Biosensor-based engineering of biosynthetic pathways

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.

A hybrid RNA-protein biosensor for high-throughput screening of adenosylcobalamin biosynthesis

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. High-throughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a high-throughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites.

Development of a gene-coded biosensor to establish a high-throughput screening platform for salidroside production

Metabolic engineering reconfigures cellular networks to produce value-added compounds from renewable substrates efficiently. However, identifying strains with desired phenotypes from large libraries through rational or random mutagenesis remains challenging. To overcome this bottleneck, an effective high-throughput screening (HTS) method must be developed to detect and analyze target candidates rapidly. Salidroside is an aromatic compound with broad applications in food, healthcare, medicine, and daily chemicals. However, there currently needs to be HTS methods available to monitor salidroside levels or to screen enzyme variants and strains for high-yield salidroside biosynthesis, which severely limits the development of microbial cell factories capable of efficiently producing salidroside on an industrial scale. This study developed a gene-encoded whole-cell biosensor that is specifically responsive to salidroside. The biosensor was created by screening a site-saturated mutagenic library of uric acid response regulatory protein binding bags. This work demonstrates the feasibility of monitoring metabolic flux with whole-cell biosensors for critical metabolites. It provides a promising tool for building salidroside high-yielding strains for high-throughput screening and metabolic regulation to meet industrial needs.

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.

Thermostable in vitro transcription-translation compatible with microfluidic droplets

BACKGROUND

In vitro expression involves the utilization of the cellular transcription and translation machinery in an acellular context to produce one or more proteins of interest and has found widespread application in synthetic biology and in pharmaceutical biomanufacturing. Most in vitro expression systems available are active at moderate temperatures, but to screen large libraries of natural or artificial genetic diversity for highly thermostable enzymes or enzyme variants, it is instrumental to enable protein synthesis at high temperatures.

OBJECTIVES

Develop an in vitro expression system operating at high temperatures compatible with enzymatic assays and with technologies that enable ultrahigh-throughput protein expression in reduced volumes, such as microfluidic water-in-oil (w/o) droplets.

RESULTS

We produced cell-free extracts from Thermus thermophilus for in vitro translation including thermostable enzymatic cascades for energy regeneration and a moderately thermostable RNA polymerase for transcription, which ultimately limited the temperature of protein synthesis. The yield was comparable or superior to other thermostable in vitro expression systems, while the preparation procedure is much simpler and can be suited to different Thermus thermophilus strains. Furthermore, these extracts have enabled in vitro expression in microfluidic droplets at high temperatures for the first time.

CONCLUSIONS

Cell-free extracts from Thermus thermophilus represent a simpler alternative to heavily optimized or pure component thermostable in vitro expression systems. Moreover, due to their compatibility with droplet microfluidics and enzyme assays at high temperatures, the reported system represents a convenient gateway for enzyme screening at higher temperatures with ultrahigh-throughput.

Optimizing enzyme properties to enhance dihydroxyacetone production via methylglyoxal biosensor development

BACKGROUND

Dihydroxyacetone (DHA) stands as a crucial chemical material extensively utilized in the cosmetics industry. DHA production through the dephosphorylation of dihydroxyacetone phosphate, an intermediate product of the glycolysis pathway in Escherichia coli, presents a prospective alternative for industrial production. However, insights into the pivotal enzyme, dihydroxyacetone phosphate dephosphorylase (HdpA), remain limited for informed engineering. Consequently, the development of an efficient tool for high-throughput screening of HdpA hypermutants becomes imperative.

RESULTS

This study introduces a methylglyoxal biosensor, based on the formaldehyde-responding regulator FrmR, for the selection of HdpA. Initial modifications involved the insertion of the FrmR binding site upstream of the -35 region and into the spacer region between the -10 and -35 regions of the constitutive promoter J23110. Although the hybrid promoter retained constitutive expression, expression of FrmR led to complete repression. The addition of 350 μM methylglyoxal promptly alleviated FrmR inhibition, enhancing promoter activity by more than 40-fold. The methylglyoxal biosensor system exhibited a gradual increase in fluorescence intensity with methylglyoxal concentrations ranging from 10 to 500 μM. Notably, the biosensor system responded to methylglyoxal spontaneously converted from added DHA, facilitating the separation of DHA producing and non-producing strains through flow cytometry sorting. Subsequently, the methylglyoxal biosensor was successfully applied to screen a library of HdpA mutants, identifying two strains harboring specific mutants 267G > T and D110G/G151C that showed improved DHA production by 68% and 114%, respectively. Expressing of these two HdpA mutants directly in a DHA-producing strain also increased DHA production from 1.45 to 1.92 and 2.29 g/L, respectively, demonstrating the enhanced enzyme properties of the HdpA mutants.

