Annals of Clinical & Laboratory Science 35:230-239 (2005)
© 2005 Association of Clinical Scientists
Global Genomic Approaches to the Iron-Regulated Proteome
Ying Liu,
Zvezdana Popovich and
Douglas M. Templeton
Department of Laboratory Medicine and Pathobiology, University of Toronto,Toronto, Canada
Address correspondence to Douglas M. Templeton M.D., Ph.D., Department of Laboratory Medicine and Pathobiology, Medical Sciences Building Rm 6302, University of Toronto, 1 King’s College Circle, Toronto M5S 1A8, Canada; tel 416 978 3972; fax 416 978 5959; e-mail doug.templeton{at}utoronto.ca.
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Abstract
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Iron interacts with cells to regulate the proteome through complex effects on gene expression. In simple organisms such as bacteria and yeast, intra- and extra-cellular iron influences gene expression through defined signal transduction pathways. In higher organisms, effects are probably mediated at the transcriptional level through secondary effects of reactive oxygen species, while post-transcriptional effects operate through well-defined pathways involving iron-regulatory proteins. To investigate the impact of iron levels on gene expression and the proteome, approaches such as differential display and subtractive hybridization have the advantage of surveying the entire geneome. However, they are technically demanding and have given way to microarray techniques. To date, numerous microarray experiments with various organisms have not yielded any definitive picture of the role of iron. Common themes throughout such studies are that both iron excess and iron depletion influence expression of proteins related to energy metabolism, cell proliferation, matrix structure, and the metabolism of iron itself. That no consistent set of genes is involved from one study to the next probably results both from the uncertainties inherent in the technique and the biological variability of the systems under study. We briefly describe two types of iron-dependent microarray experiments from our laboratory to examine major cellular targets of iron toxicity. Using Affymetrix oligonucleotide arrays with cardiac cells, we found several hundred genes whose mRNA levels were affected by iron, including an increase in several genes responding to oxidative stress and a decrease in several kinases and phosphatases. In a simpler experiment using a human liver cell line with a limited cDNA array, we targeted 13 genes affected by iron chelation. Metabolic pathway analysis shows links of 5 of these through phorbol ester responsiveness, and additional links through prostaglandin E2. We conclude that definitive understanding of the complex iron-regulated proteome requires global gene approaches and rigorous interlaboratory standardization.
(received 22 April 2005; accepted 28 April 2005)
Keywords: gene expression, microarray, genome, iron overload, iron chelation, iron-responsive elements
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Introduction
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Iron is required by living organisms for a variety of purposes related to its favourable redox properties and rich coordination chemistry. Thus, in the human organism, it is essential for the mitochondrial electron transport chain, as part of cytochromes and iron-sulfur proteins, and for nucleic acid synthesis as a cofactor in ribonucleotide reductase. The body utilizes about 20 mg of Fe per day for hemoglobin synthesis to maintain the blood’s oxygen-carrying capacity. On the other hand, iron’s redox properties contribute to its toxicity. Through participation in Fenton chemistry, iron produces reactive oxygen species (ROS) that are harmful to biological molecules. And replete iron stores also increase our susceptibility to infection; many opportunistic organisms rely on the host to supply precious iron. As might be expected of such a bioactive metal, iron also influences cellular responses and phenotype through diverse effects on gene expression. Our purpose here is to survey various studies that have taken a broad genome/proteome-based approach to understanding the role of iron in orchestrating cellular biochemistry.
Major steps in understanding iron metabolism were made over the last decade [1]. A specific role for iron in regulating genes involved in its own metabolism and trafficking at a post-transcriptional level [2–4] became textbook examples of such a mode of regulation. A perhaps reawakened interest in the metal saw classical molecular biological approaches such as differential display and subtraction cloning used to address more global effects of iron [5,6]. Recently, microarray technology has dominated such efforts. But iron remains somewhat enigmatic. Multiple effects on gene expression have been proposed but remain hard to categorize in terms of a unified response [7]. And, in view of its potential effects on multiple signaling mechanisms, iron overload, in cultured cells at least, often has surprisingly little effect. Distinguishing acute toxic responses from adaptive ones is a goal of such studies.
