4 research outputs found

    Elevated A20 promotes TNF-induced and RIPK1-dependent intestinal epithelial cell death

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    Intestinal epithelial cell (IEC) death is a common feature of inflammatory bowel disease (IBD) that triggers inflammation by compromising barrier integrity. In many patients with IBD, epithelial damage and inflammation are TNF-dependent. Elevated TNF production in IBD is accompanied by increased expression of the TNFAIP3 gene, which encodes A20, a negative feedback regulator of NF-κB. A20 in intestinal epithelium from patients with IBD coincided with the presence of cleaved caspase-3, and A20 transgenic (Tg) mice, in which A20 is expressed from an IEC-specific promoter, were highly susceptible to TNF-induced IEC death, intestinal damage, and shock. A20-expressing intestinal organoids were also susceptible to TNF-induced death, demonstrating that enhanced TNF-induced apoptosis was a cell-autonomous property of A20. This effect was dependent on Receptor Interacting Protein Kinase 1 (RIPK1) activity, and A20 was found to associate with the Ripoptosome complex, potentiating its ability to activate caspase-8. A20-potentiated RIPK1-dependent apoptosis did not require the A20 deubiquitinase (DUB) domain and zinc finger 4 (ZnF4), which mediate NF-κB inhibition in fibroblasts, but was strictly dependent on ZnF7 and A20 dimerization. We suggest that A20 dimers bind linear ubiquitin to stabilize the Ripoptosome and potentiate its apoptosis-inducing activity

    Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in Clostridioides difficile Infection

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    Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile. Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment
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