Background

Iron is an important micronutrient for all living things. In plants, it is essential for photosynthesis, respiration and other metabolic processes. Plants adjust their iron uptake from the soil to achieve the proper cellular iron concentrations. Without enough iron, plants suffer Iron Deficiency Chlorosis (IDC), which is among the most common and potentially severe nutritional stresses in plants [1]. IDC is typically not due to low amounts of iron in the soil, but rather to an unusable ferric (Fe3+) state. Too much iron is also problematic, as free iron leads to reactive oxygen species (ROS), DNA damage, and other cellular stress [2]. Therefore, iron homeostasis is tightly controlled by regulating iron uptake, transport and storage. IDC is a global problem, but is especially problematic in the calcareous soils of the Midwestern U.S., where the majority of U.S. soybeans are grown [3]. Calcareous soil is identified by the presence of calcium carbonate (or lime) and a pH higher than 7, which keeps iron in the ferric (Fe3+) state. Many high yielding soybeans are susceptible to IDC, which results in a yellowing of leaves due to reduced photosynthesis. Symptoms vary in degree of severity, but can result in total yield loss. The recommended management for IDC is growing IDC-resistant soybean lines. However, resistant lines yield lower than susceptible lines in conditions that do not favor the development of IDC. Identifying the genetic basis of IDC resistance may aid in the development of IDC tolerant lines that perform well in multiple soil types.

Plants have two mechanisms for acquiring iron from the soil (Strategy I and II). Strategy I, which occurs in most dicots including soybean, functions through the induction of the Fe-deficiency Induced Transcription Factor (FIT) in the root, which regulates Ferric-chelate Reductase (FRO) and Iron-Regulated Transporter (IRT) [4–6]. While these genes are activated in the root, it is believed that the signal activating these genes comes from an unknown factor that originates in the leaves [7, 8]. Hormones are obvious candidates for controlling signaling from the shoot to the root. Garcia et al. [9] demonstrated that the hormones ethylene and nitric oxide act early in response to IDC in the roots and are necessary for the induction of iron uptake genes. Thus far, the study of ethylene and nitric oxide function has been limited to the roots. Examining the gene expression in multiple tissues during IDC response may allow for the construction of a complete signaling pathway.

In the early 1970’s, near isogenic soybean lines (NILs), Clark (PI54833) and Isoclark, were developed that differ in their responses to iron stress [10]. The cultivar Clark (PI54833) is iron efficient and does not develop IDC symptoms in iron-limiting conditions. Isoclark is susceptible to iron stress and develops interveinal chlorosis in response to iron limitation. Gene expression comparisons between Clark and Isoclark, which share 98% genetic identity, have allowed the identification of hundreds of genes involved in iron stress responses in soybean. In the last several years, microarray analyses, RNA-Seq, introgression and association mapping, sub-NIL development and virus-induced gene silencing (VIGS) have been used to identify and characterize soybean genes that are differentially expressed during iron stress and iron stress recovery [11–15]. However, the early signaling events in the iron efficiency stress response remain elusive. Therefore, the work presented here aims to capture early transcriptional responses to iron stress in the iron efficient line Clark. We have used RNA-Seq to measure transcriptional responses one and six hours after iron stress. The differentially expressed genes (DEGs) and signaling components identified here represent new targets for soybean improvement.

Results

RNA-Seq reveals a dynamic change in gene expression in response to IDC

In order to identify early responses to iron stress, we quantified expression of genes at one and six hours post iron stress in leaves and roots of the iron efficient line Clark. While other studies have used both Clark and Isoclark, previous work [13] has demonstrated that Isoclark is iron inefficient and does not induce expression of genes involved in iron homeostasis in response to iron stress. Therefore, we limited our study to Clark. To induce iron stress, the roots of plants grown in iron-sufficient (100 μM Fe(NO3)3•9H2O) conditions for sixteen days were rinsed in distilled water and then plants were transferred into either iron-sufficient (100 μM Fe(NO3)3•9H2O) or iron-deficient (50 μM Fe(NO3)3•9H2O) conditions. Two replicates of root and 1st trifoliate leaf tissues were collected at one and six hours after transfer to sufficient or insufficient conditions for a total of eight sample types. Following RNA isolation, samples were sent to the National Center for Genomic Research for single-end RNA sequencing on an Illumina Genome Analyzer II with a read length of 36 bp. Following the bioinformatic pipeline detailed in the Materials and Methods, a total of 507,784,149 reads (252,907,004 from 8 leaf samples, 254,877,145 from 8 root samples) were mapped to the soybean genome (G. max version 1.1 [16]). The Illumina reads generated by this study were deposited in the National Center for Biotechnology Short Read Archive (NCBI SRA Bioproject accession SRP031889).

To identify genes differentially expressed in response to iron stress in each sample, we used the edgeR [17] statistical package comparing deficient and sufficient replicates at a given time point and tissue. Most RNA-Seq analysis tools provide a list of DEGs and report average expression across replicates. However, one bad replicate can extremely bias which genes are identified as differentially expressed and the level and direction of expression. Therefore, it is important to use statistical packages that report expression of all replicates and use visualization tools of raw and normalized data to verify biological and technical reproducibility of replicates. This step is particularly important in experiments such as ours, where only two replicates were used. We used the package ggplot2 (CRAN, [18]) to compare normalized gene expression in replicate data sets. In addition, ggplot2 was used to create porcupine plots [19] of significantly differentially expressed genes at multiple False Discovery Rates (FDR) (Figure 1, Additional file 1) relative to the expression of all genes. The porcupine plots use lines to connect replicate data points, allowing visual identification of any problematic data. Following these analyses, genes were considered significant if they had a fold change > 2.0 (deficient/sufficient) and a false discovery rate (FDR) <0.05. The DEGs were annotated using the SoyBase Genome Annotation Report page (http://soybase.org/genomeannotation/index.php), which provided UniRef100 [20] hit information, best A. thaliana homologs and gene ontology information inferred from A. thaliana (The Arabidopsis Information Resource [TAIR] version 10, http://www.arabidopsis.org). The DEGs and their annotations are provided for each tissue and time point (Additional files 2, 3, 4 and 5).

Figure 1
figure 1

Genes significantly differentially expressed in response to iron stress. Significantly differentially expressed genes (DEGs) (FDR < 0.05) were identified by comparing gene expression in iron deficient conditions to iron sufficient conditions (D/S). Porcupine plots were used to visualize the expression of all genes and all DEGs. Expression of all genes is shown in grey. Expression of DEGs is shown in red (repressed by iron deficiency) and blue (induced by iron deficiency). A line joins replicates of DEGs. A. DEGs from leaves after one hour of iron stress. B. DEGs from leaves after six hours of iron stress. C. DEGs from roots after one hour of iron stress. D. DEGs from roots after six hours of iron stress. E. Bar graph of the total number of repressed or induced differentially expressed genes at each tissue and time point.

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We identified 81 and 400 DEGs in response to iron stress in one hour and six hour leaves, respectively, and 360 and 129 DEGs were identified in one hour and six hour roots (Figure 1). Surprisingly, there was little overlap in the DEGs identified within the same tissue at different time points or across tissues at the same time point. Only seven genes were in common between one and six hour leaves, and another four genes were in common between one and six hour roots (Additional files 2, 3, 4 and 5). For nine of these eleven genes, the direction of expression changed between the one hour and six hour time points. Similarly, only eleven genes were in common between roots and leaves, regardless of time point. The small degree of overlap in DEGs across sample types accompanied by large changes in expression levels between time points suggests dynamic and distinct responses to iron stress occur in leaves and roots.

