Strategies for Improving Small-Molecule Biosensors in Bacteria

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Growth-based selections evaluate the fitness of individual organisms at a population level. In enzyme engineering, such growth selections allow for the rapid and straightforward identification of highly efficient biocatalysts from extensive libraries. However, selection-based improvement of (synthetically useful) biocatalysts is challenging, as they require highly dependable strategies that artificially link their activities to host survival. Here, we showcase a robust and scalable growth-based selection platform centered around the complementation of noncanonical amino acid-dependent bacteria. Specifically, we demonstrate how serial passaging of populations featuring millions of carbamoylase variants autonomously selects biocatalysts with up to 90,000-fold higher initial rates. Notably, selection of replicate populations enriched diverse biocatalysts, which feature distinct amino acid motifs that drastically boost carbamoylase activity. As beneficial substitutions also originated from unintended copying errors during library preparation or cell division, we anticipate that our growth-based selection platform will be applicable to the continuous, autonomous evolution of diverse biocatalysts in the future.

Bacterial Patterning: A Promising Biofabrication Technique

Bacterial patterning has emerged as a pivotal biofabrication technique in the biomedical field. In the past 2 decades, a diverse array of bacterial patterning approaches have been developed to enable the precise manipulation of the spatial distribution of bacterial patterns for various applications. Despite the significance of these advancements, there is a deficiency of review articles providing an overview of bacterial patterning technologies. In this mini-review, we systematically summarize the progress of bacterial patterning over the past 2 decades. This review commences with an elucidation of the definition and fundamental principles of bacterial patterning. Subsequently, we introduce the established bacterial patterning strategies, accompanied by discussions about the advantages and limitations of each approach. Furthermore, we showcase the biomedical applications of these strategies, highlighting their efficacy in spatial control of biofilms, biosensing, and biointervention. Finally, this mini-review is concluded with a summary and an outlook on future challenges and opportunities. It is anticipated that this mini-review can serve as a concise guide for those who are interested in this exciting and rapidly evolving research area.

Design of synthetic bacterial biosensors

Novel whole-cell bacterial biosensor designs require an emphasis on moving toward field deployment. Many current sensors are characterized under specified laboratory conditions, which frequently do not represent actual deployment conditions. To this end, recent developments such as toolkits for probing new host chassis that are more robust to environments of interest, have paved the way for improved designs. Strategies for rational tuning of genetic components or tools such as genetic amplifiers or designs that allow post hoc tuning are essential in optimizing existing biosensors for practical application. Furthermore, recent work has seen a rise in directed evolution techniques, which can be immensely valuable in both tuning existing sensors and developing sensors for new analytes that lack characterized sensors. Combined with advancements in bioinformatics and capabilities in rewiring two-component systems, many new sensors can be established, broadening biosensor use cases. Last, recent work in CRISPR-based dynamic regulation and memory mechanisms, as well as kill-switches for biosafety and innovative output integration concepts, represents promising steps toward designing bacterial biosensors for deployment in dynamic and heterogeneous conditions.

Special Issue "Synthetic Biology for Biosensing in Health and Environmental Applications"

Biosensors are analytical devices that utilize biological sensing elements, such as enzymes, antibodies, nucleic acids, or cells, to detect a given analyte [...].

Diboronic-Acid-Based Electrochemical Sensor for Enzyme-Free Selective and Sensitive Glucose Detection

A diboronic acid anthracene-based fluorescent system for detecting blood glucose could be used for 180 days. However, there has not yet been a boronic acid immobilized electrode to selectively detect glucose in a signal-increased way. Considering malfunctions of sensors at high sugar levels, the electrochemical signal should be increased proportionally to the glucose concentration. Therefore, we synthesized a new diboronic acid derivative and fabricated the derivative-immobilized electrodes for the selective detection of glucose. We performed cyclic voltammetry and electrochemical impedance spectroscopy with an Fe(CN)₆^3-/4- redox pair for detecting glucose in the range of 0-500 mg/dL. The analysis revealed increased electron-transfer kinetics such as increased peak current and decreased semicircle radius of Nyquist plots as the glucose concentration increased. The cyclic voltammetry and impedance spectroscopy showed that the linear detection range of glucose was 40 to 500 mg/dL with limits of detection of 31.2 mg/dL and 21.5 mg/dL, respectively. We applied the fabricated electrode to detect glucose in artificial sweat and obtained 90% of the performance of the electrodes in PBS. Cyclic voltammetry measurements of other sugars such as galactose, fructose, and mannitol also showed linear increased peak currents proportional to the concentrations of the tested sugars. However, the slopes of the sugars were lower than that of glucose, indicating selectivity for glucose. These results proved the newly synthesized diboronic acid is a promising synthetic receptor for developing a long-term usable electrochemical sensor system.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Continuous Directed Evolution of a Feedback-Resistant Arabidopsis Arogenate Dehydratase in Plantized Escherichia coli

Continuous directed evolution (CDE) is a powerful tool for enzyme engineering due to the depth and scale of evolutionary search that it enables. If suitably controlled and calibrated, CDE could be widely applied in plant breeding and biotechnology to improve plant enzymes ex planta. We tested this concept by evolving Arabidopsis arogenate dehydratase (AtADT2) for resistance to feedback inhibition. We used an Escherichia coli platform with a phenylalanine biosynthesis pathway reconfigured ("plantized") to mimic the plant pathway, a T7RNA polymerase-base deaminase hypermutation system (eMutaT7), and 4-fluorophenylalanine as selective agent. Selection schemes were prevalidated using a known feedback-resistant AtADT2 variant. We obtained variants that had 4-fluorophenylalanine resistance at least matching the known variant and that carried mutations in the ACT domain responsible for feedback inhibition. We conclude that ex planta CDE of plant enzymes in a microbial platform is a viable way to tailor characteristics that involve interaction with small molecules.

