Assembly of Genetic Circuits with the Mammalian ToolKit

Realizing Antithetic Integral Feedback Control in Mammalian Cells

Genetic circuit engineering has emerged as a powerful methodology to program the behavior of mammalian cells to respond to internal and external cues. This approach is now used to develop new therapeutics and improve production processes. However, genetic interaction networks are complex and hard to engineer rationally. Moreover, a design may fail, and it may not be possible to identify the root cause of its breakdown. Introducing designated regulatory circuitry in the form of integral feedback can introduce performance guarantees by ensuring robust and precise operation.

Orthogonal control of mean and variability of endogenous genes in a human cell line

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.