KR20180098149A - Method for diagnosis of diabetes using analysis of bacteria metagenome - Google Patents

Abstract

The present invention relates to a method for diagnosing diabetes through analysis of a bacterial metagenome, and more specifically, by analyzing a bacterial meta genome using a sample derived from a subject to analyze the increase or decrease in the content of a specific bacterium-derived extracellular vesicle, Factors, disease risk, and the course of the disease.
The extracellular vesicles secreted from bacteria in the environment are absorbed into the body and distributed in organs responsive to insulin, and they can induce or suppress diabetes by affecting metabolic functions such as insulin resistance. It is difficult to predict the cause of diabetes mellitus. Therefore, by analyzing the metagenome of the extracellular vesicles derived from bacteria using the human-derived sample according to the present invention, it is possible to diagnose the cause of diabetes and diagnose the risk of the onset, Can be diagnosed and predicted at an early stage, so that it is possible to delay the onset of the disease or to prevent the onset of the disease through proper management and diagnose the causative factor after the onset, thereby lowering the incidence of diabetes and improving the therapeutic effect.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for diagnosis of diabetes,

The present invention relates to a method for diagnosing diabetes through analysis of a bacterial metagenome, and more specifically, by analyzing a bacterial meta genome using a sample derived from a subject to analyze the increase or decrease in the content of a specific bacterium-derived extracellular vesicle, Factors, risk of onset, and disease progression.

In the 21st century, changes in living conditions such as dietary habits in the aging society have caused major changes in disease patterns in the last 50 years. In particular, although nutritional deficiency has been a problem even 50 years ago, the prevalence of metabolic diseases such as diabetes and obesity due to overeating of saturated fat has been increasing rapidly due to the recent increase in food due to economic development. The researchers have recently been approached with the issue of metabolism in relation to the occurrence of diabetes, but recent research has shown that the inflammatory response to high fat diet is related to the incidence of diabetes and obesity. Metabolic syndrome is a major risk factor for cardiovascular disease. It has a prevalence rate of 20-30% in the population, and is characterized by type 2 diabetes, hypertension, cholesterol abnormality, and hemostatic disorder due to insulin resistance. The most important pathophysiology of metabolic syndrome is diabetes characterized by insulin resistance occurring in the organs where insulin acts, such as liver, muscle, and adipose tissue. The diagnosis of diabetes is made by measuring the glucose level in the blood. Most of the cases of diabetes are very difficult to treat when the disease has already progressed, and the method of preventing the occurrence of diabetes in the high-risk group is provided by predicting the occurrence and cause of diabetes Is an efficient method.

On the other hand, the number of microorganisms that are symbiotic to the human body is 10 times more than that of human cells, and the number of microorganisms is known to be over 100 times that of human genes. Microbiota refers to microbial communities, including bacteria, archaea, and eukarya, which are present in a given settlement. Intestinal microbial guns play an important role in human physiology And is known to have a great influence on human health and disease through interaction with human cells. Bacteria that coexist in our body secrete nanometer-sized vesicles to exchange information about genes, proteins, etc., into other cells. The mucous membrane forms a physical barrier that can not pass through particles of 200 nanometers (nm) or larger and can not pass through the mucous membrane when the bacteria are symbiotic to the mucous membrane. However, since the bacterial-derived vesicles are usually 100 nanometers or less in size, The mucous membrane is freely absorbed into our bodies.

Metagenomics, also referred to as environmental genomics, can be said to be an analysis of metagenomic data obtained from samples taken in the environment (Korean Patent Publication No. 2011-0073049). Recently, 16s ribosomal RNA (16s rRNA) base sequence-based method has been able to catalog the bacterial composition of human microbial genome. The 16s rDNA nucleotide sequence of 16s ribosomal RNA can be sequenced by next generation sequencing , NGS) platform. However, in the onset of diabetes, there has been no report on the method of identifying the causative factors of diabetes through the gene metagenomic analysis in bacterial-derived vesicles derived from human organs such as blood or urine and predicting diabetes.

The present inventors extracted genes from bacterial-derived extracellular vesicles using blood and urine samples, which are samples derived from a subject, in order to diagnose the causative factors of diabetes, risk of onset, and disease progression, The present inventors have completed the present invention on the basis of the identification of bacterial-derived extracellular vesicles capable of acting as a causative factor of diabetes.

Accordingly, it is an object of the present invention to provide a method for providing information for diagnosing diabetes through metagenome analysis of bacterial-derived extracellular vesicles.

However, the technical problem to be solved by the present invention is not limited to the above-mentioned problems, and other matters not mentioned can be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, the present invention provides a method for providing information for diabetes diagnosis, comprising the steps of:

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the increase or decrease in the content of the normal-derived sample and the bacterial-derived extracellular vesicle by sequencing the PCR product.

And, the present invention provides a method of diagnosing diabetes comprising the steps of:

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the increase or decrease in the content of the normal-derived sample and the bacterial-derived extracellular vesicle by sequencing the PCR product.

The present invention also provides a method for predicting the risk of developing diabetes, comprising the steps of:

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the increase or decrease in the content of the normal-derived sample and the bacterial-derived extracellular vesicle by sequencing the PCR product.

In one embodiment of the present invention, the sample of the sample is blood or urine,

In step (c), the blood sample of Thermus, Fusobacteria, Chloroflexi, Cyanobacteria, TM7, Euryarchaeota, proteobacteria, One or more phylum bacteria selected from the group consisting of Proteobacteria, Actinobacteria, Verrucomicrobia, and Bacteroidetes, and Tenericutes isolated from urine samples of a subject. Derived < / RTI > vesicles,

(Cytophagia, Deinococci, Fusobacterias, Sphingobacterias, Flavobacterias, Alphaproteobacteria, Beta) isolated from a blood sample of a subject. (Bacillus sp.), Betaproteobacteria, TM7-3, Bacilli, Actinobacteria, Gammaproteobacteria, Clostridia, Verrucomicrobiae, and Bacteroides Bacteroidia), one or more selected from the group consisting of Mollicutes, Coriobacterias, Deltaproteobacteria, and Epsilonproteobacteria isolated from the urine samples of the subject (class) germ-derived extracellular vesicles,

The present invention relates to a method for screening a blood sample for use in the treatment of a subject suffering from a disease or condition selected from the group consisting of Aeromonadales, Deinococcales, Cytophagales, Rhizobiales, Neisseriales, (Fusobacteriales), Sphingobacterials, Sphingomonadales, Pseudomonadales, Rhodospirillales, Flavobacteriales, Fusobacterium, Fusobacteria, Fusobacteria, ), Rhodocyclales, Rhodobacterales, Gemellales, Caulobacterales, Actinomycetales, Xanthomonadales, Al, Alteromonadales, Pasteurellales, Bacillales, Burkholderiales, Lactobacillales, Clostridiales, Clostridiales, (R), RF32, Verrucomicrobiales, and Bacteroidales, Stramenopiles, Pseudomonadales, Coryobacterium species isolated from urine samples of a subject, One or more order bacterial-derived extracellular vesicles selected from the group consisting of Coriobacteriales, Desulfovibrionales, and Campylobacterales,

In addition to the aeromonadaceae, Methylobacteriaceae, Rhizobiaceae, Bradyrhizobiaceae, and Halomonadaceae isolated from the blood samples of the subject, , Cytophagaceae, Neisseriaceae, Fusobacteriaceae, Sphingomonadaceae, Weeksellaceae, Moraxellaceae, Aerococca, and so on. But are not limited to, Aerococcaceae, Pseudomonadaceae, Micrococcaceae, Propionibacteriaceae, Intrasporangiaceae, Gemellaceae, The present invention relates to a method for the treatment and / or prophylaxis of bacterial infections such as Flavobacteriaceae, Brevibacteriaceae, Rhodocyclaceae, Corynebacteriaceae, Burkholderiaceae, Rh odobacteraceae, Tissierellaceae, Caulobacteraceae, Xanthomonadaceae, Oxalobacteraceae, Staphylococcaceae, Coma spp. (Pseudomonas aeruginosa), such as Comamonadaceae, Planococcaceae, Pasteurellaceae, Actinomycetaceae, S24-7, Enterococcaceae, Basilaci For example, Bacillaceae, Prevotellaceae, Streptococcaceae, Veillonellaceae, Lactobacillaceae, Ruminococcaceae, Mogibacteriaceae, , Verrucomicrobiaceae, Lachnospiraceae, Odoribacteraceae, Bacteroidaceae, and Barnesiellaceae, urine to be tested Separated from the sample, For example, Bradyrhizobiaceae, Cellulomonadaceae, Pseudomonadaceae, Moraxellaceae, Comamonadaceae, Enterococcaceae, Clostridiaceae, ), One or more family members selected from the group consisting of Coriobacteriaceae, Rikenellaceae, Desulfovibrionaceae, and Helicobacteraceae. A bacterial-derived extracellular vesicle, or

