Displaced by Deceivers: Prevention of Biosensor Cross-Talk Is Pivotal for Successful Biosensor-Based High-Throughput Screening Campaigns

Rational Engineering of Escherichia coli Nissle 1917 as Live Biotherapeutic to Degrade Uremic Toxin Precursors

Uremic toxins (UTs) are microbiota-derived metabolites that accelerate the progression of kidney damage in patients with chronic kidney disease (CKD). One of the major UTs involved in CKD progression is p-cresol-sulfate (PCS), derived from dietary l-tyrosine (l-Tyr). Here, we engineered a probiotic strain of Escherichia coli Nissle 1917, to convert l-Tyr to the nontoxic compound p-coumaric acid via tyrosine ammonia lyase (TAL). First, a small metagenomic library was assessed to identify the TAL with the greatest whole-cell activity. Second, accessory genes implicated in the import of l-Tyr and export of PCA were overexpressed to enhance l-Tyr degradation by 106% and 56%, respectively. Last, random mutagenesis coupled to a novel selection and screening strategy was developed that identified a TAL variant with a 25% increase in whole-cell activity. Taken together, the final strain exhibits a 183% improvement over initial whole-cell activity and provides a promising candidate to degrade l-Tyr mediated PCS accumulation.

Metabolic engineering of Corynebacterium glutamicum for the production of anthranilate from glucose and xylose

Anthranilate and its derivatives are important basic chemicals for the synthesis of polyurethanes as well as various dyes and food additives. Today, anthranilate is mainly chemically produced from petroleum-derived xylene, but this shikimate pathway intermediate could be also obtained biotechnologically. In this study, Corynebacterium glutamicum was engineered for the microbial production of anthranilate from a carbon source mixture of glucose and xylose. First, a feedback-resistant 3-deoxy-arabinoheptulosonate-7-phosphate synthase from Escherichia coli, catalysing the first step of the shikimate pathway, was functionally introduced into C. glutamicum to enable anthranilate production. Modulation of the translation efficiency of the genes for the shikimate kinase (aroK) and the anthranilate phosphoribosyltransferase (trpD) improved product formation. Deletion of two genes, one for a putative phosphatase (nagD) and one for a quinate/shikimate dehydrogenase (qsuD), abolished by-product formation of glycerol and quinate. However, the introduction of an engineered anthranilate synthase (TrpEG) unresponsive to feedback inhibition by tryptophan had the most pronounced effect on anthranilate production. Component I of this enzyme (TrpE) was engineered using a biosensor-based in vivo screening strategy for identifying variants with increased feedback resistance in a semi-rational library of TrpE muteins. The final strain accumulated up to 5.9 g/L (43 mM) anthranilate in a defined CGXII medium from a mixture of glucose and xylose in bioreactor cultivations. We believe that the constructed C. glutamicum variants are not only limited to anthranilate production but could also be suitable for the synthesis of other biotechnologically interesting shikimate pathway intermediates or any other aromatic compound derived thereof.

Transcription factor-based biosensors for detection of naturally occurring phenolic acids

Phenolic acids including hydroxybenzoic and hydroxycinnamic acids are secondary plant and fungal metabolites involved in many physiological processes offering health and dietary benefits. They are often utilised as precursors for production of value-added compounds. The limited availability of synthetic biology tools, such as whole-cell biosensors suitable for monitoring the dynamics of phenolic acids intracellularly and extracellularly, hinders the capabilities to develop high-throughput screens to study their metabolism and forward engineering. Here, by applying a multi-genome approach, we have identified phenolic acid-inducible gene expression systems composed of transcription factor-inducible promoter pairs responding to eleven different phenolic acids. Subsequently, they were used for the development of whole-cell biosensors based on model bacterial hosts, such as Escherichia coli, Cupriavidus necator and Pseudomonas putida. The dynamics and range of the biosensors were evaluated by establishing their response and sensitivity landscapes. The specificity and previously uncharacterised interactions between transcription factor and its effector(s) were identified by a screen of twenty major phenolic acids. To exemplify applicability, we utilise a protocatechuic acid-biosensor to identify enzymes with enhanced activity for conversion of p-hydroxybenzoate to protocatechuate. Transcription factor-based biosensors developed in this study will advance the analytics of phenolic acids and expedite research into their metabolism.

