Research article
Global expression analysis of miRNA gene cluster and family based on isomiRs from deep sequencing data

https://doi.org/10.1016/j.compbiolchem.2010.06.001Get rights and content

Abstract

In this study, we present a global relative expression analysis of miRNAs in human miRNA gene cluster and family based on different selection schemes of isomiRs from deep sequencing data. Some divergence was observed between expression distributions based on sequence count of the most abundant isomiR and sum of all isomiR sequence counts, respectively. But the divergence showed less effect on expression patterns in miRNA gene cluster and family. Because of high sensitivity of sequencing technology and ambiguous distribution of common isomiRs among multicopy miRNA precursors and homologous genes, most gene clusters and families showed inconsistent expression levels. Inconsistent expression pattern implied expression diversification in vivo although they maybe showed close relationship during transcription and/or gene regulation. Some gene clusters and families generated mature miRNAs from the same arms and even with the same lengths, especially in gene family. The interesting expression and length distribution maybe implied strict regulation during pre-miRNA processing through cleavage of Dicer and Drosha. The study showed that the most abundant isomiR may be a better marker, and global expression analysis of miRNA gene cluster and family provide clues for analyzing relative expression levels and understanding the function relationship and mechanism for expression regulation of miRNAs in regulation network.

Introduction

MicroRNAs (miRNAs) are ∼22 nt non-coding RNA molecules that play important roles in post-transcriptional regulation in plants and animals (Bartel and Chen, 2004, Plasterk, 2006). Many miRNAs are linked as clusters on chromosomes and often transcribed from genomic DNAs as a single polycistronic transcript to provide the opportunity for different miRNAs to target several categories of genes simultaneously (Lagos-Quintana et al., 2003, Lim et al., 2003, Kim and Nam, 2006, Xu and Wong, 2008). miRNA clusters are largely present in metazoan genomes with the diversity of their distribution (Zhang et al., 2009), and they are pivotal in coordinately regulating multiple processes, including embryonic development, cell cycles and cell differentiation. They have important roles in mammalian development and human diseases such as cancer, because of their ability to repress expression of many tumor-associated proteins (Koralov et al., 2008, Mendell, 2008). For example, mir-17 cluster might be a potential human oncogene and play a role in the development of some cancers (Hayashita et al., 2005, He et al., 2005, O’Donnell et al., 2005, Diosdado et al., 2009). A cluster is usually composed of two or more related miRNA genes that might constitute a complicated regulatory network to function by targeting more or fewer special mRNAs (Yu et al., 2006). Members of miRNA cluster are not necessarily identical, but they often share sequence similarity (Aravin et al., 2003). Accumulating evidence suggests that clustered miRNAs always are transcribed as polycistrons and have similar expression patterns (Lau et al., 2001, Aravin et al., 2003, Bashirullah et al., 2003, Seitz et al., 2004, Baskerville and Bartel, 2005). If they are homologous, the miRNA cluster forms the miRNA gene family, further expanding different miRNA clusters. miRNAs in gene cluster might experience complex duplication history and might contribute to their expression specificity in different tissues or at specific times (Hertel et al., 2006, Zhang et al., 2007, Guo et al., 2009).

Because miRNA gene clusters are pivotal in co-ordinately regulating multiple processes, expression pattern based on miRNA gene cluster and gene family was performed in recent studies (Thomson et al., 2004, Jiang et al., 2006, Yu et al., 2006, Landgraf et al., 2007, Viswanathan et al., 2009). Although miRNA cluster is often located in a polycistron (Lagos-Quintana et al., 2003, Ambros, 2004, Bartel, 2004, Cullen, 2004) and co-expressed from a single promoter as a single polycistronic transcript with neighboring miRNAs and host genes (Baskerville and Bartel, 2005), consistent and inconsistent expression of members can be detected in miRNA gene clusters (Yu et al., 2006, Landgraf et al., 2007, Viswanathan et al., 2009). Recently, next-generation sequencing technologies have been widely applied to identify miRNAs at unprecedented sensitivity because they can permit unbiased, quantitive and in-depth investigation of the small RNA transcriptome (Creighton et al., 2009, Fahlgren et al., 2009). However, global and detailed expression analysis of different miRNA members in miRNA cluster and family through deep sequencing data is not clear, especially according to different selection schemes of isomiRs.

