Prime Editor (PE) is an innovative gene editing tool based on the Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein (CRISPR/Cas) system, which has revolutionized multiple fields, including genetics, medicine, and agriculture. Emerging as a successor to Base Editor (BE), PE has gained worldwide attention due to its ability to introduce base substitutions, insertions, and deletions without causing double-strand DNA breaks, which significantly reduces the risk of off-target effect and unwanted genetic change. Notwithstanding its immense potential, researchers need to address PE's long encoding sequence and low editing efficiency for its maximal applications. Researchers have been working relentlessly to explore and enhance the editing efficiency and safety of PE by modifying its protein scaffold, optimizing the guide RNA design, and identifying cellular factors that influence its activity. Improved PE variants have been developed with enhanced accuracy and efficiency as well as decreased off-target effect when compared with their initial versions, demonstrating their potential in gene editing-related applications. Several strategies have been investigated to enhance PE performance, including: ① Modifying the structure of PE proteins to increase their efficiency, specificity, and binding affinity, thereby significantly improving their editing activity. ② Optimizing the design of pegRNAs, such as modifying the length, composition, or structure, that can boost PE's editing efficiency. ③ Identifying and manipulating cellular factors, such as proteins and RNAs, that bear functional relationships with the PE system, thus greatly enhancing its gene editing capabilities. ④ Developing automated design tools to facilitate the customization of the PE system for specific applications, vastly improving its practicality in research and clinical settings. Finally, this article summarizes the applications of PE in engineering animals and plants and developing gene therapy. Despite much room for further improvement in PE, significant advances have been made in improving its editing efficiency and safety. The rapid development of Cas9 and BE for treating genetic diseases stands as compelling testimony to the potential of PE in advancing gene editing technologies and applications. With continued research and development, PE holds great promise for improving human health and well-being.

Over the past years, synthetic biology has seen significant development, establishing a typical "Design-Build-Test-Learn (DBTL)" cycle for engineering cell factories. This cycle has been becoming an enabling methodology for smart breeding to accelerate the development of biomanufacturing. In the DBTL cycle, the testing step primarily aims to evaluate the phenotypes of constructed cell factories, which can provide a large amount of data for further learning and iterative optimization. Due to the complexity of cellular metabolic networks and regulatory mechanisms, as well as the complicated associations between genotypes and phenotypes, the design and construction of cell factories have traditionally involved long-term and labor-intensive iterative experiments. In synthetic biology, the construction of cell factories with designed synthetic pathways is often combined with random mutation and evolution to build up a large screening library, which always requires a high throughput and efficient technology and equipment in the testing step. The testing step is the rate-limiting process in the entire DBTL cycle, and its efficiency is largely dependent on chassis cells themselves, as well as the throughput of bioreactors and instruments needed for their phenotype testing. Here, this review article focuses on an overview of bioreactors and instruments with different throughput scales used for the phenotype testing in synthetic biology. We introduce their characteristics and application scenarios, including single-cell detecting and screening technology as well as pico-, nano-, micro-, milli-, and liter-scale bioreactors. Moreover, this article also points out the application potential of existing phenotype testing bioreactors and instruments, and illustrates how they can be selected for specific research purposes. Finally, the challenges and perspectives for phenotype testing bioreactors and instruments are summarized, which hopefully provides a reference for a wide range of synthetic biology researchers to properly select and use phenotype testing instruments.

Two-component systems (TCSs) in bacteria, are capable of sensing and making responses to physical, chemical, and biological stimuli within and outside the cells, and subsequently induce a wide range of cellular processes through the role played by the regulatory component and the response component in combination, which is a ubiquitous signal transduction pathway. At present, an growing number of synthetic biologists have devoted their effort to using the specific and irreplaceable properties of TCSs to design biosensors with the aim of applying in optogenetics, materials science, engineering of gut microbiome, biorefining and soil improvement, and the like. The purpose of this review is to focus on the most recent research advances in the development of biosensors based on TCSs and their potential applications. At the same time, topics of great importance are discussed on how to use novel engineering methods with synthetic biology to improve the reliability and robustness of the performance of the biosensors, such as genetic remodeling, DNA-binding domain swapping, tuning of the detection threshold and isolation of phosphorylation crosstalk as well as on how to customize the signal characteristics of TCSs to meet particular needs according to the requirements of specific applications. It would be possible in the future for scientists to combine these methods with gene synthesis on a large scale and high-throughput screening in order to speed up and give synthetic biologists a hand in the discovery of TCSs with numerous uncharacterized signal inputs and the development of genetically encoded novel biosensors that may be capable of responding to a broad range of stimuli. This allows for extending the applications of the biosensors in different fields.

