Intestinal bacteria and serum metabolites capable of serving as type 2 diabetes biomarkers and application of intestinal bacteria and serum metabolites

Abstract

The invention discloses intestinal bacteria and serum metabolites which can be used as biomarkers of type 2 diabetes mellitus (prediabetes mellitus (PDM), new diagnosis diabetes mellitus (NDDM) and diabetes mellitus (P2DM) after medication) in different disease stages, and application of the intestinal bacteria and the serum metabolites. Aiming at type 2 diabetes mellitus, predictive markers including staphylococcus aureus, escherichia coli, klebsiella pneumoniae, branched chain amino acids (leucine, valine and isoleucine) and the like, especially staphylococcus aureus of which the genome contains acetolactate synthase (ALS), are efficiently screened out. The type 2 diabetes mellitus marker disclosed by the invention can be used for early warning the illness risk of type 2 diabetes mellitus and guiding the adjustment of an intestinal microbial environment; the method can be used for indicating the correlation between the intestinal microbial environment and the type 2 diabetes mellitus and is used for assisting diagnosis; and drug action target research, accurate medication, pathogenesis research and the like are realized.

Description

Technical Field

[0001] The present invention belongs to the field of medical detection, and particularly relates to intestinal bacteria and serum metabolites that can be used as biomarkers for type 2 diabetes and their applications. Background Art

[0002] Type 2 diabetes (T2D) is a chronic disease characterized by elevated blood glucose concentration and is one of the leading causes of death and disability globally. In addition, approximately 36% of Chinese adults have prediabetes, a disease state characterized by intermediate hyperglycemia and insulin resistance. Those with prediabetes will eventually develop overt T2D. Compared with prediabetic patients, untreated T2D patients have different characteristics in terms of the microbiome. In addition, the interaction of antidiabetic drugs, such as metformin, liraglutide, SGLT2 inhibitors (dapagliflozin), and insulin, with the gut microbiota and their impact on drug function are the focus of current research, which makes it complex to identify bacterial biomarkers that may be related to disease progression. Therefore, early examination, early detection, early diagnosis, and early treatment are advocated. However, due to the lack of early biological markers for diagnosing T2D, early effective treatment is often delayed.

[0003] There is evidence that the gut microbiota has changed significantly in T2D and prediabetes states. Metagenomics studies have enabled the characterization of the microbiota in T2D patients and provided insights into the interaction of functional gene abundances between the microbiota and host metabolism, and these studies have been conducted in both China and Europe. Generally speaking, T2D individuals exhibit a decrease in bacterial diversity and gene richness. Recent studies have shown that T2D is associated with an increased ratio of Bacteroidetes to Firmicutes, as well as an increase in Lactobacillus, Veillonellaceae, and Prevotella, and a decrease in butyrate-producing genera (such as Roseburia, Clostridiaceae, Clostridiales). T2D is associated with a higher colonization rate of Staphylococcus aureus and is associated with altered glucose tolerance and elevated blood glucose levels. There are also significant differences in the microbiota between prediabetic and untreated T2D patients. For example, some studies have shown that Escherichia coli is more abundant in prediabetic patients, while Bacteroides is more abundant in T2D patients. In addition, it was initially reported that the hypoglycemic effect of antidiabetic drugs can be partly attributed to certain microbial species. However, there is no study analyzing the pharmacological intervention (such as insulin and oral hypoglycemic drugs) on the microbial composition in T2D. In addition, the functional relationship between fecal microbiota and metabolites in the blood and host phenotypes in prediabetes, newly diagnosed diabetes, and diabetes after medication is not clear.

