New Strategies to Promote Macrophage Cholesterol Efflux
Bibliographic record
Abstract
The capacity of macrophages to dispose of cholesterol deposited in the atherosclerotic plaque depends on their ability to activate cholesterol efflux pathways. To develop athero-protective therapies aimed at promoting macrophage cholesterol efflux, cholesterol metabolism in THP-1 monocyte-derived macrophages has been extensively studied, but the intrinsic sensitivity of monocytes and the lack of a standardized procedure to differentiate THP-1 monocytes into macrophages have made it difficult to utilize THP-1 macrophages in the same or similar degree of differentiation across studies. The variability has resulted in lack of understanding of how the differentiation affects cholesterol metabolism, and here we review and investigate the effects of THP-1 differentiation on cholesterol efflux. The degree of THP-1 differentiation was inversely associated with ATP binding cassette A1 (ABCA1) transporter-mediated cholesterol efflux. The differentiation-associated decrease in ABCA1-mediated cholesterol efflux occurred despite an increase in ABCA1 expression. In contrast, DSC1 expression decreased during the differentiation. DSC1 is a negative regulator of the ABCA1-mediated efflux pathway and a DSC1-targeting agent, docetaxel showed high potency and efficacy in promoting ABCA1-mediated cholesterol efflux in THP-1 macrophages. These data suggest that pharmacological targeting of DSC1 may be more effective than increasing ABCA1 expression in promoting macrophage cholesterol efflux. In summary, the comparison of THP-1 macrophage subtypes in varying degrees of differentiation provided new insights into cholesterol metabolism in macrophages and allowed us to identify a viable target DSC1 for the promotion of cholesterol efflux in differentiated macrophages. Docetaxel and other pharmacological strategies targeting DSC1 may hold significant potential for reducing atherogenic cholesterol deposition.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".