KR20190043449A - Method for diagnosis of metabolic syndrome using analysis of bacteria metagenome - Google Patents

Abstract

The present invention relates to a method of diagnosing metabolic syndromes using bacterial metagenomic analysis and, more specifically, to a method of diagnosing metabolic syndromes by performing bacterial metagenomic analysis using a sample derived from a normal person and a test subject and analyzing increase/decrease in a content of extracellular vesicles originated from particular bacteria. Extracellular vesicles secreted from the bacteria existing in an environment may be absorbed into a body to directly influence immune functions and metabolic functions of our body, and metabolic syndromes are difficult to diagnose at an early stage before symptoms appear and thus are difficult to treat effectively. Accordingly, through the metagenomic analysis of extracellular vesicles derived from bacteria using a sample derived from a human body, the risk of metabolic syndromes can be predicted in advance to delay or prevent the onset of the disease through appropriate care in a group at the risk of metabolic syndromes through early diagnosis and prediction. Furthermore, the method of the present invention enables an early diagnosis of the disease even after the onset of the disease, thereby reducing the onset rate of metabolic syndromes and increasing treatment effects thereof.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for diagnosing metabolic syndrome,

The present invention relates to a method for diagnosing a metabolic syndrome through a bacterial metagenomic analysis, and more specifically, by analyzing a bacterial metagenome using a sample derived from a normal person and an examinee to analyze the increase or decrease in the content of a specific bacterium- And a method for diagnosing the syndrome.

Metabolic syndrome is a conceptualized phenomenon of various cardiovascular diseases and risk factors of type 2 diabetes as a cluster of diseases. Having metabolic syndrome increases the risk of developing diabetes with cardiovascular disease and insulin resistance. In 1998, the World Health Organization decided to call it "metabolic syndrome" instead of the term "insulin resistance syndrome" because insulin resistance has no evidence that it can explain all the elements of these symptoms. The cause is hyperglycemia because insulin can not use glucose as an energy source due to resistance even if it has insulin in the body. The main clinical features of metabolic syndrome are diabetes due to abnormal glucose metabolism, increased triglycerides due to abnormal lipid metabolism, and hypertension.

The metabolic syndrome increases the risk of cardiovascular disease and the risk of developing diabetes, so once diagnosed it should be actively treated to reduce the risk of developing these diseases. However, the prominent method of treatment is not yet clear, so prevention is very important. Because obesity is the most fundamental cause, proper weight maintenance and regular exercise should be avoided. It is also important to relax the mind by adjusting the mental and physical environment well so as not to be stressed. Studies have shown that when obese people lose weight through regular exercise, their body's insulin resistance improves, as well as the associated symptoms of diabetes, hypertension, and hyperlipidemia. It is also important to have good eating habits, and carbohydrate intake should be reduced to less than 50% of the total calories. Carbohydrates are known to be better than simple polysaccharide carbohydrates, breads and products made from uncooked grains, and brown rice.

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 that include bacteria, archaea, and eukarya in a given settlement. Intestinal microbial guns play an important role in human physiology , And it 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, It is freely absorbed into our body through the mucosa.

Metagenomics, also called environmental genomics, can be said to be an analysis of metagenomic data obtained from samples taken in the environment (Korean Patent Laid-Open Patent No. 2011-073049). 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, there has been no report on the metabolic syndrome in which metabolic syndrome is diagnosed through the metagenomic analysis of bacterial-derived vesicles in saliva-derived human parasites.

The present inventors extracted genes from bacterial-derived extracellular vesicles present in saliva, which is a sample derived from a normal person and a subject, and conducted metagenomic analysis in order to diagnose the causative factors and the risk of developing metabolic syndrome. As a result, Derived vesicles capable of acting as a causative factor of the present invention. Based on these findings, the present invention has been completed.

Accordingly, it is an object of the present invention to provide an information providing method for diagnosing a metabolic syndrome through a metagenome analysis of bacterial-derived extracellular vesicles, a method for diagnosing metabolic syndrome, and a method for predicting the risk of developing metabolic syndrome.

