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Evolutionary guided transcription factor design programs novel T cell states [ChIP-Seq]

GSE335058 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing 66 samples Submitted 2026/06/17 Platform GPL34284
Summary
Human protein-coding genes evolved via modular rearrangement of domains from ancestral genes. Here, we develop a scalable, evolutionarily guided method to assemble novel protein-coding genes from constituent domains within a protein family, termed DESynR (Domain Engineered via Synthesis and Recombination) genes. Using primary human chimeric antigen receptor T cells as a model, we find that the expression of DESynR Activator Protein-1 (AP-1) transcription factors (TFs) significantly outperforms the overexpression of natural AP-1 TFs in multiple functional assays in vitro and in vivo. Top DESynR AP-1 TFs exhibit non-intuitive domain architectures, including from TFs not canonically expressed in T cells. 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— in some cases co-opting gene expression modules from disparate cell types. We show that this reprogramming is primarily driven by differential regulation of established AP-1-bound regulatory elements, rather than unique binding. Finally, we extend the DESynR methodology to Erythroblast Transformation Specific (ETS) and Forkhead box (FOX) TFs to support the generalizability across protein families. Overall, we demonstrate that novel configurations of existing protein domains may uncover non-evolved genes that program cell states with therapeutic relevance.
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Direct links to NCBI, no account and no request form: the whole study as GSE335058_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 66 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1477198 and SRA study SRP708628. Searching any of these in the dataset finder brings you back here.

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