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1.  Systems analysis of iron metabolism: the network of iron pools and fluxes 
BMC Systems Biology  2010;4:112.
Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs.
Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.
PMCID: PMC2942822  PMID: 20704761
2.  Intracellular Iron Transport and Storage: From Molecular Mechanisms to Health Implications 
Antioxidants & Redox Signaling  2008;10(6):997-1030.
Maintenance of proper “labile iron” levels is a critical component in preserving homeostasis. Iron is a vital element that is a constituent of a number of important macromolecules, including those involved in energy production, respiration, DNA synthesis, and metabolism; however, excess “labile iron” is potentially detrimental to the cell or organism or both because of its propensity to participate in oxidation–reduction reactions that generate harmful free radicals. Because of this dual nature, elaborate systems tightly control the concentration of available iron. Perturbation of normal physiologic iron concentrations may be both a cause and a consequence of cellular damage and disease states. This review highlights the molecular mechanisms responsible for regulation of iron absorption, transport, and storage through the roles of key regulatory proteins, including ferroportin, hepcidin, ferritin, and frataxin. In addition, we present an overview of the relation between iron regulation and oxidative stress and we discuss the role of functional iron overload in the pathogenesis of hemochromatosis, neurodegeneration, and inflammation. Antioxid. Redox Signal. 10, 997–1030.
Iron Transport
Nonintestinal iron transport by transferring
Iron-bound transferrin binds the transferrin receptor for cellular iron uptake
Regulation of transferrin receptor 1 by iron regulatory element–iron regulatory protein system
Transcriptional regulation of transferrin receptor 1
Differential regulation of transferrin receptor 1 and transferrin receptor 2
Transferrin receptor 1 is regulated by hereditary hemochromatosis protein
Transferrin-independent cellular iron uptake
Intestinal iron absorption
Regulation of divalent metal transporter 1
Ferroportin is responsible for cellular iron efflux
Ferroportin associates and cooperates with ceruloplasmin
Ferroportin and hephaestin cooperate in iron efflux from intestinal cells
Iron Storage and Ferritin
Structure, tissue distribution, and importance of cytoplasmic ferritin
Iron efflux and ferritin degradation
Serum ferritin and ferritin receptor
Mitochondrial ferritin
Nuclear ferritin
Regulation of Ferritin
Iron-mediated ferritin regulation
Ferritin regulation by reactive oxygen species
Ferritin transcriptional regulation by cytokines
Ferritin regulation in erythroleukemic cells
Frataxin and Iron Homeostasis
Frataxin and Friedreich ataxia
Frataxin and mitochondrial iron traffic
Frataxin, heme synthesis, and iron–sulfur cluster biogenesis
Frataxin gene regulation
Functional Iron Overload and Human Health
Hereditary hemochromatosis
Mutant iron-responsive element-mediated iron overload
Iron regulation and neurodegeneration
Conclusions and Future Directions
PMCID: PMC2932529  PMID: 18327971
3.  Computational Reconstruction of Iron- and Manganese-Responsive Transcriptional Networks in α-Proteobacteria 
PLoS Computational Biology  2006;2(12):e163.
We used comparative genomics to investigate the distribution of conserved DNA-binding motifs in the regulatory regions of genes involved in iron and manganese homeostasis in alpha-proteobacteria. Combined with other computational approaches, this allowed us to reconstruct the metal regulatory network in more than three dozen species with available genome sequences. We identified several classes of cis-acting regulatory DNA motifs (Irr-boxes or ICEs, RirA-boxes, Iron-Rhodo-boxes, Fur-alpha-boxes, Mur-box or MRS, MntR-box, and IscR-boxes) in regulatory regions of various genes involved in iron and manganese uptake, Fe-S and heme biosynthesis, iron storage, and usage. Despite the different nature of the iron regulons in selected lineages of alpha-proteobacteria, the overall regulatory network is consistent with, and confirmed by, many experimental observations. This study expands the range of genes involved in iron homeostasis and demonstrates considerable interconnection between iron-responsive regulatory systems. The detailed comparative and phylogenetic analyses of the regulatory systems allowed us to propose a theory about the possible evolution of Fe and Mn regulons in alpha-proteobacteria. The main evolutionary event likely occurred in the common ancestor of the Rhizobiales and Rhodobacterales, where the Fur protein switched to regulating manganese transporters (and hence Fur had become Mur). In these lineages, the role of global iron homeostasis was taken by RirA and Irr, two transcriptional regulators that act by sensing the physiological consequence of the metal availability rather than its concentration per se, and thus provide for more flexible regulation.
The availability of hundreds of complete genomes allows one to use comparative genomics to describe key metabolic processes and regulatory gene networks. Genome context analyses and comparisons of transcription factor binding sites between genomes offer a powerful approach for functional gene annotation. Reconstruction of transcriptional regulatory networks allows for better understanding of cellular processes, which can be substantiated by direct experimentation. Iron homeostasis in bacteria is conferred by the regulation of various iron uptake transporters, iron storage ferritins, and iron-containing enzymes. In high concentrations, iron is poisonous for the cell, so strict control of iron homeostasis is maintained, mostly at the level of transcription by iron-responsive regulators. Despite their general importance, iron regulatory networks in most bacterial species are not well-understood. In this study, Rodionov and colleagues applied comparative genomic approaches to describe the regulatory network formed by genes involved in iron homeostasis in the alpha subclass of proteobacteria, which have extremely versatile lifestyles. These networks are mediated by a set of various DNA motifs (or regulatory signals) that occur in 5′ gene regions and involve at least six different metal-responsive regulators. This study once again shows the power of comparative genomics in the analysis of complex regulatory networks and their evolution.
PMCID: PMC1698941  PMID: 17173478
4.  Insulin/IGF-1 and Hypoxia Signaling Act in Concert to Regulate Iron Homeostasis in Caenorhabditis elegans 
PLoS Genetics  2012;8(3):e1002498.
Iron plays an essential role in many biological processes, but also catalyzes the formation of reactive oxygen species (ROS), which can cause molecular damage. Iron homeostasis is therefore a critical determinant of fitness. In Caenorhabditis elegans, insulin/IGF-1 signaling (IIS) promotes growth and reproduction but limits stress resistance and lifespan through inactivation of the DAF-16/FoxO transcription factor (TF). We report that long-lived daf-2 insulin/IGF-1 receptor mutants show a daf-16–dependent increase in expression of ftn-1, which encodes the iron storage protein H-ferritin. To better understand the regulation of iron homeostasis, we performed a TF–limited genetic screen for factors influencing ftn-1 gene expression. The screen identified the heat-shock TF hsf-1, the MAD bHLH TF mdl-1, and the putative histone acetyl transferase ada-2 as activators of ftn-1 expression. It also revealed that the HIFα homolog hif-1 and its binding partner aha-1 (HIFβ) are potent repressors of ftn-1 expression. ftn-1 expression is induced by exposure to iron, and we found that hif-1 was required for this induction. In addition, we found that the prolyl hydroxylase EGL-9, which represses HIF-1 via the von Hippel-Lindau tumor suppressor VHL-1, can also act antagonistically to VHL-1 in regulating ftn-1. This suggests a novel mechanism for HIF target gene regulation by these evolutionarily conserved and clinically important hydroxylases. Our findings imply that the IIS and HIF pathways act together to regulate iron homeostasis in C. elegans. We suggest that IIS/DAF-16 regulation of ftn-1 modulates a trade-off between growth and stress resistance, as elevated iron availability supports growth but also increases ROS production.
Author Summary
Iron plays a role in many biological processes, including energy generation and DNA replication. But to maintain health, levels of cellular iron must be just right: too much or too little iron can cause illnesses, such as anemia and hemochromatosis, respectively. Animals therefore carefully control their iron levels by regulating of iron uptake, transport, and storage within protein capsules called ferritins. But how do they coordinate this? Using the model organism C. elegans, we have discovered a network of genes and pathways that control iron homeostasis. We find that ferritin is regulated by insulin/IGF-1 signaling, which also controls growth and resistance to oxidative stress in response to harsh environmental conditions. Ferritin is also regulated by the hypoxia signaling pathway, which responds to oxygen and iron levels as well as to metabolic cues. We find that the hypoxia pathway acts as an iron sensor, a role it may also play in humans. Our work defines a network of signaling pathways that can adjust iron availability in response to a range of environmental cues. Understanding this network in C. elegans can help us to understand the causes of iron dyshomeostasis in humans, which can profoundly affect health.
