Interleukins 4 and 13 Increase Intestinal Epithelial Permeability by a Phosphatidylinositol 3-Kinase Pathway
Bibliographic record
Abstract
Interleukins 4 and 13 can affect their target cells by activation of signal transducer and activator of transcription 6 (STAT 6) or phosphatidylinositol 3-kinase (PI3K). We examined the signal transduction events involved in IL-4 and IL-13 regulation of epithelial paracellular permeability using T84 cells, a model human colonic epithelium. T84 cells treated with IL-4 or IL-13 displayed virtually identical dose- and time-dependent STAT 6 activation as assessed by electrophoretic mobility shift assay (EMSA) and decreases in transepithelial resistance (TER). STAT 6 DNA binding activity was maximal in nuclear extracts 30 min after exposure to IL-4 or IL-13, and TER was maximally reduced by 24 h post-treatment. Pretreatment of epithelia with transcription factor decoys (phosphorothioated DNA oligonucleotides containing the STAT 6 binding site) dramatically reduced STAT 6 activation as detected by EMSA, but did not attenuate the TER reduction by IL-4 or IL-13. In contrast, although the PI3K inhibitors wortmannin and LY294002 did not affect IL-4 or IL-13 STAT 6 activation, they significantly inhibited the ability of either cytokine to lower TER. Thus, we provide evidence for PI3K as the major proximal signaling event in IL-4 and IL-13 regulation of TER and speculate that pharmacological targeting of enterocytic PI3K activity may represent a means to manipulate epithelial permeability.
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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 | 0.000 |
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".