Modulating Responses of Toehold Switches by an Inhibitory Hairpin

Abstract

The toehold switch consists of a cis-repressing switch RNA hairpin and a trans-acting trigger RNA. The binding of the trigger RNA to an unpaired toehold sequence of the switch hairpin allows for a branch migration process, exposing the start codon and ribosome binding site for translation initiation. In this work, we demonstrate that responses of toehold switches can be modulated by introducing an inhibitory hairpin that shortens the length of the unpaired toehold region. First, we investigated the effect of the toehold region length on output gene expression and showed that the second trigger RNA, which binds to the inhibitory hairpin, is necessary for output gene activation when the hairpin-to-hairpin spacing is short. Second, the apparent Hill coefficient was found generally to increase with decreasing hairpin-to-hairpin spacing or increasing hairpin number. This work expands the utility of toehold switches by providing a new way to modulate their response.

Homogeneous and Universal Detection of Various Targets with a Dual‐Step Transduced Toehold Switch Sensor

“…It thus opens up a new sensing strategy with extraordinary potential for the development of easy, portable, or even point‐of‐care‐testing (POCT) assays. Previously, through the use of LacZ, green fluorescent protein (GFP), sortase, and alcohol acetyltransferase 1 (ATF1) as reporter proteins, real‐world Zika virus, gut microbiota, and norovirus RNA have been practically detected . Unfortunately, although such a promising function of the toehold switch sensor has been demonstrated, it generally requires in silico sequence design and laborious optimization of toehold switch sequence according to different individual targets .…”

Biosensing in Smart Engineered Probiotics

“…Takahashi et al. implemented paper‐based platforms using E. coli lysates and toehold switches to identify specific species of microbes in the gut microbiome . Fluorescent reporters cis ‐repressed by toehold hairpin formation were trans ‐activated by species‐specific RNAs from ten different microbes found in human microbiomes.…”

Deep Learning for RNA Synthetic Biology

“…Examples include riboswitches, riboregulators, and ribozymes, many of which hold great promise for a variety of in vitro and in vivo applications (1,5 2). Despite their appeal, the design and validation of this emerging class of synthetic biology modules have proven challenging due to variability in function that remains difficult to predict (2)(3)(4)(5)(6)(7)(8)(9). Current efforts aiming to unveil fundamental relationships between RNA sequence, structure, and behavior focus mostly on mechanistic thermodynamic modeling and lowthroughput experimentation, which often fail to deliver sufficiently predictive and actionable 10 information to aid in the design of complex RNA tools (2)(3)(4)(5)(6)(7)(8)(9).…”

“…Despite their appeal, the design and validation of this emerging class of synthetic biology modules have proven challenging due to variability in function that remains difficult to predict (2)(3)(4)(5)(6)(7)(8)(9). Current efforts aiming to unveil fundamental relationships between RNA sequence, structure, and behavior focus mostly on mechanistic thermodynamic modeling and lowthroughput experimentation, which often fail to deliver sufficiently predictive and actionable 10 information to aid in the design of complex RNA tools (2)(3)(4)(5)(6)(7)(8)(9). Deep learning, by contrast, constitutes a set of computational techniques well suited for feature recognition in complex and highly combinatorial biological problems (10)(11)(12)(13)(14), such as the sequence design space of synthetic RNA tools.…”

“…Toehold switches are a class of versatile prokaryotic riboregulators inducible by the presence of 20 a fully programmable trans-RNA trigger sequence (2-6, 15, 16). These RNA synthetic biology modules have displayed impressive dynamic range and orthogonality when used both in vivo as genetic circuit components (2,5,6), and in vitro as nucleic acid diagnostic tools using cell-free protein synthesis (CFPS) systems (3,4,15,16). Similar to other RNA synthetic biology tools, a substantial fraction of toehold switches show poor to no measurable function when tested 25 experimentally, and while efforts have been made to establish rational, mechanistic rules for improved performance based on low-throughput datasets (2-9, 15, 16), the practical utility of these approaches remains inconclusive.…”