[0004] Changes in branched-chain amino acids (BCAAs, valine, leucine, and isoleucine) in serum are one of the ways in which microorganisms affect human metabolic health. Research has shown that there is a close link between serum BCAA levels and the development of T2D, which may be due to the increased accumulation of BCAAs in adipose tissue and the liver in obesity and T2D. T2D can increase the potential of microorganisms for BCAA biosynthesis, such as Prevotella and Bacteroides fragilis, while Dialister and Butyricoccus can reduce BCAA uptake. The reasons for these elevated levels in obesity and T2D are not yet clear. Yu et al. reported that a low-isoleucine diet improves liver and fat metabolism, increases insulin sensitivity, and prevents diabetes by activating the FGF21-UCP1 axis. Charon et al. pointed out that the activity of the BCAA biosynthetic enzyme d-citrate synthase is highest in patients with T2D. In addition, Qiao et al. found that Bacteroides protects the cardiovascular system from damage by enhancing BCAA catabolism through the expression of the porA gene that degrades BCAAs. Therefore, it is necessary to explore the bacteria that promote BCAA production or catabolism in T2D and analyze their possible mechanisms, including the enzymes and genes involved in BCAA metabolism. Summary of the Invention

[0005] The first object of the present invention is to provide biomarkers for type 2 diabetes at different disease stages, and the biomarkers include intestinal bacteria and serum metabolites. Specifically, the biomarkers include at least one of Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, and branched-chain amino acids (leucine, valine, and isoleucine).

[0006] Another object of the invention is to provide the use of the above biomarkers in the preparation of screening and / or diagnostic products for type 2 diabetes.

[0007] Preferably, the product is a reagent or a kit. Further, the reagent is a reagent for metagenomic sequencing and a reagent for metabolomics analysis.

[0008] Preferably, the application is to evaluate the risk of developing type 2 diabetes by detecting the relative abundances of Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, and branched-chain amino acids in a subject's sample.

[0009] Preferably, the sample is feces and serum.

[0010] Preferably, the type 2 diabetes is type 2 diabetes at different disease stages, specifically, patients with prediabetes (PDM), newly diagnosed diabetes (NDDM), and type 2 diabetes after medication (P2DM).

[0011] Another object of the invention is to provide the use of a reagent for detecting the above biomarkers in the preparation of screening and / or diagnostic products for type 2 diabetes.

[0012] The present invention utilizes microbiome and metabolome data to form a combined biomarker panel, and efficiently screens predictive biomarkers for type 2 diabetes at different disease stages (PDM, NDDM, and P2DM). According to the results, the relevance of gut bacteria and serum metabolites to type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) is ranked from high to low as: Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, and at least one of branched-chain amino acids (leucine, valine, and isoleucine).

[0013] The microbiome biomarkers of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) of the present invention can be used to warn of the risk of type 2 diabetes, estimate the likelihood of an individual developing type 2 diabetes in the future, and guide the adjustment of the gut microbial environment. By determining whether one or two or more of the above biomarkers are present in the gut microbiota of an object, it is possible to effectively determine whether the test object is susceptible to type 2 diabetes.

[0014] Another object of the present invention is to provide a kit, which includes reagents for detecting the content of the above biomarkers. By specifically detecting the relative content of gut biomarkers, the risk of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) is evaluated, providing a new detection method for the screening and diagnosis of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM), and enabling early clinical intervention to prevent the progression of the disease.

[0015] At the same time, the above biomarkers can also be used as intervention targets to continuously monitor the health status of an individual, so as to perform early intervention when abnormal characteristics related to certain type 2 diabetes are found.

[0016] The present invention also provides the use of a drug for regulating the above biomarkers for evaluating the risk of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) in the preparation of a drug for reducing the risk of type 2 diabetes. Preferably, the present invention provides a drug, which contains a drug for reducing the risk of type 2 diabetes by regulating the relative content of the above biomarkers for evaluating the risk of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM). The drug can regulate microbiome biomarkers to balance the relative content between different genera of bacteria, reduce the risk of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM), and at the same time, the drug can regulate the content of metabolites in the gut, reduce the absorption of harmful metabolites into the enterohepatic circulation and then into the host's blood, and improve the internal environment of the individual.

[0017] The present invention also provides a model of a combination of gut bacteria and serum metabolites that can serve as biomarkers for type 2 diabetes at different disease stages (PDM, NDDM, and P2DM), and the input variables of the model are the abundances of the above-mentioned markers for assessing the risks of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM).

