Plasminogen promotes cholesterol efflux by the ABCA1 pathway
Bibliographic record
Abstract
Using genetic and biochemical approaches, we investigated proteins that regulate macrophage cholesterol efflux capacity (CEC) and ABCA1-specific CEC (ABCA1 CEC), 2 functional assays that predict cardiovascular disease (CVD). Macrophage CEC and the concentration of HDL particles were markedly reduced in mice deficient in apolipoprotein A-I (APOA1) or apolipoprotein E (APOE) but not apolipoprotein A-IV (APOA4). ABCA1 CEC was markedly reduced in APOA1-deficient mice but was barely affected in mice deficient in APOE or APOA4. High-resolution size-exclusion chromatography of plasma produced 2 major peaks of ABCA1 CEC activity. The early-eluting peak, which coeluted with HDL, was markedly reduced in APOA1- or APOE-deficient mice. The late-eluting peak was modestly reduced in APOA1-deficient mice but little affected in APOE- or APOA4-deficient mice. Ion-exchange chromatography and shotgun proteomics suggested that plasminogen (PLG) accounted for a substantial fraction of the ABCA1 CEC activity in the peak not associated with HDL. Human PLG promoted cholesterol efflux by the ABCA1 pathway, and PLG-dependent efflux was inhibited by lipoprotein(a) [Lp(a)]. Our observations identify APOA1, APOE, and PLG as key determinants of CEC. Because PLG and Lp(a) associate with human CVD risk, interplay among the proteins might affect atherosclerosis by regulating cholesterol efflux from macrophages.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".