Synthetic Biology Research Articles

Probing the orthogonality and robustness of the mammalian RNA-binding protein Musashi-1 in Escherichia coli

RNA recognition motifs (RRMs) are widespread RNA-binding protein domains in eukaryotes, which represent promising synthetic biology tools due to their compact structure and efficient activity. Yet, their use in prokaryotes is limited and their functionality poorly characterized. Recently, we repurposed a mammalian Musashi protein containing two RRMs as a translation regulator in Escherichia coli. Here, employing high-throughput RNA sequencing, we explored the impact of Musashi expression on the transcriptomic and translatomic profiles of E. coli, revealing certain metabolic interference, induction of post-transcriptional regulatory processes, and spurious protein-RNA interactions. Engineered Musashi protein mutants displayed compromised regulatory activity, emphasizing the importance of both RRMs for specific and sensitive RNA binding. We found that a mutation known to impede allosteric regulation led to similar translation control activity. Evolutionary experiments disclosed a loss of function of the synthetic circuit in about 40 generations, with the gene coding for the Musashi protein showing a stability comparable to other heterologous genes. Overall, this work expands our understanding of RRMs for post-transcriptional regulation in prokaryotes and highlight their potential for biotechnological and biomedical applications.

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

Engineering quorum sensing-based genetic circuits enhances growth and productivity robustness of industrial E. coli at low pH

BackgroundMicrobial organisms hold significant potential for converting renewable substrates into valuable chemicals. Low pH fermentation in industrial settings offers key advantages, including reduced neutralizer usage and decreased wastewater generation, particularly in the production of amino acids and organic acids. Engineering acid-tolerant strains represents a viable strategy to enhance productivity in acidic environments. Synthetic biology provides dynamic regulatory tools, such as gene circuits, facilitating precise expression of acid resistance (AR) modules in a just-in-time and just-enough manner.ResultsIn this study, we aimed to enhance the robustness and productivity of Escherichia coli, a workhorse for amino acid and organic acid production, in industrial fermentation under mild acidic conditions. We employed an Esa-type quorum sensing circuit to dynamically regulate the expression of an AR module (DsrA-Hfq) in a just-in-time and just-enough manner. Through careful engineering of the critical promoter PesaS and stepwise evaluation, we developed an optimal Esa-PBD(L) circuit that conferred upon an industrial E. coli strain SCEcL3 comparable lysine productivity and enhanced yield at pH 5.5 compared to the parent strain at pH 6.8.ConclusionsThis study exemplifies the practical application of gene circuits in industrial environments, which present challenges far beyond those of well-controlled laboratory conditions.

Категория «синтетическое» в современном естествознании

The article is devoted to rethinking the content of the category ‘synthetic’ in the conditions of modern natural science (fi rst of all, synthetic biology) and its epistemological value. In this paper the category ‘synthetic’ is explored as a product of the epistemological method. The authors carried out an exposition of ‘the synthetic’ category based on the most modern materials of scientifi c and philosophical research. The article attempts to determine the content of the category ‘synthetic’, its place and scope in modern science, by taking into account options for defi ning ‘synthetic’, its advantages and disadvantages. The works of J. Schummer, in which he develops an ontology of biochemical interactions and the epistemic possibilities arising in this connection, were taken as a basis for the classifi cation and epistemological evaluation of specifi c manifestations of ‘the synthetic’ in the scientifi c fi eld. The concept of F. Bianchini defi nes any ‘synthetic’ as ‘artifi cial’. The article shows that the category ‘synthetic’ is considered as a theoretical construct that allows for the comprehension of phenomena which epistemological status cannot be unambiguously defi ned as ‘natural’. In the process of convergence of technosciences, there is a tendency towards the emergence of the ‘synthetic’ phenomenon as the main resource through which scientifi c knowledge is obtained. Due to the achievements of synthetic biology in modern science, the problem of the relationship between ‘natural’ and ‘artifi cial’ is reshaped. This happens due to the qualitative and quantitative expansion of the phenomenon of ‘artifi cial’ / ‘synthetic’ and rethinking the differences between them. Recent advances in the synthesis of organisms and their structural parts, such as the extended genome, allows for ontologists and epistemologists signifi cantly expand the possibilities of categorizing ‘the synthetic’ and build an ontology of ‘the synthetic’.

A hierarchy of metabolite exchanges in metabolic models of microbial species and communities.

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells' metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.

Biosensing strategies using recombinant luminescent proteins and their use for food and environmental analysis.

