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MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise.

ABSTRACT: miRNAs are small non-coding RNAs able to modulate target gene expression. It has been postulated that miRNAs confer robustness to biological processes, but clear experimental evidence is still missing. Here, using a synthetic biological approach, we demonstrate that microRNAs provide phenotypic robustness to transcriptional regulatory networks by buffering fluctuations in protein levels. We construct a network motif in mammalian cells exhibiting a 'toggle-switch' phenotype in which two alternative protein expression levels define its ON and OFF states. The motif consists of an inducible transcription factor that self-regulates its own transcription and that of a miRNA against the transcription factor itself. We confirm, using mathematical modelling and experimental approaches, that the microRNA confers robustness to the toggle-switch by enabling the cell to maintain and transmit its state. When absent, a dramatic increase in protein noise level occurs, causing the cell to randomly switch between the two states.

SUBMITTER: Siciliano V

PROVIDER: S-EPMC3836244 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise.

Siciliano Velia V Garzilli Immacolata I Fracassi Chiara C Criscuolo Stefania S Ventre Simona S di Bernardo Diego D

Nature communications 20130101

miRNAs are small non-coding RNAs able to modulate target gene expression. It has been postulated that miRNAs confer robustness to biological processes, but clear experimental evidence is still missing. Here, using a synthetic biological approach, we demonstrate that microRNAs provide phenotypic robustness to transcriptional regulatory networks by buffering fluctuations in protein levels. We construct a network motif in mammalian cells exhibiting a 'toggle-switch' phenotype in which two alternati ...[more]

PMID: 24077216

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Project description:Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs - such as their degree distribution - with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness.

Dataset Information

MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise.

Publications

MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise.

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