Green tea polyphenol (−)-epigallocatechin gallate blocks epithelial barrier dysfunction provoked by IFN-γ but not by IL-4
Bibliographic record
Abstract
A characteristic of many enteropathies is increased epithelial permeability, a potentially pathophysiological event that can be evoked by T helper (Th)-1 (i.e., IFN-gamma) and Th2 (i.e., IL-4) cytokines and bacterial infection [e.g., enteropathogenic Escherichia coli (EPEC)]. The green tea polyphenol (-)-epigallocatechin gallate (EGCG) has immunosuppressive properties, and we hypothesized that it would ameliorate the increased epithelial permeability induced by IFN-gamma, IL-4, and/or EPEC. EGCG, but not the related epigallocatechin, completely prevented the increase in epithelial (i.e., T84 cell monolayer) permeability caused by IFN-gamma exposure as gauged by transepithelial resistance and horseradish peroxidase flux; EGCG did not alleviate the barrier disruption induced by IL-4 or EPEC. IFN-gamma-treated T84 and THP-1 (monocytic cell line) cells displayed STAT1 activation (tyrosine phosphorylation on Western blot analysis, DNA binding on EMSA) and upregulation of interferon response factor-1 mRNA, a STAT1-dependent gene. All three events were inhibited by EGCG pretreatment. Aurintricarboxylic acid also blocked IFN-gamma-induced STAT1 activation, but it did not prevent the increase in epithelial permeability. Additionally, pharmacological blockade of MAPK signaling did not affect IFN-gamma-induced epithelial barrier dysfunction. Thus, as a potential adjunct anti-inflammatory agent, EGCG can block STAT1-dependent events in gut epithelia and monocytes and prevent IFN-gamma-induced increased epithelial permeability. The latter event is both a STAT1- and MAPK-independent event.
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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 | 0.000 |
| 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".