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Abstract
Stochastic fluctuations at the transcriptional level contribute to isogenic cell-to-cell heterogeneity in mammalian cell populations. However, we still have no clear understanding of the repercussions of this heterogeneity, given the lack of tools to independently control mean expression and variability of a gene. Here, we engineer a synthetic circuit to modulate mean expression and heterogeneity of transgenes and endogenous human genes. The circuit, a Tunable Noise Rheostat (TuNR), consists of a transcriptional cascade of two inducible transcriptional activators, where the output mean and variance can be modulated by two orthogonal small molecule inputs. In this fashion, different combinations of the inputs can achieve the same mean but with different population variability. With TuNR, we achieve low basal expression, over 1000-fold expression of a transgene product, and up to 7-fold induction of the endogenous gene NGFR. Importantly, for the same mean expression level, we are able to establish varying degrees of heterogeneity in expression within an isogenic population, thereby decoupling gene expression noise from its mean. TuNR is therefore a modular tool that can be used in mammalian cells to enable direct interrogation of the implications of cell-to-cell variability.
Stochastic fluctuations at the transcriptional level contribute to heterogeneity in isogenic cell populations. Here, the authors engineer TuNR which modulates the variability in gene expression of endogenous human genes independent of their mean expression.
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1 University of California, San Francisco, Department of Biochemistry and Biophysics, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
2 University of California, San Francisco, Department of Biochemistry and Biophysics, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); Amyris Bio Products Portugal, Porto, Portugal (GRID:grid.266102.1)
3 University of California, San Francisco, Department of Biochemistry and Biophysics, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); Chan Zuckerberg Biohub, San Francisco, USA (GRID:grid.499295.a)