Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
PLoS One. 2010 Nov 2;5(11):e13798. doi: 10.1371/journal.pone.0013798.
MicroRNAs are small non-coding RNAs involved in post-transcriptional regulation of gene expression. Due to the poor annotation of primary microRNA (pri-microRNA) transcripts, the precise location of promoter regions driving expression of many microRNA genes is enigmatic. This deficiency hinders our understanding of microRNA-mediated regulatory networks. In this study, we develop a computational approach to identify the promoter region and transcription start site (TSS) of pri-microRNAs actively transcribed using genome-wide RNA Polymerase II (RPol II) binding patterns derived from ChIP-seq data. Based upon the assumption that the distribution of RPol II binding patterns around the TSS of microRNA and protein coding genes are similar, we designed a statistical model to mimic RPol II binding patterns around the TSS of highly expressed, well-annotated promoter regions of protein coding genes. We used this model to systematically scan the regions upstream of all intergenic microRNAs for RPol II binding patterns similar to those of TSS from protein coding genes. We validated our findings by examining the conservation, CpG content, and activating histone marks in the identified promoter regions. We applied our model to assess changes in microRNA transcription in steroid hormone-treated breast cancer cells. The results demonstrate many microRNA genes have lost hormone-dependent regulation in tamoxifen-resistant breast cancer cells. MicroRNA promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription, and therefore allows comparison of transcription activities between different conditions, such as normal and disease states.
微 RNA 是参与基因表达转录后调控的小非编码 RNA。由于初级微 RNA (pri-microRNA) 转录物的注释较差，许多微 RNA 基因表达的启动子区域的精确位置仍然是个谜。这种缺陷阻碍了我们对 microRNA 介导的调控网络的理解。在这项研究中，我们开发了一种计算方法，利用来自 ChIP-seq 数据的全基因组 RNA 聚合酶 II (RPol II) 结合模式来识别 pri-microRNA 的启动子区域和转录起始位点 (TSS)。基于 RPol II 结合模式在 microRNA 和蛋白质编码基因的 TSS 周围的分布相似的假设，我们设计了一个统计模型来模拟蛋白质编码基因高度表达、注释良好的启动子区域的 TSS 周围的 RPol II 结合模式。我们使用这个模型系统地扫描所有基因间 microRNA 上游区域，寻找与蛋白质编码基因 TSS 相似的 RPol II 结合模式。我们通过检查鉴定的启动子区域中的保守性、CpG 含量和激活组蛋白标记来验证我们的发现。我们应用我们的模型来评估甾体激素处理的乳腺癌细胞中 microRNA 转录的变化。结果表明，许多 microRNA 基因在他莫昔芬耐药的乳腺癌细胞中失去了激素依赖性调节。基于 RPol II 结合模式的 microRNA 启动子识别提供了关于转录起始的重要时间和空间测量，因此允许在不同条件（如正常和疾病状态）之间比较转录活性。
