論文

査読有り
2018年5月1日

An integrated approach of proteomics and computational genetic modification effectiveness analysis to uncover the mechanisms of flood tolerance in soybeans

International Journal of Molecular Sciences
  • Xin Wang
  • ,
  • Katsumi Sakata
  • ,
  • Setsuko Komatsu

19
5
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3390/ijms19051301
出版者・発行元
MDPI AG

Flooding negatively affects the growth of soybeans. Recently, omic approaches have been used to study abiotic stress responses in plants. To explore flood-tolerant genes in soybeans, an integrated approach of proteomics and computational genetic modification effectiveness analysis was applied to the soybean (Glycine max L. (Merrill)). Flood-tolerant mutant and abscisic acid (ABA)-treated soybean plants were used as the flood-tolerant materials. Among the primary metabolism, glycolysis, fermentation, and tricarboxylic acid cycle were markedly affected under flooding. Fifteen proteins, which were related to the affected processes, displayed similar protein profiles in the mutant and ABA-treated soybean plants. Protein levels of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), aconitase 1, and 2-oxoglutarate dehydrogenase were higher in flood-tolerant materials than in wild-type soybean plants under flood conditions. These three proteins were positioned in each of the three enzyme groups revealed by our computational genetic modification effectiveness analysis, and the three proteins configured a candidate set of genes to promote flood tolerance. Additionally, transcript levels of GAPDH were similar in flood-tolerant materials and in unstressed plants. These results suggest that proteins related to energy metabolism might play an essential role to confer flood tolerance in soybeans.

リンク情報
DOI
https://doi.org/10.3390/ijms19051301
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/29701710
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000435297000037&DestApp=WOS_CPL
ID情報
  • DOI : 10.3390/ijms19051301
  • ISSN : 1422-0067
  • ISSN : 1661-6596
  • PubMed ID : 29701710
  • SCOPUS ID : 85046134076
  • Web of Science ID : WOS:000435297000037

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