4E-BP–Dependent Translational Control of <i>Irf8</i> Mediates Adipose Tissue Macrophage Inflammatory Response
Bibliographic record
Abstract
Abstract Deregulation of mRNA translation engenders many human disorders, including obesity, neurodegenerative diseases, and cancer, and is associated with pathogen infections. The role of eIF4E-dependent translational control in macrophage inflammatory responses in vivo is largely unexplored. In this study, we investigated the involvement of the translation inhibitors eIF4E-binding proteins (4E-BPs) in the regulation of macrophage inflammatory responses in vitro and in vivo. We show that the lack of 4E-BPs exacerbates inflammatory polarization of bone marrow–derived macrophages and that 4E-BP–null adipose tissue macrophages display enhanced inflammatory gene expression following exposure to a high-fat diet (HFD). The exaggerated inflammatory response in HFD-fed 4E-BP–null mice coincides with significantly higher weight gain, higher Irf8 mRNA translation, and increased expression of IRF8 in adipose tissue compared with wild-type mice. Thus, 4E-BP–dependent translational control limits, in part, the proinflammatory response during HFD. These data underscore the activity of the 4E-BP–IRF8 axis as a paramount regulatory mechanism of proinflammatory responses in adipose tissue 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) | 0.000 | 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 | 0.001 |
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".