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 object of the present invention, the present invention provides a method for providing information for diagnosing metabolic syndrome, comprising the following steps.

(a) extracting DNA from extracellular vesicles isolated from normal and subject samples; (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 bacterial-derived extracellular vesicles with the sample derived from the normal person through sequencing of the product of the PCR (Polymerase Chain Reaction).

The present invention also provides a method for diagnosing metabolic syndrome, comprising the steps of:

(a) extracting DNA from extracellular vesicles isolated from normal and subject samples; (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 bacterial-derived extracellular vesicles with the sample derived from the normal person through sequencing of the product of the PCR (Polymerase Chain Reaction).

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

(a) extracting DNA from extracellular vesicles isolated from normal and subject samples; (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 bacterial-derived extracellular vesicles with the sample derived from the normal person through sequencing of the product of the PCR (Polymerase Chain Reaction).

In an embodiment of the present invention, in step (c), the metabolic syndrome may be diagnosed by comparing the increase or decrease in the content of extracellular vesicles derived from Fusobacteria phylum bacteria with that in saliva.

As an embodiment of the present invention, in the step (c), the content of at least one class of bacterium-derived extracellular vesicles selected from the group consisting of Fusobacterias and Halobacteria is increased or decreased in saliva It may be to diagnose metabolic syndrome.

In another embodiment of the present invention, in step (c), the aeromonadales, CW040, Burkholderiales, Xanthomonadales, Fusobacteriales, halobacteria, The increase or decrease in the content of at least one order of bacterial extracellular vesicles selected from the group consisting of Halobacteriales, Pasteurellales, and Turicibacterales was compared in saliva to determine the metabolic syndrome It can be a diagnosis.

In another embodiment of the present invention, in step (c), Paenibacillaceae, Leptotrichiaceae, Oxalobacteraceae, Burkholderiaceae, , Nocardiaceae, Comamonadaceae, Peptostreptococcaceae, Halobacteriaceae, Xanthomonadaceae, Carnobacteriaceae, and the like. , Pasteurellaceae, Fusobacteriaceae, Propionibacteriaceae, Planococcaceae, Turicibacteraceae, Corynebacterium, Corynebacterium, (Corynebacteriaceae), and Tissierellaceae, to diagnose the metabolic syndrome by comparing the increase or decrease in the content of the extracellular vesicles derived from one or more bacilli from saliva Can.

In another embodiment of the present invention, at step (c), at least one selected from the group consisting of Filifactor, Cupriavidus, Stenotrophomonas, Leptotrichia, Lautropia Such as Brevundimonas, Psychrobacter, Rhodococcus, Aggregatibacter, Veillonella, Megamonas, Neisseria, Such as Granulicatella, Sphingobium, Haemophilus, Fusobacterium, Coprococcus, Propionibacterium, Anaerococcus, The amount of one or more genus bacterial extracellular vesicles selected from the group consisting of Turicibacter, Corynebacterium, and Finegoldia is compared in saliva to determine the metabolic syndrome Diagnose .

In another embodiment of the present invention, the normal person and the subject sample are saliva,

In step (c), the extracellular vesicles derived from Fusobacteria phylum bacteria,

Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Fusobacterium and Halobacteria,

But are not limited to, Aeromonadales, CW040, Burkholderiales, Xanthomonadales, Fusobacteriales, Halobacteriales, Pasteurellales, One or more order bacterial extracellular vesicles selected from the group consisting of Turicibacterales,

But are not limited to, Paenibacillaceae, Leptotrichiaceae, Oxalobacteraceae, Burkholderiaceae, Nocardiaceae, Comamonadaceae, Peptostreptococcaceae, Halobacteriaceae, Xanthomonadaceae, Carnobacteriaceae, Pasteurellaceae, Fosobacteriaceae, and the like. Fusobacteriaceae, Propionibacteriaceae, Planococcaceae, Turicibacteraceae, Corynebacteriaceae, and Tissierellaceae, which are known in the art, One or more family bacterial-derived extracellular vesicles selected from the group consisting of