CONCLUSIONS

The methylglyoxal biosensor offers a novel strategy for constructing genetically encoded biosensors and serves as a robust platform for indirectly determining DHA levels by responding to methylglyoxal. This property enables efficiently screening of HdpA hypermutants to enhance DHA production.

Improvement of Chalcone Synthase Activity and High-Efficiency Fermentative Production of (2S)-Naringenin via In Vivo Biosensor-Guided Directed Evolution

Chalcone synthase (CHS) catalyzes the rate-limiting step of (2S)-naringenin (the essential flavonoid skeleton) biosynthesis. Improving the activity of the CHS by protein engineering enhances (2S)-naringenin production by microbial fermentation and can facilitate the production of valuable flavonoids. A (2S)-naringenin biosensor based on the TtgR operon was constructed in Escherichia coli and its detection range was expanded by promoter optimization to 0-300 mg/L, the widest range for (2S)-naringenin reported. The high-throughput screening scheme for CHS was established based on this biosensor. A mutant, SjCHS1S208N with a 2.34-fold increase in catalytic activity, was discovered by directed evolution and saturation mutagenesis. A pathway for de novo biosynthesis of (2S)-naringenin by SjCHS1S208N was constructed in Saccharomyces cerevisiae, combined with CHS precursor pathway optimization, increasing the (2S)-naringenin titer by 65.34% compared with the original strain. Fed-batch fermentation increased the titer of (2S)-naringenin to 2513 ± 105 mg/L, the highest reported so far. These findings will facilitate efficient flavonoid biosynthesis and further modification of the CHS in the future.

Genome-wide host-pathway interactions affecting cis-cis-muconic acid production in yeast

The success of forward metabolic engineering depends on a thorough understanding of the behaviour of a heterologous metabolic pathway within its host. We have recently described CRI-SPA, a high-throughput gene editing method enabling the delivery of a metabolic pathway to all strains of the Saccharomyces cerevisiae knock-out library. CRI-SPA systematically quantifies the effect of each modified gene present in the library on product synthesis, providing a complete map of host:pathway interactions. In its first version, CRI-SPA relied on the colour of the product betaxanthins to quantify strains synthesis ability. However, only a few compounds produce a visible or fluorescent phenotype limiting the scope of our approach. Here, we adapt CRI-SPA to onboard a biosensor reporting the interactions between host genes and the synthesis of the colourless product cis-cis-muconic acid (CCM). We phenotype >9,000 genotypes, including both gene knock-out and overexpression, by quantifying the fluorescence of yeast colonies growing in high-density agar arrays. We identify novel metabolic targets belonging to a broad range of cellular functions and confirm their positive impact on CCM biosynthesis. In particular, our data suggests a new interplay between CCM biosynthesis and cytosolic redox through their common interaction with the oxidative pentose phosphate pathway. Our genome-wide exploration of host:pathway interaction opens novel strategies for improved production of CCM in yeast cell factories.

Yeast-based screening platforms to understand and improve human health

Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Streptomyces-based whole-cell biosensors for detecting diverse cell envelope-targeting antibiotics

Cell envelope-targeting antibiotics are potent therapeutic agents against various bacterial infections. The emergence of multiple antibiotic-resistant strains underscores the significance of identifying potent antimicrobials specifically targeting the cell envelope. However, current drug screening approaches are tedious and lack sufficient specificity and sensitivity, warranting the development of more efficient methods. Genetic circuit-based whole-cell biosensors hold great promise for targeted drug discovery from natural products. Here, we performed comparative transcriptomic analysis of Streptomyces coelicolor M1146 exposed to diverse cell envelope-targeting antibiotics, aiming to identify regulatory elements involved in perceiving and responding to these compounds. Differential gene expression analysis revealed significant activation of VanS/R two-component system in response to the glycopeptide class of cell envelope-acting antibiotics. Therefore, we engineered a pair of VanS/R-based biosensors that exhibit functional complementarity and possess exceptional sensitivity and specificity for glycopeptides detection. Additionally, through promoter screening and characterization, we expanded the biosensor's detection range to include various cell envelope-acting antibiotics beyond glycopeptides. Our genetically engineered biosensor exhibits superior performance, including a dynamic range of up to 887-fold for detecting subtle antibiotic concentration changes in a rapid 2-h response time, enabling high-throughput screening of natural product libraries for antimicrobial agents targeting the bacterial cell envelope.

