Bibliographic record
Abstract
Abstract A set of putative novel small RNAs was recently identified as expressed in Enterococcus faecium, a major opportunistic pathogen involved in numerous healthcare-associated infections and hospital outbreaks. The aim of this study was to characterize the first functional analysis of one of them, srn0030, by phenotypic, genomic and transcriptomic approaches. By genomic analysis and RACE mapping, we revealed the presence of this RNA (previously designated as Ptet) within the 5’-untrasnlated region (UTR) of tet(M), a gene conferring tetracycline resistance through ribosomal protection. The regulatory mechanism has previously been described as transcriptional attenuation, but has actually been poorly characterized. Hence, we provide original additional data, especially the presence of three upstream transcripts of ~100, ~150 and ~230 nt within the 5’-UTR of tet(M), suggesting an alternative regulatory mechanism. The total deletion of these three transcripts causes an unexpected decreasing of tetracycline resistance in E. faecium. The attenuation mechanism was investigated, and we confirmed that the transcriptional read-through and tet(M) overexpression induced by tetracycline addition but the function of putative peptide leader on attenuation mechanism was not supported by our data. We report here new phenotypic and transcriptomic observations in E. faecium demonstrating an alternative regulatory mechanism of tet(M) gene expression.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".