How Changes in Anti-SD Sequences Would Affect SD Sequences in <i>Escherichia coli</i> and <i>Bacillus subtilis</i>
Bibliographic record
Abstract
Abstract The 3′ end of the small ribosomal RNAs (ssu rRNA) in bacteria is directly involved in the selection and binding of mRNA transcripts during translation initiation via well-documented interactions between a Shine-Dalgarno (SD) sequence located upstream of the initiation codon and an anti-SD (aSD) sequence at the 3′ end of the ssu rRNA. Consequently, the 3′ end of ssu rRNA (3′TAIL) is strongly conserved among bacterial species because a change in the region may impact the translation of many protein-coding genes. Escherichia coli and Bacillus subtilis differ in their 3′ ends of ssu rRNA, being GAUCACCUCCUUA3′ in E. coli and GAUCACCUCCUUUCU3′ or GAUCACCUCCUUUCUA3′ in B. subtilis. Such differences in 3′TAIL lead to species-specific SDs (designated SDEc for E. coli and SDBs for B. subtilis) that can form strong and well-positioned SD/aSD pairing in one species but not in the other. Selection mediated by the species-specific 3′TAIL is expected to favor SDBs against SDEc in B. subtilis, but favor SDEc against SDBs in E. coli. Among well-positioned SDs, SDEc is used more in E. coli than in B. subtilis, and SDBs more in B. subtilis than in E. coli. Highly expressed genes and genes of high translation efficiency tend to have longer SDs than lowly expressed genes and genes with low translation efficiency in both species, but more so in B. subtilis than in E. coli. Both species overuse SDs matching the bolded part of the 3′TAIL shown above. The 3′TAIL difference contributes to the host specificity of phages.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".