As temperature serves as a versatile input signal, thermoresponsive genetic controls have gained significant interest for recombinant protein production and metabolic engineering applications. The conventional thermoresponsive systems normally require the continuous exposure of heat stimuli to trigger the prolonged expression of targeted genes, and the accompanied heat-shock response is detrimental to the bioproduction process. In this study, we present the design of thermoresponsive quorum-sensing (ThermoQS) circuits to make Escherichia coli record transient heat stimuli. By conversion of the heat input into the accumulation of quorum-sensing molecules such as acyl-homoserine lactone derived from Pseudomonas aeruginosa, sustained gene expressions were achieved by a minimal heat stimulus. Moreover, we also demonstrated that we reprogrammed the E. coli Lac operon to make it respond to heat stimuli with an impressive signal-to-noise ratio (S/N) of 15.3. Taken together, we envision that the ThermoQS systems reported in this study are expected to remarkably diminish both design and experimental expenditures for future metabolic engineering applications.

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Natural products have long served as critical raw materials in chemical and pharmaceutical manufacturing, primarily which can provide superior scaffolds or intermediates for drug discovery and development. Over the last century, natural products have contributed to more than a third of therapeutic drug production. However, traditional methods of producing drugs from natural products have become less efficient and more expensive over the past few decades. The combined utilization of genome mining and synthetic biology based on genome sequencing, bioinformatics tools, big data analytics, genetic engineering, metabolic engineering, and systems biology promises to counter this trend. Here, we reviewed recent (2020-2023) examples of genome mining and synthetic biology used to resolve challenges in the production of natural products, such as less variety, poor efficiency, and low yield. Additionally, the emerging efficient tools, design principles, and building strategies of synthetic biology and its application prospects in NPs synthesis have also been discussed.

Advances in synthetic biology allow the design and manipulation of DNA from the scale of genes to genomes, enabling the engineering of complex genetic information for application in biomanufacturing, biomedicine and other areas. The transfer and subsequent maintenance of large DNA are two core steps in large scale genome rewriting. Compared to small DNA, the high molecular weight and fragility of large DNA make its transfer and maintenance a challenging process. This review outlines the methods currently available for transferring and maintaining large DNA in bacteria, fungi, and mammalian cells. It highlights their mechanisms, capabilities and applications. The transfer methods are categorized into general methods (e.g., electroporation, conjugative transfer, induced cell fusion-mediated transfer, and chemical transformation) and specialized methods (e.g., natural transformation, mating-based transfer, virus-mediated transfection) based on their applicability to recipient cells. The maintenance methods are classified into genomic integration (e.g., CRISPR/Cas-assisted insertion) and episomal maintenance (e.g., artificial chromosomes). Additionally, this review identifies the major technological advantages and disadvantages of each method and discusses the development for large DNA transfer and maintenance technologies.

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.

Withanolides are naturally occurring steroidal lactones found in certain species of the Withania genus, especially Withania somnifera (commonly known as Ashwagandha). These compounds have gained considerable attention due to their wide range of therapeutic properties and potential applications in modern medicine. To meet the rapidly growing demand for withanolides, innovative approaches such as in vitro culture techniques and synthetic biology offer promising solutions. In recent years, synthetic biology has enabled the production of engineered withanolides using heterologous systems, such as yeast and bacteria. Additionally, in vitro methods like cell suspension culture and hairy root culture have been employed to enhance withanolide production. Nevertheless, one of the primary obstacles to increasing the production of withanolides using these techniques has been the intricacy of the biosynthetic pathways for withanolides. The present article examines new developments in withanolide production through in vitro culture. A comprehensive summary of viable traditional methods for producing withanolide is also provided. The development of withanolide production in heterologous systems is examined and emphasized. The use of machine learning as a potent tool to model and improve the bioprocesses involved in the generation of withanolide is then discussed. In addition, the control and modification of the withanolide biosynthesis pathway by metabolic engineering mediated by CRISPR are discussed.

This paper will focus on analyzing the argument with appealing to nature against synthetic biology and provide a counter-argument against it through demonstrating the ambiguity of the concept of nature, denying the existence of a morally significant line between natural and non/unnatural, and disproving the allegations against synthetic biology raised by the argument appealing to nature. The paper consists of two parts following a brief introduction. The first part will describe the argument appealing to nature against synthetic biology, and identify the deficiencies of the argument per se, e.g., the ambiguity of the concept \'nature\'; and the problems in the morally significant line between the natural and the non/unnatural. The second part will discuss the allegations to synthetic biology stemming from this argument, e.g., committing metaphysical and ethical mistakes, and doing possible harms to the environment.

Hierarchical compartmentalization responding to changes in intracellular and extracellular environments is ubiquitous in living eukaryotic cells but remains a formidable task in synthetic systems. Here we report a two-level compartmentalization approach based on a thermo-responsive aqueous two-phase system (TR-ATPS) comprising poly(N-isopropylacrylamide) (PNIPAM) and dextran (DEX). Liquid membraneless compartments enriched in PNIPAM are phase-separated from the continuous DEX solution via liquid-liquid phase separation at 25 °C and shrink dramatically with small second-level compartments generated at the interface, resembling the structure of colloidosome, by increasing the temperature to 35 °C. The TR-ATPS can store biomolecules, program the spatial distribution of enzymes, and accelerate the overall biochemical reaction efficiency by nearly 7-fold. The TR-ATPS inspires on-demand, stimulus-triggered spatiotemporal enrichment of biomolecules via two-level compartmentalization, creating opportunities in synthetic biology and biochemical engineering.

Applying low-cost substrate is critical for sustainable bioproduction. Co-culture of phototrophic and heterotrophic microorganisms can be a promising solution as they can use CO2 and light as feedstock. This study aimed to create a light-driven consortium using a marine cyanobacterium Synechococcus sp. PCC 7002 and an industrial yeast Yarrowia lipolytica. First, the cyanobacterium was engineered to accumulate and secrete sucrose by regulating the expression of genes involved in sucrose biosynthesis and transport, resulting in 4.0 g/L of sucrose secretion. Then, Yarrowia lipolytica was engineered to efficiently use sucrose and produce β-caryophyllene that has various industrial applications. Then, co- and sequential-culture were optimized with different induction conditions and media compositions. A maximum β-caryophyllene yield of 14.1 mg/L was obtained from the co-culture. This study successfully established an artificial light-driven consortium based on a marine cyanobacterium and Y. lipolytica, and provides a foundation for sustainable bioproduction from CO2 and light through co-culture systems.

In recent years, genetic circuit-based regulation of metabolic flux in microbial cell factories has received significant attention. In this review, we describe a pipeline for the design and construction of genetic circuits for metabolic flux optimization. In particular, we summarize the recent advances in computationally assisted prediction of critical metabolic nodes and genetic circuit design automation. Further, we introduce strategies for constructing high-performance genetic circuits. We also summarize the latest applications of genetic circuits in the dynamic regulation of metabolism and high-throughput screening. Finally, we discuss the challenges and prospects associated with the design and construction of sophisticated genetic circuits. Through this review, we aim to provide a theoretical basis for designing and constructing high-performance genetic circuits to optimize metabolic flux.