Objectif
In many developing tissues, initially equipotent cells acquire their intended identity by exposure to extrinsic, graded signals termed morphogens, such as Sonic Hedgehog (Shh). In the ventral neural tube, Shh induces 5 progenitor domains in a precise spatial order along the dorsoventral axis of the developing spinal cord. These domains can be distinguished by the expression of different combinations of transcription factors (TFs). During their specification, the progenitors transit through several metastable states, exhibited by the expression of different combinations of TFs. In network analysis terminology these states define so-called attractors – states of equilibrium towards which global gene expression profile tends to move. We predict that ventral neural tube progenitor states correspond to such attractor states that are generated by a gene regulatory network (GRN) controlled by graded Shh signalling. We will use a series of experimental and in silico approaches to systematically decipher and model this GRN. We will perturb the GRN in the developing chick neural tube using in ovo electroporation of constructs that change the level of Shh signalling and/or the expression of specific TFs. The consequences on the transcriptome of neural cells will be assayed systematically. Factors newly identified in this analysis will be examined and their function in the transcriptional response of neural cells assessed. We will use this approach iteratively to build, challenge and refine our model, and progressively understand the principles of the network. This will define the attractor states of the system and identify the TFs that contribute to the establishment of these states. By extending our knowledge of basic mechanisms of neural tube development we hope to provide new insight that will guide therapeutic approaches to the diseased or damaged spinal cord.
Champ scientifique
Thème(s)
Appel à propositions
FP7-PEOPLE-2009-IEF
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Régime de financement
MC-IEF - Intra-European Fellowships (IEF)Coordinateur
W1B 1AL LONDON
Royaume-Uni