RNase III CLASH in MRSA uncovers sRNA regulatory networks coupling metabolism to toxin expression
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial pathogen responsible for significant human morbidity and mortality. Post-transcriptional regulation by small RNAs (sRNAs) has emerged as an important mechanism for controlling virulence. However, the functionality of the majority of sRNAs during infection is unknown. To address this, we performed UV cross-linking, ligation, and sequencing of hybrids (CLASH) in MRSA to identify sRNA-RNA interactions under conditions that mimic the host environment. Using a double-stranded endoribonuclease III as bait, we uncovered hundreds of novel sRNA-RNA pairs. Strikingly, our results suggest that the production of small membrane-permeabilizing toxins is under extensive sRNA-mediated regulation and that their expression is intimately connected to metabolism. Additionally, we also uncover an sRNA sponging interaction between RsaE and RsaI. Taken together, we present a comprehensive analysis of sRNA-target interactions in MRSA and provide details on how these contribute to the control of virulence in response to changes in metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".