<i>Staphylococcus aureus</i> Fur Regulates the Expression of Virulence Factors That Contribute to the Pathogenesis of Pneumonia
Bibliographic record
Abstract
The tremendous success of Staphylococcus aureus as a pathogen is due to the controlled expression of a diverse array of virulence factors. The effects of host environments on the expression of virulence factors and the mechanisms by which S. aureus adapts to colonize distinct host tissues are largely unknown. Vertebrates have evolved to sequester nutrient iron from invading bacteria, and iron availability is a signal that alerts pathogenic microorganisms when they enter the hostile host environment. Consistent with this, we report here that S. aureus senses alterations in the iron status via the ferric uptake regulator (Fur) and alters the abundance of a large number of virulence factors. These Fur-mediated changes protect S. aureus against killing by neutrophils, and Fur is required for full staphylococcal virulence in a murine model of infection. A potential mechanistic explanation for the impact of Fur on virulence is provided by the observation that Fur coordinates the reciprocal expression of cytolysins and a subset of immunomodulatory proteins. More specifically, S. aureus lacking fur exhibits decreased expression of immunomodulatory proteins and increased expression of cytolysins. These findings reveal that Fur is involved in initiating a regulatory program that organizes the expression of virulence factors during the pathogenesis of S. aureus pneumonia.
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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".