Unraveling the iron deficiency responsive proteome in Arabidopsis shoot by iTRAQ-OFFGEL approach.

Iron (Fe) is required by plants for basic redox reactions in photosynthesis and respiration, and for many other key enzymatic reactions in biological processes. Fe homeostatic mechanisms have evolved in plants to enable the uptake and sequestration of Fe in cells. To elucidate the network of proteins that regulate Fe homeostasis and transport, we optimized the iTRAQ-OFFGEL method to identify and quantify the number of proteins that respond to Fe deficiency in the model plant Arabidopsis. In this study, Fe deficiency was created using Fe-deficient growth conditions, excess zinc (Zn), and use of the irt1-1 mutant in which the IRT1 Fe transporter is disrupted. Using the iTRAQ-OFFGEL approach, we identified 1139 proteins, including novel Fe deficiency-responsive proteins, in microsomal fractions isolated from 3 different types of Fe-deficient shoots compared with just 233 proteins identified using conventional iTRAQ-CEX. Further analysis showed that greater numbers of low-abundance proteins could be identified using the iTRAQ-OFFGEL method and that proteins could be identified from numerous cellular compartments. The improved iTRAQ-OFFGEL method used in this study provided an efficient means for identifying greater numbers of proteins from microsomal fractions of Arabidopsis shoots. The proteome identified in this study provides new insight into the regulatory cross talk between Fe-deficient and excess Zn conditions.

Introduction

Iron (Fe) is an essential element for life on earth and is required by both plants and animals for a range of essential enzymatic and metabolic reactions. In plants, Fe is required for basic redox reactions in photosynthesis and respiration, and for many key enzymatic reactions involved in important biological processes such as DNA replication, lipid metabolism, and nitrogen fixation. According to the World Health Organization, 30% of the world’s population is affected by Fe deficiency.

To better understand the mechanisms that regulate Fe transport and homeostasis in plants, omics-based approaches have been used in tomato and Arabidopsis to gain a comprehensive understanding of growth defects caused by Fe deficiency and homeostasis., However, many molecular components in the Fe transport and homeostatic regulatory network need to be identified to bridge gaps in our understanding of the complete mechanism, especially in the dicot model plant Arabidopsis. Quantitative proteomics may provide one approach to identify the protein components of the regulatory network. In the past decade, the isobaric tags for relative and absolute quantification (iTRAQ) labeling method has been adapted to plant proteomics as a relative quantification technique; however, the number of identified proteins remains small.

To identify and quantify a greater number of proteins, Chenau et al. (2008) established a method that coupled iTRAQ with OFFGEL electrophoresis (iTRAQ-OFFGEL). The iTRAQ-OFFGEL approach is important for quantitative proteomics studies because greater amounts of sample can be loaded and separated allowing subsequent analytical steps to be optimized. In the plant proteomics field, the iTRAQ-OFFGEL method has been used to examine shifts in pI due to iTRAQ labeling; however, physiological parameters were not considered in that study.

In this study, we used the iTRAQ-OFFGEL method to analyze microsomal fractions isolated from shoots of wild-type Arabidopsis (Col-0) under conditions of Fe deficiency and excess zinc (Zn) and the irt1-1 mutant, which lacks a functional IRT1 Fe transporter, and showed that this method is an effective approach for understanding plant physiological phenomena. We identified 1139 proteins using the iTRAQ-OFFGEL method compared to only 233 proteins using the conventional iTRAQ-CEX method, which couples the iTRAQ method with peptide fractionation on a cationic exchange column. Moreover, the iTRAQ-OFFGEL method enabled the efficient identification of a large number of membrane proteins, including low-abundance proteins. We focused on the highly upregulated and downregulated microsomal proteins in Arabidopsis shoots that were identified by iTRAQ-OFFGEL to provide insight into regulatory cross talk between the conditions of Fe deficiency and excess Zn.

Results

iTRAQ-CEX analysis of Fe deficiency-responsive proteins

An analysis using iTRAQ-CEX was recently performed on microsomal fractions isolated from Arabidopsis roots to better understand the molecular and physiological effects of excess Zn on plant cells. The abundance of IRT1 and FRO2, which play pivotal roles in Fe uptake, increased in response to Fe deficiency caused by excess Zn in roots. However, the nature of regulatory cross talk between Fe and Zn remains clear. In this study, an iTRAQ-CEX analysis was performed using microsomal fractions from shoots of Arabidopsis Col-0 grown on normal MGRL medium (Normal), medium without Fe (0-Fe), and medium containing 300 µM ZnSO4 (300-Zn) and from shoots of the irt1-1 mutant grown on Normal medium to identify changes in protein expression due to Fe deficiency. To ensure that the Fe content in each type of Fe-deficient sample (0-Fe, 300-Zn, and irt1-1) was less than in Col-0 grown on Normal medium, the concentrations of 11 elements in Arabidopsis shoots were examined by inductive coupled plasma mass spectroscopy (ICP-MS) (Table S1). The Fe content was lower in all 3 types of Fe-deficient shoots than in shoots from Col-0 grown on Normal medium (Fig. 1A). The experimental scheme followed in this study is depicted in Figure 2. A total of 233 proteins were identified and quantified with a false discovery rate (FDR) of less than 1% in 3 biological replicates (Table S2). Among the identified proteins, reproducible increases in protein amounts of more than 2.0-fold were observed for only 3 proteins in the 0-Fe samples and proteins in the 300-Zn samples, while no increases in protein amounts greater than 2.0-fold were observed for the irt1-1 mutant (Table 1). Only 16 proteins decreased in amount to less than 0.50-fold in the 0-Fe samples; no proteins from the 300-Zn or irt1-1 mutant samples decreased in amount to less than 0.50-fold (Table 1). The identification of small numbers of highly responsive proteins indicates a need to adopt alternate methodologies to better understand the mechanisms of Fe homeostasis in plant cells.

