Summary
Transcription Factors (TFs) are critical regulators of many essential cellular functions such as the acquisition of cell identities in healthy tissues and their dysregulation in disease. Transcriptional activation of a gene typically requires the cooperative binding of multiple TFs, that subsequently recruit various additional cofactors. Genomics has enabled the generation of a near-complete annotation of the cis-regulatory elements and TFs binding them across cell types. Yet, the precise function of each TF in the process and how these functionalities are assembled to activate transcription is an important open question. Here we postulate that despite strong cell-type specificity, the formation of TF cooperativity modules on DNA relies on general principles that are shared across cell-types. In TFCoop we propose to formalise these organizational rules by probing the effect of hundreds of thousands of perturbations of individual TFs on the regulatory activity of their network. We will apply time-resolved nuclear depletion using optogenetics in parallel for multiple TFs of two related networks, and contrast the primary effects of their depletion genome-wide. In a complementary approach, we will develop a reductionist system to study the function of tens of thousands of individual or controlled combinations of TF motifs when inserted into the genome. We will leverage the unique properties of single molecule genomics to measure the contribution of each TF to the activity of multiple components of the regulatory system, across multiple loci simultaneously. This will be followed by factor analysis and deep learning to integrate this large collection of primary effects of TF perturbation and identify the general principles of their assembly into cooperativity networks. Upon success of the project, the resulting models will unlock the understanding of the genetic encoding of cellular identities and allow their manipulation for regenerative medicine.
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| Web resources: | https://cordis.europa.eu/project/id/101125530 |
| Start date: | 01-04-2024 |
| End date: | 31-03-2029 |
| Total budget - Public funding: | 1 990 221,00 Euro - 1 990 221,00 Euro |
Cordis data
Original description
Transcription Factors (TFs) are critical regulators of many essential cellular functions such as the acquisition of cell identities in healthy tissues and their dysregulation in disease. Transcriptional activation of a gene typically requires the cooperative binding of multiple TFs, that subsequently recruit various additional cofactors. Genomics has enabled the generation of a near-complete annotation of the cis-regulatory elements and TFs binding them across cell types. Yet, the precise function of each TF in the process and how these functionalities are assembled to activate transcription is an important open question. Here we postulate that despite strong cell-type specificity, the formation of TF cooperativity modules on DNA relies on general principles that are shared across cell-types. In TFCoop we propose to formalise these organizational rules by probing the effect of hundreds of thousands of perturbations of individual TFs on the regulatory activity of their network. We will apply time-resolved nuclear depletion using optogenetics in parallel for multiple TFs of two related networks, and contrast the primary effects of their depletion genome-wide. In a complementary approach, we will develop a reductionist system to study the function of tens of thousands of individual or controlled combinations of TF motifs when inserted into the genome. We will leverage the unique properties of single molecule genomics to measure the contribution of each TF to the activity of multiple components of the regulatory system, across multiple loci simultaneously. This will be followed by factor analysis and deep learning to integrate this large collection of primary effects of TF perturbation and identify the general principles of their assembly into cooperativity networks. Upon success of the project, the resulting models will unlock the understanding of the genetic encoding of cellular identities and allow their manipulation for regenerative medicine.Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
22-11-2024
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