[0018] The present invention also provides a method for mining biomarkers for type 2 diabetes at different disease stages (PDM, NDDM, and P2DM). Among them, the detection method for microbial content is metagenomic sequencing, and the detection method for metabolite content is liquid chromatography-tandem mass spectrometry detection.

[0019] Specifically, a method for constructing a combination model of gut bacteria and serum metabolites that can serve as biomarkers for type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) includes: performing metagenomic deep sequencing on fecal bacteria of people with type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) and healthy people, and performing serum metabolomics detection, cleaning the data and matching the database to obtain the abundances of bacteria of specific genera and metabolite contents;

[0020] Advantages of the present invention:

[0021] The present invention recruited people with type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) and healthy people respectively, performed metagenomic deep sequencing on the fecal bacteria of all people with type 2 diabetes at different disease stages (PDM, NDDM, and P2DM) and healthy people, and performed liquid chromatography-tandem mass spectrometry detection on the serum, and obtained the abundance contents of bacteria of different genera and different metabolites respectively.

[0022] The present invention efficiently screened out at least one of Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, and branched-chain amino acids (leucine, valine, and isoleucine), indicating that the biomarkers screened by the present invention have extremely high diagnostic value, can be used to warn of the risk of type 2 diabetes at different disease stages (PDM, NDDM, and P2DM), as an aid in diagnosis, as well as research on drug action targets, precision medicine, and pathogenesis. Description of the Drawings

[0023] Figure 1 For the four-group research design and main clinical data in Example 1, where A: Schematic diagram of the grouping basis of the recruited personnel and the detection of main clinical data; B; Fasting blood glucose (FBG) levels of the four groups of people; C: Glycated hemoglobin (HbA1c) levels of the four groups of people.

[0024] Figure 2 For the comparison of the microbial community compositions of the four groups in Example 2.

[0025] Figure 3 Association of four groups of gut microbial species with clinical indicators in Example 2.

[0026] Figure 4 KEGG annotation profiles of four groups of microbiomes in Example 2.

[0027] Figure 5 Differences in four groups of serum metabolites and metabolic pathway enrichment analysis in Example 3.

[0028] Figure 6 Correlation analysis of clinical indicators, differential metabolites and differential microbiota in Example 3.

[0029] Figure 7 Fecal transplantation from ALS-positive type 2 diabetic patients exacerbated high-fat diet-induced insulin resistance in rats in Example 4. Detailed implementation manners

[0030] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other implementation manners obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope protected by the present invention.

[0031] Example 1 Collection of feces from type 2 diabetes (PDM, NDDM, and P2DM) patients at different disease stages and healthy individuals

[0032] 1.1 T2D study population

[0033] All study participants in this study were recruited from Guangzhou, including 12 healthy controls (H), 12 prediabetes (PDM) patients, 12 newly diagnosed diabetes (NDDM) patients, and 21 type 2 diabetes (P2DM) patients after medication. All participants were grouped according to the diagnostic criteria of the American Diabetes Association in 2003. Patients with the following conditions were excluded: type 1 diabetes, hypertension, dyslipidemia, acute infectious diseases, cirrhosis, diarrhea, pregnancy, use of antibiotics in the past 3 months, and ingestion of probiotics in the past 6 weeks. Fecal samples were used for metagenomics analysis, and serum samples were used for metabolomics analysis, and then stored in dry ice boxes until they reached the laboratory. Then, they were stored in a -80 °C ultra-low temperature freezer in the laboratory until further use.

