A microbial marker for early warning of type 2 diabetes and its application
By detecting the expression of Bacteroidetes faecalis and rumenococci in feces, an early warning model was constructed, which solved the problem that traditional detection methods were difficult to warn of type 2 diabetes, and achieved non-invasive and extensive diabetes screening and precise treatment.
Patent Information
- Application Number
- CN202211611736.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-14
- Publication Date
- 2025-09-02
- Estimated Expiration
- 2042-12-14
AI Technical Summary
The prior art is difficult to accurately warn of the occurrence of type 2 diabetes, especially for patients with pre-diabetics. Traditional testing methods are difficult to assess the risk of disease, and there is a lack of specific bacterial markers for genetic risk factors.
A kit was developed to detect the expression of Bacillus faecalis and Rumenococci, and to use 16SrRNA sequencing technology, combined with molecular hybridization and PCR technology, to detect microbial markers in feces, and build a risk model for early warning genes and environmental factors.
It provides a non-invasive and simple detection method that can be widely used for diabetes screening in normal populations, breaking the limitations of equipment and skills, and providing new reference value for the precise treatment and early intervention of type 2 diabetes.
Smart Images
Figure CN116042875B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to a microbial marker for early warning of the occurrence of type 2 diabetes and its application. Background Art
[0002] Diabetes is believed to result from the interaction of genetic, environmental (e.g., diet, gut microbiota), and psychological factors. Over 95% of patients with type 2 diabetes have a strong family history of the disease. To date, genome-wide association studies (GWAS) have identified over 250 loci closely associated with type 2 diabetes, offering potential insights into clinical classification and risk management. Studies have shown that genetic variation in the Gpr35 gene is strongly associated with type 2 diabetes. GPR35 belongs to the orphan G protein-coupled receptor family and is primarily expressed in the gastrointestinal tract and immune cells. The Gpr35 coding region is highly polymorphic, with UCSNPs -51, -52, -38, and -40 showing significant associations with type 2 diabetes. However, due to a lack of understanding of the mechanisms by which genetic variation contributes to the complex pathological networks of metabolic disorders, translational research into precision clinical diagnosis and treatment targeting GWAS risk genes remains limited.
[0003] The human intestine contains more than 100 trillion microbial cells, which play a vital role in metabolic regulation. The structure and function of the intestinal flora are easily affected by the host's internal (genetic, psychological) and external (diet, drug) factors and are highly plastic. Recent studies have shown that the intestinal flora is involved in the occurrence and development of metabolic diseases such as obesity and type 2 diabetes induced by dietary factors. At the same time, changes in specific intestinal flora are closely related to the progression of metabolic diseases such as diabetes and the efficacy of some drugs, suggesting the potential value of flora structure characterization in the diagnosis and treatment of diabetes. The current diagnostic methods for type 2 diabetes mainly include: random (or accidental) plasma glucose testing, fasting plasma glucose testing, oral glucose tolerance test, and A1c (glycated hemoglobin). However, for patients with prediabetes, especially those with impaired glucose tolerance, traditional detection methods are difficult to accurately warn of the risk of diabetes. In addition, for diabetes warning related to genetic risk factors, there are currently no specific flora markers developed as auxiliary assessments. Summary of the Invention
[0004] In view of the deficiencies of the prior art, the present invention aims to provide a microbial marker for early warning of the occurrence of type 2 diabetes and its application.
[0005] The purpose of the present invention can be achieved through the following technical solutions:
[0006] A reagent is used in the preparation of a type II diabetes detection kit, wherein the reagent can detect the expression levels of Bacteroides faecalis and Ruminococcus.
[0007] A type II diabetes detection kit comprises reagents capable of detecting the expression amounts of Bacteroides faecalis and Ruminococcus.
[0008] Furthermore, the reagents include microbial probes or primers.
[0009] Furthermore, the microbial probe or primer can combine with Bacteroides faecalis and Ruminococcus through molecular hybridization to generate hybridization signals, and can be amplified through PCR technology.