Cell surface receptors are important membrane proteins that play a crucial role in mediating signal transduction between the intra- and extracellular environments, which sense extracellular chemical or physical stimuli through their extracellular structures to transmit and amplify signals into the cell through their transmembrane domains, ultimately leading to cellular decision-making. Cell surface receptor clustering is a key molecular mechanism for precisely recognizing extracellular signals and initiating internal signaling cascade responses. The clustering and activation of cell surface receptors are essential for various biological processes such as cell migration, proliferation, apoptosis, and differentiation. In addition, mutations in membrane receptors can lead to the abnormal activation of intracellular signaling pathways, contributing to the pathogenesis of various diseases, such as cancer, diabetes, and atherosclerosis. Given the close relevance of receptor-mediated cellular functions to health and disease, researchers have devoted great effort to exploring the biophysical principles of receptor signal transduction and activation, as well as developing diverse molecular engineering strategies for manipulating receptor activation and the corresponding cellular function. With the emergence and rapid development of chemical synthetic biology, molecular engineering tools have been developed, making the rational regulation of receptor activation much simpler as well as more precise and diverse. This review first summarizes the key functional modules involved in regulating receptor clustering, including molecular recognition, spatial organization, dynamics, and cell-selective modules. We then highlight the latest research advances in highly controllable functional modules enabling the artificial engineering of receptor clusters with dynamic aggregation, specific responsiveness, temporal and spatial resolution, and high cell selectivity. Moreover, we emphasize the emerging applications of various precise molecular strategies for artificially controlling receptor clustering to manipulate cellular phenotypes and cell fates, including immune activation and in vivo tissue regeneration. Finally, we perspective the unresolved issues and challenges in developing receptor clustering strategies, pertaining to the mechanisms of receptor clustering, designs of molecular recognition modules, limitations of clinical applications, safety and long-term in vivo uses, and the potential applications of these strategies in disease treatment.

With the developments of biotechnology and other disciplines such as computational science, synthetic biology has made great progresses in theoretical analysis, functional design, and experimental implementation, which is attracted extensively in the interdisciplinary fields such as computational biology and artificial intelligence. From the perspective of mathematical science, theories of designing various synthetic biological elements with specific functions have been emerging, such as gene switches, gene oscillators, and biological logic gates. From the perspective of technological innovation, great progresses have been made in biosynthesis and functionalization strategies such as genetic engineering and chemical modifications on proteins (enzymes) for self-assembly. The rapid developments of these related aspects have also greatly promoted the development of synthetic biology. This review specifically focuses on theoretical basis and analysis methods behind various synthetic biological networks with specific functions from the perspective of biomolecular network dynamics, including functional biological devices such as switches and oscillators, as well as factors related to mathematics and network theory, including correlations between positive and negative feedback loops and nonlinear dynamics, nonlinear factors and the causes of time delays, stability and bifurcation-related theories, and theoretical basis and analysis methods related to dynamics, such as the robustness and period tunability of periodic oscillators are also addressed, which provides theoretical analysis methods that can be used as reference for further design of more complex or easily synthesized biological devices. Therefore, synthetic biology based on dynamics can start with mathematical modeling and dynamical system theory to construct synthetic gene regulatory networks with specific functions. By applying gene editing technology and adopting reasonable assembly strategies for experimental manipulations, we can verify the theoretical designs. By analyzing gene expression profiles, the feasibility and performance of the theoretical design can be explored. Further analysis of the topology and function of synthetic gene regulatory networks, as well as relationship between dynamics and parameters can help us better understand adjustable design strategies and key factors for redesign.

