- Open Access
Characterization of the Shewanella oneidensis Fur gene: roles in iron and acid tolerance response
© Yang et al.; licensee BioMed Central Ltd. 2008
- Published: 20 March 2008
Iron homeostasis is a key metabolism for most organisms. In many bacterial species, coordinate regulation of iron homeostasis depends on the protein product of a Fur gene. Fur also plays roles in virulence, acid tolerance, redox-stress responses, flagella chemotaxis and metabolic pathways.
We conducted physiological and transcriptomic studies to characterize Fur in Shewanella oneidensis, with regard to its roles in iron and acid tolerance response. A S. oneidensis fur deletion mutant was defective in growth under iron-abundant or acidic environment. However, it coped with iron depletion better than the wild-type strain MR-1. Further gene expression studies by microarray of the fur mutant confirmed previous findings that iron uptake genes were highly de-repressed in the mutant. Intriguingly, a large number of genes involved in energy metabolism were iron-responsive but Fur-independent, suggesting an intimate relationship of energy metabolism to iron response, but not to Fur. Further characterization of these genes in energy metabolism suggested that they might be controlled by transcriptional factor Crp, as shown by an enriched motif searching algorithm in the corresponding cluster of a gene co-expression network.
This work demonstrates that S. oneidensis Fur is involved in iron acquisition and acid tolerance response. In addition, analyzing genome-wide transcriptional profiles provides useful information for the characterization of Fur and iron response in S. oneidensis.
- Acid Tolerance
- Iron Acquisition
- Iron Depletion
- Acid Tolerance Response
- Shewanella Species
Iron is the second most abundant element in the earth's crust and an essential micronutrient for virtually all organisms. The redox features of iron make it a useful cofactor for many proteins functioning in respiration, photosynthesis, nitrogen fixation and DNA biosynthesis, to name a few. However, iron also forms highly reactive oxygen species that can impose severe cellular damage [1, 2]. Hence, intracellular iron concentration must be tightly controlled. In E. coli, iron-binding transporters located in the outer and inner membranes are induced to actively import iron under iron-limited conditions . To facilitate the capture of iron, E. coli produces and releases the iron-chelating siderophores to the surrounding environment. Also, the iron storage proteins (ferritin and bacterioferritin) and non-essential iron-using proteins are repressed to release their sequestered iron . When iron is sufficient, the iron uptake and homeostatic systems are coordinately regulated by a global transcriptional factor Fur (the ferric uptake regulator), which complexes with Fe2+ and represses the transcription of a diverse array of genes . The DNA target recognized by Fe2+-Fur is a 19-bp inverted repeat sequence of GATAATGATAATCATTATC . The binding of Fur to this motif in the promoter regions of target genes effectively prevents the recruitment of the RNA polymerase holoenzyme and thus represses transcription [7, 8]. Although the major role of Fur is to regulate genes involved in iron homeostasis systems, Fur has also been demonstrated to be a pleiotropic transcriptional factor. As such, it functions in a variety of cellular processes including redox-stress responses, flagella chemotaxis, metabolic pathways, acid tolerance and virulence [9–11]. Indeed, it has been estimated that Fur directly controls the expression of over ninety genes in E. coli.
Maintaining iron homeostasis is of particular interest for a γ-proteobacterium Shewanella oneidensis for several reasons. First, S. oneidensis is capable of respiring with a variety of electron acceptors including oxygen, glycine, nitrate, nitrite, thiosulfate, elemental sulfur, fumarate, dimethyl sulfoxide (DMSO), trimethylamine N-oxide (TMAO), Fe(III), Mn(IV), Co(III), U(VI), Cr(VI) and Tc(VII) [12–16]. Unlike most other γ-proteobacteria, S. oneidensis is capable of respiring Fe(III). Therefore, iron serves as not only a protein cofactor but also an important electron acceptor for the bacterium. Second, S. oneidensis typically possesses an extraordinarily high content of cytochromes, which confers cells a characteristic pink or red color. The high content of cytochrome is suggestive of a high demand for iron, since iron is a co-factor of heme groups in the cytochromes . Lastly, the majority of S. oneidensis genes are most similar to genes of Vibrio cholerae, the γ-proteobacterium causative of a diarrheal disease called cholera . It has been established that iron acquisition genes of V. cholerae are required for successful colonization in animal models . Since Shewanella species are fish pathogens and infrequent opportunistic human pathogens [20, 21], it is possible that iron in Shewanella species plays a similar role to V. cholerae and function to signal the entry into the host.
