Scientific ReportsMicrobiologyPeer-reviewed journal article
Pro-inflammatory Pathways Contribute to Pathogenesis of Clostridioides difficile Infection in a Murine Model - A Spatial Transcriptomics Study
Niloufar Ghahari, Sanjeev Kumar NarayananiD, Ahmed AbdelKhalekiD, Deepti PillaiiD
Abstract
Clostridioides difficile (C. difficile) is a common cause of antibiotic-induced diarrhea and causes the highest number of nosocomial infections. Only two antibiotics are currently recommended for treating C. difficile infection (CDI), which may contribute to unsatisfactory treatment outcomes and an increased likelihood of recurrence. Clostridioides difficile exists as a non-pathogenic member of the human intestinal microbiome in 10-20% of the population, a phenomenon observed in mouse models after infection with bacterial spores. In this study, we aim to evaluate the difference in gene expression between symptomatic and asymptomatic mice after infection with C. difficile using spatial transcriptomics analysis. We also aim to evaluate the spatial aspect of altered genes between different layers of intestinal mucosa (superficial vs deep) and identify the key pathways. Formalin-fixed paraffin-embedded (FFPE) intestinal sections were utilized for analysis using NanoString platform to evaluate differential gene expressions in the caecum and colon. The IL-17 pathway, including Lcn2, Cxcl2, and S100a8 genes, was significantly upregulated in symptomatic mice. The IL-17 signaling pathway activated downstream signaling through NF-{kappa}B and MAPK pathways. Gene expression was significantly altered between the intestinal superficial and deep mucosal layers, highlighting layer-specific differences in gene expression patterns in the intestines of symptomatic and asymptomatic mice. Gene expression patterns in the enteric mucosa explain several clinical signs and lesions in CDI mice.
- organism: mouse
- assay: spatial transcriptomics
- research focus: host-pathogen & immune response
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 1
- FWCI
- 0.67
- Field percentile
- top 30%
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