Transcriptomic analysis of ileal tissue from Crohn's disease patients identifies extracellular matrix genes that distinguish individuals by age at diagnosis.

Crohn's disease (CD) is a debilitating gastrointestinal (GI) disorder that can impact the entirety of the GI tract. While substantial progress has been made in the medical management of CD, it remains incurable, frequently relapses, and is a significant financial and medical burden. The pathophysiology of CD is not well understood, but it is thought to arise in genetically susceptible individuals upon an environmental insult. Further elucidation of the disease etiology promises to expose additional therapeutic avenues, with the hope of reducing the burden of CD. One approach to understanding disease pathophysiology is to identify clinically relevant molecular disease subsets by using transcriptomics. In this report, we use hierarchical clustering of the ileal transcriptomes of 34 patients and identify two CD subsets. Clinically, these clusters differed in the age of the patients at CD diagnosis, suggesting that age of onset affects disease pathophysiology. The clusters were segregated by three major gene ontology categories: developmental processes, ion homeostasis, and the immune response. Of the genes constituting the immune system category, expression of extracellular matrix-associated genes, COL4A1, S100A9, ADAMTS2, SERPINE1, and FCN1, exhibits the strongest correlation with an individual's age at CD diagnosis. Together these findings demonstrate that transcriptional profiling is a powerful approach to subclassify CD patients.

Elevated Colonic Mucin Expression Correlates with Extended Time to Surgery for Ulcerative Colitis Patients.

BACKGROUND AND AIMS: Both genetic and environmental factors contribute to the development and persistence of ulcerative colitis (UC). As supported by differential responses to therapy, multiple subclasses of disease likely comprise UC. We reasoned that profiling the colonic transcriptomes may offer one approach to molecular subtype UC. METHODS: We conducted RNA-sequencing (RNA-seq) on full-thickness colonic tissues from 26 UC patients undergoing colectomy. Hierarchal clustering from transcriptomic data identified disease subsets. Subsets were characterized using differential gene expression analysis, cell type deconvolution, and network analysis. RESULTS: We identified two UC subsets that were distinguished by 957 differentially expressed genes. Cluster 1 was enriched in genes associated with intestinal epithelial cell (IEC) differentiation, while cluster 2 was enriched in genes associated with epithelial-to-mesenchymal transition (EMT) and inflammatory responses. Cluster 1 was associated with an extended time from diagnosis to colectomy [hazard ratio = 0.45 (95% CI: 0.14-0.88); p=0.03]. Of cluster 1 genes, elevated MUC5B, MUC4, and MUC2 expression displayed the strongest correlation with increased time to surgery [hazard ratio = 0.37 (95% CI: 0.11-0.61); p=0.0044]. CONCLUSIONS: Our transcriptome analysis indicates that UC can be sub-classified into at least two molecular signatures. We found that elevated mucin gene expression correlated with prolonged time to colectomy following diagnosis. This work identified MUC5B, MUC4, and MUC2 as potential prognostic indicators of disease severity, as reflected in time to surgery after diagnosis.