Development of a Novel Biosensor-Driven Mutation and Selection System via in situ Growth of Corynebacterium crenatum for the Production of L-Arginine

Engineering of L-threonine and L-proline biosensors by directed evolution of transcriptional regulator SerR and application for high-throughput screening

Amino acids are important bio-based products with a multi-billion-dollar market. The development of efficient high-throughput screening technologies utilizing biosensors is essential for the rapid identification of high-performance amino acid producers. However, there remains a pressing need for biosensors that specifically target certain critical amino acids, such as L-threonine and L-proline. In this study, a novel transcriptional regulator-based biosensor for L-threonine and L-proline was successfully developed, inspired by our new finding that SerE can export L-proline in addition to the previously known L-threonine and L-serine. Through directed evolution of SerR (the corresponding transcriptional regulator of SerE), the mutant SerRF104I which can recognize both L-threonine and L-proline as effectors and effectively distinguish strains with varying production levels was identified. Subsequently, the SerRF104I-based biosensor was employed for high-throughput screening of the superior enzyme mutants of L-homoserine dehydrogenase and γ-glutamyl kinase, which are critical enzymes in the biosynthesis of L-threonine and L-proline, respectively. A total of 25 and 13 novel mutants that increased the titers of L-threonine and L-proline by over 10% were successfully identified. Notably, six of the newly identified mutants exhibited similarities to the most effective mutants reported to date, indicating the promising application potential of the SerRF104I-based biosensor. This study illustrates an effective strategy for the development of transcriptional regulator-based biosensors for amino acids and other chemical compounds.

Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors

Transcription factor (TF)-based biosensors (TFBs) have received considerable attention in various fields due to their capability of converting biosignals, such as molecule concentrations, into analyzable signals, thereby bypassing the dependence on time-consuming and laborious detection techniques. Natural TFs are evolutionarily optimized to maintain microbial survival and metabolic balance rather than for laboratory scenarios. As a result, native TFBs often exhibit poor performance, such as low specificity, narrow dynamic range, and limited sensitivity, hindering their application in laboratory and industrial settings. This work analyzes four types of regulatory mechanisms underlying TFBs and outlines strategies for constructing efficient sensing systems. Recent advances in TFBs across various usage scenarios are reviewed with a particular focus on the challenges of commercialization. The systematic improvement of TFB performance by modifying the constituent elements is thoroughly discussed. Additionally, we propose future directions of TFBs for developing rapid-responsive biosensors and addressing the challenge of application isolation. Furthermore, we look to the potential of artificial intelligence (AI) technologies and various models for programming TFB genetic circuits. This review sheds light on technical suggestions and fundamental instructions for constructing and engineering TFBs to promote their broader applications in Industry 4.0, including smart biomanufacturing, environmental and food contaminants detection, and medical science.

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

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

Biological importance of arginine: A comprehensive review of the roles in structure, disorder, and functionality of peptides and proteins

Arginine shows Jekyll and Hyde behavior in several respects. It participates in protein folding via ionic and H-bonds and cation-pi interactions; the charge and hydrophobicity of its side chain make it a disorder-promoting amino acid. Its methylation in histones; RNA binding proteins; chaperones regulates several cellular processes. The arginine-centric modifications are important in oncogenesis and as biomarkers in several cardiovascular diseases. The cross-links involving arginine in collagen and cornea are involved in pathogenesis of tissues but have also been useful in tissue engineering and wound-dressing materials. Arginine is a part of active site of several enzymes such as GTPases, peroxidases, and sulfotransferases. Its metabolic importance is obvious as it is involved in production of urea, NO, ornithine and citrulline. It can form unusual functional structures such as molecular tweezers in vitro and sprockets which engage DNA chains as part of histones in vivo. It has been used in design of cell-penetrating peptides as drugs. Arginine has been used as an excipient in both solid and injectable drug formulations; its role in suppressing opalescence due to liquid-liquid phase separation is particularly very promising. It has been known as a suppressor of protein aggregation during protein refolding. It has proved its usefulness in protein bioseparation processes like ion-exchange, hydrophobic and affinity chromatographies. Arginine is an amino acid, whose importance in biological sciences and biotechnology continues to grow in diverse ways.

