Genome-wide microarray analysis of tomato roots showed defined responses to iron deficiency

  • Anita Zamboni1Email author,

    Affiliated with

    • Laura Zanin2Email author,

      Affiliated with

      • Nicola Tomasi2,

        Affiliated with

        • Mario Pezzotti1,

          Affiliated with

          • Roberto Pinton2,

            Affiliated with

            • Zeno Varanini1Email author and

              Affiliated with

              • Stefano Cesco3

                Affiliated with

                BMC Genomics201213:101

                DOI: 10.1186/1471-2164-13-101

                Received: 3 August 2011

                Accepted: 20 March 2012

                Published: 20 March 2012

                Abstract

                Background

                Plants react to iron deficiency stress adopting different kind of adaptive responses. Tomato, a Strategy I plant, improves iron uptake through acidification of rhizosphere, reduction of Fe3+ to Fe2+ and transport of Fe2+ into the cells. Large-scale transcriptional analyses of roots under iron deficiency are only available for a very limited number of plant species with particular emphasis for Arabidopsis thaliana. Regarding tomato, an interesting model species for Strategy I plants and an economically important crop, physiological responses to Fe-deficiency have been thoroughly described and molecular analyses have provided evidence for genes involved in iron uptake mechanisms and their regulation. However, no detailed transcriptome analysis has been described so far.

                Results

                A genome-wide transcriptional analysis, performed with a chip that allows to monitor the expression of more than 25,000 tomato transcripts, identified 97 differentially expressed transcripts by comparing roots of Fe-deficient and Fe-sufficient tomato plants. These transcripts are related to the physiological responses of tomato roots to the nutrient stress resulting in an improved iron uptake, including regulatory aspects, translocation, root morphological modification and adaptation in primary metabolic pathways, such as glycolysis and TCA cycle. Other genes play a role in flavonoid biosynthesis and hormonal metabolism.

                Conclusions

                The transcriptional characterization confirmed the presence of the previously described mechanisms to adapt to iron starvation in tomato, but also allowed to identify other genes potentially playing a role in this process, thus opening new research perspectives to improve the knowledge on the tomato root response to the nutrient deficiency.

                Background

                Iron (Fe) deficiency is a yield-limiting factor for a variety of field crops all around the world and generally results from the interaction of limited soil Fe bioavailability and susceptible genotype cultivation [1]. Iron is an important microelement for plant life due to its involvement as redox-active metal in photosynthesis, mitochondrial respiration, nitrogen assimilation, hormone biosynthesis, production and scavenging of reactive oxygen species, osmoprotection and pathogen defence [2].

                Under aerated conditions at neutral alkaline pH, the soluble Fe concentration in soil solution is very low. To cope with Fe shortage plants have developed two strategies for its acquisition. The Strategy I (all higher plants except grasses) relies on improvement of Fe uptake through acidification of soil solution by excretion of protons via a plasmalemma P-type ATPase resulting in an increased Fe solubility, reduction of Fe3+ to the more soluble Fe2+ by a FeIII-chelate reductase and plasmalemma transport of Fe2+ by the activity of a Fe transporter [3]. Some model plants used to study Strategy I are dicots such as Arabidopsis thaliana, Solanum lycopersicum and Pisum sativum [4].

                Plant responses to Fe deficiency have been recently analyzed on the basis of large-scale changes not only in transcriptome [514], but also in proteome [1520] and metabolome [17]. Results of transcriptome analysis are influenced by differences in experimental plans, plant species and microarray platforms, and thus difficult to compare and be generalized. Notwithstanding this drawback, recently, a set of 92 transcripts that robustly reflect the transcriptional response of Arabidopsis to Fe deficiency [21], has been described as the "ferrome" by Schmidt and Buckhout [21]. The "ferrome" consists of a list of transcripts considered to be involved in the basic response to iron deficiency. The ferrome is particularly enriched in genes related to heavy metal cation transport and metal homeostasis. Focusing on tomato, a plant often used as a model to study Fe deficiency (Strategy I) and a crop of economic importance, no information is available at genome-wide transcriptional level. Two proteomic characterizations of tomato roots in response to 1-week of Fe deprivation showed 23 [15] and 15 [16] differentially expressed protein spots respectively. Modifications in proteome suggest changes in energy metabolism, sulfur metabolism, response to oxidative stress and signal transduction.

                In the present work a genome-wide transcriptional characterization of tomato roots in response to Fe deficiency is presented. This approach allowed indentifying 97 differentially expressed transcripts involved in the responses to the nutritional stress. Transcriptional changes, mainly related to positive modulation of glycolysis, TCA and methionine cycle, suggest that tomato roots behave similarly to Arabidopsis under Fe deficiency. Furthermore, flavonoid biosynthesis and root morphological changes are revealed as specific tomato responses to Fe shortage.

                Results and discussion

                Responses to Fe-deficiency

                Typical responses of Fe-deficiency [22] were observed in tomato plants grown for 14 d in the presence of a low amount of Fe and thereafter subjected to 7 d of Fe deprivation. The chlorophyll content (SPAD index value) was reduced in Fe-deficient plants (Table 1). A concomitant increase in root FeIII-chelate reductase activity (Table 1) was also observed with values similar to those commonly found in roots of Fe-deficient tomato plants [23]. Furthermore, Fe-deprived tomato plants developed more lateral roots and showed an abundant production of root-hairs (Figures 1 and 2).
                Table 1

                Leaf SPAD index values and root FeIII-chelate reductase activity

                Sample

                SPAD indexa

                FeIII-chelate reductase (mol g-1 root FW h-1)b

                Fe-sufficient

                29.5 ± 0.3

                0.37 ± 0.04

                Fe-deficient

                16.8 ± 0.6

                1.41 ± 0.06

                aSPAD index value of fully expanded young leaves was determined using a SPAD-502 meter (Minolta, Osaka, Japan); mean and SD using data of the three biological replicates.

                bMean and SD of three biological replicates.

                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-101/MediaObjects/12864_2011_4003_Fig1_HTML.jpg
                Figure 1

                Shoot and root apparatus of tomato plants grown under different Fe-supply conditions. Leaf detail of Fe-deficient (A) and Fe-sufficient (B) plants. Shoot (C) and roots (E) of Fe-deficient plants and shoot (D) and roots (E) of Fe-sufficient plants.

                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-101/MediaObjects/12864_2011_4003_Fig2_HTML.jpg
                Figure 2

                Root apparatus of tomato plants grown under different Fe-supply condition. Detail of root apparatus of A) Fe-deficient and B) Fe-sufficient plants.

