Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids

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.

Engineering microbial metabolic homeostasis for chemicals production

Microbial-based bio-refining promotes the development of a biotechnology revolution to encounter and tackle the enormous challenges in petroleum-based chemical production by biomanufacturing, biocomputing, and biosensing. Nevertheless, microbial metabolic homeostasis is often incompatible with the efficient synthesis of bioproducts mainly due to: inefficient metabolic flow, robust central metabolism, sophisticated metabolic network, and inevitable environmental perturbation. Therefore, this review systematically summarizes how to optimize microbial metabolic homeostasis by strengthening metabolic flux for improving biotransformation turnover, redirecting metabolic direction for rewiring bypass pathway, and reprogramming metabolic network for boosting substrate utilization. Future directions are also proposed for providing constructive guidance on the development of industrial biotechnology.

Engineering Chimeric Chemoreceptors and Two-Component Systems for Orthogonal and Leakless Biosensing of Extracellular γ-Aminobutyric Acid

Two-component systems (TCSs) sensing and responding to various stimuli outside and inside cells are valuable resources for developing biosensors with synthetic biology applications. However, the use of TCS-based biosensors suffers from a limited effector spectrum, hypersensitivity, low dynamic range, and unwanted signal crosstalk. Here, we developed a tailor-made Escherichia coli whole-cell γ-aminobutyric acid (GABA) biosensor by engineering a chimeric GABA chemoreceptor PctC and TCS. By testing different TCSs, the chimeric PctC/PhoQ showed the response to GABA. Chimera-directed evolution and introduction of the insulated chimeric pair PctC/PhoQ*PhoP* produced biosensors with up to 3.50-fold dynamic range and good orthogonality. To further enhance the dynamic range and lower the basal leakage, three strategies, engineering of PhoP DNA binding sites, fine-tuning reporter expression by optimizing transcription/translation components, and a tobacco etch virus protease-controlled protein degradation, were integrated. This chimeric biosensor displayed a low basal leakage, a large dynamic range (15.8-fold), and a high threshold level (22.7 g L-1). Finally, the optimized biosensor was successfully applied in the high-throughput microdroplet screening of GABA-overproducing Corynebacterium glutamicum, demonstrating its desired properties for extracellular signal biosensing.

ATP biosensor reveals microbial energetic dynamics and facilitates bioproduction

Adenosine-5'-triphosphate (ATP), the primary energy currency in cellular processes, drives metabolic activities and biosynthesis. Despite its importance, understanding intracellular ATP dynamics' impact on bioproduction and exploiting it for enhanced bioproduction remains largely unexplored. Here, we harness an ATP biosensor to dissect ATP dynamics across different growth phases and carbon sources in multiple microbial strains. We find transient ATP accumulations during the transition from exponential to stationary growth phases in various conditions, coinciding with fatty acid (FA) and polyhydroxyalkanoate (PHA) production in Escherichia coli and Pseudomonas putida, respectively. We identify carbon sources (acetate for E. coli, oleate for P. putida) that elevate steady-state ATP levels and boost FA and PHA production. Moreover, we employ ATP dynamics as a diagnostic tool to assess metabolic burden, revealing bottlenecks that limit limonene bioproduction. Our results not only elucidate the relationship between ATP dynamics and bioproduction but also showcase its value in enhancing bioproduction in various microbial species.

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.

Integrating microfluidics and synthetic biology: advancements and diverse applications across organisms

Synthetic biology is the design and modification of biological systems for specific functions, integrating several disciplines like engineering, genetics, and computer science. The field of synthetic biology is to understand biological processes within host organisms through the manipulation and regulation of their genetic pathways and the addition of biocontrol circuits to enhance their production capabilities. This pursuit serves to address global challenges spanning diverse domains that are difficult to tackle through conventional routes of production. Despite its impact, achieving precise, dynamic, and high-throughput manipulation of biological processes is still challenging. Microfluidics offers a solution to those challenges, enabling controlled fluid handling at the microscale, offering lower reagent consumption, faster analysis of biochemical reactions, automation, and high throughput screening. In this review, we diverge from conventional focus on automating the synthetic biology design-build-test-learn cycle, and instead, focus on microfluidic platforms and their role in advancing synthetic biology through its integration with host organisms - bacterial cells, yeast, fungi, animal cells - and cell-free systems. The review illustrates how microfluidic devices have been instrumental in understanding biological systems by showcasing microfluidics as an essential tool to create synthetic genetic circuits, pathways, and organisms within controlled environments. In conclusion, we show how microfluidics expedite synthetic biology applications across diverse domains including but not limited to personalized medicine, bioenergy, and agriculture.

Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.

Saccharomyces cerevisiae cellular engineering for the production of FAME biodiesel

The unsustainable and widespread utilization of fossil fuels continues to drive the rapid depletion of global supplies. Biodiesel has emerged as one of the most promising alternatives to conventional diesel, leading to growing research interest in its production. Microbes can facilitate the de novo synthesis of a type of biodiesel in the form of fatty acid methyl esters (FAMEs). In this study, Saccharomyces cerevisiae metabolic activity was engineered to facilitate enhanced FAME production. Initially, free fatty acid concentrations were increased by deleting two acetyl-CoA synthetase genes (FAA1, FAA4) and an acyl-CoA oxidase gene (POX1). Intracellular S-adenosylmethionine (SAM) levels were then enhanced via the deletion of an adenosine kinase gene (ADO1) and the overexpression of a SAM synthetase gene (SAM2). Lastly, the S. cerevisiae strain overproducing free fatty acids and SAM were manipulated to express a plasmid encoding the Drosophila melanogaster Juvenile Hormone Acid O-Methyltransferase (DmJHAMT). Using this combination of engineering approaches, a FAME concentration of 5.79 ± 0.56 mg/L was achieved using these cells in the context of shaking flask fermentation. To the best of our knowledge, this is the first detailed study of FAME production in S. cerevisiae. These results will provide a valuable basis for future efforts to engineer S. cerevisiae strains for highly efficient production of biodiesel.

