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

Sager J. Gosai (), Rodrigo I. Castro (), Natalia Fuentes, John C. Butts, Kousuke Mouri, Michael Alasoadura, Susan Kales, Thanh Thanh L. Nguyen, Ramil R. Noche, Arya S. Rao, Mary T. Joy, Pardis C. Sabeti, Steven K. Reilly () and Ryan Tewhey ()
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Sager J. Gosai: Broad Institute of MIT and Harvard
Rodrigo I. Castro: The Jackson Laboratory
Natalia Fuentes: The Jackson Laboratory
John C. Butts: The Jackson Laboratory
Kousuke Mouri: The Jackson Laboratory
Michael Alasoadura: The Jackson Laboratory
Susan Kales: The Jackson Laboratory
Thanh Thanh L. Nguyen: Yale School of Medicine
Ramil R. Noche: Yale School of Medicine
Arya S. Rao: Broad Institute of MIT and Harvard
Mary T. Joy: The Jackson Laboratory
Pardis C. Sabeti: Broad Institute of MIT and Harvard
Steven K. Reilly: Yale School of Medicine
Ryan Tewhey: The Jackson Laboratory

Nature, 2024, vol. 634, issue 8036, 1211-1220

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41586-024-08070-z

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