                Comparison of root gene expression profiles in Fe-deficient and Fe-sufficient tomato plants

                Differences in root gene expression between Fe-sufficient and Fe-deficient tomato plants were obtained by a genome-wide gene expression analysis using a tomato chip developed through Combimatrix technology [24]. This chip allows monitoring simultaneously the expression of more than 25,000 tomato transcripts. Ninety-seven differentially expressed transcripts between Fe-deficient and Fe-sufficient tomato roots (75 up-regulated and 22 down-regulated) were identified by Linear Models for MicroArray (LIMMA) [25] (adjusted p-value ≤ 0.05; |FC| ≥ 2). This result obtained using a large-scale chip reinforce the idea that plant transcriptional response to Fe shortage is based on the modulation of a relative small set of genes as previously observed for the Arabidopsis "ferrome" [21].

                Manually curated annotation of the 97 differentially expressed transcripts was based on results of BlastP analysis against UniProt [26] database (Figure 3; Additional file 1) using terms of biological process of Gene Ontology (GO) [27]. Sequence grouping in functional categories according to the GO terms revealed that the most abundant functional category was "metabolic process" both for up-regulated and down-regulated transcripts (35% and 45% respectively; Figure 3). Other up-regulated transcripts belonged to "establishment of localization" (12%) and "cell wall organization and biogenesis" (8%), while for the down-regulated transcripts "response to stimulus" (9%) was one of the most representative main functional categories (Figure 3). Only up-regulated transcripts are present in the "secondary metabolic process" category (Table 2). Transcripts encoding proteins with no sequence homology to known proteins were defined as "no hits found" (12% and 5% for up-regulated and down-regulated transcripts respectively), while a similar percentage of transcripts showed homology to proteins involved in "unknown" biological process (24% and 23% respectively). A selection of differentially expressed and discussed in relation to Fe deficiency is reported in Table 2. The up-regulation and down-regulation of six differentially expressed transcripts in response to Fe deficiency were confirmed through Real-time RT-PCR experiments (Table 3).
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-101/MediaObjects/12864_2011_4003_Fig3_HTML.jpg
                Figure 3

                Functional categories distribution of differentially expressed transcripts. Distribution in main functional categories according to the GO "biological process" terms of the 75 up-regulated (A) and 22 down-regulated (B) transcripts in roots of Fe-deficient relative to Fe-sufficient plants. Percentage of transcripts is reported for each functional category.

                Table 2

                List of transcripts modulated in response to Fe-deficiency and reported in the Discussion

                #

                ProbeIDa

                Descriptionb

                UniProtIDc

                TC-IDd

                FCe

                p, value, adjf

                 

                Biological process GO:0008150

                    

                1

                TC192724_853_37_S

                SRC2

                B6SND5

                TC192724

                3.02

                0.014

                2

                TC192763_241_40_S

                Putative uncharacterized protein

                C6T3H9

                TC192763

                2.11

                0.030

                3

                TC193319_801_34_X2

                Putative uncharacterized protein

                Q40127

                TC193319

                -3.35

                0.016

                4

                TC195341_998_35_S

                Putative uncharacterized protein

                Q40127

                TC195341

                -3.33

                0.011

                5

                TC196753_1279_37_S

                D-protein

                Q8VWY8

                TC196753

                6.11

                0.028

                6

                TC197095_638_34_X2

                NtEIG-E80 protein (elicitor inducible gene product)

                Q9FXS6

                TC197095

                4.69

                0.024

                7

                TC198323_947_40_S

                Predicted protein

                B9MWQ1

                TC198323

                2.51

                0.032

                8

                TC199253_1439_39_S

                Zinc finger protein. putative

                B9SLY6

                TC199253

                2.82

                0.029

                9

                TC202360_487_40_S

                EF-1 alpha-like protein

                O49604

                TC202360

                2.15

                0.024

                10

                TC204571_463_40_S

                ATBET12, putative

                B9STJ3

                TC204571

                -2.62

                0.023

                11

                TC205207_890_35_S

                Putative uncharacterized protein

                Q40127

                TC205207

                -3.09

                0.048

                12

                TC207200_893_35_S

                VIT_00038707001

                E0CVH7

                TC207200

                2.44

                0.042

                13

                TC207407_739_37_S

                Predicted protein

                B9HQW6

                TC207407

                2.36

                0.042

                14

                TC207665_362_36_S

                Putative uncharacterized protein

                A9PCS8

                TC207665

                -2.39

                0.048

                15

                TC208712_583_40_S

                Germin-like protein

                Q5DT23

                TC208712

                2.35

                0.046

                16

                TC208745_692_37_S

                Putative uncharacterized protein

                A5C0F7

                TC208745

                4.84

                0.016

                17

                TC209321_482_35_S

                VITISV_041870

                A5C9V2

                TC209321

                6.94

                0.011

                18

                TC209504_302_40_S

                Hydrolase

                Q4PSL3

                TC209504

                2.78

                0.013

                19

                TC211515_728_35_S

                Amino acid binding protein, putative

                B9RBU8

                TC211515

                3.66

                0.014

                20

                TC212954_1137_35_S

                Predicted protein

                B9HZ36

                TC212954

                2.02

                0.046

                21

                TC213456_100_34_S

                Putative D-protein

                Q6K482

                TC213456

                4.97

                0.035

                22

                TC214599_1004_37_S

                Nodulin-like proteinAt2g16660/T24I21.7

                Q9SLF1

                TC214599

                6.87

                0.042

                23

                TC215994_489_36_S

                Putative aminotransferase, class V family protein

                Q1H8R9

                TC215994

                2.08

                0.036

                 

                Biological regulation GO: 0065007

                    

                24

                TC191806_907_40_S

                BHLH transcriptional

                Q5GA67

                TC191806

                6.56

                0.024

                25

                TC191963_1058_37_S

                Ferritin

                Q308A9

                TC191963

                -4.71

                0.008

                26

                TC194645_664_37_S

                DNA binding protein

                B9SZX2

                TC194645

                12.34

                0.015

                27

                TC198138_1325_39_S

                Thioredoxin peroxidase 1

                Q7Y240

                TC198138

                7.06

                0.046

                28

                TC203853_451_35_S

                Heterogeneous nuclear ribonucleoprotein 27C

                B9SSS5

                TC203853

                2.90

                0.028

                29

                TC206202_500_40_S

                Thioredoxin II

                B9RLX0

                TC206202

                -2.47

                0.048

                30

                TC215976_330_38_X2

                Ferritin

                Q308A9

                TC215976

                -3.64

                0.005

                 