[0010] Furthermore, the reagent can detect the expression levels of Bacteroides faecalis and Ruminococcus through 16S rRNA sequencing.
[0011] Furthermore, the 16S rRNA sequencing includes quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology.
[0012] Furthermore, the faecal Bacteroides and Ruminococcus are derived from fecal genomes.
[0013] Application of a kit in the assessment and early warning of type 2 diabetes.
[0014] Application of Bacteroides faecalis and Ruminococcus in constructing a risk model for type 2 diabetes caused by early warning genes and environmental factors.
[0015] Beneficial effects of the present invention:
[0016] This invention has creatively developed a method for early warning of type 2 diabetes associated with genetic risk factors using specific microbial markers. This method uses a microbial marker composed of Bacteroides faecalis and Gastrococcus as a warning indicator, using a significant increase in the abundance of this microbiome in feces as a warning indicator. Compared to traditional blood tests, fecal microbial testing can be widely used for diabetes screening in healthy individuals, including patients with contraindications to blood tests. This method is both simple and non-invasive, and overcomes the limitations of specific experimental equipment and experimental skills, providing new reference value for type 2 diabetes, thereby contributing to the development of precise treatment and early intervention measures for type 2 diabetes. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0018] Figure 1The weight gain and glucose metabolism indexes of wild-type (WT) and Gpr35 knockout (KO) mice of the present invention are Figure 1 A is the percentage of weight gain of mice in each group. Figure 1 B is the appearance of mice at 10 weeks old. Figure 1 C is the weight of epididymal white fat (eWAT), subcutaneous white fat (sWAT) and perirenal white fat (pWAT) of mice, Figure 1 D is the blood glucose concentration-time change curve of each group of mice in the glucose tolerance experiment. Figure 1 E is the insulin resistance index (HOMA-1R) value;
[0019] Figure 2 The figure below shows the differences in intestinal flora between wild-type (WT) and Gpr35 knockout (KO) mice of the present invention. The left figure shows the relative abundance of representative bacterial species, the middle figure shows the relative abundance value after Log2 transformation, and the right figure shows the p value and FDR value.
[0020] Figure 3 The effect of Bacteroides faecalis colonization on metabolic parameters of mice fed a normal or high-fat diet is shown in the following figure: Figure 3 A is the percentage of weight gain of mice in each group. Figure 3 B is the blood glucose concentration-time change curve of each group of mice in the glucose tolerance experiment. Figure 3 C is the daily food intake of mice, Figure 3 D is the weight of inguinal white fat (iWAT), Figure 3 E is the liver weight;
[0021] Figure 4 The effects of the present invention on the metabolic parameters of mice colonized with Ruminococcus gnavus on normal and high-fat diets are as follows: Figure 4 A is the percentage of weight gain of mice in each group. Figure 4 B is the blood glucose concentration-time change curve and area under the curve of each group of mice in the glucose tolerance experiment. Figure 4 C is food intake, Figure 4 D is the weight of epididymal white fat (eWAT), Figure 4 E is the liver weight. DETAILED DESCRIPTION
[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.
[0023] Example 1: Comparison of Glucose and Lipid Metabolism and Intestinal Microbiota Composition between Wild-Type (WT) and Gpr35 Knockout (Gpr35KO) Mice
[0024] (1) Experimental animals
[0025] WT and Gpr35KO mice (male, C57BL / 6 genetic background, 8 weeks old, purchased from Shanghai Bangyao Biotechnology Co., Ltd.) were housed in a SPF environment with free access to food and water. Experiments were conducted after 7 days of acclimation. All mice were randomly divided into four groups: a chow diet (CD, 10% kcal / fat) WT group (n=7), a high-fat diet (HFD, 60% kcal / fat, n=9) WT group (n=8), a chow diet Gpr35KO group (n=7), and a high-fat diet Gpr35KO group (n=8). Body weight and food intake were measured throughout the experiment. Energy intake was calculated by converting total dietary energy intake to energy per gram of body weight per day. At week 9, after insulin sensitivity and glucose tolerance testing, mice were sacrificed, liver and adipose tissue were weighed, and samples of liver, adipose tissue, ileocecal valve contents, and serum were collected for further analysis.
