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

Autor: Gosai SJ; Broad Institute of MIT and Harvard, Cambridge, MA, USA. sgosai@broadinstitute.org.; Harvard Graduate Program in Biological and Biomedical Science, Boston, MA, USA. sgosai@broadinstitute.org.; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA. sgosai@broadinstitute.org.; Howard Hughes Medical Institute, Chevy Chase, MD, USA. sgosai@broadinstitute.org., Castro RI; The Jackson Laboratory, Bar Harbor, ME, USA. rodrigo.castro@jax.org., Fuentes N; The Jackson Laboratory, Bar Harbor, ME, USA.; Harvard College, Harvard University, Cambridge, MA, USA., Butts JC; The Jackson Laboratory, Bar Harbor, ME, USA.; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA., Mouri K; The Jackson Laboratory, Bar Harbor, ME, USA., Alasoadura M; The Jackson Laboratory, Bar Harbor, ME, USA., Kales S; The Jackson Laboratory, Bar Harbor, ME, USA., Nguyen TTL; Department of Genetics, Yale School of Medicine, New Haven, CT, USA., Noche RR; Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA.; Yale Zebrafish Research Core, Yale School of Medicine, New Haven, CT, USA., Rao AS; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Harvard Medical School, Boston, MA, USA., Joy MT; The Jackson Laboratory, Bar Harbor, ME, USA., Sabeti PC; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.; Howard Hughes Medical Institute, Chevy Chase, MD, USA.; Department of Immunology and Infectious Diseases, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA., Reilly SK; Department of Genetics, Yale School of Medicine, New Haven, CT, USA. steven.k.reilly@yale.edu.; Wu Tsai Institute, Yale University, New Haven, CT, USA. steven.k.reilly@yale.edu., Tewhey R; The Jackson Laboratory, Bar Harbor, ME, USA. ryan.tewhey@jax.org.; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA. ryan.tewhey@jax.org.; Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA. ryan.tewhey@jax.org.
Jazyk: angličtina
Zdroj: Nature [Nature] 2024 Oct; Vol. 634 (8036), pp. 1211-1220. Date of Electronic Publication: 2024 Oct 23.
DOI: 10.1038/s41586-024-08070-z
Abstrakt: 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 body 1-3 . 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 CREs 4-8 . 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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