BET Protein Inhibition Regulates Macrophage Chromatin Accessibility and Microbiota-Dependent Colitis

Introduction In colitis, macrophage functionality is altered compared to normal homeostatic conditions. Loss of IL-10 signaling results in an inappropriate chronic inflammatory response to bacterial stimulation. It remains unknown if inhibition of bromodomain and extra-terminal domain (BET) proteins alters usage of DNA regulatory elements responsible for driving inflammatory gene expression. We determined if the BET inhibitor, (+)-JQ1, could suppress inflammatory activation of macrophages in Il10-/- mice. Methods We performed ATAC-seq and RNA-seq on Il10-/- bone marrow-derived macrophages (BMDMs) cultured in the presence and absence of lipopolysaccharide (LPS) with and without treatment with (+)-JQ1 and evaluated changes in chromatin accessibility and gene expression. Germ-free Il10-/- mice were treated with (+)-JQ1, colonized with fecal slurries and underwent histological and molecular evaluation 14-days post colonization. Results Treatment with (+)-JQ1 suppressed LPS-induced changes in chromatin at distal regulatory elements associated with inflammatory genes, particularly in regions that contain motifs for AP-1 and IRF transcription factors. This resulted in attenuation of inflammatory gene expression. Treatment with (+)-JQ1 in vivo resulted in a mild reduction in colitis severity as compared with vehicle-treated mice. Conclusion We identified the mechanism of action associated with a new class of compounds that may mitigate aberrant macrophage responses to bacteria in colitis

Linking gene expression to clinical outcomes in pediatric Crohn’s disease using machine learning

Abstract Pediatric Crohn’s disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD