Unravelling the eukaryotic post-transcriptional regulatory code

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.