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Keywords:

  • transcriptome;
  • rice;
  • root;
  • laser microdissection;
  • microarray;
  • database

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

The root system is a crucial determinant of plant growth potential because of its important functions, e.g. uptake of water and nutrients, structural support and interaction with symbiotic organisms. Elucidating the molecular mechanism of root development and functions is therefore necessary for improving plant productivity, particularly for crop plants, including rice (Oryza sativa). As an initial step towards developing a comprehensive understanding of the root system, we performed a large-scale transcriptome analysis of the rice root via a combined laser microdissection and microarray approach. The crown root was divided into eight developmental stages along the longitudinal axis and three radial tissue types at two different developmental stages, namely: epidermis, exodermis and sclerenchyma; cortex; and endodermis, pericycle and stele. We analyzed a total of 38 microarray data and identified 22 297 genes corresponding to 17 010 loci that showed sufficient signal intensity as well as developmental- and tissue type-specific transcriptome signatures. Moreover, we clarified gene networks associated with root cap function and lateral root formation, and further revealed antagonistic and synergistic interactions of phytohormones such as auxin, cytokinin, brassinosteroids and ethylene, based on the expression pattern of genes related to phytohormone biosynthesis and signaling. Expression profiling of transporter genes defined not only major sites for uptake and transport of water and nutrients, but also distinct signatures of the radial transport system from the rhizosphere to the xylem vessel for each nutrient. All data can be accessed from our gene expression profile database, RiceXPro (http://ricexpro.dna.affrc.go.jp), thereby providing useful information for understanding the molecular mechanisms involved in root system development of crop plants.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

Rice (Oryza sativa) is one of the most important crops because it is a staple food for more than half of the global population. Therefore, it is necessary to explore novel strategies for improving rice yield in order to attain a stable food supply worldwide. In the past, most efforts aimed at improving crop productivity have mainly focused on manipulation of the shoot system, such as plant height, tiller number, panicle size, grain number, size and weight (Li et al., 2003; Ashikari et al., 2005; Fan et al., 2006; Song et al., 2007; Shomura et al., 2008; Wang et al., 2008; Weng et al., 2008; Xing and Zhang, 2010). On the other hand, the root system is recently proposed as a frontier for improving crop productivity because of its pivotal roles during plant growth, e.g. acquisition of water and nutrients, soil anchorage, response to biotic and abiotic stresses, and interaction with symbiotic organisms (Gewin, 2010; Herder et al., 2010). The development of the root system involves various processes, including cell division and elongation at the primary root tip, lateral root formation, and root hair formation. During the last decade significant progress has been achieved in understanding the genes and gene regulatory networks involved in the root system of the model plant Arabidopsis (Benková and Hejátko, 2009; Fukaki and Tasaka, 2009; Iyer-Pascuzzi et al., 2009). In addition, a high-resolution spatiotemporal map of Arabidopsis root based on cell type and developmental stage-specific transcriptome data, which had been generated by a cell-sorting microarray method using tissue- and cell type-specific promoters (Birnbaum et al., 2003; Brady et al., 2007), provides useful clues on the molecular mechanisms of root development and functions (Brown et al., 2005; Petersson et al., 2009; Parizot et al., 2010; Brady et al., 2011). However, not much is known about the root system of other plant species, including rice, which differ from Arabidopsis in anatomy and morphology, particularly in radial tissue patterning, root hair patterning and cell types for lateral root formation (Hochholdinger et al., 2004; Rebouillat et al., 2009).

Microarray analysis has been established as a powerful and reliable tool to develop a system-level understanding of biological processes. In rice, microarray analyses of specific organs and/or tissues, as well as specific treatment conditions, have been performed (Kawasaki et al., 2001; Yazaki et al., 2003; Furutani et al., 2006). A large-scale gene expression profiling, covering organs and tissues at various developmental stages, led to a thorough understanding of gene functions based on the time and place of expression (Fujita et al., 2010; Wang et al., 2010; Sato et al., 2011a). Recently, microarray analysis in combination with laser microdissection (LM) has further enhanced the strategy for monitoring changes of gene expression in specific cell types, and has provided useful clues for understanding genes and/or gene networks associated with cell type-specific functions and interactions in various plant species (Nakazono et al., 2003; Casson et al., 2005; Woll et al., 2005; Dembinsky et al., 2007; Ohtsu et al., 2007; Hobo et al., 2008). In rice, cell type-specific regulatory hierarchies of gene expression were clarified by transcriptome profiling of 40 different cell types, including root cells (Jiao et al., 2009). However, the genes and gene networks specifically associated with the formation and function of the entire rice root system remain unclear.

In this study, we performed a comprehensive microarray analysis of the crown root using the LM technique and collected a total of 38 microarray data, representing various developmental stages along the longitudinal axis and distinct tissues along the radial axis. We analyzed the expression data, especially focusing on three important aspects of root development and physiological functions, namely, root cap function, lateral root formation, and water and nutrient absorption. Our high-resolution expression profiles are freely available in RiceXPro, a database for retrieving gene expression information in rice (Sato et al., 2011b), thereby providing baseline information for elucidating gene networks associated with root development and the expression of root functions.

