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Aug 09, 2023;

Machine-guided design of synthetic cell type-specific -regulatory elements.

S J Gosai, R I Castro, N Fuentes, J C Butts, S Kales, R R Noche, K Mouri, P C Sabeti, S K Reilly, R Tewhey
Author Information
  1. S J Gosai: Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  2. R I Castro: The Jackson Laboratory, Bar Harbor, ME, USA.
  3. N Fuentes: The Jackson Laboratory, Bar Harbor, ME, USA.
  4. J C Butts: The Jackson Laboratory, Bar Harbor, ME, USA.
  5. S Kales: The Jackson Laboratory, Bar Harbor, ME, USA.
  6. R R Noche: Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, USA.
  7. K Mouri: The Jackson Laboratory, Bar Harbor, ME, USA.
  8. P C Sabeti: Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  9. S K Reilly: Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
  10. R Tewhey: The Jackson Laboratory, Bar Harbor, ME, USA.
PMID: 37609287 DOI: 10.1101/2023.08.08.552077

Abstract

-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. 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 an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through and validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose across vertebrates.

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Grants

  1. R00 HG010669/NHGRI NIH HHS
  2. R01 HG012872/NHGRI NIH HHS
  3. R35 HG011329/NHGRI NIH HHS
  4. UM1 HG009435/NHGRI NIH HHS
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