A New Biosensor for Stilbenes and a Cannabinoid Enabled by Genome Mining of a Transcriptional Regulator

Ligify: Automated Genome Mining for Ligand-Inducible Transcription Factors

Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals and is publicly available at https://ligify.groov.bio.

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.

Biosensor-assisted CRISPRi high-throughput screening to identify genetic targets in Zymomonas mobilis for high d-lactate production

Lactate is an important monomer for the synthesis of poly-lactate (PLA), which is a substitute for the petrochemical plastics. To achieve the goal of high lactate titer, rate, and yield for commercial production, efficient lactate production pathway is needed as well as genetic targets that affect high lactate production and tolerance. In this study, an LldR-based d-lactate biosensor with a broad dynamic range was first applied into Zymomonas mobilis to select mutant strains with strong GFP fluorescence, which could be the mutant strains with increased d-lactate production. Then, LldR-based d-lactate biosensor was combined with a genome-wide CRISPR interference (CRISPRi) library targeting the entire genome to generate thousands of mutants with gRNA targeting different genetic targets across the whole genome. Specifically, two mutant libraries were selected containing 105 and 104 mutants with different interference sites from two rounds of fluorescence-activated cell sorting (FACS), respectively. Two genetic targets of ZMO1323 and ZMO1530 were characterized and confirmed to be associated with the increased d-lactate production, further knockout of ZMO1323 and ZMO1530 resulted in a 15% and 21% increase of d-lactate production, respectively. This work thus not only established a high-throughput approach that combines genome-scale CRISPRi and biosensor-assisted screening to identify genetic targets associated with d-lactate production in Z. mobilis, but also provided a feasible high-throughput screening approach for rapid identification of genetic targets associated with strain performance for other industrial microorganisms.

Snowprint: a predictive tool for genetic biosensor discovery

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .

Ultrahigh-throughput screening-assisted in vivo directed evolution for enzyme engineering

BACKGROUND

Classical directed evolution is a powerful approach for engineering biomolecules with improved or novel functions. However, it traditionally relies on labour- and time-intensive iterative cycles, due in part to the need for multiple molecular biology steps, including DNA transformation, and limited screening throughput.

RESULTS

In this study, we present an ultrahigh throughput in vivo continuous directed evolution system with thermosensitive inducible tunability, which is based on error-prone DNA polymerase expression modulated by engineered thermal-responsive repressor cI857, and genomic MutS mutant with temperature-sensitive defect for fixation of mutations in Escherichia coli. We demonstrated the success of the in vivo evolution platform with β-lactamase as a model, with an approximately 600-fold increase in the targeted mutation rate. Furthermore, the platform was combined with ultrahigh-throughput screening methods and employed to evolve α-amylase and the resveratrol biosynthetic pathway. After iterative rounds of enrichment, a mutant with a 48.3% improvement in α-amylase activity was identified via microfluidic droplet screening. In addition, when coupled with an in vivo biosensor in the resveratrol biosynthetic pathway, a variant with 1.7-fold higher resveratrol production was selected by fluorescence-activated cell sorting.

CONCLUSIONS

In this study, thermal-responsive targeted mutagenesis coupled with ultrahigh-throughput screening was developed for the rapid evolution of enzymes and biosynthetic pathways.

Identifying, Characterizing, and Engineering a Phenolic Acid-Responsive Transcriptional Factor from Bacillus amyloliquefaciens

Transcriptional factors-based biosensors are commonly used in metabolic engineering for inducible control of gene expression and related applications such as high-throughput screening and dynamic pathway regulations. Mining for novel transcriptional factors is essential for expanding the usability of these toolsets. Here, we report the identification, characterization, and engineering of a phenolic acid responsive regulator PadR from Bacillus amyloliquefaciens (BaPadR). This BaPadR-based biosensor system showed a unique ligand preference and exhibited a high output strength comparable to that of commonly used inducible expression systems. Through engineering the DNA binding region of BaPadR, we further enhanced the dynamic range of the biosensor system. The DNA sequences that are responsible for BaPadR recognition were located by promoter truncation and hybrid promoter building. To further explore the tunability of the sensor system, base substitutions were performed on the BaPadR binding region of the phenolic acid decarboxylase promoter (PpadC) and the hybrid promoter. This novel biosensor system can serve as a valuable tool in future synthetic biology applications.