                Cellular component organization or biogenesis GO:0071840

                31

                NP000231_1302_40_S

                Extensin-like protein Dif54

                Q43505

                NP000231

                8.22

                0.011

                32

                TC191669_1238_40_S

                Extensin-like protein Ext1

                Q8VWM5

                TC191669

                5.53

                0.003

                33

                TC204863_245_40_S

                Extensin-like protein Ext1

                Q8VWM5

                TC204863

                8.98

                0.013

                34

                TC212258_415_40_S

                Extensin-like protein Dif54

                Q43505

                TC212258

                9.13

                0.011

                35

                TC212487_279_40_S

                Extensin-like protein Ext1

                Q8VWM5

                TC212487

                6.44

                0.0004

                36

                TC214133_1133_40_S

                Extensin-like protein Dif10

                Q43504

                TC214133

                5.42

                0.024

                 

                Cellular process GO:0009987

                37

                TC207486_546_40_S

                Pollen specific actin-depolymerizing factor 2

                Q8H2B6

                TC207486

                4.81

                0.005

                 

                Establishment of localization GO: 0051234

                38

                NP796451_1558_36_S

                Root-specific metal transporter

                Q84LR1

                NP796451

                12.00

                0.024

                39

                TC191581_1150_36_S

                Iron-regulated transporter 1

                Q9XFB2

                TC191581

                9.42

                0.013

                40

                TC192292_1560_39_S

                Hippocampus abundant transcript 1 protein

                B9SG70

                TC192292

                14.79

                0.0004

                41

                TC200857_1001_40_S

                Ammonium transporter 1 member 1

                P58905

                TC200857

                2.05

                0.043

                42

                TC205660_580_35_S

                Metal tolerance protein

                B9GLJ8

                TC205660

                3.77

                0.049

                43

                TC206149_966_36_S

                Aluminum-activated malate transporter 9

                Q9LS46

                TC206149

                2.41

                0.048

                44

                TC208376_922_36_S

                Oligopeptide transporter, putative

                B9SA63

                TC208376

                17.67

                0.004

                45

                TC215768_1111_35_X2

                Aquaporin

                Q8W506

                TC215768

                -2.03

                0.023

                46

                TC215874_553_40_S

                Sec14 cytosolic factor, putative

                B9S6A7

                TC215874

                3.5

                0.009

                47

                TC216882_1121_38_X2

                Hippocampus abundant transcript 1 protein

                B9SG70

                TC216882

                9.29

                0.003

                 

                Metabolic process GO:0008152; Carbon utilization GO:0015976

                48

                TC204225_1412_35_S

                Phosphoenolpyruvate carboxylase

                P27154

                TC204225

                3.31

                0.039

                49

                TC214978_700_36_S

                Phosphoenolpyruvate carboxylase

                P27154

                TC214978

                3.74

                0.036

                 

                Metabolic process GO:0008152; Catabolic process GO:0009056

                50

                TC194584_1854_37_S

                Cysteine-type peptidase, putative

                B9STX0

                TC194584

                3.61

                0.011

                51

                TC203350_1573_40_S

                Vacuolar processing enzyme 1

                B2M1T0

                TC203350

                2.18

                0.026

                52

                TC208154_2059_38_S

                Beta-amylase PCT-BMYI

                Q94EU9

                TC208154

                -3.06

                0.042

                53

                TC215970_3405_40_S

                Protease Do-like 7

                Q8RY22

                TC215970

                4.01

                0.032

                 

                Metabolic process GO:0008152; Cellular metabolic process GO:0044237

                54

                TC192049_1645_40_S

                Sulfate adenylyltransferase

                Q43183

                TC192049

                -2.48

                0.029

                55

                TC193934_828_38_X2

                Methylthioribose kinase, putative

                B9RY82

                TC193934

                2.12

                0.023

                56

                TC194380_182_36_S

                S-adenosylmethionine-dependent methyltransferase, putative

                B9SZS6

                TC194380

                2.03

                0.045

                57

                TC195032_699_35_S

                CBL-interacting serine/threonine-protein kinase 11

                O22932

                TC195032

                -2.62

                0.030

                58

                TC198109_708_34_X2

                Phosphofructokinase, putative

                B9RRX6

                TC198109

                2.00

                0.036

                59

                TC199972_1164_40_S

                Fructose-bisphosphate aldolase

                Q2PYX3

                TC199972

                2.53

                0.022

                60

                TC201350_645_36_S

                Protein phosphatase-2C

                O82469

                TC201350

                2.10

                0.028

                61

                TC201692_547_40_S

                Xyloglucan endotransglucosylase/hydrolase 14

                B9RKL5

                TC201692

                7.29

                0.013

                62

                TC206357_361_38_S

                Catalase isozyme 1

                P30264

                TC206357

                -2.79

                0.046

                63

                TC212978_951_34_S

                Phosphoenolpyruvate carboxylase

                Q8S915

                TC212978

                3.62

                0.030

                64

                TC214826_1684_35_S

                ATP binding protein, putative

                B9RII2

                TC214826

                12.39

                0.011

                65

                TC214837_697_40_S

                Cytokinin oxidase/dehydrogenase

                C3VPM8

                TC214837

                4.64

                0.015

                66

                TC216529_896_39_S

                Peroxidase 7

                Q9SY33

                TC216529

                9.25

                0.011

                67

                TC216572_542_40_S

                Avr9/Cf-9 rapidly elicited protein 216

                Q84QE0

                TC216572

                -2.61

                0.042

                 

                Metabolic process GO:0008152; Oxidation reduction GO:0055114

                68

                TC191412_2284_38_S

                Ferric-chelate reductase

                Q6EMC0

                TC191412

                15.21

                0.008

                69

                TC191893_9_37_S

                Superoxide dismutase

                Q7YK44

                TC191893

                -2.29

                0.030

                70

                TC194139_2083_38_S

                Ferric-chelate reductase

                B9RIU2

                TC194139

                -10.76

                0.023

                71

                TC194227_76_41_X2

                Superoxide dismutase

                Q7YK44

                TC194227

                -2.20

                0.038

                72

                TC196465_645_40_X2

                Gibberellin 20 oxidase

                B9RUX2

                TC196465

                3.46

                0.009

                73

                TC199400_1132_37_S

                Peroxidase 2, putative

                B9SZA0

                TC199400

                -2.05

                0.022

                74

                TC201832_1136_40_S

                Gibberellin 20 oxidase

                B9RUX2

                TC201832

                3.00

                0.013

                75

                TC205699_961_40_S

                Chlorophyll synthase, putative

                B9RJ38

                TC205699

                7.12

                0.019

                76

                TC207549_14_41_S

                Cytochrome C oxidase polypeptide vib

                B9RJN9

                TC207549

                3.05

                0.016

                77

                TC208767_662_38_S

                Cationic peroxidase 1

                B9SWU3

                TC208767

                4.75

                0.022

                78

                TC213071_429_40_S

                Peroxidase

                Q07446

                TC213071

                -3.29

                0.032

                 

