Superantigen immune stimulation activates epithelial STAT-1 and PI 3-K: PI 3-K regulation of permeability
Bibliographic record
Abstract
Signal transducers and activators of transcription (STATs) are critical intracellular signaling molecules for many cytokines. We compared the ability of T84 epithelial cells to activate STATs in response to cytokines [interferon-gamma (IFN-gamma), interleukin (IL)-4, IL-10, and tumor necrosis factor-alpha (10 ng/ml)] and conditioned medium from superantigen [Staphylococcus aureus enterotoxin B (SEB)]-activated peripheral blood mononuclear cells (PBMC) using electrophoretic mobility shift assays (EMSA). Of the cytokines tested, only IFN-gamma caused a STAT-1 response. Exposure to SEB-PBMC-conditioned medium resulted in STAT-1 or STAT-1/3 activation, and inclusion of anti-IFN-gamma antibodies in the conditioned medium abolished the STAT-1 signal. Cells treated with transcription factor decoys, DNA oligonucleotides bearing the STAT-1 recognition motif, and then SEB-PBMC-conditioned medium displayed a reduced STAT-1 signal on EMSA, yet this treatment did not prevent the drop in transepithelial resistance (measured in Ussing chambers) caused by SEB-PBMC-conditioned medium. In contrast, the phosphatidylinositol 3'-kinase (PI 3-K) inhibitor LY-294002 significantly reduced the drop in transepithelial resistance caused by SEB-PBMC-conditioned medium. Thus data are presented showing STAT-1 (+/-STAT-3) and PI 3-K activation in epithelial cells in response to immune mediators released by superantigen immune activation. Although the involvement of STAT-1/-3 in the control of barrier function remains a possibility, PI-3K has been identified as a regulator of T84 paracellular permeability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".