Metabolic reprogramming and biosensor-assisted mutagenesis screening for high-level production of L-arginine in Escherichia coli

L-arginine is a value-added amino acid with promising applications in the pharmaceutical and nutraceutical industries. Further unleashing the potential of microbial cell factories to make L-arginine production more competitive remains challenging due to the sophisticated intracellular interaction networks and the insufficient knowledge of global metabolic regulation. Here, we combined multilevel rational metabolic engineering with biosensor-assisted mutagenesis screening to exploit the L-arginine production potential of Escherichia coli. First, multiple metabolic pathways were systematically reprogrammed to redirect the metabolic flux into L-arginine synthesis, including the L-arginine biosynthesis, TCA cycle, and L-arginine export. Specifically, a toggle switch responding to special cellular physiological conditions was designed to dynamically control the expression of sucA and pull more carbon flux from the TCA cycle toward L-arginine biosynthesis. Subsequently, a biosensor-assisted high-throughput screening platform was designed and applied to further exploit the L-arginine production potential. The best-engineered ARG28 strain produced 132 g/L L-arginine in a 5-L bioreactor with a yield of 0.51 g/g glucose and productivity of 2.75 g/(L ⋅ h), which were the highest values reported so far. Through whole genome sequencing and reverse engineering, Frc frameshift mutant, PqiB A78P mutant, and RpoB P564T mutant were revealed for enhancing the L-arginine biosynthesis. Our study exhibited the power of coupling rational metabolic reprogramming and biosensor-assisted mutagenesis screening to unleash the cellular potential for value-added metabolite production.

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

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

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.

Feedback regulation and coordination of the main metabolism for bacterial growth and metabolic engineering for amino acid fermentation

Living organisms such as bacteria are often exposed to continuous changes in the nutrient availability in nature. Therefore, bacteria must constantly monitor the environmental condition, and adjust the metabolism quickly adapting to the change in the growth condition. For this, bacteria must orchestrate (coordinate and integrate) the complex and dynamically changing information on the environmental condition. In particular, the central carbon metabolism (CCM), monomer synthesis, and macromolecular synthesis must be coordinately regulated for the efficient growth. It is a grand challenge in bioscience, biotechnology, and synthetic biology to understand how living organisms coordinate the metabolic regulation systems. Here, we consider the integrated sensing of carbon sources by the phosphotransferase system (PTS), and the feed-forward/feedback regulation systems incorporated in the CCM in relation to the pool sizes of flux-sensing metabolites and αketoacids. We also consider the metabolic regulation of amino acid biosynthesis (as well as purine and pyrimidine biosyntheses) paying attention to the feedback control systems consisting of (fast) enzyme level regulation with (slow) transcriptional regulation. The metabolic engineering for the efficient amino acid production by bacteria such as Escherichia coli and Corynebacterium glutamicum is also discussed (in relation to the regulation mechanisms). The amino acid synthesis is important for determining the rate of ribosome biosynthesis. Thus, the growth rate control (growth law) is further discussed on the relationship between (p)ppGpp level and the ribosomal protein synthesis.

MarR-type transcription factor RosR regulates glutamate metabolism network and promotes accumulation of L-glutamate in Corynebacterium glutamicum G01

Transcription factors (TFs) perform a crucial function in the regulation of amino acids biosynthesis. Here, TFs involved in L-glutamate biosynthesis in Corynebacterium glutamicum were investigated. Compared to transcriptomic results of C. glutamicum 13032, 7 TFs regulated to glutamate biosynthesis were indentifed in G01 and E01. Among them, RosR was demonstrated to regulate L-glutamate metabolic network by binding to the promoters of glnA, pqo, ilvB, ilvN, ilvC, ldhA, odhA, dstr1, fas, argJ, ak and pta. Overexpression of RosR in G01 resulted in significantly decreased by-products yield and improved L-glutamate titer (130.6 g/L) and yield (0.541 g/g from glucose) in fed-batch fermentation. This study demonstrated the L-glutamate production improved by the expression of TFs in C. glutamicum, which provided a good reference for the transcriptional regulation engineering of strains for amino acid biosynthesis and suggested further metabolic engineering of C. glutamicum for L-glutamate production.

Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum

Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.

l-arginine production in Corynebacterium glutamicum: manipulation and optimization of the metabolic process

As an important semi-essential amino acid, l-arginine is extensively used in the food and pharmaceutical fields. At present, l-arginine production depends on cost-effective, green, and sustainable microbial fermentation by using a renewable carbon source. To enhance its fermentative production, various metabolic engineering strategies have been employed, which provide valid paths for reducing the cost of l-arginine production. This review summarizes recent advances in molecular biology strategies for the optimization of l-arginine-producing strains, including manipulating the principal metabolic pathway, modulating the carbon metabolic pathway, improving the intracellular biosynthesis of cofactors and energy usage, manipulating the assimilation of ammonia, improving the transportation and membrane permeability, and performing biosensor-assisted high throughput screening, providing useful insight into the current state of l-arginine production.