These authors contributed equally to this work.
Comparative transcriptome analysis of transporters, phytohormone and lipid metabolism pathways in response to arsenic stress in rice (Oryza sativa)
Article first published online: 27 APR 2012
© 2012 The Authors. New Phytologist © 2012 New Phytologist Trust
Volume 195, Issue 1, pages 97–112, July 2012
How to Cite
Yu, L.-j., Luo, Y.-f., Liao, B., Xie, L.-j., Chen, L., Xiao, S., Li, J.-t., Hu, S.-n. and Shu, W.-s. (2012), Comparative transcriptome analysis of transporters, phytohormone and lipid metabolism pathways in response to arsenic stress in rice (Oryza sativa). New Phytologist, 195: 97–112. doi: 10.1111/j.1469-8137.2012.04154.x
- Issue published online: 24 MAY 2012
- Article first published online: 27 APR 2012
- Received: 6 February 2012, Accepted: 14 March 2012
- heavy metal transporter;
- jasmonate signaling;
- lipid metabolism;
- mRNA and microRNA sequencing;
- Oryza sativa
- Top of page
- Materials and Methods
- Supporting Information
- •Arsenic (As) contamination of rice (Oryza sativa) is a worldwide concern and elucidating the molecular mechanisms of As accumulation in rice may provide promising solutions to the problem. Previous studies using microarray techniques to investigate transcriptional regulation of plant responses to As stress have identified numerous differentially expressed genes. However, little is known about the metabolic and regulatory network remodelings, or their interactions with microRNA (miRNA) in plants upon As(III) exposure.
- •We used Illumina sequencing to acquire global transcriptome alterations and miRNA regulation in rice under As(III) treatments of varying lengths of time and dosages.
- •We found that the response of roots was more distinct when the dosage was varied, whereas that of shoots was more distinct when the treatment time was varied. In particular, the genes involved in heavy metal transportation, jasmonate (JA) biosynthesis and signaling, and lipid metabolism were closely related to responses of rice under As(III) stress. Furthermore, we discovered 36 new As(III)-responsive miRNAs, 14 of which were likely involved in regulating gene expression in transportation, signaling, and metabolism.
- •Our findings highlight the significance of JA signaling and lipid metabolism in response to As(III) stress and their regulation by miRNA, which provides a foundation for subsequent functional research.
- Top of page
- Materials and Methods
- Supporting Information
Arsenic (As) is a class I carcinogen metalloid (Neubauer, 1947), which may impose a serious threat to human health, primarily through the food chain of contaminated crops (Meharg & Hartley-Whitaker, 2002). Owing to high accumulation of As in paddy soils and an efficient uptake system for As, rice (Oryza sativa) contaminated with As presents a food safety problem because it is the principal food for over half of the world’s population (Liu et al., 2006; Zhu et al., 2008; Fendorf et al., 2010; Zheng et al., 2011). Therefore, revealing the physiological, molecular and genetic basis of As accumulation in rice is a fundamental step to develop possible control measures.
Arsenic accumulation in plants causes various toxicity symptoms, resulting in both direct and indirect effects (Verbruggen et al., 2009; Zhao et al., 2009). For example, As reduces the photosynthetic rate (Stoeva et al., 2003), perturbs the carbohydrate metabolism (Jha & Dubey, 2004), generates reactive oxygen species (ROS), inducing oxidative stress, and causes lipid peroxidation (Dat et al., 2000; Requejo & Tena, 2005). Plants possess a range of mechanisms involved in As detoxification, including metal transport, chelation and sequestration (Verbruggen et al., 2009; Zhao et al., 2010). Arsenate (As(V)) is taken up through the phosphate (Pi) transport pathway by phosphate transporters (Gonzalez et al., 2005), whereas arsenite (As(III)) is proposed to be taken up and accumulated in plant tissues by several members of the nodulin 26-like intrinsic protein (NIP) subfamily of aquaporins (Meharg & Jardine, 2003; Ma et al., 2008). One mechanism for As detoxification in plant cells at the biochemical level is complexation with a variety of thiol compounds such as glutathione or phytochelatin (PC), subsequently reducing its mobility and sequestering it into the vacuole (Raab et al., 2005). In addition, detoxification of the ROS can be achieved by synthesis of antioxidants such as superoxide dismutase, catalase and glutathione S-transferase, glutathione and ascorbate (Hartley-Whitaker et al., 2001). Previous studies have used microarray techniques to investigate transcriptional regulation of Arabidopsis thaliana and O. sativa response to As and have identified numerous differentially expressed genes (Abercrombie et al., 2008; Norton et al., 2008; Chakrabarty et al., 2009). However, little is known about the metabolic and regulatory network remodelings, or their interactions with microRNA (miRNA) in plant responses to As(III) stress. Given the difference in uptake and translocation between As(V) and As(III) – the latter is the predominant species under anaerobic conditions, such as in paddy soils (Abedin et al., 2002) – it is therefore pertinent to investigate the As(III)-responsive transcriptome by genome-wide mRNA and miRNA profiling.