PMCID: PMC3291539  PMID: 22396654
5.  Computational Modeling and Analysis of Iron Release from Macrophages 
PLoS Computational Biology  2014;10(7):e1003701.
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.
Author Summary
Iron metabolism is an important physiological phenomenon essential for sustaining life. There is a tight regulation of iron levels in humans and both deficiency and overload can lead to disorders such as anemia and hemochromatosis. Recycling of iron in human body via macrophage iron release is crucial to maintain healthy iron levels. However, a computational model is needed to quantitatively analyze the mechanism underlying a key process in iron homeostasis, which is the release of iron from the macrophages. Using mechanistic, mathematical models to simulate experimental data, we found a novel mechanism by which macrophages release iron. A comparison of experimental data with model simulations shows that a currently accepted passive-gradient mechanism cannot represent the iron-release process from macrophages. However, our model with a facilitated-transport mechanism associated with ferroportin (only known protein for iron export) accurately reproduces the iron release process. This model quantifies for the first time the detailed molecular mechanism associated with iron transport via ferroportin. This quantitative predictive model of cellular iron efflux is essential for physiologically relevant simulation of whole-body model of iron metabolism in healthy and disease states.
PMCID: PMC4083485  PMID: 24991925
6.  Iron and ferritin accumulate in separate cellular locations in Phaseolus seeds 
BMC Plant Biology  2010;10:26.
Iron is an important micronutrient for all living organisms. Almost 25% of the world population is affected by iron deficiency, a leading cause of anemia. In plants, iron deficiency leads to chlorosis and reduced yield. Both animals and plants may suffer from iron deficiency when their diet or environment lacks bioavailable iron. A sustainable way to reduce iron malnutrition in humans is to develop staple crops with increased content of bioavailable iron. Knowledge of where and how iron accumulates in seeds of crop plants will increase the understanding of plant iron metabolism and will assist in the production of staples with increased bioavailable iron.
Here we reveal the distribution of iron in seeds of three Phaseolus species including thirteen genotypes of P. vulgaris, P. coccineus, and P. lunatus. We showed that high concentrations of iron accumulate in cells surrounding the provascular tissue of P. vulgaris and P. coccineus seeds. Using the Perls' Prussian blue method, we were able to detect iron in the cytoplasm of epidermal cells, cells near the epidermis, and cells surrounding the provascular tissue. In contrast, the protein ferritin that has been suggested as the major iron storage protein in legumes was only detected in the amyloplasts of the seed embryo. Using the non-destructive micro-PIXE (Particle Induced X-ray Emission) technique we show that the tissue in the proximity of the provascular bundles holds up to 500 μg g-1 of iron, depending on the genotype. In contrast to P. vulgaris and P. coccineus, we did not observe iron accumulation in the cells surrounding the provascular tissues of P. lunatus cotyledons. A novel iron-rich genotype, NUA35, with a high concentration of iron both in the seed coat and cotyledons was bred from a cross between an Andean and a Mesoamerican genotype.
The presented results emphasize the importance of complementing research in model organisms with analysis in crop plants and they suggest that iron distribution criteria should be integrated into selection strategies for bean biofortification.
PMCID: PMC2831038  PMID: 20149228
7.  Thioredoxins, Glutaredoxins, and Peroxiredoxins—Molecular Mechanisms and Health Significance: from Cofactors to Antioxidants to Redox Signaling 
Antioxidants & Redox Signaling  2013;19(13):1539-1605.
Thioredoxins (Trxs), glutaredoxins (Grxs), and peroxiredoxins (Prxs) have been characterized as electron donors, guards of the intracellular redox state, and “antioxidants”. Today, these redox catalysts are increasingly recognized for their specific role in redox signaling. The number of publications published on the functions of these proteins continues to increase exponentially. The field is experiencing an exciting transformation, from looking at a general redox homeostasis and the pathological oxidative stress model to realizing redox changes as a part of localized, rapid, specific, and reversible redox-regulated signaling events. This review summarizes the almost 50 years of research on these proteins, focusing primarily on data from vertebrates and mammals. The role of Trx fold proteins in redox signaling is discussed by looking at reaction mechanisms, reversible oxidative post-translational modifications of proteins, and characterized interaction partners. On the basis of this analysis, the specific regulatory functions are exemplified for the cellular processes of apoptosis, proliferation, and iron metabolism. The importance of Trxs, Grxs, and Prxs for human health is addressed in the second part of this review, that is, their potential impact and functions in different cell types, tissues, and various pathological conditions. Antioxid. Redox Signal. 19, 1539–1605.
I. Introduction
A. Trx family of proteins
1. Structure and reaction mechanisms
2. Trx, Grx, and Prx family proteins in mammals
a. Trx systems
b. Grx systems
c. Peroxiredoxins
d. Trx-like proteins
B. The concept of redox signaling
C. Reversible post-translational redox modifications of protein thiols
1. Sulfenylation
2. Protein disulfides
3. Glutathionylation and cysteinylation
4. S-nitrosylation
5. Other reversible redox modifications
a. Persulfide formation
b. Methionine sulfoxidation
D. Oxidative stress in the concept of redox signaling
II. Mammalian Trx Family Proteins in Health and Disease
A. Specific pathways
1. Apoptosis
a. Cytosolic pathways
b. Mitochondrial pathways
2. Proliferation
3. Iron metabolism
a. Iron sulfur Grxs
b. Biogenesis of iron-sulfur centers
c. Regulation of iron metabolism
d. Intracellular iron distribution
B. Tissues, organ systems, and diseases
1. Development
2. Central nervous system
a. Expression profile of Trxs, Grxs, Prxs, and related proteins in the CNS
b. Trxs, Grxs, Prxs, and pathologies of the CNS
3. Sensory organs
a. Expression profile of Trx-related proteins in sensory organs
b. Pathologies of the eye
c. Pathologies related to tongue, olfactory system, and ear
4. Cardiovascular system
a. Expression pattern of Trxs, Grxs, and Prxs in cardiovascular tissue
b. Trxs, Grxs, and Prxs in pathologies of the cardiovascular system
5. Skin
6. Skeletal muscle
7. Respiratory system
a. Expression of Trx family proteins in the respiratory system
b. Trxs, Grxs, and Prxs in pathologies of the lung—interplay between ROS and inflammation
8. Infection, inflammation, and immune response
a. Expression pattern of Trx-related proteins in lymphoid tissues
b. Immune system
c. Infectious diseases
9. Metabolic and digestive system
a. Diabetes mellitus
10. Urinary tract and reproductive systems
a. Kidney
b. Urinary bladder
c. Male reproductive system
d. Female reproductive system
11. Ischemia and hypoxia
12. Cancer
a. Carcinogenesis
13. Aging
C. Therapeutic approaches
III. Concluding Remarks
PMCID: PMC3797455  PMID: 23397885
8.  Novel insights into iron metabolism by integrating deletome and transcriptome analysis in an iron deficiency model of the yeast Saccharomyces cerevisiae 
BMC Genomics  2009;10:130.
Iron-deficiency anemia is the most prevalent form of anemia world-wide. The yeast Saccharomyces cerevisiae has been used as a model of cellular iron deficiency, in part because many of its cellular pathways are conserved. To better understand how cells respond to changes in iron availability, we profiled the yeast genome with a parallel analysis of homozygous deletion mutants to identify essential components and cellular processes required for optimal growth under iron-limited conditions. To complement this analysis, we compared those genes identified as important for fitness to those that were differentially-expressed in the same conditions. The resulting analysis provides a global perspective on the cellular processes involved in iron metabolism.
Using functional profiling, we identified several genes known to be involved in high affinity iron uptake, in addition to novel genes that may play a role in iron metabolism. Our results provide support for the primary involvement in iron homeostasis of vacuolar and endosomal compartments, as well as vesicular transport to and from these compartments. We also observed an unexpected importance of the peroxisome for growth in iron-limited media. Although these components were essential for growth in low-iron conditions, most of them were not differentially-expressed. Genes with altered expression in iron deficiency were mainly associated with iron uptake and transport mechanisms, with little overlap with those that were functionally required. To better understand this relationship, we used expression-profiling of selected mutants that exhibited slow growth in iron-deficient conditions, and as a result, obtained additional insight into the roles of CTI6, DAP1, MRS4 and YHR045W in iron metabolism.
