KR102329509B1 - Composition for predicting or diagnosing diabetes risk comprising agent capable of detecting microorganisms of strain of the genus Lachnospiraceae - Google Patents

Abstract

The present invention relates to a composition for predicting or diagnosing a diabetes risk, comprising a formulation capable of detecting microorganisms of a Lachnospiraceae strain; a kit comprising the composition; a method of providing information for predicting or diagnosing a diabetes risk; and a diabetes prevention or treatment screening method. According to one aspect of the present invention, since GU174097_g, which is a Lachnospiraceae genus microorganism, is present more in a normal group than in a diabetic group and a diabetes risk group, a composition comprising a formulation capable of detecting the strain GU174097_g, a kit comprising the composition, an information providing method for predicting or diagnosing a diabetes risk, the method comprising a step of performing amplification by reacting a primer for the strain GU174097_g from a subject's sample, and a diabetes prevention or treatment screening method have an excellent effect in predicting or diagnosing the risk of diabetes or screening an agent for preventing or treating diabetes.

Description

Composition for predicting or diagnosing diabetes risk comprising agent capable of detecting microorganisms of strain of the genus Lachnospiraceae

In the present specification, Lachnospiraceae and a composition for predicting or diagnosing diabetes risk comprising an agent capable of detecting microorganisms of the family strain, a kit comprising the composition, a primer specific to the microorganism of the Lachnospiraceae family strain Disclosed are a method for providing information for predicting or diagnosing diabetes risk, including reacting, and a screening method for preventing or treating diabetes, including detecting a change in microorganisms of the Lachnospirosaceae strain in a sample of a subject.

Previous studies have identified an essential role of the bacterial community in diseases such as diabetes, obesity, psoriasis and irritable bowel syndrome. Intestinal microbial total studies related to diabetes, specifically type 2 diabetes mellitus (T2DM), have shown that the application of microbial community data, microbial collection, and genomic content to diagnosis, treatment, and eventual prevention It has been suggested that it can be used in However, microbial communities are found throughout the human body, from the gut to previously considered sterile environments such as circulating blood. Therefore, it is important to understand the dynamics or stability of microorganisms not only in the intestine but also in the entire human body. To date, information on intestinal bacteria genome and metabolite information such as short-chain fatty acid (SCFA) have been mainly investigated.

Existing from bacteria to humans, extracellular vesicles (EVs) were initially described as a means of removing waste compounds from cells, but more recently they exchange components, from nucleic acids to lipids and proteins, between cells. The focus has been on the ability to It has also been shown that extracellular vesicles serve as a means of signal transduction in homeostasis normal cells or as a result of pathological development. Compared with intestinal microbes, extracellular vesicles are more suitable for investigating the dynamics and stability of microbes through the body's intestinal barrier. Intestinal microflora-derived extracellular vesicles have recently been proposed as major communication carriers between the gut microbiota and the host, which, compared to the intestinal microflora, allows secreted extracellular vesicles to cross the intestinal wall and enter the intestinal circulation. because there is Although the fecal microbiome is a representative marker of the human microbiome, fecal samples provide limited information about the mucosal microbiome when compared to direct sampling of the entire human gut (Zmora, et al. N., et al., Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features . Cell, 2018. 174(6): p. 1388-1405. e21). Therefore, the identification of microbiome extracellular vesicles leads to an understanding of the pathophysiological mechanisms of our body. In addition, correlation analysis of microbe-derived extracellular vesicles via urine in humans and diabetes, specifically type 2 diabetes, is limited.

Under this background, the present inventors completed the present invention by performing metagenomic analysis to discover biomarkers for predicting or diagnosing the risk of diabetes.

In one aspect, an object of the present invention is to provide a biomarker for predicting or diagnosing the risk of diabetes.

In one aspect, the present invention, Lachnospiraceae ( Lachnospiraceae ) Provides a composition for predicting or diagnosing the risk of diabetes comprising an agent capable of detecting microorganisms of the family.

In another aspect, the present invention provides a kit for predicting or diagnosing diabetes risk, comprising the composition.

In another aspect, the present invention provides a method comprising: extracting genomic DNA from a sample of a subject; Reacting the extracted genomic DNA with a primer specific to the microorganism of the Lachnospiraceae strain; and amplifying the reactant; provides an information providing method for predicting or diagnosing diabetes risk, including.

In another aspect, the present invention provides a method for preventing or treating diabetes mellitus; Detecting the change of microorganisms of the Lachnospiraceae and strains in the sample of the subject before and after the candidate treatment; And when the detection amount of the microorganism of the Lachnospirosceae strain increases after the candidate substance treatment, it provides a screening method for preventing or treating diabetes, comprising the step of determining that the candidate substance is a diabetes preventive or therapeutic agent.

According to one aspect of the present invention, GU174097_g , a microorganism of the Lachnospiraceae family, is more present in the normal group than in the diabetic group and the diabetes risk group, through which the strain GU174097_g is diabetes, specifically type 2 It was confirmed that it can reduce the risk of diabetes. Therefore, a composition comprising an agent capable of detecting the strain GU174097_g , a kit comprising the composition, and a primer for the strain GU174097_g from a sample of a subject to react and amplify the risk for predicting or diagnosing diabetes The information provision method has an excellent effect in predicting or diagnosing the risk of diabetes.

1 is obtained by obtaining 16S rRNA metagenomic data with genomic DNA extracted from a urine sample of a subject according to an aspect of the present invention, and analyzing the microbial composition of each sample in 2013, 2015 and 2017 from this to alpha-diversity It is a diagram showing the result of calculating . 6P, 7P, and 8P in Fig. 1 mean data composed of samples from 2013, 2015 and 2017, respectively, and Observed OUT, ACE, Shannon, and Simpson in Fig. 1 were considered alpha diversity. will be.
Figure 2 is a road showing the change trend of each microbial genus of each sample in 2013, 2015 and 2017 from 16S rRNA metagenomic data of a subject according to an aspect of the present invention, the association between the flora and health factors through the NMDS plot is the result of the analysis. 6P, 7P, and 8P of FIG. 2 mean data composed of samples in 2013, 2015, and 2017, respectively.
3 is a KARE phenotype and insulin resistance (HOMA-IR) in the microbial composition of each sample in 2013, 2015 and 2017 using the PERMANOVA method from 16S rRNA metagenomic data of a subject according to an aspect of the present invention. It is a diagram showing the result of investigating the correlation.
4A and 4B are diagrams showing the change trend of each microbial genus of each sample in 2013, 2015, and 2017 from 16S rRNA metagenomic data of a subject according to an aspect of the present invention as an average relative abundance. Figure 4a shows microorganisms with mean relative abundance >=0.01, Figure 4b shows microorganisms with mean relative abundance <0.01, and 6P, 7P, 8P of Figures 4a and 4b denotes data composed of samples from 2013, 2015, and 2017, respectively.
5 shows the relative ratio average of the GU174097_g strain of the diabetes group (DM or T2D group), the diabetes risk group (DM_before or T2D risk group) and the normal group (Healthy) from 16S rRNA metagenomic data of subjects according to an aspect of the present invention ( It is a diagram showing the relative proportion mean). The relative ratio average is the ratio of the absolute abundance of a particular strain ( GU174097_g strain) to the total sum of the absolute abundance of all strains in the subject sample, and has a value between 0 and 1. Alltime Healthy on the x-axis of FIG. 5 is the subject who continued to be normal during the follow-up period from 2013 to 2017, the Alltime DM group is the subject who continued to be diabetic during the same period, and Worse is converted from normal to diabetic or prediabetes. refers to a subject or a subject who has converted from pre-diabetes to diabetes.
6 is a diagram showing the results of performing a network analysis of the GU174097_g strain and the KARE phenotype from 16S rRNA metagenomic data of a subject according to an aspect of the present invention.

Hereinafter, the present invention will be described in detail.