Growth-Coupled Evolutionary Pressure Improving Epimerases for D-Allulose Biosynthesis Using a Biosensor-Assisted In Vivo Selection Platform

Fast screening strategies that enable high-throughput evaluation and identification of desired variants from diversified enzyme libraries are crucial to tailoring biocatalysts for the synthesis of D-allulose, which is currently limited by the poor catalytic performance of ketose 3-epimerases (KEases). Here, the study designs a minimally equipment-dependent, high-throughput, and growth-coupled in vivo screening platform founded on a redesigned D-allulose-dependent biosensor system. The genetic elements modulating regulator PsiR expression levels undergo systematic optimization to improve the growth-responsive dynamic range of the biosensor, which presents ≈30-fold facilitated growth optical density with a high signal-to-noise ratio (1.52 to 0.05) toward D-allulose concentrations from 0 to 100 mm. Structural analysis and evolutionary conservation analysis of Agrobacterium sp. SUL3 D-allulose 3-epimerase (ADAE) reveal a highly conserved catalytic active site and variable hydrophobic pocket, which together regulate substrate recognition. Structure-guided rational design and directed evolution are implemented using the growth-coupled in vivo screening platform to reprogram ADAE, in which a mutant M42 (P38N/V102A/Y201L/S207N/I251R) is identified with a 6.28-fold enhancement of catalytic activity and significantly improved thermostability with a 2.5-fold increase of the half-life at 60 °C. The research demonstrates that biosensor-assisted growth-coupled evolutionary pressure combined with structure-guided rational design provides a universal route for engineering KEases.

Genetic Circuits for Feedback Control of Gamma-Aminobutyric Acid Biosynthesis in Probiotic Escherichia coli Nissle 1917

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate the production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Establishing a versatile toolkit of flux enhanced strains and cell extracts for pathway prototyping

Building and optimizing biosynthetic pathways in engineered cells holds promise to address societal needs in energy, materials, and medicine, but it is often time-consuming. Cell-free synthetic biology has emerged as a powerful tool to accelerate design-build-test-learn cycles for pathway engineering with increased tolerance to toxic compounds. However, most cell-free pathway prototyping to date has been performed in extracts from wildtype cells which often do not have sufficient flux towards the pathways of interest, which can be enhanced by engineering. Here, to address this gap, we create a set of engineered Escherichia coli and Saccharomyces cerevisiae strains rewired via CRISPR-dCas9 to achieve high-flux toward key metabolic precursors; namely, acetyl-CoA, shikimate, triose-phosphate, oxaloacetate, α-ketoglutarate, and glucose-6-phosphate. Cell-free extracts generated from these strains are used for targeted enzyme screening in vitro. As model systems, we assess in vivo and in vitro production of triacetic acid lactone from acetyl-CoA and muconic acid from the shikimate pathway. The need for these platforms is exemplified by the fact that muconic acid cannot be detected in wildtype extracts provided with the same biosynthetic enzymes. We also perform metabolomic comparison to understand biochemical differences between the cellular and cell-free muconic acid synthesis systems (E. coli and S. cerevisiae cells and cell extracts with and without metabolic rewiring). While any given pathway has different interfaces with metabolism, we anticipate that this set of pre-optimized, flux enhanced cell extracts will enable prototyping efforts for new biosynthetic pathways and the discovery of biochemical functions of enzymes.