The present invention relates to a method for screening a blood sample for use in a blood sample containing halomonas, Methylobacterium, Neisseria, Fusobacterium, Kaistobacter, Agrobacterium, Such as Porphyromonas, Cupriavidus, Acinetobacter, Pseudomonas, Chryseobacterium, Sphingomonas, Rothia, Micrococcus, , Enhydrobacter, Propionibacterium, Brevibacterium, Corynebacterium, Lautropia, Paracoccus, Staphylococcus, Hemophilus, Haemophilus, Catenibacterium, Anaerococcus, Prevotella, Actinomyces, Veillonella, Citrobacter, Enterococcus, (Enterococcu s), Streptococcus, Dialister, Bacillus, Lactobacillus, Bifidobacterium, Faecalibacterium, Parabacteroides, But are not limited to, Paraprevotella, Akkermansia, Ruminococcus, Adlercreutzia, Butyricimonas, Odoribacter, Coprococcus, His wife, Anaerostipes, Blautia, Bacteroides, and Epulopiscium, Rhizobium and Cupriavidus, isolated from urine samples of the test body, ), Acinetobacter, Pseudomonas, Lactobacillus, Citrobacter, Enterococcus, Paracoccus, Klebsiella, SMB53, Allo Allobaculum, O (Desulfovibrio), may comprise the step of comparing the AF12, and Plexiglas Spira (Flexispira) 1 genus (genus) derived from bacterial content increases and decreases in extracellular vesicles or more kinds selected from the group consisting of.

In another embodiment of the present invention, the blood may be whole blood, serum, plasma, or blood mononuclear cells.

Extracellular vesicles secreted from germs present in the environment are absorbed into the body to directly affect the occurrence of diabetes. Since diabetes is difficult to predict before the occurrence of symptoms, effective treatment is difficult. Therefore, Metagenomic analysis of bacterial-derived extracellular vesicles using the derived samples predicts the risk factors of diabetes and the risk factors of diabetes by early diagnosis and prediction of the risk of diabetes, And the cause of the disease can be diagnosed even after the onset, so that the progress of diabetes can be improved and the therapeutic effect can be enhanced.

FIG. 1A is a photograph of distribution patterns of bacteria and vesicles by time after oral intestinal bacteria and bacterial-derived vesicles (EV) are administered to a mouse. FIG. 1B is a photograph of blood , Liver, and various organs were extracted to evaluate the distribution patterns of bacteria and vesicles in the body.
FIG. 2 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at the phylum level by performing a metagenome analysis after separating bacterial-derived vesicles from blood of diabetic patients and normal subjects.
FIG. 3 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at the class level by performing a metagenome analysis after separating bacterial-derived vesicles from blood of diabetic patients and normal subjects.
FIG. 4 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at order level by performing a metagenome analysis after separating bacterial-derived vesicles from blood of diabetic patients and normal subjects.
FIG. 5 shows the distribution of bacterial-derived vesicles (EVs) in the diabetic patients and normal blood after the isolation of bacterial-derived vesicles and the metabolic genomic analysis to determine the diagnostic performance at the family level.
FIG. 6 shows the distribution of bacterial-derived vesicles (EVs) in which the diagnostic performance was significant at the genus level by performing a metagenome analysis after separating bacterial-derived vesicles from blood of diabetic patients and normal subjects.
FIG. 7 shows the distribution of bacterial-derived vesicles (EVs) in the diabetic patients and the normal urine after bacterial-derived vesicles were separated from the urine of the normal subjects and the diagnostic performance was significant at the phylum level by performing the metagenome analysis.
FIG. 8 shows the distribution of bacterial-derived vesicles (EVs) in the diabetic and normal urine after bacterial-derived vesicles were separated from the urine of the normal subjects.
FIG. 9 shows the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at the order level by performing a metagenome analysis after separating bacterial-derived vesicles from diabetic and normal urine.
FIG. 10 shows the distribution of bacterial-derived vesicles (EVs) in the diabetic patients and normal urine after bacterial-derived vesicles were separated from the urine of the normal subjects, and the diagnostic performance was significant at the family level by performing the metagenome analysis.
Fig. 11 shows the distribution of bacterial-derived vesicles (EVs) in the diabetic patients and the normal urine after bacterial-derived vesicles were separated from the urine of the normal subjects.

The present invention relates to a method for diagnosing diabetes through analysis of a bacterial metagenome. The present inventors extracted a gene from a bacterial-derived extracellular vesicle using a sample derived from a subject and conducted a metagenome analysis thereof, Derived vesicle that could act as an extracellular vesicle.

(A) extracting DNA from extracellular vesicles isolated from a sample of a subject;

(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And

(c) comparing the content of the normal-derived sample and the bacterial-derived extracellular vesicles by sequence analysis of the PCR product to provide information for diabetes diagnosis.

The term "diagnosis of diabetes" used in the present invention refers to a diagnosis of whether or not diabetes is likely to occur in a patient, whether the risk of developing diabetes is relatively high, the cause of diabetes, . The method of the present invention can be used to slow the onset or prevent the onset of the disease through special and appropriate management as a patient with a high risk of developing diabetes for any particular patient. In addition, the method of the present invention can be used clinically to determine treatment by early diagnosis of diabetes and by selecting the most appropriate treatment regimen.

The term " metagenome " as used herein refers to the total of genomes including all viruses, bacteria, fungi, etc. in an isolated area such as soil, It is used as a concept of a genome to explain the identification of many microorganisms at once by using a sequencer to analyze microorganisms that are not cultured mainly. In particular, a metagenome is not a genome or a genome of a species, but a kind of mixed genome as a dielectric of all species of an environmental unit. This is a term derived from the viewpoint that when defining a species in the course of omics biology development, it functions not only as an existing species but also as a species that interacts with various species to form a complete species. Technically, it is the subject of techniques that analyze all DNA and RNA regardless of species, identify all species in an environment, identify interactions, and metabolism using rapid sequencing. In the present invention, metagenomic analysis was carried out preferably using extracellular vesicles derived from bacteria isolated from blood.

The term "bacterial-derived vesicle" used in the present invention is a nano-sized substance formed of a membrane secreted by bacteria and archaea, and collectively refers to a substance having a gene derived from a bacterium in vesicles.

In the present invention, the sample of the sample may be blood or urine, and the blood may preferably be whole blood, serum, plasma, or blood mononuclear cells, but is not limited thereto.

In the examples of the present invention, the metagenomic analysis of the extracellular vesicles derived from the bacterium was performed and analyzed at the level of phylum, class, order, family, and genus, respectively To identify bacterial-derived vesicles that could actually act as a cause of diabetes mellitus.

More specifically, in one embodiment of the present invention, bacterial metagenomes were analyzed at the door level against vesicles present in a blood sample from a subject. As a result, Thermi, Fusobacteria, Chloroflexi, Cyanobacteria, TM7, Euryarchaeota, Proteobacteria, Actinobacteria, Verrucomicrobia, And Bacteroidetes germ-cell extracellular vesicles were significantly different between diabetic and normal subjects (see Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at the level of the vesicles against the vesicles present in blood samples from the subject. As a result, Cytophagia, Deinococci, Fusobacterias, Sphingobacterias, Flavobacterias, Alphaproteobacteria, Betaproteobacteria, TM7-3, The content of extracellular vesicles derived from Bacilli, Actinobacteria, Gammaproteobacteria, Clostridia, Verrucomicrobiae, and Bacteroidia strong bacteria was significantly different between diabetic and normal subjects (see Example 4).

