Epithelial-derived interleukin-23 promotes oral mucosal immunopathology.

At mucosal surfaces, epithelial cells provide a structural barrier and an immune defense system. However, dysregulated epithelial responses can contribute to disease states. Here, we demonstrated that epithelial cell-intrinsic production of interleukin-23 (IL-23) triggers an inflammatory loop in the prevalent oral disease periodontitis. Epithelial IL-23 expression localized to areas proximal to the disease-associated microbiome and was evident in experimental models and patients with common and genetic forms of disease. Mechanistically, flagellated microbial species of the periodontitis microbiome triggered epithelial IL-23 induction in a TLR5 receptor-dependent manner. Therefore, unlike other Th17-driven diseases, non-hematopoietic-cell-derived IL-23 served as an initiator of pathogenic inflammation in periodontitis. Beyond periodontitis, analysis of publicly available datasets revealed the expression of epithelial IL-23 in settings of infection, malignancy, and autoimmunity, suggesting a broader role for epithelial-intrinsic IL-23 in human disease. Collectively, this work highlights an important role for the barrier epithelium in the induction of IL-23-mediated inflammation.

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

BACKGROUND: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.