KR102478205B1 - Method of providing information for diagnosis of upper gastrointestinal tract involvement in patients with crohn’s disease - Google Patents

Abstract

The present invention relates to a method for diagnosing upper gastrointestinal tract invasion in Crohn's disease by identifying differences in the taxonomic composition of microorganisms in the intestinal microbiota of patients with Crohn's disease.

Description

Method for providing information for diagnosis of upper gastrointestinal involvement in patients with Crohn's disease

The present invention relates to a method for diagnosing upper gastrointestinal invasion in Crohn's disease patients by identifying differences in the taxonomic composition of microorganisms in the intestinal microbiota of Crohn's disease patients.

Crohn's disease (CD) is a heterogeneous disorder with a multifactorial etiology, such as genetics, host immune system, environmental factors and the gut microbiome, and is characterized by chronic relapsing transmural inflammation that can affect the gastrointestinal tract. Crohn's disease can occur in any part of the gastrointestinal tract from the mouth to the perianal region, but most often occurs in the terminal ileum and right colon. Previous studies have reported upper gastrointestinal (UGI) tract involvement in 16-34% of adults and 26-54% of children with CD. UGI tract involvement in CD presents complications such as strictures and fistula phenotypes, recurrence, and possible risks of hospitalization and surgery.

Therefore, the European Crohn's and Colitis Organization consensus guideline recommends that UGI endoscopy and radiology, such as MRI, computed tomography, and small bowel capsule endoscopy, be performed in all CD patients suspected of UGI tract involvement. However, since UGI tract invasion lacks specific clinical symptoms, it is difficult to diagnose CD patients only by methods such as imaging.

Chronic inflammation in CD patients induced by the microbiota is related to altered host and microbiome interactions and microbial imbalances. Currently, the human microbiome, including the entire microbial complement associated with the human host, is considered important in relation to biomarkers (Pascal, V. et al. (2017). A microbial signature for Crohn's disease. Gut 66, 813- 822; Douglas, G. et al. (2018). Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn's disease. Microbiome 6:13).

The inventors of the present invention completed the present invention by comparing metagenome profiles of CD patients with or without UGI tract involvement at the time of diagnosis and identifying metagenome biomarkers capable of predicting its onset.

Accordingly, an object of the present invention is to provide a method for providing information for diagnosing upper gastrointestinal invasion in Crohn's disease patients by confirming changes in microbial levels from tissue samples obtained from individuals, biomarkers, and compositions for upper gastrointestinal invasion in Crohn's disease patients. is to provide

In order to solve the above problems, the present invention analyzes the 16S rRNA genetic information of the intestinal microbiota from tissue samples obtained from individuals, and Dorea formicigenerans, Ruminococcus torques, Rumi Species of Ruminococcus gnavus, Roseburia faecis, Faecalibacterium prausnitzii, Bacteroides caccae and Bacteroides uniformis Checking the level of one or more microorganisms selected from the group consisting of; And comparing the microbial level of the step with the microbial level of the Crohn's disease patient, wherein, when the microbial level is higher than that of the Crohn's disease patient, it is determined that there is an upper gastrointestinal tract invasion, in the Crohn's disease patient A method for providing information for diagnosing gastrointestinal invasion is provided.

In addition, the present invention is a biomarker for diagnosing upper gastrointestinal invasion in patients with Crohn's disease, including one or more microorganisms selected from the group consisting of microorganisms containing 16S rRNA having 95% or more sequence homology with the nucleotide sequences of SEQ ID NOs: 1 to 8 provides

In addition, the present invention provides a composition for diagnosing upper gastrointestinal tract invasion in patients with Crohn's disease, comprising an agent for detecting a biomarker according to the present invention.

The present invention can diagnose invasion of the upper gastrointestinal tract of a patient with Crohn's disease by performing a microbiological examination on tissue obtained from a colonoscopy, and it is possible to confirm invasion of the upper gastrointestinal tract immediately upon diagnosis of Crohn's disease, so it is simple and accurate without risks such as radiation exposure. can be diagnosed In addition, it is more suitable for disease development and diagnosis than fecal samples and can provide accurate diagnosis results.

