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