More specifically, in one embodiment of the present invention, the bacterium metagenomes were analyzed at the neck level for vesicles present in blood samples from the subject, and as a result, there were found aeromonadales, Deinococcales, Cytophagales, Rhizobiales, Neisseriales, Oceanospirillales, Fusobacteriales, Sphingobacteriales, Sphingomonadales, The content of extracellular vesicles derived from the bacterial pathogens of diabetic patients and normal persons was significantly higher than that of the control group in diabetic patients and normal persons, but not in diabetic patients. (See Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at a high level against the vesicles present in blood samples from the subject. As a result, it was found that the vesicles of Aeromonadaceae, Methylobacteriaceae, Rhizobiaceae, Bradyrhizobiaceae, Halomonadaceae, Cytophagaceae, Neisseriaceae, Fusobacteriaceae, Sphingomonadaceae, Weeksellaceae, Moraxellaceae, Aerococcaceae, Pseudomonadaceae, Micrococcaceae, Propionibacteriaceae, Intrasporangiaceae, Gemellaceae, Flavobacteriaceae, Brevibacteriaceae, Rhodocyclaceae, Corynebacteriaceae, Burkholderiaceae, Rhodobacteraceae, Tissierellaceae, Caulobacteraceae, Xanthomonadaceae, Oxalobacteraceae, Staphylococcaceae, Comamonadaceae, Planococcaceae, Pasteurellaceae, Actinomycetaceae, S24-7, Enterococcaceae, Bacillaceae, Prevotellaceae, Streptococcaceae, Veillonellaceae, Lactobacillaceae, Ruminococcaceae, Mogibacteriaceae, Verrucomicrobiaceae, Lachnospiraceae, Odoribacteraceae, Bacteroidaceae and Barnesiellaceae The contents of vesicles were significantly different between diabetic and normal subjects (see Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at the genus level against the vesicles present in blood samples from the subject, and as a result, the levels of Halomonas, Methylobacterium, Neisseria, Fusobacterium, Kaistobacter, Agrobacterium, Porphyromonas, Cupriavidus, Acinetobacter, Pseudomonas, Chryseobacterium, Sphingomonas, Rothia, Micrococcus, Enhydrobacter, Propionibacterium, Brevibacterium, Corynebacterium, Lautropia, Paracoccus, Staphylococcus, Haemophilus, Catenibacterium, Anaerococcus, Prevotella, Actinomyces, Veillonella, Citrobacter, Enterococcus, Prevotella, Streptococcus, Dialister, Bacillus, Lactobacillus, There was a significant difference in the content of extracellular vesicles derived from Bifidobacterium, Faecalibacterium, Parabacteroides, Paraprevotella, Akkermansia, Ruminococcus, Adlercreutzia, Butyricimonas, Odoribacter, Coprococcus, Anaerostipes, Blautia, Bacteroides and Epulopiscium between diabetic and normal subjects See Example 4).

More specifically, in one embodiment of the present invention, the bacterial metagenomes were analyzed at the door level for the vesicles present in urine samples from the subject, and the results showed that the content of extracellular vesicles derived from bacteria from Tenericutes gum was significantly (See Example 5).

More specifically, in one embodiment of the present invention, the analysis of bacterial metagenomes on vesicles present in urine samples from the subject revealed that the content of extracellular vesicles derived from Mollicutes, Coriobacterias, Deltaproteobacteria, and Epsilonproteobacteria strains was higher than that of diabetes There was a significant difference between the patients and the normal subjects (see Example 5).

More specifically, in one embodiment of the present invention, the analysis of the bacterial metagenomes at the neck level for vesicles present in urine samples from the subject revealed that the content of extracellular vesicles derived from Verrucomicrobia and Cyanobacteria sp. (See Example 5).

More specifically, in one embodiment of the present invention, an analysis of bacterial metagenomes at the level of vesicles present in urine samples from the subject revealed that the content of Verrucomicrobia, Cyanobacteria, and bacterial-derived extracellular vesicles was significantly higher in diabetic and normal subjects (See Example 5).

More specifically, in one embodiment of the present invention, the genome-level analysis of bacterial metagenomes for vesicles present in urine samples from the subject revealed that the content of extracellular vesicles derived from Verrucomicrobia and Cyanobacteria bacteria was significantly higher in diabetic patients and normal persons (See Example 5).

The present invention is based on the results of the above-mentioned Examples, so that the bacterial-derived vesicles whose contents were significantly changed in the diabetic patients compared to the normal persons were identified by performing the metagenome analysis on the vesicle-derived extracellular vesicles isolated from blood and urine , And it was confirmed that diagnosis of diabetes can be made by analyzing the increase and decrease of bacterial-derived vesicles at each level through the metagenome analysis.

Hereinafter, preferred embodiments of the present invention will be described in order to facilitate understanding of the present invention. However, the following examples are provided only for the purpose of easier understanding of the present invention, and the present invention is not limited by the following examples.

[Example]

Example 1. Analysis of intestinal absorption, distribution, and excretion of intestinal bacteria and bacterial-derived vesicles

Experiments were carried out in the following manner to evaluate whether intestinal bacteria and bacterial - derived vesicles were systemically absorbed through the gastrointestinal tract. Fluorescence was measured at 0 min, 5 min, 3 hr, 6 hr, and 12 hr after administration of fluorescein-labeled intestinal bacteria and intestinal bacterial-derived vesicles in the stomach of mice to the gastrointestinal tract at a dose of 50 μg, respectively. As a result of observing the whole image of the mouse, it was not systemically absorbed when the bacterium was the bacterium as shown in Fig. 1A, but was systemically absorbed 5 minutes after the administration when it was bacterial-derived vesicle (EV) After 3 hours, the bladder was observed to be strongly fluorescent, indicating that the vesicles were excreted in the urinary tract. It was also found that the vesicles were present in the body for up to 12 hours of administration.

In order to evaluate the invasion pattern of intestinal bacteria and intestinal bacterial-derived vesicles after systemic absorption, 50 μg of fluorescently labeled bacteria and bacterial-derived vesicles were administered as described above, and after 12 hours Blood, Heart, Lung, Liver, Kidney, Spleen, Adipose tissue, and Muscle were extracted from the mouse. As a result of observing the fluorescence in the extracted tissues, as shown in Fig. 1B, the intestinal bacteria (Bacteria) were not absorbed by each organ, whereas the intestinal bacterial extracellular vesicles (EV) , Liver, kidney, spleen, adipose tissue, and muscle.

Example 2. Separation of vesicles from blood and urine and DNA extraction

To separate the vesicles from the blood and urine and extract the DNA, blood or urine was first placed in a 10 ml tube and centrifuged (3,500 xg, 10 min, 4 ° C) to resuspend the supernatant, I moved it to a tube. Bacteria and foreign substances were removed from the recovered supernatant using a 0.22 mu m filter, transferred to centripreigugal filters 50 kD, centrifuged at 1500 xg for 15 minutes at 4 DEG C to discard substances smaller than 50 kD, ≪ / RTI > After removing bacteria and debris using a 0.22 ㎛ filter, the supernatant was discarded using a Type 90 rotator at 150,000 x g for 3 hours at 4 ° C, and the supernatant was discarded. The pellet was dissolved in physiological saline (PBS) A vesicle was obtained.

100 μl of the vesicles isolated from the blood and urine were boiled at 100 ° C to allow the internal DNA to come out of the lipid, followed by cooling on ice for 5 minutes. Then, the supernatant was collected by centrifugation at 10,000 x g at 4 ° C for 30 minutes in order to remove the remaining suspension, and the amount of DNA was quantified using Nanodrop. Then, PCR was performed with the 16s rDNA primer shown in Table 1 below to confirm whether the DNA extracted from the bacterium was present in the extracted DNA to confirm that the gene derived from the bacterium was present in the extracted gene.

primer order SEQ ID NO: 16S rDNA
16S_V3_F 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3 ' One 16S_V4_R 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3 2

Example  3. DNA extracted from blood and urine Meta genome  analysis

After the gene was extracted by the method of Example 2, PCR was performed using the 16S rDNA primer shown in the above 1 to amplify the gene and perform sequencing (Illumina MiSeq sequencer). The result is output to the Standard Flowgram Format (SFF) file and the SFF file is converted into the sequence file (.fasta) and the nucleotide quality score file using the GS FLX software (v2.9) (20 bps) and less than 99% of the average base call accuracy (Phred score <20). After removing the low quality parts, only those with lead lengths of 300 bps or more were used (Sickle version 1.33). In order to analyze the results, Operational Taxonomy Unit (OTU) performed clustering according to sequence similarity using UCLUST and USEARCH. Specifically, clustering is performed based on sequence similarity of 94% for the genus, 90% for the family, 85% for the order, 80% for the class, and 75% for the phylum Bacteria with a sequence similarity of 97% or more were analyzed using the 16S DNA sequence database (108,453 sequence) of BLASTN and GreenGenes (QIIME).

