Functional Characterization of PsGPD in Drought Stress Response Using RNA-Seq Analysis of Transgenic Rice Plant

Article information

Plant Breed. Biotech.. 2020;8(2):131-140
Publication date ( electronic ) : 2020 June 1
doi : https://doi.org/10.9787/PBB.2020.8.2.131
1Biosafty Division, National Institute of Agricultural Science, Jeonju 54874, Korea
2Department of Forest Genetic Resources, National Institute of Forest Science, Suwon 16631, Korea
3Plant Molecular Genetics Institute, GreenGene Biotech Inc., Yongin 17058, Korea
4Crop Biotechnology Institute, Green Bio Science & Technology, Seoul National University, Pyeongchang 25354, Korea
*Corresponding author Soo-Chul Park, scpark1@snu.ac.kr, Tel: +82-33-339-5836, Fax: +82-33-339-5825
*Corresponding author Gang-Seob Lee, kangslee@korea.kr, Tel: +82-63-238-4791, Fax: +82-63-238-4704

These authors contributed equally.

received : 2020 February 2, rev-recd : 2020 March 1, accepted : 2020 March 1.

Abstract

Plants are often exposed to biotic and abiotic stresses that affect plant growth, development, and productivity. Drought is an important abiotic stress that has a particularly serious impact on plant growth and development. We transformed rice with PsGPD using Agrobacterium-mediated transformation. We generated independent PsGPD-homozygous transgenic rice plants selected as single copy/intergenic lines by the TaqMan copy number assay and by T-DNA flanking sequences. These transgenic rice plants showed improvement of drought tolerance compared to wild-type plants under drought condition. RNA sequencing analysis showed that 2,992 genes were transcriptionally affected by the PsGPD transgene or drought treatment. In total, 145 genes were modulated by the PsGPD transgene before and after drought treatment. Among these candidate genes, 4 were up- and downregulated in all four comparisons. Several genes, including Os04t0576900, Os03t0629800, and Os04t0518400 (OsPAL7), were involved in tetrapyrrole synthesis. Os09t0522200 (DREB1A), an important component in hormone signal transduction, is a transcription factor (TF) gene that plays vital roles in stress responses. We partially characterized the functions of PsGPD in the drought stress response and the role of major TFs in the drought tolerance mechanism. These genes will be useful targets for both future research and the breeding of drought tolerance in rice.

INTRODUCTION

Plants are exposed to various forms of environmental stresses both biological and abiotic. Abiotic stress is a major contributor to crop loss, reducing the average yield by more than 50% (Boyer 1982; Pandey and Shukla 2015). Climate change is considered to be a major cause of abiotic stress in crops. Climate change due to global warming is expected to seriously damage agricultural productivity and the erosion of agricultural land.

Most of the water consumed worldwide is used as agricultural water (Jarraud 2012), and drought stress has sharply reduced agricultural production (Boyer 1982). The efficiency of agricultural water use for crops is an important issue. Drought is a major abiotic stress that has severe effects on crop growth and yield (Manavalan et al. 2009; Santner et al. 2009; Zhang et al. 2010). In particular, young seedlings are highly vulnerable to drought stress because of their high water demand. Transformation of rice with genes that regulate drought-resistant cellular processes is an effective way to address future environmental disasters.

Genetically modified (GM) research is one of key solutions for the challenge of global food security, which will be caused by population growth and climate change. To develop a variety of useful GM crops that add value to new features, a technology platform for developing commercially available biotech crops has been built (Park et al. 2018). To understand the molecular mechanisms of abiotic stress and plant adaptation, it is essential to identify drought tolerance genes in young seedling. The development of plant genes that are resistant to drought stress has focused on transcription factors (TFs) in Arabidopsis thaliana. Recent studies have shown that E. coli genes involved in the metabolism of trehalose, a nonreducing disaccharide, are involved in drought tolerance mechanisms and can be used for crop development; thus, the effect of the trehalose biosynthesis genes from E. coli on the growth of rice is being investigated.

