Optimization of Heavy Metal Sensors Based on Transcription Factors and Cell-Free Expression Systems

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

A cell-based electrochemical taste sensor for detection of Hydroxy-α-sanshool

The Transient Receptor Potential Vanilloid 1 (TRPV1) has been identified as a suitable candidate for a spicy taste (Zanthoxylum plant) sensor. In this study, we investigated the response of TRPV1 expressed on human HepG2 cell membranes following stimulation with Hydroxy-α-sanshool. A three-dimensional (3D) cell-based electrochemical sensor was fabricated by layering cells expressing hTRPV1. l-cysteine/AuNFs electrodes were functionalized on indium tin oxide-coated glass (ITO) to enhance the sensor's selectivity and sensitivity. HepG2 cells were encapsulated in sodium alginate/gelatin hydrogel to create a 3D cell cultivation system, which was immobilized on the l-cysteine/AuNFs/ITO to serve as biorecognition elements. Using differential pulse voltammetry (DPV), the developed biosensor was utilized to detect Hydroxy-α-sanshool, a representative substance in Zanthoxylum bungeanum Maxim. The result obtained from DPV was linear with Hydroxy-α-sanshool concentrations ranging from 0 to 70 μmol/L, with a detection limit of 2.23 μmol/L. This biosensor provides a sensitive and novel macroscopic approach for TRPV1 detection.

In Vivo Detection of Low Molecular Weight Platform Chemicals and Environmental Contaminants by Genetically Encoded Biosensors

Genetically encoded biosensor systems operating in living cells are versatile, cheap, and transferable tools for the detection and quantification of a broad range of small molecules. This review presents state-of-the-art biosensor designs and assemblies, featuring transcription factor-, riboswitch-, and enzyme-coupled devices, highly engineered fluorescent probes, and emerging two-component systems. Importantly, (bioinformatic-assisted) strategies to resolve contextual issues, which cause biosensors to miss performance criteria in vivo, are highlighted. The optimized biosensing circuits can be used to monitor chemicals of low molecular mass (<200 g mol^-1) and physicochemical properties that challenge conventional chromatographical methods with high sensitivity. Examples herein include but are not limited to formaldehyde, formate, and pyruvate as immediate products from (synthetic) pathways for the fixation of carbon dioxide (CO₂), industrially important derivatives like small- and medium-chain fatty acids and biofuels, as well as environmental toxins such as heavy metals or reactive oxygen and nitrogen species. Lastly, this review showcases biosensors capable of assessing the biosynthesis of platform chemicals from renewable resources, the enzymatic degradation of plastic waste, or the bioadsorption of highly toxic chemicals from the environment. These applications offer new biosensor-based manufacturing, recycling, and remediation strategies to tackle current and future environmental and socioeconomic challenges including the wastage of fossil fuels, the emission of greenhouse gases like CO₂, and the pollution imposed on ecosystems and human health.

The Recent Development of Acoustic Sensors as Effective Chemical Detecting Tools for Biological Cells and Their Bioactivities

One of the most significant developed technologies is the use of acoustic waves to determine the chemical structures of biological tissues and their bioactivities. In addition, the use of new acoustic techniques for in vivo visualizing and imaging of animal and plant cellular chemical compositions could significantly help pave the way toward advanced analytical technologies. For instance, acoustic wave sensors (AWSs) based on quartz crystal microbalance (QCM) were used to identify the aromas of fermenting tea such as linalool, geraniol, and trans-2-hexenal. Therefore, this review focuses on the use of advanced acoustic technologies for tracking the composition changes in plant and animal tissues. In addition, a few key configurations of the AWS sensors and their different wave pattern applications in biomedical and microfluidic media progress are discussed.