Engineered live bacteria as disease detection and diagnosis tools

Sensitive and minimally invasive medical diagnostics are essential to the early detection of diseases, monitoring their progression and response to treatment. Engineered bacteria as live sensors are being developed as a new class of biosensors for sensitive, robust, noninvasive, and in situ detection of disease onset at low cost. Akin to microrobotic systems, a combination of simple genetic rules, basic logic gates, and complex synthetic bioengineering principles are used to program bacterial vectors as living machines for detecting biomarkers of diseases, some of which cannot be detected with other sensing technologies. Bacterial whole-cell biosensors (BWCBs) can have wide-ranging functions from detection only, to detection and recording, to closed-loop detection-regulated treatment. In this review article, we first summarize the unique benefits of bacteria as living sensors. We then describe the different bacteria-based diagnosis approaches and provide examples of diagnosing various diseases and disorders. We also discuss the use of bacteria as imaging vectors for disease detection and image-guided surgery. We conclude by highlighting current challenges and opportunities for further exploration toward clinical translation of these bacteria-based systems.

In vitro allosteric transcription factor-based biosensing

A biosensor is an analytical device that converts a biological response into a measurable output signal. Bacterial allosteric transcription factors (aTFs) have been utilized as a novel class of recognition elements for in vitro biosensing, which circumvents the limitations of aTF-based whole-cell biosensors (WCBs) and helps to meet the increasing requirement of small-molecule biosensors for diverse applications. In this review, we summarize the recent advances related to the configuration of aTF-based biosensors in vitro. Particularly, we evaluate the advantages of aTFs for in vitro biosensing and highlight their great potential for the establishment of robust and easy-to-implement biosensing strategies. We argue that key technical innovations and generalizable workflows will enhance the pipeline for facile construction of diverse aTF-based small-molecule biosensors.

Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases

Gastrointestinal (GI) diseases have a high prevalence throughout the United States. Screening and diagnostic modalities are often expensive and invasive, and therefore, people do not utilize them effectively. Lack of proper screening and diagnostic assessment may lead to delays in diagnosis, more advanced disease at the time of diagnosis, and higher morbidity and mortality rates. Research on the intestinal microbiome has demonstrated that dysbiosis, or unfavorable alteration of organismal composition, precedes the onset of clinical symptoms for various GI diseases. GI disease diagnostic research has led to a shift towards non-invasive methods for GI screening, including chemical-detection tests that measure changes in volatile organic compounds (VOCs), which are the byproducts of bacterial metabolism that result in the distinct smell of stool. Many of these tools are expensive, immobile benchtop instruments that require highly trained individuals to interpret the results. These attributes make them difficult to implement in clinical settings. Alternatively, electronic noses (E-noses) are relatively cheaper, handheld devices that utilize multi-sensor arrays and pattern recognition technology to analyze VOCs. The purpose of this review is to (1) highlight how dysbiosis impacts intestinal diseases and how VOC metabolites can be utilized to detect alterations in the microbiome, (2) summarize the available VOC analytical platforms that can be used to detect aberrancies in intestinal health, (3) define the current technological advancements and limitations of E-nose technology, and finally, (4) review the literature surrounding several intestinal diseases in which headspace VOCs can be used to detect or predict disease.

Molecular Complementarity of Proteomimetic Materials for Target-Specific Recognition and Recognition-Mediated Complex Functions

As biomolecules essential for sustaining life, proteins are generated from long chains of 20 different α-amino acids that are folded into unique 3D structures. In particular, many proteins have molecular recognition functions owing to their binding pockets, which have complementary shapes, charges, and polarities for specific targets, making these biopolymers unique and highly valuable for biomedical and biocatalytic applications. Based on the understanding of protein structures and microenvironments, molecular complementarity can be exhibited by synthesizable and modifiable materials. This has prompted researchers to explore the proteomimetic potentials of a diverse range of materials, including biologically available peptides and oligonucleotides, synthetic supramolecules, inorganic molecules, and related coordination networks. To fully resemble a protein, proteomimetic materials perform the molecular recognition to mediate complex molecular functions, such as allosteric regulation, signal transduction, enzymatic reactions, and stimuli-responsive motions; this can also expand the landscape of their potential bio-applications. This review focuses on the recognitive aspects of proteomimetic designs derived for individual materials and their conformations. Recent progress provides insights to help guide the development of advanced protein mimicry with material heterogeneity, design modularity, and tailored functionality. The perspectives and challenges of current proteomimetic designs and tools are also discussed in relation to future applications.