[0034] We grouped according to blood glucose levels, including a healthy group (Group H, n = 12), a prediabetes group (Group PDM, n = 12), a newly diagnosed diabetes group (Group NDDM, n = 12), and a type 2 diabetes group after medication (Group P2DM, n = 21) (Figure 1 A). We found that the fasting blood glucose (FBG) and glycated hemoglobin (HbA1c) levels in the PDM group, NDDM group, and P2DM group were higher than those in the healthy group (p < 0.05). In addition, the FBG level in the NDDM group and P2DM group was significantly higher than that in the PDM group. However, there was no significant difference between the NDDM group and P2DM group ( Figure 1 B - C). As can be seen from Table 1, there were also no significant differences in age, height, weight, body mass index, systolic blood pressure (SBP), diastolic blood pressure, total cholesterol (TC), triglyceride, high - density lipoprotein cholesterol (HDL - C), low - density lipoprotein cholesterol (LDL - C), albumin, and total bilirubin levels among the four groups. And the duration of diabetes in the P2DM group was about 4 - 20 years. Their fasting and post - meal C - peptide and insulin levels were different, indicating a recovery of insulin secretion. At the same time, we recorded the drugs used by 21 P2DM patients, including 8 participants using insulin injection, and the others using oral hypoglycemic drugs (e.g., metformin, acarbose, sitagliptin, voglibose, dapagliflozin, repaglinide, gliclazide, canagliflozin, alogliptin, linagliptin, and glimepiride) alone or in combination with insulin injection.

[0035] Table 1. Clinical information, characteristic indicators, and biochemical indicators of the participants (n = 57).

[0036]

[0037]

[0038] Example 2 Screening of microbial biomarkers for type 2 diabetes (PDM, NDDM, and P2DM) at different disease stages

[0039] Perform metagenomic deep sequencing on fecal bacteria of type 2 diabetes (PDM, NDDM, and P2DM) at different disease stages and healthy people, clean the data and match the database to obtain the abundance content of bacteria of specific genera.

[0040] 2.1 DNA extraction and metagenomic sequencing

[0041] Extract total genomic DNA from type 2 diabetes fecal samples using Soil DNA Kit (Omega Bio - tek, Norcross, GA, U.S.). All samples were analyzed on an Illumina NovaSeq (Illumina Inc., San Diego, CA, USA) using the NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles) according to the manufacturer's instructions (www.illumina.com).

[0042] 2.2 Sequence quality control and genome assembly

[0043] The data was analyzed on the free online Majorbio Cloud Platform (www.majorbio.com). Briefly, adapters were trimmed from paired-end Illumina reads using fastp, and low-quality reads were removed. The reads were aligned to the human genome using BWA. Metagenomic data assembly was performed using MEGAHIT. Contigs with a length ≥ 300 bp were selected as the final assembly results, and these contigs were then used for further gene prediction and annotation.

[0044] 2.3 Gene prediction and non-redundant gene catalog construction

[0045] Open reading frames (ORFs) were predicted from each assembled contig using Prodigal / MetaGene. Predicted ORFs with a length ≥ 100 bp were retrieved and translated into amino acid sequences using the NCBI translation table. A non-redundant gene catalog was constructed using CD-HIT with a sequence similarity of 90% and a coverage of 90%. The high-quality reads were aligned to the non-redundant gene catalog, and gene abundances were calculated using SOAPaligner with a similarity of 95%.

[0046] 2.4 Taxonomy and functional annotation

[0047] The representative sequences of the non-redundant gene catalog were aligned to the NR database for taxonomic annotation using Diamond with an e-value cutoff of 1e-5. KEGG annotation was performed using Diamond in the KEGG database with an e-value cutoff of 1e-5.

[0048] The present invention evaluated Alpha and Beta diversities, which represent intra-group and inter-group diversities of the microbial community, respectively. Among the four groups, there were no significant differences in the Shannon index ( Figure 2 A). In addition, Diversity-Inducing Multi-view Subspace Clustering (Dim) showed a clear separation of bacterial communities among the four groups ( Figure 2 B), indicating differences in microbial composition among the four groups.

[0049] Among the four groups, Bacteroidetes, Firmicutes, and Proteobacteria were the most abundant phyla ( Figure 2C-E), consistent with previous reports. These phyla accounted for 94.7%, 89.8%, 92.1%, and 87.7% of the samples in the H group, PDM group, NDDM group, and P2DM group, respectively. Compared with the healthy control group, the PDM group, NDDM group, and P2DM group all had an increase in Firmicutes (48.6%, 57%, and 54.4% respectively) and a decrease in Bacteroidetes (14.5%, 21.6%, and 24.7% respectively) (p < 0.05). In addition, the ratio of Firmicutes to Bacteroidetes in the PDM group, NDDM group, and P2DM group was higher than that in the H group (p < 0.05)( Figure 2 F).

