Transcriptome analysis of nitrogen-starvation-responsive genes in rice
© Yang et al.; licensee BioMed Central. 2016
Received: 25 August 2014
Accepted: 15 January 2015
Published: 3 February 2015
Nitrogen (N), a critical macronutrient for plant growth and development, is a major limiting factor in most agricultural systems. Microarray analyses have been conducted to investigate genome-wide gene expression in response to changes in N concentrations. Although RNA-Seq analysis can provide a more precise determination of transcript levels, it has not previously been employed to investigate the expression of N-starvation-induced genes.
We constructed cDNA libraries from leaf sheaths and roots of rice plants grown under N-deficient or -sufficient conditions for 12 h. Sequencing the libraries resulted in identification of 33,782 annotated genes. A comparison of abundances revealed 1,650 transcripts that were differentially expressed (fold-change ≥ 2) due to an N-deficiency. Among them, 1,158 were differentially expressed in the leaf sheaths (548 up-regulated and 610 down-regulated) and 492 in the roots (276 up, 216 down). Among the 36 deficiency-induced genes first identified via RNA-Seq analyses, 34 were subsequently confirmed by qRT-PCR. Our RNA-Seq data identified 8,509 multi-exonic genes with 19,628 alternative splicing events. However, we saw no significant difference in alternative splicing between N-sufficient and -deficient conditions. We found 2,986 novel transcripts, of which 192 were regulated under the N-deficiency.
We identified 1,650 genes that were differentially expressed after 12 h of N-starvation. Responses by those genes to a limited supply of N were confirmed by RT-PCR and GUS assays. Our results provide valuable information about N-starvation-responsive genes and will be useful when investigating the signal transduction pathway of N-utilization.
KeywordsN-starvation Oryza sativa Transcription factors Transcriptome sequencing
The macronutrient nitrogen (N) is an essential component of numerous important compounds, including amino acids, proteins, nucleic acids, chlorophyll, and some plant hormones. This element is a major limiting factor in most agricultural systems. Because the N-utilization efficiency strongly influences crop productivity, a vast amount of N fertilizers is applied to maximize yields. However, it is estimated that more than half of that N is lost from the plant–soil system, with unused N fertilizers severely polluting the environment . Thus, N-uptake efficiency must be increased to improve productivity and reduce pollution.
During periods of N-starvation, various deficiency-responsive genes function to support plant survival by increasing the level of chlorophyll synthesis , altering root architecture , improving N-assimilation , enhancing lignin content , and changing the amounts of sugars and sugar phosphates . Nitrate transporter genes (NRTs) are responsible for the high-affinity NO3 − transport system and stimulate lateral root growth. Arabidopsis NRT2.1 plays a major role in NO3 − uptake and determines root architecture by controlling lateral root formation . The ammonia transporter gene AtAmt1.1, which is highly expressed in the roots, also restructures this architecture under limited-N conditions . The plant-specific Dof1 transcription factor (TF) from maize also functions to increase N-assimilation . In Dof1-overexpressing Arabidopsis plants, genes are up-regulated under N-starvation to encode enzymes for carbon skeleton production . Those transgenic plants also show markedly elevated amino acid contents, reduced levels of glucose, and improved growth during periods of N-deficient stress . Overexpression of glutaminesynthetase1 in tobacco and maize is associated with significant gains in plant heights, dry weights, and kernel numbers [10,11]. Overexpression of NADH-glutamatesynthase in rice and alanine aminotransferase in canola and rice also causes increases in grain weights  and biomass [13,14]. An early nodulin gene, OsENOD93-1, responds to both increases and reductions in N supplies. Furthermore, transgenic rice plants over-expressing OsENOD93-1 have greater shoot dry biomass and seed yields .
Microarray analyses have been conducted to investigate genome-wide gene expression in response to changes in N conditions. Wang et al.  studied gene responses in Arabidopsis plants that were first grown for 10 d with ammonium as the sole N source, then treated with 250 mM nitrate for 20 min. That analysis identified 1,176 nitrate-responsive genes in the roots and 183 in the shoots. Peng et al.  monitored expression profiles from Arabidopsis plants grown under nitrate-limiting or -sufficient conditions. There, N-starvation altered transcript levels for 629 genes, with 340 being up-regulated and 289 down-regulated. Palenchar et al.  identified over 300 genes regulated by interactions between carbon and N signaling in Arabidopsis. Bi et al.  detected differential expression of genes under mild or severe chronic N stress. Plant responses were much more pronounced under severe conditions.
