title: Computational modeling and analysis of iron release from macrophages.
abstract: A major process of iron homeostasis in whole-body iron metabolism is the release of iron from the macrophages of the reticuloendothelial system. Macrophages recognize and phagocytose senescent or damaged erythrocytes. Then, they process the heme iron, which is returned to the circulation for reutilization by red blood cell precursors during erythropoiesis. The amount of iron released, compared to the amount shunted for storage as ferritin, is greater during iron deficiency. A currently accepted model of iron release assumes a passive-gradient with free diffusion of intracellular labile iron (Fe2+) through ferroportin (FPN), the transporter on the plasma membrane. Outside the cell, a multi-copper ferroxidase, ceruloplasmin (Cp), oxidizes ferrous to ferric ion. Apo-transferrin (Tf), the primary carrier of soluble iron in the plasma, binds ferric ion to form mono-ferric and di-ferric transferrin. According to the passive-gradient model, the removal of ferrous ion from the site of release sustains the gradient that maintains the iron release. Subcellular localization of FPN, however, indicates that the role of FPN may be more complex. By experiments and mathematical modeling, we have investigated the detailed mechanism of iron release from macrophages focusing on the roles of the Cp, FPN and apo-Tf. The passive-gradient model is quantitatively analyzed using a mathematical model for the first time. A comparison of experimental data with model simulations shows that the passive-gradient model cannot explain macrophage iron release. However, a facilitated-transport model associated with FPN can explain the iron release mechanism. According to the facilitated-transport model, intracellular FPN carries labile iron to the macrophage membrane. Extracellular Cp accelerates the oxidation of ferrous ion bound to FPN. Apo-Tf in the extracellular environment binds to the oxidized ferrous ion, completing the release process. Facilitated-transport model can correctly predict cellular iron efflux and is essential for physiologically relevant whole-body model of iron metabolism.
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America; Department of Cellular and Molecular Medicine, Cleveland Clinic, Cleveland, Ohio, United States of America.
authors.forename: Alka A
authors.initials: AA
authors.lastname: Potdar
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America.
authors.forename: Joydeep
authors.initials: J
authors.lastname: Sarkar
authors.affiliation: Department of Cellular and Molecular Medicine, Cleveland Clinic, Cleveland, Ohio, United States of America.
authors.forename: Nupur K
authors.initials: NK
authors.lastname: Das
authors.affiliation: Department of Cellular and Molecular Medicine, Cleveland Clinic, Cleveland, Ohio, United States of America.
authors.forename: Paroma
authors.initials: P
authors.lastname: Ghosh
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America.
authors.forename: Miklos
authors.initials: M
authors.lastname: Gratzl
authors.affiliation: Department of Cellular and Molecular Medicine, Cleveland Clinic, Cleveland, Ohio, United States of America.
authors.forename: Paul L
authors.initials: PL
authors.lastname: Fox
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America.
authors.forename: Gerald M
authors.initials: GM
authors.lastname: Saidel
mesh_terms: D019295:Computational Biology; D003198:Computer Simulation; D006706:Homeostasis / Q000502:physiology*; D006801:Humans; D042541:Intracellular Space / Q000378:metabolism; D007501:Iron / Q000378:metabolism*; D008264:Macrophages / Q000166:cytology / Q000378:metabolism*; D008954:Models, Biological
chemical_list: D007501:Iron