دانلود مقاله ISI انگلیسی شماره 21449
عنوان فارسی مقاله

متامیرکلاست: کشف الگوهای خوشه میرنا با استفاده از روش داده کاوی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
21449 2012 8 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
MetaMirClust: Discovery of miRNA cluster patterns using a data-mining approach
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Genomics, Volume 100, Issue 3, September 2012, Pages 141–148

کلمات کلیدی
ریز - خوشه میرنا - داده کاوی - چند یاخته ای
پیش نمایش مقاله
پیش نمایش مقاله متامیرکلاست: کشف الگوهای خوشه میرنا با استفاده از روش داده کاوی

چکیده انگلیسی

Recent genome-wide surveys on ncRNA have revealed that a substantial fraction of miRNA genes is likely to form clusters. However, the evolutionary and biological function implications of clustered miRNAs are still elusive. After identifying clustered miRNA genes under different maximum inter-miRNA distances (MIDs), this study intended to reveal evolution conservation patterns among these clustered miRNA genes in metazoan species using a computation algorithm. As examples, a total of 15–35% of known and predicted miRNA genes in nine selected species constitute clusters under the MIDs ranging from 1 kb to 50 kb. Intriguingly, 33 out of 37 metazoan miRNA clusters in 56 metazoan genomes are co-conserved with their up/down-stream adjacent protein-coding genes. Meanwhile, a co-expression pattern of miR-1 and miR-133a in the mir-133-1 cluster has been experimentally demonstrated. Therefore, the MetaMirClust database provides a useful bioinformatic resource for biologists to facilitate the advanced interrogations on the composition of miRNA clusters and their evolution patterns.

