Silencing of natural transformation by an RNA chaperone and a multitarget small RNA
Bibliographic record
Abstract
A highly conserved DNA uptake system allows many bacteria to actively import and integrate exogenous DNA. This process, called natural transformation, represents a major mechanism of horizontal gene transfer (HGT) involved in the acquisition of virulence and antibiotic resistance determinants. Despite evidence of HGT and the high level of conservation of the genes coding the DNA uptake system, most bacterial species appear non-transformable under laboratory conditions. In naturally transformable species, the DNA uptake system is only expressed when bacteria enter a physiological state called competence, which develops under specific conditions. Here, we investigated the mechanism that controls expression of the DNA uptake system in the human pathogen Legionella pneumophila We found that a repressor of this system displays a conserved ProQ/FinO domain and interacts with a newly characterized trans-acting sRNA, RocR. Together, they target mRNAs of the genes coding the DNA uptake system to control natural transformation. This RNA-based silencing represents a previously unknown regulatory means to control this major mechanism of HGT. Importantly, these findings also show that chromosome-encoded ProQ/FinO domain-containing proteins can assist trans-acting sRNAs and that this class of RNA chaperones could play key roles in post-transcriptional gene regulation throughout bacterial species.
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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) | 0.001 | 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".