Rosmarinic acid inhibits Ca2+-dependent pathways of T-cell antigen receptor-mediated signaling by inhibiting the PLC-γ1 and Itk activity
Bibliographic record
Abstract
Rosmarinic acid (RosA) is a hydroxylated compound frequently found in herbal plants and is mostly responsible for anti-inflammatory and antioxidative activity. Previously, we observed that RosA inhibited T-cell antigen receptor (TCR)- induced interleukin 2 (IL-2) expression and subsequent T-cell proliferation in vitro. In this study, we investigated in detail inhibitory mechanism of RosA on TCR signaling, which ultimately activates IL-2 promoter by activating transcription factors, such as nuclear factor of activated T cells (NF-AT) and activating protein-1 (AP-1). Interestingly, RosA inhibited NF-AT activation but not AP-1, suggesting that RosA inhibits Ca(2+)-dependent signaling pathways only. Signaling events upstream of NF-AT activation, such as the generation of inositol 1,4,5-triphosphate and Ca(2+) mobilization, and tyrosine phosphorylation of phospholipase C-gamma 1 (PLC-gamma 1) were strongly inhibited by RosA. Tyrosine phosphorylation of PLC-gamma 1 is largely dependent on 3 kinds of protein tyrosine kinases (PTKs), ie, Lck, ZAP-70, and Itk. We found that RosA efficiently inhibited TCR-induced tyrosine phosphorylation and subsequent activation of Itk but did not inhibit Lck or ZAP-70. ZAP-70-dependent signaling pathways such as the tyrosine phosphorylation of LAT and SLP-76 and serine/threonine phosphorylation of mitogen-activated protein kinases (MAPKs) were intact in the presence of RosA, confirming that RosA suppresses TCR signaling in a ZAP-70-independent manner. Therefore, we conclude that RosA inhibits TCR signaling leading to Ca(2+) mobilization and NF-AT activation by blocking membrane-proximal events, specifically, the tyrosine phosphorylation of inducible T cells kinase (Itk) and PLC-gamma 1.
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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) | 0.001 | 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".