Figure 1. Effect of Fe deficiency on Fe and Zn levels. Fe (A) and Zn (B) contents in shoots from Arabidopsis Col-0 plants grown on Normal, 0-Fe, or 300-Zn media and in shoots from irt1-1 mutant plants grown on Normal medium for 10 days. The Fe levels are presented as mean values from 3 biological replicates analyzed in triplicate. Error bars indicate the SEs of the biological replicates.

Figure 2. Experimental scheme used in this study. Microsomal fractions were extracted from 10-day-old shoots of Col-0 plants grown on Normal, 0-Fe, and 300-Zn media and irt1-1 mutants grown on Normal medium and used for iTRAQ-CEX and iTRAQ-OFFGEL analyses. The major steps involved in the iTRAQ-CEX and iTRAQ-OFFGEL analyses are presented.

Table 1. Number of proteins identified as per the expression levels using iTRAQ-CEX and iTRAQ-OFFGEL methods

Expression levels	iTRAQ-CEX	iTRAQ-OFFGEL
Col-0 0-Fe/ Col-0 Normal
More than 2.0-fold	3	66
More than 1.5-fold	5	159
More than 1.2-fold	19	330
Less than 0.833-fold	110	355
Less than 0.667-fold	36	184
Less than 0.5-fold	16	64

Efficiency of iTRAQ-OFFGEL analysis over iTRAQ-CEX

The compatibility of OFFGEL fractionation with iTRAQ labeling was reported previously., The iTRAQ-OFFGEL method has been adopted in several studies, mostly in animal systems.- In this study, a large number of proteins were identified from Arabidopsis microsomal fractions using the iTRAQ-OFFGEL method (Fig. 2). A total of 1139 proteins were identified and quantified in 3 biological replicates with an FDR of less than 1% (Table S3). The number of proteins identified using iTRAQ-OFFGEL was 4.9-fold greater than with iTRAQ-CEX, indicating a higher recovery rate of fractionated peptides.

Based on GO annotations, the proteins identified using the iTRAQ-OFFGEL method corresponded to a larger number of cellular compartments than the proteins identified using the iTRAQ-CEX method (Tables S4 ). Additionally, higher proportions of proteins belonging to “membrane and membrane bound proteins” were identified by iTRAQ-OFFGEL than by iTRAQ-CEX (Tables S4 ). Moreover, 245 proteins identified by iTRAQ-OFFGEL were predicted to have 2 or more transmembrane (TM) regions compared to only 31 proteins identified by iTRAQ-CEX based on an analysis using TMHMM Server v. 2.0 software (Fig. 3, http://www.cbs.dtu.dk/services/TMHMM/). The fractionation of iTRAQ-labeled peptides was an effective method for increasing the numbers of detectable peptides, including low-abundance peptide. Peptides derived from low-abundance proteins are often not detected by MS due to ionization suppression.

Figure 3. Proportion of TM regions corresponding to the membrane proteins identified by iTRAQ-CEX and iTRAQ-OFFGEL. Number of membrane proteins with 2 or more TM regions. TM regions were predicted using TMHMM Server v. 2.0 software (http://www.cbs.dtu.dk/services/TMHMM/). The numbers of TM regions are indicated on the x-axis and the numbers of proteins identified are indicated on the y-axis. The numbers of proteins with 2 or more TM regions identified by iTRAQ-CEX and iTRAQ-OFFGEL analyses are represented by blue and red bars, respectively.

Cross talk between Fe deficiency and excess Zn conditions

From the iTRAQ-OFFGEL analysis, growth on 0-Fe and 300-Zn caused 66 and 31 proteins, respectively, to be upregulated more than 2.0-fold (Tables 1 and 2). All of the 31 proteins that were upregulated in response to excess Zn were also upregulated more than 2.0-fold by growth on 0-Fe. In contrast, 64 and 13 proteins were downregulated to less than 0.5-fold by growth on 0-Fe and 300-Zn, respectively. Seven of these downregulated proteins were in common between growth on 0-Fe and 300-Zn (Tables 1 and 3). The functions of most of the downregulated proteins were related to photosynthesis as evidenced by the visual effects of Fe deficiency and excess Zn on physiological parameters such as the development of chlorosis. The common regulation of most of the proteins in response to Fe deficiency and excess Zn indicates regulatory cross talk activity between the 2 conditions.

Table 2. iTRAQ-OFFGEL analysis based identified proteins with more than 2.0-fold expression

