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Plant Physiol. Feb 2011; 155(2): 821–834.
Published online Dec 20, 2010. doi:  10.1104/pp.110.169508
PMCID: PMC3032469

iTRAQ Protein Profile Analysis of Arabidopsis Roots Reveals New Aspects Critical for Iron Homeostasis1,[C][W]

Abstract

Iron (Fe) deficiency is a major constraint for plant growth and affects the quality of edible plant parts. To investigate the mechanisms underlying Fe homeostasis in plants, Fe deficiency-induced changes in the protein profile of Arabidopsis (Arabidopsis thaliana) roots were comprehensively analyzed using iTRAQ (Isobaric Tag for Relative and Absolute Quantification) differential liquid chromatography-tandem mass spectrometry on a LTQ-Orbitrap with high-energy collision dissociation. A total of 4,454 proteins were identified with a false discovery rate of less than 1.1%, and 2,882 were reliably quantified. A subset of 101 proteins was differentially expressed upon Fe deficiency. The changes in protein profiles upon Fe deficiency show low congruency with previously reported alterations in transcript levels, indicating posttranscriptional changes, and provide complementary information on Fe deficiency-induced processes. The abundance of proteins involved in the synthesis/regeneration of S-adenosylmethionine, the phenylpropanoid pathway, the response to oxidative stress, and respiration was highly increased by Fe deficiency. Using Fe-responsive proteins as bait, genome-wide fishing for partners with predictable or confirmed interologs revealed that RNA processing and ribonucleoprotein complex assembly may represent critical processes that contribute to the regulation of root responses to Fe deficiency, possibly by biasing translation efficiency.

Iron (Fe) is the fourth most common element in the earth’s crust and highly abundant in almost all soil types. Its bioavailability, however, is severely restricted due to an extremely low solubility at neutral or basic pH. In plants, Fe is required for basic redox reactions in photosynthesis and respiration and for many vital enzymatic reactions associated with DNA replication, lipid metabolism, and nitrogen fixation. Fe deficiency is the most common nutritional disorder worldwide, affecting a large portion of the world’s population. Understanding how cellular Fe homeostasis is maintained provides a basis for engineering plants with enhanced Fe concentration in edible plant parts and may help to counteract malnutrition when plants are the major nutrient source in the diet.

Plants have developed sophisticated mechanisms to balance changes in Fe availability. Fe is acquired by two mutually exclusive mechanisms, referred to as strategy I and strategy II (Römheld and Marschner, 1986). In strategy II plants such as maize (Zea mays), ferric Fe is chelated by siderophores that are secreted by plant roots, and the Fe-siderophore complex is taken up by an oligopeptide transporter, YELLOW-STRIPE1 (Curie et al., 2001). Strategy II is confined to the grasses. In the strategy I plant Arabidopsis (Arabidopsis thaliana), ferric Fe is first reduced by FERRIC REDUCTION OXIDASE2 (FRO2), and the released ferrous Fe is then transported across the plasma membrane by IRON-REGULATED TRANSPORTER1 (IRT1), a member of the ZIP family (Eide et al., 1996; Robinson and Lemire, 1996; Vert et al., 2002). Both genes are regulated by a heterodimeric complex consisting of the FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) and either bHLH38 or bHLH39 (Colangelo and Guerinot, 2004; Jakoby et al., 2004; Yuan et al., 2008). All three genes are Fe responsive, indicating regulation by upstream components. The sensor for Fe has not yet been identified.

The low substrate specificity of IRT1 allows the transport of other essential and nonessential metals alongside Fe, which necessitates the concerted action of an array of transporters mediating the cellular sequestration of these “stowaway” metals in order to avoid toxicity (Korshunova et al., 1999; Vert et al., 2002; Schaaf et al., 2006; Yang et al., 2010). Some of these transporters are chiefly or entirely regulated by Fe availability, indicating that the changes in expression of the genes encoding these transporters are not controlled by actual concentration changes of the respective metal but rather in anticipation of such changes when IRT1 is up-regulated in response to Fe deficiency (Yang et al., 2010). For long-distance transport, Fe is exported from the cell by the ferroportin ortholog IREG1/FPN1 and transported in the xylem as a complex with citrate (Brown and Tiffin, 1965; Morrissey et al., 2009). The MATE transporter FRD3 has been shown to be important for the proper transport of Fe from roots to leaves. frd3 mutants showed constitutive up-regulated Fe deficiency responses, chlorotic leaves, and ectopic accumulation of Fe in the root vasculature (Rogers and Guerinot, 2002; Durrett et al., 2007). FRD3 loads citrate into the xylem, which is crucial for the transport of Fe to the shoot.

