A temporal classifier predicts histopathology state and parses acute-chronic phasing in inflammatory bowel disease patients.

Previous studies have conducted time course characterization of murine colitis models through transcriptional profiling of differential expression. We characterize the transcriptional landscape of acute and chronic models of dextran sodium sulfate (DSS) and adoptive transfer (AT) colitis to derive temporal gene expression and splicing signatures in blood and colonic tissue in order to capture dynamics of colitis remission and relapse. We identify sub networks of patient-derived causal networks that are enriched in these temporal signatures to distinguish acute and chronic disease components within the broader molecular landscape of IBD. The interaction between the DSS phenotype and chronological time-point naturally defines parsimonious temporal gene expression and splicing signatures associated with acute and chronic phases disease (as opposed to ordinary time-specific differential expression/splicing). We show these expression and splicing signatures are largely orthogonal, i.e. affect different genetic bodies, and that using machine learning, signatures are predictive of histopathological measures from both blood and intestinal data in murine colitis models as well as an independent cohort of IBD patients. Through access to longitudinal multi-scale profiling from disease tissue in IBD patient cohorts, we can apply this machine learning pipeline to generation of direct patient temporal multimodal regulatory signatures for prediction of histopathological outcomes.

Long-term activation of TLR3 by poly(I:C) induces inflammation and impairs lung function in mice.

BACKGROUND: The immune mechanisms associated with infection-induced disease exacerbations in asthma and COPD are not fully understood. Toll-like receptor (TLR) 3 has an important role in recognition of double-stranded viral RNA, which leads to the production of various inflammatory mediators. Thus, an understanding of TLR3 activation should provide insight into the mechanisms underlying virus-induced exacerbations of pulmonary diseases. METHODS: TLR3 knock-out (KO) mice and C57B6 (WT) mice were intranasally administered repeated doses of the synthetic double stranded RNA analog poly(I:C). RESULTS: There was a significant increase in total cells, especially neutrophils, in BALF samples from poly(I:C)-treated mice. In addition, IL-6, CXCL10, JE, KC, mGCSF, CCL3, CCL5, and TNFalpha were up regulated. Histological analyses of the lungs revealed a cellular infiltrate in the interstitium and epithelial cell hypertrophy in small bronchioles. Associated with the pro-inflammatory effects of poly(I:C), the mice exhibited significant impairment of lung function both at baseline and in response to methacholine challenge as measured by whole body plethysmography and an invasive measure of airway resistance. Importantly, TLR3 KO mice were protected from poly(I:C)-induced changes in lung function at baseline, which correlated with milder inflammation in the lung, and significantly reduced epithelial cell hypertrophy. CONCLUSION: These findings demonstrate that TLR3 activation by poly(I:C) modulates the local inflammatory response in the lung and suggest a critical role of TLR3 activation in driving lung function impairment. Thus, TLR3 activation may be one mechanism through which viral infections contribute toward exacerbation of respiratory disease.