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Substrate-driven reprogramming of the rhizosphere metabolome underlies enhanced tomato growth and quality in soilless cultivation

Archives of Toxicology 2026
Yu Chen, Yuyuan Chai, Xi Chen, Jing Shi

Summary

This study isolated and characterized PET-degrading bacteria from soil environments, testing their enzyme systems for PET hydrolysis activity at different temperatures and pH conditions. The findings expand knowledge of environmental bacterial communities capable of initiating PET biodegradation under natural soil conditions.

Polymers

Introduction The rhizosphere metabolome is a crucial mediator of plant-substrate interactions, yet how different cultivation substrates reprogram this metabolic interface and influence crop performance remains poorly understood. Methods Using tomato as a model, we employed non-targeted metabolomics based on solvent extraction followed by gas chromatography-mass spectrometry (GC-MS) to compare rhizosphere metabolic profiles under three substrates: conventional facility soil (SL), pinecone residue (PR), and peat substrate (PS). Results We identified and annotated 276 metabolites, with lipids and lipid-like molecules being the most abundant class (24.28%). Substrate type fundamentally reshaped the metabolome, with PR inducing the most distinct profile and PS triggering the most extensive metabolic reprogramming (277 differential metabolites). Notably, over 91% of differential metabolites were up-regulated upon tomato cultivation. The superior physicochemical properties (e.g., lower bulk density, higher nutrient availability) of organic substrates (PR and PS) were linked to both the distinct metabolic signatures and significant enhancements in tomato growth and fruit quality, particularly in PS which showed an 80.08% yield increase and a 50.19% boost in fruit vitamin C content. Discussion Our findings demonstrate that organic substrates, especially peat, enhance plant performance by activating a more diverse and functionally specialized rhizosphere metabolome, providing a mechanistic basis for optimizing substrate selection in facility agriculture.

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