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Autoimmunity and infection give rise to distict inflammatory Th17 subsets [bulk RNA-seq]

ABSTRACT: Th17 cells are a heterogeneous population that is critical for tissue homeostasis and inflammation during clearance of infections and autoimmunity. Despite substantial efforts distinguishing homeostatic and inflammatory roles of Th17 cells, the mechanism underlying the divergent functions of inflammatory Th17 cells is poorly understood. In this study, we show that the inflammatory Th17 engaged in autoimmune colitis and those involved in infection-induced colitis are two distinguishable populations illustrated by their distinct responses to a pharmacological molecule, clofazimine (CLF). Distinct from existing Th17 inhibitors, CLF selectively inhibits pro-autoimmune Th17 cells while preserving the functional state of infection-elicited Th17 cells through restricting Aldh1l2expression. Using bulk RNA sequencing, we explored transcriptional response to CLF on in vitro differentiated inflammatory and homeostatic Th17 cells. Notably, we identified a unique suppression in the transcription of signature inflammatory genes in Th17 cells with a marginal effect on those elicited by infection.

ORGANISM(S): Mus musculus

PROVIDER: GSE234593 | GEO | 2023/06/13

REPOSITORIES: GEO

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Project description:Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection This model is described in the article: Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection. Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J. PLoS ONE 2015; 10(7): e0134849 Abstract: Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions. This model is hosted on BioModels Database and identified by: BIOMD0000000583. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Autoimmunity and infection give rise to distict inflammatory Th17 subsets [bulk RNA-seq]

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