Arabidopsis Genome Initiative Code	Protein	Peptidea	Coverageb	Ratioc(Col-0 0-Fe/ Col-0 Normal)	Ratioc(Col-0 300-Zn/ Col-0 Normal)	Ratioc(irt1-1 Normal/Col-0 Normal)
AT3G57260	beta-1,3-glucanase 2	1.0	3.0	58.833 ± 35.422	7.621 ± 5.062	1.285 ± 0.602
AT2G18193	P-loop containing nucleoside triphosphate hydrolases superfamily protein	1.3	2.8	14.417 ± 9.381	20.651 ± 16.525	2.792 ± 2.269
AT4G39090	Papain family cysteine protease	2.3	12.8	12.889 ± 1.938	3.892 ± 1.034	1.000 ± 0.192
AT5G26340	Major facilitator superfamily protein	1.7	2.2	8.895 ± 4.114	4.125 ± 1.113	1.034 ± 0.139
AT5G10760	Eukaryotic aspartyl protease family protein	1.3	2.5	7.770 ± 9.065	3.901 ± 4.076	1.007 ± 0.174
AT3G52400	syntaxin of plants 122	3.7	13.0	7.158 ± 2.860	2.843 ± 0.373	1.245 ± 0.192
AT5G20230	blue-copper-binding protein	3.0	21.4	7.026 ± 1.667	4.625 ± 1.218	1.370 ± 0.387
AT3G22600	Bifunctional inhibitor/lipid-transfer protein/seed storage 2S albumin superfamily protein	1.0	6.5	6.949 ± 4.416	2.561 ± 0.940	0.950 ± 0.239
AT2G18690	unknown protein	3.0	7.8	6.173 ± 0.992	3.610 ± 0.888	1.447 ± 0.253
AT3G25610	ATPase E1-E2 type family protein / haloaciddehalogenase-like hydrolase family protein	1.0	1.2	5.788 ± 3.425	3.793 ± 2.025	0.913 ± 0.258
AT4G16370	oligopeptide transporter	12.0	16.7	5.574 ± 0.565	6.045 ± 0.946	3.341 ± 1.588
AT2G47000	ATP binding cassette subfamily B4	11.0	15.7	4.858 ± 2.582	6.587 ± 4.535	1.258 ± 0.166
AT5G06320	NDR1/HIN1-like 3	6.3	25.8	4.779 ± 0.978	2.087 ± 0.368	1.233 ± 0.033
AT1G11910	aspartic proteinase A1	10.7	26.2	4.549 ± 0.295	1.905 ± 0.343	0.917 ± 0.076
AT3G26080	plastid-lipid associated protein PAP / fibrillin family protein	1.0	3.9	4.546 ± 0.679	1.436 ± 0.639	N.D.
AT2G38290	ammonium transporter 2	1.7	4.6	4.348 ± 0.791	2.388 ± 0.422	1.167 ± 0.059
AT3G13080	multidrug resistance-associated protein 3	12.3	8.8	4.305 ± 1.467	7.100 ± 2.457	1.408 ± 0.298
AT1G02920	glutathione S-transferase 7	1.7	33.5	4.116 ± 0.382	2.573 ± 0.134	1.015 ± 0.105
AT2G23810	tetraspanin8	5.7	17.8	3.955 ± 0.866	2.079 ± 0.323	1.250 ± 0.159
AT4G27170	seed storage albumin 4	1.7	11.6	3.890 ± 2.794	0.803 ± 0.254	3.058 ± 2.330
AT3G19930	sugar transporter 4	3.3	6.7	3.722 ± 0.351	1.674 ± 0.115	1.301 ± 0.146
AT1G02930	glutathione S-transferase 6	1.7	33.2	3.693 ± 0.317	2.091 ± 0.203	0.958 ± 0.121
AT4G15610	Uncharacterised protein family (UPF0497)	3.0	14.5	3.470 ± 1.610	2.406 ± 0.911	0.906 ± 0.102
AT4G38540	FAD/NAD(P)-binding oxidoreductase family protein	2.3	4.8	3.291 ± 0.443	2.702 ± 0.507	1.156 ± 0.177
AT5G67330	natural resistance associated macrophage protein 4	4.0	8.7	3.053 ± 0.195	3.395 ± 0.023	2.203 ± 0.871
AT1G71880	sucrose-proton symporter 1	6.0	12.0	2.902 ± 0.528	2.121 ± 0.042	1.105 ± 0.042
AT4G21960	Peroxidase superfamily protein	3.7	14.6	2.899 ± 1.637	2.534 ± 1.009	3.693 ± 2.543
AT3G45140	lipoxygenase 2	5.0	5.6	2.866 ± 1.617	2.698 ± 1.668	1.105 ± 0.372
AT4G20830	FAD-binding Berberine family protein	2.0	3.0	2.684 ± 1.655	2.106 ± 1.136	1.116 ± 0.366
AT4G27160	seed storage albumin 3	2.3	15.0	2.679 ± 2.031	0.988 ± 0.191	2.716 ± 2.064
AT5G25260	SPFH/Band 7/PHB domain-containing membrane-associated protein family	3.7	21.1	2.605 ± 0.507	1.513 ± 0.403	1.118 ± 0.214
AT1G44575	Chlorophyll A-B binding family protein	6.0	28.2	2.556 ± 0.151	1.362 ± 0.164	1.653 ± 0.744
AT2G31880	Leucine-rich repeat protein kinase family protein	5.0	11.7	2.548 ± 0.420	2.196 ± 0.628	1.026 ± 0.054
AT1G08450	calreticulin 3	4.3	9.5	2.538 ± 0.893	1.514 ± 0.144	1.119 ± 0.110
AT3G01290	SPFH/Band 7/PHB domain-containing membrane-associated protein family	9.7	51.0	2.513 ± 0.868	1.766 ± 0.574	1.085 ± 0.224
AT5G11040	TRS120	5.0	4.1	2.486 ± 1.351	2.297 ± 0.945	0.870 ± 0.163
AT4G09010	ascorbate peroxidase 4	4.3	12.6	2.464 ± 0.397	1.177 ± 0.197	1.313 ± 0.443
AT5G35735	Auxin-responsive family protein	2.0	6.4	2.418 ± 1.106	1.451 ± 0.683	0.870 ± 0.380
AT2G05380	glycine-rich protein 3 short isoform	2.0	25.9	2.392 ± 0.665	3.418 ± 0.693	1.359 ± 0.303
AT4G13510	ammonium transporter 1;1	3.0	9.8	2.390 ± 1.063	1.911 ± 0.771	0.965 ± 0.084
AT1G77510	PDI-like 1-2	12.3	34.7	2.389 ± 0.266	1.603 ± 0.215	1.063 ± 0.108
AT3G26210	cytochrome P450, family 71, subfamily B, polypeptide 23	1.3	4.5	2.383 ± 1.080	1.567 ± 0.440	1.158 ± 0.195
AT1G03860	prohibitin 2	2.0	12.9	2.345 ± 0.388	1.771 ± 0.177	1.292 ± 0.254
AT4G02520	glutathione S-transferase PHI 2	6.0	43.9	2.337 ± 0.521	1.536 ± 0.533	1.006 ± 0.164
AT1G65820	microsomal glutathione s-transferase, putative	3.7	25.6	2.336 ± 0.165	1.872 ± 0.204	1.159 ± 0.096
AT3G11820	syntaxin of plants 121	5.0	18.8	2.315 ± 0.119	1.607 ± 0.145	1.114 ± 0.072
AT3G51670	SEC14 cytosolic factor family protein / phosphoglyceride transfer family protein	3.3	8.7	2.312 ± 0.027	2.130 ± 0.256	1.620 ± 0.465
AT3G62600	DNAJ heat shock family protein	5.0	14.8	2.310 ± 0.375	1.727 ± 0.323	1.326 ± 0.137
AT3G12740	ALA-interacting subunit 1	3.0	11.4	2.307 ± 0.729	1.680 ± 0.417	1.047 ± 0.116
AT2G05620	proton gradient regulation 5	2.7	20.6	2.281 ± 0.303	1.355 ± 0.151	1.321 ± 0.230
AT4G23680	Polyketidecyclase/dehydrase and lipid transport superfamily protein	3.0	21.2	2.273 ± 0.519	2.404 ± 0.171	1.265 ± 0.302
AT5G62890	Xanthine/uracil permease family protein	1.0	2.0	2.270 ± 0.858	1.722 ± 0.673	1.367 ± 0.161
AT1G47128	Granulin repeat cysteine protease family protein	4.7	10.2	2.265 ± 0.316	1.312 ± 0.117	0.976 ± 0.180
AT3G48890	membrane-associated progesterone binding protein 3	8.3	32.3	2.263 ± 0.159	1.554 ± 0.085	1.242 ± 0.154
AT1G27770	autoinhibited Ca2+-ATPase 1	3.0	4.2	2.259 ± 0.542	1.725 ± 0.334	1.186 ± 0.187
AT4G08850	Leucine-rich repeat receptor-like protein kinase family protein	18.3	18.7	2.216 ± 0.442	1.937 ± 0.641	1.154 ± 0.126
AT5G23120	photosystem II stability/assembly factor, chloroplast (HCF136)	3.3	9.2	2.215 ± 0.271	1.122 ± 0.134	1.446 ± 0.422
AT5G42020	Heat shock protein 70 (Hsp 70) family protein	2.7	49.9	2.160 ± 0.311	1.365 ± 0.255	1.069 ± 0.106
AT4G22890	PGR5-LIKE A	1.0	3.7	2.159 ± 0.294	1.244 ± 0.160	1.451 ± 0.604
AT4G34150	Calcium-dependent lipid-binding (CaLB domain) family protein	1.7	7.0	2.157 ± 0.461	1.604 ± 0.221	1.274 ± 0.205
AT5G40770	prohibitin 3	2.3	8.3	2.138 ± 0.418	1.635 ± 0.495	1.198 ± 0.162
AT1G21750	PDI-like 1-1	20.7	48.8	2.114 ± 0.493	1.569 ± 0.059	1.200 ± 0.079
AT4G16660	heat shock protein 70 (Hsp 70) family protein	8.7	9.2	2.105 ± 0.623	1.372 ± 0.107	1.108 ± 0.112
AT1G80530	Major facilitator superfamily protein	1.3	2.1	2.088 ± 0.332	2.648 ± 0.323	1.217 ± 0.440
AT2G43350	glutathione peroxidase 3	1.0	6.6	2.086 ± 0.321	1.861 ± 0.658	1.375 ± 0.185
AT2G03510	SPFH/Band 7/PHB domain-containing membrane-associated protein family	3.3	10.4	2.007 ± 0.580	1.996 ± 0.508	1.362 ± 0.451

