RESUMO
Transcription factors (TFs) reprogram cell states by exerting control over gene regulatory networks and the epigenetic landscape of a cell. Artificial transcription factors (ATFs) are designer regulatory proteins comprised of modular units that can be customized to overcome challenges faced by natural TFs in establishing and maintaining desired cell states. Decades of research on DNA-binding proteins and synthetic molecules has provided a molecular toolkit for ATF design and the construction of genome-scale libraries of ATFs capable of phenotypic manipulation and reprogramming of cell states. Here, we compare the unique strengths and limitations of different ATF platforms, highlight the advantages of cooperative assembly, and present the potential of ATF libraries in revealing gene regulatory networks that govern cell fate choices.
Assuntos
Técnicas de Reprogramação Celular/métodos , Reprogramação Celular , Epigênese Genética , Redes Reguladoras de Genes , Fatores de Transcrição , Animais , Humanos , Fatores de Transcrição/biossíntese , Fatores de Transcrição/genéticaRESUMO
Artificial transcription factors (ATFs) are precision-tailored molecules designed to bind DNA and regulate transcription in a preprogrammed manner. Libraries of ATFs enable the high-throughput screening of gene networks that trigger cell fate decisions or phenotypic changes. We developed a genome-scale library of ATFs that display an engineered interaction domain (ID) to enable cooperative assembly and synergistic gene expression at targeted sites. We used this ATF library to screen for key regulators of the pluripotency network and discovered three combinations of ATFs capable of inducing pluripotency without exogenous expression of Oct4 (POU domain, class 5, TF 1). Cognate site identification, global transcriptional profiling, and identification of ATF binding sites reveal that the ATFs do not directly target Oct4; instead, they target distinct nodes that converge to stimulate the endogenous pluripotency network. This forward genetic approach enables cell type conversions without a priori knowledge of potential key regulators and reveals unanticipated gene network dynamics that drive cell fate choices.
