KR102484695B1 - Composition for predicting obesity by evaluating the level of intestinal microorganism colony, and use thereof - Google Patents

Abstract

The present application relates to a composition for predicting obesity by evaluating the level of the intestinal microbial community and its use, and relates to a composition for predicting obesity risk, a kit for predicting obesity risk, a method for providing information for predicting obesity risk, and a method for screening obesity treatment substances. provides a way

Description

Composition for predicting obesity by evaluating the level of intestinal microorganism colony, and use thereof {Composition for predicting obesity by evaluating the level of intestinal microorganism colony, and use thereof}

It relates to a composition for predicting obesity through the evaluation of intestinal microbial community level and its use.

Since the World Health Organization defined obesity as a 'disease requiring long-term treatment', obesity has been recognized as one of the important diseases that mankind must overcome. Obesity is a phenomenon in which excess energy is accumulated in the form of body fat due to low energy consumption compared to chronically ingested nutrients, and the obesity is caused by various factors including complex genetic factors and environmental factors. Specifically, irregular eating habits, excessive food intake, lack of exercise, endocrine system diseases, genetic factors, mental factors, and drug overdose have been pointed out as realistic causes of obesity. Currently, obesity is diagnosed through a body mass index (BMI) measurement method, a bioelectrical resistance measurement method, a waist circumference measurement method, and a visceral fat measurement method using abdominal fat CT scan.

On the other hand, recent studies on obesity have paid attention to the microbial flora (microciota) present in the human intestine, and it has been reported that the microbial flora affects energy metabolism in the body and is related to obesity. The most well-known metagenome-based obesity risk assessment method is to analyze the composition ratio between Firmicutes phylum and Bacteroidetes phylum in feces, and the Firmicutes / Bacteroidetes ratio (F/B ratio) is The higher it is, the higher the risk of obesity is judged. However, in recent years, discrepancies between the F/B ratio and the obesity phenotype have been frequently reported due to pathological conditions, host genetic characteristics, and differences in the 16S rRNA target region of microbial sequences.

In addition, studies on the correlation between F/B ratio and BMI have been conducted mainly for groups with a Western-style diet, and accordingly, the above correlation may not be applied as it is in groups with other dietary habits. Therefore, there is an urgent need to develop a new obesity risk prediction technology that can replace the conventional technology having these limitations (Korean Patent Registration No. 10-1992789).

One aspect is 1 selected from the group consisting of Alistipes genus, Oscillibacter genus, Odoribacter genus, Ruminiclostridium genus, and Bifidobacterium genus. It is to provide a composition for predicting obesity risk including an agent capable of detecting more than one species of microorganisms.

Another aspect is to provide a kit for predicting the risk of obesity using microorganisms in feces containing the composition for predicting obesity.

Another aspect is the amplification product for one or more microorganisms selected from the group consisting of Alistifes genus, Oscillibacter genus, Odoribacter genus, Rumini Clostridium genus, and Bifidobacterium genus in a stool sample isolated from the subject. To provide an information providing method for predicting the risk of obesity, including the step of measuring the level.

In another aspect, one or more microorganisms selected from the group consisting of Alistifes genus, Ossillibacter genus, Odoribacter genus, Rumini Clostridium genus, and Bifidobacterium genus in a biological sample isolated from the subject are treated with a test substance doing; measuring the level of microorganisms in the biological sample; And to provide a method for screening obesity treatment substances comprising the step of comparing the level of the microorganisms with a control group not treated with the test substance.

One aspect is 1 selected from the group consisting of Alistipes genus, Oscillibacter genus, Odoribacter genus, Ruminiclostridium genus, and Bifidobacterium genus. Provided is a composition for predicting obesity risk including an agent capable of detecting more than one species of microorganisms.