In one aspect of the present invention, "diabetes risk prediction" refers to whether the subject is likely to develop diabetes, whether the likelihood of developing diabetes is relatively high, what is the causative factor of diabetes, or whether diabetes has already occurred. It may mean predicting or diagnosing. In addition, in one aspect of the present invention, "diabetes risk diagnosis" means confirming the presence or characteristics of a pathological condition for a subject, and for the purpose of one aspect of the present invention, the diagnosis is to determine whether or not diabetes occurs. can mean that The composition, kit or method according to one aspect of the present invention can be used to delay or prevent the onset of diabetes through special and appropriate management as a patient with a high risk of developing diabetes for any specific patient. In addition, the composition, kit, or method according to one aspect of the present invention can be used clinically to determine treatment by diagnosing diabetes early and selecting the most appropriate treatment method.

In one aspect of the present invention, "metagenome" is also referred to as "microgenome", and refers to the sum of genomes including all viruses, bacteria, fungi, etc. in isolated areas such as soil and animal intestines, It can be used as a concept of genome to explain the identification of many microorganisms at once using a sequencer to analyze microorganisms that cannot be cultured. In particular, the metagenome does not refer to the genome or genome of one species, but a kind of mixed genome as the genome of all species in one environmental unit. This is a term derived from the point of view that when defining a species in the process of omics development of biology, not only one functionally existing species but also various species interact with each other to create a complete species. Technically, it is the subject of a technique that uses rapid sequencing, analyzes all DNA and RNA regardless of species, identifies all species in one environment, and identifies interactions and metabolism. In one aspect of the present invention, metagenomic analysis was performed using a sample of a subject, specifically, extracellular vesicles isolated from urine.

In one aspect, the present invention provides a composition for predicting or diagnosing diabetes risk comprising an agent capable of detecting microorganisms of Lachnospiraceae and strains.

In one aspect of the present invention, the Lachnospiraceae family strain is anaerobic, spores of the order Clostridiales that ferment various plant polysaccharides with short-chain fatty acid (SCFA) and alcohol As a forming strain, the Lachnospiraceae family is related to the SCFA production pathway. Bacterial cross-feeding has a major impact on the final balance of SCFA production and efficient utilization of substrates to reach the human gut. Three different pathways are used by colony bacteria for propionate formation: the succinate pathway, the acrylate pathway, and the propanodiol pathway. In the acrylic acid pathway, lactic acid is converted to propionate, one of the SCFAs, through the activity of lactoyl-CoA dehydratase and downstream enzymatic reactions; This path seems to be a veil restricted to some members of Ner La (Veillonellaceae) and Lac North fatigue three (Lachnospiraceae) (Flint, HJ, et al., Links between diet, gut microbiota composition and gut metabolism. Proceedings of the Nutrition Society, 2015. 74(1): p. 13-22). In addition, in the propanodiol pathway characterized by the conversion of deoxy-sugar to propionate, CoA-dependent conversion of propionaldehyde to propionyl-CoA CoA-dependent propionaldehyde dehydrogenase is present in bacteria, including proteobacteria and members of the Lachnospiraceae family (Reichardt, N., et al., Phylogenetic distribution of three) Pathways for propionate production within the human gut microbiota . The ISME journal, 2014. 8(6): p. 1323).

In one aspect of the present invention, the Lachnospirose family strain is GU174097_ g strain in which the 16S rRNA gene sequence is represented by a sequence having 90% or more homology with the sequence of SEQ ID NO: 1 and having predictive or diagnostic ability of diabetes risk can be Specifically, the GU174097_ g strain contains 16S rRNA, and the V3 to V4 region (region) of the 16S rRNA gene sequence of the GU174097_ g strain, more specifically the V3 and V4 region (region) is the sequence of SEQ ID NO: 1 and 90 % or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more. It may be a sequence having the ability to predict or diagnose diabetes risk. According to an embodiment of the present invention, GU174097_g , a microorganism of the Lachnospiraceae family, is more present in the normal group than in the diabetic group and the diabetes risk group, whereby the strain GU174097_g according to one aspect of the present invention is It was confirmed that the composition containing the detectable agent has an excellent effect in predicting or diagnosing the risk of diabetes.

In one aspect of the present invention, the diabetes may be type 2 diabetes.

In one aspect of the present invention, the diabetes may satisfy one or more of the following selection criteria i) to iii):

i) fasting blood sugar greater than 100 mg/dL;

ii) oral glucose tolerance 120 min blood glucose 140 mg/dL or higher; and

iii) Glycated hemoglobin (HbA1c) level of 5.7% or higher.

In one aspect of the present invention, the i) selection criteria are specifically, fasting blood sugar of 100 mg/dL or more, 102 mg/dL or more, 104 mg/dL or more, 106 mg/dL or more, 108 mg/dL or more, 110 mg/dL or higher, 112 mg/dL or higher, 114 mg/dL or higher, 116 mg/dL or higher, 118 mg/dL or higher, 120 mg/dL or higher, 122 mg/dL or higher, 124 mg/dL or higher, or 126 mg/dL or higher dL or higher.

In one aspect of the present invention, the ii) selection criterion is specifically, a 120-minute cervical glucose load blood sugar of 140 mg/dL or more, 150 mg/dL or more, 160 mg/dL or more, 170 mg/dL or more, 180 mg/dL or more. dL or more, 190 mg/dL or more, or 200 mg/dL or more.

In one aspect of the present invention, the iii) selection criterion is specifically, glycated hemoglobin (HbA1c) level of 5.7% or more, 5.8% or more, 5.9% or more, 6.0% or more, 6.1% or more, 6.2% or more, 6.3% or more , 6.4% or more or 6.5% or more.

In one aspect of the invention, the composition may further comprise an agent capable of detecting one or more microorganisms selected from the group consisting of hafnia (Hafnia) sp and choline Cellar (Collinsella) sp. According to one embodiment of the present invention, hafnia (Hafnia) inside and choline Cellar (Collinsella) in the glycosylated hemoglobin (HbA1c) of the phenotypes associated with diabetes is that a positive correlation between the bar, so that in accordance with an aspect of the present invention composition comprising an agent capable of detecting one or more microorganisms selected from the hafnia (hafnia) inside the group consisting of strains and choline Cellar (Collinsella) in the strain has the excellent effect it in predicting or diagnosing the risk of diabetes.

In one aspect of the present invention, the microorganisms of the Lachnospiraceae family are specifically increased in the normal group compared to the diabetes group and the diabetes risk group, and Hafnia ( Hafnia ) sp. strain or Collinsella sp. strain It is identified that microorganisms are specifically reduced, and based on this, Lachnospiraceae and strains, and Hafnia spp . strains and Collinsella spp . One or more microorganisms selected from the group consisting of strains It is characterized in that it is provided as a biomarker for predicting or diagnosing diabetes risk.

In one aspect of the present invention, the detectable agent is at least one selected from the group consisting of microorganisms of the Lachnospirosaceae family, or Hafnia sp. It may refer to a substance that can be used to detect the presence of microorganisms. For example, proteins, nucleic acids, lipids, glycolipids, glycoproteins or sugars (monosaccharides , disaccharides, oligosaccharides, etc.) may be one or more selected from the group consisting of primers, probes, antisense oligonucleotides, aptamers, and antibodies capable of specifically detecting organic biomolecules.

In one aspect of the present invention, the microorganism detection agent may be an antibody, and an antigen-antibody reaction-based immunological method may be used to detect the microorganism. Analysis methods for this include western blot, ELISA (enzyme linked immunosorbent assay), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, and rocket immunoelectrolysis. Electrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence activated cell sorter (FACS), protein chip, etc., but are not limited thereto.

In one aspect of the present invention, the agent capable of detecting the microorganism may be a primer specific to one or more microorganisms selected from the group consisting of microorganisms of the Lachnospirosaceae family, or Hafnia sp. strains and Colincella sp. strains. The primer means 7 to 50 nucleic acid sequences capable of forming a base pair complementary to the template strand and functioning as a starting point for template strand copying. Primers are usually synthesized but can also be used on naturally occurring nucleic acids. The sequence of the primer does not necessarily have to be exactly the same as the sequence of the template, but only if it is sufficiently complementary to hybridize with the template. Additional features that do not change the basic properties of the primer may be incorporated. Examples of additional features that may be incorporated include, but are not limited to, methylation, encapsulation, substitution of one or more nucleic acids with homologs, and modifications between nucleic acids.

