BTK modulates p73 activity to induce apoptosis independently of p53
Bibliographic record
Abstract
Bruton's tyrosine kinase (BTK) is a key component of B cell receptor signalling. Because of this, BTK plays an important role in cell proliferation and survival in various B cell malignancies. However, in certain contexts, BTK can also have tumour suppressor functions. We have previously shown that BTK activates the p53 transcriptional activity by binding to and phosphorylating p53, as well as acting on MDM2 to reduce its inhibitory effects. This results in increased p53 functions, including enhanced cell death. Here, we report that BTK can also induce cell death and increase responses to DNA damage independently of p53. This is concomitant to the induction of p21, PUMA and MDM2, which are classic target genes of the p53 family of proteins. Our results show that these p53-independent effects of BTK are mediated through p73. Similar to what we observed in the p53 pathway, BTK can upregulate p73 after DNA damage and induce expression of its target genes, suggesting that BTK is a modulator of p73 functions and in the absence of p53. This effect allows BTK to have pro-apoptotic functions independently of its effects on the p53 pathway and thus play an important role in the DNA damage-related induction of apoptosis in the absence of p53. This provides a novel role of BTK in tumour suppression and contributes to the understanding of its complex pleiotropic functions.
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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) | 0.002 | 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".