Graphene oxide aptasensor droplet assay for detection of metabolites secreted by single cells applied to synthetic biology

Synthetic biology harnesses the power of natural microbes by re-engineering metabolic pathways to manufacture desired compounds. Droplet technology has emerged as a high-throughput tool to screen single cells for synthetic biology, while the challenges in sensitive flexible single-cell secretion assay for bioproduction of high-value chemicals remained. Here, a novel droplet modifiable graphene oxide (GO) aptasensor was developed, enabling sensitive flexible detection of different target compounds secreted from single cells. Fluorophore-labeled aptamers were stably anchored on GO through π-π stacking interactions to minimize the non-specific interactions for low-background detection of target compounds with high signal-to-noise ratios. The assay's versatility was exhibited by adapting aptamer sequences to measure metabolic secretions like ATP and naringenin. To show the case, engineered E. coli were constructed for the bioproduction of naringenin. The high signal-to-noise ratio assay (∼2.72) was approached to precisely measure the naringenins secreted from single E. coli in the droplets. Consequently, secretory cells (Gib) were clearly distinguished from wild-type (WT) cells, with a low overlap in cell populations (∼0%) for bioproduction.

Biosensor-guided discovery and engineering of metabolic enzymes

A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.

Ancestral l-amino acid oxidase: From substrate scope exploration to phenylalanine ammonia-lyase assay

In this study we assessed the applicability of the recently reported ancestral l-amino acid oxidase (AncLAAO), for the development of an enzyme-coupled phenylalanine ammonia-lyase (PAL) activity assay. Firstly, the expression and isolation of the AncLAAO-N1 was optimized, followed by activity tests of the obtained octameric N-terminal His-tagged enzyme towards various phenylalanine analogues to assess the compatibility of its substrate scope with that of the well-characterized PALs. AncLAAO-N1 showed high catalytic efficiency towards phenylalanines mono-, di-, or multiple-substituted in the meta- or para-positions, with ortho- substituted substrates being poorly transformed, these results highlighting the significant overlap between its substrate scope and those of PALs. After successful set-up of the AncLAAO-PAL coupled solid phase assay, in a 'proof of concept' approach we demonstrated its applicability for the high-throughput activity screens of PAL-libraries, by screening the saturation mutagenesis-derived I460NNK variant library of PAL from Petroselinum crispum, using p-MeO-phenylalanine as model substrate. Notably, the hits revealed by the coupled assay comprised all the active PAL variants: I460V, I460T, I460S, I460L, previously identified from the tested PAL-library by other assays. Our results validate the applicability of AncLAAO for coupled enzyme systems with phenylalanine ammonia-lyases, including cell-based assays suitable for the high-throughput screening of directed evolution-derived PAL-libraries.

Beyond rational-biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes

BACKGROUND

In contrast to modern rational metabolic engineering, classical strain development strongly relies on random mutagenesis and screening for the desired production phenotype. Nowadays, with the availability of biosensor-based FACS screening strategies, these random approaches are coming back into fashion. In this study, we employ this technology in combination with comparative genome analyses to identify novel mutations contributing to product formation in the genome of a Corynebacterium glutamicum L-histidine producer. Since all known genetic targets contributing to L-histidine production have been already rationally engineered in this strain, identification of novel beneficial mutations can be regarded as challenging, as they might not be intuitively linkable to L-histidine biosynthesis.

RESULTS

In order to identify 100 improved strain variants that had each arisen independently, we performed > 600 chemical mutagenesis experiments, > 200 biosensor-based FACS screenings, isolated > 50,000 variants with increased fluorescence, and characterized > 4500 variants with regard to biomass formation and L-histidine production. Based on comparative genome analyses of these 100 variants accumulating 10-80% more L-histidine, we discovered several beneficial mutations. Combination of selected genetic modifications allowed for the construction of a strain variant characterized by a doubled L-histidine titer (29 mM) and product yield (0.13 C-mol C-mol-1) in comparison to the starting variant.

CONCLUSIONS

This study may serve as a blueprint for the identification of novel beneficial mutations in microbial producers in a more systematic manner. This way, also previously unexplored genes or genes with previously unknown contribution to the respective production phenotype can be identified. We believe that this technology has a great potential to push industrial production strains towards maximum performance.

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.

Kinetic compartmentalization by unnatural reaction for itaconate production

Physical compartmentalization of metabolism using membranous organelles in eukaryotes is helpful for chemical biosynthesis to ensure the availability of substrates from competitive metabolic reactions. Bacterial hosts lack such a membranous system, which is one of the major limitations for efficient metabolic engineering. Here, we employ kinetic compartmentalization with the introduction of an unnatural enzymatic reaction by an engineered enzyme as an alternative strategy to enable substrate availability from competitive reactions through kinetic isolation of metabolic pathways. As a proof of concept, we kinetically isolate the itaconate synthetic pathway from the tricarboxylic acid cycle in Escherichia coli, which is natively separated by mitochondrial membranes in Aspergillus terreus. Specifically, 2-methylcitrate dehydratase is engineered to alternatively catalyze citrate and kinetically secure cis-aconitate for efficient production using a high-throughput screening system. Itaconate production can be significantly improved with kinetic compartmentalization and its strategy has the potential to be widely applicable.

