Hydrolysis of glycerophosphocholine epoxides by human group <scp>IIA</scp>, V, and X secretory phospholipases <scp>A<sub>2</sub></scp>
Bibliographic record
Abstract
Abstract This study was prompted by recent reports that epoxyeicosatrienoic (EET) and epoxyeicosatetraenoic (EEQ) acids accelerate tumor growth and metastasis by stimulation of angiogenesis, while eicosapentaenoic (EPA) and epoxydocosapentaenoic (EDP) acids inhibit angiogenesis, tumor growth, and metastasis. Cytochrome P450 epoxygenases convert arachidonic to EET, eicosapentaenoic acid to EEQ, and docosahexaenoic acid to EDP, which are found both in free form and esterified to glycerophosphocholine (GPC). Both free and esterified epoxy (EP) acids are also formed during lipid autoxidation. For biological activity, the GPC‐EP requires hydrolysis, which we presumed could occur by sPLA2s located in proximity of lipoproteins carrying the lipid epoxides. The plasma lipoproteins were isolated by ultracentrifugation and analyzed by LC/ESI‐MS. The GPC‐EPs were identified by reference to standards and to retention times of phospholipid masses. The GPC‐EP monoepoxides (corrected for isobaric ether overlaps) in stored human LDL, HDL, HDL3, or APHDL ranged from 0 to 1 nmol/mg protein, but during 4‐h incubation at 37°C increased to 1–5 nmol/mg protein. An incubation of autoxidized LDL, HDL, or HDL3 with 1 μg/ml of group V or X sPLA2 resulted in complete hydrolysis of diacyl GPC epoxide esters. Group IIA sPLA2 at 1 μg/ml failed to produce significant hydrolysis in 4 h, but at 2.5 μg/ml in 8 h yielded almost 80% hydrolysis, which represented complete diacyl GPC‐EP hydrolysis. The present study shows that group IIA, V, and X sPLA2s are capable of extensive hydrolysis of PtdCho epoxides of autoxidized plasma lipoproteins. Therefore, all three human sPLA2s were potentially capable of inducing epoxide biological activity in vivo.
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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".