No abstract

microRNAs (miRNAs) are a class of small RNAs (sRNAs) of ~21 nucleotides (nt) in length processed from foldback hairpins by DICER-LIKE1 (DCL1) or DCL4. They regulate the expression of target mRNAs by base pairing through RNA-Induced Silencing Complex (RISC). In the RISC, ARGONAUTE1 (AGO1) is the key protein that cleaves miRNA targets at position ten of a miRNA:target duplex. The authenticity of many annotated rice miRNA hairpins is under debate because of their homology to repeat sequences. Some of them, like miR1884b, have been removed from the current release of miRBase based on incomplete information. In this study, we investigated the association of transposable element (TE)-derived miRNAs with typical miRNA pathways (DCL1/4- and AGO1-dependent) using publicly available deep sequencing datasets. Seven miRNA hairpins with 13 unique sRNAs were specifically enriched in AGO1 immunoprecipitation samples and relatively reduced in DCL1/4 knockdown genotypes. Interestingly, these species are ~21-nt long, instead of 24-nt as annotated in miRBase and the literature. Their expression profiles meet current criteria for functional annotation of miRNAs. In addition, diagnostic cleavage tags were found in degradome datasets for predicted target mRNAs. Most of these miRNA hairpins share significant homology with miniature inverted-repeat transposable elements (MITEs), one type of abundant DNA transposons in rice. Finally, the root-specific production of a 24 nt miRNA-like sRNA was confirmed by RNA blot for a novel EST that maps to the 3'-UTR of a candidate pseudogene showing extensive sequence homology to miR1884b hairpin. Our data are consistent with the hypothesis that TEs can serve as a driving force for the evolution of some MIRNAs, where co-opting of DICER-LIKE1/4 processing and integration into AGO1 could exapt transcribed TE-associated hairpins into typical miRNA pathways.

BackgroundMicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20–22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.Principal FindingsWe explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis ‘orphan’ hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the “ancient” (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for “new” rapidly-evolving MIRNA genes.ConclusionsAntisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation.