bioRxivPlant BiologyPreprint
Transcriptomic expression patterns of two contrasting lowland rice varieties reveal high iron stress tolerance
S. Kar, H.-J. Mai, H. Khalouf, H. Ben Abdallah, S. Flachbart, C. Fink-Straube, A. Braeutigam, G. Xiong, L. Shang, S. K. Panda, P. Bauer
Abstract
Iron (Fe) toxicity is a major challenge for plant cultivation in acidic water-logged soil environments, where lowland rice is a major staple food crop. Only few studies addressed the molecular characterization of excess Fe tolerance in rice, and these highlight different mechanisms for Fe tolerance in the studied varieties. Here, we screened 16 lowland rice varieties for excess Fe stress growth responses to identify contrasting lines, Fe-tolerant Lachit and -susceptible Hacha. Hacha and Lachit differed in their physiological and morphological responses to excess Fe, including leaf growth, leaf rolling, reactive oxygen species generation, Fe and metal contents. These responses were mirrored by differential gene expression patterns, obtained through RNA-sequencing, and corresponding GO term enrichment in tolerant versus susceptible lines. From the comparative transcriptomic profiles between Lachit and Hacha in response to excess Fe stress, individual genes of the category metal homeostasis, mainly root-expressed, may contribute to the tolerance of Lachit. 22 out of these 35 metal homeostasis genes are present in selection sweep genomic regions, in breeding signatures and/or differentiated during rice domestication. These findings will serve to design targeted Fe tolerance breeding of rice crops. Summary statementLowland rice varieties Hacha and Lachit were selected for contrasting abilities to cope with iron excess stress. Morphological and physiological phenotypes were mirrored by molecular transcriptome changes, indicating altered metal homeostasis in the root as an adaptive tolerance mechanism in Lachit.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- approach: experimental assay
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