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Identification and profiling of novel and conserved microRNAs during the flower opening process in Prunus mume via deep sequencing

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Abstract

MicroRNAs (miRNAs) are small (20–24 nucleotide) RNAs that are critical regulators of genes involved in diverse plant processes, including development, metabolism, abiotic stress and flowering. Prunus mume is a widely cultivated ornamental plant in East Asia that blooms in early spring, even at temperatures below 0 °C. While miRNAs involved in pistil development have been identified in P. mume, few studies have profiled miRNA expression patterns during flower opening. Here, we used high-throughput sequencing and bioinformatic analysis to identify and profile miRNAs that function during flower opening in P. mume. We identified 47 conserved miRNA sequences belonging to 25 miRNA families from 92 loci in P. mume, along with 33 novel miRNA sequences from 43 loci, including their complementary miRNA* strands. The expression levels of most differentially expressed miRNAs decreased during flower opening, while miR156e-f and miR477b were upregulated at the flowering stage. We predicted 88 target genes for conserved and novel miRNAs using computational analysis and annotated their functions. Seven target genes, encoding squamosa promoter binding protein-like (SPL) and auxin response factor (ARF), scarecrow-like transcription factor (SCL) and APETALA2-like transcription factors (AP2), were verified by 5′-RACE to be the targets of miR156, miR167, miR171 and miR172, respectively. Quantitative real-time PCR validated the expression of the miRNAs and seven target genes. The results help lay the foundation for investigating the roles of miRNAs in the blooming of P. mume.

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Acknowledgments

This work was supported by the Ministry of Science and Technology (2013AA102607, 2011AA100207).

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Correspondence to Qixiang Zhang.

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Communicated by S. Hohmann.

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Wang, T., Pan, H., Wang, J. et al. Identification and profiling of novel and conserved microRNAs during the flower opening process in Prunus mume via deep sequencing. Mol Genet Genomics 289, 169–183 (2014). https://doi.org/10.1007/s00438-013-0800-6

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