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Brown, S. T.

Publications and source records attributed to Brown, S. T..

2 recordsLinked to original sources

Clostridioides difficile Toxins Alter Host Metabolic Pathway and Bile Acid Homeostasis Gene Expression In Colonic Epithelium

A major risk factor for acquiring Clostridioides difficile is antibiotic usage that disrupts a healthy microbial gut community facilitating establishment of infection. Once established, C. difficile secretes endotoxins (TcdA and TcdB) that are internalized into host colonic epithelial cells where they disrupt gut barrier function and induce hyper inflammation resulting in severe diarrhea and possibly leading to death. We employed three different platforms to explore gene expression of cells in the gut when exposed to C. difficile or its toxins, TcdA and TcdB. An antibiotic treated mouse model of C. difficile infection (CDI) was used to identify differential gene expression with a NanoString Technologies mouse inflammatory gene panel consisting of 770 genes including a subset of bile acid (BA) homeostasis and nuclear receptor genes. In the cecal tissue of mice with CDI, significant down expression was observed for genes involved in PPAR signaling, cholesterol and glucose metabolism, while a significant increase in expression was observed for IL-17 related inflammatory genes. Similarly, Caco-2 cell culture and primary human colonocytes (hCE) exposed to toxins for 24 hours showed altered expression in several PPAR regulated and cholesterol metabolic genes similar to those found in mice. These cell culture experiments also revealed significant alterations in gene expression of the FXR BA regulatory pathway. Together these data suggest that exposure to C. difficile and its toxins may alter host cholesterol metabolic processes, including BA transport and synthesis.

microbiology↗

Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses

The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MR imaging data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Here we use an open structural MR imaging dataset (ABIDE), supplemented with the Human Connectome Project (HCP), to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of 1) software tool (e.g. ANTs, CIVET, FreeSurfer), 2) cortical parcellation (DKT, Destrieux, Glasser), and 3) quality control procedure (manual, automatic). We divide our statistical analyses by 1) method type, i.e. task-free (unsupervised) versus task-driven (supervised), and 2) inference objective, i.e. neurobiological group differences versus individual prediction. Results show that software, parcellation, and quality control significantly impact task-driven neurobiological inference. Additionally, software selection strongly impacts neurobiological and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