We have introduced a systematic method for inferring miRNA functions by assessing the enrichment of likely functional target sites in gene sets. Key features of mirBridge include combining test metrics that detect different aspects of functional targeting, and a sampling algorithm for removing gene-set biases to improve estimation of statistical significance. Hundreds of human miRNA-function associations were inferred by mirBridge; some are reassuringly supported by published experiments, but many are not previously known and/or provide mechanistic insights beyond published data.
Our results provide hints about the general principles of miRNA-mediated regulation in networks. While some miRNAs could act as global regulators by repressing up to thousands of targets genome-wide (Lewis et al., 2005
), many appear to have pathway-specific functions, and these miRNAs tend to target multiple genes in the same pathway. Typically, the predicted targets of the miRNA are genes that drive pathway activity in a coherent direction (e.g. miR-16
targeting of G1-to-S-promoting genes). Such coordinate targeting could partially explain how individual miRNAs can be potent effectors of pathway activity even though the amount of repression conferred by miRNAs tends to be modest for any single target (Baek et al., 2008
; Selbach et al., 2008
; Xiao and Rajewsky, 2009
). As was observed earlier (Martinez et al., 2008
; Tsang et al., 2007
), some of our predictions (e.g. miR-1
) involve miRNAs mediating feedback and feedforward loops, whose functions include protein homeostasis and signal amplification, respectively. For example, miRNAs could be “master” regulators of pathways and thus serve as effective therapeutic targets because positive feedbacks could amplify small changes in protein concentration conferred by miRNA targeting of multiple genes. Our analysis also indicates that miRNAs can function in, and mediate cross-talk among, multiple canonical pathways, such as miR-16
’s potential roles across the cell cycle and Wnt pathways to coordinately regulate cellular growth and proliferation.
mirBridge also facilitates context-specific target prediction: one can first predict which pathways a miRNA regulates and then compile high-quality putative targets within a pathway. This strategy may be especially effective for miRNAs that function in only a few pathways, as targets predicted genome-wide may have low specificity (Lewis et al., 2005
). Additional filtering can be used to strengthen the target predictions, for example, by requiring that the putative target and the miRNA be significantly correlated in their expression using miRNA-mRNA expression data sets (Lu et al., 2005
) (Table S1i
In addition to providing functional links across miRNAs, our human miRNA-miRNA map provides, to the best of our knowledge, the first genome-wide evidence that miRNA co-targeting is prevalent, and that a handful of hub miRNA families are involved in a large fraction of the co-targeting connections. The abundance of co-targeting further suggests that while individual miRNAs may provide only modest levels of repression, combinatorial targeting by multiple miRNAs (Krek et al., 2005
) can potentially achieve a wide range of target-level modulations. Given that multiple miRNAs are expressed at different levels in any given cell type, individual genes can evolve combinations of miRNA binding sites to optimize expression levels across cell types (Bartel and Chen, 2004
). miRNA target sites are short and could thus be acquired or lost relatively quickly over evolution to fine-tune gene expression levels.
Designating a group of miRNAs as “co-targeting” does not necessarily imply that these miRNAs are co-expressed so as to regulate their common targets at the same time and place. In fact, the exact opposite is also likely: different miRNAs are responsible for controlling a given set of targets in different contexts. In general, a combination of the above scenarios is likely for individual cases, and additional data (e.g. miRNA and target expression profiles) are needed to further dissect the mechanistic basis of individual co-targeting predictions.
mirBridge is currently limited to assessing enrichment at the level of miRNA families using seed-matched motifs. But this is largely due to our lack of general understanding of miRNA-target interaction beyond seed pairing and features captured by the context score. In principle, the mirBridge methodology is general and can be applied to any combinations of gene sets, sequence motifs and site scoring metrics, including non-miRNA motifs, such as those involved in regulating mRNA stability. Given mirBridge’s ability to simultaneously correct for multiple gene-set biases, and the increasing availability of genomes and annotated gene sets, mirBridge is poised to serve as a key resource for the comprehensive functional dissection of miRNAs and other regulatory sequence motifs in genomes.