Recent developments in understanding the iron acquisition strategies of gram positive pathogens
Bibliographic record
Abstract
Iron is a versatile redox-active catalyst and a required cofactor within a diverse array of biological processes. To almost all organisms, iron is both essential and potentially toxic, where homeostatic concentrations must be stringently maintained. Within the iron-restricted host, the survival and proliferation of microbial invaders is contingent upon exploiting the host iron pool. Bacteria express a multitude of complex, and often redundant means of acquiring iron, including surface-associated heme-uptake pathways, high affinity iron-scavenging siderophores and transporters of free inorganic iron. Within the last decade, our understanding of iron acquisition by Gram-positive pathogens has expanded substantively, from the discovery of the iron-regulated surface-determinant pathway and numerous unique siderophores through to the detailed elucidation of heme-iron extraction, and heme and siderophore coordination and transfer. This review provides a comprehensive summary of the iron acquisition strategies of notorious Gram-positive pathogens and highlights how both conserved and distinct tactics for acquiring iron contribute to the pathophysiology of these bacteria. Further, a focus on recent structural and mechanistic studies details how these iron acquisition systems may be exploited in the development of novel therapeutics.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".