Role for <i>sagA</i> and <i>siaA</i> in Quorum Sensing and Iron Regulation in <i>Streptococcus pyogenes</i>
Bibliographic record
Abstract
Streptococcus pyogenes is a ubiquitous and versatile pathogen that causes a variety of infections with a wide range of severity. The versatility of this organism is due in part to its capacity to regulate virulence gene expression in response to the many environments that it encounters during an infection. We analyzed the expression of two potential virulence factors, sagA and siaA (also referred to as pel and htsA, respectively), in response to conditions of varying cell densities and iron concentrations. The sagA gene was up-regulated in conditioned medium from a wild-type strain but not from sagA-deficient mutants, and the gene was also up-regulated in the presence of streptolysin S (SLS), the gene product of sagA, thus indicating that this gene or its product is involved in density-dependent regulation of S. pyogenes. By comparison, siaA responded in a manner consistent with a role in iron acquisition since it was up-regulated under iron-restricted conditions. Although siaA expression was also up-regulated in the presence of SLS and in conditioned media from both wild-type and sagA-deficient mutants, this up-regulation was not growth phase dependent. We conclude that sagA encodes a quorum-sensing signaling molecule, likely SLS, and further support the notion that siaA is likely involved in iron acquisition.
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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".