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.

Machine learning model of the catalytic efficiency and substrate specificity of acyl-ACP thioesterase variants generated from natural and in vitro directed evolution

Modulating the catalytic activity of acyl-ACP thioesterase (TE) is an important biotechnological target for effectively increasing flux and diversifying products of the fatty acid biosynthesis pathway. In this study, a directed evolution approach was developed to improve the fatty acid titer and fatty acid diversity produced by E. coli strains expressing variant acyl-ACP TEs. A single round of in vitro directed evolution, coupled with a high-throughput colorimetric screen, identified 26 novel acyl-ACP TE variants that convey up to a 10-fold increase in fatty acid titer, and generate altered fatty acid profiles when expressed in a bacterial host strain. These in vitro-generated variant acyl-ACP TEs, in combination with 31 previously characterized natural variants isolated from diverse phylogenetic origins, were analyzed with a random forest classifier machine learning tool. The resulting quantitative model identified 22 amino acid residues, which define important structural features that determine the catalytic efficiency and substrate specificity of acyl-ACP TE.

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.

Proteomic analysis reveals that the co-ordination of cytosolic and mitochondrial pathways is beneficial for sabinene biosynthesis in engineered Saccharomyces cerevisiae

Reconstruction and optimization of biosynthetic pathways can help to overproduce target chemicals in microbial cell factories based on genetic engineering. However, the perturbation of biosynthetic pathways on cellular metabolism is not well investigated and profiling the engineered microbes remains challenging. The rapid development of omics tools has the potential to characterize the engineered microbial cell factory. Here, we performed label-free quantitative proteomic analysis and metabolomic analysis of engineered sabinene overproducing Saccharomyces cerevisiae strains. Combined metabolic analysis andproteomic analysis of targeted mevalonate (MVA) pathway showed that co-ordination of cytosolic and mitochondrial pathways had balanced metabolism, and genome integration of biosynthetic genes had higher sabinene production with less MVA enzymes. Furthermore, comparative proteomic analysis showed that compartmentalized mitochondria pathway had perturbation on central cellular metabolism. This study provided an omics analysis example for characterizing engineered cell factory, which can guide future regulation of the cellular metabolism and maintaining optimal protein expression levels for the synthesis of target products.

The dynamic-process characterization and prediction of synthetic gene circuits by dynamic delay model

Differential equation models are widely used to describe genetic regulations, predict multicomponent regulatory circuits, and provide quantitative insights. However, it is still challenging to quantitatively link the dynamic behaviors with measured parameters in synthetic circuits. Here, we propose a dynamic delay model (DDM) which includes two simple parts: the dynamic determining part and the doses-related steady-state-determining part. The dynamic determining part is usually supposed as the delay time but without a clear formula. For the first time, we give the detail formula of the dynamic determining function and provide a method for measuring all parameters of synthetic elements (include 8 activators and 5 repressors) by microfluidic system. Three synthetic circuits were built to show that the DDM can notably improve the prediction accuracy and can be used in various synthetic biology applications.

Recent Advances in Microbial Metabolic Engineering for Production of Natural Phenolic Acids

Phenolic acids are important natural bioactive compounds with varied physiological functions. They are extensively used in food, pharmaceutical, cosmetic, and other chemical industries and have attractive market prospects. Compared to plant extraction and chemical synthesis, microbial fermentation for phenolic acid production from renewable carbon sources has significant advantages. This review focuses on the structural information, physiological functions, current applications, and biosynthesis pathways of phenolic acids, especially advances in the development of metabolically engineered microbes for the production of phenolic acids. This review provides useful insights concerning phenolic acid production through metabolic engineering of microbial cell factories.

Engineering and application of LacI mutants with stringent expressions

Optimal transcriptional regulatory circuits are expected to exhibit stringent control, maintaining silence in the absence of inducers while exhibiting a broad induction dynamic range upon the addition of effectors. In the Plac /LacI pair, the promoter of the lac operon in Escherichia coli is characterized by its leakiness, attributed to the moderate affinity of LacI for its operator target. In response to this limitation, the LacI regulatory protein underwent engineering to enhance its regulatory properties. The M7 mutant, carrying I79T and N246S mutations, resulted in the lac promoter displaying approximately 95% less leaky expression and a broader induction dynamic range compared to the wild-type LacI. An in-depth analysis of each mutation revealed distinct regulatory profiles. In contrast to the wild-type LacI, the M7 mutant exhibited a tighter binding to the operator sequence, as evidenced by surface plasmon resonance studies. Leveraging the capabilities of the M7 mutant, a high-value sugar biosensor was constructed. This biosensor facilitated the selection of mutant galactosidases with approximately a seven-fold improvement in specific activity for transgalactosylation. Consequently, this advancement enabled enhanced biosynthesis of galacto-oligosaccharides (GOS).