[0050] Next, we determined the microbial composition at the genus level. We found that the P2DM group had the most unique genera (237), while the H group, PDM group, and NDDM group had 83, 145, and 49 unique genera respectively( Figure 2 G). The major genera in the healthy control group were Bacteroides (15.7%), Clostridium (16.4%), and Prevotella (5.9%). As diabetes developed, several differentially abundant pathogenic microorganisms were identified. Escherichia coli (10.6%) was abundant in the PDM group, and Klebsiella (7.6%) was abundant in the NDDM group. After treatment, the relative abundances of Escherichia coli (3.4%) and Klebsiella (1.0%) decreased significantly( Figure 2 H).

[0051] We also investigated the differentially abundant species among the four groups( Figure 2I). The potential probiotic Clostridia Phocaeicola vulgatus, Prevotella copri of the genus Prevotella, and Bacteroides stercoris, Bacteroides ovatus, and Bacteroides uniformis of the genus Bacteroides were reduced in three patient groups (PDM, NDDM, and P2DM) compared with the healthy control group (p < 0.05). The butyrate-producing bacterial population, such as Alistipes putredinis, was also reduced in the PDM, NDDM, and P2DM groups (p < 0.05). However, the relative abundances of Akkermansia muciniphila and Bifidobacterium longum, which are associated with weight loss and reduced hyperlipidemia, were increased in the PDM and P2DM groups compared with the healthy control group. We found that Escherichia coli and Klebsiella pneumoniae were more enriched in the PDM and NDDM groups. In addition, Ruthenibacterium lactatiformans was enriched in the NDDM and P2DM groups. In particular, the relative abundance of Staphylococcus aureus S. aureus in the three disease groups was increased compared with the healthy control group, especially the most in the P2DM group ( Figure 2 J).

[0052] To explore whether the increase in the abundance of S. aureus in the PDM, NDDM, and P2DM groups is related to the drug intervention method, we evaluated the differences in the microbial communities between the insulin injection group (Insulin group) and the oral hypoglycemic drug group (N_insulin group). We found that there was no significant difference in the fasting blood glucose (FBG) levels between the two groups (p > 0.05)( Figure 2 K). The abundance of S. aureus in the Insulin group was significantly higher than that in the N_insulin group (p < 0.05)( Figure 2 L). Therefore, we speculate that drug intervention may be the main factor leading to the increase in the abundance of S. aureus in the feces of T2D patients. In addition, the causal relationship between S. aureus infection and the occurrence of T2D needs further study.

[0053] Since the clinical indicators such as FBG and HbA1c levels were different among the four groups. Therefore, we constructed a correlation heatmap and a co-abundance network diagram to study the correlation between gut microbiota and clinical information( Figure 3 A, B). To determine whether the unique species are related to the clinical indicators, we performed a Spearman correlation analysis on the potential biomarker species and clinical characteristics using the data of all patients in the four groups( Figure 3(C-H). The results showed that there were significant correlations between clinical indicators, especially FBG and HbA1c levels, and the microbial composition. The abundances of Escherichia coli, Klebsiella pneumoniae, and Streptococcus salivarius were significantly positively correlated with FBG and HbA1c, while the abundances of Roseburia, Enterococcus faecalis, and Eubacterium rectale were negatively correlated with these indicators. Figure 3 A). As Figure 3 shown in B, F-H, the abundances of Bifidobacterium longum (R = 0.4246, p = 0.001), Enterococcus faecium (R = 0.4388, p = 0.0006), and Flavonifractor plautii (R = 0.4078, p = 0.002) increased in the patient groups (PDM, NDDM, and P2DM) and were positively correlated with HbA1c, FBG, and age, respectively. In addition, the abundance of S. aureus was positively correlated with FBG (R = 0.4123, p = 0.001), indicating a potential relationship between S. aureus and elevated blood glucose levels.

