Context dependency of soil microbes in mesocosm
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB54693 | ENA |
REPOSITORIES: ENA
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Project description:Plants in their natural and agricultural environments are continuously exposed to a plethora of diverse microorganisms resulting in microbial colonization of plants in the rhizosphere. This process is believed to be accompanied by an intricate network of ongoing simultaneous interactions. In this study, we compared transcriptional patterns of Arabidopsis thaliana roots and shoots in the presence and absence of whole microbial communities extracted from compost soil. The results show a clear growth promoting effect of Arabidopsis shoots in the presence of soil microbes compared to axenically grown plants under identical conditions. Element analyses showed that iron uptake was facilitated by these mixed microbial communities which also lead to transcriptional downregulation of genes required for iron transport. In addition, soil microbial communities suppressed the expression of marker genes involved in oxidative stress/redox signalling, cell wall modification and plant defense. While most previous studies have focussed on individual plant-microbe interactions, our data suggest that multi-species transcriptional profiling, using simultaneous plant and metatranscriptomics coupled to metagenomics may be required to further increase our understanding of the intricate networks underlying plant-microbe interactions in their diverse environments.
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Project description:Despite the broad use of single-cell and single-nucleus RNA sequencing in plant research, accurate cluster annotation in less studied plant species remains a major challenge due to the lack of validated marker genes. Here, using soil-grown wheat roots as a model, we generated a single-cell RNA-sequencing (scRNA-seq) atlas and annotated cluster identities in an unbiased way by transferring existing annotations from publicly available datasets in wheat, rice, maize and Arabidopsis. These cross-species orthology-based predictions were next validated using untargeted spatial transcriptomics. This information refined existing cluster annotations for different datasets across key plant model species. We then used the validated clusters to generate cell type-specific gene regulatory networks for root tissues of wheat and two other monocot crop species. By integrating all available data, including homeolog expression in wheat, we predicted reliable tissue-specific markers which are conserved across different species. In summary, we provided an annotated and validated single cell transcriptomic resource for soil-grown wheat root apical meristems and revealed conserved cell type-specific regulators and markers across species. These data expand upon previous root single cell atlas resources in crops, and will facilitate cell type annotation in non-model plant species in the future.
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