2018
DOI: 10.1016/j.pestbp.2017.06.005
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Label-free quantitative proteomics analysis of Cytosinpeptidemycin responses in southern rice black-streaked dwarf virus-infected rice

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Cited by 33 publications
(40 citation statements)
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“…A label-free quantitative proteomics analysis provided a comprehensive view about the response of RBSDV-infected rice to cytosinpeptidemycin treatment. Several abscisic acid-related proteins were regulated, suggesting that cytosinpeptidemycin may activate the abscisic acid pathway to eliminate RBSDV in rice [ 24 ]. In maize, approximately 1200 protein spots were detected and 91 DAPs were identified under RBSDV infection by 2-D method [ 25 ].…”
Section: Discussionmentioning
confidence: 99%
“…A label-free quantitative proteomics analysis provided a comprehensive view about the response of RBSDV-infected rice to cytosinpeptidemycin treatment. Several abscisic acid-related proteins were regulated, suggesting that cytosinpeptidemycin may activate the abscisic acid pathway to eliminate RBSDV in rice [ 24 ]. In maize, approximately 1200 protein spots were detected and 91 DAPs were identified under RBSDV infection by 2-D method [ 25 ].…”
Section: Discussionmentioning
confidence: 99%
“…As this plant is considered as a non-model organism, we have used the transcriptome non-redundant sequences as our reference sequence database for protein identification (proteomics informed by transcriptomics approach) and as such comparative analysis is important to illuminate complementing or contrasting trends between the different molecular levels, proteins and transcripts. Other plant proteomics studies have only utilised qRT-PCR for complementing their protein expression levels such as in Botrytis infected kiwifruit (Liu et al, 2018), virus infected rice (Yu et al, 2017) and rapeseed varieties (Wang et al, 2018). The comparison between both proteome and transcriptome as performed in this report will allow even wider comparative analysis of the expression profiles between these two omics studies.…”
Section: Discussionmentioning
confidence: 99%
“…The sequences of all differentially expressed proteins were searched according to their accession numbers in FASTA format obtained from the UniProt database (http://www.uniprot.org/). To generate the significant GO term, we analyzed the Gene Ontology (GO), which determines gene function in a standardized classification system, in three aspects: biological process, cellular components, and molecular functions . Significant correlations for GO enrichment functional item analysis were determined, and differentially expressed proteins and functional categories were identified through the Fisher's exact test under all identified proteins as the background …”
Section: Methodsmentioning
confidence: 99%