T Cell Activation Leads to Protein Kinase Cθ-Dependent Inhibition of TGF-β Signaling
Bibliographic record
Abstract
TGF-beta is an ubiquitous cytokine that plays a pivotal role in the maintenance of self-tolerance and prevention of immunopathologies. Under steady-state conditions, TGF-beta keeps naive T cells in a resting state and inhibits Th1 and Th2 cell differentiation. Because rapid generation of Th1 and Th2 effector cells is needed in response to pathogen invasion, how do naive T cells escape from the quiescent state maintained by TGF-beta? We hypothesized that stimulation by strong TCR agonists might interfere with TGF-beta signaling. Using both primary mouse CD4(+) T cells and human Jurkat cells, we observed that strong TCR agonists swiftly suppress TGF-beta signaling. TCR engagement leads to a rapid increase in SMAD7 levels and decreased SMAD3 phosphorylation. We present evidence that TCR signaling hinders SMAD3 activation by inducing recruitment of TGF-betaRs in lipid rafts together with inhibitory SMAD7. This effect is dependent on protein kinase C, a downstream TCR signaling intermediary, as revealed by both pharmacological inhibition and expression of dominant-negative and constitutively active protein kinase C mutants. This work broadens our understanding of the cross-talk occurring between the TCR and TGF-beta signaling pathways and reveals that strong TCR agonists can release CD4 T cells from constitutive TGF-beta signaling. We propose that this process may be of vital importance upon confrontation with microbial pathogens.
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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".