To develop an understanding of which genes were affected by iron stress, we began focusing on the top ten induced and repressed genes in each sample, paying particular attention to those genes with homology to Arabidopsis genes with known roles in nutrient stress responses, growth and signaling (Table 1). In one hour leaves, iron deficiency led to decreased expression of 74 of the 81 DEGs. The seven genes induced by iron deficiency included homologs of previously described Arabidopsis genes AtPLP1 (Glyma07g13790, 6E-89), AtGASA1 (Glyma14g40400, 4E-32), AtSWEET12 (Glyma05g38351, 1E-53), two homologs of AtOXS3 (Glyma11g33040 [2E-15] and Glyma18g05160 [6E-16]) and a copper amine oxidase (Glyma01g07860 [E = 0]) (Table 1). AtPLP1 is patatin-related phospholipase that is differentially expressed in response to phosphate stress [21]. AtGASA1 (GA-Stimulated in Arabidopsis) is a gibberellic acid-regulated protein and expressed in rosettes [22]. AtSWEET12 is a sucrose transporter involved in phloem loading that transfers sucrose from the leaves to nonphotosynthetic tissues elsewhere in the plant [23]. Overexpression of AtOXS3 in Arabidopsis resulted in greater tolerance to heavy metals and oxidative stress. Copper amine oxidase is upregulated in response to wounding in chickpea [24] and in response to nematode infection but not wounding, in Arabidopsis [25]. The top ten genes repressed by iron deficiency included two homologs of AtNIA1 (Glyma06g11430 [E = 0] and Glyma13g02510 [E = 0]) and a homolog of AtDXR (Glyma16g10880, E = 0). AtNIA is required for nitric oxide (NO) production [26]. NO acts an important signaling molecule for a variety of abiotic stresses including iron deficiency and drought [27]. AtDXR is localized to the chloroplast and catalyzes the first committed step of isoprenoid biosynthesis leading to the production of chlorophyll, carotenoids, ABA, brassinosteroids, cytokinins and gibberellins [28].

Table 1 Top ten significantly induced and repressed DEGs under iron stress at each time and tissue
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In one hour roots, 263 of the 360 DEGs were induced in response to iron stress. The top ten induced genes included two homologs of AtRCI3 (Glyma10g02730 [9E-120] and Glyma02g17060 [3E-118]), two homologs of AtBG1 (Glyma03g28850 [1E-138] and Glyma19g31580 [3E-137]), and three homologs of NET1D (Glyma17g27187 [2E-139], Glyma17g27135 [7E-147] and Glyma17g23660 [1E-140]). AtRCI3 (Rare Cold Inducible gene 3) is a peroxidase that is involved in salt-tolerance, dehydration and potassium deficiency signaling [29, 30]. BG1 responds to a variety of biotic stresses in Arabidopsis [31]. NET1D is an actin-binding protein highly expressed in the stele and conducting tissues of the roots [32]. Genes repressed by iron deficiency included a homolog of 2OG-Fe(II)-dependent oxygenase superfamily protein (Glyma07g18280 [4E-170]) and AtMYB121 (Glyma08g27660 [2E-62]). 2OG-Fe(II)-dependent oxygenase family members are involved in hormone synthesis in plants, particularly ethylene synthesis [33]. AtMYB121 responds to salinity stress in Arabidopsis roots [34].

Six hours after plants were transferred from iron sufficient to iron deficient media, iron deficiency induced the expression of 246 genes in leaves but repressed the expression of 154. Induced genes included a homolog of AtRD22 (Glyma06g08540 [4E-124]), WNK5 (Glyma01g32450 [E = 0]) and two homologs of ASN1/AtDIN6 (Glyma02g39320 [E = 0] and Glyma11g27480 [E = 0]). Arabidopsis AtRD22 is responsive to abscisic acid, water and salt stress [35]. The rice homolog of WNK5 (with no lysine kinase), OsWNK1, has a suspected role in abiotic stress tolerance and is involved in circadian rhythm [36]. The wheat homolog of asparagine synthetase TaASN1 has been shown to be upregulated in roots in response to salt, drought and ABA stress [37]. The top ten genes repressed by iron deficiency included three homologs of AtEXO (Glyma14g35340 [2E-146], Glyma14g35330 [5E-158] and Glyma06g10710 [9E-154]). The extracellular EXO protein is essential for cell expansion and promotes shoot and root growth [38]. AtEXO mutants have altered expression of sugar-responsive genes and increased ABA levels. It is interesting to note that eight EXO and EXO-like (EXL5) homologs were differentially expressed in response to iron stress at this time point.

In six hour roots, 52 genes were upregulated in response to iron deficient conditions while 77 genes were downregulated. The top ten genes induced in response to iron deficiency included a homolog of a wound-responsive family member (Glyma13g37760 [1E-29]), AtOMT1 (Glyma20g00604 [6E-53]), AtBOR4 (Glyma01g06774 [2E-20]), AtACO1 (Glyma05g36310 [2E-159]) and ZIP1 (Glyma13g10791 [5E-129]). AtOMT1 is involved in lignin formation and the biosynthesis of sinapate esters [39]. AtBOR4 (Borate efflux transporter 4) overexpression in rice increased tolerance to excess boron [40]. Trafficking of AtBOR4 to the outer polar domain defines the root-soil interface [41]. AtACO1 (ACC oxidase 1) is an ethylene biosynthetic gene [42, 43]. AtZIP1 functions as a zinc transporter, and is upregulated in AtIRT1 knockouts [44]. Interestingly, IRT1 and IRT2 are both ZIP family members. AtZIP1 appears to function in both iron and zinc homeostasis. Of the top ten genes repressed by iron stress in six hour roots, only one has an identified homolog in Arabidopsis, the function of which was unknown.

DEGs located within introgressed regions associated with iron inefficiency

Recently, introgression mapping was used to identify regions introgressed from the iron inefficient donor parent T203 to the iron efficient line Clark to develop Isoclark (iron inefficient). Collectively, Severin et al. [45] and Stec et al.[46] identified introgressed regions on soybean chromosomes 3, 4, 5, 8, 13 and 16 of Isoclark. Several studies have identified quantitative trait loci (QTL) for iron deficiency in soybean [15, 47–49]. However, only the studies by Mamidi et al. [49] and Lin et al. [47, 48] identified QTL on the same chromosomes as the introgressed regions. By comparing the sequences of the molecular markers used in these studies to the introgressed regions, only the QTL identified on chromosome 3 [47–49] corresponded to an introgressed region (data not shown). Five DEGs corresponded to the region on chromosome 3 [14]. In six hour leaves we identified a S-adenosyl-L-methionine-dependent methyltransferases superfamily protein (Glyma03g28490). From one hour roots we identified a βHLH038 homolog (Glyma03g28630), an ethylene responsive binding factor (AtERF15, Glyma03g31940) and a disease resistance-responsive protein (Glyma03g30360). Glyma03g28630 was recently identified as one of 12 candidate genes underlying the IDC QTL on soybean chromosome 3 by Peiffer et al. [14]. From six hour roots we identified a differentially expressed ethylene response factor (Glyma03g31940).

It is important to note that complex traits, such as IDC, can be the result of a number of small genes with minor effects. Therefore, it is worth noting the DEGs located within introgressed regions but not associated with a QTL. In six hour leaves, we identified a cell division control 6 ortholog (Glyma08g45230) and a sucrose–proton symporter (AtSUC2, Glyma16g27350). In roots at one hour, there were six DEGs of interest from introgressed regions including homologs of growth regulating factor 4 (AtGRF4, Glyma03g35010), expansin A7 (AtEXPA17, Glyma03g38480), Late embryogenesis abundant protein (AtNHL10, Glyma03g35920 and Glyma03g35980), flavin-binding monooxygenase protein (Glyma05g35430), and a glycosyl hydrolase (Glyma05g34850). In six hour roots, we identified a homolog of RNA-binding family protein (Glyma08g44150).

