  microRNAs (miRNAs) regulate gene expression at post-transcriptional level by
repressing target RNA molecules. Competition to bind miRNAs tends in turn to
correlate their targets, establishing effective RNA-RNA interactions that can
influence expression levels, buffer fluctuations and promote signal
propagation. Such a potential has been characterized mathematically for small
motifs both at steady state and \red{away from stationarity}. Experimental
evidence, on the other hand, suggests that competing endogenous RNA (ceRNA)
crosstalk is rather weak. Extended miRNA-RNA networks could however favour the
integration of many crosstalk interactions, leading to significant large-scale
effects in spite of the weakness of individual links. To clarify the extent to
which crosstalk is sustained by the miRNA interactome, we have studied its
emergent systemic features in silico in large-scale miRNA-RNA network
reconstructions. We show that, although generically weak, system-level
crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii)
can achieve high-intensity even for RNAs that are not co-regulated, (iii) are
robust to variability in transcription rates, and (iv) are significantly
non-local, i.e. correlate weakly with miRNA-RNA interaction parameters.
Furthermore, RNA levels are generically more stable when crosstalk is
strongest. As some of these features appear to be encoded in the network's
topology, crosstalk may functionally be favoured by natural selection. These
results suggest that, besides their repressive role, miRNAs mediate a weak but
resilient and context-independent network of cross-regulatory interactions that
interconnect the transcriptome, stabilize expression levels and support
system-level responses.