As used herein, the term "obesity" refers to a state in which fat tissue is excessively accumulated in the body, and the remaining calories are converted to fat and accumulated in the subcutaneous tissue or the abdominal mesentery in the body. As a conventional technique for predicting the risk of obesity, there is a method of analyzing the composition ratio between the Firmicutes phylum and the Bacteroidetes phylum, but these methods are predictive of genetic characteristics and differences in dietary habits by ethnicity. It has a limitation that accuracy may be low. Under this technical background, the present inventors confirmed that the risk of obesity can be predicted more accurately by detecting specific microorganisms at the level of the genus in a stool sample and quantitatively evaluating their level.

As used herein, the term "risk prediction" is to evaluate whether obesity is likely to occur, and to delay or prevent the occurrence of obesity through special and appropriate management of individuals at high risk of obesity, or to provide the most appropriate treatment method. By selecting, it can be used clinically to make treatment decisions. In addition, through the risk prediction, it is possible to predict the existence or characteristics of a pathological condition that may be caused in the future. For example, the term "predicting the possibility of obesity", "diagnosing the state of obesity", that is, diagnosing obesity, predicting the risk prognosis of obesity-related diseases, and determining the dosage of a drug for preventing or treating obesity , can be used interchangeably with diagnosis to determine treatment options according to the progression of obesity. The obesity-related disease is a metabolic disorder associated with obesity or may be caused by obesity, for example, metabolic syndrome, insulin deficiency or insulin-resistance related disorder, diabetes (eg, type 2 diabetes), glucose intolerance, dysbiosis Lipid metabolism, atherosclerosis, hypertension, cardiac pathology, stroke, non-alcoholic fatty liver disease, hyperglycemia, fatty liver, dyslipidemia, dysfunction of the immune system associated with overweight and obesity, cardiovascular disease, high cholesterol, elevated triglycerides, asthma, sleep apnea, osteoarthritis, neurodegeneration, gallbladder disease, syndrome X, inflammatory and immune diseases, atherogenic dyslipidemia, cancer, and the like.

In one embodiment, as a result of confirming the overall community of intestinal microbes between the obese group and the normal weight group, among a total of 32 genera detected at 70% or more in both groups, Alistipes genus, Oscillibacter ( Oscillibacter ) Genus, Odoribacter ( Odoribacter ), Ruminiclostridium ( Ruminiclostridium ), and Bifidobacterium ( Bifidobacterium ) showed a significant difference between the two groups with a p-value of 0.05 or less, and the risk of obesity and showed a negative correlation. Therefore, agents capable of detecting the microorganisms can be used to predict the risk of obesity. The microorganisms to be detected by the composition, that is, the genus Alistifes, the genus Oscillibacter, the genus Odoribacter, the genus Rumini Clostridium, and the genus Bifidobacterium are all intestinal microorganisms, compared to the normal weight group, in the obese group. , Specifically, it shows a lower detection frequency in stool samples, and the risk of obesity can be predicted by measuring the levels in these samples. On the other hand, subspecies of the Alistipes genus include, for example, Alistipes indistinctus , Alistipes finegoldii , Alistipes putredinis , and the like, , Subspecies of the genus Oscillibacter include, for example, Oscillibacter ruminantium , Oscillibacter valericigenes , and the like, and subspecies of the genus Odoribacter include, for example , Odoribacter denticanis , Odoribacter laneus , Odoribacter splanchnicus, and the like, and subspecies of the genus Rumini Clostridium include, for example, Lachinoclos Tridium Josui ( Ruminiclostridium josui ), Rachinoclostridium hungatei ( Ruminiclostridium hungatei ), and the like, and subspecies of the genus Bifidobacterium include, for example, Bifidobacterium bifidum ( Bifidobacterium bifidum ), Bifidobacterium There are Bifidobacterium longum , Bifidobacterium reuteri , and Bifidobacterium adolescentis .

