Small interfering RNAs targeting agrA and sarA attenuate pathogenesis of Staphylococcus aureus in Caenorhabditis elegans
Bibliographic record
Abstract
INTRODUCTION: The use of small interfering RNA (siRNA) gene silencing is a promising therapeutic option as it does not impose selective pressure on bacteria that is often associated with the development of resistance. The study assessed the effect of siRNA targeted to sarA and agrA in S. aureus and the relationship between the transcriptional response, biofilm formation and pathogenicity. METHODOLOGY: siRNAs designed against agrA and sarA were electroporated into methicillin-resistant and methicillin-susceptible S. aureus strains. mRNA levels, growth kinetics, biofilm formation and minimal inhibitory concentration were measured. Efficacy of siRNA in bacteria was assessed using survival assays in a C. elegans model. Differences in gene expression before and after siRNA treatment were anaysed using the paired t-test, while the log rank test was used to assess the significance of any difference among survival rates of nematodes. RESULTS: Biofilm formation decreased significantly in siRNA treated strains and growth rates of siRNA treated strains were significantly higher compared to untreated strains. We observed significant decreases in the transcriptional response in siRNA treated strains, with concomitant significant increases in the lifespan of C. elegans worms exposed to siRNA-treated versus untreated strains. CONCLUSIONS: siRNA targeted to agrA and sarA lowered mRNA transcription and pathogenicity of S. aureus.
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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".