The number of titrated microRNA species dictates ceRNA regulation

Abstract

AbstractmicroRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts—our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

Competing endogenous RNA crosstalk at system level

“…At the lowest level of diversity, we assume a homogeneous network in which µ 0 ia = 2µ 0 for each miRNA-RNA pair, with µ 0 a constant taken to be equal to 4 molecules (see Table I). (Assuming a negligible miRNA-RNA unbinding rate, this corresponds roughly to an association rate of 0.02/molecules/hour, in agreement with values reported in [37].) At the intermediate level, we discriminate (i, a) pairs interacting via k-mer pairing (for which we take µ 0 ia = µ 0 , i.e.…”

“…This implies that (i) they are driven by competition effects exclusively, and (ii) they are generated by small perturbations of RNA levels (as opposed e.g. to the models of [37,38]). Our scenario might therefore be close to a standard, homeostatic physiology in which transcription rates only undergo small variations.…”

“…To further validate this picture, we have analyzed the RNA crosstalk scenario in the cancer-specific interactomes reconstructed in [37]. These networks comprise canonical pairings only and their basic topological characteristics are noticeably distinct from those found in CLASH (see Supporting Text, Fig S12).…”

“…Generally speaking, weak individual crosstalk interactions by themselves do not necessarily imply a reduced physiological role. This is especially true in large networks, where many interactions can aggregate and perturbations can propagate by exploiting topological and kinetic heterogeneities [18,22,37]. On-going explorations of miRNA-RNA networks are indeed uncovering a high degree of hard-wired complexity [38,39].…”

miRDriver: A Tool to Infer Copy Number Derived miRNA-Gene Networks in Cancer

“…For the future versions of miRDriver, more datasets such as mutations, histone modification and chromatin accessibility datasets such as ATAC-seq could be incorporated. The current version of miRDriver does not incorporate the competing endogenous RNA (ceRNA) interactions [64]. These interactions could affect how miRNAs interact with their target genes.…”