Growth rate is a dominant factor predicting the rhizosphere effect

Author:

López José L123ORCID,Fourie Arista1ORCID,Poppeliers Sanne W M4,Pappas Nikolaos1ORCID,Sánchez-Gil Juan J4ORCID,de Jonge Ronnie4ORCID,Dutilh Bas E13ORCID

Affiliation:

1. Theoretical Biology and Bioinformatics, Department of Biology, Science for Life, Utrecht University , Utrecht, The Netherlands

2. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales , Bariloche, Rio Negro, Argentina

3. Institute of Biodiversity, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena , Jena, Germany

4. Plant-Microbe Interactions, Department of Biology, Science for Life, Utrecht University , Utrecht, The Netherlands

Abstract

Abstract The root microbiome is shaped by plant root activity, which selects specific microbial taxa from the surrounding soil. This influence on the microorganisms and soil chemistry in the immediate vicinity of the roots has been referred to as the rhizosphere effect. Understanding the traits that make bacteria successful in the rhizosphere is critical for developing sustainable agriculture solutions. In this study, we compared the growth rate potential, a complex trait that can be predicted from bacterial genome sequences, to functional traits encoded by proteins. We analyzed 84 paired rhizosphere- and soil-derived 16S rRNA gene amplicon datasets from 18 different plants and soil types, performed differential abundance analysis, and estimated growth rates for each bacterial genus. We found that bacteria with higher growth rate potential consistently dominated the rhizosphere, and this trend was confirmed in different bacterial phyla using genome sequences of 3270 bacterial isolates and 6707 metagenome-assembled genomes (MAGs) from 1121 plant- and soil-associated metagenomes. We then identified which functional traits were enriched in MAGs according to their niche or growth rate status. We found that predicted growth rate potential was the main feature for differentiating rhizosphere and soil bacteria in machine learning models, and we then analyzed the features that were important for achieving faster growth rates, which makes bacteria more competitive in the rhizosphere. As growth rate potential can be predicted from genomic data, this work has implications for understanding bacterial community assembly in the rhizosphere, where many uncultivated bacteria reside.

Publisher

Oxford University Press (OUP)

Subject

Ecology, Evolution, Behavior and Systematics,Microbiology

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