Characterization of gut microbiota profiles by disease activity in patients with Crohn's disease using data mining analysis of terminal restriction fragment length polymorphisms.

Abstract

The gut microbiota plays a significant role in the pathogenesis of Crohn’s disease (CD). In this study, we analyzed the disease activity and associated fecal microbiota profiles in 160 CD patients and 121 healthy individuals. Fecal samples from the CD patients were collected during three different clinical phases, the active (n=66), remission-achieved (n=51) and remission-maintained (n=43) phases. Terminal restriction fragment length polymorphism (T-RFLP) and data mining analysis using the Classification and Regression Tree (C&RT) approach were performed. Data mining provided a decision tree that clearly identified the various subject groups (nodes). The majority of the healthy individuals were divided into Node-5 and Node-8. Healthy subjects comprised 99% of Node-5 (91 of 92) and 84% of Node-8 (21 of 25 subjects). Node-3 was characterized by CD (136 of 160 CD subjects) and was divided into Node-6 and Node-7. Node-6 (n=103) was characterized by subjects in the active phase (n=48; 46%) and remission-achieved phase (n=39; 38%) and Node-7 was characterized by the remission-maintained phase (21 of 37 subjects; 57%). Finally, Node-6 was divided into Node-9 and Node-10. Node-9 (n=78) was characterized by subjects in the active phase (n=43; 55%) and Node-10 (n=25) was characterized by subjects in the remission-maintained phase (n=16; 64%). Differences in the gut microbiota associated with disease activity of CD patients were identified. Thus, data mining analysis appears to be an ideal tool for the characterization of the gut microbiota in inflammatory bowel disease.