Consistent biosynthesis of D-glycerate from variable mixed substrates

The use of waste streams and other renewable feedstocks in microbial biosynthesis has long been a goal for metabolic engineers. Microbes can utilize the substrate mixtures found in waste streams, though they are more technically challenging to convert to useful products compared to the single substrates of standard practice. It is difficult to achieve consistent biosynthesis in the face of the temporally changing nature of waste streams. Furthermore, the expression of all the enzymes necessary to convert mixed substrates into a product likely presents significant metabolic burden, which already plagues processes that utilize a single substrate. We developed an approach to utilize mixed feedstocks for production by activating expression of each biosynthetic pathway in the presence of its substrate. This expression control was used for two novel pathways that converted two substrates, galacturonate and gluconate, into a single product, D-glycerate. A production strain harboring both pathway plasmids produced 1.8 ± 0.3 and 1.64 ± 0.09 g L-1 of D-glycerate from galacturonate and gluconate alone, respectively. Fermentations that were fed a mixture of the two substrates, at different ratios, resulted in product titers between 1.48 ± 0.03 and 1.8 ± 0.1 g L-1. All fermentations were fed a total of 10 g L-1 substrate and there was no statistically significant difference in D-glycerate titer from the single or mixed substrate fermentations. We thus demonstrated consistent D-glycerate biosynthesis from single and mixed substrates as an example of robust conversion of complex feedstocks.

Identification of crucial roles of transcription factor IhfA on high production of free fatty acids in Escherichia coli

Transcription factor engineering has unique advantages in improving the performance of microbial cell factories due to the global regulation of gene transcription. Omics analyses and reverse engineering enable learning and subsequent incorporation of novel design strategies for further engineering. Here, we identify the role of the global regulator IhfA for overproduction of free fatty acids (FFAs) using CRISPRi-facilitated reverse engineering and cellular physiological characterization. From the differentially expressed genes in the ihfAL- strain, a total of 14 beneficial targets that enhance FFAs production by above 20 % are identified, which involve membrane function, oxidative stress, and others. For membrane-related genes, the engineered strains obtain lower cell surface hydrophobicity and increased average length of membrane lipid tails. For oxidative stress-related genes, the engineered strains present decreased reactive oxygen species (ROS) levels. These gene modulations enhance cellular robustness and save cellular resources, contributing to FFAs production. This study provides novel targets and strategies for engineering microbial cell factories with improved FFAs bioproduction.

Repurposing the mammalian RNA-binding protein Musashi-1 as an allosteric translation repressor in bacteria

The RNA recognition motif (RRM) is the most common RNA-binding protein domain identified in nature. However, RRM-containing proteins are only prevalent in eukaryotic phyla, in which they play central regulatory roles. Here, we engineered an orthogonal post-transcriptional control system of gene expression in the bacterium Escherichia coli with the mammalian RNA-binding protein Musashi-1, which is a stem cell marker with neurodevelopmental role that contains two canonical RRMs. In the circuit, Musashi-1 is regulated transcriptionally and works as an allosteric translation repressor thanks to a specific interaction with the N-terminal coding region of a messenger RNA and its structural plasticity to respond to fatty acids. We fully characterized the genetic system at the population and single-cell levels showing a significant fold change in reporter expression, and the underlying molecular mechanism by assessing the in vitro binding kinetics and in vivo functionality of a series of RNA mutants. The dynamic response of the system was well recapitulated by a bottom-up mathematical model. Moreover, we applied the post-transcriptional mechanism engineered with Musashi-1 to specifically regulate a gene within an operon, implement combinatorial regulation, and reduce protein expression noise. This work illustrates how RRM-based regulation can be adapted to simple organisms, thereby adding a new regulatory layer in prokaryotes for translation control.

Directed Evolution of 4-Hydroxyphenylpyruvate Biosensors Based on a Dual Selection System

Biosensors based on allosteric transcription factors have been widely used in synthetic biology. In this study, we utilized the Acinetobacter ADP1 transcription factor PobR to develop a biosensor activating the PpobA promoter when bound to its natural ligand, 4-hydroxybenzoic acid (4HB). To screen for PobR mutants responsive to 4-hydroxyphenylpyruvate(HPP), we developed a dual selection system in E. coli. The positive selection of this system was used to enrich PobR mutants that identified the required ligands. The following negative selection eliminated or weakened PobR mutants that still responded to 4HB. Directed evolution of the PobR library resulted in a variant where PobRW177R was 5.1 times more reactive to 4-hydroxyphenylpyruvate than PobRWT. Overall, we developed an efficient dual selection system for directed evolution of biosensors.

Fine-Tuned Gene Expression Elements from Hybrid Promoter Libraries in Pichia pastoris

As a desirable microbial cell factory, Pichia pastoris has garnered extensive utilization in metabolic engineering. Nevertheless, the lack of fine-tuned gene expression components has significantly constrained the potential scope of applications. Here, a gradient strength promoter library was constructed by random hybridization and high-throughput screening. The hybrid promoter, phy47, performed best with 2.93-fold higher GFP expression levels than GAP. The broad applicability of the novel hybrid promoter variants in biotechnological production was further validated in the biosynthesis of pinene and rHuPH20 with higher titers. The upstream regulatory sequences (UASE and URSD) were identified and applied to promoters GAP and ENO1, resulting in a 34 and 43% increase and an 18 and 37% decrease in the expression level, respectively. Yeast one-hybrid analysis showed that transcription factor HAP2 activates the hybrid promoter through a direct interaction with the crucial regulatory region UASH. Furthermore, a short segment of tunable activation sequence (20 bp) was also screened, and artificial promoters were constructed in tandem with the addition of regulatory sequence, resulting in a 61% expansion of the expression range. This study provides a molecular tool and regulatory elements for further synthetic biology research in P. pastoris.