With ‘Minghui 63’ rice, Lian et al.  applied EST microarrays to examine expression profiles under low-N stress. In seedling roots, 473 responsive genes were identified, with 115 being up-regulated and 358 down-regulated. Beatty et al.  generated transgenic rice plants that overexpress alanine aminotransferase. Comparisons of transcriptomes between the transgenic plants and controls revealed that 0.11% and 0.07% of those genes were differentially regulated in the roots and shoots, respectively. Cai et al.  analyzed the dynamics of the rice transcriptome at 1 h, 24 h, and 7 d after N-starvation treatment. In all, 3,518 genes were identified, with most being transiently responsive to such stress.
Xu et al.  performed a genome-wide investigation to detect miRNAs that responded to either chronic or transient nitrate-limiting conditions in maize. They identified miRNAs showing overlapping or unique responses as well as those that were tissue-specific. Humbert et al.  reported that the concomitant presence of N and a water deficit affected expression much more than was anticipated in maize. This research group also revealed how the interaction between those two stresses shaped patterns of expression at different levels of water stress as well as during the recovery period. Finally, Brouillette and Donovan  identified five genes that had markedly different responses to nitrogen limitations in Helianthus anomalus when compared with H. petiolaris and H. annuus.
Although microarray analyses have been extensively used for the past few decades, RNA-Seq analysis can more precisely measure transcript levels and allow for the absolute quantification of gene expression. However, RNA-Seq has not previously been employed to investigate N-deficiency-induced genes. Here, we report transcriptome profiles for 1,650 N-starvation-responsive genes from rice for which expression was altered in the roots or shoots due to an N-limitation.
Results and discussion
RNA-Seq analysis of N-deficiency stress-responsive genes
Through microarray analyses, early-responsive genes have been detected in rice roots but not in leaves when sampled after 20 min, 1 h, and 2 h of N-starvation [20,22]. Cai et al. have monitored such genes after long-term (1- and 7-d) treatments with limited-N .
Analysis of RNA-Seq data from rice seedlings
Paired-end mapped readsb
Uniquely mapped readsc
Transcript profiles of the RNA-Seq data were analyzed by calculating the reads per kilo base per million reads (RPKM). The sequenced RNA covered 33,782 annotated genes, accounting for 86.2% and 86.7% of those genes in the sheaths and roots, respectively. In addition, 2,986 novel transcripts were detected. Transcripts with low RPKM values were removed because they may not have been reliable due to low abundance or statistical faults. Among the 36,768 transcripts, 26,699 had RPKM ≥ 2. Of those, 22,992 were present in the leaf sheaths, 24,087 in the roots, and 18,319 in both. We identified 6,319 transcripts that were uniquely expressed in the sheaths (2,612) or roots (3,707).
Among the transcriptionally active transcripts, the top 500 most highly expressed were identified from the leaf sheath (Additional file 1) and roots (Additional file 2) under N-limited conditions. In both organ types, the most frequent transcripts functioned for protein synthesis, protein degradation, photosynthesis, stress responses, TFs, and DNA synthesis. Transcripts involved in lipid metabolism, transport, secondary metabolism, and amino acid metabolism were also common.
Differential expression of transcripts due to N-deficiency
Comparing transcript abundances revealed 1,650 transcripts that were differentially expressed (fold-change ≥ 2; p ≤ 0.05) due to a deficient N supply (Additional files 3 and 4). Among them, 1,158 were differentially expressed in the leaf sheaths and 492 in the roots. Of those identified in the N-deficient sheaths, 548 transcripts were up-regulated and 610 transcripts were down-regulated. In the N-deficient roots, 276 transcripts were up-regulated and 216 were down-regulated. To gain insight into the effect of N status on transcript expression profiles, we illustrated expression patterns with a heat map obtained via hierarchical cluster analysis (Additional file 5). This clustering revealed the relatedness of the various transcripts.