The lack of a dramatic effect of iron overload on gene expression notwithstanding, iron does influence a variety of cellular processes. We have recently reviewed effects that go beyond regulation of genes related to iron metabolism itself (operating at both transcriptional and translational levels) to genes grouped as affecting oxidative stress responses (eg, glutathione peroxidase, heme oxygenase-1, metallothionein); tissue fibrosis (eg, collagen, TGF-ß); energetics of metabolism (eg, aldolase, lactate dehydrogenase); and cell cycle control (eg, retinoblastoma protein, p21, various cyclins) [7].
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Signaling Evoked by Iron
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Studies with bacteria have linked iron with signal transduction mechanisms that terminate in regulating gene expression, and these may prove to be instructive for understanding iron in higher organisms. The E. coli Fur protein has homologues in many bacteria. It can act directly as a transcriptional repressor when Fe2+ is present as a cofactor, and turns off genes related to iron uptake. When iron is scarce, then, derepression alone can increase synthesis of iron transport proteins and enzymes of siderophore biosynthesis [8]. However, in other systems, positive regulation is required for synthesis of the iron transport systems. A case in point is ferric citrate signaling through the Fec proteins and TonB in E. coli. In situations of low iron, the Fec proteins are transcribed following removal of Fur repression, but the transport system is inactive. When a low concentration of ferric dicitrate is present in the cell’s vicinity, it is sensed by binding to FecA in the outer membrane. This leads to interaction of FecA with a protein complex that includes FecR and TonB on the inner membrane, and this induces a conformational change in FecR. FecR then interacts with FecI in the cell, activating it to bind to the FecA promoter, recruit RNA polymerase, and drive transcription of genes of the Fec iron transport system (FecA, FecB, FecC, etc.) that are contiguously arranged on the chromosome. This leads to increased iron uptake through FecA, which is thus both a regulator and a transporter. Again, this system is repressed by Fur at high iron concentrations. A number of other bacteria express components homologous to those of the Fec and Fur systems, and signal transduction appears to be a general theme in iron-dependent bacterial gene regulation. Another good example is the PmrA/PmrB system of Salmonella that responds to extra-cellular Fe3+ to activate a regulon involved in iron resistance [9].
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Transcriptional and Post-transcriptional Regulation
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In general, transcriptional regulation by iron in higher animals is poorly understood. Generation of ROS by iron may be a general means of gene regulation through the ROS-activated transcription factor NF-
B [10,11]. For instance, an NF-B binding site is found in the ferritin H chain gene promoter region [12]. ROS also lead to lipid peroxidation. Products of lipid peroxidation in turn activate transcription factors Sp1 and Sp3, accounting at least in part for an increased expression of a1(I) collagen [13,14]. On the other hand, hypoxia-like responses play a central role in gene regulation by iron chelators [7]. Hypoxia-inducible factor (HIF-1
) is a transcription factor that binds to a hypoxia-responsive element (HRE) in a number of target genes. The iron chelator deferoxamine (DFO) mimics the effects of hypoxia on a number of genes [15,16] including erythropoietin [17] and the transferrin receptor [18]. A possible mechanism involves the Fe(II)/2-oxoglutarate-dependent dioxygenase, which hydroxylates critical proline and arginine residues in HIF-1 under normoxic and iron-replete conditions, targeting HIF-1 for degradation by the proteasome [19,20].
Post-transcriptional regulation in eukaryotes of several genes controlling iron metabolism and energy utilization is better understood. It involves iron-regulatory elements (IRE) in mRNA, and associated IRE-binding iron-regulatory proteins (IRP) [2,3,21,22]. Iron-related genes containing IREs include those coding for ferritin, transferrin receptor, divalent metal transporter-1 (DMT-1), and ferroportin. In addition, IREs are found in the transcripts for the control enzyme of heme synthesis, 
-aminolevulinate synthase (-ALAS), and the tricarboxylic acid cycle enzymes, mitochondrial aconitase and succinate dehydrogenase.
