The 3′‐untranslated region of human interleukin‐8 mRNA suppresses IL‐8 gene expression
Bibliographic record
Abstract
Although adenosine/uridine (AU)-rich sequences in the 3'-untranslated region (UTR) of the interleukin-8 (IL-8) gene have been suggested to contribute to its post-transcriptional regulation, the molecular basis whereby this occurs still needs to be understood. To investigate the role of the 3'-UTR on human IL-8 gene regulation, chimeric reporter genes were generated by adding full length or differentially deleted 3'-UTR of the IL-8 gene to chloramphenicol acetyltransferase (CAT). Addition of the entire IL-8 3'-UTR markedly reduced CAT mRNA and protein expression in COS 7 cells. In a reporter gene study, IL-8 3'-UTR destabilized CAT mRNA, which was dependent on active transcription in COS 7 cells. A 357-base sequence (nucleotides (nt) 2387-2743 of genomic DNA) within 3'-UTR, designated e, suppressed CAT gene expression by accelerating CAT mRNA turnover. A 26-base AU-rich sequence (nt 2552-2577) within e, containing four AUUUA pentamers that form two UAUUUAUU and one UUAUUUAU octamers, did not suppress CAT gene expression. However, deletion of the AU-rich sequences attenuated the inhibitory effect of e on CAT gene expression. Elimination of the first 100 bases (nt 2386-2486) attenuated the potency of fragment e, but much weaker than elimination of the first 146 bases (nt 2387-2533). This study gives new insights in unravelling the molecular mechanisms involved in the post-transcriptional regulation of the IL-8 gene.
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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) | 0.001 | 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".