Plant pathways responding to iron stress

While these analyses identified several genes of interest, they do not highlight the major plant pathways that respond to iron deficiency. Therefore, we used the Ontologizer 2.0 software [50] to identify GO terms significantly (P < 0.05) overrepresented within our DEGs, relative to all genes in the soybean genome (Table 2). In leaves, we identified twenty-eight significantly overrepresented gene ontology biological process (BP) terms. However, many of the DEGs were associated with multiple GO terms. Therefore, any significantly overrepresented GO terms whose genes completely overlapped were mapped to the largest significantly overrepresented GO term. In leaves, the twenty-eight original BP GO terms, were reduced to fourteen. Similarly, the 45 significantly overrepresented BP GO terms identified in roots were reduced to fifteen (Table 2). The fourteen significantly overrepresented BP GO terms identified in leaves included wax biosynthesis and metabolism (GO:0010025 and GO:0010166, P = 0), defense response to bacterium (GO:0009816, P = 0), and cellular response to sucrose starvation, mannitol and sorbitol (GO:0018008, GO:0018201, GO:0043617, GO:0071325, GO:0072709, P = 0.01). When gene expression patterns are compared within these GO terms across time points (Figure 2), we see opposing expression patterns. For the GO terms DNA methylation and DNA unwinding involved in replication, gene expression is induced at one hour by iron deficiency, but repressed by six hours. For all other GO terms, we see the opposite expression pattern. Further, more significant expression changes, induced and repressed, are seen at the six hour time point.

Table 2 Overrepresented GO terms in leaves and roots DEGs
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Figure 2
figure 2

Expression changes for genes in significantly overrepresented Biological Process GO categories in leaves. To identify BP gene ontology terms overrepresented in our data sets, we combined all DEGs from leaves. Overrepresented gene ontology terms were identified using the Ontologizer 2.0 software [50] with parent–child-union analysis and Westfall-Young-Single-Step multiple testing correction, with a resampling of 1000 replicates. Since many of the DEGs were associated with multiple GO terms, any significant (P < 0.05) GO terms with completely overlapping DEGs were mapped to the larger (more DEGs) GO term. This data is shown in Table 2. Gene expression was plotted across time points (1 L, 1 hour leaves, 6 L, 6 hour leaves) and iron conditions (S, sufficient, D, deficient) to visualize changes. For each differentially expressed gene, both replicates are plotted with a line joining expression under deficient and sufficient conditions. The line is placed at the average of the two replicates within a condition. DEG significance within a time point is indicated by the intensity of the line.

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The fifteen significantly overrepresented BP GO terms identified in roots included response to oxidative stress and other stress (GO:0006950 and GO:0006979, P = 0.002), zinc ion transmembrane transport (GO:0006829 and GO:0071577, P = 0.003), response to ethylene and other stimuli (GO:0050896, GO:0070887 and GO:0071369, P = 0), cellular response to starvation (GO:0009267, P = 0.03), phenylpropanoid biosynthesis and metabolism (GO:0009698, GO:0009699, GO:0009812, GO:0009813, GO:0009962, GO:0009963, GO:0043455, GO:1900376, GO:1900378, GO:2000762, P = 0.002), immune system process (GO:0002376, P = 0.027) and steroid biosynthesis and metabolism (GO:0006694 and GO:0008202, P = 0.009) (Table 2). Comparing gene expression within these GO terms at one hour and six hour roots, again the most striking observation is the direction of expression changes (Figure 3). While most leaf DEGs were repressed at one hour in response to iron deficiency, most root DEGs are induced at one hour in response to deficiency. By six hours, the expression pattern has begun to reverse. It is also worth noting the overrepresented molecular function terms identified in roots included zinc ion transmembrane transporter activity (GO:0005385), suggesting metal ion transport has been activated in the root. In addition, protein homodimerization activity (GO:0042803), regulatory region nucleic acid binding (GO:0001067) and identical protein binding (GO:0042802) suggest a strong signaling component in root responses to IDC.

Figure 3
figure 3

Expression changes for genes in significantly overrepresented Biological Process GO categories in roots. To identify BP gene ontology terms overrepresented in our data sets, we combined all DEGs from roots. Overrepresented gene ontology terms were identified using the Ontologizer 2.0 software [50] with parent–child-union analysis and Westfall-Young-Single-Step multiple testing correction, with a resampling of 1000 replicates. Since many of the DEGs were associated with multiple GO terms, any significant (P < 0.05) GO terms with completely overlapping DEGs were mapped to the larger (more DEGs) GO term. This data is shown in Table 2. Gene expression was plotted across time points (1R, 1 hour roots, 6R, 6 hour roots) and iron conditions (S, sufficient, D, deficient) to visualize changes. For each differentially expressed gene, both replicates are plotted with a line joining expression under deficient and sufficient conditions. The line is placed at the average of the two replicates within a condition. DEG significance within a time point is indicated by the intensity of the line.

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When we compared our lists of DEGs to known iron homeostasis genes in A. thaliana[51], many were found differentially expressed in both leaves and roots (Table 3). This serves as both a control of our hydroponic iron conditions and demonstrates how quickly soybean responds to reduced iron by increasing gene expression for iron uptake and mobilization throughout the plant. While few of these genes exhibit large fold changes (>5), their function and the short response time is noteworthy. In one hour leaves, a homolog of the AtFIT1 transcription factor (Glyma09g41470, FC = -3.8) and a homolog of the phosphate transporter PHT3;1 (Glyma01g02950, FC = -2.5), were repressed by iron deficiency. By six hours, there were equal numbers of genes with homology to known iron homeostasis genes in leaves that were induced or repressed by iron deficiency. The induced genes included an ortholog of the iron transporter AtNRAMP3 (Glyma17g18010, FC = 4.2) and two AtBTS homologs, Glyma09g18770 and Glyma07g10400 (FC = 3.3 and 2.5, respectively). Only one of the four FIT1 homologs was induced (Glyma16g02320, FC = 9.2), while a homolog of Yellow Stripe-like7 (YSL7) was repressed (Glyma16g33840, FC = -2.6). In roots, the response seems to initially favor an increase in transcription, and then as the iron stress persists, transport molecules such as IRT1 and VIT1 are upregulated, presumably to scavenge as much iron as possible to restore/maintain homeostasis. In one hour roots, all but one of the differentially expressed iron homeostasis genes identified were induced by iron deficiency. These included a homolog of the transcription factors AtFIT1 (Glyma01g15930, FC = 3.7), an ortholog of the transcription factor AtBHLH038 (Glyma03g28630, FC = 2.5), ferric reduction oxidases AtFRO2 (Glyma16g03770, FC = 2.8) and AtFRO6 (Glyma05g00420, FC = 2.8), nicotianamine synthase AtNAS2 (Glyma08g18710, FC = 2.8) and the iron transporter AtVIT1 (Glyma11g08830, FC = 4.0). Glyma03g28630 was recently identified as a candidate gene underlying the IDC QTL on soybean chromosome 3 [14]. In six hour roots, the homologs of the transporters AtIRT1 (Glyma06g05460, Glyma04g05410, Glyma08g17530, Glyma13g10791, Glyma15g41620 and Glyma20g06210) and an ortholog of AtNAS2 (Glyma19g41630, FC = 2.6) were induced by iron deficiency, while homologs of AtVIT1 were repressed (Glyma08g08090, FC = -4.3 and Glyma05g24980, FC = -3.2).

Table 3 Homologs of A. thaliana iron homeostasis genes found to be significantly differentially expressed
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Identification of iron responsive gene families

In order to identify gene families responding to iron stress that may not have been identified in the previous analyses, we used BLASTP (E < 10-10) [52] and single linkage clustering [53] to group all differentially expressed genes. Using this approach, we identified 161 gene families containing 2 to 38 unique sequences (Additional file 6). Eleven gene families were identified with ten or more sequences (Groups 15, 26, 29, 34, 40, 42, 46, 53, 59, 70, 81). The majority of these gene families had largely tissue-specific expression patterns and reflected the tissues in which the largest number of DEGs were identified (one hour roots and six hour leaves). Groups 26, 59, 70 and 81 were largely specific to six hour leaves and had homology to GDSL lipases, protein kinases, cytochrome P450s and xyloglucan endotransglucosylases, respectively. Groups 15, 34, 41, 42, 46, and 53 were largely specific to one hour roots and had homology to Casparian strip membrane proteins, AP2/ERF transcription factors, peroxidases, 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenases, nucleotide binding site-leucine rich repeat resistance gene homologs and dirigent-like proteins, respectively.