Such as Filipactor, Cupriavidus, Stenotrophomonas, Leptotrichia, Lautropia, Brevundimonas, Psychrobacter, Rhodococcus, Aggregatibacter, Veillonella, Megamonas, Neisseria, Granulicatella, Sphingobium, Haemophilus, Rhodococcus, Aggregatibacter, Veillonella, Such as Haemophilus, Fusobacterium, Coprococcus, Propionibacterium, Anaerococcus, Turicibacter, Corynebacterium, and the like, (Finegoldia) of the present invention can be compared with those of the genus bacterial-derived extracellular vesicles.

In another embodiment of the present invention, in the step (c), as compared with a sample derived from a normal person,

Turicibacterales order Bacteria-derived extracellular vesicles,

Selected from the group consisting of Propionibacteriaceae, Planococcaceae, Turicibacteraceae, Corynebacteriaceae, and Tissierellaceae. One or more family bacterial-derived extracellular vesicles, or

Wherein the composition is selected from the group consisting of Coprococcus, Propionibacterium, Anaerococcus, Turicibacter, Corynebacterium, and Finegoldia Metabolic syndrome can be diagnosed when the content of one or more genus bacterial extracellular vesicles is increased.

In another embodiment of the present invention, in the step (c), as compared with a sample derived from a normal person,

Extracellular vesicles derived from Fusobacteria phylum bacteria,

Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Fusobacterium and Halobacteria,

(Fusobacteriales), Halobacteriales, and Pasteurellales, which are known in the art, such as Aeromonadales, CW040, Burkholderiales, Xanthomonadales, Fusobacteriales, Halobacteriales, One or more orders of germ-derived extracellular vesicles selected from the group,

But are not limited to, Paenibacillaceae, Leptotrichiaceae, Oxalobacteraceae, Burkholderiaceae, Nocardiaceae, Comamonadaceae, Peptostreptococcaceae, Halobacteriaceae, Xanthomonadaceae, Carnobacteriaceae, Pasteurellaceae, and Fosobacteriaceae. (Fusobacteriaceae), or a family bacterial-derived extracellular vesicle selected from the group consisting of

Such as Filipactor, Cupriavidus, Stenotrophomonas, Leptotrichia, Lautropia, Brevundimonas, Psychrobacter, Rhodococcus, Aggregatibacter, Veillonella, Megamonas, Neisseria, Granulicatella, Sphingobium, Haemophilus, Rhodococcus, Aggregatibacter, Veillonella, Haemophilus, and Fusobacterium may be diagnosed as metabolic syndrome if the content of at least one genus bacterial extracellular vesicle is decreased.

In another embodiment of the present invention, the sample may be saliva.

The extracellular vesicles secreted from the germs present in the environment are absorbed into the body and can directly affect the immune and metabolism functions. In the metabolic syndrome, it is difficult to effectively diagnose the symptoms before the diagnosis. Thus, by analyzing the metagenomic analysis of extracellular vesicles derived from bacteria using human-derived samples according to the present invention, it is possible to diagnose the risk group of metabolic syndrome early and diagnose the metabolic syndrome early, Can slow the onset of the disease or prevent the onset. In addition, it is possible to diagnose early after the onset of metabolic syndrome, thereby lowering the incidence of metabolic syndrome and improving the therapeutic effect. In addition, in patients diagnosed with metabolic syndrome, Or to prevent recurrence.

