Inflammatory bowel disease (IBD) is a chronic inflammatory condition for gastrointestinal tract. There have been numerous studies to reveal dysbiosis of fecal/mucosal microbiome composition in IBD patients but actual trend of dysbiosis is strikingly different among patient's ethnicity. In this background, the investigators have composed a prospective cohort of Korean IBD patients in a large academic referral IBD center. Using an integrated multi-omics bioinformatic analysis, the investigators aim to explore gut microbial signatures along with distinct clinical/genetic features, and their potential interplay in patients with IBD.
This prospective cohort study aims to build a gut microbiome library for Korean IBD patients, and also aims to explore gut microbial signatures along with distinct clinical/genetic features and their potential interplay using the investigator's multi-omics analysis platform. After signing the informed consent, various biological samples (fecal, mucosal and blood samples) as well as comprehensive clinical data (including psychometric evaluations using patient survey) will be obtained from patients periodically. The multi-omics investigations will comprise: metagenomics (16s RNA sequencing and whole metagenomic sequecing), metabolomics, human genomics (genome-wide SNP data, whole exome and genome sequencing), transcriptomics, proteomics, epigenetics and single-cell multi-omics analysis. In vitro and in vivo study using fecal samples will be conducted. In brief, after extracting the DNA from blood samples, whole genome sequencing will be performed and data will be compared with the previously reported variances. Novel variances or incidence of specific variances will be measured. Fecal sample will be collected and microbiome composition of each study subjects will be analyzed. Fecal microbial diversity will be measured from sequencing data of 16S ribosomal RNA which is highly preserved area of genetic information amongst microorganisms. Colonic mucosal sample will be collected when routine endoscopic surveillance is planned. Sample will be collected from diseased and normal mucosal altogether for analysis. Microbial diversity will be measured from 16S RNA sequencing data of the samples. The multi-omics data will be analyzed with comprehensive clinical metadata using an integrated bioinformatics analysis platform. Clinical data include demographics, disease characteristics and variouis patient-reported outomes data (including health-related quality of life, anxiety, depression and others). Patient-reported outcomes data will be collected using validated questionnaires periodically. Data from basic analysis will be re-explore in terms of inter-individual difference, difference between disease characteristics (such as disease phenotype, type of medical treatment and serologic markers), difference between psychosocial characteristics (such as the presence of anxiety or depression and quality of life) and etc. In addition, data from basic analysis will be used as the reference for the Korean IBD patient and it will be compared with other data obtained from the "different ethnicity" or "healthy" Korean population. In conclusion, this long term, large-scale prospective study will provide a platform for studying a field of translational medicine to reveal hidden signatures of gut microbiome underlying IBD and to identify potential biological markers in IBD.
Study Type
OBSERVATIONAL
Enrollment
1,500
Kyung Hee University Hospital
Seoul, South Korea
RECRUITINGComposition and diversity of gut microbiome
Fecal and/or intestinal mucosal samples are obtained from enrolled subjects for metagenomic sequencing. After extracting fecal or mucosal DNA, microbial composition and diversity are measured from 16sRNA high-throuput sequencing or Shotgun method. Data are compared across different disease phenotypes, such as types of disease (ulcrerative colitis versus Crohn's disease), type of treatment and the presence of psychological disorders (anxiety/depression).
Time frame: Up to 10 years
Taxonomic profiling of gut microbiome
Fecal and/or intestinal mucosal samples are obtained from enrolled subjects for metagenomic sequencing. After extracting fecal or mucosal DNA, taxonomic profiles associated with inflammatory bowel diseases and different disease phenotypes are analyzed from 16sRNA high-throuput sequencing or Shotgun method.
Time frame: Up to 10 years
Finding of single nucleotide polymorphisms (SNPs)
Blood samples are obatined from enrolled subjects for genome-wide microarray. After extracting DNA, SNPs related to inflammatory bowel diseases and different disease phenotypes are explored (Genome-wide association study \[GWAS\] statistical significance threshold, P \< 5.00\*E-08).
Time frame: Up to 10 years
Finding of serologic biomarkers
Blood samples are obatined from enrolled subjects for proteomic analysis. Serologic biomakers implicating in inflammatory bowel diseases and disease phenotypes are explored.
Time frame: Up to 10 years
Correlation between host genotyping and gut microbiome
Blood samples are obatined from enrolled subjects for genome-wide microarray. After extracting DNA, SNPs related to inflammatory bowel diseases and different disease phenotypes are explored (Genome-wide association study \[GWAS\] statistical significance threshold, P \< 5.00\*E-08). Fecal and/or intestinal mucosal samples are obtained from enrolled subjects for metagenomic sequencing. After extracting fecal or mucosal DNA, taxonomic profiles associated with inflammatory bowel diseases and different disease phenotypes are analyzed from 16sRNA high-throuput sequencing or Shotgun method. Taxonomic composition of gut microbiome are compared according to the sequecing data of host genomes .
Time frame: Up to 10 years
Liquid biopsy biosignatures assessed by single cell RNA-Seq
Blood and intestinal mucosal biopsy samples are obtained from enrolled subjects, particularly those treated with biological drugs or small molecues for single cell analysis (RNA-Seq). Data obtained from single cell analysis will be compared across different time frame (for example, before versus after specific treatment) and across differnt disease phenotypes.
Time frame: Up to 10 years
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.