Literature DB >> 23845991

Dynamic analysis of the combinatorial regulation involving transcription factors and microRNAs in cell fate decisions.

Fang Yan1, Haihong Liu, Zengrong Liu.   

Abstract

P53 and E2F1 are critical transcription factors involved in the choices between different cell fates including cell differentiation, cell cycle arrest or apoptosis. Recent experiments have shown that two families of microRNAs (miRNAs), p53-responsive miR34 (miRNA-34 a, b and c) and E2F1-inducible miR449 (miRNA-449 a, b and c) are potent inducers of these different fates and might have an important role in sensitizing cancer cells to drug treatment and tumor suppression. Identifying the mechanisms responsible for the combinatorial regulatory roles of these two transcription factors and two miRNAs is an important and challenging problem. Here, based in part on the model proposed in Tongli Zhang et al. (2007), we developed a mathematical model of the decision process and explored the combinatorial regulation between these two transcription factors and two miRNAs in response to DNA damage. By analyzing nonlinear dynamic behaviors of the model, we found that p53 exhibits pulsatile behavior. Moreover, a comparison is given to reveal the subtle differences of the cell fate decision process between regulation and deregulation of miR34 on E2F1. It predicts that miR34 plays a critical role in promoting cell cycle arrest. In addition, a computer simulation result also predicts that the miR449 is necessary for apoptosis in response to sustained DNA damage. In agreement with experimental observations, our model can account for the intricate regulatory relationship between these two transcription factors and two miRNAs in the cell fate decision process after DNA damage. These theoretical results indicate that miR34 and miR449 are effective tumor suppressors and play critical roles in cell fate decisions. The work provides a dynamic mechanism that shows how cell fate decisions are coordinated by two transcription factors and two miRNAs. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology and Clinical Implications. Guest Editor: Yudong Cai. Crown
Copyright © 2013. All rights reserved.

Entities:  

Keywords:  Apoptosis; Cell cycle arrest; Cell fate decision; MiRNAs; Protein; Transcription factors

Mesh:

Substances:

Year:  2013        PMID: 23845991     DOI: 10.1016/j.bbapap.2013.06.022

Source DB:  PubMed          Journal:  Biochim Biophys Acta        ISSN: 0006-3002


  7 in total

1.  miR-449a enhances radiosensitivity through modulating pRb/E2F1 in prostate cancer cells.

Authors:  Aihong Mao; Yang Liu; Yali Wang; Qiuyue Zhao; Xin Zhou; Chao Sun; Cuixia Di; Jing Si; Lu Gan; Hong Zhang
Journal:  Tumour Biol       Date:  2015-10-31

2.  Oscillatory expression in Escherichia coli mediated by microRNAs with transcriptional and translational time delays.

Authors:  Yuan Zhang; Haihong Liu; Jin Zhou
Journal:  IET Syst Biol       Date:  2016-12       Impact factor: 1.615

3.  Mathematical modeling of combinatorial regulation suggests that apparent positive regulation of targets by miRNA could be an artifact resulting from competition for mRNA.

Authors:  Dimpal Nyayanit; Chetan J Gadgil
Journal:  RNA       Date:  2015-01-09       Impact factor: 4.942

4.  Coordination of miR-192 and miR-22 in p53-Mediated Cell Fate Decision.

Authors:  Cheng-Yuan Sun; Xiao-Peng Zhang; Wei Wang
Journal:  Int J Mol Sci       Date:  2019-09-26       Impact factor: 5.923

Review 5.  P53/microRNA-34-induced metabolic regulation: new opportunities in anticancer therapy.

Authors:  Ding-Guo Zhang; Jun-Nian Zheng; Dong-Sheng Pei
Journal:  Mol Cancer       Date:  2014-05-21       Impact factor: 27.401

6.  A novel mathematical model of ATM/p53/NF- κB pathways points to the importance of the DDR switch-off mechanisms.

Authors:  Katarzyna Jonak; Monika Kurpas; Katarzyna Szoltysek; Patryk Janus; Agata Abramowicz; Krzysztof Puszynski
Journal:  BMC Syst Biol       Date:  2016-08-15

7.  Understanding microRNA-mediated gene regulatory networks through mathematical modelling.

Authors:  Xin Lai; Olaf Wolkenhauer; Julio Vera
Journal:  Nucleic Acids Res       Date:  2016-06-17       Impact factor: 16.971

  7 in total

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