Transcriptome and metabolome analysis of senescent rice (Oryza sativa L.) seeds: insights into the mechanism of germination vigor and seedling morphogenesis

Abstract

Seeds germination and seedlings growth are crucial factors in ensuring effective rice grain productivity. However, the mechanisms for maintaining seed vigor remains largely unknown. The seed aging phenomenon that occurs during storage poses a significant challenge to crop production, as it can lead to reduced germination rates and impaired seed vitality. The current study explored the underlying mechanisms that enable rice seeds to maintain high germination rates and seedling vigor after long - term storage. We employed transcriptomic and metabolomic techniques to identify metabolic changes and key genes associated with the aging of rice seeds during long - term storage. We utilized indicators such as imbibition rate (IR), germination rate (GR), mean germination time (MGT), germination coefficient (GC), germination index (GI), and germination potential (GP) to comprehensively assess germination activity. Traits including seedling emergence rate, seedling strength index, photosynthetic parameters, carbohydrate accumulation, and enzyme activity related to carbon metabolism were used to determine the impact of seed storage duration on seedling growth. Our research findings revealed significant differences in gene expression patterns and metabolic characteristics among seeds stored for different duration. Notably, IAA levels, the IAA/ABA ratio, and linoleic acid metabolism were identified as key factors affecting germination and seedling development. Results indicated that with the extension of storage duration, the germination potential and seedling development significantly declined. Current study provided a comprehensive understanding of the physiological and molecular mechanisms behind the germination and morphogenesis of rice seeds under different storage years. The insights gained from this study could be utilized to improve the storage and quality control of rice seeds, thereby ultimately enhancing agricultural productivity.

Keywords: Aging rice seeds; Germination vigour; Metabolome analysis; Seedling morphogenesis; Transcriptome.

Fig. 1

Oxidative stress response during germination of aging rice seeds. (A) SOD activity. (B) POD activity. (C) CAT activity. (D) APX activity. (E) MDA content. (F) H₂O₂ content. The X-axis represents the day of the sample collection after sowing. Each vertical bar with error bar represents mean ± standard deviation (n = 4). Different letters indicate significant differences between groups over the same time period. (Duncan’s test for multiple comparisons p < 0.05)

Fig. 2

Hormone levels in germination of aging rice seeds. (A) IAA content. (B) ABA content. (C) BL content. (D) SA content. (E) 12-OPDA content. (F) IPA content. (H) ACC content. (I) tZ content. (J) cZR content. (K) IAA/ABA ratio. (L) cZR/ABA ratio. Each vertical bar with error bar represents mean ± standard deviation (n = 3). Asterisks indicate significant differences between the compared two samples using a Student’s t test at *P < 0.05, **P < 0.01 and ***P < 0.001

Fig. 3

Photosynthetic pigment content of aging rice seeds at seedling stage. (A) Chlorophyll-a content. (B) Chlorophyll-b content. (C) Carotenoids content. (D) Total chlorophyll content. Each vertical bar with error bar represents mean ± standard deviation (n = 4). Different letters indicate significant differences between groups over the same time period. (Duncan’s test for multiple comparisons p < 0.05)

Fig. 4

Gas exchange parameters of aging rice seed seedlings. (A) Transpiration rate (T_r). (B) Net photosynthetic rate (P_n). (C) Intercellular CO₂ concentration (C_i). (D) Stomatal conductance (G_s). (E) Water Use Efficiency (WUE). Each vertical bar with error bar represents mean ± standard deviation (n = 4). Different letters indicate significant differences between groups. (Duncan’s test for multiple comparisons p < 0.05)

Fig. 5

KEGG enrichment analysis and GO enrichment analysis of DEGs. (A) KEGG annotations and enrichment of DEGs in 2014 vs. 2021. (B) Go analysis of DEGs in 2014 vs. 2021. X-axis means percentage and number of DEGs. Y-axis represents GO terms. (C) GO annotations and enrichment of DEGs in 2014 vs. 2021

Fig. 6

Differential metabolite analysis. (A) Metabolite class composition. Each color represents a metabolite class, and the area of the color block represents the proportion of that class. (B) Differential metabolite clustering heat map. Red means high content, green means low content. (C) KEGG enrichment analysis of differential metabolites

Fig. 7

Comprehensive analysis of metabolome and transcriptome. (A) KEGG pathway co-enriched by DEGs and DAMs. (B) KGML mutual mapping. Squares represent genes or gene products, circles represent metabolites, and diamonds represent pathway names. Red indicates up-regulation of genes or gene products or metabolites, and green indicates down-regulation of genes or gene products or metabolites

Fig. 8

Diagram of seed germination and seedling morphological establishment of rice. The content with a green background represents the measured indicators. Text in blue indicates a declining trend in aged seeds, while text in red signifies an upward trend in aged seeds