Membrane plasmalogen composition and cellular cholesterol regulation: a structure activity study
Bibliographic record
Abstract
BACKGROUND: Disrupted cholesterol regulation leading to increased circulating and membrane cholesterol levels is implicated in many age-related chronic diseases such as cardiovascular disease (CVD), Alzheimer's disease (AD), and cancer. In vitro and ex vivo cellular plasmalogen deficiency models have been shown to exhibit impaired intra- and extra-cellular processing of cholesterol. Furthermore, depleted brain plasmalogens have been implicated in AD and serum plasmalogen deficiencies have been linked to AD, CVD, and cancer. RESULTS: Using plasmalogen deficient (NRel-4) and plasmalogen sufficient (HEK293) cells we investigated the effect of species-dependent plasmalogen restoration/augmentation on membrane cholesterol processing. The results of these studies indicate that the esterification of cholesterol is dependent upon the amount of polyunsaturated fatty acid (PUFA)-containing ethanolamine plasmalogen (PlsEtn) present in the membrane. We further elucidate that the concentration-dependent increase in esterified cholesterol observed with PUFA-PlsEtn was due to a concentration-dependent increase in sterol-O-acyltransferase-1 (SOAT1) levels, an observation not reproduced by 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase inhibition. CONCLUSION: The present study describes a novel mechanism of cholesterol regulation that is consistent with clinical and epidemiological studies of cholesterol, aging and disease. Specifically, the present study describes how selective membrane PUFA-PlsEtn enhancement can be achieved using 1-alkyl-2-PUFA glycerols and through this action reduce levels of total and free cholesterol in cells.
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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".