Abstract
Background
Development of crop cultivars with high low nitrogen (LN) tolerance or nitrogen use efficiency (NUE) is imperative for sustainable agriculture development. Tibetan wild barley is rich in genetic diversity and may provide elite genes for LN tolerance improvement. Little has been known about transcriptional responses of the wild barley to chronic LN stress.
Results
In this study, transcriptomic profiling of two Tibetan wild barley genotypes, LN- tolerant XZ149 and LN-sensitive XZ56 has been conducted using RNA-Seq to reveal the genotypic difference in response to chronic LN stress. A total of 520 differentially expressed genes (DEGs) were identified in the two genotypes at 12 d after LN stress, and these DEGs could be mainly mapped to 49 metabolism pathways. Chronic LN stress lead to genotype-dependent responses, and the responsive pattern in favor of root growth and stress tolerance may be the possible mechanisms of the higher chronic LN tolerance in XZ149.
Conclusion
There was a distinct difference in transcriptional profiling between the two wild barley genotypes in response to chronic LN stress. The identified new candidate genes related to LN tolerance may cast a light on the development of cultivars with LN tolerance.
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Background
As an essential component of key macromolecules, nitrogen (N) is quantitatively the most important mineral nutrient for plants [1]. In soils, N is often the most important factor limiting plant growth, and plants frequently encounter N deficiency in their natural habitats. In the past several decades, the increasing use of N fertilizers in crop production has played a major role in increasing yields [2]. However, the main problem is the fact that crop plants only use less than half of the applied N [3], with the remaining N causing severe environmental pollution. There is thus an impending need to realize high productivity while decreasing the rate of N application. This necessitates a comprehensive understanding of molecular mechanisms underlying morphological and physiological adaptation to low N (LN) stress in crops.
Under LN stress, the sessile plants have evolved many adaptive responses. Plants improves the efficiency of N uptake by modifying root architecture, enlarging root system and enhancing the expression of high-affinity transport systems for nitrate and ammonium [4,5,6,7,8,9]. Meanwhile, N utilization efficiency could be also improved in the plants [10]. In addition, the remobilization of N from source organs might be stimulated when plants are subjected to N limitation [11], resulting in the enhancement of N re-assimilation, and maintenance of N economy in plants [12, 13]. Furthermore, the expression levels of a number of genes associated with N metabolism in plants were altered to ensure the survival or complete their life cycle [14, 15].
Tibetan annual wild barley is regarded as one of the progenitors of cultivated barley [16, 17], possessing wider genetic diversity and generally better adaption to N deficiency in comparison with cultivated barley [18]. Some wild barley genotypes with high LN tolerance were identified, providing the elite genetic materials for improving LN tolerance of barley as well as other cereal crops [19]. Two wild barley genotypes differing dramatically in LN tolerance were used for transcriptome analysis at early stage of LN stress (6 h and 48 h) in our previous study [20]. However, plants respond to nutrient deficiency by inducing or repressing different sets of genes at special time [Transcription factors and hormone signaling Expression of plant-specific Dof1 TFs has been proved to improve plant growth under LN condition [39, 40]. Several R2R3-type MYB TFs are involved in plant stress responses [41], and over-expression of OsMYB48–1 enhanced drought and salinity tolerance [ The experiment using two Tibetan wild barley accessions XZ149 and XZ56 (LN- tolerant and sensitive genotypes, respectively) was carried out in black plastic pots (5 L) in a greenhouse with natural light. The wild barley accessions were collected from Tibetan area in last century and kindly presented by professor Sun of Huazhong Agricultural University, China. The nutrition solution was used according to Quan et