ApoA-1 Mimetic Peptide ELK-2A2K2E Decreases Inflammatory Factor Levels Through the ABCA1-JAK2-STAT3-TTP Axis in THP-1–Derived Macrophages
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether the apolipoprotein A-1 (apoA-1) mimetic peptide ELK-2A2K2E regulates inflammatory cytokine expression through activating the adenosine triphosphate-binding cassette transporter A1 (ABCA1)-janus kinase 2 (JAK2)-signal transducer and activator of transcription 3 (STAT3)-tristetraprolin (TTP) signaling pathway in THP-1 macrophage-derived foam cells. METHODS AND RESULTS: The cells were treated with the apoA-1 mimetic peptide ELK-2A2K2E at different concentrations (0, 20, 40, and 80 μg/mL) or incubated with ELK-2A2K2E (40 μg/mL) for different times (0, 6, 12, and 24 hours). Our results showed that the levels of the cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and monocyte chemoattractant protein-1 (MCP-1), were decreased at both concentration- and time-dependent manners. When the cells were exposed to lipopolysaccharides and actinomycin D, ELK-2A2K2E significantly decreased the mRNA stability of inflammatory cytokines at different time points (0, 30, 60, and 120 minutes) by increasing TTP expression as analyzed by real-time quantitative polymerase chain reaction. The effect of ELK-2A2K2E on TTP was obviously blocked by the inhibition of the JAK-STAT3 pathway. Furthermore, we found that ELK-2A2K2E activated the JAK-STAT3-TTP pathway through the upregulation of ABCA1 and then decreased inflammatory cytokine expression. CONCLUSIONS: ApoA-I mimetic peptide ELK-2A2K2E increases the degradation of TNF-α, IL-6, and MCP-1 mRNA and reduces the levels of inflammatory cytokines through activating the JAK2-STAT3-TTP signaling pathway that is dependent on the upregulation of ABCA1.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".