The acquisition of Fe is aided by the induction of ARABIDOPSIS H+ ATPASE2 (AHA2), a plasma membrane-bound proton pump whose activity decreases the extracellular pH and thereby increases the solubility of Fe in the apoplast and in the rhizosphere (Santi and Schmidt, 2009). Induction of AHA2 occurs somewhat later than that of IRT1 and FRO2 and is not controlled by FIT, indicating separate control of Fe mobilization and uptake. Secretion of phenolic compounds in response to Fe deficiency has been reported for a variety of strategy I species and is thought to contribute directly or indirectly to the acquisition of Fe by chelating/reducing Fe or by affecting the microflora in the rhizosphere. Recently, this process was shown to be crucial for the reutilization of root apoplastic Fe (Jin et al., 2007). In Arabidopsis, several genes in the general phenylpropanoid pathway and genes involved in the biosynthesis of coumarins are induced upon Fe deficiency, which may be indicative for the accumulation and/or secretion of phenolic compounds (Yang et al., 2010).

A variety of metabolic alterations have been reported to occur in response to Fe starvation, such as an induction of CO2 dark fixation, an increase in amino acid and S-adenosyl-Met (SAM) biosynthesis, and an accumulation of organic acids in roots (Thimm et al., 2001; Buckhout and Thimm, 2003; Zocchi et al., 2007; López-Millán et al., 2009). Increased production of ethylene and nicotianamine (NA), an important micrometal chelator in plant cells that plays a crucial role in Fe homeostasis and transport (Klatte et al., 2009) from SAM and chelation of Fe by citrate during long-distance transport, may be the cause for some of these changes. A comprehensive picture of the metabolic events that are triggered by Fe shortage, however, is still lacking.

While the transcriptional response of roots to Fe deficiency has been expansively documented by microarray studies, limited information is available with regard to the Fe deficiency-induced changes of the root proteome. Since biological processes are ultimately controlled by proteins, interrogation of changes in the protein profile upon Fe starvation is crucial for providing an accurate picture of the events triggered by Fe deficiency. Exact quantification of differentially expressed proteins has proven difficult with gel-based approaches. Here, we present an exploration of the Fe deficiency-induced changes in the proteome of Arabidopsis roots using the iTRAQ (Isobaric Tag for Relative and Absolute Quantification) system. This approach allows the simultaneous identification and quantitative comparison of peptides by measuring peak intensities of reporter ions in tandem mass spectrometry (MS/MS) spectra. We quantitatively identified 2,882 proteins in roots of Arabidopsis, 101 of which were found to be responsive to Fe deficiency. This analysis revealed aspects potentially critical for the acclimation to Fe deficiency that were in part regulated at the posttranscriptional level and added a new layer of information regarding the responses of Arabidopsis roots to the changing availability of Fe.

RESULTS AND DISCUSSION

Quantitative Identifications of Root Proteins Using iTRAQ

Total proteins in Arabidopsis roots and changes in the protein profile upon Fe deficiency were explored using the iTRAQ technique. Proteins were extracted from roots of Fe-sufficient and Fe-deficient plants with a urea/CHAPS extraction buffer, deviating from the manufacturer’s protocol to increase protein yield and to include membrane-bound proteins. The samples were digested in solution, labeled with iTRAQ reagents, and quantitatively identified by means of LTQ-Orbitrap XL hybrid MS scans. The experimental scheme is shown in Figure 1. A total of 3,795 and 3,976 proteins were identified in the two experiments, covering a wide range of metabolic and signaling pathways (Supplemental Fig. S1; Supplemental Table S1). For both biological repeats, the false discovery rate was less than 1.1% (0.98% for experiment 1 and 1.1% for experiment 2). After merging the data obtained from the two biological replicates, a total of 4,454 proteins were identified, with an overlap of more than 80% (Fig. 2). The overlap of the technical repeats for each experiment is shown in Supplemental Figure S2. A high reproducibility is also documented by the similar patterns of strong-cation exchange separation of the two extracts (Supplemental Fig. S3). A comparison with a comprehensive proteomics study of Arabidopsis roots (Baerenfaller et al., 2008) revealed 1,028 proteins that were uniquely identified in this study, demonstrating that the iTRAQ method is partly complementary to conventional gel-based methods. As anticipated from previous studies, the majority of the most abundant proteins in Arabidopsis roots are related to basic carbohydrate metabolism. Remarkably, six proteins of the Met cycle, the SAM synthetases MAT1, MAT2, MAT3, and MAT4, the cobalmine-independent Met synthase ATMS1, and the S-adenosyl-l-homo-Cys hydrolase SAHH1, were found among the most abundant proteins, underlining the importance of this pathway in roots. The most abundant plasma membrane-bound protein identified in both biological replicates was the P-type ATPase AHA2, one of the two major H+-ATPases in roots (Santi and Schmidt, 2009; Supplemental Table S1).

Figure 1.
Experimental scheme of the iTRAQ analysis. [See online article for color version of this figure.]
Figure 2.
Venn diagram representing the overlap of the identified proteins in the two biological repeats.