al. [20]. The solution was renewed every five days, continuously aerated with pumps. Treatments were conducted on three-leaf-stage seedlings with two N levels (0.2 mM N as LN treatment, 2 mM N as control). For biomass determination, the plants were harvested at 12 d after LN treatment and separated into shoots and roots. Dry weight was recorded after the samples were dried at 105 °C for 30 min and to constant weight at 80 °C. Meanwhile fresh plant tissues were taken for use in determining nitrate reductase (NR) activity, glutamine synthetase (GS) activity and soluble protein content with three biological replications, and the content of metabolites with four biological replications. The roots of the two barley accessions under both N treatments were sampled with three biological replicates at 1 d, 2 d, 4 d, 8 d and 12 d after treatments for the time course analysis of gene HvNRT2.1 expression. For RNA-Seq analysis, the samples were taken at 12 d after treatments. Roots of four seedlings for each treatment were pooled as one biological replication. Totally eight samples [2 genotypes × 2 treatments × 2 biological replications] were taken for analysis. N concentration in plant tissue was determined using Foss Kjeltec 8400. Soluble protein content was measured as described by Andrews et al. [52]. NR and GS activity were determined according to Kaiser et al. [53] and Masclaux-Daubresse et al. [54], respectively. The metabolites were extracted and analyzed using 7890A/5975C GC–MS system (Agilent, USA) and AMDIS 32 software according to Quan et al. [22]. The total RNA was extracted using miRNeasy mini kit (QIAGEN, Germany) following the manufacturer’s specification. RNA degradation, integrity, abundances and purity were assessed for meeting the requirements [20]. cDNA libraries were constructed using the Illumina TruSeq™ RNA Sample Preparation Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. Briefly, mRNA was obtained from the total RNA using magnetic beads with poly-T oligonucleotide. Then the random fragmentation of the purified mRNA was reversely transcribed into cDNA. After ligated with the adapters on both ends, DNA fragments were selectively amplified and enriched. Subsequently, the purified PCR products were quantified using Agilent Bioanalyzer 2100 system. After cluster generation, the final cDNA library was sequenced on an Illumina NextSeq 500 platform. Raw reads with 75 bp single-end were initially processed to remove adapters sequences, empty sequences and low-quality bases, and then the Q20, Q30, GC contents, and sequence duplication level of the clean data were analyzed. Then the clean reads were mapped against the barley reference genomes using TopHat (http://tophat.cbcb.umd.edu/), and finally the map** results were analyzed to identify splice junctions. Gene expression levels were calculated by the FPKM values (fragments per kilobase of exon per million fragments mapped reads) [55]. Fold-changes were defined as normalized read count abundance for the LN-stressed samples divided by that of the control samples. To identify differentially expression genes (DEGs), the difference in expression between control and LN treatment was analyzed using the DESeq R package (1.10.1) [56]. An FDR (false discovery rate) of 0.05 was used for determining significant DEGs [57]. To validate the reliability of the RNA-Seq results, the expression of candidate genes was determined by real time PCR assay using the RNA for RNA-Seq. The first strand cDNA was synthesized using PrimerScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). The gene-specific primers, designed by primer-blast (http:/www.ncbi.nlm.nih. gov/tools/primer-blast/), were presented in Additional file 11: Table S1. All real time PCR analyses were performed on a CFX96 system (Bio-Rad USA) with two biological replicates and three technical replicates. HvGAPDH was used as an internal control. The relative expression was calculated by the comparative CT method and expressed as the fold change referred to the expression in the control plants [58]. Gene Ontology (GO) annotation and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis for the DEGs were conducted using the Blast2GO program [59] according to Zeng et al. [60]. GO terms were tested by applying tools for GO enrichment (http://systemsbiology.cau.edu.cn/agriGOv2/) at p-values ≤0.05 [61]. Venn diagram was made on jvenn (http://jvenn.toulouse.inra.fr/app/example.html) [62]. Heatmaps and hierarchical clustering were generated with genesis 1.8.1. Significant differences of gene expression between treatments were tested using a DPS statistical software, and the difference at P < 0.05 was considered as significant.Methods