Figure 1 shows (A) analysis results of alpha diversity predicted by the Chao 1 estimator, PD entire tree, and observed species in L4 versus nonL4 groups; and (B) the beta diversity analysis results measured by weighted-UniFrac distances.
Figure 2 shows the results of principal coordinates analysis (PCoA) based on (A) unweighted and (B) weighted UniFrac distance: nonL4 is indicated in blue and L4 is indicated in red (ANOSIM: R = -0.010, P = 0.477 ;R = -0.043, P = 0.898).
Figure 3 shows the taxonomic distribution of the gut microbiome and genus-level of the top 22 genera in L4 versus nonL4 groups.
Figure 4 is a histogram of computer-analyzed LDA scores for features with different abundances in the L4 (red) and nonL4 (green) groups. Horizontal bars represent effector size in each taxon. The length of the bar means the log10 transformed LDA score and is represented by a vertical dotted line. Thresholds in logarithmic LDA scores for discriminating traits were set at 1.0. Taxa of bacteria with statistically significant changes (P < 0.05) in relative abundance are listed alongside horizontal lines. The names of the taxon levels are c-class; o-order; f-family; g-genus; and s-species.
Figure 5 is a heat map showing the occurrence of markers by DiTaxa analysis between L4 and nonL4 groups. The data were grouped based on taxonomic marker placement, with columns representing each group and grouped firstly based on their phenotype and secondly based on their pattern similarity.
Figure 6 shows (A) ROC curves for the combined model calculated from the linear SVM and RF model, (B) the number of features that optimize the performance of the model, and (C) features ranked according to their contribution to classification accuracy. is shown. Traits were ranked by their frequencies selected as classifiers. The color box on the right shows the relative abundance ratio of the corresponding element in each group (Group label: nonL4 = 0, L4 = 1).
7 is a predicted diagram of the altered KEGG pathway using the PICRUSt2 assay. A total of five KEGG pathways were significantly altered in the L4 group compared to the nonL4 group; The bar plot on the left shows the average ratio of each KEGG pathway. The dot plot on the right shows the difference in mean proportion between the two groups using P-values.
Figure 8 shows the functional annotation of the predicted species in the two pathways shown in MetaCyc (blue solid line: general response, gray solid line: spontaneous or absent response, green solid line: expected response step in the species). As each reaction step in the pathway is not numerous for Ruminococcus torques species in the database, likely mechanisms involved in interactions between the gut microbiota may affect UGI tract invasion in CD.

Hereinafter, the present invention will be described in detail as an embodiment of the present invention with reference to the accompanying drawings. However, the following embodiments are presented as examples of the present invention, and if it is determined that detailed descriptions of well-known techniques or configurations may unnecessarily obscure the gist of the present invention, the detailed descriptions may be omitted. , the present invention is not limited thereby. Various modifications and applications of the present invention are possible within the scope of the claims described below and equivalents interpreted therefrom.

In addition, the terms used in this specification (terminology) are terms used to appropriately express preferred embodiments of the present invention, which may vary according to the intention of a user or operator or customs in the field to which the present invention belongs. Therefore, definitions of these terms will have to be made based on the content throughout this specification. Throughout the specification, when a certain component is said to "include", it means that it may further include other components without excluding other components unless otherwise stated.

All technical terms used in the present invention, unless defined otherwise, are used with the same meaning as commonly understood by one of ordinary skill in the art related to the present invention. In addition, although preferred methods or samples are described in this specification, those similar or equivalent thereto are also included in the scope of the present invention. The contents of all publications incorporated herein by reference are incorporated herein by reference.

The present invention analyzes the 16S rRNA genetic information of the gut microbiome from tissue samples obtained from individuals to determine Dorea formicigenerans, Ruminococcus torques, and Ruminococcus gnavus ), at least one selected from the group consisting of Roseburia faecis, Faecalibacterium prausnitzii, Bacteroides caccae and Bacteroides uniformis species checking the level of microorganisms; And comparing the microbial level of the step with the microbial level of the Crohn's disease patient, wherein, when the microbial level is higher than that of the Crohn's disease patient, it is determined that there is an upper gastrointestinal tract invasion, in the Crohn's disease patient It relates to a method for providing information for diagnosing gastrointestinal invasion.

The microorganism may be Ruminococcus torques, but is not limited thereto.

The tissue sample may be gastrointestinal tissue obtained from colonoscopy, but is not limited thereto. In particular, when diagnosing Crohn's disease through a colonoscopy, invasion of the upper gastrointestinal tract can be confirmed immediately through a biopsy, so no additional examination is required.

In addition to the above microorganisms, the age of the subject may also be a diagnostic criterion. For example, if the age of the subject is 16 years or younger, the risk of upper gastrointestinal invasion may increase, but is not limited thereto.

In addition, the present invention is a biomarker for diagnosing upper gastrointestinal invasion in patients with Crohn's disease, including one or more microorganisms selected from the group consisting of microorganisms containing 16S rRNA having 95% or more sequence homology with the nucleotide sequences of SEQ ID NOs: 1 to 8 It is about.

The microorganisms are Dorea formicigenerans, Ruminococcus torques, Ruminococcus gnavus, Roseburia faecis, Faecalibacterium prausnitzii, Bacteroi It may be one or more microorganisms selected from the group consisting of Bacteroides caccae and Bacteroides uniformis species, for example, it may be Ruminococcus torques, but is not limited thereto. don't

In addition, the present invention relates to a composition for diagnosing upper gastrointestinal invasion in a patient with Crohn's disease, including an agent for detecting a biomarker according to the present invention.

