Abstract
MicroRNAs (miRNAs) are short, non-coding single-strand RNA molecules that play important roles in plant growth, development and stress responses. Flowering time affects the seed yield and quality of soybean. However, the miRNAs involved in the regulation of flowering time in soybean have not been reported until recently. Here, high-throughput sequencing and qRT-PCR were used to identify miRNAs involved in soybean photoperiodic pathways. The first trifoliate leaves of soybean that receive the signal of light treatment were used to construct six libraries (0, 8, and 16 h under short-day (SD) treatment and 0, 8, and 16 h under long-day (LD) treatment). The libraries were sequenced using Illumina Solexa. A total of 318 known plant miRNAs belonging to 163 miRNA families and 81 novel predicted miRNAs were identified. Among these, 23 miRNAs at 0 h, 65 miRNAs at 8 h and 83 miRNAs at 16 h, including six novel predicted miRNAs at 8 h and six novel predicted miRNAs at 16 h, showed differences in abundance between LD and SD treatments. Furthermore, the results of GO and KEGG analyses indicated that most of the miRNA targets were transcription factors. Seven miRNAs at 0 h, 23 miRNAs (including four novel predicted miRNAs) at 8 h, 16 miRNAs (including one novel predicted miRNA) at 16 h and miRNA targets were selected for qRT-PCR analysis to assess the accuracy of the sequencing and target prediction. The results indicated that the expression patterns of the selected miRNAs and miRNA targets showed no differences between the qRT-PCR and sequencing results. In addition, 23 miRNAs at 0 h, 65 miRNAs at 8 h and 83 miRNAs at 16 h responded to day length changes in soybean, including six novel predicted miRNAs at 8 h and six novel predicted miRNAs at 16 h. These results provided an important molecular basis to understand the regulation of flowering time through photoperiodic pathways in soybean.
Highlights
Environmental factors and internal signals are integrated to regulate the flowering time of plants [1]
Higher plants recognize fluctuations in day length, which facilitate the coordination of the flowering time with changing seasons
The samples were continuously collected for two days, and each sample comprised the leaves from three plants
Summary
Environmental factors (day length and temperature) and internal signals (gibberellin and autonomous pathways) are integrated to regulate the flowering time of plants [1]. To explain how photoperiodic organisms perceive day-length signals, the external coincidence model has been proposed [2]. Photoreceptors, circadian clock components, bio-clock and light regulated genes are key components for day-length detection in the external coincidence model. Among the clock- and light-regulated genes, CO has been identified as a key gene in the integration of light and clock signals. The overexpression of the CO gene leads to early flowering in Arabidopsis through the regulation of the expression of downstream genes, such as FT, AP1 and LFY, regardless of the length of day light [1, 3,4,5]
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