Transcription factors (TFs) have long been known to be the predominant regulators of transcription in eukaryotes and prokaryotes, on the whole serving as activators of transcription. In the last 10
years a growing awareness of the ubiquity of miRNAs as suppressive regulators in eukaryotes has emerged [1
]. Though miRNA regulation is ubiquitous, knockdown of the miRNA pathway produces limited or unobservable phenotypes in some cell lineages [2
]. This limited effect, coupled with the small change in abundance of the average mRNA targeted by a miRNA (for example in Selbach et al.
]), lead to the suggestion miRNAs act in the majority of cell lineages to fine tune mRNA expression in a dynamic, regulatory manner, and thereby increase the robustness of the regulatory network [6
Previous work has shown that miRNAs preferentially target TFs more than other gene classes [7
], and the miRNAs themselves also appear to be regulated by TFs more than other types of gene [15
]. Thus, it appears there is an interconnected network of mutual regulation between miRNAs and TFs. Analysis of these networks for recurring motifs identified feed forward [16
] and feedback motifs [18
], both as 2-element and 3-element loops. A 2-element loop being 2 nodes mutually connected by directed edges, and 3 element loops being 3 nodes where all nodes are connected to the others by edges of either direction.
These loops appear to be important functional elements. For example 2 coupled feedback loops (LMO2, miR-223, miR-363) are necessary for normal hematopoiesis (Major personal communication), and it has been suggested these loop motifs may confer robustness to genetic regulatory networks [6
], presumably by inhibiting stochastic transcriptional perturbation using feedback and feed forward loops to regulate transcriptional “gain” and control amplification of unwanted frequencies much as these loops are used in mechanical and electrical engineering for example [27
]. The ability of these loops to confer robustness was elegantly demonstrated experimentally in Drosophila development [19
]. Li et al.
previously demonstrated removal of mir-7
had no observed phenotypic effect in Drosophila sensory cells under standard laboratory conditions [30
], just as many miRNA knockouts or knockdowns have no observable phenotype. However when Li et al.
perturbed embryos by regularly varying the temperature, miR-7
mutant flies had abnormal sensory cells, demonstrating that the hybrid feed forward/feedback motif containing miR-7
imparted robustness to sensory cell development. Li et al.
surmised miRNAs may be often used to impart robustness to regulatory networks, and this may explain the apparent lack of phenotype of many miRNA knockouts in standard laboratory settings. Hilgers et al.
] found similar miRNA regulatory motifs, where mir-263a
mutants displayed a reduced number of mechanosensory eye bristles, which they suggest is due to damage to an incoherent feedforward loop controlling apoptosis of progenitor cells.
How common are these loop motifs in regulatory networks? Is the limited phenotype of many miRNA knockdown/outs due to the prevalence of robustness-conferring miRNA:TF loops or other more complex regulatory structures? Are miRNAs less important than other classes of gene, or is their role less critical? Otherwise, is there built-in redundancy in the miRNA regulatory network of which we are unaware? Given the reports of enrichment for feedforward and feedback motifs in genetic regulatory networks [32
], and the observation of these motifs in critical cellular pathways [36
], we also touch on the presence and role of combined TF:miRNA regulatory networks including 2 and 3 element loop motifs. To our knowledge a comprehensive search for all statistically significantly enriched 3-element loop motifs has not been done before. In this analysis we do not include chromatin regulation, which is likely to be a pervasive system of suppressive regulation in the metazoan genomes. Only a small number of loci are well documented, and these utilise diverse, DNA-protein, RNA-DNA and RNA-protein specific interactions, beyond the scope of this work.
Motivated by the potential for miRNAs and TFs to act in interlinked regulatory networks we address the above questions, establishing miRNA and TFs connections from independent sources. This paper is organized as follows; first we employ text mining to find any potential functional gene class associated with miRNAs in the literature. For the next approach we employ 6 miRNA target prediction methods and following that we employ a transcription factor binding site prediction method. We extract information derived from these combined resources, and describe a resource of predicted miRNA:TF interactions in 2- and 3- element loop motifs which we make available to the community.