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Comparative Transcriptomics of Lowland Rice Varieties Uncovers Novel Candidate Genes for Adaptive Iron Excess Tolerance

  • Iron (Fe) toxicity is a major challenge for plant cultivation in acidic waterlogged soil environments, where lowland rice is a major staple food crop. Only few studies have addressed the molecular characterization of excess Fe tolerance in rice, and these highlight different mechanisms for Fe tolerance. Out of 16 lowland rice varieties, we identified a pair of contrasting lines, Fe-tolerant Lachit and -susceptible Hacha. The two lines differed in their physiological and morphological responses to excess Fe, including leaf growth, leaf rolling, reactive oxygen species generation and Fe and metal contents. These responses were likely due to genetic origin as they were mirrored by differential gene expression patterns, obtained through RNA sequencing, and corresponding gene ontology term enrichment in tolerant vs. susceptible lines. Thirty-five genes of the metal homeostasis category, mainly root expressed, showed differential transcriptomic profiles suggestive of an induced tolerance mechanism. Twenty-two out of these 35 metal homeostasis genes were present in selection sweep genomic regions, in breeding signatures, and/or differentiated during rice domestication. These findings suggest that Fe excess tolerance is an important trait in the domestication of lowland rice, and the identified genes may further serve to design the targeted Fe tolerance breeding of rice crops.

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Document Type:Article
Author:Saradia KarORCiD, Hans-Jörg MaiORCiD, Hadeel Khalouf, Heithem Ben AbdallahORCiD, Samantha Flachbart, Claudia Fink-Straube, Andrea BräutigamORCiD, Guosheng Xiong, Lianguang Shang, Sanjib Kumar PandaORCiD, Petra BauerORCiD
Parent Title (English):Plant and Cell Physiology
First Page:624
Last Page:640
Year of first Publication:2021
Release Date:2022/08/19
Impact:04.937 (2021)
Funding Information:Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC-2048/1 (project ID 390686111).
Scientific Units:Chemical Analytics
DDC classes:500 Naturwissenschaften und Mathematik / 580 Pflanzen (Botanik)
Open Access:Open Access
Signature:INM 2021/131
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International