The present invention will be described in more detail through the following examples. However, the following examples are only for specifying the content of the present invention, and the present invention is not limited thereto.

Example 1

Data Sources and Courses

We used data available through the Inflammatory Bowel Disease Multi'omics Database (http://ibdmdb.org), which provides the most comprehensive description of host and microbial activity in inflammatory bowel disease. Tissue samples collected during the initial screening colonoscopy at diagnosis were collected according to standard procedures and the V4 region of the 16S rRNA gene was PCR amplified for sequencing and transferred to the MiSeq platform (Illumina) (for detailed protocols see http://ibdmdb.org/protocols) sequenced. Subjects were divided into two groups: “nonL4” and “L4” CD patients with UGI tract involvement in disease severity.

community analysis

The obtained raw data were analyzed using Quantitative Insights Into Microbial Ecology (QIIME) version 1.9.0, which is a software that performs microbial community analysis and taxonomic classification of microbial genomes. After assigning sequences to operational taxonomic units (OTUs) with a 97% similarity threshold, UCLUST was selected for the most recent version of the Greengenes OTU database, which is a closed reference table. For diversity, samples were normalized so that all samples could be compared. The alpha diversity of the OTU library was described using Chao1, PD whole tree, and observed species and compared using Student's t-test. Distance matrices were constructed from the entire community phylogenetic tree using Unweighted and Weighted UniFrac algorithms in QIIME. Significant differences between predefined groups were analyzed using the corresponding Global-R statistics and One-way Analysis of similarities (ANOSIM) with 999 permutations.

Biomarker detection and functional analysis

To determine candidate biomarker OTUs, linear discriminant analysis effect size (LEfSe) analysis was performed with linear discriminant analysis (LDA) score titers >1.0 for features with significantly different abundances between groups. were detected.

In addition, for sequence phenotyping and biomarker detection, 16S rRNA reads were split into the most frequent variable-length subsequences to construct sequences based on 16S rRNA data processing using DiTaka software instead of the standard OUT-clustering method. A predictive model was created using linear support vector machine (SVM) and random forest classifier (RF) algorithms, and the significance of all variables was calculated and evaluated. Microbial content was estimated from each sample data using PICRUSt2, the data were functionally annotated, and the results were further analyzed with statistical analysis of taxonomic and functional profiles (STAMP v2.1.1) software. To investigate the metabolic network of the predicted organism, we used the MetaCyc database (https://MetaCyc.org), which contains data on chemical compounds, reactions, enzymes, and metabolic pathways that have been experimentally verified or reported in the scientific literature. Statistical analysis was performed using R version 3.5.1.

Example 2.

Baseline Characterization

Of the 37 potential CD patients, 4 patients with insufficient data on the severity of the disease and 7 patients who did not receive a tissue sample at diagnosis were excluded, and the remaining 26 patients were analyzed. Patients in the L4 group were diagnosed at a younger mean age of 13.0 years (IQR 10.5 to 15.5 years) compared to the nonL4 group with a mean age of 19.0 years (IQR 14.5 to 28.0 years) ( P = 0.005), with a significant difference in male versus female The ratio was 2.3 in the L4 group and 1.2 in the nonL4 group ( P = 0.650) (Table 1). Baseline CRP and Simple endoscopic score for Crohn's disease (SES-CD) scores did not show significant differences between the two groups ( P = 0.711 and P = 0.056) (Table 1). However, CD patients with UGI tract involvement had a higher ESR than patients without UGI tract involvement ( P =0.033) (Table 1). All tissue samples were obtained from the rectum and ileum (Table 1). No patients were taking medications such as corticosteroids, immunomodulators or biologics at the time of sample collection. Detailed demographic and clinical characteristics are shown in Table 1.

Taxonomic characterization

The diversity of the gut microbiome between the two groups was analyzed and the relationship between the diversity of the gut microbiome and the degree of disease was confirmed. The alpha diversity index of Chao1, PD total tree and observed species diversity is shown in FIG. 1 . All three diversity indices were higher in nonL4 compared to L4, but there was no significant difference between the two groups ( P = 0.522, P = 0.503, and P = 0.275 for Chao1, PD total tree, and observed species diversity, respectively; Fig. One). Beta diversity was further evaluated by weighted-UniFrac analysis, showing similar bacterial communities in both groups (Fig. 1). In addition, unweighted UniFrac-based Principal Coordinate Analysis (PCoA) showed that samples were clustered by entity (ANOSIM: R = -0.010; P -value = 0.477) (FIG. 2). In addition, weighted-UniFrac PCoA analysis was performed with ANOSIM (R = -0.043; P -value = 0.898) (FIG. 2).

