Integrating Small RNA Sequencing with QTL Mapping for Identification of miRNAs and Their Target Genes Associated with Heat Tolerance at the Flowering Stage in Rice
Qing Liu, Tifeng Yang, Ting Yu, Shaohong Zhang, Xingxue Mao, Junliang Zhao, Xiaofei Wang, Jingfang Dong, Bin Liu
Abstract
Although, microRNAs (miRNAs) have been reported to be associated with heat tolerance at the seedling stage in rice, their involvement in heat tolerance at the flowering stage is still unknown. In this study, small RNA profiling was conducted in a heat-tolerant variety Gan-Xiang-Nuo (GXN) and a heat-sensitive variety Hua-Jing-Xian-74 (HJX), respectively. Totally, 102 miRNAs were differentially expressed (DE) under heat stress. Compared to HJX, GXN had more DE miRNAs and its DE miRNAs changed earlier under heat stress. Plant Ontology (PO) analysis of the target genes revealed that many DE miRNAs were involved in flower development. As a parallel experiment, QTL mapping was also conducted and four QTLs for heat tolerance at the flowering stage were identified using chromosome single-segment substitution lines derived from GXN and HJX. Further, through integrating analysis of DE miRNAs with QTLs, we identified 8 target genes corresponding to 26 miRNAs within the four QTL regions. Some meaningful target genes such as LOC_Os12g42400, SGT1, and pectinesterase were within the QTL regions. The negative correlation between miR169r-5p and its target gene LOC_Os12g42400 was confirmed under heat stress, and overexpression of miR169r-5p enhanced heat tolerance at flowering stage in rice. Our results demonstrate that the integrated analysis of genome-wide miRNA profiling with QTL mapping can facilitate identification of miRNAs and their target genes associated with the target traits and the limited candidates identified in this study offer an important source for further functional analysis and molecular breeding for heat tolerance in rice.
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 February 2017.
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