Fig. 1 (a) is a photograph of the distribution pattern of bacteria and vesicles per hour after administering intestinal bacteria and bacterial-derived vesicles (EV) to a mouse, and Fig. 1 (b) It is a figure that evaluates the distribution pattern of bacteria and parasites by extracting various organs.
FIG. 2 is a graph showing the distribution of bacterial-derived vesicles (EVs) in the metabolic syndrome and normal saliva after bacterial-derived vesicles were separated from the normal saliva, and the metabolic genome analysis was carried out to determine the diagnostic performance at the phylum level.
FIG. 3 shows the distribution of bacterial-derived vesicles (EVs) in the metabolic syndrome group and the normal saliva after the bacterial-derived vesicles were separated from the normal saliva.
FIG. 4 is a graph showing the distribution of bacterial-derived vesicles (EVs) with diagnostic performance at order level by performing a metagenome analysis after separating bacterial-derived vesicles from patients with metabolic syndrome and normal saliva.
FIG. 5 shows the distribution of bacterial-derived vesicles (EVs) in the metabolic syndrome group and normal saliva after bacterial-derived vesicles were separated from the normal saliva, and the diagnostic performance was significant at the family level by performing the metagenome analysis.
FIG. 6 shows the distribution of bacterial-derived vesicles (EVs) in the metabolic syndrome and normal saliva after bacterial-derived vesicles were separated from the normal saliva, and the diagnostic performance was significant at the genus level by performing the metagenome analysis.

The present invention relates to a method for diagnosing a metabolic syndrome through the analysis of a bacterial metagenome. The present inventors extracted a gene from a bacterial-derived extracellular vesicle using a sample derived from a normal person and a subject, Derived vesicles that could act as a causative factor for the bacterial outgrowth.

(A) extracting DNA from extracellular vesicles isolated from normal and subject samples;

(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 germ-derived extracellular vesicle by sequence analysis of the PCR product to provide an information for diagnosing the metabolic syndrome.

The term " metabolic syndrome " used in the present invention is a chronic disease characterized by insulin resistance or lipid metabolism abnormality such as hyperglycemia and hypertriglyceridemia, hypertension, obesity, etc. and includes obesity, hypertension, and type 2 diabetes .

As used herein, the term " diagnosis of metabolic syndrome " means to determine whether the metabolic syndrome is likely to develop in a patient, whether the metabolic syndrome is more likely to develop, or whether the metabolic syndrome has already developed . 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 metabolic syndrome in any particular patient. In addition, the methods of the present invention can be used clinically to determine treatment by early diagnosis of the metabolic syndrome and by selecting the most appropriate treatment regime.

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 serum.

In the present invention, the sample of the normal person and the subject may be saliva, 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 Were used to identify bacterial - derived vesicles that could act as a cause of metabolic syndrome.

More specifically, in an embodiment of the present invention, the analysis of the bacterial metagenomes at the door level for vesicles present in the saliva samples from the subject revealed that the content of extracellular vesicles derived from Fusobacteria germ bacteria was significantly There was a significant difference (see Example 4).

More specifically, in one embodiment of the present invention, the analysis of the bacterial metagenomes at the level of the vesicles in the saliva samples of the subject revealed that the content of extracellular vesicles derived from Fusobacteriia and Halobacteria strong bacteria was higher in patients with metabolic syndrome and normal (See Example 4).

More specifically, in one embodiment of the present invention, the analysis of the bacterial metagenomes against the vesicles present in the saliva samples from the subject revealed that the aeromonadales, CW040, Burkholderiales, Xanthomonadales, Fusobacteriales, Halobacteriales, Pasteurellales, The amount of extracellular vesicles was significantly different between the metabolic syndrome patients and the normal subjects (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 the saliva samples from the subject. As a result, Paenibacillaceae, Leptotrichiaceae, Oxalobacteraceae, Burkholderiaceae, Nocardiaceae, Comamonadaceae, Peptostreptococcaceae, Halobacteriaceae, Xanthomonadaceae, The contents of Carnobacteriaceae, Pasteurellaceae, Fusobacteriaceae, Propionibacteriaceae, Planococcaceae, Turicibacteraceae, Corynebacteriaceae, and Tissierellaceae and bacterial-derived extracellular vesicles were significantly different between the metabolic syndrome patients and the 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 the saliva samples from the subject. As a result, it was found that the bacterial metagenomes of Filigactor, Cupriavidus, Stenotrophomonas, Leptotrichia, Lautropia, Brevundimonas, Psychrobacter, Rhodococcus, Aggregatibacter, There was a significant difference in the content of extracellular vesicles derived from bacteria in the genus Veillonella, Megamonas, Neisseria, Granulicatella, Sphingobium, Haemophilus, Fusobacterium, Coprococcus, Propionibacterium, Anaerococcus, Turicibacter, Corynebacterium and Finegoldia See Example 4).