Identification of transcription factors responding to iron stress

The reversals in gene expression found between the one and six hour time points in each tissue and the overrepresentation of GO category “regulatory region nucleic acid binding” (GO:0001067, Table 2) suggested that transcription factors play a key role in the iron deficiency stress response. Therefore, we took advantage of the SoyDB transcription factor database [54], http://casp.rnet.missouri.edu/soydb/) to identify transcription factors within our DEGs (Figure 4, Additional file 7). In one hour leaves, we identified two differentially expressed transcription factors, Glyma09g41470 and Glyma17g10820, both significantly repressed by iron deficiency (fold changes of -3.8 and -2.7, respectively). After six hours of iron deficiency, only Glyma17g10820 was still significantly differentially expressed, however it was induced by iron deficiency (fold change of 3.5). Glyma09g41470 and Glyma17g10820, encode a β-helix-loop-helix and MYB/HD-like transcription factors, respectively. Their best homologs in Arabidopsis, identified by reciprocal BLASTP [52], have no known function.

Figure 4
figure 4

Expression patterns of transcription factors significantly (FDR < 0.05) differentially expressed between iron sufficient and deficient conditions. Transcription factor families on the y-axis refer to the SoyDB transcription factor database [54] as described in the methods. Absolute fold change in gene expression is plotted on the x-axis. Multiple differentially expressed transcription factors per family are plotted. Additional details on TF expression are provided in Additional file 7: Table S7.

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In six hour leaves, we identified 39 differentially expressed transcription factors (Figure 4). These included representatives from the AP2-EREBP (2), AUX-IAA-ARF (1), β-helix-loop-helix (6), BZIP (6), C2C2 (Zn) CO-like (2), C2C2 (Zn) Dof (1), CCAAT (1), FHA (1), GRAS (1), Homeodomain/HOMEOBOX (7), MYB/HD-like (5), NAC (1), PLATZ (1), TPR (3) and WRKY (1) transcription factor families. Several of these have functions associated with defense or abiotic stress responses in Arabidopsis. Glyma06g05170 and Glyma08g14600 (fold changes of -4.35 and 3.48, respectively) are homologs of the AP2-EREBP transcription factor AtDREB2C (1E-42) (dehydration-responsive element-binding protein) which is induced by iron deficiency. AtDREB2C interacts with the BZIP transcription factor ABF2 to regulate ABA responsive gene expression [55]. Glyma16g01940 encodes a NAC transcription factor induced in response to iron deficiency (4.2-fold). Glyma16g01940 is homologous to AtNTL9 (2E-40), which regulates osmotic stress signaling [56]. Glyma02g42380 encodes a MYB transcription factor related to AtMYB73, which negatively regulates SOS induction during salt stress [57].

In one hour roots, we identified 35 differentially expressed transcription factors (Figure 4). These included representative members from eight different families such as AP2-EREBP (16), AS2 (1), β-helix-loop-helix (2), Homeodomain/HOMEOBOX (2), MYB/HD-like (6), NAC (2), PHD (2), PLATZ (2), TPR (1), and TUB (1). Of the 16 AP2-EREBP proteins expressed, five are homologs of ERF1 (Glyma19g34696 [2E-54], Glyma20g34570 [2E-71], Glyma13g18410 [9E-52], Glyma10g33060 [1E-73], and Glyma11g03910, [9E-52]). ERF1 has been shown to regulate abiotic stress responses in Arabidopsis [58]. Glyma13g34920 encodes an ERF4 homolog (3E-47), which functions as a repressor in ethylene, jasmonic acid, and abscisic acid pathways [59]. Glyma03g28630 encodes a homolog of the β-helix-loop-helix transcription factor βHLH038 (5E-52). This gene is upregulated under soybean iron stress conditions, and is thought to function as a binding partner for FIT [14]. Glyma16g01911 is most similar to AtNTL6 (6E-53), which is a plant-specific NAC that is phosphorylated by SnRK2.8 in drought-resistance [60]. The PHD family member, Glyma20g01070, is upregulated 6-fold in response to iron deficiency and is most similar to AtXLG2 (extra-large G protein 2, 1E-96), a GTP-binding protein that has been implicated in resistance to Pseudomonas syringae[61].

In six hour roots, we identified four differentially expressed transcription factors. Two were upregulated in response to iron stress, Glyma03g31940 and Glyma01g02251 (fold changes of 3.8 and 2.2, respectively), and two downregulated, Glyma18g10324 and Glyma08g43258 (fold changes of -8.5 and -2.9, respectively) (Figure 4). Only Glyma03g31940 was also significantly expressed at one hour, downregulated -3.4 fold at that time. Glyma03g31940 encodes an AP2-EREBP transcription factor (8E-54) homologous to AtERF15. Glyma01g02251 encodes a MYC2 homolog (E = 0), which is activated by the jasmonic acid signaling pathway to negatively regulate JA-mediated stress responses [62]. Glyma18g10324 and Glyma08g43258 each encode homologs of the AtWRKY6 transcription factor (1E-88 and 2E-88, respectively). AtWRKY6 has been implicated in senescence and defense [63], phosphate stress [64] and boron deficiency [65].

In addition to identifying individual transcription factors within each sample, we used overrepresentation analysis to identify transcription factor families significantly overrepresented among DEGs relative to their abundance in the soybean genome. Only the AP2-EREB transcription factor family was identified as significantly overrepresented (P < 6.88E-05) and only in one hour roots.

Identification of transcription factor binding sites overrepresented in iron-responsive genes

Transcriptional cascades happen quickly, and to explore pathway components outside of our one and six hour windows, we examined the transcription factor binding sites that were overrepresented in the promoters of our DEGs. We leveraged Clover (Cis element over representation) [66] and the TRANSFAC transcription factor database (version 2010, [67]) to identify transcription factor binding sites significantly (t < 0.05) overrepresented in promoters of DEGs relative to promoters of all predicted genes in the soybean genome.

We found 74 unique transcription factor motifs significantly overrepresented across the four tissue/time points within the DEGs. Focusing on transcription factors known to be involved in abiotic and biotic stress response, ARF, BZR1, DREB1B, HY5, MYBPH3, TGA1, and TRAB1 binding sites were all significantly overrepresented (t < 0.05) in promoters of genes differentially expressed in one hour leaves (Additional file 8). ARF (auxin response factor) has been implicated in both biotic and abiotic stress responses in several plant systems [68, 69]. In our dataset, its binding site is present in a high percentage of DEG promoters (one hour leaf, 74%; six hour leaf, 70%; one hour root, 69.2%). BZR1 is a central regulator of brassinosteroid (BR) signaling, synthesis and growth responses [70]. Soybean GmDREBa and GmDREBb are induced by cold, drought and salt in the leaves of seedlings. While expression of GmDREBc is low in leaves, it has high levels of expression in roots following drought, salt and ABA treatments [71]. HY5 (LONG HYPOCOTYL 5) is a bZIP transcription factor that has been shown to positively regulate anthocyanin biosynthesis [72]. The MYBPH3 transcription factor functions in the regulation of flavonoid biosynthesis in petunia [73] and may also be involved in salt and cold-tolerance in pea [74]. TGA1 is controlled by nitric oxide and regulates systemic acquired resistance in plants through salicylic acid (SA)–mediated signal transduction pathway [75, 76]. TRAB1, responsible for ABA regulation, is phosphorylated in response to osmotic stress and by the SnRK2 kinase in response to ABA [77].