Bacterial abundance and distribution

We analyzed the abundance and distribution of the gut microbiome between the two groups and confirmed whether the gut microbiome abundance and distribution were associated with UGI involvement in CD patients. At the genus level, Akkermansia (0.3% vs 1.8%), Haemophilus (0.2% vs 1.7%), Oscillospira (1.0% vs 1.3%), Clostridium (0.1% vs 0.9%), Lachnospira (0.1%) in the L4 group compared to nonL4. vs 0.7%), Streptococcus (0.3% vs 0.5%), Coprococcus (0.4% vs 0.5%), Ruminococcus [f__Ruminococcaceae] (0.3% vs 0.4%), Coprobacillus (0.0% vs 0.2%), Eubacterium [f__Erysipelotrichaceae] (0.0 % vs 0.2%), fewer bacteria of Epulopiscium (0.0% vs 0.2%), Selenomonas (0.0% vs 0.1%), Anaerococcus (0.0% vs 0.1%), and Desulfovibrio (0.0% vs 0.1%), and fewer Bacteroides ( 34.9% vs 33.0%), Faecalibacterium (13.7% vs 11.1%), Ruminococcus [f__Lachnospiraceae] (7.4% vs 5.4%), Prevotella (3.5% vs 0.3%), Fusobacterium (3.1% vs 2.6%), Sutterella (2.4%) vs 2.2%), Blautia (1.3% vs 0.8%), Veillonella (1.2% vs 1.1%), Dorea (0.7% vs 0.5%), Bilophila (0.6% vs 0.3%), Phascolarctobacterium (0.3% vs 0.1%), There were more bacteria of Eikenella (0.2% vs 0.1%), and Catenibacteriumwere (0.1% vs 0.0%) (FIG. 3).

Metagenome biomarker discovery

LEfSe analysis found that there was a significant difference in community composition between the two groups. As can be seen in Figure 4, the microbial composition also showed a significant difference at the order level between the groups. Pasteurellales ( P = 0.042), Sphingomonadales ( P = 0.045), Campylobacterales ( P = 0.024), and Clostridiales ( P = 0.043) showed relatively higher abundance in nonL4 (Fig. 4). Patients in the nonL4 group had members of the class Epsilonproteobacteria ( P = 0.024) and the family Campylobacteraceae ( P = 0.024), which were significantly more prominent than in the L4 group (FIG. 4). In addition, there were 7 genera that were significantly different, including Campylobacter ( P = 0.024), Prevotella ( P = 0.034), Clostridium ( P = 0.043), Coprobacillus ( P = 0.015), Slackia ( P = 0.015), and many in the nonL4 group. 0.034), and Lachnospira ( P = 0.015), and many Limnohabitans in the L4 group ( P = 0.034) (FIG. 4). At the species level, significantly more Haemophilus parainfluenza were detected in patients in the nonL4 group ( P = 0.028), and more Ruminococcus torques were detected in the L4 group ( P = 0.015) (FIG. 4 ).

Comparative taxonomic images of differentially expressed markers detected for DiTaxa and a common workflow in samples from CD patients with UGI tract involvement versus samples from patients without UGI tract involvement are shown in FIG. 5 . The taxa predicted by DiTaxa analysis of samples from CD patients with UGI tract involvement versus samples from CD patients without UGI tract involvement were the highly related Ruminococcus faecis, Coprocococcus comes (Coprococcus comes), Dorea formicigenerans, Ruminococcus torques, CCMM_s, Eubacterium hallii, Bilophila wadsworthia, Blau Blautia faecis, Ruminococcus gnavus, Alistipes putredinis, Bacteroides finegoldii, Roseburia faecis, blood Faecalibacterium prausnitzii, Roseburia inulinivorans, Lachnospira pectinoschiza, Intestinibacter bartlettii, Clostridium symbiosum (Clostridium symbiosum), Agathobacter rectalis, Roseburia intestinalis, Clostridium bolteae, Fusicatenibacter saccharivorans, Anaerostipes hadrus), Bacteroides caccae, Bacteroides uniformis, and Flavonifractor plautii (Table 2).

Among them, Dorea formicigenerans, Ruminococcus torques, Ruminococcus gnavus, Roseburia faecis, Picalibacterium prosnich (Faecalibacterium prausnitzii), Bacteroides caccae, and Bacteroides uniformis species were actually identified by the UCLUST-based method in QIIME (Table 2).

Metagenome biomarker evaluation

To further characterize the predicted values of the eight taxa identified by the LEfSe or DiTaxa methods, ROC analysis with clinical variables (age and gender at diagnosis) was performed using machine learning models (FIG. 6). When comparing the average performance of predictive models, SVM performed better: the average performance of SVM was an AUC of 0.799 or greater and an accuracy of 68.2-75.2%, while the average performance of RF was an AUC of 0.740 or less and an accuracy of 57.8-66.5%. indicated (Fig. 6). For the best-performing model architecture, the predictive performance of the linear SVM model improved when microbial features were added; However, the performance of the RF model tended to decrease. In particular, for the feature ranking method in both the linear SVM and RF models, the best two factors of Ruminococcus torques species and age at diagnosis contributed to the combined model (FIG. 6). 6 also showed the highest accuracy and increased diagnostic performance of UGI tract invasion in CD patients when the signature of Haemophilus parainfluenza species was added to the model.

