Engineering Transcription Factor BmoR Mutants for Constructing Multifunctional Alcohol Biosensors

Identification of functional sgRNA mutants lacking canonical secondary structure using high-throughput FACS screening

Coexpressing multiple identical single guide RNAs (sgRNAs) in CRISPR-dependent engineering triggers genetic instability and phenotype loss. To provide sgRNA derivatives for efficient DNA digestion, we design a high-throughput digestion-activity-dependent positive screening strategy and astonishingly obtain functional nonrepetitive sgRNA mutants with up to 48 out of the 61 nucleotides mutated, and these nonrepetitive mutants completely lose canonical secondary sgRNA structure in simulation. Cas9-sgRNA complexes containing these noncanonical sgRNAs maintain wild-type level of digestion activities in vivo, indicating that the Cas9 protein is compatible with or is able to adjust the secondary structure of sgRNAs. Using these noncanonical sgRNAs, we achieve multiplex genetic engineering for gene knockout and base editing in microbial cell factories. Libraries of strains with rewired metabolism are constructed, and overproducers of isobutanol or 1,3-propanediol are identified by biosensor-based fluorescence-activated cell sorting (FACS). This work sheds light on the remarkable flexibility of the secondary structure of functional sgRNA.

Concentration Recognition-Based Auto-Dynamic Regulation System (CRUISE) Enabling Efficient Production of Higher Alcohols

Microbial factories lacking the ability of dynamically regulating the pathway enzymes overexpression, according to in situ metabolite concentrations, are suboptimal, especially when the metabolic intermediates are competed by growth and chemical production. The production of higher alcohols (HAs), which hijacks the amino acids (AAs) from protein biosynthesis, minimizes the intracellular concentration of AAs and thus inhibits the host growth. To balance the resource allocation and maintain stable AA flux, this work utilizes AA-responsive transcriptional attenuator ivbL and HA-responsive transcriptional activator BmoR to establish a concentration recognition-based auto-dynamic regulation system (CRUISE). This system ultimately maintains the intracellular homeostasis of AA and maximizes the production of HA. It is demonstrated that ivbL-driven enzymes overexpression can dynamically regulate the AA-to-HA conversion while BmoR-driven enzymes overexpression can accelerate the AA biosynthesis during the HA production in a feedback activation mode. The AA flux in biosynthesis and conversion pathways is balanced via the intracellular AA concentration, which is vice versa stabilized by the competition between AA biosynthesis and conversion. The CRUISE, further aided by scaffold-based self-assembly, enables 40.4 g L-1 of isobutanol production in a bioreactor. Taken together, CRUISE realizes robust HA production and sheds new light on the dynamic flux control during the process of chemical production.

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.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Engineering whole-cell microbial biosensors: Design principles and applications in monitoring and treatment of heavy metals and organic pollutants

Biosensors have been widely used as cost-effective, rapid, in situ, and real-time analytical tools for monitoring environments. The development of synthetic biology has enabled emergence of genetically engineered whole-cell microbial biosensors. This review updates the design and optimization principles for a diverse array of whole-cell biosensors based on transcription factors (TF) including activators or repressors derived from heavy metal resistance systems, alkanes, and aromatics metabolic pathways of bacteria. By designing genetic circuits, the whole-cell biosensors could be engineered to intelligently sense heavy metals (Hg²⁺, Zn²⁺, Pb²⁺, Au³⁺, Cd²⁺, As³⁺, Ni²⁺, Cu²⁺, and UO₂²⁺) or organic compounds (alcohols, alkanes, phenols, and benzenes) through one-component or two-component system-based TFs, transduce signals through genetic amplifiers, and response as various outputs such as cell fluorescence and bioelectricity for monitoring heavy metals and organic pollutants in real conditions, synthetic curli and surface metal-binding peptides for in situ bio-sorption of heavy metals. We further review strategies that have been implemented to optimize the selectivity and correlation between ligand concentration and output signal of the TF-based biosensors, so as to meet requirements of practical applications. The optimization strategies include protein engineering to change specificities, promoter engineering to improve sensitivities, and genetic circuit-based amplification to enhance dynamic ranges via designing transcriptional amplifiers, logic gates, and feedback loops. At last, we outlook future trends in developing novel forms of biosensors.