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Peanut is an important oil and economic crop in China. The rainy season (April-June) in the downstream Yangtze River in China always leads to waterlogging, which seriously affects plant growth and development. Therefore, understanding the metabolic mechanisms under waterlogging stress is important for future waterlogging tolerance breeding in peanut. In this study, waterlogging treatment was carried out in two different peanut cultivars [Zhonghua 4 (ZH4) and Xianghua08 (XH08)] with different waterlogging tolerance. The data-independent acquisition (DIA) technique was used to quantitatively identify the differentially accumulated proteins (DAPs) between two different cultivars. Meanwhile, the functions of DAPs were predicted, and the interactions between the hub DAPs were analyzed. As a result, a total of 6,441 DAPs were identified in ZH4 and its control, of which 49 and 88 DAPs were upregulated and downregulated under waterlogging stress, respectively, while in XH08, a total of 6,285 DAPs were identified, including 123 upregulated and 114 downregulated proteins, respectively. The hub DAPs unique to the waterlogging-tolerant cultivar XH08 were related to malate metabolism and synthesis, and the utilization of the glyoxylic acid cycle, such as L-lactate dehydrogenase, NAD+-dependent malic enzyme, aspartate aminotransferase, and glutamate dehydrogenase. In agreement with the DIA results, the alcohol dehydrogenase and malate dehydrogenase activities in XH08 were more active than ZH4 under waterlogging stress, and lactate dehydrogenase activity in XH08 was prolonged, suggesting that XH08 could better tolerate waterlogging stress by using various carbon sources to obtain energy, such as enhancing the activity of anaerobic respiration enzymes, catalyzing malate metabolism and the glyoxylic acid cycle, and thus alleviating the accumulation of toxic substances. This study provides insight into the mechanisms in response to waterlogging stress in peanuts and lays a foundation for future molecular breeding targeting in the improvement of peanut waterlogging tolerance, especially in rainy area, and will enhance the sustainable development in the entire peanut industry.
Pubmed ID: 34456956 RIS Download
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsDatabase of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsA quantitative proteomics software package for analyzing large-scale mass-spectrometric data sets. It is a set of algorithms that include peak detection and scoring of peptides, mass calibration, database searches for protein identification, protein quantification, and provides summary statistics.
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