Alternative splicing is a common phenomenon in eukaryotic cells and contributes to protein diversity and regulation. In this study, several identified proteins were derived from alternatively spliced transcripts, comprising proteins involved in metabolism, transcriptional regulation, signaling, protein degradation, and other processes (Supplemental Table S2). In most cases, only one splice variant was identified, indicating predominant abundance of the respective isoform in roots. Some of the proteins (e.g. the Fe transporter IRT1, the haloacid dehalogenase-like hydrolase At5g38850, EUKARYOTIC TRANSLATION INITIATION FACTOR4G, and the ubiquitin ligases UBC13A, UBC13B, and UEV1C) were either regulated by Fe or have a proven role in the Fe deficiency response. UBC13A and UBC13B were shown to regulate a subset of Fe homeostasis genes and to control the morphological responses to Fe deficiency in Arabidopsis and cucumber (Cucumis sativus; Li and Schmidt, 2010). Interestingly, two Ser/Arg-rich RNA-binding proteins with putative roles in the regulation of splicing, ATSRP30 and At4g35785, are included in this list. The relatively small number of proteins derived from alternatively spliced transcripts may indicate that only a small percentage of the estimated alternatively spliced genes are translated into changes of the proteome (Wang and Brendel, 2006). However, the method used in this study may underestimate this number by mapping only proteins to the respective alternatively spliced transcripts that show sufficient diversity.

Changes in Protein and RNA Profiles Show Only a Small Overlap

For 2,882 proteins, at least two unique peptides could be identified, which permitted the quantification of their abundance. Based on a 95% confidence level, cutoff values of 1.35-fold for up-regulated proteins and of 0.62-fold for down-regulated proteins were used to define a protein as being responsive to Fe (for details, see “Materials and Methods”). Using these criteria, a total of 101 proteins were classified as being differentially expressed upon Fe deficiency, 59 of which showed increased and 42 showed decreased abundance under Fe-deficient conditions (Table I). To compare the differentially expressed proteins with transcriptional changes that are induced by Fe deficiency, we queried microarray experiments reported in the literature. Genes that appeared in at least three published microarray experiments using Fe-deficient Arabidopsis roots and the ATH1 GeneChip were considered as showing an Fe response. Based on this definition, the transcript levels of 92 genes were found to be robustly changed upon Fe deficiency (W. Schmidt and T.J. Buckhout, unpublished data). While the number of differentially expressed proteins is roughly similar to that of robustly Fe-responsive transcripts, the overlap between the changes in the proteome and transcriptome is remarkably small. Out of 92 transcripts that changed in abundance by 2-fold upon Fe deficiency, only 17 of the encoded proteins were found to be differentially expressed under Fe-deficient conditions (Table I). Different factors may contribute to the small overlap. First, proteins with low abundance might not be reliably detected and thus did not match the criteria that define them as differentially accumulated, while the transcript levels of the corresponding genes are significantly changed. Second, several proteins with high abundance are differentially expressed upon Fe deficiency, but they have important housekeeping functions and the transcript levels do not match the 2-fold change criteria used in most microarray studies. For example, the three Met synthases MAT1, MAT2, and MAT3 were found to be significantly increased upon Fe deficiency at the protein level, while the changes in transcript levels were 2.63, 1.13, and 1.55 for MAT1, MAT2, and MAT3 in previously reported microarray experiments. Thus, only MAT1 was found in the list of differentially expressed genes (Yang et al., 2010). Finally, a change in transcript abundance may or may not be translated into changes in protein level. Posttranscriptional regulatory processes such as alternative splicing, RNA processing, and other processes may affect the efficiency of translation. A generally low congruency of proteomic and transcriptional profiles has been reported previously (Gallardo et al., 2007).

Table I.
Differentially accumulated proteins and their assigned or assumed functions

Differentially Expressed Proteins Reveal New Aspects of the Fe Deficiency Response

Proteins that were differentially expressed upon Fe deficiency were enriched in the Gene Ontology categories “response to oxidative stress” (six proteins) and “SAM biosynthetic process” (three proteins; P = 1e-6–1e-4). Four marker for the Fe status, the ferric reductase FRO2, the NA synthase NAS4, and the Fe storage proteins FER1 and FER3, were found to be strongly affected by Fe deficiency both at the protein and transcript levels (Table I; Fig. 3). Consistent with our previous observations at the transcriptional level (Yang et al., 2010), the plastidic ARABIDOPSIS THALIANA NUCLEOSOME ASSEMBLY PROTEIN1 (ATABC1/NAP1), a homolog of prokaryotic SufB protein important in the repair of oxidatively damaged Fe-S clusters (Xu et al., 2005), was found to be down-regulated. ATABC1/NAP1 interacts with NAP6 and NAP7 to form a NAP1-NAP7-NAP6 complex (Xu et al., 2005). Both NAP6 and NAP7 exhibited decreased abundance in Fe-deficient roots relative to Fe-sufficient controls in both experiments, although the latter protein was slightly below the threshold used here. It has been speculated that ATABC1/NAP1 acts as a plastidic Fe sensor, adjusting the assembly or repair of Fe-S clusters to the Fe status (Xu et al., 2005). While this assumption awaits experimental confirmation, our results qualify ATABC1/NAP1 as a sensitive gene/protein marker for the Fe status of the cell. FE SUPEROXIDE DISMUTASE1, another Fe marker, showed decreased abundance that was compensated for by increased expression of COPPER/ZINC SUPEROXIDE DISMUTASE1.