From the results of the above examples, it was confirmed that the above-identified distribution parameters of the extracellular vesicles derived from bacteria can be used to predict the occurrence of the metabolic syndrome.

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 absorption, distribution, and excretion of bacteria and bacterial-derived vesicles

Experiments were carried out in the following manner to evaluate whether systemic absorption of bacteria and bacterial-derived vesicles through the mucosa was observed. Fluorescence was measured at 0 min, 5 min, 3 hr, 6 hr, and 12 hr in the gastrointestinal tract of mice by administering fluorescein-labeled bacteria and bacterial-derived vesicles to the gastrointestinal tract at a dose of 50 μg each. 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.

After the bacterial and bacterial-derived vesicles were systemically absorbed, 50 μg of the fluorescence-labeled bacteria and the bacterial-derived vesicles were administered in the same manner as described above to evaluate the pattern of invasion into various organs. The blood, heart, lung, liver, kidney, spleen, adipose tissue, and muscle were excised. As a result of observing the fluorescence in the extracted tissues, it was found that the bacteria were not absorbed by the respective organs as shown in FIG. 1B, whereas the extracellular vesicles (EV) derived from the bacteria were saliva, heart, lung, liver , Kidney, spleen, adipose tissue, and muscle.

Example 2. Separation of vesicles from saliva and DNA extraction

To separate the vesicles from the saliva and extract the DNA, saliva was first added to a 10 ml tube and centrifuged (3,500 x g, 10 min, 4 ° C) to resuspend the supernatant and transfer it to a new 10 ml 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 쨉 l of the vesicles isolated from the saliva according to the above method was boiled at 100 째 C to allow the internal DNA to come out of the lipid and then cooled on ice for 5 minutes. Next, 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 existed 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. Metagenomic analysis using DNA extracted from saliva

After the gene was extracted by the method of Example 2, PCR was performed using the 16S rDNA primer shown in Table 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: Diagnosis model of metabolic syndrome based on bacillus-derived bovine meta genome isolated from saliva

META genome sequencing was performed by separating vesicles from saliva of 215 normal subjects matched with 40 patients of metabolic syndrome and sex and age with 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, accuracy, sensitivity, and specificity.

Analysis of bacterial-derived parasites in saliva at the phylum level revealed that the diagnostic performance of the metabolic syndrome was significant when the diagnostic model was developed with the Fusobacteria germ biomarker (see Table 2 and FIG. 2).

Control group Metabolic syndrome t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity p__Fusobacteria 0.0097 0.0140 0.0039 0.0073 0.0005 0.40 0.86 0.85 0.97 0.12

Analysis of bacterial-derived vesicles in saliva at the class level revealed that the diagnostic performance of the metabolic syndrome was significant when the diagnostic model was developed with Fusobacteriia and Halobacteria bacterium biomarkers (see Table 3 and Figure 3) .

Control group Metabolic syndrome t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity c__Fusobacteriia 0.0097 0.0140 0.0039 0.0073 0.0005 0.40 0.86 0.85 0.97 0.12 c__Halobacteria 0.0013 0.0035 0.0005 0.0008 0.0052 0.41 0.83 0.85 0.97 0.09

Analysis of the bacterial parasites from the saliva level at the order level revealed that when the diagnostic model was developed with Aeromonadales, CW040, Burkholderiales, Xanthomonadales, Fusobacteriales, Halobacteriales, Pasteurellales, and Turicibacterales bacterium biomarkers, (See Table 4 and Figure 4).