Application of Metabolite-Responsive Biosensors for Plant Natural Products Biosynthesis

Plant natural products (PNPs) have shown various pharmaceutical activities, possessing great potential in global markets. Microbial cell factories (MCFs) provide an economical and sustainable alternative for the synthesis of valuable PNPs compared with traditional approaches. However, the heterologous synthetic pathways always lack native regulatory systems, bringing extra burden to PNPs production. To overcome the challenges, biosensors have been exploited and engineered as powerful tools for establishing artificial regulatory networks to control enzyme expression in response to environments. Here, we reviewed the recent progress involved in the application of biosensors that are responsive to PNPs and their precursors. Specifically, the key roles these biosensors played in PNP synthesis pathways, including isoprenoids, flavonoids, stilbenoids and alkaloids, were discussed in detail.

Biophysical and Biochemical Characterization of the Binding of the MarR-like Transcriptional Regulator Saro_0803 to the nov1 Promotor and Its Inhibition by Resveratrol

Saro_0803 is a transcriptional factor modulating the transcription of the stilbene-degrading enzyme gene nov1 in Novosphingobium aromaticivorans DSM 12444. Reportedly, Saro_0803 undergoes resveratrol-mediated dissociation from the nov1 promotor and distinguishes resveratrol from its precursors, p-coumaric acid and trans-cinnamic acid, enabling the transcriptional factor to serve as a biosensor component for regulating resveratrol biosynthesis. However, little is known about the molecular mechanisms underlying the Saro_0803 interactions with either the nov1 promotor gene or resveratrol, which undermines the potential for Saro_0803 to be further modified for improved biosynthetic performance and other applications. Here, we report the discovery of the 22 bp A/T-rich Saro_0803 binding site near the −10 box of the nov1 promotor (named nov1p22bp). As validated by molecular docking-guided mutagenesis and binding affinity assays, the Saro_0803 binding of its target DNA sequence relies on charge-predominating interactions between several typical positively charged residues and nucleic acid. Furthermore, we semi-quantified the influence of resveratrol presence on Saro_0803–nov1p22bp interaction and identified a bilateral hydrophobic pocket within Saro_0803 comprising four aromatic residues that are crucial to maintaining the resveratrol binding capability of the transcriptional factor. Our data are beneficial to understanding saro_0803′s structural and functional properties, and could provide theoretical clues for future adaptations of this transcriptional factor.

Synthetic biology-based bioreactor and its application in biochemical analysis

In the past few years, synthetic biologists have established some biological elements and bioreactors composed of nucleotides under the guidance of engineering methods. Following the concept of engineering, the common bioreactor components in recent years are introduced and compared. At present, biosensors based on synthetic biology have been applied to water pollution monitoring, disease diagnosis, epidemiological monitoring, biochemical analysis and other detection fields. In this paper, the biosensor components based on synthetic bioreactors and reporters are reviewed. In addition, the applications of biosensors based on cell system and cell-free system in the detection of heavy metal ions, nucleic acid, antibiotics and other substances are presented. Finally, the bottlenecks faced by biosensors and the direction of optimization are also discussed.

Development of a synthetic transcription factor-based S-adenosylmethionine biosensor in Saccharomyces cerevisiae

S-Adenosylmethionine (SAM) is a crucial small-molecule metabolite widely used in food and medicine. The development of high-throughput biosensors for SAM biosynthesis can significantly improve the titer of SAM. This paper constructed a synthetic transcription factor (TF)-based biosensor for SAM detecting in Saccharomyces cerevisiae. The synthetic TF, named MetJ-hER-VP16, consists of an Escherichia coli-derived DNA-binding domain MetJ, GS linker, the human estrogen receptor binding domain hER, and the viral activation domain VP16. The synthetic biosensor is capable of sensing SAM in a dose-dependent manner with fluorescence as the output. Additionally, it is tightly regulated by the inducer SAM and β-estradiol, which means that the fluorescence output is only available when both are present together. The synthetic SAM biosensor could potentially be applied for high-throughput metabolic engineering and is expected to improve SAM production.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