Two IRPs are known [3,4], designated IRP1 and IRP2. IRP1 contains a [4Fe-4S] iron-sulfur cluster that resides in a cleft between 2 domains of the protein. It is a homologue of mitochondrial aconitase, and in the holo form is a cytosolic aconitase that lacks RNA binding activity. The [4Fe-4S] cluster in m-aconitase contains a labile iron atom and is susceptible to conversion to [3Fe-4S] [23,24]. Loss of iron may lead to cluster disassembly, allowing the protein to open up into an RNA binding form. Thus, IRP1 is a bifunctional protein. When the iron-sulfur cluster is intact it exhibits aconitase activity; in iron-deficient conditions, IRE binding occurs. IRP2 is a homologue of IRP1 that lacks an iron-sulfur cluster and contains an additional 73-amino-acid motif [3,21]. The 73-amino-acid sequence confers susceptibility to iron-dependent oxidative damage [25], subsequently involving degradation by the proteasome [26]. Thus, IRP2 RNA binding activity, like that of IRP1, is increased at low iron levels; unlike IRP1 binding that requires only reconstitution of the iron-sulfur cluster, IRP2 reactivation requires new protein synthesis. IRP1 may confer a rapid response capability to the IRP/IRE system, with IRP2 reflecting longer-term adaptation.
IREs are sequences of about 30 nucleotides that occur in the untranslated regions (either 3' or 5' UTR) of mRNA [4,22]. They have a consensus structure with a base-paired stem of about 10 base pairs in length, a central unpaired loop of sequence CAGUG, and a conserved C, 5 bases upstream of this loop, that forms an unpaired bulge in the stem and is necessary for protein binding. IREs in the 5' UTR of transcripts occur in single copies, and are found in ferritin, ferroportin, m-aconitase, succinate dehydrogenase, and –ALAS mRNAs. Their binding to IRPs is increased by iron deficiency, and when they are occupied, progression of the translational machinery is blocked. IREs in the 3' UTR of TfR and DMT-1 mRNAs are not positioned to block translation. Rather, they form complexes with IRPs to stabilize the RNA against degradation. The transferrin receptor mRNA contains 5 copies of IRE [3,27], while the single IRE in DMT-1 mRNA appears to participate in a more complicated regulatory mechanism [28,29]. It is teleologically apparent why the iron storage protein ferritin would decrease and the transferrin receptor increase in conditions of low iron.
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Gene Expression Screening
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Because of the wide variety of genes that are affected by iron or iron chelation, it is desirable to use genome-wide screening of responses to derive general patterns in particular experimental or pathological circumstances. Earlier efforts have now largely given way to studies with DNA microarrays (see below). In general, microarrays offer rapid and simple screening of potentially thousands of genes, whereas the earlier methods are more laborious and technically demanding. However, it should be noted that microarrays can only provide information on the genes chosen for inclusion on the gene chip, whereas other screening methods can potentially provide information on all genes affected, whether known or unknown.