In order for a plant to be stress-resistant, gene expression of the enzyme is required (Vaidyanathan et al. 1999; Siddiqui et al. 2018). Many organisms have developed multiple mechanisms that promote survival in extreme environments. Glyceraldehyde-3-phosphate dehydrogenase (GPD) is an essential enzyme in glycolysis synthesis and gluconeogenesis. The GPD gene isolated from the oyster mushroom Pleurotus sajor-caju encodes an enzyme involved in carbon metabolism. This enzyme can also be useful for the development of crops that tolerate various forms of environmental stress, such as drought, salinity, and low temperature. When the PsGPD gene was introduced into yeast, the survival rate was higher than that of the control under various forms of environmental stress. Potato plants with the PsGPD gene can reportedly grow under salt stress conditions (Jeong et al. 2001).

To date, the function of PsGPD in the rice response to drought stress remains unknown. In a previous research, we reported on studies that are useful for T-DNA tagged rice population and the database of rice genome sequences flanking T-DNA inserts (Lim et al. 2016). In this study, we found that PsGPD positively regulates drought stress tolerance. After drought treatment, overexpression of PsGPD improved the survival rate compared to that of the wild type (WT). Furthermore, transcriptomic changes caused by drought were monitored.

MATERIALS AND METHODS

Plant materials and growth conditions

We used Oryza sativa L. japonica cv. Dongjin (provided by the Rural Development Administration) as the WT rice in these experiments. Rice transformation using Agrobacterium-mediated cocultivation, selection, and plant generation was conducted as described by Toki et al. (2006). After surface sterilization with 2% (w/v) sodium hypochlorite, seeds were washed several times with distilled water before seeding. The sterilized seeds of WT and transgenic rice plants were grown in a greenhouse with a light/dark cycle of 16 hours/8 hours at 30℃ during the day and 20℃ during the night.

Vector construction and rice transformation

We isolated full-length cDNA clones of the oyster mushroom P. sajor-caju (Jeong et al. 2001) using gene-specific primers. The following primer were designed to construct the overexpression vectors for 35S::PsGPD: GPD-XbaI-F1 (5ʹ-TCTAGAATGGTCAACGTCGGCATCAACGGG-3ʹ) and GPD-SmaI-R1 (5ʹ-CCCGGGCTACTGCGCACCGTCCTTCTCCG-3ʹ).

We inserted the amplified cDNA between XbaI/SmaI and then subcloned it into pNCGC15-BAR (derived from the binary vector pPZP200) (Fuse et al. 2001). We inserted the PsGPD gene between the CaMV35S promoter and PinII terminator in the pPZP vector, which includes the bar gene as a plant-selectable marker. The construct was introduced into Agrobacterium tumefaciens LBA4404 and transformed into rice (O. sativa L. japonica cv. Dongjin) using Agrobacterium-mediated cocultivation (Toki et al. 2006).

TaqMan copy number assay

We isolated genomic DNA from the leaf samples of WT and PsGPD-transgenic rice plants using the Genomic DNA Prep Kit for Plants (Inclone, Korea). For the endogenous control, primers and probes were obtained from a predesigned TaqMan copy number assay for the rice tubulin alpha-1 chain gene (AK102560). For the transgene, primers and probes were specific to the terminator of the NOS gene and were designed using Primer Express software (Applied Biosystems, Foster City, CA, USA). The primers used for the terminator of NOS were NOS-F 5ʹ-GCATGACGTTATTTATGAGATGGGTTT-3ʹ and NOS-R 5ʹ-TGCGCGCTATATTTTGTTTTCTATCG-3ʹ, and the NOS-probe was 5ʹ-TAGAGTCCCGCAATTAT-3ʹ. The amplification conditions consisted of one cycle of 2 minutes at 50℃ and 10 minutes at 95℃, followed by 40 cycles of 15 seconds at 95℃ and 1 minute at 60℃. In the reaction plate, we measured each sample in triplicates. We used CopyCaller software version 2.0 (Applied Biosystems, Foster City, CA, USA) to determine the copy number of the inserted T-DNA in PsGPD-transgenic rice plants.

Identification of DNA sequences flanking the T-DNA using adapter ligation-mediated PCR

The DNA sequences flanking the T-DNA were isolated using polymerase chain reaction (PCR) amplification by adaptor ligation. Genomic DNA (100 ng) cleaved with 2 units of restriction enzyme was ligated with the adapter using 5 U of T4 DNA ligase (Takara, Tokyo, Japan) at 37℃ for 2 hours.

