Engineering DNA recognition and allosteric response properties of TetR family proteins by using a module-swapping strategy

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

Targeting New Functions and Applications of Bacterial Two-Component Systems

Two-component signal transduction systems (TCSs) are regulatory systems widely distributed in eubacteria, archaea, and a few eukaryotic organisms, but not in mammalian cells. A typical TCS consists of a histidine kinase and a response regulator protein. Functional and mechanistic studies on different TCSs have greatly advanced the understanding of cellular phosphotransfer signal transduction mechanisms. In this concept paper, we focus on the His-Asp phosphotransfer mechanism, the ATP synthesis function, antimicrobial drug design, cellular biosensors design, and protein allostery mechanisms based on recent TCS investigations to inspire new applications and future research perspectives.

Genetically encoded Boolean logic operators to sense and integrate phenylpropanoid metabolite levels in plants

Synthetic biology has the potential to revolutionize biotechnology, public health, and agriculture. Recent studies have shown the enormous potential of plants as chassis for synthetic biology applications. However, tools to precisely manipulate metabolic pathways for bioproduction in plants are still needed. We used bacterial allosteric transcription factors (aTFs) that control gene expression in a ligand-specific manner and tested their ability to repress semi-synthetic promoters in plants. We also tested the modulation of their repression activity in response to specific plant metabolites, especially phenylpropanoid-related molecules. Using these aTFs, we also designed synthetic genetic circuits capable of computing Boolean logic operations. Three aTFs, CouR, FapR, and TtgR, achieved c. 95% repression of their respective target promoters. For TtgR, a sixfold de-repression could be triggered by inducing its ligand accumulation, showing its use as biosensor. Moreover, we designed synthetic genetic circuits that use AND, NAND, IMPLY, and NIMPLY Boolean logic operations and integrate metabolite levels as input to the circuit. We showed that biosensors can be implemented in plants to detect phenylpropanoid-related metabolites and activate a genetic circuit that follows a predefined logic, demonstrating their potential as tools for exerting control over plant metabolic pathways and facilitating the bioproduction of natural products.

A domain swapping strategy to create modular transcriptional regulators for novel topology in genetic network

Transcriptional regulators generate connections between biological signals and genetic outputs. They are used robustly for sensing input signals in building genetic circuits. However, each regulator can only generate a fixed connection, which generates constraints in linking multiple signals for more complex processes. Recent studies discovered that a domain swapping strategy can be applied to various regulator families to create modular regulators for new signal-output connections, significantly broadening possibilities in circuit design. Here we review the development of this emerging strategy, the use of resulting modular regulators for creating novel genetic response behaviors, and current limitations and solutions for further advancing the design of modular regulators.

Molecular basis for the transcriptional regulation of an epoxide-based virulence circuit in Pseudomonas aeruginosa

The opportunistic pathogen Pseudomonas aeruginosa infects cystic fibrosis (CF) patient airways and produces a virulence factor Cif that is associated with worse outcomes. Cif is an epoxide hydrolase that reduces cell-surface abundance of the cystic fibrosis transmembrane conductance regulator (CFTR) and sabotages pro-resolving signals. Its expression is regulated by a divergently transcribed TetR family transcriptional repressor. CifR represents the first reported epoxide-sensing bacterial transcriptional regulator, but neither its interaction with cognate operator sequences nor the mechanism of activation has been investigated. Using biochemical and structural approaches, we uncovered the molecular mechanisms controlling this complex virulence operon. We present here the first molecular structures of CifR alone and in complex with operator DNA, resolved in a single crystal lattice. Significant conformational changes between these two structures suggest how CifR regulates the expression of the virulence gene cif. Interactions between the N-terminal extension of CifR with the DNA minor groove of the operator play a significant role in the operator recognition of CifR. We also determined that cysteine residue Cys107 is critical for epoxide sensing and DNA release. These results offer new insights into the stereochemical regulation of an epoxide-based virulence circuit in a critically important clinical pathogen.

Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis

Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control, and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be nonfunctional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular "AND gate" logic system based on the B. subtilis two-subunit σ-factor, SigO-RsoA, designed to maximize future use for synthetic biology applications in B. subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potentially wider applicability of the approach to other systems and genetic backgrounds.

