Pretraining strategies for effective promoter-driven gene expression prediction.

Advances in gene delivery technologies are enabling rapid progress in molecular medicine, but require precise expression of genetic cargo in desired cell types, which is predominantly achieved via a regulatory DNA sequence called a promoter; however, only a handful of cell type-specific promoters are known. Efficiently designing compact promoter sequences with a high density of regulatory information by leveraging machine learning models would therefore be broadly impactful for fundamental research and direct therapeutic applications. However, models of expression from such compact promoter sequences are lacking, despite the recent success of deep learning in modelling expression from endogenous regulatory sequences. Despite the lack of large datasets measuring promoter-driven expression in many cell types, data from a few well-studied cell types or from endogenous gene expression may provide relevant information for transfer learning, which has not yet been explored in this setting. Here, we evaluate a variety of pretraining tasks and transfer strategies for modelling cell type-specific expression from compact promoters and demonstrate the effectiveness of pretraining on existing promoter-driven expression datasets from other cell types. Our approach is broadly applicable for modelling promoter-driven expression in any data-limited cell type of interest, and will enable the use of model-based optimization techniques for promoter design for gene delivery applications. Our code and data are available at https://github.com/anikethjr/promoter_models.

Dynamic spreading of chromatin-mediated gene silencing and reactivation between neighboring genes in single cells.

In mammalian cells genes that are in close proximity can be transcriptionally coupled: silencing or activating one gene can affect its neighbors. Understanding these dynamics is important for natural processes, such as heterochromatin spreading during development and aging, and when designing synthetic gene regulation circuits. Here, we systematically dissect this process in single cells by recruiting and releasing repressive chromatin regulators at dual-gene synthetic reporters, and measuring how fast gene silencing and reactivation spread as a function of intergenic distance and configuration of insulator elements. We find that silencing by KRAB, associated with histone methylation, spreads between two genes within hours, with a time delay that increases with distance. This fast KRAB-mediated spreading is not blocked by the classical cHS4 insulators. Silencing by histone deacetylase HDAC4 of the upstream gene can also facilitate background silencing of the downstream gene by PRC2, but with a days-long delay that does not change with distance. This slower silencing can sometimes be stopped by insulators. Gene reactivation of neighboring genes is also coupled, with strong promoters and insulators determining the order of reactivation. Our data can be described by a model of multi-gene regulation that builds upon previous knowledge of heterochromatin spreading, where both gene silencing and gene reactivation can act at a distance, allowing for coordinated dynamics via chromatin regulator recruitment.