A major strength of the present invention is the evaluation of candidate metagenome biomarkers for the prediction of UGI tract involvement in CD patients through several assays. In addition, patients with new-onset CD before the start of treatment were analyzed. Changes in microbiome community structure important in disease development tended to be more pronounced in new-onset and treatment-naïve patients compared to treated patients. Finally, the present invention focuses on mucosal-associated microbiome samples, confirming that they may be more suitable for disease development and diagnosis than stool samples.

Metagenome function analysis

In addition, the functional diversity of other putative metagenomes was evaluated using PICRUSt2 software. Pathways showing significant differences in average ratios between the L4 and nonL4 groups are shown in FIG. 7 . In L4, pathways involving thiazole biosynthesis II (p < 0.001), superpathway of thiamine diphosphate biosynthesis II (P = 0.010), and octane oxidation (P = 0.035) were more common, and L-methionine biosynthesis III (P = 0.038 ) and palmitate biosynthesis II (P = 0.050) appeared less (Fig. 7). To select pathways, the degree to which these pathways were related to the Ruminococcus torques species was evaluated. As can be seen in FIG. 7, two related MetaCyc pathways, L-methionine biosynthesis III and palmitate biosynthesis II, are related to Ruminococcus torques and may play important roles in intestinal integrity and barrier function. Yes (Fig. 8).

conclusion

This is the first study of a reliable metagenomic biomarker for UGI tract involvement in CD. The frequency of reported UGI tract involvement varies greatly. The main reason for the discrepancy in the prevalence of UGI tract lesions seems to be related to irregularly different diagnostic features in CD diagnoses, which is considered to be a low-reliability mapping of disease severity in clinical practice. However, there are currently no data on simple biomarkers for CD patients with UGI tract involvement.

Our main hypothesis is that, as altered microbial communities have been demonstrated to be essential components of intestinal inflammation in CD (Tamboli et al., 2004; Sartor, 2008), possible differences in taxonomic composition may contribute to UGI tract involvement in CD patients. It has the potential to be used as a surrogate biomarker for

In the present invention, differences in tissue microbial communities of CD patients were analyzed at the species level, and Dorea formicigenerans, Ruminococcus torques, Ruminococcus gnavus, Roseburia faecis, Faecalibacterium prausnitzii, Bacteroides caccae, Bacteroides uniformis, and Haemophilus parainfluenza strains It was identified as a predictive biomarker by the LEfSe or DiTaxa programs. Interestingly, a butyrate-producing bacterial species, Ruminococcus torques, was the common microorganism identified by the other two algorithms.

In addition, we checked whether the composition of the microbiome along with clinical predictors could predict whether a patient has UGI tract involvement or not using two different machine learning algorithms (linear SVM and RF). Moderate predictive performance was achieved for several features (8 taxa, age at diagnosis and sex), especially in linear SVM. In particular, the characteristics most influential in predicting the degree of disease were the level of Ruminococcus torques (positive correlation) species and the age at diagnosis. These consistent and reproducible results for Ruminococcus torques species suggest the possibility of using microbiome analysis as a screening tool to determine CD patients with high-risk UGI tract involvement.

Contrary to previous inconsistent results regarding the signature of Ruminococcus torques in fecal samples from patients with CD, most of the studies using tissue samples showed that the mucosa of Ruminococcus torques in CD patients compared to healthy individuals. (Ruminococcus torques) species were consistently shown to be present at high levels. In addition, our results showed that the abundance of Ruminococcus torques was significantly higher in CD patients with UGI tract involvement.

A role has not yet been suggested for the Ruminococcus torques species belonging to the Clostridium coccoides group/cluster XIVa in CD pathophysiology. They use MUC2, a mucin mainly secreted in the human intestine, as the sole carbon source, and have a strong gastric mucin-degrading ability, further demonstrating their adaptability to the human intestinal mucosal environment. Therefore, it has been suggested that excessive mucin degradation by these bacteria may contribute to intestinal diseases since access of luminal antigens to the intestinal immune system becomes possible (Ganesh et al., 2013).

In addition, the present invention found an inverse relationship between UGI tract invasion and age at diagnosis in CD. In the present invention, it was confirmed that the average age of patients with UGI tract involvement was significantly lower than that of patients without UGI tract involvement. This demonstrated a higher proportion of younger patients (≤16 years) in patients with UGI tract involvement (9.4% versus 17.8%, P = 0.005) compared to patients without UGI tract involvement (≤16 years) (Greuter et al. al., 2018) is consistent with previous studies. Another study observed a significant decrease in the proportion of Akkermansia sp. with lower mucolytic activity in CD patients younger than 16 years of age compared to patients with disease onset at a later age (Lopez-Siles et al., 2018). , which is consistent with the findings of the present invention.

