Ups and downs of a transcriptional landscape shape iron deficiency associated chlorosis of the maize inbreds B73 and Mo17
© Urbany et al.; licensee BioMed Central Ltd. 2013
Received: 13 August 2013
Accepted: 3 December 2013
Published: 13 December 2013
Improving nutrient homeostasis is a major challenge of a sustainable maize cultivation, and cornerstone to ensure food supply for a growing world population. Although, iron constitutes an important nutrient, iron availability is limited. In this respect, iron deficiency associated chlorosis causes severe yield losses every year. Natural variation of the latter trait has yet not been addressed in maize and was therefore studied in the present analysis.
In this study, we i) report about the contrasting chlorosis phenotypes of the inbreds B73 and Mo17 at 10 and 300 μM iron regime, ii) identified over 400 significantly regulated transcripts (FDR < 0.05) within both inbreds at these growth conditions by deep RNA-Sequencing, iii) linked the gained knowledge with QTL information about iron deficiency related traits within the maize intermated B73 by Mo17 (IBM) population, and iv) highlighted contributing molecular pathways. In this respect, several genes within methionine salvage pathway and phytosiderophore synthesis were found to present constitutively high expression in Mo17, even under sufficient iron supply. Moreover, the same expression pattern could be observed for two putative bHLH transcription factors. In addition, a number of differentially expressed genes showed a co-localisation with QTL confidence intervals for iron deficiency related traits within the IBM population.
Our study highlights differential iron deficiency associated chlorosis between B73 and Mo17 and represents a valuable resource for differentially expressed genes upon iron limitation and chlorosis response. Besides identifying two putative bHLH transcription factors, we propose that methionine salvage pathway and sterol metabolism amongst others; underlie the contrasting iron deficiency related chlorosis phenotype of both inbreds. Altogether, this study emphasizes a contribution of selected genes and pathways on natural trait variation within the IBM population.
KeywordsChlorosis Iron deficiency IBM population Natural variation QTL RNA-Seq Zea mays
Maize is one of the most widely grown crop plants worldwide and has become Africa's most important staple food crop . In regard of a growing world population and the increasing demand for food supply, a sustainable agriculture is of first priority. One topic concerning a sustainable maize cultivation is the improvement of its ability to cope with limiting nutrient supply.
As iron-sensitive crop maize asks for i) investigating the iron deficiency associated chlorosis that different maize genotypes display, ii) identifying the underlying genes and molecular processes, and iii) using this information to improve iron deficiency chlorosis of maize through breeding.
The processes that plants employ to efficiently access iron as well as genotypic variation of iron homeostasis itself are hitherto not completely understood. Knowledge on natural variation of iron deficiency and the chlorosis response is crucial to improve growth of crops in marginal soils, where iron deficiency frequently limits growth. Iron deficiencies are found mainly on calcareous soils  but also develop in acid soils [3, 4]. As iron is involved in the production of chlorophyll, deficiency is easily recognized by the occurrence of chlorosis symptoms, notably yellowish interveinal tissue on the younger upper leaves [3, 5]. Iron constitutes an indispensable plant nutrient and severe deficiencies cause leaves to turn completely yellow or almost white, which in turn strongly impairs plant growth and leads to high yield losses [3, 5]. This furthermore, influences nutritional crop quality and impacts on economic aspects like the need of fertilizers and the accessibility of growth areas.
Upon iron limitation, plants induce a coordinated set of responses that allow maximizing iron mobilization and uptake from the soil. Moreover, internal iron stores are utilized to allocate iron where crucial cellular processes are proceeding. The majority of plants except the grasses use the strategy I response to solubilize and absorb iron into roots when iron is limiting . As result, Fe(III) is converted into Fe(II) and subsequently transported over the plasma membrane into root cells [6, 7]. Maize and the other grasses have adopted another strategy to assimilate iron that relies on the secretion of iron chelators, non-proteinogenic amino acid derivatives, into the rhizosphere that form stable Fe(III) chelates . These Fe(III)/chelator complexes, are then subsequently imported by a specific transporter (YS1) that is located at the root surface [8, 9]. However, the further allocation of iron, its pool sizes and the fluxes within and between cells, tissues or whole organs as well as the regulating mechanisms, which orchestrate iron homeostasis, still remain elusive.
Hitherto, the knowledge on maize iron homeostasis and the involved genes is mainly derived from mutant studies or the molecular genetic analysis of monogenic differences in graminaceous species, such as rice [6, 8, 10–14]. Despite, the considerable number of identified genes and pathways in maize, their contribution to the natural phenotypic variation as well as their impact on modulating environmental adaptation is unknown. The investigation of iron deficiency chlorosis in maize using deep RNA sequencing (RNA-Seq) would allow considering the complexity of genotype and treatment related transcriptional differences [15, 16]. In this respect, identified transcriptional differences might either represent causal genes for natural variation of iron mobilization and allocation and the associated chlorosis response in maize or be regulated by the latter. Linkage of the gained information with quantitative trait loci (QTL) studies for iron efficiency related traits in the IBM population (Benke et al., unpublished) allows to pin-point differentially regulated genes that co-localize with QTL confidence intervals and thereby represent excellent candidate genes underlying this trait.
