Interferon-γ-induced increases in intestinal epithelial macromolecular permeability requires the Src kinase Fyn
Bibliographic record
Abstract
Interferon-γ (IFNγ) is an important immunoregulatory cytokine that can also decrease intestinal epithelial barrier function. Little is known about the intracellular signalling events immediately subsequent to IFNγ/IFNγ receptor interaction that mediate increases in epithelial permeability; data that could be used to ablate this effect of IFNγ while leaving its immunostimulatory effects intact. This study assessed the potential involvement of Src family kinases in IFNγ-induced increases in epithelial permeability using confluent filter-grown monolayers of the human colon-derived T84 epithelial cell line. Inhibition of Src kinase with the pharmacologic PP1 and use of Fyn kinase-specific siRNA significantly reduced IFNγ-induced increases in epithelial permeability as gauged by translocation of noninvasive E. coli (HB101 strain) and flux of horseradish peroxidase (HRP) across monolayers of T84 cells. However, the drop in transepithelial resistance elicited by IFNγ was not affected by either treatment. Immunoblotting revealed that IFNγ activated the transcription factor STAT5 in T84 cells, and immunoprecipitation studies identified an IFNγ-inducible interaction between STAT5b and the PI3K regulatory subunit p85α through formation of a complex requiring the adaptor molecule Gab2. siRNA targeting STAT5b and Gab2 reduced IFNγ-induced increases in epithelial permeability and phosphorylation of PI3K(p85α). PP1 and Fyn siRNA reduced IFNγ-induced PI3K activity (indicated by decreased phospho-Akt) and the formation of the STAT5b/PI3K(p85α) complex. Collectively, the results suggest the formation of a Fyn-dependent STAT5b/Gab2/PI3K complex that links IFNγ to PI3K signalling and the regulation of macromolecular permeability in a model enteric epithelium.
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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".