Progress in synthetic biology and nanotechnology plays at present a major role in the fabrication of sophisticated and miniaturized analytical devices that provide the means to tackle the need for new tools and methods for environmental and food safety. Significant research efforts have led to biosensing experiments experiencing a remarkable growth with the development and application of recombinant luminescent proteins (RLPs) being at the core of this boost. Integrating RLPs into biosensors has resulted in highly versatile detection platforms. These platforms include luminescent enzyme-linked immunosorbent assays (ELISAs), bioluminescence resonance energy transfer (BRET)-based sensors, and genetically encoded luminescent biosensors. Increased signal-to-noise ratios, rapid response times, and the ability to monitor dynamic biological processes in live cells are advantages inherent to the approaches mentioned above. Furthermore, novel fusion proteins and optimized expression systems to improve their stability, brightness, and spectral properties have enhanced the performance and pertinence of luminescent biosensors in diverse fields. This review highlights recent progress in RLP-based biosensing, showcasing their implementation for monitoring different contaminants commonly found in food and environmental samples. Future perspectives and potential challenges in these two areas of interest are also addressed, providing a comprehensive overview of the current state and a forecast of the biosensing strategies using recombinant luminescent proteins to come.

Developing a curriculum cluster in "Synthetic Biology" to cultivate inter-disciplinary and innovative talents for biomanufacturing

Synthetic Biology, as an emerging discipline, has gained widespread attention and is developing rapidly, profoundly impacting the fields of life sciences and biotechnology. Concurrently, as emerging engineering education programs take shape, accelerated cultivation of multifaceted innovative talents represents a new mission and imperative for higher education in China. In the context of the flourishing development of Synthetic Biology, East China University of Science and Technology has established a curriculum cluster in Synthetic Biology, focusing on microbiological drug discovery and biomanufacturing. The teaching team initially reviewed the curriculum system related to Synthetic Biology and its upstream and downstream courses. Subsequently, they expanded the core courses in Synthetic Biology, creating a curriculum cluster that encompasses not only the theoretical foundations and cutting-edge technologies but also integrates with related disciplines. Moreover, the curriculum cluster leverages lectures from renowned domestic and international professors in the State Key Laboratory of Bioreactor Engineering, and harnesses the rich resources of the Program of Introducing Talents of Discipline to Universities (the "111 plan"), aiming to enhance students' innovation capabilities. With the support of this curriculum cluster and teaching team, undergraduate students actively participate in international Synthetic Biology competitions like international genetic engineering machine competition (iGEM), consistently achieving gold awards. Furthermore, many students have applied for patents and made contributions to research paper publications. This work stands as a valuable exemplar for cultivating multifaceted talents with exceptional innovative capabilities.

Design and practice of integrating artificial intelligence into the teaching of "Synthetic Biology" under the background of discipline crossing

In recent years, artificial intelligence has been employed to empower synthetic biology, demonstrating great potential in the simulation and prediction of protein structures as well as the design and optimization of regulatory elements and metabolic networks. Integrating artificial intelligence into the teaching of Synthetic Biology is in line with the development trends of synthetic biology and can promote the cultivation of interdisciplinary high-level talents and collaborative innovation. This paper expounds the idea of integrating artificial intelligence into the teaching of Synthetic Biology from establishing interdisciplinary course contents and teaching methods, simultaneously considering the fundamentals and application of artificial intelligence in synthetic biology, cultivating independent learning and innovative practice abilities, and enhancing the ethics education related to artificial intelligence. Furthermore, a system integrating artificial intelligence with the teaching contents of Synthetic Biology is designed, which focuses on supplementing fundamentals of artificial intelligence and incorporating artificial intelligence into the classroom and experimental teaching contents of Synthetic Biology. Moreover, with the course of Synthetic Biology in Jiangnan University as an example, this paper presents the pathway of integrating artificial intelligence into the teaching of this course under the background of discipline crossing. Finally, the teaching effects are expected.

Combining systems and synthetic biology for in vivo enzymology.

Enzymatic parameters are classically determined in vitro, under conditions that are far from those encountered in cells, casting doubt on their physiological relevance. We developed a generic approach combining tools from synthetic and systems biology to measure enzymatic parameters in vivo. In the context of a synthetic carotenoid pathway in Saccharomyces cerevisiae, we focused on a phytoene synthase and three phytoene desaturases, which are difficult to study in vitro. We designed, built, and analyzed a collection of yeast strains mimicking substantial variations in substrate concentration by strategically manipulating the expression of geranyl-geranyl pyrophosphate (GGPP) synthase. We successfully determined in vivo Michaelis-Menten parameters (KM, Vmax, and kcat) for GGPP-converting phytoene synthase from absolute metabolomics, fluxomics and proteomics data, highlighting differences between in vivo and in vitro parameters. Leveraging the versatility of the same set of strains, we then extracted enzymatic parameters for two of the three phytoene desaturases. Our approach demonstrates the feasibility of assessing enzymatic parameters directly in vivo, providing a novel perspective on the kinetic characteristics of enzymes in real cellular conditions.

