Appropriate maturation and folding of 16S rRNA during 30S subunit biogenesis are critical for translational fidelity
Bibliographic record
Abstract
Ribosomal protein S5 is critical for small ribosomal subunit (SSU) assembly and is indispensable for SSU function. Previously, we identified a point mutation in S5, (G28D) that alters both SSU formation and translational fidelity in vivo, which is unprecedented for other characterized S5 mutations. Surprisingly, additional copies of an extraribosomal assembly factor, RimJ, rescued all the phenotypes associated with S5(G28D), including fidelity defects, suggesting that the effect of RimJ on rescuing the miscoding of S5(G28D) is indirect. To understand the underlying mechanism, we focused on the biogenesis cascade and observed defects in processing of precursor 16S (p16S) rRNA in the S5(G28D) strain, which were rescued by RimJ. Analyses of p16S rRNA-containing ribosomes from other strains further supported a correspondence between the extent of 5(') end maturation of 16S rRNA and translational miscoding. Chemical probing of mutant ribosomes with additional leader sequences at the 5(') end of 16S rRNA compared to WT ribosomes revealed structural differences in the region of helix 1. Thus, the presence of additional nucleotides at the 5(') end of 16S rRNA could alter fidelity by changing the architecture of 16S rRNA in translating ribosomes and suggests that fidelity is governed by accuracy and completeness of the SSU biogenesis cascade.
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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 | 0.000 |
| 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".