Biosensors for the detection of chorismate and cis,cis-muconic acid in Corynebacterium glutamicum

Corynebacterium glutamicum ATCC 13032 is a promising microbial chassis for industrial production of valuable compounds, including aromatic amino acids derived from the shikimate pathway. In this work, we developed two whole-cell, transcription factor based fluorescent biosensors to track cis,cis-muconic acid (ccMA) and chorismate in C. glutamicum. Chorismate is a key intermediate in the shikimate pathway from which value-added chemicals can be produced, and a shunt from the shikimate pathway can divert carbon to ccMA, a high value chemical. We transferred a ccMA-inducible transcription factor, CatM, from Acinetobacter baylyi ADP1 into C. glutamicum and screened a promoter library to isolate variants with high sensitivity and dynamic range to ccMA by providing benzoate, which is converted to ccMA intracellularly. The biosensor also detected exogenously supplied ccMA, suggesting the presence of a putative ccMA transporter in C. glutamicum, though the external ccMA concentration threshold to elicit a response was 100-fold higher than the concentration of benzoate required to do so through intracellular ccMA production. We then developed a chorismate biosensor, in which a chorismate inducible promoter regulated by natively expressed QsuR was optimized to exhibit a dose-dependent response to exogenously supplemented quinate (a chorismate precursor). A chorismate-pyruvate lyase encoding gene, ubiC, was introduced into C. glutamicum to lower the intracellular chorismate pool, which resulted in loss of dose dependence to quinate. Further, a knockout strain that blocked the conversion of quinate to chorismate also resulted in absence of dose dependence to quinate, validating that the chorismate biosensor is specific to intracellular chorismate pool. The ccMA and chorismate biosensors were dually inserted into C. glutamicum to simultaneously detect intracellularly produced chorismate and ccMA. Biosensors, such as those developed in this study, can be applied in C. glutamicum for multiplex sensing to expedite pathway design and optimization through metabolic engineering in this promising chassis organism.

ONE-SENTENCE SUMMARY

High-throughput screening of promoter libraries in Corynebacterium glutamicum to establish transcription factor based biosensors for key metabolic intermediates in shikimate and β-ketoadipate pathways.

Flux optimization using multiple promoters in Halomonas bluephagenesis as a model chassis of the next generation industrial biotechnology

Predictability and robustness are challenges for bioproduction because of the unstable intracellular synthetic activities. With the deeper understanding of the gene expression process, fine-tuning has become a meaningful tool for biosynthesis optimization. This study characterized several gene expression elements and constructed a multiple inducible system that responds to ten different small chemical inducers in halophile bacterium Halomonas bluephagenesis. Genome insertion of regulators was conducted for the purpose of gene cluster stabilization and regulatory plasmid simplification. Additionally, dynamic ranges of the multiple inducible systems were tuned by promoter sequence mutations to achieve diverse scopes for high-resolution gene expression control. The multiple inducible system was successfully employed to precisely control chromoprotein expression, lycopene and poly-3-hydroxybutyrate (PHB) biosynthesis, resulting in colorful bacterial pictures, optimized cell growth, lycopene and PHB accumulation. This study demonstrates a desirable approach for fine-tuning of rational and efficient gene expressions, displaying the significance for metabolic pathway optimization.

Reshaping the yeast galactose regulon via GPCR signaling cascade

Dynamically regulated systems are preferable to control metabolic pathways for an improved strain performance with better productivity. Here, we harnessed to the G protein-coupled receptor (GPCR) signaling pathway to reshape the yeast galactose regulon. The galactose-regulated (GAL) system was coupled with the GPCR signaling pathway for mating pheromone via a synthetic transcription factor. In this study, we refabricated the dynamic range, sensitivity, and response time of the GAL system to α factor by modulating the key components of the GPCR signaling cascade. A series of engineered yeasts with self-secretion of α factor were constructed to achieve quorum-sensing behaviors. In addition, we also repurposed the GAL system to make it responsive to heat shock. Taken together, our work showcases the great potential of synthetic biology in creating user-defined metabolic controls. We envision that the plasticity of our genetic design would be of significant interest for the future fabrication of novel gene expression systems.

Genetically encoded ATP and NAD(P)H biosensors: potential tools in metabolic engineering

To date, many metabolic engineering tools and strategies have been developed, including tools for cofactor engineering, which is a common strategy for bioproduct synthesis. Cofactor engineering is used for the regulation of pyridine nucleotides, including NADH/NAD+ and NADPH/NADP+, and adenosine triphosphate/adenosine diphosphate (ATP/ADP), which is crucial for maintaining redox and energy balance. However, the intracellular levels of NADH/NAD+, NADPH/NADP+, and ATP/ADP cannot be monitored in real time using traditional methods. Recently, many biosensors for detecting, monitoring, and regulating the intracellular levels of NADH/NAD+, NADPH/NADP+, and ATP/ADP have been developed. Although cofactor biosensors have been mainly developed for use in mammalian cells, the potential application of cofactor biosensors in metabolic engineering in bacterial and yeast cells has received recent attention. Coupling cofactor biosensors with genetic circuits is a promising strategy in metabolic engineering for optimizing the production of biochemicals. In this review, we focus on the development of biosensors for NADH/NAD+, NADPH/NADP+, and ATP/ADP and the potential application of these biosensors in metabolic engineering. We also provide critical perspectives, identify current research challenges, and provide guidance for future research in this promising field.

A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch

Bioprocess optimization using mathematical models is prevalent, yet the discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a significant challenge. This study proposes a novel hybrid control system called the hybrid in silico/in-cell controller (HISICC) to address PMM by combining model-based optimization (in silico feedforward controller) with feedback controllers utilizing synthetic genetic circuits integrated into cells (in-cell feedback controller). We demonstrated the efficacy of HISICC using two engineered Escherichia coli strains, TA1415 and TA2445, previously developed for isopropanol (IPA) production. TA1415 contains a metabolic toggle switch (MTS) to manage the competition between cell growth and IPA production for intracellular acetyl-CoA by responding to external input of isopropyl β-D-1-thiogalactopyranoside (IPTG). TA2445, in addition to the MTS, has a genetic circuit that detects cell density to autonomously activate MTS. The combination of TA2445 with an in silico controller exemplifies HISICC implementation. We constructed mathematical models to optimize IPTG input values for both strains based on the two-compartment model and validated these models using experimental data of the IPA production process. Using these models, we evaluated the robustness of HISICC against PMM by comparing IPA yields with two strains in simulations assuming various magnitudes of PMM in cell growth rates. The results indicate that the in-cell feedback controller in TA2445 effectively compensates for PMM by modifying MTS activation timing. In conclusion, the HISICC system presents a promising solution to the PMM problem in bioprocess engineering, paving the way for more efficient and reliable optimization of microbial bioprocesses.

