A synthetic transcription platform for programmable gene expression in mammalian cells

William C. W. Chen (), Leonid Gaidukov, Yong Lai, Ming-Ru Wu, Jicong Cao, Michael J. Gutbrod, Gigi C. G. Choi, Rachel P. Utomo, Ying-Chou Chen, Liliana Wroblewska, Manolis Kellis, Lin Zhang, Ron Weiss and Timothy K. Lu ()
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William C. W. Chen: Massachusetts Institute of Technology
Leonid Gaidukov: Massachusetts Institute of Technology
Yong Lai: Massachusetts Institute of Technology
Ming-Ru Wu: Massachusetts Institute of Technology
Jicong Cao: Massachusetts Institute of Technology
Michael J. Gutbrod: Massachusetts Institute of Technology
Gigi C. G. Choi: Massachusetts Institute of Technology
Rachel P. Utomo: Massachusetts Institute of Technology
Ying-Chou Chen: Massachusetts Institute of Technology
Liliana Wroblewska: Pfizer Inc.
Manolis Kellis: Massachusetts Institute of Technology
Lin Zhang: Pfizer Inc.
Ron Weiss: Massachusetts Institute of Technology
Timothy K. Lu: Massachusetts Institute of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-16

Abstract: Abstract Precise, scalable, and sustainable control of genetic and cellular activities in mammalian cells is key to developing precision therapeutics and smart biomanufacturing. Here we create a highly tunable, modular, versatile CRISPR-based synthetic transcription system for the programmable control of gene expression and cellular phenotypes in mammalian cells. Genetic circuits consisting of well-characterized libraries of guide RNAs, binding motifs of synthetic operators, transcriptional activators, and additional genetic regulatory elements express mammalian genes in a highly predictable and tunable manner. We demonstrate the programmable control of reporter genes episomally and chromosomally, with up to 25-fold more activity than seen with the EF1α promoter, in multiple cell types. We use these circuits to program the secretion of human monoclonal antibodies and to control T-cell effector function marked by interferon-γ production. Antibody titers and interferon-γ concentrations significantly correlate with synthetic promoter strengths, providing a platform for programming gene expression and cellular function in diverse applications.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33287-9

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DOI: 10.1038/s41467-022-33287-9

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