Results and Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

Generating root gene expression profile based on anatomical observation

Monocot plants produce numerous crown roots, a type of adventitious root that is dominant in the root system of cereals. To accurately obtain a comprehensive transcriptome data associated with root development, the crown root was separated into eight sections, namely, R0, R1, R2, R3, R4, R5, R6 and R7, representing the different growth stages along the longitudinal axis from the tip to the basal region (Figure 1a; Table S1). R0 and R1 sections correspond to the root cap and the cell division zone, including the root apical meristem, respectively. R2 corresponds to the elongation zone, where rapid cell growth by elongation and expansion occurs (Figure S1a). The formation of root hair was observed at the epidermal cells of R2 (Figure S1b). R3 represents the region behind the elongation zone and contained mature epidermal cells. The regions from R3 to R7 correspond to the maturation zone, where various events such as the development of lateral roots and gas space (aerenchyma) occur. Lateral root primordium development was visually observed at R4 by Feulgen staining (Figure S1c), suggesting that the priming of lateral root formation was already initiated at the endodermis/pericycle cell of R2 and/or R3, as described below. The aerenchyma, which is formed by lysigeny in the cortex, was first observed at R5, and became fully developed at R7 (Figure S1d). To obtain samples of R5, R6 and R7, we used the cross sections without developing lateral roots to avoid contamination with cells at different developmental stages (Figure 1a). Furthermore, in order to perform tissue type-specific transcriptome analysis, we separated the cross sections derived from R2R3 and R7 regions into three distinct tissues, namely, EpiExo (epidermis, exodermis and sclerenchyma), Cortex (approximately three layers of cortex cells) and EndStele (endodermis, pericycle and stele), respectively (Figure 1b; Table S1). The R7 cortex was not used for microarray analysis because it consisted largely of aerenchyma (non-living cells) (Figure S1d).

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Figure 1.  Description of samples used for microarray analysis. (a) Sample details for gene expression profiling of the ‘development’ data set. The crown root was divided into eight sections representing different developmental stages (R0–R7). LR, lateral root; LRP, lateral root primordium. (b) Sample details for gene expression profiling of the ‘tissue type’ data set. Cross sections at R2R3 and R7 were separated into three distinct tissues, EpiExo (epidermis, exodermis and sclerenchyma), Cortex (cortex, with approximately three layers) and EndStele (endodermis, pericycle and stele), respectively.

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Consequently, we obtained eight samples designated as the ‘development’ data set, corresponding to various stages in root development along the longitudinal axis, and five samples designated as the ‘tissue-type’ data set, corresponding to distinct radial tissue types at two different developmental stages. These two data sets consist of 38 microarray data, representing triplicate expression profiles for each sample except for one sample of the EndStele at R7. Transcriptome analysis was performed with a 44K rice microarray (Agilent Technologies, http://www.agilent.com), which contains 35 760 independent probes, corresponding to 27 201 loci published in RAP-DB (Rice Annotation Project, 2008; Sato et al., 2011a). The processed raw signal intensity was subjected to 75th percentile normalization for interarray comparison, and then transformed to log2 scale. The value was used as the normalized signal intensity. For comparison of expression patterns of each gene, we performed an additional normalization procedure. The median expression value across the data within each data set was subtracted for each probe and the gene-normalized value was assigned as a relative expression value. In this study, we used 22 297 probes corresponding to 17 010 loci, with raw signal intensity above 50 in at least three of the 38 microarray data.

Characterization of transcriptome associated with root development and tissue types

Principal component analysis (PCA) applied to the ‘development’ and ‘tissue-type’ data sets showed close clustering within biological replicates for each sample, testifying to the quality of the expression data in the two data sets (Figure 2). In the ‘development’ data set, the transcriptomes of R0 and R1 were distinctly different from the rest, and the overall gene expression gradually changed from R2 to R7. In the ‘tissue type’ data set, the transcriptome of the R2R3-EpiExo was more similar to R2R3-Cortex, as compared with R2R3-EndStele, probably because the epidermis, exodermis, sclerenchyma and cortex cells are derived from a common initial cell (dermatogen-periblem), whereas the stele, which provides the majority of the EndStele sample, is derived from a different initial cell (plerome) (Rebouillat et al., 2009). In order to understand the overall signature of gene expression in root, we performed k-means clustering to make six clusters for each data set. The gene ontology (GO) annotation, representing biological process, was used to assign each cluster to statistically significant functional categories (Figure S2; Table S2). In the ‘development’ data set, the ‘microtubule-based process’ GO term was enriched in cluster-I genes, expressed preferentially at R2, corresponding to the elongation zone. The cluster-III genes showed high expression in R0, corresponding to the root cap, and represent significant enrichment of the ‘cell wall organization or biogenesis’ GO term, reflecting the release of border cells in the root cap (as described below). Cell division-related GO terms such as ‘DNA conformation change’, ‘cell cycle’ and ‘DNA-dependent DNA replication’ were over-represented in the cluster-VI genes, which were mainly expressed at R1, including the root apical meristem, a tissue of actively dividing cells. In the ‘tissue type’ data set, it is notable that the cluster-IV genes, which were expressed preferentially at R2R3-EndStele, represent significant enrichment of the cell division-related GO terms. In Arabidopsis, initiation of the lateral root primordium is accomplished by cell division at the pericycle founder cells of the primary root (Fukaki and Tasaka, 2009). In rice, the lateral root arises from anticlinal cell divisions in the pericycle and endodermis cells of the crown root (Kawata and Shibayama, 1965). Therefore, the high activity of cell division at R2R3-EndStele could be associated with lateral root differentiation. Taken together, the developmental stage- and tissue type-specific transcriptome signatures we observed reflect various physiological and morphological events during root development.

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Figure 2.  Principal component analysis (PCA) applied to the two data sets (‘development’ and ‘tissue type’).

