Email updates

Keep up to date with the latest news and content from BMC Systems Biology and BioMed Central.

Open Access Research article

Multiple independent analyses reveal only transcription factors as an enriched functional class associated with microRNAs

Larry Croft1, Damian Szklarczyk2, Lars Juhl Jensen2 and Jan Gorodkin1*

  • * Corresponding author: Jan Gorodkin gorodkin@rth.dk

  • † Equal contributors

Author Affiliations

1 Center for Non-coding RNA in Technology and Health, Division of Genetics and Bioinformatics, IBHV, University of Copenhagen, Copenhagen, Denmark

2 NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen N, Denmark

For all author emails, please log on.

BMC Systems Biology 2012, 6:90  doi:10.1186/1752-0509-6-90

Published: 23 July 2012

Abstract

Background

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.

Results

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.

Conclusions

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 webcite.