Iron, copper, zinc, and manganese transport and regulation in pathogenic Enterobacteria: correlations between strains, site of infection and the relative importance of the different metal transport systems for virulence
Bibliographic record
Abstract
For all microorganisms, acquisition of metal ions is essential for survival in the environment or in their infected host. Metal ions are required in many biological processes as components of metalloproteins and serve as cofactors or structural elements for enzymes. However, it is critical for bacteria to ensure that metal uptake and availability is in accordance with physiological needs, as an imbalance in bacterial metal homeostasis is deleterious. Indeed, host defense strategies against infection either consist of metal starvation by sequestration or toxicity by the highly concentrated release of metals. To overcome these host strategies, bacteria employ a variety of metal uptake and export systems and finely regulate metal homeostasis by numerous transcriptional regulators, allowing them to adapt to changing environmental conditions. As a consequence, iron, zinc, manganese, and copper uptake systems significantly contribute to the virulence of many pathogenic bacteria. However, during the course of our experiments on the role of iron and manganese transporters in extraintestinal Escherichia coli (ExPEC) virulence, we observed that depending on the strain tested, the importance of tested systems in virulence may be different. This could be due to the different set of systems present in these strains, but literature also suggests that as each pathogen must adapt to the particular microenvironment of its site of infection, the role of each acquisition system in virulence can differ from a particular strain to another. In this review, we present the systems involved in metal transport by Enterobacteria and the main regulators responsible for their controlled expression. We also discuss the relative role of these systems depending on the pathogen and the tissues they infect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".