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Identification of transcripts associated with specialized functions of the root cap

Consistent with the observation of an R0-specific transcriptome signature distinct from the other regions (Figure 2), the root cap has specialized morphology and physiological functions associated with protection against mechanical damage to the root apical meristem and increased penetration into the soil (Barlow, 2003). In order to further understand genetic regulation in root cap formation and function, we identified a total of 653 R0-specific upregulated genes corresponding to 532 loci by filtering procedures of the Student’s t-test and fold change (false discovery rate, FDR < 0.05; fold change, FC > 3) against all the other samples, i.e. R1, R2, R3, R4, R5, R6 and R7 (Table S3). Among the 653 genes, we found several genes encoding starch-synthase enzymes (Figure 3a). It has also been previously reported that in Arabidopsis, starch synthase-related genes were highly expressed in the root cap, which contained gravity-perceiving columella cells (Tsai et al., 2009). We further extracted a number of genes encoding key enzymes associated with fructose/mannose and glucose/galactose matabolism (Figure 3a). Plant root cap secretes mucigel-containing sugars, which may act as a lubricant to aid root penetration into soil, and provide a significant source of carbon for microbes that colonize the rhizosphere. In maize, a high concentration of fucose was observed in the root cap (Knee et al., 2001), where the expression of several genes involved in fructose/mannose metabolism were reported to be upregulated (Jiang et al., 2006). In rice, however, the root mucigel shows a high concentration of glucose. Therefore, this suggests that preferential gene expression related not only to fructose/mannose metabolism, but also that glucose/galactose metabolism in the root cap contributes to the rice-specific signature of sugar composition in mucigel. We also observed many genes that encode cell wall modification enzymes, such as cellulose synthase, cell expansin, xyloglucan fucosyltransferase, xyloglucan galactosyltransferases and pectin methylesterase (PME) (Table S3). The root cap releases a large number of somatic cells, referred to as border cells, into the external environment through the solubilizing process of cell wall connections between cells by cell wall degradation enzymes (Stephenson and Hawes, 1994; Hawes et al., 1998). Dimethylation of pectins by PME, leading to the action of polygalacturonase, is thought to play a key role in border cell separation (Driouich et al., 2007). Therefore, the root cap-preferentially expressed genes encoding cell wall modification enzyme may be associated with the production and release of border cells.

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Figure 3.  Characterization of transcripts associated with the expression of root cap function. (a) The R0-specific expressed genes mapped onto the sugar and starch metabolism pathway. Red arrows indicate the transcripts upregulated specifically at R0. AGPase, ADP-glucose pyrophosphorylase; GAL, galactokinase; GBSS, granule-bound starch synthase; GFS, GDP-fucose synthetase; GMD, GDP-mannose 4,6-dehydratase; MPG, mannose-1-phosphate guanyltransferase; MPI, mannose-6-phosphate isomerase; PGM, phosphoglucomutase; PMM, phosphomannomutase; SS, starch synthase; UGE, UDP-glucose 4-epimerase. (b) Plant hormone network observed in the root cap. The 653 R0-specific expressed transcripts contain ethylene signaling-related genes, namely: OsETR2;1, OsCTR1;1, OsEIN3;2 and OsEIN3;3. Five ERF and one RAV subfamily genes of the AP2/EREBP superfamily, which are associated with the induction of ethylene response genes, were also involved in the 653 genes. Expression profiling of genes related to hormone metabolism and signaling revealed that auxin biosyntheis genes OsASA1, OsASA2, OsASB1 and OsTAA1;1, and OsARFs that function as activators in the auxin response, were upregulated at the root cap (Figure 4 and Table S4). On the other hand, type-A RRs, negative response regulators of cytokinin signaling, were highly expressed at the root cap, whereas type-B RRs, positive response regulators, were poorly expressed. The results indicate that the auxin pathway is activated but the cytokinin pathway is inactivated in the root cap.

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Characterization of the phytohormone-related gene network in the root cap

Among the 653 R0-preferentially expressed genes, we identified ethylene signaling-related genes, namely, OsETR2;1, OsCTR1;1, OsEIN3;2 and OsEIN3;3, and five ethylene response factor (ERF) subfamily genes of the AP2/EREBP superfamily (Figure 3b; Table S3). In Arabidopsis, five membrane receptor genes for ethylene perception, ETR1, ETR2, ERS1, ERS2 and EIN4, have been identified (Chang et al., 1993; Hua et al., 1995, 1998; Sakai et al., 1998). Ethylene receptors activate the kinase activity of CTR1, a negative regulator of the ethylene signaling pathway, in the absence of ethylene (Kieber et al., 1993). EIN2 has a pivotal role in ethylene signaling downstream of CTR1, and activates the EIN3/EIL transcription factors (Chao et al., 1997), resulting in the induction of the transcriptional cascade for ethylene response. ERF genes have been reported to participate in the plant response to ethylene and biotic stress signaling (Fujimoto et al., 2000). In particular, ERF1 is activated as the direct target by EIN3 and EIL transcription factors, and then binds to the GCC-box (AGCCGCC) resulting in the induction of ethylene-mediated transcription cascades (Solano et al., 1998). We also found a gene belonging to the RAV (related to ABI3/VP1) subfamily of AP2/EREBP (Figure 3b). In Arabidopsis, four RAV genes, namely ETHYLENE RESPONSE DNA BINDING FACTOR 1 (EDF1), EDF2, EDF3 and EDF4, were demonstrated to be rapidly induced by ethylene, and to control a subset of ethylene response (Alonso et al., 2003). Therefore, these results suggest that the ethylene-dependent signal cascade would play important roles in the function of the root cap. In Arabidopsis, ethylene upregulates indole-3-acetic acid (IAA) biosynthesis in root apical tissues, facilitating its ability to inhibit root elongation, and inducing the expression of the genes encoding enzymes involved in IAA biosynthesis, such as ANTHRANILATE SYNTHASE α1 (ASA1), ANTHRANILATE SYNTHASE β1 (ASB1) and TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1 (TAA1) (Stepanova et al., 2005, 2008; Swarup et al., 2007). Expression profiling of genes related to plant hormone biosynthesis and signaling revealed that IAA biosynthesis genes, such as OsASA1, OsASA2, OsASB1 and OsTAA1;1, as well as ethylene signaling genes, are preferentially expressed in root apical tissues, including root cap cells (Figure 4; Table S4), suggesting that ethylene-mediated auxin biosynthesis in rice root apex enhances the inhibition of root cell expansion, to ensure its proper development. AUX/IAAs and ARFs are transcriptional regulators important for auxin-mediated signaling, and are classified into six subgroups (A1, A2, A3, B1, B2 and B3) and five classes (Ia, Ib, IIa, IIb and III), respectively (Jain et al., 2006; Wang et al., 2007). We found that AUX/IAAs and ARFs showed similar expression pattern within each class and subgroup, especially at R0, indicating their functional redundancy in root cap cells. Furthermore, we detected clear cytokinin-auxin antagonistic interaction in the root apex; almost all genes encoding OsARFs belonging to class-IIa that function as activators in auxin response gene expression (Shen et al., 2010) were highly expressed at the root apex. On the other hand, genes encoding the A-type response regulator (RR), a negative regulator of cytokinin signaling (Ito and Kurata, 2006), were highly expressed at R0, whereas those encoding the B-type RR, a positive regulator of cytokinin signaling, were poorly expressed (Figure 4; Table S4). An antagonistic interaction between auxin and cytokinin regulates the balance between cell differentiation and cell division necessary for controlling root meristem size and root growth (Dello Ioio et al., 2007; Dello loio et al., 2008). Although little is known about the interaction between auxin and cytokinin in the root cap, our finding raises the possibility that an interaction between these two phytohormones might also play an important role in the maintenance of root cap cells, e.g. in the balance between the production and release of border cells.

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Figure 4.  Expression profiling of genes related to plant hormone metabolism and signaling. The relative expression values were used to construct the heat map. We also used averaged the values for genes represented by multiple probes. Green, blue and orange vertical lines indicate biosynthesis, deactivation and signaling genes, respectively. For auxin, S-A1, S-A2, S-A3, S-B1, S-B2 and S-B3 represent IAA subgroups, and C-Ia, C-IIa, C-IIb and C-III show ARF classes. For cytokinin, type-A and type-B indicate type-A OsRRs and type-B OsRRs, respectively. Additional information on the phytohormone-related genes is presented in Table S4.

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Root cap cells are important for sensing various environmental stimuli, such as gravity, light, temperature gradients, humidity, obstacles, ions and other chemicals, and for determining growth direction to ensure proper development not only of the root, but also of the whole plant body. In this analysis, we notably found that transcripts for ethylene signaling were upregulated in the root cap. The gaseous plant hormone ethylene acts as a pivotal mediator in the coordination of internal growth, defense and survival in response to various environmental stimuli (Wang et al., 2002). In addition, blocking ethylene synthesis or signaling has been found to disrupt the ability of tomato root to penetrate compact soil or even loose sand (Clark et al., 1999; Hussain et al., 1999). Therefore, the high expression signature of ethylene signaling genes in root cap cells seems to reflect two aspects of ethylene function: sensing environmental cues and subsequent modulation of root cell growth by stimulating auxin biosynthesis. Taken together, our results suggested that the gene expression network, via ethylene signal cascade, may be pivotal in controlling the expression of root cap functions to accomplish proper plant growth.

Characterization of transcripts related to lateral root initiation and development

Lateral root formation is a major determinant of root system architecture that plays an important role in whole plant growth and development. Before the initiation of lateral root primordium, priming occurs at the transitional region between the meristem and the elongation zone, referred to as the basal meristem (De Smet et al., 2007). After the lateral root primordium is formed, subsequent development is promoted for emergence and maturation. As described above, the initiation for lateral root differentiation was predicted to occur at the EndStele of R2R3 (Figure 1). To understand the genetic regulation during lateral root formation in rice, we extracted 232 genes commonly upregulated at R2R3-EndStele against R2R3-EpiExo, R2R3-Cortex and R7-EndStele by filtering procedures of the Student’s t-test and fold change (FDR < 0.05; FC > 5). Furthermore, the hierarchical cluster analysis applied to the 232 genes identified three major clusters (clusters I, II and III) with characteristic expression signatures in R0 and R1; 71 genes belonging to cluster I were not expressed in both R0 and R1, 67 genes in cluster II were expressed only in R1, and 78 genes in cluster III were expressed in both R0 and R1 (Figure 5a; Table S5). Cluster III includes a large number of genes encoding cell wall modification enzymes and cytoskeleton modification-related proteins, which play important roles in cell division and elongation (Figure 5b). In a preliminary experiment, we extracted 927 lateral root-specific expressed genes by comparing the expression profiles between samples with and without developing lateral root cells at the R5 stage, when the formation of lateral root was visually observed (Table S6). Fifty-one genes overlapped between the 232 genes and the 927 genes, and among them 43 genes belong to cluster III (Table S5). Therefore, these results suggest that most genes in cluster III would be commonly involved in cell division activity not only for lateral root initiation and development but also in the root apical meristem of the crown root. In contrast to cluster III, cluster I contains a few genes associated with cell division and elongation, but several transcription factor genes, i.e. Os07g0669500, Os03g0659700, Os09g0531600, Os05g0324600 and Os05g0466100, have been identified. In particular, Os07g0669500 encodes a member of AP2/EREBP, a rice homolog of Arabidopsis PUCHI, in which a loss of function mutation was demonstrated to disturb cell division patterns during the early developmental process of lateral root primordium, without affecting the growth and development of the primary root (Hirota et al., 2007). Os03g0659700 is similar to Arabidopsis AS2-LIKE 18/LOB DOMAIN 16 (ASL18/LBD16), ASL16/LBD29, ASL20/LBD18 and rice CROWN ROOTLESS 1/ADVENTITIOUS ROOTLESS 1 (CRL1/ARL1), which encode proteins with a conserved domain, known as the LOB domain (Inukai et al., 2005; Liu et al., 2005; Okushima et al., 2007; Lee et al., 2009). ASL18/LBD16 and ASL16/LBD29 are expressed in lateral root primordium, with possible functions associated with lateral root initiation (Okushima et al., 2007). The mutants in LBD genes, lbd16, lbd18 and lbd16 lbd18, significantly decrease the number of lateral roots (Lee et al., 2009). The loss of mutation of CRL1 reduces the number of lateral roots as well as the crown root, and the expression of the CRL1 gene was detected in the lateral root initiation area (Inukai et al., 2005). Furthermore, Os09g0531600 is a homologous gene of Arabidopsis LATERAL ROOT PRIMORDIUM 1 (LRP1), which is predicted to be involved in the early development of the lateral root meristem (Smith and Fedoroff, 1995; Krichevsky et al., 2009). These results indicate that a number of genes belonging to cluster I may play an important role specifically in the priming and initiation of the lateral root primordium. Consequently, our comprehensive data provide useful clues towards a better characterization of transcripts with specific function in the initiation of lateral root formation, and of those with specific function in cell division and elongation of lateral roots, as well as of the crown root.

