A Combination of Transcriptional and MicroRNA Regulation Improves the Stability of the Relative Concentrations of Target Genes

Riba, Andrea; Bosia, Carla; Baroudi, Mariama El; Ollino, Laura; Caselle, Michele

doi:10.1371/journal.pcbi.1003490

Cited by 50 publications

(61 citation statements)

References 43 publications

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“…Similar result has also been reported in overexpressing miR‐214 that targets MFN2 . However, in cardiomyocytes, knocked down of E2F1 suppresses mitochondrial fragmentation by regulating miR‐421 expression that targets PINK1, an important protein involved in mitophagy.Thus MFN2 is coregulated by transcription factor E2F1 and miRNA regulated by E2F1 forming closed network as described for other transcription factors and miRNA target genes . It is known that miR‐214 is activated by p53 by binding to the putative promoter ; whereas p53 is activated by E2F1 .…”

Section: Discussionsupporting

confidence: 76%

E2F1 activates MFN2 expression by binding to the promoter and decreases mitochondrial fission and mitophagy in HeLa cells

2019

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Mitofusin‐2 (MFN2) is primarily involved in mitochondrial fusion and participates in diverse biological processes. Several reports show that MFN2 is a target of different miRNAs; however, the transcriptional regulation of MFN2 has not been extensively studied. To gain insight into the transcriptional regulation of MFN2, we expressed E2F transcription factor 1 (E2F1) exogenously and observed that it increased the endogenous expression of MFN2 by binding to its putative promoter region. Although the levels of E2F1 were shown to vary during the cell cycle, the expression of MFN2 and its regulator SP1 did not change throughout the different phases, suggesting that E2F1 regulates MFN2 in a cell‐cycle‐independent manner. In the cell‐cycle phases, where the expression of E2F1 was reduced, SP1 might act in its place to regulate the expression of MFN2. We showed that E2F1 and SP1 are present as a complex on the promoter of MFN2 during the S‐phase as well as in E2F1 overexpressing cells, suggesting that they may regulate the expression of MFN2 synergistically. Furthermore, we found that E2F1 modulated mitochondrial fusion and mitophagy, likely through regulation of MFN2. Bioinformatic analysis revealed that several potential targets of E2F1 are localized in mitochondria and associated with autophagy. Collectively, these data identify the E2F1–MFN2 axis as a regulator of mitochondrial morphology and mitophagy, suggesting a potential therapeutic target for the treatment of mitochondrial disorders.

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Section: Discussionsupporting

confidence: 76%

E2F1 activates MFN2 expression by binding to the promoter and decreases mitochondrial fission and mitophagy in HeLa cells

2019

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“…Few of them, hereafter call motifs, are overrepresented and thus expected to perform regulatory functions. In particular, it has been proven that all these miRNAmediated motifs play some role in stabilizing the expression of the miRNA-target against fluctions [72][73][74][75][76]. Amongst others, a special role is performed by feedforward loops involving one miRNA, one TF and one target.…”

Section: Noise Buffering In Small Regulatory Motifsmentioning

confidence: 99%

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

Martirosyan

Giudice

Bena

et al. 2019

Computational Biology of Non-Coding RNA

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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.

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“…Multi-miRNA-multi-mRNA models have been formulated for specific modules where the targets regulate miRNA transcription, e.g., 2miRNA-2mRNA networks regulating the cell fate/differentiation decision (Lu et al 2013;Yan et al 2014), feedback loops with one miRNA and one target whose protein product regulates miRNA expression (Siciliano et al 2013), or modules where one of the targets is a TF that regulates expression of the other targets (Riba et al 2013). However, we limit our discussion to models where the target proteins have no effect on miRNA activity or concentration.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of combinatorial regulation suggests that apparent positive regulation of targets by miRNA could be an artifact resulting from competition for mRNA

Nyayanit

Gadgil

2015

RNA

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MicroRNAs bind to and regulate the abundance and activity of target messenger RNA through sequestration, enhanced degradation, and suppression of translation. Although miRNA have a predominantly negative effect on the target protein concentration, several reports have demonstrated a positive effect of miRNA, i.e., increase in target protein concentration on miRNA overexpression and decrease in target concentration on miRNA repression. miRNA-target pair-specific effects such as protection of mRNA degradation owing to miRNA binding can explain some of these effects. However, considering such pairs in isolation might be an oversimplification of the RNA biology, as it is known that one miRNA interacts with several targets, and conversely target mRNA are subject to regulation by several miRNAs. We formulate a mathematical model of this combinatorial regulation of targets by multiple miRNA. Through mathematical analysis and numerical simulations of this model, we show that miRNA that individually have a negative effect on their targets may exhibit an apparently positive net effect when the concentration of one miRNA is experimentally perturbed by repression/overexpression in such a multi-miRNA multitarget situation. We show that this apparent unexpected effect is due to competition and will not be observed when miRNA interact noncompetitively with the target mRNA. This result suggests that some of the observed unusual positive effects of miRNA may be due to the combinatorial complexity of the system rather than due to any inherently unusual positive effect of the miRNA on its target.

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A Combination of Transcriptional and MicroRNA Regulation Improves the Stability of the Relative Concentrations of Target Genes

Cited by 50 publications

References 43 publications

E2F1 activates MFN2 expression by binding to the promoter and decreases mitochondrial fission and mitophagy in HeLa cells

E2F1 activates MFN2 expression by binding to the promoter and decreases mitochondrial fission and mitophagy in HeLa cells

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

Mathematical modeling of combinatorial regulation suggests that apparent positive regulation of targets by miRNA could be an artifact resulting from competition for mRNA

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