Objective: To identify the repressor-stoperator regulatory system derived from the mycobacteriophage StarStuff and evaluate its potential as a programmable transcriptional repression element. Methods: Candidate site regions in the StarStuff genome were identified based on homologous site screening, sequence conservation analysis, and AlphaFold2-based structural prediction. Site-associated promoter-sfGFP reporter systems were constructed, and their transcriptional activities, repression responses, and DNA recognition features were analyzed using flow cytometry, site-directed mutagenesis, and an electrophoretic mobility shift assay (EMSA). Furthermore, a tobacco etch virus protease (TEV protease) recognition sequence was inserted into the StarStuff repressor to construct a protease-responsive transcriptional regulatory module. Results: A total of 24 candidate site regions were identified in the StarStuff genome. Different site-associated promoters showed markedly distinct basal expression levels, with an overall dynamic range of more than 800-fold. Representative sites exhibited different degrees of repression in response to the StarStuff repressor, and some sites showed apparent response characteristics close to first-order behavior. Site-directed mutagenesis and EMSA results demonstrated that the stoperator core sequence is essential for the specific DNA recognition by the StarStuff repressor, whereas the adjacent sequence context further influences basal promoter activity and repression output. Screening of TEV recognition sequence insertion variants showed that StarStuff-tevS (p.76) largely retained its repression activity and could switch from a repressed state to a derepressed state in the presence of TEV protease. Conclusion: The StarStuff repressor-stoperator system provides a set of transcriptional regulatory elements with graded outputs and distinct response characteristics. Moreover, this system can be further engineered into a protease-responsive regulatory module through protein engineering, providing experimental evidence for the development of programmable transcriptional regulatory elements derived from bacteriophages.
Objective: Antimicrobial resistance is increasingly recognized as a major global health threat, and lanthipeptides are regarded as promising candidates against it, owing to their distinctive mode of action and broad engineering potential. During synthetic-biology engineering of these peptides through Design-Build-Test-Learn (DBTL) cycles, a throughput bottleneck arises at the testing step: inhibition zones from agar-diffusion assays are still measured manually. To relieve it, a cascaded computer-vision algorithm is presented for automated quantification of 96-well inhibition zones, replacing the manual reading step. Methods: The algorithm comprises three cascaded stages. In stage L1, the well array is first located by a Hough circle transform; each inhibition zone is then segmented by an RF-DETR-Seg instance-segmentation model that outputs pixel-level masks. In stage L2, pixels are mapped to millimeters using the 9.0 mm well pitch of the Society for Biomolecular Screening(SBS)-standard plate, so that no ruler or reference disk is needed; merged halos are then separated by a distance-transform watershed, and equivalent diameters are reported with rule-based quality-control flags. In stage L3, only the low-confidence wells are passed to the GLM-5V vision-language model for asynchronous review, which is kept off the main measurement path. Across the three stages, well indices, segmentation masks, diameters, and quality-control labels serve as the shared interface. Results: The dataset comprises 22 plate images, of which 11 color photographs are annotated with 233 inhibition zones and divided into training, validation, and test subsets. On the annotated images, the model achieves a detection precision of 97.3%, a recall of 93.1%, and an F1 score of 95.2%, with a mean intersection-over-union of 89.6%. For diameter estimation, the predicted equivalent diameters closely match the annotations, with a mean absolute error of 0.23 mm, a relative error of 3.08%, and a Pearson correlation coefficient of 0.973. Against independent ImageJ measurements, the per-plate means show strong agreement (r = 0.941); in high-density fields, the algorithm recovers more faint, weak-boundary halos than a human reader does. Each plate is processed in about 4 s, which accelerates the reading step substantially over manual measurement. Conclusion: This pipeline turns a solid-plate inhibition phenotype into a traceable, batch-processable digital readout and fills the automation gap that remains at the testing step of lanthipeptide library screening. The approach is not specific to lanthipeptide chemistry and should transfer to other antimicrobial peptides, bacteriocins, and antibiotics screened in a 96-well format; combined with mass-spectrometry phenotyping, it can also feed structured data into the learning step of the DBTL cycle.