A blueprint for a synthetic genetic feedback optimizer

Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.

Rethinking Biosynthesis of Aclacinomycin A

Aclacinomycin A (ACM-A) is an anthracycline antitumor agent widely used in clinical practice. The current industrial production of ACM-A relies primarily on chemical synthesis and microbial fermentation. However, chemical synthesis involves multiple reactions which give rise to high production costs and environmental pollution. Microbial fermentation is a sustainable strategy, yet the current fermentation yield is too low to satisfy market demand. Hence, strain improvement is highly desirable, and tremendous endeavors have been made to decipher biosynthesis pathways and modify key enzymes. In this review, we comprehensively describe the reported biosynthesis pathways, key enzymes, and, especially, catalytic mechanisms. In addition, we come up with strategies to uncover unknown enzymes and improve the activities of rate-limiting enzymes. Overall, this review aims to provide valuable insights for complete biosynthesis of ACM-A.

Directly Evolved AlkS-Based Biosensor Platform for Monitoring and High-Throughput Screening of Alkane Production

Biosynthetic alkane using acyl-ACP aldehyde reductase (AAR) and aldehyde-deformylating oxygenase (ADO) from cyanobacteria is considered a promising alternative for the production of biofuels and chemical feedstocks. However, the lack of suitable screening methods to improve the catalytic efficiency of AAR and ADO has hindered further improvements in alkane production. Herein, a novel alkane biosensor was developed based on transcriptional factor AlkS by directed evolution, which shows sensitive dynamic response curves for exogenous long-chain alkanes as well as in situ monitoring of endogenously produced alkanes. The evolved biosensor enables high-throughput screening of alkane-producing strains from the AAR and ADO mutant library, which led to a 13-fold increase in the production of long-chain alkanes, including a 22-fold increase of C15. This study is the first to improve the alkane production through biosensors, which provides a good reference for the establishment of microbial cell factories for alkane production.

Direct evolution of riboflavin kinase significantly enhance flavin mononucleotide synthesis by design and optimization of flavin mononucleotide riboswitch

Flavin mononucleotide (FMN) is the active form of riboflavin. It has a wide range of application scenarios in the pharmaceutical and food additives. However, there are limitations in selecting generic high-throughput screening platforms that improve the properties of enzymes. First, the biosensor in response to FMN concentration was constructed using the FMN riboswitch and confirmed the function of this sensor. Next, the FMN binding site of the sensor was saturated with a mutation that increased its fluorescence range by approximately 127%. Then, the biosensor and the base editing system based on T7RNAP were combined to construct a platform for rapid mutation and screening of riboflavin kinase gene ribC mutants. The mutants screened using this platform increased the yield of FMN by 8-fold. These results indicate that the high-throughput screening platform can rapidly and effectively improve the activity of target enzymes, and provide a new route for screening industrial enzymes.

Engineering the Substrate Specificity of Toluene Degrading Enzyme XylM Using Biosensor XylS and Machine Learning

Enzyme engineering using machine learning has been developed in recent years. However, to obtain a large amount of data on enzyme activities for training data, it is necessary to develop a high-throughput and accurate method for evaluating enzyme activities. Here, we examined whether a biosensor-based enzyme engineering method can be applied to machine learning. As a model experiment, we aimed to modify the substrate specificity of XylM, a rate-determining enzyme in a multistep oxidation reaction catalyzed by XylMABC in Pseudomonas putida. XylMABC naturally converts toluene and xylene to benzoic acid and toluic acid, respectively. We aimed to engineer XylM to improve its conversion efficiency to a non-native substrate, 2,6-xylenol. Wild-type XylMABC slightly converted 2,6-xylenol to 3-methylsalicylic acid, which is the ligand of the transcriptional regulator XylS in P. putida. By locating a fluorescent protein gene under the control of the Pm promoter to which XylS binds, a XylS-producing Escherichia coli strain showed higher fluorescence intensity in a 3-methylsalicylic acid concentration-dependent manner. We evaluated the 3-methylsalicylic acid productivity of XylM variants using the fluorescence intensity of the sensor strain as an indicator. The obtained data provided the training data for machine learning for the directed evolution of XylM. Two cycles of machine learning-assisted directed evolution resulted in the acquisition of XylM-D140E-V144K-F243L-N244S with 15 times higher productivity than wild-type XylM. These results demonstrate that an indirect enzyme activity evaluation method using biosensors is sufficiently quantitative and high-throughput to be used as training data for machine learning. The findings expand the versatility of machine learning in enzyme engineering.