Taken together, mucous barrier dysfunction resulting from replacement of less mucolytic bacteria such as Akkermansia spp. with highly mucolytic bacteria such as Ruminococcus torques at an early age affects the microbial community in the intestinal mucosa. It is important for the development of UGI tract involvement in CD.

To characterize the functional role of the microbiome in the phenotype, taxa were annotated with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. This analysis showed that the L-methionine biosynthesis III and palmitate biosynthesis II pathways were reduced in relation to Ruminococcus torques, and the three KEGG pathways predicted to be increased in the L4 group were found to be related to Ruminococcus torques. This suggests that there is no relation to the species. Methionine metabolism and its metabolites and palmitate metabolic pathways in CD are currently largely unknown. Methionine is known to improve intestinal mucosal and villous morphology and barrier function. It has been reported that palmitate plays an important role in preserving the gut barrier function by regulating the secretion and function of MUC2. Another recent study demonstrated that palmitate can increase MUC2 production in gut goblet cells to produce a thick mucus gel, thereby preserving the gut barrier (Benoit et al., 2015). These findings suggest that there may be a decrease in these two protective pathways through interactions between the gut cells and the microbial community, and in particular, Ruminococcus torques strains that inhibit excessive mucus degradation in the small intestine of CD patients with UGI tract involvement. This means that it can be induced.

So far, the present invention has been looked at with respect to its preferred embodiments. Those skilled in the art to which the present invention pertains will be able to understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope will be construed as being included in the present invention.

