Synthetic Biology Applications Research Articles

Synthetic Biology-Enabled Engineering of Probiotics for Precision and Targeted Therapeutic Delivery Applications

Probiotics, defined as live microorganisms that offer health benefits when consumed in adequate amounts, have traditionally been used to maintain gastrointestinal (GI) health. They are effective in managing conditions such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and antibiotic-associated diarrhea. Recent research, however, has expanded their potential applications beyond the GI tract to include immune modulation, metabolic regulation, and mental health support. Despite these advances, conventional probiotics face limitations such as instability in harsh environments, lack of precise targeting, and ineffective control over drug release. This review explores the application of synthetic biology in addressing these limitations, emphasizing how tools such as CRISPR-Cas systems, synthetic gene circuits, and plasmid-based mechanisms enhance the stability, functionality, and specificity of engineered probiotics for therapeutic delivery. Applications of synthetic biology-enabled probiotics are examined across a range of health contexts, including GI health, cancer therapy, metabolic disorders, immune modulation, and mental well-being. Synthetic biology tools provide precise genetic modifications that improve the stability, viability, and functionality of engineered probiotics, enabling more targeted therapeutic delivery. Additionally, technologies such as encapsulation support probiotic survival, while gene circuits allow programmable, stimulus-responsive therapeutic release. As synthetic biology continues to advance, the development of programmable probiotics holds promise for personalized, targeted medicine, positioning probiotics as living therapeutics and patient-friendly alternatives to conventional therapeutic delivery systems.

Synthetic translational coupling element for multiplexed signal processing and cellular control.

Repurposing natural systems to develop customized functions in biological systems is one of the main thrusts of synthetic biology. Translational coupling is a common phenomenon in diverse polycistronic operons for efficient allocation of limited genetic space and cellular resources. These beneficial features of translation coupling can provide exciting opportunities for creating novel synthetic biological devices. Here, we introduce a modular synthetic translational coupling element (synTCE) and integrate this design with de novo designed riboregulators,toehold switches. A systematic exploration of sequence domain variants for synTCEs led to the identification of critical design considerations for improving the system performance. Next, this design approach was seamlessly integrated into logic computations and applied to construct multi-output transcripts with well-defined stoichiometric control. This module was further applied to signaling cascades for combined signal transduction and multi-input/multi-output synthetic devices. Further, the synTCEs can precisely manipulate the N-terminal ends of output proteins, facilitating effective protein localization and cellular population control. Therefore, the synTCEs could enhance computational capability and applicability of riboregulators for reprogramming biological systems, leading to future applications in synthetic biology, metabolic engineering and biotechnology.

AI in Biomedical Imaging and Diagnostics

Advances in artificial intelligence (AI) and synthetic biology have profoundly influenced biomedical research, creating transformative opportunities in imaging, diagnostics, and therapeutic engineering. In biomedical imaging, AI-driven algorithms enhance precision and accuracy, enabling automated analysis of complex datasets, real-time imaging insights, and identification of disease biomarkers. Meanwhile, synthetic biology redefines cellular engineering, particularly in T-cell research, by enabling customized functionalities, such as precision-targeted antigen recognition and tunable immune responses. The integration of AI into T-cell engineering amplifies these capabilities, facilitating the design and optimization of synthetic circuits, predictive modeling of cellular behaviors, and dynamic monitoring of therapeutic outcomes. This interdisciplinary approach is revolutionizing diagnostics and immunotherapy by streamlining the identification of disease-specific markers, improving diagnostic accuracy, and enabling real-time modulation of T-cell functionality within the tumor microenvironment. By combining AI-powered insights with synthetic biology's ability to engineer living systems, this research aims to address critical challenges in disease treatment, including tumor heterogeneity and immune evasion. This work explores the synergistic application of AI and synthetic biology in biomedical imaging and T-cell engineering, highlighting state-of-the-art technologies, their therapeutic potential, and the future landscape of personalized medicine.

Genetically Engineered Liposwitch-Based Nanomaterials.