MicroRNAs, a large family of endogenous small RNAs of c. 21–24 nucleotides in length, play a vital role in the modulation of gene expression (Bartel, 2004). In plants, cleavage of target genes by miRNAs has previously been reported as the main regulatory mechanism because of perfect or near-perfect complementarity (Rhoades et al., 2002). Plant miRNAs have been shown to regulate most of the essential physiological processes, including development, signal transduction, hormone responses, biotic and abiotic stresses (Mallory & Vaucheret, 2006; Sunkar et al., 2007; Ding & Zhu, 2009). With the improvement of computational prediction algorithms and techniques, many plant miRNAs have been identified by high-throughput sequencing and bioinformatics approaches (Meyers et al., 2006; Griffiths-Jones et al., 2008). Some rice miRNAs have been demonstrated to function in response to abiotic stresses, including drought, cold and some heavy metals (Zhao et al., 2007; Huang et al., 2009; Lv et al., 2010; Ding et al., 2011). However, knowledge of the role of miRNAs in the response to As stress in rice is still limited. This is especially the case in the early stages of plant exposure to As(III) (Tuli et al., 2010).
In this study, we used high-throughput Illumina (Illumina, San Diego, CA, USA) sequencing, which is a highly sensitive and dynamic approach for plant transcriptome studies (Marioni et al., 2008; Mortazavi et al., 2008; Wang et al., 2009), to investigate the mRNA and miRNA expression profiles of As(III)-stressed rice. The identification of As-responsive genes encoding transporter proteins, key enzymes/proteins in phytohormone signaling and lipid metabolism, provided a better understanding of the molecular mechanisms of plant response to As(III) stress.
Materials and Methods
- Top of page
- Materials and Methods
- Supporting Information
Plant growth conditions and treatments
The rice cultivar Nipponbare (Oryza sativa L. ssp japonica) was used in this study because this cv genome has been well sequenced (Goff et al., 2002). Seeds were sterilized in 30% H2O2 and germinated for 3 d at 37°C. Seedlings were grown in half-strength Hoagland nutrient solution at 28°C day/25°C night with a photoperiod of 16 h light (09:00–00:59 h) and 8 h night (01:00–08:59 h) in the glasshouse. Our pre-experiments had shown that the growth of rice seedlings was strongly inhibited by 100 μM sodium arsenite (As(III)) stress when the seedlings were treated with a series of As(III) concentrations from 10 to 100 μM. Therefore, for the As(III) treatments, 14-d-old rice seedlings were exposed to As(III) (20 and 80 μM) at 09:00 h, and materials were harvested at 0, 6 and 24 h after treatment. For each treatment, pooling of roots or shoots of the three individual plants in a sample was conducted as described previously (He et al., 2010; Aanes et al., 2011). The samples were frozen in liquid nitrogen immediately and stored at −80°C until use.
Comparisons (Supporting Information, Fig. S1) between the untreated samples (untreated root and shoot were labeled as CKR and CKS, respectively) and As(III)-treated samples (LSR and LLR refer to 20 μM As(III)-treated roots for 6 and 24 h, respectively; HSR and HLR refer to 80 μM As(III)-treated roots for 6 and 24 h, respectively; LSS and LLS refer to 20 μM As(III)-treated shoots for 6 and 24 h, respectively; HSS and HLS refer to 80 μM As(III)-treated shoots for 6 and 24 h, respectively) were performed for different time periods and dosages (please refer to Fig. S1 for details).
RNA extraction, Illumina library construction and sequencing
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions and was treated with RNase-free DNase I (New England Biolabs, Inc. Ipswich, MA, USA) to remove contaminated genomic DNA. mRNA was isolated from total RNA using Dynabeads oligo(dT) (Invitrogen). First- and second-strand cDNA was generated using Superscript II reverse transcriptase (Invitrogen) and random hexamer primers. Double-stranded cDNA was fragmented by nebulization and used for mRNA library construction according to the Illumina paired-end sample preparation protocol, using custom multiplex-indexed Solexa adaptors and sequenced as 75 × 2 using the Illumina GA Genome Analyzer paired-end pipeline. The original data set was deposited in the NCBI GEO database (access no. GSE36696).
Data processing analysis
RNA-Seq tag mapping to the rice reference genome Paired-end mRNA sequence tags were mapped to the cv Nipponbare rice genome by Burrows-Wheeler Alignment tool (BWA) (Li & Durbin, 2009) with the default parameters. Read counts used in expression analyses were based on the subset of uniquely aligned reads which also overlapped the genomic spans of the TIGR6.1 gene model. Gene expression levels within a given sample were normalized using values for a gene’s uniquely aligned read counts per kilobase of exon model per million reads (RPKM), uniquely aligning within each sample.
Bioinformatics for gene functional annotation Z-score was determined based on the RPKM according to the following formula: Z = (X – μ)/σ, where X is the RPKM of a gene for a specific tissue/time point, and μ and σ are the mean transcript expression and standard deviation of a gene across all samples, respectively. All calculations and plots were performed in the R language (Severin et al., 2010). DEGseq was used to identify differentially expressed genes (DEGs, P < 0.001) (Wang et al., 2010). In addition, a comparison was performed between DEGs at 6 and 24 h after treatment and the expression of most of the affected genes was found to be consistent at both time points, suggesting that these genes are specific to As(III) response, but not subject to circadian rhythm. Gene ontology (GO) terms of each gene were annotated based on the integrated information from TIGR6.1 and AgriGo database (Du et al., 2010). Pathway information for gene models was retrieved from the Kyoto Encyclopedia of Genes and Genomes database (KEGG, http://www.genome.jp/kegg/; Kanehisa & Goto, 2000).
