Negative Regulation of T Cell Proliferation and Interleukin 2 Production by the Serine Threonine Kinase Gsk-3
Bibliographic record
Abstract
Glycogen synthase kinase (GSK)-3 is a protein serine/threonine kinase that regulates differentiation and cell fate in a variety of organisms. This study examined the role of GSK-3 in antigen-specific T cell responses. Using resting T cells from P14 T cell receptor (TCR)-transgenic mice (specific for the lymphocytic choriomeningitis virus and H-2D(b)), we demonstrated that GSK-3beta was inactivated by serine phosphorylation after viral peptide-specific stimulation in vitro. To further investigate the role of GSK-3, we have generated a retroviral vector that expresses a constitutively active form of GSK-3beta that has an alanine substitution at the regulatory amino acid, serine 9 (GSK-3betaA9). Retroviral transduction of P14 TCR-transgenic bone marrow stem cells, followed by reconstitution, led to the expression of GSK-3betaA9 in bone marrow chimeric mice. T cells from chimeric mice demonstrate a reduction in proliferation and interleukin (IL)-2 production. In contrast, in vitro assays done in the presence of the GSK-3 inhibitor lithium led to dramatically prolonged T cell proliferation and increased IL-2 production. Furthermore, in the presence of lithium, we show that nuclear factor of activated T cells (NF-AT)c remains in the nucleus after antigen-specific stimulation of T cells. Together, these data demonstrate that GSK-3 negatively regulates the duration of T cell responses.
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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) | 0.001 | 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".