Pratibha Demiwal, Sumedh Tayade, Shri Ram Yadav, Debabrata Sircar

A metabolomics perspective on root‐derived plant immunity and phytohormone interaction

  • Cell Biology
  • Plant Science
  • Genetics
  • General Medicine
  • Physiology
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AbstractA plant's root system is essential to its survival as it supports anchorage and absorbs water and minerals from the ground. Several microorganisms interact with roots in the rhizosphere, and they can be neutral, positive, or negative. Identifying beneficial and pathogenic microorganisms and limiting or promoting their colonization is essential for roots to survive. Signaling molecules and metabolites play an important regulatory role in determining root‐microbe associations. Phytohormones modulate plant‐microbe interactions by metabolic reprogramming and transmitting long‐range signals, resulting in local and systemic resistance. Mass spectrometry‐based metabolomics provides a powerful means to study root‐microbe interactions. It provides a qualitative and quantitative approach for determining the mechanisms of root‐microbe interactions and also helps to understand pathogen‐induced metabolic reprogramming. By using metabolomics, one can identify biomarker metabolites that indicate disease resistance or sensitivity in the roots. Compared to other omics methods, metabolomics is still underutilized in root‐pathogen interaction studies. It is, therefore, vital that metabolomics is popularized to gain a better understanding of disease resistance and susceptibility. Through untargeted metabolomics, we can gain insight into plant‐microbial interactions across a variety of tissues, which helps us develop more effective crop and plant protection measures. In this review, we describe the current status and progress in metabolomics applications for studying root‐microbe interactions with an emphasis on root‐derived phytohormones and specialized metabolites. In addition, we also proposed the use of metabolomics to identify novel biomarkers during root‐microbe interactions, to distinguish resistant genotypes from susceptible ones, or to predict nondestructively early disease progression.

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