Figure 3.
Functions and subcellular distribution of marker proteins for Fe deficiency.

For some of the highly induced proteins, such as the glutathione transferase GSTL1, the germin-like protein GLP5, the cytochrome P450 CYP82C4, the oxidoreductase At3g12900, and the kelch repeat-containing protein At3g07720, the function in Fe homeostasis remains elusive. The level of the latter protein has been shown to increase upon zinc (Zn) overload, pointing to a possible role in Zn homeostasis (Fukao et al., 2009). Transcriptional profiling experiments revealed that the corresponding gene was also responsive to elevated Zn concentrations, supporting this assumption (van de Mortel et al., 2006). Zn concentrations are reportedly increased upon Fe deficiency due to the low specificity of IRT1 (Vert et al., 2002).

The ACTIN-DEPOLYMERIZING FACTORS (ADFs) are members of a small family functioning in the remodeling of the actin cytoskeleton in response to environmental cues (Bamburg, 1999; Ruzicka et al., 2007). ADF2 and ADF11 showed increased abundance upon Fe deficiency, but they have not been reported to be responsive to Fe deficiency at the transcriptional level. ADF11 was found to be distinctly expressed in developing trichoblast cells, possibly indicating a function in the changes in root hair differentiation induced by Fe deficiency.

Two out of seven Arabidopsis phytocystatins, CYS1 and CYS2, were up-regulated upon Fe deficiency. Phytocystatins belong to a superfamily of Cys proteases widely distributed among eukaryotes. Plant cystatins have well-documented roles in developmental processes and biotic and abiotic stresses (Gaddour et al., 2001; Zhang et al., 2008; Hwang et al., 2010). Root expression of CYS2 was confined to the root tip, representing the first root/soil contact point. Both genes were up-regulated in response to various abiotic stresses, indicating a possible function of CYS1 and CYS2 in Fe deficiency signaling or in the control of root development in response to environmental cues.

Fe Deficiency Alters Respiration and ATP-Coupled Transport Processes

Five proteins functioning in respiration, the cytochrome c oxidase At4g21105, the ubiquinol-cytochrome c reductase complex ubiquinone-binding protein At3g10860, and three components of mitochondrial complex I, At2g27730, At2g47690, and At4g20150 (Klodmann et al., 2010), showed increased abundance under Fe-deficient conditions (Table I; Fig. 4). Increased oxygen consumption is a “classical” response to Fe deficiency and has been described for several plant species (Espen et al., 2000). Increased respiration may be associated with an increased energy demand for transport processes, in particular for the extrusion of protons via P-type ATPases to solubilize Fe. In contrast to our results, Vigani et al. (2009) reported decreased abundance of complex I proteins in Fe-deficient cucumber roots. The difference in results might be due to the longer period of Fe starvation in the latter study, hampering the assembly of Fe-S cluster-containing proteins. The differences in the results further indicate that the increase in complex I proteins upon Fe deficiency might be transient.

Figure 4.
Fe deficiency-induced changes in processes affecting ATP production and consumption. Red arrows indicate up-regulation, and green arrows indicate down-regulation. Differentially expressed proteins are framed in boxes. AHA2 was not differentially expressed ...

In Arabidopsis, AHA2-mediated rhizosphere acidification is counteracted by nitrate uptake via ATNRT1, which catalyzes NO3/proton symport (Miller et al., 2007; Krouk et al., 2010). Arabidopsis accessions with high proton extrusion activity under Fe-deficient conditions showed a generally lower expression of ATNRT1, supporting this assumption (Santi and Schmidt, 2009). Furthermore, high nitrate levels can induce Fe chlorosis, likely due to an increase in apoplasmic pH (Smolders et al., 1997). In line with such a scenario, ATNRT1 was strongly down-regulated upon Fe starvation (Table I). Beside its function as a nitrate transporter, ATNRT1 acts as a nitrate sensor (Ho et al., 2009), and its expression is critical for the regulation of developmental processes induced by nitrate (Forde, 2002). Decreased expression of ATNRT1 may thus have an impact on root development under Fe-deficient conditions. In addition to the down-regulation of ATNRT1, two vacuolar proton ATPases, ATAVP3 and VHA-A2, were down-regulated upon Fe deficiency, contributing to an altered cellular ATP regime (Fig. 4).

