Heterogeneity in Control of mRNA Stability by AU-rich Elements
Bibliographic record
Abstract
AU-rich elements (AREs), located in the 3'-untranslated region of unstable cytokine and chemokine mRNAs, promote rapid decay of otherwise stable mRNAs and may mediate selective mRNA stabilization in response to stimulation with interleukin-1 (IL-1). AREs vary considerably, however, in both size and sequence context. To assess the heterogeneity involved in control of mRNA stability by ARE motifs, human mRNA sequences from IL-1alpha-stimulated HEK293 cells and T98G cells were screened for either instability or stability using both cDNA (950 ARE containing sequences) and Affymetrix oligonucleotide (U95Av2 GeneChip) array analysis. Although ARE-containing mRNAs exhibited a broad range of stability, IL-1alpha promoted stability in a subset of mRNAs that were unstable when transcriptionally induced by tumor necrosis factor alpha. Stabilization of granulocyte/macrophage-colony stimulating factor and IL-8 mRNAs by IL-1alpha was achieved only after 2 h of stimulation, required ongoing protein synthesis, and depended on the activation of p38 MAPK. In contrast, stabilization of Gro3 mRNA in response to IL-1alpha was achieved immediately and was insensitive to inhibitors of protein synthesis and p38 MAPK activation. In concert, these findings demonstrate that ARE sequences are functionally heterogeneous; only a subset of unstable mRNAs is sensitive to stabilization by IL-1alpha. Moreover, IL-1alpha promotes stabilization of unstable mRNAs through distinct mechanistic pathways that distinguish between specific mRNA sequences.
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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 | 0.001 |
| 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 | 0.001 | 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".