Systems analysis identifies miR-29b regulation of invasiveness in melanoma.
Systems analysis identifies miR-29b regulation of invasiveness in melanoma.
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DOI：
10.1186/s12943-016-0554-y
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发表时间：
2016-11-16
期刊：
影响因子：
37.3
通讯作者：
Crampin EJ
中科院分区：
文献类型：
--
作者：
Andrews MC;Cursons J;Hurley DG;Anaka M;Cebon JS;Behren A;Crampin EJ
在许多癌症中，microRNAs(MiRs)通过调节表型重编程过程，如上皮-间充质可塑性，促进转移进展。这可以通过靶向多个mRNA转录本的miRs来推动，从而在大组基因中诱导受调控的变化。MiR-靶数据库TargetScan和Diana-microT通过检查miR和mRNAs之间的序列互补性来预测推测的关系。然而，确定哪些miR-mRNA相互作用在内源表达水平上是活跃的，并具有生物学意义仍然是一个挑战。我们开发了一个工作流程，将TargetScan和Diana-microT预测整合到从转录丰度(RNAseq)数据计算的数据驱动关联分析中，特别是相互信息和皮尔逊相关性度量。我们使用这一工作流程来确定miR介导的mRNA抑制的假定关系，并从两条证据中获得了强有力的支持。将这一方法系统地应用于一个大型的、已发表的独特黑色素瘤细胞系的集合-路德维希·墨尔本黑色素瘤(LM-MEL)细胞系小组-我们识别了可能有助于侵袭性的假定miR-mRNA相互作用。这为进一步的体外验证研究选择感兴趣的相互作用提供了指导。TargetScan和Diana-microT支持的几种miR-mRNA调控关系显示了不同基质侵袭性的细胞系之间的不同活性。这些假定的调控关系的强烈负统计相关性与miR对靶mRNA的抑制是一致的，并表明这种miR-mRNA关系的不同活性有助于黑色素瘤侵袭性的差异。这些关系中的许多都反映在皮肤黑色素瘤TCGA数据集中，表明这些观察也显示了临床样本中分级的活动。其中几个−与癌症进展有关(miR-211、-340、-125b、miR 221和-29b)。MiR-29b-3p在黑色素瘤中的具体作用尚未得到很好的研究。我们实验验证了预测的miR-29b-3p对LAMC1、PPIC和LASP1的调节，并表明miR-29b-3p或这些mRNA靶点的失调可以影响体外细胞的侵袭性。这一分析策略提供了一种全面的、系统水平的方法来识别高通量癌症数据中的miR-mRNA调控，识别与功能表型相关性的新的假定相互作用，并可用于指导后续实验验证的实验资源。可以使用计算脚本：http://github.com/uomsystemsbiology/LMMEL-miR-miner本文的在线版本(doi：10.1186/s12943-0160554-y)包含补充材料，授权用户可以使用。
In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs. However, it remains a challenge to identify which miR-mRNA interactions are active at endogenous expression levels, and of biological consequence. We developed a workflow to integrate TargetScan and DIANA-microT predictions into the analysis of data-driven associations calculated from transcript abundance (RNASeq) data, specifically the mutual information and Pearson’s correlation metrics. We use this workflow to identify putative relationships of miR-mediated mRNA repression with strong support from both lines of evidence. Applying this approach systematically to a large, published collection of unique melanoma cell lines – the Ludwig Melbourne melanoma (LM-MEL) cell line panel – we identified putative miR-mRNA interactions that may contribute to invasiveness. This guided the selection of interactions of interest for further in vitro validation studies. Several miR-mRNA regulatory relationships supported by TargetScan and DIANA-microT demonstrated differential activity across cell lines of varying matrigel invasiveness. Strong negative statistical associations for these putative regulatory relationships were consistent with target mRNA inhibition by the miR, and suggest that differential activity of such miR-mRNA relationships contribute to differences in melanoma invasiveness. Many of these relationships were reflected across the skin cutaneous melanoma TCGA dataset, indicating that these observations also show graded activity across clinical samples. Several of these miRs are implicated in cancer progression (miR-211, -340, -125b, −221, and -29b). The specific role for miR-29b-3p in melanoma has not been well studied. We experimentally validated the predicted miR-29b-3p regulation of LAMC1 and PPIC and LASP1, and show that dysregulation of miR-29b-3p or these mRNA targets can influence cellular invasiveness in vitro. This analytic strategy provides a comprehensive, systems-level approach to identify miR-mRNA regulation in high-throughput cancer data, identifies novel putative interactions with functional phenotypic relevance, and can be used to direct experimental resources for subsequent experimental validation. Computational scripts are available: http://github.com/uomsystemsbiology/LMMEL-miR-miner The online version of this article (doi:10.1186/s12943-016-0554-y) contains supplementary material, which is available to authorized users.
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影响因子：
64.8
作者：
Baek, Daehyun;Villen, Judit;Shin, Chanseok;Camargo, Fernando D.;Gygi, Steven P.;Bartel, David P.
通讯作者：
Bartel, David P.
DOI：
10.1002/bies.201400191
发表时间：
2015-04
期刊：
BioEssays : news and reviews in molecular, cellular and developmental biology
影响因子：
--
作者：
Cloonan N
通讯作者：
Cloonan N
影响因子：
11.2
作者：
Alonso, Soledad R.;Tracey, Lorraine;Rodriguez-Peralto, Jose L.
通讯作者：
Rodriguez-Peralto, Jose L.
影响因子：
6.5
作者：
Bell, Rachel E.;Khaled, Mehdi;Levy, Carmit
通讯作者：
Levy, Carmit
DOI：
10.1111/igc.0b013e3181a48cf9
发表时间：
2009-05-01
影响因子：
4.8
作者：
Flavin, Richard;Smyth, Paul;O'Leary, John J.
通讯作者：
O'Leary, John J.