A glycosylated antitumor ether lipid kills cells via paraptosis-like cell death
Bibliographic record
Abstract
Glycosylated antitumor ether lipids (GAELs) have superior anticancer properties relative to the alkyllysophospholipid class, but there have been no studies of the mechanisms of these compounds. The prototype GAEL, 1-O-hexadecyl-2-O-methyl-3-O-(2'-amino-2'-deoxy-beta-D-glucopyranosyl)-sn-glycerol (Gln), effectively killed mouse embryonic fibroblasts (MEFs) lacking key molecules involved in caspase-dependent apoptosis, and cell death was not prevented by caspase inhibitors. Gln did not cause a loss of mitochondrial membrane potential, even in rounded-up dying cells. Gln stimulated the appearance and accumulation of LC3-II, a protein marker for autophagy, in a variety of cells, including wild-type MEFs, but not in MEFs lacking ATG5, a key protein required for autophagy. Gln induced LC3 puncta formation in Chinese hamster ovary cells stably expressing a LC3-green fluorescent protein fusion protein. Thus, Gln appears to induce autophagy. Autophagy was mTOR-independent and was not inhibited by 3-methyladenine or wortmannin. Although Gln is toxic, cellular ability to undergo autophagy was not essential for its toxicity. Furthermore, the GAEL analog 2-deoxy-C-Glc induced LC3 puncta formation but did not kill the cells. Gln, but not 2-deoxy-C-Glc, caused the accumulation of cytoplasmic acidic vacuoles in the cells. Our data suggest that GAELs may activate autophagy; however, GAELs do not kill cells by apoptosis or autophagy but rather by a paraptosis-like cell death mechanism.
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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".