Protein-protein interactions play a crucial role in biological processes such as cell signal transduction, gene expression and metabolic regulation, and thus their identification is essential for understanding these complex biological processes. Predicting protein-protein interactions is a hot topic of great significance, which can provide assistances in areas such as drug discovery and protein function research and design as well. In recent years, with the development of artificial intelligence, machine learning technologies have been applied gradually to the prediction of protein-protein interactions, which has shown good potentials. However, when processing a large amount of protein information, traditional machine learning methods are difficult to mine the intrinsic patterns and potential features, and deep learning techniques are needed. Compared with the three-dimensional structure of proteins, sequence information is easier to obtain, and the development of high-throughput sequencing technology provides abundant protein sequence information, which greatly facilitates the development of sequence-based deep learning technologies. Sequence-based deep learning models predict protein-protein interactions by learning intrinsic patterns and features from protein sequence information, which greatly improves prediction efficiency and accuracy. In this review, we focus on progress of deep learning in predicting sequence-based protein interactions, categorize, which is summarized according to the algorithmic framework and timeline, briefly describing the construction methods of datasets and the evaluation metrics of the models, discussing in detail the sequence encoding methods and common algorithmic architectures, and demonstrating the computational models based on various types of algorithms and their features and advantages. Finally, we analyze current challenges in predicting protein-protein interactions using deep learning methods, and discuss possible solutions. With the development of deep learning technology, the efficiency of predicting protein-protein interactions has increased dramatically. As a result, there is a need to develop models with stronger generalization and more robust prediction capabilities to aid the prediction of protein-protein interactions in the future.

The Diels-Alder reaction is a chemical process where a conjugated diene reacts with a dienophile to form cyclohexene. This reaction can generate up to four adjacent chiral centers and two carbon-carbon bonds simultaneously, making it an effective method to form C—C bonds. Therefore, it has attracted wide attention. Hetero-Diels-Alder (HDA) reactions, which involve heteroatoms, are an important tool for synthesizing natural heterocyclic rings. HAD reaction types are classified according to the heteroatoms involved, with the most common types being oxa Diels-Alder reaction and aza Diels-Alder reaction. At present, non-enzyme catalysts have been successfully applied to catalyze HDA reactions, which are catalyzed by chemical catalysts such as Lewis acids, metal ions, and organic molecules can do. However, compared to chemical catalysis, enzyme-catalyzed HDA reactions are favored due to their green, mild, efficient, and highly selective properties. With the discovery of enzyme-catalyzed HDA reactions in natural product biosynthetic pathways, uncovering the stereoselectivity and substrate specificity of HDA-related enzymes promotes our understandings of the relationship between sequences and functions. Additionally, it lays the foundation for further mining and modification of enzymes. However, there are several challenges need to be tackled. Firstly, although a few HDA enzymes have been studied, the vast majority are remained to be isolated and characterized. Secondly, the catalytic mechanisms of most reported HDA enzymes are not clear, and more information about their structures, key residues and catalytic processes remains to be uncovered. Thirdly, all reported cases present rather narrow substrate spectra, and the stereoselectivity is rather poor. Here, we summarize the currently known enzyme-catalyzed HDA reactions in heterocyclic natural product biosynthetic pathways, focusing mainly on those involved in the biosynthesis of pyridines and indole alkaloids. By summarizing and analyzing the entire biocatalytic pathways and catalytic mechanisms, we expect to guide further research and engineering of HAD enzymes to improve their activity and selectivity. We also hope to inspire the development of new biocatalysts for the synthesis of non-natural heterocyclic products.

Astaxanthin is a value-added terpene with strong antioxidant activity as well as other physiological functions, such as anti-cancer, enhancing immunity, eye protection, and cardio-cerebrovascular protection. Natural astaxanthin mainly comes from algae and aquatic crustaceans such as lobster shell. Astaxanthin presents with stereoisomerism and geometric isomerism, which have different biological activities and applications. Currently, astaxanthin in the market is obtained primarily through natural extraction from Haematococcus pluvialis or Xanthophyllomyces dendrorhous and chemical synthesis as well. While H. pluvialis has a long growth cycle and high light demand, leading to low biomass productivity and extraction rate for high production cost of astaxanthin, X. dendrorhous has a low astaxanthin yield and is easy to degenerate, making them challenging for the large-scale commercial production. The chemical synthesis of astaxanthin involves multiple reactions with complicated processes, producing mixed isomers and various byproducts, which consequently compromises its antioxidant capacity. Moreover, the assimilation and utilization of chemically synthesized astaxanthin in vivo is poor compared to its natural product, making it not suitable for being used by human being. With the continuous development of synthetic biology, microbial fermentation has been developed as an effective way for the commercial production of astaxanthin to better meet consumer demand. At present, astaxanthin-producing microorganisms include bacteria, fungi, and algae. This review introduces astaxanthin's structure, properties, production methods, and processes for its extraction and purification, with an emphasis on natural and engineered biosynthetic pathways. The latest progress in the production of astaxanthin by different microorganisms such as H. pluvialis, Yarrowia lipolytica and Escherichia coli is summarized, along with strategies for increasing astaxanthin production through genetic engineering and fermentation process optimization. Future metabolic engineering strategies are proposed, such as over-expression of astaxanthin synthesis genes, promoters with higher substitution intensity, subcellular localization, metabolic pathway optimization, etc, to increase astaxanthin yield for wide usage in food, medical, cosmetic and feed industries.

