The Role of +4U as an Extended Translation Termination Signal in Bacteria
Bibliographic record
Abstract
Abstract Termination efficiency of stop codons depends on the first 3′ flanking (+4) base in bacteria and eukaryotes. In both Escherichia coli and Saccharomyces cerevisiae, termination read-through is reduced in the presence of +4U; however, the molecular mechanism underlying +4U function is poorly understood. Here, we perform comparative genomics analysis on 25 bacterial species (covering Actinobacteria, Bacteriodetes, Cyanobacteria, Deinococcus-Thermus, Firmicutes, Proteobacteria, and Spirochaetae) with bioinformatics approaches to examine the influence of +4U in bacterial translation termination by contrasting highly- and lowly-expressed genes (HEGs and LEGs, respectively). We estimated gene expression using the recently formulated Index of Translation Elongation, ITE, and identified stop codon near-cognate transfer RNAs (tRNAs) from well-annotated genomes. We show that +4U was consistently overrepresented in UAA-ending HEGs relative to LEGs. The result is consistent with the interpretation that +4U enhances termination mainly for UAA. Usage of +4U decreases in GC-rich species where most stop codons are UGA and UAG, with few UAA-ending genes, which is expected if UAA usage in HEGs drives up +4U usage. In HEGs, +4U usage increases significantly with abundance of UAA nc_tRNAs (near-cognate tRNAs that decode codons differing from UAA by a single nucleotide), particularly those with a mismatch at the first stop codon site. UAA is always the preferred stop codon in HEGs, and our results suggest that UAAU is the most efficient translation termination signal in bacteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".