TFs differentially expressed in roots and leaf sheaths due to N-deficiency
Confirmation by real-time PCR
RNA-Seq results from leaf sheaths confirmed by real-time PCR
RNA-Seq log 2 (N-/N+)
RT-PCR log 2 (N-/N+)
Zinc finger protein
Ethylene-responsive transcription factor 4
Ammonium transporter protein
High affinity nitrate transporter
Ethylene-responsive transcription factor 1A
ZOS3-18 - C2H2 zinc finger protein
Zinc finger protein ZAT12
NAC domain-containing protein 67
Transporter, major facilitator family
WRKY transcription factor 46
Ethylene-responsive transcription factor
bZip, transcription factor
NAC domain-containing protein 67
Homeobox-associated leucine zipper
Heat stress transcription
B-box zinc finger family protein
MYB-related protein Zm1
Homeobox-leucine zipper protein HOX22
MADS-box transcription factor 57
Heat stress transcription factor B-1
Ammonium transporter protein
Dof zinc finger domain containing protein
B3 DNA binding domain-containing protein
TCP family transcription factor
GATA zinc finger domain-containing protein
Homeobox and START domain-containing proteins
Helix-loop-helix DNA-binding domain containing protein
AP2 domain-containing protein
RNA-Seq results from roots confirmed by real-time PCR
RNA-Seq log 2 (N-/N+)
RT-PCR log 2 (N-/N+)
Transporter, major facilitator family
Ethylene-responsive transcription factor ERF014
NAC domain-containing protein 7
MYB-related protein Hv33
LOB domain-containing protein 16
Phosphoenolpyruvate carboxylase kinase
LOB domain-containing protein 18
Zinc finger protein 1
Ammonium transporter protein
Ethylene-responsive transcription factor ERF110
NAC domain-containing protein 21/22
NAC domain-containing protein 71
Validation by GUS assays
Confirmation of RNA-Seq expression patterns by GUS assays
RNA-Seq log 2 (N-/N+) (tissue)
1.14 (leaf sheath)
1.30 (leaf sheath)
MYB family transcription factor, putative, expressed
1.54 (leaf sheath)
bHelix-loop-helix transcription factor
1.38 (leaf sheath)
Trihelix transcription factor GTL1
Analysis of alternative splicing
Alternative splicing can occur because of environmental factors. For example, expression of Wdreb2 is activated by cold, drought, salt, or exogenous ABA treatment; depending upon the source of the stress, three transcript forms may be produced . However, we found no significant difference in AS between N-sufficient and -deficient conditions, which suggests that it is not involved in the low-N stress response.
Novel transcribed regions (NTRs) validated by RT-PCR
We performed deep transcriptomic investigations with rice plants and obtained detailed expression profiles for genes involved in responses to low-N stress. These data provide valuable information about the genes (1650 transcripts) induced by N-starvation, expecially the 86 TFs that are key regulators of growth and development. We then confirmed these RNA-Seq data by conducting qRT-PCR and GUS assays of T-DNA tagging lines. In all, 8,509 multi-exonic genes could be linked with 19,628 AS events. However, we found no significant difference in alternative splicing between N-deficient samples and controls. Our data will be useful for identifying N-deficiency-induced genes and investigating the signal transduction pathway of N-utilization.
Plant materials and growth conditions
Oryza sativa L. ssp. japonica cv. Dongjin rice was used in all experiments. Seeds were surface-sterilized and germinated for two weeks in a Murashige and Skoog medium that lacked a nitrogen source. The seedlings were further grown in an N-sufficient nutrient solution at 28°C/25°C (day/night) under a 14-h photoperiod and 50 to 55% relative humidity. This hydroponic solution, refreshed every 3 d, contained 1.44 mM NH4NO3, 0.3 mM NaH2PO4, 0.5 mM K2SO4, 1.0 mM CaCl2, 1.6 mM MgSO4, 0.075 μM (NH4)6Mo7O24, 18.8 μM H3BO3, 9.5 μM MnCl2, 0.16 μM CuSO4, 0.15 μM ZnSO4, 35.6 μM FeCl3, and 74.4 μM citric acid (pH 5.0) . At the six-leaf stage, the seedlings were divided into two groups: 1) N-starvation, with the amount of NH4NO3 in the solution reduced to 0.072 mM; and 2) N-sufficient, for which the nutrient solution contained the normal N concentration of 1.44 mM. At 12 h after the treatment began, the total roots and leaf sheaths were harvested from plants in both groups. Each biological replicate constituted a pool of three plants. Two of those replicates were subjected to RNA-sequencing.
RNA extraction, preparation of cDNA library, and sequencing
Total RNA was prepared using RNAiso Reagent (Takara Bio Inc., Otsu, Japan). Quality was checked with the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Total RNA (30 μg) was used for synthesizing complementary DNA (cDNA). After the libraries were constructed, the cDNA was sequenced with the Illumina HiSeq TM 2000 according to the manufacturer’s recommendations (http://www.illumina.com).