Heterologous production of Cannabis sativa-derived specialised metabolites of medicinal significance - Insights into engineering strategies

Cannabis sativa L. has been known for at least 2000 years as a source of important, medically significant specialised metabolites and several bio-active molecules have been enriched from multiple chemotypes. However, due to the many levels of complexity in both the commercial cultivation of cannabis and extraction of its specialised metabolites, several heterologous production approaches are being pursued in parallel. In this review, we outline the recent achievements in engineering strategies used for heterologous production of cannabinoids, terpenes and flavonoids along with their strength and weakness. We provide an overview of the specialised metabolism pathway in C. sativa and a comprehensive list of the specialised metabolites produced along with their medicinal significance. We highlight cannabinoid-like molecules produced by other species. We discuss the key biosynthetic enzymes and their heterologous production using various hosts such as microbial and eukaryotic systems. A brief discussion on complementary production strategies using co-culturing and cell-free systems is described. Various approaches to optimise specialised metabolite production through co-expression, enzyme engineering and pathway engineering are discussed. We derive insights from recent advances in metabolic engineering of hosts with improved precursor supply and suggest their application for the production of C. sativa speciality metabolites. We present a collation of non-conventional hosts with speciality traits that can improve the feasibility of commercial heterologous production of cannabis-based specialised metabolites. We provide a perspective of emerging research in synthetic biology, allied analytical techniques and plant heterologous platforms as focus areas for heterologous production of cannabis specialised metabolites in the future.

Olfactory Receptors as an Emerging Chemical Sensing Scaffold

Chemical biosensors are an increasingly ubiquitous part of our lives. Beyond enzyme-coupled assays, recent synthetic biology advances now allow us to hijack more complex biosensing systems to respond to difficult to detect analytes, such as chemical small molecules. Here, we briefly overview recent advances in the biosensing of small molecules, including nucleic acid aptamers, allosteric transcription factors, and two-component systems. We then look more closely at a recently developed chemical sensing system, G protein-coupled receptor (GPCR)-based sensors. Finally, we consider the chemical sensing capabilities of the largest GPCR subfamily, olfactory receptors (ORs). We examine ORs' role in nature, their potential as a biomedical target, and their ability to detect compounds not amenable for detection using other biological scaffolds. We conclude by evaluating the current challenges, opportunities, and future applications of GPCR- and OR-based sensors.

GroovDB: A Database of Ligand-Inducible Transcription Factors

Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multilayer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data have been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides potential insights into alternative ligands and systems biology.

Metabolic and cellular engineering for the production of natural products

Increased awareness of the environmental and health concerns of consuming chemically synthesized products has led to a rising demand for natural products that are greener and more sustainable. Despite their importance, however, industrial-scale production of natural products has been challenging due to the low yield and high cost of the bioprocesses. To cope with this problem, systems metabolic engineering has been employed to efficiently produce natural products from renewable biomass. Here, we review the recent systems metabolic engineering strategies employed for enhanced production of value-added natural products, together with accompanying examples. Particular focus is set on systems-level engineering and cell physiology engineering strategies. Future perspectives are also discussed.

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.

Modular engineering of E. coli coculture for efficient production of resveratrol from glucose and arabinose mixture

Resveratrol, a valuable plant-derived polyphenolic compound with various bioactivities, has been widely used in nutraceutical industries. Microbial production of resveratrol suffers from metabolic burden and low malonyl-CoA availability, which is a big challenge for synthetic biology. Herein, we took advantage of coculture engineering and divided the biosynthetic pathway of resveratrol into the upstream and downstream strains. By enhancing the supply of malonyl-CoA via CRISPRi system and fine-tuning the expression intensity of the synthetic pathway genes, we significantly improved the resveratrol productivity of the downstream strain. Furthermore, we developed a resveratrol addiction circuit that coupled the growth of the upstream strain and the resveratrol production of the downstream strain. The bidirectional interaction stabilized the coculture system and increased the production of resveratrol by 74%. Moreover, co-utilization of glucose and arabinose by the coculture system maintained the growth advantage of the downstream strain for production of resveratrol throughout the fermentation process. Under optimized conditions, the engineered E. coli coculture system produced 204.80 mg/L of resveratrol, 12.8-fold improvement over monoculture system. This study demonstrates the promising potential of coculture engineering for efficient production of natural products from biomass.