The present study investigates the transcriptomes of the maize inbreds B73 and Mo17, which differ significantly in their chlorosis response upon low iron concentration (10 μM), in order to identify genotype and/or treatment related differentially expressed transcripts. The differences in regulation of these transcripts were further validated by qRT-PCR and the corresponding expression patterns at both, limiting (10 μM) and sufficient (300 μM) iron concentration were integrated in a physiological context. Furthermore, a pathway analysis allowed us to substantiate the impact of specific pathways and related genes onto iron deficiency associated chlorosis. Altogether, the proposed genes present interesting candidates that might contribute to natural variation and could be the basis for further functional analysis and genetic proceedings in diverse natural populations.
Impact of iron regime on the phenotype of the maize inbreds B73 and Mo17
In conclusion, B73 was characterized by a more vigorous growth under sufficient iron conditions and lower leaf chlorosis under limiting iron (Figure 1 and Additional file 1: Figure S1A). In addition, B73 samples at 300 μM iron treatment showed highest iron content and biomass trait values, except for root length, which was highest in Mo17 plants at 300 μM iron (Additional file 1: Figure S1A, B). As determined by principal component analysis of trait values, samples of both inbreds at 300 μM iron were followed by samples of B73 and finally Mo17 samples grown at 10 μM iron (Additional file 1: Figure S1B). When comparing growth at 10 versus 300 μM iron, the decrease in trait values was higher for shoot and root traits as well as for iron content of the shoot in B73 than for Mo17. Contrary, the decrease in SPAD values and correspondingly stronger leaf chlorosis was observed in Mo17 than B73 (Additional file 1: Figure S1A).
The iron responsive root transcriptome of B73 and Mo17
Differentially regulated transcripts that are significant at an experiment wide FDR <0.05 at the corresponding conditions
Condition 1 vs. condition 2
Total # significantly regulated transcripts1
Up-regulated2(in condition 1 vs. 2)
Down-regulated3(in condition 1 vs. 2)
Mo17 vs. B73 @ 300 μM
Mo17 vs. B73 @ 10 μM
Mo17 @ 10 μM vs. 300 μM
B73 @ 10 μM vs. 300 μM
Significantly regulated transcripts co-localize with relevant trait QTLs
Differentially regulated transcripts that co-localize with QTLs for iron efficiency related traits (Benke et al. unpublished)
Putative uncharacterized protein
Probable bifunctional methylthioribulose-1-phosphate dehydratase/enolase-phosphatase E1
Putative MYB DNA-binding domain superfamily protein
60S acidic ribosomal protein P1
High-affinity nitrate transport
Putative uncharacterized protein
TMEM87A protein; uncharacterized protein
Hydrophobic protein LTI6, uncharacterized protein
Esterase; uncharacterized protein
Pre-mRNA-splicing factor cwc15
Putative HLH DNA-binding domain superfamily protein; uncharacterized protein
GO-term enrichment and pathway analysis
The polymorphic landscape of Mo17 and B73 and its transcriptional equivalent
In accordance with previous studies , a high degree of polymorphisms between Mo17 and B73 was observed for the majority of chromosomes (Figure 3 and Additional file 1: Figure S5). In addition, conserved genomic regions between both inbreds with low structural variation were identified (Figure 3, Additional file 1: Figure S5) as already described elsewhere . In this respect, an extended region over five arbitrary bins on chromosome VIII (ca. 20 Mbps) was observed (Figure 3, Additional file 1: Figure S5). Remarkably, several strong transcriptional differences were observed in all four two-way comparisons for genes mapping to this region. A high number of polymorphisms could also be detected within the deduced QTL regions for SPAD values of the 5th and the 6th leaf as well as the Fe content in the shoot at both iron concentrations (Additional file 1: Figure S5). In addition, these regions harboured a large number of transcripts, characterized by high median fold changes within the two-way comparisons. The majority of these transcripts were expressed at higher levels in B73 at 300 and 10 μM iron (Additional file 1: Figure S5). Furthermore, these transcripts were induced in B73 at 10 μM and down-regulated in Mo17. The median log2FC of the mapped transcripts declined in the vicinity of the centromere but showed no distinct spatial pattern (Additional file 1: Figure S5).
Candidate genes significantly regulated within the RNA-Seq experiment (FDR < 0.05) and validated by RT-PCR
bHLH transcription factor
Putative BURP-domain protein, uncharacterized protein
O-methyl transferase, uncharacterized protein
Hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase
bHLH transcription factor
Putative oligopeptide transporter, uncharacterized protein
Putative chaperone ClpC1, uncharacterized protein
Probable bifunctional methylribose-1-phosphate dehydratase/enolase phosphatase E1
Nicotinamine aminotransferase1, uncharacterized protein
High affinity nitrate transporter
High affinnity nitrate transporter, uncharacterized protein
Glycosyl hydrolase, Invertase
Putative aquaporin protein, NIP1-1
Pathogenesis related protein 1 (PR-1), uncharacterized protein
Auxin/Dormancy related protein
Eukaryotic aspartyl protease
Obtusifoliol 14α demethylase
Putative cytochrome P450
Another interesting finding consists in a transcript encoding a putative BURP-domain protein , which is down-regulated in its expression within B73 at the 10 μM iron regime. This transcript is characterized by a very low expression in Mo17 independent of the applied iron conditions and displays exon-skipping when compared to B73 (Figure 7, Table 3 and Additional file 1: Figure S7). Finally, transcripts encoding uncharacterized proteins, O-methyl- and glycosyl-transferases, a dormancy related and auxin responsive gene, a ClpC1 homologue, an enolase, and an aspartic peptidase were validated by qRT (Figure 7, Table 3 and Additional file 1: Figures S7, S8, S9 and S10). Pseudogenes, transposable elements and transcripts giving rise to very small peptides (< 50AA) were omitted from a closer analysis.
