An Enriched Mixture of Trans-10,Cis-12-CLA Inhibits Linoleic Acid Metabolism and PGE2 Synthesis in MDA-MB-231 Cells
Bibliographic record
Abstract
Conjugated linoleic acid (CLA) isomers are potent inhibitors of mammary tumor cell growth. Evidence suggests that CLA modulates essential fatty acid (EFA) metabolism; however, it is not clear which parts of this pathway are important regulatory points modulated by CLA. Enriched mixtures of D9-cis,11-trans (D9c,11t)- and D10-trans,12-cis (D10t,12c)-18:2 were used to assess outcome measures of EFA metabolism pertaining to membrane phospholipid incorporation, tumor cell growth, and prostaglandin E2 (PGE2) synthesis in the MDA-MB-231 mammary tumor cell line. Tumor cells were treated with linoleic acid (LA), an equal mixture (Mix), or enriched preparations of D9c,11t- or D10t,12c-18:2. Treatment with Mix or the enriched mixture of D10t,12c-18:2 significantly inhibited the synthesis of arachidonic acid (AA) from LA, resulting in increased levels of LA and decreased levels of AA in membrane phosphatidylcholine and phosphatidylethanolamine (P < 0.05). LA and AA levels were not altered in cells treated with enriched D9c,11t-18:2 and were similar to those in LA control treated cells. All CLA treatments reduced [3H]thymidine uptake, an indicator of tumor cell growth, by more than one-half relative to LA controls. MDA-MB-231 cells challenged with AA in the presence of all CLA mixtures resulted in significantly reduced PGE2 synthesis relative to controls treated with LA (P < 0.05). It is evident that individual isomers exert inhibitory effects at specific steps of EFA metabolism, which correspondingly leads to a reduction in PGE2 synthesis and, ultimately, tumor growth.
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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".