In one aspect of the present invention, the primer may be a primer capable of amplifying 16S rRNA of one or more microorganisms selected from the group consisting of a microorganism of the Lachnospirose family, or a strain of Hafnia sp. and a sp. strain of Colincella. The 16S rRNA is an rRNA constituting the 30S subunit of the prokaryotic ribosome, and while most of the nucleotide sequences are highly conserved, high nucleotide sequence diversity appears in some sections. In particular, since there is little diversity among homogeneous species while diversity appears among other species, prokaryotes can be usefully identified by comparing the sequences of 16S rRNA.

In one aspect of the present invention, the Lachnospirosceae microorganism is 16S rRNA gene sequence GU174097_ g strain represented by a sequence having 90% or more homology to the sequence of SEQ ID NO: 1 and predicting or diagnosing the risk of diabetes can be Specifically, the GU174097_ g strain contains 16S rRNA, and the V3 to V4 region (region) of the 16S rRNA gene sequence of the GU174097_ g strain, more specifically the V3 and V4 region (region) is the sequence of SEQ ID NO: 1 and 90 % or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more. It may be a sequence having the ability to predict or diagnose diabetes risk.

In one aspect of the present invention, the Lachnospirosceae microorganism is a microorganism in which the 16S rRNA gene sequence is 90% or more homologous to the sequence of SEQ ID NO: 2 and is a microorganism represented by a sequence having predictive or diagnostic ability of diabetes risk. have. Specifically, the Lachnospirosceae microorganisms include 16S rRNA, and V3 to V4 regions of the 16S rRNA gene sequence of the Lachnospirosceae microorganisms, more specifically V3 and V4 regions (region) are sequenced As a sequence having more than 90%, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more homology with the sequence of number 2 It may be displayed, and may be a sequence having predictive or diagnostic ability of diabetes risk.

In one aspect of the present invention, the Hafnia sp. strain may be a microorganism in which the 16S rRNA gene sequence has 90% or more homology with the sequence of SEQ ID NO: 3 and is represented by a sequence having predictive or diagnostic ability of diabetes risk. Specifically, the Hafnia sp. strain includes 16S rRNA, and V3 to V4 regions, more specifically V3 and V4 regions, of the 16S rRNA gene sequence of the Hafnia sp. strain are the sequences of SEQ ID NO: 3 and 90% or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more. and may be a sequence having predictive or diagnostic ability of diabetes risk.

In one aspect of the present invention, the Collinsella sp. strain may be a microorganism in which the 16S rRNA gene sequence has 90% or more homology with the sequence of SEQ ID NO: 4 and is represented by a sequence having predictive or diagnostic ability of diabetes risk. Specifically, the Collinsella sp. strain includes 16S rRNA, and the V3 to V4 regions (regions) of the 16S rRNA gene sequence of the Colincella sp. strains, more specifically V3 and V4 regions (regions) are the sequence of SEQ ID NO: 4 and 90% or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more. and may be a sequence having predictive or diagnostic ability of diabetes risk.

bacteria order SEQ ID NO: GU174097_g GATGAACGCTGGCGGCGTGCCTAACACATGCAAGTCGAACGGAGTTAATCGGAAAAGGAG
TTAGCTTGCTAATAACAATTCGGATTGACTTAGTGGCGGACGGGTGAGTAACGCGTGGGT
AACCTGCCCCATACAGGGGGATAACAGTTAGAAATGACTGCTAACACCGCATAACATGGC
GGGGGCGCATGACCCTGCCATCAAAAATTTATTGGTATGGGATGGGCCCGCGTCTGATTA
GCTAGTTGGTAAGGTAACGGCCTACCAAGGCGACGATCAGTAGCCGGCTTGAGAGAGTGG
ACGGCCACATTGGGACTGAGACACGGCCCAAACTCCTACGGGAGGCAGCAGTGGGGGATA
TTGGACAATGGGGGCAACCCTGATCCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTA
TGTAAAGCTCTATCAGCAGGGAAGATAATGACAGTACCTGACCAAGAAGCACCGGCTAAA
TACGTGCCAGCAGCCGCGGTAATACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGT
AAAGGGAGCGTAGGCGGTCTGCCAAGTCTGATGTGAAAACCCGGGGCTCAACCCCGGGAG
TGCATTGGAAACTGGCGGACTGGAGTGTCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGG
TGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACT
GACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACCGCC
GTAAACGATGAATACTAGGTGTCGGGGCACAAAAGTGCTCCGGTGCCGTCGCAAACGCAG
TAAGTATTCCACCTGGGGAGTACGTTCGCAAGAATGAAACTCAAAGGAATTGACGGGGAC
CCGCACAAGCGGTGGAGCATGTGGATTAATTCGACGCAACGCGAAGAACCTTACCTGTCC
TTGACATCCCCCTGACCGGTGAGTAACGTCACCTTTCCTACGGGACAGGGAGACAGGTG
GTGCATGGTTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCA
ACCCCTATTCCCAGTAGCCAGCGGCCAGGCCGGGCACTCTGGGGAGACTGCCGGGGATAA
CCCGGAGGAAGGCGGGGATGACGTCAAATCATCATGCCCCTTATGGGCAGGGCTTCACAC
GTGCTACAATGACAGCCACAAAGGGATGCGAACCTGCGAGGGGGAGCGAACCCCAGAAAA
GCTGTCCCAGTTCGGATTGTAGTCTGCAACCCGACTACATGAAGCTGGAATCGCTAGTAA
TCGCGAATCAGAATGTCGCGGTGAATACGTTCCCGGGTCTTGTACACACCGCCCGTCACA
CCATGGGAGATGGATATGCCCGAAGTCAGTGACCTAACCGCAAGGGAGGAGCTGCCGAAG
GTGGAGCCATTAACTGGGGTG One Lachnospirose GATGAACGCTGGCGGCGTGCTTAACACATGCAAGTCGAACGGACCCATATTGAAACCTAGTGATATATGGGTTAGTGGCGGACGGGTGAGTAACGCGTGGGCAACCTGCCTTACACAGGGGGATAACACTTAGAAATAGGTGCTAAAACCGCATAAGCGCACAGCTTCGCATGAAGCGGTGTGAAAAACTCCGGTGGTGTAAGATGGACCCGCGTCTGATTAGCTTGTTGGCGGGGTAACGGCCCACCAAGGCGACGATCAGTAGCCGGCCTGAGAGGGCGGACGGCCACATTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTAATTCGTTACGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGAGTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGCGCCAAGTCTGGAGTGAAATGCCGCAGCTTAACTGCGGAACTGCTTTGGAAACTGGCGGACTAGAGTGCGGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGGCTTACTGGACCGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGATTACTAGGTGTCGGGGCCCTAAGGGGCTTCGGTGCCGCCGCAAACGCATTAAGTAATCCACCTGGGGAGTACGTTCGCAAGAATGAAACTCAAAGGAATTGACGGGGACCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCTGCCCTTGACATCCGGATGAATAAGGGGTAATGCCCTTAGCCCTTCGGGGCATCCGAGACAGG TGGTGCATGGTTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCTTCAGTAGCCAGCATTCAGGATGGGAACTCAGGAGAGACTGCCCGGGACAACCGGGAGGAAGGCGGGGATGACGTCAAATCATCATGCCCCTTATGGGCAGGGCTACACACGTGCTACAATGGCGAAAACAGAGGGAAGCGAGCCGGTGACGGTAAGCAAACCCCAAAAACATCGTCCCAGTTCGGACTGTAGTCTGCAACCCGACTACACGAAGCCGGAATCGCTAGTAATCGCGGATCAGAATGCCGCGGTGAATACGTTCCCGGGTCTTGTACACACCGCCCGTCACACCATGGGAGTCGGAAATGCCCGAAGTCAGTGACCTAACCTTTAAGGGAGGAGCTGCCGAAGGTGGAGCCGGTAACTGGGGTG 2 hafnia ATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGTAGCACAAGAGAGCTTGCTCTCTGGGTGACGAGCGGCGGACGGGTGAGTAATGTCTGGGAAACTGCCTGATGGAGGGGGATAACTACTGGAAACGGTAGCTAATACCGCATGACGTCTTCGGACCAAAGTGGGGGACCTTCGGGCCTCACGCCATCAGATGTGCCCAGATGGGATTAGCTAGTAGGTGGGGTAATGGCTCACCTAGGCGACGATCTCTAGCTGGTCTGAGAGGATGACCAGCCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGTAAAGTACTTTCAGCGAGGAGGAAGGCATTGTGGTTAATAACCACAGTGATTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGATTAAGTCAGATGTGAAATCCCCGAGCTTAACTTGGGAACTGCATTTGAAACTGGTCAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCTGTAAACGATGTCGACTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTAACGCGTTAAGTCGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCAGAGAATTTAGCAGAGATGCTTTAGTGC CTTCGGGAACTCTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCCTTTGTTGCCAGCACGTAATGGTGGGAACTCAAAGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCATATACAAAGAGAAGCGAACTCGCGAGAGCAAGCGGACCTCATAAAGTATGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCAAAAGAAGTAGGTAGCTTAACCTTCGGGAGGGCGCTTACCACTTTGTGATTCATGACTGGGGTG 3 Collinsella GATGAACGCTGGCGGCGCGCCTAACACATGCAAGTCGAACGGCACCCCTCTCCGGAGGGAAGCGAGTGGCGAACGGCTGAGTAACACGTGGAGAACCTGCCCCCTCCCCCGGGATAGCCGCCCGAAAGGACGGGTAATACCGGATACCCCGGGGTGCCGCATGGCGCCCCGGCTAAAGCCCCGACGGGAGGGGATGGCTCCGCGGCCCATCAGGTAGACGGCGGGGTGACGGCCCACCGTGCCGACAACGGGTAGCCGGGTTGAGAGACCGACCGGCCAGATTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATCTTGCGCAATGGGGGGAACCCTGACGCAGCGACGCCGCGTGCGGGACGGAGGCCTTCGGGTCGTAAACCGCTTTCAGCAGGGAAGAGTCAAGACTGTACCTGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCCCGGCAGGCCGGGGGTCGAAGCGGGGGGCTCAACCCCCCGAAGCCCCCGGAACCCCGCGGCTTGGGTCCGGTAGGGGAGGGTGGAACACCCGGTGTAGCGGTGGAATGCGCAGATATCGGGTGGAACACCGGTGGCGAAGGCGGCCCTCTGGGCCGAGACCGACGCTGAGGCGCGAAAGCTGGGGGAGCGAACAGGATTAGATACCCTGGTAGTCCCAGCCGTAAACGATGGACGCTAGGTGTGGGGGGACGATCCCCCCGTGCCGCAGCCAACGCATTAAGCGTCCCGCCTGGGGAGTACGGCCGCAAGGCTAAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCAGCGGAGCATGTGGCTTAATTCGAAGCAACGCGAAGAACCTTACCAGGGCTTGACATATGGGTGAAGCGGGGGAGACCCCGTGGCCGAGAGGAGCCCATACAGGTGGTGCATGGCTGTCGT CAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCCGCCGCGTGTTGCCATCGGGTGATGCCGGGAACCCACGCGGGACCGCCGCCGTCAAGGCGGAGGAGGGCGGGGACGACGTCAAGTCATCATGCCCCTTATGCCCTGGGCTGCACACGTGCTACAATGGCCGGTACAGAGGGATGCCACCCCGCGAGGGGGAGCGGATCCCGGAAAGCCGGCCCCAGTTCGGATTGGGGGCTGCAACCCGCCCCCATGAAGTCGGAGTTGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATGCGTTCCCGGGCCTTGTACACACCGCCCGTCACACCACCCGAGTCGTCTGCACCCGAAGTCGCCGGCCCAACCGAGAGGGGGGAGGCGCCGAAGGTGTGGAGGGTGAGGGGGGTG 4

In one aspect of the present invention, the composition may be applied to a sample of a subject, and the sample may be urine, but is not limited thereto.

In another aspect, the present invention provides a kit for predicting or diagnosing the risk of diabetes comprising an agent capable of detecting microorganisms of the Lachnospiraceae family. The description of the Lachnospirose family strain, microorganism detection agent, diabetes, diabetes risk prediction or diagnosis is the same as described above.

In one aspect of the present invention, the kit may be applied to a urine sample of a subject, and specifically, the kit may predict or diagnose the risk of diabetes in a non-invasive manner.

In one aspect of the present invention, the kit may further include one or more collection devices for sampling selected from the group consisting of absorbent pads, cotton swabs, droppers, spoons, swabs, and toothpicks for collecting samples from a subject. As long as it can be collected, it is not limited thereto.

In one aspect of the present invention, the kit may further include instructions.

In one aspect of the present invention, in the manual, when the detection amount of microorganisms of Lachnospiraceae and strains obtained from the subject's sample is lower than that of the normal control sample, predicting or diagnosing diabetes may be described, or the subject's If the detection amount of one or more microorganisms selected from the group consisting of Hafnia sp. strain and Collinsella sp. strain obtained from the sample is higher than that of a normal control sample, predicting or diagnosing diabetes may be described, or the risk of diabetes It may be described to take a sample, more specifically urine, from a subject in order to predict or diagnose

In another aspect, the present invention provides a method comprising the steps of extracting genomic DNA from a sample of a subject; Reacting the extracted genomic DNA with a primer specific to the microorganism of the Lachnospiraceae strain; and amplifying the reactant; provides an information providing method for predicting or diagnosing diabetes risk, including. The description of the Lachnospirose family strain, primer, diabetes, diabetes risk prediction or diagnosis is as described above. In addition, the description of the composition for predicting or diagnosing the risk of diabetes may be applied to the information providing method.

The information providing method according to an aspect of the present invention may include extracting genomic DNA from a sample of a subject.

In one aspect of the present invention, the sample of the subject in the genomic DNA extraction step is collected from the body of a diabetic patient or a person expected to be diabetic, and samples such as tissues, cells, whole blood, serum, plasma saliva or urine, etc. may include, and specifically, the sample may be the subject's urine, but is not limited thereto.

The conventional method used for predicting or diagnosing the risk of diabetes has a problem of causing additional stress due to invasive blood collection and fasting blood glucose measurement. However, the information providing method according to one aspect of the present invention is a non-invasive method, particularly, A urine sample may be used to reduce the subject's stress.

In one aspect of the present invention, the genomic DNA extraction may be performed by applying a general technique known in the art.

The information providing method according to an aspect of the present invention may include reacting the extracted genomic DNA with a primer specific to the microorganism of the Lachnospiraceae strain.

In one aspect of the present invention, the Lachnospirose family strain is GU174097_ g strain in which the 16S rRNA gene sequence is represented by a sequence having 90% or more homology with the sequence of SEQ ID NO: 1 and having predictive or diagnostic ability of diabetes risk can be Specifically, the GU174097_ g strain contains 16S rRNA, and the V3 to V4 region (region) of the 16S rRNA gene sequence of the GU174097_ g strain, more specifically the V3 and V4 region (region) is the sequence of SEQ ID NO: 1 and 90 % or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more. It may be a sequence having the ability to predict or diagnose diabetes risk.

In one aspect of the present invention, the primer reaction step further comprises reacting a primer specific to one or more microorganisms selected from the group consisting of Hafnia sp. strain and Collinsella sp. strain to the extracted genomic DNA. can be included as

The information providing method according to an aspect of the present invention may include amplifying the reactant.

In one aspect of the present invention, in the step of amplifying the reactant, the method for amplifying the reactant may be performed using general amplification techniques known in the art, for example, polymerase chain reaction, SYBR real-time PCR, reverse transcription-polymerase chain reaction, multiplex PCR , touchdown PCR, hot start PCR, nested PCR, booster PCR, real-time PCR, fractional display PCR, rapid amplification of cDNA ends, inverse PCR, vectorret PCR, TAIL-PCR, ligase chain reaction, repair chain reaction, transcription -Mediated amplification, self-maintaining sequence replication, or selective amplification of a target sequence may be used, but is not limited thereto.

In one aspect of the present invention, the method for providing information may further include comparing the amount of the amplification product with the amplification product of a normal control sample after the amplification step.

In one aspect of the present invention, the information providing method predicts that there is a risk of diabetes when the amplification product for the microorganisms of the Lachnospiraceae and strain of the subject sample is lower than that of the normal control sample or is diagnosed as having diabetes can do. Alternatively, in one aspect of the present invention, the method for providing information is that the amplification product for one or more microorganisms selected from the group consisting of Hafnia sp. strain and Collinsella sp. strain of the subject sample is higher than that of a normal control sample. If it is high, it can be predicted to be at risk for diabetes or diagnosed as having diabetes.