Much of iron homeostasis in S. oneidensis remains to be elucidated. The Fur gene of S. oneidensis shares 74% identity with the E. coli homolog. Previous transcriptomic studies of the fur mutants under anaerobic and aerobic conditions indicate that it regulates a variety of biological processes including iron uptake [22, 23]. A highly conserved Fur binding motif has also been predicted. These results are consistent with a role of S. oneidensis Fur in iron acquisition. However, responses of the fur mutant to iron depletion have not yet been examined. It is also unclear how disruption of the pleiotropic regulator Fur affects other cellular processes. In addition, although Fur is regarded as the master regulator for iron response, it has been shown in Vibrio cholerae and Staphylococcus aureus that a number of genes are regulated by iron independently of Fur [24, 25]. It is interesting to investigate such systems in S. oneidensis.
In the present study, we performed physiological and transcriptomic studies to characterize the function of S. oneidensis Fur. Our results demonstrated that Fur played a role in iron acquisition and acid resistance response. We also discovered that a number of genes including genes related to anaerobic energy transport were regulated by iron rather than Fur. This work provides useful information in understanding the complicated complex molecular networks coordinating the bacterial response to iron.
Phenotypic analyses of the fur mutant
For microarray experiments, mid-log phase grown fur mutant was treated with iron chelator for one hour and then ferrous sulfate. The concentration of 160 uM iron chelator was used for iron depletion during the microarray experiments because growth was clearly inhibited, but not completely abolished at this concentration (Fig. 2). Samples were collected at multiple time points during iron depletion and repletion and used for global transcriptomic analyses. To allow for comparison of any pairs of samples, S. oneidensis genomic DNA was used as a common reference in each microarray experiment (see Methods and Materials for details). This strategy has been successfully employed in several studies [29–32]. In addition, since S. oneidensis fur mutant in previous studies was generated from a rifampin-resistant strain (DSP10) instead of wild-type S. oneidensis MR-1, we decided to repeat the microarray comparison between the fur mutant and wild-type under aerobic condition, using the fur mutant derived from MR-1. Therefore, MR-1 was grown in LB to mid-logarithmic phase, RNA was prepared and used for microarray experiments. This allowed for comparison of gene expression profiles of the fur mutant and MR-1 (see below). To evaluate the reliability of microarray data, quantitative RT-PCR was performed on a few selected genes (Additional file Additional file 1). A high Pearson correlation coefficient of 0.92 was observed between RT-PCR and microarray results.
Representative genes that displayed significant up- or down-expression in the fur mutant
Ratio (p value)
siderophore biosynthesis proteinx
heme exporter protein CcmD
TonB system transport protein ExbB1
TonB system transport protein ExbD1
ferrous iron transport protein A
hemin ABC transporter, periplasmic hemin-binding protein
hemin ABC transporter, permease protein
hemin ABC transporter, ATP-binding protein
heme transport protein
iron-regulated outer membrane virulence protein
TonB-dependent receptor, putative
TonB-dependent heme receptor
siderophore biosynthesis protein, putative
siderophore biosynthesis protein, putative
ferric alcaligin siderophore receptor
sodium: alanine symporter family protein
TonB-dependent receptor, putative
TonB-dependent receptor, putative
ferric vibriobactin receptor
bacterioferritin subunit 1
translation initiation factor IF-1
ribosomal protein L4
ribosomal protein L11
ribosomal protein L27
ribosomal protein L32
ribosomal protein L34
ribosomal protein L35
alcohol dehydrogenase, zinc-containing
quinone-reactive Ni/Fe hydrogenase, large subunit
NADH: ubiquinone oxidoreductase, Na translocating
cold shock domain family protein
DNA-binding response regulator
transcriptional regulator, LysR family
sensory box histidine kinase/response regulator
transcriptional regulator, LysR family
sensory box protein
formate hydrogenlyase transcriptional activator, putative
regulator of sigma D
Iron-responsive and Fur-independent biological processes
Gene co-expression network
Genes with the highest fold changes are not necessarily the most important genes for a given condition, since there usually is a dissociation between gene expression and physiological phenotype . Moreover, a large number of significantly regulated genes are linked to hypothetical proteins. To gain more insights into their functions, a gene co-expression network was constructed (Additional file Additional file 2A). Microarray data have frequently been used for gene annotation based on the observation that genes with similar expression patterns are more likely to be functionally related, that is, “guilt by association”, despite the fact that co-expression does not necessarily indicate a causal relationship at the transcriptional level. This fact is exploited in the “majority-rule” method of network annotation in which a gene is annotated based on the most commonly occurring functions of its co-expressed partners.
