Projektbeschreibung
Licht in die posttranskriptionelle Regulierung der Genexpression bringen
Genome enthalten Gene, die für Proteine kodieren. Die Genaktivität und somit die Proteinexpression sind streng reguliert: Nicht alle Zellen exprimieren jederzeit alle Proteine. Das Wissen zur Regulierung der Genaktivität in Zellen ist entscheidend, um die Funktion von Zellen zu verstehen. Nachdem ein Gen in RNS transkribiert wurde, regulieren posttranskriptionelle Mechanismen die RNS-Stabilität und die Geschwindigkeit, mit der RNS in Proteine übersetzt wird. Diese hochkomplexen Schritte sind unzureichend erforscht und somit weitestgehend unbekannt. Das Ziel des ERC-finanzierten Projekts EPIC besteht darin, anhand des bekannten einzelligen Modellsystems, der Hefe Saccharomyces cerevisiae, und weiterer eukaryontischer Pilzarten neues Licht in die posttranskriptionelle Regulierung der Proteinexpression zu bringen. Das Team wird Hochdurchsatztechnologien, synthetische Biologie und Deep Learning einsetzen, um die Sprache der Genregulation zu entschlüsseln und das (Neu-)Schreiben von Genomen zu ermöglichen.
Ziel
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.
Wissenschaftliches Gebiet
- natural sciencesbiological sciencessynthetic biology
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesbiological sciencesgeneticsRNA
- natural sciencesbiological sciencesgeneticsgenomes
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-ERC-SYG - HORIZON ERC Synergy GrantsGastgebende Einrichtung
9052 ZWIJNAARDE - GENT
Belgien