[0026] (2) Intraperitoneal glucose tolerance test
[0027] After 9 weeks of HFD feeding, all mice were fasted overnight and intraperitoneally injected with D-glucose (2 g / kg body weight). The tail tip venous blood glucose levels were measured at 0, 15, 30, 60, and 120 minutes after administration.
[0028] (3) Bacterial 16S rRNA sequencing
[0029] Bacterial DNA was extracted from the contents of the mouse ileocecal valve using a fecal genomic DNA extraction kit (Solarbio). DNA concentration and purity were then determined using a Onedrop instrument and agarose gel electrophoresis. PCR amplification was performed using 2.5 ng of diluted genomic DNA as a template using barcoded 16SV4 universal primers (515F-806R) and the Hot Start Colorless Master Mix high-efficiency, high-fidelity enzyme. The PCR products were assayed for DNA concentration, and then mixed at equal concentrations based on PCR product concentration. After thorough mixing, the products were purified and recovered using a PCR Purification Kit. The recovered products underwent a second round of amplification, and the amplified products were tested. Qualified results were then sequenced on an Illumina MiSeq. Because the raw data obtained using the Illumina MiSeq sequencing platform may contain some low-quality data, which may interfere with the analysis results, the raw data was preprocessed before further analysis. The specific steps are as follows: 1) For the raw data from high-throughput sequencing, each sample data was first separated according to the barcode information, so that the primer sequence could be extracted for sequencing quality control; 2) Sequences that passed the quality check were aligned using the Ribosomal Database Project (RDP) Classifier 2.3 and the Silva database to determine the taxonomic level of each sequence (kingdom, class, phylum, order, family, genus, species). 3) Finally, Mothur was used to classify the sequences into operational taxonomic units (OTUs). Based on a sequence similarity of 97%, the sequences were divided into OTUs, and the abundance profile of OUTs was generated based on the number of sequences.
[0030] (4) Data analysis methods
[0031] The experimental data are expressed as mean ± SEM and analyzed using GraphPad Prism 8 software. The data were analyzed using t-test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, *p < 0.05 indicates that the data differences are statistically significant.
[0032] Example 2: Effects of Bacteroides caccae on metabolic parameters in mice fed a normal and high-fat diet
[0033] 2.1 Bacterial culture
[0034] The Bacteroides faecalis (ATCC 43182) powder was reconstituted with sterile water and added to autoclaved ATCC medium 1490 liquid medium. The culture was incubated in a three-gas incubator (10% CO2, 10% H2, 80% N2, 37°C) for 48 hours. The culture was washed with pre-reduced sterile PBS under anaerobic conditions. 10 ml of the culture was centrifuged at 3000 rpm / min for 3 minutes, the supernatant was discarded, and the culture was diluted to 5×10 with pre-reduced sterile PBS under anaerobic conditions. 8 CFU / mL.
[0035] 2.2 Bacteroides faecalis colonization
[0036] Male C57BL / 6J mice (8 weeks old) were housed under the same SPF conditions. After acclimation, they were randomly divided into four groups: WT+CD group (n=6), WT+CD group (WT+CD(+)) (n=6), WT+HFD group (n=8), and WT+HFD group (WT+HFD(+)) (n=8). Each group received a freshly prepared Bacteroides faecalis suspension (200 μL) orally every other day for two weeks. Fecal samples were collected 14 days later for PCR confirmation of successful colonization.
[0037] 2.3 Analysis of Glucose and Lipid Metabolism Indices
[0038] The analysis method of glucose and lipid metabolism indicators is the same as that in Example 1 and will not be repeated here.
