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

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. 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, Fe and metal contents. These responses were likely of genetic origin as they were mirrored by differential gene expression patterns, obtained through RNA-sequencing, and corresponding GO term enrichment in tolerant versus susceptible lines. 35 genes of the metal homeostasis category, mainly root-expressed, showed differential transcriptomic profiles suggestive of an induced tolerance mechanism. 22 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 targeted Fe tolerance breeding of rice crops.

SEC14-GOLD protein PATELLIN2 binds IRON-REGULATED TRANSPORTER1 linking root iron uptake to vitamin E

“…Protein descriptions were downloaded from The Arabidopsis Information Resource ( www.arabidopsis.org ). GO term enrichment was conducted and represented as described ( Kar et al, 2021 ). The proteomics data are available via ProteomeXchange with identifier PXD032079.…”

Effects of endophytic Bacillus spp. on accumulation and distribution of iron in the shoots of lowland rice grown under iron toxic conditions

“…Other strategies allow for tolerance of higher iron content within the plant: (1) through deposition of iron in specific tissues such as roots or old leaves, chelation, and sequestration to the vacuole (includer/avoidance type) (Aung et al., 2019; Briat et al., 2010; Majerus et al., 2007; Kar et al., 2021), or (2) through enzymatic detoxification of iron‐induced radical formation in both symplast and apoplast (includer/tissue tolerance type) (Becana et al., 1998; Fang et al., 2001; Wu et al., 2017).…”

A2TEA: Identifying trait-specific evolutionary adaptations

“…This includes forward genetics approaches such as identifying causative genes for advantageous mutant phenotypes, 33 finding common regulators for several stresses via traditional transcriptomics, 34 usage of Quantitative Trait Locus (QTL) mapping and Genome-wide association studies (GWAS) incl. potential integration with expression data, 35 the combination of expression data with functional information and clustering methods 36,37 and also machine learning based approaches that employ transcriptomic or phenomic data as the basis of their candidate gene predictions. 38,39 The underlying methods are manifold and include approaches such as Bulked-Segregant analysis, 40 k-means clustering, 41 WGCNA, 42 co-expression networks 43 and set analyses of DEGs often in combination with pathway or GO term enrichment analyses.…”