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.

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.

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.

Cheating the Cheater: Suppressing False-Positive Enrichment during Biosensor-Guided Biocatalyst Engineering

Transcription factor (TF)-based biosensors are very desirable reagents for high-throughput enzyme and strain engineering campaigns. Despite their potential, they are often difficult to deploy effectively as the small molecules being detected can leak out of high-producer cells, into low-producer cells, and activate the biosensor therein. This crosstalk leads to the overrepresentation of false-positive/cheater cells in the enriched population. While the host cell can be engineered to minimize crosstalk (e.g., by deleting responsible transporters), this is not easily applicable to all molecules of interest, particularly those that can diffuse passively. One such biosensor recently reported for trans-cinnamic acid (tCA) suffers from crosstalk when used for phenylalanine ammonia-lyase (PAL) enzyme engineering by directed evolution. We report that desensitizing the biosensor (i.e., increasing the limit of detection) suppresses cheater population enrichment. Furthermore, we show that, if we couple the biosensor-based screen with an orthogonal prescreen that eliminates a large fraction of true negatives, we can successfully reduce the cheater population during the fluorescence-activated cell sorting. Using the approach developed here, we were successfully able to isolate PAL variants with ∼70% higher k_cat after a single sort. These mutants have tremendous potential in phenylketonuria (PKU) treatment and flavonoid production.

Effective use of biosensors for high-throughput library screening for metabolite production

The development of fast and affordable microbial production from recombinant pathways is a challenging endeavor, with targeted improvements difficult to predict due to the complex nature of living systems. To address the limitations in biosynthetic pathways, much work has been done to generate large libraries of various genetic parts (promoters, RBSs, enzymes, etc.) to discover library members that bring about significantly improved levels of metabolite production. To evaluate these large libraries, high throughput approaches are necessary, such as those that rely on biosensors. There are various modes of operation to apply biosensors to library screens that are available at different scales of throughput. The effectiveness of each biosensor-based method is dependent on the pathway or strain to which it is applied, and all approaches have strengths and weaknesses to be carefully considered for any high throughput library screen. In this review, we discuss the various approaches used in biosensor screening for improved metabolite production, focusing on transcription factor-based biosensors.

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.

Whole-cell screening of oxidative enzymes using genetically encoded sensors

Biocatalysis is increasingly used for synthetic purposes in the chemical and especially the pharmaceutical industry. Enzyme discovery and optimization which is frequently needed to improve biocatalytic performance rely on high-throughput methods for activity determination. These methods should ideally be generic and applicable to entire enzyme families. Hydrogen peroxide (H2O2) is a product of several biocatalytic oxidations and its formation can serve as a proxy for oxidative activity. We designed a genetically encoded sensor for activity measurement of oxidative biocatalysts via the amount of intracellularly-formed H2O2. A key component of the sensor is an H2O2-sensitive transcriptional regulator, OxyR, which is used to control the expression levels of fluorescent proteins. We employed the OxyR sensor to monitor the oxidation of glycerol to glyceraldehyde and of toluene to o-cresol catalysed by recombinant E. coli expressing an alcohol oxidase and a P450 monooxygenase, respectively. In case of the P450 BM3-catalysed reaction, we additionally monitored o-cresol formation via a second genetically encoded sensor based on the phenol-sensitive transcriptional activator, DmpR, and an orthogonal fluorescent reporter protein. Single round screens of mutant libraries by flow cytometry or by visual inspection of colonies on agar plates yielded significantly improved oxidase and oxygenase variants thus exemplifying the suitability of the sensor system to accurately assess whole-cell oxidations in a high-throughput manner.

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

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.

Development of a Biosensor Platform for Phenolic Compounds Using a Transition Ligand Strategy

The time-consuming and laborious characterization of protein or microbial strain designs limits the development of high-performance biocatalysts for biotechnological applications. Here, transcriptional biosensors emerged as valuable tools as they allow for rapid characterization of several thousand variants within a very short time. However, for many molecules of interest, no specific transcriptional regulator determining a biosensor's specificity is available. We present an approach for rapidly engineering biosensor specificities using a semirational transition ligand approach combined with fluorescence-activated cell sorting. In this two-step approach, a biosensor is first evolved toward a more relaxed-ligand specificity before using the resulting variant as the starting point in a second round of directed evolution toward high specificity for several chemically different ligands. By following this strategy, highly specific biosensors for 4-hydroxybenzoic acid, p-coumaric acid, 5-bromoferulic acid, and 6-methyl salicylic acid were developed, starting from a biosensor for the intracellular detection of trans-cinnamic acid.