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Figure 5.  Characterization of candidate genes associated with lateral root initiation and development. (a) Hierarchical cluster analysis applied to the normalized signal intensities of the 232 genes upregulated specifically at R2R3-EndStele. The 232 genes were divided into three clusters (I, II and III) based on the characteristic expression signature at R0 and R1. (b) Functional grouping of cluster-I and -III genes. The relationship between genes and functional categories is summarized in Table S5.

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Characterization of phytohormone-related genes associated with lateral root formation

It is well known that auxin is a key stimulatory hormone that regulates lateral root formation in Arabidopsis. The transcriptional factor genes related to the initiation step of lateral root formation, namely, PUCHI, ASL18/LBD16, ASL16/LBD29 and ASL20/LBD18, are regulated by ARF (Hirota et al., 2007; Okushima et al., 2007; Lee et al., 2009). In addition, other hormones such as cytokinin, brassinosteroids and ethylene were recently demonstrated to be associated with lateral root formation (Fukaki and Tasaka, 2009). In order to uncover the function of hormones on lateral root formation, we surveyed the expression profile of genes associated with hormone biosynthesis and signaling, and then identified unique expression trends in the R2R3-EndStele in auxin and brassinosteroids. Auxin biosynthesis and signaling-related genes tended to be expressed preferentially at R2R3-EndStele (Figure 4; Table S4). In Arabidopsis, gain-of-function mutations of AUX/IAAs dramatically reduce the number of lateral roots (Tian and Reed, 1999; Rogg et al., 2001; Fukaki et al., 2002; Tatematsu et al., 2004; Yang et al., 2004; Uehara et al., 2008). It has also been reported that IAA14 interacts with ARF7 and ARF19, and that a double mutation, arf7 arf19, severely impaired lateral root formation (Fukaki et al., 2002; Okushima et al., 2005; Wilmoth et al., 2005). Furthermore, the regulatory network via miR390, TAS3-derived trans-acting short-interfering RNAs (tasiRNAs) and their targets, namely, ARF2, ARF3 and ARF4, was recently demonstrated to maintain proper definition in regulating lateral root growth (Marin et al., 2010). In contrast, it remains unclear whether auxin-mediated signaling via transcriptional regulators such as AUX/IAAs and ARFs plays a central function for lateral root formation in rice. However, our finding that a number of AUX/IAAs and ARFs are preferentially expressed at R2R3-EndStele provides strong evidence that the auxin-associated mechanism of lateral root initiation is conserved between rice and Arabidopsis. In the R2R3-EndStele, genes encoding a series of enzymes associated with brasinosteroid biosynthesis were highly expressed, indicating that the brassinosteroids promote lateral root formation (Figure 4; Table S4). Brassinosteroids functionally interact with auxin and cause overlapping effects in plant developmental processes (Nakamura et al., 2003, 2006; Li et al., 2005; Nakamoto et al., 2006). Bao et al. (2004) reported that in Arabidopsis, exogenous brassinosteroids act synergistically with auxin to promote lateral root formation, and hypothesized that the promotion of lateral root formation is the result of increasing acropetal auxin transport (from the base to the tip). Although it is unclear whether brassinosteroids directly promote lateral root formation or affect auxin transport at the root tip, our finding indicated that R2R3-EndStele, including pericycle and endodermis cells, where lateral root initiation occurs, would show high brassinosteroid levels because of preferential expression of genes encoding enzymes for brassinosteroid biosynthesis (Figure 4; Table S4). Very recently, it has been reported that auxin stimulates DWARF4 expression and brassinosteroid biosynthesis in Arabidopsis (Chung et al., 2011). Therefore the synergistic regulation of auxin and brassinosteroids in lateral root formation may be initiated with the stimulation of brassinosteroid biosynthesis at R2R3-EndStele by auxin-mediated signal. In the process, auxin transport at the root tip as well as in the basal region, and/or an unidentified cascade mechanism, could be induced to promote lateral root formation.

Global expression profiling of water and nutrient transport protein genes

One of the most important roles of plant roots is the absorption of water and nutrients from the rhizosphere and distribution in the shoot, which is mainly accomplished by the activity of a wide variety of transporter and channel proteins. Roots show morphological and physiological changes during the developmental process of the longitudinal axis. It has been demonstrated that the major site for absorption is uniquely determined by each nutrient (Harrison-Murray and Clarkson, 1973; Ferguson and Clarkson, 1976; Tatsumi, 1982; Häussling et al., 1988; Yamaji and Ma, 2007). For example, the uptake of magnesium and calcium is much higher in the apical than in the basal region, whereas that of silicon takes place preferentially at the basal zone. In order to elucidate the major site for water and nutrient uptake, we investigated the expression profile of 371 genes encoding predicted transport proteins in the ‘development’ data set (Table S7). The hierarchical clustering applied to 371 genes revealed that the expression of many transporter genes was regulated in a developmental stage-dependent manner along the longitudinal axis, with the majority expressed preferentially at R2–R7, where formation and development of root hair and vascular system, major components for water and nutrient uptake, were observed (Figure 6a).

