Integrated metabolome and transcriptome analysis of differences in quality of ripe <i>Lycium barbarum</i> L. fruits harvested at different periods

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Abstract

Background

Wolfberry is well-known for its high nutritional value and medicinal benefits. Due to the continuous ripening nature of Goji berries and the fact that they can be commercially harvested within a few weeks, their phytochemical composition may change during the harvesting process at different periods.

Result

The involved molecular mechanisms of difference in fruit quality of ripe Lycium barbarum L. harvested at four different periods were investigated by transcriptomic and metabolomics analyses for the first time. According to the results we obtained, it was found that the appearance quality of L. barbarum fruits picked at the beginning of the harvesting season was superior, while the accumulation of sugar substances in L. barbarum fruits picked at the end of the harvesting season was better. At the same time the vitamin C and carotenoids content of wolfberry fruits picked during the summer harvesting season were richer. Ascorbic acid, succinic acid, glutamic acid, and phenolic acids have significant changes in transcription and metabolism levels. Through the network metabolic map, we found that ascorbic acid, glutamic acid, glutamine and related enzyme genes were differentially accumulated and expressed in wolfberry fruits at different harvesting periods. Nevertheless, these metabolites played important roles in the ascorbate–glutathione recycling system. Ascorbic acid, phenolic substances and the ascorbate–glutathione recycling system have antioxidant effects, which makes the L. barbarum fruits harvested in the summer more in line with market demand and health care concepts.

Conclusion

This study laid the foundation for understanding the molecular regulatory mechanisms of quality differences of ripe wolfberry fruits harvested at different periods, and provides a theoretical basis for enhancing the quality of L. barbarum fruits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-024-04751-z.

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Most cited references 61

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Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

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The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).

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KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

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Minoru Kanehisa (2019)

In this era of high‐throughput biology, bioinformatics has become a major discipline for making sense out of large‐scale datasets. Bioinformatics is usually considered as a practical field developing databases and software tools for supporting other fields, rather than a fundamental scientific discipline for uncovering principles of biology. The KEGG resource that we have been developing is a reference knowledge base for biological interpretation of genome sequences and other high‐throughput data. It is now one of the most utilized biological databases because of its practical values. For me personally, KEGG is a step toward understanding the origin and evolution of cellular organisms.

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Author and article information

Contributors

Wenkong Yao: yaowenkong@163.com

Mei Feng: feng_m@nxu.edu.cn

Journal

Journal ID (nlm-ta): BMC Plant Biol

Journal ID (iso-abbrev): BMC Plant Biol

Title: BMC Plant Biology

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2229

Publication date (Electronic): 2 February 2024

Publication date PMC-release: 2 February 2024

Publication date Collection: 2024

Volume: 24

Electronic Location Identifier: 82

Affiliations

[1 ]College of Enology and Horticulture, Ningxia University, ( https://ror.org/04j7b2v61) Yinchuan, 750021 Ningxia China

[2 ]Ningxia Modern Facility Horticulture Engineering Technology Research Center, Yinchuan, 750021 Ningxia China

[3 ]Ningxia Key Laboratory of Modern Molecular Breeding of Dominant and Characteristic Crops, Yinchuan, 750021 Ningxia China

Article

Publisher ID: 4751

DOI: 10.1186/s12870-024-04751-z

PMC ID: 10835843

PubMed ID: 38302892

SO-VID: 87ec26b4-b114-4a9a-99e9-47f98a11e87d

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 5 February 2023

Date accepted : 16 January 2024

Funding

Funded by: National Science Foundation of Ningxia

Award ID: 2022AAC03095

Award Recipient : Deshuai Liu Miao Yuan Ye Wang Li Zhang Wenkong Yao Mei Feng

Custom metadata

ScienceOpen disciplines: Plant science & Botany

Keywords: wolfberry,fruit quality,picking time,abscisic acid,glutamic acid,ascorbate–glutathione recycling system

Data availability:

ScienceOpen disciplines: Plant science & Botany

Keywords: wolfberry, fruit quality, picking time, abscisic acid, glutamic acid, ascorbate–glutathione recycling system

Integrated metabolome and transcriptome analysis of differences in quality of ripe Lycium barbarum L. fruits harvested at different periods

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Abstract

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Supplementary Information

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Plant MYBs

Most cited references 61

Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

KEGG: kyoto encyclopedia of genes and genomes.

Toward understanding the origin and evolution of cellular organisms

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