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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Pathogen Interactions
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1455344
This article is part of the Research Topic Unraveling Molecular Mechanisms of Citrus Resistance to Huanglongbing View all articles

Identifying the Earliest Citrus Responses to Candidatus Liberibacter asiaticus (CLas) Infection: A Temporal Metabolomics Study

Provisionally accepted
  • 1 Citrus Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida, United States
  • 2 University of Florida, Gainesville, United States

The final, formatted version of the article will be published soon.

    The global citrus industry faces a great threat from Huanglongbing (HLB), a destructive disease caused by 'Candidatus Liberibacter asiaticus' (CLas) that induces significant economic losses without any known cure. Understanding how citrus plants defend against HLB, particularly at the early stages of infection, is crucial for developing long-term solutions. This study investigated the earliest metabolic responses of fresh citrus leaves to CLas infection using untargeted metabolomics and machine learning models. HLB-tolerant and HLB-sensitive cultivars were compared to analyze their biochemical reactions within 48 hours post-infection. HESI/Q-Orbitrap MS analysis identified temporal differential metabolites, revealing distinct metabolic pathways activated in response to CLas infection. Both cultivars responded by increasing specific metabolite concentrations, such as flavonoids, within 2 hours post-infection, but the HLB-tolerant cultivar maintained higher levels throughout the 48-hour period. This early metabolic activity could influence long-term plant health by enhancing disease resistance and reducing pathogen impact. These findings provide potential biomarkers for breeding HLB-resistant cultivars and offer valuable insights for developing sustainable management strategies to mitigate the impact of HLB on the citrus industry, ensuring its long-term productivity and economic viability.

    Keywords: Huanglongbing (HLB), temporal metabolomics, Early biomarkers, machine learning, Citrus

    Received: 26 Jun 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Li, Wang, Gmitter and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yu Wang, University of Florida, Gainesville, United States

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