IFN-γ drives inflammatory bowel disease pathogenesis through VE-cadherin-directed vascular barrier disruption.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder with rising incidence. Diseased tissues are heavily vascularized. Surprisingly, the pathogenic impact of the vasculature in IBD and the underlying regulatory mechanisms remain largely unknown. IFN-γ is a major cytokine in IBD pathogenesis, but in the context of the disease, it is almost exclusively its immune-modulatory and epithelial cell-directed functions that have been considered. Recent studies by our group demonstrated that IFN-γ also exerts potent effects on blood vessels. Based on these considerations, we analyzed the vessel-directed pathogenic functions of IFN-γ and found that it drives IBD pathogenesis through vascular barrier disruption. Specifically, we show that inhibition of the IFN-γ response in vessels by endothelial-specific knockout of IFN-γ receptor 2 ameliorates experimentally induced colitis in mice. IFN-γ acts pathogenic by causing a breakdown of the vascular barrier through disruption of the adherens junction protein VE-cadherin. Notably, intestinal vascular barrier dysfunction was also confirmed in human IBD patients, supporting the clinical relevance of our findings. Treatment with imatinib restored VE-cadherin/adherens junctions, inhibited vascular permeability, and significantly reduced colonic inflammation in experimental colitis. Our findings inaugurate the pathogenic impact of IFN-γ-mediated intestinal vessel activation in IBD and open new avenues for vascular-directed treatment of this disease.

Application of machine learning algorithms for multiparametric MRI-based evaluation of murine colitis.

Magnetic resonance imaging (MRI) allows non-invasive evaluation of inflammatory bowel disease (IBD) by assessing pathologically altered gut. Besides morphological changes, relaxation times and diffusion capacity of involved bowel segments can be obtained by MRI. The aim of this study was to assess the use of multiparametric MRI in the diagnosis of experimentally induced colitis in mice, and evaluate the diagnostic benefit of parameter combinations using machine learning. This study relied on colitis induction by Dextran Sodium Sulfate (DSS) and investigated the colon of mice in vivo as well as ex vivo. Receiver Operating Characteristics were used to calculate sensitivity, specificity, positive- and negative-predictive values (PPV and NPV) of these single values in detecting DSS-treatment as a reference condition. A Model Averaged Neural Network (avNNet) was trained on the multiparametric combination of the measured values, and its predictive capacity was compared to those of the single parameters using exact binomial tests. Within the in vivo subgroup (n = 19), the avNNet featured a sensitivity of 91.3% (95% CI: 86.6-96.0%), specificity of 92.3% (95% CI: 85.1-99.6%), PPV of 96.9% (94.0-99.9%) and NPV of 80.0% (95% CI: 69.9-90.1%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.003) and performing significantly worse compared to none of the single values. Within the ex vivo subgroup (n = 30), the avNNet featured a sensitivity of 87.4% (95% CI: 82.6-92.2%), specificity of 82.9% (95% CI: 76.1-89.7%), PPV of 88.9% (84.3-93.5%) and NPV of 80.8% (95% CI: 73.8-87.9%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.015), exceeded by none of the single parameters. In experimental mouse colitis, multiparametric MRI and the combination of several single measured values to an avNNet can significantly increase diagnostic accuracy compared to the single parameters alone. This pilot study will provide new avenues for the development of an MR-derived colitis score for optimized diagnosis and surveillance of inflammatory bowel disease.