Phenotypic responses upon iron regime
As outlined above, the maize inbreds B73 and Mo17 showed a contrasting chlorosis response upon limiting iron regime (Figure 1A, B, Additional file 1: Figure S1A, B). Our observations might reflect that Mo17 cannot deal as efficiently as B73 with available iron, even far above limiting concentrations. In addition, the evaluation of the morphological values of shoot and root showed a stronger morphological impact of decreasing iron concentration for Mo17 than for B73 (Figure 1C-F, Additional file 1: Figure S1A, B). These might also be due to an altered iron homeostasis of Mo17, which even at 300 μM tries to maximise iron uptake by increasing root length as well as branching (Benke et al., unpublished). Despite an increase of Mo17 root length, root weight stays significantly lower at both iron treatments, when compared to B73. Correspondingly, the genetic basis of root differences between B73 and Mo17 might influence iron homeostasis and a potential cross-talk between the underlying pathways might exist as shown for Arabidopsis thaliana. If higher biomass of B73 is also a result of a more efficient iron and in general nutrient homeostasis cannot be clearly answered. Interestingly, the iron content in the shoot of both genotypes was not different at limiting iron but lower in Mo17 than in B73 at 300 μM available iron (Figure 1G, Additional file 1: Figure S1A). This in consequence, might indicate that either Mo17 displays a significantly altered iron allocation, a deregulation of iron deficiency signal pathways or impairment in iron store build-up. Although, B73 is characterized by a more vigorous growth under 300 μM treatment, iron content of the shoot was not drastically increased under these conditions. Nevertheless, iron deficiency asscoiated chlorosis is significantly lower for B73 than for Mo17, which might indicate, in addition to other morphological differences, a more efficient iron homeostasis of B73. The nature of the iron deficiency signals, the linked molecular responses, as well as their variation between different genotypes still remain unresolved and should be addressed by further experiments. In this respect, physiological investigations of siderophore production, pool sizes and fluxes as well as of corresponding siderophore/Fe(III)-chelate ratios of a diverse germplasm set might provide answers to these issues.
A transcriptomic compendium of B73 and Mo17 at two different iron regimes
Apart from transcriptional differences a high number of polymorphisms could be detected between both inbreds, which is in concordance to previous studies . Correspondingly, a striking drop in polymorphisms on the long arm of chromosome VIII was observed as previously described  (Figure 3, Additional file 1: Figure S5). In contrast to this study , a considerable number of differentially regulated transcripts has been detected (Figure 3, Additional file 1: Figure S5). The very low level of differential gene expression between B73 and Mo17 in the region on chromosome VIII described by Springer et al., (2009) results from comparative analysis by Affimetrix 17 K microarray data of seedling, embryo and endosperm tissue  without any variation in iron regime. In this respect, the observed strong regulation differences (Figure 3, Additional file 1: Figure S5) in the present study are likely due to the action of trans-acting factors, being polymorphic between B73 and Mo17. Moreover, this emphasizes a putative involvement of regulated transcripts in processes linked to efficient iron homeostasis or stress response in general. The relative low number of polymorphisms between both inbred are likely due to the fact that B73 and Mo17 are identical by descent for this region . However, as recent studies could show that a maize diversity set displays sequence variation in the corresponding chromosome section [25, 27], a selective fixation during the breeding process [28, 29] or a general lack of structural variation in this region seems to be unlikely. Further investigations of this polymorphic  and transcriptional landscape, using a maize diversity set grown at different iron regimes, might provide extended information about genes underlying iron efficiency not only of the IBM population but of maize in general.
Concerning transcriptional differences in the iron responsive signatures of both inbreds, altogether 412 significantly regulated genes over all two-way comparisons could be identified (Figure 2, Table 1, Additional file 1: Table S3). These differences were validated by qRT-PCR for several known [5, 6] as well as novel candidate genes (Table 3). Overall, a highly significant correlation (p < 0.001, R2 = 0.77) was observed (Figure 6), which confirms the quality of the presented transcriptional profiling.