Genetic code expansion (GCE) is an advanced and rapidly evolving technology in chemical biology and synthetic biology. By enabling the site-specific incorporation of noncanonical amino acids(ncAAs) into proteins through genetic encoding, GCE expands beyond the limitations of the 20 canonical amino acids in side-chain structure and chemical reactivity, providing a powerful synthetic biology tool for the precise engineering and functional regulation of biotherapeutics. The biomedical applications of GCE are systematically reviewed, with an emphasis on strategies that use genetically encoded ncAAs to achieve precise protein modification, function expansion, and controllable regulation. Representative applications include homogeneous drug conjugation, half-life extension, covalent protein drugs, viral vector functionalization, vaccine development, and the programmable control of engineered cells. These advances provide an important technical foundation for the development of new therapeutics, vaccine design, and cell therapy. Finally, the major challenges facing the biomedical translation of GCE are discussed, and its future potential is considered in light of advances in orthogonal translation system optimization, genome recoding, and AI-assisted design.
Live biotherapeutic products (LBPs) based on engineered microorganisms are pioneering a paradigm shift in next-generation disease therapeutics. LBPs hold the promise of achieving targeted colonization at disease sites, sensing pathological signals, and continuously and precisely releasing therapeutic payloads in vivo. Consequently, they can effectively overcome key bottlenecks such as short drug half-lives, high systemic toxicity, and poor tissue penetration. Focusing on three core intervention scenarios—solid tumors, the intestinal mucosa, and peripheral natural barriers (including the skin and facial organs)—this paper systematically reviews the rational selection strategies and engineering principles of current microbial chassis for live biotherapeutics. It highlights that due to significant microenvironmental differences across various physiological sites in vivo, the development of live microbial drug chassis has transitioned from the simple application of model organisms to microenvironment-driven customized designs. Building on this, the review further anticipates that the next generation of chassis will evolve toward dynamic biosafety control, multi-strain synergistic synthetic consortia, and intelligent computational capabilities, thereby accelerating the translational application of LBPs in precision medicine.
Metabolic diseases, including type 2 diabetes, obesity, and hyperuricemia, are characterized by dysregulated energy metabolism and have become a growing global public health challenge. Conventional therapeutic strategies mainly rely on fixed-dose and time-dependent drug administration, which are often insufficient to cope with the dynamic and multifactorial metabolic fluctuations that occur during disease progression. Advances in synthetic biology have provided new opportunities for precision treatment of metabolic diseases. Through the construction of artificial gene circuits with “sense-process-respond” capabilities, engineered cells can thereby detect exogenous physical or chemical stimuli as well as endogenous physiological and pathological signals in real time and precisely regulate the expression and release of therapeutic molecules. On this basis, living therapeutics developed from engineered cells or microbes are capable of long-term persistence in vivo, sustained therapeutic intervention, and interactions with host metabolic networks and the gut microbiota, enabling dynamic and adaptive regulation of metabolic disorders. This review summarizes recent advances in living therapeutics for metabolic diseases, with a focus on programmable gene circuit-driven regulatory strategies, and further discusses the major challenges and future perspectives in this rapidly evolving field.
Microbial cell therapy, as the fourth-generation biopharmaceutical successor to antibody drugs, is entering a new phase of intelligent and precision medicine through deep integration with synthetic biology. Synthetic biology has established a methodological framework for constructing highly controllable, genetically engineered, therapeutic microbial strains by leveraging core technologies such as microbial chassis identification and screening, precision genome editing, and intelligent genetic circuit design. Currently, genetically engineered bacteria applied in microbial cell therapy have achieved significant preclinical breakthroughs in areas including tumor-targeted therapy, immunomodulation, and metabolic diseases, enabling in situ drug synthesis and precise microenvironmental regulation. This article systematically reviews the research progress of microbial cell therapy in the intelligent design, efficient genome editing, and disease treatment applications using genetically engineered microbial strains, with an emphasis on analyzing the core bottlenecks in in vivo colonization, biosafety control, and clinical translation regulation. Based on the current progress and challenges, future directions are proposed to focus on the exploration of non-model microbial chassis, construction of multi-strain synergistic systems, development of multiple biosafety containment strategies, and establishment of industrialization quality control standards compatible with novel regulatory frameworks.
