Macrophage-activating lipopeptide-2 downregulates the expression of ATP-binding cassette transporter A1 by activating the TLR2/NF-кB/ZNF202 pathway in THP-1 macrophages
Bibliographic record
Abstract
Macrophage-activating lipopeptide-2 (MALP-2) has been shown to promote the development of atherosclerosis. ATP-binding cassette transporter A1 (ABCA1), a transmembrane protein, plays a critical role in mediating cholesterol export from macrophages to apolipoprotein A-I (apoA-I). However, whether MALP-2 can regulate the expression of ABCA1 is still largely unknown. The aim of this study was to explore the effects of MALP-2 on ABCA1 expression in THP-1 macrophages and the underlying mechanisms. Our results showed that the treatment of cells with MALP-2 decreased ABCA1 level and suppressed cholesterol efflux in both concentration- and time-dependent manners. The contents of intracellular cholesterol were significantly increased in the presence of MALP-2. Moreover, MALP-2-mediated inhibition of ABCA1 expression was abolished by siRNA of either Toll-like receptor 2 (TLR2) or nuclear factor κB (NF-κB). A similar effect was produced by treatment with the NF-κB inhibitor pyrrolidine dithiocarbamate. In addition, MALP-2-induced activation of NF-κB markedly increased zinc finger protein 202 (ZNF202) level, and ZNF202 siRNA impaired the effects of MALP-2 on ABCA1 expression. Taken together, these results suggest that MALP-2 can decrease ABCA1 expression and subsequent cholesterol efflux through activation of the TLR2/NF-κB/ZNF202 signaling pathway in THP-1 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) | 0.001 | 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".