[0039] Example 3: Effects of Ruminococcus gnavus on metabolic parameters in mice fed a normal and high-fat diet
[0040] 3.1 Bacterial culture
[0041] Ruminococcus gnavus (ATCC 29149) powder was reconstituted with sterile water and plated on autoclaved Columbia blood agar plates (jx601, Shandong Top Biotechnology Co., Ltd.). The plates were then cultured in a three-gas incubator (10% CO2, 10% H2, 80% N2, 37°C) for 48 hours. The culture medium was washed with sterile water under anaerobic conditions, and 10 ml of the culture medium was centrifuged at 4000 rpm / min for 10 minutes. The supernatant was discarded and diluted to 5 × 10 with sterile saline under anaerobic conditions. 8 CFU / min.
[0042] 3.2 Ruminococcus colonization
[0043] Male C57BL / 6J mice (8 weeks old) were housed under the same SPF conditions. After acclimation, they were randomly divided into four groups: WT+CD group (n=8), WT+CD group (WT+CD(+)) (n=8), WT+HFD group (n=8), and WT+HFD group (WT+HFD(+)) (n=8). They were given a freshly prepared quadruple antibiotic solution (ABX) containing 0.2g ampicillin, 0.2g metronidazole, 0.2g neomycin sulfate, and 0.1g vancomycin hydrochloride dissolved in 400mL of water for 10 days (replaced every 3 days) to deplete the mice's intestinal flora. Feces were collected and confirmed by PCR for depletion of the intestinal flora. The immunization groups were gavage-treated with a freshly prepared Ruminococcus suspension (200µL per mouse per day) for two weeks. Fecal samples were collected 14 days later to confirm successful colonization.
[0044] 3.3 Analysis of Glucose and Lipid Metabolism Indices
[0045] The analysis method of glucose and lipid metabolism indicators is the same as that in Example 1 and will not be repeated here.
[0046] Bacterial 16SrRNA sequencing includes quantitative PCR, gene chips, second-generation high-throughput sequencing, Panomics or Nanostring technology.
[0047] like Figure 1-4 As shown, Gpr35 gene polymorphism is a risk site for type 2 diabetes. In the present invention, Gpr35 gene knockout can aggravate the weight gain and glucose metabolism disorder of high-fat diet-induced obese mice, but has no significant effect on low-fat diet mice, suggesting that diet and genetic factors jointly affect the occurrence and development of type 2 diabetes. - / - There are extensive differences in the composition of the intestinal flora between mice and wild-type mice, among which the differences in Bacteroides faecalis and Ruminococcus are the most significant. Subsequently, we conducted a single-bacteria colonization experiment on wild-type mice and found that this microbial combination can increase the sensitivity of mice to high-fat diet-induced obesity and glucose metabolism disorders, and produced similar effects to Gpr35 deficiency, suggesting a correlation between genetic defects and intestinal flora disorders. Therefore, this microbial combination can be used as an early warning or diagnostic factor for genetic risk-related type 2 diabetes. The present invention uses the abundance of microorganisms in feces as a warning indicator. Compared with the traditional blood test method, fecal flora detection can be widely used for diabetes screening in normal people, including patients with contraindications to blood tests. This method is simple and non-invasive, and breaks the limitations of specific experimental equipment and experimental skills. It provides a new reference value for the diagnosis of type 2 diabetes, thereby contributing to the formulation of precise treatment and early intervention measures for type 2 diabetes.
[0048] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
[0049] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.
Claims
1. Use of a reagent in preparing a type II diabetes detection kit, characterized in that: The reagent is capable of detecting the expression levels of Bacteroides faecalis and Ruminococcus; Type II diabetes is caused by genetic and environmental factors.
2. Use of a reagent according to claim 1 in preparing a type II diabetes detection kit, characterized in that: The reagent can detect the expression levels of Bacteroides faecalis and Ruminococcus through 16S rRNA sequencing.
3. Use of a reagent according to claim 2 in preparing a type II diabetes detection kit, characterized in that: The 16S rRNA sequencing includes quantitative PCR, gene chip, and second-generation high-throughput sequencing.
4. Use of a reagent according to claim 1 in preparing a type II diabetes detection kit, characterized in that: The Bacteroides faecalis and Ruminococcus bacteria were derived from fecal genomes.