Control group Metabolic syndrome t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity o__Aeromonadales 0.0006 0.0020 0.0001 0.0003 0.0029 0.22 0.83 0.86 0.97 0.15 o__CW040 0.0007 0.0022 0.0002 0.0004 0.0009 0.25 0.84 0.86 0.97 0.15 o__Burkholderiales 0.0223 0.0333 0.0070 0.0056 0.0000 0.32 0.87 0.86 0.96 0.21 o__Xanthomonadales 0.0026 0.0053 0.0009 0.0017 0.0005 0.36 0.83 0.86 0.97 0.12 o__Fusobacteriales 0.0097 0.0140 0.0039 0.0073 0.0005 0.40 0.86 0.85 0.97 0.12 o__Halobacteriales 0.0013 0.0035 0.0005 0.0008 0.0052 0.41 0.83 0.85 0.97 0.09 o__Pasteurellales 0.0349 0.0461 0.0167 0.0219 0.0004 0.48 0.85 0.86 0.97 0.12 o__Turicibacterales 0.0009 0.0014 0.0028 0.0032 0.0024 3.02 0.88 0.88 0.97 0.27

Analysis of the bacteria derived from saliva vesicles in the (family) level, Paenibacillaceae, Leptotrichiaceae, Oxalobacteraceae, Burkholderiaceae, Nocardiaceae, Comamonadaceae, Peptostreptococcaceae, Halobacteriaceae, Xanthomonadaceae, Carnobacteriaceae, Pasteurellaceae, Fusobacteriaceae, Propionibacteriaceae, Planococcaceae, Turicibacteraceae, Corynebacteriaceae, and Tissierellaceae And bacterium biomarkers, the diagnostic performance of the metabolic syndrome was significant (see Table 5 and FIG. 5).

Control group Metabolic syndrome t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity f__Paenibacillaceae 0.0001 0.0004 0.0000 0.0000 0.0006 0.07 0.83 0.86 0.98 0.12 f__Leptotrichiaceae 0.0035 0.0070 0.0008 0.0023 0.0000 0.24 0.87 0.86 0.97 0.18 f__Oxalobacteraceae 0.0060 0.0190 0.0015 0.0018 0.0008 0.25 0.83 0.86 0.97 0.15 f__Burkholderiaceae 0.0086 0.0244 0.0024 0.0043 0.0007 0.27 0.85 0.86 0.96 0.18 f__Nocardiaceae 0.0004 0.0009 0.0001 0.0003 0.0007 0.30 0.83 0.85 0.97 0.09 f__Comamonadaceae 0.0066 0.0133 0.0022 0.0033 0.0001 0.34 0.84 0.86 0.98 0.09 f__Peptostreptococcaceae 0.0020 0.0070 0.0007 0.0007 0.0091 0.35 0.83 0.86 0.98 0.09 f__Halobacteriaceae 0.0013 0.0035 0.0005 0.0006 0.0015 0.35 0.83 0.85 0.97 0.09 f__Xanthomonadaceae 0.0026 0.0053 0.0009 0.0017 0.0005 0.36 0.83 0.86 0.97 0.12 f__Carnobacteriaceae 0.0062 0.0109 0.0025 0.0048 0.0013 0.40 0.83 0.87 0.98 0.15 f__Pasteurellaceae 0.0349 0.0461 0.0167 0.0219 0.0004 0.48 0.85 0.86 0.97 0.12 f__Fusobacteriaceae 0.0062 0.0096 0.0030 0.0053 0.0076 0.49 0.85 0.85 0.96 0.09 f__Propionibacteriaceae 0.0061 0.0066 0.0129 0.0083 0.0001 2.11 0.86 0.89 0.98 0.30 f__Planococcaceae 0.0027 0.0033 0.0062 0.0053 0.0009 2.27 0.85 0.90 0.97 0.39 f__Turicibacteraceae 0.0009 0.0014 0.0028 0.0032 0.0024 3.02 0.88 0.88 0.97 0.27 f__Corynebacteriaceae 0.0159 0.0211 0.0863 0.0839 0.0000 5.42 0.91 0.90 0.98 0.42 f __ [Tissierellaceae] 0.0028 0.0056 0.0169 0.0187 0.0002 6.07 0.89 0.90 0.98 0.39

Analysis of bacterial parasites in saliva at the genus level revealed that there was a significant increase in the number of parasites in the saliva, When diagnostic models were developed with bacterium biomarkers from Propionibacterium, Anaerococcus, Turicibacter, Corynebacterium, and Finegoldia, the diagnostic performance of metabolic syndrome was significant (see Table 6 and FIG. 6).

