Research article
Characterization of the proneural gene regulatory network during mouse telencephalon development
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* Corresponding authors: Christopher J Portier portier@niehs.nih.gov - François Guillemot fguille@nimr.mrc.ac.uk
- † Equal contributors
1 Environmental Systems Biology Group, Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, RTP, NC 27709, USA
2 Division of Molecular Neurobiology, National Institute for Medical Research, The Ridgeway, Mill Hill, London, UK
3 Constella Health Sciences, Durham, NC 27713, USA
4 INSERM U682, Avenue Molière, 67200 Strasbourg, France
BMC Biology 2008, 6:15 doi:10.1186/1741-7007-6-15
Published: 31 March 2008Abstract
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.