In one aspect of the present invention, the diabetes may be type 2 diabetes.

In one aspect of the present invention, the diabetes may satisfy one or more of the following selection criteria i) to iii):

i) fasting blood sugar greater than 100 mg/dL;

ii) oral glucose tolerance 120 min blood glucose 140 mg/dL or higher; and

iii) Glycated hemoglobin (HbA1c) level of 5.7% or higher.

In one aspect of the present invention, the i) selection criteria are specifically, fasting blood sugar of 100 mg/dL or more, 102 mg/dL or more, 104 mg/dL or more, 106 mg/dL or more, 108 mg/dL or more, 110 mg/dL or higher, 112 mg/dL or higher, 114 mg/dL or higher, 116 mg/dL or higher, 118 mg/dL or higher, 120 mg/dL or higher, 122 mg/dL or higher, 124 mg/dL or higher, or 126 mg/dL or higher dL or higher.

In one aspect of the present invention, the ii) selection criterion is specifically, a 120-minute cervical glucose load blood sugar of 140 mg/dL or more, 150 mg/dL or more, 160 mg/dL or more, 170 mg/dL or more, 180 mg/dL or more. dL or more, 190 mg/dL or more, or 200 mg/dL or more.

In one aspect of the present invention, the iii) selection criterion is specifically, glycated hemoglobin (HbA1c) level of 5.7% or more, 5.8% or more, 5.9% or more, 6.0% or more, 6.1% or more, 6.2% or more, 6.3% or more , 6.4% or more or 6.5% or more.

In another aspect, the present invention provides a method for preventing or treating diabetes mellitus; Detecting the change of microorganisms of the Lachnospiraceae and strains in the sample of the subject before and after the candidate treatment; And when the detection amount of the microorganism of the Lachnospirosceae strain increases after the candidate substance treatment, it provides a screening method for preventing or treating diabetes, comprising the step of determining that the candidate substance is a diabetes preventive or therapeutic agent. The description of the Lachnospirose family strain, primer, diabetes, diabetes risk prediction or diagnosis is as described above.

In one aspect of the present invention, Lachnospiraceae in the sample in the presence and absence of a candidate for preventing or treating diabetes, microorganisms of the family strain , or Hafnia spp . and Collinsella sp. strains consisting of As a method of comparing changes in one or more microorganisms selected from the group, it can be applied to screening for preventing or treating diabetes. In other words, the three (Lachnospiraceae) and microorganisms of the strains in the presence and absence of diabetes prevention or treatment of candidate samples within Lac North fatigue, or hafnia (Hafnia) sp and choline Cellar (Collinsella) one selected from the group consisting of sp After comparing the two by measuring the degree or relative abundance of the above microorganisms, when the candidate substance is present, if the colony formation or relative abundance of microorganisms of the Lachnospirosaceae strain is further increased, the candidate substance is used to prevent diabetes Or it can be determined as a therapeutic agent, or when the candidate substance is present, the degree or relative abundance of one or more microorganisms selected from the group consisting of Hafnia sp. strain and Colincella sp. strain is further reduced. It can be considered as a treatment.

In one aspect of the present invention, the candidate material may include various low molecular weight compounds, high molecular weight compounds, nucleic acid molecules (eg, DNA, RNA, PNA, etc.), proteins, sugars, lipids, etc. that can be a preventive or therapeutic agent for diabetes. can, but is not limited thereto.

Hereinafter, the configuration and effect of the present invention will be described in more detail by way of examples. However, the following examples are provided for illustrative purposes only to aid understanding of the present invention, and the scope and scope of the present invention are not limited thereto.

[Example 1] Metagenome analysis

[Example 1-1] Cohort and study design

To discover biomarkers for predicting or diagnosing diabetes risk, metagenome analysis was performed. A related KARE (Korean Association Resource, Ansan, Anseong Community-Based Community-Based Cohort Project) study was conducted on two local communities: a rural community in Anseong in Korea and an urban community in Ansan City. It started as part of the Korean Genome Epidemiology study. Urine samples were obtained from 315 subjects in 2013, 2015 and 2017 belonging to the two cohorts for metagenomic analysis. Metagenomic 16S rRNA sequences were obtained from each of 315 individuals by using extracellular vesicles (EV) isolation and DNA extraction from the sample.

[Example 1-2] EV Isolation and DNA Extraction from Human Samples

The urine sample obtained in Example 1-1 was differentially centrifuged with a microcentrifuge (Labogene 1730R; BMS, Korea) at 10,000 g at 4° C. for 10 minutes to separate extracellular vesicles (EV) (Lee, EY) , et al., Global proteomic profiling of native outer membrane vesicles derived from Escherichia coli . Proteomics, 2007. 7(17): p. 3143-3153). The supernatant was filtered through a 0.22 μm filter to remove bacteria, foreign substances and wastes. Then, for DNA extraction, the isolated extracellular vesicles (EV) were boiled at 100° C. for 40 minutes and centrifuged at 13,000 rpm at 4° C. for 30 minutes to remove remaining suspended particles and impurities. Then, the supernatant was collected and DNA was isolated using the PowerSoil® DNA Isolation Kit (MO BIO Laboratories; Carlsbad, CA, USA) according to standard DNA extraction protocols. And the DNA of each sample was quantified using the QIAxpert system (Qiagen, Hilden, Germany).

[Example 1-3] Metagenomic sequence analysis from extracted DNA

Paired-end sequence pairs for each subject targeted the V3-V4 region of the bacterial 16S rRNA gene. The primers widely used to amplify the V3-V4 region, the F341 and R805 primers (SEQ ID NOs: 5 and 6) in Table 2 below, have a minimum overlap of 11, a maximum error rate of 10%, and a minimum length of 10 CUTADAPT software. detected and removed.

division order SEQ ID NO: F341 5'-CCTACGGGNGGCWGCAG -3' 5 R805 5'-GACTACHVGGGTATCTAATCC -3' 6

The DNA isolated in Example 1-2 was merged using CASPER software having a mismatch rate of 0.27 after the metagenomic sequence was amplified using the F341 and R805 primers, and as a result, the nucleotide sequence of 350-550 bp in length Data were generated (Kwon, et al., 2014). After the merged sequence was duplicated (dereplicate), the chimeric sequences were detected and removed using VSEARCH software as a Silva Gold reference database for the chimera. Using the de novo picking method, an OUT (operating taxonomic unit) table was obtained as a result of a sequence identity threshold of 97%.

[Example 2] Analysis of microbial composition using 16S rRNA

[Example 2-1] Prediction of functional profile from 16S rRNA metagenomic data

The functional potential of a microbial community can be predicted from phylogeny. PICRUSt and Tax4fun use evolutionary modeling to predict the metagenome from 16S data and the EzTaxon reference genome database. The SILVA-based 16S rRNA profile was transformed into a taxonomic profile of a prokaryotic KEGG organism. A linear transformation was realized by a pre-computed associative matrix. The estimated abundance of KEGG organisms was then normalized by the 16S rRNA copy number obtained from NCBI genome annotations. Finally, the normalized taxonomic abundance was used to linearly combine the pre-calculated functional profiles of KEGG organisms for the prediction of the functional profile of the microbial community.

[Example 2-2] Analysis of microbial composition

Alpha and beta diversity was calculated by QIIME. Then, a recursive curve was obtained with 10,000 sparse sequences. Bray-Curtis, Unweighted UniFrac was considered beta diversity, Observed OUT, ACE, Shannon and Simpson were considered alpha diversity, and the results are shown in FIGS. 1, 2, 4A and 4B.

In FIG. 1, alpha-diversity is indicated, and 6P, 7P, and 8P in FIG. 1 mean data composed of samples in 2013, 2015, and 2017, respectively. As shown in FIG. 1 , as a result of analyzing the microbial composition from the metagenomic data from the subject samples in 2013, 2015 and 2017 of Example 1-1, the alpha diversity decreased during the follow-up period, which had an effect on the age effect. may be due to

In addition, Figure 2 shows the change trend of each genus according to each year. As shown in FIG. 2 , as a result of analyzing the association between the flora and health factors through the NMDS plot, the samples collected in 2013, 2015, and 2017 had a slight difference in the flora, and each genus of the flora according to the period change was confirmed.