Comparison between functional and gene expression data in iron deficiency highlighted the complementary utility of these two approaches to identify important functional components. This should be taken into consideration when designing and analyzing data from these type of studies. We used this and other published data to develop a molecular interaction network of iron metabolism in yeast.
PMCID: PMC2669097  PMID: 19321002
9.  The capacity for multistability in small gene regulatory networks 
BMC Systems Biology  2009;3:96.
Recent years have seen a dramatic increase in the use of mathematical modeling to gain insight into gene regulatory network behavior across many different organisms. In particular, there has been considerable interest in using mathematical tools to understand how multistable regulatory networks may contribute to developmental processes such as cell fate determination. Indeed, such a network may subserve the formation of unicellular leaf hairs (trichomes) in the model plant Arabidopsis thaliana.
In order to investigate the capacity of small gene regulatory networks to generate multiple equilibria, we present a chemical reaction network (CRN)-based modeling formalism and describe a number of methods for CRN analysis in a parameter-free context. These methods are compared and applied to a full set of one-component subnetworks, as well as a large random sample from 40,680 similarly constructed two-component subnetworks. We find that positive feedback and cooperativity mediated by transcription factor (TF) dimerization is a requirement for one-component subnetwork bistability. For subnetworks with two components, the presence of these processes increases the probability that a randomly sampled subnetwork will exhibit multiple equilibria, although we find several examples of bistable two-component subnetworks that do not involve cooperative TF-promoter binding. In the specific case of epidermal differentiation in Arabidopsis, dimerization of the GL3-GL1 complex and cooperative sequential binding of GL3-GL1 to the CPC promoter are each independently sufficient for bistability.
Computational methods utilizing CRN-specific theorems to rule out bistability in small gene regulatory networks are far superior to techniques generally applicable to deterministic ODE systems. Using these methods to conduct an unbiased survey of parameter-free deterministic models of small networks, and the Arabidopsis epidermal cell differentiation subnetwork in particular, we illustrate how future experimental research may be guided by network structure analysis.
PMCID: PMC2759935  PMID: 19772572
10.  Iron homeostasis and toxicity in retinal degeneration 
Iron is essential for many metabolic processes but can also cause damage. As a potent generator of hydroxyl radical, the most reactive of the free radicals, iron can cause considerable oxidative stress. Since iron is absorbed through diet but not excreted except through menstruation, total body iron levels build up with age. Macular iron levels increase with age, in both men and women. This iron has the potential to contribute to retinal degeneration.
Here we present an overview of the evidence suggesting that iron may contribute to retinal degenerations. Intraocular iron foreign bodies cause retinal degeneration. Retinal iron buildup resulting from hereditary iron homeostasis disorders aceruloplasminemia, Friedreich’s Ataxia, and panthothenate kinase associated neurodegeneration cause retinal degeneration. Mice with targeted mutation of the iron exporter ceruloplasmin have age-dependent retinal iron overload and a resulting retinal degeneration with features of age-related macular degeneration (AMD). Post mortem retinas from patients with AMD have more iron and the iron carrier transferrin than age- matched controls.
Over the past ten years much has been learned about the intricate network of proteins involved in iron handling. Many of these, including transferrin, transferrin receptor, divalent metal transporter 1, ferritin, ferroportin, ceruloplasmin, hephaestin, iron regulatory protein, and histocompatibility leukocyte antigen class I-like protein involved in iron homeostasis (HFE) have been found in the retina. Some of these proteins have been found in the cornea and lens as well. Levels of the iron carrier transferrin are high in the aqueous and vitreous humors. The functions of these proteins in other tissues, combined with studies on cultured ocular tissues, genetically engineered mice, and eye exams on patients with hereditary iron diseases provide clues regarding their ocular functions.
Iron may play a role in a broad range of ocular diseases, including glaucoma, cataract, AMD, and conditions causing intraocular hemorrhage. While iron deficiency must be prevented, the therapeutic potential of limiting iron induced ocular oxidative damage is high. Systemic, local, or topical iron chelation with an expanding repertoire of drugs has clinical potential.
PMCID: PMC2093950  PMID: 17921041
11.  HIF-1 Regulates Iron Homeostasis in Caenorhabditis elegans by Activation and Inhibition of Genes Involved in Iron Uptake and Storage 
PLoS Genetics  2011;7(12):e1002394.
Caenorhabditis elegans ftn-1 and ftn-2, which encode the iron-storage protein ferritin, are transcriptionally inhibited during iron deficiency in intestine. Intestinal specific transcription is dependent on binding of ELT-2 to GATA binding sites in an iron-dependent enhancer (IDE) located in ftn-1 and ftn-2 promoters, but the mechanism for iron regulation is unknown. Here, we identify HIF-1 (hypoxia-inducible factor -1) as a negative regulator of ferritin transcription. HIF-1 binds to hypoxia-response elements (HREs) in the IDE in vitro and in vivo. Depletion of hif-1 by RNA interference blocks transcriptional inhibition of ftn-1 and ftn-2 reporters, and ftn-1 and ftn-2 mRNAs are not regulated in a hif-1 null strain during iron deficiency. An IDE is also present in smf-3 encoding a protein homologous to mammalian divalent metal transporter-1. Unlike the ftn-1 IDE, the smf-3 IDE is required for HIF-1–dependent transcriptional activation of smf-3 during iron deficiency. We show that hif-1 null worms grown under iron limiting conditions are developmentally delayed and that depletion of FTN-1 and FTN-2 rescues this phenotype. These data show that HIF-1 regulates intestinal iron homeostasis during iron deficiency by activating and inhibiting genes involved in iron uptake and storage.
Author Summary
Due to its presence in proteins involved in hemoglobin synthesis, DNA synthesis, and mitochondrial respiration, eukaryotic cells require iron for survival. Excess iron can lead to oxidative damage, while iron deficiency reduces cell growth and causes cell death. Dysregulation of iron homeostasis in humans caused by iron deficiency or excess leads to anemia, diabetes, and neurodegenerative disorders. All organisms have thus developed mechanisms to sense, acquire, and store iron. We use Caenorhabditis elegans as a model organism to study mechanisms of iron regulation. Our previous studies show that the iron-storage protein ferritin (FTN-1, FTN-2) is transcriptionally inhibited in intestine during iron deficiency, but the mechanisms regulating iron regulation are not known. Here, we find that hypoxia-inducible factor 1 (HIF-1) transcriptionally inhibits ftn-1 and ftn-2 during iron deficiency. We also show that HIF-1 activates the iron uptake gene smf-3. Transcriptional activation and inhibition by HIF-1 is dependent on an iron enhancer in the promoters of these genes. HIF-1 is a known transcriptional activator, but its role in transcriptional inhibition is not well understood. Our data show that HIF-1 regulates iron homeostasis by activating and inhibiting iron uptake and storage genes, and they provide insight into HIF-1 transcriptional inhibition.
PMCID: PMC3240588  PMID: 22194696
12.  Structural and functional protein network analyses predict novel signaling functions for rhodopsin 
Proteomic analyses, literature mining, and structural data were combined to generate an extensive signaling network linked to the visual G protein-coupled receptor rhodopsin. Network analysis suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking.
Using a shotgun proteomic approach, we identified the protein inventory of the light sensing outer segment of the mammalian photoreceptor.These data, combined with literature mining, structural modeling, and computational analysis, offer a comprehensive view of signal transduction downstream of the visual G protein-coupled receptor rhodopsin.The network suggests novel signaling branches downstream of rhodopsin to cytoskeleton dynamics and vesicular trafficking.The network serves as a basis for elucidating physiological principles of photoreceptor function and suggests potential disease-associated proteins.
Photoreceptor cells are neurons capable of converting light into electrical signals. The rod outer segment (ROS) region of the photoreceptor cells is a cellular structure made of a stack of around 800 closed membrane disks loaded with rhodopsin (Liang et al, 2003; Nickell et al, 2007). In disc membranes, rhodopsin arranges itself into paracrystalline dimer arrays, enabling optimal association with the heterotrimeric G protein transducin as well as additional regulatory components (Ciarkowski et al, 2005). Disruption of these highly regulated structures and processes by germline mutations is the cause of severe blinding diseases such as retinitis pigmentosa, macular degeneration, or congenital stationary night blindness (Berger et al, 2010).
