Dynamic transcriptome and metabolome analyses of two types of rice during the seed germination and young seedling growth stages

Abstract

Background: Seed germination and young seedling growth are important agricultural traits for developing populations of both irrigated and directly seeded rice. Previous studies have focused on the identification of QTLs. However, there are few studies on the metabolome or transcriptome of germination and young seedling growth in rice. Results: Here, an indica rice and a japonica rice were used as materials, and the transcripts and metabolites were detected during the germination and young seedling growth periods on a large scale by using RNA sequencing and a widely targeted metabolomics method, respectively. Fourteen shared transcripts and 15 shared metabolites that were continuously differentially expressed in the two materials were identified and may be essential for seed germination and young seedling growth. Enrichment analysis of differentially expressed genes in transcriptome expression profiles at different stages indicated that cell wall metabolism, lipid metabolism, nucleotide degradation, amino acid, etc., were enriched at 0-2 days, and most of the results are consistent with those of previous reports. Specifically, phenylpropanoid biosynthesis and glutathione metabolism were continuously enriched during the seed germination and young seedling growth stages. Next, KO enrichment analysis was conducted by using the differentially expressed genes of the two materials at 2, 3 and 4 days. Fourteen pathways were enriched. Additionally, 44 differentially expressed metabolites at 2, 3 and 4 days were identified. These metabolites may be responsible for the differences in germination and young seedling growth between the two materials. Further attention was focused on the ascorbate-glutathione pathway, and it was found that differences in ROS-scavenging abilities mediated by some APX, GPX and GST genes may be directly involved in mediating differences in the germination and young seedling growth speed of the two materials. Conclusions: In summary, these results may enhance the understanding of the overall mechanism of seed germination and young seedling growth, and the outcome of this study is expected to facilitate rice breeding for direct seeding.

Integrating GWAS and transcriptomics to identify genes involved in seed dormancy in rice

“…Dataset GSE115371 provided gene expression data at 0 h, 1 h, 3 h, 12 h, 1 d, 2 d, 3 d and 4 d timepoints after imbibition, for an Australian variety (Amaroo); only timepoints from aerobically grown samples were used (Narsai et al 2017). DEGs were identi ed by comparing expression at different timepoints to expression at 0 h. Dataset SRP277875 provided expression data for a japonica variety (02428) and an indica variety (YZX) at 2, 3, and 4 days after imbibition compared with a 0 h timepoint (Yang et al 2020). The data of the japonica variety was used for expression comparison of candidate genes (Fig.…”

“…To further nd eSNPs associated with seed germination, we compared expression in the japonica and indica varieties in dataset SRP277875 (Yang et al 2020). More than twice as many genes were upregulated in the japonica than in the indica variety (Online Resource 5), suggesting that the germination difference between these two rice subpopulations can be caused by the differential expression of genes.…”

Dynamic Transcriptomic and Metabolomic Analyses of Madhuca pasquieri (Dubard) H. J. Lam During the Post-germination Stages

“…However, Wang et al ( 2020 ) only identified 57 metabolites in mung bean by untargeted metabolome analysis performing gas chromatography-mass spectrometry (GC-MS), and most of the metabolites were sugar metabolism compounds, amino acid metabolism compounds, tricarboxylic acid (TCA), and other organic acid metabolism compounds. Seven hundred thirty metabolites were detected in the germination and post-germination growth stages of the two varieties of rice by widely targeted metabolome, including 32 substances and their derivatives, among which flavone (74, 10.1%), organic acids (67, 9.2%), amino acid derivatives (60, 8.2%), nucleotide and its derivates (57, 7.8%), and flavone C-glycosides (44, 6.0%) accounted for the largest proportion (Yang et al, 2020 ). This indicated that the widely targeted metabolome method could identify more metabolites than the untargeted metabolome method, and the metabolites of rice were far more than that of M. pasquieri , which may be related to the different species and germination stages.…”

“…For example, 78.7–83.9% of the reads were mapped on the mung bean reference genome (Wang et al, 2020 ). The average rates of mapped transcripts for the indica rice and japonica rice in the Nipponbare reference genome were 80.94 and 80.27%, respectively (Yang et al, 2020 ). This is because M. pasquieri lacks genomic information and only full-length transcriptome sequences are used as reference sequences, while the third-generation sequencing uses mixed samples, which may filter some low-quality reads during assembly, and the transcript length is longer, leading to a relatively lower mapped rate with the single RNA-Seq samples.…”

“…Widely targeted metabolomics is a new method that can accurately detect hundreds of target metabolites, and is broadly used in plants, e.g., Arabidopsis ( Arabidopsis thaliana ) (Sawada et al, 2017 ), rice (Yang et al, 2019b ), and apple ( Malus domestica ) (Xu et al, 2020 ). Moreover, it has proven that the combination analysis of metabolome and transcriptome data can effectively reveal the biosynthetic mechanisms of the main metabolic pathway of post-germination growth in plants (Yang et al, 2020 ). Therefore, the combination of widely targeted metabolomics and transcriptomics is very necessary for the in-depth understanding of the post-germination growth of M. pasquieri .…”