Descripción del proyecto
Arrojar luz sobre la regulación postranscripcional de la expresión génica
Los genomas contienen genes que codifican proteínas. La actividad génica, y por ende la expresión de las proteínas, está muy regulada: no todas las células expresan todas las proteínas todo el tiempo. Comprender cómo controlan las células la expresión génica es crucial para entender su funcionamiento. Luego de la transcripción de un gen en ARN, los mecanismos postranscripcionales regulan la estabilidad del ARN y la velocidad a la que se traduce en proteínas. Estos pasos muy complejos se han estudiado poco y, por tanto, se desconocen en gran medida. El equipo del proyecto EPIC, financiado por el CEI, pretende arrojar luz sobre la regulación postranscripcional utilizando el sistema modelo de célula única establecido, la levadura «Saccharomyces cerevisiae», y otras especies fúngicas eucariotas. El equipo empleará tecnologías de alto rendimiento, biología sintética y aprendizaje profundo para descubrir el lenguaje de la regulación genética y permitir la (re)escritura de genomas.
Objetivo
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.
Ámbito científico
- natural sciencesbiological sciencessynthetic biology
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesbiological sciencesgeneticsRNA
- natural sciencesbiological sciencesgeneticsgenomes
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC-SYG - HORIZON ERC Synergy GrantsInstitución de acogida
9052 ZWIJNAARDE - GENT
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