Reactive Lipid Species from Cyclooxygenase-2 Inactivate Tumor Suppressor LKB1/STK11
Bibliographic record
Abstract
LKB1, a unique serine/threonine kinase tumor suppressor, modulates anabolic and catabolic homeostasis, cell proliferation, and organ polarity. Chemically reactive lipids, e.g. cyclopentenone prostaglandins, formed a covalent adduct with LKB1 in MCF-7 and RKO cells. Site-directed mutagenesis implicated Cys210 in the LKB1 activation loop as the residue modified. Notably, ERK, JNK, and AKT serine/threonine kinases with leucine or methionine, instead of cysteine, in their activation loop did not form a covalent lipid adduct. 4-Hydroxy-2-nonenal, 4-oxo-2-nonenal, and cyclopentenone prostaglandin A and J, which all contain alpha,beta-unsaturated carbonyls, inhibited the AMP-kinase kinase activity of cellular LKB1. In turn, this attenuated signals throughout the LKB1 --> AMP kinase pathway and disrupted its restraint of ribosomal S6 kinases. The electrophilic beta-carbon in these lipids appears to be critical for inhibition because unreactive lipids, e.g. PGB1, PGE2, PGF2alpha, and TxB2, did not inhibit LKB1 activity (p > 0.05). Ectopic expression of cyclooxygenase-2 and endogenous biosynthesis of eicosanoids also inhibited LKB1 activity in MCF-7 cells. Our results suggested a molecular mechanism whereby chronic inflammation or oxidative stress may confer risk for hypertrophic or neoplastic diseases. Moreover, chemical inactivation of LKB1 may interfere with its physiological antagonism of signals from growth factors, insulin, and oncogenes.
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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) | 0.002 | 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".