Interferon‐gamma signals via an ERK1/2‐ARF6 pathway to promote bacterial internalization by gut epithelia
Bibliographic record
Abstract
The barrier function of the epithelium lining the intestine is essential for health by preventing the free passage of colonic bacteria into the mucosa. Epithelia treated with interferon (IFN)-γ display increased bacteria transcytosis. Much is known of how IFNγ affects the tight junction and paracellular permeability, yet its role in modifying transcellular traffic of commensal bacteria remains poorly understood. Using immunoblotting, ELISA and immunolocalization, IFNγ was found to activate extracellular regulated kinase (ERK)1/2 in the human colon-like T84 epithelial cell line. Pharmacological inhibition of MEK/ERK1/2 signalling with U0126 significantly inhibited IFNγ-induced increases in the transcytosis of non-invasive Escherichia coli (strain HB101). IFNγ treatment enhanced epithelial internalization of E. coli, some of which subsequently escaped the enterocyte. Molecular analyses revealed that ERK1/2 inhibition prevented activation of the ADP-ribosylation factor (ARF)-6, a protein associated with endocytosis, and that siRNA knock-down of ARF6 expression reduced IFNγ-induced E. coli internalization into T84 cells. None of these interventions affected the drop in transepithelial resistance caused by IFNγ. Thus, increased transcellular passage may be a major component of IFNγ-induced increases in epithelial permeability, and ERK1/2 and ARF6 are presented as important molecules in IFNγ-evoked transcytosis of bacteria across gut epithelia.
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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) | 0.002 | 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".