Dataset Information

Identification of novel maize miRNAs by measuring the precision of precursor processing.

ABSTRACT:

Background

miRNAs are known to play important regulatory roles throughout plant development. Until recently, nearly all the miRNAs in maize were identified by comparative analysis to miRNAs sequences of other plant species, such as rice and Arabidopsis.

Results

To find new miRNA in this important crop, small RNAs from mixed tissues were sequenced, resulting in over 15 million unique sequences. Our sequencing effort validated 23 of the 28 known maize miRNA families, including 49 unique miRNAs. Using a newly established criterion, based on the precision of miRNA processing from precursors, we identified 66 novel miRNAs in maize. These miRNAs can be grouped into 58 families, 54 of which have not been identified in any other species. Five new miRNAs were validated by northern blot. Moreover, we found targets for 23 of the 66 new miRNAs. The targets of two of these newly identified miRNAs were confirmed by 5'RACE.

Conclusion

We have implemented a novel method of identifying miRNA by measuring the precision of miRNA processing from precursors. Using this method, 66 novel miRNAs and 50 potential miRNAs have been identified in maize.

SUBMITTER: Jiao Y

PROVIDER: S-EPMC3214924 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundIt has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a "context-sensitive" Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification.ResultsThe probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this "trained" CSHMM was evaluated by: (a) cross-validation estimates using known human sequences, (b) predictions on a dataset of known miRNAs, and (c) prediction on a dataset of non coding RNAs. The CSHMM is compared with two recently developed methods, miPred and CID-miRNA. The results suggest that the CSHMM performs better than these methods. In addition, the CSHMM was used in a pipeline that includes filters that check for the presence of EST matches and the presence of Drosha cutting sites. This pipeline was used to scan and identify potential miRNAs from the human chromosome 19. It was also used to identify novel miRNAs from small RNA sequences of human normal leukocytes obtained by the Deep sequencing (Solexa) methodology. A total of 49 and 308 novel miRNAs were predicted from chromosome 19 and from the small RNA sequences respectively.ConclusionThe results suggest that the CSHMM is likely to be a useful tool for miRNA discovery either for analysis of individual sequences or for genome scan. Our pipeline, consisting of a CSHMM and filters to reduce false positives shows promise as an approach for ab initio identification of novel miRNAs.

Project description:BackgroundThe plant miRNAs represent an important class of endogenous small RNAs that guide cleavage of an mRNA target or repress its translation to control development and adaptation to stresses. MiRNAs are nuclear-encoded genes transcribed by RNA polymerase II, producing a primary precursor that is subsequently processed by DCL1 an RNase III Dicer-like protein. In rice hundreds of miRNAs have been described or predicted, but little is known on their genes and precursors which are important criteria to distinguish them from siRNAs. Here we develop a combination of experimental approaches to detect novel miRNAs in rice, identify their precursor transcripts and genes and predict or validate their mRNA targets.ResultsWe produced four cDNA libraries from small RNA fractions extracted from distinct rice tissues. By in silico analysis we selected 6 potential novel miRNAs, and confirmed that their expression requires OsDCL1. We predicted their targets and used 5'RACE to validate cleavage for three of them, targeting a PPR, an SPX domain protein and a GT-like transcription factor respectively. In addition, we identified precursor transcripts for the 6 miRNAs expressed in rice, showing that these precursors can be efficiently processed using a transient expression assay in transfected Nicotiana benthamiana leaves. Most interestingly, we describe two precursors producing tandem miRNAs, but in distinct arrays. We focus on one of them encoding osa-miR159a.2, a novel miRNA produced from the same stem-loop structure encoding the conserved osa-miR159a.1. We show that this dual osa-miR159a.2-osa-miR159a.1 structure is conserved in distant rice species and maize. Finally we show that the predicted mRNA target of osa-miR159a.2 encoding a GT-like transcription factor is cleaved in vivo at the expected site.ConclusionThe combination of approaches developed here identified six novel miRNAs expressed in rice which can be clearly distinguished from siRNAs. Importantly, we show that two miRNAs can be produced from a single precursor, either from tandem stem-loops or tandemly arrayed in a single stem-loop. This suggests that processing of these precursors could be an important regulatory step to produce one or more functional miRNAs in plants and perhaps coordinate cleavage of distinct targets in the same plant tissue.