Validation of the sequencing data by quantitative reverse transcription PCR (qRT-PCR) was carried out according to Xiao & Chye (2011a). Specific primers for each selected gene and rice 18S rRNA internal control are listed in Table S1.
miRNA processing MicroRNA analysis was based on retrieved results from Deep-Sequencing Small RNA Analysis Pipeline (DSAP) websites (Huang et al., 2010). Custom R scripts were used for clustering and graphical representation. Rice miRNA–target pair information of computational prediction and degradome-Seq data were downloaded from plant microRNA database (PMRD) (Lindow et al., 2007; Zhang et al., 2010) and starBase (Yang et al., 2011). Taking into account the fact that miRNA may down-regulate the expression level of its target mRNA (Rhoades et al., 2002), there should be a negative correlation between miRNA and target mRNA expression profiles. We computed the expressional correlation between the miRNAs and target mRNA (Wang & Li, 2009). The miRNA–mRNA pairs were considered to be biologically relevant if the Pearson correlation coefficients between them were in the range −1 to −0.5.
Analysis of total arsenic in rice
After harvest, plant materials for each treatment were separated into roots and shoots, carefully washed and subsamples (c. 0.1 g) were digested in a microwave oven (MARS 5; CEM Microwave Technology Ltd, Matthews, NC, USA) with nitric acid. Arsenic concentrations in the digest solutions were determined by inductively coupled plasma mass spectrometry (Agilent ICP–MS 7500ce; Agilent Technologies, Santa Clara, CA, USA; Zheng et al., 2011). In general, As concentrations in the rice seedlings showed a common pattern: root As concentration was higher than shoot As concentration, and they both increased with either exposure time or solution As concentration (Fig. S2a,b).
- Top of page
- Materials and Methods
- Supporting Information
A snapshot of rice mRNA profiling under As(III) stress
To investigate the genomic significance of rice response to As(III) stress, the mRNA and small RNA expression profiles in As(III)-treated and untreated rice seedlings were analyzed and compared. Using Illumina high-throughput sequencing, 3.4–9.2 million 75 bp paired-end mRNA reads were generated in the analyzed samples, and 1.01–2.54 million unique-type reads were mapped uniquely to the rice genomic location, with 67.1–86.0% of the mapped reads located in the annotated genic regions (Table S2). The expression of 31 441 (54.6%) genes was detected in at least one sample (Fig. S3a), and 13 893 (24.1%) and 3665 (6.4%) genes were detected in all samples and one of the samples, respectively (Fig. S3b), indicating that the expressed genes were comparable among samples.
After comparisons between untreated and As(III)-treated samples (Fig. S1), the expression of 7865 DEGs was found to be significantly changed after As(III) treatment in root and shoot samples in either a time- (Fig. S4a) or dosage-dependent manner (Fig. S4b). The 2081 DEGs in both roots and shoots showed qualitative and quantitative differences. Two main groups with four clusters of samples were identified by their transcriptome similarity, indicating that the transcriptomes of different treatments within shoots or roots were clustered together (Fig. 1a and Table S3). The effect of dosage on the root samples was more significant than that of time, whereas the situation was reversed for the shoots, which was further confirmed by the principal component analysis (PCA) analysis (Fig. 1b).
Expression profiles of As(III)-responsive transporter proteins
To elucidate the molecular mechanisms of As(III) uptake and transportation in rice, we analyzed the expression profiles of rice genes encoding various types of transporter (Fig. 2a), specifically the citrate transporter (Zhao et al., 2010), aquaporin (Sakurai et al., 2005), the ATP-binding cassette (ABC) (Verrier et al., 2008), P-type ATPase (Baxter et al., 2003), phosphate transporter (PT) (Liu et al., 2011), and metal transporters (MT) (Migeon et al., 2010). The hierarchical clustering showed that 273 out of 473 transporter genes were regulated upon As(III) treatment in either roots or shoots (Fig. 2b). In general, 106 As(III)-responsive transporter DEGs were deemed to be significantly different after As(III) treatments, including 62 DEGs in the roots, 21 in the shoots and 23 in both roots and shoots (Fig. 2c,d and Table S4).
In root samples, 27 of the 85 As(III)-responsive transporter genes were found to be regulated upon treatment with 20 μM As(III) for 6 (LSR) or 24 h (LLR) (Fig. 2c). Eleven ABC subfamily G (ABCG) transporters were up-regulated under As(III) stress in the LSR or LLR treatments (Fig. 2c). OsABCG43 was elevated threefold in the LLR treatment (Fig. 2c,e). OsPDR20 was up-regulated ninefold in both the LSR and LLR treatments (Fig. 2c,e). OsABCC9 was elevated 30-fold in the LSR treatment and ninefold in the LLR treatment (Fig. 2c). OsABCB5 was shown to be up-regulated seven- and ninefold in the LSR and LLR treatments, respectively (Fig. 2c,e).
Two P-type ATPase genes (OsACA1 and OsALA4) and one phosphate transporter (OsPT19) gene were observed to be significantly up-regulated in the LSR and LLR treatments (Fig. 2c,e). Among the eight metal transporters differentially expressed in the roots, only the OsZIP8 gene was up-regulated ninefold in the LLR treatment (Fig. 2c,e). By contrast, the expression of genes encoding the citrate transporter and aquaporin was considerably down-regulated (Fig. 2c,f). In particular, two genes encoding citrate transporters (OsLsi2 and Os10g39980) were down-regulated c. twofold in the LSR and eightfold in the LLR treatment (Fig. 2c,f). Moreover, 16 genes encoding aquaporin transporters were also down-regulated in the LSR and LLR treatments (Fig. 2c). The expression of OsLsi1 showed three- and fivefold reductions in the LSR and LLR treatments (Fig. 2c,f), and OsLsi6 was reduced 23- and twofold in the LSR and LLR treatments, respectively (Fig. 2c,f).