a Peptide indicates average number of assigned peptides. bCoverage indicates average percentage of assigned peptides to the predicted protein. cThe values were calculated as the ration of 115 (Col-0 0-Fe) to 114 (Col-0 Normal), 116 (Col-0 300-Zn) to 114 (Col-0 Normal), or 117 (irt1-1 Normal) to 114 (Col-0 Normal). Microsomal proteins from the Col-0 grown on Normal, 0-Fe or 300-Zn media and from the irt1-1 mutant grown on Normal medium were labeled with iTRAQ-114, 115, 116 and 117 reagents, respectively. Proteins that increased more than 2.0-fold were selected and ordered based on fold change values in Col-0 0-Fe/ Col-0 Normal. However, the response of proteins in the Col-0 300-Zn/ Col-0 Normal and irt1-1 Normal/ Col-0 Normal were ordered as per their position in fold change values in Col-0 0-Fe/ Col-0 Normal. Data were mean ± SD of 3 independent experiments. N.D. means protein and/or iTRAQ reporter ions were not detected.

Table 3. iTRAQ-OFFGEL analysis based identified proteins with less than 0.5-fold expression

Arabidopsis Genome Initiative Code	Protein	Peptide a	Coverage b	Ratio c(Col-0 0-Fe/Col-0 Normal)	Ratio c(Col-0 300-Zn/ Col-0 Normal)	Ratio c(irt1-1 Normal /Col-0 Normal)
AT3G16240	delta tonoplast integral protein	2.3	0.2	0.171 ± 0.058	0.378 ± 0.053	1.116 ± 0.386
AT1G12230	Aldolase superfamily protein	3.3	0.1	0.182 ± 0.009	0.371 ± 0.058	0.811 ± 0.324
AT4G12980	Auxin-responsive family protein	2.3	0.1	0.245 ± 0.070	0.415 ± 0.027	0.777 ± 0.179
AT5G14120	Major facilitator superfamily protein	4.0	0.1	0.247 ± 0.031	0.451 ± 0.070	0.883 ± 0.204
ATCG00490	ribulose-bisphosphate carboxylases	20.7	0.5	0.283 ± 0.034	0.622 ± 0.050	0.849 ± 0.125
AT3G23810	S-adenosyl-l-homocysteine (SAH) hydrolase 2	1.3	0.2	0.301 ± 0.044	0.505 ± 0.071	0.966 ± 0.196
AT2G17695	Unknown protein	2.3	0.1	0.311 ± 0.271	N.D.	N.D.
AT1G30120	pyruvate dehydrogenase E1 beta	2.7	0.1	0.314 ± 0.033	0.492 ± 0.043	0.793 ± 0.316
AT1G01620	plasma membrane intrinsic protein 1C	4.0	0.2	0.316 ± 0.056	0.753 ± 0.152	1.167 ± 0.070
AT4G28750	Photosystem I reaction centre subunit IV / PsaE protein	2.0	0.3	0.319 ± 0.066	0.528 ± 0.058	0.844 ± 0.051
AT1G09340	chloroplast RNA binding	6.3	0.2	0.328 ± 0.088	0.759 ± 0.121	1.439 ± 0.302
AT5G64040	photosystem I reaction center subunit PSI-N, chloroplast, putative / PSI-N, putative (PSAN)	3.7	0.2	0.335 ± 0.051	0.532 ± 0.075	0.791 ± 0.120
ATCG01060	iron-sulfur cluster binding;electron carriers;4 iron, 4 sulfur cluster binding	1.3	0.1	0.340 ± 0.090	0.563 ± 0.127	0.939 ± 0.160
AT4G13770	cytochrome P450, family 83, subfamily A, polypeptide 1	3.3	0.1	0.341 ± 0.030	0.427 ± 0.109	0.746 ± 0.232
AT1G30380	photosystem I subunit K	1.0	0.1	0.348 ± 0.109	0.515 ± 0.059	0.800 ± 0.082
AT1G67090	ribulosebisphosphate carboxylase small chain 1A	3.3	0.4	0.348 ± 0.011	0.728 ± 0.105	0.867 ± 0.158
AT1G77590	long chain acyl-CoA synthetase 9	3.7	0.1	0.348 ± 0.062	0.601 ± 0.063	0.796 ± 0.238
AT1G09310	Protein of unknown function, DUF538	8.3	0.4	0.351 ± 0.074	0.564 ± 0.066	1.941 ± 0.566
ATCG00340	Photosystem I, PsaA/PsaB protein	7.7	0.1	0.356 ± 0.096	0.552 ± 0.080	0.891 ± 0.070
AT5G44020	HAD superfamily, subfamily IIIB acid phosphatase	9.0	0.3	0.358 ± 0.087	0.781 ± 0.156	1.194 ± 0.071
AT3G05900	neurofilament protein-related	17.0	0.2	0.362 ± 0.090	0.556 ± 0.104	1.091 ± 0.147
AT4G30950	fatty acid desaturase 6	2.0	0.0	0.369 ± 0.047	0.506 ± 0.032	0.659 ± 0.167
AT1G01790	K+ efflux antiporter 1	9.7	0.1	0.375 ± 0.005	0.815 ± 0.148	0.865 ± 0.132
AT3G55800	sedoheptulose-bisphosphatase	2.0	0.1	0.378 ± 0.196	0.675 ± 0.081	0.869 ± 0.034
AT1G13930	Involved in response to salt stress. Knockout mutants are hypersensitive to salt stress.	4.3	0.3	0.379 ± 0.035	0.502 ± 0.061	1.073 ± 0.073
