Small non-coding RNAs in Streptomyces coelicolor
Bibliographic record
Abstract
In bacteria, small RNAs (sRNAs) make important regulatory contributions to an ever increasing number of cellular processes. To expand the repertoire of known sRNAs, we sought to identify novel sRNAs in the differentiating, multicellular bacterium Streptomyces coelicolor. We describe a combined bioinformatic and experimental approach that enabled the identification and characterization of nine novel sRNAs in S. coelicolor, including a cis-encoded antisense sRNA. We examined sRNA expression throughout the S. coelicolor developmental cycle, which progresses from vegetative mycelium formation, to aerial mycelium formation and finally sporulation. We further determined the effects of growth medium composition (rich versus minimal medium) on sRNA gene expression, and compared wild-type sRNA expression profiles with those of four developmental mutants. All but two of the sRNAs exhibited some degree of medium dependence, with three sRNAs being expressed exclusively during growth on one medium type. Unlike most sRNAs characterized thus far, several sRNA genes in S. coelicolor were expressed constitutively (apart from during late sporulation), suggesting a possible housekeeping role for these transcripts. Others were expressed at specific developmental stages, and their expression profiles were altered in response to developmental mutations. Expression of one sRNA in particular was dependent upon the sporulation-specific sigma factor sigma(WhiG).
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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 | 0.000 | 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 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".