Fe Deficiency Affects Translation

A subset of translation elongation factors (ELF5A-1 and ELF5A-3) and initiation factors (At2g05830 and EIF4G) showed increased abundance when plants were subjected to Fe deficiency (Table I). This stands in contrast to the decreased expression of some components of the ribosomes. Current thinking suggests that the composition of ribosomal proteins is highly dynamic and possibly affected by environmental signals (Schippers and Mueller-Roeber, 2010). Mutations in some of the ribosomal proteins were shown to cause developmental defects, implying critical functions of these proteins in developmental processes. Some of the Fe-responsive elongation/initiation factors have strong predicted interologs to ribosomal subunits. It is thus tempting to speculate that differential expression of ribosomal proteins can control mRNA preference and may bias the efficiency of translation toward a subset of genes that are crucial for the acclimation to Fe deficiency. Alternatively, the increase in ELF5A-1 and ELF5A-3 upon Fe deficiency may be explained by other reported functions of these proteins, such as cell growth, senescence, and xylem development (Liu et al., 2008; Ma et al., 2010). Interestingly, the translation initiation factor At2g05830 is regulated by the transcription factor FIT (Colangelo and Guerinot, 2004).

SAM Is a Central Metabolite in Fe-Deficient Roots

Several proteins related to the synthesis/consumption of SAM showed increased abundance upon Fe deficiency, placing SAM in a central position in the metabolism of Fe-deficient plants (Fig. 5). SAM is synthesized from l-Met by S-Met adenosyltransferase (MAT). SAM synthetases are massively up-regulated at the protein level upon Fe starvation, in accordance with previous proteomic studies in tomato (Solanum lycopersicum), cucumber, and Chlamydomonas (Reinhardt et al., 2006; Li et al., 2008; Li and Schmidt, 2010). The protein abundance of three out of four Arabidopsis MAT isoenzymes, MAT1, MAT2, and MAT3, was increased by Fe deficiency. Two enzymes with a proposed function in the salvage of l-Met from methylthioadenosine, ARD2 and At5g53850, were also induced by Fe starvation. SAM is an important methyl donor in transmethylation reactions and a substrate for the biosynthesis of NA from three molecules of SAM by NICOTIANAMINE SYNTHASE (NAS), encoded by four isogenes in Arabidopsis (Shojima et al., 1990; Herbik et al., 1999). Ectopic expression and mutations of NA synthases have detrimental effects on Fe homeostasis (Brumbarova and Bauer, 2005; Cassin et al., 2009; Klatte et al., 2009). In strategy II plants, NA is a direct precursor of phytosiderophores (Mori, 1999). Thus, induction of NAS genes under Fe-deficient conditions appears to be a central and conserved response. NAS4 was among the most up-regulated proteins, suggesting that NA synthesis is depleting the pool of SAM under conditions of Fe deficiency. Together, these data demonstrate that SAM is a central player in the metabolism of Fe-deficient roots as a precursor of NA besides its function as methyl group donor.

Figure 5.
Metabolic processes associated with proteins that are up-regulated upon Fe deficiency. The Met salvage cycle regenerates Met and SAM in several steps. SAM is synthesized by MAT from Met. SAM is then converted to methylthioadenosine (MTA) and aminocyclopropane-1-carboxylate ...