Twelve of 44 DEGs were regulated in the shoots upon treatment with 20 μM As(III) for either 6 h (LSS) or 24 h (LLS; Fig. 2d). Among them, five aquaporin genes were all elevated c. twofold in the LSS or LLS treatment (Fig. 2g). The other two aquaporin genes (OsPIP1;3 and OsTIP1;2) were down-regulated three- or fivefold (Fig. 2h). In comparison to the up-regulation of 11 ABC transporter DEGs in root samples, only one ABCB-type transporter OsABCB7 gene was up-regulated threefold in LSS (Fig. 2d,g), and another two ABC transporters (OsABCF6 and OsABCI5) were down-regulated two- to threefold in the LSS treatment (Fig. 2d,h). One P3A-ATPase gene (OsAHA7) was up-regulated in both the LSS and LLS treatments (Fig. 2d,g), and OsNRAMP1 was up-regulated c. twofold in the LLS treatment (Fig. 2d,g).
When the seedlings were treated with a high concentration (80 μM) of As(III), 72 of the 85 transporter DEGs in the roots were observed to be regulated in either 6 h (HSR) or 24 h (HLR; Fig. 2c). Among them, no citrate transporters were differentially expressed, and most of the aquaporins were down-regulated in the HSR and HLR treatments. However, the expression level of OsNIP3:2 was greatly increased (30-fold) in the HSR and HLR treatments, and OsNIP1;1 was also up-regulated 15-fold in the HSR treatment and 80-fold in the HLR treatment (Fig. 2c). In addition, OsHMA5 and OsHMA9 were considerably up-regulated in both the HSR and HLR treatments (Fig. 2c). Four PT genes (OsPT5, OsPT8, OsPT19 and OsPT23) were significantly up-regulated, whereas the other three PT genes (OsPT1, OsPT15 and OsPT26) were down-regulated in either the HSR or the HLR treatment (Fig. 2c). Among the ABC transporters, there were nine ABCG-type transporters found to be up-regulated in the HSR or HLR treatments, but the other four genes (OsABCG5, OsABCG14, OsABCG42_2 and OsABCG18) were significantly down-regulated (Fig. 2c).
Expression patterns of genes involved in phytohormone pathways and lipid metabolism under As(III) stress
From the KEGG, we observed that 3628 As(III)-responsive genes could be annotated to 228 pathways (Table S5). Among them, phytohormone (specifically JA) and lipid metabolism pathways were two of the most significant pathways identified. To demonstrate the utility of these data in understanding their specific functions in rice response to As(III) stress, we therefore focused on analyses of genes involved in these pathways.
Jasmonate pathway The expression levels of 35 As(III)-responsive DEGs in the JA pathway, with 19 in the roots, three in the shoots and 13 in both roots and shoots (Fig. 3a–c, Table S6), were significantly different. Among them, the differential expression of 10 and 11 genes in the roots, and seven and 21 genes in the shoots, were located in the JA biosynthesis and signaling pathways, respectively (Fig. 3b,c). In particular, the transcript abundance of OsDAD1;2 was up-regulated c. twofold in the LSR and LLR treatments, and 28- and ninefold in the HSR and HLR treatments, respectively (Fig. 3d,e). The expression of OsDAD1;3 was also up-regulated over twofold in the HLS treatment (Fig. 3f), and the mRNA of OsLOX2;1 was up-regulated threefold in the LSR and LLR treatments, and sixfold in the HSR treatment (Fig. 3d,e). Another tandem duplication gene, OsLOX2;3, was up-regulated sixfold in the LSR treatment, 16-fold in the HSR treatment and fivefold in the HLR treatment (Fig. 3d,e). In the shoots, two genes, OsLOX2;2 and OsLOX2;4, encoding lipoxygenase were up-regulated in the HLS treatment (Fig. 3f). The expressions of OsAOS1, OsAOS2 and OsAOC were significantly up-regulated in either roots or shoots upon As(III) treatment (Fig. 3d,f). Moreover, the expressions of OsOPCL1 and OsKAT were up-regulated after As(III) treatment (Fig. 3d–f). In the JA signaling pathway, the expression of OsJAR1 was up-regulated fivefold in the HSR treatment and twofold in the HLR treatment (Fig. 3e). The COI1 gene was not detected as a significant DEG. By contrast, five JAZ genes (OsJAZ1-OsJAZ2 and OsJAZ4-OsJAZ6) were up-regulated in the roots in the LSR and LLR treatments, but down-regulated in the shoots in the HSS and HLS treatments (Fig. 3d). In addition, the expression of OsMYC2 was down-regulated twofold in both the HSR and HLR treatments (Fig. 3b).
Other phytohormones To investigate the involvement of other phytohormones in the rice response to As(III) stress, we further analyzed the expression profiles of genes related to plant hormones, including auxin, GAs, cytokinins (CKs), brassinosteroids (BRs), ethylene (ET), and ABA. Among the 324 phytohormone-related genes, 275 rice orthologs were expressed in our experiment, but only 78 DEGs (47 in the roots, 13 in the shoots and 18 in both roots and shoots) were found to be significant in the rice response to As(III) stress (Table S7).
In the ABA biosynthesis pathway, OsNCED2 and OsNCED1 were greatly induced in the roots upon As(III) treatment (Table S7). The expression of GA2ox3 in the GA biosynthesis pathway was up-regulated two to fivefold in As(III)-treated root samples, whereas other DEGs in the GA signaling pathway were observed to be down-regulated (Table S7). Genes involved in the BR biosynthesis were consistently down-regulated under As(III) stress; however, two DEGs (Os06g39880 and Os02g11020) involved in the BR deactivation pathway were up-regulated three- to 34-fold in the HSR and HLR treatments (Table S7), indicating decreased BR accumulation in the roots upon As(III) treatment. In the BR signaling pathway, OsBRI1 was also down-regulated in the roots. Some genes involved in the biosynthesis of CK (OsLOG), ET (OsACS2) and auxin (OsASA2 and OsASB1) were up-regulated in root samples (Table S7), suggesting that As(III) stress may induce the accumulation of these hormones.