AT4G16155	dihydrolipoyl dehydrogenases	3.0	0.2	0.381 ± 0.124	0.507 ± 0.095	0.808 ± 0.351
AT4G15630	Uncharacterised protein family (UPF0497)	2.0	0.1	0.387 ± 0.082	0.544 ± 0.009	0.900 ± 0.063
AT1G68530	3-ketoacyl-CoA synthase 6	1.7	0.0	0.394 ± 0.104	0.600 ± 0.207	1.080 ± 0.396
AT3G16950	lipoamide dehydrogenase 1	2.0	0.2	0.404 ± 0.059	0.551 ± 0.076	0.785 ± 0.266
AT3G63160	Unknown protein	4.3	0.5	0.406 ± 0.015	0.611 ± 0.094	0.832 ± 0.162
AT4G31780	monogalactosyldiacylglycerol synthase 1	2.0	0.0	0.409 ± 0.138	0.565 ± 0.157	0.905 ± 0.131
ATCG00730	photosynthetic electron transfer D	1.3	0.1	0.410 ± 0.061	0.492 ± 0.072	0.874 ± 0.094
AT1G55670	photosystem I subunit G	2.3	0.1	0.410 ± 0.112	0.581 ± 0.056	0.887 ± 0.068
ATCG00350	Photosystem I, PsaA/PsaB protein	10.7	0.1	0.411 ± 0.067	0.609 ± 0.117	1.067 ± 0.115
AT3G61260	Remorin family protein	13.3	0.7	0.412 ± 0.043	0.628 ± 0.056	0.814 ± 0.222
AT5G16620	hydroxyproline-rich glycoprotein family protein	4.0	0.1	0.412 ± 0.135	0.620 ± 0.155	0.898 ± 0.257
AT3G44890	ribosomal protein L9	6.0	0.3	0.414 ± 0.130	0.575 ± 0.006	0.890 ± 0.072
AT1G31330	photosystem I subunit F	7.0	0.3	0.416 ± 0.101	0.576 ± 0.026	0.883 ± 0.078
AT1G52230	photosystem I subunit H2	1.0	0.1	0.417 ± 0.052	0.534 ± 0.078	0.824 ± 0.319
ATCG00540	photosynthetic electron transfer A	5.3	0.2	0.425 ± 0.034	0.596 ± 0.112	0.958 ± 0.184
AT4G34620	small subunit ribosomal protein 16	3.3	0.4	0.428 ± 0.158	0.654 ± 0.300	0.943 ± 0.336
AT3G45780	phototropin 1	7.7	0.1	0.434 ± 0.098	0.595 ± 0.061	1.019 ± 0.127
AT2G38040	acetyl Co-enzyme a carboxylase carboxyltransferase alpha subunit	7.3	0.1	0.437 ± 0.111	0.803 ± 0.031	0.825 ± 0.129
AT1G20620	catalase 3	4.7	0.2	0.446 ± 0.154	0.723 ± 0.142	0.726 ± 0.319
AT1G34430	2-oxoacid dehydrogenases acyltransferasefamily protein	11.0	0.3	0.449 ± 0.017	0.576 ± 0.052	0.899 ± 0.279
AT3G25860	2-oxoacid dehydrogenases acyltransferase family protein	12.3	0.4	0.451 ± 0.031	0.552 ± 0.093	0.892 ± 0.291
AT2G39730	rubiscoactivase	5.7	0.4	0.453 ± 0.079	0.652 ± 0.192	0.885 ± 0.319
AT3G54890	photosystem I light harvesting complex gene 1	2.7	0.1	0.455 ± 0.029	0.813 ± 0.141	1.446 ± 0.325
AT4G22710	cytochrome P450, family 706, subfamily A, polypeptide 2	6.3	0.1	0.461 ± 0.031	0.753 ± 0.110	0.895 ± 0.158
AT4G02770	photosystem I subunit D-1	3.7	0.5	0.461 ± 0.056	0.614 ± 0.050	0.911 ± 0.077
AT3G28860	ATP binding cassette subfamily B19	2.7	0.1	0.464 ± 0.058	0.685 ± 0.164	1.105 ± 0.156
AT3G27160	Ribosomal protein S21 family protein	1.7	0.1	0.467 ± 0.096	0.621 ± 0.128	0.816 ± 0.140
ATCG00720	photosynthetic electron transfer B	2.3	0.2	0.471 ± 0.070	0.656 ± 0.158	1.029 ± 0.091
AT3G16400	nitrile specifier protein 1	3.0	0.1	0.474 ± 0.169	0.730 ± 0.101	1.031 ± 0.230
AT4G38690	PLC-like phosphodiesterases superfamily protein	7.7	0.2	0.475 ± 0.061	0.714 ± 0.156	1.082 ± 0.179
AT3G54210	Ribosomal protein L17 family protein	1.3	0.1	0.482 ± 0.057	0.670 ± 0.071	0.892 ± 0.131
AT1G22740	RAB GTPase homolog G3B	3.0	0.2	0.484 ± 0.101	0.645 ± 0.115	0.999 ± 0.204
ATCG00780	ribosomal protein L14	5.7	0.5	0.486 ± 0.124	0.678 ± 0.186	0.865 ± 0.242
AT4G00400	glycerol-3-phosphate acyltransferase 8	2.7	0.1	0.488 ± 0.019	0.844 ± 0.093	1.252 ± 0.058
AT3G27020	YELLOW STRIPE like 6	1.7	0.0	0.490 ± 0.046	0.698 ± 0.022	0.960 ± 0.110
AT4G25650	ACD1-like	2.0	0.0	0.491 ± 0.052	0.622 ± 0.012	0.583 ± 0.290
AT1G61520	photosystem I light harvesting complex gene 3	3.3	0.2	0.491 ± 0.187	0.677 ± 0.199	1.007 ± 0.204
AT4G12800	photosystem I subunit l	2.7	0.2	0.497 ± 0.127	0.578 ± 0.086	0.849 ± 0.051
AT2G30520	Phototropic-responsive NPH3 family protein	7.0	0.1	0.499 ± 0.042	0.562 ± 0.124	1.006 ± 0.211