Traditional anti-infection strategies have long focused on eradicating individual pathogens, with insufficient attention paid to the social defense mechanisms driven by microbial communities. Microbial cooperative resistance is not an isolated genetic mutation of single cells, but rather an ecological defense network built upon public goods sharing, three-dimensional spatial isolation, quorum sensing-mediated communication, and cross-species protection. With the development of synthetic biology, the anti-infection paradigm is shifting from simple chemical or physical killing to micro-ecological network intervention. This review systematically summarizes the progress of synthetic biology strategies for intervening against cooperative resistance: implementing signal disruption via quorum quenching systems; degrading biofilms that act as spatial barriers using engineered phages expressing depolymerases; selectively eliminating resistant clones through sequence-specific CRISPR-Cas antimicrobials; and introducing engineered live biotherapeutic products for niche replacement. Furthermore, this review analyzes the issues faced by engineered agents in clinical translation, such as in vivo delivery barriers and biosafety risks, and discusses the “anti-resistance” adaptive evolutionary game driven by pathogenic communities based on the “Red Queen hypothesis”. Finally, it indicates that future intervention strategies should focus on dynamic and adaptive micro-ecological remodeling, aiming to provide engineered solutions for the treatment of multidrug-resistant chronic infections.
As synthetic biology advances from prokaryotic systems to more complex eukaryotic contexts, conventional gene engineering strategies based primarily on DNA sequence are increasingly insufficient to meet the demands of sophisticated regulation required in biomanufacturing and precision medicine. In eukaryotic genomes, gene function is profoundly governed by epigenetic regulation mediated by DNA modifications, histone post-translational modifications (PTMs), and higher-order chromatin architecture. Such regulatory systems exhibit combinatorial complexity, hierarchical spatiotemporal organization, and reversible heritability, not only defining transcriptional states but also enabling the stable transmission of epigenetic information through parental histone recycling during DNA replication. However, the intrinsic complexity of epigenetic regulatory networks has limited the establishment of systematic engineering principles, constraining the rational reprogramming of eukaryotic systems. Against this background, synthetic epigenetics has emerged as a bottom-up framework integrating concepts from synthetic biology and epigenetics. This field aims to achieve programmable control of chromatin states through epigenome editing and the construction of artificial chromatin regulatory systems, thereby providing experimental platforms for dissecting the establishment and maintenance of epigenetic information. Moreover, it offers new strategies and targets for epigenetic intervention and rewriting. Importantly, synthetic epigenetics is poised to shift biological engineering paradigms from sequence-centric genome editing toward chromatin-state programming, establishing a theoretical and technological foundation for the rational design of complex living systems. Recent advances in synthetic epigenetics are systematically summarized in this review, with focus placed on endogenous epigenetic modification editing, engineering of higher-order chromatin organization, and de novo construction of synthetic epigenetic regulatory systems. Moreover, their emerging applications in large-scale gene regulation, epigenetically driven evolution, and engineered biological manufacturing platforms are discussed.
In recent years, cancer therapy has been shifting from traditional cytotoxic strategies toward precision regulation and immune remodeling; however, it remains constrained by off-target toxicity, immune evasion, and a lack of spatiotemporal control. By coupling photosensitive proteins with functional modules to translate external light signals into programmable intracellular responses, optogenetics offers a novel technological paradigm for high-resolution tumor modulation. This review systematically delineates the recent advances in optogenetics for cancer therapy, focusing on light-responsive modules and regulatory strategies, with a special emphasis on its applications in both cell-intrinsic tumor regulation and extrinsic immune system modulation. At the tumor cell level, optogenetic systems enable precise control over cell fate by inducing programmed cell death pathways, including apoptosis, pyroptosis, and necroptosis. At the immunomodulatory level, optogenetics allows for the dynamic regulation of T-cell activation, intracellular signal transduction, and the functionality of engineered immune cells, while further enhancing immune cell migration and localized infiltration. Additionally, translational factors critical for in vivo applications, such as optical signal transmission and delivery strategies, are discussed. Overall, by utilizing light as a programmable external input, optogenetics provides a powerful framework for the synergistic regulation of tumor cells and the immune system, demonstrating significant translational potential for precision and localized therapeutics.
Mitochondria are the energy centers of eukaryotic cells, playing essential roles in cellular metabolism, growth, development, aging, and death. Mitochondria possess a circular DNA, which could encode subunits of the respiratory chain complexes. In contrast, most mitochondrial proteins are encoded by the nuclear genome. Mutations in either mitochondrial DNA (mtDNA) or nuclear DNA encoding mitochondrial proteins lead to impaired energy metabolism. This, in turn, causes mitochondrial dysfunction and triggers mitochondrial diseases. Conventional treatments mainly relieve symptoms, but cannot fundamentally correct mtDNA mutations, leading to limited efficacy. Gene therapy approaches deliver normal mitochondrial DNA to replace mutant mitochondrial DNA, but they have safety and efficacy limitations. Mitochondrial gene editing is another potential therapeutic strategy. However, its inherent drawbacks, such as off-target effects, low efficiency, and restricted targeting, limit its clinical translation. In contrast, mitochondrial transplantation, an emerging strategy that directly supplies functional mitochondria, reduces the proportion of mutated mtDNA and holds the potential to cure mitochondrial diseases. This paper systematically reviews the donor sources, transplantation methods, and clinical applications of mitochondrial transplantation in typical mitochondrial diseases, such as cardiomyopathy and encephalomyopathy.