Structural features of sensory two component systems: a synthetic biology perspective

All living organisms include a set of signaling devices that confer the ability to dynamically perceive and adapt to the fluctuating environment. Two-component systems are part of this sensory machinery that regulates the execution of different genetic and/or biochemical programs in response to specific physical or chemical signals. In the last two decades, there has been tremendous progress in our molecular understanding on how signals are detected, the allosteric mechanisms that control intramolecular information transmission and the specificity determinants that guarantee correct wiring. All this information is starting to be exploited in the development of new synthetic networks. Connecting multiple molecular players, analogous to programming lines of code, can provide the resources to build new sophisticated biocomputing systems. The Synthetic Biology field is starting to revolutionize several scientific fields, such as biomedicine and agriculture, propelling the development of new solutions. Expanding the spectrum of available nanodevices in the toolbox is key to unleash its full potential. This review aims to discuss, from a structural perspective, how to take advantage of the vast array of sensor and effector protein modules involved in two-component systems for the construction of new synthetic circuits.

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.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.

Screening microbially produced Δ9-tetrahydrocannabinol using a yeast biosensor workflow

Microbial production of cannabinoids promises to provide a consistent, cheaper, and more sustainable supply of these important therapeutic molecules. However, scaling production to compete with traditional plant-based sources is challenging. Our ability to make strain variants greatly exceeds our capacity to screen and identify high producers, creating a bottleneck in metabolic engineering efforts. Here, we present a yeast-based biosensor for detecting microbially produced Δ⁹-tetrahydrocannabinol (THC) to increase throughput and lower the cost of screening. We port five human cannabinoid G protein-coupled receptors (GPCRs) into yeast, showing the cannabinoid type 2 receptor, CB2R, can couple to the yeast pheromone response pathway and report on the concentration of a variety of cannabinoids over a wide dynamic and operational range. We demonstrate that our cannabinoid biosensor can detect THC from microbial cell culture and use this as a tool for measuring relative production yields from a library of Δ⁹-tetrahydrocannabinol acid synthase (THCAS) mutants.

Microbial production of cannabinoids promises a cheaper and more sustainable route to these important therapeutic molecules, but strain improvement and screening is challenging. Here, the authors develop a yeast-based Δ9-tetrahydrocannabinol (THC) biosensor for screening microbial mutant libraries.

Toward bioproduction of oxo chemicals from C1 feedstocks using isobutyraldehyde as an example

Oxo chemicals are valuable chemicals for synthesizing a wide array of industrial and consumer products. However, producing of oxo chemicals is predominately through the chemical process called hydroformylation, which requires petroleum-sourced materials and generates abundant greenhouse gas. Current concerns on global climate change have renewed the interest in reducing greenhouse gas emissions and recycling the plentiful greenhouse gas. A carbon–neutral manner in this regard is producing oxo chemicals biotechnologically using greenhouse gas as C1 feedstocks. Exemplifying isobutyraldehyde, this review demonstrates the significance of using greenhouse gas for oxo chemicals production. We highlight the current state and the potential of isobutyraldehyde synthesis with a special focus on the in vivo and in vitro scheme of C1-based biomanufacturing. Specifically, perspectives and scenarios toward carbon– and nitrogen–neutral isobutyraldehyde production are proposed. In addition, key challenges and promising approaches for enhancing isobutyraldehyde bioproduction are thoroughly discussed. This study will serve as a reference case in exploring the biotechnological potential and advancing oxo chemicals production derived from C1 feedstocks.

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.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

Signal Amplification of araC pBAD Using a Standardised Translation Initiation Region

araC pBAD is a genetic fragment that regulates the expression of the araBAD operon in bacteria, which is required for the metabolism of L-arabinose. It is widely used in bioengineering applications because it can drive regulatable and titratable expression of genes and genetic pathways in microbial cell factories. A notable limitation of araC pBAD is that it generates a low signal when induced with high concentrations of L-arabinose (the maximum ON state). Herein we have amplified the maximum ON state of araC pBAD by coupling it to a synthetically evolved translation initiation region (TIR^EVOL). The coupling maintains regulatable and titratable expression from araC pBAD and yet increases the maximal ON state by >5-fold. The general principle demonstrated in the study can be applied to amplify the signal from similar genetic modules.