Fusion of intrinsically disordered and globular proteins is a powerful strategy to create functional nanomaterials. However, the immutable nature of genetic encoding restricts the dynamic adaptability of nanostructures postexpression. To address this, we envisioned using a myristoyl switch, a protein that combines allostery and post-translational modifications─two strategies that modify protein properties without altering their sequence─to regulate intrinsically disordered protein (IDP)-driven nanoassembly. A typical myristoyl switch, allosterically activated by a stimulus, reveals a sequestered lipid for membrane association. We hypothesize that this conditional exposure of lipids can regulate the assembly of fusion proteins, a concept we term "liposwitching". We tested this by fusing recoverin, a calcium-dependent myristoyl switch, with elastin-like polypeptide, a thermoresponsive model IDP. Biophysical analyses confirmed recoverin's myristoyl-switch functionality, while dynamic light scattering and cryo-transmission electron microscopy showed distinct calcium- and lipidation-dependent phase separation and assembly. This study highlights liposwitching as a viable strategy for controlling DP-driven nanoassembly, enabling applications in synthetic biology and cellular engineering.

Chiral nanoenzymes: synthesis and applications.

Chiral nanoenzymes are a new type of material that possesses both chiral nanostructures and enzymatic catalytic activity. These materials exhibit selectivity in their catalytic activity towards organisms due to the introduction of chiral features in nanomaterials and have inherent chiral discrimination in organisms. As synthetic enzymes, chiral nanoenzymes offer significant advantages over natural enzymes. Due to their unique chiral structure and distinctive physicochemical properties, chiral nanoenzymes play an important role in various fields, including biology, medicine, and environmental protection. Their strong stereospecificity and biocompatibility make them useful in disease therapy, biosensing, and chiral catalysis, setting them apart from conventional and natural enzymes. In recent years, the design of synthetic methods and biological applications of chiral nanoenzymes has received significant attention and extensive research among scientists. This paper provides a systematic review of the research progress in the discovery, development, and application of chiral nanoenzymes in the last decade. Additionally, it presents various applications of chiral nanoenzymes, such as disease therapy, biosensing, and chiral catalysis. Finally, the challenges and future prospects of chiral nanoenzymes are discussed.

Route to Measure Exact Parameters of Bio-Nanostructures Self-Assembly

Artificial bio-nanocoatings, primarily composed of proteins, offer a broad range of applications across various fields thanks to their unique properties. Proteins, as major components of these structures, enable a high degree of customization, such as mutations, conjugation with other molecules or nanoparticles, or the inclusion of an enzymatic activity. Their ability to self-assembly simplifies the production of bio-nanocoatings, making this process efficient and environment-friendly. Despite these advantages, a comprehensive understanding of the underlying self-assembly mechanism is lacking, and the reaction rates governing this process have not been characterized. In this article, we introduce a novel method to determine the key parameters describing the self-assembly mechanism of bio-nanostructures. For the first time, this approach enables an accurate calculation of the autocatalytic and self-inhibitory parameters controlling the process. Through mathematical modeling, our method enhances the understanding of how the protein-based nanocoatings form and opens new avenues for their application in nanotechnology and synthetic biology. Improved control over the self-assembly processes may enable the development of nanomaterials optimized for specific functions, such as drug delivery, biosensing, and bioactive surface fabrication.

Crystal Structure and Mutagenesis of an XYP Subfamily Cyclodipeptide Synthase Reveal Key Determinants of Enzyme Activity and Substrate Specificity.

Cyclodipeptide synthases (CDPSs) catalyze the synthesis of diverse cyclodipeptides (CDPs) by utilizing two aminoacyl-tRNA (aa-tRNA) substrates in a sequential ping-pong reaction mechanism. Numerous CDPSs have been characterized to provide precursors for diketopiperazines (DKPs) with diverse structural characteristics and biological activities. BcmA, belonging to the XYP subfamily, is a cyclo(l-Ile-l-Leu)-synthesizing CDPS involved in the biosynthesis of the antibiotic bicyclomycin. The structural basis and determinants influencing BcmA enzyme activity and substrate selectivity are not well understood. Here, we report the crystal structure of SsBcmA from Streptomyces sapporonensis. Through structural comparison and systematic site-directed mutagenesis, we highlight the significance of key residues located in the aminoacyl-binding pocket for enzyme activity and substrate specificity. In particular, the nonconserved residues D161 and K165 in pocket P2 are essential for the activity of SsBcmA without significant alteration of the substrate specificity, while the conserved residues F158 as well as F210 and S211 in P2 are responsible for determining substrate selectivity. These findings facilitate the understanding of how CDPSs selectively accept hydrophobic substrates and provide additional clues for the engineering of these enzymes for synthetic biology applications.

Quantitative Characterization of Gene Regulatory Circuits Associated With Fungal Secondary Metabolism to Discover Novel Natural Products.