In six hour leaves, ABF1, ABZ1, Alfin1, ARF, AtMYB15, AtMYB77, BZR1, C1, DREB1B, E2F, HY5, KNOX3, LIM1, MYBAS1, NAC6, OSBZ8, P, RAV1, TGA1 and TRAB1 binding sites were significantly (t < 0.05) overrepresented in the promoters of our DE genes and all have reported roles in stress responses (Additional file 9). ABF (ABA-responsive elements binding factor) is ABA and stress inducible, and in turn, activates ABREs (ABA-responsive elements) in response to abiotic stress [78]. ABZ1 (anaerobic basic leucine zipper) was isolated from a tomato cDNA library enriched for anaerobically induced genes [79]. The soybean genome contains six genes identified as Alfin1-type PHD finger protein and their expression responds differentially to drought, salt, cold and ABA treatments when expressed transgenically in Arabidopsis [80]. Like ARF, Alfin binding sites are highly represented in our DEG promoters (six hour leaves, 74.3%; one hour roots, 70.6%). Myb factors have been implicated in a variety of biotic and abiotic stress responses [81]. Transgenic AtMYB15 can confer improved tolerance to drought and salt stress in Arabidopsis [82]. AtMYB77 expression responds to wounding, pathogen infection, abiotic stress and hormone treatment [68]. The E2F transcription factor regulates the cell cycle and DNA replication [83, 84]. Atwood et al. [11] and O’Rourke et al. [13] found that DNA replication was inhibited in iron efficient soybean lines. HAHB4, a HD-Zip transcription factor, regulates crosstalk between ethylene and drought signaling in sunflower [85]. APETALA 2/ethylene-responsive element binding factor (AP2/ERF) family includes four major subfamilies: the AP2, RAV, ERF and DREB subfamilies and many have been shown to play a role in abiotic stress [86]. Binding sites for two of those subfamilies (RAV and DREB) were overrepresented in the promoters of genes from the six hour leaf time point. Knotted1-like homeobox (KNOX) genes are involved in plant morphogenesis, and barley KNOX3 has been shown to be regulated by the ethylene signaling pathway [87]. Lignin plays an important role in mechanical support, water transport and pathogen resistance. NtLIM1 encodes a Pal-box binding protein involved in lignin biosynthesis [88]. Tobacco NtMYBAS1 is involved in phenylpropanoid biosynthesis [89], which has long been known to be stress-induced [90]. Soybean GmNAC6 is induced by both endoplasmic reticulum-stress and osmotic-stress signaling to promote cell death [91]. In rice, the bZIP class Abscisic acid Responsive Element (ABRE)-binding factor, OSBZ8 has been shown to function in ABA signaling and in salt stress [92]. The P transcription factor in maize is involved in flavonoid biosynthesis, leading to the production of a red phlobaphene pigment [93, 94]. AtRAV1, a RAV (Related to ABI3/VP1) transcription factor family gene has been shown to positively regulate leaf senescence, and is induced in response to ethylene and methyl jasmonate [95]. DREB1B, TGA1, and TRAB1 binding sites were all overrepresented in both one hour and six hour leaves.

In our DE genes from one hour roots, ABZ1, Alfin1, AtMYB77, BZR1, HAHB4, MYBPH3, P, TGA1a, TGA1b, TGA2, TRAB1 and WRKY11 binding sites were overrepresented (Additional file 10). Of these 11 families, only CBNAC, TGA2 and WRKY11 binding sites are unique to one hour roots. Calmodulin-regulated transcription factors and NAC transcription factors in general have been show to function in both biotic and abiotic stresses [96, 97]. TGA2 is involved in salicylic acid signaling in Arabidopsis [98]. WRKY11 is a negative regulator of basal defense responses in Arabidopsis [99]. Six hour DE root genes had P, RAV1, TGA1a and TGA1b binding sites overrepresented (Additional file 11). There are 14 transcription factor family binding sites overrepresented in all four time points and tissues, and 40 overrepresented in at least two time points and tissues.

Discussion and conclusions

A complicated molecular network exists to maintain iron homeostasis, as metals are necessary for many metabolic processes yet toxic to cells in high concentrations. The mechanisms for sensing deficiencies and interactions with general stress and defense pathways are poorly understood. Previous work demonstrated that iron deficiency is sensed in the leaves and that an unknown leaf signal regulates the expression of iron uptake genes in the root [7, 8]. In order to capture signaling between the root and shoot, and to identify genes acting early in efficient responses, we sampled leaves and roots from the same plants, one and six hours after the onset of iron stress. One of our first observations was the dynamic difference between roots and shoots. At one hour of iron stress, few genes were differentially expressed in leaves but many were already changing expression in roots. By six hours, more genes were differentially expressed in the leaves, and a massive shift was seen in the direction of gene expression in both roots and shoots. Further, there was little overlap in the DEGs found in each tissue and time point.

Stein and Waters [100] and Waters et al. [101] used the Arabidopsis genome array to measure gene expression in roots and rosettes (respectively) of the same plants 24 and 48 hours after iron deficiency using two different Arabidopsis ecotypes, differing in the speed of their iron deficiency response time [100]. In the faster Kas-1 ecotype, greater differential gene expression was observed in roots (1504 DEGs) than rosettes (130 DEGs). In the slower Tsu-1 ecotype, the number of DEGs was approximately equal between roots and rosettes (630 and 690, respectively). In the faster Kas-1, 40% and 31% of DEGs were expressed in both time points in roots and rosettes, respectively. In contrast, only 16% and 10% of DEGs from Tsu-1 roots and rosettes were common to both time points. Another interesting difference between ecotypes was that only Kas-1 root DEGs were significantly overrepresented with abiotic and biotic stress associated GO terms. In contrast, only Tsu-1 rosettes were overrepresented with abiotic and biotic stress GO terms. In our study, approximately equal numbers of DEGs were identified in roots and leaves, with very little overlap in time points, mirroring Tsu-1 responses [100, 101]. In our experiment however, we found most GO terms associated with abiotic stress significantly overrepresented in the root, mirroring Kas-1. Another interesting difference is that Stein and Waters [100] found that FRO2 expression in Kas-1 significantly increased 16 hours after iron deficiency treatment, while FRO2 expression in Tsu-1 was significant only after 48 hours. In our study, we found that soybean homologs of FIT and FRO2 were induced by iron deficiency in both one and six hour roots suggesting Clark may have a faster iron efficiency response than either of the Arabidopsis ecotypes. Differences in the timing of FRO2 expression could also be due to different experimental protocols. While our study reduced available iron under hydroponic conditions, Stein and Waters [100] removed iron from the growing media completely.

To our knowledge, no other gene expression analyses have been performed on the early stages of iron deficiency examining root and shoot response simultaneously. Therefore, we expanded our comparisons to other iron deficiency experiments focused on single tissues. Buckhout et al. [102] grew Arabidopsis ecotype Landsberg erecta in Fe-sufficient hydroponic conditions, moved them into Fe-free media and collected roots 0, 0.5, 1, 6 and 24 hours later. At one hour, they identified 36 DEG (18 induced, 18 repressed). By six hours, 60 DEGs (50 induced, 10 repressed) were found. Similar to our study, very little overlap in gene expression was found between time points. However, we found greater differential gene expression in one hour roots, with 263 of 360 DEGs induced by iron deficiency. Yang et al. [103] grew two Arabidopsis accessions (Col-0 and C24) on agar media, switched to Fe-deficient agar and collected roots three days later for microarray analysis. Their goal was to identify core iron-stress response genes in Arabidopsis and categorize them into functional modules. They identified 130 and 44 genes upregulated and downregulated, respectively, in response to iron deficiency. All but one gene overlapped with the iron-stress responsive genes found in the Buckhout study [102].

We also examined studies done in response to other nutrient deficiencies. Hermans et al. [104] looked at the effect of magnesium stress on Arabidopsis roots and mature leaves 4, 8 and 28 hours after the removal of magnesium from the media. In four-hour roots, 89 of the 97 DEGs were induced by magnesium deficiency. By eight hours, 120 of 123 genes were induced and by 28 hours only 3 of 8 genes were induced by magnesium deficiency. In the leaves, 145 of 155, 104 of 106 and 286 of 410 were induced at 4, 8 and 28 hours respectively by magnesium deficiency. Their time points do not allow for direct comparison with the Clark iron response, but the pattern is reminiscent in that roots show much activity early and taper off, while leaves have larger expression changes as stress persists.

