Apolipoprotein AI tertiary structures determine stability and phospholipid‐binding activity of discoidal high‐density lipoprotein particles of different sizes
Bibliographic record
Abstract
Human high-density lipoprotein (HDL) plays a key role in the reverse cholesterol transport pathway that delivers excess cholesterol back to the liver for clearance. In vivo, HDL particles vary in size, shape and biological function. The discoidal HDL is a 140-240 kDa, disk-shaped intermediate of mature HDL. During mature spherical HDL formation, discoidal HDLs play a key role in loading cholesterol ester onto the HDL particles by activating the enzyme, lecithin:cholesterol acyltransferase (LCAT). One of the major problems for high-resolution structural studies of discoidal HDL is the difficulty in obtaining pure and, foremost, homogenous sample. We demonstrate here that the commonly used cholate dialysis method for discoidal HDL preparation usually contains 5-10% lipid-poor apoAI that significantly interferes with the high-resolution structural analysis of discoidal HDL using biophysical methods. Using an ultracentrifugation method, we quickly removed lipid-poor apoAI. We also purified discoidal reconstituted HDL (rHDL) into two pure discoidal HDL species of different sizes that are amendable for high-resolution structural studies. A small rHDL has a diameter of 7.6 nm, and a large rHDL has a diameter of 9.8 nm. We show that these two different sizes of discoidal HDL particles display different stability and phospholipid-binding activity. Interestingly, these property/functional differences are independent from the apoAI alpha-helical secondary structure, but are determined by the tertiary structural difference of apoAI on different discoidal rHDL particles, as evidenced by two-dimensional NMR and negative stain electron microscopy data. Our result further provides the first high-resolution NMR data, demonstrating a promise of structural determination of discoidal HDL at atomic resolution using a combination of NMR and other biophysical techniques.
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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".