In agroecosystems, microorganisms have rich, diverse, and complex ecological functions, so-called emergent properties, which refer to novel characteristics that emerge in complicated systems as their complexity increases. Interestingly, although emergent functions originate from the joint action of multiple species, the number of species required for triggering such a phenomenon is not so large, typically less than ten. This not only provides the possibility of using synthetic bacterial communities (SynComs) to explore the generation of emergent functions, but also makes it possible to use SynComs to modify the symbiotic microbiota of plant hosts. Seed microbiome, which consists of the earliest microbial residents of a plant, has much simpler community structures compared with either rhizosphere or phyllosphere microbiome. Considering the priority effect, however, it is believed that the seed microbiome plays an important role in the evolution and assembly of plant symbiotic microbial communities, which is currently overlooked. Under particular conditions, even if the members of the seed microbiome have become rare species or disappeared in the later stage, they may still affect the development of plant symbiotic microbiome with legacy effect. Notably, limited by the dry and oligotrophic microhabitat conditions, biofilms should be the main morphology for the microbes existing on the surface of and inside seeds. Applying functional synthetic microbial communities as the seed coatings or biofilms may be the most effective intervention strategy for plant microbiome manipulations, as it targets the most critical period of the early development of plant-associated microbiota. In the context of smart agriculture, the integration of seed chip technologies and drone intelligent platforms could facilitate the high-throughput field characterization and application of SynComs, enabling the discovery of functional SynComs with specific emergent properties that work with plant seeds. Therefore, the application of synthetic bacterial biofilms, or coatings, provides a feasible approach, and is expected to bring breakthrough for the development of microbe-crop breeding technology.

In recent years, extracellular vesicles have received increasing attention due to their close association with the occurrence and development of diseases. As mechanism underlying the regulation of extracellular vesicles on the development of diseases and other kinds of biological functions has been explored persistently, their utilization as drug carriers has also been tested by scientists for targeted therapy. Extracellular vesicles as drug carriers have several intrinsic advantages compared to artificial carriers, such as higher biocompatibility, lower immunogenicity, better capacity for biofilm fusion, and particular natural homing effect on intracellular communications. However, the biomedical applications of extracellular vesicles also face challenges with their complicated surface modification, poor drug loading capacity and low product yield. Engineered extracellular vesicles refer to the artificial modification of natural extracellular vesicles to particularly fit with target recipient cells or tissues, which can achieve precise delivery of contained functional molecules and support production at a large scale, thus showing a broad prospect for their biomedical applications. Synthetic biology technology can realize the de novo design and reengineering of chassis cells to support the standardized and modular synthesis of extracellular vesicles. This article first reviews the methods and applications of surface modification and functional molecule encapsulation of extracellular vesicles, and then summarizes strategies for their preparation and production, such as extraction, and purification. In the second section, we envision the role of synthetic biology in promoting the customized design and synthesis of engineered extracellular vesicles to further facilitate fine control on attributes, improve efficiency, and expand applications to make them widely used in human health as soon as possible.