Advances in Synthetic-Biology-Based Whole-Cell Biosensors: Principles, Genetic Modules, and Applications in Food Safety

A whole-cell biosensor based on synthetic biology provides a promising new method for the on-site detection of food contaminants. The basic components of whole-cell biosensors include the sensing elements, such as transcription factors and riboswitches, and reporting elements, such as fluorescence, gas, etc. The sensing and reporting elements are coupled through gene expression regulation to form a simple gene circuit for the detection of target substances. Additionally, a more complex gene circuit can involve other functional elements or modules such as signal amplification, multiple detection, and delay reporting. With the help of synthetic biology, whole-cell biosensors are becoming more versatile and integrated, that is, integrating pre-detection sample processing, detection processes, and post-detection signal calculation and storage processes into cells. Due to the relative stability of the intracellular environment, whole-cell biosensors are highly resistant to interference without the need of complex sample preprocessing. Due to the reproduction of chassis cells, whole-cell biosensors replicate all elements automatically without the need for purification processing. Therefore, whole-cell biosensors are easy to operate and simple to produce. Based on the above advantages, whole-cell biosensors are more suitable for on-site detection than other rapid detection methods. Whole-cell biosensors have been applied in various forms such as test strips and kits, with the latest reported forms being wearable devices such as masks, hand rings, and clothing. This paper examines the composition, construction methods, and types of the fundamental components of synthetic biological whole-cell biosensors. We also introduce the prospect and development trend of whole-cell biosensors in commercial applications.

Electrochemical Cell-Free Biosensors for Antibody Detection

We report here the development of an electrochemical cell-free biosensor for antibody detection directly in complex sample matrices with high sensitivity and specificity that is particularly suitable for point-of-care applications. The approach is based on the use of programmable antigen-conjugated gene circuits that, upon recognition of a specific target antibody, trigger the cell-free transcription of an RNA sequence that can be consequently detected using a redox-modified probe strand immobilized to a disposable electrode. The platform couples the features of high sensitivity and specificity of cell-free systems and the strength of cost-effectiveness and possible miniaturization provided by the electrochemical detection. We demonstrate the sensitive, specific, selective, and multiplexed detection of three different antibodies, including the clinically-relevant Anti-HA antibody.

Rapid and Finely-Tuned Expression for Deployable Sensing Applications

Organisms from across the tree of life have evolved highly efficient mechanisms for sensing molecules of interest using biomolecular machinery that can in turn be quite valuable for the development of biosensors. However, purification of such machinery for use in in vitro biosensors is costly, while the use of whole cells as in vivo biosensors often leads to long sensor response times and unacceptable sensitivity to the chemical makeup of the sample. Cell-free expression systems overcome these weaknesses by removing the requirements associated with maintaining living sensor cells, allowing for increased function in toxic environments and rapid sensor readout at a production cost that is often more reasonable than purification. Here, we focus on the challenge of implementing cell-free protein expression systems that meet the stringent criteria required for them to serve as the basis for field-deployable biosensors. Fine-tuning expression to meet these requirements can be achieved through careful selection of the sensing and output elements, as well as through optimization of reaction conditions via tuning of DNA/RNA concentrations, lysate preparation methods, and buffer conditions. Through careful sensor engineering, cell-free systems can continue to be successfully used for the production of tightly regulated, rapidly expressing genetic circuits for biosensors.

Design and biofabrication of bacterial living materials with robust and multiplexed biosensing capabilities

