The two‐component system ArlS–ArlR is a regulator of virulence gene expression in <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Staphylococcus aureus is a major human pathogen that produces many virulence factors in a temporally regulated manner controlled by at least two global virulence regulatory loci (agr and sarA). We identified previously a two-component system, ArlS-ArlR, that modifies the activity of extracellular serine protease and may be involved in virulence regulation. Here, we show that mutations in either arlR or arlS increase the production of secreted proteins [alpha-toxin (Hla), beta-haemolysin, lipase, coagulase, serine protease (Ssp)] and especially protein A (Spa). Furthermore, the pattern of proteins secreted by both mutants was strikingly different from that of the wild-type strain. Transcriptional fusions showed that expression of hla, ssp and spa was higher in both mutants than in the wild-type strain, indicating that the arl operon decreases the production of virulence factors by downregulating the transcription of their genes. The arl mutation did not change spa expression in an agrA mutant or in a sarA mutant, suggesting that both the sarA and the agr loci are required for the action of arl on spa. Northern blot analyses indicated that the arl mutation increased the synthesis of both RNA II and RNA III, but decreased sarA transcription. Finally, arl was not autoregulated, but its expression was stimulated by agr and sarA. These results suggest that the Arl system interacts with both agr and sarA regulatory loci to modulate the virulence regulation network.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".