Feroniellin A-induced autophagy causes apoptosis in multidrug-resistant human A549 lung cancer cells
Post-publication record
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Bibliographic record
Abstract
During the screening of natural chemicals that can reverse multidrug resistance in human A549 lung cancer cells resistant to etoposide (A549RT-eto), we discovered that Feroniellin A (FERO), a novel furanocoumarin, shows toxicity toward A549RT-eto cells in a dose- and time-dependent manner. FERO reduced the expression of NF-κB, leading to downregulation of P-glycoprotein (P-gp), encoded by MDR1, which eventually sensitized A549RT-eto cells to apoptosis. FERO specifically diminished transcription and promoter activity of MDR1 but did not inhibit the expression of other multidrug resistance genes MRP2 and BCRP. Moreover, co-administration of FERO with Bay11-7802, an inhibitor of NF-κB, accelerated apoptosis of A549RT-eto cells through decreased expression of P-gp, indicating that NF-κB is involved in multidrug resistance. Conversely, addition of Z-VAD, a pan-caspase inhibitor, blocked FERO-induced apoptosis in A549RT-eto cells but did not block downregulation of P-gp, indicating that a decrease in P-gp expression is necessary but not sufficient for FERO-induced apoptosis. Interestingly, we found that FERO also induces autophagy, which is characterized by the conversion of LC3 I to LC3 II, induction of GFP-LC3 puncta, enhanced expression of Beclin-1 and ATG5, and inactivation of mTOR. Furthermore, suppression of Beclin-1 by siRNA reduced FERO-induced apoptosis in A549RT-eto cells and activation of autophagy by rapamycin accelerated FERO-induced apoptosis, suggesting that autophagy plays an active role in FERO-induced apoptosis. Herein, we report that FERO reverses multidrug resistance in A549RT-eto cells and exerts its cytotoxic effect by induction of both autophagy and apoptosis, which suggests that FERO can be a useful anticancer drug for multidrug-resistant lung cancer.
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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".