In addition, according to Figures 4a and 4b two major genera, Bacteriodes genus and Akkermansia genus decreased during the follow-up period. This suggests that the clinical significance of genera changes during the observation period needs to be treated as repeated measurement data.

[Example 3] Diabetes marker strain discovery through metagenomic analysis

The following analysis was performed to discover biomarkers for predicting or diagnosing diabetes risk using the metagenome and microbial composition analysis results obtained from Examples 1 and 2, and the diabetes type 2 diabetes (T2D, type -2 diabetes) and the level of insulin resistance are defined as follows. First, to investigate insulin resistance with the KARE phenotype, insulin resistance (homeostatic model assessment, HOMA-IR) was calculated with fasting blood glucose and fasting insulin levels (Pisprasert, V., et al., Limitations in the use of indices using glucose and insulin levels to predict insulin sensitivity: impact of race and gender and superiority of the indices derived from oral glucose tolerance test in African Americans . Diabetes care, 2013. 36(4): p. 845-853). The definitions of the diabetes group (T2D group), the diabetes risk group (T2D risk group), and the normal group (Healthy group) among the samples of Example 1 are as follows.

- Diabetes group (T2D group): Fasting blood sugar of 126 mg/dL or higher, Oral glucose tolerance 120-minute blood sugar 200 mg/dL or higher, or HbA1c level of 6.5% or higher

- Diabetes risk group (T2D risk group): Fasting blood sugar of 100 mg/dL or more, Oral glucose tolerance 120-minute blood sugar 140 mg/dL or more, or HbA1c level of 5.7% or more

- Normal group (Healthy group): a sample that does not belong to the T2D group and the T2D risk group

At this time, samples taking diabetes drugs were also classified into the T2D group, DM_binary1 is a phenotype that distinguishes the T2D group from the T2D risk group and the normal group, and DM_binary2 is a phenotype that distinguishes the normal group from the T2D and T2D risk group.

[Example 3-1] Analysis of microbial variance explained by KARE phenotype

To analyze the microbial multivariate explained by the KARE phenotype, PERMANOVA (Permutational multivariate analysis of variance) method was performed. The PERMANOVA is a non-parametric method of multivariate analysis of variance based on pairwise distances (Anderson, MJ, A new method for non-parametric multivariate analysis of variance . Austral ecology, 2001. 26(1)) : p. 32-46). The R package pldist is used to obtain microbial multivariate for a subject in repeated measurements of the microbial profile. The pldist summarizes the within-subject changes in the composition of the microbiome and then compares these compositional changes by subject. These dissimilarities depend on a new transformation of relative abundance, which has been extended to two or more points of view and merged with several dissimilarities. Bray-Curtis and kulczynski distances were used for this dissimilarity, and PERMANOVA was performed on the biochemistry-related KARE phenotype with R package adonis. In addition, the phenotypes were averaged for the values in 2013, 2015, and 2017, and the results are shown in FIG. 3 .

3 is a result of examining the correlation between KARE phenotype and insulin resistance (HOMA-IR) in the microbial composition using the PERMANOVA method. It was confirmed that (WBC) level, hematocrit (Hct, hematocrit) level, DM_binary1 and age were significantly related to microbial changes during the follow-up period. In addition, glycated hemoglobin (HbA1c) and DM_binary1 could significantly explain the multivariate of microbial changes from 2013 to 2017 in cohort subjects, suggesting that microbial changes are associated with diabetes.

[Example 3-2] Correlation analysis and network analysis of flora and diabetes

In order to discover biomarkers for predicting or diagnosing diabetes risk, the following correlation analysis and network analysis of the flora and diabetes were performed.

First, in the correlation analysis, for each genus and species, a generalized linear mixed model was used to find the association with diabetes, and the linear mixed model was used to find the association with the KARE phenotype. Complex symmetric structures with different scale parameters for each time point during the follow-up period were assumed to be covariance structures, and a sandwich estimator was used to find a robust estimate of the covariance matrix. The problems of several tests were adjusted to the false discovery rate (FDR) by the Benjamin Hochberg method. Genus with a correlation coefficient greater than 0.6 and an FDR less than 0.05 were considered statistically significant.

In addition, for taxa and functional genes with an FDR less than 0.1 in association analysis and KARE phenotype to account for microbial multivariate, network analysis was performed to find potential relationships between phenotypes, functional genes and taxa. The edge width is -log ₁₀ of the p-value. When Rho is positive, the edge color is displayed in red, and when Rho is negative, it is displayed in blue. Blue nodes indicate microbiota and KARE phenotypes are indicated in red. The R package visNetwork (version 2.0.8) was used for this visualization.

From the above analysis results, the association between the diabetes (DM) group and the unclassified Lachnospiraceae microorganism GU174097_g was found. Table 3 below shows that GU174097_g was more present in the normal group than in the T2D group and the T2D risk group.

Phenotype Genus Estimate StdErr DF p-value FDR DM_binary2 GU174097_g -189.13 46.6324 735 0.00006 0.00393

In addition, as shown in FIG. 5 , during the period of 2013 to 2017, GU174097_g was more present in normal subjects (normal group (Healthy)) than diabetic subjects (T2D group (DM) and T2D risk group (DM_before)), and normal It was confirmed that there was relatively more presence in subjects who switched to diabetes or prediabetes or those who switched from prediabetes to diabetes. Through this, it was found that the relative abundance of GU174097_g decreased as the subject's condition worsened, that is, the risk of diabetes increased or had diabetes.

On the other hand, hafnia (Hafnia) in and Colin Cellar (Collinsella) in the diabetes - glycosylated hemoglobin (HbA1c) of the associated phenotypes are correlated as in Table 4, microbial metabolism of GU174097_g can not be distinguished from the others in . To the results in Table 4 is a hafnia (Hafnia) and choline in Sela (Collinsella) shows a trick requires diabetes, and specifically to lead to claim 2 diabetes GU174097_g or other factors and interactions.

Phenotype Genus Estimate p-value FDR HbA1c Hafnia 0.0020 1.76E-08 5.74E-05

In addition, according to Table 5 below, GU174097_g had a positive correlation with the 60-minute oral glucose tolerance insulin level. This means that GU174097_g can help the homeostatic cycle of insulin levels in the human body. In addition, although the fasting insulin level and the coefficient of insulin resistance (HOMA-IR) are not significantly significant, they have a negative correlation, which can alleviate the insulin resistance and reduce the risk of diabetes. Glycated hemoglobin (HbA1c) levels and oral glucose tolerance 60 min and 120 min blood glucose levels also support the above assay results positively.

Phenotype Rho p-value FDR Ins60 0.0950 0.0018 0.0026 HbA1c -0.0434 0.1548 0.1872 Glu0 0.0306 0.3165 0.3647 Ins0 -0.0301 0.3236 0.3721 HOMA-IR -0.0270 0.3756 0.4255 Glu60 -0.0229 0.4536 0.5045 Ins120 0.0177 0.5622 0.6096 Glu120 0.0072 0.8138 0.8424 BMI -0.0070 0.8186 0.8469

In previous studies, patients with type 2 diabetes could not produce enough insulin, but patients with impaired glucose tolerance were able to produce higher amounts of insulin than patients with type 2 diabetes and insulin sensitivity, and type 2 diabetes patients could The amount of insulin was small, but not fast enough to control blood sugar well. In addition, people with normal insulin sensitivity have a moderate elevation of insulin, which is sufficient to maintain homeostasis in blood glucose, and this difference is maximized at 60-minute oral glucose tolerance insulin levels (Lee, S., et al., Intact). glucagon-like peptide-1 levels are not decreased in Japanese patients with type 2 diabetes . Endocrine journal, 2009: p. 0910220351-0910220351). These results confirm that the difference in the relative abundance of GU174097_g is maximized between the normal group and the T2D risk group in FIG. 5 in that the level of the relative abundance of GU174097_g is related to the subject's insulin sensitivity did. In addition, GU174097_g had a strong positive correlation with insulin levels at 60 minutes of oral glucose tolerance, but there was no clear correlation with insulin levels at 120 minutes of oral glucose tolerance, so the results are consistent with the results shown in Table 5 above.