Unraveling the roles of plant specialized metabolites: using synthetic biology to design molecular biosensors

Plants are a rich source of specialized metabolites with a broad range of bioactivities and many applications in human daily life. Over the past decades significant progress has been made in identifying many such metabolites in different plant species and in elucidating their biosynthetic pathways. However, the biological roles of plant specialized metabolites remain elusive and proposed functions lack an identified underlying molecular mechanism. Understanding the roles of specialized metabolites frequently is hampered by their dynamic production and their specific spatiotemporal accumulation within plant tissues and organs throughout a plant's life cycle. In this review, we propose the employment of strategies from the field of Synthetic Biology to construct and optimize genetically encoded biosensors that can detect individual specialized metabolites in a standardized and high-throughput manner. This will help determine the precise localization of specialized metabolites at the tissue and single-cell levels. Such information will be useful in developing complete system-level models of specialized plant metabolism, which ultimately will demonstrate how the biosynthesis of specialized metabolites is integrated with the core processes of plant growth and development.

Recent trends in biocatalysis

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

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.

Dietary Change Enables Robust Growth-Coupling of Heterologous Methyltransferase Activity in Yeast

Genetic modifications of living organisms and proteins are made possible by a catalogue of molecular and synthetic biology tools, yet proper screening assays for genetic variants of interest continue to lag behind. Synthetic growth-coupling (GC) of enzyme activities offers a simple, inexpensive way to track such improvements. In this follow-up study we present the optimization of a recently established GC design for screening of heterologous methyltransferases (MTases) and related pathways in the yeast Saccharomyces cerevisiae. Specifically, upon testing different media compositions and genetic backgrounds, improved GC of different heterologous MTase activities is obtained. Furthermore, we demonstrate the strength of the system by screening a library of catechol O-MTase variants converting protocatechuic acid into vanillic acid. We demonstrated high correlation (R² = 0.775) between vanillic acid and cell density as a proxy for MTase activity. We envision that the improved MTase GC can aid evolution-guided optimization of biobased production processes for methylated compounds with yeast in the future.

Design, Evolution, and Characterization of a Xylose Biosensor in Escherichia coli Using the XylR/xylO System with an Expanded Operating Range

Genetically encoded biosensors are extensively utilized in synthetic biology and metabolic engineering. However, reported xylose biosensors are far too sensitive with a limited operating range to be useful for most sensing applications. In this study, we describe directed evolution of Escherichia coli XylR, and construction of biosensors based on XylR and the corresponding operator xylO. The operating range of biosensors containing the mutant XylR was increased by nearly 10-fold comparing with the control. Two individual amino acid mutations (either L73P or N220T) in XylR were sufficient to extend the linear response range to upward of 10 g/L xylose. The evolved biosensors described here are well suited for developing whole-cell biosensors for detecting varying xylose concentrations across an expanded range. As an alternative use of this system, we also demonstrate the utility of XylR and xylO as a xylose inducible system to enable graded gene expression through testing with β-galactosidase gene and the lycopene synthetic pathway. This evolution strategy identified a less-sensitive biosensor for real applications, thus providing new insights into strategies for expanding operating ranges of other biosensors for synthetic biology applications.

Engineering and application of a biosensor with focused ligand specificity

Cell factories converting bio-based precursors to chemicals present an attractive avenue to a sustainable economy, yet screening of genetically diverse strain libraries to identify the best-performing whole-cell biocatalysts is a low-throughput endeavor. For this reason, transcriptional biosensors attract attention as they allow the screening of vast libraries when used in combination with fluorescence-activated cell sorting (FACS). However, broad ligand specificity of transcriptional regulators (TRs) often prohibits the development of such ultra-high-throughput screens. Here, we solve the structure of the TR LysG of Corynebacterium glutamicum, which detects all three basic amino acids. Based on this information, we follow a semi-rational engineering approach using a FACS-based screening/counterscreening strategy to generate an l-lysine insensitive LysG-based biosensor. This biosensor can be used to isolate l-histidine-producing strains by FACS, showing that TR engineering towards a more focused ligand spectrum can expand the scope of application of such metabolite sensors.