Preface to the special issue “Synthetic Biology and Bioproduction in Plants”

Preface to the special issue “Synthetic Biology and Bioproduction in Plants”

High-Speed Imaging of Giant Unilamellar Vesicle Formation in cDICE.

Giant unilamellar vesicles (GUVs) are widely used as in vitro model membranes in biophysics and as cell-sized containers in synthetic biology. Despite their ubiquitous use, there is no one-size-fits-all method for their production. Numerous methods have been developed to meet the demanding requirements of reproducibility, reliability, and high yield while simultaneously achieving robust encapsulation. Emulsion-based methods are often praised for their apparent simplicity and good yields; hence, methods like continuous droplet interface crossing encapsulation (cDICE), which make use of this principle, have gained popularity. However, the underlying physical principles governing the formation of GUVs in cDICE and related methods remain poorly understood. To this end, we have developed a high-speed microscopy setup that allows us to visualize GUV formation in real time. Our experiments reveal a complex droplet formation process occurring at the capillary orifice, generating >30 μm-sized droplets and only in some cases GUV-sized (∼15 μm) satellite droplets. According to existing theoretical models, the oil-water interface should allow for the crossing of all droplets, but based on our observations and scaling arguments on the fluid dynamics within the system, we find a size-selective crossing of GUV-sized droplets only. The origin of these droplets remains partly unclear; we hypothesize that some small GUVs might be formed from large droplets sitting at the second interface. Finally, we demonstrate that proteins in the inner solution affect GUV formation by increasing the viscosity and altering the lipid adsorption kinetics. These results will not only contribute to a better understanding of GUV formation processes in cDICE but ultimately also aid in the development of more reliable and efficient methods for GUV production.

Biosafety risks, control, and biocontainment of engineered microalgae

Microalgae, with the ability to harness solar energy to fix CO2 and convert it into organic compounds, have emerged as promising green cell factories. With the rapid development of cutting-edge biotechnologies, the research and application of photosynthetic microalgae have been expanding, leading to comprehensive and in-depth engineering of microalgae. The synthetic biology and genome editing technologies have enabled the applications of microalgae in medicine, agriculture, food, energy, and the environment. However, the survival and spreading of engineered microalgae in the natural environment pose potential safety risks to ecosystems and human health. To curb the risks caused by the spreading of engineered microalgae in the environment, biosafety policies should be formulated for engineered microalgae and the prevention and control technologies should be developed. Toward this goal, researchers have developed biocontainment systems, including positive strategies such as the design of toxic protein-based kill switches and passive strategies such as knocking out essential genes to construct the strains with nutritional deficiencies, thereby spatially containing engineered microalgae. This article summarizes the application of cutting-edge biotechnologies in the engineering of microalgae, the biosafety risks and management regulations associated with the escape of engineered microalgae, and the progress in novel biocontainment technologies established for engineered microalgae. Finally, this article gives insights into the future development direction of microalgae biocontainment.

Synthetic biology in plants

Synthetic biology in plants

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits.

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates dis-continuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

Directed evolution of an orthogonal transcription engine for programmable gene expression in eukaryotes.

T7 RNA polymerase has enabled orthogonal control of gene expression and recombinant protein production across diverse prokaryotic host chassis organisms for decades. However, the absence of 5' methyl guanosine caps on T7 RNAP derived transcripts has severely limited its utility and widespread adoption in eukaryotic systems. To address this shortcoming, we evolved a fusion enzyme combining T7 RNAP with the single subunit capping enzyme from African swine fever virus using Saccharomyces cerevisiae. We isolated highly active variants of this fusion enzyme, which exhibited roughly two orders of magnitude higher protein expression compared to the wild-type enzyme. We demonstrate the programmable control of gene expression using T7 RNAP-based genetic circuits in yeast and validate enhanced performance of these engineered variants in mammalian cells. This study presents a robust, orthogonal gene regulatory system applicable across diverse eukaryotic hosts, enhancing the versatility and efficiency of synthetic biology applications.

Research progress in bacterial cellulose synthase subunit diversity and fiber structure formation

Bacterial cellulose (BC) is the glucose polymer produced by bacterial metabolism. The bacterial cellulose synthase (BCS) is the key enzyme for catalyzing the formation of BC. The cooperation between different submits of BCS is necessary for the intracellular formation and extracellular secretion of BC. This review summarized the BC-producing strains and the differences of BCS among different strains. Furthermore, we detailed the BC synthesis mechanism, the interactions between BCS subunits, and the relationship between the structural characteristics of strains and the formation of highly ordered fiber structures. A comprehensive insight into the mechanism of BC synthesis and secretion will supply more strategies for optimizing the BC synthesis via methods of synthetic biology.

