Large scale investigation in yeast, to identify novel gene(s) involved in mRNA non-stop decay pathway
Bibliographic record
Abstract
Nonstop decay is an mRNA degradation pathway involved in identifying and eliminating transcripts that lack an in frame stop codon.Nonstop mRNAs are identified at the first round of translation when the ribosome reaches 3' mRNA and stalls and subsequently recruits other factors involved in NSD machinery for mRNA degradation.This process keeps the cell safe from possible harmful of the truncated proteins.Compared to other RNA degradation pathways, very little is known about the NSD mechanism.In order to identify novel genes involved in NSD, we first performed a large-scale analysis in Saccharomyces cerevisiae, and identified 68 gene candidates.From these results we picked three helicases, NAM7, ECM32, and SKI2 to further investigate their role in the NSD process.Spot test and colony count assay confirmed the role of selected candidates in NSD.The abundance of the nonstop mRNA was then evaluated using qRT-PCR method, and it was confirmed that the deletions of the selected candidates had no significant effect on nonstop mRNA at the transcriptional level when compared to the wildtype strain.Negative genetic interaction revealed the association between candidate genes and translational regulation genes.The results of this study confirm the role of candidates in NSD but further research to characterize these novel candidates is needed. List of abbreviations μLMicro liters oC Degrees Celsius
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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 | 0.001 |
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".