Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
Nucleic Acids Res. 2012 Oct;40(18):8818-34. doi: 10.1093/nar/gks657. Epub 2012 Jul 13.
MicroRNA (miRNA) target hubs are genes that can be simultaneously targeted by a comparatively large number of miRNAs, a class of non-coding RNAs that mediate post-transcriptional gene repression. Although the details of target hub regulation remain poorly understood, recent experiments suggest that pairs of miRNAs can cooperate if their binding sites reside in close proximity. To test this and other hypotheses, we established a novel approach to investigate mechanisms of collective miRNA repression. The approach presented here combines miRNA target prediction and transcription factor prediction with data from the literature and databases to generate a regulatory map for a chosen target hub. We then show how a kinetic model can be derived from the regulatory map. To validate our approach, we present a case study for p21, one of the first experimentally proved miRNA target hubs. Our analysis indicates that distinctive expression patterns for miRNAs, some of which interact cooperatively, fine-tune the features of transient and long-term regulation of target genes. With respect to p21, our model successfully predicts its protein levels for nine different cellular functions. In addition, we find that high abundance of miRNAs, in combination with cooperativity, can enhance noise buffering for the transcription of target hubs.
微小 RNA (miRNA) 靶标枢纽是一类可以同时被相对大量 miRNA 靶向的基因，miRNA 是一类非编码 RNA，可介导转录后基因沉默。尽管靶标枢纽调控的细节仍知之甚少，但最近的实验表明，如果 miRNA 的结合位点彼此接近，那么一对 miRNA 可以协同作用。为了检验这一假说及其他假说，我们建立了一种新方法来研究集体 miRNA 抑制的机制。这里提出的方法将 miRNA 靶标预测和转录因子预测与文献和数据库中的数据相结合，为所选靶标枢纽生成调控图谱。然后，我们展示了如何从调控图谱中推导出动力学模型。为了验证我们的方法，我们以第一个被实验证明的 miRNA 靶标枢纽之一 p21 为例进行了案例研究。我们的分析表明，miRNA 具有独特的表达模式，其中一些 miRNA 具有协同作用，可以微调靶基因的瞬时和长期调控特征。对于 p21，我们的模型成功预测了其在九种不同细胞功能下的蛋白质水平。此外，我们发现高丰度的 miRNA 与协同作用相结合，可以增强靶标枢纽转录的噪声缓冲能力。
