ABCA1 Protein Enhances Toll-like Receptor 4 (TLR4)-stimulated Interleukin-10 (IL-10) Secretion through Protein Kinase A (PKA) Activation
Bibliographic record
Abstract
BACKGROUND: ABCA1 is known to suppress proinflammatory cytokines. RESULTS: ABCA1 activates PKA and up-regulates anti-inflammatory cytokine IL-10. Elevated PKA transforms macrophages to M2-like phenotype. Disrupting lipid rafts by statins MCD, and filipin recuperates ABCA1 phenotype and likely functions downstream of ABCA1. CONCLUSION: By modulating cholesterol, ABCA1 activates PKA. This generates M2-like macrophages. SIGNIFICANCE: ABCA1 does not simply suppress inflammatory response. It promotes M2-like activation and facilitates resolution. Nonresolving inflammatory response from macrophages is a major characteristic of atherosclerosis. Macrophage ABCA1 has been previously shown to suppress the secretion of proinflammatory cytokine. In the present study, we demonstrate that ABCA1 also promotes the secretion of IL-10, an anti-inflammatory cytokine critical for inflammation resolution. ABCA1(+/+) bone marrow-derived macrophages secrete more IL-10 but less proinflammatory cytokines than ABCA1(-/-) bone marrow-derived macrophages, similar to alternatively activated (M2) macrophages. We present evidence that ABCA1 activates PKA and that this elevated PKA activity contributes to M2-like inflammatory response from ABCA1(+/+) bone marrow-derived macrophages. Furthermore, cholesterol lowering by statins, methyl-β-cyclodextrin, or filipin also activates PKA and, consequently, transforms macrophages toward M2-like phenotype. Conversely, cholesterol enrichment suppresses PKA activity and promotes M1-like inflammatory response. As the primary function of ABCA1 is cholesterol removal, our results suggest that ABCA1 activates PKA by regulating cholesterol. Indeed, forced cholesterol enrichment in ABCA1-expressing macrophages suppresses PKA activation and elicits M1-like response. Collectively, these findings reveal a novel protective process by ABCA1-activated PKA in macrophages. They also suggest cholesterol lowering in extra-hepatic tissues by statins as an anti-inflammation strategy.
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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) | 0.003 | 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".