The first PCR was conducted with 5 mL of digestion/ligation mixture, 10 mL of 2× PCR premix (SolGent, Korea), 0.5 pmol of Ada1 (TAATACGACTCACTATAGC), and either the LB1 (TTCAAGCACGGGAACTGGCATGAC) or RB1 (AAGCTTAGCTTGAGCTTGGATCAGATTGTC) primer, The cycling conditions were as follows: a first step at 95℃ for 5 minutes, followed by 20 cycles of 94℃ for 30 seconds and 68℃ for 2 minutes and a final step at 72℃ for 5 minutes. Next, the second PCR was performed using 5 mL of the first PCR product, 0.5 pmol of Ada2 (GACTCACT ATAGCAATTAAC), and either the LB2 (GTTTCTGGCAGCTGGACTTC) or RB2 (TTGTCGTTTCCCGCCTTCAG) primer. The cycling conditions were as follows: a first step at 95℃ for 5 minutes; 39 cycles of 30 seconds at 95℃, 30 seconds at 60℃, and 2 minutes at 72℃; and a final step at 72℃ for 5 minutes. The PCR products were loaded onto a 2% agarose gel, purified using DF buffer (RBC), and sequenced with the LB2 or RB2 primer.

Drought treatment of rice plants

WT and transgenic rice plants (single copy/intergenic/homozygous transgenic rice with 35S::PsGPD) were grown to four weeks old in a greenhouse (with a light/dark cycle 16 hours/8 hours and a temperature of 30℃ during the day and 20℃ during the night).

WT and transgenic rice plants (four weeks old, grown in pots) were subjected to drought stress by withholding water for 2-4 days and recovered by rewatering. After 13-27 days, we counted the number of plants that survived without withering. Drought tolerance was assessed in terms of the proportion of surviving plants after the recovery period. The experiment was repeated three times.

cDNA library construction and RNA sequencing

PsGPD-transgenic and WT plant under normal (WT–control, PsGPD–control) or drought stress (WT-drought and PsGPD-drought) were used for RNA sequencing analysis in replicates.

Total RNA was isolated from leaves using the Hybrid R RNA Extration Kit (GeneAll, Korea). We purified and fragmented mRNA using the TruSeq RNA Sample Kit Sample Prep Kit (Illumina, San Diego, CA, USA) based on the manufacturer’s instructions. First-strand cDNA was synthesized by reverse transcriptase with random primers. Second-strand cDNA was synthesized using DNA polymerase I and RNase H. We performed the following steps: an end-repair reaction, an A single-base addition, and adaptor ligation. The products were purified and enriched by PCR and immobilized on a proprietary flow cell surface. The libraries were sequenced using the Illumina NovaSeq6000 platform. Eight RNA sequencing (RNA-seq) libraries were sequenced with 2.7 to 3.0 Gb reads per library.

Data processing

Hisat2, SAMtools, and dexseq_count.py were used for the RNA-seq data analysis. Raw sequence reads were mapped to the rice genome sequence (IRGSP-1.0) in the RAP database (http://rapdb.lab.nig.ac.jp). Reads in FASTQ files were aligned using hisat2 based on the Hisat2 and Bowtie2 implementations with data-cufflinks options (Kim et al. 2013). In the program, SAMtools (version 1.2) was used for indexing and sorting of bam and sam files. To extract exons from the transcripts, we defined nonoverlapping exonic regions with dexseq_prepare_annotation.py and then extracted the per-exon read counts with the script dexseq_count.py using the annotated GTF file (Anders et al. 2015) in the DEXseq package. The program returned one file for each biological replicate with the exon counts. We tested the differential exon usage with the DEXseq package in Bioconductor (https://www.bioconductor.org/).

