Divergent directed evolution of a TetR-type repressor towards aromatic molecules

Engineered transcription factor-binding diversed functional nucleic acid-based synthetic biosensor

Engineered transcription factors (eTFs) binding diversed functional nucleic acids (dFNAs), as innovative biorecognition systems, have gradually become indispensable core elements for building synthetic biosensors. They not only circumvent the limitations of the original TF-based biosensing technologies, but also inject new vitality into the field of synthetic biosensing. This review aims to provide the first comprehensive and systematic dissection of the eTF-dFNA synthetic biosensor concept. Firstly, the core principles and interaction mechanisms of eTF-dFNA biosensors are clarified. Next, we elaborate on the construction strategies of eTF-dFNA synthetic biosensors, detailing methods for the personalized customization of eTFs (irrational design, rational design, and semi-rational design) and dFNAs (SELEX, modifying and predicting), along with the exploration of strategies for the flexible selection of signal amplification and output modes. Furthermore, we discuss the exceptional performance and substantial advantages of eTF-dFNA synthetic biosensors, analyzing them from four perspectives: recognition domain, detection speed, sensitivity, and construction methodology. Building upon this analysis, we present their outstanding applications in point-of-care diagnostics, food-safety detection, environmental monitoring, and production control. Finally, we address the current limitations of eTF-dFNA synthetic biosensors candidly and envision the future direction of this technology, aiming to provide valuable insights for further research and applications in this burgeoning field.

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

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

The opportunistic pathogen Pseudomonas aeruginosa infects the airways of people with cystic fibrosis (CF) 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.

Microbial biosensors for diagnostics, surveillance and epidemiology: Today's achievements and tomorrow's prospects

Microbial biosensors hold great promise for engineering high-performance, field-deployable and affordable detection devices for medical and environmental applications. This review explores recent advances in the field, highlighting new sensing strategies and modalities for whole-cell biosensors as well as the remarkable expansion of microbial cell-free systems. We also discuss improvements in robustness that have enhanced the ability of biosensors to withstand the challenging conditions found in biological samples. However, limitations remain in expanding the detection repertoire, particularly for proteins. We anticipate that the AI-powered revolution in protein design will streamline the engineering of custom-made sensing modules and unlock the full potential of microbial biosensors.

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Design and Characterization of a Generalist Biosensor for Indole Derivatives

Transcription factor (TF)-based biosensors are useful synthetic biology tools for applications in a variety of areas of biotechnology. A major challenge of biosensor circuits is the limited repertoire of identified and well-characterized TFs for applications of interest, in addition to the challenge of optimizing selected biosensors. In this work, we implement the IclR family repressor TF TtgV from Pseudomonas putida DOT-T1E as an indole-derivative biosensor in Escherichia coli. We optimize the genetic circuit utilizing different components, providing insights into biosensor design and expanding on previous studies investigating this TF. We discover novel physiologically relevant ligands of TtgV, such as skatole. The broad specificity of TtgV makes it a useful target for directed evolution and protein engineering toward desired specificity. TtgV, as an indole-derivative biosensor, is a promising genetic component for the detection of compounds with biological activities relevant to health and the gut microbiome.

Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.

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.