Example 4. Analysis of Bacillus-derived &lt; RTI ID = 0.0 &gt;

Metagenomic sequencing was performed by separating vesicles from blood of 122 normal persons matched with age and gender of 61 diabetic patients by the method of Example 3 above. For the development of the diagnostic model, first the p value between the two groups was less than 0.05 and the difference between the two groups was more than 2 times, and the logistic regression analysis was used to determine the diagnostic performance index AUC under curve, sensitivity, and specificity.

Diagnostic models were developed with one or more biomarkers selected from Thermi, Fusobacteria, Chloroflexi, Cyanobacteria, TM7, Euryarchaeota, Proteobacteria, Actinobacteria, Verrucomicrobia, and Bacteroidetes germ bacteria as a result of analysis of bacterial-derived vesicles in the blood at the phylum level. , The diagnostic performance for diabetes was significant (see Table 2 and Figure 2).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity p __ [Thermi] 0.0022 0.0050 0.0000 0.0000 0.0000 0.01 0.71 0.98 0.00 0.70 1.00 0.04 p__Fusobacteria 0.0059 0.0110 0.0000 0.0000 0.0000 0.01 0.77 0.84 0.26 0.71 0.81 0.39 p__Chloroflexi 0.0007 0.0022 0.0000 0.0000 0.0015 0.01 0.67 0.99 0.00 0.50 1.00 0.00 p__Cyanobacteria 0.0167 0.0646 0.0001 0.0001 0.0055 0.01 0.91 0.84 0.76 0.87 0.88 0.65 p__TM7 0.0041 0.0084 0.0001 0.0001 0.0000 0.03 0.76 0.86 0.24 0.66 0.78 0.30 p__Euryarchaeota 0.0013 0.0034 0.0001 0.0001 0.0001 0.06 0.66 0.99 0.00 0.60 1.00 0.04 p__Proteobacteria 0.3781 0.1300 0.1119 0.1119 0.0000 0.30 0.99 0.96 0.97 0.94 0.94 1.00 p__Actinobacteria 0.1072 0.0686 0.0357 0.0357 0.0000 0.33 0.90 0.83 0.95 0.89 0.84 1.00 p__Verrucomicrobia 0.0171 0.0185 0.0481 0.0481 0.0000 2.81 0.93 0.90 0.68 0.95 0.94 0.70 p__Bacteroidetes 0.0857 0.0653 0.2915 0.2915 0.0000 3.40 1.00 1.00 1.00 0.98 0.91 1.00

Bacterial-derived vesicles in blood were analyzed at the class level and were found to be selected from bacteria such as Cytophagia, Deinococci, Fusobacterias, Sphingobacterias, Flavobacterias, Alphaproteobacteria, Betaproteobacteria, TM7-3, Bacilli, Actinobacteria, Gammaproteobacteria, Clostridia, Verrucomicrobiae and Bacteroidia (Table 3 and FIG. 3), the diagnostic performance for diabetes was significant when the diagnostic model was developed with one or more biomarkers.

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity c__Cytophagia 0.0011 0.0028 0.0000 0.0000 0.0000 0.01 0.70 0.94 0.08 0.60 0.97 0.09 c__Deinococci 0.0022 0.0050 0.0000 0.0000 0.0000 0.01 0.71 0.98 0.00 0.70 1.00 0.04 c__Fusobacteriia 0.0059 0.0110 0.0000 0.0000 0.0000 0.01 0.77 0.84 0.26 0.71 0.81 0.39 c__Sphingobacteriia 0.0021 0.0048 0.0000 0.0000 0.0000 0.01 0.72 0.97 0.08 0.72 0.97 0.09 c__Flavobacteriia 0.0048 0.0070 0.0000 0.0000 0.0000 0.01 0.88 0.83 0.71 0.81 0.75 0.61 c__Alphaproteobacteria 0.0644 0.0444 0.0010 0.0010 0.0000 0.02 1.00 0.99 1.00 1.00 1.00 0.96 c__Betaproteobacteria 0.0486 0.0428 0.0014 0.0014 0.0000 0.03 0.97 0.94 1.00 0.97 0.88 1.00 c__TM7-3 0.0040 0.0084 0.0001 0.0001 0.0000 0.03 0.76 0.86 0.24 0.63 0.75 0.30 c__Bacilli 0.1423 0.0723 0.0196 0.0196 0.0000 0.14 0.98 0.97 1.00 0.95 0.91 1.00 c__Actinobacteria 0.0993 0.0686 0.0310 0.0310 0.0000 0.31 0.88 0.83 0.95 0.88 0.88 0.96 c__Gammaproteobacteria 0.2630 0.1112 0.1093 0.1093 0.0000 0.42 0.96 0.94 0.97 0.91 0.91 1.00 c__Clostridia 0.1904 0.1130 0.4011 0.4011 0.0000 2.11 0.97 0.93 1.00 0.97 0.97 1.00 c__ Verrucomicrobiae 0.0168 0.0183 0.0481 0.0481 0.0000 2.86 0.93 0.90 0.71 0.95 0.94 0.70 c__Bacteroidia 0.0769 0.0670 0.2914 0.2914 0.0000 3.79 1.00 1.00 1.00 0.98 0.91 1.00

Analysis of the bacterial-derived vesicles in the blood at the order level showed that the bacterial-derived vesicles in the blood were classified as Aeromonadales, Deinococcales, Cytophagales, Rhizobiales, Neisseriales, Oceanospirillales, Fusobacteriales, Sphingobacteriales, Sphingomonadales, Pseudomonadales, Rhodospirillales, Flavobacteriales, Rhodocyclales, Rhodobacterales, Gemellales, Caulobacterales, Actinomycetales, Diagnostic performance for diabetes was significantly improved when one or more biomarkers selected from Xanthomonadales, Alteromonadales, Pasteurellales, Bacillales, Burkholderiales, Lactobacillales, Clostridiales, RF32, Verrucomicrobiales, and Bacteroidales were selected And Fig. 4).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity o__Aeromonadales 0.0007 0.0022 0.0000 0.0000 0.0003 0.00 0.71 0.98 0.11 0.56 0.88 0.09 o__Deinococcales 0.0014 0.0039 0.0000 0.0000 0.0001 0.00 0.68 0.98 0.00 0.51 1.00 0.04 o__Cytophagales 0.0011 0.0028 0.0000 0.0000 0.0000 0.01 0.70 0.94 0.08 0.60 0.97 0.09 o__Rhizobiales 0.0251 0.0233 0.0001 0.0001 0.0000 0.01 0.98 0.96 1.00 0.98 0.97 0.96 o__Neisseriales 0.0073 0.0181 0.0000 0.0000 0.0000 0.01 0.79 0.84 0.39 0.75 0.75 0.35 o__Oceanospirillales 0.0023 0.0060 0.0000 0.0000 0.0001 0.01 0.73 0.93 0.11 0.66 0.91 0.13 o__Fusobacteriales 0.0059 0.0110 0.0000 0.0000 0.0000 0.01 0.77 0.84 0.26 0.71 0.81 0.39 o__Sphingobacteriales 0.0021 0.0048 0.0000 0.0000 0.0000 0.01 0.72 0.97 0.08 0.72 0.97 0.09 o__Sphingomonadales 0.0214 0.0241 0.0002 0.0002 0.0000 0.01 0.99 0.94 0.95 0.95 0.91 0.87 o__Pseudomonadales 0.1607 0.1018 0.0015 0.0015 0.0000 0.01 1.00 0.99 1.00 1.00 0.97 1.00 o__Rhodospirillales 0.0031 0.0063 0.0000 0.0000 0.0000 0.01 0.76 0.90 0.21 0.58 0.78 0.22 o__Flavobacteriales 0.0048 0.0070 0.0000 0.0000 0.0000 0.01 0.88 0.83 0.71 0.81 0.75 0.61 o__Rhodocyclales 0.0017 0.0039 0.0000 0.0000 0.0000 0.01 0.71 0.98 0.03 0.66 1.00 0.04 o__Rhodobacterales 0.0070 0.0097 0.0001 0.0001 0.0000 0.01 0.88 0.82 0.74 0.80 0.78 0.61 o__Gemellales 0.0012 0.0039 0.0000 0.0000 0.0014 0.01 0.70 0.97 0.16 0.50 0.94 0.13 o__Caulobacterales 0.0056 0.0081 0.0001 0.0001 0.0000 0.02 0.86 0.80 0.74 0.83 0.81 0.57 o__Actinomycetales 0.0807 0.0680 0.0013 0.0013 0.0000 0.02 0.98 0.98 0.97 1.00 1.00 1.00 o__Xanthomonadales 0.0026 0.0052 0.0000 0.0000 0.0000 0.02 0.76 0.89 0.18 0.78 0.84 0.26 o__Alteromonadales 0.0009 0.0022 0.0000 0.0000 0.0000 0.02 0.70 0.93 0.05 0.56 1.00 0.09 o__Pasteurellales 0.0074 0.0148 0.0002 0.0002 0.0000 0.03 0.83 0.83 0.58 0.82 0.81 0.43 o__Bacillales 0.0535 0.0665 0.0015 0.0015 0.0000 0.03 0.97 0.92 0.92 0.93 0.84 0.96 o__Burkholderiales 0.0393 0.0396 0.0013 0.0013 0.0000 0.03 0.96 0.93 1.00 0.97 0.88 1.00 o__Lactobacillales 0.0866 0.0504 0.0176 0.0176 0.0000 0.20 0.95 0.93 0.97 0.88 0.81 1.00 o__Clostridiales 0.1899 0.1130 0.4007 0.4007 0.0000 2.11 0.97 0.93 1.00 0.97 0.97 1.00 o__RF32 0.0002 0.0009 0.0005 0.0005 0.0010 2.48 0.74 0.97 0.00 0.60 0.94 0.00 o__Verrucomicrobiales 0.0168 0.0183 0.0481 0.0481 0.0000 2.86 0.93 0.90 0.71 0.95 0.94 0.70 o__Bacteroidales 0.0769 0.0670 0.2914 0.2914 0.0000 3.79 1.00 1.00 1.00 0.98 0.91 1.00