Microbial genetic circuits are vital for regulating gene expression and synthesizing bioactive compounds. However, assessing their strength and timing, especially in multicellular fungi, remains challenging. Here, an advanced microfluidic platform is combined with a mathematical model enabling precise characterization of fungal gene regulatory circuits (GRCs) at the single-cell level. Utilizing this platform, the expression intensity and timing of 30 transcription factor-promoter combinations derived from two representative fungal GRCs, using the model fungus Aspergillus nidulans are determined. As a proof of concept, the selected GRC combination is utilized to successfully refactor the biosynthetic pathways of bioactive molecules, precisely control their production, and activate the expression of the silenced biosynthetic gene clusters (BGCs). This study provides insights into microbial gene regulation and highlights the potential of platform in fungal synthetic biology applications and the discovery of novel natural products.

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced DeepInverton, a deep learning model that identifies invertons across a broad phylogenetic spectrum without using sequencing reads. By analyzing 68 733 bacterial genomes and 9382 metagenomes, we have uncovered over 200 000 nonredundant invertons and have also highlighted their abundance in pathogens. Additionally, we identified a post-Cambrian Explosion increase of invertons, paralleling species diversification. Furthermore, we revealed that invertons regulate diverse functions, including antimicrobial resistance and biofilm formation, underscoring their role in environmental adaptation. Notably, the majority of inverton identifications by DeepInverton have been confirmed by the in vitro experiments. The comprehensive inverton profiles have deepened our understanding of invertons at pan-genome and pan-metagenome scales, enabling a broad spectrum of applications in microbial ecology and synthetic biology.

Current Technologies in Snake Venom Analysis and Applications

This comprehensive review explores the cutting-edge advancements in snake venom research, focusing on the integration of proteomics, genomics, transcriptomics, and bioinformatics. Highlighting the transformative impact of these technologies, the review delves into the genetic and ecological factors driving venom evolution, the complex molecular composition of venoms, and the regulatory mechanisms underlying toxin production. The application of synthetic biology and multi-omics approaches, collectively known as venomics, has revolutionized the field, providing deeper insights into venom function and its therapeutic potential. Despite significant progress, challenges such as the functional characterization of toxins and the development of cost-effective antivenoms remain. This review also discusses the future directions of venom research, emphasizing the need for interdisciplinary collaborations and new technologies (mRNAs, cryo-electron microscopy for structural determinations of toxin complexes, synthetic biology, and other technologies) to fully harness the biomedical potential of venoms and toxins from snakes and other animals.

No two clones are alike: characterization of heterologous subpopulations in a transgenic cell line of the model diatom Phaeodactylum tricornutum

BackgroundConjugation-based episome delivery is a highly efficient method used to transfer DNA into the diatom Phaeodactylum tricornutum, facilitating the production of recombinant proteins and high-value metabolites. However, previous reports have indicated phenotypic heterogeneity among individual cells from clonally propagated exconjugant cell lines, potentially affecting the stability of recombinant protein production in the diatom.ResultsHere, we characterized the differences between subpopulations with distinct fluorescence intensity phenotypes derived from a single exconjugant colony of P. tricornutum expressing the enhanced green fluorescent protein (eGFP). We analyzed the expression cassette sequence integrity, plasmid copy number, and global gene expression. Our findings reveal that lower copy numbers and the deletion of the expression cassette in part of the population contributed to low transgene expression. Gene co-expression analysis identified a set of genes with similar expression pattern to eGFP including a gene encoding a putative Flp recombinase, which may be related to variations in fluorescence intensity. These genes thus present themselves as potential candidates for increasing recombinant proteins production in P. tricornutum episomal expression system.ConclusionsOverall, our study elucidates genetic and transcriptomic differences between distinct subpopulations in a clonally propagated culture, contributes to a better understanding of heterogeneity in diatom expression systems for synthetic biology applications.

Dual-Plasmid Mini-Tn5 System to Stably Integrate Multicopy of Target Genes in Escherichia coli.