Read alignment and assembly
RNA-Seq reads were aligned to the rice reference genomes by the TopHat2 program . That program analyzes the RNA sequences to identify splice junctions between exons by using the ultra-high-throughput short-read aligner Bowtie . Each read was mapped with Cufflinks, which assembled the alignments within the Sequence Alignment/Map file into transfrags . The assembly files were then merged with reference transcriptome annotations into a unified annotation for further analysis .
Expression levels for each gene were calculated by quantifying the Illumina reads according to the RPKM method . Replicates were examined independently for statistical analysis. Genes that were differentially expressed by at least two-fold were tested for False Discovery Rate correlations at p-values ≤ 0.05 . We also selected any transcripts with RPKM ≥ 2 in at least one cDNA library. Heat maps illustrating patterns for differentially expressed genes were generated as described by Severin et al. .
Gene Ontology (GO) term analysis and discovery of alternatively spliced exons
Gene Ontology terms were examined by applying tools for GO enrichment (http://amigo.geneontology.org/cgi-bin/amigo/term_enrichment ) and Blast2GO , at p-values ≤ 0.05. Six basic modes of AS were identified by Cufflinks software, in which differentially spliced exons were detected by comparing pairs of gene models annotated to the same locus .
Identification of novel transcripts
Paired-end reads were mapped to the genome with a spliced-read mapper. Afterward, the reference annotations were used to generate faux-read alignments that covered the transcripts. Those alignments were used together with the spliced-read alignments to produce a reference genome-based assembly. Finally, this assembly was merged with the reference annotations and “noisy” read mappings were filtered, resulting in all reference annotation transcripts in the output as well as novel transcripts .
Total RNA was isolated from seedling leaf sheaths and roots, using RNAiso Reagent. For first-strand cDNA synthesis, 1 μg of total RNA was reverse-transcribed in a total volume of 25 μL that contained 10 ng of oligo(dT) 12–18 primer, 2.5 mM dNTPs, and 200 units of AMV Reverse Transcriptase (Promega, Madison, WI, USA) in a reaction buffer. The samples were diluted 10 times prior to PCR. Gene-specific primers were designed using the Oligonucleotide Properties Calculator, or OligoCalc (http://basic.northwestern.edu/biotools/OligoCalc.html). Real-time PCR was performed with 3 μL of template cDNA, 1 μL of forward primer (5 pmol), 1 μL of reverse primer (5 pmol), and 5 μL of SYBR Green mix (Qiagen, Hilden, Germany). Conditions included 5 min of pre-denaturation at 95°C, then 45 cycles of 10 s at 95°C and 20 s at 60°C, followed by steps for dissociation curve generation (15 s at 95°C, 60 s at 60°C, and 15 s at 95°C). To examine the expression of novel transcripts, we performed semi-quantitative RT-PCR with OsUbiquitin as the internal reference to equalize the quantity of RNA. After 28 cycles of amplification, PCR products were resolved on a 2% agarose gel and stained with ethidium bromide. All primers are listed in Additional file 7.
Histochemical GUS-staining was performed according to the method of Jeon et al. . Five-d-old seedlings were cut into approximately 1-cm pieces and submerged in a staining solution containing 0.5 M Na2HPO4 (pH 7.0), 0.5 M NaH2PO4 (pH 7.0), 0.1% TritonX-100, 0.5 M EDTA (pH 8.0), 1% DMSO, 0.1% X-gluc (5-bromo-4-chloro-3-indolyl-β-d-glucuronic acid/cyclohexylammonium salt), 1 mM K3[Fe(CN)6], 1 mM K4[Fe(CN)6], and 5% methanol. The samples were then incubated at 37°C for 12 h. Afterward, the staining solution was replaced with 70% (w/v) ethanol at 65°C to remove the chlorophyll.
Availability of supporting data
Illumina sequence data are available from NCBI under Short Read Archive accession SRP045923.
Novel transcribed region
Polymerase chain reaction
Quantitative reverse transcription polymerase chain reaction
Reads per kilobase per million reads
We thank Ki-Hong Jung, Su-Zhen Li, Xiao-Jin Zhou, and Qiu-Xue Zhang for their valuable discussions. We also thank Kyungsook An for generating the transgenic lines and handling the seed stock, and Priscilla Licht for editing the English composition of the article. This work was supported in part by grants from the Next-Generation BioGreen 21 Program (No. PJ01108001); the Basic Research Promotion Fund, Republic of Korea (NRF-2007-0093862); and Kyung Hee University (20120227) to G. An.
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