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

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.

Figure 1

Abundance of text-mined proteins co-occurring with miRNAs in the literature by functional category. All text-mined proteins compared to proteins co-occurring together with miRNAs in the literature, grouped by eggNOG functional category. P-values are calculated from a Fisher Exact test comparing all proteins to proteins which co-occur with miRNAs.

Figure 2

Abundance of 6 different miRNA target prediction methods by functional category. All proteins compared to 6 miRNA target prediction methods’ proteins by eggNOG functional category. P-values are calculated from a Fisher Exact test comparing all proteins to each methods’ predicted target proteins.

Figure 3

Functional enrichments for high scoring TarBase genes. (A) All TarBase genes compared to scored TarBase genes by eggNOG functional category. (B) Enrichment for transcription and signal transduction functional categories by score of TarBase source literature. P-values are calculated from a Fisher Exact test comparing all genes to high scoring TarBase genes.

Figure 4

Conserved transcription factor binding site densities in mouse and human promoter regions. Conserved transcription factor binding site density for mouse and human transcription factor, non transcription factor and miRNA promoter regions compared to syntenic intronic regions. P-values are calculated from a Fisher Exact test comparing non TF promoter regions with miRNA and TF promoter regions.

Figure 5

In-degree and out-degree histograms for human TF:miRNA predicted networks. (A) log10(in-degree) histogram for all human nodes in the TF:miRNA predicted network. (B) log10(out-degree) histogram for all human nodes in the TF:miRNA predicted network.