IC4R001-RNA-Seq-2015-26714321

Project Title

Using RNA-seq to Profile Gene Expression of Spikelet Development in Response to Temperature and Nitrogen during Meiosis in Rice (Oryza sativa L.)

The Background of This Project

Rice reproductive development is sensitive to high temperature and soil nitrogen supply, both of which are predicted to be increased threats to rice crop yield. Rice spikelet development is a critical process that determines yield, yet little is known about the transcriptional regulation of rice spikelet development in response to the combination of heat stress and low nitrogen availability. In this project, the Researchers profiled gene expression of rice spikelet development during meiosis under heat stress and different nitrogen levels using RNA-seq.

Plant Culture & Treatment

Figure 1. Table 1. Summary of transcriptome sequencing.

• Ganxin203, a super-hybrid early rice (Oryza sativa L. ssp. indica) variety, was grown in hydroponic conditions in 2014 at High-Tech Agricultural Science and Technology Park of Jiangxi Agricultural University (latitude: 28° 46 0 N, longitude: 115° 50 0 E, altitude: 48.80m), Jiangxi Province, China.
• Plants were subjected to one of four treatments composed of two factors, nitrogen and temperature:

1. NN: normal nitrogen level (165 kg ha -1 , as the control) with normal temperature (30°C, as the control);
2. HH: high nitrogen level (264 kg ha -1 ) with high temperature (37°C);
3. NH: normal nitrogen level and high temperature;
4. HN: high nitrogen level and normal temperature.

Illumina Sequencing

• Total RNA from young florets undergoing meiosis was isolated using TRIzol reagent (Invitrogen) according to the manufacturer’s protocol. For transcriptome sequencing and assembly, RNA from all four treatments were mixed and pooled equally to obtain more sequence infor-mation, however, each treatment was subjected individually to digital gene expression (DGE) sequencing.
• Oligo(dT) beads were used to isolate poly(A) + mRNA from total RNA, and mRNA were disrupted into short fragments using fragmentation buffer. These short fragments were used as templates for random hexamer primer to synthesize first-strand cDNA.The second-strand cDNA was synthesized by adding buffer, dNTPs, RNase, and DNA polymerase I.
• The library was sequenced using an Illumina HiSeq TM 2000 platform, performed at the Beijing Genomics Institute. The raw reads were stored in a fastq format.

Research Findings

• In this study, researchers obtained a total of 52,250,482 clean reads (accumulated nucleotides, 4,702,543,380 bp), which were assembled into 106,229 contigs with Q20 percentage and GC content of 96.32%, and 52.98%, respectively, and then the contigs were assembled into 76,103 unigenes, with a mean length of 520 bp.
• Researchers annotated the transcriptome by blasting all the distinct unigene sequences against NR, NT, Swiss-Prot, KEGG, COG, and GO databases by BLASTX with a cut-off E-value of 10 −5 . This resulted in a total of 75,807 unigenes (99.61% of all unigenes) that were above the cut-off value (Table 2). 60,788 unigenes were annotated by NR (79.88% of all unigenes; Table 2), and 75,593 (99.33%), 34,776 (45.70%), 31,311 (41.14%), 18,041 (23.71%), and 44,131 (57.99%) unigenes were annotated by NT, Swiss-Prot, KEGG, COG, and GO databases, respectively.

Figure 2. GO analysis of unigenes.

• We classified the functions of the predicted genes using Gene Ontology (GO) assignments. Based on sequence homology, 44,131 unigenes and 304,589 sequences,were categorized into 57 functional groups (Fig 2). In each of the three main categories (biological process, cellular component, and molecular function) of the GO classification, the major subcategories were: “metabolic process”, “cellular process”, and “single-organism process” for biological process, “cell”,“cell part”, and “organelle” for cellular components, and “binding”, “catalytic activity”, and “transporter activity” for molecular function.
• To validate the expression profiles obtained by RNA-seq, researchers performed RT-qPCR analysis of 10 randomly selected DEGs . For all 10 genes, they found the same expression profiles as the original RNA-seq data, suggesting that the RNA-seq data obtained for the DEGs analysis was credible.

Figure 3. Table 1. Summary of transcriptome sequencing.

Figure 4. GO analysis of unigenes.

Labs working on this Project

• Key Laboratory of Crop Physiology, Ecology and Genetic Breeding, Ministry of Education, College of Agronomy, Jiangxi Agricultural University, Nanchang, 330045, China
• Southern Regional Collaborative Innovation Center for Grain and Oil Crops, Hunan Agricultural University, Changsha, 410128, China