On cross-conditional and fluctuation correlations in competitive RNA networks

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Motivation: In competitive endogenous RNA (ceRNA) networks, different mRNAs targeted by the same miRNA can ‘cross-talk’ by absorbing miRNAs and relieving repression on the other mRNAs. This creates correlations in mRNA expression even without direct interaction. Most previous theoretical study of cross-talk has focused on correlations in stochastic fluctuations of mRNAs around their steady state values. However, the experimentally known examples of cross-talk do not involve single-cell fluctuations, but rather bulk tissue-level changes between conditions, such as due to differentiation or disease. In our study, we quantify for the first time both fluctuational and cross-conditional cross-talk in chemical kinetic models of miRNA–mRNA interaction networks. We study the parameter regions under which these different types of cross-talk arise, and how they are affected by network structure.

Results: We find that while a network may support both fluctuational and cross-conditional cross-talk, the parameter regimes under which each type of cross-talk tends to emerge are rather different. Consistent with previous studies, fluctuational cross-talk occurs when miRNA and mRNA expression levels are ‘balanced’, whereas cross-conditional cross-talk tends to emerge when average miRNA levels are high and average mRNA levels are low. Conversely, cross-conditional miRNA cross-talk—a little-discussed phenomenon—is greatest when miRNA levels are low and mRNA levels are high. We show that the parameter ranges where cross-talk is maximized can, to some degree, be predicted based on network structure. Indeed, we find that the dominant effect of network structure on correlations happens through the effect of network structure on the overall balance between miRNA and mRNA expression. However, it is not the only effect, as we find that the density of connections between miRNAs and mRNAs in larger networks increases cross-talk without altering the expression balance.

Conclusion: Our results deepen the theoretical understanding of cross-talk in ceRNA networks, and have implications for the experimental identification of ceRNA cross-talk phenomena.

Availability and Implementation: Simulation software available upon request.


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