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Figure 6.  Global expression profiles of nutrient and water transport protein genes. (a) Hierarchical cluster analysis applied to the relative expression values of the 371 predicted transport protein genes in the ‘development’ data set. (b) k-means cluster analysis was applied to the relative expression values of the 371 transport protein genes in the ‘tissue type’ data set. The numbers in parentheses indicate the number of genes belonging to each cluster. (c) Expression profile of family genes encoding various transport proteins. For genes with multiple probes, we used the average of the relative expression values to construct the heat map.

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We further investigated the expression patterns of 371 genes in the ‘tissue type’ data set, so as to predict the histological localization and function of the transporters and channels in roots. The genes were classified into six clusters (I, II, III, IV, V and VI) by a k-means algorithm (Figure 6b; Table S7). The genes belonging to clusters I, II and III showed preferential expression at EpiExo, Cortex and EndStele, respectively, indicating their distinct roles in the radial transport system of water and nutrients, at least in the case of plasma membrane-localized transporters; cluster I may have a role in the initial uptake and transport from the rhizosphere to root cortex cells, cluster II may be involved in radial transport through the cortex cell layers and cluster III may play a role in transport into the xylem vessel. The expression profile of the genes belonging to the three representative clusters in various organs and tissues further clarified that cluster-I genes are preferentially expressed in root as compared with cluster-II and -III genes (Figure S3), suggesting that a root-specific transporter would be needed for the initial uptake of nutrients from the rhizosphere into the root, perhaps because the transport system in the root surface should overcome drastic water and nutrient gradients between internal cells and external soil conditions, in contrast to other systems such as cell-to-cell transport. Interestingly, cluster-IV genes are mainly expressed in R7-EndStele rather than the tissues of R2R3 (Figure 6b). The casparian strip is a band of tissue impregnated with lignin and suberin on cell walls of endodermis and exodermis cells, thereby preventing the diffusion of water and nutrient into the stele. Therefore, a symplastic system is required for water and nutrient transport across the casparian strip. In rice, it is developed between the exodermis and sclerenchyma at approximately 40 mm from the tip (corresponding to EpiExo), and in the endodermis at approximately 10 mm from the tip (corresponding to EndStele) (Morita et al., 1996; Cai et al., 2011). We also found that genes belonging to cluster IV showed high expression at the basal region, corresponding to R5–R7 (Figure S4). The results suggested that the casparian strip could have developed from R5 at least at the EndStele, thereby affecting the expression of the cluster-IV genes in modulating the radial transport system, depending on root developmental processes along the longitudinal axis. In addition to the casparian strip, we also observed aerenchyma development from R5 to R7 regions, which would change the transport system (Figure 1). Consistent with these events in root development, we found a number of transporter genes, which were expressed preferentially at R2–R4 and R5–R7 regions, respectively (Figure 6a).

In order to examine the nutrient substance-dependent expression pattern of transporter genes, we performed expression profiling of family genes encoding various transport proteins, namely: plasma membrane H+-ATPase transporter (OSAs; Zhu et al., 2009), ammonium transporter (OsAMTs; Sonoda et al., 2003; Suenaga et al., 2003), nitrate transporter (OsNRTs; Lin et al., 2000; Plett et al., 2010), phosphate transporter (OsPTs; Paszkowski et al., 2002), iron transporter (OsYSLs and OsIRTs; Koike et al., 2004; Ishimaru et al., 2006), sulfate transporter (OsSULTRs; Buchner et al., 2004), aquaporin (OsNIPs, OsPIPs, OsTIPs and OsSIPs; Sakurai et al., 2005, 2008) and HKT-type transporter (OsHKTs, Hauser and Horie, 2010). Except for OSAs, most of the transporter family genes were preferentially expressed in R2–R7 (Figure 6c). The plasma membrane H+-ATPase encoded by OSAs is a primary transporter that plays a central role in transport across the plasma membrane and activates a wide variety of secondary transporters, not only for water and nutrients but also for assimilates such as amino acids and sugars (Sondergaard et al., 2004). Therefore, a uniform expression trend of OSAs across all root samples seemed to reflect the pleiotropic functions of H+-ATPase. Interestingly, we also found that genes belonging to the same families tended to show similar expression patterns; for example, OsPTs and OsYSLs were expressed mainly at the R2–R5 region in the ‘development’ data set; OsPTs, OsYSLs, OsIRTs and OsAMTs showed high expression signatures at EpiExo; and OsNIPs, OsPiPs and OsTIPs were mainly expressed in the Cortex. On the other hand, OsNRTs and OsSULTRs exhibited a wide variety of expression patterns in both the ‘development’ data set and the ‘tissue type’ data set. These results appear to reflect the difference in radial transport systems of nutrients: for example, the major site for uptake and transport of phosphate and iron may be the root tip region (R2–R5), and radial transport might require the symplastic pathway via the active transport protein, at least at the epidermis, exodermis and sclerenchyma region. In contrast, the absorption of nitrate and sulfate may occur over the whole region (R2-R7), and might require a symplastic pathway in the entire process of radial transport. Therefore, the spatiotemporal expression profile of transporter genes may be useful for elucidating the activity site of the symplastic pathway in radial transport systems.

Recently, it has been demonstrated that cell type-specific expression of AtHKT1;1 in stele cells of mature root increased salt tolerance by reducing shoot Na+, but constitutive expression of the gene did not (Møller et al., 2009). This implies that it is more effective to manipulate the transport process in specific cell types for the modification of solute accumulation than to indiscriminately engineer the whole cells. OsHKT1;5, a rice homologue of AtHKT1;1, was clarified to be expressed at the R7-EndStele (Figure 6c): it is therefore a potential target for improving salt tolerance in rice. Taken together, the comprehensive expression profile of transporter genes should provide important clues for understanding the radial transport system of each nutrient as well as useful information for manipulating stress tolerance under saline, alkaline and acid paddy fields, and for the removal of metals and metalloids in toxic paddy fields by phytoremediation.