One of the aims of this study was to identify genes directly involved in the uptake and utilization of iron in soybean. A recent review by Kobayashi and Nishizawa [51] generated a comprehensive list of genes known to be involved in iron homeostasis responses in higher plants. We used this data to identify homologous sequences, responding to iron deficiency, in our RNA-Seq data. The gene expression changes are similar to what has been shown in other plants. In total, we identified 29 DEGs with homology to known iron deficiency genes. In the soybean root, we see all the components of the iron transport machinery induced; AHA11, FRO2, FRO6, FIT1, IRT1, VIT1 (at one hour), and NAS2. VIT1 functions in moving iron into vacuoles for storage [105]. The repression of VIT1 at six hours of iron stress suggests that the roots are switching from storage to uptake and mobilization of stored iron as the stress persists.

In Arabidopsis, most of the genes involved in iron homeostasis have been found in both leaves and roots, with AtFIT1 notably missing from leaves [106]. It is interesting that in soybean, not only are multiple copies of FIT1 expressed in leaves, but they are differentially expressed at both one and six hours of iron stress. Yellow Stripe-like 1, 2 and 3 are well characterized in iron-transport [107, 108], while the function of YSL7 in metal transport is only putative. We observe YSL7 responding to iron deficiency in leaves, but no other YSL homologs were significantly affected. AtBTS has been shown to function in leaf iron homeostasis [109], but more studies have been conducted on its role in the roots. Strangely, we do not see BTS in our root data, but this may be due to timing of the experiment as BTS levels are elevated in Arabidopsis roots by 24 hours of iron stress [109]. Such differences between soybean and Arabidopsis in iron response are worth investigating further.

One novel approach we used to characterize iron deficiency responses in soybean was to identify gene families among our differentially expressed genes (Additional file 6). One hour roots had a number of differentially expressed gene families with ten or more sequences. Interestingly, several of these were related to the development and maintenance of Casparian strips and all were induced by iron deficiency. Group 15 contained 13 DEGs from one hour roots orthologous to Casparian strip membrane domain proteins (CASPs) 1, 3 and 5. These proteins were identified by Roppolo et al. [110] for their role in the development of Casparian strips. Group 53 contained nine DEGs from one hour roots with homology to dirigent-like proteins, including orthologs of ENHANCED SUBERIN 1 (ESB1). Recently, Hosmani et al. [111] found that ESB1 was required for the formation of Casparian strips. Group 41 contained 18 DEGs with homology to peroxidases in one hour roots. Lee et al. [112] found that peroxidase AtPER64 was also required for timely formation of Casparian strips. Other peroxidases (AtPER03, AtPER09, AtPER15, AtPER37, AtPER39, AtPER40, AtPER49, AtPER72) were also induced in the root endodermis relative to the rest of the root. Given these results we examined the rest of the differentially expressed genes for other genes that could function in Casparian strips. The top DEG in one hour root is a homolog of a type III peroxidase, RCI3 (Glyma10g02730), which has been shown to contribute to ROS production in potassium deficiency [29]. Four other homologs of RCI3 are also highly induced in one hour root (Glyma02g17060, Glyma03g36620, Glyma12g10850 and Glyma06g45910). An ortholog of respiratory burst oxide homolog F (AtRBOHF, Glyma05g00420) was also induced by iron stress in one hour roots. Lee et al. [112] found that RBOHF was also localized to Casparian strips and was required for their formation. Further, Lee et al. hypothesized that CASP proteins provide a scaffold for RBOHF to produced hydrogen peroxide which is then used by the peroxidases to polymerize lignins to form Casparian strips. The Casparian strip has been shown by Perls/DAB staining in frd3 mutants to act as a barrier to Fe within the root [113]. Additionally, we see seven homologs of NET1D (AT1G03080; Glyma17g27187, Glyma17g23660, Glyma17g27135, Glyma13g07360, Glyma07g36351, Glyma10g14860 and Glyma15g21211) upregulated in response to iron deficiency. The NET1D family has recently been shown to be an actin-binding protein highly expressed in the stele and conducting tissues of the roots [32]. Given that Casparian strips are thought to control the passage of water and mineral nutrients into the vascular system, which would then need to pass through the stele into the xylem, it interesting to speculate that increased expression of these genes in response to iron stress could facilitate the uptake of iron.

We also identified a putative family (Group 42) of 9 2OG-Fe(II)-dependent oxygenases differentially expressed in one hour roots. While seven of these were induced by iron deficiency, two were repressed. Reciprocal best BLASTP [52] identified two orthologs of FERULOYL-COA 6’ HYDROXYLASE 1 (AtF6’H1, Glyma03g23770 and Glyma07g12210), both induced by iron deficiency. Schmid et al. [114] found that F6’H1 was required for coumarin synthesis and was also induced by iron deficiency. Recently, Rodríguez-Celma et al. [115] demonstrated that Arabidopsis excretes phenolic compounds, such as coumarin, in response to low iron. Fourcroy et al. [116] found that AtPDR9 was induced by iron deficiency in Arabidopsis and was required for the secretion of coumarin compounds aiding in iron acquisition. Therefore, we examined our DEGs to identify similar genes. We found differential expression of a number of these genes in one hour roots including orthologs of pleiotrophic drug resistance protein AtPDR9 (Glyma17g03863 and Glyma07g36166), AtPDR11 (Glyma19g37760), AtPDR12 (Glyma13g43860) and caffeic acid O-methyltransferase AtOMT1 (Glyma04g40591 and Glyma06g14210). Further, we found the GO terms associated with coumarin (GO:00098040 and phenylpropanoid (GO:0009698) biosynthesis and metabolism were significantly overrepresented in one hour roots. This suggests that secretion of coumarins is essential for iron uptake in soybean as well.

The second main aim of this study was to identify signaling genes that regulate iron deficiency responses. The virtual on/off switch of gene expression we observed between one and six hours suggests a prominent role for transcription factors in establishing and regulating early iron-stress responses in roots and leaves. Our analyses of the transcription factor families within our DEG list led to interesting results. Within the 970 DEGs in this study, there were 80 transcription factors representing 18 families. We found two transcription factors, both downregulated, in one hour leaves but 35 transcription factors representing ten families that were either induced or repressed in roots at the same time. As was true for DEGs, we saw this pattern reverse by six hours with 39 transcription factors representing 15 families differentially expressed in leaves and only four transcription factors in roots.

Transcription factor binding sites, which were overrepresented in our DEGs, correlated with this pattern as well, with six hour leaves containing the highest number of unique transcription factor binding sites, and binding sites within six hour leaf transcripts greater than six hour root transcripts. Surprisingly, the degree of overlap between transcription factor family binding sites across time points and tissues was larger than might have been expected given the differences in gene expression across time points and tissues. There were 40 transcription factor binding sites that were significantly overrepresented (t < 0.05) in at least two time/tissue gene lists out of 74 transcription factor binding sites significantly overrepresented in at least one time point or tissue. Many of the transcription factor families corresponding to the significant transcription factor binding sites were not identified as significantly differentially expressed themselves, suggesting that the complexity of the early stress response is greater than what we captured.

