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Quantitative comparison between the rhizosphere effect of Arabidopsis thaliana and co-occurring plant species with a longer life history.

Martinus SchneijderbergXu ChengCarolien FrankenMattias de HollanderRobin van VelzenLucas SchmitzRobin HeinenRene GeurtsWim H van der PuttenThiemo Martijn BezemerTon Bisseling
Published in: The ISME journal (2020)
As a model for genetic studies, Arabidopsis thaliana (Arabidopsis) offers great potential to unravel plant genome-related mechanisms that shape the root microbiome. However, the fugitive life history of this species might have evolved at the expense of investing in capacity to steer an extensive rhizosphere effect. To determine whether the rhizosphere effect of Arabidopsis is different from other plant species that have a less fugitive life history, we compared the root microbiome of Arabidopsis to eight other, later succession plant species from the same habitat. The study included molecular analysis of soil, rhizosphere, and endorhizosphere microbiome both from the field and from a laboratory experiment. Molecular analysis revealed that the rhizosphere effect (as quantified by the number of enriched and depleted bacterial taxa) was ~35% lower than the average of the other eight species. Nevertheless, there are numerous microbial taxa differentially abundant between soil and rhizosphere, and they represent for a large part the rhizosphere effects of the other plants. In the case of fungal taxa, the number of differentially abundant taxa in the Arabidopsis rhizosphere is 10% of the other species' average. In the plant endorhizosphere, which is generally more selective, the rhizosphere effect of Arabidopsis is comparable to other species, both for bacterial and fungal taxa. Taken together, our data imply that the rhizosphere effect of the Arabidopsis is smaller in the rhizosphere, but equal in the endorhizosphere when compared to plant species with a less fugitive life history.
Keyphrases
  • plant growth
  • microbial community
  • arabidopsis thaliana
  • transcription factor
  • cell wall
  • gene expression
  • dna methylation
  • machine learning
  • high resolution
  • single cell
  • genome wide