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. 2025 Aug 15;25(1):1076.
doi: 10.1186/s12870-025-07030-7.

Transcriptome and hormones metabolome joint analyses reveal the key molecular mechanism of Sweetpotato storage roots sprouting

Affiliations

Transcriptome and hormones metabolome joint analyses reveal the key molecular mechanism of Sweetpotato storage roots sprouting

Fan Ding et al. BMC Plant Biol. .

Abstract

Sprouting ability is an important indicator for evaluating the production performance of sweetpotato. However, the storage roots sprouting ability varies among different varieties. Currently, research on the sweetpotato storage roots sprouting (SPSRS) mainly focuses on physiology, rarely studies on the key metabolism and regulatory genes during the sprouting process. In this study, we compared transcriptomes and hormone metabolites in sprout buds of two sweetpotato varieties with contrasting sprouting abilities (strong-sprouting vs. late-sprouting) at 0 and 3 days post-harvest. In the SPSRS process of strong-sprouting variety (0d_vs_3d), a total of 2902 differentially expressed genes (DEGs), including 2411 up-regulated and 491 down-regulated genes were identified. But they were detected only, 45 up-regulated genes and 126 down-regulated genes between 0d and 3d of late-sprouting variety. Gene Ontology (GO) analysis showed that the DEGs between 0d and 3d in strong-sprouting variety and late-sprouting variety were mainly related to photosynthesis, light harvesting (GO:0009765), photosystem II (GO:0009523), and chlorophyll-binding (GO:0016168). In the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, DEGs in strong-sprouting variety were mainly associated with photosynthesis-antenna proteins. Both GO analysis and KEGG analysis indicated that DEGs in strong-sprouting variety (0d_vs_3d) were primarily related to photosynthesis. Hormone metabolites detection results showed that low abscisic acid (ABA) concentrations are crucial for SPSRS, while high indole-3-acetic acid (IAA) concentrations play a promoting role. Gibberellins (GAs) and cytokinins (CKs) are not key hormones for SPSRS. During the SPSRS process, IbGA2ox, IbGA20ox, and IbGA3ox were involved in regulation of GAs, while IbAMI1 was involved in regulation of IAA, and IbCYP707A1 and IbUGT73B2 were involved in regulation of ABA. Our study results also suggest that IbCYP707A1 is a key gene for SPSRS.

Keywords: Hormones metabolite; Storage roots sprouting; Sweetpotato; Transcriptome.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This article does not contain any studies with human participants or animals. The collection materials of the plants, complies the relevant institutional, national, and international guidelines and legislation. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Stereomicroscopic Images and Scale Representations of SPSRS in Two Sweetpotat Varieties
Fig. 2
Fig. 2
The Heatmap of Differential hormone detected during Sweetpotato Storage Root Sprouting. MNS17-0d and MNS17-3d represent the storage roots of sweetpotato variety MNS17 which placed in a 25 °C environment for 0 days and 3 days after harvest, respectively. The same applies to NS88. The notation used in the following text remains consistent. The figure presents the standardized data, with color intensity corresponding to values within the range of − 1.5 to 1.5
Fig. 3
Fig. 3
Principal component analysis (PCA) of differential hormones during sprouting in different sweetpotato varieties
Fig. 4
Fig. 4
Correlation analysis of hormones in sweetpotato. * means P < 0.05, ** means P < 0.01, *** means P < 0.001
Fig. 5
Fig. 5
Differential Gene Expression Analysis at Different Time Points after Harvest in Different Sweetpotato Varieties. (a) Bar Chart Illustrating Statistics of Differentially Expressed Genes, and (b) Venn Diagram Depicting Differentially Expressed Genes
Fig. 6
Fig. 6
Differential Gene Expression Volcano Plot
Fig. 7
Fig. 7
The differentially expressed genes of the two varieties show highly significant enrichment in 10 GO categories. The numbers next to the bar graphs indicate the percentage of differentially expressed genes enriched in the corresponding GO term compared to the total number of differentially expressed genes enriched in the respective first-level category
Fig. 8
Fig. 8
KEGG Pathway Classification of Differentially Expressed Genes (MNS17-0d_vs_MNS17-3d)
Fig. 9
Fig. 9
Process Diagram Analysis of Key Hormones and Genes in Sweetpotato Storage Root Sprouting. The heatmap of genes is displayed with the values of log10 (FPKM), where the numerical values in the graph represent FPKM values
Fig. 10
Fig. 10
The qPCR validation and correlation analysis with RNA-seq for 14 differentially expressed genes

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