                Metabolic process GO:0008152; Primary metabolic process GO:0044238

                79

                TC197609_880_35_S

                Zinc finger protein. putative

                B9T6Q0

                TC197609

                3.54

                0.014

                80

                TC192838_1967_36_S

                Endo-1,4-beta-glucanase

                B9RLZ9

                TC192838

                2.45

                0.042

                 

                Metabolic process GO:0008152; Secondary metabolic process GO:0019748

                81

                TC197109_626_37_S

                Flavonoid 3-hydroxylase, putative

                B9T1C6

                TC197109

                2.56

                0.024

                82

                TC198786_1057_37_S

                UDP-glucose:glucosyltransferase

                B6EWY6

                TC198786

                2.19

                0.022

                83

                TC203267_704_38_S

                UDP-glucose:flavonoid glucoside 1,6-glucosyltransferase

                C5NN14

                TC203267

                2.76

                0.013

                84

                TC212095_566_35_S

                UDP-glucose:flavonoid glucoside 1,6-glucosyltransferase

                C5NN14

                TC212095

                3.69

                0.018

                 

                Response to stimulus GO:0050896

                85

                TC193192_66_41_X2

                Peroxidase 4

                B7UCP4

                TC193192

                -3.86

                0.029

                86

                TC208216_282_40_S

                Pit1 protein

                Q40539

                TC208216

                4.71

                0.019

                87

                TC195700_1019_40_S

                Peroxidase

                B9VRK9

                TC195700

                -2.67

                0.013

                 

                No hits found

                88

                TC203837_663_35_S

                No hits found

                 

                TC203837

                2.06

                0.046

                89

                TC204355_604_36_S

                No hits found

                 

                TC204355

                -3.09

                0.038

                90

                TC207055_310_37_S

                No hits found

                 

                TC207055

                3.05

                0.030

                91

                TC209134_260_40_S

                No hits found

                 

                TC209134

                30.06

                0.007

                92

                TC209988_335_40_S

                No hits found

                 

                TC209988

                6.82

                0.013

                93

                TC211287_216_38_S

                No hits found

                 

                TC211287

                7.63

                0.011

                94

                TC212074_241_40_S

                No hits found

                 

                TC212074

                6.44

                0.023

                95

                TC212933_306_37_S

                No hits found

                 

                TC212933

                9.59

                0.011

                96

                TC214074_254_40_S

                No hits found

                 

                TC214074

                2.22

                0.041

                97

                TC215128_252_35_S

                No hits found

                 

                TC215128

                3.86

                0.029

                aID of TomatoArray2.0 probes

                bDescription of each transcript. Bold discussed; italics undiscussed.

                cUniProtID [26] of the first hit obtained by BlastP analysis

                dID of the TC of DFCI Tomato Gene Index (Release 12.0) [92]

                eFold change value

                fadjusted p-value

                Table 3

                Real-time RT-PCR validation of a set of genes differentially expressed in microarray analysis

                TC ID

                Description

                Real-time RT-PCR (ratio)

                Microarray

                TC208376

                Oligopeptide transporter, putative

                17.87 ± 4.35

                17.67

                NP796451

                Root-specific metal transporter

                15.11 ± 3.97

                12.00

                TC191581

                Iron-regulated transporter 1

                3.07 ± 0.59

                9.42

                TC216882

                Hippocampus abundant transcript 1

                protein, putative

                8.69 ± 1.53

                9.29

                TC205660

                Metal tolerance protein

                1.55 ± 0.44

                3.77

                TC194139

                Ferric-chelate reductase, putative

                -12.14 ± 3.15

                -10.76

                TC ID, description, Real-time RT-PCR relative expression value (Fe-deficient vs. Fe-sufficient) and microarray fold change value (Fe-deficient vs. Fe-sufficient). Real time data were normalized on the EF1a gene and were performed 4 times on 3 independent experiments. Real-time RT-PCR data are expressed as mean ± SD.

                Sixty-one of 97 transcripts are ascribable to adaptive responses to Fe deficiency involving Fe homeostasis, metabolic process, oxidative stress responses, root morphological modification, transport processes, hormone metabolism and signaling. The others are hardly related to specific role showing homology to protein without a specific biological process or lacking homology to known protein ("no hits found").

                Fe homestasis

                Our transcriptional analysis confirmed that roots of Fe-deficient tomato plants overexpressed genes involved in Fe uptake and reduction, including the transcripts encoding IRON-REGULATED TRANSPORTER (IRT) [2830] and FeIII-chelate reductase (FRO) [31]. The tomato bHLH protein (encoded by LeFER) plays a role in Fe-deficiency responses through the expression of these two tomato Fe mobilization genes belonging to the Fe uptake systems of the Strategy I plants [29, 32, 33]. Our data show the up-regulation of the FER transcript (#24) in Fe-deficient roots, which is in agreement with the positive modulation of Fe-uptake-related transcripts such as LeFRO1 (#68), LeIRT1 (#39) and a transcript encoding a NATURAL RESISTANCE-ASSOCIATED MACROPHAGE PROTEIN1 (LeNRAMP1; #38) [29, 32]. A positive modulation of FER and Fe mobilization proteins (IRT1, NRAMP1 and FRO1) was not found in two proteomics studies performed in tomato roots grown in conditions similar to those used in the present work [15, 16]. Authors justified these results as related to the features of proteomic approach, which was not sensitive enough to detect FER and not well suited for membrane-bound proteins. However, functional characterizations of fer mutant proved that FER controls the expression of the iron-uptake genes [29, 32, 33]. A transcriptional behaviour similar to that described in the present work was observed for Arabidopsis orthologous genes in Fe-starved roots [5, 8, 11, 34].

                Our data show a strong down-regulation of another ferric chelate reductase (#70). Previous results indicated that the same transcript (TC194139 of the Release 12.0, corresponding to TC124302 of the Release 9.0) specific to the Solanum lycopersicum genome, is only slightly regulated by Fe and that its function is not essential for Fe uptake [34]. Our results showing a negative regulation of this FRO transcript in response to Fe-deficiency in roots, also quantified by Real-time RT-PCR experiment, confirm that this gene does not play a crucial role in deficiency-induced Fe uptake and suggest the involvement in other biological process.

