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利用高通量分析重建小鼠肝脏中微小RNA介导的调控网络的综合分析。

Integrated analyses to reconstruct microRNA-mediated regulatory networks in mouse liver using high-throughput profiling.

作者信息

Hsu Sheng-Da, Huang Hsi-Yuan, Chou Chih-Hung, Sun Yi-Ming, Hsu Ming-Ta, Tsou Ann-Ping

出版信息

BMC Genomics. 2015;16 Suppl 2(Suppl 2):S12. doi: 10.1186/1471-2164-16-S2-S12. Epub 2015 Jan 21.

Abstract

BACKGROUND

MicroRNAs (miRNAs) simultaneously target many transcripts through partial complementarity binding, and have emerged as a key type of post-transcriptional regulator for gene expression. How miRNA accomplishes its pleiotropic effects largely depends on its expression and its target repertoire. Previous studies discovered thousands of miRNAs and numerous miRNA target genes mainly through computation and prediction methods which produced high rates of false positive prediction. The development of Argonaute cross-linked immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) provides a system to effectively determine miRNA target genes. Likewise, the accuracy of dissecting the transcriptional regulation of miRNA genes has been greatly improved by chromatin immunoprecipitation of the transcription factors coupled with sequencing (ChIP-Seq). Elucidation of the miRNA target repertoire will provide an in-depth understanding of the functional roles of microRNA pathways. To reliably reconstruct a miRNA-mediated regulatory network, we established a computational framework using publicly available, sequence-based transcription factor-miRNA databases, including ChIPBase and TransmiR for the TF-miRNA interactions, along with miRNA-target databases, including miRTarBase, TarBase and starBase, for the miRNA-target interactions. We applied the computational framework to elucidate the miRNA-mediated regulatory network in the Mir122a⁻/⁻ mouse model, which has an altered transcriptome and progressive liver disease.

RESULTS

We applied our computational framework to the expression profiles of miRNA/mRNA of Mir122a⁻/⁻ mutant mice and wild-type mice. The miRNA-mediated network involves 40 curated TFs contributing to the aberrant expression of 65 miRNAs and 723 curated miRNA target genes, of which 56% was found in the differentially-expressed genes of Mir122a--mice. Hence, the regulatory network disclosed previously-known and also many previously-unidentified miRNA-mediated regulations in mutant mice. Moreover, we demonstrate that loss of imprinting at the chromosome 12qF1 region is associated with miRNA overexpression in human hepatocellular carcinoma and stem cells, suggesting initiation of precancerous changes in young mice deficient in miR-122. A group of 9 miRNAs was found to share miR-122 target genes, indicating synergy between miRNAs and target genes by way of multiplicity and cooperativity.

CONCLUSIONS

The study provides significant insight into miRNA-mediated regulatory networks. Based on experimentally verified data, this network is highly reliable and effective in revealing previously-undetermined disease-associated molecular mechanisms. This computational framework can be applied to explore the significant TF-miRNA-miRNA target interactions in any complex biological systems with high degrees of confidence.

摘要

背景

微小RNA(miRNA)通过部分互补结合同时靶向许多转录本,并已成为基因表达的关键转录后调节因子类型。miRNA如何实现其多效性作用很大程度上取决于其表达及其靶标库。先前的研究主要通过计算和预测方法发现了数千种miRNA和众多miRNA靶基因,这些方法产生了较高的假阳性预测率。Argonaute交联免疫沉淀结合高通量测序(CLIP-Seq)技术的发展提供了一个有效确定miRNA靶基因的系统。同样,通过转录因子的染色质免疫沉淀结合测序(ChIP-Seq),剖析miRNA基因转录调控的准确性也有了很大提高。阐明miRNA靶标库将有助于深入了解miRNA通路的功能作用。为了可靠地重建miRNA介导的调控网络,我们利用公开可用的基于序列的转录因子-miRNA数据库(包括用于TF-miRNA相互作用的ChIPBase和TransmiR)以及miRNA靶标数据库(包括miRTarBase、TarBase和starBase)建立了一个计算框架,用于miRNA-靶标相互作用。我们应用该计算框架来阐明Mir¬122a-/-小鼠模型中miRNA介导的调控网络,该模型具有转录组改变和进行性肝病。

结果

我们将计算框架应用于Mir¬122a-/-突变小鼠和野生型小鼠的miRNA/mRNA表达谱。miRNA介导的网络涉及40个经过整理的转录因子,这些转录因子导致65个miRNA和723个经过整理的miRNA靶基因异常表达,其中56%在Mir¬122a-/-小鼠的差异表达基因中被发现。因此,该调控网络揭示了突变小鼠中先前已知的以及许多先前未识别的miRNA介导的调控。此外,我们证明12qF一染色体区域印记缺失与人类肝细胞癌和干细胞中miRNA过表达相关,这表明在缺乏miR-122的幼鼠中癌前变化开始出现。发现一组9个miRNA共享miR-122靶基因,表明miRNA与靶基因之间通过多重性和协同性产生协同作用。

结论

该研究为miRNA介导的调控网络提供了重要见解。基于实验验证的数据,该网络在揭示先前未确定的疾病相关分子机制方面高度可靠且有效。这种计算框架可以应用于以高度置信度探索任何复杂生物系统中重要的TF-miRNA-miRNA靶标相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90f7/4331712/dc523afc38ef/1471-2164-16-S2-S12-1.jpg

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