Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.
Journal:
Journal of agricultural and food chemistry
PMID:
39536264
Abstract
Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Californian multi-environment trials (2017-2022) were assessed using liquid chromatography-mass spectrometry. In total, 3104 proteins were identified across all of the samples. Location, genotype, and year explained 26.7, 17.1, and 14.3% of the variance in the relative abundance of individual proteins, respectively. Sixteen proteins with storage, DNA/RNA binding, or enzymatic functions were 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 the 2017-18 and 2021-22 years. Individual protein abundances were reasonably predictive (RMSE = 1.25-2.04%) for total, alcohol-soluble, and malt protein content and malt fine extract. This study illustrates the role of the environment in the barley proteome and the utility of proteomics and machine learning to predict grain/malt quality.