Differentially regulated genes resulted from a combination of the most widely used analytical approaches for the analysis of RNA-Seq data, notably RPKM/FPKM based cufflinks, as well as raw count data based edgeR and DeSeq proceedings. These methods can be divided into two concepts referring to library size (edgeR and DeSeq) or distribution adjustment of read counts (cufflinks) . Both edgeR and DeSeq rely on the hypothesis that most of the genes are not differentially expressed and propose a scaling factor for data normalization . In contrast cufflinks uses library size and gene length normalization . Beside these differences in normalization, determination of differentially expressed genes also relies on slightly different statistical tests. This in consequence results DE genes that are specific for single analysis approaches (Additional file 1: Figure S1). Nevertheless, most significant genes are conserved among the different approaches. DeSeq appears to be most stringent in the determination of DE genes, which might be due to the fact that several transcripts are characterized by low expression and elevated biological variation between biological replicates. These in consequence will be discarded by the DeSeq algorithm. In contrast, edgeR seems to be liberal for lowly expressed genes but compensates for this by being more conservative with strongly expressed genes . Recently, cufflinks and the RPKM normalization, which are still widely used, were described to be ineffective in the context of differential transcriptome analysis and should be abandoned . In contrast, DeSeq and edgeR normalization methods and data analysis were described to be more robust in the presence of different library sizes and widely different library compositions, both of which are typical of real RNA-seq data . Finally, combination of these different, approaches, as presented in this study, offers the possibility to access significant DE genes in single approaches as well as conserved DE genes among all used approaches. In consequence, we propose the use of an experiment wide FDR significance threshold (FDR < 0.05) to unify the results of such a multi statistical transcriptome survey as a robust gene selection criterion. In this respect, the two genotype specific comparisons yielded the highest number of differentially expressed genes (Table 1). Although, the overlap between treatment and genotype comparisons was low seven out of 19 significantly regulated genes within Comparison 4 were also identified as such in Comparison 1 (Figure 2). The low number of significantly regulated genes within Mo17 in the experiment wide and the single statistical test approaches in response to the applied iron regimes (Comparison 3) provides further evidence that Mo17 is unable to efficiently respond upon low iron availability. Furthermore, the overlap detected between the genotype specific comparisons at both iron regimes might indicate that in addition to the treatment responsive genes, other factors for example such, influencing genotype specific root and shoot development and morphology might also contribute to overall stress tolerance and in consequence might also impact the iron deficiency associated chlorosis response. Whether the differential expression of the identified transcripts is causal for the observed phenotypic differences, or if it is merely a consequence of factors acting up-stream of these genes and modulating their expression needs further functional investigation. Nevertheless, principal component analysis of significant transcripts and their expression patterns allows to clearly separate genotype and treatment samples as well as differentiate a pronounced iron deficiency response for B73, which is not the case for Mo17 (Additional file 1: Figure S1C). This result further substantiates that Mo17 is unable to efficiently respond upon low iron availability.
Differentially expressed transcripts co-localize with QTLs for iron related traits in the IBM population
A first hint, if the identified genes might not only contribute to phenotypic differences between both inbreds as result of genetic background but also to the natural variation of iron efficiency related traits within the IBM population relies in the projection of the corresponding genes onto the genetic map with corresponding QTLs (Benke et al., unpublished). Indeed, 27 genes co-localized within QTL confidence intervals, detected by Benke et al. (Figure 3, Table 2 and Additional file 1: Figure S3). The majority of these genes arose from the genotype specific comparisons (Table 2). However, it is noteworthy that two genes from the comparison of B73 grown at the 10 vs. 300 μM iron regime also mapped to QTL confidence intervals (Table 2). These candidates (GRMZM2G574782 and GRMZM2G423972) encode a putative bifunctional methylribose-1-phosphate dehydratase/enolase phosphatase E1 (DEP1) and a formate dehydrogenase isoform, respectively (Table 2). As both genes are important for methionine salvage pathway, a prerequisite for an efficient phytosiderophore synthesis [6, 31–33], they represent excellent candidates underlying natural variation of iron efficiency within the IBM population (Table 2).
Analysis of iron deficiency associated chlorosis by an integrative approach using transcriptome, functional and pathway information
This hypothesis is further strengthened by a GO-term enrichment and a pathway analysis that highlights the importance methionine, related processes (Figures 4 and 5, Additional file 1: Figure S4) in concordance to comparable studies in other plants [34–39]. Correspondingly, Mo17 displayed higher stress levels even at high iron regime as consequence of an inefficient iron homeostasis (Additional file 1: Figure S4). As amino-acid and carboxylic acid metabolism yield necessary backbones for phytosiderophore synthesis [5, 32], an up-regulation as observed for Mo17 at both iron regimes fortifies the hypothesis of inefficient iron deficiency response for Mo17, independent of physiological iron supply. Even the compensation response of Mo17, increasing amino-acid metabolism and nucleotide as well as methionine salvage pathways,  together with a reduced polyamine synthesis in order to achieve a higher metabolite flux towards phytosiderophore production, as deduced from the transcriptional profile, fails to complement the inefficient iron response (Figure 5, Additional file 1: Figure S4). In contrast, B73 induces nucleotide and methionine salvage pathways only upon limiting iron supply, and does not require modulating polyamine synthesis (Figure 5, Additional file 1: Figure S4). Further evidence for this hypothesis is provided by the up-regulation of metal handling pathways and corresponding transcripts independent of iron concentration in Mo17 (Figure 5, Additional file 1: Figure S4).