Fe Deficiency Induces the Phenylpropanoid Pathway

Three proteins from the flavonoid pathway, Phe ammonia-lyase, catalyzing the initiation of the general phenylpropanoid pathway, and the coumarate:CoA ligases 4CL1 and 4CL2, mediating the last step of this pathway, were robustly induced by Fe deficiency. Strong up-regulation at the transcript and protein levels was also noted for F6′H1, an oxidoreductase that was shown to be involved in coumarin synthesis by catalyzing the synthesis of 6′-hydroxyferuloyl-CoA from feruloyl-CoA (Kai et al., 2008). Caffeoyl-CoA O-methyltransferase, mediating the preceding conversion of caffeoyl-CoA to ferulyl-CoA, was also induced by Fe deficiency. This step represents a further sink for SAM, which is converted into S-adenosyl-l-homo-Cys (Fig. 5). Mutations in caffeoyl-CoA O-methyltransferase and in F6′H1 were shown to cause reductions in the levels of scopoletin and its β-glucoside scopolin in the roots (Kai et al., 2008), implying that both enzymes are involved in the biosynthesis of scopoletin. To prove whether scopoletin and/or scopolin levels were increased upon Fe deficiency, root extracts from Fe-sufficient and Fe-deficient plants were analyzed by ultra-performance liquid chromatography-MS/MS. The analysis of root extracts revealed that scopoletin, but not scopolin, accumulates in roots under conditions of Fe deficiency (Supplemental Fig. S4). Scopolin could not be detected by ultra-performance liquid chromatography-MS/MS in Arabidopsis root due to its low abundance. Neither of the two substances was found in root exudates of Fe-sufficient or Fe-deficient plants. The induction of enzymes catalyzing the biosynthesis of 6′-hydroxyferuloyl-CoA may be indicative of increased lignification. UDP-GLUCOSYL TRANSFERASE 72E1, conjugating lignin monomers (Lim et al., 2005), and HYDROXYCINNAMOYL-COA SHIKIMATE/QUINATE HYDROXYCINNAMOYL TRANSFERASE, involved in lignin biosynthesis, were up-regulated by Fe deficiency. In addition, LACCASE7, annotated as being involved in lignin degradation, was strongly down-regulated both at the transcript and protein levels. Lignin biosynthesis is increased by a variety of stresses, including supraoptimal concentrations of Zn and copper, and by the presence of unfavorable metals such as cadmium, aluminum, and nickel (Mao et al., 2004; van de Mortel et al., 2006; Kovácik and Klejdus, 2008; Xue et al., 2008; Kovácik et al., 2009; Moura et al., 2010). Lignification alters cell wall properties and may serve to restrict the long-distance transport of these metals. This is in line with an increased lignification of the endodermis in response to Zn overload in Thlaspi caerulescens (van de Mortel et al., 2006). However, enzymes that catalyze the synthesis of monolignins were neither at the protein level nor at the transcript level found to be induced under conditions of Fe deficiency. This leaves other possibilities open for the fate of the increased levels of phenolic compounds. Reutilization of apoplasmic Fe from cell walls by secreted phenolics, as reported by Jin et al. (2007), represents a possible function.

Clustering of Fe-Responsive Proteins Predicts Posttranscriptional Regulation of the Fe Deficiency Response

In order to decipher functional networks, we performed clustering of Fe-responsive bait proteins and potential partners based on predicted or confirmed interactions. The Interactome 2.0 database (http://bar.utoronto.ca/interactions/cgi-bin/Arabidopsis_interactions_viewer.cgi) was used to generate potentially functional modules (for details, see “Materials and Methods”). To be added to a cluster, a fished protein had to have high or medium interolog confidence to at least two bait proteins. Proteins that were differentially expressed were used as bait. The database query resulted in a network composed of 58 proteins, 19 of which were “bait” proteins (Fig. 6). Proteins in this cluster were strongly enriched in the Gene Ontology categories “mRNA metabolic process” (P < 1e-10), “protein RNA complex assembly,” and “translation initiation” (P < 1e-6–1e-4). In particular, five proteins with predicted or experimentally verified functions in mRNA splicing were part of the network, providing a possible explanation for the small overlap of differentially expressed genes and proteins (Supplemental Table S3). Moreover, 14 proteins were related to protein translation and seven proteins had functions in protein/vesicle transport, further supporting a regulation of protein synthesis, possibly biased toward a preference for mRNAs with functions that are crucial under Fe-deficient conditions.

Figure 6.
Clustering of Fe-responsive proteins. Shown are predicted and confirmed interactions of Fe-responsive bait proteins (pink nodes) with proteins that were not differentially expressed under Fe-deficient conditions (white nodes). Only proteins with high ...

CONCLUSION

The use of reporter ions in MS/MS scans provides a means to accurately quantify changes in protein abundance. Our analysis of changes in the protein profile of Arabidopsis roots upon Fe deficiency suggests new components of the Fe deficiency response. The highest accumulation was observed for proteins involved in SAM and NA synthesis, placing SAM in a central position in the metabolism of Fe-deficient roots. Proteins involved in the phenylpropanoid pathway robustly accumulated upon Fe deficiency, probably mediating the synthesis of phenolic compounds that act in the recycling of apoplastic Fe. We further found that respiration was up-regulated and energy-consuming transport processes were down-regulated at the protein level, possibly to ensure Fe mobilization via ATPase-mediated proton extrusion and energization of Fe uptake via IRT1. Protein-protein interaction networks with Fe-responsive proteins as bait revealed that posttranscriptional processes, in particular RNA processing and mRNA preference, might represent important control hubs for the regulation of the root responses to Fe deficiency. The incongruence of proteomic and transcriptomic data suggests that changes in protein profiles upon Fe deficiency provide information that is complementary to transcriptional profiling studies, revealing potentially crucial aspects of the Fe deficiency response.