Control group Metabolic syndrome t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity g__Filifactor 0.0013 0.0067 0.0000 0.0001 0.0081 0.04 0.85 0.86 0.97 0.15 g__Cupriavidus 0.0041 0.0115 0.0005 0.0010 0.0000 0.12 0.84 0.87 0.98 0.15 g__Stenotrophomonas 0.0019 0.0051 0.0004 0.0014 0.0005 0.21 0.84 0.87 0.98 0.12 g__Leptotrichia 0.0030 0.0069 0.0008 0.0023 0.0005 0.26 0.86 0.85 0.96 0.15 g__Lautropia 0.0085 0.0244 0.0022 0.0044 0.0007 0.26 0.85 0.86 0.97 0.18 g__Brevundimonas 0.0010 0.0022 0.0003 0.0005 0.0001 0.27 0.83 0.87 0.98 0.12 g__Psychrobacter 0.0002 0.0007 0.0001 0.0002 0.0074 0.30 0.83 0.86 0.98 0.12 g__Rhodococcus 0.0004 0.0009 0.0001 0.0003 0.0009 0.30 0.83 0.85 0.97 0.09 g__Aggregatibacter 0.0029 0.0081 0.0009 0.0025 0.0061 0.31 0.84 0.86 0.98 0.12 g__Veillonella 0.0278 0.0436 0.0089 0.0117 0.0000 0.32 0.87 0.88 0.97 0.24 g__Megamonas 0.0008 0.0017 0.0003 0.0006 0.0010 0.34 0.84 0.86 0.98 0.12 g__Neisseria 0.0329 0.0608 0.0120 0.0261 0.0011 0.37 0.83 0.87 0.98 0.15 g__Granulicatella 0.0061 0.0108 0.0024 0.0047 0.0013 0.39 0.83 0.87 0.98 0.15 g__Sphingobium 0.0005 0.0011 0.0002 0.0003 0.0078 0.45 0.83 0.86 0.97 0.12 g__Haemophilus 0.0307 0.0415 0.0150 0.0190 0.0005 0.49 0.85 0.86 0.97 0.12 g__Fusobacterium 0.0062 0.0096 0.0030 0.0053 0.0076 0.49 0.85 0.85 0.96 0.09 g__Coprococcus 0.0016 0.0020 0.0033 0.0029 0.0035 2.04 0.87 0.85 0.96 0.15 g__Propionibacterium 0.0060 0.0066 0.0129 0.0083 0.0001 2.13 0.87 0.89 0.98 0.30 g__Anaerococcus 0.0012 0.0041 0.0034 0.0038 0.0035 2.86 0.84 0.87 0.98 0.15 g__Turicibacter 0.0009 0.0014 0.0028 0.0032 0.0024 3.02 0.88 0.88 0.97 0.27 g__Corynebacterium 0.0159 0.0211 0.0863 0.0839 0.0000 5.42 0.91 0.90 0.98 0.42 g__Finegoldia 0.0008 0.0026 0.0105 0.0122 0.0001 13.32 0.92 0.92 0.99 0.45

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 metabolic syndrome using analysis of          방균 <130> MP18-011 <150> KR 1020170135468 <151> 2017-10-18 <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 (4)

