Proposed approach (experimental / theoretical / computational)

Regulation of gene transcription in higher eukaryotes is accomplished through the involvement of transcription start site (TSS)-proximal (promoters) and -distal (enhancers) regulatory elements. It is now well acknowledged that enhancer elements play an essential role during development and cell differentiation, while genetic alterations in these elements are a major cause of human disease. The classical definition of enhancers implies the property to activate gene expression at a distance, while promoters induce local gene expression. However, this basic dichotomy has been challenged by broad similarities between promoters and enhancers. Using high-throughput enhancer reporter assays, the team of Salvatore Spicuglia, at the TAGC laboratory demonstrated that a subset of gene-promoters, termed Epromoters, actually works also as bona fide enhancers and regulates distal gene expression. Subsequent studies from our and other labs have since suggested that Single Nucleotide Polymorphisms (SNPs) affecting distal gene expression are significantly enriched within Epromoters. Overall, these findings have significant implications for the understanding of complex gene regulation in normal development and open the intriguing possibility that physiological traits or disease-associated variants lying within a subset of (E)promoters might also directly impact distal gene expression. Previous work has suggested that the intrinsic and external factors controlling the promoter and enhancer activity of a given Epromoter are not necessarily the same. To understand the mechanistic bases of the proximal versus distal regulation, the Spicuglia team recently developed a dual reporter assay allowing us to simultaneously measure the promoter and enhancer activity of a given Epromoter. This approach will allow the assessment of hundreds of wild-type or mutated Epromoters in parallel, including natural genetic variants, in human lymphoblastoid cells. In the present project, the PhD student will develop deep learning approaches to computationally model the sequence determinants of enhancer and promoter activity in Epromoters. Based on these models, he/she will infer the grammatical rules dictating the enhancer versus promoter activity of Epromoters and prioritize genetic variants according to their impact on regulatory potential and transcriptional activity in a large panel of genotyped individuals. Predictions will be assessed experimentally by biologists of the Spicuglia team in a back-and-forth strategy until the development of a stable model of the genetic determinants influencing enhancer versus promoter activity.