MATERIALS AND METHODS

Plants and Growth Conditions

Arabidopsis (Arabidopsis thaliana) plants were grown in a growth chamber on an agar-based medium as described by Estelle and Somerville (1987). Seeds of the accession Columbia were obtained from the Arabidopsis Biological Resource Center (Ohio State University). Seeds were surface-sterilized by immersing them in 5% (v/v) NaOCl for 5 min and 70% ethanol for 7 min, followed by four rinses in sterile water. Seeds were placed onto petri dishes and kept for 1 d at 4°C in the dark, before the plates were transferred to a growth chamber and grown at 21°C under continuous illumination (50 μmol m−2 s−1; Phillips TL lamps). The medium was composed of (mm): KNO3 (5), MgSO4 (2), Ca(NO3)2 (2), KH2PO4 (2.5) and (μm): H3BO3 (70), MnCl2 (14), ZnSO4 (1), CuSO4 (0.5), NaCl (10), Na2MoO4 (0.2), FeEDTA (40), solidified with 0.3% Phytagel (Sigma-Aldrich). Suc (43 mm) and 4.7 mm MES were included, and the pH was adjusted to 5.5. After 10 d of precultivation, plants were transferred to fresh agar medium either with 40 μm FeEDTA (+Fe plants) or without Fe and with 100 μm 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine sulfonate (ferrozine; −Fe plants) to trap residual Fe. Plants were grown for 3 d on Fe-free medium before analysis.

Protein Extraction

Roots from +Fe plants and –Fe plants (13 d old) were ground in liquid nitrogen and suspended in 10× volume of precooled acetone (−20°C) containing 10% (v/v) TCA and 0.07% (v/v) 2-mercaptoethanol. Proteins were then precipitated for 2 h at −20°C after thorough mixing. Proteins were collected by centrifuging at 35,000g (JA-20 108 rotor; Beckman J2-HS) at 4°C for 30 min. The supernatant was carefully removed, and the protein pellets were washed twice with cold acetone containing 0.07% (v/v) 2-mercaptoethanol and 1 mm phenylmethanesulfonyl fluoride and a third time with cold acetone without 2-mercaptoethanol. Protein pellets were dried by lyophilization and stored at −80°C or immediately extracted using protein extraction buffer composed of 6 m urea, 50 mm triethylammonium bicarbonate, pH 8.5, and 2% CHAPS for 1 h at 6°C under constant shaking. Protein extracts were centrifuged at 19,000g for 20 min at 10°C. The supernatant was then collected, and the protein concentration was determined using a protein assay kit (Pierce).

In-Solution Trypsin Digestion and iTRAQ Labeling

Total protein (100 μg) was reduced by adding dithiothreitol to a final concentration of 10 mm and incubated for 1 h at room temperature. Subsequently, iodoacetamide was added to a final concentration of 40 mm, and the mixture was incubated for 1 h at room temperature in the dark. Then, dithiothreitol (10 mm) was added to the mixture to consume any free iodoacetamide by incubating the mixture for 1 h at room temperature in the dark. Proteins were then diluted by 50 mm triethylammonium bicarbonate and 1 mm CaCl2 to reduce the urea concentration to less than 0.6 m and digested with 40 μg of modified trypsin (Promega) at 37°C overnight. The resulting peptide solution was acidified with 10% trifluoroacetic acid and desalted on a C18 solid-phase extraction cartridge.

Desalted peptides were then labeled with iTRAQ reagents (Applied Biosysterms) according to the manufacturer’s instructions. Control samples (proteins extracted from roots of +Fe plants) were labeled with reagent 114; samples from Fe-deficient roots were labeled with reagent 117. Two independent biological experiments with three technical repeats each were performed. The reaction was allowed to proceed for 1 h at room temperature. Subsequently, treated and control peptides were combined and further fractionated offline using high-resolution cation-exchange chromatography (PolySulfoethyl A, 5 μm, 200-Å bead). In total, 40 fractions were collected and combined into 20 final fractions according to the peak area. Each final fraction was lyophilized in a centrifugal speed vacuum concentrator. Samples were stored at −80°C.

MS/MS Analysis

Nano-HPLC-MS/MS analysis was performed on a nanoAcquity system (Waters) connected to an LTQ-Orbitrap XL hybrid mass spectrometer (Thermo Electron) equipped with a PicoView nanospray interface (New Objective). Peptide mixtures were loaded onto a 75-μm i.d., 25-cm length C18 BEH column (Waters) packed with 1.7-μm particles with a pore size of 130 Å and were separated using a segmented gradient in 90 min from 5% to 40% solvent B (acetonitrile with 0.1% formic acid) at a flow rate of 300 nL min−1 and a column temperature of 35°C. Solvent A was 0.1% formic acid in water.