The agent capable of detecting the microorganisms includes organic biomolecules such as proteins, nucleic acids, lipids, glycolipids, glycoproteins or sugars (monosaccharides, disaccharides, oligosaccharides, etc.) that are specifically present in the microorganisms in the sample. Detectable primers, probes, antisense oligonucleotides, aptamers, antibodies, and the like can be used.

In one embodiment, the agent capable of detecting the microorganism may be a primer capable of detecting the microorganism. Preferably, the primer specifically detects the genomic sequence (eg, 16S rRNA) of the corresponding microorganism and does not specifically bind to the genomic sequence of another microorganism.

As used herein, the term "primer" refers to a nucleic acid sequence of 7 to 50 that can form a base pair complementary to the template strand and functions as a starting point for copying the template strand. Primers are usually synthetic, but may be used from 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 is sufficiently complementary to allow hybridization with the template. Additional features may be incorporated that do not change the basic properties of the primer. Examples of additional features that can be incorporated include, but are not limited to, methylation, capping, substitution of one or more nucleic acids with cognate counterparts, and modifications between nucleic acids. As used herein, the term "16s rRNA" is an rRNA constituting the 30S subunit of prokaryotic ribosomes, and most of the base sequences are highly conserved, while high base sequence diversity appears in some sections. In particular, since there is little diversity among homologous species, but there is diversity between different species, prokaryotes can be usefully identified by comparing 16S rRNA sequences.

In one embodiment, the primers can be used to amplify the 16S rRNA sequence conserved in the microorganism, and the presence of the microorganism can be detected through whether a desired product is produced as a result of the sequence amplification. A sequence amplification method using primers may use various methods known in the art. For example, polymerase chain reaction (PCR), reverse transcription-polymerase chain reaction (RT-PCR), multiplex PCR, touchdown PCR, hot start PCR, nested PCR, Booster PCR, real-time PCR, differential display PCR (DD-PCR), rapid amplification of cDNA ends (RACE), inverse polymerase chain reaction , vectorette PCR, TAIL-PCR (thermal asymmetric interlaced PCR), ligase chain reaction, repair chain reaction, transcription-mediated amplification, self-maintained sequence replication, selective amplification of the target sequence can be used, It is not limited thereto.

In addition, the agent capable of detecting the microorganism may be an antibody, and the microorganism may be detected using an immunological method based on an antigen-antibody reaction. Analysis methods for this include Western blot, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion method, and rocket immunoelectrolysis Phosphoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, FACS (Fluorescence activated cell sorter), protein chip, etc., but are not limited thereto.

In addition, molecular and immunological methods widely used in the art may be used to detect microorganisms according to an embodiment.

When the risk of obesity is predicted using the composition for predicting the risk of obesity according to one aspect, it is possible to determine whether or not the currently maintained diet induces obesity, so that a person with a normal weight can control an appropriate diet so that he or she does not become obese. It can effectively prevent obesity.

Another aspect provides a kit for predicting obesity risk including the composition for predicting obesity.

Since the kit for predicting obesity risk includes or uses the above-described composition for predicting obesity as it is, the description of common information between the two is omitted.

In one embodiment, the kit includes not only detection agents such as primers, probes, antisense oligonucleotides, aptamers, and antibodies for detecting the microorganisms, but also one or more other component compositions suitable for the analysis method, solutions, or device may be included.

Specifically, the kit may be a RT-PCR kit, a microarray chip kit, or a protein chip kit. In one embodiment, the kit including the primer specific to the microorganism may include essential elements for performing an amplification reaction such as PCR. For example, the PCR kit includes a test tube or other appropriate container, reaction buffer, deoxynucleotides (dNTPs), enzymes such as Taq-polymerase reverse transcriptase, DNase, RNAse inhibitors, DEPC-water, sterile water, and the like. In another embodiment, the kit may be applied to a biological sample isolated from an individual, and the sample may be, for example, a stool or fecal sample. The kit includes a collecting device for sample collection, and the collecting device may be, for example, a brush, an absorbent pad, a cotton swab, a pipette, a swab, or a syringe.

