Kinetic Modelling of Competition and Depletion of Shared miRNAs by Competing Endogenous RNAs

Abstract

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.

Competing endogenous RNA crosstalk at system level

“…This finding agrees with the proposed role of kinetic heterogeneities in creating favourable paths in the miRNA-RNA network through which perturbations can efficiently propagate, as discussed e.g. in [17,27].…”

“…to fluctuations in miRNA levels. An extended comparison of different crosstalk measures can be found in [17]. Starting from Eq (2), one can derive an analytical expression allowing for the convenient computation of χ ij for each (i, j) pair in any miRNA-RNA network specified by a given set of kinetic parameters (see Supporting Text, Section 1).…”

“…Susceptibilities are bound to be larger when RNAs are more sensitive to changes in miRNA levels, i.e. in the so-called susceptible region at intermediate values of β [17]. Representative susceptibility distributions derived by solving Eq (6) and describing the CLASH network's crosstalk pattern for different degrees of TH and BH are displayed in Fig 2a and in Supporting Text, Supplementary Fig S4.…”

“…In some cases (e.g. in presence of specific patterns of correlation between transcription rates), fluctuations can be reduced leading to more finely tuned expression levels [17,18,22,23]. In general, though, crosstalk tends to amplify target variability, especially when different species are transcribed independently [18].…”

“…In silico studies of small motifs, summarized e.g. in [16,17], have characterized how the strength, selectivity and directionality of miRNA-mediated RNA crosstalk are modulated by kinetic and topologic ingredients, leading to highly adjustable output profiles [18][19][20], differential processing of intrinsic and extrinsic heterogeneities [21][22][23][24], stabilization of protein levels [25,26] and long-range effects [27], both at steady state and during transients [28]. Experimental evidence, however, suggests that, in order to fully develop its potential, RNA crosstalk presupposes rather specific conditions, either in terms of the size of the perturbation required to generate a significant response [29][30][31] or in terms of molecular abundances and kinetic parameters [32][33][34] (see e.g.…”