Traditionally, signal transduction networks have been studied by combining biochemical and genetic experiments addressing the relations among a small number of components. More recently, large throughput experiments using different techniques like two hybrid or co-immunoprecipitation coupled to mass spectrometry have added a new level of complexity (Ito et al, 2001; Gavin et al, 2002, 2006; Ho et al, 2002; Rual et al, 2005; Stelzl et al, 2005). However, in these studies, space, time, and the fact that many interactions detected for a particular protein are not compatible, are not taken into consideration. Structural information can help discriminate between direct and indirect interactions and more importantly it can determine if two or more predicted partners of any given protein or complex can simultaneously bind a target or rather compete for the same interaction surface (Kim et al, 2006).
In this work, we build a functional and dynamic interaction network centered on rhodopsin on a systems level, using six steps: In step 1, we experimentally identified the proteomic inventory of the porcine ROS, and we compared our data set with a recent proteomic study from bovine ROS (Kwok et al, 2008). The union of the two data sets was defined as the ‘initial experimental ROS proteome'. After removal of contaminants and applying filtering methods, a ‘core ROS proteome', consisting of 355 proteins, was defined.
In step 2, proteins of the core ROS proteome were assigned to six functional modules: (1) vision, signaling, transporters, and channels; (2) outer segment structure and morphogenesis; (3) housekeeping; (4) cytoskeleton and polarity; (5) vesicles formation and trafficking, and (6) metabolism.
In step 3, a protein-protein interaction network was constructed based on the literature mining. Since for most of the interactions experimental evidence was co-immunoprecipitation, or pull-down experiments, and in addition many of the edges in the network are supported by single experimental evidence, often derived from high-throughput approaches, we refer to this network, as ‘fuzzy ROS interactome'. Structural information was used to predict binary interactions, based on the finding that similar domain pairs are likely to interact in a similar way (‘nature repeats itself') (Aloy and Russell, 2002). To increase the confidence in the resulting network, edges supported by a single evidence not coming from yeast two-hybrid experiments were removed, exception being interactions where the evidence was the existence of a three-dimensional structure of the complex itself, or of a highly homologous complex. This curated static network (‘high-confidence ROS interactome') comprises 660 edges linking the majority of the nodes. By considering only edges supported by at least one evidence of direct binary interaction, we end up with a ‘high-confidence binary ROS interactome'. We next extended the published core pathway (Dell'Orco et al, 2009) using evidence from our high-confidence network. We find several new direct binary links to different cellular functional processes (Figure 4): the active rhodopsin interacts with Rac1 and the GTP form of Rho. There is also a connection between active rhodopsin and Arf4, as well as PDEδ with Rab13 and the GTP-bound form of Arl3 that links the vision cycle to vesicle trafficking and structure. We see a connection between PDEδ with prenyl-modified proteins, such as several small GTPases, as well as with rhodopsin kinase. Further, our network reveals several direct binary connections between Ca2+-regulated proteins and cytoskeleton proteins; these are CaMK2A with actinin, calmodulin with GAP43 and S1008, and PKC with 14-3-3 family members.
In step 4, part of the network was experimentally validated using three different approaches to identify physical protein associations that would occur under physiological conditions: (i) Co-segregation/co-sedimentation experiments, (ii) immunoprecipitations combined with mass spectrometry and/or subsequent immunoblotting, and (iii) utilizing the glycosylated N-terminus of rhodopsin to isolate its associated protein partners by Concanavalin A affinity purification. In total, 60 co-purification and co-elution experiments supported interactions that were already in our literature network, and new evidence from 175 co-IP experiments in this work was added. Next, we aimed to provide additional independent experimental confirmation for two of the novel networks and functional links proposed based on the network analysis: (i) the proposed complex between Rac1/RhoA/CRMP-2/tubulin/and ROCK II in ROS was investigated by culturing retinal explants in the presence of an ROCK II-specific inhibitor (Figure 6). While morphology of the retinas treated with ROCK II inhibitor appeared normal, immunohistochemistry analyses revealed several alterations on the protein level. (ii) We supported the hypothesis that PDEδ could function as a GDI for Rac1 in ROS, by demonstrating that PDEδ and Rac1 co localize in ROS and that PDEδ could dissociate Rac1 from ROS membranes in vitro.
In step 5, we use structural information to distinguish between mutually compatible (‘AND') or excluded (‘XOR') interactions. This enables breaking a network of nodes and edges into functional machines or sub-networks/modules. In the vision branch, both ‘AND' and ‘XOR' gates synergize. This may allow dynamic tuning of light and dark states. However, all connections from the vision module to other modules are ‘XOR' connections suggesting that competition, in connection with local protein concentration changes, could be important for transmitting signals from the core vision module.
In the last step, we map and functionally characterize the known mutations that produce blindness.
In summary, this represents the first comprehensive, dynamic, and integrative rhodopsin signaling network, which can be the basis for integrating and mapping newly discovered disease mutants, to guide protein or signaling branch-specific therapies.
Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein-coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein–protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease-associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.
PMCID: PMC3261702  PMID: 22108793
protein interaction network; rhodopsin signaling; structural modeling
13.  Regulation of cellular iron metabolism 
Biochemical Journal  2011;434(Pt 3):365-381.
Iron is an essential but potentially hazardous biometal. Mammalian cells require sufficient amounts of iron to satisfy metabolic needs or to accomplish specialized functions. Iron is delivered to tissues by circulating transferrin, a transporter that captures iron released into the plasma mainly from intestinal enterocytes or reticuloendothelial macrophages. The binding of iron-laden transferrin to the cell-surface transferrin receptor 1 results in endocytosis and uptake of the metal cargo. Internalized iron is transported to mitochondria for the synthesis of haem or iron–sulfur clusters, which are integral parts of several metalloproteins, and excess iron is stored and detoxified in cytosolic ferritin. Iron metabolism is controlled at different levels and by diverse mechanisms. The present review summarizes basic concepts of iron transport, use and storage and focuses on the IRE (iron-responsive element)/IRP (iron-regulatory protein) system, a well known post-transcriptional regulatory circuit that not only maintains iron homoeostasis in various cell types, but also contributes to systemic iron balance.
PMCID: PMC3048577  PMID: 21348856
ferritin; ferroportin; iron-regulatory protein 1 (IRP1); iron-regulatory protein 2 (IRP2); iron–sulfur cluster (ISC); transferrin receptor (TfR); Abcb, ATP-binding cassette, subfamily B; ALA, 5-aminolevulinic acid; ALAS, ALA synthase; β-APP, β-amyloid precursor protein; BMP, bone morphogenetic protein; c-aconitase, cytosolic aconitase; C/EBPα, CCAAT/enhancer-binding protein α; Cfd1, cytosolic Fe–S cluster-deficient protein 1; Cul1, Cullin 1; Dcytb, duodenal cytochrome b; DHBA, dihydroxybenzoic acid; DMT1, divalent metal transporter 1; ER, endoplasmic reticulum; FBXL5, F-box and leucine-rich repeat protein 5; FLVCR, feline leukaemia virus, subgroup C, receptor; Grx, glutaredoxin; H, heavy; HIF, hypoxia-inducible factor; HO-1, haem oxygenase 1; Hpx, haemopexin; IOP1, iron-only hydrogenase-like protein 1; IRE, iron-responsive element; IRIDA, iron-refractory iron deficiency anaemia; IRP, iron-regulatory protein; ISC, iron–sulfur cluster; CIA, cytosolic ISC assembly; Isu, iron–sulfur cluster scaffold homologue; L, light; Lcn2, lipocalin 2; LIP, labile iron pool; m-aconitase, mitochondrial aconitase; MRCKα, myotonic dystrophy kinase-related Cdc42 (cell division cycle 42)-binding kinase α; Nar1, nuclear architecture-related protein 1; Nbp35, nucleotide-binding protein 35; Nfs, nitrogen fixation homologue; NTBI, non-transferrin-bound iron; PCBP1, poly(rC)-binding protein 1; Rbx1, Ring-box 1; ROS, reactive oxygen species; SDH, succinate dehydrogenase; Skp1, S-phase kinase-associated protein 1; SLC, solute carrier; STAT3, signal transducer and activator of transcription 3; Tf, transferrin; TfR, Tf receptor; UTR, untranslated region
14.  HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology 
We present HepatoNet1, a manually curated large-scale metabolic network of the human hepatocyte that encompasses >2500 reactions in six intracellular and two extracellular compartments.Using constraint-based modeling techniques, the network has been validated to replicate numerous metabolic functions of hepatocytes corresponding to a reference set of diverse physiological liver functions.Taking the detoxification of ammonia and the formation of bile acids as examples, we show how these liver-specific metabolic objectives can be achieved by the variable interplay of various metabolic pathways under varying conditions of nutrients and oxygen availability.