                Together with the positive modulation of LeFER, LeFRO1 and LeIRT1, we observed for the first time a high up-regulation of another bHLH transcript (#26); this result suggests that like in Arabidopsis [35] also in tomato plants the response to Fe deficiency through FER activity may need the interaction with another bHLH protein. BlastP analysis against TAIR database [36] using protein sequence obtained from the predicted coding sequence of the TC194645 showed the highest sequence homology with the protein encoded by AtbHLH38 (score: 120; Evalue: 9 E-28; identity: 35%; positives: 57%) known to interact with FIT, the Arabidopsis orthologous of tomato FER [35].

                The down-regulation of two ferritin transcripts (#25 and 30) is in line with the negative regulation of ferritin genes observed in roots of Fe-depleted Arabidopsis plants [5, 8, 11]. It has been suggested that ferritins can be involved in Fe homeostasis [37] with a main role of plastidial Fe [11]. Arabidopsis nodulin-like genes have been recently described to be putatively involved in Fe transport and storage under metal cation sufficiency [38]. Transcriptional levels of Arabidopsis nodulin-like genes were down-regulated at least until 72-h of Fe-deficiency, while two other nodulin-like genes were not modulated in response to different Fe conditions [38]. On the other hand in tomato roots we recorded a positive modulation of two transcripts encoding protein with a nodulin-like domain (#9 and 22). BlastP analysis against TAIR database [36] using protein sequence obtained from the predicted coding sequence of the TC214599 (#22) showed the highest sequence homology with the protein encoded by At3g43660 (score: 325; Evalue: 2 E-29; identity: 54%; positives: 68%) which is one of the two Arabidopsis genes not modulated by iron [38].

                Metabolic processes

                Carbohydrate metabolism

                As observed in Arabidopsis root microarray analyses [5, 8, 11] glycolysis-related genes are positively modulated in Fe-deficient roots. These transcriptional data fit well with the increased activity of glyceraldehyde-3-phosphate dehydrogenase (GADPH), pyruvate kinase (PK), and phosphofructokinase (PFK) recorded in response to Fe starvation in cucumber roots [39]. Furthermore, increased levels of protein related to glycolysis under Fe shortage were recently reported in sugar beat [17] and Medicago truncatula [20] roots. All together these evidences are consistent with the idea of a shift from anabolic to catabolic metabolism. In the present work, a fructose-bisphosphate aldolase (FBP aldolase) (#59) and a PFK (#58) transcripts were up-regulated under Fe deficiency, further confirming changes in primary metabolism in response to Fe starvation. The positive modulation of transcript encoding a PFK, an enzyme catalysing a protogenic reaction, supports the role of glycolysis in different process such as production of ATP and H+ for H+-ATPase, reducing equivalents for ferric chelate reductase and of phosphoenolpyruvate (PEP) [40]. Up-regulation of three phosphoenolpyruvate carboxylase (PEPC) transcripts (#48, 49 and 63) agrees with results of several proteomic and physiological studies on the response to Fe deficiency in tomato [15, 16, 41], sugar beat [17] and Medicago truncatula roots [20], showing a positive modulation of proteins involved both in glycolysis and TCA cycle. PEPC activity, through pyruvate consumption, can keep active the glycolytic pathway and give a contribution to the control of cytosolic pH [40, 41]. In Fe deficiency, starch catabolism was reported to be enhanced both at transcriptional [5] and protein level [16]. Our analysis revealed a down-regulation of a transcript encoding a protein showing homology to a potato chloroplastic ß-amylase (#52) involved into starch degradation in plastids [42]. Since starch catabolism mediated by this enzyme occurs in plastids, the negative modulation of ß-amylase could be ascribed to other causes than an accelerated glycolysis.

                The positive modulation of a cytochrome C oxidase (#76) is in line with the transient induction of electron-transport-chain genes observed in Arabidopsis [5] and consistent with the enhancement of respiration rate observed in cumber [39] and sugar beat [43] Fe-deficient roots. This behaviour was interpreted as an attempt to increase energy production through oxidative phosphorylation. However, more recently it has been suggested that the increased respiration rate in root segments of Fe-starved cucumber plants should not be interpreted as an increase in mitochondrial activity but rather as the result of an increase in the number of less efficient mitochondria and of the induction of different O2-consuming reaction [44].

                Methionine cycle

                Nicotianamine (NA) is considered to be a key molecule for long-distance transport of Fe in plants [45]. Proteomic analysis of Fe-starved tomato roots [16] showed a positive modulation of proteins related to metabolism of methionine (e.g. methionine synthase), a precursor of nicotianamine. An up-regulation of a transcript encoding a methylthioribose kinase (MTK, #55), another enzyme of the methionine cycle, was observed in our transcriptional analysis. A positive modulation of MTK transcripts was also recorded in roots of rice, a Strategy II plant species, under Fe deficiency [46, 47], although in this case, related to the necessity to increase the production of mugineic acid.

                A down-regulation of a sulfate adenylyltransferase (ATPS; ATP sulfurylase) gene (#54) was observed in this work. However, the opposite was found in Arabidopsis [11]. The connection between the sulphur nutritional status and capability to respond to Fe shortage has been demonstrated in both Strategy I and Strategy II plants [30, 48]. Recently an increase in methionine content related to phytosiderophore synthesis without significant changes in ATPS activity was described in Fe-deficient barley roots [48]. Interestingly, in a recent study of Medicago truncatula root proteome [20] two enzymes related to biosynthesis of cysteine and methionine were negatively affected by Fe deficiency.

                Protein turnover

                Response to Fe starvation induced the accumulation of gene transcripts related to protein turn-over, including protease (#53) and peptidase (#50 and 51) involved in protein degradation, and a gene encoding a heterogeneous nuclear ribonucleoprotein (#28) that can act in the pre-mRNA metabolism preceding protein synthesis.

                The activity of these genes can be related to molecular events controlling plant responses to abiotic stresses such as nutritional deficiencies [49]. A general increase in protein synthesis was reported as a response to Fe-deficiency in cucumber roots [50]; furthermore, protein recycling in response to Fe starvation was suggested by analysis of expression profiles of soybean [13] and proteomic changes in cucumber [18] and Medicago troncatula [20] roots. Transcriptomic data presented here are in line with the idea that, under Fe deficiency, N recycling reactions take place, possibly related to the necessity of additional anaplerotic source of C and N [18, 20].