The intertwined adoption of synthetic biology and 3D bioprinting has the potential to improve different application fields by fabricating engineered living materials (ELMs) with unnatural genetically-encoded sense & response capabilities. However, efforts are still needed to streamline the fabrication of sensing ELMs compatible with field use and improving their functional complexity. To investigate these two unmet needs, we adopted a workflow to reproducibly construct bacterial ELMs with synthetic biosensing circuits that provide red pigmentation as visible readout in response to different proof-of-concept chemical inducers. We first fabricated single-input/single-output ELMs and we demonstrated their robust performance in terms of longevity (cell viability and evolutionary stability >15 days, and long-term storage >1 month), sensing in harsh, non-sterile or nutrient-free conditions compatible with field use (soil, water, and clinical samples, including real samples from Pseudomonas aeruginosa infected patients). Then, we fabricated ELMs including multiple spatially-separated biosensor strains to engineer: level-bar materials detecting molecule concentration ranges, multi-input/multi-output devices with multiplexed sensing and information processing capabilities, and materials with cell-cell communication enabling on-demand pattern formation. Overall, we showed successful field use and multiplexed functioning of reproducibly fabricated ELMs, paving the way to a future automation of the prototyping process and boosting applications of such devices as in-situ monitoring tools or easy-to-use sensing kits.

Hybrid Nucleic Acid-Quantum Dot Assemblies as Multiplexed Reporter Platforms for Cell-Free Transcription Translation-Based Biosensors

Cell-free synthetic biology has emerged as a valuable tool for the development of rapid, portable biosensors that can be readily transported in the freeze-dried form to the point of need eliminating cold chain requirements. One of the challenges associated with cell-free sensors is the ability to simultaneously detect multiple analytes within a single reaction due to the availability of a limited set of fluorescent and colorimetric reporters. To potentially provide multiplexing capabilities to cell-free biosensors, we designed a modular semiconductor quantum dot (QD)-based reporter platform that is plugged in downstream of the transcription-translation functionality in the cell-free reaction and which converts enzymatic activity in the reaction into distinct optical signals. We demonstrate proof of concept by converting restriction enzyme activity, utilized as our prototypical sensing output, into optical changes across several distinct spectral output channels that all use a common excitation wavelength. These hybrid Förster resonance energy transfer (FRET)-based QD peptide PNA-DNA-Dye reporters (QD-PDDs) are completely self-assembled and consist of differentially emissive QD donors paired to a dye-acceptor displayed on a unique DNA encoding a given enzyme's cleavage site. Three QD-based PDDs, independently activated by the enzymes BamHI, EcoRI, and NcoI, were prototyped in mixed enzyme assays where all three demonstrated the ability to convert enzymatic activity into fluorescent output. Simultaneous monitoring of each of the three paired QD-donor dye-acceptor spectral channels in cell-free biosensing reactions supplemented with added linear genes encoding each enzyme confirmed robust multiplexing capabilities for at least two enzymes when co-expressed. The modular QD-PDDs are easily adapted to respond to other restriction enzymes or even proteases if desired.

Variability in cell-free expression reactions can impact qualitative genetic circuit characterization

Cell-free expression systems provide a suite of tools that are used in applications from sensing to biomanufacturing. One of these applications is genetic circuit prototyping, where the lack of cloning is required and a high degree of control over reaction components and conditions enables rapid testing of design candidates. Many studies have shown utility in the approach for characterizing genetic regulation elements, simple genetic circuit motifs, protein variants or metabolic pathways. However, variability in cell-free expression systems is a known challenge, whether between individuals, laboratories, instruments, or batches of materials. While the issue of variability has begun to be quantified and explored, little effort has been put into understanding the implications of this variability. For genetic circuit prototyping, it is unclear when and how significantly variability in reaction activity will impact qualitative assessments of genetic components, e.g. relative activity between promoters. Here, we explore this question by assessing DNA titrations of seven genetic circuits of increasing complexity using reaction conditions that ostensibly follow the same protocol but vary by person, instrument and material batch. Although the raw activities vary widely between the conditions, by normalizing within each circuit across conditions, reasonably consistent qualitative performance emerges for the simpler circuits. For the most complex case involving expression of three proteins, we observe a departure from this qualitative consistency, offering a provisional cautionary line where normal variability may disrupt reliable reuse of prototyping results. Our results also suggest that a previously described closed loop controller circuit may help to mitigate such variability, encouraging further work to design systems that are robust to variability. Graphical Abstract.

