Activation of p53 in anoxic freshwater crayfish, <i>Faxonius virilis</i>
Bibliographic record
Abstract
Tumor suppressing transcription factor p53 regulates multiple pathways including DNA repair, cell survival, apoptosis and autophagy. Here, we studied the stress-induced activation of p53 in anoxic crayfish (Faxonius virilis). Relative levels of target proteins and mRNAs involved in the DNA damage response were measured in normoxic control and anoxic hepatopancreas and tail muscle. Phosphorylation levels of p53 were assessed using immunoblotting at sites known to be phosphorylated (serine 15 and 37) in response to DNA damage or reduced oxygen signaling. The capacity for DNA binding by phosphorylated p53 (p-p53) was also measured, followed by transcript analysis of a potentially pro-apoptotic downstream target, the etoposide induced (ei24) gene. Following this, both inhibitor (MDM2) and activator (p19-ARF) protein levels in response to low-oxygen stress were studied. The results showed an increase in p-p53 levels during anoxia in both hepatopancreas and tail muscle. Increased transcript levels of ei24 support the activation of p53 under anoxic stress. Cytoplasmic accumulation of Ser15 phosphorylated p53 was observed during anoxia when proteins from cytoplasmic and nuclear fractions were measured. Increased cytoplasmic concentration is known to initiate an apoptotic response, which can be assumed as a preparatory step to prevent autophagy. The results suggest that p53 might play a protective role in crayfish defense against low-oxygen stress. Understanding how anoxia-tolerant organisms are able to protect themselves against DNA damage could provide important clues towards survival under metabolic rate depression and preparation for recovery to minimize damage.
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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".