scholarly journals Comparative Phosphoproteomic Analysis of Barley Embryos with Different Dormancy during Imbibition

2019 ◽  
Vol 20 (2) ◽  
pp. 451 ◽  
Author(s):  
Shinnosuke Ishikawa ◽  
José Barrero ◽  
Fuminori Takahashi ◽  
Scott Peck ◽  
Frank Gubler ◽  
...  
Keyword(s):  
Large Scale ◽  
Barley Grain ◽  
Scale Analysis ◽  
Motif Analysis ◽  
Water Imbibition ◽  
Grain Dormancy ◽  
Large Scale Analysis ◽  
Multiple Protein ◽  
Barley Grains ◽  
The Right

Dormancy is the mechanism that allows seeds to become temporally quiescent in order to select the right time and place to germinate. Like in other species, in barley, grain dormancy is gradually reduced during after-ripening. Phosphosignaling networks in barley grains were investigated by a large-scale analysis of phosphoproteins to examine potential changes in response pathways to after-ripening. We used freshly harvested (FH) and after-ripened (AR) barley grains which showed different dormancy levels. The LC-MS/MS analysis identified 2346 phosphopeptides in barley embryos, with 269 and 97 of them being up- or downregulated during imbibition, respectively. A number of phosphopeptides were differentially regulated between FH and AR samples, suggesting that phosphoproteomic profiles were quite different between FH and AR grains. Motif analysis suggested multiple protein kinases including SnRK2 and MAPK could be involved in such a difference between FH and AR samples. Taken together, our results revealed phosphosignaling pathways in barley grains during the water imbibition process.

Comparative Phosphoproteomic Analysis Reveals a Decay of ABA Signaling in Barley Embryos during After-Ripening

2019 ◽  
Vol 60 (12) ◽  
pp. 2758-2768 ◽  
Author(s):  
Shinnosuke Ishikawa ◽  
Jos� M Barrero ◽  
Fuminori Takahashi ◽  
Hirofumi Nakagami ◽  
Scott C Peck ◽  
...  
Keyword(s):  
Protein Kinases ◽  
Seed Dormancy ◽  
Large Scale ◽  
Major Protein ◽  
Aba Signaling ◽  
Motif Analysis ◽  
Large Scale Analysis ◽  
Aba Sensitivity ◽  
Barley Grains

Abstract Abscisic acid (ABA) is a phytohormone and a major determinant of seed dormancy in plants. Seed dormancy is gradually lost during dry storage, a process known as ‘after-ripening’, and this dormancy decay is related to a decline in ABA content and sensitivity in seeds after imbibition. In this study, we aimed at investigating the effect of after-ripening on ABA signaling in barley, our cereal model species. Phosphosignaling networks in barley grains were investigated by a large-scale analysis of phosphopeptides to examine potential changes in response pathways to after-ripening. We used freshly harvested (FH) and after-ripened (AR) barley grains which showed different ABA sensitivity. A total of 1,730 phosphopeptides were identified in barley embryos isolated from half-cut grains. A comparative analysis showed that 329 and 235 phosphopeptides were upregulated or downregulated, respectively after ABA treatment, and phosphopeptides profiles were quite different between FH and AR embryos. These results were supported by peptide motif analysis which suggested that different sets of protein kinases are active in FH and AR grains. Furthermore, in vitro phosphorylation assays confirmed that some phosphopeptides were phosphorylated by SnRK2s, which are major protein kinases involved in ABA signaling. Taken together, our results revealed very distinctive phosphosignaling networks in FH and AR embryos of barley, and suggested that the after-ripening of barley grains is associated with differential regulation of phosphosignaling pathways leading to a decay of ABA signaling.

scholarly journals Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  
Keyword(s):  
Natural Product ◽  
Large Scale ◽  
Scale Analysis ◽  
Computational Tools ◽  
Natural Product Discovery ◽  
Large Scale Analysis ◽  
Metabolite Annotation

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

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