Bacterial-derived vesicles in the blood were analyzed at the family level and found to be in the order of Aeromonadaceae, Methylobacteriaceae, Rhizobiaceae, Bradyrhizobiaceae, Halomonadaceae, Cytophagaceae, Neisseriaceae, Fusobacteriaceae, Sphingomonadaceae, Weeksellaceae, Moraxellaceae, Aerococcaceae, Pseudomonadaceae, Micrococcaceae, Propionibacteriaceae, Intrasporangiaceae, Gemellaceae, Flavobacteriaceae, Brevibacteriaceae, Rhodocyclaceae, Corynebacteriaceae, Burkholderiaceae, Rhodobacteraceae, Tissierellaceae, Caulobacteraceae, Xanthomonadaceae, Oxalobacteraceae, Staphylococcaceae, Comamonadaceae, Planococcaceae, Pasteurellaceae, Actinomycetaceae, S24-7, Enterococcaceae, Bacillaceae, Prevotellaceae, Streptococcaceae, Veillonellaceae, Lactobacillaceae, Ruminococcaceae, Mogibacteriaceae, When a diagnostic model was developed with one or more biomarkers selected from Verrucomicrobiaceae, Lachnospiraceae, Odoribacteraceae, Bacteroidaceae, and Barnesiellaceae and bacteria, (See Table 5 and FIG. 5).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity f__Aeromonadaceae 0.0007 0.0022 0.0000 0.0000 0.0003 0.00 0.71 0.98 0.11 0.53 0.88 0.09 f__Methylobacteriaceae 0.0077 0.0098 0.0000 0.0000 0.0000 0.00 0.91 0.82 0.79 0.85 0.75 0.70 f__Rhizobiaceae 0.0115 0.0149 0.0001 0.0001 0.0000 0.00 0.92 0.82 0.89 0.87 0.75 0.91 f__Bradyrhizobiaceae 0.0024 0.0062 0.0000 0.0000 0.0000 0.00 0.74 0.93 0.08 0.78 0.97 0.09 f__Halomonadaceae 0.0022 0.0060 0.0000 0.0000 0.0001 0.01 0.72 0.94 0.08 0.62 0.91 0.13 f__Cytophagaceae 0.0011 0.0028 0.0000 0.0000 0.0000 0.01 0.70 0.94 0.08 0.60 0.97 0.09 f__Neisseriaceae 0.0073 0.0181 0.0000 0.0000 0.0000 0.01 0.79 0.84 0.39 0.75 0.75 0.35 f__Fusobacteriaceae 0.0045 0.0090 0.0000 0.0000 0.0000 0.01 0.74 0.93 0.08 0.64 0.94 0.17 f__Sphingomonadaceae 0.0209 0.0240 0.0002 0.0002 0.0000 0.01 0.98 0.93 0.97 0.95 0.91 0.96 f __ [Weeksellaceae] 0.0036 0.0060 0.0000 0.0000 0.0000 0.01 0.85 0.81 0.61 0.78 0.81 0.61 f__Moraxellaceae 0.0655 0.0546 0.0006 0.0006 0.0000 0.01 1.00 0.98 0.97 0.98 0.94 0.96 f__Aerococcaceae 0.0046 0.0082 0.0000 0.0000 0.0000 0.01 0.87 0.80 0.74 0.71 0.63 0.52 f__Pseudomonadaceae 0.0951 0.0802 0.0009 0.0009 0.0000 0.01 1.00 0.98 1.00 1.00 0.97 1.00 f__Micrococcaceae 0.0191 0.0239 0.0002 0.0002 0.0000 0.01 0.97 0.89 0.95 0.89 0.81 0.91 f__Propionibacteriaceae 0.0136 0.0168 0.0001 0.0001 0.0000 0.01 0.96 0.89 0.92 0.92 0.84 0.96 f__Intrasporangiaceae 0.0033 0.0063 0.0000 0.0000 0.0000 0.01 0.83 0.83 0.50 0.66 0.72 0.39 f__Gemellaceae 0.0012 0.0039 0.0000 0.0000 0.0014 0.01 0.71 0.96 0.16 0.51 0.91 0.13 f__Flavobacteriaceae 0.0012 0.0035 0.0000 0.0000 0.0002 0.01 0.70 0.97 0.11 0.61 0.94 0.09 f__Brevibacteriaceae 0.0014 0.0030 0.0000 0.0000 0.0000 0.01 0.72 0.97 0.08 0.67 0.97 0.04 f__Rhodocyclaceae 0.0017 0.0039 0.0000 0.0000 0.0000 0.01 0.71 0.98 0.03 0.66 1.00 0.04 f__Corynebacteriaceae 0.0224 0.0313 0.0003 0.0003 0.0000 0.01 0.91 0.84 0.84 0.89 0.88 0.70 f__Burkholderiaceae 0.0018 0.0044 0.0000 0.0000 0.0000 0.01 0.78 0.89 0.29 0.65 0.75 0.26 f__Rhodobacteraceae 0.0069 0.0093 0.0001 0.0001 0.0000 0.01 0.88 0.82 0.74 0.80 0.78 0.61 f __ [Tissierellaceae] 0.0038 0.0066 0.0001 0.0001 0.0000 0.01 0.82 0.80 0.53 0.70 0.72 0.48 f__Caulobacteraceae 0.0056 0.0081 0.0001 0.0001 0.0000 0.02 0.86 0.80 0.74 0.83 0.81 0.57 f__Xanthomonadaceae 0.0024 0.0052 0.0000 0.0000 0.0000 0.02 0.76 0.87 0.21 0.77 0.84 0.22 f__Oxalobacteraceae 0.0267 0.0394 0.0006 0.0006 0.0000 0.02 0.89 0.87 1.00 0.86 0.81 1.00 f__Staphylococcaceae 0.0376 0.0571 0.0008 0.0008 0.0000 0.02 0.93 0.88 0.89 0.87 0.78 0.83 f__Comamonadaceae 0.0086 0.0102 0.0002 0.0002 0.0000 0.02 0.84 0.78 0.87 0.85 0.78 0.91 f__Planococcaceae 0.0056 0.0074 0.0001 0.0001 0.0000 0.03 0.93 0.87 0.87 0.87 0.84 0.65 f__Pasteurellaceae 0.0074 0.0148 0.0002 0.0002 0.0000 0.03 0.83 0.83 0.58 0.82 0.81 0.43 f__Actinomycetaceae 0.0034 0.0072 0.0001 0.0001 0.0000 0.04 0.76 0.71 0.58 0.64 0.59 0.39 f__S24-7 0.0029 0.0100 0.0001 0.0001 0.0032 0.05 0.73 0.87 0.21 0.51 0.75 0.22 f__Enterococcaceae 0.0059 0.0081 0.0003 0.0003 0.0000 0.06 0.86 0.79 0.84 0.67 0.53 0.78 f__Bacillaceae 0.0083 0.0104 0.0005 0.0005 0.0000 0.06 0.78 0.77 0.37 0.77 0.75 0.57 f__Prevotellaceae 0.0271 0.0306 0.0017 0.0017 0.0000 0.06 0.86 0.82 0.82 0.88 0.84 0.96 f__Streptococcaceae 0.0358 0.0273 0.0023 0.0023 0.0000 0.06 0.92 0.88 0.97 0.87 0.81 1.00 f__Veillonellaceae 0.0121 0.0117 0.0016 0.0016 0.0000 0.13 0.78 0.79 0.39 0.83 0.84 0.43 f__Lactobacillaceae 0.0313 0.0286 0.0129 0.0129 0.0000 0.41 0.81 0.86 0.13 0.61 0.66 0.17 f__Ruminococcaceae 0.0882 0.0769 0.1927 0.1927 0.0000 2.18 0.94 0.91 1.00 0.96 0.94 1.00 f __ [Mogibacteriaceae] 0.0003 0.0011 0.0008 0.0008 0.0000 2.35 0.92 0.92 0.53 0.76 0.78 0.57 f__Verrucomicrobiaceae 0.0168 0.0183 0.0481 0.0481 0.0000 2.86 0.93 0.90 0.71 0.95 0.94 0.70 f__Lachnospiraceae 0.0427 0.0326 0.1669 0.1669 0.0000 3.91 1.00 1.00 1.00 1.00 1.00 1.00 f __ [Odoribacteraceae] 0.0012 0.0028 0.0046 0.0046 0.0000 4.00 0.92 0.91 0.87 0.91 0.91 0.83 f__Bacteroidaceae 0.0300 0.0240 0.2583 0.2583 0.0000 8.60 1.00 1.00 1.00 1.00 1.00 1.00 f __ [Barnesiellaceae] 0.0006 0.0019 0.0057 0.0057 0.0000 8.87 0.99 0.97 1.00 0.96 0.91 1.00

