Integrated QTL-seq and transcriptome analyses identify a DUF1677 candidate gene with a Predicted Kelch β-propeller-like fold associated with iron deficiency response in rice
Structural analyses indicated that OsFe2200 adopts a putative Kelch β-propeller-like fold, while additional structural comparisons identified β-propeller- and Kelch-like proteins as moderate confidence homologs, suggesting that OsFe2200 is a structurally conserved but sequence divergent protein.
Abstract
Iron deficiency is a major abiotic constraint in rice production, especially in direct-seeded and aerobic cultivation systems, yet the genes controlling tolerance, especially at the seedling stage, remain poorly understood. In this study, we screened 116 rice genotypes under iron deficiency hydroponics system, selecting RA23 (tolerant) and Lalat MAS (susceptible) as parents for an F
2
mapping population. QTL-seq of the extreme F
2
bulks identified two QTLs,
qFe2.1
and
qFe9.1
, supported by both ΔSNP-index and G′ statistic analysis. RNA-seq of the parental lines under iron deficiency further narrowed down these intervals to 14 candidate genes. Among these, an uncharacterised gene
OsFe2200 (Os02g0152200 / LOC_Os02g05810)
, a DUF1677-family gene, showed the strongest signal, i.e., ~ 80-fold higher expression in the susceptible parent, confirmed by qRT-PCR. Structural analyses indicated that
OsFe2200
adopts a putative Kelch β-propeller-like fold, while additional structural comparisons identified β-propeller- and Kelch-like proteins as moderate confidence homologs, suggesting that
OsFe2200
is a structurally conserved but sequence divergent protein. The
OsFe2200
promoter contains iron-responsive motifs and a 21 bp insertion specific to RA23, which may contribute to its lower expression in the tolerant line, though this remains to be tested directly. Haplotype analysis in the Bengal and Assam Aus Panel showed greater phenotypic variability associated with
OsFe2200,
the haplotype harbouring a shared mutation with RA23 (D90V) was superior and significantly different for the number of crown roots but was non-significant for SPAD values. Together, these results identify
OsFe2200
as a strong putative candidate gene for iron deficiency tolerance in rice, providing a starting point for future functional studies and breeding programmes.
Background: Domain of Unknown Function 1645 (DUF1645) is a conserved but poorly characterized plant gene family whose evolutionary history and roles in stress adaptation remain unclear. We performed an integrated genomic, evolutionary, transcriptomic, and Quantitative Trait Locus (QTL) characterization of the DUF1645 f...
Pei-Pei Su, Zhi-Qun Que, Xin Song et al.· Genes· 0 citations
Spermidine (Spd) is a pivotal polyamine involved in plant growth and stress tolerance, but its genetic regulation in soybeans (Glycine max) remains unclear. In this study, we quantified seed Spd content in 208 soybean accessions across two years by RP-HPLC and performed GWAS. Spd showed continuous variation and a hig...
Jie Wang, Xun-Chao Zhao, Yuan Li et al.· Journal of Agricultural and...· 0 citations
OsLSD3 is identified as the strongest candidate among the examined members for further investigation of plant height- and grain-related traits, while OsLSD2 and OsLSD4 represent additional candidates for grain-trait regulation.
A comprehensive genome-wide analysis to characterize the 19-member OsDMP family in rice provides a robust framework for understanding OsDMP evolution and expression regulation, revealing transcriptional responsiveness to abiotic stress and providing candidate genes for future functional validation and application in br...
Soil salinization is a major constraint on rice production, and severe salinity levels lead to pronounced yield losses. Therefore, elucidating the genetic architecture of salinity tolerance and identifying candidate genes underlying this trait are crucial for molecular design breeding.
In this study, a popul...
Wei Xu, Chun-Yan Ju, Xiao-Ding Ma et al.· Frontiers in Plant Science· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.