Lipid metabolism In all, 59 lipid biosynthesis DEGs, including 27 in the roots, 17 in the shoots and 15 in both roots and shoots (Fig. 4a–c, Table S8), were deemed to be significantly different after As(III) treatment. Forty-two genes encoding key enzymes in the lipid metabolism pathway were observed to be differentially regulated in the roots (Fig. 4b), including malonyl-CoA ACP transacylase (MAT), 3-hydroxyacyl-ACP dehydrase (HAD) and enoyl-ACP reductase (ENR) in the plastids, 3-hydroxyacyl-CoA dehydrogenase (KCR), lysophosphatidate acyltransferase (LPAAT) and phosphatidate phosphatase (PP) in the endoplasmic reticulum (ER), multifunctional protein (MFP) and COMATOSE (CTS) in the peroxisome, and acyl-CoA-binding protein (ACBP) and monoacylglycerol lipase (MAGL) in the cytosol (blue words in Fig. 4a). By contrast, 32 genes were found to be differentially regulated in the shoots (Fig. 4c), including acetyl-CoA carboxylase (ACC), 3-ketoacyl-ACP reductase (KAR), 3-ketoacyl-ACP synthase II (KASII), fatty acyl-ACP desaturase (FAD), fatty Acyl-ACP thioesterase (FAT) in the plastids, ketoacyl-CoA synthase (KCS), enoyl-CoA reductase (ECR) and glycerol-3-phosphate acyltransferase (GPAT) in the endoplasmic reticulum, acetyl-CoA acyltransferase (KAT) in the peroxisome, and triacylglycerol lipase (TAGL) and long-chain acyl-CoA synthetase (LACS) in the cytosol (yellow words in Fig. 4a). In the prokaryotic lipid synthesis pathway, two KAR (Os07g07440 and Os10g31780), one ENR (Os08g37874) and two FAD (Os03g18070 and Os03g53010) genes were up-regulated in the LSR and LLR treatments. OsFAD7 was up-regulated twofold in the LSR treatment and threefold in LLR. The mRNA levels of one KAR (Os07g07440) and one FAD gene (Os03g53010) were up-regulated > 10-fold in the LSR and LLR treatments (Fig. 4b,d). In comparison, the expressions of two KAR (Os07g07440 and Os10g31780) genes and one ENR gene (Os08g37874) were up-regulated > 20- and 10-fold in the HSR and HLR treatments, respectively (Fig. 4b,e).
In the eukaryotic pathway, which occurs in the ER, nine genes in the LSR or LLR treatment and 12 genes in the HSR or HLR treatment were found to be As(III)-responsive (Fig. 4b). Among them, three KCS (Os03g12030, Os03g14170 andOs11g37900), one KCR (Os06g04220) and two GPAT (Os05g20100 and Os05g38350) genes were up-regulated three- to 11-fold in the LSR treatment (Fig. 4b,d), whereas in the peroxisome, MFP (Os06g04220) was up-regulated > fourfold in the LSR, HSR and HLR treatments (Fig. 4b,d,e). Few genes in lipid metabolism were found to be affected in the shoot samples by As(III) treatment; one exception was OsFAD8, which was induced fourfold in the HLS treatment (Fig. 4c).
MicroRNA profiling of rice under As(III) stress and their correlations with target mRNAs
Recently, miRNAs have emerged as key players in plant developmental and stress activities as well as adaptation to heavy metal stress (Sunkar & Zhu, 2004; Janeczko et al., 2005; Moldovan et al., 2010). Thus, whether miRNAs were involved in plant response to As(III) stress became one of the essential issues to be addressed. From our sequencing data, we generated 4.2–6.7 million 35 bp single-end small RNA reads (Table S9), and identified 97–146 miRNAs belonging to 41–50 miRNA families as predicted by the miRBase database. The miRNA data were further analyzed to identify As(III)-responsive miRNAs. In general, the expression of 36 miRNAs showed significant alterations in response to As(III) treatments. Twenty-five miRNAs (22 families) were observed in the roots (Fig. 5a and Table 1), whereas 30 miRNAs (23 families) were observed in the shoots (Fig. 5c and Table 2).
Among the 25 root As(III)-responsive miRNAs, 12 miRNAs were observed to be down-regulated in either low- or high-concentration As(III) treatments (Fig. 5a and Table 1). Specifically, the expression level of miR156j was down-regulated 16–50-fold in the roots, whereas the expression of miR820c was down-regulated sixfold in the HLR treatment and threefold in the shoots (Table 2). However, miR168a showed opposite patterns in the root and shoot samples, that is, up-regulated threefold in the LLR treatment but down-regulated sixfold in the LSS treatment (Table 2). miR393 was up-regulated ninefold in the LLS treatment, threefold in the LSS treatment and sevenfold in the HSS treatment. In addition, miR162a and miR390 were observed to be up-regulated after As(III) treatments (Table 1). Meanwhile, three miRNAs (miR394, miR2106 and miR535) were found to be up-regulated in the HLR treatment (Fig. 5a, Table 1). By contrast, among the 30 As(III)-responsive miRNAs in the shoots, 10 miRNAs were down-regulated after either low- or high-concentration As(III) treatments (Fig. 5c). In particular, three miR166 subfamily members (miR166h, miR166l and miR166n) and miR319b were down-regulated in all As(III) treatments, and miR156j was down-regulated in the HSS treatment (Table 2). Three miRNAs (miR812j, miR1428e-3p and miR1876) were specifically up-regulated in the HSS treatment (Fig. 5c), and miR394 and miR1876 were elevated twofold in either the LLS or the HSS treatment (Table 2).