Novel candidates and old cues with new twists contribute to the iron responsive transcriptome of B73 and Mo17
A closer analysis of phytosiderophore and strategy II related pathways substantiates the picture of a severely flawed pathway in Mo17 when compared to B73. Whilst the majority of Yang cycle genes  as well as those being involved in phytosiderophore synthesis  are already up-regulated at 300 μM iron in Mo17, all of these are only induced by B73 to similar transcript levels than Mo17 upon limiting iron (Figure 5). A particularly striking fact is that Mo17 shows constitutively high expression levels for these genes at both iron regimes (Figure 5). Intriguingly, NAS1, which was previously thought to be one of three NAS isoforms in maize , was identified as being differentially expressed in the comparison of B73 grown at 10 versus 300 μM iron and represented by two different gene identifiers (Table 3, Additional file 1: Figure S7, S8, S9 and S10). A closer look at the corresponding chromosome region and a BLAST search manifested that two distinct regions harbored all together five NAS1 genes (Additional file 1: Figure S6). The two detected differentially regulated isoforms map to chromosome 9 and are separated by another NAS1 homologue that also shows differences in read coverage between B73 and Mo17. Moreover, the high sequence homology between these isoforms prohibits the correct affiliation of RNA-Seq reads and therefore the detection of truly differentially expressed isoforms. Nevertheless, the deduced read coverage in our study suggests a preferential expression of the isoforms GRMZM2G385200 and GRMZM2G034956 in B73 (Additional file 1: Figure S6). The drop in read coverage within the NAS1 isoform GRMZM2G312481 in B73 in contrast to Mo17 points to an expression that is specific for Mo17 (Additional file 1: Figure S6). However, as the high homology of the corresponding genetic loci hampers the validation of these results by qRT-PCR, other approaches like pyro-sequencing based assays have to be applied in order to fully understand the specific spatio-temporal regulation pattern. In addition to NAS1, also NAAT and a DEP1 homologous transcript showed transcriptional differences within both inbreds upon iron deficiency (Figure 7, Table 3 and Additional file 1: Figures S8 and S10). In contrast to Mo17, which displayed elevated expression levels independent of iron conditions for the corresponding genes, B73 induced the latter only upon iron deficiency to equal levels than Mo17 (Figure 7, Additional file 1: Figures S8 and S10). Correspondingly, we suggest that a transcription factor regulating expression of these and other downstream genes, is either impaired in its regulatory function or its own expression upon the iron deficiency signal.
Interestingly, two bHLH transcription factors, which were significantly induced in B73 upon limited iron showed elevated transcript levels at Mo17 across both iron regimes (Figure 7). These regulators show high homology to IRO2 from rice [23, 40, 41] as well as PYE from Arabidopsis and IRO3 from rice, respectively . The gene, GRMZM2G057413 might encode the maize IRO2 homologue, which is responsible for the induction of iron deficiency related genes, including those involved in nicotianamine synthesis [6, 23]. The high levels of IRO2 in Mo17 even at 300 μM provide an explanation for the elevated expression of genes related to methionine salvage pathway and nicotianamine synthesis [42, 43]. In addition, read coverage within the corresponding gene model exceeded the current one for 20 kb downstream of the putative transcription start (Figure 7, Additional file 1: Figure S7). Surprisingly, read coverage was high in Mo17 in a region far away from the putative transcription start site but at the vicinity of another gene (GRMZM2G057506) with no mapped reads. Interestingly the rice homologue also possesses a bHLH domain (data not shown). Altogether, our data indicate that the gene model for the maize locus GRMZM2G057314 might have to be revised and furthermore that this locus might undergo alternative splicing. In addition, qRT validated higher expression levels for Mo17 even at 300 μM iron but also suggested a further significant induction at 10 μM iron for Mo17 (Figure 7). High IRO2 levels in rice result from the action of the up-stream iron deficiency responsive element-binding factor 1 (IDEF1)[42, 43]. Although, a slight difference in IDEF1 could be detected by qRT (data not shown), the expression pattern within the RNA-Seq data was not significantly different at an FDR < 0.05. Intriguingly, the second bHLH transcription factor showed as closest homologues the rice IRO3 gene and PYE (Popeye) of Arabidopsis. Induction of this gene in B73 upon limiting iron and its expression profile, which is comparable to GRMZM2G057413 in Mo17 (Figure 7) indicates a contribution to efficient iron homeostasis. AtPYE, is known to be involved in maintaining iron homeostasis under low iron conditions and regulating root hair morphology of the strategy I plant Arabidopsis . OsIRO3, which also shows high homology to GRMZM2G350312 has been described as an iron regulated bHLH transcription factor that plays an important role for Fe homeostasis in rice by acting as negative regulator of the Fe deficiency response . The transcription profile of GRMZM2G350312, as observed in this study is identical to the one of IRO3in rice. In this respect, the corresponding maize gene might act analogous to OsIRO3. If both maize transcription factors act independent of each other or are both controlled by higher ranking factors like IDEF1 needs further investigation. Moreover, as bHLH transcription factors often form homo- and/or hetero-dimers [45, 46], a possible interaction of these genes might impact on iron deficiency associated chlorosis of both inbreds. A more essential question that has to be addressed by further experiments is, if solely internal cellular iron levels act as iron deficiency signal and trigger the IDEF1/IRO2 and IRO3 like regulation networks or if other mechanisms are involved. An e-QTL study of the identified iron responsive transcription factors (GRMZM2G057413 and GRMZM2G350312) would provide first answers.
