2021
DOI: 10.3389/fpls.2021.738261
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Multi-Omics Analyses Reveal the Regulatory Network and the Function of ZmUGTs in Maize Defense Response

Abstract: Maize is one of the major crops in the world; however, diseases caused by various pathogens seriously affect its yield and quality. The maize Rp1-D21 mutant (mt) caused by the intragenic recombination between two nucleotide-binding, leucine-rich repeat (NLR) proteins, exhibits autoactive hypersensitive response (HR). In this study, we integrated transcriptomic and metabolomic analyses to identify differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) in Rp1-D21 mt compared to t… Show more

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Cited by 10 publications
(11 citation statements)
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“…Besides, conjoint analysis (coupling comparative cytology with transcriptomic and metabolomic approaches) to understand the mechanisms underlying Solanum nigrum L. response to cadmium toxicity revealed key differentially expressed genes and metabolites, including laccase, peroxidase, D-fructose, and cellobiose, that were associated with cell wall biosynthesis, implying that the cell wall biosynthesis pathway plays a central role in Cd detoxification in Solanum nigrum ( Wang et al, 2022 ). Combined transcriptomic and metabolomic approaches applied in maize to analyze gene regulatory networks modulating Rp1-D21 mutant-mediated hypersensitive pathogen defense response revealed that four uridinediphosphate-dependent glycosyltransferase (UGT) ( ZmUGTs) genes were highly expressed, whilst the SA biosynthesis and phenylpropanoid biosynthesis pathways were induced at both the transcriptional and metabolic levels, suggesting that ZmUGT genes may be involved in maize defense response by regulating SA homeostasis ( Ge et al, 2021 ). Earlier, the epigenetic-based amalgamation of multi-omics approaches elucidated the critical role DNA methylation and play in lipid biosynthesis regulation and spatio-temporal modulation of ROS during cotton fiber development ( Wang et al, 2016 ).…”
Section: Omics Technologies Integrated With Modern Plant Breeding Methods In a Systems Biology Approach For Crop Improvementmentioning
confidence: 99%
“…Besides, conjoint analysis (coupling comparative cytology with transcriptomic and metabolomic approaches) to understand the mechanisms underlying Solanum nigrum L. response to cadmium toxicity revealed key differentially expressed genes and metabolites, including laccase, peroxidase, D-fructose, and cellobiose, that were associated with cell wall biosynthesis, implying that the cell wall biosynthesis pathway plays a central role in Cd detoxification in Solanum nigrum ( Wang et al, 2022 ). Combined transcriptomic and metabolomic approaches applied in maize to analyze gene regulatory networks modulating Rp1-D21 mutant-mediated hypersensitive pathogen defense response revealed that four uridinediphosphate-dependent glycosyltransferase (UGT) ( ZmUGTs) genes were highly expressed, whilst the SA biosynthesis and phenylpropanoid biosynthesis pathways were induced at both the transcriptional and metabolic levels, suggesting that ZmUGT genes may be involved in maize defense response by regulating SA homeostasis ( Ge et al, 2021 ). Earlier, the epigenetic-based amalgamation of multi-omics approaches elucidated the critical role DNA methylation and play in lipid biosynthesis regulation and spatio-temporal modulation of ROS during cotton fiber development ( Wang et al, 2016 ).…”
Section: Omics Technologies Integrated With Modern Plant Breeding Methods In a Systems Biology Approach For Crop Improvementmentioning
confidence: 99%
“…Previously, SA accumulation and HR severity were measured in Rp1-D21/+ mutants crossed to different genetic backgrounds (Ge et al, 2021). We calculated the correlation between SA accumulation and lesion severity in these Rp1-D21/+ mutants.…”
Section: Ics1 Is Required For Hypersensitive Response-induced Sa Bios...mentioning
confidence: 99%
“…Calculation of the correlation between SA levels and lesion severity in Rp1-D21/+ mutants of different genetic backgrounds was performed by reanalysis of the data presented in (Ge et al, 2021). Total and free SA values were obtained by measuring the height of each bar from the graph in Figure 5A (Ge et al, 2021) using a ruler. Correlation analyses of SA accumulation means, and the lesion severity scores in Figure 5B for Rp1-D21/+ in each genetic background, were performed using the "=correl()" equation in Microsoft Excel.…”
Section: Growth Conditionsmentioning
confidence: 99%
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