6 illustrates the correlation between the results of network analysis of the insulin level and the blood glucose level and the strong positive correlation. A negative correlation was found between glycated hemoglobin (HbA1c) levels and oral glucose tolerance 60 min insulin levels. This is consistent with previous clinical studies and the network analysis results.

Overall, GU174097_g , a microorganism of the Lachnospiraceae family, is more present in the normal group than in the diabetic group and the diabetes risk group. A composition comprising an agent capable of detecting the strain GU174097_g, including the composition A kit, an information providing method for predicting or diagnosing the risk of diabetes, including the step of amplifying by reacting a primer for the strain GU174097_g from a sample of a subject, has an excellent effect in predicting or diagnosing the risk of diabetes.

<110> Seoul National University R&DB Foundation Industry-Academic Cooperation Foundation, Dankook University <120> Composition for predicting or diagnosing diabetes risk comprising agent capable of detecting microorganisms of strain of the genus Lachnospiraceae <130> 20p102/ind <160> 6 <170> KoPatentIn 3.0 <210> 1 <211> 1461 <212> DNA <213> Unknown <220> <223> 16S rRNA sequence from the family Lachnospiraceae <400> 1 gatgaacgct ggcggcgtgc ctaacacat caagtcgaac ggagttaatc ggaaaaggag 60 ttagcttgct aataacaatt cggattgact tagtggcgga cgggtgagta acgcgtgggt 120 aacctgcccc atacagggg ataacagtta gaaatgactg ctaacaccgc ataacatggc 180 gggggcgcat gaccctgcca tcaaaaattt attggtatgg gatgggcccg cgtctgatta 240 gctagttggt aaggtaacgg cctaccaagg cgacgatcag tagccggctt gagagagtgg 300 acggccacat tgggactgag acacggccca aactcctacg ggaggcagca gtgggggata 360 ttggacaatg ggggcaaccc tgatccagcg acgccgcgtg agtgaagaag tatttcggta 420 tgtaaagctc tatcagcagg gaagataatg acagtacctg accaagaagc accggctaaa 480 tacgtgccag cagccgcggt aatacgtatg gtgcaagcgt tatccggatt tactgggtgt 540 aaagggagcg taggcggtct gccaagtctg atgtgaaaac ccggggctca accccgggag 600 tgcattggaa actggcggac tggagtgtcg gagaggtaag cggaattcct agtgtagcgg 660 tgaaatgcgt agatattagg aggaacacca gtggcgaagg cggcttactg gacgataact 720 gacgctgagg ctcgaaagcg tggggagcaa acaggattag ataccctggt agtccacgcc 780 gtaaacgatg aatactaggt gtcggggcac aaaagtgctc cggtgccgtc gcaaacgcag 840 taagtattcc acctggggag tacgttcgca agaatgaaac tcaaaggaat tgacggggac 900 ccgcacaagc ggtggagcat gtggattaat tcgacgcaac gcgaagaacc ttacctgtcc 960 ttgacatccc cctgaccggt gagtaacgtc acctttccta cgggacaggg gagacaggtg 1020 gtgcatggtt gtcgtcagct cgtgtcgtga gatgttgggt taagtcccgc aacgagcgca 1080 acccctattc ccagtagcca gcggccaggc cgggcactct ggggagactg ccggggataa 1140 cccggaggaa ggcggggatg acgtcaaatc atcatgcccc ttatgggcag ggcttcacac 1200 gtgctacaat gacagccaca aagggatgcg aacctgcgag ggggagcgaa ccccagaaaa 1260 gctgtcccag ttcggattgt agtctgcaac ccgactacat gaagctggaa tcgctagtaa 1320 tcgcgaatca gaatgtcgcg gtgaatacgt tcccgggtct tgtacacacc gcccgtcaca 1380 ccatgggaga tggatatgcc cgaagtcagt gacctaaccg caagggagga gctgccgaag 1440 gtggagccat taactggggt g 1461 <210> 2 <211> 1444 <212> DNA <213> Unknown <220> <223> 16S rRNA sequence from the Lachnospiraceae family <400> 2 gatgaacgct ggcggcgtgc ttaacacat caagtcgaac ggacccatat tgaaacctag 60 tgatatatgg gttagtggcg gacgggtgag taacgcgtgg gcaacctgcc ttacacaggg 120 ggataacact tagaaatagg tgctaaaacc gcataagcgc acagcttcgc atgaagcggt 180 gtgaaaaact ccggtggtgt aagatggacc cgcgtctgat tagcttgttg gcggggtaac 240 ggcccaccaa ggcgacgatc agtagccggc ctgagagggc ggacggccac attgggactg 300 agacacggcc cagactccta cgggaggcag cagtggggaa tattgcacaa tgggggaaac 360 cctgatgcag cgacgccgcg tgagtgaaga agtaattcgt tacgtaaagc tctatcagca 420 gggaagaaaa tgacggtacc tgagtaagaa gccccggcta actacgtgcc agcagccgcg 480 gtaatacgta gggggcaagc gttatccgga tttactgggt gtaaagggag cgtagacggt 540 gcgccaagtc tggagtgaaa tgccgcagct taactgcgga actgctttgg aaactggcgg 600 actagagtgc gggaggggta agcggaattc ctagtgtagc ggtgaaatgc gtagatatta 660 ggaggaacac cggtggcgaa ggcggcttac tggaccgtaa ctgacgttga ggctcgaaag 720 cgtggggagc aaacaggatt agataccctg gtagtccacg ccgtaaacga tgattactag 780 gtgtcggggc cctaaggggc ttcggtgccg ccgcaaacgc attaagtaat ccacctgggg 840 agtacgttcg caagaatgaa actcaaagga attgacgggg acccgcacaa gcggtggagc 900 atgtggttta attcgaagca acgcgaagaa ccttacctgc ccttgacatc cggatgaata 960 aggggtaatg cccttagccc ttcggggcat ccgagacagg tggtgcatgg ttgtcgtcag 1020 ctcgtgtcgt gagatgttgg gttaagtccc gcaacgagcg caacccctat cttcagtagc 1080 cagcattcag gatgggaact caggagagac tgcccgggac aaccgggagg aaggcgggga 1140 tgacgtcaaa tcatcatgcc ccttatgggc agggctacac acgtgctaca atggcgaaaa 1200 cagagggaag cgagccggtg acggtaagca aaccccaaaa acatcgtccc agttcggact 1260 gtagtctgca acccgactac acgaagccgg aatcgctagt aatcgcggat cagaatgccg 1320 cggtgaatac gttcccgggt cttgtacaca ccgcccgtca caccatggga gtcggaaatg 1380 cccgaagtca gtgacctaac ctttaaggga ggagctgccg aaggtggagc cggtaactgg 1440 ggtg 1444 <210> 3 <211> 1465 <212> DNA <213> Unknown <220> <223> 16S rRNA sequence from the genus Hafnia <400> 3 attgaacgct ggcggcaggc ctaacacat caagtcgagc ggtagcacaa gagagcttgc 60 tctctgggtg acgagcggcg gacgggtgag taatgtctgg gaaactgcct gatggagggg 120 gataactact ggaaacggta gctaataccg catgacgtct tcggaccaaa gtgggggacc 180 ttcgggcctc acgccatcag atgtgcccag atgggattag ctagtaggtg gggtaatggc 240 tcacctaggc gacgatctct agctggtctg agaggatgac cagccacact ggaactgaga 300 cacggtccag actcctacgg gaggcagcag tggggaatat tgcacaatgg gcgcaagcct 360 gatgcagcca tgccgcgtgt atgaagaagg ccttcgggtt gtaaagtact ttcagcgagg 420 aggaaggcat tgtggttaat aaccacagtg attgacgtta ctcgcagaag aagcaccggc 480 taactccgtg ccagcagccg cggtaatacg gagggtgcaa gcgttaatcg gaattactgg 540 gcgtaaagcg cacgcaggcg gttgattaag tcagatgtga aatccccgag cttaacttgg 600 gaactgcatt tgaaactggt cagctagagt cttgtagagg ggggtagaat tccaggtgta 660 gcggtgaaat gcgtagagat ctggaggaat accggtggcg aaggcggccc cctggacaaa 720 gactgacgct caggtgcgaa agcgtgggga gcaaacagga tagataccc tggtagtcca 780 cgctgtaaac gatgtcgact tggaggttgt gcccttgagg cgtggcttcc ggagctaacg 840 cgttaagtcg accgcctggg gagtacggcc gcaaggttaa aactcaaatg aattgagggg 900 ggcccgcaca agcggtggag catgtggttt aattcgatgc aacgcgaaga accttaccta 960 ctcttgacat ccagagaatt tagcagagat gctttagtgc cttcgggaac tctgagacag 1020 gtgctgcatg gctgtcgtca gctcgtgttg tgaaatgttg ggttaagtcc cgcaacgagc 1080 gcaaccctta tcctttgttg ccagcacgta atggtgggaa ctcaaaggag actgccggtg 1140 ataaaccgga ggaaggtggg gatgacgtca agtcatcatg gcccttacga gtagggctac 1200 acacgtgcta caatggcata tacaaagaga agcgaactcg cgagagcaag cggacctcat 1260 aaagtatgtc gtagtccgga ttggagtctg caactcgact ccatgaagtc ggaatcgcta 1320 gtaatcgtag atcagaatgc tacggtgaat acgttcccgg gccttgtaca caccgcccgt 1380 cacaccatgg gagtgggttg caaaagaagt aggtagctta accttcggga gggcgcttac 1440 cactttgtga ttcatgactg gggtg 1465 <210> 4 <211> 1427 <212> DNA <213> Unknown <220> <223> 16S rRNA sequence from the genus Collinsella <400> 4 gatgaacgct ggcggcgcgc ctaacacat caagtcgaac ggcacccctc tccggaggga 60 agcgagtggc gaacggctga gtaacacgtg gagaacctgc cccctccccc gggatagccg 120 cccgaaagga cgggtaatac cggatacccc ggggtgccgc atggcgcccc ggctaaagcc 180 ccgacgggag gggatggctc cgcggcccat caggtagacg gcggggtgac ggcccaccgt 240 gccgacaacg ggtagccggg ttgagagacc gaccggccag attgggactg agacacggcc 300 cagactccta cgggaggcag cagtggggaa tcttgcgcaa tggggggaac cctgacgcag 360 cgacgccgcg tgcgggacgg aggccttcgg gtcgtaaacc gctttcagca gggaagagtc 420 aagactgtac ctgcagaaga agccccggct aactacgtgc cagcagccgc ggtaatacgt 480 agggggcgag cgttatccgg attcattggg cgtaaagcgc gcgtaggcgg cccggcaggc 540 cgggggtcga agcggggggc tcaacccccc gaagcccccg gaaccccgcg gcttgggtcc 600 ggtaggggag ggtggaacac ccggtgtagc ggtggaatgc gcagatatcg ggtggaacac 660 cggtggcgaa ggcggccctc tgggccgaga ccgacgctga ggcgcgaaag ctgggggagc 720 gaacaggatt agataccctg gtagtcccag ccgtaaacga tggacgctag gtgtgggggg 780 acgatccccc cgtgccgcag ccaacgcatt aagcgtcccg cctggggagt acggccgcaa 840 ggctaaaact caaaggaatt gacgggggcc cgcacaagca gcggagcatg tggcttaatt 900 cgaagcaacg cgaagaacct taccagggct tgacatatgg gtgaagcggg ggagaccccg 960 tggccgagag gagcccatac aggtggtgca tggctgtcgt cagctcgtgt cgtgagatgt 1020 tgggttaagt cccgcaacga gcgcaacccc cgccgcgtgt tgccatcggg tgatgccggg 1080 aacccacgcg ggaccgccgc cgtcaaggcg gaggagggcg gggacgacgt caagtcatca 1140 tgccccttat gccctgggct gcacacgtgc tacaatggcc ggtacagagg gatgccaccc 1200 cgcgaggggg agcggatccc ggaaagccgg ccccagttcg gattgggggc tgcaacccgc 1260 ccccatgaag tcggagttgc tagtaatcgc ggatcagcat gccgcggtga atgcgttccc 1320 gggccttgta cacaccgccc gtcacaccac ccgagtcgtc tgcacccgaa gtcgccggcc 1380 caaccgagag gggggaggcg ccgaaggtgt ggagggtgag gggggtg 1427 <210> 5 <211> 17 <212> DNA <213> Artificial Sequence <220> <223> F357 <400> 5 cctacgggag gcagcag 17 <210> 6 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> R519 <400> 6 gtnttacngc ggckgctg 18

