Apolipoprotein-A1 transports and regulates MMP2 in the blood
Bibliographic record
Abstract
Synthesized in the liver and intestines, apolipoprotein A1 (APOA1) transports cholesterol in high density lipoproteins from atherosclerotic lesions to the liver, protecting against atherosclerotic plaque rupture. Here, we show that proMMP2 (zymogen of matrix metalloproteinase-2) circulates associated with APOA1 in humans and APOA1-expressing mice. This is noteworthy because MMP2 is the most abundant MMP in blood, and MMPs promote atherosclerotic plaque rupture. Artificial intelligence (AlphaFold)-based modeling suggested that APOA1 and MMP2 interact; direct interactions were confirmed using five orthogonal interaction assays, showing that APOA1 binds to MMP2 catalytic and hemopexin-like domains. APOA1 inhibited MMP2 autolysis and allosterically increased MMP2 activity—an effect specifically reproduced by plasma from humans and APOA1-expressing mice but not albumin nor plasma from APOA1 knockout mice. These function-altering interactions with APOA1 may increase MMP2 bioavailability and lay the foundation for future research on how apolipoproteins and MMPs influence atherosclerotic plaque rupture, independently of cholesterol transport. APOA1 may protect against atherosclerotic plaque rupture by removing cholesterol from plaques and proteases such as MMP2 promote rupture. Here, the authors show that APOA1 interacts with MMP2 in a way which may affect rupture independently of cholesterol.
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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) | 0.001 | 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".