ECCO Grant Untangling the gut phagosome dynamics of the relapse/remission cycle in ulcerative colitis

Abstract

Abstract Background and Aims The course of ulcerative colitis (UC) is usually characterized by intertwining periods of remission and relapse. Most microbiome UC studies focus on the gut bacteriome. However, recent studies on animal models suggest that phage-mediated bacterial cell lysis may play a role in sustaining gut inflammation, proving the phageome (collection of bacteriophages) should not be ignored in this disease. Therefore, this project aims to: 1) characterize the gut phageome of patients with UC who suffer from frequent relapses, 2) compare temporal dynamics of gut the phageome between patients and healthy controls, 3) To model interactions between bacteriome, mycobiome, phageome on one hand, and lifestyle factors (diet, stress levels, medication) on the other 4) To infer host status (relapse versus remission of the disease) based on phageome composition, and using interactions inferred in Objective 3. Methods The cohort comprises 24 patients with UC (14 of whom suffered from relapse) and 10 healthy controls, with stool samples collected monthly for a year. Phageome profiling will be achieved via shotgun sequencing of phage genomes from the same stool samples. This dataset will be interrogated together with bacterial (16S rRNA) and fungal (ITS) amplicon data, which have just been generated. Available metadata include disease symptoms, diet, stress levels, work productivity, medication, and faecal calprotectin. Dynamic time-wrapping algorithms will be used to align time series of microbial members with similar temporal dynamics. Dynamic Bayesian Network modelling will infer interactions between microbiome members and predict future relapse. Anticipated Impact The phageome is the often neglected (“dark matter”) part of the microbiome, but has defining interactions with bacteria. We will expand knowledge on inter-species dynamics during relapse and remission cycles of UC. This will potentially lead to new microbiome-related relapse predictors, which may contribute to new prognostic tools in UC, and potentially inform therapeutic avenues.