Transcription Factor-Based Biosensors for Detecting Pathogens

Microorganisms are omnipresent and inseparable from our life. Many of them are beneficial to humans, while some are not. Importantly, foods and beverages are susceptible to microbial contamination, with their toxins causing illnesses and even death in some cases. Therefore, monitoring and detecting harmful microorganisms are critical to ensuring human health and safety. For several decades, many methods have been developed to detect and monitor microorganisms and their toxicants. Conventionally, nucleic acid analysis and antibody-based analysis were used to detect pathogens. Additionally, diverse chromatographic methods were employed to detect toxins based on their chemical and structural properties. However, conventional techniques have several disadvantages concerning analysis time, sensitivity, and expense. With the advances in biotechnology, new approaches to detect pathogens and toxins have been reported to compensate for the disadvantages of conventional analysis from different research fields, including electrochemistry, nanotechnology, and molecular biology. Among them, we focused on the recent studies of transcription factor (TF)-based biosensors to detect microorganisms and discuss their perspectives and applications. Additionally, the other biosensors for detecting microorganisms reported in recent studies were also introduced in this review.

Self-assembled nanoparticle-enzyme aggregates enhance functional protein production in pure transcription-translation systems

Cell-free protein synthesis systems (CFPS) utilize cellular transcription and translation (TX-TL) machinery to synthesize proteins in vitro. These systems are useful for multiple applications including production of difficult proteins, as high-throughput tools for genetic circuit screening, and as systems for biosensor development. Though rapidly evolving, CFPS suffer from some disadvantages such as limited reaction rates due to longer diffusion times, significant cost per assay when using commercially sourced materials, and reduced reagent stability over prolonged periods. To address some of these challenges, we conducted a series of proof-of-concept experiments to demonstrate enhancement of CFPS productivity via nanoparticle assembly driven nanoaggregation of its constituent proteins. We combined a commercially available CFPS that utilizes purified polyhistidine-tagged (His-tag) TX-TL machinery with CdSe/CdS/ZnS core/shell/shell quantum dots (QDs) known to readily coordinate His-tagged proteins in an oriented fashion. We show that nanoparticle scaffolding of the CFPS cross-links the QDs into nanoaggregate structures while enhancing the production of functional recombinant super-folder green fluorescent protein and phosphotriesterase, an organophosphate hydrolase; the latter by up to 12-fold. This enhancement, which occurs by an undetermined mechanism, has the potential to improve CFPS in general and specifically CFPS-based biosensors (faster response time) while also enabling rapid detoxification/bioremediation through point-of-concern synthesis of similar catalytic enzymes. We further show that such nanoaggregates improve production in diluted CFPS reactions, which can help to save money and extend the amount of these costly reagents. The results are discussed in the context of what may contribute mechanistically to the enhancement and how this can be applied to other CFPS application scenarios.

Biotechnology Applications of Cell-Free Expression Systems

Cell-free systems are a rapidly expanding platform technology with an important role in the engineering of biological systems. The key advantages that drive their broad adoption are increased efficiency, versatility, and low cost compared to in vivo systems. Traditionally, in vivo platforms have been used to synthesize novel and industrially relevant proteins and serve as a testbed for prototyping numerous biotechnologies such as genetic circuits and biosensors. Although in vivo platforms currently have many applications within biotechnology, they are hindered by time-constraining growth cycles, homeostatic considerations, and limited adaptability in production. Conversely, cell-free platforms are not hindered by constraints for supporting life and are therefore highly adaptable to a broad range of production and testing schemes. The advantages of cell-free platforms are being leveraged more commonly by the biotechnology community, and cell-free applications are expected to grow exponentially in the next decade. In this study, new and emerging applications of cell-free platforms, with a specific focus on cell-free protein synthesis (CFPS), will be examined. The current and near-future role of CFPS within metabolic engineering, prototyping, and biomanufacturing will be investigated as well as how the integration of machine learning is beneficial to these applications.