Using a combination of different approaches, we also observed evidence of hormone-related signaling in our data. Recently, Garcia et al. [117] demonstrated that genes involved in iron deficiency responses, such as AtFIT, AtBHLH38, AtFRO2, AtIRT1, AtNAS1, AtNAS2 and AtFRD3, were induced in response to IDC, ethylene, and nitric oxide (NO). Our results also confirm a role for ethylene and NO in IDC responses. In one hour roots, we observed differential expression of eleven genes involved in ethylene production, all of which were induced by iron deficiency. These encode ten oxidoreductases (Glyma02g34201, Glyma04g07480, Glyma04g07490, Glyma07g18280, Glyma08g18011, Glyma15g33740, Glyma15g41000, Glyma16g12830, Glyma18g43136 and Glyma19g31460) and a 1-amino-cyclopropane-1-carboxylate synthase (ACC, Glyma08g03400). ACC is converted to ethylene by ACC synthase and ACC oxidase [118]. While ethylene synthesis appeared to be induced by iron deficiency in one hour roots, APETALA 2/ethylene-responsive element binding protein (AP2-EREBP) transcription factors were repressed. In six hour roots, genes involved in response to ethylene (GO:0050896, GO:0070887 and GO:0071369, P=0) were significantly overrepresented and induced by iron deficiency. This included homologs of the ethylene sensors AtEIN4 (Glyma20g21780), AtEIN3 (Glyma17g31940), AtERF15 (Glyma03g31940) and AtETR2 (Glyma20g34420). Glyma03g31940 (AtERF15) has an extreme fold change of -3.4 in one hour root to 3.8 in six hour roots. The chitinase AtChiB (Glyma02g04820), PDF1.2 (Glyma03g32520) and PR4 (Glyma03g40770), which are all induced by ethylene [119, 120], were repressed in six hour roots. The promoters of genes differentially expressed in six hour roots had an overrepresentation of the AP2-EREBP DREB1B transcription factor binding site (P<1.6E-6). The induction of the ethylene biosynthetic enzyme 1-aminocyclopropane-1-carboxylate oxidase (ACO1, Glyma05g36310) in six hour roots, suggests that ethylene may be required for sustained IDC signaling in soybean.

NO has protective effects on iron-stressed organisms, ranging from animals to plants [121]. Homologs of proteins involved in NO synthesis and nitrate transport such as AtNIR1 (Glyma02g14910 and Glyma07g33570), AtNRT1 (Glyma11g03430) and AtNIA1 (Glyma06g11430 and Glyma13g02510) were repressed by iron deficiency in one hour leaves, but homologs of the nitrate transporters NRT1-2 (Glyma08g407340 and Glyma08g40740) and NRT1-7 (Glyma01g04830) were induced in six hour leaves. We also found that binding sites of the NO regulated transcription factor TGA1 [75, 76] were overrepresented in one hour leaves. These findings suggest NO synthesis is occurring mainly in the leaves. NO can interact with iron-sulfur cluster enzymes such as ribonuclease reductase, aconitase and NADH dehydrogenase to inhibit DNA synthesis and mobilize stored iron reserves. Aconitase ACO3 (Glyma14g12315) was induced in one hour roots while NADH dehydrogenase NAD2 (Glyma0886s50) was down in six hour roots. In Arabidopsis and cucumber, ACO1 (ACC oxidase) is involved in ethylene synthesis, has also been shown to increase NO production, and we see ACO1 (Glyma05g36310) induced in six hour roots [9]. We also see two nitrate transporters differentially expressed, but in opposite direction in roots after six hours of iron stress; AtNRT2.4 (Glyma12g08380, FC=2.95) and AtNRT1-5 (Glyma01g40850, FC=-4.38).

We observed other interesting signaling pathways changing in response to iron deficiency including genes involved in the sucrose efflux pathway. SWEET transport proteins act redundantly to mediate sucrose efflux in Arabidopsis [23]. atsweet11;12 double mutants were defective in phloem loading, had reduced growth, and had increased sucrose levels in the leaves. In addition, expression of SWEET proteins was tightly correlated with other sucrose synthesis and transport genes including sucrose phosphate synthase (AtSPS4F) and a sucrose transporter (AtSUC2). In our experiments we observed repression of two SWEET13 sucrose transporters (Glyma05g38340 and Glyma08g01310), a homolog of AtSPS3F (Glyma14g03300), and a homolog of AtSPS4F (Glyma15g03300) in the leaves one hour after iron stress. However, a homolog of AtSWEET12 (Glyma05g38351) was induced by iron stress in one hour leaves. By six hours after iron stress, none of the soybean SWEET genes were differentially expressed but a homolog of AtSUC2 (Glyma16g27350) was induced by iron stress. Similarly, genes involved in response to sucrose starvation were significantly overrepresented in six hour leaves (GO:0043617, P < 0.05). Alterations in sucrose efflux could signal stressful conditions from shoot to root by limiting root growth and potentially affecting nutrient uptake.

Identifying genes involved in sugar signaling ties in with previous work from our group investigating the role of DNA replication in iron deficiency stress responses. O’Rourke et al. [13] found that genes involved in DNA replication were repressed in leaves in response to long term (14 days) iron stress in the iron efficient line Clark. Atwood et al. [11] found that DNA replication genes were differentially expressed between two near isogenic lines (Clark and Isoclark) that differed in their iron efficiency. Silencing of the DNA replication gene GmRPA3c (Replication protein A subunit 3) in Isoclark, to mimic expression in Clark, resulted in improved IDC symptoms and significantly reduced growth. RNA-Seq of silenced plants revealed GmRPA3c silencing resulted in massive transcriptional reprogramming with genes involved in defense and immunity, circadian rhythm, photosynthesis, protein modifications, growth and iron uptake and transport significantly differentially expressed. We hypothesized [11] this response was controlled by the SnRK1/TOR complex which is regulated by sucrose and heavy carbon load [122]. Activation of SnRK1, initiates a phosphorylation relay [123, 124] that inhibits components of the SnRK1/TOR pathway including the E2F transcription factor that controls the cell cycle and DNA replication [83]. In the analysis reported here, we see differential expression of genes involved in sucrose transport and DNA replication. In addition, we find that E2F transcription factor binding site (M01114) is significantly overrepresented (P=0) in six hour leaves and that a homolog of AtETG1 (E2F TARGET GENE 1) is repressed by iron deficiency.

In a general sense, the SnRK1/TOR signaling pathway is controlled by nutrient and energy availability inside the cell, but it remains unclear how external and endogenous signals regulate nutrient and energy status. Recently Schröder et al. [125–127] characterized the extracellularly located EXO and EXO-like (EXL) family in Arabidopsis. While EXO is required for growth under standard conditions, EXL1, EXL2 and EXL4 function to slow growth during low carbon availability. Lisso et al. [38] used exo T-DNA mutants and EXO overexpression in exo mutants coupled with sucrose and trehalose feeding studies to study the function of EXO. They found that exo mutants grew slowly, regardless of sugar levels, suggesting EXO modifies sugar responses during seedling growth. Further, EXO regulated the expression of a number of sugar responsive genes including AtDIN6 and AtSUC2. They hypothesized that EXO could link extracellular and intracellular carbon and sugar signaling. In our six hour leaf data, we identified eight homologs of EXO and EXL5 (Glyma02g37060, Glyma04g10880, Glyma06g10700, Glyma06g10710, Glyma10g32250, Glyma14g35330, Glyma14g35340 and Glyma20g35370) repressed in response to iron deficiency with fold changes ranging from -3.3 to -45.2. Similarly, three homologs of AtDIN6 (Glyma02g39320, Glyma11g27480 and Glyma18g06840) and the homolog of AtSUC2 mentioned above were all induced by iron deficiency. These data suggest that SWEET and EXO proteins regulate the SnRK1/TOR signaling pathway in response to iron deficiency. Further, recent work by Xiong et al. [128] shows that both sucrose and glucose signaling are components of the SnRK1/TOR pathway. Our data adds support to the model proposed in Atwood et al. [11] that iron deficiency in Clark is regulated by SnRK1/TOR signaling.

The coordination of growth and developmental pathways with stress responses makes sense, however many IDC studies have focused on long term stress responses. In soybean, even short term IDC has a long lasting effect on yield [129]. While iron efficient soybean lines would seem preferable, they generally yield lower than iron inefficient lines in iron sufficient conditions [130], again suggesting a link between the regulation of growth and development and nutrient stress. Given this response, IDC tolerant soybean lines are not employed by farmers unless completely necessary. Therefore, research needs to focus on translating expression studies to the identification of target genes for crop improvement. While our analysis identified hundreds of DEGs, identifying those genes responsible for greater stress tolerance that have little or no impact on yield is an important challenge for the future.

