Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo

Bernardo P. Almeida, Christoph Schaub, Michaela Pagani, Stefano Secchia, Eileen E. M. Furlong and Alexander Stark ()
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Bernardo P. Almeida: Vienna BioCenter (VBC)
Christoph Schaub: Genome Biology Unit
Michaela Pagani: Vienna BioCenter (VBC)
Stefano Secchia: Genome Biology Unit
Eileen E. M. Furlong: Genome Biology Unit
Alexander Stark: Vienna BioCenter (VBC)

Nature, 2024, vol. 626, issue 7997, 207-211

Abstract: Abstract Enhancers control gene expression and have crucial roles in development and homeostasis1–3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.

Date: 2024
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DOI: 10.1038/s41586-023-06905-9

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