bioRxivPlant Biology
ALUMINUM RESISTANCE TRANSCRIPTION FACTOR 1 (ART1) contributes to natural variation in rice Al resistance.
J. D. Arbelaez, L. G. Maron, T. O. Jobe, M. A. Pineros, A. N. Famoso, A. R. Rebelo, N. Singh, Q. Ma, Z. Fei, L. V. Kochian, S. R. McCouch
Abstract
Transcription factors (TFs) mediate stress resistance indirectly via physiological mechanisms driven by the genes they regulate. When studying TF-mediated stress resistance, it is important to understand how TFs interact with different genetic backgrounds. Here, we fine-mapped the aluminum (Al) resistance QTL Alt12.1 to a 44 Kb region containing six gene models. Among them is ART1, which encodes a C2H2-type zinc finger TF required for Al resistance in rice. The parents of the mapping population, Al-resistant Azucena (tropical japonica) and Al-sensitive IR64 (indica), showed similar ART1 expression levels but extensive sequence polymorphism within the ART1 coding region. Using reciprocal near-isogenic lines (NILs) in the Azucena and IR64 genetic backgrounds, we examined how allele-swapping Alt12.1 would affect plant responses to Al. Analysis of global transcriptional responses to Al stress in roots of the NILs alongside their recurrent parents demonstrated that the ART1 from Al-resistant Azucena led to greater changes in gene expression in response to Al when compared to the ART1 from IR64 in both genetic backgrounds. The presence of the ART1 allele from the opposite parent affected the expression of several genes not previously implicated in rice Al tolerance. We highlight examples where putatively functional variation in cis-regulatory regions of ART1-regulated genes interacts with ART1 to determine gene expression in response to Al. This ART1-promoter interaction may be associated with transgressive variation for Al resistance in the Azucena x IR64 population. These results illustrate how ART1 interacts with the genetic background to contribute to quantitative phenotypic variation in rice Al resistance.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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