                Secondary metabolism

                Phenolic compounds are reported as components of root exudates in Fe-deficient Strategy I plants. These molecules are involved in chelation and/or reduction of rhizospheric insoluble Fe [51, 52]. Recently, phenolics have been proposed to selectively influence rhizospheric microorganisms and be involved in the reutilization of apoplastic Fe [53]. Our results showed an up-regulation of a flavonoid-3-hydroxylase (#81) gene and of three genes putatively involved into flavonoid glycosylation (#82, 83 and 84). Two out of the last three genes showed sequence homology to a Catharanthus roseus flavonoid glucoside 1,6-glucosyltransferase catalysing 1,6-glucosylation of flavonol and flavone glucosides [54]. A positive modulation of genes related to general phenylpropanoid pathway (e.g. genes encoding PAL and 4CL) was reported in Fe-deficient Arabidopsis roots [11]. Data of the present work underline the up-regulation of transcripts involved in a more specific branch of phenolic pathway supporting the idea that Fe-starved roots might operate flavonoid secretion into the rhizosphere in order to promote Fe acquisition [55].

                Oxidative stress responses

                Many proteins involved in antioxidative defence response contain Fe in heme group or coordinated to the thiol group of cysteine. The modulation of transcripts and proteins related to oxidative stress response in roots of Fe-starved plants seems to depend on the species and the experimental conditions [5, 13, 17, 19]. However, catalase (CAT) and peroxidase (POX) activities are known to be depressed under Fe deficiency conditions [56] in tomato leaves. Our data showed a main down-regulation of transcripts encoding thioredoxin (TRX) (#29) and detoxifying enzymes catalase (CAT; #62), superoxide dismutase (#69 and 71), peroxidase (POX; #73, 78, 85 and 87). Two other POX transcripts (#66 and 77) and a thioredoxin peroxidase gene (#27) were, conversely, up-regulated. At protein level, a decrease of a CAT was reported while some POXs showed higher levels in response to Fe deficiency in tomato roots [15]; a different response of peroxidase isoforms has also been reported in sunflower [57]. Taken together, data of the present work suggest a different role in response to nutrient stress condition between the thioredoxin (ABB) and POX isogenes and are in agreement with previous results obtained in Medicago truncatula [20] and sugar beat [17] Fe-deficient roots.

                A germin-like transcript (#15) was up-regulated under Fe deficiency, similarly to what has been observed in the tomato root proteome analysis [15]. The positive modulation of a germin protein reported in this proteomic study was justified hypothesizing its role in producing hydrogen peroxide for apoplastic Fe reduction or in other stress response on the basis of sequence similarity to a Nicotiana attenuata germin protein [15, 58].

                Root morphological adaptation

                Morphological modifications in roots of Fe-deficient plants are well documented [59, 60]. Root hairs proliferation and development of transfer cells were described in Fe-starved tomato plants [29, 6163]. Enhanced formation of lateral roots and root hairs was also recorded in our experiment (Figure 1). Extensin proteins seem to be involved in this latter phenomenon; in fact, we observed a strong positive modulation of LeExt1 (#32, 33 and 35), LeDif10 (#36) and LeDif54 (#31 and 34). It was reported that these three genes encode extensin, a structural protein putatively conferring physical characteristics of the cell wall [64], and act during root hair formation in tomato due their predominant expression in root hair cells [64, 65].

                The overexpression of an endo-1,4-β-glucanase (#80) and a xyloglucan endotransglucosylase/hydrolase protein (#61) transcripts could be involved into the cell wall loosening [66] associated to root morphological adaptation to Fe deficiency, as previously observed in a tomato root proteomic analysis [16]. These changes in root morphology are also supported by the up-regulation of a transcript showing homology to a tobacco actin-depolymerizing factor (#37) related to the pollen tube elongation [67]. Here, we report a negative modulation of a transcript (#45) showing homology to the tobacco aquaporin PIP2 [68] suggesting a role of the PIP2 tomato protein in Fe-related morphological root changes. It has been suggested that PIP aquaporins can play a role not only in root water uptake but also in root development [68]. Transgenic plants exhibiting RNAi of PIP2 aquaporins showed a significant increase in the length of primary roots [68].

                Transport processes

                Among the positively modulated transcripts belonging to "transport" functional category, a stronger modulation (more than 17 times) of an oligopeptide transporter (OTP) gene (#44) was observed under Fe shortage. BlastP analysis against TAIR database [36] using protein sequence obtained from the predicted coding sequence of the TC208376 (#44) showed the highest sequence homology with the protein encoded by AtOPT3 (score: 541; Evalue: E-154; identity: 83%; positives: 90%). Positive modulation of AtOPT2 and AtOPT3 has been recorded in Fe-deficient Arabidopsis roots [8, 11, 21, 69]. The plant members of OTP family have been described to have different functions in transport physiology such as long-distance sulphur distribution, nitrogen mobilization, metal homeostasis, and heavy metal sequestration. OPTs can transport glutathione, metal-chelates and peptides [70]. It was hypothesized that some plants OTPs are able to transport Fe-chelates and Fe-NA suggesting a role of these proteins in long-distance metal transport in planta [71, 72]. A similar function can be hypothesized for the tomato up-regulated OPT transcript, due to the previously described positive modulation of genes related to NA synthesis (see methionine cycle paragraph).

                Another up-regulated gene involved in transport phenomena in response to Fe-deficiency is LeAMT1 (#41). This tomato gene, firstly isolated from a root hair cDNA library [73] was root-specifically expressed and positively regulated by ammonium (NH4 +) in root hairs [73, 74]. This result suggests the presence of a linkage between NH4 + uptake and Fe shortage. It has been demonstrated that NH4 +-dependent rhizosphere acidification can improve Fe availability in the rhizosphere [75]. Interestingly, nitrate acquisition is limited under Fe deficiency [20, 76].

                Furthermore, favouring ammonium uptake with respect to nitrate could reduce competition for reducing equivalent between nitrogen and Fe acquisition. This is also in agreement with the hypothesized N-recycling in Fe-deficient plant roots [20].

                Fe-deficient tomato roots strongly overexpressed two transcripts (#40 and 47) encoding a protein sharing features of major facilitator superfamily (MSF) with putative transport activities. However, on the basis of their sequence homology it was not possible to hypothesize an involvement in transport of a specific metabolite or mineral nutrient.

                A positive modulation of a gene encoding a metal tolerance protein (MTP; #42) was also recorded. A similar behaviour was described in Fe-deficient Arabidopsis roots [8, 11] and interpreted in the light of low specificity of IRT1 transporters, which can transport different metals into the Fe-deficient plants. MTP genes might therefore play a role in the detoxification of zinc ions taken up absorbed under Fe deficiency conditions [11].

                Fe deficiency in tomato induced the expression of a transcript (#43) encoding a protein showing sequence homology to Arabidopsis vacuolar aluminium-activated malate transporter (ALMT) 9 [77]. An increase in organic acids concentration mainly citric and malic acids in plant roots under Fe starvation has been reported for many plant species [78]. However, a decrease of malate levels was observed in tomato root tips after 15-d of Fe deficiency while higher contents were recorded in leaves and xylem sap [41]. The tomato malate transporter gene might be involved in malate fluxes at intracellular level and/or in long-distance transport in planta.