Using the computational prediction and degradome-Seq data (Zhang et al., 2010; Yang et al., 2011), we further computed the expression correlation between the miRNAs and their predicted mRNA targets (Wang & Li, 2009). Among the 2467 candidate miRNA–mRNA pairs identified, only 237 and 128 pairs in the roots and shoots, respectively, were found to be biologically relevant (Table S10). In particular, the correlation coefficients of two transporter–miRNA pairs, two lipid–miRNA pairs and three JA–miRNA pairs in the roots (Fig. 5b), and five transporter–miRNA pairs, one lipid–miRNA pair and one JA–miRNA pair in the shoots (Fig. 5d) ranged from −1 to −0.5.
Dynamic expression of transcription factors under As(III) stress
To further investigate the transcriptional regulation of As(III)-responsive genes, we identified transcription factors (TFs) that may mediate in the rice response to As(III) stress. According to the Database of PlantTFDB 2.0 (Zhang et al., 2011), there are 2424 TFs in 56 subfamilies in rice cv Nipponbare. In our mRNA-seq data, 1477 (60.9%) TF genes were detected and 468 (19.3%) TFs were differentially expressed. Consistent with the whole genome gene expression patterns, HSR and HLS expressed the lowest number of TF genes (1005 and 955), but with the highest super-high expression gene numbers (65 and 44). Of the 468 (42 subfamilies) differentially expressed TFs, 230 (36 subfamilies) were in the roots, 103 (31 subfamilies) were in the shoots and 135 (22 subfamilies) were in both the roots and shoots (Table S11).
In the roots, low As(III) stress (20 μM) up-regulated a small number of TF genes at 6 and 24 h after treatment (64 and 44 genes, respectively) (Fig. 6a). However, high As(III) concentration (80 μM) considerably increased the number of induced TF genes (101 and 80 genes, respectively). Similar changes were observed in the down-regulated genes, that is, more TFs were differentially expressed as the As(III) concentration increased (Fig. 6a). Moreover, we noted that among the up-regulated TFs in the roots, 18 were elevated after 6 h As(III) treatment in both low and high concentrations and remained up-regulated after 24 h. In particular, six NAM, ATAF and CUC (NAC) and four WRKY TF genes were significantly up-regulated (Table S11), suggesting that NAC and WRKY families are likely TFs responsible for the regulation of As(III)-responsive genes in the roots. Unexpectedly, the expression of one helix–loop–helix (HLH) TF (Os04g31290) was significantly repressed in the roots (Table S11). In comparison with the roots, we found that few TFs were differentially regulated in the shoots after As(III) treatment (Fig. 6a,b, Table S11).
Clustering analyses were performed to further explore the relationship between the As(III)-responsive TFs and DEGs. Results showed that the differentially expressed TF genes were grouped into two clusters, and that four groups were well correlated with the same topology of DEGs (Figs 2a, 6c). Therefore, the dynamics of accumulation of TFs after As(III) stress were particularly well resolved in our mRNA-seq data, and specific subfamilies of TFs were preferentially expressed after As(III) treatment. Meanwhile, the family-specific expression profiles among the time course treatments showed that only the expression of the NAC subfamily was significantly induced in the roots of the LSR and LLR treatments, whereas the WOX subfamily was significantly down-regulated in the LSS and LLS treatments. In addition, the C2H2, HSF, NAC, NF-X1, VOZ and WRKY subfamilies were highly up-regulated in the roots, the GRF, HRT-like, MYB, MYB-related, NF-YA, NY-YC, Nin-like, STAT, TALE, Whirly, YABBY and bHLH subfamilies were down-regulated in the roots of the HSR and HLR treatments, and the ARR-B, B3, CPP, GATA, GRF, HB-other, NF-YA, SRS and WOX subfamilies were down-regulated in the HSS and HLS treatments (Fig. 6d).
To further confirm the expression of As(III)-responsive genes in our Illumina sequencing analyses, we have selected some representative genes, including three transcription factors (Os05g37060, Os09g25070 and Os11g29870), for qRT-PCR analyses (Fig. S5). The results confirmed the differential expression of As(III)-responsive genes in mRNA-seq analysis, further supporting the reliability of our sequencing data.
- Top of page
- Materials and Methods
- Supporting Information
Pollution with transition metals and metalloids is an increasing environmental problem, and As, in particular, is extremely toxic to plants. Although numerous investigations have been carried out to understand the physiological, molecular and genetic bases of tolerance of plants to As(V) (Abercrombie et al., 2008; Norton et al., 2008; Chakrabarty et al., 2009; Verbruggen et al., 2009), the mechanisms of As(III) uptake, transport and accumulation in plants as well as the transcriptional regulation of these processes remain to be further elucidated. There is therefore an urgent need to address these questions, especially given the fact that As(III) is the predominant As species not only in paddy soils but also in rice grains (Li et al., 2009a). Previous studies using microarray techniques to investigate transcriptional regulation of plant response to As stress have identified numerous differentially expressed genes (Chakrabarty et al., 2009; Rai et al., 2011), including the differential expression of genes related to the sulphur assimilation pathway, changes in which have also been detected in this study. By making use of the novelty and specificity of the Illumina sequencing approach, we specifically observed the As(III)-responsive genes in the pathways of heavy metal transportation, phytohormone biosynthesis and lipid metabolism, as well as transcription factors and miRNAs, which have not been previously linked with rice response to As(III) stress.
