MiR-RACE, a New Efficient Approach to Determine the Precise Sequences of Computationally Identified Trifoliate Orange (Poncirus trifoliata) MicroRNAs

BACKGROUND: Among the hundreds of genes encoding miRNAs in plants reported, much more were predicted by numerous computational methods. However, unlike protein-coding genes defined by start and stop codons, the ends of miRNA molecules do not have characteristics that can be used to define the mature miRNAs exactly, which made computational miRNA prediction methods often cannot predict the accurate location of the mature miRNA in a precursor with nucleotide-level precision. To our knowledge, there haven't been reports about comprehensive strategies determining the precise sequences, especially two termini, of these miRNAs. METHODS: In this study, we report an efficient method to determine the precise sequences of computationally predicted microRNAs (miRNAs) that combines miRNA-enriched library preparation, two specific 5' and 3' miRNA RACE (miR-RACE) PCR reactions, and sequence-directed cloning, in which the most challenging step is the two specific gene specific primers designed for the two RACE reactions. miRNA-mediated mRNA cleavage by RLM-5' RACE and sequencing were carried out to validate the miRNAs detected. Real-time PCR was used to analyze the expression of each miRNA. RESULTS: The efficiency of this newly developed method was validated using nine trifoliate orange (Poncirus trifoliata) miRNAs predicted computationally. The miRNAs computationally identified were validated by miR-RACE and sequencing. Quantitative analysis showed that they have variable expression. Eight target genes have been experimentally verified by detection of the miRNA-mediated mRNA cleavage in Poncirus trifoliate. CONCLUSION: The efficient and powerful approach developed herein can be successfully used to validate the sequences of miRNAs, especially the termini, which depict the complete miRNA sequence in the computationally predicted precursor

Characterization of microRNAs Identified in a Table Grapevine Cultivar with Validation of Computationally Predicted Grapevine miRNAs by miR-RACE

BACKGROUND: Alignment analysis of the Vv-miRNAs identified from various grapevine cultivars indicates that over 30% orthologous Vv-miRNAs exhibit a 1-3 nucleotide discrepancy only at their ends, suggesting that this sequence discrepancy is not a random event, but might mainly derive from divergence of cultivars. With advantages of miR-RACE technology in determining precise sequences of potential miRNAs from bioinformatics prediction, the precise sequences of vv-miRNAs predicted computationally can be verified with miR-RACE in a different grapevine cultivar. This presents itself as a new approach for large scale discovery of precise miRNAs in different grapevine varieties. METHODOLOGY/PRINCIPAL FINDINGS: Among 88 unique sequences of Vv-miRNAs from bioinformatics prediction, 83 (96.3%) were successfully validated with MiR-RACE in grapevine cv. 'Summer Black'. All the validated sequences were identical to their corresponding ones obtained from deep sequencing of the small RNA library of 'Summer Black'. Quantitative RT-PCR analysis of the expressions levels of 10 Vv-miRNA/target gene pairs in grapevine tissues showed some negative correlation trends. Finally, comparison of Vv-miRNA sequences with their orthologs in Arabidopsis and study on the influence of divergent bases of the orthologous miRNAs on their targeting patterns in grapevine were also done. CONCLUSION: The validation of precise sequences of potential Vv-miRNAs from computational prediction in a different grapevine cultivar can be a new way to identify the orthologous Vv-miRNAs. Nucleotide discrepancy of orthologous Vv-miRNAs from different grapevine cultivars normally does not change their target genes. However, sequence variations of some orthologous miRNAs in grapevine and Arabidopsis can change their targeting patterns. These precise Vv-miRNAs sequences validated in our study could benefit some further study on grapevine functional genomics