Abstract
Variation in the DNA sequence upstream of bacterial promoters is known to affect the expression levels of the products they regulate, sometimes dramatically. While neutral synthetic insulator sequences have been found to buffer promoters from upstream DNA context, there are no established methods for designing effective insulator sequences with predictable effects on expression levels. We address this problem with Degenerate Insulation Screening (DIS), a novel method based on a randomized 36-nucleotide insulator library and a simple, high-throughput, flow-cytometry-based screen that randomly samples from a library of 436 potential insulated promoters. The results of this screen can then be compared against a reference uninsulated device to select a set of insulated promoters providing a precise level of expression. We verify this method by insulating the constitutive, inducible, and repressible promotors of a four transcriptional-unit inverter (NOT-gate) circuit, finding both that order dependence is largely eliminated by insulation and that circuit performance is also significantly improved, with a 5.8-fold mean improvement in on/off ratio.
Highlights
The composability of complex genetic devices from well-characterized, basic DNA parts is a central premise of synthetic biology [1]
Degenerate Insulation Screening (DIS) is a novel promoter insulation protocol based on amplification with degenerate primers and screening to compare against reference expression levels
This method is motivated by the fact that there are many possible mechanisms by which an upstream sequence might affect promoter expression, but any given such mechanism is likely to pertain to only a small fraction of sequences
Summary
The composability of complex genetic devices from well-characterized, basic DNA parts is a central premise of synthetic biology [1]. This principle has been a driving force behind the creation of modular genetic devices and their curation into community repositories (e.g., [2,3,4]), as well as the development of computational tools for predictive design (e.g., [5,6,7,8]). Successful genetic regulatory networks are often arrived at by repeated iterations or by combinatorial construction and screening of a library of variants of the intended network, from which one or a few high-performing variants are selected [1, 11].
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