Intestinal Engineered Probiotics as Living Therapeutics: Chassis Selection, Colonization Enhancement, Gene Circuit Design, and Biocontainment

Intestinal probiotics are often used for the in situ treatment of diseases, such as metabolic disorders, tumors, and chronic inflammatory infections. Recently, there has been an increased emphasis on intelligent, customized treatments with a focus on long-term efficacy; however, traditional probiotic therapy has not kept up with this trend. The use of synthetic biology to construct gut-engineered probiotics as live therapeutics is a promising avenue in the treatment of specific diseases, such as phenylketonuria and inflammatory bowel disease. These studies generally involve a series of fundamental design issues: choosing an engineered chassis, improving the colonization ability of engineered probiotics, designing functional gene circuits, and ensuring the safety of engineered probiotics. In this review, we summarize the relevant past research, the progress of current research, and discuss the key issues that restrict the widespread application of intestinal engineered probiotic living therapeutics.

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.

Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions

Genetic sensors with unique combinations of DNA recognition and allosteric response can be created by hybridizing DNA-binding modules (DBMs) and ligand-binding modules (LBMs) from distinct transcriptional repressors. This module swapping approach is limited by incompatibility between DBMs and LBMs from different proteins, due to the loss of critical module-module interactions after hybridization. We determine a design strategy for restoring key interactions between DBMs and LBMs by using a computational model informed by coevolutionary traits in the LacI family. This model predicts the influence of proposed mutations on protein structure and function, quantifying the feasibility of each mutation for rescuing hybrid repressors. We accurately predict which hybrid repressors can be rescued by mutating residues to reinstall relevant module-module interactions. Experimental results confirm that dynamic ranges of gene expression induction were improved significantly in these mutants. This approach enhances the molecular and mechanistic understanding of LacI family proteins, and advances the ability to design modular genetic parts.

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.

Engineering Alternate Ligand Recognition in the PurR Topology: A System of Novel Caffeine Biosensing Transcriptional Antirepressors

Recent advances in synthetic biology and protein engineering have increased the number of allosteric transcription factors used to regulate independent promoters. These developments represent an important increase in our biological computing capacity, which will enable us to construct more sophisticated genetic programs for a broad range of biological technologies. However, the majority of these transcription factors are represented by the repressor phenotype (BUFFER), and require layered inversion to confer the antithetical logical function (NOT), requiring additional biological resources. Moreover, these engineered transcription factors typically utilize native ligand binding functions paired with alternate DNA binding functions. In this study, we have advanced the state-of-the-art by engineering and redesigning the PurR topology (a native antirepressor) to be responsive to caffeine, while mitigating responsiveness to the native ligand hypoxanthine-i.e., a deamination product of the input molecule adenine. Importantly, the resulting caffeine responsive transcription factors are not antagonized by the native ligand hypoxanthine. In addition, we conferred alternate DNA binding to the caffeine antirepressors, and to the PurR scaffold, creating 38 new transcription factors that are congruent with our current transcriptional programming structure. Finally, we leveraged this system of transcription factors to create integrated NOR logic and related feedback operations. This study represents the first example of a system of transcription factors (antirepressors) in which both the ligand binding site and the DNA binding functions were successfully engineered in tandem.

The Bipartisan Future of Synthetic Chemistry and Synthetic Biology

Synthetic biology holds great potential for sustainable chemical synthesis, yet is limited to accessing a relatively small area of chemical space. By interfacing this new technology with the versatility and scope of synthetic chemistry, the best of both worlds can be harnessed to drive a green chemical industry.

Engineered protein switches for exogenous control of gene expression

There is an ongoing need in the synthetic biology community for novel ways to regulate gene expression. Protein switches, which sense biological inputs and respond with functional outputs, represent one way to meet this need. Despite the fact that there is already a large pool of transcription factors and signaling proteins available, the pool of existing switches lacks the substrate specificities and activities required for certain applications. Therefore, a large number of techniques have been applied to engineer switches with novel properties. Here we discuss some of these techniques by broadly organizing them into three approaches. We show how novel switches can be created through mutagenesis, domain swapping, or domain insertion. We then briefly discuss their use as biosensors and in complex genetic circuits.

Application of combinatorial optimization strategies in synthetic biology

In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often impeded by our limited knowledge of the optimal combination of individual circuits. For example, a fundamental question in most metabolic engineering projects is the optimal level of enzymes for maximizing the output. To address this point, combinatorial optimization approaches have been established, allowing automatic optimization without prior knowledge of the best combination of expression levels of individual genes. This review focuses on current combinatorial optimization methods and emerging technologies facilitating their applications.