Evolutionary Design of Choline-Inducible and -Repressible T7-Based Induction Systems

Systematic promoter design for plasmid-encoded S-adenosylmethionine sensing systems

S-adenosylmethionine (SAM) is an important biomolecule that mainly acts as a methyl donor and plays many roles in a variety of biological functions. SAM is also required for the biosynthesis of valuable methylated compounds, but its supply is a bottleneck for these biosynthetic pathways. To overcome this bottleneck and to reconfigure SAM homeostasis, a high-throughput sensing system for changes in intracellular SAM availability is required. We constructed a plasmid that can detect the factors that can alter SAM availability using minimal components. It does so by placing a fluorescent protein under a promoter controlled by endogenous MetJ, a transcription factor that represses its own regulons upon binding with SAM. Next, to validate SAM-responsive behavior, we systematically reconstructed 10 synthetic promoters with different positions and with different number of metbox sites. We found that a position between the -35 box and the -10 box was the most effective for repression and that this setup was suitable for detecting the genetic or environmental factors that can deplete and recover the intracellular SAM availability. Overall, the response patterns of the synthetic MetJ-regulated promoters characterized in this study may be useful for the development of better SAM biosensing systems.

Imparting As(III) Responsiveness to the Choline Response Transcriptional Regulator BetI

The development of a low-cost and user-friendly sensor using microorganisms to monitor the presence of As(III) on earth has garnered significant attention. In conventional research on microbial As(III) sensors, the focus has been on transcription factor ArsR, which plays a role in As(III) metabolism. However, we recently discovered that LuxR, a quorum-sensing control factor in Vibrio fischeri that contains multiple cysteine residues, acted as an As(III) sensor despite having no role in As(III) metabolism. This finding suggested that any protein could be an As(III) sensor if cysteine residues were incorporated. In this study, we aimed to confer As(III) responsiveness to BetI, a transcriptional repressor of the TetR family involved in osmotic regulation of the choline response, unrelated to As(III) metabolism. Based on the BetI structure constructed using molecular dynamics calculations, we generated a series of mutants in which each of the three amino acids not critical for function was substituted with cysteine. Subsequent examination of their response to As(III) revealed that the cysteine-substituted mutant, incorporating all three substitutions, demonstrated As(III) responsiveness. This was evidenced by the fluorescence intensity of the downstream reporter superfolder green fluorescent protein expression regulated by the operator region. Intriguingly, the BetI cysteine mutant maintained its binding responsiveness to the natural ligand choline. We successfully engineered an OR logic gate capable of responding to two orthogonal ligands using a single protein.

MetJ-Based Mutually Interfering SAM-ON/SAM-OFF Biosensors

SAM (S-adenosylmethionine) is an important metabolite that operates as a major donor of methyl groups and is a controller of various physiological processes. Its availability is also believed to be a major bottleneck in the biological production of numerous high-value metabolites. Here, we constructed SAM-sensing systems using MetJ, an SAM-dependent transcriptional regulator, as a core component. SAM is a corepressor of MetJ, which suppresses the MetJ promoter with an increasing cellular concentration of SAM (SAM-OFF sensor). The application of transcriptional interference and evolutionary tuning effectively inverted its response, yielding a SAM-ON sensor (signal increases with increasing SAM concentration). By linking two genes encoding fluorescent protein reporters in such a way that their transcription events interfere with each other's and by placing one of them under the control of MetJ, we could increase the effective signal-to-noise ratio of the SAM sensor while decreasing the batch-to-batch deviation in signal output, likely by canceling out the growth-associated fluctuation in translational resources. By taking the ratio of SAM-ON/SAM-OFF signals and by resetting the default pool size of SAM, we could rapidly identify SAM synthetase (MetK) mutants with increased cellular activity from a random library. The strategy described herein should be widely applicable for identifying activity mutants, which would be otherwise overlooked because of the strong homeostasis of metabolic networks.

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Transcription Factors as Evolvable Biosensors

One of the most prominent features of genetically encoded biosensors (GEBs) is their evolvability-the ability to invent new sensory functions using mutations. Among the GEBs, the transcription factor-based biosensors (TF-biosensors) is the focus of this review. We also discuss how this class of sensors can be highly evolvable and how we can exploit it. With an established platform for directed evolution, researchers can create, or evolve, new TF-biosensors. Directed evolution experiments have revealed the TF-biosensors' evolvability, which is based partially on their characteristic physicochemical properties.

