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Plant-microbiome-based biostimulants: Mechanistic insights into disease suppression and sustainable crop productivity
1Research and Development Cell, Lovely Professional University, Phagwara, Punjab 144411, India; School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, Punjab 144411, India.
Abstract:
Plant-associated microbiomes are becoming widely accepted as being part of the regulation of crop health, productivity and resilience to growing biotic stress. This review aims to synthesize current knowledge on the ecological organization, mechanistic basis, and engineering potential of plant microbiomes in disease suppression and sustainable crop productivity under biotic stress. In this context, microbiome-derived biostimulants, bioinoculants and bioactive compounds are emerging as promising eco-friendly strategies to enhance plant health and stress resilience. The review is a synthesis of current progress in the composition, functional properties, and ecological processes of plant microbiomes in the rhizosphere, phyllosphere, and endosphere. Mechanistically, microbiome-mediated disease suppression works via nutrient competition (e.g., siderophore-mediated iron binding), generation of antimicrobial metabolites (e.g., DAPG, lipopeptides, VOCs), niche exclusion by biofilm production, and regulation of plant defense responses via induced systemic resistance (ISR) and defense priming. These activities include the stimulation of pattern-stimulated defense system, such as Ca2 + influx, reactive ROS build-up, MAPK signaling, and regulation of defense-associated genes. Multi-omics studies have shown that the functionality of microbiomes is too specific to a situation, and it depends on host genotype, environmental factors, and networks of microbial interactions. New approaches like synthetic microbial consortia and microbiome engineering have potential to improve disease control but are limited by ecological variability and field variability. Further development of mechanistic insights and predictive models will be critical in the ability to apply microbiome-based interventions to scalable and robust agricultural systems.
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