RNA-Based Regulation of Transcription and Translation of Aureusvirus Subgenomic mRNA1
Bibliographic record
Abstract
Cucumber leaf spot virus (CLSV) is an aureusvirus (family Tombusviridae) that has a positive-sense RNA genome encoding five proteins. During infections, CLSV transcribes two subgenomic (sg) mRNAs and the larger of the two, sg mRNA1, encodes coat protein. Here, the viral RNA sequences and structures that regulate transcription and translation of CLSV sg mRNA1 were investigated. A medium-range RNA-RNA interaction in the CLSV genome, spanning 148 nucleotides, was found to be required for the efficient transcription of sg mRNA1. Further analysis indicated that the structure formed by this interaction acted as an attenuation signal required for transcription of sg mRNA1 via a premature termination mechanism. Translation of coat protein from sg mRNA1 was determined to be facilitated by a 5'-terminal stem-loop structure in the message that resembled a tRNA anticodon stem-loop. The results from mutational analysis indicated that the 5'-terminal stem-loop mediated efficient base pairing with a 3'-cap-independent translational enhancer at the 3' end of the message, leading to efficient translation of coat protein from sg mRNA1. Comparison of the regulatory RNA structures for sg mRNA1 of CLSV to those used by the closely related tombusviruses and certain cellular RNAs revealed interesting differences and similarities that provide evolutionary and mechanistic insights into RNA-based regulatory strategies.
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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 | 0.000 |
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".