Among the earlier approaches, Ye and Connor [6,30] used suppression subtraction hybridization (SSH) to generate cDNA of human astrocytoma cell mRNA after exposing the cells to either conditions of high iron loading (100 µg/ml for 48 hr, as ferric ammonium citrate (FAC)) or chelation with DFO. In this approach [31], DNA is digested by restriction enzymes into small fragments and a pool (the tester) is divided in half and tagged with 2 different adapters at the 5' end. Each pool is hybridized with an excess of cDNA (the driver). Based on hybridization kinetics, more abundant species in the tester are enriched. Then the hybridization products are mixed for a second round of hybridization, followed by 2 rounds of PCR with primers to the adapters. The PCR products are ligated into vectors and cloned in E. coli. Genes expressed at higher levels in the tester are recovered at higher frequency. To identify down-regulated genes, the experiment is repeated with driver and tester reversed. Clearly the approach is time consuming and technicaly demanding. In a DFO experiment with astrocytoma cells [30], following elimination of repeat or hybridizationally similar sequences and reverse Northern blot analysis, forward subtraction yielded cDNA fragments increased by DFO, and backward subtraction yielded those decreased. Twelve mRNA species were increased by DFO (5 confirmed by Northern blotting) and 29 were decreased (most confirmed by Northern blots). Transcripts known to be regulated by iron, such as ferritin and Tfr, were not identified by this approach. However, a parallel SSH study with iron-treated astrocytes performed by the same authors [6] revealed the expected increase in ferritin mRNA. DFO down-regulated some genes involved in energy production, such as cytochrome c oxidase and NADH:ubiquinone oxidoreductase, and some related to protease function. The authors noted disconnection between gene expression and protein function, as iron overload has been reported to decrease activity of cytochrome c oxidase. However, it is also possible that optimal iron levels are needed for expression of a given gene, and both increase or decrease of iron may suppress expression. A case in point is TGF-ß in cardiac cells [32], which is decreased by both iron loading and chelation. With iron overloaded astrocytes, SSH identified 18 transcripts that were increased by iron loading and 19 that were decreased. Overall, no general pattern was obvious, but some genes involved in energy production, such as -enolase, were actually decreased by iron exposure.
An approach taken by Barisani et al [5] was conventional differential display. Anchored primers are designed to bind to the 5' boundary of the poly-A tail of a transcript to be reverse-transcribed. Following reverse transcription with additional upstream primers of arbitrary sequences, mRNA sub-populations are visualized by denaturing PAGE. This allows direct side-by-side comparison of mRNAs between or among related cells. As in the SSH approach, differentially expressed PCR products are selected, eluted, cloned, sequenced, and analyzed with reverse Northern blotting. HepG2 human hepatocarcinoma cells were treated for 3 to 7 days with 40 µg/ml iron, again as FAC. Time-dependent decreases in apolipoprotein B100 mRNA and increases in aldose reductase and semaphorin transcripts were demonstrated, but the significance of this is unclear and the method seems rather blunt.
A high-throughput method for genomic screening of iron-responsive genes in Salmonella was reported by Bjarnason et al [33]. Digested genomic Salmonella DNA was ligated into a plasmid with the luminescence luxCDABE operon reporter gene, and this random promoter library was reintroduced into Salmonella. Luminescence was observed in low and high iron media, and clones with differential expression of 3-fold or more were selected for further study. This approach follows expression from any promoter, whether of known or unknown genes, if they are represented in the constructed library. The use of a luminescent reporter allows monitoring changes of expression from a given promoter (clone) in real time. The authors identified 182 promoters affected by increased iron exposure and 298 by iron depletion. They concluded that 7% of the Salmonella genome may be regulated directly or indirectly by iron, and both Fur-regulated and Fur-independent genes are involved. These results are biologically significant, but compared to microarray methods the approach is laborious, requiring construction of libraries, screening, and DNA sequencing. With resort to commercial micro-arrays, the synthesis and hopefully the confirmation by sequencing of the targets have already been done by the supplier of the array.
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Microarray Methods–Plants and Bacteria
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Microarray methods have been exploited well in studies of lower organisms, as selected examples will illustrate. Arabidopsis has become a prototype in studies of plant genetics. An array of about 6,000 Arabidopsis cDNAs was used to study the response of roots and shoots to growth in iron-deficient defined medium [34]. Generally, about 100 genes were depressed and a similar number induced in one day by both tissues. The numbers in both categories rose at later times. Genes of many classes were represented, but the main conclusions were that respiration increases in response to a need to increase iron import, but the demand is exceeded in roots, and oxidative phosphorylation is supplemented by increased carbon import and anaerobic metabolism.