Intracellular biosensor-based dynamic regulation to manipulate gene expression at the spatiotemporal level

The use of intracellular, biosensor-based dynamic regulation strategies to regulate and improve the production of useful compounds have progressed significantly over previous decades. By employing such an approach, it is possible to simultaneously realize high productivity and optimum growth states. However, industrial fermentation conditions contain a mixture of high- and low-performance non-genetic variants, as well as young and aged cells at all growth phases. Such significant individual variations would hinder the precise controlling of metabolic flux at the single-cell level to achieve high productivity at the macroscopic population level. Intracellular biosensors, as the regulatory centers of metabolic networks, can real-time sense intra- and extracellular conditions and, thus, could be synthetically adapted to balance the biomass formation and overproduction of compounds by individual cells. Herein, we highlight advances in the designing and engineering approaches to intracellular biosensors. Then, the spatiotemporal properties of biosensors associated with the distribution of inducers are compared. Also discussed is the use of such biosensors to dynamically control the cellular metabolic flux. Such biosensors could achieve single-cell regulation or collective regulation goals, depending on whether or not the inducer distribution is only intracellular.

A Versatile Transcription Factor Biosensor System Responsive to Multiple Aromatic and Indole Inducers

Allosteric transcription factor (aTF) biosensors are valuable tools for engineering microbes toward a multitude of applications in metabolic engineering, biotechnology, and synthetic biology. One of the challenges toward constructing functional and diverse biosensors in engineered microbes is the limited toolbox of identified and characterized aTFs. To overcome this, extensive bioprospecting of aTFs from sequencing databases, as well as aTF ligand-specificity engineering are essential in order to realize their full potential as biosensors for novel applications. In this work, using the TetR-family repressor CmeR from Campylobacter jejuni, we construct aTF genetic circuits that function as salicylate biosensors in the model organisms Escherichia coli and Saccharomyces cerevisiae. In addition to salicylate, we demonstrate the responsiveness of CmeR-regulated promoters to multiple aromatic and indole inducers. This relaxed ligand specificity of CmeR makes it a useful tool for detecting molecules in many metabolic engineering applications, as well as a good target for directed evolution to engineer proteins that are able to detect new and diverse chemistries.

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.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Force in numbers: high-throughput screening approaches to unlock microbial transport

Biological membranes are inherently complex, making transport processes in microbial cell factories a significant bottleneck. Lack of knowledge on transport proteins' characteristics and the need for advanced technical equipment often hamper transporter identification and optimization. For these reasons, moving away from individual characterization and towards high-throughput mining, engineering, and screening of transporters is an increasingly attractive approach. Superior transporters can be selected from large libraries by coupling their activity to growth, for substrates that function as feedstocks or toxic compounds. Other compounds can be screened thanks to recent advances in the design and deployment of synthetic genetic circuits (biosensors). Furthermore, novel strategies are rapidly increasing the repertoire of biomolecule transporters susceptible to high-throughput selection methods.

Transcription factor-based biosensors: a molecular-guided approach for natural product engineering

Natural products and their derivatives offer a rich source of chemical and biological diversity; however, traditional engineering of their biosynthetic pathways to improve yields and access to unnatural derivatives requires a precise understanding of their enzymatic processes. High-throughput screening platforms based on allosteric transcription-factor based biosensors can be leveraged to overcome the screening bottleneck to enable searching through large libraries of pathway/strain variants. Herein, the development and application of engineered allosteric transcription factor-based biosensors is described that enable optimization of precursor availability, product titers, and downstream product tailoring for advancing the natural product bioeconomy. We discuss recent successes for tailoring biosensor design, including computationally-based approaches, and present our future outlook with the integration of cell-free technologies and de novo protein design for rapidly generating biosensor tools.