Another large cluster contains many c-type cytochromes (e.g. CymA, MtrA, MtrC and OmcA) and their related genes such as heme exporters and alcohol dehydrogenases (Fig. 6B). Many of them were considered as iron-responsive but Fur-independent, as discussed in the previous section. When the Gibbs Motif Sampler was applied, a palindromic sequence of “AAATGTGATCNNGNTCACA NTT” (Fig. 6B) was identified with statistical significance. This sequence is almost identical to the known binding motif of an E. coli global transcriptional factor Crp (AAATGTGA TCTAGATCACA TTT) . In S. oneidensis, crp mutants are deficient in reducing Fe(III), Mn(IV), nitrate, fumarate and DMSO as electron acceptors . The identification of a highly conserved Crp-binding site at the promoters of genes in this cluster provides an explanation for the phenotypes of crp mutants. Crp might act as a master regulator of anaerobic energy transport; its inactivation leads to repression of multiple branches of anaerobic energy transport pathway. Notably, the regulation of Crp to anaerobic energy transport is iron-responsive but Fur-independent, since most genes in this cluster shows little expression difference between the fur mutant and wild-type.
The primary objective of the present study was to characterize the function of the pleiotropic regulator Fur and the iron response in S. oneidensis. Fur involvement in iron acquisition was evident, as shown by our physiological and transcriptomic studies. On the other hand, there were iron-responsive and Fur-independent systems (e.g. a large group of genes related to anaerobic energy transport). The identification of Crp binding site in Fig. 6B is consistent with the role of Crp in anaerobic energy transport and supports the notion that Crp is the master regulator of this bioprocess. The identification of a number of transcriptional factors in this cluster is worthy of future investigation. Over 200 genes of regulatory functions have been annotated in S. oneidensis genome (http://www.tigr.org), yet only a small number of them have been examined experimentally. The involvement of the identified regulators in anaerobic energy transport, once verified, will contribute to the understanding of the complex branched respiratory systems in S. oneidensis and hence the potential utilization of Shewanella species to remediate U.S. DOE uranium-contaminated sites.
Bacterial strains and plasmids
To generate a fur deletion mutant from MR-1, a pDS3.1-fur suicide plasmid described in  was used. This plasmid contains the 5'- and 3'-flanking regions of Fur locus, with a 241-bp internal fragment of Fur locus removed. The plasmid was transformed into E. coli WM3064 strain prior to conjugal transferring into MR-1. Correct in-frame deletion was verified by sequencing PCR products using primers outside the DNA recombination region. The sequences of the primers are: (5'-GCA AGT ACA GCC GTT ATT CAC TCC-3') and (5'-GTA TCC AAA GGA TGC AAC TGG-3').
MR-1 and the fur mutant were grown to mid-log phase and diluted 1:100 into 300 ul fresh LB or M1 liquid medium. A Type FP-1100-C Bioscreen C machine (Thermo Labsystems) was used to measure the growth every thirty minutes. All physiological studies were done in triplicates so that average and standard error could be calculated. Different concentrations of iron chelator were prepared by dissolving 2, 2'-dipyripyl in water. For iron repletion, ferrous sulfate was used at the concentration of 200 uM.
Four biological replicates of MR-1 and the fur mutant were grown in LB to mid-log phase (OD600=0.6) and cells were collected. For the fur mutant, cells were also sampled at 1, 5, 10, 20, 40, and 60 minutes after adding 2, 2'-dipyridyl to a final concentration of 160 uM. Subsequently, ferrous sulfate was added to a final concentration of 200 uM. Cells were collected at 1, 5, 10, 20, 40, and 60 minutes thereafter. Total RNA was extracted using Trizol Reagent (Invitrogen) as described previously . RNA samples were treated with RNase-free DNase I (Ambion, Inc.) to digest residual chromosomal DNA and then purified with RNeasy Kit (Qiagen) prior to spectrophotometric quantification at 260 and 280 nm.