Another aspect is the amplification product for one or more microorganisms selected from the group consisting of Alistifes genus, Oscillibacter genus, Odoribacter genus, Rumini Clostridium genus, and Bifidobacterium genus in a stool sample isolated from the subject. To provide an information providing method for predicting the risk of obesity, including the step of measuring the level.

Since the information providing method for predicting the risk of obesity includes or uses the above-described composition for predicting obesity as it is, the description of common information between the two is omitted.

In one embodiment, the method may predict that the risk of obesity is high when the amount of the amplification product of the microorganism in the sample is lower than that of the amplification product of the microorganism in the normal control sample.

The subject may be a vertebrate, may be a mammal, amphibian, reptile, bird, etc., may be a mammal, for example, may be a human ( Homo sapiens ). According to an embodiment, one or more microorganisms selected from the group consisting of the genus Alistifes, the genus Oscillibacter, the genus Odoribacter, the genus Rumini Clostridium, and the genus Bifidobacterium are detected from a sample obtained from an Asian. it could be

In another aspect, one or more microorganisms selected from the group consisting of Alistifes genus, Ossillibacter genus, Odoribacter genus, Rumini Clostridium genus, and Bifidobacterium genus in a biological sample isolated from the subject are treated with a test substance doing; measuring the level of microorganisms in the biological sample; And it provides a method for screening obesity treatment substances comprising the step of comparing the level of the microorganisms with a control group not treated with the test substance.

Since the method for screening the anti-obesity substance includes or uses the above-described composition for predicting obesity as it is, the description of common information between the two is omitted.

In one embodiment, the method measures the level or degree of colony formation of the microorganisms in a sample in the presence and absence of a test substance for preventing or treating obesity, respectively, compares both, and then, when the test substance is present, the number of microorganisms If the level or degree of cluster formation is further increased, the test substance may be selected as a preventive or therapeutic agent for obesity.

The test substance may include various low-molecular-weight compounds, high-molecular weight compounds, nucleic acid molecules (eg, DNA, RNA, PNA, etc.), proteins, sugars, and lipids that can be used for the prevention or treatment of obesity.

According to the composition according to one aspect, there is an effect of predicting the risk of obesity with a small amount of stool. In addition, the intestinal microbial community according to one aspect is significantly related to the degree of obesity, which can improve the accuracy of diagnosis compared to conventional studies conducted on intestinal microorganisms, and can be used as a target for the development of new drugs for preventing or treating obesity. .

1 shows the diversity of microorganisms in stool samples of an obese group and a normal weight group by principal component analysis.
Figure 2 shows the difference in microbial composition in stool samples between an obese group and a normal weight group at the phylum level.
Figure 3 shows the difference in microbial composition in stool samples between an obese group and a normal weight group at the class level.
4 is a bar graph showing microbial genera having a significant difference in abundance between groups among microorganisms in stool samples of an obese group and a normal weight group.
Figure 5a is a boxplot showing the ratio of Allisipes genus in stool samples from an obese group and a normal weight group.
Figure 5b is a boxplot showing the ratio of the genus Oscillibacter in stool samples of the obese group and the normal weight group.
Figure 5c is a boxplot showing the ratio of the genus Odoribacter in stool samples from an obese group and a normal weight group.
5D is a boxplot showing the ratio of the genus Rumini Clostridium in stool samples from an obese group and a normal weight group.
Figure 5e is a boxplot showing the ratio of the genus Bifidobacterium in stool samples from an obese group and a normal weight group.

Hereinafter, a preferred embodiment is presented to aid understanding of the present invention. However, the following examples are provided to more easily understand the present invention, and the content of the present invention is not limited by the following examples.

