Modulation of Iron Availability at the Host-Pathogen Interface in Phagocytic Cells
Bibliographic record
Abstract
This review summarizes recent data on iron metabolism in macrophages, with a special emphasis on possible bacteriostatic and bactericidal consequences for intracellular pathogens. It includes the role of biological chelators and transporters in normal macrophage physiology and antimicrobial defense. Iron is an essential metal cofactor for many biochemical pathways in mammals. However, excess iron promotes the formation of cytotoxic oxygen derivatives so that systemic iron levels must be tightly regulated. The mechanism of iron recycling by macrophages including iron efflux from erythrocyte-containing phagosomes, iron release from macrophages, and entry into the transferrin (Tf) cycle remain poorly understood. Ferroportin expression in the liver, spleen, and bone marrow cells appears to be restricted to macrophages. Mutant mice bearing a conditional deletion of the ferroportin gene in macrophages show retention of iron by hepatic Kupffer cells and splenic macrophages. Hepcidin is induced by lipopolysaccharide (LPS) in mouse spleens and splenic macrophage in vitro and appears to mediate the LPS-induced down-regulation of ferroportin in the intestine and in splenic macrophages, suggesting that inflammatory agents may regulate iron metabolism through modulation of ferroportin expression. The host transporter Nramp1 may compete directly with bacterial divalent-metal transport systems for the acquisition of divalent metals within the phagosomal space. The ultimate outcome of these competing interactions influences the ability of pathogens to survive and replicate intracellularly. This seems particularly relevant to the Salmonella, Leishmania, and Mycobacterium spp., in which inactivating mutations in Nramp1 abrogate the natural resistance of macrophages to these pathogens.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".