Fragile X-related Protein FXR1P Regulates Proinflammatory Cytokine Tumor Necrosis Factor Expression at the Post-transcriptional Level
Bibliographic record
Abstract
Tumor necrosis factor (TNF) is regulated post-transcriptionally by the AU-rich element (ARE) within the 3'-untranslated region of its mRNA. This regulation modulates translational efficacy and mRNA stability. By using a cRNA probe containing the TNF ARE sequence, we screened a macrophage protein expression library and identified FXR1P. Macrophages that we generated from FXR1 knock-out mice had enhanced TNF protein production compared with wild type macrophages following activation. Expression of several other proteins that are regulated by ARE sequences was also affected by FXR1P deficiency. A GFP-ARE reporter that has green fluorescent protein (GFP) expression under control of the 3'-untranslated region of TNF mRNA had enhanced expression in transfected macrophages deficient in FXR1P. Finally, we found that the ablation of FXR1P led to a dramatically enhanced association of the TNF mRNA with polyribosomes demonstrating the important role of FXR1P in the post-transcriptional regulation of TNF expression. Our data suggest that release of this repression by FXR1P occurs during lipopolysaccharide-induced macrophage activation. Finally, complementation of the knock-out macrophages with recombinant FXR1P resulted in decreased TNF protein production, supporting our findings that FXR1P operates as a repressor of TNF translation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".