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Proteomic analysis of oil body membrane proteins accompanying the onset of desiccation phase during sunflower seed development.

ABSTRACT: A noteworthy metabolic signature accompanying oil body (OB) biogenesis during oilseed development is associated with the modulation of the oil body membranes proteins. Present work focuses on 2-dimensional polyacrylamide gel electrophoresis (2-D PAGE)-based analysis of the temporal changes in the OB membrane proteins analyzed by LC-MS/MS accompanying the onset of desiccation (20-30 d after anthesis; DAA) in the developing seeds of sunflower (Helianthus annuus L.). Protein spots unique to 20-30 DAA stages were picked up from 2-D gels for identification and the identified proteins were categorized into 7 functional classes. These include proteins involved in energy metabolism, reactive oxygen scavenging, proteolysis and protein turnover, signaling, oleosin and oil body biogenesis-associated proteins, desiccation and cytoskeleton. At 30 DAA stage, exclusive expressions of enzymes belonging to energy metabolism, desiccation and cytoskeleton were evident which indicated an increase in the metabolic and enzymatic activity in the cells at this stage of seed development (seed filling). Increased expression of cruciferina-like protein and dehydrin at 30 DAA stage marks the onset of desiccation. The data has been analyzed and discussed to highlight desiccation stage-associated metabolic events during oilseed development.

SUBMITTER: Thakur A

PROVIDER: S-EPMC4854339 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Proteomic analysis of oil body membrane proteins accompanying the onset of desiccation phase during sunflower seed development.

Thakur Anita A Bhatla Satish C SC

Plant signaling & behavior 20150101 12

A noteworthy metabolic signature accompanying oil body (OB) biogenesis during oilseed development is associated with the modulation of the oil body membranes proteins. Present work focuses on 2-dimensional polyacrylamide gel electrophoresis (2-D PAGE)-based analysis of the temporal changes in the OB membrane proteins analyzed by LC-MS/MS accompanying the onset of desiccation (20-30 d after anthesis; DAA) in the developing seeds of sunflower (Helianthus annuus L.). Protein spots unique to 20-30 D ...[more]

PMID: 26786011

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Project description:Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.

Dataset Information

Proteomic analysis of oil body membrane proteins accompanying the onset of desiccation phase during sunflower seed development.

Publications

Proteomic analysis of oil body membrane proteins accompanying the onset of desiccation phase during sunflower seed development.

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