[Example]

Example 1. Experiment subject and sample collection

Stool samples were collected from 675 Koreans without serious diseases, of which 13 underweight, 158 overweight, and 7 missing anatomical records were excluded from this experiment. 214 adults with a BMI of 25 or more were set as the obese group, and 283 adults with a BMI within the normal range (18.5 to 23) were set as the control group (normal weight group). The BMI of the two groups showed a significant difference with P-value < 0.0001, and the average age of the obese and normal weight groups was 48 years, and there was no significant difference. Obesity categories were classified based on the WHO Asia-Pacific criteria. After collecting feces using a sterilized cotton swab, they were stored at room temperature while mixed with a preservation solution.

[Table 1]

Then, after moving the stored sample to the laboratory, genomic DNA was extracted from the sample using a stool DNA preparation kit, and the amount of DNA was quantified using Nanodrop. The extracted DNA was PCR amplified using 515F/806R primers targeting the V4 region of bacterial 16S rRNA, and the nucleotide sequence data of the amplification products were sequenced using Illumina's MiSeq.

Meanwhile, the sequences of barcode fusion primers used for amplification are shown in Table 1 below. The library was prepared by a dual indexing method, and the index sequence of 8 bp used was indicated by X in the base sequence of Table 1.

[Table 2]

Thereafter, from the sequenced nucleotide sequence data, after removing unpaired single reads, only high quality reads were filtered in consideration of the quality score. Each read pair was merged into one read, and Operational Taxonomy Unit (OTU) clustering was performed according to sequence similarity using sklearn as a sequence clustering algorithm. Subsequently, taxonomic analysis was performed by assigning bacteria with a sequence similarity of 99% or more to each OTU using SILVA 132 as a 16S rRNA taxonomy database. QIIME2 was used as a tool for overall analysis, and the microbial community diversity indicators of the obese and normal weight groups were identified and the distribution of microorganisms at the level of phylum, class, and genus was analyzed.

Example 2. Flora analysis in stool samples

(2.1) Results of Diversity Index and Principal Component Analysis

In order to identify the overall microbial patterns present in the samples of the obese and normal weight groups, first, the diversity of microbial communities was compared, and the results are shown in Table 3 below. Here, Faith_pd is phylogenetic diversity, Observed_OTUs is the number of observed Operational Taxonomic Units (OTUs), and Shannon index is species diversity.

[Table 3]

As shown in Table 3, as a result of analyzing the diversity indicators of the microbial community, it was confirmed that the normal weight group was significantly higher than the obese group in all indicators.

Next, Emperor Principal Coordinates Analysis (PCoA), principal component analysis, was performed using QIIME2.

Figure 1 shows the diversity of microorganisms in stool samples of the obese and normal weight groups by principal component analysis.

(2.2) Microbial composition analysis at the Phylum and Class level

Figure 2 shows the difference in microbial composition in stool samples between the obese group and the normal weight group at the phylum level, and Figure 3 shows the difference in microbial composition in the stool samples between the obese group and the normal weight group at the class level. will be.

As a result of checking the distribution ratio of microbial phyla detected by 1% or more for each sample, there was no significant difference in the abundance of microorganisms between each group. On the other hand, as a result of confirming the distribution ratio of microorganisms at the river level, there was a significant difference in the composition of intestinal microorganisms between the two groups. Specifically, as a result of confirming the distribution ratio of microbial classes detected at 1% or more for each sample, both groups were Bacteroidia , Clostridia , Gammaproteobacteria, and Negativicutes . , Actinobacteria ( Actinobacteria ) were abundantly present in the order of river. However, the abundance between the two groups for classes Bacteroidia, Clostridia, and Actinobacteria showed significant differences.

Example 3. Analysis of the correlation between the distribution of intestinal microorganisms and obesity in Asians

In order to more accurately determine the association between fecal flora and obesity, intestinal microflora was analyzed at the genus level. Specifically, among 578 genera of microorganisms detected in the feces of a total of 497 people including the normal weight group and the obese group, genera showing a detection frequency of 70% or more were selected and shown in Table 4.

