Adipocyte Enhancer-binding Protein-1 Promotes Macrophage Inflammatory Responsiveness by Up-Regulating NF-κB via IκBα Negative Regulation
Bibliographic record
Abstract
Nuclear factor kappaB (NF-kappaB) subunits comprise a family of eukaryotic transcription factors that are critically involved in cell proliferation, inflammation, and apoptosis. Under basal conditions, NF-kappaB subunits are kept under inhibitory regulation by physical interaction with NF-kappaB inhibitors (IkappaB subunits) in the cytosol. Upon stimulation, IkappaB subunits become phosphorylated, ubiquitinated, and subsequently degraded, allowing NF-kappaB subunits to translocate to the nucleus and bind as dimers to kappaB responsive elements of target genes. Previously, we have shown that AEBP1 enhances macrophage inflammatory responsiveness by inducing the expression of various proinflammatory mediators. Herein, we provide evidence suggesting that AEBP1 manifests its proinflammatory function by up-regulating NF-kappaB activity via hampering IkappaBalpha, but not IkappaBbeta, inhibitory function through protein-protein interaction mediated by the discoidin-like domain (DLD) of AEBP1. Such interaction renders IkappaBalpha susceptible to enhanced phosphorylation and degradation, subsequently leading to augmented NF-kappaB activity. Collectively, we propose a novel molecular mechanism whereby NF-kappaB activity is modulated by means of protein-protein interaction involving AEBP1 and IkappaBalpha. Moreover, our study provides a plausible mechanism explaining the differential regulatory functions exhibited by IkappaBalpha and IkappaBbeta in various cell types. We speculate that AEBP1 may serve as a potential therapeutic target for the treatment of various chronic inflammatory diseases and cancer.
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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".