Potential roles of transporters in rice response to As(III)
After uptake into plants, As(III) could be transformed to other species such as As(V) and some complexation of As (Zhao et al., 2010). Previous studies using plants exposed to As have uncovered some potential transporters involved in delivering As species across plant cells. They are mainly phosphate transporters such as AtPht1;1 and AtPht1;4 (Shin et al., 2004), and silicon transporters such as OsLsi1 (Yamaji et al., 2008) and OsLsi2 (Ma et al., 2006, 2008; Li et al., 2009b). Our data revealed that other than these known transporters, the expression of a new phosphate transporter, OsPT19, was significantly up-regulated by As(III) in a root-specific manner (Fig. 2). In addition to OsPT19, we also observed that the expression of many other phosphate transporters, such as Os03g05620, was significantly elevated after As(III) exposure, indicating their activation by the As(III)treatment. Previous studies have already suggested that the citrate transporters and aquaporins are involved in As(III) uptake and translocation. For example, OsLsi2, a homolog of plant citrate transporters, has been proposed to transport As(III) from the exodermis and the endodermis toward the stele (Ma et al., 2006, 2008; Li et al., 2009b). However, our data showed that two genes encoding citrate transporters (Os03g01700 and Os10g39980) were specifically down-regulated in the roots, but not in the shoots (Fig. 2). Similar to the expression of OsLsi1, other differentially expressed aquaporins were also down-regulated at low concentrations of As(III). These results appear to support the idea that that they are likely to function in the plant response to low concentrations of As(III). Since the homology of these transporters in Arabidopsis has already been shown to be involved in mediating As(III) stress (Bienert et al., 2008; Kamiya et al., 2009), our results indicate that they may have evolutionarily conserved functions across plant species. Nevertheless, the part these transporters play in As(III) transportation needs to be further experimentally validated in the near future.
Another As(III) detoxification approach in plants is to complex As(III) with thiol-rich compounds such as glutathione or PCs, and subsequently to reduce its mobility and sequester it into the vacuole by the ABC transporters (Zhao et al., 2010). To date, two ABC transporters in Arabidopsis (AtABCC1 and AtABCC2) have been reported to mediate As(V) tolerance in plants (Song et al., 2010). In rice, however, only OsABCC9 has been recently demonstrated to function in response to Cr(VI) (Dubey et al., 2010). Our findings suggest that, in addition to OsABCG9, many other ABC transporters (Fig. 2) were significantly up-regulated as a result of As(III) stress in either roots or shoots, implying that they may be potential targets for As(III) detoxification. In fact, some of these transporters, such as OsABCG43 (Oda et al., 2011), OsNRAMP1 (Takahashi et al., 2011) and OsHMA5 (Ogawa et al., 2009), have already been implicated in plant response to the heavy metal cadmium (Cd(II)). Consistent with this (Verbruggen et al., 2009), our data therefore further support that As(III) and Cd(II) many share a similar sequestration mechanism.
These results reveal the differential expression patterns of various transporters in rice in response to As(III) stress, suggesting that they may have distinct roles in either uptake and/or translocation of As(III) in planta. Although the significance of their up-regulation or down-regulation needs to be further validated, our data present new candidate transporters, which may provide a new insight into the mechanism of As(III) transportation in rice.
The association of phytohormone pathways with rice response to As(III) stress
Recently, the cross-talk between heavy metals and phytohormones such as ET and JA has been extensively demonstrated through measuring the hormone contents of plants after heavy metal treatments (Maksymiec et al., 2005; Maksymiec, 2007). In the case of JA, in particular, it has been suggested that in addition to the induction of JA biosynthesis, heavy metals (including Cd, Cu and Zn) and JA had similar effects in relation to the promotion of the expression of some genes, for example, VSP2, MAPK and CDC25 as well as those involved in glutathione metabolism (Xiang & Oliver, 1998; Mira et al., 2002; Agrawal et al., 2003; Bleeker et al., 2006). However, it is still unknown whether As(III) stress can also trigger the production and/or downstream signaling of phytohormones. Our analyses consistently suggested that the genes involved in the JA biosynthesis pathway were considerably up-regulated in both the roots and shoots after As(III) treatments (Fig. 3), providing evidence to support the finding that JAs are accumulated when the plants were exposed to As(III). In the downstream signaling pathway, our data showed that although the expression of OsCOI1 was unaffected by As(III) stress, the expression of the eight JAZs was significantly increased. The up-regulated expression of JAZs upon As(III) stress was similar to that of exogenous JA application in Arabidopsis (Browse et al., 2007), suggesting that the transcriptional activation may be associated with the degradation of JAZ proteins by JA or As(III) stress. However, the expression of OsMYC2, a downstream JA-responsive TF was induced only at low concentration of As(III), instead, it was repressed at high concentration of As(III). The reduced expression of OsMYC2 at high concentrations of As(III) may be a result of the disruption of entire cellular activities, including transcription by the action of excess As(III). Alternatively, it is also possible that the expression of As(III)-induced downstream genes in the JA signaling pathway is controlled by TFs other than OsMYC2 under high As(III). Considered together, these results imply that JA biosynthesis and signaling are activated by As(III) stress, which further support the important role the JA pathway plays in the plant response to As(III). Moreover, we also observed that, other than JA, some genes encoding enzymes important for the synthesis of ET, ABA and CK were simultaneously activated by As(III), which is indicative of the increased production of these hormones. This evidence hints that the rice response to As(III) stress may overlap not only with the JA pathway, but also with other phytohormones such as ET, ABA and CK. In response to As(III) stress, all these pathways (JA, ET, ABA and CK) may cooperatively inhibit the growth and developmental processes of plants, and accordingly enhance plant tolerance to As(III).
