The cis-regulatory logic of the ground state for neural specification

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This project aims to characterise a state of cells specified to become neural, pre-placodal and/or early epiblast, which also appears to accompany the transition from pluripotency to a "primed" state of ES cells that predisposes them to a neural or neural crest-like fate. We will use chick embryos as a model (which offers a number of important advantages) and a combination of new techologies in parallel, which should enable us very efficiently to construct a Gene Regulatory Network of gene interactions that define this state. Specifically we will:

1. use RNA-seq to compare the transcriptome of embryonic cells exposed to signals from the organiser, of pre-placodal ectoderm and of epiblast from pre-streak embryos;

2. use ChIP-seq to identify active enhancers in the same samples. This combination of approaches will allow us to prioritise genes, selecting those represented in both datasets and common to the 3 conditions;

3. starting with the transcription factors (TFs) represented in the above selected set, we will validate their expression at appropriate stages of development by in situ hybridisation;

4. make reporter constructs for enhancers for the selected, in-situ validated TFs and test them in vivo using a rapid cloning and screening strategy;

5. use bioinformatics to analyse the enhancers to identify key transcription factors; these will be related back to 1 and 2 above;

6. use NanoString analysis for testing interactions between key sets of transcription factors to generate a preliminary interactome (GRN).

7. analyse the transition from pluripotency to the "primed" state of chick ES cells as in 1 and 2 above, and the genes and enhancers compared to the draft GRN.

8. use the information from 5-7 above iteratively to refine the network.

We expect to be able to generate a fairly complete GRN within 3 years with only 2 postdocs.

This project is not only multidisciplinary but also cuts across 7 key BBSRC strategic priority areas:
ageing, animal health, the 3Rs, synthetic biology, technology development, data driven biology, systems approaches.
The benefits to academics in many disciplines are summarised above. Briefly:
The methodology should be applicable to many problems: a biological question is transformed into inter-related genomewide screens and data then used to reduce the number of important genes for further study (no "candidate genes"). The GRN is a model with predictive power and will made public on www.Biotapestry.org, for users to explore the effects of changing the state of different genes in the network, etc. It can therefore serve as an important teaching/training aid students and professionals in many disciplines. The "omics" and perturbation (NanoString) data will be made publicly available and make up resources useful for reference for many problems and also help to annotate the genome and Gene Ontologies. Using different tools on the same biological problem will make these resources amenable for cross-reference, increasing their value for Systems Biology applications. All of these benefits should occur during the project or shortly after its end. Beyond this, the project could generate information to understand cell fate transitions more generally, contributing to the generation of tools facilitating the manipulation of cell fate in vitro or in vivo by pointing at crucial genes and interactions.

The project should also have benefits outside academia although it is likely to take a little longer for these to bear fruit.
Specifically we can envisage the most likely benefits to include:
* interdisciplinary training and provision of highly skilled individuals:
* training of PDRAs employed on the grant will not only equip them with scientific skills, but also with transferable skills applicable to other areas including organisational, cross-disciplinary interaction, problem solving, modelling complex scenarios. This will contribute to the UK economy by providing highly skilled personnel for the private sector
* improve international reputation of UK science and collaboration

The network generated can be used for teaching and training medical, veterinary and other practitioners and general public.
* it will be an interactive teaching tool - useful for dissemination, teaching about cell fate/stem cells, etc.
* particularly useful for modelling situations
* moving towards a comprehensive model of human and animal physiology - the network is adaptable to other situations involving these genes
* young scientists at 6th form level - these will be attracted to our labs through the Nuffield and similar schemes

Concerning the 3Rs, a predictive model can help to streamline the design of biological experiments and thus reduce the number of animals required to test hypotheses. Our publicly available data will also help because it will be a unique example of datasets matched for the same biological situation which will have much greater value.

Medical, veterinary and other animal and human health applications, especially regeneration and repair:
* the genes could potentially help to identify endogenous stem cells that could be harnessed for repair or other therapies
* the genes and their regulatory mechanisms could be manipulated to control cell fate in vivo or in vitro
* the information generated has potential to help generate patient-specific cells specified as pre-neural for therapy or to test treatments