Methods

Growth conditions

Soybean (Glycine max (L.) Merr.) line Clark (PI 548533) was germinated on germination paper for one week at the USDA-ARS green house at Iowa State University. Seedlings were transferred to hydroponics with iron sufficient media (100 μM Fe(NO3)3•9H2O) and 3% CO2 as described by Chaney et al. [131], with volumes adjusted for 10 L buckets. Nine days after being placed in hydroponics, the roots of all seedlings were rinsed six times in diH2O and transferred to either Fe sufficient or deficient nutrient solutions (100 μM vs. 50 μM Fe(NO3)3•9H2O). Chaney et al. [131] demonstrated that these nutrient solutions distinguished iron efficient and inefficient cultivars and mimicked IDC symptoms in the field. These growth conditions have also been used in other soybean iron deficiency studies [11–15]. Whole roots and the 1st trifoliate of plants were harvested at one hour and six hours after transfer into the separate Fe conditions and flash frozen in liquid nitrogen. Two biological replicates were harvested for each sample. As samples were collected before the onset of IDC, A15 (iron efficient) and T203 (iron inefficient) control plants were grown to verify expected IDC symptoms in Fe-deficient conditions. The tissues used in this study were the same tissues used by Atwood et al. [11] to study the effect of iron deficiency on DNA replication genes.

RNA isolation

Flash frozen tissue was ground in liquid nitrogen with a mortar and pestle. RNA was extracted using a Qiagen® RNeasy® Plant Mini Kit (Qiagen®, Germantown, MD). The manufacturer’s protocol was followed using ~300 mg of ground tissue which was lysed using the RLT buffer and tubes were incubated at 56°C for two minutes with 800 rpm shaking to aid in tissue disruption. RNA was treated with an Ambion® TURBO DNA-free™ kit (Ambion®, Austin, TX) to remove all contaminating DNA. RNA quality was analyzed using an Agilent® 2100 Bioanalyzer TM (Agilent®, Santa Clara, CA). RNA was considered to be of good quality if the RNA was not degraded or was only marginally degraded. Equal amounts of RNA from three plants were pooled for each biological replicate prior to sequencing. In addition, the same RNA samples were used by Atwood et al. [11] to measure differential gene expression of Replication Protein A subunits by quantitative reverse transcription polymerase chain reaction.

RNA-Seq and data analysis

Sequencing was performed at the National Center for Genome Resources on an Illumina Genome Analyzer II as described by Peiffer et al.[14]. In brief, 16 multiplex libraries were prepared from two biological replicates of the eight samples (one and six hour samples of roots grown in sufficient or deficient Fe conditions and leaves grown in sufficient or deficient Fe conditions). Libraries were sequenced for 36 cycles to produce a total of 507,784,149 single-end reads. TopHat (version 2.0.3, [132]) was used to align paired reads to the Williams 82 reference genome sequence using default settings (version G. max 1.1, [16]). The program samtools [133] was used to remove unreliably mapped reads. The resulting mapping files (bam) were imported into the statistical program R (R Development Core Team 2006) using the Bioconductor package Rsamtools [134]. The Bioconductor package rtracklayer [135] was used to import the gene feature file corresponding to G. max version 1.1 [16]. The package GenomicRanges [136] was used to count reads and output a matrix containing gene counts for each sample. Genes with counts per million (cpm) < 1 in at least two of the four samples being compared were eliminated from the analyses. Count tables for all genes are provided in Additional files 12, 13, 14 and 15. The Bioconductor package edgeR [17, 137–139] was used for single factor, pairwise comparisons to calculate normalization factors, estimate tagwise dispersion and determine differential expression (DE). Differential expression compared iron sufficient conditions to the iron deficient conditions (D/S). The R graphics program ggplot2 [18] was used to compare sample replicates for technical reproducibility (data not shown) and to create porcupine plots comparing gene expression of DEGs and their replicates to all other expressed genes. Data was visualized at multiple FDR (Figure 1, Additional file 1). Following visual assessment, DE genes were considered significant if their fold change was greater than two with a false discovery rate (FDR) <0.05.

Annotation of DEGs

DEGs were annotated using the SoyBase Genome Annotation Report page (http://soybase.org/genomeannotation/index.php). In brief, primary proteins of G. max version 1.1 were compared to the UniRef100 protein database (version 11/26/2012, [20] and all predicted proteins from the A. thaliana genome (The Arabidopsis Information Resource version 10) using BLASTP (E<10-6, BLAST version 2.2.27, [52]. BLAST reports from Uniref100 were parsed using custom Perl scripts to identify the top BLASTP hit and the most informative BLASTP hit. Informative BLASTP hits were identified by removing hits containing the key words predicted, hypothetical, related, and scaffold. Custom Perl scripts were used to assign gene ontology (GO) biological process and molecular function terms [140] information from the top A. thaliana hit to the corresponding soybean protein. To identify gene ontology terms overrepresented among DEGs from each tissue, we used Ontologizer 2.0 software [50] with parent-child-union analysis and Westfall-Young-Single-Step multiple testing correction, with a resampling of 1000 replicates [141]. DEGs from each time point within a tissue were combined. The gene ontology information from Arabidopsis (described above) was used to create a gene associate file for soybean for use with Ontologizer.

In order to identify transcription factors present within the DE genes, we took advantage of the SoyDB transcription factor database [54]. However, the database had not been updated to reflect changes in G. max version 1.1 [16]. Best reciprocal BLASTP ([52], E<10-6) was used to compare all predicted proteins in G. max 1.0 to all predicted proteins from G. max 1.1. Custom Perl scripts were then used to assign transcription factors in SoyDB to a G. max 1.1 identifier. Of the 5,683 transcription factors present in SoyDB, 5,124 (90%) were assigned G. max 1.1 identifiers. To identify overrepresented transcription factor families, a Fisher’s exact text [142] was used with a Bonferroni correction [143] (P<0.05) to compare the number of times each transcription factor family was found within the DEGs relative to representation in the soybean genome.

To identify soybean orthologs of known iron homeostasis genes in Arabidopsis, we leveraged the work of Kobayashi and Nishizawa [51], which identified genes involved in iron regulation, uptake and/or translocation. To identify orthologous sequences, the corresponding protein sequences were used for best reciprocal BLASTP ([52], E<10-6) against all predicted primary proteins in G. max 1.1 [16]. The Arabidopsis proteins were involved in iron regulation, uptake and/or translocation. To account for whole genome duplication events in soybeans’ evolutionary history, each Arabidopsis protein was allowed to hit two soybean proteins. Soybean proteins were considered putative orthologs if they identified the original Arabidopsis query sequence.

Identification of differentially expressed gene families

Single linkage clustering (as described by [53]) was used identify gene families that could be acting in iron deficiency responses. In short, protein sequences corresponding to all differentially expressed genes were compared against themselves using BLASTP ([52], E < 10-10). Proteins with overlapping BLAST reports were assigned to groups representing potential gene families. For genes of interest, orthology to genes in Arabidopsis was confirmed using best reciprocal BLASTP ([52], E < 10-10).

Transcription factor binding site analysis

Clover (Cis element over representation), [66] was used in conjunction with the TRANSFAC transcription factor database (version 2010, [67]) to identify transcription factor binding sites that were significantly (t < 0.05) overrepresented in promoters of DEGs when compared to the promoters of all predicted genes in the soybean genome. Custom Perl scripts were used to mine the soybean gene features file [16] (http://www.phytozome.net) and identify 1000 bases of promoter sequence for each predicted gene. Promoters were defined as the 1000 base pairs upstream of the start ATG. Promoters less than 1000 bases or containing two or more ambiguous bases (N) were removed from the analyses. Clover [66] was run using a t-value cutoff of t < 0.05. The promoters of all predicted proteins in the soybean genome were used to correct for oversampling.