Project description
Shedding light on post-transcriptional regulation of gene expression
Genomes contain genes that encode proteins. Gene activity, and thus protein expression, is highly regulated: not all cells express all proteins all the time. Understanding how cells control gene activity is crucial to understanding how cells function. After a gene is transcribed into RNA, post-transcriptional mechanisms regulate RNA stability and the rate at which RNA is translated into proteins. These highly complex steps are understudied and hence largely unknown. The ERC-funded EPIC project aims to shed light on post-transcriptional regulation using the established single-cell model system, the yeast Saccharomyces cerevisiae, and other eukaryotic fungal species. The team will employ high-throughput technologies, synthetic biology and deep learning to unravel the language of gene regulation and enable the (re)writing of genomes.
Objective
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.
Fields of science
- natural sciencesbiological sciencessynthetic biology
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesbiological sciencesgeneticsRNA
- natural sciencesbiological sciencesgeneticsgenomes
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC-SYG - HORIZON ERC Synergy GrantsHost institution
9052 ZWIJNAARDE - GENT
Belgium