a Peptide indicates average number of assigned peptides. bCoverage indicates average percentage of assigned peptides to the predicted protein. cThe values were calculated as the ration of 115 (Col-0 0-Fe) to 114 (Col-0 Normal), 116 (Col-0 300-Zn) to 114 (Col-0 Normal), or 117 (irt1-1 Normal) to 114 (Col-0 Normal). Microsomal proteins from the Col-0 grown on Normal or 0-Fe or 300-Zn media and from the irt1-1 mutant grown on Normal medium were labeled with iTRAQ-114, 115, 116, and 117 reagents, respectively. Proteins that decreased less than 0.5-fold were selected and ordered based on fold change values in Col-0 0-Fe/ Col-0 Normal. However, the response of proteins in the Col-0 300-Zn/ Col-0 Normal and irt1-1 Normal/ Col-0 Normal were ordered as per their position in fold change values in Col-0 0-Fe/ Col-0 Normal. Data were mean ± SD of 3 independent experiments. N.D. means protein and/or iTRAQ reporter ions were not detected.

Regulation of protein expression in the irt1-1 mutant

Of the 7 proteins identified by the iTRAQ-OFFGEL method that were induced more than 2.0-fold in the irt1-1 mutant, 6 proteins were also expressed more than 2.0-fold in Col-0 plants grown on 0-Fe medium (Table S3). However, no proteins in the irt1-1 mutant were reduced in amount to less than 0.5-fold. The number of highly responsive proteins was less in the irt1-1 mutant than in Col-0 grown on 0-Fe or 300-Zn media or in Col-0 grown on Normal medium (Table 1). Moreover, photosystem I was affected less in the irt1-1 mutant than in Col-0 grown on 0-Fe medium (Table 3). These results indicate a broad effect of mineral stress on protein expression levels rather than a limited effect caused by the disruption of just one Fe transporter.

Discussion

Overview of proteins upregulated more than 2.0-fold in all 3 Fe-deficient conditions

Two transporter proteins were identified among the proteins that were upregulated by more than 2.0-fold in all 3 Fe-deficient conditions; that is, growth on 0-Fe and 300-Zn media and in the irt1-1 mutant. Oligopeptide transporter (OPT3, AT4G16370) was upregulated by 5.574-, 6.045-, and 3.341-fold by growth on 0-Fe medium and 300-Zn medium and in the irt1-1 mutant, respectively (Table 2). OPT3 is involved in the transport of small peptides that may have roles in nutrition., OPT3 plays a critical role in the maintenance of whole-plant Fe homeostasis and Fe nutrition in developing seeds, and it plays an important role in shoot-to-root signaling for the regulation of Fe deficiency responses in roots. Stacey et al. (2006) showed that OPT3 expression was enhanced by Fe limitation. The high level of OPT3 expression under Fe-deficient conditions shown in this study provides further support for the role of this protein in defense against Fe deficiency stress. The expression of another important transporter protein, natural resistance-associated macrophage protein 4 (NRAMP4, AT5G67330), was increased by 3.053-, 3.395-, and 2.203-fold by growth on 0-Fe medium and 300-Zn medium and in the irt1-1 mutant, respectively (Table 2). NRAMP4 is also upregulated under Fe-deficient conditions in both shoots and roots. OPT3 and NRAMP4 may serve as backup systems for Fe homeostasis because they were also upregulated in the irt1-1 mutant on Normal medium.

