Fig. 4 miRNAs providing robustness in gene expression noise . (a ) Simple target regulation by a transcription factor and an incoherent feed-forward loop involving miRNA. (b) The miRNA and its corresponding target are under the control of different TFs. (Solid arrows represent the activation process and round-headed arrows represent the inhibition process). (c) Target expression distribution in various network motifs presented in (a) and(b) , where introducing the miRNA regulation leads to suppression of noise (II ) in comparison to the situation (I ) when miRNA is absent or activated by different TFs. Adapted from Osella et al74.
They created an incoherent FFL (Fig. 4a, right panel ), where the transcription factor activates both the target gene and the miRNA that inhibits the target. miRNA being an extrinsic noise source is expected to increase the fluctuations in the gene expression. However, the probability distribution of protein expression level displays that miRNA regulation affects the mean of the distribution and reduces the coefficient of variation (Fig. 4c, left panel (situation-II) ). They demonstrated that compared to open circuits (Fig. 4b ) where target and miRNA are under the control of different TFs, an incoherent FFL (Fig. 4c, right panel (situation-II) ) showed a lesser degree of fluctuations.74 Such regulation is important especially in network motifs with positive feedbacks, where small perturbation in the signal, might drive the system to different protein steady states and affect the cellular fate decisions.
These theoretical studies are crucial to understanding networks where miRNAs are involved in TF-gene motifs that regulate cell fate decisions and any dysregulation in the corresponding motif components can lead to cancer. For example, miR-34a is a tumor suppressor miRNA and regulates several targets in cell proliferation, apoptosis, senescence such as MYCN, BCL2, SIRT1, E2F3, etc.75 p53 is a transcription factor for mir-34a, and in several cancer types, mir-34a is found to be downregulated.75 Another example is the network motif involving the protooncogene c-Myc, mir-17-92 (OncomiR), and their target transcription factor E2F in a feed-forward loop.14,76Abnormality in the c-Myc, as well as mir-17-92 expressions, is linked with several cancer phenotypes.77,78