<110> University-Industry Cooperation Group of Kyung Hee University <120> METHOD OF PROVIDING INFORMATION FOR DIAGNOSIS OF UPPER GASTROINTESTINAL TRACT INVOLVEMENT IN PATIENTS WITH CROHN'S DISEASE <130> PN2007-316 <160> 27 <170> KoPatentIn 3.0 <210> 1 <211> 253 <212> DNA 213 <Dorea formicigenerans> <400> 1 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgta gacggctgtg 60 caagtctgaa gtgaaaggca tgggctcaac ctgtggactg ctttggaaac tgtgcagcta 120 gagtgtcgga gaggtaagtg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gcttactgga cgatgactga cgttgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 2 <211> 253 <212> DNA <213> Ruminococcus torques <400> 2 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgta gacggagtgg 60 caagtctgat gtgaaaaccc ggggctcaac cccgggactg cattggaaac tgttcatcta 120 gagtgctgga gaggtaagtg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gcttactgga cagtaactga cgttgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 3 <211> 253 <212> DNA <213> Ruminococcus gnavus <400> 3 tacgtagggg gcaagcgtta tccggattta ctgggtgtaa aggggagcgta gacggcatgg 60 caagccagat gtgaaagccc ggggctcaac cccgggactg catttggaac tgtcaggcta 120 gagtgtcgga gaggaaagcg gaattcctgg tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gctttctgga cgatgactga cgttgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 4 <211> 253 <212> DNA 213 <Roseburia faecis> <400> 4 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgca ggcggtgcgg 60 caagtctgat gtgaaagccc ggggctcaac cccggtactg cattggaaac tgtcgtacta 120 gagtgtcgga ggggtaagtg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gcttactgga cgataactga cgctgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 5 <211> 197 <212> DNA <213> Faecalibacterium prausnitzii <400> 5 aaggcaagtt ggaagtgaaa tccatgggct caacccatga actgctttca aaactgtttt 60 tcttgagtag tgcagaggta ggcggaattc ccggtgtagc ggtggaatgc gtagatatcg 120 ggaggaacac cagtggcgaa ggcggcctac tggggcaccaa ctgacgctga ggctcgaaag 180 tgtgggtagc aaacagg 197 <210> 6 <211> 253 <212> DNA <213> Bacteroides caccae <400> 6 tacggcggat ccgagcgtta tccggattta ttgggtttaa aggggagcgta ggcggattgt 60 taagtcagtt gtgaaagttt gcggctcaac cgtaaaattg cagttgatac tggcagtctt 120 gagtgcagta gaggtgggcg gaattcgtgg tgtagcggtg aaatgcttag atatcacgaa 180 gaactccgat tgcgaaggca gctcactgga gtgtaactga cgctgatgct cgaaagtgtg 240 ggtatcaaac agg 253 <210> 7 <211> 253 <212> DNA <213> Bacteroides caccae <400> 7 tacggaggat ccgagcgtta tccggattta ttgggtttaa aggggagcgta ggcggattgt 60 taagtcagtt gtgaaagttt gcggctcaac cgtaaaattg cagttgatac tggcagtctt 120 gagtgcagta gaggtgggcg gaattcgtgg tgtagcggtg aaatgcttag atatcacgaa 180 gaactccgat tgcggaggca gctcactgga gtgtaactga cgctgatgct cgaaagtgtg 240 ggtatcaaac agg 253 <210> 8 <211> 253 <212> DNA 213 <213> <400> 8 tacggaggat ccgagcgtta tccggattta ttgggtttaa aggggagcgta ggcggacgct 60 taagtcagtt gtgaaagttt gcggctcaac cgtaaaattg cagttgatac tgggtgtctt 120 gagtacagta gaggcaggcg gaattcgtgg tgtagcggtg aaatgcttag atatcacgaa 180 gaactccgat tgcgaaggca gcctgctgga ctgtaactga cgctgatgct cgaaagtgtg 240 ggtatcaaaa agg 253 <210> 9 <211> 253 <212> DNA <213> Ruminococcus faecis <400> 9 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgta gacggagtgg 60 caagtctggt gtgaaaaccc ggggctcaac cccgggactg cattggaaac tgtcaatcta 120 gagtaccgga gaggtaagcg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gcttactgga cggtaactga cgttgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 10 <211> 253 <212> DNA <213> Coprococcus comes <400> 10 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgta gacggctgtg 60 taagtctgaa gtgaaagccc ggggctcaac cccgggactg ctttggaaac tatgcagcta 120 gagtgtcgga gaggtaagtg gaattcccag tgtagcggtg aaatgcgtag atattgggag 180 gaacaccagt ggcgaaggcg gcttactgga cggtaactga cgttgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 11 <211> 109 <212> DNA <213> CCMMs <400> 11 tacgtaggtg gcgagcgtta tccggaatta ttgggcgtaa agaggggagca ggcggcacta 60 agggtctgtg gtgaaagatc gaagcttaac ttcggtaagc catggaaac 109 <210> 12 <211> 143 <212> DNA <213> Eubacterium hallii <400> 12 tgctcggcta gagtacagga gaggcaggcg gaattcctag tgtagcggtg aaatgcgtag 60 atattaggag gaacaccagt ggcgaaggcg gcctgctgga ctgttactga cgctgaggca 120 cgaaagcgtg gggagcaaac agg 143 <210> 13 <211> 90 <212> DNA <213> Bilophila wadsworthia <400> 13 tccgtagata tctggaggaa caccggtggc gaaggcggcc acctggacgg taactgacgc 60 tgaggtgcga aagcgtgggt agcaaacagg 90 <210> 14 <211> 118 <212> DNA 213 <br><br><br> <400> 14 tacgtagggg gcaagcgtta tccggattta ctgggtgtaa agggagcgta gacggcgcag 60 caagtctgat gtgaaaggca ggggcttaac ccctggactg cattggaaac tgctgtac 118 <210> 15 <211> 253 <212> DNA <213> Alistipes putredinis <400> 15 tacggaggat tcaagcgtta tccggattta ttgggtttaa agggtgcgta ggcggtttga 60 taagttagag gtgaaatttc ggggctcaac cctgaacgtg cctctaatac tgttgagcta 120 gagagtagtt gcggtaggcg gaatgtatgg tgtagcggtg aaatgcttag agatcataca 180 gaacaccgat tgcgaaggca gcttaccaaa ctatacctga cgttgaggca cgaaagcgtg 240 gggagcaaac agg 253 <210> 16 <211> 141 <212> DNA 213 <Bacteroides finegoldii> <400> 16 tacggaggat ccgagcgtta tccggattta ttgggtttaa aggggagcgta ggtggattgt 60 taagtcagtt gtgaaagttt gcggctcaac cgtaaaattg cagttgatac tggctgtctt 120 gagtacagta gaggtgggcg g 141 <210> 17 <211> 98 <212> DNA <213> Roseburia inulinivorans <400> 17 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgca ggcggaaggc 60 taagtctgat gtgaaagccc ggggctcaac cccggtac 98 <210> 18 <211> 124 <212> DNA <213> Lachnospira pectinoschiza <400> 18 agaggcaagt ggaattccta gtgtagcggt gaaatgcgta gatattagga ggaacaccag 60 tggcgaaggc ggcttgctgg actgtaactg acactgaggc tcgaaagcgt ggggagcaaa 120 cagg 124 <210> 19 <211> 252 <212> DNA <213> Intestinibacter bartlettii <400> 19 tacgtagggg gctagcgtta tccggattta ctgggcgtaa agggtgcgta ggcggtcttt 60 taagtcagga gtgaaaggct acggctcaac cgtagtaagc tcttgaaact ggaggacttg 120 agtgcaggag aggagagtgg aattcctagt gtagcggtga aatgcgtaga tattaggagg 180 aacaccagta gcgaaggcgg ctctctggac tgtaactgac gctgaggcac gaaagcgtgg 240 ggagcaaaca gg 252 <210> 20 <211> 253 <212> DNA <213> Roseburia inulinivorans <400> 20 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgca ggcggagggc 60 taagtctgat gtgaaagccc ggggctcaac cccggtactg cattggaaac tggtcatcta 120 gagtgtcgga ggggtaagtg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gcttactgga cgataactga cgctgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 21 <211> 143 <212> DNA <213> Clostridium symbiont <400> 21 tgtttaactg gagtgtcgga gaggtaagtg gaattcctag tgtagcggtg aaatgcgtag 60 atattaggag gaacaccagt ggcgaaggcg acttactgga cgataactga cgttgaggct 120 cgaaagcgtg gggagcaaac agg 143 <210> 22 <211> 253 <212> DNA <213> Agathobacter rectalis <400> 22 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgca ggcggtgcgg 60 caagtctgat gtgaaagccc ggggctcaac cccggtactg cattggaaac tgtcgtacta 120 gagtgtcgga ggggtaagcg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gcttactgga cgataactga cactgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 23 <211> 118 <212> DNA 213 <Roseburia intestinalis> <400> 23 tacgtatggt gcaagcgtta tccggattta ctgggtgtaa aggggagcgca ggcggtacgg 60 caagtctgat gtgaaagccc ggggctcaac cccggtactg cattggaaac tgtcggac 118 <210> 24 <211> 253 <212> DNA <213> Clostridium bolteae <400> 24 tacgtaggtg gcaagcgtta tccggattta ctgggtgtaa agggagcgta gacggcgaag 60 caagtctgaa gtgaaaaccc agggctcaac cctgggactg ctttggaaac tgttttgcta 120 gagtgtcgga gaggtaagtg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 gaacaccagt ggcgaaggcg gcttactgga cgataactga cgttgaggct cgaaagcgtg 240 gggagcaaac agg 253 <210> 25 <211> 195 <212> DNA <213> Fusicatenibacter saccharivorans <400> 25 tacgtagggg gcaagcgtta tccggattta ctgggtgtaa agggagcgta gacggcaagg 60 caagtctgat gtgaaaaccc agggcttaac cctgggactg cattggaaac tgtctggctc 120 gagtgccgga gaggtaagcg gaattcctag tgtagcggtg aaatgcgtag atattaggaa 180 gaacaccagt ggcga 195 <210> 26 <211> 181 <212> DNA <213> Anaerostipes hadrus <400> 26 tacgtagggg gcaagcgtta tccggaatta ctgggtgtaa agggtgcgta ggtggtatgg 60 caagtcagaa gtgaaaaccc agggcttaac tctgggactg cttttgaaac tgtcagactg 120 gagtgcagga gaggtaagcg gaattcctag tgtagcggtg aaatgcgtag atattaggag 180 g 181 <210> 27 <211> 81 <212> DNA <213> flavonifractor plautii <400> 27 taaagggcgt gtaggcggga ttgcaagtca gatgtgaaaa ctgggggctc aacctccagc 60 ctgcatttga aactgtagtt c 81

