Placental autophagy regulation by the BOK-MCL1 rheostat
Bibliographic record
Abstract
Autophagy is the catabolic degradation of cellular cytoplasmic constituents via the lysosomal pathway that physiologically elicits a primarily cytoprotective function, but can rapidly be upregulated in response to stressors thereby inducing cell death. We have reported that the balance between the BCL2 family proteins BOK and MCL1 regulates human trophoblast cell fate and its alteration toward cell death typifies preeclampsia. Here we demonstrate that BOK is a potent inducer of autophagy as shown by increased LC3B-II production, autophagosomal formation and lysosomal activation in HEK 293. In contrast, using JEG3 cells we showed that prosurvival MCL1 acts as a repressor of autophagy via an interaction with BECN1, which is abrogated by BOK. We found that MCL1-cleaved products, specifically MCL1c157, trigger autophagy while the splicing variant MCL1S has no effect. Treatment of JEG3 cells with nitric oxide donor SNP resulted in BOK-MCL1 rheostat dysregulation, favoring BOK accumulation, thereby inducing autophagy. Overexpression of MCL1 rescued oxidative stress-induced autophagy. Of clinical relevance, we report aberrant autophagy levels in the preeclamptic placenta due to impaired recruitment of BECN1 to MCL1. Our data provided the first evidence for a key role of the BOK-MCL1 system in regulating autophagy in the human placenta, whereby an adverse environment as seen in preeclampsia tilts the BOK-MCL1 balance toward the build-up of isoforms that triggers placental autophagy.
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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".