[0054] Next, we performed KEGG pathway analysis to understand the potential functional genes associated with the microbial communities of the four groups. The NMDS results showed that the four groups exhibited different functional compositions. Figure 4 A). In addition, consistent with the microbial composition results, the P2DM group had the most abundant unique KOs (reaching 220), followed by the NDDM group (103), the PDM group (81), and the H group (51). Figure 4 B). The four groups were significantly enriched in metabolism, environmental information processing, genetic information processing, cellular processes, human diseases, and organismal systems. Figure 4 C). At the same time, the tertiary KEGG pathway genes related to functional metabolism showed that the four groups showed differences in module enrichment. Figure 4 D-J). For example, the microbial communities of PDM patients were enriched in the phosphorylation transfer system, fructose and mannose metabolism, biofilm formation of Escherichia coli, and propionate metabolism. The microbes of the NDDM group were enriched in ABC transporters, glycerolipid metabolism, benzoate degradation, and inositol phosphate metabolism. Figure 4 D). At the same time, we found that the KEGG modules involved in metabolic pathways, such as alanine, aspartate, and glutamate metabolism; the citric acid cycle; and butyrate metabolism, were significantly lower in the patient groups (PDM, NDDM, and P2DM) than in the healthy control group (p < 0.05).

[0055] The microbial genes involved in the "valine, leucine, and isoleucine biosynthesis" pathway increased in the patient groups (NDDM and P2DM groups). Figure 4G). We found that the acetolactate synthase ALS protein (EC 2.2.1.6), the first key enzyme in BCAA synthesis, was also highly enriched in the microbiome of the patient group ( Figure 4 H). As Figure 4 shown in I, insulin resistance increased in the patient group (p < 0.05). Furthermore, by tracing the enzyme source in the KEGG database, we found that the acetolactate synthase ALS protein and BCAA transaminase were secreted by S. aureus, which has genes encoding the biosynthesis of valine, leucine, and isoleucine (K01652, gene: ilvG). Further, BCAA mainly plays a role in the development of T2D, indicating that S. aureus can promote insulin resistance by promoting the excretion of serum BCAA. Therefore, controlling S. aureus infection can help slow down the formation of BCAA in T2D.

[0056] To explore the potential pathological effects of S. aureus on the P2DM phenotype, we evaluated the microbial functions of P2DM patients. In this study, compared with the healthy control group, the P2DM group had more microbial genes involved in "S. aureus infection" ( Figure 4 E-F). Combining these observations, we believe that S. aureus infection can affect T2D patients by regulating the amino acid levels related to glycolipid metabolism and insulin resistance.

[0057] Example 3 Screening of Key Differential Metabolites in Type 2 Diabetes (PDM, NDDM, and P2DM) at Different Disease Stages

[0058] Untargeted metabolomics detection was performed on the sera of type 2 diabetes (PDM, NDDM, and P2DM) at different disease stages and healthy individuals, and the data were cleaned and matched with the database to obtain the contents of specific metabolites.