The liver has a pivotal function in metabolic homeostasis of the human body. Hepatocytes are the principal site of the metabolic conversions that underlie diverse physiological functions of the liver. These functions include provision and homeostasis of carbohydrates, amino acids, lipids and lipoproteins in the systemic blood circulation, biotransformation, plasma protein synthesis and bile formation, to name a few. Accordingly, hepatocyte metabolism integrates a vast array of differentially regulated biochemical activities and is highly responsive to environmental perturbations such as changes in portal blood composition (Dardevet et al, 2006). The complexity of this metabolic network and the numerous physiological functions to be achieved within a highly variable physiological environment necessitate an integrated approach with the aim of understanding liver metabolism at a systems level. To this end, we present HepatoNet1, a stoichiometric network of human hepatocyte metabolism characterized by (i) comprehensive coverage of known biochemical activities of hepatocytes and (ii) due representation of the biochemical and physiological functions of hepatocytes as functional network states. The network comprises 777 metabolites in six intracellular (cytosol, endoplasmic reticulum and Golgi apparatus, lysosome, mitochondria, nucleus, and peroxisome) and two extracellular compartments (bile canaliculus and sinusoidal space) and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. We performed flux-balance analyses to validate whether for >300 different metabolic objectives a non-zero stationary flux distribution could be established in the network. Figure 1 shows one such functional flux mode associated with the synthesis of the bile acid glycochenodeoxycholate, one important hepatocyte-specific physiological liver function. Besides those pathways directly linked to the synthesis of the bile acid, the mevalonate pathway and the de novo synthesis of cholesterol, the flux mode comprises additional pathways such as gluconeogenesis, the pentose phosphate pathway or the ornithine cycle because the calculations were routinely performed on a minimal set of exchangeable metabolites, that is all reactants were forced to be balanced and all exportable intermediates had to be catabolized into non-degradable end products. This example shows how HepatoNet1 under the challenges of limited exchange across the network boundary can reveal numerous cross-links between metabolic pathways traditionally perceived as separate entities. For example, alanine is used as gluconeogenic substrate to form glucose-6-phosphate, which is used in the pentose phosphate pathway to generate NADPH. The glycine moiety for bile acid conjugation is derived from serine. Conversion of ammonia into non-toxic nitrogen compounds is one central homeostatic function of hepatocytes. Using the HepatoNet1 model, we investigated, as another example of a complex metabolic objective dependent on systemic physiological parameters, how the consumption of oxygen, glucose and palmitate is affected when an external nitrogen load is converted in varying proportions to the non-toxic nitrogen compounds: urea, glutamine and alanine. The results reveal strong dependencies between the available level of oxygen and the substrate demand of hepatocytes required for effective ammonia detoxification by the liver.
Oxygen demand is highest if nitrogen is exclusively transformed into urea. At lower fluxes into urea, an intriguing pattern for oxygen demand is predicted: oxygen demand attains a minimum if the nitrogen load is directed to urea, glutamine and alanine with relative fluxes of 0.17, 0.43 and 0.40, respectively (Figure 2A). Oxygen demand in this flux distribution is four times lower than for the maximum (100% urea) and still 77 and 33% lower than using alanine and glutamine as exclusive nitrogen compounds, respectively. This computationally predicted tendency is consistent with the notion that the zonation of ammonia detoxification, that is the preferential conversion of ammonia to urea in periportal hepatocytes and to glutamine in perivenous hepatocytes, is dictated by the availability of oxygen (Gebhardt, 1992; Jungermann and Kietzmann, 2000). The decreased oxygen demand in flux distributions using higher proportions of glutamine or alanine is accompanied by increased uptake of the substrates glucose and palmitate (Figure 2B). This is due to an increased demand of energy and carbon for the amidation and transamination of glutamate and pyruvate to discharge nitrogen in the form of glutamine and alanine, respectively. In terms of both scope and specificity, our model bridges the scale between models constructed specifically to examine distinct metabolic processes of the liver and modeling based on a global representation of human metabolism. The former include models for the interdependence of gluconeogenesis and fatty-acid catabolism (Chalhoub et al, 2007), impairment of glucose production in von Gierke's and Hers' diseases (Beard and Qian, 2005) and other processes (Calik and Akbay, 2000; Stucki and Urbanczik, 2005; Ohno et al, 2008). The hallmark of these models is that each of them focuses on a small number of reactions pertinent to the metabolic function of interest embedded in a customized representation of the principal pathways of central metabolism. HepatoNet1, currently, outperforms liver-specific models computationally predicted (Shlomi et al, 2008) on the basis of global reconstructions of human metabolism (Duarte et al, 2007; Ma and Goryanin, 2008). In contrast to either of the aforementioned modeling scales, HepatoNet1 provides the combination of a system-scale representation of metabolic activities and representation of the cell type-specific physical boundaries and their specific transport capacities. This allows for a highly versatile use of the model for the analysis of various liver-specific physiological functions. Conceptually, from a biological system perspective, this type of model offers a large degree of comprehensiveness, whereas retaining tissue specificity, a fundamental design principle of mammalian metabolism. HepatoNet1 is expected to provide a structural platform for computational studies on liver function. The results presented herein highlight how internal fluxes of hepatocyte metabolism and the interplay with systemic physiological parameters can be analyzed with constraint-based modeling techniques. At the same time, the framework may serve as a scaffold for complementation of kinetic and regulatory properties of enzymes and transporters for analysis of sub-networks with topological or kinetic modeling methods.
We present HepatoNet1, the first reconstruction of a comprehensive metabolic network of the human hepatocyte that is shown to accomplish a large canon of known metabolic liver functions. The network comprises 777 metabolites in six intracellular and two extracellular compartments and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. Taking the hepatic detoxification of ammonia as an example, we show how the availability of nutrients and oxygen may modulate the interplay of various metabolic pathways to allow an efficient response of the liver to perturbations of the homeostasis of blood compounds.
PMCID: PMC2964118  PMID: 20823849
computational biology; flux balance; liver; minimal flux
15.  Iron metabolism in copper-deficient swine 
Journal of Clinical Investigation  1968;47(9):2058-2069.
The way in which iron is handled by the duodenal mucosa, the reticuloendothelial system, the hepatic parenchymal cell, and the normoblast was investigated in copper-deficient swine.
Copper-deficient swine failed to absorb dietary iron at the normal rate. Increased amounts of stainable iron were observed in fixed sections of duodenum from such animals. When 59iron was administered orally, the mucosa of copper-deficient animals extracted iron from the duodenal lumen at the normal rate, but the subsequent transfer to plasma was impaired.
When intramuscular iron supplements were given to copper-deficient pigs, increased amounts of iron were found in the reticuloendothelial system, the hepatic parenchymal cells, and in normoblasts (sideroblasts). Hypoferremia was observed in the early stages of copper deficiency, even though iron stores were normal or increased. When red cells that were damaged by prolonged storage were administered, the reticuloendothelial system failed to extract and transfer the erythrocyte iron to the plasma at the normal rate. Administration of copper to copper-deficient animals with normal iron stores resulted in a prompt increase in the plasma iron.
The observed abnormalities in iron metabolism are best explained by an impaired ability of the duodenal mucosa, the reticuloendothelial system, and the hepatic parenchymal cell to release iron to the plasma. It is suggested that copper is essential to the normal release of iron from these tissues. This concept is compatible with the suggestion made by others that the transfer of iron from tissues to plasma requires the enzymatic oxidation of ferrous iron, and that the plasma copper protein, ceruloplasmin, is the enzyme (ferroxidase) which catalyzes the reaction.
Because excessive amounts of iron were found in normoblasts, it is suggested that an additional defect in iron metabolism affects these cells and plays a major role in the development of anemia. As a result of the proposed defect, iron cannot be incorporated into hemoglobin and, instead, accumulates as nonhemoglobin iron.