The LTQ-Orbitrap XL hybrid mass spectrometer was operated in positive ionization mode. The MS survey scan for all experiments was performed in the Fourier transform cell recording a window between 350 and 1,600 mass-to-charge ratio (m/z). The resolution was set to 60,000 at m/z 400, and the automatic gain control was set to 500,000 ions. The m/z values triggering MS/MS were put on an exclusion list for 90 s. The minimum MS signal for triggering MS/MS was set to 5,000. In all cases, one microscan was recorded. For high-energy collision dissociation, the applied acquisition method consisted of a survey scan to detect the peptide ions followed by a maximum of three MS/MS experiments of the three most intense signals exceeding a minimum signal of 5,000 in survey scans. For MS/MS, we used a resolution of 7,500, an isolation window of 2 m/z, and a target value of 100,000 ions, with maximum accumulation times of 400 ms. Fragmentation was performed with normalized collision energy of 50% and an activation time of 30 ms. For each experiment, three technical repeats were performed.

Database Search and Quantification

The 2.3.02 version of the Mascot software (Matrix Science) was used to simultaneously identify and quantify proteins. In this version, only unique peptides used for protein quantification can be chosen, which is more precise to quantify proteins. Searches were made against the Arabidopsis protein database (TAIR9_pep_20090619, 33,410 sequences; ftp://ftp.arabidopsis.org/home/tair/Sequences/blast_datasets/TAIR9_blastsets/) concatenated with a decoy database containing the randomized sequences of the original database. For each technical repeat, spectra from the 20 fractions were combined into one MGF (Mascot generic format) file after loading the raw data, and the MGF file were searched. For biological repeats, spectra from the three technical repeats were combined into one file and searched. The search parameters were as follows: trypsin/P was chosen as the enzyme with two missed cleavages allowed; fixed modifications of carbamidomethylation at Cys, variable modifications of oxidation at Met and iTRAQ 4plex at Tyr; peptide tolerance was set at 10 ppm, and MS/MS tolerance was set at 0.6 D. Peptide charge was set Mr, and monoisotopic mass was chosen. iTRAQ 4plex was chosen for quantification during the search simultaneously.

The search results were passed through additional filters before exporting the data. For protein identification, the filters were set as follows: significance threshold P < 0.05 (with 95% confidence) and ion score or expected cutoff less than 0.05 (with 95% confidence). For protein quantitation, the filters were set as follows: “weighted” was chosen for protein ratio type (http://mascot-pc/mascot/help/quant_config_help.html); minimum precursor charge was set to 1 and minimum peptides was set to 2; only unique peptides were used to quantify proteins. Summed intensities were set as normalization, and outliers were removed automatically. The peptide threshold was set as above for homology.

Statistical Analysis

Proteins with significant changes in abundance upon Fe deficiency were selected using a method described by Cox and Mann (2008). In brief, the mean and sd from the log2 ratios of the 2,882 quantified proteins overlapping in both biological repeats was calculated. Next, 95% confidence (Z score = 1.96) was used to select those proteins whose distribution was removed from the main distribution. For the down-regulated proteins, the confidence interval was −0.13256 (mean ratio of the 2,882 proteins) − 1.96× 0.288001 (sd), corresponding to a protein ratio of 0.616837. Similarly, for the up-regulated proteins, the mean confidence interval was calculated (mean ratio + 1.96× sd), corresponding to a protein ratio of 1.349032. Protein ratios outside this range were defined as being significantly different at P = 0.05. The cutoff value for the down-regulated proteins was 0.62-fold and for the up-regulated proteins was 1.35-fold.

Protein Fishing Based on Protein-Protein Interactions

Protein fishing was done using the MACCU software (http://maccu.openfoundry.org/) based on the Interactome 2.0 (Geisler-Lee et al., 2007) downloaded from The Arabidopsis Information Resource database (ftp://ftp.arabidopsis.org/home/tair/Proteins/Interactome2.0/). Using 101 Fe-responsive proteins as bait, a protein was considered as a potential partner and part of the cluster when it had two or more high- or medium-confidence interaction relationships with Fe-responsive proteins.

Supplemental Data

The following materials are available in the online version of this article.

Supplementary Material

[Supplemental Data]

Acknowledgments

LTQ-Orbitrap data were acquired at the Academia Sinica Common Mass Spectrometry Facilities located at the Institute of Biological Chemistry, Academia Sinica. Expert technical help was provided by Chin-Wen Chen (Proteomics core, Institute of Plant and Microbial Biology), Ming-Hsin An and Chao-Yu Pan (Bioinformatics core, Institute of Plant and Microbial Biology), and Ya-Yun Liao and Huang Tze Yu (Schmidt laboratory). We thank Cole Lu (Schmidt laboratory) for artwork and Prof. Thomas J. Buckhout (Humboldt University Berlin) for microarray meta-analysis and critical comments on the manuscript. We further thank Dr. Suh-Yuen Liang (National Research Program for Genomic Medicine Core Facilities for Proteomics and Glycomics, Academia Sinica) and Prof. Ming Yang (Department of Computer Science, Nanjing Normal University) for providing expert advice for protein quantification and statistical analysis.

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