[0059] 3.1 For metabolite extraction

[0060] Suspend the serum (200 μL) in a solvent (methanol:acetonitrile = 1:1, v:v, 1 mL) and vortex for 1 minute. The mixture is subjected to ultrasonic irradiation in a low-temperature environment, then stored at -20 °C for 2 hours, and centrifuged at 10,000 rpm for 20 minutes at 4 °C. The supernatant is discharged in a freeze-vacuum concentrator, and 200 μL of a complex solution (acetonitrile:H2O = 1:1, v:v) is added for re-dissolution. The mixture is vortexed for 1 minute and centrifuged at 10,000 rpm for 30 minutes at 4 °C. The supernatant is taken out and placed in a sample vial for LC-MS / MS analysis. The sample is analyzed and processed in positive and negative ion modes. The chromatographic column used is Thermoscientific TMAccucore TM C18 Column (100×2.1 mm, 2.6 μm), with the column temperature set at 40 °C; the injection volume is 2 μL; gradient elution: time: 0, 2, 10, 15, 18, 20, 25 min; mobile phase A (%) : 95, 95, 60, 40, 10, 95, 95; mobile phase B (%) : 5, 5, 40, 60, 90, 5, 5; the flow rate is 0.3 mL / min. The mobile phase composition is: in positive ion mode: 0.1% formic acid aqueous solution and acetonitrile; in negative ion mode: 5 mmol / L ammonium acetate aqueous solution and acetonitrile. Mass spectrometry conditions: electrospray ionization (ESI) parameters: capillary temperature (Capillary Temp): 300 °C. Auxiliary gas heater temperature (Aux Gas Heater Temp): 350 °C. Sheath gas flow rate (Sheath Gas Flow Rate): 45 Arb. Auxiliary gas flow rate (Aux Gas Flow Rate): 10 Arb. Spray voltage (Spray Voltage): 3 kV. Scanning mode: full scan range (Full MS Scan Range): 100 - 800 m / z. Resolution: 120,000. Data-dependent secondary scan (DD-MS2) resolution: 30,000. Collision energy (Collision Energy): 40. 10 μL of the supernatant of each sample is mixed with the QC sample to evaluate the repeatability and stability of the LC-MS / MS analysis process.

[0061] 3.2 Metabolomics data processing

[0062] The raw data collected from LC-MS / MS is imported into Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) for data processing. Metabolites are identified by integrating several databases, including BGI Library, mzCloud, and ChemSpider (HMDB, KEGG, and LipidMaps).

[0063] Disturbances in the gut microbiota affect BCAA levels, and changes in serum BCAA levels are associated with T2D. Therefore, we evaluated whether serum BCAA is involved in the impact of S. aureus infection on glucose and lipid metabolism. We collected data from 47 patients and performed metabolomic analysis of serum BCAA. Orthogonal partial least squares discriminant analysis plots showed a clear separation of serum metabolites among the H and PDM, PDM and NDDM, and NDDM and P2DM populations, indicating significant differences in the serum metabolite composition among the PDM, NDDM, and P2DM groups ( Figure 5 A-B, E-F, I-J).

[0064] We also identified key metabolite biomarkers using adjusted p < 0.05 and |log2(FC)| > 1 (FC: fold change) criteria. Fifteen, 31, and 18 different metabolites were identified between the H and PDM, PDM and NDDM, and NDDM and P2DM groups, respectively. Among these metabolites, BCAAs (leucine, isoleucine, and valine), aromatic amino acids (AAAs: phenylalanine, tyrosine, and tryptophan), and bile acids (cholic acid, 7-ketodeoxycholic acid, muricholic acid, chenodeoxycholic acid, cholic acid, deoxycholic acid, taurocholic acid, taurodeoxycholic acid, glycocholic acid, and glycochenodeoxycholic acid) showed significant differences among the groups ( Figure 5 C,G,K,M-P).

[0065] By functional enrichment analysis, tyrosine metabolism, primary bile acid biosynthesis, valine, leucine, and isoleucine biosynthesis, and phenylalanine metabolic pathways were found to be significantly enriched ( Figure 5 D, H, L). To gain insight into BCAA biosynthesis in the development of T2D, we quantitatively analyzed the levels of valine, leucine, isoleucine, and total branched-chain amino acids ( Figure 5 M-P). We found that valine and BCAA levels were higher in the PDM, NDDM, and P2DM groups than in the H group (p < 0.05). Isoleucine levels were higher in the PDM and NDDM groups. Leucine levels did not show significant differences among these groups (p > 0.05). These results suggest that amino acid metabolism by gut microbiota may regulate the levels of circulating serum BCAA, which is associated with the severity of diabetes; thus, reducing the intake of BCAA-rich foods can lower the incidence of T2D.