Real-time PCR

For first-strand cDNA synthesis, we used 1 mg of total RNA with oligo (dT) primer and RevertAid H Minus Reverse Transcriptase (Fermentas) in a 20-mL mixture, according to the manufacturer’s instructions. The rice cDNA mixture was then diluted 5 times. The PCR was conducted in a solution with a final volume of 20 mL, containing 2 mL of cDNA, 0.2 pmol/mL gene-specific primers, and 10 mL of 2× PCR mixture (GeneAll, Korea), with two technical replicates. The PCR conditions were as follows: 5 minutes at 95℃; 39 cycles of 95℃, 39 cycles of 95℃ for 30 seconds, 55℃ for 30 seconds, and 72℃ for 30 seconds; and 72℃ for 5 minutes. Real-time PCR was performed on a CFX-96 real-time PCR system (Bio-Rad, Hercules, USA), following the manufacturer’s instructions. The expression of target genes was normalized to that of the action gene (Livak and Schmittgen 2001).

Gene ontology (GO) and pathway enrichment analyses

Up- and downregulted (≥ 4-fold) genes were selected at the nominal level of significance (P-value ≤ 0.05). All differentially expressed genes were annotated with the PANTHER classification system (http://www.pantherdb.org) (Mi et al. 2019). Functional categories of every gene and pathway were assigned using KEGG (https://www.genome.jp/kegg/tool/map_pathway1) as the classification source (Kanehisa and Goto 2000).

RESULTS

Screen of single copy/intergenic/homozygous transgenic rice plants

Expression of the P. sajor-caju GPD gene is strongly induced by various forms of stress, such as saline, dry, low-temperature, and high-temperature conditions. Transgenic potato plants that overexpress the PsGPD gene can increase their tolerance to salt stress (Jeong et al. 2001). To investigate the function of PsGPD in rice under drought stress, we generated transgenic rice plants containing PsGPD driven by the CaMV35S promoter (Fig. 1A). Single copy transgenic rice plants were screened using TaqMan copy number analysis (Fig. 1B) (Cho et al. 2014). Transgenic plants with T-DNA inserted into the intergenic region are less likely to be affected by surrounding genes; therefore, we examined whether the transformed gene was inserted into an intergenic location to avoid altering the expression of surrounding genes. We selected intergenic transgenic rice plants by T-DNA flanking sequence analysis (Table 1). Furthermore, we used homozygous lines of intergenic and single copy insertion transgenic rice plants selected by the TaqMan copy number assay (Fig. 1B). We obtained 19 independent lines of single copy/intergenic/homozygous transgenic rice plants that contained PsGPD.

Fig. 1

The PsGPD-OX vector and molecular identification of transgenic rice. (A) Schematic of the overexpression construct. RB: right border, CaMV35S: cauliflower mosaic virus 35Spromter, TNOS: nopaline synthase gene terminator, Bar: selection marker, LB: left border. (B) Copy number assay by quantitative RT-PCR.

Analysis of the integration site of T-DNA based on flanking sequences.

Overexpression of PsGPD improves drought tolerance at the young seedling stage

To characterize the function of PsGPD in response to drought stress, we generated WT and transgenic rice plants. The four-week-old WT and transgenic rice plants had water withheld for two to four days (d) and then were rehydrated.

As shown in Fig. 2, after drought treatment, the WT plants showed serious withering and damage; after 13-27 days, their survival rate was at 18%. The PsGPD-transgenic rice plants showed less wilting and better recovery than the WT plants.

Fig. 2

Drought treatment between wild-type and PsGPD-overexpressing rice at the young seedling stage. (A) Phenotype of transgenic rice under drought. (B) Survival rate of transgenic rice.

After three weeks of drought treatment, PsGPD-transgenic rice plants appeared relatively healthy and vigorous. After rewatering, the survival rate of PsGPD-transgenic rice plants was ∼90%. These results suggest that overexpression of PsGPD increases the drought tolerance of rice.

Transcriptomic analysis of PsGPD-transgenic rice plants under drought conditions

To study how transformation with PsGPD confers drought tolerance to rice, we conducted RNA-seq analysis of genotype 71671. In total, eight samples with two biological replicates were used for RNA-seq analysis. Total RNA extracted from leaves of PsGPD-transgenic and WT plants under normal and drought stress conditions (WT–control, PsGPD–control, WT–drought, and PsGPD–drought) was used for RNA-seq in replicates.