PMCID: PMC297366  PMID: 5675426
16.  Sequence analysis of dolphin ferritin H and L subunits and possible iron-dependent translational control of dolphin ferritin gene 
Iron-storage protein, ferritin plays a central role in iron metabolism. Ferritin has dual function to store iron and segregate iron for protection of iron-catalyzed reactive oxygen species. Tissue ferritin is composed of two kinds of subunits (H: heavy chain or heart-type subunit; L: light chain or liver-type subunit). Ferritin gene expression is controlled at translational level in iron-dependent manner or at transcriptional level in iron-independent manner. However, sequencing analysis of marine mammalian ferritin subunits has not yet been performed fully. The purpose of this study is to reveal cDNA-derived amino acid sequences of cetacean ferritin H and L subunits, and demonstrate the possibility of expression of these subunits, especially H subunit, by iron.
Sequence analyses of cetacean ferritin H and L subunits were performed by direct sequencing of polymerase chain reaction (PCR) fragments from cDNAs generated via reverse transcription-PCR of leukocyte total RNA prepared from blood samples of six different dolphin species (Pseudorca crassidens, Lagenorhynchus obliquidens, Grampus griseus, Globicephala macrorhynchus, Tursiops truncatus, and Delphinapterus leucas). The putative iron-responsive element sequence in the 5'-untranslated region of the six different dolphin species was revealed by direct sequencing of PCR fragments obtained using leukocyte genomic DNA.
Dolphin H and L subunits consist of 182 and 174 amino acids, respectively, and amino acid sequence identities of ferritin subunits among these dolphins are highly conserved (H: 99–100%, (99→98) ; L: 98–100%). The conserved 28 bp IRE sequence was located -144 bp upstream from the initiation codon in the six different dolphin species.
These results indicate that six different dolphin species have conserved ferritin sequences, and suggest that these genes are iron-dependently expressed.
PMCID: PMC2603009  PMID: 18954429
17.  Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions 
A human alveolar macrophage genome-scale metabolic reconstruction was reconstructed from tailoring a global human metabolic network, Recon 1, by using computational algorithms and manual curation.A genome-scale host–pathogen network of the human alveolar macrophage and Mycobacterium tuberculosis is presented. This involved integrating two genome-scale network reconstructions.The reaction activity and gene essentiality predictions of the host–pathogen model represent a more accurate depiction of infection.Integration of high-throughput data into a host-pathogen model followed by systems analysis was performed in order to elucidate major metabolic differences under different types of M. tuberculosis infection.
Mycobacterium tuberculosis (M. tb) is an insidious and highly persistent pathogen that affects one-third of the world's population (WHO, 2009). Metabolism is foundational to M. tb's infection ability and the ensuing host–pathogen interactions. In addition, M. tb has a heterogeneous clinical presentation and can infect virtually every tissue. Depending on the location of the infection, different metabolic pathways are active and inactive in both the host and pathogen cells. In this study, we sought to model the host–pathogen interactions of the human alveolar macrophage and M. tb as well as detail the metabolic differences in specific infection types using genome-scale metabolic reconstructions (Figure 4A).
Genome-scale metabolic reconstructions are knowledge bases of all known metabolic reactions of a given organism. Reconstructions have been shown to elucidate the mechanistic genotype-to-phenotype relationship through the integration of high-throughput and physiological data (Oberhardt et al, 2009). Genome-scale reconstructions are converted into mathematical models under the constraints-based reconstruction and analysis (COBRA) platform (Becker et al, 2007). COBRA models use network stoichiometry and steady-state mass balances to define a solution space of potential flux states that a network can take. Thus, the COBRA approach does not require kinetic parameters.
Recently, the global human metabolic network, Recon 1, has been reconstructed (Duarte et al, 2007). To understand the metabolic host–pathogen integrations of M. tb with its human host, we first tailored the global human metabolic network into a cell-specific metabolic reconstruction of the human alveolar macrophage. This was carried out using established computational algorithms (Becker and Palsson, 2008; Shlomi et al, 2008) and manual curation to confirm the included and excluded reactions. The human alveolar macrophage reconstruction, iAB-AMØ-1410, accounts for 1410 genes, 3012 intracellular reactions, and 2572 metabolites (Figure 4C). iAB-AMØ-1410 was able to accurately predict maximum ATP and NO production rates obtained from experimental data (Griscavage et al, 1993; Newsholme et al, 1999).
The second step to studying host–pathogen interactions was integration of the human alveolar macrophage reconstruction with an existing genome-scale metabolic model of M. tb, iNJ661 (Jamshidi and Palsson, 2007). Interfacial constraints were set to create a phagosomal environment that was hypoxic, nitrosative, rich in fatty acids, and poor in carbohydrates. From the onset, it was apparent that some oxygen (<15% of in vitro uptake) was required for proper simulations. In addition, algorithmic tailoring of the M. tb biomass objective function was performed to better represent an infectious state. The integrated host–pathogen metabolic reconstruction was dubbed iAB-AMØ-1410-Mt-661.
Analysis of the integrated host–pathogen metabolic reconstruction resulted in three main findings. First, by setting interfacial constraints and tailoring the biomass objective function, the solution space better represents an infectious state. Without adding artificial constraints to the host portion of the integrated model, the iAB-AMØ-1410 solution space is greatly reduced (Figure 4B). Macrophage glycolysis and nitric oxide production are up-regulated and macrophage ATP production, nucleotide synthesis, and amino-acid metabolism are suppressed. In addition, M. tb glycolysis is suppressed and isocitrate lyase is up-regulated for generation of acetyl-CoA. Fatty acid oxidation pathways and production of mycolic acids are increased, while production of nucleotides, peptidoglycans, and phenolic glycolipids are reduced. The modified solution space of the alveolar macrophage and M. tb better represents the infectious state.
Second, the host-pathogen model more accurately predicts M. tb gene deletion tests than the current in vitro model, iNJ661. The host-pathogen model predicted 11 essential genes and 37 unessential genes differently than iNJ661. A total of 22 of the differentially predicted genes have been experimentally characterized (Sassetti and Rubin, 2003; Sohaskey, 2008). The host-pathogen model correctly predicted 18 of the 22 genes. Thus, iAB-AMØ-1410-Mt-661 is a more accurate platform for studying infectious states of M. tb.
Finally, we sought to determine metabolic differences in both the macrophage and M. tb between three different types of infection: latent, pulmonary, and meningeal. Transcription profiling data of the macrophage for the three infections (Thuong et al, 2008) were integrated in the context of the host–pathogen network to elucidate the reaction activity of the three infections. There was wide heterogeneity in the three infection states; some of these differences are highlighted. Macrophage hyaluronan synthase and export were only active in the pulmonary infection. This is potentially interesting from a pharmaceutical viewpoint as hyaluronan has been implicated as a potential carbon source for extracellular M. tb (Hirayama et al, 2009). In addition, we detected metabolic activity differences in M. tb pathways that have been previously discussed as potential drug targets (Eoh et al, 2007; Boshoff et al, 2008). Polyprenyl metabolic reactions were only active in the latent state infection, while de novo synthesis of nicotinamide cofactors was only active in latent and meningeal M. tb infections.
Host-pathogen modeling represents a novel approach for studying metabolic interactions during infection. iAB-AMØ-1410-Mt-661 is a more accurate platform for understanding the biology and pathophysiology of M. tb infection. Most importantly, genome-scale metabolic reconstructions can act as scaffolds for integrating high-throughput data. Particularly, in this study we were able to discern reaction activity differences between different infection types.
Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host–pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host–pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host–pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host–pathogen network and were able to describe three distinct pathological states. Integrated host–pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.
PMCID: PMC2990636  PMID: 20959820
computational biology; host–pathogen; Mycobacterium tuberculosis; systems biology; macrophage
18.  Mechanisms of mammalian iron homeostasis 
Biochemistry  2012;51(29):5705-5724.
Iron is vital for almost all organisms because of its ability to donate and accept electrons with relative ease. It serves as a cofactor for many proteins and enzymes necessary for oxygen and energy metabolism, as well as for several other essential processes. Mammalian cells utilize multiple mechanisms to acquire iron. Disruption of iron homeostasis is associated with various human diseases: iron deficiency resulting from defects in acquisition or distribution of the metal causes anemia; whereas iron surfeit resulting from excessive iron absorption or defective utilization causes abnormal tissue iron deposition, leading to oxidative damage. Mammals utilize distinct mechanisms to regulate iron homeostasis at the systemic and cellular levels. These involve the hormone hepcidin and iron regulatory proteins, which collectively ensure iron balance. This review outlines recent advances in iron regulatory pathways, as well as in mechanisms underlying intracellular iron trafficking, an important but less-studied area of mammalian iron homeostasis.
