ArticleNature communications2025
Predicting rhizosphere-competence-related catabolic gene clusters in plant-associated bacteria with rhizoSMASH.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrative genomic and phenotypic analyses reveal broad-spectrum antifungal activity and biocontrol potential in Bacillus ayatagriensis.Functional & integrative genomics · 2026Article
- Revealing Functional Traits of Insect Pest Suppressive Rhizobacterial Strains Through Comparative Genomics.Environmental microbiology · 2026Article
- A 'Genome-First' Framework for Next-Generation Bioinputs: From Functional Mining to Rational Synthetic Microbial Communities.Environmental microbiology · 2026Review
- BiG-SCAPE 2.0 and BiG-SLiCE 2.0: scalable, accurate and interactive sequence clustering of metabolic gene clusters.Nature communications · 2026Article
- Calcium phosphate-solubilizing bacteria promote growth and alleviate phosphorus deficiency in French marigold with minimal impact on the rhizosphere microbiome.Environmental microbiome · 2026Article
- Roots: metabolic architects of beneficial microbiome assembly.Plant physiology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Plants release a substantial fraction of their photosynthesized carbon into the rhizosphere as root exudates 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 integrate published information on catabolic genes in bacteria that contribute to their rhizosphere competence and develop the rhizoSMASH algorithm for genome-synteny-based annotation of rhizosphere-competence-related catabolic gene clusters (rCGCs) in bacteria with 58 curated detection rules. Our analysis reveals heterogeneity in rCGC prevalence both across and within plant-associated bacterial taxa, indicating extensive niche specialization. Furthermore, we demonstrate the predictive value of the presence or absence of rCGCs for rhizosphere competence in machine learning with two case studies. rhizoSMASH provides an extensible framework for studying rhizosphere bacterial catabolism, facilitating microbiome-assisted breeding approaches for sustainable agriculture.
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Registered trials
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