Microarray hybridization, scanning and quantification
cDNA was produced in a first-strand reverse transcription (RT) reaction and labeled with Cy5 dUTP (Amersham Biosciences) by direct labeling in the presence of random hexamer primers (Invitrogen). S. oneidensis MR-1 genomic DNA (gDNA) was amplified by Klenow (Invitrogen) and Cy3 dUTP was incorporated into the product (Amersham Biosciences). Fluorescein labeled probes were purified using a PCR purification kit (Qiagen). Slides were pre-hybridized at 50°C for about one hour to remove unbound DNA probes in a solution containing 50% (V/V) formamide, 9% H20, 3.33% SSC (Ambion, Inc.), 0.33% sodium dodecyl sulfate (Ambion, Inc.), and 0.8 μg/μL bovine serum albuminin (BSA, New England Biolabs). Slides were hybridized at 50°C overnight with Cy5- and Cy3- labeled probes in the above solution, with 0.8 μg/μL herring sperm DNA (Invitrogen) replacing BSA to prevent random binding. Pre-hybridization and hybridization were carried out in hybridization chambers (Corning). Slides were then washed on a shaker at room temperature in the following order: 7 min. in 1x SSC, 0.2% SDS; 7 min. in 0.1x SSC, 0.2% SDS; and 40 sec. in 0.1x SSC.
A ScanArray Express Microarray Scanner (PerkinElmer) was used to scan slides. Fluorescence and background intensity were quantified using ImaGene 6 software (BioDiscovery, Inc.). All spots in which signal vs. background ratios were less than 3 were discarded.
Microarray data analysis
GeneSpring 7.2 (SiliconGenetics) was used to remove outliers, perform LOWESS normalization and for analysis of statistical significance via a two-way t test for two independent conditions. To calculate the ratios over different time points, samples during iron depletion (namely, C1', C5', C10', C20', C40' and C60') were compared to samples without addition of chelator (C0'), and samples during the iron repletion (F1', F5', F10', F20', F40' and F60') were compared to C60' (1' is 1 min., 5' is 5 min., etc.). Genes with |log3 ratio| ≥1 with p<0.05 are considered significant. To construct a gene co-expression network from temporal gene expression profiles, a Random Matrix Theory (RMT) based algorithm as described in [41, 42] was employed. Applying the RMT method to the microarray data revealed a Pearson correlation coefficient of 0.87 as the minimal threshold to construct gene co-expression network. Since gene co-expression networks are known to be hierarchical , higher thresholds have been used to recognize larger functional modules. As a result, nineteen modules were distinguished (Additional file Additional file 2A). The software Pajek  was used to visualize the gene co-expression network. To identify the common motif, the Gibbs Motif Sampler  was employed according to its manual. The promoter regions of genes in the heat shock cluster were scanned to identify RpoH-binding site. For Crp-binding site, the recursive model of the Gibbs Motif Sampler was used to scan the upstream intergenic regions.
Real-time RT-PCR analysis
Real-time quantitative reverse transcription-PCR (RT-PCR) was performed as described previously , except that iQ SYBR green supermix (Bio-Rad) was used instead of SYBR green I.
Microarray data accession
The microarray data are available at http://www.cs.clemson.edu/~luofeng/yang/fur.html.
The authors thank Zamin Yang, Tingfen Yan and Xueduan Liu for technical assistance. This work was supported by The United States Department of Energy under the Genomics: GTL Program through the Shewanella Federation, Microbial Genome Program and Natural and Accelerated Bioremediation Research Programs of the Office of Biological and Environmental Research, Office of Science. Oak Ridge National Laboratory is managed by University of Tennessee-Battelle LLC for the Department of Energy under contract DE-AC05-00OR22725. Feng Luo is supported by the NSF grant EPS-0447660.
This article has been published as part of BMC Genomics Volume 9 Supplement 1, 2008: The 2007 International Conference on Bioinformatics & Computational Biology (BIOCOMP'07). The full contents of the supplement are available online at http://0-www.biomedcentral.com.brum.beds.ac.uk/1471-2164/9?issue=S1.
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