Aging is a complex systemic biological process driven by multiple factors, including genomic instability, epigenetic alterations, metabolic imbalance, and chronic inflammation, and serves as a fundamental basis for numerous age-related diseases. Traditional anti-aging research has mainly relied on single-target interventions or empirical drug screening, both of which exhibit substantial limitations in dynamic regulation and systemic intervention. In recent years, the rapid development of synthetic biology, centered on the “Design-Build-Test-Learn (DBTL)” paradigm, has provided new engineering-oriented strategies for anti-aging research, encompassing gene editing and epigenetic reprogramming, metabolic pathway reprogramming and energy homeostasis regulation, synthetic cell therapies, and anti-aging vaccines. Technologies such as CRISPR-based screening, synthetic gene circuits, and partial cellular reprogramming are driving aging research from correlation analysis toward dynamic regulation. Meanwhile, strategies including metabolic engineering, mitochondrial transplantation, and thylakoid engineering demonstrate the potential for systematic reconstruction of cellular energy networks. In parallel, emerging approaches such as senolytic CAR-T therapy, engineered stem cells, and anti-aging vaccines have shown promise for long-term modulation of the aging microenvironment. Overall, synthetic biology is shifting anti-aging research from traditional passive interventions toward system-level programmable regulation, and may provide new theoretical foundations and technological pathways for extending healthy lifespan.
Human proteins hold significant value in disease mechanism dissection and biomedical development. However, their natural sources are limited, and direct research using human cells faces ethical constraints and technical bottlenecks. Consequently, utilizing heterologous hosts for the recombinant expression of human proteins has become a critical alternative. Saccharomyces cerevisiae has emerged as the preferred heterologous host for expressing human proteins owing to its well characterized genetic background and ability to perform eukaryotic post translational modifications. Nevertheless, the expression of human genes in wild type yeast still suffers from challenges such as low expression yield, insufficient humanization of post translational modifications, poor stability of engineered strains, and the inability of some human proteins to recapitulate their physiological functions in human cells. Accordingly, this review systematically summarizes four major strategies for optimizing the expression of human genes in Saccharomyces cerevisiae: gene adaptation modification, transcriptional and translational optimization, optimization of secretion and folding efficiency, and humanization of post translational modifications. It also presents recent advances in three major application areas: food and functional ingredients, biomedicine and disease research, and gene function research. Furthermore, current limitations and challenges are analyzed, and future trends are prospected. This review aims to provide a systematic technical framework and theoretical basis for efficiently obtaining human proteins and dissecting their functions.
The formal implementation of the Regulations on the Clinical Research and Clinical Translational Application of Novel Biomedical Technologies (State Council Order No. 818) has established a dual-track regulatory framework in China’s biomedical sector, under which pharmaceuticals and novel biomedical technologies are regulated through parallel pathways. Synthetic biomedical applications exhibit both the material characteristics of pharmaceutical products and the technical characteristics of clinical interventions. Some applications further present hybrid attributes that cannot be readily classified within a single regulatory regime, thereby giving rise to institutional challenges concerning legal characterization, regulatory pathway selection, and regulatory coordination. Drawing upon major application scenarios of synthetic biology in medicine, this study analyzes the normative classification and regulatory logic of synthetic biomedical applications under the dual-track framework. It argues that the pharmaceutical registration regulatory model and the novel biomedical technology regulatory model correspond respectively to the governance of product risks and clinical conduct risks. Using oncolytic microorganism-based hybrid applications as a representative case, the article further examines the regulatory dilemmas arising from blurred boundaries, fragmented allocation of regulatory authority, and insufficient data coordination between the two tracks. On this basis, the study proposes the establishment of dynamic classification rules, cross-agency collaborative review mechanisms, and conditional data mutual recognition mechanisms, with a view to facilitating institutional coordination between clinical exploration and industrial translation in synthetic biomedical applications.