P-loop containing nucleoside triphosphate hydrolases superfamily protein (AT2G18193) showed the strongest upregulation, with increases of 14.417-, 20.651-, and 2.792-fold in response to growth on 0-Fe medium and 300-Zn medium and in the irt1-1 mutant, respectively (Table 2). The superfamily, which includes AT2G18193, includes 4 of the 6 primary classes of enzymes as defined by their Enzyme Commission number. Among them, the S1 family functions in signal transduction, the S2 family consists of DNA-binding proteins or transporter proteins, and the S3 family includes transferases, ATP-dependent kinases, and sulfotransferase. Fe deficiency may trigger a signal transduction pathway that upregulates the expression of transporters and other transferases to increase the efficiency of Fe transport. Moreover, this may help in the stabilization of DNA molecules.

Peroxidase superfamily protein (AT4G21960) expression was increased by 2.899-, 2.534-, and 3.693-fold in response to growth on 0-Fe medium and 300-Zn medium and in the irt1-1 mutant, respectively (Table 2). In addition to the known defense-related induction pathway, peroxidase superfamily genes are induced via other signal transduction pathways. Fe deficiency is also known to affect different peroxidase isoenzymes to varying degrees. For example, Fe induces the generation of H2O2 leading to oxidative stress in sunflower. Thus, Fe deficiency, as well as excess Zn, may play a role in inducing a signaling pathway that directs defense against Fe deficiency stress.

Cross talk between Fe-deficient and excess Zn conditions

We also showed that the expression of some of the identified proteins was higher with growth on 0-Fe and 300-Zn media, but was not induced to the same level as in the irt1-1 mutant. Major facilitator superfamily protein (AT5G26340) was highly upregulated in response to growth on 0-Fe and 300-Zn media. ZINC-INDUCED FACILITATOR1 (ZIF1), which encodes a member of the major facilitator superfamily of membrane proteins, is involved in a novel mechanism of Zn sequestration possibly by the transport of a Zn ligand or Zn ligand complex into vacuoles. The Zn content in shoots grown under Fe-deficient or excess Zn conditions increased to a level greater than in Col-0 shoots grown on Normal medium (Fig. 1B). The stronger expression of AT5G26340 under Fe-deficient and excess Zn conditions suggests a role in the sequestration of Zn into vacuoles. Multidrug resistance-associated protein 3 (MRP3, AT3G13080), a transporter protein, was highly upregulated by 4.305- and 7.100-fold by growth on 0-Fe and 300-Zn media, respectively. Cadmium, nickel, arsenic, cobalt, and lead exposure induced MRP3 expression in Arabidopsis roots and shoots. Previously, a slight difference in MRP3 expression was observed in response to treatment with higher levels of Zn and Fe. MRP3 is also thought to have separate binding sites for different substrates., Therefore, MRP3 might be able to transport both metal conjugates and non-metal-containing toxic compounds. The increased expression of MRP3 observed in this study suggests a role in assisting the transport of Fe under Fe-deficient conditions. However, further evidence is needed to confirm the mechanism underlying increased MRP3 expression under Fe-deficient conditions.

Syntaxin of plants 122 (SYP122, AT3G52400) was also highly expressed under both 0-Fe and 300-Zn growth conditions. SYP122 was upregulated 7.158- and 2.843-fold by growth on 0-Fe and 300-Zn media, respectively. SYP121 (AT3G11820), which has functional redundancy with SYP122, showed 2.315- and 1.607-fold increases in expression with growth on 0-Fe and 300-Zn media, respectively. SYP122 and SYP121 are key element of the soluble N-ethylmaleimide-sensitive factor protein attachment protein receptor (SNARE) complex on target membranes and can bind the SNARE complex to a vesicle to establish docking. SYP121 is involved in localization of the K+ channel KAT1 and aquaporin PIP2;5 transporter to the plasma membrane., Therefore, SYP122 might be involved in the localization of Fe transporters to the plasma membrane, although SYP122-interacting proteins that might assist the transport of Fe, or other metals whose accumulation is enhanced under Fe-deficient conditions, have not been reported.

Blue copper (Cu) binding protein (AT5G20230) was also highly expressed with 7.026- and 4.625-fold increases in expression with growth on 0-Fe and 300-Zn media, respectively. This protein may function to sequester Cu, a potentially toxic element that is also an essential cellular catalyst for redox reactions. Elemental analysis showed that the accumulation of Cu was higher in shoots grown on 0-Fe and 300-Zn media (Table S1), indicating that AT5G20230 might also play a role in the sequestration of excessive Cu caused by Fe deficiency and excess Zn. This potential role was further supported by the lack of an increase in blue Cu binding protein expression in the irt1-1mutant corresponding to the levels of Cu in the irt1-1 mutant.

Conclusions

We showed that iTRAQ-OFFGEL is an efficient method to identify and quantify a large number of proteins from microsomal fractions of Arabidopsis shoots. The number of proteins identified in this study using conventional iTRAQ-CEX was less than half the 521 proteins identified in an iTRAQ-CEX analysis of roots in our previous study. In contrast, we identified a 4.9-fold greater number of proteins using the iTRAQ-OFFGEL method. Moreover, many cellular compartments were targeted by our iTRAQ-OFFGEL analysis. To determine the impact of the iTRAQ-OFFGEL method on protein recovery, we also applied the method to a microsomal fraction from Arabidopsis roots. Overall, the number of proteins identified in roots was increased but it was not as large as that observed using the iTRAQ-CEX method (Y. Fukao, personal communication). The identification of a slightly smaller number of proteins in shoots may be due the abundance of chloroplast proteins in addition to storage, ribosomal, and cytoskeletal proteins. Peptides from chloroplast proteins might be preferentially detected by MS over low-abundance proteins due to ionization suppression, thereby decreasing the total number of proteins identified. We confirmed that the higher-resolution peptide separation of the iTRAQ-OFFGEL method is more effective for protein samples that include high-abundance proteins. In this study, higher expression levels of metal ion transporters such as NRAMP4, OPT3, MRP3, and other membrane proteins such as SYP122 under both Fe-deficient and excess Zn conditions could be detected using the iTRAQ-OFFGEL method. Our study provides new insight into the regulatory cross talk that occurs in response to Fe deficiency and excess Zn, although a detailed analysis is needed to fully understand the regulatory network involved in this cross talk.