Plant materials and LN treatment
Physiological measurement
cDNA library construction and sequencing for RNA-seq
Identification of the DEGs and validation of RNA-Seq by real time PCR
Statistical analysis
Abbreviations
- ACO:
-
Aminocyclopropane-1-carboxylate oxidase
- Arg:
-
Arginine
- Asn:
-
Asparagine
- ASN:
-
Asparagine synthetase
- DEGs:
-
Differentially expressed genes
- FDR:
-
False discovery rate
- FPKM:
-
Fragments per kilobase of exon per million fragments mapped reads
- Gln:
-
Glutamine
- GO:
-
Gene Ontology
- GS:
-
Glutamine synthetase
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- LN:
-
Low nitrogen
- LRR:
-
Leucine-rich repeat domain containing kinase
- N:
-
Nitrogen
- NAGK:
-
N-acetyl-l-glutamate kinase
- NR:
-
Nitrate reductase
- PAE:
-
Pectin acetylesterase
- PE:
-
Pectinesterase
- PEL:
-
Pectate lyase
- PKs:
-
Protein kinases
- RLK:
-
Receptor-like kinase
- TFs:
-
Transcription factors
- TPP:
-
Trehalose-6-phosphate phosphatase
- WAK:
-
Wall associated kinase
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Acknowledgments
We thank Prof. Dongfa Sun (Huazhong Agricultural University, China) for providing Tibetan wild barley accessions.
Funding
We deeply thank Natural Science Foundation of China (31620103912), China Agriculture Research System (CARS-05) and Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP) for their financial supports to this research, including experimental implementation, sampling and data analysis.
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The datasets used and/or analyzed during the current study are available from the first author on reasonable request.
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XQ and JZ designed and performed research. XQ and GC analyzed data. XQ and GZ wrote the paper. All authors have read, edited and approved the current version of the manuscript.
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Additional files
Additional file 1:
Figure S1. Temporal expression of the HvHRT2.1 gene in XZ149 and XZ56 under low N stress. The relative expression was calculated by the expression of LN stress divide by that of control. Different lowercase represents significant differences according to the Duncan’s multiple range, P < 0.05, n = 4. Primers of HvNRT2.1 and GAPDH for real time PCR are listed in Table S1. (DOCX 16 kb)
Additional file 2:
Table S2. Summary of map** reads of the RNA-Seq (XLSX 11 kb)
Additional file 3:
Table S3. The FPKM value of 520 DEGs in XZ149 and XZ56 (XLSX 113 kb)
Additional file 4:
Table S4. Gene accession numbers and sequences of 520 DEGs (XLSX 478 kb)
Additional file 5:
Table S5. DEGs at 12 d after low N stress in XZ149 and XZ56 (XLSX 160 kb)
Additional file 6:
Figure S2. Hierarchical cluster of DEGs at three time points in XZ149 and XZ56. The samples and treatments are displayed above each column. Genes are displayed by different colors and relative levels of expression are showed by a color gradient from low (green) to high (red). (PNG 142 kb)
Additional file 7:
Table S6. DEGs consistently expressed at three time points under low N stress in each genotype (XLSX 13 kb)
Additional file 8:
Figure S3. GO annotation and enrichment analysis (PNG 65 kb)
Additional file 9:
Table S7. Enzymes and sequences in one KEGG pathway (XLSX 12 kb)
Additional file 10:
Table S8. Traits related with N metabolism in two wild barley genotypes XZ149 and XZ56 under low and normal N levels (DOC 40 kb)
Additional file 11:
Table S1. The primers used in real time PCR (DOC 53 kb)
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Quan, X., Zeng, J., Chen, G. et al. Transcriptomic analysis reveals adaptive strategies to chronic low nitrogen in Tibetan wild barley. BMC Plant Biol 19, 68 (2019). https://doi.org/10.1186/s12870-019-1668-3
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DOI: https://doi.org/10.1186/s12870-019-1668-3