Distribution, biosynthesis, and synthetic biology of phenylethanoid glycosides in the order Lamiales

Distribution, biosynthesis, and synthetic biology of phenylethanoid glycosides in the order Lamiales

Substrate-Multiplexed Assessment of Aromatic Prenyltransferase Activity.

An increasingly effective strategy to identify synthetically useful enzymes is to sample the diversity already present in Nature. Here, we construct and assay a panel of phylogenetically diverse aromatic prenyltransferases (PTs). These enzymes catalyze a variety of C-C bond forming reactions in natural product biosynthesis and are emerging as tools for synthetic chemistry and biology. Homolog screening was further empowered through substrate-multiplexed screening, which provides direct information on enzyme specificity. We perform a head-to-head assessment of the model members of the PT family and further identify homologs with divergent sequences that rival these superb enzymes. This effort revealed the first bacterial O-Tyr PT and, together, provide valuable benchmarking for future synthetic applications of PTs.

A novel Affi-Cova magnetic nanoparticles for one-step covalent immobilization of His-tagged enzyme directly from crude cell lysate

Owing to the rapid advancement of in vitro synthetic biology, functional carriers capable of covalently binding target proteins from crude lysates under mild conditions have garnered escalating attention. Herein, a magnetic nanoparticle with affinity/covalent bifunction (MNP@Affi-Cova) was developed for the direct covalent immobilization of the recombinant enzyme of His-tagged birA (r-birA) from crude cell lysates in a single step. This innovative approach is attributed to the presence of chelated Ni2+ ions and epoxy groups on the surface of the beads. The fabricated magnetic nanoparticles were characterized by SEM, FT-IR spectrum, and zeta potential. The application conditions and stability of the MNP@Affi-Cova beads were systematically evaluated. Notably, the MNP@Affi-Cova beads exhibited a covalent capture efficiency of 91.25 μg r-birA/mg beads from a cell lysate supernatant containing 2.62 mg/mL crude protein. The immobilized r-birA exhibited significantly enhanced pH and thermal stability compared to the free counterpart. Additionally, the reusability of the immobilized r-birA on MNP@Affi-Cova demonstrated the retention of 76.1 % of its initial activity over ten cycles. These results suggest that the MNP@Affi-Cova presents considerable potential as a support for the covalent immobilization of recombinant His-tagged enzymes directly from crude lysates, thereby circumventing the labor-intensive purification process typically required before enzyme immobilization.

Treatment of neurologic pathology and inflammation in Machado-Joseph disease through in vivo self-assembled siRNA.

Machado-Joseph disease, also known as Spinocerebellar ataxia type 3 (MJD/SCA3), is a fatal autosomal dominant hereditary ataxia characterized by cerebellar ataxia resulting from the abnormal expansion of CAG repeats in exon 10 of the ATXN3 gene. Presently, there is no effective treatment for SCA3. Small interfering RNAs (siRNAs) are emerging as potential therapeutic strategies to specifically target the disease-causing mutant ATXN3 (mATXN3) protein. However, the delivery efficiency of siRNAs remains a major obstacle for clinical application, particularly in brain disorders. This study aimed to develop a synthetic biology strategy to reprogram the host liver as a tissue chassis to induce and deliver in vivo self-assembled siRNAs (IVSA-siRNAs) to target the ATXN3 gene. A synthetic construct directed by a cytomegalovirus promoter was designed to encode a neuron-targeting rabies virus glycoprotein tag and mATXN3-siRNA. After intravenous injection, the synthetic construct was taken up by mouse livers, which were then reprogrammed to enable the self-assembly, production, and secretion of small extracellular vesicles (sEVs) encapsulating mATXN3-siRNA. The sEV-encapsulated mATXN3-siRNA was further transported through the endogenous circulating system of sEVs, crossing the blood-brain barrier and reaching the cerebellar cortex and spinal cerebellar tract, where they silenced the ATXN3 gene. Treatment with the synthetic construct for 8 or 12 weeks led to significant improvements in motor balance ability and reduction of cerebellar atrophy in YACMJD84.2 transgenic mice. The number of Purkinje cells in the cerebellar cortex was significantly increased, and the loss of myelin basic protein was reduced. Moreover, the quantity of neurotoxic nuclear inclusion bodies and the expression of glial fibrillary acidic protein, which promotes neuroinflammation in activated astrocytes, were decreased significantly. The synthetic construct facilitated the generation and delivery of IVSA-siRNA to the cerebellar cortex and spinal cerebellar tract, thereby inhibiting the expression of mATXN3 protein. This treatment successfully addressed motor impairments, alleviated neuropathological phenotypes, and mitigated neuroinflammation in YACMJD84.2 transgenic mice. Our strategy effectively overcomes the primary challenges associated with siRNA therapy for cerebellar ataxia, offering a promising avenue for future clinical treatments.