The Antiproliferative Effect of EPA in HL60 Cells is Mediated by Alterations in Calcium Homeostasis
Bibliographic record
Abstract
Studies show that n-3 polyunsaturated fatty acids (PUFA) inhibit proliferation and induce apoptosis in cancer cells. Recent reports indicate that this effect is due to activation of the unfolded protein response (UPR). However, what causes this activation has been unclear. We examined the effects of eicosapentaenoic acid (EPA) on the human leukemia cell line HL60 and the econazole (Ec) resistant HL60 clone E2R2. Ec depletes Ca(2+) from the ER and blocks Ca(2+) influx in mammalian cells, leading to activation of the UPR and apoptosis. EPA inhibited growth of HL60 cells strongly, while E2R2 cells were much less affected. Gene expression analysis of HL60 cells revealed extensive changes in transcripts related to the ER homeostasis, Ca(2+)-homeostasis and cell cycle/apoptosis. Protein levels of phosphorylated eIF2alpha, a selective translation inhibitor and UPR hallmark, activating transcription factor 4 (ATF4) and sequestosome-1 were moderately increased, whereas the cell cycle/progression protein cyclin D1 was decreased in HL60. In contrast, EPA concentrations that strongly inhibited and caused activation of the UPR in HL60 cells had no effect on the expression level of these UPR markers in E2R2 cells. Given that the only known difference between these cells is Ec-resistance, our results strongly suggest that the inhibitory effect of EPA on HL60 cells is initially meditated through alterations of the Ca(2+)-homeostasis followed by activation of the UPR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.001 |
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".