Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis.

BACKGROUND: Personalized medicine requires finding relationships between variables that influence a patient's phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn's disease. RESULTS: We present here a method to identify interactions based on canonical correlation analysis. We show that the model is the most important factor to identify relationships between blocks using a dataset of Crohn's disease patients with longitudinal sampling. First the analysis was tested in two previously published datasets: a glioma and a Crohn's disease and ulcerative colitis dataset where we describe how to select the optimum parameters. Using such parameters, we analyzed our Crohn's disease data set. We selected the model with the highest inner average variance explained to identify relationships between transcriptome, gut microbiome and clinically relevant variables. Adding the clinically relevant variables improved the average variance explained by the model compared to multiple co-inertia analysis. CONCLUSIONS: The methodology described herein provides a general framework for identifying interactions between sets of omic data and clinically relevant variables. Following this method, we found genes and microorganisms that were related to each other independently of the model, while others were specific to the model used. Thus, model selection proved crucial to finding the existing relationships in multi-omics datasets.

Dissecting Common and Unique Effects of Anti-α4ß7 and Anti-Tumor Necrosis Factor Treatment in Ulcerative Colitis.

BACKGROUND AND AIMS: Vedolizumab is an anti-α4ß7 antibody approved for the treatment of ulcerative colitis [UC]. Although it is assumed that vedolizumab blocks intestinal homing of lymphocytes, its effects on different intestinal cell populations are not fully stablished. In order to establish the unique mechanisms of action of vedolizumab in UC patients, we compared its effects to those induced by anti-tumour necrosis factor [TNF]. METHODS: Patients with active UC [endoscopic Mayo score >1] starting vedolizumab [n = 33] or anti-TNF [n = 45] and controls [n = 22] were included. Colon biopsies [at weeks 0, 14 and 46] and blood samples [at weeks 0, 2, 6, 14, 30 and 46] were used for cell phenotyping, transcriptional analysis [qPCR], and to measure receptor occupancy. RESULTS: Vedolizumab, in contrast to anti-TNF, significantly reduced the proportion of α4ß7+ cells within intestinal T subsets while preserving the percentage of α4ß7+ plasma cells. The marked decrease in α4ß7 did not change the percentage of colonic αEß7+ cells [at 46 weeks]. Both vedolizumab and anti-TNF significantly downregulated inflammation-related genes in the colon of responders [Mayo score < 2]. Moreover, both treatments significantly decreased the percentage of intestinal, but not blood, total lymphocytes [T and plasma cells], as well as the proportion of α4ß1+ cells within intestinal T lymphocytes. CONCLUSIONS: Our data show that while vedolizumab and anti-TNF block two unrelated targets, they induce remarkably similar effects. On the other hand, vedolizumab's unique mechanism of action relies on blocking intestinal trafficking of α4ß7 T cells, despite effectively binding to B and plasma cells that express α4ß7.

Intestinal Inflammation Modulates the Epithelial Response to Butyrate in Patients With Inflammatory Bowel Disease.

BACKGROUND: Butyrate-producing gut bacteria are reduced in patients with active inflammatory bowel disease (IBD), supporting the hypothesis that butyrate supplementation may be beneficial in this setting. Nonetheless, earlier studies suggest that the oxidation of butyrate in IBD patients is altered. We propose that inflammation may decrease epithelial butyrate consumption. METHODS: Non-IBD controls and IBD patients were recruited for the study. Stool samples were used for short-chain fatty acid and bacterial butyryl CoA:acetate CoA-transferase quantification. Colonic biopsies and ex vivo differentiated epithelial organoids (d-EpOCs) treated with butyrate and/or tumor necrosis factor alpha (TNFα) were used for analyzing the expression of transporters MCT1 and ABCG2, metabolic enzyme ACADS, and butyrate receptor GPR43, and for butyrate metabolism and consumption assays. RESULTS: We observed that lower stool content of butyrate-producing bacteria in active IBD patients did not correlate with decreased butyrate concentrations. Indeed, the intestinal epithelial expression of MCT1, ABCG2, ACADS, and GPR43 was altered in active IBD patients. Nonetheless, d-EpOCs derived from IBD patients showed SLC16A1 (gene encoding for MCT1 protein), ABCG2, ACADS, and GPR43 expression levels comparable to controls. Moreover, IBD- and non-IBD-derived d-EpOCs responded similarly to butyrate, as assessed by transcriptional regulation. TNFα significantly altered SLC16A1, ABCG2, and GPR43 transcription in d-EpOCs, mimicking the expression profile observed in biopsies from active IBD patients and resulting in reduced butyrate consumption. CONCLUSIONS: We provide evidence that the response to butyrate is not intrinsically altered in IBD patients. However, TNFα renders the epithelium less responsive to this metabolite, defeating the purpose of butyrate supplementation during active inflammation.