Comparative analysis revealed that 2,992 genes were transcriptionally affected by PsGPD transgene or drought treatment. In WT plants, 1,825 and 865 genes (total 2,690 genes) were up- and downregulated, respectively, under drought stress conditions. In comparison, in the transgenic rice plants, the expression of 1,115 genes (754 upregulated and 361 downregulated) was changed by drought treatment, compared to normal conditions. There were fewer changes in expression under drought stress in PsGPD-transgenic rice than in WT plants. This result indicates that several genes were activated due to overexpression of PsGPD, regardless of stress (Fig. 3A). For both WT and PsGPD-transgenic rice under drought stress, 992 genes overlapped (Fig. 3B), and the remaining 123 genes could be regulated only in the transgenic rice plants after drought treatment.

Fig. 3

The PsGPD transgene and drought stress-modulated genes identified through RNA-seq analysis of genotype 71671. (A) Number of genes affected by the PsGPD transgene and drought stress. (B) Overlapping analysis of genes up- and downregulated by PsGPD or drought stress.

The expression levels of 345 and 1,185 genes were changed in the PsGPD-transgenic rice under normal and drought stress conditions, respectively, compared to the levels WT rice under the same conditions (Fig. 3A). Among these genes, 145 genes overlapped, indicating that they were regulated by the PsGPD gene, regardless of drought stress (Fig. 3B). Table 2 lists these coexpressed genes, which can be considered putative target genes of PsGPD.

Genes affected by both the PsGPD transgene and drought stress.

Enriched GO terms and pathways

To find significantly enriched gene ontology (GO) terms, we used both 123 and 145 genes regulated by the PsGPD gene under drought-stressed and untreated conditions, respectively. The top five enriched GO terms in the biological process category were metabolic and cellular processes, biological regulation, response to stimulus, and localization (Fig. 4). Among cellular components, cells, organelles, cell junctions, and protein-containing complexes were highly ranked. The enriched GO terms among molecular functions were classified into four categories: catalytic activities, binding, transcription regulators, and transporter activities (Fig. 4). Moreover, we also found four pathways using genes affected by both the PsGPD gene and drought treatment, including biosynthesis of secondary metabolites, metabolic pathways, plant–pathogen interactions, and KEGG signaling pathway (Fig. 5).

Fig. 4

GO term enrichment analysis of genes affected by PsGPD transgene and drought stress treatment. Differentially expressed genes (fold change ≥ 4 and P-value ≤ 0.05) were annotated using the PANTHER classification system (v.14.0). GO was used as the classification source. N71671_n17DJ: PsGPD_Control Vs. WT_Control, S71671_S17DJ: PsGPD_Drought Vs. WT_Drought, S17DJ_n17DJ: WT_Drought Vs. WT_Control, S71671_n71671: PsGPD_Drought Vs. PsGPD_Control.

Fig. 5

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of genes affected by the PsGPD transgene and drought stress treatment.

Identification of candidate target genes of PsGPD

In total, 145 genes were transcriptionally modulated by the PsGPD transgene before and after drought treatments (Fig. 3A). Functional analysis revealed that the enriched pathways included the biosynthesis of secondary metabolites and metabolic pathways, while enriched GO terms included the response to stimulus and metabolic process.

Among these candidate genes, 4 were up- and downregulated in all four comparisons. Several genes, including Os04t0576900, Os03t0629800, and Os04t0518400 (OsPAL7), were involved in tetrapyrrole synthesis. Os09t0522200 (DREB1A), an important component in hormone signal transduction, is a TF gene that plays vital roles in the stress response.

To validate the RNA-seq, we performed qRT-PCR of the four selected drought-responsive genes. Comparison of the expression values of the RNA-seq and qRT-PCR results under the control and drought stress conditions indicated consistent correlation. The expression of the obviously upregulated genes (i.e., Os03t0629800, Os04t0518400, and Os09t0522200) was highly induced in PsGPD-transgenic rice plants. Os04t0576900 expression in PsGPD-transgenic rice plants was also significantly lower than that in the WT rice plants (Fig. 6). Finally, the expression pattern found through qRT-PCR of the four selected genes supported the results obtained through the RNA-seq analysis. We partially examined the functions of PsGPD in the drought stress response and the role of major TFs in the drought tolerance mechanism. These genes will be useful targets for both future research and the breeding of drought tolerance in rice.

Fig. 6

qRT-PCR of genes affected by both the PsGPD transgene and drought stress for comparison of the RNA-seq data. n17DJ: WT_Control, S17DJ: WT_Drought, n71671: PsGPD_Control, S71671: PsGPD_Drought.

