Plant & Animal Genomes XIX Conference

January 15-19, 2011
Town & Country Convention Center
San Diego, CA

Deep Sequencing Of Vitis vinifera SmallRNAs Reveals Widespread Production Of Non-Canonical, Hairpin-Derived MicroRNA-Like Molecules.

V Piccolo¹ , Erica Mica² , M. Enrico Pè² , Graziano Pesole^3,4 , David S. Horner¹

¹  Dipartimento di Scienze Biomolecolari e Biotecnologie, Università degli Studi di Milano via Celoria 26, 20133 Milano, Italy

²  Scuola Superiore Sant’Anna, 56127 Pisa, Italy

³  Istituto Biomembrane e Bioenergetica, Consiglio Nazionale delle Ricerche, via Amendola 165/A, 70125 Bari, Italy

⁴  Dipartimento di Biochimica e Biologia Molecolare

Distinct models have been proposed for the biogenesis of microRNAs, transactivating siRNAs (ta-siRNAs), heterochromatic siRNAs (het-siRNAs) and natural-antisense siRNAs (nat-siRNAs) in plants. Various bioinformatics tools enable the identification of these classes of RNAs through the recognition of precise mapping patterns of smallRNA deep sequence data on genome sequences.
We have analyzed smallRNA Next Generation Sequencing data from the Grapevine, Vitis vinifera with novel bioinformatics tools. In addition to canonical microRNAs, our approach identifies many genomic loci showing patterns of generation of smallRNAs that suggest extensive redundancy and physiological overlap between the classical mechanisms of smallRNA biogenesis. For example, phased, strand specific production of mature miRNA-like molecules from hairpin precursors is observed at many loci, while several hairpin precursors consistently produce precisely excised 24 base smallRNAs, Furthermore, we observe phased smallRNA production from many loci annotated as protein coding. Many of these ta-siRNA-like molecules are predicted to be capable of targeting mRNAs.
Our results indicate that the distinctions typically drawn between different classes of smallRNAs (and their biogenetic mechanisms) may, in some cases, be rather arbitrary and unrepresentative of biological reality. Furthermore, the bioinformatics strategy presented here is able to identify non-canonical miRNA-like precursors in addition to loci following strict rules for microRNA annotation. Accordingly, our approach is likely to be helpful in the study of miRNA-gene evolution in plants.