bioRxivPlant BiologyPreprint
Antibiotics and copper drive compartment-specific dysbiosis and functional reprogramming in tomato microbiomes
Fabiano José PerinaiD, Vanessa E Thomas, Toi KetehouliiD, Sameerika Dissanayaka Mudiyanselage, Erica M. GossiD, Samuel J. MartinsiD
Abstract
Plant-associated microbiomes help sustain plant immunity and productivity, yet the degree to which agricultural antimicrobials reshape these microbial networks and alter disease outcomes remains poorly characterized. Here, we quantified how chemical inputs such as copper and streptomycin trigger distinct, compartment-specific dysbiosis in tomato (Solanum lycopersicum), fundamentally decoupling microbiome-mediated immunity from pathogen defense. We demonstrated that chemical disturbance correlates with increased susceptibility to bacterial spot caused by Xanthomonas perforans. Notably, streptomycin-induced dysbiosis increased epidemic intensity, characterized by physiological and growth trade-offs, including reduced fruit number, mass, and seed weight, alongside a decline in photosynthetic gas exchange; while copper-induced dysbiosis had intermediate effects. Chemical perturbation restructured microbiomes in a niche-dependent manner based on amplicon profiling: seeds and the phyllosphere showed the greatest instability, including higher dispersion, taxon turnover, and network reorganization under streptomycin, whereas rhizosphere communities remained more deterministic but were structurally reshaped by copper. Rhizosphere metagenomics further revealed enrichment of antibiotic-resistance functions under streptomycin treatment and of metal-tolerance and oxidative-stress functions under copper treatment, along with shifts in genes linked to cell-envelope remodeling and redox metabolism. These findings identify microbiome dysbiosis as a mechanistic bridge between chemical stress and plant disease, and support crop protection strategies that preserve microbiome integrity.
- organism: plant (other)
- assay: metagenomics
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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