Molecular Traits and Functional Exploration of BES1 Gene Family in Plants

Sun, Zhenting; Liu, Xingzhou; Zhu, Weidong; Huang, Lin; Chen, Xiugui; Li, Yan; Ye, Wuwei; Yin, Zujun

doi:10.3390/ijms23084242

Cited by 10 publications

(6 citation statements)

References 38 publications

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“…The overexpression of OsBES1-3 and OsBES1-5 in rice increased the root growth of transgenic lines under drought stress, and transgenic Arabidopsis lines with overexpressed cotton GhBES1-4 showed salt tolerance [51]. The overexpression of PtrBES1-7 in Populus trichocarpa enhanced tolerance to drought stress by improving the ability to scavenge reactive oxygen species and antioxidant enzymes superoxide dismutase and peroxidase [54,55]. Previous studies showed that BES1 genes were induced by different abiotic stresses such as drought, heat, salinity, cold and ABA [56].…”

Section: Discussionmentioning

confidence: 99%

A Comparison of the Physiological Traits and Gene Expression of Brassinosteroids Signaling under Drought Conditions in Two Chickpea Cultivars

Felagari,

Bahramnejad,

Siosemardeh

et al. 2023

Agronomy

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This study aimed to investigate the effects of drought stress at the flowering stage on the physiological and molecular responses of the genes involved in the brassinosteroid pathway of two chickpea cultivars (ILC1799: drought tolerant, and ILC3279: drought sensitive). The drought resulted in significant reductions in chlorophyll a, chlorophyll b, total chlorophyll and carotenoid content in both cultivars, and had significantly lesser effects on the tolerant cultivar, Samin, compared to that of ILC3279. However, the relative water content, the osmotic potential and the cell membrane stability were less affected by drought in both cultivars. The proline content and peroxidase activity increased significantly under drought stress in both cultivars, with a higher amount in Samin (ILC1799). Members of the BES1 family positively mediate brassinosteroid signaling and play an important role in regulating plant stress responses. The expression of these genes was analyzed in chickpea cultivars under drought. Further, a genome-wide analysis of BES1 genes in the chickpea genome was conducted. Six CaBES1 genes were identified in total, and their phylogenetic tree, gene structures, and conserved motifs were determined. CaBES1 gene expression patterns were analyzed using a transcription database and quantitative real-time PCR analysis. The results revealed that the expression of CaBES1 genes are different in response to various plant stresses. The expression levels of CaBES1.1, CaBES1.2, CaNAC72 and CaRD26 genes were measured by using qRT-PCR. The relative expression of CaBES1.2 in the drought conditions was significantly downregulated. In contrast to CaBES1.1 and CaBES1.2, the expression of CaRD26 and CaNAC72 showed a significant increase under drought stress. The expression of CaRD26 and CaNAC72 genes was significantly higher in the Samin cultivar compared to that of ILC3279 cultivars.

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Section: Discussionmentioning

confidence: 99%

A Comparison of the Physiological Traits and Gene Expression of Brassinosteroids Signaling under Drought Conditions in Two Chickpea Cultivars

Felagari,

Bahramnejad,

Siosemardeh

et al. 2023

Agronomy

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“…A study conducted by He et al [91] revealed that OsPIP1 encoding aquaporin interacts with other proteins to promote water uptake and seed germination. Furthermore, BES1/BZR1, a family of Brassinosteroids transcriptional regulators, were recently proposed to regulate plant development [92], kernel size in rice [50] and maize [51] through interaction with several proteins [93].…”

Section: The Qgl1-1 Bfsg Qtl Harbors Genes Involved In Post-embryonic...mentioning

confidence: 99%

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

Kabange,

Dzorkpe,

Park

et al. 2023

Plants

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This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint-strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]–High-Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and QTLs for grain length, co-detected by all GAPIT models on chromosomes (Chr) 1–3, 5, 7, and 11, were qGL1-1BFSG (AX-95918134, Chr1: 3,820,526 bp) explains 65.2–72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5,623,288 bp) for grain width explains 15.5–18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18,023,142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: 2,118,477 bp) of the observed PVE. For the grain length-to-width ratio (LWR), the qLWR2BFSG (AX-274833045, Chr2: 10,000,097 bp) explains nearly 15.2–32% of the observed PVE. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28,484,619 bp) and explains 32.8–54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8–54% of PVE. Putative candidate genes pooled from major QTLs for each grain trait have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

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“…A study conducted by He, et al [91] revealed that OsPIP1 encoding aquaporin interacts with other proteins to promote water uptake and seed germination. Furthermore, BES1/BZR1, a family of Brassinosteroids transcriptional regulator, were recently proposed to regulate plant development [92], kernel size in rice [50] and maize [51] through interaction with several proteins [93].…”

Section: The Grain Width Thickness and Lwr-associated Qtls Qgw1-1 Bsf...mentioning

confidence: 99%

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

Kabange,

Dzorkpe,

Park

et al. 2023

Preprint

View full text Add to dashboard Cite

This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]–High Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and quantitative trait loci (QTLs) for grain length, co-detected by all GAPIT models on (Chr) 1–3, 5, 7, and 11), were qGL1-1BFSG (AX-95918134, Chr1: 3820526 bp) explains 65.2%–72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5623288 bp) for grain width explains 15.5%–18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18023142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: AX-154787777 bp) of the observed PVE. For t length-to-width ratio, the qLWR2BFSG (AX-274833045, Chr2: 10000097 bp) explains nearly 15.2%–32% of PVE for LWR. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28484619 bp) and explains 32.8%–54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8%–54% of PVE. Putative Candidate genes pooled from major QTLs for each grain traits have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

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Molecular Traits and Functional Exploration of BES1 Gene Family in Plants

Cited by 10 publications

References 38 publications

A Comparison of the Physiological Traits and Gene Expression of Brassinosteroids Signaling under Drought Conditions in Two Chickpea Cultivars

A Comparison of the Physiological Traits and Gene Expression of Brassinosteroids Signaling under Drought Conditions in Two Chickpea Cultivars

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

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