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Integrated Network Analysis Reveals Distinct Regulatory Roles of Transcription Factors and MicroRNAs

Overview
Journal RNA
Specialty Molecular Biology
Date 2016 Sep 9
PMID 27604961
Citations 13
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Abstract

Analysis of transcription regulatory networks has revealed many principal features that govern gene expression regulation. MicroRNAs (miRNAs) have emerged as another major class of gene regulators that influence gene expression post-transcriptionally, but there remains a need to assess quantitatively their global roles in gene regulation. Here, we have constructed an integrated gene regulatory network comprised of transcription factors (TFs), miRNAs, and their target genes and analyzed the effect of regulation on target mRNA expression, target protein expression, protein-protein interaction, and disease association. We found that while target genes regulated by the same TFs tend to be co-expressed, co-regulation by miRNAs does not lead to co-expression assessed at either mRNA or protein levels. Analysis of interacting protein pairs in the regulatory network revealed that compared to genes co-regulated by miRNAs, a higher fraction of genes co-regulated by TFs encode proteins in the same complex. Although these results suggest that genes co-regulated by TFs are more functionally related than those co-regulated by miRNAs, genes that share either TF or miRNA regulators are more likely to cause the same disease. Further analysis on the interplay between TFs and miRNAs suggests that TFs tend to regulate intramodule/pathway clusters, while miRNAs tend to regulate intermodule/pathway clusters. These results demonstrate that although TFs and miRNAs both regulate gene expression, they occupy distinct niches in the overall regulatory network within the cell.

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References
1.
Krol J, Loedige I, Filipowicz W . The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 2010; 11(9):597-610. DOI: 10.1038/nrg2843. View

2.
Gerstein M, Kundaje A, Hariharan M, Landt S, Yan K, Cheng C . Architecture of the human regulatory network derived from ENCODE data. Nature. 2012; 489(7414):91-100. PMC: 4154057. DOI: 10.1038/nature11245. View

3.
Das J, Mohammed J, Yu H . Genome-scale analysis of interaction dynamics reveals organization of biological networks. Bioinformatics. 2012; 28(14):1873-8. PMC: 3492000. DOI: 10.1093/bioinformatics/bts283. View

4.
Stenson P, Mort M, Ball E, Howells K, Phillips A, Thomas N . The Human Gene Mutation Database: 2008 update. Genome Med. 2009; 1(1):13. PMC: 2651586. DOI: 10.1186/gm13. View

5.
Goh K, Cusick M, Valle D, Childs B, Vidal M, Barabasi A . The human disease network. Proc Natl Acad Sci U S A. 2007; 104(21):8685-90. PMC: 1885563. DOI: 10.1073/pnas.0701361104. View