Interplay between iron homeostasis and virulence: Fur and RyhB as major regulators of bacterial pathogenicity
Bibliographic record
Abstract
In bacteria-host interactions, competition for iron is critical for the outcome of the infection. As a result of its redox properties, this metal is essential for the growth and proliferation of most living organisms, including pathogenic bacteria. This metal is also potentially toxic, making the precise maintenance of iron homeostasis necessary for survival. Iron acquisition and storage control is mediated in most bacteria by the global ferric uptake regulator (Fur) and iron-responsive small regulatory non-coding RNAs (RyhB in the model organism Escherichia coli). While the role of these regulators in iron homeostasis is well documented in both pathogenic and non-pathogenic bacteria, many recent studies also demonstrate that these regulators are involved in the virulence of pathogenic bacteria. By sensing iron availability in the environment, Fur and RyhB are able to regulate, either directly or indirectly via other transcriptional regulators or modulation of intracellular iron concentration, many virulence determinants of pathogenic bacteria. Iron is thus both a nutritional and regulatory element, allowing bacteria to adapt to various host environments by adjusting expression of virulence factors. In this review, we present evidences that Fur and RyhB are the major regulators of this adaptation, as they are involved in diverse functions ranging from iron homeostasis to regulation of virulence by mediating key pathogen responses such as invasion of eukaryotic cells, toxin production, motility, quorum sensing, stress resistance or biofilm formation. Therefore, Fur and RyhB play a major role in regulating an adaptative response during bacterial infections, making them important targets in the fight against pathogenic bacteria.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".