The B Cell Antigen Receptor Regulates the Transcriptional Activator β-Catenin Via Protein Kinase C-Mediated Inhibition of Glycogen Synthase Kinase-3
Bibliographic record
Abstract
Beta-catenin is a transcriptional activator that is regulated by glycogen synthase kinase-3 (GSK-3). GSK-3 is constitutively active in unstimulated cells where it phosphorylates beta-catenin, targeting beta-catenin for rapid degradation. Receptor-induced inhibition of GSK-3 allows beta-catenin to accumulate in the cytoplasm and then translocate to the nucleus where it promotes the transcription of genes such as c-myc and cyclin D1. Wnt hormones, the best known regulators of beta-catenin, inhibit GSK-3 via the Disheveled protein. However, GSK-3 is also inhibited when it is phosphorylated by Akt, a downstream target of phosphatidylinositol 3-kinase (PI3K). We have previously shown that B cell Ag receptor (BCR) signaling leads to activation of PI3K and Akt as well as inhibition of GSK-3. Therefore, we hypothesized that BCR engagement would induce the accumulation of beta-catenin via a PI3K/Akt/GSK-3 pathway. We now show that BCR ligation causes an increase in the level of beta-catenin in the nuclear fraction of B cells as well as an increase in beta-catenin-dependent transcription. Direct inhibition of GSK-3 by LiCl also increased beta-catenin levels in B cells. This suggests that GSK-3 keeps beta-catenin levels low in unstimulated B cells and that BCR-induced inhibition of GSK-3 allows the accumulation of beta-catenin. Surprisingly, we found that the BCR-induced phosphorylation of GSK-3 on its negative regulatory sites, as well as the subsequent up-regulation of beta-catenin, was not mediated by Akt but by the phospholipase C-dependent activation of protein kinase C. Thus, the BCR regulates beta-catenin levels via a phospholipase C/protein kinase C/GSK-3 pathway.
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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 | 0.001 |
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".