Evidence of translation efficiency adaptation of the coding regions of the bacteriophage lambda
Bibliographic record
Abstract
Deciphering the way gene expression regulatory aspects are encoded in viral genomes is a challenging mission with ramifications related to all biomedical disciplines. Here, we aimed to understand how the evolution shapes the bacteriophage lambda genes by performing a high resolution analysis of ribosomal profiling data and gene expression related synonymous/silent information encoded in bacteriophage coding regions.We demonstrated evidence of selection for distinct compositions of synonymous codons in early and late viral genes related to the adaptation of translation efficiency to different bacteriophage developmental stages. Specifically, we showed that evolution of viral coding regions is driven, among others, by selection for codons with higher decoding rates; during the initial/progressive stages of infection the decoding rates in early/late genes were found to be superior to those in late/early genes, respectively. Moreover, we argued that selection for translation efficiency could be partially explained by adaptation to Escherichia coli tRNA pool and the fact that it can change during the bacteriophage life cycle.An analysis of additional aspects related to the expression of viral genes, such as mRNA folding and more complex/longer regulatory signals in the coding regions, is also reported. The reported conclusions are likely to be relevant also to additional viruses.
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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".