<i>Enterococcus faecium</i> inhibits NF-κB/NLRP3/Caspase-1 signaling pathway to antagonize enterotoxigenic <i>Escherichia coli</i>-mediated inflammatory response
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC) can cause intestinal inflammation and diarrhea in yaks, which has a negative impact on their economic value. In recent years, probiotics have gained increasing attention as a pure, natural, nontoxic, harmless, and residue-free additive. However, the underlying mechanisms by which probiotics safeguard against ETEC are not completely elucidated. This study aimed to investigate the protective effect of Enterococcus faecium ( E. faecium) against ETEC infection in mice through oral gavage. Morphological changes were examined through light microscopy. The expressions of inflammatory cytokines (IL-1β, IL-6, TNF-α, IL-10, NF-κB, and NLRP3), tight junction protein (ZO-1, Claudin-1), and pyroptosis (Caspase-1, Caspase-4, and gasdermin D (GSDMD)) were detected using immunohistochemistry and quantitative real-time PCR. The results indicate that ETEC infection triggers the activation of inflammation-related pathways (NF-κB) and NLRP3 inflammasome, leading to the expression of a large number of inflammatory cytokines. Additionally, the activation of NLRP3 leads to the release of GSDMD activation through Caspase-1, ultimately resulting in inflammatory injury and pyroptosis. Feeding mice E. faecium early resulted in an increase in the expression of tight junction protein, a reduction in inflammatory cytokines, and alleviation of inflammatory injury and pyroptosis in intestinal tissues. Our research indicates that E. faecium has the ability to antagonize ETEC and provide protection to the gastrointestinal mucosa in mice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".