Transcriptional biosensors represent powerful tools for the screening of vast strain libraries, but the broad ligand specificity of some transcriptional regulators (TRs) can prohibit such applications. Here authors present the engineering of a LysG-based biosensor with a focused ligand specificity to isolate L-histidine-producing strains.

Control strategies to manage trade-offs during microbial production

When engineering microbes to overproduce a target molecule, engineers face multiple layers of trade-offs to allocate limited cellular resources between the target pathway and native cellular systems. These trade-offs arise from limited free ribosomes during translation, competition for metabolic precursors, as well as the negative relationship between production and growth rate. To achieve high production performance, microbes need to spontaneously make decisions in the dynamic and heterogeneous fermentation environment. In this review, we discuss recent advances in microbial control strategies that are used to manage these trade-offs and to improve microbial production. This review focuses on design principles and compares different implementations, with the hope to provide guidelines to future microbial engineering.

Directed evolution of Anabaena variabilis phenylalanine ammonia-lyase (PAL) identifies mutants with enhanced activities

There is broad interest in engineering phenylalanine ammonia-lyase (PAL) for its biocatalytic applications in industry and medicine. While site-specific mutagenesis has been employed to improve PAL stability or substrate specificity, combinatorial techniques are poorly explored. Here, we report development of a directed evolution technique to engineer PAL enzymes. Central to this approach is a high-throughput enrichment that couples E. coli growth to PAL activity. Starting with the PAL used in the formulation of pegvaliase for PKU therapy, we report previously unidentified mutations that increase turnover frequency almost twofold after only a single round of engineering.

Engineering intracellular malonyl-CoA availability in microbial hosts and its impact on polyketide and fatty acid synthesis

Malonyl-CoA is an important central metabolite serving as the basic building block for the microbial synthesis of many pharmaceutically interesting polyketides, but also fatty acid-derived compounds including biofuels. Especially Saccharomyces cerevisiae, Escherichia coli, and Corynebacterium glutamicum have been engineered towards microbial synthesis of such compounds in recent years. However, developed strains and processes often suffer from insufficient productivity. Usually, tightly regulated intracellular malonyl-CoA availability is regarded as the decisive bottleneck limiting overall product formation. Therefore, metabolic engineering towards improved malonyl-CoA availability is essential to design efficient microbial cell factories for the production of polyketides and fatty acid derivatives. This review article summarizes metabolic engineering strategies to improve intracellular malonyl-CoA formation in industrially relevant microorganisms and its impact on productivity and product range, with a focus on polyketides and other malonyl-CoA-dependent products.Key Points• Malonyl-CoA is the central building block of polyketide synthesis.• Increasing acetyl-CoA supply is pivotal to improve malonyl-CoA availability.• Improved acetyl-CoA carboxylase activity increases availability of malonyl-CoA.• Fatty acid synthesis as an ambivalent target to improve malonyl-CoA supply.

Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms

Low yield and low titer of natural products are common issues in natural product biosynthesis through microbial cell factories. One effective way to resolve such bottlenecks is to design genetic biosensors to monitor and regulate the biosynthesis of target natural products. In this review, we evaluate the most recent advances in the design of genetic biosensors for natural product biosynthesis in microorganisms. In particular, we examine strategies for selection of genetic parts and construction principles for the design and evaluation of genetic biosensors. We also review the latest applications of transcription factor- and riboswitch-based genetic biosensors in natural product biosynthesis. Lastly, we discuss challenges and solutions in designing genetic biosensors for the biosynthesis of natural products in microorganisms.

Transcription Factor Engineering for High-Throughput Strain Evolution and Organic Acid Bioproduction: A Review

Metabolic regulation of gene expression for the microbial production of fine chemicals, such as organic acids, is an important research topic in post-genomic metabolic engineering. In particular, the ability of transcription factors (TFs) to respond precisely in time and space to various small molecules, signals and stimuli from the internal and external environment is essential for metabolic pathway engineering and strain development. As a key component, TFs are used to construct many biosensors in vivo using synthetic biology methods, which can be used to monitor the concentration of intracellular metabolites in organic acid production that would otherwise remain “invisible” within the intracellular environment. TF-based biosensors also provide a high-throughput screening method for rapid strain evolution. Furthermore, TFs are important global regulators that control the expression levels of key enzymes in organic acid biosynthesis pathways, therefore determining the outcome of metabolic networks. Here we review recent advances in TF identification, engineering, and applications for metabolic engineering, with an emphasis on metabolite monitoring and high-throughput strain evolution for the organic acid bioproduction.