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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 (T...

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Autores principales: Tu, Kang, Yu, Hui, Hua, You-Jia, Li, Yuan-Yuan, Liu, Lei, Xie, Lu, Li, Yi-Xue
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2764428/
https://www.ncbi.nlm.nih.gov/pubmed/19671526
http://dx.doi.org/10.1093/nar/gkp638
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author Tu, Kang
Yu, Hui
Hua, You-Jia
Li, Yuan-Yuan
Liu, Lei
Xie, Lu
Li, Yi-Xue
author_facet Tu, Kang
Yu, Hui
Hua, You-Jia
Li, Yuan-Yuan
Liu, Lei
Xie, Lu
Li, Yi-Xue
author_sort Tu, Kang
collection PubMed
description 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.
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spelling pubmed-27644282009-10-20 Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms Tu, Kang Yu, Hui Hua, You-Jia Li, Yuan-Yuan Liu, Lei Xie, Lu Li, Yi-Xue Nucleic Acids Res Computational Biology 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. Oxford University Press 2009-10 2009-08-10 /pmc/articles/PMC2764428/ /pubmed/19671526 http://dx.doi.org/10.1093/nar/gkp638 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ 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.
spellingShingle Computational Biology
Tu, Kang
Yu, Hui
Hua, You-Jia
Li, Yuan-Yuan
Liu, Lei
Xie, Lu
Li, Yi-Xue
Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms
title Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms
title_full Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms
title_fullStr Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms
title_full_unstemmed Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms
title_short Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms
title_sort combinatorial network of primary and secondary microrna-driven regulatory mechanisms
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2764428/
https://www.ncbi.nlm.nih.gov/pubmed/19671526
http://dx.doi.org/10.1093/nar/gkp638
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