Characterization of the proneural gene regulatory network during mouse telencephalon development

被引:81
作者
Gohlke, Julia M. [2 ]
Armant, Olivier [1 ,4 ]
Parham, Frederick M. [2 ]
Smith, Marjolein V. [3 ]
Zimmer, Celine [1 ]
Castro, Diogo S. [1 ]
Nguyen, Laurent [1 ]
Parker, Joel S. [3 ]
Gradwohl, Gerard [4 ]
Portier, Christopher J. [2 ]
Guillemot, Francois [1 ]
机构
[1] Natl Inst Med Res, Div Mol Neurobiol, London NW7 1AA, England
[2] Natl Inst Environm Hlth Sci, Mol Toxicol Lab, Environm Syst Biol Grp, Res Triangle Pk, NC 27709 USA
[3] Constella Hlth Sci, Durham, NC 27713 USA
[4] INSERM, U682, F-67200 Strasbourg, France
基金
英国医学研究理事会;
关键词
D O I
10.1186/1741-7007-6-15
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: The proneural proteins Mash1 and Ngn2 are key cell autonomous regulators of neurogenesis in the mammalian central nervous system, yet little is known about the molecular pathways regulated by these transcription factors. Results: Here we identify the downstream effectors of proneural genes in the telencephalon using a genomic approach to analyze the transcriptome of mice that are either lacking or overexpressing proneural genes. Novel targets of Ngn2 and/or Mash1 were identified, such as members of the Notch and Wnt pathways, and proteins involved in adhesion and signal transduction. Next, we searched the non-coding sequence surrounding the predicted proneural downstream effector genes for evolutionarily conserved transcription factor binding sites associated with newly defined consensus binding sites for Ngn2 and Mash1. This allowed us to identify potential novel co-factors and co-regulators for proneural proteins, including Creb, Tcf/Lef, Pou-domain containing transcription factors, Sox9, and Mef2a. Finally, a gene regulatory network was delineated using a novel Bayesian-based algorithm that can incorporate information from diverse datasets. Conclusion: Together, these data shed light on the molecular pathways regulated by proneural genes and demonstrate that the integration of experimentation with bioinformatics can guide both hypothesis testing and hypothesis generation.
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页数:18
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