The use of high-throughput sequencing methods for plant microRNA research.

MicroRNA (miRNA) acts as a critical regulator of gene expression at post-transcriptional and occasionally transcriptional levels in plants. Identification of reliable miRNA genes, monitoring the procedures of transcription, processing and maturation of the miRNAs, quantification of the accumulation levels of the miRNAs in specific biological samples, and validation of miRNA-target interactions become the basis for thoroughly understanding of the miRNA-mediated regulatory networks and the underlying mechanisms. Great progresses have been achieved for sequencing technology. Based on the high degree of sequencing depth and coverage, the high-throughput sequencing (HTS, also called next-generation sequencing) technology provides unprecedentedly efficient way for genome-wide or transcriptome-wide studies. In this review, we will introduce several HTS platform-based methods useful for plant miRNA research, including RNA-seq (RNA sequencing), RNA-PET-seq (paired end tag sequencing of RNAs), sRNA-seq (small RNA sequencing), dsRNA-seq (double-stranded RNA sequencing), ssRNA-seq (single-stranded RNA sequencing) and degradome-seq (degradome sequencing). In particular, we will provide some special cases to illustrate the novel use of HTS methods for investigation of the processing modes of the miRNA precursors, identification of the RNA editing sites on miRNA precursors, mature miRNAs and target transcripts, re-examination of the current miRNA registries, and discovery of novel miRNA species and novel miRNA-target interactions. Summarily, we opinioned that integrative use of the above mentioned HTS methods could make the studies on miRNAs more efficient.

Long non-coding RNAs: a novel endogenous source for the generation of Dicer-like 1-dependent small RNAs in Arabidopsis thaliana.

The biological relevance of long non-coding RNAs (lncRNAs) is emerging. Whether the lncRNAs could form structured precursors for small RNAs (sRNAs) production remains elusive. Here, 172 713 DCL1 (Dicer-like 1)-dependent sRNAs were identified in Arabidopsis. Except for the sRNAs mapped onto the microRNA precursors, the remaining ones led us to investigate their originations. Intriguingly, 65 006 sRNAs found their loci on 5891 lncRNAs. These sRNAs were sent to AGO (Argonaute) enrichment analysis. As a result, 1264 sRNAs were enriched in AGO1, which were then subjected to target prediction. Based on degradome sequencing data, 109 transcripts were validated to be targeted by 96 sRNAs. Besides, 44 lncRNAs were targeted by 23 sRNAs. To further support the origination of the DCL1-dependent sRNAs from lncRNAs, we searched for the degradome-based cleavage signals at either ends of the sRNA loci, which were supposed to be produced during DCL1-mediated processing of the long-stem structures. As a result, 63 612 loci were supported by degradome signatures. Among these loci, 6606 reside within the dsRNA-seq (double-stranded RNA sequencing) read-covered regions of 100 nt or longer. These regions were subjected to secondary structure prediction. And, 43 regions were identified to be capable of forming highly complementary long-stem structures. We proposed that these local long-stem structures could be recognized by DCL1 for cropping, thus serving as the sRNA precursors. We hope that our study could inspire more research efforts to study on the biological roles of the lncRNAs in plants.

Next-generation sequencing of dsRNA is greatly improved by treatment with the inexpensive denaturing reagent DMSO.

dsRNA is the genetic material of important viruses and a key component of RNA interference-based immunity in eukaryotes. Previous studies have noted difficulties in determining the sequence of dsRNA molecules that have affected studies of immune function and estimates of viral diversity in nature. DMSO has been used to denature dsRNA prior to the reverse-transcription stage to improve reverse transcriptase PCR and Sanger sequencing. We systematically tested the utility of DMSO to improve the sequencing yield of a dsRNA virus (Φ6) in a short-read next-generation sequencing platform. DMSO treatment improved sequencing read recovery by over two orders of magnitude, even when RNA and cDNA concentrations were below the limit of detection. We also tested the effects of DMSO on a mock eukaryotic viral community and found that dsRNA virus reads increased with DMSO treatment. Furthermore, we provide evidence that DMSO treatment does not adversely affect recovery of reads from a ssRNA viral genome (influenza A/California/07/2009). We suggest that up to 50 % DMSO treatment be used prior to cDNA synthesis when samples of interest are composed of or may contain dsRNA.