Croft Larry, Szklarczyk Damian, Jensen Lars Juhl, Gorodkin Jan
Center for Non-coding RNA in Technology and Health, Division of Genetics and Bioinformatics, IBHV, University of Copenhagen, Copenhagen, Denmark.
BMC Syst Biol. 2012 Jul 23;6:90. doi: 10.1186/1752-0509-6-90.
Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods.
In the first approach text mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3'UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring TarBase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes.
We see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.
长期以来,转录因子(TFs)一直被认为是真核生物和原核生物转录的主要激活因子。随着人们越来越意识到微小RNA(miRNAs)作为真核生物中抑制性调节因子的普遍性,这表明miRNAs和TFs之间可能存在相互、优先、自我调节的连接。在这里,我们使用文本挖掘、TF启动子结合位点和6种不同的miRNA结合位点预测方法,研究了TFs和miRNAs与其他基因之间以及它们彼此之间的连接。
在第一种方法中,对PubMed摘要的文本挖掘揭示了miRNAs与TFs和信号转导基因类别之间具有统计学意义的关联。其次,对人和小鼠3'UTR中的miRNA靶标的预测显示,仅TFs有富集,但在信号转导或其他基因类别的预测方法中并不一致。此外,通过人工检查原始论文,对986个TarBase条目的随机样本进行了实验证据评分,观察到TFs的富集随着评分增加。低评分的TarBase条目,其中实验证据与预测的miRNA靶标的miRNA:mRNA表达呈反相关,似乎在任何程度上都没有选择真正的miRNA靶标。我们人工验证的文本挖掘结果还表明,与其他基因类别相比,miRNAs可能被更多的TFs激活,因为文献中7%的miRNA:TF共现是TFs激活miRNAs。当我们第三发现与其他基因类别相比,miRNA和TF基因中预测的保守TF结合位点有富集时,这一点得到了证实。
我们使用几种独立的方法发现了miRNAs和TFs之间连接的富集,这表明存在一个相互激活和抑制调节的网络。我们还使用预测的miRNA和TF结合位点构建了小鼠和人类的调控网络(包含2环和3环基序),并且我们开发了一个网络服务器来搜索和显示这些环,可在http://rth.dk/resources/tfmirloop上供社区使用。