A robust genetic toolbox for fine-tuning gene expression in the CO2-Fixing methanogenic archaeon Methanococcus maripaludis

Libraries of well-characterized genetic elements for fine-tuning gene expression are essential for biological and biotechnological research and applications. The fast-growing and genetically tractable methanogen, Methanococcus maripaludis, is a promising host organism for biotechnological conversion of carbon dioxide and renewable hydrogen into fuels and value-added products, as well as fundamental biological studies of archaea. However, the lack of molecular tools for gene expression has hindered its application as a workhorse to fine-tune gene and metabolic pathway expressions. In this study, we developed a genetic toolbox, including libraries of promoters, ribosome binding sites (RBS), and neutral sites for chromosomal integration, to facilitate precise gene expression in M. maripaludis. We generated a promoter library consisting of 81 constitutive promoters with expression strengths spanning a ∼104-fold dynamic range. Importantly, we identified a base composition rule for strong archaeal promoters and successfully remodeled weak promoters, enhancing their activities by up to 120-fold. We also established an RBS library containing 42 diverse RBS sequences with translation strengths covering a ∼100-fold dynamic range. Additionally, we identified eight neutral sites and developed a one-step, Cas9-based marker-less knock-in approach for chromosomal integration. We successfully applied the characterized promoter and RBS elements to significantly improve recombinant protein expression by 41-fold and modulate essential gene expression to generate corresponding physiological changes in M. maripaludis. Therefore, this work establishes a solid foundation for utilizing this autotrophic methanogen as an ideal workhorse for archaeal biology and biotechnological studies and applications.

Dynamic Metabolic Control: From the Perspective of Regulation Logic

Establishing microbial cell factories has become a sustainable and increasingly promising approach for the synthesis of valuable chemicals. However, introducing heterologous pathways into these cell factories can disrupt the endogenous cellular metabolism, leading to suboptimal production performance. To address this challenge, dynamic pathway regulation has been developed and proven effective in improving microbial biosynthesis. In this review, we summarized typical dynamic regulation strategies based on their control logic. The applicable scenarios for each control logic were highlighted and perspectives for future research direction in this area were discussed.

Towards universal synthetic heterotrophy using a metabolic coordinator

Engineering the utilization of non-native substrates, or synthetic heterotrophy, in proven industrial microbes such as Saccharomyces cerevisiae represents an opportunity to valorize plentiful and renewable sources of carbon and energy as inputs to bioprocesses. We previously demonstrated that activation of the galactose (GAL) regulon, a regulatory structure used by this yeast to coordinate substrate utilization with biomass formation during growth on galactose, during growth on the non-native substrate xylose results in a vastly altered gene expression profile and faster growth compared with constitutive overexpression of the same heterologous catabolic pathway. However, this effort involved the creation of a xylose-inducible variant of Gal3p (Gal3pSyn4.1), the sensor protein of the GAL regulon, preventing this semi-synthetic regulon approach from being easily adapted to additional non-native substrates. Here, we report the construction of a variant Gal3pMC (metabolic coordinator) that exhibits robust GAL regulon activation in the presence of structurally diverse substrates and recapitulates the dynamics of the native system. Multiple molecular modeling studies suggest that Gal3pMC occupies conformational states corresponding to galactose-bound Gal3p in an inducer-independent manner. Using Gal3pMC to test a regulon approach to the assimilation of the non-native lignocellulosic sugars xylose, arabinose, and cellobiose yields higher growth rates and final cell densities when compared with a constitutive overexpression of the same set of catabolic genes. The subsequent demonstration of rapid and complete co-utilization of all three non-native substrates suggests that Gal3pMC-mediated dynamic global gene expression changes by GAL regulon activation may be universally beneficial for engineering synthetic heterotrophy.

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.

Bayesian Optimization for Design of Multiscale Biological Circuits

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

Engineering 3-Hydroxypropionic Acid Production from Glucose in Yarrowia lipolytica through Malonyl-CoA Pathway

3-Hydroxypropionic acid (3-HP) is an important intermediate compound in the chemical industry. Green and environmentally friendly microbial synthesis methods are becoming increasingly popular in a range of industries. Compared to other chassis cells, Yarrowia lipolytica possesses advantages, such as high tolerance to organic acid and a sufficient precursor required to synthesize 3-HP. In this study, gene manipulations, including the overexpression of genes MCR-NCa, MCR-CCa, GAPNSm, ACC1 and ACSSeL641P and knocking out bypass genes MLS1 and CIT2, leading to the glyoxylate cycle, were performed to construct a recombinant strain. Based on this, the degradation pathway of 3-HP in Y. lipolytica was discovered, and relevant genes MMSDH and HPDH were knocked out. To our knowledge, this study is the first to produce 3-HP in Y. lipolytica. The yield of 3-HP in recombinant strain Po1f-NC-14 in shake flask fermentation reached 1.128 g·L^-1, and the yield in fed-batch fermentation reached 16.23 g·L^-1. These results are highly competitive compared to other yeast chassis cells. This study creates the foundation for the production of 3-HP in Y. lipolytica and also provides a reference for further research in the future.

Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor

A cross-ribosome binding site (cRBS) adjusts the dynamic range of transcription factor-based biosensors (TFBs) by controlling protein expression and folding. The rational design of a cRBS with desired TFB dynamic range remains an important issue in TFB forward and reverse engineering. Here, we report a novel artificial intelligence (AI)-based forward-reverse engineering platform for TFB dynamic range prediction and de novo cRBS design with selected TFB dynamic ranges. The platform demonstrated superior in processing unbalanced minority-class datasets and was guided by sequence characteristics from trained cRBSs. The platform identified correlations between cRBSs and dynamic ranges to mimic bidirectional design between these factors based on Wasserstein generative adversarial network (GAN) with a gradient penalty (GP) (WGAN-GP) and balancing GAN with GP (BAGAN-GP). For forward and reverse engineering, the predictive accuracy was up to 98% and 82%, respectively. Collectively, we generated an AI-based method for the rational design of TFBs with desired dynamic ranges.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Programming Super DNA-Enzyme Molecules for On-Demand Enzyme Activity Modulation

Dynamic interactions of enzymes, including programmable configuration and cycling of enzymes, play important roles in the regulation of cellular metabolism. Here, we constructed a super DNA-enzymes molecule (SDEM) that comprises at least two cascade enzymes and multiple linked DNA strands to control and detect metabolism. We found that the programmable SDEM, which comprises glucose oxidase (GOx) and horseradish peroxidase (HRP), has a 20-fold lower detection limit and a 1.6-fold higher reaction rate than free enzymes. An SDEM can be assembled and disassembled using a hairpin structure and a displacement DNA strand to complete multiple cycles. An entropically driven catalytic assembly (catassembly) enables different SDEMs to switch from an SDEM with GOx and HRP cascades to an SDEM with sarcosine oxidase (SOX) and HRP cascades in over six orders of magnitude less time than without the catassembly to detect different metabolisms (GO and sarcosine) on demand.

Dynamic upregulation of the rate-limiting enzyme for valerolactam biosynthesis in Corynebacterium glutamicum

Valerolactam is a monomer used to manufacture high-value nylon-5 and nylon-6,5. However, the biological production of valerolactam has been limited by the inadequate efficiency of enzymes to cyclize 5-aminovaleric acid to produce valerolactam. In this study, we engineered Corynebacterium glutamicum with a valerolactam biosynthetic pathway consisting of DavAB from Pseudomonas putida to convert L-lysine to 5-aminovaleric acid and β-alanine CoA transferase (Act) from Clostridium propionicum to produce valerolactam from 5-aminovaleric acid. Most of the L-lysine was converted into 5-aminovaleric acid, but promoter optimization and increasing the copy number of Act were insufficient to significantly improve the titer of valerolactam. To eliminate the bottleneck at Act, we designed a dynamic upregulation system (a positive feedback loop based on the valerolactam biosensor ChnR/Pb). We used laboratory evolution to engineer ChnR/Pb to have higher sensitivity and a higher dynamic output range, and the engineered ChnR-B1/Pb-E1 system was used to overexpress the rate-limiting enzymes (Act/ORF26/CaiC) that cyclize 5-aminovaleric acid into valerolactam. In glucose fed-batch culture, we obtained 12.33 g/L valerolactam from the dynamic upregulation of Act, 11.88 g/L using ORF26, and 12.15 g/L using CaiC. Our engineered biosensor (ChnR-B1/Pb-E1 system) was also sensitive to 0.01-100 mM caprolactam, which suggests that this dynamic upregulation system can be used to enhance caprolactam biosynthesis in the future.

Metabolic engineering for sustainability and health

Bio-based production of chemicals and materials has attracted much attention due to the urgent need to establish sustainability and enhance human health. Metabolic engineering (ME) allows purposeful modification of cellular metabolic, regulatory, and signaling networks to achieve enhanced production of desired chemicals and degradation of environmentally harmful chemicals. ME has significantly progressed over the past 30 years through further integration of the strategies of synthetic biology, systems biology, evolutionary engineering, and data science aided by artificial intelligence. Here we review the field of ME from its emergence to the current state-of-the-art, highlighting its contribution to sustainable production of chemicals, health, and the environment through representative examples. Future challenges of ME and perspectives are also discussed.

De novo design of the global transcriptional factor Cra-regulated promoters enables highly sensitive glycolysis flux biosensor for dynamic metabolic control

Glycolytic flux is a fundamental index in microbial cell factories. A glycolytic flux biosensor that can monitor glucose metabolism efficiency is a promising strategy in rewiring metabolic flux to balance growth and biosynthesis. A key design feature of the glycolytic flux biosensors is the interaction between the global transcriptional factor Cra and its regulated promoters. However, overexpression and mutation of Cra has unpredictable effects on global metabolism in Escherichia coli. Therefore, new orthogonal biosensor design strategies should be developed to circumvent metabolic issues. In this report, the promoters in glycolytic flux biosensor were replaced with synthetic promoters of varying strengths or phage-derived promoters, and the Cra DNA-binding sites were deployed into promoters at different positions and distances to yield biosensors. The de nova biosensors that depended on Cra could sense Fructose-1,6-diphosphate (FBP) with broad dynamic ranges and low basal leakage. Then the negative-response biosensors were applied to fine-tune the target ATP synthesis gene, leading to the desired increase in pyruvate production (the highest 9.66 g/L) and cell growth. Moreover, the membrane synthesis gene plsC was also dynamically activated by the positive-response biosensor, leading to effective accumulation of lycopene in the cell membrane and a 50-fold increase in lycopene titre (100.3 mg/L) when compared with the control strain, demonstrating the effective and broader usages of our biosensors.