In addition, a putative oligopeptide transporter (GRMZM2G400602) could also be identified that showed an induction in its expression within the RNA-Seq experiment for both genotypes upon limiting iron conditions (Figure 7, Table 3). This regulation pattern could be validated by qRT, although induction upon 10 μM iron regime in replicate B was stronger for Mo17 than for B73 (Figure 7). One might speculate, that this transport protein, which only shows weak homology to the yellow stripe like (YSL) transporter clade  might be involved in the intracellular transport of phytosiderophores, their precursors or related iron complexes. A putative function in phytosiderophore export to the rhizosphere can be excluded, as the maize homologues  of the corresponding gene in rice OsTOM1 differ from this putative oligopetide transporter.
An intriguing finding is the strong expression difference of a putative obtusifoliol-14α demethylase (GRMZM2G096029), which undergoes a down-regulation in B73 upon limiting iron, but displays nearly no expression in Mo17 (Figure 7, Table 3). As this gene is involved in sterol biosynthesis, and as it has been previously described that the relative abundance of Δ5-sterols correlates with aluminum tolerance in rice , a potential consequence of the extremely low transcript expression in Mo17 could be an altered sterol composition of root membranes. As low sterol abundance seems to correlate with higher membrane permeability and an accumulation of metal ions , higher sterol abundance in B73 root tissue might improve its iron efficiency by reducing undesired metal accumulation and toxicity.
In addition, the regulation pattern of the gene GRMZM2G106980 encoding a putative BURP-domain could be due to developmental processes impacted by the stress response as described elsewhere . Moreover, expression of a ClpC1 homologous chaperone gene could also be validated by qRT-PCR (Additional file 1: Figure S8). AtClpC1, has been identified as being responsible for the Arabidopsis mutant phenotype irm1, showing typical Fe-deficiency chlorosis . In this respect, ClpC1 is involved in leaf iron homeostasis, presumably via chloroplast translocation of some nuclear-encoded proteins which function in Fe transport. The homologous gene in maize might fulfill similar functions. The high transcript levels of two cytochrome P450 genes, involved in polyphenol synthesis, in Mo17 at both iron regimes points to an iron deficiency compensation response (Additional file 1: Figures S7 and S10). In this respect, a flawed iron deficiency chlorosis response in Mo17 might lead at both iron conditions to increased production of phenols that, after being excreted to the rhizosphere, solubilize Fe(III) from the soil . Whether this Strategy I related mechanism supports iron assimilation by phytosiderophores  remains theoretical. Another, explanation would be a general stress response of Mo17, which in consequence results in elevated phenol production .
Over 400 significantly regulated transcripts at two iron regimes within both inbreds were identified that besides novel candidate genes included known iron responsive loci. The integration of QTL information and transcriptome data emphasize a contribution of the above mentioned genes to natural variation of the investigated traits. Further analysis of the proposed candidate genes and pathways in a diverse germplasm set will extend our understanding about the impact of the latter to natural trait variation in maize. The presented data is a valuable resource for researcher investigating iron deficiency response in graminaceous and non-graminaceous plants and represents a vantage point for the generation of molecular markers in order to improve iron deficiency chlorosis resistance in maize.
Plant material, hydroponic growth and root tissue sampling
Seeds of the maize genotypes B73 and Mo17 were obtained from the Maize Genetics Cooperation Stock Center (http://maizecoop.cropsci.uiuc.edu/). Maize seeds were sterilized with a 3% NaClO solution for three minutes and then treated with 60°C hot water for another five minutes. Seeds for each genotype were germinated in Petri dishes (Greiner Bio-One GmbH, Frickenhausen, Germany) between two filter paper sheets moistened with saturated CaSO4 solution in the dark at room temperature. After six days of imbibition, germinated seeds were transferred to a continuously aerated nutrient solution with nutrient concentrations as described  and supplied with 100 μM Fe(III)-EDTA for the following seven days. Afterwards, half of the pots representing a genotype group were shifted to either limiting [10 μM Fe(III)-EDTA] or non-limiting [300 μM Fe(III)-EDTA] iron conditions. The nutrient solution was exchanged every third day. Four plants of each genotype were grown in one 5 liter pot until the 28th day in a growth chamber at a relative humidity of 60%, light intensity of 170 μmol m-2⋅s-1 in the leaf canopy, and a day-night temperature regime of 16 h/24°C and 8 h/22°C, respectively. In order to correct for the biological variation, the experiment was carried out two times each with three pots harbouring four plants of a given genotype at the mentioned iron conditions.
