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Barley grain proteome assessment using multi-environment trial data and machine learning

ABSTRACT: Proteomics can be used to assess individual protein abundances, which could reflect genotypic and/or environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples from a Californian multi-environment trial were assessed using liquid chromatography-mass spectrometry. A total of 3105 proteins were identified across all samples. Location, genotype, and year explained 26.7%, 17.1%, and 14.3% of variance in the relative abundance of individual proteins, respectively. Sixteen proteins with storage, DNA/RNA binding, or enzymatic functions were found to be significantly higher/lower in abundance (compared to the overall mean) in the Yolo 3 and Imperial Valley locations, Butta 12 and LCS Odyssey genotypes, and 2017-18 and 2021-22 years. Individual protein abundances were reasonably predictive (RMSECV=1.25-2.04%) for total, alcohol-soluble, and malt protein content and malt fine extract. This study illustrates the role of the environment on the barley proteome and the utility of proteomics and machine learning to predict grain/malt quality.

INSTRUMENT(S): timsTOF HT

ORGANISM(S): Hordeum Vulgare (ncbitaxon:4513)

SUBMITTER: Glen Patrick Fox Christine Diepenbrock

PROVIDER: MSV000096087 | MassIVE | Mon Oct 14 16:09:00 BST 2024

SECONDARY ACCESSION(S): PXD056792

REPOSITORIES: MassIVE

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Barley grain proteome assessment using multi-environment trial data and machine learning

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