Development of a Transcription Factor-Based Diamine Biosensor in Corynebacterium glutamicum

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Directed Evolution of Protein-Based Sensors for Anaerobic Biological Activation of Methane

Microbial alkane degradation pathways provide biological routes for converting these hydrocarbons into higher-value products. We recently reported the functional expression of a methyl-alkylsuccinate synthase (Mas) system in Escherichia coli, allowing for the heterologous anaerobic activation of short-chain alkanes. However, the enzymatic activation of methane via natural or engineered alkylsuccinate synthases has yet to be reported. To address this, we employed high-throughput screening to engineer the itaconate (IA)-responsive regulatory protein ItcR (WT-ItcR) from Yersinia pseudotuberculosis to instead respond to methylsuccinate (MS, the product of methane addition to fumarate), resulting in genetically encoded biosensors for MS. Here, we describe ItcR variants that, when regulating fluorescent protein expression in E. coli, show increased sensitivity, improved overall response, and enhanced specificity toward exogenously added MS relative to the wild-type repressor. Structural modeling and analysis of the ItcR ligand binding pocket provide insights into the altered molecular recognition. In addition to serving as biosensors for screening alkylsuccinate synthases capable of methane activation, MS-responsive ItcR variants also establish a framework for the directed evolution of other molecular reporters, targeting longer-chain alkylsuccinate products or other succinate derivatives.

Accelerating the design of pili-enabled living materials using an integrative technological workflow

Bacteria can be programmed to create engineered living materials (ELMs) with self-healing and evolvable functionalities. However, further development of ELMs is greatly hampered by the lack of engineerable nonpathogenic chassis and corresponding programmable endogenous biopolymers. Here, we describe a technological workflow for facilitating ELMs design by rationally integrating bioinformatics, structural biology and synthetic biology technologies. We first develop bioinformatics software, termed Bacteria Biopolymer Sniffer (BBSniffer), that allows fast mining of biopolymers and biopolymer-producing bacteria of interest. As a proof-of-principle study, using existing pathogenic pilus as input, we identify the covalently linked pili (CLP) biosynthetic gene cluster in the industrial workhorse Corynebacterium glutamicum. Genetic manipulation and structural characterization reveal the molecular mechanism of the CLP assembly, ultimately enabling a type of programmable pili for ELM design. Finally, engineering of the CLP-enabled living materials transforms cellulosic biomass into lycopene by coupling the extracellular and intracellular bioconversion ability.

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.

Modulating Sensitivity of an Erythromycin Biosensor for Precise High-Throughput Screening of Strains with Different Characteristics

Genetically encoded biosensors are powerful tools for product-driven high-throughput screening in synthetic biology and metabolic engineering. However, most biosensors can only properly function in a limited concentration cutoff, and the incompatible performance characteristics of biosensors will lead to false positives or failure in screening. The transcription factor (TF)-based biosensors are usually organized in modular architecture and function in a regulator-depended manner, whose performance properties can be fine-tuned by modifying the expression level of the TF. In this study, we modulated the performance characteristics, including sensitivity and operating range, of an MphR-based erythromycin biosensor by fine-adjusting regulator expression levels via ribosome-binding site (RBS) engineering and obtained a panel of biosensors with varied sensitivities by iterative fluorescence-assisted cell sorting (FACS) in Escherichia coli to accommodate different screening purposes. To exemplify their application potential, two engineered biosensors with 10-fold different sensitivities were employed in the precise high-throughput screening by microfluidic-based fluorescence-activated droplet sorting (FADS) of Saccharopolyspora erythraea mutant libraries with different starting erythromycin productions, and mutants representing as high as 6.8 folds and over 100% of production improvements were obtained starting from the wild-type strain and the high-producing industrial strain, respectively. This work demonstrated a simple strategy to engineer biosensor performance properties, which was significant to stepwise strain engineering and production improvement.

Synthetic biology-based bioreactor and its application in biochemical analysis

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

Biosystem design of Corynebacterium glutamicum for bioproduction

Corynebacterium glutamicum, a natural glutamate-producing bacterium adopted for industrial production of amino acids, has been extensively explored recently for high-level biosynthesis of amino acid derivatives, bulk chemicals such as organic acids and short-chain alcohols, aromatics, and natural products, including polyphenols and terpenoids. Here, we review the recent advances with a focus on biosystem design principles, metabolic characterization and modeling, omics analysis, utilization of nonmodel feedstock, emerging CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) tools for Corynebacterium strain engineering, biosensors, and novel strains of C. glutamicum. Future research directions for developing C. glutamicum cell factories are also discussed.

Development of a Transcriptional Factor PuuR-Based Putrescine-Specific Biosensor in Corynebacterium glutamicum

Corynebacterium glutamicum is regarded as an industrially important microbial cell factory and is widely used to produce various value-added chemicals. Because of the importance of C. glutamicum applications, current research is increasingly focusing on developing C. glutamicum synthetic biology platforms. Because of its ability to condense with adipic acid to synthesize the industrial plastic nylon-46, putrescine is an important platform compound of industrial interest. Developing a high-throughput putrescine biosensor can aid in accelerating the design-build-test cycle of cell factories (production strains) to achieve high putrescine-generating strain production in C. glutamicum. This study developed a putrescine-specific biosensor (pSenPuuR) in C. glutamicum using Escherichia coli-derived transcriptional factor PuuR. The response characteristics of the biosensor to putrescine were further improved by optimizing the genetic components of pSenPuuR, such as the response promoter, reporter protein, and promoter for controlling PuuR expression. According to the findings of the study, pSenPuuR has the potential to be used to assess putrescine production in C. glutamicum and is suitable for high-throughput genetic variant screening.

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.

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.

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.

Construction and Application of a High-Throughput In Vivo Screening Platform for the Evolution of Nitrile Metabolism-Related Enzymes Based on a Desensitized Repressive Biosensor

Transcription factor (TF)-based biosensors are expected to serve as powerful tools for the high-throughput screening of biocatalytic systems; however, most of them respond to ligands in a narrow concentration range, which limits their application. In this study, we constructed a heterogenous niacin biosensor using the repressive TF BsNadR and its target promoters from Bacillus subtilis. The fine-tunable output of the niacin biosensor was expanded to a wide range of niacin concentrations (0-50 mM) through desensitization engineering, which was suitable for the accurate identification of differences in enzyme activity. Structural mechanism analysis indicated that weakening the affinity of BsNadR with the ligand niacin and with DNA alters its regulatory properties. Based on the desensitized niacin biosensor, a high-throughput in vivo screening platform was developed for evolving nitrile metabolism-related enzymes. The evolved nitrilase, amidase, and nitrile hydratase with 6.6-, 2.1-, and 21.3-fold improvements in activity were achieved, respectively. In addition, these mutants also exhibited elevated activity toward other cognate substrates, indicating the broad applicability of the screening platform. This study not only provided a universal high-throughput screening platform for different nitrile metabolism-related enzymes but also demonstrated the advantages of repressive biosensors and the vital role of desensitization engineering of the TF in the development of high-throughput screening platforms for enzymes.