Widespread pre-translational regulation of the inclusion of signal peptides in human proteins
Bibliographic record
Abstract
Signal peptides (SP) are cleavable N-terminal protein motifs used co-translationally for entry of nascent polypeptides into the secretory pathway. Their co-translational cleavage prevents their extensive post-translational regulation and flexibility in their usage is made possible by the control of their inclusion at a pre-translational level. To characterize this regulation on a transcriptome scale, we analyzed the level and mechanisms of inclusion of the 3298 most likely human SP-encoding genes, 47% of which alternatively express their SP. Analysis of RNA-seq data across different normal human tissues indicates that pre-translational regulation of the SP differs depending on tissue-coverage of the gene, with alternative SP genes more likely to be widely expressed than constitutive SP genes. SP inclusion represents a new metric to measure functional gene expression and its deregulation in disease. Our analysis supports the extensive use of pre-translational regulation of SP inclusion, with functional consequences and implications for biomarker discovery.
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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 | 0.000 | 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".