Machine-guided design of cell-type-targeting cis-regulatory elements

• 成果类型: Article
• 署名作者: Gosai, Sager J.; Castro, Rodrigo I.; Fuentes, Natalia; Butts, John C.; Mouri, Kousuke; Alasoadura, Michael; Kales, Susan; Nguyen, Thanh Thanh L.; Noche, Ramil R.; Rao, Arya S.; Joy, Mary T.; Sabeti, Pardis C.; Reilly, Steven K.; Tewhey, Ryan
• 署名单位: Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Harvard University; Howard Hughes Medical Institute; Jackson Laboratory; Harvard University; University of Maine System; University of Maine Orono; Yale University; Yale University; Yale University; Harvard University; Harvard Medical School; Harvard University; Harvard T.H. Chan School of Public Health; Yale University; Tufts University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-5767
• DOI: 10.1038/s41586-024-08070-z
• 发表日期: 2024-10-31
• 页码: 1211-1220
• 关键词: transcription; variants; binding; dissection; enhancers; genome

摘要

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing and stimulus responses, which collectively define the thousands of unique cell types in the body1-3. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for these intended purposes has arisen naturally. Here we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell-type specificity. We take advantage of innovations in deep neural network modelling of CRE activity across three cell types, efficient in silico optimization and massively parallel reporter assays to design and empirically test thousands of CREs4-8. Through large-scale in vitro validation, we show that synthetic sequences are more effective at driving cell-type-specific expression in three cell lines compared with natural sequences from the human genome and achieve specificity in analogous tissues when tested in vivo. Synthetic sequences exhibit distinct motif vocabulary associated with activity in the on-target cell type and a simultaneous reduction in the activity of off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs from massively parallel reporter assay models and demonstrate the required literacy to write fit-for-purpose regulatory code. A generalizable framework to prospectively engineer cis-regulatory elements from massively parallel reporter assay models can be used to write fit-for-purpose regulatory code.