PMCID: PMC3572738  PMID: 22703180
19.  Iron-Responsive miR-485-3p Regulates Cellular Iron Homeostasis by Targeting Ferroportin 
PLoS Genetics  2013;9(4):e1003408.
Ferroportin (FPN) is the only known cellular iron exporter in mammalian cells and plays a critical role in the maintenance of both cellular and systemic iron balance. During iron deprivation, the translation of FPN is repressed by iron regulatory proteins (IRPs), which bind to the 5′ untranslated region (UTR), to reduce iron export and preserve cellular iron. Here, we report a novel iron-responsive mechanism for the post-transcriptional regulation of FPN, mediated by miR-485-3p, which is induced during iron deficiency and represses FPN expression by directly targeting the FPN 3′UTR. The overexpression of miR-485-3p represses FPN expression and leads to increased cellular ferritin levels, consistent with increased cellular iron. Conversely, both inhibition of miR-485-3p activity and mutation of the miR-485-3p target sites on the FPN 3′UTR are able to relieve FPN repression and lead to decreased cellular iron levels. Together, these findings support a model that includes both IRPs and microRNAs as iron-responsive post-transcriptional regulators of FPN. The involvement of microRNA in the iron-responsive regulation of FPN offers additional stability and fine-tuning of iron homeostasis within different cellular contexts. MiR-485-3p-mediated repression of FPN may also offer a novel potential therapeutic mechanism for circumventing hepcidin-resistant mechanisms responsible for some iron overload diseases.
Author Summary
Cellular iron homeostasis is maintained by a sophisticated system that responds to iron levels and coordinates the expression of targets important for balancing iron export and uptake with intracellular storage and utilization. Ferroportin is the only known cellular iron exporter in mammalian cells and plays a critical role in both cellular and systemic iron balance. Thus the ability to regulate cellular iron export is of great interest in the search for therapeutic strategies to control dysregulated iron homeostasis, iron overload disorders, and conditions affected by cellular iron concentrations such as antimicrobial resistance. During iron deprivation, repression of ferroportin levels reduces iron export and preserves cellular iron. Ferroportin translation is known to be repressed by iron regulatory proteins that bind to the 5′UTR, yet alternative mechanisms that can post-transcriptionally regulate ferroportin have not been previously reported. Here, we find that miR-485-3p is induced during iron deficiency and represses ferroportin by directly targeting its 3′UTR, and further experimental evidence supports a model that includes both iron regulatory proteins and microRNAs as post-transcriptional regulators of ferroportin. These findings demonstrate a novel role for microRNAs in the cellular response to iron deficiency and can have therapeutic implications for various diseases of iron homeostasis.
PMCID: PMC3616902  PMID: 23593016
20.  Siderophore-mediated iron trafficking in humans is regulated by iron 
Siderophores are best known as small iron binding molecules that facilitate microbial iron transport. In our previous study we identified a siderophore-like molecule in mammalian cells and found that its biogenesis is evolutionarily conserved. A member of the short chain dehydrogenase family of reductases, 3-OH butyrate dehydrogenase (BDH2) catalyzes a rate-limiting step in the biogenesis of the mammalian siderophore. We have shown that depletion of the mammalian siderophore by inhibiting expression of bdh2 results in abnormal accumulation of cellular iron and mitochondrial iron deficiency. These observations suggest that the mammalian siderophore is a critical regulator of cellular iron homeostasis and facilitates mitochondrial iron import. By utilizing bioinformatics, we identified an iron-responsive element (IRE; a stem-loop structure that regulates genes expression post-transcriptionally upon binding to iron regulatory proteins or IRPs) in the 3′-untranslated region (3′-UTR) of the human BDH2 (hBDH2) gene. In cultured cells as well as in patient samples we now demonstrate that the IRE confers iron-dependent regulation on hBDH2 and binds IRPs in RNA electrophoretic mobility shift assays. In addition, we show that the hBDH2 IRE associates with IRPs in cells and that abrogation of IRPs by RNAi eliminates the iron-dependent regulation of hBDH2 mRNA. The key physiologic implication is that iron-mediated post-transcriptional regulation of hBDH2 controls mitochondrial iron homeostasis in human cells. These observations provide a new and an unanticipated mechanism by which iron regulates its intracellular trafficking.
PMCID: PMC3567482  PMID: 22527885
Mammalian siderophore; IRE-IRP regulation; hemochromatosis
21.  The Iron Metallome in Eukaryotic Organisms 
Metal ions in life sciences  2013;12:241-278.
This chapter is focused on the iron metallome in eukaryotes at the cellular and subcellular level, including properties, utilization in metalloproteins, trafficking, storage, and regulation of these processes. Studies in the model eukaryote Saccharomyces cerevisiae and mammalian cells will be highlighted. The discussion of iron properties will center on the speciation and localization of intracellular iron as well as the cellular and molecular mechanisms for coping with both low iron bioavailability and iron toxicity. The section on iron metalloproteins will emphasize heme, iron-sulfur cluster, and non-heme iron centers, particularly their cellular roles and mechanisms of assembly. The section on iron uptake, trafficking, and storage will compare methods used by yeast and mammalian cells to import iron, how this iron is brought into various organelles, and types of iron storage proteins. Regulation of these processes will be compared between yeast and mammalian cells at the transcriptional, post-transcriptional, and post-translational levels.
PMCID: PMC3924584  PMID: 23595675
eukaryote; heme; iron metallome; iron-sulfur cluster; iron trafficking; metal homeostasis
22.  Iron Deprivation in Synechocystis: Inference of Pathways, Non-coding RNAs, and Regulatory Elements from Comprehensive Expression Profiling 
G3: Genes|Genomes|Genetics  2012;2(12):1475-1495.
Iron is an essential cofactor in many metabolic reactions. Mechanisms controlling iron homeostasis need to respond rapidly to changes in extracellular conditions, but they must also keep the concentration of intracellular iron under strict control to avoid the generation of damaging reactive oxygen species. Due to its role as a redox carrier in photosynthesis, the iron quota in cyanobacteria is about 10 times higher than in model enterobacteria. The molecular details of how such a high quota is regulated are obscure. Here we present experiments that shed light on the iron regulatory system in cyanobacteria. We measured time-resolved changes in gene expression after iron depletion in the cyanobacterium Synechocystis sp. PCC 6803 using a comprehensive microarray platform, monitoring both protein-coding and non-coding transcripts. In total, less than a fifth of all protein-coding genes were differentially expressed during the first 72 hr. Many of these proteins are associated with iron transport, photosynthesis, or ATP synthesis. Comparing our data with three previous studies, we identified a core set of 28 genes involved in iron stress response. Among them were genes important for assimilation of inorganic carbon, suggesting a link between the carbon and iron regulatory networks. Nine of the 28 genes have unknown functions and constitute key targets for further functional analysis. Statistical and clustering analyses identified 10 small RNAs, 62 antisense RNAs, four 5′UTRs, and seven intragenic elements as potential novel components of the iron regulatory network in Synechocystis. Hence, our genome-wide expression profiling indicates an unprecedented complexity in the iron regulatory network of cyanobacteria.
PMCID: PMC3516471  PMID: 23275872
iron homeostasis; expression profiling; regulation; non-coding RNA; cyanobacteria
23.  Altered Ferritin Subunit Composition: Change in Iron Metabolism in Lens Epithelial Cells and Downstream Effects on Glutathione Levels and VEGF Secretion 
Ferritin subunit composition is cell-type specific and controls iron metabolism in cells. In this study, alterations of subunit composition in lens epithelial cells had profound effects on cellular homeostatic mechanisms dependent on iron metabolism.
The iron storage protein ferritin is necessary for the safe storage of iron and for protection against the production of iron-catalyzed oxidative damage. Ferritin is composed of 24 subunits of two types: heavy (H) and light (L). The ratio of these subunits is tissue specific, and alteration of this ratio can have profound effects on iron storage and availability. In the present study, siRNA for each of the chains was used to alter the ferritin H:L chain ratio and to determine the effect of these changes on ferritin synthesis, iron metabolism, and downstream effects on iron-responsive pathways in canine lens epithelial cells.
