Long non-coding RNAs (lncRNAs) are crucial players regulating many biological processes in plants. However, limited knowledge is available regarding their roles in kiwifruit ripening and softening. In this study, using lncRNA-seq technology, 591 differentially expressed (DE) lncRNAs (DELs) and 3107 DE genes (DEGs) were identified from kiwifruit stored at 4 °C for 1, 2, and 3 weeks in comparison with non-treated control fruits. Of note, 645 DEGs were predicted to be targets of DELs (DEGTLs), including some DE protein-coding genes (such as β-amylase and pectinesterase). DEGTL-based GO enrichment analysis revealed that these genes were significantly enriched in cell wall modification and pectinesterase activity in 1 W vs. CK and 3 W vs. CK, which might be closely related to the fruit softening during low-temperature storage. Moreover, KEGG enrichment analysis revealed that DEGTLs were significantly associated with starch and sucrose metabolism. Our study revealed that lncRNAs play critical regulatory roles in kiwifruit ripening and softening under low-temperature storage, mainly by mediating the expression of starch and sucrose metabolism and cell wall modification related genes.
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http://dx.doi.org/10.3390/plants12051070 | DOI Listing |
Plant Physiol
December 2024
National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Joint International Research Laboratory of Germplasm Innovation & Utilization of Horticultural Crops, National R&D Centre for Citrus Preservation, College of Horticulture and Forestry Science, Huazhong Agricultural University, Wuhan 430070, P.R. China.
Kiwifruit (Actinidia chinensis), a recently commercialized horticultural crop, is rich in various nutrient compounds. However, the regulatory networks controlling the dynamic changes in key metabolites among different tissues remain largely unknown. Here, high-resolution spatiotemporal datasets obtained by ultraperformance liquid chromatography-tandem mass spectrometry methodology and RNA-seq were employed to investigate the dynamic changes in the metabolic and transcriptional landscape of major kiwifruit tissues across different developmental stages, including from fruit skin, outer pericarp, inner pericarp, and fruit core.
View Article and Find Full Text PDFFoods
November 2024
School of Food Science and Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China.
The kiwifruit () is an important nutritional and economic fruit crop. However, the short edible window period of kiwifruit has seriously affected its market value. 1-Methylcyclopropene (1-MCP), as a novel ethylene inhibitor, is widely applied to delay fruit ripening and senescence.
View Article and Find Full Text PDFBMC Plant Biol
November 2024
Florida Research & Education Center, IFAS, University of Florida, Apopka, FL, 32703, USA.
Kiwifruit (Actinidia spp.), celebrated for its unique flavor and rich nutritional content, is a globally popular fruit. This fruit requires post-harvest ripening before consumption.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
College of Agriculture, Guangxi University, Nanning 530004, China. Electronic address:
Advances in the detection and mapping of DNA methylation redefine our understanding of the modifications as epigenetic regulation. In plants, the most prevalent DNA methylation plays crucial and dynamic roles in a wide variety of processes, such as stress responses, seedlings growth, fruit ripening and so on. Here, we discuss firstly the changes of DNA methylation (CG, CHG, and CHH) dynamic in plants.
View Article and Find Full Text PDFPlant J
December 2024
Graduate School of Environmental and Life Science, Okayama University, Okayama, 700-8530, Japan.
Previous research on the ripening process of many fruit crop varieties typically involved analyses of the conserved genetic factors among species. However, even for seemingly identical ripening processes, the associated gene expression networks often evolved independently, as reflected by the diversity in the interactions between transcription factors (TFs) and the targeted cis-regulatory elements (CREs). In this study, explainable deep learning (DL) frameworks were used to predict expression patterns on the basis of CREs in promoter sequences.
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