Analysis of Soybean Root Proteins Affected by Gibberellic Acid Treatment Under Flooding Stress

Oh, Myeong Won; Nanjo, Yohei; Komatsu, Setsuko

doi:10.2174/0929866521666140403122602

Cited by 10 publications

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“…Taken together, these results suggest that the regulation of cell wall-related proteins, such as XTH, by exogenous calcium might promote the elongation of soybean roots under flooding stress. In this study, except for XTH, other cell wall-related proteins such as glycosyl hydrolase, which was decreased in flood-stressed soybean, was exhibited a similar tendency in previous study (Oh et al, 2014a ).…”

Section: Resultssupporting

confidence: 88%

“…These changes have effect on the calcium signaling under flooding stress and appear to be contributed flooding-responsive mechanism. Various calcium-related proteins, including annexin, calreticulin, calcium-binding EF-hand family protein, and calcium-dependent lipid-binding family protein, are changed in soybean under flooding stress (Oh et al, 2014a ). Taken together, these results indicate that calcium has functions as a key signaling regulator in response to flooding by controlling calcium-related proteins in soybean.…”

Section: Introductionmentioning

confidence: 99%

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Gel-free proteomic analysis of soybean root proteins affected by calcium under flooding stress

Nanjo

Komatsu

2014

Front. Plant Sci.

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Soybean is sensitive to flooding stress and exhibits reduced growth under flooding conditions. To better understand the flooding-responsive mechanisms of soybean, the effect of exogenous calcium on flooding-stressed soybeans was analyzed using proteomic technique. An increase in exogenous calcium levels enhanced soybean root elongation and suppressed the cell death of root tip under flooding stress. Proteins were extracted from the roots of 4-day-old soybean seedlings exposed to flooding stress without or with calcium for 2 days and analyzed using gel-free proteomic technique. Proteins involved in protein degradation/synthesis/posttranslational modification, hormone/cell wall metabolisms, and DNA synthesis were decreased by flooding stress; however, their reductions were recovered by calcium treatment. Development, lipid metabolism, and signaling-related proteins were increased in soybean roots when calcium was supplied under flooding stress. Fermentation and glycolysis-related proteins were increased in response to flooding; however, these proteins were not affected by calcium supplementation. Furthermore, urease and copper chaperone proteins exhibited similar profiles in 4-day-old untreated soybeans and 4-day-old soybeans exposed to flooding for 2 days in the presence of calcium. These results suggest that calcium might affect the cell wall/hormone metabolisms, protein degradation/synthesis, and DNA synthesis in soybean roots under flooding stress.

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Section: Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Gel-free proteomic analysis of soybean root proteins affected by calcium under flooding stress

Nanjo

Komatsu

2014

Front. Plant Sci.

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“…Potential genes Glyma15g18430 and Glyma15g18450 are close to SNP Gm15_15220084. Glyma15g18430 reported by Won Oh et al (2014) has differentially changed soybean root proteins with gibberellic acid treatment under flooding stress. It belongs to the glycosyl hydrolases family (PF01301) and involves in carbohydrate metabolic process (GO: 0005975).…”

Section: Resultsmentioning

confidence: 99%

Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean

et al. 2019

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Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.

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“…75 Furthermore, ABA efficiently delayed ethyleneinduced and GA-promoted cell death. 76 In soybean, GA and ethylene improved the development of soybean root at the early stage of flooding stress; 50,77 however, these factors could not improve survival ratio after water removal.…”

Section: ■ Transgenic Soybean Overexpressing the Gene That Codes The ...mentioning

confidence: 98%

Proteomic Techniques and Management of Flooding Tolerance in Soybean

2015

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Climate change is considered a major threat to world agriculture and food security. To improve the agricultural productivity and sustainability, the development of high-yielding stress-tolerant, and climate-resilient crops is essential. Of the abiotic stresses, flooding stress is a very serious hazard because it markedly reduces plant growth and grain yield. Proteomic analyses indicate that the effects of flooding stress are not limited to oxygen deprivation but include many other factors. Although many flooding response mechanisms have been reported, flooding tolerance mechanisms have not been fully clarified for soybean. There were limitations in soybean materials, such as mutants and varieties, while they were abundant in rice and Arabidopsis. In this review, plant proteomic technologies are introduced and flooding tolerance mechanisms of soybeans are summarized to assist in the improvement of flooding tolerance in soybeans. This work will expedite transgenic or marker-assisted genetic enhancement studies in crops for developing high-yielding stress-tolerant lines or varieties under abiotic stress.

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Analysis of Soybean Root Proteins Affected by Gibberellic Acid Treatment Under Flooding Stress

Cited by 10 publications

References 0 publications

Gel-free proteomic analysis of soybean root proteins affected by calcium under flooding stress

Gel-free proteomic analysis of soybean root proteins affected by calcium under flooding stress

Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean

Proteomic Techniques and Management of Flooding Tolerance in Soybean

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