Primary metabolomics analyses and detection of citrus “huanglongbing” disease based on UHPLC-MS/MS and machine learning

Abstract

‘Candidatus Liberibacter asiaticus’ is the major agent associated with citrus “huanglongbing” (HLB) disease, which is the most destructive citrus disease and has caused serious losses to citrus industry worldwide. Ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS)-based nontargeted metabolomics and machine learning algorithms were developed for identifying HLB disease in different citrus varieties and growing seasons. In this study, 52 (28 up-regulated and 24 down-regulated) and 33 (26 up-regulated and 7 down-regulated) differential metabolites were screened in Navel orange (Citrus sinensis Osbeck) and Ponkan (Citrus reticulata Blanco cv. Ponkan) leaves, respectively. The variable importance in projection (VIP) algorithm was then used to select the common differential metabolites in HLB diseased samples, and a total of 19 differential metabolite variables were obtained from Navel orange and Ponkan varieties (mainly including primary metabolites such as D-ribose, D-threonate, L-ornithine). Finally, support vector machine (SVM) model based on the metabolites with significant features performed the best for the prediction of citrus HLB disease, with a classification accuracy of 100 %. The results showed that the proposed method was able to provide important and common information about citrus host-'Ca. L. asiaticus' interactions. They also demonstrated that combing untargeted metabolomics with machine learning can be effective tools for distinguishing citrus HLB infection (from asymptomatic to symptomatic) in different growing stages and cultivars.

Plant phenotyping relevance

UHPLC-MS/MSメタボロミクスと機械学習を組み合わせ、柑橘のHLB感染状態を識別する手法を開発・評価しており、病害状態の推定が中心的な貢献である。

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