مقدمه انگلیسی

MicroRNAs (miRNAs) are endogenous small non-coding RNA molecules of 21–23 nucleotides (nt) long in length. They play important roles in gene regulation via the RNA interference pathway [1], [2], [3] and [4]. As a wide range of investigations has been conducted on miRNA genes, a consensus scenario of miRNA biogenesis has been currently revealed. Initially, miRNA genes are transcribed from intergenic or intronic regions by RNA polymerase II [5] or III [6], and generate primary miRNA transcripts (pri-miRNAs) in the nucleus [2]. Within the same organelle, these transcripts are processed by the RNase III Drosha endonuclease, which is associated with its co-factor DGCR8 complex, into precursor forms (pre-miRNAs) about 70–90 nt [7] and [8]. The canonical pre-miRNAs are 70–90 nt long and fold-back to form stem-loop structures, which are characteristic secondary structures of miRNAs. Subsequently, these miRNA molecules are exported as single hairpins into the cytoplasm by the aid of Exportin 5 (XPO5) [9]. Cleaved by another RNase III Dicer enzyme, pre-miRNA hairpins are processed into double-stranded mature miRNA duplexes [10]. Preferentially, one of the single-strand mature miRNA is incorporated into the RNA-induced silencing complex (RISC) or different ribonucleoproteins (miRNPs), while the remaining strand is degraded rapidly [2] and [11]. Primarily depending on the degree of complementarity in sequences, the binding of miRNAs to the 3′ untranslated regions (3′ UTR) of their mRNA targets gives rise to two down-regulation mechanisms, mRNA degradation and translation repression. Up to date, a number of examples of mRNA cleavage instances have been reported in plant, and alternatively translation repression is the main mechanism observed in animal cells. A substantial amount of literature has demonstrated miRNAs as crucial negative regulators in diverse physiological and developmental processes at the post-transcriptional level. In 1993 when the first miRNA lin-4 was identified in Caenorhabditis elegans, the negative regulation pair between lin-4 and its target lin-14 was thought of as an individual case [12]. In fact, miRNAs have not gained the attention of researchers until a second similar system of let-7 was observed in C. elegans [13], and then its homologous transcripts were extensively investigated in animal genomes. Thereafter, a considerable body of evidence suggests that miRNAs play important gene-regulatory roles related to organism development, cell differentiation, and tumor suppression and oncogenesis [1], [12] and [13]. Currently, newly discovered miRNA genes either by experimental or computational approaches have steadily increased as evident by the amount of records in the miRBase registry [14]. In recent years, many studies have attempted to provide insight into the biogenesis, expression, targeting and evolution of individual miRNA genes in different species. Some well-studied examples in humans include, for instance, mir-196 which governs the cleavage of homeobox (HOX) gene clusters [15]; mir-375 which targets Myotrophin (MTPN), and both mir‐196 and mir‐375 are related to glucose-stimulated insulin secretion and exocytosis [16]; and mir-143 which regulates adipocyte differentiation [17]. All these studies focused on the discovery of the biological functions of limited individual miRNA genes, not the clustered miRNAs. Up until the present, a handful of miRNA clusters has been reported in animal genomes. To the best of our knowledge, Altuvia et al. was the first group that identified conserved regions of miRNA clusters systematically [18]. Then, Yu et al. [19] adopted the same method to enlarge the extent of conserved miRNA cluster, and thus checked the expression profile of identified human miRNA clusters. To group two or more miRNA genes in chromosomal distance within at most 3000 nt, they used available 326 human miRNA genes from the miRBase registry for clustering analysis. Finally, they identified 51 clusters composed of 148 miRNA genes and created 9 distinct paralogous clusters. Furthermore, accumulating studies have illustrated that clustered miRNA genes located on polycistronic transcripts might be expressed at similar levels and coordinately involve in an intricate regulatory network. These miRNA clusters are usually derived from polycistrons with lengths from few hundred nucleotides to almost a million base pairs [20] and [21]. For instance, the mir-17 cluster and its paralogous clusters are one of the well-studied cases. In 2004, Tanzer et al. have tried to reconstruct the phylogenetic evolution of the mir-17 cluster family mainly in nine metazoan genomes and have concluded that: (i) mir-17 miRNA cluster consisting of six precursor miRNAs was within about 1 kb distance on chromosome 13 in humans and (ii) at least three paralogous clusters were related to the mir-17 cluster family, which are mir-17-92, mir-106-92, and mir-106-25, and governed by tandem duplications [22]. Meanwhile, several studies have further demonstrated that the mir-17 cluster family plays an important role in cell proliferation, organism development and cancer oncogenesis [23] and [24]. Although the entire regulatory mechanisms of clustered miRNA genes remain largely uncharacterized, it is likely that these miRNA clusters may function more efficiently in a complicated miRNA-mediated network than individual miRNAs alone [25]. Many resources were developed to investigate miRNA genes. However there is no any resource that emphasizes an efficient and comprehensive investigation of miRNA clusters. Formerly, miRNA clusters were arbitrarily defined by a fixed distance, but there was no report systematically investigating the conservation patterns of clustered miRNA genes across metazoan species. In this study, we introduced a data mining approach to efficiently discover highly conserved sets of miRNA genes upon miRNA clusters, and to facilitate researches on the conservation pattern of the clustered miRNA genes. Based on our previous homologous search of miRNA genes in animal genomes, we first performed the identification of miRNA clusters (MirClust). This identification is based on miRNA classes with respect to different maximum inter-miRNA distances (MIDs) discretely ranging from 1 kb to 50 kb. Despite the singleton miRNA classes, we utilized the FP-growth algorithm to efficiently discover the conserved co-occurrence of miRNA clusters among the miRNA clusters defined under the same MID. The FP-growth algorithm is one of highly efficient data mining methods for discovering frequent co-occurrence patterns from huge datasets such as biological sequences and gene-expression data [26]. It has been applied so far to gain insights into various bioinformatic studies [27], [28] and [29]. We have now constructed a database (MetaMirClust) for interrogating the origin and conservation of miRNA clusters on a species-wide scale (http://fgfr.ibms.sinica.edu.tw/MetaMirClust/). Researchers can choose proper distances to determine a miRNA cluster for individual species or specific miRNA cluster between different MIDs for comparison. This study is the first attempt to make a feasible dataset for surveying the different recruitments of miRNA genes in homologous miRNA clusters and for comparison of the miRNA gene compositions/structures of miRNA clusters conserved in metazoan species.

نتیجه گیری انگلیسی

Although current public databases and previous studies have managed to recognize miRNA clusters on a restricted and arbitrary defined manner, our study provides not only a comprehensive investigation of clustered miRNA genes but also the feasibility of examining the conservation pattern of miRNA clusters across metazoan genomes. Distinctive features of this study include: (i) an efficient discovery of evolutionarily conserved miRNA clusters, (ii) an extensive study of miRNA cluster properties in fifty-six metazoan species, (iii) a potential interrogation of recruitment process of miRNA genes in paralogous cluster families, including identification of new paralogous clusters in a family. Meanwhile, we further conducted a series of biological experiments to validate the notion that a miRNA cluster is transcribed in a polycistronic transcript and is also co-expressed in the mature form level. Subsequently, this study could further promote studies on miRNA clusters such as biological functions of miRNA circuitry networks and transcription regulatory elements of these clustered miRNAs. All results obtained in this study are available to browse on-line through http://fgfr.ibms.sinica.edu.tw/MetaMirClust/ or in Supplementary Data S1.

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