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.

In vivo protein-based biosensors: seeing metabolism in real time

Biological homeostasis is a dynamic and elastic equilibrium of countless interlinked biochemical reactions. A key goal of life sciences is to understand these dynamics; bioengineers seek to reconfigure such networks. Both goals require the ability to monitor the concentration of individual intracellular metabolites with sufficient spatiotemporal resolution. To achieve this, a range of protein or protein/DNA signalling circuits with optical readouts have been constructed. Protein biosensors can provide quantitative information at subsecond temporal and suborganelle spatial resolution. However, their construction is fraught with difficulties related to integrating the affinity- and selectivity-endowing components with the signal reporters. We argue that development of efficient approaches for construction of chemically induced dimerisation systems and reporter domains with large dynamic ranges will solve these problems.

Recent Advancements in Nanobiosensors: Current Trends, Challenges, Applications, and Future Scope

In recent years, there has been immense advancement in the development of nanobiosensors as these are a fundamental need of the hour that act as a potential candidate integrated with point-of-care-testing for several applications, such as healthcare, the environment, energy harvesting, electronics, and the food industry. Nanomaterials have an important part in efficiently sensing bioreceptors such as cells, enzymes, and antibodies to develop biosensors with high selectivity, peculiarity, and sensibility. It is virtually impossible in science and technology to perform any application without nanomaterials. Nanomaterials are distinguished from fine particles used for numerous applications as a result of being unique in properties such as electrical, thermal, chemical, optical, mechanical, and physical. The combination of nanostructured materials and biosensors is generally known as nanobiosensor technology. These miniaturized nanobiosensors are revolutionizing the healthcare domain for sensing, monitoring, and diagnosing pathogens, viruses, and bacteria. However, the conventional approach is time-consuming, expensive, laborious, and requires sophisticated instruments with skilled operators. Further, automating and integrating is quite a challenging process. Thus, there is a considerable demand for the development of nanobiosensors that can be used along with the POCT module for testing real samples. Additionally, with the advent of nano/biotechnology and the impact on designing portable ultrasensitive devices, it can be stated that it is probably one of the most capable ways of overcoming the aforementioned problems concerning the cumulative requirement for the development of a rapid, economical, and highly sensible device for analyzing applications within biomedical diagnostics, energy harvesting, the environment, food and water, agriculture, and the pharmaceutical industry.

Achieving spatially precise diagnosis and therapy in the mammalian gut using synthetic microbial gene circuits

The mammalian gut and its microbiome form a temporally dynamic and spatially heterogeneous environment. The inaccessibility of the gut and the spatially restricted nature of many gut diseases translate into difficulties in diagnosis and therapy for which novel tools are needed. Engineered bacterial whole-cell biosensors and therapeutics have shown early promise at addressing these challenges. Natural and engineered sensing systems can be repurposed in synthetic genetic circuits to detect spatially specific biomarkers during health and disease. Heat, light, and magnetic signals can also activate gene circuit function with externally directed spatial precision. The resulting engineered bacteria can report on conditions in situ within the complex gut environment or produce biotherapeutics that specifically target host or microbiome activity. Here, we review the current approaches to engineering spatial precision for in vivo bacterial diagnostics and therapeutics using synthetic circuits, and the challenges and opportunities this technology presents.

Enzyme directed evolution using genetically encodable biosensors

Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.

Development and Characterization of Indole-Responsive Whole-Cell Biosensor Based on the Inducible Gene Expression System from Pseudomonas putida KT2440

Indole is a biologically active compound naturally occurring in plants and some bacteria. It is an important specialty chemical that is used as a precursor by the pharmaceutical and chemical industries, as well as in agriculture. Recently, indole has been identified as an important signaling molecule for bacteria in the mammalian gut. The regulation of indole biosynthesis has been studied in several bacterial species. However, this has been limited by the lack of in vivo tools suitable for indole-producing species identification and monitoring. The genetically encoded biosensors have been shown to be useful for real-time quantitative metabolite analysis. This paper describes the identification and characterization of the indole-inducible system PpTrpI/P_{PP_RS00425} from Pseudomonas putida KT2440. Indole whole-cell biosensors based on Escherichia coli and Cupriavidus necator strains are developed and validated. The specificity and dynamics of biosensors in response to indole and its structurally similar derivatives are investigated. The gene expression system PpTrpI/P_{PP_RS00425} is shown to be specifically induced up to 639.6-fold by indole, exhibiting a linear response in the concentration range from approximately 0.4 to 5 mM. The results of this study form the basis for the use of whole-cell biosensors in indole metabolism-relevant bacterial species screening and characterization.