Claims (18)

A composition for predicting or diagnosing diabetes risk,
The composition comprises an agent capable of detecting microorganisms of Lachnospiraceae and strains,
The Lachnospirose family strain is a GU174097 _g strain comprising the 16S rRNA gene sequence of SEQ ID NO: 1,
The diabetes is type 2 diabetes, diabetes risk prediction or diagnosis composition. delete delete According to claim 1,
The diabetes is a composition for predicting or diagnosing diabetes risk, which satisfies one or more of the following selection criteria of i) to iii):
i) fasting blood sugar greater than 100 mg/dL;
ii) oral glucose tolerance 120 min blood glucose 140 mg/dL or higher; and
iii) Glycated hemoglobin (HbA1c) level of 5.7% or higher. According to claim 1,
The composition hafnia (Hafnia) sp and choline Cellar (Collinsella), diabetes risk prediction or diagnostic composition comprising from the group consisting of strains in the additional agents capable of detecting one or more microorganisms selected. According to claim 1,
The detection agent is at least one selected from the group consisting of primers, probes, antisense oligonucleotides, aptamers and antibodies specific to microorganisms, diabetes risk prediction or diagnosis composition. A kit for predicting or diagnosing diabetes risk, comprising the composition of any one of claims 1 and 4 to 6. 8. The method of claim 7,
The kit is applied to a urine sample of a subject, diabetes risk prediction or diagnosis kit. 8. The method of claim 7,
The kit further comprises instructions,
In the manual, when the detection amount of microorganisms of Lachnospiraceae and strains obtained from the subject's sample is lower than that of the normal control sample, predicting or diagnosing diabetes is described, Diabetes risk prediction or diagnosis kit. 10. The method of claim 9,
In the above instructions, if the detection amount of one or more microorganisms selected from the group consisting of Hafnia sp. strain and Collinsella sp. strain obtained from a sample of a subject is higher than that of a normal control sample, predicting or diagnosing diabetes is described, diabetes risk Predictive or diagnostic kits. A method for providing information for predicting or diagnosing diabetes risk, the method comprising:
extracting genomic DNA from the subject's sample;
Reacting the extracted genomic DNA with a primer specific to the microorganism of the Lachnospiraceae strain; and
Including; amplifying the reactant;
The Lachnospirose family strain is a GU174097 _g strain comprising the 16S rRNA gene sequence of SEQ ID NO: 1,
The diabetes is type 2 diabetes, the information providing method for predicting or diagnosing the risk of diabetes. delete delete 12. The method of claim 11,
The diabetes is to satisfy one or more of the following selection criteria of i) to iii), information providing method:
i) fasting blood sugar greater than 100 mg/dL;
ii) oral glucose tolerance 120 min blood glucose 140 mg/dL or higher; and
iii) Glycated hemoglobin (HbA1c) level of 5.7% or higher. 12. The method of claim 11,
The information providing method is after the amplification step,
The method of providing information, further comprising the step of comparing the amount of the amplification product with the amplification product of a normal control sample. 16. The method of claim 15,
The information providing method is predicting that there is a risk of diabetes or diagnosing that there is diabetes when the amplification product for the microorganisms of the Lachnospiraceae family strain of the subject sample is lower than that of the normal control sample, information providing method . 16. The method of claim 15,
The primer reacting step further includes reacting the primers specific to one or more microorganisms selected from the group consisting of hafnia (Hafnia) sp and choline Cellar (Collinsella) in the genomic DNA isolates the extracted,
The information providing method predicts that there is a risk of diabetes if the amplification product for one or more microorganisms selected from the group consisting of Hafnia sp. strain and Collinsella sp. strain of the subject sample is higher than that of the normal control sample, or A method for providing information, which is to diagnose as having diabetes. delete

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