[Table 4]

As shown in Table 4, there were a total of 32 genera detected by 70% or more in both groups, and among them, between the normal weight group and the obese group, Alistipes genus ( Alistipes ), Oscillibacter genus ( Oscillibacter ), Lombus Microorganisms of the genera Romboutsia , Sutterella , Odoribacter , Ruminiclostridium , Prevotella , and Bifidobacterium have a p-value It was confirmed that a significant difference was shown at 0.05 or less.

4 is a bar graph showing microbial genera showing significant differences in abundance between groups among microorganisms in stool samples of an obese group and a normal weight group. As shown in FIG. 4, in the case of the Alistipes genus, which showed a detection rate of about 90% in both groups, the normal weight group showed an abundance of 2.66% and the obese group showed an abundance of 1.72%.

5A to 5E are boxplots showing differences in the relative abundance of microorganisms in stool samples of an obese group and a normal weight group. As shown in Figures 5a to 5e, Alistipes genus, Oscillibacter genus, Odoribacter genus, Ruminiclostridium genus, Bifidobacterium genus case , compared to the normal weight group, it was confirmed that it was not present in relative abundance in the obese group.

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting.

<110> GENINUS Inc. <120> Composition for predicting obesity by evaluating the level of intestinal microorganism colony, and use thereof <130> PN134763KR <160> 2 <170> KoPatentIn 3.0 <210> 1 <211> 71 <212> DNA <213> artificial sequence <220> <223> forward primer 515F <220> <221> variation <222> (33)..(40) <223> n is 8bp index sequence <400> 1 aatgatacgg cgaccaccga gatctacacg ctnnnnnnnn tatggtaatt gtgtgycagc 60 mgccgcggta a 71 <210> 2 <211> 64 <212> DNA <213> artificial sequence <220> <223> reverse primer 806R <220> <221> variation <222> (25)..(32) <223> n is 8bp index sequence <400> 2 caagcagaag acggcatacg agatnnnnnn nnagtcagcc agccggacta cnvgggtwtc 60 taat 64

Claims (11)

Odoribacter ( Odoribacter ) A composition for predicting obesity risk comprising an agent capable of detecting microorganisms, wherein the composition is applied to a stool sample of Asians, a composition for predicting obesity risk. The method according to claim 1, wherein the composition is Alistipes genus, Oscillibacter genus, Ruminiclostridium genus, and Bifidobacterium genus One or more selected from the group consisting of genus Bifidobacterium A composition for predicting obesity risk further comprising an agent capable of detecting microorganisms. The method according to claim 1, wherein the agent capable of detecting the microorganism is a primer, a probe, an antisense oligonucleotide, an aptamer or an antibody specific to microorganisms, obesity risk prediction composition. The composition according to claim 3, wherein the primer is a primer capable of amplifying 16s rRNA of the microorganism. The method according to claim 1, wherein the composition is applied to a stool sample, obesity risk prediction composition. A kit for predicting the risk of obesity comprising the composition for predicting the risk of obesity of claim 1. The kit for predicting obesity risk according to claim 6, wherein the kit is an RT-PCR kit, a microarray chip kit, or a protein chip kit. An information providing method for predicting the risk of obesity, comprising measuring the level of an amplification product for a microorganism of the genus Odoribacter in a stool sample isolated from an individual, wherein the individual is Asian. The method according to claim 8, further comprising the step of measuring the level of an amplification product for one or more microorganisms selected from the group consisting of genus Alistifes, genus Oscillibacter, genus Ruminiclostridium, and genus Bifidobacterium A method for providing information to predict the risk of obesity. The method according to claim 8, wherein the step of measuring the level of the amplification product further comprises calculating a relative ratio of all microorganisms in the stool sample. The method according to claim 8, wherein the risk of obesity is predicted to be high when the level of the amplification product for the microorganism is lower than that of the normal control group.

Images