Bacterial-derived vesicles in the blood were analyzed at the genus level. As a result, it was found that the vesicle-derived vesicles from the blood were analyzed at the genus level, and the results were as follows: Halomonas, Methylobacterium, Neisseria, Fusobacterium, Kaistobacter, Agrobacterium, Porphyromonas, Cupriavidus, Acinetobacter, Pseudomonas, Chryseobacterium, Sphingomonas, Rothia, Micrococcus, Enhydrobacter, Propionibacterium, Corynebacterium, Lautropia, Paracoccus, Staphylococcus, Haemophilus, Catenibacterium, Anaerococcus, Prevotella, Actinomyces, Veillonella, Citrobacter, Enterococcus, Prevotella, Streptococcus, Dialister, Bacillus, Lactobacillus, Bifidobacterium, Faecalibacterium, Parabacteroides, Paraprevotella, Akkermansia, Ruminococcus, Adlercreutzia, Butyricimonas, When diagnostic models were developed with one or more biomarkers selected from bacterium of the genus Odoribacter, Coprococcus, Anaerostipes, Blautia, Bacteroides, Epulopiscium, and Escherichia, the diagnostic performance for diabetes was significant (see Table 6 and FIG. 6).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity g__Halomonas 0.0020 0.0058 0.0000 0.0000 0.0002 0.00 0.71 0.94 0.05 0.61 0.94 0.13 g__Methylobacterium 0.0037 0.0065 0.0000 0.0000 0.0000 0.01 0.85 0.88 0.61 0.65 0.59 0.48 g__Neisseria 0.0045 0.0115 0.0000 0.0000 0.0000 0.01 0.75 0.92 0.11 0.64 0.94 0.13 g__Fusobacterium 0.0045 0.0090 0.0000 0.0000 0.0000 0.01 0.74 0.93 0.08 0.64 0.94 0.17 g__Kaistobacter 0.0009 0.0030 0.0000 0.0000 0.0020 0.01 0.72 0.97 0.11 0.54 0.88 0.09 g__Agrobacterium 0.0018 0.0053 0.0000 0.0000 0.0003 0.01 0.72 0.96 0.11 0.65 0.94 0.09 g__Porphyromonas 0.0015 0.0037 0.0000 0.0000 0.0000 0.01 0.72 0.93 0.13 0.59 0.91 0.09 g__Cupriavidus 0.0172 0.0367 0.0001 0.0001 0.0000 0.01 0.83 0.73 0.89 0.76 0.63 0.87 g__Acinetobacter 0.0369 0.0442 0.0003 0.0003 0.0000 0.01 0.99 0.96 0.95 0.98 0.94 1.00 g__Pseudomonas 0.0912 0.0794 0.0008 0.0008 0.0000 0.01 1.00 0.99 1.00 1.00 0.97 1.00 g__Chryseobacterium 0.0022 0.0035 0.0000 0.0000 0.0000 0.01 0.83 0.83 0.53 0.75 0.78 0.43 g__Sphingomonas 0.0159 0.0185 0.0001 0.0001 0.0000 0.01 0.97 0.90 0.97 0.92 0.84 0.96 g__Rothia 0.0056 0.0091 0.0000 0.0000 0.0000 0.01 0.88 0.87 0.68 0.73 0.69 0.52 g__Micrococcus 0.0093 0.0154 0.0001 0.0001 0.0000 0.01 0.83 0.76 0.71 0.84 0.84 0.52 g__Enhydrobacter 0.0271 0.0286 0.0002 0.0002 0.0000 0.01 0.96 0.89 0.92 0.97 0.91 0.91 g__Propionibacterium 0.0136 0.0168 0.0001 0.0001 0.0000 0.01 0.96 0.89 0.92 0.92 0.84 0.96 g__Brevibacterium 0.0014 0.0030 0.0000 0.0000 0.0000 0.01 0.72 0.97 0.08 0.67 0.97 0.04 g__Corynebacterium 0.0224 0.0313 0.0003 0.0003 0.0000 0.01 0.91 0.84 0.84 0.89 0.88 0.70 g__Lautropia 0.0013 0.0041 0.0000 0.0000 0.0007 0.02 0.70 0.97 0.11 0.53 0.91 0.09 g__Paracoccus 0.0050 0.0070 0.0001 0.0001 0.0000 0.02 0.86 0.83 0.63 0.73 0.75 0.52 g__Staphylococcus 0.0370 0.0566 0.0008 0.0008 0.0000 0.02 0.93 0.87 0.89 0.87 0.75 0.83 g__Haemophilus 0.0067 0.0140 0.0002 0.0002 0.0000 0.02 0.83 0.84 0.47 0.82 0.84 0.43 g__Catenibacterium 0.0046 0.0098 0.0001 0.0001 0.0000 0.03 0.78 0.83 0.32 0.71 0.78 0.39 g__Anaerococcus 0.0008 0.0019 0.0000 0.0000 0.0000 0.03 0.70 0.97 0.03 0.58 0.94 0.09 g __ [Prevotella] 0.0020 0.0045 0.0001 0.0001 0.0000 0.04 0.71 0.97 0.03 0.61 0.97 0.09 g__Actinomyces 0.0031 0.0069 0.0001 0.0001 0.0000 0.04 0.75 0.72 0.50 0.61 0.59 0.39 g__Veillonella 0.0052 0.0088 0.0002 0.0002 0.0000 0.04 0.73 0.86 0.21 0.70 0.88 0.22 g__Citrobacter 0.0065 0.0090 0.0004 0.0004 0.0000 0.06 0.80 0.77 0.63 0.66 0.59 0.52 g__Enterococcus 0.0053 0.0076 0.0003 0.0003 0.0000 0.06 0.85 0.81 0.76 0.67 0.53 0.65 g__Prevotella 0.0271 0.0306 0.0017 0.0017 0.0000 0.06 0.86 0.82 0.82 0.88 0.84 0.96 g__Streptococcus 0.0332 0.0272 0.0022 0.0022 0.0000 0.07 0.90 0.87 0.97 0.85 0.81 1.00 g__Dialister 0.0036 0.0060 0.0003 0.0003 0.0000 0.08 0.72 0.94 0.05 0.69 0.94 0.09 g__Bacillus 0.0049 0.0077 0.0004 0.0004 0.0000 0.09 0.75 0.90 0.24 0.67 0.78 0.22 g__Lactobacillus 0.0308 0.0283 0.0128 0.0128 0.0000 0.42 0.80 0.86 0.13 0.61 0.63 0.13 g__Bifidobacterium 0.0140 0.0220 0.0296 0.0296 0.0000 2.12 0.90 0.93 0.16 0.92 0.94 0.17 g__Faecalibacterium 0.0388 0.0430 0.0867 0.0867 0.0000 2.23 0.91 0.89 0.79 0.88 0.88 0.70 g__Parabacteroides 0.0051 0.0078 0.0117 0.0117 0.0000 2.29 0.87 0.88 0.29 0.76 0.81 0.22 g__Paraprevotella 0.0005 0.0015 0.0012 0.0012 0.0000 2.61 0.88 0.91 0.05 0.83 0.88 0.04 g__Akkermansia 0.0167 0.0184 0.0481 0.0481 0.0000 2.88 0.93 0.90 0.71 0.95 0.94 0.70 g__Ruminococcus 0.0078 0.0159 0.0235 0.0235 0.0000 3.02 0.97 0.93 1.00 0.94 0.94 1.00 g__Adlercreutzia 0.0008 0.0018 0.0023 0.0023 0.0000 3.03 0.88 0.91 0.16 0.96 0.97 0.00 g__Butyricimonas 0.0005 0.0015 0.0017 0.0017 0.0000 3.54 0.91 0.93 0.16 0.92 0.94 0.13 g__Odoribacter 0.0007 0.0022 0.0030 0.0030 0.0000 4.32 0.96 0.96 0.92 0.90 0.84 0.96 g__Coprococcus 0.0103 0.0128 0.0453 0.0453 0.0000 4.42 0.97 0.94 0.97 1.00 1.00 1.00 g__Anaerostipes 0.0002 0.0008 0.0013 0.0013 0.0000 5.29 0.93 0.93 0.74 0.96 0.94 0.57 g__Blautia 0.0059 0.0071 0.0499 0.0499 0.0000 8.50 1.00 1.00 1.00 1.00 1.00 1.00 g__Bacteroides 0.0300 0.0240 0.2583 0.2583 0.0000 8.60 1.00 1.00 1.00 1.00 1.00 1.00 g__Epulopiscium 0.0001 0.0003 0.0012 0.0012 0.0000 10.70 0.99 0.98 0.95 0.96 0.91 0.96 g__Escherichia 0.0001 0.0002 0.0814 0.0814 0.0000 887.16 1.00 1.00 1.00 1.00 1.00 1.00