A method for providing information for the diagnosis of metabolic syndrome, comprising the steps of:
(a) extracting DNA from extracellular vesicles isolated from normal and subject samples;
(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 bacterial-derived extracellular vesicle with the sample derived from the normal person through sequencing analysis of the product of PCR (Polymerase Chain Reaction).
The method according to claim 1,
Wherein the normal person and the subject sample are saliva,
In step (c), the extracellular vesicles derived from Fusobacteria phylum bacteria,
Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Fusobacterium and Halobacteria,
But are not limited to, Aeromonadales, CW040, Burkholderiales, Xanthomonadales, Fusobacteriales, Halobacteriales, Pasteurellales, One or more order bacterial extracellular vesicles selected from the group consisting of Turicibacterales,
But are not limited to, Paenibacillaceae, Leptotrichiaceae, Oxalobacteraceae, Burkholderiaceae, Nocardiaceae, Comamonadaceae, Peptostreptococcaceae, Halobacteriaceae, Xanthomonadaceae, Carnobacteriaceae, Pasteurellaceae, Fosobacteriaceae, and the like. Fusobacteriaceae, Propionibacteriaceae, Planococcaceae, Turicibacteraceae, Corynebacteriaceae, and Tissierellaceae, which are known in the art, One or more family bacterial-derived extracellular vesicles selected from the group consisting of
Such as Filipactor, Cupriavidus, Stenotrophomonas, Leptotrichia, Lautropia, Brevundimonas, Psychrobacter, Rhodococcus, Aggregatibacter, Veillonella, Megamonas, Neisseria, Granulicatella, Sphingobium, Haemophilus, Rhodococcus, Aggregatibacter, Veillonella, Such as Haemophilus, Fusobacterium, Coprococcus, Propionibacterium, Anaerococcus, Turicibacter, Corynebacterium, and the like, Wherein the content of at least one genus bacterial-derived extracellular vesicle selected from the group consisting of Pseudomonas aeruginosa and Finegoldia is compared.
3. The method of claim 2,
In the step (c), in comparison with a sample derived from a normal person,
Turicibacterales order Bacteria-derived extracellular vesicles,
Selected from the group consisting of Propionibacteriaceae, Planococcaceae, Turicibacteraceae, Corynebacteriaceae, and Tissierellaceae. One or more family bacterial-derived extracellular vesicles, or
Wherein the composition is selected from the group consisting of Coprococcus, Propionibacterium, Anaerococcus, Turicibacter, Corynebacterium, and Finegoldia A method for providing information for the diagnosis of metabolic syndrome characterized by diagnosing metabolic syndrome when the content of at least one genus bacterial extracellular vesicle is increased.
3. The method of claim 2,
In the step (c), in comparison with a sample derived from a normal person,
Extracellular vesicles derived from Fusobacteria phylum bacteria,
Extracellular vesicles derived from one or more classes of bacteria selected from the group consisting of Fusobacterium and Halobacteria,
From the group consisting of Aeromonadales, Burkholderiales, Xanthomonadales, Fusobacteriales, Halobacteriales, and Pasteurellales from the group consisting of Aeromonadales, Burkholderiales, One or more selected bacterial out-of-cell vesicles,
But are not limited to, Paenibacillaceae, Leptotrichiaceae, Oxalobacteraceae, Burkholderiaceae, Nocardiaceae, Comamonadaceae, Peptostreptococcaceae, Halobacteriaceae, Xanthomonadaceae, Carnobacteriaceae, Pasteurellaceae, and Fosobacteriaceae. (Fusobacteriaceae), or a family bacterial-derived extracellular vesicle selected from the group consisting of
Such as Filipactor, Cupriavidus, Stenotrophomonas, Leptotrichia, Lautropia, Brevundimonas, Psychrobacter, Rhodococcus, Aggregatibacter, Veillonella, Megamonas, Neisseria, Granulicatella, Sphingobium, Haemophilus, Rhodococcus, Aggregatibacter, Veillonella, A diagnosis of metabolic syndrome characterized by a decrease in the content of at least one genus bacterial extracellular vesicle selected from the group consisting of Haemophilus and Fusobacterium, / RTI &gt;

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