[0066] To further investigate whether the changes in valine, leucine, and isoleucine biosynthesis are correlated with clinical indicators and differential bacteria, we performed Spearman correlation assessment. We found a close association among these metabolites, differential bacteria, and clinical indicators ( Figure 6A). Additionally, we found that BCAA and valine levels were positively correlated with FBG and HbA1c levels, isoleucine level was positively correlated with SBP, and tryptophan and tyrosine levels were negatively correlated with TC and LDL-C levels ( Figure 6 B). In particular, we found that leucine level was positively correlated with the abundance of Flavonifractor plautii. Valine, isoleucine, and BCAA levels were positively correlated with the abundances of Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus, and negatively correlated with the abundances of B. cellulosilyticus, B. ovatus, B. xylanisolvens, B. uniformis, and other potential probiotic species ( Figure 6 C-M). Overall, these data suggest that the abundance of S. aureus, FBG and HbA1c levels, and BCAA levels were higher in the T2D group, indicating that the relationship between S. aureus and glycolipid metabolism might be mediated by BCAA levels.

[0067] Example 4 Fecal microbiota transplantation to detect the effect of fecal microbiota of P2DM patients with and without ALS protein on glucose metabolism in rats

[0068] Genomic DNA was extracted from fecal samples of healthy control group and P2DM. Primer sequences of S. aureus 16S rRNA gene (ALS, ilvG) in the online NCBI (https: / / www.ncbi.nlm.nih.gov / tools / primer-blast) were used. Amplification of bacterial genes was determined using SYBR Green PCR Master Mix (Invitrogen) and ABI 7500 Real-Time PCR System (Applied Biosystems).

[0069] To determine whether ALS promotes S. aureus-mediated glycolipid metabolism disorder, we used real-time fluorescence quantitative PCR technology to extract genomic DNA from fecal samples of H and P2DM patients. Primer sequence analysis of Staphylococcus aureus 16S rRNA gene (ALS, ilvG) was performed using the NCBI online database (https: / / www.ncbi.nlm.nih.gov / tools / primer-blast). Amplification of bacterial genes was measured using SYBR Green PCR (Invitrogen) with the ABI 7500 Real-Time PCR System (Applied Biosystems). We gavaged rats with feces (with or without ALS protein) from type 2 diabetic patients ( Figure 7A, B). Compared with the rats receiving feces from healthy humans, the rats receiving fecal gavage of ALS-positive feces showed more severe glycolipid metabolism disorders, manifested as decreased insulin levels, increased fasting blood glucose levels, increased insulin resistance, and increased levels of inflammatory cytokines (TNF-α and IL-6), but there was no significant difference in serum endotoxin levels among the three groups ( Figure 7 C-H). At the same time, we found that compared with the rats receiving fecal gavage of feces without ALS protein, the rats receiving fecal gavage of ALS-positive feces had exacerbated liver injury, significantly increased intestinal permeability, and valine and BCAA levels ( Figure 7 I-N). Generally speaking, the above results indicate that ALS protein plays an important role in promoting branched-chain amino acid synthesis and inducing S. aureus-mediated insulin resistance.

[0070] As described above, only the specific embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A type 2 diabetes biomarker, characterized in that, The biomarkers are Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae and / or branched-chain amino acids.

2. Use of the biomarker according to claim 1 in the preparation of a screening and / or diagnostic product for type 2 diabetes.

3. Use of a reagent for detecting the biomarker according to claim 1 in the preparation of a screening and / or diagnostic product for type 2 diabetes.

4. The application according to claim 3, characterized in that The products are reagents and test kits.

5. The use according to claim 4, wherein the reagents are metagenomic sequencing reagents and metabolomics analysis reagents.

6. The application according to claim 3, wherein The application is to assess the risk of type 2 diabetes by detecting the abundance of Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae and branched-chain amino acids in subject samples.

7. The use according to claim 6, characterized in that The samples were stool and serum.

8. The application according to any one of claims 3-7, characterized in that, The type 2 diabetes described is type 2 diabetes at different disease stages, specifically prediabetes patients, newly diagnosed diabetes patients and type 2 diabetes patients after medication.

9. Use of a drug that regulates the biomarker according to claim 1 in the preparation of a drug for reducing the risk of type 2 diabetes.

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