Reduced Surface Expression of Epithelial E-Cadherin Evoked by Interferon-Gamma Is Fyn Kinase-Dependent
Bibliographic record
Abstract
Interferon gamma (IFNγ) is an important regulatory cytokine that can exert a pro-inflammatory effect in the gut, where it has been shown to increase epithelial permeability via disruption of the tight junctions. Here we investigated the potential for IFNγ to regulate the adherens junction protein E-cadherin, an important mediator of normal epithelial tissue function, using the model T84 human colonic epithelial cell line. IFNγ (10 ng/ml) stimulated increased internalization of E-cadherin as assessed by immunofluorescence microscopy; internalization was reversed when cells were treated with PP1 (125 nM), a Src kinase-selective inhibitor. Immunoprecipitation studies demonstrated loss of E-cadherin from membrane fractions following IFNγ treatment and a corresponding increase in cytosolic E-cadherin and its binding partners, p120-catenin and beta-catenin: effects that were Src-kinase dependent. E-cadherin and p120-catenin phosphorylation was increased by IFNγ treatment and siRNA studies showed this was dependent upon the Src-kinase isoform Fyn. E-cadherin ubiquitinylation and subsequent proteasomal degradation stimulated by IFNγ was found to be dependent upon Fyn and the E-cadherin-selective ubiquitin ligase, Hakai. Use of Fyn and Hakai siRNA inhibited the internalization of E-cadherin as shown by immunoblotting and confocal fluorescence microscopy. Finally, IFNγ treatment resulted in a more fragile T84 cell monolayer with increased cell detachment in response to physical stress, which was prevented by PP1 and siRNA targeting Fyn or Hakai. Collectively, these results demonstrate a Fyn kinase-dependent mechanism through which IFNγ regulates E-cadherin stability and suggest a novel mechanism of disruption of epithelial cell contact, which could contribute to perturbed epithelial barrier function.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".