Objective
CRISPR development has enormously accelerated genetic engineering principles, and precise methods to modify small alleles (such as base or prime editing) are now available. However, generating large genomic changes still presents enormous challenges. Large modifications, such as gene transfers, are performed generally with viral vectors, which have been associated with toxicities in the clinic, and often lack versatility needed for basic science experimentation. Newer CRISPR-based techniques for gene transfer suffer from significant efficacy and safety problems when used for large message writing.
The overall goal of SCRIBE is to create new strategies for gene writing and define their molecular principles. These new writers will use RNA to both encode and transfer the message. The SCRIBE strategies will take advantage of the retrotransposon capacity for writing genes from RNA, and the precision of CRISPR in addressing specific sites of the genome. Thus, the find function will be dominated by CRISPR components, and copy-paste activity will be executed by retroelement components. To develop and optimize such a technology, we will use evolutionary analysis to select those retroelements with the highest activity and orthogonality, and modulate their message writing capacity by engineering their components. We will test various CRISPR and retrotransposon combinations, and adapt both of them to converge into a unified molecular machine. We will use artificial intelligence applied to protein design and a novel concept of synthetically oriented evolution to accelerate emergence of the new function. Finally, we will deploy new gene writing principles for RNA-based in vivo gene delivery.
In sum, we will develop a new family of tools for engineering life. The real breakthrough will be the establishment of gene writing as a simple and general method for both research advancement and applied purposes.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencesbiological sciencesgeneticsRNA
- natural sciencesbiological sciencesgeneticsgenomes
You need to log in or register to use this function
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
08002 Barcelona
Spain