                Hormone metabolism and signaling

                The role of plant hormones in the regulation of Fe deficiency responses has been extensively studied [79, 80]. As for Arabidopsis Col-0 accession, we identified the positive modulation of a methionine cycle gene, MTK gene, that could be related not only to NA synthesis (see above) but also to the recycling of methylthioadenosine during ethylene production [11]. This is in agreement with the hypothesized involvement of ethylene and/or auxin in control of hair root production under Fe deficiency [79]. Furthermore, data of the present work suggest the involvement of other hormones (such as gibberellin and cytokinin) in Fe deficiency response in tomato roots. Concerning gibberellin, we recorded the up-regulation of two gibberellin oxidase 20 (GA20OX) transcripts (#72 and 74). They can regulate root morphological changes through the synthesis of active GA1. Indeed, it has been reported that GAs are related to tomato root growth [81]. In addition, it was shown that the expression of a tomato SlGA20ox1:GUS construct in Arabidopsis localized also in columella of secondary roots [82], thus suggesting a role in secondary root formation. The same authors also reported that the expression of the construct was positively affected in cotyledons, hypocotyls and roots by benziladenine, a synthetic cytokinin [82]. However cytokinins were described as negative regulators of root Fe uptake mechanism in Arabidopsis through a root-growth dependent pathway [83]. Cytokinins could play a similar role also in tomato roots. This hypothesis is supported by the up-regulation of a cytokinin oxidase/dehydrogenase (CKX) gene (#65) under Fe starvation. CKX genes are involved in regulation of plant growth and development through the control of the cytokinin concentration [84].

                Focusing on signal transduction, we observed the up-regulation of a transcript (#60) encoding a protein with sequence homology to the MCP5, a protein phosphatase-2C (PP2C) of Mesembryanthemum crystallinum [85]. McMCP5 is expressed in roots and is induced in response to salt and drought stresses [85]. The tomato PP2C gene could play a role in signal transduction of stress nutrient condition as Fe starvation. The positive modulation of a gene encoding a SRC2 protein containing a C2 domain (#1) suggests a putative involvement of Ca as secondary messenger. Focusing on Ca, in our experiment we also identified a down-regulated gene (#57) in Fe-starved roots showing homology to an Arabidopsis gene (AtSOS2) encoding a CBL-interacting serine/threonine-protein kinase 11. Arabidopsis SOS2 interacts with the Ca binding protein SOS3 (SALT OVERLY SENSITIVE 3), thus controlling K and Na homeostasis and the response to salt stress [86, 87]. In addition a negative modulation of a gene (#67) showing homology to a tobacco Avr/Cf-9 rapidly elicited (ACRE) transcript encoding a PP2C was observed [88]. Our data confirm the results obtained with proteome analysis of tomato roots in response to Fe-deficiency, where changes in the levels of proteins involved in signal transduction were reported [16]. The observed transcriptional changes in tomato roots can be the result of the perception of nutrient stress condition and the following signal transduction.

                Taken together, these results suggest that Fe deficiency responses are, at least in part, dependent on hormonal balance modifications possibly resulting from signal perception and transduction.

                Conclusion

                Ninety-seven differentially expressed transcripts were identified comparing root transcriptional profiles of Fe-deficient and Fe-sufficient tomato plants. Tomato roots respond to Fe deficiency by modulating the expression of a number of transcripts similar to the model plant Arabidopsis. The comparison of tomato Fe-responsive transcript set with the Arabidopsis "ferrome" [21], encompassing 92 transcripts that robustly represent the response to Fe shortage, confirms the involvement of the well know homologous key regulatory elements (e.g. bHLHs) controlling the expression of transcripts related to Fe uptake and translocation (e.g. IRT and FRO). As showed by Arabidopsis "ferrome" [21], tomato roots modulate transcripts involved in homeostasis of Fe and heavy metal cations (e.g. IRT, NRAMP, MTP, ferritin) and others cation (e.g. AMT). Both plant species require the up-regulation of transcripts related to glycolysis (e.g. PFK) and methionine cycle (e.g. MTK), the latter pathway being putatively linked to NA biosynthesis in response to Fe deficiency. Fe-NA complexes could be transported both in tomato and Arabidopsis plants through OPTs during the response to Fe shortage. Here we describe, for the first time, the modulation of a specific branch of phenolic (flavonoids) biosynthesis in response to Fe deficiency. In addition, tomato roots seem to be more characterized by root morphological adaptation, mainly linked to hair root production, as suggested by the strong up-regulation of extensin transcripts.

                Therefore, this transcriptional study, while confirming evidence coming from proteomic studies, allowed identifying new putative targets for further functional investigations on the response to Fe deficiency in tomato roots.

                Methods

                Plant material, growth conditions and RNA extractions

                Tomato seedling (Solanum lycopersicum L., cv. 'Marmande superprecoce' from DOTTO Spa, Italy), germinated for 6 days on filter paper moistened with 1 mM CaSO4, were grown for 14 days in a continuously aerated nutrient solution (pH adjusted at 6.0 with 1 M KOH) as reported by Tomasi at al. [22] with 5 μM Fe (Fe-EDTA); thereafter, most of the plants were transferred for a further week to a Fe-free nutrient solution (Fe-deficient) and some tomato plants were transferred for a week to a nutrient solution containing 100 μM Fe-EDTA (Fe-sufficient plants) as control. Nutrient solutions were renewed every three days. The controlled climatic conditions were the following: day/night photoperiod, 16/8 h; light intensity, 220 μE m-2s-1; temperature (day/night) 25/20°C; RH 70 to 80%.

                At the end of the growing period (27 days), Fe-deficient tomato plants clearly showed visible symptoms of Fe deficiency yellowing of the fully expanded apical leaves and proliferation of lateral roots and root hairs and increase in the diameter of the sub-apical root zone. 24 hours before harvesting, all nutrient solutions (both for Fe-deficient and Fe-sufficient plants) were renewed and the pH was adjusted to 7.5 with 10 mM Hepes-KOH. The pH of the growing medium was adjusted to this value to mimic as close as possible the conditions that are occurring in Fe-deficiency-inducing soil. However, in order to favour an equilibrate development of tomato plants growing in the Fe-free nutrient solution, the exposure to the pH of 7.5 was limited to the last two days.

                Roots of Fe-deficient and Fe-sufficient tomato plants (27 d-old) were harvested five hours after the beginning of light phase. The collected roots were immediately frozen in liquid nitrogen and stored until further processing at -80°C. The collection was repeated in three independent cultivations and the roots from six plants were pooled for each treatment.

