IC4R003-RNA-Seq-2015-26576634

Project Title

Figure 1. Table 1. Summary of transcriptome sequencing.

RNA-seq reveals differentially expressed genes of rice (Oryza sativa) spikelet in response to temperature interacting with nitrogen at meiosis stage

The Background of This Projec

• Seed setting rate, one of the most important components of rice grain yields, is significantly affected by high temperature during pollen mother cell meiosis. However, limited information about differentially expressed genes of rice spikelet in response to high temperature is available at meiosis stage. Moreover, no reports demonstrate that nitrogen level can regulate the effects of high temperature at meiosis stage on rice production.
• In this study, the researchers used Illumina RNA-seq technology to profile different gene expression of ricespikelet from a conventional strain Zhong531 treated by high temperature and high nitrogen level at meiosis stage, to gain insights into molecular events associated with temperature and nitrogen. The overall objective of this study was to increase our understanding of the heat response in rice spikelet and provided good candidate genes for crop. improvement.

Plant Culture & Treatment

• One rice (Oryza sativa L. ssp. indica) strain, Zhong531, was chosen for this study. The trials were conducted between March and August in 2014, in net house at High-Tech Agricultural Science and Technology Park ofJiangxi Agricultural University (latitude: 28° 46′ N, longitude: 115° 50′ E, altitude: 48.80 m), Nanchang, Jiangxi Province, China.
• This study received four treatments: 1) NN: normal nitrogen level (165 kg ha −1 ) with natural temperature (30 °C); 2) HH: high nitrogen level (330 kg ha −1 ) with high temperature (37 °C); 3) NH: normal nitrogen level and high temperature; and 4) HN: high nitrogen level and natural temperature, respectively.

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Illumina Sequencing

• Total RNA was extracted using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. The total RNA samples from the four treatments were mixed and pooled (with equal amount of RNA from each treatment) as one sample for transcriptome sequencing (paired ends sequencing, 90 bp) to obtain as much gene expression information as possible, but they were subjected individually to conduct digital gene expression (DGE) sequencing.

• The library products were ready for sequencing via Illumina HiSeqTM 2000 at the Beijing Genomics Institute (BGI, http://www.genomics.cn/index; Shenzhen, China). Image data output from sequencing machine were transformed by base calling into sequence data, called raw data or raw reads, and was stored in fastq format.

Research Findings

• Illumina paired-end sequencing generated a total of 61,733,120 raw reads (Table 1). After filtration, 52,553,536 clean reads with accumulated length of 4,729,818,240 bp were remained for further analysis. The Q20 percentage was 96.13 %, and the GC percentage was 54.24 %. These clean reads were assembled into 101,597 contigs with a mean length of 378 bp. The N50 of contigs was 708 bp. These contigs were further assembled by paired-end joining and gap-filling, and clustered into unigenes. Finally, we obtained 72,667 unigenes, with a mean length of 537 bp. The N50 of unigenes was 747 bp. The size distribution indicated that the lengths of the 148 unigenes were more than 3,000 nt.

• All of the unigenes were compared to the sequences in public databases, including NR, the Nucleic acid data bank (NT), Swiss-Prot, KEGG, COG, and GO database, using BLASTX with a cutoff e-value of 10 −5 (Table 2). A total of 72,463 unigenes (99.68 % of all unigenes) returned a significant BLAST result.
• GO assignments were used to classify the functions of the predicted unigenes. Based on sequence homology, 43,180 unigenes and 299,435 sequences could be categorized into three main categories with a total of 57 functional groups (Fig. 3). In each of the three main categories (biological process, cellular component, and molecular function) of the GO classification, “metabolic process”, “cell”, and “binding” were dominant. We also noticed a high-percentage of genes in the categories of “cellular process”, “cell part”, and “catalytic activity”.
• The differentially expressed genes (DEGs) were identified in different samples. The following significant DEGs were identified: (a) between samples ZHN and ZHH, 1,072 and 1,637 genes were up- and down-regulated, respectively; (b) between samples ZNH and ZHH, 424 and 698 genes were up- and down-regulated, respectively; (c) between samples ZNH and ZHN, 4,638 and 2,824 genes were up- and down-regulated, respectively; (d) between samples ZNN and ZHH, 188 and 813 genes were up- and down-regulated, respectively; (e) between samples ZNN and ZHN, 1,581 and 1,839 genes were up- and down-regulated, respectively; (f ) between samples ZNN and ZNH, 389 and 906 genes were up- and down-regulated, respectively.

Labs working on this Project

• Key Laboratory of Crop Physiology, Ecology and Genetic Breeding, Ministry of Education, College of Agronomy, Jiangxi Agricultural University,QingShanHu District, Nanchang, Jiangxi Province 330045, China