The efficiency of valuable metabolite production by engineered microorganisms underscores the importance of stable and controllable gene expression. While plasmid-based methods offer flexibility, integrating genes into host chromosomes can establish stability without selection pressure. However, achieving site-directed multicopy integration presents challenges, including site selection and stability. We introduced a stable multicopy integration method by using a novel dual-plasmid mini-Tn5 system to insert genes into Escherichia coli's genome. The gene of interest was combined with a removable antibiotic resistance gene. After the selection of bacteria with inserted genes, the antibiotic resistance gene was removed. Optimizations yielded an integration efficiency of approximately 5.5 × 10-3 per recipient cell in a single round. Six rounds of integration resulted in 19 and 5 copies of the egfp gene in the RecA+ strain MG1655 and the RecA- strain XL1-Blue MRF', respectively. Additionally, we integrated a polyhydroxybutyrate (PHB) synthesis gene cluster into E. coli MG1655, yielding an 8-copy integration strain producing more PHB than strains with the cluster on a high-copy plasmid. The method was efficient in generating gene insertions in various E. coli strains, and the inserted genes were stable after extended culture. This stable, high-copy integration tool offers potential for diverse applications in synthetic 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.

Ten Years of the Synthetic Biology Summer Course at Cold Spring Harbor Laboratory.

The Cold Spring Harbor Laboratory (CSHL) Summer Course on Synthetic Biology, established in 2013, has emerged as a premier platform for immersive education and research in this dynamic field. Rooted in CSHL's rich legacy of biological discovery, the course offers a comprehensive exploration of synthetic biology's fundamentals and applications. Led by a consortium of faculty from diverse institutions, the course structure seamlessly integrates practical laboratory sessions, exploratory research rotations, and enriching seminars by leaders in the field. Over the years, the curriculum has evolved to cover essential topics such as cell-free transcription-translation, DNA construction, computational modeling of gene circuits, engineered gene regulation, and CRISPR technologies. In this review, we describe the history, development, and structure of the course, and discuss how elements of the course might inform the development of other short courses in synthetic biology. We also demonstrate the course's impact beyond the lab with a summary of alumni contributions to research, education, and entrepreneurship. Through these efforts, the CSHL Summer Course on Synthetic Biology remains at the forefront of shaping the next generation of synthetic biologists.

Enzymatic Characterization of SpPAL Genes in S. polyrhiza and Overexpression of the SpPAL3.

Phenylalanine ammonia-lyase (PAL, EC 4.3.1.5) catalyzes the deamination of phenylalanine, which is the initial step in the biosynthesis of phenylpropanoids. It serves as a crucial enzyme that facilitates the transfer of carbon from primary to secondary metabolism in plants. Duckweed is regarded as a promising chassis plant in synthetic biology research and application, due to its being rich in secondary metabolites and other advantages. The genes encoding PAL in Spirodela polyrhiza (L.) Schleid, the giant duckweed, were investigated in this study. Three SpPAL genes (SpPAL1-SpPAL3) were identified and cloned. All of them were successfully expressed in E. coli, and their recombinant proteins all showed PAL activity. In addition, SpPAL1 and SpPAL2 proteins could also utilize tyrosine as substrate, although the activity was low. A qRT-PCR analysis demonstrated that the expression of SpPAL3 was most pronounced in young fronds. It was found that the expression of SpPAL1 and SpPAL3 was significantly induced by MeJA treatment. Overexpression of SpPAL3 in Lemna turionifera inhibited the growth of fronds and adventitious roots in the transgenic plants, indicating the importance of SpPAL3 in duckweed besides its involvement in the secondary metabolism.

Multilevel Systematic Optimization To Achieve Efficient Integrated Expression of Escherichia coli.

Genomic integration of heterologous genes is the preferred approach in industrial fermentation-related strains due to the drawbacks associated with plasmid-mediated microbial fermentation, including additional growth burden, genetic instability, and antibiotic contamination. Synthetic biology and genome editing advancements have made gene integration convenient. Integrated expression is extensively used in the field of biomanufacturing and is anticipated to become the prevailing method for expressing recombinant proteins. Therefore, it is pivotal to strengthen the expression of exogenous genes at the genome level. Here, we systematically optimized the integrated expression system of Escherichia coli from 3 aspects. First, the integration site slmA with the highest expression activity was screened out of 18 sites in the ORI region of the E. coli BL21 (DE3) genome. Second, we characterized 16 endogenous promoters in E. coli and combined them with the T7 promoter. A constitutive promoter, Plpp-T7, exhibited significantly higher expression strength than the T7 promoter, achieving a 3.3-fold increase in expression levels. Finally, to further enhance the T7 expression system, we proceeded with overexpression of T7 RNA polymerase at the chassis cell level. The resulting constitutive efficient integrated expression system (CEIES_Ecoli) showed a 2-fold increase in GFP expression compared to the pET3b recombinant plasmid. Therefore, CEIES_Ecoli was applied to the integrated expression of nitrilase and hyaluronidase, achieving stable and efficient enzyme expression, with enzyme activities of 22.87 and 12,195 U·mL-1, respectively, comparable to plasmid levels. Overall, CEIES_Ecoli provides a stable and efficient method of gene expression without the need for antibiotics or inducers, making it a robust tool for synthetic biology, enzyme engineering, and related applications.

