Information theory and machine learning illuminate large‐scale metabolomic responses of Brachypodium distachyon to environmental change. (11th March 2023)

• Record Type: Journal Article
• Authors: Mahood, Elizabeth H.; Bennett, Alexandra A.; Komatsu, Karyn; Kruse, Lars H.; Lau, Vincent; Rahmati Ishka, Maryam; Jiang, Yulin; Bravo, Armando; Louie, Katherine; Bowen, Benjamin P.; Harrison, Maria J.; Provart, Nicholas J.; Vatamaniuk, Olena K.; Moghe, Gaurav D.
• Is Part Of: Plant journal. Volume 114:Number 3(2023)
• Page Start: 463
• Page End: 481
• Publication Date: 2023-03-11
• Subjects: computational biology -- metabolomics -- mass spectrometry -- abiotic stress -- mycorrhizal symbiosis -- Brachypodium; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.16160
• Languages: English
• ISSNs: 0960-7412

Abstract

SUMMARY: Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC‐MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC‐MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ–condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)‐based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML‐based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co‐accumulation analysis further identified condition‐specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio‐Analytic Resource for Plant Biology website (https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi‐bin/efpWeb.cgi ), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation. Significance Statement: The inability to identify a vast majority of peaks detected in untargeted metabolomics studies creates a bottleneck in understanding how plants respond metabolically to stress. Here, using Brachypodium distachyon as a model, we employed information theory and machine learning to assess how leaf and root metabolomes and specific chemical structural classes change under stress, revealing novel insights.