Redefining the IBDs using genome-scale molecular phenotyping.
Terrence S FureyPraveen SethupathyShehzad Z SheikhPublished in: Nature reviews. Gastroenterology & hepatology (2019)
The IBDs, Crohn's disease and ulcerative colitis, are chronic inflammatory conditions of the gastrointestinal tract resulting from an aberrant immune response to enteric microbiota in genetically susceptible individuals. Disease presentation and progression within and across IBDs, especially Crohn's disease, are highly heterogeneous in location, severity of inflammation and other phenotypes. Current clinical classifications fail to accurately predict disease course and response to therapies. Genome-wide association studies have identified >240 loci that confer risk of IBD, but the clinical utility of these findings remains unclear, and mechanisms by which the genetic variants contribute to disease are largely unknown. In the past 5 years, the profiling of genome-wide gene expression, epigenomic features and gut microbiota composition in intestinal tissue and faecal samples has uncovered distinct molecular signatures that define IBD subtypes, including within Crohn's disease and ulcerative colitis. In this Review, we summarize studies in both adult and paediatric patients that have identified different IBD subtypes, which in some cases have been associated with distinct clinical phenotypes. We posit that genome-scale molecular phenotyping in large cohorts holds great promise not only to further our understanding of the diverse molecular causes of IBD but also for improving clinical trial design to develop more personalized disease management and treatment.
Keyphrases
- genome wide
- ulcerative colitis
- gene expression
- clinical trial
- dna methylation
- emergency department
- oxidative stress
- randomized controlled trial
- intensive care unit
- newly diagnosed
- machine learning
- open label
- genome wide association
- single cell
- patient reported outcomes
- deep learning
- copy number
- case report
- artificial intelligence
- phase ii
- drug induced
- big data
- childhood cancer