Computational Strategies to Enhance Cell-Free Protein Synthesis Efficiency

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements.

Intracellular Construction of Organelle-like Compartments Facilitates Metabolic Flux in Escherichia coli.

The formation of well-designed synthetic compartments or membraneless organelles for applications in synthetic biology and cellular engineering has aroused enormous interest. However, establishing stable and robust intracellular compartments in bacteria remains a challenge. Here, we use the structured DIX domains derived from Wnt signaling pathway components, more specifically, Dvl2 and Axin1, as building blocks to generate intracellular synthetic compartments in Escherichia coli. Moreover, the aggregation behaviors and physical properties of the DIX-based compartments can be tailored by genetically embedding a specific dimeric domain into the DIX domains. Then, a pair of interacting motifs, consisting of the aforementioned dimeric domain and its corresponding binding ligand, was incorporated to modify the client recruitment pattern of the synthetic compartments. As a proof of concept, the human milk oligosaccharide lacto-N-tetraose (LNT) biosynthesis pathway was selected as a model metabolic pathway. The fermentation results demonstrated that the co-compartmentalization of sequential pathway enzymes into intracellular compartments created by DIX domain, or by the DIX domain in conjunction with interacting motifs, prominently enhanced the metabolic flux and increased LNT production. These synthetic protein compartments may provide a feasible and effective tool to develop versatile organelle-like compartments in bacteria for applications in cellular engineering and synthetic biology.

Single-molecule force spectroscopy of toehold-mediated strand displacement

Toehold-mediated strand displacement (TMSD) is extensively utilized in dynamic DNA nanotechnology and for a wide range of DNA or RNA-based reaction circuits. Investigation of TMSD kinetics typically relies on bulk fluorescence measurements providing effective, bulk-averaged reaction rates. Information on individual molecules or even base pairs is scarce. In this work, we explore the dynamics of strand displacement processes at the single-molecule level using single-molecule force spectroscopy with a microfluidics-enhanced optical trap supported by state-of-the-art coarse-grained simulations. By applying force, we can trigger and observe TMSD in real-time with microsecond and nanometer resolution. We find TMSD proceeds very rapidly under load with single step times of 1 µs. Tuning invasion efficiency by introducing mismatches allows studying thousands of forward/backward invasion events on a single molecule and analyze the kinetics of the invasion process. Extrapolation to zero force reveals single step times for DNA invading DNA four times faster than for RNA invading RNA. We also study the kinetics of DNA invading RNA, a process that in the absence of force would rarely occur. Our results reveal the importance of sequence effects for the TMSD process and have relevance for a wide range of applications in nucleic acid nanotechnology and synthetic biology.

Synonymous codon substitutions modulate transcription and translation of a divergent upstream gene by modulating antisense RNA production

Synonymous codons were originally viewed as interchangeable, with no phenotypic consequences. However, substantial evidence has now demonstrated that synonymous substitutions can perturb a variety of gene expression and protein homeostasis mechanisms, including translational efficiency, translational fidelity, and cotranslational folding of the encoded protein. To date, most studies of synonymous codon-derived perturbations have focused on effects within a single gene. Here, we show that synonymous codon substitutions made far within the coding sequence of Escherichia coli plasmid-encoded chloramphenicol acetyltransferase (cat) can significantly increase expression of the divergent upstream tetracycline resistance gene, tetR. In four out of nine synonymously recoded cat sequences tested, expression of the upstream tetR gene was significantly elevated due to transcription of a long antisense RNA (asRNA) originating from a transcription start site within cat. Surprisingly, transcription of this asRNA readily bypassed the native tet transcriptional repression mechanism. Even more surprisingly, accumulation of the TetR protein correlated with the level of asRNA, rather than total tetR RNA. These effects of synonymous codon substitutions on transcription and translation of a neighboring gene suggest that synonymous codon usage in bacteria may be under selection to both preserve the amino acid sequence of the encoded gene and avoid DNA sequence elements that can significantly perturb expression of neighboring genes. Avoiding such sequences may be especially important in plasmids and prokaryotic genomes, where genes and regulatory elements are often densely packed. Similar considerations may apply to the design of genetic circuits for synthetic biology applications.