DISCUSSION

In this study, we generated independent single copy/intergenic/homozygous transgenic rice with 35S::PsGPD. We partially characterized the functions of PsGPD in the drought stress response and the role of major TFs in the drought tolerance mechanism. Transgenic plants with T-DNA inserted into the intergenic region are less likely to be affected by surrounding genes; therefore, we examined whether the transformed gene was inserted into an intergenic location to avoid altering the expression of surrounding genes. Comparison of the expression values of the RNA-seq and qRT-PCR results under the control and drought stress conditions indicated consistent correlation. The expression of the obviously upregulated genes (i.e., Os03t0629800, Os04t0518400, and Os09t0522200) was highly induced in PsGPD-transgenic rice plants. These genes will be useful targets for both future research and the breeding of drought tolerance in rice.

ACKNOWLEDGEMENTS

This study was supported by grants from the Rural Development Administration’s Agenda Program (Project No. PJ013562), the New Breeding Technologies Development Program (Project No. PJ01479302) and the Next-Generation Bio-Green 21 Program (Project No. PJ01367503),  Rural Development Administration, Republic of Korea. Our authors are also truly grateful to all researchers who participated in this study.

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Article information Continued

Fig. 1

The PsGPD-OX vector and molecular identification of transgenic rice. (A) Schematic of the overexpression construct. RB: right border, CaMV35S: cauliflower mosaic virus 35Spromter, TNOS: nopaline synthase gene terminator, Bar: selection marker, LB: left border. (B) Copy number assay by quantitative RT-PCR.

Fig. 2

Drought treatment between wild-type and PsGPD-overexpressing rice at the young seedling stage. (A) Phenotype of transgenic rice under drought. (B) Survival rate of transgenic rice.

Fig. 3

The PsGPD transgene and drought stress-modulated genes identified through RNA-seq analysis of genotype 71671. (A) Number of genes affected by the PsGPD transgene and drought stress. (B) Overlapping analysis of genes up- and downregulated by PsGPD or drought stress.

Fig. 4

GO term enrichment analysis of genes affected by PsGPD transgene and drought stress treatment. Differentially expressed genes (fold change ≥ 4 and P-value ≤ 0.05) were annotated using the PANTHER classification system (v.14.0). GO was used as the classification source. N71671_n17DJ: PsGPD_Control Vs. WT_Control, S71671_S17DJ: PsGPD_Drought Vs. WT_Drought, S17DJ_n17DJ: WT_Drought Vs. WT_Control, S71671_n71671: PsGPD_Drought Vs. PsGPD_Control.

Fig. 5

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of genes affected by the PsGPD transgene and drought stress treatment.

Fig. 6

qRT-PCR of genes affected by both the PsGPD transgene and drought stress for comparison of the RNA-seq data. n17DJ: WT_Control, S17DJ: WT_Drought, n71671: PsGPD_Control, S71671: PsGPD_Drought.

Table 1

Analysis of the integration site of T-DNA based on flanking sequences.

Line Chromosome Insertion site Insertion type Gene ID
71663 Chr04 29845424-29847167 Intergenic Os04t0581266-00 upstream 2.3kb
71664 Chr04 29346227-29348282 Intergenic Os04t0581100-01 upstream 6.561kb
71671 Chr04 29577793-29578251 Intergenic Os04t0576850-00 upstream 4.282kb
71847 Chr01 13476206-13476371 Intergenic Os01t0339400-00 upstream 8.546kb

Table 2

Genes affected by both the PsGPD transgene and drought stress.

ID n17DJz) n71671z) S17DJz) S71671z) Description
Os04t0576900 1 1430.8 1 111 Protein kinase, core domain-containing protein
Os03t0629800 1 70.3 1 324.7 Conserved hypothetical protein
Os04t0518400 136.65 1176.28 43.15 223.95 Similar to phenylalanine ammonia-lyase (fragment)
Os09t0522200 21.8 169.45 38.3 1166.35 DRE-binding protein 1A

z)n17DJ: WT_Control, S17DJ: WT_Drought, n71671: PsGPD_Control, S71671: PsGPD_Drought.