Autophagy regulates trophoblast invasion by targeting NF-κB activity
Bibliographic record
Abstract
Preeclampsia is one of the most serious complications of pregnancy, affecting 5-10% of parturients worldwide. Recent studies have suggested that autophagy is involved in trophoblast invasion and may be associated with defective placentation underlying preeclampsia. We thus aimed to understand the mechanistic link between autophagy and trophoblast invasion. Using the two most commonly used trophoblast cell lines, JEG-3 and HTR-8/SVneo, we inhibited autophagy by ATG5 and beclin-1 shRNA. Conversion of LC3-II was evaluated in ATG5 and beclin-1 knock-down cells in the presence of the lysosomal protease inhibitors E-64d and pepstatin A, to detect the efficiency of autophagy inhibition. Upon autophagy inhibition, we measured cell invasion, activity of NF-κB and related signaling pathways, MMP-2, MMP-9, sFlt-1, and TNF-α levels. Autophagy inhibition increased the invasiveness of these trophoblastic cell lines and increased Akt and NF-κB activity as well as p65 expression. Of note, an NF-κB inhibitor significantly attenuated the trophoblast invasion induced by autophagy inhibition. Autophagy inhibition was also associated with increased MMP-2 and MMP-9 levels and decreased the production of sFlt-1 and TNF-α. Collectively, our results indicate that autophagy regulates trophoblast invasiveness in which the NF-κB pathway and MMP-2, MMP-9, sFlt-1 and TNF-α levels are affected.
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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) | 0.001 | 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".