Example 5: Analysis of bacterial-derived vesicle metagenomes isolated from urine based diabetes diagnosis model

In the method of Example 3, metagenomic sequencing was performed after separating vesicles from urine of 134 healthy persons matched with age and gender of 60 diabetic patients. For the development of the diagnostic model, first the p value between the two groups was less than 0.05 and the difference between the two groups was more than 2 times, and the logistic regression analysis was used to determine the diagnostic performance index AUC under curve, sensitivity, and specificity.

Analysis of bacterial-derived vesicles in the urine at the phylum level revealed that the diagnostic performance for diabetes was significant when the diagnostic model was developed with the Tenericutes germ biomarker (see Table 7 and Figure 7).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity p__Tenericutes 0.0051 0.0101 0.0013 0.0013 0.0001 0.26 0.71 0.97 0.21 0.50 0.89 0.05

Analysis of urine bacterial parasites at the class level revealed that diagnostic performance against diabetes was significantly higher when one or more biomarkers selected from Mollicutes, Coriobacterias, Deltaproteobacteria, and Epsilonproteobacteria were selected (See Table 8 and FIG. 8).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity c__Mollicutes 0.0051 0.0101 0.0013 0.0013 0.0001 0.3 0.71 0.97 0.21 0.50 0.89 0.05 c__Coriobacteriia 0.0130 0.0112 0.0370 0.0370 0.0027 2.9 0.80 0.93 0.42 0.76 0.92 0.41 c__Deltaproteobacteria 0.0015 0.0025 0.0045 0.0045 0.0012 3.0 0.72 0.94 0.26 0.48 0.97 0.14 c__Epsilonproteobacteria 0.0003 0.0013 0.0049 0.0049 0.0003 15.0 0.77 0.97 0.37 0.58 1.00 0.23

Analysis of urine bacterial parasites at the order level showed that diagnostic performance against diabetes was improved when one or more biomarkers selected from Stramenopiles, Pseudomonadales, Coriobacteriales, Desulfovibrionales, and Campylobacterales were selected (See Table 9 and Fig. 9).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity o__Stramenopiles 0.0022 0.0051 0.0000 0.0000 0.0010 0.00 0.76 0.92 0.16 0.49 0.86 0.18 o__Pseudomonadales 0.1477 0.1423 0.0664 0.0664 0.0000 0.45 0.75 0.88 0.30 0.63 0.67 0.59 o__Coriobacteriales 0.0130 0.0112 0.0370 0.0370 0.0027 2.86 0.81 0.91 0.42 0.78 0.93 0.29 o__Desulfovibrionales 0.0007 0.0018 0.0043 0.0043 0.0002 5.85 0.74 0.95 0.37 0.65 0.88 0.18 o__Campylobacterales 0.0003 0.0013 0.0049 0.0049 0.0003 14.97 0.76 0.98 0.33 0.57 1.00 0.29

Analysis of urine bacterial vesicles at the family level revealed that one or more biomarkers selected from Bradyrhizobiaceae, Cellulomonadaceae, Pseudomonadaceae, Moraxellaceae, Comamonadaceae, Enterococcaceae, Clostridiaceae, Coriobacteriaceae, Rikenellaceae, Desulfovibrinaceae and Helicobacteraceae, , The diagnostic performance of diabetes was significant (see Table 10 and Figure 10).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity f__Bradyrhizobiaceae 0.0017 0.0043 0.0004 0.0004 0.0009 0.21 0.74 0.97 0.24 0.53 0.80 0.21 f__Cellulomonadaceae 0.0010 0.0025 0.0002 0.0002 0.0011 0.24 0.72 0.93 0.07 0.44 0.98 0.05 f__Pseudomonadaceae 0.0789 0.0754 0.0347 0.0347 0.0000 0.44 0.75 0.89 0.39 0.70 0.83 0.32 f__Moraxellaceae 0.0687 0.0941 0.0317 0.0317 0.0001 0.46 0.70 0.91 0.20 0.51 0.93 0.11 f__Comamonadaceae 0.0057 0.0091 0.0124 0.0124 0.0001 2.18 0.70 0.95 0.22 0.61 0.95 0.21 f__Enterococcaceae 0.0106 0.0108 0.0233 0.0233 0.0050 2.20 0.69 0.97 0.15 0.61 0.95 0.16 f__Clostridiaceae 0.0137 0.0122 0.0331 0.0331 0.0000 2.42 0.80 0.95 0.51 0.76 0.83 0.47 f__Coriobacteriaceae 0.0130 0.0112 0.0370 0.0370 0.0027 2.86 0.78 0.89 0.37 0.84 1.00 0.37 f__Rikenellaceae 0.0017 0.0028 0.0074 0.0074 0.0000 4.49 0.79 0.96 0.41 0.59 0.85 0.32 f__Desulfovibrionaceae 0.0007 0.0018 0.0043 0.0043 0.0002 5.85 0.76 0.97 0.37 0.57 0.93 0.16 f__Helicobacteraceae 0.0002 0.0012 0.0047 0.0047 0.0003 27.36 0.76 0.98 0.32 0.57 1.00 0.26

An analysis of urine bacterial parasites at the genus level revealed that one of the bacteria selected from Rhizobium, Cupriavidus, Acinetobacter, Pseudomonas, Lactobacillus, Citrobacter, Enterococcus, Paracoccus, Klebsiella, SMB53, Allobaculum, Desulfovibrio, AF12, When diagnostic models were developed with the above biomarkers, the diagnostic performance of diabetes was significant (see Table 11 and Figure 11).