Phenotypic evaluation of chlorosis symptoms of the 5th and the 6th leaf, measured as SPAD units (SPAD5, 6), was carried out 25 days after germination for individual plants with a SPAD meter (Konica Minolta SPAD 502, Langenhagen, Germany). All other traits analysed in this study were recorded at harvest (28 days after germination). Root length (RL) and root weight (RW) were measured for all plants in one pot as one sample and root tissue was immediately frozen in liquid nitrogen at harvest for subsequent RNA-Seq analysis. Shoot length (SL) was measured independently for each plant and shoot dry weight (SDW) was evaluated after drying the shoot material for 7 days at 70°C. Dried shoot material of all the plants from one pot was pooled to one sample of each of the two experimental replications. Afterwards, shoot samples were ground and measured for Fe concentration using inductively coupled plasma optical emission spectrometry ICP-MS (Elan 6000, Perkin Elmer Sciex, Rodgau, Germany).
RNA isolation, library construction and RNA-Seq run on Illumina HiSeq2000
After harvest and phenotypic evaluation, frozen root tissue for all plants in one pot was used for RNA isolation. Root tissue samples from three pots each with four plants of a specific genotype at a given iron regime were pooled for each biological replicate (two independent experiments) and ground in a mortar under liquid nitrogen. About 200 mg of tissue from both biological replicates was subjected to total RNA isolation using the Qiagen RNAeasy Plant Mini Kit (QIAGEN, Hilden, Germany). RNA concentration was measured by the QuBit broad range RNA assay kit (Life Technologies GmbH, Darmstadt; Germany) and the QuBit fluorimeter (Life Technologies GmbH, Darmstadt; Germany). Integrity of isolated total RNA and possible DNA contamination was checked on 1.2% agarose gels. DNA contamination was subsequently eliminated, using the Ambion DNA-free TM kit, (Life Technologies GmbH, Darmstadt; Germany). Successful DNAse treatment was monitored on the Agilent Bioanalyser (Agilent Technologies, Böblingen; Germany). Further processing of RNA included an rRNA depletion step using the RiboMinusTM Plant Kit (Life Technologies GmbH, Darmstadt; Germany) and the monitoring of depleted RNA using the Agilent Bioanalyser pico Chip (Agilent Technologies, Böblingen; Germany). RNA-seq libraries were prepared from depleted samples according to the recommendations of the supplier (TruSeq RNA sample preparation v2 guide; Illumina). Libraries were quantified by fluorometry, immobilized and processed onto a flow cell with a cBot (Illumina), followed by sequencing-by-synthesis with TruSeq v3 chemistry on a HiSeq2000 system. Raw sequencing data was processed with Illumina software CASAVA (ver. 1.8.2). Raw data files can be accessed as FASTQ files via the Short Read Archive at NCBI (http://www.ncbi.nlm.nih.gov/sra) under the BioProject ID PRJNA187035.
Transcriptomic data analysis
Raw RNA-Seq reads were analysed, using the statistical software R and reads having a Phred score of equal to, or less than 20 in more than 30 percent of the cycles were removed . After creating an indexed reference with Bowtie2 v.2.0.0-beta 6[56, 57] and the current maize sequence (ZmB73_RefGen_v2, http://ftp.maizesequence.org) as well as the reference annotation file (ZmB73_5a, http://ftp.maizesequence.org) , high quality reads were aligned to the reference using TopHat v.2.0.3 with standard settings [17, 59]. The resulting BAM files were sorted and indexed with SAMtools v.1.4 as well as processed to SAM files. The further transcript assembly and calling of differentially expressed transcripts was carried out, using four different procedures. For transcript assembly by cufflinks v.2.0.2, sorted SAM files were used either by providing a annotation file for reference annotation based transcript assembly (RABT) in order to rapidly identify novel transcripts  or without any annotation reference as previously described . The further analysis was performed using cufflinks v.2.0.2 with default settings . In this respect, transcript abundances is calculated in Fragments Per Kilobase of exon per Million fragments mapped (FPKM), which is analagous to Reads Per Kilobase of exon per Million fragments mapped (RPKM) when analysing single-read runs. Subsequent proceeding of Cufflinks output with Cuffcompare, Cuffmerge and Cuffdiff with default settings resulted in determination of differentially expressed transcripts after data normalization in order to correct for differences in both library sizes and gene length. Furthermore, sorted and indexed BAM files were processed with the statistical software R v.2.15.1 and the R package EasyRNASeq by providing an annotation object created by the R packages EasyRNASeq, biomaRt, GenomicRanges and GenomicFeatures. Correspondingly, calling the easyRNASeq() function of the R package EasyRNASeq resulted in tables with raw count data that can be further analysed by the packages DeSeq or edgeR using as output format option within the easyRNASeq() function “DeSeq” or “edgeR”, respectively. The derived count tables for the R packages DeSeq and edgeR were computed per gene models. Identification of differentially expressed transcripts was carried out as previously described [18, 19, 63, 64]. In this respect, normalisation of count data is achieved using the DeSeq functions estimateSizeFactors() and sizeFactors() in order to determine a normalization factor as well as following the trimmed Mean of M-values (TMM) approach using the edgeR function calcNormFactors().
