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Review
. 2009 Apr;31(4):435-45.
doi: 10.1002/bies.200800212.

The interplay between transcription factors and microRNAs in genome-scale regulatory networks

Natalia J Martinez  1 , Albertha J M Walhout
Affiliations
Review

The interplay between transcription factors and microRNAs in genome-scale regulatory networks

Natalia J Martinez et al. Bioessays. 2009 Apr.

Abstract

Metazoan genomes contain thousands of protein-coding and non-coding RNA genes, most of which are differentially expressed, i.e., at different locations, at different times during development, or in response to environmental signals. Differential gene expression is achieved through complex regulatory networks that are controlled in part by two types of trans-regulators: transcription factors (TFs) and microRNAs (miRNAs). TFs bind to cis-regulatory DNA elements that are often located in or near their target genes, while miRNAs hybridize to cis-regulatory RNA elements mostly located in the 3' untranslated region of their target mRNAs. Here, we describe how these trans-regulators interact with each other in the context of gene regulatory networks to coordinate gene expression at the genome-scale level, and discuss future challenges of integrating these networks with other types of functional networks.

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Figures

Figure 1
Figure 1
TF and miRNA-containing regulatory networks. (A) Transcriptional network (TF →target). (B) Post-transcriptional network (miR-NA →target). (C) Integrated gene regulatory network. Nodes: green circles, TFs; red rectangles, miRNAs; gray diamonds, other protein-coding genes. Edges: black arrows, transcriptional activation; black blunted arrows, transcriptional repression; red blunted arrows, post-transcriptional repression. Examples of in- and out-degrees: 1, out-degree of TF is 8; 2, in-degree of target gene is 2; 3, in-degree of target gene (in this case a TF) is 5; 4, out-degree of miRNA is 2. (D) Summary of presence of hub nodes in transcriptional and post-transcriptional networks.
Figure 2
Figure 2
Cartoon depicting coherent and incoherent feedback and feed-forward motifs. Note that for feed-forward loops, other arrangements between regulators and targets are possible. Green circles, TFs; red rectangles, miRNAs; gray diamonds, other protein-coding genes; black arrows, activation; black blunted arrows, repression. LIN-28 is an RNA-binding protein. ESC TFs: embryonic stem cell TFs Nanog, Oct4, Tcf3 and Sox2.
Figure 3
Figure 3
Network circuits allow the spreading of regulatory effects. (A) TF and miRNAs that participate in feedback loops are highly connected and not only regulate each other but also each others’ targets. In this example, an upstream signal activates the miRNA, which in turns represses all its direct targets, including the TF in the feedback loop. As a result, all downstream targets of the TF are also repressed (indirect targets of the miRNA). Red blunted arrows, post-transcriptional repression; black dashed arrows, inhibition of transcriptional activation; gray diamonds, protein-coding genes; red rectangle, miRNA; green circle, TF. (B) MiRNAs tend to target hubs in protein-protein interaction networks, hence spreading its regulatory effects to large set of proteins. In this example, an upstream signal activates the miRNA, which in turn represses the hub (direct target). As a result, all protein-protein interactions between the hub and other nodes (indirect targets) are inhibited. Red blunted arrow, post-transcriptional repression; blue dashed lines, inhibition of protein-protein interactions; red rectangle, miRNA; gray diamonds, protein-coding genes.
Figure 4
Figure 4
Integration of functional data into “meta network models”.
See this image and copyright information in PMC

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