Strengthening microbial cell factories for efficient production of bioactive molecules

High demand of bioactive molecules (food additives, antibiotics, plant growth enhancers, cosmetics, pigments and other commercial products) is the prime need for the betterment of human life where the applicability of the synthetic chemical product is on the saturation due to associated toxicity and ornamentations. It has been noticed that the discovery and productivity of such molecules in natural scenarios are limited due to low cellular yields as well as less optimized conventional methods. In this respect, microbial cell factories timely fulfilling the requirement of synthesizing bioactive molecules by improving production yield and screening more promising structural homologues of the native molecule. Where the robustness of the microbial host can be potentially achieved by taking advantage of cell engineering approaches such as tuning functional and adjustable factors, metabolic balancing, adapting cellular transcription machinery, applying high throughput OMICs tools, stability of genotype/phenotype, organelle optimizations, genome editing (CRISPER/Cas mediated system) and also by developing accurate model systems via machine-learning tools. In this article, we provide an overview from traditional to recent trends and the application of newly developed technologies, for strengthening the systemic approaches and providing future directions for enhancing the robustness of microbial cell factories to speed up the production of biomolecules for commercial purposes.

Directed evolution of linker helix as an efficient strategy for engineering LysR-type transcriptional regulators as whole-cell biosensors

Whole-cell biosensors based on transcriptional regulators are powerful tools for rapid measurement, high-throughput screening, dynamic metabolic regulation, etc. To optimize the biosensing performance of transcriptional regulator, its effector-binding domain is commonly engineered. However, this strategy is encumbered by the limitation of diversifying such a large domain and the risk of affecting effector specificity. Molecular dynamics simulation of effector binding of LysG (an LysR-type transcriptional regulator, LTTR) suggests the crucial role of the short linker helix (LH) connecting effector- and DNA-binding domains in protein conformational change. Directed evolution of LH efficiently produced LysG variants with extended operational range and unaltered effector specificity. The whole-cell biosensor based on the best LysG^E58V variant outperformed the wild-type LysG in enzyme high-throughput screening and dynamic regulation of l-lysine biosynthetic pathway. LH mutations are suggested to affect DNA binding and facilitate transcriptional activation upon effector binding. LH engineering was also successfully applied to optimize another LTTR BenM for biosensing. Since LTTRs represent the largest family of prokaryotic transcriptional regulators with highly conserved structures, LH engineering is an efficient and universal strategy for development and optimization of whole-cell biosensors.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Spatial-temporal regulation of fatty alcohol biosynthesis in yeast

BACKGROUND

Construction of efficient microbial cell factories is one of the core steps for establishing green bio-manufacturing processes. However, the complex metabolic regulation makes it challenging in driving the metabolic flux toward the product biosynthesis. Dynamically coupling the biosynthetic pathways with the cellular metabolism at spatial-temporal manner should be helpful for improving the production with alleviating the cellular stresses.

RESULTS

In this study, we observed the mismatch between fatty alcohol biosynthesis and cellular metabolism, which compromised the fatty alcohol production in Saccharomyces cerevisiae. To enhance the fatty alcohol production, we spatial-temporally regulated fatty alcohol biosynthetic pathway by peroxisomal compartmentalization (spatial) and dynamic regulation of gene expression (temporal). In particular, fatty acid/acyl-CoA responsive promoters were identified by comparative transcriptional analysis, which helped to dynamically regulate the expression of acyl-CoA reductase gene MaFAR1 and improved fatty alcohol biosynthesis by 1.62-fold. Furthermore, enhancing the peroxisomal supply of acyl-CoA and NADPH further improved fatty alcohol production to 282 mg/L, 2.52 times higher than the starting strain.

CONCLUSIONS

This spatial-temporal regulation strategy partially coordinated fatty alcohol biosynthesis with cellular metabolism including peroxisome biogenesis and precursor supply, which should be applied for production of other products in microbes.

Synthetic biology promotes the capture of CO2 to produce fatty acid derivatives in microbial cell factories

Environmental problems such as greenhouse effect, the consumption of fossil energy, and the increase of human demand for energy are becoming more and more serious, which force researcher to turn their attention to the reduction of CO2 and the development of renewable energy. Unsafety, easy to lead to secondary environmental pollution, cost inefficiency, and other problems limit the development of conventional CO2 capture technology. In recent years, many microorganisms have attracted much attention to capture CO2 and synthesize valuable products directly. Fatty acid derivatives (e.g., fatty acid esters, fatty alcohols, and aliphatic hydrocarbons), which can be used as a kind of environmentally friendly and renewable biofuels, are sustainable substitutes for fossil energy. In this review, conventional CO2 capture techniques pathways, microbial CO2 concentration mechanisms and fixation pathways were introduced. Then, the metabolic pathway and progress of direct production of fatty acid derivatives from CO2 in microbial cell factories were discussed. The synthetic biology means used to design engineering microorganisms and optimize their metabolic pathways were depicted, with final discussion on the potential of optoelectronic-microbial integrated capture and production systems.

A Synthetic Biosensor for Detecting Putrescine in Beef Samples

Biogenic amines (BAs) are toxicological risks present in many food products. Putrescine is the most common foodborne BA and is frequently used as a quality control marker. Currently, there is a lack of regulation concerning safe putrescine limits in food as well as outdated food handling practices leading to unnecessary putrescine intake. Conventional methods used to evaluate BAs in food are generally time-consuming and resource-heavy with few options for on-site analysis. In response to this challenge, we have developed a transcription factor-based biosensor for the quantification of putrescine in beef samples. In this work, we use a naturally occurring putrescine responsive repressor-operator pair (PuuR-puuO) native to Escherichia coli. Moreover, we demonstrate the use of the cell-free putrescine biosensor on a paper-based device that enables rapid low-cost detection of putrescine in beef samples stored at different temperatures. The results presented demonstrate the potential role of using paper-based biosensors for on-site testing, particularly as an index for determining meat product stability and quality.

