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Mass spectrometry-based proteomic analysis to characterise barley breeding lines

Source :
Theses: Doctorates and Masters
Publication Year :
2023

Abstract

Barley is a key ingredient in the malting and brewing industry, and it is the fourth most important crop being cultivated worldwide. The protein content of the barley grain is one of the main components determining the quality and nutritive value of the food and beverages prepared from barley. Mass spectrometry-based proteomic analysis is a valuable tool that can guide and inform plant breeding strategies and crop improvement programs. Understanding the proteome changes in barley grain under different growing locations, the impact of different environmental conditions and its relationship with malting characteristics have the potential to inform breeding programs to achieve high-quality malt. Moreover, hordeins, the major barley storage proteins, are among the known triggers of coeliac disease (CD). Therefore, investigating the changes in the overall grain proteome, especially hordeins provides valuable insight from a food safety perspective. This thesis focuses on the proteomic investigation of barley grain to understand differences due to genetic and environmental factors and how these differences impact end use application after food processing steps such as malting. In Chapter 2 of this thesis, the proteome and malting characteristics of three different barley genotypes grown in three different locations in Western Australia were measured by applying a bottom-up proteomics workflow. First, using discovery proteomics, 1,571 proteins were detected and in the next step, by applying a global proteome quantitation workflow, 920 proteins were quantified in barley samples. Data analysis revealed that growing location outweighed the impact of genetic background, and samples were clustered into two major groupings of northern and southern growing locations. Also, a relationship between proteome measurements and malting characteristics using weighted gene co-expression network analysis (WGCNA) were investigated. The statistical analysis showed that both the genotypes and

Details

Database :
OAIster
Journal :
Theses: Doctorates and Masters
Notes :
Bahmani, Mahya
Publication Type :
Electronic Resource
Accession number :
edsoai.on1410020270
Document Type :
Electronic Resource