Primary cultures of canine lens epithelial cells were used. The cells were transfected with custom-made siRNA for canine ferritin H- and L-chains. De novo ferritin synthesis was determined by labeling newly synthesized ferritin chains with 35S-methionine, immunoprecipitation, and separation by SDS-PAGE. Iron uptake into cells and incorporation into ferritin was measured by incubating the cells with 59Fe-labeled transferrin. Western blot analysis was used to determine the presence of transferrin receptor, and ELISA was used to determine total ferritin concentration. Ferritin localization in the cells was determined by immunofluorescence labeling. VEGF, glutathione secretion levels, and cystine uptake were measured.
FHsiRNA decreased ferritin H-chain synthesis, but doubled ferritin L-chain synthesis. FLsiRNA decreased both ferritin H- and L-chain synthesis. The degradation of ferritin H-chain was blocked by both siRNAs, whereas only FHsiRNA blocked the degradation of ferritin L-chain, which caused significant accumulation of ferritin L-chain in the cells. This excess ferritin L-chain was found in inclusion bodies, some of which were co-localized with lysosomes. Iron storage in ferritin was greatly reduced by FHsiRNA, resulting in increased iron availability, as noted by a decrease in transferrin receptor levels and iron uptake from transferrin. Increased iron availability also increased cystine uptake and glutathione concentration and decreased nuclear translocation of hypoxia-inducible factor 1-α and vascular endothelial growth factor (VEGF) accumulation in the cell-conditioned medium.
Most of the effects of altering the ferritin H:L ratio with the specific siRNAs were due to changes in the availability of iron in a labile pool. They caused significant changes in iron uptake and storage, the rate of ferritin synthesis and degradation, the secretion of VEGF, and the levels of glutathione in cultured lens epithelial cells. These profound effects clearly demonstrate that maintenance of a specific H:L ratio is part of a basic cellular homeostatic mechanism.
PMCID: PMC2941172  PMID: 20805568
24.  Serum Iron Levels and the Risk of Parkinson Disease: A Mendelian Randomization Study 
PLoS Medicine  2013;10(6):e1001462.
In this study, Mendelian randomization was used to study genes known to modify iron levels, and the effect of iron on Parkinson's disease (PD) risk was estimated. Based on estimates of the genetic effects on both iron and PD obtained from the largest sample meta-analyzed to date, the findings suggest that increased iron levels in the blood are associated with a 3% reduction in the risk of Parkinson's disease for every 10 µg/dL increase in iron. The results of this analysis have potentially important implications for future research into the prevention of Parkinson's disease.
Please see later in the article for the Editors' Summary
Although levels of iron are known to be increased in the brains of patients with Parkinson disease (PD), epidemiological evidence on a possible effect of iron blood levels on PD risk is inconclusive, with effects reported in opposite directions. Epidemiological studies suffer from problems of confounding and reverse causation, and mendelian randomization (MR) represents an alternative approach to provide unconfounded estimates of the effects of biomarkers on disease. We performed a MR study where genes known to modify iron levels were used as instruments to estimate the effect of iron on PD risk, based on estimates of the genetic effects on both iron and PD obtained from the largest sample meta-analyzed to date.
Methods and Findings
We used as instrumental variables three genetic variants influencing iron levels, HFE rs1800562, HFE rs1799945, and TMPRSS6 rs855791. Estimates of their effect on serum iron were based on a recent genome-wide meta-analysis of 21,567 individuals, while estimates of their effect on PD risk were obtained through meta-analysis of genome-wide and candidate gene studies with 20,809 PD cases and 88,892 controls. Separate MR estimates of the effect of iron on PD were obtained for each variant and pooled by meta-analysis. We investigated heterogeneity across the three estimates as an indication of possible pleiotropy and found no evidence of it. The combined MR estimate showed a statistically significant protective effect of iron, with a relative risk reduction for PD of 3% (95% CI 1%–6%; p = 0.001) per 10 µg/dl increase in serum iron.
Our study suggests that increased iron levels are causally associated with a decreased risk of developing PD. Further studies are needed to understand the pathophysiological mechanism of action of serum iron on PD risk before recommendations can be made.
Please see later in the article for the Editors' Summary
Editors' Summary
Parkinson disease is a degenerative disorder of the central nervous system caused by the death of dopamine-generating cells in the substania nigra, a region of the midbrain. The earliest symptoms are usually movement-related and include tremor, slow movements, and difficulty walking, and later cognitive and behavioral problems may arise, with dementia commonly occurring in the advanced stages of the disease. Parkinson disease affects around ten million people world-wide and incidence increases with age, with men more affected than women. To date, the causes of Parkinson disease remain unknown although a combination of genetic and environmental factors is thought to play a role. Identifying possible modifiable risks is an important step in the possible prevention of Parkinson disease.
Why Was This Study Done?
Previous studies have shown a possible association between lower blood levels of iron in people with Parkinson disease compared with controls, although the quality of these studies makes this finding difficult to interpret. So in this study, the researchers used a mendelian randomization approach to investigate whether there was any evidence of an effect of blood iron levels on the risk of Parkinson disease and if so to further explore the direction and scale of any link. Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in situations where it is inappropriate to perform a randomized controlled trial.
What Did the Researchers Do and Find?
The researchers estimated the effect of blood iron levels on the risk of Parkinson disease using three polymorphisms in two genes, HFE and TMPRSS6. For each polymorphism, they performed a meta-analysis combining the results of studies investigating the genetic effect on iron levels, which included almost 22,000 people from Europe and Australia, and a meta-analysis of studies investigating the genetic effect on the risk of Parkinson disease, which included a total of 20,809 people with Parkinson disease and 88,892 controls from Europe and North America. They then performed three separate mendelian randomization analyses to estimate the effect of iron on Parkinson disease for the three polymorphisms. By combining the three estimates, they obtained a statistically significant odds ratio of 0.97 for Parkinson disease per 10 µg/dl increase in iron, corresponding to a 3% reduction in the risk of Parkinson disease for every 10 µg/dl increase in blood iron. Since genotype influences on blood iron levels represent differences that generally persist throughout adult life, the combined mendelian randomization estimate reflects an effect of iron over the course of a lifetime.
What Do These Findings Mean?
These findings suggest that increased iron levels in the blood are associated with a 3% reduction in the risk of Parkinson disease for every 10 µg/dl increase in iron. This finding is important as it suggests that increased blood iron levels may have a protective effect against Parkinson disease, although the underlying mechanism remains unclear. Furthermore, although mendelian randomization is an increasingly used approach to address the issue of classical confounding, there may be remaining confounding factors specific of mendelian randomization that may influence the interpretation of this study. Nevertheless, the results of this analysis have potentially important implications for future research into the prevention of Parkinson disease. Further studies on the underlying mechanisms are needed before any specific treatment recommendations can be proposed.
Additional Information
Please access these Web sites via the online version of this summary at
The National Institutes of Neurological Disorder and Stroke, MedlinePlus, and NHS Choices have several pages with comprehensive information on Parkinson disease
Wikipedia gives an explanation of mendelian randomization (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
PMCID: PMC3672214  PMID: 23750121
25.  A Computational Model of Liver Iron Metabolism 
PLoS Computational Biology  2013;9(11):e1003299.
Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.
Author Summary
Iron is an essential nutrient required for healthy life but, in excess, is the cause of debilitating and even fatal conditions. The most common genetic disorder in humans caused by a mutation, haemochromatosis, results in an iron overload in the liver. Indeed, the liver plays a central role in the regulation of iron. Recently, an increasing amount of detail has been discovered about molecules related to iron metabolism, but an understanding of how they work together and regulate iron levels (in healthy people) or fail to do it (in disease) is still missing. We present a mathematical model of the regulation of liver iron metabolism that provides explanations of its dynamics and allows further hypotheses to be formulated and later tested in experiments. Importantly, the model reproduces accurately the healthy liver iron homeostasis and simulates haemochromatosis, showing how the causative mutation leads to iron overload. We investigate how best to control iron regulation and identified reactions that can be targets of new medicines to treat iron overload. The model provides a virtual laboratory for investigating iron metabolism and improves understanding of the method by which the liver senses and controls iron levels.
PMCID: PMC3820522  PMID: 24244122

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