Computational predicting novel MicroRNAs in tomato and validating with RT-PCR

MicroRNAs (miRNAs) are a newly discovered class of nonprotein-coding small RNAs with the length of ∼21 nucleotides that regulate gene post-transcriptional expression in animals and plants. By far, the researches have indicated that miRNAs may play multiple roles in plant growth and development. It is difficult to identify some miRNAs by experimental methods because of their low expressional levels and tissue specificity, while bioinformatics is an effective strategy in the prediction of this kind of miRNAs. In this study, we presented an approach of expressed sequence tag (EST) analysis for predicting novel miRNAs as well as their targets in tomato (Lycopersicon esculentum). The database of tomato ESTs was compared with previously known miRNA sequences of other plants using BLAST to search for potential miRNAs. Eight potential miRNAs were found following a range of filtering criteria, including stem-loop structure, mismatches, the content of A + U, minimal folding free energy indices, and others with subsequent validated by touchdown RT-PCR assay in fruit tissue. Three unknown miRNAs, LemiR157a, LemiR172i, and LemiR399, which were not reported in previous study, were found in tomato. Tomato mRNA database was further compared with the newly identified miRNA sequences with BLAST, and 42 potential targets of miRNAs were identified. According to the annotations of tomato mRNAs provided by the website (http://ted.bti.cornell.edu/digital/sRNA), miRNA target genes were classified into four groups, in which transcription factors regulating growth and development, signal pathway transduction, and metabolism of tomato plants were in the majority.