Bacterial gene expression studies promise important new insight into the biology of human pathogens. The transcriptome of Neisseria meningitidis was studied to understand the dependence of this important human pathogen on iron availability [35]. Cultures were depleted of iron with DFO; then 100 µM ferric nitrate was added for 5 hr to see which suppressed genes were reactivated. One hundred and fifty-three genes were up-regulated by addition of iron, and 80 were down-regulated. Many of these genes were Fur-regulated, including some previously unknown to be Fur-dependent but confirmed to be so by gel shift assays. One conclusion was that such gene expression profiling might be useful to identify new iron-governed regulons in bacteria. In a different approach with the intestinal pathogen Campylobacter jejuni, early response to iron-repletion was compared to subsequent adaptation [36]. When iron-deficient organisms were supplied with iron, 647 genes were affected within 15 min, whereas only 208 genes were differentially expressed when iron-rich and iron-deficient conditions were compared at steady state. In general, genes involved in iron acquisition and oxidative stress response were down-regulated in both scenarios, while expression of genes associated with energy metabolism was increased. Changes in protein glycosylation were also revealed in this study, which may explain differences in virulence dependent upon intestinal iron availability. Further, use of a Fur mutant identified 53 Fur-regulated genes, including some that were not previously known to be so [36].
Whole-genome microarray analysis of the gram-negative animal pathogen Pasteurella multocida demonstrated that genes involved in energy metabolism and electron transport were generally decreased, and those of iron-binding and iron-transport were increased during 2 hr of iron deprivation [37]. The authors noted that 27% of the genes affected by iron deficiency in this whole-genome study had no known function, underlining the power of the microarray approach to explore new territory when a complete genome is available. In another example with an animal pathogen, 66 putative open reading frames of the virulence plasmid of the immunopathogen Rhodococcus equi were examined on a DNA microarray [38]. One group of genes, including those associated with the virulence-associated protein (vap) family, was induced by iron restriction, whereas a second group of magnesium-regulated genes was down-regulated.
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Microarray Methods–Higher Animals
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Among the initial microarray studies of iron-responsive genes was that of human HL-60 pro-monocytes by Alcantara et al [39]. The cells were tested following stimulation to differentiate with phorbol ester. Using an early commercial array that tested only 43 genes, mostly relevant to apoptosis or cell cycle regulation, the authors found that 11 genes were suppressed by DFO. Each was confirmed by RT-PCR, Northern, and/or Western analysis, and a significant general conclusion could be drawn: iron supports expression of genes involved in regulating cell cycle progression and apoptosis, and thus is fundamentally involved in the differentiation process of a hematopoietic cell line. Despite the small number of genes tested, this remains one of the most biologically instructive studies to address general genomic regulation by iron.
Muckenthaler et al [40] developed the "Iron-Chip" specifically to probe patterns of human gene regulation by iron. They initially selected about 115 genes based on "literature searches ... microarray experiments performed on filters that contain approximately 20,000 human nonredundant expressed sequence tags ... and gene lists from published microarray studies that address metabolic pathways of interest" [40]. From this and other published reports [41,42], it is difficult to evaluate the selection process. However, this specialized chip has been used with some interesting results, at least in the hands of its developers, and more genes have been added in later versions. Initial implementation of the chip used the human HeLa cell line exposed to various stresses including peroxide, nitric oxide, hemin, 100 µ M FAC, and DFO. Genes increased by iron loading with hemin or FAC included heme oxygenase-1 and several heat shock proteins. In addition to the IRP-regulated transferrin receptor gene, a decrease with FAC was seen in metallothionein-2. Lysyl oxidase and c-jun were increased more than 3-fold by DFO, and HIF-1 was unchanged by either treatment. Interestingly, similar profiles were seen with DFO and HeLa cells over-expressing the hemochromatosis gene, Hfe [40], suggesting a role of the protein in decreasing an intracellular iron pool involved in signaling, perhaps the so-called labile iron pool. The IronChip was used to study gene regulation more extensively in Hfe-deficient mice [41]. The hepatic regulator of iron metabolism, hepcidin, was decreased in Hfe–/– mice and its regulation by iron was altered. This was accompanied by dysregulation of the intestinal redox iron transporters Ireg1 and DcytB, indicating additional regulatory defects involved in hemochromatosis. Profiling gene expression in human epithelial cells, the IronChip revealed that infection with Neisseria menigitidis produces changes similar to those in uninfected cells in iron-deficient conditions [42].