From its first report in late 2019 to September 2023, the Corona Virus Disease 2019 (COVID-19) pandemic had accumulatively infected at least 770 million people worldwide, and caused a death toll surpassing 6.96 million. The super-spreading of COVID-19 posed a huge challenge to healthcare systems all over the world, and led to a global economic recession. Later research confirmed that the causative agent is a novel coronavirus, nomenclature Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This virus possesses a similar genomic structure, and displays many common harmful characteristics of SARS-CoV and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). Due to its acute contagiousness, relevant genetic research and antiviral drug development must be carried out in laboratories with the biosafety level 3 or above, which is insufficient worldwide. Moreover, cost for such experiments is very high, and the processes are tedious, time-consuming and laborious. To overcome these limitations and enable relevant research work conducted in laboratories with the biosafety level 2, global researchers have constructed a series of SARS-CoV-2 replicons using cutting-edge reverse genetic techniques. By in vitro transcription and electroporation or liposome-related transfection, mRNAs or plasmids carrying replicons can be delivered into diverse susceptible host cells. These replicons can replicate, transcribe, and translate inside permissive cells through their transcription and translation machines. With one or more structural genes deleted, no functional virion can form to lose virus infectivity. At the same time, reporter genes, such as green fluorescent protein (GFP) and/or luciferase gene, can be inserted into the replicons to report the levels of virus replication and transcription, indicating the functions of genes or the effectiveness and efficacy of antiviral compounds. By co-transfection with trans-complemental proteins, such as stomatitis virus glycoprotein, virions can be formed with a feature of the single infection of cells without SARS-CoV-2 receptors, such as angiotensin-converting enzyme 2 (ACE2), or transmembrane serine protease 2 (TMPRSS2). Resistance-selecting genes can be introduced into the proper position of replicons to select stable cell lines expressing replicons. Thus, cell lines stably expressing replicons can be obtained through multiple passages in the presence of selectable drugs, such as G418. In this article, we summarize synthesis methods for constructing SARS-CoV-2 replicons, such as in vitro ligation by type Ⅱ or ⅡS restriction enzymes, Bacterial Artificial Chromosome (BAC), yeast transformation-associated recombination (TAR), and cyclic polymerase extension reactions (CPER). Moreover, we review single-cycle and stably expressed SARS-CoV-2 replicon systems, which provide a solid foundation for studying the SARS-CoV-2 gene function, pathogenesis, virus-host interactions, vaccine assessment, and large-scale high-throughput screening of antiviral drugs to contain transmission of this pandemic.

Advances in organoid technology have opened new paths for developing cancer models that more closely resemble the cell composition and pathophysiology characteristics of patients. Patient-derived tumor organoids maintain histopathology and genetic/phenotypic characteristics of original tumors after multiple passages, which can not only be used as an excellent model for screening new anticancer drugs, but also predict the clinical response of patients through drug sensitivity testing, providing a reliable basis for individualized precision treatment of cancer patients. By constructing an organoid biobank for each patient, a variety of therapeutic regimens such as targeted drugs and individual/combined chemotherapy drugs can be screened. Combined with single-cell sequencing and bulk transcriptome sequencing analysis, the sensitivity of each patient to different drugs can be predicted, which can provide a reference for clinical medication, and promote the progress of individualized precision treatment for cancer patients. Guided by engineering principles, synthetic biology offers unique tools to reconstruct spatial and dynamic signals to regulate intercellular communications. In clinical cancer treatment, synthetic biology mainly employs rational artificial design to synthesize a large number of therapeutic gene circuits, which are eventually implanted into the patient body with the assistance of vectors to correct the original circuits with defective functions and achieve the ultimate goal of disease treatment. The rapid development of synthetic biology has provided new paths and methods for developing tumor organoids, including how to engineer organoids to reconstruct spatial and dynamic signals, maintain cell homeostasis, and regulate intercellular communications. In this review, the construction process of tumor organoids and their applications in synthetic biology are summarized. The current limitations of tumor organoids in terms of construction efficiency, standardization, automation, and accuracy are discussed. Finally, we discuss the prospects of synthetic biology in engineering tumor organoids with complicated structures for specific functions.

The CRISPR-Cas system consists of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins, which has become the focus of molecular diagnosis because it recognizes and cleaves specific DNA or RNA sequences. Using Cas proteins (Cas12, Cas13, Cas14, Cas3, etc.) combined with signal amplification and transformation techniques (fluorescence, potentiometric, colorimetric, lateral flow assay, etc.), researchers have developed many diagnostic platforms with high sensitivity, good specificity, and low cost, which provide a new tool for detecting pathogen nucleic acids. This review presents the biological mechanism and classification of the CRISPR-Cas system, and also summarizes existing technologies for detecting pathogenic nucleic acids based on the trans-cleavage activity of Cas proteins, commenting their properties, functions and application scenarios, with future applications prospected based on the functional characteristics of the CRISPR-Cas system, which is expected to become an ideal detection platform for other multiple targets.