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However, &lt;5% of signals obtained from liquid chromatography tandem mass spectrometry (LC\u2010MS\/MS) can be identified, limiting our understanding of how metabolomes change under biotic\/abiotic stress. To address this challenge, we performed untargeted LC\u2010MS\/MS of leaves, roots, and other organs of <jats:italic>Brachypodium distachyon<\/jats:italic> (Poaceae) under 17 organ\u2013condition 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)\u2010based 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\u2010based 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\u2010accumulation analysis further identified condition\u2010specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio\u2010Analytic Resource for Plant Biology website (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/bar.utoronto.ca\/efp_brachypodium_metabolites\/cgi-bin\/efpWeb.cgi\">https:\/\/bar.utoronto.ca\/efp_brachypodium_metabolites\/cgi\u2010bin\/efpWeb.cgi<\/jats:ext-link>), 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.<\/jats:p>","DOI":"10.1111\/tpj.16160","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T08:42:57Z","timestamp":1678178577000},"page":"463-481","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Information theory and machine learning illuminate large\u2010scale metabolomic responses of <i>Brachypodium distachyon<\/i> to environmental change"],"prefix":"10.1111","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7087-6580","authenticated-orcid":false,"given":"Elizabeth H.","family":"Mahood","sequence":"first","affiliation":[{"name":"Plant Biology Section, School of Integrative Plant Science Cornell University  Ithaca NY 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