Combinatorial Metabolic Engineering Strategies for the Enhanced Production of Free Fatty Acids in Escherichia coli

In this study, we evaluated the effects of several metabolic engineering strategies in a systematic and combinatorial manner to enhance the free fatty acid (FFA) production in Escherichia coli. The strategies included (i) overexpression of mutant thioesterase I ('TesA^R64C) to efficiently release the FFAs from fatty acyl-ACP; (ii) coexpression of global regulatory protein FadR; (iii) heterologous expression of methylmalonyl-CoA carboxyltransferase and phosphoenolpyruvate carboxylase to synthesize fatty acid precursor molecule malonyl-CoA; and (iv) disruption of genes associated with membrane proteins (GusC, MdlA, and EnvR) to improve the cellular state and export the FFAs outside the cell. The synergistic effects of these genetic modifications in strain SBF50 yielded 7.2 ± 0.11 g/L FFAs at the shake flask level. In fed-batch cultivation under nitrogen-limiting conditions, strain SBF50 produced 33.6 ± 0.02 g/L FFAs with a productivity of 0.7 g/L/h from glucose, which is the maximum titer reported in E. coli to date. Combinatorial metabolic engineering approaches can prove to be highly useful for the large-scale production of FA-derived chemicals and fuels.

Establishing an Autonomous Cascaded Artificial Dynamic (AutoCAD) regulation system for improved pathway performance

Endogenous metabolic pathways in microbial cells are usually precisely controlled by sophisticated regulation networks. However, the lack of such regulations when introducing heterologous pathways in microbial hosts often causes unbalanced enzyme expression and carbon flux distribution, hindering the construction of highly efficient microbial biosynthesis systems. Here, using naringenin as the target compound, we developed an Autonomous Cascaded Artificial Dynamic (AutoCAD) regulation system to automatically coordinate the pathway expression and redirect carbon fluxes for enhanced naringenin production. The AutoCAD regulation system, consisting of both intermediate-based feedforward and product-based feedback control genetic circuits, resulted in a 16.5-fold increase in naringenin titer compared with the static control. Fed-batch fermentation using the strain with AutoCAD regulation further enhanced the naringenin titer to 277.2 mg/L. The AutoCAD regulation system, with intermediate-based feedforward control and product-triggered feedback control, provides a new paradigm of developing complicated cascade dynamic control to engineer heterologous pathways.

Optimization of Ultrahigh-Throughput Screening Assay for Protein Engineering of d-Allulose 3-Epimerase

d-Allulose is the corresponding epimer of d-fructose at the C-3 position, which exhibits a similar taste and sweetness to sucrose. As a low-calorie sweetener, d-allulose has broad application prospects in the fields of medicine, food, and so on. Currently, the production method of d-allulose is mainly the enzymatic conversion of d-fructose by d-allulose 3-epimerase (DAEase). However, the limited specific activity and thermal stability of DAEase restrict its industrial application. Herein, an ultrahigh-throughput screening assay based on the transcription factor PsiR was extensively optimized from the aspects of culture medium components, screening plasmid, and expression host, which enhanced the correction between the fluorescent readout and the enzyme activity. Then, the error-prone PCR (epPCR) library of Clostridium cellulolyticum H10 DAEase (CcDAEase) was screened through the above optimized method, and the variant I228V with improved specific activity and thermal stability was obtained. Moreover, after combining two beneficial substitutions, D281G and C289R, which were previously obtained by this optimized assay, the specific activity of the triple-mutation variant I228V/D281G/C289R reached up to 1.42-fold of the wild type (WT), while its half-life (T1/2) at 60 °C was prolonged by 62.97-fold. The results confirmed the feasibility of the optimized screening assay as a powerful tool for the directed evolution of DAEase.

A p-Coumaroyl-CoA Biosensor for Dynamic Regulation of Naringenin Biosynthesis in Saccharomyces cerevisiae

In vivo biosensors that can convert metabolite concentrations into measurable output signals are valuable tools for high-throughput screening and dynamic pathway control in the field of metabolic engineering. Here, we present a novel biosensor in Saccharomyces cerevisiae that is responsive to p-coumaroyl-CoA, a central precursor of many flavonoids. The sensor is based on the transcriptional repressor CouR from Rhodopseudomonas palustris and was applied in combination with a previously developed malonyl-CoA biosensor for dual regulation of p-coumaroyl-CoA synthesis within the naringenin production pathway. Using this approach, we obtained a naringenin titer of 47.3 mg/L upon external precursor feeding, representing a 15-fold increase over the nonregulated system.

Application of quorum sensing system in microbial synthesis of valuable chemicals: a mini-review

With advantages of low substrates cost, high optical purity of end products and environmentally friendly fermentation process, microbial production of valuable chemicals grow rapidly. Compared with static microbial strain engineering strategies, such as gene deletion, overexpression and mutation, dynamic pathway regulation is a new approach that balances cellular growth and chemical production. Quorum sensing is a natural microbial communication system responsible for cell-density-related cell behaviors. Accordingly, quorum sensing systems can be employed to achieve dynamic regulation in microorganisms without the need for manual intervention or the use of chemical inducers. In this review, natural quorum sensing systems are firstly summarized. Then, recent progress in using quorum sensing circuits in the field of metabolic engineering is highlighted. The current application challenges of quorum sensing systems and future perspectives in microbial synthesis of chemicals are also discussed.

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.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.