Human protein-coding genes evolved via rearrangement of domains from ancestral genes. We develop a scalable, evolutionarily guided method to assemble novel genes from constituent domains within a protein family, termed DESynR (domain engineered via synthesis and recombination) genes. In primary human T cells, DESynR activator protein-1 (AP-1) transcription factors (TFs) significantly outperform natural AP-1 TFs across in vitro and in vivo antitumor assays. DESynR AP-1 TFs induce broad transcriptional and epigenetic reprogramming and establish non-natural T cell states that optimize features of exhaustion, effector and cytotoxic function, and persistence-sometimes co-opting gene modules from disparate cell types. Reprogramming is primarily driven by differential regulation of established AP-1-bound regulatory elements rather than unique binding. Finally, we screen DESynR erythroblast transformation-specific (ETS) and forkhead box (FOX) TFs to support generalizability across protein families. Overall, we demonstrate that reconfiguring existing protein domains may uncover non-evolved genes that program therapeutically relevant cell states.
Oliver Takacsi-Nagy, Sivakanthan Kasinathan, Austin Hartman et al.· Cell· 0 citations
Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer–gene regulatory interactions from single-cell datasets has been challenging. Here we introduce a family of classification models, scE2G, to predict enhancer–gene regulation. These models use features from single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) or multiomic RNA and ATAC-seq data, and are trained on a CRISPR perturbation dataset including >10,000 evaluated element–gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped expression quantitative trait loci and genome-wide association study variant–gene associations and demonstrate state-of-the-art performance at prediction tasks across several cell types and categories of perturbations. We apply scE2G to build maps of enhancer–gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte count. The scE2G models will enable accurate mapping of enhancer–gene regulatory interactions across thousands of human cell types. scE2G is a family of models that predict enhancer–gene regulatory interactions from single-cell datasets and enable mapping of these interactions across diverse cell types and tissues.
Maya U. Sheth, Wei-Lin Qiu, X. Ma et al.· Nature Genetics· 0 citations