Robust and flexible platform for directed evolution of yeast genetic switches

A wide repertoire of genetic switches has accelerated prokaryotic synthetic biology, while eukaryotic synthetic biology has lagged in the model organism Saccharomyces cerevisiae. Eukaryotic genetic switches are larger and more complex than prokaryotic ones, complicating the rational design and evolution of them. Here, we present a robust workflow for the creation and evolution of yeast genetic switches. The selector system was designed so that both ON- and OFF-state selection of genetic switches is completed solely by liquid handling, and it enabled parallel screen/selection of different motifs with different selection conditions. Because selection threshold of both ON- and OFF-state selection can be flexibly tuned, the desired selection conditions can be rapidly pinned down for individual directed evolution experiments without a prior knowledge either on the library population. The system's utility was demonstrated using 20 independent directed evolution experiments, yielding genetic switches with elevated inducer sensitivities, inverted switching behaviours, sensory functions, and improved signal-to-noise ratio (>100-fold induction). The resulting yeast genetic switches were readily integrated, in a plug-and-play manner, into an AND-gated carotenoid biosynthesis pathway.

Evolution-guided engineering of small-molecule biosensors

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely baker's yeast Saccharomyces cerevisiae. From a single round of mutagenesis of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis,cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion-of-function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a prokaryotic chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.

In Vitro Transcriptional Regulation via Nucleic-Acid-Based Transcription Factors

Cells execute complex transcriptional programs by deploying distinct protein regulatory assemblies that interact with cis-regulatory elements throughout the genome. Using concepts from DNA nanotechnology, we synthetically recapitulated this feature in in vitro gene networks actuated by T7 RNA polymerase (RNAP). Our approach involves engineering nucleic acid hybridization interactions between a T7 RNAP site-specifically functionalized with single-stranded DNA (ssDNA), templates displaying cis-regulatory ssDNA domains, and auxiliary nucleic acid assemblies acting as artificial transcription factors (TFs). By relying on nucleic acid hybridization, de novo regulatory assemblies can be computationally designed to emulate features of protein-based TFs, such as cooperativity and combinatorial binding, while offering unique advantages such as programmability, chemical stability, and scalability. We illustrate the use of nucleic acid TFs to implement transcriptional logic, cascading, feedback, and multiplexing. This framework will enable rapid prototyping of increasingly complex in vitro genetic devices for applications such as portable diagnostics, bioanalysis, and the design of adaptive materials.

Programmable T7-based synthetic transcription factors

Despite recent progress on synthetic transcription factor generation in eukaryotes, there remains a need for high-activity bacterial versions of these systems. In synthetic biology applications, it is useful for transcription factors to have two key features: they should be orthogonal (influencing only their own targets, with minimal off-target effects), and programmable (able to be directed to a wide range of user-specified transcriptional start sites). The RNA polymerase of the bacteriophage T7 has a number of appealing properties for synthetic biological designs: it can produce high transcription rates; it is a compact, single-subunit polymerase that has been functionally expressed in a variety of organisms; and its viral origin reduces the connection between its activity and that of its host's transcriptional machinery. We have created a system where a T7 RNA polymerase is recruited to transcriptional start sites by DNA binding proteins, either directly or bridged through protein–protein interactions, yielding a modular and programmable system for strong transcriptional activation of multiple orthogonal synthetic transcription factor variants in Escherichia coli. To our knowledge this is the first exogenous, programmable activator system in bacteria.

Rapid Diversification of BetI-Based Transcriptional Switches for the Control of Biosynthetic Pathways and Genetic Circuits

Synthetic biologists are in need of genetic switches, or inducible sensor/promoter systems, that can be reliably integrated in multiple contexts. Using a liquid-based selection method, we systematically engineered the choline-inducible transcription factor BetI, yielding various choline-inducible and choline-repressive promoter systems with various input-output characteristics. In addition to having high stringency and a high maximum induction level, they underwent a graded and single-peaked response to choline. Taking advantage of these features, we demonstrated the utility of these systems for controlling the carotenoid biosynthetic pathway and for constructing two-input logic gates. Additionally, we demonstrated the rapidity, throughput, robustness, and cost-effectiveness of our selection method, which facilitates the conversion of natural genetic controlling systems into systems that are designed for various synthetic biology applications.