IsomiRs, the population of variants of known miRNAs because of imprecise and alternative cleavage of Dicer and Drosha during pre-miRNA processing, have recently been identified from high-throughput DNA sequencing data (Ruby et al., 2006, Landgraf et al., 2007, Kuchenbauer et al., 2008, Morin et al., 2008a, Morin et al., 2008b, Guo and Lu, 2010). The multiple miRNA variants might influence the miRNA half-life, subcellular localization, and miRNA target specificity especially for 5’ end variation (Borel and Antonarakis, 2008). Although the biological consequence of isomiRs remains to be determined, isomiRs from a single hairpin precursor could be a way of broadening the regulatory network. But these isomiRs, especially for isomiRs with fewer clones, may be also caused from large numbers of hairpins through non-Dicer processing or degradation (Guo and Lu, 2010). In the study, we analyzed relative expression levels of miRNAs in miRNA cluster and gene family based on isomiRs by reanalyzing deep sequencing data from SOLiD™ system. In order to discover detailed expression information, two kinds of analysis methods combining isomiRs were performed: sequence count of the most abundant isomiR and sum of all isomiR sequence counts.

Section snippets

Material and methods

Original sequencing data of small RNAs in human tissue sample generated by SOLiD™ System were obtained from Applied Biosystems (http://SOLiDsoftwaretools.com/gf/project/srna/). We reanalyzed the deep sequencing data based on strict analysis method according to Guo and Lu (2010). In the study, we denoted the miRNA precursors by mir-# and the mature miRNAs by miR-# in accordance with miRBase database (version 14.0, http://www.mirbase.org/). If sequence count of the most abundant isomiR of miRNA

Overview of miRNAs

A total of 55 human miRNAs more than 1000 sequence counts were obtained from deep sequencing data based on the most abundant isomiR. 47 miRNA genes were identified in gene cluster and/or gene family (Table 1, Table 2). Of these, both miR-#-5p and miR-#-3p from the 3 kinds of miRNA precursors (hsa-mir-199a-1, hsa-mir-199a-2 and hsa-mir-376b) were characterized as mature miRNAs according to ratio of miRNA:miRNA* (<2). Due to multiple isomiRs with various 5′ and/or 3′ terminus, common isomiRs

Discussion

In the study, isomiRs would be divided equally among multicopy miRNA precursors and homologous genes if they were located in common regions with indistinguishable sequences (Fig. 1). However, even though among multicopy precursors (such as between hsa-mir-24-1 and hsa-mir-24-2), specific isomiRs also could be detected with different 5′ and/or 3′ terminus (Fig. 1A). Especially among homologous miRNA genes, specific isomiRs were easy to be identified based on multiple sequence alignment (Fig. 1

Acknowledgements

The work is supported by the project 30871393 from National Natural Science Foundation of China and funded by Tsinghua National Laboratory for Information Science and Technology (TNList) Cross-discipline Foundation.

References (42)

  • J. Yu et al.

    Human microRNA clusters: genomic organization and expression profile in leukemia cell lines

    Biochem. Biophys. Res. Commun.

    (2006)
  • V. Ambros

    The functions of animal microRNAs

    Nature

    (2004)
  • J.E. Babiarz et al.

    Mouse ES cells express endogenous shRNAs, siRNAs, and other Microprocessor-independent, Dicer-dependent small RNAs

    Genes Dev.

    (2008)
  • D.P. Bartel et al.

    Micromanagers of gene expression: the potentially widespread influence of metazoan microRNAs

    Nat. Rev. Genet.

    (2004)
  • S. Baskerville et al.

    Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes

    RNA

    (2005)
  • C. Borel et al.

    Functional genetic variation of human miRNAs and phenotypic consequences

    Mamm. Genome

    (2008)
  • C.J. Creighton et al.

    Expression profiling of microRNAs by deep sequencing

    Brief Bioinf.

    (2009)
  • M.J.L. de Hoon et al.

    Cross-mapping and the identification of editing sites in mature microRNAs in high-throughput sequencing libraries

    Genome Res.

    (2010)
  • B. Diosdado et al.

    MiR-17-92 cluster is associated with 13q gain and c-myc expression during colorectal adenoma to adenocarcinoma progression

    Br. J. Cancer

    (2009)
  • N. Fahlgren et al.

    Computational and analytical framework for small RNA profiling by high-throughput sequencing

    RNA

    (2009)
  • M.R. Friedlander et al.

    Discovering microRNAs from deep sequencing data using miRDeep

    Nat. Biotechnol.

    (2008)
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