Dataset Information

Discrimination of the expression of paralogous microRNA precursors that share the same major mature form.

ABSTRACT:

Background

MicroRNAs (miRNAs) are a class of small non-coding RNAs generated from endogenous transcripts that form hairpin structures. The hairpin precursor is processed into two mature miRNAs that form major/minor duplexes. Mature miRNAs regulate gene expression by cleaving mRNA or repressing protein translation. Numerous miRNAs have been discovered via deep sequencing. Many miRNAs are produced from multiple genome sites. These miRNAs are grouped into paralogous families of miRNAs that generate the same major mature form within organisms. Currently, no method of distinguishing the expression of these miRNAs is available.

Results

In the present study, strategies were developed to discriminate and quantify the expression of paralogous miRNA precursors. First, paralogous miRNA precursors that were differentially expressed in tissues were identified through analysis of the coexpression scores of their major and minor forms based on deep sequencing data. Then the precursors were identified by monitoring the expression of their host gene or minor form using real-time PCR. Finally, precursors were identified by assessing the expression of clusters of miRNA members. These approaches were used to distinguish miR-128-1 and miR-128-2 as well as miR-194-1 and miR-194-2. The mechanism of transcription related to the differential expression of miR-194-1 and miR-194-2 was also investigated.

Conclusion

This is the first report to distinguish paralogous miRNA copies by analyzing the expression of major-minor pairs, the host gene, and miRNA clusters. Discriminating paralogous precursors can provide useful information for investigating the mechanisms that regulate miRNA gene expression under different physiological and pathological conditions.

SUBMITTER: Wang M

PROVIDER: S-EPMC3940925 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Discrimination of the expression of paralogous microRNA precursors that share the same major mature form.

Wang Minghua M Wang Weiping W Zhang Ping P Xiao Juanjuan J Wang Jianguo J Huang Chaoqun C

PloS one 20140303 3

<h4>Background</h4>MicroRNAs (miRNAs) are a class of small non-coding RNAs generated from endogenous transcripts that form hairpin structures. The hairpin precursor is processed into two mature miRNAs that form major/minor duplexes. Mature miRNAs regulate gene expression by cleaving mRNA or repressing protein translation. Numerous miRNAs have been discovered via deep sequencing. Many miRNAs are produced from multiple genome sites. These miRNAs are grouped into paralogous families of miRNAs that ...[more]

PMID: 24594692

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Project description:Background: Cancers are commonly characterised by hypoxia and also by global reductions in the levels of mature microRNAs. We have examined the hypothesis that hypoxia might mediate this reduction through repressive effects on microRNA biogenesis proteins. Methods: Breast cancer cell lines were exposed to hypoxia and manipulations of hypoxia inducible factor (HIF) and HIF hydroxylase activity. The effects of hypoxia on the mRNA and protein levels of enzymes involved in microRNA biogenesis (Dicer, Drosha, TARPB2, DCGR8, XPO5) was determined by RT PCR and immunoblotting. The effect of hypoxia on microRNAs was determined with microarray studies, RT PCR and reporter assays. Results: In breast cancer lines there was significant reduction of Dicer mRNA and protein levels in cells exposed to hypoxia. This effect was independent of HIF but dependent on the HIF hydroxylase PHD2 and was partly mediated by feedback effects via microRNAs. Furthermore, several other proteins with critical roles in microRNA biogenesis (Drosha, TARBP2 and DCGR8) also showed significant and co-ordinated repression under hypoxic conditions. Despite these substantial alterations no, or modest, changes were observed in mature microRNA production Conclusion: These observations provide further and important interfaces between oxygen availability and gene expression and a potential mechanistic explanation for the reduced levels of microRNAs observed in some cancers. They provide further support for the existence of feedback mechanisms in the regulation of the microRNA biogenesis pathway and the relative stability of microRNAs. MCF7 cells were treated with three different conditions. Treatment-1: MCF7 cells were exposed to hypoxia (0.1% O2) for 48 h and harvested for RNA extraction (n=3). Treatment-2: MCF7 cells were exposed to normoxia for 48 h and harvested for RNA extraction (n=3). Treatment-3: Dicer inhibition in MCF7 cells by transient transfection of siRNAs targeting Dicer. Cells were transfected with 20 nM siRNA duplexes (Shanghai GenePharma Co., Ltd, China), using Lipofectamine 2000 reagent (Invitrogen) following the manufacturer’s protocol. A second transfection was carried out after 24 h following the same protocol. Cells were harvested 24 h after the second transfection and used for RNA extraction (n=3). RNA integrity was assessed using the Agilent 2100 Bioanalyzer. Affymetrix miRNA 3.1 Array Strip was used for RNA analysis. This array consisted probe sets unique to human mature and pre-miRNA hairpins. A detailed protocol can be found in the miRNA 3.1 Array Strips technical manual (Affymetrix). In summary, 100-300 ng of total RNA was used to synthesise double stranded cDNA using random hexamers. The cDNA was then amplified to produce antisense cRNA, which was then reverse transcribed in a second cycle of cDNA synthesis. The second cycle incorporates dUTP into the cDNA sequence, which allows it to be fragmented using uracil DNA glycosylase and apurinic/apyrimidic endonuclease I. Following biotinylation, these fragments were hybridised overnight to a Affymetrix miRNA 3.1 array. The arrays were then washed, stained using a fluorescently-labelled antibody, and scanned using a high-resolution scanner. Intensity data were analysed using Partek® software (Partek Inc.). Data were normalised by quantile normalisation and log 2 transformed. Differential expression was determined by ANOVA and corrected for false discovery.

Project description:BackgroundMicroRNAs are known to be generated from primary transcripts mainly through the sequential cleavages by two enzymes, Drosha and Dicer. The sequence of a mature microRNA, especially the 'seeding sequence', largely determines its binding ability and specificity to target mRNAs. Therefore, methods that predict mature microRNA sequences with high accuracy will benefit the identification and characterization of novel microRNAs and their targets, and contribute to inferring the post-transcriptional regulation network at a genome scale.Methodology/principal findingsWe have developed a method, MiRmat, to predict the mature microRNA sequence. MiRmat is essentially composed of two parts: the prediction of Drosha processing site and the identification of Dicer processing site. Based on the analysis of microRNAs from 12 species, we found that the patterns of free energy profiles are conserved among vertebrate microRNA hairpins. Therefore, we introduced in our method the free energy distribution pattern of the downstream part of pri-microRNA secondary structure and Random Forest algorithm to predict the mature microRNA sequence. Based on the evaluation on an independent test dataset from 10 vertebrates, MiRmat was shown to identify 77.8% of the Drosha processing sites and 92.8% of the Dicer sites within a deviation of 2 nt. In a more stringent evaluation by excluding the microRNAs sharing the same family between the training set and test set, MiRmat kept a rather well performance of 71.9% and 87.2% of the identification rate on the Drosha and Dicer site respectively, which represents the ability to deal with the novel microRNA family. MiRmat outperforms other state-of-the-art methods and has a high degree of efficacy for the prediction of mature microRNA sequences of vertebrates.ConclusionMiRmat was developed for identifying microRNA mature sequence(s) by introducing the free energy distribution of RNA stem-loop structure and the Random Forest algorithm. We prove that MiRmat has better performance than the existing tools and is applicable among vertebrates. MiRmat is freely available at http://mcube.nju.edu.cn/jwang/lab/soft/MiRmat/.

Dataset Information

Discrimination of the expression of paralogous microRNA precursors that share the same major mature form.

Background

Results

Conclusion

Publications

Discrimination of the expression of paralogous microRNA precursors that share the same major mature form.

Similar Datasets

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