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Original Studies with Liver and Heart Cells
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Three basically different microarray platforms are in current use, where the spotted target sequence is full length or partial cDNA, oligonucleotides (typically about 60-mer sequences) derived from libraries of digested cDNA, or shorter synthetic oligonucleotides typified by the Affymetrix GeneChips. cDNA arrays are preferentially used with a 2-color protocol (for example, control and treatment cDNA samples are labeled with distinguishable fluorophores and hybridized together on one array), whereas in the Affymetrix format a single sample is hybridized to each array. In principle, oligonucleotide arrays have several advantages over cDNA arrays, including higher specificity, more uniform melting temperatures, and more uniformity of spots. Affymetrix GeneChips consist of up to 16 probes representing each transcript, each a 25-mer unit, that together span the length of the gene. Perfect match sequences and mismatch (1-base change) probe pairs are used by Affymetrix to minimize non-specific cross hybridization. The major advantage of Affymetrix GeneChips over non-synthetic types of microarrays is the minimized chip-to-chip variation resulting from the use of synthetic targets. On the other hand, synthetic oligonucleotides are expensive compared to direct spotting on cDNA arrays, and this limits the number of experiments that most laboratories will conduct. Several head-to-head comparisons of different platforms have been performed, but there is no consensus about a preference for cDNA or oligonucleotide arrays [43–45].
We used the rat genome U34A chip from Affymetrix to analyze approximately 7,000 full-length sequences and 1,000 expressed sequence tag (EST) clusters in myocytes isolated from ventricles of 1- to 3-day-old rat pups. The cells were treated with 20 µg/ml iron, as FAC, for 72 hr. One hundred and seventy genes or ESTs were up-regulated and 380 genes or ESTs were down-regulated, by at least 2-fold. Genes increased by iron loading included oxidative stress response genes such as glutathione peroxidase, glutathione S-transferase A, catalase, metallothioneins I and II, and heat shock protein 60s. Surprisingly, heat shock protein 70s was unchanged. Genes decreased by iron loading included those coding transferrin receptor, HIF-1, glyceraldehyde phosphate dehydrogenase (GAPD), cyclin B1, collagen type 1, and fibronectin. Down-regulation of the IRE-regulated transferrin receptor served as a positive control for these experiments, and changes in the other gene transcripts were consistent with data published previously by us [32] and others [40,46]. An interesting observation from our gene list is that several protein phosphatases and kinases were down-regulated by iron, such as phosphatases 2A, 2B, 2C, protein tyrosine phosphatase, phosphoglycerate kinase 1, phos-photidylinositol 4-kinase, serine threonine kinases 3 and 39, mitogen activated protein kinase kinase 1, and the stress-related mitogen activated protein kinase 14 (p38). Down-regulation of both kinases and phosphatases is consistent with the idea that many phosphatases are induced by the kinases they regulate [47], achieving a negative feedback loop. Another observation is that a number of mRNAs for growth factors were decreased by iron loading, including transforming growth factor ß3, several forms of vascular endothelial growth factor, platelet-derived growth factor A chain, and heparin binding EGF-like growth factor. Insulin-like growth factors IGF-I and IGF-II were increased, and the potentially inhibitory IGF binding protein-3 was decreased.
In a different approach, we used a 2-color experiment with the Ontario Cancer Institute’s (OCI) 1.7 k human cDNA microarray, containing 1,700 human genes and (or) ESTs, to study the effects of iron chelation on liver cells. Each gene was spotted in duplicate, and the experiment was repeated 4 times. Illustrative of the problems of using semi-commercial approaches, gene lists were changed between experiments, many genes were not verified, and technical backup was lacking. Nevertheless, when HepG2 (human hepatocarcinoma-derived) cells were treated with 1 mM DFO for 24 hr, the expression of 13 genes was changed at least 2-fold. These genes are listed in Table 1
. In this limited survey, 3 genes (ie, AKR1C2, ALDH1A1, and GAPD) changed in the opposite direction upon iron loading in the Affymetrix experiment with cardiac myocytes. This lends credence to the iron-dependence of these genes.