Graphical Abstract

Enriching intracellular macrolides in Escherichia coli improved the sensitivity of bioluminescent sensing systems

A repressor protein MphR and an enhanced green fluorescent protein (eGFP) were used to construct a bioluminescent sensing system for macrolide analysis in Escherichia coli host cells. We deleted TolC, an efflux pump for macrolides in E. coli, to promote the intracellular accumulation of macrolides. The binding constant (K_1/2) of the sensing system constructed in an E. coli strain was decreased up to 33-fold with deleted TolC, and its sensitivity to the macrolides erythromycin, azithromycin, roxithromycin, and pikromycin was increased. The limit of detection of the bioluminescent sensing system for serum azithromycin was 4.1 nM. The ability to detect serum azithromycin concentrations was confirmed by analyzing photographs using ImageJ software. We also developed a novel sensing system for the immune suppressor FK506, another macrolide that is frequently prescribed. Deleting TolC also significantly improved the sensitivity of this sensing system. Bioluminescent sensing systems constructed in TolC mutants were sensitive to various macrolides, indicating their potential for clinical application with hand-held devices.

Recent advances in high-throughput metabolic engineering: Generation of oligonucleotide-mediated genetic libraries

The preparation of genetic libraries is an essential step to evolve microorganisms and study genotype-phenotype relationships by high-throughput screening/selection. As the large-scale synthesis of oligonucleotides becomes easy, cheap, and high-throughput, numerous novel strategies have been developed in recent years to construct high-quality oligo-mediated libraries, leveraging state-of-art molecular biology tools for genome editing and gene regulation. This review presents an overview of recent advances in creating and characterizing in vitro and in vivo genetic libraries, based on CRISPR/Cas, regulatory RNAs, and recombineering, primarily for Escherichia coli and Saccharomyces cerevisiae. These libraries' applications in high-throughput metabolic engineering, strain evolution and protein engineering are also discussed.

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.

Fluorescence-Activated Cell Sorting as a Tool for Recombinant Strain Screening

Metabolic engineering of microbial cells is the discipline of optimizing microbial metabolism to enable and improve the production of target molecules ranging from biofuels and chemical building blocks to high-value pharmaceuticals. The advances in genetic engineering have eased the construction of highly engineered microbial strains and the generation of genetic libraries. Intracellular metabolite-responsive biosensors facilitate high-throughput screening of these libraries by connecting the levels of a metabolite of interest to a fluorescence output. Fluorescent-activated cell sorting (FACS) enables the isolation of highly fluorescent single cells and thus genotypes that produce higher levels of the metabolite of interest. Here, we describe a high-throughput screening method for recombinant yeast strain screening based on intracellular biosensors and FACS.

Development of Synthetic Riboswitches to Guide the Evolution of Metabolite Production in Microorganisms

The untranslated region (UTR) of prokaryotic mRNA contains riboswitches, which are gene regulating modules. Riboswitches can be used as biosensors to regulate the expression of a gene or an operon depending on the intracellular level of a target molecule and consequently modulate the cellular responses. In evolutionary engineering, riboswitch-based biosensors have been widely applied for high-throughput screening or selection of target phenotypes. Evolutionary approaches can overcome the limitations of rational approaches in metabolic engineering. Previous studies have reported synthetic riboswitches equipped with novel aptamers and marker genes based on a deep understanding of the operation mechanism of the riboswitch. Here, we introduce the development process of novel synthetic riboswitches for applications in metabolic engineering.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

Porphyrin Molecules Decorated on Metal-Organic Frameworks for Multi-Functional Biomedical Applications

