Apolipoprotein A-1 Binding Protein Inhibits Inflammatory Signaling Pathways by Binding to Apolipoprotein A-1 in THP-1 Macrophages
Bibliographic record
Abstract
BACKGROUND: It has previously been demonstrated that apolipoprotein A-1 (apoA-1) binding protein (AIBP) promotes apoA-1 binding to ATP-binding cassette transporter A1 (ABCA1) and prevents ABCA1 protein degradation so as to inhibit foam cell formation. Because apoA-1 inhibits inflammatory signaling pathways, whether AIBP has an inhibitory effect on inflammatory signaling pathways in THP-1-derived macrophages is investigated. METHODS AND RESULTS: Analysis of inflammation-related gene expression indicated that AIBP decreased lipopolysaccharide (LPS)-mediated macrophage inflammation. AIBP significantly prevented NF-κB nuclear translocation. Further, AIBP prevented the activation of mitogen-activated protein kinases (MAPKs), including p38 MAPK, extracellular-signal regulated kinase and c-Jun N-terminal kinase. AIBP decreased MyD88 expression at both mRNA and protein levels, but did not have any effect on TLR4 expression. Moreover, treatment with both AIBP and apoA-1 decreased the abundance of TLR4 in the lipid raft fraction. AIBP lacking 115-123 amino acids (∆115-123), however, did not have such effects as described for intact AIBP. In addition, knockdown of ABCA1 inhibited the effects of AIBP on inflammatory factor secretion. CONCLUSIONS: These results suggest that AIBP inhibits inflammatory signaling pathways through binding to apoA-1 and stabilizing ABCA1, and subsequent alteration of lipid rafts and TLR4 in the cell membrane.
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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 | 0.001 |
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".