Conclusion

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

In order to gain a better understanding of the formation and function of the root system, we performed genome-wide transcriptome analysis of the rice root via a combined LM and microarray approach. We highlighted genes and gene networks that play central roles in morphological and physiological aspects of root development, such as root cap function and lateral root formation. We also clarified synergistic and antagonistic interactions among phytohormones not only in lateral root formation but also in the functions of the root cap in sensing and transmitting external cues, followed by the modulation of root growth. Furthermore, profiling of transporter and channel genes provides comprehensive insights into the mechanism of water and nutrient absorption in rice root. Recently, the root system has been hailed as the key to a new ‘green revolution’, so that improvement of the root system has become an important strategy for crop breeding to overcome various stress conditions and achieve increasing yield worldwide (Gewin, 2010; Herder et al., 2010). However, at present, little is known about potential root genes underlying stable yield in crop plants. Therefore, our comprehensive gene expression profile will be very useful not only for elucidating gene regulatory networks of the root system but also for exploring a valuable gene in forward- and/or reverse-genetics approaches, which could lead to novel strategies for crop improvement. All gene expression profiles described here are available via RiceXPro (Figure 7), a database for retrieving gene expression information for rice (Sato et al., 2011b; http://ricexpro.dna.affrc.go.jp), thereby facilitating access to our resources on gene expression.

image

Figure 7.  Pictograph representing the gene expression profile in the root system, as shown in the RiceXPro database. The scheme presents the expression pattern of OsHKT1;5 in the ‘development’ and ‘tissue type’ data sets, based on the raw signal intensity.

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Experimental Procedures

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

Plant materials and growth conditions

Oryza sativa L. ssp. japonica cultivar Nipponbare was used in this study. The seeds were sterilized with 70% ethanol solution and 1% sodium hypochlorite solution, imbibed in distilled water in the dark at 25°C for 2 days, and transferred onto a nylon net floated in distilled water in a growth chamber (60% humidity; 14-h light at 30°C and 10-h dark at 25°C). After 3 days, the seedlings were transferred to a nutrient solution (based on Yoshida nutrient solution; pH 5.5; Yoshida et al., 1976). The pH of the nutrient solution was adjusted using 1 n NaOH and maintained using 2-(N-morpholine)-ethanesulphonic acid MES (buffer). The solution was renewed every 2 days. Root samples were obtained from 10-day-old seedlings. In order to obtain root samples with the same developmental stage, we selected crown roots with lengths of more than 5 cm, and with emerging lateral roots at approximately 15–18 mm from the root tip.

Anatomical observation

For the observation of epidermal cell and root hair, the crown roots were immersed in clearing solution of chloral hydrate/glycerol/water (8:1:2, w/v/v) and examined under a microscope (DM2500; Leica, http://www.leica.com). The formation of lateral root primordium was confirmed by Feulgen staining with Schiff’s solution, according to the method described by Hoecker et al. (2006), and observed using the Zeiss Stemi 2000-C stereo microscope (Zeiss, http://www.zeiss.com). For observation of aerenchyma, the fresh roots were embedded in 5% agar, cut into 75-μm-thick sections using a vibration microtome (HM650V; ThermoFisher Scientific, http://www.thermofisher.com) and examined under a light microscope (ECLIPSE E600; Nikon, http://www.nikon.com).

Sample collection with LM

The crown roots were manually separated into eight sections corresponding to R0, R1, R2, R3, R4, R5, R6 and R7 samples (Table S1). The samples representing the ‘development’ and ‘tissue type’ data sets were fixed in 99.5% cold ethanol and 99.5% cold acetone, respectively. Sample fixation and paraffin embedding were performed using the microwave processor (Energy Beam Sciences, http://www.ebsciences.com) according to Takahashi et al. (2010). The paraffin-embedded samples were cut into 12–20-μm-thick sections using a microtome (RM2255; Leica). Laser microdissection was performed in all samples except for R2 and R3 using the Veritas Laser Microdissection System LCC1704 (Arcturus, now Molecular Devices, http://www.moleculardevices.com).

RNA extraction and microarray analysis

Total RNA was extracted from the collected cells with a Pico-Pure™ RNA isolation kit (Arcturus, now Molecular Devices), according to the manufacturer’s protocol. The quantity and quality of the obtained RNAs were checked with the Agilent 2100 Bioanalyzer (Agilent Technologies, http://www.agilent.com). One-color spike-mix was added to the total RNA prior to the labeling reaction, and labeling was performed using a Quick Amp Labeling Kit, One-Color (Agilent Technologies) in the presence of cyanine-3 (Cy3)-CTP, according to the modified manufacturer’s protocol. The Cy3-labeled cRNA was purified by RNeasy Mini Kit (Qiagen, http://www.qiagen.com), and the quantity was examined with a NanoDrop ND-1000 UV-VIS spectrophotomer (NanoDrop Technologies, http://www.nanodrop.com). A total of 1100 ng Cy3-labeled cRNA was fragmented and hybridized on a slide of rice 4 × 44K microarray RAP-DB (G2519F#15241; Agilent Technologies) at 65°C for 17 h. Hybridization and washing of the hybridized slide were performed according to the manufacturer’s instructions. Slides were scanned on an Agilent G2505B DNA microarray scanner, and background correction of the Cy3 raw signals was performed using the feature extraction 10.5.1.1 (Agilent Technologies).

