Oxidative tyrosylation of high density lipoproteins impairs cholesterol efflux from mouse J774 macrophages: role of scavenger receptors, classes A and B
Bibliographic record
Abstract
Studies were designed to test whether tyrosylation of high-density lipoprotein (HDL(T)) modifies its metabolic features. HDL(T) was less effective than native HDL in promoting cholesterol efflux from J774-AI macrophages. Cell association with fluorescent HDL(T)-apolipoprotein and the uptake of HDL(T)-[(3)H]cholesteryl hexadecyl ether were enhanced by 50% in comparison with native HDL. In addition, neutral cholesterol ester hydrolase (nCEH) activity in J774-AI, which controls the hydrolysis of cholesteryl ester stores to provide free cholesterol for cellular release, declined in the presence of HDL(T). In vitro displacement experiments revealed the ability of HDL(T) to compete with oxidized and acetylated LDL, known as ligands of scavenger receptor (SR) class B type I/II. Similarly, treatment with a blocking antibody to SR-BI/II reduced the cell association of HDL(T) and native HDL by 50%. The addition of polyinosinic acid, an inhibitor of SR class A, reduced the cell association of HDL(T) without affecting that of native HDL. These findings provide evidence that HDL(T) can compete with modified LDL, bind SR-BI/BII and internalize cholesterol ester. Furthermore, the impaired capacity of HDL(T) in promoting cholesterol efflux from J774-AI was accompanied by diminished nCEH and enhanced recognition by SR-AI/II, which appears to involve the transport of cholesterol into cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".