Inflammatory bowel diseases (IBD), such as Crohn's disease, are chronic, immunologically mediated disorders that have severe medical consequences. The current hypothesis is that these diseases are due to an overly aggressive immune response to a subset of commensal enteric bacteria. Studies to date on IBD have suggested that the disorder may be caused by a combination of bacteria and host susceptibility;however the etiologies of these diseases remain an enigma. In this application, we propose to develop and demonstrate the ability to profile Crohn's disease at an unprecedented molecular level by elucidation of specific biomarkers (bacterial strains, genes, or proteins) that correlate to disease symptoms. To achieve this goal, we will employ a multidisciplinary approach based on metagenomic and metaproteomic molecular tools to elucidate the composition of the commensal microbiota in monozygotic twins that are either healthy or exhibit Crohn's disease (for concordant, both are diseased;for discordant, one is healthy and one is diseased). The central hypotheses of this proposal are (1) that specific members and/or functional activities of the gastrointestinal (GI) microbiota differ in patients with Crohn's disease as compared to healthy individuals, and (2) that it will be possible to elucidate microbial signatures which correlate with the occurrence and progression of this disease by integration of data obtained from 16S rRNA-based molecular fingerprinting, metagenomics, and metaproteomics approaches. To address these hypotheses, five specific aims are proposed: 1) Obtain data on community gene content (metagenome) in a subset of healthy twins and twins with Crohn's Disease to assess potential differences in the metabolic capabilities of the gut microbiota associated with CD, 2) Obtain data on community protein content (metaproteome) in a subset of healthy twins and twins with Crohn's Disease to assess the state of expressed proteins associated with CD, 3) Prospectively obtain biopsy and fecal samples from a cohort of 80 patients undergoing surgical resections for the treatment of inflammatory bowel according to HMP guidelines, 4) Extend our efforts in molecular fingerprinting, metagenomics, and metaproteomics to a larger population to further explore the relationship between changes in gut microbial community structure and function in CD. These studies will include: (i) completion of the metagenomic and metaproteomic characterization of the gut microbiota from the Swedish monozygotic twins, (ii) evaluation of gut microbiota stability over time, as measured in fecal samples from a group of individuals from the Swedish twin registry using molecular fingerprinting approaches, (iii) molecular fingerprinting and metaproteomic comparison of microbial community composition in intestinal biopsies from multiple CD patient cohorts, and (iv) molecular fingerprinting and metagenomic analysis of the less abundant members of the gut microbiota in healthy individuals and CD patients, and 5) Apply various statistical clustering and classification methods to correlate/associate microbial community composition, gene and protein content with patient metadata, including metabolite profiles and clinical phenotype. The ultimate goal of these efforts is to identify novel biomarkers for non-invasive diagnostics of CD and to eventually identify drug targets (i.e. bacterial strains) for cure or suppression of disease symptoms.
This study aims to unravel the contribution of the bacteria that normally inhabit the human gastrointestinal tract to Crohn's disease by using a multidisciplinary approach to study changes in the structure and function of gut microbial communities in three sets of patient cohorts who have Crohn's disease. These results will be compared with those obtained from the study of healthy individuals and have the potential to identify new biomarkers of disease severity, location, and progression.
| Liu, Zhenqiu; Hsiao, William; Cantarel, Brandi L et al. (2011) Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data. Bioinformatics 27:3242-9 |
