Engineered poly (A)-surrogates for translational regulation and therapeutic biocomputation in mammalian cells

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Brief intro:

  • Author: Jiawei Shao, Shichao Li, Xinyuan Qiu, Jian Jiang, Lihang Zhang, Pengli Wang, Yaqing Si, Yuhang Wu, Minghui He, Qiqi Xiong, Liuqi Zhao, Yilin Li, Yuxuan Fan, Mirta Viviani, Yu Fu, Chaohua Wu, Ting Gao, Lingyun Zhu, Martin Fussenegger, Hui Wang & Mingqi Xie
  • Journal: Cell Research
  • Doi: https://www.doi.org/10.1038/s41422-023-00896-y
  • Publication Date: 2024 Jan 4

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Abstract

Here, we present a gene regulation strategy enabling programmable control over eukaryotic translational initiation. By excising the natural poly-adenylation (poly-A) signal of target genes and replacing it with a synthetic control region harboring RNA-binding protein (RBP)-specific aptamers, cap-dependent translation is rendered exclusively dependent on synthetic translation initiation factors (STIFs) containing different RBPs engineered to conditionally associate with different eIF4F-binding proteins (eIFBPs). This modular design framework facilitates the engineering of various gene switches and intracellular sensors responding to many user-defined trigger signals of interest, demonstrating tightly controlled, rapid and reversible regulation of transgene expression in mammalian cells as well as compatibility with various clinically applicable delivery routes of in vivo gene therapy. Therapeutic efficacy was demonstrated in two animal models. To exemplify disease treatments that require on-demand drug secretion, we show that a custom-designed gene switch triggered by the FDA-approved drug grazoprevir can effectively control insulin expression and restore glucose homeostasis in diabetic mice. For diseases that require instantaneous sense-and-response treatment programs, we create highly specific sensors for various subcellularly (mis)localized protein markers (such as cancer-related fusion proteins) and show that translation-based protein sensors can be used either alone or in combination with other cell-state classification strategies to create therapeutic biocomputers driving self-sufficient elimination of tumor cells in mice. This design strategy demonstrates unprecedented flexibility for translational regulation and could form the basis for a novel class of programmable gene therapies in vivo.

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