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Dryad

Linking genomic and phenotypic traits to interaction outcomes in a synthetic phyllosphere community

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Sep 22, 2026 version files 208.28 KB

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Abstract

Microbial community function is difficult to predict from the properties of individual members, in part because the same species can behave differently in isolation and in a community. Using a 16-member synthetic bacterial community isolated from the tomato phyllosphere, with the pathogen Pseudomonas syringae pv. tomato, we combined whole-genome sequencing, phenotypic profiling, and reciprocal spent-media assays spanning over 600 single-species and community-level combinations to ask what predicts the outcome of environmentally mediated interactions. Genomic annotations, metabolic breadth, growth strategy, and phylogenetic relatedness were all poor predictors of interaction outcomes in nutrient rich media, and cross-validated models did not perform better than a mean-only baseline. Community-conditioned media reduced growth substantially more than the average of their member strains predicted, but never more than the most inhibitory member, so that community effects fell consistently between the average and the strongest single-member effect; the number of member strains did not predict suppression, indicating that composition rather than inoculum size drives these effects. Although this community suppresses P. syringae on plants, the pathogen was indistinguishable from commensal members in cell-free assays, both in its sensitivity to community filtrates and in the effect of its own filtrate, indicating that protection is not reducible to the chemical environment the community generates. On tomato seedlings, pathogen challenge raised disease scores on bare plants but not on plants carrying a community, whereas no individual strain altered disease outcome. Across scales, interaction outcomes were governed by the composition of the resident community rather than by the identity, traits, or ecological role of any single member, indicating that the community, together with its host context, should be considered for predicting microbiome function.