                Ferric-chelate reduction

                To determine the root capacity to reduce Fe(III)-EDTA, accordingly to Pinton et al. [89] roots of a single intact (Fe-sufficient or Fe-deficient) tomato plants were incubated in the dark at 25°C for 60 min in 50 mL of an aerated solution containing CaSO4 0.5 mM, BPDS 0.5 mM, Hepes-KOH 10 mM (pH 5.5) and 0.25 mM of Fe(III)-EDTA. Thereafter, the absorbance of the solutions at 535 nm was measured at intervals of 15 min and the amount of Fe(III) reduced calculated by the concentration of the Fe(II)-BPDS3 complex formed, using an extinction coefficient of 22.1 mM-1 cm-1.

                Microarray analysis

                Transcriptional analysis was carried out using a Combimatrix [24], produced by the Plant Functional Genomics Center, University of Verona [90]. The chip (TomatoArray2.0) carries 25,789 nonredundant probes (23,282 unique probes and 2,507 probes with more than one target) randomly distributed in triplicate across the array, each comprising a 35-40-mer oligonucleotide designed using the program oligoarray 2.1 [91]. The source of sequence information included tentative consensus sequences (TCs) derived from the DFCI Tomato Gene Index [92] Release 12.0 and expressed sequence tags. Eight bacterial oligonucleotide sequences provided by CombiMatrix, 8 probes designed on 8 Ambion spikes and 40 probes based on Bacillus anthracis, Haemophilus ducreyi and Alteromonas phage sequences were used as negative controls. Complete description of chip is available at the Gene Expression Omnibus [93] under the series entry (GPL13934).

                Total RNA was isolated using the Spectrum™ Plant Total RNA kit (Sigma-Aldrich) and quantified by spectrophotometry using NanoDrop™ 1000 Tem Scientific). RNA quality was evaluated using Agilent 2100 Bioanalyzer (Agilent). Total RNA (1 μg) was amplified and labelled using the RNA ampULSe kit (Kreatech). After checking the quantity and quality of aRNA by spectrophotometry using NanoDrop™ 1000 (Thermo Scientific) and the quality subsequent labelling, 4 μg of labelled aRNA was hybridized to the array according to the manufacturer's recommendations [24]. Pre-hybridization, hybridization, washing and imaging were performed according to the manufacture's protocols. The array was scanned with an Axon GenePix® 4400A scanner (MDS Analytical Technologies).

                Analysis of raw data was performed using the open source software of the Bioconductor project [94, 95] with the statistical R programming language [96, 97]. Background adjustment, summarization and quantile normalization were performed using limma package [25]. Differentially expressed probes were identified by linear models analysis [25] using limma package and applying Bayesian correction, adjusted p-value of 0.05 and a |FC| ≥ 2. All microarray expression data are available at the Gene Expression Omnibus [93] under the series entry (GSE31112). Genes were grouped in main functional categories according to the "biological" terms of the Gene Ontology [27] assigned to each tomato TC or EST (Release 12.0) on the basis of the results of BlastP analysis [98] against the UniProt database [26] (Additional file 1). Genes without significant BlastP results were classified as "no hits found" (Evalue < 1e-8; identity > 40%).

                Real-time RT-PCR experiments

                0.5 μg of total RNA (checked for quality and quantity using a spectrophotometer NanoDrop™ 1000 (Thermo Scientific), followed by a migration in an agarose gel) of each sample was retrotranscribed using 1 pmol of Oligo d(T)23VN (New England Biolabs, Beverly, USA) and 10 U M-MulV RNase H for 1 h at 42°C (Finnzymes, Helsinki, Finland) following the application protocol of the manufacturers. After RNA digestion with 1 U RNase A (USB, Cleveland, USA) for 1 h at 37°C, gene expression analyses were performed by adding 0.16 μl of the cDNA to the realtime PCR complete mix, FluoCycleTM sybr green (20 μl final volume; Euroclone, Pero, Italy), in a DNA Engine Opticon Real-Time PCR Detection (Biorad, Hercules, USA). Specific primers (Tm = 58°C) were designed to generate 80-140 bp PCR products (Additional file 2). Three genes were used as housekeeping to normalized the data: elongation factor 1-alpha EF1a (X14449; TC203463; forward: 5'- TGGATATGCTCCAGTGCTTG-3'; reverse: 5'-TTCCTTACCTGAACGCCTGT-3'), histone H1 (AJ224933; TC192148; forward: 5'- CAAAGGCCAAAACTGCTACC-3'; reverse: 5'-AGGCTTTACAGCTGCTTTCG-3') and ubiquitin Ubi3 (X58253; TC196208; forward: 5'-AGCCAAAGAAGATCAAGCACA-3'; reverse: 5'-GCCTCTGAACCTTTCCAGTG-3'). Each Real-Time RT-PCR was performed 4 times on 3 independent experiments; analyses of real-time result were performed using Opticon Monitor 2 software (Biorad, Hercules, USA) and R [93] with the qpcR package [99]. Efficiencies of amplification were calculated following the authors' indications [100]: PCR efficiencies were 99.15%, 89.16% and 87.25%, for EF1a, H1 and Ubi3 genes, respectively. The efficiencies for TC191581, TC192292, TC194139, TC216882, TC205660, TC208376 and NP796451 were respectively 94.75, 85.03, 98.92, 91.55, 92.70, 96.61 and 92.43%. The reported Real time data were normalized on the EF1a gene. Gene expression data were illustrated considering the differences in the amplification efficiency of PCR and using the gene expression levels in roots of Fe-sufficient plants as reference; applying the following formula:

                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-101/MediaObjects/12864_2011_4003_Equa_HTML.gif

                Where: Ex or y is the percentage value of PCR efficiency for the amplification of the gene x or y, respectively; Ct(x+Fe) Ct for the control treatment (+Fe) and the considered gene (x); C t (x-Fe); Ct for the treated roots (-Fe) and the considered gene (x); C t (y+Fe) Ct for the control treatment (+Fe) and the housekeeping gene (y); C t (y-Fe) Ct for the treated roots (-Fe) and the housekeeping gene (y).

                Declarations

                Acknowledgements

                This work was supported by grants: MIUR (FIRB-Futuro in Ricerca and PRIN), Cariverona and Internal Projects UniBZ.

                Authors’ Affiliations

                (1)
                Department of Biotechnology, University of Verona
                (2)
                Department of Agriculture and Environmental Sciences, University of Udine
                (3)
                Faculty of Science and Technology, Free University of Bolzano

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