Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms.
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.
Tu
Kang
K
Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, PR China.
Yu
Hui
H
Hua
You-Jia
YJ
Li
Yuan-Yuan
YY
Liu
Lei
L
Xie
Lu
L
Li
Yi-Xue
YX
D000465:Algorithms; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D016014:Linear Models; D035683:MicroRNAs / Q000378:metabolism*; D034622:RNA Interference; D014157:Transcription Factors / Q000378:metabolism*
D035683:MicroRNAs; D014157:Transcription Factors