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The transcriptomic landscapes of rice cultivars with diverse root system architectures grown in upland field conditions
Author(s) -
Kawakatsu Taiji,
Teramoto Shota,
Takayasu Satoko,
Maruyama Natsuko,
Nishijima Ryo,
Kitomi Yuka,
Uga Yusaku
Publication year - 2021
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/tpj.15226
Subject(s) - biology , transcriptome , oryza sativa , quantitative trait locus , phenotype , gene , plant genetics , japonica , botany , phenotypic trait , genetics , gene expression , regulation of gene expression
SUMMARY Root system architecture affects plant drought resistance and other key agronomic traits such as lodging. However, although phenotypic and genomic variation has been extensively analyzed, few field studies have integrated phenotypic and transcriptomic information, particularly for below‐ground traits such as root system architecture. Here, we report the phenotypic and transcriptomic landscape of 61 rice ( Oryza sativa ) accessions with highly diverse below‐ground traits grown in an upland field. We found that four principal components explained the phenotypic variation and that accessions could be classified into four subpopulations ( indica , aus , japonica and admixed) based on their tiller numbers and crown root diameters. Transcriptome analysis revealed that differentially expressed genes associated with specific subpopulations were enriched with stress response‐related genes, suggesting that subpopulations have distinct stress response mechanisms. Root growth was negatively correlated with auxin‐inducible genes, suggesting an association between auxin signaling and upland field conditions. A negative correlation between crown root diameter and stress response‐related genes suggested that thicker crown root diameter is associated with resistance to mild drought stress. Finally, co‐expression network analysis implemented with DNA affinity purification followed by sequencing analysis identified phytohormone signaling networks and key transcription factors negatively regulating crown root diameter. Our datasets provide a useful resource for understanding the genomic and transcriptomic basis of phenotypic variation under upland field conditions.