Ribosome biogenesis mediates the translational increase of non-optimal codon transcripts during IFN stimulation
Bibliographic record
Abstract
Abstract The interferon response is a signaling pathway unique to vertebrates that links the innate and adaptive immune responses. Interferons signal through a cascade of factors including the JAK-STAT pathway to induce the transcription of hundreds of interferon-stimulated genes (ISGs). Although the main interferon signal transduction pathways and ISGs have been elucidated, translational regulation of ISG transcripts is not fully understood. Prior work demonstrated that ribosomal protein RPL28 negatively regulates a subset of ISGs; however, we find that this effect may be due to a reduction in overall ribosome availability. Multi-omics analysis of RNA-seq and LC-MS/MS data reveal proteins, including several ISGs, that are translationally up-regulated in IFN-β-stimulated cells depleted of ribosome biogeneis factor BOP1. Analysis of codon usage demonstrates a significant reduction in codon optimality for proteins that are translationally up-regulated during BOP1 knockdown and IFN-β stimulation. Using reporter constructs, we demonstrate that codon non-optimal reporters are translated more than codon-optimized reporters in BOP1-depleted IFN-β cells. We propose that ribosome biogenesis regulates translational fine-tuning of integral protein production to ensure optimal interferon responses.
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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".