Macrophages and Epithelial Cells Mutually Interact through NLRP3 to Clear Infection and Enhance the Gastrointestinal Barrier
Bibliographic record
Abstract
Activation of the nod-like receptor protein 3 (NLRP3) leads to the release of the proinflammatory cytokine IL-1β, which then facilitates pathogen control by macrophages. The role of NLRPs in controlling infection of epithelial cells is not well understood. Our hypothesis was that activation of the NLRP3 inflammasome in colonic epithelial cells would promote macrophage-mediated epithelial recovery after infection with the pathogen Citrobacter rodentium. We devised a co-culture model using mouse colonic epithelial cells (CMT-93) and macrophages (J774A.1) during infection with C. rodentium. Inflammasome was activated using LPS and ATP and inhibited by YVAD. We assessed cytokine secretion (ELISA), macrophage recruitment and pathogen penetration (immunofluorescence), and epithelial barrier integrity (transepithelial electrical resistance). Macrophages were recruited to the apical membrane of epithelial cells, associated with tight junctions, promoted epithelial barrier recovery, and displaced C. rodentium. While NLRP3 was expressed in infected epithelial cells, IL-18 or IL-1β secretion remained unchanged. Supernatants from infected epithelial cells promoted infection clearance by macrophage; while this was inflammasome-independent, ATP significantly improved epithelial barrier recovery. The inflammasome appears to promote epithelial barrier function, independent of IL-18 and IL-1β secretion. Inflammasome activation in macrophages plays a dual role of promoting pathogen clearance and improving epithelial barrier integrity.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".