Riboregulation of bacterial and archaeal transposition
Bibliographic record
Abstract
The coexistence of transposons with their hosts depends largely on transposition levels being tightly regulated to limit the mutagenic burden associated with frequent transposition. For 'DNA-based' (class II) bacterial transposons there is growing evidence that regulation through small noncoding RNAs and/or the RNA-binding protein Hfq are prominent mechanisms of defense against transposition. Recent transcriptomics analyses have identified many new cases of antisense RNAs (asRNA) that potentially could regulate the expression of transposon-encoded genes giving the impression that asRNA regulation of DNA-based transposons is much more frequent than previously thought. Hfq is a highly conserved bacterial protein that plays a central role in posttranscriptional gene regulation and stress response pathways in many bacteria. Three different mechanisms for Hfq-directed control of bacterial transposons have been identified to date highlighting the versatility of this protein as a regulator of bacterial transposons. There is also evidence emerging that some DNA-based transposons encode RNAs that could regulate expression of host genes. In the case of IS200, which appears to have lost its ability to transpose, contributing a regulatory RNA to its host could account for the persistence of this mobile element in a wide range of bacterial species. It remains to be seen how prevalent these transposon-encoded RNA regulators are, but given the relatively large amount of intragenic transcription in bacterial genomes, it would not be surprising if new examples are forthcoming. WIREs RNA 2016, 7:382-398. doi: 10.1002/wrna.1341 For further resources related to this article, please visit the WIREs website.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".