Comparative analysis of non-coding RNAs in the antibiotic-producing Streptomyces bacteria
Bibliographic record
Abstract
BACKGROUND: Non-coding RNAs (ncRNAs) are key regulatory elements that control a wide range of cellular processes in all bacteria in which they have been studied. Taking advantage of recent technological innovations, we set out to fully explore the ncRNA potential of the multicellular, antibiotic-producing Streptomyces bacteria. RESULTS: Using a comparative RNA sequencing analysis of three divergent model streptomycetes (S. coelicolor, S. avermitilis and S. venezuelae), we discovered hundreds of novel cis-antisense RNAs and intergenic small RNAs (sRNAs). We identified a ubiquitous antisense RNA species that arose from the overlapping transcription of convergently-oriented genes; we termed these RNA species 'cutoRNAs', for convergent untranslated overlapping RNAs. Conservation between different classes of ncRNAs varied greatly, with sRNAs being more conserved than antisense RNAs. Many species-specific ncRNAs, including many distinct cutoRNA pairs, were located within antibiotic biosynthetic clusters, including the actinorhodin, undecylprodigiosin, and coelimycin clusters of S. coelicolor, the chloramphenicol cluster of S. venezuelae, and the avermectin cluster of S. avermitilis. CONCLUSIONS: These findings indicate that ncRNAs, including a novel class of antisense RNA, may exert a previously unrecognized level of regulatory control over antibiotic production in these bacteria. Collectively, this work has dramatically expanded the ncRNA repertoire of three Streptomyces species and has established a critical foundation from which to investigate ncRNA function in this medically and industrially important bacterial genus.
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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 | 0.001 | 0.001 |
| 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".