Statistical analysis

The processed raw signal intensity of all probes (45 151) were subjected to 75-percentile normalization with GeneSpringGX11 for interarray comparison (Agilent Technologies) and transformed to log2 scale. After normalization, we extracted 35 760 independent probes corresponding to 27 201 annotated loci published in RAP-DB (Rice Annotation Project, 2008). For comparison of the expression patterns of each gene, we performed an additional normalization procedure. The median expression value across the data within each data set was subtracted for each probe and the gene-normalized value was assigned a relative expression value. We performed an unpaired Student’s t-test and PCA using relative expression value with the GeneSpringGX11. In the Student’s t-test, P values were adjusted for multiple testing by Benjamini and Hochberg’s method for correcting FDR. We performed hierarchical and k-means cluster analyses based on an uncentered Pearson correlation algorithm for hierarchical clustering and Euclidean algorithm for k-means with GeneSpiringGX11. The heat map for expression profiling of hormone-related genes and transporter genes was constructed in excel (Microsoft, http://www.microsoft.com) format.

Gene annotation used for the analyses

The gene annotation used in this study was obtained from the RAP-DB (Rice Annotation Project, 2008), PLANT TRANSCRIPTION FACTOR DATABASE (Pérez-Rodríguez et al., 2010) and Cell Wall Navigator (Girke et al., 2004). The phytohormone biosynthesis/signaling genes were derived from Hirano et al. (2008). The probe set for water and nutrient transporter genes were selected based on InterPro (http://www.ebi.ac.uk/interpro/index.html) and several published studies. The identity (ID) converter in RAP-DB was used to confirm RAP-DB Os IDs and MSU LOC_Os IDs, when necessary.

GO enrichment analysis

A total of 16 822 differentially expressed genes in the ‘development’ and 7596 in the ‘tissue type’ data sets were statistically identified by anova (FDR < 0.05) and fold change analysis (FC > 3 for ‘development’ and FC > 5 for ‘tissue type’ in at least one pair among all samples within each data set), and divided into six clusters by a k-means algorithm with GeneSpringGX11. We then used the agriGO (Du et al., 2010) at default setting to examine the significant enrichment GO terms for each cluster in the ‘development’ and ‘tissue type’ data sets.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

We thank Dr Hirokazu Takahashi (Nagoya University) for suggestions on LM procedures, Dr Mitsuhiro Obara (JIRCAS) for suggestions on the net float system in water culture and Ms Ritsuko Motoyama (NIAS) for microarray analysis. This work was supported mainly by a grant from the Ministry of Agriculture, Forestry and Fisheries (MAFF) of Japan (Genomics for Agricultural Innovation, RTR0002) and partly by a grant from the Bio-oriented Technology Research Advancement Institution (Promotion of Basic Research Activities for Innovative Biosciences) to TA and MN.

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  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information
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Accession number

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

The data discussed in this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (Edgar et al., 2002), and are accessible through GEO Series accession number GSE30136.

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Results and Discussion
  5. Conclusion
  6. Experimental Procedures
  7. Acknowledgements
  8. References
  9. Accession number
  10. Supporting Information

Figure S1. Anatomical observation of the rice crown root.

(a) A crown root showing different developmental stages. On the right panel, single epidermal cells in R1, R2 and R3 are highlighted in yellow. The white and black scale bars represent 200 mm and 50 mm, respectively.

(b) Root hair formation was observed in R2, R3 and R4 sections. The black scale bars represent 50 mm.

(c) Lateral root formation in R4 section as indicated by arrows was detected by Feulgen staining. The lateral roots were emerged in R6 section. The white scale bar shows 200 mm.

(d) Cross-sections of the crown root at R2-R7. Aerenchyma formation was firstly observed in R5 (position indicated by an arrowhead) and clearly visible in R6 and R7. The black scale bar represents 50 mm.

Figure S2. Overall gene expression signatures for the ‘Development’ (a) and ‘tissue-type’ (b) datasets obtained by k-means clustering and GO enrichment analysis. A total of 16,822 differentially expressed genes in the ‘development’ and 7,596 in the ‘tissue-type’ datasets were statistically identified by ANOVA (FDR < 0.05) and fold change analysis (FC >3 for ‘development’ and FC > 5 for ‘tissue type’ in at least one pair among all samples within each dataset), and divided into 6 clusters by k-means algorithm with GeneSpringGX11. We then used the AgriGO (Du et al., 2010) at default setting to examine the significant enrichment GO terms for each cluster in the 2 datasets. The major enriched GO terms were extracted and the frequencies of categorized genes are shown as bar graphs. The background (BG) value represents the percentage of genes with terms in all genes. Asterisks indicate significant overrepresented GO terms in each cluster (FDR<0.05).

Figure S3. Expression patterns of transport protein genes in root and shoot of 10-day old seedlings grown in culture medium (a), and in various organs and tissues of rice plants grown in the paddy field (b). Microarray analysis of root and shoot of 10-day-old seedlings was performed with 3 replicates. We used the publically available data for expression profiles of various organs and tissues (Sato et al., 2011a). Based on the expression pattern in the ‘tissue-type’ dataset, the 371 transport protein genes were classified into the six clusters (Figure 6b). The averaged relative expression values (log2) of genes in clusters I, II and III are shown here.

Figure S4. Expression patterns of transport protein genes in the ‘development’ dataset. Based on the expression pattern in the ‘tissue-type’ dataset, the 371 transport protein genes were classified into six clusters, I, II, III, IV, V, and VI (Figure 6b). The ‘development’ dataset was used for hierarchical clustering based on the relative expression values of the genes in each cluster. The numbers in parentheses indicate the number of genes belonging to each cluster.

Table S1. Details of samples used in transcriptome analysis of rice root via laser microdissection and microarray analysis approaches.

Table S2. Significant enrichment GO terms in each cluster generated by k-means clustering in the ‘development’ and ‘tissue-type’ datasets.

Table S3. List of 653 genes preferentially expressed in root cap.

Table S4. List of genes associated with phytohormone biosynthesis and signaling.

Table S5. List of 232 genes associated with lateral root formation.

Table S6. List of 927 genes associated with lateral root development at R5 stage.

Table S7. List of genes encoding various transport proteins in clusters I, II, III, IV, V and VI.

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