Control group diabetes t-test Training Set Test Set Taxon Mean SD Mean SD p-value Ratio AUC Sensitivity Specificity AUC Sensitivity Specificity g__Rhizobium 0.0030 0.0043 0.0000 0.0000 0.0000 0.00 0.91 0.86 0.74 0.86 0.83 0.67 g__Cupriavidus 0.0137 0.0551 0.0007 0.0007 0.0078 0.05 0.74 0.92 0.19 0.54 0.76 0.11 g__Acinetobacter 0.0564 0.0928 0.0157 0.0157 0.0000 0.28 0.70 0.96 0.17 0.64 1.00 0.17 g__Pseudomonas 0.0751 0.0734 0.0317 0.0317 0.0000 0.42 0.77 0.88 0.45 0.74 0.83 0.44 g__Lactobacillus 0.0367 0.0433 0.0736 0.0736 0.0008 2.00 0.64 0.98 0.26 0.75 0.93 0.28 g__Citrobacter 0.0011 0.0018 0.0028 0.0028 0.0045 2.47 0.65 0.95 0.26 0.48 0.98 0.06 g__Enterococcus 0.0090 0.0097 0.0227 0.0227 0.0023 2.51 0.68 0.95 0.29 0.78 0.88 0.17 g__Paracoccus 0.0026 0.0043 0.0067 0.0067 0.0015 2.62 0.70 0.94 0.14 0.72 0.93 0.22 g__Klebsiella 0.0019 0.0034 0.0051 0.0051 0.0003 2.72 0.61 0.98 0.14 0.66 0.98 0.33 g__SMB53 0.0031 0.0048 0.0182 0.0182 0.0000 5.92 0.86 0.97 0.64 0.88 0.93 0.89 g__Allobaculum 0.0002 0.0007 0.0019 0.0019 0.0045 8.88 0.62 0.97 0.21 0.70 0.95 0.33 g__Desulfovibrio 0.0005 0.0016 0.0042 0.0042 0.0001 8.90 0.71 0.98 0.29 0.69 1.00 0.28 g__AF12 0.0002 0.0012 0.0041 0.0041 0.0000 22.63 0.63 0.98 0.26 0.84 1.00 0.50 g__Flexispira 0.0000 0.0000 0.0040 0.0040 0.0000 0.70 1.00 0.38 0.63 1.00 0.39

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. There will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

<110> MD Healthcare Inc. <120> Method for diagnosis of diabetes using analysis of bacteria          metagenome <130> MP17-352 <150> KR 10-2017-0025085 <151> 2017-02-24 <160> 2 <170> KoPatentin 3.0 <210> 1 <211> 50 <212> DNA <213> Artificial Sequence <220> <223> 16S_V3_F <400> 1 tcgtcggcag cgtcagatgt gtataagaga cagcctacgg gnggcwgcag 50 <210> 2 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> 16S_V4_R <400> 2 gtctcgtggg ctcggagatg tgtataagag acaggactac hvgggtatct aatcc 55

Claims (2)

(a) extracting DNA from an extracellular vesicle isolated from a sample of a subject;
(b) performing PCR using the primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for the extracted DNA; And
(c) comparing the increase and decrease in the content of the normal human-derived sample and the bacterial-derived extracellular vesicle through sequence analysis of the PCR product,
The sample of the sample is blood or urine,
In step (c), the thermo, Fusobacteria, Chloroflexi, Cyanobacteria, Euryarchaeota, Proteobacteria, One or more phylum bacterium-derived cells selected from the group consisting of Actinobacteria, Verrucomicrobia, and Bacteroidetes, and Tenericutes isolated from urine samples of a subject. Outside parcels,
(Cytophagia, Deinococci, Fusobacterias, Sphingobacterias, Flavobacterias, Alphaproteobacteria, Beta) isolated from a blood sample of a subject. (Bacillus sp.), Betaproteobacteria, Bacilli, Actinobacteria, Gammaproteobacteria, Clostridia, Verrucomicrobiae, and Bacteroidia, One or more classes of bacteria selected from the group consisting of Mollicutes, Coriobacterias, Deltaproteobacteria, and Epsilonproteobacteria isolated from sample urine samples, Derived &lt; / RTI &gt; vesicles,
The present invention relates to a method for screening a blood sample for use in the treatment of a subject suffering from a disease or condition selected from the group consisting of Aeromonadales, Deinococcales, Cytophagales, Rhizobiales, Neisseriales, (Fusobacteriales), Sphingobacterials, Sphingomonadales, Pseudomonadales, Rhodospirillales, Flavobacteriales, Fusobacterium, Fusobacteria, Fusobacteria, ), Rhodocyclales, Rhodobacterales, Gemellales, Caulobacterales, Actinomycetales, Xanthomonadales, Al, Alteromonadales, Pasteurellales, Bacillales, Burkholderiales, Lactobacillales, Clostridiales, Clostridiales, , Verrucomicrobiales, and Bacteroidales, Stramenopiles, Pseudomonadales, Coriobacterials, and Staphylococcus species isolated from urine samples of a subject, ), Desulfovibronales, and campylobacterales, in order to prevent the growth of the cells,
In addition to the aeromonadaceae, Methylobacteriaceae, Rhizobiaceae, Bradyrhizobiaceae, and Halomonadaceae isolated from the blood samples of the subject, , Cytophagaceae, Neisseriaceae, Fusobacteriaceae, Sphingomonadaceae, Weeksellaceae, Moraxellaceae, Aerococca, and so on. But are not limited to, Aerococcaceae, Pseudomonadaceae, Micrococcaceae, Propionibacteriaceae, Intrasporangiaceae, Gemellaceae, The present invention relates to a method for the treatment and / or prophylaxis of bacterial infections such as Flavobacteriaceae, Brevibacteriaceae, Rhodocyclaceae, Corynebacteriaceae, Burkholderiaceae, Rh odobacteraceae, Tissierellaceae, Caulobacteraceae, Xanthomonadaceae, Oxalobacteraceae, Staphylococcaceae, Coma spp. But are not limited to, Comamonadaceae, Planococcaceae, Pasteurellaceae, Actinomycetaceae, Enterococcaceae, Bacillaceae, Prevotellaceae, Streptococcaceae, Veillonellaceae, Lactobacillaceae, Ruminococcaceae, Mogibacteriaceae, Berukomikerae, Lactobacillus, (Lactnospiraceae), Odoribacteraceae, Bacteroidaceae, and Barnesiellaceae, isolated from the urine sample of the subject. B & B The present invention relates to the use of a compound of formula (I) or a pharmaceutically acceptable salt thereof for the preparation of a medicament for use in the treatment or prevention of cancer, such as, for example, radyrhizobiaceae, Cellulomonadaceae, Pseudomonadaceae, Moraxellaceae, Comamonadaceae, Enterococcaceae, Clostridiaceae, One or more family members of a bacterium selected from the group consisting of Coriobacteriaceae, Rikenellaceae, Desulfovibrionaceae, and Helicobacteraceae, Extracellular vesicles, or
The present invention relates to a method for screening a blood sample for use in a blood sample containing halomonas, Methylobacterium, Neisseria, Fusobacterium, Kaistobacter, Agrobacterium, Such as Porphyromonas, Cupriavidus, Acinetobacter, Pseudomonas, Chryseobacterium, Sphingomonas, Rothia, Micrococcus, , Enhydrobacter, Propionibacterium, Brevibacterium, Corynebacterium, Lautropia, Paracoccus, Staphylococcus, Hemophilus, Haemophilus, Catenibacterium, Anaerococcus, Prevotella, Actinomyces, Veillonella, Citrobacter, Enterococcus, (Enterococcu s), Streptococcus, Dialister, Bacillus, Lactobacillus, Bifidobacterium, Faecalibacterium, Parabacteroides, But are not limited to, Paraprevotella, Akkermansia, Ruminococcus, Adlercreutzia, Butyricimonas, Odoribacter, Coprococcus, His wife, Anaerostipes, Blautia, Bacteroides, and Epulopiscium, Rhizobium and Cupriavidus, isolated from urine samples of the test body, ), Acinetobacter, Pseudomonas, Lactobacillus, Citrobacter, Enterococcus, Paracoccus, Klebsiella, Alobaculum, Lactobacillus, Allobaculum, De sulfobibrio, and Flexispira. The method for providing information for diagnosis of diabetes according to claim 1, wherein the content of at least one genus bacterium-derived extracellular vesicle is selected from the group consisting of:

The method according to claim 1,
Wherein the blood is whole blood, serum, plasma, or blood mononuclear cells.

Images