydroxypentadecanedioic acid, and positively correlated with PE-NMe(20:4 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z)) and PS(22:6 / 22:4). Therefore, proanthocyanidins can regulate neuroinflammation and obesity in the hippocampal tissue of mice through four intestinal flora or metabolic hosts, Lactobacillus, Lachnospiraceae_NK4A136_group, Candidatus_Saccharimonas, and Desulfovibrio, to produce four metabolites, Osmaronin, 6-Hydroxypentadecanedioic acid, PE-NMe(20:4 / 22:6), and PS(22:6 / 22:4), and can be used as biomarkers for obesity-related metabolic dysfunction.

[0059] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. Biomarkers of proanthocyanidins for intervention of obesity-related metabolic dysfunction, characterized in that: The biomarkers include swainsonine, 6-hydroxypentadecanedioic acid, PE-NMe (20:4 / 22:6), PS (22:6 / 22:4), Lactobacillus, Lachnospiraceae_NK4A136_group, Candidatus_Saccharimonas, and Desulfovibrio.

2. A method for screening biomarkers according to claim 1, characterized in that: The following steps are involved: (1) Mice were randomly divided into three groups: control group CON, high-fat group HFD, and proanthocyanidin group PAs. The HFD and PAs groups were fed with a high-fat purified diet with a fat energy supply ratio of 60%, and the CON group was fed with a high-fat control diet with a fat energy supply ratio of 10% for 8 weeks. (2) The PAs group was gavaged with Pas 100 mg / kg / d, and the CON and HFD groups were gavaged with an equal dose of normal saline every day for 8 weeks. Samples were collected after the gavage. (3) Extract the total microbial genome from the colon content samples, perform high-throughput sequencing, and analyze the diversity, abundance, and differential flora of the microbial flora to obtain differential flora; (4) extracting metabolites from colon content samples and performing non-targeted metabolomics analysis to obtain differential metabolites; (5) Based on the differences in microbiota and metabolites, the correlation between intestinal microbiota and obesity metabolic function, and between intestinal metabolites and obesity metabolic function were analyzed to screen out biomarkers for the intervention of proanthocyanidins in obesity-related metabolic dysfunction.

3. The screening method according to claim 2, characterized in that It also includes measuring biochemical indicators related to serum lipid metabolism; the biochemical indicators related to serum lipid metabolism include alanine aminotransferase, aspartate aminotransferase, blood sugar, triglyceride, total cholesterol, low-density lipoprotein and high-density lipoprotein content.

4. The screening method according to claim 2, characterized in that It also includes determining the expression levels of inflammatory factors and related genes in hippocampal tissue; wherein the inflammatory factors include: interleukin-1β, lipopolysaccharide and tumor necrosis factor; the related genes include ULK1, Sirt1, Becline-1, P62, Atg3, Lc3, FGF21.

5. The screening method according to claim 2, characterized in that The step (4) of extracting metabolites from the colon content sample is as follows: weigh 50 mg of the colon content sample, add 400 μL of extraction solution to extract metabolites, grind using a low-temperature frozen tissue grinder for 6 minutes, and then perform low-temperature ultrasonic extraction for 30 minutes; place the sample at -20°C for 30 minutes, centrifuge at 4°C and 13000×g for 15 minutes, and take the supernatant for LC-MS analysis.

6. The screening method according to claim 5, characterized in that The extract has a volume ratio of methanol to water of 4:1 and contains 0.02 mg / mL of internal standard L-2-chlorophenylalanine.

7. The screening method according to claim 2, characterized in that The non-targeted metabolomics analysis includes LC-MS differential metabolite analysis, HMDB compound classification, and KEGG pathway enrichment analysis.

8. The screening method according to claim 7, characterized in that The criteria for determining differential metabolites in the LC-MS differential metabolite analysis were VIP>1 and P<0.

05.

9. Use of the biomarker according to claim 1 in developing products for preventing or alleviating obesity-related metabolic dysfunction.

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