Transcriptional regulation of rice response to As(III) stress by miRNA and transcription factors
More recently, miRNAs have emerged as key players in plant responses and adaptation to heavy metal stress (Sunkar & Zhu, 2004; Janeczko et al., 2005; Moldovan et al., 2010). In IR64 rice only one miRNA, miR394, has been reported to be down-regulated by As(III) or As(V) (Tuli et al., 2010). Thus, global analysis of miRNAs involved in the rice response to As(III) was another important scientific question. Here, we reported that 12 and six new miRNAs were down-regulated and up-regulated, respectively, in the roots treated with short- and long-term As(III) (Fig. 5). We may therefore consider that the expression of target genes of these miRNAs is likely to be turned on or off in response to As(III) stress. However, the expression of miR394 was up-regulated in response to long-term As(III) stress, which was inconsistent with previous findings (Tuli et al., 2010). One possible explanation for this inconsistency is the differences between this work and previous studies in terms of the As(III) concentrations, the treatment times and/or the plant variety used.
Since few miRNAs were reported to be As(III)-responsive, we further compared the expression patterns of these As(III)-related miRNAs to those of various abiotic stresses. In rice plants, at least 22 and 19 miRNAs have been identified as cold- and Cd(II)-responsive, respectively (Lv et al., 2010; Ding et al., 2011). In addition, many Arabidopsis miRNAs, such as miR168, miR171 and miR396, are regulated by abiotic stresses such as salinity, drought and cold (Liu et al., 2008). Our data suggest that five miRNAs – miR166, miR156, miR171, miR168 and miR396 – are responsive to both As(III) and other abiotic stresses. Hence, they are very likely to be involved in the plant response to these stresses, including heavy metal or metalloid stress.
On the other hand, the data concerning the clustering of the differentially expressed TFs into two clusters with four groups corresponded well with the clustering of all expressed genes (Fig. 6). Our results further demonstrate that some As(III)-responsive TFs displayed differential expression patterns in the roots and shoots under high concentration of As(III). For example, most TF subfamilies show the opposite response to high As(III) stress in the shoots, despite similar up- and down-regulation of TF subfamilies under high As(III) stress in the roots. Our results reveal that under As(III) stress, the transcriptional regulation of As(III)-responsive genes underwent a major reprogramming. The identified 468 differentially expressed TFs are excellent candidates for future functional genomic studies to dissect the regulation of rice genes in response to heavy metals or metalloids. Moreover, although they were also regulated by other abiotic stresses, there were some differentially expressed TFs that appeared to be As(III)-responsive, which may further link the As(III) stress to the existing stress response pathways.
To the best of our knowledge, this study is the first to use high-throughput sequencing technology to study transcriptomes of plant response to heavy metals or metalloids. Overall, by genome-wide transcriptome and miRNA analyses in rice seedlings treated with As(III), we found a large number of potentially interesting genes in relation to As(III) stress, especially As(III)-responsive transporters and TFs. The change in the expression of genes related to lipid metabolism and phytohormone pathways after As(III) exposure was striking, indicating that rice invests more energy and resources into immediate defense needs than into normal growth requirements. In addition, the results of the miRNA–mRNA and TF–mRNA comparison have expanded our understanding of the transcriptional regulation of As(III)-responsive genes in rice.
- Top of page
- Materials and Methods
- Supporting Information
We thank Prof. Alan J. M. Baker (The University of Melbourne) for critical reading of the manuscript. This work was supported by the Major Science and Technology Project of Ministry of Agriculture of the People’s Republic of China (no. 2009ZX08009-002B) and the National Natural Science Foundation of China (no. 30970548 and 40930212).
- Top of page
- Materials and Methods
- Supporting Information
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- Top of page
- Materials and Methods
- Supporting Information
Fig. S1 Schematic representation of the experimental procedure for transcriptome profiling of the rice response to As(III).
Fig. S2 Arsenic content in rice after As(III) treatment.
Fig. S3 Global patterns of 10 samples of gene expression.
Fig. S4 Number of differentially expressed genes (DEGs) modulated in response to As(III) treatments of varying times and dosages.
Fig. S5 Comparison of Illumina sequencing data and qRT-PCR results.
Table S1 List of primers used for qRT-PCR analysis
Table S2 Summary of mapping reads of the mRNA-seq
Table S3 List of 2081 differentially expressed genes (DEGs) both root and shoot samples
Table S4 List of differentially expressed genes (DEGs) encoding As transporter proteins
Table S5 KEGG pathways enriched in differentially expressed genes (DEGs)
Table S6 List of differentially expressed genes (DEGs) in JA biosynthesis and signaling pathways.
Table S7 List of differentially expressed genes (DEGs) in phytohormone biosynthesis and signaling pathways
Table S8 List of differentially expressed genes (DEGs) in lipid metabolism
Table S9 Summary of mapping reads of the miRNA-seq
Table S10 Expression profiling of miRNA and their target mRNA
Table S11 List of differentially expressed genes (DEGs) encoding rice transcription factors (TFs)
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