Claims (7)

By analyzing the 16S rRNA genetic information of the intestinal microbiome from tissue samples obtained from individuals, Dorea formicigenerans, Ruminococcus torques, Ruminococcus gnavus, and Rosé Levels of one or more microorganisms selected from the group consisting of Roseburia faecis, Faecalibacterium prausnitzii, Bacteroides caccae and Bacteroides uniformis species Checking; and
Comparing the microbial level of the step with the microbial level of the Crohn's disease patient; including,
A method of providing information for diagnosing upper gastrointestinal invasion in Crohn's disease patients, wherein the microbial level is determined to have upper gastrointestinal invasion when the level of the microorganism is higher than that of Crohn's disease patients. According to claim 1,
The method of providing information for diagnosing upper gastrointestinal invasion in patients with Crohn's disease, wherein the microorganism is Ruminococcus torques. According to claim 1,
The tissue sample is a method of providing information for diagnosing upper gastrointestinal invasion in patients with Crohn's disease, which is a gastrointestinal tissue obtained from a colonoscopy. The method of claim 1, wherein the risk of upper gastrointestinal invasion increases when the subject's age is 16 years or younger. A biomarker for diagnosing upper gastrointestinal invasion in Crohn's disease patients, including one or more microorganisms selected from the group consisting of microorganisms containing 16S rRNA having nucleotide sequences of SEQ ID NOs: 1 to 8; Crohn's disease patients, including a formulation for detecting A composition for diagnosing upper gastrointestinal tract invasion. The method of claim 5, wherein the microorganism is Dorea formicigenerans, Ruminococcus torques, Ruminococcus gnavus, Roseburia faecis, Upper gastrointestinal invasion of patients with Crohn's disease, which is one or more microorganisms selected from the group consisting of Faecalibacterium prausnitzii, Bacteroides caccae and Bacteroides uniformis species Diagnostic composition. delete

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