Nucleic Acids Research Advance Access originally published online on August 10, 2009
Nucleic Acids Research 2009 37(18):5969-5980; doi:10.1093/nar/gkp638
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Nucleic Acids Research, 2009, Vol. 37, No. 18 5969-5980
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms
1Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031 and 2Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai 200235, PR China
*To whom correspondence should be addressed. Tel: 86-21-54920086; Fax: 86-21-54065058; Email: yxli{at}sibs.ac.cn
Correspondence may also be addressed to Lu Xie. Tel: 86-21-61313672; Fax: 86-21-54065058; Email: xielu{at}scbit.org
Correspondence may also be addressed to Lei Liu. Tel: 86-21-54065020; Fax: 86-21-54065058; Email: liulei{at}scbit.org
Received August 26, 2008. Revised July 17, 2009. Accepted July 17, 2009.
Recent miRNA transfection experiments show strong evidence that miRNAs influence not only their target but also non-target genes; the precise mechanism of the extended regulatory effects of miRNAs remains to be elucidated. A hypothetical two-layer regulatory network in which transcription factors (TFs) function as important mediators of miRNA-initiated regulatory effects was envisioned, and a comprehensive strategy was developed to map such miRNA-centered regulatory cascades. Given gene expression profiles after miRNA-perturbation, along with putative miRNA–gene and TF–gene regulatory relationships, highly likely degraded targets were fetched by a non-parametric statistical test; miRNA-regulated TFs and their downstream targets were mined out through linear regression modeling. When applied to 53 expression datasets, this strategy discovered combinatorial regulatory networks centered around 19 miRNAs. A tumor-related regulatory network was diagrammed as an example, with the important tumor-related regulators TP53 and MYC playing hub connector roles. A web server is provided for query and analysis of all reported data in this article. Our results reinforce the growing awareness that non-coding RNAs may play key roles in the transcription regulatory network. Our strategy could be applied to reveal conditional regulatory pathways in many more cellular contexts.