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Related Experiment Video

Updated: Jun 7, 2025

Metabolomic Analysis of Barley by Gas Chromatography/Mass Spectrometry
08:15

Metabolomic Analysis of Barley by Gas Chromatography/Mass Spectrometry

Published on: November 8, 2024

375

Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Maany Ramanan1, Harmonie Bettenhausen2, Gabriela Grigorean3

  • 1Department of Food Science & Technology, University of California, Davis, California 95616-5270, United States.

Journal of Agricultural and Food Chemistry
|November 13, 2024
PubMed
Summary
This summary is machine-generated.

Proteomics analysis of barley grain reveals environmental factors significantly impact protein abundance. This protein data can accurately predict grain and malt quality, offering valuable insights for crop improvement.

Keywords:
barleyhordeinmachine learningproteomicstimsTOF LC-MS

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Area of Science:

  • Agricultural Science
  • Biochemistry
  • Proteomics

Background:

  • Proteomics offers a method to analyze individual protein levels in barley grain.
  • Protein abundance can be influenced by genetic and environmental factors, potentially impacting grain and malt quality.
  • Understanding these influences is key for optimizing barley cultivation and processing.

Purpose of the Study:

  • To investigate the impact of genotype, location, and year on the barley grain proteome.
  • To identify specific proteins whose abundance varies with these factors.
  • To assess the predictive power of individual protein abundances for grain and malt quality traits.

Main Methods:

  • Liquid chromatography-mass spectrometry (LC-MS) was used to analyze 3104 proteins in 79 barley grain samples.
  • Samples were sourced from Californian multi-environment trials spanning 2017-2022.
  • Statistical analysis was performed to determine the variance explained by location, genotype, and year.

Main Results:

  • Location, genotype, and year accounted for 26.7%, 17.1%, and 14.3% of protein abundance variance, respectively.
  • Sixteen proteins with storage, DNA/RNA binding, or enzymatic functions showed significant abundance variations across different locations, genotypes, and years.
  • Individual protein abundances demonstrated good predictive capability for total protein, alcohol-soluble protein, malt protein content, and malt fine extract (RMSECV = 1.25-2.04%).

Conclusions:

  • Environmental factors play a significant role in shaping the barley grain proteome.
  • Proteomics, combined with machine learning, is a powerful tool for predicting barley grain and malt quality.
  • This approach can aid in developing improved barley varieties and optimizing cultivation practices.