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Table 1. Gene expression altered by iron chelation in HepG2 human hepatocarcinoma cells. Confluent cell cultures were treated with 1 mM deferoxamine (DFO) for 24 hr. Genes showing 2-fold changes av eraged over 4 independent experiments with 1.7 K OCI microarrays are listed. Gene symbols are shown in parentheses.
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Critical issues not discussed here are how the data should be normalized [we used Lowess normalization in the OCI experiments and the GC-RMA algorithm from ArrayAssist software (Stratagene, La Jolla, CA) with the Affymetrix data], and how they should be combined from multiple experiments to test statistical significance. There is a well-established means of reporting such data adopted by many journals (Minimum Information About a Microarray Experiment (MIAME) [48]) that acknowledges the biological variation among cell types and subtle changes in conditions, and is intended to facilitate interlaboratory comparisons. MIAME requires details of (a) experimental design, (b) array design, (c) sample selection, (d) hybridization protocol, (e) image analysis, and (f) normalization and controls for comparison. It still seems optimistic to think that this information will be sufficient to consolidate data from many laboratories. Here, we describe briefly a further stage in the analysis that the richness of data from microarray experiments facilitates.
Using the minimal set of 13 genes suggested to change in the OCI cDNA experiments, we used PathwayAssist software (Stratagene) to uncover links among the genes differentially expressed. Many different approaches can be explored in this package. We only present, as an example, an analysis of shortest-path connections of the 13 genes altered in Table 1
, based on small-molecule regulators and minimization of connections (Fig. 1). Ten of the 13 gene products interconnect by one step through known small molecule regulators. The role of PGE2 in the generated scheme deserves comment. It has been found that iron down-regulates prostaglandin E2 (PGE2) expression [49] and DFO increases prostaglandin synthesis by increasing cyclooxygenase-2 expression [50]. PGE2 in turn has been shown to increase an insulin-like growth factor binding protein (IGFBP4) in chondrocytes [51] (compare IGFBP1 in Table 1) and is linked to increased polyamine synthesis and spermidine/spermine N1-acetyltransferase activity in lymphocytes [52]. Additionally, SAT, GAPD, IGFBP1, and ENO2 are all increased by DFO (Table 1). The pathway analysis reveals a linkage through a phorbol ester (PMA)-dependent (eg, protein kinase C) mechanism, though stathmin (STMN1) is regulated contrary to expectation. Such analyses are highly speculative, but illustrate the richness of data that can be generated for closer scrutiny by global microarray experiments. The consistency of these observations with the cited reports supports the veracity of the array data.
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Concluding comments
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In general, no clear or consistent patterns of iron-dependent gene expression have been uncovered by genome-wide screening of cells treated with iron or iron chelators, and many reports have focused more on methods than results. Such approaches are often validated by demonstrating regulation of genes already known to be iron-dependent from classical biochemical methods. Otherwise, individual genes revealed in specific studies require validation by additional approaches such as real-time PCR and Northern blotting, and significance must be demonstrated at the protein level. Novel genes, ie, those not already known to be iron-dependent, may have idiosyncratic responses dependent on specific cell types and under conditions of culture and treatment. This underscores the need for reporting-protocols, such as MIAME. Pre-microarray methods have not generally proven to be very responsive to detecting genes known or strongly suspected to be influenced by iron, and microarrays yield large numbers of candidates with the suspicion of large numbers of false positives. Nevertheless, iron is a metal that clearly affects numerous biological processes, and as methods for genome-wide screening become more refined, reproducible, and robust, patterns of iron-dependent gene regulation with general applicability to many cell types may emerge.
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Abstract
Introduction