Materials and Methods

Plant material and sample preparation

Arabidopsis (Arabidopsis thaliana) ecotype wild type Col-0 and irt1-1 mutant seeds were germinated on sterile plates of MGRL medium containing 2.3 mM MES-KOH, pH 5.7, 1.0% (w/v) sucrose, and 1.2% agar. The Col-0 and irt1-1 mutant were given different treatments i.e., normal MGRL medium (Normal), without Fe (0-Fe) and with 300 µM ZnSO4 (300-Zn). The seedlings were grown for 10 d at 22 °C under 16h light/ 8h dark conditions. Shoots (approximately 0.2 g fresh weight) were harvested and homogenized with buffer A (50 mM HEPES-KOH, pH 7.5, 5 mM EDTA, 400 mM sucrose, and protease inhibitor cocktail). The homogenates were centrifuged at 1,000×g at 4 °C for 20 min, and the supernatants were centrifuged at 8,000×g at 4 °C for 20 min. The supernatants were centrifuged at 100,000×g at 4 °C for 60 min to prepare the microsomal fraction. The pellets were washed by buffer A twice under the same condition and then dissolved in iTRAQ buffer (AB SCIEX). The protein concentration was determined using Nano drop (Thermo Scientific) by taking the OD. Three replicates were prepared from 3 independent experiments of plants grown at different times.

Determination of metal content

Ten-day-old shoots were harvested from Col-0 and irt1-1 mutant, that were given different treatments i.e. Normal, 0-Fe and 300-Zn for Col-0 and irt1-1 on Normal. Harvested shoots were dried at 60 °C for 2 d. Dried samples weighing more than 10 mg were digested with ultrapure HNO3 using a Microwave Digestion System (Milestone General). Elemental content in the digests was determined by ICP-MS (Agilent technology). Measurement was performed with 3 independent biological replicates.

In-solution trypsin digestion and iTRAQ labeling for iTRAQ-CEX

Each 20 µL of microsomal protein fraction (2.5 mg mL–1) was reduced by tris-(2-carboxyethyl) phosphine at 60 °C for 60 min and then alkalized by methyl methanethiosulfonate at room temperature for 10 min. Samples were digested using 10mL of trypsin (1 mg mL–1) at 37 °C for 16 h. The peptides from Col-0 grown on Normal medium, 0-Fe, 300-Zn medium and irt1-1 mutant grown on Normal medium, were labeled with iTRAQ-114, -115, -116, and -117 reagents, respectively, at room temperature for 60 min. For iTRAQ-CEX analysis, the mixed peptides were manually separated by 25, 50, 75, 100, 200, 350, and 1000 mM KCl using strong cation exchange (AB SCIEX) and then desalted on Sep-Pak C18 cartridges (Waters). The labeled peptides were concentrated by a vacuum concentrator. However, for iTRAQ labeling coupled with OFFGEL fractionation, the mixed peptide mixture was directly desalted on Sep-Pak C18 cartridges (Waters) followed by lyophilization to concentrate the peptides and then subjected to OFFGEL fractionation.

iTRAQ-OFFGEL fractionation analysis

The trypsin digested and iTRAQ labeled peptides were mixed with the supplied OFFGEL buffer to obtain a 3.60 mL sample solution, which was then subjected to isoelectric focusing using immobilized pH gradient strips in the liquid phase. The peptides were separated into 24 fractions with a 3100 OFFGEL fractionator (Agilent Technologies) using a 24 cm IPG gel, pH 3-10 (GE Healthcare) at 4500 V for 50,000 Vh at 50 µA, according to the manufacturer’s instructions. The fractionated peptides were automatically purified using C-TIP (AMR); the details are described in Fukao et al. (2013).

LC-MS/MS analysis

iTRAQ analysis was performed on the LTQ Orbitrap XL-HTC-PAL-Paradigm MS4 system. The iTRAQ-labeled peptides were loaded on the column (75mm internal diameter, 15 cm; L-Column; CERI) using a Paradigm MS4 HPLC pump (Michrom Bioresources) and an HTC-PAL autosampler (CTC Analytics). Buffers were 0.1% (v/v) acetic acid and 2% (v/v) acetonitrile in water (A) and 0.1% (v/v) acetic acid and 90% (v/v) acetonitrile in water (B). A linear gradient from 5% to 45% B for 70 min was applied, and peptides eluted from the column were introduced directly into an LTQ Orbitrap XL mass spectrometer (Thermo Scientific) with a flow rate of 200 nL min–1 and a spray voltage of 2.0 kV. The range of the mass spectrometric scan was mass-to-charge ratio 450 to 1500, and the top 3 peaks were subjected to tandem mass spectrometry analysis. The obtained spectra were compared against data in The Arabidopsis Information Resource 10 (TAIR10; http://www.arabidopsis.org/) using the MASCOT server (version 2.4; Matrix Science) via Proteome Discover software (version 1.3; Thermo Scientific). The following search parameters were used for database searching: threshold set-off at 0.05 in the ion-score cutoff; protein identification cutoff set to 2 assigned spectra per predicted protein; peptide tolerance at 10 ppm; tandem mass spectrometry tolerance at ±0.2 Da; peptide charge of 2+ or 3+; trypsin as the enzyme and allowing up to one missed cleavage; iTRAQ label and methyl methanethiosulfonate on Cys as a fixed modification; and oxidation on Met as a variable modification. iTRAQ data for 3 biological replicates were analyzed by MASCOT, and the data only with FDR of less than 1% were used for subsequent analysis. Only proteins that were identified in all 3 independent experiments were considered. Statistical analysis was conducted using the Student t test.