Differentially expressed transcripts were called for specific two-way comparisons for each analysis pipeline (Additional file 2). In this respect, Mo17 transcript expression was compared to B73 at 300 μM and at 10 μM iron supply. In addition, Mo17 as well as B73 transcript expression at 10 μM iron was compared to that present at 300 μM. In order to standardize selection of differentially expressed transcripts the threshold of significance was set to an experiment wide false discovery rate (FDR)  lower than 0.05 for all tests. Only transcripts significant at this experiment wide FDR throughout all four statistical approaches for corresponding two-way comparisons were further analysed.
Genetic map and QTL intervals (Benke et al., unpublished) were used for the projection of the physical positions of differentially expressed and significant genes onto the corresponding map. Information on physical positions, sequences accession numbers and molecular characteristics were retrieved from corresponding web resources [58, 66]. Transcripts showing expression values in all four statistical approaches were projected with their experiment wide median log2FC for specific two-way comparisons onto the physical map for each chromosome.
GO-term enrichment and pathway analysis
Based on version 5b of the filtered gene set of the maize genome, sequences of significantly regulated transcripts within all four two-way comparisons (FDR < 0.05) throughout all four statistical approaches were determined. An unique protein identifier from the UniProt Knowledgebase data set  was identified for each transcript using BLAST software  with an expectation cutoff value of 1e-10. Based on the unique protein identifier, gene ontology (GO) terms were assigned. Singular GO term enrichment analysis  was carried out using the maize transcript sequence file version 5a as reference. To determine significantly enriched GO terms between the significantly regulated genes within the RNA-Seq approach and the reference a hypergeometric test with a level of false discovery rate lower than 0.05 was applied . GO-terms showing a fold enrichment within the last quartile of all values for each category are plotted in Figure 4.
Visualisation of transcript expression differences within specific pathways was carried out, using MapMan v.3.5.1R2. Genes that were identified as differentially expressed within two-way comparisons were used as input together with their median log2FC across all statistical tests. In order to use the mapping file provided by the MapMan homepage (Zm_GENOME_RELEASE_09, http://mapman.gabipd.org), gene identifiers were converted into transcript identifiers by adding the extension “_T01”. Moreover, custom iron homeostasis pathway files (Additional files 3 and 4) were used.
Variant calling and data adjustment
In order to detect polymorphisms between B73 and Mo17, TopHat v.2.0.3 was used to generate BAM files out of all FASTQ files of a given genotype across both iron treatments and the two replications. By using SAMtools v.1.4 BAM files were sorted and indexed. Variant calling was conducted, using the mpileup function with default settings together with the current maize sequence (ZmB73_RefGen_v2) as reference. The SAMtools output was further processed with SAMtools/BCFtools v. v.1.4 into VCF files . VCF files were filtered using R. Only polymorphisms having an average read depth above 10 and being clearly called as homozygous (a single PL value of 0) were kept. The number of polymorphisms (SNPs and INDELs) was computed for arbitrary bins (size = 4 MBps) for each chromosome and plotted along the physical coordinates.
RT-PCR and selection of candidate genes
Candidate gene selection criteria were the following: i) pseudogenes, transposable elements and transcripts giving rise to very small peptides (< 50AA) were omitted from validation by RT-PCR, ii) all genes significantly regulated in the comparison of B73 grown at 300 and 10 μM iron (Comparison 4 – 11 genes), except Nas1 and Fdh, which were represented by multiple isoform as well as the significantly regulated genes of the comparsion of Mo17 grown at 300 and 10 μM iron (Comparison 3 – 3genes) were included in the RT-PCR validation, iii) together with genes significant at the comparisons of both genotypes at 300 and 10 μM iron (Comparison 1 and 2 – 6 genes), respectively and ranging with their experiment wide median P-values within the upper quartile of the corresponding distribution. In addition, 20 known iron responsive genes were also integrated in the validation approach (Additional file 1: Table S4). Total RNA for RT-PCR analysis was extracted from B73 and Mo17 root tissue as described above. After DNAse treatment (Ambion DNA-free, Invitrogen) cDNA synthesis was carried out using Reverse Transcriptase (SuperScript VILO, Life Technologies GmbH, Darmstadt; Germany) and random hexamer primers. Expression analysis by RT-PCR was conducted according to manufacturer’s instructions (DyNAmo ColorFlash SYBR Green qPCR Kit, Biozym Scientific GmbH, Hessisch Oldendorf; Germany) using Actin as an internal control to normalize transcript abundance. Corresponding primers and PCR conditions for candidate gene amplification are given in Additional file 1: Table S4.
We would like to thank Maarten Koornneef for valuable discussion and critical reading of the manuscript. We also would like to thank the MGCSC for providing seeds of the IBM population. Moreover, we thank Nicole Kliche-Kamphaus, Andrea Lossow, and Nele Kaul for excellent technical support. In addition, we also would like to thank Astrid Draffehn for critical reading of the manuscript and helpful suggestions. Part of the work was carried out in the Department for Genetics and Plant Breeding headed by Maarten Koornneef. Funding was granted by Deutsche Forschungsgemeinschaft and the Max Planck Society.
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