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April 2nd, 2025
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Wageningen University
bioinformatics
biorxiv

Predicting Rhizosphere Competence Related Catabolic Gene Clusters in plant-associated bacteria with RhizoSMASH

Li, Y.Open in Google Scholar•Sun, M.Open in Google Scholar•Raaijmakers, J. M.Open in Google Scholar•Mommer, L.Open in Google Scholar•Zhang, F.Open in Google Scholar•Song, C.Open in Google Scholar•Medema, M. H.Open in Google Scholar

Plants release a substantial fraction of their photosynthesized carbon into the rhizosphere as root exudates, a mix of chemically diverse compounds that drive microbiome assembly. Deciphering how plants modulate the composition and activities of rhizosphere microbiota through root exudates is challenging, as no dedicated computational methods exist to systematically identify microbial root exudate catabolic pathways. Here we used and integrated published information on catabolic genes in bacterial taxa that contribute to their rhizosphere competence. We developed the RhizoSMASH algorithm for genome-synteny-based annotation of rhizosphere-competence-related catabolic gene clusters (rCGCs) in bacteria by means of a set of 58 knowledge-based logic detection rules carefully curated through sequence similarity network analysis. Our analysis revealed large heterogeneity of rCGC prevalence both across and within plant-associated bacterial taxa, indicating extensive niche specialization. Furthermore, we validated that the presence or absence of rCGCs in bacterial genomes reflects their catabolic capacity and is predictive for their rhizosphere competence by aligning rhizoSMASH results with paired genome/metabolome datasets of rhizobacterial taxa. RhizoSMASH provides an extensible framework for studying rhizosphere bacterial catabolism, allowing targeted selection of beneficial bacterial taxa for microbiome-assisted breeding approaches for sustainable agriculture.

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