Proteasomal degradation of caspase‐6 in 17β‐estradiol‐treated neurons
Bibliographic record
Abstract
In primary cultures of human neurons, 17beta-estradiol (17beta-E2) prevents caspase-6-mediated cell death and induces a caspase inhibitory factor (CIF) inhibiting active caspase-6 (Csp-6) in vitro. Here, we show that treatment of neurons with 17beta-E2 results in a proteasomal-dependent but ubiquitin-independent degradation of endogenous and exogenous active Csp-6 in live neurons and in cell free assays, respectively. We further show that the proteasomal-dependent degradation of Csp-6 is not required for its inhibition. Using several protease inhibitors, we find that leupeptin, E-64, and ALLN prevent inhibition of recombinant active Csp-6 (R-Csp-6) in 17beta-E2-treated neuronal protein extracts. Because all three protease inhibitors have the ability to inhibit cysteine proteases, we believe that a cysteinyl protease activity may be required for 17beta-E2-mediated inhibition of active Csp-6. However, we exclude caspases, calpains, and cathepsins as potential cysteinyl proteases involved in the 17beta-E2-mediated Csp-6 inhibition. The results suggest that a proteolytic activity inhibited by leupeptin, E-64, and ALLN is needed to inhibit Csp-6 and that the inhibited Csp-6 is subsequently degraded by the proteasome. The mechanism of 17beta-E2-mediated inhibition of Csp-6 is different from the ubiquitin-dependent proteasomal degradation of Csp-3 and Csp-7 by XIAP and cIAP2 but consistent with the mechanism of Baculovirus p35 inhibition of caspases.
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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 | 0.001 |
| 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".