Metal-organic frameworks (MOFs) have been widely used as porous nanomaterials for different applications ranging from industrial to biomedicals. An unpredictable one-pot method is introduced to synthesize NH2-MIL-53 assisted by high-gravity in a greener media for the first time. Then, porphyrins were deployed to adorn the surface of MOF to increase the sensitivity of the prepared nanocomposite to the genetic materials and in-situ cellular protein structures. The hydrogen bond formation between genetic domains and the porphyrin' nitrogen as well as the surface hydroxyl groups is equally probable and could be considered a milestone in chemical physics and physical chemistry for biomedical applications. In this context, the role of incorporating different forms of porphyrins, their relationship with the final surface morphology, and their drug/gene loading efficiency were investigated to provide a predictable pattern in regard to the previous works. The conceptual phenomenon was optimized to increase the interactions between the biomolecules and the substrate by reaching the limit of detection to 10 pM for the Anti-cas9 protein, 20 pM for the single-stranded DNA (ssDNA), below 10 pM for the single guide RNA (sgRNA) and also around 10 nM for recombinant SARS-CoV-2 spike antigen. Also, the MTT assay showed acceptable relative cell viability of more than 85% in most cases, even by increasing the dose of the prepared nanostructures.

Directed evolution of biofuel-responsive biosensors for automated optimization of branched-chain alcohol biosynthesis

The biosynthesis of short-chain alcohols is a carbon-neutral alternative to petroleum-derived production, but strain screening operations are encumbered by laborious analytics. Here, we built, characterized and applied whole cell biosensors by directed evolution of the transcription factor AlkS for screening microbial strain libraries producing industrially relevant alcohols. A selected AlkS variant was applied for in situ product detection in two screening applications concerning key steps in alcohol production. Further, the biosensor strains enabled the implementation of an automated, robotic platform-based workflow with data clustering, which readily allowed the identification of significantly improved strain variants for isopentanol production.

Improvement of a synthetic live bacterial therapeutic for phenylketonuria with biosensor-enabled enzyme engineering

In phenylketonuria (PKU) patients, a genetic defect in the enzyme phenylalanine hydroxylase (PAH) leads to elevated systemic phenylalanine (Phe), which can result in severe neurological impairment. As a treatment for PKU, Escherichia coli Nissle (EcN) strain SYNB1618 was developed under Synlogic's Synthetic Biotic™ platform to degrade Phe from within the gastrointestinal (GI) tract. This clinical-stage engineered strain expresses the Phe-metabolizing enzyme phenylalanine ammonia lyase (PAL), catalyzing the deamination of Phe to the non-toxic product trans-cinnamate (TCA). In the present work, we generate a more potent EcN-based PKU strain through optimization of whole cell PAL activity, using biosensor-based high-throughput screening of mutant PAL libraries. A lead enzyme candidate from this screen is used in the construction of SYNB1934, a chromosomally integrated strain containing the additional Phe-metabolizing and biosafety features found in SYNB1618. Head-to-head, SYNB1934 demonstrates an approximate two-fold increase in in vivo PAL activity compared to SYNB1618.

Evolving Small-Molecule Biosensors with Improved Performance and Reprogrammed Ligand Preference Using OrthoRep

Genetically encoded biosensors are valuable for the optimization of small-molecule biosynthesis pathways, because they transduce the production of small-molecule ligands into a readout compatible with high-throughput screening or selection in vivo. However, engineering biosensors with appropriate response functions and ligand preferences remains challenging. Here, we show that the continuous hypermutation system, OrthoRep, can be effectively applied to evolve biosensors with a high dynamic range, reprogrammed activity toward desired noncognate ligands, and proper operational range for coupling to biosynthetic pathways. In particular, we encoded the allosteric transcriptional factor, BenM, on OrthoRep such that the propagation of host yeast cells resulted in BenM's rapid and continuous diversification. When these cells were subjected to cycles of culturing and sorting on BenM activity in the presence and absence of its cognate ligand, muconic acid, or the noncognate ligand, adipic acid, we obtained multiple BenM variants that respond to their corresponding ligands. These biosensors outperform previously engineered BenM-based biosensors by achieving a substantially greater dynamic range (up to ∼180-fold induction) and broadened operational range. The expression of select BenM variants in the presence of a muconic acid biosynthetic pathway demonstrated sensitive biosensor activation without saturating response, which should enable pathway and host engineering for higher production of muconic and adipic acids. Given the streamlined manner in which high-performance and versatile biosensors were evolved using OrthoRep, this study provides a template for generating custom biosensors for metabolic pathway engineering and other biotechnology goals.