Descrizione del progetto
Gettare nuova luce sulla regolazione post-trascrizionale dell’espressione genica
I genomi contengono geni che codificano proteine. L’attività genica, e quindi l’espressione delle proteine, è altamente regolata: non tutte le cellule esprimono sempre tutte le proteine. Comprendere come le cellule controllano l’attività genica è fondamentale per comprendere il funzionamento delle cellule. In seguito alla trascrizione in RNA di un gene, i meccanismi post-trascrizionali ne regolano la stabilità e la velocità con cui viene tradotto in proteine. Queste fasi molto complesse sono poco studiate e quindi in gran parte sconosciute. Il progetto EPIC, finanziato dal CER, mira a gettare nuova luce sulla regolazione post-trascrizionale utilizzando l’affermato sistema modello monocellulare, il lievito Saccharomyces cerevisiae, e altre specie fungine eucariotiche. Il team impiegherà tecnologie ad alta produttività, biologia sintetica e apprendimento profondo per svelare il linguaggio della regolazione genica e consentire la (ri)scrittura dei genomi.
Obiettivo
Genomes encode instructions for cells to regulate gene activity in response to their environment. However, and despite its importance for biology, medicine and biotechnology, the underpinning regulatory code remains undeciphered. Gene regulation consists of two major steps. First, genes are transcribed into mRNA. Second, post-transcriptional mechanisms regulate mRNA stability and the rate at which it is translated into proteins. This second step of gene regulation is still poorly understood because relevant parameters such as mRNA half-life, mRNA protein binding and subcellular localization are difficult to assay. The lack of understanding of post-transcriptional regulation implies that we still do not have a complete picture of the regulatory code. In EPIC, we exploit the advantages of the model eukaryote Saccharomyces cerevisiae and other species covering a broad evolutionary range to derive the first comprehensive sequence-based model of eukaryotic gene regulation. EPIC integrates the complementary expertise of 3 teams. It combines innovative high-throughput technologies (Pelechano) to probe post-transcriptional regulation at an unprecedented scale across a broad range of species and conditions with synthetic biology to massively test regulatory sequences (Verstrepen). Deep learning on these data allows us to build predictive models and unravel complex regulatory instructions (Gagneur). Ultimately, EPIC will enable us to decipher the actual language of gene regulation and facilitate (re)writing genomes. EPIC will enable understanding and predicting regulation, and ultimately phenotype, from DNA, closing a major gap in basic biology, while also opening exciting avenues for applications in biotechnology and medicine, from pinpointing disease-causing mutations to rational design of genes, RNAs and cells.
Campo scientifico
- natural sciencesbiological sciencessynthetic biology
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesbiological sciencesgeneticsRNA
- natural sciencesbiological sciencesgeneticsgenomes
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC-SYG - HORIZON ERC Synergy GrantsIstituzione ospitante
9052 ZWIJNAARDE - GENT
Belgio